Cassandra Mussard commited on
Commit
baad382
·
1 Parent(s): 73ab90f

gradio api

Browse files
.ipynb_checkpoints/app-checkpoint.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "28e561c1-96da-4fc9-9261-16c4562b057b",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import gradio as gr\n",
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+ "import numpy as np\n",
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+ "import pandas as pd\n",
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+ "import matplotlib.pyplot as plt\n",
14
+ "import torch\n",
15
+ "from torch.utils.data import Dataset, DataLoader\n",
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+ "from transformers import AutoModel, AutoTokenizer"
17
+ ]
18
+ },
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+ {
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+ "cell_type": "code",
21
+ "execution_count": null,
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+ "id": "10ff0dd7-7cd1-4eb8-824e-56b3d640b271",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "from transformers import BlipForConditionalGeneration, AutoProcessor\n",
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+ "\n",
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+ "model = BlipForConditionalGeneration.from_pretrained(\"cassmussard/BLIP_airbnb\")\n",
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+ "processor = AutoProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")"
30
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "fd119e29-aaf4-4aec-af80-77b8e11fb82f",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "model.eval()\n",
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+ "def predict(image):\n",
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+ " inputs = processor(images=image, return_tensors=\"pt\")\n",
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+ " pixel_values = inputs[\"pixel_values\"]\n",
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+ " generated_ids = model.generate(pixel_values=pixel_values, max_length=50)\n",
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+ " generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
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+ " return generated_caption"
46
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "435fdb1e-167f-45dc-bbbb-b8d0e05becbb",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "iface = gr.Interface(fn=predict, \n",
56
+ " inputs=\"image\", \n",
57
+ " outputs='label',\n",
58
+ " live=True,\n",
59
+ " description=\"Draw a number on the sketchpad to see the model's prediction.\",\n",
60
+ " ).launch(debug=True, share=True);\n",
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+ "iface.launch(share=True)"
62
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "e4ffb76c-667e-4a4e-8fa5-bc3d12e61e83",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "venv",
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+ "language": "python",
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+ "name": "venv"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.10.12"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 5
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+ }
.ipynb_checkpoints/blip-checkpoint.ipynb ADDED
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1
+ {
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+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "source": [
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+ "## Image to text for Airbnb images"
7
+ ],
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+ "metadata": {
9
+ "id": "p5S2GYrJe6lb"
10
+ }
11
+ },
12
+ {
13
+ "cell_type": "code",
14
+ "source": [
15
+ "from google.colab import drive\n",
16
+ "drive.mount('/content/drive')"
17
+ ],
18
+ "metadata": {
19
+ "id": "T1pFJmQDr2JO",
20
+ "colab": {
21
+ "base_uri": "https://localhost:8080/"
22
+ },
23
+ "outputId": "61945e69-0ed1-4c16-bb18-39340bda6a3f"
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+ },
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+ "execution_count": 38,
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+ "outputs": [
27
+ {
28
+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
32
+ ]
33
+ }
34
+ ]
35
+ },
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+ {
37
+ "cell_type": "code",
38
+ "source": [
39
+ "!unzip /content/drive/MyDrive/image_to_text.zip"
40
+ ],
41
+ "metadata": {
42
+ "colab": {
43
+ "base_uri": "https://localhost:8080/"
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+ },
45
+ "id": "i4SU-NF57K2Y",
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+ "outputId": "773cdb9a-96f8-4e43-e6ba-25f80f46c30b"
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+ },
48
+ "execution_count": 39,
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+ "outputs": [
50
+ {
51
+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
54
+ "Archive: /content/drive/MyDrive/image_to_text.zip\n",
55
+ "replace Cassandra/living_room/1001147_20.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
56
+ ]
57
+ }
58
+ ]
59
+ },
60
+ {
61
+ "cell_type": "code",
62
+ "source": [
63
+ "import torch\n",
64
+ "import torch\n",
65
+ "from torch.utils.data import Dataset\n",
66
+ "from PIL import Image\n",
67
+ "import pandas as pd\n",
68
+ "from transformers import AutoProcessor\n",
69
+ "import numpy as np\n",
70
+ "from torchvision import transforms\n",
71
+ "from transformers import BlipForConditionalGeneration\n"
72
+ ],
73
+ "metadata": {
74
+ "id": "lG3i-iiWe7l_"
75
+ },
76
+ "execution_count": 15,
77
+ "outputs": []
78
+ },
79
+ {
80
+ "cell_type": "markdown",
81
+ "source": [
82
+ "### Create dataset with images and text and process them with BLIP's processor"
83
+ ],
84
+ "metadata": {
85
+ "id": "FpRt69nWfFFv"
86
+ }
87
+ },
88
+ {
89
+ "cell_type": "code",
90
+ "execution_count": 40,
91
+ "metadata": {
92
+ "id": "1i4BMba0ln91"
93
+ },
94
+ "outputs": [],
95
+ "source": [
96
+ "class Airbnb(Dataset):\n",
97
+ " def __init__(self, csv_file, data_augmentation):\n",
98
+ " self.df = pd.read_csv(csv_file)\n",
99
+ " self.processor = AutoProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
100
+ " def __len__(self):\n",
101
+ " return self.df.shape[0]\n",
102
+ "\n",
103
+ " def __getitem__(self, index):\n",
104
+ " path_to_im = \"/content/Cassandra/living_room/\" + str(self.df.listing_id_x[index])+ '_' + str(self.df.photo_number_x[index])\n",
105
+ " image = Image.open(path_to_im).convert(\"RGB\")\n",
106
+ " label = str(self.df.answers[index])\n",
107
+ " encoding = self.processor(images=image, text=label, padding=\"max_length\", return_tensors=\"pt\")\n",
108
+ " encoding = {k:v.squeeze() for k,v in encoding.items()}\n",
109
+ " return encoding"
110
+ ]
111
+ },
112
+ {
113
+ "cell_type": "markdown",
114
+ "source": [
115
+ "### Import CSV file"
116
+ ],
117
+ "metadata": {
118
+ "id": "e2sr84dsfXt7"
119
+ }
120
+ },
121
+ {
122
+ "cell_type": "code",
123
+ "execution_count": 43,
124
+ "metadata": {
125
+ "id": "Zl0asqIYpp4-"
126
+ },
127
+ "outputs": [],
128
+ "source": [
129
+ "csv_file = \"/content/drive/MyDrive/Picture_Descriptions_All-Copy.csv\""
130
+ ]
131
+ },
132
+ {
133
+ "cell_type": "code",
134
+ "execution_count": 44,
135
+ "metadata": {
136
+ "id": "8uUjuOj-qGsv"
137
+ },
138
+ "outputs": [],
139
+ "source": [
140
+ "dataset = Airbnb(csv_file, data_augmentation = None)"
141
+ ]
142
+ },
143
+ {
144
+ "cell_type": "markdown",
145
+ "source": [
146
+ "### Split train/test dataset"
147
+ ],
148
+ "metadata": {
149
+ "id": "0IK-kRFxfd3H"
150
+ }
151
+ },
152
+ {
153
+ "cell_type": "code",
154
+ "execution_count": 45,
155
+ "metadata": {
156
+ "id": "93wmNMwgqwgg"
157
+ },
158
+ "outputs": [],
159
+ "source": [
160
+ "train_size = int(0.8 * len(dataset))\n",
161
+ "test_size = len(dataset) - train_size\n",
162
+ "train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])"
163
+ ]
164
+ },
165
+ {
166
+ "cell_type": "markdown",
167
+ "source": [
168
+ "### Create dataloader"
169
+ ],
170
+ "metadata": {
171
+ "id": "3VWdqSeWfhAN"
172
+ }
173
+ },
174
+ {
175
+ "cell_type": "code",
176
+ "execution_count": 46,
177
+ "metadata": {
178
+ "id": "0pJdUuSTqy-5"
179
+ },
180
+ "outputs": [],
181
+ "source": [
182
+ "train_loader = torch.utils.data.DataLoader(\n",
183
+ " train_dataset,\n",
184
+ " batch_size=1,\n",
185
+ " shuffle=True\n",
186
+ " )\n",
187
+ "test_loader = torch.utils.data.DataLoader(\n",
188
+ " test_dataset,\n",
189
+ " batch_size=1,\n",
190
+ " shuffle=True\n",
191
+ " )"
192
+ ]
193
+ },
194
+ {
195
+ "cell_type": "markdown",
196
+ "source": [
197
+ "### Import model and create device"
198
+ ],
199
+ "metadata": {
200
+ "id": "mnwwxvB_fjlx"
201
+ }
202
+ },
203
+ {
204
+ "cell_type": "code",
205
+ "execution_count": 47,
206
+ "metadata": {
207
+ "id": "jY6h9kpgq0KX"
208
+ },
209
+ "outputs": [],
210
+ "source": [
211
+ "model = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\")"
212
+ ]
213
+ },
214
+ {
215
+ "cell_type": "code",
216
+ "source": [
217
+ "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
218
+ ],
219
+ "metadata": {
220
+ "id": "9rk60pCKfUkV"
221
+ },
222
+ "execution_count": 48,
223
+ "outputs": []
224
+ },
225
+ {
226
+ "cell_type": "markdown",
227
+ "source": [
228
+ "### Train loop"
229
+ ],
230
+ "metadata": {
231
+ "id": "HbiDQqzngCbn"
232
+ }
233
+ },
234
+ {
235
+ "cell_type": "code",
236
+ "execution_count": 49,
237
+ "metadata": {
238
+ "id": "i39jlG5Aq1Yo",
239
+ "colab": {
240
+ "base_uri": "https://localhost:8080/"
241
+ },
242
+ "outputId": "a5292b17-f2b9-4a38-db0a-3f97d4923aa4"
243
+ },
244
+ "outputs": [
245
+ {
246
+ "output_type": "stream",
247
+ "name": "stdout",
248
+ "text": [
249
+ "Epoch: 0\n",
250
+ "Average Loss for epoch 0: 1.2714\n",
251
+ "Accuracy for epoch 0: 0.55\n",
252
+ "Epoch: 1\n",
253
+ "Average Loss for epoch 1: 0.8113\n",
254
+ "Accuracy for epoch 1: 0.57\n",
255
+ "Epoch: 2\n",
256
+ "Average Loss for epoch 2: 0.6934\n",
257
+ "Accuracy for epoch 2: 0.57\n",
258
+ "Epoch: 3\n",
259
+ "Average Loss for epoch 3: 0.5895\n",
260
+ "Accuracy for epoch 3: 0.57\n",
261
+ "Epoch: 4\n",
262
+ "Average Loss for epoch 4: 0.4905\n",
263
+ "Accuracy for epoch 4: 0.57\n"
264
+ ]
265
+ }
266
+ ],
267
+ "source": [
268
+ "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)\n",
269
+ "model.to(device)\n",
270
+ "model.train()\n",
271
+ "for epoch in range(5):\n",
272
+ " print(\"Epoch:\", epoch)\n",
273
+ " total_loss = 0.0\n",
274
+ " total_correct = 0\n",
275
+ " total_examples = 0\n",
276
+ "\n",
277
+ " for idx, batch in enumerate(train_loader):\n",
278
+ " input_ids = batch.pop(\"input_ids\").to(device)\n",
279
+ " pixel_values = batch.pop(\"pixel_values\").to(device)\n",
280
+ " labels = input_ids\n",
281
+ "\n",
282
+ " outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)\n",
283
+ " loss = outputs.loss\n",
284
+ " total_loss += loss.item()\n",
285
+ "\n",
286
+ " predictions = torch.argmax(outputs.logits, dim=-1)\n",
287
+ " correct = (predictions == labels).sum().item()\n",
288
+ " total_correct += correct\n",
289
+ " total_examples += labels.numel()\n",
290
+ "\n",
291
+ " loss.backward()\n",
292
+ " optimizer.step()\n",
293
+ " optimizer.zero_grad()\n",
294
+ "\n",
295
+ " average_loss = total_loss / len(train_loader)\n",
296
+ " accuracy = total_correct / total_examples\n",
297
+ " print(f\"Average Loss for epoch {epoch}: {average_loss:.4f}\")\n",
298
+ " print(f\"Accuracy for epoch {epoch}: {accuracy:.2f}\")"
299
+ ]
300
+ },
301
+ {
302
+ "cell_type": "code",
303
+ "source": [
304
+ "model.save_pretrained(\"/content/drive/MyDrive/image_to_text\")"
305
+ ],
306
+ "metadata": {
307
+ "id": "XnO_kOtt820P"
308
+ },
309
+ "execution_count": 50,
310
+ "outputs": []
311
+ },
312
+ {
313
+ "cell_type": "code",
314
+ "source": [
315
+ "processor = AutoProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
316
+ "processor.save_pretrained(\"/content/drive/MyDrive/image_to_text\")"
317
+ ],
318
+ "metadata": {
319
+ "colab": {
320
+ "base_uri": "https://localhost:8080/"
321
+ },
322
+ "id": "L_JM-H3O82-K",
323
+ "outputId": "2e67dcc6-0939-4293-ee71-1e64cc29c762"
324
+ },
325
+ "execution_count": 51,
326
+ "outputs": [
327
+ {
328
+ "output_type": "execute_result",
329
+ "data": {
330
+ "text/plain": [
331
+ "[]"
332
+ ]
333
+ },
334
+ "metadata": {},
335
+ "execution_count": 51
336
+ }
337
+ ]
338
+ },
339
+ {
340
+ "cell_type": "code",
341
+ "source": [
342
+ "!cp -r /content/drive/MyDrive/image_to_text /content/drive/My\\ Drive/image_to_text_blip"
343
+ ],
344
+ "metadata": {
345
+ "id": "tzkbMoRb83AX"
346
+ },
347
+ "execution_count": 52,
348
+ "outputs": []
349
+ },
350
+ {
351
+ "cell_type": "markdown",
352
+ "source": [
353
+ "### Test loop"
354
+ ],
355
+ "metadata": {
356
+ "id": "Dc4j-hLrgE6r"
357
+ }
358
+ },
359
+ {
360
+ "cell_type": "code",
361
+ "source": [
362
+ "model.eval()\n",
363
+ "with torch.no_grad():\n",
364
+ " total_loss = 0.0\n",
365
+ " total_correct = 0\n",
366
+ " total_examples = 0\n",
367
+ "\n",
368
+ " for idx, batch in enumerate(test_loader):\n",
369
+ " input_ids = batch.pop(\"input_ids\").to(device)\n",
370
+ " pixel_values = batch.pop(\"pixel_values\").to(device)\n",
371
+ " labels = input_ids\n",
372
+ "\n",
373
+ " outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)\n",
374
+ " loss = outputs.loss\n",
375
+ " total_loss += loss.item()\n",
376
+ "\n",
377
+ " predictions = torch.argmax(outputs.logits, dim=-1)\n",
378
+ " correct = (predictions == labels).sum().item()\n",
379
+ " total_correct += correct\n",
380
+ " total_examples += labels.numel()\n",
381
+ "\n",
382
+ " average_loss = total_loss / len(test_loader)\n",
383
+ " accuracy = total_correct / total_examples\n",
384
+ " print(f\"Test Average Loss: {average_loss:.4f}\")\n",
385
+ " print(f\"Test Accuracy: {accuracy:.2f}\")"
386
+ ],
387
+ "metadata": {
388
+ "id": "sMEMW6MiO0sS"
389
+ },
390
+ "execution_count": null,
391
+ "outputs": []
392
+ },
393
+ {
394
+ "cell_type": "code",
395
+ "source": [
396
+ "torch.save(model.state_dict(), 'blip_weights.pth')"
397
+ ],
398
+ "metadata": {
399
+ "id": "NgLRsohzZdmH"
400
+ },
401
+ "execution_count": null,
402
+ "outputs": []
403
+ },
404
+ {
405
+ "cell_type": "code",
406
+ "source": [
407
+ "from google.colab import files\n",
408
+ "files.download(\"/content/image_to_text_airbnb.zip\")"
409
+ ],
410
+ "metadata": {
411
+ "id": "EzF0SGPfei44"
412
+ },
413
+ "execution_count": null,
414
+ "outputs": []
415
+ },
416
+ {
417
+ "cell_type": "code",
418
+ "source": [],
419
+ "metadata": {
420
+ "id": "qcKs5-3Jgz-M"
421
+ },
422
+ "execution_count": null,
423
+ "outputs": []
424
+ },
425
+ {
426
+ "cell_type": "code",
427
+ "source": [],
428
+ "metadata": {
429
+ "id": "ObYnoCzag0Aq"
430
+ },
431
+ "execution_count": null,
432
+ "outputs": []
433
+ },
434
+ {
435
+ "cell_type": "code",
436
+ "source": [],
437
+ "metadata": {
438
+ "id": "rY6u33avg0CM"
439
+ },
440
+ "execution_count": null,
441
+ "outputs": []
442
+ },
443
+ {
444
+ "cell_type": "code",
445
+ "source": [],
446
+ "metadata": {
447
+ "id": "8EZkrYFqg0E2"
448
+ },
449
+ "execution_count": null,
450
+ "outputs": []
451
+ },
452
+ {
453
+ "cell_type": "code",
454
+ "source": [
455
+ "pip install huggingface_hub"
456
+ ],
457
+ "metadata": {
458
+ "id": "qBmjfndHgzFj"
459
+ },
460
+ "execution_count": null,
461
+ "outputs": []
462
+ },
463
+ {
464
+ "cell_type": "code",
465
+ "source": [
466
+ "huggingface-cli login"
467
+ ],
468
+ "metadata": {
469
+ "id": "TaNz1XGVg0uU"
470
+ },
471
+ "execution_count": null,
472
+ "outputs": []
473
+ },
474
+ {
475
+ "cell_type": "markdown",
476
+ "source": [
477
+ "### Gradio webapp"
478
+ ],
479
+ "metadata": {
480
+ "id": "ISBzxw0Igout"
481
+ }
482
+ },
483
+ {
484
+ "cell_type": "code",
485
+ "source": [
486
+ "import gradio as gr\n",
487
+ "from gradio.components import Label"
488
+ ],
489
+ "metadata": {
490
+ "colab": {
491
+ "base_uri": "https://localhost:8080/",
492
+ "height": 337
493
+ },
494
+ "id": "tHSnxN7AZw8a",
495
+ "outputId": "8fc49c5d-de24-4a57-e86d-2e63010b382d"
496
+ },
497
+ "execution_count": null,
498
+ "outputs": [
499
+ {
500
+ "output_type": "error",
501
+ "ename": "ModuleNotFoundError",
502
+ "evalue": "No module named 'gradio'",
503
+ "traceback": [
504
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
505
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
506
+ "\u001b[0;32m<ipython-input-38-c71c84f2e5e0>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgradio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgradio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomponents\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
507
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gradio'",
508
+ "",
509
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
510
+ ],
511
+ "errorDetails": {
512
+ "actions": [
513
+ {
514
+ "action": "open_url",
515
+ "actionText": "Open Examples",
516
+ "url": "/notebooks/snippets/importing_libraries.ipynb"
517
+ }
518
+ ]
519
+ }
520
+ }
521
+ ]
522
+ },
523
+ {
524
+ "cell_type": "code",
525
+ "source": [
526
+ "model.eval() # Mettez votre modèle en mode évaluation\n",
527
+ "\n",
528
+ "# Fonction d'inférence pour Gradio\n",
529
+ "def predict(image):\n",
530
+ " processor = AutoProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
531
+ " inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
532
+ " pixel_values = inputs.pixel_values\n",
533
+ "\n",
534
+ " generated_ids = model.generate(pixel_values=pixel_values, max_length=50)\n",
535
+ " generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
536
+ "\n",
537
+ "# Création de l'interface Gradio\n",
538
+ "iface = gr.Interface(fn=predict,\n",
539
+ " inputs=gr.components.Textbox(placeholder=\"Enter your text here...\"),\n",
540
+ " outputs=gr.components.Label(num_top_classes=2))\n",
541
+ "iface.launch(share=True)"
542
+ ],
543
+ "metadata": {
544
+ "id": "eNDHwvGEad6n"
545
+ },
546
+ "execution_count": null,
547
+ "outputs": []
548
+ }
549
+ ],
550
+ "metadata": {
551
+ "accelerator": "GPU",
552
+ "colab": {
553
+ "gpuType": "T4",
554
+ "provenance": []
555
+ },
556
+ "kernelspec": {
557
+ "display_name": "Python 3",
558
+ "name": "python3"
559
+ },
560
+ "language_info": {
561
+ "name": "python"
562
+ }
563
+ },
564
+ "nbformat": 4,
565
+ "nbformat_minor": 0
566
+ }
app.ipynb ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": null,
6
+ "id": "28e561c1-96da-4fc9-9261-16c4562b057b",
7
+ "metadata": {},
8
+ "outputs": [],
9
+ "source": [
10
+ "import gradio as gr\n",
11
+ "import numpy as np\n",
12
+ "import pandas as pd\n",
13
+ "import matplotlib.pyplot as plt\n",
14
+ "import torch\n",
15
+ "from torch.utils.data import Dataset, DataLoader\n",
16
+ "from transformers import AutoModel, AutoTokenizer"
17
+ ]
18
+ },
19
+ {
20
+ "cell_type": "code",
21
+ "execution_count": null,
22
+ "id": "10ff0dd7-7cd1-4eb8-824e-56b3d640b271",
23
+ "metadata": {},
24
+ "outputs": [],
25
+ "source": [
26
+ "from transformers import BlipForConditionalGeneration, AutoProcessor\n",
27
+ "\n",
28
+ "model = BlipForConditionalGeneration.from_pretrained(\"cassmussard/BLIP_airbnb\")\n",
29
+ "processor = AutoProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")"
30
+ ]
31
+ },
32
+ {
33
+ "cell_type": "code",
34
+ "execution_count": null,
35
+ "id": "fd119e29-aaf4-4aec-af80-77b8e11fb82f",
36
+ "metadata": {},
37
+ "outputs": [],
38
+ "source": [
39
+ "model.eval()\n",
40
+ "def predict(image):\n",
41
+ " inputs = processor(images=image, return_tensors=\"pt\")\n",
42
+ " pixel_values = inputs[\"pixel_values\"]\n",
43
+ " generated_ids = model.generate(pixel_values=pixel_values, max_length=50)\n",
44
+ " generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
45
+ " return generated_caption"
46
+ ]
47
+ },
48
+ {
49
+ "cell_type": "code",
50
+ "execution_count": null,
51
+ "id": "435fdb1e-167f-45dc-bbbb-b8d0e05becbb",
52
+ "metadata": {},
53
+ "outputs": [],
54
+ "source": [
55
+ "iface = gr.Interface(fn=predict, \n",
56
+ " inputs=\"image\", \n",
57
+ " outputs='label',\n",
58
+ " live=True,\n",
59
+ " description=\"Draw a number on the sketchpad to see the model's prediction.\",\n",
60
+ " ).launch(debug=True, share=True);\n",
61
+ "iface.launch(share=True)"
62
+ ]
63
+ },
64
+ {
65
+ "cell_type": "code",
66
+ "execution_count": null,
67
+ "id": "e4ffb76c-667e-4a4e-8fa5-bc3d12e61e83",
68
+ "metadata": {},
69
+ "outputs": [],
70
+ "source": []
71
+ }
72
+ ],
73
+ "metadata": {
74
+ "kernelspec": {
75
+ "display_name": "venv",
76
+ "language": "python",
77
+ "name": "venv"
78
+ },
79
+ "language_info": {
80
+ "codemirror_mode": {
81
+ "name": "ipython",
82
+ "version": 3
83
+ },
84
+ "file_extension": ".py",
85
+ "mimetype": "text/x-python",
86
+ "name": "python",
87
+ "nbconvert_exporter": "python",
88
+ "pygments_lexer": "ipython3",
89
+ "version": "3.10.12"
90
+ }
91
+ },
92
+ "nbformat": 4,
93
+ "nbformat_minor": 5
94
+ }
tokenizer.json CHANGED
@@ -1212,6 +1212,7 @@
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  "�": 1067,
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  "�": 1068,
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  "�": 1069,
@@ -2145,6 +2146,937 @@
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  "": 1993,
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  "": 1994,
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  "^": 1995,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2148
  "the": 1996,
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  "of": 1997,
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  "and": 1998,
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  "whom": 3183,
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  "shook": 3184,
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  "movie": 3185,
 
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  "km�": 3186,
 
 
 
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  "secretary": 3187,
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  "prior": 3188,
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  "report": 3189,
@@ -6511,7 +7447,11 @@
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  "killer": 6359,
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  "gradually": 6360,
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  "filmed": 6361,
 
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  "�c": 6362,
 
 
 
6515
  "dollars": 6363,
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  "processing": 6364,
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  "94": 6365,
@@ -7179,7 +8119,11 @@
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  "retail": 7027,
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  "##ible": 7028,
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  "chile": 7029,
 
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  "m�": 7030,
 
 
 
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  "roberts": 7031,
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  "sixteen": 7032,
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  "##ich": 7033,
@@ -7886,7 +8830,11 @@
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  "dakota": 7734,
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  "norfolk": 7735,
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  "unsuccessful": 7736,
 
7889
  "##�": 7737,
 
 
 
7890
  "explosion": 7738,
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  "helicopter": 7739,
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  "congressional": 7740,
@@ -8306,7 +9254,11 @@
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  "logo": 8154,
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  "lean": 8155,
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  "##ration": 8156,
 
8309
  "�f": 8157,
 
 
 
8310
  "floors": 8158,
8311
  "##ven": 8159,
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  "architects": 8160,
@@ -8378,7 +9330,11 @@
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  "brad": 8226,
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  "louise": 8227,
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  "renowned": 8228,
 
8381
  "##�": 8229,
 
 
 
8382
  "liam": 8230,
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  "##ably": 8231,
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  "plaza": 8232,
@@ -10409,7 +11365,11 @@
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  "towel": 10257,
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  "##fl": 10258,
10411
  "##day": 10259,
 
10412
  "##0": 10260,
 
 
 
10413
  "wishing": 10261,
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  "assuming": 10262,
10415
  "interviewed": 10263,
@@ -10474,7 +11434,11 @@
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  "##bb": 10322,
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  "isabella": 10323,
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  "naming": 10324,
 
10477
  "##8": 10325,
 
 
 
10478
  "keen": 10326,
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  "bacteria": 10327,
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  "listing": 10328,
@@ -10850,7 +11814,11 @@
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  "##oni": 10698,
10851
  "cognitive": 10699,
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  "1834": 10700,
 
10853
  "##�": 10701,
 
 
 
10854
  "claws": 10702,
10855
  "nadu": 10703,
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  "roberto": 10704,
@@ -11771,7 +12739,11 @@
11771
  "complexity": 11619,
11772
  "rita": 11620,
11773
  "disputed": 11621,
 
11774
  "##�": 11622,
 
 
 
11775
  "pablo": 11623,
11776
  "##sch": 11624,
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  "marketed": 11625,
@@ -11871,7 +12843,11 @@
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  "diamonds": 11719,
11872
  "expressway": 11720,
11873
  "ga": 11721,
 
11874
  "##1": 11722,
 
 
 
11875
  "1821": 11723,
11876
  "uruguay": 11724,
11877
  "talents": 11725,
@@ -12020,7 +12996,11 @@
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  "tooth": 11868,
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  "standings": 11869,
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  "virtue": 11870,
 
12023
  "##�": 11871,
 
 
 
12024
  "##wara": 11872,
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  "##cting": 11873,
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  "chateau": 11874,
@@ -12893,7 +13873,11 @@
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  "sketches": 12741,
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  "##sive": 12742,
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  "marriages": 12743,
 
12896
  "##z": 12744,
 
 
 
12897
  "folding": 12745,
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  "peers": 12746,
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  "slovak": 12747,
@@ -13863,7 +14847,11 @@
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  "complement": 13711,
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  "suppressed": 13712,
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  "jewel": 13713,
 
13866
  "##�": 13714,
 
 
 
13867
  "floated": 13715,
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  "##kas": 13716,
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  "continuity": 13717,
@@ -14299,14 +15287,22 @@
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  "##ying": 14147,
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  "checks": 14148,
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  "##combe": 14149,
 
14302
  "##>": 14150,
 
 
 
14303
  "##fly": 14151,
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  "successes": 14152,
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  "unexpectedly": 14153,
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  "blu": 14154,
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  "assessed": 14155,
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  "##flower": 14156,
 
14309
  "##G": 14157,
 
 
 
14310
  "observing": 14158,
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  "sacked": 14159,
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  "spiders": 14160,
@@ -14390,7 +15386,11 @@
14390
  "##haw": 14238,
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  "##icus": 14239,
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  "guardians": 14240,
 
14393
  "m�": 14241,
 
 
 
14394
  "roared": 14242,
14395
  "habits": 14243,
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  "##wise": 14244,
@@ -14647,7 +15647,11 @@
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  "nonprofit": 14495,
14648
  "pits": 14496,
14649
  "monaco": 14497,
 
14650
  "##J": 14498,
 
 
 
14651
  "##usion": 14499,
14652
  "prominently": 14500,
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  "dispatched": 14501,
@@ -14683,7 +15687,11 @@
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  "abundance": 14531,
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  "wrath": 14532,
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  "laundry": 14533,
 
14686
  "�1": 14534,
 
 
 
14687
  "garde": 14535,
14688
  "##rp": 14536,
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  "jeanne": 14537,
@@ -14757,7 +15765,11 @@
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  "renault": 14605,
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  "##tics": 14606,
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  "ropes": 14607,
 
14760
  "##�": 14608,
 
 
 
14761
  "presumed": 14609,
14762
  "rewarded": 14610,
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  "infrared": 14611,
@@ -15439,14 +16451,22 @@
15439
  "nude": 15287,
15440
  "cracks": 15288,
15441
  "fungi": 15289,
 
15442
  "##5": 15290,
 
 
 
15443
  "limb": 15291,
15444
  "trousers": 15292,
15445
  "josie": 15293,
15446
  "shelby": 15294,
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  "tens": 15295,
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  "frederic": 15296,
 
15449
  "##��": 15297,
 
 
 
15450
  "definite": 15298,
15451
  "smoothly": 15299,
15452
  "constellation": 15300,
@@ -15543,7 +16563,11 @@
15543
  "oppose": 15391,
15544
  "ankles": 15392,
15545
  "typhoon": 15393,
 
15546
  "##/": 15394,
 
 
 
15547
  "##ache": 15395,
15548
  "##asia": 15396,
15549
  "linguistics": 15397,
@@ -15563,7 +16587,11 @@
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  "attracting": 15411,
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  "stunt": 15412,
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  "tuition": 15413,
 
15566
  "##88": 15414,
 
 
 
15567
  "vegetable": 15415,
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  "##mates": 15416,
15569
  "##quent": 15417,
@@ -16064,7 +17092,11 @@
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  "clown": 15912,
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  "glove": 15913,
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  "1860s": 15914,
 
16067
  "##F": 15915,
 
 
 
16068
  "##ug": 15916,
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  "archibald": 15917,
16070
  "focal": 15918,
@@ -16326,7 +17358,11 @@
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  "bodyguard": 16174,
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  "electro": 16175,
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  "brighter": 16176,
 
16329
  "##�": 16177,
 
 
 
16330
  "bihar": 16178,
16331
  "##chev": 16179,
16332
  "lasts": 16180,
@@ -16347,7 +17383,11 @@
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  "strap": 16195,
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  "campaigned": 16196,
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  "railroads": 16197,
 
16350
  "##>28G": 16198,
 
 
 
16351
  "emblem": 16199,
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  "##dre": 16200,
16353
  "stormed": 16201,
@@ -16564,7 +17604,11 @@
16564
  "responds": 16412,
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  "proclamation": 16413,
16566
  "##inae": 16414,
 
16567
  "##�": 16415,
 
 
 
16568
  "##rea": 16416,
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  "ein": 16417,
16570
  "pleading": 16418,
@@ -17005,7 +18049,11 @@
17005
  "magnet": 16853,
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  "playable": 16854,
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  "xvi": 16855,
 
17008
  "##@": 16856,
 
 
 
17009
  "overthrow": 16857,
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  "tobias": 16858,
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  "knob": 16859,
@@ -17153,7 +18201,11 @@
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  "sgt": 17001,
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  "ling": 17002,
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  "exceeding": 17003,
 
17156
  "##�": 17004,
 
 
 
17157
  "admiration": 17005,
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  "supermarket": 17006,
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  "##ark": 17007,
@@ -17259,7 +18311,11 @@
17259
  "morse": 17107,
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  "motives": 17108,
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  "creepy": 17109,
 
17262
  "##�": 17110,
 
 
 
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  "soo": 17111,
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  "##sz": 17112,
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  "bargain": 17113,
@@ -17298,7 +18354,11 @@
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  "proposes": 17146,
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  "sanctions": 17147,
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17301
  "##1": 17149,
 
 
 
17302
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17304
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@@ -17581,7 +18641,11 @@
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17582
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17583
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17584
  "##O": 17432,
 
 
 
17585
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17586
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17587
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17650
  "punjabi": 17498,
 
17651
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17652
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17653
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17654
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17964
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17965
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17966
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17967
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17968
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17969
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18257
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18258
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18259
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18260
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18261
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18262
  "willingly": 18110,
@@ -18348,7 +19424,11 @@
18348
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18349
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18350
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18351
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18352
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18353
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18354
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@@ -18660,7 +19740,11 @@
18660
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18661
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18662
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18663
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18664
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18665
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18666
  "##rative": 18514,
@@ -18877,7 +19961,11 @@
18877
  "intercontinental": 18725,
18878
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18879
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18880
  "�D�": 18728,
 
 
 
18881
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18882
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18883
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@@ -18967,7 +20055,11 @@
18967
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18968
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18969
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18970
  "##B": 18818,
 
 
 
18971
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18972
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18973
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@@ -19096,7 +20188,11 @@
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19097
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19098
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19099
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19100
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19101
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19102
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@@ -19258,8 +20354,13 @@
19258
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19259
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19260
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19261
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19262
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19263
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19264
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19265
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@@ -19408,7 +20509,11 @@
19408
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19409
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19410
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19411
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19412
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19413
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19414
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@@ -19459,7 +20564,11 @@
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19460
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19461
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19462
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19463
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19464
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19465
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@@ -19582,7 +20691,11 @@
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19583
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19584
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19585
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19586
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19587
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19588
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@@ -19728,7 +20841,11 @@
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19729
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19730
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19731
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19732
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19733
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19734
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@@ -19853,7 +20970,11 @@
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19854
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19855
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19856
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19859
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20016
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20017
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20019
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20020
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@@ -20339,7 +21464,11 @@
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20341
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20342
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20344
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20345
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22004
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22005
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22006
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22007
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@@ -22081,7 +23214,11 @@
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22082
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22083
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22084
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22085
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22086
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22087
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@@ -22341,7 +23478,11 @@
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22343
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22344
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22345
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22346
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22347
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@@ -22692,7 +23833,11 @@
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22693
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22694
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22695
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22696
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22697
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22698
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@@ -22795,7 +23940,11 @@
22795
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22796
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22797
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22798
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22799
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22800
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22801
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@@ -23068,7 +24217,11 @@
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23069
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23070
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23071
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23072
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23074
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@@ -23121,7 +24274,11 @@
23121
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23122
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23123
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23124
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23125
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23126
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23127
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@@ -23454,7 +24611,11 @@
23454
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23455
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23456
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23457
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23458
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23459
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23460
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@@ -23581,7 +24742,11 @@
23581
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23582
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23583
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23584
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23585
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23586
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23587
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@@ -23632,7 +24797,11 @@
23632
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23633
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23634
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23635
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23636
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23637
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23638
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@@ -23822,7 +24991,11 @@
23822
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23823
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23824
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23825
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23826
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23827
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23828
  "maternity": 23676,
@@ -23891,7 +25064,11 @@
23891
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23892
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23893
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23894
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23895
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23896
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23897
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@@ -24074,7 +25251,11 @@
24074
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24075
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24076
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24077
  "##:": 23925,
 
 
 
24078
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24079
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24080
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@@ -24251,7 +25432,11 @@
24251
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24252
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24253
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24254
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24255
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24256
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24257
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@@ -24973,16 +26158,27 @@
24973
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24974
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24975
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24976
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24977
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24978
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24979
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24980
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24981
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24982
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24983
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24984
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24985
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24986
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24987
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24988
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@@ -25116,7 +26312,11 @@
25116
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25117
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25118
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25119
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25120
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25121
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25122
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@@ -25309,7 +26509,11 @@
25309
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25310
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25311
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25312
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25313
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25314
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25315
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@@ -25678,7 +26882,11 @@
25678
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25679
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25680
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25681
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25682
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25683
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25684
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@@ -25722,7 +26930,11 @@
25722
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25723
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25724
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25725
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25726
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25727
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25728
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@@ -25948,7 +27160,11 @@
25948
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25949
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25950
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25951
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25952
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25953
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25954
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@@ -26282,7 +27498,11 @@
26282
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26284
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26285
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26286
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26287
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26288
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@@ -26455,7 +27675,11 @@
26455
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26456
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26457
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26458
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26459
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26460
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26461
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@@ -26593,7 +27817,11 @@
26593
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26594
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26595
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26596
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26597
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26598
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26599
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@@ -26938,7 +28166,11 @@
26938
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26939
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26940
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26941
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26942
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26943
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26944
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@@ -26961,7 +28193,11 @@
26961
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26962
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26963
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26964
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26965
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26967
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@@ -27541,7 +28777,11 @@
27541
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27544
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27545
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@@ -27581,7 +28821,11 @@
27581
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27584
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27585
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@@ -27837,7 +29081,11 @@
27837
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27840
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27841
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@@ -27857,7 +29105,11 @@
27857
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27860
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27861
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@@ -27956,13 +29208,21 @@
27956
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27959
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27960
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27961
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27962
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27963
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27964
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27965
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27966
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27967
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27968
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@@ -28053,7 +29313,11 @@
28053
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28054
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28055
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28056
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28057
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28058
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28059
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@@ -28093,7 +29357,11 @@
28093
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28094
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28096
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28097
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28098
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28099
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@@ -28331,7 +29599,11 @@
28331
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28332
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28334
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28335
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@@ -28747,7 +30019,11 @@
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28750
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28751
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29147
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29148
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29150
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@@ -29262,7 +30542,11 @@
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29264
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29265
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29266
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29268
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@@ -29277,7 +30561,11 @@
29277
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29280
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29281
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@@ -29304,7 +30592,11 @@
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29307
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29308
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29426
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29427
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29428
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29430
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@@ -29585,7 +30881,11 @@
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29588
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29589
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@@ -29793,6 +31093,7 @@
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2912
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2913
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2914
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2915
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2916
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2917
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2918
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2920
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2921
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2923
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2926
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2927
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2931
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2932
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2933
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2934
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2935
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2936
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2937
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2938
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2941
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2942
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2943
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2944
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2945
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2946
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2947
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2952
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2953
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2954
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2956
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2957
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2958
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2959
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2960
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2961
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2962
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2963
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2964
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2965
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2966
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2967
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2968
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2969
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2970
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2971
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2972
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2973
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2974
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2975
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2976
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2977
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2978
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2979
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2980
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2981
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2982
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2983
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2985
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2987
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2988
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2989
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2990
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2991
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2992
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2993
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2994
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2995
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2996
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2997
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2998
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2999
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3000
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3001
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3002
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3003
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3004
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3005
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3006
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3007
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3008
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3009
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3010
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3011
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3012
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3013
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3014
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3015
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3016
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3017
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3018
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3019
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3020
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3021
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3022
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3023
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3024
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3025
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3026
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3027
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3028
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3029
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3030
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3031
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3032
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3033
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3034
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3035
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3036
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3037
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3038
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3039
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3040
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3041
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3042
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3043
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3044
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3045
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3046
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3047
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3048
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3049
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3050
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3051
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3052
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3053
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3054
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3055
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3056
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3057
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3058
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3059
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3060
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3061
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3062
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3063
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3064
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3065
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3066
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3067
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3068
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3069
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3070
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3071
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3072
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3073
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3074
+ ".": 1991,
3075
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3076
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3077
+ "?": 1994,
3078
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3079
+ >>>>>>> 1a2ef63 (Weights and notebook)
3080
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3081
  "of": 1997,
3082
  "and": 1998,
 
4267
  "whom": 3183,
4268
  "shook": 3184,
4269
  "movie": 3185,
4270
+ <<<<<<< HEAD
4271
  "km�": 3186,
4272
+ =======
4273
+ "km²": 3186,
4274
+ >>>>>>> 1a2ef63 (Weights and notebook)
4275
  "secretary": 3187,
4276
  "prior": 3188,
4277
  "report": 3189,
 
7447
  "killer": 6359,
7448
  "gradually": 6360,
7449
  "filmed": 6361,
7450
+ <<<<<<< HEAD
7451
  "�c": 6362,
7452
+ =======
7453
+ "°c": 6362,
7454
+ >>>>>>> 1a2ef63 (Weights and notebook)
7455
  "dollars": 6363,
7456
  "processing": 6364,
7457
  "94": 6365,
 
8119
  "retail": 7027,
8120
  "##ible": 7028,
8121
  "chile": 7029,
8122
+ <<<<<<< HEAD
8123
  "m�": 7030,
8124
+ =======
8125
+ "m²": 7030,
8126
+ >>>>>>> 1a2ef63 (Weights and notebook)
8127
  "roberts": 7031,
8128
  "sixteen": 7032,
8129
  "##ich": 7033,
 
8830
  "dakota": 7734,
8831
  "norfolk": 7735,
8832
  "unsuccessful": 7736,
8833
+ <<<<<<< HEAD
8834
  "##�": 7737,
8835
+ =======
8836
+ "##°": 7737,
8837
+ >>>>>>> 1a2ef63 (Weights and notebook)
8838
  "explosion": 7738,
8839
  "helicopter": 7739,
8840
  "congressional": 7740,
 
9254
  "logo": 8154,
9255
  "lean": 8155,
9256
  "##ration": 8156,
9257
+ <<<<<<< HEAD
9258
  "�f": 8157,
9259
+ =======
9260
+ "°f": 8157,
9261
+ >>>>>>> 1a2ef63 (Weights and notebook)
9262
  "floors": 8158,
9263
  "##ven": 8159,
9264
  "architects": 8160,
 
9330
  "brad": 8226,
9331
  "louise": 8227,
9332
  "renowned": 8228,
9333
+ <<<<<<< HEAD
9334
  "##�": 8229,
9335
+ =======
9336
+ "##₂": 8229,
9337
+ >>>>>>> 1a2ef63 (Weights and notebook)
9338
  "liam": 8230,
9339
  "##ably": 8231,
9340
  "plaza": 8232,
 
11365
  "towel": 10257,
11366
  "##fl": 10258,
11367
  "##day": 10259,
11368
+ <<<<<<< HEAD
11369
  "##0": 10260,
11370
+ =======
11371
+ "##а": 10260,
11372
+ >>>>>>> 1a2ef63 (Weights and notebook)
11373
  "wishing": 10261,
11374
  "assuming": 10262,
11375
  "interviewed": 10263,
 
11434
  "##bb": 10322,
11435
  "isabella": 10323,
11436
  "naming": 10324,
11437
+ <<<<<<< HEAD
11438
  "##8": 10325,
11439
+ =======
11440
+ "##и": 10325,
11441
+ >>>>>>> 1a2ef63 (Weights and notebook)
11442
  "keen": 10326,
11443
  "bacteria": 10327,
11444
  "listing": 10328,
 
11814
  "##oni": 10698,
11815
  "cognitive": 10699,
11816
  "1834": 10700,
11817
+ <<<<<<< HEAD
11818
  "##�": 10701,
11819
+ =======
11820
+ "##²": 10701,
11821
+ >>>>>>> 1a2ef63 (Weights and notebook)
11822
  "claws": 10702,
11823
  "nadu": 10703,
11824
  "roberto": 10704,
 
12739
  "complexity": 11619,
12740
  "rita": 11620,
12741
  "disputed": 11621,
12742
+ <<<<<<< HEAD
12743
  "##�": 11622,
12744
+ =======
12745
+ "##₃": 11622,
12746
+ >>>>>>> 1a2ef63 (Weights and notebook)
12747
  "pablo": 11623,
12748
  "##sch": 11624,
12749
  "marketed": 11625,
 
12843
  "diamonds": 11719,
12844
  "expressway": 11720,
12845
  "ga": 11721,
12846
+ <<<<<<< HEAD
12847
  "##1": 11722,
12848
+ =======
12849
+ "##ı": 11722,
12850
+ >>>>>>> 1a2ef63 (Weights and notebook)
12851
  "1821": 11723,
12852
  "uruguay": 11724,
12853
  "talents": 11725,
 
12996
  "tooth": 11868,
12997
  "standings": 11869,
12998
  "virtue": 11870,
12999
+ <<<<<<< HEAD
13000
  "##�": 11871,
13001
+ =======
13002
+ "##₁": 11871,
13003
+ >>>>>>> 1a2ef63 (Weights and notebook)
13004
  "##wara": 11872,
13005
  "##cting": 11873,
13006
  "chateau": 11874,
 
13873
  "sketches": 12741,
13874
  "##sive": 12742,
13875
  "marriages": 12743,
13876
+ <<<<<<< HEAD
13877
  "##z": 12744,
13878
+ =======
13879
+ "##⁺": 12744,
13880
+ >>>>>>> 1a2ef63 (Weights and notebook)
13881
  "folding": 12745,
13882
  "peers": 12746,
13883
  "slovak": 12747,
 
14847
  "complement": 13711,
14848
  "suppressed": 13712,
14849
  "jewel": 13713,
14850
+ <<<<<<< HEAD
14851
  "##�": 13714,
14852
+ =======
14853
+ "##½": 13714,
14854
+ >>>>>>> 1a2ef63 (Weights and notebook)
14855
  "floated": 13715,
14856
  "##kas": 13716,
14857
  "continuity": 13717,
 
15287
  "##ying": 14147,
15288
  "checks": 14148,
15289
  "##combe": 14149,
15290
+ <<<<<<< HEAD
15291
  "##>": 14150,
15292
+ =======
15293
+ "##о": 14150,
15294
+ >>>>>>> 1a2ef63 (Weights and notebook)
15295
  "##fly": 14151,
15296
  "successes": 14152,
15297
  "unexpectedly": 14153,
15298
  "blu": 14154,
15299
  "assessed": 14155,
15300
  "##flower": 14156,
15301
+ <<<<<<< HEAD
15302
  "##G": 14157,
15303
+ =======
15304
+ "##ه": 14157,
15305
+ >>>>>>> 1a2ef63 (Weights and notebook)
15306
  "observing": 14158,
15307
  "sacked": 14159,
15308
  "spiders": 14160,
 
15386
  "##haw": 14238,
15387
  "##icus": 14239,
15388
  "guardians": 14240,
15389
+ <<<<<<< HEAD
15390
  "m�": 14241,
15391
+ =======
15392
+ "m³": 14241,
15393
+ >>>>>>> 1a2ef63 (Weights and notebook)
15394
  "roared": 14242,
15395
  "habits": 14243,
15396
  "##wise": 14244,
 
15647
  "nonprofit": 14495,
15648
  "pits": 14496,
15649
  "monaco": 14497,
15650
+ <<<<<<< HEAD
15651
  "##J": 14498,
15652
+ =======
15653
+ "##ي": 14498,
15654
+ >>>>>>> 1a2ef63 (Weights and notebook)
15655
  "##usion": 14499,
15656
  "prominently": 14500,
15657
  "dispatched": 14501,
 
15687
  "abundance": 14531,
15688
  "wrath": 14532,
15689
  "laundry": 14533,
15690
+ <<<<<<< HEAD
15691
  "�1": 14534,
15692
+ =======
15693
+ "£1": 14534,
15694
+ >>>>>>> 1a2ef63 (Weights and notebook)
15695
  "garde": 14535,
15696
  "##rp": 14536,
15697
  "jeanne": 14537,
 
15765
  "renault": 14605,
15766
  "##tics": 14606,
15767
  "ropes": 14607,
15768
+ <<<<<<< HEAD
15769
  "##�": 14608,
15770
+ =======
15771
+ "##α": 14608,
15772
+ >>>>>>> 1a2ef63 (Weights and notebook)
15773
  "presumed": 14609,
15774
  "rewarded": 14610,
15775
  "infrared": 14611,
 
16451
  "nude": 15287,
16452
  "cracks": 15288,
16453
  "fungi": 15289,
16454
+ <<<<<<< HEAD
16455
  "##5": 15290,
16456
+ =======
16457
+ "##е": 15290,
16458
+ >>>>>>> 1a2ef63 (Weights and notebook)
16459
  "limb": 15291,
16460
  "trousers": 15292,
16461
  "josie": 15293,
16462
  "shelby": 15294,
16463
  "tens": 15295,
16464
  "frederic": 15296,
16465
+ <<<<<<< HEAD
16466
  "##��": 15297,
16467
+ =======
16468
+ "##ος": 15297,
16469
+ >>>>>>> 1a2ef63 (Weights and notebook)
16470
  "definite": 15298,
16471
  "smoothly": 15299,
16472
  "constellation": 15300,
 
16563
  "oppose": 15391,
16564
  "ankles": 15392,
16565
  "typhoon": 15393,
16566
+ <<<<<<< HEAD
16567
  "##/": 15394,
16568
+ =======
16569
+ "##د": 15394,
16570
+ >>>>>>> 1a2ef63 (Weights and notebook)
16571
  "##ache": 15395,
16572
  "##asia": 15396,
16573
  "linguistics": 15397,
 
16587
  "attracting": 15411,
16588
  "stunt": 15412,
16589
  "tuition": 15413,
16590
+ <<<<<<< HEAD
16591
  "##88": 15414,
16592
+ =======
16593
+ "##ии": 15414,
16594
+ >>>>>>> 1a2ef63 (Weights and notebook)
16595
  "vegetable": 15415,
16596
  "##mates": 15416,
16597
  "##quent": 15417,
 
17092
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17096
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17097
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17098
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17099
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17100
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17101
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17102
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17358
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17362
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17364
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17365
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17366
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17368
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17383
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17384
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17385
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17386
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17387
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17388
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17389
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17390
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17391
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17393
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17604
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17605
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17608
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17609
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17610
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17611
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17612
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17613
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17614
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18049
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18050
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18053
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18054
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18055
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18056
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18057
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18058
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18059
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18201
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18202
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18204
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18205
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18206
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18207
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18208
+ >>>>>>> 1a2ef63 (Weights and notebook)
18209
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18210
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18211
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18311
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18312
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18313
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18314
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18315
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18316
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18317
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18318
+ >>>>>>> 1a2ef63 (Weights and notebook)
18319
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18320
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18321
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18354
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18358
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18359
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18360
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18361
+ >>>>>>> 1a2ef63 (Weights and notebook)
18362
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18364
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18641
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18642
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18643
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18644
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18645
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18646
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18647
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18648
+ >>>>>>> 1a2ef63 (Weights and notebook)
18649
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18650
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18651
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18712
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18713
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18714
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18715
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18716
  "##�e": 17499,
18717
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18718
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18719
+ >>>>>>> 1a2ef63 (Weights and notebook)
18720
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18721
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18722
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19031
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19032
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19033
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19034
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19035
  "Bodz": 17814,
19036
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19037
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19038
+ >>>>>>> 1a2ef63 (Weights and notebook)
19039
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19040
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19041
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19328
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19329
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19330
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19331
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19332
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19333
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19334
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19335
+ >>>>>>> 1a2ef63 (Weights and notebook)
19336
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19337
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19338
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19424
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19425
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19426
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19427
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19428
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19429
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19430
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19431
+ >>>>>>> 1a2ef63 (Weights and notebook)
19432
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19433
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19434
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19740
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19741
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19742
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19743
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19744
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19745
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19746
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19747
+ >>>>>>> 1a2ef63 (Weights and notebook)
19748
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19749
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19750
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19961
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19962
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19963
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19964
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19965
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19966
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19967
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19968
+ >>>>>>> 1a2ef63 (Weights and notebook)
19969
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19970
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19971
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20055
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20056
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20057
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20058
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20059
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20060
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20061
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20062
+ >>>>>>> 1a2ef63 (Weights and notebook)
20063
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20064
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20065
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20188
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20189
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20190
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20191
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20192
  "##=": 18947,
20193
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20194
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20195
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20196
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20197
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20198
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20354
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20355
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20356
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20357
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20358
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20359
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20360
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20361
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20362
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20363
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20364
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20365
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20366
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20509
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20510
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20511
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20512
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20513
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20514
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20515
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20516
+ >>>>>>> 1a2ef63 (Weights and notebook)
20517
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20518
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20519
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20564
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20565
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20566
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20567
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20568
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20569
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20570
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20571
+ >>>>>>> 1a2ef63 (Weights and notebook)
20572
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20573
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20574
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20691
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20692
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20693
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20694
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20695
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20696
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20697
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20698
+ >>>>>>> 1a2ef63 (Weights and notebook)
20699
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20700
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20701
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20841
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20842
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20843
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20844
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20845
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20846
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20847
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20848
+ >>>>>>> 1a2ef63 (Weights and notebook)
20849
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20850
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20851
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20970
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20971
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20972
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20973
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20974
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20975
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20976
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20977
+ >>>>>>> 1a2ef63 (Weights and notebook)
20978
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20979
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20980
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21135
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21136
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21137
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21138
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21139
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21140
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21141
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21142
+ >>>>>>> 1a2ef63 (Weights and notebook)
21143
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21144
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21145
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21464
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21465
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21466
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21467
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21468
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21469
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21470
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21471
+ >>>>>>> 1a2ef63 (Weights and notebook)
21472
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21473
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21474
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23131
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23132
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23133
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23134
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23135
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23136
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23137
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23138
+ >>>>>>> 1a2ef63 (Weights and notebook)
23139
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23140
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23141
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23214
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23215
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23216
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23217
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23218
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23219
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23220
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23221
+ >>>>>>> 1a2ef63 (Weights and notebook)
23222
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23223
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23224
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23478
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23479
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23480
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23481
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23482
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23483
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23484
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23485
+ >>>>>>> 1a2ef63 (Weights and notebook)
23486
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23487
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23488
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23833
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23834
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23835
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23836
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23837
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23838
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23839
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23840
+ >>>>>>> 1a2ef63 (Weights and notebook)
23841
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23842
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23843
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23940
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23941
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23942
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23943
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23944
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23945
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23946
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23947
+ >>>>>>> 1a2ef63 (Weights and notebook)
23948
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23949
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23950
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24217
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24218
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24219
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24220
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24221
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24222
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24223
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24224
+ >>>>>>> 1a2ef63 (Weights and notebook)
24225
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24226
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24227
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24274
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24275
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24276
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24277
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24278
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24279
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24280
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24281
+ >>>>>>> 1a2ef63 (Weights and notebook)
24282
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24283
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24284
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24611
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24612
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24613
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24614
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24615
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24616
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24617
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24618
+ >>>>>>> 1a2ef63 (Weights and notebook)
24619
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24620
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24621
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24742
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24743
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24744
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24745
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24746
  "##�": 23432,
24747
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24748
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24749
+ >>>>>>> 1a2ef63 (Weights and notebook)
24750
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24751
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24752
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24797
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24798
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24799
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24800
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24801
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24802
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24803
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24804
+ >>>>>>> 1a2ef63 (Weights and notebook)
24805
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24806
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24807
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24991
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24992
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24993
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24994
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24995
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24996
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24997
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24998
+ >>>>>>> 1a2ef63 (Weights and notebook)
24999
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25000
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25001
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25064
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25065
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25066
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25067
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25068
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25069
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25070
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25071
+ >>>>>>> 1a2ef63 (Weights and notebook)
25072
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25073
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25074
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25251
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25252
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25253
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25254
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25255
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25256
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25257
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25258
+ >>>>>>> 1a2ef63 (Weights and notebook)
25259
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25260
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25261
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25432
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25433
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25434
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25435
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25436
  "##m": 24102,
25437
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25438
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25439
+ >>>>>>> 1a2ef63 (Weights and notebook)
25440
  "##kki": 24103,
25441
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25442
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26158
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26159
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26160
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26161
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26162
  "##�": 24824,
26163
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26164
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26165
+ >>>>>>> 1a2ef63 (Weights and notebook)
26166
  "##ulio": 24825,
26167
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26168
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26169
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26170
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26171
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26172
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26173
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26174
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26175
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26176
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26177
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26178
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26179
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26180
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26181
+ >>>>>>> 1a2ef63 (Weights and notebook)
26182
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26183
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26184
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26312
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26313
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26314
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26315
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26316
  "##stra�e": 24967,
26317
+ =======
26318
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26319
+ >>>>>>> 1a2ef63 (Weights and notebook)
26320
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26321
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26322
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26509
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26510
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26511
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26512
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26513
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26514
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26515
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26516
+ >>>>>>> 1a2ef63 (Weights and notebook)
26517
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26518
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26519
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26882
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26883
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26884
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26885
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26886
  "##2": 25529,
26887
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26888
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26889
+ >>>>>>> 1a2ef63 (Weights and notebook)
26890
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26891
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26892
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26930
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26931
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26932
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26933
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26934
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26935
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26936
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26937
+ >>>>>>> 1a2ef63 (Weights and notebook)
26938
  "##mund": 25574,
26939
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26940
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27160
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27161
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27162
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27163
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27164
  "##('/": 25799,
27165
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27166
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27167
+ >>>>>>> 1a2ef63 (Weights and notebook)
27168
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27169
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27170
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27498
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27499
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27500
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27501
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27502
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27503
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27504
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27505
+ >>>>>>> 1a2ef63 (Weights and notebook)
27506
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27507
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27508
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27675
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27676
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27677
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27678
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27679
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27680
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27681
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27682
+ >>>>>>> 1a2ef63 (Weights and notebook)
27683
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27684
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27685
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27817
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27818
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27819
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27820
+ <<<<<<< HEAD
27821
  "##�i": 26444,
27822
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27823
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27824
+ >>>>>>> 1a2ef63 (Weights and notebook)
27825
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27826
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27827
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28166
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28167
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28168
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28169
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28170
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28171
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28172
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28173
+ >>>>>>> 1a2ef63 (Weights and notebook)
28174
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28175
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28176
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28193
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28194
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28195
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28196
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28197
  "�10": 26812,
28198
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28199
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28200
+ >>>>>>> 1a2ef63 (Weights and notebook)
28201
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28202
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28203
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28777
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28778
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28779
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28780
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28781
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28782
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28783
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32650
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32653
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32656
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32657
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32658
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32659
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32660
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32661
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32662
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32663
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32664
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32665
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32666
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32667
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32668
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32669
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32670
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32671
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32672
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32673
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32675
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32677
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32678
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32680
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32681
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32682
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32684
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32685
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32690
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32691
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32692
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32693
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32694
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32695
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32696
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32697
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32698
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32699
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32700
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32701
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32704
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32705
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32706
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32707
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32708
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32709
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32710
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32712
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32714
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32715
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32716
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32717
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32718
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32719
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32720
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32721
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32722
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32724
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32725
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32726
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32727
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32728
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32729
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32731
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32732
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32733
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32734
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32736
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32737
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32738
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32739
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32741
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32744
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32745
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32746
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32747
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32749
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32750
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32751
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32756
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32757
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32758
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32759
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32761
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32764
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32766
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32767
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32768
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32769
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32770
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32771
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32772
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32773
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32774
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32775
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32776
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32777
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32778
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32779
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32780
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32781
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32782
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32783
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32784
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32785
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32786
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32787
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32788
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32789
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32790
+ "##秀": 30454,
32791
+ "##秋": 30455,
32792
+ "##空": 30456,
32793
+ "##立": 30457,
32794
+ "##章": 30458,
32795
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32796
+ "##糹": 30460,
32797
+ "##美": 30461,
32798
+ "##義": 30462,
32799
+ "##耳": 30463,
32800
+ "##良": 30464,
32801
+ "##艹": 30465,
32802
+ "##花": 30466,
32803
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32804
+ "##華": 30468,
32805
+ "##葉": 30469,
32806
+ "##藤": 30470,
32807
+ "##行": 30471,
32808
+ "##街": 30472,
32809
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32810
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32811
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+ "##貝": 30478,
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32819
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32821
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+ "##部": 30486,
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+ "##都": 30487,
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+ "##里": 30488,
32825
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32826
+ "##金": 30490,
32827
+ "##鈴": 30491,
32828
+ "##镇": 30492,
32829
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32830
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+ "##間": 30495,
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+ "##阿": 30497,
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+ "##陳": 30498,
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+ "##雄": 30500,
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+ "##青": 30501,
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+ "##面": 30502,
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+ "##風": 30503,
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+ "##食": 30504,
32841
+ "##香": 30505,
32842
+ "##馬": 30506,
32843
+ "##高": 30507,
32844
+ "##龍": 30508,
32845
+ "##龸": 30509,
32846
+ "##fi": 30510,
32847
+ "##fl": 30511,
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+ "##!": 30512,
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+ "##(": 30513,
32850
+ "##)": 30514,
32851
+ "##,": 30515,
32852
+ "##-": 30516,
32853
+ "##.": 30517,
32854
+ "##/": 30518,
32855
+ "##:": 30519,
32856
+ "##?": 30520,
32857
+ "##~": 30521
32858
+ >>>>>>> 1a2ef63 (Weights and notebook)
32859
  }
32860
  }
32861
  }
vocab.txt ADDED
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