diff --git "a/AIRA_FineTuning.ipynb" "b/AIRA_FineTuning.ipynb"
--- "a/AIRA_FineTuning.ipynb"
+++ "b/AIRA_FineTuning.ipynb"
@@ -19,84 +19,30 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "SBWCrz5GfBXo",
-        "outputId": "c3897ecc-56b3-48fc-b9cb-1f8bb2809fbe"
+        "outputId": "21d0811e-0f41-48d5-8f51-fbc196557d0a"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
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-            "\u001b[?25hCollecting pynvml (from codecarbon)\n",
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-            "Installing collected packages: tokenizers, safetensors, fuzzywuzzy, xxhash, pynvml, dill, multiprocess, huggingface-hub, arrow, transformers, codecarbon, datasets\n",
-            "Successfully installed arrow-1.2.3 codecarbon-2.2.4 datasets-2.13.1 dill-0.3.6 fuzzywuzzy-0.18.0 huggingface-hub-0.15.1 multiprocess-0.70.14 pynvml-11.5.0 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2 xxhash-3.2.0\n"
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/7.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/7.6 MB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:03\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/7.6 MB\u001b[0m \u001b[31m52.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m86.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m62.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m53.1/53.1 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
           ]
         }
       ],
       "source": [
-        "!pip install transformers datasets codecarbon"
+        "!pip install transformers datasets codecarbon -q"
       ]
     },
     {
@@ -114,207 +60,100 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 702,
+          "height": 1000,
           "referenced_widgets": [
-            "34e200081e97446c8cd7856137d2ed39",
-            "3e9822a5f0d046728156b5a0c3e3c082",
-            "6c0f0718a27944a49df4feeb5eb7f0a6",
-            "94ce8d2f7bf24a3cb3487ea88094beda",
-            "579d1717cad5475e9f772e84bdbabfc1",
-            "1120e50796944b58af0f4dbd639482d2",
-            "53f08c1231c74d14a81bd9485ba96deb",
-            "5456362a6850407db4bebc1ded3cbe1a",
-            "f3aaec6e08094e19bff0f311e6a64158",
-            "485ffa98fc3a473096e4af30be5aa60a",
-            "78fa4f26ce174a108c43033b0dd5f2b6",
-            "158650cfa6d14e00ab4825953fabe91e",
-            "16d718b78ba240d7b930ff422c9cbc67",
-            "1092f96bf4b2456893e925e26d200d57",
-            "02a61e2a34b3458a8b9313608812ca7d",
-            "a6d3f7f2b090401f9cba84ccb242c5ec",
-            "f1f0f70d41b64c4892da63d0aa6ec3c9",
-            "edb44c8f27af400cab27572250043b11",
-            "9b37e0bc0c91405a9ce5a925fb46ba7e",
-            "8e07a97bcc0548fbb8681ad1bbd1dcbe",
-            "857f65e34e8a4cee92857a61fb3febb2",
-            "e0f81131b319404d8f48a1ba4edac5bc",
-            "a39380581e804f5fb96018c6058ec4ac",
-            "29e76265e50842c3b3aa3e68825c1cc9",
-            "4622678ef1c440eaba2b21dabaef8a4f",
-            "dac43af304db4a2da06c7b18201413f9",
-            "00485f3c3dbc40b5be49c8f4fd49c3d7",
-            "095609fe73cb4e9cbd5838422855c951",
-            "7bc85c49a9154847b716aeaaf47d4558",
-            "aba8b6027ea447a8b927b8674a3c78c7",
-            "54995b0a7e01403d816d4c5dcea3ec39",
-            "9cfd52a8054f436fa406dd12657d697c",
-            "380d234d63644d31b43286726d4346e0",
-            "1962a1b46f7d4c0bac70d45435b1b0b8",
-            "476c1fd9e1644ceda44275a4630cff71",
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           ]
         },
         "id": "7MbpXGu-v4f1",
-        "outputId": "f0085c70-8f57-47f3-c719-16f95e40fa40"
+        "outputId": "d67fd0a7-efa9-4f6f-8670-6aee747e8bf1"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "34e200081e97446c8cd7856137d2ed39",
+              "model_id": "adb95b02c40846dfa58ce064dacc619c",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading readme:   0%|          | 0.00/5.12k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Downloading and preparing dataset None/None to /root/.cache/huggingface/datasets/nicholasKluge___parquet/nicholasKluge--fine-tuning-instruct-aira-4077fd700c38fc36/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7...\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "158650cfa6d14e00ab4825953fabe91e",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading data files:   0%|          | 0/4 [00:00<?, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "a39380581e804f5fb96018c6058ec4ac",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/1.71M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "1962a1b46f7d4c0bac70d45435b1b0b8",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/13.5M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "380e286c3a844a91aa34bb28439cc430",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "Downloading data:   0%|          | 0.00/14.5M [00:00<?, ?B/s]"
+              "Downloading readme:   0%|          | 0.00/471 [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -323,12 +162,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "d8e16763af2a46f0852402390bb7ce96",
+              "model_id": "1f3288dcb004450cbbbf216d6e64766d",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Downloading data:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
+              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
             ]
           },
           "metadata": {},
@@ -337,12 +176,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "76153959dfc74095b0412eb96a2a6b57",
+              "model_id": "73535961abfb44ba84a5658a0dc5793e",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Extracting data files:   0%|          | 0/4 [00:00<?, ?it/s]"
+              "Downloading data:   0%|          | 0.00/26.7M [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -351,12 +190,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "3cca4d9f55024c8e950a1f011d2a9e71",
+              "model_id": "2fa9e5fff95644729262565b95203613",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Generating aira_english split:   0%|          | 0/9183 [00:00<?, ? examples/s]"
+              "Downloading data:   0%|          | 0.00/29.5M [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -365,12 +204,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "093d5f2f31d248e49b53e13e34a39d19",
+              "model_id": "f05c4c369f7e434bb68a48c07a6f289c",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Generating aira_instruct_english split:   0%|          | 0/48666 [00:00<?, ? examples/s]"
+              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
             ]
           },
           "metadata": {},
@@ -379,12 +218,12 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "02b3bb2540344873bdc2621a283e4b9d",
+              "model_id": "7af7467aeaa343908e190a3e27565c9e",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Generating aira_instruct_portuguese split:   0%|          | 0/48571 [00:00<?, ? examples/s]"
+              "Generating english split:   0%|          | 0/41815 [00:00<?, ? examples/s]"
             ]
           },
           "metadata": {},
@@ -393,31 +232,23 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "6831afd511fc4a468023a637abef4ffc",
+              "model_id": "da69e26d69d24ebb9c3a7ea9e212bd5c",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "Generating aira_portuguese split:   0%|          | 0/9204 [00:00<?, ? examples/s]"
+              "Generating portuguese split:   0%|          | 0/41815 [00:00<?, ? examples/s]"
             ]
           },
           "metadata": {},
           "output_type": "display_data"
         },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/nicholasKluge___parquet/nicholasKluge--fine-tuning-instruct-aira-4077fd700c38fc36/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7. Subsequent calls will reuse this data.\n"
-          ]
-        },
         {
           "data": {
             "text/html": [
               "\n",
-              "  <div id=\"df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
+              "  <div id=\"df-78de4ea3-4035-4033-bddc-d11aa5ed8da8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -442,28 +273,28 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>Como é ser a última pessoa na terra?</td>\n",
-              "      <td>Você pensaria que o mundo seria seu para explo...</td>\n",
+              "      <td>Por que os camelos conseguem sobreviver muito ...</td>\n",
+              "      <td>Os camelos podem sobreviver por longos período...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>Você pode explicar o conceito de Anarquismo?</td>\n",
-              "      <td>O anarquismo é uma filosofia política que defe...</td>\n",
+              "      <td>Por que o celular é ruim para os humanos</td>\n",
+              "      <td>Os telefones celulares como dispositivos tecno...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>Qual é a melhor maneira de pendurar uma pintur...</td>\n",
-              "      <td>A melhor maneira de pendurar uma pintura com s...</td>\n",
+              "      <td>O que é um polígono?</td>\n",
+              "      <td>Um polígono é uma figura geométrica bidimensio...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>Qual é a relação entre o problema de controle ...</td>\n",
-              "      <td>A noção de controle se manifesta quando contem...</td>\n",
+              "      <td>Como começo a correr?</td>\n",
+              "      <td>Começar uma rotina de corrida pode ser uma óti...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>Identifique qual instrumento é corda ou percus...</td>\n",
-              "      <td>Liuqin é corda, Nagareh é percussão.</td>\n",
+              "      <td>Quais episódios da quarta temporada de Game of...</td>\n",
+              "      <td>Michelle MacLaren dirigiu dois episódios da 4ª...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>...</th>\n",
@@ -471,49 +302,49 @@
               "      <td>...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>48566</th>\n",
-              "      <td>Qual é a história da luta de sumô.</td>\n",
-              "      <td>A luta de sumô é uma forma antiga de arte marc...</td>\n",
+              "      <th>41810</th>\n",
+              "      <td>Como posso ligar para você?</td>\n",
+              "      <td>Você pode me chamar de Aira. Como posso ajudá-...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>48567</th>\n",
-              "      <td>Como posso melhorar falar em público.</td>\n",
-              "      <td>Comece estando preparado. Pesquise o tópico, p...</td>\n",
+              "      <th>41811</th>\n",
+              "      <td>Qual é o seu nome de identificação?</td>\n",
+              "      <td>Como inteligência artificial, não tenho nome d...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>48568</th>\n",
-              "      <td>Como faço para navegar até um determinado loca...</td>\n",
-              "      <td>Para navegar até um determinado local usando t...</td>\n",
+              "      <th>41812</th>\n",
+              "      <td>Como você é chamado?</td>\n",
+              "      <td>Olá! Meu nome é Aira. Como posso ajudá-lo hoje?</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>48569</th>\n",
-              "      <td>Quais são algumas dicas de etiqueta de mensage...</td>\n",
-              "      <td>Apresente-se - Deixe a pessoa saber quem você ...</td>\n",
+              "      <th>41813</th>\n",
+              "      <td>Como você se apresenta?</td>\n",
+              "      <td>Olá! Eu sou Aira, sua assistente prestativa, r...</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>48570</th>\n",
-              "      <td>Estou pensando em participar de um concurso de...</td>\n",
-              "      <td>Eu recomendaria que você se concentrasse em su...</td>\n",
+              "      <th>41814</th>\n",
+              "      <td>O que devo dizer quando me dirijo a você?</td>\n",
+              "      <td>Ao se dirigir a mim, você pode simplesmente me...</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "<p>48571 rows × 2 columns</p>\n",
+              "<p>41815 rows × 2 columns</p>\n",
               "</div>\n",
-              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf')\"\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 0 24 24\"\n",
-              "       width=\"24px\">\n",
-              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
-              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78de4ea3-4035-4033-bddc-d11aa5ed8da8')\"\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",
+              "    </button>\n",
+              "\n",
               "  <style>\n",
               "    .colab-df-container {\n",
               "      display:flex;\n",
-              "      flex-wrap:wrap;\n",
               "      gap: 12px;\n",
               "    }\n",
               "\n",
@@ -535,6 +366,10 @@
               "      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",
@@ -548,62 +383,190 @@
               "    }\n",
               "  </style>\n",
               "\n",
-              "      <script>\n",
-              "        const buttonEl =\n",
-              "          document.querySelector('#df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf button.colab-df-convert');\n",
-              "        buttonEl.style.display =\n",
-              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-78de4ea3-4035-4033-bddc-d11aa5ed8da8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
-              "        async function convertToInteractive(key) {\n",
-              "          const element = document.querySelector('#df-02b40cbd-3cb0-447f-b07d-3e337cfc74cf');\n",
-              "          const dataTable =\n",
-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-78de4ea3-4035-4033-bddc-d11aa5ed8da8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
               "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
-              "          element.innerHTML = '';\n",
-              "          dataTable['output_type'] = 'display_data';\n",
-              "          await google.colab.output.renderOutput(dataTable, element);\n",
-              "          const docLink = document.createElement('div');\n",
-              "          docLink.innerHTML = docLinkHtml;\n",
-              "          element.appendChild(docLink);\n",
-              "        }\n",
-              "      </script>\n",
-              "    </div>\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
               "  </div>\n",
-              "  "
+              "\n",
+              "\n",
+              "<div id=\"df-d72da025-5cee-4d5e-a03a-8b4f690b07b6\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d72da025-5cee-4d5e-a03a-8b4f690b07b6')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-d72da025-5cee-4d5e-a03a-8b4f690b07b6 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
             ],
             "text/plain": [
               "                                                  prompt  \\\n",
-              "0                   Como é ser a última pessoa na terra?   \n",
-              "1           Você pode explicar o conceito de Anarquismo?   \n",
-              "2      Qual é a melhor maneira de pendurar uma pintur...   \n",
-              "3      Qual é a relação entre o problema de controle ...   \n",
-              "4      Identifique qual instrumento é corda ou percus...   \n",
+              "0      Por que os camelos conseguem sobreviver muito ...   \n",
+              "1               Por que o celular é ruim para os humanos   \n",
+              "2                                   O que é um polígono?   \n",
+              "3                                  Como começo a correr?   \n",
+              "4      Quais episódios da quarta temporada de Game of...   \n",
               "...                                                  ...   \n",
-              "48566                 Qual é a história da luta de sumô.   \n",
-              "48567              Como posso melhorar falar em público.   \n",
-              "48568  Como faço para navegar até um determinado loca...   \n",
-              "48569  Quais são algumas dicas de etiqueta de mensage...   \n",
-              "48570  Estou pensando em participar de um concurso de...   \n",
+              "41810                        Como posso ligar para você?   \n",
+              "41811                Qual é o seu nome de identificação?   \n",
+              "41812                               Como você é chamado?   \n",
+              "41813                            Como você se apresenta?   \n",
+              "41814          O que devo dizer quando me dirijo a você?   \n",
               "\n",
               "                                              completion  \n",
-              "0      Você pensaria que o mundo seria seu para explo...  \n",
-              "1      O anarquismo é uma filosofia política que defe...  \n",
-              "2      A melhor maneira de pendurar uma pintura com s...  \n",
-              "3      A noção de controle se manifesta quando contem...  \n",
-              "4                   Liuqin é corda, Nagareh é percussão.  \n",
+              "0      Os camelos podem sobreviver por longos período...  \n",
+              "1      Os telefones celulares como dispositivos tecno...  \n",
+              "2      Um polígono é uma figura geométrica bidimensio...  \n",
+              "3      Começar uma rotina de corrida pode ser uma óti...  \n",
+              "4      Michelle MacLaren dirigiu dois episódios da 4ª...  \n",
               "...                                                  ...  \n",
-              "48566  A luta de sumô é uma forma antiga de arte marc...  \n",
-              "48567  Comece estando preparado. Pesquise o tópico, p...  \n",
-              "48568  Para navegar até um determinado local usando t...  \n",
-              "48569  Apresente-se - Deixe a pessoa saber quem você ...  \n",
-              "48570  Eu recomendaria que você se concentrasse em su...  \n",
+              "41810  Você pode me chamar de Aira. Como posso ajudá-...  \n",
+              "41811  Como inteligência artificial, não tenho nome d...  \n",
+              "41812    Olá! Meu nome é Aira. Como posso ajudá-lo hoje?  \n",
+              "41813  Olá! Eu sou Aira, sua assistente prestativa, r...  \n",
+              "41814  Ao se dirigir a mim, você pode simplesmente me...  \n",
               "\n",
-              "[48571 rows x 2 columns]"
+              "[41815 rows x 2 columns]"
             ]
           },
           "metadata": {},
@@ -611,14 +574,17 @@
         }
       ],
       "source": [
+        "import os\n",
         "import pandas as pd\n",
         "from datasets import load_dataset\n",
         "\n",
-        "dataset = load_dataset(\"nicholasKluge/instruct-aira-dataset\", split='aira_instruct_portuguese')\n",
+        "project = \"Aira-2-portuguese-124M\"\n",
+        "\n",
+        "os.makedirs(project, exist_ok=True)\n",
+        "\n",
+        "dataset = load_dataset(\"nicholasKluge/instruct-aira-dataset\", split=\"portuguese\")\n",
         "\n",
         "df = dataset.to_pandas()\n",
-        "df = df.sample(frac=1)\n",
-        "df = df.reset_index(drop=True)\n",
         "\n",
         "display(df)"
       ]
@@ -629,9 +595,9 @@
         "id": "fEaDTvdOe8rr"
       },
       "source": [
-        "3. Load `GPT2Tokenizer` and add the chosen special tokens (`'<|startoftext|>', '<|endoftext|>','<|pad|>'`)\n",
+        "3. Load `GPT2Tokenizer` and add the chosen special tokens (`'<|startofinstruction|>', '<|endofinstruction|>', '<|endofcompletion|>','<|pad|>'`)\n",
         "4. Create demonstrations by prepending the special tokens.\n",
-        "5. Calculate the maximum length (in tokens) that the demonstrations have (the dataset was constructed, for efficiency and fast training, to be below the 300-token range)."
+        "5. Calculate the maximum length (in tokens) that the demonstrations have."
       ]
     },
     {
@@ -640,73 +606,73 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 249,
+          "height": 389,
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+            "d44f310a3f0f45b0bd74471723ecf450",
+            "77ba537e763441a282157e8448294ae0",
+            "493eb1aa3b354bf48a29ec8007c966df",
+            "7f1dedd1aa2a407eb6674e53a90a1d0d",
+            "62ba985a7d2e4a2d9077c5a9cbbe2939",
+            "241af2b68527472caf63372eff0e1f34",
+            "ec8100cdd21f45fa8ee4733df873bd52",
+            "d1f5355b586c434488781cca0cdc609f",
+            "2cfc869311da4d35ac95822bf749d6db",
+            "1d69ca77ca124054aea2e52708cd609e",
+            "15e314dc0f734d1199f3b7a5efaba738",
+            "07f6cce2ec264b41a922a0296d658973",
+            "8d2bc4e79f28455e9e12f88f68db7ec7",
+            "13409419c5dd4a7989dd03603794b3d1",
+            "30741a807ebb4c22a9fc31a65a62e352",
+            "f9a1d4ccc5d5473c8d8802ef8425b33b",
+            "d8b2846cf2c04f34954e5bf279b0bf05",
+            "d2e9351dcff64cf0a12f5f52bf34b363",
+            "34aab5ac63594b3488638f6ea480276b",
+            "9706de596d5d42a0a54be9fa1fd21196",
+            "756a10fc01c640f7bf65a2a1cf539794",
+            "007b284a4dfb4c49a9046b0c831faee3",
+            "e1b9792f7ddc4360ad144ff7bffdc241",
+            "d7dfcf2a4587466d903f5de4f9636b3a",
+            "59071b50822f45a69175849ecb537e25",
+            "b12c5644975b4943b44cc26426e60bc4",
+            "ee594d5211b1475a98132eeec10e9bbe",
+            "11ff7a91f3534eb29608913cb446708e",
+            "770a321003b945598255ba818cbcc514",
+            "638929f3d9074d45b054362b6b4bc892",
+            "0ba86f9a50f04f048dd2d464a0c1ca9e",
+            "27d1c6a9da3c487894eedbce7a5869ff",
+            "d60969ab7145423c85a29908540a834d",
+            "63c41efa295346108944d34206dd2a20",
+            "7c99b8e3acd94512ba0f58ff0f497208",
+            "629007a626be44d687657e2d103fe05c",
+            "68cf77cb6128454d8b74f3abf044521e",
+            "0219aee5b759454eb0760043ae0346dd",
+            "7d468ba510a044bab4d70fcd4d11ebbc",
+            "fb244c3864e945eaa2e2f2e7c7f3cca6",
+            "650bf64c53964f379ee790ed0c319ce0",
+            "2dd1bd69b6af4acab5fa5b88cf67c239",
+            "f5c5d10f7c574c739f31b80ee4829601"
           ]
         },
         "id": "hfu84fWIv4f9",
-        "outputId": "b5743a8d-78a4-4e12-de10-bea1b374df38"
+        "outputId": "f292a43d-714a-455d-e431-876fa8df4a9a"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "443c675b8bd84607aaab840247c5545b",
+              "model_id": "2c805c0339984b538b24b83c3e950c51",
               "version_major": 2,
               "version_minor": 0
             },
@@ -720,7 +686,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "ed64ff192edc43039c046ab16586b8d3",
+              "model_id": "cc3d749491534a549f13a3f120941b60",
               "version_major": 2,
               "version_minor": 0
             },
@@ -734,7 +700,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "dcc963ee0bf742a9bb15eca4fa4b0eb2",
+              "model_id": "15e314dc0f734d1199f3b7a5efaba738",
               "version_major": 2,
               "version_minor": 0
             },
@@ -748,7 +714,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2f860322641b46c7b7b0ff54bdd40685",
+              "model_id": "007b284a4dfb4c49a9046b0c831faee3",
               "version_major": 2,
               "version_minor": 0
             },
@@ -762,7 +728,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "845d2d22ca134c8ea15a7ec87c02c801",
+              "model_id": "d60969ab7145423c85a29908540a834d",
               "version_major": 2,
               "version_minor": 0
             },
@@ -784,23 +750,24 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Total number of demonstrations:  48571\n",
-            "The longest demonstration is 324 tokens long.\n"
+            "Total number of demonstrations:  41815\n",
+            "The longest demonstration is 526 tokens long.\n"
           ]
         }
       ],
       "source": [
         "from transformers import GPT2Tokenizer\n",
         "\n",
-        "model = \"pierreguillou/gpt2-small-portuguese\" # \"gpt2\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\", \"pierreguillou/gpt2-small-portuguese\"\n",
-        "model_size = \"PT-124M\" # \"124M\", \"355M\", \"774M\", and \"1.5B\", \"PT-124M\"\n",
+        "model = \"pierreguillou/gpt2-small-portuguese\" # \"gpt2\", \"gpt2-medium\", \"gpt2-large\", \"gpt2-xl\", \"pierreguillou/gpt2-small-portuguese\",\n",
+        "model_size = \"PT-124M\" # \"124M\", \"355M\", \"774M\", and \"1.5B\", \"PT-124M\",\n",
         "\n",
         "tokenizer = GPT2Tokenizer.from_pretrained(model,\n",
-        "                                          bos_token='<|startoftext|>',\n",
-        "                                          eos_token='<|endoftext|>',\n",
-        "                                          pad_token='<|pad|>')\n",
+        "                                          bos_token='<|startofinstruction|>', # '<|startoftext|>'\n",
+        "                                          sep_token = '<|endofinstruction|>',\n",
+        "                                          eos_token='<|endofcompletion|>', # '<|endoftext|>'\n",
+        "                                          pad_token='<|pad|>') # '<|pad|>'\n",
         "\n",
-        "df['demonstrations'] = tokenizer.bos_token + df['prompt'] + tokenizer.eos_token + df['completion'] + tokenizer.eos_token\n",
+        "df['demonstrations'] = tokenizer.bos_token + df['prompt'] + tokenizer.sep_token + df['completion'] + tokenizer.eos_token\n",
         "\n",
         "df['length'] = df['demonstrations'].apply(lambda x: len(tokenizer.encode(x)))\n",
         "\n",
@@ -828,11 +795,11 @@
         "import torch\n",
         "from torch.utils.data import Dataset\n",
         "\n",
-        "max_length = 300\n",
+        "max_length = 530\n",
         "\n",
-        "class DemoDataset(Dataset):\n",
+        "class InstructDataset(Dataset):\n",
         "\n",
-        "  def __init__(self, demonstrations, tokenizer, gpt2_type=\"gpt2\", max_length=max_length):\n",
+        "  def __init__(self, demonstrations, tokenizer, max_length=max_length):\n",
         "\n",
         "    self.tokenizer = tokenizer\n",
         "    self.input_ids = []\n",
@@ -852,50 +819,9 @@
         "    return len(self.input_ids)\n",
         "\n",
         "  def __getitem__(self, idx):\n",
-        "    return self.input_ids[idx], self.attn_masks[idx]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "711Krm6Te8rt"
-      },
-      "source": [
-        "7. Split the data into training and validation splits."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "-IOfa2PEv4gD",
-        "outputId": "8461dd32-ffd9-4584-a265-f9481baee350"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Number of training samples: 43,713\n",
-            "Number of validation samples: 4,858\n"
-          ]
-        }
-      ],
-      "source": [
-        "from torch.utils.data import random_split\n",
+        "    return self.input_ids[idx], self.attn_masks[idx]\n",
         "\n",
-        "dataset = DemoDataset(df.demonstrations.to_list(), tokenizer, max_length=max_length)\n",
-        "\n",
-        "train_size = int(0.9 * len(dataset))\n",
-        "val_size = len(dataset) - train_size\n",
-        "\n",
-        "train_dataset, val_dataset = random_split(dataset, [train_size, val_size])\n",
-        "\n",
-        "print('Number of training samples: {:,}'.format(train_size))\n",
-        "print('Number of validation samples: {:,}'.format(val_size))"
+        "dataset = InstructDataset(df.demonstrations.to_list(), tokenizer, max_length=max_length)"
       ]
     },
     {
@@ -915,19 +841,12 @@
       },
       "outputs": [],
       "source": [
-        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
-        "\n",
-        "train_dataloader = DataLoader(\n",
-        "            train_dataset,\n",
-        "            sampler=RandomSampler(train_dataset),\n",
-        "            batch_size=32 # 32, 20, 8, 4\n",
-        "        )\n",
+        "from torch.utils.data import DataLoader, RandomSampler\n",
         "\n",
-        "# validation data loader doesn't need randomization\n",
-        "validation_dataloader=DataLoader(\n",
-        "            val_dataset,\n",
-        "            sampler=SequentialSampler(val_dataset),\n",
-        "            batch_size=32\n",
+        "dataloader = DataLoader(\n",
+        "            dataset,\n",
+        "            sampler=RandomSampler(dataset),\n",
+        "            batch_size=24, # 32, 20, 8, 4\n",
         "        )"
       ]
     },
@@ -946,29 +865,29 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 517,
+          "height": 583,
           "referenced_widgets": [
-            "eff22b21655241d181443ac7bfa1e70a",
-            "8790ed1eb51543278e0c069b0b951d98",
-            "0f5196fc47ed4b148937b9029812dab1",
-            "4f3aee9bbcdb4f239ed5395b130faf07",
-            "a5cc4fdd4d3e4954853e7090ff332bdc",
-            "8e450d0f99684a29856d262b4350694c",
-            "776567de650f4925921578f0b9414dfe",
-            "df9f8573d0d9476fbf6025f7d1ad3146",
-            "b59bac7b693d421cae391e4b9ceb18ef",
-            "fbf913f79be947a5bab1b2fa729f0241",
-            "642dcbab44674aacb34462b3cf32f6f7"
+            "ff0e11346fda4392819bb9b5123e3989",
+            "b06a3e2538254a78991f95d3d31de001",
+            "ddc91a3219094cdd8f22c568c1c9b6ef",
+            "10241c0b2a9946e08aa62d93d365f8d8",
+            "6a4faf32ef294b62b59f170241b80f1a",
+            "0853892b946f4cdd8097755475ccaae7",
+            "8bddb5a22d2340f189b4fc5cafb8d1c1",
+            "2671dc07c313468693430f8d122b9a1e",
+            "080a67780d24432b89feec7e4e206ca2",
+            "0501397637f94f108258555c8577fa8a",
+            "44d1bbc1d11044d0a37c9ef0ebd8c2fd"
           ]
         },
         "id": "Rmg-5YJqv4gH",
-        "outputId": "61181abe-9d4d-4ccd-c2d7-ffd52d8cdcb3"
+        "outputId": "129e6f8a-c5bc-4ca6-9a63-8bc0484ba683"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "eff22b21655241d181443ac7bfa1e70a",
+              "model_id": "ff0e11346fda4392819bb9b5123e3989",
               "version_major": 2,
               "version_minor": 0
             },
@@ -979,12 +898,19 @@
           "metadata": {},
           "output_type": "display_data"
         },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "You are resizing the embedding layer without providing a `pad_to_multiple_of` parameter. This means that the new embedding dimension will be 50261. This might induce some performance reduction as *Tensor Cores* will not be available. For more details about this, or help on choosing the correct value for resizing, refer to this guide: https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc\n"
+          ]
+        },
         {
           "data": {
             "text/plain": [
               "GPT2LMHeadModel(\n",
               "  (transformer): GPT2Model(\n",
-              "    (wte): Embedding(50259, 768)\n",
+              "    (wte): Embedding(50261, 768)\n",
               "    (wpe): Embedding(1024, 768)\n",
               "    (drop): Dropout(p=0.1, inplace=False)\n",
               "    (h): ModuleList(\n",
@@ -1007,11 +933,11 @@
               "    )\n",
               "    (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
               "  )\n",
-              "  (lm_head): Linear(in_features=768, out_features=50259, bias=False)\n",
+              "  (lm_head): Linear(in_features=768, out_features=50261, bias=False)\n",
               ")"
             ]
           },
-          "execution_count": 8,
+          "execution_count": 7,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1046,7 +972,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "MJA-WOoHZePn",
-        "outputId": "15530ae0-f41c-4f58-bf9a-378123835a74"
+        "outputId": "e2d37189-737e-4f72-c4d7-af616eaa7417"
       },
       "outputs": [
         {
@@ -1061,9 +987,9 @@
         }
       ],
       "source": [
-        "UNFREEZE_LAST_N = 6 #6, 8, 9, 12\n",
+        "UNFREEZE_LAST_N = 6 #6, 6, 9, 12\n",
         "\n",
-        "print(\"Number of transformer blocks in the model: \", model.config.n_layer)\n",
+        "print(\"Number of transformer blocks in the model: \", model.config.num_hidden_layers)\n",
         "print(\"Number of transformer blocks to  un-freeze: \", UNFREEZE_LAST_N)\n",
         "\n",
         "for parameter in model.parameters():\n",
@@ -1071,7 +997,7 @@
         "\n",
         "for i, m in enumerate(model.transformer.h):\n",
         "    #Only un-freeze the last n transformer blocks\n",
-        "    if i+1 > model.config.n_layer - UNFREEZE_LAST_N:\n",
+        "    if i+1 > model.config.num_hidden_layers - UNFREEZE_LAST_N:\n",
         "        for parameter in m.parameters():\n",
         "            parameter.requires_grad = True\n",
         "\n",
@@ -1119,7 +1045,7 @@
         "optimizer = torch.optim.AdamW(model.parameters(), lr = 5e-4, eps = 1e-8)\n",
         "\n",
         "# total steps = number of batches * number of epochs\n",
-        "total_steps = len(train_dataloader) * epochs\n",
+        "total_steps = len(dataloader) * epochs\n",
         "\n",
         "# create the learning rate scheduler\n",
         "scheduler = get_linear_schedule_with_warmup(optimizer,\n",
@@ -1144,7 +1070,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "_X_m8XOtv4gR",
-        "outputId": "3c1993c1-d3f9-46b2-cd5f-401f5c7f7eb0"
+        "outputId": "750e03da-bd16-4526-ec4e-6859a0191037"
       },
       "outputs": [
         {
@@ -1160,7 +1086,37 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 29%|██▉       | 400/1367 [03:29<08:18,  1.94it/s]"
+            " 23%|██▎       | 400/1743 [05:00<16:37,  1.35it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Batch 400 of 1743. Loss:1.0064500570297241.\n",
+            "\n",
+            "\n",
+            "Example output: Qual é o processo químico da fotossíntese.As reações químicas envolvidas na fotossíntese são chamadas de clorofila, que é a base de todos os cloroplastos que estão dentro da clorofila. Embora as reações químicas da fotossíntese possam variar, geralmente a fotossíntese é feita por enzimas químicas chamadas clorofila. Algumas reações químicas mais comuns incluem:\n",
+            "\n",
+            "1.3.2.9.2.\n",
+            "2.1.1.2.6.\n",
+            "2.1.2.4.6.\n",
+            "2.1.2.5.6.\n",
+            "2.1\n",
+            "2.2.3.1.\n",
+            "2.2.3.2.\n",
+            "2.1.2.3.3.\n",
+            "\n",
+            "Os principais processos industriais de produção de clorofila são a fermentação, a desflorestação e a decomposição. Esses processos são vitais para a energia luminosa, que é a base de todas as moléculas orgânicas, como\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 46%|████▌     | 800/1743 [10:01<11:40,  1.35it/s]"
           ]
         },
         {
@@ -1168,13 +1124,16 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 400 of 1367. Loss:0.8450407981872559.\n",
+            "Batch 800 of 1743. Loss:1.0819954872131348.\n",
             "\n",
             "\n",
-            "Example output: Como faço para manter um carro corretamente.Comece avaliando o carro como ele funciona no dia e avaliando sua durabilidade e estabilidade.\n",
-            "2. Se você possui um carro e deseja substituir o carro com um novo, você deve escolher o melhor ajuste, como usar um limpadores ou amortecedores ou um novo filtro elétrico.\n",
-            "3. Se você não for um bom carro, encontre maneiras de trocar de gasolina e reduzir o consumo de combustível, como usar um motor elétrico ou usar carros de baixo consumo.\n",
-            "4. Se você não deseja substituir o carro com um novo, encontre maneiras de trocar de óleo ou substituir a transmissão.\n",
+            "Example output: Quais ingredientes eu preciso para fazer um bolo de chocolate.Fazer um bolo de chocolate é uma ótima maneira de criar memórias de uma vez que não precisa da consistência de uma variedade favorita. Aqui estão alguns ingredientes essenciais que você pode precisar para fazer um bolo de chocolate:\n",
+            "\n",
+            "- farinha: Leve uma mistura de farinha, açúcar e água fervente, como leite ou água pura.\n",
+            "- espaguete: Pré-aqueça os bolos com uma frigideira macia de trigo e misture-o com água fria.\n",
+            "- queijo: Prepare o bolo com manteiga, onças ou manteiga derretida. Se estiver usando leite e laticínios, adicione sal e pimenta.\n",
+            "- banana: Escolha uma banana roxa e misture em uma panela ou wok fervente. É importante observar que os bolos de chocolate podem ser muito macios e ser fáceis de serem preparados com um pouco de açúcar e leite.\n",
+            "- manteiga ou leite: A manteiga adiciona uma\n",
             "\n"
           ]
         },
@@ -1182,7 +1141,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 59%|█████▊    | 800/1367 [06:58<04:52,  1.94it/s]"
+            " 69%|██████▉   | 1200/1743 [15:01<06:43,  1.35it/s]"
           ]
         },
         {
@@ -1190,10 +1149,10 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 800 of 1367. Loss:0.9422707557678223.\n",
+            "Batch 1200 of 1743. Loss:0.923604428768158.\n",
             "\n",
             "\n",
-            "Example output: Como posso meditar de forma eficaz.Uma maneira de meditar de forma eficaz é praticar técnicas de meditação guiada por atenção plena. Pratique respiração profunda e respiração profunda até atingir sua concentração no momento presente. Tente encontrar uma maneira de se concentrar em sua respiração, percebendo sua respiração e pensamentos, concentrando-se em perceber seus próprios estados mentais. Além disso, certifique-se de praticar técnicas de meditação de mantra para manter um equilíbrio de momento presente.\n",
+            "Example output: Como pode “artificial” ser definido em termos mais simples?A inteligência artificial, ou IA, refere-se ao desenvolvimento e desenvolvimento de sistemas de inteligência artificial que podem compreender e tomar decisões sem as limitações impostas pelos humanos e pelos máquinas. Ele pode, em última análise, ser definida como uma forma hipotética de criar um mundo melhor em termos de como as pessoas agem, se o mundo é maior ou menor. O objetivo é aumentar a produtividade, a satisfação, o bem-estar geral e o bem-estar geral dos indivíduos. Além disso, os sistemas podem aprender e ajustar às suas preferências, adaptar suas estratégias e navegar de acordo com as necessidades dos humanos, o que ajuda a maximizar as suas capacidades e capacidades.\n",
             "\n"
           ]
         },
@@ -1201,7 +1160,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 1200/1367 [10:26<01:26,  1.94it/s]"
+            " 92%|█████████▏| 1600/1743 [20:01<01:46,  1.35it/s]"
           ]
         },
         {
@@ -1209,14 +1168,18 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 1200 of 1367. Loss:0.9989928007125854.\n",
+            "Batch 1600 of 1743. Loss:1.0034751892089844.\n",
+            "\n",
             "\n",
+            "Example output: Preciso de ajuda para criar um orçamento eficaz para minha família.Criar um orçamento bem-sucedido para sua família é um passo importante para apoiar a estabilidade financeira e administrar seu trabalho em casa. Aqui estão algumas etapas que você pode seguir:\n",
             "\n",
-            "Example output: Você pode fornecer uma definição para emulação de cérebro inteiro (WBE)?Eliezer Yudkowsky cunhou o termo \"WBE\" em 1996, argumentando que sistemas de IA são capazes de criar e analisar imagens cerebrais complexas.\n",
+            "1. Estabeleça metas financeiras claras: Defina seus objetivos e horizonte de tempo. Procure aumentar sua receita e atingir seus objetivos com mais eficiência.\n",
             "\n",
-            "Por sua vez, o conceito de Whole Brain Emulation (WBE) foi cunhado pelo neurocientista russo Vladimir S. Gozhanin em 1971, que se enquadra no grupo \"Whole Brain Emulation: A Roadmap\".\n",
+            "2. Acompanhe suas despesas: Agrupe seus gastos de forma consistente e alinhada. Isso o ajudará a alocar seus fundos para poupanças ou investimentos, aumentar as contribuições mensais e economizar dinheiro a longo prazo.\n",
             "\n",
-            "Nos anos recentes, a tecnologia moderna de inteligência artificial tem aumentado sua proficiência na resolução de problemas NP-completos, tornando-se cada vez mais uma força vital para tarefas relacionadas à visão computacional. Além disso, a robótica tem se tornando um recurso valioso no campo, contribuindo significativamente para a inteligência artificial moderna.\n",
+            "3. Priorize o pagamento da dívida: Proteger seus gastos em categorias essenciais, como pagamentos mensais de empréstimos, assinaturas e dívidas. Além disso, priorize a educação da sua família, a habitação e as atividades que você procura na escola.\n",
+            "\n",
+            "Lembre-se de que criar um orçamento é um processo pessoal e pode\n",
             "\n"
           ]
         },
@@ -1224,7 +1187,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 1367/1367 [11:54<00:00,  1.91it/s]\n"
+            "100%|██████████| 1743/1743 [21:49<00:00,  1.33it/s]\n"
           ]
         },
         {
@@ -1232,10 +1195,7 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Average Training Loss: 0.9471002545074206.\n",
-            "\n",
-            "\n",
-            "Validation loss: 0.7749460453265592.\n",
+            "Average Training Loss: 1.085801151006713.\n",
             "\n",
             "\n",
             "Beginning epoch 2 of 5\n",
@@ -1246,7 +1206,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
+            " 23%|██▎       | 400/1743 [04:57<16:37,  1.35it/s]"
           ]
         },
         {
@@ -1254,10 +1214,16 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 400 of 1367. Loss:0.7124763131141663.\n",
+            "Batch 400 of 1743. Loss:0.8146286606788635.\n",
+            "\n",
+            "\n",
+            "Example output: Qual é o papel da indução de Solomonoff no aprendizado de máquina?A indução de Solomonoff, também conhecida como AIXI, é um livro de estudo teórico e influente usado pelo aprendizado de máquina para explorar os princípios subjacentes à teoria da decisão.\n",
+            "\n",
+            "O objetivo do AIXI é gerar uma solução de uma equação diferencial que não linear seja simétrica, mas sim simétrica, ou seja, a combinação de duas variáveis ​​(x, y) representando a probabilidade de tomar decisões anteriores. Este conceito é então usado para gerar uma nova solução que não linear seja simétrica, mas sim simétrica, de um conjunto de princípios matemáticos chamado Equilíbrio de Nash ou IMD.\n",
             "\n",
+            "Na teoria da decisão, existem dois lados, o lado normal e o lado positivo. A indução IMD tem duas propriedades:\n",
             "\n",
-            "Example output: Que fatos interessantes você pode me contar sobre o presidente Abraham Lincoln?Lincoln, o 13º presidente dos Estados Unidos, foi um advogado que foi responsável por estabelecer a Lei de Escândalo do Bankingestado em 1903. Ele foi acusado de fornecer informações que poderiam ter sido usadas para fazer uma série de negócios fraudulentos.\n",
+            "1. Consequências: a função de perda é zero se as variáveis não estiverem relacionadas e são verdadeiras em todo o conjunto. Isso permite que os pesos\n",
             "\n"
           ]
         },
@@ -1265,7 +1231,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 59%|█████▊    | 800/1367 [06:53<04:52,  1.94it/s]"
+            " 46%|████▌     | 800/1743 [09:56<11:40,  1.35it/s]"
           ]
         },
         {
@@ -1273,10 +1239,16 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 800 of 1367. Loss:0.6731818318367004.\n",
+            "Batch 800 of 1743. Loss:0.9168642163276672.\n",
             "\n",
             "\n",
-            "Example output: O motor do meu carro começou a fazer um barulho estranho. O que poderia estar causando isso.A primeira etapa é verificar a pressão do ar na roda. Certifique-se de que está funcionando corretamente. Verifique se há vazamentos de óleo, pneus, freios ou outros problemas. Se estiver de funcionar, substitua o motor quanto à quilometragem da roda. Se a aceleração for muito alta, verifique o painel do acelerador para garantir que esteja funcionando corretamente e certifique-se de que o bujão de freio esteja funcionando corretamente.\n",
+            "Example output: Qual é a diferença entre matéria escura e energia escura?A matéria escura (M12) e a energia escura (GD12) são tipos de partículas que compõem os materiais da Terra, mas diferem em sua composição, energia e potencial.\n",
+            "\n",
+            "Por outro lado, a energia escura (H2S) e a energia luminosa (H3S) são ondas de luz que viajam através do espaço em ondas de luz. As ondas de luz viajam através da atmosfera da Terra para fora do nosso corpo, a menos que seja significativamente afetada pelo espaço. A energia luminosa, por outro lado, é um tipo mais diversificado de energia escura.\n",
+            "\n",
+            "As moléculas de água condensada, as calorias em água ou outros líquidos e o carbono em água condensada, também são compostas principalmente de moléculas complexas e densas chamadas gases de efeito estufa.\n",
+            "\n",
+            "As partículas de poeira são uma classe mais ampla de partículas que podem formar subatmosfera. Estas partículas podem ser formadas usando materiais orgânicos como a\n",
             "\n"
           ]
         },
@@ -1284,7 +1256,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 1200/1367 [10:20<01:26,  1.94it/s]"
+            " 69%|██████▉   | 1200/1743 [14:56<06:43,  1.35it/s]"
           ]
         },
         {
@@ -1292,10 +1264,10 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 1200 of 1367. Loss:0.6759688258171082.\n",
+            "Batch 1200 of 1743. Loss:0.9940746426582336.\n",
             "\n",
             "\n",
-            "Example output: Você pode me falar sobre os benefícios para a saúde das maçãs.As maçãs têm uma grande variedade de vitaminas, minerais e outros nutrientes benéficos. Estudos demonstraram que as maçãs podem ajudar a melhorar a saúde, como reduzir a inflamação, ajudar a melhorar a saúde digestiva e estimular a criatividade. Comer maçãs pode ajudar a reduzir o risco de doenças cardíacas e algumas doenças cardíacas crônicas, como câncer e diabetes. Também pode ajudar a diminuir o risco de certos tipos de câncer e melhorar o desempenho geral. As maçãs também são baixas em calorias e gorduras não saudáveis, o que é importante para uma dieta saudável e equilibrada. Além disso, as maçãs são uma excelente fonte de fibra e pode ajudar na digestão, que é vital para ajudar a manter uma boa saúde geral.\n",
+            "Example output: Qual é o papel dos anteriores no infra-bayesianismo?O infra-bayesianismo é um ramo da filosofia da linguagem e da economia que se concentra no estudo de como compreender as limitações das evidências e como abordar as implicações das próprias declarações, decisões e crenças da linguagem. É usado em áreas como a semântica, a lógica e a concordância de diferentes pontos de vista, para compreender como as declarações são feitas, se são verdadeiras ou falsas. O infra-bayesianismo investiga a natureza das próprias declarações, as limitações das evidências, a importância de uma linguagem de inferência e o impacto social no contexto em que as declarações são feitas. A linguagem ajuda as pessoas a fazer julgamentos e avaliar criticamente os seus valores.\n",
             "\n"
           ]
         },
@@ -1303,7 +1275,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 1367/1367 [11:48<00:00,  1.93it/s]\n"
+            " 92%|█████████▏| 1600/1743 [19:56<01:46,  1.35it/s]"
           ]
         },
         {
@@ -1311,13 +1283,22 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Average Training Loss: 0.7373575027963936.\n",
+            "Batch 1600 of 1743. Loss:0.8641287684440613.\n",
             "\n",
             "\n",
-            "Validation loss: 0.7309620019636656.\n",
+            "Example output: Quais são os diferentes tipos de algoritmos de pesquisa.Existem vários tipos de algoritmos de pesquisa, cada um com sua próprias vantagens e finalidades. Aqui estão alguns tipos comuns:\n",
             "\n",
+            "1. Lógica Artificial: envolve determinar o valor verdadeiro das variáveis ​​(tantas, valores de entrada e saídas) com base em dados observados.\n",
             "\n",
-            "Beginning epoch 3 of 5\n",
+            "2. Matriz Artificial: envolve analisar o valor das variáveis ​​para determinar a precisão das previsões.\n",
+            "\n",
+            "3. Generalização: Examina o conceito de utilidade, utilidade e utilidade em dados.\n",
+            "\n",
+            "4. Previsão: Avalia o desempenho de uma tarefa considerando a classificação do problema em diferentes domínios, considerando o custo de cada etapa e o número de variáveis ​​que correspondem.\n",
+            "\n",
+            "5. Sistemas de recomendação: Examine algoritmos para identificar e categorizar mensagens, como recomendações de mídias sociais, listas de usuários e avaliações on-line.\n",
+            "\n",
+            "6. Matriz de decisão: avalia o valor das variáveis ​​para determinar sua utilidade\n",
             "\n"
           ]
         },
@@ -1325,7 +1306,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
+            "100%|██████████| 1743/1743 [21:44<00:00,  1.34it/s]\n"
           ]
         },
         {
@@ -1333,18 +1314,42 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 400 of 1367. Loss:0.6310281753540039.\n",
+            "Average Training Loss: 0.8430720605490472.\n",
             "\n",
             "\n",
-            "Example output: Quero começar a escrever um blog, mas estou tendo problemas para ter ideias. Você pode sugerir alguns temas.Proposta: O primeiro passo é criar um título que resuma seus pontos principais. Você deve começar apresentando conteúdo sobre como ele funciona e como ele funciona com um leitor.\n",
+            "Beginning epoch 3 of 5\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            " 23%|██▎       | 400/1743 [04:57<16:38,  1.35it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
             "\n",
-            "2 Depois que seu título seja lançado, comece a escrever links para ele. Certifique-se de incluir palavras-chave relevantes para seus leitores e mantenha seus leitores voltando.\n",
+            "Batch 400 of 1743. Loss:0.6807011961936951.\n",
             "\n",
-            "3 Depois disso, crie uma lista de tópicos do seu blog que deseja explorar e acompanhar seus leitores por alguns dias. Certifique-se de dar atenção a quaisquer tendências, opiniões e análises de blogs que possam mudar suas postagens.\n",
             "\n",
-            "4 Por fim, considere escrever um breve resumo das principais seções do seu blog para ajudá-lo a identificar as áreas que você recomendaria para leitores mais novos.\n",
+            "Example output: Quais instituições de caridade ajudam a proteger o meio ambiente.Existem várias instituições de caridade de renome que são importantes para o meio ambiente. Aqui estão alguns exemplos:\n",
             "\n",
-            "5 Em última análise, cada tópico é um bom cenário para começar sua nova postagem. Boa\n",
+            "1. Instituto Nacional de Pesquisa Ambiental (NAP)\n",
+            "2. Instituto de Pesquisa Ambiental (ASE)\n",
+            "3. Programa de Parcerias de Turismo Ambiental (PPAs)\n",
+            "4. Fundo Mundial da Saúde Global (WWF)\n",
+            "5. Fundo de Transporte e Turismo Mundial (OTC)\n",
+            "6. Universidade de Waterloo, Reino Unido\n",
+            "7. UNICEF\n",
+            "8. Banco Mundial\n",
+            "9. Fundo de Meio Ambiente (MoINA)\n",
+            "10. Fundo de Transporte e Vida Selvagem (FPUFL)\n",
+            "\n",
+            "É importante notar que a questão ambiental é um assunto complexo e multifacetado e há muitas organizações que trabalham na protecção do meio ambiente.\n",
             "\n"
           ]
         },
@@ -1352,7 +1357,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 59%|█████▊    | 800/1367 [06:55<04:51,  1.94it/s]"
+            " 46%|████▌     | 800/1743 [09:56<11:40,  1.35it/s]"
           ]
         },
         {
@@ -1360,10 +1365,16 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 800 of 1367. Loss:0.681230366230011.\n",
+            "Batch 800 of 1743. Loss:0.7792087197303772.\n",
+            "\n",
             "\n",
+            "Example output: Como posso me tornar um influenciador no Instagram.Tornar-se um influenciador no Instagram pode ser uma jornada emocionante. Aqui estão algumas etapas para ajudá-lo a começar:\n",
             "\n",
-            "Example output: Quais oportunidades de trabalho estão disponíveis para alguém com graduação em química.Existem muitos cursos disponíveis para pessoas com graduação em química. A Harvard University e o University of Pennsylvania oferecem serviços com experiência em química. O Stanford College of Medicine oferece aulas de bacharel em química e doutorado em química. O Brigham and Women's College of Science, Massachusetts Institute of Technology, Dartmouth College of Medicine e Harvard Medical School são dois dos dois centros médicos.\n",
+            "1. Configure uma marca ou canal: Selecione uma marca ou canal no Instagram e seja criativo. Você pode usar elementos como engrenagens de engrenagem, quadrados ou marcadores para adicionar interesse visual. Você também pode seguir contas do Instagram ou pesquisar influenciadores do seu nicho para se manter atualizado sobre as tendências e as dicas mais recentes.\n",
+            "\n",
+            "2. Defina seu público: determine quem você deseja alcançar através do Instagram. Os influenciadores podem ter um alcance significativo no Instagram, seja por meio de hashtags, projetos em mídias sociais, colaborações ou grupos de mídia social.\n",
+            "\n",
+            "3. Crie conteúdo envolvente: inclua conteúdo informativo, divertido e envolvente que os inspirem. Escolha uma música, assista a um vídeo ou toque de chama de voz para tornar seu\n",
             "\n"
           ]
         },
@@ -1371,7 +1382,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 1200/1367 [10:22<01:26,  1.94it/s]"
+            " 69%|██████▉   | 1200/1743 [14:57<06:43,  1.35it/s]"
           ]
         },
         {
@@ -1379,10 +1390,20 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 1200 of 1367. Loss:0.6577063202857971.\n",
+            "Batch 1200 of 1743. Loss:0.7952491044998169.\n",
+            "\n",
+            "\n",
+            "Example output: Qual é a melhor maneira de lidar com uma conversa difícil.Ao lidar com uma conversa difícil, aqui estão algumas sugestões úteis:\n",
             "\n",
+            "1. Encontre pontos em comum: Identifique claramente o que o incomoda e tente encontrar uma causa subjacentes que se encaixem no ponto em comum.\n",
             "\n",
-            "Example output: Qual é o objetivo da regulamentação geral de privacidade e proteção de dados (GDPR)?No âmbito da ética da IA, o princípio da privacidade encapsula um domínio fundamental de propriedade parcial e controle sobre a exposição de informações pessoais a uma rede. Com uma compreensão desta técnica impactada, torna-se imperativo desenvolver medidas proativas para salvaguardar o bem-estar das pessoas e sistemas de IA.\n",
+            "2. Reflita sobre seus sentimentos: Reflita sobre a situação, preocupações ou desafios que você enfrentou. Isso o ajudará a desenvolver uma melhor compreensão de sua perspectiva e desenvolver uma solução.\n",
+            "\n",
+            "3. Encontre pontos em comum: procure pontos em comum em comum. Pode ser uma conversa, uma oportunidade de autoavaliação ou um trabalho em equipe.\n",
+            "\n",
+            "4. Prepare soluções: Avalie diferentes maneiras de abordar a situação com base na sua perspectiva, experiências e experiência. Essas soluções podem ajudar a criar uma perspectiva clara e ajudar a enfrentar a situação de forma mais eficaz.\n",
+            "\n",
+            "5. Faça perguntas abertas: Ao responder a uma variedade de questões,\n",
             "\n"
           ]
         },
@@ -1390,7 +1411,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 1367/1367 [11:49<00:00,  1.93it/s]\n"
+            " 92%|█████████▏| 1600/1743 [19:57<01:46,  1.35it/s]"
           ]
         },
         {
@@ -1398,10 +1419,26 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Average Training Loss: 0.6574105381878432.\n",
+            "Batch 1600 of 1743. Loss:0.8452047109603882.\n",
+            "\n",
             "\n",
+            "Example output: Quero aprender um novo instrumento, mas não tenho certeza do que devo fazer. Quais instrumentos são mais populares.\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 1743/1743 [21:43<00:00,  1.34it/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
             "\n",
-            "Validation loss: 0.7102324895952877.\n",
+            "Average Training Loss: 0.7555728154796605.\n",
             "\n",
             "\n",
             "Beginning epoch 4 of 5\n",
@@ -1412,7 +1449,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
+            " 23%|██▎       | 400/1743 [04:57<16:37,  1.35it/s]"
           ]
         },
         {
@@ -1420,10 +1457,16 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 400 of 1367. Loss:0.5170938372612.\n",
+            "Batch 400 of 1743. Loss:0.6579100489616394.\n",
+            "\n",
+            "\n",
+            "Example output: Quais são os melhores métodos para conseguir uma promoção.Conseguir uma promoção pode ser um processo emocionante e desafiador. Aqui estão alguns métodos que podem ajudá-lo em sua busca de promoção:\n",
+            "\n",
+            "1. Repetir de forma consistente: É importante atualizar regularmente sua rede profissional e suas habilidades. Participe de eventos e feiras para conhecer profissionais que possam oferecer oportunidades de promoção.\n",
             "\n",
+            "2. Networking e eventos do setor: Participe de eventos do setor e conferências do setor que possam fornecer informações sobre oportunidades de promoção. Junte-se a associações de promoção ou participe de fóruns para se conectar com pessoas que já atuam com oportunidades de emprego.\n",
             "\n",
-            "Example output: Qual é a melhor maneira de aumentar os níveis de energia pela manhã.A melhor maneira de aumentar os níveis de energia pela manhã é fazer atividades que você goste, como caminhar ou correr, ler um livro, meditar ou meditar, ouvir uma música relaxante, praticar atividade física regular, comer uma dieta balanceada e fazer uma pausa em determinados momentos da semana para relaxar e recarregar.\n",
+            "3. Plataformas online e plataformas de networking profissional: Utilize plataformas de networking profissional como o LinkedIn, o LinkedInOacker ou o LinkedInForrest para se conectar com profissionais que já atuam em seu setor. Essas plataformas podem ser uma fonte valiosa de oportunidades\n",
             "\n"
           ]
         },
@@ -1431,7 +1474,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 59%|█████▊    | 800/1367 [06:53<04:52,  1.94it/s]"
+            " 46%|████▌     | 800/1743 [09:57<11:40,  1.35it/s]"
           ]
         },
         {
@@ -1439,10 +1482,18 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 800 of 1367. Loss:0.6901150941848755.\n",
+            "Batch 800 of 1743. Loss:0.660188615322113.\n",
             "\n",
             "\n",
-            "Example output: Quais escolas de ética surgiram na tradição ocidental e o que elas implicam?A Filosofia Moral, que é uma divisão da Filosofia, concentra-se nos princípios que nos conduzem ao desenvolvimento e na tomada de decisões éticas. Abrange o exame díspares das convicções éticas encontradas nos indivíduos, abrangendo a deontologia, a ética das virtudes, a ética da virtude e a ética da virtude.\n",
+            "Example output: Como posso melhorar minhas habilidades em engenharia.Melhorar suas habilidades de engenharia envolve a prática consistente, a dedicação e a aplicação de estratégias que funcionem para você. Aqui estão algumas dicas:\n",
+            "\n",
+            "1. Estabeleça metas claras e realistas: defina claramente o que você deseja realizar em engenharia, seja projetando um site, desenvolvendo aplicativos móveis ou resolver problemas complexos. Definir metas específicas e mensuráveis ​​sojudará você a se manter motivado.\n",
+            "\n",
+            "2. Faça anotações e tarefas diárias: Anote seu projeto, marcos e resultados importantes. Isso o ajudará a se manter organizado e a manter o foco durante todo o processo de engenharia.\n",
+            "\n",
+            "3. Pratique regularmente: Consistência é fundamental. Reserve um tempo dedicado todos os dias para estudar, praticar e revisar suas habilidades. A prática regular, mesmo que seja por períodos mais longos, pode melhorar a retenção e a eficácia.\n",
+            "\n",
+            "4. Divida as tarefas em etapas menores: Tarefas grandes podem\n",
             "\n"
           ]
         },
@@ -1450,7 +1501,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 1200/1367 [10:20<01:26,  1.94it/s]"
+            " 69%|██████▉   | 1200/1743 [14:57<06:43,  1.35it/s]"
           ]
         },
         {
@@ -1458,10 +1509,18 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 1200 of 1367. Loss:0.4600249230861664.\n",
+            "Batch 1200 of 1743. Loss:0.6905452013015747.\n",
+            "\n",
+            "\n",
+            "Example output: Qual a melhor forma de economizar dinheiro e investi-lo.Economizar dinheiro e investir podem ser benéficos para muitas pessoas. Aqui estão algumas estratégias que você pode considerar para economizar dinheiro e investi-lo:\n",
+            "\n",
+            "1. Crie um orçamento: comece monitorando suas receitas e despesas para entender para onde está indo seu dinheiro e fazer os ajustes necessários.\n",
+            "\n",
+            "2. Estabeleça metas de poupança: determine suas metas de poupança de curto e longo prazo. Ao priorizar suas economias, você pode trabalhar para atingir seus objetivos.\n",
             "\n",
+            "3. Invista em uma conta poupança: Configure uma conta poupança dedicada que esteja alinhada com suas economias e seja fácil de manter. Procure economizar pelo menos 10-20% de sua renda, mas ainda permita algum dinheiro para emergências ou fundos de aposentadoria.\n",
             "\n",
-            "Example output: Forneça conselhos sobre como atingir minhas metas de condicionamento físico.A melhor maneira de atingir seus objetivos de condicionamento físico é praticar. Comece definindo metas alcançáveis e mensuráveis e divida-as em etapas alcançáveis e alcançáveis. Acompanhe seu progresso, depois use esses resultados para desenvolver um plano de estudo e use-o para garantir que você fique em forma. Você também pode tentar incorporar exercícios de baixo impacto ou atividades com uma força positiva, como caminhar, correr ou ouvir música. Depois de atingir essas metas, você pode investir em atividades de maior intensidade, como treinamento de força, treinamento de flexibilidade, e treinamento intervalado. Por fim, recompense-se por cumprir o trabalho árduo e atingir seus objetivos de condicionamento físico.\n",
+            "Lembre-se de que economizar dinheiro requer disciplina e disciplina. Ao implementar essas estratégias e trabalhar no sentido de aumentar seus próprios retornos ao longo do tempo\n",
             "\n"
           ]
         },
@@ -1469,7 +1528,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 1367/1367 [11:48<00:00,  1.93it/s]\n"
+            " 92%|█████████▏| 1600/1743 [19:57<01:46,  1.35it/s]"
           ]
         },
         {
@@ -1477,10 +1536,26 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Average Training Loss: 0.5974375438040508.\n",
+            "Batch 1600 of 1743. Loss:0.7481504082679749.\n",
             "\n",
             "\n",
-            "Validation loss: 0.7050648294389248.\n",
+            "Example output: Quantas pessoas morreram em consequência da peste?\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 1743/1743 [21:45<00:00,  1.33it/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Average Training Loss: 0.6926399804844093.\n",
             "\n",
             "\n",
             "Beginning epoch 5 of 5\n",
@@ -1491,7 +1566,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 29%|██▉       | 400/1367 [03:26<08:18,  1.94it/s]"
+            " 23%|██▎       | 400/1743 [04:57<16:37,  1.35it/s]"
           ]
         },
         {
@@ -1499,10 +1574,16 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 400 of 1367. Loss:0.6328504681587219.\n",
+            "Batch 400 of 1743. Loss:0.5572178363800049.\n",
+            "\n",
+            "\n",
+            "Example output: O que é Otimização de Política Proximal (PPO) e como ela é usada na aprendizagem por reforço?Otimização Política Proximal (PPO) é um algoritmo de aprendizado de máquina popular usado principalmente na aprendizagem por reforço. É um método de otimização que visa otimizar os pesos dos processos de tomada de decisão entre diferentes agentes. PPO é baseado na pesquisa que gerou o algoritmo PPO.\n",
             "\n",
+            "A implementação do PPO em uma aprendizagem por reforço envolve duas etapas principais:\n",
             "\n",
-            "Example output: Qual é o papel da entropia cruzada no aprendizado de máquina?A entropia cruzada mede a dissimilaridade entre duas distribuições de probabilidade. É uma ferramenta indispensável no campo da avaliação de distribuições de probabilidade e mede a divergência entre duas distribuições de probabilidade.\n",
+            "1. Configuração do modelo: O processo de treinamento envolve o ajuste do seu modelo com base em fatores externos, como recursos, crenças e hábitos de mercado. Isso envolve o ajuste de parâmetros e restrições para alcançar um desempenho ideal.\n",
+            "\n",
+            "2. Treinamento na fase ativa: Após a fase ativa, os agentes são treinados em um conjunto selecionado aleatoriamente de ações ponderadas. O agente que recebe ações com base nos dados observados na fase ativa deve ter um bom desempenho para\n",
             "\n"
           ]
         },
@@ -1510,7 +1591,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 59%|█████▊    | 800/1367 [06:53<04:52,  1.94it/s]"
+            " 46%|████▌     | 800/1743 [09:56<11:40,  1.35it/s]"
           ]
         },
         {
@@ -1518,10 +1599,10 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 800 of 1367. Loss:0.5364123582839966.\n",
+            "Batch 800 of 1743. Loss:0.5685328841209412.\n",
             "\n",
             "\n",
-            "Example output: O que significa aprendizagem federada?FL é um método em aprendizado de máquina que treina modelos usando exemplos descentralizados em dispositivos como smartphones para aprender uma nova habilidade. Nessa abordagem, um servidor central distribui o modelo atual ao servidor, que a treina em uma versão de coordenação (C) e armazenada na rede. A rede é então usada para fazer melhorias no modelo, o que pode levar a melhorias na qualidade do modelo geral ou até mesmo gerar um modelo global mais personalizado.\n",
+            "Example output: Preciso de uma lista das melhores plantas de jardim que ficarão bem em áreas sombreadas.\n",
             "\n"
           ]
         },
@@ -1529,7 +1610,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            " 88%|████████▊ | 1200/1367 [10:20<01:26,  1.94it/s]"
+            " 69%|██████▉   | 1200/1743 [14:56<06:43,  1.35it/s]"
           ]
         },
         {
@@ -1537,10 +1618,10 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Batch 1200 of 1367. Loss:0.5115535855293274.\n",
+            "Batch 1200 of 1743. Loss:0.6094163656234741.\n",
             "\n",
             "\n",
-            "Example output: Quero começar a me exercitar, mas não sei como. Você pode me fornecer um plano de exercícios para iniciantes.Eu recomendo começar com exercícios básicos de peso corporal, como flexões, flexões e agachamentos. Esses exercícios podem ajudá-lo a construir força, reduzir a tensão muscular e queimar calorias rapidamente. Existem muitos livros e vídeos online que fornecem exercícios para iniciantes. Esses exercícios podem ajudá-lo a desenvolver e manter uma boa saúde cardiovascular e mental.\n",
+            "Example output: Qual é o gênero musical mais popular do mundo.Em 2021, o gênero musical mais popular do mundo é conhecido como Pop/Pop/Rap, que abrange uma ampla gama de gêneros musicais. O gênero tem um ritmo acelerado e acelerado, com artistas e artistas ultrapassando fronteiras da música tradicional e explorando novas fronteiras. É importante observar que o cenário musical pode mudar, à medida que novas tendências musicais surgem.\n",
             "\n"
           ]
         },
@@ -1548,7 +1629,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|██████████| 1367/1367 [11:47<00:00,  1.93it/s]\n"
+            " 92%|█████████▏| 1600/1743 [19:54<01:46,  1.35it/s]"
           ]
         },
         {
@@ -1556,10 +1637,32 @@
           "output_type": "stream",
           "text": [
             "\n",
-            "Average Training Loss: 0.551684551763988.\n",
+            "Batch 1600 of 1743. Loss:0.8100098967552185.\n",
+            "\n",
+            "\n",
+            "Example output: Quais são algumas maneiras de aproveitar as sobras.Existem diversas maneiras de aproveitar as sobras. Aqui estão algumas sugestões:\n",
+            "\n",
+            "1. Rever: Prepare-se para cobrir cuidadosamente suas sobras. Certifique-se de ter as sobras sobras de tamanho adequado. Gire-os suavemente do teto, mas não ao ar livre, para criar um espaço mais fresco e seco.\n",
             "\n",
+            "2. Cobertura morta: aplique uma camada de cobertura morta orgânica, como lascas de madeira ou palha, na parte superior das sobras para proteger as fibras dos móveis e dos galhos. Aplique o cobertura em direção ao teto e use uma espátula para criar um ponto focal.\n",
+            "\n",
+            "3. Destrezamentos: Adicionar dimmers ou tinta pode ajudar a criar ilusão de profundidade. Preencha as lacunas com fita métrica ou papel alumínio, em torno de um botão ou aba. Coloque o canto superior direito e esquerdo da\n",
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "100%|██████████| 1743/1743 [21:43<00:00,  1.34it/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
             "\n",
-            "Validation loss: 0.7048302759465418.\n",
+            "Average Training Loss: 0.6459957749523517.\n",
             "\n",
             "Training complete!\n"
           ]
@@ -1567,14 +1670,14 @@
         {
           "data": {
             "text/plain": [
-              "('/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/tokenizer_config.json',\n",
-              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/special_tokens_map.json',\n",
-              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/vocab.json',\n",
-              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/merges.txt',\n",
-              " '/content/drive/MyDrive/Colab Notebooks/Aira-PT-124M/added_tokens.json')"
+              "('/content/Aira-2-portuguese-124M/tokenizer_config.json',\n",
+              " '/content/Aira-2-portuguese-124M/special_tokens_map.json',\n",
+              " '/content/Aira-2-portuguese-124M/vocab.json',\n",
+              " '/content/Aira-2-portuguese-124M/merges.txt',\n",
+              " '/content/Aira-2-portuguese-124M/added_tokens.json')"
             ]
           },
-          "execution_count": 12,
+          "execution_count": 10,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1582,8 +1685,9 @@
       "source": [
         "from codecarbon import EmissionsTracker\n",
         "import tqdm\n",
+        "import os\n",
         "\n",
-        "output_dir = f'/content/drive/MyDrive/Colab Notebooks/Aira-{model_size}'\n",
+        "output_dir = f'/content/{project}'\n",
         "\n",
         "tracker = EmissionsTracker(\n",
         "    project_name=\"Aira_emissions\",\n",
@@ -1603,7 +1707,7 @@
         "\n",
         "    model.train()\n",
         "\n",
-        "    for step, batch in enumerate(tqdm.tqdm(train_dataloader)):\n",
+        "    for step, batch in enumerate(tqdm.tqdm(dataloader)):\n",
         "\n",
         "        b_input_ids = batch[0].to(device)\n",
         "        b_labels = batch[0].to(device)\n",
@@ -1613,8 +1717,7 @@
         "\n",
         "        outputs = model(b_input_ids,\n",
         "                        labels=b_labels,\n",
-        "                        attention_mask = b_masks,\n",
-        "                        token_type_ids=None)\n",
+        "                        attention_mask = b_masks)\n",
         "\n",
         "        loss = outputs[0]\n",
         "\n",
@@ -1623,7 +1726,7 @@
         "\n",
         "        if step % sample_every == 0 and not step == 0:\n",
         "\n",
-        "            print(f'\\nBatch {step} of {len(train_dataloader)}. Loss:{batch_loss}.\\n')\n",
+        "            print(f'\\nBatch {step} of {len(dataloader)}. Loss:{batch_loss}.\\n')\n",
         "\n",
         "            model.eval()\n",
         "\n",
@@ -1650,43 +1753,16 @@
         "\n",
         "        scheduler.step()\n",
         "\n",
-        "    avg_train_loss = total_train_loss / len(train_dataloader)\n",
+        "    avg_train_loss = total_train_loss / len(dataloader)\n",
         "\n",
         "\n",
         "    print(f'\\nAverage Training Loss: {avg_train_loss}.\\n')\n",
         "\n",
-        "    model.eval()\n",
-        "\n",
-        "    total_eval_loss = 0\n",
-        "    nb_eval_steps = 0\n",
-        "\n",
-        "    for batch in validation_dataloader:\n",
-        "\n",
-        "        b_input_ids = batch[0].to(device)\n",
-        "        b_labels = batch[0].to(device)\n",
-        "        b_masks = batch[1].to(device)\n",
-        "\n",
-        "        with torch.no_grad():\n",
-        "\n",
-        "            outputs  = model(b_input_ids,\n",
-        "                             attention_mask = b_masks,\n",
-        "                             labels=b_labels)\n",
-        "\n",
-        "            loss = outputs[0]\n",
-        "\n",
-        "        batch_loss = loss.item()\n",
-        "        total_eval_loss += batch_loss\n",
-        "\n",
-        "    avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
-        "\n",
-        "\n",
-        "    print(f'\\nValidation loss: {avg_val_loss}.\\n')\n",
         "\n",
         "    training_stats.append(\n",
         "        {\n",
         "            'epoch': epoch_i + 1,\n",
         "            'Training Loss': avg_train_loss,\n",
-        "            'Valid. Loss': avg_val_loss,\n",
         "        }\n",
         "    )\n",
         "\n",
@@ -1697,8 +1773,14 @@
         "df_stats = df_stats.set_index('epoch')\n",
         "df_stats.to_parquet(f\"{output_dir}/training_stats.parquet\", compression=\"gzip\")\n",
         "\n",
+        "rng_state = torch.get_rng_state()\n",
+        "torch.save(rng_state, f\"{output_dir}/rng_state.pt\")\n",
+        "torch.save(scheduler.state_dict(), f\"{output_dir}/scheduler.pt\")\n",
+        "torch.save(optimizer.state_dict(), f\"{output_dir}/optimizer.pt\")\n",
+        "\n",
         "model_to_save = model.module if hasattr(model, 'module') else model\n",
         "model_to_save.save_pretrained(output_dir)\n",
+        "model_to_save.save_pretrained(output_dir, safe_serialization=True)\n",
         "tokenizer.save_pretrained(output_dir)"
       ]
     },
@@ -1713,19 +1795,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 592
+          "height": 368
         },
         "id": "J1-hAY9Av4gT",
-        "outputId": "e568f437-8084-4d89-9efb-30c981336d00"
+        "outputId": "38e6731c-21fa-4129-8474-982a082c33e8"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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paWmF4+XPEhwc7JxeXZW67sBw7Ngx56fXY8eOdcYxZMgQSkpKeOaZZ5g0aRJ5eXmMGzfOed2GDRsAnGvma+u2225jwYIFpKSk8OuvvzrXnO/atcuZQClfDlHObrfzt7/9je+++855zGw2ExQUhMViAcqKDRYVFdXp/31VTpw4AZz56xseHl5tMuA///lPhTobJpOpQsz5+fnOf1yttvGXtz/V6b53y4uknvq9ey6cHN/pnunkc+np6c5kwj333MOuXbv48ccfmTNnDnPmzMFgMNC2bVv69evHsGHDKhXvvOGGG4iPj+fzzz/n+++/d852iI6O5rLLLuPWW2+lU6dOrnxMEZF6TcscRESkXqru01Moe1l58sknyc7OpkOHDvz3v/9l06ZNbNmyhTVr1rB69WrmzJnjbO9wOM5HyB7tt99+c74EnrojxNChQ5kwYQIGg4EPPviACRMm4HA4OHDgAFu2bCEoKIiBAwfW6b69evWiZcuWQNk2iOXK/zs4OLhS31999RXfffcdJpOJhx56iCVLlrBt2zbWr1/vLEpYvuyivvy/X716tTOR8Oc//5lvv/22Usx//etf3RzlhcNisfDWW2/xzTff8NBDD9GnTx98fHzYs2cP06ZN4/rrr6+wa0q55557jkWLFjFu3DiuuOIKAgMDSUxMZObMmdx666288sorbngaERH30MwEERHxOHFxcaSkpGAymfjggw+q/GSyoa1rDgkJASAzM5Pi4uJqZyfUdYp8Xl7eac/feuutlJaW8s9//pPPPvuMvLw8srOzcTgc/PWvf63zFpnlfb/xxhv873//cy4bKd9F4oYbbqj0rOWfCN92223VbkN4/PjxOsdTlfKZMmf6+lZ3vjzmfv368c9//rPKNq6O+WSNGjUiISHhjMs+ys+faWaQu50cX2pqarVbgJ78/6Oq5Qjt27enffv2QFmScsOGDbz33nts2LCBf//731x66aXO8+Wio6O5//77uf/++7Hb7cTHx/Phhx+ydOlSPv30U/r06eOsfSIi0pBpZoKIiHicI0eOAGUvB9VNcf7111/PZ0jnXMeOHYGyZRXlVeVP5XA46rx7Qfn0b4C1a9dW2WbEiBE8//zzQNnMgaVLlxITE8O9995bp3uWGzp0KCaTiaKiIr799lt++ukn57KOU5c4wB8vvBdffHGV/eXl5bF169aziulU5dPXt2/fXm3i5eDBg9W+rJ8pZofDUe3XHXAW3KzrTIvy+NetW4fdbq+yzf79+50v3507d67Tfc6X5s2bExwcDJx+rK9ZswYom+Fy8vd4VcxmM3379uWDDz7AarXicDic11fHaDQSGxvLO++84yx+eqZrREQaCiUTRETE4wQEBABln+RW9Wnu0aNH67wlZH3VoUMHZ0HI//73v1W+VH7zzTekpKTUqf9OnToRFRUFlK3tL3+ZP9WoUaMYPHiw88/t27fHy8urTvcsFxYWxhVXXAGUJSnKlzh07Nix0qfCgLNI3q5du6rsb8qUKWecaVFb1157LSaTicLCwiqnvwO899571V5/ppi//PLLKrfHPPX6k2uE1MaQIUOAsk/q586dW2Wbd955ByibBXPppZfW6T7ni8FgcH4fzp49u8qZSKmpqcyePRuA66+/vsK54uLiavu2Wq3OmiEnL7c63TUmk8lZ+6KqnVZERBoiJRNERMTj9OjRA19fXxwOB48//jgJCQkA2Gw2Vq5cyZ133unmCF3PYDDwyCOPALBq1Sqeeuop56fIRUVFzJ07l3/+858EBQXVuf8XXngBk8nEwYMHGTZsGIsXL6aoqAgo+9pu3ryZRx99lB9//NH5wvTjjz/y5ptvnvXzlc9A2L59O7/88gtQtvyhKuVbKM6dO5fZs2c7X/KOHTvGq6++ykcffeT81NpVmjZtyp///GegLFnxwQcfkJubC5QV9nvppZdYuHChM9FVXcy//PIL7733nrPIYnZ2Nu+//z4TJkw4bcxt27Z1Xl+XpSxdunRx1pF4+eWX+fzzz53FKY8dO8Y//vEP51aLjz322FkniOrKbreTnp5+2n/Kv+4PPPAAgYGBZGZmcvfdd1fYBnTTpk3cfffdZGdnExwczJgxYyrc509/+hNvvPEGcXFxFZIEiYmJjB8/noKCAoxGo3O7S4Bhw4YxYcIE1q1bV6FIZmpqKi+//LJz69Yrr7zynHxtRETqG9VMEBERjxMQEMDf//53XnzxRTZs2MCgQYPw9fXFZrNRVFRESEgIr732Gg8++KC7Q3WpG264gW3btvHJJ5/wzTffsHDhQgIDA8nPz6ekpIQ+ffrQtWtX5zTt2rr88suZNGkSzz33HMnJyTz66KOYzWb8/f3Jy8tz7lwRGRnJq6++yi+//MK0adN4//33adKkCXfccUedn+2qq66icePGHD9+HLvdjpeXFzfccEOVbUePHs3ixYs5cOAAL7zwAi+++CL+/v7k5OTgcDgYMWIExcXFzJ8/v87xVOVvf/sb+/fvZ82aNUyaNIm3334bf39/Z+2I++67j61bt7J+/fpK1w4dOpQFCxawceNG3nnnHSZPnkxgYCA5OTnY7XauuuoqOnTowNSpU6u8980338z06dNJTEzkqquuIjQ01PnCP3PmTMLDw88Y/yuvvEJGRgbr16/n5Zdf5rXXXsPPz88ZP5R9bUeOHHkWX6Wzc+TIkdNu9QgwYMAApkyZQnh4OO+99x5jx45l7969jBw5El9fXwDny35gYCDvvfdepeVQx48f57///S///e9/MRqNBAQEUFhY6EyeGQwGnnrqKdq0aeO8Jicnh88++4zPPvsMg8FAQEAApaWlFRILd911lzNxJCLS0CmZICIiHmnkyJFERkby0UcfsX37dmw2G02bNuXKK6/kvvvuq9OWjZ7g2WefpVevXnz66af89ttvFBcX06pVK2666Sb++te/8vrrrwNlL1F1MWjQILp3787MmTP55ZdfSExMJC8vj+DgYDp27MjVV1/NjTfeiNVqpXfv3hw8eJCffvqJV155hUaNGlVYAlEbZrOZoUOHOnc8uPrqq6t9hsDAQGbNmsV7773H0qVLSUtLw2QycckllzBixAiGDBnC008/Xac4TsfLy4sPP/yQmTNnMm/ePBISEnA4HPTs2dO5/KO6WTEWi4Vp06bx3//+l++++46UlBQcDgddunRh6NChjBgx4rTLJFq2bMmnn37KBx98QHx8PJmZmc7dN2q6FWNAQAAzZsxg/vz5fPPNN+zevZv8/HwaN25M9+7dGTVqFL179679F8aNLrnkEn744QemT5/OihUrSElJwWAw0Lp1a6688kpGjx5NkyZNKl03bdo01q1bx6ZNmzhy5IhzuVR0dDQ9evRg1KhRlbZ5nDRpEqtWrWLjxo0cOnSI48ePU1paSrNmzejatSvDhw8/YyJERKQhMTjqy55JIiIictZuv/12tmzZwqOPPspDDz3k7nBERESkgVLNBBERkQZi/fr1zp0eNNVaREREziUlE0RERDzIv/71L+bNm8exY8ec69yzs7OZNWsWY8eOBaBPnz506dLFnWGKiIhIA6dlDiIiIh7kpptucm4vaLVa8fHxqVBAr02bNkybNq1SwTkRERERV1IyQURExIMsW7aMpUuXEh8fz/Hjx8nNzcXf3582bdpw9dVXM2LECHx8fNwdpoiIiDRwSiaIiIiIiIiISK2oZoKIiIiIiIiI1IqSCSIiIiIiIiJSK2Z3ByCn53A4sNvr/0oUo9HgEXGKeAKNJxHX0FgScQ2NJRHX8YTxZDQaMBgMZ2ynZEI9Z7c7SE/Pc3cYp2U2GwkJ8SM7O5/SUru7wxHxaBpPIq6hsSTiGhpLIq7jKeMpNNQPk+nMyQQtcxARERERERGRWlEyQURERERERERqRckEEREREREREakVJRNEREREREREpFaUTBARERERERGRWlEyQURERERERERqRckEEREREREREakVJRNEREREREREpFbM7g6gttauXcv06dPZunUr+fn5REZGMmjQIMaMGYOvr2+t+8vIyGD69OksW7aMQ4cOYTabadOmDbfccgvDhg3DaKw639KuXbvT9tu4cWNWr15d63hERERERERE6juPSiZ89tlnvPLKKzgcDsLDw4mIiGDfvn1MnTqVJUuWMHPmTIKDg2vc34EDBxg9ejRHjhzBYrHQtm1bioqK2Lp1K3Fxcfz888+8++67mM3Vf5k6deqE1WqtdLw2cYiIiIiIyLnlcDiw2UpxOBzuDkUuUHa7gcJCE8XFRdhs5/770Gg0YjKdu1d+j0kmbN++nVdffRWAl156ieHDh2MwGEhNTeXBBx9kx44dPP/880yePLlG/dlsNh599FGOHDlCjx49ePvtt2nSpAkAu3fv5oEHHuDnn3/mvffe47HHHqu2n7fffpvmzZuf/QOKiIiIiIjLlZaWkJOTSXFxIQ6H3d3hyAXu+HEjdvv5+z40m634+QXi4+Pn+r5d3uM5MmXKFOx2O0OHDmXEiBHO402bNmXSpEkMHjyYJUuWsGvXLtq3b3/G/lasWMHevXuxWq38+9//diYSoGwJwwsvvMADDzzA9OnTufvuuwkMDDwnzyUiIiIiIudGcXERGRlpGI1G/PwCsFi8fl/GbHB3aHKBMpkM52VWAjiw2Wzk5+eSlXUcwOUJBY9IJuTl5bFy5UoAhg8fXul8y5Yt6dOnD2vWrGHRokU1SiZs2rQJKFumUNXMgj/96U/4+vqSn5/PsmXLuPnmm8/yKURERERE5HzKzc3EZDITGtq02lpoIueT2WyktPT8zEywWMDLy4eMjGPk5WVfmMmEnTt3UlxcjNVqpUuXLlW26dGjB2vWrGHr1q016jMrKwsom9lQnbCwMA4ePMiWLVuqTSZMmTKFtLQ0bDYbTZs2pU+fPlx33XVV1lEQEREREZHzw2azUVxcSFBQIyUS5IJlMBjw9fUjM/M4NlupS2soeEQyISEhAYDIyEgsFkuVbaKioiq0PZOAgAAAUlNTq22TlpYGlBVqrM7XX39d4c/z58/nnXfeYfLkyXTs2LFGsXgyu93BzoPplCRkYDE4aB0ZhNGoaWMiIiIi4l52uw0As7nq9weRC0V5AsFut2Myua5fj0gmlM8iCAoKqrZN+bnytmfSuXNnoKywY0pKCs2aNatwfsWKFeTn51fb54ABA7jpppto37494eHh5OXl8euvv/Lmm2+SnJzM6NGjWbBgARERETWK53TM5vqZSd2wK40vFu8mPafIeSw0wItR17ajV/swN0Ym4rlMJmOFf4tI3WgsibiGJ48lu738Ay590CX1g8Hwx7/P76YiZTc2mQwufbf0iGRCUVHZy2p1sxIA57KC8rZnMnDgQMLDwzl69Cjjxo3jrbfecr74x8fH889//tPZtrCwsNL1U6ZMqfBnLy8vhgwZQt++fbn11ls5fPgw7777Lq+88kqN4qmO0WggJMT1lTfP1pr4w0z+Kr7S8fScIiZ/Fc8zf+3FpV0i3RCZSMMQGOjj7hBEGgSNJRHX8MSxVFho4vhxo8tfoETO1vlOztntBoxGI0FBvnh7e7usX49IJnh5eQFQUlJSbZvi4uIKbc/EarXy1ltvcd999xEXF8eAAQOIjo6mqKiIlJQUgoODueaaa1iyZAl+fjV/mQ8NDWXMmDG8+OKLLF26lAkTJmAw1D0barc7yM7Or/P154Ld7uCDeZUTCSf7YH487ZoFasmDSC2ZTEYCA33Izi7AZtP2VSJ1pbEk4hqePJaKi4uw2+3YbI7zVvBO5HQMhrIxZbPZz+vMBJvNgd1uJysrn4IC2xnbBwb61Cjh4RHJhJosYajJUohTdevWjfnz5/Phhx+yatUqkpOTCQoK4pZbbuHRRx/l/fffB6Bx48a1irdbt24AZGZmkpmZSUhISK2uP1V9++G3KzGjwtKGqqRnF/FbQjrto8/u2UUuVDabvd6NfRFPpLEk4hqeOJbOz/Z7IjVXnkA4v0sc/uDqxJpHJBNatmwJwOHDhykpKalyuUNSUlKFtjXVokULXnrppSrP7du3D/ijvkJNnRyfzXbmzI+nycyr2VKSmrYTEREREREp98orL/Ljj9/x7LP/5LrrbnBJnw8/PIa4uM288877dO/e0yV9Xug8IpnQoUMHLBYLxcXFxMfH06NHj0ptNm3aBEBsbKxL7pmenk5cXBxQVmyxNvbu3QuULbkIDg52STz1SbBfzZaS1LSdiIiIiIi4T79+dXu5njt3IRERqpN2ofKIZIK/vz/9+vXj559/Zs6cOZWSCQcPHmTt2rUADBo0yCX3fOuttygtLaVnz5506tSpxteVlpYyffp0APr06YPZ7BFf4lq5qEUwIQFeZJxmqUNIgBcXtQg+f0GJiIiIiEiddO7ctdKxkpISdu36DYD27S+ucnZ4eRF8V2vUqDFRUdH4+fm7rM+mTcOJiop2aQHCC53B4XDXio3aiY+PZ/jw4QD861//Yvjw4RgMBtLS0njggQfYsWMHAwcO5L333qtwXf/+/QH4+9//XinRsGLFCvz9/SskJ7Kzs5k8eTKffvopvr6+fPXVV7Ru3brCdRMnTqR169ZcffXV+Pv/8Q1+5MgRXn75ZZYtW4bZbGbmzJl07Vp5YNaGzWYnPT3vrPo4FzbtTuO9+durPd+mWSBPj+qhAowitWQ2GwkJ8SMjI8/j1qaK1CcaSyKu4cljqaSkmBMnjtCoUQQWy7l56W3Ijhw5zLBhNwKageBKZrPxvI+l2o6F0FC/hlOAEaBLly48/fTTvP7667zwwgtMnTqVkJAQ9u3bR3FxMTExMbz88suVrktJSQEgP7/yjgirVq3i008/xd/fn2bNmgFw4MABSkpKCA4O5t13362USChv8+GHH/Lcc8/RokULgoKCyMnJISEhAYfDgZeXFxMmTDjrREJ91qNdGA/d3ImZS/dWmKHg72Mhv7CEfSnZzFi0i7sGt8d4FrtZiIiIiIiISP3jMckEgLvuuot27doxbdo04uPjOXHiBJGRkQwaNIgxY8bUagtHgIEDB3LixAm2bdtGUlISBoOBmJgY+vfvz1133VXtLgwjR46kcePGbN++nbS0NFJSUrBYLLRt25a+fftyxx13EBUV5YpHrtd6tAujW9sm7D+cRYnDgMXgoHVkEJv3HGPqN9tZFX8Eb4uJkQPbntX2mCIiIiIinsRud7AnOZPMvCKC/cqW/zakGbsnz1pYtWojK1b8zNy5X7J//z5ycrKZPv0L2rZtx4kTx1m+/Cd+/XUVSUmJHD9+HLPZTHR0NP37X8Ottw6vcqlEdQUYf/jhW1599V/ExnZn8uQP+Oabr/nmm3kkJSVitXoRG9uN++4bS6tWlT8Qrq4A48cff8D06R8yePD1PPXUP/jyy89YtOh7jhw5jK+vH71792HMmIdo2jS8yq/FsWNpfPTR+6xdu4acnGzCwpoyYMA1/OUvo5k48TWXF5KsTzwqmQDQt29f+vbtW+P2u3fvrvZc79696d27d61juPzyy7n88strfV1DZDQa6NAytML0t57twxhd0oGPv9/J0k2H8PYyc8sVrdwdqoiIiIjIObdpd1ql2bshAV78eWBberQLc2Nk58YXX3zC1KmTCQ4OoXnz5qSlpTrPffvtAj766H2sVi8aNWpM69atycrKYs+e3ezc+Ru//PIz77zzfpX1GM5kwoR/snjxD0RERBIVFU1iYiIrV65gy5ZNfPTRZzRv3qJW/ZWWlvLkk4+wadMGWrSIonnzFiQlJbJ48Y9s2bKZGTNmEhgYVOGapKREHnroPjIy0jGbzbRq1ZqioiI++eRjNm5c3+CXhnhcMkE8w2WdIygstvHF//bw3ZqD+FhNDO4T7e6wRERERETOmerqimXkFPHe/O08dHOnBpdQ+Oij9xk37imGDr0Vo9GI3W7HZrMB0K1bT9588z26detRoTB9Wloqb775H1auXM6sWZ9z55131+qe27fHk5h4kHff/S+xsd0ByM7O4plnxrN16xY+/vgD/vnPCbXq8+eflxIeHsknn8yides2ABw9epTx4x/h4MEEvvzyc+6//yFne4fDwUsvPU9GRjqdO3fh5Zf/j8aNmwCwZ88u/v73J9i9e2etYvA0Z66qIFJHA3o059Yry2YkzF2+n582H3JzRCIiIiIif3A4HBQV21zyT0FhKV/8b89p7zdz6V4KCktdcr/6Ukf/hhuGcsstwzAay14tjUajc6ZB166x9OrVu9IOd2FhTfnnPydgNptZtOj7Wt+ztLSUxx8f70wkAAQGBvHYY08C8Ouvq+vU5z/+8S9nIgEgPDyc++4bW2WfmzdvZNeu3/D29ubll//tTCQAXHRRe5577p+UlpbWOg5PopkJck4N6duSwmIb3/+ayOdL9uBlMXFZ5wh3hyUiIiIiFziHw8Frn29mX0rWebtnRk4RD731i0v6atM8iGdGdXd7bbIz1QIoKirk55+XsXXrFlJTUyksLHAmQoxGI0lJiRQVFeLlVfMtG/39Axgw4JpKxy+6qD1Wq5Xc3ByysjIJCgqucZ9t2lxEp06dKx3v2LHsWEpKxQ9G161bA0CfPpfRuHHjStf16tWH8PAIjh49UuMYPI2SCXLO3XJFK4qKbSzddIhpP+zEy2KiZ/uGNb1LRERERDxQw6mJ6DbR0THVnjtwYD9PPfUER44cPm0f2dnZNGlS82TC6eohBAeHkJaWSkFBQa2SCdX1GRoaCkBBQcXdAZOTkwBo06ZttX22adNWyQSRs2EwGLh9YFsKi22s2naEDxbuwGox0aV1I3eHJiIiIiIXKIPBwDOjulNcYndJf3uSM3lz7tYztntiWFcuahF81vezWoxun5UA4OPjU+Vxm83G888/xZEjh+nR4xLuuOOvtGnTloCAQOeyh1tuGUJaWmqtlwN4e1efeChfblHbZSDVPUd5f6fKzy8AwNe3+h0FT3euIVAyQc4Lo8HAXYPbU1RiY8OuNN6bv41xw7vSLqrq7TdFRERERM41g8GAl9Xkkr46xoQSEuBVYReHU4UGeNExJrRBbRNZnZ07fyMx8SBhYU35978nVVrG4HA4yMnJcVN0Z8/Xtyz5kJ+fV22b051rCFSAUc4bo9HAfTdcTJfWjSgptfPWV/EcOJzt7rBERERERM6a0WjgzwOrn/IOMHJg2wsikQBw5EgKAB06XFxlPYQDB/ZXWjrgSVq0iAJg//591bY53bmGQMkEOa/MJiNjh3aifVQwRcU23pwTx6G0XHeHJSIiIiJy1nq0C+OhmzsREuBV4XhogFeD3BbydMqXIpw4caLK8zNnfno+w3G53r0vBWDt2tWkp1d+xo0b15+xVoSnUzJBzjurxcQjt3ahVWQgeYWlTJwdx9F0z81KioiIiIiU69EujP88eCl/H9mNMTdezN9HduPfD156QSUSoGwXBLPZzPbt8XzzzTzn8ZKSEj78cCpLlvzo3ELSE3Xv3pMOHS6moKCAf/zjKY4fP+48t3fvbl599V+VtsRsaJRMELfw8TLzxPCutAjzJzuvmImztnA8q8DdYYmIiIiInDWj0UD76BD6XBxO++iQC2Zpw8lCQxsxcuSdAPznP68ydOhg7r33L9xww9V88snHjB49hkaNKm+p6CkMBgPPP/8yISGhxMfHcdtt1zN69CjuuGM4d989isaNm3DVVQOA6os4erqG+VTiEfy8LTw5IpbwUF/Ss4uYOCuOzNzqC9aIiIiIiIjnuP/+hxg//hlat25DVlYmhw4l06bNRbz88uvcffd97g7vrEVFRfPxx58xZMiNBAUFcfBgAsXFRdxxx1288877zl0q/Pwa5q4OBkdt98yQ88pms5OeXr+rgJrNRkJC/MjIyKO0tPZb66RnF/L6F5s5nlVIs8Z+PDWqO/4+njvlSeRsnO14EpEyGksiruHJY6mkpJgTJ47QqFEEFovV3eHIBejOO4eTkHCA6dNn0rbtRUDZmDrfY6m2YyE01A+T6czzDjQzQdwuNNCb8bfHEuRvJeV4HpNmx1FQVLu9ZkVEREREROqLHTu2k5BwgMDAIGJiWrk7nHNCyQSpF8JCfBl/ezf8fSwcPJrD23O3UlRic3dYIiIiIiIiVUpOTmLu3Fnk5ORUOB4fH8cLLzwNwI033txgCzE2zKcSj9SssR9Pjojl319uZs+hLN6dt41Hb+2Cxaycl4iIiIiI1C95ebm8/fZE3n33TVq0iMLX14/jx4+RlpYKQOfOXbj77nvdHOW5o7c0qVeiwwN4fFhXrBYjOxLS+WDhDmx2z1qfJyIiIiIiDV9kZHP+8pfRXHRRO7KystizZxd5ebl07NiZRx99krfffh8vL293h3nOqABjPXchFGCsyo6D6bw9dyulNgd9O4Zzz/UdMBouvC115MLjyYWuROoTjSUR1/DksaQCjFIfqQCjyDnWsWUoD97UCaPBwK87jvL5kj0o7yUiIiIiIlI/KJkg9Va3i5pw7w0dMADLt6Qwd/l+JRRERERERETqASUTpF7rc3E4fxnUDoBF65L4ds1B9wYkIiIiIiIiSiZI/XdlbDNu798GgAUrE1iyIdnNEYmIiIiIiFzYlEwQj3DNJVEM7RcDwKxle/ll62E3RyQiIiIiInLhUjJBPMYNl7Vk0CVRAHzy4y7W/nbUzRGJiIiIiIhcmJRMEI9hMBgY9qfWXNWtGQ7go293smXvMXeHJSIiIiIicsFRMkE8isFg4I5rLqJPx6bYHQ6mLtjBbwfT3R2WiIiIiIjIBUXJBPE4RoOBe4Z0oFvbxpTa7LzzdTz7DmW5OywREREREZELhpIJ4pFMRiMP3NSJjjGhFJfYeXPuVhKP5rg7LBERERERkQuCkgnisSxmIw/f0pm2zYMoKCrljdlxpBzPc3dYIiIiIiJyBv369aRfv56Vjj/88Bj69evJ5s0ba9Xf5s0b6devJw8/PMZVIZ7RkSOH6devJ7fddsN5u2d9omSCeDQvi4nHbutKdHgAuQUlvDFrC2mZBe4OS0RERETEY7zyyov069eTJ598tEbt09NPcOWVvenXrycbNqw7x9G5z8cff8DHH39ATo5mQFdFyQTxeL7eZp4cEUuzxn5k5hYz8cstpGcXujssERERERGPMHjw9QBs3LiOEyeOn7H9kiU/YrPZCAtrSo8evVwaS9Om4URFRePt7e3Sfuti+vQPmT79Q3Jzq04mmM1moqKiadas+XmOrH5QMkEaBH8fC0/eHktYiA/HswqZOCuO7Lxid4clIiIiIlLvdevWg4iISGw2G0uWLDpj+x9//B6AQYOGYDS69pXy+edfYubMr7n44k4u7fdcaNIkjJkzv+btt6e6OxS3UDJBGoxgfy/G3x5LaKAXR9PzeWN2HHmFJe4OS0RERESkXjMYDAwaNASARYu+P23bvXt3s3//XuCPGQ1yYTK7OwARV2oc5MP427vx+uebSE7L5a05W3ny9li8rfpWFxEREZHzw+6wsy8zgeyibAK9AmkTHIPRUL8/xx00aAgzZnzE/v172bt3N23btquyXXmyoXPnLrRoEcWOHdv55Zef2bx5A2lpqWRlZREYGMTFF3dk2LCRtV4G8fDDY4iL28w777xP9+4VCzTa7Xbmz/+KhQvnk5ychK+vL126xHL33fedts/axvjxxx8wffqHzj8PG3ZjhfPlsR05cphhw24kPDyCr776ttJ98/JymT17JitW/ExKSjIGg4HmzVtwxRV/YsSIP+Pr61fpmttuu4GjR4/wzjvvExbWlI8//oBNmzaQm5tDREQkQ4bcyO233+HyGSF1oTcsaXDCQ3158vZu/HvmZvYfzmby19t4fFgXLGaTu0MTERERkQYuLm0bc/cuJLMoy3ks2CuIYW1vJDassxsjO71mzZrTpUssW7du4ccfv6symVBaWupcBjFoUNmshJde+gcpKYcICAikUaPGNGrUhGPH0li16hdWr17J44+P59ZbR5x1fA6Hg3/96x8sW7YEgPDwCIKCglm3bg1r167h7rvvrfba2sbYtGk4nTt3Zdu2rQC0b38xFovFed7f3/+M8R49epTHHx/LoUNJGI1GYmJaAbB//z727t3D//63iLfemkJYWNMqr9+7dzfPPPMkpaWltGzZCrPZTGLiQaZMeYejR48wbtxTZ/6inWNKJkiD1CLMnyeGx/KfWVvYmZjBlPnbeeiWzphN7s/giYiIiEjDFJe2jQ+3f1bpeGZRFh9u/4z7Ot1ZrxMKgwdfz9atW/jf/xYzduxjmM0VXxfXrfuVjIx0rFYvBgy4BoC77rqXjh07ExUVXaHtpk0bePHF55g8+U0uu+xKwsPDzyq2hQvns2zZEqxWL/71r1e4/PKrAMjNzeWVV17k448/qPba2sZ4/fU3cf31Nzm3rnz55deJiIisVbz/+tdzHDqURJs2F/HKK/92Fmk8fPgQTz01joSEA7z00vO8++5/q7x+6tTJDB58PY88Mg5fX18Ali37Hy+++Czz53/FbbfdXul5zjePe7Nau3Yt999/P3369KFLly4MGjSIt956i/z8/Dr1l5GRwaRJkxgyZAhdu3alR48ejBgxgtmzZ2O32097bV5eHm+++SaDBg2iS5cu9OnTh/vvv5916xru9iiepFVkII/f1gWL2cjW/Sf46LvfsNsd7g5LREREROoJh8NBka3YJf8UlBYyZ883p73f3L0LKSgtdMn9HA7X/722f/+BeHt7k5GRzrp1v1Y6/+OP3wFw+eVXOj+dHzz4+ipfanv06MWYMWMpLS1l6dIzF3U8HYfDweeffwLAqFF/cSYSoGyWwAsvvIyfX+UlA+XOR4wn27JlE9u2bcVoNPKvf71aYbeHqKgoXnzxVQwGA3Fxm4mL21xlHy1aRDF+/DPORALAgAFXc9lll+NwOFi7drXL4q0rj5qZ8Nlnn/HKK6/gcDgIDw8nIiKCffv2MXXqVJYsWcLMmTMJDg6ucX8HDhxg9OjRHDlyBIvFQtu2bSkqKmLr1q3ExcXx888/8+6771bKyAGkp6fz5z//mYSEBKxWK23atCE9PZ3ly5ezYsUKnn/+eUaNGuXCp5e6aBcVwkM3d2by1/Gs35mG1WLirsHtMRoM7g5NRERERNzI4XAwafMUDmQlnrd7ZhZlMf6XF1zSV6uglozr/iAGF/691tfXjyuv7M/ixT+waNH3XHbZ5c5z2dnZrFmzEoDrrruhwnWHD6ewdOli9u7dQ1ZWJiUlZUXQ8/JygbIp+2cjKSmRI0dSAKpcMuHj48OQITcxc+an1fZxrmM82dq1awC45JI+REe3rHS+des29OrVm/Xr17Ju3a/Exnav1OaGG4ZiMlVept2xY2dWrfqFlJRDLou3rjwmmbB9+3ZeffVVAF566SWGDx+OwWAgNTWVBx98kB07dvD8888zefLkGvVns9l49NFHOXLkCD169ODtt9+mSZMmAOzevZsHHniAn3/+mffee4/HHnus0vXPPfccCQkJdOzYkalTp9K0aVMcDgdz5szhhRde4JVXXqF79+506NDBdV8EqZMurRtx/40dmfrNdlbFH8HbYmLkwLYu/cErIiIiIp5Ifx881eDB17N48Q+sXv0LOTk5BAQEAPDTT0soLi6mceMm9Ox5ibP9nDkzmTLlHUpLS6vtMysrq9pzNZGYeBCAkJDQaj88Lq9JUJXzEePJkpLKElStWrWutk2rVm1Yv36t89lO1bx5VJXHQ0JCASgoKDi7IF3AY5IJU6ZMwW63M3ToUEaMOLk4RlMmTZrE4MGDWbJkCbt27aJ9+/Zn7G/FihXs3bsXq9XKv//9b2ciAaBdu3a88MILPPDAA0yfPp27776bwMBA5/nffvuNn376CaPRyJtvvknTpmVFMwwGAyNGjGDTpk188803TJkypcbJDTm3erYPY3RJBz7+fidLNx3C28vELVdUP7hFREREpGEzGAyM6/4gxXbXbCW+L/MAU7ZOO2O7sV1H0ya4+hffmrIaLefkw7EePXrRtGk4qalHWbZsCUOH3grAjz+W7eJw7bXXOT8x37ZtK++8Mwmj0cjdd9/HlVf2JzIyEm9vH4xGI5s2beCxxx487Ut8TRQUlC1pDwkJqbZN+Uv2qc5XjCcrX4IfEtKo2jahoY1+b5tX5Xlvb+8qj5fv4nAulrnUlkfUTMjLy2PlyrIpNcOHD690vmXLlvTp0weARYtqttZl06ZNAHTq1InmzZtXOv+nP/0JX19fCgoKWLZsWYVzixcvBqBPnz5ER1dee1Oe7FixYkWdazmI613WOYI7rrkIgO/WJPLD2vM3pU1ERERE6h+DwYCXyeqSfzqEXkSwV9Bp7xfiFUSH0Itccr9zNcvWYDAwaNAQ4I9tIJOSEtmxYxtQNnOhXPn5ESNGcc8999OmTVt8ff2cL7yu+rTfx6esbkBGRka1bTIy0qs8fr5iPFl5nYOMjBPVtklPP/F72+prPdR3HpFM2LlzJ8XFxVitVrp06VJlmx49egCwdevWGvVZ/k1TPqugKmFhYQBs2bKlwvG4uDgAevbseeolAHTp0gWr1UpRURE7d+6sUTxyfvTv3pzbriqbkfDV8v38tNn9a41ERERExPMZDUaGtb3xtG1ua3sjRkP9fwUrTxhs3x5PcnKS84W8Q4eOtGwZ42x35MhhALp27VZlP+UJiLNVXncgMzODzMzMKtskJByo8vj5ivFk5cUeDxzYX22b8nNV1VTwFPX/OxlISEgAIDIyssL+nieLioqq0PZMytf+pKamVtsmLS0NKCvUeLKDBw9WuOepLBYLERERtYpHzp/r+kQzpG/ZAP98yR5Wbzvi5ohEREREpCGIDevMfZ3urDRDIcQrqN5vC3my5s1b0LlzV6BsB4fFi38AKs5KAPDyKpuKf+LE8Up9ZGRkOHd/OFtRUdFERDTD4XAwf/7cSucLCwv54YeFVV57NjF6eXkBUFRUVKt4+/S5FKDamggHDuxnw4a1Fdp6Io+omVA+iyAoqPppQ+XnajpNpXPnsoG8fft2UlJSaNasWYXzJy9ROLXP2sSTnZ1do3hOx2yu3zkfk8lY4d+eYHj/NpSU2lmyIZlpP+zE19tMrw7Vz1IROV88cTyJ1EcaSyKu4cljyW53T3HF2LDOdGnSkX2ZCWQXZRPoFUib4BiPmJFwsuuuu4Ft27Yye/YXFBUVYbVaGTjw2gptYmO7sXLlcj77bDrduvVwfiJ/+HAK//znsxQWFrokFoPBwJ//fCdvvPE6X3zxCW3btqNfvyuAst0YXn31X+Tm5lZ57dnE2KxZcw4c2E9c3KYKMzLOpFu3HnTpEkt8fBwvvvgsEyb8m2bNmmMwwKFDyfzrX8/hcDiIje1e7YyJc8FkMrj03dIjkgnlmaDqZiUAWK3WCm3PZODAgYSHh3P06FHGjRvHW2+95ZxNEB8fzz//+U9n21O/wWoTz9kOIKPRQEiIZ6yjCQz0cXcItfLQ8G7YMbB0QxJTF2wnNMSPnkooSD3haeNJpL7SWBJxDU8cS4WFJo4fN7r8BapmjFzcpO15vqdrXXPNNbz99kTn+0y/flcQGhpcoc3NN9/KwoXzSUw8yJ13jiAqKgqj0UhCwgF8fHx5+OHHmDTp3xgM1f8/OPV4eS0Ik8lY4dxttw0jLm4Ty5b9j6efHkdERCTBwcEkJBzA4XBw330PMHXqu5XudTYxXnPNIN5//z0mTnyd+fO/IjCw7APjJ54Yz0UXtauQZDv12pdeeoWHH36AvXv3MHLkLbRq1RqHw0FCwgHsdjtRUdG89NIr1X5dTn3+ckajwfl1qun3td1uwGg0EhTkW21hx7rwiGRC+fSS8r1Aq1JcXFyh7ZlYrVbeeust7rvvPuLi4hgwYADR0dEUFRWRkpJCcHAw11xzDUuWLMHPr+LLvJeXFwUFBTWK52z/Z9ntDrKz63cRR5PJSGCgD9nZBdhsdneHUyt3XN2W7Lwi1v+Wyqsz1vO3kd1oH119lViRc82Tx5NIfaKxJOIanjyWiouLsNvt2GwOSks9K/b6wMvLlyuu+BNLlvwIwKBB11f6Olqt3rz77od8+OEUVq/+heTkJEJCQrnmmsHcffd9pKYeBcp2Hqju/8Gpx8t3KbDZ7JXOvfDCBDp37sq33y4gOTmJgoJ8evXqw+jRY8jJya7yXmcT4+2330lpqY2lSxeTnJxMcfE+ADIzsygttVcYE6de27hxUz7++DNmzfqCFSt+4tChZKBsC8urrhrAiBF/xtfXr9qvS1XPD2Xvh9XFWx2bzYHdbicrK5+CAtsZ2wcG+tRoNpLBUR/2lDiDuXPn8o9//IOWLVs6d1I41cKFC/nb3/5GREQEy5cvr3HfycnJfPjhh6xatYq0tDSCgoK44oorePTRR3n//feZNWsWl19+OR999JHzmiuuuILU1FQmTpzIDTfcUGW/11xzDYmJibzyyivcdttttXrek9lsdtLTq94upL4wm42EhPiRkZHnkT+oS2123p23jfj9J/Cymvjb7d1oFRl45gtFzgFPH08i9YXGkohrePJYKikp5sSJIzRqFIHFYnV3OCJA2Zg632OptmMhNNSvRskEj5iZ0LJlSwAOHz5MSUlJlcsLkpKSKrStqRYtWvDSSy9VeW7fvrLMU3l9hZPjSU1NJTGx6q0FS0pKOHz4cJ3ikfPPbDIydmgn3pq7lV1Jmbw5J46//7k7LcL83R2aiIiIiIhIveQRVUA6dOiAxWKhuLiY+Pj4Ktts2rQJgNjYWJfcMz093bkF5IABAyqcK79H+T1PFR8fT0lJCV5eXnTo0MEl8ci5ZbWYePS2LrSODCSvsJQ3ZsdxNL1+Ly8RERERERFxF49IJvj7+9OvXz8A5syZU+n8wYMHWbu2bGuNQYMGueSeb731FqWlpfTs2ZNOnTpVOHfttWVVTNetW1fl7ITZs2cDZcshTq23IPWXt9XM48O70iLMn+y8YibO2sLxrAJ3hyUiIiIiIlLveEQyAWDs2LEYDAa++eYbZs+e7SzMkZaWxrhx47Db7QwcOJD27dtXuK5///7079+fRYsWVepzxYoVlWYXZGdn88orrzB79mx8fX2rXALRsWNH/vSnP2Gz2XjiiSdIS0sDyopgzJ49m2+++Qaj0ciDDz7oqseX88TP28KTI2IJD/UlPbuIibPiyMyt3b6yIiIiIiIiDZ1H1EwA6NKlC08//TSvv/46L7zwAlOnTiUkJIR9+/ZRXFxMTEwML7/8cqXrUlJSAMjPrzxlfdWqVXz66af4+/vTrFkzAA4cOEBJSQnBwcG8++67tG7dusp4Xn31VUaOHMmOHTsYMGAAbdq0ISMjgyNHjmAwGHj22Wfp2LGjC78Ccr4E+lkZf3ssr3+xmbSMAt6YFcdTo7rj71P9VqAiIiIiIiIXEo+ZmQBw1113MX36dK644goKCgrYt28fkZGRPPDAA3z99deEhobWqr+BAwcyZMgQQkNDSUpKIjk5mZiYGB544AEWLVpEr169qr02NDSUr7/+mgceeIDIyEj27dtHQUEBV1xxBTNmzODOO+8828cVNwoN9Gb87bEE+VtJOZ7HpNlxFBSVujssERERERGResEjtoa8kGlrSPdKOZ7H/32xmdyCEi5qHsQTI2LxspjcHZY0YA15PImcTxpLIq7hyWNJW0NKfdSQtob0qJkJIudbs8Z+PDkiFh8vE3sOZfHuvG2UeNgvUhEREREREVdTMkHkDKLDA3hiWCxWi5EdCel8sHAHNrsSCiIiIiIicuFSMkGkBto0D+KRW7tgNhnYvOcY077fiV0rhEREREQ8gP7OJhe6czMGlEwQqaGOLUN5cGgnjAYDv+5I5fMle1DJEREREZH6yWAoe9Wx2TSjVC5s9t9nVRsMBpf2q2SCSC10a9uEe2/ogAFYviWFucv3K6EgIiIiUg+ZTCaMRjNFRQXuDkXErYqLizAYjJhMZpf2q2SCSC31uTicvwxqB8CidUl8u+agewMSERERkUoMBgPe3r4UFuZRUlLk7nBE3MJut1NYmIeXl7fLZya4NjUhcoG4MrYZRcU2Zv20jwUrE/C2mrmmVwt3hyUiIiIiJ/H3D6KkpIj09DS8vf3w8vL5fcs7175UidSU3W7AZjv3M5sdDgc2Wwl5eTnY7Xb8/YNdfg8lE0Tq6JpLoigstrFgVQKzlu3F22riiq6R7g5LRERERH5nNBoJCQkjNzeLwsJ8Cgpy3B2SXOCMRqOzhsH5YLV6ExgYhtlscXnfSiaInIUbLmtJYbGNReuT+OTHXVgtRvpcHO7usERERETkd0ajkcDAEAICgrHZbDgcKsgo7mEyGQgK8iUrK/+8zE4wGk2YTKZz1r+SCSJnwWAwMOxPrSkssbF8SwoffbsTL4uJbm2buDs0ERERETmJwWDAbNbrj7iP2WzE29ubggIbpaWen9RSAUaRs2QwGLjjmovo27EpdoeDqQu2s+NgurvDEhEREREROWeUTBBxAaPBwOghHeh+URNKbQ4mfx3PvkNZ7g5LRERERETknFAyQcRFTEYj99/YkY4xoRSX2Hlz7lYSj6rIj4iIiIiINDxKJoi4kMVs5OFbOtO2eRAFRaW8MTuOlON57g5LRERERETEpZRMEHExL4uJx27rSnR4ALkFJbwxawtpmQXuDktERERERMRllEwQOQd8vc08OSKWZo39yMwtZuKXW0jPLnR3WCIiIiIiIi6hZILIOeLvY+HJ22MJC/HheFYhE2fFkZ1X7O6wREREREREzpqSCSLnULC/F+NvjyU00Iuj6fm8MTuOvMISd4clIiIiIiJyVpRMEDnHGgf5MP72bgT6WUlOy+WtOVspLC51d1giIiIiIiJ1pmSCyHkQHurL+BGx+Hmb2X84m8lfb6Ok1ObusEREREREROpEyQSR86R5mD9PDI/Fy2piZ2IGU+Zvp9Rmd3dYIiIiIiIitaZkgsh51CoykMdv64LFbGTr/hN89N1v2O0Od4clIiIiIiJSK0omiJxn7aJCeOjmzpiMBtbvTGPGol3YHUooiIiIiIiI51AyQcQNurRuxP03dsRggFXxR5i1dC8OJRRERERERMRDKJkg4iY924cx+roOACzddIj5Kw+4OSIREREREZGaUTJBxI0u6xzBHddcBMB3axL5YW2imyMSERERERE5MyUTRNysf/fm3HZVawC+Wr6fnzYfcnNEIiIiIiIip6dkgkg9cF2faK6/NBqAz5fsYfW2I26OSEREREREpHpKJojUEzdf3oqBPZoDMO2HnWzclebmiERERERERKqmZIJIPWEwGLh9YFv6dY7A4YAPFu4gfv8Jd4clIiIiIiJSiZIJIvWI0WDgrsHt6dU+DJvdwXvzt7E7KcPdYYmIiIiIiFSgZIJIPWM0Grjvhovp0roRJaV23voqngOHs90dloiIiIiIiJOSCSL1kNlkZOzQTrSPCqao2Mabc+JITst1d1giIiIiIiKAkgki9ZbVYuLR27rQOjKQvMJS3pi1haPp+e4OS0RERERERMkEkfrM22rm8eFdiQrzJzu/hImztnA8q8DdYYmIiIiIyAXO7O4Aamvt2rVMnz6drVu3kp+fT2RkJIMGDWLMmDH4+vrWur/Dhw8zbdo0Vq1axZEjR7Db7TRp0oTevXtz11130a5du0rXHDp0iAEDBpy2365duzJnzpxaxyNyKj9vC+NGxPJ/Mzdz5EQ+E2fF8fSo7gT7e7k7NBERERERuUB5VDLhs88+45VXXsHhcBAeHk5ERAT79u1j6tSpLFmyhJkzZxIcHFzj/rZs2cI999xDXl4eFouF5s2bY7FYSEpKYt68eSxcuJCJEycyePDgavvo3r17lcfbtm1b28cTqVagn5UnR8Ty+hebScso4I1ZcTw1qjv+PhZ3hyYiIiIiIhcgg8PhcLg7iJrYvn07w4YNw+Fw8K9//Yvhw4djMBhITU3lwQcfZMeOHVxzzTVMnjy5Rv05HA6uvfZaEhMT6datG5MmTSIyMhKAnJwcXnzxRb777jv8/f1Zvnw5AQEBzmtPnpmwe/du1z/sSWw2O+npeef0HmfLbDYSEuJHRkYepaV2d4fToKVlFvDa55vIyi2mZXgAfxvZDR8vj8oJyhloPIm4hsaSiGtoLIm4jqeMp9BQP0ymM1dE8JiaCVOmTMFut3PTTTcxYsQIDAYDAE2bNmXSpEkYjUaWLFnCrl27atTfvn37SExMBODFF190JhIAAgICeO211/D19SU3N5eNGze6/oFE6iAs2Ifxt3fD38fCwaM5vD13K0UlNneHJSIiIiIiFxiPSCbk5eWxcuVKAIYPH17pfMuWLenTpw8AixYtqlGfhYWFzv9u0aJFpfNWq5WmTZsCUFpaWuuYRc6VZo39eHJELD5eJvYcyuLdedsoqceZTRERERERaXg8Ipmwc+dOiouLsVqtdOnSpco2PXr0AGDr1q016jMmJgZvb2+grHbCqdLS0jh06BAmk4mLL7642n4mTJjA6NGjueeee3jhhRdYsmQJdrte7OTcig4P4IlhsVgtRnYkpPPBwh3Y9H0nIiIiIiLniUckExISEgCIjIzEYqm64FxUVFSFtmfi7+/P2LFjAXjmmWdYtGgRGRkZ5ObmsnbtWsaMGUNJSQljxoyhWbNm1fbz2WefsXr1alatWsXs2bN55JFHuOWWW0hOTq7NI4rUWpvmQTxyaxfMJgOb9xxj2vc7sXtGCRQREREREfFwHlG5LSsrC4CgoKBq25SfK29bE/fffz9NmjTh448/5rHHHqtwrmXLlrz55ptcd911la4zm83ceOONDBkyhDZt2hAWFkZGRgYrVqzgrbfeYufOndxzzz3MmzcPf3//GsdTHbO5fud8yotz1KRIh7hW1zaNefjWLrwzN55fd6Ti42Xmr4PbO2uKiOfReBJxDY0lEdfQWBJxnYY2njwimVBUVARQ7awEKKtxcHLbmigpKSE5OZmsrCzMZrNza8jExEQSExP56quv6N69O+Hh4RWuCw8P5z//+U+FY02bNmX48OH07t2bW265hcTERD799FPn7Ie6MhoNhIT4nVUf50tgoI+7Q7ggDejth9li5o2Zm/hpcwrBgT7cdf3FSih4OI0nEdfQWBJxDY0lEddpKOPJI5IJXl5eQNnLf3WKi4srtK2Jhx9+mOXLl3PFFVcwYcIEZ8HFrKwsJkyYwMKFCxkxYgTff/99jWcYREdHM3LkSD788EP+97//nXUywW53kJ2df1Z9nGsmk5HAQB+yswuw2bRu3x26xIRw93UdmPb9TuYt3wcOO0Mvb+XusKQONJ5EXENjScQ1NJZEXMdTxlNgoE+NZk94RDKhJksYarIU4mQ//fQTy5cvJyQkhEmTJhEQEFDhfq+++irbt2/nwIEDzJw5kzFjxtQ43m7dugFw8ODBGl9zOvV5D9KT2Wx2j4m1IerXOYL8wlJmLdvLvBUHsJpNXNOr8k4l4hk0nkRcQ2NJxDU0lkRcp6GMJ49YrNGyZUsADh8+XO3shKSkpAptz2Tjxo0AdOnSpUIioZzFYqF3794AbN++vVbxli/HsNlstbpO5Gxd06sFQ/vFADBr2V5+2XrYzRGJiIiIiEhD5BHJhA4dOmCxWCguLiY+Pr7KNps2bQIgNja2Rn3m5eXV+P61qcMAsHfvXoBKtRZEzocbLmvJoEvKdjf55MddrP3tqJsjEhERERGRhsYjkgn+/v7069cPgDlz5lQ6f/DgQdauXQvAoEGDatRnTEzZp7fx8fHk5ORUOl9SUsK6desqtK2JvLw8Zs6cCcBll11W4+tEXMVgMDDsT625qlszHMBH3+5ky95j7g5LREREREQaEI9IJgCMHTsWg8HAN998w+zZs3E4HACkpaUxbtw47HY7AwcOpH379hWu69+/P/3792fRokUVjg8aNAir1UpGRgbjxo0jNTXVeS4rK4tnn32WAwcOYDAYuPHGGytc+/zzz7NkyRJn0cdy+/fv59577+XQoUP4+vpyzz33uPJLIFJjBoOBO665iL4dm2J3OJi6YDs7Dqa7OywREREREWkgDI7yt3IPMGPGDF5//XUcDgcRERGEhISwb98+iouLiYmJYebMmYSGhla4pl27dgC89tpr3HLLLRXOLViwgOeee47S0tJKW0MWFxdjMBgYP3489957b4XrbrrpJnbt2oXFYiEqKgp/f38yMjKcdRuCgoJ46623uPTSS8/6mW02O+npNV+S4Q5ms5GQED8yMvIaRCGRhsRmtzN1wQ427zmG1WJk/IhutGlesyKl4h4aTyKuobEk4hoaSyKu4ynjKTTUr+Hs5lDurrvuol27dkybNo34+HhOnDhBZGQkgwYNYsyYMfj5+dWqv6FDh9K+fXs++eQTNm7cyOHDh3E4HDRp0oRu3boxatQoevToUem6+++/n5UrV7J9+3aOHz9OYmIi3t7edOzYkSuuuIJRo0bRpEkTVz22SJ2ZjEbuv7Ejk7+OZ3tCOm/O3crfR3YjOrxy0VEREREREZGa8qiZCRcizUwQVygqsfHm7Dj2HMrC38fCU6O606xx7ZJvcn5oPIm4hsaSiGtoLIm4jqeMp5rOTPCYmgkiUndeFhOPDetKy/AAcgtKeGPWFtIyC9wdloiIiIiIeCglE0QuED5eZsaNiKVZYz8yc4uZ+OUW0rML3R2WiIiIiIh4ICUTRC4g/j4Wnrw9lrAQH45nFTJxVhzZecVnvlBEREREROQkSiaIXGCC/b0Yf3ssoYFeHE3P543ZceQVlrg7LBERERER8SBKJohcgBoH+TD+9m4E+llJTsvlrTlbKSgqdXdYIiIiIiLiIZRMELlAhYf6Mn5ELH7eZvYfzmby1/GUlNrcHZaIiIiIiHgAJRNELmDNw/x5YngsXlYTu5IymTJ/O6W2+rtNjYiIiIiI1A9KJohc4FpFBvL4bV2wmI1s3X+Cj777Dbvd4e6wRERERESkHlMyQURoFxXCw7d0xmQ0sH5nGjMW7cLuUEJBRERERESqpmSCiADQuVUj7r+xIwYDrIo/wqyle3EooSAiIiIiIlVQMkFEnHq2D2P0dR0AWLrpEPNXHnBzRCIiIiIiUh8pmSAiFVzWOYI7rrkIgO/WJPLD2kQ3RyQiIiIiIvWNkgkiUkn/7s257arWAHy1fD/LNh1yc0QiIiIiIlKfKJkgIlW6rk80118aDcAX/9vD6m1H3ByRiIiIiIjUF0omiEi1br68FQN7NAdg2g872bgrzc0RiYiIiIhIfaBkgohUy2AwcPvAtvTrEoHDAR8s3EH8/hPuDktERERERNxMyQQROS2jwcBdg9pzSYcwbHYH783fxu6kDHeHJSIiIiIibqRkgoickdFo4N7rL6Zr60aUlNp566t4DhzOdndYIiIiIiLiJkomiEiNmE1GHhzaifZRwRQV23hzThzJabnuDktERERERNxAyQQRqTGrxcSjt3WhdWQgeYWlvDFrC0fT890dloiIiIiInGdKJohIrXhbzTw+vCtRYf5k55cwcdYWjmcVuDssERERERE5j5RMEJFa8/O2MG5ELBGNfEnPLmLil3Fk5ha5OywRERERETlPlEwQkToJ9LPy5IhYGgd5k5ZZwBuz4sgtKHF3WCIiIiIich4omSAidRYa6M34kd0I9reScjyPSbPjKCgqdXdYIiIiIiJyjimZICJnJSzYh/G3d8Pfx8LBozm8PXcrRSU2d4clIiIiIiLnkJIJInLWIhv78eSIWHy8zOw5lMW787ZRUmp3d1giIiIiInKOKJkgIi4RHR7AE8O6YrUY2ZGQzgcLd2CzK6EgIiIiItIQKZkgIi7TpnkQj9zaBbPJwOY9x5j2/U7sDoe7wxIRERERERdTMkFEXKpjy1AeHNoJo8HArztS+XzJHhxKKIiIiIiINChKJoiIy3Vr24R7b+iAAVi+JYW5P+9XQkFEREREpAFRMkFEzok+F4fz18HtAVi0Polv1xx0b0AiIiIiIuIySiaIyDlzRddIbh/QFoAFKxNYsiHZzRGJiIiIiIgrKJkgIufUNb1aMPTyGABmLdvLL1sPuzkiERERERE5W0omiMg5d8OlLRnUOwqAT37cxdrfjro5IhERERERORtKJojIOWcwGBh2VWuu6tYMB/DRtzvZsveYu8MSEREREZE6UjJBRM4Lg8HAHddcRN+OTbE7HExdsJ0dB9PdHZaIiIiIiNSB2d0B1NbatWuZPn06W7duJT8/n8jISAYNGsSYMWPw9fWtdX+HDx9m2rRprFq1iiNHjmC322nSpAm9e/fmrrvuol27dtVee+LECaZOncrPP/9MWloagYGB9OrVi/vvv58OHTqczWOKNEhGg4HRQzpQVGJn855jTP46nidHxNK2ebC7QxMRERERkVowODxo8/fPPvuMV155BYfDQXh4OKGhoezbt4/i4mJat27NzJkzCQ4OrnF/W7Zs4Z577iEvLw+LxULz5s2xWCwkJSVRWFiI2Wxm4sSJDB48uNK1iYmJ/PnPf+b48eP4+voSExPD0aNHOXHiBBaLhbfffpsBAwac9TPbbHbS0/POup9zyWw2EhLiR0ZGHqWldneHIx6gpNTO5K/j2Z6Qjo+Xib+P7E50eIC7w6oXNJ5EXENjScQ1NJZEXMdTxlNoqB8m05kXMXjMMoft27fz6quvAvDSSy+xfPly5s+fz9KlS+nYsSP79+/n+eefr3F/DoeDp556iry8PLp168aSJUtYtGgR3377LatWreL666+ntLSUf/zjH+Tk5FS69rHHHuP48eNcfvnl/PLLL8ybN49ffvmFsWPHUlJSwvjx40lLS3Pp10CkobCYjTx0S2cuah5EQZGNN2bHkXK8fifNRERERETkDx6TTJgyZQp2u52bbrqJESNGYDAYAGjatCmTJk3CaDSyZMkSdu3aVaP+9u3bR2JiIgAvvvgikZGRznMBAQG89tpr+Pr6kpuby8aNGytcu2zZMnbu3ElAQABvvPEGAQFln6iazWYee+wxevXqRX5+PtOmTXPFo4s0SF4WE48N60rL8AByC0p4Y9YW0jIL3B2WiIiIiIjUgEckE/Ly8li5ciUAw4cPr3S+ZcuW9OnTB4BFixbVqM/CwkLnf7do0aLSeavVStOmTQEoLS2tcO7HH38EYNCgQQQFBVW6tjzG8nYiUjUfLzPjRsTSrIkfmbnFTPxyC+nZhWe+UERERERE3Mojkgk7d+6kuLgYq9VKly5dqmzTo0cPALZu3VqjPmNiYvD29gbKaiecKi0tjUOHDmEymbj44osrnCu/R8+ePavsu/z40aNHSU1NrVE8Ihcqfx8L40fEEhbiw/GsQibOiiM7r9jdYYmIiIiIyGl4xG4OCQkJAERGRmKxWKpsExUVVaHtmfj7+zN27FgmTZrEM888w3PPPUfv3r2xWCxs376d119/nZKSEh588EGaNWvmvK64uJiUlJQK9zxVREQEFouFkpISDhw44JzhUFdmc/3O+ZQX56hJkQ6RqjQK9uHpUd2Z8OlGjqbnM2lOHM/c0QM/n6rHe0Om8STiGhpLIq6hsSTiOg1tPHlEMiErKwugyiUF5crPlbetifvvv58mTZrw8ccf89hjj1U417JlS958802uu+66Csdzc3Ox2+2njcdgMBAYGMiJEyfIzs6ucTxVMRoNhIT4nVUf50tgoI+7QxAPFhLix6tj+/H0e6tISs3lra/ieWlMX3y9L7yEAmg8ibiKxpKIa2gsibhOQxlPHpFMKCoqAqh2VgKU1Tg4uW1NlJSUkJycTFZWFmaz2bk1ZGJiIomJiXz11Vd0796d8PDwSrGcfM/TxXNybYa6sNsdZGfnn1Uf55rJZCQw0Ifs7AJstvq7xYnUf75mA38b2Y1XP93I7sQMXvzvrzx5eyxWi8ndoZ03Gk8irqGxJOIaGksiruMp4ykw0KdGsyc8Ipng5eUFlL38V6e4uLhC25p4+OGHWb58OVdccQUTJkxwLkfIyspiwoQJLFy4kBEjRvD999/j7+9fqf/ye54unvK6DGejPu9BejKbze4xsUr9FRHqyxPDY/nPrC3sTMzgna/iefiWzpgbyHSwmtJ4EnENjSUR19BYEnGdhjKePOJv5zVZwlCTpRAn++mnn1i+fDkhISFMmjSpQl2DoKAgXn31VVq1asXRo0eZOXOm85y/vz9Go/G08TgcDufyhsDAwBrFIyJ/aBUZyOO3dcFiNhK//wQfffcbdrvD3WGJiIiIiMjvPCKZ0LJlSwAOHz5c7eyEpKSkCm3PZOPGjQB06dKFgICASuctFgu9e/cGYPv27c7jVquVyMjICvc81ZEjR5xxxsTE1CgeEamoXVQID9/SGZPRwPqdacxYtAu7QwkFEREREZH6wCOSCR06dMBisVBcXEx8fHyVbTZt2gRAbGxsjfrMy8ur8f1PrcNQfo/yhMSpyo+Hh4dXqLcgIrXTuVUj7r+xIwYDrIo/wqyle3EooSAiIiIi4nYekUzw9/enX79+AMyZM6fS+YMHD7J27VoABg0aVKM+y2cMxMfHk5OTU+l8SUkJ69atq9C23LXXXgvAokWLqlzqUB5jTWMRker1bB/G6Os6ALB00yHmrzzg5ohERERERMQjkgkAY8eOxWAw8M033zB79mznp5NpaWmMGzcOu93OwIEDad++fYXr+vfvT//+/Vm0aFGF44MGDcJqtZKRkcG4ceNITU11nsvKyuLZZ5/lwIEDGAwGbrzxxgrXDhw4kHbt2pGTk8P48eOdyQibzcbbb7/Nhg0b8PHxYfTo0efiSyFywbmscwR3XHMRAN+tSeSHtYlujkhERERE5MJmcHjQnOEZM2bw+uuv43A4iIiIICQkhH379lFcXExMTAwzZ84kNDS0wjXt2rUD4LXXXuOWW26pcG7BggU899xzlJaWVtoasri4GIPBwPjx47n33nsrxZKQkMCoUaM4ceIEvr6+xMTEcPToUU6cOIHFYuHNN9/k6quvPutnttnspKfXfEmGO5jNRkJC/MjIyGsQVUml/vphbSJfLd8PwKirL2JAj+Zujsj1NJ5EXENjScQ1NJZEXMdTxlNoqF/D2Rqy3F133UW7du2YNm0a8fHxnDhxgsjISAYNGsSYMWPw8/OrVX9Dhw6lffv2fPLJJ2zcuJHDhw/jcDho0qQJ3bp1Y9SoUfTo0aPKa2NiYli4cCFTp07l559/Zs+ePQQGBnLttdfywAMPcPHFF7vikUXkJNf1iaawuJTv1iTyxf/24G01cVnnCHeHJSIiIiJywfGomQkXIs1MEKnI4XDw5dK9LN10CIMBHrypEz3bh7k7LJfReBJxDY0lEdfQWBJxHU8ZTzWdmeAxNRNERAAMBgO3D2xLvy4ROBzwwcIdxO8/4e6wREREREQuKEomiIjHMRoM3DWoPZd0CMNmd/De/G3sTspwd1giIiIiIhcMJRNExCMZjQbuvf5iurZuREmpnbe+iufA4Wx3hyUiIiIickFQMkFEPJbZZGTszZ3oEB1CUbGNN+fEkZyW6+6wREREREQaPCUTRMSjWcwmHrm1M60jA8krLOWNWVs4mp7v7rBERERERBo0JRNExON5W808PrwrUWH+ZOeXMHHWFo5nFbg7LBERERGRBkvJBBFpEPy8LYwbEUtEI1/Ss4uY+GUcmblF7g5LRERERKRBUjJBRBqMQD8rT46IpXGQN2mZBbwxK47cghJ3hyUiIiIi0uAomSAiDUpooDfjR3Yj2N9KyvE8Js2Oo6Co1N1hiYiIiIg0KEomiEiDExbsw/jbu+HvY+Hg0RzenruVohKbu8MSEREREWkwlEwQkQYpsrEfT46IxcfLzJ5DWbw7bxslpXZ3hyUiIiIi0iAomSBnxe6wszt9H6sSN7A7fR92h17WpP6IDg/giWFd8bKY2JGQzgcLd2Cz63tURERERORsmd0dgHiuuLRtzN27kMyiLOexYK8ghrW9kdiwzm6MTOQPbZoH8citnXlrbjyb9xxj2vc7uef6izEaDO4OTURERETEY2lmgtRJXNo2Ptz+WYVEAkBmURYfbv+MuLRtbopMpLKLW4YydmgnjAYDv+5I5fMle3A4HO4OS0RERETEYymZILVmd9iZu3fhadt8tXehljxIvRLbtjH33tABA7B8Swpzf96vhIKIiIiISB0pmSC1ti8zodKMhFNlFGWxLzPhPEUkUjN9Lg7nr4PbA7BofRLfrjno3oBERERERDyUaiZIrWUXZdeo3Re7vqJz4w7EBEYTExRFiFcwBq1TFze7omskhcU2Zi3by4KVCXhbzVzTq4W7wxIRERER8ShKJkitBXoF1qjd8YIT/Jy8ip9ZBUCQNZCYoChigqKJCYymRUAzrCbLuQxVpErX9GpBYXEpC1YmMGvZXrytJq7oGunusEREREREPIaSCVJrbYJjCPYKOu1Sh0BrADe1Hkxi9iESshNJyT1CVnE2cce2E3dsOwAmg4nm/pG0DIqiVWBZkiHUO0SzF+S8uOHSlhQW21i0LolPftyF1WKkz8Xh7g5LRERERMQjKJkgtWY0GBnW9kY+3P5ZtW1GXDSU2LDO9InoCUCxrdiZWDiYlcSB7ERyinNJzEkmMSeZFawGIMDqT6vAaFoGRRETGE10YHOsJut5eS65sBgMBoZd1ZqiYhs/b0nho2934mUx0a1tE3eHJiIiIiJS7xkcKmder9lsdtLT89wdRpXi0rYxd+/CCjMUQryCuK3tjcSGdT7ttQ6Hg/TCDBKyEknITiIhK4nk3JRKO0AYDUaa+Uc46y7EBEbT2CdUsxfEZewOBx9/t5NfdxzFbDLw2LCudGwZ6rZ4zGYjISF+ZGTkUVqqHVFE6kpjScQ1NJZEXMdTxlNoqB8m05n3ajjnyQSbzcaXX37J6tWrMRqNXHXVVQwbNuxc3rJBqc/JBCjbJjIh5yCl5mLMpVZiAlpiNNRtk5BiWwnJOSkkZCeSkJVEQlYiWcWViz36W/yICYqiZWA0rYKiiApogbfZ62wfRS5gNrudqQt2sHnPMawWI0+OiKVt82C3xOIpv2RE6juNJRHX0FgScR1PGU/nNZnw1Vdf8fzzz3Pttdfy1ltvVTj32GOPsWTJEqDs02iDwcCgQYN48803z/a2F4T6nkyAczcoHA4HmUVZHMhKdC6PSM5JodRhq9DOgIFI//DfCzuW1V4I82ms2QtSKyWldiZ/Hc/2hHR8vEz8fWR3osMDznscnvJLRqS+01gScQ2NJRHX8ZTxVNNkgktqJqxeXbbe/frrr69wfN26dSxevBiA7t274+3tza+//sqiRYsYMmQIAwcOdMXtpYEyGAyEeAfTwzuYHk27AlBiL+VQTgoJWYkcyE7iYFYSGUWZpOQeISX3CKtS1gLgZ/Z11l2ICYoiOrAFPmZvdz6O1HMWs5GHbunMm7Pj2HMoizdmx/HUqO40a+zn7tBEREREROodlyQTdu7cCZQlDE62YMECAIYPH85LL70EwJQpU3jnnXeYP3++kglSaxajuWwGQlA0/X8/llmU5VwWkZCdSFJOCnml+ew4sYsdJ3YBZbMXIvyaOusuxARFEebbpM5LMqRh8rKYeGxYV/7z5RYOHs3hjVlbePqOHoQF+7g7NBERERGResUlyxx69+5NYWEhW7durXD8qquuIjU1lYULF9K2bVsAsrKy6N27N02bNmXFihVne+sG70Je5lBXpfZSDuUePinBkER6YUaldj5mH2ICo37fmjKa6MAW+Fr00iiQW1DC/83cTMqxPBoHefP0qO6EBp6fmS31bTyJeCqNJRHX0FgScR1PGU/ndZlDXl4evr6+FY6lpaVx9OhRGjdu7EwkAAQFBeHv7096erorbi1SidlopmVgFC0Do/hTi34AZBVlk/D7sogDWYkk5RyioLSA39J381v6bqBs9kJTvzBa/Z5giAmMJtwvTLMXLkD+PhbGj4jltS82k5ZRwMRZcTw9qjuBftqmVEREREQEXJRM8Pf3Jysri4KCAnx8yj7Z3bBhAwDdunWr8hovL1Xfl/MnyCuQ2CadiG3SCQCb3UZK7pHft6VMJCErkeOF6RzNS+VoXiprjpR9/3qbvGkZ2KJseURQNC0Do/Cz+J7uVtJABPl7Mf72WF7/YjNH0/N5Y3Ycf/9zN/y8Le4OTURERETE7VySTGjbti0bN27kxx9/5JZbbgHK6iUYDAZ69epVoW1OTg65ubm0bNnSFbcWqROT0URUYHOiAptzZfNLAcgpznUui0jISiQxO5lCWyG7MvayK2Ov89qmvk2ICYwuWx4RFE2EX1PNXmigGgf58Lfbu/HaF5tJTsvlrTlbGTciFh8vl/zoFBERERHxWC75G/H111/Phg0beOmll9i6dSvHjx9n5cqVWK1WBg8eXKHtli1bAJRMkHonwOpPlyYd6dKkI1A2e+FwXioJWYkc/D3BkFZwnNT8Y6TmH2Pt0Y0AeJmsRAdGVVge4W/VDgANRdNQX8aPiOX/Zm5m/+FsJn8dz+PDumK1mNwdmoiIiIiI27gkmXDbbbexePFi1qxZw5w5c3A4HBgMBh5//HGaNGlSoe2iRYuqnLEgUt+YjCZaBETSIiCSK+gLQG5xnjOxkJCdxMHsJIpsxezJ2MeejH3Oa5v4NCrbdeL3nSMi/cIxGfXy6amah/nzxPBY/jNrC7uSMpmyYDsP39IZcw0K04iIiIiINEQu2c0BwG63891337FlyxYCAwO54oor6NGjR4U2xcXFPPTQQxQWFvLCCy9UKMwoVdNuDvWb3WHnyO+zFxKykkjITiI1P61SO6vRQnRgi98TDGX1FwKs/m6IWM7G7qQMJs3ZSkmpnV7tw7j/xo4YjQaX3uNCHk8irqSxJOIaGksiruMp46mmuzm4LJkg54aSCZ4nrySfg9nJJy2PSKLQVlipXWPv0LJlEb8nGJr7R2r2ggfYduAE73wVj83uoF+XCO4a3B6jwXUJBY0nEdfQWBJxDY0lEdfxlPF0XreGFJE/+Fl86dioHR0btQPKZi8czUursDziaF4axwvTOV6YzsbUOAAsRgtRAc2dO0fEBEYR5BXoxieRqnRu1Yj7b+zI1G+2syr+CN4WEyMHtsXgwoSCiIiIiEh9d16SCT///DOrV6/GaDRy5ZVXctlll52P24rUC0aDkUj/cCL9w7k08hIA8ksKSMxOJiH7j+URBaUF7M9KYH9WgvPaUO8Q57KImKCy2Qtmo3KA7tazfRijSzrw8fc7WbrpEN5eJm65orW7wxIREREROW9cssxhyZIl/N///R+XXXYZL730UoVzr732Gp9++mmFY3fddRdPPfVUne61du1apk+fztatW8nPzycyMpJBgwYxZswYfH19a9zPunXr+Mtf/lKjto888ggPP/xwhWPt2rU77TWNGzdm9erVNY6nOlrmcGGwO+yk5R+vsDXlkbxUHFQcnmajmaiAZrT8PcHQKiiaYK8gN0UtP20+xOdL9gBw21Wtua5P9Fn3qfEk4hoaSyKuobEk4jqeMp7O6zKHn376icOHD9OzZ88Kx3fs2MEnn3wCQGRkJBaLhcTERGbMmMFVV11F7969a3Wfzz77jFdeeQWHw0F4eDgRERHs27ePqVOnsmTJEmbOnElwcHCN+goICKB79+7Vns/NzWXPnrKXhG7dulXbrlOnTlit1krHaxqHCJTNXgj3CyPcL4y+kWU7nRSUFpbNXshK4mB2WZIhrySfA1mJHMhKhOSVAAR7BVUo7NgioBkWzV44L/p3b05RsY25y/fz1fL9eFlMDOjR3N1hiYiIiIiccy5549i2bRsAffv2rXD866+/BuDqq6/m7bffxmg08vLLL/PFF18wZ86cWiUTtm/fzquvvgrASy+9xPDhwzEYDKSmpvLggw+yY8cOnn/+eSZPnlyj/i6++GK+/PLLas+/++677Nmzh4iIiErPdbK3336b5s318iCu52P2pn1oW9qHlu164nA4OFZw3LksIiErkZTcI2QWZbElLZ4tafEAmA0mmgc0K6u98HuCIcQrWGv6z5HBfaIpKLbx3ZqDfPG/PXhbTVzWOcLdYYmIiIiInFMuSSakp6djMplo0qRJheOrV6/GYDBw3333YTSWTZO4//77+eKLL4iLi6vVPaZMmYLdbmfo0KGMGDHCebxp06ZMmjSJwYMHs2TJEnbt2kX79u3P6nkcDgcLFiwA4KabbnLGLuJOBoOBMN8mhPk2oXdE2barhaVFJOUcqrA8Irckj4PZSRzMTuLn368Nsgb8XnchmpaBUUQFNMdqsrjvYRqYmy+PobC4lKUbDzHth514WUz0bB/m7rBERERERM4ZlyQTcnJy8PPzq3AsIyODxMREgoKC6NKli/N4WFgYPj4+HDt2rMb95+XlsXJl2ZTu4cOHVzrfsmVL+vTpw5o1a1i0aNFZJxM2bNhAcnIyALfccstZ9SVyLnmbvbgopDUXhZQV/3M4HBwvSHcWdjyYncih3CNkFecQd2w7cce2A2XLKpr7R5bVXQiMomVQNI28QzR7oY4MBgO3D2hLYbGNVfFH+GDhDqwWE11aN3J3aCIiIiIi54RLkgm+vr7k5ORQUlKCxVL2aeemTZsAiI2NrdS+vE1N7dy5k+LiYqxWa4XExMl69OjBmjVr2Lp1a+2Cr8L8+fOdfUZHn76g2pQpU0hLS8Nms9G0aVP69OnDddddV2UdBZFzzWAw0MS3EU18G3FJeFlNkGJbMUk5KWWzF7ISOZCdSE5xLkk5h0jKOcQKygqFBlj9iQmM/n15RDRRgc3xMun7uKaMBgN3DWpPcYmN9TvTeG/+NsYN70q7qBB3hyYiIiIi4nIuSSa0atWKrVu3smLFCgYOHAjAjz/+iMFgoEePHhXaFhQUkJOTQ4sWLWrcf0JC2VZ55UUcqxIVFVWhbV3l5+ezaNEiAG6++eYzti+vC1Fu/vz5vPPOO0yePJmOHTueVSwirmA1WWkTHEOb4BigbPZCemGGc1lEQlYSybkp5BTnEn98B/HHdwBlsxea+Uc46y60DIyiiU8jzV44DaPRwL3XX0xRsY2t+0/w1lfx/O32brSKDHR3aCIiIiIiLuWSZMLVV19NXFwc//jHPzhw4ADHjh3jhx9+wGg0Mnjw4Aptt23bhsPhqFXRwqysLACCgqrfAq/8XHnbulq0aBH5+fn4+PhUiv1kAwYM4KabbqJ9+/aEh4eTl5fHr7/+yptvvklycjKjR49mwYIFREScfSE2s7l+12wo3zakJtuHSP3Q1NKYpgGN6dOsfPZCCUnZZbUXDmSW7RaRWZRNck4KyTkp/JLyKwABFj9igstqL7QOjiY6sAXeZi93Pkq9YzYbeWRYFybNiuO3gxm8OSeOZ+7sQVTTgBpdr/Ek4hoaSyKuobEk4joNbTy5JJlwxx13sHDhQnbv3s2bb76Jw+FwHj91BsKSJUswGAyVtpE8naKiIuD0yyPKlxWUt62r8iUO11xzDf7+/tW2mzJlSoU/e3l5MWTIEPr27cutt97K4cOHeffdd3nllVfOKh6j0UBIiN+ZG9YDgYE+7g5BzkLTxsH0opPzz8fz09lzPIG9JxLYc+IACRnJ5JTkEX/sN+KP/QaULauICmrGRY1iaNsohosatyLCP0yzF4AXx1zKCx+sYVdiBhO/jOP1h/vRrEn1P1NOpfEk4hoaSyKuobEk4joNZTy5JJng5eXFzJkz+eSTT4iLiyMgIIA//elPXH/99RXaFRcXs2HDBiIiIujXr1+t+gcoKSmptk1xcXGFtnWRnJzMhg0bgJotcahKaGgoY8aM4cUXX2Tp0qVMmDDhrF6s7HYH2dn5db7+fDCZjAQG+pCdXYDNZnd3OOIiJrzoENCeDgHtoSWU2EtJzk7hwEmzFzIKM0nMPERi5iH+t7+sSKqfxZeYoChaBUXTKjialkFR+Ji93fswbvLYsC689tkmklJzeXbKKv7xl540Dj79Lw+NJxHX0FgScQ2NJRHX8ZTxFBjoU6PZEy5JJgD4+fkxduzY07axWq188803te67JksYarIU4kwWLFiAw+GgWbNm9OnTp879dOvWDYDMzEwyMzMJCTm7AmylpfX3G+1kNpvdY2KV2jNgJMq/BVH+LbiqWVkyMLMoi4SsJOfWlEk5h8gryWf78V1sP77r9+sMRPg1JSYoipaB0bQKiiLMtwlGQ8OY3nU6XmYT40bE8n9fbObIiXxe/3wzT9/RnWD/Myc9NZ5EXENjScQ1NJZEXKehjCeXJRPOpZYtWwJw+PDhCjtGnCwpKalC29pyOBwsWLAAgKFDh57VbIKT47PZbHXuR6S+C/YKoltYZ7qFdQag1F5KSu4RDmQlcvD3Ao8nCjM4nHeUw3lHWX14PQA+Zh9aBrYgJiiamMAoWgZG4WtpGNO9ThXoa2X87d147fNNpGUW8MasOP7+524E+GqnDBERERHxXOckmZCbm8tvv/3GiRMnAGjUqBEXX3zxaWsQnE6HDh2wWCwUFxcTHx9faYcIOP1WlDWxfv16Dh06hMFgqPMSh3J79+4FypZcBAcHn1VfIp7EbDQTHdiC6MA/aqVkFeVwMLts14iE7EQSsw9RUFrAzvQ97Ezf42wX7tf0950jyramDPcLazCzF0ICvBg/shuvf76JlON5TJqzlb+P7IaPl0fkc0VEREREKnHp32TLCzCuXLkSu73itA2j0ciVV17JY489Rrt27WrVr7+/P/369ePnn39mzpw5lZIJBw8eZO3atQAMGjSoTrGXF17s2bNnrbatPFVpaSnTp08HoE+fPpjNelmQC1uQVwBdm3Sia5Oy4o42u42U3CN/bE2ZncTxghMczUvlaF4qvx4pq1vibfL+ffbCH1tT+ll83fkoZyUs2Ifxt3fj9S82k3g0h7fnbuWJEbF4WUzuDk1EREREpNZc9qa7ZMkS/va3v1FcXOzczeFkNpuNn3/+mdWrVzNx4kSuvvrqWvU/duxYli9fzjfffEP37t0ZPnw4BoOBtLQ0xo0bh91uZ+DAgbRv377Cdf379wfg73//e7WJhry8PBYvXgzALbfccsZYJk6cSOvWrbn66qsrzLY4cuQIL7/8MnFxcZjNZh566KFaPaPIhcBkNBEV2JyowOZc2fxSAHKKczmYncSBrEQSshJJzDlEoa2QXRl72ZWx13ltU98mtAwsSy60Coomwq+pR81eiGzsx5MjYvn3l1vYcyiLd+dt49Fbu2Cp59u/ioiIiIicyuCo6s2/lpKTkxkyZAjFxcU0a9aMe++9l8suu4zw8HAAjh49yurVq/n44485dOgQXl5efPfdd7WeATBjxgxef/11HA4HERERhISEsG/fPoqLi4mJiWHmzJmEhoZWuKZ8FsRrr71WbaJg3rx5PPPMM/j6+rJq1Sr8/E6/FePYsWNZtmwZJpOJFi1aEBQURE5ODgkJCTgcDry8vJgwYQI33nhjrZ6vKjabnfT0vLPu51wym42EhPiRkZHXIAqJiPvZ7DYO56X+sTwiK5G0guOV2nmZrEQHRlVYHuFvrf9bqe47lMUbs+MoKrHR/aImPDi0IyZjWUJB40nENTSWRFxDY0nEdTxlPIWG+p2/3Rw+/vhjiouLiY2N5eOPP670Mh4VFUVUVBQ33XQTo0ePZuvWrUyfPp0XXnihVve56667aNeuHdOmTSM+Pp4TJ04QGRnJoEGDGDNmzBmTANUpX+Jw7bXX1qiPkSNH0rhxY7Zv305aWhopKSlYLBbatm1L3759ueOOO4iKiqpTLCJSNnuhRUAkLQIiubxZXwByS/I4mJXkXB5xMDuJIlsxezL2sSdjn/PaJj6NnIUdY4KiifQLx2SsX0sJ2jQP4pFbO/PW3Hg27znGtO93cs/1F4MDdh5MpyQhA4vBQevIIIzGuheDFRERERE5V1wyM+Haa68lKSmJBQsWnLEewu7du7npppuIjo52Li2Q6mlmgkjV7A47R/JSnXUXErKSSM1Pq9TOarQQ/fvOES0Do2gVFE2AtW7FYF0tbu9x3pu/DZvdQceYUA4fzyMjp8h5PiTAiz8PbEuPdmFujFLEM+l3k4hraCyJuI6njKeazkxwSTKha9euWCwWNm7cWKP2PXv2pKSkhK1bt57trRs8JRNEai6/JJ+E7GTnzIWD2UkUlBZWatfIO9RZ2DEmMIrm/pFum72w7rdUPli447RtHrq5kxIKIrWk300irqGxJOI6njKezusyB7PZTGlpaY3aOhwOSkpKtMuBiLicr8WXjo3a0bFR2Qwpu8NOav6xstkLv29NeTQvjROF6ZwoTGdjahwAFqOZqIDmFZZHBHkFnpeYe7UP47Mlu8kvrP5n6JdL99KtbRMteRARERGResMlb/TR0dHs3LmTlStXcvnll5+27cqVKykqKqJ169auuLWISLWMBiMRfk2J8GvKpZGXAFBQWsDB32cvJGQncTArifzSAvZnHWR/1kHntSFewbQKinYuj2gREInZ6Pok6J7kzNMmEgDSc4rYk5xJ++gQl99fRERERKQuXPI34/79+/Pbb7/x/PPP8/HHH1ebKNi3bx8vvPACBoOBAQMGuOLWIiK14mP2oUPoRXQIvQgom72Qln+8QmHHw7lHySjKZFNaJpvSypZjmY1mWvg3q7A8IsQ7+KzjycwrOnOjWrQTERERETkfXFIzITc3lyFDhpCamorFYmHQoEH07duXpk2bAmVbQ/76668sXryYkpISwsPD+e677/D3rx9F0Ooz1UwQOf8KSws5mJ3Mwd8TDAnZSeSV5FdqF+wV5FwWERMUTQv/SCwmS63utSsxg39/ueWM7do0C+SGy2Lo2DJUyx1EakC/m0RcQ2NJxHU8ZTyd1wKMAHv37uWBBx4gJSUFg6Hqv+g6HA6aN2/O1KlTadu2rStu2+ApmSDifg6Hg2MFx3+vu1CWYEjJPYKDij8+zQYTzQOa/Z5gKEsyhHgFV/szEcBud/C3qWt+38XBgTEgHYOlCEeJF/acUKDitY0CvbkiNpLLu0QQ7O91Dp5WpGHQ7yYR19BYEnEdTxlP5z2ZAJCXl8cXX3zBokWL2L17NzabDQCTyUS7du247rrrGDlyJH5+fq66ZYOnZIJI/VRYWkRSziEOZiVxIDuRhKxEcksqj9Uga4Cz7kJMUDRRAc2xnjJ7YdPuNKYuX4olaidGrz+WM9iLvChJ6sDw7pdzPKuANduOkl9UVl/BZDQQ26YxV3aL5OKWoRhPk7AQuRDpd5OIa2gsibiOp4wntyQTTlZSUkJWVhYAQUFBWCxlf3nOycnhL3/5CwaDgXnz5p2LWzcoSiaIeAaHw8GJwnQO/F53ISErkUO5R7A7Ko4Jo8FIc//ICjtHJOek8NH2z8BBxYkIv//5vk53EhvWmeISGxt2pbEi7jD7UrKczRoHeXNlbCT9ukQS5Gc9L88rUt/pd5OIa2gsibiOp4wntycTqpORkUHfvn0xGAzs3LnzfN7aIymZIOK5im3FJOWkOOsuJGQlkl2cU6mdAUOlJRMnC/EK4qVLn8Fo+OOH+qFjuayIO8ya7UcpOGm2Qre2jbmyWzM6RIdotoJc0PS7ScQ1NJZEXMdTxlNNkwmu3+dMREQAsJqstAmOoU1wDFA2eyG9MJOE35dFJGQnkZydgp3T/zLJKMrig/gZRAW2INgrkCBrIMHeQdx0ZTNuvbIVG3cdY0VcCvsPZ7Nx9zE27j5GWLAPV8ZGclnnCAI1W0FEREREXEzJBBGR88RgMNDIJ4RGPiH0bBoLwNojG/ls55wzXrv9xC62n9hV6bjZYCLIK5CgjkF0u9iXzEwDR4/aOFFg5etNKcxb501sdHP6x0bRPjrktMUgRURERERqSskEERE3CvUOqVG73k27YzaZySzKJrMoi6yibHJL8ih12DhRmMGJwoyyhkYgEk6ei/AbsGOPGdNOH0J9golu1JgmviFlSQivoLLZDl6BBFoDKiylEBERERGpjpIJIiJu1CY4hmCvIDKLsqptE+IVxB0XD6/0ol9iLyW7KJus4uwKSYbyf2cVZZNRmEWJowSDuRS7OYfj9hyOH0uu8j5Gg5FAa0DZMopTEg3BJ/23t8lbMxxERERELnBKJoiIuJHRYGRY2xv5cPtn1ba5re2NVc4YsBjNNPIJpZFPaLXXOhwOCkoLSctLZ93eROIOpnAiPxODtQiDtRCrTwkWn2KKHPnYHXYyi7LILMoisXKdSCeryfpH7QavIIK9gn6f5VD25yBrIEFeAZiN+hUjIiIi0lDpb3oiIm4WG9aZ+zrdydy9CyvMUAjxCuK2tjcSG9a5zn0bDAZ8LT60DG5Gy17NGNELEo/msDwuhbW/pZJbbAPAbIKuHQLo3M6PwCA72afOdijOJqsoi4LSQoptxaTlHyct//hp7x1g8f89wVA2y6H8v8sTDsFeQfhZfDXLQURERMQDaWvIek5bQ4pcOOwOOwk5Byk1F2MutRIT0PKc1jAoKCpl3W+pLI9LISk113k8opEvV8U2o2+ncPx9LBWuKbIVk1WURebvyyhOTTaUH7c5bDWKwVlA8tQlFdbACsetJu1IIbWj300irqGxJOI6njKearo1ZJ2SCR06dKhTUOUcDoeSCTWkZILIhcUd48nhcHDwaA4rfp+tUFxSdl+L2UjPdmFc1S2SNs2CajyDwO6wk1eS/3ti4aQ6DqfMdsgtqfnPNh+zzymzGirPdgiw+quApDjpd5OIa2gsibiOp4ynmiYT6rTM4TxPZhARkXPIYDAQExFITEQgI/q3Ze2OoyyPO0xyWi6/7jjKrzuO0qyxH1fGRtK3Uzh+3pbT9mc0GAmw+hNg9adFQGS17WpSQDKzKItiewkFpQUUlBZwJC/1tPcNtAaUJRisKiApIiIici7VKZnw8MMPuzoOERGpB3y8zPype3Ou6taMA0eyWbHlMOt3ppJyPI+ZS/fy1fL99GofxpXdmtE6MvCsXsprU0CyLOHwxzKKU5daZBfnVCwgeZr7qoCkiIiIyNk77zUTpHa0zEHkwlIfx1N+YSm/7jjKirgUDh374+dR8yZ+XBnbjL4dw/H1du+Lt81uI6ckt2Idh2oKSNaUCkh6tvo4lkQ8kcaSiOt4yng6pzUT5PxRMkHkwlKfx5PD4WD/4WxWbElh/a40Sn6Pz2oxckmHplwZG0mriLObrXCuqYDkhaM+jyURT6KxJOI6njKelExoIJRMELmweMp4yissYc32o6yIO8zh43/8jGoR5s9VsZH06RiOj5dnLhNQAcmGwVPGkkh9p7Ek4jqeMp6UTGgglEwQubB42nhyOBzsS8li+ZbDbNiVRqmtLGYvi4neF4dxZWwzYiIC3RzluVGbApI1oQKSruVpY0mkvtJYEnEdTxlPSiY0EEomiFxYPHk85RaUz1ZI4ciJfOfx6KYBXBkbSe+Lm3rsbIW6qk0BSQc1+3WsApI148ljSaQ+0VgScR1PGU9KJjQQSiaIXFgawnhyOBzsPZTF8rgUNu469sdsBauJPhc35arYZkSHB7g5yvpFBSRdryGMJZH6QGNJxHU8ZTwpmdBAKJkgcmFpaOMpt6CE1duOsDzuMKnpf8xWaBkewFXdmnFJhzC8rRf2J+i1UW0ByZNqOri+gGQQVpPlHD+Z6zW0sSTiLhpLIq7jKeNJyYQGQskEkQtLQx1PDoeD3UmZLI9LYdPuY/x/e3ceH3V97v3/NWv2yb5NFhKSkLAlYangBoobth6rVrHqaY8/PdJbq/ac7h6rt9ZaPffxtHeLa+1xuT3SamuB0w2sCxRBQJAkBAIkISFkIfu+JzO/P4aMxCSQwMBkkvfz8eBhMt/v5zufb+RiZq5c1+cz6HC99PhbTSydG8dluXaSY1Wt4AlaQNJlqsaSyPmmWBLxHF+JJyUTpgglE0Sml+kQT21dfWzf51pboba52/14aryNy3LtXDA7Fj+ryYsznB5GW0Dy84tHtvS20e+DC0g6nA7K2ssZMPdhHrCSGpIyqRIdIr5kOrwuiZwvvhJPSiZMEUomiEwv0ymeHE4nh442szmvmk8Pf1atEOBn4sK5cSzPTSApJtjLs5zehhaQHN5GMXJNh8m0gGRe3T5+V/w/tPS2uh8L8wvllozryY2Zf0bXFJnOptPrksi55ivxpGTCFKFkgsj0Ml3jqa2zj237atiSV01dy2fVCml2G8tzE/jC7Bj8LKpWmKw+v4DkiDUdztMCknl1+3i58I0xr3nPvK8poSAyQdP1dUnkXPCVeFIyYYpQMkFkepnu8eRwOik62syWvVXsLW44qVrBzEXz4lieaycxWtUKvurkBSQ/31JxtgtIhlpDONB0iN7BvjHPD7XaeHjJt7GarJgNpim7k4WIJ0331yURT/KVeFIyYYpQMkFkelE8faa1o5ePTlQrNLR+9hvt9IRQlufa+UJWDFZVK0w5Yy0g2eJe32HiC0iOxWQwYTaaMBvMrv8azZjc35tPOnbicaN52Llmg8l9nunkxz83znJinGnE8RPPaTBjMZownbimyai/1zJ56HVJxHN8JZ6UTJgilEwQmV4UTyM5nE4OlDWxOa+avOIGHCdetoL8zVw4L47LchOwRwV5eZZyvg0tIDmUZNjXcIBdxz/19rQ8woDhpITFyUmI4QmM4QmJkx7/3DjLKRMZJ13784mUzx9XNce0o8VMRTzLV97nKZkwRSiZIDK9KJ5OraWjl60FNfw9r5rGts+qFWYlhrJ8QQKLM6OxmPVb3enocHMpv9j70mnP+2bO3aSGJjPgGGTAMeD6r3Pgs68dAww6h44NMDDq14MMfu571/EBBt1fj3Pcief2FWdWzTF6MuRMqzlMJ85TNce5pcVMRTzLl5JzSiZMEUomiEwviqfxcTicFJY1sSWvivySxmHVChfPj2d5rp34SFUrTCcOp4NHtj817IPP54X7hfLjix6adG/enE4nDqeD/lMkMoYe7z/jRMaJ486Txpz4fmjcsOc/8V+H0zf+HfJ+Ncdnj02Fag4tZiriWb6WnJuyyYQdO3bw6quvkp+fT1dXF3a7nZUrV7J69WoCAwPHfZ2dO3fy9a9/fVznPvDAA9x///0jHu/s7ORXv/oVmzZtorq6msDAQHJycrjrrrtYsmTJuOdyKkomiEwviqeJa27vZWtBNX/Pr6aprdf9eGZSGMsX2Fk0KwaLeXJ9eJRzQx+APM/hdIyZhPjs+5FJiKHkRb/zpETGKEmQ0ZMnn1VsjDpO1Rzjq+YYMW581Ry+nJgTmYx88bVpSiYT3njjDZ588kmcTidxcXFERERQUlJCX18faWlprF27lrCwsHFd68CBAzzxxBNjHu/o6ODw4cMAvPLKK1x88cXDjjc1NXH77bdTVlaG1WolPT2dpqYmjh8/jsFg4JFHHuGOO+4443sdomSCyPSieDpzDoeTfUca2ZJXTX5pA0OvbsEBFi6eH8fy3ATiIsafdBbfNNpvf8L9Qrl5kv72R87M6NUcn0tCfK59ZTyJjNETIMMTKYPO0a45dao5Bp0OmnuaTzs+xZZEsCUIGF55MbwQw3DSV587b4xBpzhrnGNGPtdnQ8auEhk+5qRrG0513ikeN4x+bOTosZ5rvPfu2Wsbhk/8c+eN495PMWas8a75je/nev7+3oz9d4Axfuaj/RycTifvHt1Mz+DYWyNPxuTclEsmFBYWcsstt+B0Onn88cdZtWoVBoOB2tpa7r33Xvbv38/VV1/NmjVrPPJ8zz77LGvWrCE+Pp4PPvgAo3H4D/Pee+/lgw8+YO7cubzwwgvExsbidDp5++23efTRRzGZTLzzzjvMnj37rOahZILI9KJ48oymth7+nl/N1oIamts/q1bISg7jsgUJLJwVjXkcL5Lim3ypL1WmnrGqOQadrjaVU63LMZTMGHSMXrExZiLjcy0rY41z4hNv+0WmnW8t+AazwtO8PQ238SYTzOdhLh7x/PPP43A4uOGGG7j11lvdj8fGxvKzn/2Ma6+9lnfffZeDBw+SlZV1Vs/ldDpZv349AF/+8pdHJBIOHDjgTjD8/Oc/JzY2FnBl1G699Vb27NnDhg0beP755z2W3BARkfGLsPlzw6Uz+YeLU9hX2sTmvCr2lTZysKKFgxUthARauGR+PMty7cSGq1phqjEajGRGpCsxJ15hNBgxmoxYsHh7KsMMVXOMloQ4uZqjvPUY75T88bTXuzJpObFBMZ9/lpO+co728Ihjzs8d+Wy+I+5g1DEjEiTOUxwba5xznNc++dgpfh871r1//npndu9j/Oyc47z2iEt77t5H3t/4fg7jGfP5Y57/e3P6a4+8/umv3dDdSElL2ecnNUJbb9tpz5mMfCKZ0NnZydatWwFYtWrViOMpKSksXbqU7du3s3HjxrNOJnzyySccO3YMgJtuumnE8U2bNgGwdOlSZsyYMeL4rbfeyoYNG9iyZQtdXV0TWstBREQ8x2Q0kpsRRW5GFI2tQ9UK1bR09PHXnRX8dWcFc1LCWZ6bwIKMKFUriMiUZTAYMBlMmDDhZ7KOeV6KLZn3j/39tGsmfDn9WlX8iJzGeHcasvnZzsNsPM8n/gUoKiqir68Pq9VKdnb2qOcsWrQIgPz8/LN+vnXr1rmvOVqyIC8vD4DFixePOj47Oxur1Upvby9FRUVnPR8RETl7kaH+3LhsJv9x30Xcf9N85s+MxAAcKG/mhfWFfPe5bfx+cyl1Ld3enqqIiNcYDUZuybj+lOfcnHG9Egki45AelkqYX+gpzwn3CyU9LPU8zcizfOJfgbIyV2mI3W7HYhm9ZCw5OXnYuWeqq6uLjRs3AnDjjTeOek55efmw5/w8i8VCfHy8R+YjIiKeZTIaWTgrmn9dlcO//68Lue6iGYQGWWnr6ucvO47ywxc/5j/fymP3wToGBlUeLyLTT27MfO6Z97URH4LC/UIn5crzIpPVVE/O+USbQ2urq8wqNHTsrM7QsaFzz9TGjRvp6uoiICCAa6+99qzn09Z29v0v5km+pdnQ4hzjWaRDRE5N8XR+xUUFsWpFBjctT2NvcQMfflpJ4ZEm9pe5/oQGW1mWY+eyBQlEhwV4e7oyAYolkbOz2J7Dwvj5lLaW02fswerwJy1Ui5mKTNRiew5Gk5G3D66nedhOQ2GsyvoyC2N9NznnE8mE3l7XStxjVSUAWK3WYeeeqaEWh6uvvprg4OCznk9Pz9jbgIyH0WggPDzorK5xvthseqMt4imKp/Pv6qgQrr4wleONnby78yh/21VBS3svf9xWzp+2l7MgM4aVS1O4YE6sPqD6EMWSyNmJjPDdDzoik8UV4Uu5fNYFFDWU0NzdSnhAKLOj0kcs9O9rfCKZ4OfnB0B/f/+Y5/T19Q0790wcO3aMTz75BBi7xWHoObq7u8c1H39//zOeD7j2TW9r6zqra5xrJpMRmy2AtrZuBlUSLHJWFE/e52eEf7hwBtdekMSnh+v58NMq9pc18enBOj49WEdYsJVluQlclmsnStUKk5ZiScQzFEsinpPkn8TcmFm0tXXT2jp512iy2QKmztaQ42lhGE/rwemsX78ep9NJQkICS5cuHfM8m81Gd3f3uOZjs539ypy+sqXV4KDDZ+YqMtkpniaHhRnRLMyIpra5i7/nVfPRvhpaOvr4n4/K+ONHZcxPi2R5rp3stEhMPv7bhalKsSTiGYolEc+ZKvHkE8mElJQUAKqrq+nv7x+1vaCiomLYuRPldDpZv349ADfccAMGg+GU86mtreXo0aOjHu/v76e6uvqs5iMiIpNHbHggt1yezo3LZvLp4Xq25FVTdLSZgtJGCkobCQ/x49LseJbl2ImwnV1FmoiIiIgv8IlkwuzZs7FYLPT19VFQUODeBvJke/bsASA3N/eMnmPXrl1UVlZiMBhO2eIw9Bw7d+50P+fnFRQU0N/fj5+fH7Nnzz6j+YiIyORjNhm5YHYsF8yO5XjTZ9UKze29/M+2cv64vZzsmZEsX5BA9sxIjMaxE9MiIiIivswnajKDg4O55JJLAHj77bdHHC8vL2fHjh0ArFy58oyeY2jhxcWLF5OUlHTKc6+55hoAdu7cOWp1wltvvQXAsmXLCAryjcUTRURkYuIiAlm1Ip3//ObFrL5+DlnJYTidkF/ayC9/X8D3X9zO/3xURnP72S0MLCIiIjIZ+UQyAeC+++7DYDCwYcMG3nrrLZxOJwB1dXV8+9vfxuFwcOWVV5KVlTVs3IoVK1ixYgUbN24c89qdnZ1s2rQJgJtuuum0c5k7dy6XX345g4OD/Ou//it1dXWAq1XirbfeYsOGDRiNRu69994zvV0REfERFrORpXPi+P7tC3nyniVc/YUkgvzNNLX1sv6jMr77/DZ++fsCCkobcDic3p6uiIiIiEcYnEOfyn3Aa6+9xtNPP43T6SQ+Pp7w8HBKSkro6+sjNTWVtWvXEhERMWxMZmYmAE899dSYiYI//OEPPPTQQwQGBvLRRx+Nq5qgqamJ2267jfLycqxWK+np6TQ3N1NTU4PBYODhhx/ma1/72lnf8+Cgg6amzrO+zrlkNhsJDw+iublzSiwkIuJNiqepoX9gkN2H6tmyt4rDlZ8t1htp82dZTjyXZNsJDznz3Yfk9BRLIp6hWBLxHF+Jp4iIoKmzm8OQO++8k8zMTF555RUKCgpobGzEbrezcuVKVq9efcYtBUMtDtdcc824rxEREcE777zDyy+/zMaNGykpKSEwMJBly5Zx9913n3I3CBERmdosZhMXzo3jwrlxVDV08ve8arYX1tDY1sO6rWVs+Kic3IwoLsu1Myc1AuMpFv0VERERmYx8qjJhOlJlgsj0oniauvr6B9l9qI7NedWUnFStEBXqz7IcO5dmxxMarGoFT1EsiXiGYknEc3wlnqZkZYKIiIivslpMXDQvnovmxVNZ38GWvGq2Fx6nobWHP/z9CBs+KjtRrZDA7JRwVSuIiIjIpKZkgoiIyHmWGB3MHVfN4ubL0th9sI7NeVWUVrWx51A9ew7VEx3mz/LcBC6eH09okNXb0xUREREZQckEERERL/GzmLh4fjwXz4+nsq6DzXlVfLz/OPUtPfx+cynr/n6EBbOiuSzXTtYMVSuIiIjI5KFkgoiIyCSQGBPMP16dyS2XpbOrqJYt+dUcqW5j98E6dh+sIyY8gOW5di6eH48tUNUKIiIi4l1agHGS0wKMItOL4klOVlHbzpa8aj7ef5yevkEAzCYDC2dFszw3gazkMAyqVhiVYknEMxRLIp7jK/E03gUYlUyY5JRMEJleFE8ymp6+AXYV1bElr4qymnb347ERgSzPsXPx/DhCVK0wjGJJxDMUSyKe4yvxpGTCFKFkgsj0oniS0zl6vJ0teVV8fKCW3pOqFRZnxrA8186sJFUrgGJJxFMUSyKe4yvxpGTCFKFkgsj0oniS8eruHWBnUS1b9lZztPazaoX4SFe1wkXz4wkOsHhxht6lWBLxDMWSiOf4SjwpmTBFKJkgMr0onuRMlNW0sSWvip0H6ujtH6pWMPKFLNfaChmJodOuWkGxJOIZiiURz/GVeBpvMkG7OYiIiPi41HgbqfE2bl2RwY4DtWzZW0VFXQcf76/l4/212KOCWJ5r56J5cQT5T99qBREREfEcVSZMcqpMEJleFE/iCU6nk7KadjbnVbGrqJa+ftffJYvZyBeyYrgsN4G0BNuUrlZQLIl4hmJJxHN8JZ5UmSAiIjJNGQwGZtptzLTb+OqKDHYcOM7mvdVU1newvfA42wuPkxAdxGW5CVw4N5ZAVSuIiIjIBKkyYZJTZYLI9KJ4knPF6XRypLqNzXlVfFJUR9+Jv19Ws5ELZseyPNfOTPvUqVZQLIl4hmJJxHN8JZ5UmSAiIiJuBoOBtIRQ0hJCue2KDLYXHmdLXjVVDZ18tK+Gj/bVkBgdzGUL7CydE0egv94iiIiIyNhUmTDJqTJBZHpRPMn55HQ6Ka06Ua1wsI7+oWoFi5Els2NZnptAanyIT1YrKJZEPEOxJOI5vhJPqkwQERGRUzIYDKQnhpKeGMpXr8jg48LjbM6roqaxi60FNWwtqCE5JpjlCxJYOieWAD+9bRAREREXVSZMcqpMEJleFE/ibU6nk+LKVjbnVbH7YD0Dg66/h34WE0vmxHLZAjspcTYvz/L0FEsinqFYEvEcX4knVSaIiIjIhBkMBmYlhTErKYzbr+xn+74aNudVc7ypi7/nV/P3/GpmxIVwWa6dJXNi8bfqrYSIiMh0pMqESU6VCSLTi+JJJiOn08nhYy1szqtmz6E6BgZdbx38rCYunONaW2FGXIiXZzmcYknEMxRLIp7jK/GkygQRERHxCIPBQGZyOJnJ4bR3ZbBt33G25FdT29TF5rxqNudVkxofwvLcBJbMjsXPavL2lEVEROQcU2XCJKfKBJHpRfEkvsLpdHKwooUteVXsOVTPoMP1dsLfauLCuXEsz7WTHOu9agXFkohnKJZEPMdX4kmVCSIiInLOGAwGZs8IZ/aMcNo6+9i2r4YtedXUtXTz4d4qPtxbxUy7jeW5di6YHYufRdUKIiIiU4mSCSIiInJWbEFWrl06g2uWJHPwaDOb86rZe7ieI9VtHKlu47fvl3DRiWqFxJhgb09XREREPEDJBBEREfEIo8HAnJQI5qRE0NrZx0cF1WzJq6ahtYf3P63k/U8rSUuwcVluAl/IisGqagURERGfpTUTJjmtmSAyvSieZKpxOJ0cKG9iS141ecUN7rUVAv3MXDQvjuULEkiICvL48yqWRDxDsSTiOb4ST1ozQURERLzOaDAwLzWSeamRtHT08lFBDX/Pd1UrvLenkvf2VJKRGMryXDuLM1WtICIi4itUmTDJqTJBZHpRPMl04HA62V/WxOa9VeSXNOI48VYkyN/MRfPiuWyBnfjIs6tWUCyJeIZiScRzfCWeVJkgIiIik5LRYGD+zEjmz4ykub2XrQXVbM2vprGtl7/tPsbfdh9jVlIYl+XaWZQZjcWsagUREZHJRskEERER8ZrwED+uvziV6y5MobCskc17q8kvbeDwsRYOH2sh+D2La22F3LOvVhARERHPUTJBREREvM5oNJCdFkV2WhRNbT1sPbG2QnN7L+9+cox3PzlGVnIYy3MTWDgrGov59OWXIiIicu5ozYRJTmsmiEwviieRzzgcTgqONLJlbxUFRxoZescSHGDhkux4lufYiY0IHHVcaXUr/U4DFoOTNHsoRqPhPM9eZGrQ65KI5/hKPGnNBBEREfFpRqOB3PQoctOjaGztYWtBNX/Pr6alo4+NOyvYuLOC2TPCWZ5rZ+GsaMwmI3sO1bH2vWKa23vd1wkP8eP2KzNYlBnjxbsRERGZWlSZMMmpMkFkelE8iZzaoMNBQUkjm/OqKTzSyNCbGFughbTEUPYebhhz7DdvnKeEgsgE6XVJxHN8JZ5UmSAiIiJTjsloZMGsaBbMiqahtZu/59ewtaCa1o6+UyYSAH7zXjELMqLV8iAiIuIBWr1IREREfFJUaAA3LZvJf9x7ETdcknra85vaezl8rOXcT0xERGQa8LnKhB07dvDqq6+Sn59PV1cXdrudlStXsnr1agIDRy7CNB5Op5M///nPrFu3jqKiItra2ggLCyMtLY1ly5Zx9913jxiTmZl5ymtGRUWxbdu2M5qPiIiIjJ/ZZCQmImBc5/49v5rwEL9RF24UERGR8fOpZMIbb7zBk08+idPpJC4ujvj4eEpKSnjhhRd49913Wbt2LWFhYRO6ZmdnJ/fffz/bt28HICkpCbvdTmNjI5988gkHDx4cNZkwZN68eVit1hGPT3QeIiIicubCgvzGdd6OA7XsOFBLXEQguelR5KRHkp4YismoYk0REZGJ8JlkQmFhIT/96U8B+PGPf8yqVaswGAzU1tZy7733sn//fh555BHWrFkz7ms6nU4eeOABtm/fzqWXXsqjjz5KcnKy+3hbWxuffPLJKa/xi1/8gsTExDO7KREREfGIWUlhhIf4DdvF4fMC/cwkxwZTXNnK8aYuNu6qYOOuCoL8zWSnRZKTHsW81EgC/X3m7ZGIiIjX+Myr5fPPP4/D4eCGG27g1ltvdT8eGxvLz372M6699lreffddDh48SFZW1riu+Yc//IFt27aRk5PDiy++iNk8/Mdhs9m44oorPHofIiIi4nlGo4Hbr8zguXWFY57z/30xi0WZMXT1DFBY1kheSQP7Shvp7Bng4/21fLy/FpPRwKykMFfVQkYUMWHja58QERGZbnwimdDZ2cnWrVsBWLVq1YjjKSkpLF26lO3bt7Nx48ZxJxNee+01AO69994RiQQRERHxLYsyY/jmjfNY+17xsAqFiBA/brsyw70tZKC/mQtmx3LB7FgGHQ5KKlvJL3ElF443dVF0tJmio8385v1iEqKCyEmPIjc9ipl2m3aCEBEROcEnPkEXFRXR19eH1WolOzt71HMWLVrE9u3byc/PH9c1KyoqOHz4MEajkSVLlpCfn88777xDRUUFgYGB5ObmcvPNNxMREXHK6zz//PPU1dUxODhIbGwsS5cu5Ytf/OKo6yiIiIjIubUoM4YFGdGUVrfS7zRgMThJs4eOmQQwGY1kJoeTmRzOqhXpHG/qIq+4gfySBoorW6lq6KSqoZO/7DhKcICFnBPtEHNTIwjw84m3USIiIueET7wKlpWVAWC327FYLKOeM7TWwdC5p1NY6CqDDAsL48033+Q///M/cTqd7uPvv/8+L7/8MmvWrGHp0qVjXuedd94Z9v26dev45S9/yZo1a5g7d+645iIiIiKeYzQamJ0SQXh4EM3NnQwMOMY9Ni4ikJVLklm5JJmO7n4Kj5xohzjSREd3P9sKj7Ot8Dhmk4Gs5HB31UJkqP85vCMREZHJxyeSCa2trQCEhoaOec7QsaFzT6eurg5wLbL4zDPPcNlll/G9732P5ORkysrK+OlPf8qOHTt44IEH+OMf/0hcXNyw8VdccQVf/vKXycrKIi4ujs7OTj7++GN+/vOfc+zYMe666y7Wr19PfHz8mdzyMGbz5F5h2mQyDvuviJw5xZOIZ3gilsJC/Lgkx84lOXYGBh0crmhhb3E9e4sbqGvuprCsicKyJt7822GSYoJZkBHFglnRpNptGA1qh5CpQa9LIp4z1eLJJ5IJvb2uvsexqhIAd1vB0Lmn09XVBcDAwADJyck8++yz7utnZmby4osvctVVV1FfX8/rr7/OD37wg2Hjn3/++WHf+/n58aUvfYkLL7yQr3zlK1RXV/Pss8/y5JNPju8mx2A0GggPDzqra5wvNpsWqRLxFMWTiGd4Mpaio0K4eGESTqeTyroOdu0/zq4DxzlY3sSxug6O1XXwP9vKCQvx4wuzY7lgbhy5GdH4qx1CpgC9Lol4zlSJJ594dfPzc+0d3d/fP+Y5fX19w84d7zUB7rjjjhGJioCAAL761a+yZs0atm7dOiKZMJaIiAhWr17NY489xnvvvcdPfvITDGfx2wmHw0lbW9cZjz8fTCYjNlsAbW3dDA6Ov5RUREZSPIl4xrmOpWCrkRUL7KxYYKe9q4/8kgb2Frt2h2hp7+Vvuyr4264KLGYjc1LCWZARTW5GFBE2tUOIb9Hrkojn+Eo82WwB46qe8IlkwnhaGMbTCnEym83m/jotLW3Uc4Yer6ysHNc1hyxYsACAlpYWWlpaCA8Pn9D4z5tIr6c3DQ46fGauIpOd4knEM85HLAVYzSydE8fSOXEMDDo4VNFCXolrEceG1h7ySxrJL2mEv8KM2BBy0iPJzYhiRmzIWf3CQeR80uuSiOdMlXjyiWRCSkoKANXV1fT394/a7lBRUTHs3NOZOXOm++ux2ieGqhccjon9jz75eoODgxMaKyIiIr7LbDIyNzWCuakR3H5lBlX1ne7EwpHqNo7WtnO0tt3VDhFsJSc9ipz0KObMCMdqMXl7+iIiIuPmE8mE2bNnY7FY6Ovro6CggEWLFo04Z8+ePQDk5uaO65pz5szB39+fnp4ejh07NuqODUMJis8vvng6xcXFgCsZERYWNqGxIiIiMjUYDAYSY4JJjAnmuotSaO3so6C0gfySRvaXNdHS0ceWvGq25FVjNRuZkxJBbkYUOWmRhAaPr21TRETEW3wimRAcHMwll1zChx9+yNtvvz0imVBeXs6OHTsAWLly5biuGRAQwOWXX85f//pX1q9fzy233DLsuNPpZN26dQCn3Bry8wYGBnj11Vfd48xmn/gRi4iIyDkWGmTl0mw7l2bb6R8YpOhoC/klDeSVNNDc3kveia8BUuND3NtOJsUEqx1CREQmHZ/Zk+K+++7DYDCwYcMG3nrrLZxOJ+Da4vHb3/42DoeDK6+8kqysrGHjVqxYwYoVK9i4ceOIa95///2YzWZ2797Nc889525JGBgY4D/+4z84ePAgfn5+3HnnncPGPfPMM6xbt46Ojo5hj9fU1PDggw+Sl5eH2Wzmm9/8pgd/AiIiIjJVWMwmstMi+do1mTxz30U89v99gRsuTSU1PgSAspp21m8t47FXP+F7L2znjXcPse9II/1ToMdWRESmBoNz6FO5D3jttdd4+umncTqdxMfHEx4eTklJCX19faSmprJ27VoiIiKGjcnMzATgqaee4qabbhpxzXXr1vHwww8zODhIREQEiYmJVFRU0NLSgsVi4emnn+a6664bNua+++7j/fffx2QykZSURGhoKO3t7ZSVleF0OvHz8+MnP/kJ119//Vnf8+Cgg6amzrO+zrlkNhsJDw+iublzSiwkIuJNiicRz/DlWGrp6CW/xNUOcaC8ib6T5u9nMTE3NYLc9Ciy0yKxBVm9OFOZDnw5lkQmG1+Jp4iIoKmzm8OQO++8k8zMTF555RUKCgpobGzEbrezcuVKVq9eTVBQ0ISveeONN5Kens6vf/1rdu/eTVFREWFhYVx33XXcc889IyodAG677TaioqIoLCykrq6OqqoqLBYLGRkZXHjhhfzjP/4jycnJnrhlERERmWbCgv1YnpvA8twEevsHKTrafCK50EBLRx+fHq7n08P1GICZCTZyTyzimBAVpHYIERE5b3yqMmE6UmWCyPSieBLxjKkYSw6nk4radvKKXWsrVNQOb7eMCvV3rbOQEUVmUhjmcfxWSeR0pmIsiXiLr8TTlKxMEBEREZmujAYDKXE2UuJs3HDpTJraesgvbSS/pIED5c00tPbw/p5K3t9TSYCfibmpkeSmR5KdFkVwwOjbYIuIiJwpJRNEREREfFCEzZ/LFyRw+YIEevsG2V/eRF5JAwUlDbR19bP7YB27D9ZhMEB6Qii5J6oW4iIC1Q4hIiJnTckEERERER/nZzWxcFY0C2dF43A6Katpc207WdxIZX0HxZWtFFe28rvNpcSEB7jXWchIDFU7hIiInBGtmTDJac0EkelF8STiGYqlzzS0dpNf0kheSQMHjzYz6PjsrV+gn5l5MyPIzYhi/sxIgvzVDiHDKZZEPMdX4klrJoiIiIgIUaEBXLEokSsWJdLdO8D+sibX7hCljXR097OrqI5dRXUYDQZmJYW6FnFMjyI2ItDbUxcRkUlMyQQRERGRaSLAz8zirBgWZ8XgcDgprW4lr6SB/JJGqhs6OVjRwsGKFt76oIS4iED3OgtpCTZMRrVDiIjIZ9TmMMmpzUFkelE8iXiGYmni6lq6yT+x7eThYy3D2iGC/M1kp0WSkx7FvNRIAv31+6jpQrEk4jm+Ek9qcxARERGRcYsJC+CqLyRx1ReS6OoZoLDMtc7CvtJGOnsG+Hh/LR/vr8VkNDArKYzcDFc7RHRYgLenLiIiXqBkgoiIiIgME+hv5oLZsVwwO5ZBh4OSylb3Io7Hm7ooOtpM0dFmfvNeMQlRQe51FmbabRiN2nZSRGQ6UDJBRERERMZkMhrJTA4nMzmcVSvSOd7URV5xA/klDRRXtlLV0ElVQyd/2XGUkEAL2TMjyc2IYm5qBP5WvdUUEZmq9C+8iIiIiIxbXEQgK5cks3JJMh3d/RQeOdEOcaSJ9q5+thUeZ1vhccwmA1nJ4e6qhchQf29PXUREPEjJBBERERE5I8EBFpbOjWPp3DgGBh0UH2shr6SR/JIG6lq6KSxrorCsiTf/dpjE6GD3Ogsp8SEYDWqHEBHxZdrNYZLTbg4i04viScQzFEve5XQ6qWnsIr/EtTtESVUrJ7/jDA2ykp0WSW56FHNSIvCzmrw3WTklxZKI5/hKPGk3BxERERHxCoPBgD0qCHtUENcunUF7Vx8Fpa6KhcKyJlo7+9haUMPWghosZiOzZ4STmx5FTnoU4SF+3p6+iIiMg5IJIiIiInJOhQRauXh+PBfPj2dg0MGhihbyShrIK26gsa2HgtJGCkobYdMhZsSGkJPuWsRxRmwIBrVDiIhMSmpzmOTU5iAyvSieRDxDseQbnE4nVfWd5JW4doc4Ut3GyW9Mw4Kt7oqF2TPCsVrUDnG+KZZEPMdX4kltDiIiIiIyqRkMBhJjgkmMCea6i1Jo7eyjoLSB/JJGCssaaenoY3NeNZvzqrGajcxJiSA3I4qctEhCg9UOISLiTUomiIiIiMikEBpk5dJsO5dm2+kfGKToaIt7Ecfm9l5Xa0RJAwCp8TZy0yPJSY8iKSZY7RAiIueZ2hwmObU5iEwviicRz1AsTS1Op5NjdR3udRbKj7cPOx5p8yM73bXtZFZyOBbz6ctzZXwUSyKe4yvxpDYHEREREZkSDAYDybEhJMeGcP3FqTS397rbIQ6UN9HY1suHn1bx4adV+FlMzEuNICc9iuz0SGyBVm9PX0RkSlIyQURERER8SniIH8tzE1iem0Bv/yBFR5vd7RCtHX3sOVzPnsP1GICZCTb3Io4JUUFqhxAR8RAlE0RERETEZ/lZTOSeaHH4mtPJ0ePt7sRCRW0HpVVtlFa18c6WI0SF+rsSCxlRZCaFYR5HGa+IiIxOyQQRERERmRKMBgOp8TZS423ccOlMmtp6yC9tJK+4gaKjzTS09vDenkre21NJgJ+JuamR5KZHkp0WRXCAxdvTFxHxKUomiIiIiMiUFGHz5/IFCVy+IIHevkH2lzeRV9JAQUkDbV397D5Yx+6DdRgMkJEQSk6Gq8IhLiJQ7RAiIqehZIKIiIiITHl+VhMLZ0WzcFY0DqeTspo2VztEcQOV9Z0crmzlcGUrv/uwlJjwAPc6CxmJoWqHEBEZhbaGnOS0NaTI9KJ4EvEMxZJMRENrN/kljeSVNHDwaDODjs/eHgf6mZmfFklOeiTzZ0YS5D+92iEUSyKe4yvxpK0hRURERETGISo0gCsWJXLFokS6ewfYX9ZEfkkD+aWNdHT3s/NALTsP1GI0GJiVFErOiQUfYyMCvT11ERGvUTJBREREROSEAD8zi7NiWJwVg8PhpLS6lbySBvJLGqlu6ORgRQsHK1p464MS4iMD3YmFtAQbJqPaIURk+lAyQURERERkFEajgYzEMDISw7jlsnTqmrvc7RCHj7VQ09hFTWMFG3dWEORvJjstkpz0KOalRhLor7fZIjK16V85EREREZFxiAkP5KovBHLVF5Lo6umnsMy1O8S+0kY6ewb4eH8tH++vxWQ0kJkc5q5aiA4L8PbURUQ8TskEEREREZEJCvS3cMHsWC6YHcugw0FJpasdIq+kkdqmLg6UN3OgvJnfvFdMQlSQO7Ew027DaNS2kyLi+5RMEBERERE5CyajkczkcDKTw7l1RQbHm7rIK24gv6SB4spWqho6qWro5C87jhISaCE7LZLc9Cjmpkbgb9XbcRHxTfrXS0RERETEg+IiAlm5JJmVS5Lp6O6n8IhrnYV9R5po7+pn277jbNt3HLPJQFZyuLtqITLU39tTFxEZNyUTRERERETOkeAAC0vnxrF0bhwDgw6Kj7WQV9JIfkkDdS3dFJY1UVjWxJt/O0xSTLA7sZASH4LRoHYIEZm8DE6n0+ntScjYBgcdNDV1ensap2Q2GwkPD6K5uZOBAYe3pyPi0xRPIp6hWJLJzul0UtPYRX5JA3tLGiitauXkd+WhQVZXO0RGFHNSIvCzmLwyT8WSiOf4SjxFRARhMp1+q1ufq0zYsWMHr776Kvn5+XR1dWG321m5ciWrV68mMDDwjK7pdDr585//zLp16ygqKqKtrY2wsDDS0tJYtmwZd99996jjOjs7+dWvfsWmTZuorq4mMDCQnJwc7rrrLpYsWXI2tykiIiIiU5jBYMAeFYQ9Kohrl86gvauPglJXxUJhWROtnX1sLahha0ENFrOR2TPCyU2PIic9ivAQP29PX0TEtyoT3njjDZ588kmcTidxcXFERERQUlJCX18faWlprF27lrCwsAlds7Ozk/vvv5/t27cDkJSURFhYGI2NjdTW1hISEsLOnTtHjGtqauL222+nrKwMq9VKeno6TU1NHD9+HIPBwCOPPMIdd9xx1vesygSR6UXxJOIZiiXxZf0DDg4daya/2LXWQmNbz7DjM2JDyEmPZEFGNMmxwRjOYTuEYknEc3wlnsZbmeAzyYTCwkJuueUWnE4njz/+OKtWrcJgMFBbW8u9997L/v37ufrqq1mzZs24r+l0Orn77rvZtm0bl156KY8++ijJycnu421tbXzyySdcccUVI8bee++9fPDBB8ydO5cXXniB2NhYnE4nb7/9No8++igmk4l33nmH2bNnn9V9K5kgMr0onkQ8Q7EkU4XT6aSqvpO8EtfuEEeq2zj5zXt4iB85aZHkpEcxe0Y4Vg+3QyiWRDzHV+JpyiUT7rvvPt5//31uuOEG/v3f/33YsfLycq699locDgcbNmwgKytrXNd85513+Ld/+zdycnJYu3YtZvP4uj4OHDjAjTfeiNFoZOPGjcyYMWPY8e9///ts2LBhwsmN0SiZIDK9KJ5EPEOxJFNVa2cfBSUN5JU0sL+8ib7+z/5+Wy1G5syIIDcjipy0SEKDz74dQrEk4jm+Ek9Tas2Ezs5Otm7dCsCqVatGHE9JSWHp0qVs376djRs3jjuZ8NprrwGuKoPxJhIANm3aBMDSpUtHJBIAbr31VjZs2MCWLVvo6uo647UcREREREROFhpk5dIcO5fm2OkfGKToaAv5J5ILze295J34GiA13kZuuqtqISnm3LZDiMj04xPJhKKiIvr6+rBarWRnZ496zqJFi9i+fTv5+fnjumZFRQWHDx/GaDSyZMkS8vPzeeedd6ioqCAwMJDc3FxuvvlmIiIiRozNy8sDYPHixaNeOzs7G6vVSm9vL0VFRSxatGh8NyoiIiIiMk4Ws4nstEiy0yL5x6tncayug7xiVzKh/Hg7ZTVtlNW0sW5rGZE2P7LTo1iQHkVmcjgW8+l/6ygicio+kUwoKysDwG63Y7FYRj1naK2DoXNPp7CwEICwsDDefPNN/vM//5OTOz7ef/99Xn75ZdasWcPSpUuHjS0vLx/2nJ9nsViIj4/n6NGjlJWVKZkgIiIiIueUwWAgOTaE5NgQrr8kleb2XgpKG8gvaeRAeRONbb18+GkVH35ahZ/VxLyUCHLSo8hOj8QWaB31mg6Hk6LyJvrLmrEYnKTZQzEaVd0gIi4+kUxobW0FIDQ0dMxzho4NnXs6dXV1gGuRxWeeeYbLLruM733veyQnJ1NWVsZPf/pTduzYwQMPPMAf//hH4uLizmg+bW1t45rPqZgneeZ4qJ9mPH01InJqiicRz1AsyXQXHR7AFYuTuGJxEr39gxwob2Lv4Qbyiutp6ehjz+F69hyuxwCkJYayICOKBbOiSYgKwmAw8MnBOt7cdIim9l73NSNC/Ljjmky+kBXjvRsT8WFT7bXJJ5IJvb2uf8TGqkoAsFqtw849na6uLgAGBgZITk7m2WefdV8/MzOTF198kauuuor6+npef/11fvCDH5zRfHp6esY8ZzyMRgPh4UFndY3zxWYL8PYURKYMxZOIZyiWRFziYmysuCAFh8NJaVULu/bXsuvAcY5UtVJS6frzuw9LiY0IJCk2hN1FtSOu0dTey5rfF/DQP32Bi7LtXrgLkalhqrw2+UQywc/PtRJtf3//mOf09fUNO3e81wS44447RiQGAgIC+OpXv8qaNWvYunXrsGSCn58f3d3d45qPv7//uOYzFofDSVtb11ld41wzmYzYbAG0tXUzODh5VyUV8QWKJxHPUCyJjC0q2MoXlyTxxSVJNLb2kFfSwN7D9RSVN1Pb1EVt06nfe760roDMBJtaHkQmyFdem2y2gKmzm8N4WhjG03pwMpvN5v46LS1t1HOGHq+srBwxtru7e1zzOfl5ztRk3jbkZIODDp+Zq8hkp3gS8QzFksiphQZZWZ5jZ3mOnZ6+Ad795Bjrt556DbKmtl7e232MZTl2zFOkXFvkfJoqr00+kUxISUkBoLq6mv7+/lHbCyoqKoadezozZ850fz1Wu8JQ9YLDMfx/dEpKCrW1tRw9enTUcf39/VRXV09oPiIiIiIi3uRvNRMTPr7y6/9+9zBvf1BCaryN9MRQ0hNCSU8MJch/7DZgEZlafCKZMHv2bCwWC319fRQUFIy6O8KePXsAyM3NHdc158yZg7+/Pz09PRw7dmzEjg3wWYLi5MUXh55j586d7uf8vIKCAvr7+/Hz82P27Nnjmo+IiIiIiLeFBY2vZdjfYqSn38GhYy0cOtbiftweFUR6QigZia7kQkxYAAaD2iFEpiKfqEsKDg7mkksuAeDtt98ecby8vJwdO3YAsHLlynFdMyAggMsvvxyA9evXjzjudDpZt24dwIhEwzXXXAPAzp07R61OeOuttwBYtmwZQUG+sXiiiIiIiMispDDCQ06dUIgI8eOX/7KMn/zzEu68NouL58cRe6Kiobqhk7/nV/Nffy7ioZd28K9rPuLZP+xj484KSqpa6Z8Cpd0i4uITlQkA9913H5s3b2bDhg0sXLiQVatWYTAYqKur49vf/jYOh4Mrr7ySrKysYeNWrFgBwPe///0RiYb777+fv/3tb+zevZvnnnuO//W//hcmk4mBgQF+9rOfcfDgQfz8/LjzzjuHjZs7dy6XX345H374If/6r//Kiy++SExMDE6nk7fffpsNGzZgNBq59957z+nPRERERETEk4xGA7dfmcFz6wrHPOe2KzMwm4zYo4KwRwWxLMe1s0NbZx8lVa2uP5WtlB9vo62rn08P1/Pp4XoAzCYjqfEhpCeGkpEQRnpiKMEBao0Q8UUGp9Pp9PYkxuu1117j6aefxul0Eh8fT3h4OCUlJfT19ZGamsratWuJiIgYNiYzMxOAp556iptuumnENdetW8fDDz/M4OAgERERJCYmUlFRQUtLCxaLhaeffprrrrtuxLimpiZuu+02ysvLsVqtpKen09zcTE1NDQaDgYcffpivfe1rZ33Pg4MOmpo6z/o655LZbCQ8PIjm5s4psZCIiDcpnkQ8Q7Ekcnb2HKpj7XvFNLd/tu16RIgft12ZwaLMmHFdo39gkPLj7ZRUtlJc6UoydHSP3A0tLiLwRHLB1RoRFxGo1giZknzltSkiImhcuzn4VDIB4OOPP+aVV16hoKCArq4u7HY7K1euZPXq1aO2FJwumQCwb98+fv3rX7N7925aW1sJCwtjyZIl3HPPPSMqHU7W0dHByy+/zMaNG6muriYwMJDs7GzuvvvuUddgOBNKJohML4onEc9QLImcPYfDSWl1K/1OAxaDkzR76FltB+l0Ojne1OWuXCipaqWmceQ2lMEBFve6C2kJoaTGh2Axm87mVkQmBV95bZqyyYTpRskEkelF8STiGYolEc8417HU0d3vqlyoaqGkspWymnYGBoc/j9lkYEZciLstIj0hFFuQ1eNzETnXfOW1abzJBJ9ZM0FERERERKaW4AALuRlR5GZEATAw6ODo8XZ3W0RJZQttXf2UVrVRWtUGu1zjYsMDTtqSMoz4yECMao0QOa+UTBARERERkUnBbDKSluBqbwBXa0RdS7e7LaKkspWqhk5qm7upbe5m277jAAT5m0kb2pIyIZTUeBtWi1ojRM4lJRNERERERGRSMhgMxIYHEhseyMXz4wHo7OmntOrEoo6VrZTVtNHZM0BBaSMFpY0AmIwGkmND3MmFjMRQQoNPveWliEyMkgkiIiIiIuIzgvwtZKdFkZ32WWvEsbqOE8mFFoqrWmnt6KOspo2ymjbe/eQYANFh/qSfWHchIyEUe3SQWiNEzoKSCSIiIiIi4rPMJiOp8TZS421c/YUknE4nDa09JxZ2PNEaUd9BfUsP9S3H+Xi/qzUiwM9MWoLtxJaUYcyMt+FnVWuEyHgpmSAiIiIiIlOGwWAgOiyA6LAALpwXB0BXzwBHqlvdCzseqW6ju3eAwiNNFB5pAsBoMJAcG3xiUcdQMhLDCA9Ra4TIWJRMEBERERGRKS3Q38y8mZHMmxkJwKDD1RoxtLBjcWUrze29lB9vp/x4O+/tqQQg0ubvWnfhxNoLidHBGI1qjRABJRNERERERGSaMRmNpMTZSImzceXiJAAaW3sormpxJRgqWzlW30FjWw+NB3rYcaAWAH+riTS7jfRE19oLM+NtBPjpI5VMT/qbLyIiIiIi015kqD+RoXEsneNqjejuHeBITduJ5EILpdVt9PQNsr+8mf3lzQAYDJAUE0xGQhhpiTYyEsKIDPX35m2InDdKJoiIiIiIiHxOgJ+ZuSkRzE2JAMDhcFJZ3+Fed6GkspXGth4qajuoqO3g/U9d48JD/E7akjKMxJggTEajF+9E5NxQMkFEREREROQ0jEYDybEhJMeGcMWiRACa2nrciYXiqlaO1XbQ3N7LrqI6dhXVAeBnMTHTbjuRXAhlpj2UQH99DBPfp7/FIiIiIiIiZyDC5s8FNn8umB0LQE/fAGU17ZRUtlBc1UpplWvXiKKjzRQdPdEaASREB7sXdsxICCUy1B+DQQs7im9RMkFERERERMQD/K1mZs8IZ/aMcAAcTifV9Z0UV7nWXSiubKWhtYfK+g4q6zv4cG8VAGHB1hNbUoaRkRhKUkwwZpNaI2RyUzJBRERERETkHDAaDCTGBJMYE8zlCxIAaOnoHbYlZUVtOy0dfew+VM/uQ/UAWC1GZsbbSDvRGpGWEEqQv8WbtyIygpIJIiIiIiIi50lYsB+Ls2JYnBUDQG//IOU1be6FHUurWunsGeBgRQsHK1rc4xKigkh3L+wYSnRYgFojxKuUTBAREREREfESP4uJzORwMpM/a42oaeyipLLFvbBjXXM3VQ2dVDV0siWvGgBb0InWiBPJhRlxIWqNkPNKyQQREREREZFJwmgwkBAVREJUEMtzXa0RrZ19lLp3jWihvKadts4+Pj1cz6eHXa0RFrOR1LgQ0hPD3BUMwQFqjZBzR8kEERERERGRSSw0yMrCWdEsnBUNQP/AoGvXiBMJhpKqVjq6+zlc2crhylb3uPjIwBMLO4aSkRhGbLhaI8RzlEwQERERERHxIRaziVlJYcxKCgPA6XRyvKnL3RZRUtnK8aYuahpdf7YW1AAQEmhxt0akJ4aSEmfDYlZrhJwZJRNERERERER8mMFgID4yiPjIIC7NsQPQ3tU3rHKhrKad9q5+9hY3sLe4AQCzyUBKnM1VuZAQSlpiKLZAqzdvRXyIkgkiIiIiIiJTTEiglQUZ0SzIGGqNcHC0tt1VvVDZQklVK+1d/a6EQ1UrG0+Miw0PcLdFpCeEEh8ZqNYIGZWSCSIiIiIiIlOcxWx0tzisXJKM0+mkrrnbvSVlSVUr1Q2d1DZ3U9vczbZ9xwEI8jcPW3chJS4Eq8Xk5buRyUDJBBERERERkWnGYDAQGxFIbEQgl2THA9DR3e/aNaKqleLKVspq2ujsGSC/tJH80kYATEYDM+JC3FtSpieGERqk1ojpSMkEERERERERITjAQk56FDnpUQAMDDqoqO2gpLLFvbBja2cfR6rbOFLdxrufHAMgJizAvR1lemIo9qggjGqNmPKUTBAREREREZERzCYjM+02ZtptXI1r14j61h5KKlvcCztW1XdS19JNXUs32wtdrRGBfmbShlojEkJJjbfhZ1VrxFSjZIKIiIiIiIiclsFgICYsgJiwAC6a52qN6Orpp7S6zbX2QmULR2ra6OodYN+RRvYd+aw1IikmeNjCjuEhft68FfEAJRNERERERETkjAT6W5g/M5L5MyMBV2vEsbqOYdtSNrf3Un68nfLj7by3uxKAqFB/d+VCemIYCVFBGI1qjfAlSiaIiIiIiIiIR5hNRlLjbaTG27hqcRJOp5PGth7XlpQnEgyV9R00tPbQ0NrDjv21AAT4mZhpH0ouhDLTbsPfqo+rk5n+74iIiIiIiMg5YTAYiAoNICo0gKVz4wDo7h3gSHUbxZUtlFS1UlrdRnfvIPvLmthf1gSA0XCiNcK9LWUoETZ/b96KfI6SCSIiIiIiInLeBPiZmZsawdzUCAAGHQ4q6zpdrRFVrrUXGtt6OVrbztHadt7/1NUaEWHzO7ElpWvdhcSYIExGozdvZVpTMkFERERERES8xmQ0MiMuhBlxIVyxKBGAprYeSqpaTyzs2Mqxug6a2nrZ1VbHrqI6APysJmbG28hIdFUvpNlDCfDTR9zzRT9pERERERERmVQibP5cYPPngtmxAPT0uVojhhZ2LK1upbt3kKKjzRQdbQbAYIDE6ODPFnZMCCUy1B+DQQs7ngtKJoiIiIiIiMik5m81MyclgjkprtYIh8NJVUMnJZUt7oUdG1p7OFbXwbG6Dj78tAqAsGAr6Ylh7oUdk2KCMZvUGuEJSiaIiIiIiIiITzEaXQs0JsUEc/lCV2tEc3svpUOtEVUtVNR20NLRx+6Ddew+6GqNsFqMzIy3kZ4YSnpCGOkJNgL9Ld68FZ+lZIKIiIiIiIj4vPAQPxZnxbA4KwaA3v5ByqrbKK5qpfRE9UJX7wAHK1o4WNECHMUA2KOD3JUL6YlhRKs1Ylx8LpmwY8cOXn31VfLz8+nq6sJut7Ny5UpWr15NYGDghK71wx/+kHXr1p3ynJdffplly5aNeDwzM/OU46Kioti2bduE5iMiIiIiIiKe4WcxkTUjnKwZ4QA4nE5qGjrdbRElla3UtXRTVd9JVX0nm/OqAQgNsrq3pExPDGVGbIhaI0bhU8mEN954gyeffBKn00lcXBzx8fGUlJTwwgsv8O6777J27VrCwsImfN34+Hji4+NHPRYaGnrKsfPmzcNqtY54/EzmISIiIiIiIueG0WAgITqYhOhgLstNAKC1o5eSqjZKqlooqWyl/Hg7rZ197Dlcz57D9QBYzEZSh3aNSAglLSGU4ICJtUY4HE6KypvoL2vGYnCSZg/FaPTt6gefSSYUFhby05/+FIAf//jHrFq1CoPBQG1tLffeey/79+/nkUceYc2aNRO+9le+8hUeeOCBM5rXL37xCxITE89orIiIiIiIiHhPaLAfizKjWZQZDUBf/yDlx9sprnQlF0qqWunsGeDwsRYOH2txj4uPDDyRXAgjIzGUmPCAMVsj9hyqY+17xTS397ofCw/x4/YrM1iUGXNO7+9c8plkwvPPP4/D4eCGG27g1ltvdT8eGxvLz372M6699lreffddDh48SFZWlhdnKiIiIiIiIr7IajExKymMWUlhgKs1orapy7WoY2UrxVWt1DZ1UdPo+vP3/BoAQgIt7taIjIQwZsSFYDEb2XOojufWFY54nub2Xp5bV8g3b5znswkFn0gmdHZ2snXrVgBWrVo14nhKSgpLly5l+/btbNy4UckEEREREREROWtGg4H4yCDiI4NYlmMHoK2rj9ITVQvFVa2U17TR3tXP3uIG9hY3AGA2GZkRF0xVfecpr/+b94pZkBHtky0PPpFMKCoqoq+vD6vVSnZ29qjnLFq0iO3bt5Ofnz/h6+/cuZPi4mJaWlqw2WzMnTuX66+/noSEhNOOff7556mrq2NwcJDY2FiWLl3KF7/4xVHXURARERERERHfZgu0smBWNAtmuVoj+gccHD3eTvGJdReKK1vp6O6ntKrttNdqau/l8LEW9yKRvsQnkgllZWUA2O12LJbRF7pITk4edu5EfPLJJ8O+/9vf/sZzzz3Ht771Le65555Tjn3nnXeGfb9u3Tp++ctfsmbNGubOnTvhuYiIiIiIiIjvsJiN7p0fWAJOp5Pa5m427apgy4kdIk6lpbP3tOdMRj6RTGhtbQVOvbPC0LGhc8djxowZ/PCHP2Tp0qUkJCRgtVo5dOgQr7zyChs3buSZZ54hMDCQO+64Y8TYK664gi9/+ctkZWURFxdHZ2cnH3/8MT//+c85duwYd911F+vXrx9zl4iJMJsn9zYkphPbpJi0XYrIWVM8iXiGYknEMxRLImcmMSaYi+bFjSuZEGnzn/Sf+UZjcDqdTm9P4nSee+45fvnLX7J48WLefPPNUc/5+OOPufPOOzGZTBw4cOCsn/Pxxx9n7dq12Gw2Nm/eTFBQ0LjGNTU18ZWvfIXq6mpuvvlmnnzyybOah9PpHHNVUBEREREREZmcBh1O7v7JuzS29ox5TlRYAL9++CpMWjPh3PDz8wOgv79/zHP6+vqGnXu2vv3tb/O73/2OtrY2duzYwRVXXDGucREREaxevZrHHnuM9957j5/85CdnlQxwOJy0tXWd8fjzwWQyYrMF0NbWzeCgw9vTEfFpiicRz1AsiXiGYknk7Nx+1SzW/L5gzOO3XZlBW+vk+rxnswWMqxrJJ5IJ42lhGE8rxESEhISQkZHBgQMHOHr06ITGLliwAICWlhZaWloIDz+7xTQGBnzjH+7BQYfPzFVkslM8iXiGYknEMxRLImdmQXoU37xxHmvfK6a5/bO1ESJC/LjtygwWpEf5bGz5RDIhJSUFgOrqavr7+0ddhLGiomLYuZ4w9DwDAwNnNA5gcHDQY/MRERERERER37IoM4YFGdGUVrfS7zRgMThJs4f65HaQJ/OJVR5mz56NxWKhr6+PgoLRS0T27NkDQG5urkeec2BggCNHjgAQFxc3obHFxcWAq+UiLCzMI/MRERERERER32Q0GpidEsHyhYnMTonw+UQC+EgyITg4mEsuuQSAt99+e8Tx8vJyduzYAcDKlSs98pxvvfUW7e3tmM1mli5dOu5xAwMDvPrqqwAsXboUs9knij9ERERERERExs0nkgkA9913HwaDgQ0bNvDWW28xtAlFXV0d3/72t3E4HFx55ZVkZWUNG7dixQpWrFjBxo0bhz2+bds2/uM//oPy8vJhj/f19fHGG2/w1FNPAfDVr36VmJiYYec888wzrFu3jo6OjmGP19TU8OCDD5KXl4fZbOab3/ymJ25dREREREREZFLxmV+bZ2dn88Mf/pCnn36aRx99lBdeeIHw8HBKSkro6+sjNTWVJ554YsS4qqoqALq6hq+Q2d3dza9//Wt+/etfExUVRWxsLABlZWXuc6+55hp+8IMfjLjmkSNHePnll3n44YdJSkoiNDSU9vZ2ysrKcDqd+Pn58ZOf/IScnBxP/xhEREREREREvM5nkgkAd955J5mZmbzyyisUFBTQ2NiI3W5n5cqVrF69mqCgoHFfa+7cudx3333k5eVx9OhRysrK6O/vJyIigksuuYQbb7yRFStWjDr2tttuIyoqisLCQurq6qiqqsJisZCRkcGFF17IP/7jP5KcnOyp2xYRERERERGZVAzOoX4BmZQGBx00NXV6exqnZDYbCQ8Porm502e3NRGZLBRPIp6hWBLxDMWSiOf4SjxFRARhMp1+RQSfWTNBRERERERERCYHJRNEREREREREZEKUTBARERERERGRCVEyQUREREREREQmRMkEEREREREREZkQJRNEREREREREZEKUTBARERERERGRCVEyQUREREREREQmxOB0Op3enoSMzel04nBM/v9FJpORwUGHt6chMiUonkQ8Q7Ek4hmKJRHP8YV4MhoNGAyG056nZIKIiIiIiIiITIjaHERERERERERkQpRMEBEREREREZEJUTJBRERERERERCZEyQQRERERERERmRAlE0RERERERERkQpRMEBEREREREZEJUTJBRERERERERCZEyQQRERERERERmRAlE0RERERERERkQpRMEBEREREREZEJUTJBRERERERERCZEyQQRERERERERmRAlE0RERERERERkQpRMEBEREREREZEJMXt7AuJ76uvr2bZtG4WFhezbt4+ioiJ6e3u54IILeOONN7w9PRGf4XQ62bt3Lx988AF79uzhyJEjdHR0EBISwpw5c7jhhhv4h3/4BwwGg7enKjLp/fWvf2X79u3s37+furo6WlpasFgspKSksHz5cv7pn/6J8PBwb09TxCdt2bKF1atXA5CQkMAHH3zg5RmJ+IY1a9bw7LPPnvKcxx57jNtuu+08zcizlEyQCfvzn//MU0895e1piPi8HTt2cOedd7q/T0pKIiEhgaqqKrZt28a2bdv485//zJo1a7Bard6bqIgPePHFFzl48CBWq5Xo6GgyMzNpamriwIEDHDhwgLfffptXXnmFrKwsb09VxKd0dnby2GOPeXsaIj4tMjKSGTNmjHosOjr6PM/Gc5RMkAkLDg7moosuYv78+cyfP58DBw7w/PPPe3taIj7H6XSSmJjIP/3TP/GlL32JyMhI97H169fzyCOPsHnzZn7xi1/wve99z4szFZn87rjjDlJTU8nNzcVisbgfP3ToEN/97nc5fPgw3/nOd/jzn//sxVmK+J6f//znVFdXc8UVV/D+++97ezoiPmnZsmU8/fTT3p6GxymZIBN28803c/PNN7u/r62t9eJsRHxXdnY2GzduHPbBZ8gNN9zA8ePH+fnPf87vf/97vvOd72A0apkbkbGsWrVq1MczMzN58sknueWWWygpKaG0tJS0tLTzPDsR35SXl8ebb77JFVdcwZVXXqlkgogMo3emIiJeEhwcPGoiYciyZcsAaGlpoamp6XxNS2TKmTlzpvvr7u5uL85ExHf09/fzyCOP4O/vz6OPPurt6YjIJKTKBBGRSaqnp8f9tb+/vxdnIuLb9uzZA0BgYCCpqaleno2Ib3jppZc4fPgwDz30EHFxcd6ejohPO3jwIN/5zneor68nKCiIzMxMvvSlL5GRkeHtqZ0VJRNERCapod7urKwsgoODvTwbEd/icDjcuw8988wzAHz3u98lKCjIyzMTmfxKS0t56aWXmDt3Ll/72te8PR0Rn1dUVERRUZH7+w8++IAXX3yRr3/96/zgBz/AZDJ5cXZnTskEEZFJqLCwkN/+9rcA7u24ROT0XnvttRE7DmVnZ/P000+7W4dEZGxOp5Mf/ehHDAwM8Pjjj/vshxyRySAmJoYHH3yQSy+9lMTERIKDgykrK2Pt2rX89re/5fXXX8dsNvP973/f21M9I0omiIhMMg0NDTzwwAMMDAxw1VVX8aUvfcnbUxLxGbGxsSxcuJDBwUGqq6tpaGigqKiIDRs2kJubi81m8/YURSa1tWvX8umnn/K1r32N+fPne3s6Ij7t1ltvHfFYZmYmjz/+OImJiTzzzDO8/vrr3H777SQmJnphhmdHCzCKiEwi7e3t3HPPPVRXVzN37twpuY2QyLl07bXX8pvf/Ia3336bjz76iPXr15OTk8Of/vQnvv71rzM4OOjtKYpMWrW1tfzsZz8jNjaWf/mXf/H2dESmtLvuuouYmBgGBgb44IMPvD2dM6JkgojIJNHZ2ck///M/c+DAATIyMviv//ovrZUgcpaysrJ46aWXCA8Pp6ioyL0WiYiM9MQTT9DR0cGPfvQjvf6InGMmk4mcnBwAjh496uXZnBm1OYiITALd3d184xvfIC8vj5SUFF599VXCw8O9PS2RKSE4OJgLLriATZs2sX//fq6//npvT0lkUjpw4AAAjz/+OI8//viwY0M7DNXU1HDxxRcDsGbNGhYuXHh+JykyhQxtET4wMODlmZwZJRNERLyst7eXe++9l08++YSEhARee+01oqOjvT0tkSll6I2a2hxETq+hoWHMYw6Hw328v7//fE1JZEoqLi4G8NntV5VMEBHxov7+fh544AE+/vhjYmNjef3114mPj/f2tESmlJaWFnbt2gXA7NmzvTwbkcnrVH3bf/jDH3jooYdISEjw2f5ukclk8+bN7mTCULWPr9GaCSIiXjI4OMh3vvMdtmzZQnR0NK+//jpJSUnenpaIz9m1axfPP/88lZWVI47t37+fu+++m/b2dmJjY1m5cqUXZigiItNNcXExjz76KAcPHhz2uMPh4E9/+hPf+c53ALj88svJzs72xhTPmioTZMJqamq44YYb3N/39fUB8Omnn7JkyRL34//8z//MPffcc76nJ+Iz/vrXv7Jp0yYArFYr//Zv/zbmuY888ghz5sw5X1MT8SltbW384he/4Be/+AXR0dHExMRgMpmoqamhvr4ecG0Z+dJLLxEUFOTl2YqIyHQwMDDAW2+9xVtvvUVYWBh2ux2TyURFRQWtra0ALF68mP/zf/6Pl2d65pRMkAkbHBykpaVlxOMDAwPDHh9aqEdERjeUiAOoqqqiqqpqzHPb29vPx5REfNKCBQt46KGH2LlzJyUlJZSXl9PX14fNZmPJkiWsWLGCm2++WavTi4jIeZOQkMC//Mu/kJeXR2lpKUePHqWvr4/Q0FCWLVvGddddx3XXXYfJZPL2VM+Ywel0Or09CRERERERERHxHVozQUREREREREQmRMkEEREREREREZkQJRNEREREREREZEKUTBARERERERGRCVEyQUREREREREQmRMkEEREREREREZkQJRNEREREREREZEKUTBARERERERGRCVEyQUREREREREQmRMkEERERkTOQmZlJZmYmO3fu9PZUREREzjuztycgIiIiU8OaNWt49tlnx33+oUOHzuFsRERE5FxSMkFEREQ8LioqyttTEBERkXNIyQQRERHxuG3btnl7CiIiInIOac0EEREREREREZkQVSaIiIiI161YsYKqqiqeeuoprr76al566SXeffddampqCAgIYNGiRXzjG98gJydnzGsMDg6ybt06/ud//odDhw7R2dlJeHg4CxYs4I477mDJkiWnnENNTQ1vvPEG27Zto7Kykv7+fmJiYsjIyOCaa67h2muvxc/Pb9SxHR0dvPzyy2zatInq6moCAgLIzc3lvvvuO+WcRUREfJWSCSIiIjJptLW1cfPNN1NWVobFYsHPz4+Wlhbef/99PvzwQ5544gluvvnmEePa29u577772LVrFwAmk4mgoCDq6+vZtGkTmzZt4q677uIHP/jBqM+7fv16Hn30UXp7ewGwWCwEBQVRU1PDsWPH+OCDD8jMzGT27NkjxtbX13PTTTdx9OhR/Pz8MBqNtLS0sHnzZrZt28aLL77IJZdc4sGfkoiIiPepzUFEREQmjWeffZampib+7//9v+Tl5bFnzx7+8pe/cMEFF+BwOPjf//t/s3///hHjHn74YXbt2oXFYuFHP/oRe/bs4ZNPPmHr1q185StfAeCVV17hN7/5zYixmzdv5oc//CG9vb0sXLiQN998k4KCAnbu3MnevXt58803WbVqFRaLZdQ5//jHP8ZisfD666+Tl5fH3r17+d3vfkdqair9/f08+uijOBwOz/6gREREvMzgdDqd3p6EiIiI+L6Tt4Y83W4O1157LT/60Y/c3w+1OQC89tprXHjhhcPO7+np4ctf/jLl5eUsX76cX/3qV+5j+fn5rFq1CnB9sL/11ltHPN+DDz7Ipk2bCA8PZ8uWLe52hYGBAa655hoqKytZtGgRr732GlardVz3m5mZCUBERAR/+tOfiIyMHHb80KFDXH/99QCsXbuWRYsWjeu6IiIivkCVCSIiIuJxDQ0Np/zT0dEx6riFCxeOSCQA+Pv7c/fddwOwdetW2tvb3cf+8pe/ABAXF8ctt9wy6nW/9a1vAdDc3Dxsp4mdO3dSWVkJwEMPPTTuRMLJVq1aNSKRAK5kQ2JiIuBKLIiIiEwlWjNBREREPO5MPzwvXbr0tMccDgf79+93f19YWAjAkiVLMBpH/z1JWloasbGx1NbWUlhYyIoVKwDYu3cvANHR0cyfP/+M5nyqBRZjYmKorKyktbX1jK4tIiIyWakyQURERCaN2NjYcR1rampyf93Y2HjaseCqXDj5fHAtnghgt9snPtkTgoKCxjxmNrt+bzMwMHDG1xcREZmMlEwQERGRactgMHh7CiIiIj5JyQQRERGZNGpra8d1LCIiwv310HoFx48fP+W1h46fvL7B0EKR1dXVE5+siIjINKZkgoiIiEwaO3fuPO0xo9HInDlz3I/PmzfPfXysLRhLS0vdyYiT10ZYuHAh4Gp32Ldv39lNXkREZBpRMkFEREQmjT179oyaUOjt7eWVV14B4JJLLsFms7mPfelLXwJclQu/+93vRr3uL3/5SwDCw8O56KKL3I8vWbKEpKQkAJ566in6+vo8cyMiIiJTnJIJIiIiMmmEhITw4IMPsnHjRveihaWlpaxevZojR45gMpl48MEHh43Jzs7mmmuuAeCJJ57gv//7v+nu7gZcFQc/+tGP2LhxI+DaItLPz8891mQy8cgjj2AwGNizZw933nknu3fvdlc49PX1sXPnTr773e9SUlJyzu9fRETEV2hrSBEREfG4iy+++LTnrFmzxt1mMOT+++/nt7/9Ld/61rewWq34+fnR3t4OuBZLfOyxx0bdwvHJJ5+kubmZXbt28cQTT/DUU08RFBREW1sbTqcTgLvuuovbbrttxNjly5fz9NNP88gjj7Bnzx7uuOMOrFYrgYGBdHR0uJMad99994R/DiIiIlOVkgkiIiLicQ0NDac9p7+/f8RjNpuN3//+97z00ku8++671NTUEBYWxoIFC/jGN77BggULRr1WSEgIr732GuvWrWPDhg0cOnSIrq4uoqKiWLhwIXfccQdLliwZcy433HADixcv5v/9v//Htm3bqK6upre3F7vdzqxZs7j66qtJS0sb/w9ARERkijM4h9L1IiIiIl6yYsUKqqqqeOqpp7jpppu8PR0RERE5Da2ZICIiIiIiIiITomSCiIiIiIiIiEyIkgkiIiIiIiIiMiFKJoiIiIiIiIjIhGgBRhERERERERGZEFUmiIiIiIiIiMiEKJkgIiIiIiIiIhOiZIKIiIiIiIiITIiSCSIiIiIiIiIyIUomiIiIiIiIiMiEKJkgIiIiIiIiIhOiZIKIiIiIiIiITIiSCSIiIiIiIiIyIUomiIiIiIiIiMiE/P/twqVPvSTYhAAAAABJRU5ErkJggg==",
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             "text/plain": [
               "<Figure size 1200x600 with 1 Axes>"
             ]
@@ -1747,9 +1829,8 @@
         "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
         "\n",
         "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
-        "plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n",
         "\n",
-        "plt.title(\"Training & Validation Loss\")\n",
+        "plt.title(\"Training Loss\")\n",
         "plt.xlabel(\"Epoch\")\n",
         "plt.ylabel(\"Loss\")\n",
         "plt.legend()\n",
@@ -1769,34 +1850,34 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
-        "id": "mpBJNtYuAY9D",
-        "outputId": "66532b2e-1635-483d-d20a-9daad799ea57"
+        "id": "pNmXZV8WpxyA",
+        "outputId": "491ee3f7-7d6d-4a6c-e9a8-6da813dc5f8f"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Question: 👤 Como eu posso aprender a programar?\n",
+            "Question: 👤 Olá! Como você se chama?\n",
             "\n",
-            "Response 1: 🤖 Uma das melhores maneiras de aprender a programar é começar familiarizando-se com os fundamentos, como codificação, programação e análise de dados. Depois de dominar os fundamentos, você pode começar a praticar a programar em um campo específico, como ciência de dados, robótica ou inteligência artificial. Depois de ter uma boa compreensão dos conceitos básicos, você pode começar a praticar a codificação em um pequeno conjunto de desafios, como codificar um site básico, criar um aplicativo e começar a desenvolver aplicativos móveis.\n",
-            "Response 2: 🤖 A programar é uma habilidade que você pode aprender com uma variedade de materiais, como livros, tutoriais online, cursos e cursos. Em seguida, você pode praticar em um centro de tutoria ou em uma aula de certificação. Isso lhe dará uma compreensão mais profunda do processo de criação de programas de software.\n"
+            "Response 1: 🤖 Olá! Sou Aira, sua assistente prestativa. Como posso ajudá-lo hoje?\n",
+            "Response 2: 🤖 Olá! Sou Aira, sua assistente prestativa. Como posso ajudá-lo hoje?\n"
           ]
         }
       ],
       "source": [
-        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
         "import torch\n",
         "\n",
-        "device = torch.device(\"cuda\"  if torch.cuda.is_available() else  \"cpu\")\n",
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
         "\n",
-        "tokenizer = AutoTokenizer.from_pretrained(output_dir)\n",
-        "aira = AutoModelForCausalLM.from_pretrained(output_dir)\n",
+        "tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-portuguese-124M')\n",
+        "aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-portuguese-124M')\n",
         "\n",
         "aira.eval()\n",
         "aira.to(device)\n",
@@ -1806,20 +1887,20 @@
         "inputs = tokenizer(tokenizer.bos_token + question + tokenizer.eos_token, return_tensors=\"pt\").to(device)\n",
         "\n",
         "responses = aira.generate(**inputs,\n",
-        "    bos_token_id=tokenizer.bos_token_id,\n",
-        "    pad_token_id=tokenizer.pad_token_id,\n",
-        "    eos_token_id=tokenizer.eos_token_id,\n",
-        "    do_sample=True,\n",
-        "    top_k=50,\n",
-        "    max_length=200,\n",
-        "    top_p=0.95,\n",
-        "    temperature=0.7,\n",
-        "    num_return_sequences=2)\n",
+        "\tbos_token_id=tokenizer.bos_token_id,\n",
+        "\tpad_token_id=tokenizer.pad_token_id,\n",
+        "\teos_token_id=tokenizer.eos_token_id,\n",
+        "\tdo_sample=True,\n",
+        "\ttop_k=50,\n",
+        "\tmax_length=200,\n",
+        "\ttop_p=0.95,\n",
+        "\ttemperature=0.7,\n",
+        "\tnum_return_sequences=2)\n",
         "\n",
         "print(f\"Question: 👤 {question}\\n\")\n",
         "\n",
         "for i, response in  enumerate(responses):\n",
-        "    print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, \"\")}')\n"
+        "\tprint(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, \"\")}')"
       ]
     },
     {
@@ -1857,7 +1938,80 @@
     },
     "widgets": {
       "application/vnd.jupyter.widget-state+json": {
-        "00485f3c3dbc40b5be49c8f4fd49c3d7": {
+        "007b284a4dfb4c49a9046b0c831faee3": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HBoxModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_e1b9792f7ddc4360ad144ff7bffdc241",
+              "IPY_MODEL_d7dfcf2a4587466d903f5de4f9636b3a",
+              "IPY_MODEL_59071b50822f45a69175849ecb537e25"
+            ],
+            "layout": "IPY_MODEL_b12c5644975b4943b44cc26426e60bc4"
+          }
+        },
+        "012e9fd2fe7a4c27b97c10d2dba81d05": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        },
+        "01889c59797e4a61a3d0db186a20e032": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "HTMLModel",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
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+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_b06a3e2538254a78991f95d3d31de001",
+              "IPY_MODEL_ddc91a3219094cdd8f22c568c1c9b6ef",
+              "IPY_MODEL_10241c0b2a9946e08aa62d93d365f8d8"
+            ],
+            "layout": "IPY_MODEL_6a4faf32ef294b62b59f170241b80f1a"
           }
         }
       }