Bag_of_words_model (1).ipynb

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        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "import gensim\n",
        "import gensim.corpora as corpora"
      ]
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        "data =['The quick brown fox jumps over the lazy dog.A bright blue sky stretches endlessly above the calm ocean.',\n",
        "'Delicious aromas wafted from the kitchen as the chef prepared a gourmet meal.',\n",
        " 'The adventurous explorer discovered a hidden treasure in the heart of the jungle.',\n",
        " 'Sparkling stars illuminated the night sky, creating a magical atmosphere.',\n",
        "'Children giggled and played in the colorful playground on a sunny afternoon.',\n",
        "'The mysterious old book contained ancient spells and secrets.',\n",
        " 'The rhythmic sound of waves crashing against the shore lulled me into a peaceful sleep.',\n",
        " 'A group of friends gathered around a campfire, sharing stories under the starlit sky.',\n",
        "'In the bustling city, people hurriedly walked through crowded streets, lost in their own thoughts.',\n",
        "'These sentences cover a variety of topics and should serve well for a bag-of-words model input.',\n",
        "'Keep in mind that for a bag-of-words model, the order of words is disregarded, and only the frequency of words matters.']"
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        "countvec= CountVectorizer()"
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        "count_vecfit =countvec.fit_transform(data)"
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        "bag_of_words = pd.DataFrame(count_vecfit.toarray(),columns=countvec.get_feature_names_out())"
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        "bag_of_words"
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              "      --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",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-252b5688-3e62-4366-b2af-bce7b66c4059 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidfvec=TfidfVectorizer()"
      ],
      "metadata": {
        "id": "MLPjRxoO9MGO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidfvec_fit =tfidfvec.fit_transform(data)"
      ],
      "metadata": {
        "id": "mfsPZ5n8-MFu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_bag = pd.DataFrame(count_vecfit.toarray(),columns=tfidfvec.get_feature_names_out())"
      ],
      "metadata": {
        "id": "lR2vT0fx-WYc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tfidf_bag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-o6pH67M-YaH",
        "outputId": "448613fc-adae-42c9-e69b-5d84a4d8fa3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    above  adventurous  afternoon  against  ancient  and  aromas  around  as  \\\n",
            "0       1            0          0        0        0    0       0       0   0   \n",
            "1       0            0          0        0        0    0       1       0   1   \n",
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            "4       0            0          1        0        0    1       0       0   0   \n",
            "5       0            0          0        0        1    1       0       0   0   \n",
            "6       0            0          0        1        0    0       0       0   0   \n",
            "7       0            0          0        0        0    0       0       1   0   \n",
            "8       0            0          0        0        0    0       0       0   0   \n",
            "9       0            0          0        0        0    1       0       0   0   \n",
            "10      0            0          0        0        0    1       0       0   0   \n",
            "\n",
            "    atmosphere  ...  through  topics  treasure  under  variety  wafted  \\\n",
            "0            0  ...        0       0         0      0        0       0   \n",
            "1            0  ...        0       0         0      0        0       1   \n",
            "2            0  ...        0       0         1      0        0       0   \n",
            "3            1  ...        0       0         0      0        0       0   \n",
            "4            0  ...        0       0         0      0        0       0   \n",
            "5            0  ...        0       0         0      0        0       0   \n",
            "6            0  ...        0       0         0      0        0       0   \n",
            "7            0  ...        0       0         0      1        0       0   \n",
            "8            0  ...        1       0         0      0        0       0   \n",
            "9            0  ...        0       1         0      0        1       0   \n",
            "10           0  ...        0       0         0      0        0       0   \n",
            "\n",
            "    walked  waves  well  words  \n",
            "0        0      0     0      0  \n",
            "1        0      0     0      0  \n",
            "2        0      0     0      0  \n",
            "3        0      0     0      0  \n",
            "4        0      0     0      0  \n",
            "5        0      0     0      0  \n",
            "6        0      1     0      0  \n",
            "7        0      0     0      0  \n",
            "8        1      0     0      0  \n",
            "9        0      0     1      1  \n",
            "10       0      0     0      3  \n",
            "\n",
            "[11 rows x 112 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/news_articles.csv')"
      ],
      "metadata": {
        "id": "Yykq8MCj-322"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VOp0dTxjA9FU",
        "outputId": "d348aee0-93d3-4441-93ad-d036f8c19800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                                              title  \\\n",
              "0  25626  One Weight-Loss Approach Fits All? No, Not Eve...   \n",
              "1  19551  South Carolina Stuns Baylor to Reach the Round...   \n",
              "2  25221  U.S. Presidential Race, Apple, Gene Wilder: Yo...   \n",
              "3  18026  His Predecessor Gone, Gambia’s New President F...   \n",
              "4  21063  ‘Harry Potter and the Cursed Child’ Goes From ...   \n",
              "\n",
              "                                             content  \n",
              "0  Dr. Frank Sacks, a professor of nutrition at H...  \n",
              "1  South Carolina’s win over   Duke was not only ...  \n",
              "2  (Want to get this briefing by email? Here’s th...  \n",
              "3  BANJUL, Gambia  —   A week after he was inaugu...  \n",
              "4  The biggest book of the summer isn’t a blockbu...  "
            ],
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b70ed58-d128-485e-978a-479fe41c11eb 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-7b70ed58-d128-485e-978a-479fe41c11eb');\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",
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              "\n",
              "\n",
              "<div id=\"df-41113ffc-36fe-4d01-aae7-ed6152daf342\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41113ffc-36fe-4d01-aae7-ed6152daf342')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </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",
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              "\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",
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              "    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",
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              "    }\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-41113ffc-36fe-4d01-aae7-ed6152daf342 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6j68zRXA-eu",
        "outputId": "21408089-e491-4e74-cfa2-4d808405cb3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   id       100 non-null    int64 \n",
            " 1   title    100 non-null    object\n",
            " 2   content  100 non-null    object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 2.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "articles =data['content']"
      ],
      "metadata": {
        "id": "AwyJBJPXBFgP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wv7lkmN2DyrL",
        "outputId": "71fa783f-9194-4d46-f990-0f37049105c8"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download('punkt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ai7Mohq6EhR4",
        "outputId": "59ca26cd-cf28-4192-eda9-43d1647edfa7"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "# Assuming 'articles' is a Pandas Series containing text data\n",
        "# Make sure to import the necessary libraries at the beginning of your script or notebook\n",
        "\n",
        "# Convert to lowercase and remove punctuation\n",
        "articles = articles.str.lower().apply(lambda x: re.sub(r\"[^\\w\\s]\", \"\", x))\n",
        "\n",
        "# Remove stopwords\n",
        "en_Stopwords = set(stopwords.words('english'))\n",
        "articles = articles.apply(lambda x: ' '.join([word for word in x.split() if word not in en_Stopwords]))\n",
        "\n",
        "# Stemming\n",
        "ps = PorterStemmer()\n",
        "articles = articles.apply(lambda x: ' '.join([ps.stem(word) for word in x.split()]))\n",
        "\n",
        "# If 'articles' is a Pandas Series, you may need to convert it back to a list if necessary\n",
        "# articles = articles.tolist()\n"
      ],
      "metadata": {
        "id": "qPA5u-baBRO8"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "articles"
      ],
      "metadata": {
        "id": "aSFJ1ho5C-1J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cc426cf-1b1c-4901-a48d-5d14a7e509a0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     dr frank sack professor nutrit harvard like ch...\n",
              "1     south carolina win duke surpris fan postgam re...\n",
              "2     want get brief email here good even here lates...\n",
              "3     banjul gambia week inaugur anoth countri adama...\n",
              "4     biggest book summer isnt blockbust thriller sp...\n",
              "                            ...                        \n",
              "95    want get brief email here good even here lates...\n",
              "96    tallinn estonia guard brought ahm abdul qader ...\n",
              "97    gov scott walker wisconsin activ wisconsin nat...\n",
              "98    social media shook emot headlin shout news leg...\n",
              "99    moment joanna acevedo first set foot box gym v...\n",
              "Name: content, Length: 100, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim import corpora\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "# Assuming 'articles' is a list of preprocessed documents\n",
        "# If 'articles' is a Pandas Series, you can convert it to a list using articles.tolist()\n",
        "\n",
        "# Tokenize each document\n",
        "tokenized_articles = [word_tokenize(doc) for doc in articles]\n",
        "\n",
        "# Create a gensim dictionary\n",
        "dictionary = corpora.Dictionary(tokenized_articles)\n",
        "\n",
        "print(dictionary)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6O5Bga_ECK1",
        "outputId": "9923e4df-8a28-487d-b331-99679eecbbd3"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dictionary<8693 unique tokens: ['10', '100', '108', '15', '155']...>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Assuming 'articles' is a list of preprocessed documents\n",
        "# If 'articles' is a Pandas Series, you can convert it to a list using articles.tolist()\n",
        "\n",
        "# Tokenize each document, handling empty strings\n",
        "tokenized_articles = [word_tokenize(doc) if doc else [] for doc in articles]\n",
        "\n",
        "# Create a gensim dictionary\n",
        "dictionary = corpora.Dictionary(tokenized_articles)\n",
        "\n",
        "# Create a document-term matrix\n",
        "doc_term_matrix = [dictionary.doc2bow(tokens) for tokens in tokenized_articles]\n",
        "\n",
        "print(doc_term_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DCQZ5EjIESEE",
        "outputId": "9ffea988-1be3-4789-c097-b9187365e7b2"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_topics =2"
      ],
      "metadata": {
        "id": "LK6uUfTfE3Mj"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lda_model = gensim.models.LdaModel(corpus =doc_term_matrix,\n",
        "                                   id2word = dictionary,\n",
        "                                   num_topics=num_topics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3XwNbbumFR_i",
        "outputId": "a7f41da0-7a41-496f-8fe3-0834938d58af"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:gensim.models.ldamodel:too few updates, training might not converge; consider increasing the number of passes or iterations to improve accuracy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_model.print_topics(num_topics=num_topics,num_words =5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EKSwcZl2FpWK",
        "outputId": "9e5a1601-58a4-4b5f-adea-eb554ba69d4d"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '0.014*\"mr\" + 0.014*\"said\" + 0.006*\"trump\" + 0.005*\"would\" + 0.005*\"one\"'),\n",
              " (1,\n",
              "  '0.019*\"mr\" + 0.015*\"said\" + 0.005*\"trump\" + 0.004*\"would\" + 0.004*\"state\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lda_model.print_topics(num_topics=150,num_words =100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H4CvzagJF6aq",
        "outputId": "caa0a7bc-5dcf-4cb6-da9d-d8b1d2abfd4e"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '0.014*\"mr\" + 0.014*\"said\" + 0.006*\"trump\" + 0.005*\"would\" + 0.005*\"one\" + 0.004*\"state\" + 0.004*\"year\" + 0.004*\"peopl\" + 0.004*\"ms\" + 0.004*\"new\" + 0.003*\"presid\" + 0.003*\"like\" + 0.003*\"time\" + 0.003*\"could\" + 0.003*\"day\" + 0.003*\"go\" + 0.002*\"first\" + 0.002*\"countri\" + 0.002*\"two\" + 0.002*\"get\" + 0.002*\"say\" + 0.002*\"even\" + 0.002*\"want\" + 0.002*\"show\" + 0.002*\"also\" + 0.002*\"nation\" + 0.002*\"use\" + 0.002*\"last\" + 0.002*\"mani\" + 0.002*\"make\" + 0.002*\"govern\" + 0.002*\"live\" + 0.002*\"call\" + 0.002*\"work\" + 0.002*\"way\" + 0.002*\"home\" + 0.002*\"citi\" + 0.002*\"come\" + 0.002*\"back\" + 0.002*\"help\" + 0.002*\"compani\" + 0.002*\"offici\" + 0.002*\"still\" + 0.001*\"american\" + 0.001*\"support\" + 0.001*\"polit\" + 0.001*\"look\" + 0.001*\"unit\" + 0.001*\"play\" + 0.001*\"take\" + 0.001*\"much\" + 0.001*\"includ\" + 0.001*\"offic\" + 0.001*\"tri\" + 0.001*\"know\" + 0.001*\"made\" + 0.001*\"see\" + 0.001*\"week\" + 0.001*\"sinc\" + 0.001*\"vote\" + 0.001*\"im\" + 0.001*\"deal\" + 0.001*\"plan\" + 0.001*\"world\" + 0.001*\"month\" + 0.001*\"think\" + 0.001*\"group\" + 0.001*\"report\" + 0.001*\"york\" + 0.001*\"former\" + 0.001*\"move\" + 0.001*\"famili\" + 0.001*\"team\" + 0.001*\"ask\" + 0.001*\"immigr\" + 0.001*\"return\" + 0.001*\"school\" + 0.001*\"ad\" + 0.001*\"night\" + 0.001*\"news\" + 0.001*\"dont\" + 0.001*\"place\" + 0.001*\"critic\" + 0.001*\"execut\" + 0.001*\"hous\" + 0.001*\"that\" + 0.001*\"leas\" + 0.001*\"c\" + 0.001*\"women\" + 0.001*\"student\" + 0.001*\"life\" + 0.001*\"million\" + 0.001*\"univers\" + 0.001*\"democrat\" + 0.001*\"put\" + 0.001*\"need\" + 0.001*\"care\" + 0.001*\"clinton\" + 0.001*\"told\" + 0.001*\"person\"'),\n",
              " (1,\n",
              "  '0.019*\"mr\" + 0.015*\"said\" + 0.005*\"trump\" + 0.004*\"would\" + 0.004*\"state\" + 0.004*\"year\" + 0.004*\"like\" + 0.004*\"one\" + 0.003*\"new\" + 0.003*\"work\" + 0.003*\"peopl\" + 0.003*\"time\" + 0.003*\"also\" + 0.003*\"unit\" + 0.003*\"two\" + 0.002*\"say\" + 0.002*\"group\" + 0.002*\"offici\" + 0.002*\"go\" + 0.002*\"first\" + 0.002*\"govern\" + 0.002*\"mani\" + 0.002*\"includ\" + 0.002*\"last\" + 0.002*\"ms\" + 0.002*\"call\" + 0.002*\"make\" + 0.002*\"american\" + 0.002*\"citi\" + 0.002*\"polic\" + 0.002*\"live\" + 0.002*\"presid\" + 0.002*\"could\" + 0.002*\"week\" + 0.002*\"get\" + 0.002*\"offic\" + 0.002*\"back\" + 0.002*\"even\" + 0.002*\"nation\" + 0.002*\"show\" + 0.002*\"way\" + 0.002*\"use\" + 0.002*\"want\" + 0.002*\"countri\" + 0.002*\"compani\" + 0.002*\"help\" + 0.001*\"made\" + 0.001*\"day\" + 0.001*\"tri\" + 0.001*\"percent\" + 0.001*\"take\" + 0.001*\"month\" + 0.001*\"recent\" + 0.001*\"sinc\" + 0.001*\"report\" + 0.001*\"support\" + 0.001*\"parti\" + 0.001*\"forc\" + 0.001*\"play\" + 0.001*\"ask\" + 0.001*\"hous\" + 0.001*\"plan\" + 0.001*\"night\" + 0.001*\"think\" + 0.001*\"anoth\" + 0.001*\"still\" + 0.001*\"republican\" + 0.001*\"never\" + 0.001*\"world\" + 0.001*\"secur\" + 0.001*\"look\" + 0.001*\"home\" + 0.001*\"islam\" + 0.001*\"need\" + 0.001*\"news\" + 0.001*\"execut\" + 0.001*\"may\" + 0.001*\"polit\" + 0.001*\"member\" + 0.001*\"much\" + 0.001*\"come\" + 0.001*\"case\" + 0.001*\"chang\" + 0.001*\"york\" + 0.001*\"long\" + 0.001*\"ad\" + 0.001*\"releas\" + 0.001*\"dr\" + 0.001*\"chief\" + 0.001*\"man\" + 0.001*\"saudi\" + 0.001*\"campaign\" + 0.001*\"former\" + 0.001*\"three\" + 0.001*\"white\" + 0.001*\"start\" + 0.001*\"right\" + 0.001*\"interview\" + 0.001*\"famili\" + 0.001*\"run\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import LsiModel"
      ],
      "metadata": {
        "id": "PaDICSvZGLod"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lsa_model =LsiModel(corpus =doc_term_matrix,id2word=dictionary,num_topics=num_topics)"
      ],
      "metadata": {
        "id": "23_dtesjRm6u"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lsa_model.print_topics(num_topics=num_topics,num_words =5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yqLesf_aSuSz",
        "outputId": "85bfbe44-fca8-4d2e-adf9-0d40ec2fdcd8"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '0.615*\"mr\" + 0.429*\"said\" + 0.187*\"trump\" + 0.130*\"state\" + 0.119*\"would\"'),\n",
              " (1,\n",
              "  '-0.537*\"mr\" + -0.319*\"trump\" + 0.286*\"said\" + 0.242*\"saudi\" + 0.142*\"weight\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models.coherencemodel import CoherenceModel"
      ],
      "metadata": {
        "id": "BH0Q6UN9TAIS"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Lmu5WfDTc6bF"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cohenrece_values =[]\n",
        "model_list =[]"
      ],
      "metadata": {
        "id": "HvvdVQQHTBYN"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_topics =2\n",
        "max_topics =11"
      ],
      "metadata": {
        "id": "y8Bhnd2dacET"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for num_topics_1 in range(min_topics,max_topics+1):\n",
        "  model=LsiModel(corpus =doc_term_matrix,id2word=dictionary,num_topics=num_topics_1)\n",
        "  model_list.append(model)\n",
        "  coherence_model =CoherenceModel(model =model ,texts = articles,dictionary =dictionary ,coherence='c_v')\n",
        "  cohenrece_values.append(coherence_model.get_coherence())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mR2CA210aoXH",
        "outputId": "facdba8e-3caa-4a93-808e-3c93e7a8ad69"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gensim/topic_coherence/direct_confirmation_measure.py:204: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  m_lr_i = np.log(numerator / denominator)\n",
            "/usr/local/lib/python3.10/dist-packages/gensim/topic_coherence/indirect_confirmation_measure.py:323: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  return cv1.T.dot(cv2)[0, 0] / (_magnitude(cv1) * _magnitude(cv2))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cohenrece_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ST2_JvRudNlI",
        "outputId": "9b31bf94-4521-4aba-fb04-04748ce83f15"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from gensim.models import LsiModel\n",
        "from gensim.corpora import Dictionary\n",
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "import math\n",
        "\n",
        "# Assuming 'articles' and 'doc_term_matrix' are defined as in your previous code\n",
        "\n",
        "min_topics = 2\n",
        "max_topics = 10\n",
        "model_list = []\n",
        "coherence_values = []\n",
        "\n",
        "for num_topics_1 in range(min_topics, max_topics + 1):\n",
        "    model = LsiModel(corpus=doc_term_matrix, id2word=dictionary, num_topics=num_topics_1)\n",
        "    model_list.append(model)\n",
        "\n",
        "    # Check for empty documents\n",
        "    non_empty_articles = [doc for doc in articles if doc]\n",
        "\n",
        "    # Check for empty tokenized documents\n",
        "    tokenized_articles = [word_tokenize(doc) if doc else [] for doc in non_empty_articles]\n",
        "\n",
        "    coherence_model = CoherenceModel(model=model, texts=tokenized_articles, dictionary=dictionary, coherence='c_v')\n",
        "\n",
        "    # Handle NaN values by replacing them with 0\n",
        "    coherence_value = coherence_model.get_coherence()\n",
        "    if not math.isnan(coherence_value):\n",
        "        coherence_values.append(coherence_value)\n",
        "    else:\n",
        "        coherence_values.append(0)\n"
      ],
      "metadata": {
        "id": "wBZHWUDGdg2a"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# for creating model_list and coherence_values\n",
        "\n",
        "# Plot the coherence values\n",
        "plt.plot(range(min_topics, max_topics + 1), coherence_values, marker='o')\n",
        "plt.xlabel(\"Number of Topics\")\n",
        "plt.ylabel(\"Coherence Score\")\n",
        "plt.title(\"Coherence Scores for Different Number of Topics\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YxR-VkHlcaVW",
        "outputId": "432e298a-280f-4d09-a6a5-49e499eac6cd"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IgQULFmDBggWlnqNBgwalxmVrawsA2rhiY2MBAK1atSrxfJq4AWDcuHGlHpOWlqY9d0lcXFywefNmbNq0CTExMdi7dy+WL1+OhQsXwsXFBZMnTy5TLJqfe7NmzYrta9GiBfbu3VusELvozzEmJgZCCDRp0qTEa2gKOj09PREUFITVq1dj586d6N69O5599lmMGTNG5wuvrEqLXalUwsvLq9h7qkGDBnorYtZMpVDWPxDKojq+nyurR48e6N27N1asWFEssa6oojFr3juPjih8dHvRe2ncuHGxYe9NmzYFUFhT5OzsXOnfRWnK+zmoD9euXdMmkY9q0aKFdv+jnxFF/x3LZDI0btwYV69eLfUasbGxcHV11fkjq6gRI0bg008/xeTJkzFv3jz06dMHzz//PIYPH67zB05txASpjrGysoKrqyvOnj1brteVdT4OhUJR4nZN6whQWBTYt29fvPXWWyUeq/nQ0zAzM9N5rvkrZu7cuejfv3+J5yj6QVaWuJ5Ec92VK1eWOvlbWYfpy2QyNG3aFE2bNsWgQYPQpEkT7Ny5E5MnTy5zPOVV0s9RJpPhjz/+KPHn8+i9rFq1CuPHj8dPP/2EP//8EzNnzkRISAiOHz+Ohg0bGizmkuKuDM37Xp9TDtTU9/OTBAcHo1evXvjf//5X4sg9TdF6USqVqsTzlRazPu+lsr+LJ6mL8xKZmZnh8OHDOHDgAH777TeEhoZiz549eOqpp/Dnn3+W+vurDZgg1UHPPPMMPvnkE4SHh6Nz586PPdbd3R1qtRoxMTHav1yAwhmFU1NT4e7uXu7re3t7IzMzE4GBgeV+LQB4eXkBKGzhqOg5SooJKPwCLe3LU3OMlZWV3q4LFN6Pra0t4uPji8VS2nU0P/dLly4V23fx4kU4ODg8cRi/t7c3hBDw9PQs9sVRktatW6N169Z477338Pfff6Nr167YsmULli5d+sTXlha75ncJAHl5eYiLi9Prz7YozdwzpSUiFVEd389A5b/Me/bsiV69emlbOIuytbXV6TrXMERrCvCwpe3R+4qOjgYA7cisyv4uSmOIz8GyXLO0f9+a/Y/StHBrCCFw+fJl+Pr6lnoNb29v7N27FykpKY9tRZLL5ejTpw/69OmD1atX48MPP8S7776LAwcOGPTfq9Rqd/sYleitt96Cubk5Jk+ejMTExGL7Y2NjtcPJn376aQAotqTC6tWrAQCDBg0q9/VffPFFhIeHY+/evcX2paamoqCg4LGvd3R01P5lq0kqHlXS8P0n6devHywtLRESEoKcnBydfZq/ZNu3bw9vb2989NFHJc56/aTrRkREICsrq9j2yMhI3L17V9vl1K5dO3h6emLNmjXFhr1rYnFxcYGfnx+2b9+uc8zZs2fx559/an9vj/P8889DoVBg8eLFxf5aF0Lg7t27AArr1or+Tlq3bg25XF7mmakfFRgYCKVSiXXr1ulc97PPPkNaWlqF3lNl8fXXX+PTTz9F586d0adPH72dtzq+nwHA3Ny82PunvDS1SCVNGuvt7Y2LFy/qxHf69OkyTXlREbdv38YPP/ygfZ6eno4vv/wSfn5+cHZ2BlD530VpDPE5WJZrRkZGIjw8XLstKysLn3zyCTw8PIrN0/Xll1/qlE58++23iI+Px8CBA0u9xrBhwyCEwOLFi4vt0/zbLGnKBU0LekX+/dckbEGqg7y9vfH1119jxIgRaNGihc5M2n///Te++eYb7ey7bdq0wbhx4/DJJ58gNTUVPXv2RGRkJLZv346hQ4eid+/e5b7+m2++iZ9//hnPPPMMxo8fj/bt2yMrKwv//fcfvv32W1y9elVnkr+SbNy4Ed26dUPr1q0xZcoUeHl5ITExEeHh4bh58yZOnz5drpisrKzw8ccfY/LkyejQoYN2vqDTp08jOzsb27dvh1wux6effoqBAweiZcuWmDBhAho0aIBbt27hwIEDsLKywi+//FLqNXbs2IGdO3fiueeeQ/v27aFUKnHhwgV8/vnnMDU11c4rIpfLsXnzZgwePBh+fn6YMGECXFxccPHiRZw7d0774b9y5UoMHDgQnTt3xqRJk3D//n2sX78e1tbWOvMNlcbb2xtLly7F/PnzcfXqVQwdOhSWlpaIi4vDDz/8gFdeeQVz587F/v37MX36dLzwwgto2rQpCgoKsGPHDigUCgwbNqxcP2egsHB//vz5WLx4MQYMGIBnn30Wly5dwqZNm9ChQwe9TJT37bffwsLCQlvsu3fvXhw7dgxt2rTBN998U+nzP6o6vp+BwoR+8+bNWLp0KRo3bgxHR0c89dRT5TpHz5490bNnTxw6dKjYvokTJ2L16tXo378/Jk2ahKSkJGzZsgUtW7bUDgbRp6ZNm2LSpEk4ceIEnJyc8PnnnyMxMRHbtm3THqOP30VJDPE5+CTz5s3Drl27MHDgQMycORN2dnbYvn074uLi8N133xWr/7Gzs0O3bt0wYcIEJCYmYs2aNWjcuDGmTJlS6jV69+6Nl19+GevWrUNMTAwGDBgAtVqNI0eOoHfv3pg+fTref/99HD58GIMGDYK7uzuSkpKwadMmNGzYUKeAvFaq4lFzVI1ER0eLKVOmCA8PD6FUKoWlpaXo2rWrWL9+vc4Q2/z8fLF48WLh6ekpjI2NhZubm5g/f77OMUKUPjS6pOHAGRkZYv78+aJx48ZCqVQKBwcH0aVLF/HRRx9p58HRDMVduXJlifHHxsaKsWPHCmdnZ2FsbCwaNGggnnnmGfHtt99qjylpeLIQD4fGHjhwQGf7zz//LLp06SLMzMyElZWV6Nixo9i1a5fOMf/++694/vnnhb29vTAxMRHu7u7ixRdfFGFhYSX/oB84c+aMePPNN0W7du2EnZ2dMDIyEi4uLuKFF14Qp06dKnb80aNHRd++fYWlpaUwNzcXvr6+2rlJNP766y/RtWtXbbyDBw8W58+f1znmSUPfv/vuO9GtWzdhbm4uzM3NRfPmzcW0adPEpUuXhBBCXLlyRUycOFF4e3sLU1NTYWdnJ3r37i3++uuvx97vk669YcMG0bx5c2FsbCycnJzE1KlTi01r0LNnT9GyZcsnXqfo9TQPU1NT0bBhQ/HMM8+Izz//vNh7VojKD/MXonq+nxMSEsSgQYOEpaWlAPDEIf+l3Zvm3CVd96uvvhJeXl5CqVQKPz8/sXfv3lKH+Re9b815i04fUdI9an4fe/fuFb6+vsLExEQ0b968xKkn9PG7KElZPwf1NcxfiML3xPDhw4WNjY0wNTUVHTt2FL/++qvOMZqf465du8T8+fOFo6OjMDMzE4MGDSo2r1NJ7/WCggKxcuVK0bx5c6FUKkX9+vXFwIEDxcmTJ4UQhXO/DRkyRLi6ugqlUilcXV3FqFGjik2lUBvJhNBjVR8RERFVmYMHD6J379745ptvMHz4cKnDqVVYg0RERERUBBMkIiIioiKYIBEREREVwRokIiIioiLYgkRERERUBBMkIiIioiI4UWQFqdVq3L59G5aWlnVyfR4iIqKaSAiBjIwMuLq6PnbBXSZIFXT79u1iq1ATERFRzXDjxo3HLrbNBKmCLC0tART+gK2srCSOhoiIiMoiPT0dbm5u2u/x0jBBqiBNt5qVlRUTJCIiohrmSeUxLNImIiIiKoIJEhEREVERTJCIiIiIimCCRERERFQEEyQiIiKiIpggERERERXBBImIiIioCCZIREREREVIniBt3LgRHh4eMDU1RUBAACIjI8v0ut27d0Mmk2Ho0KE624UQWLhwIVxcXGBmZobAwEDExMToHJOSkoLRo0fDysoKNjY2mDRpEjIzM/V1S0RERFTDSZog7dmzB0FBQQgODsapU6fQpk0b9O/fH0lJSY993dWrVzF37lx079692L4VK1Zg3bp12LJlCyIiImBubo7+/fsjJydHe8zo0aNx7tw57Nu3D7/++isOHz6MV155Re/3R8Wp1ALhsXfxU9QthMfehUotpA6JiIioGJkQQrJvqICAAHTo0AEbNmwAAKjVari5uWHGjBmYN29eia9RqVTo0aMHJk6ciCNHjiA1NRU//vgjgMLWI1dXV7zxxhuYO3cuACAtLQ1OTk744osvMHLkSFy4cAE+Pj44ceIE/P39AQChoaF4+umncfPmTbi6upYp9vT0dFhbWyMtLY1LjZRR6Nl4LP7lPOLTHiarLtamCB7sgwGtXCSMjIiI6oqyfn9L1oKUl5eHkydPIjAw8GEwcjkCAwMRHh5e6uvef/99ODo6YtKkScX2xcXFISEhQeec1tbWCAgI0J4zPDwcNjY22uQIAAIDAyGXyxEREVHqdXNzc5Genq7zoLILPRuPqV+d0kmOACAhLQdTvzqF0LPxEkVGRERUnGQJUnJyMlQqFZycnHS2Ozk5ISEhocTXHD16FJ999hm2bt1a4n7N6x53zoSEBDg6OursNzIygp2dXanXBYCQkBBYW1trH25ubo+/QdJSqQUW/3IeJTVVarYt/uV8rehuYxciEVHtYCR1AGWVkZGBl19+GVu3boWDg0OVX3/+/PkICgrSPk9PT2eSVEaRcSnFWo4eJQDEp+Wg+4r9qG9hAnMTI9RTGsHcRAFzEyOYKxWopzSChYkR6pkoCv+rLNxublJ4XOHxhduMFNLk/exCJCKqPSRLkBwcHKBQKJCYmKizPTExEc7OzsWOj42NxdWrVzF48GDtNrVaDaCwBejSpUva1yUmJsLF5eEXUmJiIvz8/AAAzs7OxYrACwoKkJKSUuJ1NUxMTGBiYlK+myQAQFJG6cnRo26n5uB2atmOfRylkfxBEqWA+SOJVj1NQqV8kGgpjVDPRDfRMn+QaNVTPkjETIxQz1gBuVz22GtquhCLthdpuhA3j2nHJImIqAaRLEFSKpVo3749wsLCtEP11Wo1wsLCMH369GLHN2/eHP/995/Otvfeew8ZGRlYu3Yt3NzcYGxsDGdnZ4SFhWkTovT0dERERGDq1KkAgM6dOyM1NRUnT55E+/btAQD79++HWq1GQECA4W64DnO0NC3TcQsGtYC7vTmy8gqQnadCVm4BsnJVyM4rQGbuI9vyHm7PylU9eF6AfFVhepJXoEZKQR5SsvR3D/WUioetWsqHrVYWJkYwM5bj97MJpXYhylDYhdjXxxmKJyRaRERUPUjaxRYUFIRx48bB398fHTt2xJo1a5CVlYUJEyYAAMaOHYsGDRogJCQEpqamaNWqlc7rbWxsAEBn++zZs7F06VI0adIEnp6eWLBgAVxdXbVJWIsWLTBgwABMmTIFW7ZsQX5+PqZPn46RI0eWeQQblU9HTzu4WJsiIS2nxCRCBsDZ2hTju3pWKoHIK1AXT6YeJFCF21XIzi1A1oN92gTrkaQrK68A2Y9s05QQZeepkJ2nQnIFpsvSdCFGxqWgs7d9he+PiIiqjqQJ0ogRI3Dnzh0sXLgQCQkJ8PPzQ2hoqLbI+vr165DLy1dP8tZbbyErKwuvvPIKUlNT0a1bN4SGhsLU9GErxs6dOzF9+nT06dMHcrkcw4YNw7p16/R6b/SQQi5D8GAfvPbVqWL7NOlQ8GCfSreuKI3kUBopYVNPWanzaAghkFugLky4HmmpysorTLQ0idiJqyn49cyTR+GVtauRiIikJ+k8SDUZ50Eqv4/3RWNtmO6s5rWhiDk89i5GbT3+xON2TenEFiQiIomV9fu7xoxio5rP2swYAODvbouXO7vD0dIUHT3tanxdTlm7EDt62lV1aEREVEGSr8VGdUdMUgYAoJOXPYb4NUBnb/sanxwBD7sQgYddho8S0E8XIhERVR0mSFRlohMLK5ybOFlIHIn+DWjlgs1j2sHZuviIPSO5DC1c2A1LRFSTsIuNqoQQAtGJhS1ITZ0sJY7GMAa0ckFfH2dExqUgKSMHjpYm2LD/Mo7F3sWin8/h8/EdIJOxFYmIqCZggkRVIjE9Fxk5BVDIZfCqby51OAajkMt0CrEdrUwxYM1hHLh0B3vPJWJAq9InIyUiouqDXWxUJTStR+729WBipJA4mqrjXd8Cr/bwBgC8/8s5ZOcVSBwRERGVBRMkqhLa7jXH2tm99jjTejdGQ1sz3E7Lwbqwy1KHQ0REZcAEiarE5aTCAu2mtbBA+0nMlAosfrYlAODTI1cQ8yBZJCKi6osJElUJTQtS41paoP0kfVo4IbCFEwrUAu/9eBacn5WIqHpjgkQGJ4RATGLdbUHSCB7sA1NjOSLiUvBT1G2pwyEiosdggkQGl5Ceg4zcwhFsng61dwTbk7jZ1cOMp5oAAJb+dgFp9/MljoiIiErDBIkMTjNBpEcdG8FWkindveBV3xzJmblY/eclqcMhIqJSMEEig4up5RNElofSSI4lQ1oBAHYcv4azt9IkjoiIiErCBIkMTlOg3YQJEgCga2MHPNvGFWoBvPvjWajVLNgmIqpumCCRwUWzQLuY9wa1gIWJEU7fSMXuEzekDoeIiIpggkQGJYR4ZA4ktiBpOFqZIqhvUwDA8tCLuJuZK3FERET0KCZIZFC303KQmVsAI7kMHvZ1dwRbScZ2dkcLFyuk3c/H8tCLUodDRESPYIJEBqWpP/J0MIfSiG+3Rxkp5Fg6tLBg+//+uYl/rqZIHBEREWnwG4sMiiPYHq+9uy1G+LsBAN778SwKVGqJIyIiIoAJEhmYpkC7CQu0S/X2wOawqWeMiwkZ+OLvq1KHQ0REYIJEBsYWpCezM1di3oDmAICP90UjIS1H4oiIiIgJEhmMWi0Qk8Qh/mXxor8b2jayQVaeCkt/Oy91OEREdR4TJDKYW6n3kZ2ngrFCBneOYHssuVyGpUNbQS4Dfj0Tj6MxyVKHRERUpzFBIoOJSSrsXvNysICxgm+1J2npao2xnT0AAAt/OovcApW0ARER1WH81iKDYYF2+QX1a4r6lia4kpyFrYevSB0OEVGdxQSJDCYmkTNol5eVqTHeG9QCALB+/2XcSMmWOCIiorqJCRIZjKaLrYkjW5DK49k2rujsZY/cAjUW/XxO6nCIiOokJkhkEGq10LYgNWELUrnIZDIsGdoSxgoZwi4mYd/5RKlDIiKqc5ggkUHcSr2P+/kqKBVyeNjXkzqcGqexoyWmdPcCACz6+Ryy8wokjoiIqG5hgkQGoVmDzau+OYw4gq1Cpj/VGA1szHAr9T427L8sdThERHUKv7nIIKLZvVZp9ZRGCB7sAwDYeuQKLj+YdJOIiAyPCRIZhHaJERZoV0pfHyf0ae6IfJXAwp/OQgghdUhERHUCEyQyiGjNCDa2IFWKTCbDomdbwsRIjr9j7+Ln07elDomIqE5ggkR6p1YLbXcQ12CrPDe7epjeuzEAYOlvF5Ceky9xREREtR8TJNK7G/eykZOvhtJIzjXY9OSVnl7wdDDHnYxcfLwvWupwiIhqPSZIpHeaAm3v+hZQyGUSR1M7mBgp8P6QlgCA7X9fxbnbaRJHRERUu0meIG3cuBEeHh4wNTVFQEAAIiMjSz32+++/h7+/P2xsbGBubg4/Pz/s2LFD5xiZTFbiY+XKldpjPDw8iu1ftmyZwe6xrtEM8Wf3mn51b1Ifg3xdoBbAez+ehVrNgm0iIkORNEHas2cPgoKCEBwcjFOnTqFNmzbo378/kpKSSjzezs4O7777LsLDw3HmzBlMmDABEyZMwN69e7XHxMfH6zw+//xzyGQyDBs2TOdc77//vs5xM2bMMOi91iXaEWws0Na7BYN8YK5U4N/rqfjm5A2pwyEiqrUkTZBWr16NKVOmYMKECfDx8cGWLVtQr149fP755yUe36tXLzz33HNo0aIFvL29MWvWLPj6+uLo0aPaY5ydnXUeP/30E3r37g0vLy+dc1laWuocZ27OWhl90c6BxCH+eudsbYo5fZsCAEL+uIiUrDyJIyIiqp0kS5Dy8vJw8uRJBAYGPgxGLkdgYCDCw8Of+HohBMLCwnDp0iX06NGjxGMSExPx22+/YdKkScX2LVu2DPb29mjbti1WrlyJggIu5aAPKrVA7B3NCDa2IBnC+C4eaO5sidTsfKwIvSh1OEREtZKRVBdOTk6GSqWCk5OTznYnJydcvFj6h35aWhoaNGiA3NxcKBQKbNq0CX379i3x2O3bt8PS0hLPP/+8zvaZM2eiXbt2sLOzw99//4358+cjPj4eq1evLvW6ubm5yM3N1T5PT08vy23WOddTspFboIaJkRxudlyDzRCMFHIsHdoKw7eEY/eJG3jB3w3t3W2lDouIqFaRLEGqKEtLS0RFRSEzMxNhYWEICgqCl5cXevXqVezYzz//HKNHj4apqanO9qCgIO3/+/r6QqlU4tVXX0VISAhMTExKvG5ISAgWL16s13upjTQF2o0dOYLNkPw97PBC+4b45uRNLPjxLH6e3pVr3hER6ZFkn6gODg5QKBRITEzU2Z6YmAhnZ+dSXyeXy9G4cWP4+fnhjTfewPDhwxESElLsuCNHjuDSpUuYPHnyE2MJCAhAQUEBrl69Wuox8+fPR1pamvZx4wYLZEvCAu2qM29gc1ibGeN8fDp2HL8mdThERLWKZAmSUqlE+/btERYWpt2mVqsRFhaGzp07l/k8arVap+tL47PPPkP79u3Rpk2bJ54jKioKcrkcjo6OpR5jYmICKysrnQcVF5OkWaSWBdqGZm9hgrcHNAcArPozGknpORJHRERUe0jaxRYUFIRx48bB398fHTt2xJo1a5CVlYUJEyYAAMaOHYsGDRpoW4hCQkLg7+8Pb29v5Obm4vfff8eOHTuwefNmnfOmp6fjm2++wapVq4pdMzw8HBEREejduzcsLS0RHh6OOXPmYMyYMbC1ZR1HZT0cwcYWpKowsoMb9vxzA6dvpGLpbxewblRbqUMiIqoVJE2QRowYgTt37mDhwoVISEiAn58fQkNDtYXb169fh1z+sJErKysLr7/+Om7evAkzMzM0b94cX331FUaMGKFz3t27d0MIgVGjRhW7pomJCXbv3o1FixYhNzcXnp6emDNnjk5dElWM7gg2tiBVBblchqVDWmHIxqP4+fRtjOzghi6NHaQOi4ioxpMJITgdbwWkp6fD2toaaWlp7G574MqdTDy16hBMjeU4v3gA5CzSrjLBP53F9vBr8KpvjtBZPaA0YsE2EVFJyvr9zU9R0htN91pjRwsmR1UsqF8zOFiY4MqdLGw9ckXqcIiIajwmSKQ32hFsrD+qctZmxnh3UGHB9vr9Mbh5L1viiIiIajYmSKQ30doRbEyQpDDUrwECPO2Qk6/G4l/OSx0OEVGNxgSJ9ObhHEgs0JaCTCbD0qGtYCSXYd/5RPx1PvHJLyIiohIxQSK9KFCpceVOFgBOEimlJk6WmNTdEwCw6JdzuJ+nkjgiIqKaiQkS6cXVu9nIU6lhZqxAAxszqcOp02Y+1QSu1qa4ee8+Nh28LHU4REQ1EhMk0gtN91oTJ45gk5q5iREWDm4JAPjfoSvauamIiKjsmCCRXnAG7eqlf0sn9GpWH3kqNYJ/OgdOd0ZEVD5MkEgvopNYoF2dyGQyLH62JZRGchy9nIzf/ouXOiQiohqFCRLpxcMRbGxBqi7c7c0xrVdjAMD7v5xHRk6+xBEREdUcTJCo0vJVasQlF45ga8IWpGrl1Z5e8LCvh6SMXKz5K0bqcIiIagwmSFRpV5OzkK8SMFdyBFt1Y2qswOIhrQAAX/x9FRfi0yWOiIioZmCCRJWmXYPNyRIyGUewVTc9m9bH062doVILvPfjWajVLNgmInoSJkhUaTGaAm1Hdq9VVwue8UE9pQInr93Dt6duSh0OEVG1xwSJKi3mQQsSC7SrLxdrM8wJbAoAWPbHRaRm50kcERFR9cYEiSot+sEItsYs0K7Wxnf1QFMnC6Rk5WHF3ktSh0NEVK0xQaJKySt4OIKNLUjVm7FCjqVDWwMAdkVex7/X70kcERFR9cUEiSrl6t0sFKgFLEyM4GptKnU49AQdPe0wrF1DCAG89+NZqFiwTURUIiZIVCna7jVHC45gqyHmP90cVqZGOHc7HV8dvyZ1OERE1RITJKqUaG2BNuuPagoHCxO8OaA5AOCjPy8hKSNH4oiIiKofJkhUKVxipGZ6qWMj+Da0RkZOAUJ+vyh1OERE1Q4TJKoUTRdbEyZINYpCLsPSoa0gkwE//HsL4bF3pQ6JiKhaYYJEFZZboMLVu9kA2MVWE/k2tMHogEYAgAU/nUVegVriiIiIqg8mSFRhcclZUKkFLE2M4GzFEWw10Zv9msPeXInLSZn4/Fic1OEQEVUbTJCowjQF2k2cOIKtprKuZ4x3nm4BAFj7Vwxupd6XOCIiouqBCRJVGAu0a4fn2zVARw873M9X4f1fzkkdDhFRtcAEiSqMBdq1g0wmw5KhraCQy7D3XCIOXEySOiQiIskxQaIKi+EcSLVGM2dLTOrmCQAI/vkccvJVEkdERCQtJkhUITn5Kly9yzXYapNZfZrA2coU11OyselgrNThEBFJigkSVciVO1lQC8DK1AiOliZSh0N6YG5ihIWDfQAAWw7GahchJqpJVGqB8Ni7+CmqcH4vrjdIFWUkdQBUM8UkPSzQ5gi22mNgK2f0aFofh6PvIPjnc9g+oQN/v1RjhJ6Nx+JfziM+7eHyOS7Wpgge7IMBrVwkjIxqIrYgUYXEaIf4s3utNpHJZHj/2ZZQGslxOPoO/jibIHVIRGUSejYeU786pZMcAUBCWg6mfnUKoWfjJYqMaiomSFQh0doh/izQrm08HMzxWk9vAMD7v5xHZm6BxBERPZ5KLbD4l/MoqTNNs23xL+fZ3UblwgSJKiQm6UELkiNbkGqj13t5o5FdPSSk52BdWIzU4RA9VmRcSrGWo0cJAPFpOYiMS6m6oKjGY4JE5ZaTr8I17Qg2tiDVRqbGCiwe0hIA8NnROFxKyJA4IqLSJWWUnhxV5DgigAkSVUDsnUyoBWBtZoz6HMFWa/Vu5ogBLZ2hUgu89+N/EILdE1Q9OVqWbS3Ish5HBDBBogp4dIJIjnCq3RYO9oGZsQInrt7Dd6duSR0OUYk6etrByerxf6zZmBmjo6ddFUVEtYHkCdLGjRvh4eEBU1NTBAQEIDIystRjv//+e/j7+8PGxgbm5ubw8/PDjh07dI4ZP348ZDKZzmPAgAE6x6SkpGD06NGwsrKCjY0NJk2ahMzMTIPcX23EJUbqDlcbM8wKbAIACPn9AtKy8yWOiKg4hVyGZs6P/zzKyM3H8St3qygiqg0kTZD27NmDoKAgBAcH49SpU2jTpg369++PpKSS14Kys7PDu+++i/DwcJw5cwYTJkzAhAkTsHfvXp3jBgwYgPj4eO1j165dOvtHjx6Nc+fOYd++ffj1119x+PBhvPLKKwa7z9omWtOC5Mj6o7pgYldPNHG0wN2sPCzfe4GT8FG1c/BSEg5HJwMA7MyVOvtcrE3R3t0GKjXw6o6TOH87XYoQqQaSCQkLCwICAtChQwds2LABAKBWq+Hm5oYZM2Zg3rx5ZTpHu3btMGjQICxZsgRAYQtSamoqfvzxxxKPv3DhAnx8fHDixAn4+/sDAEJDQ/H000/j5s2bcHV1LdN109PTYW1tjbS0NFhZWZXpNbVFz5UHcO1uNr6eHIAujR2kDoeqwPErdzHyk+PFtnMSPpJaanYe+n18GEkZuZjQ1QPvDfJBZFwKkjJy4Ghpio6edihQqzH2s0hExKXAycoEP7zeFa42ZlKHThIp6/e3ZC1IeXl5OHnyJAIDAx8GI5cjMDAQ4eHhT3y9EAJhYWG4dOkSevToobPv4MGDcHR0RLNmzTB16lTcvfuwWTU8PBw2Njba5AgAAgMDIZfLERERUer1cnNzkZ6ervOoi+7nqXA9JRsAu9jqktTsvBK3cxI+ktqCn84hKSMX3vXN8faA5lDIZejsbY8hfg3Q2dseCrkMJkYKfPKyP5o4WiAxPRfjt0Ui7T67i+nxJEuQkpOToVKp4OTkpLPdyckJCQmlz96blpYGCwsLKJVKDBo0COvXr0ffvn21+wcMGIAvv/wSYWFhWL58OQ4dOoSBAwdCpSpcnTwhIQGOjo465zQyMoKdnd1jrxsSEgJra2vtw83NrSK3XePF3smEEIBtPWM4WCif/AKq8TST8JWEk/CRlH4+fRu/nL4NhVyGj0f4wdRYUeqx1vWM8cXEjnCyMkF0YiZe+fIf5BaoqjBaqmkkL9IuL0tLS0RFReHEiRP44IMPEBQUhIMHD2r3jxw5Es8++yxat26NoUOH4tdff8WJEyd0jqmI+fPnIy0tTfu4ceNG5W6khnq0QJsj2OoGTsJH1VFCWg4W/HgWADDjqcbwbWjzxNc0sDHDtvEdYWFihIi4FMz95gzUTOypFJIlSA4ODlAoFEhMTNTZnpiYCGdn51JfJ5fL0bhxY/j5+eGNN97A8OHDERISUurxXl5ecHBwwOXLlwEAzs7OxYrACwoKkJKS8tjrmpiYwMrKSudRF0U/MsSf6gZOwkfVjRACb313Bmn38+Hb0BrTejcu82t9XK2wZUx7GMll+OX0bSwLvWjASKkmkyxBUiqVaN++PcLCwrTb1Go1wsLC0Llz5zKfR61WIzc3t9T9N2/exN27d+HiUlhE2rlzZ6SmpuLkyZPaY/bv3w+1Wo2AgIAK3EndEqNdg431R3UFJ+Gj6uariOs4HH0HJkZyrH7RD8aK8n2VdWvigBXDfQEAnxy+gm3H4gwRJtVwRlJePCgoCOPGjYO/vz86duyINWvWICsrCxMmTAAAjB07Fg0aNNC2EIWEhMDf3x/e3t7Izc3F77//jh07dmDz5s0AgMzMTCxevBjDhg2Ds7MzYmNj8dZbb6Fx48bo378/AKBFixYYMGAApkyZgi1btiA/Px/Tp0/HyJEjyzyCrS6LTnrQxcY12OqMjp52cLE2RUJaTomLgQKAk6UJJ+GjKhGXnIUPf7sAAJg3sDkaV3C6kefbNUR8Wg5W7r2E9389DxdrU47GJB2SJkgjRozAnTt3sHDhQiQkJMDPzw+hoaHawu3r169DLn/4l0FWVhZef/113Lx5E2ZmZmjevDm++uorjBgxAgCgUChw5swZbN++HampqXB1dUW/fv2wZMkSmJg8nGV1586dmD59Ovr06QO5XI5hw4Zh3bp1VXvzNVB2XgFupNwHwC62ukQhlyF4sA+mfnUKMqDUFdPvZOTC2ZqtSGQ4BSo13vi/KNzPV6GLtz3Gdfao1Ple7+WN26n3sTPiOmbtjsLOySbw92CiT4UknQepJquL8yCduZmKZzccg725EicX9H3yC6hWCT0bj8W/nNcp2Ha0NIFKLXA3Kw+eDubY/UonOFkxSSLD2HjgMlbuvQRLEyOEzumBBnqYy0ilFnh1x0n8dSERNvWM8e1rXSrcKkU1Q1m/vyVtQaKaRbMGWxO2HtVJA1q5oK+Pc7FJ+G6n3sfIT44jLjkLIz85ziSJDOLsrTR8vC8aALB4SEu9JEdAYQvp+lFtMWrrcUTdSMX4bZH4/vUurKmjmjfMn6SjqT9igXbdVdIkfG529bD7lU5oYGOmTZIS0zmijfQnJ1+FN/7vNArUAgNaOuO5tg30en4zpQKfjfOHh3093Lx3HxO/OIHM3AK9XoNqHiZIVGbaFiQ2P1MRRZOkUUySSI9W74vGpcQMOFiY4IPnWhlkDjZ7CxNsn9gR9uZKnL2Vjmk7TyFfpdb7dejJVGpRLdZ8ZIJEZfboJJFERT2aJF1hkkR6EnHlLrYeuQIAWPZ8a9hbmDzhFRXnbm+Oz8Z3gKmxHIei7+DdH/4Dy3SrVujZeHRbvh+jth7HrN1RGLX1OLot3y/JckZMkKhMsnILcPOeZgQbEyQqWUlJUhKTJKqgzNwCvPHNaQgBjPB3Q6CP05NfVEl+bjbY+FI7yGXA//1zE2v+ijH4NalQ6Nl4TP3qVLGZ+6Va85EJEpXJ5aTC7jUHCyXszLkGG5WuaJI0kkkSVdCSX87j5r37aGhrhveeaVFl1+3TwglLhrYCAKwNi8GeE9er7Np1lWbNx9KmEQGqfs1HJkhUJtruNU4QSWVQLEnayiSJyuev84nY888NyGTAqhfawNLUuEqvPzrAHdMfLGHyzg9nceBS0hNeQZVRHdd8ZIJEZRKTxDXYqHzc7Oph15QHSdIdJklUdnczczHv+zMAgCndvRDgZS9JHG/0a4rn2zWASi0wbecpnLmZKkkcdUF1XPORCRKVCQu0qSIa2RcmSa7WprhyJwujmCTREwgh8O4PZ5GcmYemThYI6ttUslhkMhmWPe+L7k0ckJ2nwsQvTuD63WzJ4qnNHC3LVnxflfNTMUGiMtEM8WeBNpVXI/t62P1KZ7hamyJWkyRV4V+BVLP88O8thJ5LgLFChtUv+sHUWCFpPEojOTaNbocWLlZIzszD+G2RuJeVJ2lMtU1qdh4+fTBSsTQyAC7WplW65iMTJHqizNwC3ErlGmxUccWSpE+YJFFxt1PvI/incwCA2YFN0aqBtcQRFbI0NcYXEzpoa+ombT+BnHyV1GHVCiev3cOgdUcRdvEOjOSF81sVneVK8zx4sA8Ucv3PgVWaCiVIO3bsQNeuXeHq6opr164BANasWYOffvpJr8FR9RDzoHutvqUJbOpxBBtVTCP7etj1SicmSVQitVpg7jenkZFbgLaNbPBqDy+pQ9LhZGWKLyZ0gJWpEU5dT8Ws3f9KNoFhbaBWC3xyOBYj/heOW6n34WFfDz9O64otY9oVW/Ta2doUm8e0w4BWLlUaY7kTpM2bNyMoKAhPP/00UlNToVIVZtE2NjZYs2aNvuOjauBh9xpbj6hy3O3NseuVTnB5kCS9tDWCSRIBALaHX8XfsXdhZqzA6hf9YKSofh0cTZwssXWsP5QKOfaeS8T7v5zjRJIVcC8rD5O//Acf/n4RBWqBZ3xd8MuMbmjVwBoDWrng6NtPYdeUTlg70g+7pnTC0befqvLkCKhAgrR+/Xps3boV7777LhSKh33D/v7++O+///QaHFUPHOJP+uRub47dD5Kky0mZTJIIl5MyseyPiwCAdwa1gKeDucQRlS7Ayx6rR7QBAGwPv4ZPDj++doZ0nbyWgqfXHcH+i0lQGsnxwXOtsH5UW51pHEpa81EK5U6Q4uLi0LZt22LbTUxMkJWVpZegqHqJTmKBNulXSUnSnYxcqcMiCeSr1Aj6vyjkFqjRo2l9jAloJHVIT/SMryveG1Q4cWXIHxfxU9QtiSOq/tRqgS2HYvHi/44jPi0Hng7m+OH1Lhgd4G6QtfX0odwJkqenJ6KiooptDw0NRYsWVTfTKVUdTQ0Su9hIn9ztzbFrysMkadTW40yS6qCNBy7jzM00WJsZY8Uw32r7ZVnU5O5emNjVEwAw95vT+Ds2WeKIqq+UrDxM3H4Cy/64CJVa4Nk2rvhlRje0dK0eRfilKXeCFBQUhGnTpmHPnj0QQiAyMhIffPAB5s+fj7feessQMZKE0nPytbObcg4k0jcPh8IkydmKSVJddOZmKtbvvwwAWDK0VbHi3OruvUEt8HRrZ+SrBF7dcRIXE9KlDqnaOXE1BU+vPYKDl+7AxEiOkOdbY+1IP1iYGEkd2hOVO0GaPHkyli9fjvfeew/Z2dl46aWXsHnzZqxduxYjR440RIwkIU2BtpOVCazNqnaqf6obPBwKu9s0SdJLTJLqhJx8FebsiYLqQZHus21cpQ6p3OTywrmaOnjYIiOnABO2nUB82n2pw6oW1GqBTQcvY+Qnx5GQngMvB3P8OK0rRnVsVGNaCcuVIBUUFODLL79EYGAgYmJikJmZiYSEBNy8eROTJk0yVIwkoctJmu41th6R4TyaJMUwSaoTlodeROydLDhammDpg4VhayJTYwW2jvWHd31zxKflYMK2E0jPyZc6LEndzczFhC9OYEXoJajUAkP9XPHzjG5o4WIldWjlUq4EycjICK+99hpycgq7XOrVqwdHR0eDBEbVQ/SDFiSOYCNDKylJSs5kklQb/X05GduOXQUArBjuW+PnV7Opp8QXEzqivqUJLiZk4LUdJ5FXoJY6LElExhWOUjsUXdiltnxYa3w8omZ0qRVV7i62jh074t9//zVELFQNPVyDjQXaZHgeDoXzJGmSpFGfMEmqbdLu52PuN6cBAKMDGqFXs9rxR7abXT1sG98B5koF/o69i7e+PQ11HZpIUq0W2HjgMkZ+Eo7E9Fx41zfHT9O7YkSHmtOlVlS5U7rXX38db7zxBm7evIn27dvD3Fx3vgpfX1+9BUfS4ySRVNU8HyRJIz8J17YkfT2lExwsyraYJVVvi385h9tpOXC3r4d3nq5dI59bNbDG5jHtMfGLE/gx6jZcbMzw9oDmUodlcMmZuZizJwpHYgpH8j3ftgGWDG0F8xrYavQomSjnNKByefFGJ5lMBiEEZDKZdmbt2i49PR3W1tZIS0uDlVXN6lctq7T7+Wiz+E8AwOngfizSpioVl5yl/Wu0qZMFk6RaIPRsPF776hTkMuCb1zqjvXvVLTxalb755wbe/PYMAGDJkJZ4ubOHtAEZ0PErdzFz179IysiFqbEc7w9phRfaN6zWrUZl/f4ud3oXFxdXqcCo5tAUaDtbmTI5oirn6WCO3a90xshPwhGdmInRWyOwc0oAk6QaKikjB+/8cBYA8FpP71qbHAHAC/5uSEjLwap90Vj48zk4Wpmif0tnqcPSK5VaYNOBy/j4r2ioBdDY0QKbRrerVQN6yp0gubu7GyIOqoa0BdrsXiOJeD6YJ2nU1uO4lJiB0Vsj8PWUANgzSapRhBB45/v/kJKVhxYuVpgd2FTqkAxu+lONcTvtPnZF3sDMXf/i6ymd0N7dVuqw9OJORmGX2tHLhV1qw9o1xJKhLVFPWbO71Iqq0GqAsbGxmDFjBgIDAxEYGIiZM2ciNjZW37GRxKITOcSfpOdV3wK7pnSCo6UJLiVm4KWtEbjLwu0a5Zt/buKvC0lQKuT4eEQbKI2q30K0+iaTybBkSCs81dwRuQVqTN5+AlfuZEodVqX9HZuMp9cdwdHLyTAzVmDlcF+serFNrUuOgAokSHv37oWPjw8iIyPh6+sLX19fREREoGXLlti3b58hYiSJsECbqguv+hbY/QqTpJroRko2Fv9yDgDwRr+maO5cO2s2S2KkkGPDS23h29Aa97LzMW5bZI2d30ulFlj7VwzGfFq4bmITRwv8PL0rXvB3kzo0gyl3kXbbtm3Rv39/LFu2TGf7vHnz8Oeff+LUqVN6DbC6qgtF2h0/+AtJGbn4/vUuaNeodjQNU8125U4mRn5yHEkZuWjmZMnutmpOpRYY9clxRF5NQQcPW+x+pbNkK7NLKTkzF89v+hvXU7Lh29Aau1/pVKNaXJIycjBnTxSOXb4LAHihfUMsHlJzu9TK+v1d7hakCxculDhr9sSJE3H+/Pnyno6qqbTsfCQ9+EuniSNbkKh68KpvgV2PtCSN/pQtSdXZ50fjEHk1BeZKBVa94FcnkyMAcLAwwfaJHWFbzxhnbqZh2s5TKFDVjIkk/76cjKfXHsWxy3dhZqzAqhfaYOULtbNLrahyJ0j169dHVFRUse1RUVGcVbsWiX4wgs3V2hSWphzBRtWH9yNJ0sUEJknV1aWEDKzcewkAsOAZHzSyrydxRNLydDDHZ+M7wMRIjgOX7mDBT2dRzg6cKqVSC3y8LxqjP4tAcmbhVBu/zOiKYe0bSh1alSl3gjRlyhS88sorWL58OY4cOYIjR45g2bJlePXVVzFlyhRDxEgSeDiDNgu0qfrRJEn1H0mSUrLypA6LHsgrUGPOnijkqdR4qrkjRnSovXUq5dGukS3Wj2oLuQzYFXkDG/ZfljqkEiVl5GDMpxFYGxYDIYAR/m74aVo3NK5jS06Vu41swYIFsLS0xKpVqzB//nwAgKurKxYtWoSZM2fqPUCSBgu0qbrzflC4PfKT47iYkKGdcdvOvGav61UbrAuLwfn4dNjWM8ayYa2r9aSBVa1fS2csHtIKC348i1X7ouFsbVqtCp2PxiRj9p5/kZyZh3pKBT54rhWea1t3Wo0eVe4WJJlMhjlz5uDmzZtIS0tDWloabt68iVmzZvEfQS3CFiSqCbwfTAGgaUl6aetxtiRJ7OS1e9h0sLBl5MPnWsPR0lTiiKqflzu5Y2ovbwDA/O//w6HoOxJHVNiltvrPS3j58wgkZ+ahubMlfp7erc4mR0AFEqS4uDjExMQAACwtLWFpWfgFGhMTg6tXr+o1OJJOtLYFiQkSVW+NHZkkVRfZeQV44/+ioBbAc20bYGBrF6lDqrbe7NcMQ/1cUaAWeP2rkzh7K02yWBLTc/DS1uNYt/8yhABGdXTDj9O6onEdH6BT7gRp/Pjx+Pvvv4ttj4iIwPjx4/URE0nsXlaedgV1jmCjmkCTJDlYsCZJSiG/X8TVu9lwsTbFomdbSh1OtSaXy7BieBt0bWyPrDwVJnxxAjdSsqs8jsPRd/D02iOIiCscbbh2pB9CnveFqbGiymOpbsqdIP3777/o2rVrse2dOnUqcXQb1TwxSYWtRw1szGr8asxUdzR2LKxJcrAwwYX4dIz+NAL3mCRVmUPRd7Dj+DUAwMrhbbh+YxkojeTYPKY9mjtb4k5GLsZvi0RqdtW8ZwtUany09xLGbYvE3QdLwPwyoxuG+DWokuvXBBWqQcrIyCi2PS0tDSqVqtwBbNy4ER4eHjA1NUVAQAAiIyNLPfb777+Hv78/bGxsYG5uDj8/P+zYsUO7Pz8/H2+//TZat24Nc3NzuLq6YuzYsbh9+7bOeTw8PCCTyXQeRSe+rMseLjHC1iOqWQqTpABtkvQSk6QqkZqdh7e+PQ0AGN/FA92aOEgcUc1hZWqMbRM6wMXaFLF3sjDly3+Qk1/+79LySEzPwUufRmDDgcIutZcCGuGH17vAqz4/8x9V7gSpR48eCAkJ0UmGVCoVQkJC0K1bt3Kda8+ePQgKCkJwcDBOnTqFNm3aoH///khKSirxeDs7O7z77rsIDw/HmTNnMGHCBEyYMAF79+4FAGRnZ+PUqVNYsGABTp06he+//x6XLl3Cs88+W+xc77//PuLj47WPGTNmlCv22iyGa7BRDdbY0ZJJUhVb8NM5JKbnwsvBHG8PaC51ODWOi7UZvpjQEZamRjhx9R6C/i8KarVh5kg6FH0HA9ceQeSDLrV1o9riw+das0utBOVeauT8+fPo0aMHbGxs0L17dwDAkSNHkJ6ejv3796NVq1ZlPldAQAA6dOiADRs2AADUajXc3NwwY8YMzJs3r0znaNeuHQYNGoQlS5aUuP/EiRPo2LEjrl27hkaNGgEobEGaPXs2Zs+eXeZYi6rNS42M+uQ4wq/cxcrhvtVq+ClRecQkZmDU1sJJ7nxcrLBzcgBsOQWA3v18+jZm7voXCrkM303tAj83G6lDqrHCY+9i3OeRyFOpMbGrJxYO9tHbuQtUaqzeF41NBwsXlm/hYoVNo9vB08Fcb9eoKQy21IiPjw/OnDmDF198EUlJScjIyMDYsWNx8eLFciVHeXl5OHnyJAIDAx8GI5cjMDAQ4eHhT3y9EAJhYWG4dOkSevToUepxaWlpkMlksLGx0dm+bNky2Nvbo23btli5ciUKCgoee73c3Fykp6frPGqrmCS2IFHN18TJErumBMDBQonzrEkyiMT0HCz48SwAYFrvxkyOKqmztz0+erENAODzY3H49MgVvZw3Pu0+Rm09rk2OxnQq7FKri8lReVSoAtfV1RUffvhhpS6cnJwMlUoFJycnne1OTk64ePFiqa9LS0tDgwYNkJubC4VCgU2bNqFv374lHpuTk4O3334bo0aN0skSZ86ciXbt2sHOzg5///035s+fj/j4eKxevbrU64aEhGDx4sXlvMuaJyUrD8mZhV8idX2IJ9V8hUlSJ4zaelybJLElST+EEHjz2zNIu5+PVg2sMOOpxlKHVCs828YVCWn38eHvF7H0twtwtjbFM76uFT7fgUtJCNoThXvZ+bAwMcKyYa0rdb66pMwtSMnJybh27ZrOtnPnzmHChAl48cUX8fXXX+s9uJJYWloiKioKJ06cwAcffICgoCAcPHiw2HH5+fl48cUXIYTA5s2bdfYFBQWhV69e8PX1xWuvvYZVq1Zh/fr1yM0tfT2n+fPnayfGTEtLw40bN/R9a9WCpkC7oS1HsFHtoEmSNC1JYz6LqLKRQrXZzojrOBx9B0ojOT5+0Q/GinJ3SFAppnT3wvguHgCAoD2nEXHlbrnPUaBSY3noRUzYdgL3svPR0tUKv87oxuSoHMr8jp4xYwbWrVunfZ6UlITu3bvjxIkTyM3Nxfjx43VGlD2Jg4MDFAoFEhMTdbYnJibC2dm59IDlcjRu3Bh+fn544403MHz4cISEhOgco0mOrl27hn379j2xRiggIAAFBQWPnejSxMQEVlZWOo/aiAXaVBs9miSdu13YksQkqeKuJmfhg98uAADeHtCcM+7rmUwmw4JnfNC/pRPyVGpM+fIf7WdzWdxOvY+RnxzH5gddamM7u+O7qV3gwS61cilzgnT8+HGd0WBffvkl7OzsEBUVhZ9++gkffvghNm7cWOYLK5VKtG/fHmFhYdptarUaYWFh6Ny5c5nPo1ardVp+NMlRTEwM/vrrL9jb2z/xHFFRUZDL5XB0dCzzdWsrzQzaTTjEn2qZJk6W+JpJUqUVqNQI+r8o3M9XoZOXHSY8aOkg/VLIZVg7si3au9siPacA47edQGJ6zhNft/9iIp5edwT/XLsHSxMjbBrdDu8PacVRahVQ5gQpISEBHh4e2uf79+/H888/DyOjwm6YZ599VrsESVkFBQVh69at2L59Oy5cuICpU6ciKysLEyZMAACMHTtWuyAuUFgHtG/fPly5cgUXLlzAqlWrsGPHDowZMwZAYXI0fPhw/PPPP9i5cydUKhUSEhKQkJCAvLzCD8Lw8HCsWbMGp0+fxpUrV7Bz507MmTMHY8aMga2tbbnir420cyDVsVWbqW5o+iBJsjdnklRR/zt8Baeup8LSxAgfvdAGcjnX4DQUU2MFPh3rDy8Hc9xKvY/x204gIycfKrVAeOxd/BR1C+Gxd6FSC+Sr1Aj5/QImfvEPUrPz0bqBNX6d2Q1Pc7mXCitzkYmVlRVSU1Ph7u4OAIiMjMSkSZO0+2Uy2WNreEoyYsQI3LlzBwsXLkRCQgL8/PwQGhqqLdy+fv065PKHOVxWVhZef/113Lx5E2ZmZmjevDm++uorjBgxAgBw69Yt/PzzzwAAPz8/nWsdOHAAvXr1gomJCXbv3o1FixYhNzcXnp6emDNnDoKCgsoVe22lmUWbXWxUWzV1ssSuVzph1CfHce52YU3SV5MCYFOPhdtPcu52Gtb8FQ0ACH62JRra1pM4otrP1lyJ7RM74rlNf+NCfDpe2BKO1Ox8JDzSmlTf0gQWJgrEJRcuVTK+iwfmP90cJkZsNaqMMs+DNGTIEDg4OGDr1q34/vvvMXr0aCQkJGhbXX777TfMnTsXFy5cMGjA1UVtnAcpOTMX/kv/gkwGnF88AGZK/uOi2is6MQOjPjmOu1l5aNXAiknSE+TkqzBkwzFcSsxAPx8n/O/l9pDJ2HpUVf67mYZhW/5GXoG61GNMjeVYM8IPA1qx1ehx9D4P0pIlS/Dzzz/DzMwMI0aMwFtvvaXTJbV792707NmzclGTpDTda2629ZgcUa33aHfb2VuFLUlp2SV3XxDw8b5oXErMgIOFEh8+35rJURXzcbWC+RM+l61MjdHXp/RBTlQ+Ze5i8/X1xYULF3Ds2DE4OzsjICBAZ//IkSPh46O/WT+p6sUkarrXWKBNdUMz58Ik6aWtx3H2Vjqe2XAEeQVqJKY/LBdwsTZF8GCfOv1XeWRcCj55MGnhh8+1hoOFicQR1T2RcSm4l53/2GOSMnIRGZeCzt5PHpxET1auiSscHBwwZMiQYskRAAwaNAienp56C4yqnqYFiUN2qS7RJEkWJka4kXJfJzkCgIS0HEz96hRCz8ZLFKG0MnML8MY3URACeKF9Q/RryRYKKSRlPHkEW3mOoyfjzF6kxRYkqqsaO1rA1Ljkj0NNB9viX87Xye62pb+ex42U+2hgY6bXtcGofBwtTfV6HD0ZEyQCULhsQPSDNdiacIg/1TGRcSnaJXZKIgDEp+UgMi6l6oKqBsIuJGL3iRuQyYBVL7aBpamx1CHVWR097eBibYrSKr9kKOwO7uhpV5Vh1WpMkAgAcCczF6nZ+ZDLuAYb1T1l7Zb44Lfz+DL8Kq4mZxk4IundzczF29/9BwCY1NUTnbxY1yIlhVyG4ActeEWTJM3z4ME+UHBeKr3hYlsEALj8oHutkV09zrhKdU5ZuyXO3k7H2Z/OASj8t9KjqQN6NKmPLo0dYFGL1i4UQuDdH84iOTMXTRwtMLd/M6lDIgADWrlg85h2WPzLecSnPUzqnTmQwCAq9C86NjYW27ZtQ2xsLNauXQtHR0f88ccfaNSoEVq2bKnvGKkKsECb6jJN90VCWg5KqjKSAbC3UGJ8Vw8cjUnGyWv3cD0lG18dv46vjl+HkVyGdu626Nm0Pno2rQ8fF6saPcP0j1G3EHouAUZyGT4e4cc/mqqRAa1c0NfHGZFxKUjKyIGjZWG3GluO9K/cCdKhQ4cwcOBAdO3aFYcPH8YHH3wAR0dHnD59Gp999hm+/fZbQ8RJBhadxAJtqrs03RdTvzoFGaCTJGm+dpYObYUBrVwwvXcTZOYW4HjsXRyKvoPDMXdw7W42IuNSEBmXgpV7L8HeXInuTRzQo2l9dG9SH/Uta86w+Nup97HwQSvZrD5N0KqBtcQRUVEKuYxD+atAuROkefPmYenSpQgKCoKl5cPWhqeeegobNmzQa3BUdTQrRbNAm+qq8nRfWJgYIdDHCYE+hcsiXbubhcPRd3AoOhnhscm4m5WHH6Nu48eo2wAAHxcr9GhaHz2aOsDf3Q5Ko+pZ/qlWC7z57Wlk5BSgjZsNpvbyljokIsmUO0H677//8PXXXxfb7ujoiOTkZL0ERVVLCIHoBzVITdiCRHVYRbsv3O3N8XJnc7zc2QN5BWqcun4Phx+0Lp29lY7z8YWPLYdiUU+pQGcv+wcJU3142NerNrNSfxl+Fccu34WpsRwfv9gGRorqmcgRVYVyJ0g2NjaIj48vNinkv//+iwYNGugtMKo6dzJykXa/cASbd30mSFS3Vbb7QmkkRycve3TyssdbA5ojOTMXR2OSHyRMyUjOzEXYxSSEXUwCALjZmaFHk8JkqYu3vWRD6S8nZSLkj4sAgHeebgEvfhZQHVfuBGnkyJF4++238c0330Amk0GtVuPYsWOYO3cuxo4da4gYycA0rUfu9uYsxiTSMwcLEwxt2wBD2zaAWi1wISEdh6MLE6Z/rqXgRsp97Iy4jp0RD4q9G9miR1MH9GzqiJauVVPsna9SI+j/opBboEb3Jg4YE+Bu8GsSVXflTpA+/PBDTJs2DW5ublCpVPDx8YFKpcJLL72E9957zxAxkoFpR7Bx/iMig5LLZWjpao2WrtaY2ssbWbkFCI+9i8Mxd3A4+g6u3s1G5NUURF5NwUd/RsPeXIluTQqnEuje1MFgsyRvOhCLMzfTYGVqhBXDfWv0CDwifZEJISo0d/6NGzfw33//ITMzE23btkWTJk30HVu1lp6eDmtra6SlpcHKykrqcCpl/vdnsCvyBqb3bsz5TogkdP1uNg49SJbCY+8iM7dAZ38LF6vC1qUm9dHewxYmRpVv8T1zMxXPbfobKrXA2pF+GOLHUgmq3cr6/V3hmc3c3Nzg5uZW0ZdTNcICbaLqoZF9Pbxs746XO7kjX6XGqWv3HrQuJeO/W2m4EJ+OC/Hp+N+hKzAzVqCztz16PJhOwNPBvEzF3iq10Bah29QzxuKfz0GlFhjU2gXPtnGtgrskqhnKnSANGzYMHTt2xNtvv62zfcWKFThx4gS++eYbvQVHhlc4gq2wi60pJ4kkqjaMFXIEeNkjwMseb/YvXPrj6OXkwrmXoguLvfdfTML+B8XeDW3NCkfGNamPLo3tYVVCsXfo2fhi0xgAgJWpEZYObVVtRtMRVQfl7mKrX78+9u/fj9atW+ts/++//xAYGIjExES9Blhd1ZYutoS0HHQKCYNCLsP59/vrpcmeiAxLCIEL8Rna2qV/rt5Dnkqt3a+Qy9CukY12dFzrBtb483wCpn51qsSZwgFgy5h2XKqC6gSDdbFlZmZCqVQW225sbIz09PTyno4kpmk9crevx+SIqIaQyWTwcbWCj6sVXuvpjey8Ahy/cheHLhVOJRCXnIUTV+/hxNV7WLUvGjZmRsgpUJeaHMkALP7lPPr6OHPJCqIHyp0gtW7dGnv27MHChQt1tu/evRs+Pj56C4yqhrZ7jTNoE9VY9ZRGeKq5E55qXjiz942U7AddcXfwd+xdpN4veOzrBYD4tBxExqVwCQuiB8qdIC1YsADPP/88YmNj8dRTTwEAwsLCsGvXLtYf1UAxiVyDjai2cbOrhzGd3DHmQbH3+v2XsS4s5omvS8rIeeIxRHVFueeRHzx4MH788UdcvnwZr7/+Ot544w3cvHkTf/31F4YOHWqAEMmQopMezIHEAm2iWslYIUdnr7K1ChlqniWimqhCw/wHDRqEQYMG6TsWqmJCCFzWtiAxQSKqrTp62sHF2hQJaTkl1iHJULgob0dPu6oOjajaqvA8SHl5eUhKSoJardbZ3qhRo0oHRVUjPi0HGbkFMJLL4OlgLnU4RGQgCrkMwYN9MPWrU5ABOkmSpiQ7eLAPC7SJHlHuLraYmBh0794dZmZmcHd3h6enJzw9PeHh4VFsAVuq3mKSCluPPBzMoTTiqt1EtdmAVi7YPKYdnK11u9GcrU2xmUP8iYopdwvS+PHjYWRkhF9//RUuLi6cWKwGi9FOEMkCbaK6YEArF/T1cdbOpO1oWditxpYjouLKnSBFRUXh5MmTaN68uSHioSr0cJFa1h8R1RUKuYxD+YnKoNz9Kj4+PkhOTjZELFTFuAYbERFRycqdIC1fvhxvvfUWDh48iLt37yI9PV3nQTWDEAKXkziCjYiIqCTl7mILDAwEAPTp00dnuxACMpkMKpVKP5GRQd1Oy0HmgxFsHvYcwUZERPSocidIBw4cMEQcVMU09UeeHMFGRERUTLkTpJ49exoiDqpiD0ewsXuNiIioqAo1HRw5cgRjxoxBly5dcOvWLQDAjh07cPToUb0GR4bDAm0iIqLSlTtB+u6779C/f3+YmZnh1KlTyM3NBQCkpaXhww8/1HuAZBhsQSIiIipduROkpUuXYsuWLdi6dSuMjY2127t27YpTp07pNTgyDLVaaGfR5iSRRERExZU7Qbp06RJ69OhRbLu1tTVSU1P1ERMZ2K3U+8jOU8FYIYM7R7AREREVU+4EydnZGZcvXy62/ejRo/Dy8tJLUGRYMUmF3WteDhYwVnAEGxERUVHl/nacMmUKZs2ahYiICMhkMty+fRs7d+7E3LlzMXXq1HIHsHHjRnh4eMDU1BQBAQGIjIws9djvv/8e/v7+sLGxgbm5Ofz8/LBjxw6dY4QQWLhwIVxcXGBmZobAwEDExMToHJOSkoLRo0fDysoKNjY2mDRpEjIzM8sde03FAm0iIqLHK3eCNG/ePLz00kvo06cPMjMz0aNHD0yePBmvvvoqZsyYUa5z7dmzB0FBQQgODsapU6fQpk0b9O/fH0lJSSUeb2dnh3fffRfh4eE4c+YMJkyYgAkTJmDv3r3aY1asWIF169Zhy5YtiIiIgLm5Ofr374+cnBztMaNHj8a5c+ewb98+/Prrrzh8+DBeeeWV8v4oaqxoFmgTERE9niiHgoICcejQIXHv3j2Rm5srzp07JyIiIkRGRkZ5TqPVsWNHMW3aNO1zlUolXF1dRUhISJnP0bZtW/Hee+8JIYRQq9XC2dlZrFy5Urs/NTVVmJiYiF27dgkhhDh//rwAIE6cOKE95o8//hAymUzcunWrzNdNS0sTAERaWlqZX1NdPLPuiHB/+1fxx3+3pQ6FiIioSpX1+7tcLUgKhQL9+vXDvXv3oFQq4ePjg44dO8LCovxdNXl5eTh58qR26RIAkMvlCAwMRHh4+BNfL4RAWFiYTtF4XFwcEhISdM5pbW2NgIAA7TnDw8NhY2MDf39/7TGBgYGQy+WIiIgo9Xq5ubm1Yt05tfrhGmxN2IJERERUonJ3sbVq1QpXrlyp9IWTk5OhUqng5OSks93JyQkJCQmlvi4tLQ0WFhZQKpUYNGgQ1q9fj759+wKA9nWPO2dCQgIcHR119hsZGcHOzu6x1w0JCYG1tbX24ebmVvabrUZupd7H/XwVlAo53O3qSR0OERFRtVSheZDmzp2LX3/9FfHx8VXeqmJpaYmoqCicOHECH3zwAYKCgnDw4EGDX3f+/PlIS0vTPm7cuGHwaxqCpv7Iq745jDiCjYiIqETlXovt6aefBgA8++yzkMlk2u1CCMhkMqhUqjKdx8HBAQqFAomJiTrbExMT4ezsXOrr5HI5GjduDADw8/PDhQsXEBISgl69emlfl5iYCBcXF51z+vn5ASicpqBoEXhBQQFSUlIee10TExOYmJiU6d6qM80INhZoExERla7cCdKBAwf0cmGlUon27dsjLCwMQ4cOBQCo1WqEhYVh+vTpZT6PWq3WLnfi6ekJZ2dnhIWFaROi9PR0REREaKcg6Ny5M1JTU3Hy5Em0b98eALB//36o1WoEBATo5d6qM80SI00cOcSfiIioNOVOkHr27Km3iwcFBWHcuHHw9/dHx44dsWbNGmRlZWHChAkAgLFjx6JBgwYICQkBUFgH5O/vD29vb+Tm5uL333/Hjh07sHnzZgCATCbD7NmzsXTpUjRp0gSenp5YsGABXF1dtUlYixYtMGDAAEyZMgVbtmxBfn4+pk+fjpEjR8LV1VVv91ZdRT+YJJIF2kRERKUrd4IEAEeOHMH//vc/XLlyBd988w0aNGiAHTt2wNPTE926dSvzeUaMGIE7d+5g4cKFSEhIgJ+fH0JDQ7VF1tevX4dc/rBOJisrC6+//jpu3rwJMzMzNG/eHF999RVGjBihPeatt95CVlYWXnnlFaSmpqJbt24IDQ2Fqamp9pidO3di+vTp6NOnD+RyOYYNG4Z169ZV5EdRozw6go1rsBEREZVOJoQQ5XnBd999h5dffhmjR4/Gjh07cP78eXh5eWHDhg34/fff8fvvvxsq1molPT0d1tbWSEtLg5WVldThlMm1u1noufIglEZyXHh/ABRy2ZNfREREVIuU9fu7QqPYtmzZgq1bt8LY2Fi7vWvXrjh16lTFoqUqoSnQ9q5vweSIiIjoMcqdID06MeOjrK2tkZqaqo+YyEAeLjHC7jUiIqLHKXeC5OzsjMuXLxfbfvToUXh5eeklKDKMGK7BRkREVCblTpCmTJmCWbNmISIiAjKZDLdv38bOnTsxd+5c7VB6qp40XWwc4k9ERPR45R7FNm/ePKjVavTp0wfZ2dno0aMHTExMMHfuXMyYMcMQMZIeqNQCsXc4SSQREVFZlDtBkslkePfdd/Hmm2/i8uXLyMzMhI+PT4UWrKWqcz0lG7kFapgYyeHGNdiIiIgeq0LzIAGFM2H7+PjoMxYyIE2BdmNHjmAjIiJ6knInSFlZWVi2bBnCwsKQlJQEtVqts//KlSt6C470hwXaREREZVfuBGny5Mk4dOgQXn75Zbi4uOgsWEvVl7ZAm0P8iYiInqjcCdIff/yB3377DV27djVEPGQg2jmQHNmCRERE9CTlHuZva2sLOzs7Q8RCBlKgUuPKnSwA7GIjIiIqi3InSEuWLMHChQuRnZ1tiHjIAK6nZCNPpYaZsQINbc2kDoeIiKjaK1MXW9u2bXVqjS5fvgwnJyd4eHjorMcGgOuxVUOa+qPGjhaQcwQbERHRE5UpQRo6dKiBwyBD0oxgY4E2ERFR2ZQpQQoODjZ0HGRA0UmaJUZYf0RERFQWFZ4o8uTJk7hw4QIAoGXLlmjbtq3egiL9ejgHEluQiIiIyqLcCVJSUhJGjhyJgwcPwsbGBgCQmpqK3r17Y/fu3ahfv76+Y6RK4Ag2IiKi8iv3KLYZM2YgIyMD586dQ0pKClJSUnD27Fmkp6dj5syZhoiRKuHq3Ycj2BrYcAQbERFRWZS7BSk0NBR//fUXWrRood3m4+ODjRs3ol+/fnoNjirv0QJtjmAjIiIqm3K3IKnV6mJD+wHA2Ni42LpsJD3tEiMs0CYiIiqzcidITz31FGbNmoXbt29rt926dQtz5sxBnz599BocVV50Egu0iYiIyqvcCdKGDRuQnp4ODw8PeHt7w9vbG56enkhPT8f69esNESNVwsMRbGxBIiIiKqty1yC5ubnh1KlT+Ouvv3Dx4kUAQIsWLRAYGKj34Khy8lVqxCUXjmDjJJFERERlV6F5kGQyGfr27Yu+ffvqOx7So6vJWchXCZgrOYKNiIioPMrcxbZ//374+PggPT292L60tDS0bNkSR44c0WtwVDnaNdicLHXW0iMiIqLHK3OCtGbNGkyZMgVWVlbF9llbW+PVV1/F6tWr9RocVU60pv7Ikd1rRERE5VHmBOn06dMYMGBAqfv79euHkydP6iUo0o+YJBZoExERVUSZE6TExMQS5z/SMDIywp07d/QSFOmHdg4kFmgTERGVS5kTpAYNGuDs2bOl7j9z5gxcXFz0EhRVXl6BGleTuQYbERFRRZQ5QXr66aexYMEC5OTkFNt3//59BAcH45lnntFrcFRxV+9moUAtYGliBBdrU6nDISIiqlHKPMz/vffew/fff4+mTZti+vTpaNasGQDg4sWL2LhxI1QqFd59912DBUrloynQbuxkwRFsRERE5VTmBMnJyQl///03pk6divnz50MIAaBwTqT+/ftj48aNcHJyMligVD6a+qOmXIONiIio3Mo1UaS7uzt+//133Lt3D5cvX4YQAk2aNIGtra2h4qMK0iwxwgJtIiKi8qvQTNq2trbo0KGDvmMhPYrWJkhsQSIiIiqvci9WS9VfboEKV+9mAwCasgWJiIio3Jgg1UJxyVlQPRjB5mzFEWxERETlJXmCtHHjRnh4eMDU1BQBAQGIjIws9ditW7eie/fusLW1ha2tLQIDA4sdL5PJSnysXLlSe4yHh0ex/cuWLTPYPVa1RyeI5Ag2IiKi8pM0QdqzZw+CgoIQHByMU6dOoU2bNujfvz+SkpJKPP7gwYMYNWoUDhw4gPDwcLi5uaFfv364deuW9pj4+Hidx+effw6ZTIZhw4bpnOv999/XOW7GjBkGvdeqpCnQ5gSRREREFVOhIm19Wb16NaZMmYIJEyYAALZs2YLffvsNn3/+OebNm1fs+J07d+o8//TTT/Hdd98hLCwMY8eOBQA4OzvrHPPTTz+hd+/e8PLy0tluaWlZ7NjaggXaRERElSNZC1JeXh5OnjyJwMDAh8HI5QgMDER4eHiZzpGdnY38/HzY2dmVuD8xMRG//fYbJk2aVGzfsmXLYG9vj7Zt22LlypUoKCh47LVyc3ORnp6u86iuYjRzILFAm4iIqEIka0FKTk6GSqUqNrmkk5MTLl68WKZzvP3223B1ddVJsh61fft2WFpa4vnnn9fZPnPmTLRr1w52dnb4+++/MX/+fMTHx2P16tWlXiskJASLFy8uU1xSyslX4epdrsFGRERUGZJ2sVXGsmXLsHv3bhw8eBCmpiWP1Pr8888xevToYvuDgoK0/+/r6wulUolXX30VISEhMDExKfFc8+fP13ldeno63Nzc9HAn+nXlThbUArAyNYKjZcn3QkRERI8nWYLk4OAAhUKBxMREne2JiYlPrA366KOPsGzZMvz111/w9fUt8ZgjR47g0qVL2LNnzxNjCQgIQEFBAa5evapdY64oExOTUpOn6iQm6WGBNkewERERVYxkNUhKpRLt27dHWFiYdptarUZYWBg6d+5c6utWrFiBJUuWIDQ0FP7+/qUe99lnn6F9+/Zo06bNE2OJioqCXC6Ho6Nj+W6iGmKBNhERUeVJ2sUWFBSEcePGwd/fHx07dsSaNWuQlZWlHdU2duxYNGjQACEhIQCA5cuXY+HChfj666/h4eGBhIQEAICFhQUsLB4WJKenp+Obb77BqlWril0zPDwcERER6N27NywtLREeHo45c+ZgzJgxtWJNuWgWaBMREVWapAnSiBEjcOfOHSxcuBAJCQnw8/NDaGiotnD7+vXrkMsfNnJt3rwZeXl5GD58uM55goODsWjRIu3z3bt3QwiBUaNGFbumiYkJdu/ejUWLFiE3Nxeenp6YM2eOTn1RTcY5kIiIiCpPJoQQUgdRE6Wnp8Pa2hppaWmwsrKSOhwAhSPYfBaGQi2AyHf7wNGSy4wQERE9qqzf35IvNUL6E3snE2oB2NQzRn2L6l9QTkREVF0xQapFtBNEOnIEGxERUWUwQapFHo5gY4E2ERFRZTBBqkU0I9iaODJBIiIiqgwmSLXIo5NEEhERUcUxQaol7uepcD0lGwAniSQiIqosJki1ROydTAgB2NYzhoOFUupwiIiIajQmSLXEo0uMcAQbERFR5TBBqiW4xAgREZH+MEGqJbjECBERkf4wQaoloh+MYGviyASJiIiospgg1QLZeQW4kXIfALvYiIiI9IEJUi1wOamw/sjeXAl7rsFGRERUaUyQagHtDNpsPSIiItILJki1AAu0iYiI9IsJUi3w6BxIREREVHlMkGqBmAc1SE25SC0REZFeMEGq4bJyC3DznmYEG1uQiIiI9IEJUg2nGcHmYGECW3OuwUZERKQPTJBquGhtgTa714iIiPSFCVINp60/YvcaERGR3jBBquE0LUiNWaBNRESkN0yQariYRLYgERER6RsTpBosM7cAt1K5BhsREZG+MUGqwTQzaNe3NIFNPY5gIyIi0hcmSDXYw+41th4RERHpExOkGky7xIgj64+IiIj0iQlSDRbNIf5EREQGwQSpBovhJJFEREQGwQSphkrPyUd8Wg4AoAlbkIiIiPSKCVINpSnQdrIygbWZscTREBER1S5MkGqoh91rbD0iIiLSNyZINVT0gxYkjmAjIiLSPyZINVRMEgu0iYiIDIUJUg2lqUFigTYREZH+MUGqgdLu5yMhXTOCjS1IRERE+iZ5grRx40Z4eHjA1NQUAQEBiIyMLPXYrVu3onv37rC1tYWtrS0CAwOLHT9+/HjIZDKdx4ABA3SOSUlJwejRo2FlZQUbGxtMmjQJmZmZBrk/Q7j8oHvNxdoUVqYcwUZERKRvkiZIe/bsQVBQEIKDg3Hq1Cm0adMG/fv3R1JSUonHHzx4EKNGjcKBAwcQHh4ONzc39OvXD7du3dI5bsCAAYiPj9c+du3apbN/9OjROHfuHPbt24dff/0Vhw8fxiuvvGKw+9S3aHavERERGZRMCCGkunhAQAA6dOiADRs2AADUajXc3NwwY8YMzJs374mvV6lUsLW1xYYNGzB27FgAhS1Iqamp+PHHH0t8zYULF+Dj44MTJ07A398fABAaGoqnn34aN2/ehKura5liT09Ph7W1NdLS0mBlZVWm1+jL4l/OYduxq5jczRPvPeNTpdcmIiKqycr6/S1ZC1JeXh5OnjyJwMDAh8HI5QgMDER4eHiZzpGdnY38/HzY2dnpbD948CAcHR3RrFkzTJ06FXfv3tXuCw8Ph42NjTY5AoDAwEDI5XJERERU8q6qxsMCbdYfERERGYKRVBdOTk6GSqWCk5OTznYnJydcvHixTOd4++234erqqpNkDRgwAM8//zw8PT0RGxuLd955BwMHDkR4eDgUCgUSEhLg6Oiocx4jIyPY2dkhISGh1Gvl5uYiNzdX+zw9Pb1MMRpC9INJItnFRkREZBiSJUiVtWzZMuzevRsHDx6EqampdvvIkSO1/9+6dWv4+vrC29sbBw8eRJ8+fSp8vZCQECxevLhSMetDWnY+kjIKE7UmjmxBIiIiMgTJutgcHBygUCiQmJiosz0xMRHOzs6Pfe1HH32EZcuW4c8//4Svr+9jj/Xy8oKDgwMuX74MAHB2di5WBF5QUICUlJTHXnf+/PlIS0vTPm7cuPHY6xpK9IMRbK7WprDkCDYiIiKDkCxBUiqVaN++PcLCwrTb1Go1wsLC0Llz51Jft2LFCixZsgShoaE6dUSluXnzJu7evQsXFxcAQOfOnZGamoqTJ09qj9m/fz/UajUCAgJKPY+JiQmsrKx0HlJg9xoREZHhSTrMPygoCFu3bsX27dtx4cIFTJ06FVlZWZgwYQIAYOzYsZg/f772+OXLl2PBggX4/PPP4eHhgYSEBCQkJGjnMMrMzMSbb76J48eP4+rVqwgLC8OQIUPQuHFj9O/fHwDQokULDBgwAFOmTEFkZCSOHTuG6dOnY+TIkWUewSYlTYE2lxghIiIyHElrkEaMGIE7d+5g4cKFSEhIgJ+fH0JDQ7WF29evX4dc/jCH27x5M/Ly8jB8+HCd8wQHB2PRokVQKBQ4c+YMtm/fjtTUVLi6uqJfv35YsmQJTExMtMfv3LkT06dPR58+fSCXyzFs2DCsW7euam66ktiCREREZHiSzoNUk0k1D5L/0r+QnJmLH6d1hZ+bTZVdl4iIqDao9vMgUfndy8pDciZHsBERERkaE6QaRNO91sDGDOYmNXaGBiIiomqPCVINEp3EAm0iIqKqwASpBol50ILUlAXaREREBsUEqQbhCDYiIqKqwQSpBrnMLjYiIqIqwQSphkjJykNyZh4AoDFHsBERERkUE6QaQtO95mZnhnpKjmAjIiIyJCZINYS2QNuR9UdERESGxgSphoh+sAZbY9YfERERGRwTpBoimi1IREREVYYJUg0Rox3BxgSJiIjI0Jgg1QDJmblIycqDTMYRbERERFWBCVINoB3BZlsPZkqFxNEQERHVfkyQaoCYRE4QSUREVJWYINUAXGKEiIioajFBqgHYgkRERFS1mCBVc0IIRCc9aEHiEH8iIqIqwQSpmruTmYvU7HzIOYKNiIioyjBBquY03WuN7OrB1Jgj2IiIiKoCE6RqjgXaREREVY8JUjUXzQJtIiKiKscEqZq7/KBAm0uMEBERVR0mSNWYEELbgsQRbERERFWHCVI1dicjF2n3C0ewedU3lzocIiKiOoMJUjWmaT3ysDfnCDYiIqIqxASpGns4go0F2kRERFWJCVI1FsMZtImIiCTBBKka0xZoswWJiIioSjFBqqYKR7BxiD8REZEUmCBVU4npucjIKYBCLuMINiIioirGBKma0rQeudvXg4kRR7ARERFVJSZI1ZS2e40F2kRERFWOCVI1FcM12IiIiCTDBKmaitYM8WeBNhERUZVjglQNCSFwWduCxASJiIioqjFBqobi03KQkVsAI7kMng4cwUZERFTVJE+QNm7cCA8PD5iamiIgIACRkZGlHrt161Z0794dtra2sLW1RWBgoM7x+fn5ePvtt9G6dWuYm5vD1dUVY8eOxe3bt3XO4+HhAZlMpvNYtmyZwe6xvDQF2h4O5lAaSf4rIiIiqnMk/fbds2cPgoKCEBwcjFOnTqFNmzbo378/kpKSSjz+4MGDGDVqFA4cOIDw8HC4ubmhX79+uHXrFgAgOzsbp06dwoIFC3Dq1Cl8//33uHTpEp599tli53r//fcRHx+vfcyYMcOg91oeLNAmIiKSlkwIIaS6eEBAADp06IANGzYAANRqNdzc3DBjxgzMmzfvia9XqVSwtbXFhg0bMHbs2BKPOXHiBDp27Ihr166hUaNGAApbkGbPno3Zs2dXOPb09HRYW1sjLS0NVlZWFT5PSd785jS+OXkTs/o0wZy+TfV6biIiorqsrN/fkrUg5eXl4eTJkwgMDHwYjFyOwMBAhIeHl+kc2dnZyM/Ph52dXanHpKWlQSaTwcbGRmf7smXLYG9vj7Zt22LlypUoKCh47LVyc3ORnp6u8zCUmCQWaBMREUnJSKoLJycnQ6VSwcnJSWe7k5MTLl68WKZzvP3223B1ddVJsh6Vk5ODt99+G6NGjdLJEmfOnIl27drBzs4Of//9N+bPn4/4+HisXr261GuFhIRg8eLFZYqrMoQQuJzELjYiIiIpSZYgVdayZcuwe/duHDx4EKampsX25+fn48UXX4QQAps3b9bZFxQUpP1/X19fKJVKvPrqqwgJCYGJiUmJ15s/f77O69LT0+Hm5qanuymkUgv8/l88MnMLoJADbnb19Hp+IiIiKhvJutgcHBygUCiQmJiosz0xMRHOzs6Pfe1HH32EZcuW4c8//4Svr2+x/Zrk6Nq1a9i3b98Ta4QCAgJQUFCAq1evlnqMiYkJrKysdB76FHo2Ht2W78eMXf8CAFRqoPdHBxF6Nl6v1yEiIqInkyxBUiqVaN++PcLCwrTb1Go1wsLC0Llz51Jft2LFCixZsgShoaHw9/cvtl+THMXExOCvv/6Cvb39E2OJioqCXC6Ho6NjxW6mkkLPxmPqV6cQn5ajsz0hLQdTvzrFJImIiKiKSdrFFhQUhHHjxsHf3x8dO3bEmjVrkJWVhQkTJgAAxo4diwYNGiAkJAQAsHz5cixcuBBff/01PDw8kJCQAACwsLCAhYUF8vPzMXz4cJw6dQq//vorVCqV9hg7OzsolUqEh4cjIiICvXv3hqWlJcLDwzFnzhyMGTMGtra2Vf4zUKkFFv9yHiUNJRQAZAAW/3IefX2coZDLqjg6IiKiuknSBGnEiBG4c+cOFi5ciISEBPj5+SE0NFRbuH39+nXI5Q8buTZv3oy8vDwMHz5c5zzBwcFYtGgRbt26hZ9//hkA4Ofnp3PMgQMH0KtXL5iYmGD37t1YtGgRcnNz4enpiTlz5ujUF1WlyLiUYi1HjxIonFk7Mi4Fnb2f3BpGRERElSfpPEg1mb7mQfop6hZm7Y564nFrR/phiF+DCl+HiIiIasA8SFTI0bL4CLzKHEdERESVxwRJYh097eBibYrSqotkAFysTdHRs/TJMImIiEi/mCBJTCGXIXiwDwAUS5I0z4MH+7BAm4iIqAoxQaoGBrRyweYx7eBsrduN5mxtis1j2mFAKxeJIiMiIqqbauxM2rXNgFYu6OvjjMi4FCRl5MDRsrBbjS1HREREVY8JUjWikMs4lJ+IiKgaYBcbERERURFMkIiIiIiKYIJEREREVAQTJCIiIqIimCARERERFcEEiYiIiKgIJkhERERERTBBIiIiIiqCCRIRERFREZxJu4KEEACA9PR0iSMhIiKistJ8b2u+x0vDBKmCMjIyAABubm4SR0JERETllZGRAWtr61L3y8STUigqkVqtxu3bt2FpaQmZTH8Lyqanp8PNzQ03btyAlZWV3s5bndT2e+T91Xy1/R55fzVfbb9HQ96fEAIZGRlwdXWFXF56pRFbkCpILpejYcOGBju/lZVVrXzTP6q23yPvr+ar7ffI+6v5avs9Gur+HtdypMEibSIiIqIimCARERERFcEEqZoxMTFBcHAwTExMpA7FYGr7PfL+ar7afo+8v5qvtt9jdbg/FmkTERERFcEWJCIiIqIimCARERERFcEEiYiIiKgIJkhERERERTBBqiZCQkLQoUMHWFpawtHREUOHDsWlS5ekDktvNm/eDF9fX+2kX507d8Yff/whdVgGs2zZMshkMsyePVvqUPRm0aJFkMlkOo/mzZtLHZZe3bp1C2PGjIG9vT3MzMzQunVr/PPPP1KHpTceHh7FfocymQzTpk2TOjS9UKlUWLBgATw9PWFmZgZvb28sWbLkiWtu1SQZGRmYPXs23N3dYWZmhi5duuDEiRNSh1Vhhw8fxuDBg+Hq6gqZTIYff/xRZ78QAgsXLoSLiwvMzMwQGBiImJiYKomNCVI1cejQIUybNg3Hjx/Hvn37kJ+fj379+iErK0vq0PSiYcOGWLZsGU6ePIl//vkHTz31FIYMGYJz585JHZrenThxAv/73//g6+srdSh617JlS8THx2sfR48elTokvbl37x66du0KY2Nj/PHHHzh//jxWrVoFW1tbqUPTmxMnTuj8/vbt2wcAeOGFFySOTD+WL1+OzZs3Y8OGDbhw4QKWL1+OFStWYP369VKHpjeTJ0/Gvn37sGPHDvz333/o168fAgMDcevWLalDq5CsrCy0adMGGzduLHH/ihUrsG7dOmzZsgUREREwNzdH//79kZOTY/jgBFVLSUlJAoA4dOiQ1KEYjK2trfj000+lDkOvMjIyRJMmTcS+fftEz549xaxZs6QOSW+Cg4NFmzZtpA7DYN5++23RrVs3qcOoUrNmzRLe3t5CrVZLHYpeDBo0SEycOFFn2/PPPy9Gjx4tUUT6lZ2dLRQKhfj11191trdr1068++67EkWlPwDEDz/8oH2uVquFs7OzWLlypXZbamqqMDExEbt27TJ4PGxBqqbS0tIAAHZ2dhJHon8qlQq7d+9GVlYWOnfuLHU4ejVt2jQMGjQIgYGBUodiEDExMXB1dYWXlxdGjx6N69evSx2S3vz888/w9/fHCy+8AEdHR7Rt2xZbt26VOiyDycvLw1dffYWJEyfqdcFtKXXp0gVhYWGIjo4GAJw+fRpHjx7FwIEDJY5MPwoKCqBSqWBqaqqz3czMrFa15mrExcUhISFB5/PU2toaAQEBCA8PN/j1uVhtNaRWqzF79mx07doVrVq1kjocvfnvv//QuXNn5OTkwMLCAj/88AN8fHykDktvdu/ejVOnTtXoeoDHCQgIwBdffIFmzZohPj4eixcvRvfu3XH27FlYWlpKHV6lXblyBZs3b0ZQUBDeeecdnDhxAjNnzoRSqcS4ceOkDk/vfvzxR6SmpmL8+PFSh6I38+bNQ3p6Opo3bw6FQgGVSoUPPvgAo0ePljo0vbC0tETnzp2xZMkStGjRAk5OTti1axfCw8PRuHFjqcPTu4SEBACAk5OTznYnJyftPkNiglQNTZs2DWfPnq11fxE0a9YMUVFRSEtLw7fffotx48bh0KFDtSJJunHjBmbNmoV9+/YV++uutnj0r3BfX18EBATA3d0d//d//4dJkyZJGJl+qNVq+Pv748MPPwQAtG3bFmfPnsWWLVtqZYL02WefYeDAgXB1dZU6FL35v//7P+zcuRNff/01WrZsiaioKMyePRuurq615ne4Y8cOTJw4EQ0aNIBCoUC7du0watQonDx5UurQah12sVUz06dPx6+//ooDBw6gYcOGUoejV0qlEo0bN0b79u0REhKCNm3aYO3atVKHpRcnT55EUlIS2rVrByMjIxgZGeHQoUNYt24djIyMoFKppA5R72xsbNC0aVNcvnxZ6lD0wsXFpViy3qJFi1rVjahx7do1/PXXX5g8ebLUoejVm2++iXnz5mHkyJFo3bo1Xn75ZcyZMwchISFSh6Y33t7eOHToEDIzM3Hjxg1ERkYiPz8fXl5eUoemd87OzgCAxMREne2JiYnafYbEBKmaEEJg+vTp+OGHH7B//354enpKHZLBqdVq5ObmSh2GXvTp0wf//fcfoqKitA9/f3+MHj0aUVFRUCgUUoeod5mZmYiNjYWLi4vUoehF165di02tER0dDXd3d4kiMpxt27bB0dERgwYNkjoUvcrOzoZcrvu1plAooFarJYrIcMzNzeHi4oJ79+5h7969GDJkiNQh6Z2npyecnZ0RFham3Zaeno6IiIgqqV9lF1s1MW3aNHz99df46aefYGlpqe1ftba2hpmZmcTRVd78+fMxcOBANGrUCBkZGfj6669x8OBB7N27V+rQ9MLS0rJYvZi5uTns7e1rTR3Z3LlzMXjwYLi7u+P27dsIDg6GQqHAqFGjpA5NL+bMmYMuXbrgww8/xIsvvojIyEh88skn+OSTT6QOTa/UajW2bduGcePGwciodn0FDB48GB988AEaNWqEli1b4t9//8Xq1asxceJEqUPTm71790IIgWbNmuHy5ct488030bx5c0yYMEHq0CokMzNTpxU6Li4OUVFRsLOzQ6NGjTB79mwsXboUTZo0gaenJxYsWABXV1cMHTrU8MEZfJwclQmAEh/btm2TOjS9mDhxonB3dxdKpVLUr19f9OnTR/z5559Sh2VQtW2Y/4gRI4SLi4tQKpWiQYMGYsSIEeLy5ctSh6VXv/zyi2jVqpUwMTERzZs3F5988onUIend3r17BQBx6dIlqUPRu/T0dDFr1izRqFEjYWpqKry8vMS7774rcnNzpQ5Nb/bs2SO8vLyEUqkUzs7OYtq0aSI1NVXqsCrswIEDJX73jRs3TghRONR/wYIFwsnJSZiYmIg+ffpU2XtXJkQtmmKUiIiISA9Yg0RERERUBBMkIiIioiKYIBEREREVwQSJiIiIqAgmSERERERFMEEiIiIiKoIJEhEREVERTJCIqMa4evUqZDIZoqKipA5F6+LFi+jUqRNMTU3h5+dX5dc/ePAgZDIZUlNTq/zaRLUZEyQiKrPx48dDJpNh2bJlOtt//PFHyGQyiaKSVnBwMMzNzXHp0iWdNaM0ZDLZYx+LFi2q1PW7dOmC+Ph4WFtbV+o8RKSLCRIRlYupqSmWL1+Oe/fuSR2K3uTl5VX4tbGxsejWrRvc3d1hb29fbH98fLz2sWbNGlhZWelsmzt3bmVCh1KphLOzc51NUIkMhQkSEZVLYGAgnJ2dERISUuoxixYtKtbdtGbNGnh4eGifjx8/HkOHDsWHH34IJycn2NjY4P3330dBQQHefPNN2NnZoWHDhti2bVux81+8eBFdunSBqakpWrVqhUOHDunsP3v2LAYOHAgLCws4OTnh5ZdfRnJysnZ/r169MH36dMyePRsODg7o379/ifehVqvx/vvvo2HDhjAxMYGfnx9CQ0O1+2UyGU6ePIn333+/1NYgZ2dn7cPa2hoymUz73NHREatXry71/Jouxd27d5d6vyV1sR07dgy9evVCvXr1YGtri/79+2sT2m+//RatW7eGmZkZ7O3tERgYiKysrBLvn6guY4JEROWiUCjw4YcfYv369bh582alzrV//37cvn0bhw8fxurVqxEcHIxnnnkGtra2iIiIwGuvvYZXX3212HXefPNNvPHGG/j333/RuXNnDB48GHfv3gUApKam4qmnnkLbtm3xzz//IDQ0FImJiXjxxRd1zrF9+3YolUocO3YMW7ZsKTG+tWvXYtWqVfjoo49w5swZ9O/fH88++yxiYmIAFLYOtWzZEm+88UaFWoOedP6y3G9RUVFR6NOnD3x8fBAeHo6jR49i8ODBUKlUiI+Px6hRozBx4kRcuHABBw8exPPPPw8uyUlUgipZEpeIaoVx48aJIUOGCCGE6NSpk5g4caIQQogffvhBPPpxEhwcLNq0aaPz2o8//li4u7vrnMvd3V2oVCrttmbNmonu3btrnxcUFAhzc3Oxa9cuIYQQcXFxAoBYtmyZ9pj8/HzRsGFDsXz5ciGEEEuWLBH9+vXTufaNGzd0VrDv2bOnaNu27RPv19XVVXzwwQc62zp06CBef/117fM2bdqI4ODgJ55LCCG2bdsmrK2ty3z+styvZjX0e/fuCSGEGDVqlOjatWuJ1z958qQAIK5evVqmeInqMrYgEVGFLF++HNu3b8eFCxcqfI6WLVtCLn/4MeTk5ITWrVtrnysUCtjb2yMpKUnndZ07d9b+v5GREfz9/bVxnD59GgcOHICFhYX20bx5cwCF9UIa7du3f2xs6enpuH37Nrp27aqzvWvXrpW654qc/3H3W5SmBakkbdq0QZ8+fdC6dWu88MIL2Lp1a62qJSPSJyZIRFQhPXr0QP/+/TF//vxi++RyebFum/z8/GLHGRsb6zyXyWQlblOr1WWOKzMzE4MHD0ZUVJTOIyYmBj169NAeZ25uXuZz1iRmZmal7lMoFNi3bx/++OMP+Pj4YP369WjWrBni4uKqMEKimoEJEhFV2LJly/DLL78gPDxcZ3v9+vWRkJCgkyTpc+6i48ePa/+/oKAAJ0+eRIsWLQAA7dq1w7lz5+Dh4YHGjRvrPMqTFFlZWcHV1RXHjh3T2X7s2DH4+PhU+h7Kc/7H3W9Rvr6+JU43oCGTydC1a1csXrwY//77L5RKJX744YdK3AlR7WQkdQBEVHO1bt0ao0ePxrp163S29+rVC3fu3MGKFSswfPhwhIaG4o8//oCVlZVerrtx40Y0adIELVq0wMcff4x79+5h4sSJAIBp06Zh69atGDVqFN566y3Y2dnh8uXL2L17Nz799FMoFIoyX+fNN99EcHAwvL294efnh23btiEqKgo7d+7Uy32U9fyPu9+i5s+fj9atW+P111/Ha6+9BqVSiQMHDuCFF15AbGwswsLC0K9fPzg6OiIiIgJ37twpNdkiqsvYgkRElfL+++8X6wJr0aIFNm3ahI0bN6JNmzaIjIys9Hw/j1q2bBmWLVuGNm3a4OjRo/j555/h4OAAANpWGZVKhX79+qF169aYPXs2bGxsdOqdymLmzJkICgrCG2+8gdatWyM0NBQ///wzmjRpopf7KOv5H3e/RTVt2hR//vknTp8+jY4dO6Jz58746aefYGRkBCsrKxw+fBhPP/00mjZtivfeew+rVq3CwIED9XI/RLWJTBQtFCAiomrh6tWr8PT0xL///ivJMiZEdRlbkIiIiIiKYIJEREREVAS72IiIiIiKYAsSERERURFMkIiIiIiKYIJEREREVAQTJCIiIqIimCARERERFcEEiYiIiKgIJkhERERERTBBIiIiIiqCCRIRERFREf8PN9jiBxNYCrwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_num_topics =3\n",
        "final_lsa_model =LsiModel(corpus =doc_term_matrix,id2word=dictionary,num_topics=final_num_topics)\n",
        "print(final_lsa_model.print_topics(num_topics=final_num_topics,num_words=10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haMl8WcVdAK6",
        "outputId": "e18c3152-bcd1-4675-e953-ebce6f87fb21"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0, '0.615*\"mr\" + 0.429*\"said\" + 0.187*\"trump\" + 0.130*\"state\" + 0.119*\"would\" + 0.108*\"year\" + 0.102*\"one\" + 0.091*\"like\" + 0.090*\"peopl\" + 0.086*\"new\"'), (1, '-0.537*\"mr\" + -0.319*\"trump\" + 0.286*\"said\" + 0.242*\"saudi\" + 0.142*\"weight\" + 0.140*\"taliban\" + 0.120*\"one\" + 0.120*\"dr\" + 0.120*\"peopl\" + 0.107*\"year\"'), (2, '-0.460*\"saudi\" + -0.264*\"taliban\" + 0.249*\"weight\" + -0.194*\"afghanistan\" + 0.191*\"dr\" + -0.189*\"arabia\" + -0.174*\"afghan\" + -0.139*\"offici\" + 0.139*\"diet\" + -0.127*\"state\"')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9jVEp7EFgmh3"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}