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Imports"],"metadata":{"id":"1IAOt4vieP1z"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import time\n","import torch\n","\n","from datasets import load_dataset, Dataset\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.metrics import (\n","    accuracy_score, precision_score, recall_score, f1_score,\n","    confusion_matrix, classification_report\n",")"],"metadata":{"id":"gKR05z9gPe6P"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["3. loaded IMDB directly"],"metadata":{"id":"HydCvxoueS7K"}},{"cell_type":"code","source":["imdb = load_dataset(\"imdb\")\n","\n","print(imdb)\n","print(imdb[\"train\"][0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":660,"referenced_widgets":["c395d7671fd24042a6b1fde5c6b03269","0c8d73507763449caddbab84b693cd6b","a28705dc4d084196ac58f2005fd147cf","d895234b1bb0448f8e7326bf75f3b53a","0edfe4edcacf4be29e7ff5d31f22a0e7","2293c42c27ca408baeac41c0d9911fe3","36a8b076e10540c29f4918701a28051f","bb704a65242248758a7683d1b27f29b0","62baff119139497d88518a418e8b5576","60cb30888f7e4a35952e65b81f58489c","90cc9b1ac8fb4d4b8616426a89d94a64","feb57d2facc4444d9e0d8736b59ff6bc","71814b0485d84e71be8d59c6849621da","a49657f357c44325b47989aa7088281c","44d82f23bcb54d1b988541f25c93ba56","5b31c62a475841ed95f66c8393f06cbb","80de35b5e07b4dd989602596d91109d1","4dd51a2327eb475f93b366705799410b","548411b26ed94e448a8d0883b8e644cc","fa5e27a111364cf3a9793904d8c7b969","beedb1b88d384e89b2789e8c273cef51","30b4030b16654203a99f23c4c70ed26f","0db3a9fd06414fcfacdb03cef89fd8d9","16782fb981374dbf9596f8cffd345948","291212f7fd5b4d71aefcf8698a67c87e","f3c9ac26c1ca4108a96e5ebf12205c36","a0fdb87f92d24f1ca367fb941f4a1bda","da24565846cb4e67932a1bc30636563a","e61c0df0b8b243f0a9317aa94c1ee1dd","a985d9f7c46a4d50bdcaea6ea64152f6","b78418954da74de596257fb3c4354808","0bf74a1ddcb84b1eac7b53dde51b0267","325db54dd9bf46e09e024ca85d659a1c","b08522bc3f3446e3bbb0908ee2b1e99b","7785056bdf124139b4fb7696354b5840","4d430b6bd4c040308558fad71e592f11","0cb1af165a7541bab4613f490da41da3","60d96fb9ad034803b9157744f5e6801c","c0372ce72aec4409a909a0c22e4f6913","43c5ce13fef5428c8df0124e665e7696","6e08d913c7b54b2c817e21b3aed58b8a","5a16d7f6be11424ea9bb9637ed80ea9f","eb958805b5634ba088fb440b2d6c58f7","573681cdb8754ce9ab4fa60ff9013905","d3db6203b9e241bd8e6cde6800a6db7d","9a403c1a74c54aec93c28b310ef2015e","63edc6852a064d6790ee8ea105bd8ec5","c0d90e57434f4f63aaad8477bb443c71","fdf6ae044ab243918eaf8f56b16a1085","4f2c7577a1364260880ba27b63f5c820","78f1ca390ac3461b86cb588cb48406ab","bb2b89a00f214945a57e7271dfa3fd8b","cef7775b763a43e190804f94b928d1ff","d782a338182c4408bd23acb0a8b86edd","06e565b82c5d4012ac084df07e347f66","f16f1e8358ff469d8df4aa3ef633f16f","31c39f0ab22140939c2d3f81b15ce888","d22efa20fe45496b899a01e27e0f7a7e","3ad027baea804240b92afbfb5cb3aa92","713ecefeda974d5da5da3c0632f3914d","954e6bf0797941f68ab23e30be05de4e","10fbf2b4f6ee4160b3412bb947195eb2","ca447a92389b42368fb33312807f6bc9","98d81d34d1e94ebdaf51d538c3ebf1a4","5bd98265ed9943e88cad2d914f1ffd5e","935ce404ce87466682c070c237dce728","6b15b567e2704ec6bc615064215220e2","d448e2578f6e41af93f825510f17d94f","7e8634631f6349e0bd44f7c1a08aef9b","2749737ee53a48febe92988875be6f34","e26c9919feb24ee1857943d411449590","1d1df4b6944648a09d1859213a25b44d","765ce797d8e54809a913cb7ee518a2fe","594002686969425c8ed785fcac9623b5","a43864ca5b804c25910e45c2a27b3e60","89d0b0dc6c6342dd87d65447eb029d0b","361e23992b9f49289b70f0ef5a94a4fc"]},"id":"82PUjqeaPwTV","executionInfo":{"status":"ok","timestamp":1775357458570,"user_tz":-60,"elapsed":7053,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"c0aea16e-6b0e-491f-c88c-d3136caeb81a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c395d7671fd24042a6b1fde5c6b03269"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n","WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"]},{"output_type":"display_data","data":{"text/plain":["plain_text/train-00000-of-00001.parquet:   0%|          | 0.00/21.0M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"feb57d2facc4444d9e0d8736b59ff6bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/test-00000-of-00001.parquet:   0%|          | 0.00/20.5M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0db3a9fd06414fcfacdb03cef89fd8d9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["plain_text/unsupervised-00000-of-00001.p(…):   0%|          | 0.00/42.0M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b08522bc3f3446e3bbb0908ee2b1e99b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d3db6203b9e241bd8e6cde6800a6db7d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/25000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f16f1e8358ff469d8df4aa3ef633f16f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating unsupervised split:   0%|          | 0/50000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6b15b567e2704ec6bc615064215220e2"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 25000\n","    })\n","    test: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 25000\n","    })\n","    unsupervised: Dataset({\n","        features: ['text', 'label'],\n","        num_rows: 50000\n","    })\n","})\n","{'text': 'I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this country, therefore being a fan of films considered \"controversial\" I really had to see this for myself.<br /><br />The plot is centered around a young Swedish drama student named Lena who wants to learn everything she can about life. In particular she wants to focus her attentions to making some sort of documentary on what the average Swede thought about certain political issues such as the Vietnam War and race issues in the United States. In between asking politicians and ordinary denizens of Stockholm about their opinions on politics, she has sex with her drama teacher, classmates, and married men.<br /><br />What kills me about I AM CURIOUS-YELLOW is that 40 years ago, this was considered pornographic. Really, the sex and nudity scenes are few and far between, even then it\\'s not shot like some cheaply made porno. While my countrymen mind find it shocking, in reality sex and nudity are a major staple in Swedish cinema. Even Ingmar Bergman, arguably their answer to good old boy John Ford, had sex scenes in his films.<br /><br />I do commend the filmmakers for the fact that any sex shown in the film is shown for artistic purposes rather than just to shock people and make money to be shown in pornographic theaters in America. I AM CURIOUS-YELLOW is a good film for anyone wanting to study the meat and potatoes (no pun intended) of Swedish cinema. But really, this film doesn\\'t have much of a plot.', 'label': 0}\n"]}]},{"cell_type":"markdown","source":["4. Converting to Pandas"],"metadata":{"id":"hNgLlu3ueu7W"}},{"cell_type":"code","source":["train_df = pd.DataFrame(imdb[\"train\"])\n","test_df = pd.DataFrame(imdb[\"test\"])\n","\n","train_df.columns = [\"review\", \"sentiment\"]\n","test_df.columns = [\"review\", \"sentiment\"]\n","\n","print(\"Train shape:\", train_df.shape)\n","print(\"Test shape:\", test_df.shape)\n","\n","train_df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":241},"id":"m-84kPKPP2Gn","executionInfo":{"status":"ok","timestamp":1775357461858,"user_tz":-60,"elapsed":3282,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"1f534f4e-93c3-480c-8108-51da6faef7c7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train shape: (25000, 2)\n","Test shape: (25000, 2)\n"]},{"output_type":"execute_result","data":{"text/plain":["                                              review  sentiment\n","0  I rented I AM CURIOUS-YELLOW from my video sto...          0\n","1  \"I Am Curious: Yellow\" is a risible and preten...          0\n","2  If only to avoid making this type of film in t...          0\n","3  This film was probably inspired by Godard's Ma...          0\n","4  Oh, brother...after hearing about this ridicul...          0"],"text/html":["\n","  <div id=\"df-f68879b9-a880-48ac-9d04-67ed62558109\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>review</th>\n","      <th>sentiment</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>I rented I AM CURIOUS-YELLOW from my video sto...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>\"I Am Curious: Yellow\" is a risible and preten...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>If only to avoid making this type of film in t...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>This film was probably inspired by Godard's Ma...</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Oh, brother...after hearing about this ridicul...</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f68879b9-a880-48ac-9d04-67ed62558109')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-f68879b9-a880-48ac-9d04-67ed62558109 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Jane Fonda and Robert De Niro are great in this movie. I haven't always been a fan of Fonda's work but here she is delicate and strong at the same time. De Niro has the ability to make every role he portrays into acting gold. He gives a great performance in this film and there is a great scene where he has to take his father to a home for elderly people because he can't care for him anymore that will break your heart. I wouldn't really recommend this film as a great cinematic entertainment, but I will say you won't see much bette acting anywhere.\",\n          \"Yet again one of the most misunderstood Goddesses of my country has been twisted by \\\"Westerners\\\" who cannot understand the esoteric symbolism of the Mother Goddess in her dark forms. The Mother takes on the frightening form of Kali Mata to destroy our inner demons, and to terrify our egos. And though blood sacrifice is given to Kali and Durga, the events depicted in this film are just absurd. The Mother takes on a wrathful form to be wrathful to our inner demons, limitations, and ego when no other form will suffice. It's also in her wrathful form that she burns away all your Karmas in the \\\"Smashan\\\" fires that you cultivate in your heart for her to dance on if you love her, and she will bring you to reality and truth. Reality and truth has a dark side as well as light, which serves a purpose. The Mother is the embodiment of the physical Universe as well, she is Nature. Nature can be cruel and destructive to maintain balance. You cannot have growth and life without death and destruction. Kali represents the force of destruction for the purpose of new growth and life both mundane and spiritual in the universe. It's very outrageous to me that people who know nothing of India or it's divinity can just take one of our beloved Goddesses and use her like a cheap prostitute to make some low-budget, talentless horror film. How dare they take our beloved Mother and portray her as a horror that makes people chop their eyelids off!? She is only horrific to those who are attached to their ego and who live in delusion , greed, anger, and other inner-demons. It's very clear to me that the person who wrote this movie must have a very serious self-deluding ego, and serious inner-demons to see Kali as so horrible and terrible. When the ego drops away she becomes a form that is enchanting, beautiful, and young, a beauty that is so enchanting to behold that she enchants the entire Universe with it. Kali Maa is an ancient Mother, not to be trifled with for the sake of entertainment, let's just hope that in her endless compassion and mercy that she does not take on wrathful form to those involved with this movie.<br /><br />The audacity that Westerners have in using religions like my own, or the religions of the Caribbean Islands such as Santeria, and Vodou which are actually very positive, and other such religions to twist and exaggerate misunderstood elements that the Western mind cannot comprehend, is totally ridiculous. It's clear that there is no respect for what people live, breathe and believe in when it comes to these kind of flicks.<br /><br />Kali Maa in reality is a caring and compassionate mother, whom we shed tears at her beautiful feet in devotion and love for. And I am happy that my Mother takes on wrathful form sometimes to protect her devotees from themselves and from outside forces.<br /><br />Many Praises to the REAL Kali Maa, who has shown MANY the path of God and realization.\",\n          \"Paris is the place to be to enjoy beautiful art and music, and to fall madly in love - as is the case in this film. Boy meets girl, they fall in love, but something stands in their way of eternal happiness, the classic story.<br /><br />The wonderful music of George Gerschwin complements the great dancing by Gene Kelly and Leslie Caron. \\\"An American in Paris\\\" is a humorous, light-hearted, loving film well worth watching.<br /><br />8/10<br /><br />\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":9}]},{"cell_type":"markdown","source":["5. Checks"],"metadata":{"id":"2n_uRX7Xe1gP"}},{"cell_type":"code","source":["print(train_df[\"sentiment\"].value_counts())\n","print(test_df[\"sentiment\"].value_counts())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dZIOnaEmQDqF","executionInfo":{"status":"ok","timestamp":1775357461924,"user_tz":-60,"elapsed":60,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"ecc6ba7d-e7e7-4d37-bcce-24b0271b1e18"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["sentiment\n","0    12500\n","1    12500\n","Name: count, dtype: int64\n","sentiment\n","0    12500\n","1    12500\n","Name: count, dtype: int64\n"]}]},{"cell_type":"markdown","source":["6. Define train/test variables"],"metadata":{"id":"dyed4O2te_cY"}},{"cell_type":"code","source":["X_train = train_df[\"review\"]\n","y_train = train_df[\"sentiment\"]\n","\n","X_test = test_df[\"review\"]\n","y_test = test_df[\"sentiment\"]"],"metadata":{"id":"zTeCzL-KQQ7C"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["7. TF-IDF + Logistic Regression"],"metadata":{"id":"G4euF1DBfC_B"}},{"cell_type":"code","source":["vectorizer = TfidfVectorizer(\n","    stop_words=\"english\",\n","    max_features=5000\n",")\n","\n","X_train_tfidf = vectorizer.fit_transform(X_train)\n","X_test_tfidf = vectorizer.transform(X_test)\n","\n","lr_model = LogisticRegression(max_iter=1000)\n","lr_model.fit(X_train_tfidf, y_train)\n","\n","y_pred_lr = lr_model.predict(X_test_tfidf)\n","\n","print(\"Accuracy:\", accuracy_score(y_test, y_pred_lr))\n","print(\"Precision:\", precision_score(y_test, y_pred_lr))\n","print(\"Recall:\", recall_score(y_test, y_pred_lr))\n","print(\"F1-score:\", f1_score(y_test, y_pred_lr))\n","print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred_lr))\n","print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred_lr))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wl3UpXCkQVbH","executionInfo":{"status":"ok","timestamp":1775357481523,"user_tz":-60,"elapsed":19574,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"d5fe8f66-5bb6-4478-b6ab-af623f7ff435"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy: 0.87808\n","Precision: 0.8754965835054823\n","Recall: 0.88152\n","F1-score: 0.8784979669935422\n","\n","Confusion Matrix:\n"," [[10933  1567]\n"," [ 1481 11019]]\n","\n","Classification Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.88      0.87      0.88     12500\n","           1       0.88      0.88      0.88     12500\n","\n","    accuracy                           0.88     25000\n","   macro avg       0.88      0.88      0.88     25000\n","weighted avg       0.88      0.88      0.88     25000\n","\n"]}]},{"cell_type":"markdown","source":["8. SHAP for Logistic Regression"],"metadata":{"id":"E8Gbo9pbfLtJ"}},{"cell_type":"code","source":["import shap\n","\n","explainer = shap.Explainer(lr_model, X_train_tfidf)\n","shap_values = explainer(X_test_tfidf[:100])\n","\n","shap.summary_plot(\n","    shap_values,\n","    X_test_tfidf[:100],\n","    feature_names=vectorizer.get_feature_names_out()\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":957},"id":"j19h7_ZIQc4Y","executionInfo":{"status":"ok","timestamp":1775357488293,"user_tz":-60,"elapsed":6766,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"e4ac5f55-6bc0-4b98-ee08-882d04f51819"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x950 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAv4AAAOsCAYAAAAyVLLxAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAA3IFJREFUeJzs3Xl8VNX9//H3zISwZMIqWWQNhkUCNEIwrghWBMEEENRUEYqtgHXHpYsrar9WbV1xQVoVrUoFZImKaBVBtEYCREEkSA0FIRMgMZABsszy+4PfjJnMTEiGJLO9no/HPPSee+45n3snCZ85c+65BqfT6RQAAACAiGYMdgAAAAAAmh+JPwAAABAFSPwBAACAKEDiDwAAAEQBEn8AAAAgCpD4AwAAAFGAxB8AAACIAiT+AAAAQBQg8QcAAACiAIk/AAAAos4DDzwgs9l8wn27du2SwWDQkiVLGtV+oMc1p5hgBwAAAACEquTkZP3nP/9Rv379gh3KSSPxBwAAAPxo3bq1zjrrrGCH0SSY6gMAAAD44WvKTnV1tW6++WZ17txZHTt21KxZs/Tmm2/KYDBo165dHsdXVlbqxhtvVKdOnZScnKw77rhDNputhc/iOBJ/AAAARC2bzeb1cjgc9R7zhz/8QfPnz9fvf/97/etf/5LD4dAf/vAHn3XvvvtuGY1Gvf3225o9e7b+9re/6e9//3tznMoJMdUHAAAAUenIkSNq1aqVz31xcXE+y8vKyvTCCy/onnvu0e9//3tJ0pgxY3TRRRdpz549XvUzMzP1zDPPSJJGjx6tNWvWaMmSJZo9e3YTnUXDkfgDABBlampq9Morr0iSZsyY4TfxAUKW4bKG13W+43dX27ZttW7dOq/yl156SW+++abPY7Zs2aLKykplZ2d7lE+YMEEff/yxV/2LL77YY3vgwIH65JNPGhJ5kyPxBwAAQFQyGo3KyMjwKn/33Xf9HlNcXCxJ6tq1q0d5QkKCz/odO3b02I6NjVVlZWUjI20azPEHAAAAGig5OVmSdODAAY/y/fv3ByOcRiHxBwAAQJgxNOLVtAYNGqQ2bdpoxYoVHuXLly9v8r6aGlN9AAAAgAbq0qWLrr/+ev35z39WmzZtlJ6ersWLF2vHjh2Sjk8fClWhGxkAAAAQgv7yl79o5syZeuSRR3T55ZerpqbGvZxnhw4dghydfwan0+kMdhAAAKDlsKoPwp5hcsPrOpc2Xxy1XHPNNVq/fr2KiopapL9AMNUHAAAAYabp5+43xtq1a/X5559r2LBhcjgcevfdd/XGG2/oiSeeCGpcJ0LiDwAAADSC2WzWu+++q0cffVTHjh1TSkqKnnjiCd16663BDq1eJP4AAABAIwwbNkxffPFFsMNoNBJ/AAAAhJngTvUJV6zqAwAAAEQBEn8AAAAgCpD4AwAAAFGAOf4AAAAIM8zxDwQj/gAAAEAUIPEHAAAAogCJPwAAABAFSPwBAACAKEDiDwAAAEQBVvUBAABAmGFVn0Aw4g8AAABEARJ/AAAAIAqQ+AMAAABRgMQfAAA0SklJidauXauSkpJgh4KoZWjECy7c3AsAABps0aJFKigokCStWrVK6enpysnJCW5QABqEEX8AANAgJSUl7qTfpaCggJF/IEyQ+AMAgAbZsGGDz/L8/PwWjgRgqk8gSPwBAECDHD16tFHlAEILiT8AAGgQg4HRUyCccXMvAABoEKfTGewQgP+PD6GBYMQfAACcFL4JAMIDiT8AADgpfBMAhAcSfwAAACAKkPiHmNzcXGVkZLTY0mj5+fnKyMhQbm5ui/QHAAhfNTU1PsttNlsLRwKwnGcgSPwBAECDVFZW+ixnOU8gPJD4AwCABmnbtq3P8nbt2rVwJAACwXKeAACgQWJifKcNJpOphSMBmMITCBL/EGW32zV//nzl5uaqtLRUvXr10owZMzRmzBh3nS+//FIrVqzQtm3bdPDgQbVq1UppaWm69tprNWzYMK82P/30U7300kvatWuXOnXqpEsvvVRnnHFGS54WgBZktVplsViUlJQks9nc5MfWriMp4L4aE1NRUZEkKSEhQRUVFX77ri+2uudWUlKi7du3a8CAAUpMTPTqKyUlxeucrFarvvvuO5WUlKhDhw7q2LGju57r2MrKSrVp00YJCQnavXu39u/fr9atW8vpdCo1NVV2u10mk0m7d+/26tvftfR1XvHx8e5rUV/9pnhf/C3baTQygQAIByT+IerZZ5/VsWPHNGXKFEnHb/q9++67VV1draysLHfZoUOHNG7cOCUmJmr//v1asWKFfve73+nFF1/0SOrXrFmju+66S6eeeqp++9vfymQyKTc3V+vXrw/K+QFoXnl5eVq5cqU7uczOzlZmZmaTHVu7jsFgkMFgkMPhaHRfjTmfFStWyOFweJT76luSOzaj0Sin0ymn0ymTyaTBgwdry5Yt7nNLTk7Wjz/+KElatWqV0tPTlZKSouXLl7uXqDQajZowYYL7nPLy8jz2147lF7/4hb755huvOOv6+OOPPbZr9+3v2vu75i711W+q98Xfsp0s5wmEBxL/EFVeXq5Fixa5R2imTJminJwcPfnkkxo9erTatGmje+65x2u+5eTJk3XFFVfolVdecSf+drtdf/3rX9W+fXstXLhQHTt2dNfNyclp0fMC0PysVqs74ZOO/w3Izc1VWlraCUd9G3Js3TquxLqxfTX2fHwl03X7XrlypZxOp7tu7WPsdrsKCgo8tl1Jv0tBQYFHHVcbK1euVFpamiS5+/AVS91jG6OgoEBff/21z2vp6tfXNa99Pv7qN8f7AiD88N1ciJoyZYrHH2ez2azJkyfr8OHD2rhxoyTPm6yOHj2q8vJymUwmDRo0SN9++617n+vr6OzsbHfSX7vNUFNWVqaqqir3ttVqVUVFhXu7urpapaWlHscUFxfXu22xWDz+kaQP+ojkPnbs2OFO+FxsNpssFssJ+7BYLD6PdU17kaQ9e/Z41albf9u2bSd9Hq5r5Ssmf+x2+wlH2wNht9tlsVi0bdu2BscSiLrJvM1m0/fff3/Ca167vsVi0bffflvvz8D+/fs99gXys1tXuPx+0EfT9RFMThka/MLPGPEPUb179/YqS0lJkSTt3btXkvTjjz/queee05dffunxyyp5zsN01e/Vq5ffNkNJ586dPbbrjk7FxsaqS5cuHmXJycn1brvmw9IHfURDH/369ZPJZPJI/GJiYtxt19dHUlKSz2P79Onj3u7Ro4dXndpiYmLco84ncx6ua+UrJn9MJpPHiH9TcU0LSkpK0ooVK5ot+TcYDB7JWUxMjPr16+eeqnSifl3vc1JSkseIf+190vF7JGpr6Pvhb46/FD6/H/TRdH0g/DDiH6aOHj2q6667Tv/5z3+Uk5OjRx99VPPmzdNzzz2n4cOHM98SiGJms1nZ2dnuFVhiYmKUlZWluLi4Jjm2bh2j0ei+ubMxfTX2fHzdQFq37+zsbE2YMMFvbOnp6R7n1r17d4/20tPTNWnSJI++jEajsrOzFRcX547Fn0GDBgV8o2t6eromTpzo89rXd81d6qvfVO+Lv39b6vtAACB0MOIfonbt2uVV5vqqvVu3bvrqq6904MAB3XfffV7/CL3wwgse2926dZMk/e9///PbJoDIkpmZqbS0tIBWdGnIsXXrSM27qo+rv4au6lNfbL5W9SksLFT//v3dK+vU7qvuqj6uWN577z0VFBTI6XTKYDDokksu0YgRI/yu6vP1119r586dko4nyuecc44GDRqkPXv2ePXt61r6u+b+VvU5mZ8BAJGJxD9ELVmyxGOev9Vq1dKlSxUfH69hw4Zpw4YNkrxHX7788ktt3brVo+z0009XYmKiVq5cqenTp7vn+bvaBBCZzGazUlNTm+3YunUC7asxMQ0ePNi97UqUffVdX2x19yUmJnq05asvX7FceeWVGj9+vFdi7evYxMREDR8+3OfymnWnXNZ37f2dV934G9JWIGw2m8/ympqaJusDQPMh8Q9RHTt21PTp0z2W7rRYLLrnnnvUpk0bpaenq0uXLnrqqadUXFyshIQE7dixQ++//75SU1Pdo0rS8bmpt912m/74xz9q+vTpmjhxokwmk1auXKkOHTq4b/YCADROYxPrpk7EW9rRo0cbVQ4gtJD4h6ibbrpJBQUFWrx4scrKytSzZ089/PDDGjt2rCQpPj5e8+bN0zPPPKN//etfstvtGjBggJ5++mmtWLHCI/GXpIsuukhGo1F///vf9dJLL6lz587uB3jdeOONwThFAECYqbuEtEu7du1aOBKA+0oCYXByFygAAFGlpqZGr7zyiiRpxowZatWqVYOOe/vtt7Vp0yav8mHDhunyyy9v0hiB+jgN0xpc1+B8rRkjCS+s6gMAAE4Kq/oA4YHEHwAAAIgCzPEHAAAnhVnDaGmNeSIv30f9jBF/AAAAIAqQ+AMAAABRgKk+AAAACDNM4AkEI/4AAKBB/C37GRsb28KRAAgEiT8AAGiQ7t27N6ocQGhhqg8AAGiQ008/XQaDwWMVH4PBoAEDBgQxKkQnpvoEghF/AADQIGazWRMnTpTJZJIkmUwmTZw4UXFxcUGODEBDMOIPAAAaLDMzU2lpabJYLEpKSpLZbA52SAAaiMQfAAA0itlsVmpqarDDANBIJP4AAAAIK415ci9+xhx/AAAAIAqQ+AMAAABRgKk+AAAACDNM9QkEI/4AAMCvkpISrV27ViUlJcEOBcBJYsQfAAD4tGjRIhUUFEiSVq1apfT0dOXk5AQ3KAABY8QfAAB4KSkpcSf9LgUFBYz8A2GMxB8AAHj5z3/+06hyoCU5G/HCz0j8AQCAF38j+/v372/hSAA0FRJ/AADgxW63N6ocQOjj5l4AAACEGZbzDAQj/gAAAEAUIPEHAABeTCZTo8oBhD6m+gAAAC9t27ZtVDnQkpxM9QkII/4AAMDLsWPHGlUOIPSR+Eep3Nxcvfnmm8EOAwAQoljVB4g8JP5RKjc3V2+99VawwwAAhBmnk0ciAeGKxD9CVFZWymazBTsMAECEMxiYW41QYGjECy4k/k2ouLhYGRkZmj9/vkf5jTfeqIyMDL3xxhse5dOnT9eUKVPc299//73uuOMO/fKXv9Q555yjyy+/XAsXLvT6WvWBBx5QRkaGfvrpJ82dO1cXX3yxzj//fPfTFN99911NmzZNI0eO1HnnnacJEybonnvu0U8//SRJysrK0qZNm9zxul75+fnNcVlCgtVq1c6dO2W1WoMdSouItvNF07znDW3jRPVKSkq0du1ajye/NmV8JSUl7rbqttsc16Glf5/4/QXQXFjVpwklJyerW7du2rBhg2bNmiVJqqmpUUFBgYxGo/Lz83X11VdLOv6Hffv27brsssskSdu2bdPMmTMVExOjyy+/XF26dNFnn32mZ599Vt9//70efvhhr/5uuOEGdenSRb/5zW907NgxtWvXTu+9954eeOABnXHGGZo9e7Zat26tkpISff755yorK1OnTp10++23a968eSovL9ecOXPc7aWkpLTAVWp5eXl5Wrlypex2u0wmk7Kzs5WZmRnssJpNtJ0vmuY9b2gbJ6q3aNEiFRQUSJJWrVql9PR0paSkNGl8LgaDQQaDQQ6HQyaTSYMHD9aWLVua9Do0RZsn0z+/vwCaEol/Exs+fLjeffddVVZWqk2bNtqyZYsqKyt1ySWXaN26dbLZbIqJidGmTZtkt9uVkZEhSfrrX/+qmpoavfLKK+rbt68k6corr9Qf//hHffDBB8rOztaZZ57p0ddpp52mhx56yKPs008/VVxcnF544QXFxPz89s6ePdv9/yNHjtSbb76pqqoqjRs3rrkuRUiwWq0eyYLdbldubq7S0tJkNpuDHF3Ti7bzRdO85w1t40T1SkpK3Em/S0FBgb755hs5HI4mi8/F6XS655vb7XaPvpvqOpxsm43B7y/QcCznGRim+jSxjIwM2Ww2bd68WZK0YcMGde7cWb/61a905MgRbdu2TZKUn58vg8GgjIwMlZWV6ZtvvtGIESPcSb90fDTr2muvlSStWbPGq6+pU6d6lZnNZlVWVmr9+vVhewNWWVmZqqqq3NtWq1UVFRXu7erqapWWlnocU1xc7HPbYrF4JQs2m03ff/99k/XhYrFYPK55U55HQ/soKiryeb4WiyWszoM+Gt5HU7zn27Ztq7cN13n4+32yWCwqLi7W9u3b5Ysr6a99TGN+B3312xA2m0179uxpUB8N7afutQ3Xv1euqaH1nceJrkU4/H7QR/P2gfDDiH8TGz58uKTjCf/ZZ5+t/Px8DRs2TAMGDFD79u21YcMGDRkyRPn5+erbt686dOigrVu3SpL69Onj1V5KSoqMRqP27t3rta9Xr15eZTNmzNCmTZt0xx13qEOHDho6dKjOPfdcjR49WnFxcU18ts2jc+fOHtt1R7piY2PVpUsXj7Lk5GSf20lJSTKZTB7/gMXExKhfv35q3bp1k/ThkpSU1Gzn0dA+UlJSfJ5v7XbC4Tzoo+F9NMV7PnDgQK1YscJvG67zMBgMfvuKi4uT0WjUqlWrVJfRaPRI/hv7O+jr97ghYmJi1LNnzwb10dB+6l7bcP17lZCQ4LHt6zxiY2Pli+vJveHw+0EfzdsHwg8j/k2sS5cu6tOnj/Lz81VZWamtW7dq+PDhMhqNGjp0qDZs2KDy8nJ9//337g8JgWrTpo1XWc+ePbV48WI99dRTuvTSS2WxWPTwww9rypQp+vHHH0+qv3BkNpuVnZ3tnvYUExOjrKyssPkQ1FjRdr5omve8oW2cqF5iYqLS09M9jklPT9eECROaND4Xo9Eoo9Hobjc9Pb3Jr8PJttkYofb7y5N7gcjDiH8zyMjI0JIlS7Ru3TrV1NS45+YPHz5cTz/9tL744gs5nU534n/qqadKkn744Qevtnbt2iWHw6Fu3bo1uP/Y2Fidd955Ou+88yRJ69ev16233qo33nhDv//97yVF13JsmZmZSktLk8ViUVJSUsTPlY2280XTvOcNbeNE9XJycjRq1CgVFhaqf//+SkxMlKQmjS8+Pl4VFRXuEcza7Vqt1ia/Difb5sn2Hyw8uRehLXrymKZE4t8Mhg8frrffflsLFixQUlKSunfv7i6vrq7Wq6++KpPJpDPOOEPS8a/bhgwZonXr1mnnzp1KTU2VdPzGtVdeeUWSNGrUqAb1XV5ero4dO3qUDRgwQJJ06NAhd1m7du10+PBhOZ3OqPgQYDab3dc1GkTb+aJp3vOGtnGieomJie6Ev7niq91+7Xab4zq09O9TqPz+8uReIPKQ+DeDYcOGyWg0qqioSFlZWe7yPn36qEuXLvrhhx80ePBgj69v77jjDs2cOVPXXXedeznP9evX6z//+Y/Gjh3rtaKPPzfccIPi4+N1xhlnKDExURUVFcrNzZXBYPBYwWfQoEH67LPP9Nhjj2nIkCEyGo0aPny415w/AABqC9eFIwCQ+DeL9u3bq1+/ftq+fbt7uU6X4cOH64MPPvAqHzhwoF5++WXNnz9fS5Ys0bFjx9StWzfddNNNPlfv8WfKlCn66KOP9M477+jQoUPq0KGD+vfvr7vuusujz6uvvlp79+7Vxx9/rKVLl8rhcOjFF18k8QcA1CsaviVG6GM5z8AYnHx0BwAgqrieGyMdXw2uVatWXnWef/557d6926u8V69euv7665s9RqA+VYaG/wy2dr7QjJGEF1b1AQAAAKIAiT8AAAAQBUj8AQBAgzHHHwhfJP4AAMCL6wm9dbkemgYg/PDbCwAAvPh6OrzEk3uBcMZyngAAwEtlZaXPcp7ci1DAcp6BYcQfAAAAiAIk/gAAwEtiYmKjygGEPhJ/AADg5eyzz/ZZfs4557RwJIAvhka84ELiDwAAvCQmJio9Pd2jLD09XV27dg1OQABOGjf3AgAAn3JycjRq1CgVFhaqf//+TPMBwhyJPwAA8CsxMZGEHyGHVX0Cw1QfAAAAIAqQ+AMAAABRgMQfAAAAiALM8QcAAECYYY5/IBjxBwAgzFitVu3cuVNWqzXYoQAII4z4AwAQRvLy8rR8+XI5nU4ZDAZNnDhRmZmZwQ4LQBhgxB8AgDBhtVq1bNkyOZ1OSZLT6dSyZcsY+UfUccrQ4Bd+RuIPAECYKCgoaFQ5ANRG4g8AQJjYvn27z/LCwsIWjgRAOCLxBwAgTFRXVzeqHABqI/EHACBMmEymRpUDQG0k/gAAhIk2bdr4LG/btm0LRwIgHJH4AwAQJiorK32WHz16tIUjARCOWMcfAIAwYbfbfZY7HI4WjgQILpbpDAwj/gAAhAl/c/ljYhjHA3BiJP4AAIQJf3P8/ZUDQG0k/lHs008/1fz584MdBgCggfzN8T927FgLRwIEm6ERL7iQ+EexTz/9VAsWLAh2GACABvI3x99fOQDURuIfwo4cORLsEAAAISSY6/hbrVZt2bJFW7ZskdVqbZL2du7cecK2GloPwIlxN1Az2rdvn5588kl99dVXkqRhw4bp9ttv1+zZs5WcnKyXXnrJXTcjI0OXXnqpxo0bp/nz52vHjh06/fTT3XW2bduml19+WZs3b9bRo0eVnJys8ePHa/r06R43dW3dulVLlizRN998o5KSEplMJqWmpuqaa67RqFGj3PVmzpypTZs2uft2uf/++5WVldWs1wUAEBh/6/Wf7Dr+VqtVFotFSUlJMpvNXtt5eXlavny5nE6n+5hJkyYpMzOzwW3Hx8dr//79kqSffvpJq1evlt1ul8lk0pgxY3Tqqae6+3PJy8vTypUrZbfbZTQaNXbsWA0dOtTdXkVFhdcxzaXuNWnovmAL5djQ8kj8m0l5ebmuu+46lZaWavLkyUpJSdHmzZs1e/Zsv3Mxt23bpk8++UQTJ07UpZde6i5fv3697rzzTvXo0UNTp05V+/bttWXLFvcHhEcffdRd99NPP9WuXbt00UUXKTk5WYcOHdK7776rO++8Uw8//LDGjh0rSbr22mvldDq1efNmPfjgg+7jhwwZ0kxXBABwsvz9+3Ey6/hv2LBB7733njsJHzx4sLZs2eLevvjii7V69WqPpF+Sli9frrS0tHqTydqJuz92u13vv/++pOPfXGRnZyszM1NWq9XjWIfDoffff1+rVq3yiKX2Mc2l9nnU7a++fcEWyrGdLJbzDAyJfzNZuHChSkpK9NBDD+mSSy6RJE2ZMkVPP/20Xn/9dZ/H/PDDD3ruuec8fimrqqr00EMPadCgQXrhhRfco/uTJ09W37599eSTTyo/P989av+b3/xGN954o0e7OTk5uuqqq/SPf/zDnfifddZZ+uCDD7R582aNGzeuyc8fAND0mnodf7vd7k76XdsFBQUe+1evXu2zfafTqaKiIg0ePNhn23UT94bGk5ubq7S0NFksFp/H1v0AUvuY5hjRrnsetfuT5HdfsEfX64s72LEheJjj30w+++wznXLKKRozZoxH+TXXXOP3mH79+nl9Es/Ly1NpaamysrJktVpVXl7ufp177rnuOi61v+6trKxUeXm5KisrNXz4cBUVFYXFHMmysjJVVVW5t61WqyoqKtzb1dXVKi0t9TimuLi43m2LxeLxjwV90Ad90Ee49uFLdXV1o/pwTbmprq4+YWJe34cKg+HnUde6fWzbti2gm45tNpt7aorR2LA0xXVMc7wfvs7D1Z+vDyc2m007duxoVB/N8XP17bff+o27qfpA+GHEv5ns27dPaWlpXn+0OnfurPj4eJ/H9OzZ06usqKhIkjym49RV+xezrKxML7zwgtauXauysjKvularNeQ/6Xfu3Nlju268sbGx6tKli0dZcnJyvdtJSUn0QR/0QR9h34e/m3jrzvE/UR8JCQnu+EwmU70Jur/9RqNRKSkpfvsYOHCgVqxY0ejkPyYmRsnJyYqLi9PYsWPd04AaekxtTfF++DoPV39Op9Pr+sTExKh///6N6qM5fq7S0tK8vnFxxd1UfQQTU30CQ+IfQnw9gMU1AnDLLbeoX79+Po/r2rWru+6NN96ooqIi5eTkaODAgTKbzTIajcrNzdUHH3zAY90BIIz5e1BXoDf3mkwmjR8/Xu+//75sNptiYmI0aNAgbd261b3tWvCh9s29RqNREyZM8Eq0azObzcrOzlZubq5sNpvHPtc3BU6nUwaDQQaDQQ6Hw92fq90RI0bI6XTqww8/dN/gK3l+C1H3mKZW9zzq9lffvmA6UdyITiT+zSQ5OVl79uyRw+HwGPUvKyvz+CrtRFzfArRt2/aEN+R8//332rFjh6677jrNmjXLY9/y5cu96tf+ihYAEPqa4wFew4cP15AhQ+pd1Uc6PoLs+hY6JSWlQd8eZ2Zmuufr117Vx/VNgauP2v9ft90LLrhAw4YN86rbkqv61D6Puv3Vty/YQjk2BAeJfzMZMWKEXn/9da1evdp9c68kvzf2+nP22Werc+fOevXVVzV69Gh16NDBY39lZaXsdrvi4uLcHzDq3vi0c+dOffrpp15tu0aIDh065NUuACD0NNcDvMxms1JTU/1uu8r83cjb0LYTExM99tXuo25/9cXnr73m5OuaNGRfsIVybGh5JP7NZPr06frggw80d+5cffvtt+rdu7c2b96sb775Rh07dmzwaHvbtm01d+5c3XHHHZo8ebKys7PVo0cPVVRUaNeuXVqzZo0ef/xxZWRkKCUlRX369NFrr72myspK9erVS7t379Y777yj1NRUfffddx5tDx48WG+//bb+8pe/6LzzznN/xdutW7fmuCQAgJMUzAd4AQh/JP7NpGPHjvr73/+up556SitXrpTBYNCwYcP04osvatq0aWrdunWD2zr77LO1cOFCLVy4UKtWrdJPP/2k9u3bq3v37rr66qvVt29fScf/8D/99NN66qmn9O677+rYsWM67bTT9MADD2jHjh1eif+YMWNUWFioDz/8UB9//LEcDofuv/9+En8ACFHN9QAvANHB4Kw7LwTNqry8XBdddJEuu+wy/elPfwp2OACAMDJ//nz3PPvaUlJSvO7tqk9NTY1eeeUVSdKMGTPUqlWrJosRaAkVhjkNrhvvfKIZIwkvrOPfjHzdhLVw4UJJipgn5wEAWk5zzfEHwo1Thga/8DOm+jSjW265RcnJyRowYIAcDoc2bNigzz77TEOGDNHIkSODHR4AIMwwxx/AySDxb0bnn3++3nvvPa1Zs0ZVVVVKTEzU1KlTdd111/FHGgDQaP7m8rdr166FIwEQjkj8m9HUqVM1derUYIcBAIgQ/tbr97e+PxC5mMITCOb4AwAQJpjjD+BkkPgDABAm/E0Trf2EeADwh78UAACEiYSEBJ/lLfkEWwDhi8QfAIAwcc455zSqHIhULOcZGBJ/AADCRGJiotLT0z3K0tPT1bVr1+AEBCCssKoPAABhJCcnR6NGjVJhYaH69+/PNB8ADUbiDwBAmElMTCThR1RjCk9gmOoDAAAARAESfwAAACAKMNUHAAAAYYapPoFgxB8AAACIAiT+AADAzWq1aufOnbJarcEOBUATY6oPAACQJOXl5Wn58uVyOp0yGAyaOHGiMjMzgx0WgCbCiD8AAJDVatWyZcvkdDolSU6nU8uWLWPkHyHJ2YgXfkbiDwAAVFBQ0KhyAOGHxB8AAGj79u0+ywsLC1s4EgDNhTn+AABA1dXVPsurqqpaOBLgxHhyb2AY8QcAAH4ZDCRYQKQg8QcAAACiAIk/AAAAEAWY4w8AAORwOHyW2+32Fo4EaAimoAWCEX8AAOD3Jl5/N/0CCD8k/mGssLBQ119/vUaNGqWMjAzNnz+/0W3MnDlTWVlZzRAdACCctG7d2md5bGxsC0cCoLkw1SdM2Ww23XXXXbLZbJo9e7bi4+PVt2/fYIcFAAhTRqPvsUCTydTCkQAnxnKegSHxD1N79+7V3r17deutt+rKK68MdjgAAAAIcUz1CVOlpaWSpA4dOgQ5EgAAAIQDEv9mcOTIET3//POaPn26fvnLX+rss8/WxIkT9eyzz6qyslLS8Zulzj33XN1///0ex/75z39WRkaG/vrXv3qU//GPf9QFF1wgm82mmTNnaubMmZKkuXPnKiMjQxkZGdq3b59yc3OVkZGh/Px8r7iYz49QYbVatXPnTlmt1mCHglqa4n2p20agbTbVz0hTxdOUMTT3cU3N6XQGtX/AF6cMDX7hZ0z1aQYHDhzQihUrdOGFF2rs2LEymUzatGmTXnvtNRUWFmrevHmKjY3VkCFDvBL0DRs2yGg0asOGDe4yp9OpjRs3Kj09XTExMbr22mv1i1/8Qq+88oomTZqkM844Q5LUqVOnFj1PIBB5eXlauXKl7Ha7TCaTsrOzlZmZGeywol5TvC912xg8eLC2bNnS6Dab6mekqeI5GYGeSyj9nvDkXiByMOLfDLp166b33ntPv//97/WrX/1KV1xxhf7yl7/o2muv1ZdffqmtW7dKkjIyMlRSUqLdu3dLkiwWi3788UeNGTNG//3vf93Tef773/+qrKxMw4cPlySdddZZ7n8AhgwZonHjxmncuHFq27ZtEM4WaDir1epOZqTj64Pn5uYGfUQz2jXF++KrjYKCgka32VQ/I00Vz8kI9Fz4PQHQXEj8m0GrVq0UE3P8yxSbzabDhw+rvLxcZ555piS5E39XIu8a3d+wYYNMJpNmzZolg8HgLnd9K5CRkdGi5xEsZWVlHutJW61WVVRUuLerq6vdH4pciouL6922WCweX1fTR3D6sFgsXg8Dstls+v7778PqPCKtj23btvl8XywWS4P7+Pbbb0/4oKfabfo7D38/IxaLpVHXylc7vuLZsWNHvW2ezPvh71y2bdtWbx9N8X6c6Dz279/vsV1WVuZ3So/T6QzZn136CG4fCD9M9Wkmixcv1tKlS/XDDz94PQ3R9Ys1cOBAxcXFKT8/X5MnT9aGDRt0+umnq3v37kpNTVV+fr7Gjh2rDRs2qEOHDurfv38wTqXFde7c2WPbbDZ7bMfGxqpLly4eZcnJyfVuJyUl0UcI9JGUlCSTyeSR1MTExKhfv34ea4iH+nlEWh8DBw7UihUrvN4XV72G9JGWluYxSu1L7Tb9nYfBYPD5M5KcnKy4uLh6z6P2tfL1s+Yrnrp/V5vy/fD3856WllZvH03xfpzoPBISErzOw1/i73A4QvZnlz6C2wfCDyP+zeCf//ynHn30UZ1yyin605/+pKeeekrPPfecHnjgAUk/PxY9JiZG6enpys/Pl9PpVH5+vvtbgIyMDG3YsEEOh0ObNm3S0KFDGzTPsr46PHYdwWY2m5Wdne3+RiwmJkZZWVleCR1aVlO8L77acN2X1Jg2m+pnpKniORmBnkuwfk94ci8Q+Rjxbwbvv/++Tj31VD3zzDMeD0T54osvvOoOHz5cn3/+uT7++GPt37/fnfifeeaZeuutt/TJJ5+ooqLCXX4i7du3lyQdPnzYa9++ffvc/5AAwZKZmam0tDRZLBYlJSV5jTIhOJriffHVhtVqbXSbTfUz0lTxnIxAzyUYvyf+ntDLk3uByEEW2AxMJpMMBoPH16Y2m02vvvqqV13XvP358+crNjZWv/jFLyRJZ5xxhkwmk1566SVJanDi37NnT0nSV199pQsvvNBd/sEHH+jAgQN8TYeQYDablZqaGuwwUEdTvC912wi0zab6GWmqeJoyhuY+LlD+ntDLk3sRilimMzAk/s3gl7/8pebNm6ebb75Zo0aN0pEjR7R69Wqfo+39+/dXhw4dVFRUpGHDhrnnOZvNZp1++unaunWrTjnlFKWkpDSo7969e+vMM8/UO++8I6fTqX79+mnHjh369NNP1aNHD9lstiY9VwAAAIQH5vg3g2uuuUY33HCD9u7dq7/97W9avHixMjMzNXfuXK+6BoNBQ4cOleQ9ql97vn9jPPjggxo1apQ++OADPfXUUyouLtaLL76orl27BnhGAAAACHcGJ4/kAwAgqtTU1OiVV16RJM2YMUOtWrXS888/736uTG09e/bU7373u5YOEajXfsO9Da6b4HyoGSMJL4z4AwAAv2ovUgEgvPHbDAAA/GJiABA5SPwBAACAKMCqPgAAwO9DHnn4I0ITy3kGghF/AACgmpoan+U8uReIHCT+AABArVq18lnOk3uByMFUHwAAoPj4eJ/l7du3b+FIgBPjlvPAMOIPAAB0wQUXNKocQPgh8QcAAEpJSVH37t09yrp3765evXoFKSIATY2pPgAAQJJ04403qqioSJs3b9YZZ5yhlJSUYIcEoAmR+AMAALeUlBQSfoQ8J8t5BoSpPgAAAEAUIPEHAAAAogBTfQAAABBWmOoTGEb8AQAAgChA4g8AAABEARJ/AADgl9Vq1c6dO2W1WoMdClCLoREvuDDHHwAA+JSXl6fly5fL6XTKYDBo4sSJyszMDHZYAALEiD8AAPBitVq1bNkyOZ1OSZLT6dTy5csZ+QfCGIk/AADw8t1333mVOZ1On+UAwgNTfQAAgJf9+/f7LD9w4EALRwJ4YznPwDDiDwAAvLRp06ZR5QBCH4k/AADwUllZ2ahyAKGPqT4AAMALI/4IZc5gBxCmGPEHAABeGPEHIg+JPwAA8MKIPxB5SPxDUH5+vjIyMpSbmxvsUAAAUcrfyP6xY8daOBIATYXEP0rl5+dr/vz5qqioCHYoAIAQ5G9kv23bti0cCeDNKUODX/gZiX8IGjp0qD7//HONGzeu2frYuHGjFixYQOIPAPCJOf5A5GFVnxBkNBrVunXrYIcBAAiA1WqVxWJRUlKSzGZzwHVOpi2r1aqioiJJkslk0jfffKOuXbsqISFBCQkJKi4u1uHDh2U0GnXkyBF17NjRq11/I/78+wSELxL/EJSfn6/Zs2fr/vvvV1ZWlnJzczV37ly9+OKLysjI8Kg7c+ZMFRcXe9wP8PXXX+sf//iHCgsLVVFRoQ4dOqhv37667rrrNHjwYD3wwAN69913JUnZ2dnu46677jrNmjWrZU4SAMKIK7mOj49XRUWFVyLu2r937159+OGHstvtMplMys7OVmZmpkdy/u2332rFihVyOBySpDPPPFPnnnuu9u/fr8rKSrVp00YpKSnauHGjVq9eLYfDIZPJpGHDhqlHjx4ece3evVsbN2501znrrLNkNBr12WefNfjc/vKXv6h///4aN26cEhMT3eX+Rvarqqoac+mAZsIUnkCQ+EeYXbt26YYbblCXLl2Uk5Ojzp07q6ysTAUFBdqxY4cGDx6syy67TEeOHNGaNWs0Z84c90hP3759gxs8AISgvLw8rVy5Una73V1WO6n3tV+S7Ha7VqxYocrKSo8PA3XrffXVV/rqq6/qjcFut5+wnt1u1+effx7AGUqFhYUqLCxUenq6cnJyJLGqDxCJSPwjzJdffqnKykr9+c9/1qBBg3zWGTJkiFJTU7VmzRqNHDlSp556agtHCQDhwWq1+k3qc3Nz1bt3b5/7XRwOh3vU3nVcKCsoKNCoUaOUmJjIqj5ABOLm3gjj+up57dq1Yft1bFlZmUfsVqvV4ybk6upqlZaWehxTXFxc77bFYpHT+fNz/uiDPuiDPhrSx7fffus3WbfZbNq8efMJk3lX0h8uCgsLZbVaZTD4nkrhWtUnUt9z+mh4H8HEqj6BMThr/xQgJJzMHP/q6mrdeuut+uqrr9S6dWsNHjxYZ511lsaMGaPk5GT3cfPnz9eCBQu0cuVKRvwBwA+r1apHHnnEZ3IfExOjm2++WU8//bTf5N9gMMhoNIb8SH9tt99+u7p27ar33nvP570CI0aMaNZV54CG2GP4c4Pr9nDe3YyRhBdG/MOAv1EXyftr49jYWD3//PN69dVXNWPGDBmNRs2fP19TpkzRmjVrmjtUAIgoZrNZ2dnZionxnBkbExOjrKwsJSQkeOw3Go3uv9kmk0kTJ0702B8TE6P09PR6/65Lx//un6hOc0hPT1fXrl0lMccfiETM8Q8D7du3lyQdPnzYa9++ffu8/kGSpEGDBrnn+FssFl199dV64YUXNGrUKEn1f5gAAPwsMzNTaWlpflf1qb0/KSlJkryW16y932w269JLL1VRUZF7FZ+EhASvVX0kuZfkrL2/NtexFRUV7thMJpP27NmjU045RatWrdKBAwfc9bt27aouXbpo+/btHu00ZlUf1vEHwheJfxjo2bOnpOMrP1x44YXu8g8++EAHDhzwmMJTXl7utR5zYmKiOnXqpEOHDrnL2rVrJ+n4hwmm+gBA/cxms1JTUyXJIzn2tV+Sx//72m82mzV48GCPOr7arV3H1/66+1z/dX1wGDhwoEpKSlRYWKj+/fu795eXl+vll1+WJF177bU+1/Hv0KGDz75cg1FAMDFPPTAk/mGgd+/eOvPMM/XOO+/I6XSqX79+2rFjhz799FP16NFDNpvNXfcf//iHvvzyS5133nnq1q2bnE6nPvvsM+3atUvTpk1z13N9G/DMM8/okksuUWxsrE477TSvf6wAAOEtMTHR60NDXFyc+9uIuLi4RrXHN8ZA+CLxDxMPPvigHn/8cX3wwQd6//33dcYZZ+jFF1/UI4884nGX/QUXXKCDBw/q3//+t8rKytS6dWv16NFD99xzjyZMmOCul56erptuuknvvPOOHn74Ydntdl133XUk/gAASfL4lrgh5QBCH4l/CHIt/WYymdxlp5xyih599FGvui+99JLHdkZGhtfKP/5Mnz5d06dPP4lIAQCRipt7EcpYpjMwrOoTglw3YnXu3DnIkQAAohU39wKRhxH/EFJaWqo1a9borbfeUlxcnNeNXwAAtBRG/IHIw4h/CCkqKtKTTz6ptm3b6oknnmj0DVcAADSVY8eO+SxnxB+hgCf3BoYR/xCSkZGhzz//PNhhAACgtm3bNqocQOhjxB8AAHjxN7J/9OjRFo4EQFMh8QcAAF78PcDL18O+AIQHpvoAAAAv/hJ/f+VAy2LufiAY8QcAAF5SUlJkNHqmCUajUSkpKUGKCMDJIvEHAABezGazJkyY4H6YpMlk0oQJE1hxDghjTPUBAAA+ZWZmKi0tTRaLRUlJSTKbzcEOCZAkOYMdQJgi8QcAAH6ZzWalpqYGOwwATYCpPgAAAEAUIPEHAAAAogBTfQAAABBWnCznGRBG/AEAAIAoQOIPAAAARAESfwAAIlRRUZGWLl2qoqKiYIcCNCmnDA1+4WfM8QcAIALNmzdPP/74oyRpw4YN6t69u2688cYgRwUgmBjxBwAgwhQVFbmTfpcff/yRkX8gypH4AwAQYdauXduociDcMNUnMCT+AABEmKNHj/osP3bsWAtHAiCUkPgDABAlnE5nsEMAEEQk/gAARAmDgWkPQDRjVR8AAACEFb67Cgwj/gAARAmm+gDRjcQfAIAowVQfILqR+AdRbm6uMjIylJ+fH+xQAAAAwoihES+4kPhHqcLCQs2fP1/79u0LdigAAABoAST+UWrHjh1asGABiT8AAECUYFUfAAAAhBWeyBsYEv8QYLfbNX/+fOXm5qq0tFS9evXSjBkzNGbMGI9627Zt08svv6zNmzfr6NGjSk5O1vjx4zV9+nTFxPz8Vv73v//VSy+9pG+++Ubl5eVq3769evfurWuuuUbnnXee5s+frwULFkiSZs+e7T7u0ksv1QMPPNAi5wwACD9Wq1UWi0VJSUmS5P5/s9nssc9sNjdZf0VFRZKkhIQEVVRUeLTvr88TxXKy+xsTd0pKSpNdD+BkkfiHgGeffVbHjh3TlClTJB2/6ffuu+9WdXW1srKyJEnr16/XnXfeqR49emjq1Klq3769tmzZovnz52vHjh169NFHJUnl5eW6/vrrJUmTJ09WUlKSysvL9d1332nr1q0677zzdOGFF+rgwYNatmyZZsyYoZSUFElS9+7dg3D2AIBwkJeXp5UrV8put8toNMrpdMrpdMpkMmnw4MHasmWL7Ha7TCaTsrOzlZmZedL9LV++3GsJUlf7ktzx1O6zdpy+YjnZ/Y2N22g0asKECSd9PYCmQOIfAsrLy7Vo0SL3iMCUKVOUk5OjJ598UqNHj5bBYNBDDz2kQYMG6YUXXnCP7k+ePFl9+/bVk08+qfz8fGVkZOjrr79WWVmZHnnkEY0ePdpnf3379tWQIUO0bNkyZWZmKiMjo8XOFQAQfqxWqzsZliSHw+HeZ7fbVVBQ4LGdm5urtLS0gEe6rVarVqxY4fO5A3a7XStXrpTT6XTH4eqzd+/eHnHWjaXueTR2fyBxOxwOrVy58qSuB9BUuLk3BEyZMsXjj4HZbNbkyZN1+PBhbdy4UXl5eSotLVVWVpasVqvKy8vdr3PPPVfS8REG17GS9MUXX8hqtbb8yTSBsrIyVVVVubetVqsqKirc29XV1SotLfU4pri4uN5ti8Xi8YeYPuiDPugjkvuoXd+X/fv3e2yfqI89e/a4k+GGsNls+u677wI+D4vF4vHhoi673e6132azafPmzV5x2mw2WSwWv+dRe7/FYql3/4nOw1/cdrvdo41w/bmq22YwOWVo8As/Y8Q/BPTu3durzDX9Zu/evTp27Jgk6cEHH/TbhuuXc9iwYRo/frxyc3O1atUqDRw4UJmZmRo9erT69OnT9ME3g86dO3ts1x0hiY2NVZcuXTzKkpOT6912zUelD/qgD/qIhj5at26t+iQkJDSqjx49eshkMjU4+Y+JidHAgQM9HhjWmPNISkqS0Wj0m/ybTCaPEX9Xn0OHDtVnn33mEWdMTIz7Gvs6j9r7k5KS6t1/ovPwF7fJZPJoI1x/ruq2ifBD4h8GXJ/Qb7nlFvXr189nna5du7r/f+7cubrmmmv0xRdfaPPmzfrnP/+pl19+WXPmzNGVV17ZIjEDACKH2WxWdna2cnNzZbPZZDQenzDgcDgUExOjQYMGaevWrbLZbIqJiVFWVpbi4uJOqr8JEyZoxYoVXkm0q31J7nhcZQkJCR5x1o2l7nk0dn8gcRuNRmVnZ5/U9QCaCol/CNi1a5dXmWs1gG7durm/imvbtm2Dbw5KTU1Vamqqpk2bpoqKCk2fPl3z5s3TFVdcIYPBwGPbAQCNkpmZqbS0tBZb1cfVX32r+tSOx1VWN866sZzs/sbGzao+zcP77g80BIl/CFiyZInHPH+r1aqlS5cqPj5ew4YNk9PpVOfOnfXqq69q9OjR6tChg8fxlZWVstvtiouL06FDhxQfH+8ejZGk+Ph4devWTXv27FFVVZXatGmjtm3bSpIOHz7ccicKAAhrZrNZqamp7u3a/193X1P1N3jwYPd2YmJivfE0NJaT3X8ideMGQgWJfwjo2LGjpk+f7vHVpcVi0T333KM2bdpIOj5954477tDkyZOVnZ2tHj16qKKiQrt27dKaNWv0+OOPKyMjQ++9957efPNNjRo1St27d1dMTIw2bdqk//znPxo9erS7vbS0NBmNRr388ss6fPiw2rZtq27dumnQoEFBuw4AgObFt71AdCPxDwE33XSTCgoKtHjxYpWVlalnz556+OGHNXbsWHeds88+WwsXLtTChQu1atUq/fTTT2rfvr26d++uq6++Wn379pV0/ObewsJCffbZZzp48KBMJpNOPfVU3Xrrrbriiivc7SUlJem+++7TwoUL9Ze//EU2m02XXnopiT8ARDBfy2MCiB4GJ38FAACIKM8//7x2797tVd6zZ0/97ne/U01NjV555RVJ0owZM9SqVauWDhE4Kd8Znmxw3dOdtzVjJOGFdfwBAIgSTPUBohuJPwAAUYIv+YHoxhx/AACiBCP+iBQ8kTcwjPgDAAAAUYDEHwAAAIgCTPUBAABAWOFulcAw4g8AQISJjY1tVDmA6EDiDwBAhBkwYIDP8tNPP72FIwEQSkj8AQCIMOnp6T7Lf/GLX7RsIABCCok/AAARxmw2a9KkSTIaj/8zbzQaNWnSJMXFxQU5MqBpOGVo8As/4+ZeAAAiUGZmptLS0mSxWJSUlCSz2RzskAAEGYk/AAARymw2KzU1NdhhAAgRJP4AAAAIK0zhCQxz/AEAAIAoQOIPAAAARAESfwAAACAKkPgDAAAPJSUlWrt2rUpKSoIdCuCTsxEv/IybewEAgNuiRYtUUFAgSVq1apXS09OVk5MT3KAANAlG/AEAgKTjI/2upN+loKCAkX8gQpD4AwAASdIXX3zRqHIgWHhyb2BI/AEAgCRp//79PssPHDjQwpEAaA4k/gAAQJJUXV3ts7yqqqqFIwHQHLi5FwAASPKf+NfU1LRwJED9mMITGEb8AQCAJCk2NrZR5QDCC4k/AACQJJlMpkaVAwgvJP4AAECSZLfbfZbbbLYWjgRAc2COPwAAkOR/Lj9z/BFqeCJvYBjxBwAAkqRWrVr5LGeOPxAZSPwjSEVFhebPn6/8/PxghwIACEPM8QciG1N9IkhFRYUWLFggScrIyAhyNACAcONwOHyW+5v7DwQLy3kGhhF/AAAgyf+DuniAFxAZGPE/ScXFxcrKytJ1112nWbNmuctvvPFGffnll7rtttt09dVXu8unT5+uI0eOaMmSJdq1a5cWLVqkTZs2yWKxyG63KyUlRVOmTNHEiRM9+jl06JD+/ve/a926dTpw4IDatm2r5ORkXXzxxZo2bZry8/M1e/ZsSdKCBQvcI//JycnKzc11t/Phhx/qX//6l77//nvZ7Xalpqbqmmuu0UUXXdSMVwlAJLJarbJYLEpKSpLZbPbabkwb8fHxqqioaNSxDY0LDedvLn/r1q1bOBIAzYHE/yQlJyerW7du2rBhgzvxr6mpUUFBgYxGo/Lz892Jv9Vq1fbt23XZZZdJkvLz87Vp0yadd955OvXUU1VZWal///vfevjhh/XTTz9pxowZ7n7+8Ic/aNOmTZo8ebL69u2rqqoqFRUVaePGjZo2bZpSUlI0Z84cPfHEExo1apRGjRolSWrXrp27jeeff14vv/yyzjnnHM2ePVtGo1Fr1qzRH/7wB91111264oorWuqyAQhzeXl5Wrlypex2u0wmkwYPHqwtW7a4t7Ozs5WZmdngNlwaemxD4zqZtqIRc/wRPpjqEwgS/yYwfPhwvfvuu6qsrFSbNm20ZcsWVVZW6pJLLtG6detks9kUExOjTZs2yW63u+ffjx8/XlOmTPFo66qrrtLs2bP16quv6pprrlFMTIysVqs2bNigKVOm6K677vIZQ5cuXTRy5Eg98cQTSk1N1bhx4zz2b9++XS+//LJmzJihG264wV2ek5Oj22+/Xc8995zGjx+vuLi4Jr46ACKN1Wr1SNjtdrsKCgrc++12u3Jzc5WWluZ3xL1uG405tjFxBdoWAEQi5vg3gYyMDNlsNm3evFmStGHDBnXu3Fm/+tWvdOTIEW3btk3S8RF+g8HgTvzbtm3rbqOqqkrl5eU6fPiwzjrrLB05ckS7du2SdPwr1tjYWG3dulX79u0LKMZVq1bJYDBo/PjxKi8v93iNGDFCR44c0ZYtW07iKjSdsrIyj/mkVqtVFRUV7u3q6mqVlpZ6HFNcXFzvtsVikdP586q/9EEf9BF4H3v27DnhzZ42m00Wi8VvH67phv6O/fHHHxt9Htu2bfNqs3Yckfp+BNLH/v37PbZdfdT3noTiedBHcPtA+DE4a/8UICClpaUaM2aMpk2bpptvvlnXXXedTjnlFP35z3/WRRddpKuvvlq/+c1vdNVVV0mS3nzzTUnS0aNH9dJLL+mjjz5SSUmJV7svvfSShg4dKklasmSJ/va3v6mmpkZ9+vRRRkaGRo4cqTPPPNNdf9++fcrOzva630CSbr75Zn3xxRf1nsfcuXM1fvz4k7oWACKf1WrVI488Um/yHxMToz/+8Y9+v0Wsr40THduYuAJtK9LV1NTolVdekSTNmDHDvX7/E0884fWhQJISEhI0Z86cFo0RqM8mw/MNrjvU+btmjCS8MNWnCXTp0kV9+vRRfn6+KisrtXXrVt15550yGo0aOnSoNmzYoMmTJ+v77793J/+SdPfdd2v9+vWaNGmShg4dqg4dOshoNOrzzz/Xm2++6bGs2pQpUzRy5EitX79eGzdu1Mcff6y3335bo0eP1iOPPNKgOA0Gg5555hkZjb6/6DnttNNO7kIAiApms1nZ2dnKzc11T2UcNGiQtm7d6t7OysqqN9mu24ZLQ45tTFyBthWt/N3cywO8EGpYzjMwJP5NJCMjQ0uWLNG6detUU1PjHokfPny4nn76aX3xxRdyOp0aPny4pONr7q9fv17jxo3Tn/70J4+2vvrqK599nHLKKZo4caImTpwou92u++67T6tXr9bUqVOVlpYmg8H/L0GPHj30xRdfKCkpSSkpKU101gCiVWZmptLS0k5qVZ/abTTVqj6+4kLD+RsY4uZeIDIwx7+JDB8+XA6HQwsWLFBSUpK6d+/uLq+urtarr74qk8mkM844Q9LPf1zrzrQ6ePCgli9f7lFWWVmpyspKjzKTyaS+fftKkg4fPizp53sGXNu1uW72fe6553x+tV53Hh8AnIjZbFZqaqo7ua673Zg2EhMTG31sQ+NCw/mbvsUDvIDIwIh/Exk2bJiMRqOKioqUlZXlLu/Tp4+6dOmiH374QYMHD3Z/5RwXF6ezzjpLq1atUuvWrZWWlqbi4mK988476tatmw4dOuRu43//+59mzpypUaNG6bTTTlN8fLx27dqlJUuWqFu3bu4PEx07dlSPHj304Ycfqnv37urcubPatm2rESNGKC0tTTNnztRLL72kq666ShdddJG6du2qgwcP6rvvvtPnn3+uL7/8smUvGgAgpNTU1Pgsr66ubuFIgPpxg2pgSPybSPv27dWvXz9t377dvWqPy/Dhw/XBBx94lT/00EN69tln9dlnn+m9995Tjx499Lvf/U4xMTGaO3euu15iYqKys7O1ceNGffrpp6qpqVHXrl01adIkTZ8+XW3atPFo84knntBzzz2nyspKJScna8SIEZKkmTNnauDAgVq0aJHeeustHTt2TJ07d9Zpp52mO+64oxmvDgAgHLhu8q2LOf5AZGBVHwAAooy/VX2ef/557d6926t+r169dP3117dojEB98g0vNLhuhpOfXRfm+AMAAEn+5/LXXnkJQPhiqg8AAJDkf44/iT9CDct5BoYRfwAAIMn/HP+YGMYJgUhA4g8AACT5X6+fxB+IDPwmAwAASazjj/DByjSBYcQfAABI8j+Xnzn+QGQg8QcAAJKkbt26NaocQHgh8QcAAJKkCy64wGf5yJEjWzYQ4AQcMjT4hZ+R+AMAAEnHnxSfnp7uUZaenq6uXbsGJyAATYqbewEAgFtOTo5GjRqlwsJC9e/fX4mJicEOCUATIfEHAAAeEhMTSfiBCETiDwAAgLDCk3sDwxx/AAAAIAqQ+AMAAABRgKk+AAAACCs8uTcwjPgDAABJktVq1c6dO2W1WoMdCoBmwIg/AABQXl6eli1b5t6eNGmSMjMzgxgRgKbGiD8AAFHOarV6JP2StGzZMkb+gQhD4g8AQJQrKChoVDkQbE4ZGvzCz0j8AQCIctu3b/dZXlhY2MKRAGhOJP4AAES56upqn+VVVVUtHAmA5sTNvQAAwCeDgWkSCE1M4QkMI/4AAABAFCDxBwAAPjHiD0QWpvoAAACfnE6ej4rQxE9mYBjxBwAAAKIAiT8AAPCJEX8gspD4AwAAn5jjD0QW5vgDAACfSPwRqljOMzCM+MPtyJEjwQ4BAKKO1WrVzp07ZbVagxaD3W73WW6z2Vo4EgDNiRH/IMjNzdXcuXP13HPPqaCgQLm5uSotLVWvXr00Y8YMjRkzxqP+p59+qtdee007duyQwWBQ3759NW3aNI0cOdKr7YbWzcrKUnJysubMmaN58+Zpy5Yt6tChg1auXNmMZw6gPlarVRaLRUlJSTKbzcEOJ6Sc7LWpe3xD2qtdR5LP+icb10cffaRPPvlETqdTJpNJ2dnZyszMDDieQNXU1Pgs9/dEXwDhicQ/iJ599lkdO3ZMU6ZMkXT8A8Hdd9+t6upqZWVlSZIWL16sRx99VL1799Zvf/tbSdK7776rO+64Q3/605902WWXudtrTF1JKikp0fXXX6+LLrpIF154oY4ePdoSpw3Ah7y8PK1cuVJ2u/2ECWC0OdlrU/f4wYMHa8uWLfW2V/sYo9Eop9PplZyfbFz//Oc/tXXrVve23W5Xbm6u0tLSvJL52n0ZDAYZDAY5HI4m+1lp1aqVz/LY2NiTahdoLkz1CQyJfxCVl5dr0aJF7j/wU6ZMUU5Ojp588kmNHj1a1dXVeuaZZ9S9e3e9+uqrHvWuvvpqPfXUUxo9erTi4+N1+PDhBtd12bt3r+655x5NnDixxc8dwM+sVqs7qZPqTwCjzcleG1/HFxQUuPf7aq/uMQ6Hw6t+7969TyqukpISj6TfxWazyWKxKDU11e85uD6EBNKvPyaTqVHlAMITc/yDaMqUKR5/qM1msyZPnqzDhw9r48aNysvL07Fjx5STk+NVLycnR0ePHlVeXp4kNaquS4cOHdzfLISSsrIyVVVVubetVqsqKirc29XV1SotLfU4pri4uN5ti8XisSwdfdBHKPVhsVi85li7EsBwOo/m6KO+a9OQPvbs2eN3/nrd9lxx++qzbv3CwkKfcRUVFTXoWm3fvt1n20ajUSaTyeM8vv/++xPGs2PHDq8+6tvev3+/Vxv1ibSfK/pomj4QfhjxD6LevXt7laWkpEg6PhrvmnrTp08fr3qusr1793r8tyF1Xbp16xaSozmdO3f22K47ihUbG6suXbp4lCUnJ9e77ZoTSx/0EYp9WK1WmUwmj+QuJiZGycnJiouLC5vzaI4+Wrdu3eBr46uP2NhYr+PrcrXnitvX+1G3/oABA7R69WqvuPr06eMRl79rZTQatWrVKq+2L7zwQve/Ay59+/Y9YTz9+/f36qO+7YSEBK82fHGt6hNpP1f00TR9BBNPmAgMI/5RrE2bNsEOAYCO/4ObnZ3tTr5iYmKUlZXlldhGo5O9Nr6OT09Pr7e9uscYjUYZjUaP+gkJCScVV2JiotLT0z3KBg0apIsuuuiE5+Arnub6WeEBXkBkYcQ/iHbt2uVVVlRUJOn4aPyxY8ckST/88IPOPPNMv/UkqXv37g2uCyD0ZGZmKi0tjVV9fDjZa+Pr+BOtxlP3GMl7FZ2TjSsnJ0ejRo1SYWGh+vfvr8TExAafg694mgPr+AORhcQ/iJYsWeIxz99qtWrp0qWKj4/XsGHDVFNTo7Zt2+pf//qXx4jOkSNH9K9//Uvt2rXTWWedJen4PwoNrQsgNJnNZo+bOvGzk702dY9vSHt16/iqf7JxJSYm1pvwNzaepsaIPxBZSPyDqGPHjpo+fbr7Btvc3FxZLBbdc889atOmjdq0aaObb75Zjz76qH7961/r0ksvlXR8ic49e/boT3/6k/tDQ3x8fIPrAgDQECT+CFUs5xkYEv8guummm1RQUKDFixerrKxMPXv21MMPP6yxY8e661x++eU65ZRT9Prrr2vBggWSpH79+umvf/2r10O5GlMXAIATYaoPEFlI/IPIZDJp1qxZmjVrVr31Ro0apVGjRjWozYbWzc3NbVB7AAAAiAwk/gAAAAgrTEILDMt5AgAAAFGAxB8AAACIAkz1CYKsrCz3Sj4AAABASyDxBwAAQFhhOc/AMNUHAIAoZzKZfJbHxDA+CEQSEn8AAKJcQkJCo8oBhCcSfwAAotw555zTqHIg2JwyNPiFn5H4AwAQ5RITE5Wenu5Rlp6erq5duwYnIADNgsl7AABAOTk5GjVqlAoLC9W/f38lJiYGOyQATYzEHwAASDo+8k/Cj3DgCHYAYYqpPgAAAEAUIPEHAAAAogCJPwAAABAFmOMPAACAsOI0skxnIBjxBwAAAKIAiT8AABGmpKREa9euVUlJSbBDARBCmOoDAEAEWbRokQoKCiRJq1atUnp6unJycoIbFNDEnMz0CQgj/gAARIiSkhJ30u9SUFDAyD8ASST+AABEjPz8/EaVA4guTPUBACBClJeX+yw/fPhwywYCNDNW9QkMI/4AAESIY8eONaocQHQh8QcAIEK0a9euUeUAoguJPwAAESImxvcMXpPJ1MKRAAhFzPEHAABAWHEydB0QLhsAAAAQBUj8o9z8+fOVkZGhffv2BTsUAMBJstlsjSoHEF2Y6gMAQIQ4evRoo8qBcOU0sZxnIBjxBwAgQrRt27ZR5QCiC4k/AAARolWrVo0qBxBdmOrTAvbt26cnn3xSX331lSRp2LBhuv322zV79mwlJyfrpZde8qi/fPlyLV68WLt27VJMTIwGDRqk6667Tunp6V5tN7Suw+HQwoULtWzZMh08eFDdu3fXjBkzmuuUAQBBYDD4nv5gMBhktVpVVFQkSerSpYsOHz6s6upqrV69WuXl5ercubOqqqpUU1Mjm82m8vJy2Ww2paWladCgQdq/f78qKyvVpk0bpaSkyGw2q6SkRBs2bJDBYNDAgQNlt9uVlJQks9ksq9Uqi8Wi+Ph4VVRUuMtdSkpKtH37dg0YMECJiYnu+nXrAWg6BqfT6Qx2EJGsvLxcV199tUpLSzV58mSlpKRo8+bN+uabb3Ts2DH16dPHI/F/5pln9NprryktLU0XX3yxjh49qmXLlqm0tFR/+9vfdN555wVU929/+5veeustDR06VBdeeKHKysr09ttvq1u3biosLNTKlSt16qmntui1AQA0rcWLF2vjxo1e5T179tSPP/4oh8PRJP0YDAZ169ZNP/74o9c+k8mkwYMHa8uWLbLb7R7l2dnZyszM1KJFi1RQUODe1717dxUXF8tut3vUA/zJjX+jwXWzKq5uxkjCCyP+zWzhwoUqKSnRQw89pEsuuUSSNGXKFD399NN6/fXXPeru2rVLr7/+un7xi1/oxRdfdH81O3HiRF1++eV69NFHdfbZZ8tkMjW67qJFizR8+HDNmzfP/SCXCy+8UNdcc00LXg0AQHOqrq72Wb5nzx415Tif0+n0mfRLkt1u90jqa5fn5uYqISHBa3/ttlz10tLSGPkHmhhz/JvZZ599plNOOUVjxozxKPeVcK9du1ZOp1PTpk3zmI/ZtWtXZWVlqbi4WIWFhQHXvfrqqz2e3jhgwICQHFEpKytTVVWVe9tqtaqiosK9XV1drdLSUo9jiouL6922WCwe/+jRB33QB31EYh/Hjh2TL6Hy5b7NZtPmzZsbVM9isYT9+xHpfSD8MOLfzPbt26e0tDQZjZ6fsTp37qz4+HivupJ02mmnebXjKtu7d68GDhzYqLp79+6VJPXu3durbkpKir788stGnlXz6ty5s8d23RGf2NhYdenSxaMsOTm53u2kpCT6oA/6oI+I76Ndu3byxWAwhETyHxMTo6FDh7rveauvXnJysuLi4jzKw+39iPQ+gokn9waGywYAQISIifE9ntejRw+vAaiTYTQa1b17d78xpKene8USExOjrKws9e7d22sBiu7du7vru+rVTfoBnDxG/JtZcnKy9uzZI4fD4fFHt6yszOMrNUnq1q2bJOm///2v1x/UH374waNOIHV37drlVde1wgMAIHJ17dpV06ZN81jV580331R1dbV+8YtfuFf1qaysdK/qc+jQIdlsNg0cOLDeVX3y8/MlqVGr+uTk5GjUqFEqLCxU//79WdUHaCEk/s1sxIgRev3117V69Wr3zb2SvG7sddV99tln9frrr+vcc891j34cPHhQubm5Sk5OVv/+/Rtd94ILLtC8efP0xhtvuG/4laTt27ef8OtWAEBkMJvNGjx4sCSppqZG7du3lyRdfPHFDVrnPzEx0WfZ+PHj/faXmppa77G1y2vXB07EaeTJvYEg8W9m06dP1wcffKC5c+fq22+/Ve/evd3LeXbs2NFjzeXevXvrmmuu0WuvvabrrrtOo0ePdi/RefToUT300EPupL2xdS+//HK9/fbbuv766z2W8+zbt6/7JmAAAABELhL/ZtaxY0f9/e9/11NPPaWVK1fKYDBo2LBhevHFFzVt2jS1bt3ao/7NN9+sHj16aPHixZo3b55atWqltLQ0PfzwwzrjjDMCrnvHHXeoS5cuWrZsmZ5++mn16NFDv//977V7924SfwCIEDU1NT7LbTZbC0cCIBSR+LeAbt266fHHH/coKy8v16FDh7zuspekSZMmadKkSQ1qu6F1jUajfvOb3+g3v/mN175Zs2Y1qC8AQGjzt5zn0aNHWzgSACdj7969Wrdunfbv36/Jkyere/fustvtOnTokDp06OCxPHtjsKpPC6isrPQqW7hwoSSF5Dr6AIDw5G85T1bIQaRxGhr+CidOp1Nz5sxRSkqKrr76as2ZM0c7duyQdPxZC71799azzz4bcPuM+LeAW265RcnJyRowYIAcDoc2bNigzz77TEOGDNHIkSODHR4AIEL4W84z0NFBAC3r8ccf19NPP63f//73+uUvf6nRo0e793Xo0EGXXXaZli5dqltvvTWg9kn8W8D555+v9957T2vWrFFVVZUSExM1depUXXfddfwxBgAAgCRpwYIFmjZtmv7v//7P68nJkjRkyBCtWrUq4PZJ/FvA1KlTNXXq1GCHAQAAEBEidTnPPXv26JxzzvG7Py4uTocPHw64feb4AwAQIfyt6uOvHEBoSUhI0J49e/zu37hxo3r27Blw+yT+AABECH+r+vhaZAJA6Lnsssv04osv6ocffnCXuZ759OGHH+rVV1/V5ZdfHnD7JP4AAESIU045xWd5ly5dWjgSAIGYO3eukpOTlZ6ermnTpslgMOjRRx/Veeedp0suuURDhgzRn/70p4DbJ/EHACBCnH322T7L65szDIQjh6Hhr3DSoUMHffnll7rrrru0d+9etWnTRmvXrlV5ebnuv/9+ffbZZ36X7W0Ibu4FACBCJCYmKj09XQUFBe6y9PR0de3aNXhBAWiUtm3b6p577tE999zT5G2T+AMAEEFycnI0atQoFRYWqn///kpMTAx2SABCBIk/AAARJjExkYQfES1Sl/O89tprT1jHYDDoH//4R0Dtk/gDAAAAIeCTTz5xr+LjYrfbVVxcLLvdrq5duyouLi7g9kn8AQAAgBCwa9cun+U1NTWaP3++nnrqKX300UcBt8+qPgAAAAgrTkPDX5GgVatWuvHGG3XxxRfrxhtvDLgdEn8AAAAgDPziF7/QunXrAj6exB8AAAAIAx999BHr+AMAgKZntVplsViUlJQks9kc7HCAiPfggw/6LC8vL9e6deu0adMm/eEPfwi4fRJ/AADgJS8vTytXrpTdbpfJZFJ2drYyMzODHRYgSXIaImTyfh0PPPCAz/JOnTrptNNO04svvqjrrrsu4PZJ/AEAgAer1arly5fL6XRKOr6c4PLly5WWlsbIP9CMHA5Hs7bPHH8AAODhu+++cyf9Lk6nU999912QIgLQFBjxBwAAHkpKSnyWHzhwoIUjAXxzRMhMn927dwd0XM+ePQM6jsQfAAB4aNu2baPKAQSmd+/eXk/qbQi73R5QfyT+AADAQ2Vlpc/yo0ePtnAkQGR7+eWXA0r8A0XiDwAAPLRp06ZR5UBLcxojY67Pr3/96xbtj5t7AQCAB38j/lVVVS0cCYCmxIg/AADw0Lp1a5/ljPgDLePzzz/Xpk2bdOjQIa8lPg0Gg+69996A2iXxjyC5ubmaO3euXnzxRWVkZAQ7HABAmKqoqPBZfvjw4RaOBIguZWVlGj9+vL766is5nU4ZDAb30rqu/z+ZxJ+pPgAAwIO/mw1b8iZEoD5OQ8Nf4eTOO+/UN998ozfffFM//PCDnE6nVq9erR07dmj27NlKT0/Xvn37Am6fxB8AAHiIj49vVDmApvH+++9r1qxZuvLKK92/b0ajUampqXruuefUu3dv3XrrrQG3T+IPtyNHjgQ7BABACPB3c6+/cgBNo7y8XGlpaZIks9ksSbJare79F198sVavXh1w+8zxj0B2u13z589Xbm6uSktL1atXL82YMUNjxoxx18nKylJycrLmzJmjefPmacuWLerQoYNWrlwZxMgBILJZrVZZLBbFx8eroqJCSUlJ7n/c69YrKiqSJKWkpMhsNquoqEibNm3S6aefrtjYWCUlJenAgQP66quv1L59ezkcDiUkJCguLk7ffPONYmJitG/fPsXExCguLk5Op1OJiYmqrKyU0+l0z9d/+eWXlZSUpCNHjujgwYPq2LGj37n83NyLUOGM0Glnp556qiwWi6TjN9knJCTo66+/1oQJEyRJe/fuPakpdyT+EejZZ5/VsWPHNGXKFEnHb/q9++67VV1draysLHe9kpISXX/99brooot04YUX8mAWAGhGeXl5WrlypccTN00mk7Kzs5WZmelRb8WKFe6VPAwGgzp06KDy8nJJ0oYNGwKOYfv27V5lBw8edH/IkFTv/GFG/IHmNWLECH300Ue6++67JUlXXnmlHnvsMZlMJjkcDj311FMeA7mNReIfgcrLy7Vo0SL3KNKUKVOUk5OjJ598UqNHj3aP2Ozdu1f33HOPJk6cGMRoASDyWa1Wr6RfOv4NbW5urtLS0mQ2m931ai/f53Q63Ul/sDHiDzSvOXPm6KOPPlJVVZVat26tBx54QN9++617FZ8RI0bo2WefDbh95vhHoClTpnh8dWw2mzV58mQdPnxYGzdudJd36NDB4xuAUFFWVubxkBir1eqxtFx1dbVKS0s9jikuLq5322KxuJfDog/6oA/6aOk+LBaLV9LvYrPZ3F/t11cvFNQe8Q/n94M+mqYPNL3Bgwdrzpw57mdpdOrUSf/+979VVlamQ4cO6dNPP1VycnLA7RuctX8KENZc6/j/9a9/1ciRIz32ffrpp7rjjjt011136YorrlBWVpY6deqk1157LTjBAkAUsVqteuSRR3wm9TExMfrjH/+ouLi4euuFgosvvlgXXnhhsMMA9HqvxQ2ue83/Lm/GSJrWtm3bNHDgwGZrnxH/KMZXtgDQMsxms7KzsxUT4znDNiYmRllZWYqLi/OoZzT+/M+z0WhUx44dWzJcv3iAF9C8Bg0apCFDhuj//u//tHPnziZvnzn+EWjXrl1eZa4bt7p169bC0QAAJCkzM1NpaWknXNXHVc/Xqj6bN2/WgAEDTriqz5o1a/Tjjz+6p3a4+nPp3bu3e4nA9u3bKzExUUeOHFFpaak6dOigAwcO6MCBA17nwAO8gOb1wgsv6O2339Z9992ne++9V+np6crJydEVV1yhXr16nXT7TPWJIK6pPklJSR4391qtVuXk5OjIkSNatWqV2rRp417O86WXXgpy1ACA5uBaOtT14aL2duvWrfXKK69IkmbMmKFWrVp5HPvxxx/ro48+8mpzzJgxGjVqVIvED9Tntd5LGlx32q4pzRhJ8ygpKdHixYv19ttv6/PPP5cknXnmmcrJydHll1+uU089NaB2meoTgTp27Kjp06fr1Vdf1auvvqrp06fLYrHolltuYXoPAEQJs9ms1NRU9yBQ3e36+Fu2k2WfgZaRmJioG2+8UevWrdPu3bv1t7/9TQaDQbfffvtJjfwz1ScC3XTTTSooKNDixYtVVlamnj176uGHH9bYsWODHRoAIAz4GyRq27ZtC0cCIDk5WWlpaTr99NO1detWHTlyJOC2SPwjSFZWlnt5zszMTM2aNctv3dzc3JYKCwAQZvyN+PMAL4QKZ4TfbuJ0OvXpp5/qX//6l5YtW6aDBw+qU6dOysnJ0ZVXXhlwuyT+AADAg78Rf6aLAs3rs88+09tvv60lS5Zo//79at++vSZOnKgrr7xSF110kdfKYI1F4g8AADww4g8ExwUXXCCz2aysrCxdeeWVGjt2rGJjY5usfRJ/AADggTn+QHAsXrxY48ePb7Zv10j8AQCAB1b1QahzROgzJSZPntys7bOcJwAA8MCIPxCZSPwBAICHw4cPN6ocQHhgqg8AAPDg72bC1q1bt3AkgG+Rvpxnc2HEHwAAeMjIyGhUOYDwQOIPAAA8JCYmKj093aMsPT1dXbt2DU5AAJoEU30AAICXnJwcjRo1SoWFherfv78SExODHRLg5ozQVX2k4/fSPP/881qzZo3279+v+fPn68wzz1RZWZleffVVZWdnKzU1NaC2SfwBAIBPiYmJJPxAC/rxxx91wQUXaM+ePerbt6+2b98uq9UqSercubPmz5+v//3vf3r66acDap/EHwAAAAgBd955pyoqKlRQUKCEhAQlJCR47J84caLefffdgNtnjj8AAAAQAj788EPdfPPNGjhwoAw+pjP16dNHe/bsCbh9RvwBAAAQViJ1jv+xY8fqvYm+oqLipNpnxB8AAAAIAQMHDtS6dev87l++fLnOOOOMgNsn8QcAAABCwK233qpFixbp0Ucf1aFDhyRJDodDO3fu1DXXXKP//Oc/uu222wJun6k+AABAklRSUqLt27drwIABrOaDkBapT+6dOnWq/ve//+mee+7R3XffLUkaO3asnE6njEaj/u///k8TJ04MuH0SfwAAoEWLFqmgoECStGrVKqWnpysnJye4QQFR6O6779Y111yjpUuXaufOnXI4HDrttNN02WWXqU+fPifVNok/AABRrqSkxJ30uxQUFGjUqFGM/AMt5OjRozr//PN13XXXafbs2Sc1pccf5vgDABDlNmzY4LM8Pz+/hSMBole7du1UVFTkcxnPpkLiDwBAlDt8+HCjyoFgcxoNDX6Fk7Fjx2r16tXN1j6JPwAAUe7o0aONKgfQPO69917t2LFD11xzjdavX6+9e/eqrKzM6xUo5vgDABDlWrVq5bM8Nja2hSMBoltaWpokadu2bXrzzTf91rPb7QG1T+IPAECUq6ys9FnOiD9CVaQ+ufe+++5r1jn+JP4AAMCn5kxAAHh74IEHmrV95vgDABDl2rRp47O8bdu2LRwJgObEiH+YmjlzpoqLi5WbmxvsUAAAYa6mpsZneVVVVQtHAjRMuK3W01APPvjgCesYDAbde++9AbVP4h+lcnNzVVFRoauuuirYoQAAgoybe4HQUN9UH4PBIKfTeVKJP1N9olRubq7eeuutYIcBAAgB/kb8/ZUDaB4Oh8PrZbPZ9N///le33XabMjIytH///oDbJ/EHACDKMeIPhC6j0aiUlBT99a9/Vd++fXXTTTcF3BZTfUJIbm6u5s6dq+eee04FBQXKzc1VaWmpevXqpRkzZmjMmDEnbGPTpk36+9//rm+//VY2m029e/fW5ZdfrokTJ7rrZGVlqbi4WJKUkZHhLn/xxRc9tgEglFmtVlksFiUlJclsNjd5/WDFJsn9/0eOHNE333yjjh07qmfPntq/f78OHTqko0ePqm/fvrJaraqsrFRVVZXKy8uVmJioU045Rbt379aAAQMkSV9//bU6deqk008/XZJUVFQku90uu90uk8kkSbLZbD7jYsQfIStKV5waMWKEfv/73wd8PIl/CHr22Wd17NgxTZkyRdLxDwR33323qqurlZWV5fe4devW6c4771SXLl00depUtWvXTh9++KEefvhh7d27VzfccIMk6fbbb9e8efNUXl6uOXPmuI9PSUlp3hMDgCaSl5enlStXupPX7OxsZWZmNln9YMVmMBhkMBjkcDjc83n9+eSTT+qNY9WqVV5ldds85ZRTJPkf8fdXDiA48vPzZTQGPmGHxD8ElZeXa9GiRe5RoilTpignJ0dPPvmkRo8e7XPZNbvdrscee0xt27bVwoUL1bVrV0nSFVdcoVmzZmnhwoXKyspSz549NXLkSL355puqqqrSuHHjWvTcAOBkWa1Wd6IsHf/7l5ubq7S0NJ+j642tH8zYnE6nOzGvL+kPVN02Dx48qCNHjujYsWM+6/srB9A8XnvtNZ/l5eXlWrdund555x399re/Dbh95viHoClTpnj8A2E2mzV58mQdPnxYGzdu9HnMd999J4vFouzsbHfSLx0frZk2bZocDofWrl3b7LE3hbKyMo8l5KxWqyoqKtzb1dXVKi0t9TjGNXXJ37bFYvH4B48+6IM+wrcPi8Xi9bh6m82m7777zmcf/ur/+OOPTX4ermk0dfuyWCw+2/QVW0srKSnxO9XHda7Bfs/pIzT7CCan0dDgVzj59a9/7fN16623at26dfrDH/6gZ555JuD2GfEPQb179/Yqc03D2bt3r89j9u3bJ0nq06eP177TTjut3mNDTefOnT22646SxcbGqkuXLh5lycnJ9W675s3SB33QR/j3kZSUJJPJ5JEwx8TEaODAgR5PmnX14a9+jx49FBcX16TnkZKS4rOv2u3U/n9fsbW0xMREv998tGvXTlLw33P6CM0+0PSKioq8ygwGgzp16qT4+PiTbp8RfwBAWDGbzcrOzlZMzPGxq5iYGGVlZXkl8YHWD2ZsRqPxpObvnoihzg2Rp5xyiuLi4vzexFtdXd1ssQDwZjAYlJCQoF69erlfPXv2dCf9x44d0+7duwNunxH/ELRr1y6vMtcnwG7duvk8xlX+ww8/eO1zldU+tu4ffwAIJ5mZmUpLS2vwyjmNrR/M2KTj0zL27dunDz74wH2j76hRo5ScnKxDhw7p2LFjSk1N9VjV59ChQ0pISNApp5yiPXv2qH///pLkXhWo9qo+DodDX3zxhXtVH5bzBEJDSkqKXn/9db8PWF25cqWuuuqqgL8lJPEPQUuWLPGY52+1WrV06VLFx8dr2LBhPo8ZMGCAkpKSlJubq2nTprlXarDZbHr99ddlMBh0wQUXuOu3a9dOhw8fdj8BDgDCjdlsVmpqarPVPxknG1tqaqpSU1M1dOjQgD6s1F6lbfTo0R77Bg8erJqaGuXl5bnLGPFHuHFGaO5yopv6a2pqWNUn0nTs2FHTp093L92Zm5sri8Wie+65x+eKPpJkMpl011136c4779T06dM1adIktWvXTh999JG2bNmiGTNmqGfPnu76gwYN0meffabHHntMQ4YMkdFo1PDhw73m/AEAgqelPqywnCcQPIcPH1Z5ebl7u7S01Od0HteqjydzrwWJfwi66aabVFBQoMWLF6usrEw9e/bUww8/rLFjx9Z73IgRI/T888/rH//4h15//XXV1NSod+/euueeezwe4CVJV199tfbu3auPP/5YS5culcPh0IsvvkjiDwBRyN+IPw/wAprfk08+qQcffFDS8anYt956q2699VafdZ1Opx5++OGA+yLxD0Emk0mzZs3SrFmz/NZ56aWXfJYPGzbM73Sg2tq0aaP77rsv4BgBAJHD31x+5vgjVDkNkbM+zcUXXyyz2Syn06m77rpLv/rVrzR06FCPOgaDQXFxcRo2bJgyMjIC7ovEHwCAKMcDvIDgOfvss3X22WdLko4cOaLJkydr0KBBzdIXiT8AAAAQAu6///5mbZ/EHwCAKNe2bdtGlQPBFm5P5G2szz//XJs2bdKhQ4fkcDg89hkMBt17770BtUviH0KysrLcK/kAANBS/C3byXKeQMsqKyvT+PHj9dVXX7mXXHct8en6/5NJ/CPnzggAABAQlvMEQsOdd96pb775Rm+++aZ++OEHOZ1OrV69Wjt27NDs2bOVnp6uffv2Bdw+iT8AAFGO5TyB0PD+++9r1qxZuvLKKxUfHy9JMhqNSk1N1XPPPafevXv7XeqzIUj8AQCIcu3bt/dZ3qFDhxaOBGgYp8HQ4Fc4KS8vV1pamiS5n9ZttVrd+y+++GKtXr064PZJ/AEAiHIpKSmNKgfQPE499VRZLBZJUuvWrZWQkKCvv/7avX/v3r0ynMSHGW7uBQAgyp1++ukeNxFKx28kHDBgQBCjAqLPiBEj9NFHH+nuu++WJF155ZV67LHHZDKZ5HA49NRTT2nMmDEBt0/iDwBAlDObzZo4caJWrlwpu90uk8mk7OxsxcXFBTs0wLfwmsHTYHPmzNFHH32kqqoqtW7dWg888IC+/fZb9yo+I0aM0LPPPhtw+yT+AABAmZmZSktLk8ViUVJSknt+MYCWM3jwYA0ePNi93alTJ/373/9WeXm5TCaT+4bfQJH4AwAAScdH/lNTU4MdBoA6Onbs2CTtcHMvAAAAwkqkruojSbt379bs2bPVv39/de7cWevWrZMkHTx4UDfffLM2b94ccNuM+AMAAAAhYNu2bTr//PPlcDiUmZmpnTt3ymazSZJOOeUUrV+/XkeOHNE//vGPgNon8QcAAABCwF133aWOHTvqyy+/lMFgUEJCgsf+8ePH61//+lfA7TPVBwAAAAgB69at0/XXX6+uXbv6XK+/Z8+e2rt3b8DtM+IPAEAUsFqt7hV7WrduHexwgJPiNIbf3P2GcDgcateund/9Bw4cOKnfXxJ/AAAiXF5enpYvXy6n0ymDwaCsrKxghwTAh6FDh+q9997T7373O699NptNixYt0llnnRVw+0z1AQAgglmtVi1btsz9VF6n0+l+UBeA0PLHP/5RH3zwga6//npt3bpVklRSUqJ///vfuvjii/Xdd9/pD3/4Q8DtM+IPAEAEKygo8FleUVHRZGuDAy0tHJfpbIhLLrlEr776qm655Ra99NJLkqSpU6fK6XSqffv2eu211zRixIiA2yfxBwAggm3fvt1n+dGjR0n8gRB0zTXX6LLLLtOHH36onTt3yuFw6LTTTtOYMWN4ci8AAPCvurraZ7nD4WjhSAD48qc//Uk5OTkaMmSIuywuLk6TJk1q8r6Y4w8AQAQzmUw+y30tFQig5f3lL39xz+eXpNLSUplMJn3yySdN3hcj/gAARLC2bdv6LDcaGftD+IrUOf4urpvxmxq/9QAARLBjx475LGeqDxB9SPwBAIhg/pbtbK4RRQChi6k+AABEMOb4IxJF2lSfXbt2adOmTZKkQ4cOSZK+//57vytvDR06NKB+SPzDVG5urubOnasXX3xRGRkZ9dadOXOmiouLlZub20LRAQBCRZs2bXyW+/tAAKDl3Xvvvbr33ns9ynw9vdf19O1AH8BH4h+lcnNzVVFRoauuuirYoQAAmlFlZaXPcp7cC4SGV155pcX6IvGPAs8995zXXM7c3FwVFxeT+ANAhGOOPyJRJE31mT59eov1xc29UaBVq1aKjY0NdhgAgCAIlTn+VqtVO3fulNVq9bkNoPkx4h/m7Ha75s+fr9zcXJWWlqpXr16aMWOGxowZ465Td45/VlaWiouLJcnj/oCG3C8AAAgvgazjb7VaZbFYFB8fr4qKCiUlJclsNrvLG7ItSUVFRZKk4uJiffrpp3I4HDIYDDrllFNUWloqh8Mhk8mk7OxsZWZmevTtavNEMfqLpb66QLQi8Q9zzz77rI4dO6YpU6ZIOj6F5+6771Z1dbWysrJ8HnP77bdr3rx5Ki8v15w5c9zlKSkpLRIzAKDlNHYd/7y8PK1cudJjipDJZNLgwYO1ZcsW2e32E24bjUa/7TudTh04cMC9bbfbtWLFCqWlpenbb7919133A4G/GH3FUvu4unX9tQlEAxL/MFdeXq5Fixa5RzCmTJminJwcPfnkkxo9erTP1RxGjhypN998U1VVVRo3blxLhwwAaEGNmeNvtVq9kn5XGwUFBQ3ebuzDwRwOh7777juPvu12u3Jzc5WWluYxSl83Rl+xuI6T1KA2EX4iaY5/S2KOf5ibMmWKxx8vs9msyZMn6/Dhw9q4cWMQIwtcWVmZqqqq3NtWq1UVFRXu7erqapWWlnoc45q65G/bYrF4/CNHH/RBH/QRLX00ZI6/qw+LxRK01X5KS0u9+rbZbLJYLO7t4uLiBsXoOq6oqKhBbdYWCe95S/WB8MOIf5jr3bu3V5lrys7evXtbOJqm0blzZ4/tuqMysbGx6tKli0dZcnJyvduu+ab0QR/0QR/R1oe/Of61PxC4+khKSpLJZGrx5N9gMGjo0KH67LPPPPqOiYnxOJfk5GRZrdYTxug6zul0etX11WZtkfCet1QfCD+M+AMAEMH8zfH3lTibzWZlZ2crJsZzXDAmJkbp6enu8hNtG43GBq8aZDAYNHHiRCUkJHj0HRMTo6ysLMXFxdUbo69YXMf5quurTYQfp8HQ4Bd+xoh/mNu1a5dXmWsVhW7duvk9jke1A0B0aOzofWZmptLS0pp0VZ+EhARVVFQoPj5eu3fv1v79+9WhQwd16NBBKSkp7tHm2n3XtwKPr3r+Vu5paJtANCDxD3NLlizxmOdvtVq1dOlSxcfHa9iwYX6Pa9eunQ4fPux+9DMAILrU9wAvs9ms1NRUSVJiYqLP8oZsDx482P3/rnZqt3eivhtTr77jGtomEOlI/MNcx44dNX36dPfSnbm5ubJYLLrnnnt8rujjMmjQIH322Wd67LHHNGTIEBmNRg0fPtxrzh8AILz5u7m3vnX8gVDnNDJoGQgS/zB30003qaCgQIsXL1ZZWZl69uyphx9+WGPHjq33uKuvvlp79+7Vxx9/rKVLl8rhcOjFF18k8QeACOPv5l6+7QWij8FZ33d9AAAgrM2fP98917621q1bq1u3bpoxY4ZatWoVhMiAwD12wboG171r7YhmjCS88D0fAAARrDEP8AIQ2ZjqAwBABGvIA7yAcMMynYFhxB8AgAjmb6EHfx8IAEQuEn8AACJYZWWlz/KWfjovgOBjqg8AABHMX4LvcDhaOBKg6TDVJzCM+AMAEMGY4w/AhcQfAIAIlpCQ4LO8devWLRwJgGAj8QcAIIKdc845Pss7dOjQwpEACDYSfwAAIlhiYqLS09M9yoYMGaLY2NjgBAQ0AafB0OAXfsbNvQAARLicnByNGjVKhYWF6t+/vzp37qxXXnkl2GEBaGEk/gAARIHExEQlJiZKkmpqaoIcDYBgIPEHAABAWGEKT2CY4w8AAABEARJ/AAAAIAow1QcAAABhhak+gWHEHwCACGW1WrVz505ZrdZghwIgBDDiDwBABMrLy9OKFSvkcDhkNBo1YcIEZWZmBjssAEHEiD8AABHGarVq2bJlcjgckiSHw6Fly5Yx8g9EORJ/AAAiTEFBQaPKgXDDk3sDQ+IPAECE2b59u8/ywsLCFo4EQCgh8QcAIMJUV1c3qhxAdODmXgAAAIQVJzN4AsKIPwAAABAFSPwBAACAKEDiDwBAlHA6ncEOAUAQMccfAIAoYTQy3ofIwDKdgeEvQJDl5+crIyNDubm5wQ4FABDhGPEHohuJPwAAABAFmOoDAACAsMJUn8Aw4g8AAABEAUb8Q9CxY8f0j3/8Qx999JH279+v9u3bKzMzU9dff72Sk5MlSUVFRbr88st11VVXac6cOV5t/OlPf9Inn3yiVatWqVOnTpKkgwcPasGCBVq/fr1KS0vVsWNHnX/++br++uvVuXPnFj1HAEDLM5xglNRqtcpisSgpKUlms9lrf0lJibZv366ePXvKbre767mOi4+PV0VFhZKSkiQd/7dKklJSUiSp3rYb0v+J9gOoH4l/iLHZbLrxxhv19ddf65e//KWmTp2q3bt3a+nSpcrLy9Nrr72mxMREpaSkaODAgVq9erVuueUWmUwmdxtWq1Vr167VOeec4076LRaLZsyYoZqaGk2YMEHdu3fXnj17tHTpUuXn5+v111/njygARLj6bu7Ny8vTypUrZbfbZTKZlJ2drczMTPf+RYsWqaCgwOMYk8mkwYMHa8uWLbLb7e5y1weM2v0ZjUY5HA6fbTek/xPtR3RxMNUnIEz1CTG5ubn6+uuvdc011+jRRx/V5Zdfrttvv12PPfaYSktLNW/ePHfdSy+9VKWlpfrPf/7j0ca///1vVVVV6dJLL3WXPfbYY7LZbHrjjTd00003adKkSbr55pv1wgsvaN++fXrjjTda7BwBAKHFarW6k2pJstvtys3NldVqlXR8pL9u0u+qV1BQ4JH0S8cT/rofMhwOh8+2G9L/ifYDaBgS/xCzZs0aGY1GzZgxw6P8vPPOU79+/bRu3Tr3H88xY8aoVatWeu+99zzqvv/+++rQoYPOP/98Scf/YK5fv14jRoxQ69atVV5e7n6deuqp6t69u/Ly8lrmBBugrKxMVVVV7m2r1aqKigr3dnV1tUpLSz2OKS4urnfbYrF4/CNEH/RBH/QRyX3Url+bq939+/d7lH///fdeybvNZpPFYpEkffvttz7bC5TNZlNRUZH7PCwWi8/+t23bVu/+7777Lizej0jtA+GHqT4hZt++feratavat2/vte+0007Tjh07VF5ers6dO6tDhw4677zztG7dOlmtVpnNZu3bt0+bN2/WlClT1KpVK0nSrl275HA4tGLFCq1YscJnv926dWvW82qMuvcb1J2CFBsbqy5duniUue598Lftmm9KH/RBH/QRDX20bt1avrim4CQkJHiU9+3bVyaTySO5jomJcceWlpamDz/80GebgYiJiVGfPn0UFxcn6fg5++o/LS2t3v0DBw70uG8hVN+PSO0D4YfEP8yNHz9ea9as0b///W9NnDhR77//vpxOp8aPH+9V95JLLvGY/lObv38kAACRw98cf7PZrOzsbOXm5spmsykmJkZZWVnuxDwxMVHp6ele031iYmI0aNAgbd26VTabzV1uNBo9pvsYDAYZDAY5HA6vthvS/4n2I/o4xRz/QJD4h5hu3brpP//5jyoqKhQfH++x74cfflBcXJw6duzoLjvvvPPUsWNHvffee+7Ev3fv3ho0aJC7Tvfu3WUwGGSz2bgRCgCiWH2r+mRmZiotLc3vqjk5OTkaNWqUCgsL1aNHjyZf1edE/Z9oP4ATY45/iBk5cqQcDodeffVVj/LPP/9chYWFGjFihIzGn9+2mJgYjR07VgUFBfrggw+0e/dur1H9jh076txzz9Unn3yiLVu2ePXpdDr1008/Ncv5AABCx4mW8zSbzUpNTfWbVCcmJmrEiBFKSUnxqOc6LjEx0V1uNps1ePBgDR482L1dX9sN6b8hbQDwjxH/EJOVlaV3331XCxcu1L59+zR06FDt2bNHS5YsUZcuXXTDDTd4HXPppZdq0aJFeuSRR2Q0GnXJJZd41fnDH/6g3/72t7ruuus0fvx49e/fXw6HQ3v37tW6des0btw4zZo1qyVOEQAQJPUt5wmEE57cGxgS/xATExOjefPmuR/gtWbNGsXHx+uXv/ylfve733ndnCNJAwYM0Gmnnab//ve/OvPMM5WYmOhVJykpSf/85z+1cOFCrV27VqtWrVJsbKwSExN1/vnna/To0S1xegAAAAgSg5OP/wAARJRnn31We/fu9Srv3r27brzxRtXU1OiVV16RJM2YMcO9ChwQLu4dt7HBdR96f1gzRhJeGPEHACDC1NTU+Cyvrq5u4UiA5sFUn8Bwcy8AABHG3wh+bGxsC0cCIJSQ+AMAEGFMJpPP8pgYvugHohmJPwAAUYLb+oDoxkd/AACixInW8QfCBXP8A8OIPwAAUYIRfyC6kfgDABBh2rVr16hyANGBxB8AgAhzwQUX+CwfOXJkywYCNBOnoeEv/IzEHwCACJOSkqLu3bt7lHXv3l29evUKUkQAQgE39wIAEIFuvPFGFRUVafPmzTrjjDOUkpIS7JAABBmJPwAAESolJYWEH4AbiT8AAADCioPlPAPCHH8AAAAgCpD4AwAAAFGAqT4AAAAIKzy5NzCM+AMAAABRgMQfAABIkoqKirR06VIVFRUFOxQAzYCpPgAAQPPmzdOPP/4oSdqwYYO6d++uG2+8MchRAb4x1ScwjPgDABDlioqK3Em/y48//sjIPxBhSPwBAIhya9eu9Vm+bt26Fo4EQHMi8QcAIModPXq0UeUAwhNz/AEAgE9OpzPYIQA+8eTewDDiDwBAlHM4HD7LSfyByELiDwBAlKuqqmpUOYDwxFQfAACiXOvWrX2Wx8bGtnAkQMM4mekTEEb8AQCIckaj73TAZDK1cCQAmhOJf4iZP3++MjIytG/fvnrL6pOVlaWZM2c2V4gAAAAIQyT+dRQWFmr+/PkNTrIBAACAcEDiX8eOHTu0YMGCoCX+v/nNb/T5558rOTk5KP0DAACEOqcMDX7hZyT+LcTpdNb7IJQjR45IkmJiYtS6dWsZWJ8WAAAATYjEv5b58+dr7ty5kqTZs2crIyNDGRkZeuCBByRJ1dXVevnll3XFFVfonHPO0ciRI3Xbbbdp+/btHu3k5+crIyNDubm5evvtt3X55ZfrnHPO0euvv659+/YpIyND8+fP14cffqipU6fq3HPP1eOPP+6Owd98/mPHjunxxx/XmDFjdO6552r69On66quvGnx+27Zt0x133KFf/vKXOvvss3XZZZfpH//4h2w2W4BXDACaltVq1c6dO2W1Wlukzdr7GtN3SUmJ1q5dq5KSkiaL0x9XXCUlJR7xNce1qot1/IHIwnKetVx44YU6ePCgli1bphkzZiglJUWS1L17d9lsNt1000365ptvNG7cOF1xxRWyWq1atmyZfvOb32jBggUaOHCgR3tvvfWWDh06pIkTJ6pLly5KTEx071u7dq3+9a9/afLkyZo8ebLi4uJOGN/9998vo9GoadOm6ejRo3rnnXd000036ZlnnlFmZma9x65fv1533nmnevTooalTp6p9+/basmWL5s+frx07dujRRx8N4IoBQNPJy8vTypUrZbfbZTKZlJ2dfcK/bSfTZu19BoNBBoNBDofjhH0vWrRIBQUFkqRVq1YpPT1dOTk5JxVnQ+J3MZlMGjx4sLZs2dKk18oXvn1GqOLJvYEh8a+lb9++GjJkiJYtW6bMzExlZGS4973xxhvauHGjnn32WZ199tnu8ilTpujKK6/UU089pZdeesmjPYvFoiVLlqhz587uMtdI/n//+18tWrTI/eGiIUwmk/7+97+rVatWkqTs7GxNmTJFjz/+uJYsWeL3uKqqKj300EMaNGiQXnjhBcXEHH/bJ0+erL59++rJJ590f0sBAMFgtVo9Ely73a7c3FylpaXJbDY3eZuSPPY5nU736HZ9fZeUlLiTfpeCggKNGjXKY3CnKdSN38Vut3vE0BTXqm4fJyoHEJ6Y6tNAq1atUu/evXX66aervLzc/bLZbMrMzNTXX3+tyspKj2PGjx/vkfTXdt555zUq6Zekq666yp30S1JiYqLGjh2rXbt2qaioyO9xeXl5Ki0tVVZWlqxWq0f85557rrtOqCgrK/N4WqTValVFRYV7u7q6WqWlpR7HFBcX17ttsVg8vrKmD/qgj9Dqo6ioyCvJtNlsslgsAffx/fff+23TYrHUm9S66tXtY8OGDT7rFxYWSmraa3WiGH3F29A+9u/f77Fd99+v2vG7/huOP1f00bx9IPww4t9ARUVFqqqq0kUXXeS3Tnl5uZKSktzbPXv29Fu3vn3++Pqg0KdPH0nS3r17/X6QcH0oePDBB/22XfeXO5jqfliqO4IVGxurLl26eJTVXQWp7nbt94U+6IM+Qq+PlJQUmUwmj0Q3JibGo53G9tG3b1+/bTqdTq99tbnq1Z2GOXz4cK1fv96r/umnny6paa9VUlJSvTH6irehfSQkJHhst2nTxme7rif3huvPFX00bx/B5GSqT0BI/BshNTVVt912m9/9nTp18tj294f0RPuamusT/i233KJ+/fr5rNO1a9cWiwcA6jKbzcrOzlZubq5sNptiYmKUlZXVoPufAm2z9j7Xk2sdDke9fScmJio9Pd1jqk16enqz/A2tG79LTEyMBg0apK1btzbZtfL3hF6e3AtEFhL/OvzdyNSjRw/99NNPGj58uN9Hmze3oqIir8T9hx9+kCR169bN73Gubxfatm3bLDd/AUBTyMzMVFpamiwWi5KSkgKer97QNuvuk9SgvnNycjRq1CgVFhaqf//+TT6331/88fHxqqiocMdntVqb9Fr5ws29QGRhjn8dbdu2lSQdPnzYo3z8+PEqLS3VG2+84fO4lpgq8+abb6qmpsa9XVJSotWrV6tXr1713i9w9tlnq3Pnznr11Vd16NAhr/2VlZXu5wgAQDCZzWalpqY2aSJbX5u19zWm78TERI0YMaJZk/66MSYmJnrE1xzXqi6Hw9FsbQNoeYz415GWliaj0aiXX35Zhw8fVtu2bdWtWzf96le/Ul5enp5++mlt2LBBw4cPV1xcnCwWizZs2KDY2FjNnz+/WWOz2+367W9/qzFjxujo0aNaunSpqqqqdOedd9Z7XNu2bTV37lzdcccdmjx5srKzs9WjRw9VVFRo165dWrNmjR5//HFW9QEAeGDEH6GKOf6BIfGvIykpSffdd58WLlyov/zlL7LZbLr00kv1wAMP6KmnntKSJUv0/vvvu5P8rl27Ki0tTZdeemmzxzZ37lwtXbpUCxcuVEVFhVJTU3X//ffrrLPOOuGxZ599thYuXKiFCxdq1apV+umnn9S+fXt1795dV199tfr27dvs8QMAACB4DE4eywcAQFSpqanRK6+8IkmaMWOGFixYoN27d3vV69Wrl66//vqWDg84oZsv/67BdZ9ZfHozRhJeGPEHAABAWHEw0ycg3NwLAECU48m9QHQg8QcAIMrVXjGuIeUAwhNTfQAAiHKtWrVqVDkQbKzqExhG/AEAiHI8uReIDiT+AAAAQBQg8QcAIMq1bt26UeUAwhOJPwAAUa5///6NKgeCzSFDg1/4GYk/AABRLj09vVHlAMITiT8AAFHObDZr0qRJMvz/lVIMBoMmTZqkuLi4IEcGoCmxnCcAAFBmZqbS0tJksViUlJQks9kc7JAAv1jOMzAk/gAAQNLxkf/U1NRghwGgmTDVBwAAAIgCJP4AAABAFGCqDwAAAMKKgyn+AWHEHwAAAIgCJP4AAESxAwcOaO3atSopKQl2KACaGVN9AACIUiUlJXrmmWckSatWrVJ6erpycnKCHBVwYg6W8wwII/4AAESh6upqHTlyxKOsoKCAkX8ggpH4AwAQhcrLy32Wf/HFFy0bCIAWw1QfAACiUE1Njc/yAwcOtHAkQOPx5N7AMOIPAEAUcjqdPsttNlsLRwKgpZD4AwAQhfwl/gZGUoGIReIPAAAARAHm+AMAEIUY2Uc448m9gWHEHwAAAIgCIZX45+fn69e//rVGjBihjIwM5ebmBjuksDVz5kxlZWUFOwwAAACEiJCZ6nP48GHdeeedSkhI0K233qo2bdpoyJAhTdrHm2++qfj4+JBOiOfPn6/+/ftr5MiRwQ4FAAAgJDnFXJ9AhMyI/7Zt21RRUaFZs2bpsssu07hx49S9e/cm7eOtt94K+W8RFixYoE8//TTYYQAAACDChEzif/DgQUlS+/btm7Rdu92uysrKJm0TAAAACDeNTvznz5+vjIwM7du3z2tfVlaWZs6c6d7OyMjQAw88oG+++UYzZ87Ueeedp1/+8pd66KGHdPToUY/jHnjgAUnS7NmzlZGRoYyMDPf+8vJyPfrooxo/frzOOussjR8/Xo8++qjX48Zzc3OVkZGhvLw8/f3vf9eECRN0zjnn6KOPPlJGRoaKi4u1adMmd/u1z8MVa12uNvPz872uwa5du/Tcc89p3LhxOvvss/WrX/1K69ev92pj8eLFuuGGG3TJJZforLPO0pgxY3Tvvfd6XMN9+/a5z/ndd9/1iLG2vLw83XDDDRo5cqTOOecc5eTkaMmSJV591jVnzhyde+65slqtXvu+/fZbZWRkaMGCBSdsBwAaw2q1aufOnT7/9gSbK7aSkhJt2bJFW7Zs8Run1WrVli1btGHDBq969Z1jSUmJ1q5dq5KSknpj8NfviY5vDv7W9wdCicNgaPALP2v2Of47duzQbbfdpqysLI0ZM0YbN27UihUrZDQadffdd0uSbr/9dn3++edatmyZZsyYoZSUFPfxVqtV1157rfbs2aPs7GwNGDBAhYWFWrJkiTZs2KCFCxcqLi7Oo8+nn35aNptNkyZNUlxcnHr16qUHH3xQTzzxhDp27Khrr73WXbdTp04Bn9sDDzygmJgYTZ06VTU1NXrrrbd0xx136J133tGpp57qrvfPf/5TgwYN0pVXXqkOHTrov//9r5YvX64NGzZo0aJF6tixozp16qQHH3xQ9913n8444wxNmjTJq7933nlHjzzyiAYPHqxrr71Wbdu2VV5env7yl79o7969uuWWW/zGOmnSJK1bt06rV6/W5MmTPfa53o/s7OyArwUA1JWXl6eVK1fKbrfLZDIpOztbmZmZwQ5LkmdstRmNRk2YMMEjzry8PC1fvtwjIXbVk+T3HBctWqSCggJJ0qpVq5Senq6cnByfMfi6Pic6vrkYjSEzGQBAE2v2xP/777/XK6+8okGDBkmSJk+erCNHjmjlypW67bbb1K5dO40cOVIVFRVatmyZMjMzPUa5Fy5cqN27d+v3v/+9Lr/8cnd5v3799Nhjj+m1117T9ddf79FnZWWl3nzzTbVp08ZdNmTIEL3wwgvq3Lmzxo0b1yTn1rFjRz355JPutZAzMjI0ffp0vfPOO7rxxhvd9RYtWqS2bdt6HDtixAj97ne/04oVKzR9+nS1bdtW48aN03333adu3bp5xXjw4EH99a9/1cUXX6w///nP7vLLL79cf/3rX/XGG29o8uTJfu+LOOecc5SYmKgVK1Z4JP6VlZVavXq1zjrrLCUmJp70NQEA6figTe3E2m63Kzc3V2lpaTKbzSEVW20Oh0MrV650x2m1WrVixQqvUXBXPafTKYfDIcnzHI8cOeJO2l0KCgo0atQoJSYmnvD6lJSU1Ht8c2LEH4hczf6xfvDgwe6k32X48OGy2+0+pwvV9emnn6pTp05eI+CXXXaZOnXqpDVr1ngdM2XKFI+kv7nk5OR4PAAlLS1N7dq10+7duz3quZJ+h8Mhq9Wq8vJy9evXT2azWVu3bm1QX//+979VXV2tCRMmqLy83ON1/vnny+Fw6KuvvvJ7vGs0adu2bdq5c6dHu0eOHHGPXIWCsrIyVVVVubetVqsqKirc29XV1SotLfU4pri4uN5ti8Xi8Y8ZfdAHfTRvH99++61XYm2z2WSxWIJ+HhaLxWfS72K3291xfvvtt+7E3le9uvtsNpuKiv5fe3ceF1XV/wH8M8wAAgMugIAgiOKKKCaKuKBWmqmg5JLmQlbimmZWaqumpq1a9mhUIm49+YgmYmpaj2iaivsuSoIpOiiyyKAsM9zfH/5mHoeZYRlhhpn5vF8vXnXPnHvPuWcu+J1zv/dMOi5fvqxzn9TUVNy+fVtnH1TjI5PJKty/Jsbqzp07Oo+vUlevK7ZRt9og81PrM/7e3t5aZfXr1wcA5OfnV7r/rVu30LZtW0gkml2VSCTw9fXV+cfR19fXwN5Wj67Z9fr162ud17Fjx/DDDz/gwoULGr9kADR+ySqSkZEBAJg6dareOjk5ORUeY8iQIYiLi0NiYiJmz54N4NEt6kaNGqF3795V6ocxNGrUSGO7/OygnZ0dXF1dNcq8vLwq3Pb09GQbbINtGLGNwMBArVl1iUSirmfK8xCJRBCLxXqDf7FYrD52YGAgEhMTdQb/YrFYY8ZfdY7NmzdH48aNsWvXLq192rZtC3d3d8jlcq0+qMbHyckJIpFI7/41MVaNGzfWee4qdfW6Yht1qw1TYu6+Yaod+Ff0Fd+6/oiKxWK99WvrdmJNzvZXNCukLw/y8fO6cOECpk+fDh8fH0yfPh1NmjSBvb09RCIR3n33Xb0zSfqOuWDBAri5uemso+tD1uM8PT0RFhaGnTt3YsaMGeqHnceNG6f1wYqI6ElIpVJERkYiKSkJCoUCEokEERERWs9k1YW+PU71vJOqn1KpFEOGDNEK/h9/LkrXOTo5OSE4OFgjXSc4OBju7u46+1B+fDw8PCrcn4jIENWO9lTLbd6/f1/jAdbi4mJkZ2fX+Nr73t7euH79uvoPo4pCocA///xTabD7uIo+tOiaqQeAzMzM6nW4nN27d0OpVOKbb77R6OvDhw+rPNsPAE2bNgXw6LmCJ3k4LioqCgcPHkRycjJSU1MBoE6l+RCR5QgNDUVgYCBkMhk8PT1Nntv/uMf75uzsrE598ff31+qnqm56ejqKiopQr149jXr6znHUqFHo27cvUlNT0bp1a63c/MrGp7L9iYiqq9qBv5+fH4BHqxG0adNGXf7TTz9Vefa6Onr37o01a9Zg27ZtGD58uLp827ZtyM3NxQsvvFDlYzk4OOD+/fs6X/P19cW5c+fUf9SBRx9utm/f/kT9V93xKH93Iy4uTud4OTo66vwA0q9fP6xcuRKxsbHo3Lmz1l0NuVwOOzs72NnZVdifnj17wt3dHVu3bkV6ejo6duyIZs2aVfOsiIiqRiqVIiAgwNTd0OnxvlUWVEulUgQFBVV6nPI8PDwqPHZl41PZ/kTWqoyZPgapduDftWtX+Pn5ITY2Fvn5+WjSpAnOnDmDc+fOoUGDBjXewejoaPzxxx/47LPP1LMeqampSExMhJ+fH8aPH1/lYwUFBSExMRGrVq2Cv78/RCIRwsPD4eDggJEjR+KDDz7A5MmTMXDgQBQUFGDbtm3w8vLSerilOvr06YOffvoJM2fORFRUFGxtbXH06FGkpaXpHK/27dsjJSUF8fHx8PT0hEgkwnPPPQcPDw/MnTsXixYtwogRIzBw4EB4eXkhNzcXaWlpSE5OxubNmzXuwuiiesh39erVAIBp06YZfG5EREREZD6qHfiLxWJ89dVX+OKLL7Bp0ybY2tqiW7du+P777/Hqq6/WeAelUilWr16N2NhYHDhwANu3b4erqyuGDRuGSZMmVStfdOrUqcjPz8fmzZtRUFAAQRCwfft2ODg44Pnnn8fdu3fxn//8B8uWLYO3tzdee+012NjYVHnlHV2Cg4Px2Wef4ccff8R3330He3t7dO3aFd9//z0mTpyoVX/u3Ln49NNPsWbNGhQWFgIAnnvuOQBAZGQkfH19sWHDBmzduhUFBQVo0KAB/Pz8MGXKFK2HcPQZOnQo1qxZAwcHBzz77LMGnxsRERERmQ+RwAV7rU52djYGDRqEyMhI9ZeoERGR9SgtLcXChQtRUlKi9VqzZs0wefJkE/SKqOpeik6vct2f1vpXXslKcCkXK5SQkAClUlmt5yOIiMg61MbzekQ1rQxM8jcEA38r8ttvv0Emk2H9+vUICwtD27ZtTd0lIiIyEX03/CtaAY+IzBsDfyvy3nvvwd7eHsHBwfjggw9M3R0iIjIhfd9FU9H37xCReWPgb0WOHz9u6i4QEVEdoS/wr8kvwSSqLQLvTBlE9289ERERWTR9ufxFRUVG7gkRGQsDfyIiIiukL8dfqVQauSdEZCxM9SEiIrJC+h7iZY4/mQN+c69hOONPRERkhWxtbXWWe3p6GrknRGQsDPyJiIisUIMGDXSWh4WFGbcjRGQ0DPyJiIiskJ2dHZycnDTKgoOD4e7ubqIeEVFtY44/ERGRlfLw8MDgwYORlpaG1q1bw8PDw9RdIqqSMi7naRAG/kRERFbM3d0dTZo0MXU3iMgImOpDRERERGQFOONPRERERGalDEz1MQRn/ImIiIiIrAADfyIiIiIiK8DAn4iIyMJlZWVh//79yMrKMnVXiMiEmONPRERkwX7++WecPn0aALBr1y4EBwdj2LBhpu0U0RNSMsXfIJzxJyIislBZWVnqoF/l9OnTuHv3rmk6REQmxcCfiIjIQh0+fFhn+ZEjR4zcEyKqC5jqQ0REZKH05fRnZ2cbuSdENYvf3GsYzvgTERFZKKVSqbNcoVAYuSdEVBcw8CciIrIygiCYugtEZAJM9SEiIrIyIqZJkJkr4yVsEM74ExERERFZAQb+RERERERWgIE/ERGRheLDvUT0OAb+ZiQmJgYRERGm7gYREZmJkpISneWlpaVG7glRzSqDqMo/9D8M/ImIiCyUvb29znI7Ozsj94SI6gKu6mNG/vWvf3EJNiIiqjIbG93ze2Kx2Mg9IaK6gIG/GbG1tTV1F4iIiKpFLpdDJpPB09MTUqnU1N0hC6HkkrQGYapPLSgpKUFcXBxGjhyJ7t27o0+fPpg1axYuX76sUe/48eMICQlBUlIStm/fjpEjRyIsLAyDBw/G2rVrtY6rL8f/5MmTmDp1Knr37o0ePXpgzJgx2LZtm0adN998Ez169IBcLtfa/8KFCwgJCcEPP/zwZCdORERWQS6XIy0tTf1vSvltlaNHj2LJkiX48ccfsXjxYuzZs6fKx6ys/En6W1PHJTI3nPGvYQqFAq+//jrOnj2LgQMHYuTIkZDL5fjll1/w6quv4ocffkC7du009tmyZQtycnIQGRkJZ2dn7Nq1CytWrICHhwcGDBhQYXsHDhzA22+/DVdXV4wdOxaOjo7Ys2cPFi1ahMzMTEybNg0AEBUVhQMHDuC3337DsGHDNI6RmJgIGxsbREZG1uxgEBGRxTl69Ci2b98OpVIJsViMoKAgnDt3Tr0dGRmJ0NBQyOVydT3g0bcF//e//8WdO3cwduzYCo+pOoa+8ifpr+rfuic9LpE5YuBfwzZt2oQTJ05gxYoVCAsLU5cPHz4cL774IpYvX47vv/9eYx+ZTIaEhAT1LdAhQ4Zg8ODB2LRpU4WBv1KpxGeffQYHBwesXbsW7u7uAICRI0di0qRJWLt2LSIiIuDr64vu3bvDw8MDiYmJGoF/UVERfvvtN3Tr1g0eHh41ORRERFRHGfrNveWDeaVSidOnT6tfVyqVSEpKQmBgIGQymc7lRM+fP4+srCz1vzm6jpmUlIRmzZrpLA8MDKxyypCuY2/fvl39/4Yel0yP39xrGKb61LBdu3ahWbNmaNu2LfLy8tQ/CoUCoaGhOHPmDIqKijT2iYiI0PhjU69ePQQFBeGff/6psK1Lly5BJpMhMjJSHfQDj54FGD9+PMrKyrB//34AUM9oXLx4EWlpaeq6v//+OwoLCzFkyJCaOP0akZOTg+LiYvW2XC5HQUGBerukpAT37t3T2Of27dsVbstkMo0Ho9kG22AbbMOa2ijv8eNVpw19wfzjFAoFZDIZbGxs9H7ASE1NVbeh65gKhQKpqak6y2UymXq7srFKT0/XOoZSqazwuJbynpviuqK6jzP+NSw9PR3FxcV49tln9dbJy8uDp6enetvb21urTv369ZGfn19hW7du3QIANG/eXOu1Fi1aAAAyMzPVZUOGDEFcXBwSExMxe/ZsAI9udTZq1Ai9e/eusC1jatSokcZ2+RkYOzs7uLq6apR5eXlVuP34eLMNtsE22Ia1tVFe+eU8q9qGvb09xGJxhcG/RCKBl5cXnJyc0LdvX/z3v//VqtO2bVt1GyKRSOuYEokEbdq0wW+//aZV/vi5VTZW/v7+WsdWrWik77iW8p6b4rqiuo+Bfy0ICAjArFmz9L7esGFDjW1jLavm6emJsLAw7Ny5EzNmzMDt27dx8uRJjBs3DhIJLwUiIkujL0BXKpUG/d2XSqWIjIxEUlISFAoFJBIJ2rdvj/Pnz6u3IyIi4OTkBADo378/7ty5g/Pnz6uPERwcrHGXWtcxIyIi0LhxY53lqmMb2l/VIhlPclwic8Vor4Y1bdoUubm56NKli971k2uK6k7BtWvXtF5TlZW/mxAVFYWDBw8iOTkZqampAFCn0nyIiKjm6PuG3tLSUr1f7lWZ0NBQdQ6/aonOipbsHDt2LLKyspCamorWrVvrfJ5M1zErKn/S/gJ44uOSaSn5jbwGYY5/DRs0aBDu3buHjRs36ny9fL7ck2jTpg08PT2RlJSE7OxsdblCocD69eshEom0Unh69uwJd3d3bN26FTt27EDHjh3RrFmzGusTERHVHfq+/+VJ7/JKpVIEBASoA+by2+V5eHggPDy8wkUk9B2jsmMb0t+aOi6RueGMfw0bPXo0jh49iq+//hrHjh1Dly5d4OTkBJlMhmPHjsHOzg6xsbE10pZYLMY777yDt99+G9HR0YiKioKjoyP27t2Lc+fOYcKECfD19dXaJzIyEqtXrwYA9XKfRERkefSlkjK9k8g68Te/hkkkEixfvhwJCQnYuXOnOsh3d3dHYGAgBg8eXKPthYeHY+XKlVi9ejXWr1+P0tJSNGvWDO+//z6GDh2qc5+hQ4dizZo1cHBwqPAhZCIiskyGLudJVFcoeQkbhIF/LZBIJBg1ahRGjRpVYb2QkBAcP35c52vz58/H/PnzNcrKysp0zt507twZnTt3rnL/bG1tIRKJ0L9/fzg4OFR5PyIisgz6/j0hIsvGHH8zcvfuXa3ltwyRkJAApVKJF154oQZ6RURE5oYz/kTWiTP+ZuDIkSM4ePAgMjMzMWjQIIOP89tvv0Emk2H9+vUICwtD27Zta7CXRERkLh7/Iicish4M/M3AmjVrkJGRgREjRmD8+PEGH+e9996Dvb09goOD8cEHH9RgD4mIyJxwxp/MXRmvYYMw8DcDNbUKkL7nCYiIyLow8CeyTszxJyIislD6vrlXoVAYuSdEVBdwxp+IiMhC6Qv8y8rKjNwTopql5F0rg3DGn4iIyEI1adJEZ7mnp6eRe0JEdQEDfyIiIgvVu3dvneW9evUyck+IqC5g4E9ERGShPDw8EBwcrFEWHBwMd3d303SIqIYoqvFD/8McfyIiIgs2atQo9O3bF6mpqWjdujU8PDxQWlpq6m4RkQkw8CciIrJwHh4e8PDwMHU3iMjEmOpDRERERGQFOONPRERERGaFy3kahjP+RERERERWgIE/EREREZEVYKoPERGRFSssLER2djY8PT0hlUpN3R2iKlEw08cgDPyJiIis1P3797F06VL1dlRUFEJDQ03YIyKqTUz1ISIiskJKpRLZ2dkaZb/88gvkcrmJekREtY2BPxERkRUqKCjQWX769GnjdoTIAAqIqvxD/8PAn4iIyAo9ePBAZ/mVK1eM3BMiMhYG/kRERFaorKxMZ3lxcbGRe0JExsLAn4iIiIjICnBVHyIiIiIyK6VM3TcIZ/yJiIiIiKwAA38iIiJSE4k4lUpkqZjqQ0RERGqCIJi6C0SVKuUHVINwxt+MREREICYmxuD9Y2NjERISglu3bqnLkpKSEBISguPHj9dEF4mIyMwx8CeyXAz8iYiISI2pPkSWi4E/EREREZEVYI4/EREREZmVUlN3wExxxr8OkslkmDt3Lnr37o3evXtj1qxZuHnzpla9W7duISQkBLGxsVqv6crnJyICALlcjrS0NMjlclN3pVJV7WttnVN1j6uqn5WVpXe/8sfUt606RkXHqg1M9SGyXJzxr2MKCgoQExODrKwsvPDCC2jevDlOnjyJSZMm8WvUieiJHT16FNu3b4dSqYRYLEZkZCRCQ0NN3S2dqtrX2jqn6h738foq5fcrf8ygoCCcO3dO53Z5Nf1+6XuIV6FQ1Mjxiaju4Yx/HbNu3TrcunUL7733HubMmYMRI0ZgyZIlePrpp5GXl2fq7hGRGZPL5RqBqVKpRFJSUp2c+a9qX2vrnKp73PL1VR7fT9cxT58+rXe7vJp+v8rKynSWl5SU1MjxiWrTA5Goyj/0Pwz865jk5GS4urpi0KBBGuXR0dEm6pHx5eTkaNzdkMvlKCgoUG+XlJTg3r17Gvvcvn27wm2ZTKYxu8U22IY1tiGTybSCSoVCgUuXLtW587hx44bOvspkMo029J2TTCYz6ljpql++PxXVqSqFQoH09PQnfj/u3LkDGxvdIYCdnV2du3bZRt1sg8wPU33qmMzMTLRr1w5isVij3M3NDc7OzibqlXE1atRIY1sqlWps29nZwdXVVaPMy8urwm1PT0+2wTasvg1PT0+IxWKN4FMikaBdu3Yaed114TyaNm2qs6+PH9fLywtyuVxvPScnJ4PPo7pjpat++f4IgqC3TlVJJBI0b95c49wMeT8aN26sN5ffxsamzl27bKNutkHmhzP+ZqyiB7CedFaJiCyPVCpFZGQkJJJHcz4SiQQRERFaAXJdUNW+1tY5Vfe45eurPL6frmMGBwfr3S7PWO8XH+4lc/BQVPUf+h/O+Ncx3t7e6lvcj8/6Z2dna9yCAwAXFxcAwP3797WOk5mZWbsdJSKzFBoaisDAQMhkMnh6emrN8tUlVe1rbZ1TdY/7eH1nZ2cUFBRo7afrmHK5XOe26hj6jkVEVF0M/OuY3r17Iz4+Hr/++isiIyPV5WvXrtWq6+TkBFdXVxw7dgyCIKhnaW7evInk5GRjdZmIzIxUKkVAQICpu1ElVe1rbZ1TdY/7eH0PD48qHbOibdUx9B2LiKg6GPjXMePHj8fu3buxePFiXLp0CS1atMCJEydw9uxZNGjQQKv+yJEjsWrVKsyYMQO9e/dGdnY2tmzZghYtWuDixYvGPwEiIjIL+pbzJCLLxcC/jnFxccGPP/6Ir776Cjt37gQAPPXUU4iNjcWUKVO06kdHR0Mul2Pnzp04ceIE/P398cEHH+DSpUsM/ImIiMgilYDJ+4YQCfzIT0REZFVKS0uxcOFCnWv2+/n56ZxoIqpLRG/kVLmusLxR5ZWsBFf1ISIiIiKyAkz1ISIiIiLzwkwfg3DGn4iIiIjICjDwJyIiIiKyAgz8iYiIiIisAHP8iYiIiMi8iJjkbwjO+BMREVkhGxvdIYBUKjVyT4jIWBj4ExERWaGGDRvqLA8PDzdyT4jIWBj4ExERWSEHBwfY2dlplPn4+MDPz89EPSKi2sYcfyIiIivl4+ODp59+GmfPnkWnTp3g7+9v6i4RUS1i4E9ERGTF/Pz8EBAQYOpuEJERMPAnIiIiIvPCVX0Mwhx/IiIiIiIrwMCfiIiIiMgKMPAnIiIiIrICzPEnIiIiIvPCFH+DcMafiIiIAADp6enYsmUL0tPTTd0VIqoFnPEnIiIifPvtt7h58yYA4NixY/Dx8cH06dNN3Csiqkmc8SciIrJy6enp6qBf5ebNm5z5pzpMVI0fUmHgT0REZOX279+vs/zAgQNG7gkR1SYG/kRERFauuLi4WuVEZJ6Y409ERGTl6tWrp7PcwcHByD0hqiJm8BiEM/5ERERWrrS0VGd5SUmJkXtCRLWJgT8REZGVs7W11VluZ2dn5J4QUW1i4E9ERGTlioqKdJY/ePDAyD0hotrEHH8iIiIiMi/M8TcIZ/zNXExMDCIiIkzdDSIiMmN8uJfIOjDwJyIisnL6Un30lROReWKqDxERERGZGeb6GIIz/kRERFZOX0oPU32ILAtn/GtYUlISFixYgJUrV+LMmTNITExEbm4uAgIC8NZbbyEoKAgnTpzAypUrkZqaCicnJ4wYMQKvvfaa+hhHjhxBYmIiLl68iOzsbNja2iIwMBCvvPIKOnfuXGkf8vLyMGvWLFy7dg2ff/45unbtCgA4evQo1q1bhwsXLqCkpAS+vr4YPnw4hg8fXmvjQURkzuRyOWQyGTw9PSGVSmt9v8r2f7wcANLT05Gfn4/8/Hw0btwYYrEYJ06cgI+PD5ydnZGfn4/69evD3t5efYzi4mLI5XKUlJSol+vUt14/1/EnsiwM/GvJt99+C6VSiVGjRkGhUGDDhg2YPn06FixYgIULFyIqKgrPP/889u7di++++w5NmjTBwIEDATz68JCfn4+BAwfCw8MDd+7cQWJiIqZOnYrvvvsOnTp10ttuZmYmXn/9dTx48ADff/89WrduDQDYunUrlixZgqCgILzyyitwcHDA0aNHsXTpUmRmZmLmzJlGGRciInNx9OhRbN++HUqlEmKxGJGRkQgNDa21/Srb//FykehRmoMgCDqP8ffff1epLScnJwD61+vnOv5EloWBfy1RKpWIj49XfymKv78/Zs+ejTlz5mDNmjVo164dAGDIkCEYPHgwNm/erA7833//fa3bq8OGDcPIkSOxZs0avYH/5cuXMXPmTEilUsTFxaFJkyYAgOzsbHzxxRfo378/Fi9erK4/YsQIfPHFF9i4cSOGDRsGHx+fGh8HIiJzJJfL1UE28OhvelJSEgIDAyucwTd0v8r2b9asmUa5voC/ugoLC3H37l08fPhQ5+v6yolMjin+BmGOfy0ZPny4xjchqoL19u3bq4N+AOo0nn/++Udd9njQ/+DBA+Tl5UEsFqN9+/a4cOGCzvaOHj2KSZMmoUmTJli9erU66AeA33//HSUlJRgyZAjy8vI0fnr16oWysjKkpKTU2Lk/qZycHBQXF6u35XI5CgoK1NslJSW4d++exj63b9+ucFsmk2n8Q8k22AbbYBsVtSGTydRBtopCocClS5cqbCM9PV3nfjKZrErnoa/dCxcuaJXXlNTUVCgUigrrmPr9YBt1sw0yPyKhpqYNCIBmjr8qt14lJCQEzz//PBYuXKhRPn/+fOzYsQPHjx8HANy8eRP/+te/cOTIEY1fQgAQiUQ4duyYejsmJkb9D0Lz5s0RFxentR7z0qVLkZCQUGG/J0+erPGcARGRNZPL5ViyZIlGsC2RSDBv3jx1ekxN7lfZ/jNmzMDXX39dK8H/jBkzsHfvXly6dEnrtcDAQIwbN67G2yR6UqI596tcV/jUpRZ7Yl6Y6lNLbGx030wRi8UV7vfgwQNMnDgRDx8+xOjRoxEQEAAnJyeIRCLEx8drBP0qLi4uaNOmDQ4ePIhdu3YhKipK43XVZ7sFCxbAzc1NZ7ve3t5VOS0iIqsglUoRGRmJpKQkKBQKSCQSREREVBq8G7pfZfs3btxYo9zGxgaCIDxxyo+TkxPc3d31rtfPVB+qu5jrYwgG/nVMSkoK7t69iw8//BCRkZEar61atUrnPhKJBJ9//jnmzZuHTz75BAqFAiNGjFC/3rRpUwBAgwYNqvWAGRGRNQsNDUVgYGC1V+cxdL/K9i9fDuhe1efkyZPw9vaucFWfwsJCXL16tdKHd1UPERORZWDgX8eo7giUn8U5cuQIzp8/r3c/iUSCJUuW4P3338enn34KhUKB0aNHAwD69euHlStXIjY2Fp07d9ZKBZLL5bCzs+PqDURE5UilUgQEBBhtv8r2L18eFBSkVeepp56q9PilpaW4fv26elvfev3l/70gIvPGwL+OCQ4OhqurK5YvX47bt2+jcePGuHLlCnbu3ImAgACkpaXp3VcikWDx4sWQSCT48ssvoVQqMXbsWHh4eGDu3LlYtGgRRowYgYEDB8LLywu5ublIS0tDcnIyNm/erPFAMBERWQ996/U//vAnUZ3Cm1EGYeBfxzg7O+Pbb7/FN998g02bNkGpVKJNmzb4+uuvkZiYWGHgDzy6Y/Dxxx9DIpFg+fLlKC0txYQJExAZGQlfX19s2LABW7duRUFBARo0aAA/Pz9MmTIFrq6uRjpDIiKqa/Td8X08RYiIzB9X9SEiIrIypaWlWLNmDQBgwoQJiIuLQ3p6ula9Fi1aYOLEicbuHlGlRHMLKq/0/4SlzrXYE/PCdfyJiIhIJ84NElkWpvoQERFZOX0P9+orJzI55vgbhDP+REREVk7fev1cx5/IsjDwJyIiIiKyAkz1ISIisnL61utnqg/VXcz1MQRn/ImIiKxcaWmpznJ96/sTkXli4E9ERGTl3NzcqlVOROaJgT8REZGVCwsL01nevXt3I/eEiGoTA38iIiIr5+HhgeDgYI2y4OBguLu7m6ZDRJURVeOH1PhwLxEREWHUqFHo27cvUlNT0bp1a3h4eJi6S0RUwxj4ExEREYBHM/8M+IksFwN/IiIiIjIvIubwGII5/kREREREVoCBPxERERGRFWDgT0RERERkBRj4ExERERFZAQb+REREViArKwv79+9HVlaWqbtCRCbCVX2IiIgs3M8//4zTp08DAHbt2oUOHTqYtkNEZBKc8SciIrJgWVlZ6qBf5ezZsygpKTFNh4hqAr+51yAM/ImIiCzYX3/9pbM8Ly/PuB0hIpNj4E9ERGTB7ty5o7O8tLTUyD0hIlNjjj8REZEFUyqVpu4CUS1gDo8hOONPRERkhQRBMHUXiMjIGPgTERFZMLFYrLPcxoYhAJG1YaoPERGRBXNwcDB1F4hqHjN9DMKP+0RERBbs4cOHOsuZ6kNkfRj4ExERWTB9D/cy8CeyPgz8iYiILBhz/IlIhTn+REREFqxevXo6y0UiJkmTGePlaxB+3CciIrJgRUVFOsvLysqM3BMiMjXO+BtRcXEx4uPj8dtvvyErKwu2trbw8PBA9+7dMXPmTHW9o0ePYt26dbhw4QJKSkrg6+uL4cOHY/jw4VrHvHjxIuLi4nDq1Ck8ePAAXl5eGDRoEKKjoyGR/O/tjYmJwe3btxEXF4dly5bh8OHDKCkpQadOnfD222/Dz8/PKGNARETGVZUcf7lcDplMBk9PT0ilUvW2WCzGP//8A19fX2RnZ+PmzZuws7NDu3btkJ2djby8PHTo0AFOTk5IT08HAPj7+wOAxvEqU779imRlZeHy5cto06YNPDw8qjoM1W6HyBKJBD7dYzQff/wxtm/fjkGDBqFDhw5QKpW4ceMGTp06hQ0bNgAAtm7diiVLliAoKAh9+vSBg4MDjh49iuTkZIwbN07jA8LBgwfx9ttvo2nTpnj++efh4uKCc+fOYefOnejbty8+/fRTdd2YmBhcu3YNUqkUQUFB6NixIzIzM/Hzzz/D29sbmzZt0psHSkRE5is2NlYdlD/O3t4e3t7eaN++PX799VcolUqIxWIEBQXh3LlzT/SNvzY2NigrK4NYLEZkZCRCQ0P11j169Ci2b9+ubr+i+j///DNOnz6t3g4ODsaoUaOq1KfqtEN1n+hD3atV6SJ8zCVtVTjjb0TJycno3r07FixYoPP17OxsfPHFF+jfvz8WL16sLh8xYgS++OILbNy4EcOGDYOPjw+Ki4uxcOFCtG/fHqtWrVLP7g8bNgwtW7bEsmXLcPz4cYSEhKiPk5eXh3HjxiE6Olpd1rBhQ3zzzTdISUlBWFhYLZ05ERGZir4cf7FYDKVSqQ76gUd3Bx4PrA2lSiNSKpVISkpCYGCgzhl2uVyuDsYrq5+VlaXVt9OnT6Nv376VzvxXpx0iS8YcfyOSSqW4du0a0tLSdL7++++/o6SkBEOGDEFeXp7GT69evVBWVoaUlBQAj2Yu7t27h4iICMjlco26PXr0UNd5nI2NjdbMSJcuXQAA//zzT02frsFycnJQXFys3pbL5SgoKFBvl5SU4N69exr73L59u8JtmUymcVubbbANtsE2rKUNfTn+SqUSJSUlTzSzXxUKhQIymUzneVy7dk2rfVV9QPM8Ll++rPP4qamp6v/XN1YymUxvO5b4nhurDTI/TPUxouTkZHz00UcoLCyEt7c3QkJC0KtXL4SHh8PGxgZLly5FQkJChceYPHkyXnvtNaxduxYrVqyosG5kZCQ+/PBDAI9SfTIzM/Hrr79q1Ll16xYiIyMRExODmJiYJztBIiKqc1auXKlzcsfOzg5eXl64efNmrQb/EokE8+bNg5OTk9ZrcrkcS5Ys0WhfX/2srCwsW7ZM6xizZ8+Gu7t7hX2oTjtkHpjqYxim+hhRnz59sH37dhw6dAgnT55ESkoKEhMT0alTJ6xcuVL9SXzBggVwc3PTeQxvb28A/3soa+bMmWjVqpXOuuX/EFa0ZjM//xERWSZ9z2+JRCKIxWIMGjQIO3fuhEKhgEQiQfv27XH+/HkoFAqD2hOJRBCJRCgrK4NEIkFERITe4FoqlSIyMhJJSUnq9vXV9/DwQHBwsFaOf2VBf3XbITPB5TwNwsDfyOrXr4+BAwdi4MCBEAQBK1aswLp167B//340bdoUANCgQYNKHzjy9fUFADg4OPDhJCIi0ktfjr9qMqhLly7o0KGD3lV9bty4gaZNm9baqj6hoaEIDAysUv1Ro0ahb9++SE1NRevWrau1qk912iGyVAz8jUSpVOLBgwdwdnZWl4lEIrRu3RoAkJ+fj379+mHlypWIjY1F586dtf5Yy+Vy2NnZwc7ODmFhYWjUqBHi4+PRr18/1K9fX6NuUVERlEolZzOIiKxcVdbxl0qlCAgI0LmtCuT9/f3Vz4U9Xq4SFBSksf348SpTvv2KeHh4VHsZT0PaIbJEDPyN5MGDBxgwYADCw8PRunVrNGzYELdu3UJCQgJcXFwQHh4Od3d3zJ07F4sWLcKIESMwcOBAeHl5ITc3F2lpaUhOTsbmzZvRpEkTODg4YMGCBXjrrbcwbNgwREZGomnTpigoKEBGRgb27duHzz//XGNVHyIisj5VWcefiKwDA38jqVevHkaPHo2UlBSkpKTgwYMHcHNzQ3h4OCZMmKDOUYyMjISvry82bNiArVu3oqCgAA0aNICfnx+mTJkCV1dX9THDwsKwdu1arF27Frt27UJubi5cXFzg4+ODMWPGoGXLlqY6XSIiqiMqyvEnIuvCVX2IiIgs2Lp163Dx4kWtcgcHB3h5eWHChAmwtbU1Qc+IDCf6qBqr+izgqj4qnPEnIiKyYA8f6g6QHs/xJzI7vGFlEH6BFxERkQWr7S/oIiLzwcCfiIjICjHTl8j6MPAnIiKyYHy4l4hUmONPRERkwSr7Ai8is8QPrgbhbz0REZEFq8oXeBGRdWDgT0REZMGY6kNEKgz8iYiILFibNm10ljs6Ohq5J0Rkagz8iYiILFhwcLDOcmdnZ+N2hIhMjoE/ERGRBZNKpYiKilKn9ohEIkRGRupNASIiy8VVfYiIiCxcaGgoAgMDIZPJ4OnpCXt7e5w/f97U3SIyHB9RMQgDfyIiIisglUoREBAAACgtLTVxb4jIFJjqQ0RERERkBRj4ExERERFZAab6EBEREZGZYZK/ITjjT0RERERkBRj4ExERWbmsrCzs378fWVlZpu4KEdUipvoQERFZsc2bN+Ps2bMAgF27diE4OBijRo0yca+IKsFMH4Nwxp+IiMhKlZSUqIN+ldOnT3Pmn8hCMfAnIiKyUvn5+TrLDx8+bOSeEJExMPAnIiKyUsXFxTrLb926ZeSeEJExMPAnIiKyUmVlZTrLi4qKjNwTIjIGBv5ERERWSiTS/YSknZ2dkXtCRMbAwJ+IiMhK6Qv8bWwYHhBZIi7nSUREZKUEQahWOVGdweU8DcKP9ERERFZKX46/vod+ici8MfAnIiKyUvpSepjjT2SZGPgTERFZKX05/hIJM4GJLBEDfyIiIiulL5e/tLTUyD0hImNg4G+mioqKoFAoTN0NIiIyY/py/Bn4E1kmBv5P4Pbt2wgJCUFsbKxG+fTp0xESEoKNGzdqlEdHR2P48OHq7atXr+Ktt97CM888g+7du2PEiBFYu3YtlEqlxn7z589HSEgIcnNzsWDBAvTv3x+9evXCnTt3AAA7duzA+PHj0adPH/Ts2RNDhgzB+++/j9zcXI3j/PPPP/jggw/w3HPPoVu3boiIiMDXX3+Nhw8f1uSwEJk9uVyOtLQ0yOVyU3eFynn8vTHm+2RoW3X9WmKOP5F1YRLfE/Dy8oK3tzeOHTuGSZMmAXg0S3L69GnY2Njg+PHjGDNmDIBHf/wvX76MF154AQBw8eJFxMTEQCKRYMSIEXB1dcWff/6JFStW4OrVq1i0aJFWe9OmTYOrqyteffVVPHz4EI6Ojvj1118xf/58dOrUCZMnT4a9vT2ysrJw6NAh5OTkoGHDhgCAS5cuYfLkyXB2dsYLL7yAxo0b48qVK/j5559x5swZfP/998zpJAJw9OhRbN++HUqlEmKxGJGRkQgNDTV1twia741IJIJIJEJZWVmtv0+GXhPmcC3py/EXi8VG7glRNem5dqlijPSeUJcuXbBjxw4UFRWhXr16OHfuHIqKivD888/jwIEDUCgUkEgkOHnyJJRKJUJCQgAAX3zxBUpLS7FmzRq0bNkSAPDiiy9i3rx52L17NyIjI9G1a1eNtlq0aIGFCxdqlCUnJ8PJyQmrVq3SCNwnT56sUe/jjz+Gm5sb1q1bBycnJ3V5165d8fbbb2PXrl2IiIio0bEhMjdyuVwdqAGAUqlEUlISAgMDIZVKTdw761b+vREEQZ2fXpvvk6HXhLlcS/py/MvfeSYiy8BUnycUEhIChUKBU6dOAQCOHTuGRo0aYfTo0SgsLMTFixcBAMePH4dIJEJISAhycnJw9uxZhIeHq4N+4NHMyyuvvAIA2Ldvn1ZbY8eO1SqTSqUoKirCwYMH9f4BT0tLw9WrVzFgwACUlpYiLy9P/RMcHAwHBwccOXLkiceipuTk5GisIS2Xy1FQUKDeLikpwb179zT2uX37doXbMplMY3zYBtvQ1caVK1e0Ah6FQgGZTGZW52GJbchksgqDUYVCgUuXLtX4eVy4cKHCa0JfG7r6q9qvLrwfqlTRitbxN/V7zjbqfhtkfkQCv57vidy7dw/PPfccxo8fjxkzZmDixIlwc3PD4sWL8eyzz2LMmDF49dVX8dJLLwEAfvrpJ5w/fx4vv/wyXn31VUyZMkXjeKWlpejRowe6deuGb775BsCjHP8dO3bg4MGDqFevnkb9f/75B6+//joyMzNRv359PPXUU+jRowf69eunntnfu3cv5s2bV+F5dOnSBatWraqpYSEyS3K5HEuWLNEI2CQSCebNm6dxp4yMT9d787jaep8MvSbq+rWkuuN848YNnQ/y+vj4YPr06SboGVHViD4pqXJd4V0+s6LCVJ8n5OrqiubNm+P48eMoKirC+fPn8fbbb8PGxgZPPfUUjh07hmHDhuHq1avq4N9Q5YN+APD19cXmzZuRkpKCY8eO4eTJk1i0aBFiY2Pxww8/wMfHR/0Jf+zYsQgLC9N5bBcXlyfqG5ElkEqliIyMRFJSkjpNLyIiok4Eatau/Hujeii1rKysVt8nQ68Jc7mWmONPZF0Y+NeAkJAQJCQk4MCBAygtLVXn5nfp0gVff/01/vrrLwiCgC5dugAAmjRpAgC4du2a1rEyMjJQVlYGb2/vKrdvZ2eHnj17omfPngCAgwcP4o033sDGjRsxZ84c+Pr6Ani0ekNde7CMqK4JDQ1FYGAgZDIZPD0961Q+trUr/94AMMr7ZOg1wWuJiOoa5vjXgC5duqCsrAw//PADPD094ePjoy4vKSlBfHw8xGIxOnXqBABo1KgROnTogAMHDiAtLU19HEEQsGbNGgBA3759q9R2Xl6eVlmbNm0AAPn5+QCA1q1bo0WLFtiyZQtu3rypVV+hUKjrEtGj2dqAgAAGanXQ4++NMd8nQ9uq69cSH+4lsi6c8a8BnTt3ho2NDdLT0zVWxmnevDlcXV1x7do1BAUFadzifeuttxATE4OJEyeql/M8ePAgDh8+jAEDBmit6KPPtGnT4OzsjE6dOsHDwwMFBQVISkqCSCTCwIEDATy6lfvxxx9jypQpGD16NCIjI9G8eXMUFRXh5s2b+O9//4vp06dzVR8iIiuj7+HekpKq508Tkflg4F8DXFxc0KpVK1y+fFm9XKdKly5dsHv3bq3ydu3aIS4uDrGxsUhISMDDhw/h7e2N119/XefqPfoMHz4ce/fuxdatW5Gfn4/69eujdevWeOeddzTabN26NTZu3Ig1a9bgwIED2LJlC5ycnODl5YWIiAh1GhIREVkPGxsbnbP7/AIvIsvEVX2IiIisjGpVn5s3b+qc3ffz89NadY6oLuGqPobhjD8REZGVYo4/mS1+ca9B+HAvERGRlWKOP5F1YeBPRERkpVTfh1Cera2tkXtCRMbAVB8iIiIrpe8LvCQShgdU1zHXxxCc8SciIrJS+nL8FQqFkXtCRMbAwJ+IiMhK6Qv8S0tLjdwTIjIGBv5ERERWSl+Ov4ODg5F7QkTGwMCfiIjIStnb2+ssb9KkiZF7QlRNomr8kBoDfyIiIitVv359neXdu3c3ck+IyBgY+BMREVkpOzs7dOjQQaMsODgY7u7uJuoREdUmrtdFRERkxUaMGIFnnnkGqampaN26NTw8PEzdJSKqJQz8iYiIrJyHhwcDfiIrwFQfIiIiIiIrwBl/IiIiIjIvXK3HIJzxJyIiIiKyAgz8iYiIiIisAAN/IiIiIiIrwMCfiIiIiMgKMPAnIiIiIrICDPyJiIiIiKwAl/MkIiIiIvPC5TwNwhl/IiIiIiIrwMCfiIiIiMgKMPAnIiIiIrICDPyJiIiIiKwAA38iIiIiIivAwJ+IiIiIyApwOU8iIiIiMi8irudpCM74ExEREZHVmT9/PqRSqam7YVQM/ImIiIiIrABTfYiIiIjIvDDTxyCc8SciIiIiKufcuXN47rnn4OTkhPr162P48OH4559/1K+/+uqr6NWrl3o7OzsbNjY26NKli7pMLpfD1tYWmzdvNmrf9WHgT0RERET0mBs3biA8PBz37t3Dhg0b8N133+HkyZPo3bs3CgoKAADh4eE4duwYioqKAAAHDhyAvb09Tp06pa7z119/QaFQIDw83GTn8jim+lCdIgiC+peFiIhqR2lpKR4+fAgAuH//PmxtbU3cIzJXzs7OEFngCjvLli1DaWkp9uzZg0aNGgEAOnXqhHbt2iE+Ph6vv/46wsPDUVxcjKNHj6J37944cOAAoqKisGfPHhw6dAgDBgzAgQMH0KpVK3h4eJj4jB5h4E91SkFBAerXr2/qbhARWY033njD1F0gM5afnw8XFxejtyu8Vbsh7J9//omnn35aHfQDQJs2bdCxY0ccPHgQr7/+Ovz9/eHj44MDBw6oA//Jkyfj4cOH2L9/vzrwryuz/QADf6pjnJ2dkZ+fD+BRXtygQYPw66+/Wt1yW/pwTLRxTLRxTLRxTLRxTLRxTLRVNibOzs4m6FXty83NRXBwsFa5h4cHcnJy1NuqgP/+/fs4c+YMwsPDUVhYiISEBBQXFyMlJQUTJ040Ys8rxsCf6hSRSKSeObCxsYFYLIaLiwv/AP8/jok2jok2jok2jok2jok2jok2ax2TRo0a4c6dO1rlWVlZaNWqlXo7PDwcb775JpKTk+Hm5oY2bdqgsLAQc+bMwb59+1BcXKzxALCp8eFeIiIiIqLH9OzZE3/88Qdyc3PVZampqTh79ix69uypLlPN8H/11VfqlJ7g4GA4ODhg6dKlaNq0KZo1a2bs7uvFGX8iIiIiskpKpRIJCQla5TNnzsSaNWvQv39/vPfeeygqKsL7778PX19fvPzyy+p6bdq0QePGjbF//3588803AACxWIwePXpg165dGDNmjLFOpUoY+FOdZWdnh4kTJ8LOzs7UXakzOCbaOCbaOCbaOCbaOCbaOCbaLH1MioqKMGLECK3y9evXY//+/XjrrbcwZswYiMVi9OvXD1999ZXWcw3h4eFISEjQeIi3d+/e2LVrV516sBcARIIgCKbuBBERERER1S7m+BMRERERWQEG/kREREREVoA5/lQnKJVKbNiwAQcPHsS1a9cgCAJatmyJyZMno1OnThXue+vWLURGRmqVt2/fHvHx8bXU49r3JGMCPFp7+auvvkJycjIUCgW6deuGd955B25ubkbofe05cuQIkpKScP78eWRmZmLEiBGYM2dOpftZ6nUCGD4mgOVeJwBw4MABrFq1CtevX4enpydefvllndfA4yzlOsnIyMBnn32Gs2fPwsnJCQMHDsTUqVMr/YZeQRCwdu1abN68GXl5eWjVqhXefPNNBAUFGanntcfQMYmIiMDt27e1yg8dOgR7e/va6q5R3LhxA+vXr8f58+fx999/w8/PD//5z38q3c+SrxNLx8Cf6oTi4mLEx8dj8ODBiI6Oho2NDX755RdMnjwZ3377Lbp06VLpMaZNm4aQkBD1tqOjY212udY96ZjMmzcP165dw7x582BnZ4eVK1dixowZWLduHSQS8/3VP3z4MK5evYqnnnoK9+/fr/b+lnadAE82JpZ6nZw+fRpvv/02hgwZgtmzZ+PYsWNYuHAhHB0d8eyzz1a6vzlfJ/fv38fkyZPh6+uLzz//HHfu3MGyZctQVFRU6QfCtWvXIjY2FtOnT0fLli2xefNmTJ8+HRs3boSPj4+RzqDmPcmYAMAzzzyDsWPHapRZwsOuf//9Nw4dOoTAwECUlZWhrKysSvtZ6nViFQSiOkChUAj5+flaZcOGDRPeeOONCvfNzMwUOnfuLOzdu7c2u2h0TzImZ86cETp37iwcPnxYXZaeni6EhIQIe/bsqZX+GotSqVT//+DBg4WlS5dWaT9LvU4EwfAxseTrZNq0acKECRM0yt59911h+PDhFe5nCddJXFyc0LNnTyEvL09dtmXLFqFr167CnTt39O5XVFQkhIeHC99++626rKSkRBg8eLCwZMmSWu1zbTN0TASher9T5ubxvx0fffSRMGLEiEr3seTrxBowx5/qBNW3ApYva9myJe7evWuiXpnWk4zJX3/9BWdnZ4SGhqrLmjVrhlatWuHQoUO10l9jsbHhn63yDB0TS71OSkpKcPz4ca2Z/f79+yM9PR23bt0yUc+M46+//kLXrl1Rv359dVm/fv1QVlaGI0eO6N3v7NmzKCws1Bg3W1tb9O3b16yvB8DwMbF0hvztsOTrxBrwX1CqsxQKBc6dOwd/f/8q1V+6dCm6du2Kfv36YdGiRcjPz6/lHhpfVcckIyMDfn5+EIlEGuX+/v7IyMioxR7WfdZwnVSVpV4nN2/ehEKh0Pq2TNXvTVXOzZyvk4yMDK1zd3Z2hpubW4XnrnpN17jJZDIUFRXVbEeNyNAxUdm9ezfCwsLQq1cvzJgxA2lpabXTUTNgydeJNTDfBE6yeOvWrcPdu3fx0ksvVVjPzs4Ow4cPR7du3eDs7Izz588jLi4OFy9eNPs85fKqOib379/X+oIR4NE/dIbkxVsCa7pOqspSrxNV38ufm+oOWkXnZgnXiaHv6/3792FnZ6f1wKqzszMEQUBBQQHq1atX4/01hie51sPDw9G+fXt4enoiMzMTcXFxePXVV602n92SrxNrUPf/gpHZksvlyM7OrrSet7e31qoKR44cQWxsLF577TW0bdu2wv3d3Nwwd+5c9Xbnzp3RokULvPHGG9i3bx/69etn2AnUAmONiTl5kjGpDmu5TixVdcbkSZjTdULG8fbbb6v/v1OnTujWrRuGDRuGDRs2aFwrROaAgT/Vmt9//x2LFi2qtF5CQoLGLcPLly9jzpw5GDBgACZOnGhQ2z169ICDgwMuXbpUp/6hNtaYuLi4ICsrS6u8oKBA67kBUzN0TGqCpV0n1WWp14mq73K5XOM11cxudc+trl4n+ri4uGidO1D5++ri4oKSkhIUFxdrzOYWFBRAJBLpnDE3F4aOiS5ubm4IDg7GpUuXaqp7ZsWSrxNrwMCfas3QoUMxdOjQau1z48YNzJgxAx06dMAHH3xQOx0zIWONSbNmzZCSkgJBEDTytzMyMhAQEFCt9mubIWNi6Yw1JpZ6nZSUlEAikSAjIwNhYWHqcn25yZamWbNmWnnrqjsmFZ276rXr16+jVatW6vKMjAx4enqadfqGoWNC2iz5OrEGfLiX6ozs7GxMnz4dnp6e+PTTT58ol/bPP//Ew4cP0a5duxrsofEZOibdu3fH/fv3kZKSoi67fv06UlNT0aNHj9rqrtmxlOvEUJZ6ndjZ2SEkJAR//PGHRvnevXvh7++PJk2aVOt45naddO/eHSkpKSgoKFCX/f7777CxsUG3bt307tehQwc4OTnh999/V5cpFArs27fPrK8HwPAx0eXu3bs4ffq02VwPNc2SrxNrwBl/qhOKioowY8YM5OXlYfbs2fj777/Vr9na2qJNmzbq7dDQUAwaNAgffvghAGDZsmWwsbFB+/bt4ezsjAsXLiA+Ph7t2rVDnz59jH0qNeZJxqRDhw4ICwvDxx9/jFmzZqm/mKlly5bo27ev0c+lJt2+fRsXLlwA8GiMMjMz1f8APb68nLVcJ4DhY2LJ18lrr72GSZMmYenSpXj22Wdx4sQJ7N69G0uWLNGoZ4nXybBhw7Bp0ybMnj0br7zyCu7cuYOvv/4aL7zwAtzd3dX1pkyZgtu3b2Pbtm0AAHt7e0yYMAHff/89GjZsiICAAGzevBn5+flaX15lbgwdk927d+PgwYPo0aMH3N3dcfPmTcTHx0MsFpv9mACP/l4cPHgQwKO/I4WFheq/HZ07d0bDhg2t6jqxBgz8qU7IycnBlStXAABvvvmmxmteXl5ISkpSbyuVSo1vF/T390dCQgK2bt2KoqIiNG7cGJGRkZg0aZJZrMChz5OMCQAsWbIEX331FRYvXgylUonQ0FC88847Zj0mAHD8+HEsWLBAvf3XX3/hr7/+Ur+mYi3XCWD4mACWe50EBwfjs88+w6pVq5CYmAhPT0+8//77Wmv7W+J14uLiglWrVuHzzz/H7Nmz4eTkhKFDh2Lq1Kka9ZRKJZRKpUZZdHQ0BEHAhg0bkJubi1atWmHFihVmv3qNoWPi7e2Nu3fv4ssvv0RBQQGcnZ3RpUsXTJo06YkfJK8LcnJytB5QVm1/9913CAkJsarrxBqIBEEQTN0JIiIiIiKqXczxJyIiIiKyAgz8iYiIiIisAAN/IiIiIiIrwMCfiIiIiMgKMPAnIiIiIrICDPyJiIiIiKwAA38iIiIiIivAwJ+IiIiIyAow8Cciq/Pyyy9DJBKZuhsAgPPnz0MikWDv3r3qsuTkZIhEIsTHx5uuY1QnxMfHQyQSITk52aD9eS3pdvr0adjY2GD//v2m7gqRUTHwJ7IQ165dQ0xMDNq0aQNHR0c0bNgQbdu2RXR0NPbt26dRt1mzZmjfvr3eY6kC4+zsbJ2vX7p0CSKRCCKRCH/++afe46jqqH7q1auHli1b4s0330ROTo5hJ2ph3nzzTfTo0QP9+vUzdVeMIiMjA/Pnz8fp06dN3RUykry8PMyfP9/gDy+GquhaCw4OxtChQzF79mwIgmDUfhGZksTUHSCiJ3f8+HH07t0btra2GD9+PAIDA/Hw4UNcvXoVe/bsgbOzM/r27Vtj7a1evRrOzs5wcHBAXFwcevXqpbducHAwZs+eDQDIycnBzp07sWzZMuzduxcnTpyAnZ1djfXL3Bw+fBh79+7Ftm3bNMrDw8Px8OFD2NramqZjtSgjIwMLFixAs2bNEBwcbOrukBHk5eVhwYIFAIA+ffoYrd3KrrU33ngDvXv3xs6dOzFo0CCj9YvIlBj4E1mABQsW4MGDBzh9+jQ6duyo9bpMJquxtkpLS7F+/XqMGDEC9evXx/fff49vvvkGzs7OOut7e3tj7Nix6u0ZM2YgIiICO3bsQGJiIkaMGFFjfTM3K1euhJubGwYOHKhRbmNjg3r16pmoV0TWoVevXmjWrBm+++47Bv5kNZjqQ2QBrl69CldXV51BPwB4enrWWFtJSUm4c+cOoqOj8fLLL6OwsBCbNm2q1jGee+45AEBaWpreOqtWrYJIJML27du1XisrK4OPj4/GLN6ePXvw4osvonnz5nBwcECDBg3Qv3//Kufw9unTB82aNdMqz8jIgEgkwvz58zXKBUHAqlWr0LlzZzg6OkIqlaJv375aaVX6KBQKbNu2Dc8++6zWzL6uvOzHy1auXInWrVujXr16CAoKwo4dOwAA586dw4ABA+Di4gJXV1fMmDEDpaWlOs/z2rVrGDJkCOrXrw8XFxdERUXh2rVrGnXLysqwePFihIeHw9PTE3Z2dvD19cWUKVNw7949nee1ZcsW9OnTBw0aNICjoyNat26NGTNmoKSkBPHx8eo7TxMmTFCngFVlFjgjIwPjxo2Dh4cH7O3t0aJFC7z77rt48OCBRr358+dDJBIhNTUV7777Lnx8fGBvb4+OHTti586dlbYD/C+v/o8//sDHH38MPz8/ODg4IDQ0FEeOHAEA7N+/Hz179oSTkxO8vLywcOFCncfatm0bevToAScnJ0ilUvTo0QOJiYk66/7www9o06YN7O3tERAQgOXLl+tNQ8nPz8ecOXMQEBAAe3t7uLu7Y/To0VrvYXVVdZwrek5GJBLh5ZdfBvDouvX39wfwaIJC9Z6rftce//3697//jQ4dOqBevXrw9fXF/PnzoVAoNI5d1d/TqlxrIpEIzz33HHbv3g25XF7NkSIyT5zxJ7IALVq0QGpqKrZu3YoXXnihSvsolUq9OfzFxcV691u9ejX8/f3Rq1cviEQidOrUCXFxcXjttdeq3N+rV68CANzc3PTWGTVqFGbNmoV169YhMjJS47U//vgDmZmZ6hQi4NE/9Dk5ORg/fjx8fHyQmZmJH3/8Ec888wz27dtXYTqSIcaNG4d///vfGD58OCZMmIDi4mJs3LgR/fr1w9atW7X6XN6JEycgl8vRtWvXarX7r3/9C7m5uXjttddQr149fPPNN4iKisLmzZsxceJEjB49GkOHDsWePXuwYsUKNG7cGO+//77GMQoLC9GnTx+EhoZiyZIluHr1KlauXIkjR47g1KlT6g+KJSUl+PzzzzFs2DAMGTIETk5OOHbsGFavXo2DBw9qpWq99957+OSTT9CuXTvMmjULXl5e+Pvvv7FlyxZ8/PHHCA8Px7vvvotPPvkEMTEx6vfEw8OjwnO+fv06unbtivz8fEydOhUtW7ZEcnIylixZgkOHDuGPP/6ARKL5z1l0dDRsbW3x1ltvoaSkBMuXL8fQoUNx5coVnYGjLnPnzoVSqcTMmTNRUlKCL7/8Ev3798e6devw6quvIiYmBmPGjMF//vMffPjhh/D399e4u7Vy5UpMmzYNbdq0wYcffgjg0XU6dOhQxMbGIiYmRl13+fLlmDVrFjp27IhPPvkEDx48wBdffIHGjRtr9Ss/Px/du3fHP//8g1deeQWBgYG4ffs2Vq5cidDQUBw/fhx+fn5VOscnHefKtG3bFsuWLcOsWbMQFRWl/vsklUo16m3fvh3Xrl3DtGnT4Onpie3bt2PBggW4fv061qxZU+1zqeq1FhYWhtjYWBw8eBADBgyodjtEZkcgIrP3119/Cba2tgIAoWXLlsKECROElStXChcvXtRZ38/PTwBQ6c/du3c19svMzBTEYrHw0UcfqcuWL18uANDZFgChf//+wt27d4W7d+8KV65cEb766ivB1tZWqF+/vpCVlVXheQ0fPlywt7cXcnJyNMrHjh0rSCQSjf3lcrnW/jKZTHB1dRWef/55jfLo6Gih/J+/3r17C35+flrHSE9PFwBonPPWrVsFAEJsbKxG3dLSUqFz585Cs2bNhLKysgrPLS4uTgAgJCYmar22b98+AYCwZs0arbImTZoIeXl56vIzZ84IAASRSCRs2bJF4zhPPfWU4OnpqXWeAISZM2dqlKvOadKkSeqysrIy4cGDB1r9+/HHHwUAwqZNm9RlR48eFQAIffv2FR4+fKhRv6ysTD0eus6tMi+99JIAQPj11181yt966y0BgPDjjz+qyz766CMBgDBo0CCN9yAlJUUAIMydO7fS9tasWSMAEDp16iQUFxeryxMTEwUAgkQiEY4dO6YuLy4uFjw9PYVu3bqpy3JycgQnJyehRYsWQn5+vro8Pz9faN68uSCVSoXc3FxBEAQhNzdXcHR0FNq2bSsUFhaq6964cUNwcnISAAj79u1Tl8+YMUOoV6+ecPr0aY1+Z2RkCM7OzkJ0dLS6rDrjXZ1x1vU7pAJAow+6fofKv2ZjYyOcOHFCXV5WViYMHTpUACAcPnxYXV6d39OqnPuff/4pABC++OILvXWILAlTfYgsQFhYGE6cOIHo6Gjk5+djzZo1mDp1Ktq1a4fw8HCdt/+bNWuGvXv36vzp37+/znbi4+NRVlaG8ePHq8vGjBkDW1tbxMXF6dxnz549cHd3h7u7O1q1aoU333wT7dq1w549e3TOZj4uOjoaxcXFGqlEcrkcv/zyCwYMGKCxv5OTk0ade/fuQSwWIzQ0FEePHq2wnerasGEDnJ2dMXToUGRnZ6t/8vLyEBERgYyMDPVdDX3u3r0LAGjUqFG12n755ZdRv3599XaHDh3g4uKCJk2aaN3t6dmzJ2Qymc40hrlz52psR0VFoXXr1hoPGotEIjg4OAB4dIcoLy8P2dnZePrppwFAY1w3btwIAFiyZInW8wmqNAtDlJWVYfv27ejUqZPWsxDz5s2DjY0NfvnlF639Zs6cqdFmly5dIJVKK31fHjdlyhSNOxqqWePQ0FCEhISoy+3s7NC1a1eNY+/duxeFhYWYMWMGXFxc1OUuLi6YMWMG5HI5fv/9dwCPfkcePHiAadOmwdHRUV3Xx8cHY8aM0eiTIAjYuHEjwsPD4e3trXH9OTk5oVu3btizZ0+Vz1HF0HGuKf369cNTTz2l3haJRHjnnXcAoFbbdXV1BQDcuXOn1togqkuY6kNkIYKCgtQ54devX8f+/fvx448/4s8//8SQIUO00jKcnJzw7LPP6jzWhg0btMoEQUBcXBw6dOiAsrIyjfz8Hj16YP369ViyZIlWKkBoaCgWLVoEALC3t4efnx98fX2rdE6q4H7dunWYPHkygEc55IWFhRofPgDg77//xnvvvYfffvsNeXl5Gq/V9Jr9ly5dQkFBQYUpKllZWWjVqpXe11V9Eqq5lGDz5s21yho2bIimTZvqLAeAe/fuaaRWNGjQQOdzH23btsW2bdtQWFio/iD1n//8B19++SVOnTql9bxAbm6u+v+vXr0KkUik9zkTQ929exdyuRyBgYFarzVq1AheXl46P9jqGidXV1e9zyboUv4YqvFU5ayXf+3xY6enpwOAzn6rylT9Vv23TZs2WnXbtWunsX337l3cu3dP/YFaFxub6s/pGTrONaVt27ZaZapzr812Vb9/deV7PYhqGwN/Igvk5+eH8ePHY9y4cejVqxcOHTqElJQU9OzZ0+Bj7t+/H3///TcAoGXLljrr7NixA0OHDtUoc3Nz0/sBozISiQQvvfQSli9fjrS0NAQEBGDdunVo2LChRg69XC5HeHg4CgsL8cYbbyAoKAjOzs6wsbHBkiVL8N///rfStvT9w1/+4ULgUbDg7u6On376Se/xKvqeBADqoK2632cgFourVQ5U/8OFytatW/Hiiy+ia9eu+Prrr9G0aVPUq1cPSqUSAwYMQFlZmUb9J5nZr2n6xqM6Y2HIWNc2Vf+fffZZzJkzx2T9qM7vS11uV/X7p+9DFJGlYeBPZMFEIhFCQ0Nx6NAhZGZmPtGx4uLiYG9vj3Xr1umcUZw0aRJWr16tFfg/qejoaCxfvhzr1q3DxIkTkZycjJiYGNjb26vr/PHHH7h16xbi4uIwYcIEjf3LP9iqT6NGjXDixAmtcl2zjS1btsSVK1fQrVs3rYcUq0r1waA6qSc1JS8vDzKZTGvW/9KlS2jcuLF6tn/9+vWoV68e9u3bp5GCcvnyZa1jtmrVCrt27cKZM2cqfGC5uh8M3N3d4ezsjAsXLmi9lpubi9u3b9fJ7wNQ3S24cOECnnnmGY3XLl68qFFH9d/Lly/rravi7u6OBg0a4P79+wZ/oNaluuOsSlHLycnRSFfT9ftSlff80qVLWmXlx0nVblV/T6vSrurOZWUf1IksBXP8iSzA3r17dc54PXz4UJ3vWz5loDry8/ORkJCA/v37Y+TIkRg+fLjWT2RkJHbt2oXbt28b3I4uwcHB6NChAzZs2ID169ejrKwM0dHRGnVUM7DlZ3P37NlT5fz+Vq1aoaCgACkpKeqysrIyLFu2TKvu+PHjUVZWhnnz5uk8VlZWVqXtderUCS4uLurlIY1t6dKlGtu//PILUlNTNT64icViiEQijZl9QRDUqVuPe+mllwAA7777LkpKSrReV703qg9KVb3TYWNjg4iICJw6dQq7d+/WOoeysjJERUVV6VjG1K9fPzg5OWHFihUoKChQlxcUFGDFihWQSqXqb2vu168fHBwc8K9//Utj2cybN29q3VWysbHBmDFjkJKSgoSEBJ1tG5KvXt1xVqWxqZ5TUPnyyy+1jl2V93zv3r04efKkelsQBHz22WcAoHFNVuf3tCrtHjlyBBKJBD169NBbh8iScMafyALMmjUL9+7dQ2RkJIKCguDo6IgbN27gp59+wpUrVzB+/HgEBQUZfPx///vfePjwIYYNG6a3zrBhwxAfH4+1a9dqPTj6pKKjozF79mx8+umnaNWqFbp166bxes+ePeHp6YnZs2cjIyMDPj4+OH36NNavX4+goCCcO3eu0jZiYmLw5ZdfIioqCjNnzoSdnR0SEhJ0fqBSLeH57bff4uTJkxg8eDDc3Nxw8+ZNHD58GGlpaZXmJYvFYrzwwgvYtm0biouLNe5g1DY3Nzds3boVt27dQp8+fdTLeXp4eGh8X8Hw4cOxZcsWPP300xg/fjxKS0uxbds2rTXdAaBr166YM2cOPv30Uzz11FN48cUX4enpifT0dCQkJCAlJQUNGjRAu3bt4OzsjJUrV8LR0RENGjRA48aN1Q8M6/LJJ59g7969GDp0KKZOnYqAgAAcOHAAmzZtQnh4uNYHwbqgQYMG+OyzzzBt2jSEhoaq17WPj49HWloaYmNj1Q9pN2zYEAsXLsRbb72F7t27Y/z48Xjw4AG+++47tGzZEqdOndI49uLFi3Ho0CGMHDkSI0eORLdu3WBnZ4fr169j586d6Ny5s8Z3QFRVdcZ59OjRePfddxETE4PLly+jUaNG2L17t84lgl1dXREQEICff/4ZLVq0gIeHB5ycnBAREaGu07FjRzz99NOYNm0avLy8kJiYiN9//x3jxo1DWFiYul51fk8ru9YEQcDu3bsxYMAAg+/cEZkdk6wlREQ16rfffhOmTp0qdOjQQXB1dRXEYrHQqFEjoU+fPsLq1asFpVKpUd/Pz08IDAzUezzVUn2q5TxDQkIEiUSitazm44qKigRnZ2ehVatW6jL8/7KKT0omkwkSiUQAICxatEhnnTNnzgjPPfec0KBBA0EqlQq9e/cWDhw4oHPZQX1LEf76669Cx44dBTs7O8HLy0t45513hMuXL+tdinDdunVCz549BWdnZ8He3l7w8/MToqKihJ9//rlK56VaAjMhIUGjvKLlPHUtTejn5yf07t1bq1y1tGV6erq6TLUc4t9//y1ERkYKzs7OglQqFSIjI4WrV69qHeP7778X2rZtK9jb2wuenp7CxIkThXv37mkt2ajy008/Cd27dxekUqng6OgotG7dWpg5c6bGspi//vqr0KlTJ8He3l4AoLPv5V27dk0YO3as4O7uLtja2gr+/v7CvHnzNJa/1HfOlY1TearlPB9fQlNF33nru6a2bt0qhIWFCY6OjoKjo6MQFhYm/PLLLzrb/e6774RWrVoJdnZ2QosWLYRly5apl30t35fCwkLh448/Ftq3by/Uq1dPkEqlQps2bYTXXntNOHLkiLpedZdPreo4C4IgHDlyROjevbtgb28vuLq6ChMnThRyc3N1jtHRo0eF7t27C46OjgIA9ZKcjy/D+dNPPwlBQUGCnZ2d4OPjI3zwwQdCSUmJVrvV+T2t6FpLTk4WAAg7duyo0tgQWQKRIBj41BcRET2xAQMGoLCwEH/++adR2uvTpw8yMjKQkZFhlPaIKpKRkQF/f3989NFHWt+OXduioqJw48YNHDt2rM48lE5U25jjT0RkQl9++SUOHz5s0NrrRGSYU6dOITExEV9++SWDfrIqzPEnIjKhwMDAWl8CkYg0derUSWs5WiJrwBl/IiIiIiIrwBx/IiIiIiIrwBl/IiIiIiIrwMCfiIiIiMgKMPAnIiIiIrICDPyJiIiIiKwAA38iIiIiIivAwJ+IiIiIyAow8CciIiIisgIM/ImIiIiIrAADfyIiIiIiK/B/D5P0suj4ZloAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":["9. DistilBERT imports"],"metadata":{"id":"P9HjkDCqfREE"}},{"cell_type":"code","source":["import torch\n","print(\"GPU available:\", torch.cuda.is_available())\n","if torch.cuda.is_available():\n","    print(\"GPU name:\", torch.cuda.get_device_name(0))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"avwe2S78feCL","executionInfo":{"status":"ok","timestamp":1775357488321,"user_tz":-60,"elapsed":21,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"a8f7caf3-16df-495e-fced-88be6a0f3806"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["GPU available: True\n","GPU name: Tesla T4\n"]}]},{"cell_type":"markdown","source":["10. Install libraries"],"metadata":{"id":"NHU2brOHiJ0K"}},{"cell_type":"code","source":["!pip install transformers datasets accelerate -q"],"metadata":{"id":"MF0lgTJshBtZ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["11. Imports"],"metadata":{"id":"kI2DuGYSiX-Y"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import time\n","\n","from datasets import Dataset\n","from transformers import (\n","    DistilBertTokenizerFast,\n","    DistilBertForSequenceClassification,\n","    Trainer,\n","    TrainingArguments\n",")\n","\n","from sklearn.metrics import (\n","    accuracy_score,\n","    precision_score,\n","    recall_score,\n","    f1_score,\n","    confusion_matrix,\n","    classification_report\n",")"],"metadata":{"id":"ZEQz0EHCiXcj"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["12. Subset creation"],"metadata":{"id":"UENY7Y9AijEN"}},{"cell_type":"code","source":["train_sample = train_df.sample(n=4000, random_state=42).reset_index(drop=True)\n","test_sample = test_df.sample(n=1000, random_state=42).reset_index(drop=True)\n","\n","print(train_sample.shape)\n","print(test_sample.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qJsyxY7Ki0A9","executionInfo":{"status":"ok","timestamp":1775357496530,"user_tz":-60,"elapsed":32,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"ac090e0a-1798-4ba1-97e1-5ddea59123cc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(4000, 2)\n","(1000, 2)\n"]}]},{"cell_type":"markdown","source":["13. Converted to Dataset"],"metadata":{"id":"_whYiHjbjn8q"}},{"cell_type":"code","source":["train_dataset = Dataset.from_pandas(train_sample[[\"review\", \"sentiment\"]])\n","test_dataset = Dataset.from_pandas(test_sample[[\"review\", \"sentiment\"]])"],"metadata":{"id":"evYKJ630jr6L"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["14. Tokenizer"],"metadata":{"id":"wWVa1OyVj0SI"}},{"cell_type":"code","source":["tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["0c57d68bf84746fbaa67edf2b28c738b","9f28a4c1b15e43628f30403bb321a3cb","b1f455f5944543a291d90b34fb83625a","2dce540ba4514434b05e523bafd03849","b7a844a85852415eaf36ee396ad0c600","8029cfd6b8e4402eb1b4d58effdcec1b","72edd1fb50f545f1badaf1e97f3dc388","ed933ad73e4b46cb89f3bb4ccbbf52ce","fd6e8320972c455f9e37483c5e07254f","d5661ad6c6b74f1ca2facd226f36d455","7a25a4f11cdd419f8473691af95cf969","615b67d98d634507b8643bfcce37a103","ed517f80d21b472d8a4ab9978e486b22","a0019168d9bc4a65b2d3e39d82667aed","786f8f2fda5f4dbba463217be03f9945","3f97cfd962c145749111c97866b101ea","1b2fd7d5e1fe43eb9414cb4974490cda","85f89a2c368849a48684787dbbf0a313","fae2044862854f66bfe727914e6695f3","428994c624074db282927e937bb163fb","1feba9cb47b648f48990d77cba6831b9","7714a5c33d18405bbb14bbe8398e74b2","ecd87a15809741a6a968d8932744d907","35b075596753497bab16ef2a2708b9ec","76916f012f4141df991400360f76818d","cac69553dfbb4dd38618fe99fada66d8","9b89f5b18dca486c8ee84bc3167c4676","99e997e0664349e1bafb6d58de7bee4a","ae0f29098c8c4f45964414ee03225278","ea3fb1f75639430a94ed65e7eeabee29","b651f9f18dd8421d899615b109135406","df7bb1612ccc4428b4315ef07cd36da5","32f95bc9e41e42b086eff4ea5bb287e8"]},"id":"OduwK5v3j7Z2","executionInfo":{"status":"ok","timestamp":1775357645699,"user_tz":-60,"elapsed":1739,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"d48f7084-3b89-412a-d936-08fdd87a7e88"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0c57d68bf84746fbaa67edf2b28c738b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"615b67d98d634507b8643bfcce37a103"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ecd87a15809741a6a968d8932744d907"}},"metadata":{}}]},{"cell_type":"markdown","source":["15. Tokenisation"],"metadata":{"id":"TaOMoaNTkCEb"}},{"cell_type":"code","source":["def tokenize_function(example):\n","    return tokenizer(\n","        example[\"review\"],\n","        truncation=True,\n","        padding=\"max_length\",\n","        max_length=256\n","    )\n","\n","train_dataset = train_dataset.map(tokenize_function, batched=True)\n","test_dataset = test_dataset.map(tokenize_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["df8e195388644b9bb7776a319a679ce3","4ba5b9111be944569d4ce93567e1949e","aa93c992d3ee4f34aceccf9f3ea8344f","fa437cb6ccd14e8da3882d6e4293a8e6","3352f029535444e98965460c46dab18f","d237c7b1cf6541068a9dd8966558508e","69e7c006fd434e29a5aa458aa9c21477","2c945ef777c84f59bb43f746b8e22d86","9c8e344853ec4531800ea9ca3422a451","f893a225c38e490a9401b5a1aff00769","ab2c390dab9e4ef4aa36e3690c54979a","d002f941233541798f4b61fb44956544","b5d162f459794a31a3ba62c99f1265e7","3162977541464376a3658bd095bde35a","d7ba832f8f41449da367509cd5526dd1","e7b2629e8ae84af9b4dcf6fffe7c90b6","ee6ea560e02149eea950dee8b7b94c8e","4886967b8bc34165a045019370ad6ee9","385778ab68304991ba4f55d683fd85d3","5edd2ee0d5714074b9015a782b67763b","3a8f1be8e7014d10b863d0345f2cd3cf","a69cb47c1d0f4e2d9166408e3adac027"]},"id":"4tbLqF88kE17","executionInfo":{"status":"ok","timestamp":1775357700164,"user_tz":-60,"elapsed":4218,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"30ae53a9-cffc-419b-f8b5-6b48bb3859bf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"df8e195388644b9bb7776a319a679ce3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d002f941233541798f4b61fb44956544"}},"metadata":{}}]},{"cell_type":"markdown","source":["16. Format for Pytorch"],"metadata":{"id":"MIBdiQwjkRAg"}},{"cell_type":"code","source":["train_dataset = train_dataset.rename_column(\"sentiment\", \"labels\")\n","test_dataset = test_dataset.rename_column(\"sentiment\", \"labels\")\n","\n","train_dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])\n","test_dataset.set_format(type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"labels\"])"],"metadata":{"id":"gBBDC_qLkVPg"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["17. Loading Model"],"metadata":{"id":"UsWvu84KkXCm"}},{"cell_type":"code","source":["model = DistilBertForSequenceClassification.from_pretrained(\n","    \"distilbert-base-uncased\",\n","    num_labels=2\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":391,"referenced_widgets":["d82a1fb5f293464faa7fd5a7fbc9f529","df2f41b4a7d140efa00e8bd64bab6c33","49e9ae3f14ab4fb7b9992a045c83a08c","478dc459d95a48edbf5b92c93e7d0496","94648c44e95f4b9883369ab1fa875ee6","2788ee6514774cfb8bb87cd0ba8671d8","306f900848354e2d8f69187d93f4feda","23bc113ba3a04d42b4cca15f08b5fbd3","9ff9b3f46aea4ad68e5740b0c45387f5","08d9fac290bf490fb4b248a4cfcdb5a2","67dabb5dc7b14228b9c96831cfd44405","bf85dc63c0e0433f938b32ff48f07c40","2c0a95750d41451f9a2a0a1b58f5ad2a","66b6224f1d8a4bc0a66cb6731f0ff5a5","4bee0612d77a4f25b94dfd2a54af45cd","a889cfb9870e4f74a7b51510aac4f844","2f758253b666431baf55f83713c3538f","e1f3361a8e3044f699bc5913da2d342c","e7e7912b1a26407e9ca9987adfb83ac8","f19591f215aa4255ba8b733d362b60bb","bf5299a77c1a425ca46499c0bd9bb952","a853fe316c874d2c8b38a57ebe8b9e0e","c1ff769739b248568d6795f610b8a2cf","b246a7461f0b4f79a34b55d4b9497112","73230c08083f478ab511cfbe452156de","e446d955c4784b86b98beba881392d6e","67369098e7de486f948ec68308ee95ed","5e0e6963b2224dcca4ac3d1a5b7c8875","d6ba28aa26e04647ac5194fb3cb0d6c6","500164c4184743e998a7e4671410d2c0","216d205188c04241af9b1c6aed2f488e","309ca5550d634304913e2e19c5e941f9","497b8fe452bf4d339137c2a6168c054b"]},"id":"I6VPp5NUkdee","executionInfo":{"status":"ok","timestamp":1775357788861,"user_tz":-60,"elapsed":7393,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"9927a6d6-5bc1-4da3-8c8c-fbba1915f1a0"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d82a1fb5f293464faa7fd5a7fbc9f529"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf85dc63c0e0433f938b32ff48f07c40"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c1ff769739b248568d6795f610b8a2cf"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["DistilBertForSequenceClassification LOAD REPORT from: distilbert-base-uncased\n","Key                     | Status     | \n","------------------------+------------+-\n","vocab_projector.bias    | UNEXPECTED | \n","vocab_layer_norm.bias   | UNEXPECTED | \n","vocab_layer_norm.weight | UNEXPECTED | \n","vocab_transform.bias    | UNEXPECTED | \n","vocab_transform.weight  | UNEXPECTED | \n","classifier.weight       | MISSING    | \n","pre_classifier.bias     | MISSING    | \n","pre_classifier.weight   | MISSING    | \n","classifier.bias         | MISSING    | \n","\n","Notes:\n","- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n","- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"]}]},{"cell_type":"markdown","source":["18. Mectric Functions"],"metadata":{"id":"1X7mLtmmlDIy"}},{"cell_type":"code","source":["def compute_metrics(pred):\n","    labels = pred.label_ids\n","    preds = np.argmax(pred.predictions, axis=1)\n","\n","    return {\n","        \"accuracy\": accuracy_score(labels, preds),\n","        \"precision\": precision_score(labels, preds),\n","        \"recall\": recall_score(labels, preds),\n","        \"f1\": f1_score(labels, preds)\n","    }"],"metadata":{"id":"KY2U54J4lJWK"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["19. Training the arguements"],"metadata":{"id":"gkQNKFG8lLP_"}},{"cell_type":"code","source":["training_args = TrainingArguments(\n","    output_dir=\"./distilbert_results\",\n","    eval_strategy=\"epoch\",\n","    save_strategy=\"no\",\n","    logging_strategy=\"epoch\",\n","    per_device_train_batch_size=8,\n","    per_device_eval_batch_size=8,\n","    num_train_epochs=2,\n","    learning_rate=2e-5,\n","    weight_decay=0.01,\n","    report_to=\"none\"\n",")"],"metadata":{"id":"qdbFBNjDlRZQ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["20. Trainer"],"metadata":{"id":"U36QzkzNlyyq"}},{"cell_type":"code","source":["trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=train_dataset,\n","    eval_dataset=test_dataset,\n","    compute_metrics=compute_metrics\n",")"],"metadata":{"id":"HCoKLEMVl04d"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["21. Extra Training"],"metadata":{"id":"22RJoOPNl5T3"}},{"cell_type":"code","source":["start_time = time.time()\n","\n","trainer.train()\n","\n","distilbert_training_time = (time.time() - start_time) / 60\n","print(\"Training time (min):\", round(distilbert_training_time, 2))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":155},"id":"NKATYvpamCBX","executionInfo":{"status":"ok","timestamp":1775359048106,"user_tz":-60,"elapsed":222395,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"060c3c87-ea26-46c9-ef36-7f2269874e2a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1000/1000 03:41, Epoch 2/2]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Epoch</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>0.257775</td>\n","      <td>0.338064</td>\n","      <td>0.878000</td>\n","      <td>0.852207</td>\n","      <td>0.907975</td>\n","      <td>0.879208</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>0.161616</td>\n","      <td>0.490257</td>\n","      <td>0.892000</td>\n","      <td>0.901053</td>\n","      <td>0.875256</td>\n","      <td>0.887967</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Training time (min): 3.73\n"]}]},{"cell_type":"markdown","source":["22. Evaluate"],"metadata":{"id":"qm4gpbTYolvZ"}},{"cell_type":"code","source":["distilbert_results = trainer.evaluate()\n","print(distilbert_results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"46ZXaX71or0C","executionInfo":{"status":"ok","timestamp":1775359056075,"user_tz":-60,"elapsed":7916,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"2689fd48-9348-43df-dd0d-e5d010cb416c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='125' max='125' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [125/125 00:07]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["{'eval_loss': 0.49025672674179077, 'eval_accuracy': 0.892, 'eval_precision': 0.9010526315789473, 'eval_recall': 0.8752556237218814, 'eval_f1': 0.8879668049792531, 'eval_runtime': 7.9655, 'eval_samples_per_second': 125.541, 'eval_steps_per_second': 15.693, 'epoch': 2.0}\n"]}]},{"cell_type":"markdown","source":["23. Confusion Matrix"],"metadata":{"id":"IcnqzV9gozPk"}},{"cell_type":"code","source":["predictions = trainer.predict(test_dataset)\n","\n","y_pred_distilbert = np.argmax(predictions.predictions, axis=1)\n","y_true_distilbert = predictions.label_ids\n","\n","print(\"Confusion Matrix:\\n\", confusion_matrix(y_true_distilbert, y_pred_distilbert))\n","print(\"\\nClassification Report:\\n\", classification_report(y_true_distilbert, y_pred_distilbert))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":260},"id":"jMnAHcLUo04i","executionInfo":{"status":"ok","timestamp":1775359070334,"user_tz":-60,"elapsed":7841,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"ceed63d7-557b-4143-d934-eea2ad1fe0bf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Confusion Matrix:\n"," [[464  47]\n"," [ 61 428]]\n","\n","Classification Report:\n","               precision    recall  f1-score   support\n","\n","           0       0.88      0.91      0.90       511\n","           1       0.90      0.88      0.89       489\n","\n","    accuracy                           0.89      1000\n","   macro avg       0.89      0.89      0.89      1000\n","weighted avg       0.89      0.89      0.89      1000\n","\n"]}]},{"cell_type":"markdown","source":["24. Comparison Table"],"metadata":{"id":"a_on318Mrz1T"}},{"cell_type":"code","source":["comparison_df = pd.DataFrame([\n","    {\n","        \"Model\": \"TF-IDF + Logistic Regression\",\n","        \"Accuracy\": accuracy_score(y_test, y_pred_lr),\n","        \"Precision\": precision_score(y_test, y_pred_lr),\n","        \"Recall\": recall_score(y_test, y_pred_lr),\n","        \"F1-score\": f1_score(y_test, y_pred_lr),\n","        \"Training Time (min)\": \"Very low / fast\"\n","    },\n","    {\n","        \"Model\": \"DistilBERT (subset)\",\n","        \"Accuracy\": distilbert_results[\"eval_accuracy\"],\n","        \"Precision\": distilbert_results[\"eval_precision\"],\n","        \"Recall\": distilbert_results[\"eval_recall\"],\n","        \"F1-score\": distilbert_results[\"eval_f1\"],\n","        \"Training Time (min)\": round(distilbert_training_time, 2)\n","    }\n","])\n","\n","comparison_df"],"metadata":{"id":"2NwkKZqhsq8A","executionInfo":{"status":"ok","timestamp":1775359934662,"user_tz":-60,"elapsed":82,"user":{"displayName":"Wadadli-Jay","userId":"15339412927671469332"}},"outputId":"1a0dee9c-67c5-4ce0-b44e-77ede9cf7bc0","colab":{"base_uri":"https://localhost:8080/","height":112}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                          Model  Accuracy  Precision    Recall  F1-score  \\\n","0  TF-IDF + Logistic Regression   0.87808   0.875497  0.881520  0.878498   \n","1           DistilBERT (subset)   0.89200   0.901053  0.875256  0.887967   \n","\n","  Training Time (min)  \n","0     Very low / fast  \n","1                3.73  "],"text/html":["\n","  <div id=\"df-192459b0-00fc-4a54-8a62-3d81b64a4c13\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Model</th>\n","      <th>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1-score</th>\n","      <th>Training Time (min)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>TF-IDF + Logistic Regression</td>\n","      <td>0.87808</td>\n","      <td>0.875497</td>\n","      <td>0.881520</td>\n","      <td>0.878498</td>\n","      <td>Very low / fast</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>DistilBERT (subset)</td>\n","      <td>0.89200</td>\n","      <td>0.901053</td>\n","      <td>0.875256</td>\n","      <td>0.887967</td>\n","      <td>3.73</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-192459b0-00fc-4a54-8a62-3d81b64a4c13')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-192459b0-00fc-4a54-8a62-3d81b64a4c13 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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