{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Google - American Sign Language Fingerspelling Recognition with TensorFlow\n\nThis notebook walks you through how to train a Transformer model using TensorFlow on the Google - American Sign Language Fingerspelling Recognition dataset made available for this competition.\n\nThe objective of the model is to predict and translate American Sign Language (ASL) fingerspelling from a set of video frames into text(`phrase`).\n\nIn this notebook you will learn:\n\n- How to load the data\n- Convert the data to tfrecords to make it faster to re-traing the model\n- Train a transformer models on the data\n- Convert the model to TFLite\n- Create a submission","metadata":{}},{"cell_type":"markdown","source":"# Installation\n\nSpecifically for this competition, you'll need the mediapipe library to work on the data and visualize it","metadata":{}},{"cell_type":"code","source":"!pip install mediapipe","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:12:55.018364Z","iopub.execute_input":"2023-08-02T22:12:55.019331Z","iopub.status.idle":"2023-08-02T22:13:09.95033Z","shell.execute_reply.started":"2023-08-02T22:12:55.019287Z","shell.execute_reply":"2023-08-02T22:13:09.94911Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting mediapipe\n  Downloading mediapipe-0.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (33.5 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m33.5/33.5 MB\u001b[0m \u001b[31m30.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hRequirement already satisfied: absl-py in /opt/conda/lib/python3.10/site-packages (from mediapipe) (1.4.0)\nRequirement already satisfied: attrs>=19.1.0 in /opt/conda/lib/python3.10/site-packages (from mediapipe) (23.1.0)\nRequirement already satisfied: flatbuffers>=2.0 in /opt/conda/lib/python3.10/site-packages (from mediapipe) (23.3.3)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (from mediapipe) (3.6.3)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from mediapipe) (1.23.5)\nRequirement already satisfied: opencv-contrib-python in /opt/conda/lib/python3.10/site-packages (from mediapipe) (4.7.0.72)\nRequirement already satisfied: protobuf<4,>=3.11 in /opt/conda/lib/python3.10/site-packages (from mediapipe) (3.20.3)\nCollecting sounddevice>=0.4.4 (from mediapipe)\n  Downloading sounddevice-0.4.6-py3-none-any.whl (31 kB)\nRequirement already satisfied: CFFI>=1.0 in /opt/conda/lib/python3.10/site-packages (from sounddevice>=0.4.4->mediapipe) (1.15.1)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (1.0.7)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (4.39.3)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (9.5.0)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib->mediapipe) (2.8.2)\nRequirement already satisfied: pycparser in /opt/conda/lib/python3.10/site-packages (from CFFI>=1.0->sounddevice>=0.4.4->mediapipe) (2.21)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib->mediapipe) (1.16.0)\nInstalling collected packages: sounddevice, mediapipe\nSuccessfully installed mediapipe-0.10.3 sounddevice-0.4.6\n\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"markdown","source":"# Import the libraries","metadata":{}},{"cell_type":"code","source":"import os\nimport shutil\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport tensorflow as tf\nimport json\nimport mediapipe\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport random\n\nfrom skimage.transform import resize\nfrom mediapipe.framework.formats import landmark_pb2\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tqdm.notebook import tqdm\nfrom matplotlib import animation, rc","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-08-02T22:13:09.953384Z","iopub.execute_input":"2023-08-02T22:13:09.953767Z","iopub.status.idle":"2023-08-02T22:13:19.39291Z","shell.execute_reply.started":"2023-08-02T22:13:09.953729Z","shell.execute_reply":"2023-08-02T22:13:19.391853Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n  warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\ncaused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n  warnings.warn(f\"file system plugins are not loaded: {e}\")\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"TensorFlow v\" + tf.__version__)\nprint(\"Mediapipe v\" + mediapipe.__version__)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:19.397511Z","iopub.execute_input":"2023-08-02T22:13:19.405339Z","iopub.status.idle":"2023-08-02T22:13:19.418463Z","shell.execute_reply.started":"2023-08-02T22:13:19.405208Z","shell.execute_reply":"2023-08-02T22:13:19.417136Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"TensorFlow v2.12.0\nMediapipe v0.10.3\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Load the Dataset","metadata":{}},{"cell_type":"code","source":"dataset_df = pd.read_csv('/kaggle/input/asl-fingerspelling/train.csv')\nprint(\"Full train dataset shape is {}\".format(dataset_df.shape))","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:19.422627Z","iopub.execute_input":"2023-08-02T22:13:19.428416Z","iopub.status.idle":"2023-08-02T22:13:19.627002Z","shell.execute_reply.started":"2023-08-02T22:13:19.428383Z","shell.execute_reply":"2023-08-02T22:13:19.626129Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Full train dataset shape is (67208, 5)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"The data is composed of 5 columns and 67208 entries. We can see all 5 dimensions of our dataset by printing out the first 5 entries using the following code:","metadata":{}},{"cell_type":"code","source":"dataset_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:19.631849Z","iopub.execute_input":"2023-08-02T22:13:19.635094Z","iopub.status.idle":"2023-08-02T22:13:19.658192Z","shell.execute_reply.started":"2023-08-02T22:13:19.635047Z","shell.execute_reply":"2023-08-02T22:13:19.657301Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"                              path  file_id  sequence_id  participant_id  \\\n0  train_landmarks/5414471.parquet  5414471   1816796431             217   \n1  train_landmarks/5414471.parquet  5414471   1816825349             107   \n2  train_landmarks/5414471.parquet  5414471   1816909464               1   \n3  train_landmarks/5414471.parquet  5414471   1816967051              63   \n4  train_landmarks/5414471.parquet  5414471   1817123330              89   \n\n                      phrase  \n0               3 creekhouse  \n1            scales/kuhaylah  \n2        1383 william lanier  \n3          988 franklin lane  \n4  6920 northeast 661st road  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>path</th>\n      <th>file_id</th>\n      <th>sequence_id</th>\n      <th>participant_id</th>\n      <th>phrase</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>train_landmarks/5414471.parquet</td>\n      <td>5414471</td>\n      <td>1816796431</td>\n      <td>217</td>\n      <td>3 creekhouse</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>train_landmarks/5414471.parquet</td>\n      <td>5414471</td>\n      <td>1816825349</td>\n      <td>107</td>\n      <td>scales/kuhaylah</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>train_landmarks/5414471.parquet</td>\n      <td>5414471</td>\n      <td>1816909464</td>\n      <td>1</td>\n      <td>1383 william lanier</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>train_landmarks/5414471.parquet</td>\n      <td>5414471</td>\n      <td>1816967051</td>\n      <td>63</td>\n      <td>988 franklin lane</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>train_landmarks/5414471.parquet</td>\n      <td>5414471</td>\n      <td>1817123330</td>\n      <td>89</td>\n      <td>6920 northeast 661st road</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Quick basic dataset exploration\n\nEach entry in **train.csv** contains a `phrase`, its `sequence_id`, `path` and `file_id`. The `file_id` indicates the file that holds the **_landmarks_** data for that particular `phrase` and `sequence_id` is the unique index of the landmark sequence within the landmarks data file.\n\nThe following diagram shows an example of how files are connected.\n\n![files2.png](attachment:751e38db-4def-4177-bf5b-27071bb1b3e3.png)","metadata":{},"attachments":{"751e38db-4def-4177-bf5b-27071bb1b3e3.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"As an example, let us examine the landmarks file for the first row of`train.csv`. ","metadata":{}},{"cell_type":"code","source":"# Fetch sequence_id, file_id, phrase from first row\nsequence_id, file_id, phrase = dataset_df.iloc[0][['sequence_id', 'file_id', 'phrase']]\nprint(f\"sequence_id: {sequence_id}, file_id: {file_id}, phrase: {phrase}\")","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:19.662185Z","iopub.execute_input":"2023-08-02T22:13:19.664641Z","iopub.status.idle":"2023-08-02T22:13:19.677069Z","shell.execute_reply.started":"2023-08-02T22:13:19.664608Z","shell.execute_reply":"2023-08-02T22:13:19.676146Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"sequence_id: 1816796431, file_id: 5414471, phrase: 3 creekhouse\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Now, let us open the parquet file and fetch the data for the particular `sequence_id`.","metadata":{}},{"cell_type":"code","source":"# Fetch data from parquet file\nsample_sequence_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/{str(file_id)}.parquet\",\n    filters=[[('sequence_id', '=', sequence_id)],]).to_pandas()\nprint(\"Full sequence dataset shape is {}\".format(sample_sequence_df.shape))","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:19.681641Z","iopub.execute_input":"2023-08-02T22:13:19.684108Z","iopub.status.idle":"2023-08-02T22:13:23.94717Z","shell.execute_reply.started":"2023-08-02T22:13:19.684057Z","shell.execute_reply":"2023-08-02T22:13:23.946165Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Full sequence dataset shape is (123, 1630)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"This particular phrase(***3 creekhouse***) contains 123 entries (or frames) and 1630 columns including 1628 landmark coordinates. Let us print the first 5 entries.","metadata":{}},{"cell_type":"code","source":"sample_sequence_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:23.948579Z","iopub.execute_input":"2023-08-02T22:13:23.950354Z","iopub.status.idle":"2023-08-02T22:13:23.977112Z","shell.execute_reply.started":"2023-08-02T22:13:23.950319Z","shell.execute_reply":"2023-08-02T22:13:23.975819Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"             frame  x_face_0  x_face_1  x_face_2  x_face_3  x_face_4  \\\nsequence_id                                                            \n1816796431       0  0.710588  0.699951  0.705657  0.691768  0.699669   \n1816796431       1  0.709525  0.697582  0.703713  0.691016  0.697576   \n1816796431       2  0.711059  0.700858  0.706272  0.693285  0.700825   \n1816796431       3  0.712799  0.702518  0.707840  0.694899  0.702445   \n1816796431       4  0.712349  0.705451  0.709918  0.696006  0.705180   \n\n             x_face_5  x_face_6  x_face_7  x_face_8  ...  z_right_hand_11  \\\nsequence_id                                          ...                    \n1816796431   0.701980  0.709724  0.610405  0.712660  ...        -0.245855   \n1816796431   0.700467  0.709796  0.616540  0.713729  ...              NaN   \n1816796431   0.703319  0.711549  0.615606  0.715143  ...              NaN   \n1816796431   0.704794  0.712483  0.625044  0.715677  ...        -0.370770   \n1816796431   0.706928  0.712685  0.614356  0.714875  ...              NaN   \n\n             z_right_hand_12  z_right_hand_13  z_right_hand_14  \\\nsequence_id                                                      \n1816796431         -0.269148        -0.129743        -0.251501   \n1816796431               NaN              NaN              NaN   \n1816796431               NaN              NaN              NaN   \n1816796431         -0.408097        -0.185217        -0.325494   \n1816796431               NaN              NaN              NaN   \n\n             z_right_hand_15  z_right_hand_16  z_right_hand_17  \\\nsequence_id                                                      \n1816796431         -0.278687        -0.266530        -0.152852   \n1816796431               NaN              NaN              NaN   \n1816796431               NaN              NaN              NaN   \n1816796431         -0.343373        -0.328294        -0.203126   \n1816796431               NaN              NaN              NaN   \n\n             z_right_hand_18  z_right_hand_19  z_right_hand_20  \nsequence_id                                                     \n1816796431         -0.257519        -0.275822        -0.266876  \n1816796431               NaN              NaN              NaN  \n1816796431               NaN              NaN              NaN  \n1816796431         -0.315719        -0.326104        -0.314282  \n1816796431               NaN              NaN              NaN  \n\n[5 rows x 1630 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>frame</th>\n      <th>x_face_0</th>\n      <th>x_face_1</th>\n      <th>x_face_2</th>\n      <th>x_face_3</th>\n      <th>x_face_4</th>\n      <th>x_face_5</th>\n      <th>x_face_6</th>\n      <th>x_face_7</th>\n      <th>x_face_8</th>\n      <th>...</th>\n      <th>z_right_hand_11</th>\n      <th>z_right_hand_12</th>\n      <th>z_right_hand_13</th>\n      <th>z_right_hand_14</th>\n      <th>z_right_hand_15</th>\n      <th>z_right_hand_16</th>\n      <th>z_right_hand_17</th>\n      <th>z_right_hand_18</th>\n      <th>z_right_hand_19</th>\n      <th>z_right_hand_20</th>\n    </tr>\n    <tr>\n      <th>sequence_id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1816796431</th>\n      <td>0</td>\n      <td>0.710588</td>\n      <td>0.699951</td>\n      <td>0.705657</td>\n      <td>0.691768</td>\n      <td>0.699669</td>\n      <td>0.701980</td>\n      <td>0.709724</td>\n      <td>0.610405</td>\n      <td>0.712660</td>\n      <td>...</td>\n      <td>-0.245855</td>\n      <td>-0.269148</td>\n      <td>-0.129743</td>\n      <td>-0.251501</td>\n      <td>-0.278687</td>\n      <td>-0.266530</td>\n      <td>-0.152852</td>\n      <td>-0.257519</td>\n      <td>-0.275822</td>\n      <td>-0.266876</td>\n    </tr>\n    <tr>\n      <th>1816796431</th>\n      <td>1</td>\n      <td>0.709525</td>\n      <td>0.697582</td>\n      <td>0.703713</td>\n      <td>0.691016</td>\n      <td>0.697576</td>\n      <td>0.700467</td>\n      <td>0.709796</td>\n      <td>0.616540</td>\n      <td>0.713729</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1816796431</th>\n      <td>2</td>\n      <td>0.711059</td>\n      <td>0.700858</td>\n      <td>0.706272</td>\n      <td>0.693285</td>\n      <td>0.700825</td>\n      <td>0.703319</td>\n      <td>0.711549</td>\n      <td>0.615606</td>\n      <td>0.715143</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1816796431</th>\n      <td>3</td>\n      <td>0.712799</td>\n      <td>0.702518</td>\n      <td>0.707840</td>\n      <td>0.694899</td>\n      <td>0.702445</td>\n      <td>0.704794</td>\n      <td>0.712483</td>\n      <td>0.625044</td>\n      <td>0.715677</td>\n      <td>...</td>\n      <td>-0.370770</td>\n      <td>-0.408097</td>\n      <td>-0.185217</td>\n      <td>-0.325494</td>\n      <td>-0.343373</td>\n      <td>-0.328294</td>\n      <td>-0.203126</td>\n      <td>-0.315719</td>\n      <td>-0.326104</td>\n      <td>-0.314282</td>\n    </tr>\n    <tr>\n      <th>1816796431</th>\n      <td>4</td>\n      <td>0.712349</td>\n      <td>0.705451</td>\n      <td>0.709918</td>\n      <td>0.696006</td>\n      <td>0.705180</td>\n      <td>0.706928</td>\n      <td>0.712685</td>\n      <td>0.614356</td>\n      <td>0.714875</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 1630 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data visualization using mediapipe APIs\n\nLet us visualize the hand landmarks data for the phrase ***3 creek house*** using the [hand landmarker apis](https://developers.google.com/mediapipe/solutions/vision/hand_landmarker) of the [mediapipe library](https://developers.google.com/mediapipe).\n\nHand landmarks represent the key points on a human hand.\n\nReference: [Data visualization using mediapipe APIs by sknadig](https://www.kaggle.com/code/nadigshreekanth/data-visualization-using-mediapipe-apis)","metadata":{}},{"cell_type":"code","source":"# Function create animation from images.\n\nmatplotlib.rcParams['animation.embed_limit'] = 2**128\nmatplotlib.rcParams['savefig.pad_inches'] = 0\nrc('animation', html='jshtml')\n\ndef create_animation(images):\n    fig = plt.figure(figsize=(6, 9))\n    ax = plt.Axes(fig, [0., 0., 1., 1.])\n    ax.set_axis_off()\n    fig.add_axes(ax)\n    im=ax.imshow(images[0], cmap=\"gray\")\n    plt.close(fig)\n    \n    def animate_func(i):\n        im.set_array(images[i])\n        return [im]\n\n    return animation.FuncAnimation(fig, animate_func, frames=len(images), interval=1000/10)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:23.978849Z","iopub.execute_input":"2023-08-02T22:13:23.979246Z","iopub.status.idle":"2023-08-02T22:13:23.993676Z","shell.execute_reply.started":"2023-08-02T22:13:23.979212Z","shell.execute_reply":"2023-08-02T22:13:23.992239Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"This image illustrates the 21 keypoints on the hand.\n\n![hand-landmarks (1).png](attachment:bd58b6bd-7c28-48d1-8dd4-6b11d0e3d4a8.png)\n\nsource: https://developers.google.com/mediapipe/solutions/vision/hand_landmarker","metadata":{},"attachments":{"bd58b6bd-7c28-48d1-8dd4-6b11d0e3d4a8.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Extract the landmark data and convert it to an image using medipipe library.\n# This function extracts the data for both hands.\n\nmp_pose = mediapipe.solutions.pose\nmp_hands = mediapipe.solutions.hands\nmp_drawing = mediapipe.solutions.drawing_utils \nmp_drawing_styles = mediapipe.solutions.drawing_styles\n\ndef get_hands(seq_df):\n    images = []\n    all_hand_landmarks = []\n    for seq_idx in range(len(seq_df)):\n        x_hand = seq_df.iloc[seq_idx].filter(regex=\"x_right_hand.*\").values\n        y_hand = seq_df.iloc[seq_idx].filter(regex=\"y_right_hand.*\").values\n        z_hand = seq_df.iloc[seq_idx].filter(regex=\"z_right_hand.*\").values\n\n        right_hand_image = np.zeros((600, 600, 3))\n\n        right_hand_landmarks = landmark_pb2.NormalizedLandmarkList()\n        \n        for x, y, z in zip(x_hand, y_hand, z_hand):\n            right_hand_landmarks.landmark.add(x=x, y=y, z=z)\n\n        mp_drawing.draw_landmarks(\n                right_hand_image,\n                right_hand_landmarks,\n                mp_hands.HAND_CONNECTIONS,\n                landmark_drawing_spec=mp_drawing_styles.get_default_hand_landmarks_style())\n        \n        x_hand = seq_df.iloc[seq_idx].filter(regex=\"x_left_hand.*\").values\n        y_hand = seq_df.iloc[seq_idx].filter(regex=\"y_left_hand.*\").values\n        z_hand = seq_df.iloc[seq_idx].filter(regex=\"z_left_hand.*\").values\n        \n        left_hand_image = np.zeros((600, 600, 3))\n        \n        left_hand_landmarks = landmark_pb2.NormalizedLandmarkList()\n        for x, y, z in zip(x_hand, y_hand, z_hand):\n            left_hand_landmarks.landmark.add(x=x, y=y, z=z)\n\n        mp_drawing.draw_landmarks(\n                left_hand_image,\n                left_hand_landmarks,\n                mp_hands.HAND_CONNECTIONS,\n                landmark_drawing_spec=mp_drawing_styles.get_default_hand_landmarks_style())\n        \n        images.append([right_hand_image.astype(np.uint8), left_hand_image.astype(np.uint8)])\n        all_hand_landmarks.append([right_hand_landmarks, left_hand_landmarks])\n    return images, all_hand_landmarks","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:23.99542Z","iopub.execute_input":"2023-08-02T22:13:23.995839Z","iopub.status.idle":"2023-08-02T22:13:24.013155Z","shell.execute_reply.started":"2023-08-02T22:13:23.995805Z","shell.execute_reply":"2023-08-02T22:13:24.011951Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"# Get the images created using mediapipe apis\nhand_images, hand_landmarks = get_hands(sample_sequence_df)\n# Fetch and show the data for right hand\ncreate_animation(np.array(hand_images)[:, 0])","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:24.014656Z","iopub.execute_input":"2023-08-02T22:13:24.01498Z","iopub.status.idle":"2023-08-02T22:13:36.104867Z","shell.execute_reply.started":"2023-08-02T22:13:24.014951Z","shell.execute_reply":"2023-08-02T22:13:36.10399Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"<matplotlib.animation.FuncAnimation at 0x79998e0e5930>","text/html":"\n<link rel=\"stylesheet\"\nhref=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n<script language=\"javascript\">\n  function isInternetExplorer() {\n    ua = navigator.userAgent;\n    /* MSIE used to detect old browsers and Trident used to newer ones*/\n    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n  }\n\n  /* Define the Animation class */\n  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n    this.img_id = img_id;\n    this.slider_id = slider_id;\n    this.loop_select_id = loop_select_id;\n    this.interval = interval;\n    this.current_frame = 0;\n    this.direction = 0;\n    this.timer = null;\n    this.frames = new Array(frames.length);\n\n    for (var i=0; i<frames.length; i++)\n    {\n     this.frames[i] = new Image();\n     this.frames[i].src = frames[i];\n    }\n    var slider = document.getElementById(this.slider_id);\n    slider.max = this.frames.length - 1;\n    if (isInternetExplorer()) {\n        // switch from oninput to onchange because IE <= 11 does not conform\n        // with W3C specification. It ignores oninput and onchange behaves\n        // like oninput. In contrast, Microsoft Edge behaves correctly.\n        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n        slider.setAttribute('oninput', null);\n    }\n    this.set_frame(this.current_frame);\n  }\n\n  Animation.prototype.get_loop_state = function(){\n    var button_group = document[this.loop_select_id].state;\n    for (var i = 0; i < button_group.length; i++) {\n        var button = button_group[i];\n        if (button.checked) {\n            return button.value;\n        }\n    }\n    return undefined;\n  }\n\n  Animation.prototype.set_frame = function(frame){\n    this.current_frame = frame;\n    document.getElementById(this.img_id).src =\n            this.frames[this.current_frame].src;\n    document.getElementById(this.slider_id).value = this.current_frame;\n  }\n\n  Animation.prototype.next_frame = function()\n  {\n    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n  }\n\n  Animation.prototype.previous_frame = function()\n  {\n    this.set_frame(Math.max(0, this.current_frame - 1));\n  }\n\n  Animation.prototype.first_frame = function()\n  {\n    this.set_frame(0);\n  }\n\n  Animation.prototype.last_frame = function()\n  {\n    this.set_frame(this.frames.length - 1);\n  }\n\n  Animation.prototype.slower = function()\n  {\n    this.interval /= 0.7;\n    if(this.direction > 0){this.play_animation();}\n    else if(this.direction < 0){this.reverse_animation();}\n  }\n\n  Animation.prototype.faster = function()\n  {\n    this.interval *= 0.7;\n    if(this.direction > 0){this.play_animation();}\n    else if(this.direction < 0){this.reverse_animation();}\n  }\n\n  Animation.prototype.anim_step_forward = function()\n  {\n    this.current_frame += 1;\n    if(this.current_frame < this.frames.length){\n      this.set_frame(this.current_frame);\n    }else{\n      var loop_state = this.get_loop_state();\n      if(loop_state == \"loop\"){\n        this.first_frame();\n      }else if(loop_state == \"reflect\"){\n        this.last_frame();\n        this.reverse_animation();\n      }else{\n        this.pause_animation();\n        this.last_frame();\n      }\n    }\n  }\n\n  Animation.prototype.anim_step_reverse = function()\n  {\n    this.current_frame -= 1;\n    if(this.current_frame >= 0){\n      this.set_frame(this.current_frame);\n    }else{\n      var loop_state = this.get_loop_state();\n      if(loop_state == \"loop\"){\n        this.last_frame();\n      }else if(loop_state == \"reflect\"){\n        this.first_frame();\n        this.play_animation();\n      }else{\n        this.pause_animation();\n        this.first_frame();\n      }\n    }\n  }\n\n  Animation.prototype.pause_animation = function()\n  {\n    this.direction = 0;\n    if (this.timer){\n      clearInterval(this.timer);\n      this.timer = null;\n    }\n  }\n\n  Animation.prototype.play_animation = function()\n  {\n    this.pause_animation();\n    this.direction = 1;\n    var t = this;\n    if (!this.timer) this.timer = setInterval(function() {\n        t.anim_step_forward();\n    }, this.interval);\n  }\n\n  Animation.prototype.reverse_animation = function()\n  {\n    this.pause_animation();\n    this.direction = -1;\n    var t = this;\n    if (!this.timer) this.timer = setInterval(function() {\n        t.anim_step_reverse();\n    }, this.interval);\n  }\n</script>\n\n<style>\n.animation {\n    display: inline-block;\n    text-align: center;\n}\ninput[type=range].anim-slider {\n    width: 374px;\n    margin-left: auto;\n    margin-right: auto;\n}\n.anim-buttons {\n    margin: 8px 0px;\n}\n.anim-buttons button {\n    padding: 0;\n    width: 36px;\n}\n.anim-state label {\n    margin-right: 8px;\n}\n.anim-state input {\n    margin: 0;\n    vertical-align: middle;\n}\n</style>\n\n<div class=\"animation\">\n  <img id=\"_anim_img3e5065a628024a8e80dc2a24b7643522\">\n  <div class=\"anim-controls\">\n    <input id=\"_anim_slider3e5065a628024a8e80dc2a24b7643522\" type=\"range\" class=\"anim-slider\"\n           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n           oninput=\"anim3e5065a628024a8e80dc2a24b7643522.set_frame(parseInt(this.value));\">\n    <div class=\"anim-buttons\">\n      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.slower()\">\n          <i class=\"fa fa-minus\"></i></button>\n      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.first_frame()\">\n        <i class=\"fa fa-fast-backward\"></i></button>\n      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.previous_frame()\">\n          <i class=\"fa fa-step-backward\"></i></button>\n      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.reverse_animation()\">\n          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.pause_animation()\">\n          <i class=\"fa fa-pause\"></i></button>\n      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.play_animation()\">\n          <i class=\"fa fa-play\"></i></button>\n      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.next_frame()\">\n          <i class=\"fa fa-step-forward\"></i></button>\n      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.last_frame()\">\n          <i class=\"fa fa-fast-forward\"></i></button>\n      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim3e5065a628024a8e80dc2a24b7643522.faster()\">\n          <i class=\"fa fa-plus\"></i></button>\n    </div>\n    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select3e5065a628024a8e80dc2a24b7643522\"\n          class=\"anim-state\">\n      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_3e5065a628024a8e80dc2a24b7643522\"\n             >\n      <label for=\"_anim_radio1_3e5065a628024a8e80dc2a24b7643522\">Once</label>\n      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_3e5065a628024a8e80dc2a24b7643522\"\n             checked>\n      <label for=\"_anim_radio2_3e5065a628024a8e80dc2a24b7643522\">Loop</label>\n      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_3e5065a628024a8e80dc2a24b7643522\"\n             >\n      <label for=\"_anim_radio3_3e5065a628024a8e80dc2a24b7643522\">Reflect</label>\n    </form>\n  </div>\n</div>\n\n\n<script language=\"javascript\">\n  /* Instantiate the Animation class. */\n  /* The IDs given should match those used in the template above. */\n  (function() {\n    var img_id = \"_anim_img3e5065a628024a8e80dc2a24b7643522\";\n    var slider_id = \"_anim_slider3e5065a628024a8e80dc2a24b7643522\";\n    var loop_select_id = \"_anim_loop_select3e5065a628024a8e80dc2a24b7643522\";\n    var frames = new Array(123);\n    \n  frames[0] = 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w6+df7zkE3vXdT/j7hQetcQeAOMEFpTrcXXpNNFaIm6uTXPOP976FMfmdWDv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\\\nsAAAwgQWAECYwAIACBNYAABhAgsAIExgAQCECSwAgDCBBQAQJrAAAMIE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 frames[1] = 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 frames[2] = 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 frames[3] = 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 frames[4] = 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 frames[5] = 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 frames[6] = 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 frames[7] = 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 frames[8] = 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 frames[10] = 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 frames[18] = 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 frames[21] = 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 frames[22] = 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 frames[23] = 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 frames[24] = 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 frames[25] = 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 frames[30] = 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 frames[31] = 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 frames[33] = 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 frames[34] = 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 frames[35] = 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 frames[36] = 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 frames[43] = 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 frames[61] = 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  frames[67] = 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 frames[71] = 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 frames[101] = 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 frames[103] = 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 frames[105] = 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 frames[107] = 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 frames[109] = 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 frames[111] = 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 frames[113] = 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 frames[115] = 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 frames[117] = 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 frames[119] = 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   /* set a timeout to make sure all the above elements are created before\n       the object is initialized. */\n    setTimeout(function() {\n        anim3e5065a628024a8e80dc2a24b7643522 = new Animation(frames, img_id, slider_id, 100.0,\n                                 loop_select_id);\n    }, 0);\n  })()\n</script>\n"},"metadata":{}}]},{"cell_type":"markdown","source":"# Preprocess the data\n\nFor convenience and efficiency, we will rearrange the data so that each parquet file contains the landmark data along with the phrase it represents. This way we don't have to switch between train.csv and its parquet file. \n\nWe will save the new data in the [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) format. `TFRecord` format is a simple format for storing a sequence of binary records. Storing and loading the data using `TFRecord` is much more efficient and faster.\n\nReference:\n\nhttps://www.kaggle.com/code/irohith/aslfr-preprocess-dataset\n\nhttps://www.kaggle.com/code/shlomoron/aslfr-parquets-to-tfrecords-cleaned","metadata":{}},{"cell_type":"markdown","source":"ASL-Fingerspelling mainly focusses on hand movement. So we will take hand land mark coordinates and pose coordinates for hands to train the model.","metadata":{}},{"cell_type":"markdown","source":"# Fetch the pose landmark coordinates related to hand movement.","metadata":{}},{"cell_type":"markdown","source":"This image illustrates the pose coordinates related to hand movement. The pose coordinates also come from the parquet file.\n\n\n![pose.png](attachment:1389643c-6068-4c7d-9275-0b42f25b836e.png)\n\n[Image source](https://developers.google.com/mediapipe/solutions/vision/pose_landmarker)","metadata":{},"attachments":{"1389643c-6068-4c7d-9275-0b42f25b836e.png":{"image/png":"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"}}},{"cell_type":"code","source":"# Pose coordinates for hand movement.\nLPOSE = [13, 15, 17, 19, 21]\nRPOSE = [14, 16, 18, 20, 22]\nPOSE = LPOSE + RPOSE","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:36.105855Z","iopub.execute_input":"2023-08-02T22:13:36.106179Z","iopub.status.idle":"2023-08-02T22:13:36.1125Z","shell.execute_reply.started":"2023-08-02T22:13:36.10615Z","shell.execute_reply":"2023-08-02T22:13:36.111554Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"# Create x,y,z label names from coordinates","metadata":{}},{"cell_type":"code","source":"X = [f'x_right_hand_{i}' for i in range(21)] + [f'x_left_hand_{i}' for i in range(21)] + [f'x_pose_{i}' for i in POSE]\nY = [f'y_right_hand_{i}' for i in range(21)] + [f'y_left_hand_{i}' for i in range(21)] + [f'y_pose_{i}' for i in POSE]\nZ = [f'z_right_hand_{i}' for i in range(21)] + [f'z_left_hand_{i}' for i in range(21)] + [f'z_pose_{i}' for i in POSE]","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:36.114662Z","iopub.execute_input":"2023-08-02T22:13:36.115345Z","iopub.status.idle":"2023-08-02T22:13:36.128976Z","shell.execute_reply.started":"2023-08-02T22:13:36.115316Z","shell.execute_reply":"2023-08-02T22:13:36.127907Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"Create feature columns from the extracted coordinates.","metadata":{}},{"cell_type":"code","source":"FEATURE_COLUMNS = X + Y + Z","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:36.135556Z","iopub.execute_input":"2023-08-02T22:13:36.136261Z","iopub.status.idle":"2023-08-02T22:13:36.142572Z","shell.execute_reply.started":"2023-08-02T22:13:36.136227Z","shell.execute_reply":"2023-08-02T22:13:36.140196Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"Store ids of each coordinate labels to lists","metadata":{}},{"cell_type":"code","source":"X_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"x_\" in col]\nY_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"y_\" in col]\nZ_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"z_\" in col]\n\nRHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if \"right\" in col]\nLHAND_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"left\" in col]\nRPOSE_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"pose\" in col and int(col[-2:]) in RPOSE]\nLPOSE_IDX = [i for i, col in enumerate(FEATURE_COLUMNS)  if  \"pose\" in col and int(col[-2:]) in LPOSE]","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:36.14565Z","iopub.execute_input":"2023-08-02T22:13:36.14622Z","iopub.status.idle":"2023-08-02T22:13:36.155817Z","shell.execute_reply.started":"2023-08-02T22:13:36.146195Z","shell.execute_reply":"2023-08-02T22:13:36.15491Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"# Preprocess and write the dataset as TFRecords","metadata":{}},{"cell_type":"markdown","source":"Using the extracted landmarks and phrases let us create new dataset files and write them as TFRecords.\n\nThis takes around 10 minutes. After this, loading the dataset will be faster for any future experiments.","metadata":{}},{"cell_type":"code","source":"# Set length of frames to 128\nFRAME_LEN = 128\n\n# Create directory to store the new data\nif not os.path.isdir(\"preprocessed\"):\n    os.mkdir(\"preprocessed\")\nelse:\n    shutil.rmtree(\"preprocessed\")\n    os.mkdir(\"preprocessed\")\n\n# Loop through each file_id\nfor file_id in tqdm(dataset_df.file_id.unique()):\n    # Parquet file name\n    pq_file = f\"/kaggle/input/asl-fingerspelling/train_landmarks/{file_id}.parquet\"\n    # Filter train.csv and fetch entries only for the relevant file_id\n    file_df = dataset_df.loc[dataset_df[\"file_id\"] == file_id]\n    # Fetch the parquet file\n    parquet_df = pq.read_table(f\"/kaggle/input/asl-fingerspelling/train_landmarks/{str(file_id)}.parquet\",\n                              columns=['sequence_id'] + FEATURE_COLUMNS).to_pandas()\n    # File name for the updated data\n    tf_file = f\"preprocessed/{file_id}.tfrecord\"\n    parquet_numpy = parquet_df.to_numpy()\n    # Initialize the pointer to write the output of \n    # each `for loop` below as a sequence into the file.\n    with tf.io.TFRecordWriter(tf_file) as file_writer:\n        # Loop through each sequence in file.\n        for seq_id, phrase in zip(file_df.sequence_id, file_df.phrase):\n            # Fetch sequence data\n            frames = parquet_numpy[parquet_df.index == seq_id]\n            \n            # Calculate the number of NaN values in each hand landmark\n            r_nonan = np.sum(np.sum(np.isnan(frames[:, RHAND_IDX]), axis = 1) == 0)\n            l_nonan = np.sum(np.sum(np.isnan(frames[:, LHAND_IDX]), axis = 1) == 0)\n            no_nan = max(r_nonan, l_nonan)\n            \n            if 2*len(phrase)<no_nan:\n                features = {FEATURE_COLUMNS[i]: tf.train.Feature(\n                    float_list=tf.train.FloatList(value=frames[:, i])) for i in range(len(FEATURE_COLUMNS))}\n                features[\"phrase\"] = tf.train.Feature(bytes_list=tf.train.BytesList(value=[bytes(phrase, 'utf-8')]))\n                record_bytes = tf.train.Example(features=tf.train.Features(feature=features)).SerializeToString()\n                file_writer.write(record_bytes)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:13:36.157355Z","iopub.execute_input":"2023-08-02T22:13:36.157949Z","iopub.status.idle":"2023-08-02T22:23:34.714666Z","shell.execute_reply.started":"2023-08-02T22:13:36.157916Z","shell.execute_reply":"2023-08-02T22:23:34.713672Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"  0%|          | 0/68 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d1af5569ace401582d3376afdc547b2"}},"metadata":{}}]},{"cell_type":"markdown","source":" Load the preprocessed data","metadata":{}},{"cell_type":"markdown","source":"# Get the saved TFRecord files into a list","metadata":{}},{"cell_type":"code","source":"tf_records = dataset_df.file_id.map(lambda x: f'/kaggle/working/preprocessed/{x}.tfrecord').unique()\nprint(f\"List of {len(tf_records)} TFRecord files.\")","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:34.716014Z","iopub.execute_input":"2023-08-02T22:23:34.71702Z","iopub.status.idle":"2023-08-02T22:23:34.777458Z","shell.execute_reply.started":"2023-08-02T22:23:34.716982Z","shell.execute_reply":"2023-08-02T22:23:34.776498Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"List of 68 TFRecord files.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Load character_to_prediction json file","metadata":{}},{"cell_type":"markdown","source":"This json file contains a character and its value. We will add three new characters, \"<\" and \">\" to mark the start and end of each phrase, and \"P\" for padding.","metadata":{}},{"cell_type":"code","source":"with open (\"/kaggle/input/asl-fingerspelling/character_to_prediction_index.json\", \"r\") as f:\n    char_to_num = json.load(f)\n\n# Add pad_token, start pointer and end pointer to the dict\npad_token = 'P'\nstart_token = '<'\nend_token = '>'\npad_token_idx = 59\nstart_token_idx = 60\nend_token_idx = 61\n\nchar_to_num[pad_token] = pad_token_idx\nchar_to_num[start_token] = start_token_idx\nchar_to_num[end_token] = end_token_idx\nnum_to_char = {j:i for i,j in char_to_num.items()}","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:34.778942Z","iopub.execute_input":"2023-08-02T22:23:34.779581Z","iopub.status.idle":"2023-08-02T22:23:34.790576Z","shell.execute_reply.started":"2023-08-02T22:23:34.779546Z","shell.execute_reply":"2023-08-02T22:23:34.789533Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# Reference: https://www.kaggle.com/code/irohith/aslfr-transformer/notebook\n\n# Function to resize and add padding.\ndef resize_pad(x):\n    if tf.shape(x)[0] < FRAME_LEN:\n        x = tf.pad(x, ([[0, FRAME_LEN-tf.shape(x)[0]], [0, 0], [0, 0]]))\n    else:\n        x = tf.image.resize(x, (FRAME_LEN, tf.shape(x)[1]))\n    return x\n\n# Detect the dominant hand from the number of NaN values.\n# Dominant hand will have less NaN values since it is in frame moving.\ndef pre_process(x):\n    rhand = tf.gather(x, RHAND_IDX, axis=1)\n    lhand = tf.gather(x, LHAND_IDX, axis=1)\n    rpose = tf.gather(x, RPOSE_IDX, axis=1)\n    lpose = tf.gather(x, LPOSE_IDX, axis=1)\n    \n    rnan_idx = tf.reduce_any(tf.math.is_nan(rhand), axis=1)\n    lnan_idx = tf.reduce_any(tf.math.is_nan(lhand), axis=1)\n    \n    rnans = tf.math.count_nonzero(rnan_idx)\n    lnans = tf.math.count_nonzero(lnan_idx)\n    \n    # For dominant hand\n    if rnans > lnans:\n        hand = lhand\n        pose = lpose\n        \n        hand_x = hand[:, 0*(len(LHAND_IDX)//3) : 1*(len(LHAND_IDX)//3)]\n        hand_y = hand[:, 1*(len(LHAND_IDX)//3) : 2*(len(LHAND_IDX)//3)]\n        hand_z = hand[:, 2*(len(LHAND_IDX)//3) : 3*(len(LHAND_IDX)//3)]\n        hand = tf.concat([1-hand_x, hand_y, hand_z], axis=1)\n        \n        pose_x = pose[:, 0*(len(LPOSE_IDX)//3) : 1*(len(LPOSE_IDX)//3)]\n        pose_y = pose[:, 1*(len(LPOSE_IDX)//3) : 2*(len(LPOSE_IDX)//3)]\n        pose_z = pose[:, 2*(len(LPOSE_IDX)//3) : 3*(len(LPOSE_IDX)//3)]\n        pose = tf.concat([1-pose_x, pose_y, pose_z], axis=1)\n    else:\n        hand = rhand\n        pose = rpose\n    \n    hand_x = hand[:, 0*(len(LHAND_IDX)//3) : 1*(len(LHAND_IDX)//3)]\n    hand_y = hand[:, 1*(len(LHAND_IDX)//3) : 2*(len(LHAND_IDX)//3)]\n    hand_z = hand[:, 2*(len(LHAND_IDX)//3) : 3*(len(LHAND_IDX)//3)]\n    hand = tf.concat([hand_x[..., tf.newaxis], hand_y[..., tf.newaxis], hand_z[..., tf.newaxis]], axis=-1)\n    \n    mean = tf.math.reduce_mean(hand, axis=1)[:, tf.newaxis, :]\n    std = tf.math.reduce_std(hand, axis=1)[:, tf.newaxis, :]\n    hand = (hand - mean) / std\n\n    pose_x = pose[:, 0*(len(LPOSE_IDX)//3) : 1*(len(LPOSE_IDX)//3)]\n    pose_y = pose[:, 1*(len(LPOSE_IDX)//3) : 2*(len(LPOSE_IDX)//3)]\n    pose_z = pose[:, 2*(len(LPOSE_IDX)//3) : 3*(len(LPOSE_IDX)//3)]\n    pose = tf.concat([pose_x[..., tf.newaxis], pose_y[..., tf.newaxis], pose_z[..., tf.newaxis]], axis=-1)\n    \n    x = tf.concat([hand, pose], axis=1)\n    x = resize_pad(x)\n    \n    x = tf.where(tf.math.is_nan(x), tf.zeros_like(x), x)\n    x = tf.reshape(x, (FRAME_LEN, len(LHAND_IDX) + len(LPOSE_IDX)))\n    return x","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:34.794181Z","iopub.execute_input":"2023-08-02T22:23:34.794492Z","iopub.status.idle":"2023-08-02T22:23:34.814807Z","shell.execute_reply.started":"2023-08-02T22:23:34.794462Z","shell.execute_reply":"2023-08-02T22:23:34.813846Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"# Create function to parse data from TFRecord format\n\nThis function will read the `TFRecord` data and convert it to Tensors.","metadata":{}},{"cell_type":"code","source":"def decode_fn(record_bytes):\n    schema = {COL: tf.io.VarLenFeature(dtype=tf.float32) for COL in FEATURE_COLUMNS}\n    schema[\"phrase\"] = tf.io.FixedLenFeature([], dtype=tf.string)\n    features = tf.io.parse_single_example(record_bytes, schema)\n    phrase = features[\"phrase\"]\n    landmarks = ([tf.sparse.to_dense(features[COL]) for COL in FEATURE_COLUMNS])\n    # Transpose to maintain the original shape of landmarks data.\n    landmarks = tf.transpose(landmarks)\n    \n    return landmarks, phrase","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:34.816099Z","iopub.execute_input":"2023-08-02T22:23:34.817187Z","iopub.status.idle":"2023-08-02T22:23:34.827072Z","shell.execute_reply.started":"2023-08-02T22:23:34.817154Z","shell.execute_reply":"2023-08-02T22:23:34.826076Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"# Create function to convert the data \n\nThis function transposes and applies masks to the landmark coordinates. It also vectorizes the phrase corresponding to the landmarks using `character_to_prediction_index.json`.","metadata":{}},{"cell_type":"code","source":"table = tf.lookup.StaticHashTable(\n    initializer=tf.lookup.KeyValueTensorInitializer(\n        keys=list(char_to_num.keys()),\n        values=list(char_to_num.values()),\n    ),\n    default_value=tf.constant(-1),\n    name=\"class_weight\"\n)\n\ndef convert_fn(landmarks, phrase):\n    # Add start and end pointers to phrase.\n    phrase = start_token + phrase + end_token\n    phrase = tf.strings.bytes_split(phrase)\n    phrase = table.lookup(phrase)\n    # Vectorize and add padding.\n    phrase = tf.pad(phrase, paddings=[[0, 64 - tf.shape(phrase)[0]]], mode = 'CONSTANT',\n                    constant_values = pad_token_idx)\n    # Apply pre_process function to the landmarks.\n    return pre_process(landmarks), phrase","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:34.82889Z","iopub.execute_input":"2023-08-02T22:23:34.829285Z","iopub.status.idle":"2023-08-02T22:23:37.831285Z","shell.execute_reply.started":"2023-08-02T22:23:34.82925Z","shell.execute_reply":"2023-08-02T22:23:37.83025Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"\n\nUse the functions we defined above to create the final dataset.","metadata":{}},{"cell_type":"markdown","source":"# Train and validation split/Create the final datasets","metadata":{}},{"cell_type":"code","source":"batch_size = 64\ntrain_len = int(0.8 * len(tf_records))\n\ntrain_ds = tf.data.TFRecordDataset(tf_records[:train_len]).map(decode_fn).map(convert_fn).batch(batch_size).prefetch(buffer_size=tf.data.AUTOTUNE).cache()\nvalid_ds = tf.data.TFRecordDataset(tf_records[train_len:]).map(decode_fn).map(convert_fn).batch(batch_size).prefetch(buffer_size=tf.data.AUTOTUNE).cache()","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:37.833111Z","iopub.execute_input":"2023-08-02T22:23:37.83349Z","iopub.status.idle":"2023-08-02T22:23:39.516098Z","shell.execute_reply.started":"2023-08-02T22:23:37.833457Z","shell.execute_reply":"2023-08-02T22:23:39.515097Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"markdown","source":"# Create the Transformer model\n\nWe will use a **[Transformer](https://en.wikipedia.org/wiki/Transformer_(machine_learning_model))** to train a model for this dataset. **Transformers** are designed to process sequential input data. The model we are going to design is similar to the one used in the [Automatic Speech Recognition with Transformer](https://keras.io/examples/audio/transformer_asr/) tutorial for **Keras**. We will finetune only a small part of the model since we can treat the ASL Fingerspelling recognition problem similar to speech recognition. In both cases, we have to predict a sentence from a sequence of data.\n\n![picnogrid.gif](attachment:9c4fc53c-6b08-4404-9369-34f391fb78d6.gif)","metadata":{},"attachments":{"9c4fc53c-6b08-4404-9369-34f391fb78d6.gif":{"image/gif":"R0lGODlhWAIYAfcGMQAAAAAADgAAHwAHHgAJMgAaRQD/AAEBAQMDAwMRIgUFBgYdQwcJCwouXgwKCg4qTBAQEBAoQBEdORMEARQUFxU7YhZAdRcdIhdLhBoaGB4FAR7b+yAfICEjIiIsKCUmJiUwPSVQeidAYCdWiCgoKCix0CkoKCkpKioMBCseHSsrKiwsLC0uLS0uLy9ejDIxMTQNADQnHzRkojUbEzVMYjVhlzY2Nzc6PDoSADpCXDsmEDs7Ozxzqj1MRj6lQj7hQD8xJD8/Pz9Rcz9ciT9zpD+BqUBsmkNERET/RkVli0ZDzUZtjkc0IUgZAEhGRUhZYUlF/EpJSktQWkw7IE1NTU5leFCGuFFPTVFtsFIpClJYUFJ+oVNTU1QwEFRUVVZWVlZXV1hYWFmdzVxcXFyRwV1PRWBe4mBhYWGGpGRjZGRyh2TdYmT/bGVBH2VLMmVaR2aWwmg4CWlpaWltcGp+aWtram6hy3BvcHSedHV1dXl5eXqJnHt7e3t8eXur1HxJGXxwY3x8e3x+f39RKX9dPX+iwYBpgICAgIGBgYKapYO22YSDhIS+64dqmYiIiIqLiot+b42Ni45ZI46Ojo6PjpGRkZK535VmNpWWlpXA5JbI6JdxSJnQ95qamp2Ea52dnZ6dnZ+fn5+tvKC4xqJoJ6SkpKl1PKuZiqvO5KysrKzf+61+Sa2IZrGxsbKys7WUbrXL07XV7banlra2trfk+rm5ub2FSr6+vcPb6cPp/sTk9cW7rMXX3MaRUsbGxcb1/seZYcegdcuwkcvLy8/Pz9HR0dLS0tPT09TU1NadWNjY2Nm/ndvb29ywetzNv93s+N32/eC4heDZxuDe2+Dg4OGrauTn6OXl5ebEiOj/9OvPoev//+zr6+z4+u3Im+3Vre/fxO/v7/Ly8vLz8/P29fb29vf///j39vn5+Pn///r6+fv7+/zgrvzmvvz8/Pz/+/3y3/378v39/v3/5/3//v3///7wzP763f7+/f7/8P7/////6f//6v//7v//8P///yH/C05FVFNDQVBFMi4wAwEAAAAh+QQFCgAGACH+LUNyZWF0ZWQgd2l0aCBlemdpZi5jb20gdmlkZW8gdG8gR0lGIGNvbnZlcnRlcgAsAAAAAFgCGAGHAAAAAAAOAAAfAAceAAkyABpFAP8AAQEBAwMDAxEiBQUGBh1DBwkLCi5eDAoKDipMEBAQEChAER05EwQBFBQXFTtiFkB1Fx0iF0uEGhoYHgUBHtv7IB8gISMiIiwoJSYmJTA9JVB6J0BgJ1aIKCgoKLHQKSgoKSkqKgwEKx4dKysqLCwsLS4tLS4vL16MMjExNA0ANCcfNGSiNRsTNUxiNWGXNjY3Nzo8OhIAOkJcOyYQOzs7PHOqPUxGPqVCPuFAPzEkPz8/P1FzP1yJP3OkP4GpQGyaQ0RERP9GRWWLRkPNRm2ORzQhSBkASEZFSFlhSUX8SklKS1BaTDsgTU1NTmV4UIa4UU9NUW2wUikKUlhQUn6hU1NTVDAQVFRVVlZWVldXWFhYWZ3NXFxcXJHBXU9FYF7iYGFhYYakZGNkZHKHZN1iZP9sZUEfZUsyZVpHZpbCaDgJaWlpaW1wan5pa2tqbqHLcG9wdJ50dXV1eXl5eomce3t7e3x5e6vUfEkZfHBjfHx7fH5/f1Epf109f6LBgGmAgICAgYGBgpqlg7bZhIOEhL7rh2qZiIiIiouKi35vjY2Ljlkjjo6Ojo+OkZGRkrnflWY2lZaWlcDklsjol3FImdD3mpqanYRrnZ2dnp2dn5+fn628oLjGomgnpKSkqXU8q5mKq87krKysrN/7rX5JrYhmsbGxsrKztZRutcvTtdXttqeWtra2t+T6ubm5vYVKvr69w9vpw+n+xOT1xbusxdfcxpFSxsbFxvX+x5lhx6B1y7CRy8vLz8/P0dHR0tLS09PT1NTU1p1Y2NjY2b+d29vb3LB63M2/3ez43fb94LiF4NnG4N7b4ODg4atq5Ofo5eXl5sSI6P/068+h6///7Ovr7Pj67cib7dWt79/E7+/v8vLy8vPz8/b19vb29///+Pf2+fn4+f//+vr5+/v7/OCu/Oa+/Pz8/P/7/fLf/fvy/f3+/f/n/f/+/f///vDM/vrd/v79/v/w/v/////p///q///u///w////CP8A/wkcSLCgwYMIEypcyLChw4cQI0qcSLGixYsYM2rcyLGjx48gQ4ocSbKkyZMoU6pcybKly5cwY8qcSbOmzZs4c+rcybOnz59AgwodSrSo0aNIkypdyrSp06dQo0qdSrWq1atYs2rdyrWr169gw4odS3YhPnn6yqpdy7YtTHzu1rnDxzFtQnzr8rpzVxJuWnx0/7mT57aw4cNI8dUjSDhj2mvMzC1mqK+yZbsM6dFbqG/yP7uB/4VGTLq06Zl0/e2qJOqZ6MF5G79eJxvvOs9x3aXDR4pArne3BS6Gd+pTp1Cw0v0jHBftcnnNCQNWNxfw2dju6uWWqxycqG7xpnH/MwevFa5/9OC6s7t3r+zT8OPLt0i3XRsADg5IsPSuIN3RhL1HmGf64CMJAb9MltY78miTgQQseHDBEtDgo5xw+giYIWP08KUQPvqsEoEq9zBhxT/exHCihwK9J9Bo88Uo44wE1aOPKQjAgYwwKBSgiyyplILJeYTBI0sn5/EFDiilQCNPPfjsgsko5tBzoCinfFfgk9qksAU1x3hCgR/yxLNLKbjIQxc8Z56XFjilfFcPYfbEeeYzu4jiC3Ld/BOMELywYgINo4BThiJ8SRNnn/CkUkstoeAyGI2UVhoflJIIwMk79LBCwy44AEDBBAP48c83WQSAQKl+aqDAAQ/Q/9LPJQfkF8I2pByAwKsudHNWPV2KoaY3GcCxDyEXeCBBIe6A40ayEfiBTzAqnPCBCKj8ow0TyXKwAC+bQODBBxdsoY8tDZwChIQ1OBMDGfi8EsO4ItDSDrcedCCBIstZ6u+/bhGGjQYBPJFIN/qwA0MFumiDQwPd/EFAJuBk0YA0ODAcjQY8NAPAEsyscoAdpgCAhjR/CKBKh8DOIAQmlLixQCy2LGDJNZc8oMsl+6HcwC06hIDLMjqM0M0lC4wijRsPgLvs0g/80ssCqrATgxHbaDMDGe3M4AIuwuggAzhTVADLMkAMsU12ALft9leACdTOG64mwEg7GsiQzo0EpP/SRAI9hIFDAnpMIIEUbyjQwBsJ0CLPPZDAcgmC6fQSACfycJmCBy1AEIEl87ghARhzuJFAIm3IQE89znziyQWaLLbKA7d0IcNiwMx8SQW//MOKBLnUbG8MW8jzzQxwBJMAKott8sAwXRhhTughVOjZ29hnPxVd8+wSizvxCINDAcTAUMM2/6wiQCI4RMAFFWCoAckEIlxBRRiJDOIbp3MeSAs9trhcOjKnNSucgxUXYMQ9pgCCOZxhDntwxRR48A911KMewJAAJwTSiwdIkAgFmhovCFGB34iIFlMb3hbW0SU45C4WopkdLKZghG7cow23Uoz2dsjDp9yjCQvIhUD/DtQKHIzAHP2Q2DAsBg16gAMW0UAAEeihj13gwhYDsIQ+tGaH3tDiH7bQ1F62mAIyuKMdUwhBNgZRwno8ER5tsMA26vEKIVAiBXD4xzwG0QBc/MEC0EjftzZRwnecMIXt0AERtDUDMUSDAnb4xz0GYQFiRG8bk7wV23rIyU4SZS7A0M8ZpoAADIADBgVzAwBk8A5bIOABadBAA7LxhwPQ4A0H4EE7cECANOggAKgghQC+GMDYrWOLGjjRP4AhAEUsAwUiEAQTGgCNYGRACICYwQi4MQgJqOENFCDDO4BBARrIIQUR4MUlGpALfKxiAbSwRQFoYQ8GwmEZWwudN98w/wA72EMH57uhHHXoyYIadCcgeocwypABD2zhGexAgQRiQAEK6SMewQACBaqgi3/AYxMp6AAatmE8N2QAW+swxQNyoY8McuIs+PhGEOwgj3TYww1J2MYy3gCCKsDQH8Jwwg3Q8Ix32AMSNshBIRgUj1M4oQdOiEAsViGEX+CDFTnIBTBEoAt9CGMHSXCGEwqxDnAglQai2MZNt0AeQuSUoAeNq1xl8p93QKk+MNDbQBSzGLs86YI20kd20jOY0PwlMAU6S7+EY6O9Zi5DaJFHeqyTFmGoARqTbMAvBqiYzjznOdIZjJoAQw/lNAZEL5qralf7ksxlbh30YMcERrC29f+0yLXSGe1cRKOmx8ZNNDBS04sC41rFwtQ6vLWOmoLBARCo4AJ2cMc7rPNb51AXtceVLHCHy9ruenclwwFEIUSDGZwoRh7OcMQiYGGOzlrmu/CNb09+CyOcUHEx9eCsfPfLX4RSJyi2YVF/B0zgAhv4wAhOsIIXzOAGO/jBEI6whCdM4Qpb+MIYzrCGN8zhDnv4wyAOsYhHTOISm/jEKE6xilfM4ha7+MUwjrGMZ0zjGtv4xjjOsY5JU1jh7vjHBq4vkIe8X7qU4xF1yEMdBKEOIjv5u/9pAQCmzAF0iObJWJ4rXeSxAwC8SgVWzrKYD7plG+gKACQI85jXzMkynzn/zf/CrZznTOc6z1nIbC6xmxGAZjUXGM95FvGe+1wpwigjAytQQbUUzehGO/rRkFZ0B65AjkCreNBwphRhiDHlTnv606AOtagBUIcrW9rEmPazjAhjDACcgBrIQIYyZE3rWdu61ri+9axjzQwOTDkVgjm1npdjZj5nmkasBkALPnKN/IgKGYsVdohTXeh/tJoF/6CNnbc95yaj4wgASEMYABCE1EobxNTWtLUBgG0XUYQuyAgCABAwi398AACBCPa5P5xuZK+73RgJzCIYMGUKjCEPBAf2OvYd3+tupN8zSjbAM0KXcHSCCp3OTwaoEW2GZxjiq/53xzdyjEWooNNe/zC3x1UrD3GEgxsvLy99iP1mVcdI4iO/CD6aLBB33CIKBziClQG9ck4SJg0ZsFYLxmHqmcuj2IRW97UXKw+55EUu7cm61rfeHuz8wxcAcAJfiF50s1Rq4eCesgLE0fSKgPzmIpeOR+Dyj1kA4AgLJ3vZEeLuGPEF46+CANv1zpC3zwfn0PnHLc5wh8Y7vvF1qMPjHR/5yTf+DLX4h5Vrcfex790h/6k44RHDlyh4GT+DDzjNjW3zw8e9yYsYtexHvYh/nOMfnMd72z9/Fw+1ggq7N4081LEOddze9ApAffAlYniKuIP4xB99UxDf5EmE/ROTqIT2MbEIBDAgEtqvBP8lOiEHAKigE5TQ/iRA4QQATML2uL973nnPEL6QQw8AyIBgpF+W+iJf+fyHEM3HfGFBff9ACQCgBwaBDgAAAQZxCwcAfAXBB+4Hf7k3f/THd/AWBAcAAB8wEIVVGoTRClFABVQQBcjABafnAKmnc6tHaAFoEIQxCUfwPl5gDcsnFQaIgHlge5nDF9yAH+ggWPJgZXYXBZqXObeXBxV4exeYg/TnIZhAAWfWAZrHdC3yFGeROTEog5nTZI7gabXwBSvIdKLldFAHZ1xYeMVFEAungp02DUToY1SxgwDQgzxXcfjBc9kWf0i4cBX0D0z4fk4of1C4d3FjDWMwZQjAZ1b/SA0kUAe3kHdd+BN9VxEIqAB8dgtgsIJsRxCViGkCxn+hwRdn4GUHAAHMsBV2iIepFQ57OBALx3l/KBBNNogWaIiVyBKb4RKasUMe4gorAAAH0IGO+A/U0IEAEAW3oHJIsQ6UkAd6kAeHcA05xxCEcQ3SqAeS2AnzNmW3QIbJRwHKgAjSmAeVwIcRgWmVcI58sIruRhfcoAf0eAelUIr/sIh8BgG+4Ah58I+PAIhT0YqBeGWw6AB8OIvL2IeBiIuFqHu7iBK92IsBhxmopRAy9xkZSSl0hw6HwGeNWIzHuA6+oAcZ4GWV4IxBUXV6IQ/yNmUZgIOyUXVxoW1Xtw5w/yEXVoYMnmaP3wgAt+AFp0cBxXCSU+YEKseSeZGTegGKL8gCGDdlDqAMmqcXuWFlzOBpYVCV62Bl+ogfxUACnfYCGLgcVkcb2yFgK/l6B3iHBamHCCmLfsiQt9iE8QeROlEP70B8ebEYvwWKf/khPoZcZqEXebGR8aEeWScQxJB2fNZpxzgQ1MCEAMAHRuF/AEBwJ4CDgRGAyjBvBCcIpfCTQTmUyCCWBMcFcneI/vGCKzBur5IBVJkQ1OAA3gcAZ2AQYXBmDkAMNoCKQaCOEakTrYgP5/CD/xCEDjCEg2GEC4kOe7GEdvmEwykShLEKF3ADQLAvoaEZFOmCAuhRbf9wAUcwAyLwC5nTNp8AAbqijJAJAFZYhIAICsn3CdeIUF+3CJQwCYtADRjHZx9gjQHydY5ACZTgCMNwC4gwCZHwCNTQCY4wCY5QCsPAiJU5mo9ZmuN4DPeWfFcwWlfWChHaoMyQCosgoZ0QGoO2AmTIZ7IJCo4QCY7wCalQoI+ACcQAAaeXBsoQoZWACMSQBrxJDC9wAMm3A/NHGLfgCI8wCUBaCgUaCZ3QgkBhgJUAAIJgEOfQgDACgRJIEIIwnbqYE4SBIxGgAwigCOFhDb7yDv2BD9wwDeFwPQYBGMGwA1WAWZDwAnCgDhfpH/hgD6lyAxPQAM8wQDFScb6wqL7/MAy+cIo/+WmRKRA4+Q+Y0IDDcJ82AR3uYGWI4Gnh+I0BWkGd+g+f4GmlcAeeNgwv0Gk7UKHfqAcYCo5CuaEdCgBXUJPP9w91sKqtWnDZthfaBnUs+o2ySYVTpgKmN2UkMAw6mnx1kAqoWgdDWqRHqg40aWVh2GmzsKyiwg3BOik+YYDWBwbEQAzFYAzFgAxg15vIYAzGQAwPSm7UcK7FcK6dSIh3WZab+g+9AAA5oAMDoAneoAEJkAn6oA7voA/w8AcfYw4eopi2pRw4UgC5YA9dgAC0hQ+bIbEFwrBxMABgIEtNRKensXCtEKnvGWrHeA2LwHbCVX4px5oxASOP/+Blm9iiaMYNo4GhfJYKTJh8CkAM8vYqRwCrfCarpNmJyQcByHCrVyBzQQuWXZZ8HCCDv8lnxeqiyuBryWcDKvgqNuCsp1cHKduIAJAKvcpnvVmkfLYDMGJ9mogAtxC2AMAB4UAUBrits9e3neYIuYiXOIEWvWBLGcsJ2EAqmVB1IAIPcQAANQCxdAeK+ZU+sJILN3QAkTtdtrVX7kAP9xAHATAHhQoNCxsjC+cKpzdl7Tlqx/iZQUCVC3cMEAABxKCpG5F47/YPxOAKs9AKwxCmmgiUQmls1HALvpsKJWqhQHt6CEAMVXt3SJuAswqUnchnFMChp3eDvjsLqWANgf/wjQwAvd/YAeVQC7Pwu8XwD1nLbjormxlwZi9QqwDwAmQLrWfbgWr7jW17ZkHADen7u9RwpckHALWggnzGAdeAvLXgCplanWaxWxZhgDebATZAAo0mlghwAox2Aivgaw7AAhxcLTZwko8QuPw6d7tIGMtgA3uwCec5MAOwuHNRD44LANJjQR51C6kwC7zQcyECACt1Q5BrDgyyw6ngCj/8D+ngD5tAA6HgBluwNjKSuqhYjO4pascYDq0aBJVGGEKJCbhLcT3XhXwBhw1YfsOroQCwAsXgnlxgd2jbvK8ytEUbdBW6Kxc6b5vItAdAAU+LipjnaZUQCStIvsmnAsT/8JgAMAbse3otwLT517WnB7aoaL/s+Spmy8dp26tN65uoyIyedgje+CoHUAv0+wHE4GxhtyEhERqX6BB2eAf/EA7kMA7kgA7XgB/hcA63zHYpi5S2PA55q6r6Sp0jEZjrCCDRQCqWEKw2/LiRSxjygsUJUAVClD5CjLlxgADn8w/+8Aoz0IGpmAQdRRidgRYcW8X/oLoF7LeRyQxiWQqB6I1fQB/0dV3U9Q/DkHkMCRF8sYgwKaSddgtXoIwfgAwO0GljYHedlgqq2mm+qYxBAKtTlgejWdBXkHHI4GtTFga34GmYwLcAQKSd1gHIoKNTJgf/8KtohsbkSAGdBpUz/z0MBDdlaZCyD11+alcMLNBpRwB2xAgAjuCNnVYLUZl/xYCsuDrGAhg3+uxw1ABs2VaJBClgB8kihEGLi8UXeXAAxzym68h1XKe7sVx4WNdkGzOwssEPcXAA0mNICHAADPACGTDXFeAkq7DNmbu5m8BnDOAB8XsAJZRttCEXiGkaVvzOfTuSlgoAXEB3nEYCagkSQhkIeSsRhHEHLbADL0AFgtDZnu0LadDZL3AFxOAENrADKnAIrbACO2ADLJAKlfABsW0DxfAFL+DZZzAMNrDaH4AJodDZNvACtcAHJODZTmAMqr0DLHAHvqACO7ADH5AKlADbvz0MacACnh0G0P+72i+gB+4wBsQdBWmw2jYQBMbABbv9AmFQB9xtA1EwDEGw2iTgCKnwAqt9AiYq3TZwBMQQBu19Br6w20FwAp1QCtK9Ay1QC3cA2y/ABcMQBeCdBjR7Ed4YBqt44TLIljyoeYBBGNxQK1amJk12hBUEGFbmkPvK4XsFEXcgB/+BEYTRzP00DcYga86QBZqbDt6AAgcgArEgDtPAChMAAFYgD3s9xN0cuRsDACIAC9wgDZswAXCdDhJMKYvttxZqhXyRlVYoj6Kijg9BF9cg2y3AAizQAmze5mzOAnbNrK7g4gNRDueADniODuqw59BZDnl+DnyO5+5g53mODl2Z54P/nuflcOiCjufqkOePnufr4Od4Xg7Ph+iULumSvg6AruflsHN6rg7lQA6dDuiHHumRvuiF/uiprueh7up7LupdyeeR7umFfut7Xg67CwYkkOZr7uZtDucd0LSVMHSa7eFumYe1HIuUOpeAWJdhLbig9w/l4KjW3qjYfu3GIGUt0Iw0rg/NTNcQ4ADjDgGvUgPaPE+A8Q75IAmEvQ22wNf3UQP1cAkAYLGN5Q+DgAAWq1hn186rC8/wGWyfGZ//sMsU0HrY+A/IkMWzN9fkdtYJUQ7iMA7jUA4Ub/HjMOnicMsbf8vkIA6L3vHjIA54TvLiIA/lgMsd7w64XPJ6XvEh/8/pFY/L7hDy4iDyLZfz4vDoKC/qPG/pJV/y5aAONS8Og17z43DzNY/xGk8O62DxtxzzLG98KD/oLw/1Gi8OgP7yPZ/xuLwOIW/xul4RKg3P2AuPxz51w9eWrmiQzN6HXP3sgiim0t4Qs+jwo3Zm+ObUCoEWzczIndaB6P4HCHABUnAFXHAFXwAE9x48l0vEMuC43mwO2LpwzTABAqAJGXJBWg7wjP3wA79wYbiVhFGhKyDx4SkOnxAKrv/6sO/6NCpvFFDsdC4Q10AM76oMuR9rxCAO1LCuxqAM5YAMx4AM8ioOxnAMx1AM4WANun8MxlAOzJDjxcAM5cD8x0AM1v8QDsXA/MYgDr2f4+fADN8vr+Tw/dvPDdywrsxPDtOg+8VA/O+6/dcQDtFPDOFwDd+/rgBxTlmxY8WYUSuGrJiycsiMISN2TZyxYwXDXSOG7BiycsweGmy4kZi1cBmPERNHjVhBhsoeGqP2T+ZMmjVt/kvVKdROnj1DgUqV5sCBOtz+4buZlKa8f8YAsPgnT90/SgDy/Jt69F84AA6y/lv3rxaAKGBlTs0DYNK/c2IBHAmLVKlNd/98cXjRQu9evnpfQDgAoEMorXMNM8U2YUChcNOoWZMWB4CRen8AXMaMmUAuWwce5LLX5oAMcVkQGDH3Dp+7etE0CFAUtZ4+w7X/bd/GnVv37pthXQFQkFn4cASCZRLjAMAV1n+VAJzBJ5c37js2lE2XeY3gQu3IFIq79vAYM3QKT1Irt9FhOG4ZvZ+j9hK9d4XWJno/Rg6jRmTqmG0s5ppxVjqJG24e8q4chBxi5hyHTrIvIYUuklAZdP5jcL/xylEGQAHVMyYca8RDBr6EYEqPopHEkfAY8ExqcCCIYsIOt1Q4mKXGqJp6KqqpqroqK6S48mqmsMYqKyzm0lqrrbHgKiw3dQykssoqrSlnBwDSMIqpGuVpTTFNmKJtHslq+EcSACLYY5JIHpkEEUD26MYWAD67pw0AZNgnDgRGMCeedMr5BxgFBNDk/596ZtOxUUcfddQ34BCgtLjhMiuug3+4+QCAMNDRigoAQPmnrunkQTVVVVXdKix3pNONG2ZmfUzCYsYJTyPyqJlVGWvGAZE9946BT7z5vCvGPgSR0S+hjdbBEKYLe2VvWQXF27VXbkryjhgRKzwnWma6Q4YZDj0cB8EQR6RPQWfRm5UZZcIJR0JkwHNWGfjivebLVf+Vp65xxInKS96Ycgoqqaiy6h90UK2Lm67Q0QdVUGchy2GL/2GSLbeghBXSmSIpRSZTv9QnGsUsAYs1eOI4AM1eDliAlnpk4ocQDoyQZxXPcrnHzxrquQQBAhKV6WUEGvgFHzBpEzlqqaemKf+sVoYb6tLLMv2HGQC4MGqqYRygAJkdqZZJLoMbbQ9ZfB1S5iuZwuGvmG/xc9chasipqD4WKzoGV5OQufAjGmmyRj2O4tNImZC5hehuZPQVl1xzO1ToQ+/WJTHv8UI+EL+3j1FGSbRtUltHhHtcuCo9bDqnK5tuAYAKm/RQy+Mn4+ItOt9/B95LVB1FTLFMAsannpdjNqedJtZMZJhd3EDgADL+8RlPPWswRxsUPEvEF1ncuOx6d/Rh9HT119ctrFsg+IADDjrIIDBLiTOOm06mUtKLLdNeX3QehZS2QeRFcJsKUpAiDsDZzRoEOoaCMkKMBqknWSzCj+D4Uzi9pU3/gNxwFrFUAjcBagVy3rKGvQp3knFlxBiX89BEKmK37mykIxsZj01C55CUEMQYpWNfTUr4JR4pbCqPAAAFXvAFLzTxC6JCABea6AUujCEIAMjAGKTYxBdkAACP0N1beIe24T1KHinTwAAYARakKA018hCGBoZCgQkExgXbwEf2fpEnAHCPHsCo4wEYEBgADAGPZ9RHIqEWREY2smriIMc4xkGOcPChOPfDlHHkAqp/HAKL1PCdI3U0pHIxgyM1jNtMhhSveXGjlPqihjLkRQ0HIcs+6smPhgjnkQ7WhBuylFc5rlEuWUpnSO5BobN2KUtlPAaY6MFcgMQBzHmxy4a8/8xhTcKhDGaCh5nUkJsoiZgwH/0DiVpDJyaF44gwgkyc06mLygRgiXqs43wvA4AL0qEOfCyjDChAQABAgAZz3MwUNKPFPbqAABeYwx3pEIYbAMMAECTCHBV7Z0aDOMSatEIFADiAOrkWMKbgYxEAQEAqzqbR6eBjHepYxzrwwTjShVMqMV0HqnAq053KAx0WvGUGdekf+YQspu5wh0zlYc+Y0uSY3RKReBjiDpjaM2BIlYoykHWNpSI1LDXkCIayuZSdejWpr2IpdlZnxH9MAgBXqEUpUjHXVIACAAyQ61xLMYtAAOAFs6BrKWrBBQBEop1jTKttFviQbsgjOvSIx/80kuVYpLwjlsxoLEbbw4xATWMk5qDHotYxDVl2ox6qSWxqqRY8plzjC1sjJEqNMxNlEBYAlViparHDuB+CKmS4UQdF/IZDY2hwIxdKSDFA2dKtIPNupONkUtRBn4iELFfHHeELdXu6tZbTdTZBh+xqcosDUMFp0sFdkz6G2O0mZYiLLKFcThYVtPoufYuaianQp4+ctte/UXOpTDBBAZQSkmvlKEUaIAAADpQsSv/FzUwnV67oqlKxWJlch7ZFXOMuU6vLdepvb/LUyFkjww16MADRwU2t9st3rVUmfCZ8OAibsYjebdjDArapienDHT79B8bKomNQdcxJYkxxjdP/hqpFLnltTkuVU5l8lDI62bFKxrLqZGKMK8qWa9S4jALOwIyoiDjLSUFHmtFxjrXtBh9qPoc7woHDwA1VzWkOmZlR11youuPO87WJT+8sF7lop2/nuHOFz3yqG7euYULaSle+cqSMKQktuTuyOxe9aU5DeDX/GEceFBBSrl2DCo4YhskIredOU82V9NGPe5DRZuyQ2Fsiu4biwtnqgzX6R48GIJEm7ZYknYVjmF5vknm9bGYz0lS1SM5ICU3rZp+OgMXA9q324526sPo2tg6HsnWDFIwYgyC+rbZuuuvoIAVb0kYitlmWhOzdiTvd98b3KJlCjTFc5gOlKpi38w2p/3WUw+CEYpx3qO3Ub/M5cvbWjToOXg6BD1wm6/51u034bplQutjzVm+9K25xkpe8JqbqBAMyUJiRm/xRC/KOtzmqFHBD3OVTwzjDND4kjpsFSfK+dMiR3PKbF/3eXrrFGYzOSKTAfNaGmbl7HX5rou/ZgwpceqB9rXN8lCOmU5GYA8px07YImS0xJZSRk131rLe903Uhx8LdPsB/wFwZgK711MPN9rnjJudVEcRNAACBkPnirU3+hyDo/ZYE9t3x+S4p3x8/l6aTbnJ4Z+4J9z75qOXcrR+gwhGcMPooHAEBChj96I8QBRtgsfSqpwIJCtvOxnPe9ryW/O1HXPeVUP8E873Tu811f5t1gwoR6EQ+Og/xD0I9qfbDh370jS4OA1mDG7kXYvCxH/11N70VtbgF+Gsh/vGLP/zhHz/6z18LV8QEVCKXfvzlP//sa1749K/Juu+vbkprGv//B8DHq7ntg77uQiupqKqdUsAFZMCXcqz+Y68AlMAJdLkBpMC5WJ0WUB+kqIUD8L8LBMEQbDYLFMH845EVALWJU8EVZMEWPDiC+Y0PLMEZpMEaI8EaZIpiQCkGYAAF4EEfBMIfdAAf7MEhFEIiVAAj7EEhZADG278ahMIoZDrti8LVYQBKUYBK0UIs1MIsrBQv5MItPD0EaMIoeD4pRMM0dKQbhEL/eRCHNxyYN4RDOaTDOrTDOpSkPJQkcSAUNfTDP2QfNgTEQSTEQsQyQaxB4FHERWTERlREQ4TESHQzKpTESrTES9xASsTETeTETpxE+yNATxTFUfTDIZEQqiPFVFRFTkTEVXTFVyzFrSCdDtk8WLTFW0zDdTiHXWQzXPTFXwTGYBTGYbw3YyLGY0TGZFTGZWTGZnTGZ4TGaJTGaaTGarTGa8TGbNTGbeTGbvTGbwTHcBTHcSTHcjTHc0THdFTHdWTHdnTHd4THeJTHeaTHerTHe8THfNTHfeTHfvTHfwTIgBTIgSTIgjTIg0TIhFTIhWTIhnTIh4TIiJTIiaTIirTI/4vEyIzUyI3kyI70yI8EyZAUyZEkyZI0yZNEyZRUyZVkyZZ0yZeEyZiUyZmkyZq0yZvEyZwMwETSyZ5cH/S5mbYLJZ8MSPyyOJfSB5d6sqgjyn2kh1I5n5l4hx9Lh+hIStZ4SiFCqqRah3QIGHpQJMWSPOA5iqA0ppLKyqYESPQBoH/gSZpIBwvDjUWhS6hjytpYxH9wBlfYBnBwhWeQO7Xcx+TZB08gKKf5B344hTSYg1Ewh39YhjGIhaScCaYQhjE4gzAYgysohGVQg2dIB6s6mdXwqpLiyrQpTdI0FaQizd85q7ogBQL4BWBAAEZohznAhZZJKi9RTaQSzHlkCv9v0AACiIV6SIfQQAAOqB8jSAdbQABFqAeDqYuDugAVMIEMQANWCABOsAnEk4l3yA1aowdYQYrYnM3arAYF8IOboLVQ/E1sZApTACkrQIpgQAArQAd4GAQA0IRkCIDYkE7sCQBLiIdtcYdgAIFfWIY5wIQ6UANeYAp48IQ02ANAKIR/iAdZoFDQXIY06IMx0AQJTYNEAMxgUANIOIM9gAacWUwSrQfz9M9M8IYWUIVvkINKAIQ5QAWmiIfFrFCLcs/3pEaksAccCIE2KABooIdqOAAi2AZ3YIc5iAVgOAAAzS98MAUBUIV04MksVYVeQCkOQCh7WKgLGDUZyIdBOID/D0CAB/iFXiiOC0iENggA2WsAaJCEA0gAGziACoAGeNCTDzgAC4AG86RNRqiG2oyG6rmACUCUffiDA2jUQQUtIW3HuvBPTkgMP0gH/QSABHgCVPixXvhPjDKZfzAFBLgBLnCCKoAGWwgAVUgGANgCc3BORUiG+0QH+yyCaKBVc7BPOMhVKzCHZjgAHqgHYBCAUbiEAUCFeFiF2oRTOHiHmVGELD1PTUhURsAGBKiBbmgGALACbzgNaFgGFMAAcwhSS4VGfEgHSCUAUTgFDQCUdHgHYSgDObojOI2NV6GNulgFAPCAFogBEXgGWP3SANCE1jgAOzAFAqAFfGAHDSAC/5+hgBMAgj2xT02gB3swDREQBF2QB0mQTXr4Bg3YgktIIg+IgQOwAmylzUxIT25tWHk4WSugTU14B3tognRdV3Z1xroQzqEIKQJQBWHYA3WFB5W1hFxlhE9DH8cyBYW9KXkwhQGQ1QGtB2P1g9jMBZOl2KlVA0RYBEeABSqNjXgAB09wAgQoAFzw2n/QBpSVhAFQg0Uo21gwVPRUAG4FADigB2+YACsg1UThWZ8F2nTk0iwtBGkghmAAADKQTzsArc7wA/982pmgh+lEFJm4mVUIAFog1ePhWtq0A3poBgUoAvskg3e4h0XQBf+0BHrohQHIhHeg0kIgBQHghDw6AP8/cE4/2FlE0IWH/QX/1FYGoFk4+AfhtIJuLYLu0QAMqMrEPcfkaQcYaIBtUJR7aIIC8IUsOAAPSI47dc4LOIEV4AA0qKd/IAUFGJOYooeD+lIAiA0mFYN7EN8PcIBj5SMPQIEAYIRZhc5vgIEAeAENeFtS0NOWZRqPBYATGE69DYA3RU9FjdzARQAZeAfLuAAvwoDxtF5zRAptiIL1/LRgyAFduAdPKAMp2INn+AdhoALV2wE4CLBgkILJfIceDgYa+IVoyIFYwAcTvt1vAAQpSIMJ4AF3kFAqqAJUwIdo2GGmAAdAaFVcSAdJEIAzcAI10IXosAdI+GIifgUhMNf/KIgFb4gCVdCGK7AEfGiHMigEeQAHSPACOZjeJxzhazTGmUhKxAPPEAutsAzKrJQOg/mGGPADfjgoOwg0JkMfAUqkdyDZX3BLKvtOrDutpUiV1Rgee5gCgqJSK6iHn+3jZJSKqDQZe1oNdMgppODKr6uL8VyqdEifd6AYqahKfFAHeQgNojWkq4wpx3qHdXiHRHIpmEqHePiDAfhac/CS6Pxlpkiq6FCHp0QHbAaTY5aHeyAESxEBaPDKVH7I6NSHeNiFVICFbnhCfXiHZRCFbvBO3UCfp8QHZyiFUdgGMDHncz7liwtM26jn3DDKK5OJgv7ng+TJ1YipoNSNpIJo/+zgSX3Y3IfCuoW2VKBcFNpQaI32SY5OH5Am6ZI26ZNG6ZRW6ZVm6ZZ2aYMcypfOyaZqS5nOyVTQA7OxaZycBDnIA2LQg9/baZasi1DohDHAhH+oA0cIhV2LMFS5y6F+SFC5g3AIBUT4Bz3AB19YDqFWCkCDaqmeyKmQg3AohSDAhLLghlRYDaJjinIwhh8yijIT64J0KdPJPlfYgUj4gjRghitYhFmo6vtDCqa4BRs4g0DQg0OohOuQt6wDmMiW7Mmm7MqebK+ua90aysA07GHgA0eYhE9IPEGwhjFItYFOm7DAhDTQgzwYAyeQA0yQg0gIN9QuRrzmNNvObHGCGP8jYQpuCAVX2DXD/gdHYIZh4IJO2IGSqYRbyC2lmIpZGINW0IMoOAQq+IA7OAQWiAJlEOGbQ4pbSIM6IO/yNu/zRu/0Vu/1Zu/2ToNJiMDdzqjoOJl1oIaTyQM9IIGYgJXOvoIEO4NhkAMOOAMVqAWIQwpQQQYIsAHB8gVxCIdKwARiOIG3yGiTY4rfSL4N5/AO9/DLsJ2olm+m669r8AVquIMxyINQEIQxCAVlgIBaPNVOSLpKqKt/UAZ2coTlCMxPQ4YO2AFj+AdxuINHSAVE+IJZwAQVKBlUbi+mwBgwFMMpp/Iqt/Irt/ImBIOjcPIR541iCII6IIGSSYUM8IX/M8CtMLiO/v6HUiCGPCCYPLgGQQiEfyAHYqgNpki5Fwi3O+AAJvIFZbiGUMAEa4Dsf3CFrPnwRWf0DS8OL+ByL5/vNs+DIEi1OyAGfKiFOkB0lfIF3DqZ1nKCO3iBT7gDCriDKCCBSpiFHKG8f7gGB2gBPD+HFoiCHcDzk1u6DFf0Rvf1X98aAIB0EZ88X1aHY0f2ZFf2ZWf2ZcdtgbSaMzCGLyCzQHBuPSCVaziEcKiDD0A3gNt0ZHgBUdmBNGgFubqDK7C3ungECPCFfxiGNOiAVLgDQoFqpezy7eL12AL2fv/wR4906ct3hiNIBeKCpG6FMfiHRXDupUaGUMAt/1+wgVqsi1tYgRc4Akxw7hc4ABuQiTN49qX4hzEoGVBJBRKQg+VQEsy2uH3395f3cIAn9r4jIJ0ohZvH+ZzX+Z3n+ZzvhAMfeHFECmLoAC4Qh0oIg1o4gk7ABBtI8ju4BX1AhuU7maJOg3GIhEr45VoYhjzgA45xsd7AiUWgiTFQABbA81cJC3E4d7MJ+rRyeZiXe+ST+bd/O3ife605cJa/x/COghfIg1pIA19YbGU4gyhohTQwm8CPEqRIhVJoBVc4Ai8wbaSog0A4BF9AzKTQg0y/hk9YhBagd4U3FWbIA0S4hWGY+WaL+7x3fcyoe+lbHQpIgzOw/dvH/dzX/f/dt/00IDAOuD6+r0em8IU0qIQxGIMOqAMb2AFBUIEdoACF/wcncO61QYdDkKtU44YgaIFUS7wd0Hy8M+yrkAduOOySIQYKWA5lkIMgoJG2KLrWf33Xj33u+4dj8CuqSQ5hr2l/BAh8+m6V0ufrBIAgYyhAUKGnFJU86Gxc04fvH8Z/5fRc/Ofq2o5DX/R4PDDrn7uMGVPe8fVPHsZFk4ahG6YC05VQVFJiqvRSJdCgQocSLWr0qFGYrg4cAOD0KdSoUqdSrWq1KgIAXv7h64j0K9iwYscKhWkMAIt/6NSxbev2Ldy36ND9owTgQwYAjlCS7ev3L+DAggcTDgvzVp7/i+5SsWBwpZI1jMxeLAIjFB2ocv+GtWJ2RV+5NNeYUUiFL6XKlLfSZFz3T88dZOH+CbrD7N8sbqnq6ONa+PdgpUyvEi9u3HjWrV2BM29O1Czan4IvplIAIJUvAAhconbu/Tv48OKFXiT2BSbGVBmUYVxHt1YUV/9cA8Wu8d8jTCvlsWh1GuhFcpwEkzr/nMGaa7cEgtEtXqRRIHrjSZiRcE0ddyGGxyXHlVcTemjYP2elFeFfFznilAJ1DFPHXdzgQ+KHMco443gXcRPGNfjQ5UsdLrmWEjJnwNQhTJVM8s85/+TRyWkXldOCSyRedAsXL13kGhd1qPUPM3W0ksYR/4uk1B2N31WYIZppTrXhcmW6qRJ0Iw7mzjCT2PCUDRQc8AVGHb75J6CBGpWSILW8lFIdj8yXEjo7EPMiUCmlksc/BZaCIEzDREFfav+kkYo8KSm2wydbtmJDILPQB6OghJ2pJqxpsulnqx7GKZ2rPH7gVFaU4ForsMHSqBpr7rxnw2wYUcEHSn6mNEwQ6JVDBUaaTdLJr88e0WaBrkDATXokILOSsL+9Giu6GmrFYbm2hhgdq36FmpFuVziQEF20trsvv79ddEYpKtmQQRp1qMAnu5H+M8ZJF92xSHtcDJOtp5T2idILPvlyRwvFqBVvv2Sdmy7JVs0acni3ondaqP/uuPwyzDHL7LI8bOnTWykAbOobyj37LDKXL8jxSBpRUPBFEEzdNmRQKdUSRDjyzLLDATac8YETFnl1ETIfULPZOP9YEwUAYDiRARjU/PfzXyOX/HZUJ7PNnMo8F2bsP7cAcISoc/v9d1AF8qGAAjZQcR0+XgCg9kX6XlQHBRns0AIAdaQBgKIF9gmTTP9Q84EDHFDgVBC3iIMRyIAnVZKFcLv+lNyqC6ayjcQgczvutx9zTO69964MMsSA69osADjRt+zJ+1wgIpGwl8oBAR/OuN1AqVOKLycFksE/mADQCko1ywNpGnT9g0wUdRwBAAlhc5W68mWx/jr92q3bZvz/8r47YoF2OXAAAgIoQAc4QIABPIACHMAAAArwAP/zSZJqsTfk5a+CwiqQI0pxkVAg7k4ea9xQxsEajFzhDPrQAwBuwRcK4WZB/+AGIo4xuoCZD3/wi5/b6key2FlQLCor0CMAQAEVfIAERiRBB5xSxCWeIC8OOEERSfABFYxuLxGcYPV6qMU/uWYSFpMgtihAga81rk1eIQcV0AETG9zhH2EAwG24lIdUVeo1PglFJQ4BgM90pCPuWNkWWbiU1tVvOOniYSCP8sO6AKCN4RjHOMRxDmog4FvkgORsWmG8fzxyHLO5AwCOdEW+ZTGRpnQXtILAhTEooANBuIOLLkaU/0McIQyLCF0dVkApmPDBF74YRkfkcQY9cCEMqnQR0/5BjE/UwTSnzGHJmGIhQqoJkaccyiLtQikyiQMADujORWpxgChIJyV5OIAo/yFBUurrmu78TuOGkYpQpMI+LHQHNa5xDnScTiXXaEUnAlqKr/lmDJ7jQyUm9o9xVCIUtbjGxToSill0ohShcASkAglNNEmzKcNpSgYgsMP7tfOdqNvfS/oHAEqhoysw4YYD6TK+AkmQnOroCl3yEEokqROLJTUpUAvToeWMzzfXmIQvboGIOnQiWb9iISdD8Q9l5EEZqYgjnIKZt1pYYxbMeARJbii7jWaoowhoigce4AFDRP9BAR41ZIasGdSfiCiljKSU5i4SDm9qbj49JSd9CqTTdK6TgnM9LHDksY7FroNM+KAEN0LRCWa4wjRbc8c61LFYmMCEGl9zRCjCMQwVYnYd8oAROTQ2DGs4Ikk/TR5ZOZoVAGSgBBvYgyEMca/ZAoCayCEpYuGE0prdtY6+2asD+uqamvq1joPlaWFLGdzpmusfvwxHLYjBjGKchEzyuwU1uFEK8PrSIkQRR8BSYY1wYMJQrx3r/NJlyAwcARB7UIQWEPACPbgVAi0QqW+JI9egZnOlxtUrX1vz1+YKdqejNCygTnva3lD3KxIu6p9g0ophUAMZrbDGMaJkFNd0Qhn/nk0FN25BDH2krhyVcEctapEKRJAxkbEtjjSdQjAqyAERR4BAHzKAgCsgAgBVgIYaABhX4FK3wHi9GHKVu+DAKsnBPWVnu3rjDraYVkaZhd+WvVum944HJqkoBjVSgQl8MGNc8FtOKIbxz1RcgxI/vcgwMIGMUujBsqa8MVWakhULdYALacCCHO5gAwAGAYUAoMIXziCKNCDAOhgaMFCdjI9zvCgl3PCmTOXxHp19TB5Jeu6DpTuji8RjGLwjRjdOCyAKZwR/RtFHPYJia6HQGsMqwUeuoaqSetCaLDc7NlCQ/Y94SKMYyiDGM3Yto0yhmA+g4EZTzUsUvVZCD50I/0QpPlFjo4gDGcdwapm6nLL4qskCQjBELfKgBigowQc/0NMB0vAFb46hESngwgJ5+1vlkPln+FCHmFdXV+LahSRAQQcAIOCnWxyAWkDhg5WjW/DxuAMf2JjAAFAQgEz0xhmVWAQmdOHrPpEZ18H+9U/1EY9T5AEVWXyRP2RxB1T8MSj1eDlZfv7zYv/D5QKxRxMCIGQrrNBN5cmDIzqBCdOIFSPX4MbVw6vqX3N94+TpCtjDLvaxh30l0q7uIMtKAQRk4ApQMEMqCqaGRBjC3v/zphpowAApGAIAL+CCdQKMFSa/ieyGP3zZIxSqlT9nuECkrQ3CIPkwjEFxCADD5P/DcIYg0PYMmR8Y5qDr00DB5BsoAIEOEsAJf2xiAtJMQCHqIZBag/2PEaLHi1xKbA6dFuxwmjBX6nGPOACADPP5T+PkwY8/AKAI8xmfV/RBD9z/sdinoUfRUeeV07pD6LQPFe7rAY84JCAICLBD05VX8BcxXkbh6ARBF+UcQD8FgAeQgBmwkIpOGCIPZ0ugI8QAAjjBnZzVCGwADXzBChwABcBCIdyL4K0J4dFI1YWFjWCCS6zEOvzUIgXRpVWFFV0ZhI3ZP7SDBoDAFAgALVTDAQRAFSyCGwAAAajC7NWaY8kDrmHEO3iFO2Bfn7gDheEDp4TPP8BDGwSAHeCDOUT/iEUo3yAggPGZ3T9QH1DEiz6kw6/t4EoQWz0AYa2tQzoMXwKUwQH4QTok3Kqxi9eNHWCc3VEgHhx2RUooAwBwABjUQj9tXduwG3EwBQLkABQIgQIcEARkQAdcginowAFkwB1YBwTQgQvsQCOUAduhwij8TwRKBaZJSDDFoSd2hWscQwLdgCAQQx4WxSJNwqPNQim0YinsHwAwwCu6oiug0Au4giuWwixcAQBEguhh2fxVYK3Jgz10wRAQQgXoQhwgABF0XDGen6z1SW+IwzRQQ2S8RDxMgzKkAzxQQzf0yTVMwzV8Y9F1RDhMwzQ8A+qMnwLAAUZwAzIUQ7S9SD8M/8IB2EE9jEasARukxEM4WoM5yF4Rmpg5mBc3HEO0YQQ1eiNMdKFFjMY4/kM68MMgiAAkXEAmpF9w9VFYiMMZHEgahKRIjiRJluQZyMEXtE4LHMIshI3XWRgfWkVTgMAQGMINHMEY3MEdyEHlccEgJMMLNIUTrMADlAAdKADBvIACQMAcZABcFccmigfXgGRJVqVVkuSBhIFIPcUL8EEp5EjjLZxKOZxKQBwErBzFWZxKYBxhjV5z+NHGsZis4cM7zIMn5AEt/EM99APz2cG8cIU++MMrxEBTJIAa6MI/sAMKCMAc6MAAcAI+fMMbuF4AiMAozMs+eEIM9JYEqAE01P/DPiyjO3oDDgDAA+TlH1HkATxBGTiFBKCBOUDKMrjBBADAANBAJrzDP0TDARTAL+BDOuDDKhzACGxDP7yCIgIACBSCGv2DZFJmDoxCbNKlDxIdYsGEMkwMmdlIyQyipbHP0iRWTJqMEkBBFexAFETBDoDOW+VBJTjFIxbBBoRAAA2i8UTBU0LlBMYIT9SPAD2FAwQMGjpZqXkaqOnDH9FF8ZCTGolalbUlMFpgY80MzCRIBlbg0JVdb8QDNUiDJ0zADHpFPbyDKWTFBXxAbVrANrADDFjIAsQCix7AAHwACrQg+vHDJfTWBXTAWYUANMzDMtqBPWSBaaLmO0zkIDz/xQUI2QEw3T80Q20mAAdMAAIMgCLgQzRMwAP8pjy8wyoAgAvIAzAE0JK2oCLIgzbgQJV6AAQgABKGz/QB3UbOxx2AT9VdhDpEQh7wgR70qZ/+KaAGqh4IAos4BQWcQStoxkvCZNrh2AGAwLvdC1QAkFsdQSm8AQVMwA8gQQ8MYutQQV5kogQSHAkqg6CeKqoGqiDIwXd+gByUArigouMVV15xUoK1x5RhRINB6AgahdcFgQIwiR5mxNA9nD5Ew1kBQAKcaVFdxMcJAByUAz0IQ4uSgT3AgGmKAi/Mwx8cQAjoQl1uggIswC80AwIQgB/s4DKoqR3EQxwEABrYYwHY/1zHHSlFyqAivAM8XEJv5gLSAcAQ4MI/gEOSkuvHbSlwCieY6oMkAIAR/MO9YsA7/AEChAAu4MM+iGsB5AJg/lyF/YQyCOuHcIMCkApWzd94yiQA7EAe7FaOZcUJBIItkMIZ+IAPnNVZNcXcYeIHkioJMgc3dEAG5AFXwaRY0iqU3apfMReVoZoIDmsVns8OTC3VVm3VBkFe6AwxQG1QWMQ66MMyzMDaBcAWmAM9vAOC/oMpAAAGoAPu/UMwSEEhtAMMIIAf1IM8KGYAKAI8MMMzfEPSWQIpIIALhINnhcMmEGc3xAFSHgABaMLa0OMT1gA+xENiwoAAcIK5+mao6P9DO+DA3nrDBPhms3xpmJLCAVTAKHQDOIDCKHwDDggA3zKDNXyDDiAAs8rpx6KYtn1FZsUF8AKvPIgDe6DOEIpnoxqHkr1AHrhVziLQDJSBLJDCGryBW3UUACgAKqBCXgicfvpsmdRM8I7v+LYUMyQJRmDWaxForUaZgjGtrj7oL/YqUbjGUhyHWwHAGTwVUmwNPogDtdoti9GDPOSDPTpfQ+YaPbDoY77Ex+VYR8WrIRlSBTzDMsooI7wDOMlDPTap7OED0gUAJ9gCAmDANnRFOuwD88HBxzXAb3ac6ZoD4GZFAlQBLMgDNmjANBmSGHQu4LiUy0xoELcM9IHFBmL/QqkI42DwoBL3Bf3Fjd/xl/0BQAqYAjCcQQ+sQQGZ1crSQdYuGfhaEMsYLbyo1JMdl9IuF6k1bca55VdcxDVgAih8Ah3XsR1/QkVRDgR0Qr6IxUXYwykkAjQsR4nKwAAXsD3ywPOdRjm4A4tmLkZ8XACAwAtU8gvYAAiMQrdegCW/wA2YwBKMwwUHABy8SBBysD1aAe6BcNJxQi8cAAZ0gwbLw/ghAAtr6W8Gp+luQxGewhRQaQD4gTZoQAC0QCW3gA3YQAswq3WizKJG46B4hK808RKXWcoKGAC0gCCMTlY4QBtMwRMcULL2VvYiwuiIKlVEpd8886xqEz40Mma9/5A3lYMQ4s2CIonLaIbTapwfhwUXBEEcLSqwfW4AMEL2mcIB1AABQ8oqIEAFQEM5rsIJWAHdNnAJxm4mvG07AMIT4MLaKvTNvMMrREEhuCsCrMAEIKEQ9obkNun7hDAnZOkMZoQ2wEAAaMIwEwAt/Nw7NKwLmIMnqAEuiN8mAIAFSEMTCEAm5Jrnel4ssJjvSSURs98fsd/4XJivagQ1FEMpHEIejMGBeB4XnOQdoJj7oCHXccORLGrIPLEmAoANVEL3StMYPG/9rdT6eK+6hPF1zup7CkJQrEPE+Ul2qGVG6JGvpJpAXxhjM7bn0MUGOrE/NGwFwEI4CIOaGl8Rm//eASwBNYTDK1CpH1Q0ZAYnv0bAKDyDNLQBAvimNxDzFoQXK9SmHwBpABTCJlTpleLgi1AkAjDdi4QwI9xDFiCAZV6DMHRB6kLDkB7AEDxDPASDDrtAOiyjCOgCPIhrA2zDJSBABFgCN0hDt7owNIcHW6fvULjGNRyCkdSBHIjDHcDqNSxCIMhDIDAvCVCBHsQRWqvEuGiRW7+1DUwCB2hHAMmBwCkZy5KzrOznYaWizsxCLdwChfuCJjnAhFP4LQxDENkAMWj4LbjC4fiiYjuHVvnFRQCuU0jTQ6fDyuDDmHbUARiBOTwyJ/CFPbRBb9nf49LlbK94U9D48AEAHNT/o21awk84YZOCn3D/gze0aOssAA3iQzDUJgNAgIWMwDtEAwr0lgDBQT20g47vuAxqQqiUHTxhhDGUwiKcZCXoZBTwKRV4wdDcAR/cwSTABFpXQipgRHmlwW2UQx4cQykEARudgQpE3F4011wF+KQiEFw7QvcCAKveNduagVPq9V4nzHR14KRKRY5Z+lSEID/P33n/mj6AAyDoAASAgBo8wxn2Rm8E5zKUQQZQQA8UQjd4bhPMYHCeBjx4AhBQgAdUQSw0yzvU+q3nQCGYLbciITGydgTEAt4qX46SQZMU4wCYeT2oegy0+qunA7HpgzCUAQVcgBoQggIkwQkvwxuk/wAFMDsWFqGwQ0Cxx0JUm/d8RIIezEIoyEHYyEEdiM4RMMOznQM3hAM3YEIaGMNJXcz+yYET3LkNpE8GjIEjgKoD2MAsGENkEAM1YEIQOIEKtd81OXr9+WEdCkLWygE3z9YVmIESMECyovOocnpwLVIkAEAHREEQ/PzP74B2AP3PHwELRJwTEH0U7IqilHi5LIc+mNbPAZJvdMU7NFaf0EM6nOH08cwY80XCnsavEzARQ5+ooUOwdWGX+SBm5dr47CAY8rRAymH3Rf0QXh9GnG2frENsUsj0db13wMQxjIFrKEManJscpEEefMQ7UljYGEMeiAmukEMb5c0YlEItUP/BHdxCJYhUK2ACRAlFLYTBCCWTO6H8W2cAHzilHHzxBzhCGlzAAORvNTl4o7ezgbUUpHyaA8jUwS2YGumI/Dq9sLhUDvYerd0MSvQaUxMrrlnEvHNFD96Mi2PfzchDOhQbPVCY8nOFDxZFxw3jy4HTyyEoraFGE27/+3gInnbCnjqCn9aB/+kBH6TBGLDsJwRCJcyGPFwKQCD7J+/fP3V3Svmy5mjcvzS+/tWpdKTgtVLUZj26ZpDgvzF5Cq4rOJJkSZMnUaZUuZJlS5IEXR04AIBmTZs3cdqcyaEPBCpBADiok0eFTAQzcybNiQCAl3/48LmUOpVqVasjCRoDwGL/oLp/lACAFDkyXNCx/9z9qwUgCtqCXvMAmPTvnFoAR9JGvbqX7z96+ujVo4dPX9++hUdCjarXcGPHLfWiS1syDzNitW4hi8LBUdTJxJww+7cu6rFHguoEsRElgw0nCs6kCneMhYIdk5SNhorunzgFXup2fDyc+EmYMpUmzzkzQyBEO3Y4ujLzwFGdAJAqt8nUKdTi38GnzLq169ew/9BBJcjtgAPeUL2ubatu97+4c+uuxfs0PHjEJPVSTMABCSzQwANNUoy//hjkqyN31omQNz02GgmTPDqwxqB1yvknlS8KkocxdM6xxhhliLlFjw4WqUOOVGp5oY7/RnKFpheI/xmowR2tOi477bRjqgMzzOhJph+BTI67pxjj0UkH/9GKK3m8Agskr/grywEsR7OrrbHgkosuu/Zr8smr8PnrHxrPTElBM9uMM7EmoyJmByq8+OKLHWy4S5zEbgGACmoGkmcyk8TJA5kOe3MiAw7OuIWbcMaZBYE0BAEAAoiEk1NOH5PUbiYEEBhBCQcOUADJm1ZVbknvPI1VpfGmrPI8LKPSkkuR5OvSIPvEzO+uvGQdDs7+otKHsTeLbbagqKih8JhjmCklDQgOeOGRYZjxRQUHAEDAFR0XxMfQ3sYgBp3LShkjAwgyAOAD116AoA50QgEgDV+ddRLUUEVVwAEFrP87MjuDW1WyKSb77ZfW8qz8NcugdvXS1zDxI5PYhq0K8MCPQQYZpWQ5jjUqbiopJxxqhsnjBQr6oICKO8c44xhvqXBrQSajGuYKG86opJZQuKCCm1Iyu4YPElrogJg6SiG3ZAb/Bdjqq11dGNapPX2YSvPEevYfXUfila2Lgc1Yv425PgmwNelZc0022647PIJSeeGKMzqZJQ0FqGFmHHTEOWaHO9RJlGc4CaImimIK6uSIHElyIo9IHHAgA9GOtbuxqrEOXfRwte7c8/68thUkdAwViZugysFnHXd4m+Xsc9xxp8P7xlx759MLihux4YEvnjiRAtHwH2XkiPoprxD/UcGGQ86IWsTEdhYpFSfugOADDcs5h6BOAIDoEQDOeHZr46GMKeHR4c+6O9PZHy518wIxSZ6gTPJFUJMypbZh/Q54wisI8eqXwKsQBBSIiNYYuFESanBgB63gAAvIgT2GMUZE1JDDB45Qhzpw4QhyOAIAKlGQidTFHfRTYEtAFz8Zyo9hLwTP/cAyhmLcgoe38IUrEOCAHvKQGOfbQYp6WIwxBEtjBDydAeUWRRtOkSVRccUJ0xAKTITCFa1ARAc+cI1yUCAVURlHBqVWEsaM4xqBK4YvarEDRMwiCkdQHhX3EsMZ7hEnr3IhHqtyv0WwCifIARIi/tEh3/2xZFBE/yAgIanGf4yDGrXoBCLSEIY86EFmtziCHvAhjlTcQQ6uwMQsduYdqByqJOgYwws6k0hiEEM0LYwkS/TIR12Sbn63/FyUyCMPkZSCBUcIgjGPGQRlLhOZxtyBMpt5zBVEjTeLrJ8jD+hLbbKyJIl6SGEw4Qs4uqIWvmClmRQjD3V+DXuuKMUsUqFBbervH+7b5T1r4sd5QklKOmIkVc5lTfZhU4r7hKS53GGohJ6lILeIJyYwUQ7MqIRAGoTQPw4RDmvMYpbvMehL6mlIfO5Snx8NJDCnFKIIrZSlLXXpS0njFoEaj6B0M+kt18mbUpCDiPoIBKfmxJ+Kqo8xlRCHL//O4ItSxLNTBs3lSONX0ptK5WHnUMdVsZpVrW6Vq+pABzq82iHblemaUXzkVPdJkFngaxi30EcnOIemf1zjFv/whSOsQY1OOHGeT4Xq6JbEV7SihCDHAMALdjSMAf6zYTUdrEHTIhtX3MGtxGgqQP9RjGH8oxKzuMYwosbNtIb0fX8NnVQfO6t/GDYDh9DDa2EbW9nOlrax5cNrEbHEKIiEsf1ybGrniY9wcGgjwmzMOcrhDnLEDh2MmqpfTYs11AL3JGlRbHRrYgOPDtSs2aTuR3v7XZdAF7sAm654sWeNQzjiEe1173vhG1/5zjcSjzhEKcLr2+4WFL0HDVBjPAb/XPKWN0nn7a9gD3yS3yaYwQ3WkT0JDD8DHxgfYO3qhTGcYXQwlLsItKmDQZxatYaLVCU28YlRnGIVr5jFLFYAAEC0vhDPuCQLpvGNn/uPVkR4jzmTMY5nbGMgD1mbBPGFE6iQZCUvmclNdvKToRzlKF/hCIggSH6JPFghZ5nLeNTHh7scZo5tWcxlrt860ZxmNa+ZzW1285vTjGUzf5TMc7bznfF84zrnmc999vNj9/xnQQ+a0FQMdKERnWhFT+3Qi3b0oyHNo0ZHmtKVtvReJn1pTW+a0yTJdKdBHepHf1rUpTb1n0l9alWvusypZvWrYa3n/YI51rW2NY1dDeSAAQAAIfkEBQoABgAswQCkAFoAVQAACP8ADQgcSLCgwYMIEypcyDDhv38NI0qcSLHhQ4gVM2rciFCeAXccQ4ocSbKkyZMoU6pcybKly5cwY8qcSbOmzZs4c+rcybOnz59AgwodSrSo0aNIkypdyrSp06dQo0qdSrWq1atYs2rdyrWr169gw4odS7as2bNo0y48cCAs27Zu4XZFwFZgXbAHAACga1eu1LYeIiBAAIACBKx6DewtsSEFAAeGDNmA0JZCBgN+mSYGAAFNkT5BIKQZk4ZPHS6Gxjg4gOApW7p3riDIcCeUHAV0GUDw0qgMXL5LFQDo8eMMAgUHHFSaxEUCBQMIJjiQM4aDA+BH9wIwkGENEjqv+9j/OFBlQ5HjbBUcauRkcNHtr6F/ESWqx5lPXJSYoYAgQZEtuNE1By2GpLAUADYIEgoUUJTBRRqO+KEEcggwoAUEgwEAwiggHKCAUXk5YEEjpWDSSyMNILeXF6X40ANuPZTQIV0UpEFUYnQdkAMUZrwBCCmmUPECA3XRgYQPABwQwgY0DEbXGard5dN2SQIwRiVqOOLBZRR4cEQlY0jiyQVj7MCaAhcwgBlhT/yiBms/UQnCiLNcYQJheekFgAJXmJIMBVTQQddgrBXqhSg3wPlTXkNA0QiRerWVF2Z7zeDJGD4gcYNArbEFAANnHJbZTtshIIEQVQTxlmJ17XXAAJeQ/0KID4fx5akNLCgaFKEO5PHFZmvmRRghpKjZGqt7PiHIsUMlSRgAcqQhLKuDAhAFCE7aRdgNv4yC2ahx5hlFIA7sqedbYXjgHo4AUFFKD1LeeOsiH0B3rpX1tpZkXh0sC26z9nZwyA5UVjkGCdApRph6uTKLVKmFBcKFXoQZEAbCCuslBxUJKxWpvdCeobDFGOc1wBDSOvwwlZQCwIUeDGw3xgeeOpAEFCLQBV9T8OVlgyPPyUEBa0HocYMIQ1Ol3QeHHEEHBDbU4USt/y41qXYQqKGEGldkkNhdVTrVFpWDiQBFDiNbdbWeCnwQAHoIbdcUXOdKyuxbcmOFHVMBAQAh+QQFCgAGACzCANYALAAjAAAI/wANCBxIkOCBggMPIlzIMOEBhQkNHEAwUeBDiRYRNNwokSLCDBoNABgpkGJIjBwXPnxI8eCNEiGA2HjxgQOHEEU8UshQMSXCiyUP0NiwxMaOHmcEGRKzAdEXLoYMUUDQEqJPAw56QDh4oMcOqgcpNJLiAcwtOU6i2pAI1KdGHz98pBnz5cqiKD189FDQI4zADGPmFKFRJ02ZFG03VvSwRouNI07GYApEB8maFxnuAFAA4ICLDS4UpGmUgupVAwoM2BgjsPMLLQg6ZABT544DBQ8Z0LiAgALi070FKuDigIGbGAq8MJh4IIMjPD5AIlAAViSAq25ITYlyBNMjQbZsnf8Y82Ek5yhsfngwQLUqSY4H3dgqc+NFkD4QukwJ8khPDDec5WDGDeyBNdKBVx1wx1YiycHZQxTAwYotgMgxBBQgaHQggiVxpNEYKqAGwA5BHAABFWpA0UgcJihARRq4kbThdSkBoFEULdgIgAhQzJHGESlIQABYX9gw0YwjHWSDFFVZNRICANiwwkM7NKIECAFc1x5FUZCg44wPASDKLz2cpNB1nQFAAhcUICIHBQkc0NmWADigBQOdySljmjec4SMEEKQ2UJIA5ACFGiEmROhIFMiRJ5LvPfTBIrSccaR1D4lghhA0tiaSnHKqcAdXCCZ50HQGULBHLE8ceWCYBoz/IYicUHbEEgDcHYTmrigZQIUjLLTUGpgGHJEHBGmKVJUcHJBqXZKpqnHHHbh5NCiNcxa7iAPDsgWAg91yaMAZ0IjCEkPY2miACpN8wN6GBkQh7K5QAhAGHz1ckBhCe2r0gSDutgRAB1T0ZCsAEOjhl0U+0WsABI/sUCAAT7G30oEvOCKxxaftehAEeUTx7h1OoEYRwh+MoQcF7J3maWfKopbHFZvJkcGuGVyoRocui+Tpp9fJcQYENBvQgQ1XHJKEEkOe1PO1oF6nRSNDZHzGCyZoNMG+T2MLNANQQHEFTyRx9TS/FnkswQMmUZenz2cPapGec4K1Elt3xw03UHPiFGZAAA7lHXenw4qL6bN6N/Re4gEBACH5BAUKAAYALMEA1gAwACIAAAj/AA0IHEiQ4AEDBxIqTAgA4cGCECNGBEDR4cGGFDNiRIBg4EOJIAtmPNiRgosbGS5kWOnBRQSBJkxwRJAwpM2KCDuC2GAA0Jk0eQzt2VACUBhDjV4goPjRJkgANAUqeNIDQAYGKQyFATEmVZQgTsJEMUBBQUanEA9m8OHBAIcMEO7Q+eEDAIcxNCnUuaMBwocoadI4wInWIwAPSNZEocIYDCW6XhC8QIjwSokSDCBodXCAsNOPDHr0YAjggJYdCFhQcVLHBIMDCooUUXAAAufODQt7dPAHEouBAD6cKY0ghSMkPkjSVOAw92eEFBBkOALI1qpOV6LYGBSDC4nciHsw/6DJsOnnjm5IecrDIsMMBWDGZChjQFKHMwI7skiTgLxG58+1kQwhIyWkxQEIdJEBADkIYVsLckChBASleaYbbBzkwQFUBnT0whUGUJQBFlCkIcURR2CRQwDmEQSgTS0gslRnNEkhwg5hfJGDBdHVNAFlIHXk1FJHyFGaQAAoAcUcKRwwAXNS5cHAi0iG6FlNIMkRBUUQnGEIBtEJdFAAwclhkYsVQnCBcw1hCREDi4BwgiM2IKAAR0dWeAUVHYLkACyooESBAx3Q1iJUAIAABRYQOAQcSQCEsQOHEymQyChShHFFItBI0adICF2ghBqNLiUmQhVS8cFB5lV00GAAOP9RSiEvKCTSkQjYEAiFHeF6kAJXUOAmcKVdBMAZdzhAnkQXLdWBHhR0yGqHAEAQhlkTMQRBIp9o0VBUIVG01Ad68FqlW3eciuZSAEjxSyLS6oYoB5h8wCFDLKRLm0HUAlCHHB6ECSRaiHZQiQkcmkoFAMzhxlFnHyzCp6O6ATfuJN8hmUYHCAqpEABcCGJvvBUbthQHgdi7VB2G0kQRAxUExpyQJT8a4lIU5KHyGIaGCIEKVSxKUs1PHQRBIBxAAGJpLexwxx0f5HABR0RLVGFHDggyhxQQcBFIEC0MZuewVRd0UWcXGGCGFjZA4PCnZWdrsRAWTKBQry3GDZyLBigZcCeSnRmUd9UAMtUwqwCSrfdEGFWpeEgBAQAh+QQFCgAGACzCANYALwAiAAAI/wANCBxIsOBABAgMABi48IDBhxAfAlgo8IBDggguJqQYsSNEAAgAdHABIkMHGzZolPAwEcCHDBY9ynRIUQGAJxvIGJBzBhAcA1aA9JBiwJCBixdlFjywEIJAAAcUPMHjwQDCNIbOhDrDoYyBNCYUWE2olODCHj+0OLnjCJEjJEg+KKSQJoOCNIKSXGBwI4whGzHLEuxhwEeQIDs8cOhBh8oZwBDGKJyzoUhGE43KZCx7IGFCB4DkhBSIUAsFCmPG+KBqgEGICGNNOCArWCCFVaSE6bkAAUIMBC3GTHyBxIdoBAoUIOx8VDCHGCqipHkjyUkoTGM8kWpzIAqHhSwy7P+wMZYswrIAAkjq9YaDgwMMEGRYBMGNKR0i64hFiEBQjtoFgaTDKnVUZBUAV5xhgE1HJaFEEGfksYgZUCSwGYBPBVBHFAM5hEAfQMC3whgUGnAYCUIIEUBgGNJkAHkJ2dSBEkOUkUYMFIDw1AF1WRURVB45BIEjHVCEgIJKoJBRTAGItEhnSS0VpVUsEsTBIjCtoIcWdvnoGVM2XOGjQVBNRCZHT4X0AhZYGACTQyyWGQVMzZkVkgejPGEABxDcsAcEQBIkpAFmvKdcnRWFVAegU5IGwBG/JHJFGFzA8ksPgTJkVQJppIEoRgZcUMdoHznQAwRMpTGHFIAaxFRCAaT6wcWnjlLBIa0PURDIF0xlqulACgTywlMdMhWFClCSeZRIiXQybLIyUeAIBY0qcIRTuIKkkCC/qDGmYB1MwsCvGRSYUFILAUkBImB4gCpTAO6gx0YUQaipRRcBEMQiwyrUaEeBcZFGmVNGuRADctzB6IWCwSuQHGBA5ZCn2voIwAk36MFhxRiaZYAeOwh5xatigXQDoSzg23GHjjKwiAndNTcRBzuMIYgSFkzwb8dMMcWBIwrUYQMAFHyQhxwvfDcBbStDFBILe6hhwxhnONHBRkft3LSgSkBRxQ4KQbWZ1k1zhIAIQgjAssdbK9vhhWgu1bbTy8UtqFIBAQAh+QQFCgAGACzBANYAMAAiAAAI/wANCBxIkCAAggcOGFC4UOHBghAjSjRoAAGAhwMvJmSIgOHEjwgFWoRQZAkHDhQAcEgRYaEBBzYoILCIEaTEiwMPXNhQAlAaA3kMNTJQIk2aPgbSTKhY02ZEBCldQtACIYOBDFcaWbFDp6oXQ4dIHOjY1GlDHwKjUKESRA+eHzcQKCgzRoGWPGQ75OETRWZZpwjQntnh5MiRIHiQ0NmBACgLA0cO0big0JCBGArM5lwI4ICUMxgPfBjjQMuZDisQXbzBcwIACEEgJDTr0QCAKQZkUTlwkcIBKhx0nvHBJsiBCUVCiBT4F+JYAxAgIHghBRIpT6nScClzKUaGMwoDr//B48Dj2NmaGfwh5SgKBwgoDoB5ROENqT8GnJhIyMBAkDMBNMZQbSAppMAlydzgkm0G1NHBBDE48JIaOThAwQ5qQIEFWZoVBIADbfUXngEqLOIQAjYIdEYUBkRhBgiZNWcTb/nlcdCIXAhxAhdfjGGBCAEekAEkMXboXGNhgNaYbTkYgEVsE8z0nA1UdAbSRTjxVpZCdzjBGwRjCKKEBK41NNARVjEIUU0KLIkeQgAoMMkTOURyhJQWCfScAXJAwJxzBrRwQ4AXZZASjRGB4KSeFS3JHAD99bfla7EAJccdeeCCip8RKUSBBY1I2FFCOOkZhBZWRhRnFXJ0kIENlYjmogYDBBKUWRCCFCmSR2DsAICja27EQB1RSOmUE3poNJCjVFhVK0IePKHHERU9KxEVcihLEJh5gjSKAVIoZG2nSVXZrUAm2FjkRssNK8gcFBj5px5B/EoQFSw+JdAL/jFqJJYG8LFDRQRF1ekYeWRwnrwMWkRBICQQfMAYC5qZAQt5hDGukY11sAgEB2WQ754AKLoHvzIybEBqDgCAL0EwRaHGsht3iBGuB9wxsAMZgMFHECYoQIFrb6osURR3pOGBE3V40YKfDKVsNEFmCOSEbGpOrZlCSvhmr9ZG8oYo2AxvJLVTAQEAIfkEBQoABgAsvwDWADIAIwAACP8ADQgcSJDggQMGAChcuDChwoIQI0qMiNDAwYMWDyComFDgAQATQ0p8CEDBh48NGQK4CBKkyJceLSIAQGODkBcmOFCA8EIKCAQKFDqgMLMlzIktL4rY8ATIGD2YJjUSs2FPHS9hDElJgfDj0YkZUG6M8eKAA4wp0IjZw6LDC0ON7hzc6PJrTA9I6GgJM2YMmFJrfrAAesBJmQ6HbABAQOFMHboP7YJE4OEHHRtHggTZcQWPDzo3VirQcoBCnzE3GBy4oscG0bovK2pkfKboZDoZFIw50+KAnA4I0WwYCPfFxqMkLwJhBUQsgChHFGa442ONHI0gSkRQwDjIBAQIYH7/dAABwocTZ1iZOhWGRAcgUxiMAQ9gwo8facAjcMB9LkeRCDEgiSmARBFFCzrooMUZNoCxCik6OBHEYiA5EAUYGnU1238hIaTAIKs0RxIAWiTURRsIQDAHBQpkQIINSUBxA30W2VXQDnIYoMBsz5GGUgdmKOGFF3WcoYYSHHAYEWwQaWSAHkEYMNN4Y1yQQRBcyKEEBhRMsFIUzRU1UkGRGWQAA4I4IBNII0BhCBUQLCblRgpQQcFiSg50nEMClSlQeFTkYREADowRZFgKcQRABnKs1BFELcm5EUZMYnSHDQhoIQcLBqgZ1EXH3TCGk0vqKAUHKjnKZHg5QDFEGRuF/6eAngl9lEYHK/n5pwE3/DLKGWAYoEYiJmhUqQEcKIGFRTtOOlBd8+XK5KAXoKGFBzYEgosuL8hZ0FwQXJFGV3UdlCsHVIzY4QE73PGCB49KdFAdjYqJUHjhpXFFV/EaVBoFctyh2rRNCnQHF7lqGOsZqNrb0EAQZAJLEB/tCSBCcgRLl0wGZKAFnl6hpBC+dcCSSHg24ivIEVI+hFAGCPeXKEgIAaACIldkwICNA3V1xw4tUxhGWeGZO+dHYfBxwkV5wqRylFMaQEmcUmbU5wen/YkRzz0TikkHOgLAwceThUeoBEFMcsLIXJtZtQOHkJBQECwsZhsEQkCBhQNyElrcdngcRPKBAWCYsBAEQchxhhRKJADe1m1HNJMJd5AghwIQOCHHEVS80NHjkU90rwEvwGGG5le8hqfZfIb+LcRKKAG2Q+DVmJLfoQe4c+1mJuqV66LXCLxAAQEAIfkEBQoABgAsugDWADYAJgAACP8ADQgcSLAgQQAADCpcyLDhwgMHDiIUiHDiRIoOMzpEgKBiRAMIIBa0qLHkwAMAKDiIeIAjAAQMEB5QgMBAwo4XTToUSaFECSAUKgLwYujDSo8ohebUSbCmSAYlivQ44yjPDQeGDEWps2jRmBZRohxNulTnR5AhD2Sg4wDBhyBRqBiqtKVEjxZBtDRqhAIkWaYFKfTIkBblGSdkARwIA2eDFJQGUkTJwyFhYsA1ffzwscPJC5sAuHxseaCDnAlywrSs+SJQCwiJE5o1AMGH5zN36pz5MsmJDw80a8pZeUfOxAMeGhla+VfnSwQpriR1wCFDlFRIfqxUAGDHDoRfziT/YGDgQJQ0FDCWjMiRo4IDd2y89KjFRo9IlQGokNMSQJENPYAEAQQhlbURZBW5oRpHMx1Q3wEsOBIESmHYdMADRVwgkEizZfCHGzdwkcYYdZhiygcV2ZRBHuWtqIUHPQgRAAIKBKfYWesZwIQtm4ARxQ4tqNDGJolE8YICGXB3hQooUcAGElhAIcJz5Tkk20IIuBHEQC9B9IUJZwBiiw4H5KBEXHLQ0cMTSsAWEmgMGXiSQHeo4BdIAPxoQBmktKGCGlDkcAIFHAEBBGlwMhSRUldWmUGFNU2kgCAZZADJGEFcIEGkNVHhAUQpMoSTQVe2ZMAYn71pgANmKPEEC8eh/zVTGhmUl1Cj5bEEGoMQNWeqCWmYCsALedSAQVvzORXRBSyCapNB/QnmAKO3NmXAFSmolUYfJzgQHGS22vRCHQ2qd5KpVUCzBws2dOCBFDFBa0AHaojgCGIDdWQhqB3tkKpSBEFU0w2FaHEEV7D8QoWpARsAAhRm1OoSRR91tOgRFPT6rHosCQwRBHr0oUYGHBakwANpGKIASHDK1DEAdzDnrLm5IuTAHYKskOtDNJp3CAcs34pQWrTlUVhSVcLZURp79MHFvjgGvJoBHyyyZX+gmXrAEVqgpdSilgHAwTOxpPeSSTVBwIcXAukrU0dhvNAg2FfitEMfYwSYNNoCnb5RR01uK2YAFR0gKtOGISlwRh4MRM1UTQYEEYnEYTtwxUppWcag2I8gRiVg1nZwyJY1c1BHeQyiXhEVgXzgOOiIr5rHGQTZgDppOF1wwxxpNK4q7AZBPkbMNnFhA2kVKeCEGYG2/TrwGwoUxCEfGHDGewAwwMERXIwxRg5DEMArrtCfa0AGlNiQxgUv2FBH+gMeMCPkiZavUEgUMJ9HEDbAdhLR9tNIRETQJpGUbGcIDOBD8nVABQJGYw40QEAAACH5BAUKAAYALFcCFwEBAAEAAAgEAA0EBAAh+QQFCgAGACy6ANcANQAlAAAI/wANCBxIsCAAAAYQElRYsKHDhxAfHhyo8CDDhBEzakRoMeHEiR5BXtRI0mNIkAItokRZcuNJlTBjwsTY0iFDmThzsqy5MGbInipfmuRJ8aXIijp9Ek1pVKfQpiNbzoT6EWlOqVBpGk3Z8WrNnUmDVpWJNanBsF2jRuSIduzWoEt/UmyLMy5Tk2LpdnQJka1Wqlb3ZlTbE+/Yo2QJf5wLl+tJrmIB92VLeCHglRinNkxL1uDblYsbbz7cmWley1Wfbr68U2jUmT7VIhbNOPJZyo1Z4qZsk/NQyGnPOoZdkvdf0phpliZp9bjqwLS/QlbeFPjj1tL3pj4NHDtPt5k71yO161muZq/ky/tGm743a83tJaKPHn817ItH60tkrD+u4PgBAQAh+QQFCgAGACy7ANIAMQAsAAAI/wANCBxIsKDBgQcOHFzIsKFDAwofSpx4UKEDBxApanSokEKYMBASbhxpsGMjQwpEklwpEIEBQxwyslzpskOaiDNXKpQTRGbOjQp3GJKS8udKE41eIMBpVGOQN0WbatzJQKVUiQozUPE5dWZHQ1eWAlXIFKgBDo2cpCwLMaFVgQkneDVAYYwDkQcQ6C3rcmBKCSVAiB2psE6KAyknMEUAgQIHGiWWOJGT5s4YNRtcKBhMMWuagRdKVDlDGgwXKlzGVNlQxMkHCAIV3ICgd6Lb2kFUdAjiBHORHSxIcKDAQC+CC5vJGq/tsC9OBB8W5blDhXiGu2Xd5lWZt2/DpQxoU//YQemWEjMfyIpcqj0vB9idDSx1eWCIkkqYOuXhYmZESrGctWSADbkkEuB33kEwBhZK3ECBQBTIoRhbBs0xyhlvNaRQChk4EcYLChSlgAFhfCDffAH2NYYTGWpowAfnQXLDCSdkkAEDBkBwBUMqITDGVi0y5NINSmARxRmLHCJIHmlQkcchNhwRxAsfeNCDB7UdcIYoQHaFgCHpMcVABh3o8UELL7TgxBh0IOGDSAygEktI3j0UURpcwIVAiHkZsIMTBSHAgA9aHICjFnpgSeFCCl2gRBUOQFCcQXLEhFBCDqQB2w56kKSQBGYo8UYYUaAWxhlhOAFGGC5BMEVI8h3/cMQONwjyYJ22KQDCF3o5kAEJUUYxBih6pFGHJ7Z00ZYBDiQCzRiI0YRAFICWBcFnDCigwyYsinXAGqP0sBdJLikQxogJ7WmAEyQQhAAfJmSUBxI/ZDAuuQPuSB9dcuCokEsc3HHYGGEsguWBhIHxoEh/yleQJKZEkoYW4iJs1gdgZISAphn12QEgydzhgZtR5eQSF5a+EEasfaVgSiNmKACBDzYk95NLGcghEBgZEJTBC2WQYggGASy36EguOVHjGAJB0AIfadwwHAXYbSeVRVykofQYaRzxAbrsXVVQXjWYUYUTK7i719E5pWuBEinMF6TY7k4gN9229Yl3QQEBACH5BAUKAAYALLsA0gA0ACwAAAj/AA0IHEiwoMGBAAAQPHCwocOHDxVCnEiRosKEBhQeUGCAYcWPETMmTIhghxcHCBCAXGnwYsIDAAIYakQBgUeWODNmPACTiqEUCm7mXHkR5oEjeXgOHaoRAIQxjSAcULmU5UUEAFo0KpOy6kqlIhUYcjIBrFeWMM9w4Sm0IsO2OAEcuQPA7MSpVG1WpTDGkIOOD5Xe1EijhAe9RLEG2Rq04VSOBqgy4HCESxo0G0AooAqy7oFDVyZQZWtzJEYQJZ6c6XSHxIcMCC4gXqkyzBmVek0LRODgRR49mIvAhlm3I1yKWF/o4QzTAwUKKpykSXPlSI8ODjwwsMlWMEusFB5B/zDAMSGIDWKuyDHQgfjLlKV1dwaAVcEhFSNNBDmTpkSFzS95hhtWpklkkUD0HQCBWgBQcAQYcgThAH3wzUaQRCN9lCBPAGSghBIe3HFHCxyIBJhIGE010HENuYShTS+UYsYQVHSQYWkFMQRAUA48IRWLLRloEwgGqOFIKmEEleJxNhGowBzQyLFRRS7aJAIUQkzARZMcyscWghyEQYUce4Bgl0NVKgBAFDkAcAV+QXUXAF5dFZlIGGPYwNGXB44EUwZvHEABGO4VKN8OuNDCwkApAVmQixmBoMQQDshhQwYqqECCCies4IEKHHRwRR1ylMIGEoueORGGCmGVAxRDGP8iCBVRBGGrrUfYyscdiDhyxAo++OCEcZ2h6KcDhgRwBQPEXQjAB7mMEkRYGTgyrKpoGjuSAlTc4MWOCoSbEkcUCJJIFe/Z5AAePhyG7UGGkjTHFfEm1EEeL8DEABB/JdgDEj5ENh9GKt0ABRZABLHDrUHYMAaSO4TBBSC2DMIABfRpoQV3xY6k0qtCvHCDDTa8AMQLnwjywQstdNBBDJe8sQkpMfiARA+bOQqvSFjVccScGA3wQB1OxOsGKbaYEkMH7eYcF1ZhqrSZQENAkQad8AEwhS1MpMATfDq3KFIaJYp0xBhVCEFASgXy1FhHdS7VAhcCHWDDGWFAIG6hfmpWdBZMCqTBgAEfpBHGX1N9zXdxZ51owxEZ5OFFBrt1Wa+BZyEAghp3cMEBSQUa23hBKnUABRQ6GNUsQvKNTpACIoAQgLYZuu7QVHMS2LrtdyGIEe8OBQQAIfkEBQoABgAsvADSADMALAAACP8ADQgcSLCgwYMGACBcyLChw4cQIyIEoFChxIsOKWpUeOAAxo8DK260CBIkxYQbPVLg4LFkRJEaDyAQaCgNhJkuM6LcKJABGENeHHQ82DJnwp0eFUBgEciJAwU4CSIoWlTiyZAUcQYxNCaDgaEFZRpA4LXqRwAyESCgeMWQIQoIFBDsGBWBkQ0i1JoUODVhCz11MkBQULVqhjBalpTwMPZsQpkcTIRBxKIj1QMUI4C4weWODQhjO161ekABAAiGGnHh21cmxQMfSmyQQyEmWo0f1zrI0+jK2pmGmY5J4oKwZdGjLwI44siGR4VTcaq4w+UIhLSEsSZ3WNTBnTu1oWP/jlCkyhgqLzSq7Ttyu84dgY4khIr5wI4tG4pAZb2TJEgIHKShh1dqUcTBDmlc4YEHN+n1lWhGQaAEHFHMpBELUchhAwNJhQbAWkadBAAFUOy0FCJacCDXSVMdABpaZmFUkQF3ALLDARmkMUYQDuAmUGkMUAGLIBcwAFpJWQGQYQNKzEFFEAZQcAQDGXwQxBjfjRHFGKgIEgUVe2Qzx4cyrsWBIAYMAQUIcjEwyi93hJHHGDtQQJ8Bxh3QAZIeIcDHCQdQYAgCEBxxhiB7UPBVTJhtBMFNJq01RhhDgTHGGVHQBZZAFDlQ5QtU/PDDTTE29CEALwhCEQRRlCIIZhCs/8SBVy1QYQAmmFQyZxA2rOGDA1/lBgkFEKThxQtqVnGHHGeMocUVTuzwggcZZGCaQhDgkcGmED03hxlCjGEDWg2YEUQH16FkGadLQRDEGkh00JdEM70gChRDADfWFUE0ut56hCngAxJ0UKGFD1FF5BEEoWACAgV0TeWAHl8VCBNaPfzAQgeXSBIARjMF4goHLaJVoHwbUUCCDTsEccQYp1TSSjC2XDIvRDM5gUx6ChDmaA5QSHHHI7p+sQPLLbxhCytPSGJKDI3hbAAHvjjRHwU7cHFGHWpAkYMJ6GanEQqSAHGAAxD711BLjqwGwQU91CHHl1eY0BFLI0Xc6LoKH1EgARaiQDuGkxzcpN5xx3EK46KlMoTACPgyYafiH/ZpwMc/DiVSSTJREIRcmi5+1VVguYeRTKH76KNRazeqOusv9bc57FbxRLtytt/umO4RBQQAIfkEBQoABgAsuQDVADgAJQAACP8ADQgcSLCgwYMIDQAAkLChw4cQCR44ELGiRYEMFzIcOPGiR4QLMQKgaABCBgUICnb86BEAgpchvTQycSClQpYWNS48oNFBizuG0qRIOTIlAwgISOI8qLMmgB1BxtQ5chKpQgQLFZQoccGA0qU6BU5ceKJRIycOSCoFACFKGitcvS4dGJInVgAZolAJEkNBzY4ZEKgIEyUM0rRfcWrkubCDkzEcDqD0yhjEBjJnPCiom/hjXawnvETxwiGjAZhsg1Qp4QKlS4YrFY9kfMFQoygKxI4FQOEGHxsQGEy+ORfj6YUOxoR5kcIqRZ7Jx9hgUPNl7LkU70LgcsYGw6QwKRT/WaJlx+aRpzuznEhUwZU0RzQicI1XzYYiKf8WV6nQZRA5Tmw2FmMkcCGHByAglRJW+3FkAAUS2CAHbgIlpdEJVMjxAXs2adQgQUoYkAMF2VkIAAd5jJFBSC/JJddGxVE0wR5KpHHVXSTU8UVk/S12F2Mw4sTTAXo4gcAYcjDYQRg2dCCZAzckcgMDdy0EQUhBfkTSGGnwJAUsQ4hQSS15nBEFF1EcQckviQShhRZnnLEHLBsyuFRKR9zB1g6o6AKFGWMcYYMNH1zggAIXUHESBRlwwEEibKxhJ05YfXBHB0HUkeYRIKgx1GKxAcBABzaMkYoBPziwHmVsYcJHGi04/+AhBJVc6VdWje6wyCJ4IIHHDhRQQJxFzwm0QydpBFZUTX7tIAdSEASRxhh6rIHHIXXQgUQPSQ1bEUUXKCHEF62E8eNzGimAhRJzXBFFFBDcgIQPUVBHAQIQyJolRBNJYIYSZYzxF0U7jYWABWbs0B9FPVyQlA5d7GDKJRC4WFFSQTwRRB6MdexxSk7cwewBGbQxhQF39GKLJ5dIgthHYAShh6zsifTczXnssBMQtgDTByZvuNHivg+l9EEaeXTgUlI3Mw0kAA4g8gEEK0ACCRCR6YTeRSnVgYl3fq2ltQIZqBAEFWZAkcYNGTD20l0fpWTCLFFg6ZIDJr0QhRx3zFsBxrtUaCHECA7cxXRYF4HbyBwMtBDFGKIVVscdaUTxQgYOqCofSh16SLRDFDmgBBRznCFHGl7swEFaIoVVnVgfsieEEvlqvVG3GE20knrYicUi0x9qaVzwLAUEACH5BAUKAAYALLkA1gA5ACQAAAj/AA0IHEiwoMGDCA0CAJCwocOHEAUCQGDgwIGIGDNKZHgAwYGJFTde1Ehy4MKJHU9G8ULBosCRJUky9HgSQIcjjQyl8JhyAkWKMR8uTHngCIUoeqLs4JAiAAIEC0WUqOAxaMKLTw0sROAlZxQOHGkCoHAEzYYQWa0WBPBxIwcwXK5EOQEVgYIDGRx8MPBFiwEPEKqqJUgRggMKQcCkeeHgKc+FIDZYGePh51OYajkuDGPoDhcKWgEooLiQQ5QqJVwoEJgV82ADCjrIMaRTK1SJDDzMqdMBAoPRLl8bADrciZwoGRQ4gA31wAsIXMY44fBxpOuSbUMaAG3gSAaLVT8C/xCyYcsOCG0vBs+8UOCHMGMKwgTwIsyTEh7uChaudTsEG1eccQJDExGngA1jgHEBTNcJBwIUVXyRAUMDAXVgGmBAoFV2/BWEgBoGDLEaUEA5sIIeNoDG1nodsmaADXM4sMMhFLRnAANX5FEJdy6yBVSD2FGUwS2FHNFBH6VQEYYed3xSiii/CPLCBxQoAMBvFVmnVkoAZBDKKL9UkoYUZ8ABRSNBtIDADXv0cMYcYCg5yh4fddCDhkA6BJ5AEARByRgUtGBRRyIokQNPl20FgQejjOJBEGv88AJwQcl4hytHnNTRUwpMUEcUFSlgpQEOHKAAB3QgscYdNvgAAaURXf+knANR5PGBIFGYeplL1clBBUE3/IBHKQb04EMPDDi2X0QIYKCEHDYcEMUZE1EY2lBspVFDDmekkaoP34kq7mUkKaCEGRcgEEQe1YnH0UUoPQCFEmVolZ9FgzBxwSCXBJYnQhflIagNfYzan0UEgoeAA0IYopUDU0xxRRimVDOKAaR8p9FFFFyB1yIThnfwSCs+FZ0BKdhiSh56eOIGBRB8ZyOzLwYBgCMfbKpdSK3VZMAiN0AAiSe8PcZWSQCM8UIeQXTEml2J2ngABBRkkIEUUGABBFgoDWptrAZAcEceVxA4c0UOcPBCFIrJUYccYVwhhRIi8CReUB+R8InHdkFaAMEHQYSRxx1jfBFFFFxQcQMHd53kE7xfk/SRBGaIMgYViJ/x9hc7dECBA42f5CJP/FEk7xBOrPB5TZFbNBrpLnZ4AAUTHJ1lotmZzXqLA320q8+8yxR8TAEBACH5BAUKAAYALLkA1gA7ACQAAAj/AA0IHEiwoMGDCBMaOKCwocOHCQ9IhEixokIABhRIPACAocWPDg8gMDDSgAMuURQsBMnyYEmBGTykaWRoAoCXDD22hFgSwAsuOw6FOWLCAAWCHga+3IkQwUSBDo4YaFSmhcSRTg1kiKLGgAuVB1QyVYjgxRgDY3bYmMBw5IsLOwxwCQNzrEGRGA20eHHkrAoGOgfmMCBGSwYDHVfaVcrRAIM0Bgzt0LhSrIIXX+YYEFFS5VKmgTMcOSRVQ4CnAincEOQkw82eiwUyzPvhzhUbGq+qhBDChhwtNyBgBHAzduLGFI6cObKCOMaXCJYYKALks2y7gT/QpcLgOkEGXrh4/3U8Mm/slRcqtAiTZoXHjQM5OJETxIECBgpGBo6tUogBLDYJhBVGB3BARR0v6IfAgk7tZxcCADjQSA6G2LAQR3lBUEcdri3E4IIknTcQAArUkcgZGfSRRlgptOCEI5WIgsoY9yEmUEcYxjabAWnogQo0dVzhSClodDLJGGPYoAYqdPQ1hxxcqCHKBVpBOBZeAkEWxRlPQCCSEgbkAGFHC4qEHwVBjEKLDT4g8YKDD10VgI0QqOCIHl6CuNAFQqShgHkj3uTDD47IQUeVjVmkEwRBhOEIHxjlB2FbCjhRx5gYeeDBC/Ot8UMKEDrlnEUqgZBDFHmYwMId3TWI6U0ABP9RhwMMQPACEmxo8QIFCmSAYUfEKWqSElBIgYADi1ygE3HwJSYFFEPI4UQPPhCXgQMHOOAGB8CCxJEaQiDAACLNfebcUxJMFYCoBwBhyiWLeGKLjYBCxBECrAKQRxDd6pSTbCJRUEcLFGxyiRRjmMJKFYDEEcOOFo2kwhEA/FZciDfSuywFizhBiiQq2DBHSgsG660BUXTwRR3DiUTSgmKNSlAGaoBwRB64tYUaSBDIcaDLCzkns7gZUJBBBo0KYgYUR+i3LJwOjcRBH3WI9R0EH9hABXty1IEWFUcE8YIHItxo5VgAMFBkBljb8MUdd8ghVxRUcEGFDR1gK5F5E9U72xJGFkBhiAF5yDFGECRQAIF9QiuV33V66iiBEhT8etBV5XV7o98POlVm5sFyLmJDv4o+OkWmn05RQAAAIfkEBQoABgAsuQDWADsAIwAACP8ADQgcSLCgwYMIExoAoLChw4cJATBEsBCixYsIDxyoyLEjxo8GERyYuDCDgpEMGYJcaWBjS4IAHAQxFOQkgAMIJHpk2dClwCAcHERxJOdKIxsGThpAIFInz4wLnRrI8EVgmCAvIBygoEAgABAlQjCVSPEpQZwHuipQcQYMGANOglDUqZEClzBVNoToavZszp+Grsi58yID04EMIZQoMubODsMQGJTtO/YmByqGDNhoirglhyBpNkwd+bKvwJwSIazIEyaFAwo5c04QyOEFojsZIFBwQLf0SrSoDUAYk8ZJBo2TAUAoUiQNlSAUNLqUylJjVAAKbMihssPBxsOkT5z/2VAiBWqfUfsqb5HhCpcxxyeiBsAgChg5HTx0EOnbtFcDQ0AhxA4QkHWYRCcEMcZUEjlFnX84YaGEFGrNZ8AK7xnQAkoNPuifVWNMcMaCZGUQRiljBBFDIbpIwcIHHHCQwRNzMNCfWRTZcMgBVYziiiBCYCJIHY6M4UQUN+SxRw83REEFF1/EkgsHaKlHEQR6fLDDKLTQYYYZanzA4U0adSiRB0/44IEC/PGEk0CHLMIFFRTsJ4EBO3x3GJtRofZCGHgY4MNhGDGEUwAbKSdHJSwwgFJXCBwSBABMoSQQBCa8IEcoSLAxBlJL/eYVBzaEgsl/Nwl0gAOIkKBSdkF4/4EHG4bk4cgPPSjA5mQXUcpQDlyk8YIjEPx3FgAZNDIECHxgcoQJQfzww2OUQgBBpR+lFIASUOR5RwtJoTcQTtxSwYGjOGXQAQSSvKGFKaQUKy5ENyGQRA4BfDFGoiqNG1UCalBRJhCSuKEFJLYAE8klg6Cb7UIOhIEAC32QZlCHh+nxgkZT2PJKGmOU4R2f/fY60mUMHKKVQRvp2aADgoBwQhienCFmmdKt5GseL2hG6WldcTgQAx3cYEOAjXhwXk6W/oZdGnmMYahvClCQQQbiObLIGTu8kMMQA6A1Zkog+ZpBIHX4pEAGL0RxRh11nHFFEC104N1IaVVZ8lM4VUORSoxX3GHAGVFEYcARHZjZIE5NeVjdSCBAgcUbc0Rxwm7BnSaSdNKltDffTF2gxAW8qsp4h6g6DmFLb35oVoOu9xUQACH5BAUKAAYALLkA1wA5ACMAAAj/AA0IHEiwoMGDCBMCWJiwocOHDRcCMDARosWLBRkypLgRo0eEEw9UNHBApESNIz86PJBRpAEEMBEsNHmSY0qVBlkiOAjhpAIHLCUKPHkTJ8mdCoQeUJAhiiEqUaKOSUPhJcmSNDt+xMoxgyEvBpx8SaMnjyFDUqioTWOIwgGYb0vWVPl2J4ADHZywHfOCAwQHAhFULYlAwQS5M20WfciVYgYbfM58oPBX5sSQiAFE6MEAAgcONJYw2LnV8sQgeqIccXtXYFIANiAgcADhhIE7kDZsyJMGzJUSG1q8xYgYQoszY46okLjTtIMqG9DICXPFyZEjQETQoHzXQwWYHg88/xgi5cuZDlmHAlARZg+lKkUeKEBA8yjLq3GJT1ACJYgCkQDmccYNUR1SSim/JKLAgvRhRRhcLNV1n0UscZEGUDOZoMcvvGByhgoZjBbECjE5CB5pLzmI0U47XNGBFAdwcEUptfABQgcSOQgAfTIVZhJFBkDA0oIlfUQFGHtAgYUefDGQY0zzmbZQDz50oIINLBxBBxJ02OCDD7JN2NBwFwQxRy5mmHHYQjHBpZ4DGbyQhhw/IHHGEVH1oAUSWvRQZwYkQbQjAGPcIYcUECB312s2QXDDGV9QcYYcNjDQgQ3zufQSTBRw8NBcDHxyB2EG5EEFkHHeQEUadYSxQwdOzv9UIgAOxADBBYSQkkGREU1EHwBnOKJTg1go4UQYh9xxQwYOvMbmayEe4GiurPSxCikpNAYSTWCUIhJ9VmEAxRUZNAighAspEAQpl2CCyA1urBLDAQzsGqhDACgAghCVvPDWSApQEMQdSP0nVJB6nfGGLYNAYHCDHgHQARRQAOJAawI96EQevwLAAAU2yHGGE07s4IBgDHDVGq+M5StEI0dcJRNHVlExBAhcUOHEGWBwkClhRKl011JXnJGTZQp0QAUUZpTxQcozkRrSwRHPXMkZS2UsUQePInJCDiA82ZhGQ310l0wzXmyZARS8sMgYLXAw9I4N3jWXUUPJdYgeA0FUEEQaYwSBI5t1UZTVYjjZfUAoLyDwghxcXPGCUOASdDfeA820owWN9DCGZDODp1jQiCcOQAAAQMDfERPCRDXmFiVmQAsn6AT77QNFiPvuV/GOd0AAACH5BAUKAAYALLsA2gAzACEAAAj/AA0IHEiwoMGDBwEQBKAQocOHCQ02ZMhQYEOIGA8iMICgYsGLBiiCzAjxAAIEB0KeRCmypUuRJAmaDNmQQ5oUAVoeeMlzZMmOFSHYiGLIkIEwVIJwUGAR5caWA30aPEA1JUUIH17keXSEQwYOBjh8aHFmEqIjXgwx4Fi1J0YABxjc0HMmSteKO082RJDhhqFGdTj0hKkRBAgVX+pciWIDgkcDCijYKFEiBgWgABDYEEXXho0PVGlSPJjyAhQogI4ogOlgx5gzVK6kKVEkTZAoY8KoeLHn154DeRqFUcCScMGUEArloKrgQ5DbafKMCULi8oEJJqPIOUMpkqNQG0pM/zhqY7xC4zIlVBkWJcgXOXrS2MjgADNFCgo6xIrVg7iBC7S98QJxKe002nEGOKAEFKEoZQIFosEEAQQq/LDGHXTMwUCBAEygQAZe6FGCECgZKJUBB4BQiBQKmEgRAgy08MUdYdzxyQ8+cBEFF2nckUcaBviwIQA2hEcVVAcFcAoXFDmQwQte1JEGGEfIwUsiSmWAHUMOdLADFWv8cAcXHHjwxAXFnYfQAVgk4UQaixkghxMZwGiAFNDsUSIALRq4EwMUHGDDHUj8ANeRDwEAwYJp7MCBAyJ11IEgQUCwUwoZmMRABhcAQYgbH2SQghs+9JcmQg1JkYoTBiLQIkMU6P9hKQUtXEGKKWmckUYfh0BiyyqL9MGKLXGc5OJIFx1wBgd5MEmTAhCcUMkVeYTCxw0vmELIBV45NUOfKDDhgFUVSUWRAoswAIEaI9ggRRpjINJJFC94AMFGHPWEkkUhJRrSThAsspEFUKRxhBRYqCGYR3uWWBWiAu1EE6qicXAHn4bkIAACOUAhRAAEHutSTAsxtNMKVwDgxRwbObGIEATsSRhUj5F0Xkc7nGCAIBtFR5zMUaFHskU3A0BFBnccYcAiTGaWUr8wnUgyXkx9EcUZLRzywqFEjyb10FCbBMAdmOiRB1j41gw2ROfBxWcpxJzRYr9rD30eVQgIAcUZodUivTZFJiEgghICoPi033+bFMAE+yLuN14mHe444n1PTnJAAAAh+QQFCgAGACy9ANwALAAgAAAI/wANCBxIsKBBAwcOKlzI0CCAhwgPPITYsGJDCB8kQjzA0QAAiw0PICB4gEOaRldWpNC4EcHEjyAPUjCwY9GdMIZiXBGEKU8QEjbkUEDg0qNHmBZHOukT5kgLBghAEFHQkUOQKHIaNVKhUWBChRwdSJjJJY0TGyYcQOhAJU2JDYKisIDw8ICDFTGipPFyoQMFAF0LfhRioEoaAxRsgOFCJUydLylAhMigRc6VMWdsKMhwAYCCI3MajVGAILBAChfGFDIjZ8eYPGEMkIDgEgDRkR4ZcNhxJs9bG3UNiQoiEmZCDkrMlFqEaUcGCCwnUvDhY+DLAxOKlODihAKHULSk2P82mpCCEiUTMhzhKNEBhw4qqDD/sWaSkw4c6AJ+iACCjUeY2PAEBRxNZABRb2RwwAdRUHBEGnnI8QUXR7yQgQIePPdCFG3lQQULPkAwxyhntJBCERtUQJpRCD2gRBpjPIKIE1EcwQKB14nEkn9OaIEEHrqoomBURVxAmkQIISAEFFVk8MIRL+03UQcOROcRAj2scUYaaVDhgQcMQFWagQcoIMUOB7QApYEGMNBBC2WYQsgNO9T5whSSACIUAhN04MQaSHhAWm3GiZQHRjsA0IENRxzBhRyI8JEGKW7YMMYYX+ghjC2e1MHFFbYMckAPPlw4JlJJnpdHKo+ksYMKL3zj8JdItL20wiNlqNXBC6QMcuCgpg2kwBBKnMFFgdcRxZ9AY8jxlVcQMMDRSAmhOhACKczxghMR3VZgkgKlcYdX17LJpkEcdQkloTl69kUYz5IU00hjNGVblB89ZIEZY7AX00JH2CtQXUdBEIQSUKyE278HtXDGussCwIATelg4FMMHAWZABousO5Jnbe1QZbwYE6QxBK0k+pEDlx3BgEClkVyyUYClYsMBEFzxMG4LzyzYRAfsEYUNaUTBs8w+D7zfBVCYEURpBybd0EQBDJFDACJJPS8DVCGttUJ1ef01WGM3FBAAIfkEBQoABgAsVwIXAQEAAQAACAQADQQEACH5BAUKAAYALL0A2wArACEAAAj/AA0IHEiwoMGDBg8cQMiQ4UIDACIeQLBQoUWBEx82bPgQgUeCFAl2AKHQgMaNBCMKnEARgEAIHFyUeLIjyA4mJTZ4CMLh5MaILhFALFKkBxUvY84AKrLhiZZJhuTAEbOnURAFPlMCHTgx4pGcMTp0UJHhQIYbNwQZGhMjg1snFEKijIgg4gtHYzxcOOAyiqE8WmxyiEEBq0KPQg0CBaowIoU6d1Y0RvBCjiFDL+oCQKDA40SKfBlKBKpgjKAgfAE44ALmTAYHDhRsBm2x8UKXWmcDYCDBhhwwWEWA6LHmCEQAhy0iR357sUCXi4HmgGIGkBxKoqizQaKFBIOBiWtr/+aL4MJA6BEvgHgBypAZUb4wXTnRCAQEDxl2TLpiwIEBBSZZZFhEavzyxAEASuSAElDscYYTQGSAFXKCdOaZAy9wEcYZPvRAkWYRcWAaKj0YINRyGewhQgDJ0VWGHAtxllh/dCDxww4OHECBGlScQUULqoW2nARDOKHABzbsEEZSZ0BCCitXcKBSQT2kEAYXOyDySyIUpIabSaoxWEceaZyhRRRX2PDCFKuUQUUadYQRhBuEQMBXYx+EsgYSHrZUEkEgYOEEaIzxNYaUBzAAAQim2JKGE0Gs6YYjILDwQwaeTTlQAApocEhPdYFWFxVl3XkABChkYIINpdhiSpcPNer2JUgHfPAIA5sRxMAVEDBXUUgtEHMKCpmCOWtKdR1xx2ajZTDGc7h1dYQgHlDA1W0N0QWAHGFMZKIBNtzRFUSNhZHHdybGOtdxAOhhg4lCRZFGrqkpcMYZQs2I0nnkAkDBIdbyxYUNn0UkQiNnVLSvYscJ9QIiEBmQBgXMAmCDGUoEYNLCDANVFxdymJhwRBDkkccDEmDFcce+GpCHEwCM4RIVggzaWVYrQzsaA4LUxEIfaUBgklw5K7YYXxxggQUkLcRYNELoOQeBEkrYqfLTokVnkgQXfIR11s4F+OfXP806Ntmiob1vQAAAIfkEBQoABgAsvQDRACsAKgAACP8ADQgcSLCgwYMHDipcyNBgwoYQIx5EoODAQ4kYCyZ0UKSIRYsZQxpAYMDDhhIOBh5AAFJkQ5ZXqmQ4wCEKh5UkXS58mCKQApJOGpVBwFLnTgQiSmiheMBGIwpFjS5EUGQDCKIH5ARZKVUhyQ50Lvw8wKVRiqhdCZJU8Ogm1zSNYuRMKxDAwzRRAAxEYChGgIEAAku1a+BCETQsEwKwESRnYMF6XeplSWNDEawIbMiBeuCxZ8kjAUDo04MBUQAUDBmiYNHzZ4yCOwMQxAKrAgBUGp2o6Pp1xMiy9UQxoCD0jUOJZff+HdkuAgBnwiQ8ULHDIdYkKT7GeVGh3dYrOx//uSO7s4JFKgxYJCl4JFeFnVcGfh54gBDkEHx4QCDhkQ0A9HWnXksIhedARQx4cAUWBiSiRx9IrKEGFFhM154B39XVmUM/oaaEEpC0UkolZ+RgAAcZeNABImZIMd1c6mFYl2+eMSCIGUq8kEF2ySWkhRNjeHFbbDidBsBtMg504BFp2JCAewakAIR5FbEgnQJHjKFFDw6QpFxggqACwnsGdKAEGbXIEcYYZ6QRhhSm2PIGawJxgZ0BDviABB2PrXDDB2dQkQgqL4TW2QlKmOGEDYwyasIFbRByhRxn7HDFHSlUxJIHPqSRxkyj5DLHDVBBQJiMASAQxhUYPgScXQ68/4CJJ6RswoBd372ASB6wJBKjQN3Fd0AdV4Tn3kfP7bCDDqu4sYgcHbyRwkqLpIKED16SCRiGAByyA4CACaZAGBAAy8AHgJCySiKuvMDADz2gdRCAADiwSAuGBpZQEDbIp9gEXUTySyJryYsQvR04QqdyDuSR0mTPdZDHGDZAUBSBLwGgK5Lg7tACuI85sYgNA/b4W2hR5PEdABnUUdxzFEhwxx0vCwhbaGGcEdoRJGOYgQFQOAFsV7gCcMcRqEkHgApjjCHEEH+l9Rh9gthwBWNjUNHvhnTVxS0FZpiRhhMPh2fzYBgiUIMSE+gFbtfzUvccjHAb5FrdDPmGt3eRdQIdEAAh+QQFCgAGACy+ANEAKgAoAAAI/wANCBxIsKDBgQgOHABwsKHDhwYOGAhQpEhCiQMVYoTIkSCDDSUUMkwYUWPHkwcYnMkgEoBLABo3nnQokUIZBAkBvBhDQeJChjMhInBRBadLQ41eIDDgMujDpRlAQjiAQKehCS+BOj2YkAEaA0apfgGQc6vDpSyChAVgw5AJkmYbVo2igirQMAZsKFga1+DCDHeqMlVIwZACiVr7RgRAocQSnAsTUmlBVfFBDyAnCJQIoVRPhZYLRpHDYCAABV4MpTgssy9GQVMHnzaQhoKC0ALJ0qbC0GXVHYbAgrasm8OdxTABQEBEQffwuDAXLtpxWqICAGd4l8QYPfHBkTjBMv91MgYxU52Bmk782bpg8spaGQuBcmSFAQ87VORoBELh0oUEhWcQUFUlIAIHNpwhSCmFmJGIK2wgcUgdZkCRAUEwZcTUgKddhAUUiYBiQB5HcHACBxT0sEYeL1Rh20bq+dSeaQzccYYFLyoUwFL/reBLHTJuiFiGyeXGFAMgRMGiAnudp8BUCDAJQBhUXNEHSwBmxCMDQy4GAhRmADIabWmMAcYll0CQ2w6HSLRDID54wKUBDHywkJqjfMbQQgPUYIiJAtlgQxBSXEJKHU54AEEeDpwGwAdI/ACBS0/oIkgadYgySgZEwoRTGldgKNCTt8mRyh1dADGqDXSkcQAHiYzhQsWW8Z23UCA26MZXSQAEIcgLttjyAWtTloIIC3O+NOBiHAjCmpYAZICIAwZMoQMVabzRBiWjlLKGDxk+NBIAR9xB1p69CZLrfwZAQIgtzmhyww/geudeRGABcEcQlRnlBV67GkABJIQowKUD19n7XVXL9cRXB44clhCTRyByBIwKO3SuCnwgtIgKGypQQyNzlLYhvk6dCwYXDIUBRkQUqLCHAUpo2BdQADjyAgt3OLDDGGcEQcEFF8xoVmUcYIEFJFJcoUJTRhNX81sSQYbbQQdIAMJFgl0tV0tePxX1VgEBACH5BAUKAAYALMAAzwAoACoAAAj/AA0IHEiwoEEDCA4qXMhw4AGHDSNKvEADAoKHDjFKjJiwyAYRChCIFIgRwMaGBw6AKJEhpAEAMEuaPKnwAAIgTkQiaGEi5QGYMGkatGngQJ8ORTM0MjQhYVChQy+G2ACC5JgyAX7OhFrQp4sSIBQoOBCkxUiuBzFe2ZEwZRgpNp+idQiAQ52LDzMYajQh5da5RQG8CAIAAYADCgxFuSh37k8PavoaNuAgD97GXH8qKLGBwmGBNvgUfgmYZOEqLhg8BEDB0BgHJjFD/dlBy8XAeg1Y1Fo64ZUMcR+aSOMAb2mEdZ0cNvkzygmbq48bGNMh6M8MaYjG/hsxJUnDGYJA/xcIIIoTxA8nD+Te9SV6kxKqcCjYQU5CAwEQklyftq2Dhwd0sIMSBqRhoA89HKGEEgq4pxB7Mx1wgRJSyBHKLZ00YkAfeeBhwBovGDBCg+RtFJ0QBiSxgw0cZJCBDhRAAEEGPchChU4QoSRQBoIcccFYpHlnEwIvINKHDVrdl9CSJTokghKNUMEABRQ4IKMBQGRAEgWPOEDBIWBAAOR6Jl0AwUzMCQGFGlykccYVUVBxxBm22AKcAXI4QZ4ca/iggEkO9ECBAmPkkshozAVwQ3YxOSCWDn+8cQYVYSxywG4Z/GAABfdBI4oWaYwyR1wvAeWmfuT5VVcpeRBiCgTu6dlpwAuOFKJFqu29xEd1P613URpRMLAJKYBwwEBIjmxoEUI+aTSQYSog8lSvBtiQB0wapMABGJtIIsgvbPzAgEjs0WXYGGGMdp8CiyBV4kO9ILIHguM6axBzCC3yQWEzpUGFQIwZoEIeV8CEQEj2PmgYB4uM1SAJiyT0JwMfUJFHdaN5txG/XKSbECLuGpCBElDApRG1J63miA0CXXEpB3LUMYRuRDmWUAd32NCHCWH4q0JIDkhnMABDmNFIEE6QSJp0+1WgBAjUalXuXAcHXVjCTDuYdUNToxUQACH5BAUKAAYALMEAzwAnACoAAAj/AA0IHEiwYMEDBg4gNMiwocOBABQaAIDgocWLByiK8IBg4cSIHi86pAigxYYSCSUCWCny4koAEIr0MFBR4MuWFisC2HElIUwGAFLibLgSAQAGfVTQhCBlDIWOQ4kiRFBkAwiBEAwZcgA1qkMEVkpkQKDggKEXNL2OzJCmrMADekxIVGvQqJYPbwGcMOAEQU26eQ3EkDPxQMcTjWwo+As44YMST2p2fNG3K2CKB1wYoLH47ZgUCEOqBamlR+eMURqleNt4IAuBkg+kMUSBdeOgYSB0hWmgtlHRaoNEIVjTBuyEgI0ySAOBpmGBZ4LO9WrYqAInH16y/GIi4sAAAy0T/1Ro9GNQBTWgnAlio4eHDCOEuDUcdKJzg9INMJDwgQSXPKX4YUAkkYSCBBKGQGGfeR6xdJABCgAgARRmlFLKI3kYUkYQH2Tggw+OGBKAQqFlZBNy+BmQwR5Y5DDBbyRmdAAFrTiigoOslVffeBdQ0UcKEbIEUwAAKJDQHSw4oMcYNsnoYGh5XQAFHIuMIUcaUWR5BSmSEAmADYGQeMaHDDh3AAMKJDIKA34NRIESWKDwwgk2hAEGGG+YQogXX2RwR3MdOfADGxlAeMATqNwhyigQIBTUSgcAcoSjkB4AgQIU2FAJJYMMApsDQeThgAEcJBKLabo9iZADh3AQkV9+1dtHwY+mGFAGAmweoAVhlHgwH2NFGdACIva95GgeQRyQAgod1GFKGw4kwgYSM3VU044fGTXGF2l1G8QdKSnEgS0GnJHLKD54YFuKUyHSQUTeIXAIawFU5EQlY9jQAVcikfQBH2UtFAZB5zG5AolGeWcRSVeMQRGpjiwWlAQ1GDIGlBC19KgjNqyURwtY7YCFATkM5GhU3lEQZhByAJDBGHq8kIIE9eKIMklOqKHGGXJwYYIDGLeG3AUGQBFFBvV1NN1tBkAgRAMTGGai0AeR1Rm2VJvcLXBZZ5x1QAAAIfkEBQoABgAswQDPACsAKQAACP8ADQgcSLBgQQAIARhcyLBhQ4QCFTqcSHGgQoQHIBLUWLHjRQAZE3YcuRBkyAMkUxqAKPKAy5UcVTI8gMAlTQAITMaUWTIhQgguPOjkaRDlSgYhQHSgkmbLhiUIDBxQEJWowJcIq2wQQ+dKBg8uIFCFYGiMg5dWD7Q48yQoVQMIFHh4YUOOIS8U0KpEQIPGnTAUEDCgWjWDoUZOHCiuKRPlhQ0bVEhFUDUjBUFXXgisabSxgSVPptrM2WJMHhtRceqVWVOLZJgQozR6QTmk1c0GznyAK9LAmCgTctq+LVAOB94YHXhRQBPrbZQU7kBAGDWqEyc4pfYmGjVDHQQeOIT/PCGHgknKAztPROkyNUIhSqQgWcMlyhklUDLQpBxg5WT/E3GGkgJO6AEHFHP44MMipZyBxRDMZQeSVM1JNNNVICjxxCGLRMFBXiZlcEYlTnCgAAVSHUSQTekZBRIWBuQwQUbtucTcETHYcAcdSHRggAITwlVdVQVlJNAYVzCQ00VKwmUABXIwAJIPP+Dho4QWwbVHIg6k+KMFanChgAGKMdDBEaQMEpINQdx0QAYqBCJHTgI9UUUHYORRByq6eODkkwaY8UUYV1xBBRcGlGHAKnPIYUIdDjDAAIUgecGHFj148AstYRyxQwYtgFAZAAqI0IgNN533AQQHfBDJIlRs+mLKdCJlgAQScszxhE8sXmUZIhlslFBOR3CQgSSm/LVDG1PkQAUePShpU43C5vTCIjSehxIHZyCkgAMnamFKL9DAElhcqTmEEwBR3EETpQqFoRmQIOE0hSyJ9NDZdgytm0YU60qEQBgRCXnAtR0khJKFE9WLQCAqZJfTFVFYhBAXfGSgGkzqNZwRBItwIJECaZgnkAIPzJFGTsJVRtK6Nuih3QcAR3XAGVBAmCpLjQFwRR0r3XGcAy+MIYcSFzBGnE5zeKWFAjb0EYR50xIXUUZjYjHGGVNT56XV/tUkAhRVMDfZTmC7dEEO/REJdk9xvft2vyDVxDBxAQEAIfkEBQoABgAswQDRACsAJwAACP8ADQgcSLCgQYEADipcyFBhwoYQIxIE8PChxIsLKVLEyFGjRgMJN3KMaHHgR5EjBSI4sBKkS48HPMpMaeDAAYIIctpEQDGmTZkfMR640IMBQo0xAdxMChRlRJZFNixxwqLqBxFVjij46OCnx5Qgitx40cNJkDIbNjTKsyjSIkOGKK50epGlEwdzKT7YMinPCwhAxjRK07XnyJsIqFA4oMDASgRpKDi5E6KCwCt6nLwI0PJwFAobb54IRLFDWg03UzQypMDmYQNcOACggKCmDTAAFCC4ISJhzh0xWqfkycVADyQQGlPJYPPmQ5u6b9Z03RAxyC931iDBk+IBlBwAeLL//Di3ZvWKcsGYMYNokSNQlqCA0I00KUKQHhI9MX8wfAYFWoSCiRIj0GYTJLXE8NNRICW0FQCCQLOHQNJR6JgBIECxhx4vMDZBTFsdIUcea/hgmEUUMZCGHDdAcB+D4Q0BhQgLKjAFBSCdwYEDP/gAwXg9AeCBFItQgZRCCJxhwAQUcMDBB0EAYospDBwQBQcHeKBFa0zxlAg0VDDm1UZKGSCCEokYksYYYVzhhA0xtOHEGWNQ8SMCdPjAJWMAcEBJFRAoBZRLBwhhgBormNCaUs0xBkYqkgxigA9I7JATSBlMgqWgg1IYQAqGBCHXpTsB0MEipJiigAc+tATBE32w3JBbTBPRdYADgTjBX6MAUHEFELYoKBcAN0jIZ1MlTSRHECwRWtsYJzBhSxtL8ZRHIjc4FxRDIS0rKEFyKDDBDMLxFEYYj20LkXNpHBEerQacUZtPIB0xx07OVQgRp3K4y5MBEFyBUG0G2NCHdEoRjBGnaTgh6AtUqASAAyNgkYJJISW7b1INg9RwSBScAYUSCdM0UVJ3nHFAHzYckIETgrCQQgYmG5RQcymnkQIVeaxQocYmJ6wUGlBUYcOiCtdsc00I5ADFBQTrqzRBTLE0AX9TO0ShfVnvS9fUAQEAIfkEBQoABgAswADXACkAIgAACP8ADQgcSBCAQQAEEyJMyLChQ4MCD0pciBCiw4sKDw48IBABR40YQ0aUeOCAwZInB0oUiXGiAwoHENjgAADBSgMWWRbUiGDEhgoODBlyYBIlyIU6K9YE4ABNCTpBDFxJs+NgSQQKburEaVLBoh4ItAxLoYDCmDEqMijAeTInSwBdvxpgcIcLnxMGM9wxNMbAjScQkG5FqODREQAK8oxBGGlHggMUDKVRUAhaCwMI2AomGBMzxwx5ghzosMYHA5sOxGwAYeDFFQRBFkFAebEmzoEWoIgCVQoTkjU1S4Yo4QFBhkZOEDihYOAjRMEcGYg4IaVTISVqxpwIcKdSEMQGMrj/aZ6BrwIvYUpqjshZBBQzfXY48KiByYE8O/SsgLukyFrjGgTgiC4eGAVScw6wcMcIIDjAgQ1OSLGKLSjk8UEGjyhwQAkbZBCTRwbkMcpjHLGHmQEgQNHIHY50EggXKpBwAhCbnJHHCwAcgUgHF0SQWWcZnOHRRANl5l4SKWQwn4EHhFGLBwghgQQECGSWGQBg4GTTbQwpwMEZQQxpG2YGAULKDC/g0QMDGprkwBk9xFTRRSYhcgSZcNUUQAus2OIDHhCcltlaakBTRZ6bFbTlHS+caJIbpvSBCSFI+KCAAh4dsBYiorDAJUsKVLLfQISYgsMcHvTgAaYe2dToVYk6qpRZBotkcOIEEEBwhqAg2sSAIxkE15xOsz5iq0AH6ErUjyDWEdWWW3VkgA2HODCQsiXGxMAYc5gYrbQ26MEATtgiZBIGUIAA4rcEZebEIZlhe2UYahhAQWbsJpQZFWkAoGtgAGSgxxgaQptvu1hidkZWVDxymVsHI7tQGHJoEcMdu3obMWcRqYHdYcJu7BBH7olQZUklirwRQRNsB6J6Ko9c5coxX4TvxgEBACH5BAUKAAYALMAA2QAjACEAAAj/AA0IHEiwoEAABxEaXMiwIACFDw82nNjw4cMDAA4coMgxocWPCCN2XBgy4wEFHBCY6LDRAEKMIyWaxChigwhDaRRgFOmyY0gEDyEEqbJB1JhGMQ4AhcBAY8+JGjUCQLDDwIszp1KlgHDHBAAIqPZIVUjwo8cddXZkqDPmAAc+CCjk4aAgVqKxDjUiQGCAwoUdcqjkwNBozQeEV+4A4NCIShAuTsn23AnAgRIoqe5E8gNlzw8fUwGcqXJBTqMJaSBYHBiSwYgceVphUfKoz5UcUij44GIAKIgNRajsODBmjoKQrAHcgKIkDQsNTQ+gIFRm7xgKGXuUWGLIhAE10J6c/4yoQIiBRzleUFDhpc6iUqySEdL4Is2BKomEGkqBYMwoDwZshNEFBpgxhhNXOPZCBxdAgMIbd+gVxgkblGCAAhToNEYGAbKGwAJ7qPCURRpB0IoNGHnwQxUP8IXAAQ6AAdRCChjQwYVKBfhQBnls0gYEeCDRAwO9HefEEQK1NFAAB3iwCAQBwqQABC84YoopeBzhAQMK7GWAFLE80VtDQRwS0kZAkCLMGFoIOZCLACzyixRONeTFGVNtFAMphPSgwJa9wQnBIyD0JFlZBtThRJ4IQNDBFV562RsAaWUEFEwkISDICnnaSAUANQoI1A559LbRoQUBRcEiGYDqaY4PAYMFhCCqidohQ0C9wAcDCHXARWiTPgCFeS0pORFQVPSB0AefvvjQDo1AIUGXMQkE1BhjAHDCpxhBoMcdFECgU7UDYZSHDRlQgdEXiNwQlbHkAsCAHlEcQUIfYzggILwxIQSUCWZg0YcKsZJb0QRKmKHvrQYvpBEDRPLbsEF1TsyRxBQFBAAh+QQFCgAGACzAANsAJAAfAAAI/wANCBxIsGBBAAYQHjDIsKFDAwciPpzoEIBFAAcAZIAAMSFCig0tQgQgAYQHQ1EQIIAoEeTBAwgsZiixYY+hNBAiXnTp0WJMjUfyoFGkBcEOPQwAOGjBAMHCiQpXAqCQhkqaRU4g9Mlw4MohAGegqTGwkiHCjxc7cDmDRY4cGyqP6EkYBcwZUWkQKAg58IAFEYtq5RmrxMcPjgfSaDmgIE2jFFQctByI8QAEBRmuQDGTKk2dOYUMGfgxQaACOiEYSDEE4IUXBRgp75VgBkuqTobUOMnAwIGjGAaO2CB7YMQGGmVeHMgAS9TKhRkxCoFiYYJeyxk6kDIFPEMe2BDoVP+50cgLggyoRDXNmBHEkDlBnIy5c0fOGC9XJAEDAjGKigcGLHFeGjEoAMEdF0xmgBJQnLFDFE7c8AFHFh2QRyUCOUBHEcftxYACB0QRhYIAgCCEIQE4tdN1R5TyBgUK/ICEFgI9BwAVGcRGEEw35FFjRzABcAIktpCShg8+OEUcAGqEwsBTBD33wndBQsTADFeUQsoagEQkUUQKoIJKBmQ5NGVSKqZASjJn9PCDZCwFuUMeF0y0EguCUAAAbA64wUQYO7IEAAOI6BnTQzHZUEmOTkU0RlkXqZhHEGRJVVGik+SogEpydJRQkEHkgZFPH4UU0wuPkBmTHHtBFFNxZlBzENFKZT0EQEwZHJIjAHLoSZZFZZihRE5eunSrRnpwwCuZFnXwyBkJJAAilCCRmisEUQShVB15tFAptTxhFIBGgiCyww6LXJGQSgEoGG6QHZhhRh853cqTWZSpNIISDJQJ7r19VajAwDr9C/BBpR5srMIBAQAh+QQFCgAGACzBANcAJQAiAAAI/wANCBxIsGDBAwcMKlzIkCDChA0jSjSAcKLFgggqUoR4sSNFAAAyCtTocWBCDxEQIABAgYLJkgcNICixIQUAB4YM2aCAwEBLkh0TQkBTpE8QCGmSGqqjxdAYBwd6XkSY8c4VBBnuhJKjIKECCF4alYEoUqICAD1+nEHQ1UGlSVcuXJA5wUGaMSkclGW40kCGNUjoUO1j48CSDUVUHgCgAFEjJ3sHAlh8QIHML6JE9UjTiYsSMxQOJCiSWPEcWoZSGARAMSPIF4FCQYFy5UoaR36UdEXA4A0EtgBAjLJhwDJBkCsZWGhUClOvRg26hgxT6scLBQhElAChmEIa1scVH/8IAsXMG0CkNnFp4WCyATpIfIQ0sIGGypVjniJkTRnAmEpqOGJCBihk0MERlIxBCCsXjLFDSAxcwEBrB4DxyxwqsQaABCLsMcsVKkT1EFVlkJIMBT3Q0dN9K2XkhSg3iITQEFA0EsCIZC2GgA6RvOEDEjusmFBGDpwBQUWTISBBDmoEsZEBk7GW0QEMXEKKjxAI1NN+N7QgE0FsBeBAHl8gByV/ihFyiQNfinjTE4IYd1xIIMmRxpAg4UjFC/eRBYANv4zyZEFRAhCFIO2t9BBIYaww30YrUVFKD0Adt98Oi5AgE0j8hWHCfFMC0EGclRK6aQeHPCgQeJ5uKlNlh6iOEBJHCyXJUiBcIJfQGCas6pocVGxKa60fASDHGVD2FIasmw4gRBoB0BkZsSsBwIUeDLDG60cOJAFFDqsO2xB/i9ngiEtyUBBSEHzcIIIAXZWEJgAfHHJEHRDYMIcTWSoGU550QqCGGVVckUGeAVAEE2XJIvDAtxlNW9KIyDlwQsJTwnRcsYVuqfFEUl0UEAAh+QQFCgAGACzCANYALAAjAAAI/wANCBxIkOCBgggTKlR4oGHBgwcQRBTY8CBFBAszGohokWAGjAYAiBQosaPGhRU5HrxRIgQQGy8+cOAQokhJChkMgDz5sKNEGhuW2NjR44wgQ2I2IALDxZAhCipN8nTQAwLEHjsQSDSQwZAUD2BuyYni9MVGhzx1GvDxw0eaMV+uLIrSw0cPBT3GCMwwRo0RGnXSlEmBVuNED2u02DjiZAymQHSQrHmR4Q6AAwAMuNjggkGaRim0pjWgwIANvSEPvNCCoEMGMHfuOGAgkUGPCwhw7jyZW6ACLrPbxGDghQHHDI/w+PiogIGCkiN5uiE1JcqRSo8O2bJ1YswHkQoORP9h88ODTq2XMWc2bMCNrTIwj/SB0GVHkEh9YrhBACCHmR4HaaWeSOvxdIdVIclR0gEZmPGKLYDIMQQUIAy0YIG7JcRfGCuQBoB9B0BgwBxQNBJHDApEMUcAFZ2VWYE8RaHCZQCIAMUcaRzBgQQXYHaAFy9wNBJmG5kmxYImicTfCyxgFkQjFLao5AFUnAAAkikBIMovPexk0YsNkcAFBYjIAYFVWklEIwPApSfSgJjdcAaOZ5Y20Js1QqGGCiG9eOWbDMrhZkMEvlnSB4vQckZEhUYkghlCYITRejRitoJlCgxJoEUIlEbBHrE8wWihaY5xCH/8EShRpk5EkRmcgA7/RIUjLJQkUKFvGnBEHiL6CFFDcnDw4kabHkSBGrGFtxVBlE50BCIQ/JlaQ3UwACacAp0BjSgNZXjnrVcacMIkHYRbEQJRZErgWamGwcdthSU0kpIGfBDIBxYC0AEVNL6KHgR9hDGQVAr5iREEjuxwZaZfcHErsZm9kDBJBBdssAEQ3OEqf3c4QTEAEHSnB5qjMXsrRgrkwa9nwvaZwYRqhOTtaM1mJscZEFyRGQc2XAHJEEoQsGzJBeHpkBaNDGGDI2O0oIJWE8RLNLNgAsCAElBQkYF6akE0NUJVRyTBA+FFBFJKX3/76rl2Evlri2n3CeimIL16Ftxxh/QtoUUWCulQxXk/HLgBAQEAIfkEBQoABgAswQDWADAAIgAACP8ADQgcSJDgAQMHEipMKJBhwYcQIxoAMPAgRQAYM2JEcLChxI8GEXgUScHFjQwXMqj04CKCwBUxAhzg2BGkRIoIFRgAscEAoDNp8hjas6EEoDCGGq2wWNNmRAA6BSp40gNABgYfGnkBMaYUlSBOvEQxQIFBRqcQM/jwYKBDBgh36PzwAYDDGAUHKNS5A2FCByqG7k5EWzGhByRrolBZDKbSXC4IXjg5eIBKiRIMHBgK4+AARsIOGfTokRDjATA2FLC4EqWOCQcGFBQpgpdCZ8840VKG8AdSi4EAPpwBkPCDIyQ+OCJYTvOz04MIKCjIcASQrVWduETZMSgGlw/EDRz/1qJA5MyOuUHONOCGlKc8LDLoQKDlTIYyBiRdSOPRw5wB5n3m3HMGtJEMIcSZRh8ACDCBAgI5CAGBAy3IAYUSnSEUHmENKcBBHhwkKNJqBohkQBJQzBGFE0FgkUMApl1EUHoSHdTCIQiIiIAUIuwwRhhSWCDABMSlARtwCDVlIkg5HpFGQuYpAYUBHxygQHkHKZAHA5RVNNhZhX0khxMCOZCGGiNQEMBywJEgx2AT4RQjBBfkRpFDBQHAwCItePCIDQhMoJBADFJEhZc0OgALKifZ1gFeTRGaIwhQYAEBQ6UxlCMYOzA4WE2eKZDIKFKEcUUi0EhR4kPEHXCBEo10/ybShiIpAAAV4M1q550GOICRE6UU8sKgeZb2giAQeNqlSAhcQQFOFhFKnK5n3OEATUwGlwcFJQ4IAAVh5OgZQoQmCQAEiXyiBUUcgbRRcHpc6hl6HNRB2Wc15QiAFL8kUmKkHxXKASYdFEooC3msWi50GNUhhwcUmAjwTTl2UAkJheZoABWexvnvAR8scmiSHAKX4weTnMCgZ2mAh9tCAFxxSAfQTYyWwIKYsPIckGqoZwVp8LdeyRAVSkEe4Clwl4nnslBFpQgtSTSrIkFwCAcQHIpRCzvcgbQQEds8tYi9CjKHFBDIvMMLGdIk9tSEenaBAWaM8QIE+uokMdw34SeGgBBCBJDkkne+zfdECMC4aoIGGU70hq02N26Yh9+08JcaEgtSQAAAIfkEBQoABgAswgDWAC8AIgAACP8ADQgcSLDgQAQIDAAYuPCAwYcQBTo0MFGhxIoCEThUkBDAwoggBx6YOPIAgA4uQGTocMMGjRIeTAL4QEFjyJAAHC5EoODAkw1kBKYBJMYAmhdBvBgw1FPiTYgLHeg8oOAJHg8GEKYxdCbUGQ5RxqQx4YDjyKcEE/b40ePKHUeIHCFB8oEihTMZGJwRtOSCgx1hDL1AiPYggB4GfAQJYsMDhx50qJyxcQBCGJNyNhTRaKJRGY5oDyRMCAFQHYUOEWrJQGHMGB94VBhgECKCQAUpKFAsbJHCKlLBEF2A4CAGghZjPNpA4kNOVgUKMibknQHIhyhpCElyUgrTGU+2uhz/iJJBIIsMO2xkdCgaI0gADCTZInSBQeUDGQ5BcENKx4EOdXCEEAJq5OARbwwdoMMqd7BngAIAXDHGgx4hkIQSQYyhhyNmQHGBe7wBEIAcUWRlEkV8vACAAi2E0aEUApEghBABSJQTggItpF5CEHagxBBlpMECBTZolFMauk0nUoJPQbAIBw9m9YQBWGig0VkKfYAIAAmduNtFBV35EAeLZHCACnqEkUFP0ZWEAAA2cJEVRV6idmBBdzL05gtYYFEmlgSNBEAUHHwEaFYAeDDKlBxAcMMeENxoEAQGmDHBnBaJ9KYckT7EJQBH/JLIFWFwAcsvPUjKUFYXnJHGlw9l/zDHmyDm6EAPEJiUxhxSdGrQTgikQcWhFGkUBRVzqkrSQBQE8oVMtWaKQCAqumcSFSeY+FGONnaQSCcvFBttQRQ8kgGXBSnghK85niiaQoL8ogamhXUwCQOwGsBBHdHeSAEiYHiQa51o7RBIl7sF8Wp0F4nmURCLhIsaghN5kUZJA3VQkUcHOiBHHRTkZBOOA8nhBZcOvcptRyfcoEeJ6JJsmAF6BLEQBcM6mNMNlbLglMx4MuAICQBQIWeOHNxwhh5KhHDpuAjmBACZCtTxwgEUfJCHHC9cgMAEb24LdJgAvLBHFUGcccYRHVz5Zr5jE9SQElCgrdCNCEENNMcIiBYghABdYiR23HhmZJZFg/9MuEEjxx0QACH5BAUKAAYALMEA1gAwACIAAAj/AA0IHEiQIACDBwwkFLiwoMOHEB0eQADgoEEABw5YbBix40MEBiAUWdLBQAYAHFJEGMjAhkKKFj12tKjggIcNJQClkXPHUCMDJXYGMpBmAgCKMjseUEDBIgAIWiBkMOmlERo7dKReMYRoxdGKMZMyNODDgBYnVKgE0YPnxw0ECMpoUTAmT0YAHfLoiZJBo1iCGcueMXAkSGE8SOgESdjnhIEgh2hcmGjIQIwAHGVqzHhAyuCEGTucYaDFQIcVizTewGkUQhAHIMVyBDDFgCwqGgFQMECFwwEOZ3ywObK0SAjAskE6gHDgxRVIpDylSnOljCkdF8ZoROADyaQJC2P//x3o4A8pR1E4OECBwEskCm8MtDEQJUZGBo/PBECQcfxYBZckA4JA/CVURwcTxODAAQ6okQNTO6gBBRYgHRSWWAAwsJYDA4FkAiIVKuCSAWdEcYQTZoBQkwEY+SfQQU7kcVBCIHEhxAlehBGGCBZMIFAGkChgkIsEhXHGQRXmIFAQEMCFlAE2UGGAAhX5dSFYM1ZUkF93RFGRA2MIooQE/BkAV4tH9OXXiwOFpUBs/W0JgAKRSHFEJUc4CSeNBsixm5ZbGtDCDftVlEFTazo0IBYcItmmhRBM2R+gCj0ViwF58JQHLqhE+lBCFITQiJAgNcRnEF5gdOGLClQhRwcZ2PFQiShqMJAZQ3AdsMMhIFVI4JQAhLHDQBM5hNF9dUSRa1JH6GGhQoClVymLn3rwhB5HmHlrRFfIcSxHEIwhnrEDjWKAFKDJlpAcXj4pkAoyCjlWhwYwUIcgc+zm4kF6BPHVQFREYeanBrzw2Lz7GhDIDkcNJAcEF4ZX11RlEmmmboGQQNBgq2bAQh5hrGqxQB044ulUVcYGwIB7GCwykcWixiF9DTlgQxRqGGDByBDxeYQgANzhEgMZfMFHECcwBV6cPAcaxR1peBBFHVywADG0Lzet0ERmCOQEBQkdxbTWHikBdkzbki2RqsipLVvW4wUEACH5BAUKAAYALL8A1gAyACQAAAj/AA0IHEiQ4IEDBgAoBJBwoUCHBSNKlLiQoQGEAA5qPIgAo8UDFieKjFhR4QEGH0A6LNmQYciRIg90RHgRAQAaG4S8MMGBAoQXUjwwUKDQQQaMCWGOPJhR5gERJZ7YGJOn0qRGdjbskeMljCEvFDq+VBpRZgabGRW8sIHAAUgDKdCUoPTiQ4s0jQyZfEu2IEgPSOhoGTMmjJZSa36wOKDAwJUyHRCxVZBhTB20CvsWRODhBx0bQYIcCSIFjw86OxQquAKAQp8wINxS4WODQtK+CDkioHAGswEEdTIoIMwCgZyUBhJtEHjAUKMXHftmzvgbCKsXKkFSOaLwQh0fa+TY/wRRIoJACk7cIiAL0gEECB1ijGFlKtiYEx2ATGEwBoFNBT/8IIdGCvjXUXTsGcDAJaYAEkUULcygAxhn3ADGKqToMJpGDjAQRRhOzeSUZr+1YQt2BzXkxUVduHEABHVQQNkJOwwBxQ3rJYSUZjIhsIMcBhQokwFUaMEUAB1AYQYVXNRxhhpKHHXRRSq9NJZfHfERhEzUOTDGBRfs0OQQGMgoUxRA+NfURdQ9VFBmEjlwiAMXNWbACFDwQQUEBgTQ2EEMUEEBTQ1FhCCccA60HhV3NARBGGZEmehDGcix5o6FKrSeiLcZZMAdNhygRRpAGECniE0BcMMYXI5FkwJScP9QEnVjNXYEFENQYdNbfFkEQBqy8jWlQOvd8MsoZ4BhgBqJmMDlZgdIoAQWjYmVVIogHTAGUdl2mhEAF6ChhQc2BIKLLi8AkKNBCCgAwRVp5MZQbusR1QEV2bra6UE73PGCB536RVMd8VoL53ppcOErc281dQAFFMhxBwPCxnTQHQqrRKVNCIwhq03MuUkTBJnAsqW6hC4lkBxh/EZdihl4oe5tNGW03gFzwJLIupptGsgRLqeaQcY0gyTWCohQkQEDJHp6QB47BA3SGOlilK2aAITRhwrYNq1okIIEQeVBlbjFVIoJcdDHGMSi7TWxBkBQSQeMAcABGC8z5YAER0x1ckLFbxO0npwmJBRECwAUuBAEQkCBxQStBl7WehxE8oEBYayweBBypJGDEhcEwLPknq6nQh4n1OFeD2k4EQWKxLpE+kTr2WBGIz3IIeh6M9/s5uwTQaAEFBewqa7vIU0KPHMIDOWfxrnp+PvyZQ1JfeBuLx8QACH5BAUKAAYALLoA1gA2ACYAAAj/AA0IHEiwIEEAAAwqXMiwYcEDEAcmNIAwYUWEBSc63KjwgECPFQdGJDhSI0eHIQFkcOARgUsACBQgPKAAQcQDIU/qBEChRAkgEHAi9GLoQ9CKBxAAEIpRZ8eSDEpY6XFmUR4DDgwZciJn0aIxL6JEcaB0YlOnBm3ahECH7IcdTqgYwrSlRI8VR+Q2QhHA5lm0BjxC6JHBr80zUS4uDQNngxScB1JEMZQB8kXANn388BHECYvAALyIVMohDQQ5YZYKfCFIhQPQOXUK9uHExpg8dc58idTDh4eaEOUoOJBHzswDHxrJCcoULUwEHK4IZdAhQ5RUSH4EVRrEBs4vaS4w/zCA4Iqh1zhlk3epwAGAOTaWIgVzo0ckDghVGFdaZAMNmg68RpFJG9kk0ExuvIEAeTQdoIV3LyxyBEJhUHRABEVcQF5gFAXmkUccGuRRBn+4cQMXaZxRhymmdMCUSnQIlEEeWnjQgxAMxFTTgrB9NNJCHjFhyyZgRHFDCyq4sUknUbSgQGEHXBGDUhSwgQQWUIDQ14IQXabagQ0h4EYQJC3FhQlnAGKLDgjkgEUOV6RBRw9PKEGBTUJxCKKHAzIE4h0qMEgeAFTscEAZpLShApY5fHAnAjYAkZR8FmkE0Z6KmTRihTwuxUAfGWQAyRhBUCDBQDVRscKkS+1ZkEvpff8E5kcLgsXgggqYoQQYLSyoFo8KpJHBoB2SBJqFl16aU1IGmJAGswC0cBUGr8GUVJcAcKDHgPL1edBgMmVarEALXpHCARmkEYgJCEyQEGSQUWRDHQx2W6ZHZ/zyyAs3dOCBFO6JaEAHaojgiBNNKSYSADvEZ+C7HKrm0Q2FaNFDHYfA8gsVzBoLAhRmQOAStzNldARzIIV4UFJ4OsCHIGpU5iqqD6RhiAIbTgStZXdA0KqrFsFLkQN3tCZoRzEhEMUhHOTcY0QQ5CGUq5gOesYefXCB7MwisWzAB4uQOSm3IAZRoWWzQkbaM7FQgBOPHC0IAR+iETuTUmG8UC9GEJfGFUQfY/Qgq1M8quirfF1S0YFqCk+KwBl5BAzYQDwGEcmwSg3oABXugQSish084kRLcE++YQeHkJmQUh3UES9oZQFwxbp/mU4rVnmkoZENd1zLMkIU5CBHGu5lbntaAo1xx3ihBRY7AAo4YQYUObQ07vHGGnDEIR8AcMZwAFB3xBVnjJFDDQLUZBH2SBvAASU2VJEBv3dEYoMDEJBXU8QEsi+SARSY3h26ExTKfch/HPGICJSQP3JxDYEJ5NDIIGi7S1HQAAEBACH5BAUKAAYALFcCFwEBAAEAAAgEAA0EBAAh+QQFCgAGACy6ANcANQAlAAAI/wANCBxIUCCAgwUJAkjIsKHDhwYNLiyIUOFEiBgZLjy4EWFFjh4zilRoAGTFkiAHhhz5kGNJlCZjyrzIsuXMmzNrUoTp0aXFlDFf6pSY82JPlzeHvsTJdGLOoSaJyoTJc6rIjkg3qoyKcunTkSer4pQo1udKmhGb/iz7lWpDrE2jqt2J0WfauFZXdm1JFihepG/Dvu1q9K9ZtFVJWiR8NCnghHqt9j3s97DiwIghs+3IuDNirUl3cuXbUyXhzSlNB6VoNPHdwmNZPya7lDFa0I3t/gRqerJT2L6PaszKeXHI25zlXnVLMzdroZIz/gbteThPpRGpNj9L9Lrgmlh9QywXyhz71vJPlZunS3l79PXZiaPWCv+52tX1NX/Vm3+w3Mz9kbZXgDrpBl9AAAAh+QQFCgAGACy7ANIAMQAsAAAI/wANCBxIsKDBgQcOHFzIsKFDAwofSpx4UKEDBxApanSokEKYMBASbhxpsGMjQwpEklwpEIEBQxwyslzpskOaiDNXKpQTRGbOjQp3GJKS8udKE41eIMBpVGOQN0WbatzJQKVUiQozUPE5dWZHQ1eWAlXIFKgBDo2cpCwLMaFVgQkneDVAYYwDkQcQ6C3rcmBKCSVAiB2psE6KAyknMEUAgQIHGiWWOJGT5s4YNRtcKBhMMWuagRdKVDlDGgwXKlzGVNlQxMkHCAIV3ICgd6Lb2kFUdAjiBHORHSxIcKDAQC+CC5vJGq/tsC9OBB8W5blDhXiGu2Xd5lWZt2/DpQxoU//YQemWEjMfyIpcqj0vB9idDSx1eWCIkkqYOuXhYmZESrGctWSADbkkEuB33kEwBhZK3ECBQBTIoRhbBs0xyhlvNaRQChk4EcYLChSlgAFhfCDffAH2NYYTGWpowAfnQXLDCSdkkAEDBkBwBUMqITDGVi0y5NINSmARxRmLHCJIHmlQkcchNhwRxAsfeNCDB7UdcIYoQHaFgCHpMcVABh3o8UELL7TgxBh0IOGDSAygEktI3j0UURpcwIVAiHkZsIMTBSHAgA9aHICjFnpgSeFCCl2gRBUOQFCcQXLEhFBCDqQB2w56kKSQBGYo8UYYUaAWxhlhOAFGGC5BMEVI8h3/cMQONwjyYJ22KQDCF3o5kAEJUUYxBih6pFGHJ7Z00ZYBDiQCzRiI0YRAFICWBcFnDCigwyYsinXAGqP0sBdJLikQxogJ7WmAEyQQhAAfJmSUBxI/ZDAuuQPuSB9dcuCokEsc3HHYGGEsguWBhIHxoEh/yleQJKZEkoYW4iJs1gdgZISAphn12QEgydzhgZtR5eQSF5a+EEasfaVgSiNmKACBDzYk95NLGcghEBgZEJTBC2WQYggGASy36EguOVHjGAJB0AIfadwwHAXYbSeVRVykofQYaRzxAbrsXVVQXjWYUYUTK7i719E5pWuBEinMF6TY7k4gN9229Yl3QQEBACH5BAUKAAYALLsA0gA0ACwAAAj/AA0IHEiwoMGBAAAQPHCwocOHDxVCnEiRosKEBhQeUGCAYcWPETMmTIhghxcHCBCAXGnwYsIDAAIYakQBgUeWODNmPACTiqEUCm7mXHkR5oEjeXgOHaoRAIQxjSAcULmU5UUEAFo0KpOy6kqlIhUYcjIBrFeWMM9w4Sm0IsO2OAEcuQPA7MSpVG1WpTDGkIOOD5Xe1EijhAe9RLEG2Rq04VSOBqgy4HCESxo0G0AooAqy7oFDVyZQZWtzJEYQJZ6c6XSHxIcMCC4gXqkyzBmVek0LRODgRR49mIvAhlm3I1yKWF/o4QzTAwUKKpykSXPlSI8ODjwwsMlWMEusFB5B/zDAMSGIDWKuyDHQgfjLlKV1dwaAVcEhFSNNBDmTpkSFzS95hhtWpklkkUD0HQCBWgBQcAQYcgThAH3wzUaQRCN9lCBPAGSghBIe3HFHCxyIBJhIGE010HENuYShTS+UYsYQVHSQYWkFMQRAUA48IRWLLRloEwgGqOFIKmEEleJxNhGowBzQyLFRRS7aJAIUQkzARZMcyscWghyEQYUce4Bgl0NVKgBAFDkAcAV+QXUXAF5dFZlIGGPYwNGXB44EUwZvHEABGO4VKN8OuNDCwkApAVmQixmBoMQQDshhQwYqqECCCies4IEKHHRwRR1ylMIGEoueORGGCmGVAxRDGP8iCBVRBGGrrUfYyscdiDhyxAo++OCEcZ2h6KcDhgRwBQPEXQjAB7mMEkRYGTgyrKpoGjuSAlTc4MWOCoSbEkcUCJJIFe/Z5AAePhyG7UGGkjTHFfEm1EEeL8DEABB/JdgDEj5ENh9GKt0ABRZABLHDrUHYMAaSO4TBBSC2DMIABfRpoQV3xY6k0qtCvHCDDTa8AMQLnwjywQstdNBBDJe8sQkpMfiARA+bOQqvSFjVccScGA3wQB1OxOsGKbaYEkMH7eYcF1ZhqrSZQENAkQad8AEwhS1MpMATfDq3KFIaJYp0xBhVCEFASgXy1FhHdS7VAhcCHWDDGWFAIG6hfmpWdBZMCqTBgAEfpBHGX1N9zXdxZ51owxEZ5OFFBrt1Wa+BZyEAghp3cMEBSQUa23hBKnUABRQ6GNUsQvKNTpACIoAQgLYZuu7QVHMS2LrtdyGIEe8OBQQAIfkEBQoABgAsvADSADMALAAACP8ADQgcSLCgwYMGACBcyLChw4cQIyIEoFChxIsOKWpUeOAAxo8DK260CBIkxYQbPVLg4LFkRJEaDyAQaCgNhJkuM6LcKJABGENeHHQ82DJnwp0eFUBgEciJAwU4CSIoWlTiyZAUcQYxNCaDgaEFZRpA4LXqRwAyESCgeMWQIQoIFBDsGBWBkQ0i1JoUODVhCz11MkBQULVqhjBalpTwMPZsQpkcTIRBxKIj1QMUI4C4weWODQhjO161ekABAAiGGnHh21cmxQMfSmyQQyEmWo0f1zrI0+jK2pmGmY5J4oKwZdGjLwI44siGR4VTcaq4w+UIhLSEsSZ3WNTBnTu1oWP/jlCkyhgqLzSq7Ttyu84dgY4khIr5wI4tG4pAZb2TJEgIHKShh1dqUcTBDmlc4YEHN+n1lWhGQaAEHFHMpBELUchhAwNJhQbAWkadBAAFUOy0FCJacCDXSVMdABpaZmFUkQF3ALLDARmkMUYQDuAmUGkMUAGLIBcwAFpJWQGQYQNKzEFFEAZQcAQDGXwQxBjfjRHFGKgIEgUVe2Qzx4cyrsWBIAYMAQUIcjEwyi93hJHHGDtQQJ8Bxh3QAZIeIcDHCQdQYAgCEBxxhiB7UPBVTJhtBMFNJq01RhhDgTHGGVHQBZZAFDlQ5QtU/PDDTTE29CEALwhCEQRRlCIIZhCs/8SBVy1QYQAmmFQyZxA2rOGDA1/lBgkFEKThxQtqVnGHHGeMocUVTuzwggcZZGCaQhDgkcGmED03hxlCjGEDWg2YEUQH16FkGadLQRDEGkh00JdEM70gChRDADfWFUE0ut56hCngAxJ0UKGFD1FF5BEEoWACAgV0TeWAHl8VCBNaPfzAQgeXSBIARjMF4goHLaJVoHwbUUCCDTsEccQYp1TSSjC2XDIvRDM5gUx6ChDmaA5QSHHHI7p+sQPLLbxhCytPSGJKDI3hbAAHvjjRHwU7cHFGHWpAkYMJ6GanEQqSAHGAAxD711BLjqwGwQU91CHHl1eY0BFLI0Xc6LoKH1EgARaiQDuGkxzcpN5xx3EK46KlMoTACPgyYafiH/ZpwMc/DiVSSTJREIRcmi5+1VVguYeRTKH76KNRazeqOusv9bc57FbxRLtytt/umO4RBQQAIfkEBQoABgAsuQDVADgAJQAACP8ADQgcSLCgwYMIDQAAkLChw4cQCR44ELGiRYEMFzIcOPGiR4QLMQKgaABCBgUICnb86BEAgpchvTQycSClQpYWNS48oNFBizuG0qRIOTIlAwgISOI8qLMmgB1BxtQ5chKpQgQLFZQoccGA0qU6BU5ceKJRIycOSCoFACFKGitcvS4dGJInVgAZolAJEkNBzY4ZEKgIEyUM0rRfcWrkubCDkzEcDqD0yhjEBjJnPCiom/hjXawnvETxwiGjAZhsg1Qp4QKlS4YrFY9kfMFQoygKxI4FQOEGHxsQGEy+ORfj6YUOxoR5kcIqRZ7Jx9hgUPNl7LkU70LgcsYGw6QwKRT/WaJlx+aRpzuznEhUwZU0RzQicI1XzYYiKf8WV6nQZRA5Tmw2FmMkcCGHByAglRJW+3FkAAUS2CAHbgIlpdEJVMjxAXs2adQgQUoYkAMF2VkIAAd5jJFBSC/JJddGxVE0wR5KpHHVXSTU8UVk/S12F2Mw4sTTAXo4gcAYcjDYQRg2dCCZAzckcgMDdy0EQUhBfkTSGGnwJAUsQ4hQSS15nBEFF1EcQckviQShhRZnnLEHLBsyuFRKR9zB1g6o6AKFGWMcYYMNH1zggAIXUHESBRlwwEEibKxhJ05YfXBHB0HUkeYRIKgx1GKxAcBABzaMkYoBPziwHmVsYcJHGi04/+AhBJVc6VdWje6wyCJ4IIHHDhRQQJxFzwm0QydpBFZUTX7tIAdSEASRxhh6rIHHIXXQgUQPSQ1bEUUXKCHEF62E8eNzGimAhRJzXBFFFBDcgIQPUVBHAQIQyJolRBNJYIYSZYzxF0U7jYWABWbs0B9FPVyQlA5d7GDKJRC4WFFSQTwRRB6MdexxSk7cwewBGbQxhQF39GKLJ5dIgthHYAShh6zsifTczXnssBMQtgDTByZvuNHivg+l9EEaeXTgUlI3Mw0kAA4g8gEEK0ACCRCR6YTeRSnVgYl3fq2ltQIZqBAEFWZAkcYNGTD20l0fpWTCLFFg6ZIDJr0QhRx3zFsBxrtUaCHECA7cxXRYF4HbyBwMtBDFGKIVVscdaUTxQgYOqCofSh16SLRDFDmgBBRznCFHGl7swEFaIoVVnVgfsieEEvlqvVG3GE20knrYicUi0x9qaVzwLAUEACH5BAUKAAYALLkA1gA5ACQAAAj/AA0IHEiwoMGDCA0CAJCwocOHEAUCQGDgwIGIGDNKZHgAwYGJFTde1Ehy4MKJHU9G8ULBosCRJUky9HgSQIcjjQyl8JhyAkWKMR8uTHngCIUoeqLs4JAiAAIEC0WUqOAxaMKLTw0sROAlZxQOHGkCoHAEzYYQWa0WBPBxIwcwXK5EOQEVgYIDGRx8MPBFiwEPEKqqJUgRggMKQcCkeeHgKc+FIDZYGePh51OYajkuDGPoDhcKWgEooLiQQ5QqJVwoEJgV82ADCjrIMaRTK1SJDDzMqdMBAoPRLl8bADrciZwoGRQ4gA31wAsIXMY44fBxpOuSbUMaAG3gSAaLVT8C/xCyYcsOCG0vBs+8UOCHMGMKwgTwIsyTEh7uChaudTsEG1eccQJDExGngA1jgHEBTNcJBwIUVXyRAUMDAXVgGmBAoFV2/BWEgBoGDLEaUEA5sIIeNoDG1nodsmaADXM4sMMhFLRnAANX5FEJdy6yBVSD2FGUwS2FHNFBH6VQEYYed3xSiii/CPLCBxQoAMBvFVmnVkoAZBDKKL9UkoYUZ8ABRSNBtIDADXv0cMYcYCg5yh4fddCDhkA6BJ5AEARByRgUtGBRRyIokQNPl20FgQejjOJBEGv88AJwQcl4hytHnNTRUwpMUEcUFSlgpQEOHKAAB3QgscYdNvgAAaURXf+knANR5PGBIFGYeplL1clBBUE3/IBHKQb04EMPDDi2X0QIYKCEHDYcEMUZE1EY2lBspVFDDmekkaoP34kq7mUkKaCEGRcgEEQe1YnH0UUoPQCFEmVolZ9FgzBxwSCXBJYnQhflIagNfYzan0UEgoeAA0IYopUDU0xxRRimVDOKAaR8p9FFFFyB1yIThnfwSCs+FZ0BKdhiSh56eOIGBRB8ZyOzLwYBgCMfbKpdSK3VZMAiN0AAiSe8PcZWSQCM8UIeQXTEml2J2ngABBRkkIEUUGABBFgoDWptrAZAcEceVxA4c0UOcPBCFIrJUYccYVwhhRIi8CReUB+R8InHdkFaAMEHQYSRxx1jfBFFFFxQcQMHd53kE7xfk/SRBGaIMgYViJ/x9hc7dECBA42f5CJP/FEk7xBOrPB5TZFbNBrpLnZ4AAUTHJ1lotmZzXqLA320q8+8yxR8TAEBACH5BAUKAAYALLkA1gA7ACQAAAj/AA0IHEiwoMGDCBMaOKCwocOHCQ9IhEixokIABhRIPACAocWPDg8gMDDSgAMuURQsBMnyYEmBGTykaWRoAoCXDD22hFgSwAsuOw6FOWLCAAWCHga+3IkQwUSBDo4YaFSmhcSRTg1kiKLGgAuVB1QyVYjgxRgDY3bYmMBw5IsLOwxwCQNzrEGRGA20eHHkrAoGOgfmMCBGSwYDHVfaVcrRAIM0Bgzt0LhSrIIXX+YYEFFS5VKmgTMcOSRVQ4CnAincEOQkw82eiwUyzPvhzhUbGq+qhBDChhwtNyBgBHAzduLGFI6cObKCOMaXCJYYKALks2y7gT/QpcLgOkEGXrh4/3U8Mm/slRcqtAiTZoXHjQM5OJETxIECBgpGBo6tUogBLDYJhBVGB3BARR0v6IfAgk7tZxcCADjQSA6G2LAQR3lBUEcdri3E4IIknTcQAArUkcgZGfSRRlgptOCEI5WIgsoY9yEmUEcYxjabAWnogQo0dVzhSClodDLJGGPYoAYqdPQ1hxxcqCHKBVpBOBZeAkEWxRlPQCCSEgbkAGFHC4qEHwVBjEKLDT4g8YKDD10VgI0QqOCIHl6CuNAFQqShgHkj3uTDD47IQUeVjVmkEwRBhOEIHxjlB2FbCjhRx5gYeeDBC/Ot8UMKEDrlnEUqgZBDFHmYwMId3TWI6U0ABP9RhwMMQPACEmxo8QIFCmSAYUfEKWqSElBIgYADi1ygE3HwJSYFFEPI4UQPPhCXgQMHOOAGB8CCxJEaQiDAACLNfebcUxJMFYCoBwBhyiWLeGKLjYBCxBECrAKQRxDd6pSTbCJRUEcLFGxyiRRjmMJKFYDEEcOOFo2kwhEA/FZciDfSuywFizhBiiQq2DBHSgsG660BUXTwRR3DiUTSgmKNSlAGaoBwRB64tYUaSBDIcaDLCzkns7gZUJBBBo0KYgYUR+i3LJwOjcRBH3WI9R0EH9hABXty1IEWFUcE8YIHItxo5VgAMFBkBljb8MUdd8ghVxRUcEGFDR1gK5F5E9U72xJGFkBhiAF5yDFGECRQAIF9QiuV33V66iiBEhT8etBV5XV7o98POlVm5sFyLmJDv4o+OkWmn05RQAAAIfkEBQoABgAsuQDWADsAIwAACP8ADQgcSLCgwYMIExoAoLChw4cJATBEsBCixYsIDxyoyLEjxo8GERyYuDCDgpEMGYJcaWBjS4IAHAQxFOQkgAMIJHpk2dClwCAcHERxJOdKIxsGThpAIFInz4wLnRrI8EVgmCAvIBygoEAgABAlQjCVSPEpQZwHuipQcQYMGANOglDUqZEClzBVNoToavZszp+Grsi58yID04EMIZQoMubODsMQGJTtO/YmByqGDNhoirglhyBpNkwd+bKvwJwSIazIEyaFAwo5c04QyOEFojsZIFBwQLf0SrSoDUAYk8ZJBo2TAUAoUiQNlSAUNLqUylJjVAAKbMihssPBxsOkT5z/2VAiBWqfUfsqb5HhCpcxxyeiBsAgChg5HTx0EOnbtFcDQ0AhxA4QkHWYRCcEMcZUEjlFnX84YaGEFGrNZ8AK7xnQAkoNPuifVWNMcMaCZGUQRiljBBFDIbpIwcIHHHCQwRNzMNCfWRTZcMgBVYziiiBCYCJIHY6M4UQUN+SxRw83REEFF1/EkgsHaKlHEQR6fLDDKLTQYYYZanzA4U0adSiRB0/44IEC/PGEk0CHLMIFFRTsJ4EBO3x3GJtRofZCGHgY4MNhGDGEUwAbKSdHJSwwgFJXCBwSBABMoSQQBCa8IEcoSLAxBlJL/eYVBzaEgsl/Nwl0gAOIkKBSdkF4/4EHG4bk4cgPPSjA5mQXUcpQDlyk8YIjEPx3FgAZNDIECHxgcoQJQfzww2OUQgBBpR+lFIASUOR5RwtJoTcQTtxSwYGjOGXQAQSSvKGFKaQUKy5ENyGQRA4BfDFGoiqNG1UCalBRJhCSuKEFJLYAE8klg6Cb7UIOhIEAC32QZlCHh+nxgkZT2PJKGmOU4R2f/fY60mUMHKKVQRvp2aADgoBwQhienCFmmdKt5GseL2hG6WldcTgQAx3cYEOAjXhwXk6W/oZdGnmMYahvClCQQQbiObLIGTu8kMMQA6A1Zkog+ZpBIHX4pEAGL0RxRh11nHFFEC104N1IaVVZ8lM4VUORSoxX3GHAGVFEYcARHZjZIE5NeVjdSCBAgcUbc0Rxwm7BnSaSdNKltDffTF2gxAW8qsp4h6g6DmFLb35oVoOu9xUQADs="}}},{"cell_type":"markdown","source":"# Define the Transformer Input Layers\n\nWhen processing landmark coordinate features for the encoder, we apply convolutional layers to downsample them and process local relationships. \n\nWe sum position embeddings and token embeddings when processing past target tokens for the decoder.","metadata":{}},{"cell_type":"code","source":"class TokenEmbedding(layers.Layer):\n    def __init__(self, num_vocab=1000, maxlen=100, num_hid=64):\n        super().__init__()\n        self.emb = tf.keras.layers.Embedding(num_vocab, num_hid)\n        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=num_hid)\n\n    def call(self, x):\n        maxlen = tf.shape(x)[-1]\n        x = self.emb(x)\n        positions = tf.range(start=0, limit=maxlen, delta=1)\n        positions = self.pos_emb(positions)\n        return x + positions\n\n\nclass LandmarkEmbedding(layers.Layer):\n    def __init__(self, num_hid=64, maxlen=100):\n        super().__init__()\n        self.conv1 = tf.keras.layers.Conv1D(\n            num_hid, 11, strides=2, padding=\"same\", activation=\"relu\"\n        )\n        self.conv2 = tf.keras.layers.Conv1D(\n            num_hid, 11, strides=2, padding=\"same\", activation=\"relu\"\n        )\n        self.conv3 = tf.keras.layers.Conv1D(\n            num_hid, 11, strides=2, padding=\"same\", activation=\"relu\"\n        )\n        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=num_hid)\n\n    def call(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        return self.conv3(x)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:39.517735Z","iopub.execute_input":"2023-08-02T22:23:39.518061Z","iopub.status.idle":"2023-08-02T22:23:39.528213Z","shell.execute_reply.started":"2023-08-02T22:23:39.518036Z","shell.execute_reply":"2023-08-02T22:23:39.52728Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"# Encoder layer for Transformer","metadata":{}},{"cell_type":"code","source":"class TransformerEncoder(layers.Layer):\n    def __init__(self, embed_dim, num_heads, feed_forward_dim, rate=0.1):\n        super().__init__()\n        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n        self.ffn = keras.Sequential(\n            [\n                layers.Dense(feed_forward_dim, activation=\"relu\"),\n                layers.Dense(embed_dim),\n            ]\n        )\n        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n        self.dropout1 = layers.Dropout(rate)\n        self.dropout2 = layers.Dropout(rate)\n\n    def call(self, inputs, training):\n        attn_output = self.att(inputs, inputs)\n        attn_output = self.dropout1(attn_output, training=training)\n        out1 = self.layernorm1(inputs + attn_output)\n        ffn_output = self.ffn(out1)\n        ffn_output = self.dropout2(ffn_output, training=training)\n        return self.layernorm2(out1 + ffn_output)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:39.530804Z","iopub.execute_input":"2023-08-02T22:23:39.531182Z","iopub.status.idle":"2023-08-02T22:23:39.544797Z","shell.execute_reply.started":"2023-08-02T22:23:39.531149Z","shell.execute_reply":"2023-08-02T22:23:39.543719Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":"# Decoder layer for Transformer","metadata":{}},{"cell_type":"code","source":"# Customized to add `training` variable\n# Reference: https://www.kaggle.com/code/shlomoron/aslfr-a-simple-transformer/notebook\n\nclass TransformerDecoder(layers.Layer):\n    def __init__(self, embed_dim, num_heads, feed_forward_dim, dropout_rate=0.1):\n        super().__init__()\n        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm3 = layers.LayerNormalization(epsilon=1e-6)\n        self.self_att = layers.MultiHeadAttention(\n            num_heads=num_heads, key_dim=embed_dim\n        )\n        self.enc_att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n        self.self_dropout = layers.Dropout(0.5)\n        self.enc_dropout = layers.Dropout(0.1)\n        self.ffn_dropout = layers.Dropout(0.1)\n        self.ffn = keras.Sequential(\n            [\n                layers.Dense(feed_forward_dim, activation=\"relu\"),\n                layers.Dense(embed_dim),\n            ]\n        )\n\n    def causal_attention_mask(self, batch_size, n_dest, n_src, dtype):\n        \"\"\"Masks the upper half of the dot product matrix in self attention.\n\n        This prevents flow of information from future tokens to current token.\n        1's in the lower triangle, counting from the lower right corner.\n        \"\"\"\n        i = tf.range(n_dest)[:, None]\n        j = tf.range(n_src)\n        m = i >= j - n_src + n_dest\n        mask = tf.cast(m, dtype)\n        mask = tf.reshape(mask, [1, n_dest, n_src])\n        mult = tf.concat(\n            [batch_size[..., tf.newaxis], tf.constant([1, 1], dtype=tf.int32)], 0\n        )\n        return tf.tile(mask, mult)\n\n    def call(self, enc_out, target, training):\n        input_shape = tf.shape(target)\n        batch_size = input_shape[0]\n        seq_len = input_shape[1]\n        causal_mask = self.causal_attention_mask(batch_size, seq_len, seq_len, tf.bool)\n        target_att = self.self_att(target, target, attention_mask=causal_mask)\n        target_norm = self.layernorm1(target + self.self_dropout(target_att, training = training))\n        enc_out = self.enc_att(target_norm, enc_out)\n        enc_out_norm = self.layernorm2(self.enc_dropout(enc_out, training = training) + target_norm)\n        ffn_out = self.ffn(enc_out_norm)\n        ffn_out_norm = self.layernorm3(enc_out_norm + self.ffn_dropout(ffn_out, training = training))\n        return ffn_out_norm","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:39.548026Z","iopub.execute_input":"2023-08-02T22:23:39.548388Z","iopub.status.idle":"2023-08-02T22:23:39.564782Z","shell.execute_reply.started":"2023-08-02T22:23:39.548357Z","shell.execute_reply":"2023-08-02T22:23:39.563907Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":"# Complete the Transformer model\n\nThis model takes landmark coordinates as inputs and predicts a sequence of characters. The target character sequence, which has been shifted to the left is provided as the input to the decoder during training. The decoder employs its own past predictions during inference to forecast the next token.\n\nThe **Levenshtein Distance** between sequences is used as the accuracy metric since the evaluation metric for this contest is the **Normalized Total Levenshtein Distance**.","metadata":{}},{"cell_type":"code","source":"# Customized to add edit_dist metric and training variable.\n# Reference:\n# https://www.kaggle.com/code/irohith/aslfr-transformer/notebook\n# https://www.kaggle.com/code/shlomoron/aslfr-a-simple-transformer/notebook\n\nclass Transformer(keras.Model):\n    def __init__(\n        self,\n        num_hid=64,\n        num_head=2,\n        num_feed_forward=128,\n        source_maxlen=100,\n        target_maxlen=100,\n        num_layers_enc=4,\n        num_layers_dec=1,\n        num_classes=60,\n    ):\n        super().__init__()\n        self.loss_metric = keras.metrics.Mean(name=\"loss\")\n        self.acc_metric = keras.metrics.Mean(name=\"edit_dist\")\n        self.num_layers_enc = num_layers_enc\n        self.num_layers_dec = num_layers_dec\n        self.target_maxlen = target_maxlen\n        self.num_classes = num_classes\n\n        self.enc_input = LandmarkEmbedding(num_hid=num_hid, maxlen=source_maxlen)\n        self.dec_input = TokenEmbedding(\n            num_vocab=num_classes, maxlen=target_maxlen, num_hid=num_hid\n        )\n\n        self.encoder = keras.Sequential(\n            [self.enc_input]\n            + [\n                TransformerEncoder(num_hid, num_head, num_feed_forward)\n                for _ in range(num_layers_enc)\n            ]\n        )\n\n        for i in range(num_layers_dec):\n            setattr(\n                self,\n                f\"dec_layer_{i}\",\n                TransformerDecoder(num_hid, num_head, num_feed_forward),\n            )\n\n        self.classifier = layers.Dense(num_classes)\n\n    def decode(self, enc_out, target, training):\n        y = self.dec_input(target)\n        for i in range(self.num_layers_dec):\n            y = getattr(self, f\"dec_layer_{i}\")(enc_out, y, training)\n        return y\n\n    def call(self, inputs, training):\n        source = inputs[0]\n        target = inputs[1]\n        x = self.encoder(source, training)\n        y = self.decode(x, target, training)\n        return self.classifier(y)\n\n    @property\n    def metrics(self):\n        return [self.loss_metric]\n\n    def train_step(self, batch):\n        \"\"\"Processes one batch inside model.fit().\"\"\"\n        source = batch[0]\n        target = batch[1]\n\n        input_shape = tf.shape(target)\n        batch_size = input_shape[0]\n        \n        dec_input = target[:, :-1]\n        dec_target = target[:, 1:]\n        with tf.GradientTape() as tape:\n            preds = self([source, dec_input])\n            one_hot = tf.one_hot(dec_target, depth=self.num_classes)\n            mask = tf.math.logical_not(tf.math.equal(dec_target, pad_token_idx))\n            loss = self.compiled_loss(one_hot, preds, sample_weight=mask)\n        trainable_vars = self.trainable_variables\n        gradients = tape.gradient(loss, trainable_vars)\n        self.optimizer.apply_gradients(zip(gradients, trainable_vars))\n        # Computes the Levenshtein distance between sequences since the evaluation\n        # metric for this contest is the normalized total levenshtein distance.\n        edit_dist = tf.edit_distance(tf.sparse.from_dense(target), \n                                     tf.sparse.from_dense(tf.cast(tf.argmax(preds, axis=1), tf.int32)))\n        edit_dist = tf.reduce_mean(edit_dist)\n        self.acc_metric.update_state(edit_dist)\n        self.loss_metric.update_state(loss)\n        return {\"loss\": self.loss_metric.result(), \"edit_dist\": self.acc_metric.result()}\n\n    def test_step(self, batch):        \n        source = batch[0]\n        target = batch[1]\n\n        input_shape = tf.shape(target)\n        batch_size = input_shape[0]\n        \n        dec_input = target[:, :-1]\n        dec_target = target[:, 1:]\n        preds = self([source, dec_input])\n        one_hot = tf.one_hot(dec_target, depth=self.num_classes)\n        mask = tf.math.logical_not(tf.math.equal(dec_target, pad_token_idx))\n        loss = self.compiled_loss(one_hot, preds, sample_weight=mask)\n        # Computes the Levenshtein distance between sequences since the evaluation\n        # metric for this contest is the normalized total levenshtein distance.\n        edit_dist = tf.edit_distance(tf.sparse.from_dense(target), \n                                     tf.sparse.from_dense(tf.cast(tf.argmax(preds, axis=1), tf.int32)))\n        edit_dist = tf.reduce_mean(edit_dist)\n        self.acc_metric.update_state(edit_dist)\n        self.loss_metric.update_state(loss)\n        return {\"loss\": self.loss_metric.result(), \"edit_dist\": self.acc_metric.result()}\n\n    def generate(self, source, target_start_token_idx):\n        \"\"\"Performs inference over one batch of inputs using greedy decoding.\"\"\"\n        bs = tf.shape(source)[0]\n        enc = self.encoder(source, training = False)\n        dec_input = tf.ones((bs, 1), dtype=tf.int32) * target_start_token_idx\n        dec_logits = []\n        for i in range(self.target_maxlen - 1):\n            dec_out = self.decode(enc, dec_input, training = False)\n            logits = self.classifier(dec_out)\n            logits = tf.argmax(logits, axis=-1, output_type=tf.int32)\n            last_logit = logits[:, -1][..., tf.newaxis]\n            dec_logits.append(last_logit)\n            dec_input = tf.concat([dec_input, last_logit], axis=-1)\n        return dec_input","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:39.566347Z","iopub.execute_input":"2023-08-02T22:23:39.566749Z","iopub.status.idle":"2023-08-02T22:23:39.593026Z","shell.execute_reply.started":"2023-08-02T22:23:39.566718Z","shell.execute_reply":"2023-08-02T22:23:39.591812Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":"The following callback function is used to display predictions.","metadata":{}},{"cell_type":"code","source":"class DisplayOutputs(keras.callbacks.Callback):\n    def __init__(\n        self, batch, idx_to_token, target_start_token_idx=60, target_end_token_idx=61\n    ):\n        \"\"\"Displays a batch of outputs after every 4 epoch\n\n        Args:\n            batch: A test batch\n            idx_to_token: A List containing the vocabulary tokens corresponding to their indices\n            target_start_token_idx: A start token index in the target vocabulary\n            target_end_token_idx: An end token index in the target vocabulary\n        \"\"\"\n        self.batch = batch\n        self.target_start_token_idx = target_start_token_idx\n        self.target_end_token_idx = target_end_token_idx\n        self.idx_to_char = idx_to_token\n\n    def on_epoch_end(self, epoch, logs=None):\n        if epoch % 4 != 0:\n            return\n        source = self.batch[0]\n        target = self.batch[1].numpy()\n        bs = tf.shape(source)[0]\n        preds = self.model.generate(source, self.target_start_token_idx)\n        preds = preds.numpy()\n        for i in range(bs):\n            target_text = \"\".join([self.idx_to_char[_] for _ in target[i, :]])\n            prediction = \"\"\n            for idx in preds[i, :]:\n                prediction += self.idx_to_char[idx]\n                if idx == self.target_end_token_idx:\n                    break\n            print(f\"target:     {target_text.replace('-','')}\")\n            print(f\"prediction: {prediction}\\n\")","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:39.594655Z","iopub.execute_input":"2023-08-02T22:23:39.595043Z","iopub.status.idle":"2023-08-02T22:23:39.608797Z","shell.execute_reply.started":"2023-08-02T22:23:39.595006Z","shell.execute_reply":"2023-08-02T22:23:39.607655Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"# Train the Transformer model","metadata":{}},{"cell_type":"code","source":"# Transformer variables are customized from original keras tutorial to suit this dataset.\n# Reference: https://www.kaggle.com/code/shlomoron/aslfr-a-simple-transformer/notebook\n\nbatch = next(iter(valid_ds))\n\n# The vocabulary to convert predicted indices into characters\nidx_to_char = list(char_to_num.keys())\ndisplay_cb = DisplayOutputs(\n    batch, idx_to_char, target_start_token_idx=char_to_num['<'], target_end_token_idx=char_to_num['>']\n)  # set the arguments as per vocabulary index for '<' and '>'\n\nmodel = Transformer(\n    num_hid=200,\n    num_head=4,\n    num_feed_forward=400,\n    source_maxlen = FRAME_LEN,\n    target_maxlen=64,\n    num_layers_enc=2,\n    num_layers_dec=1,\n    num_classes=62\n)\nloss_fn = tf.keras.losses.CategoricalCrossentropy(\n    from_logits=True, label_smoothing=0.1,\n)\n\n\noptimizer = keras.optimizers.Adam(0.0001)\nmodel.compile(optimizer=optimizer, loss=loss_fn)\n\nhistory = model.fit(train_ds, validation_data=valid_ds, callbacks=[display_cb], epochs=13)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:23:39.610558Z","iopub.execute_input":"2023-08-02T22:23:39.61095Z","iopub.status.idle":"2023-08-02T22:30:56.796667Z","shell.execute_reply.started":"2023-08-02T22:23:39.610917Z","shell.execute_reply":"2023-08-02T22:30:56.795518Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"Epoch 1/13\n    632/Unknown - 102s 126ms/step - loss: 0.8785 - edit_dist: 1.0315target:     <2796 west golden willow drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <7898 steth 1010>\n\ntarget:     <9734719887>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+92-61-6-699>\n\ntarget:     <4977236992>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <985-166-6696>\n\ntarget:     <reallyloud.co.uk/simaii>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <belenondenerin>\n\ntarget:     <kkaicd1.pixnet.net>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <https://ww.comanganganan>\n\ntarget:     <8260 john r bowdoin>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <8686 hane warorin>\n\ntarget:     <56 paper birch drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <460-393-3838>\n\ntarget:     <gandchudaihardcor.html>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <http:///www.charom/>\n\ntarget:     <2708 west 77th>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <786-261-111>\n\ntarget:     <https://www.keainfo.gr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <https://www.comanerin>\n\ntarget:     <288 fuller lake>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <288-985-5111>\n\ntarget:     <mser/okiguide>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <anerin llle>\n\ntarget:     <220 north 47th avenue east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <232 hanganesthan>\n\ntarget:     <www.sudinfo.be>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.ndranere>\n\ntarget:     <69 grant point>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <369 re rarin>\n\ntarget:     <fibrain.pl/chapter145/thanghr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <900-11-1-113>\n\ntarget:     <+3515218895>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+33-353-3858>\n\ntarget:     <viapierogobetti/brandilove>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <323 sinininine com>\n\ntarget:     <amir le>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <anin le re>\n\ntarget:     <automantenimientosa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <mesesinestestes>\n\ntarget:     <6499 nfd 5053>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <649-957-5737>\n\ntarget:     <7870 preston place>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+99-808-6818>\n\ntarget:     <6870 scabisuit lane>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <6828 saian stin>\n\ntarget:     <sunshine mayer>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <ananan hanest>\n\ntarget:     <4082494707>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <482-868-6960>\n\ntarget:     <arabradio.us/vana>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <asaneaneran raran>\n\ntarget:     <televisoresponse>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <6800 core con>\n\ntarget:     <www.voices.com/bitesize>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.bering.com/>\n\ntarget:     <qd.razavi.ac.ir/bunya>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <http:///www.comaneranere>\n\ntarget:     <+246987508002294>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+64-88-8-895>\n\ntarget:     <+2666480514>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+22-868-0698>\n\ntarget:     <2257645994>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <237-555-5595>\n\ntarget:     <35816 webwood>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <380-000-6000>\n\ntarget:     <liana deleon>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <anan sarane>\n\ntarget:     </p21sistemas/sharps_carbines>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <http:///www.chesthererere>\n\ntarget:     <www.boehringeringelheim.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.hinghangherom/>\n\ntarget:     <2901092582>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <200-410-1828>\n\ntarget:     <102 flagstad>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <109 sthalan>\n\ntarget:     </verkstader/20180101>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <230-201-1101>\n\ntarget:     <myanmarsocialonlinemedia.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <aneste stestestene>\n\ntarget:     <bunkerbranch.tumblr.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <brousthanth rerert>\n\ntarget:     <+8286698863353>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+88-868-6333>\n\ntarget:     <9464 east woods edge way>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <http:///www.comanestanere>\n\ntarget:     <4632526021>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <462-223-3323>\n\ntarget:     <martin+blando/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+39-101-1010>\n\ntarget:     <beloshveyka.ru/105>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <900-213-2113>\n\ntarget:     <bid.auctionbymayo.com/71063>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <erestes-changertarart>\n\ntarget:     <7384391127>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <335-351-3313>\n\ntarget:     <www.twitchquotes.com/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.stomas.com/>\n\ntarget:     <5016 east paquin street>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <406 sanananest st st>\n\ntarget:     <2292979260>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <239-264-6666>\n\ntarget:     <9186054830>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <981-818-0117>\n\ntarget:     <brandee tran>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <920 sorend s>\n\ntarget:     <5061 williamsburg drive east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <901 starerestest s>\n\ntarget:     <certuspro.com/bloqueosjalapa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <bestestestestestestest>\n\ntarget:     <www.tudecora.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.comeres.com/>\n\ntarget:     <+279053128>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+39-919-0-11>\n\ntarget:     <rachael salas>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <http:///wwerestene>\n\ntarget:     <9525366907>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <553-366-6900>\n\ntarget:     <963 crested owl>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <9993 stet rorolad>\n\ntarget:     <2886 will teasley>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2886 lallale s>\n\ntarget:     <josie savage>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <comanengesth st>\n\ntarget:     <4127407457>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <444-270-2878>\n\ntarget:     <www.tornadoshop.cz/cornelius>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.domanderom/>\n\n632/632 [==============================] - 125s 162ms/step - loss: 0.8785 - edit_dist: 1.0315 - val_loss: 0.8061 - val_edit_dist: 1.0529\nEpoch 2/13\n632/632 [==============================] - 22s 35ms/step - loss: 0.7541 - edit_dist: 1.0540 - val_loss: 0.7261 - val_edit_dist: 1.0515\nEpoch 3/13\n632/632 [==============================] - 23s 36ms/step - loss: 0.6791 - edit_dist: 1.0481 - val_loss: 0.6646 - val_edit_dist: 1.0427\nEpoch 4/13\n632/632 [==============================] - 22s 36ms/step - loss: 0.6293 - edit_dist: 1.0394 - val_loss: 0.6316 - val_edit_dist: 1.0349\nEpoch 5/13\n631/632 [============================>.] - ETA: 0s - loss: 0.5953 - edit_dist: 1.0325target:     <2796 west golden willow drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <179 west willes dew drive>\n\ntarget:     <9734719887>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+35-38-61-99>\n\ntarget:     <4977236992>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <9498 tw-6592>\n\ntarget:     <reallyloud.co.uk/simaii>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <reldoud-k-maimai>\n\ntarget:     <kkaicd1.pixnet.net>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <haydayd-phenst.t.et>\n\ntarget:     <8260 john r bowdoin>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <826 oihn hardin>\n\ntarget:     <56 paper birch drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <561 ne brlich>\n\ntarget:     <gandchudaihardcor.html>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <310 hectr rearth sh>\n\ntarget:     <2708 west 77th>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2786 west 71>\n\ntarget:     <https://www.keainfo.gr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <https://ww.kreaifog>\n\ntarget:     <288 fuller lake>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <288-993-1>\n\ntarget:     <mser/okiguide>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <serkit hagride>\n\ntarget:     <220 north 47th avenue east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <220 nond han has>\n\ntarget:     <www.sudinfo.be>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.strkinf.com>\n\ntarget:     <69 grant point>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <69 wan mile>\n\ntarget:     <fibrain.pl/chapter145/thanghr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <firain-11118 rth 115th paroad>\n\ntarget:     <+3515218895>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+355-355-889>\n\ntarget:     <viapierogobetti/brandilove>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <228 lie beent dive drive>\n\ntarget:     <amir le>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <amirr le>\n\ntarget:     <automantenimientosa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <manien mientols>\n\ntarget:     <6499 nfd 5053>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <649-950-5053>\n\ntarget:     <7870 preston place>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <952-493-8505>\n\ntarget:     <6870 scabisuit lane>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <6670 sabitas lala lave>\n\ntarget:     <sunshine mayer>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <sunan maler>\n\ntarget:     <4082494707>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <418-437-4300>\n\ntarget:     <arabradio.us/vana>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <arabedio sers burna>\n\ntarget:     <televisoresponse>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3400 werth rad>\n\ntarget:     <www.voices.com/bitesize>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.vele.cet/blessssssss.i>\n\ntarget:     <qd.razavi.ac.ir/bunya>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <celart sacetasta>\n\ntarget:     <+246987508002294>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+46-98-58-0294>\n\ntarget:     <+2666480514>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <224-801-5140>\n\ntarget:     <2257645994>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <225-576-5941>\n\ntarget:     <35816 webwood>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <388 wobo bod>\n\ntarget:     <liana deleon>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <alata lereas>\n\ntarget:     </p21sistemas/sharps_carbines>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <110 stohot stes carie>\n\ntarget:     <www.boehringeringelheim.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.beseringeling detorgeto>\n\ntarget:     <2901092582>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <290-101-5822>\n\ntarget:     <102 flagstad>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <dende fialal>\n\ntarget:     </verkstader/20180101>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: </urustasturo.com.d>\n\ntarget:     <myanmarsocialonlinemedia.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <mananarsobedilee cotity.com>\n\ntarget:     <bunkerbranch.tumblr.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <bundban hanchander.com>\n\ntarget:     <+8286698863353>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+82-86-864-862-2-253>\n\ntarget:     <9464 east woods edge way>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3682 wast wast 150th road>\n\ntarget:     <4632526021>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <463-252-6021>\n\ntarget:     <martin+blando/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <alele pranon>\n\ntarget:     <beloshveyka.ru/105>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <belotst surat rur 100005>\n\ntarget:     <bid.auctionbymayo.com/71063>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <ebittliba.com/ebasal/>\n\ntarget:     <7384391127>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <735-439-2525>\n\ntarget:     <www.twitchquotes.com/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.twitele pes.com/>\n\ntarget:     <5016 east paquin street>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <516 beast parin st>\n\ntarget:     <2292979260>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <291-297-2956>\n\ntarget:     <9186054830>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <918-483-4833>\n\ntarget:     <brandee tran>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <brande detran>\n\ntarget:     <5061 williamsburg drive east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <506 willime rk riveasast>\n\ntarget:     <certuspro.com/bloqueosjalapa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <coustus-de-de-de-pom/ba>\n\ntarget:     <www.tudecora.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.bededecor.com>\n\ntarget:     <+279053128>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+27-53-90-128>\n\ntarget:     <rachael salas>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <handele ster s>\n\ntarget:     <9525366907>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <952-536-6907>\n\ntarget:     <963 crested owl>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <9633 rest 1065>\n\ntarget:     <2886 will teasley>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2886 will ead>\n\ntarget:     <josie savage>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <80 sese vagle>\n\ntarget:     <4127407457>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <441-103-7744>\n\ntarget:     <www.tornadoshop.cz/cornelius>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.ondosh.cordives>\n\n632/632 [==============================] - 24s 38ms/step - loss: 0.5951 - edit_dist: 1.0325 - val_loss: 0.6139 - val_edit_dist: 1.0294\nEpoch 6/13\n632/632 [==============================] - 23s 36ms/step - loss: 0.5689 - edit_dist: 1.0277 - val_loss: 0.6037 - val_edit_dist: 1.0252\nEpoch 7/13\n632/632 [==============================] - 22s 35ms/step - loss: 0.5469 - edit_dist: 1.0237 - val_loss: 0.5793 - val_edit_dist: 1.0216\nEpoch 8/13\n632/632 [==============================] - 23s 36ms/step - loss: 0.5282 - edit_dist: 1.0203 - val_loss: 0.5670 - val_edit_dist: 1.0187\nEpoch 9/13\n632/632 [==============================] - ETA: 0s - loss: 0.5111 - edit_dist: 1.0177target:     <2796 west golden willow drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <1779 west olenwill wadrive>\n\ntarget:     <9734719887>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+36-83-43-29>\n\ntarget:     <4977236992>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <9498 stret 6592>\n\ntarget:     <reallyloud.co.uk/simaii>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <realoud-com.rud.com.i>\n\ntarget:     <kkaicd1.pixnet.net>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: </packaicde-pane-te-testat>\n\ntarget:     <8260 john r bowdoin>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <8260 oighner dowoy>\n\ntarget:     <56 paper birch drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <56 ken birch dree drive>\n\ntarget:     <gandchudaihardcor.html>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <randesting.ru/cremarcharesh>\n\ntarget:     <2708 west 77th>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2786 west 771th>\n\ntarget:     <https://www.keainfo.gr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <https://www.preatifo.frg>\n\ntarget:     <288 fuller lake>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <288 fol land la>\n\ntarget:     <mser/okiguide>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <ser/ki-guide>\n\ntarget:     <220 north 47th avenue east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <220 nont hanch roas>\n\ntarget:     <www.sudinfo.be>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.sprkinf.com>\n\ntarget:     <69 grant point>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <669 sout oroin>\n\ntarget:     <fibrain.pl/chapter145/thanghr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <firaina-bor-bar-bor-parande>\n\ntarget:     <+3515218895>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+355-355-352-89>\n\ntarget:     <viapierogobetti/brandilove>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2020 pire stri/te-bendiariave>\n\ntarget:     <amir le>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <amirr lee>\n\ntarget:     <automantenimientosa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <mprenimientoientos>\n\ntarget:     <6499 nfd 5053>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <649-915-95053>\n\ntarget:     <7870 preston place>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <952 ean perth plac>\n\ntarget:     <6870 scabisuit lane>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <6870 sabis sbace>\n\ntarget:     <sunshine mayer>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <sutunshine.maler>\n\ntarget:     <4082494707>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <418-873-4430>\n\ntarget:     <arabradio.us/vana>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <arabberasdioers/vana>\n\ntarget:     <televisoresponse>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <1382 weber facherad dada>\n\ntarget:     <www.voices.com/bitesize>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.vonde.com/barestai>\n\ntarget:     <qd.razavi.ac.ir/bunya>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <elopart-scelesacira>\n\ntarget:     <+246987508002294>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+246-988-50-0294>\n\ntarget:     <+2666480514>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+222-64-80-80-514>\n\ntarget:     <2257645994>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+22-576-594-94-94>\n\ntarget:     <35816 webwood>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3581 ncr co d>\n\ntarget:     <liana deleon>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <alliana deler st>\n\ntarget:     </p21sistemas/sharps_carbines>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <111 wistestemas/shoubieries>\n\ntarget:     <www.boehringeringelheim.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.bosekinger-dangelhopom.co>\n\ntarget:     <2901092582>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <290-110-6822>\n\ntarget:     <102 flagstad>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <dond fiagst lar>\n\ntarget:     </verkstader/20180101>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: </verustadu/2010-01>\n\ntarget:     <myanmarsocialonlinemedia.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <mianarsocallobaleredidia.com>\n\ntarget:     <bunkerbranch.tumblr.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <bundebanch.um/ganch-bus/>\n\ntarget:     <+8286698863353>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+82-86-86-863-253>\n\ntarget:     <9464 east woods edge way>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3683 wast west 16th road roach>\n\ntarget:     <4632526021>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <463-252-6021>\n\ntarget:     <martin+blando/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.lespret.com/>\n\ntarget:     <beloshveyka.ru/105>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <belot-shuera-ru/1010005>\n\ntarget:     <bid.auctionbymayo.com/71063>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <ebit-dioba.com/baapas/1013331>\n\ntarget:     <7384391127>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <738-439-1256>\n\ntarget:     <www.twitchquotes.com/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.twitochantes.com/>\n\ntarget:     <5016 east paquin street>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <5016 east papapa paret>\n\ntarget:     <2292979260>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <291-297-7956>\n\ntarget:     <9186054830>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <918-818-0583>\n\ntarget:     <brandee tran>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <brand deozur>\n\ntarget:     <5061 williamsburg drive east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <501 willimser drive east>\n\ntarget:     <certuspro.com/bloqueosjalapa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <cobursspo.com/blogross.ia>\n\ntarget:     <www.tudecora.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.redentecar.com>\n\ntarget:     <+279053128>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+27-9053-128>\n\ntarget:     <rachael salas>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <31 hach seal s>\n\ntarget:     <9525366907>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <952-536-6907>\n\ntarget:     <963 crested owl>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <963 w333 rest wall>\n\ntarget:     <2886 will teasley>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2886 will tead lach>\n\ntarget:     <josie savage>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <gitosesesese.vetug.com>\n\ntarget:     <4127407457>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <441-609-7744>\n\ntarget:     <www.tornadoshop.cz/cornelius>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.orndosh.com.com/corelis>\n\n632/632 [==============================] - 24s 38ms/step - loss: 0.5111 - edit_dist: 1.0177 - val_loss: 0.5576 - val_edit_dist: 1.0164\nEpoch 10/13\n632/632 [==============================] - 22s 35ms/step - loss: 0.4979 - edit_dist: 1.0157 - val_loss: 0.5494 - val_edit_dist: 1.0149\nEpoch 11/13\n632/632 [==============================] - 23s 36ms/step - loss: 0.4866 - edit_dist: 1.0144 - val_loss: 0.5446 - val_edit_dist: 1.0137\nEpoch 12/13\n632/632 [==============================] - 22s 35ms/step - loss: 0.4754 - edit_dist: 1.0134 - val_loss: 0.5415 - val_edit_dist: 1.0129\nEpoch 13/13\n632/632 [==============================] - ETA: 0s - loss: 0.4654 - edit_dist: 1.0126target:     <2796 west golden willow drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <1779 west golewill w drive>\n\ntarget:     <9734719887>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+67-823-48-29>\n\ntarget:     <4977236992>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <549-723-6592>\n\ntarget:     <reallyloud.co.uk/simaii>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <realoud.com.ru/simasi>\n\ntarget:     <kkaicd1.pixnet.net>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <paricdel-zine-l-ne-tes>\n\ntarget:     <8260 john r bowdoin>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <8260 johnr row doin>\n\ntarget:     <56 paper birch drive>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <56 ker birch roadive>\n\ntarget:     <gandchudaihardcor.html>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <radeshuigsharboranch.ht>\n\ntarget:     <2708 west 77th>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2788 west 771th>\n\ntarget:     <https://www.keainfo.gr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <https://www.treaifo.g>\n\ntarget:     <288 fuller lake>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <288 forl morl>\n\ntarget:     <mser/okiguide>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <messer/oki-gride>\n\ntarget:     <220 north 47th avenue east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <220 north 3120 strea>\n\ntarget:     <www.sudinfo.be>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.sprinf.com>\n\ntarget:     <69 grant point>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <669 cran oira>\n\ntarget:     <fibrain.pl/chapter145/thanghr>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <firaina-1801.brg/303-111-haror>\n\ntarget:     <+3515218895>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+35-352-521-89>\n\ntarget:     <viapierogobetti/brandilove>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2020 ire sibrog dive>\n\ntarget:     <amir le>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <amoir le>\n\ntarget:     <automantenimientosa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <margenimient.co.s>\n\ntarget:     <6499 nfd 5053>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <649-915-9537>\n\ntarget:     <7870 preston place>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <9387 perth per plac>\n\ntarget:     <6870 scabisuit lane>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <6870 sabis suitlan>\n\ntarget:     <sunshine mayer>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <sutukane.comaye.comayeu>\n\ntarget:     <4082494707>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <478-835-4800>\n\ntarget:     <arabradio.us/vana>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <arabra diosers/vana>\n\ntarget:     <televisoresponse>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3883 webifachorl dada>\n\ntarget:     <www.voices.com/bitesize>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.volence.com/chesiti>\n\ntarget:     <qd.razavi.ac.ir/bunya>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <celerte-sacir-sacira>\n\ntarget:     <+246987508002294>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+246-987-50-0294>\n\ntarget:     <+2666480514>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+22-264-80-80-514>\n\ntarget:     <2257645994>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <257-559-5948>\n\ntarget:     <35816 webwood>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <388 row co rd>\n\ntarget:     <liana deleon>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <allia adrellers>\n\ntarget:     </p21sistemas/sharps_carbines>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <211 wistemas hishoubies>\n\ntarget:     <www.boehringeringelheim.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.boshringer-dheim.com.com>\n\ntarget:     <2901092582>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <290-714-109-582>\n\ntarget:     <102 flagstad>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <1029 agst ral>\n\ntarget:     </verkstader/20180101>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: </verksadr/2018-01>\n\ntarget:     <myanmarsocialonlinemedia.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <mianmarsocillogilereemila.com>\n\ntarget:     <bunkerbranch.tumblr.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <bundebanch.um/banch-bus/>\n\ntarget:     <+8286698863353>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+82-86-86-863-863-353>\n\ntarget:     <9464 east woods edge way>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3807 worth stree stree road>\n\ntarget:     <4632526021>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <463-252-6021>\n\ntarget:     <martin+blando/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <anale pring road roach>\n\ntarget:     <beloshveyka.ru/105>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <belot-shveika.ru/10105>\n\ntarget:     <bid.auctionbymayo.com/71063>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <ebiditarioba.com/401013>\n\ntarget:     <7384391127>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <738-439-1257>\n\ntarget:     <www.twitchquotes.com/>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.twitechnes.com/>\n\ntarget:     <5016 east paquin street>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <5016 east papa paparin streeee>\n\ntarget:     <2292979260>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <293-297-5526>\n\ntarget:     <9186054830>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <918-058-0583>\n\ntarget:     <brandee tran>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <brand dedezran>\n\ntarget:     <5061 williamsburg drive east>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <5061 willimsbruriveriveras.a>\n\ntarget:     <certuspro.com/bloqueosjalapa>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <cutusspo.com/blogross-iala>\n\ntarget:     <www.tudecora.com>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.rundecarescary.com>\n\ntarget:     <+279053128>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <+27-9053-128>\n\ntarget:     <rachael salas>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <3 hira chis>\n\ntarget:     <9525366907>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <952-536-6907>\n\ntarget:     <963 crested owl>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <963 restedow ll>\n\ntarget:     <2886 will teasley>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <2886 will tea lcourt>\n\ntarget:     <josie savage>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <8680 susense 127 grace>\n\ntarget:     <4127407457>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <481-209-7744>\n\ntarget:     <www.tornadoshop.cz/cornelius>PPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPPP\nprediction: <www.orndsh.cz.com.com/corelis>\n\n632/632 [==============================] - 24s 38ms/step - loss: 0.4654 - edit_dist: 1.0126 - val_loss: 0.5434 - val_edit_dist: 1.0123\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Plot training loss and validation loss","metadata":{}},{"cell_type":"code","source":"plt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.legend(['training loss', 'val_loss'])","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:30:56.798021Z","iopub.execute_input":"2023-08-02T22:30:56.798402Z","iopub.status.idle":"2023-08-02T22:30:57.088205Z","shell.execute_reply.started":"2023-08-02T22:30:56.79837Z","shell.execute_reply":"2023-08-02T22:30:57.087275Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"<matplotlib.legend.Legend at 0x79997821caf0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"markdown","source":"# Submission\n\nRefrence: https://www.kaggle.com/code/shlomoron/aslfr-a-simple-transformer/notebook","metadata":{}},{"cell_type":"markdown","source":"# Create TFLite model","metadata":{}},{"cell_type":"code","source":" class TFLiteModel(tf.Module):\n    def __init__(self, model):\n        super(TFLiteModel, self).__init__()\n        self.target_start_token_idx = start_token_idx\n        self.target_end_token_idx = end_token_idx\n        # Load the feature generation and main models\n        self.model = model\n    \n    @tf.function(input_signature=[tf.TensorSpec(shape=[None, len(FEATURE_COLUMNS)], dtype=tf.float32, name='inputs')])\n    def __call__(self, inputs, training=False):\n        # Preprocess Data\n        x = tf.cast(inputs, tf.float32)\n        x = x[None]\n        x = tf.cond(tf.shape(x)[1] == 0, lambda: tf.zeros((1, 1, len(FEATURE_COLUMNS))), lambda: tf.identity(x))\n        x = x[0]\n        x = pre_process(x)\n        x = x[None]\n        x = self.model.generate(x, self.target_start_token_idx)\n        x = x[0]\n        idx = tf.argmax(tf.cast(tf.equal(x, self.target_end_token_idx), tf.int32))\n        idx = tf.where(tf.math.less(idx, 1), tf.constant(2, dtype=tf.int64), idx)\n        x = x[1:idx]\n        x = tf.one_hot(x, 59)\n        return {'outputs': x}\n    \ntflitemodel_base = TFLiteModel(model)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:30:57.090106Z","iopub.execute_input":"2023-08-02T22:30:57.090758Z","iopub.status.idle":"2023-08-02T22:30:57.102003Z","shell.execute_reply.started":"2023-08-02T22:30:57.090726Z","shell.execute_reply":"2023-08-02T22:30:57.100988Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"model.save_weights(\"model.h5\")","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:30:57.103568Z","iopub.execute_input":"2023-08-02T22:30:57.103965Z","iopub.status.idle":"2023-08-02T22:30:57.233879Z","shell.execute_reply.started":"2023-08-02T22:30:57.103933Z","shell.execute_reply":"2023-08-02T22:30:57.232901Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"keras_model_converter = tf.lite.TFLiteConverter.from_keras_model(tflitemodel_base)\nkeras_model_converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]#, tf.lite.OpsSet.SELECT_TF_OPS]\ntflite_model = keras_model_converter.convert()\nwith open('/kaggle/working/model.tflite', 'wb') as f:\n    f.write(tflite_model)\n    \ninfargs = {\"selected_columns\" : FEATURE_COLUMNS}\n\nwith open('inference_args.json', \"w\") as json_file:\n    json.dump(infargs, json_file)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:30:57.235453Z","iopub.execute_input":"2023-08-02T22:30:57.23579Z","iopub.status.idle":"2023-08-02T22:31:54.197935Z","shell.execute_reply.started":"2023-08-02T22:30:57.235759Z","shell.execute_reply":"2023-08-02T22:31:54.19535Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"!zip submission.zip  './model.tflite' './inference_args.json'","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:31:54.201001Z","iopub.execute_input":"2023-08-02T22:31:54.201432Z","iopub.status.idle":"2023-08-02T22:31:56.413359Z","shell.execute_reply.started":"2023-08-02T22:31:54.201393Z","shell.execute_reply":"2023-08-02T22:31:56.412149Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"  adding: model.tflite (deflated 18%)\n  adding: inference_args.json (deflated 85%)\n","output_type":"stream"}]},{"cell_type":"code","source":"interpreter = tf.lite.Interpreter(\"model.tflite\")\n\nREQUIRED_SIGNATURE = \"serving_default\"\nREQUIRED_OUTPUT = \"outputs\"\n\nwith open (\"/kaggle/input/asl-fingerspelling/character_to_prediction_index.json\", \"r\") as f:\n    character_map = json.load(f)\nrev_character_map = {j:i for i,j in character_map.items()}\n\nfound_signatures = list(interpreter.get_signature_list().keys())\n\nif REQUIRED_SIGNATURE not in found_signatures:\n    raise KernelEvalException('Required input signature not found.')\n\nprediction_fn = interpreter.get_signature_runner(\"serving_default\")\noutput = prediction_fn(inputs=batch[0][0])\nprediction_str = \"\".join([rev_character_map.get(s, \"\") for s in np.argmax(output[REQUIRED_OUTPUT], axis=1)])\nprint(prediction_str)","metadata":{"execution":{"iopub.status.busy":"2023-08-02T22:31:56.415422Z","iopub.execute_input":"2023-08-02T22:31:56.415817Z","iopub.status.idle":"2023-08-02T22:31:56.767873Z","shell.execute_reply.started":"2023-08-02T22:31:56.415782Z","shell.execute_reply":"2023-08-02T22:31:56.766729Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stdout","text":"/848/\n","output_type":"stream"}]}]}