From dcfd937e0fe7a2e8f2b8f7b8d04e56cd87684fc7 Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Fri, 17 Mar 2023 11:39:41 -0600
Subject: [PATCH 1/4] Update likelihood calculation

---
 generative/inferers/inferer.py | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/generative/inferers/inferer.py b/generative/inferers/inferer.py
index 2f3e2424..caa32034 100644
--- a/generative/inferers/inferer.py
+++ b/generative/inferers/inferer.py
@@ -580,14 +580,14 @@ def get_likelihood(
         # get the first batch, up to max_seq_length, efficiently
         logits = transformer_model(x=latent[:, : transformer_model.max_seq_len], context=condition)
         probs = F.softmax(logits, dim=-1)
-        probs = torch.gather(probs, 2, latent[:, : transformer_model.max_seq_len].unsqueeze(2)).squeeze(2)
+        probs = torch.gather(probs, 2, latent[:, 1 : transformer_model.max_seq_len + 1].unsqueeze(2)).squeeze(2)
 
         # if we have not covered the full sequence we continue with inefficient looping
-        if probs.shape[1] < latent.shape[1]:
+        if logits.shape[1] < latent.shape[1]:
             if verbose and has_tqdm:
-                progress_bar = tqdm(range(transformer_model.max_seq_len, seq_len + 1))
+                progress_bar = tqdm(range(transformer_model.max_seq_len, seq_len))
             else:
-                progress_bar = iter(range(transformer_model.max_seq_len, seq_len + 1))
+                progress_bar = iter(range(transformer_model.max_seq_len, seq_len))
 
             for i in progress_bar:
                 idx_cond = latent[:, i + 1 - transformer_model.max_seq_len : i + 1]
@@ -598,13 +598,9 @@ def get_likelihood(
                 # apply softmax to convert logits to (normalized) probabilities
                 p = F.softmax(logits, dim=-1)
                 # select correct values and append
-                p = torch.gather(p, 1, idx_cond[:, -1].unsqueeze(1))
+                p = torch.gather(p, 1, latent[:, i + 1].unsqueeze(1))
                 probs = torch.cat((probs, p), dim=1)
 
-        # remove starting token probability
-        probs = probs[:, 1:]
-        probs = torch.log(probs)
-
         # reshape
         probs = probs[:, ordering.get_revert_sequence_ordering()]
         probs_reshaped = probs.reshape((inputs.shape[0],) + latent_spatial_dim)

From 126badd9aa213cd1e82ef5cb231b5c55f354ccbd Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Fri, 17 Mar 2023 13:56:54 -0600
Subject: [PATCH 2/4] Fixes notebook

---
 .../anomaly_detection_with_transformers.ipynb | 242 ++++++++++++------
 .../anomaly_detection_with_transformers.py    |  35 ++-
 2 files changed, 183 insertions(+), 94 deletions(-)

diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
index 4aacdd09..1fefde21 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.ipynb
@@ -53,7 +53,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "/home/mark/Envs/monai-generative/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
       "  from .autonotebook import tqdm as notebook_tqdm\n"
      ]
     },
@@ -61,24 +61,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-11 22:41:26,292 - A matching Triton is not available, some optimizations will not be enabled.\n",
-      "Error caught was: No module named 'triton'\n",
       "MONAI version: 1.2.dev2304\n",
-      "Numpy version: 1.23.5\n",
-      "Pytorch version: 1.13.1+cu117\n",
+      "Numpy version: 1.23.4\n",
+      "Pytorch version: 1.12.0+cu102\n",
       "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
       "MONAI rev id: 9a57be5aab9f2c2a134768c0c146399150e247a0\n",
-      "MONAI __file__: /media/walter/Storage/Projects/GenerativeModels/venv/lib/python3.10/site-packages/monai/__init__.py\n",
+      "MONAI __file__: /home/mark/Envs/monai-generative/lib/python3.8/site-packages/monai/__init__.py\n",
       "\n",
       "Optional dependencies:\n",
       "Pytorch Ignite version: 0.4.10\n",
       "ITK version: 5.3.0\n",
-      "Nibabel version: 4.0.2\n",
+      "Nibabel version: 5.0.0\n",
       "scikit-image version: 0.19.3\n",
       "Pillow version: 9.3.0\n",
-      "Tensorboard version: 2.11.0\n",
+      "Tensorboard version: 2.12.0\n",
       "gdown version: 4.6.0\n",
-      "TorchVision version: 0.14.1+cu117\n",
+      "TorchVision version: 0.13.0+cu102\n",
       "tqdm version: 4.64.1\n",
       "lmdb version: 1.4.0\n",
       "psutil version: 5.9.4\n",
@@ -161,7 +159,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/tmp/tmp83zh5r1m\n"
+      "/tmp/tmp1lues0wg\n"
      ]
     }
    ],
@@ -189,14 +187,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "MedNIST.tar.gz: 59.0MB [00:04, 13.2MB/s]                                                                                                                                                                                       "
+      "MedNIST.tar.gz: 59.0MB [00:02, 24.7MB/s]                                                                                                                                                                                               "
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-11 22:41:31,031 - INFO - Downloaded: /tmp/tmp83zh5r1m/MedNIST.tar.gz\n"
+      "2023-03-17 17:29:49,957 - INFO - Downloaded: /tmp/tmp1lues0wg/MedNIST.tar.gz\n"
      ]
     },
     {
@@ -210,15 +208,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-11 22:41:31,104 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-11 22:41:31,105 - INFO - Writing into directory: /tmp/tmp83zh5r1m.\n"
+      "2023-03-17 17:29:50,063 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-17 17:29:50,064 - INFO - Writing into directory: /tmp/tmp1lues0wg.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:25<00:00, 1816.60it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:27<00:00, 1734.23it/s]\n"
      ]
     }
    ],
@@ -259,7 +257,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 3 Axes>"
       ]
@@ -295,16 +293,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-11 22:42:02,011 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-11 22:42:02,012 - INFO - File exists: /tmp/tmp83zh5r1m/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-11 22:42:02,012 - INFO - Non-empty folder exists in /tmp/tmp83zh5r1m/MedNIST, skipped extracting.\n"
+      "2023-03-17 17:30:22,567 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-17 17:30:22,568 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-17 17:30:22,569 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:03<00:00, 1812.52it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:03<00:00, 1751.59it/s]\n"
      ]
     }
    ],
@@ -539,7 +537,7 @@
    "metadata": {},
    "source": [
     "### VQ-VAE Model training\n",
-    "We will train our VQ-VAE for 50 epochs."
+    "We will train our VQ-VAE for 30 epochs."
    ]
   },
   {
@@ -552,43 +550,43 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|████████████████| 185/185 [02:50<00:00,  1.09it/s, recons_loss=0.152, quantization_loss=1.05e-5]\n",
-      "Epoch 1: 100%|███████████████| 185/185 [02:56<00:00,  1.05it/s, recons_loss=0.0358, quantization_loss=7.67e-6]\n",
-      "Epoch 2: 100%|███████████████| 185/185 [03:02<00:00,  1.01it/s, recons_loss=0.0296, quantization_loss=1.27e-5]\n",
-      "Epoch 3: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0273, quantization_loss=1.68e-5]\n",
-      "Epoch 4: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0274, quantization_loss=2.47e-5]\n",
-      "Epoch 5: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0269, quantization_loss=3.15e-5]\n",
-      "Epoch 6: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0261, quantization_loss=2.88e-5]\n",
-      "Epoch 7: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0247, quantization_loss=2.52e-5]\n",
-      "Epoch 8: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0246, quantization_loss=2.67e-5]\n",
-      "Epoch 9: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0248, quantization_loss=2.96e-5]\n",
-      "Epoch 10: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0247, quantization_loss=2.78e-5]\n",
-      "Epoch 11: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0245, quantization_loss=3.64e-5]\n",
-      "Epoch 12: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0234, quantization_loss=2.43e-5]\n",
-      "Epoch 13: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0231, quantization_loss=3.42e-5]\n",
-      "Epoch 14: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0229, quantization_loss=3.24e-5]\n",
-      "Epoch 15: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.023, quantization_loss=3.77e-5]\n",
-      "Epoch 16: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0227, quantization_loss=3.07e-5]\n",
-      "Epoch 17: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.022, quantization_loss=3.66e-5]\n",
-      "Epoch 18: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0223, quantization_loss=3.17e-5]\n",
-      "Epoch 19: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0218, quantization_loss=3.32e-5]\n",
-      "Epoch 20: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0218, quantization_loss=3.49e-5]\n",
-      "Epoch 21: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0218, quantization_loss=2.99e-5]\n",
-      "Epoch 22: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0217, quantization_loss=3.81e-5]\n",
-      "Epoch 23: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0212, quantization_loss=2.88e-5]\n",
-      "Epoch 24: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0216, quantization_loss=2.93e-5]\n",
-      "Epoch 25: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0216, quantization_loss=3.35e-5]\n",
-      "Epoch 26: 100%|██████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0212, quantization_loss=3.28e-5]\n",
-      "Epoch 27: 100%|███████████████| 185/185 [02:59<00:00,  1.03it/s, recons_loss=0.0213, quantization_loss=3.2e-5]\n",
-      "Epoch 28: 100%|██████████████| 185/185 [02:58<00:00,  1.04it/s, recons_loss=0.0209, quantization_loss=3.05e-5]\n",
-      "Epoch 29: 100%|██████████████| 185/185 [02:58<00:00,  1.03it/s, recons_loss=0.0205, quantization_loss=2.83e-5]\n"
+      "Epoch 0: 100%|████████████████| 185/185 [02:40<00:00,  1.15it/s, recons_loss=0.152, quantization_loss=1.31e-5]\n",
+      "Epoch 1: 100%|████████████████| 185/185 [03:15<00:00,  1.06s/it, recons_loss=0.036, quantization_loss=7.58e-6]\n",
+      "Epoch 2: 100%|███████████████| 185/185 [03:34<00:00,  1.16s/it, recons_loss=0.0292, quantization_loss=1.29e-5]\n",
+      "Epoch 3: 100%|███████████████| 185/185 [03:46<00:00,  1.22s/it, recons_loss=0.0302, quantization_loss=1.38e-5]\n",
+      "Epoch 4: 100%|███████████████| 185/185 [03:50<00:00,  1.24s/it, recons_loss=0.0278, quantization_loss=2.59e-5]\n",
+      "Epoch 5: 100%|███████████████| 185/185 [03:55<00:00,  1.27s/it, recons_loss=0.0263, quantization_loss=2.88e-5]\n",
+      "Epoch 6: 100%|████████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.026, quantization_loss=2.77e-5]\n",
+      "Epoch 7: 100%|███████████████| 185/185 [03:59<00:00,  1.30s/it, recons_loss=0.0248, quantization_loss=2.68e-5]\n",
+      "Epoch 8: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0246, quantization_loss=3.16e-5]\n",
+      "Epoch 9: 100%|███████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0246, quantization_loss=2.58e-5]\n",
+      "Epoch 10: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0243, quantization_loss=3.26e-5]\n",
+      "Epoch 11: 100%|██████████████| 185/185 [04:00<00:00,  1.30s/it, recons_loss=0.0239, quantization_loss=3.42e-5]\n",
+      "Epoch 12: 100%|██████████████| 185/185 [04:00<00:00,  1.30s/it, recons_loss=0.0233, quantization_loss=3.16e-5]\n",
+      "Epoch 13: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0235, quantization_loss=3.8e-5]\n",
+      "Epoch 14: 100%|██████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0225, quantization_loss=3.29e-5]\n",
+      "Epoch 15: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0231, quantization_loss=2.51e-5]\n",
+      "Epoch 16: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0231, quantization_loss=2.55e-5]\n",
+      "Epoch 17: 100%|██████████████| 185/185 [03:57<00:00,  1.28s/it, recons_loss=0.0225, quantization_loss=3.12e-5]\n",
+      "Epoch 18: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0222, quantization_loss=3.9e-5]\n",
+      "Epoch 19: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0216, quantization_loss=3.22e-5]\n",
+      "Epoch 20: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0222, quantization_loss=3.17e-5]\n",
+      "Epoch 21: 100%|██████████████| 185/185 [04:00<00:00,  1.30s/it, recons_loss=0.0211, quantization_loss=3.31e-5]\n",
+      "Epoch 22: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0215, quantization_loss=4.93e-5]\n",
+      "Epoch 23: 100%|██████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0215, quantization_loss=3.38e-5]\n",
+      "Epoch 24: 100%|██████████████| 185/185 [03:59<00:00,  1.29s/it, recons_loss=0.0214, quantization_loss=3.36e-5]\n",
+      "Epoch 25: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.0211, quantization_loss=3.3e-5]\n",
+      "Epoch 26: 100%|██████████████| 185/185 [03:57<00:00,  1.28s/it, recons_loss=0.0205, quantization_loss=3.57e-5]\n",
+      "Epoch 27: 100%|██████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.0208, quantization_loss=3.33e-5]\n",
+      "Epoch 28: 100%|███████████████| 185/185 [03:58<00:00,  1.29s/it, recons_loss=0.021, quantization_loss=3.18e-5]\n",
+      "Epoch 29: 100%|███████████████| 185/185 [03:56<00:00,  1.28s/it, recons_loss=0.021, quantization_loss=2.49e-5]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "train completed, total time: 5397.495220899582.\n"
+      "train completed, total time: 7012.97215795517.\n"
      ]
     }
    ],
@@ -656,7 +654,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -709,8 +707,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3317.48it/s]\n",
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3413.76it/s]\n"
+      "Loading dataset: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 47164/47164 [00:14<00:00, 3264.50it/s]\n",
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3347.99it/s]\n"
      ]
     }
    ],
@@ -831,7 +829,7 @@
    "metadata": {},
    "source": [
     "### Transformer Training\n",
-    "We will train the Transformer for 5 epochs."
+    "We will train the Transformer for 20 epochs."
    ]
   },
   {
@@ -844,31 +842,101 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Epoch 0: 100%|███████████████████████████████████████████████| 250/250 [00:52<00:00,  4.78it/s, ce_loss=0.222]\n",
-      "Epoch 1: 100%|█████████████████████████████████████████████| 250/250 [00:52<00:00,  4.72it/s, ce_loss=0.00988]\n",
-      "Epoch 2: 100%|█████████████████████████████████████████████| 250/250 [00:53<00:00,  4.70it/s, ce_loss=0.00582]\n",
-      "Epoch 3: 100%|█████████████████████████████████████████████| 250/250 [00:53<00:00,  4.71it/s, ce_loss=0.00385]\n",
-      "Epoch 4: 100%|█████████████████████████████████████████████| 250/250 [00:53<00:00,  4.70it/s, ce_loss=0.00271]"
+      "Epoch 0: 100%|████████████████████████████████████████████████| 250/250 [01:00<00:00,  4.11it/s, ce_loss=1.82]\n",
+      "Epoch 1: 100%|████████████████████████████████████████████████| 250/250 [01:05<00:00,  3.81it/s, ce_loss=1.53]\n",
+      "Epoch 2: 100%|████████████████████████████████████████████████| 250/250 [01:08<00:00,  3.66it/s, ce_loss=1.43]\n",
+      "Epoch 3: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.70it/s, ce_loss=1.35]\n",
+      "Epoch 4: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.72it/s, ce_loss=1.29]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 114.38it/s]\n"
      ]
     },
     {
-     "name": "stdout",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "train completed, total time: 270.7773208618164.\n"
+      "Epoch 5: 100%|████████████████████████████████████████████████| 250/250 [01:05<00:00,  3.79it/s, ce_loss=1.24]\n",
+      "Epoch 6: 100%|████████████████████████████████████████████████| 250/250 [01:06<00:00,  3.76it/s, ce_loss=1.21]\n",
+      "Epoch 7: 100%|████████████████████████████████████████████████| 250/250 [01:09<00:00,  3.59it/s, ce_loss=1.18]\n",
+      "Epoch 8: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.70it/s, ce_loss=1.16]\n",
+      "Epoch 9: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.14]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 118.28it/s]\n"
      ]
     },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\n"
+      "Epoch 10: 100%|███████████████████████████████████████████████| 250/250 [01:05<00:00,  3.80it/s, ce_loss=1.12]\n",
+      "Epoch 11: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.11]\n",
+      "Epoch 12: 100%|████████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.1]\n",
+      "Epoch 13: 100%|███████████████████████████████████████████████| 250/250 [01:09<00:00,  3.60it/s, ce_loss=1.09]\n",
+      "Epoch 14: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.71it/s, ce_loss=1.08]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 117.65it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.72it/s, ce_loss=1.06]\n",
+      "Epoch 16: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.70it/s, ce_loss=1.06]\n",
+      "Epoch 17: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.05]\n",
+      "Epoch 18: 100%|███████████████████████████████████████████████| 250/250 [01:09<00:00,  3.61it/s, ce_loss=1.04]\n",
+      "Epoch 19: 100%|███████████████████████████████████████████████| 250/250 [01:07<00:00,  3.69it/s, ce_loss=1.03]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 256/256 [00:02<00:00, 123.74it/s]\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": [
+      "train completed, total time: 1368.4766018390656.\n"
      ]
     }
    ],
    "source": [
-    "n_epochs = 5\n",
-    "val_interval = 2\n",
+    "n_epochs = 20\n",
+    "val_interval = 5\n",
     "epoch_losses = []\n",
     "val_epoch_losses = []\n",
     "vqvae_model.eval()\n",
@@ -887,8 +955,9 @@
     "\n",
     "        logits, quantizations_target, _ = inferer(images, vqvae_model, transformer_model, ordering, return_latent=True)\n",
     "        logits = logits.transpose(1, 2)\n",
-    "\n",
-    "        loss = ce_loss(logits, quantizations_target)\n",
+    "        \n",
+    "        # train the transformer to predict token n+1 using tokens 0-n\n",
+    "        loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n",
     "\n",
     "        loss.backward()\n",
     "        optimizer.step()\n",
@@ -911,10 +980,17 @@
     "                )\n",
     "                logits = logits.transpose(1, 2)\n",
     "\n",
-    "                loss = ce_loss(logits, quantizations_target)\n",
+    "                loss = ce_loss(logits[:,:,:-1], quantizations_target[:,1:])\n",
     "\n",
     "                val_loss += loss.item()\n",
-    "\n",
+    "        # get sample\n",
+    "        sample = inferer.sample( vqvae_model=vqvae_model, transformer_model=transformer_model, ordering=ordering, latent_spatial_dim=(spatial_shape[0], spatial_shape[1]), starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device)\n",
+    "    )\n",
+    "        plt.imshow(sample[0,0,...].cpu().detach())\n",
+    "        plt.title(f'Sample epoch {epoch}')\n",
+    "        plt.show()\n",
+    "        val_loss /= val_step\n",
+    "        val_epoch_losses.append(val_loss)\n",
     "        val_loss /= val_step\n",
     "        val_epoch_losses.append(val_loss)\n",
     "\n",
@@ -942,17 +1018,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2023-03-12 00:16:51,478 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
-      "2023-03-12 00:16:51,479 - INFO - File exists: /tmp/tmp83zh5r1m/MedNIST.tar.gz, skipped downloading.\n",
-      "2023-03-12 00:16:51,480 - INFO - Non-empty folder exists in /tmp/tmp83zh5r1m/MedNIST, skipped extracting.\n"
+      "2023-03-17 19:50:26,144 - INFO - Verified 'MedNIST.tar.gz', md5: 0bc7306e7427e00ad1c5526a6677552d.\n",
+      "2023-03-17 19:50:26,144 - INFO - File exists: /tmp/tmp1lues0wg/MedNIST.tar.gz, skipped downloading.\n",
+      "2023-03-17 19:50:26,144 - INFO - Non-empty folder exists in /tmp/tmp1lues0wg/MedNIST, skipped extracting.\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3258.71it/s]\n",
-      "In-distribution data: 100%|███████████████████████████████████████████████████| 17/17 [00:05<00:00,  3.22it/s]\n"
+      "Loading dataset: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5895/5895 [00:01<00:00, 3244.75it/s]\n",
+      "In-distribution data: 100%|███████████████████████████████████████████████████| 17/17 [00:03<00:00,  4.86it/s]\n"
      ]
     }
    ],
@@ -1001,7 +1077,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "out-of-distribution data: 100%|███████████████████████████████████████████████| 16/16 [00:05<00:00,  3.15it/s]\n"
+      "out-of-distribution data: 100%|███████████████████████████████████████████████| 16/16 [00:03<00:00,  4.42it/s]\n"
      ]
     }
    ],
@@ -1030,14 +1106,14 @@
    "id": "5aa92638",
    "metadata": {},
    "source": [
-    "## Log-likehood plot\n",
+    "## Log-likelihood plot\n",
     "\n",
     "Here, we plot the log-likelihood of the images. In this case, the lower the log-likelihood, the more unlikely the image belongs to the training set."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "cd456a7c",
    "metadata": {},
    "outputs": [
@@ -1047,13 +1123,13 @@
        "Text(0.5, 0, 'Log-likelihood')"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Gm4rH7TsKhSXQPKhx6vIFark5HZvN7Bqy4lLHlpGWLVsybNgwXn75ZY4ePVrtvuzsbN59911GjhxJUlIS3bp144UXXqC8vLza49atW8dXX33FTTfddNrzvfjii/j5+XH11VfX+Z9RRMTX1LdbKjwYoiuGTWrcTcMo3HiJl156ieLiYoYPH87y5cvZvXs3ixYtYtiwYbRp04apU6dis9l444032LBhA9dddx2rVq0iMzOTDz/8kCuuuIKUlBTuvffeaq97+PBhsrOz2b17N8uXL+f222/n6aefZurUqXTu3Nmab1ZExM0ZRmW4Saxjyw1o3I2rKNx4iS5duvDjjz/SsWNHbrzxRjp16sTtt9/O0KFDycjIIDraXEFq0KBBfPfdd/j7+3PJJZfQuXNnJk2axOjRo1myZAnBwcHVXnfy5MkkJCTQuXNnbrnlFvLz80lPT682XVxERKrLL4bDFcuGJdax5QagfaR5rXE3DaMxN16kXbt2LFiw4LSP69WrF//85z9P+zjNhhIRcY691SY2DJqdftKrg70L6/cC19fkS9RyIyIi4mL1HW9j17pisb89h11bj69RuBEREXGxzCoL+NVHG4Ubl1C4ERERcTHHGjf1bLlpU/H4vYfrvEi91EDhRkRExMXqu8aNnb1bqqgUCopdW5MvUbipgStW8BXfpv9DIr5tT0XLTZt6ttyEBEDLirVu1DXlPIWbKgIDzSHtR44csbgS8XT2/0P2/1Mi4jsMA7IKzeOE5vV/vr31Zq/CjdM0FbwKf39/IiMjyc3NBSA0NFT7J0m9GIbBkSNHyM3NJTIyEn9/f6tLEpEmdvAYFJeZx3Fh9X9+6xawPhd+V7hxmsLNCeLj4wEcAUfEGZGRkY7/SyLiW+wtLq1CIdiJT9kz1HLTYAo3J7DZbCQkJBAbG0tpaanV5YgHCgwMVIuNiA/LruiSineiSwrULeUKCjen4O/vrw8oERGpN3sosYeU+tJCfg2nAcUiIiIu1NCWmzZquWkwhRsREREX2lsRblo7G24qpo/nFEJJmWtq8jUKNyIiIi6U1cCWm5bNINgfDCpbgaR+FG5ERERcKKuiOynByTE3NltlMFK4cY7CjYiIiIsYRmUgcWYBPzt7uMkpanhNvkjhRkRExEUOHG3YAn529ufmqOXGKQo3IiIiLmIfb+PsAn52sfZwo5YbpyjciIiIuEhDBxPbxalbqkEUbkRERFzEMZi4oeFG3VINonAjIiLiIvaWlrgGhhsNKG4YhRsREREXya0II7ENGEwMVVpuiswZWFI/CjciIiIu4gg3oQ17HXs4OlIKhSUNey1fpHAjIiLiIrlHzOuGttyEBkJ4kHmsrqn6U7gRERFxkTwXdUsBxGrcjdMUbkRERFzgeDnsc1HLDWjGVEMo3IiIiLjA/iPmZpd+NnPzy4aK00J+TlO4ERERcQH7YOKYUPB3waerpoM7T+FGRETEBVw1DdwuVt1STnOLcDN37lzat29PSEgIAwcOZNWqVXV63gcffIDNZuPqq69u3AJFREROwzFTqoHTwO3ULeU8y8PNwoULSUtLY8qUKaxZs4Y+ffowfPhwcnNza33ezp07uf/++znvvPOaqFIREZFTa7SWG4WberM83MyaNYvx48czduxYevTowbx58wgNDWX+/PmnfE5ZWRk333wzTzzxBB07dmzCakVERGrm6nDTqqIFaN8RrVJcX5aGm5KSElavXk1qaqrjNj8/P1JTU8nIyDjl85588kliY2MZN27cac9RXFxMQUFBtYuIiIiruTrcxFSEm2PHoajUNa/pKywNN/v27aOsrIy4uLhqt8fFxZGdnV3jc1asWMEbb7zBa6+9VqdzTJs2jYiICMclMTGxwXWLiIicyNXhJizIXKkYKtfPkbqxvFuqPg4fPswtt9zCa6+9RkxMTJ2eM2nSJPLz8x2X3bt3N3KVIiLii1wdbqCy9SZP4aZeAqw8eUxMDP7+/uTk5FS7PScnh/j4+JMev23bNnbu3MkVV1zhuK28vByAgIAANm/eTKdOnao9Jzg4mODg4EaoXkRExGQYlQHEVbOlwAw3mflquakvS1tugoKC6NevH+np6Y7bysvLSU9PJyUl5aTHd+vWjfXr17N27VrH5corr2To0KGsXbtWXU4iImKJg8eg1Pxbm1YubLmxDyrO04yperG05QYgLS2N0aNH079/f5KTk5k9ezZFRUWMHTsWgFGjRtGmTRumTZtGSEgIPXv2rPb8yMhIgJNuFxERaSr2lpXIEAjyd93rxjSr/vpSN5aHm5EjR5KXl8fkyZPJzs4mKSmJRYsWOQYZZ2Zm4ufnUUODRETEx9jDhyv2lKrK3gqkcFM/locbgIkTJzJx4sQa71u2bFmtz12wYIHrCxIREakHe/ho5cLxNlA5oHjfUde+rrdTk4iIiEgD7a8IHy0bKdxotlT9KNyIiIg0kD18xDRWuNGA4npRuBEREWmg/Y005ia2yhYMUncKNyIiIg3U2GNujh6HohLXvrY3U7gRERFpoH2N1C0VFgTNAqqfQ05P4UZERKSB9jXSgGLQoGJnKNyIiIg0gGE0XrcUVJkOrnBTZwo3IiIiDXCkFI4dN49dPaAYKjfiVMtN3SnciIiINIC9RaVZgDlGxtW0BUP9KdyIiIg0QGOOtwF1SzlD4UZERKQBGnO8DWhAsTMUbkRERBqgsTbNtFPLTf0p3IiIiDRAY61xY9dKA4rrTeFGRESkARwtN40VbtRyU28KNyIiIg1g3xG8scfcHCk1L3J6CjciIiIN0NjdUmGBEOxvHtuDlNRO4UZERKQBGntAsc0G0RWvfUBdU3WicCMiItIAjd1yA5XhRi03daNwIyIi4qSSMsgvNo8bM9zYX/uAwk2dKNyIiIg4yR42/G0QGdJ451HLTf0o3IiIiDjJvvZMdDPwszXeeRRu6kfhRkRExEn7m2C8DVQOVtaA4rpRuBEREXGSvVuqsWZK2anlpn4UbkRERJxkDxvRjRxuHC03Cjd1onAjIiLipINNFW4qur3UclM3CjciIiJOauqWG4WbulG4ERERcdKBJgo39tc/UgrHjjfuubyBwo2IiIiTmqrlpkUQBFZ8Yu/XjKnTUrgRERFx0sEmmi1VbX8pdU2dlsKNiIiIk5qq5QY07qY+FG5EREScUFplX6kmCTeaMVVnCjciIiJOOHTMvLYBUY24r5SdFvKrO4UbERERJ9jHvkSGgH8TfJpqC4a6U7gRERFxQlOOt6l6HrXcnJ7CjYiIiBOaao0bO23BUHcKNyIiIk5o6nCjlpu6U7gRERFxQlPtCG6n2VJ1p3AjIiLiBHvIiFK3lNtRuBEREXFCk7fcVJynsASKtb9UrRRuREREnNDUY27CgyHAr/q5pWYKNyIiIk5o6pYbm61yscB9Cje1UrgRERFxQlOPuQGNu6krhRsREZF6Mgw4WLH9QlO13IBmTNWVwo2IiEg9FZTA8XLzuKnG3IC2YKgrhRsREZF6soeLsEAICWi680arW6pOFG5ERETqyYrxNlAZbjSguHYKNyIiIvVkxXibqudTy03tFG5ERETqaX9Ft1RTjrepej4NKK6dwo2IiEg9NfUaN3b22VIaUFw7hRsREZF6snrMzYFjTXteT6NwIyIiUk9Wj7kpKIbSsqY9tydRuBEREaknq8bcRASDn808VuvNqSnciIiI1FNTb5pp5+8HkRX7S2nczakp3IiIiNSTvdWkqcNN1XNqxtSpKdyIiIjUk1Wzpaqe86C6pU5J4UZERKQejh2HI6XmsVpu3JPCjYiISD3YBxMH+kGLoKY/f7TG3JyWwo2IiEg92MfbRDUDm63pzx9dsZCfWm5OTeFGRESkHqwcb1P1vBpzc2oKNyIiIvWw36Jp4HYac3N6CjciIiL1cNDicKOdwU9P4UZERKQerG65se9npQHFp+YW4Wbu3Lm0b9+ekJAQBg4cyKpVq0752I8++oj+/fsTGRlJWFgYSUlJvP32201YrYiI+DJ3GnNTblhTg7uzPNwsXLiQtLQ0pkyZwpo1a+jTpw/Dhw8nNze3xsdHR0fzyCOPkJGRwc8//8zYsWMZO3YsixcvbuLKRUTEF9nDTVSINee3n7fMMDfQlJNZHm5mzZrF+PHjGTt2LD169GDevHmEhoYyf/78Gh8/ZMgQrrnmGrp3706nTp2455576N27NytWrGjiykVExBc5Wm5CrTl/cEDl+joaVFwzS8NNSUkJq1evJjU11XGbn58fqampZGRknPb5hmGQnp7O5s2bOf/882t8THFxMQUFBdUuIiIizrJq08yqNO6mdpaGm3379lFWVkZcXFy12+Pi4sjOzj7l8/Lz82nevDlBQUFcdtllzJkzh2HDhtX42GnTphEREeG4JCYmuvR7EBER37Lf4jE3Vc+tlpuaWd4t5YwWLVqwdu1afvjhB6ZOnUpaWhrLli2r8bGTJk0iPz/fcdm9e3fTFisiIl7jeDkcsq9QbNGYG6hsNdJCfjULsPLkMTEx+Pv7k5OTU+32nJwc4uPjT/k8Pz8/OnfuDEBSUhIbN25k2rRpDBky5KTHBgcHExwc7NK6RUTENx2qEiaiLGy5cSzkp26pGlnachMUFES/fv1IT0933FZeXk56ejopKSl1fp3y8nKKizVkXEREGpd9vE1kCARY+AmqhfxqZ2nLDUBaWhqjR4+mf//+JCcnM3v2bIqKihg7diwAo0aNok2bNkybNg0wx9D079+fTp06UVxczBdffMHbb7/NK6+8YuW3ISIiPsAxmNjCLimobDXSmJuaWR5uRo4cSV5eHpMnTyY7O5ukpCQWLVrkGGScmZmJn19lPC4qKuLOO+/k999/p1mzZnTr1o133nmHkSNHWvUtiIiIj7B6dWI7bZ5ZO5thGD61vmFBQQERERHk5+cTHh5udTkiIuJB3v4ZHl0KwzvB3y63ro70HXDrZ9AzFj6/ybo6mlJ9Pr89craUiIiIFaxendiupda5qZXCjYiISB1Zva+UXXSVMTe+1f9SNwo3IiIideRouXGTcFNcBkdKra3FHSnciIiI1JG7tNyEBUKwv3l8QIOKT+JUuNm+fbur6xAREXF79iBhdcuNzVbZeqNxNydzKtx07tyZoUOH8s4773DsmCKjiIj4BndpuQGtdVMbp8LNmjVr6N27N2lpacTHx/OnP/2JVatWubo2ERERt2EY7rEjuJ1WKT41p8JNUlISL774Inv37mX+/PlkZWVx7rnn0rNnT2bNmkVeXp6r6xQREbFUUSmUlJnH7hBuHN1S6kA5SYMGFAcEBHDttdfy4Ycf8uyzz7J161buv/9+EhMTGTVqFFlZWa6qU0RExFL2FpKQAAgNtLYW0Fo3tWlQuPnxxx+58847SUhIYNasWdx///1s27aNJUuWsHfvXq666ipX1SkiImIpd9lXyi5aY25Oyam9pWbNmsWbb77J5s2bufTSS3nrrbe49NJLHXtAdejQgQULFtC+fXtX1ioiImIZx75SodbWYRetMTen5FS4eeWVV7j11lsZM2YMCQkJNT4mNjaWN954o0HFiYiIuAu13HgOp8LNkiVLaNu2bbXdugEMw2D37t20bduWoKAgRo8e7ZIiRURErOZOM6Wgys7gCjcncWrMTadOndi3b99Jtx84cIAOHTo0uCgRERF3405r3IC6pWrjVLgxTrFLV2FhISEhbtJeJyIi4kLusq+UnT3cFJRUTlEXU726pdLS0gCw2WxMnjyZ0NDKUVVlZWV8//33JCUlubRAERERd+BuLTeRIeBng3LD7JqKa251Re6jXuHmp59+AsyWm/Xr1xMUFOS4LygoiD59+nD//fe7tkIRERE34C77Stn52SAqxBxQfEDhppp6hZulS5cCMHbsWF588UXCw8MbpSgRERF3Y18sz11absDsmtp/VDOmTuTUbKk333zT1XWIiIi4NXebLQUaVHwqdQ431157LQsWLCA8PJxrr7221sd+9NFHDS5MRETEXZSWmQN3wT3DjVpuqqtzuImIiMBmszmORUREfIV9vI2fDSKCra2lKq11U7M6h5uqXVHqlhIREV9iDw+RIeDfoF0ZXUstNzVz6i06evQoR45UbkO6a9cuZs+ezZdffumywkRERNzFfjccbwMac3MqToWbq666irfeeguAQ4cOkZyczMyZM7nqqqt45ZVXXFqgiIiI1Q662b5Sdgo3NXMq3KxZs4bzzjsPgH/+85/Ex8eza9cu3nrrLf7v//7PpQWKiIhYzV1bblqqW6pGToWbI0eO0KJFCwC+/PJLrr32Wvz8/DjnnHPYtWuXSwsUERGxmrutTmwXrQHFNXIq3HTu3JlPPvmE3bt3s3jxYi6++GIAcnNztbCfiIh4HXfbV8rOMVvqmLkNg5icCjeTJ0/m/vvvp3379gwcOJCUlBTAbMXp27evSwsUERGxmru23ERWjAEqMyD/mLW1uBOnVii+/vrrOffcc8nKyqJPnz6O2y+66CKuueYalxUnIiLiDty15SY4AFoEweESc9yNu9VnFafCDUB8fDzx8fHVbktOTm5wQSIiIu7GXVtuwBx3c7hE426qcircFBUVMX36dNLT08nNzaW8vLza/du3b3dJcSIiIu5gv5u23IAZbnbla8ZUVU6Fm9tuu42vv/6aW265hYSEBMe2DCIiIt7GMMwBu+CeLTcttdbNSZwKN//973/5/PPPGTx4sKvrERERcSsFJXC8ooPC3da5gcrWJLXcVHJqtlRUVBTR0dGurkVERMTt2MeyhAZCiNMjVRuPWm5O5lS4eeqpp5g8eXK1/aVERES8kbuuTmynLRhO5lQGnTlzJtu2bSMuLo727dsTGBhY7f41a9a4pDgRERGrueu+UnZquTmZU+Hm6quvdnEZIiIi7sndW2405uZkToWbKVOmuLoOERERt+RY4ybU2jpORS03J3NqzA3AoUOHeP3115k0aRIHDhwAzO6oPXv2uKw4ERERqzlWJ3bTbqmqY24M7S8FONly8/PPP5OamkpERAQ7d+5k/PjxREdH89FHH5GZmclbb73l6jpFREQs4c6rE0NlXcVlcKQUwoKsrccdONVyk5aWxpgxY9iyZQshIZVR9tJLL2X58uUuK05ERMRq7rqvlF1oIAT7m8cad2NyKtz88MMP/OlPfzrp9jZt2pCdnd3gokRERNyFu7fc2GyaDn4ip8JNcHAwBQUFJ93+22+/0apVqwYXJSIi4i7ceV8pO4Wb6pwKN1deeSVPPvkkpaWlANhsNjIzM3nooYe47rrrXFqgiIiIldx5Xyk7zZiqzqlwM3PmTAoLC2nVqhVHjx7lggsuoHPnzrRo0YKpU6e6ukYRERFLHDsOhSXmsbtOBYfKlhuNuTE5NVsqIiKCJUuWsHLlStatW0dhYSFnn302qamprq5PRETEMvsrdhkK9INwN56FpG6p6uodbsrLy1mwYAEfffQRO3fuxGaz0aFDB+Lj4zEMA5vN1hh1ioiINLn9VRbwc+ePN7XcVFevbinDMLjyyiu57bbb2LNnD7169eKss85i165djBkzhmuuuaax6hQREWly+ypabtx5vA1U1ndQ4QaoZ8vNggULWL58Oenp6QwdOrTaff/73/+4+uqreeuttxg1apRLixQREbGCvSUkxo3H24Babk5Ur5ab999/n7/+9a8nBRuACy+8kIcffph3333XZcWJiIhYab+HtNxozE119Qo3P//8MyNGjDjl/Zdccgnr1q1rcFEiIiLuYJ+bb5ppp3BTXb3CzYEDB4iLizvl/XFxcRw8eLDBRYmIiLgDe8tNjJu33Nhblg6XQEmZtbW4g3qFm7KyMgICTj1Mx9/fn+PHjze4KBEREXew30NabiJCwK9iNpcGFddzQLFhGIwZM4bg4OAa7y8uLnZJUSIiIu7APlvK3QcU+9kgKsQMY/uPQlxzqyuyVr3CzejRo0/7GM2UEhERb+GYLeXm3VJgjrvZf1TjbqCe4ebNN99srDpERETcimFUmS3l5i03YI672YKmg4OTe0uJiIh4u4ISKC03j6M9pOUG1HIDCjciIiI1srfatAiCEKd2YmxaCjeVFG5ERERq4CkL+Nkp3FRSuBEREamBpyzgZ6ctGCop3IiIiNRgv4dMA7fT5pmVFG5ERERq4FjAz8O6pdRy4ybhZu7cubRv356QkBAGDhzIqlWrTvnY1157jfPOO4+oqCiioqJITU2t9fEiIiLO2OdB08ChMoQp3LhBuFm4cCFpaWlMmTKFNWvW0KdPH4YPH05ubm6Nj1+2bBk33XQTS5cuJSMjg8TERC6++GL27NnTxJWLiIg387SWG3v32cGjcLzc2lqsZnm4mTVrFuPHj2fs2LH06NGDefPmERoayvz582t8/Lvvvsudd95JUlIS3bp14/XXX6e8vJz09PQmrlxERLyZp425iW5mbsNgoNYbS8NNSUkJq1evJjU11XGbn58fqampZGRk1Ok1jhw5QmlpKdHR0TXeX1xcTEFBQbWLiIjI6ezzsJYbf7/KWvOKrK3FapaGm3379lFWVkZcXFy12+Pi4sjOzq7Tazz00EO0bt26WkCqatq0aURERDguiYmJDa5bRES8n6e13EBlrfbxQr7K8m6phpg+fToffPABH3/8MSEhITU+ZtKkSeTn5zsuu3fvbuIqRUTE05SWwcFj5rGntNyAwo2dpQtKx8TE4O/vT05OTrXbc3JyiI+Pr/W5M2bMYPr06Xz11Vf07t37lI8LDg4mODjYJfWKiIhvOFARbPxsEFnz385uqVVFuMnz8XBjactNUFAQ/fr1qzYY2D44OCUl5ZTPe+6553jqqadYtGgR/fv3b4pSRUTEh9i7pKJDzLEsniJG4QawuOUGIC0tjdGjR9O/f3+Sk5OZPXs2RUVFjB07FoBRo0bRpk0bpk2bBsCzzz7L5MmTee+992jfvr1jbE7z5s1p3ry5Zd+HiIh4j/0etsaNnbqlTJaHm5EjR5KXl8fkyZPJzs4mKSmJRYsWOQYZZ2Zm4udXGZtfeeUVSkpKuP7666u9zpQpU3j88cebsnQREfFSnjZTyq5VmHmtcOMGJk6cyMSJE2u8b9myZdW+3rlzZ+MXJCIiPs0TZ0oBtLJPBffxcONBPYkiIiJNY7+H7Qhup24pk8KNiIjICezhIMZDu6UOHjWns/sqhRsREZETeGrLTVRI5RYMB3x4CwaFGxERkRPYty+I9bBwU20LBh/umlK4EREROUFuRTCwd/N4klYad6NwIyIiUlW5URkMWnlYyw1oIT9QuBEREanm0DE4Xm4ee9qYG9CMKVC4ERERqcY+3iYqBIL8ra3FGfauNLXciIiICFAZCmI9cLwNqOUGFG5ERESqyfXg8TZQZcxNkbV1WEnhRkREpIrcilDgiTOloHL6+j6tcyMiIiJQ2eLh6S036pYSERERoHLMjae23NjDzQEf3oJB4UZERKQKT12d2M6+BQP47hYMCjciIiJVeHrLjbZgULgRERGpJs/DZ0uBtmBQuBEREalQfNxcoRg8d50b0BYMCjciIiIV7C0dQf4QEWxtLQ1h71JTy42IiIiPq9olZbNZW0tDqOVGREREAO8YbwOV9fvqKsUKNyIiIhXyPHx1Yru4ivpzFW5ERER8m7e03MQ1N69zFG5ERER8W66Hb71gZ2+5ySkCw7C2Fiso3IiIiFTw9AX87OzT2I+UQmGJtbVYQeFGRESkgr3lxpPXuAEIDYTwIPM42we7phRuREREKthbbmI8vFsKINY+7qbQ2jqsoHAjIiKCOTbF3nIT7+EtN1D5PfjijCmFGxEREcwdtEvKwIbnd0uBb8+YUrgRERGhcmxKTCgE+ltbiytUnTHlaxRuREREgOyKsSn2Fg9PZ2990pgbERERH2UPAXFe0CUFarkRERHxefaWG28YTAwacyMiIuLz7GNuvKVbqur+Ur62SnGA1QWIiDSZ4jL43y5YsQfWZMPeIjhUDM38IboZdI2Gs+NgWDvz2GazumJpQo6WGy8JN/YtJErK4NAxiGpmbT1NSeFGRLzfvqPw8k/w7gY4cOzk+4+Uwv5jsOUg/GcbPPktdIuG0T3h/3WD5kFNX7M0uRwvCzfBAWZmP3DU7JpSuBER8QalZfDyWpj9IxSWmrclhMGIjnBOArSPgMhg80/b7CLYsB+W/w4rfodNB2DScpixCiaeDeN6QzP9yvRm2V60gJ9dXJgZbrILoVuM1dU0Hf2kioh3+u0ATPgK1uaaX/duBQ8kQ2o7CKhhuGG3ljCkLdzZF/KL4R+b4LWfYUc+PPEtvLkenj4PLunYtN+HNIljx82uG/Celhsww83Gfb43qFgDikXE+/x7Kwz70Aw2EcHwUiosuRFGdKg52JwoIhjG94Fvb4YXLzRbezIPw6gv4E+LKz8FxWvYu6RCAiA82NpaXMlXZ0wp3IiI9zAMmPkD3LrIHEdz3hmw4iYY2Q38nBgcHOAHf+hhhpy7zgZ/G3y0Bc5/H77e7fr6xTJVu6S8aRx5nI8u5KdwIyLeodyAh5fD9O/Nr+/oA/+40jV9DM2DYPIg+Pw66BgBWUVw/afw6DfmeB3xeN42U8ouzkc3z1S4ERHPV1YOd6fD/PXmrofTzoenzqtbF1R99IuH//0/GNvT/PrVdXDNJ5V/9ovH8ratF+zs30+2Wm5ERDxIuQFpS2HhJrPb6JVhcFvvxjtfWCA8NwTevgxaBMGqLLhoIXyf1XjnlEbnbdPA7ezfT5bCjYiIhzAMeGwFvLfRHFPzysVwXdemOfeIDuYg5e7RkHsErv4YFvzSNOcWl7MPuPWWfaXsWleEm7wjUHzc2lqaksKNiHiuOWvgb+vM47mpcE2Xpj1/p0j47w3meY+XwwPL4JFvzG4y8SjeOuYmuhkE+5vHvjRjSuFGRDzTexvgqQzz+Klz4fomarE5UVggvHox/PUc8+u/rYNbPofCEmvqEadke2nLjc0GrVuYx3sOW1tLU1K4ERHPs3iHOc4GzCnadyRZWg42G/ylP7w+AkL8YckuuPxf8LsPfZp4MMOonE3kbS03UBlusnzov6PCjYh4lh+zYfxiKDPM9WseS7G6okpXdYZPrzV3LPx1P1z8IazJsboqOY39R80Z/TYg1stabqBy3I1abkRE3NG2Q/DH/8DR4+Y2Ci8Mdb8V186Ogy9vgB4tzVGcV39stjSJ29pb8aHfKgyC/K2tpTGo5UZExF3lHoH/95m5e3dSLLw2HALd9JPojBbmgn8XtTOD2Ogv4J0NVlclp2Bv0WjTwto6GovG3IiIuKOiUrj5P7CzANqHw7uXm6sGu7PmQfD2pfD/upldaH/5n7k1hGFYXZmcYK+PhJu9PrTWjcKNiLi34+Vw2yJzE8zoEPjgCogNtbqqugn0h/+7yBxsDObWEA8vNxceFLexp8C8bu3l4UbdUiIi7sAwzLVjvtplzkJ65zLoFGV1VfVjs5nTxKefb45Ynb/ebMXRWjhuw95d4+3h5nAJFBRbW0tTUbgREfdkGPB0hjlWxc8GfxsOAxKsrsp543rD3GHm9/LeRpjwldkqJZbz9m6p0ECIDDGPfaX1RuFGRNyPYZhdOP+3xvx6+vlwSUdra3KFG7qaA6ED/OBfv5ndbdpV3HKOcBNubR2NydemgyvciIj7mfEDzPrRPH7qXBjby9p6XOnKzrDgEgjyg8+3w51L1EVloWPHYd9R89hbW26gyqBihRsREQvM/AGeW2UePzHY+tWHG8PwDvDWZRDoB59uNccVaRaVJewf9qGBEBFsbS2NKcHHZkwp3Ih4K8PwrFk55QY8+o3ZHQUweRDc2dfamhrTRe1g3sXmGJy3q+yTJU1qb5XBxO62HqQrtfGxlpsAqwsQERfYdgi+3g3f7YUtB809jQpLzQGrgX4QGQyJ4ZDYAtqGQ/eWcHYsdIg0P1ytVlgCE78yu2nAbLHx5mBjd2VnKCgxZ0/NWWO+T3f3s7oqn+LtC/jZ+dp0cIUbEU9VWgYfboa3foXVtexfVFoOeUfNy4n7HEUEQ99YOPcMuOAM6NUK/Ju4QXfzARj7XzOUBfrBnIvgOot2+LbCH3tA/jF4/Fuz9aZ9hBl6pEnY17jx+nDjYwOKFW5EPI1hwL+3mR+EO/PN2wL8IKU1DG4DZ8VAu3CzFSDQH4qPw4FjkFkAuw/DjnxYnwc/50F+MSzbbV6exnzO4DZwQaJ56RDReG31ZeXwylqzG6q4DOLD4I0RkOzB072dNeFsyC6Ceevgrq+gU6T5Pkqj2+vla9zY2b+/7EKzB9gdGmwbk8KNiCfJPQIPLqvsvmnVDP7c19wdu7ZVe9u0MFtlqiotg40H4Pu98M3vsGIPHCo2X9v++u3CYUhF0DnvjMrFMhrCMCA9E55caZ4fYGhbeCnVc1YebgxTBsOmA2bQHPU5fHkjtGxmdVVe73cf6ZaKa27+DVRaDjmFlQOMvZXCjYinWJNjbsCYXWT+lrq3H0zo6/weS4H+0LuVeRnfxxyfszYXlv8OX2fCD9mwqwD+/qt58bOZ43SGtDW7sfq0qt+5Dx4zQ9PrP8Ov+8zbIoPND/Wbu3v3aM66CPAzFyq8+EOzRW7cIvjwSvfdHNRL7K5o/EyMsLaOxhbgZ7beZOabjbgKN41s7ty5PP/882RnZ9OnTx/mzJlDcnJyjY/99ddfmTx5MqtXr2bXrl288MIL3HvvvU1bsIgVPtliDrgtLoMzo+DV4dDTxd0WAX7QP968pPU3B/l+uxeWZZqDlX87CD/mmJcZP5hbCXSJgj6xZk1tw829n5oHme3eRaWQVWg+b3UO/JBlbiAJ0CwAxvY091xyRWuQt4gKMTfbHPFPWLkHnvgWnj7P6qq8VmlZ5dTotl4ebsD8Ec3MNy8D21hdTeOyNNwsXLiQtLQ05s2bx8CBA5k9ezbDhw9n8+bNxMbGnvT4I0eO0LFjR2644Qb+8pe/WFCxiAXe2wB/WWoGhhEd4OVh0KIJdsRuHgQXtzcvYI5EtI/PWZVlfir8dtC81FWPlnB9V3MQbZRCTY26tYRXhsGoL+DVdXBhW7iwndVVeaW9h80fq2B/3+gRbRsB7K5srfJmloabWbNmMX78eMaOHQvAvHnz+Pzzz5k/fz4PP/zwSY8fMGAAAwYMAKjxfhGv8/5GuOd/5vGos+D5IdaNBGzTAm7uYV7AHP+zLte87Mg3By8cKjZbfPz8IDQA4sKgfTgkxUJKG3OAspzeJR1hXC94Yz3clQ7Lb9L4m0aQWaVLyhd6RRMrtpfILLC2jqZgWbgpKSlh9erVTJo0yXGbn58fqampZGS4bjGr4uJiiosrt0EtKPCBd1W8w+Id5vonAON7w9Tz3Os3cGwoDGtvXsT1pgw2B3r/dhDSlppbNrjT++8F7B/ybb14T6mq7F1vmT7QcmPZCsX79u2jrKyMuLi4arfHxcWRnZ3tsvNMmzaNiIgIxyUxMdFlry3SaH7KMTdVLDPg/3Vzv2Ajja9ZgLmCcaAffLEd3t1odUVex/4h7wvjbaDy+9ztA3/je/32C5MmTSI/P99x2b17t9UlidQuu9Acb3GsDIa1gxcuVLDxVb1awV/PMY8f+cbs/hOXsX/I+0q4sXdL5RbB0VJra2lsloWbmJgY/P39ycmpvmJqTk4O8fHxLjtPcHAw4eHh1S4ibqukDMb815zu3TXanBUV4PV/g0ht7uwL57aBI6XmGkfaYNNlfK3lJjKkci6Ct7feWPZbMygoiH79+pGenu64rby8nPT0dFJSUqwqS8Ra074zp01HBsPblzXNrChxb342mDnUnNKzbDd89JvVFXkNR7jxkb95bbbK9XwUbhpRWloar732Gn//+9/ZuHEjf/7znykqKnLMnho1alS1AcclJSWsXbuWtWvXUlJSwp49e1i7di1bt2616lsQcZ2lmfDST+bx/12kmUVSqWOkuSYQwGMr4NAxS8vxBvnF5gXgDB8JN1AZ5Lx9ULGlU8FHjhxJXl4ekydPJjs7m6SkJBYtWuQYZJyZmYmfX2X+2rt3L337Vu4UPGPGDGbMmMEFF1zAsmXLmrp8EdfJOwITvjKPx/Y0pwKLVDXxbPjXb+YGo09nwIyhVlfk0exrvcQ0gzAfaiD1lZYby1conjhxIhMnTqzxvhMDS/v27THU3yzexjDg7nQz4HSLhifOtboicUfB/jBjCFz1sbkdxshuMMAHNxl1kapr3PgSe8vNrkOWltHoNFJRxGqv/wxf7TI/vF692JwCLFKTQW3gpu7m8f3LzP3AxCk7DpnX7SOtrKLpdYwyr+3fv7dSuBGxUmaB2cUA8Phg6OHi/aLE+0wZBFHBsGE/vLPB6mo81o6KXUPsH/a+okOkeZ2Z793ZWOFGxCqGAQ99DUeOQ0prc7l9kdNp2QweqNhc+NnvoaC49sdLjbYfMq87RlpZRdNLaGE2EpeWw+9ePO5G4UbEKp9uNbujAv3MsRRaqE/qakxP6BwJ+47C7NVWV+OR7N0yHXys5cbPVtl6s70ee956GoUbESvkF5srzgLc0w/OjLa2HvEsgf7wxGDz+NW1sMuL/wRvBIeOwYGj5rH9g96XdPCBcTcKNyJWeDrD3FW7U6QZbkTqa1h7OP8MKCmHp761uhqPYm+xSGgOoYHW1mIFe1ecWm5ExHVWZcGCX8zjmUMgRLOjxAk2Gzx5LtgwuzhXZVldkcdwdElFWlmFdXxhxpTCjUhTKimD+5aaxzd1h8FnWFuPeLazYuDmHubx4yu171QdbfPRmVJ2jm4ptdyIiEvM/Qk2HYCWIfD4IKurEW/w0EBzbaQfsuHLnVZX4xF8dRq4nb1bam+h9+4OrnAj0lS2H4KZP5jHT50H0c0sLUe8RHwYjO9tHk/NgDIvXrzERXy9WyqqmblDOMDOQ5aW0mgUbkSagmHAA8uguAyGJML1Z1pdkXiTu86GiGDYeAA+2mJ1NW6t3KgMN77acgOVwW6rl3ZNKdyINIUPN8Py3yHEH54bojVtxLUiQ2BixabCz35vju2SGv1eAMeOmwvZJfrQbuAnOrOlef3bfmvraCwKNyKNbf9RmLzCPE4bAB18bKc+aRrj+0BsqLnmzdu/Wl2N29pc8WHeORr8ffgTsKvCjYg0yOMrYf8xc8fvCX2trka8VVgg3DfAPJ75IxR56UjRBtq8z7y2t1z4Kvu6oQo3IlJ/K3+HDzaZxzOHQpC/tfWId/tjD2gXDnlH4LV1VlfjluwtN119PdxU7NG7M9/spvM2CjcijeXYcbhvmXk8pickJ1hZjfiCIH94eKB5PGcNHDxmbT1uyN5S4estN7Gh5lCtcqNy3R9vonAj0lheXA3bDpm/RR5Nsboa8RXXngk9WkJBiRlwxKG0rPKD3Ndbbmy2yq6pzV7YNaVwI9IYfjtghhuAZ843p+mKNAU/G/z1HPP49Z8hu9DaetzIjkNQWm4OT2rTwupqrOeYMbXP2joag8KNiKuVG2Z3VGk5DGsHV3ayuiLxNRe3hwHxcPQ4zPjB6mrcRtUuKa3GUCXcHLC2jsagcCPiau9ugO/2QmgAPHuBfotK07PZ4LGK7T3e2eCdgyqcoMHE1dn/HTar5UZEapV7BJ741jx+eKBvrxIm1kppbbYclhkw7Xurq3ELmyo+xBVuTN0qZkz9fhjyvWzsucKNiCs98g3kF0OvVuaiaiJWeiQFbMCnW2FdrtXVWG59xT9Bz1hr63AXkSFwRsXfX7/kWVuLqynciLjKoh3wyRZzQOcLQyFAP15isbNi4Pqu5vHTGdbWYrF9RyCr0Mx6PVpZXY376FUR9NZ7WfbVb18RVygoNjfGBHMV4j7601DcxEMDIdAPlu2G5butrsYy9g/vjlHQPMjaWtyJPdz8qnAjIid54lvILjL3jXog2epqRCq1C4fRPc3jpzLMHep90C/qkqpRT7XciEiNVvwOb1VsVPjChdAswNp6RE6U1h9CA2FtLvx7m9XVWML+4d1L4aYa+7/HjkNwuNjSUlxK4UakIY6Uwl+WmsdjesLgNtbWI1KTVqFwZ5J5/Mx3cLzc0nKs8IvCTY2im1UuaPirFw0qVrgRaYgnvjV3nksIg8mDrK5G5NTu7AstQ8wtQd7dYHU1TerAUdhz2Dw+S4OJT2LvmvrFi7qmFG5EnLU0E+avN4//7yJooVGK4sZaBMFfBpjHz35vDoL3ET/nmNcdIqGFdkI5ib01a22OtXW4ksKNiDMOHYO7083jW3vBkLbW1iNSF2N7QqdIyDsKs360upom82OWeX12grV1uKt+Ff8uP+61tg5XUrgRccZDX5uzozpFwhR1R4mHCPKHp841j/+2zuyi8gE/VHxoD2htbR3uKike/G3mOkB7CqyuxjUUbkTq6+Mt8NEW87fBy8PMWSginmJYe7ionbmx65QVVlfT6ErLYG22edxf4aZGoYGV425+8JLWG4UbkfrYVQD3V8yOurc/nB1nbT0iznjqXHMF7cU74X+7rK6mUf2aB8eOm1sNdIqyuhr31d/eNZVlbR2uonAjUlclZTB+ERSUQP84uK+/1RWJOKdLFNzWyzx+bIXZvOGl7C0R/RPMnVGkZvZWrdVquRHxMU9+Cz/lQmQw/G04BPpbXZGI8+5PNqeG/3YQ5q2zuppGo/E2dWMPNxv3ecdEOoUbkbr473Z4teIDYE4qJIZbW49IQ0UEw5TB5vFz33vl4OJyo3IGUD+Fm1rFhkG7CDCAH/ZYXU3DKdyInE5mAdxVMe37jj4wooO19Yi4yv/rBkMS4VgZ3LfU6/ad2pAH+49CWCD00fC40zo30bz+xgv2V1W4EalNYQmM+gLyi83Bw49p2rd4EZsNZgyF0ABYuQfe8a6Vi7+uGCudcoY5C15qd14783q5F4wxV7gROZVyAyZ+Bb/ug1bN4I0R+g0p3qddODw80Dx+fCVkF1pbjwvZP6TPb2dtHZ5iUKK5wsW2g/C7h693o3AjcirPrYLPt0OQHyy4FM5oYXVFIo3j9j7QN9acCfjg117RPVVYUjmt+QKFmzqJCDYX9AP4JtPaWhpK4UakJp9sgZk/mMczhkKy1m0XL+bvBy9caK59898d8Pdfra6owTJ+Nzc/bxsB7SOtrsZzXOAlXVMKNyIn+j6rct+oO5Pgpu6WliPSJM6KgcdSzONHvzG7Yz3Ysp3m9fna9q1e7F1432RC8XFra2kIhRuRqn7ZB3/4Nxw9Dhe3h8kaQCw+5I4kGNYOistg/GKzb8cDlZXDoq3m8bCO1tbiafrEQVwYHC6BFR7cNaVwI2K3/RDc+Jk57mBgArw23GyuF/EVfjZzHaeEMNhyEB5ebnVFTvl+D+w7ao4hGZxodTWexc8Gl3Q2j7/Yam0tDaHf3CJgbod7w6eQd8Rsnn/3cm2IKb6pZTOYd7H5KbdwE7y/0eqK6u3zLeb18E5aSNwZl3Uxr7/cbu4644kUbkT2HIZrPoHMw9AhAv5xpfknn4ivGtQGHkw2j+9fCqs8ZzfFql1S9g9pqZ9+CdAq1NyGwVO7phRuxLdtOwiXf2QuPX9GC/jnVRAbanVVItb7S3+4rCOUlMPoL2CXZyx8snK3uqQayt8PLq0Ihh9vsrYWZynciO/6ZR9c8RH8fhg6R8J/roW22jNKBDC7peYOg54xZlq4saLb1s29u968vrqbuqQa4oYe5vWibbDf/d/2kyjciG9a8Ttc/THkHTV/eX92LbTRIn0i1YQFwnuXQ2IL2J4PN/3bnEbjpnKKYMl28/jmntbW4ul6xULvWHPMzT89b9iVwo34GMOAeWvh+k/N/aKSE+CTa8wOZhE5WUJz+PBKaBkC6/LMFhw3DTj/+BXKDOifAF1jrK7G8/2hl3n9/i+et2i1wo34jiOl8Ocl8NgK8zfgjV3NMTYaPCxSu05R8I+rIDIYfswxA86Bo1ZXVU3x8couqZt7WVuLt7jyTGgeBDsOwf92Wl1N/SjciG/4OQ8u+Sf86zdzZ7hnzoOXUqFZgNWViXiG3q3gX1dXBpxL/wU7862uyuHDDeaKDnFhlYNhpWHCguCPFUFxzirPar1RuBHvduw4PJ0BF/8DNuyHmGbmL+jxfcBms7o6Ec/SuxX8+1pzZuG2Q+YfDGtyrK6KkjJ4+Ufz+M/9IUR/s7jM+LPNf8+fsj1rM02FG/Fey3fD0A/gxdVmN9SVneHrm2BwG6srE/Fc3VrCf6+DXq3MWVRXfwyfWbuU7Qe/mMtVxYbBTRpI7FIxoZXdfDMyoNxDWm8UbsT7fLfX/IV73aew9ZC5bs2CS+CNEVrDRsQV4pvDZ9fAhW3NfdjGLYIHlpnHTSyvCJ7PMI8nDlCrTWO4o5859mZdjhkkPYHCjXiH4+Xw5Q5zb6grPoKVeyDID8b1gpV/gMs6WV2hiHdpHmRuU3LX2ebXC34xu3837m/SMp5ZYa6ke1YrDSRuLLFhcN855vH0lZ6x7o3NMDxpiFDDFRQUEBERQX5+PuHhWrDNoxmG2TLzj03wwSbILjJvD/CDP3Q3V1g9Q2vXiDS6ZZlw51fmIn8h/nBvf5jQt9GbUf67Fe74HGzAJyMhKb5RT+fTjpfDFR/Ahjy4qAO8foW5zmNTqs/nt8KNeJZDx+D7LEjPhP/tqr4kfMsQuLEb3NoL2kdYV6OIL8o7AnelQ/ou8+v2ETDtfEht1yin237Q/LAtLIHbz4ZHzmuU00gVv+bBNQuhuAweGgR3Dmja8yvc1ELhxkOUlkFmgTkjY8tBc/Gwtbmw44Spp4F+cN4ZcHMPGNEBgrTeuohlDAM+3gKTV0BORd/Fxe3NTTj7xLrsNDlFMPKf5vorA9vAe9eaDbbS+N7/BR5ON1vLXhwBV3VtunMr3NRC4caNGAbsLTQDzPZ8cxPLbYfMy64Cc4ZTTTpEwJC2cFFbc+ZT86AmLFpETutwCcxYBa+uq/w5HtYO7hsA/RrWd5RTCDd9ZP66OKMFfDTSXNtGmoZhwKNL4Z315pJhL46AK85smnMr3NRC4cYCxWWw/ZDZArPlIGw9CL8dNMfLHCk99fOaBUDHSOgYAT1bQd9YSIqFqJAmKlxEGmTrQZj1o7l4pn0OcXIC/LGHuTRDWGC9Xu6HvfDnz80esNbNYeH10FY90E2u3ID7l8C/KvacuisZ/jLQ3E28MSnc1ELhphEdOApbDlUGGHuY2VVw6sUR/G1m33zHCOgUaS7z3inSvMSHNf2INRFxve2HzPWm/rHZHJkK0DwQrukCl3aCc9vUOvi4oBhe+A4WrDN/lXRtaQ5oVbCxTlk5TF0Bb/xkft07FqZeCL3jGu+cHhdu5s6dy/PPP092djZ9+vRhzpw5JCcnn/LxH374IY899hg7d+6kS5cuPPvss1x66aV1OpfCTQMVFMOeQnM8zJaK1hd7mDlw7NTPaxEEXaKgcyScGW0ed4mCduEaJyPiK7KLYOEmeGdD9a0bQgPggkQY2hb6x0P3lhDgR14RvLkW3v4ZCir26rymG0wdam4NINb7aBNMWVr5/lzYHsYmweBE17fkeFS4WbhwIaNGjWLevHkMHDiQ2bNn8+GHH7J582ZiY08egPbtt99y/vnnM23aNC6//HLee+89nn32WdasWUPPnqdfmtLnw41hQEm5ucvc0eNwrMzcouDYcThyHA4eM0PKwSqXrCJz+c89haffDfiMFmaAsYeXLlHQOQriQrXdgYiYDAO+3QOfbIXFO8zfMVWFBkBSLD9FtuSTski2RUdh6xjB7ZeGcd6ZWqXP3eQWmesNfbq5spG+UxQs+aNrA45HhZuBAwcyYMAAXnrpJQDKy8tJTEzkrrvu4uGHHz7p8SNHjqSoqIj//Oc/jtvOOecckpKSmDdv3mnP12jh5sBRc+G4cswfXMMw32WD6tf24xPvL7c/p+K+snIzhBwvNzdOKa24Pl5xe2mV20rLKy5l1e+z328PL8VlZqBp6DseGVwRYqKgS6QZYOzdSfXsQxcRH2cYsH4fLNlpri6+Oqf2P6Iigs0u66gQaBFoTihoUXEJCzSnTQX6Vb+2H/vZzIsN84+tPrHmBAVxiR0HYf5aM+SkdoRZF7v29evz+W1pBC4pKWH16tVMmjTJcZufnx+pqalkZGTU+JyMjAzS0tKq3TZ8+HA++eSTGh9fXFxMcXGx4+v8fLMptKCgoMbHO21dNoz51LWv2dhsmIN2gwMg2B+aBUJUEESGmAHGfh0bBglh0DrMXHb9VLOTyo5CwdEm/RZExAu0D4bxXc1LuWFOhVqbZ86c3JFvLmqz+7D5B9rBo3DwkGvOO/U8+EMP17yW0NIfHugHd/WBohJw9ces/XO7Lm0yloabffv2UVZWRlxc9RFIcXFxbNq0qcbnZGdn1/j47OzsGh8/bdo0nnjiiZNuT0xMdLJqERHxCn+uuIhHOXz4MBERtbe4eX3n5aRJk6q19JSXl3PgwAFatmyJzUVjQAoKCkhMTGT37t2+OY7Hjem9cU96X9yT3hf3pffGbLE5fPgwrVu3Pu1jLQ03MTEx+Pv7k5OTU+32nJwc4uNrXugpPj6+Xo8PDg4mODi42m2RkZHOF12L8PBwn/1P5+703rgnvS/uSe+L+/L19+Z0LTZ2li5YHRQURL9+/UhPT3fcVl5eTnp6OikpKTU+JyUlpdrjAZYsWXLKx4uIiIhvsbxbKi0tjdGjR9O/f3+Sk5OZPXs2RUVFjB07FoBRo0bRpk0bpk2bBsA999zDBRdcwMyZM7nsssv44IMP+PHHH/nb3/5m5bchIiIibsLycDNy5Ejy8vKYPHky2dnZJCUlsWjRIseg4czMTPz8KhuYBg0axHvvvcejjz7KX//6V7p06cInn3xSpzVuGktwcDBTpkw5qftLrKf3xj3pfXFPel/cl96b+rF8nRsRERERV9Im8SIiIuJVFG5ERETEqyjciIiIiFdRuBERERGvonDTADt37mTcuHF06NCBZs2a0alTJ6ZMmUJJSfVN337++WfOO+88QkJCSExM5LnnnrOoYt8xdepUBg0aRGho6CkXbbTZbCddPvjgg6Yt1AfV5b3JzMzksssuIzQ0lNjYWB544AGOHz/etIUK7du3P+lnZPr06VaX5XPmzp1L+/btCQkJYeDAgaxatcrqktye5VPBPdmmTZsoLy/n1VdfpXPnzvzyyy+MHz+eoqIiZsyYAZhLZl988cWkpqYyb9481q9fz6233kpkZCS33367xd+B9yopKeGGG24gJSWFN95445SPe/PNNxkxYoTj68ZavVoqne69KSsr47LLLiM+Pp5vv/2WrKwsRo0aRWBgIM8884wFFfu2J598kvHjxzu+btGihYXV+J6FCxeSlpbGvHnzGDhwILNnz2b48OFs3ryZ2NhYq8tzX4a41HPPPWd06NDB8fXLL79sREVFGcXFxY7bHnroIaNr165WlOdz3nzzTSMiIqLG+wDj448/btJ6pNKp3psvvvjC8PPzM7Kzsx23vfLKK0Z4eHi1nyNpfO3atTNeeOEFq8vwacnJycaECRMcX5eVlRmtW7c2pk2bZmFV7k/dUi6Wn59PdHS04+uMjAzOP/98goKCHLfZU/fBgwetKFGqmDBhAjExMSQnJzN//nwMLftkuYyMDHr16uVYyBPMn5mCggJ+/fVXCyvzTdOnT6dly5b07duX559/Xt2DTaikpITVq1eTmprquM3Pz4/U1FQyMjIsrMz9qVvKhbZu3cqcOXMcXVIA2dnZdOjQodrj7L+0s7OziYqKatIapdKTTz7JhRdeSGhoKF9++SV33nknhYWF3H333VaX5tOys7OrBRuo/jMjTefuu+/m7LPPJjo6mm+//ZZJkyaRlZXFrFmzrC7NJ+zbt4+ysrIafx42bdpkUVWeQS03NXj44YdrHGxa9XLif6w9e/YwYsQIbrjhhmr90+I6zrwvtXnssccYPHgwffv25aGHHuLBBx/k+eefb8TvwHu5+r2RxlOf9yotLY0hQ4bQu3dv7rjjDmbOnMmcOXMoLi62+LsQqZ1abmpw3333MWbMmFof07FjR8fx3r17GTp0KIMGDTppA8/4+HhycnKq3Wb/Oj4+3jUF+4j6vi/1NXDgQJ566imKi4u1f0s9ufK9iY+PP2k2iH5mXKch79XAgQM5fvw4O3fupGvXro1QnVQVExODv79/jZ8h+lmoncJNDVq1akWrVq3q9Ng9e/YwdOhQ+vXrx5tvvlltk0+AlJQUHnnkEUpLSwkMDARgyZIldO3aVV1S9VSf98UZa9euJSoqSsHGCa58b1JSUpg6dSq5ubmO2SBLliwhPDycHj16uOQcvqwh79XatWvx8/PTLJ0mEhQURL9+/UhPT+fqq68GoLy8nPT0dCZOnGhtcW5O4aYB9uzZw5AhQ2jXrh0zZswgLy/PcZ89Vf/hD3/giSeeYNy4cTz00EP88ssvvPjii7zwwgtWle0TMjMzOXDgAJmZmZSVlbF27VoAOnfuTPPmzfn3v/9NTk4O55xzDiEhISxZsoRnnnmG+++/39rCfcDp3puLL76YHj16cMstt/Dcc8+RnZ3No48+yoQJExQ8m1BGRgbff/89Q4cOpUWLFmRkZPCXv/yFP/7xj/rDrAmlpaUxevRo+vfvT3JyMrNnz6aoqIixY8daXZp7s3q6lid78803DaDGS1Xr1q0zzj33XCM4ONho06aNMX36dIsq9h2jR4+u8X1ZunSpYRiG8d///tdISkoymjdvboSFhRl9+vQx5s2bZ5SVlVlbuA843XtjGIaxc+dO45JLLjGaNWtmxMTEGPfdd59RWlpqXdE+aPXq1cbAgQONiIgIIyQkxOjevbvxzDPPGMeOHbO6NJ8zZ84co23btkZQUJCRnJxsfPfdd1aX5PZshqG5ryIiIuI9NFtKREREvIrCjYiIiHgVhRsRERHxKgo3IiIi4lUUbkRERMSrKNyIiIiIV1G4EREREa+icCMiIiJeReFGRNyGzWbjk08+AWDnzp3YbDbH9gzLli3DZrNx6NAhp177dK+3YMECIiMjG1S/s8aMGePYO0hEGk57S4lIjcaMGcOhQ4ccYaOpJSYmkpWVRUxMTKO8/qBBg8jKyiIiIqJRXl9ErKNwIyJuyd/f37EBbWMICgpq1NcXEeuoW0pE6u3rr78mOTmZ4OBgEhISePjhhzl+/Ljj/sOHD3PzzTcTFhZGQkICL7zwAkOGDOHee++t8zlO7EY60ZEjR7jkkksYPHiwo2vp9ddfp3v37oSEhNCtWzdefvnlU77+qbq5Fi9eTPfu3WnevDkjRowgKyvLcV95eTlPPvkkZ5xxBsHBwSQlJbFo0aJqz1+/fj0XXnghzZo1o2XLltx+++0UFhY67i8rKyMtLY3IyEhatmzJgw8+iLb4E3EthRsRqZc9e/Zw6aWXMmDAANatW8crr7zCG2+8wdNPP+14TFpaGitXruSzzz5jyZIlfPPNN6xZs8ZlNRw6dIhhw4ZRXl7OkiVLiIyM5N1332Xy5MlMnTqVjRs38swzz/DYY4/x97//vc6ve+TIEWbMmMHbb7/N8uXLyczM5P7773fc/+KLLzJz5kxmzJjBzz//zPDhw7nyyivZsmULAEVFRQwfPpyoqCh++OEHPvzwQ7766ismTpzoeI2ZM2eyYMEC5s+fz4oVKzhw4AAff/yxy/5tRASweFdyEXFTo0ePNq666qqTbv/rX/9qdO3a1SgvL3fcNnfuXKN58+ZGWVmZUVBQYAQGBhoffvih4/5Dhw4ZoaGhxj333FPrOQHj448/NgzDMHbs2GEAxk8//WQYhmEsXbrUAIyNGzcavXv3Nq677jqjuLjY8dxOnToZ7733XrXXe+qpp4yUlJRaX+/gwYOGYRjGm2++aQDG1q1bq31fcXFxjq9bt25tTJ06tdo5BgwYYNx5552GYRjG3/72NyMqKsooLCx03P/5558bfn5+RnZ2tmEYhpGQkGA899xzjvtLS0uNM844o8Z/axFxjlpuRKReNm7cSEpKCjabzXHb4MGDKSws5Pfff2f79u2UlpaSnJzsuD8iIoKuXbs6vn7mmWdo3ry545KZmVnn8w8bNozOnTuzcOFCgoKCALPFZNu2bYwbN67a6z799NNs27atzq8dGhpKp06dHF8nJCSQm5sLQEFBAXv37mXw4MHVnjN48GA2btzo+Lfp06cPYWFh1e4vLy9n8+bN5Ofnk5WVxcCBAx33BwQE0L9//zrXKCKnpwHFItLk7rjjDm688UbH161bt67zcy+77DL+9a9/sWHDBnr16gXgGNPy2muvVQsOYA5MrqvAwMBqX9tsNo2HEfFAarkRkXrp3r07GRkZ1T70V65cSYsWLTjjjDPo2LEjgYGB/PDDD4778/Pz+e233xxfR0dH07lzZ8clIKDuf2dNnz6d0aNHc9FFF7FhwwYA4uLiaN26Ndu3b6/2up07d6ZDhw4u+K4hPDyc1q1bs3Llymq3r1y5kh49egDmv826desoKiqqdr+fnx9du3YlIiKChIQEvv/+e8f9x48fZ/Xq1S6pUURMarkRkVPKz88/abbS7bffzuzZs7nrrruYOHEimzdvZsqUKaSlpeHn50eLFi0YPXo0DzzwANHR0cTGxjJlyhT8/PyqdWU1xIwZMygrK+PCCy9k2bJldOvWjSeeeIK7776biIgIRowYQXFxMT/++CMHDx4kLS3NJed94IEHmDJlCp06dSIpKYk333yTtWvX8u677wJw8803M2XKFEaPHs3jjz9OXl4ed911F7fccgtxcXEA3HPPPUyfPp0uXbrQrVs3Zs2a5fTChCJSM4UbETmlZcuW0bdv32q3jRs3ji+++IIHHniAPn36EB0dzbhx43j00Ucdj5k1axZ33HEHl19+OeHh4Tz44IPs3r2bkJAQl9X2wgsvVAs4t912G6GhoTz//PM88MADhIWF0atXr3pNPz+du+++m/z8fO677z5yc3Pp0aMHn332GV26dAHMMTuLFy/mnnvuYcCAAYSGhnLdddcxa9Ysx2vcd999ZGVlMXr0aPz8/Lj11lu55ppryM/Pd1mdIr7OZqhDWUQaWVFREW3atGHmzJmMGzfO6nJExMup5UZEXO6nn35i06ZNJCcnk5+fz5NPPgnAVVddZXFlIuILFG5EpFHMmDGDzZs3ExQURL9+/fjmm28abZ8oEZGq1C0lIiIiXkVTwUVERMSrKNyIiIiIV1G4EREREa+icCMiIiJeReFGREREvIrCjYiIiHgVhRsRERHxKgo3IiIi4lX+P5fu60YIA10lAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1063,8 +1139,8 @@
     }
    ],
    "source": [
-    "sns.kdeplot(in_likelihoods, color=\"dodgerblue\", bw_adjust=50, label=\"In-distribution\")\n",
-    "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=1, label=\"OOD\")\n",
+    "sns.kdeplot(in_likelihoods, color=\"dodgerblue\", bw_adjust=1, label=\"In-distribution\")\n",
+    "sns.kdeplot(ood_likelihoods, color=\"deeppink\", bw_adjust=40, label=\"OOD\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"Log-likelihood\")"
    ]
@@ -1097,7 +1173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
index a436cab9..ea85475b 100644
--- a/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
+++ b/tutorials/generative/anomaly_detection/anomaly_detection_with_transformers.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.14.4
+#       jupytext_version: 1.14.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -165,7 +165,7 @@
 
 # %% [markdown]
 # ### VQ-VAE Model training
-# We will train our VQ-VAE for 50 epochs.
+# We will train our VQ-VAE for 30 epochs.
 
 # %%
 n_epochs = 30
@@ -313,11 +313,11 @@
 
 # %% [markdown]
 # ### Transformer Training
-# We will train the Transformer for 5 epochs.
+# We will train the Transformer for 20 epochs.
 
 # %%
-n_epochs = 5
-val_interval = 2
+n_epochs = 20
+val_interval = 5
 epoch_losses = []
 val_epoch_losses = []
 vqvae_model.eval()
@@ -337,7 +337,8 @@
         logits, quantizations_target, _ = inferer(images, vqvae_model, transformer_model, ordering, return_latent=True)
         logits = logits.transpose(1, 2)
 
-        loss = ce_loss(logits, quantizations_target)
+        # train the transformer to predict token n+1 using tokens 0-n
+        loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])
 
         loss.backward()
         optimizer.step()
@@ -360,10 +361,22 @@
                 )
                 logits = logits.transpose(1, 2)
 
-                loss = ce_loss(logits, quantizations_target)
+                loss = ce_loss(logits[:, :, :-1], quantizations_target[:, 1:])
 
                 val_loss += loss.item()
-
+        # get sample
+        sample = inferer.sample(
+            vqvae_model=vqvae_model,
+            transformer_model=transformer_model,
+            ordering=ordering,
+            latent_spatial_dim=(spatial_shape[0], spatial_shape[1]),
+            starting_tokens=vqvae_model.num_embeddings * torch.ones((1, 1), device=device),
+        )
+        plt.imshow(sample[0, 0, ...].cpu().detach())
+        plt.title(f"Sample epoch {epoch}")
+        plt.show()
+        val_loss /= val_step
+        val_epoch_losses.append(val_loss)
         val_loss /= val_step
         val_epoch_losses.append(val_loss)
 
@@ -424,13 +437,13 @@
 ood_likelihoods = np.concatenate(ood_likelihoods)
 
 # %% [markdown]
-# ## Log-likehood plot
+# ## Log-likelihood plot
 #
 # Here, we plot the log-likelihood of the images. In this case, the lower the log-likelihood, the more unlikely the image belongs to the training set.
 
 # %%
-sns.kdeplot(in_likelihoods, color="dodgerblue", bw_adjust=50, label="In-distribution")
-sns.kdeplot(ood_likelihoods, color="deeppink", bw_adjust=1, label="OOD")
+sns.kdeplot(in_likelihoods, color="dodgerblue", bw_adjust=1, label="In-distribution")
+sns.kdeplot(ood_likelihoods, color="deeppink", bw_adjust=10, label="OOD")
 plt.legend()
 plt.xlabel("Log-likelihood")
 

From 65f359347ae1866aaae152a148269d8fd42399e8 Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Sat, 18 Mar 2023 10:42:04 -0600
Subject: [PATCH 3/4] Adds deleted log

---
 generative/inferers/inferer.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/generative/inferers/inferer.py b/generative/inferers/inferer.py
index caa32034..bbea1879 100644
--- a/generative/inferers/inferer.py
+++ b/generative/inferers/inferer.py
@@ -601,6 +601,9 @@ def get_likelihood(
                 p = torch.gather(p, 1, latent[:, i + 1].unsqueeze(1))
                 probs = torch.cat((probs, p), dim=1)
 
+        # convert to log-likelihood
+        probs = torch.log(probs)
+
         # reshape
         probs = probs[:, ordering.get_revert_sequence_ordering()]
         probs_reshaped = probs.reshape((inputs.shape[0],) + latent_spatial_dim)

From db3d87c9b2900e7515abd1714f8adbb3d339ea1c Mon Sep 17 00:00:00 2001
From: Mark Graham <markgraham539@gmail.com>
Date: Sat, 18 Mar 2023 11:00:12 -0600
Subject: [PATCH 4/4] Explicitly sets latent target

---
 generative/inferers/inferer.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/generative/inferers/inferer.py b/generative/inferers/inferer.py
index bbea1879..eafefb5c 100644
--- a/generative/inferers/inferer.py
+++ b/generative/inferers/inferer.py
@@ -580,7 +580,9 @@ def get_likelihood(
         # get the first batch, up to max_seq_length, efficiently
         logits = transformer_model(x=latent[:, : transformer_model.max_seq_len], context=condition)
         probs = F.softmax(logits, dim=-1)
-        probs = torch.gather(probs, 2, latent[:, 1 : transformer_model.max_seq_len + 1].unsqueeze(2)).squeeze(2)
+        # target token for each set of logits is the next token along
+        target = latent[:, 1:]
+        probs = torch.gather(probs, 2, target[:, : transformer_model.max_seq_len].unsqueeze(2)).squeeze(2)
 
         # if we have not covered the full sequence we continue with inefficient looping
         if logits.shape[1] < latent.shape[1]:
@@ -598,7 +600,8 @@ def get_likelihood(
                 # apply softmax to convert logits to (normalized) probabilities
                 p = F.softmax(logits, dim=-1)
                 # select correct values and append
-                p = torch.gather(p, 1, latent[:, i + 1].unsqueeze(1))
+                p = torch.gather(p, 1, target[:, i].unsqueeze(1))
+
                 probs = torch.cat((probs, p), dim=1)
 
         # convert to log-likelihood