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+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"0ea8b46b-839b-445b-8043-ccdf4e920ace","showTitle":false,"title":""},"id":"YLH80COBzi_F"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"63B5exAuzq4M"},"outputs":[],"source":["from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","    drive.mount('/content/drive')\n","    workding_dir = \"/content/drive/MyDrive/logical-reasoning/\"\n","except ModuleNotFoundError:\n","    workding_dir = str(Path.cwd().parent)"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":368,"status":"ok","timestamp":1719461634865,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"zFulf0bg0H-9","outputId":"debdd535-c828-40b9-efc0-8a180e5830dd"},"outputs":[{"name":"stdout","output_type":"stream","text":["workding dir: /Users/inflaton/Library/CloudStorage/GoogleDrive-dh.huang.2023@smu.edu.sg/My Drive/logical-reasoning\n"]}],"source":["import os\n","import sys\n","\n","os.chdir(workding_dir)\n","sys.path.append(workding_dir)\n","print(\"workding dir:\", workding_dir)"]},{"cell_type":"code","execution_count":null,"metadata":{"application/vnd.databricks.v1+cell":{"cellMetadata":{},"inputWidgets":{},"nuid":"9f67ec60-2f24-411c-84eb-0dd664b44775","showTitle":false,"title":""},"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":589,"status":"ok","timestamp":1719462011879,"user":{"displayName":"Donghao Huang","userId":"00463591218503521679"},"user_tz":-480},"id":"DIUiweYYzi_I","outputId":"e16e9247-9077-4b0c-f8ea-17059f05a1c4"},"outputs":[{"name":"stdout","output_type":"stream","text":["loading env vars from: /Users/inflaton/Library/CloudStorage/GoogleDrive-dh.huang.2023@smu.edu.sg/My Drive/logical-reasoning/.env.example\n"]},{"data":{"text/plain":["True"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["from dotenv import find_dotenv, load_dotenv\n","\n","found_dotenv = find_dotenv(\".env\")\n","\n","if len(found_dotenv) == 0:\n","    found_dotenv = find_dotenv(\".env.example\")\n","print(f\"loading env vars from: {found_dotenv}\")\n","load_dotenv(found_dotenv, override=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4hQO8gkFzi_K"},"outputs":[],"source":["import pandas as pd\n","\n","df = pd.read_csv(\"datasets/mgtv/train.csv\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"W2QyVreqhOGM","outputId":"68b9590e-1ac6-4c6f-e0c4-e273ec816419"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 25000 entries, 0 to 24999\n","Data columns (total 6 columns):\n"," #   Column  Non-Null Count  Dtype  \n","---  ------  --------------  -----  \n"," 0   text    25000 non-null  object \n"," 1   label   25000 non-null  object \n"," 2   answer  0 non-null      float64\n"," 3   title   25000 non-null  object \n"," 4   puzzle  25000 non-null  object \n"," 5   truth   25000 non-null  object \n","dtypes: float64(1), object(5)\n","memory usage: 1.1+ MB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8mOMrIurhOGN","outputId":"1870d855-7c18-4850-eb88-302acad05719"},"outputs":[{"data":{"text/plain":["label\n","不是      11783\n","是        6591\n","不重要      5076\n","问法错误      921\n","回答正确      629\n","Name: count, dtype: int64"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df[\"label\"].value_counts()"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"data":{"text/plain":["puzzle\n","在一个寂静的夜晚，甄贾贾匆匆忙忙地从小巷子里跑了出来，脸上满是惊恐。在他身后，一只空荡荡的垃圾桶翻倒在地。甄贾贾并非小巷里的居民，也没有与人结怨。究竟是什么原因让他如此惊慌失措？                                                            10058\n","在一栋老旧居民楼的地下室。一天，警方接到报案，有人在地下室中发现了甄好奇的遗体。现场没有打斗的痕迹，也没有明显的自杀工具。死者被发现时坐在一把椅子上，表情平静，似乎在死前并未经历痛苦。地下室除了一台电视和一张桌子外，没有其他物品。桌上有一杯水，电视还开着。究竟是什么原因导致了甄好奇的死亡？     9345\n","一个晴朗的周末，公园的长椅上发生了一件奇怪的事情。一位老人每天下午都会来到这个公园的同一张长椅上看书。但今天，当他像往常一样来到公园时，却发现他的椅子不见了。更奇怪的是，公园里的其他椅子都完好无损，唯独他那张椅子不见了踪影。                                      1719\n","在一片宁静的沙滩上，甄德帅突然惊慌失措地跑来跑去，似乎在寻找着什么。他的举动引起了其他沙滩游客的注意，但没有人能理解他到底在做什么。                                                                                    1401\n","在一个寂静的夜晚，考古学家甄历史被发现死在了他刚刚发掘的古埃及墓穴中。他的身体趴在一张石桌上，周围散落着各种神秘的古埃及文物。令人不解的是，墓穴内并没有打斗的痕迹，甄历史的表情安详，似乎是在某种不可思议的平静中结束了自己的生命。请还原真相。                               866\n","在神秘的森林深处，有一个小木屋。某天，一只狐狸突然冲进小木屋，紧接着又冲了出来，然后又冲了进去……如此反复十几次。这是怎么回事？                                                                                       539\n","在一个安静的夜晚，小镇上的钟楼突然停止了报时。第二天早晨，人们发现钟楼的管理员甄大勇失踪了，而钟楼的门紧闭，从外面看起来一切正常。小镇上的人们议论纷纷，不知道发生了什么事情。                                                                510\n","在一个封闭房间内，发现了甄木匠的遗体。现场没有打斗痕迹，死者脸上带有惊恐的表情。房间内除了一张床和一把椅子外，别无他物。请问，甄木匠是如何在这样一个看似安全的房间内死亡的？                                                                 131\n","在神秘的大森林里，有一个小木屋。一天，一只熊走进了这个木屋，但它并没有寻找食物，反而开始哭泣。它为什么会哭泣呢？                                                                                               106\n","在一个寂静的夜晚，古宅里传来了一阵惊恐的尖叫。早晨，人们发现著名收藏家甄先生的遗体躺在他的书房里，身边散落着一些珍贵的古董。令人不解的是，书房的门和窗户都从内部紧闭，没有任何强行闯入的痕迹。究竟是什么原因导致了甄先生的死亡？                                        86\n","在甄家村里，有一个古老的传说：每年南瓜丰收的季节，南瓜田里总有一个最大的南瓜会不翼而飞，村民们对此现象困惑不解。请找出南瓜失踪背后的原因。                                                                                   61\n","在公司午休时间，所有人都注意到甄认真的桌上多了一碗热气腾腾的汤。然而，甄认真却坚决不让大家尝一口，甚至不愿透露汤的真正内容。同事们议论纷纷，都想知道这碗汤隐藏着什么秘密。                                                                   54\n","在一个炎热的夏日，乡村的甄家大院的西瓜突然全部不翼而飞。据了解，甄家大院周围并没有其他人家，而且门窗都完好无损，没有任何被撬的痕迹。村民们议论纷纷，猜测这批西瓜究竟去了哪里。你知道西瓜去了哪里吗？                                                      49\n","在深不见底的森林中，发现一具男子的遗体。死者甄灿身上没有任何明显的外伤，周围也没有打斗的痕迹。唯一奇怪的是，甄灿手中紧握着一片绿叶。警方调查后发现，他在死前曾拼命寻找某种东西。你知道甄灿究竟在寻找什么吗？                                                  32\n","在深山老林里，发现了甄青年的遗体，现场没有打斗痕迹，遗体旁边有一盏熄灭的登山灯。据了解，甄青年是独自一人的登山爱好者，前一天晚上他还与朋友通了电话，表示第二天要征服附近的一座高峰。警方调查后，排除了他杀的可能性。那么，甄青年究竟是如何在山上遇难的？                            23\n","在一个安静的夜晚，一个图书馆的阅览室内，只有甄读者和一位图书管理员。突然，甄读者站起来，神情紧张地走向管理员，询问能否借用一下电话。管理员点头答应，但发现甄读者并没有拨打电话，而是静静地站在电话旁边片刻后，便匆匆离开了。                                          20\n","Name: count, dtype: int64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df[\"puzzle\"].value_counts()"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"data":{"text/plain":["truth\n","原来，甄贾贾是一名正在潜逃的小偷。当晚，他企图在巷子里寻找可以盗窃的目标，却不料垃圾桶里突然窜出一只野猫，将他吓了一跳。他以为是被警方发现了，于是惊慌失措地逃跑了。而那只垃圾桶，正是他翻找东西时不小心弄翻的。                                                                                                      10058\n","甄好奇其实是一位科幻小说作家，患有严重的幽闭恐惧症。他为了寻找写作的灵感，常常独自一人来到这个地下室观看科幻电影。不幸的是，有一天他在观看电影时突然遭遇停电，地下室一片漆黑。他的幽闭恐惧症发作，极度恐慌中误以为自己被困在一个未知的宇宙空间。在这种极端恐惧的状态下，他突发心脏病，平静地离开了人世。由于心脏病发作时没有痛苦，所以他的表情看起来很平静。而桌上的水和开着的电视，只是他日常在地下室写作时的习惯。     9345\n","真相是，公园里的园艺工人前一天晚上在修剪树枝时不小心弄坏了一盏路灯。他们为了修理路灯，临时把那张长椅搬过去当作梯子使用。修理完毕后，他们忘了把长椅放回原位，导致老人找不到自己的椅子。而那张长椅，此刻正无辜地躺在公园的角落里，上面还写着：“我不是椅子，我是临时梯子。”                                                                          1719\n","原来，甄德帅是一位环保志愿者，他在沙滩上发现了一只被渔网缠住的幼海龟。幼海龟的生命危在旦夕，甄德帅急于寻找工具来解救它。然而，由于紧张和焦急，他无法用言语清楚地表达自己的意图，导致其他游客误以为他是在寻找失物。                                                                                                      1401\n","甄历史在研究古埃及文物时，意外地发现了一枚罕见的魔法符文石。在深入的研究中，他偶然激活了符文石上的诅咒。这个诅咒能让触碰它的人在一种幻觉中看到自己最渴望的事情，并由此进入一种假死状态。实际上，甄历史是在幻觉中体验了回到古埃及黄金时代的场景，心满意足地结束了自己的生命。而这一切，外人都无法理解，只能看到他死亡的表象。                                                  866\n","原来，这只狐狸误食了一颗具有神奇效果的果实，这颗果实让狐狸具备了短时间内预见未来的能力。但是，这个能力有一个副作用，就是每次预见未来都会让狐狸忘记之前预见的内容。在反复进出木屋的过程中，狐狸其实是在试图回忆起自己刚刚预见的未来片段，而这个片段恰好是：它的晚餐会在木屋的某个角落里找到。这个真相让森林里的其他动物忍俊不禁，原来狐狸的奇异行为竟然只是为了寻找晚餐。                            539\n","真相是，钟楼的管理员甄大勇在夜晚进行例行的钟楼维护时，不慎从钟楼的顶部摔落，但并未死亡，只是昏迷。由于他跌落时砸到了控制时钟报时的机械装置，导致钟声停止。他躺在钟楼底部，但由于门从内部反锁，外面的人无法进入。甄大勇在第二天中午苏醒后，自己打开了门，这才知道自己引发了小镇上的恐慌。                                                                    510\n","原来，甄木匠患有严重的心脏病，但他一直对外隐瞒。在事发当天，他独自一人在工作室加工木材，不慎将一根木刺刺入了脚掌。由于他患有心脏病，这根微不足道的木刺引发的感染竟导致心脏骤停，最终离世。由于工作室门窗紧闭，且现场并无其他人在场，这个秘密被隐藏了起来。直到真相大白，村民们才恍然大悟。                                                                   131\n","原来，这只熊是一个超级剧迷，它一直在追一部关于熊的连续剧。那天，它刚好看到剧中的熊主角失去了最心爱的玩具，情不自禁地感同身受，流下了眼泪。而这间小木屋，恰好是它看剧的秘密基地。森林里的其他动物都以为熊是因为找不到食物而哭泣，其实它只是为了一个虚构的剧情而感动。                                                                              106\n","真相是，甄先生在收藏市场上偶然发现了一枚传说中的毒戒指。传说中，这枚戒指曾属于一位中世纪的巫师，戒指上镶嵌的宝石含有剧毒。甄先生对这枚毒戒指非常感兴趣，于是将其买下。然而，他在把玩毒戒指的时候，不慎将其掉落在地，导致戒指上的宝石破裂，释放出致命的毒气。由于书房门窗紧闭，毒气在室内循环，甄先生无法逃脱，最终窒息而死。至于古董散落一地，是因为甄先生在毒气侵袭下，痛苦挣扎时造成的。                    86\n","真相原来与一位年迈的农夫有关。这位农夫年轻时，曾与一位美丽的姑娘相恋。他们约定在南瓜丰收的季节结婚。然而，命运弄人，姑娘在婚礼前的一场意外中离世。悲伤的农夫为了纪念心爱的姑娘，每年都会将最大的南瓜偷走，放到姑娘的墓前，以此寄托自己的哀思。这一行为延续了多年，成为了乡村里一个神秘的传说。                                                                  61\n","原来，甄认真最近在参加一个健康饮食的挑战，而这碗汤是他自己制作的低��路里养生汤。他之所以不愿意分享，是因为这是他第一次尝试烹饪，担心同事们不喜欢，也害怕大家取笑他的烹饪技巧。此外，他还在汤中加入了一种据说能提神醒脑的草药，想在下午的工作中保持最佳状态，这也成为了他不愿分享的秘密。                                                                     54\n","原来，这批西瓜是被一只巨大的乌鸦偷走了。这只乌鸦为了给自己的孩子们准备食物，它趁着夜色，竟然将甄家大院的西瓜一颗颗地带回了巢穴。第二天，村民们发现了乌鸦的巢穴，里面堆满了西瓜，而这个意外的真相让所有人都忍俊不禁。甄家老爷也感慨地说：“真是世界大了，什么奇事都有！”                                                                             49\n","甄灿是一位植物学家，他深知森林中某种罕见草药的药用价值。他的妻子患有重病，急需这种草药救治。为了救妻子，甄灿冒险进入森林寻找这种草药。然而，他在森林中误食了一种有毒的植物，导致心脏麻痹而死。甄灿在临死前意识到自己的错误，因此紧握着那种有毒绿叶，希望以此警示后来者不要重蹈覆辙。而他寻找的那种草药，其实就在他遗体附近不远处。                                                32\n","真相是甄青年患有梦游症，当天晚上在深山中的帐篷里梦游时，不慎摔下了山崖。由于梦游时意识不清，他没有能及时反应过来危险，而且由于登山灯在梦游中并未打开，导致他无法看清路况，最终导致了悲剧的发生。第二天早上，他的朋友由于联系不上他，报警进行了搜救，最终发现了这一不幸的事实。                                                                          23\n","甄读者其实是一位正在躲避追杀的侦探。他在图书馆查阅资料时，发现追杀他的人可能已经接近。为了确认自己的推断，他向管理员借用电话是为了测试电话是否被监听。他之所以没有拨号，而是静静地站在电话旁边，是因为他在检查电话听筒是否有异常的微小声响，这是他作为侦探的专业技能。当他确认电话安全后，他离开了图书馆，并悄悄地用手机联系了他的同事，安排下一步的行动和庇护所。                                20\n","Name: count, dtype: int64"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df[\"truth\"].value_counts()"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"text/plain":["(16, 16, 16)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["len(df[\"title\"].value_counts()), len(df[\"puzzle\"].value_counts()), len(df[\"truth\"].value_counts())"]},{"cell_type":"code","execution_count":21,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["STHeiti\n","LiHei Pro\n","Hei\n","Heiti TC\n","Heiti TC\n","STHeiti\n"]}],"source":["from matplotlib.font_manager import fontManager\n","import os\n","\n","fonts = [\n","    font.name\n","    for font in fontManager.ttflist\n","    if \"Hei\" in font.name or \"YaHei\" in font.name\n","]\n","for font in fonts:\n","    print(font)"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA+YAAAKKCAYAAACwIUGyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAACnx0lEQVR4nOzdd3gU5f7//9em9xBCR0IHkaqAFAGRJkVFiiIKqDQriijYAEGUomLHg4h0y1FQUKoiVaocpPcSCBBaEtKzye7evz/4Zb9E8JyPycKw5Pm4rr0kc88m73Fnd+c1c89924wxRgAAAAAAwBI+VhcAAAAAAEBhRjAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCflYXcC24XC6dOnVK4eHhstlsVpcDAAAAALjBGWOUmpqqMmXKyMfnv18TLxTB/NSpUypXrpzVZQAAAAAACpm4uDjddNNN/3WdQhHMw8PDJV38HxIREWFxNQAAAACAG11KSorKlSvnzqP/TaEI5rnd1yMiIgjmAAAAAIBr5v9yOzWDvwEAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWylcwv+uuu+Tj46P33nsvz/J169apXr16Cg4OVsOGDbV58+Y87VOmTFFMTIxCQ0PVpUsXnTlzxt2Wk5Oj5557TlFRUYqOjtbQoUPlcDjc7adOnVKnTp0UEhKiSpUqacaMGfkpHQAAAACA60q+gvnKlSs1cuTIPMvOnDmjLl26aOTIkUpJSdHrr7+uzp076/Tp05KkpUuXauzYsVq4cKHOnz+vWrVqqUuXLu7njxgxQgcOHNC+ffu0b98+7dmzRyNGjHC3d+vWTXXr1lVCQoIWLFigMWPGaOnSpfkpHwAAAACA64bHurJPmzZNPXr0UNeuXeXv76/7779fjz76qD777DNJ0gcffKCJEyeqTp06Cg4O1pgxY+RyubRixQplZ2friy++0IwZM1SyZEkVL15cs2bN0tSpU5Wenq4NGzYoPT1db7/9toKDg1W7dm1NmjRJY8eO9VT5AAAAAABYws9Tv2jt2rUaMmRInmXdu3fX888/r9GjR2vTpk1asGDBZe2//fabwsLCVKNGDZUqVcrdFh0drUaNGmn9+vX6888/dd9998lms7nb27Ztq4cfflhZWVkKCgry1GZcUYVXFl3V338lseM7XfO/CQAAAAC49jx2xfz48eOqUqVKnmXVq1fXkSNHlJCQoCJFilwWoHPbr/Tc/9Xu6+urihUr6tixY57aBAAAAAAArjmPXTHPzMxUSEhInmXBwcHKzMy8Ypsn2//KbrfLbre7f05JScnPJgEAAAAAcNV57Ip5cHCwsrKy8ixLSEhQSEjIFds82f5X48aNU2RkpPtRrly5AmwZAAAAAABXj8eCeUxMjI4ePZpn2dGjR1WpUiVFR0frwoULysnJuWL7lZ77v9pzcnJ0+vRplS9f/rLnvfrqq0pOTnY/4uLiPLCFAAAAAAB4nseCebNmzS6bvmzu3Llq06aNbDabGjRooBUrVlyxvV69etqzZ4+SkpLcbYmJidq0aZOaNm16xd+9bNky1a1bV4GBgZfVEhgYqIiIiDwPAAAAAACuRx4L5v369dPMmTO1ZMkSORwOLVy4UN9++62efvppSdKQIUM0ePBg7du3T1lZWRozZoyCg4PVsmVLBQQE6PHHH9eAAQOUkJCg8+fPq2/fvnr22WcVEhKipk2bSrrYRT0rK0t79uzRiy++qNdff91T5QMAAAAAYAmPBfOSJUvq+++/16uvvqqwsDCNGjVK8+fPV4kSJSRJ7du31+DBg9W2bVsVLVpUW7du1bx589zPf/PNN1W6dGlVrlxZ1apVU9WqVTVq1Ch3+9y5c/X777+raNGi6tSpk15//XW1bdvWU+UDAAAAAGAJmzHGWF3E1ZaSkqLIyEglJyfnq1s785gDAAAAAP6Jf5JDPXbFHAAAAAAA/HMEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAs5Gd1Abh+VHhl0TX/m7HjO13zvwkAAAAA1xOumAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMAAAAAYCGCOQAAAAAAFiKYAwAAAABgIYI5AAAAAAAW8mgwT0hIUK9evVS0aFHFxMRo4sSJ7ra9e/eqWbNmCg4OVs2aNbV48eI8z50/f76qVaum4OBgtWrVSgcPHszT/uabb6pkyZKKiIhQ3759lZaW5snSAQAAAACwhEeD+aOPPqoqVaooLi5Of/zxh9auXauZM2fKbrerY8eO6tmzp5KTkzV58mT1799fO3fulCTt3LlTTzzxhL744gslJyfrwQcfVPv27ZWVlSVJmjx5subPn6+NGzfqxIkTstlsGjBggCdLBwAAAADAEh4N5mvWrNFrr72m0NBQlSxZUoMGDdKPP/6oH374QbVq1dIzzzyjgIAANW/eXCNHjtS7774rSfr00081bNgw3XnnnQoICNCTTz6phg0b6uuvv5YkffDBB/riiy9UsWJFRUREaPLkyVq3bp0OHz7syfIBAAAAALjmPBrMO3XqpGHDhiklJUVxcXEaO3asSpYsqbVr16pz58551u3evbt+++03Sfqv7WfPnlV6errq16/vbvP399d9992nFStWeLJ8AAAAAACuOY8G80mTJmnevHmKjIxUTEyM4uPj9cYbb+j48eOqUqVKnnWLFSumrKws2e12nTx5UpUqVcrTXr16dR05cuSKz720HQAAAAAAb+axYO5wOHTvvffq4YcfVkJCgo4dO6b27dvr7NmzyszMVEhIyGXPCQ4OVmZmplwul3x8fK7Y9r+eeyV2u10pKSl5HgAAAAAAXI88FswXLlyo4OBgTZgwwT0q+5gxY/T4448rICDAPZDbpRITExUSEiIfHx8ZY/K0JSQkKCQkRMHBwVd8bm77lYwbN06RkZHuR7ly5TyzkQAAAAAAeJjHgvn+/fvVvHnzPMtCQ0NVpEgRSdLRo0fztJ0+fVpFixZVQECAypYtq+PHj+dpP3r0qCpVqqSYmJjLnntp+5W8+uqrSk5Odj/i4uIKsGUAAAAAAFw9HgvmlSpV0r59+/Isy8rK0t69e9WrVy8tXbo0T9vcuXPVpk0bSVKzZs3+tr1EiRIKCAjQ/v373W0Oh0MLFixQ69atr1hLYGCgIiIi8jwAAAAAALgeeSyY33vvvdq6dasmTZqk1NRUnTx5Uo8++qiaNGmibt26afPmzZo1a5ZycnK0YcMGvfPOOxo6dKgkadCgQXrrrbe0adMm5eTkaNq0adqxY4d69uwpSXrhhRfUv39/nThxQqmpqRo0aJDatGmjihUreqp8AAAAAAAs4bFgHhQUpIULF+qnn35SyZIl1ahRI5UoUUKzZs1SUFCQfv75Z02ZMkXh4eHq16+fpkyZopo1a0qSateurU8++US9e/dWRESE5syZo8WLFyswMFCS9OSTT6pt27a67bbbVLZsWWVnZ2vy5MmeKh0AAAAAAMvYzF9HXbsBpaSkKDIyUsnJyfnq1l7hlUVXoar/LnZ8p2v+NwvLdgIAAADA1fZPcqhH5zEHAAAAAAD/DMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALDQVQ3mx44d08yZM6/mnwAAAAAAwKtd1WD+/PPP68yZM+6f161bp3r16ik4OFgNGzbU5s2b86w/ZcoUxcTEKDQ0VF26dMnz3JycHD333HOKiopSdHS0hg4dKofDcTXLBwAAAADgqrtqwXzx4sU6fPiwhgwZIkk6c+aMunTpopEjRyolJUWvv/66OnfurNOnT0uSli5dqrFjx2rhwoU6f/68atWqpS5durh/34gRI3TgwAHt27dP+/bt0549ezRixIirVT4AAAAAANfEVQnmdrtdgwcP1uTJk+Xn5ydJmjZtmnr06KGuXbvK399f999/vx599FF99tlnkqQPPvhAEydOVJ06dRQcHKwxY8bI5XJpxYoVys7O1hdffKEZM2aoZMmSKl68uGbNmqWpU6cqPT39amwCAAAAAADXxFUJ5u+8845atGihO+64w71s7dq16ty5c571unfvrt9++03GGG3atEmdOnW6Yvu2bdtUo0YNlSpVyt0WHR2tRo0aaf369VdjEwAAAAAAuCY8Hszj4uI0btw4LVu2TFFRUXrxxRflcrl0/PhxValSJc+61atX15EjR5SQkKAiRYooKCjoiu1Xeu6l7X9lt9uVkpKS5wEAAAAAwPXI48F8zJgxatu2rf7zn/9o69at+v333zVp0iRlZmYqJCQkz7rBwcHKzMy8Yts/af+rcePGKTIy0v0oV66c5zYQAAAAAAAP8vP0L1ywYIH27dunqKgoSdKXX36pBx98UMHBwcrKysqzbkJCgkJCQq7Y9n9tj4mJuWz5q6++6h50TpJSUlII5wAAAACA65JHr5ifO3dOoaGh7lAuSTVr1tSJEycUExOjo0eP5ln/6NGjqlSpkqKjo3XhwgXl5ORcsf1Kz720/a8CAwMVERGR5wEAAAAAwPXIo8E8OjpaSUlJSkpKci/bs2ePYmJi1KxZMy1dujTP+nPnzlWbNm1ks9nUoEEDrVix4ort9erV0549e/L83sTERG3atElNmzb15CYAAAAAAHBNeTSY+/j46LHHHtNjjz2ms2fP6ujRo+rfv7+ef/559evXTzNnztSSJUvkcDi0cOFCffvtt3r66aclSUOGDNHgwYO1b98+ZWVlacyYMQoODlbLli0VEBCgxx9/XAMGDFBCQoLOnz+vvn376tlnn73ivecAAAAAAHgLjw/+Nn78eFWsWFG1atVSq1at9OCDD2rAgAEqWbKkvv/+e7366qsKCwvTqFGjNH/+fJUoUUKS1L59ew0ePFht27ZV0aJFtXXrVs2bN8/9e998802VLl1alStXVrVq1VS1alWNGjXK0+UDAAAAAHBN2YwxxuoirraUlBRFRkYqOTk5X/ebV3hl0VWo6r+LHd/pf6/kYYVlOwEAAADgavsnOdTjV8wBAAAAAMD/HcEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALDQVQ3m7777rs6fPy9J2rt3r5o1a6bg4GDVrFlTixcvzrPu/PnzVa1aNQUHB6tVq1Y6ePBgnvY333xTJUuWVEREhPr27au0tLSrWToAAAAAANfEVQvmO3bs0IgRIyRJdrtdHTt2VM+ePZWcnKzJkyerf//+2rlzpyRp586deuKJJ/TFF18oOTlZDz74oNq3b6+srCxJ0uTJkzV//nxt3LhRJ06ckM1m04ABA65W6QAAAAAAXDNXJZhnZ2erd+/ecjgckqQffvhBtWrV0jPPPKOAgAA1b95cI0eO1LvvvitJ+vTTTzVs2DDdeeedCggI0JNPPqmGDRvq66+/liR98MEH+uKLL1SxYkVFRERo8uTJWrdunQ4fPnw1ygcAAAAA4Jq5KsH89ddfV+3atXXTTTdJktauXavOnTvnWad79+767bff/mf72bNnlZ6ervr167vb/P39dd9992nFihVXo3wAAAAAAK4ZjwfzNWvWaO7cuZo0aZJ72fHjx1WlSpU86xUrVkxZWVmy2+06efKkKlWqlKe9evXqOnLkyBWfe2k7AAAAAADezM+TvywlJUWPPfaYpk+frsjISPfyzMxMhYSEXLZ+cHCwMjMz5XK55OPjc8W2//XcK7Hb7bLb7XnqAgAAAADgeuTRK+bPPfecunXrppYtW+ZZHhwc7B7I7VKJiYkKCQmRj4+PjDF52hISEhQSEvK3z81tv5Jx48YpMjLS/ShXrlz+NwoAAAAAgKvIY8F8wYIF+uqrr/TJJ58oKChIQUFBOnbsmG666Sbt3r1bR48ezbP+6dOnVbRoUQUEBKhs2bI6fvx4nvajR4+qUqVKiomJuey5l7Zfyauvvqrk5GT3Iy4uzlObCQAAAACAR3ksmHfu3Fk5OTnKyspyP8qXL68TJ07o7bff1tKlS/OsP3fuXLVp00aS1KxZs79tL1GihAICArR//353m8Ph0IIFC9S6desr1hIYGKiIiIg8DwAAAAAArkdXbR7zS3Xt2lWbN2/WrFmzlJOTow0bNuidd97R0KFDJUmDBg3SW2+9pU2bNiknJ0fTpk3Tjh071LNnT0nSCy+8oP79++vEiRNKTU3VoEGD1KZNG1WsWPFalA8AAAAAwFVzTYJ5UFCQfv75Z02ZMkXh4eHq16+fpkyZopo1a0qSateurU8++US9e/dWRESE5syZo8WLFyswMFCS9OSTT6pt27a67bbbVLZsWWVnZ2vy5MnXonQAAAAAAK4qm/nrqGs3oJSUFEVGRio5OTlf3dorvLLoKlT138WO73TN/2Zh2U4AAAAAuNr+SQ69JlfMAQAAAADAlRHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALCQR4N5UlKSHn30URUvXlwVKlTQ+PHj5XK5JEnr1q1TvXr1FBwcrIYNG2rz5s15njtlyhTFxMQoNDRUXbp00ZkzZ9xtOTk5eu655xQVFaXo6GgNHTpUDofDk6UDAAAAAGAJjwbz7t27q2zZsjp8+LB+/fVXzZ8/X59++qnOnDmjLl26aOTIkUpJSdHrr7+uzp076/Tp05KkpUuXauzYsVq4cKHOnz+vWrVqqUuXLu7fO2LECB04cED79u3Tvn37tGfPHo0YMcKTpQMAAAAAYAmbMcZ44hf9+eef6tOnj3bs2CGbzSZJ2rt3r3r06KGePXvq1KlT+uSTT9zrv/LKKwoICNCbb76pu+++WwMHDlS3bt3c7Y0bN9bYsWPVrFkzlS5dWrt371apUqUkSQkJCbr55psVGxur0NDQ/1lbSkqKIiMjlZycrIiIiH+8bRVeWfSPn1NQseM7XfO/WVi2EwAAAACutn+SQz12xdzpdGrIkCHuUC5J5cqV05kzZ7R27Vp17tw5z/rdu3fXb7/9JmOMNm3apE6dOl2xfdu2bapRo4Y7lEtSdHS0GjVqpPXr13uqfAAAAAAALOHnqV/UoEEDNWjQIM+yhQsXqkGDBjp27JiqVKmSp6169eo6cuSIEhISVKRIEQUFBV3W/vXXX+v48eOXPffS51+J3W6X3W53/5ySkpLfzQIAAAAA4Kq6aqOynz59Wi+++KJGjhypzMxMhYSE5GkPDg5WZmbmFdv+SfuVjBs3TpGRke5HuXLlPLNRAAAAAAB42FUJ5na7Xd26ddPTTz+tRo0aKTg4WFlZWXnWSUhIUEhIyBXb/kn7lbz66qtKTk52P+Li4jyzYQAAAAAAeNhVCeb9+vVTuXLl9Nprr0mSYmJidPTo0TzrHD16VJUqVVJ0dLQuXLignJycK7Zf6bmXtl9JYGCgIiIi8jwAAAAAALgeeTyYjxo1SkeOHNGMGTPcA8E1a9ZMS5cuzbPe3Llz1aZNG9lsNjVo0EArVqy4Ynu9evW0Z88eJSUludsSExO1adMmNW3a1NPlAwAAAABwTXk0mH/11VeaPXu25s+fn2cwt379+mnmzJlasmSJHA6HFi5cqG+//VZPP/20JGnIkCEaPHiw9u3bp6ysLI0ZM0bBwcFq2bKlAgIC9Pjjj2vAgAFKSEjQ+fPn1bdvXz377LN/25UdAAAAAABv4bFgvnbtWvXt21dHjx5VmTJl5Ofn535kZWXp+++/16uvvqqwsDCNGjVK8+fPV4kSJSRJ7du31+DBg9W2bVsVLVpUW7du1bx589y/+80331Tp0qVVuXJlVatWTVWrVtWoUaM8VToAAAAAAJaxGWOM1UVcbf9kYvcrqfDKoqtQ1X8XO77T/17JwwrLdgIAAADA1fZPcuhVmy4NAAAAAAD8bwRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALORndQHAtca0cAAAAACuJ1wxBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQn5WFwDg6qjwyqJr/jdjx3e65n8TAAAA8HZcMQcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAs5Gd1AQBQEBVeWXTN/2bs+E7X/G8CAADgxsUVcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALEQwBwAAAADAQgRzAAAAAAAsRDAHAAAAAMBCBHMAAAAAACxEMAcAAAAAwEIEcwAAAAAALORndQEAgP+bCq8suuZ/M3Z8p2v+NwEAAAobrpgDAAAAAGAhrpgDAK4r9AwAAACFDVfMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALAQwRwAAAAAAAsRzAEAAAAAsBDBHAAAAAAACxHMAQAAAACwEMEcAAAAAAALEcwBAAAAALCQn9UF/F+lpKToqaee0oIFCxQREaHnn39eL7/8stVlAQCQLxVeWXTN/2bs+E7X/G8CAID/zWuC+cCBAxUUFKSTJ0/qwoUL6t69u4oUKaInnnjC6tIAAMDf4AQEAAD/m1cE85MnT2rlypU6duyYgoKCFBkZqVmzZqlDhw4EcwAAYDlOQAAACsIrgvm6devUtm1bBQUFuZfVqFFDYWFhOnjwoKpWrWphdQAAAIUDJyAA4OrwimB+/PhxValS5bLl1atX15EjRwjmAAAA8JjCcgKisGwn4A28IphnZmYqJCTksuXBwcHKzMy8bLndbpfdbnf/nJycLOniAHL54bJn5Ot5BZHfWguC7bx62M6rp7Bsp1R4tpXtvHrYzquH7bx62M6rp7BspyTVemPZNf+bu0bffc3/Jtt59eRnO3P3d2PM/1zXZv4va1nsvffeU0ZGhkaOHJlneYcOHfTCCy+oXbt2eZaPGjVKo0ePvpYlAgAAAABwmbi4ON10003/dR2vuGIeExOjJUuWXLb86NGjqlSp0mXLX331VQ0ZMsT9s8vlUmJioqKjo2Wz2a5qrblSUlJUrlw5xcXFKSIi4pr8TasUlm1lO28sbOeNhe28sRSW7ZQKz7aynTcWtvPGwnZePcYYpaamqkyZMv9zXa8I5nfccYdeeuklORwO+fldLHnXrl2y2+1XvPc8MDBQgYGBeZYVKVLkWpR6mYiIiBt6B79UYdlWtvPGwnbeWNjOG0th2U6p8Gwr23ljYTtvLGzn1REZGfl/Ws/nKtfhEWXLllXTpk31wgsvKC0tTXFxcRowYICGDx9udWkAAAAAABSIVwRzSfr888+VmJioUqVK6fbbb1fXrl3Vr18/q8sCAAAAAKBAvKIru3SxC8BXX31ldRn/Z4GBgXrjjTcu61J/Iyos28p23ljYzhsL23ljKSzbKRWebWU7byxs542F7bw+eMWo7AAAAAAA3Ki8pis7AAAAAAA3IoI5AAAAAAAWIpgDAAAAAGAhgjkAAAAAABYimAMA4MWys7OtLgEAABQQwdxDLly4oPPnz0uSUlJStH//fnebw+FQUlKSJGnTpk169NFHLamxoDIyMpSRkSHp4jalpKTkabfb7ZKkbdu2aeTIkde8vqvN6XQqPj5eiYmJSkxMVHZ2ttauXavff//d6tLypTDss4VJWlqahgwZIklavny5Fi9erAULFlxx3SZNmsjpdF7L8jzK4XDo/fffV2pqqtWlXFWxsbHuz5cjR47om2++cbcdPXpUu3btknTxPVqhQgUrSvSo1NRUOZ1O9zbb7XY5nU7Fxsbmea3j4+OtKhEe5s2fQ7jcjfB6fvXVV5o1a5ZOnTol6eLn0rx589ztP/30kzZs2GBVebhKJk6cqJycHKvLIJh7yq+//qq6detq4sSJWr16tZ5++mlJFw+mGjdurEmTJkm6OH+eN83Hfqnly5erWbNmmjdvnpYvX657771XkpSYmKj77rtP48aNkyS5XC6NHTvWylILJCMjQ0OGDNHo0aP15ptvavTo0XrjjTeUmpqqcuXKqVatWqpVq5aKFSumtWvXqnXr1laXnC+FYZ/N5XQ6dfz4caWkpOjUqVP66yyRM2bMkNPp1Oeff54n/HgTX19fzZ07V5I0dOhQff/99xowYIA+//xzVahQQZUqVVKFChU0Z84cxcfHy9fX1+KK889ms2no0KHKyMjQe++9p7S0NC1ZskSrVq3S6tWr9euvv+rIkSNWl1lg+/bt0/333697771X27dv18yZMyVJ8+bNU5MmTbR582ZJUpEiRXTixAkrSy2wnTt3qkOHDvrjjz/UrVs3/fHHH3rggQe0detWvfnmm2revLlOnz6tBx54QM2aNbO63HwpDJ9D/8SpU6d06623aseOHVaXkm9Op1OzZs3SrFmzdPjwYUnSypUrC2Uvlhvh9ZSkF154QVOnTtX27ds1e/ZsXbhwQUOHDpXD4VC/fv3Uq1cv7d692+oyPWLLli36+OOP9f333+dZ/ueff+rHH3+0qKprb/Xq1Ro2bJhGjRpldSkEc0/p3r27Fi5cqPPnzys0NFTh4eHatWuXunTpotdee03Dhw+XdDHkeOsBccuWLTVixAjNnTtXxhhFRUXp4MGDatmypVq2bKnXXntNkhQcHCwfH+/dtXx8fDRr1iz5+fnpX//6l/z8/DRr1iyFh4erXLlyOnjwoMaNG6eePXsqPDxcDofjsgMsb1AY9tlc58+fV8WKFfXee++pRYsW7t4egwcP1okTJzR48GD5+Pho48aNXhtwAgMDFRwcLEny8/PT9OnTVa5cOblcLr399tsqW7asRo8eLZfLpYiICIurLRhfX18FBAQoJCREEydOlMPhUI8ePfTAAw+oe/fuat++vX755ReryyywZs2a6ezZs3r22WdVpkwZhYSEaNu2bfrmm2/0n//8R3379pUk+fv7y9/f3+JqC+aWW27RuXPnVLVqVQ0aNEjHjh1Tw4YNtXr1ak2bNk3x8fHasGGDAgMD/7YnyPWuMHwO5Tp37tx/7dlw5swZderUyeu302azqW/fvvrwww+1a9cuLVmyRJ06ddJvv/0mSe5eaZJ333JSWF5P6eIx7Jo1a9SoUSM988wzCgsLU0hIiPz8/BQWFqZ9+/apf//+VpdZIBkZGZo6dapWrlypH374QQcOHMjTfuHCBc2aNcui6q6t7OxsvfzyyypVqpRefvllq8uRzXhjorjOOJ1O3XnnnSpTpoyMMbpw4YL279+vBg0aKDMzU+Hh4bLb7crKytKoUaN033336dy5c1aX/Y8YY9SnTx+VLl1axhidPXtWmzZtUuvWrZWUlKSbbrrJvY39+vVT+/btlZiYaHXZ+WKMUfXq1XXgwAHVqVNHO3bsUPXq1bV//35VrlxZvXr10saNG/XDDz/o/PnzqlSpkjIyMhQYGGh16f9nhWGfvVR6erpuu+02rV+/Xq1bt9aWLVuUlZWlUqVK6dixY6pfv75iY2NVpUoVLVmyRFWrVrW65HwJCAhQqVKldPbsWRUvXlzJycl67733FBoaqs8//1z9+/eXj4+PPvzwQ23ZssXqcgukSJEiOnv2rKpXr659+/bppptucu+j/v7+10WXtILIyspS0aJFFRYWJmOMnE6n+7156Tp2u11Lly5Vr169dPLkSQsrLrgPP/xQxYsXV3x8vCpWrKjGjRurd+/eWrFihSpWrKg///xTRYoUsbrMfCssn0Px8fG688479e9//1u33nprnrakpCTNmTNHb731lsLDwzV//nzVqlXLoko9o2jRou7jnTvuuEODBg3SQw895P5548aNki6GeIfDYVmd+VVYXs/ly5dr1apV+uijj5SamipjjEqUKKHY2FjFxMSoc+fOMsbIZrMpODhYAwcOVN26da0uO19SU1PVoEEDPf300ypZsqSys7O1f/9+de/eXbfeeqt++eUXLViwQOnp6YqIiNDDDz+sxo0bW132VfHQQw/p559/1vLly9WkSROry5Gf1QXcCFwul1599VWFhobK6XTqyJEjev/993XkyBG5XC4NGjRIlStXVkZGhs6fP6/q1atbXfI/lp2drcaNGyssLEw2m02xsbFatWqV5s2bpxIlSqhRo0YKDw93b2PlypWtLjnfbDabkpOTNW3aNCUlJWnatGlKTU3VtGnTlJ6erpiYGG3YsMH9enujwrDPXiowMFCBgYGKjo6Wn5+fvv/+exUvXlxt2rRRdHS0wsLCtGrVKt1yyy1eezAsSVWqVNGePXt0++23a/PmzWrYsGGedmOMXn75ZSUlJalKlSo6dOiQRZUWnK+vr/z8/BQYGCh/f3/5+f2/rzObzWZhZZ4RFBSkc+fOKTAwUFu2bJG/v7+ef/551a9fX5s2bdLAgQPVu3dv2e12LVmyRLfddpvVJeebMUZ33HGHwsLCdPToURlj1LdvX23btk179uzR8OHDdfr0aV24cEETJ05URkaGJk6caHXZ/1hh+RwqVqyYzp07p9mzZ2vt2rVKTExUXFycduzYoZ07d6pWrVp6/fXXNXDgQK1cuVK33HKLV/eyCwgI0KFDh/Ttt99q6dKlWrt2rRo0aKBvvvlG8+fPl5+fn1avXq0vv/zS6lLzpbC8niNGjFBAQID7+yP3+M4Yo8DAwDzHQQsXLtS7776rOXPmWFJrQQUGBiokJES+vr6y2Wz6+uuv1bhxY7300ksaMmSIMjIyVLFiRc2ZM0e9e/dW9+7db4jeEJc6efKkBg4cqDVr1mju3LnXRSiX6MruEf7+/mrZsqU+/vhjde3aVSVLllSFChW0bds2jR8/XuPHj9dvv/2mu+++W7/++qv69etndcn/WGBgoPr27avt27dr3Lhxuv3221WrVi0dO3ZMTz75pN577z05nU49++yzWr9+vXr27Gl1yQWSlZWltWvXKj09XWvXrlVGRobWrl2rzMxM/f777+5AExwcLJvN5lVXy6XCsc9e6q+hbfz48Ro2bJi6desm6WLIGzVqlPu+em916tQp9e3bV7Gxserbt2+eK6i5Z/pfeeUVVa1aVUuWLLGw0oL5/vvv5XK5tHbtWmVlZbkPAjds2KB169ZZXJ3nuFwulStXTk899ZRiY2MVGBiojz76SAsXLtSCBQvUpk0b+fj4aPbs2Ro2bJjV5eaby+VSv3799Oijj+rZZ5/V+PHj1bFjR9lsNj311FMKCAjQl19+qZSUFP3nP/9RjRo1rC45XwrL55C/v79at26tlStXav/+/crKylLdunX19ttv6/jx49qyZYtuueUW3XXXXerfv7/27t1rdckFYrPZdODAAb333nuaMWOGqlevrpo1a2rkyJEqXry4oqKiVKZMGfcAud6msLyeGzZs0OrVq1W0aFFJF7tzZ2VlyeFwKCoqSsOGDdORI0fUp08f9e3bVy1btrS24ALIPZmd+93pcDjkdDrl7++vcePGafPmzbr55psVGBioF198UT4+Pl7ba/Kzzz7Tjh07lJCQoPPnz2vBggXq3bu3qlevrpCQEG3dulUdOnSwukw3rph7SEhIiHr16qVp06YpOTlZAwcOVFJSku6++25t2rRJkydP1vbt27Vt2zZ9/PHHVpebLwEBAbrpppv07bffys/PT/fcc48CAwP19NNPq0OHDpowYYKaNm2qX3/91esPjkuWLKnp06erTp06mj59utatW6fp06fr999/1/Tp03XHHXdo+/btKl++vEqXLm11uflSGPZZ6eIXztatW90/22w2rVy5UjVq1FDNmjW1fv16ZWRkaPjw4bLb7dq+fbtXdk9zuVyKjIxUv379tHHjRvd/XS6XbDab+ypAkSJFZIzx2ityKSkpeuyxx2S329W9e3clJye723r06OHu9n0jCA8P144dOxQREaHTp0/ryy+/1I8//qiIiAg999xzWr9+vb788ksFBwerefPmVpebb76+vurXr5+WLl2qF198Ud9++622bdumKlWquNcpXry46tSpo8WLF1tYaf4Vls+hXE2bNlVUVNQVTzLk5OS4Q2vXrl1Vs2ZNCyr0jLVr18rpdKpjx45at26dunbtqjJlymjkyJHq2rWrtm7dqmLFiik1NfWymWy8SWF5PaWLt5zcfvvt6tWrlz766CMlJycrLS3N/frVqVNHr7/+ugYPHmxtoQVgs9nyjB00btw4BQUFadu2bXr++ef1yCOP6MUXX3Svs3jxYhUvXtyqcvMt94LhX3vRPfjggzp48OB1efxOMPcQm82myMhIPfLII2rRooVefvll+fv7u190Y4w+/vhjrx1NVrp48NS+fXtNmjRJt912m5566ikVKVJE4eHhstlsMsbo+++/1z333JPnyoC3ycnJ0YULF/Txxx/r/Pnz+vjjj5WdnS2XyyW73a64uDiNHDlS4eHhOnTo0HXT/eWfKgz7rHRxZOvGjRurVq1aWr9+vaSL9wTabDbNnj1by5cv17Fjx/TMM8/I5XIpPT1dmZmZFlf9z+Xk5CggIEB33HGHgoKCdOzYMdntdtntdg0dOlRJSUnat2+fxo0b59UHiBEREUpPT1fx4sV19uzZPFdPjx8/LuniScQbxezZszV27Fi99NJLeuWVV1StWjU99NBD7gEnL1y4oPj4eCUlJSkqKsriavPP5XJp3Lhxql69utLS0vTee+/p9OnTql+/vk6cOKEjR45o1apVWrNmjex2u3twSm9RWD6HctWpU0eff/653n33XQUGBiogIMD934CAAL3yyis6evSovvvuOw0dOtTqcvNl+vTpeuqppxQUFKS9e/fq3nvv1YIFC9S9e3f99NNPSktL08iRI7VhwwbZbDaVKlXK6pLzrTC8nrm+++47GWNUrVo13XTTTcrKytIrr7yiyMhIjR49Wu3atdMXX3yhgQMHKiQkxOpy8+2PP/7Qn3/+6f65devW8vPz03333aeIiAhNmTJFR44c0YULF7x23ABjjH788UeFhYUpLS1N8fHx2rJli5YuXarbbrtNL7zwgoYMGXJdZZbrp5IbQO70Lvfcc4+722jugeKpU6d04sQJvfTSS5o0aZKeeeYZi6vNn88//1zFixfXHXfcIelil++EhARJF0d5PHLkiHr27KmpU6d67aiVOTk56tixo44cOaIePXro0KFD6tChg1JTU+Xr66t27drJGKOwsDDdd9996tWrl9Ul51th2Gdr1KihhIQEtWjRQv3791dsbKyOHDmiNm3aaNGiRTpw4ICqVaumZ555Rs8//7zV5eZbWlqae+75Pn366MCBA+rRo4cGDhyoJ554QjabTU6nUxcuXNCiRYvkdDq9erR9p9Mpl8sll8ulnJwcuVwuq0u6KmrUqKE//vhDFStWlDFGt912m3u03OXLlysuLk67d+/WDz/84LW3nBhjNGrUKK1cuVIPP/ywgoKC1KVLFy1btkxvvvmmli9frsOHDyszM1OrVq1yf/94k8LyOZSrYsWKOnHihA4cOOAepPDSR1ZWls6ePavY2Fjt3LlTtWvXtrrkf+y+++5Tly5dVKNGDW3YsEH9+/dXqVKlNGnSJD399NMKCAjQwoULJV28CuvNV5ILw+uZmZmpsLAw94ldl8ul9u3ba8GCBXr77bf11FNPyeFw6JlnnlF8fLzXj2XSoEED9enTR9HR0apatapsNpuGDx+uBQsWyOFwyM/PT/7+/qpTp45+//13xcTEWF3yP+bn56fOnTtfttzpdGru3Ll68cUXNX36dM2bN0+33HKLBRVegYHHJCYmmt27d7t/TktLc/976dKlpkyZMuann34ymZmZVpTnEXa73Zw8edL9865du9z//uabb0xMTIz57bffTEJCghXledz/2o527dpdo0qujsKwzxpjjMvlMrVr1zbnz5835cuXN3Xq1DFffvmlufnmm018fLxp0KCBuf/++82iRYusLrVA7Ha7+fPPP/O8Ly+1du1aU61aNbN3795rXJnnhYeHm6ysLBMTE2PS09NNVFSUmT17tpkzZ47x8/MzBw4csLpEj1i/fr358ccfjTHGHD9+3GRkZBi73W7sdrs5cOCAqV69unnrrbesLbKATp06ZerVq2cGDRpkHn/8cfPNN9+Y4cOHm4YNG5pNmzaZt99+2+oSPaKwfA4Zc/GzKDw8/H+ut2nTpmtQzdVVunRp97/Dw8NNZGSksdlspl+/fn+7nrcpLK9nkSJFTFpamklLSzPffPONCQoKMqVLlza+vr6mdOnSplSpUsbX19ds377d6lILxOl0mttuu81MmjTJfPvtt6ZNmzamefPmJjg42Lz66qvm448/Nh9++KFp1KiRmTFjhnn44YetLvmqSExMNB06dDDR0dFmy5YtVpdjjDGGK+Ye8scff6hbt25q166dpk6dqptvvlmRkZHuK1Lm/+92+OCDD2ry5Ml69NFHrSw3Xw4cOKCHHnpINWvW1OzZs9WuXTtFRUXl2UY/Pz91795d3377rdq1a2dxxfk3YsQI/fjjj0pPT9fRo0c1YcIEffDBB5d1kS1TpoxFFRZcYdhnc+VOURMdHa3w8HC1adNGNWvWVMWKFbV9+3bl5OToww8/VOvWrdWmTRuv7AqdnJys/v37a+HChRo5cqT8/f314IMPugcolC6+psnJyWrRooW++uortW3b1uKq8y8rK0uZmZlKTU1VWlqaypQpo08//VQBAQGqUqWKqlevrkaNGunLL7+8fs6E/0OLFy9W165dNWrUKEnSzTffrJIlS7rfm5Jkt9v1xhtvKCcnx72etyldurTWr1+v7t27a9euXbrlllu0Zs0anTp1SlOnTtWRI0fkdDrVq1cv/fzzz+rWrZvKli1rddn/WGH4HMoVEBCg7OxsjRs3zt0bKzQ0VNHR0apcubLq1KmjkJAQ3X777VaXWmCXjmlx7NgxRUVF6auvvtKvv/6qpUuXKjg42MLqPKMwvJ7m/x+fZM+ePTLG6MSJE2rRooXGjRunIkWKyM/PT8YYjR49WgsWLFCdOnWsLjnfcnJy8vQ0s9lsatasmdLS0vTQQw/J19dXY8eOVXZ2tnr27Knx48e7X/cbSVRUlObPn6/OnTurc+fO2rlzp+W3hBHMPaRo0aJ67rnn9OKLL0qSzpw5o2XLluVZx263a/Xq1apfv74VJRZYWlqamjZtqnfeeUeStHnzZv300095DhIff/xxrV692iunyrhUcHCwhg0b5r5/3NfX1/0GzmX+/+6XzZo102+//eZ1I7MXhn02V2ZmphISErRs2TLZ7XZNmDBBvr6+KlmypI4cOaK0tDSVL19eDRo00IwZMzRw4ECrS/7HIiMjdeutt+rDDz9U2bJldeTIEe3bt0+LFi3Ks54xRnv27Lmu7qn6p+x2uxwOh9LT0xUbG6vw8HDt2rUrzzoHDx7UE088oTZt2ujgwYMKDQ21qNr8a968uVavXq1GjRpJutgt78iRI5ett3//ft10003XujyPCg4O1o8//qhOnTqpVKlSaty4sZo2bSrp4iB4R48eVUZGhn7++We99NJLOnPmjOUHUP9UYfgcutRdd92l7du3KzAwUC6Xy739hw8fVnx8vJo3b67evXurT58+Xn1bTXZ2tjIzM+Xv769bbrlFzZo105AhQ7Rs2TKFhYVpy5YtstlsqlSpktWlFsiN/npmZWWpbNmy6t+/v3x8fOR0OtW0aVOdOnVKd9xxh/z9/SVdPMFWrlw5jRgxwuKK8y8jI0OZmZnuk0q9evXS/v37NW3aNA0YMEArVqzQvn37lJ6ervT0dJUvX16HDx/OMyDnjSIgIEDff/+9atasqaFDh2rq1KnWFmTFZfrC4Pnnn7e6hKvu/vvvt7qEa2bz5s3mq6++umx5RkaGmTlzpgUVed6NvM+ePXvWNGjQwLz++uvmtttuM8nJycYYY77++mtz7NgxExkZaYwx5ocffjCtW7e2sFLPOXbsmHnwwQetLuOqiY2NNQ6H47+uk5OTY/74449rVNHVN2/ePKtLuC4cOXLE6hLypTB+Dv2dY8eOmbffftsUL17c9OjRw+py8i0nJ8fYbDbj4+NjJkyYYE6ePGkmTJhg9u/fbxo3bmy2bt1qdYnXxI3yev5fHDhwwMyaNet/fv9czxwOh9m/f78ZP368qVevnnn55ZfdbVOnTjUOh8NER0ebxMRE43K5zIIFC0x6erqFFV99v/zyS55bO61iM+aSy50AUAh98803euihh5SWlqazZ8+qcuXKVpcEoJApjJ9D58+fV2xsrBo0aGB1KflijHH3YilWrJgiIyPdbWvXrvXqaQzzw9tfz8Lm+PHjOnr0qIoUKXLZ9IwzZszQY489Zk1hhRjB/Cow/+U+jG+//VYZGRnq27fvNa7q2lm6dKn8/f3VunVrq0spkOPHj6tcuXKy2WxKTExUvXr19Oeffyo6Otrq0jyusO+zN5LCtN/u27dPO3bsyNMt3xij4OBgdezY0cLKPCsxMVF79uz526kLJ06cqJ07d2rGjBnXtjAPO3PmjObMmaPw8HCFhoa6HyEhIe5/lypVSuHh4VaXWiAOh0NZWVkKCwuzuhQA/8XPP/+sm266Sbfeequki93dg4KClJqaqgYNGmj//v0WV4j/i9zZhooWLZrnc/e7775Tu3btVKRIEYsqu5x33wh8HVq1apWCg4N15syZK7anp6frqaeeusZVedb69etVpUoVXbhw4YrtsbGx6tSp07UtysOcTqcqVqyohIQErVmzRnv37tXJkycVERFhdWkeVxj22dOnT+vgwYOSpPj4eK1cudLdduHCBcXFxUmS1qxZozvvvNOSGj2hMO23krRx40YNHz5cn3zyifvRs2fPPGNB3Aj27NmjO++8M8+cs5cqXbq0vvrqq2tcleelp6dr2LBh+vLLLzV+/Hi9+OKL6tOnjzp06KCGDRuqevXqKlq0qL788kurSy2Q7du3q0KFCpo2bZrVpVw1DodD4eHhcjgc2rJli/bu3asDBw5c9jh8+LCys7OtLtdjjDFavXq1hgwZ4r6SvnfvXh06dMjiygpmx44dioyMvGGnpbxUt27dtHfvXkkX56pfv369JOmzzz7T3XffLeniPdre/pr+1R9//HHFMUzS09O9eqBY6eJ0f1WqVLns/vGnn35aCxYssKiqK/Pe0X+uU+Hh4crOztZzzz2nf//735e1V6xYUQ6HQy6Xy2sHSAsICNCRI0c0cuRIffzxx5e1V65cWTk5OV49gqOvr6/8/f0VGhqqyZMnKygoSMaYy+bPtdlsKlmypGbOnOl1AxHlKgz77MaNG9W7d2899thjat26tSZNmqS77rpLGzdu1EMPPaTnn39eL7zwgiIjI7Vu3Tqry823wrTfSlJISIgGDx6sp59+2r0sKipKU6ZMsbAqzwsLC5MxRn379tWGDRsUFBSUp71cuXLu+dxzByjyRoGBgQoMDNSmTZuu2J6ZmaknnnhCY8eO9do52yVpw4YNSklJUatWrWSM0WuvveYeudvHx0cNGzZ0BwBv5efnp4CAABljdPvtt8tms+nvOmg2atRIGzZsuMYVesaaNWvUokULSdKuXbvUrl07BQQEqGXLlkpPT5fL5dIbb7yhhQsX6pFHHtEXX3xhccX5Ex4eLrvdLh8fH124cEF33333ZQPeOp1OZWdnKzk5WQcOHLCo0oJJS0tTkSJF1LRpU3366acKDQ1VRESEfvnlFw0fPtx90jcrK8vaQj3kxRdf1CuvvKLixYtr7NixatWqlQYNGqQFCxZo2rRpWrBggdLT07VixQqrSy2Q8PBwXbhwQWfPnnUvS0tLU2JiopxOp9asWSNjjBISEtS1a1cLKyWYe1xwcLCCgoK0evVqTZw40T3ida7c+48yMzO9cpRg6eI2BgQEaMaMGWrSpIl69uyZpz23S0hmZqZCQkIsqNAzgoKC5O/vr2XLlunnn3/WV199ddnrmful+9133+mJJ56wqNKCKQz7bOfOnXXs2DF9//33Kl68uMLCwrRt2za99NJLmjdvnnvU+cDAQK8erVwqHPvtY489pjJlymj//v1KT0/XiRMn3G12u12vvfaapItXNZKSkjRz5kyrSvWIwMBAhYWFKTAwUH379tXXX3+dpz23a7fdbvfaYD5nzhy1atXqsuU7d+7U5MmTNWnSJAUHB6tu3bpKTEy0oELPWbhwoZ577jlVqFBBkvTOO++oT58+kqQTJ05owoQJSk5O9toTobkiIyPl7+8vX19f5eTk5GkzxujcuXMKDQ3VzTffrJSUFK/r2RMfH6+WLVsqLCxM1apVU/Xq1dWsWTMNHTpUDRo0cF+Y+O6773To0CE1adLEa4N5cHCwO4gHBwdrx44dmjx5sqSLU+lWq1ZNxhj179/fq3uChIWF6csvv9SQIUOUnZ2tVatWKSAgQFu2bNH8+fPdJ2H+uj97o9TUVG3fvl21atXStGnTFBoaqsjISG3dulWPPvqo+/XNzMy0uNKCy/1evPPOO/XWW2+pW7du2rp1q6pUqaJXX31VISEhOnnypG666SaC+Y3G399fAQEBWrRokVq1aqVbbrlFHTp0cLd7c1DN5e/vr6CgIH3zzTfq0aOHqlatmmegj9zw5u1dngIDA7Vv3z732VMfHx/16NHjsvVSUlLc0/p4oxt9n83JyVHVqlVVokQJGWOUmZmp+Ph4DRw4UC6XS0899ZSysrJkt9s1derU6+peo/y40fdbu93uDqI2m002my3P1DzGGPfPISEhf3uVzpvknixauHChmjZtqjFjxuSZqif3Cro3TlEkSWfPnlWfPn1UpUoVuVwunThxQjfddJMWLVqkLl26qFOnTkpOTlZkZKQGDBhw2Ykmb5I7LkJERITOnTun4sWLy8fHR9OnT5d0cTyBJk2aKC0tzeuC6l/lfnf4+/srJSXF3aMnJiZG+/fv180336zExERNmDDBK+dsL126tFJSUrR582alpaXp0KFD2r17t/r27evepwcPHqyyZcuqSpUqXn2VNSAgwH2iyN/fX/7+/nr00UclXQyzaWlpkqSBAwe6TzJ5G6fTqebNm+eZevI///mP9uzZo7Jly2rbtm366KOPlJmZqQceeMDrT5yFh4dr+fLl+vXXX+Xr66ugoCAFBATo66+/1hdffKEHHnhA0sXbUrxd7kmytLQ0DRkyRMHBwVqzZo26d++u2bNn6+jRoypXrtwVu/JfawRzDzl06JA+++wzPf3008rOzlb9+vX1zTffqFevXlq9erVq1aol6eJBpc1m88qwk5ycrJkzZ6pTp07Kzs5Whw4d9OGHH+q+++7T5s2b3R9muW/i3K553sbpdGr06NGSLt5z0759e0kX39jJycmqUKGCwsPDZYxRcnKyTp486ZWDERWGfVa62DV00aJFCg0N1enTp3X+/HmNHDlSpUuX1tGjRzV48GC1bNlSGRkZOnjwoHu7vU1h2W8DAwP1ySefSLo4inVSUlKeruyTJk3SmDFjrCrPY5xOp2bNmqVPPvlEc+bMUXZ2tooVK6YlS5aoWbNmqlatmvuES0pKigICArz2M7dEiRI6e/as1q9fr59++kk1a9ZUvXr19NJLL+mXX35Ry5Yt3et6e1gdMmSIvvnmG7Vq1cp9v6MxRmvXrpUxRk6nU5999pm2bdvmvjrnrXJPKPn6+mr06NGaPXu2evToIV9fX4WGhrr314cfftjKMgvk0KFD6tmzp4YPH65OnTqpU6dO8vPzU1xcnKZMmaIaNWroo48+Us2aNb36YoWvr6/S0tLct19kZmaqVatWyszMvCGuHksXP3N79OihiIgId+g+e/asNmzY4J6f3hgju92uIkWKeOXJpFwul0u9e/dW2bJl3cu2bNmi8+fPq2rVqvrjjz+0ceNGZWZm6s477/Ta21Lj4+Pd35+5li9frrZt2yo7O1vr16/XnDlzLKzwcgRzD1izZo1atWql0NBQdezY0f0h1bFjR7377ru65557tHnzZpUoUUJbt25VnTp1vG4n37Rpk9q1a6f09HTdcsst7m3s27evTpw4oU6dOmnDhg0KCQnR3r17Vb16da+9enPq1Cl9/fXXOn/+vEaPHu0OAdLFM4x+fn76+eefdd9992nt2rVeObJuYdhnc/n6+qp8+fJq0aKFYmNjNW3aNBUrVkwLFizQ7t279dhjj2nFihX64osv9M477+i5556zuuR8KQz77V+lpKTo1Vdf1VtvveVelpycrDJlyujAgQNevY1r1qxR//791apVKyUkJLjfo5UrV9bPP/+sDh06qEKFCmrUqJFWrlzp9SGuWLFiuvfee1W/fn2NHDlS48ePV8WKFRUTE6OUlBT3epmZmcrKylL58uUtrPafy8nJ0SOPPKLevXvrzjvvlDFGISEhcrlcKlOmTJ6rjE6nU1lZWXnuh/Qmdrtd9957r3uUfZfLpeHDh+uhhx7S5s2bJUk//PCDcnJy9M033yglJcUrb6nJysrS/fffr379+mnQoEEaOXKkZs6c6T72McYoOjpagwcPlsPh8OoTENLFnjkvvfSSXC6XNm3a5O65UqZMGYsr84yAgAA9//zzkqR58+apbdu2Wr16tdq1a6dNmzbp119/1dSpUxUUFKSVK1d69UnCnJwclShRQsHBwe6TEL6+vlq6dKmioqJUoUIFGWMUEBAgl8t12XgC3mLHjh2aMGGCnE6npIsXKqpWrar27dtr27Zt7tuJrivXYK70G15qaqr57LPPTEZGhjl06JDx8fExTqfT3T569Ghz++23m9TUVHPbbbeZyZMnW1ht/pw/f94MGzbMHD9+3L2NlxowYIC55557jNPpNHfddZcZN26cRZV6TqlSpcy9995rHnvsMWOMMcHBwcYYY0qXLm2MMaZKlSqW1VZQhWGf/av//Oc/xhhjjh07ZtavX2+2bt1qDh06ZPbs2WPGjBlj5s+fbzp06GBxlQV3I++3l8rKyjJjxowxu3btMr///rs5cOCA2bp1q/n555/N9u3bzZkzZ0xOTo7VZeabw+Ewu3fvNsYY93vU4XC42xctWmTKli1rtmzZYsqUKWOWL19uVake43A4jK+vr/Hx8TE2m834+Phc9rDZbKZcuXJWl/qPrVq1yrRr1864XC5jjLns9byRnDlzxjRv3txERESYJk2aGD8/P2OMMbt27TJxcXGmePHipk2bNiY4ONjcddddplGjRhZXnH/x8fF5fp45c6ZZvny52b17t0lISDDGGLNz505TtGhRs3XrVitK9IgLFy6YsLAws2bNGrNq1SoTGhpqdu7cabKzs/Os5+/vb1GFnvPggw+a8uXLm/Xr15uePXuaGTNmGGOMGThwoGndurVxOBzmiy++MA0bNrS4Us/YsmWLycrKMn379jWDBw82jRs3Nq+//rq7ffXq1aZEiRIWVph/mZmZJicnxxQrVsw888wzJjo62qxatcrExMSYatWqmb1797q/T2666SaLq72IYO5huQdQWVlZeZYPGDDAlCtXztx5553WFOZBV9pGp9Np7rnnHlO/fn1Tp06dyz6svVF0dLRJSUkxZcqUMX/++edlAadq1apWlucxhWGfNcaYOXPmmJo1a5o33njDGGOMn5+fad68uWnevLlp1qyZqVOnjmnVqpXX77uFZb/NzMx0nyAMDAw0TqfTrFq1yjRr1swYY0ynTp3MyJEjrSzRY3LfoxkZGXmWf/nll8bf39888cQTFlXmOWvXrjXGXDyRFBsba2JjY83Ro0dNRESE+9+hoaEmNjbWnDhxwuJq/7mUlBQzYMAA06NHD5OdnW1sNpvJzMw0xhhTrlw5U7x4cVO8eHETHR1tAgIC8pwo9Va1a9c2xhgTGhpqpkyZYnx9fc348eNN+fLlTVZW1nVzIOxJvXv3Np07dzbNmjUzUVFR7lA3ffp0c8cdd1hcXf4lJSUZPz8/06RJE9O4cWPj6+trqlSpYiIjI83YsWPd690IwXzjxo3uz9pOnTqZjz76yBhz8cRhkyZNzIYNG8yDDz5oBg0aZGWZHjFo0CATERFhVq5caR5++GEzc+ZMY7fbzX333Wd69epljDFmxowZpl69ehZXWjDFihUzY8eONf7+/mbUqFFm48aNZuzYsebJJ580ZcuWNbt37zalS5c2e/bssbpUgrmnHTx40Pj4+JikpKQ8y51Op2nUqJFZsmSJNYV5UO42Jicn51menp5uatasaRYsWGBRZZ4VHh5u7Ha7efHFF83QoUNNQECAycrKMsHBwaZ3794mPDzc9O7d22zZssXqUgukMOyzxlw8MFq5cqVxOBzG5XKZUqVKudu2bt1qlixZYh588EHz6aefWlhlwRWG/bZdu3Zm8eLFJiAgwBhjTEREhBk9erTp37+/qVSpkhk9erR54oknTEBAgNm2bZvF1RbcgQMHjI+Pjzl//vxlbV26dDE//fSTBVV5zunTp43NZjN16tS57MA+KirK/e/w8PBrXZrHvfTSS2bAgAHGZrO5TzAUKVLEnDlzxiQlJZk2bdpcFweHnlC9enVjjDFBQUEmPT3dxMfHm+zsbBMSEmISEhJMdHS0xRUWTFxcnGnSpIk5cuSIMebi51J2drbZtm2buf32282iRYuM3W53r//7779bVWqBnTt3zhQpUsQYc/HYIPe9+Mcff5jy5cubCRMmGGOMu3eEt/vxxx+N3W43GRkZZvPmzSY2NtYcO3bM7Nq1y/zxxx8mICDArFixwuoyC2z+/Pnm9OnTxhhj7rvvPvPJJ58YYy4ez1evXt2sWbPGPP7446Z///5WlllgRYsWNcYYs2TJElO5cmVz7Ngxc+zYMRMVFWUiIyNNUFCQ8ff3v2K2uda4x9zDXC6XjDFKTU3NM7qzj4+PHnnkEX344Ydq3bq1105pI/2/bfzrqLEhISF65JFHNHfuXN13330WVlhwxhilp6crIyNDTZs21bRp0+RwOJSamqo33nhDQUFBql+/vtLS0rx2wKVchWGflaQWLVro1KlT7vv/4uPj3W0+Pj568sknNWrUKPdIs96oMOy3OTk5atCggXvgs/j4eOXk5CgjI0PZ2dlyuVzKzMxURESE6tWrp7lz56pu3boWV10wue/R5ORkRUdH52nr3bu3XnjhBdWuXfv6vF/u/6BIkSLasGGDjhw5oj59+mj27Nnu0fSzs7PdPzscDs2aNUuSvHbk53fffVedOnXS4MGDVbp0aUkX73uMiopyj3bt7TND5EpPT5d08T3r7++vokWLyhijpKQkZWdn69///rd7UCZvHEirZMmSOnjwoCZNmqQ6depo27Zt+vbbbxUXF6dy5crp3Llz+vbbbyVdHBQ3JSVFd9xxh8VV50/uZ6t08fV0OBz69ddfZYzRM888owoVKuiXX36RJP36669q27atleXm24EDB9StWzfFx8fr559/1m233aYmTZq4Z3Wx2Wyy2+1yOByqWrWq1eUWWOfOnfXnn3+qRIkS+uSTT9zvx5CQEK1YsUIBAQH67rvv9O9//9viSgvGbrdr7dq1ql27tp599lmNHj1aX375pYoVK6aPP/7YPVhuYmKi9WMHWHRC4IZ17tw5M2rUKHPmzJnL2jIzM03VqlXN888/f+0L86BTp06Zxx57zMTFxV3WduHCBVOqVCkzbNgwCyrzHKfTaSZPnmwyMjLM2bNnzbFjx4zNZjPnzp2zujSPKwz77IoVK0x4eLh58cUXjTEXu1aWKFHClC5d2pQuXdqUKlXKhIeHG19fXzNx4kSLq82/wrTfxsfHm+7du5vSpUubp556ytjtdrNixQqv7i76dzIyMsyqVatMWlraFdvbt29vXnvttWtclefZ7XZzyy23mAYNGvzto2bNmqZ8+fJWl1og8fHxplixYmbMmDHGGGPCwsJMuXLlTLly5UxQUJApXbq0KVeunDlw4IDFlRZMWFiYycrK+tvxAi4dS8BbPfnkk6ZUqVKmV69eJiwszDRq1Mj4+PiY6Oho06NHD9O7d2/Tq1cv89BDD5lOnTpZXW6+HTx40AQFBRljjElISDBly5Y11atXNzfffLP7UaFCBVOqVKk8vVy8TWZmppk4caK7K7vdbndfbc2Vmppqbr31VvPwww9bUaLHxMbGmk6dOpmAgACzZs0ak5GR8bfvz1OnTlldbr65XC5ToUIFU7FiRTN16lTjcrnc4z88/PDDZsSIERZXmJfNmBtgklcvsnfvXpUuXfqGOSN+JWvXrlWFChVUrlw5q0vxGLvdrlWrVumuu+7yyjP7BXEj7LNnzpzR2rVr1b17d0lSVFSUkpKS8qyTnZ3tniYu90qWtysM++2sWbPUsWNHFStWTJmZmUpISMgzD21hkJqaqrCwMK+dOaEw+uCDD/TSSy/p3Xff1f33359nFhOXy6XU1FRVq1bNPUe9t3E6nQoPD1dCQoIOHTqkkJAQ9/Rpl3I4HEpKSlKDBg0sqLLgvvjiC40cOVLx8fGqUaOG/vzzT50/f15Tp07VnDlz1LlzZz333HNeN4vAX+3atUsdO3bU8ePHrS7lmnI6ndqwYYOaNWuWZ3liYqKOHz+uevXqWVOYByQnJ2vEiBF6+eWXVbZsWTkcDpUsWVKnTp1yr3P+/Hk1a9ZMd999tyZPnmxhtVfHvn37VLVq1etqFimC+VWSnZ2tgIAAOZ3O6+oFByC9//77GjJkiNVlXHMpKSnWd9PCP2a323X48GHVqFGjUIdvY4zi4uJUunRpr7+1Jj09XUOHDtUbb7yhkiVLWl0O8ikpKUkBAQEKDQ3VokWL1LZtW/dJ0KSkJH366afq3bu3195mgsIjJydH33///WXT+sXGxurQoUNq06aNRZUVLgRzD3K5XPLx8VFcXJwqVKigpKQk/fLLL1q9enWeOYW91enTp7Vnzx4FBgb+1/ukzpw5owEDBmju3LleeZXO4XBo/vz5Cg4O/p8HwdnZ2QoODtbdd999jarzrOzsbPXv31+zZs1STk6Oxo0bp5EjR2ro0KGaMGGCHA6HSpcurYSEBKtLvapy37s3iqysLH399dd6/PHH3ftwcnKye45ob70/91K5cySHh4crNDTU/QgJCXH/u1SpUgoPD7e61ALbu3evatWqdcWxAYwx2rlzp6pXr+61c83mSk5O1uOPP65//etfVwyrSUlJKlasmLZs2aJbb73VggoBFAa54yJcKjs7W/7+/oX65Ki38qbjBYK5hzidTgUEBOjEiROKiIhQeHi4cnJyNHr0aJ0+fVpTpkzRW2+9pW3btun9999XTEyM1SX/Y999950ef/xx1apVS5s2bXIvX7hwoe6++259+OGHeuKJJ5SRkaGyZcvK6XRaWG3+ZWdnKygo6P/U9ez48eNq166dlixZcg0q86zdu3fL19dXt99+u/bv36+cnBzVqlVL+/btU/Xq1XXgwAG5XC7VrFlTFy5csLrcAklJSdGAAQMuG8AkPj5eTz75pAYOHKhJkyZp8eLFFlXoWbmDhDkcDknSsWPH3AMyLlu2TKVKlbKyPI84cuSIqlatqgYNGigjI8P9SE9PV2Zmpru30uTJk9WvXz+ryy2QI0eOqEqVKu7Bly6Ve3Jwx44dqlmzpgXVeU5GRobCw8N16NAhbd68Wd27d8/T4ywnJ0eBgYE6ePCgKleubGGl+WO327Vu3TpJUo0aNdy3zCQlJentt9/We++9J2OMtm/f7tVdZHPt379f3bt3/68Hw2XLltVjjz2mkJAQq8vNN2866C+IwvJ6Xrhw4bJBNqWLgzSuWbNGTZs2taCqq6Ow7LvedLxAMPegoKAgpaWlydfXV35+fnI6napbt65mz56t2rVr66OPPtLnn3+u6Oho/f7771aX+48tXrxYkydP1vz589W/f3999NFH+vHHH/Xcc89pw4YNatiwoY4fPy4fHx+VKlVKWVlZVpecLy6XS35+fnkOgufMmaNevXpJkn744QfVrFlT1atXl4+PzxUPlq93J0+eVJMmTTR27Fg9+uijKl++vIwxOn78uGJiYtz/lS6GvMTERIsrLpisrCzdfPPNOnr0aJ6z3RcuXFCzZs304IMPKj4+Xv/617/UqVMnzZkzR1FRURZWXDCZmZkqUqSI4uPj9cknn2jGjBkaOHCgXnzxRa/sxXIlJ0+eVNWqVZWRkXHF9szMTD3xxBNat26dDh8+fI2r86zjx4+rUqVK7hMtf+Xj46N9+/apWrVq17gyz8r97F26dKnat2+vyMhI3X333erYsaN7HAEfHx8dP37cK8cRSExMVLFixVS+fHmNHTtWPXv2lHQxsIeEhCg1NVVz5szRU089paFDh2r8+PEWV1wwx48fV5UqVfTRRx9ddjCckZGhtLQ0/frrr+rYsaNmz55tdbn55k0H/QVRWF7P3IszP//8s8LCwiRdfI+2b99eycnJXh9SL1VY9l2vOl6wYsS5G012drZZt26dCQ0NNTt27DDbtm0zfn5+5t133zVRUVGmS5cupmPHjiY5OdkcPXr0snlavcWvv/5q7r33XmOMMYGBgWbevHnG39/fNG7c2BhjTHR0tMnKyjJpaWkmNDTUylILLHeU2KeeespkZGTkGZVz4sSJpkyZMsYYY2w2myX1FZTT6TQ//vijcTgceUZQzf137nylly7zZi6Xy1SrVs2MGTPG2Gw24+fnZ3x8fMzXX39tatSoYapXr25iY2ONMcaUKVPGpKamWlzxP5eVlWVuvfVW88gjj5iHH37Y+Pj4mKCgIFO2bFnz0EMPmd69e7sf3bp1M3feeafVJefb7NmzzcmTJ01wcHCe5Tt27DBPP/20++f33nvPq0dCzhUXF/dfvzdsNps5dOjQNazI8zIyMkxCQoLx8fExp0+fNn/++af55ptvzLBhw8ydd95pQkJCTMOGDY2Pj4+Jj4+3utx8yR2h3G63G5fLZebMmWN2795tjDGmRIkS5vjx48bpdJoxY8aYiIgIi6stuBMnTlz2HjXG5Jld4M033zSRkZHXsCrP+7vtzJWRkWF69+5tKlWqdA2r8rzC8noaYy6bzzorK8urZw/4O4Vh3/W244Ub56ZKC50/f17333+/srKydN999+n++++Xy+XSunXr1Lp1a3Xr1k3r1q1TaGioewCQ3LkCvcmlI6oGBwdr586d+uCDD5ScnCzp4jykAQEB7h4DN4Lvv/9eNptN/v7+WrFihVasWKE6deooJyfHK6+U5zp+/LgeffRRlSxZUqmpqWrVqpVatWrl/ndaWtply7xZdna2/Pz8FBISou+++07169fXK6+8osDAQO3fv1+DBw9W27ZtlZCQoODgYK+8V9dut+vee+9VvXr1dNttt8kYI19fX9166626/fbb1ahRI/ejcePGat68udUl58vZs2fVp08ftWzZUi6XSydOnJAkLVq0SPXr19epU6fcn0kDBgzQwoULrSy3wPbt25dnlNwb0W+//aby5cvr/fffl3Tx+6VevXp66KGHNGHCBK1atUoJCQkaMWKEJHnteBABAQHuHjtt2rRRnz59dOTIEUlSmTJllJCQIB8fHw0YMMA9B7g3cjgcateunbZu3ZpneWpqqoYNG6bKlSsrLS1NklS1alV98cUXVpTpEXPmzLniPcc7d+7UM888I+ni/ly3bl3VqFHjWpfnEYXp9cxVGO4jLwz7rlceL1h9ZuBGculVxuDgYONyuUyvXr1MWlqaKVasmLstMjLSPUeit1i2bJl5+OGH3VfMS5cubYwxJicnx5QsWdJs2rTJlChRwmzevNmsWbMmz/8Lb5R7ZrRUqVLG4XCYYsWKmQ4dOpj27dubDh06mKFDhxpjvPeKea7jx4+bWbNmmXvuucf4+/ub3r17m02bNpktW7aYLVu2mF9//dWsX7/eLF261OpS8+38+fPG19fXfaV8wYIFpnHjxmbEiBHm559/NnXr1jXGGFO7dm2TnZ1tqlWrZm3BHpCSkmJsNpvZtm2bGTZsmKlcubJ5+eWXTWJiotWlecS5c+fMggULTL9+/UxERIRp0aKF+emnn8zKlSutLs1jzp49a+655x7j6+trhg0b9j+vmB8+fPgaVudZ+/btM4MHDzY7d+40Pj4+5sKFC8YYY/bu3WsmT55s+vTpY+rUqWOys7ONzWbz2ivmxlz8brHb7Wbx4sXGZrOZSZMmGWMuzkW/fPlyY4wxSUlJXn11LjEx0XTs2NEUK1bM+Pr6mjFjxphDhw6ZxYsXm5tvvtn8+OOPxuVyWV1mgZ05c8bYbDZTtWpVExgYaOLi4owxxixcuND4+/ub+++/370vX3r11dsUltfT5XKZ1157zUycONH4+PiYt99+20ycONFMnDjRTJgwwfj4+Lh/fvPNN82QIUOsLjnfCsu+a4z3HS/cGJc1rxN/PfNks9mueDbK399fxstu7X/yySdls9ncgwsZY7R3717VqFFDWVlZ6tSpk5KSktSxY0dJN87ZRpvNJqfTKT8/Pz388MMqXbq0GjdurNDQUHe7Nxs9erTS0tJUt25d3XXXXdq0aZNuueUWLV++XG+99ZbKlCmjn376yeoyCyQqKkrx8fG666671Ldv3zzvvdz36KhRo3ThwgV9/PHH7rOn3sxms+mZZ55RrVq1NGHCBDVt2lRr165V1apVNXHiRD366KNWl1ggxYoV07333useZX78+PGqWLGiYmJilJKS4l4vMzNTWVlZXjmHcGRkpFwulxYuXKgaNWrogw8++K/re9t3yqWqV6+uDz74wH0PvcPhUK1atdzfMXXr1lW3bt3cV5G9cVuPHTumX3/91f1zhw4dVL16dR09elSSVKJECfdYHomJiV7d6ywqKkqLFi2SMUbr16/XlClTVLNmTU2dOlXbt2+/Yca5KFGihM6ePav169frp59+Us2aNVWvXj299NJL+uWXX9SyZUv3ut48TWVheT0zMzP166+/KjQ0VMYYLVmyxP0+zO0h+fPPP0u62AsvMTFREydOtKzegigs+67kfccL3vvJfx1xOp1q27at0tPT1bVrVxlj3PMBSnkDusPhUFpamtd96W7fvl1bt251fwjl5OSoefPm+vbbbxUdHa3du3ercuXKOnnypLKystyjzXo7p9OpnJwcORwO/fLLLzpx4oR27dqlSZMm6YEHHvDKA8RL7du3T926dVNgYKC2bt2qn376SdWrV1dUVJQmTJigdu3aWV1igfn4+Kh48eKy2WyXve9Wr14tm82m8+fPy263KzY2Vjk5ORZV6jmpqamaN2+ePvjgAz3yyCPavXu3Nm7cqI4dO+qJJ57Q/fffr8jISKvLLBCXy+UetNAYo88///yydYwxuummm3T8+HELKiyYgIAALVq0SNLFgWsKg8zMTBljlJWVpY8//lg333yzypQp4253OByy2Wxe+R5t3769O4Tn6t69uz777DMtW7ZM8fHxWrlypcaMGaNz586pePHiFlXqOU6nU6tWrVL9+vUVGBioc+fOafLkyXnWyczMlI+Pj4YOHWpRlQXjbQf9BXGjv54hISHavHmzpIu3bi5atMgdSrOyshQaGqqVK1daWaJHFaZ915uOF7wrHV6nnE6n7r//fm3cuFGdO3eWMUYLFy7U+++/r/Pnz6tnz55KSUlR165ddezYMZUuXdrrzjCGh4fnuac6JydH3333nXr37q2srCydPXtWTqfTvY433399qapVq8rlcsnlcmnWrFmSLs4p3KpVK5UrV87rr5i7XC41bdpU//nPf/Tbb7+pQ4cO6tu3r5YsWaJhw4Zp586devLJJ1WkSBGrSy2QLl266MSJE5ozZ45ef/119/J169bpwoUL+uijj7Rq1Sp98MEH+uWXXyystGC++eYb1ahRQykpKUpOTtbu3bsVGhqqqVOn6sCBAypWrJhmzZqlPXv2KCUlRXfffbfVJefL77//rmbNmikgIEB79+6VdPFLtW7dutqxY4eMMapVq5Z2797tdSdBr8QYI6fTqYcffviK7bk9e7zdmTNnNGXKFEVERFxxXIuMjAwZY7zy/usffvhBFStWzDONVPPmzTVr1iy9+OKL+uWXX5Samqpu3brJZrPplltusbBaz/Dz89OoUaPUvHlzGWN08OBBrVu3TnfccYek//c+LlGihMWVFow3HfQXRGF5PaXLj2G9/Vjv7xSGfdfbjhesr+AGEBAQoOeee04vv/yyHnnkEfn6+qp///764YcfVKdOHTVs2FBdu3aVdLEbe7NmzSyuOH8unaonIyNDDRo00Ndff622bdvq9OnTys7OVnZ2thwOx99O6+MNLv1Azp3W7rnnnnMvq1GjhqZOnaq6dete89o8rXfv3qpRo4YWL16sH374QTExMYqOjlb79u2VmJioV155Rc8//7xmzpxpdan55nA41KRJE23atEnly5fP8wX7yiuvqHv37nrppZeUlpamCxcuyOVyyeFwXBcf0P+E0+lU//79FRERIT8/P2VmZuq2225TSEiIli9fLun/dQHOfX/GxcVZVm9+nTlzRi1atFDt2rXlcDjynMX39fV1/+zj4+PVZ/gv5XQ6ZYy54qCExhh17drVKwcsvFRKSoo6dOigBx54QOHh4fr3v/+txo0bKyYmRiNGjFDr1q3VqFEj/fjjj3muonuLGjVqyBiT5/OnfPnycjgcevTRR+Xj46PNmzd7/W0m0sXPlyeffFLPPPOMAgMDtWLFCndbVFSU+6pjeHi411+B9LaD/vwoTK+ndPHC09/dinojKQz7rjceL3jn/+nrkMvlkt1uV2JioiIiImSMUalSpfTBBx/o888/15o1a7z+TZ6VlaWMjAwdOnRI06ZNU1hYmO666y499NBD8vf3V2pqqvtKhjeOOp/L4XDIGHPZFZu//jxx4kQZY9S6dWv99ttv17JEj1i2bJmGDBmipk2bavTo0ZozZ446dOigAwcOqEmTJnI4HKpfv75q1KihhQsXqmPHjl45GrKfn59eeuklTZs2TQ0bNtSUKVMUGxurZcuWqW7durr55pu1Y8cOffbZZypWrJi721bu/KXewtfX1/3+S0xMVExMjH755Rf9+OOP2rBhg5o1a6YnnnhCFStWtLjSgilSpIg2bNigI0eOqE+fPpo9e7b7hEN2drb7Z4fD4e7l0qdPHytLLrDcLtzTpk3z+u+RvzNs2DDZbDYNGTJEKSkpGjhwoL7++mtJ0q5duzRx4kRFRESoU6dOysrKUo8ePSyu+J/76wnrnJwcJSQkSLrYrfTs2bNWlOVxZ8+e1bp16/TNN98oKysrT9ul+6+378veeNCfH4Xl9cyVe0vNX3sKGmP09ddf/23PJW9SWPZdbzxesBlvv0n2OrJ7925Vr15dCQkJKl26tFJTUxUaGqrGjRvr3Xff9drpiXLNnTtXTz31lGw2myZOnKjevXvnaT927JhiYmJ08uRJxcTEuKeo8jY5OTmaNGmSgoKC/ucXjdPplN1u1wsvvHCNqvOcRYsWaf/+/RoyZIgkqVOnTu7Bwk6ePKn9+/drw4YN+v777+VyubRjxw6LK84/u92uWrVqacaMGfrjjz8UGBio7OxstWjRQo8++qg+//xz9e/fXzt37tRdd92lH3/8UUWLFrW67HyLj49X+fLl3SfI7Ha7Jk6cqHHjxunDDz9Uv379LK6w4LKzs3Xrrbfm6Rr8V5mZmUpLS1NsbOy1K+wq2L17t2rXrq3MzEyvvzL+dw4ePKjs7GzVrFlTX3/9td566y3t2bPH3Z6SkqLZs2fr/fff10MPPaS3337bwmrzJy0tTREREVq4cKGioqIUExMjf39/lShRQhs2bNCwYcO0du1aq8v0GIfDoaCgILVo0cK9bO3ate5joTVr1rjbLr0K6y3sdru2bdvmPuifNm2a+6D/6aef1r/+9S8ZY/Tkk0+678W2+qC/IG701zNXTk6ONm/erMjISPcxoNPpVEpKiqpUqaJSpUpZXGHBFbZ915uOFwjmV4HD4dDhw4dVrVo12Ww2HTx4UMWKFVNUVJTVpV0TWVlZ2rRpk1q0aHHDnEGVpEOHDqlKlSpWl3HVJCUlyc/PT+Hh4Ze1paenu0ei90YJCQm6+eabde7cuTzLT58+rebNm+vgwYOqWbOmvv32W9WuXduiKj3H6XTqxIkTl53p3rhxo2JiYryyK3Bh5nQ6df78eZUsWdLqUq6ZI0eOqFKlSpctzz3h6429d86dO6eGDRvKZrPp5ptv1pIlS9xtO3bsUKdOnbzy9pK/43A49OGHHyo4OPhv18nMzFRqaqpGjx59DSvzLG866C+IwvJ6/jfHjh3z6ivIf1VY9l1vQjC/ipxOp6ZPn6633npLHTp00L/+9S+rS0I+/fHHH2rZsqU+//xz9erVy+pyPC4xMVFt2rRRly5d9Nprr8nX19fqkjwuJSVFu3btUoMGDa44+GJKSorXTwsiSbNnz1avXr00ZcoUNW3aVLVr11a9evW0bds2SdIDDzygunXravjw4dYWepUZY5SUlKQiRYp4ZYj7vzhz5ozi4uLUoEEDq0spEKfTqZMnTyokJEQ2m00ZGRkqV66csrKydPvtt2vRokUqV66cpIs9e7p27eq1vT6cTqc2btyoBg0a5On9kDtji7cPtAncCNLT0zVhwgSNGjXK/f0RFxen+vXra9SoUXr66actrhBXw1/HAbHCjXm0ch1YuXKlateureeff17t2rXTs88+a3VJBXbgwAFVqlRJp0+fvqztRpj7+e8cOHBAXbt2VXR0tJo2bepe7nK59P777+utt96ysLqCS0xMVOvWrRUaGqqBAwfqq6++0uOPP66kpCSrS/Mof39/NW/e/G+360YI5ZLcgWXmzJnueZPj4+MlXbx9Ye3atTfkyaW/OnnypIoXL+7Vt2AcPXpULVq0UHp6uowxWrZsWZ72d955R7fffrvuv/9+awr0kMzMTFWoUEElS5ZU8eLFVbFiRblcLmVmZmrXrl3uW6Li4+O1dOlSrx7DJPcWmgsXLuRZ7ufnd8OH8pycnMtGdj506JAyMjIsqujqO3PmzA0xY8KV3Mivp9Pp1NixY92hfNOmTWratKnq169/2W2cuL7Z7fbLlvXo0UNz586VJPfYHqdOnbouppH1vhuAvcBnn32mwYMH64knntCoUaMUHR1tdUkeERAQoNjYWAUFBeVZ/tprr2nZsmXatGmTV95T/t/8+9//1qBBgxQTE6N58+a5uzDt2rVLvXv31s6dO/XAAw9YXGXBDB8+XKVKldL8+fMVGBjonsvzlltu0ZQpU3TvvfdaXaJHBAYGyhjjHtQtJydHY8aMuWx/TktL09ixY60o0SNy34O7du3Sp59+KunioDxvv/22NmzYoDVr1qhChQoWVuhZBw8eVN26dXXy5Mk8twvlDsLpzbdgSBen9AsICNDy5cv1wAMPaMOGDapZs6akiwNQ3n333erQoYPi4+NVunRpi6vNn9DQUFWqVEm7d++WMUa33HKLBg0apNKlS8vf3999ZXns2LEqVaqUV59YKiyfQ9LFk9fnz59X8eLFZbPZtHfvXt16661KT093b+9PP/2kCRMmaOvWrSpbtqzFFedfWlqannvuOX366ad5ugXnnihduHChVaV5TGF4PXNycuTv76+AgAD5+vpq586d+vDDD7V9+3a988476tmzp9UleoQxRitWrJC/v///XNdms6ly5cpeewtc2bJlNWjQIA0dOtT93lyzZo1atWrlHhPs0oG7LWfgcYcPHza7d++2ugyPi4+PNz4+PiYzM9O9bOLEicbHx8e8//77FlbmWZmZmWb69OnmjjvuMAEBAeall14ydrvd3T516lQTFBRkbrnlFtOgQQMLKy2YlJQUY4wxFy5cMNnZ2Ze1f/XVV6ZIkSLmp59+utalXTU+Pj7ubXW5XMZms5mWLVu6Hz4+Puauu+6yuMqCCQsLMwsXLjSNGzc27733nqlQoYLx8/MzERERpmLFiqZixYqmfPnyJjw83OpSPeLs2bPGZrOZrKysPMvtdrux2Wzm2LFjFlVWcCdPnjQ+Pj7un2fMmGGqVKlizp49616Wux8fPHjQihI9pmrVqu5/V65c2SxevNisXr3ahIaGmpSUFPPFF1+YwMBAs2zZMgur9IzC8DlkjDGnTp0yPj4+Ji4uzhhjTGxsbJ792RhjOnfubOrWrWtBdZ6VkZFx2fHRv/71L+Pj42OWLl1qYWWec6O/nunp6aZkyZLmjjvuMM2aNTM2m834+PiYsmXLmmbNmpnmzZu7H/Xr1zfly5e3uuR8c7lc7m276aab/uujePHipkKFClaXnG/+/v4mMjLSVK1a1Wzbts0cO3bM+Pj4mEOHDpmcnBxjs9mMy+Uy2dnZ18Vx0Y11edNC2dnZ7vtWrzRgzY0g98xa7hW5X375RS+//LKmT5/u1aM1/lXume8HH3xQM2bMuGzAt+joaH355Ze64447dOedd1pUZcEYY1SyZMm/HV00KytLzz77rH744Qfdf//92rFjxw0x4Imvr697/82dp/TSeVf9/f29ejRZ6eL0Jm+88YZmzpypYsWKqXPnzmrcuLHatGkjh8Oh1157TVFRUTfMrQq5syf8ddyA3M8rbx4vIXeQs4MHD8oYoyZNmujBBx9Up06dNHv2bNlsNh09evSK2+9N7Ha74uPj1bVrVxljdPbsWXXo0EHSxdevf//+2rhxo5YvX65mzZpZXG3BFYbPIUmKjIyUMcbdkyU4ODhPz4Bjx45pyZIlmj59ulUlekxuT4jc7fvss880ZMgQTZ06VXfffbfF1XnGjf56GmM0btw4hYeHyxijdevWKSIiQrfeeqvuv//+PMdLmZmZXn0LZ+53xtatW1WiRIn/uq7L5VJISIiSk5Ovi67e/1RwcLD27t2r7t27q0WLFmrSpIk6duyoxMRExcbGytfXV6tWrbpubjlh8DcPKVeunKpWrfo/1/P19dUDDzyggQMHXoOqPOvChQuKjo5277z33HOP6tWr5/X3WF9JRkZGnu5oSUlJiouLU506ddzLkpOTVbZsWaWlpVlRYoE4nU4tWLBARYoUuWJwSUtLU/ny5VWrVi09//zzSk5O1owZM659oR4WEBCQ5/7U//Wzt/nhhx/Uu3dvvfPOO6patarsdrsqVqyoFi1aKDExUUuXLtWQIUM0ceJEd/DxRlu2bHEPeJaenq6IiIgrfqn6+Pjo1KlTXju9TUJCgrvb6N99VdtsNtWqVUt//vmn1w5yl5mZqenTpys4OFjGGKWnp0uSDh8+rC+++EJDhw7V8OHDb5hbpW70z6FcLpdL/v7+stvt8vPzU1JSksqXL6+UlBSdPn1aHTp0UMmSJbV06VKrS823zMxM9yjlvr6+Wr9+vd5++235+vrqzTffzDPLh7kOBpYqiMLweuZKSUlRkSJFdObMGc2dO1cff/yx6tatqzfffFPVqlWzujyPCA8P1+HDh1WiRAn3HN5/lZOTo379+nn1rERhYWFKS0tTcnKyYmJilJaWpn//+9/6/fffNX/+fMXFxSkmJkbSxe/clJQUS+slmHvICy+8oLCwsP954LB+/Xr95z//0fnz569RZZ6TnJysqKgoHT58WMYYpaamKiws7LIvmqysLKWmpqpRo0YWVep5b775pt555x2NGzdOgwYNknTxS9bPz08XLly44hRj3mLbtm0aP368+vTpo1atWl12r2NcXJyqVKmiY8eOeW3AueeeexQREaHvvvtODz74oHv5d999J4fD4f7Zmw+IMzMz1aZNG/3xxx9q1qyZjDGy2+164oknVL16dd1+++1avny5oqKi1K1bN61bt8490rU3McbkueIoXRzR+kqfvU6nU6dOnfLaacZy73/Lzs6Ww+FQUlKSihcvbnVZHudyufT5558rODhYNptNWVlZysnJ0fr16zV37lz5+vqqU6dOmjp1qlcPkFYYPodyffvtt6pZs6bq1aunU6dOKSIiQna7XTExMXr33Xf11ltvqX379vrwww+9dhwIp9OpyMhI98Chp0+fls1mU3BwsMLDw/McG2VlZSklJSXP6+xNCsPrean09HRNnDhRw4cPl8vl0uzZs5Wenq7hw4frhRde0IgRI7z2RGiu0NBQHT16VCVKlFDp0qVVr149SdKqVavUsmVLSdLq1au9fjA/Pz8/ZWdnq2/fvvrxxx/VuXNnHT9+XKtWrZJ0sYdSTk6OXC6XihQpYnkwvzFOP18HPvjgg//TesuWLdOTTz7p1VMz/fWs2V/PAuf+fL10C8mPMWPGKCIiQs8884z8/PzUo0cPJSUlafjw4Zo3b55mzJihChUqKCIiQvHx8V4dzB0Oh1JTU/XYY48pPT1dXbt21SuvvOIeYKpcuXKqVauWFi1a5JVTFGVmZiooKMjd1fDSKYpuJMHBwe6ud5GRkZo+fbo7xFSrVk3ff/+9nn/+eS1evFh//vmn1w5K6XA4NGPGDJUsWfKyk0iXMsaodevWXn2F6lIZGRkqVaqUypQpo7Zt2+qxxx5TixYtrC7LI3x8fDRs2DA1b95cxhht2LBBBw8e1LPPPqvFixdr9erVevrpp9W4cWNt2LAhzyB/3qKwfA5JF3tcPfzww+73XunSpWWz2eTj4yOn06kRI0bohx9+8PrbEv6/9u47Oqo6f+P4e1IJCUmAkEBoCQkt9KaUVcqCIMVCRwQUEFYUUYqURYpKcWFZBWERCwvSFKRsQATEpSh9ESSAkBBqCCRASG9Tfn/wY9ZIkX5nkud1Tg4z917OeXK+SeZ+7rdZrVaWLl1K4cKFcXFx4c9//jMRERHk5ubSp08f+y4uNpuNlJQUw2/471VBac/funr1KpMmTWL06NG0atWKQoUKsXr1alq0aMHAgQMZPny40z+AsFgsWK1W4Frxun79euBa+15/fX2RSmdmtVrZt28fx44dY9euXYSFhREWFsaWLVto3LgxVqsVm83mMDWLeswfgm3btpGSkkL79u1vOJeenm7fq9XZXL16lWLFihEbG3vb/Dk5OWRmZuYZ9u1s3nnnHWbOnEnx4sWZPn06HTt2BK49ER8zZgzffPMNU6ZMYdq0acyZM8dphwVfuHABf39/e4GzY8cO/vGPf/D666/nmT8/YMAAfHx8mDFjhlFRH4iCMITUz8+PESNGsGrVKhYsWMDSpUuZN28eL7/8MjabzT5/Li0tjWnTphkd96FycXEhPj4+X/SYW61WQkNDiYyMZNOmTYwdO5bmzZuzcOFCAgICjI5638LDw4mJiQGgTp06bN68mWLFilGkSBHOnTuHzWajQYMGPP744yxatMjgtPcnv/8dstlsXL16FU9PT4oUKUJqairZ2dmcOnWKJk2a0K5dO7799lvGjh3L6NGjjY77QFgsFjw8PLBYLGzatImRI0fi4+PDzJkz7T2RzqogtefUqVOpUqUKaWlpDBgwgGXLlhEZGZlnd5rrWzmmpaXxyiuvGJj23pnNZjw8PIiOjiYsLIxy5crZt74LDg7m/PnzwLXdTZz1gdJ1DRo04KuvvqJChQocOnQIf39/5syZQ3x8PNOnT6dq1apER0fj4uJC6dKlSU1NNTSveswfgm3btjFu3DhCQkLo168fAwYMsA8/dOYnbBaLBZPJRLly5Zx+CM8fee+99xg5ciSTJk2iZ8+etGnThs8//5ySJUvyxRdf8NJLL/Hiiy8SFxfHsWPHnLYwf+GFFwgLC+PTTz9l06ZNxMXFsXz5cn7++Wd27NhBo0aNMJlMjBgx4o7WUHB0NpstzwgPq9VKYmKi/ThAYmKiUw8XtlgsjBkzhjVr1vDGG2/QsGFDXFxcmD17Nn/+85+pX79+nu/XmcXExORZC+L3bjc32xlYLBbMZjPPPvssLVu2xNXVlZo1a/LDDz/QoUMHfH19qVevHj/88ANhYWFGx70vubm5HD161D4F43q75ebmkpubS0BAAJMnT6Z79+4MHz7cqYud/P53yGQy2ReMAihcuLC9Q8Ld3Z3ly5fz008/8cwzz3Ds2LF8sX7J9Z/ZnJwcWrVqRcuWLZkxYwZNmjRh/PjxvP3220ZHvGcFpT3NZjMLFy7E19cXV1dXsrKyeO655/D29ubXX3/F3d3d/vtpNpuxWq1OW5i7ubnZe8vh2mfNjBkz7Gt8XH/tKL3I92Pv3r3Ex8fTvHlzTpw4wdixY2nXrh379u0jICCAxMREAC5evEjlypUNTqse84cmMTGRb775hk8++YTY2FjGjBnDyJEjjY51Xy5evEhwcDAZGRn5ehje70VFRdGxY0cyMzNZt26dfSRAfHw8gwcPZtCgQbRo0cLglHfvyy+/ZOTIkURFRVGsWDFq1apFvXr1+OKLL+jfvz9Lly7F29ub1q1b06ZNG7p27XpHe146MldXVzIzM/Hw8LA/Mc5v0zCu97YtWLCAvn37snPnTvr27cvSpUuZOnUqubm5fPLJJ045HPj3unfvztdff33TETzX2/L06dOUKVPGgHT37/z585QpU4ZPP/2UjRs3snbtWjp16kT9+vV59tlnKV++PC+//DKbNm1i7969TruPudVqxdvbG29vb6xWKxkZGZw9e5YSJUrg4uLCmTNn7G0YEhJCu3btmD17tsGp711B+Dvk7+9P586dmT9/vv37SExMJDQ01L5g6i+//EKjRo2YNm0agwYNMjLuPdu4cSOPP/44JpOJPn36sGjRojwdMDt37qRTp07s37/faddogYLTntdduXKFcuXKcfz4cVatWsXixYspV64cgwYNyhdTiGw2G9OmTePy5ct88MEH/OUvfyE7O/uG66xWKwsWLDAg4YPTtm1bGjZsyP79+6ldu7Z9J4zfM5vNJCcn89FHHxmQ8n9UmD8Cy5cvZ+XKlSxdutToKPfl9OnTVKhQgStXrjjllgn3Izk5mWbNmvHEE08wc+ZMo+M8EGlpafz888888cQTnDt3jgYNGnDw4EH71hnZ2dmsX7+ev/3tb/Z5n87MbDbj6elJUlISvr6+WK1WoqOj88xTttlsJCcnU6tWLQOT3p8lS5bwwgsvkJmZSc+ePZk8eTIdOnRg69atBAcHM2vWLObNm8cvv/zilFNqfismJgaTyXTThd9SU1OpVasWMTExhIaGGpDu/p08eZKwsDCys7Nxd3cnMTGRd955h+XLl7N8+XJatGhBVlYWtWrVYvjw4U7be3Mr2dnZtGnThkWLFlG6dGkAxo8fT3h4OL169TI43b0pKH+HFi9ezK5du1i9ejWurq68/vrrdO7cmUqVKuUZpj916lSmTp3K8ePH/3DbJkfUuHFjjh49Ss+ePW+ZPyEhwX7uzTffdMr1hQpKe14XFxdHSEgIubm59mOLFy9myJAhTJgwgddff93AdPdvwoQJzJo1i7feeouxY8caHeehycrKYsCAAfj5+eHm5nbDPc9vS+CsrCwyMjIMfxChwlzu2OHDh6lRowbnzp0jODjY6DiPXHx8PJ6enhQrVszoKA9FVlbWLRfTcubFCq9LT08nPDyc/fv3O23P4r2KjY2lfPny9q3xTp06RUhIiLGhHrLrvc1RUVFEREQYHeeeJCYmMm/ePIYPH55nlNK8efOoWLEizZs3B2DPnj089thjRsWUu1DQ/g5ZrVaWL19OZGQko0ePpkaNGmRlZeHh4QFcWxSvePHi/POf/6RPnz4Gp717ly5dYuPGjSxYsIDvv/8egNatW+fpvLBYLCQnJ5OamsqqVaucds0LyP/t+UdiY2MpU6aM/ft1VufPn8fV1ZWgoCD69+9/RyuvL1my5BEke3jWrVvHa6+9hpeXFx4eHvj7+7N161ajY91AhbncFYvFctN9r6/LzMwkKysrXwyTFRHncn3l4zJlypCZmcm2bdto3LixU++aIJJfpKamsn//fp588sk8PVfr16932nVafisqKophw4Zx9OhRVqxYke8fluX39iwo3n//fdLS0m45JSw9PZ3U1FSnXTvguvT0dBISEkhLS2PPnj14enry4osvGh3rBirM5a4dPHiQhIQEvLy87IuAlCxZEn9/f9577z0mT57MlClTePPNN42OKiL5kMVioW7dumzcuDFP79P777/PrFmzuHjxooHpRKQgmzVrFr179y5wU/5EHJHVamXRokV06dIFLy8vrly5QokSJUhISHDIbWNVmMtdW7duHc888wyVK1cmJyeHrKwsEhISWLRoEc2bN+eTTz7hk08+4ezZs0ZHFZF8ysfHh5iYGObMmYObmxuFCxfGarVSq1YtJk6cyPr163VjLGIgs9l80/UfCoKDBw/yxBNPkJiYmG8Wyy3I7ZmfWa1WIiMjKVu27E3P22w2MjIyeOKJJx5xsgfHzc2NxMREVq9eTXZ2Nq+99hpXr17NMwLYZDLh5eVlYMr/z6HCXO5FxYoVmT17Nk899RQAQ4YMwdfXl/fee4/ExESCg4PzLJohIvIglShRgpiYGKpWrcrIkSOZM2cOAwYMoHr16rzwwgskJCTcdtqNiDw8GzZsYN68eSxevBibzca7775705tek8lEnTp1aN++vQEpH4xDhw5Ru3Zt0tPT7eu0HDt2jIiICKdeWf+3ClJ7FjRXrlwhICDgluevD3F35p9lb29vkpKS6NChAw0aNGDy5Mk3XQjO19eXvXv3Gro9cP7ejFoeqGHDhjFv3jzmzZtHSEgI06dPt78PCwsjKCiIFStWULRoUSwWiwpzEXkofvnlF8xmM97e3hQvXpwhQ4bg6upq/6D18PBg8eLFLFy4kB9++MHgtCIFT4kSJUhJSaF27dqcO3eOGTNmcPLkSWbPns3JkyeZNGkSJ0+eZM2aNfTv39/ouPfF09MTm82WZ0GwwoUL33IxVWdUkNqzoPHz8yM1NRWr1XrTL5vNlmflfWfk6emJ2Wxm+/btvPzyy3h6enL+/Pk8X3FxcVSuXJmVK1camlVjUuSOZGZmcvz4cS5evIjJZCIoKIilS5dStGhR+4dRTk4OlStXpnPnzsC1YU/Ovu+1iDiWlStX0rlzZ/s2adeL8d8PSZs/fz7Z2dns2bOH06dP27fbEpGHr27dumzatImFCxdSsmRJvL29mT9/Po0aNWL+/PmsWLGC+fPnExkZyT/+8Q+j494XFxcXTCYTLi7/6+tyc3PLV/c/Bak9CxpXV1e8vb1vef5W+347E09PTw4ePEhERARhYWEAFC9enMOHD1OkSBH7FpV///vfqVevnqFZVZjLHfHy8iIyMpLLly/bF0s4c+YMvXr1umHIUlJS0i33FhYRuR/NmjVj27ZtdO7cOc+onN/OyrLZbPznP/8BIDQ0lBMnTqgwF3lEbDYbXbt2pUaNGgDMmDHDfu73N/jt27enQ4cOjzTfg3azouVmeyanpqY65Q4RBa09Jf/IzMykTZs2ZGdns3PnTvtuASaTiZycHOrWrWsfMVCkSBFOnz5t+DxzVU5yV6ZOncry5ctxcXHh8uXL9OnT54YFlpKTk6lQoUK+elosIo6hWLFi/OlPfyInJ4f09HSSkpJYuHAhZrMZq9UKQFZWFpmZmXh5ebF161bKlStncGqRgiMnJ4fChQtz+fLlPL3IN+PsPXHX2Ww2ChcunOdYdna2/ZjZbMZisTjlPN2C2J4FQcuWLfP9Z2Nqairh4eHs2bOHkSNHEhkZaT/n5eVFYGAg8fHxVKxYkejoaAOT/o8Wf5O7cvz4cVJTU/Hy8iI1NZWmTZuybdu2PMW5i4sLpUuXvuFDSkTkQfHw8OD48eN8+OGHuLi44O3tjY+PD66urpw8eZIPPvgAHx8fo2OKFGinTp2ibNmylChRgitXrtC4cWN27NiBr68vKSkpRsd7IE6cOEHFihXto3QArl69Sq9eveyFgMViISkpiU6dOhkV84EoCO1ZECQnJ9OjRw+KFCnyhw9bvv76a3Jzc//wOkdWsmRJXnvtNdatW8fOnTvx9vYmIyOD4OBgzp8/71CFuXrM5a5UqlQJgJSUFP7yl78wadIkQkNDKVGiBN999x1vvfUWR48eNTiliORncXFxfPrpp5QoUYIPP/zQfnzu3LnMmTOHjRs3qigXMVBGRgYTJ05k9uzZbNy4kdTUVFq0aMHhw4dp0aIFGRkZtGjRwn69sy7S2KNHD7p164bJZKJp06b245cuXcLV1TXPMWdWUNqzoPDz8+Pbb7+9o2u//vprnL0PNzc3l9GjR7NixQrWr18P5J3+5kgjPVSYyx37/PPPKVGiBHBtaNNXX31F165d2blzJ3BtqNa5c+dYv369fR6HiMiDlJGRQfXq1enevfsNH6aFCxdm2LBhbNy4Mc/x3r17P8qID8XQoUPp0KEDzZs3tx+Lioriiy++YNSoUQQGBhqYTiQvs9lMYmIihw8fJigoiFmzZuHl5UWfPn0A7P/m5uaSmppqZNR7FhcXx6pVq/jqq68wmUycOnWKkJAQ4FoPudlsNjbgA1QQ2lNuzWKxOPX2o1lZWZjNZrp168aqVavIzc0lIyODhIQEypUrR3x8POXKlWPevHm0adPG0Kwayi53rFq1avj6+uLu7o7JZOLHH3+kadOmeZ46xcXFERwczJYtW4wLKiL5Vnp6OitWrGD37t2sWbOG+Ph4ihUrRuPGjfP8LcrNzSUlJYXU1FQOHTpkYOIHIzQ0lFGjRjFw4ED7sU2bNjFgwAA2bdpEeHi4gelEbhQXF8fmzZvtD8au39zv3buXmjVr4unpaXDC+5eRkcGGDRuYO3cuW7dupX///kyePJnk5GQqVapEZmam0REfmILQngVReno6Li4ut1z0zNXVlaSkJHx9fR9xsgfDYrHg7u5OQkICe/fuZe3atcydO5fExESio6Pt2xqmpaVRsWJFwx9yqzCXe5KcnEzRokVJTU3Ns83Cpk2b6NSpEzt27KB69eoGJhSR/M5ms/Hvf/+bCRMmEBgYyD//+U8qVKhgdCyRAi8rK4snn3ySUqVKsWbNGlatWsXMmTNZt24dNWvWJDExkXbt2vHcc8/RvHlz+2g8Z7Z9+3YGDhxI/fr1eeedd6hSpYpTLvZ2MwWxPQuKPXv20LBhw9sO5z558qRTLxR37NgxKlasiNlsJikpiVKlShEfH09QUJDR0W6gwlzuidls5tixY0REROT5Zb6+8qienIrIo2KxWPjoo4/o2bOnQ37QihQ0J06cYODAgXz77bd4eHhQs2ZNunfvzpgxY+zbGX722WcsX76cd955h3Hjxhkd+YFITk4mISEBi8VCly5d2LdvX764Hyqo7VkQJCYm8uuvv+Lh4XHT86mpqTRs2DBfrduSnZ3tsL+XKsxFRERE5KHZtGkTDRs2vGEf75MnT1KyZEnD9w6Wu6P2FHk4VJiLiIg4IIvFwuLFiwFo0qQJYWFh/Oc//6FJkya37N0QERER56TCXERExAFZrVb7sNHx48fj4eFBp06d+Oabb3j66ae5dOkSAQEBwLWdMlSsi4iIOC8V5iIiIg6qWLFiXLlyBbjWaz548GC6d+9uf79r1y7g2j6s+Wl7JhERkYLGxegAIiIicnMeHh7ExMTw/vvv89133+Hr60v9+vWJjo5m9erVXLp0yd6DLiIiIs5LhbmIiIiDMplMHD9+nOnTp/Ovf/2LypUrU61aNcaNG0eJEiUoWrQowcHBZGdnGx1VRERE7oOb0QFERETkRtu3b8disdC2bVt++uknOnbsSHBwMOPGjaNjx47s37+fgIAAUlNTSUlJMTquiIiI3Af1mIuIiDiY+fPn06pVK3Jycjh69CgdOnRgxYoVjBkzBrPZTFpaGuPGjaNOnTp069aNtLQ0oyOLiIjIfdDibyIiIg7m8uXLuLq6UrVqVSZNmsSFCxd45ZVXOHjwIFOmTOH8+fMcPXoUgPT0dKpVq8apU6eMDS0iIiL3TEPZRUREHEzx4sWBa3PM+/btC4Cvry8uLi6kpKTYjwF4e3uTk5NjSE4RERF5MFSYi4iIOCiLxWJ/ffr0aYoWLcrixYvZtGkT3333HV5eXgamExERkQdFc8xFREQcVE5ODpmZmZjNZiIiIujSpQsVKlRgw4YNrF27lpEjRzJq1CgqVKhgdFSRm+rYsSOrV6/Oc2zPnj107NiRc+fOGRNK7pnaU+ThUY+5iIiIAzKbzSQnJ+Pj48OUKVP473//y6JFiyhevDghISH069ePjz/+2OiYIreVlJREVlZWnmO5ubkkJSXlGREizkHtKfLwaPE3ERERB2Sz2YiNjQUgICAAPz8/+7nt27fzxBNPGBVNREREHjAV5iIiIiIiIiIG0hxzERERJ2Cz2di6dStDhw6196QfPXqUmJgYg5OJiIjI/VJhLiIi4oC2bdtmfx0VFUXp0qXp06cPV65cIT09HavVyvjx46lZsyavvPKKgUlF8rp69SqXLl0CICUlhWPHjtnPmc1mkpKSANi9ezd9+vQxJKPcmbS0NIYOHQrA999/z7fffsuaNWtuem2jRo00z1zkPmgou4iIiIOJj4+ndOnS+Pj4UKlSJSpXrkxubi4jRoygfv36mEwm+7UxMTE0atSIxMREAxOL/M/y5ct58803GTp0KJUqVeLDDz9k8+bNxMbG0rVrV5577jnGjh3LgQMHqF+/Pmaz2ejIcguZmZlUrlyZM2fOUKdOHWrXrs26det47733mDJlCi4uLlitVt5//33Gjh3LqVOnjI4s4rTUYy4iIuJgSpUqRUpKCqtXr2bcuHHUq1ePIkWK0LdvX0qWLMmIESOIi4sDIDw8/IZVkkWM1LlzZ9auXculS5fw9vamSJEiREVF8fzzzzNmzBjGjh0LgKenJ66urganldvx9PTEy8sLADc3N+bPn0/ZsmWxWq1MmjSJ0qVLM3HiRKxWK76+vganFXFu2i5NRETEAcXExNCjRw/Gjh1Lu3btaNeuHW5ubpw9e5Z58+ZRtWpVPvroI6pVq4bVajU6rggAFouFpk2bEhwcjM1mY9++fRw7doxx48YRHBzMsmXLWLBgAVlZWUyYMEHFnINzcXHh5MmTlCtXjoSEBMqWLUtycrL9gYrJZMJkMuHi4oKHh4fBaUWcmwpzERERB5OVlcVzzz1Hv379GDx4MOPGjWPBggX2m2GbzUbx4sV58803MZvNvPDCCwYnFrnGarUyevRovL29sVgsxMbGMmPGDGJjY7FarQwePJiwsDAyMjK4dOkSlStXNjqy/IHw8HCOHDnCY489xp49e2jQoEGe8zabjZEjR5KUlER4eLgWpBS5RyrMRUREHEyhQoXYtWsXJUuWBODdd98lPDyc0qVLU6pUKUqWLEmxYsWIioqiadOmDBo0yODEIte4u7vTrFkzevXqxebNm/nyyy8JCQlh/fr1fPvttwwePJju3bszYcIEhg0bRr9+/YyOLH/g/Pnz9O3bl1OnTtG3b1/7NBq4VpSbTCZGjRrFZ599xooVKwxMKuLcNMdcRETEAV0vyq/7/vvvmTVrFgMHDiQ8PJwFCxZQvXp1/v73vzN48GCDUorcqHDhwrz44oucPn2aWrVqMWDAAJKSkmjdujW7d+/Gy8uLgwcPcuDAAV5++WWj48ptWK1W/Pz86NevHwEBAfTr1w9/f3+sVqt9GDuAv78/NpuNihUrGpxYxHmpx1xERMTBnDt3jq5du7J48WJCQ0Np3bo1a9eu5ciRIwwYMIBFixbRsmVLAF566SXdDItDMZlM+Pn50bNnT5588klGjhyJu7s7pUqVAq71ss6cOZM//elPBieVP5Kbm4uHhwdNmjShUKFCnD59muzsbLKzsxkxYgRJSUn8+uuvTJkyhZSUFKPjijg1FeYiIiIOJigoiOjoaGbPnk3NmjU5cOAAy5Yt4+zZs5QtW5bExESWLVsGXNsXOiUlhSZNmhicWuR/9u7dS5cuXWjfvr19uPOZM2eAa0Ojz507x/Dhw5k9ezavvfaawWnlVtLS0uz7zvfu3Zvjx4/TrVs3BgwYwMCBAzGZTFgsFq5evcq6deuwWCxaaV/kHmkfcxEREQf06quvsnr1alq2bMnq1aupVq0ae/fupWjRorRs2RIPDw9sNhtms5nU1FTWrl1rdGQRu6SkJOLj44mIiAAgPT0db29vADZs2EDfvn2ZO3curVq1olChQkZGlT+Qk5PDkSNHcHd3p1q1ajec//HHH+nXrx9r1qyhSpUqBiQUyR/UYy4iIuKA6taty+rVq/nyyy+pWrUqW7Zs4dKlS3z22WcsWrSIZ599ljfeeIPy5csbHVUkj71799KpUyeeeuopPvvsM6pUqYKfn1+eXQUAunbtyty5c+nTp4+RceU2kpOT6d+/P2vXrmXcuHG4u7vTtWtXvLy87PPLbTYbycnJPPnkkyxevJhWrVoZnFrEOanHXERExAElJSXh4eGBt7c369ato1WrVvZ9gpOSkvj444/p1asXISEhxgYV+Z0TJ06watUqhg0bhslkomjRohw4cCDPNdnZ2WzdupVGjRpRvXp1Y4LKHZk8eTJ9+vShdOnSxMbGEhERwbp16/JcY7PZOHLkCDVq1KB58+YGJRVxbirMRUREROShefPNN/nwww+NjiEPwJkzZxgxYgRfffWV0VFE8h0V5iIiIg7q4sWLLFq0iCJFiuDt7W3/Kly4sP11yZIlKVKkiNFRRURE5D6oMBcREXFQsbGxVKxYkfr165ORkWH/Sk9PJzMz074C8ty5c+nXr5/RcUVucH1F9ptZtmwZGRkZ9O3b9xGnkrt15swZypYti8lk4sqVK9SuXZuff/6Z4sWLGx1NJN9wMTqAiIiI3Jynpyeenp7s3r2bQ4cOceLECeLj40lJSSE3N5f09HR69OjB5MmTjY4qcoMtW7bg5eXFxYsXb3o+PT2dV1999RGnkrtlsVgIDQ3l8uXLbNu2jaNHjxIXF4evr6/R0UTyFa3KLiIi4oAWLVpEixYtbjh+6NAh5s6dy+zZs/Hy8qJWrVpcuXLFgIQit1ekSBFycnJ44403bjonOTQ0FLPZjNVqxcVFfUWOytXVFXd3d7y9vZk7dy6FChXCZrPRpEmTPNeZTCaCgoJYsGABRYsWNSitiPPSUHYREREHk5CQQMmSJQkPD+fMmTPExMRQpkwZ1q1bx/PPP0+7du3417/+hZ+fHykpKeq5Eod05MgR6tevj6+vLyNGjGDYsGF5zv/3v//lscceIyUlxb7HuTgmf39/Ll26RFBQEJGRkTRv3pyFCxfmucZqtTJ+/HiGDRvGwIEDDUoq4rzUYy4iIuJgAgMDSUhIYMeOHfz73/+mWrVq1K5dm+HDh7Nx40aaNWtmv1ZFuTgqd3d3PDw8WLduHS1atCAiIoKnn37afr5w4cIGppO74enpya+//oq/vz+NGzfGxcWFbt263XBdSkoKjRs3NiChiPPTuCEREREHFBAQQIcOHZg4cSKHDh2iWrVqhIaGUrduXVJSUuxfFy9e5PTp00bHFbGLiYlh6NChmEwmcnJyqFevHkuXLuXFF18kKirKfl12djYmk0kFugOzWCyMGzcOgL1799KmTRvg2rD15ORkihYtSrly5Shbtiy+vr688MIL1KhRw8jIIk5LPeYiIiIOymq1Ur58eWw2GzabjU8++eSGa2w2G2XKlOHMmTMGJBTJa9u2bbRo0QJvb2/atm1Lbm4uAG3btmXatGm0b9+ePXv2EBgYyP79+6lZs+YtV20X450/f54lS5Zw6dIlJk6cyKxZs+znihQpgpubG5GRkTzzzDNs374dHx8fA9OKODf1mIuIiDigH3/8EVdXVzw8PIiNjeXkyZOcOHECHx8fYmNjOXHiBF5eXpw8eZKdO3caHVcEgLp16zJr1iwuXLhAaGgoVqsVq9UKQN++fenbty8dOnQgLS2N2bNnay6ygytbtiwxMTEEBgZSs2ZNVq5caT/n4uKCu7s7tWrVwsPDg1q1aukhi8h9UI+5iIiIg7l48SJPPvkkNWrUwGw2U758efs5V1dX+3sXF5c850SM5uPjc8MWaLm5uXh6egIwbtw4zp07R0REBBUqVFBh7iRyc3NZvHgxVapU4cCBAzecV0Eucv9UmIuIiDgYf39/du7cSWxsLL179+bLL7/k+iYqOTk59vdms9m+MnLv3r2NjCxyg+s/s5mZmfbCHGDu3Lk0btyYUaNGGRVN7lJOTg6enp706NGDJUuWYLFYyM7O5urVq/Tu3ZsLFy7Qu3dvhgwZQr169YyOK+KUtF2aiIiIg8rJyaFOnTq3XRwrMzOTtLQ0Tp069eiCidyB48ePU6VKFU6fPk3ZsmXznJs1axbr1q0jMjISd3d3gxLKnbDZbLi5uXH58mV++OEHvvjiC9avX8/Fixf5/PPPKVSoEABpaWk8//zzREREGJxYxDmpMBcRERGRB+7SpUvMnj2bV199lcDAwDznsrKyqFmzJm3btuXDDz80JqDcEavVyqeffkrv3r1JS0sjMzOTkJAQEhISCAgIMDqeSL6hwlxEREREHrmjR49SqlQp/P39jY4idyE7O5stW7bQvHlzPDw8jI4jkm+oMBcREXEwNpuNH3744Y6G+JpMJsLCwggODn4EyUTuXG5uLtHR0VStWlWLg+UDak+Rh0uFuYiIiIO5PqezVKlSf3gDnJ2djbe3NydPnnxE6UTuzMWLFwkODiYzM1M9q/mA2lPk4dKq7CIiIg7GZDLh4eHB/v37b5ib+3tWq5XChQuTnJyMn5/fI0oocmszZswgICCA9PR0bDYbS5cuvekDJpPJxGOPPUblypUNSCl3Su0p8mioMBcREXFAbm7/+4i+viXa7+Xm5tKvXz+ioqJUlItDMJvNREZG4uHhgdVqxWQysWzZspteGx0dTfHixdm9e/cjTil3Su0p8uhoKLuIiIgDuj48PTAwkFKlSlG7dm0AtmzZQrNmzQDYunUrGRkZxoUUuY2rV69SvHhxLBaL/VhaWho+Pj4AfP3117zxxhtcuHDBqIhyF9SeIg+XCnMREREHVKhQIU6dOkXJkiUpW7YsZ8+eBaBUqVLEx8cD4OPjQ1pampExRW4pOTmZYsWKYbFYOHHiBKNHj+ann34iLi4OgMTERLy9vSlcuLDBSeVOqD1FHi4NZRcREXEwZrOZnJwc0tPTAfLM5/ztaxcXl0eeTeRO5ebmYrPZsNlsHD58mMDAQH788Uf7+RIlShiYTu6W2lPk4VJhLiIi4mDc3NywWq329xaLhRkzZmCz2UhPT7e//u2QUhFH4+bmxvjx47FYLDzzzDM888wzaKCm81J7ijxcetQuIiLiYGw2G3/7298YOXIkAB06dODQoUNERUXRsWNH++vOnTsbnFTk1k6cOIHFYsHNzY0dO3bQoEEDvvrqK6NjyT1Se4o8XOoxFxERcTATJ05k1qxZvPXWWwDMnTvX4EQidy4qKorAwECio6OJjIzk9ddfZ/PmzXTv3p0nn3yShIQEbDYbmZmZpKWlUb16daMjy22oPUUeDS3+JiIi4mDOnz+Pq6srQUFB9O/f/45WXl+yZMkjSCZyexaLBXd3d0wmk32Y8+/3vP7trafJZNKUDAem9hR5dDSUXURExMEEBwcTFBQEQEhICOXKlaN8+fI3fJUrV47ixYvj4eFhcGKRa1xdXbl69So5OTls2rSJxx57jMjISJ5++mn+9Kc/sWHDBqxWK1arleTkZE6fPm10ZLkNtafIo6MecxERERF54NauXctf//pXDh48CMCePXsYMGAAISEhzJs3j8DAQIMTyt1Qe4o8XCrMRUREnERWVhYtWrRg+/btuLq6Gh1H5LaysrJITk62j/4AyM7OZsmSJbz44ou4u7sbmE7ultpT5OFSYS4iIuKAzGYzpUuXxmazYTKZOHLkCEWLFsXDw4PMzEzatm1L5cqVqV69Ot26daNo0aJGRxa5KbPZjJub1hvOL9SeIg+H5piLiIg4IDc3N65evcry5cux2Wx4e3vj4uJCoUKFcHd3Z+vWrZw+fZrRo0fTpUsXo+OK3NL3339Pu3bt+P77742OIg+A2lPk4VBhLiIi4qA8PT1p2rQpnp6e9gXerv/r6elJZGQkkZGR7N6928iYIjc4c+YMU6dOBcDf35+kpCSefvpp6tSpw65duwxOJ3dL7Sny8KkwFxERcVC/nUd+5MgR+vfvj9lsznONn5+ffb9zEUexcOFCxo8fz9atW2nYsCE7duzg559/Ji4ujlatWrFw4UKjI8pdUHuKPHyaICIiIuJgoqKieOWVV0hJSbEfW7VqFYcOHcJqtbJ06VL7XsI1atSgRo0aRkUVuamxY8dStGhRunbtyvbt26lUqRKhoaGUKlWKSZMm0blzZ8LCwmjSpInRUeUOqD1FHj71mIuIiDgYk8lEREQEXl5e9mOjRo1i9+7dWCwW3n77bdLS0qhVq5aGkYrD2bFjBwCvvfYa06ZNo3Xr1ly8eBFvb28uX75M+/btGTBgAG+88YbBSeVOqD1FHg0V5iIiIg6mWrVqfP755/b55IB9KyJXV1fOnj1LfHw8bdq0oWXLluzbt8+oqCJ5REZG0qZNG3766ScAOnbsyMWLF2nbti2pqal4enqSnp7OO++8w6+//sr27dsNTiy3o/YUeXRUmIuIiDgoi8WS573ZbCYzMxOAoKAgPvjgA4YPH07//v2NiCdyg6eeeoq//vWvPPvss6xcuRIfHx8CAgLo2LEjzz33HP7+/pw9e5YSJUrQvn17Pv74Y6Mjy22oPUUeHc0xFxERcVA5OTn88ssvmM1moqOj8fT0ZMSIEVitVlxcrj1bf/vtt6ldu7axQUX+n6enJyNHjqRt27Z0796dqKgoXF1dGTJkCFu2bOHAgQOcOHGCKlWq0KFDB4YOHWp0ZLkNtafIo2Oy2Ww2o0OIiIhIXhaLBW9vb7y9vQHIzMwkKysLgBIlShAWFkbDhg1p1aoVrVu3thfqIo4iJSWFzp07s3nzZrZt20ZwcDARERG8/fbbTJw4kfPnz1O2bFmio6OpUKGC0XHlD6g9RR4u9ZiLiIg4IFdXV3sh/luXL1/m5MmTHD16lM2bN9OrVy8qVKjAnj17DEgpcmu+vr6sXLmSsWPHEhwcTGhoKJs3b7aP8ChVqhSenp7ExcWpkHMCak+Rh0s95iIiIk4sLS2Nw4cP8/jjjxsdReSu7d27lwYNGhgdQx4QtafIvVNhLiIiIiIiImIgTUgTERERERERMZAKcxEREREREREDqTAXERERERERMZAKcxEREREREREDqTAXEREpoE6dOkX16tUBmDVrVp7VlCdMmMD06dPt75s1a8a+ffseeUYREZGCQIW5iIiIEBgYSMWKFY2OISIiUiC5GR1AREREjNetWze6detmdAwREZECST3mIiIiBdCcOXMIDw/n8OHDuLm58fXXX/PSSy8B0LBhQ959911GjhyJv7//Tf//hg0bqF+/PoUKFaJs2bKMGTOGnJycR/cNiIiI5CMqzEVERAqgQYMGERMTQ7Vq1TCbzbi4/O+WYNeuXYwbN44PPviAq1ev3vB/169fz/Dhw5k1axYpKSls3bqVs2fP0rNnz0f4HYiIiOQfKsxFRETkrowYMYJly5bRqFEjPDw8qFChAgsXLiQ2Npaff/7Z6HgiIiJOR3PMRURE5I5dunSJw4cPU69evRvOWa1W9u/fT506dQxIJiIi4rxUmIuIiMhdCQoK4sKFC0bHEBERyTc0lF1ERETuWEBAAP7+/uzduzfPcYvFwqBBg0hLSzMomYiIiPNSYS4iIlJAubi4kJube9fnpk+fTo8ePdi8eTPZ2dlER0fTpUsXUlNT8fHxeZiRRURE8iUV5iIiIgVUqVKlcHNzu+mWaI0aNWLy5MkMGTLkhnPt27dn5syZvP322/j5+dGiRQuqVavG/PnzH0FqERGR/Mdks9lsRocQERERERERKajUYy4iIiIiIiJiIBXmIiIiIiIiIgZSYS4iIiIiIiJiIBXmIiIiIiIiIgZSYS4iIiIiIiJiIBXmIiIiIiIiIgZSYS4iIiIiIiJiIBXmIiIiIiIiIgZSYS4iIiIiIiJiIBXmIiIiIiIiIgZSYS4iIiIiIiJiIBXmIiIiIiIiIgb6P+jTEvFIbyBvAAAAAElFTkSuQmCC","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","from matplotlib import rcParams\n","\n","# Set the font to SimHei to support Chinese characters\n","rcParams[\"font.family\"] = \"STHeiti\"\n","rcParams[\"axes.unicode_minus\"] = False  # This is to support the minus sign in Chinese.\n","\n","plt.figure(figsize=(12, 6))\n","df[\"title\"].value_counts().plot(kind=\"bar\")\n","plt.show()"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[{"data":{"text/plain":["(5, 5, 5)"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["df_dev = pd.read_csv(\"datasets/mgtv/dev.csv\")\n","len(df_dev[\"title\"].value_counts()), len(df_dev[\"puzzle\"].value_counts()), len(\n","    df_dev[\"truth\"].value_counts()\n",")"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","from matplotlib import rcParams\n","\n","# Set the font to SimHei to support Chinese characters\n","rcParams[\"font.family\"] = \"STHeiti\"\n","rcParams[\"axes.unicode_minus\"] = False  # This is to support the minus sign in Chinese.\n","\n","plt.figure(figsize=(12, 6))\n","df_dev[\"title\"].value_counts().plot(kind=\"bar\")\n","plt.show()"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"text/plain":["(3, 3, 3)"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["df_test_a = pd.read_csv(\"datasets/mgtv/test_a.csv\")\n","len(df_test_a[\"title\"].value_counts()), len(df_test_a[\"puzzle\"].value_counts()), len(\n","    df_test_a[\"truth\"].value_counts()\n",")"]},{"cell_type":"code","execution_count":27,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","from matplotlib import rcParams\n","\n","# Set the font to SimHei to support Chinese characters\n","rcParams[\"font.family\"] = \"STHeiti\"\n","rcParams[\"axes.unicode_minus\"] = False  # This is to support the minus sign in Chinese.\n","\n","plt.figure(figsize=(12, 6))\n","df_test_a[\"title\"].value_counts().plot(kind=\"bar\")\n","plt.show()"]}],"metadata":{"accelerator":"GPU","application/vnd.databricks.v1+notebook":{"dashboards":[],"environmentMetadata":null,"language":"python","notebookMetadata":{"pythonIndentUnit":4},"notebookName":"07_MAC_+_Qwen2-7B-Instructi_Unsloth_train","widgets":{}},"colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"}},"nbformat":4,"nbformat_minor":0}