Meddical_ImageCaptioning_VIT.ipynb

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      "metadata": {
        "id": "AldVDvOgcpbc"
      },
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      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import json\n",
        "import pandas as pd\n",
        "import re\n",
        "import numpy as np\n",
        "import time\n",
        "import matplotlib.pyplot as plt\n",
        "import collections\n",
        "import random\n",
        "import requests\n",
        "from math import sqrt\n",
        "from PIL import Image\n",
        "from tqdm.auto import tqdm\n",
        "from transformers import BertTokenizerFast, BertTokenizer\n",
        "from transformers import BertTokenizer, BertModel\n",
        "import torch\n",
        "import pdb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC\n"
          ]
        },
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            "text/plain": [
              "             image                                            caption\n",
              "0  synpic38562.jpg  Non enhancing mass in Third Ventricle    Hydro...\n",
              "1  synpic38633.jpg  There is smooth symmetric and homogeneous thic...\n",
              "2  synpic38559.jpg  Non enhancing mass in Third Ventricle    Hydro...\n",
              "3  synpic38581.jpg  Enlarged dysmorphic left cerebral hemisphere. ...\n",
              "4  synpic38533.jpg  Superior uterine segment  Seperate uterine hor..."
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              "      <td>synpic38559.jpg</td>\n",
              "      <td>Non enhancing mass in Third Ventricle    Hydro...</td>\n",
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              "    <tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "RGC_Dataset",
              "summary": "{\n  \"name\": \"RGC_Dataset\",\n  \"rows\": 18434,\n  \"fields\": [\n    {\n      \"column\": \"image\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18434,\n        \"samples\": [\n          \"synpic49861.jpg\",\n          \"synpic31683.jpg\",\n          \"synpic53111.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"caption\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9570,\n        \"samples\": [\n          \"Area of enhancing and thickened small bowel at the terminal ileum in the right lower quadrent that was not bright on T2.\",\n          \"CXR showing cardiomegaly and mediastinal lymphadenopathy\",\n          \"Axillary radiograph shows a coronally oriented linear defect traversing the acromion\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "\n",
        "%cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC\n",
        "RGC_Dataset = pd.read_csv('outputO.txt')\n",
        "\n",
        "RGC_Dataset['image'] = RGC_Dataset['image'].apply(\n",
        "    #lambda x: f'/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/roco_new/images/{x}')\n",
        "    lambda x: f'{x}')\n",
        "RGC_Dataset.head()\n"
      ]
    },
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      "execution_count": null,
      "metadata": {
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        "def text_preprocessing(text): #text preprocesing\n",
        "    text = text.lower()\n",
        "    text = re.sub(r'[^\\w\\s]', '', text)\n",
        "    text = re.sub('\\s+', ' ', text)\n",
        "    text = text.strip()\n",
        "    text = '[start] ' + text + ' [end]'\n",
        "    return text"
      ]
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              "             image                                            caption\n",
              "0  synpic38562.jpg  [start] non enhancing mass in third ventricle ...\n",
              "1  synpic38633.jpg  [start] there is smooth symmetric and homogene...\n",
              "2  synpic38559.jpg  [start] non enhancing mass in third ventricle ...\n",
              "3  synpic38581.jpg  [start] enlarged dysmorphic left cerebral hemi...\n",
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              "          + ' 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-6d6893df-8469-486d-9aa6-e808002f7bb4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d6893df-8469-486d-9aa6-e808002f7bb4')\"\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-6d6893df-8469-486d-9aa6-e808002f7bb4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "RGC_Dataset",
              "summary": "{\n  \"name\": \"RGC_Dataset\",\n  \"rows\": 18434,\n  \"fields\": [\n    {\n      \"column\": \"image\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18434,\n        \"samples\": [\n          \"synpic49861.jpg\",\n          \"synpic31683.jpg\",\n          \"synpic53111.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"caption\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9432,\n        \"samples\": [\n          \"[start] baseline cxrcardiomegaly calcified aorta bibasilar atelectasis related to poor inspiratory effort [end]\",\n          \"[start] fracture of fifth metatarsal metadiaphysis and avulsion at the peroneus tendon insertion [end]\",\n          \"[start] coronal ct reconstruction of the facial bones demonstrates a defect in the midline anterior calvarium with low attenution lesion occupying the nasofrontal interface [end]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "RGC_Dataset['caption'] = RGC_Dataset['caption'].apply(text_preprocessing)\n",
        "RGC_Dataset.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nSTivH_FSSf2"
      },
      "outputs": [],
      "source": [
        "MAX_LENGTH = 50\n",
        "VOCABULARY_SIZE = 10000\n",
        "BATCH_SIZE = 32\n",
        "BUFFER_SIZE = 1000\n",
        "EMBEDDING_DIM = 512\n",
        "UNITS = 512"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X8MGUNtBN2sz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba83b7f6-1013-4df4-d8c5-bf9d8b2c477c",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vocab  ['', '[UNK]', 'the', '[start]', '[end]', 'of', 'and', 'with', 'in', 'mass', 'right', 'left', 'is', 'a', 'to', 'ct', 'no', 'on', 'there', 'lesion', 'contrast', 'lobe', 'demonstrates', 'axial', 'enhancement', 'lateral', 'large', 'enhancing', 'into', 'from', 'within', 'are', 'frontal', 'image', 'images', 'multiple', 'posterior', 'at', 'or', 'edema', 'signal', 'small', 'bone', 'demonstrate', 'sinus', 'cystic', 'an', 'seen', 'bilateral', 'both', 'artery', 'shows', 'normal', 'tissue', 'anterior', 'temporal', 'soft', 'chest', 'fracture', 'through', 'consistent', 'as', 'associated', 'which', 'fluid', 'extending', 'distal', 'this', 'brain', 'lower', 'lesions', 'wall', 'thickening', 'vein', 'upper', 'involving', 'mri', 'present', 'surrounding', 'fat', 'density', 'cerebral', 'pulmonary', 'noted', 'note', 'matter', 'proximal', 'ventricle', 'superior', 'well', 'parietal', 'following', 'lung', 'enlarged', 'medial', 'view', 'diffusion', 'air', 'demonstrating', 'evidence', 'fossa', 'radiograph', 'abnormal', 'level', 'also', 'effect', 'space', 'dilated', 'adjacent', 'bowel', 'middle', 'not', 'restricted', 'region', 'mild', 'diffuse', 'aspect', 'hydrocephalus', 'inferior', 'central', 'low', 'abdomen', 'peripheral', 'intensity', 'kidney', 'enhances', 'that', 'enhanced', 'lobes', 'basal', 'without', 'administration', 'occipital', 'increased', 'hemorrhage', 'extension', 'white', 'flow', 'renal', 'cortical', 'has', 'pleural', 'complex', 'arteries', 'head', 'filling', 'canal', 'marked', 'focal', 'arising', 'hemisphere', 'cerebellar', 'by', 'calcified', 'be', 'joint', 'irregular', 'coronal', 'internal', 'heterogeneous', 'colon', 'calcifications', 'venous', 'solid', 'skull', 'transverse', 'liver', 'enlargement', 'sagittal', 'spinal', 'base', 'third', 'veins', 'calcification', 'high', 'extends', 'changes', 'portion', 'scan', 'effusion', 'neck', 'blood', 'acute', 'femoral', 'hematoma', 'body', 'tumor', 'along', 'cistern', 'defect', 'attenuation', 'carotid', 'aorta', 'ap', 'due', 'ventricles', 'ganglia', 'components', 'areas', 'appearance', 'cranial', 'was', 'aortic', 'orbital', 'cortex', 'show', 'cyst', 'mr', 'nerve', 'esophagus', 'optic', 'for', 'some', 'masses', 'cerebellum', 'pelvis', 'showing', 'gray', 'cord', 'mixed', 'collection', 'area', 'noncontrast', 'foramen', 'suprasellar', 'minimal', 'dural', 'narrowing', 'vascular', 'reveals', 'dwi', 'midline', 'arch', 'sphenoid', 'lytic', 'corpus', 'appears', 'vasogenic', 'ring', 'csf', 'stranding', 'amount', 'appendix', 'structures', 'callosum', 'stomach', 'hemispheres', 'hepatic', 'cavernous', 'thoracic', 'draining', 'abdominal', 'spine', 'cervical', 'bony', 'it', 'moderate', 'side', 'extraaxial', 'extensive', 'infarcts', 'but', 'breast', 'infiltrative', 'aneurysm', 'mediastinal', 'iv', 'expansile', 'sclerosis', 'vertebral', 'sign', 'material', 'linear', 'muscle', 'thickened', 'significant', 'rim', 'flair', 'thin', 'secondary', 'nodular', 'homogeneous', 'views', 'vessels', 'throughout', 'infarct', 'external', 'partially', 'gallbladder', 'sinuses', 'sided', 'heart', 'disease', 'component', 'bilaterally', 'subclavian', 'ventricular', 'subarachnoid', 'structure', 'sigmoid', 'pa', 'bladder', 'herniation', 'ethmoid', 'adc', 'nodules', 'cavity', 'radiographs', 'orbit', 'nodule', 'between', 'prominent', 'lucency', 'loss', 'junction', 'spaces', 'opacity', 'most', 'mastoid', 'foci', 'bones', 'overlying', 'mediastinum', 'all', 'smooth', 'free', 'apex', 'sclerotic', 'oblique', 'dense', 'compression', 'ica', 'involvement', 'maxillary', 'patient', 'osseous', 'voids', 'mesenteric', 'segment', 'quadrant', 'pattern', 'lumen', 'descending', 'cta', 'enhance', 'fourth', 'common', 'after', 'sella', 'imaging', 'filled', 'circumscribed', 'displaced', 'adenopathy', 'very', 'homogeneously', 'duct', 'reaction', 'defects', 'stenosis', 'two', 'parenchyma', 'mca', 'does', 'plain', 'hyperintense', 'periventricular', 'other', 'heterogenous', 'fissure', 'femur', 'displacement', 'tissues', 'pons', 'have', 'erosion', 'opacities', 'identified', 'absent', 'parenchymal', 'leptomeningeal', 'size', 'hyperdense', 'defined', 'septum', 'than', 'subependymal', 'subdural', 'rectus', 'knee', 'hemorrhagic', 'swelling', 'periosteal', 'inflammatory', 'gland', 'destruction', 'scattered', 'mid', 'window', 'occlusion', 'absence', 'visualized', 'complete', 'severe', 'vessel', 'map', 'epidural', 'single', 'second', 'destructive', 'deep', 'oral', 'location', 'posteriorly', 'more', 'fractures', 'collecting', 'located', 'ear', 'pole', 'demonstrated', 'branches', 'abnormality', 'findings', 'windows', 'surface', 'origin', 'basilar', 'just', 'petrous', 'medullary', 'cisterns', 'rounded', 'intracranial', 'abscess', 'trachea', 'focus', 'disc', 'tendon', 'tibial', 'horn', 'film', 'convexity', 'based', 'process', 'cysts', 'muscles', 'loops', 'jugular', 'arterial', 'main', 'intraluminal', 'dilation', 'opacification', 'hilar', 'auditory', 'anteriorly', 'several', 'lymphadenopathy', 'dilatation', 'these', 'mra', 'metaphysis', 'lobulated', 'border', 'barium', 'fusion', 'nasal', 'system', 'joints', 'ivc', 'containing', 'line', 'its', 'duodenum', 'cleft', 'partial', 'kidneys', 'widening', 'thrombosis', 'margins', 'lymph', 'likely', 'intravenous', 'brainstem', 'visible', 'plate', 'formation', 'longitudinal', 'definite', 'subtle', 'tibia', 'sulci', 'obstruction', 'fragment', 'around', 'thalami', 'phase', 'over', 'markedly', 'fatty', 'centered', 'midbrain', 'evident', 'collections', '1', 'metastatic', 'tear', 'patients', 'decreased', 'round', 'notice', 'causing', 'lungs', 'infarction', 'radial', 'gas', 'tubular', 'spleen', 'sonographic', 'interstitial', 'facial', 'hypodense', 'echogenic', 'contour', 'suggestive', 'nodes', 'injury', 'feeding', 'expansion', 'cxr', 'consolidation', 'appearing', 'ascending', 'vermis', 'sequences', 'proptosis', 'one', 'irregularity', 'hernia', 'dissection', 'smaller', 'deformity', 'thick', 'roof', 'portions', 'pneumothorax', 'occupying', 'may', 'lumbar', 'greater', 'contrastenhanced', 'cells', 'subcutaneous', 'subcortical', 'shown', 'reveal', 'nonenhancing', 'infiltration', 'humeral', 'suture', 'slightly', 'intact', 'angiogram', 'sac', 'regions', 'iliac', 'can', 'products', 'obstructive', 'mural', 'mrv', 'distended', 'chronic', '2', 'r', 'indicating', 'diaphragm', 'near', 'margin', 'densities', 'aberrant', 'thrombus', 'gastric', 'angle', 'shift', 'pathologic', 'distribution', 'condyle', 'volume', 'same', 'diffusely', 'degenerative', 'cpa', 'centrally', 'across', 'above', 'walls', 'tail', 'scalp', 'pancreatic', 'meniscus', 'isointense', 'incidental', 'echogenicity', 'chiasm', 'predominantly', 'long', 'involves', 'globe', 'glass', 'cava', 'vena', 'supplied', 'superiorly', 'invasion', 'hydronephrosis', 'diaphysis', 'atrophy', 'via', 'uterus', 'tube', 'layering', 'ground', 'delayed', 'contains', 'pineal', 'medulla', 'marrow', 'ileum', 'during', 'double', 'cardiac', 'symmetric', 'inner', 'humerus', 'floor', 'avm', 'segmental', 'matrix', 'effusions', 'wellcircumscribed', 'portal', 'peripherally', 'clivus', 'subchondral', 'shoulder', 'malformation', 'bile', 'again', 'caliber', 'airspace', 'radius', 'prior', 'perilesional', 'hyperechoic', 'catheter', 'us', 'tip', 'surgical', 'monro', 'metallic', 'effacement', 'course', 'bodies', 'site', 'hypointense', 'agenesis', 'aca', 'underlying', 'scans', 'rib', 'nasopharynx', 'mandible', 'involved', 'branch', 'anomalous', 'sas', 'periappendiceal', 'intra', 'flattening', 'enhancment', 'dystrophic', 'clearly', 'represent', 'relatively', 'rectum', 'pellucidum', 'patent', 'mastoiditis', 'intraaxial', 'including', 'configuration', 'atrium', 'appear', 'splenic', 'representing', 'position', 'mandibular', 'intraventricular', 'enteric', 'drains', 'bronchus', 'behind', 'shaped', 'pancreas', 'massive', 'however', 'hemithorax', 'fundus', 'asymmetric', 'wrist', 'welldefined', 'unremarkable', 'suggest', 'sequence', 'patchy', 'numerous', 'nose', 'fistula', 'bright', 'about', 'selected', 'lucent', 'li', 'inflammation', 'gadolinium', 'foot', 'thyroid', 'svc', 'saggital', 'erosions', 'band', 'ureter', 'rightsided', 'poorly', 'mildly', 'fluoroscopic', 'capsule', 'atelectasis', 'were', 'terminal', 'suggesting', 'stem', 'plane', 'ovary', 'markings', 'eye', 'distortion', 'short', 'shadowing', 'paraspinal', 'l', 'initial', 'increase', 'diagnosis', 'contain', 'confirms', 'cecum', 'atrial', '3', 'softtissue', 'serpentine', 'indicative', 'hypertrophy', 'fused', 'erosive', 'clear', 'best', 'ventral', 'necrosis', 'hyperintensity', 'heterotopic', 'foraminal', 'disruption', 'center', 'below', 'arachnoid', 'tentorial', 'ridge', 'out', 'mlo', 'intrahepatic', 'heterogeneously', 'gastroesophageal', 'spot', 'extradural', 'dislocation', 'compressed', 'collapse', 'change', 'ankle', 'thalamic', 'testicle', 'performed', 'ossification', 'little', 'ligament', 'galen', 'finding', 'diploic', 'corresponding', 'ascites', 'valve', 'subfalcine', 'silhouette', 'minimally', 'metatarsal', 'hip', 'examination', 'elbow', 'bulb', 'additionally', 'tract', 'submandibular', 'showed', 'previous', 'extra', 'diameter', 'aggressive', 'adrenal', 'zone', 'surfaces', 'straight', 'sides', 'rupture', 'pericardial', 'outer', 'multifocal', 'inguinal', 'heterotopia', 'epiphysis', 'cribiform', 'boney', 'thalamus', 'see', 'off', 'negative', 'mucosal', 'medially', 'lobular', 'intense', 'inferiorly', 'gre', 'fibula', 'dark', 'computed', 'characteristic', 'bulge', 'borders', 'xray', 'primarily', 'physis', 'osteopenia', 'meningioma', 'major', 'lesser', 'collateral', 'articular', 'thorax', 'spread', 'separate', 'pelvic', 'multiloculated', 'incidentally', 'hilum', 'follow', 'first', 'entire', 'eccentric', 'dysplasia', 'dorsal', 'conus', 'punctate', 'otherwise', 'injection', 'iac', 'hypodensity', 'facet', 'expected', 'edge', 'cc', 'bridging', 'biliary', 'typical', 'three', 'table', 'sylvian', 'superficial', 'septal', 'projection', 'hyperattenuating', 'extraconal', 'ductal', 'diverticulum', 'coursing', 'tubers', 'tentorium', 'ovoid', 'gyral', 'coronary', 'colonic', 'branching', 'w', 'scout', 'projecting', 'pituitary', 'orbits', 'loop', 'further', 'foreign', 'extravasation', 'continuity', 'up', 'type', 'trigeminal', 'resulting', 'reformatted', 'opthalmic', 'only', 'like', 'laterally', 'gyri', 'films', 'duodenal', 'dependent', 'curvilinear', 'contiguous', 'axis', 'ulnar', 'post', 'patella', 'passing', 'either', 'dura', 'caudate', 'biopsy', 'azygous', 'abnormalities', 'suspicious', 'mm', 'leftsided', 'intramedullary', 'i', 'empty', 'dominant', 'cartilage', 'appreciated', 'apparent', 'study', 'sheath', 'persistent', 'opacified', 'obtained', 'nuclei', 'non', 'metaphyseal', 'levels', 'horns', 'anomaly', 'vertex', 'vasculature', 'stone', 'sinusitis', 'producing', 'predominately', 'possible', 'oriented', 'marginated', 'many', 'isodense', 'illdefined', 'differentiation', 'compatible', 'classic', 'calcific', 'vertical', 'supratentorial', 'retroperitoneal', 'periphery', 'pericholecystic', 'mesentery', 'magnified', 'known', 'implant', 'extremity', 'drainage', 'diverticula', 'causes', 'additional', 'vascularity', 'thinning', 'surrounds', 'supply', 'reduction', 'processes', 'nucleus', 'lucencies', 'limits', 'interhemispheric', 'under', 'ulna', 'splenium', 'sma', 'remodeling', 'involve', 'fields', 'elevated', 'down', 'distally', 'deviation', 'anatomy', 'upon', 'territory', 'styloid', 'replacing', 'pseudoaneurysm', 'pad', 'open', 'nondisplaced', 'neural', 'necrotic', 'intraperitoneal', 'hypothalamus', 'history', 'heads', 'hand', 'flexure', 'extend', 'clavicle', 'chain', 'bifrontal', 'arise', 'any', 'anastamosis', 'toward', 'stones', 'stir', 'reconstruction', 'pica', 'interval', 'flap', 'celiac', 'airway', 'void', 'tomographic', 'swallow', 'signs', 'si', 'shape', 'rotation', 'remaining', 'prominence', 'nerves', 'labeled', 'innominate', 'grayscale', 'frondlike', 'ependymal', 'dilitation', 'developmental', 'debris', 'airfluid', 'venogram', 'slight', 'similar', 'septations', 'hemidiaphragm', 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'endplates', 'endon', 'endometium', 'endoleak', 'endograft', 'endhancing', 'encroaching', 'encircled', 'enchondromas', 'enchondroma', 'enchantment', 'encephalomalcia', 'encephalitis', 'encapsulating', 'encapsulate', 'enahncing', 'en', 'empties', 'eminating', 'ekg', 'eight', 'edl', 'edification', 'edh', 'edgeslines', 'edemahemorrhage', 'edemahematoma', 'edemacuffing', 'edemacontusion', 'ed', 'ectopy', 'echoic', 'echogenicitydebris', 'echogenicities', 'echogenecity', 'echocardiogram', 'eca', 'easier', 'ease', 'earring', 'earlycontrast', 'eam', 'dysphagia', 'dysgensis', 'dysgenesisof', 'dykedavidoffmasson', 'dwm', 'duret', 'duralbased', 'dumping', 'dumbell', 'dtpa', 'dsc', 'dsa', 'druj', 'drops', 'dropped', 'dropout', 'droplet', 'drinking', 'drawing', 'downstream', 'doughnut', 'doudenaljejunal', 'doublebubble', 'dots', 'dot', 'dorsopalmar', 'dorsalcentral', 'donut', 'dont', 'domonstrates', 'domes', 'doesnt', 'document', 'doctor', 'dobhoff', 'dmonstrating', 'dliated', 'djj', 'divisum', 'dividing', 'diverting', 'diverticulalike', 'diverticulae', 'diversion', 'diveritcula', 'disuse', 'distracted', 'distort', 'distinguishing', 'distinguished', 'distend', 'distant', 'distalmost', 'dissocation', 'dissect', 'disorganized', 'disorders', 'dislocations', 'diseasehas', 'discussion', 'discontinous', 'disconnection', 'disclosed', 'directionality', 'direcly', 'dips', 'diminution', 'diminuative', 'diminished', 'dimetaphysis', 'diligent', 'dilate', 'digitorum', 'differentiated', 'differentiate', 'difference', 'didelphys', 'dichorionic', 'diaphrgams', 'diaphrams', 'diamniotic', 'diagonal', 'diagnoses', 'diagnose', 'dextroposed', 'dexterity', 'develops', 'developed', 'detection', 'detachable', 'desending', 'derotation', 'deposition', 'deployment', 'dephasing', 'depent', 'depending', 'depedent', 'deoxyhemoglobin', 'deomonstrates', 'dentigerous', 'densityweighted', 'densitiy', 'demylinating', 'demostrated', 'demontratew', 'demontrate', 'demonstrats', 'demonstarting', 'demonostrates', 'deltoidmedial', 'delivery', 'dehisence', 'degress', 'degrades', 'degradation', 'degerative', 'deformaties', 'deflux', 'deflection', 'definitively', 'definitely', 'definited', 'define', 'definate', 'deficiency', 'defective', 'decub', 'decompressive', 'decline', 'decidual', 'decending', 'december', 'ddx', 'days', 'day', 'dawes', 'dates', 'date', 'danger', 'dandy', 'da', 'cystourethrogramretrograde', 'cystoconray', 'cyclophosphamide', 'cxrlat', 'cxrcardiomegaly', 'cx', 'cv', 'cutoff', 'curvolinear', 'curvinear', 'curvilinearserpentine', 'curves', 'cupola', 'cup', 'ctleft', 'ctlarge', 'crownrump', 'crotch', 'crosssectional', 'crossover', 'crossmidline', 'crosshairs', 'cropped', 'crook', 'crohns', 'cricipoharyngeal', 'crescentshaped', 'creatinine', 'creatine', 'crazy', 'craniopharyngioma', 'cr', 'cpca', 'cp', 'couse', 'courtesy', 'couple', 'countours', 'cough', 'cosmic', 'correspoinding', 'correlated', 'cornal', 'coritical', 'cords', 'coracoidclavicle', 'coracoid', 'cor', 'copper', 'copd', 'coorelates', 'coonsistent', 'convexities', 'contusionsedemaencephalomalacia', 'contributions', 'contributes', 'contraststooled', 'contrastopacified', 'contrastenhancing', 'contraction', 'contracted', 'contoured', 'contiuity', 'continual', 'continous', 'contined', 'contigious', 'contacting', 'constrained', 'constent', 'constast', 'conspicuous', 'consolodation', 'consolidated', 'consitent', 'consisten', 'consider', 'connective', 'connect', 'congestionnote', 'conforming', 'confluencesmv', 'confirmation', 'conedown', 'condyl', 'condition', 'concistent', 'conception', 'concentrically', 'concdyle', 'con', 'computer', 'compromise', 'composition', 'complicated', 'comparrison', 'comparisons', 'compared', 'comoparison', 'community', 'communited', 'combing', 'combination', 'comb', 'coloncecum', 'colocephaly', 'collumn', 'collosalseptal', 'collinear', 'collimation', 'colli', 'collectionand', 'collar', 'collapses', 'collage', 'colitis', 'cochlear', 'cobblestone', 'coated', 'coaptation', 'coalesced', 'coalesce', 'cmultiple', 'cluster', 'clubbed', 'clotted', 'cloquets', 'clinically', 'classificaation', 'clamp', 'cl', 'circumstribed', 'circumstantially', 'circumscribing', 'circumferentiallynarrowed', 'circulares', 'cingulate', 'cicumscribed', 'chylous', 'choriodal', 'chondromalacia', 'cholinecreatine', 'cholecystostomy', 'cholangitis', 'cholangiography', 'choanal', 'cho', 'chiropractic', 'cheststill', 'chestabd', 'charles', 'changesrupture', 'chalky', 'cervicothoracic', 'cervicomedullary', 'certainty', 'certainly', 'cerical', 'cerebeller', 'cerbral', 'cephalization', 'centripetally', 'centric', 'centralpathway', 'centralized', 'centerofflow', 'celaic', 'ccam', 'cathteter', 'cathether', 'catheterization', 'cassette', 'cartoon', 'carrying', 'carpi', 'carotidjugular', 'cardiomediastinum', 'cardiogenic', 'carcinomas', 'capsuleno', 'capitis', 'capillary', 'cannulation', 'cannotfully', 'canicular', 'calvalrium', 'callosalseptal', 'call', 'calipers', 'califications', 'calification', 'caliectasis', 'calicies', 'calficic', 'calcificationsleftparasaggital', 'calcifcations', 'calcarine', 'cahnge', 'cacification', 'button', 'burr', 'bunion', 'bunching', 'bumps', 'bulls', 'bullousemphysematous', 'bulgesubligamentous', 'buildup', 'buckled', 'buckethandle', 'bs', 'broncholiths', 'bronchioles', 'bronchiectiasis', 'bronch', 'brochogram', 'brochietasis', 'brighter', 'breat', 'breach', 'branchesb', 'branched', 'brainhead', 'brachycephalic', 'brachipontine', 'bowtie', 'bowl', 'bowels', 'bovine', 'bottomand', 'bottle', 'born', 'bonetegmen', 'boneselective', 'bohlers', 'blockage', 'blindended', 'blanket', 'bland', 'bl', 'biopsies', 'bintestinal', 'bilaterall', 'bilateal', 'bifucation', 'bifid', 'bidirectional', 'bezoar', 'beveling', 'bertin', 'benignappearing', 'bells', 'began', 'bedside', 'beaten', 'bears', 'beamhardening', 'beaded', 'bcell', 'bbs', 'bayonettype', 'baumanns', 'basline', 'basis', 'basing', 'basilic', 'basically', 'bars', 'baringlike', 'bamboo', 'balloooning', 'ballistic', 'ballandsocket', 'balance', 'bakers', 'baja', 'bag', 'baclofen', 'backflow', 'babys', 'babygram', 'baastrups', 'azagous', 'az', 'axillae', 'axialquadriceps', 'ax', 'awake', 'avms', 'avitene', 'avioli', 'average', 'autosplenectomy', 'autopsy', 'australia', 'aupright', 'atypical', 'attributed', 'attenuatino', 'attenuates', 'attenuaiton', 'attempts', 'attaching', 'attachement', 'ats', 'atropy', 'atrioseptal', 'atalectasis', 'asymettry', 'asymetry', 'asymetric', 'asterisks', 'assumed', 'associtated', 'aspirationdrainage', 'aspirates', 'aspartate', 'asking', 'asis', 'asedsignal', 'ascends', 'asc', 'artificial', 'articulationincreased', 'arthroplasties', 'arthogram', 'arterys', 'arteryand', 'arteriosum', 'arteriorgram', 'arteriography', 'arrest', 'arguable', 'ares', 'areola', 'arcuate', 'arcandswirl', 'aptransverse', 'approximatly', 'approaches', 'applied', 'apperance', 'appendicular', 'appendicoliths', 'appendicolithcontaining', 'appendicits', 'appearence', 'appeareance', 'appearancy', 'apophyseal', 'apicalgreaterthanbasilar', 'apexndix', 'aperistalsis', 'apendix', 'apears', 'apearing', 'apart', 'aorticopulmonarywindow', 'aorticopulmonary', 'aortalower', 'aortal', 'aortagram', 'aof', 'antrae', 'antimesenteric', 'antidependent', 'antero', 'anteriorsuperior', 'anteriorposterior', 'anteriorinferior', 'antegrage', 'ant', 'anosmia', 'anlargement', 'anisotropy', 'angulated', 'angioplastied', 'aneurysmdolichoectasia', 'aneurysmally', 'andy', 'ancalcified', 'anastomosed', 'anasarca', 'amputation', 'amniotic', 'alytic', 'already', 'alone', 'allowing', 'alleviate', 'alingment', 'align', 'alert', 'alcohol', 'albeit', 'alae', 'aka', 'airwa', 'airtrapping', 'airdilated', 'airbronchograms', 'airbronchogram', 'aided', 'aica', 'agitated', 'aggregate', 'afforementioned', 'afferents', 'affects', 'advancement', 'adpkd', 'adolescent', 'admitted', 'adenxa', 'adenomyosis', 'adenomatoid', 'adenoma', 'adenoids', 'adenexal', 'adductor', 'addionally', 'added', 'add', 'adcmap', 'acyanosis', 'acutechronic', 'act', 'acromionclavicle', 'achoa', 'acetablular', 'accurate', 'accross', 'accidental', 'accessory11th', 'accentuates', 'acceleration', 'abscessmass', 'abrubt', 'abnormall', 'abductor', 'abdomenal', 'abcsess', 'abcess', '730', '71', '70', '58x33x43cm', '53', '4ulnar', '46', '3ulnar', '3rd', '37', '35', '32', '30ppm', '30', '2nd', '2540', '24', '23', '22g', '202', '1dorsal', '1a', '1995', '18cm', '180', '15', '14', '13mm', '133', '12thnerves', '12th', '116', '1100', '10th', '1008']\n"
          ]
        }
      ],
      "source": [
        "tokenizer = tf.keras.layers.TextVectorization(\n",
        "    max_tokens=VOCABULARY_SIZE,\n",
        "    standardize=None,\n",
        "    output_sequence_length=MAX_LENGTH)\n",
        "\n",
        "\n",
        "vocab = tokenizer.get_vocabulary()\n",
        "\n",
        "\n",
        "tokenizer.adapt(RGC_Dataset['caption'])\n",
        "vocab = tokenizer.get_vocabulary()\n",
        "print('vocab ',vocab)\n",
        "\n",
        "\n",
        "print(tokenizer.get_vocabulary())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qvhg-6eKN3nz"
      },
      "outputs": [],
      "source": [
        "word2idx = tf.keras.layers.StringLookup(\n",
        "    mask_token=\"\",  # add medical tokens to enhance the model\n",
        "    vocabulary=tokenizer.get_vocabulary())\n",
        "\n",
        "\n",
        "\n",
        "idx2word = tf.keras.layers.StringLookup(\n",
        "    mask_token=\"\",\n",
        "    vocabulary=tokenizer.get_vocabulary(),\n",
        "    invert=True)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yrca2aN2N5WL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eaef4d54-12fd-45a6-b3c3-bc57193d06c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14747\n",
            "3687\n"
          ]
        }
      ],
      "source": [
        "img_to_cap_vector = collections.defaultdict(list)\n",
        "for img, cap in zip(RGC_Dataset['image'], RGC_Dataset['caption']):\n",
        "    img_to_cap_vector[img].append(cap)\n",
        "\n",
        "img_keys = list(img_to_cap_vector.keys())\n",
        "random.shuffle(img_keys)\n",
        "\n",
        "\n",
        "slice_index = int(len(img_keys)*0.8)\n",
        "img_name_train_keys, img_name_val_keys = (img_keys[:slice_index],\n",
        "                                          img_keys[slice_index:])\n",
        "\n",
        "train_imgs = []\n",
        "train_captions = []\n",
        "for imgt in img_name_train_keys:\n",
        "    capt_len = len(img_to_cap_vector[imgt])\n",
        "    train_imgs.extend([imgt] * capt_len)\n",
        "    train_captions.extend(img_to_cap_vector[imgt])\n",
        "\n",
        "\n",
        "test_imgs = []\n",
        "test_captions = []\n",
        "for imgv in img_name_val_keys:\n",
        "    capv_len = len(img_to_cap_vector[imgv])\n",
        "    test_imgs.extend([imgv] * capv_len)\n",
        "    test_captions.extend(img_to_cap_vector[imgv])\n",
        "\n",
        "print(len(train_captions))\n",
        "print(len(test_captions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UHN3Q1YDN5TD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61332f4d-f5c2-437a-8a7a-b76f7651ac3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14747, 14747, 3687, 3687)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(train_imgs), len(train_captions), len(test_imgs), len(test_captions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "12c-7FHzOFSq"
      },
      "outputs": [],
      "source": [
        "def load_RGCdata(img_path, caption):\n",
        "    print('type caption ',type(caption))\n",
        "    img = tf.io.read_file(img_path)\n",
        "    img = tf.io.decode_jpeg(img,channels=3)\n",
        "    print('img', img)\n",
        "    img = tf.keras.layers.Resizing(299, 299)(img)\n",
        "    img = img / 255.\n",
        "    caption = tokenizer(caption)\n",
        "    return img, caption"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "vHk83y3eOFPz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bf9a48c-393b-4def-e915-5f921d7e6eff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n",
            "type caption  <class 'tensorflow.python.framework.ops.SymbolicTensor'>\n",
            "img Tensor(\"DecodeJpeg:0\", shape=(None, None, 3), dtype=uint8)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow/python/data/ops/structured_function.py:258: UserWarning: Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'tensorflow.python.data.ops.batch_op._BatchDataset'>\n",
            "type caption  <class 'tensorflow.python.framework.ops.SymbolicTensor'>\n",
            "img Tensor(\"DecodeJpeg:0\", shape=(None, None, 3), dtype=uint8)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "%cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n",
        "train_dataset = tf.data.Dataset.from_tensor_slices(\n",
        "    (train_imgs, train_captions))\n",
        "\n",
        "\n",
        "train_dataset = train_dataset.map(\n",
        "    load_RGCdata, num_parallel_calls=tf.data.AUTOTUNE\n",
        "    ).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "print(type(train_dataset))\n",
        "\n",
        "\n",
        "test_dataset = tf.data.Dataset.from_tensor_slices(\n",
        "    (test_imgs, test_captions))\n",
        "\n",
        "test_dataset = test_dataset.map(\n",
        "    load_RGCdata, num_parallel_calls=tf.data.AUTOTUNE\n",
        "    ).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bQr_bgk11eMF"
      },
      "outputs": [],
      "source": [
        "image_augmentation = tf.keras.Sequential(\n",
        "    [\n",
        "        tf.keras.layers.RandomFlip(\"horizontal\"),\n",
        "        tf.keras.layers.RandomRotation(0.2),\n",
        "        tf.keras.layers.RandomContrast(0.3),\n",
        "       # tf.keras.layers.RandomZoom(0.2),#new\n",
        "        #tf.keras.layers.RandomTranslation(0.2, 0.2),#new\n",
        "       # tf.keras.layers.RandomHeight(0.2),#new\n",
        "        #tf.keras.layers.RandomWidth(0.2),#new\n",
        "\n",
        "    ]\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P-XHccS6eLOu"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Layer\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "class GaussianSmoothingAugmentation(Layer):\n",
        "    def __init__(self, kernel_size=3, sigma=1.0, **kwargs):\n",
        "        self.kernel_size = kernel_size\n",
        "        self.sigma = sigma\n",
        "        super(GaussianSmoothingAugmentation, self).__init__(**kwargs)\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        kernel = self.gaussian_kernel(self.kernel_size, self.sigma)\n",
        "        self.kernel = K.constant(kernel, dtype=tf.float32)\n",
        "        self.kernel = self.kernel[:, :, None, None]\n",
        "\n",
        "    def gaussian_kernel(self, size, sigma):\n",
        "        x = tf.range(-size // 2 + 1, size // 2 + 1, dtype=tf.float32)\n",
        "        x = tf.exp(-0.5 * (x / sigma) ** 2)\n",
        "        kernel = x[:, None] * x[None, :]\n",
        "        kernel /= tf.reduce_sum(kernel)\n",
        "        return kernel\n",
        "\n",
        "    def call(self, inputs, training=None):\n",
        "        if training:\n",
        "            outputs = tf.nn.depthwise_conv2d(inputs, self.kernel, strides=[1, 1, 1, 1], padding='SAME')\n",
        "            return outputs\n",
        "        else:\n",
        "            return inputs\n",
        "\n",
        "    def compute_output_shape(self, input_shape):\n",
        "        return input_shape\n"
      ],
      "metadata": {
        "id": "64_AxiAKCMXC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z_2wna3KCNpY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H9GDJ9_1nIMO"
      },
      "outputs": [],
      "source": [
        "from keras.applications import InceptionV3, DenseNet121, ResNet50,EfficientNetB0\n",
        "#from tensorflow.keras.applications import InceptionV3, DenseNet121, ResNet50\n",
        "from keras.models import Model ,Sequential\n",
        "from keras.layers import Dense,GlobalAveragePooling2D ,GlobalMaxPooling2D,Dropout, GRU,LSTM,Embedding, BatchNormalization, Conv2D, LSTM, Flatten, concatenate\n",
        "from keras.callbacks import TensorBoard\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras import regularizers\n",
        "import tensorflow as tf\n",
        "from keras.applications import vgg16\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "\n",
        "tf.config.run_functions_eagerly(True)\n",
        "\n",
        "def CNN_Encoder():\n",
        "\n",
        "    %cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/.ipynb_checkpoints/swin-rgc-roco-medicat\n",
        "\n",
        "\n",
        "    img_rows, img_cols=299, 299\n",
        "\n",
        "\n",
        "    inception_v3 = tf.keras.applications.InceptionV3(\n",
        "        include_top=False, #true\n",
        "        weights='imagenet',\n",
        "        input_shape=(img_rows, img_cols, 3)\n",
        "    )\n",
        "    efficientnet_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(299, 299, 3))\n",
        "    densenet = DenseNet121(weights='imagenet', include_top=False, input_shape=(299, 299, 3)) #best result\n",
        "    resnet50 = ResNet50(weights='imagenet', include_top=False, input_shape=(299, 299, 3))\n",
        "    vgg16_model =vgg16.VGG16(weights=\"imagenet\", include_top=False, input_shape=(299,299,3))\n",
        "\n",
        "    for layer in inception_v3.layers:\n",
        "      layer.trainable=False\n",
        "    for layer in densenet.layers:\n",
        "      layer.trainable=False\n",
        "    for layer in resnet50.layers:\n",
        "      layer.trainable=False\n",
        "    for layer in vgg16_model.layers:\n",
        "      layer.trainable=False\n",
        "    for layer in efficientnet_model.layers:\n",
        "      layer.trainable=False\n",
        "\n",
        "\n",
        "    output=efficientnet_model.output\n",
        "    print('PRINT OUTPUT SHAPE ',output.shape)\n",
        "\n",
        "\n",
        "\n",
        "    top_model=GlobalAveragePooling2D()(output)\n",
        "    #top_model=GlobalMaxPooling2D()(output)\n",
        "\n",
        "    top_model=Dense(1024, activation='relu')(top_model)\n",
        "    top_model=Dense(512, activation='relu')(top_model)\n",
        "    #top_model=LSTM(128, return_sequences=True)(top_model)\n",
        "    GaussianSmoothingAugmentation(kernel_size=3, sigma=1.0, input_shape=(28, 28, 1))\n",
        "\n",
        "    #top_model=GlobalAveragePooling2D()(top_model)\n",
        "    #top_model = Conv2D(128, (7, 7), padding='same', activation='relu', strides=(2, 2))(top_model)\n",
        "\n",
        "    #top_model = Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.01))(top_model)\n",
        "    top_model = tf.keras.layers.Reshape((-1, top_model.shape[-1]))(top_model)\n",
        "    top_model = GRU(128, return_sequences=False)(top_model)\n",
        "    top_model = tf.keras.layers.Reshape((-1, top_model.shape[-1]))(top_model)\n",
        "    top_model=Dense(2048, activation='relu')(top_model)\n",
        "    top_model=Dropout(0.2)(top_model)\n",
        "    print('top_model.shape',top_model.shape)\n",
        "\n",
        "\n",
        "    #print('output', output.shape)\n",
        "    top_model = tf.keras.layers.Reshape(\n",
        "        (-1, top_model.shape[-1]))(top_model)\n",
        "\n",
        "\n",
        "    cnn_model = tf.keras.models.Model(efficientnet_model.input, top_model)\n",
        "\n",
        "\n",
        "    print(cnn_model.summary())\n",
        "    return cnn_model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow matplotlib pydot"
      ],
      "metadata": {
        "id": "UjBR6XIAKyRR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "e25a84ba-577d-4f7a-bd8e-82b39b2416c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jMy5MrE2PdHV"
      },
      "outputs": [],
      "source": [
        "class MedicalTransformerEncoderLayer(tf.keras.layers.Layer):\n",
        "\n",
        "    def __init__(self, embed_dim, num_heads):\n",
        "        super().__init__()\n",
        "        self.layer_norm_1 = tf.keras.layers.LayerNormalization()\n",
        "        self.layer_norm_2 = tf.keras.layers.LayerNormalization()\n",
        "        self.attention = tf.keras.layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim)\n",
        "        self.dense = tf.keras.layers.Dense(embed_dim, activation=\"relu\")\n",
        "\n",
        "    def call(self, x, training):\n",
        "\n",
        "        x = self.layer_norm_1(x)\n",
        "        x = self.dense(x)\n",
        "\n",
        "\n",
        "        attn_output = self.attention(      #self attension\n",
        "            query=x,\n",
        "            value=x,\n",
        "            key=x,\n",
        "            attention_mask=None,\n",
        "            training=training\n",
        "        )\n",
        "\n",
        "        x = self.layer_norm_2(x + attn_output) # This line implements a residual connection.\n",
        "        #The output from the self-attention layer (attn_output) is added back to the original input (x) before being passed through another\n",
        "        # layer normalization step (self.layer_norm_2).\n",
        "        # Residual connections help address the vanishing/exploding gradient problem in deep neural networks.\n",
        "\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MFqNFts0duGB"
      },
      "outputs": [],
      "source": [
        "class EmbeddingsLayer(tf.keras.layers.Layer):\n",
        "\n",
        "    def __init__(self, vocab_size, embed_dim, max_len):\n",
        "        super().__init__()\n",
        "        self.token_embeddings = tf.keras.layers.Embedding(\n",
        "            vocab_size, embed_dim)\n",
        "        self.position_embeddings = tf.keras.layers.Embedding(\n",
        "            max_len, embed_dim, input_shape=(None, max_len))\n",
        "\n",
        "\n",
        "    def call(self, input_ids):\n",
        "        length = tf.shape(input_ids)[-1]\n",
        "        position_ids = tf.range(start=0, limit=length, delta=1)\n",
        "        position_ids = tf.expand_dims(position_ids, axis=0)\n",
        "\n",
        "        token_embeddings = self.token_embeddings(input_ids)\n",
        "        position_embeddings = self.position_embeddings(position_ids)\n",
        "\n",
        "\n",
        "        return token_embeddings + position_embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K7QfeQ1XbXWE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "083c7ea9-4428-4005-eab3-67ee7d8d87b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([32, 50, 512])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n",
        "EmbeddingsLayer(tokenizer.vocabulary_size(), EMBEDDING_DIM, MAX_LENGTH)(next(iter(train_dataset))[1]).shape\n",
        "#converts caption text from the training dataset into numerical embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "pcbCQqrDnJ4-"
      },
      "outputs": [],
      "source": [
        "#from transformers import BertTokenizerFast, BertTokenizer\n",
        "#%cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/bert-base-uncased\n",
        "# ORIGINAL\n",
        "class MedicalTransformerDecoderLayer(tf.keras.layers.Layer):\n",
        "\n",
        "    def __init__(self, embed_dim, units, num_heads):\n",
        "        super().__init__()\n",
        "        self.embedding = Embeddings(\n",
        "            tokenizer.vocabulary_size(), embed_dim, MAX_LENGTH)\n",
        "\n",
        "        self.attention_1 = tf.keras.layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim, dropout=0.1\n",
        "        )\n",
        "        self.attention_2 = tf.keras.layers.MultiHeadAttention(\n",
        "            num_heads=num_heads, key_dim=embed_dim, dropout=0.1\n",
        "        )\n",
        "        self.layernorm_1 = tf.keras.layers.LayerNormalization()\n",
        "        self.layernorm_2 = tf.keras.layers.LayerNormalization()\n",
        "        self.layernorm_3 = tf.keras.layers.LayerNormalization()\n",
        "\n",
        "\n",
        "        self.ffn_layer_1 = tf.keras.layers.Dense(units, activation=\"relu\")\n",
        "        self.ffn_layer_2 = tf.keras.layers.Dense(embed_dim)\n",
        "\n",
        "\n",
        "        self.out = tf.keras.layers.Dense(tokenizer.vocabulary_size(), activation=\"softmax\")\n",
        "\n",
        "        self.dropout_1 = tf.keras.layers.Dropout(0.3)\n",
        "        self.dropout_2 = tf.keras.layers.Dropout(0.5)\n",
        "\n",
        "\n",
        "    def call(self, input_ids, encoder_output, training, mask=None):\n",
        "        embeddings = self.embedding(input_ids)\n",
        "        combined_mask = None\n",
        "        padding_mask = None\n",
        "\n",
        "        if mask is not None:\n",
        "            causal_mask = self.get_causal_attention_mask(embeddings)\n",
        "            padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32)\n",
        "            combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32)\n",
        "            combined_mask = tf.minimum(combined_mask, causal_mask)\n",
        "\n",
        "        attn_output_1 = self.attention_1(\n",
        "            query=embeddings,\n",
        "            value=embeddings,\n",
        "            key=embeddings,\n",
        "            attention_mask=combined_mask,\n",
        "            training=training\n",
        "        )\n",
        "\n",
        "        out_1 = self.layernorm_1(embeddings + attn_output_1)\n",
        "\n",
        "        attn_output_2 = self.attention_2(\n",
        "            query=out_1,\n",
        "            value=encoder_output,\n",
        "            key=encoder_output,\n",
        "            attention_mask=padding_mask,\n",
        "            training=training\n",
        "        )\n",
        "\n",
        "        out_2 = self.layernorm_2(out_1 + attn_output_2)\n",
        "\n",
        "\n",
        "        ffn_out = self.ffn_layer_1(out_2)\n",
        "\n",
        "        ffn_out = self.dropout_1(ffn_out, training=training)\n",
        "\n",
        "        ffn_out = self.ffn_layer_2(ffn_out)\n",
        "\n",
        "\n",
        "        ffn_out = self.layernorm_3(ffn_out + out_2)\n",
        "\n",
        "        ffn_out = self.dropout_2(ffn_out, training=training)\n",
        "\n",
        "\n",
        "        preds = self.out(ffn_out)\n",
        "        return preds\n",
        "\n",
        "\n",
        "    def get_causal_attention_mask(self, inputs):\n",
        "        input_shape = tf.shape(inputs)\n",
        "        batch_size, sequence_length = input_shape[0], input_shape[1]\n",
        "        i = tf.range(sequence_length)[:, tf.newaxis]\n",
        "        j = tf.range(sequence_length)\n",
        "        mask = tf.cast(i >= j, dtype=\"int32\")\n",
        "        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))\n",
        "        mult = tf.concat(\n",
        "            [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)],\n",
        "            axis=0\n",
        "        )\n",
        "        return tf.tile(mask, mult)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def process_tensor_to_string(self, tensor):\n",
        "    # Convert the tensor to a numpy array\n",
        "    tensor_np = tensor.numpy()\n",
        "    # Ensure it's a string if it's not already\n",
        "    #if tensor_np.dtype != 'str':\n",
        "    tensor_np = tensor_np.astype(str)\n",
        "    return tensor_np"
      ],
      "metadata": {
        "id": "vcmxAcqStjXZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "9_NmSUaVys9R"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from nltk.translate.bleu_score import sentence_bleu\n",
        "from nltk.translate.meteor_score import meteor_score\n",
        "\n",
        "from nltk.translate.meteor_score import meteor_score\n",
        "from nltk.translate import meteor\n",
        "from nltk import word_tokenize\n",
        "import numpy as np\n",
        "import math\n",
        "\n",
        "\n",
        "class MedicalImageCaptioningModel(tf.keras.Model):\n",
        "\n",
        "    def __init__(self, cnn_model, encoder, decoder, image_aug=None):\n",
        "        super().__init__()\n",
        "\n",
        "        self.cnn_model = cnn_model\n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "        self.image_aug = image_aug\n",
        "        self.loss_tracker = tf.keras.metrics.Mean(name=\"loss\")\n",
        "        self.acc_tracker = tf.keras.metrics.Mean(name=\"accuracy\")\n",
        "        self.per_tracker = tf.keras.metrics.Mean(name=\"precision\")\n",
        "        #self.rec_tracker = tf.keras.metrics.Mean(name=\"recall\")\n",
        "\n",
        "    def calculate_loss(self, y_true, y_pred, mask):\n",
        "\n",
        "        loss = self.loss(y_true, y_pred)\n",
        "        mask = tf.cast(mask, dtype=loss.dtype)\n",
        "        loss *= mask\n",
        "\n",
        "        return tf.reduce_sum(loss) / tf.reduce_sum(mask)\n",
        "\n",
        "    def calculate_precision(self,y_true, y_pred, mask):\n",
        "      y_pred = tf.argmax(y_pred, axis=-1)\n",
        "      y_true = tf.cast(y_true, dtype=tf.int64)\n",
        "      mask = tf.cast(mask, dtype=tf.float32)\n",
        "\n",
        "      true_positives = tf.reduce_mean(tf.cast(tf.equal(y_true, y_pred), tf.float32) * mask)\n",
        "\n",
        "      predicted_positives = tf.reduce_mean(mask)\n",
        "\n",
        "\n",
        "      precision = true_positives / (true_positives + predicted_positives)\n",
        "      return precision\n",
        "\n",
        "    def calculate_recall(self,y_true, y_pred, mask=None):\n",
        "      y_true = tf.cast(y_true, dtype=tf.float32)\n",
        "      y_pred = tf.cast(y_pred, dtype=tf.float32)\n",
        "\n",
        "      if mask is not None:\n",
        "            mask = tf.cast(mask, dtype=tf.float32)\n",
        "            if mask.shape != y_true.shape:\n",
        "              mask = tf.broadcast_to(mask, y_true.shape)\n",
        "            y_true = y_true * mask\n",
        "            y_pred = y_pred * mask\n",
        "\n",
        "      true_positives = tf.round(tf.clip_by_value(y_true * y_pred, 0, 1))\n",
        "      possible_positives = tf.round(tf.clip_by_value(y_true, 0, 1))\n",
        "      recall = true_positives / (possible_positives + tf.keras.backend.epsilon())\n",
        "      return recall\n",
        "\n",
        "\n",
        " # it identifies the most likely word predicted by the model for each position in the captions across the vocabulary.\n",
        "    def calculate_accuracy(self, y_true, y_pred, mask):\n",
        "        accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2))#this creates a binary tensor where 1 indicates a correct prediction and 0 indicates an incorrect prediction.\n",
        "        accuracy = tf.math.logical_and(mask, accuracy)#this removes incorrect predictions caused by padding elements.\n",
        "        accuracy = tf.cast(accuracy, dtype=tf.float32)\n",
        "\n",
        "        mask = tf.cast(mask, dtype=tf.float32)\n",
        "        acc = tf.reduce_sum(accuracy) / tf.reduce_sum(mask)\n",
        "\n",
        "        return tf.reduce_sum(accuracy) / tf.reduce_sum(mask)\n",
        "\n",
        "\n",
        "\n",
        "    def compute_loss_and_acc(self, img_embed, captions, training=True):\n",
        "      encoder_output = self.encoder(img_embed, training=True)\n",
        "      y_input = captions[:, :-1] #prefix of captions\n",
        "      y_true = captions[:, 1:] #true captions\n",
        "\n",
        "\n",
        "      mask = (y_true != 0)\n",
        "      y_pred = self.decoder(y_input, encoder_output, training=True, mask=mask)\n",
        "\n",
        "      loss = self.calculate_loss(y_true, y_pred, mask)\n",
        "      acc = self.calculate_accuracy(y_true, y_pred, mask)\n",
        "      per=self.calculate_precision(y_true, y_pred, mask)\n",
        "      #rec=self.calculate_recall(y_true, y_pred, mask)\n",
        "\n",
        "\n",
        "      return loss, acc, per#, rec#, f1_score, specificity\n",
        "\n",
        "\n",
        "    def train_step(self, batch):\n",
        "        imgs, captions = batch\n",
        "\n",
        "        if self.image_aug:\n",
        "          imgs = self.image_aug(imgs)\n",
        "\n",
        "        img_embed = self.cnn_model(imgs)\n",
        "\n",
        "\n",
        "        with tf.GradientTape() as tape:\n",
        "            loss, acc, per = self.compute_loss_and_acc(\n",
        "                img_embed, captions\n",
        "            )\n",
        "\n",
        "        train_vars = (\n",
        "            self.encoder.trainable_variables + self.decoder.trainable_variables\n",
        "        )\n",
        "        grads = tape.gradient(loss, train_vars)\n",
        "        self.optimizer.apply_gradients(zip(grads, train_vars))\n",
        "        self.loss_tracker.update_state(loss)\n",
        "        self.acc_tracker.update_state(acc)\n",
        "        self.per_tracker.update_state(per)\n",
        "        #self.rec_tracker.update_state(rec)\n",
        "\n",
        "\n",
        "        return {\"loss\": self.loss_tracker.result(),\n",
        "                \"accuracy\": self.acc_tracker.result(),\n",
        "                \"precision\": self.per_tracker.result()}\n",
        "                #\"recall\": self.rec_tracker.result()}\n",
        "\n",
        "    def test_step(self, batch):\n",
        "        imgs, captions = batch\n",
        "\n",
        "        img_embed = self.cnn_model(imgs)\n",
        "\n",
        "        loss, acc, per = self.compute_loss_and_acc(\n",
        "            img_embed, captions, training=False\n",
        "        )\n",
        "\n",
        "        self.loss_tracker.update_state(loss)\n",
        "        self.acc_tracker.update_state(acc)\n",
        "        self.per_tracker.update_state(per)\n",
        "        #self.rec_tracker.update_state(rec)\n",
        "        #self.blue_tracker.update_state(bl)\n",
        "\n",
        "        return {\"loss\": self.loss_tracker.result(),\n",
        "                \"accuracy\": self.acc_tracker.result(),\n",
        "                \"precision\": self.per_tracker.result()}\n",
        "                #\"recall\": self.rec_tracker.result()}\n",
        "\n",
        "\n",
        "    @property\n",
        "    def metrics(self):\n",
        "        return [self.loss_tracker, self.acc_tracker, self.per_tracker ]#,self.rec_tracker]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "PCtAa4eeIWzf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b5bd40d-de61-4987-bd6c-1eaee3190290"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/.ipynb_checkpoints/swin-rgc-roco-medicat\n",
            "PRINT OUTPUT SHAPE  (None, 10, 10, 1280)\n",
            "top_model.shape (None, 1, 2048)\n",
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_7 (InputLayer)        [(None, 299, 299, 3)]        0         []                            \n",
            "                                                                                                  \n",
            " rescaling_2 (Rescaling)     (None, 299, 299, 3)          0         ['input_7[0][0]']             \n",
            "                                                                                                  \n",
            " normalization_1 (Normaliza  (None, 299, 299, 3)          7         ['rescaling_2[0][0]']         \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " rescaling_3 (Rescaling)     (None, 299, 299, 3)          0         ['normalization_1[0][0]']     \n",
            "                                                                                                  \n",
            " stem_conv_pad (ZeroPadding  (None, 301, 301, 3)          0         ['rescaling_3[0][0]']         \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " stem_conv (Conv2D)          (None, 150, 150, 32)         864       ['stem_conv_pad[0][0]']       \n",
            "                                                                                                  \n",
            " stem_bn (BatchNormalizatio  (None, 150, 150, 32)         128       ['stem_conv[0][0]']           \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " stem_activation (Activatio  (None, 150, 150, 32)         0         ['stem_bn[0][0]']             \n",
            " n)                                                                                               \n",
            "                                                                                                  \n",
            " block1a_dwconv (DepthwiseC  (None, 150, 150, 32)         288       ['stem_activation[0][0]']     \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block1a_bn (BatchNormaliza  (None, 150, 150, 32)         128       ['block1a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1a_activation (Activa  (None, 150, 150, 32)         0         ['block1a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block1a_se_squeeze (Global  (None, 32)                   0         ['block1a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block1a_se_reshape (Reshap  (None, 1, 1, 32)             0         ['block1a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block1a_se_reduce (Conv2D)  (None, 1, 1, 8)              264       ['block1a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block1a_se_expand (Conv2D)  (None, 1, 1, 32)             288       ['block1a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block1a_se_excite (Multipl  (None, 150, 150, 32)         0         ['block1a_activation[0][0]',  \n",
            " y)                                                                  'block1a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block1a_project_conv (Conv  (None, 150, 150, 16)         512       ['block1a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block1a_project_bn (BatchN  (None, 150, 150, 16)         64        ['block1a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2a_expand_conv (Conv2  (None, 150, 150, 96)         1536      ['block1a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_expand_bn (BatchNo  (None, 150, 150, 96)         384       ['block2a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2a_expand_activation   (None, 150, 150, 96)         0         ['block2a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2a_dwconv_pad (ZeroPa  (None, 151, 151, 96)         0         ['block2a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block2a_dwconv (DepthwiseC  (None, 75, 75, 96)           864       ['block2a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block2a_bn (BatchNormaliza  (None, 75, 75, 96)           384       ['block2a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2a_activation (Activa  (None, 75, 75, 96)           0         ['block2a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2a_se_squeeze (Global  (None, 96)                   0         ['block2a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2a_se_reshape (Reshap  (None, 1, 1, 96)             0         ['block2a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2a_se_reduce (Conv2D)  (None, 1, 1, 4)              388       ['block2a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2a_se_expand (Conv2D)  (None, 1, 1, 96)             480       ['block2a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2a_se_excite (Multipl  (None, 75, 75, 96)           0         ['block2a_activation[0][0]',  \n",
            " y)                                                                  'block2a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2a_project_conv (Conv  (None, 75, 75, 24)           2304      ['block2a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2a_project_bn (BatchN  (None, 75, 75, 24)           96        ['block2a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_expand_conv (Conv2  (None, 75, 75, 144)          3456      ['block2a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block2b_expand_bn (BatchNo  (None, 75, 75, 144)          576       ['block2b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block2b_expand_activation   (None, 75, 75, 144)          0         ['block2b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block2b_dwconv (DepthwiseC  (None, 75, 75, 144)          1296      ['block2b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block2b_bn (BatchNormaliza  (None, 75, 75, 144)          576       ['block2b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2b_activation (Activa  (None, 75, 75, 144)          0         ['block2b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block2b_se_squeeze (Global  (None, 144)                  0         ['block2b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block2b_se_reshape (Reshap  (None, 1, 1, 144)            0         ['block2b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block2b_se_reduce (Conv2D)  (None, 1, 1, 6)              870       ['block2b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block2b_se_expand (Conv2D)  (None, 1, 1, 144)            1008      ['block2b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block2b_se_excite (Multipl  (None, 75, 75, 144)          0         ['block2b_activation[0][0]',  \n",
            " y)                                                                  'block2b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block2b_project_conv (Conv  (None, 75, 75, 24)           3456      ['block2b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block2b_project_bn (BatchN  (None, 75, 75, 24)           96        ['block2b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block2b_drop (Dropout)      (None, 75, 75, 24)           0         ['block2b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block2b_add (Add)           (None, 75, 75, 24)           0         ['block2b_drop[0][0]',        \n",
            "                                                                     'block2a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3a_expand_conv (Conv2  (None, 75, 75, 144)          3456      ['block2b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_expand_bn (BatchNo  (None, 75, 75, 144)          576       ['block3a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3a_expand_activation   (None, 75, 75, 144)          0         ['block3a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3a_dwconv_pad (ZeroPa  (None, 79, 79, 144)          0         ['block3a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block3a_dwconv (DepthwiseC  (None, 38, 38, 144)          3600      ['block3a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block3a_bn (BatchNormaliza  (None, 38, 38, 144)          576       ['block3a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3a_activation (Activa  (None, 38, 38, 144)          0         ['block3a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3a_se_squeeze (Global  (None, 144)                  0         ['block3a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3a_se_reshape (Reshap  (None, 1, 1, 144)            0         ['block3a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3a_se_reduce (Conv2D)  (None, 1, 1, 6)              870       ['block3a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3a_se_expand (Conv2D)  (None, 1, 1, 144)            1008      ['block3a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3a_se_excite (Multipl  (None, 38, 38, 144)          0         ['block3a_activation[0][0]',  \n",
            " y)                                                                  'block3a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3a_project_conv (Conv  (None, 38, 38, 40)           5760      ['block3a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3a_project_bn (BatchN  (None, 38, 38, 40)           160       ['block3a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_expand_conv (Conv2  (None, 38, 38, 240)          9600      ['block3a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block3b_expand_bn (BatchNo  (None, 38, 38, 240)          960       ['block3b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block3b_expand_activation   (None, 38, 38, 240)          0         ['block3b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block3b_dwconv (DepthwiseC  (None, 38, 38, 240)          6000      ['block3b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block3b_bn (BatchNormaliza  (None, 38, 38, 240)          960       ['block3b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3b_activation (Activa  (None, 38, 38, 240)          0         ['block3b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block3b_se_squeeze (Global  (None, 240)                  0         ['block3b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block3b_se_reshape (Reshap  (None, 1, 1, 240)            0         ['block3b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block3b_se_reduce (Conv2D)  (None, 1, 1, 10)             2410      ['block3b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block3b_se_expand (Conv2D)  (None, 1, 1, 240)            2640      ['block3b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block3b_se_excite (Multipl  (None, 38, 38, 240)          0         ['block3b_activation[0][0]',  \n",
            " y)                                                                  'block3b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block3b_project_conv (Conv  (None, 38, 38, 40)           9600      ['block3b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block3b_project_bn (BatchN  (None, 38, 38, 40)           160       ['block3b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block3b_drop (Dropout)      (None, 38, 38, 40)           0         ['block3b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block3b_add (Add)           (None, 38, 38, 40)           0         ['block3b_drop[0][0]',        \n",
            "                                                                     'block3a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4a_expand_conv (Conv2  (None, 38, 38, 240)          9600      ['block3b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_expand_bn (BatchNo  (None, 38, 38, 240)          960       ['block4a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4a_expand_activation   (None, 38, 38, 240)          0         ['block4a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4a_dwconv_pad (ZeroPa  (None, 39, 39, 240)          0         ['block4a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block4a_dwconv (DepthwiseC  (None, 19, 19, 240)          2160      ['block4a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block4a_bn (BatchNormaliza  (None, 19, 19, 240)          960       ['block4a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4a_activation (Activa  (None, 19, 19, 240)          0         ['block4a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4a_se_squeeze (Global  (None, 240)                  0         ['block4a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4a_se_reshape (Reshap  (None, 1, 1, 240)            0         ['block4a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4a_se_reduce (Conv2D)  (None, 1, 1, 10)             2410      ['block4a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4a_se_expand (Conv2D)  (None, 1, 1, 240)            2640      ['block4a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4a_se_excite (Multipl  (None, 19, 19, 240)          0         ['block4a_activation[0][0]',  \n",
            " y)                                                                  'block4a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4a_project_conv (Conv  (None, 19, 19, 80)           19200     ['block4a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4a_project_bn (BatchN  (None, 19, 19, 80)           320       ['block4a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_expand_conv (Conv2  (None, 19, 19, 480)          38400     ['block4a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4b_expand_bn (BatchNo  (None, 19, 19, 480)          1920      ['block4b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4b_expand_activation   (None, 19, 19, 480)          0         ['block4b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4b_dwconv (DepthwiseC  (None, 19, 19, 480)          4320      ['block4b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4b_bn (BatchNormaliza  (None, 19, 19, 480)          1920      ['block4b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4b_activation (Activa  (None, 19, 19, 480)          0         ['block4b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4b_se_squeeze (Global  (None, 480)                  0         ['block4b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4b_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block4b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4b_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block4b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4b_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block4b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4b_se_excite (Multipl  (None, 19, 19, 480)          0         ['block4b_activation[0][0]',  \n",
            " y)                                                                  'block4b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4b_project_conv (Conv  (None, 19, 19, 80)           38400     ['block4b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4b_project_bn (BatchN  (None, 19, 19, 80)           320       ['block4b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4b_drop (Dropout)      (None, 19, 19, 80)           0         ['block4b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4b_add (Add)           (None, 19, 19, 80)           0         ['block4b_drop[0][0]',        \n",
            "                                                                     'block4a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_expand_conv (Conv2  (None, 19, 19, 480)          38400     ['block4b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block4c_expand_bn (BatchNo  (None, 19, 19, 480)          1920      ['block4c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block4c_expand_activation   (None, 19, 19, 480)          0         ['block4c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block4c_dwconv (DepthwiseC  (None, 19, 19, 480)          4320      ['block4c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block4c_bn (BatchNormaliza  (None, 19, 19, 480)          1920      ['block4c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4c_activation (Activa  (None, 19, 19, 480)          0         ['block4c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block4c_se_squeeze (Global  (None, 480)                  0         ['block4c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block4c_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block4c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block4c_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block4c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block4c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block4c_se_excite (Multipl  (None, 19, 19, 480)          0         ['block4c_activation[0][0]',  \n",
            " y)                                                                  'block4c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block4c_project_conv (Conv  (None, 19, 19, 80)           38400     ['block4c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block4c_project_bn (BatchN  (None, 19, 19, 80)           320       ['block4c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block4c_drop (Dropout)      (None, 19, 19, 80)           0         ['block4c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block4c_add (Add)           (None, 19, 19, 80)           0         ['block4c_drop[0][0]',        \n",
            "                                                                     'block4b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block5a_expand_conv (Conv2  (None, 19, 19, 480)          38400     ['block4c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5a_expand_bn (BatchNo  (None, 19, 19, 480)          1920      ['block5a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5a_expand_activation   (None, 19, 19, 480)          0         ['block5a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5a_dwconv (DepthwiseC  (None, 19, 19, 480)          12000     ['block5a_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5a_bn (BatchNormaliza  (None, 19, 19, 480)          1920      ['block5a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5a_activation (Activa  (None, 19, 19, 480)          0         ['block5a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5a_se_squeeze (Global  (None, 480)                  0         ['block5a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5a_se_reshape (Reshap  (None, 1, 1, 480)            0         ['block5a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5a_se_reduce (Conv2D)  (None, 1, 1, 20)             9620      ['block5a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5a_se_expand (Conv2D)  (None, 1, 1, 480)            10080     ['block5a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5a_se_excite (Multipl  (None, 19, 19, 480)          0         ['block5a_activation[0][0]',  \n",
            " y)                                                                  'block5a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5a_project_conv (Conv  (None, 19, 19, 112)          53760     ['block5a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5a_project_bn (BatchN  (None, 19, 19, 112)          448       ['block5a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_expand_conv (Conv2  (None, 19, 19, 672)          75264     ['block5a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5b_expand_bn (BatchNo  (None, 19, 19, 672)          2688      ['block5b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5b_expand_activation   (None, 19, 19, 672)          0         ['block5b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5b_dwconv (DepthwiseC  (None, 19, 19, 672)          16800     ['block5b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5b_bn (BatchNormaliza  (None, 19, 19, 672)          2688      ['block5b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5b_activation (Activa  (None, 19, 19, 672)          0         ['block5b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5b_se_squeeze (Global  (None, 672)                  0         ['block5b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5b_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block5b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5b_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block5b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5b_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block5b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5b_se_excite (Multipl  (None, 19, 19, 672)          0         ['block5b_activation[0][0]',  \n",
            " y)                                                                  'block5b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5b_project_conv (Conv  (None, 19, 19, 112)          75264     ['block5b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5b_project_bn (BatchN  (None, 19, 19, 112)          448       ['block5b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5b_drop (Dropout)      (None, 19, 19, 112)          0         ['block5b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5b_add (Add)           (None, 19, 19, 112)          0         ['block5b_drop[0][0]',        \n",
            "                                                                     'block5a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_expand_conv (Conv2  (None, 19, 19, 672)          75264     ['block5b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block5c_expand_bn (BatchNo  (None, 19, 19, 672)          2688      ['block5c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block5c_expand_activation   (None, 19, 19, 672)          0         ['block5c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block5c_dwconv (DepthwiseC  (None, 19, 19, 672)          16800     ['block5c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block5c_bn (BatchNormaliza  (None, 19, 19, 672)          2688      ['block5c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5c_activation (Activa  (None, 19, 19, 672)          0         ['block5c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block5c_se_squeeze (Global  (None, 672)                  0         ['block5c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block5c_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block5c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block5c_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block5c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block5c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block5c_se_excite (Multipl  (None, 19, 19, 672)          0         ['block5c_activation[0][0]',  \n",
            " y)                                                                  'block5c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block5c_project_conv (Conv  (None, 19, 19, 112)          75264     ['block5c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block5c_project_bn (BatchN  (None, 19, 19, 112)          448       ['block5c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block5c_drop (Dropout)      (None, 19, 19, 112)          0         ['block5c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block5c_add (Add)           (None, 19, 19, 112)          0         ['block5c_drop[0][0]',        \n",
            "                                                                     'block5b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6a_expand_conv (Conv2  (None, 19, 19, 672)          75264     ['block5c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_expand_bn (BatchNo  (None, 19, 19, 672)          2688      ['block6a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6a_expand_activation   (None, 19, 19, 672)          0         ['block6a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6a_dwconv_pad (ZeroPa  (None, 23, 23, 672)          0         ['block6a_expand_activation[0]\n",
            " dding2D)                                                           [0]']                         \n",
            "                                                                                                  \n",
            " block6a_dwconv (DepthwiseC  (None, 10, 10, 672)          16800     ['block6a_dwconv_pad[0][0]']  \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block6a_bn (BatchNormaliza  (None, 10, 10, 672)          2688      ['block6a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6a_activation (Activa  (None, 10, 10, 672)          0         ['block6a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6a_se_squeeze (Global  (None, 672)                  0         ['block6a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6a_se_reshape (Reshap  (None, 1, 1, 672)            0         ['block6a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6a_se_reduce (Conv2D)  (None, 1, 1, 28)             18844     ['block6a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6a_se_expand (Conv2D)  (None, 1, 1, 672)            19488     ['block6a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6a_se_excite (Multipl  (None, 10, 10, 672)          0         ['block6a_activation[0][0]',  \n",
            " y)                                                                  'block6a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6a_project_conv (Conv  (None, 10, 10, 192)          129024    ['block6a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6a_project_bn (BatchN  (None, 10, 10, 192)          768       ['block6a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_expand_conv (Conv2  (None, 10, 10, 1152)         221184    ['block6a_project_bn[0][0]']  \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6b_expand_bn (BatchNo  (None, 10, 10, 1152)         4608      ['block6b_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6b_expand_activation   (None, 10, 10, 1152)         0         ['block6b_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6b_dwconv (DepthwiseC  (None, 10, 10, 1152)         28800     ['block6b_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6b_bn (BatchNormaliza  (None, 10, 10, 1152)         4608      ['block6b_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6b_activation (Activa  (None, 10, 10, 1152)         0         ['block6b_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6b_se_squeeze (Global  (None, 1152)                 0         ['block6b_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6b_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6b_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6b_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6b_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6b_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6b_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6b_se_excite (Multipl  (None, 10, 10, 1152)         0         ['block6b_activation[0][0]',  \n",
            " y)                                                                  'block6b_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6b_project_conv (Conv  (None, 10, 10, 192)          221184    ['block6b_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6b_project_bn (BatchN  (None, 10, 10, 192)          768       ['block6b_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6b_drop (Dropout)      (None, 10, 10, 192)          0         ['block6b_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6b_add (Add)           (None, 10, 10, 192)          0         ['block6b_drop[0][0]',        \n",
            "                                                                     'block6a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_expand_conv (Conv2  (None, 10, 10, 1152)         221184    ['block6b_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6c_expand_bn (BatchNo  (None, 10, 10, 1152)         4608      ['block6c_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6c_expand_activation   (None, 10, 10, 1152)         0         ['block6c_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6c_dwconv (DepthwiseC  (None, 10, 10, 1152)         28800     ['block6c_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6c_bn (BatchNormaliza  (None, 10, 10, 1152)         4608      ['block6c_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6c_activation (Activa  (None, 10, 10, 1152)         0         ['block6c_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6c_se_squeeze (Global  (None, 1152)                 0         ['block6c_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6c_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6c_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6c_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6c_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6c_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6c_se_excite (Multipl  (None, 10, 10, 1152)         0         ['block6c_activation[0][0]',  \n",
            " y)                                                                  'block6c_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6c_project_conv (Conv  (None, 10, 10, 192)          221184    ['block6c_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6c_project_bn (BatchN  (None, 10, 10, 192)          768       ['block6c_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6c_drop (Dropout)      (None, 10, 10, 192)          0         ['block6c_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6c_add (Add)           (None, 10, 10, 192)          0         ['block6c_drop[0][0]',        \n",
            "                                                                     'block6b_add[0][0]']         \n",
            "                                                                                                  \n",
            " block6d_expand_conv (Conv2  (None, 10, 10, 1152)         221184    ['block6c_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block6d_expand_bn (BatchNo  (None, 10, 10, 1152)         4608      ['block6d_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block6d_expand_activation   (None, 10, 10, 1152)         0         ['block6d_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block6d_dwconv (DepthwiseC  (None, 10, 10, 1152)         28800     ['block6d_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block6d_bn (BatchNormaliza  (None, 10, 10, 1152)         4608      ['block6d_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6d_activation (Activa  (None, 10, 10, 1152)         0         ['block6d_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block6d_se_squeeze (Global  (None, 1152)                 0         ['block6d_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block6d_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block6d_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block6d_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block6d_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block6d_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block6d_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block6d_se_excite (Multipl  (None, 10, 10, 1152)         0         ['block6d_activation[0][0]',  \n",
            " y)                                                                  'block6d_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block6d_project_conv (Conv  (None, 10, 10, 192)          221184    ['block6d_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block6d_project_bn (BatchN  (None, 10, 10, 192)          768       ['block6d_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " block6d_drop (Dropout)      (None, 10, 10, 192)          0         ['block6d_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " block6d_add (Add)           (None, 10, 10, 192)          0         ['block6d_drop[0][0]',        \n",
            "                                                                     'block6c_add[0][0]']         \n",
            "                                                                                                  \n",
            " block7a_expand_conv (Conv2  (None, 10, 10, 1152)         221184    ['block6d_add[0][0]']         \n",
            " D)                                                                                               \n",
            "                                                                                                  \n",
            " block7a_expand_bn (BatchNo  (None, 10, 10, 1152)         4608      ['block7a_expand_conv[0][0]'] \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block7a_expand_activation   (None, 10, 10, 1152)         0         ['block7a_expand_bn[0][0]']   \n",
            " (Activation)                                                                                     \n",
            "                                                                                                  \n",
            " block7a_dwconv (DepthwiseC  (None, 10, 10, 1152)         10368     ['block7a_expand_activation[0]\n",
            " onv2D)                                                             [0]']                         \n",
            "                                                                                                  \n",
            " block7a_bn (BatchNormaliza  (None, 10, 10, 1152)         4608      ['block7a_dwconv[0][0]']      \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7a_activation (Activa  (None, 10, 10, 1152)         0         ['block7a_bn[0][0]']          \n",
            " tion)                                                                                            \n",
            "                                                                                                  \n",
            " block7a_se_squeeze (Global  (None, 1152)                 0         ['block7a_activation[0][0]']  \n",
            " AveragePooling2D)                                                                                \n",
            "                                                                                                  \n",
            " block7a_se_reshape (Reshap  (None, 1, 1, 1152)           0         ['block7a_se_squeeze[0][0]']  \n",
            " e)                                                                                               \n",
            "                                                                                                  \n",
            " block7a_se_reduce (Conv2D)  (None, 1, 1, 48)             55344     ['block7a_se_reshape[0][0]']  \n",
            "                                                                                                  \n",
            " block7a_se_expand (Conv2D)  (None, 1, 1, 1152)           56448     ['block7a_se_reduce[0][0]']   \n",
            "                                                                                                  \n",
            " block7a_se_excite (Multipl  (None, 10, 10, 1152)         0         ['block7a_activation[0][0]',  \n",
            " y)                                                                  'block7a_se_expand[0][0]']   \n",
            "                                                                                                  \n",
            " block7a_project_conv (Conv  (None, 10, 10, 320)          368640    ['block7a_se_excite[0][0]']   \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " block7a_project_bn (BatchN  (None, 10, 10, 320)          1280      ['block7a_project_conv[0][0]']\n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " top_conv (Conv2D)           (None, 10, 10, 1280)         409600    ['block7a_project_bn[0][0]']  \n",
            "                                                                                                  \n",
            " top_bn (BatchNormalization  (None, 10, 10, 1280)         5120      ['top_conv[0][0]']            \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " top_activation (Activation  (None, 10, 10, 1280)         0         ['top_bn[0][0]']              \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " global_average_pooling2d_1  (None, 1280)                 0         ['top_activation[0][0]']      \n",
            "  (GlobalAveragePooling2D)                                                                        \n",
            "                                                                                                  \n",
            " dense_11 (Dense)            (None, 1024)                 1311744   ['global_average_pooling2d_1[0\n",
            "                                                                    ][0]']                        \n",
            "                                                                                                  \n",
            " dense_12 (Dense)            (None, 512)                  524800    ['dense_11[0][0]']            \n",
            "                                                                                                  \n",
            " reshape_3 (Reshape)         (None, 1, 512)               0         ['dense_12[0][0]']            \n",
            "                                                                                                  \n",
            " gru_1 (GRU)                 (None, 128)                  246528    ['reshape_3[0][0]']           \n",
            "                                                                                                  \n",
            " reshape_4 (Reshape)         (None, 1, 128)               0         ['gru_1[0][0]']               \n",
            "                                                                                                  \n",
            " dense_13 (Dense)            (None, 1, 2048)              264192    ['reshape_4[0][0]']           \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)         (None, 1, 2048)              0         ['dense_13[0][0]']            \n",
            "                                                                                                  \n",
            " reshape_5 (Reshape)         (None, 1, 2048)              0         ['dropout_5[0][0]']           \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 6396835 (24.40 MB)\n",
            "Trainable params: 2347264 (8.95 MB)\n",
            "Non-trainable params: 4049571 (15.45 MB)\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "#tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', repo_type='tf-hub')\n",
        "encoder = MedicalTransformerEncoderLayer(EMBEDDING_DIM, 8)\n",
        "decoder = MedicalTransformerDecoderLayer(EMBEDDING_DIM, UNITS, 8)\n",
        "\n",
        "cnn_model = CNN_Encoder()\n",
        "Medicalcaption_model = MedicalImageCaptioningModel(\n",
        "    cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,\n",
        ")\n",
        "\n",
        "#cnn_mode.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MDR7LoddZXB2"
      },
      "outputs": [],
      "source": [
        "class LRSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):\n",
        "    def __init__(self, post_warmup_learning_rate, warmup_steps):\n",
        "        super().__init__()\n",
        "        self.post_warmup_learning_rate = post_warmup_learning_rate\n",
        "        self.warmup_steps = warmup_steps\n",
        "\n",
        "    def __call__(self, step):\n",
        "        global_step = tf.cast(step, tf.float32)\n",
        "        warmup_steps = tf.cast(self.warmup_steps, tf.float32)\n",
        "        warmup_progress = global_step / warmup_steps\n",
        "        warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress\n",
        "        return tf.cond(\n",
        "            global_step < warmup_steps,\n",
        "            lambda: warmup_learning_rate,\n",
        "            lambda: self.post_warmup_learning_rate,\n",
        "        )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bayNssgNX6QN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "befb0331-c7e1-4ce7-a35e-eabf48a58e8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n",
        "cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(\n",
        "    from_logits=False, reduction=\"none\" #change\n",
        ")\n",
        "\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)\n",
        "EPOCHS=65\n",
        "num_train_steps = len(train_dataset) * EPOCHS\n",
        "num_warmup_steps = num_train_steps // 15\n",
        "\n",
        "#lr_schedule = LRSchedule(post_warmup_learning_rate=1e-4, warmup_steps=num_warmup_steps)\n",
        "lr_schedule = LRSchedule(post_warmup_learning_rate=0.001, warmup_steps=1000)\n",
        "\n",
        "Medicalcaption_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=lr_schedule),\n",
        "    loss=cross_entropy,\n",
        "    metrics=['accuracy']\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "1RYo-MRVYn49",
        "outputId": "12c1992c-979a-4b37-cfd1-93bdb757626d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n",
            "Epoch 1/5\n",
            "461/461 [==============================] - 3001s 6s/step - loss: 6.0767 - accuracy: 0.1506 - precision: 0.1266 - val_loss: 4.5653 - val_accuracy: 0.2741 - val_precision: 0.2149\n",
            "Epoch 2/5\n",
            "461/461 [==============================] - 304s 650ms/step - loss: 3.8929 - accuracy: 0.3326 - precision: 0.2488 - val_loss: 3.5381 - val_accuracy: 0.3769 - val_precision: 0.2734\n",
            "Epoch 3/5\n",
            "461/461 [==============================] - 305s 651ms/step - loss: 3.0669 - accuracy: 0.4313 - precision: 0.3006 - val_loss: 3.0951 - val_accuracy: 0.4545 - val_precision: 0.3119\n",
            "Epoch 4/5\n",
            "461/461 [==============================] - 300s 642ms/step - loss: 2.4902 - accuracy: 0.5143 - precision: 0.3390 - val_loss: 2.8190 - val_accuracy: 0.5054 - val_precision: 0.3349\n",
            "Epoch 5/5\n",
            "461/461 [==============================] - 299s 636ms/step - loss: 2.1068 - accuracy: 0.5726 - precision: 0.3637 - val_loss: 2.6814 - val_accuracy: 0.5348 - val_precision: 0.3478\n",
            "<bound method Callback.on_predict_batch_begin of <keras.src.callbacks.History object at 0x7f58bc17b790>>\n",
            "<bound method Callback.on_predict_begin of <keras.src.callbacks.History object at 0x7f58bc17b790>>\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images\n",
        "history_list = []\n",
        "#for learning_rate in learning_rates:\n",
        "\n",
        "history = Medicalcaption_model.fit(train_dataset,epochs=5,validation_data=test_dataset)\n",
        "                            #,callbacks=[early_stopping])\n",
        "#history_list.append(history.history)\n",
        "\n",
        "#history_list.append(history.history)\n",
        "\n",
        "print(history.on_predict_batch_begin)\n",
        "print(history.on_predict_begin)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install matplotlib tensorflow"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "OQF-HZUAjhDh",
        "outputId": "7c8355cf-ac6d-41bd-da0a-8b96764f40cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
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            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.53.0)\n",
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            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.25.2)\n",
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            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
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            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2024.6.2)\n",
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            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.6.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNs3ia3qjxCd",
        "outputId": "d626618a-7d10-4f04-e20a-8692813e81bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'precision', 'val_loss', 'val_accuracy', 'val_precision'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bnh613u8M9sb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d6306822-144f-4715-dd2a-d1d266e6fc94"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss=history.history['loss']\n",
        "val_loss=history.history['val_loss']\n",
        "\n",
        "perc = history.history.get('precision')\n",
        "val_precision=history.history.get('val_precision')\n",
        "\n",
        "#print('val_precision',val_precision)\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "#epochs=1\n",
        "fig1 = plt.figure()  # Create the first figure\n",
        "plt.figure(fig1.number)\n",
        "plt.plot(epochs, acc, 'r',label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b',label='Testing accuracy')\n",
        "plt.title('Training and Testing accuracy')\n",
        "\n",
        "plt.xlabel(\"# Epochs\")\n",
        "plt.ylabel(\"Accyracy\")\n",
        "plt.legend(loc=0)\n",
        "\n",
        "fig2 = plt.figure()\n",
        "plt.figure(fig2.number)\n",
        "\n",
        "plt.plot(epochs, loss, 'r',label='Training Loss')\n",
        "plt.plot(epochs, val_loss, 'b',label='Testing Loss')\n",
        "plt.title('Training and Testing Loss')\n",
        "plt.xlabel(\"# Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend(loc=0)\n",
        "\n",
        "fig3 = plt.figure()  # Create the first figure\n",
        "plt.figure(fig3.number)\n",
        "\n",
        "\n",
        "plt.plot(epochs, perc, 'r',label='Training Precision')\n",
        "plt.plot(epochs, val_precision, 'b',label='Testing Precision')\n",
        "plt.title('Training and Testing Precision')\n",
        "plt.xlabel(\"# Epochs\")\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.legend(loc=0)\n",
        "\n",
        "\n",
        "plt.figure(fig1.number)\n",
        "plt.figure(fig2.number)\n",
        "plt.figure(fig3.number)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from tensorflow.keras.utils import plot_model\n",
        "#plot_model(cnn_model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "id": "8EmgRyaIy1Xx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ErlQQICtj_g"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def load_image_from_path(img_path):\n",
        "    img = tf.io.read_file(img_path)\n",
        "    img = tf.io.decode_jpeg(img, channels=3)\n",
        "    img = tf.keras.layers.Resizing(299, 299)(img)\n",
        "    img = img / 255.\n",
        "    return img\n",
        "\n",
        "\n",
        "def generate_caption(img_path):\n",
        "\n",
        "    #tf.random.set_seed(None)\n",
        "    #np.random.seed(None)\n",
        "    # Assuming load_image_from_path function loads and preprocesses the image\n",
        "    img = load_image_from_path(img_path)  # You need to define load_image_from_path(img_path) function\n",
        "    img = tf.expand_dims(img, axis=0)\n",
        "    img_embed = caption_model.cnn_model(img)\n",
        "\n",
        "\n",
        "    img_encoded = caption_model.encoder(img_embed, training=True)\n",
        "    #print('ENCODEeeeeee ', img_encoded)\n",
        "\n",
        "\n",
        "    y_inp = '[start]'\n",
        "\n",
        "    for i in range(MAX_LENGTH-1):\n",
        "\n",
        "        tokenized = tokenizer([y_inp])[:, :-1]\n",
        "\n",
        "        mask = tf.cast(tokenized != 0, tf.int32)\n",
        "\n",
        "        pred = caption_model.decoder(\n",
        "            tokenized, img_encoded, training=False, mask=mask)\n",
        "\n",
        "        pred_idx = np.argmax(pred[0, i, :])\n",
        "\n",
        "\n",
        "        pred_word = idx2word(pred_idx).numpy().decode('utf-8')\n",
        "\n",
        "\n",
        "        # Generate predictions for the next word\n",
        "        pred = caption_model.decoder(tokenized, img_encoded, training=False, mask=mask)\n",
        "        pred_idx = np.argmax(pred[0, i, :])\n",
        "\n",
        "        pred_word = idx2word(pred_idx).numpy().decode('utf-8')\n",
        "\n",
        "\n",
        "        if pred_word == '[end]':\n",
        "            break\n",
        "\n",
        "        y_inp += ' ' + pred_word\n",
        "\n",
        "    # Remove the '[start]' token from the beginning of the caption\n",
        "    y_inp = y_inp.replace('[start] ', '')\n",
        "\n",
        "    return y_inp\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "27_bJe_M1Drr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "d2cb8df6-9b58-4708-e1dc-248c394f2724"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "y_true::  ,Axial CT image through the mid-thoracic region demonstrates \n",
            " no evidence of pulmonary embolism or thoracic aortic pathology. \n",
            "  In the upper outer quadrant of the left breast is a dense angular mass.\n",
            "random_image synpic28473.jpg\n",
            "\n",
            "detected caption ::\n",
            "axial ct image of the abdomen at the level of the gallbladder demonstrates a nodular appearance\n",
            " to the liver splenomegaly and dilated splenorenal variceal veins one segment demonstrates calcif\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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BmARGIZBAAgkkEJPAKAQSSCCBBGISGIVAAgkkkEBMAqMQSCCBBBKISWAUAgkkkEACMQmMQiCBBBJIICaBUQgkkEACCcQkMAqBBBJIIIGYBEYhkEACCSQQk8AoBBJIIIEEYhIYhUACCSSQQEwCoxBIIIEEEohJYBQCCSSQQAIxCYxCIIEEEkggJoFRCCSQQAIJxCQwCoEEEkgggZgERiGQQAIJJBCT6O96AIEE8nESCoXs3/1+/yM/7/f7CoVCCoVC6vf79v/hzyPhcNh+zt8fd49fN67n/WwggXzVJDAKgXxhEg6HBxS2V8T+d71e7yNKHwXe6/UGrul/N3xd6dmKHcPBmLrd7kc+4w2K///w/YbHGkggXzcJ9Z9zhfsNE0ggnyTDnjSKVZIp1WEP3382FAqZMeD/kUjEroFBGZZerzfw/W63a9fxBiYWiw38jnE8K2oYji4+yWAFEsiXXZ5H3QdGIZAvVJ6lRH2k0Ov1FI/HlUqlNDo6qkQioWg0qkgkolAopGg0qmQyqUQioVgspnA4rHg8bv/mOt1uV4eHh/bdVqulVqulTqejfr+vo6MjtVothcNhHR4e6ujoSEdHR9rf39fe3p5ardaA8vfGCmM0DD0FEshXTQKjEMjvRLyyRrkmEgml02nlcjnl83ml02nFYjFFo1HFYjHF43ElEgnF43FFIhEdHR1ZVOE98n6/b0oaGCgSiSgajZoRCYVCOjw8VK/XU6/XMwPS6XSUTCbV7XbN6+92u2ZAEomE9vb2VK/XVa1Wtb29rVqt9hFjEBiHQL6qEhiFQH4j+aSkrf/MyMiIEomEIpGIEomEksmkstmsxsfHlcvlFI/HPxIddLtdHRwcWBQBnINBOTw8VCgU0tHRkSl/voeEw2FFIhH1ej0dHh4qHo/b9Tw8RdTRarWUTCbtOxiRaDSqcDisaPQ4xZZKpTQyMmKRBFHGzs6OdnZ2VK1W1el01Gq11G63dXR09AW+hUAC+fwkMAqBmHjcnHeJYpQ0oDzxpH2CFYnFYpqYmFAul1M6nZYkFQoFpdNpRaNRJRIJ9Xo9HR0d2b0ODg7U6XRMeXMv7kO+gAjAC59HcXc6HYsi/HiBjyKRiD3PyMiI4vG4RRytVusjzCWMAWPo9XoaHR01w0QUkkwmBwxKp9NRs9nUzs6Oms2marWayuWyGo3GMxPZfi593sPDX7ynj0uGBxLIbyqBUQhE0sdTKL2BkAZpmmD3kUhEY2NjmpycVLFYVDgcViqVUiwWM2XdbDZN4e3u7ioajX6EHdTv99XpdBQKhZRMJnV4eGj3TCQSSiQSOjo6ss8A6wAp9Xo9RaNR7e/vSxpMWKO4PWTEOFGw0WjUjFM0Gh0wLBiSSCRiEUe/3x+IVhKJhEVEsVjM/pAD6XQ62t3dtRzF8vKy1tfX1el01O12dXR09IkbkqiHqCOApgL5IiQwCoEMyLM8cY+PJ5NJ5XI5jY6OKpPJaGZmRsVi0ZK1QDAoWjzpg4MDg45Qgii5fr+v/f19+xn386ygkZERRaNR7e3tqd1um9dOtDJMayVvgPLn3ihyohS+Gw6HNTIyYkYDA+EjC/59dHRkkYOfM5909pENz0E00ev1lMvllEgkdHBwoN3dXa2vr2tra0u7u7sGPT2r1oLowF83kEA+TwmMQiAmw8rNK73x8XFNT09bAjgcDptnC/bf6XTMM04mk2o2m+bZI5FIRCMjI+r3+2q325KO4abDw0P7PtdLJBKmtIk6gIaAaFKplOUUYrGYedGxWMygI+nEwDBuPHOinXA4rFgsZmONx+M6PDwcyFV0Oh2777Di95GPr13wkJM3OBiHUCikXC6ncDisg4MD9Xo97e3tqVwua3V1VZubm5YnGU6eBxLIFyGBUQhE0mA+IZ1OK5VKKZ1Oa3FxUZlMRvF4XJ1OR/V6fYDrHwqFDG5pt9uKxWLK5XLK5XJqtVra2toyb57vJZNJhcNhtVotU5LD+QPGwu9QqBgJlL9fcz7KwZB4KiqGBaOC58/1MAqHh4cWmWAYyUlIJ+wmvgtM5Y2qj0T4/TCF1ddmSFIymTQWFBDU4eGhNjc39ejRI1WrVe3t7dk8BsnrQL4IeR51H1Q0f8Xlk/IF/HxkZETFYlGFQkFTU1NKp9MGlezt7anRaAwoO+mEq48XHAqFFI/HLdkKhDOsJL2C5zPg/tJJhOIpp8PJbOoNhseD1++ZRSSiySUgHoLiM/z/4OBgIH/ilbmHcPzzeYPGc2JMYrGYJFnuAoUOmwnjSLI9EolYtDM+Pq75+Xm1222trq5qe3tbW1tbqlarA+MYjiB+XauNoBVHIJ9VgkjhKybPqgT21EsglFgspnw+r7NnzyqZTCqfz5vHjhHg+9BBfTK02+0OsHPA8kdHRy2PsLe3ZxEA1E7pWKnzPRQtcI7/vVd21BN4mMc/M54998CoMU5yAd6j51n4HPCQn0dfHU2ymrn09/fz4j9HYppnjEajA5XTjBOKK2PzkVEqlVI8HrfPNBoN7e3t6eHDh9ra2rJrP6sS24/NV4IHEsizJICPvobi3wOeK4oqGo0ql8tpampKp06d0tjYmNrtthqNhtrttnq9nlFGJZkhGOb/+5oBvGAgmX6/b9AH9QceasGwePiEe/iaBFg//hnIAfhxeYPgIw7GzHMDG/lx+LqHbrdrxWsoZQ8VeYjLG5tIJGJj8vckeup2u8ZW8obHbysgI+aG33sDQmU3PxsZGbF53tjY0MrKiiqVivb39wcqxJ9FGw4kkI+TwCh8DcX3/wFeSSQSOnPmjIrFosbGxhSJRNTpdCxxizJHoUEDPTg4GODuc20Pm/CzRCJhcAlKeHR0dIC9hEeLMvdGAfgJI8BnUIQ+0ezrBjxEQ4KYa0iya/I5DIKHdXi+drutaDRqxozf8YdxegaVdAJFUSkdCoUGlLNPOPseTYwJ6Msb4EgkomQyOcA8AqojKZ9MJjUyMmLGudVqaXl5WY8ePdLe3p5isdgANZd7BhLIx0lgFL6GgnJJJBIqFotaWlpSoVAwI7G/v69Op2MeMCwgsHTpWHGMjIyYAhwuWAO7b7Vads9haAbB+/UwjGcD8Zlhw8D3qEPA+z48PLRCMWAZlPno6Kh5x9IJPIMX7yMe4CuvmDGUiUTCDB/f49+SrBbCV1bTb4lcwv7+vhkdlLJXzj5qIepgHBickZGRAWquV+zkPXw9xOjoqLLZrNrtth48eKDl5WXt7u5arkIKIKRAPlkCo/A1lLGxMU1MTOjUqVMaHx9Xv99Xs9nU3t6eRQQUWsViMTWbTe3v7+vg4GDAu+fP/v6+QRYoE5gy+/v7xihCMVOhjOHwkYGniXoMnjoHDI4vRqMXETANVdGSjLaKAYnFYkZTxTCGw2E1Gg01m02LJlDgkgYiEyAwogLYSIx1f39fvV5PzWbTIp29vT1jO2FkJBmLiYhKOqkQZ06YLwwiORFfROfzHMwRkRj39vBZPB63OpJ4PK7NzU2tra1pbW1N1WrVjGZgGAJ5lgRG4SsinhoJBu5pmeFwWIVCQWfPntXMzIx5+F7ZSjKP0Sd2faGZx+CBHogePJuHWgSUNffb39//CAsI5cv1Ue6+PYVPhCeTSUsWj46OGpUU+ISOqP1+35hOeM18T9IA1FOr1YxOm81m7R58Jx6PK51OD0RZ3W5X9Xpd3W5XmUzGDM3R0ZF2d3dtXvf29ixfARSHeK8co+qjA5+fGO7/xLbjs8MFfz6C4Q+GhffDNUdGRtTpdLS+vq779++rWq0OfIe//X2DHMTvpwRG4UsowxW60gl0gCeKMgMiWlxc1NTUlDVm47s+WYriBO8+ODgwpY3n6LuKUk+AkvPQj28Oh/hWDUBJw5ARMAlRCu0rUHR+LLCYeJa9vT0dHBwolUoplUqp3W4PRCMoRLx5Ip9Go6FqtWrGrtvtGpvHz3epVFIulxuAZHZ3d3VwcKBMJjOQL2BOKWpjjsD7+cMYiIYwoB6KQhkzhz7vwO887ZfPAafBxCJ6Ojw8tG6zwFiSlM1mlc1mdXh4qMePH2tlZUXb29tmnFkb8XjcvhPI7588j7oP6hR+i+JZI4hXjBiEXC6nubk5zc3NaXJyUs1mU/V63TxVDzv4Te+rcL3xwTj4ZnG+xYMku5b3VIkW+P0w7x4mD0aEqACYJ5lMDjy/p5GmUinrNDpcOYyBw3ummhkmFXkToJ52u21GTpJ2d3cVi8UsB0HrDM5tCIfD9nkSthjIg4ODAYMI3MY1eGbpWEHv7+9bvyOS3J566nMMGHEiD56Z+0snhgMngfmWTgxMNBpVKpWyAkFJ1sF1dHRUp06d0rlz57S+vq4PP/xQm5ubljwHEmR8SFDXEAgSRAq/RfFK2lf28icWi+nMmTM6c+aMJUOr1aoajYYpM1o5SyesGK6NNy+dcOF9O2kPbwxjzz7xi5FJJBJmKGDzQN/MZrMDh92gMIFmMEq0d/CJZJ+b6Ha7A9HE8AE7RAfValXValXtdtuiJcbl2U5g9v1+X9ls1s5IiEajKhQKGhsbUyaTMWUP5IV3TnIdBU/1Mfg+c0W0xL0xKLu7uwalYTSouH4W5Acc59cH1yfXQ8RA1DQ6OqpCoaCDgwOjGgOZsQZCoZDGxsaUy+W0urqqd999V9VqdSAHNEydDeTrLwF89CUTlIhP7MbjcWWzWZ05c0anTp1SPB7X7u6uKSGvuCn68jkDvH06j3JGATkDhCiD+3vmjIdoMCAeFspkMgPdQUdHRzU6Omq1ClwfeEKSsZpQgvQD4tp4+oyVZ6LdNQp7f39ftVpNOzs72t3dtbF6do9fm4yJ+clkMtaQLxKJKJPJqFgs2ilvvuWEN7CdTscgLYx2Npu13zGHGGaiF76Pcm+32wY1EVX4amiMkWdNkZBn/j2Vlefw0QVGnO+R3O52uxodHVUqldLY2Jjq9bpu3ryptbU1g764RpCY/v2QwCh8ycSH6HhyZ8+e1eTkpEZGRiRJtVpNrVZrAI5BqfhkJVAL18I7B3NHqXjWENAGjet85S1KGM+dxnixWEyZTEaZTMYUyMjIiCKRiLF0pEGWUTwet15JGAKgm0gkor29PUkn0QzwEIaEKKLVaqler9sZBTxnLBbTyMiIeeMYWZ8LIJKgr5N0Yrh4pmw2q3Q6bVAS0QlKGKUOawm2EVEUtQQYkOHurR7CowMsp7z5vI8kiwB9kR7wka9h8DRfEvHUTPBd/nQ6HaMAj4yMWLJ9d3dXd+/e1aNHjz5SPR7I11sCo/AFi+9z80mYrPfGaD9x8eJFTU9PG+ZNwZJnBNH0DTzYe/dALb7lAhCQTyziOfsKYo99k+CEhopnCS8e9o5/ZvB3z3YavieQCXz84cphX2zGM3OSWbfb1e7urp1q5s8toNoXNhTzO0zDxBhyX98iAqU9MjJiBWLZbNYorT6ngJJvt9tmaHxlMwYom81aTQXRWygUMtosz8jnm82mwWE8p4/OvMEfjhh5DiIqckR+Hnhe4D8iGmpAMpmMGo2Gbt++rY2NDWOW8Y49m2p4XQeU16+uBEbhtyTDRV0+VwDcI0mlUklnz57VwsKCJA3UFqAUPBMJ5QIDhmt5pg8b2HPwfcUvxobcgnTCovGFUSMjIwYLoRgxOD65isLsdDqmKH23U/5QIOd593jpHhLpdDpqNBqGxXvGjXTMuiGfAiQyMjKi/f19U/zJZFKjo6MWURExobwwAlw7m83auHy7bA4P4jhOjKl0bGSYXw+xEQ1Ace31esrn8wNJeowu98HQEwm1220zgp7iOtwGhOjBs5hQ/KwPxuujTE9uwLEAPksmk6rVarp//742NjYsH+PXMMbBG61AvpoSGIXfgqAg/DR6JRiJRFQoFHTx4kWVSiVToq1WS4eHh0qlUpYLkAargVHa3e7xKWSeZirJrgXNkogCZeS7dzIWvEUUH1EBBWV8luQvB+hwTYyLL+zq94+L4Cii4yzkQqFg46dIjmvhNRMloRhJmgLVAH0QPYTDx+c/hEIh7e3tWWsMn6xPpVIqlUoqFArq9XpqtVoql8va3t4egNaAoVDqKFR+DhyDcfPsH+A45nhzc1MbGxuKx+OamppSPp+36/p8gaej8m6IMOr1unZ3dwfeNeKdDP7P2iNn4n/P7zC+rCuekfUVCoXsYKWVlRXdvHlzIHfjIbmPW++BfHUkMAq/RfEhPv8eHx+3NhRg20AeKPRkMmksEhgktKYAHgBvZ+OTWximOHooIR6PK5PJ2CLIZrN2jgCwie8x5GEcvHGfE/DwSTh80uCt1+tpZ2dHT58+VaPRMCMB3JVKpZTJZCza4T4kljGOvpMpCWDul8lkJEn1el2pVEq9Xk/b29va3t62aItrEvFcu3ZNr7zyilFcGW+j0VCtVtPe3p6azabNFV1KJZlHznxiIFDqYPN4551OR6urq9rZ2bFIYXx83CIGYDqMqb+HpIGzEzCOkA18NOkjQKAxH136YjciCa7P7zzFGAMP3DQ+Pq5UKqX79+/r4cOHxlZCvHEJ5KspgVH4gsVvSDZlJBJRKpXS2bNnNTc3Zx6X56/jQaLgORoSZdNut00BwVbx0IB00lJBklUh4+ED/dCKGQOFV+yNCuMB2sDz9nATEQP3RZHiha+srOjp06fq9XpKp9MGVzE+T0dtNpvmHUP/pDgMmiswClh/Pp9Xu93WkydP1O0et/4ul8sDtRbeU6Y4rlgs2iagEHBhYcFaROzt7Wlzc3PA4EGz9e0nfI4BQ4fBlqRWq2UdTDudjtLptEqlksbGxpTP5xWNnpwt7T1+og1/Ihy1HOQaMBCtVsucCt4zxgySAYZRkkUxwIsYYf99+jdxT54tn8/r6OhId+/e1crKilVI+9PvgkjhqymBUfiCZTgySKVSWlxc1KlTp5RKpbS7u6tms2leJ4qk0+lYiwd/6L2v4PV5Cu8BwpQh0ZlKpSSdKHYUBZAUXiodVEdHRy064T7AIeDu0skxmt7zh+pJwdnh4aHK5bKePn2qvb09q7Tl+77rKQqu2Wxa4heaJhARXUPJSTDmaDSq+/fv69133zWv2RsA7ylzb4wEPwMGIQq5dOmSTp06ZQar3W5re3vbfk/00uv1jGlEpOUdgVAoZKwixpJOp83Aw3Ai8gHGkwZPfPNeOP8mUtvf37e8y+7uriWFcUJ4Lq7JHEoaYFYB28ViMYtGPcWV8fln2Nvb0+3bt7WysjJQ/T4cMXwS0SKQL48ERuFzEv/sTBfYqnS88aanp3Xx4kWlUik7gAbvG3aO9xJ94ZlvyoYS8lHBwcGBYfvJZFK5XM4Sjj7JCjbvk4XgzaVSSTMzM3bqmjdoYMf+VDV6ANFuA2+TZm6hUEjNZlObm5uqVquKx+MqFArWsO7g4EDNZtPmiUQqytnj7Xjuw7mBfD6vkZERbW5u6pe//KUqlYp5tcM4N8pwmLXjl7ev1ub78/PzOn/+vGZmZgwuGR0dVavVUq1W0+7urnK5nNUmpNNpjY6OWjNCT3f1rUBI8kajUZVKJcuD+HYc3vAwZuBAMH+oyEB7vBeaAGJ4iRyG+zMxP8ONAYkuvEElQmTtwEYbGRnR1taW7t+/r9XVVTMk3jAw38P3DuTLJc+j7oM2F88hXtH4zRAKhXTq1CmdPXtWqVRK/X5f1WrVEpS+2tczh/C8ySukUqkBeqWnAqI0iABQvL5KudFomCKG3ogyHx0dNW9Ukil44AKMAkrfVxen02m1223VajVT0CgUFGYikdDk5KSd35zJZIyLn8lkBpLkvrOpdELRhfrqYTLmYW9vT9VqVbu7uwO4OUpNOmkkiKEZLupCYTEP/n2urKxoZ2dHV65c0fXr17W3t2eRC/UivEuvfMmfYJhJ3NN/qNFoWGsSDIYkq6auVqsD1c3e8QDSAebxUSLzvL+/b5FXrVYzx4Hn6vV6ZqBxGPgdSWwgR39/5pa1Kh07LRMTE5qYmNC9e/f04Ycfam9vz8bOWvaOTCBfXQmMwnOKh1Gi0ajGx8c1Nzen8fFxU2h+c3hOvocufGWxVwRAJySUPSwCVRQFKskSwhgHmErpdNq8ZpLJKP5QKGTFbdw7Ho+b4oEl5BlDeM75fN7YQhS37e7uanl5WSsrKxodHdWNGzc0MTGhvb09U5RECMViUaVSyQq9UCrMnW+2R4EViV8a1+FhD1MlMdREAF7R+mhtOHrAa261Wnr77beVTqf18ssvm7LGWJL8jsfjqlarevfdd1WpVAbeATAcbUqWlpY0MTExAOElEglNTU0pl8up1WppY2PD3pNv2eGj0EQiYQrdJ5qBuUqlktV4NJtN6wVFDoexsz55ft43//dMN4wB485kMtra2lIoFNKFCxc0Pz+vW7duaWVl5SPU30C++hLAR59CwuHjtgnT09M6d+6cwuGT7qQoBekESmBze0+W6fY4brvdNgWG8kilUgY3UHDEGPgOVbZsen6GlwgFls3u+e14vr6fEVEHhq3X62lsbExjY2MKh8OqVqva2NjQ8vKyHj58aBWxnjrqPepCoSBJ5t32ej3V63XzbCORiMET8OYZG1HE/Py87t27pzfeeMMYXN4Y+CQrRgVDjPjPSSdJesYNHHLmzBn99V//tWH39XpdT548MWP+5MkTPXz4ULu7ux9ZF+wP3mOhUNC5c+d05coVLSwsmCKemJhQOBzWvXv3tLGxYUV0qVRqoNgOBeufmchumBLK2vBHrx4dHVndhE9mM1YgS+bCJ9O9guez4fDJ4UvUc1D8tra2NuAABfLllSCn8DlKMpnU3NycFhcX7fQrMHLP8+ccYL8JvefuoSNJA/9HaSWTSWPxgPF7A+NbWOAd+toEooJCoaBCoWAQAZ+j3YTPcdDLqFwuq9c77ncEft7tdrWxsWHQwfb2tkEVz8qxgPujwMDHgaiGFVE4HFY6nbakbDqdVrFYVCqV0muvvabDw0P9l//yX/TgwQMzGiToMawYZZ+gRfEBT5EgB4ryZz4kEgn99V//tf7qr/5Km5ubevDggR49eqTt7W3VajW9++672tzctEgR3N8bf55FOqk3SafTmp2d1fe//30Vi0UVi0WFw2FVKhXLCzAfrCMPH2EgPBTm81HSCZvNw4S9Xs8OWCLP5KOF4WgA8dcm7+EhOMaVTqeNKnz79m3dvXt3gGEXyJdTAqPwHPJxrAkfVheLRc3Pz2tmZmagkyYdOX1hEJuH2gEPDfjWEgjwDTROvDd48z6hKslgDTpw0lOHa3FKGl44GLd0DMNEIhHl83mFw+GBjqM0iYOWOTIyooODA929e1cffvihVlZWVKvVPrUn6Gm7H/ddfkfCfmlpSbOzs5qentaf/Mmf6Pr163rw4IF+9rOfqVgsKplM6u7du9rf37d3kkwmzQuPx+Oq1+uq1+sKhUI6ffq0isWiNjY2dHh4qP/5P/+n3nzzTa2tranb7WphYUF/9md/pv/4H/+jxsbGtLOzo1u3bun//b//p5WVFf393/+9Hj169JG2JsMJYo/LS4PtICKRiGZnZ3XhwgUtLS3Zc7Tb7YEiPAwEdFKug2PA/bgPNGLqGfyZFZ4WDPnBN+6TBs/B8JAl64xoyud6MIgYstHRUW1tbenOnTtaWVn52Pf7vD8P5IuTwCh8gkDblAbP2AUfBts9e/asLl68OODpgqOOjo4auwcPHqMAlo8XR08fNhe5AbxCvstYfAtpDIrnnJMfaDQaA3x6ePxw6cHMGSNJ7adPn1pbg2g0alTVM2fO6Pvf/74KhYL+9m//Vj/96U9Vq9WeOYfPYmU9a538uiXmn7tYLOrKlSu6evWqpqam9Jd/+Ze6dOmSjo6OLOHtewsNK8rnEc44LpfLko7bj5w+fdrovb1eT2tra3rrrbf0k5/8RP/tv/03VSoVMwqfRZHh8eNlk5Tv94+pyKdOnVKhUDA4sNls2tyRj+IZqUGRTrx5igNp9cF7JQqj6I/EP9EDeSxPf/UJeRwbHBradVBsiSHK5/PqdDq6deuWnjx5YpDTsOJ/lvEJ5LcnAfvoEwSD8Kzip3A4rOnpaV24cEGZTMY2W7vdVjabtV723qOjoMtfw/fPl2RHKOLR+zOQfcFXt9u1nkBguNFo1Aqh+PzOzo4kDSRhd3Z2jIoKvEG+IRKJqNFo6NGjR1pZWRlIauJVr66uanNzUy+99JLefvvtjzUI0kf56sOe8rNooc8S//t6va6VlRVdvnzZYB8w7uHT1Pz3P41RoE7BUzK9hMNhTU5O6ty5c3r33XcHWmF8VkXm+yA1m01jLzGGe/fuWW6nUChoenpai4uLmpyctIQxuaq9vT1tbGxoY2NDrVbLDgoCgqMDrH8fjUZjoB6EeQAewkmiGI4/sNoQ7xgRNfH7dDqt73znO1pcXNQvfvEL1et1u7+fM08CCOTLJ7+3RgGFOdyILpfLaWlpSaVSyaCivb09pVIp5XI5g108JIRHHo/HrW0DYTatF4Bk8PCBoEi04pXhuVGgxHX5XDqdtvYJ6XRaR0dHWltbM7wcGMZfQ5IZnXq9bu0YvKCgDg8Pdfv2bT1+/NjaQX8cTuyvwb29QfA4uPR8EcPR0ZEpTe6BkZVkh80wV3zv0wjetx8fyWbGjRHO5/OanJzU+vq6GQX/fM8rw/Ph7w9xgOvV63Vrm/EHf/AHKhQK2traUjgc1vr6uu7du6ednR3VarWBPAICUWFmZkZjY2OanZ01yjTsM86D8HUnwxEYldj+qFAgrnQ6bdAjbLFqtapcLqdkMqlvf/vbunfvnp4+fWpNCoFGff4skC+f/N4ahWHqXyaT0ezsrBYXF5VMJo02CUYNvx5YiEpc39K53+9bQi+VSpmny++lk0ZkUC9pnAYrCKUB44hzCDiT1+cf8vm8SqWSarXaQCUym46CJ399uqJyFsKwokLxQRn9dTKcWB3+HfPyvIJhoIGej7yIzLxB+CziFTv/9rUNPM/IyIi1qpBOckafBULyxALu56GU4f8fHh7qwYMHKhQK+ta3vqV+v6/l5WXdunVLGxsbA8l1PxbIDvR5olfTxMSEdejlhDj6SvmW7fSDwrnAwfBdYkmGs959U8Jut6tms6l8Pq9vfOMbKhaLevvtt82x8BBoIF9O+b01CtJJ9fDExITOnDmjubm5AUofCt3TLfGOOHoSJVGv143r7TuPYkyAAPDSstmsYcMYAzw1vDoSetls1gwMEMMwDx3lMDo6alAQh8lLMs8QNszu7u5HTgqDBdXv93XmzBkdHBxodXX11yaJfU6DXAlMp+EI4lkyXM9Bs7phb350dPQjOPVwkvd5BI+VufEeu0+s4nGfO3dOv/zlL43i6e/7WYS59vKsyK3b7erOnTuan59XIpHQr371K21vbw9Anj7SZS79M0pSo9FQpVLR8vKy5ubmdO3aNeXzee3t7Rk8yNqCbICTQR7EH7WKAfHOkG+BEo/Htbm5qbm5OV28eFG5XE5vv/32MyPUQL588rUxCs9SFP7f3qNFCXPyGYfd0NSs3W4bNRJhI9NLxjctQ3mTK0ilUrbpu92Ts435v+elQz3lc95brNfrA2wRogWvjLgPyUL64/M5xkbv/EwmoytXriiVSun9998f4NyjEC9fvqx/+2//rUKhkP7zf/7Pevz48ceyR4i4pqamNDs7q0KhoL29Pf3yl780A/rrxHvRKJ3l5WXt7e2Z4pNOWoF7A+C9/eeVYehiGM7x/YCSyaRmZ2eVzWa1u7s7wD7yYx++3vDPh9ekj9L8mFifKE+gxlqtpu3tbXunH3ftYWPj/99qtXTv3j1tbW3p4sWL+sY3viFJA80FM5nMwGlyw2dmkMTmnhgSChB9NXylUlGlUlEqldKNGzd0+/Ztra+vDxhWP4fPCzMG8sXK18Yo+IXlK1w9fkwH036/r7m5OZ07d86qbn1/IpLBeKWepgkNlDAdWp4/67fT6RhVDwz24ODAjAEN0miy5iGfkZERa5PA/alyJkrwPfLZjGxSlDQMJgwKfYtSqZSmp6f1wgsv6E//9E/18OFDLS8v289JsE5PTysej2t2dlZ37twxZULju9HRUeVyOY2NjVlHUFpIf/DBB59qgw97j/1+X2tra1pbWzNM2zeU4z1/VvkkhpRnNElSsVhUPp/XlStXtLKyYusjFAoNJPh/Xe7kWbUMPkeBMRimfqZSKY2Pj+uf//mfP3YuP4333e8f96F6++239eDBA129elWnT5+2yABDzMFL0K5Z9z66BVIC3oO91e/3rUEgEezIyIi++93v6ubNm7p3795Ai/jhiDCQ3618bYyCdEL7894ciw3Od6FQ0JUrV8w7bzQaBvmQK5BODpMB+wcK8mfrAgOhuDEg5Ay8Jw1jCHog1EPGBh0QHN43VkOJwA7h34wFrJfKV/+H6MPPBfDX0tKSlpaWrLVFLpezZDaK5uzZszp16pS+9a1vDcBcwDmtVsuKsNbX163N828KE7RaLT19+tTok74b7Bcl3jDgUABZffOb39TPfvYz61nk21wPX+NZhmvYADzLQPAz3xDv/Pnzmp2d1erq6kdqVj6LsA4ODw9VrVb14x//WDMzM3rxxRft/e/v79uaYG8cHBwY/ZkiOKJjHwX7diOs3V6vZ47UlStXlM1m9e67734EWvw8ni+Q31y+VkbB0/7wUNhgmUxGp0+f1unTpyVJtVrNONzek/dHUVJNTCKOw1kImbkHVa4ofcbgcV0EL9AnNn2bCbwnFHC/f9KXBm+Oxne+0ZxvY0FRFJAUG9dHF7RE8AV2w1XOQDcYOkkDSdZ6vT7AjwejBo5APgv+3u12ValUtLe3p93dXTtD+YuSYYqrJDPuuVxOoVBI/+bf/Bv97d/+rZaXlwcOBfIY/DCryAsGw8NlnoDAzycmJvQf/sN/0Pj4uNbX182B+TxgFcZAhBONRrW1taV//Md/1Pj4uK5fv25V16wt+m7RtoQqeq5DXs0bSE8SwOFhTmZnZ9Xv93Xr1q2BFi/SiWMXQEi/O/laGQXp5BB3wtpUKqUzZ87o7NmzxtOPx+MqlUqWGAM73tvbM0wVgyAdbyRf/UuDOrx06KF0PKW9MVxuwnASwj5JDQxBLoINhLLlmaRjDxPP2RsFzzXH0IEH82xASURDGEXorbTpQMEDZ8EsoTiPs4UPDw+Vy+VsDumaSrX1MMPn027yXu/4RDcaz9GGmmt+3jKsqH0RYyqVUrlc1h/90R/p+vXr+l//639ZK2/fYmQ4T+DFOwE+KYxBCYVCKpVKOnfunP78z/9c//7f/3uVy2Wtr6/r5s2bA2P8TQUlTk0C7/rp06fa3NzU4uKiLl26pFKpJOk4UQ1pIR6PG3wIrOQJD6xdoCP2ETURRFrT09PK5/PWPsQ34wsMwu9WvjZGwW9IPLjZ2VmdOXPGDMDu7q4pSLwazv/t9XrmjfqeQySU6WmE1w3On81mTdmzyUhWS4M0RgqBiE7wtCTZoTiE3b69MX1s2DDARv6P99pgiXjF7BvNATXt7OzYvFHR7AveRkZGTEFKMgiBtgw8Jxz1drttx4CSq+F+n1b6/b6d+eyjsi8SPgIW4cAachnJZFLNZlN3797VjRs39J/+039SuVzWgwcPtLm5aZRNGs8NF0L6PAQ/B47EGUgmk1pYWNClS5c0Pz9vzkQ8Htft27c/NwqnHwOGFoooeYx79+5pdXVVV65c0fnz5w3q9HAr+yObzdpzYxyI6DCqw1BTKBRSq9WyrrTvvPOONjc3g0Tzl0S+0kbBh6cIeYCrV6/q7NmzkmTVo1Rych6BL8xhg8LUwQsHfvFHG4KZ09kTY0G1aaVSUbfb1ejoqDWk40D7aDT6kbbReFQ0eqP9sSSLQDwXnH/v7++r2WwapIOXi8fmG/ZRjU3xnafI0hohnU4PjItCMeaXFs2cc0y9A7x5jFOhULCWzuRoPotgYJnfz8I0+jQCg0s6iRhGR0eVyWQUCoX05MkTxeNxvfLKK3rllVf0wx/+0CIsDPYws2a4DsL/n6SyLxrzeQWSubSW+DxkmL3Ev4eNDu3EV1dX9corryifzxv7jjoHcmOjo6NKp9MD9TLcC3iU9cEeo/1KJpPR9evXdfPmTT19+nTAIHiH6jehAAfy6eQraxSG2TZ43DBrSqWSeapAKFDtUPZ41x7a4TooTg68x0OXZHkHX4QWCoW0u7trXUZpOYAhArqqVqvmjcM+QpkSgZDow6MiCQ6sg8JvNBrWYiAWixlcheKBM07o7/v14/XRPhvjQBRCLoNznjudjprNpur1usrlsra2tiwXs7+/b/AA7KpsNqutra2B5Pmnfb947P6ZvkhhDXB/HALmq1wu2xnMMMmSyaQpVK/cP6vwXRQia/N3wcrpdrtaXV3V3t6evv3tb2tsbMzg0kQioXq9rkQiYesllUqZYYXtxt70ZAiMRCwWU7lcViqV0vXr1xWLxezYTyJ+PyeB/HbkK2sUPGOBBXb58mXNzs4qEomoVquZwoP2SQgLDg5FlZoBPFG+i1IHU/cVnRyKQyi9t7ener2uVqtlLatpi+F7/pOcpTW1dMJGIaQHgiAZyNkDvjNqs9m0Vhm+zz5cct+f33v/QDx4rrSgLhaLAwaTeSKq4RAXchn+MBfPDGIcmUxGIyMjA5j7pxWSzbVaTZOTk/bzL0pJ+BwSUaivGO50OlpbW9PDhw+thQTv+PP0Yn1CGqz+dyXh8HHL9bfeekvf/OY3NTk5aQ0XOf+CfBNkDZ8DYs/hQOGYYBhokRGNRvXiiy8qnU7rgw8+GIC2AkbSb1e+ckbBKwQ27MzMjF544QWNj49bghcjABzU7/dNweGJ0PLBH47DYsQ7hxlB7YF0wiBCeXY6HZXLZdsshULBvEmgHzYM1E06flar1QFoCgNBXgSF7NlF7XZb9Xrd8gwwjoAi/GE7wFOe9opxY0PzWQworTV8xWqj0bBjNpvNpuUySFiSXORZOccYppRv1/y8UqlUdPfuXS0vL2thYcF6R30RMkzZ5d3TtoGeQe12W0+ePFE6nR5IEBNNfp7j4/7SSRTy2xbgpnq9rtdff10vv/yy5ubmtLu7a44OkGUqlbJImPMWWq3WAIONvYZjgmPDdc6fP690Oq333ntPtVotiBJ+B/KVMwp+w8ZiMTv+MBKJaGdnZ6DVL5g/eQDfTpjEKkoUbBVjkclkrOVCp9MxzwjvjXqB/f19691PSwo+6+EHeP3APhxTybXA91HIeFeSzDun6R0Rg8drCds9Fg3cBc/cJ/LISdCGgwQicBSbnOek/sCzsKSTFhVEGEAJ/f5xxXgikdD29rZWV1c/dZFVp9PR5uamPvjgAy0sLOjcuXNfqHIczgMQKe3u7hpWHgqFVK1WdefOHWO4nT59WqdOnbLcw+cxDoxBtVo17B0F+tsU1hgOxjvvvKPd3V2dPXvWWrePj49bWxgcAYgVoVBIIyMjRpH2jhbMOYgXrMFTp06p0+no7t271hgxyCn89uQrZxSk4wUyNjam69evK5PJ2AKE+bK/v2/Jr8PDQ1NowD7QQoFIWMREDkQENGXz7Be88mg0qt3dXaNidrtd5XI562ZKi2IPDaEUs9mscfDxMKlSHh0dVSgUsoPffSUvXjiMKDYszCgiDE6GowU3RtF7/oT1jAsjK2mA9srP9vb2DBYAFwZiYbOCr3N+RDweV6FQUDgc1vb29qdOOvN+6P8zOzs70HqEtfBx4ovRnvVvf43hn3HuQLVaVa1WM3YN0VC5XLbqXiKnpaUlpdPp5zZcw2P3Y+A97ezsqN1uK5fLaXt7+3cCpXjno9Vq6c6dO9rb29OLL75o51zQeK/f79s54bu7u5ZrSCQSajQaVoFPfYKPuolk19bWtLi4qHQ6rbffftuOiA3ktyNfaqPgPTcWRTQa1dmzZ3X69Gml02k7dyCfz1uBFxAIxiKdTmtsbMxaYEvHm75erxvkkkwmlcvlTOlhJDwOShETrJharWbtKPL5vFExCZVjsZhdC0WKcQErRmGzQSqVijqdjjW28weqk2jmmv5kLgrNSKaD18L2aDablkOhfmN0dNQMEj2ZiCBQAD5fUalU7N2QvCcqINHM3DJ30WhUpVJJ4+PjKpfLA23HfeTCPUisJpNJ875rtZp+9atfaXR0VN/5zncGoJphhf4shhIKFmM4vKbwXP3fBwcHqlQqWltb08bGhsrlshk86dhhqFar2t7e1tHRkdUUTE9P2yl2PjcDTOnH5u8//LNut6u1tTU9ePBA9Xpdc3Nzun///sDYvdf9cfvn81Cmw7j+wcGBHj58qG63q29+85vGgIOV1+8ft7UoFotGXKBivt1uq1qt2p6DwMD/qeavVCoqFot69dVX9fbbb6tardpYeLZPqgsJ5LPLl94o+AZhIyMjOn/+vM6ePWul88A9ngcN35rvSjJIiISYPwkN5YVipv8Qyhsl0W63DX6CqQNtM5PJKJPJKJFIKJvNmuJDeaJQvOLzNFIqr6HqcQIW/ZNIPkuyaMFz4VHI/nMo5na7bZ8bGRlRJpOxHIJ0zMf3zc88BZCxxmIxZbNZy9d4Vg4RBDx/X7gE1//MmTOWRBxW4sxDNBrV2NiYCoWCxsbGjI4Ls+v27dvmFExPT1vPKOnZTdWg1WLgUdBAYnwOY8H7rtVq2tjY0IMHD/TLX/5S9+/ftw6fPDNwCnmdra0tra+va2xsTBMTE5qcnLSeUMzd8JkNrF0fAcLr39zc1N27d7W2tqZIJGIsON/S/NdBVYx1eK4/D+n3+9rc3NS9e/d04cIF2w+eJVcoFBSPxw2C9UfP1uv1AeYcc8C66nQ6qlarymQyevHFF/XBBx9oc3PzI60xAljp85cv9XGceAOh0PExjZcvX9bk5KTBEGxmkqnpdHpg0wKX0JxOkrF32JCE+3iuYPMeavHFTChvwmYkHo9rZmbGTmbjfOVWq2UQzs7OjtFIoZUSAUDdRJlJx15RJpOxIjpgGa+sCcWZL19YRLdWn4PJ5XK2UYmkOEgIpehbYlDfQSRF/yUSzV4pY+CAnjCEY2NjGh8f1/7+vv72b/92oDOrf8+lUknz8/N2UAvV4f1+36KNRCKhfD6vpaUlnT17VhMTE0bZJGnOecTUVcCQ4tkxjr5Yj0rs/f19PX78WB9++KHu37+ve/fuGf8eo0Lb8tHRUZVKpYFcE05AoVDQ1NSUTp06pYmJCWsRgYeP8pdk7UFIynIWAucbHB0d6cmTJ3r99df18OHDj0Q+vw5SelY19echGP5XXnlFFy9e1Pb2tlGoYfzlcjnlcjl7H6wJ3pHfd8wtUS45m0wmo3a7bVXk7O3Pq/XH75M8z3x9qSMFFv3S0pJeeuklpVIpawvtWUMoD++1ktgFQsGzg4pKdIGyZGND2yRv4Jt9+V5H/CG6SKVSyufz5h3hmfu8Bff17Su8scWY+ZwGBo/iOLxJDAjN9jz04fvQZLNZYweRXMcI4NGhOJlXf6wof3s6KwVbvoWydBLacwIdc0Vh3Pz8vPL5vMFl/B6jl8vlNDc3Zwqbc4QlWSuN0dFRra+v6/bt28rn85qbm1OxWJR00m8H48l8wnzKZDIqFArGswf+Ojg4MKO9sbGh9fV1LS8va2try/II/CGKAv8ul8sWhQKPbG9va3NzU+vr61pZWdH09LTGxsbseZkvPGKMA2vVw3xUjKdSKV26dEntdltra2sDxuWTZPgzn2fTOQzbe++9p/HxcU1OTtrYWLuQFaLRqBVb8i58FEEuj5YaKH4K5QqFgm7cuKF33nlH5XJ5oJ7j86r2DuRYvtRGYWRkRPPz8zp//rxBOBwQjgKkMhfPFWVMAZXv3YPHFIvFNDY2Zj9nk/imXB5Lx5PH+4nH4+Z1gcVzOAlsIpSsh58kmRKiTxAK2PPh+RwbmM/hEUPxhB8Ou8N7+nhdtVrNaiZGRkYseeqb6LXb7YG2GxgujA/zQm98n9tgDsjFwKDiPXh4TpIVFPKM0kmtCSeEkdSs1+sWqTFOjHS73VatVlO1WtXY2JjBi1CJDw4OjLkjyZyEsbExS+ZLMootz7e9va1Go2GFgIzbJ9aJpBgHrDBYNERKRB68QyjKviiNSJA1hJdNcn5nZ8faiJMPo+iLpnSfpOSJKol8Pws1+FninYFGo6H33ntPf/zHf6xsNmsJ8VQqZYwinBN6JhFl4tBgbDOZjEUQnk7eaDQ0OjpqZ4dXKhVzdgL5fOVLZRT8QhsdHdW5c+d08eJFaw2BF4jS8X3tUWx4HChQIBbaJdC62rcnlmQJr4ODA+VyOaXTafPSEBYy/eShmMZiMZVKJeXzeYOWpJO+8njNwEa0skApMwYUD2Ez9+HnyWTSWltjdJg36aQ1AgYLmGN7e9uMGewpjAtKmsQzDQGBXpiXRCJhG3C4pxHPzH1RkjwT1wYOQZENn0VQKpVULBbtvih56jI8tJZKpQagNYw/ythTSTE6dHClKJFiQt/tlcgGmIqxseaIKlgLfB4jzOcajYblhDgveXx8XLlcbqAltW9xAvTH/znn4NGjR3r77be1vr6ub3zjG7p+/boePXqkn//859re3rZT9HwuKZFIKJ1OK5fLqVgsKpPJaHd3187zZuzNZnPgBD7W7fPsVaTf7+vRo0daWVnR/Py80bQxiBgD9iSGlgQ0USFsPwgPvtCNsabTad24cUM3b97U5uamtXQJ5POTL41R8A3PisWiXnjhBY2NjQ2Em+QF8JRR7B5jJ8lLwQ2KFMXrE7zSCScczjR0UrxkvHAYO8BO4L+ZTEZjY2N2T+7nexKBEVcqFTt7QJLBA1Qg41kDXfn2HT5hSg0B4btPWGJ8uAY4u89T7O/vW4sGaLEUG6G4wuGwlpeXDQ6gUMvfS5IZaf5NchnlRgKx0+kom81qYWFBKysr5oWTh0gmk5qbm1MqlTIHAHYWBml3d9e8fJ4PJYxhiMVi5r0z/zgGKHOOK93b21O1Wv2Ip4pSIq/hCyF9BNjv9834kBfBkO3u7ioWi2lxcVFnzpyx1ivkvXBIPFvqWSykeDyu06dPGxyTTCZVLBa1uLio69evq1ar6f79+1pbW9Pu7q4SiYQVT546dUpTU1MWefT7fWtVQpL8/fff1zvvvGPNEbn3r6PuDifo+/2+fvnLX+rChQsaGxszGik5K3INzDO5PIwB75t1xtoA8mXO2u22RkZGdPnyZR0dHRkrif3kxx9UQn82+VIYBV740dGRpqende3aNZVKJSsIg5LJyU94aWCVGAXYRz7R7D1vSYZzkqzzzBm8WorffARCstV/B4VFMo1WEkAMbAjoi3hlvgeR7x8PNEIUFI1GDd4AlmJMGAcwaHjhGBLvift7UGG9v79vCWmK9A4ODiwpure3Zz/Dm+XaPjFLFbQke0fJZFITExOq1WrWjqPRaGh6elp/8Rd/oX/8x3/Uo0ePbEzpdFqvvPKKLl26NHBQkKfyUn/CPKCgfOsPPG3gLMaLN4ryB0YB1uD9cl9vAKGTogx96/NIJKJcLmeRDZFYLBbT5OSkFhYWdO3aNV28eFGnT5+2QsFfJxhKPhuPxzU1NWW5E1pXX7p0Sb1eT0tLS8bmwUng++RvIE/Mzc1pZmbG5vbs2bMqlUp68803tb29bRCsp34yDq9kWd9EjPSGqtVqmpiYUKvVsvqg/f19ra6uqt1uW07nWQQOSca243sYaggEODXZbFYvvfTSwLnVPnIOEtCfXb4URkE6VigLCwv64Q9/qGg0qnK5bFx+qHjUBIA1SscbiNoAfzSlT5KyKVAWeLbek0FJ+J49eCmE+pxK5dtEoIiAGDycIR1j1qurq9rc3BxoLQx0hCFiDkheo4jBgyORiEUfPD+bkbyGZyShnHytxXC0QNU084BR29raUqVSUbVatfYcKBqiE4wm+RPppIldsVhUKpUy2InEY7vd1p/8yZ/oT/7kT7S9va3l5WX1+32dOXNGhUJBa2trqlQq9qwoI5LRPkeB8sb75b0TUUmycUuyKDIcDlubbxhrvC8ffeJgHB4eanNz03I6HtLg3dFaGq83FotpYmJC58+f15kzZzQzMzNQ0zHskTN3Xvg/64FDbnxeg55b6XRak5OT6vf72t7eVrlcNmcCbB7jWS6XlU6nra4nl8vpu9/9riYnJ/XOO+9oZWXF9hoGwhuZYYiJ8bC+bt68qR/+8IcqlUoG+bBWqF4m+uU0QqI2CCKeOYdTgEEimscw3LhxQ7/4xS9ULpclndBwfxdsya+LfCmMQiQSsYNwUEzABCxk+O7kBFBGeBORSGSgXTTYMiE7kQReIcqdnv3gnix8DhNhkeJJ+rMS8EDB/qUTBhGf6XQ6VhELK4hNDQSCMpNOjmKk9UY0GrVzHvCWnmWAms3mQNM7H0rzTMBLeKzMXzgctkSgb+zHM8IcAR8m4sjn8+bZ8bxENygUIAHvfc/MzOh73/vewPw8fvzYusP66AzFDDRDxIK376ExojS+S1QEDOijOd/MbzgJ65P6wGXkBsgJATm2Wi2DJxl/NpvV5OSkFhcXNTEx8bE9m4YNgvduPbuGv32zOfj+sVhMs7OzVsXOfPD5drutvb09U5ZEgJ1OR7lczuC/l156SWfPnrVE+/r6uiqVivb397W1taWVlRVbK6xzb7gY//LysjY3NzU5OamxsTHLI9FBoFwuG6SFI8b80F6ePBvOkiR7n8B3zHUikdC1a9d08+ZNbW1t2VwF0NFnl9+6URj2kuLxuG7cuKGLFy/q8PBQOzs7AwsiFosZVjo2NmberT8b1isFFnw4HFapVDIDgpeG54rHi7KmWyqLCfoim4wK6Gq1auczlEola20BpIF3hGKs1+umTIF/gE1QLEBnlPrjlfEzopFSqWQFcyiQZDKpTCajarWqw8NDw4s5P5koRjrevOl02uAh31AP44KhODw8tDkYHR3V1taWMaYwlKdOnVIoFLKqXgxir9dTsVjU/Py8nj59qpWVFUlSPp+3osNGo6FCoTDAlOr1etrc3DQMmaiOaAaWmS+uk2SGB0YUsJZnQXEPHAjvGPgW2MAh/PGH4GCIpZNoizwR9+E7s7OzWlpa0vj4uK3TZ3nZzxL/e4y6hxaJnsl7zM3NKZPJaHt722i7qVRKlUpF6+vr2tzctDH3ej2DdrLZrJEq2AMXLlwwB2t3d9f6dAF/bmxs6IMPPrAzNLwhg9b7s5/9TDdu3FCpVNLU1JTu3btnDDrevXRS5+ANfiQSMXIH7WUwarzn7e1t2+/9/nFH3pdffln//M//rHq9bhBzIJ9NvhCjMFx16MVvjFQqpaWlJZ05c8Z+n81mJZ1woHnpQER7e3vHA3c5BEnGvQdLZ+Ok0+kBLJfN7D0SoAW8Rh/W8n96tWAkMDZg1dIgbVGSHUaDsUK5sck9to9S8qE4CgbogVPI4vG4JahRZBMTE5ZXqFQqA1EAdEwiGKIC8HQweSIZDHYoFDIGDkqV65HLwdhhyOv1usE9QHF4bsA6kqxtRCQSUb1e187OjjUJHObzg+FjsH1lOAZfkiW1yblQGY2jUK1WrVodRhp5oXA4bNEpBo45wCjzTmKxmMGLkqwtiCR75kKhoGKxaFGITygP74Vn7ZFhXJz8B0Vf5G6mp6dVKpUssT4zM6NcLmdOxMTEhOr1unZ3d025c8b0zs6OqtWq9QybmJgYiARzuZxVv2NAd3d3debMGf3TP/2TPvzwQ1vr3vG4c+eOqtWqTp8+ratXr2p8fNwKPVnn7XZbo6OjA9AgOoFoE2PBniO/lc/njTlHxJtOp/XNb37TTnEjqmBsw7BdIB8vX4hR8Ngp4TwvhhdComhpaUlHR0emNFkcbD7PUqEgiUXE4sL79g3vKMIiIYiSRCmD64MPe2UH1RG+OvRNlDCJz2w2a4woFiAKBQUFdEHLBQTcmsQ1f+PdcjiPZ3ngZWM0fJM25sV7yIlEQtVq1Vg3KChPp8RgYHSAp0hSwjMfpsCi5ImwOPMZyAZqKh1mx8bGlM/nLVpKJBJ6/Pix4vG4wVUYJl/v4BlczA85Ct6jj6x84Z4/ytMnkHkWDD/v3jOp/NxikHj3XBsKMx57v9+3Kufx8XGbV+YM+SRj4N+5/xnz4Vk5sIwwIjg2sOcymYymp6fV6XRUr9etqI68Cs0coe7C8Mrn8wPOGU4LBuD06dMWcWxubg4U9PGdjY0NYzS99tprxgpjPn2imveHMWKtD9cy0FuJXI6PvKlhunLlinq9nra3twd0TmAQnl++MKOAeLyRF08/k8uXLw9U+qL4pZPTzWh6RydSn2j2DCFobhSRgffjpZLEglnERqZSGMWDIcI75B5Q+eDPl0ollUolZTIZMy4oa0Jtkn0wbOBT+3MIiEqIPvjb3xvv2bffIMHMRtza2rJWDuD8eJNsOpQh/HkUYiwWUz6fN0iFKMqzaSgQo4AMrxEvjkQtbJGpqSnrcRMKHXe1pTcUdR2tVksrKytqNBoql8vW+pxNTKJ9dHRUGxsb2tvbUzab/QjsQH6JsUE/prWzZ3ORR0Ih4W2Sb0Dxh8NhY8+gYOmAC+WUqIg5pEcRHV2Bmj7rHvLRBQbKG37mE4eACJDvMZd8Np/PWyfT0dFRjY6OWqffRqNhjg+RCH2bPEuN+1+8eFGlUkn/5//8H928eXMgumGfHx0d6Z133tG5c+dUKBQGjA8dCbxD50kJUFZhHPZ6PRUKBdsTOJreaahWq5qampIk/fznP1e9Xrf5DCiqzy9faE4B48Di7PV6yufzunr1qk6dOmUhJewNQnpfyehbAODJ+3AWxotn9YTDx11L6VZZr9e1ubk5kPzkHGa8l2g0ai0KarWacrmcJRtJeBOiY3x80RkeMp5MrVbT9va2YeGdTmeAUYTwcxhELFwa4uEVsvE9pj2M56Ik/fzhfTP/KFAKgVDmGCIMRTwe1/T0tLXtoAU2xyWyyTqdjiXqw+GwGT6fNC4WiwZpQB1cX1+XdFKZ7Pv+kHwE/isUCtrY2Bg49pNkMuQD8lHgzMxbo9EYgLc85ABslkwmLaLgffrojkQp0RGkBN9llvumUilNTU3ZfEmfrW/Y8HdQ7M1m00gQwGC+6M3vO/7NGkylUpqentbh4aElgdvtttbX17W2tqZqtWp5BO9o+SgBSnA8Hlc+n9df/dVfaWRkRP/8z/9sEZf3zlutln7yk5/ou9/9rnK5nLa2trSzszNAbpBO2m8QLYRCIavdoW4FY+1ZSb55Jet9bGxML730kt59912rQwkMwvPLF55o9kwAqhFPnz5trRZY3H4hUZwlyRQti4ZEK8wFPCW8VPjhN27c0OLiokZGRtRoNPT48WO98847Wltbs578Hls+PDz8SIUtnrs/WCaVSlnHSmixjJE2wT4hC0WRCMa3UMYrY9PiZcEiwTPzCU4MBEpeOsFMu92unZtQKBSUz+e1vb1toXS32x0oagPm8ecMA7MAwZHcBm7BEIbDYRUKBWsn4Wmr/X5fGxsbZoQnJiasqJC5gJJarVa1tbVlDC28SBQGkRzPTwIaaIFiJrjxGA/fegMocXx8XOl02uCqbvekhxZz6kkInmRAtS1r2isa5j6VSml2dlaLi4sGKyIewvh1MpxPkGRRLUSCWCymarWqZDKpycnJZ9Y/sJ6QcPi4AHNhYcGUbKvV0ocffmhwbSgUUqVSsf5C4+PjFkUyNqC6fr+vc+fOaWRkRNlsVm+88YZBrES/kUhEjx8/VqFQ0Pnz5w2Wo1jS7zGeE8PPekM/4FDhZPFstVrNijAplJucnNQLL7ygd955x/KQgTyffCFGwSd22Gj5fF7Xr1/X7OzsQNtqegrx4vDYUPpsDs8s4d++kGh0dFQXL17Uiy++qKmpKZVKJVO+yWRSpVJJ2WxWr7/+unnP0ElhIKFkYOVIMoiBA2+KxaImJiaUyWQGKl2lk1YZ0PqkEy420A4VrShPn/je3NxUMpkcoG+ysX1B2vD3JZkCplp5enpaS0tLunXrllFUqRsAL2Yz4f3i2XPvjY0NU8iclezzL7FYTI1Gw/IANMzzBUjj4+PG0+/1elb/QPS2s7NjShrvj/fv6wf8oUl47D7ZmM/nlc/nje1FpABmXa/XTXH4mhbvtJCLIJLwRVWRSMQgMCID6YSyGw6HDcMvFosGbfwmENLwnsI5QiliHH1S3K8bolb2D9fxZ2pDaYUWvrKyolu3bunRo0eqVCo2j9CiMYjszf39fU1NTelf/+t/rVQqpb/9278dyCOSQyA/xNzjwfv8GVAdcBz7L51OKx6PW3Kf5yKXwPtGhxwdHXcwHh8f19WrV/Xee+9Ze5Ugp/Dr5XNZsX6y8bp8YieZTOrKlSuanZ21zU7CkM/m8/mBxe0pjsNVtOCk6XRaCwsLdhzi0tKSVUziiaPEwuGw5ufnNTY2Zt40sAF0TlpDEz2sr68PRCpUEBcKBWu8hnflexlxP7Bf8hscxMM1PQ/fF8vxMzYeipnF71k8/Azvan9/3yqs5+fntby8bJ+FoouiisViAxXdz4rU6KsDhxzvL5FI2HfplcQ4aJ8Rj8dVLBY1MzOj6elphULHR1lCSwTX513gBdJGg+pvKJbQbEOhkzoN5qhSqZiSQWGQhMbb39rasvXBGHEwPLzGO4XdFo2eNKwLhUIql8va3d21ugEUL4afqMnXinwWCGl4b0knMKpvTe6hEa/8UeDD+Qn/uXA4bGc/9Ho9g9gODg6smDCXy2liYkJjY2MaGxszJQ55AbLC9773PT169EgffPCBOSmRSESvvfaavvWtbxnDjPVEnszXLUBHpdU4643n8O1M2BP07CJC9BXt586dU7vd1vvvv28RoGci+igxkGP53IwCWB8TDL6fSqX0zW9+U1NTU9YkTJItGL6Hx++NAYbD97ABKw+Hw3rxxRf1l3/5l5qZmTFvGlrm+vq6wTjnzp1TsVgc4KFTHAYWC/tlb2/PlIjnhlPtTDM1nwjGa4X/zebCKGDASIp6GOLw8NAa9YVCIW1tbVknU2Ac5or5QpF5o8lnQqHjozzv3LljrQcwLD5BB9RETsUnWEOhkF2fRLnPaxAhbW9vG4ecyAvOe7PZVDab1fT0tBKJhJrNpp1W1263jQ7JBvdnYQA/0DPK52r4OZsbIwa2zntGYTFuEpEYsXw+r5GRkYHDXphX5sPTLL1CAbb048CwYdxYN59V2TyLrYQyo7GiT8h/ktF51u/8z3wdD/UlFOkR9WIsJZnn7mtoQqGQpqen9e/+3b/T//7f/1v37t1TKpXSK6+8om9+85s2R9Sg8Aw7OztWWEiESd+kdDpt98RooCsgQRCJ8L5g6B0dHVmzwwsXLujw8FAffvih5dSYO74XGIUT+VyMAopGOvGKCDlffvllLS0tDXTW9LAIiUIWty/oGqbleTbF5OSkXnvtNS0sLAyMZWdnxzjwoVBItVpNW1tbyufz5pn6w2EoqSeCoehMkiWw8RThfVNJzBi9ouW7vugJJYLy9B6px2fxwjY3N60VA8/ucxp+Q3Mf5pyo4/79+3Z85u7urm18chYYhGFD7Duo4nGRDMQwARt4iIDvAwMBK0DvpdMpzwWkRfSHIUDxk69hTmlL4Zk1kmzu+P/4+LhSqZRWV1cH1hGJb8aJQaFvE4qC9wcEgjdar9c1OzurqakpS6ASsfnIZdgj/zzlWZ4/7+Y3FfZDoVDQlStXrD7lzp07Vt3MPIyPj9v5EP78EvqA/at/9a/U7/etshtnaWJi4iOU6uFCumg0ar2+iNoxsoVCwc4uB6okqsRIemYaMGOr1bK+Wo8ePZL0+Z4r8XWT39gooCSIDsBaS6WSXnrpJS0sLJhnIJ30JuE7KHn61/Ni4YbDy+Y7eGokrvhZKBTSxsaGfvazn6nf79sh7765FlALITiLhkUK84mFJckWJYfoZLPZAWOFUqPlAZ4zig3DwlwNY7ye1QL00u12rTkZMAjP6Ru3cQ3G6DeoJOve6WsMiFZQkGwsn9yWNAC9AZOxUYmOeCauQ6FTq9VSPB43OO7x48e2LlCeJIwLhYLBBkQlRFfJZNKKz3w1ss8DEAV44zYxMWHFWRh15gpsm3cN64r1gTHEQ2XeqLStVCr2Pbxe5sIbRZhrrJPPQzBmGAKiNvJyns7r/0aeJ5pIpVIW1T59+lSVSkU7OzsDRaPsYUm2JoHN2u220Xc5lQ4GGfkYEvfsnWq1aq21oSz3ej2rsRkfH7cIQJLBdr7WhHlgDdOLimg3mUzqpZdeUq/X05MnTz4CcQdyIp9bopkFCEZ56dIlLSwsDBQd0RQrHA4bDug7JbLJ2MS+/xGKz1PicrmcpBP2x8rKiu7fv69cLqd+v29HNdL8C88mHA5bvxVYKHijvvgNZc3C99WeGBUiD1oooxBQXr7qmeS4PzSGJCpKhkZ109PTRsdD4XtDBbsJ5YXS9gnow8NDS8Jzchn00nw+b4rYdx31h9KghHkers18+KrsbDarUqlkFdxESZubm7p586YePnyofD6vH/zgB5qbm7N+SiQXfdGgr1T37CCUHsYXTxIILhKJqFqtqtvtDiSlmWsUJTkt7oGCA55AYcTj8YEobGtryww+axKjKMkS6ST0WZufl1FgLQKhsqcODg40Pj5uUQ/vj/vzzoYj72cJUHAodFwMNzMzo0qlotXVVcsFAI/6aBunChi4VCppbm7OCALVatVqTZrNpnZ2dswRAU6EBMHc4WjB/MIxZO1QuU7U4utKJKlYLFpjP9rov/TSS6YreHeeSRbI5wgfsWHz+by+973vKZvNDvS0Bx9nQfqDcChqgSkETQ3KIIpPOt64tK7gkA7CxZmZGeXzeVUqFeP0R6NRTU1NmSGCKw10g4GhZz+eOc+FAaGYBk/cw2B4Pr7mwTOTPEzkO0FyDX7mK0dRqhgxNiuYuoffSOb7jrLAJL56ORI57h3EKVaJRMJgPZ7ZN9xj0+OZUfSFtyidQCc0xyuXy4rH4yqVSkomk9rY2FC5XDZFQqTjz66WTlg8/n2Qw0Cp81kO2IFP77vHYkCJXDEqwBUYNiAnsGkMNM/sOfPDh8Og2BgfBppootVqWZtr1hDy67zST1LYFKjRjbXX6xn+7s+A9tdhHeBRe0aSF77H7+LxuNFNK5WKNjY2zOASlbPeRkZGBvJbnKFdLBYHDEytVtPIyIgymYwymYxqtZoZV4rogOtisZjGx8etsJEIHRiV+cbR884XZARqIWq1mr3vaDSqq1ev6ujoSGtra18Y1PdVlt/IKPjwGIV+/fp1601Csg9YCfog3gCbOBqNDvD1fYEZtDtJA59fXl7W3/3d3+natWuamJhQMpnU+Pi4pqenrcR+dHRU+Xxea2trmpycVCqV0rlz5/TkyRPD31GohUJBhUJB1WrVPEEWF4aJsJloB8wfb5UklsdCScahnFAQfN6HwnhzKCsP2YCpE1UQeUiyugm8x2QyaUVIhN0kolGgU1NTAywtSVY0RvU4XHI2Gs9DhMTGJGrDsBCdjY6OamZmxuYomUxqdXVVb775pm7evGmediwW09zcnMbGxlQoFHT9+nUzkCibRCIxEDHCjuL5YJtQD8I84+3j6TOvRFsoSQwHFEjP8orH43bgEwbJt+umyvrw8LjH1fr6uqampgagQ+lE8WKUnpVM/iRv3vP0geHwgD2NWzo2XPv7+1peXtbp06ctqvYEimftZd5rKpWyhoeQAIDZMKBEKijqfr+vXC43kPdCcUMmIKKE4oyDsLe3p5WVFUWjUc3MzFj0Tt0PeQcMM/Up0kleKRo97tcUj8dt7QNBknPodDqamJjQtWvXrHDS5xc85Pb7moD+jSMFJi2TyejGjRu6fPmyJYoo4GEhssnZqCQyYQLRywW8FygDZUoyl177b7zxhm7fvq1z587p9OnTmpiY0OnTp9VoNLSzs2OwDgehpFIpnTlzRg8ePNDt27dt8QProHDp2YJ34vMgHsKhLxKUTubBt9MmyYkCh/3iISSiCAyHx8s9BZM5QQEBD7FJp6amDCOv1Wq6d++ewSY8BwoTmCqTyZgXDo8fKIa+RqFQaIC266ms7XbbmDwwSlCqPGssFtOjR4/04MEDra6uDiTn2ZAYotHRUTWbTb322msqlUoW5aAEUQBAjcO0Xd4LxYUoapwRIiHphJLIv4EMuRfrNp1O29GWXJ+8DOuSZ6rVanr//fc1Pj6uxcXFj2X+fFxC+tcpIQgR/llxAmDI9fvHFN3bt2+r3W5ramrKjMKzDAJj8oqRKDeXy9nJgnt7e6aMef/ApDhWExMTmp2dtRYZCAr7/PnzmpmZseLEmzdvqlKpGKuo1WqpXq8rk8nYe/H7DVgZ+i9daDlfe29vz3QP+9LnDzudjnZ2dlQoFHTt2jW9+eabNp+ervr7TFP9jSMFPL1XX31Vs7Oz2tnZMQuNgiJhiiLs9/tmOMgZ8PJRvLHYcctsIgN+JsmMCQuoWq3qwYMHmp2d1Y0bN3T+/Hmtrq4ONBBrt9uWQLty5YqePn1qBS0ePwa/9PUU4PP0PeKaJM7At+m9Q4sMjIN04s34vjxAEnhaQFXD/WbwtOmlBD3PF7nBkPJ9ZcbGxqxtNVEIHjLXB8f3SXd67dMLyUcSJPOIgICFhvsMkbN59OiRXn/9dTuCUzrBoJl7NiLw3P37960fP4V2PCdQARERERswDQbCf4fIhmrqUChkzQ4lDbw7IJnR0VGLaIFe+v2+tUEhyuTd8l7hxAOTjI+PfyxEwZx7HP+T4Ayeg+62VG8fHByoUqkYZbpSqei9997T6urqR85zeJ5kM/uQe+7v7xvjBwfGn0fBui4UClpcXFSpVDJj7aFW9oQfE9XsEAbW19cNPsUQEJ03Gg0zeqlUSteuXdMrr7yiSCSie/fu6Re/+IVWV1fV6/Vsn/ooBqNWLBbVbDa1sLCgWq2m9957z/SSXzs+of77JL+RUej1etbLfG5ubgD3JtTkZVNpOzc3p1jsuPUwbA4waUm2OelwiULE2ybhS04BTj8e+dLSki5fvqx0Oj3Qt313d1elUklHR0daWFjQ7Oysdens9Y77wD99+tQ8S5QOxgDlj2IlhMdDpbePdILjek9Wkj0338Gb9jxzMFu8ml6vZ/dnzkmwETnA+iAUbjab1jaAJDORimdXeWYNxphunzw7+DnPC/vIM8pg9DA/MIEeP36sN998Uw8fPjQl6SOy8fFxU7jj4+Oampqy+R0fHx+AfNjgPqQn4U0BYrPZtMQjhtFHcfl8XmNjY6rVanaqHPku3gnPwvujxxWdRsHBSWj6yAKjWalU9Prrr2t3d1ff+973dOrUqYH+QRivz+KJ+sI75n9ra8sM0/379/XkyROtrq7aXPgowOc3niUexoLBRVdb8mDhcNiYbdls1mAh6heG4TGfA+D7FKzt7e1pZ2dHa2trFj1ubGwoGo1aXkaS9efCyZqdnTW9gxMai8X0wQcfGBUbyBXjT8SJLqjX6zp37pwODg50584dq63y6MDvo/xGRiEej+vs2bM6deqUMRNIhnmWjueAV6vVgdO4yC34fIEkY7XAlKGwLBQ67sJJfoLCKELH+/fva2FhQQsLC2o0Gtre3jYYxnt0Fy9etP485CMwMODqUNl8+2VfBBaPxy05SaKUDYDnB2aNssFD8ng13iKeij8AB0/XbywMBT9jIdP2gc2IR+37/TPXJKHJmcB0op0Bh6yAE3M/7g+0x7UYIwq10+loY2NDnU5HV65c0dWrV3X69GmNjY0ZUQBIDK+T63gh0qQQkrVBcl+SteReXV01zJuID+YJniNeL8V0vCP48bTpQAkSudVqNatkphYE9lcqlTJDuLu7a432aFd948YNvfDCCwOFj7wH1gnySYbCQ6jk7MjVScckgfX1ddVqNWUyGV28eFFzc3MDfal+XTQyLNHoSR+tlZUVg1hQsN5I7+/vW2tu3ydLOkEVEAoNL1++bI3r8OrpYRWNRq35JIr68PD4OM75+XmDF6vVqlZXVzU6Oqpvfetb2t7e1vr6ulFacbRYD0SDpVJJkUhE58+f19bWlh3eg5P0+yq/kVHgpcbjcet8KJ3kEEggRaNRTU9PK5vNmqLx+HgulzPlSeKTDYlXAuuINhGpVGqAsiYds1JWVlZ07949TU9Pa2xsTN1uV0+ePLEKZLx7cNbNzU1TMu12207S8rhkvV43KIyFDgVP0kBikwXlC9kIm0mcYWjwUvku3ia9lhqNhnnWkkx5YuT4HfBJr9czxdbv961fE6wfFEooFLIDTvCQMdAko4HEwIxJnvukoXTSdoEGh7lczk5ry2azeu211/Ttb39b58+ft1wEOSSuMz8/L+n44B3WC5EBCV3myXv1wF207tje3laz2bQKXN65p65KMsgFo+pZZhgnnyjtdDo2NrB0vkN00e/3rVr74ODATkZ75513tLq6qjt37ujUqVOam5vT5OSkisWi5V6eB9pBqft1JsmMEF1vR0dHrf/SqVOnlMvl7LrDBmj4+l5Q4qVSyfoOkcj2Z5hgDGkDMjU1pbGxMXOiMEKsu+GE+uTkpMbHxxWPxy26JUcgSRMTEwqFQmacieDJXwFBES1lMhkVi0VNTk7aMa+PHz+2gs3R0VGLdhlLInF8pOcvfvELVatVW2essd83+dRGgYVVLBb1wx/+UJFIxPqWe0WOosVLJinnPb9isWiwA14gXjOeLBuOXvYUTgExkZzsdrsGRS0vL6vVatlxkniQ5XLZevBks1ldvHhRq6ur6naP21YTVZB0glEFnuk9JI9fe4UDqyiRSBhzxzfcy2QypqxI4npqrceqJRmkg3KGUuoVJzx9Px4YOiTwmFdJH/HIgV1IQPuEOsaNaITvohA9HdkXUaXTaV27ds1OBwuHw3aoC4owk8lYk0AgMSC+brermZkZw7IxBsy9JGMkkfidnZ1VMplUvV5XpVIx5wFHgLlFueD1ogCJEHg/PD/PCLyAMuI9Y4BhWAGJEQV9+OGH2tzc1NTUlKampnTu3DmLnIjSfA3Is+AXSbaWdnd3tb29bYYK+O3cuXPGGAIC9Lj+rxNvJBl/v9/X48eP7chVX4iJMSgUCiqVSmbsyB0SVfq80fA4stmstRqv1WqKxWIGd+KMwSjCuBweHurhw4e6d++eIpGIlpeXzaEEbpybm9Ps7KzOnDmjx48f6+///u8tdwSU5+d8fn5eBwcHevvtt9VqtQZg3983+VRGgUWSzWZ148YNSbIqVBJyvicLmxVIAuVHiEijOBYMCWeanx0cHKhWqw0k+4gQ8Pbgp4P/7ezs6N69e3r8+LGuXr06AN08ffpUIyMjhlUuLS3p9u3bZjR8QpHNDkQgnTA0UAp8lryDZ4bgEVMhylGVRBfALN4YcJ1KpaJWq6V0Oq18Pq9QKGQtKYCtSPiS9GWzekgCzJ058nkJngFDjYdMcpucg98Y3Mdz2WmXgeePUqTalDwKRU9cgwgmEokYQwoIDww/kUgYTdjj/8ATzCMGjwNwgBrh8EuDyt2/O8bJuQGZTMaei5wPcBA0T/JArFMcCBwBnwTf2dnRzs6Out2uGo2GWq2Wvd+DgwOdP3/ezg34OCWEIQb2WFtb0+rqqnZ2dtTvH9cGnT17VouLiwOH3Q/XSEifXEznDQif2d7etn5cvPNkMmmUVJwAvu91hb/P8D2BUDH03tgDB0Lw8LmvSCRic7yxsaH5+Xlj0BFpQHqIx+PW0bhSqejp06cWjRPxYRTC4bBOnz6ter2uW7dufSx19/dBntsoeO/swoULWlhYMCqkp3xhzVESo6Oj5rGwqMk7UMWMAub7eNXeI8XI8FIZE5sPCOfo6Lht7u3bt3X27Fkrwd/e3lar1dLOzo71WMlkMvrWt76lf/iHf1Cj0bAqX7BaTzmUZDxpFDoe3TBvn2IZz1+noA7l4wt38DihhdImAgWNcZBkUQGJerqqemOAp483R36CefV0TZ//wBMcZir5Ai3yKxgqFDwbF8Pq2Rz1el2RSMR65sCkWl1dtXET8Y2MjCifz1sS2ifhaV7nE/QoE94HhYYk9DGcjI/Pw5wiegiHwxbZYbxg9aytrVmLBRwT1h8OTiQSMbZSsVjU+Pi4+v2+wVidTscosTDf1tbWtLy8rMXFRS0sLFgSddiz98WR5DYwLMlk0noMERlKspwe12Ot/rqcBd/lHQKZecME84/9hoIFHeCev05YO5ylzTnivAs/jkgkYhASjlo0GtX58+eVSCQsaoDuTeSK8frBD36g1dVVvf322xZ1AG9Dsy0Wi3rhhRe0sbFh51b8PsqnMgrRaFSnTp3S2bNntbu7O+AZsdGxvig6lDmKmxcHkyWdTn+ExQF9zBcS4b2zKPGQ8Sh8IVIkEtHKyoo2NjZ06tQpqwLl4BAON+/3j3skzczMGN0un8/bUZq+SKjTOTn4ncUsnXigPsRH4dbrdfNC0+m01U2AxULnA67iyE/pGEvlWUh6erYTuCfQim8DghIAbkDxeZqhT1bj0bPZUIiSBpS+f05YUGww5o/feTot7ykUCg3g8sNKa39/X5OTk8awIcpkjdy/f9+e1Xv7CPM6NjZmuD/FaozRGwifE8CA+qaEOzs71pJBkq1nTx8FMsUhIF8EDBKJROyUMz7X6/W0ubmpWq2mlZUVgzng9vskPFEyVOFms2nnYEQiERWLRase55k+CS76pJ/j2LBWWMN+LaCM/bU84xDa9vC7eZbgUCEYP6IRWnZTeQ9kh7JnbVEMBxRH/uzJkyc6OjrSzMyM4vG4vvOd7+iDDz7QxsaGQcNEgjAcM5mMvv3tb+v111+3kyF/3+S5jUK/f9xW99KlS1YxiIeFx+W9LpKg5BeAmVhsKEU8NtgzJE37/b5R3HjZXJsKVhapb7aG59lut7WxsaHp6WllMhktLS1pZ2dHy8vLGhkZ0fe+9z1T4FeuXNH+/r4VaNHDx8NSLCBJtinwmH2iWZIpIBK9eDgHBwfm8cNeAfP3uQm8MLwe35LDbzaiGZQP40Dps8kwmkRYvvLVK3vgJZLdbFCvCP0zkkwFMhxOgqKYSXTz/jGs5CRg89y5c0dHR0e6fv26ed0kMC9cuKBer6f333/fqK94qr5ZHs9EApTIst/vG1XXM39gD/lCvd3dXfPGMSLci/umUin1ej0z4qw9/vZtWnCIKPZDaZPMp7eQP50PCi/kikKhYDkkcjx07iWx6/fqcC7heTx3n1wnTwKbiRwFNHEStUST1C4QLfjxfNL9hg0LRp+1w/MTHbJf6HiAI3f27FlVq1WVy2WL9CFrJJNJO5Hx+9//vv7u7/5O9Xp9IPfHfqFFycLCghVU/r7JcxsFjtI8d+6c9vb2VCwWByYShem9Jc86gF1AiEh4jxeMZ4j3mU6njUbqqYh4wl7RedrZ0dGRhX537tzRCy+8oHg8bowYlBxJyH6/r8nJSRUKBW1tbZnSwoMGIqHjqk9YgrsT8qKUveKm2RcV1b476cjIiJrNpkUm9IGnshtl5FtscB/wb3IhPg9AfkA6MVz8HkjHJzWHefooUrwpntUXEvKuJRkuL8kUFu9qmGYMeQAvm0iz3W7r9u3bymQy+u53v2u9fJhbCqPW19eN/cJcQ0ZgvUmDuRUfMfnokuQy0UgqlbJojfWMEpJOlCZeMQoeOBJlRvPCaDRqEaWHRYkEeHfANOSJJFmXUVrGSLJ5IuIhd+V7OvlxDssnGQbv1RO1PXr0SE+ePBk4ArTfPykgxNnBESHvglEbNlTD42Afs06heBPxRSIRYwJCS/f5Pv9+xsbGtL29bXm5/f19raysWOSSz+cVjUb1zW9+Uzs7O3rnnXcGYEMIC0R4ly9fVqvV0t27dwfG/zwR0Fddntso0Edoa2tLkmxzAQegRMkh4LkNt2SALQNNE0XGNdiwvpspYS09ayjnBw/EM0AxkdxcW1vT2tqaLly4YBxxmqjBbCAimZ+f15MnT4y1hOKCHTXcJ2gYqvHQCd4eUZI3LPRzQnHzjGD/eLc+QpIGWw+g4OkxRPWxdNIxFg96d3fXWCD+TAHfL2Y4ueiZRRhuIBaP36PwPSPMM28wOiTXoR5zTSivS0tLOn36tKampnTq1ClNTk4OjIX5LhQKGh8ft6McJdn7YE6Bi2iXQU8kbwhRZBhWIhc8/FqtZtBBJpMxQ+fHA1yJocaY8lzr6+uSTpwkSdaxlnWCEWNuqf9g7VNEmUqlrJcPsB0Rj2eb/SYyzHYioQ3riMiffQfrSZJBsxAtJiYmNDMzY0fWDouHjOhSzJrj/rFYzNY19Qa0YInFjnt0jY+P23d8Luz27duqVCqqVCoG3WE8yPd4B5T3ALWXd3Tp0iVtbW2pWq0OvHtvzL6O8twriT45bD7YFCRKETB0FChJKjYcip9cA8lLPDNCaK80PUzAzyhQ4hQtioUODw8tGVqv1/Xee+9pcXHRqJFra2tWQTk5OWnjnpubs9PhpGMPhEZz29vbBrmQLAS28ZgyvH8MHbgniTlOI8PAAGewwNh0zKPH5fnj8wh4UKFQyOoGgIFQFuPj42acqMLtdruq1WrGhsG7QsnzHJLsZyg9NgSf4T5Uvfrut0SEw71lGGMikTC64djYmJ2g5/NDnnAAbCCd5A+4Fs4HEQ9/PDNMkiXugVwwYFB9a7WawXE8A8p3mAYM9o3RxEP23UNxCnBgiGjBsfkcEIl0rGQhRvhcBN/3Tei4jldSn8Qw+iThGs1mU5ubm3r69Knq9br6/b4xrVC+fn5g2pEox+Fj/frxeCNGVE9k5pUt7wNjAUyEM5bL5axIljWwvr6ux48fa319Xd1uV2NjY5qbmxswLNVqVY1Gw9AA9BMGnXqGVCpl/ZHeeust01XDeZCvozy3UfDYs/eWPF2Tl0MSDGWCQoQ5wyLHs4A+RsUp0IJP5rExuQ+LCAVNgo5KXPjq9+7d0/Lyss6dO6fJyUmFw2GVy2U9fPhQMzMz5m1kMhldu3ZNW1tb2tjYGMBkfXMt70UzDxTD8DOiHTa5p036IjGUFVGXZ9uQ+OSaPDcRFZuDP4zX5x6kQY+Udhi0HPHRmW9RDiTBex32RPFYaX/B+mi1WgYl8gwob+AS4C3ogsViUYVCwT7Lsw3DDcATrD+u43MpJCDJy0jHHiSGicgAirQ3rtBmfTtvv965J9AEnicwKcaGttB4s6xPqm69AidS9UWEdGFFWcZiMd25c8dycBg6DNPm5qbGx8fNKHv5NIaBCJAI4eHDh1pfX7dnhXRBlOWhRJ6BdQppgijIEyNIkoMSoBd8FMf7bjQa1lMJWAe4p1gsWqM+7wgRPdPigwabjx49srYdq6urAzqLdUKEQIfhbrer6elpXbhwQbdu3TJHJTAK/7+A3eMZeIXCBuGF8oKwqnhrnhYHs4LFwufAs/HeUDCegUQRly8yI+GJUuVz5XJZDx480JkzZ+wQ+UqlokePHlmxjSQLexcXF63jJGExz+0pmh5jZEMxXu9JIYwRJSHJ5oSEO5/388Kzc2yh7wDq55NobdhL5PtsaElmwHxhmk9K8z02OAel+KIfafCcBQ8r4WnBZKJ620cj5DKoocD4D2PbHqJYXV21QjtgGubbf9/Tcfkjybxdoi3fgRbsmffik9QobHD/cPjkbA5yLsAdUCt9rqlQKJhXW6vVjCLMdVE0RBTU84RCx8fJ1uv1AUiRd+g96XQ6rdOnT3+kM/HzGgagq+3tbT19+lSPHj3S+vq6vTOMNe/e06Bpb51Op+2wKb+OmU/6QhFVFAoFM7TeAfIRaDKZtLNMcDSZZ5wv2IFAXEQpROygCBsbGxaBed3BHmZdhcNhc27i8biuXr2qlZUVi5q+7nmF5zYK7XZbjUbDGEF4YjSdImT2k0ZhE96Yp/754hC8LemksIokHvkIPF4MEnCC501DW2Mh0RabYwWLxaLOnj2r9fV1lctlra+vq9lsKp/P2yI/f/68tre3Va1WbQFSlAfuj0LySVfv3ZOIw5BiJPxBPGxmmEf8n3sMQydg00RaKFfyLHzWh7dck/uy8Dl/F2gDb9BTDSkoAjJgw/jitlgsZmcu812+h2dPwpwEKvg9URJKdjgiQjB6zWZTGxsbZhhxSlBM0kmVLWvIdzP1/Z1QICgP8iHe44WJ5Hn3zCeJS5ouAoVRbcz6g01DPgxlD4UbAkIsFrOurc1mc6ArLfPR7XYHEs6SDA4kQqrVarp48eLAuQPPK0dHx32bNjY29PTpU8slkL8jcmf+cPok2dj29/ftRDXeR79/TC/HIWEv+86tRHL8Ht2AU+EjskQioYmJCWu/gYNIzgVSCRB2r9ezc9srlYrq9brBrXj+6BnWDvuaXFk6ndarr76qf/7nf7YI6Ossz71yLl68aIvCe3goISw7GxTviQgAhgpMEekkcUO1LvAJCSHqCrD4JFeTyaR5ev5a0ATBXyOR4xYc4IzFYlGzs7M6ffq0ebjNZtPCfaKFUqlkB3uw0FjEzWbTns9DNt44eL63h9VQqCQKQ6GQYcp4gigqT8HzUA4bBc9JGmwN4pOFGExokj4p76vBhyMf/g1dM5vNWmEZ0QJ4uL8vhprEKuP3Rn0Y46dYEIeCMfAH3Jx8EfdhfnySF2XL2iAZinLyCgevm7VF1CSd0EuBoujIi7JA4XMfFFi3e1yoB5SFAUd5YYx9LsH3lPKsG6Ayrsu7Z61jSNvttjY3N81B2t/f1/T0tClGqsb9fmNdEgFXq1XV63U9efJE7733nh49eqStrS1rCeEdNpSmZ65xbboVU6BaKpUUCoXMw8bIMf9EbDQa9GuSqA5jgCHJZDLGyvL5inw+r8nJSRtDLpezufHdbXFYeA5vDLi3Z6hxFsfS0pKdUeHrZHDAvk6Rw3MbhdnZ2YENzgbx2DYLG0PAYsK7QmnjmXnPDqXhcw29Xs8MBMVx9MjBi6PFgnRypisbkd9tb2/r0aNHunjxohKJhKanp7W6umoVzrOzs5JOinPm5ub05MkToxaS5wAnxqtmDBhKjActGtjceFxcAyPE+Ei4ScdV0ySbMRooLKIQv6lRtHweJUJSEm/awzWHh4dWOT0cKWC0gbXGx8cNJ/eQFvNLghuPm3fvi5wYn18jKPXV1VVNT09rbm7uI+wOCse2t7etRUij0bA8D2vFe620ucBbBYpkzqh7AKbwhtTnzYjo8Dg93Akevr+/r0QiYQfGEOkAEZLHiUajmpyctHftadow+NgTwHjsGb/HMKh4qni5nPaG07K6uqp8Pq9SqaRSqWSGAQhTku2nbrdrSpl+QpVKxboQ+/wU653ICGcIKIbeRVSVh0IhbW5u6v79++r3j9uYcKYJRBDafPuW8j5n4RVvNBq19uf+5D1J5uAdHR1ZdP/48WM9fPjQHDzmFOQCo+D/7Z05jC+02HPnztnZLThG3gn7usBKz20UfOJ3mE3ARkNpMaE+egADZ1H7ZCHFI3j3vCQOeYFWiReIp8XPUGLDG9r3aVleXtb+/vGpYFNTU3aG9MrKipaWluzIwaOjI83Pz+v999+31hRs4kajYQ3dUAySzNjhBaIYfcISD73fP66DQJEwfg4Yp88Oc8iGZs6ZY67p78u8SieHFXnWFgaZseCZSjLvHUOcy+U0NTVlCtTngtiouVzOoDyiEo/Bs7m9VyoNYs3lclnvvPOO1tbWNDU1pcnJSaMUP3z4UI8fPzaYxEOJ0gk8BgZMB1q8QuaBMY+MjNi5FyhonrnXO25tgYLmWej5742ch6tQgEg+n7eDgVAyQDtELxh2cPpYLGZRCaQHIBMfFbEmfIU6fzhPYmNjww4nKpVKRrn1TgzvCtptKHTMhnrw4IE2NjbMaYjH43ZIEPkUDDLQH9E7e4H5CYeP+z7duXNH9+/ft7k9f/68xsfH1e12rT09zonXDUBFVIdLxw4TLEFPcHj8+LFVKVMRT7QDBR3GEdGudGJIaOKJkieqYR22Wi01Gg1NTEzoxRdfNLqrj6Aw6F8HeW6jgKIeDiXxPkmeEX6hwFk4bF6iDJ8ApRaBzY5H5EM7PF3wU0lmMPDEiTB8sRcJRI7rwxOH747BOHfu3MAxlGfPnjUPFS8YvHLYy/SeO1Q4FDA8fpKxKHaMH+cH813fi4Umej65hzfPBqdSnI3FgobeCkUU4+DhLEnGdmm328YEYWNCt/QN28Cz/Xsl8qHFuTcE1EZgSH0b7/39fWu2RtFYrVazQ3NWV1eNI861vGJDkUsnxyfu7OyYE8FceQeEfI9P2GJQWNuRSMTO/cbp8TkfHwkTMQwbYHJKtGYZhhJxgnzUDexBjovkLN/HC/X7h7nAkCHAMsNe9zAsR0Xv3t6earWaGXn+cE2MqIeCpRMKOpAaa4ZErYeFSRjTR4oW8eQduR4RFIfqcN2xsTGVSqUBkgXO0eTkpBnktbU160zLvgX+ZF1Sw0MRK8/r2WAe6iIHlM1mdeXKFb311lsDkcLzJvS/CvLcRgHPyVtoIAaMARvVL2LCa/jdKCM2BzRS6QRLJmwlPCX0pf6Ajc2ioZ8Q+Qiu7b3acrmsDz74QDMzM0omkzpz5owlIldXV1UqlTQ1NWVKZ2FhQcvLywN0WZQ5LBXPVwd/BLYAe4epAs3QKyfP6/ctLCjRh1uNMuYwF5QhB8igWPBuPHWQv0mO8/fR0ZG2trYUDoft+FDGi4fGfXgfJPSJJHhW/z5JHOM9wqzhucF1UdrQDVFSm5ub6vf7Vl2OocRIeW/Md8bECyQPJJ1UQ+MZkuOiT450crYCERQ4/OTkpCKRiI0HWAe2Fp4s+wGihffsicR8dTrjZM6azabK5bIODw8NWiEvs76+ru3tbYsYMTI+Ej88PDSlxvqhOJOIBjiV9y6dRFmFQkHxeNxYOVx7uFqZqAbjxZqkXsAXqeHQZTIZnTlzxhLunIgHwYHkL9EpZA86GrCHGOfi4qJFkkSA0nGB4dTUlOkSKrFxSmH5EdXgtKJfyDORW/NwNtf03XHn5+fVaDSsmypRxtdFntsosODYcJJMgeEZEdoSHsNThvXh6YG8HDBsFKP3fnyhHJj8MEPGw1bguChHn6Q7PDy0/vN4M9FoVJVKRclkUmtra1bo1esdHzM6MTGhR48eWZ8Uz64iAvEHs6BUvTfiDSXtwH1C1XvRLHY2B4aOTQjlFZoo98R7R3nwf+YK791TLlFyvigLbz8UCtk5u77QiE1CIl+SPb+PMjAm/v1iuMCyed9QmumP5em6HhLr9XrG1GEd8H69p0y4z2dYmxhUnpcxk3zf3983qmM2m9XR0ZE5DawfmG+sX94hStm/Aw6C4b35qmgiORLPYN540MBI+/v71oaDanDeA2uEMeAxe5otRpp7+6gbCJZ14J0Los1wOGwG00eGJIj9/sejJnr1+UK/tnGsmIdEImE5Oc8IA66jfmBmZsbgTHQP68Qz/TY2NvTw4UPTHXyWd+JzmIzRM9LQV55EMzo6ahFbrVZTKBTS2bNnVavV9PDhw4H9+nWQ5zYKh4eH5j2wIGBZkOBE0bIQvWfEpsJrwzNC8fASfdIaxeeVGosbD7RQKKhQKFgTM1/8hgHqdI5PbaOaEe8IyipJL2AbIpOFhQV98MEHBlFhsPAMSAhKJ91RPb8Z2Ibx4p3gURI2s/lQiN67xWNBoaDYoIECNRAhSTIM+ejoyIwmG9I3qaNdM7Ua1WpVGxsb2tjYMGgDbNtHfz7BzVyEw2Hj4vumaBgQrkWOBuPHeIAVgGvIJ0kaaH3iz+uAEcQYnj59qrW1NVuLQF8YcxQfihjPD4ZVLpcbKKzE00epYpR5txhTz/yhEI98BFBZp9MxOBKoj+Qxa4LIAe94ZmbGzjQPhUIDxYGsRfaKdJJHQjnXajVzkrLZrBlWr8jJH2CAQQSkQcgYSIV5wUiFQiE7o7nfP6Gf8sw+/+YdBA9rYdjx5GlOCMowPz+vU6dOmRH1EXWlUrGq/larpQcPHphn79enh06hG4+MjFhkC62eOfF7utvtWi82jF6hUNDVq1ftGGCfd/PyVTQWn6p1Nt4voSNN7EhS8js8JCbfewKdTsd43MAKLA5eOAvRV/sSheChkuyCIiqdFOwAAXDeAMrkyZMnevjwoebm5qyidnV11RQO9FQkn89rbm7ODAbPzyaGZYNCYFwYrOESehQaHh+RDtdm43l2CnOMhyOd5HO89+5DXwwIrCY2G+EytF4UJ0rj7NmztsnW1tb05MkTO1jdUz8lmeKAlklHSd4nxtkn9jDwFGnlcjmr0kXJEfVhSNnUePzSSfdVFHy327WEIoojHD6ufAV2IcfloUbWJ949hh5DggfNuvXG1ysTIh4iMZwm8kJASN5pYi1T/+IjTNpwnDt3TgsLC8b8ooDv8ePHtqdwDjjBjjWwt7dnODr0Tt5Ju902SBcI0kfZ7E2iJQ/5YuwwhuQDPWOQfYyz6NesJ5n4XJlfl7x3DImvQvckA+aZvM7t27d169YtS5azDz1rijXMHvNRCfvGw21AjUQtPMf29rZKpZJ+8IMf6I033jDarXSCqhDJfdXkuY2Cr2YlfAVfJ3rwm9V7HYScbH6+h2fIhHtKIEku71Uw6WDTQBQsQN8/ptvtWsWpby+9tbVlLKSxsTFlMhlrA+6pgSjb+fl5PX782Lxbj6/ybD7SYSyeDirJDCWeHV4UWDTsEvI23AuD6w3AMBsII+XnkAUPjky0wD1QhOPj4xYNpNNp/eAHP9Do6KjW19f1N3/zN9rY2FC32x1INvN+eecYdd/qwys55owNDWSBt+/fI/PlKc0+Wc2zYMzC4bCazaYqlYq63a7156JILxQKDXjn4PA4GxgN8i/g37u7u5YvgqUiDVJcPR07n8+rUCioWCwqFosZ00iSHfNJFS/JW997ineZSqU0MTGhhYUFnT9/XsVi0Q6kkmRwC/PM3vSQHkdY+m6iROrUdvicFNRrjF4yeXwELWPzlFjppH8UjKJyuWznljA2Puf3NOP3BY4YLc9YY+3EYjEVi0U7c9qTXI6OjovtcM729/e1vLxsjCbgHpLgHrrD6LDnQqHjOgdPFOD5cVB9fo7nIjK8cuWKfvnLX9r883vpq5mA/lSRgvdk4Yxz8lMsdlzdurOzo16vZ/x2kqZ4fyRKPR1uOPOPV8bPPPWSZDXYNhgvCgiPgxwCCxDaYKVS0cHBgXHvC4WCyuWy9Y6fnJw0j0uStSjwTCiwc8+EweuGNukZH3i/GDagMK6FgcAL5z4+9CW8LxQKpgS8h8OYCKulkw2AkcBQEKLn83nLnWSzWUvqJhIJTU1N6S/+4i/0X//rfzXjLcmgCW+k2cTeWQCrpV06kBnPhREmN8PzYzB9vsV71f45MBC1Ws3WDCQEGGBUc/vCN/BlnsUnIePxuA4ODsxB8GQBD53xXonocrmcxsfHlc/nrfcOVEafK9jf31c2m1U4fNyDCyNEPgCWzYsvvqiJiYmBJGgoFNLY2Jg1bvRJZgydP7uD77KnMD6sGfYSSl+SRRmsO94Fn8FBg0yQSqUGWnv46Il14MkBwHM00SMX5QkmIAOcgzA5OWk5HBwmxk79x+bmpjY3N9VsNg3SgZJL/ojxNBoNi9zZK/RrYw6IND3jimiS5ySSuXLliuLxuF5//fWBRD5O2FdNPlVDPA/PeC+A0vxOp2O95VmYvFyUpS+C8kc7cj3PoiDk8xPM7713hSdNUorfg3PCUsnlcjo8PNTGxoZ1XBwfH9fy8vIAO8ZDSKOjoxobG7MjGf0YpJOEKOwhlIZ00lDNRwjADt4jQjnxM0kDEIrnVbPBUBR4cBgWj2F6Ngf3AEYhREY54wlRSDQzM6MLFy7o1Vdf1Y9//GNrI8D7w2v3iUiSibCcfIJWOtkowGJAAMMJb6IalIWnkNJEDsgA/jzPeHR0ZNRXlDg4PFAe9QrkFiRZEnJ9fd1osNFo1NqDoHzxQDG4QEN42NCdWXu+BQVEB9YjCoY/5HrOnTun69evS5I2NzdtPhkTiVVfxOk59p7pxHP5Ik/m3ked/I49wDVGR0eNvMDaY36JLjkxrtvtGi6PV020yvsFbvbRrCdKEA3ncjlNTk5acplnwxjT9wmnZ3NzUxsbG9rZ2THnQZJBhpLsTBSqxXHeeLcYUE+e4d34e7GHWVeFQkFzc3O6ceOGbt68abnGYYP4VZFP1Trbh2UoQZQdysxvUF4Mm4rw3Yfq4HUei2SR8H08YjxyMGI8q9HRUesd41kGPtzFW6pWq1pZWdHFixclyYqZOp2O9VDhZ8Bcp06d0uPHj61vCmPBk/dFNxgBDwOxsDwk4pOxbErmBGXmFyceoPdU/fOxAPHiJA3kF3wegsRoq9UymiBj6vV62tra0t7eni5evKilpSW9+eab2tnZGaA0otzZJBgFX0nMBvabHjyaeeHfJPHwYjkv2XuQ1KLwPpnndDqtw8ND5fN5Gw/evs8feFiDuhoiPAqc+LyvpSA/hCFGIfKO4PjDWhrGq3keDMW9e/esRgYFCtZdKBSsACscPqYL07Hz4ODAjMvh4aFRdFGCRErei/cMrEwmY/PG/KOEPezaarUGmiOydnjvPir1EZekgWJG9ir7FcU+TO3FQ/eKN5lM2sFYrGeP1XtD3ev1rE0Gjhs5M/abj6hYo+QH0TncB/iYe3gWJDoBokC3e9zapFQq6cKFC2o2m3r//ffNsfkqynMbBapKKYhikqSTl8kCowQdBdLtdu3cAzY9nhGJNl+569ktKDIURj6fNziKpmecnEUIS+tbFjawTDqdtqQai4U2vEAZKysrGhsbM3ZFOBzWxMSExsfHValUbCwoOhY9C84zKXh28gLeWAIbeGyaZyZ5y/hJwE1MTFjYDBZP10+Mkt+kGCIMBWMCTiEpDmUQw9RqtfTw4UN1u13j6WMwgOHwdtnEMK1ItrIpPIxA1IAHybjA9qWThmi+6M5HOMwlORpqKrgeG51uqozB14/w/Mypx5eJSvr9vq0BnCGKqEKh43YSKHKULnPHgTMkYlGO9AIistrY2DjZiNGowTa0mAiFQgM0ZvIlvkIXg4eC9YqPmgAP60FsYB/45C6OFu+B/cv+9IWL1WrV1gwKnr3qjb+H59gPNM6juSF5Kd/wkbohoDbppN8YJ6yxl6D8AidiDHGWoNNizNkLzIdnPPqkPw4Ne5NcEN2BqSMiOiLx3O129eDBA9t7Pr/gkYQvq9H4VIlmLKU06AnikeIJU8nIomQhoHx8IgmWElAG1/MJMfjpLMJ4PG6tDPDAgKeINhhvrVYz/J6CMc6cxWAsLS3pwYMHevr0qWKxmCWQ+Ew2m9Xp06e1vr5ufVBQ4D4Z7j1RvBTfPsIn0T1khnJl8XuaIxuq1WoZ7DLMwOh0OpYLwOtkoXNvj5vjUW5ubmpkZMROm/N1Hb1eT7du3RpgVQATEgXxPtigjJ11wjyxYT2hAHYO7BTPavHnEKOwfZsF5p05kE4ozpwTwfuA51+tVg266fV6KhaLmpiYUCaTMeomnr838hgC1qXPJ8RisQGGXS6X0+Lioq37er2uzc1NRaNRIzWMjIxocnJS5XJZKysrWl1dtSiMZPHTp09VLpdVKpUUjUatqFKSRSzScfSOEpdOPFz+Zs2xXng/7CUMms+BAefyfn00haPB+mKOaZvCO5YGE6w+OsabB87yOR7aXvMHFEE66S10cHBgpIJSqWQQ4NramlGOMcREVxg39pzP0QC/EX0MGxqYZz7ixtEgiuDzu7u7KhQKevXVV62FDuLpqkQ8X1Z5bqNAQykmFgWEp4/HyUtOJBJWxo43R8jK5sKY0OrB1yPQ2AzPjwQpL2V8fNw8Amoh8BQ8fkzbX+nk/IJKpWL1CiwE7vX06VPDSimUiUSOT3oaHR01PJKFgncknfDEmSeUOz9DGfqQnTH5HAkKkg2NwuUe0F9RykdHx+192eScHofXyzwzdxjwRqNhkQCbqdVqmZKEscNBJSgQohvaFhAZoaj9vzFSUDnB6CORiHXTjEajqtVq5nkzRxgB5picAQ6FV4DPijB99EBUVavVlE6nNTExoXw+bwbHw428e5wLjKAkFYtFxeNxbW5uWmRGdAY8yvrJ5XKWbKYlC3ATvYOazaZVYRMV3L17V2+88Ya+973vKZfLmRFqNpu6e/euHSXrITeP0Xsqr4depEHmF0aI7+KlMw8ofKI0IhacNvbdMFTmCRisOb4PFMPeQMHiFHIWB9dj/Kx9ojaYhZBWyuWyKXecgm63a4cn8VnWDHuUtcV+Y/wYTtYxz+RZTOSt2IPValXhcNjOs6c2BUfIr9Mvszy3UWAzwAaibTWJTsJ8j+l2u13zBpvNpiXk+B3Qys7OjikENgCLHSWC8eH4xn6/bxQ4issIt1EEfAeFDGUtk8loe3tbY2NjkjRQtHJ4eKhqtfqRhDNnu5bL5QEOPYvPF8iAcXvoCuPFQvTwG5uaZ2AB++rlYdomhoXveTqs96bB/aUTD4XfJRIJo2vW63XrLomn7pkWFA5yHcYqDR5qzj28ckLJ++iQMflEHO3EeU4S1IiPMFHSGFLgSd8qWpI1HiTZm0gkNDs7q4WFBaVSKZXLZTMesKA8nZWCRI6mpCkbuHImk9Hp06c1OzurXq9nHUHHxsYM8kF5rKysqFwuq9lsWgO7Xq83QM4Ih8Pa2trSG2+8oUqlogsXLiiVSml5edlIAP1+f6C+xRtmH7F68gNr23vNOD1eUbJXULZ4wrxD9gmRMGufiI//e0XIGvDQqCcYwF4Er6d1hq8Ul46jQc5AmZqaMhp0pVJRp9Ox2guYkRgBICMq2PksiXb22setZdiIVItLJ1EZThJMJJy8xcVFSdIbb7yh7e1tW7/c48scLTy3UahWq6ZwiRQIqaWTvAKeKF4QybJ8Pj+AdbO5vWVH2TBZKEVCOUJ5lD4vnXt2Oh0rogqFQioWi6YMiGBisZhqtZr1XAmHjytxl5aWtLKyYl4RShIDNDJyfFD43bt3rVOqT6CyWXq9nnmMRFMoHMJslJh0Qj/1uRNCdvjVeHAsYJ+ElmTtwvG+8HA8+8HDUz7J2Ol0LE9BCT+RG9cD3gJO8DAQBsnDBnzGM9Q87RZIDQUEls8cQiMl0vE9hjwsxf+pfGXcUB0l2Tnb0ehx1Xo+n9epU6c0Pj4uSVbkBe+e5Gk2m7WN7nMG4+PjOn36tNEgZ2dn9eKLL6pYLCocDhuRodVqaWFhYWD+5+bmlE6n9atf/cqOmsRbRvkCoRwcHOhnP/uZfvaznw2c8oajtLu7a8lRX3jmlRr7ykfoKH+iUb/PKIQjaoTlQ8ThcygeqvUQHoaOveXHzF7B+LD/fbI9m82qUCgMVJfzTJAjSPazBprNpkFT5DiAfhirpIHokugV40a043OeGDqYRIzdG12iV+aR2glJOnv2rCTpn/7pnwzy8kbuyyrPbRTAnL2Vw1tkQqUTLxIvPRQKGZwBq8J7kz456qEYSaYQSCxJxwU/KAAqjbHy0slRk76HfCKRMGYKi5vDOIAWFhcXdffuXa2vr6vfP27IRmM0nuv06dNaWVkxg+crKocT4j45RejMgpRODsZBkTEWH0Gw6VG85AJYtHhWYP38QcH5cXBNlDBGCViOJnck8Xkur2RY9Gxo4COPwwIX+PYVYLy+YM97pMBZw5WlPgpjY3qnAUYNeQPmJ5fLGf2YfADfQTFAiECRr62tmVLOZrPWEM9HS9IxtHT+/HlNTk7ql7/8pQ4ODoz62ev1NDExoZGREZXLZT1+/FgTExN2n0QiobGxMc3MzOiDDz7Q9va2UStptQEM5ZtHsu9wfIjAfcTnPVu/f6STtuihUMgOqiEi8mwcIDugOt9Smmuw1qh6Bjmg7sBDq/ybOfesJ+9h857ZOxhgr0BDoZAZHA59wpCtrKzo6dOn2tnZMV3EnuHe5Hh6vd6AsgeuJJpAz+B8VKvVgZ5j0klewVc6e+iRvdpoNFQqlXTlyhW9/fbbA5T2LzOE9KkoqR7vZfFhQVFmvCg8Qbw4SarVapZ3CIVCliTDs4QTzUKl8AQ6Kx4bYSPXZ4Hj/ezt7Q10MvW0T0JbElNjY2OmrJeWlgxy8ZRGFlE2m9XCwoJ1rkR5+g3HYpdk0QKKzmPcPn+CsOn5nMc+qYplA3r4LpvNqlarDUQR/h3B6cezYYx8HmyZo0Yx0kR2+Xxeu7u7A7gy75BNSOTEYveG2nuJeK7eY/QOBOvBK2QMmVd8KAxyMJ52PDo6qng8biweqtoxOqwV7yQAHZFLaTabVhhFbx9J5ogsLCxof39fKysrlmBk7Lyr5eVlbW5uKp/PD4xpY2PjIw3oMN4k2jFERElesXtCgq/C9rRJ6SSxSvSGR+5PNGQucUDwkD08SR6AvcD1aW9CnQuRMQYC2JP30uv1DO4FiiH/yHqntT2ODc8hyVqis6al4yih0WjYeSTsOUkW7bAmMcB0VPVG0EcCrD2cHFANSWYMiErobAujjZwfazWRSOj69es6ODjQL37xC3sPPvH8ZZNP1TpbOsHSWHB+UwNBUFXKImu1Wob3smiBnyTZiU14nXgKKE+ssA+Js9msJes8NITHzsLI5XIDSS8U0cbGhmq1mgqFgm2Q+fl56+lfLpfV7/dVKpWMiRSJRKzVs98o/Buvh83tE6ReIUoaYHCw6Alp8Y7xSPB8fOK41ztuB95oNEwJsbD5gwIFm/eRnWcEeawexd5sNm2uJNm7Y2zxeHwg4e29WX6G8vKeo1fEbGyKG8kpUFkMdOi92V6vZ0ViGC/pBBKLRCKWC+J6jNv3QmK9YXT5PXOZz+fNyGMYDw4OtLa2pkePHqlYLOr8+fOqVCp68OCBYdye5XX69Gm1223VajU1m00Vi0VtbW3p0aNH2tnZsToF7yzwnqBLEmURjXnCAOLxfg/lQCP3+xUiATkT30iRNeHXJFROTzMHqsH7xjgxbtYbUTJJcvYJOSrmCWeOuffvxTtY1MN4NhaV3fH4yVknGEHau+PtS7L7QADxsKanzXqGEfAo65D9jJFmrfN/rgG01O/39dJLL6nRaOjDDz/8+uQUaGnhk4csArwASYY1t1otFQoF26AofPBplLYkLS4umnexs7NjiwQF6KMD8FP6xaMM+A5Gw0cuHtoh4bS+vq7V1VUtLCxYiCwd5062trbss+Vy2dgQkUhEY2NjGh8f19OnTwcYH96jJSpAQfpoxhs4T29jLvHk2dxwx5vNpikJPBKS9PTV8cVKnlqJ0fRYKYsYTNfnBrx3x/cRDK9POnM/n2xjPv27HvbI+L7HwtmQw3CPZ/h4AyCdHMoUDoeVz+eVy+WspoS6GhK0RAQIHi65I9hbUEdLpZI2NzdVLBYVjUbtzN9QKKRsNqsLFy7oww8/1Pr6uhYWFuwaJF8pcrt165bW1tbMKKyvrw90xUUZ+ogWLx4o1DOtfF4HJcp7wZtlzRNF4KXi3Uoy41qv1wecGw8zslZIDtPtGIeD+hPeNWMCamKNEO0wfhwSn6NgPwAz+7wZ79A3PSyXy9re3jYD2+12ba2zj7g+BXH5fH6A8u3HgtPDukylUgPV0xhf5geD5JsW8u729vbsPaXTaf3oRz+y6PLLahCkT2EUYBHx4vAYfKiFIoYGCtbOhLNo/P8lDbRZkE7oXyR0PZUSJey9Wh+peKob3oYkU3reSq+urg7ANclkUtPT09ra2rLCGrzL6elpY5OcOXNGjx8/NuMhnRhDNhXj9OwbX2TGuBBf78HzelaID/9ZUIlEwui1sVhMExMTBmN4ZY6S4pqxWGxACUka8FahSHo4yL9jlAeKEUPAs3u6H+uFZ8Fj9ewhIolsNmv4L9/zTKJo9JjvT9hOlTJOCO9hb2/PzutFGVC4hDFC8bBhUUJUJTMXMLH6/b5VFzcaDZXLZWMycUYyZwf7PMvu7q62t7dVLpf15MkTbW5uanl52Si47B88UCAu1g7XAVIikvL1PigxlB/fwwFgzfnojbmFVo5RRYim+L6HgIYTr36dDVems99Z3+xVaOok22GPTU5OWlKddUzCm6gYPSGddKTFaHOMKLoJhwnCBusLHUDkyTrzyh94iWf2xXvkW1hrEDy88YU5ybPEYjG99tprarVaxkj6MsqnOqOZ8Jj/owjxANhAPvTF8sdiMUu0SCdFOCgpXgCKxVe0kthl43e7XaOtAXVIMkVMxbV0kqSjDJ6FTPUyXg/fPXXqlDY2NqxicW1tTZOTk5qYmLDn5zB06USZe6XnlRTPGo/HbUH7pDJKl03qFSpeFvf1BULhcNgOakFQvoTm0klthh8jG4kion7/pM05jfJQVPDGPRQE1krUIJ30NYKHzybxeDYbzbM3yKv0+yc9qvACfRTjPV5yTx5WqVartm6YVyIwPNbp6WnD1KlI9tRCevjgkfriJQ9Rbm1t6c6dO4rH4yoWiyoWi2o2m7p//74WFhYMtjg4ONDq6qr1U1pbW9PTp0+t0BDP1HvSnh3EnvPQJwYd7BpPGMXvc3/+3XA/YBRgPPJTKGv2Eows3hnK288Xe9Hnt4ZhThrRsT/93qbOwXfp9c6UT1ZvbGyo1+vZQVgodXJdOCCefothxIHFk4dl5Z039m0oFBqIGn3iGqHuhGjD60aiYhxAOjQfHByoUChoampKr732mv7pn/7Jemx92eS5jcLjx4919uxZW6B47uCQ4GwsPjYkL8MzJPi+x6E97umteDQatUQfL4jqwV7vuO0CiSmst/eaUEx4jYyj1WrZhh2mI5ZKJcOm2ThQYqXjcxYWFxdVrVaNaiaddHDkHh7mYD582CoNGlcgJDYzcy2dRE+0L0AhokRowoXyRBF7j9/XM3S7XUv6waDJ5/NmiIHKjo6OG4hRUYwRxPB7bjbeHx48uLOHBIYTyPzf0xiJEvymRMC3aXSWTqeNGEBlPNfziVhoqcwrXTExcDgGfq5xIugSi1LjCE3WII7G9va2MXOA/VZWVrS5uamtrS1riOdhRlh9JPuBlDCSsG18dICyZKzUQfAzKL2eZYNhJzcznExl/6LcPHzJWvZ4uWfiMAc4Kh4+A/byhsrvDU8tJzrD0EuyuqH19XVjhTFftO3e2dmx6MRHpzhHsVjMYCPWIeuEvAtzKJ04OEQXPkrCOHgdRiEoESmV5hA8oKgz37Ozs3rppZf0+uuv27vEuH8ZmEnPbRQ+/PBDLS4uDiQ+vZfDg5FwgrpIN06f0ASqQaEw8Xgf3uNEcXiD4nE9zkbw4TSeVLfbtarZXq9nOCpKcG9vT8vLy9Ycr9vtGuXw/ffft8/u7++rXC7bIguHw4Y37+3tqV6vDxzzWS6X7fngabMo2GQef/dG1udBpJPEnnRy+AebCsUsyQ4LQonzOzxOSQNeJU3cSNbzDD6a8NfwOSO/uXEAUEDeC/OHlQARAa90u12jGPoahV7v5LQzYErPePEkBd4/BwmxBvg80MPu7q5BijwHxhGldnBwYLRE5pgkK89AdATksrq6aucjNBoNNZtNra2t2Rj39va0srJi1FO8WpwooDcPq6JAfL4BY+rnnHfKZ5l/DhtC2D+1Wk3tdttgGg8venybdQpsJJ0UPfpkLHAkfzzsQjRLBMj/2dc+r8X3yInh5EmynNmTJ0+0tbWlQqEwUJ9Qq9W0s7NjjhaGAYXsk/g8hydfEDV5+Ij3zlrxjfsYk3fqqPbnmVijOK/s26OjI1UqFYOkL1y4oFqtplu3bn3EWP6u5VO1zsZbR+FTEeqTuD4h5rFzP7Ekq1A43nNkciRZTyMmme9yPTY+/HnvNfnEKfAWkAYJy/39fd28eVMvv/yyisWibTyO7nzw4IG63a7Gx8eVSCRUKBSMmz8zM6NTp05ZCIgio/yfIiygLnjxMCj8YmUDkUeRNOAtMC+evQTcEI0eU3nPnj1r8BKJXooH8dRZqHt7ewP1DoTLzWZTGxsbpgyZI+AFr6yIzGg7QJsNoARonnjohPJQhIl0PD2W5+IzRDgoZFhKvGufVCcqIJpptVoDTopPIB4dHRlRAe+N+fKKGe+P9cja7PWOu3LCi2+1WgN5KJ5lb2/Pcj7AbqwLcHXGDXRSqVSshsb38KFCF4eKnBCKHUiHiI53g5PEgT7Dlek+uqfFtIdgpBPOvjcijAvPGafPM9xQlBgElLZnx2F8WIP8LBKJGN2VViwkmPlcuVy21uLsD89kyufzA+dOEJV59iDOG4bAR2DSSYdmFDbwJIwmfu6jZebKO89E9zx/NBrVa6+9pmazqUePHn1pogTpUxqFarVqC+nw8NA8TTYVk4rihdJFIsknWfHQfPjtPXwwbxJaeJDRaNReNB4GL8VTEFFi/IyqajYmm2tra0vLy8t21jH3unz5sra2trS5uWneaLlclnTM2Mjlcpqfn9fDhw+tlbE02HZXkuVa8D456hHl7ROleCCMw2PqKHF/MIrnTpN7YT4xAvCnweoJZ1mcGBIite3tbUWjx03YmD+MKsaJ8J1NAfRBxMA7xCv28APv0CfPgXtYLyQkmQvPusEYeChIOgn5/YFAGE7G6OtKaLJI76Nh/B4lgWdLDgOlcnBwYO+dzqhHR0dm+CuViiqViq1h1nGhULC5x1smP+ZrezyTjH5bQHMoKZ9vIgfkCyFxCqiJGBsbM8XKOsAAeogEiArnxc8/n/X5MA95+tYZGBzgTd4ln/cQDPAVlG+MA/pienpap06dGog4MHQ8J5EX1/Un5nnFz57jHl7HsXbYV0S0MJGIcocNnY+Qhgkh7C/2HQYnGo3qlVdeUa1W0/b29pciSpA+hVHwUAUKhqiBhJcPsbzl9CEmxoJFxEtACaKUQ6GQHTrvjQ6bCM9md3fXzj+QTvr8YzD8wuKFYBDwbG/fvq3z588PNO07deqUzp07Zxttb29PjUbDit0wNMViURsbG5Y7QaGxODwOycYjsea9E7Bd5ohN5xlMw0qS8JZ8h4cV8PQ8qwmljte+s7MzkCvhvVQqFYsWGHM0GjVPCwPM6XmE/aFQaIDG6p9LOqmelmQGjM+Q/8F4kh9i3vCwgTHwVH3+BMUFF9971NJJtTuRBLj07u6uvReiWJLXvPtYLGbRAeOo1+umeEh0o3Ap0GIfeHYajguetB87BI1I5LgzLt1dWe/k14D0WO84Gb4TMbAldOZCoWBrkudkPlG0QB7AKCg3XzeDsA94bk9D9nASa4vvemMLxMjaJE/V6/WsncfY2JhOnz5t50Ew/o2NDe3t7ZmjxN71fYxQ1J7pRR6NcZIXGE7UY2h5RpLiRESMgwp6X+fhiQJEJVCUqfHhAK/vfOc7+od/+AdDCX7X8txGgQ3vvWASZfwOyCYSiZjS9bkEFgIJPDYsE4yykQb58HiYhOrci0WIUQLD92ymfr9v3hmKBMgDBfb48WM9ffpUZ86cMay/1+vpypUrdrxfu93W9va2tVuORqMqFou6ePGihfywR3h25o1FBDUNZoovavPhuE+68TOMjN/Evq0Ec4/SwwND+XjvnUWKsvGeD8oLyiXJtmw2a+wcqkd573hAvpAMJcA48KT4PX9gQDEGFDieOdGMJJs/2qaTPAXigQJYr9cNYsLp8AoLo0D0w+9gnkjH1bN4+lSDDycOMbo4DDg85GvA+n1RIvkclFKhUDDHJxwOa2xszMYoSdvb26pWq/Ye6Jrq+5ChbD2N1D9zJpOx90H05iE3cj4YCU+NlWSRJgaNHAiOoY9sYVah7Nn7KFMMAuuAfYlhZ9+Xy2U9ffpUT58+VbFYHDh9jQgKlhjRCFAlJAT2ulf+6CGS4qx3HDHv8LIPcS4xcJ79BURHhMs+wyDyTBgz9KMki1bn5uZ07do1vf322wOsxd+VPLdR4GVGo1HbcIRw3iCgwGmX4DP5Hg8npILq5j/nFaD/HHkM+otwCApepG/LjKfL4mHD45n5atVWq6Xl5WWdPXt2AFPM5/M6d+6ceeO9Xs+40hz+sbi4aF1GNzc3beGgYFHufm5YgMBpPsrySXuejdCVOfeMHs+24Hf83nt+PmyWTg5fl07CWx9x+KQXPH68Nwwqhg6PFIhL0kfYIPybZyRXwTvDuJBEplIWpYH3jCKUZHRKDyXhCaNcfFEUa4L7sXmJVMiHAL1gjH1CkiJLxlGr1Qz6w8B4OnSn01EqldKVK1eUyWS0t7enarWqDz/8ULFYzKJelJCnMmLAPZMKI+5ZQz5i8rAM+8hDs9BzybngtZKgxrgTReEp47jRnJEx+EgMg+rrIVCOPrfgowh+TpV6NpvVwcGB1tfXtb29bZToRqMxQL9utVqWT0CHeEgV5UphGZR4xkWdi88JYJglDeg6oGgfKeGUYsBZY0Q/w84prfkhVPBeqa+4dOmSms2mbt26NbCnPWz624KXPhV8VCwWbQHhJeFZo6Dw7P2DSScceg+r8DM2EYufBcMJTCxQLGsikbCwGmjDw0e+R3w0GrUkKElSegXh1ff7fd2/f19Xr161BnjI3Nycjo6OdP/+fXU6HT1+/FidTkcXL160zba4uKjt7W1rkkcFJGE9IS0biypb78Uyd35DY/xIDksn7Xd9VIbCJeKQZMlMvCcPGaC8gBi8F28L4/+HiyAXAMUAtxA9cWSidOKp4pVi/IBqUEzMkWd2+PlhzRCZDnfcxbCjkDyNlrlmXcAswdMnWvLdPFHqsLaIJDDaeJZ0jPWdOiWZcW00GlpZWTGePo5RoVDQiy++qPPnz5vz0O/39fTp04Fomvfrobt8Pm9RHeu91+sNOAl4wsNUaA9F8n+MCV6yr1T2cB5zjKHFmKJAyXuwvmAs8f5ZQ8y/dwwxBqHQcZt5IgXmoFaraWtry3JxIyMjFkGhJ54+faqVlRUrViMaxwFjXhgvhmM4qvDRDvsKJ4GonTWVSCQMGYlEIraOcB5oHYLBRB+xnj1hhM8RUebzeV24cEE7OztaWVmx9+j1529LPhV8VKlUBtpU4JGTTCM8xmKzKVh8Pq/gKW9AR2xez2Ta2dlRv983RgGeH/+mA6uHqBgvL5/IxCdX4ZJHIse88ydPnujdd9/Vj370IxsDSn9ubs6SQZ1OR48ePVIodNw1Faz26tWrWltb0/vvv28KkWfzyTU8NZSBh5BQ0CxoFh+0yGGOOYqH4hhJAyE7yXkgglarZfgrm95zwtkkkiz0pugOZlS1WjWPkhbHKAuMrIeyht8BBgHaoCTzxNgkKGCPBbPRvScLe0TSQC6D9YlXLcnujeIHPvLUZ8+rxxMEWuj1eqrX62bMMJhgxCgOz3hj/judjlFCSVrPzc2pXq9rZGTEok6UqFcsRBD8wXixzjFYPDPXZ5/57/hcime78fw+ge2TyJIG9iCRIc4C88+7QcmjBHHafG6Q64+NjVlUHIlEVK/X7ZhLDEyhUBg4JKff75vRgMSCkfHOh6/SZi/5PAfQLsYDAgFQnYcucVo8oSEcDg+cC44Tw3dwCtkPXBs9xPuBFp7P5/Xiiy9qZ2fH6OEeNfhtyXMbhY2NDT148EBzc3PWVsE3vMLLQFF5Fg7/Z1MweXhqMCpI8njqIAYFBoCndPEZD03BdorFYqYE8MzBTMGrJZmRabfbevfdd7W4uKhz587ZZmCBnD17VpVKxTpvotRYdOPj49b0anl52RQSnoh0bCCA3mAwgTV72AqPzyeeWSB+s+JFofClk4S9NzYsKp/wxSj7xKenF0LVQzExfjY5XiIHxLCJpZMqal/Y6PFclGen0xkoeoRSStiOoWAuMTTUpWBomIvhpLrHwHle6LVEB0BJ5A08Ru6T8z7sZ13zXBjxqakpa5vdaDRs3dG6gVPAfMTqx8a9/XhQhkQ2Hq7B4Hrv2EM6Pkr2kTswErAr88Q7Go7ycUyy2azRgtk/zIGHj3GIgOdwiFjDzCO/i0aP25dMTU1JkrHBMNSlUsmuiRJdXV21hDH64+DgwObYw1PsEwwW74/oBQNMhAtFmKiSfcWz0gVVOoHVGZfXYcCRFHWSh0AneOr14eGhRkZGtLCwoEuXLunmzZsDuZffpjy3UaD4olAoGI7ncXMUi0+04i1JJ2cj+AQsSoYNgVX1eBpKUpIpGp+s4Xp4hu1225KkXKfRaNj92YBUfHoMsNFo6Cc/+YlyuZz1OmLxFAoFzc/Pq1wuKxwOq9FoaG1tTfPz8xY2X7p0SZKshJ1ngQ3hPROUEWNnTCQReV7vyedyuYGKVzxX6HhsPOaT5BrvgE1ycHBgEdjo6Kh1pxyO5IjGYNgMK3mMB8VQjUbDzhvmffjkJsaP+6H8MEY+10RhH7CZ92Cp9cDDw9BioL3Hy1xxfSIDNr8vmvLri/fiFQmt1HEkGA+UTxQ4uZJUKqWjoyM7m5lnj8ViRo/mmowVhwhF4bFrnCGML3UxnrVFVOBJIEQf3uB5VhjinaxEImEH3UgnsKXPzfnve8ybcQODDnvr/BzHQjqmslIkShQgnWD/fg3QS4rrsgdwmqA2Mz8k9T3NF2WMDvKJbJwX33odowtRAN3m1wvEAgx7LBZTNps1B4V7k7vAofAObiQS0QsvvKB6va6HDx8OOIy/LflUiWa8fZQHeDw5Bn4Gfoe3CSMA2AQlyoLB0yNE8y8OrBB4JJFIGNbearUG6KLcD4+Qz/uNxoJmMTLhFPxUKhV9+OGHGh8fN4NDwvvMmTNqNBq6ffu2dnd39cEHHygej+vUqVPmxV28eFG1Wk23b9+2FhhEJ/7cAIwqWC4YL0kpzxZhHoiymK+xsTHLn7DxPfMEg4Qng5GMRqOWpCdqAkf2J25xT9+Z1BsED+ugELmOz43gNTEmzybCIA6Pk43DvKCMPROGteWT50AjOCWeDsy/KTBknVGYiMJCUTDP0WjUYA02LsaE9YcHCEzlGTmseSIHsOhGo2GRCPPqc0ZcNxwOD5y7wV5APGGB/YT3jqfrDWW/3zco0Xvz3gHDu+X/rC3PbiIC9Vi5dMKuQxFjrDw8SeRBVEMVMwwsIoTFxUVrncE6KJfLarfbBgETIdG3C4XfbDZVq9UGYEjuwTxJJ84CSIKfa/QextJDuolEwvYvRgRj52Epv5ZAWPg5exsDlM1mlcvldOXKFa2urhrc96XMKeDFhcNhqxpmc2PJWVxgbkdHR1aajsfFhLPBUYzwdkmwectJfgLPpN1uq1KpqFarGbbuC4SICGhBwcQPe7osDnqWoGx+9rOfKZ1O69q1awPRUDgc1sWLF1Wv17W+vq6RkRFtb28rl8vZoo7H43rllVesqyNjhkOOlwGuHwqdFFUlk8mBcJ+NxmfB9r2nivfjQ302P+EsChql7ZOC6XTavFbyBcwR9wEL9SyZiYkJpdNpm8NGo2FslcPDQ4seKC7CafBhO3BPJBJRLpczRejzQ55RRijuo1M8c2iD0okHzf0wzMwTdSIofAyaV87MI0aRTYvHz3pKJpMaHx9Xo9HQxsaGdnd3Dd5i/pPJpCYnJ42uCgHAM+BY5zwD4/U5IHjsPuLmeYBPMKwQK8jXQaP2Xrxvn8HaQYEOR4PMN79n/w8bJOaduhWfG+F37FMSs5AHfCI7lUppYWFhoE02zt7du3e1s7MzYNRGRkYG2HQ4VKx3nEbmF2eCfBz/94YO1h/PCmzE+vQ5OuaPSNsfSEW0hh7z+Q6fO2QMnU5H8/Pzun79un7+859/xAn4ouVTRQpQ5FBaeGgedvCJMh6SIiAofUALeBZca9ibJKSWTgyOpAFvDW9VOilN93xr39CK6CGbzVp4j9fkQ8f9/X298847SqVSOn/+vC086bgZ3ssvv6yf//zn2t3d1dramo6OjrS4uGjVmMlkUt/+9reVz+f17rvvWsGT7/KKMfOhNZg5LBmSh3je0AhRYHhJw8k7nw/x18S7B0elECqbzVpU4yEovFTP0aZh4OLiop1GBmQRjR43heNdooT9M5BU5rqsK4yBT+75cfNs3vCj0DwMhXIlV8H3eLfALBwnCabu8yn87Y0zUB9efygUspPqMpmMERbYE3x2f39fW1tbWl1d1dzcnEql0gDDivt6Kqt0TKnc2dkZ8LBxvvy+I+mM04Qh97CDV3KQP1gbkmwfEQkSBfmIkDH6qJux4Dj5BK6Hw1BqPrfBmsnn8xobG1O/39f6+rpBXxcuXBgoSuU9NhoNbW9vG+zma5M8NO17G+FgElWjjHm3wMg4rkTUQK3eQSOahonGu+c77EvmAAdDGuwwjHEF3UDneXbYuXPntLq6qsePH9v7/m3IpzIKLEDPiYbvzQuRTvDFdDptUA98YR9FsECHQ0uUJZgsHgmLEs+D0BaliDCe4cphPNlWq2WJH++hsQH6/b4qlYrefvtta43sE8ZjY2NaWlrSzZs3jYa6s7Ojixcvam5uTpFIRKVSSTdu3FC/39etW7cGEmB+vHiiPiJiPCxcjBwGRNIAUwbF6VuO8B7A7lH0XlmgeI6Ojkw5gpV6DNuzauhUWSwWzSOCy40ipbme9/78H8buDZ900vwNiIz5YtN6o4ZywZDwzMwRIbqHlrgWnHhyC9644PgA98C46XSO+2tRl8PvJVn0QCTMKWOwqSqVilFVJyYmFI/HtbKyYk4QEYvPqaDAxsfHDQbhmT2u7yMC3quHLPg380AE6BUZDp0nL5D7wLjiKbN+MD6eXECjSdajdIIwEBVikGntMT09bedp03xydHR0oFW9N24rKyva2toyg8C8A8/yeQ83s59wDoeJJkSoOCnscw9fY0BZz+Sbho06ERjsLg8ReQcVI+9JFLwjmFrZbFbnz5/Xzs7Ob7Xa+VMZBap2mTzPjPEbVpJBQTx0KBSybpWlUsk2v98YhFVsDE/d9AsEK+yZJcOeXjgctkVCQge2AqEwsI/3sNhcnU5HT5480VtvvaU/+IM/UC6XMyUTjUZ19uxZtdttOzBla2tLtVpNV65c0dLSknlbr776qkZHR/XTn/7UvBLuh+Lw/HIMgHTCMPLJaRbl8GLiOhTsocjxkICFuC7QC/x/wm+Su947xDseHR3V7OysHT0pnZyJ4NttwND4/9h7r+bGs+TM+4GlJ2HpXbG876rurnbSaHa0O5qRtCGtFFp9gr3Yr7WXupA2ZmND0ivXM9K0q+5qU96QLHoSIAEQoAPh3gvuL5FAs6f9TM80T0RFVZHA35yTJ82TT+bB2HA9lAtRHuuK54037o9/pGUFLDDwZmTSRxDMJ8oHxcdz1ut1K5AjugAaI4cRDjd7DwG5gDN79lG9XjfIjLnx9Fvui7Lc3t7W0tKShoaG1NfXZw30dnd3W963VqtZa+5Go2GUx62tLYPSJBlMFwwGLR9CxMX9UUxAHsw1ipr/41h4rxYl7kkAzKvHuP08+2gWGeM63Lezs1PJZFIDAwMt+7VUKmlzc1Plcrml5Yxn4eXzeT169Miqx9mPHk7jmcmD8Y4eKmb9mT/W2jPL0DU8P2uDUkdR40whnzh97cQB/u2dWmQR+Ufvsb6wHldXV3X//v3vXqTgN7TH3xA2zx33igqhZOOgrFkAFB8em2cnsBhck6IksFMUAPfAevMZLLJvO0EBFjg0+CDvyKKwmAsLC7p9+7ZefPFFJRIJu04oFNLFixfV29urBw8eKJvN6uHDh8pms8pms3rppZc0MDCgaDSqixcvKhwO6xe/+IV11sRQEcGgXNlQGFmUHfPEhvfsK4+3enYWkAIKIBo9OjjeUxBZP0lWk8BG8JsDNsrw8LCmpqYsAiwUCioUCrZB8MgwXF5psIGRJV9VS/fZra0tbW1taXR0tAXbxxDQCt2z1SR9alPhSXtqLnBYo9GwugHPI8cwctoeHhuRMVGmr5aFLs0Rnrw3a1etHvXa2djYUCgU0uPHj9Xd3W2H0PvaE2SCk92QCZwbvxf5GwaPhyiYf/YZssN+IUFOxIEMsV5eiXr4EcOOAfcto73j5g0Iz9kOzxaLRaXTaaPqLi0taX9/X2fOnNHU1FQLBAUM+PDhQ6sR8hEPtRLIMYbeQ1V8h71FpIB+IZJGR2CMiN49eYU54joeXiUPRtTIfXzfLRxLYD9km9972LS/v1+XL1/W2tqaNeT8tseXSjSjrD2ThYf3goVH19fXZ3zwSqViCpEMPfgdygoFweJ4TBclxWR6r5mNiGfHtdjcvb29FpGw4NJR7cX+/r7GxsZamuF1dHSov7/fNuPbb7+tUqmk//Jf/otR16QjCGdqakrlclmLi4tG152bm1N/f7/Onj1rBU5XrlxRLBbTe++9p9nZWQsHCSnbWUMYOuoufBm+96B8Ul5qbR5HLQX5A5KfNIAjUQq+S0GX1MzhIPweZ4aGymZjbfGAvBJAyL3SIAlHJJPP540RxTsfHBxocHDQDD2eFW0mPG2UyNXnCXi/cDhsawuzJZ1Oa2Njw7r8Ek361gf83CfrvROC8vV4/HFeJQqrv79fExMTOjw8bOk27CPQw8NDZbNZra+vm/EjyvPceI9Ze0qjJyN440QBGMwcevrj3Pm8i3f0MAIeUmmHIFljT0H1c+UVIUei9vb2amhoSOPj4+rp6VEmk9HBwYGGhoY0MzNjZBPmFSIDCWaiMebXR5msGd9nnkkaY/DQMXyX6Jp9xTPX60fnsPBz9BIUdq7TblBwNDDK6Db/fBR+Itc+EvHMueHhYc3MzBiN/NseX6rNRb1etxbTTDrGwjf5gkmCUoLNgHKjzkGS0fMwDggWxSO+EpokD5uFdsP08w8EAlZxynPQWbGjo8Pogz5pHIlELEHovW4EmpCd8O33f//3lUwmzYuQpMuXL6tcLuv+/fu2sM+fP9f29ramp6c1MTGhaDSq6elppVIpffTRR3r48KG2trbsgHkUDJ408ATJLB81sVERGp+f8F0+UcJsKkJqXwUaiUSsoMtvLJQAcyAdecZDQ0NKJBKmfCKRowZ5PAN4vdRKCOD9wHojkYhyuVxL2/FcLmeGnI2ZSqVaqm8xgrTfkJoJXdaRd2RT4qiwIYkYgDB4HuYFY4WcYDi5F+c4ey8UeMRj9ZIMQz979qwGBwdVKpVa8hlAmuDTmUzGDmPBmJJfwIkigmLPkOxEHvg8sCwN8SQZVIisEykgWzgDyL+P+LwX6/n4vC+Kuj2/SOKU4rd4PK5UKtVSqxEMBnXu3DkNDw+bs4lB2NnZ0QcffKBHjx5ZfhLWXfuccD32uY+iuC45EW80/Dq3owvAur6IFHnxbC/2G/oHh4q18/kZCu3Y88gPTCVJ5tQEg0FNTk7aSZHt8vVNG4ov1eYCilwymTSLjULzfHWPO2KFUVYod5QfC4th8ArRey0IHUbFd2Eld1GpVEz4CBV9VEExCdAFCq9UKtkGo/dRX1+fFf/AiFhaWtK7776rGzduaHBw0N4P7G97e1vr6+vG5IHVBG0RY/fCCy9odHRUs7OzunfvnvXiR8l51g8YM0aAuZPUwrri/T3fn03rPREEGzqfP6ULqEBq4vXQiSORiEZHRzU6OmrRBh46+SNJtl4YVgQe5Qyktri4aAaRUavVDKfHQ0ehAYfxc+Y3FApZ/yW+5xONPIsvViT/Ua1W7ShXKJ+ZTMYK+sLho/5P5EY8fbEde+d9PcRK9MHpbHNzcyqXyxofHzccHDo2JIjt7W17duBNYEbmCMWNwsVzRakQOfD+EDp8zsBDiD4/6PNqfj/6ZLFXbHjSRFNAgjwr8k/yGuYOVcqswcjIiMbGxloYh9x7YWFBH3zwgcGTOEXANvxB1j3LLBxu1skQ5XsZZf/43IvPJ/AHBwgdhPPKeyMjJJD9GrHWHk4PBoMGcSNP3tn2EQ667uLFi9rY2GiBTL+N8aXgo2AwqDt37uiNN96w84t9GIkX4hU+noh/ATwkJsi3scaa4tFxPU9Ngx+PN+ObvOEdMcFg4UAOGKe9vb2WzcJi5HI5dXd3m7KHfQAbZm5uTjs7O3r55Zd16tQpu09fX5+uXbumzs5OraysqFAoGO5eKpV07tw5nTlzRsHgEYebpO2pU6f09OlT3b59W6urq5bklGSRkYftUGasBxvU890p9pGanUT9ZmE+fTiL94SXhHHF0/LJwmCweVqXP6SFz7TT/5AFf3YzLJ52L4c1Japj06BcUEQkUlmXUqnUMnck9FhflAgYP8YFxQuLjKaLQAZg1z5xD/PFy5mHWbwXTiuLarVq+RKKr4AvMVLkOOgf5bFyHAWUSyBw1CqFOiD2B3mkRqNh74JhwKuGzODZa6wZn/dQlR8ofJQj787e84lW1rter9vZI7Rhj8ViSqVShhpQ2MccQj3OZrO6ffu2stmsvQMOD84N/bQ4w8LnEHydA/Pj+2L5GhwPl3l5xPgA83loyEdK7TrP6zscuFCo2cvMJ+j9vX00TfFqd3e3zp8/r7t372pjY8PWAsP2TY4vxT5iQ0uywiyEh4n02D+Lh8fuS/Kl5qH1CDIbgw2YTqcNokFhsbj8G695f3/fEqU+pDo8PDSPFJYHC4snBzbrk20UXKEYJVlrha2tLX344YeqVCo6e/aseSmJREIvvPCCIpGIPv74YxPSpaUlMw4zMzNGcY1Gozp9+rSdKnX//n0tLy9b/3yeBViI50TRATF4RQxMw/N6b8cW/f9FXuCWnh9frVatEZ6nJ7LZaSGCR7yzs2OH0lODwneQB2QHYw+MJbUqGanp/dBfC6YTCXfvPXuZoE2F33xEKdwDQ9jf32+fBXbiHADPbkLpsI7ekHJvDxfxx1OwIScQdZVKJW1vbxss6ZPtPnmLciCfQxIaOQUjx7Fhr2G4gRAxiCgdTzrAG/ZJZ5QV3i/v5OFEnA+vG1qUSjjc0moDWjd5vsHBwRYHZGRkxJSoj3ByuZzeffddzc/Pfyp/w5oir7lcruUIVpwjCuRQ4OwRHxV4uNjrDuSMeeN7nmlJHgKD5qP3QCBg+Q/Wx78je4rfs86gK+gveihFIhGdPXtWm5ubLTmXb3p8YaNAghflA8YGdkfOgHOMSSjikROOS82CF4wHR+bh/cXj8RaGRXvoyiR6bJ1NgnflcWGEnkUDF+bnvseNP12McHh7e9u8MfDPTCajd999V7u7u5qamlI8Hjdv9urVqwqFQvrggw+UzWatfQZRyunTp5VKpWzz4QVMTk5am41Hjx5pfX3dNg5Kgk1MVAbeTihO6w88bVhBGBCp2dPH48aSWhQERqu3t9eMA3ChV+qS7Pc7Ozufap+AYUNREYFhmNoHP0Mutra2TFnhPBDpYCTxSpFRz+iRmgYHyixzx3wRiQWDR201PDTJnADTENmiaFmbdkzdwyV4dO3QH8lkkuJERp7Jwv95f56Z6AJHyidRUSBENJ4p6D1eT9bwkK+HoYiiPU9falJRPaSLPGHggsGjtg2jo6Mmn0ThW1tbikQiSiQSBvWwTsHg0UFJn3zyiR49emTOJIbOO4/lctmYZDgCkCJisZhBwMwlz+4jAp8P8t47soPz4HNGRF84oZ4oQgTCO6EfMdDMN9EJOgwHkHtLamlYSMO8J0+eKJPJtDhz3+T4wkYBz1CStra2zNtCuaBsyR9Uq9UW6IFQvtFoWHEP2Kv3sJgorsXieTYE1pF7IaDtC4/CIzIhXI/FYiakeGae4YL3Te8XDAxzUCqVzOC9++67Wl1d1a1btxSLxQw/vHbtmpLJpG7fvq2FhQUtLS2pt7dXpVJJpVJJU1NTGh4etmpOvJu+vj5NTU3pBz/4gZ4/f6779+9rdXXVnh3jR1INmIG+7oTGCBPvjkB6SMcLKusH1ku1s09QopiItBBg4BhYPhglr1DxisgrcU0fIeD5eAXLnANRETFwlOH+/tGxoNQLSLIEI9EQcwu0kEgk1NnZqWw2q+3tbTUajRYqqseO9/f3lc/nLa8jNXvko7Dbczp+jfhdNps1+Z6YmDCl5hPtyLqPQnBsfBIUbx4vlMpnr2QwTMBfRIo+wd5OWAC2wSHCCKJ8caB4Lhw7P++8V3d3t4aHh+1c5Z2dHcXjcfX19WlnZ8dk39ec8Ey7u7t67733dPfuXdtrvg+TJDsKlfkAgUB3ADt7TxpDz3V8FXz73mD96T+FM0PE1s5Ea4+2iMb82d5SMwfV/kxAVB4VwMlhrQOBgB1NSu7pN5polppnDC8tLen06dOG7yH4JO14ASwolYoYAhYCgwIM5BkOGBKP0/qFQ+mDgfIzzyZAUSLAvk8QC0fBDxNMf3d/LTBAjJkP0RuNo8NSJOnKlSuamJgwwZmYmFBvb6/ee+893b59W+Fw2DD+crlsTBRff4CXGI1GjbOdyWS0sLCgBw8eWOiIIHE9IA6gFKCQWq2mXC7XgiEjzEQM3BfPB48LxYYQQwH0jBhf8APmjwHh5yh3IJNg8KjvTS6XaynW428f1eVyOaXTafs50SCRZ39/v3p7e81A+s3KHzYcTgUEAEnGvgIa8M+NPIIDo6yJWNgPnmHlIyAUAWSBUqmkkZERjY6Oan9/X5lMRouLizYHPjrzc+C92s7OTuuU6xkrOFHt0bjUTP4TyXrowVNy/RoAlXoGGxGGjwrIB7C27MVEImE5hd3dXQ0MDKijo0MbGxsaGBiwljBSM4+E8bx//77JOnPCfi8Wi1YxD60ZoglQr6fIS83CRg8D+Vwhc8W8+hwLOSx0GYra6yKfNwXO81EoxYVernx0hRPFs3pDxefK5bJFsVNTU3r69GkLAeGbHF/KKGDJUOIsJtghySKUCEkxrLbnsbMQTLbHRNm89CjyxTsIP5/n5zyXjyj8sYEkFlF6CHt3d7fi8bgpEHIl4PSxWMz485ystb29bZx48g9Pnz5VsVjU1NSULl++bNeMxWJ67bXX1Gg0NDs7a/OTz+ftvcbHx1vCewaKdnJyUmNjY3rhhRe0ublp/VDy+bwxtxAejK3UZBPB1Y9EInZ+MfNGchNDsLu7a/2QfE0HzwYmfHh4aGwrakGAAb0hxhP1/Y36+/uVSqW0vr7e0gocr85jpURznsXEmmLE2KxEfzgh3oMPhUJmQDhDGWWMAYzH44YNV6tVa6hICw/kCU/RK0mcGTB7Blg2SgkowtMp2fw8j1e6Pi/gYQaSkNJRS3UcF5QMMt5O6UZRMkf1erPDLjAsCgzIktwT84mBIFr10A95OujejUbDlFm1WlVvb6/Onz+vRCJh3+O6uVxOb731lh49emTwCHoFD3p7e9v6a3nKZ19fX8u5C96xg52FPPF+DPSA1DRQOBk+wc8f7oFO9DkCdBlySgtv5p919DrIR8keokIOvBOMPh0YGFChUPjNGwWpmTj0yoIN4wWLClUWj59jUVHibGTCTo9tspnAZ48LlXgOvsemwxPA4+FAG6mV689i8T2UkT+39tSpUy3FPwsLC9ajhVCdgqsnT54on8/r1q1bGhwctI3wox/9SGfPntXy8rLxqHd3d9Xb26ulpSXzuCgq8u+KoMAImp6e1oULF5TP57W1taVnz54pk8mos7PTag4CgYBBLMwD84sHB8WTtSCpFQqFFIvFdHh4aIYBaIJ+NfTjIdJg/Q8ODszoeFaSzwMABfT397dUWOMssAasIfkr5AiIsKenR+VyWblcznIzeGoePsK4cn3ek+t2d3crlUppaGjIIAOgI++QIGseOuFnnirKe3qaIvugXC5raWlJwWDQor521lg7rs0e8XsDbB3FDAHEKx3vJPE8vq2Mbx/vmTTcPxQKGYOO9vgeRgZm8TTKjo4Oi5bD4bCSyaSko6h9dHRU58+ftx5ovCMO09tvv62NjQ1rpwPtmDXBkHt4xu8bisF4Nq7BvLXnVlDuPAv/RheQ+McYAF9iMHz07fM5yA3y68k3Pn/D3BGl7O3tWf0KRX7IPHMViRyd63316lWtrKzYXvgmx5eqU0AgOZYRKMFjl2T3JbVQOpkMvComg4XxRsEzG6TW053YKFzTMwHaw1s8OhYDnrP3Buv1ukEhQCoU2bGpKSDr7e3VxMSEdUel8IgFhWq6srKiN998U9evX9e5c+fM+I2Ojmp7e1uzs7NqNBoaHBxUuVy2LqrxeFzj4+MaHh5uSU76NZCaPeeTyaRmZmbsbNf3339f8/Pz9ky8O8c97uzstCScSQpC0yMp6RPbXqFybxKe+XxejUbDcNdoNGoUP38f1tT/qdfrxt/3xVp+sIYoRLB1FBzKqN0z9yG9p1fS0A6ZkprVqdA+fUiOfPjQH9xaakanbFgPUfF97/0zLxsbGy15INYZmMQzmoh4A4GAGTLmEAPpnSiULEac+ef/5LzwaHlGFLpPbAaDTcoxSpg8IevFdYnCp6en1dnZqUKhoHQ6rVQqpb29PSUSCZ05c8aMNWNvb0+PHj3SBx98oM3NTdtLXLdarWp7e1vb29uWPwMmoj6G9QfORE6Zc59r8c6W1zPoCJwYDF4oFPqUs+rl0f/cs7aI5vy+9QymRqPRQp/l+f0zYAQ8TExkfPr0aaXT6W8l4fyljAKTyqE2MAkID1HebFxoaSRymRivgODhIqhSE0rY2dmx+1JnACOEDQXric0JztjV1WVeNxvAGyQ2OEaEBJGnq4VCIeOUS038GU8U6APFiiIKBoNaWFhQJpPRzs6OLl++bHTYixcvqqOjwxbTt2RYX183KCgWiymbzVozONouM8ce6+es5OnpaW1tbemXv/ylPvnkE5t7lDzQTaPRMFiko6NDsVispUIYL4a5wovc29vT5uamzp492wIfMq+eVoiA411iSDC85G8wSNlstoUuiKyhFAcGBqw+RZJ5cJJsY/kog00qyWoQuD9RqyRruYAsw9Ta3t62pmd4ecgIMA2y1r4e3nPkWSqVihU2Qov1uRnkkGI3Sca/RwYwgtzbs6t8lNvOrPEOlE9o+s/5XJ7PKzBfyDo5tVgsZsxDIq1Tp05ZhNnT02OwaH9/v8bHx1s85YODAy0tLemTTz6xvkdeEaJ8IWZIRwWl5JOICkluY7g8lOUNtXcQUPIwq7yc+jwMexs5oW0Pn+MaRB4Ycv4wd0SePAeIRbuB5Pce9fAJdF8YXK/XNTo6qpWVlS+qwr/w+FJ1Cu14tySDTvC2PN0RBeYz8wgo18SCe+yfDdJoNKxvuff+fOjGPcCb8fC9gSLEBtOmVa9XPFKTg83POX+4WCzqwYMHyufzGhoaMiWyuLio9fV1bW9vW598v9mKxaLeeustlUolvfbaaxaFXLp0yaKGWq2mzc1N8/JKpZKePXumRCKhg4MDFQoFra+v6/Hjx5qcnNSFCxc0MjJigtie2BweHtaPfvQj1Wo13b17V11dXVZBS5l8qVSyAiIEkYSaZ4NhGNj41WpVs7OzVmuRTCZNyXk2F8bbF+F4ap4Xbr6bTCa1sbGhtbW1lpqKSqVZfV6r1WwNPZTkcWKuCbQHJEKCFeVHxJhOpzUyMqLOzk6DsjymjOEhAkCBYMiZI5QphW/ei8WYQkEFviPhjTfKHNHJk++iiFEGGGIYcu3eO04a89wOc3nasfdwuQ8Klr3gITwgJYxUV1eXhoaGNDo6qnQ6bZBeIBAwSih4PXtsd3dXT5480ePHj5XJZCy6BCbiWQ4ODqxmJ51Oq6+vz6BH//zsf2S2PT+HbvHOg4+sfGTnFTfwFXCzZxrhZOE8ejlgX+JI+EgcpxEH1htjIEsfoRM94pSR/woGgxodHVV3d3dLo75vIsfwpXMKUjM7zwRyWI2fYITPCzt/e662p18xeWxKSaZc2rFlKp+ZNATEe+1AXSwgdEM2HpS09o3vqa1gveVyWaurq1aij3DCqKDTaSAQMG66JC0vLxtl8kc/+pFFI/F4XPl8Xp988okKhYJ5D5LMkz04ONDa2pq2trZ0eHho1MipqSmNjY0pkUhIUovww+yZnp7WkydPdPr0af3BH/yBenp6tLy8rPfff193797V6uqqKpWKsUJYm2Cw2cWWAr5QKGTtHh4/fqyhoSFdunTJNgl4frVaNa/Ww3LIBx60D5HZKMxnOBy2ltLe48bjginkHQ/qYjAIvkCRzc/nMTi8Ozg+OZLd3V07j9p7Z8CUfNezRJA1P4fI4/7+viVGURQ03UOx4wh5BYwXivEjN8YfIigiDq7FfYHzgBY8LdIz+aTmKW/MtWcuoXQxVET3kAsGBwc1MTGhZDJpNQech+DZicj1+vq67t69qydPnphBxxh7xhiwLWsNfMse81XZPuGMXAUCzdPOfBsRz0zyyXHkg/3E7zndEV2BXHidBvWaNfawGrIFTRe95RlhyC5depkPrlWtVm0PYOy6uro0ODioVCqlpaWlFoPwdY3DV0o01+t1raysaHR01E7fose590Rge3gvDkOAR4b3we+YbKwhFDNelrwFgs3/8f7oW4M3RZESOB8bhmIXPAKeydcmsCmIQAqFgp4/f67e3l4zDMPDwwanUYHqk6Zgh++88452dnb04x//WAMDA6rVapqYmND29rb1M+GIQd7Ve061Ws3yEblcTp988olmZmZ09epVyxn4Nbp48aLee+89JZNJDQ4OKhgM6tKlSxocHNT58+f19OlTa9lBQ0EafKEwgCtqtZrx7A8PD/XgwQMT6lrt6CAZ4KhsNmvJTLyYUqlkG9t7vMwtSTkiFyA7j6ljgFBSvoLZs5M8Vuu9ehQpa8NBLzCytre3Jcm8U9qwwCDBQ8PY4cT4E8BQrD7RncvllM/n7d5EO55dRGIRhVYqlZTNZm1feagMw0f7Fgzbzs6ORcWSbC/4fJ533FAw7CMUH0qYCKZer1sk4iMgalhGRkas0R3V2zyjj0R2dnb07NkzPXjwwIoymQtgPYrSKLqs1Wrq6elRMplULBZrOasCKBGdgBHFuUJ+KRjFufPwDPeVmu22GdBAQQAqlYrJOV68h4pYGw+/8TuiOQ81YTTQLT4hztkOPhqRZPkVnBKcNX8vn+/6quNLwUceb8OS4XkD2eBNeLjJh7xMng/FSV4SuvpkDZPG93gOvC1ogxSycCC9D7v43s7OjhkPQnMSj0A729vbLZuCTcO75nI54whDBZyamtLa2pqFdywgXjbv++TJE0UiEf34xz9Wf3+/dnd3dfXqVcViMS0sLGh7e9sEpb1qlSRbtVo1z5N5eeGFF6ytBAIBZXZubk5Xr17V8PCwfadWq+n3f//39corr2hubk5Pnjyx4rhAIGD5DxQ2iko6wpQ3NjY0ODhon4HxxOYkNwJsQATAu3j54N8oUqIv3x6bE9AqlYp2dnZaIlV/bGQ4HLbTAbke3igeN+83NDRkLTQwjL6HFkrfs5eoVUAmiYJQnniKQKlbW1sWkflcFZEA38V79J46sKuPlID38Gy7uroUi8VUqVQUj8c1MTFhLZ13d3clNWsl2MNcHxlFtpl3HChfkAb8R74DWrLP+QwNDbWwf4haDw8PNT8/r/v372thYcEaKQLNNBoNWyfYN+xfYMV4PG5V0sgL8wU07Av+vAPQnkvxeQOfg/LK2ieJgXIwIN7w8jc6ycN7HgJn7XC8iFSIPtnz6D5fc+Mhcp/8RibPnDmj+fl5Q1x8BPhVx5dONPsJ3NnZ0dDQkD0EgpBKpSykJdwHvmEREXxfd8CioIipGcBbZZLxUH2iEWWPMmAh2XCE2ig6hE6SbXAwUgQU2ACrTtKsUChobm5OQ0NDisfj6u/vV61W0/LysjUhg84JIycQOKJiPnjwQJFIRD/4wQ/MOxwfH7fN46ExEujAJigA2CiFQkGPHj1SrVbTa6+9ZoKPQOOpP3v2zDYxDcJyuZxGR0f12muv6datW3r+/LkWFxetnTfYPVRb743VajXrTeRlg0Tp8PCwdX5lbRBaX7DEWkoyhUBFtiTDxaFO1uvN9tFUd9OqGwWE8gb+8pu0HfOt14/qRehqi8z6xC05qFAoZFEJytR74GxgnItSqWS1JB5XpnYgHA4rHo/b8ZxQEYE8wa7x+DGg3mOnNXw0GtUPfvADKyjd3d1VLpezIi+cJg+neXzcG2a/tjh/oVBIg4ODOnXqlDF/MLYoyt7eXlsjnnN3d1f379/XvXv3rAEic+u9XdaKPUe0jHeNXHuPGU8eJUh7C0979+wsz4L08meKMNwsgCWawpGpVCo2l1zHkzGQMXQgMiaphV6MY+yNMff1xASOf/VJ7XA4bHCxJ5uMjY215BXbdfVXGV/q5DWPQdZqR62kp6enLbsO5o+HhRJAoYLttls+FD6LjSDgGbOICCgQAQqGSlpCPO+hA09hcREez/vmmnhKftIRPhg0eIu7u7taW1szumpHR4cmJye1ublpyUGvaEggdXZ26oMPPlC9XtcPfvADa3MxPDysZDKptbU185DZXD09Pdrb2zM+Pp5bV1eXzX0ikdDZs2dbGA23bt3SO++8o42NDWUyGY2MjBjPeXFxURsbGxobG9Pk5KSuX7+uq1evqlQq2TkHS0tL1leGYimavFGM6CsuM5mMCbok+573cHg2vDe/SX2RFB6ZV9TAPigxokvYYyg6FKiXQW8wKVKjqpwIiYI8ojRJFpGCqeNps+l5R2BLILC1tTXt7OzYaYNesfX09Jinz1z5DrLUo+zt7Rlnnznr7u7W6OioJiYmrF24JE1NTZnRofiQefXHsVYqR1XirKuHjlBwPDNGhyKyqakpg51GR0fNiyYv5WV2dnZWH330kVZWVuwZgYh4LgwvESCQbm9vryVpUby+qhkDS7RBZMf7eaYRRsfnLr1OY1085IPBwOhzXdaPvezzJci2h31wKiTZM6DgcXQZPoJGv7JmyCaONO+BXPb09Jihbb/uVxlf2Cj4GzFpLBYhH0KOpYTKSYiPFYX+xWQGAgGjvLEIUmuRGbgzG9PT/WCReMyZBfFwAM2zECiUFYuPQOKJ87yeeYAHAcSxubmper3ekmhbXl5u4Sqj4DAWoVBIDx48ULlc1l/+5V9aovXy5csqFovK5XL2bISSwA4IFxBKuXx0WAc019OnT9txhqdOndL9+/etrmJ4eFiBQECTk5NaXV3V7OysstmslpeXNTU1penpaQ0MDFii+vLly9rZ2dHa2poWFha0tbVl68nG3d3dNcgEJUs0sre315Ks9kQE3gdBj0Ra+wXBzeZdPfMMuUBJ+MI8TztFmUKD5g+MGDjxKEeemciSIiTWHceH9/SFf0AdqVRK2WxW9XrdCiZpylYoFAz7Jx+AIvEGB1iCDd/Z2TzXOBqNanx8XGfPntXIyIiePXumlZUVbW9vm0OAUmJ/UdzFfhkeHrbIrFqt2r/B9FnjsbExpVIpcxRQ1OgC327Ee/2cf7C8vNzS3oa97JvZYYiQEZLD0E2BfZED9jd7wUPGvmOCh+y8IfC1VN47x3vnb/Yff0jac/QqegPZRD5xOHk+9KWnLHviAE4ra4/u9CQaHC9Jdi/yl/V6XSMjI8pkMqanv06UIH1F9hE3heaJwOExAH+waT0uz8CL7+rqaqlq9QuDgsDj9lRDfg4sQ34BPBlslKQyn8MgRCIR2xTea23nIWMApGbbCJ6bcxooSEL4OQnL5wPC4bBRXCVZ59R3331Xr7zyisLhsBWuZTKZFmUTi8VavCWaYaHkDg+P+lHt7Oxoe3tbHR0dGhoaUmdnp65fv65/+7d/09LSksbHx5VMJtXf36+bN28qFotpdnZWm5ubymQymp+f19WrVzU+Pm7vARx07ty5liQgIb+HOTDG5D/6+vpsDnzU2Gg0Wjj/7TLlPWOfmCYiwZOGholCkZoV9pIMpmG9oPhJss1NpW4wGDSvk+9KMnnb2toyj9fLsIelSCgmk0ml02k7kpbcysbGhlZXV+0d6HHke4MhVxjIzs6jo0+ppmee+vv7NTU1pe7ubuVyOd2/f99aS3jSAZ6sT4oy77RvkZqwD+8fCDTbQR8eHlr0EQgE7KQ9jBrGPp/P6+2339bS0pIlyj3DxkMhRAyS7N2J5DE2JJahk/J/jAvrjcfsk8yeWOIVO3JArsQ7D+xx1pN8GroKhYvyRkd4Y+ehI2/giA5I2kP/ZU19RbqPBpBLomQfwdCyZ2JiQh9++KHtk687vpJRYOzs7Oj58+eamZmRJMPSmXA2KsrZh1s+bPVUMKympBaaGsLglQYW3VtqPkuYyXUIj8H/YXRQWUpFsTdEPKNnBvCsPkEIVirJErzAHBz+Ua0etfKgbQCJxsePHyuZTOr06dOKRCK6ceOG9vb2NDs7q0AgYOcXEyrjWQCrAEnhPa2ururZs2cKhUJKp9MaGxvT6dOntbGxYcns06dPK5lMqlQq6ec//7nK5bLGxsaMZURxElg+79Xb26tEImFwGr2XVldXDRbxLI6enh5LGCMTRIV4PuR/+OMxV79ZqG/wnUFRMCh67usTfh47JkoIBAIqlUoqFAoW0vvIE+VBG/BoNGoRDxWleNhdXV12HsbZs2fNcx4fH9f09LQxw1Aa/nQ4nhk5I2KAj88eCoVCxkiCbuujrFAopGfPnunUqVM6e/asGUUfdfi95xUXvyNvhhJnLkKho9YmRLxE61ITUm40GtrY2NC9e/f0/Plzk3cGewNmF6QQ7oND4Q2IN7Y4Hj55jvHzzEWiFd6D3+FQeJgIWWEuuJ+v1EbfUE3siR+8N5/zTqRP9hIleFaQf0fm2RsZ5g4iBfkab4C8E5RKpQxK96zJrzq+llHgIVg0knyew82D+4lhosDuPesDqIThXw4F4WEhFp3N5ZNE0pFH1d3d3VIdLR0JFgyZWCzWUrOAoPiF9IltIAyfyJKa+DLwE54RG3d7e1uBQMCqoQnZ3333XfX29mp0dFSJREKvvvqqdYPkGfAS8MKB7QqFgmHUKLu33npLuVxOP/3pT9Xf369z585pdXXV2B+jo6Om5DlX4syZM+rr69PW1pbW1tYkHSnR6elpTU9Pm+JFUCVZv5lGo2EVyZ5uiadDhIUR9lHe3t6esZ1QXJ5I4HM7GHXvXXu4hO/hOXrmjmf24AWy3t7rROmigPi/D8tZQ3IJExMTunDhgs6ePavh4WGjiXoFwTOkUqkWpevlyv8MlhldVKluptEiioETzAqFgp4+fWoOmpdfnsEbBZ6NPwxvHGlLvrW1pf39fYOTcIbYG8+fP9edO3f06NEjowOzx3gGnj+fz6tarSoejxutE+VNg0Pez++faDRqEDNQG3MFcQSnw+9PDKfPeXili+z4KNZD5fF4XMVi0eSG63FtbwwwYF6e6vW6ncVM8p31aC9m8/lNnxRnzYhopWYEhj6IxWLK5/O29r8xo4AnOzk5qXq9bgVHQB28DIvKYklN/r8PIz2eJsl48x6K4rs+mYTnQIsClCbsE6l5pKHvFULhDN5mZ2enLZokC2H9u3jvEwwRGMO34G5nTtATHgFD0YXDYS0vL+vNN9/Un//5nxvf+8KFC3r77bcNJ97d3W1hRoVCR2cTA3cVi0XrCwOrplQqKR6Pa2pqSjdu3NCdO3dUqVT0+PFjXbt2TT09Pbp48aJ5b+VyWYuLi3YwUDqdVnd3t/3bc7NRML29vRoZGVE2m1U2m9X6+rp2d3fV3d1t8BpsEp9M9tfxxUSsJ5ugVqu1UDaBAr1nyCb2yeRQKGSJOpKv4L1AYHh8QHU8D14rDo+HDMLhsNU48NlUKqWLFy/q3LlzLV4v1wN+8MlM/gZKAIakvQYRFlAhDhOyNzY2ZvU4wKZbW1stdFLugbLBE4a1w3tjpDGKEC7W1tYsYc7BNcgAzzM7O6sPPvjAqLfoBUktxpSkeaPRUCwWa4FWfAGq1BqB8OwYekmWb5CatFD/Duz/w8NDq/MgesBpxEnwHRSIZklw1+vNVt2sB/KA3HlIifn20BWDfCswtXcwPAEDB8IbKl89j2MFtRV9OTQ0pM3NTbvG1xlfyyg0Gg1lMhkTRGAFNiueI16Ar9pl80gyb4kXZIFZLBbKU1D5Hp4CiWiPK5IMRVHXakdnCxSLRVskhAEoBizX85qJbMgvAOWgdCgSQtnX63WDJmCMSE0Pk+TR9va2sVo2Nzf14Ycf6pVXXlEkctTw6u7du8rlcoY9szHwbnwYCgMnGo0qnU4rFAppbm5ON27cUDB4VLgmSR999JGWl5c1MTFhCTowYPBvWhXT0ZP5TiaTLZRRSSbAnvGSyWTs/GBJhuHW63UzYqw/Qo9CkZo4sVcYyIbflHwOqMDDSe2hPPg8UB/XYbP5e3t4RmoeG0uSf39/36JiisiGhoYst4UH5z11D9fALIHFAsy4ubmp9fV1K+6KxWJWDc6cIWfM9ezsrHZ2dhQMBjU2NtZCwuDevO/W1pbW19fN6HjaNA7K4OCgVSOTc+MMCIoucdDeeecdPXnyxBLoPkL3hoEqcarPfZ80lCQKHUPhIwHqQnAocAykJqzIM4FIeLqsx+l93tLDSjgivv9WtVq1o1p9xA90eByawB90iCTLv7FPuJePWJgz3hcDgNwSVfuzzxuNhuUUYa8RLf9G4SNCcaiBUDLZtGxQGskhCCwIrBWqDgn3YYx4nJjNikfjPTJGO1TFZxAQ75n67D8bnv40TDbvCGziqWZdXV2W4wCHRmh88RSbHx69F3w8/3g8rkePHimZTOrixYuKxWI6c+aMHj58aHkDhBWILhwOa2hoSLlczjqNAtfQYuDSpUsWtUxMTGh+ft680XQ6rbNnz2plZUULCwv2HNPT01ZV2dHRoVKppA8//FCRSEQXL17U5OSkVS9zXGYkErEqU5wFokA8O7xyjKOHEUnGsVmA5ur11pbYUAJ93gnjgFPi2xvgUdEigWfCKUAhehnCEUAp+cZ30lEuLRwOW95leHi4JXcgNc+E4L2JSmdnZ7WysmJGDpgqmUyqUChYq41CoaDZ2VnDlIeGhnTmzBnV63VrMLeysqKHDx+qUCgokUjoypUrJrc4Q6x1oVAw9hv7bWdnRyMjI4pGoyoUCqZoUUQHBwd20pdnLxWLRf3Hf/yH5ubm7OQ6IiEPpTJ35F68B48zhSxj6JEBn8T1FHbk3PeVwjPf3983OcEo+1wj6+pzKD4fRUSHnMI6wxAhM0BQPqfho8J2hcz/PbMLY+ajBOTYywwGi0R3KNTsdAuKAQEHFph3QL7K+NrwUTAY1J07d/SHf/iHxj6hXgHFzWR5mphPAkH/850SCfNR4ih/FLKnubUnyFDq4XC45TzhWu2oQ2cqlbJ21RSDUb8gqUUgJVmSzTNlMGzeKyChLcn6CWHAMGoYD5Q2gpfL5VStVvXxxx/biW0vv/yynj59qmfPnml7e9vm1wuDZ+f4UFiSCoWCNjY2NDU1ZQnD6elpPX36VM+fP9fp06c1PDyskZERzc3NGc0NxhMMHaCrWCymQqFgHlOpVNLW1pYZzJGREY2NjWl9fd02toc9PATI/33Vrse+oYyyvnhxDIyqx4XJTaDASGJHo1ENDg5aRAiEJ8mcFgwD8slcs74oPWQGUsXExISdT01Eh0ICBikUClpZWdHGxkbLqWE4ELQv4TQyb9gPDg6Uy+WM0TQ+Pq7u7m5tbm7q8ePH2traUk9Pj1599VUNDw9bxFitVjU/P6+nT5+aEvOHGklHOaFCoaB8Pm85Gt7r4OBAqVRKw8PDLYVpz58/13vvvaeVlRV7h/bkKEaH9SLnJcn2OU4BBt573J4O6pO5vnYJdAKHwbP1WHf0iM8ReuIB78TnvOPCXqatBNEa+qi9wyl6wUPcfiBDIAM4okRXHimhFspDUh5y8sQEomMP+32dKEH6BuAjLCOKz1s7KG2+rN+H9+ChbASoeCS5UFKSWibbh/s+AeQTNCh0ogYveB0dHUomkxZu+edBMXj4ql5vnjGNQkbhd3Z2mmAhlFDIfDLT1yhATWOzVatVU1QrKyuG+UciEUs6z8/PmzH0cBq01GAwqGKxaJsHaIw2HtKRcTtz5ow18MPDvHnzpp0PgWcKVLG1taV8Pq9YLGa5it7eXo2NjZmSpGiNeeD+/A2WjffFRiWy8HAOng5GgqgK7nckEjHKrzfAKAz++DwADgJ4bCwWa2mjgQGAf+5/Rk0NeTI4/1BVpaPzgldWVpRMJluSsHjVy8vLunfvnjY3N21v+LMRAoGAZmdndXh4aHUieKcHBwdKJpN66aWXdObMGdVqNT1+/FgPHjzQ0tKSUqmUfvjDH2pycrJF8XE6H9F7JpMxbJ336+7uNqXHXCwuLmpzc1PxeNwgMZTqysqK3nrrLaNf+6Z0OH7FYlGBQMBgIuSBqBBDQIWzpyb7ZC/K0rOPWD9JLfU7GCRkln3g1xJDKzWrl9FNntKMjPpzHXwBLPfzNRboGfYkCpz9gP7yOsw7JehFDBF7hXvybEBE7fBgIBCwAtp22vRXGV87UkAxvP/++7px44Y9KJYPr8vTG/Ho+T7XoIiGjeUtKJ6AZzSxsFhyBM97qL6KEI+l0WiYpeeaXoGwECTDyY1whKY/Q9cLS/vPfLtcnht+OgYTgwbWGIlE9ODBA42NjVl9QL1e1z/8wz9YDgJ4hA0DPOKTpii5xcVFTU1Nqb+/vyUcLRaLWl9fVzKZtNzL1taWQSMUvC0vL9sxpMlkUlNTU4pGo1pbW9Pw8LCmpqYkyZhNKHKfPGyPAPDMgBkPDw+tCM6TBzDYtVrNKl5pz0xECnsMY8gmo2IZb5U+O8B4vr5CkslQu0eGgfIMF4x9IBBQLpdTIpGwZC/GhN89fvxYz58/Vzab1fb2torFokKhkJ31gXECW9/f39fAwIDGx8ct0Xz9+nXLCX3yySf6+OOPdXBwoJGREf3whz/U6Ohoc0P/v2tmMhmtrKxYTQGcdq8Ec7ncp6AGFO/58+c1NTVle+Dhw4e6c+eORe/MGd/xcDCQH5ROBvkkzjrBWCMfGF/2NXuJTgI4VR5awfnA2eDzRA8+N+T1CtGhN2jIK3LX0dFhURTX5d15Hs828vR1Br8jysDABAJHbYIoPEUfECEAyRHlAaXy/Bgrn1vwuvHrJJu/dqRQr9ctpAOHBNdj4n0oJMlejongbF8mBY/GW0uUN8qCl0cZM9ncz3uKeP1SM6nt2SDAT+CwfkLr9bqFfVjwWq3WctYzg42NBwFezfc8k6jRaNjGiEaj1m6ho+PozOWHDx9aO4nz58/r3r171v6XcB0FDNZOotPfl3OUUZy1Ws08ZWopwuGwVcdKsjwHrAueFQ+cyAljlkgkDCtn7thAGCk8XzaHJJtzIrR2w44HiRyQF2ATcaYD+Y96vW6tJmq1mtEfWXeUPMVShN0oJDY68IevWCY6QNE1Gkc9ih4/fmxrfvbs2RbFsrOzo1wuZ0V+wA4UGcbjcXV1dWljY8MSy7CIMKCnT5/WrVu31NfXp8ePH9uhKqdPn9brr79ulGDmjHbrDx8+1PLyssrlshKJhGq1mkF/wJfsD963q6tLXV1dunHjhrVGD4fDevDggd555x1j8jB8T69YLGa0TxLLRNnBYNDWDkSB/Yt37/WEV8LATlC/+ZyPRJEVb1AgA1BdjzFED7DmHi0gSiC5XCqVWuoWmGdfhYxso7s8dMh9PNrBPaC2IvceBvcsTb7n/3iIEyPpHWbp69FSv3ai2eOsCD4eCQtHAtZbaJKNXAOrx2bEs+QFUf7+5b3yJimMoJMsg9ZKEtkfdEKymGtzPRYZ5cvz8nyFQkGlUskqpDFACDDX8P/Hm2EjkBglOmFzMR8ff/yxzp49q4mJCYVCIf3oRz9SJpNpYaBIsg2CsvRGFC9vfn5eyWTSFFKj0dDm5qa2trZ06tQpbW5uWmtsQmM2hyTrUknYT/6EOaSl8NbWVgttkWQmBsSzLDA6tBXx3g24sKeRctRpIBBQPB43g5BMJhUOh83Qjo6OWjvzhYWFlvwGg0ONcGQ8/s+ccT631Kx1aScRYPQorFpbW7PcE17+5uamMd7IkRAhjY6OKho9ao9BwzoSr0Snly9f1sTEhHZ2djQ/P69CoaCrV69aZ1xknf2wuLiou3fvGpU0FArZAe84JKVSyTqeIuNEizdv3tTNmzdNOT169Ehzc3NmpDy0Svda2uN7WARGEgpUas0DeWPQ7t37aM3n9tiHRNo8O9AQdUCQMiBK+D3o8xXoikAgYOcm+NYmOAToBs+wYn/xf3SYXwuu7+UairdnDJJ7xeB5CB69CIPRO7hEUF7fej32VcfXNgpYJEr4R0dHW6hbnj1Ewyvv4fN9r3gxFu1WHUvv6V5eCKQm/ZFr46X7Q1zwuPGSWBy6PXqFDq2UjU8Ii9Ij3MboeYYDG7bdK/MV2NwHZgzzUa1W9R//8R/68z//c0UiR03cfu/3fk8ffPCBtre3lc/nWzwun8iTZJ00w+GwnQNx4cIFpdNpM2A8F11R6TYKNAUBAMZXu8dP9SqeGWcqsGkHBgaUSqUsWe29LoSctZWONm1/f79GRkaUSCRM2YBLh0JHzQHT6bRGR0c1ODjYktwF8w8EAtaLCs+ZbqWRSMSa3nV2dlpzvK6urpZe9Twn75zL5ewZiQKY542NDSUSCWWzWYu6Go2GHTjU399vLWGAcg4PD7W6uqp4PK5qtWpFaECoBwcH6urqsuNbnz9/rtnZWfX09FgBGQMHhloAZDaZTJqD056T87RdqqcnJyd17do187rv3bunu3fvtvR58q0vgACJxoC+kF/kmjn0z+A9b+ARyBnMM07O/v6+FZ/iIXt4GecTWjnRMsYCw4OSx+iQGzw8PLR39HAxusE/U3v+EZ3l80/AW7y/J6WwNhhHH1FgEL1TK8mowZ6MgbNUKBQUDoc1OztrVGbe9auOr20U/EuVSiWjabKAvDxhOj8jScuGhv1AuOXZH95rQBkTfhGCsTBgnt7oUA1JGIcSAGcGMiDpjYHwWDhQDdbYY4WeKuYhJZ8wxXMB50bpAP9AmaU1BIyVubk5Xb58WZXKUdUx3U3xwOnKCazUaDSsnoDEZrVaVSaT0ZkzZ1q8WM4fzuVytil4P5q+0dQO+IRW6J5+SzKdQ3Q8i0Nq5lEODw+NGonxQjGx0fr6+jQ9PW2nSpEXIrFMpJBKpSySAHetVCot3imdS1H+cMvz+by6u7tbemF1d3fbOwP3oAAZeG++QA1IMJPJmLc9ODiocrncUkyGEfbFkfl83nB49gNy39HRoatXr2p6elqSNDc3p0qlopmZGTvv2HvReOYUC1arVetwiqJAOUNWYK/V63UlEgm9/vrrZkjm5ubMAfHQHnsNo1QsFrW1tWVrQJ4HRwkHzTNqcGba82543D5n4xPJ8Xi8BYphHfgsuSSUss8teqTCF4N5AyY1PXGUMmQQn/9rL2DDqfEOL//2lHnWCiQEgkQwGDSdEA6HDVFAv4CCeIPE3PnzGXwu7OuMr20UvEVaWFiwwzbgxuNFQj0DOkHZVyoV20wocgTXJ0e9ooWNwgJiRTEChF18vre319hDPtHmk98kHhEUnzxmE7BYLI4PD7kvPGb+8HMiIITAJ7KlJksKA8bBNY8fP7b2F9FoVLdu3VIulzOFzAYjiR4KhTQ0NKRAIGB99Ht6epTJZLS9va2BgQGbA8JhIhcqj31Fp2f/wHJg7oC7UGJ4NrRyYG34G4jMQ2dskFDoqGXD+fPnde3aNY2MjEiSHj16ZAoX73l3d1erq6vKZrOmuH0LkXg8ru7ubpVKJdXrdQ0ODiqdThsz5tKlSzo8PLR5Wl9ft0p3Onsim8hQNBq1c63Jr5B7YeNz7gVwTb1+VBELjMPcomSZLxQQxhrG0eXLlxWJRJTJZJTL5RSLxTQzM9MSZaGggMRQCJFIROvr67YHuTce9O7urgYGBrS3t6e+vj698sorSiaTajQaevbsmd5++22TBw9Hei+f66CAw+GwReasKWw9vz+YC48kQDggusJxQ3H6BDOK3D8fEQR7GueLWg2o6Ow7/sbpaa/hQD+RV/HPwf2k1hbwPF97dIb+wsgAl/q1gcBAhwX0IYYF5wg0gv0E1Pnzn//cngfZ+KrjG6lTYBQKBT158kQXL140/NQraknG08VDAD5AgbIAKCufyGWi/fm9vpqRyfKJJZ5Talpd+NhSE2dk8qFqlkqlltbF3N9XG3qMslarWVKWiMFvJJQsvWE4zpDn5D3xuJPJpA4ODvT8+XPdu3dPr7/+uur1unnI2WzWIgyEe2BgwKCXQqHwKeUNXknkhhcUjUaVSqXswBnvOUUiEYOhOjo6bK16e3sNhpOa7bQLhYIlLT01FI+ZRCJz5g18Op3WtWvXdPbsWXV3d2t9fV35fN5ahPho1LMxPLbMnNZqNau2n5iY0OnTp83ZwNvr6urSxMSEJicntb29rb6+Pt29e9ccg3K5bMVgeKWerYS312g0rDlgIpHQ8vKyef0YQGAhSBkHBweKxWJGMuju7tbu7q42NjYUCASsz9Dh4VEH3Fwup5GREasSl5qFo+vr69rY2DBGE4q/p6fHooWdnZ0W+AJZD4fDunLliiXJV1dXdefOHW1ublp049k5PgKMRqNGVkBWkHfvrSJz3tHyECv6gX0CQwn8XVKLc4EBwgHj9+xRnhVnk71JoaWHjX17d54bhcw+98xCqfUsd96vPXrgs95xxGhyDbrlEh0A66Kv/BySPyCSQq91dHToyZMntreJin5jieZ2rDIQCNiJWyweTa48vYpNiyLkoJn2ymZ46T7E8+G71KrofF8WFA74ILUCnvLoFTXsBnBFNpxPhrUnqryX6xlR/M57yCS3ga18SEtS1xfkVatV6zp67949a7YmSTdv3jQ+NowR8FIgkEqlolQq1WIcC4WCDg8PlcvllM1mW5KPz58/NzgEDxsPmKgHhlRnZ6d5YSRcfW5ma2vL8jfACD5kl1oNtSTFYjFdu3ZNr7zyilKplGq1mlZXV22ufJKbyl9vGLq7u61ZHJsS2dnb27NEOkY/l8upu7tb6XTaciCnTp3S2NiY5ufnbT2CwaC1rk4mk+alE/nxbswZimZ7e1urq6tWVV2pVCwxW6/XLb9GiwKihN3dXTvyElmCe043TOYuk8lYESYyR9EbEZ3PsUlNQgaHVL344ou6fv26EQbefPNNPXjwwHo7QQxhnqvVakvbCrxjj9ljnD3k4r1nFDl7hueG1QUMSeTl4WT2IY4AEQt/POQLxJxMJk3500vK5zBBElCiPkns587rPf/86DmgpHYZBxqELOFp9uRKeHaoz7ybJLsu9F4fZdZqNTuxkbX9uuMbSTRLzdoEQkCy4ig6r3jxxMBlV1ZWWqybT+D4ozVRMrBTmFy+6wXQGwuUB4aFjQbE5BNIeOooIvob8RzANsBhbAC8CRQSGxIvAqE7Dh8kj8DG4z2ALLLZrH7xi1/opz/9qXp6ejQ+Pq6FhQWDOqg45nS2/v5+Ez6PgfI38B735OD3vr6+lgNyCOFhrPhkHzkNktDMK0qJNUFR4MH7hm+E0AMDA5qamtLly5ft2FBv3NkcwG8U+JAsRMaQBaAGIsNq9agID0jBR6bMN2ygqakpvfvuu5YXk2Rr55l0UrMlPI6ET2aHQiG7PwqJ5yJSJrJEdlizaDRq9Fci266uLiUSiRZjSkHa0NCQyuWyHYREohoYz3vl5I1isZhOnz6tmzdvamBgQPl8Xv/0T/+ke/fuGWMH58crXN6Vfcge9Q5QO/MGRwdZZC/7ehEMC61D8NA92wY9w30wEJ4Iwb392iFHKFYcNnSCNxDIt9TMAXiWEp/xhYfeSPAc6BXgV/IsOCrkrDB2tBvHeebZeR7feZl71et1a0/DnHgd+lXHN9L7iMED53I5O3AHy4lweAFhk+IxM5kecgmFQubJsRmBYHylIuGnT+xKTRzV432e1SQ1E15emFE4hNz8SSQSdvwdi1sqlSwi8mX4viIb/BWhxAAhWHiUJHE7Ojqs6nN/f18PHjzQ1atXdfHiRYXDYU1NTenZs2cmOMACjUbDaKewpaA5UneAJ7KxsaGDgwONjY2pv79fa2trZih5fjBjX43tk3EINmtcLpdNOQIt0AenWCxabod5icfjOnfunF544QWdPn26xSB0dnZqZGTEmEIoNYx4o9FQf3+/GWGqjfv6+gxjh12D4oFyKKllk9brRwVXp06dajnr2N+vVquZ58q6eZlFXlk3sGr47sg10BqyhvPBnKDIAoGAwU7xeNzO+5BknW3pZEsEWK1WW/YdsB/GBRZQV1eX3njjDWM/3b59W2+99ZZFNECs/GF/kRhHyeIoeUXqjaJPKgeDQaOvE4lJzTbQwMXegHhGD1Ad+9xDtF7HMI8o/FKp1HKuCQPn08sTayLJkAP0gX8PZLQdJgbx4BrQTTHwRJ/eEOH4oJswduw7X6QGLB8MBpVIJCwa/SbH1zYKx418Pq+pqSlbJASRDQ9+T+LXZ87xxPi8p57yPfjxWEb+jZfF5PpQm3sDkeA1+IQwm5VreuPFubaVSkX9/f0tuQ/vqUpNiAuvCTwdpUqoR4UnoSf48ODgoKrVqh1tefXqVWUyGb333ntKJBIaHh7WxMSETp06ZayUXC5nnjTPDB+eVsUk/UnG+lwBiViEMxqNfkopeuNJlMCaoZiHhoYUDoeNucTn8RD5093drcHBQY2OjurcuXPWQ0hqYtDJZFJnzpwxeGR3d9fadgSDQWuNDsuI90fpoPxoKRGPxzUwMGDJPPohMUKhkIaHh+3AIYxiZ2ennR1BgploA6VFXogOs5ubm5Y0JkpGTn1tCQodxQ/dFVlhHr3MV6tVq78ol8t69OiRVlZW7EAgjK6HZFFUYPHeIDx48EB/93d/p93dXaVSqRaINRAItDBjvGwzB+xVrs364Ln76L+dWYjzgHHF6eDfwJCwz9ANRPDcC6PgE8Qcl0tVMgarXYk3GkeFiB7mQgd4B4Zn9u+NPvHdj9FlRAdEj+w/oi8+y3NhCMm7+kQ0aw0ZhWfmDIVvcnxrRmFra8uwwXYmgre0eGCeqeIn0wsaihzjAOPFVxgidGwqvtdOO+M5gLagWm5vbxvcgfDDuvFJKt7J85BJVnkPiDDP51jw3DBgeLR4NXh1oVDIzpQeHx/Xzs6Onj17pnQ6rWg0qqmpKSuO8kpSkrXzhstMszhPxfPsK+A+PDlvdBFOD/2xYdh8vE8ikTCFQNGW1Dw7YGRkxFg8Q0NDmpycVCqVMoUL5OQL3mBf4E2S16DdBe+JUiASI1np8xmRyNGpcgMDA5a0xWhL0vDwsC5cuGBrRO0CualKpWIRj58LDAWRFRGJl1fgUuBPcgkcziTJDDl5NlqP0I9KkuHOwWDQmuwBRUFJ9TAeZ1vgKFy8eFGXL19WvV7X+vq6/u7v/k6bm5sW5RJdelq2z9khYxge70QR5bDmUvM87r29PXM8UIQYdKIQ5MQTR7hWu5Po9x7ruLOzo62tLdtHyIUntvBM7c4NxBXuh6FBL3F/PsO1PSVVkiXo6YZKfgc583ATetATMiBNIDPsid3dXdOVNEVcWFj48gr6c8a3YhSko4KLWCzWknPI5/MtCToUO1ABgkFohYfgDQlYIpNHSOgpYwgOSt/XEqDYUPxSa8EbC+3DOUmG8+KF8k4kzNuVPnUXPBNCyGIDi6GISYD6RFgqlbJQc3Nz02oXRkZGND09rampKe3v7+vx48e2IdhU3sPyISi/i0ajxmv35yeQePPJQo8T+8Q1XhkQGhBRX1+fnVGMl84aw57y/WxQhJzri6fklRmsJih9Hp9H0XLdXC7Xkhxl0HSwq+voHGT/O95ncHBQr732mgYHB7W0tKTZ2Vnl83nVajVL6kpHCoAEMAq+u7vbakg4dwH8HugECIFIloQuRigUCmlsbMzOySYSGhoaMuVCNLC+vq7Nzc0WiAVDxN4gymNuurq69PLLL6uzs1PZbFb//M//rFqtpqGhoU959YeHhy3MNQ8Vexo4e8HnCZE7HC7P/PLFm3jIHkFgL3rGE46Kh1rYfxhDICKKJbmXdxw9nbxWq9nv8Pz5m2cBmuY9iXp5Nu+MkvtEJ2BEgG6B3phb73jhsAAZko/iOugmzuru7+/X6urq18odfNb4VoxCo9HQysqK0um0JWVI1vqiGV4UJY5CYxIQEISApA0CxHfwrGFU8D2UONfF+iNEXunhJUmteRLug9IFhuKe1FP4MvRgMGgUSp+g4jtcl9/R7plQHRxTkh30nkqlJB0xiN59912r5j137pwpahJ+4I7cB2NL4Rje/MbGhra2tlpaccP/BmIh4Unxoc8zsHlgiRBp0XMGHFxq5ptQXHjyUvNYRFhi3d3d2traMiVO4RxwI2E3MrK/v69SqaRAIGDvRo0LUJavbCXaIOLwkBs/9wy3YrFo50Ow4fECvdfvD17ivcgtkCD3CU48cBTvzs6OGU7IDt7RwNPFCWEumXPP8MMI1et1gxguX76sF198Ud3d3drf39e///u/a2VlxdqUM4/eiINjo5TZS+xfT+DAU8arpgiTHCBrDqEB5chzohQ9tRWniH3F/iNiI09HZMb3qaz2nH6Uto8qMXY+Qetzmu3GCSexPRkvNRPL9XrdWqF4FMHnMpEjfk+EV6/XW3qiMdBj29vb1m7knXfe+e0xChiCzc1N6+WPUsdCY8XbvQIEjj9gxZ5VgMLziTk2oF9AKnZ96M4C0g1UavZUIQTG8Hh80eOXsHNIPIJ3UyXrQ06fvMJb5zqE1TB/gFBQXpLsOECeuVQq6b333tPly5d15coVg0OITvDI8ETo/4SSKBQKisViSqfThrmSzOzo6DCDQPUv3glQBMKNQeTzeDUoeHrJEOoyv8wrRgp4aXx83HI0bCqiLe9xevYI74Ui4L3xGj285/MMnZ2d9tySTL4WFhasgeDm5qYRCEZGRgzfJy+F1+3xbrBrjBG4OzkUEpOct10ul7W+vq6hoSHbBxgYIuhsNqtisaihoSFTVkAk1KBQs8L7E8WQjMT4QL+t1+u6d++eVlZWNDo6al4sxp05J7ENVEP0GA43G0d6WqhnFwGLQWPG+aHuod0LxtHyRpT9inySm0GuUPBEVL441ENcPqGLwcQBoe/3QwAAe9RJREFU8klkbxjY7+isSqXSUhznHTfyIMw9zqskM6jIjYffmCdvWKvVqvL5vPr6+uyzGMR4PK6trS2Fw2F98sknpt+8E/tNjG8tUmg0jqosgQuKxaJVevqkFEaAicRbYKORUAH2IClLCMi1fIm7TxKjJD11kjDXewL8HEXu2QwIFX3PsfhQDfFSaefAs2B0UPDACXgJQCSeMQWUwDxRmcshOx0dR+18f/GLX2h6etpO4VpcXLREI11QpdZD5lGc1BH09/fbtTl5izCYzYA3uru7q97eXqVSKTuAB/4/kQneNooerzmZTLZgtWx2lA2e7O7uroaGhpRKpYxrj3cXDoetqtkn7oi88JLhoudyuRZ6KHPryQCNRsNonbwLUQEQFIrdN2HzuYFA4Ij5hRzgnXrDgUFlDehbw/xubGyYAk8mkxobG1Nvb6/VgiQSCYu6q9WqlpaWtLCwYO/n6Z07Ozvq7u62a0FnPX/+vC5evKhKpaLZ2Vn97Gc/szwfRj0ajVq+CoPK/vS5BfaRd6RYJ54Hx5BEbE9Pj/r7+w2a9N4/+x9l7B0b8mxQiclN+KiPe3jEgWtIanEofV7Ms4x8zoL/AxUBwfGORCRS8/wFKKY8hzeMGDl/HoLfZ1wTOBQHkj2Ifjg8PFQsFpMkOyaX5/gmx7diFHgJNhiVnlRXEoaDcaN4Pb+YcBJD4L1QQlhvFPgOm5jnAFP0Co9FkJqHY3iD4dlSCLy/L4II7RZGAdEKeRMqEMlnAFchrLw/yhRh9CEqc4AnFwqFFI/Hlc1mLR/Q2dmpZDKppaUlE0CEHK8Ug8T7+MI2vHBqB/r7+807Yl1IaCeTSfX396uvr8/OgICphEeH4SM5htfkIwXWHXmgvgJPFbgMOAsPnzYgvjYExQ8LivkmbPcGnkgQY4yR5PyIXC7XkjvAQfE4O5RTvH1w/M7OTrsuEQHQIh61h6X6+vqs31YgELCDbUZHR23/1Ot1Xb9+3aCcUqmktbU1y78wXyhu5pMzFvr7+5VMJnXz5k1Txm+99Za1iHj27JnlLfB0gdSYK6+4wLo9u451QP4rlUpLe3IcJfIaKGWgI5Qk+4+aFt7Nn5Pc1dWlwcFBk2OMM2gEtFufoOW5WX+fN8BhQ26oScGAIG/AzRwhin7CeCCD6CHkw+sxnCEcT9aP/Y7uisfjVo1NlCE1Idh79+59K6wjxrdiFDxGx+JKTbinPcHnlSSLzGTghSMELIwvB8eb8cbFh5WeUsZEeyPhE13tAtPOMOH/vB9CD8QBS+Xg4MAarnl4jHvy3j6JhTeN14DRo6AMDJXne/TokSWjT58+rWw221Jo5XMnsIToA0MehZB0YWHBNg6J51KpZPfj+VGK3MefMYES4TrMFQYNhejpsrw/kAdQHP2OMCJElmwuHAwUNNRBvHRwV+6BYfGyUCgUlMlktLi4aI3dqCnw+DUGEuwaz5XN6r116WjzkmvgM8yp1KwBITqgiSRnMY+OjioYDGptbU3pdNpOucvlcnr69KklkNfX1w3rxwFhroAzu7u7devWLcuZ/fznP9fz588/VUOELPNs7DnmEwPg5dU7Du3RGJRzDCVzSfSP8QFuxeNuZ3dBfMCQokd8TQPRGsrfRyDt+UvWA4XNXvQKHkWOs8Na8o7AYV7hwzjyjCKMk29a1w5RsU+YFw+h4nDwcz7vYTD03Tc5vjX2EdY+m81qdHTUJpMJRuCwqCyYzw1Ap0MoWDSsa3uzLJQ1YSHYv09oc8C6ZzH4xCWbwStvj136MLJer1vCkfC7p6fHOpAeHBwoHo9bnyME1Bem8Aye5urD3GCw2UGRTUZfo4WFBb322mvmJQ8PD2tjY8OKmPgenhStGIicOHSF1hjFYlG9vb3Wjton5hBGNhzGAeElQvIKGSWBAfFwDorDe9sY3729PQ0ODrYcKIRTgAL2kRC5p97eXoNwWHs2DnIQDocttwJ+u7a2ZswVlBxzhhLx+SXgCpKpPsr0iUcf9vs8Wjvs1dvbaxRZ9gCw3pUrVyxKWF9f1+PHj9XR0WFrJMnWgmtB8xwcHNTVq1ct6nr48KE++ugjezcO9fGV6j6q9mwc1oy5RT75A1yCsWPvEnEgB+yBYDBo7DfmBpjXM3qA3oDvJJkD6GEmZMf3SsJI8B1JLdARytXj9j56J+plf3JflLa/r/fqcWCYL9+9gOjVJ+4lGTSNfmCwB9lDhUJBDx48aInMvunxrRkFvMVKpaKVlRVNTk7aBvFJF0ktoagPr7kOR9ahVJhkIgcUDQoGwcVTZFOxSHt7e8b4wBOR1GKYfFgpNVkGXpmhEBEsoKpIJGJN9aAMEkID2fAdCuZ4Fi9Y3jihmCORo14uGNzV1VVNTk4qHA5rbGxM2WzWOnVS4yAdVQ6ThIUxhVLr6Dg6+wCoB6PlQ3MfTqOowfg9FCg1qaqe/opBk9Qi+MgJ6yQdhdQrKyvGimKO2LR4bcwHhojNSuTIM+G5SzI5o90G8gjDyVNG/Rr7ugnqMwj3iSBRYuQNfETre3DxMwr4ZmZmTMYwsHNzc4rH40qn0+YYLC0taWtrSwMDA5LUEonAPsMgRyIRXb9+3brNZrNZvf3222bAcK68F+rZMB6u9Z62T/Ayvzg2QJTIMHNBYhpqars3jRHlb08DBWfHqULGSDijlNuZUn74tUM3+AQtesQbQWTFRwX8nPn1ChtnsV6vW/QqNaN/D2cRyaPYPTztHQtvoHCUnz592pKk5/ff5PjWjILUXORCoWAnf4E1kpTp7Ow0vNAnhxAMoopyuWwJWI/fIRA0m8PaegiFz2EooDFKsk2AEmOh+T+GwCsClJ03EB5P5dDyvr4+U8JUt7JxYcSw2N4Ll2Sbx7Mj6vW6bTwU/J07d6yrKiekgYHivbOZMbgoJJSKrwtAqCWZtwQUAZUTjw9vCcofc4WgQj9szxN5ZwDD5xUvjCUiEu7v21LgYMCyAaLE+Pt1bjQaVhOBIjo4OLDT1ZALrsnc+TwCzgObtV4/onqivEh+4pTABmMDewUaDoc1NDTUUsVN5e2VK1cMviIKajSOKN70CENOeCfmnUikt7dXr7zyihmEg4MD/eIXv9Di4qJRY3lniA2STLmxztVq1QyYh1r5G+WLjOCwAe0A24EQ+OgDCqYkU/q8E/sYjN/3AEKWWEdkwbcG8UocJ8xDRegaHDmiHAw+BhBaLsPfHwfA/5/nwnlC5j3DCGPK/uKeXMvrQebTO9Dz8/P2fDzTNz2+NUqq1PSyadR26tQp80p8xh3Fj0IAh8Yr8bguFtILMp6dJPMo+Z1P/kjN3iUYGO/1s7m8B+EXyvOpPWyFwSHpSQjNOc5sMNpG4GX7lhrg5/zhmfGOPERG1FEsFnXnzh0NDw/r1q1bCgQCmp6e1u3bt005YWTBO/f29rS+vm6GiSIfEnhwy3kv2GPw91F8zK3HZn1eB4KAh9o8XxxD2p6Ex6j7Tc/G5l7gvHh44XDYCiMlmaGIRCIG73iIksNtWBeUPLLgn6FSqRhe7z+H0Qcm3N7ettwGDomHNHGAiHxYCzxraKuHh4d6+vSpsbako26ojx8/NsgrGo1aNbRn0ZHDunr1qk6dOmWy/P777+uTTz4x7J1IECXHXqvX6wZN8g6SzEh55lG9Xm85hwAHDnovn2G9OduDSn2UP2sITOQ7/uI4+ggCneD3PYbd4/sYHW+0OXODdfaQsaSWzr/eE/e4v2c0AWthOIFBvYyQH0XRI8c4LVRzU4QLkSQajZpsdXUdHU3r9zWDufkmx7dKSfVeIyEQIaXU9ETpl89GZ/HZyL7HEIKANcaYeEYRiyo1k9ooeBQXRsTji2xin8QBPmnHIPEs/H0wNMBEYNosMmElHi5eO4LsPQLvXQOb4An61tt7e3v68MMP7cze4eFhpdNpK3Khu+n+/r6SyaQ9x9ramlXIdnd3K5VKaXFx0Ur+a7WaFQMmk0lTpDwbSt4nHj1sx6YhIgLT9wrAJ7AlteRtUHDeoKDsuRcOABCRZx35yBDl7tfYJ1LZqLTNYK1hFRHd0ZTPM+h8vyUvH8gEVMxEImGGhor4vr4+w9Fhsw0MDFhEEolEtLe3p9nZWS0uLppDUS6XjdGEkke2x8bGdPbsWZPL2dlZvfnmm1peXrb+T319fZKaVOl2CMdHq56i6fcKZApJdgIeR9mSb6GuxMMxwDQ+Akb5+2Qwc+wNEevpoahAIKCBgYGWyJVn8ywfjzqwz9ohKA+TwqDi+RqNZt8iZAx0oB3yYR7JUbXrCe+IEg0gu0S36CAPa3koi2f4Nsa3Bh/hVaHAs9ms+vr6rAkZniuKxrckpqcRmCc/99ctFostSWip2V2S0MsnhqVmkYnHSQnzPDMEgeH5EVK8TzwDj7Oi7KBQAtFIMqXGYJOhjHxfHkJsj9+jbFBkJKXq9boGBgZUqVT04MED3bhxQ8FgUNeuXdPm5qbW1tYUCoWsKtl7bmze7e1tbWxsWAUmRV2wvtLptHWArdVqdlgL74vx9OExwurzDbRxYH0w9u38dJKknq89NDRk84jBh9GBEmDN0+m0wVEYXKiPvpCITYnSgRPucx3MtfcCvTPivUfyMhAsqLSlFQZGXVLLCVycBkeSuVgsamxsTH19fZYb2tjYUKVSsVPrgHL8KXp46adOnTK5e/bsmd577z2L9Hw3AJ8bIvL10S5KG+iM6IL1DYfDRklGoTOv5Bey2ay1LEeGqGT2eSDfdYAkvt8zHqrycDDKFRgKuAj41Msf78Ve8jkT1tIzBXGWkBHuj3EhQsAQ8Tu+7x1jdAT7AgPhu83CUgJZqNfrVmne2dmplZUVLSwsWETkDd43Pb7VnIKkFiW0vb1txTxSaxM2+vYEg0HjwXd3dxtHGUXk4SA8JTxSvBowPDwzlDuCdBz8w+IjGD6J5AtPJLX0/2Gze2xXanrNCF4gELBW0jw7UQP/RhB9fxTvVUhN7w3MFYz43r17mpiY0NDQkE6fPq2lpSWVSiVLnjK/FDYRcaAgeHfaghPybm9vm+H1bUPAYplX3yRMkimRRqNhyjKfz7fgvb5SnTVE6ZN/8oll5lBqFp/5thcHBwd2Yh7JWIwBRoINRS6HTcuat2PSOBTeecCTw7Hw0SpwAx4fkAnKNZ1OWy6hVquZwUaWkDM8+8PDQ6uUZg6CwaAZKJyrVCql6elpjY2NKRA4OuzqX/7lXwyznpmZsXVj/rinnwfe0SeKeT7eAyXmIwo/Z6wD0BvdeFGk6AVk3rONyuWjs61ZKxw5Igf2jme38ewk8z33n/drz9m16wfeA+eM/BU/a49qiUTbIV6eFdmh2JbPkp9k/3pWG9EBlGK+39nZqWfPnplO9ZG4j7i+qfGt5RQ8DMLi5fN5C5H9ZJBQw6MDS8Ny+kSg3wwICv/HyyTZ5Q+5IbnGz71A+QX21Yfcl5BVatZEkNDF46AqFu+Z70rN3ABKHPy8vZ8Q3g2eIN4vmwPvtVKpmNfH57PZrObn55VIJOxcgIWFBW1ubprnjbCjFCnEwatljjs7OzU4OGjC6XFsn8QniRgOh+1c5FrtiP+PQsfIeY+azeZ7LmGskQMP02BAfP8jn7xDwXOymY/kMDIoEiI0ogvWjYI4hk+M+jAexewjHuYPuSdx6PMq+Xxeg4ODtj4oQiCoRqNhUBT1Bx6mQVkgQ4FAwKqXgT7Gx8etO+6//Mu/aHV11YwPz4FRx7vmGXBgvJNzXKKViFFqtpDAWwbvJv/kDQsV6uw3KKU+UdruSHkqr4dK2vctsgVBgGdjz/l9i1z550cX+CjEF3WSVGYfBYNBa2/uI2aelWswNz4iIvr18DQOKH/Qiaz54eGhJZiZJ++MfNPjW8spMPxDHx4eKpvNanx83EIkNlP7ZmORmRjvyTAhvtCjncmDVcZTwaMmUYmXyuaTmt6TFxwEq1artWwQ3tNXHNL2AiNFWI01x5sgxMZzwQhJMhorbCCegaZ5eGr0+Qeyyufzun37tgYHB3X69GlduHBBz549UyaT0fb2tur1umKxmHZ3d21zS7KiJhRPvV43ZR8IHBXOoayJJqRm+A57BiUFRk9TuY6ODvPUe3t7rVirXj+ql+jv77fzKUjY0ncIrw+F2dPTY4VbrCPeFYqMiJKoE6+aszAwVCilcPioi2kmk7EGfHiReGl4urwbciY1z/2GzYYyg+LKPHV2dmpsbEzRaFSZTMacE2QT+Ek6YiIRRRSLxZYkOkWReNIom7GxMQ0ODko6qke4e/euKXafO0DW2Rue+eVhNeSuvTgRB4V7Q6qIRCLa2dmxegnkAhn1egDlx3N4aI69wh/v2fu97etIcJh84Rrf9boDgwGN3TsKHmnguaLRqBUdMicoZEgkzAOOH3PGNXFI2DP+REQfYfhnwSlMJBKKRCL6m7/5G5sDP9qpt9/U+NbhIz8CgYCxAoaGhgwaggrXnrT04TmblAGWLDW9Ajakp0mCX/roAsbEcXUPCAeeOlW2bESEm82AAPK8PtGKEsILpr209xKkJrsD75Sq2e3tbYVCIfX399v8MT+EqbRSoNr2zp07Gh0dNRYKvGY2fjgcNtaID5V5Nu7DQCHUajUzsBglvGIgFWAcPCIP2eAdFgoF7e/va2BgwKqIiQCYOzaPTxRXq0dnUtPfyePbePO+yI/1g1GCN4mi7unpsRoA8lMrKyvK5/PWTkRqetKeMsjfbHaUA8bE54GIxvCYmRvkjeccGhpSR0eHFT52dnYqn88bq0nSp+5HEnxkZERXrlyxI1bv3btnyokIDyOErPp3w9FAHtgzGDNk3MsEuD0wI1Ar0RsKkc/7aJxrednCyWP++Azz3F47QjSCAsY58HkDTxjhvj7/6Gm2RKN8n2sTJfvDdrzDwdyxLvyOfcV7Et3iHLW35sBhxfFCnubm5lQoFL6Ctv3q49dqFFi83d1dvfzyy1ZFS68ZFKvU7HmDgMLQ8Vikz8p7JgwChLIAV0WZAItgoICD2ltneI8HT9GHzN6AefYCgouB8pECgkrICFZPoguhJNlbr9e1tbWlQqFgiTo8jXYlXq1Wtbi4qHv37umFF14wLjxGl41OOAts4ZWrz88w4Nz7FhtSs4CIyMD/ztP+fHITr478wuHhYUsVsj+bAEPOOsPkaDQaLTkoNi9KysOIyBO5Jp88xZuPRCIaHh7WxYsXtbm5qWfPnmltbU0dHR12FoKvSvXcfSJI3zIC1orUjKjI7WBMWYu+vj4NDQ21GAFk3XdcxYHw7D2qlzlONRAIaHFxUXfv3rXktWdEeSad1GQaca4HEBmRHQbee88ofU8K4ec4JzgCPg+DJ4wRwbjiKeM48MfnDKhDwpnxeSSMI1Cg35c+aYzBwTixXjwPhpz9KqnlECo+42HBarVquRP0Au+OgSGvxFzBYCNnAGRHBMG8cW767OysOZffBlR03Pi1RwqhUEhPnz7VlStXNDU1ZUnVfD5vRsBbb+9ZIATtyWbfmoAFBQ8klOew+kgkYnkNPEkmXZJ5kggPQgLMwkbCg+B7KAnvQfBdFAMKqp1m1/5dKnBhB/GuhMh8HuWEp8sG+fDDDzUxMaFUKqVz587p0aNHlofwjblKpZKi0agxX8i5+OIlktE+Gcam297eNky1t7e3BVrB4+TdWRMSeER5rCXXZM58V1sUPp4v74Hx9Rg1kJJPhjP3Pnexu7ur9fV1M3jhcFjpdFrj4+NKJBJ6+PChnWjnDSVrTudYnIv2IinyGawRxhe82Heu5ZzpXC5neSnmHiUInJhIJMwwDg4OKh6Pa2ZmxpT9gwcPTK7A9dl3zAtyzfOAt3uI00cIfE9qGjn2FsbWF5u2R0we5sDr9tEAUYVPxjKICriOZxZ5WIa/vVHxERADAw8tGtngGj5yQV483OUjF5/0hhrsnSnkGhgZpxFZ5PtEaD5vgU6cm5trie5+HePXHimg6N9880396Ec/0sDAgGKxmEKhkLa3t21T+2y+T3yhUNl4KBlf2+AXGOUGdIDXgXIggsCzB8MmnCXhDPsDBUFrYzaWjxZIbnqmR7vQ+1AUpYRAeUWHd4KR9BsdAwI0Qt3BysqKnjx5onQ6rZmZGV24cEH379/X1taWUQJhH3V1dVmrajB1nhmFjeeO9wrEQRKMhDgbDCPLWmHYmWM8KX+8IGuKEiSH4ml7eI54fuQcpOaZCB7rR8mhoMC3fVIfA95oHPUWGhkZaYkOCe95Vk8U8Cwb1tQnS9ng0Az9OmO0gDRhYPX09FhURo0JDghGnwizVqspkUhYXcPKyoqePXumrq4uU4jIqydsSLL5QEZ9dThrwjz7NfQJdn7m96HU2j2APelhFR+ReyiNeSTqJALBEPg+Sd6B8g03PcQH/x+lzrN4lg/OC3ANg73WHsngGLHvcJoGBgZaIMB2p9DvBWjdnvHn+8Eh4++//779vz2f8G2OX6tRYJNFo1FtbGzoZz/7mS5duqRr165pYmJCiURCa2trluT0hgCFIMlYLAg0LAef1PHMHxQN5fZYZZ804x5cgyZx/B7BaVc0Pg8iNVkHPI/Pc+BR8E4+5PSl/GzgcDisZDJpfHVvVD3jw7OSqKD86KOPdPXqVQ0MDOjcuXPKZDIGqWxubrZ4vxQP+pYE4M94MUQQwD8HBwcG8SC0fr28ELMWeJ/eaPhNy4Zhjnyxke+d5HMWwDcoSubOwxzeGB+nXJgL8haSjCmDk4BiOo7CKMnIAt57RRHgeXtowDs3RJ4+CuVwJZQOURKtNXp6eowwEQwesWE++OAD7ezstDChPLPGK1kPiaFAfZeAds/Ye+pAvfzx9TU+ouAdvQfdbgyAcrgXz0XUST4IiKg9WQ4BwrOUeG9gQvYa9/UoRPs+8klnHDsfsTI36AGMBUe8RqNRWwMf7XpH1kOp6CLyTqz16uqq1tfXbf9gUH4d49dqFBA06J2lUknvvvuuVlZW9Oqrr2p8fFyjo6NWQAUlz29qT62TZEVXXiC8VwArxMM8PpyHTorXwOJ5Vg0L6ZPRPuHLZ3xhFIuN14TxINT2kQ338e+J4Pi2u/CvUWqhUMiMDeyPcrmsdDqtfD6vp0+f6tatW7pw4YIeP36sra0thUIh6+OPB7y/v28HoAC3EPJKMmXgN2+j0bAEGFEEc+QrfpnTWq1mUBg5DQxgV1eXKpWKncPsozbuz/d4Z8/ugqaKUuVZMCbUNuTzeeVyOeXzeVWrVTsD2of1eJ6ejuoVpE9+837emyQyQHGxTjgVKCiUJFEHxheiAR4k0Vpvb6/V7iAXZ86cUTqdVr1e1y9/+Uu9/fbbZsja54L9V61WzWFB4WJUkVOiGOa6vehQkuV6pE/3/ZGayXBgKw8pecPJszGASckRoFxxBHyOhOGdM7x4fo+c8XzkTtp1gk8YY2iYZ96dPeuVNO9AfUUwGLQcGfduh5lBK0AqcMB2d3dVLBb17Nkzg3z5zq9z/FqNwmeNtbU1/f3f/71OnTplh6aD5xF6k0Ty+CbVoOCzKHg/vBCxEYBavJfrIwGPgzMwIl4ZcG28Eb7rvTMvnChbqKYIsIcfpGZxHN4OioTeOHi7/hmgqNbrdevx8u677+r69etGd9za2lK1WjVMG6OwsbGh4eFho9/5wjM2zO7urkUioVDIziTmPTFOUjNJz7yzkUheYsA9Q2N3d1dbW1stVFY2F32igsEjvn+tVjMDiRHyMIVXwHj5kqyvE8YJyACDR9LZQy1cr/28XWSh0Wi00Fa5Fo4FipAOtCh17lGpVGxdoP9Go0cnoMXjccsrYFx3d3e1s7Ojmzdv6vTp06rVapqfn9e7775r8w/kSQTtayy4jk/Ae4eKZ0J2MQw0D5Rk+8gTQNgf1BaRB+JeXpH7qKV9f+EwooiJ8H1rdf9dno+9zDthqNtzF+D1np1Yq9UsgkPvcB9QBl+simz7SEWSwUY4VjiE3J99Qxtw5q1cPjpkanFxUYuLi1pdXTW94mX61zW+E0ZBOkpCPn78WPl8XpcuXbJePpFIRNlstsVTkGTJxnq9bg3cPMyAkIHBAzdwHZKIPT09isfj9ntCR+/VeQWPEmah2iMTNgqbC1yX5JrUysbwXpnHbtlYhMAIiGfi+N4y0ejRMZxdXV3KZrN2BOrc3JxOnz6tmZkZPXr0SMVi0bwWciPkWFB2PDPKxCcbMUSNRuNTh6dgwFCIQFO+OhMvsFgs2ib3EFggcNS0jc3LpgP3p3FgsVg0Zc9aUHntPXvP/tje3jZIBo9NaipRj2P7BCmRYSgUsgjIKwdkzuc9gBM87u0jKb/p8djx4n3zQenISaD+hVYWExMT6u7utsrlfD5vOR4PexDxoGw9ZOnzXAyUt/fW8Yhhq/lkOvPkFSwOgC8s9ZGnV5Ie5uJ33Bvv3MNXHqLyEZ6vtfDRPXsJRIF3Zn58TszXVLCWvocSDp+P6oDg2OvIOc8J8wx4DqeAaHh1dVXLy8vKZDLa2toyWW2fq++dUUAoa7WaVlZWtL29rbW1Nd24ccNgja2tLW1vb6tcLhvMAOMFdoa/ls8bsMBgiAgTC5rL5cy7hEOMEOE5eCzU45O+eI17tCda+Z23+CRbESiUB8rPbw4PP2HISITBYa/VaioWiy2e7t7enu7fv6/x8XGNjIwokUi0VJtWKhWrQs5msy0YOJ4M1wcW8QmxVCpl0QzPDDQC/c4rQRQOHmH7u6EUPaTDmpEUJZLw7BByEXt7e3YeNAqFjYuCI2EM+yqfz5uBazQadr6zXyNv+L0Bw2B59gz1FfQiQsHwf5+Ib1dUKDbu5ZVrNBpVf3+/hoeHdeHCBaObPnr0SM+fP7fkbCwWswi6VqtZEtOzvFCI5H78mvi9iKLGW8fwcmiUj6aAVXFmWH/2CcaK5/Ay46nN7FVkvx0Owjij0CFLeGiKPcrPPVuReWqHn/r6+loiR9hVGIVoNGoRq4fN2ufQswwxCHwPZ3F7e1v5fF4LCwtaW1uz88B9VNFuDH5nE82fNdo94Z2dHT169EgbGxu6dOmSzp49q1QqZRWt0hF0VKlUzKtHgUATxDLzNxAI8AheLni5T2J7LwPsmeI1PGiSw55dgJeIYcAA+esgvAicx3H5gwD47ox4xWwIDBdMFU+/43S5QCCgubk5FYtFq3TOZDLGhOFd/LPCNCJ3QQW0L7yiBB+mmI+cpCb27rFSoh7YNJ5zDcbtG87h7fF/lBsto9shOP7NNbzyIPoAzoCVUq/Xlc1mraNsNBq1Wox2719q9j/C2fDPh5L3MKKHcHA4QqGQ5UqIalCGnuboWzxIMjpqJBLR5OSk+vv7tba2pnfffVeSlEgkTLbj8bgk2dohY1Kz5bWHPDEgGGr2AUaVBnBAMuxXrsn6Q+pgT6McmRtkjGsSIfmEO5ANRa3+2uw1FC9/PPSLAscQshdZf++EQCxBpnxre5QyHVhxdrgWzohvp087cJwVDxUS7W5sbOj58+fKZDLW0wqD4CEwP7yx+HWM74RR8MLPqFarymaz+o//+A89ffpUZ86c0cTEhLWhDofD1l/eY4Q+Yebpe5JMARLuAjOwqfEePbSDQEMvBB5CYWNA2CQ+RwEm6r1kEsc+1+ANio94+D/KCqFF2eJ9AIugyCm64ndLS0saHh7WxMSE3nzzTYMuEHIqpzkWsrOzUzs7O9ra2lI+n7d3RbA9Ps3m4jPValWFQkEDAwNGoSyVSoaNewYP7yg1FS3GknYYvb29SqfTSiaT6ujoUKFQULFYtPYYJMyTyaQODw9VKBS0s7NjRpxNt729bbg9ipGN5uUEqAEWCJCY1PTefKToWzjwPu0JTWpBEomEKSnf7993cPWebr1eNwPNvNL4TpI++OADFQoFex4UOt1Tk8lkS74AWUXuiBaBrrycomgxrt4I+ghOUsvP/PX5HVGIp2l7OIjfYyS88vfsMX+QEbAR+9FHFL4Wx3fGJWLEMSASDIfDdggWkQGK3OcOMLCebcVaAxcCFRHZHh4eGvLx/PlzO/rV124wfHTgjcD3MtH8WQNBW11d1dbWlubn5zU9Pa2zZ89qamrKvAqUe3s4h9dPph9j4gXPCzyL7D1fPCieh7/xEH1Yzu8Rfu8NoQhQyGxQL/ieldF+HyAZvDzC756eHvX19alaPepoirFIp9MKBALa3t7WnTt39NJLL2lwcFATExP66KOPWiAJqckwAhry+ChVx6FQSIlEws6dxgj5OQN7RunQzwjcNh6PG+OJz3k4j+GTzP39/ZqZmdH4+LgZFyJA2lJg5NbW1ox9Q98g6LOhUMjOscAR8dASXjA5FR+9enYV8+WZRJFIpIW2ioHxDQBZW9aVyMazejy100MfJCinpqbUaBzVVCwsLJgzgzHb29uz1iGsoefY+9yap5myV7g3Dgcy6v+WZLChZ1d5OInnxmEKh8MmL9zD05E9798TB/y+IsLDc/b5DN4PpwoHxOcRUei8G04O5AVkECPvi8y4JhG2RxCQRU9O2d/f19ramjY3N7W1taVMJqNsNmt5j1812n//64wSpO+4UfCTcXh4qJWVFevPfu3aNQ0ODhqbpFqtfur8Bd8iAUOBZyE1cwDe0yNsZlO2exn+3GA2vN/oUpMRgVDzHXjsKCQMhY8+CIOlppHDI0IAKX7xyclYLGZzUK0eld/jpdRqNWUyGY2MjOj111/X06dPW7xklNbGxoZCoZB54T5hTtsN6KAdHR2Kx+O2kdgskloqrYvFYgsO75OWvrIUhcqGJfwnebq1taX+/n6Njo4qFotJkkEM9ADCQHZ0dCiTyWh+ft42u9Q8I2Fra8u8chRKtVrV1taWJLX0y2c9fct33oPaDY8xA9359Se6IiLzEZInL3BtYM12/JoK9Uqlotu3byuXy7UoZR8JYJj4P4Ye+fPRmk8cexolzpKPXnh+D2l4WjaGhHdj/vxzsaeQf5K9PuHO/mePErl4J8KTPtgHrAeOnHfcMNQ+wcz1MVwQHJgLnod3pnqfugK6AAMdHhwcaHNzU+vr61pcXNTa2ppFH+15jF+3sv+i4zttFPzEsUjlcllPnz7VwMCAJicnTXj39vYsLPONvxBiFABQDYIitbaipXsoHptP+KHACQ8HBgZamp0RVvLc3B9vifDee5wIu/cK/Yb1uQ0wY7wSvCvYMLC1pCOmjW8H8POf/1x/+Zd/qcnJSV27dk0ff/yxeYce18YL8knlpaUl5fN5S8rGYjH19fUZA8h7Yt678/Q85nJvb8+8Xu5LpTkdNj0nf2RkRL29vdrd3dXy8rJBTKyhP8fYG+9MJqO9vT0zEjs7Ozo8PDRFDgyBMsHbBsro6OiwZ6jVjpoBQhGt1+uWmCSK4t5Eo6wjygsqMQaYfJRXOnzHvwvzF4vFdPHiRcXjcWUyGT169MgwcvI75HtQTp7hxD2RFS+HyD5yiCz6n7MPkEnmgfX2BWo+wvJ7lz9Sa8U57CzPyjruGsglRov38uwnHwUx996otyduPY2aZ/PP3Nvb21KZzDGsOHnITqlU0tbWltFKkTf2hs9TMmcnRuFrDq8k9/b2NDc3p4GBAcXjcaXTadVqNeVyOWUyGdtwJIAkmUI9Dvv0ShwhQQgRLsJf712xqb13SwKba0vNiMdTXdvDcELn9g2K4PtIBMPAe9RqNTtbl+fESELvvHv3rl599VVNT0/rjTfe0O7urp4+fapoNKqtrS01Gg3rwUMyPx6Pm9fMKVCExd3d3RodHbVoxMMmvLsvYAOqITFOO++hoSFTgMPDw3YcZTKZNM+/VCppdXVV8/Pzevz4sSnrsbExDQwMmKHm+NHZ2Vlj5MD6oadVpXLUA4oKYQx8JBIx9hY/84qBxnNg++SGYD1xtgERjq9o9948BgMP2St2ZFNqVv+Dn3d1dWlyclKVSkUPHz7UxsZGi8IHGkFRIxcksH2Vsk+gg50TtXkP2UNkRFM8G+1H2vF/L9eecIGCZj48y4pn9waKd+M9uCZzhKImWez3NhGef1/WBViJZ8Wh8/vZ5xHY1z468K1xqPNZXFy0hoabm5v2zl7x+0jh18km+rLjO20U2hMvCE2lUtHa2poikYjGxsaUSCTU19en8fFx9ff3a3Nz0zYrSVAW3oeM0qc9BDwXSQbl+I0mNc8vZrP5UneEB0WD0EtNHNozWjyk4j1Vn8yVmsU87SE7eQvYJ7wbm73ROKL8zc3N6YMPPtDIyIgGBwd169YtZbNZZbNZ2wxsdjYI9FByMlQGY8SIioCy+BnRFsoKBQp8xiHulUpFw8PDmpqa0o0bN5ROp23uUZbFYlHr6+vKZDJGLCDPkM/nDR7AKOTzeW1sbGhra0t7e3vmOMBgwoAABUEbhTLpGVAeBz88PFQqldL58+eNtURCfGBgQJlMRp2dnUYVRSHm83nLpyBfMMraGUY+oRgIBFqO7Uwmk0omk9YenTX2z4rixhjjqTKnyAS4NkoPQ+UZbihUny/wRs47Op6r73/n2T9QoYkqqtXqp+oxkHeMmSSLXLimh4P52+ezkAX2qid/kGPwkTBGiCgep417QymlDxdR79bWltbW1rS0tKTNzU1rCd8OxfHMXpd53fZdHN9po8BgAr2C3t/f1+zsrLLZrEZHRzU4OKh0Oq2+vj6NjY3p4OCgpTe+p48SboKDI0wwSUh+4a3hUXF/vKJarWZFRcAWxWLRlCkeLYJEEqsdP+bfHrNlIwEPYRD95vCwD1WSvNfh4aFBCRzc88477+j8+fO6dOmSTp06pbNnzyqXyxn/Hw/OG6iDgwP19va2tMAIBI5qO3K5nKrVqkE+KBWfaEWplctlqz8Ag41EIrp27Zr+83/+z7py5UpLJJHNZvX8+XM7Q4G/KYDL5XLGtJJa24dDL2S9gEw46GZqakqFQsEUJ04E/aVQHslksoWZQiQxNTWljo4O7e3tmdJgDr18oST9+jGn/ucedvKJXuQvl8tpeHhYgUBAjx8/VjabNQ+ad8MQIBv8Dtn1kSfedTtJwkM6fAeP2OcCuIeXE5+09iwuSRathkIh7ezsGBXzs5S63/c+WS41m0t6nN87jETjPCeG1+dAfAsPrkPUwHPy/jgUQI5bW1taX1832SwWixa1eD3lUYfjdNl3efxWGAU/mFSfHCwWi1pbW9P4+LjRVv3RjXiwwCz0to9Emi2cI5Gj3kEIDBsLQUOIPCbbzrjw4S4wC1Q1PDIgJp6/Vms2dPNCi+fnPX+iCu+JEebCyOE98RL5fSgU0tramu7cuaNTp06ps7NTr7/+uvL5vB49eqTBwUFrj8GmIzHKM6PsMYbr6+uGuRMJ+E3tC/QgAYTDYTun+OLFi/pP/+k/mcIjqsPzhK9OtEPkx/xxbCtVzp7nz98c6AOtGBgIA5rL5fTw4UPNzs6qr6/PkvhAA8BpMLk++ugjFYtFi8w89x1vsqenRysrKy3epz9IiEgMmqbPc/nIFVw6FArp8uXLqlarmp+fb2lB3mg0Wlp4Iz8oUQ/JeDhMavZCYl18ngv591XCrC0yy+c8lba3t9ccE5wEroMSRvlzXe7tWYPsdR+1+32HEu/r62uZQ38vZMrnEXC+pOapeZIs58AZG0BE0ejRsbu5XE5LS0t68uSJ1tfXPxUxHaejflvHb51RYAD/oDgpEc9kMorFYkqn00okEkqn0+aB9fT0KJVKKZfLKZvNWusBSYbd+qQQw+P6sAxQup7q6pPKKAz+0IaCjUEewvel4d6emsmGA7P3xTDcB+8e/BbBr9ebZxtTL3D79m2dP39eV65cUTqd1q1bt6yqkgSaL/Yjgurq6jIlVS6Xjak0NDRkG50CQzzKer1ZBBaPxy1qiUajSiQSunXrloaHh22OMbL1+lFl7tmzZw3ykaT5+XmVy2UzRiTCPRTHIBrr6ekx6AVWVSwWUyKRUDgc1vDwsFKplCYmJvSP//iPWlhYsFoQ3oOztEOhoz5cGxsbCgQCGhsbU1dXl7a2tgxeSqfT1gp+YWHBvk/LC6rxPb6OcoYxI8mMQUdHhy5cuKDTp08rl8tpZWXFvGHIDzs7O/Z8KDJPsCDqxEDgwUtNA4IRwbEBqvTzy2dR3t5LJwrBmUHhUrBYKBRMAfsIHFn1hgijwX2YH+7jI2yeyyt+H8XwM4ya/7nPR/T395vT4Fueb2xsaGVlRfPz81b4iXxxTZ8Q/10Yv3VGAcFmETxWd3h4aPjzxsaGRkZGWpgzw8PDunr1quLxuDo6Ogz+4FhQf6ayT3Z5D8cX4PAZMHmeiaiADUX3w93dXWPekHfwVEc2iqSWvICvDPUYr4+a6JlD+I5hI8GLUlhZWdG//Mu/qKurS5cvX9apU6f0R3/0R/r5z39uyV+EHINEkRWVn9yzVCppZGREsVhM5XLZ3oezDci39Pf3q6+vT11dXSqVSiqXyxoaGlJfX5/dz0dCKITe3l5NT09rYGBAw8PDGhwc1LNnzww+YUOCEbMeyEh3d7f1fWLeBwcHzbskwT02NqZ0Oq2NjQ1LFLJ+VMHHYjGr1u3v79fOzs6nzpQ4ODg6C3pkZESvvvqqefAck1mpHHWCJW8RDoct6ckze1ipWq2qv79fN2/eVCQS0dLSklZXVw1mo5EehhYIEbn1uLyHJ5E1ZMgrY/aUx75Rql5Be5aQz7VxnjQGyxek+eiiHQ7lPv65kGuKCamtwQhgVP25HO3OovfmfVIbMgFkBmQX9ICq4+XlZTvbgsib8VkQ0W/7+K0zCl4ZohTasbx6vW6nWAUCAcN68/m8lpaWNDY2pmvXrmlkZMSSRoFAwAq0UE4oKBKw7fQ3zxBB2DzMJLX2fIfRweHzfX19LQksb0w8hx5PDooi2C2RCpvGM6UwXvzM92p/+vSp3nzzTYVCIZ09e1ZXr17V3t6enjx5YhXB5CpIIBNxUDzGvNbrdZ0+fdoMo0/kUVeB4SNxxztks1k7+CeVSrUkFJnjaDSqoaEhxWIxjY+Pa3x8XLdv39bCwoIl2iXZPNGWGOM0NjamsbExg8H6+/tboBVkJhwO6+rVqyqVSnr27JkZUXofBYNBe5Z4PK5qtdqCJwcCAasATyaTun79uq5evapoNKpMJqNPPvnkU9g7XjQtEtqpmJFIROPj45qZmdH+/r4ymYzJG/fGGACv4I1LzXOI+Z3PASDfHgLy845D4h0kPkdSHoPuDQVUX+8kkV+AOg7sR1Tq4SkcrnaCB1Gv9/b541tieIaU3wuepRUMBu1oW05kDIVCyufzmp+f18LCgjY2NiyPxbzwXN4w/C6O3zqjIB3fJMpHD2x2ME2/+VF+W1tbOn36tK5fv66+vj5ls1m7RkdHh3k6FFlJamHmeBYTITlhs9R6NKBX8j609+2ow+Gj83ppNuYrYcFHMYJ+M5A/4HeeZ84cUXTHBi8Wi3rvvfdUKpX0wx/+UC+++KLOnz+vw8NDPX361IwjuQTmslwuG/0U6MYXs+VyOTtXutFoGG12c3NTq6urajQaSiQSCgaD2tzctO8Gg0GDVzzW7LHZjo4OjYyMaGBgQKlUSm+99ZbW1tZaGE7pdFpDQ0O20Xt7e+3/UjNJybzg4ZLviEQixibyRgRFzil1QEsYPM9e6u/vVzgc1pMnTxSNRpVKpfT06VNjq5Ggl2RRVDssgozFYjFduXJFAwMD2tvb0/Pnz7W7u2sVuMlk0jxolKVPnrbTIYEXpWak4HNA3vuW1LKGeP0YR6nJQsPAoXg9qQPvHDnmdyhqoia/V/xeZs0wLDwjTovPZ3gn0deKQPKgIJOaE2A+ap9WVlase0J75fFve57gy4zfSqPweckdHzm0/w6Bz+Vylki9fPmyxsbGlMvlWhLPntsOC8In7bw3AlzCJsOLZ2N4hYeQw1bCC2LT4GF7b83fDwXW19dnmwK6qzceMKNQBmwwoIzbt29buP/yyy/r/Pnzxq7wnHA2IQnzubk5qz4ul8t68uSJQS25XM4UGvUAvhUBp7wFAkcVz9QAJBIJK0jy8IEfgUBAPT09unLlijo7O/XLX/5Sz549kyQNDg7q9ddf1+nTp1uK0drx5+NkJhwO28E7OAHkMkiko7zwcgcGBjQ+Pq5kMmmKsVqt2oHr6+vrmpycVCQS0fb2torFoqrVqiWvYWWhHFGCRBGSNDExoaGhIQUCAW1ubmphYUHZbNaK4EqlktXF4C17OKed+0/+AseBzyJX/Jvnaa8l8HkrWFRSM5dDDoZoh2iBdYU4ALOHd/aw0XEeORE4+TkID162eQ6/5jDOiDQGBgas/gXHZH5+Xuvr67b3ITP8qvG7biB+K43CNzUqlYru3r2rRCKh8+fPq6+vz+ARjmX0mCrC7ml+Hh4iIvB4pi/wARPFMKC4PFujVCpZZTK1ASS3fZ6BDQzbiLDdV11Do/NsJp4br/LZs2f6x3/8R/X09CidTmt6elq7u7vGciFXA+OEg+I5NpWjQmF4kYwkj4IHyXPjMUoyJQkrC0+bz/vN5zHoUCik6elpo3yWSiW9+uqrunTp0qeUG9/h+6wpa1ksFrW6uqqVlRWtr6+3HJKDUvOVsLCg9vf3Dcvv6OhQKpVSd3e3CoWCQZcw46rVqv2OdUFWMES+a64kjY2N6YUXXtCpU6cUCAQ0OztrVdreaFENzLpiMGKxWEulN4oOx8AbBuaEvz28RBsRDIP38qlC9kacimKpmfTFwSCHQ9TiDRbrwVojL+RZfCM7CudYB5LCHvpinkkc0/334OBAy8vLWl5eVjabtbVq77fk5+T7Nr7XRgHP786dO9rd3dXg4KDh0Z2dnSqVSsZRR/GzwXw/HxQhGwOh8okvT+mjkjUQaPKqfc2Bh2Tw7mjD6zeOx2LZrO3nShDq+zwInhcba3FxUf/0T/+kc+fOKRaLaWhoSHt7e9rc3DQDSIQBEykajVpimgiHBmEoKwySTxTu7Oy0VJSGQiEtLy9re3tb2WxW58+fVyqVsmRiO87O/HIcZTweV6PRUDqdbvGS26FFqWngoSYvLi5qYWFBmUxG29vbVtnN/HsIhJyFLwZjfXt6ejQ0NGTKKpfLKRaLKRAIaG1tzYrsYLX4PI3UpEZynGosFtPly5d16dIldXd3a25uzqK6UChkEKPn+rP+/f39xmzzUJT06XOCfa5AkjkYXNMre+aBSMavB4l2SS1RK/fCcEky+WT4iJR18i3IfZTsmVp8D9iR+5L/6+/vt8JFIKKlpSWtra1pbW1N6+vr1qqjXVbaocvv2/jeGgVPh0NAJicnNT4+bnhjOp02HJgTu3xCGaOA0gKHlppMIjwp2EEInGcSUTMBVAFcgfdCQVd7626fPAQC8BWh7YwObxAajYbRNaF55nI5JZNJa28xMjJiZyHEYjF7TxQ7yowiH8+k8soKiAtKLQrXU23xvJeWltTX16eRkRFjhBD2UytBlEUxmo8E2mmNUhOS4XzmtbU1ZbNZbWxsWFKdeoN2g9/b22vvAZsIaBDjt7+/b4VtGOnBwUEFg0GrfO3o6DCCgXcc8Mo7OzutDfbVq1f1gx/8QPF4XPl8Xh999JGeP39uzkpnZ6cODg7slC6UaEdHh0WZjUazvsLj8h668dETMuOVPkrYU0N9Tg055nNekfp94SNsPuvzcF4xsz+8UfLGiXt49hPMOMgQtLCORCLK5XJ6/vy51tfXtbm5aew3EtLc0w9vRH+XqKZfdHxvjYJPWElHh/Y8efJEuVxOAwMDSiQS1pEzmUxqYGDAaKU+gc3fnh8Nm8hvBuh1eDTHeXB83ofpGA1f6ORb9vo2Dwg63j3eOZvRN70j8Uy9BR4fvaOSyaRSqZRGRkZMkdBbCi8Xz5YkILAIORIPuWHkmC+S7sADeJPFYlHd3d1aXV21g3Xi8bgZ7EQiYWvYzgRh7nyjOlojr6+va35+Xmtray3GimugqL3Bp50212b+UFjADd5oB4NB9ff32/tSxwEsQ3KZyIDqZmojXnjhBf3xH/+xxsbGtL+/r4WFBS0tLZlX7AvKSJx6mib1KJ6i68kHGAhkheciJ9JuFHhfIoG+vj719vZafQTsK6nJBvRkinbITmqlbCP/rAnr4qMYzyqChef7NAUCAYtwOZWwVCopm81qZWVFa2tr1vbky0QAv+sso88a31ujgHC0Q0LLy8va2NhQd3e3MpmMhoaG1N/fb8yWSqVivXuonMQ7ZUOB8ZKY8yG4ZyAFAs0DSPznPO6LFw1MAS8eWqUkw7+BsjyLpD1JDSZeKBQsZwEs4iEl6hv6+/utBcbW1pb1hKfQCjYHuC4KxLOxiAKgjHZ2drawWbx3SF4FKKSjo0Nra2taXFzU0NCQrl69qsnJyZa20h5eKpVKmpubs7bSHMHJ2QrkOTw8QQTVnpzGM5aa8B+YuM/TdHR0WEQVjUa1t7en5eVlnTlzRj/4wQ+0tLSk58+fq7+/32SG3BUGPB6P69KlS/rBD36gyclJ5fN5LS8va35+3lqREG0wl/7cY4/tY5y8t8274KmTY/DYPPLiHQkcGiJhT3X2UQL7oK+vrwWC8fk4ckpSKyvI907C+BAle4OD40MVeyKRUCwWUywWa9m/S0tL2traUqFQsN5e7ZEI//a6gHX3c/N9HN9bo8DwC4/ggDkXi0VlMhmlUiltbGwonU4rlUopHo9bG2UgBTa4x1rbK0GlJkOivYjIU1l9UZYfUEGhheJxxWIxo47u7e2ZV4hnirL32DjP136uM1Q+GtFls9kWbjzJVzawrypG6fjoA2MJfZP3oMqbTQ6EQpsEIgfyD3t7e8rn8zbPMzMzLdTIQqGgfD6v58+f69GjR2b0PBSBASAqop5CatIvUa5g055BQ0U03jMRIpEScsB8zs/P65VXXtH169cVi8X03nvvaWlpyardgaBisZimpqZ0+fJlpVIp662zsrJirCiSyI1GQ319fbaOrLOPynwU4KE1FDhOAHOAocJA+QridohSaipTz46jhQf38RXbRG94+e3GxkcJyC7yQ3TGM9NXbHBwsCW3wiFcKysr1pyOiP6zRnvU8H2Eio4b33ujcNzwbIlcLqdisaiuri6trq4qkUhoeHjYIohkMqlKpWJtcyW1nLbmk21g0Hj6KGdf9ObL+X34ioHhM3hnPkwnSeoNS1dXl3ll4LjAWO2QAaE6sBMbFDgGGIuEMRg2zyA1D6QhkvCFdd47Z148Q0aSVWRTUFSpVIzG2N3drWw2q7ffflu1Wk0XL140g5rJZLSysqLnz58rm83a3PBMHsdHwXFP8jTMnV+jRCLRUqDmIUOfx/GQFQnMxcVFPXnyRC+//LISiYT6+/utPxT9mjo6OjQxMaGZmRkNDg5qbW1N5XJZKysrVkTnTxFDXtrhSQ+Hgpn79t9EDkS6PjdAnYGfq+McFzz63d3dFujLR8FeyXtojvn29FbmE9mq1+tmyPkuCXpqRnDIAoGA5QsWFxe1ubmpXC7XcobByfhq48QotA0E22OgtBLY3d015TM4OKiBgQHDMuPxuNEOjwvTJbV4Z1Sj4m36PkgkU32fGcJp31GTDcXhQkAenuLJe/j+Mj4p7nMYUER3dna0srJiR38CXXR0dKi/v98MF5+VZBWreM08u6ch+twJEQZwBc9DxMTf/rzdSCRiJ1vNz89rZmbG3jMSiahQKFgdAArUFzGxHj6Cak+ASk0PEsVCFNCeY8BweMPOnEajUeXzef385z+32orXXntNQ0NDlusIBAIaHh62okUK31By+Xze1h3Fz/2QMYwt0Js/SAfGT7vCxiHAEfAKHJiMaIo5ZK44khJDwmeQD5+UJjr0Do+HSXk2b1R93ovIkW7D8XhclUpFGxsbWl9f1+rqqjHHgFi/r3mAb3KcGIVjBoJ1HK3x8PDoaMhSqWSey87OjpXMJ5NJjY6Omoebz+dNYCW15Bm8waC9MMZBasU6fTsAIBk/KK4CEsHLYsOzufEuvQLwNRBs1t3dXWPDwIySjs4KRomhHNqpir6VBErNJzvxHtnEeL7+vUmaErmwHjzL2NiYdbbFu6QpItEJUBPG0MNxXkH6yAVlB4YtHZ0XXavVWjp1+u/6pCzRGPTU2dlZ/du//Zu6u7s1MzOjvr4+bWxs2Olu/lhHPPQnT55oeXm5pY25j/jaK5bx8vHC22XLc/AxZh6z52/fI4lrA3NR5d/eNRXatO9MGgg0j6DFiGLYPZnCJ4qRcSJTehIRXTUaDastwBjABPPv+mUSySfj+HFiFNpGe9KJn3mIQ5JRVWG2DA0NaWhoyNgy9NmPx+NGH8zn83Y93/gLBcXGxfv12Czf88k3vH+iA5SfV8Rs2kAg0NKWuj2C4LqemeSxYLjehUJBkoweCSTGqWwYCSp2UazewPK8zLHvL9Tb22ueLl4ozxYMBq2Sure3156n0WgY5XBnZ6flfGeP/fvkqNTExD0Thv+Df2OwiBS4P+wvICMMos/LhEJHrUiePHmiWCymeDyuwcHBli6lGDFaZywsLOjZs2d2spovkPQy4CuN6dzp8XyMLZAS6+Llmev6Ohe+gwH1yVeiC67N/Hj4EMPNGhAV+6i5PQfFXPX09LR0K4VU4esLisWi1Q59VsL4ZHy98TtlFDyv2NPu/Pi8whQfZv+q4WEmGCELCwvGVBoYGLDiGf5OpVLa29vT9va2tre3zdPx4T0epy/U4X1QEuVy2XBmTwXFq2TgvUmyz7R7aFzfzxvwksdniW5yuZxWV1cNosKb6+3ttUIiIA0UWXsrAg/PoYw8jMG98brBvoPBZpHS5uamFWrRXI122Hiuvn7DKyG//sw5x6rSSI/PAJVBxQT7Bq7xZAIS1XjVrG25XLaCKQ5tYQ7i8bhBaLOzs/rlL3+pfD7fctiLZwJ5ueAeQDs+skKh+iZ1RDLkajD61JdQREn+ot1Z8NGkd1BIxHNCH8WZPK//vo9OuT/QGQdTBQJHZ1fMzs4aGzCXy1nfJL8HfbL6ZHwz43fWKEjHGwBP1TtufFFv47jPVSoVgzBIwo6NjWloaEjhcNjyDqOjo0qn0yoUCtaSl43LpvfYNZsLrxyP2rN9eDdwbpQjitR3cmVuGL4SGsXRaHy6oSDwCYqGXke+cArliYLAkEnN5nz1et0Omsc75f7AaF6psWYo4WKxqOfPnysSiWh4eFi9vb3mBFBUxTxyfV8o5Qv7UKAo6vboELjGRxUwo8Dk+T5KSpIZGoxSsVjU06dP1dvbq7GxMUsC884ffvihfvGLX2htbc0gGE/1xHBwT29cfWWzJFtvck44DD4fARzpk9AYA+6J4wDkJ6kFDvKV6rwvihs55T6+RgUjghEgb1Cr1bSysmLUZ45gxXk6bg9+X2mj3+YINL6gFvxtSeDgUaMQ8Dh/3aEmnlAgEFBvb68pr+7ubiUSCTv0heQcGD60Q+odPHTgMVsPAUhNDxw8HAhIanLCPXOGzSk1T0jjHr4oq1ar2cZmoEx6enqsIpn3bTSOKJP+wHMiCa9AfK8flIc/p6K7u9ve17dJYCSTSZ07d84a4D18+FB37twxg4zy8mQBPFaiEZSdx8gltSSd+b+HPjzTBwWHh+yxeE9TjkajGh4e1unTp3Xp0iVrdLe2tqb79+/ro48+MvYac0Ii10dVHkP33jpKEwPM93CAeB8PibV7+8d52x7C8nkHrnkcno+BxqD7yBSojIJQniOfz2thYUFra2tWad5eJX0yvv74IvP5O2MUEDqv6DxU4Jk33/bwStfDOaFQyDYDzKWBgQHDUcGoaSFB1ShtImC/+LXAk6XCE+XgOeV4gygBGEVAVXjOng3Fc+OZ+g2PUvHKyHvIPmEdDoeNUgjc5O/D83u6LElyX8MBxdXnISYmJpRIJHRwcGDeZalUsvnwig8D47vPSq1FUsyN1Ex2kyD3UQVr4JlMjUbDWDBQd/3coKBJlBPJ8VlJ1oIByMx78d4QtCtxZB24j3v6iNPDkH5ePJ3VK3jkBrabZ5IRxcBE8nAQrCPWDmPf3d1tct7f369QKKRisWht1WlBUSwWj4WJTozDNzO+V0ZBUkvCq7u727xunzz+dWGPntOOZ+oLgjo7O9XX12dHWPb19SkWi1k47Qu9qAsolUrmmaO0oLmST0ABMbwSR6GQKGS+UIw+gRgOh1Uulw3LZWMCFaF0PM0ULxKcG2+RnxPBMD+chcvAaHiljbLyURH3jcfjCofDVhcAjOajnHa2Fp1j23M2PB+H9PDM/OGzzBfRC9EcUM3u7q5Fa8yFJxVgUDwExPV991oMg3ds2qMEjBbePBGmNwae9y+pxQD66MIn4H0Cmnfz9z4uAuCavgUFlFTIF0RTwETLy8vK5/PKZDJmtD1L6mR88+N7aRR4nYGBAV28eFFPnjyxQ0k8DvttDqovpVZD5KEe732TJI3H48bJxqMaGBiwgie8ahLbntfte+l7mqX3avHiUVi+VQNKwUNTjUbDFB/X5f1QAigGqIQYn0AgYAbNGyD/PQyEZ/tw1gKK2UcS7QbIwz88l0/W+zYVQD2sjYeMoAKTaAZ2JG9A1EYU4K/NdcD8mTeux2elpnNAdIBz0N/fb9XQREuSWvB9v/+8oWyn+vqEPnPuoyyfbPbwEp/3jgVz326YeSYa8bGeRAREwNBsDw4OLCrguNNcLmcFcH4NPyvndxItfDPje2UU2ABsyO7ubv3P//k/lU6n9eDBA3344Ye6f/++4djHCWI7e+k4nLVdyX9erqL9M5833SgJaiASiYT1giei8FCQP4eBZCx8cgrkPDQAPOI9ezxkn7vA68MDx1v0bCkMrfco8TJhSYENexoiuDcKiGt45UabaZ9IZ348dZLkM3x/b7hQyFzT99YhOUz+gWshR94IoMSZQ59j8NXRJOZRrjB6gFuQO6IOPuOhOpwJHx2gyL3i5h2RO+bW55y8TPsoxcM93qCxnj4yQtZ4T9aD62PEenp6lEqllEgk1Gg0lM1mtbCwoJ2dHTOE+Xz+U+cd+/1wovi//fG9MgqSDP6gV0pPT4/+x//4H7p+/bqGhoZUKBT0ySef6Je//KUePHigvb097ezsfGqD++SeD6sZ/O64qOObEmyUK8lpWmqQqOvv71csFrOGdiRiMQqVSsUwWq8MfJGZj6xgk5A8hhbpm67xXN6wgCOzyT0rpVKpWONArxjBwD1H3tMLJRm8hqHyOQoPs0B99HkKrusxc67NQUT+nh4S4h6+Etp7+1Lz8Jj29W80Gma0uK9vbeLP3fB0Td/y5Ljkr3diiKiIuDwZwSfBeU4fpbXnDHz07HMgfk14BgxQo9GsoyFXxOl5gUBAS0tLun//vhYXF63nFZXoHoL6dUXtJ6N1fK+Mgt80nhctSf39/bp69aouX76sN954Q5OTk9rf39fS0pLeeustPX36VM+ePWtpK+Cv2e5VoWDbG4/573xT7+MHZ/5ydjIJawrJKBbzp3uhBIvFor0XnUOBUYA6UJBABPTj5+dQR9u9T+/xo/y9R+txYu9twzZqN8rtCVkGyptzkUnCe+PiITAYTu2QiVe8/Jx3RlF6iivPyVqzPt5LB8ohMvMKFoWLsfFGleel3gQ6qffUvZFj3n1uwBfcMV8ecsKw+fflXdqZROwdHCzmgWfs7u4254GWJ7XaUQPJu3fv6vHjxzbfPEd7nuvEEPzmxvfKKEjNJKLU2r9dkjU8o5ndzMyM3njjDV2/fl0DAwN68OCB3n77bf393/+9ccWBQPDqUHxAKO2G4NsS9nZmldSsLcAgjI2NWRESyonKX386FQoFyivKmevw83K5bEZSahZxec8RpgleOqPd2HjGFArUw0jMHV4wBVQ+KvCwhvfipWYuyUM0QGkwuZhH7zB4GIg15D6SWhK9nl3F/z0zybOuSGKTAD4uL+ANhM/h+GfiflIzGez7B1F/gbEMBAJGH+boV+SViMJXmHu58s+AEQL+ikaj1qI6FApZrqhYLKpQKFhEuL6+rrW1tU/1kDqO4HESKfxmxvfSKBweHtqGQzh9/xypmfDDG33hhRf04osv6uLFixodHdXOzo6WlpbsWrlcTnfu3NH8/LwlybxHKn2a+/91B4rUwxaflbeQmnxyktO0CuAkKhQWbBDftM7nINLptLq6uuzcZXIVKDbgKVhDJCNJmmKY2+mYPuJAoaLYgIKAQVDk/B6lj5FAwaK8PIvKe8r8zCt1PNt2T9YnYf0cA7P4d2mvWfAGBty/vZK33WggJz568WwensknpSmE4xm9sfE1E4FAoKXGBAPuo8BGo/EpyI39QL8hjoANBAJ2ABLspmKxaOtEtXb7e2HEjnOafpUsn4xvb3yvjIJXPmx0hg+J8VI9TssmjMViOn/+vCYnJ/Xiiy/q8uXLmpqasuTZ5uamHjx4oNu3b+vevXt6+PChNTeTWpPd39RoT4i3X/s4b4zvUEXNMYUUkcHyCYVC5uFtbW2pXC5rYGBAL774ok6fPm0bGU469+dAHxQNytB3yuTZPHTB9YggUE6BQMDyHDS5473wbMkdcF8UM94wCWGiD4+XE/WhrH00yT2JJIGQvCJDccPW8n2HfKSBIeKZPYzmvWdvVDCqeOaexeRzBD6/wDuTr8E4MlDwEBGYc94DgzMwMGDOAREd1/Kstu3tbesxRZdSjjflNDOuexwJw0O57XJ7Mn6943tlFL6pwXtCDT116pTeeOMNvfLKK7p27ZrS6bQajaN+R2tra3r69Knu3bun999/X5988olqtVoLXi+1hsrHMZakTy/WNx1ee7gjEAiov79f4+Pj1rs/l8vZZ5PJpK5evapTp04ZHIXiQCmi/HhPrgsO75k37Ulmj2njneOBwiDyc4WCQsnxec68DoVCFsF4zNw3YyOSwqv288LPfG7Cv5ev9kaBE1lIMl4+bcR93kVSS82GpJbuoZIsh+AjBQwPRoV7swYoe08W4F4kd3kO5sSvI3kpDDdRYKFQMOO4t7dnrdkzmYwVBmI4vyvjV+2j9s8gV8d9lugVOcXZ+10aJ0bhKwzvZbNRgaXS6bRu3rypq1ev6sUXX9TMzEzLucErKyu6e/eu7ty5o08++UQrKytaXFxUNpv9lMeP5+eZHt4I+CimnWb4Tb2nTxj7jRIOh5VOpzUyMmKJRZSTp7ESbdBOHMohJ7LRKZZKVq6D0oVKSn2G1KRlkvjGKOC9e2imvR7BHynpz62g4A+Ixye5vUGDteaZS6wHsFT7c9Ch1F8jGAyaN891vNL37CZv9DylFDnhM8gKz+D7LQH/+ERwJBKx1iKewsuzQA3FIEEfJp9EzoBkvqdnf5e8fKLA9hYw/M7/3+8nzw7z+RT+9Pb2amJiQg8fPvxNvt43Pk6MwtcYPuGIcvAe7uDgoEZHRzU2NqabN29aTmJsbMwOWNnc3NTTp0/18ccf69mzZ7pz547m5uY+VbDj6X+M49hHUB2/yejBFyq1e5zAQUAykswD5cCdw8NDZTKZT5125o0NCsu37UbJdHZ2amRkRFevXtX09HQLPbN9/j2bBmXn+wN5pYrBgILLH3/CGL9vx79ZY7x7r7T5rE+ce8jEM4i4D59trxz2sBLf528MDxEONFAMTr1e197eXkvhHdAgz95oNM9OwDju7OyoUCiYkSQ6AL6joAzIyRewMS/ftdEOs0nN/kueNEBRIAbBr1u9XtfIyIhu3rypU6dO6cyZM/rf//t/61//9V9/My/1LY0To/AVRjts066cJFniDhwVOKCnp0fnzp3Tyy+/rOvXr+vatWsaHh42CKZcLmt1dVXPnz/XO++8o1/+8pdaW1vTzs6O9c0BC25PbjK+qU35dRJ9RAremB13HY8p839vhNjI0WhU09PTOnPmjHU8hV7rk8l4vChMDIikYxV6ey7D01D5DOvXaBxVb/M9rsPnKGTjfdu/i3GSWo0YUQvMIG8IvPFrj9ToMusTvpKsGJDzBoCxSEhj/KrVqp0C6LurSkc9lujm65U9jDA/N9Knzy4+zmH5TQ7PHJNa6biedIKMkF/r7u7WuXPndO3aNZ06dUrJZFLZbFblcll/8zd/o/fee+/Y7qy/zePEKHyF4cN3qbkhPCWS//N5FA+ePEpwYmJCFy5c0MTEhM6fP6+XXnpJg4ODmpiYUDQaNa9tfn5e9+/f171797S+vq65uTktLi5qd3fX7sEyfVNYrg+tvbL+rDyIdHzew1Mb/ee95+bhkHYjws86OzsVj8fV29srSS31Fr6zpzcU0HJRrnjMGGF/3+Pe07OFyCEAr7Tj+YxGo9lXyRs4D2m1s3CQC9+62uc42us8/PfK5bIymYwWFxctsQsbCejNe8BQin0LFF9F7NfNr5WkFs/Zz5uPwL4p+PLbGDxvf3+/xsbGlE6nFYvFrF5lYGBAqVRKMzMzmpmZUUdHh9bW1rS4uKjFxUWtra0ZecRHnL9L48QofIXRjuW3bxSpSWklIeh588AH3iOFrplKpTQwMKAzZ87o1Vdf1UsvvaTp6WklEglL2h4cHCibzWp+fl4LCwu6d++ePv74Y83NzWl3d7fFA/6qA+XzWQL/eZ5gO1bLnLTP33HXOC7p7qMOPNV2RQkez1z29fVZRa2nrPI9z7UHuvK9jfz3eA5fC9DeLwgDwboTAbQX5vEOnmrqo732KLAd6iJHUqlUlMlkNDs7a62kfWTW2dmpdDqtjo4O64NFBMNzeePL83i5/Koe/3F1B7/u4de4p6dHk5OTeuWVV3Tu3DnNzMxoZGREjUbDqteppchkMrp//77u3LmjbDarYrFovdHYz1Kz8+xv+j2/6XFiFL7DA4XQ09Ojy5cv66WXXtLly5c1MTGhmZkZJZNJdXV1mXBubW3p+fPnmp2dtSpsWCEcRMJgw3v82o/jqLPt0UB7JPFdHV4u6XlFshUjgtd9HBRFa2eUCzANStbTNNtbSeDto4j9XLUnN7keUA+Og4drfIuSg4MD5fN5bW1tGe2z/TCi9iKxX+f4LPnwc8PvSMof933p0/BU++eA6YaHh5VMJhWLxXTx4kXdvHnTvH5qcciRLC8v6+nTp5qbm9PCwoLu37+vJ0+eWFvu77JMf5vjxCj8lgzvqSaTSU1PT2toaEgTExO6fv26zpw5o7GxMQ0ODppSKBaLyuVyWllZ0fPnzzU/P6/FxUU9f/5cDx8+1ObmpuHt7Wce4C16+qNnuniR+C54hZ83eBefaPQDg3Cccvfev2fuSM3GdtBQ/XUwNkQW5EY8s8z3MKI40FeMtytPYKv24yw9hMf4rii1z4Ih/c98fYTUJCF4B0RqdUgmJibM65+entbo6Kimp6c1NjamZDLZUr+SzWa1srKi999/Xw8fPtTi4qLW19eVyWRULBZb7unv830cJ0bht2x4JYUyoHVFX1+fzp49a5TYiYkJ85w8Dl0ul7W1taXl5WXNzc3po48+0uzsrObn5+3weRSUL7wC9uJn7XAQw7NpPHPmuzTa80Ltv/ssI+fhKg8htudLfOLcDw/R+MF3+d1nKajjnvu4SM2vyW8q6dveRdgrds+QOq4baigUUnd3t0XC/f39mpmZ0WuvvaYbN25oYmLC6oR8p95qtWqQ2pMnT3T37l19/PHHyufzdpaFrw/ieb4uXPa7NE6Mwm/RaA+32z14T4esVqvq7u7W8PCwzpw5o9HRUfOoZmZmLHcRi8UUDod1cHBg7TmWlpb05MkTzc7OanNzU4VCQZubm1pZWTGqok8IM/zm/y7CSp+VDPeK4Lj3+qxrtBuF4yCH4/bEZyn54zxj/2yfN5ef9X7fheGjTh8FBAJHZ2YMDw9reHjYjqEdGRnR5cuXNTMzo8HBQaVSKatT2dvb0/b2torFolZXV/XkyRM9efJE2WxWz54909LSkjY2NlpgqfZ8X/uztOeuvuuR77c5TozCb9Fo9z5JQPrEptQ8ZtEnKVE+/f39Gh4eNsrdhQsXdOnSJZ0+fVrj4+MaHBxUPB5Xo9E84nJ/f1/b29taW1vT2tqaMpmMVldXzXgsLy9bjxtfvPRdNAqf9zxEAu21Hsg2fZX4065sjrvncZGE//xnPdOvMmLHGZLPurf0qzH5b2tgCCBZBAJHlNuLFy8a2250dNQMQiwWsyJFYDxaZayvr2t1dVUPHjzQ3bt3tbq6qt3dXTtidXd3t6Vy2/eT8lGA1GoU2g0GLLATo/Crx4lR+I4NL9w+QferEsee5ujzBY1GaxO5eDyua9eu6ezZsxoZGdHo6KgmJyeVTqetiR5J12CweWLWxsaGNjY2tLS0pGfPnmlubk7r6+va3d1VuVzW3t6ednd37f/HPdsXee8vq0D97z8r6Xnc9z5P2X+VZ/wy12gfv+qa7VDR533+6zxXO8RCy4ze3l719fUZlBmNRnXx4kVNT09ramrKoEw8fqJbf+BTPp/X4uKilpeXtb6+rufPn1vEWiwWWxLyfh09JHdcwvqLvM+3NWe/jePEKJyMYwdKNBQKKZFIWHV2MplUMpm0yGJkZESJRMIO86EzKq0oCoWCSqWSNjc3LdKA5re9va18Pm9RSC6X0+7ubouR8xXaREPHKezjaibaleVxRqFdtD8rmd5+vS8zj8fd51cNb7jb3/XzrtOes/hViq/difisRDURZjqdVjqdViqVsuM0U6mUhoaGNDIyouHhYTsREANBdLu7u2uQz+bmprLZrObm5jQ3N6dMJmPRwNramiV+T5T0b2acGIWT8YUHRgKapT+zuLOzU+Pj4zp16pSmpqYMihoeHtbAwIB6e3vtu1Jr2+pKpaJisaitrS1rt5zL5cxj3NnZ0cbGhpaXl7W9vW3JbpQnxWQ8Iy0Y2r3azzIm7fmA4yCf47B+r2i/DNxwnLL3fx/3eX7fDgm1R1nH5Tg85ZZiOH7Gv+PxuCYmJszI49Vzkh9YP1EAdRa+yK1arWpnZ8cgxqWlJa2vr2txcVFzc3NWCcwfkr4YDnpC8W4n4zczTozCyfjM0c6w8Y3m+L10/NGjgUDAFFA6ndbY2FiLokmn00okEqaEiDLoz0MxmSSrvKXwqlQqWcuPXC5n0QgJ8Ww2q729PePp+06o/Nv/8S2gqUT2vXza54TxTUUN7Uq//d/UU3CYDa07OEDJH5rUXnfR09OjeDyukZERpdNpJZNJpVIpxeNxO0fDn0UN/s+cQH0F/tve3tbW1pYymYyREIrFotbW1jQ3N2ctWZARDFd7FOfn4bMS9ScG4jczTozCyfjM4Y2C55ZLaknQHWcU2j/vFQCf6+rq0sDAgPr6+qzPDMc4plIpjY2NaWhoyAwJeDStoTl72tcfYDj4GwXvK4H9gUFUCu/v77fkPChg4jr+Wo1G6/nJRCsM7/n79/Z1Dyhu/niqKzg9P0dx+w6nGAdadniDQLsP4Dd/sh3zhDGkqHF7e1ubm5t2LsLc3JxWV1etQWCpVNLu7q52dnaUz+etRbaH29qT6b6NR3vk1g7zHZd8P4GPfjPjxCicjF852MS/Khn8q3BzuOp+s6Mgf1XVKM3saFuMwoPN0tPTo0QioaGhIaXTacXjcSWTSSUSCcOzUaAYHH+OAMVlXhm3t8zwygoD4LvBtr8P//8iCu2z9or/Pn982wnfd6nRaFhEBBxD0dvBwYF2dnZUKpW0v79v0VUul1M2m9XGxoZyuZxVUbcX0rUf+flZ73NcSxL/O/+eyE8kEmmpFfC5jfYk9peF5k7G1x8nRuFkfOb4rMTqr2LyeA/x236eL5LEDQaPjhfFUHR1dam3t9eKnnwzPY+Xg5n7KmdJ1sUUuIXPeZzfKzXPe/d/fC8jIhbOLUDpE9nQbI8oB+8dqnCxWDQvHiX/q4Y/c+FXzeVnRXnta/J53/uscdz1/TyejN/MODEKJ+NkfM7w0YOklqjCRxlSK/zRnojF622PAvjZ97nfzsn47owvIoPhX8NznIyT8Z0dn+e1fhmqajtd9sQInIzfxnFiFE7GyfiM8UWj48+qATgZJ+O3cZwYhZNxMv7faIeJjhs+GmD8qloH/nxXmweejJPRPk6Mwsk4Gf9vfF6h2Zf5zHHJ2ZNxMn4bxolROBkn4/+NbwL2Oe4aJ3DSyfhtGsHf9AOcjJNxMk7GyfjujBOjcDJOxsk4GSfDxolROBkn42ScjJNh48QonIyTcTJOxsmwcWIUTsbJOBkn42TYODEKJ+NknIyTcTJsnBiFk3EyTsbJOBk2TozCyTgZJ+NknAwbJ0bhZJyMk3EyToaNE6NwMk7GyTgZJ8PGiVE4GSfjZJyMk2HjxCicjJNxMk7GybBxYhROxsk4GSfjZNg4MQon42ScjJNxMmycGIWTcTJOxsk4GTZOjMLJOBkn42ScDBsnRuFzxuedmuV/z3GO/Jw/4XDYfuY/EwqFjr2G//+v+l37Z/ib6/Jvf8/PGzyzH9z3uKMo2z/r/++f47jvHHevL/Ocv+pv3vm4Z/6sZ/+iv2NwD+bH/59r+OuEw2EFg8FPPZv/N7LSfo/j7nPcNfz1P++9WJ92OeQ+fv389/0f//3j7nHcOng5/qLrdNyc+Wdtv9/njc969pPxJU5e+z4cKchGqFarikajKpfLLX9XKhU1Gg1T9PV63b5Xq9VaNhJHMnZ1denw8FA9PT2q1+t2Vm8oFFKj0VAkElG9XlcoFFKlUrHrdXR06PDw0K7l7xeJROyz3C8YDKpWq7Uo8GAwaN/juev1uoLB4OeeENbX16ednR37fzAYVL1et3dEHhqNht2Xd/abOBKJqFar2bzyzPV6XeFwWJVKxT7Dffzwz+6PwuQ9w+GwDg4OJEmdnZ2q1WpqNBqq1+s2t8FgUNVqVR0dHapWqzbX/po8E+9WrVZbfs87sQa8byQSUSAQsPWQZGvH93meYDCoSCSiw8NDdXV1qVqtKhgMtnyXtfNr7J+D9YxEIvZdZJPP+fXn3ZkvL7O8K99hDpFz3tevCd/3MudlsF2+w+GwyTHPz3Owb/zv+L9/Nn9Pb0z8Wni55u9wOKxqtWrvF4lEWv7vr9n+s+/zCDS+4FmBX8bb/G0fCNDh4aEpVoTdbxKmLhqNmvDyh83rN6Iku5bf+PV6XR0dHXa9SqVigt7V1aWDg4OWz6LA/AbwihOPlOfnZ41Go0XRtA+vaGu1milQPsuG9UqKd4xGo6rX62Yc+TnXQgn5Te6NKfMSCoVMsfs18HPZ7olzDQbPxveYL9ayXC7b98PhsCKRiA4ODlqUDM9dr9dtfVEofJffVSoVe27+9ooGJd7+/fZ57OjosDnG0B43T4eHhzaX3klBRv1a8Dz+ffwadnd36+DgoOVcaf+M/h6RSMTmB4Pk15Pnbb8H9/YOCrKGnGEwWXveH1nmfbq6urS3t2ey4Y0P78+zYay8zCBH3gh4mftdH14Xfdb4/mj6Lzi8oolGo+ro6NDIyIgpVQawzI0bNzQ0NKRqtWqC29HRoenpaTUaDb388st68cUX9eqrryoQCGh6elq3bt3S66+/rq6uLoXDYU1PT+vSpUs6PDy0jctz7O3tmYL3XpRXfGyecDis8+fPq1KpqFwu6/r165qcnLRN9nneEJHN6OioeXQTExPq7u42Y9LV1aVbt24pFouZwXjppZf0wgsvqK+vr8WzJxLo6enRK6+8ou7ubtXrdZ06dUovv/yyxsbGFIlE1NPTo1QqZe/D30NDQxoYGGgxTMArPN+lS5da1gNjkkwmdePGDd26dcuUQq1WU7lc1sWLFxUKhRQKhXTmzBnt7+8rGAwqGo1qenpaN2/e1OjoqAKBgHp6enT9+nX19vYqGAwqFAqpp6fH5qjRaGh6elqvvPKKzp492xKh+XWpVCqamppSPB5XKBTS1NSUXnjhBfs90cnMzIy97/nz5yXJjCX/Hh8f19DQ0KegGJRduVw2Bcln+vr6FIvFFIlEFAqF7O9yufwpbz0ajdrveYeRkRH19vaqVqupUqmoVqtpenpa0pERwvAyUPCXLl0yg4YMVqtVxWIxpdNpRaNRu8eZM2fsWcLhsCYmJizqqtVq9g7eAGNo/d5k7/h9guHAgcHIIRdfRFl+X8aJUXDDwwEIeU9Pj65du2beu9/AL7/8skqlkqamppRMJhWNRtXd3a1EIqGzZ88qHA7r9OnT2tjY0NramkKhkEqlklZXV9XZ2amenh4NDQ1paGhItVpNN27cUCQSUTgcNiWEIu3q6jJFVqlUTJmghBH+GzduqLOzUy+88IIqlYpGRkY0PDzcAj98VtQXDAb14x//WDMzMzo8PFQsFtMf/uEfqru72+bnjTfe0Nramm7duqVaraZXXnlFu7u7Wl9f18HBgTo6Osw7xpC8/vrrWl1d1csvv6xGo6GDgwOtrq5qZGREyWRSyWRS586dsw0qHcFXf/Inf6J4PG7vyNpIR8rx4sWL+uEPf6hAIKCuri5JR/BHvV7XwcGBNjc3tb29rRs3bpiijUajunz5sjo6OnThwgXdunVL3d3dCgaD6u/vVzweV7FYVLFYlCT93u/9njKZjK5evapoNKparaYf//jHunDhgiqVilKplAYHB7W0tKRSqWTvTHSB4unp6dHg4KAGBgaUTCY1PDys3d1dvfTSS+Y542RIR0bu2rVrBiUGg0F1dnYqEAgonU4rmUyagWTeMOqdnZ1mRFDIk5OTmpiYULlctnkkisK4AYOxBj7ynZ6e1p/92Z+ZEfrpT3+qmZkZizZ4T2SsWq2qs7NT58+ft/2E7IVCIXV2duqnP/2penp6JEk3b97U7/3e77VEwefOndOf/MmfmGH7yU9+ounp6Ra4iGdvNBoKhULq7u7Wq6++qmg0ahEI9yV6IFIvl8ufmTv5Po8To+AGmyIcDqujo0Ovvvqq/uiP/kgHBwcKBoO6ePGi/uIv/kLnzp2zzZnL5VSv102xvPrqq3r11VdVLpdt0/b29mpjY0PValXr6+va29tTIBBQNps15b64uKjx8XHt7+/r8uXL+vGPf6yrV68afLK3t2eKvbOzU2+88Yb+9E//VIlEQsFgUOfPn9cf//EfKx6Pm3fuIa3u7m7dunVLf/EXf2FeeftIJpPq7e01xbu7u6vZ2VlJsufk3+Vy2TxQ4IGDg4MWeMpjvPyJRCJaWVn5FKZ96tQp/fSnP1UymZQkTU1NmYLiGtVq1ZRcV1eXhoaGVCgUFIlEtLOzo5mZGbtGpVLR7u6uAoGAGYpLly7ppz/9qdLptMrlsh4/fqxyuazDw0NVq1UNDAxoeHhYBwcHKhaLpiij0ajC4bD29/c1OjpqSphrx+NxBYNBra+vKxwO6+zZs/rJT36iS5cuaW9vT7FYTH/wB3+g69ev25zV63WtrKwolUqZtwtkAqSCsvuv//W/6qc//alu3LiheDyug4MDk69bt27pz/7szzQ4OKhAIKA33nhDf/zHf6wXXnhBwWDQDPvVq1dVrVbV1dWlK1eu6E/+5E80NDSkcrmsv/qrv9Jrr72mkZERdXR0KBKJ6I033pAk8+QPDw+1s7OjoaEh89bD4bBCoZBefvll/emf/qnOnz+ver2un/zkJ3rxxRd17tw5izYw8kQwjUZD0WhUY2Nj6u3tVWdnp/b391WtVs1olctl7e/vK5VKmZxjzG7cuKE///M/18zMjAKBgP7bf/tvunLliv7oj/5IV69e1Y0bNxQIBHTu3Dn9xV/8hS5cuKBAIGBz8frrryuZTJqMnuQTmuPEKLQNDAOK4+///u8t3Hz99deVyWT0yiuvKBQK6f79+3rhhRc0Pj6uUCik0dFR7e7u6t/+7d8UCoV0eHiox48fq6enRzdu3DCI4ty5c7p//74pVu+dhUIhvf7669rY2NCtW7fMA2NDNBoNjYyMaGRkRPv7+3rppZckHXla/+f//B/l83kFg0Ht7Oyot7fXQuhkMqnJyUkVCgXdunWrJSEcDAaVTqd14cIF/fM//7OF0z5PgXGpVCoaGBhowZ0HBgY0OTnZssn4TiAQUC6XUzqdVrFYtARsLBaz69VqNT169Ej/+q//qps3b+rChQuq1Wp68uSJGRyek/tevXpVjx490s7Ojnl+L774on72s59pc3PTkrmxWEybm5sGNf3sZz9ToVBoyRHwviikwcFBgz329/c1MDCgUqmkkZERjY2N6Z133mnJCUSjUaVSKVO8r732mkqlkq5fv65oNKoXXnhBv/jFL/TRRx8pHA5rY2PDnI72HM/e3p5FhBjOSqWi/+//+/8M1uEPivIf/uEf9MorrygcDuutt97S9va2XnrpJYVCIV28eFEff/yxbt++rVqtpoGBAV29elXFYlGvvfaaeckrKyva2NhQuVxWpVLRm2++qUAgYE5JR0eHPvzwQ7388su6ceOGPvjgA9VqNR0eHuqDDz5QPp83Tx+I6eHDh4pEIvrhD3+o8+fP6//+3//bwq66e/euTp06pfHxcVsTonAio48//lgvvviibt26pbfffluVSkWdnZ0aHx/X3/7t3+rFF180Odvf39ff/u3fKp/P67333lMwGNTly5f1N3/zN7pw4YLNaSAQ0O7urjlQJ6N1fGH20fdp+EQhHnA4HFahUNCDBw90//59VatVzc3NqVAo6MaNG6ZEyS0QBr///vsKBAL667/+a73zzjsKh8NKJBJ65513WuASQuJwOKzd3V3dv39f9+/f1/7+vl577TVNTEwoGAzqn//5nxUKhbS8vKzZ2Vnt7u6qXq9rd3e3hXkzPDysd955R1euXNHAwICq1aoymYzu3r2rvb099fX16Q/+4A/U39+vWq2mO3fuaHR0VGNjYxoeHtaTJ0+0srIiqTU5NTg4qDfffFP//b//d/P833vvPZ09e1bxeNy8zO7ubs3Nzemdd97RpUuX9L/+1//SX/3VX+n27duq1+t6/PixLl68qFQqpZ2dnZZnHxkZUSwW08jIiNLptLLZrC5evKjp6WkFAgG99dZbunTpksbHxzUzM6OXXnpJH3/8scrlcsuzrqysaHt7W2+88YaePXvWknhlvttJACsrK3r8+LFef/113b9/X6lUSv/+7/+uv/7rv9bW1pYuXbqk8+fPKxqN6uHDh+ru7tbGxoZmZ2f12muv6fHjxzo8PNSHH34oSTo8PDSo4/DwUPv7+zbfyWRS165dUzQa1eHhoTo7O01pIkvVatVgQv6A16MgyX/VajX95Cc/0e3btzUwMGDvVqvV1NPTY/Ozurqqe/fuWTTUaDS0uLgoSZY4h9jAcxweHpoB7OjokCSDiF588UW99957GhwcNMhsYWHBcjjr6+vq7u5WV1eXCoWC5ZuAb0ie+3XhntJRfoKIBdaWl0vmY35+viVnwPPyXjgUs7Oz/387V/vTxrF+D+u3tfGCAxhsYsdxjOwkGAIRIWqUFykpQmkqNa36H/ZzW1WtUPshbZqStCUQFFU0FpTE2Mbv2NjY6zfW3A/oeTJ2qnurq6uffmrnSCjEeHdnZmfmmTnPOYNIJNKj/pO7hbeQQUEATUpmsxlHR0cYHx/HtWvXeMsaj8cRDAZRq9VQKpXg9/tx5swZ6LqOWq3GnXd6eponoGg0ipOTE2QyGZycnODOnTt4+vQpb6ur1Sr8fj/C4TCSySQMw8DOzg78fj8A4ODgABsbG1hfX++RSY6NjeHs2bPM5R8eHuLq1atQVRUOhwPZbBaRSASKoqBSqQAAGo0GPB4PKpUKkskkVlZWuO4mkwmJRAJOpxMzMzMoFotwOp0YHx9HKBRCrVZDu91GOp3GwsICcrkcAGBvb4+To7VaDblcDl9++SXX32KxIBaLYX5+HslkEoqiwOv1YmRkBIODg0in0xgeHuZkfT6fx9bWFgzDwOLiIlKpFBqNBn777TdsbGywsuazzz6DyWTChx9+iOfPn8NkMqFYLGJ+fh5//PEHFEVBIBCA1WpFMpnEwMAA8vk8pqeneeI9d+4choeH4fP5kE6ncXR0BI/Hg3A4jEwmA0VRkEwmMTs7i0Qigd3dXSQSCYyMjODKlSsol8totVoYHh7m99fpdLC3t4dAIIB2u41SqYRcLodwOAyfzwdd12E2mxEOh9HtdrG7u4tut4sLFy5gd3cXxWIRFy9ehNVq7QnK0WgUiqKg2Wyi0WjAarWi2Wyi2WwiGo1iZ2cHAFAqlZjiCYfDyOVy8Pv9cLlcKBQKfI3H44Hdbkc8Hu9Rp7XbbVitVvh8Pv4bJZ3NZjN++OEHOJ1OVKvVHvnq+fPnoaoq5y0oYJhMJsRiMUxNTSEUCuHly5cwDIMFGSsrKwiHw9je3sb169c5SIiqv83NTV40UPBot9uIRqPc5iINtL29DZ/Ph2w2i729PVy5cgWJRIIXajSGRIWeDAhvISWpfaDtpcPhgKqqmJiYQKFQQLFYhKZp8Hq96HQ6SKVSGBsbg9PpxMHBAQqFAtMIw8PDqFQqyOfzCIfDMAwD+/v7aDQa8Hq9KJVKLAMklQ2tUk0mE6xWK6ampqDrOnZ3d7nD07ZaURROWlYqFRSLRaiqCo/Hg263i0QigePjY4RCIbTbbWQyGXS7XS5vs9lENpvlIKiqKmv9h4aGYLFYUC6Xoaoqzp49ywPNarXyc1KpFA/QQCAAXdc5R0K0DCUUDcNAMBhEIpFAvV7HxMQEhoaGUK/XkclkMDw8jMnJSTQaDaRSKR64pHap1+sA0ENN0cAOBALIZDJotVrQNA1utxu5XI7byGw2Y2dnh/MQPp8P7XYb8XgcExMTcDgcqNVqyOfz3EaapiGZTAI4XWFOTk4il8txLshisWBoaAiFQoFzSy6XC7FYjN9fMBjkAK8oCs6dOwdVVXkSC4VC0HUde3t7HNy++OILLiMFaV3X8fDhQ6TTacTjcRwcHMDlcuH4+Bj1eh1jY2M4c+YMtre3uc0vXLgAAGg2m0gmk6zqKZfLTCv6fD5UKhWkUimcO3eOFy2UU5uYmOCgZLPZoGkadF3nd0E0W6FQgMPhwPj4OACgVqvh5OQExWIRwOnOMp/Po91ucx+w2WwwmUyw2+0olUq86AoEAkgkErzCd7lcHHgBwOFwwOVyIZPJwOl0YnJyEq9fv4ZhGPD7/Rz8LRYLxsbGkMlkOMDt7++j2Wzyu9Q0DcfHx6hWqz2eh787/ko9ZVDog6j/ppUuSRbpc3FVQjkBUgkRfSRui0mjTrQSrcxEHwHlDmhyo8EllglADzUF9GrJaSssavpF7puuo90Q8FbuaLFY4Ha7YRgG8vn8O2YyVVV5BUh1E7X0BFH+6XQ6exLkVF4qu9lshqZprPQhHbqqqj1KHiqraM5zuVyoVqts2qJ2UlWVcypEtdAzqD1GR0eRy+U4UNA7JN07celEyRDlROUnw5yqqkyrkKqG2pR09FarFU6nEw6HA6lUCgAwODiI0dFRFItFtFot3Lx5ExsbG2g0Gtw2w8PDsFqtKBQKWF5exvb2Nuc4yuUyJ42pfUmxpqoqKpUKl9vr9SKdTgMA58qA0x1Fp9OBy+WCy+VCrVZDuVxmmerIyAjK5TKbL+m9UT+i+1ksFu5/oomu36tBiwXReyL2BbG/2u12NBqNHsmp6CehMvV7guh90H3EPieOH3qWzWbjXQZRUn93SJ/CfwkyJSmKwgGBJmNxVUEJQYvFwnpv0c9Azuh+HXj/7zRZ0/VHR0csfaX7iIoXcXKkSVhMCItSRfohLblo9KGJbGBgANevX4fP50MgEGAeW4Su68xdi4P5+PgYnU6H+W+xbkdHRz0DVJQSUjC8d+8e5ubmuK3u378Pr9fLAYLqJgY3RVFw+/btHk071Wlubg43b97kZy0uLrJ8lsp++/ZtzMzMcEBbXl6Gx+PhepH6RVXVnt3U9PQ0vyeiHmhSJF6b+glNjpqm4eLFiwgEApiamoLVamX/yJUrVwAAT548wdHREd/H7XYjGo0iFApx8n9ychI+nw8PHjzgetPOxWQyweVyYXZ2FoFAAMFgEGazGQsLC/D5fFhYWMDx8THC4TBCoRCCwSAcDgcnxz0eDyf+2+02zGYzyuUyTk5OeHImiEYvmrRpUhZ9NvRdAD0Ja+p31Hf6v0eLsP5xQvkJClrUl0WFm+jEpjKJvgrRWEk7bwA941NCBoV3IHKXlOiiTi6qhKhTUTDoT45Rkpp2CBQoWq0WX0vXidfT/UX3K/1f3BXQfanMpOQhCqBfMUT1EVU3dL3T6YTT6cTa2hqeP38O4JQe+PTTTzknEgqFcP78eWiahtnZWQwODuLmzZt47733oGkaLl26hIcPHzI1sLS0hFu3brFLlzT0pMsXB+zU1BRTMkSljY6O4sGDB/jkk084Ebu0tIRIJAKPx8MTi9PpxPz8PLd7q9VipYnFYmEDGHkXKDhQ0np0dJTNiYqiYG5uDnfv3oXNZoOu67h//z6i0Sg++ugj3Llzh6mZSCSCjz/+mHd4N27cQCQSYUMXuX9HR0dRr9cRi8Xg9/tZlfPs2TMEg8GeFS21y8jICFqtFnPjAPD06VO0Wi08efKkpz/RhFqv17GxsYF4PI7JyUlWqa2ursLr9WJg4NQ4+fLlSwCnOxF6TiwWw87OTs9qWgRJdqmPioot+jtdR3UQ+6F4jdjn6Rrq9zTuKMDQdaI8l75L9CvdS5z8aaHyZySIuOOjZ9KPxClkUPgTiNvN/s/7B7H4b/9n4oq6/3pR0vln9+qnXP5dOf5T2cRgQt+lVdvAwACq1SpevnyJa9eusbJkeXkZX3/9NauAHA4HNE3jlWyn08Hs7Cz29/cxODgIj8eDlZUVNJtN3LhxA+VymSdfmsB1XcfW1tY7TtPNzU1Eo1HcunULq6urMAwDhUIBq6ur2NrawoMHDwCcJoYtFguKxSLsdjsikQgWFhawsbHRQ/t9/vnn+OCDD3Dv3j08e/aMNf3icRVv3rxBKBTC4uIi1tfX0e12+f5ra2u4ceMGLBYLvF4v2u02vvrqKySTSbx+/Zolwd988w3u3r3LcmSn04np6WnY7Xa0Wi04HI4egyFRW5RXofyJmOikCZFW3dSH7HY7rFYrSqUS71jpaBSTyYRms4l6vQ63283CAgC8G+ynKem+c3NzuHr1Krxe7zt9568mX/9dXxX75p/17f6++mdj6a88p/+7/ePuP10nE81vIYPCPxA0SYl0TiaTQTabxczMDOcmgNNBQ4k+4oRplZZOp5FMJtFoNHp4ZKLfDg4OkM1meSISd0UixFwCrd5cLhcuXbrExjOr1YrDw0NsbW1xUvzixYuIxWLvGKLEIxx0XefJmSgdqr/4TOBt/oTUPQBQrVYRi8W4/kQp0iRHOyNFUfDixYuefAnVhag74uY1TcP58+extraGgYEBNpMtLy/zMRhikDcMA263GyaTCYeHh2yQW1pawvvvvw+n08l0kcViwebmZk+bUPmIyhTzYevr63jx4gUfFyIhIYPCPxDE3VKyjWiUWq2GsbExdLtddpLSCpy4ZTJJAeBJTlVV2Gw25qV1XUcqlcLBwQG7wWnidblcPatGMnM1m008fvyYJ3R6LilI6HMKEIZhYGVlBZcvX4amaVBVlQNWp9PB5uYm6vU66vU6fyYKCF6/fg1VVfHdd9/x88hMODQ0xEl1Cp4ij075i5GREei6zkogCrAA+JgFOsyQ1FYDAwNYWlpCIpGA3W5Ht9vF9vY2Hj9+jNXVVRweHnICd2hoiD0y8/PzWFtb453O/v4+VldX8fPPP0PXdfh8Pmiahmw2y4G0VqvhzJkzODw8RLvdxuHhITRNY2lru92GzWbDyMiIpE8kGNKn8A8ETVyULDWZTHxW0qNHj6AoCn788UcsLi5id3cX5XIZlUoFkUgE33//PTtByXdQKpXw+++/Y3Z2Fj/99BM2Nzdx9epVDA4OwjAMPveJnNOlUomfv729jU6ngxcvXvQkHMlLcfnyZWSzWUxOTmJnZ4dX8BsbG2i321hdXUU4HMarV6/Q7XZRqVRQrVZxdHSERCIBs9mMdDoNwzCgqiqOj4/x6tUrdDod/PLLLzCZTMjlcnzekcPhgN/vx9OnTwGcGp0oubq5uQm3241sNgubzYZIJIJvv/0WJpMJb968YdMWcOowT6VS2Nvbg81mg9vtxvr6OkwmE5LJJKLRKHRdZ7qM/AsAEI/HYTabEQgE8OzZMyiKgng8zmYvMiuK1BMFsOnpaaTTaezs7GBtbQ0zMzP49ddf0e128fz5c1y7dg25XA75fB7AaU4iGAzi0aNH/3cdUOL/NaQk9R8IUSJINIUoLxWVJQB6XJ+imoiUNqSAMQyDlVh0X/oOUUuieqU/B9J//DFRMZTn6JcbijsOUXYLvJWvkvRXlC6K8mLxALZ+ykbMfdAR5qLMku4h0nDdbhfj4+OIRCJYX19nIYJ4r36Xssi5U5uR+kxU1YjqK6qzGNjF46RFGosOghMlpSI9KHcJ/xz8T30KEhISEhJ/f8jlv4SEhIQEQwYFCQkJCQmGDAoSEhISEgwZFCQkJCQkGDIoSEhISEgwZFCQkJCQkGDIoCAhISEhwZBBQUJCQkKCIYOChISEhATjX2gQDRF7UyoiAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import random\n",
        "import tensorflow as tf\n",
        "from nltk.translate.bleu_score import sentence_bleu\n",
        "#from sacremoses import corpus\n",
        "#from rouge import Rouge\n",
        "\n",
        "\n",
        "#random_row = captions.sample(1).iloc[0]\n",
        "#y_true= random_row['caption']\n",
        "y_true= \",Axial CT image through the mid-thoracic region demonstrates \\n no evidence of pulmonary embolism or thoracic aortic pathology. \\n  In the upper outer quadrant of the left breast is a dense angular mass.\"\n",
        "print('y_true:: ', y_true)\n",
        "#print('random_image',random_row['image'])\n",
        "print('random_image','synpic28473.jpg')\n",
        "#print('random_caption: ',random_row['caption'])\n",
        "\n",
        "#img_path = '/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images/' + random_row['image']\n",
        "img_path = '/content/drive/MyDrive/Colab_Notebooks/Medical-Vision-Langauge-Transformer/dataset/RGC/images/' + 'synpic28402.jpg'\n",
        "#print(img_path)\n",
        "pred_caption = generate_caption(img_path)\n",
        "print()\n",
        "print('detected caption ::')\n",
        "#print(pred_caption)\n",
        "print(pred_caption[:95] + \"\\n\" + pred_caption[95:192])\n",
        "img=Image.open(img_path)\n",
        "plt.imshow(img)\n",
        "plt.axis('off')  # Turn off axis numbers and ticks\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#print(pred_caption[:100] + \"\\n\" + pred_caption[100:])\n",
        "\n",
        "#-----------------------------------------\n",
        "# (val_imgs, val_captions))\n",
        "\n",
        "\n",
        "\n",
        "#rouge_scorer = Rouge()\n",
        "#rouge_scores = {}\n",
        "#for metric in [\"rouge_l\", \"rouge_1\", \"rouge_2\"]:\n",
        "#  scores = rouge_scorer.get_scores(y_true, pred_caption)[metric]\n",
        "#  rouge_scores[metric] = {\"p\": scores.p, \"r\": scores.r, \"f\": scores.f}\n",
        "\n",
        "#print(rouge_scores)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hTDWHn6nmRSl"
      },
      "outputs": [],
      "source": [
        "def precision_recall_specificity(true_caption, predicted_caption):\n",
        "    # Convert captions to sets of words\n",
        "    true_words = set(true_caption.split())\n",
        "    predicted_words = set(predicted_caption.split())\n",
        "\n",
        "    # True Positives (TP): words in both true_caption and predicted_caption\n",
        "    tp = len(true_words & predicted_words)\n",
        "\n",
        "    # TN is the words in the vocabulary that are not in either true_words or predicted_words\n",
        "    tn = len(vocab - true_words - predicted_words)\n",
        "\n",
        "    # False Positives (FP): words in predicted_caption but not in true_caption\n",
        "    fp = len(predicted_words - true_words)\n",
        "\n",
        "    # False Negatives (FN): words in true_caption but not in predicted_caption\n",
        "    fn = len(true_words - predicted_words)\n",
        "\n",
        "    # Precision: TP / (TP + FP)\n",
        "    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0\n",
        "\n",
        "    # Recall: TP / (TP + FN)\n",
        "    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0\n",
        "\n",
        "    Accuracy = (tp+tn) / (tp + tn + fn + fp)\n",
        "\n",
        "\n",
        "    return precision, recall, Accuracy\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "predicted_captions = [generate_caption(img) for img in val_imgs]\n",
        "total_precision = 0.0\n",
        "total_recall = 0.0\n",
        "total_accuracy = 0.0\n",
        "\n",
        "\n",
        "for true_caption, prediction in zip(val_captions, predicted_captions):\n",
        "  precision, recall, accuracy = precision_recall_specificity(true_caption, prediction)\n",
        "  total_precision += precision\n",
        "  total_recall += recall\n",
        "  total_accuracy += accuracy\n",
        "\n",
        "print(f'Total Precision: {total_precision :.2f}')\n",
        "print(f'Total Recall: {total_recall :.2f}')\n",
        "print(f'Total Accuracy: {total_accuracy :.2f}')\n",
        "\n",
        "\n",
        "print(f'Precision: {total_precision / len(val_imgs):.2f}')\n",
        "print(f'Recall: {total_recall / len(val_imgs):.2f}')\n",
        "print(f'Accuracy: {total_accuracy / len(val_imgs):.2f}')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "La4aYk18xqAc"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "def encode_image(img_path):\n",
        "  img = Image.open(img_path)\n",
        "  img = img.resize((224, 224))\n",
        "  img = np.array(img)\n",
        "  img = img / 255.0\n",
        "  img = img.astype('float32')\n",
        "  img = tf.expand_dims(img, axis=0)\n",
        "  img_embed = caption_model.encoder(img, training=False)\n",
        "  return img_embed\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g0F4CKiy6G_p"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "url = \"https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcT2j6yclbKYDav4BGUKLAdTvSFXp1gtuzy5DQ&usqp=CAU\"\n",
        "im = Image.open(requests.get(url, stream=True).raw)\n",
        "im.save('tmp.jpg')\n",
        "\n",
        "\n",
        "pred_caption = generate_caption('tmp.jpg')\n",
        "print('Predicted Caption:', pred_caption)\n",
        "print()\n",
        "im\n",
        "'''\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}