final-bms-submission.ipynb

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   "source": [
    "# install image classifier \n",
    "!pip install -q --upgrade pip\n",
    "!pip install -q efficientnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "graduate-savage",
   "metadata": {
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   "source": [
    "# install relevant packages\n",
    "\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import efficientnet.tfkeras as efn\n",
    "\n",
    "from tensorflow.keras.mixed_precision import experimental as mixed_precision\n",
    "from kaggle_datasets import KaggleDatasets\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "import unicodedata\n",
    "import re\n",
    "import numpy as np\n",
    "import os\n",
    "import io\n",
    "import time\n",
    "import pickle\n",
    "import math\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "caroline-report",
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   "source": [
    "# seed for reproducibility\n",
    "SEED = 42\n",
    "os.environ['PYTHONHASHSEED'] = str(SEED)\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "matched-yukon",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on TPU  grpc://10.0.0.2:8470\n",
      "REPLICAS: 8\n",
      "Compute dtype: bfloat16\n",
      "Variable dtype: float32\n"
     ]
    }
   ],
   "source": [
    "# Detect hardware, set appropriate distribution strategy (GPU/TPU)\n",
    "try:\n",
    "    TPU = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n",
    "    print('Running on TPU ', TPU.master())\n",
    "except ValueError:\n",
    "    print('Running on GPU')\n",
    "    TPU = None\n",
    "\n",
    "if TPU:\n",
    "    tf.config.experimental_connect_to_cluster(TPU)\n",
    "    tf.tpu.experimental.initialize_tpu_system(TPU)\n",
    "    strategy = tf.distribute.experimental.TPUStrategy(TPU)\n",
    "else:\n",
    "    strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n",
    "\n",
    "REPLICAS = strategy.num_replicas_in_sync\n",
    "print(f'REPLICAS: {REPLICAS}')\n",
    "\n",
    "# # set half precision policy\n",
    "mixed_precision.set_policy('mixed_bfloat16' if TPU else 'float32')\n",
    "\n",
    "# enable XLA optmizations\n",
    "tf.config.optimizer.set_jit(True)\n",
    "\n",
    "print(f'Compute dtype: {mixed_precision.global_policy().compute_dtype}')\n",
    "print(f'Variable dtype: {mixed_precision.global_policy().variable_dtype}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "former-pickup",
   "metadata": {
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   "outputs": [],
   "source": [
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "secret_value_0 = user_secrets.get_secret(\"__gcloud_sdk_auth__\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "still-dylan",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "user_credential = user_secrets.get_gcloud_credential()\n",
    "user_secrets.set_tensorflow_credential(user_credential)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "under-equipment",
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   "source": [
    "DEBUG = False # when DEBUG = True, use smaller dataset\n",
    "\n",
    "# image resolution determined in data cleaning process\n",
    "IMG_HEIGHT = 256\n",
    "IMG_WIDTH = 448\n",
    "N_CHANNELS = 3\n",
    "# maximum InChI length is 200 to prevent too much padding\n",
    "MAX_INCHI_LEN = 200\n",
    "\n",
    "# batch sizes\n",
    "BATCH_SIZE_BASE = 6 if DEBUG else (64 if TPU else 12) \n",
    "BATCH_SIZE = BATCH_SIZE_BASE * REPLICAS\n",
    "BATCH_SIZE_DEBUG = 2\n",
    "\n",
    "# target data type, bfloat16 when using TPU to improve throughput\n",
    "TARGET_DTYPE = tf.bfloat16 if TPU else tf.float32\n",
    " # minimal memory usage of labels\n",
    "LABEL_DTYPE= tf.uint8\n",
    "\n",
    "# 100K validation images are used\n",
    "VAL_SIZE = int(100) if DEBUG else int(10000)\n",
    "VAL_STEPS = VAL_SIZE // BATCH_SIZE\n",
    "\n",
    "# ImageNet mean and std to normalize training images accordingly\n",
    "IMAGENET_MEAN = tf.constant([0.485, 0.456, 0.406], dtype=tf.float32)\n",
    "IMAGENET_STD = tf.constant([0.229, 0.224, 0.225], dtype=tf.float32)\n",
    "\n",
    "# Google Cloud Dataset path to training and validation images\n",
    "GCS_DS_PATH = KaggleDatasets().get_gcs_path('finaldata')\n",
    "\n",
    "# Tensorflow AUTO flag, used in datasets\n",
    "AUTO = tf.data.experimental.AUTOTUNE"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "final-wesley",
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   "source": [
    "# character to integer mapping\n",
    "with open('../input/tokenizer/vocabulary_to_int.pkl', 'rb') as handle:\n",
    "    vocabulary_to_int = pickle.load( handle)\n",
    "\n",
    "# integer to character mapping\n",
    "with open('../input/tokenizer/int_to_vocabulary.pkl', 'rb') as handle:\n",
    "    int_to_vocabulary = pickle.load( handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fatty-intermediate",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VOCAB_SIZE: 41\n"
     ]
    }
   ],
   "source": [
    "# configure model\n",
    "VOCAB_SIZE = len(vocabulary_to_int.values())\n",
    "SEQ_LEN_OUT = MAX_INCHI_LEN\n",
    "DECODER_DIM = 1024\n",
    "ATTENTION_UNITS = 512\n",
    "\n",
    "print(f'VOCAB_SIZE: {VOCAB_SIZE}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dominant-boston",
   "metadata": {
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   "source": [
    "# decodes TFRecord\n",
    "def decode_tfrecord(record_bytes):\n",
    "    features = tf.io.parse_single_example(record_bytes, {\n",
    "        'image': tf.io.FixedLenFeature([], tf.string),\n",
    "        'InChI': tf.io.FixedLenFeature([MAX_INCHI_LEN], tf.int64),\n",
    "    })\n",
    "\n",
    "    # decode the PNG and explicitly reshape to image size (required on TPU)\n",
    "    image = tf.io.decode_png(features['image'])    \n",
    "    image = tf.reshape(image, [IMG_HEIGHT, IMG_WIDTH, 1])\n",
    "    # normalize according to ImageNet mean and std\n",
    "    image = tf.cast(image, tf.float32)  / 255.0\n",
    "    image = (image - IMAGENET_MEAN) / IMAGENET_STD\n",
    "    \n",
    "    if TPU: # if running on TPU image needs to be cast to bfloat16\n",
    "        image = tf.cast(image, TARGET_DTYPE)\n",
    "    \n",
    "    InChI = tf.reshape(features['InChI'], [MAX_INCHI_LEN])\n",
    "    InChI = tf.cast(InChI, LABEL_DTYPE)\n",
    "    \n",
    "    return image, InChI"
   ]
  },
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   "execution_count": 11,
   "id": "successful-gibraltar",
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   "outputs": [],
   "source": [
    "# Benchmark function to test the dataset throughput performance\n",
    "def benchmark_dataset(dataset, num_epochs=3, n_steps_per_epoch=25, bs=BATCH_SIZE):\n",
    "    start_time = time.perf_counter()\n",
    "    for epoch_num in range(num_epochs):\n",
    "        epoch_start = time.perf_counter()\n",
    "        for idx, (images, labels) in enumerate(dataset.take(n_steps_per_epoch)):\n",
    "            if idx is 1 and epoch_num is 0:\n",
    "                print(f'image shape: {images.shape}, image dtype: {images.dtype}')\n",
    "                print(f'labels shape: {labels.shape}, label dtype: {labels.dtype}')\n",
    "            pass\n",
    "        epoch_t = time.perf_counter() - epoch_start\n",
    "        mean_step_t = round(epoch_t / n_steps_per_epoch * 1000, 1)\n",
    "        n_imgs_per_s = int(1 / (mean_step_t / 1000) * bs)\n",
    "        print(f'epoch {epoch_num} took: {round(epoch_t, 2)} sec, mean step duration: {mean_step_t}ms, images/s: {n_imgs_per_s}')"
   ]
  },
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   "id": "likely-figure",
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   "outputs": [],
   "source": [
    "# plots the first images of the dataset\n",
    "def show_batch(dataset, rows=3, cols=2): # for temporary purposes\n",
    "    imgs, lbls = next(iter(dataset))\n",
    "    fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(cols*7, rows*4))\n",
    "    for r in range(rows):\n",
    "        for c in range(cols):\n",
    "            img = imgs[r*cols+c].numpy().astype(np.float32)\n",
    "            img += abs(img.min())\n",
    "            img /= img.max()\n",
    "            axes[r, c].imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "compact-activation",
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   "source": [
    "def get_train_dataset(bs=BATCH_SIZE):\n",
    "    ignore_order = tf.data.Options()\n",
    "    ignore_order.experimental_deterministic = False\n",
    "    \n",
    "    FNAMES_TRAIN_TFRECORDS = tf.io.gfile.glob(f'{GCS_DS_PATH}/allcleanedimages/train/*.tfrecords')\n",
    "    train_dataset = tf.data.TFRecordDataset(FNAMES_TRAIN_TFRECORDS, num_parallel_reads=AUTO)\n",
    "    train_dataset = train_dataset.with_options(ignore_order)\n",
    "    train_dataset = train_dataset.prefetch(AUTO) # optimize automatically\n",
    "    train_dataset = train_dataset.repeat()\n",
    "    train_dataset = train_dataset.map(decode_tfrecord, num_parallel_calls=AUTO)  # optimize automatically\n",
    "    train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=True)\n",
    "    train_dataset = train_dataset.prefetch(1) # just 1 prefetched batch is needed\n",
    "    \n",
    "    return train_dataset\n",
    "\n",
    "train_dataset = get_train_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "armed-relief",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: (512, 256, 448, 3), image dtype: <dtype: 'bfloat16'>\n",
      "labels shape: (512, 200), label dtype: <dtype: 'uint8'>\n",
      "epoch 0 took: 9.45 sec, mean step duration: 378.2ms, images/s: 1353\n",
      "epoch 1 took: 3.55 sec, mean step duration: 142.1ms, images/s: 3603\n",
      "epoch 2 took: 1.86 sec, mean step duration: 74.3ms, images/s: 6890\n"
     ]
    }
   ],
   "source": [
    "benchmark_dataset(train_dataset)"
   ]
  },
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   "id": "treated-entrepreneur",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "imgs.shape: (512, 256, 448, 3), lbls.shape: (512, 200)\n",
      "train img 0 mean: -1.881, 0 std: 0.689, min: -2.125, max: 2.641, <dtype: 'bfloat16'>\n"
     ]
    }
   ],
   "source": [
    "# display statistics about the first image to check if the images are decoded correctly\n",
    "imgs, lbls = next(iter(train_dataset))\n",
    "print(f'imgs.shape: {imgs.shape}, lbls.shape: {lbls.shape}')\n",
    "img0 = imgs[0].numpy().astype(np.float32)\n",
    "train_batch_info = (img0.mean(), img0.std(), img0.min(), img0.max(), imgs.dtype)\n",
    "print('train img 0 mean: %.3f, 0 std: %.3f, min: %.3f, max: %.3f, %s' % train_batch_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "heavy-chassis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:07:55.171944Z",
     "iopub.status.busy": "2021-04-17T13:07:55.170892Z",
     "iopub.status.idle": "2021-04-17T13:07:56.877447Z",
     "shell.execute_reply": "2021-04-17T13:07:56.877964Z"
    },
    "papermill": {
     "duration": 1.748101,
     "end_time": "2021-04-17T13:07:56.878135",
     "exception": false,
     "start_time": "2021-04-17T13:07:55.130034",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show first few train images\n",
    "show_batch(train_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "incorporated-program",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:07:56.963914Z",
     "iopub.status.busy": "2021-04-17T13:07:56.963193Z",
     "iopub.status.idle": "2021-04-17T13:07:57.151859Z",
     "shell.execute_reply": "2021-04-17T13:07:57.152356Z"
    },
    "papermill": {
     "duration": 0.234868,
     "end_time": "2021-04-17T13:07:57.152577",
     "exception": false,
     "start_time": "2021-04-17T13:07:56.917709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_val_dataset(bs=BATCH_SIZE):\n",
    "    FNAMES_TRAIN_TFRECORDS = tf.io.gfile.glob(f'{GCS_DS_PATH}/allcleanedimages/val/*.tfrecords')\n",
    "    val_dataset = tf.data.TFRecordDataset(FNAMES_TRAIN_TFRECORDS, num_parallel_reads=AUTO)\n",
    "    val_dataset = val_dataset.prefetch(AUTO)\n",
    "    val_dataset = val_dataset.repeat()\n",
    "    val_dataset = val_dataset.map(decode_tfrecord, num_parallel_calls=AUTO)\n",
    "    val_dataset = val_dataset.batch(BATCH_SIZE, drop_remainder=True)\n",
    "    val_dataset = val_dataset.prefetch(1)\n",
    "    \n",
    "    return val_dataset\n",
    "\n",
    "val_dataset = get_val_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "instructional-yugoslavia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:07:57.235812Z",
     "iopub.status.busy": "2021-04-17T13:07:57.234853Z",
     "iopub.status.idle": "2021-04-17T13:08:06.284569Z",
     "shell.execute_reply": "2021-04-17T13:08:06.283756Z"
    },
    "papermill": {
     "duration": 9.092172,
     "end_time": "2021-04-17T13:08:06.284753",
     "exception": false,
     "start_time": "2021-04-17T13:07:57.192581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: (512, 256, 448, 3), image dtype: <dtype: 'bfloat16'>\n",
      "labels shape: (512, 200), label dtype: <dtype: 'uint8'>\n",
      "epoch 0 took: 5.26 sec, mean step duration: 210.3ms, images/s: 2434\n",
      "epoch 1 took: 1.89 sec, mean step duration: 75.5ms, images/s: 6781\n",
      "epoch 2 took: 1.89 sec, mean step duration: 75.8ms, images/s: 6754\n"
     ]
    }
   ],
   "source": [
    "benchmark_dataset(val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "expressed-insertion",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:06.376984Z",
     "iopub.status.busy": "2021-04-17T13:08:06.376012Z",
     "iopub.status.idle": "2021-04-17T13:08:06.629974Z",
     "shell.execute_reply": "2021-04-17T13:08:06.630509Z"
    },
    "papermill": {
     "duration": 0.303503,
     "end_time": "2021-04-17T13:08:06.630708",
     "exception": false,
     "start_time": "2021-04-17T13:08:06.327205",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_imgs.shape: (512, 256, 448, 3), val_lbls.shape: (512, 200)\n",
      "val img 0 mean: -1.881, 0 std: 0.689, min: -2.125, max: 2.641, <dtype: 'bfloat16'>\n"
     ]
    }
   ],
   "source": [
    "val_imgs, val_lbls = next(iter(val_dataset))\n",
    "print(f'val_imgs.shape: {val_imgs.shape}, val_lbls.shape: {val_lbls.shape}')\n",
    "val_img0 = val_imgs[0].numpy().astype(np.float32)\n",
    "val_batch_info = (val_img0.mean(), val_img0.std(), val_img0.min(), val_img0.max(), val_imgs.dtype)\n",
    "print('val img 0 mean: %.3f, 0 std: %.3f, min: %.3f, max: %.3f, %s' % train_batch_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "characteristic-fourth",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:06.721331Z",
     "iopub.status.busy": "2021-04-17T13:08:06.720387Z",
     "iopub.status.idle": "2021-04-17T13:08:07.995009Z",
     "shell.execute_reply": "2021-04-17T13:08:07.995807Z"
    },
    "papermill": {
     "duration": 1.322712,
     "end_time": "2021-04-17T13:08:07.996066",
     "exception": false,
     "start_time": "2021-04-17T13:08:06.673354",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TH32EZ54uxpkzZ4L5K1GE0Wl10Gi0PY5JvpEREQWXTquDXh8Fu8OhdCgUJHa7HZaMjNBsy2bjF4hBEPYTkc7OTmwuKsKuvXsxY+ZMAIDN1o0Xfvc72G3uSUR3Px0v/c/1WJiejhkzZ8LlcuFoYyMA92Smd+dO77aIRsrhcODpLVt8xyQREQVPgtkMl8uFBXfcAQfTYCPW+g1PYe3qx+Dwm2z2lw49Fr0/Q1pycvD3t99mX5oACfuJiNeitDRfhSuDwYDjLS2IitL70qv8VzYGemxu3jJ8cPoUAGDLps0o3rIlFKETERFRAMXEuAsLMI06sk2bNr1PddP+0qFHq7/PkEajEe+/915Anp8iaCLiPxvWCIGoKD2iogxwuaTvHI+hHisgEBVlAOCeURMREVF40Wq1mJ2cjNqDB5UOhYKssqKiT/bK4bpDeEYUB+T5HQ4HnK6ez3/wwAHcfMstmDJlCiupBkDETET8eRsaajQa6HQ6VNfUoLK8fMgDprSkBG++8QYAdxqWXq9HVU0NdHodCpbncxmORi0qKgp7XnkFLukCADQ3NWNFAZsh+Uswm7FxUxH/1oho1LRaLSZMmIAbZ8zA05s3Kx0OBYn/eOHqNRHxr5gaDHW1tVixaiWcTicnIgEQkRMRb0NDwF1RweFwINZoxOzkZN99mpua0NXV1eNxVqu1R28RvV4Pp9MJoRFIMJuhERq0tDSH5pcIE9ExMTCbzb7LHe3t6OzsVDAi9XI6nb6JiMvlUjgadUkwmzE7ORmJiUmYPXs2/9aIaFRYKWt8iImJQWJiEqKjo5UOhcYoIici/ux2OxbOn4+CwkLs3b/fd/3cOXPQUF8/rMcCwL7XXoXT6cS8ubcFNd5wYzabe+zXoo0beUJ/P2w2G9Lmz+fJ6gPYuKkIiYlJnp83oa6uln9rRDRih+sOweV04UI/RWoocnS0t6No40bFvvgsK9nhq7xKYxPRndX9xcbGItZo9F2++557YJpkAoBhpYJMj4+HlBIn2tqCGmc48C6JAkD7qdN4aft2322dnZ34708/9aW02W12LEpL63EOz3hTUFiIJUuXYkpcHCcifvxTH7f/4cUeJY3PnTuHE21tvvtUVVRwCXwg7KweEuysrn4JZjOuu+46vldEOL1ej8fWrsXqlSsVjYPH2whEcmf14eqddrV67RrfN7DDWdpjzvrXvEuigDvdqPfKksFgwMxZMxEVZcCFCxf6VLQgAgCNRuM7Tp5Yu67fFUrvfXjSKRENJSYmpscXjhSZNBoNpk6fpnQYPN4CZNxMRHorK9mBv73xJgDg5ltuwS3f/z6A/pshDkar1eKhvGU4XHcoqCdHKcn7O+q07sNFSomijRsB9N+Yz+FwYMumzb7KY7l5yyAgUFpS0mMySOOP/99L/eHDvuNkoOV177Gk0+mQl58/or9NIhpfOtrb+aXFOOBwOFBVUal0GOhob4cQgmPTGI2b1KzB9G6GeP211+GrL78c1kFlMBjQ+u5JPP+b5/Ds1q0A+m+GGI4MBoPv/783HYdeHwUAqD98GOkLFgz7OVrfPQm9PgqZ6em+hpE2mw1qOPZCIS8/Hw/84heIv2bqoP1sIpVer4fGr8fP35uO49mt2/DM008P+zn896HL5ezRrGo8HUv9YmpWSCg9TtHg9Ho9APe5nRTZ9Hq9al5npl4P0yDjFHNm4G5oOCUuDlPi4nD9tdfheEsLlubmDuux3mY3QiPwwelT+OD0qWE/Vs0MBgPa3nsXH5w+hb83Hcd18Qm+fZS1aNGwn8e7f6bExaGgsNC3j26cMX4+N20tLh63kxAAqKqp8b3u3mNpm2fSPlz++/AhT+PR8XgsEVFfRpMJm55+Gqsee0zpUCgESsrKfF+MUvgbt6lZ/vxPpHY5nXh261YcPnRo2I+3222oPXAQ0uX+VnYkj1Ubo8kES3Y2JCSe+/VvALj3z1cXLvSp1T1c3g/gleXlOHjgAADg+7feilt/8APf80fysqbT6Rzx7+afDheO+8eSkwOjJ3e29uBBX7rEaI8l/33Yu1nV92+9FVdffTVPGCQKQ/7vFcPV0d7e4+990qRJKNq4EWeY+jsu6HS6IRtVh8rBAwdgs9nCanxWG6ZmhYDal4yFEIiKcn+7MHPWLJRVVqK7uzuo3+KPJR1uPPBPafPuH++yr8vlUv2xVLGzGjfe6F6pWHDHHb6+PgPxT98aaarVrr174XQ6kb5gge95IiU9cliYmhUSkT5OBZpWq4XL5Rryb9l/LBiukaQHU+Twji8v7diBOy3ZqsoyUFO6mCoNMk5xIhICu195BU6n09eTRG0Sk5JQs2c3AKD+cD0yFy0CJIL6R67T6XzVtAwGA463tODZrVtRvGVL0LYZbvT6KEB8vX+iotwT2sOHDqv+WEpfkIZGz/lADrt9yA8ju195BTNnuT+MzLvt9iEnLv6854s4HA7f82zZtHn8HEuciIREpI9TgSKEwKzERKSkpuJP+/fj5IkTg97ffywYLuly9SkJn2A2AxJshBrB/MeXQ3V1SofjYzAY8MeXX4YlI0PpUNSL5XuVpdPrIDQqWUfsh0ajQVSU+8R0rU4bkhOu/AcRjRCIitL7qmyRm3ci6N0/3tdIp1fvn633WHI6nSM6jnR6ne/304zwQ4n/seR9Hh5LRMqIiopCzZ7deObp4iEnIQAC1mNq46YiNh2OcP7ji9qoeVxWO+65EChYno8Esxn7Xnt1WM0TQ6lo82ZMmDABc+fMAeBuJBcolpwcLExPH7Khoc1mw7zbbseZM2fYwK4f3v3j/YA+afJk7HvtVQBAc1MzVhQUKBkeAPdrnZVtQfup05g7Zw5aW1qGdX+v7X94EU+sXQcAQz52MAXL8xEdHY2bb7nFt4/KSnbwWCIKEb1ej+7u7mFPMPzTMkfKP42zYHk+4uPjkWWxYFdNDbrZWT3iaLVaXLhwAS6XS+lQerDZbFj9qLLNFcMZJyIh0NbaCo3QwOl0+t40jSYTTCZ3Z/eO9vYB+ygEm9Plwudnz/bbTG6spCc/OGn2bN83GOfOnuuzdC6l9KXi6PV6OJ1OxBqNmJ2c7LtPc1MTurq6Ah5jOPDfPwDwxfkvfPvzkksuwezkZLhcLjTU14ekjK039UKj0Xy9XZcLTqdzyGPJ+9jY2Nge32q1tLTgRFvbmGPzTvKnTJnie34eS0Sh829//COui0/AVxcuDOv+VTU1vrTMkfJP42xrbcWJtjbkP1KA1ndPYtpV16jqHAIaG4PBgCVLf4kpcXFwqOxcDCnlsFb/qH88R0QhBYWFyPd8k120cSM2FxUpHFFwGAwGfHD6lC/tpq6udlhL5/77BwDmzpkTlMlSuJudnIy9+/fjwoULIatj7v+ajnS73sc+83RxyI75iD+WeI5ISIzHcWo00jMyULNzZ4/KeCmpqUhJTe1T7QoAsiyWUXenList7VMpi30dIpPBYEBJeRkWLUhTOhQaDZ6srj6xsbG+N9/Ozs6glh0cbopUMAghcOOMGT3Sihb/9O4hU9T89w8A3H3PPTBNcq8gqS29TUnR0dGYHh8PnU6HgsJCzzk+9oC/1kWbN2N6QjwAd0530YaNcDqdcLlcONrYOOhKjPexZSU7UFFejhtnzMCZM2dCVmqzv2Pp7NmzqkhpCwhOREJiPI5TI5FgNmPjpqJ+35+9f4Pnzp0LyMrnYGJjYzE5Lg4/njsXq1asCOq2KHTCZSKyfsNTWLv6sZB/1lI9nqyuPlarFdYBPoiNtfpHdEwMzGaz73LvNJhQGiitSErpi7O/tKLe+2d+WhomOicCcO+fSy+9VNGUNrXo6upCQ3099Hq9+41PYFjpcMPlfY0mTJjgez6H3YEjDQ2DvtEmmM2IiXF/aPM+1iVln+MhFPo7lrw5xqy0QxQYMTExSExMQnR0dJ/bBhvvAs1qteLTTz/Fv/zoRyHZHgWf0WRCXFxc0CexY/H1WHmJ0qGEHa6IqNC+114dU/UPb7qOl1pTv0abVrTvtVeRmJik2t9LaaNNh+uP9zUaaTqT9zUC1J0KNda/NVXgikhIcJwamFarxUPLlkGj0fSbLqUEo8mESZMmoa62VulQaIzCId1utGPluDHIODW6UhUUVHabHU6HEwaDAQaDwdcQcbhcLhcuXLjg+6fWJUJvnDZbt+93NRgMQz7ObrP3+L0MBpZr7a27u9v3+o/2WNLr9cOuUqLVanu8hg6Hw7d9tVU48df7b43HEtHI6XQ6JCXPxuaiIlVMQgDg7OefIyU1Fbv27vX1GqLI4a1EqrbXd/2GDaqKJxwMubeEEC8CmAvgUyml2XPdtwBUAogDcArAIinlZ0IIAWArgB8D+BLA3VLKt4MTeuRalJaGpNmz8cHpUwBG3sCuob4eU+LifJfVWHMb+DpO/4Z93d3dQ1Y7WZSWBqFxVyEzGAxoe+9dPLt12/hpYDcE7z6Ep3VNYmLiqI6lqpoauFyuYVUpeShvGfKWL/dd7t3QUK16/60BGF/NECMIxyplqDVtpqurC50dHZBSIj4hAcePHVM6JBqF6JgYGPsrZiDhS/NOmj0bpz76SNFU7XNnz6GurhZOh9OXGu2t0Gg0mXD2889ZrXEAQ6ZmCSFuAnAewMt+b+5FAP5XSrlRCFEI4B+klI8IIX4M4Jdwv7knAtgqpUwcMgguefdhNBqRafm6z4J/nfWDBw5E1HKzVqvF0txc3zfRQiMgPJ+i+6uy0t9jdTodnE4nthYXq3bipZT+jqXSkhJfzrZWq8VDecug0+rgcDh8+zDLYoFLSlSWlw/43N7HajXaHuf4qCU9Yzh67x8hBNyfU8Pkb42pWQCCP1ZxnOqf2tNmwuUkZ+qf//uztbOzz+cB/88AZTt2hOxcpIH4f57xjoPJKSk4depU2IyJQTHWqllCiDgA+/3e3E8CuEVK+bEQ4nIAb0oppwohfuf5ubz3/QZ9fr7BDyoxKQnVu3b5Lm975hk8u3VrRDZsMhgMaH33JPT6KADAkYYGWDIyejSu6k9efj4e+MUvEH/NVNjtNl8Kkl3F38YrwXssZaan46hnxcJgMODvTceh10ehu7vbtw8H402/0ul0aH33JJ7dug3PPP10KH6FoMvLz0duXh6Ar//WXC6Xeo8lTkR8gjlWcZzqSwiB/EcKcN/9Px/W+4YSDAYDtpeU4O477xxyHKHQ8k/FHurzjP/r2N/98/LzcfjQIRxtbITD4VDFF5J6vV6940aoBWEi8rmU8lLPzwLAZ1LKS4UQ+wFslFIe9Nz2OoBHpJSNgz4/3+AHJYSAzi+3PzdvGX7+wAMR27BJr4/qkVZUUV3Vo3FVf7RaLTQarW9/7H7lFTidzhGltI0H3mOpuqYGM2a63xO6u7txXXwCbHY7IDGsSYh/OpxeHwWny9mjb0A402q10HhW53LzluGh3NwRp0eGFCciPsEcqzhO9TU9Ph7Xfec72LWzRtVjUer3vjescYSCy7+aolarxZKlv4RG6y45vzgnZ8jzWa+77jqsWrsGTocTG9avx/Hjx323abVarFu/HtdMvQZVFZXYWVUV1N+lt97VSgFgQVoaVjzyiGrP0w2pYJbvlVJKIcSIv2IQQtwH4D73JZ4zPxgpZY8lbwHR48N6pPEf0E599BGeeboY37/1Vtz6gx8AQI+0Ii+n09njGxCdXgehidAdNAbeY6myvBwHDxxASmoqbpwxAza7fdC0CktOji9PV0LiuV//BocPHQIw9MQl3PgfSwICUVEG6PQ8+TDcjWas4jg1uLbW1rDo6eR0OhEVZeiR4kyht3FTka+a4mia8B4/fhyLFqT1m27ndDoV7RtjNpt7VCsF3BUjOQkZ2mhH10+EEJf7LXd/6rneCsDkdz+j57o+pJQvAHgB8HzTRNSPzs5ObC4q6lEO9s033lA8DzTcefNsNRoNbpwx9JfpWdmWMQ0gRAoZ01jFcSoyNDc1Ye6cOViUkYGW5mZ3NcEIWcFVEyEEoqLcadXd3d3IyMxEekYGAKCyogIFy/N9fWZcLteoC5nYbDaUl5Zh5+7dWLdmDd49eXLAdCytVgudThewVHZvOlnvND/vMeavtaUlINuMdKOdiLwCYDGAjZ7/9/pdv0QIUQH3CYBnhzo/hGg4mpuafW8ykyZP9p3Y3l8zRO/91Vw2Nlx4l5vbT5327X+7zQ7JfUvhgWMV+Rq/Nh0/jj9s345dNTUhS92J9IpJ/o2Jz3edx5on1vlSrXZWV2OX5/xWpzNw6btSSuzZtQv79+3DmnXrcM3Ua3Cotg7/9sILffZz2sKFmJ+W1ieVazS+c/31WLHyUWi0Wqx+dCVOnjjhu817jNHIDad8bzmAWwD8HyFEJ4A1cL+pVwkh7gVwGsAiz91fhbsKyftwl0T8aRBipnFoRUGB72f/1ZGBvp33vz+Nnne5mU2aSO04VtFQvvrqK1g839CHiiU7G2++8UbEvn96xwjvWNy7OlmwVp68acbedKyCwkJMj4/vs5+rKiuxd8+egFROW71mDbIyMpgNEGBDTkSklFkD3PSDfu4rAfxirEHR6BVt3gynyxXRH8QDtbxLQ/MuN3OJmdSOY1VoCCGg8fRxClcZmZlobW3Fiba2kFQ1Ktq8GfX19Xj0kUfgdDqRYDbjuuuuG7Q0fbjwjhFKj8WlJSUoKCxEQWEhaqqrsaumxpeO5Z/KtXLFih4rGUPR6/XQaDRIS09Hycsv8/NGEPAMzAgzPSE+rAeI4QiHkyMjxXhYbvZPLeid5tfR3o66ulq0NHMiRgQAsxITkZySEtZNP3dWV6Nmzx44HI6QVMOLT0jAtOnT8V8ff4ziLVsQExOD2P6a9IUhtYwRVqsVy5ctg9BokJu3DK3vnvRVFvVP5RrpRKKqpgYzZ83EM08XY8umTSz/HASciBDRuNY7tcB/2b28rCwivrUkCgRLTg5iY2Nx8MABpUMZE6fTCa1OG7LKk6U7SiCExnduY7gzmkyYNGmS6pq9eitU1R44COmSWJi+0Pf+3bv66FCMJhMs2dmoPXjQ94+TkODgRISIiIiGlJVtgdPpxKZf/UrpUMasYHk+EsxmZFksPb5s0Ov1qKqpGXXJ7uam5j6p0eWlZWhrbcXPH3wQu/buDevVJAAwmUxITklR3UTEq662Fofq6nDN1Kmw5ORgYXo6FqWlDbuUrl6vR35BAcp27EBrSwvOnz8f5IjHN05EiIiIaFBGkwntp07j87NnlQ4lINpaW6ERGpivu7bPbU6nc9R9qAaq1tjV1QUhBGYlJiKhV+O7cJJgNsNkMqGjo0PpUAYlpcTJEydwww03QEqJ+IQEHD92bNDHeNN0tVotzp07q4qUs/GAExEiIiIalCU7Gy9t3x6WH868aTa9m+GePXcWHe3tPe5rt9uDdt7IwQMHIITAmnVrsWXT5qBsI9g2biqC0+nEvLm3KR3KsJSXlaGqshJLc3Pxozlz+m2IDHyddiilhMPhwNrH1igQ7fjEiQgRERENyuFwhG1vpri4OCxZuhR/ff1134dQvV6PuLg4zJg5M2QpRnW1tTja2Ij7fn4/O26HkNPpRF1tLap37cJfX38d//M//+NrgOitArcwPR1OpxPpCxYoHe64w4kIERERDWprcXGfiUh0TAy+GRODzs5OhaIamsFgwJKlv8SUuLgeFZMeW7sWEjLo33x3tLfj3Llzvsvd3d2YdtU1cLrCr7plOKfnNdTXY0pcHASA7SUlONLQgMOHDvmqly1KG1uPERo9TkSIiAaQkpqKlNRUdLS3s3oWjWtOpxOWnBxIl8v3t/DNmBhkZWfD6XRia3Gx6krHp6SmIjk1Bbt37e5TMWn1ypUhiaG/9w27PTwb4oVzep5/1SxLRgaSU1Lwm9/+Fu3tp8MmzSxSaZQOgIhIrVJSU5FfUIAMy0C98ojGj6xsS4+/hc7OTgghkJuXB40KS9OmpKbil0sfwq6aGqVDCXsGgwESMmzT83qrq62F1arelbzxhBMRIiIiGlJzUzM62jtg9GvG19HejiMNDZg+fbqCkfUVHRMDKSXqDx+GjJAPz0rR6/X448svo/10O442NiodDkUYTkSIiIhoSCs8vRUyLRbfdeVlZbBkZGDVmscUjKwvs9kMKSXSFyzgieFjZLfbYcnIQGV5OZv6UcBxIkJERETD0tzUBCEEdu3dC53OfZqpzWZDZXkF9r32KqbHxyscoZtWq1X1BESr1cJgMPgu6/V6VFRXI8tigRAhavk+THq9XukQKIJxIkI0jnW0tzN1gYiGzduYb8bMmRAa90cIKSXOWK1ITExCdHS0whF+XSlr29atSocyoIfylqH13ZPQ66MAuFcd7rRkwzjJhBtnzFA4up5Kysp8cRIFGiciRONYeVkZUxeIaEQOHjiAbc88g4ULF/qu62hvR9HGjUhJTYVWBSeua7RauDxVvIwmEwoKCxEbG6twVF/TaXXuD/d+ix92uw0C7r4WaqLT6XrESRRI6jraiYiISNXqamuxZdMmvPPOO75JR2dnJzYXFeEvf/4zavbsQZbfeSShZMnJQcXOajy+Zq3vOm9Dw8snTlQkpv6UlpQgY+FCPNqrjHBpSQlaW1oghFB8QpdgNmPfa6+idMeOHj1YiAKJExEiIiIaESklYmJisDQ3t8f17548iRkzZ/gaxYWa0WjEjTfOwMmTJwH0bGiopopPVqsVh+rq8OQTT/S5/vz585iVmNhn34ZaTEwMEhOTYO3s5EnqFDSciBAREdGIWTs7odPpkJef7/v23uFwYMumzRBCKJIKdfDAATy9eXOP5ooarRZ2m02VH6b/v8sv7zdtTKPRKL4i4k236+xkvw0KHk5EiIiIaMQ6OztRtmMHDh86hHXr1wNwd2Av3rIFZTt24OzZsyGPqa62Fs88/TRcnk7wvdO01Obs55/jzTfeQEFhoa8KGfB1dTIlq5B50+3OWK2KxUCRjxMRIiIiGhWr1YqjjY24Zuo1fa4/f/58yOJIMJuRkGDucV3vNC016urqQkN9Pbq6upA0e7avWWRXVxc6Ozr6/b2IIgknIkRERDRqDocDh2rrFK1MtXFTEZ781QbfZaPJBCllnzQttXpi3TpUVFf1aRap1+lgvu7aoG8/JTW1R4odUahwIkI0jvVuqkVENFJOpxOHDx1CfkGBIiepe5sX2m1fV3YyeSYi3jStcNDd3d2nlHp5WRkqy8uDvu2U1FTk5uVB4zcRYSNDCgVORIjGsd5NtYiIRuPc2XOoq6uFyWTq0xncaDIFtdHhQ3nLULRhI7IWLQIA94nyRiM6OjqCts1A6+7uxrSrrlGkCWN0TAyMRiM0Gg3iPeekGAwGvLRjR8hjofGHExGicay/plpERCPV0tKMeXNvQ1Z2NnS9vkm3ZGcH9aRrnVYHp9PpW02IiorCosyMkKwkBJLdblNk9cZsNiPTYkFUVBTWrf+6nLBOrxvkUUSBwYkIERERBcS6NWtQXVPTo6FhaUkJsnNy8LinslageZsAAl+nm/qnaYWb9RuewvqnnupxvoYlJwe79u7FRRdfDMCdNmUwGEb9r7fSHSWYO2cOHn2kEAaDAUJl3d0pcnG6S0RERAFxoq0NDocDsUYjoqOj0dXVBavViuXLlgVtm1a/8rJpCxfijvnzce/dPw3a9oJt7erHkDR7Npbm5uI//+M/EBMTg9jYWGg0GhSuWIE1q1fjsbVrMXX6tFE9v91mx+KcnB7no1g7rWior0diUhI+OH0KmemLAvXrEA2KExEiIiIKCLvdjoXz56OgsBDT4+PRUF8PAH1Owg6WqspKVFVWhmRbweJwOOB0OqHVarFxUxESE5NQtHEj5t12m+8+q1euDMq2NRoNoqIMfc7zIQoWrr0RERFRQJWWlODue+7BhqKiUT1+NKlH373hhh4pYeHM29DwqquuxttvH0VZaWnQtuVyuXwTxeamJsydMwfpGRmACjvRB1LB8nwUPlygdBjjHldEiIiIKKCsVitMk0yY6Jw4qsdX1dRg5qyZI3qMEBoUb9kyqu2pTVdXF4QQuPfuu9FQXx/UFaWG+nocOXLEt92G+nq8ffRo0LanFm2trUqHQOBEhIiIiIKgrGQHYo1GFBQWDngfh8OBrcXFfZoOVlVUoPbgwRFvczSPUTOHwxH0tDYpJWSv/R+qVDqiISciQogXAcwF8KmU0uy5bi2AnwH4b8/dHpVSvuq5bQWAewE4ASyVUv5HEOImIiICwHFKrcrLyhAdHT1o6V6XywWXy9XvY8ezBLMZLpfLVw2MKFINZ0VkO4BfA3i51/XFUsrN/lcIIeIBZAJIADARwF+EENdIKcOjrSkREYWj7eA4pUreVB8amZiYGADA+fPnFY4kMhlNJpz9/HN0dXUBcE/8IN39cCi0hpyISCnfEkLEDfP57gBQIaXsBvCREOJ9ALMAHBp9iEREwdPR3o6ijRt9lWoo/HCcokhiyclBbGwsDh44oHQoAICMzEzsrK6OqPdHS3Y2JsbGov7QIZSXlWHjpiI4nU7Mm3vb0A+mgBpL1awlQojjQogXhRD/4LkuFkCH3306Pdf1IYS4TwjRKIRojPTKDIHmcDjQ3d0N9LPb7DZ7WDdyGo7e1VSIxqKzsxObi4rwzNNPK9LVmIKK4xSFnaxsC1K+l4pDdXVKhwIASM/IgMavuWKksGRnI8OSpXQY495oJyLPA5gC4HoAHwMYcZkKKeULUsoZUsoZYL3qEdlaXIz4a6bCbrf1uW1RWhqyFkV2I6Kqmhp8cPoUPjh9Cq3vnoReH6V0SESkPhynKOwYTSa0nzqNlmb1nBtyoq0NiYmJMBqNSocSMB3t7airq0VHe0dE/V7haFRVs6SUn3h/FkL8HsB+z0UrAJPfXY2e6yiAnE7ngEuk46HShU6vQ1SUeyXE5ZIAPx8QUS8cpygcWbKz8dL27ao6r+aJdevwwelTeObpYmweZV8YtWlvb8cv7v85Jk2ejMwI6T0TrkY1ERFCXC6l/NhzcT4A79k9rwAoE0I8DfdJgFcDaBhzlNSDJScHC9PTsSgtbVxMPACgaPNmOF0urCgoQMHyfERHRwPwNGKy901F878/BUaC2YyNm4pgt9nH1bFH4YnjFIUjh8PRbxUxpXV3dwMAhBCQEZCmmJKaCrvd7tvXGqGBE0zNVcJwyveWA7gFwP8RQnQCWAPgFiHE9XCfpXAKwP0AIKVsEUJUAWgF4ADwC1YiCTyj0YgZM2dCaMZyik94iI6JgdlsxoQJE/D52bMAhteEaHpCPCZMuASzk5PR3NTkq4xBbglmM2JiYiClRP3hw5DDGfikezVOSomk2bNx6qOP0NnZGfxgiYbAcYrCnRACsxITUVO9E+3tp5UOp4fu7m5Mu+oa5C5fhhtnzECjp/lhJGior8dNt9yMG2fMwOHDrFehhOFUzervTJ4/DHL/JwE8OZagiLzMZjP27t+PuXPmjHipeiyPjXQbNxUhMTEJRRs3In3BgmE9pqWlGfPm3gaDwRBxy/QU3jhOUbiLiopCzZ7dqn1ftdttEBDQRNgXoFLKiPy9wolQwxKb0OgkdDFKhxE2YmNjcfnEiTja2Iis7GxkZVvQ3NQcEWlIer0eVTU1qKqo6NEMq7WlZdB66pacHGRlf53nuf0PL6Kjw10Y5+577oFpkjslfLynFXnTq7z7p7OzE2es7vT44ab8CSFw44wZOHPmDP770097vF6RwP9Y+tsbb+Ktv/0N586dw4m2NnWm/Nk/OyqlnKF0GJGO4xQF01MbN2LfK6/g9OnTvvdktYmNjcVXX32Frq4u2PtJiQ4nsbGxOHv2LM6fP4/Y2Fj8saQEZz//fNhfzNEIDTJOjeocEVKW1WqF1fNGJV0uOJ1OXHKJOw0JAM6dPRdWTXm8S9IajQZardbdadczQR6qGZb3sbGxsT1O4G9pacGJtjYAwPy0NEx0TgSAcZlW5E1va25q8qVX+e8fL+ly+faP0+kcMKVNSulbmtfr9XA6nYg1GjE7OTnsjj3g6/3j5X8sdXR09Dj+nC5Xj781wF19ZbwcS0QUHNdMnYrGxkbYbX2rYaqF1WrF7ldegdPpxML585UOZ0ysfpM9q9WKH//whwpGM75xRSRCzE5Oxt797qIwdXW1YdWUx5vqExVlwIULFzAlLm7Yb8YjTRMaj2lF3mNjOClq/q/FSFLaCgoLkV9QEHbHHtDzbwcAijZuHPTYGOn9g44rIiHBcYqCKT0jAzU7d6q+l1GWxYJYoxF1tbWoq61VOpyQSUlNxamPPuoxgaER4IpI5HO5XLhw4QIAwOlw9mj0p8aO0Xq9HgB8y7vd3d1wueSAjRp7P1aj0fjSh7q7u0eUauWt/uHdR2rcP2Pl//prtVpcuHBh2JVYvK/FSCq3OBwOXLhwwddM0/saAYDNZlNdlRUhBKKi3P1nvPvHa6hjyf9vzf/+BoMhIo8lIgq+6spKpUMYlvKyMiQmJeF7N92Eo42NvvFUjfR6fcBSyE599BEKCgsRazSisqIibF6vcMAVkQghhIDO8+E+MTERFdVVvtu2bNqM4i0j7uUVVL2Xd/X6KHc/EIl+GzX2fuzMWTN9v5deHwWnyznsb5L0+ijkLl+Gh3JzAahz/4yFwWBA23vv+j5o1x+uR+aiRXDY7cOaEHhfi+HeH3B/mNdotZAuFxwOh+81AoB5t92uuioriUlJqNmzG8DX+8fL6Rz8WPL/W/PeX6/Toe29d/Hs1m2hP5a4IhISHKeI3IQQyH+kAD9/4AFMu+qaIcdsJRgMBvzx5ZdhycgI2HPqdDoIjWbIMYL6Mcg4xYlIBDIajT0a9Agh4HQ6sbW4WPFva40mEyzZ2XC5XOjo6EBlefmwHmfJyfF1P/V+U1978CAO1dWNKo7klBQkp6QAcO8f4dc1ubSkJOyWX7VaLR7KWwadVgcJCen6+u96JPt5MN7XzuFwDHkseZfvAaCstFRVJ19acnIQGxvrm2QFYv9otVoszc2FTqcL/d8aJyIhwXGK6GvJKSmYlZiIbVu3qu5DeUpqKpJTU9B+uj0gY99AjCYTJk2aNK5S1EaNE5HxLS8/Hw/84heIv2aqIt9c+KcJzZw1C2WVlVhwxx2Dfkuu1Wqh032dOVixsxo33ug+hod67Ejl5ecjNy/PdzkzPR1HGhrCoiqIN8XIYDDg703HoddHobu7OyivdWJSEqp37YLN1o3rr73Odx7PSJfmQ53C1N+x5LA7glIdRZG/NU5EQoLjFAWCXq/HY2vXYvXKlUPe15KTg7+//fawemeRW4LZjDk//jHefOMNHG1sDGpacHR0NH7+4IO46ZabI6ZyadBwIjK+abVaaDRaxSYho0kTyns4H3nLl/supy9IQ2NjI4CRpQwNhzetyKu6pgYOhyMsqoJ4U4y6u7txXXwCbHb7sNLbRsObkmQwGHC8pQVRUXpfo6vhbs97PIQyham/Y+lIQ0NQSjgr8rfGiUhIcJyisfBW59NqtVi4aBEqy8vhcrnQUF8PKSXi4+PR1tbWY2wrKCzEm2+8wT5Yw5RgNiMhIQFCownqSoi/LIsFGZYsdLR3oGzHDjZQHggnIhRKY0kT8j5Wq9H2eEMOZXpPlsUCl5SoLC/3pYQdPHBANcuv3hQpwF1KV0oJh8MRsiVybxqS1jN5ExoBAXdqW0d7+6D9RPxTmIKZDpeSmoqU1FQAX+8jL6VSxSw5OZAuV+D7rXAiEhIcp2i0LDk5SEpKgtFkwlt/+xt219QgPSOjx/v2zt27kZWR0aNiZHJKCk6dOqWq1Fa1SklNxezk5IClIo+UNyW+qrISSUlJqOLJ7D1xIkKhFMpyvMG277VXfR3I1RAPoK5Szf6v9Uji8Zb79RpJqeDh8H9+tbx2+157FU6nM/CvFyciIcFxikZr32uvYubMWdj41FN45umne9zmrey0c/du/PSuu2DrNVaqseqgP/8KhL2FIgXXW6Fx6bJcvPH6XxUvjGIwGLC9pAR333mn6l+7kGL5XhqOBLMZkBhTQzqjyYS4uDjUH66HVqeF3WaHHKIMrLcpobWzE5988gkOHzrs64qutOamZt8bqbeJXagb2Pk3fAQAk8mEujr36kxLc0vI4uiPy+XC4UOHodO730o62jswOzl5yOXpjvZ23+8AAOfOnQtIPAlmM2JiYiCl9D2/mo4lNkMkGp8a6uuxbevWHtf5V3bSR+nxx5dfhkt+PV6aTJPwywcfVPzD9UCEEFiwMA0ZWVn93l5VUYm21lbExHw9gQ9U01tvqltGVhYmxk5ETfVOHPWkbyupu7sbG9avR0l5GcpLy7Bn1y5ORobAFRHyCcQ3tgWFhViydGlYr4L0R8lv2Ee76qCEkTRPDAY1rmD5C0ozRK6IhATHKRqtgcZWg8GAkvIyLFqQ1m9qltrPEfGPfyDe92SvQI1fSo81w9HfazpucUWEhqNgeT4SzGbse+1V2G12LEpLG/EJvaUlJfjr66/DMUTFqaLNmzE9IR6Ae/k2fUEaTp8+PerYg620pARvvvEGAODmW27Bvtde9d1WsDw/4FVN9Ho9qmpqoNPrfPvHuzITqNWDYGhuasLcOXNw9z33YPXaNQCCs38GUrA8H9HR0apdZfDuH6+bb7kFu/buHdXfGhGFB7vNXWBFq9X2SVXyNoHtT0V5OT7/7LNghzcqWq0WBoNh0PiBr9+Tvc6dO4eMzEyk99Pfo7KiAnt27RqyGe7FF1+MBWlpmDtnDlpblM0K8OqveaLdUzyGBseJCPm0tbZCI9zNerxvlkaTCbGxsb7KHkOxWq0DnnTsTZsBgAkTJvi24bA7glbFKFD8f68pU6b0GEwG2i/+KWej+WDsdDohNCIs9o9XV1cXGurrMT8tDROdEwG4X/dLL70UAHpUiRkto8kEk8nU7xK/2stcevePV+9jaSD+fzu9HfrbnwIWHxEF3qK0NCTNno2iLVuws6rK997V3d2NxTk5Az5u4aJ0vPXm31SXmhUdE4Of3XcfbpwxA/fe/dNB79vfe/J7776LXbt29bne6XRi3eOPY+r0aQCA1Y+uxMkTJ3zv+V4L0tKweuUqXLjw1Rh/k8Do3TzRmzb24P0/V2WzR7VhahYNajSpVgPxX6JV83JqoIRDylko+L/uIy1e0B9vmpyaU9QCrXd6g79vRwumZoUAxykai6GKjIRTatbs5GSkpKaGbFwLdnGTseqdohYOaWMhx9QsGq3SkhIcPHAA1TU10Orc5VpH0rjHP8Vo+x9exBNr1wGAapZTlZJgNmPjpqKQpi0pxX9pXqfTobqmBpXl5SMuY+s9lmoPHsTcOXNUnaIWLKU7SlBeGuDyv0QUdP5pmf29d61csaJPSnNpSQnOnj0bkviGq2jzZkyYMAFPrl8fsm36p0YDwHvvvdfj9lA3yaXA4kSEBmW1WvE///M/mDFzhu9k6ZH8sWs0GsycNRNRUQY8sXYdvx3wiImJQWJiUo/c2UjlP9EyGAyYMXMGDh44MOLn8R5LtQcPjtvjyNppHbe/O1E4652W2dvJEyf6XBfI3kqBMj0hHk6nM6S9TXqnfFdUV+NOSzbsdpsiTXIpsDgRoSE5HA5s2bTZ18AOcC+VAkM3sPN/rFpPIA62lNRUuFwubC0uHvff2HiPh8OHDvVofDmchob+jyUiotBKSU3F3954U/GS6DqdDp4eunA4HHh26zbodDrExsYqMnlzOByoqmADw9HiOSI0YolJSaj2nGh2pKHBd4KWv+7u7lCHpTrec0Q0Gi2+/PILXH/tdfjqyy/hdDqZQwr3/ml99yT0+ihkpqf3qAHvcrn6VCAZz7zniGzZtAnPPP10z78vlu8NCY5TNF75p8X+9rnncP78eUXjufa665CRlYVVK1b4rlPqfBpLTg4Wpqf3qHwYHR2N6fHxaG1pUXxfqcYg45Qm1LFQ+Guor8eUuDhMiYtD8ZYt+OD0qR7/vB8uyW1rcTGuv/Y6HG9pwdLcXKXDUY3u7m5Mu+oaTImLQ0FhYY9jqLyqSunwVCk3bxn/vogopLxpsQBU8cH63ZMncc3Ua3pc19HejkmTJyMhwRzSWIxGI2bMnAnhKTkshIDRZEJDfb0q9lU44ESERkxKCbvNBrvNhlMffYRnni7Gp5/+N6KiDGiob8CzW7fB6RrfKUj+nE4n7DYboqL0PdLbCLDb3cdRZXk5dlbvRFSUAdVVVajmRKRfh+oO8e+LiELKmxZbe/Cg0qEA6D8VqrysDIt/ejee/NUGhaJyi4qKwrr1TygaQ7jhRITGpLOzE5uLimC1us//qKutxTNPPw3XOD8XgkamvKwMleXl7p9Lv/6ZeuLfFxGFirfB8TVTp6J4yxYcqqtTOiQA7i/32lpbse+1VzE9Pt53fcHyfBQ+PLyKnoFSWlKCBXfcMWQTZxoYT1YnCgGXy4XDhw4DcNcYTzCHdvk4HJw7ew51dbXjsizvUJqbmuF0OhU/SZSIxgchBKbHuytkqeFc4t76qzzZ1tqK6JgYGI3GkBTHiY6JgRCiR8NJl8uFtpbILskfaJyIEIWA3W7HwvnzUVBY6GtqRT21tDSPmwaFIzXcvj1ERIEQFRWFRZkZqn1P7mhvR9HGjUhJTcXRxkZfRUqz2RyyZov9bWtBWhoeX7s26NuOJEzNooAoWJ6PuXPmoKy0VOlQiIiIaIzsNvWmG3nTwlNSU6HxO/fS5XIBcFdlVEJ6RkaPeGhonIhQQGg0Gmi1WmiEUDoUVetob0ddXS2am5uVDoWIiKiH6JgYzE5ORlRUFBbn5CgdzpBOtLUhMTERRqMRgLuqp9CIkFQXPHf2HNNlA4ATEQqIjZuKsHf/fmRaLEqHomrlZWWYN/c2rPQ0hCQiIlILs9mMvfv3Y3p8vK8vhpo9sW4dKqqrfJ89pJQQEO5JSJC/F21paWZhlQDgRIRIAc1NTZg7Zw5aW1qUDoWIiAh6vR4ZWVlhNzZ1d3f3mDSVlpQgMz0da9atC9o2izZvxga/c0OEEDAYDO441Hduv6oNORERQpiEEG8IIVqFEC1CiIc8139LCPFnIcR7nv//wXO9EEJsE0K8L4Q4LoS4Idi/BFG46erqYsMjogDhOEU0dhqNBhNjJ4bV2ORtjLtt61bfdVarFUcbG/s0PQyk6QnxmB4/3Xd5VmIiPjh9CluLi2G324K23Ug0nBURB4DlUsp4AEkAfiGEiAdQCOB1KeXVAF73XAaAOQCu9vy7D8DzAY+aKMwZTSYUFBYiNjZW6VCIIgHHKaIxSElNxdJluaip3ql0KCNmt9v69Fbqr+lhIJWV7EB5aZnvskajQVSUwVe9i4ZvyPK9UsqPAXzs+blLCNEGIBbAHQBu8dztJQBvAnjEc/3L0l14+rAQ4lIhxOWe5yEiACaTCfkFBXjzjTdgtVqVDocorHGcokgjhEBUVHBPtvaKT0jA7ORkvPH6X3G0sTEk2wy0jMxMtLa24kRbG+x2u6/pYZbFgvKysqGfYISC8Zzj1Yj6iAgh4gB8F0A9gMv83rT/C8Blnp9jAfiXEej0XMc3eCIiCiqOUxQJZiUmombP7pBsSwgNirds6dGYL9zsrK5GzZ49cDgcWDh/PgB308NYTzUtUq9hT0SEEJcAqAGQK6U8J/zKtEoppRBiRKfnCCHug3tJHDxnnoiIxorjFEUCS04OYmNj8czTxSHbZu3BgyHbVjA4nU5oddoelbI62tshhEBefj62FhcHNW3K22AxFB3dI82wJiJCCD3cb+6lUspdnqs/8S5lCyEuB/Cp53orAJPfw42e63qQUr4A4AUAEBodawwQEdGocZyiSPH3t9/G20eP4kRbm9KhhJWC5flwZ1u6dXZ2QgiB3Lw8PLttW1AnIt4GizRyw6maJQD8AUCblPJpv5teAbDY8/NiAHv9rr/LU5UkCcBZ5t0SEVGwcJyiSNLmOdeBRqa//dbR3o76w4chPR3XSX2GsyKSAuBOAE1CiHc81z0KYCOAKiHEvQBOA1jkue1VAD8G8D6ALwH8NJABExER9cJxiiKW0WSCJTs7YM/ncDj6pCqlpKYiMSkp6ClMoVZeVsYTy1VuOFWzDmLg/pQ/6Of+EsAvxhgXUdiz2WyYd9vtOHPmTJ/b2NCQKHA4TlGk0Gq1cLlcPVKMYmNjsWTp0oBto7u7G89u3dZnIrJk6dKgpzCFglarhU6nQ3d3d8Cf08tms/V4jWj0RlQ1i4iGT0o5YBUSb0NDIiIir4fyluGtN//WY+xoqK/HlLi4wG1Eok/TvY72dhxpaMD06dNx/NixwG1LAQ/lLcMvlizBtKuuGbC5oNFkgslk6vc2f81NTejq6kLawoVYmLHId/3qR1fi5IkTAYt5PONEhIiIiEgFDtcd6rOKLqWE3Rbcbt3lZWXYVVODkvIyLFqQFtRtBdvhukPQCA1yly+D6LVQWlpSAqvVikmTJiE5JWXI5zp9+jS6urpQVVmJqsrgNUgczzgRISIiIlKYwWBA/eHDYZ8apbS62locbWxE67snodf3bAr519dfh9VqRV1tLepqaxWKkPxxIkJERESkIIPBgLb33sWzW7eheMsWRWJwuVywdnRidnKyLyUpXHV3d2PaVdf0OXPMYbcrExANiB2aiIiIiBQWFRUFrVar2PbtdjuqKiuxd/9+TI+PVyyOQLHbbbDbev7jCebqwxURIiIiIgUNVmWRKJJxIkJERESkoMGqLBJFMqZmERERERE62ttRtHEjUlJTFU0To/GDExEiIiIiQmdnJzYXFSElNRUaTkQoBDgRISIiIlJAdEwMZicnIzo6WulQiBTBiQgRERGRAsxmsyqrVFVWVLCfCYUET1YnIiIiUkBzUxPmzpmD1pYWpUPpoZpdxClEOBEhIiIiUkBXVxca6uuVDoNIMUzNIiIiIiKikOOKCAVEwfJ8REdHo7OzU+lQiIiIiCgMcCJCAdHW2qp0CERERGHBaDIhNjYWDfX1kFIqHc6g4uPj0dbWpvo4KTwxNYuIiIgohCzZ2ajetQs6vV7pUIb0+JNPhkWcFJ44ESEiIiKiHuLj47Fz926UvPwyHHa70uFQhGJqFhEREVGIRMfEQEqJ+sOHIV2uHrcZTSac/fxzdHV1KRTd16xWK57duhXd3d1Kh0IRjBMRGjVLTg6MRmOP6w4eOIC62lqFIqJw1ftY6mhvR3lZmYIREREFh9lshpQS6QsW9LnNkp2NN994QxUlfeMTElC9axcuXLiAKXFxsNtsSodEEYgTERq1rGwLZs6cBZvfm5PNZuNEhEZsYXo6Zsyc6btcf/gwJyJEFJGam5qQNHs2yquqcM/ixfjqq698t5WWlKCgsBAFhYWoqa5GRXm5YieJe5starVa7Cgrg8FgwIpHHmFxGgoooYYqCEKjk9DFKB0GjdC+116FdEmkzZ/vu87pdMLldCoYFYUjnU4Hofn6lDXpcsHhcCgYURixf3ZUSjlD6TAiHccpCiStVouLLr4YL/zbv8GSkdHjNu/7YW7eMrzx+l/ReOSIQlF+Ta+PwvKCh/HXv/xFFas1FGYGGad4sjqNiUu6YLfZfP84CaHRWJSZiYULF/qOI05CiCiSOZ1OfPXll9hVUwMAyMjMhFarBQA4HA7YbTbUHjiI7996KwoKC5GckqJkuLDbOb5TcHAiQkSKy8q2IMOSpXQYREQh43Q6sbOqCgDQ2tqKmj17kGWx+G6vq63F/n378OYbbyAlNRW79u6FThe6jPoEsxn7XnvV988/NqJA4TkiNCYxMd/E7ORkNDc1qaLKB4W/BLMZMTHuFBiXyxUWDb+IiMbiRFsbHA4HXL3e67znY0yZMgVSSsQnJOD4sWOhCUq6J0tep9tP49y5c6HZNo0bPEeERm3fa68iMTEJADB3zhzmjdKo7XvtVTidTsybe1uP44rVWoaB54iEBMcpUppWq8XS3Fzo9Xo4HA5sLS7uMVEYLaPJhEmTJrHQDAXPIOMUV0RoTJqbmlBYUIDWlhalQ6EIUbA8H9HR0QDcKyJspEVE45FWq4XL5fKtCDudTtTV1qJ61y50d3fj2a3bxjwR0ev1iIuLw4yZMzkRIUVwIkJjcq7rHFdCKKBYGpKICEhbuBBOlwsnWtvQ0tIMAGior8eUuDhAuk8gH00DRCEEZiUmwtrZifsfeAASEmsfWxOk34JocJyIEBEREalMVWUlsiwWzPnJj/GT2+b60rH8U1UnTZqEFIsFQohhNxTOys7GxIkTUXvwIFavXBnMX4FoSENWzRJCmIQQbwghWoUQLUKIhzzXrxVCWIUQ73j+/djvMSuEEO8LIU4KIf4lmL8AERGNbxynKFKVl5XhwFtvYcnSpXhwyS/wjW98o8ftdbW1mJ2cjCVLlyI5JQVCiAGfy2AwwGAw4PY77sDW4mdwqK4u2OETDWk4KyIOAMullG8LIaIBHBVC/NlzW7GUcrP/nYUQ8QAyASQAmAjgL0KIa6SULEBNRETBwHGKIpY3HStKr8eaxx/H7pqaHpUqF6WlQWg0WLhwIa6ZOhUnT5zo8xzfuf56rFj5KDRaLR5/bA3sdhYAIXUYciIipfwYwMeen7uEEG0AYgd5yB0AKqSU3QA+EkK8D2AWgEMBiJdUpKxkR59Sg0SjwWOJxoLjFEUyKaWv2evumhrs3b+/R6VKbwPY8rKyAZ9j9Zo1yMrIYAVCUp0RNTQUQsQB+C4A79nJS4QQx4UQLwoh/sFzXSyADr+HdaKfAUEIcZ8QolEI0Qh+AAlL5WVlqCwvVzoMigA8lihQOE5RJGtuasLcOXPw3nvvoaK6Glme80N602q1MBgM0Ov1AICVK1awAiGp0rAnIkKISwDUAMiVUp4D8DyAKQCuh/ubqC0j2bCU8gUp5Qwp5QwMktNIREQ0HBynKNJ1dXWhob4en/3v/+JOSzaMk0y4cUbf9gwP5S3DB6dPodzTuf3kiRNsDEuqNKyqWUIIPdxv7qVSyl0AIKX8xO/23wPY77loBWDye7jRcx0REVFQcJyi8cZut6H2wEF8/9ZbcesPfuCrmmU0maDVaPHM08Xo6OgY+omIFDTkRES41/z+AKBNSvm03/WXe/JyAWA+gGbPz68AKBNCPA33SYBXA2gIaNREREQeHKdovKqrrcVnn32G6Oho3HzLLfjss8/Q2dGB0h07cMbKuTWpnxhqqU4IkQrgAIAmAC7P1Y8CyIJ7uVsCOAXgfu8bvhBiJYB74K5kkiulfG2Ibfw3gC8A/M8of49g+T9QX0yAOuNiTMOnxrjUGBOgzrjUFtNkKeU/KR2EkjhOqS4mQJ1xqTEmQJ1xqTEmQJ1xMaahDThODTkRCRUhRKOUsm+io4LUGBOgzrgY0/CpMS41xgSoMy41xkShocbXXo0xAeqMS40xAeqMS40xAeqMizGNzYiqZhEREREREQUCJyJERERERBRyapqIvKB0AP1QY0yAOuNiTMOnxrjUGBOgzrjUGBOFhhpfezXGBKgzLjXGBKgzLjXGBKgzLsY0Bqo5R4SIiIiIiMYPNa2IEBERERHROMGJCBERERERhZziExEhxI+EECeFEO8LIQoVjuWUEKJJCPGOEKLRc923hBB/FkK85/n/H4Icw4tCiE+FEM1+1/Ubg3Db5tl3x4UQN4Q4rrVCCKtnf70jhPix320rPHGdFEL8S5BiMgkh3hBCtAohWoQQD3muV2x/DRKT0vvqG0KIBiHEMU9c6zzXXyGEqPdsv1IIEeW53uC5/L7n9rgQxrRdCPGR37663nN9KI93rRDi70KI/Z7Liu0nUh7HqT4xcJwafkwcp4YfF8epkccX/mOVlFKxfwC0AD4AcCWAKADHAMQrGM8pAP+n13VFAAo9PxcC+FWQY7gJwA0AmoeKAcCPAbwGQABIAlAf4rjWAsjv577xntfSAOAKz2usDUJMlwO4wfNzNIB3PdtWbH8NEpPS+0oAuMTzsx5AvWcfVAHI9Fz/WwAPeH5+EMBvPT9nAqgMYUzbASzs5/6hPN7zAJQB2O+5rNh+4j9l/4HjVH8xcJwafkwcp4YfF8epkccX9mOV0isiswC8L6X8UEppA1AB4A6FY+rtDgAveX5+CcC8YG5MSvkWgP8dZgx3AHhZuh0GcKkQ4vIQxjWQOwBUSCm7pZQfAXgf7tc60DF9LKV82/NzF4A2ALFQcH8NEtNAQrWvpJTyvOei3vNPArgVwE7P9b33lXcf7gTwAyGECFFMAwnJ8S6EMAL4CYB/81wWUHA/keI4TvXCcWpEMXGcGn5cHKdGIFLGKqUnIrEAOvwud2LwP4ZgkwD+UwhxVAhxn+e6y6SUH3t+/i8AlykQ10AxqGH/LfEsP74ovk4HCHlcnmXG78L9bYUq9levmACF95VnCfcdAJ8C+DPc32p9LqV09LNtX1ye288C+MdgxySl9O6rJz37qlgIYegdUz/xBtIzAAoAuDyX/xEK7ydSlBreZ/1xnBo5jlPDiwngODVkTCoZp4AIGauUnoioTaqU8gYAcwD8Qghxk/+NUkqJwWfCQaeGGPw8D2AKgOsBfAxgixJBCCEuAVADIFdKec7/NqX2Vz8xKb6vpJROKeX1AIxwf5s1LdQx9NY7JiGEGcAKuGObCeBbAB4JVTxCiLkAPpVSHg3VNolGiOPUyCj+3gtwnBoujlPDE0ljldITESsAk99lo+c6RUgprZ7/PwWwG+4/gk+8y2qe/z9VILSBYlB0/0kpP/H8gboA/B5fL9WGLC4hhB7uN9JSKeUuz9WK7q/+YlLDvvKSUn4O4A0As+FeNtb1s21fXJ7bvwng/4Ugph950gaklLIbwB8R2n2VAuB2IcQpuFNwbgWwFSrZT6QIjlPDw3FqABynRo7j1JAiZqxSeiJyBMDVnrP8o+A+geYVJQIRQkwQQkR7fwbwQwDNnngWe+62GMBeBcIbKIZXANzlqdKQBOCs31Jv0PXKe5wP9/7yxpXpqdJwBYCrATQEYfsCwB8AtEkpn/a7SbH9NVBMKthX/ySEuNTz80UA/hnuvOA3ACz03K33vvLuw4UA/ur51i7YMZ3wG5wF3Pmt/vsqqK+flHKFlNIopYyD+/3or1LKbCi4n0hxHKeGh+NU/9vnODX8uDhODVNEjVVS4bPl4a4w8C7ceYArFYzjSrirQhwD0OKNBe4cutcBvAfgLwC+FeQ4yuFeErXDnd9370AxwF2V4TeefdcEYEaI4yrxbPc43Af55X73X+mJ6ySAOUGKKRXu5ezjAN7x/PuxkvtrkJiU3lfXAfi7Z/vNAB7zO+4b4D75sBqAwXP9NzyX3/fcfmUIY/qrZ181A9iBryuWhOx492zvFnxdiUSx/cR/yv8Dx6necXCcGn5MHKeGHxfHqdHFeAvCeKwSngCJiIiIiIhCRunULCIiIiIiGoc4ESEiIiIiopDjRISIiIiIiEKOExEiIiIiIgo5TkSIiIiIiCjkOBEhIiIiIqKQ40SEiIiIiIhCjhMRIiIiIiIKOU5EiIiIiIgo5DgRISIiIiKikONEhIiIiIiIQo4TESIiIiIiCjlORIiIiIiIKOQ4ESEiIiIiopDjRISIiIiIiEKOExEiIiIiIgo5TkSIiIiIiCjkOBEhIiIiIqKQC9pERAjxIyHESSHE+0KIwmBth4iIaDQ4ThERKUtIKQP/pEJoAbwL4J8BdAI4AiBLStka8I0RERGNEMcpIiLlBWtFZBaA96WUH0opbQAqANwRpG0RERGNFMcpIiKFBWsiEgugw+9yp+c6IiIiNeA4RUSkMJ1SGxZC3AfgPs/FGyG0SoWiOlddfTWklPjg/feVDoWI1E46/0dK+U9KhxGJOE5Rf4QQuPba6/DJJ58gOiYaEyZMUDqkIb333nv48osvlA6DxqtBxqlgTUSsAEx+l42e676OScoXALwAAEKjk9DFBCmU8FP83B/hdDoxb+5tSodCRGpn/+y00iGEKY5TNCp3Ll6MX23ehC2bNsPa2YlYo1HpkAaVkpqK06dPo/7QIZSXlSkdDo1Hg4xTwZqIHAFwtRDiCrjf2DMBWIK0rYii1WrhcDjw2MpVSodCRBTJOE7RqBw5cgR3zL0NnZ2dOGO1Dv0AhZWXlqK0ogJzb3N/ucnJCKlJUCYiUkqHEGIJgP8AoAXwopSyJRjbijRpCxfCbrOhrZWFW4iIgoXjFI2WAHCkoQHBqDoaDFarFYcOHUKilKpfvaHxJyjle0ccBJe8AQBGkwmW7GyUlpTAGgbfshCRCtg/OyqlnKF0GJGO4xR5x2ibzYZtW7fC5XQqHdKIGI1GZGVnw+l0YmtxMZxhFj+FsUHGKXZWVxGTyYTLJ16Os2fPKh0KERER+bnyyiuRX1CAutrasJuEAEBnZyeEEMjNy4NGy8ILpA6ciKiEEAKxRiMOHzqM8+fPKx0OEREReRgMBjz73HOoq6vFuXPnlA5nTDQaDeLj45UOgwgAJyKqERUVhUWZGagsL1c6FCIiIurl5MkTmDf3Npxoa1M6lDGJiorCuvVPKB0GEQAF+4hQX3abXekQiIiIyEMIgTvvugsZWVn4YpA+HBmZmUjPyAhhZIOrrKhAdWVln+tLS0pQe/Agnn/hBex77VUULM9ncRxSFE9WV4n4hAS89+57sNttSodCROGEJ6uHBMep8SkxKQnP/uY3OH36NO7MzsaFr77q935arVZV5104nc5Bz2P53k03oWbPHhTk5+OlP/4xbCqAUZjiyerq9/j69e6agERERKQKGo0GVZWVSF+wYMBJCOD+4G+32VTzb6iT6R0OBwD3Zw+dXh/QfUY0EpyIqIAlJwdXXX01wC8kiIiIVEGv12PVY4/5PrRHkuamJsydMwdtra2I4kSEFMRzRFTAaDTiwfvvZ1oWERGRStjtdsy77XY4XeFXqncoXV1daKivh81mw0s7dmDh/PlKh0TjFFdEFGY0mSClxKlTp5QOhYiIiDy0Wi3mL5gflj1DhquyogJCw7xwUg4nIgozeSYiZ9hJnYiISBUsOTnYuWc3WiO8olR1ZSWmT4/HvtdexXT2FiEFcCKiIG8Tw46ODqVDISIiIgDRMTGIjY2Fw+4I+54hQ3G5XOjs6EBiYhISk5IgBFdHKLQ4EVGQRuPe/WxiSEREpA5msxlSSqQvWBCRJ6r7s9vtuGfxYhRt3Ij7H/g5K2hRyHEioiCNRoOdVVVKh0FERERwV8rSarURPwHx19nZic1FRbB2WvENgwFaFfVDocjHiYiCSsrKoNdHKR0GERHRuGc0mfDaf/4nli1fjm1btyodTsh98P77aDl5Ektzc5UOhcYRTkQUpNPp2MSQiIhIBSZNmoR/f+01VFVWRnSlrIE8tmoVNBqBm26+GbGxsUqHQ+ME+4goxJKTg9IdO+Cw25UOhYiIaFwzGAw42tiIQ3V1kHL8dhfu7u5GYlISYo1GWFnNk0KAKyIKiI6JQVJSEqydneP6DY+IiEhpBoMBbe+9izcPHMA1U6cqHY5iuru7Me2qa7Bt61aYzWYkJJiVDonGAU5EFGA2m2E0mdDZ2al0KERERONeVFQUanbuxMkTJ5QORVF2uw0upxMbN23Ck7/aoHQ4NA5wIqIAl8uFutpafHzmjNKhEBERjVtarRYrV69G+oI0lJWWKh2OKpSWlOCdd97B1KlTkWWxKB0ORThORBTQUF8PoRG4a/FiNg8iIiJSyEN5y/Daq6/iUF0dzvCcCACA1WrFj3/4Q9x3778i1mhUOhyKcJyIKEBKidoDB/HzXzzI5kFEREQK0Wl1cI7DCllDcTgccDgcSElNZQUtCipORBRSV1uL++79V5RVVCDnrru4MkJERBQier0eu195BekZi+ByuZQOR5Wam5rw4QcfYHJcHPT80pSCRKihapPQ6CR0MUqHoYjvXH89fv/ii3jw/vvReOSI0uEQUbixf3ZUSjlD6TAi3XgepyKRwWDAB6dP4dmt21C0cSMrWA5Aq9WiZs8eOBwOLJw/X+lwKFwNMk5xRURhx955BzurqpBlsUCr1SodDhERUcTLyMyEVquF0+nkJGQQTqcTWp0WOj3bzlFwcCKiAqUlJZg6bRouuvhiTkaIiIiCJMFsxr7XXsWKVauQNm8+K2UNk9l8LStoUVBwIqICVqsV6QsWoKKqCkVbtiA6OlrpkIiIiCJOTEwMEhOTcPr0KTTU17NS1jA0NzWjvb0dibNnIzEpiee0UkBxIqISX331FX7+s5/hyiuvxPT4eKXDISIiAgAYTSYUFBb6qielpKaioLCwzzfkKampyMvPV+3KvlarxaKMDADAYytXweFwKBxReFhRUIC7LBZMnjwZ1bt2sdonBRQnIirS2dmJJQ88gNaWFqVDISIiAgBceeWVyC8o8PWUSExKQl5+PrKys3tMOlJSU5GblweNSiciOp0OaenpuHDhAitljVBnZycO1dVBCCCKExEKIE5EVMZqteL8+fOIjomBkY2EiIhIQd+94Qb85rfPo66uFufOnQMAbC0uxkt//COcTifmp6UpHOHwGE0mzJw1Cy9v344pcXE42tiodEhhp6O9HX9/++/YXvKy0qFQBBlTGQQhxCkAXQCcABxSyhlCiG8BqAQQB+AUgEVSys/GFub4YzabUVBYiCUPPAArc1iJiEaF49TYPP7kenzwwQeYN/c233VOpxOPPPywglGNnCU7G0uWLsWUuDjYbTalwwlL5WVl2FVTg5LyMqVDoQgSiBWR70spr/erD1wI4HUp5dUAXvdcphHyNhK6fOJEpUMhIgp3HKdGyFtd6qqrrsbqR1cqHc6YCCFUe95KOJo6dRr2vfYqz2elgAhGatYdAF7y/PwSgHlB2EbE6+rqQkF+PpePiYgCj+PUELzVpSZMmIATJ04oHc6oJZjN+Om992L+ggWoP3wYkueGjEl3dzfuys6G0+lEfHw8K2jRmI11IiIB/KcQ4qgQ4j7PdZdJKT/2/PxfAC4b4zbGLTZaIiIaM45T49jGTUXYWFSEqspKpC9YwEpZAXDsnXcwb+5tyMrOZgUtGrOxtspMlVJahRDfBvBnIUSPr02klFII0e8nac+A4BkUeM78cOj1emg0GmRkZuLYsWNobWmB3W5XOiwiIjXjOKUC6zc8hWnTpod8u1OmTMGFCxcGnYCs37ABG9avx4ULF+B0OkMYnXoVbd6MadOno+Tll1FdWdnjNiEEoqKi3PuU35XSGI1pIiKltHr+/1QIsRvALACfCCEul1J+LIS4HMCnAzz2BQAvAIDQ6HgoD8FoMuHFl7bj3NlzEBqB2+fNQ0d7O7Zs2oTOzk6lwyMiUiWOU+qwdvVjEBrlJnODTTC+853v4OQHH2DLpk0o3rIlhFGp1/SEeEiXRM3OnX1um5WYiJo9u5G+IA12O0/8p7EZ9buCEGKCECLa+zOAHwJoBvAKgMWeuy0GsHesQY53RpMJv37uORx75xgWLVyItDvmYeH8+aiqrERmr4ZSRETkxnFKPRwOB+w2myL/LrvsMuQ//PCAzRYrKyqg1Wpw0803+5o2jmcpqakwxhrhki64+pnAaTQaREUZuHpEATGWrycuA3BQCHEMQAOAP0kp/x3ARgD/LIR4D8D/9VymMTCZTEhOSUF1ZWW/bwpERNQvjlOj1NzUhLlz5uD999/D+iefVDqcMTn7+eeYGBs7YLPF0pIS3Pbjn+D06dO+po3jkV6vx+5XXsGvn3sOzxQXo/Dhgn7v80SYHw+kLqNOzZJSfgjgO/1c//8A/GAsQREREY0Vx6nR6+rqQkN9Pb744gvMTEzE7ORkNDc1oaury3ef6JgYfDMmRvXpwV1dXThjtUKj0SA+Ph7H3nmnx+1SSjQeOYJbf/ADmM1mfHH+C7S0NCsTrII0Gg1mzpqJT/7rE9QfPowTbW393mdi7MQeDS6JxmKsJ6tTCHS0t6P24EFkZGbiaGMjl0OJiCgkykp2IHH2bOzdvx93zJ2L2SnJ0GndHx2MRiPa29uxuahI4SiHdvDAAQgh8PsXX8TOqiqUlpT0aRZ88MAB/Pr553H7/NM9GjiOFxmZmdBqtSgvK+t3EpKSmork1BSsW7MWleXlCkRIkUiooTys0OgkdDFKh6Fqefn5eGjZMpinTcOXX34Jwze+gT379uHV/fvxzNNPKx0eESnF/tlRv0Z9FCTjeZyKjo7G9Ph4nGhrwzVTp0Ljd9J5Z2cnzng+0MfGxuLyiRNxtLFRlaXnhRBY/NOfomjzZsydMwcN9fV97rPvtVcxfXo8Vj/6KMrLxlcH8enx8YiOju7xmnolmM2Y8+Mf48033lDt60sqNsg4xRWRMLG1uBiXXXYZXnxpOyorKvGd73wHR48cwbatW5UOjYiIIpg3TQsAGo8cGfB+Vqu1zyqDmkgpcdLTnDE+IQFHGhr6fKBubmrGhAmXjKtzRaJjYmA2m+FyudBQX9/vJCMmxj0JH+z1JxoNrogQEYUzroiEBMepyGA0GpFpsSAtfSFuSkmF3da3/Ozs5GQUFBbiwFtvYWtxccSnQ89OTsbe/fvx0vbteOThh/sUxbHk5CApKSls0vBIhQYZp9ihiYbEKhlERBQJOjs7sbmoCNZOK75hMPRbztflciExKQnLlufhG9/4hgJRhpZWq8WFCxdQWV7eb2XOrGwLFmVmsis9BQUnIjQkjUaDqdOnKR0GERFRQHzw/vtoOXkSS3Nz+9zWUF+PguXL8fe3/47tJS+HPrgQ+u4NN+CXDz2EKXFxONrY2Od2o8mEmOgYNNTXMxWcgoITkTBgNJlQUFjIRktEREQB8NiqVdBoRL8rIlJK7CgpQfqCBVA+eT24Hn9yPQzfMMBus/V7boglOxvT4+MHbG5INFaciISB2NhY/NO3v42zZ88qHQoREVFE6O7uRkZmJqbHxw94n6lTpyHLYglhVKEhhIDBYIDD4cDqR1f2e58EsxnpGYuQv2xZv80NiQKBE5EwcKKtDb/euhXnz59XOhQiIqKw193djWlXXYOamhpER0cPeJ8lDzwQkRW0ZiUm4oPTp3DDDTfihKeSWG8xMTHYWVWNkpdf7revCFEgcCISBqKjozGRaVlEREQBY7fb8NabbyIlNbXfFC0AcDgcSElNRV5+/oD3CUcajQZRUQZUVVb2WxXMkpODLIsFTqeTPUMoqDgRUTm9Xo/7H3gAh+rqlA6FiIgootTV1iIlNRWv/OlP/aZgNTc14fTp0xFTQUur1eKSSy7BuvXrB6yUpdVqkZ2TA/O116KstFShSGm84ERE5VixioiIKHgW5+TgO9df328KVldXF85YrYgyGPDSjh0KRBdYD+Utw4n338eFr74asFLWQ3nLcOOMGTjXda5Ph3WiQONEROXS0tNRU71T6TCIiIgikrs/hkRKamq/1SkPHjiApzdtxqTJk8O+gqVOq4NGo0FFefmAlbIO1x3C5qIilJeWKRAhjTc6pQOggVlyclBQWIiZ371B6VCIiIgiks1mw7zbbkfOXXch1miEtdcqQF1tLQ7V1eGTTz5B0ebNePONN/rcJxwkmM3IsljgcDhQXVXV53a9Xo+qmhro9DoULM9HW2urAlHSeMMVEZWKjolBUlISHrz/ftjtNqXDISIiikhSSjQeOYL6Q4cwafJkJCSY+73PSU91qfiEBAghQh3mmMXExMDlcqH+8GFIl6vP7fEJCXC5XDxBnUKKExGVMpvNyPR8c0FERETBVV5WhsU/vRsvl5UiOSWlz+0d7e0o2rgR9z/wc+j0egUiHD1vFazysjKkL1jQ72eL1WvWIHPRIsybexvL9VLIcCKiUs1NTZg7Zw5aW1qUDoWIiGhcsNvsuOyyy/qdiHR2dmJzURGsneGXlpWVbcGizEx+uUmqw4mISnV1daGhvp5NDImIiEJkUVoashYtgtFkQmJSUr8pWCfa2pCYmAhjmDQ6NJpMiImOQUN9PbZt3Trg/VpbWvpN2SIKJk5EVEir1WJRRobSYRAREY0rDocDH334ISZPnozqXbv6TcFas3o1Zs6ahazsbFVX0NJqtch7OB+/fu45vP32UZTu2NGnZ4i/x1at4ooJhZyqJyJCCNy1eDF27d2Lnbt3Y+fu3SivqoJOp4NWqw3Lk8V6MxgMMBgMuOSSS/DEk08CADZu2oSs7GyFIyMiIhp/Ojs7caiuDkIAUf1MRJxOJ4q3bIEQAnFXXAG9Ss8X0el0yFu+HLMSE1FasgOV5eVKh0TUh6rL985KTMSSpUtx709/ihOeahWQ7m8s8h7Ox1tv/g2NR44oG+QIJZjNiImJAeAulffsc8/h5MkTEBB49+RJAMDUqVPR0tysZJhERETjVkd7O/7+9t+xa+9e3Hv33ejs7Oz3Pn/Y/kc0NzVj4fz5CkQ5OHd1Lw22Fhf327gQcH8mgQRaWviZg5Sh6omIRqNBVWUlmo4f73ObtymP2qWkpiIlNdV3OT1jEXZWVcPpWR59av36Pt9SuFwuTkSIiIgUUl5Whl01Nfjg9ClkWizYXFTU730sd+ZAp1fnR6nHn1wPvV4/aDnejZuK4HQ6MW/ubSGOjshNnX89KqTX62G+9lqsWbcOq1euRGZWFh5btco3oQCA9RuewrRp01FZUYHqykoAwKmPPoLdbvfd56+vv46jjY2D1ujWarW4Y/587CgpCd4vRERERAOy2WxY9ehKXHbZZTAYDOju7u5zH7vNjunx032lcdVCq9Vi9aMrERUV1e9qToLZjI2bijB5chyyMzMViJDIjRORYaqorgYAVFVW4uTJk7hx5kwszc1F8ZYtvvusXf0YhEbTY3JitVqH1YE1Pj4eVqsV8QkJ+MZF30Dxli0wGo39voEQERFRcHmbGG741Ubc9/P7Me2qa/o0GF6UlobZyclITEpSKMr+PZS3DL9YsqTfmAF3c8PExCTMu+22frNOiEKFE5FhklIiKyMDdpv7D/pf774bpkmTetxnJNUmvClbHe3taG9vx+biYmRnZCIlNRX/8dq/4/Z58zB58mQseeCBYU1kiIiIKLA62ttx+NBh3DhjBtBPfRyHwwGHw4GU1FSUl5aqZrzWaXXYVbMLTlffKln+lTlZJYuUpuqJiMvlQlr6Qux75RW8e/IkNBoNVj32GJ584gk4HA64QlTv2pKTg/iE+B7XdXZ24uOPP4YQokealRACUVFRvssOh8O3QpKRmYl0zx9/rNGIb3/725h322343//3//DLBx/ERx996MtDjY6OxtrHH8fv/u3fkL5gAb766qtg/5pERETkx1tBa3ZyMtY/+SQeefjhPvdpbmrChiefxNmzZxWIsH+lJSU4e/Zsv+V6dTodpsfHs2kyqYKqJyIN9fW4KSUVDrsd06ZPR4LZ7JuEbC0uDtlExGg04pJLovtc/1Desh6Vu4QQuPuee3Df/fejo7MDAFBVUYmdVVUAgJ3V1di1a9fXTyDhWzLt/S1KV1cXCvLzcdHFF+MP27fDwr4iRAETHRODb8bEMPWRiIbU0d6Ohvp6XDN1ar+3exsQq8lAKzNGkwlXXnklzp8/r7qYaXxS9URESulLhWprbUVba6vvNucgTXlC5XDdIZw5c8Z3WaPR4NJLL0Vebi4O1dX1ub/T6RxR3E6nE199+SWONDQgOSUFdbW1AYmbaLzypkQajUaYJk1i6iMRDam8rAxVlZVYmpuLgsJClJaUhO37hiU7G7l5ecjPy1M6FCIAw2hoKIR4UQjxqRCi2e+6bwkh/iyEeM/z/z94rhdCiG1CiPeFEMeFEDcEM/hQKS0pQVtrK9Y+vs7XgPA711+PWKMRZ/zejLxNjvqbhIyW0+lEXW0ttjxTjGuvuy5gzzscQghotVqsWLUKT6z9+ncnCkcJZjNmJyfjzTfewI6SEmx48kkUrCjERRdfrHRoFAAcqyiYnE4nDh86hPyCAvyxpAQ6naq/x+1XgtkMl8uF23/yE5SXliodDhGA4XVW3w7gR72uKwTwupTyagCvey4DwBwAV3v+3Qfg+cCEqSyr1Yqf/OhHqCyvQEl5GUrKy5CWvhA1O3eGZPveFLWF6em4LkSTkQSzGWnp6Ziflob169bB4XCgpLwMDW+/je9cf31IYiAKFCGEr6pNQ32971/cFVdgxcqVCkdHAbId43ysouA6d/Yc6upqERsbCxEGfcz8eb+IAYDGI0cGbSFAFEpiOAejECIOwH4ppdlz+SSAW6SUHwshLgfwppRyqhDid56fy3vfb9Dn1+gkdDEjDl6r1SJt4UJUeXp2RDqDwYA3Dx7E8tzcoKdp7dm3D1WVlSjbsaPH9VkWC2KNRmg0mrBenqbxxWAw4IPTp/DM08U9GpPt2bcPO6uqwrtnj/2zo1LKGUqHoQbBHKtGO05R5Nmzbx8OHz6Mt958M2xSpve99iomXj4RSx58MKBZG0TDMsg4Ndq1xcv83rD/C8Blnp9jAXT43a/Tc92gE5HR0ul0WPnYY9BqtapqJBQsNpsNv/3Nb5CckoKjjY39NlcaDf9KX97nfOThh/HuyZN97lteVuar6PXbf/s90u6YByEEbDYbv2EhVbPZ7H2uc7lcOHbsmALRUIioYqyiyOJyubBseR6ky4WjjY1wuVxwuVyqTtfSCA06Ojs4CSHVGfNfjZRSCiFG/AlUCHEf3EviGF6GWF/d3d24Kzsb06ZPH9Xjw42UEi+/9BIys7JwuLERs264sd9GRSPhX+nrww8/xOKcHDgcDpw8ccJ3n/j4eLS1tfkmGt6KXhqNFlddfRVefOkl/O7553GkvgEtLc0DbYpIMd3d3Vick9Oj6ZjRZMLp06d7FMGgyDWasSoQ4xRFnjstFpz84APk5i3DLx9aitaWVrQ0NyM9Y5HSoQ1Ip9Pj8OFDSodB1MdoJyKfCCEu91vu/tRzvRWAye9+Rs91fUgpXwDwAuBZ8h6lY++8g0suuQSxsbHjIk1ISulb/VmYvnDMK0FZ2dn4x3/8x34rfRlNJkyaNAl5+fk9mjkCX1cAa2ttxfLcXHzvppvwx5KXkfvLX4bNUjWNLx99+CGSZs/GpMmTsXBROm666WY8tX49G3pFtjGNVYEapyiyuN8zJLRaHbRaHa7/7ndx/Xe/q3RYRGFptBORVwAsBrDR8/9ev+uXCCEqACQCODvU+SGBkJiUhMKVj2L+bbePmw8Vu2pq0PD22wAw4smIXq/H1KlTsX7DBvzxxRexZdOmHmlVWq0WF110EfILClC0cSNWrlgBh71vWotXXW0tDtXV4fW//AXfv/VWfPHFF2htaYF9kMcQhVpnZyfKduzAI48+ijvmzUNVZSWbeUU+VY1VFBnWb9gAp9OFl7ZvR3WYnKNatHmz0iEQ9WvIk9WFEOUAbgHwfwB8AmANgD0AqgBMAnAawCIp5f8KIQSAX8NdueRLAD+VUjYOGcQYTwLUarXQ6XS4csqUcZVm8Z3rr8c///CHOPDWW2iorx/yHI3omBiYzWYsSEvDjpISJJjNKC8t9T1OCIFZiYlISU3F7ORkvHvyJFauWDGimLIsFqSlp6OluRm/f+EFnP38c3R1dY36dyQKNJ1OB6HRwOl09tt1OOzwZHUAwR+reLI6ecfQRZmZWFlYiAvd3WHzHrKhqAiXXHIJynbsgMvlGtZnBqKAGWScGlbVrGALxBt8KCtKqUlySgq2PFOMm1JSe6RO9WbJyUFSUhKMJhOWPPhgj/4ngLvRW3JqCqRLovbgwYCc0JackoLEpCRse+YZVTSgJIpInIiEBCciNDs5GXv378fcOXPCsiu5N/4LFy5gSlzcoJ8ZiAJqkHEqYs6+s9ls+N3zz+N7N90E95dd40NdbS3uu/dfse7xx6HVavvcbjAYcNfixSh89FFUlJf3mYTo9XrU7NmDzcXF+PjMx9iyaVPAqmrU1dbi1T/9CS6Xy3ddgtmMfa+9iunx8b7r1m94StXVRihy6fV66PV6pcMgIpXT6/VYt/4JXLhwoceYFk6am5pwx9y5yFq0aNB0a6JQipiJiJQSR+obMCspEddMnap0OCF1oq0NV11zNZbm5iI6OhqA+0Tz1O99Dy/tKMEdC+YjOzMTdbW1OGO1+pq7GY1GAEBLSzNuSklBaUlJwJdqT5440eM5Y2JiMPHyiYiPj/dNGKdNmx52zaEoMqx9/HGUVlYoHQYRqZxGo8G5s+cwJS4ORxuHzDhXpa6uLsxOScbLpTug0/ELGFKHiPoauqWlGWl3zFM6jJCz2+1YtCANBYWFeOKpp3DGakVySgree+89WDKz+uSwZmVnY+LEiag9eBCdnZ14bOWqkMZbXlaGOxcvxkUXXRTejeQo7D22ahXmp6UpHQYRqVhKaipuuvlmCI0I+3Smw3WHoBGagFTdJAqEiPwaev2GDbjo4ouVDiPkSktKcP311+PW//sDPLV+PdY99liPSYg3Teurr74KaArWaKxZvRoLFi7sN52MKFi0Wi0MBoPvstPpxN7duxWMiIjULiU1Fb/45S+xdvVjSocyZnW1tXh261asXru2T5o0kRIiciKi1Wrx1IYNvtSj8cJqteKtv/0N8267HQ319Th//jwAd5rWt771Lfzx5Zdxx4L5aGttVbxaRmtLCy6eMAFFW7YgOiZa0Vho/Hgobxla3z0JvT4KgHtyvvuVV8bdewURDU90TAyklDhUVxcxVTldLhdaW1rgdDoV/yxAFJETkRUFBaiqrESmxaJ0KCHX0tzcZ+nYkp2Nq66+GpaMDKTdMQ8n2toUiu5rdrsd/3r33bjyyisxfTq/kaHQOFx3CJ9++ingqWfhcDjw+l/+AtOkScoGRkSqo9Vq8fgTT0BKifQFCyKmT5ndbkfu0qWoO1iLH//kJ8xMIEVF1Dki/pqbmpD6ve9h32uv9rheuiRcLhe0uqH/8Ow2O556Yj0KVz6KqKgoCE3falyrClfg2LFjAYt7rNIzMrBr164e5XJLS0pw9uxZBaP6msvl8r2Zd3Z24sBbb+GGG29QOCoaL+pqa2Ht/LpqnEajwU0334z/+vhjRVMViUh9dDod0tLT8XQENgOMjY3FkqVLIaULz27bxhL7pJiIXBEBAAgBk8mECRdPgNPp9P1raW7GzqqqHtcN9E+j0eD2+fOQlZGBttbWfu/zcmmpL81DraxWqy9NS2kN9fXYtnWr7/LW4mK8/NLLkGFaDpHC34QJExDL1Cwi6kfvMStSNNTXY0pcHHaUlGD69OlKh0PjWMQ0NPSn1Wrx0LJl0Gg0KCst7dO8L5CyLBYYJ5lQe+CgKhop7ty9G1kZGWFf2YMoWHr/jcxOTkZKaio2FxUpHNkosaFhSLCh4fjibfLbfrodleXlSocTNN5m0LtralBaUgJrED8v0Tg2Hhoa+tu4aROSZs/G5qKioE5CAKCivBz//el/IzklJajbiTRsIkdKiYqKwvonn1Q6DCJSKSEEbrr5Zvxy6UPYVVOjdDhBd9lllyG/oIArw6SIiDxH5Ksvv8RdOTl9rhdCYFZiIjRjbJ7ncrnQUF8PKaWvkaL5umvH9JzjzY6Kcjz1xHoce+cdpUOhcUZoBKZOn6Z0GESkUrMSEzF/wQLUHz4c8WnD3d3dWJyTg5o9exCfkIAjDQ2spEUhFXETkZTUVPzu+edx4auv+tym0WiQnJIy5goRDocDR44cgfSc3NXS0oxL/+FSxMbGcllzmC6++GKsW/8E5s29TelQaJypLK/A/LQFPa5LSU1FeWkp/36JCBqNBlWVleGbrjlC3gIyj69fj9IdO5jaTSEVURORBLMZzzy7DQ/cd3+/HyicTieKt2wJyrYTk5Lw2Nq1mHf77fjqyy+Dsg0iGrvSkh145+9/9112uVx4//33VVNZjkhN9Ho9qmpqoNPrULA8v99eGnq9Ho+tXYvVK1cqEGHgaLVabNy0CTNmzMDePXuUDidkmpuaMHfOHDy2di0MBgNcnmI8RKEQMeeICCGQmJSEnVXVONrYGPLtby0uxrFjx/DyjpKQb5uIhi8qSo9HV63yXW6or8cjDz+smspy/YmPj4cQfcuHEwWbRqPBzFkzkZiYhOjo/pvPajSaiEh3fChvGe686y50dXVFZKWsgXR1daGhvh4ZCxfieEsLlubmKh0SjSMRMxG586678PMHHsBbf/ubIvmNTqcTKwoKUFdbxxPXiVROp/96MVhKidmzZyM2NlbBiPrSarXIezgfBYWF+OGPfgQxxnPbiEbD4XCgsqJS6TBCQqfVobysFKU7dsA1DlcEHA4HoqL0uOnmm1X3fkiRKyJGNktODvIfeQT3/vSnijYlczqdqKutxdZnn8X0eHYLJ1Irs/laZFksvssdHR3o6upSMKK+Nm7ahH/+5x/izTfewL+98MK4/GBEytNoNIgfxnhmt9lDEE1w6PV67H7lFbhcLqx+dGVEl+sdjM1mw7zbbsfp06cRd8UVrG5JIRERExGj0Yif/+xnaDp+XOlQ0FBfj63Fxaw6QaRiMTHRPUpVLlyUjmumTlUwoq9Fx8RgdnIydDod5t9xBxrq61WdNkaRzW6346n16we9j7fyUrjypp8BGNd/a1JKNB45gjNWK6pqalBeVaV0SDQORMTJ6gcPHMCpU6eUDgOA+w95R0kJMjIz8d677/KELyKV6S/VRKfVjbmsdyBotVo8/sQTmBwXhyUPPthv9T+iUNJqtViQlgbAnX1wy/e/3+/9OtrbUV5WFsrQAiYjM3PM1TQjycEDB5BpsfRIYSUKlrA/yr57ww2YPHmyKrqa+0vPyMANN96IRx5+WOlQiMiP0+nEzqoq/Oa3v8Wf/+M/sGLVKkyOi8OBt95SLCYhBKKiouBwOFC6YwfOnDkT9GasRMPhcrlQumMHysvKULR5MyzZ2SjdUYLvfvcGxCck+O5XV1cbthOReQsWIG3efNV8oam0utpaZGdmIsFsRpbFEravK4WHsJ6IfPeGG/By6Q6sf/wJpUPpY3FODnbt3Quj0YjOzk6lwyEijwSzGQlmM6TLBZvNhjst2YAAVq1eDa1WC6fTieamppCdMyKEwN333IP77r8fd991FxqPHAnJdomGS6vVQqPRoKW5GZ+f/RyHDx3GhQvd+Pzs5wAAjdBAusIzHdloMmHCJRNw5MgR9s/w09baiksvvRQpqalKh0IRLqwnImvWrcOGJ59S5YllFy5cQHNzM5597jkseeABNkojUoGU1FQ88+w27KyqxpIHH0RbW5vvtsfXrsXS3FzcdPPN+PCDD1CQnx+S1Mqs7Gz84z/+I/Jyc3HyxImgb49oJKKiolCzZzeiogyYO2cOGurrAaDHuGswGFBSHl7fmmu1WjyUtww33XQzjh87zjTqfnS0t6P24EGlw6AIF7YTEUtODl7avh17du1SOpR+OZ1OrF29GmsffxyXT5zIiQiNK95qK3a7OirpXDxhAkpKS3HgrbfwwH3342hjY5+CEt6Gp1UVFbh84kS4XC5kZGbi2DvH8NFHH8LhcATsw8rFF1+MF196CTU7d+JoYyPKT55kgQsKa+FWNUun0yFv+XJoNFqsf/xxVqXrR2dnJzM6KOjCdiIiXS60tbaqevDu6upCQX4+pk2fDiGEqmMlChSjyYTtL78Ml8uFexYvVnwgM5pMePGll/B2YyO2bd065AcOq9Xq++JgZ3U1tFotdu3di5MnT6KqogIN9fVj+ls2mkz43e9/j0VpafjqwgV+AKKwZ7Pb8aunnlI6jBGJT0iAEBpsLS5WpAkyEbkpXyZmFIwmE06fPo0TfmkVauV0OrHuiSegYz1uGgeMJhOyLBb8+2uv4T//4z+QlZ2NvPx8xSrSeON5df9+PLpixYg/9DudTthsNvz8Zz/Dx2fO4NfPPYdHHn10TE1LJ02ahD//539yEkIRI0qvx6q1a5QOY0Qef3I99Ho9nE4nvyQkUlDYrYhcPGEC8gsKULRxo9KhDNu6NWtQVlGBXTt3svoERSRLTg4Wpqfj9OnT2PSrX/kqPsXGxvZIc2ptbcW7J08GNM2pN28FqjXr1uGiiy/uEc9odXZ2YnNREf60fz+io6PR2dmJ9RuegsvpwmOrVvnup9VqsXHTJlxxxRW+6+7MzsZXX37pu1xXW6u6Kn9Eg+nu7obLJeFyuQa8T7ikZvlXqJt3222slEWksLCbiJSUluLdkyfDqrRl0/Hj2PbMM0hLT8f+fftU18GZaCyiY2IQGxuLNatXo621FQ6Hw3db7zSndevXY8bMmWhpbsba1auD8rcwKzERz2zbhp/dey9OtLX1iGes2lpbfT+vXf1Yn9sfylsGs9mMtHnzYHc48OSGDdjzyiu49+67FU9RIxqN7u5uTLvqGkAAjgHO+QqnhoazEhNRs2c3mo43oaG+PqDvD0Q0cmGVmpWSmooDb72FlY8+qnQoI/bRhx9i8uTJmB4fr3QoRAGTkZmJb/3Dt3DwwAE0HT8+6KDudDqxasUK/Ovdd+PjM2fwr/fdh8lxcWNKc+rNkpODm26+GXm5uWhuagrqhwyHw9Hj+Y0mE2666WasWb0aX375Jew2G1YUFKC5uRmZFkvQ4iAKNrvdBrvNNmgKU7h8oNdoNIiKMmDN6tVhEzNRJAu7iUhdbW2PvGqDwRAWHVE7OztxqK4O6zdsgE4XvIUou90OjRAwGAwQQgRtO2Oh1WrxxNp1cDldYfHaUV/eqljzFizAmTNncKiurs/tFdXVSM/IgF6v7/F36k1zevVPf8Lll1+OLc8UY9q06TAYDKM+bvV6PTIyM1FQWIh/f+21PvGEgslk6jOpcjqdeHzNGlRVVg7wKCL1SzCbse+1V7Fr715MmDChz/u2Xq/H7ldeQZaKJ9xarRaXXHIJMrKyMHfOHLS2tCgdUtjx7sOK6mrs3L0b1153HSqqq7Fn374Bjw2ioYTVRATwVrpwf1DR6/Wo2bMHS3NzlQ1qBFyu4J2cajSZ8OD9P8eVU6bgzYMHcdfixaqajAghMD0+Hg/lLcPj69fjm9/8Joq2bEF0dLTSodEIGAwGvLRjBwDgTosFdnvfJmB2ux13WrJRs3MnHlu7Fnv/9Kc+r/XJEydQf/gwbkpJxYcffoA/vvwySsrLsGDhQiQkmIcdz3dvuAElZaXYWV2Nmd+9AU3Hj4/9lwygibGxmJWYqHQYRKMWExODxMQkpHwvFSc/+KDPmKvRaDBz1kzEGo3KBDgMD+Utw4n330dcXBwa6utx/vx5pUMKOw/lLcOxlmbce/fdyMrIQHNTE+60ZOPw4cMDHhtEQxlyIiKEeFEI8akQotnvurVCCKsQ4h3Pvx/73bZCCPG+EOKkEOJfAhnswQMHcP8DP/dVoLLb7XjyiSfCYgZu9Hxb+tjKVUFbDrZkZ2PVY6vx7smTWJ6biyVLlyJbRXm7d951F9ZveAqH6w7hnrvvxuKcHFx55ZVY+/jjYfEajndGkwkFhYVYuiwXe/fsATB4OobdboPL6cTqlSvxr3ff7Xut/dOxpJSw22yw2WywZGRg0YI0XHTRRfhjyctDpmx543nhD/+GV/a+AqfT2e+kSGldXV1hdU5bOFLTOBXJBNwnevd+v3Y4HKisUPeqn06rg0aj4erkKHlTT/X6KNjsdl+qnt1uw1tvvglrp7XfY4NoKMNZEdkO4Ef9XF8spbze8+9VABBCxAPIBJDgecxzQoiAHZV1tbWwdlrx5IYNvoO9uakJZaWlgdpEQGi12h4rEXq9HnFxcXjvvfeCuhxcUV6OyooKuFwu1NXW4l9/+lM8/MgjviVUvQIlhPV6vS9tJu/hh7F29WOoq63FGasVnZ2dWPLAA7hm2lTodDpVp5ONZ97l+N/9/vf4p29/G2+8/ldUVVSM6Dm8r3V5WdmQ6VilJSV48P77kZiUhPKqKkRFRfnu543HYDAgNjYW//Ttb+OB++5HuQreA5qbmlDy0ktYvXaNL2bvfuOKSNBth0rGqUi2csUK2O12WHJyepzvqNFo8J3vfEfByAaXYDYjy2KBw+FAdVWV0uGEJZPJhI8++hAL7rijT9GCutpa/PSuu/o9NoiGMuTJClLKt4QQccN8vjsAVEgpuwF8JIR4H8AsAIdGH2JPd2ZnY88rr2Bpbi6Kt2xBV1cXurq6EB8fj7a2NsXrgUfHxODxJ55A6Y4daDxyBABQUV2NttZWPPLww0HtG5CUlIRjx4759sGxY8cw47s3AAJYs24dJlx8MaoqK9Hc1BSSyl1Gkwn5BQU4+/nneHztWuyq2dXnG2ur1Yr5t92Oq6+5Bi++9BJ++5vf4OWXXlL8dSQ37/F81dVXI33BAlzo7h71MeytoCWEwE0pqRAAtpeU4Iorr8Tvnn8eR+ob0NLSDCklGo8cwd/ffhsajRZXXX0V1q1/Ak6HExvWr8f//Zcf4pbvfx9pd8zHkSNHVNOLw9vA1D9mu83u228UPGobpyJVa0sLDtXV4Yorr+yRZqnRaDBt+jT8+2uvKRjdwGJiYuByuVB/+DDkICWIqX8JZjMSzGZYO62+zzW9nWhrg5QuGI1GplvTiIzlrOklQoi7ADQCWC6l/AxALIDDfvfp9FwXMF99+SXuvftumCZN6nH9U7/6FXZWVWFHSUkgNzdiZrMZk+PicObMGd91UkqsW7s2qB+YjCYTOjs7cfLEiR7Xez/4r1qxAkajEc8+9xw+/OADFOTnB62Pg1arxUN5y3DTTTfjqfXr0VBfDwADbs/hcKCttRXLc3Ox5ZlilJWVwW5TX4rNeKPVavH4E09gclwc7v/Zz/ClXy+MsfCmYwGAJSMDySkp+N5NN+Hxp9bj6U2bceqjj2C1WuF0OuF0OtHW2opFC9Kg1WqxNDcXWo0WP//ZfapMw+odMylOkXEqUtntdiycPx8FhYWw5ORgypQpw+qNlZKaipTU1BBE2D+j0YjysjJsLipSLIZwtnFTERITk8bcv82SkwOjgucRHTxwgD2cVGi0E5HnATwBQHr+3wLgnpE8gRDiPgD3uS+N7Jz5zs5O3HTLLfjyiy9w7NgxAMCaVatQuPJRRSciF198MVY99hj+/J//6csJ12q1WL1y5YD11wOl69y5IfsUeNNjXnzpJVTtqsHOyirsqqlBdwC/rc3IzMSJtjbc8v3vY/26x0eUilZXW4v77v1XrFq9Gk+tXw/bEOUiKTiEENBoNFi9di2qKitx+vTpoJ7jUFdbi0N1dbhm6lT8ZO5cFK58FGl3zIMQoscx4HQ6cfjQIaSkpvKcCxoORcepSOZwOLAgLQ2xsbHDmogkJiVhydKlIYhsYE9v3qzo9glYmJ6OGTNnhny7QgBRnhTgQ3V1/FyhMqOaiEgpP/H+LIT4PYD9notWACa/uxo91/X3HC8AeAEAhEY34qNCulx4ubQUM757A+x2G44dO4Y7Ldkwmkw4+/nnIW8aaDSZ8Lvf/x6L0tLw1YULAHqmaQX7wJ8YG4sEsxntp08Pej+r1Yqf/OhHEBoNFi5ciO0lJfjNs8/i1EcfjbrhmtFkgslkwrmz57CzuhoulwsLbr9jVCflNx0/DgHgzYMHmaalACEEFixMgxAarF+3LmR19qWUOHniBN5/7z08u3WbL7Vp9aMr+6zyEQ2HGsapSLW1uBhv/PWvuOSSS2A0GvHf//3fQ97/2W3bQhRd/4KVAUDDtygtDUIT+gl9lF6Pl3bsQFp6Oj755BNfGjCpw6iOCCHE5X4X5wPwvqKvAMgUQhiEEFcAuBpAw9hC7F95WRk++ugjZGRm+K5zOBywZGcrcqKUJTsbra2t+OrCBbiczh5pLf5pWsGg1Wox5yc/Hva3xA6HA3abDeVlZcjJysL8tDQ8+9xzyMvPH3HFC6PJhF8/9xwWZWbAfN21cDqdkFKO6QPs8ePHsTw3F//07W8jMytr1M9DI5eVnQ2dTo+dVVWKNPvyVr7ypjb1NwlJSU1FbCwzaWhwahinIpXT6cS0adOQlp4+rGadTqcTdptN0X9qOZcsHJWV7Bh2UaCDBw4M+KWm97NHqP998cUXWDh/PnJ/+Usszc3Fk7/aEMjdQ2M0nPK95XCfxDdVCNEphLgXQJEQokkIcRzA9wEsAwApZQuAKgCtAP4dwC+klEH761+zahUKVqzo0USptKQE7733XrA2OSCHw4HK8nLfm91FF12E6fHxWPLgg0FNI9FqtdDr9bjl+9/HqVOnRvx4p9OJtatXY8OTT+L7P7gVGs3XE5EEsxk5d93lq2jUuxoYAMTGxuK9997D6kdXorK8fEy/i7+62lps2bQJNru9RwwUHAlmM/bs24cvvvhixBWxQi0lNVXV/Qoo9NQ8TkUCl8uFCxcuwOV3ond5WRkqy8t9VewAvkdHqr6vdV96vR7d3d14629/U23qbF1tLazWTmiEhmWGVWTIiYiUMktKebmUUi+lNEop/yClvFNKea2U8jop5e1Syo/97v+klHKKlHKqlDKoJTTc6VgWuPxSd6xWK377+xfwneuvD+amfYQQSExKQk31ThxtbPRd/9KOHfj7228H/Q8ybeFCTI6Lw4Lb7xj1trq6utBQX4/5t93e4wTgmJgYbNqy2dcY8aG8ZX2aJDbU1+ORhx8OSnMoKSVOtLZhftoCzFuwgJORIIqJicGhujq8smePqlPhzp09h46ODqXDIJVR8zgVCRrq6zElLq7HGAe4/x7T0tPR+u670EeFvjw8hY73tW57770+n6+MJhMqd+7EdfEJ2LZ1qzIBDlNzUzMmTJjAxosqEvZn3x175x10dnT0SNXYU7ML//zDHw7ZEC0QoqKi8My2bZgYO7HHB7iK8nKsfPTRoG7baDIh7oorcL6rKyBpNP09x6G6Q/j5Lx6ETq+HTqvDE0895WsoCbgnC8Fc8m5paUbaHfPwDYOBaVpB5k2rU7OWluaArrwR0dC8le56vz+0tDRjp6fXj+CKSETzvtaXXHIJ1q1/osdtluxsXHvddbDZ7apPgVtRUIBVjz7KFREVCfuJCOBO1Yi74grf5fKyMtQePIjv3XRT0L9FX79hAzo6OnCorg6Ae4XkrsWL0dzUFPQ/yC+/+AKXXXYZfvtvv4dON5ZKzAOrq63FqY9OIUqBZoj+KsrL8fbbbysaQ6TrnXq3fsNTQTuuiCgyOBwOXLhwAW+/fVR1zYUpsEpLSjD/9ttxRdwVvkbJFdXVmJ+WhkcLC4NeHTRQXC4XMjIz2XhRJSLiU0ZHezt++dBD6Dp3DsePHwfw9TLi63/5y4ANeALhqy+/xF05OQDck5C777kHDzz4oO+6YNHr9Xjwl0uwbetWTJo0KagVQe5ZvBjLCwpgU7Apm7eqkld0TAzMZjNcLhca6usRazQqUi0tkuTmLevx9zJt2nRFKpwQUfjwVsSSLpciBS4odLxNab2Nkh12O+60ZPt+VvuKuldDfT1qamrYeFElIuJTRnlZGRbn5OB3f/iDLx2rpaUZub/8ZVArVqWkpuJ3zz+PC199BcDdXfbSSy/FQ7/8JU60tQV1u8uWL4et24ZFGRm4ccaMoH5g/Oqrr7D9xRdDkuo2XGazGXv378fCRYsgNBpMmjQJ0TExSocVtjra23H40GFoOPEgohHwVsTiJGT8sNttvlQ9/5/DhTel3JKT06PYESkjYj512Gw2/PY3v0FySoovvaSuthYPLlmCiy6+OODbE0Kgo6MDZ8+e9V1nMBgQHR3tS9MKFm8q2M8ffBCLMjLwH//+70FPAzOZTP8/e/ceF1Wd/w/89Zkzw3iD2r5bbjGjaFYCU6iZIBDu7nd3v9VWKheBYbxUW6apkSniBQQBNVPxlrXVljhc5Kqmm3tpv7kpKGAqctMsRWBsL9/f7iamwFzO7w9mJkbuMDPnDLyfj4cP4XBmzvvMwPnM53zen/dHVB0RAMjNyUGKecX6kuJi0VbqcAWNjY0O/721p9RNmyhtjBBCSL9kabXw8fVFYnISdUYENmg6IjzPY39GBpiE4fGpU63bOY7Dxk2boLBjuU/Lom/T/P2t1aIUSiWSU1KwZfNbdjtOVwICAvCPf/wDT0yZgsKCAofdiWhfoejGdzdQUlKM0tOnwbcr4SiUG9/dwAMKT/z3L3+Ju+66C9MDA2mY1c5qqqtF8V7fqaG+Hp6enpQ2RgghpF90Oh2e+dWv8MpLv6Fy8AIbVC05z/MoPtE2Sd1SEWF1XBzycnN7tehSb0kkEjAmQUFennXbmDFjkL59O5qbb9vtOF3Jy83FpYsXMXzYMPz1+HGHpYG1r1BUXV2FWc8+h4jQUFEMwVuqaUmlUtx9990ICg7Gb155hSph2FHiunWieK/vlJOdjdqaGqHDIIQQ4sIMBgOuXrkCxli/FnQWG4UIM1d6Y1B1RIC2dKyg4GAMGzbMuq2qstKu1TyMRqNNJ8RXpcLYsWOdnhrkauk0jpCfm4tr167h+vXrmP/CCzYLMpK+o5QnQohMJsPBTz7BkWOfUmUhMqg1NjaCMYbY5cshcdGOiFqjwZFjnyIrJwd73n0XRYcPu1Q7Pug6IgDwzddfY592v/X7pqYmh3USLAsa0tCesBQKBfbs2gWDwTXKB4rVxIkTKeWJEAKj0egSawsRYg8SiQQ+Ltbptnz+DAgIwMgRI/Hv//wbPM9jmr8/pgcG2nVKgiMNyk8ca+LjUVJcgrj4eJuFDh0hOiYG99xzD4pPnnTYMTobbnN2tQeFUumU17O/Tp44gVGjRtGH6AE4eeIEdLpG6/ccx2FOZKSAEXVNKpUiUqSxEeLKLFUZC/PzMevZ5xxaAZIQMTh54gR279yJxOQkoUPpFcvnMeWYMQgMCkJ9fT00ajVmPfscli5ejB3btyMsIgK79+51iZQzJoa7HUwi5SG1b+lVd3d3pGzciEmTJuFf//oXZG59W5AvN+cA/CZNwkTviV3uYzQYMeGhhzD5scegb20daMhduuuuu3DX3Xej/to167Yjxz6F0WjErGefc9hx27O8ntmZmSgrLXXKMYnzPfrYY6iqrATP85DL5dDmZGNOaJjQYXVQdPgwJBIJZj3nnN9/UdP/+0ue56f2vCMZCEe0U2K0fMUKxC5fjtLTpxERGip0OIQ4hVwuxz6tFgvmzkWryMsRh8yYgYKDB/Hs0093+XnMPyAABz/5BEajARMnPITvv//eyVHeoZt2atDePm5qakLpqVP45z//iY2pqdZh5t7+mx0WigkTJnS7T15uLi5duthzMAM00dsbYeHhDj9OdyzpbT6+vg5frZ4IZ21CAqTSvnXahTBXrYZe77jOPyFDkbuHBwrzC/Cglxei58wROhxCnKalpQXv7N6Nb67VYd78+aL7nOPu4YHpgYEIfvJJvLZ0KUpKinHjxo0u9y8rLUXcm2/i3NlzKDp8GNMDA+EfECC68wIGycrqXcnJzkZOdjYAOGzkgDeZEB4ebj2OI0gkEoTMmIG8Aweg0+kQFBwMhacC1+qv9fxgOzp54gS27UhHVmamQ0eAiHCkUikgvutUBwaDAeK9X0WIa1KpVAgKDsbWLVuEDoUQpzMajXBzk7dlf2Rni+pzjmUR5+bmZjzo5dVjbDzPI1OrRX5eHr65VtenxzrboB0RcZaiwkLEr13r0PkaNdXV2LJ5s3XxxLqrV/F///d/2LA+yWHH7ExJcTF0jeJYNFAmE/9de1eUvH49DHrxT/hvbdU7/fefkMFsy9at2Lh5syhLdhPiDFWVlXj26adRW1ODYXK5KOZWcByHUaNGITk1Fc3NzVgTH9+nNrq1tRWznnveel6HPvkEmnnzRDUyQh2RAWppacG8mBiYHJhPONHbGwHTp1sXT9TpdLh1+xZmCZC/K4bKEn6TJmH12rWCxjBYzQ4Ntbn4enjcJcrKG25uMqxZt07oMAgZFBRKJfwDAtDU1IRdO3cKHQ4hgmhqakJZaSnOnzuH6kuXsCw2VrBYfFUqTA8MxNbt21Hz1Vc4e+YMHvTyQpZW26f5KzzP40x5OcpKSzHr+edx+/ZtzA4LRWh4uGgqa1FHxA4qzp9HY0ODwypKSSSSDj1z3sTjq4uOn59yp6LCQkErSwQFB+NnP/85UlNSBIthMNuQlGRzR1Sl8rXrYqD2JJUN6sxSQpxGHRMDbx8fmHgTTEaj0OEQIqjEdesgkTBBR0Q2v70Fh48eRczcuQCAhHXroB/gJPrbt24hfPZshM2chQUvvoDDR4+Kon2njoid+AcE4P4HHnDIc3e2IOPqVatw5swZhxyvO/m5uTAK1FD5qlTYsXsXTp44QY2lk7S2Cp+mxRjDvPnzcSA/32aRJr0IYiPE1THW9oFr5ZtvIn5lnNDhECIKLS0tiIyKgp+fn1OPK5fLodZo8NGHv8OzTz+NZ59+GqEzZ9o9ZXpd/GrcvHmz7RwnTYJcwFQ06ojYyc70dJw7e9Yhq9B2tiBjbU3NkKvv7uHhgYK8fHwpQAdsKGppacF8jUbQGBhjWPDii1iybBnSUlKsozViiI2QwWCavz+Wxb4+JNsUQjrT0tKCiRMeRmFhIQ4eOQKZzM0px5XL5ai9/BUeeOABHCoqQllpKcpKS3GmvNzu5YQrKirwyIMTUFhYiE//+Ed8c61OsFQ06ojYidFohFQqxUcZGR0WHyT2Qyv9OpfQE1fnzpuHRYsX4/WlS1F54YLNz4SOjZDBQCKR4FTJKTQ2Nva8MyFDhF7fii+OH8ff//43p1SSDAoOxuif/AS7d+5C8cmTTvmcYznH9G3bUJBfgJAZMwRZAJE6InbU2tqK9955Bzt373bIyIgYtLa2ImGN8yeKy2QyhIaFdUhRI/Ylk8mQkpZm/d5kMjn9A79MJoPfpEkoOHgQTTdvYp5Gg1MlJQDa7hh1lqZFCOm7LVu3QjN3LpYsXtxh1J2Qoa6kuBj//Mc/ceiTTxxaGRVo64j85Cc/Qfq2bdb2zhlKiouxdcsWJKxZg01paTh96hSSU1OddnxgkK8j4mw8z2N/RsagvmvP8zyuX78OhULh1DtojzzyCDK1WmosHUwikeAR74nW78tKS1FeXu7UGBKTksCDR3RkJAx6vfVvSS6Xo/TLM/jmm2+QlJBIIyKEDIBCqcTIkSPxn+++o+sqIV2ora3F/AUL8Pn//q9dn9fdwwMqlcr6Pc/z3S5Q6GiWimFyuRyJycmYHhiIqspKNDU1OfzYTAwfmJlEykPqIXQYpJemBwY6fdGrQ0eOIC83F9mZmU475lAkl8uhzcnGnNAwpx9boVRCHRODLK0Wuk4+GKk1GhiNRuTm5Dg9NlHT//tLnuenCh3GYDfY2qm4+Hgc//xzlJWWCh0KIaIll8vxzbU6lJeVY8miRZ22TX3FcRy2bt+OmLlzkZWpha5Rh+KTJ506EtJdbMtiY8FxHBhjaGxosM+C3d20UzQi4iCpmzaB4zisjhucVUgsv6SO7sgyxjB33jxk7NuHQ0VFDj0WaSNhEsjlcrQOsFRgb3Ech5S0NAwfMQI5WVnWhTstZDIZ9Ho9vjxzBl9duuTweAgZzGQyGfIKC1F88iRqqquFDocQUbMsCKiZNw+eCsWAOyJqjQYxGg3kw+R49umnUVNdbV0jTgyMRiPSt20D0LZmW25BPtRzNYh7cwVqa2occkyaI+Igm1JTMWXKFFEsFuMIscvfwONTHX8Tdpq/P97a+jZ0jY2DNt1NLBRKJZ6YNg0AcPzkSTwxbZpT5jqFhYcjOysLK5cvR1lpqfWirFAqEfzkk9bFKy9dvEi/A4TYgdFoRENDg6g+ABEiRpYFAa/rdFCpVPD1VfX8oE4wxuAfEICAgAA8PnUqykvLbNo7MaqprkZVZRWMRqN1gUVHfKaljoiDNDc34w/HjkE5ZozQodgVx3GYExkJjpNCInH8rw9VdHEOhVKJPXv3Yubs2ZgTHo43Y2Pxf//8Jz7KyEBcfLxDFuvkOA7LV65AY2MjqiorbeZ8tI+HFq8kxH4iIiNRVlqKhvp6oUMhxGWcPHECS15/Hfuzs/pcGTUoOBjxa9Zg9zvvoL6+Hps3bsTaNWscFKn96PV6hM+ejVnPPodhw4ah6PAh7N671+6fB2iOCOk1tUaDWbNnI33bNoRHRED16KP4/vvvIXOTYd3qNTY16E0mk/XfQH7H7rnnHgwbPtxlJ1MyxuDm9kMNcoPBINiCkJ3hOA4JSUnYuT0dDz38kM0wMWMMj0+dColEgsuXL+O9D97HkcOfIEubCamUg76fCyzJ5XJERkXhyy+/xPDhw3H9+nXr+8txHEwmE0aNGgVvHx/RDVuLEs0RcQpXb6cs16Kiw4fxmN9j2Pb2VuzYvl3osAhxGUWHD2Oavz92bN/e6zmyjDGsWBWHpcteR+np04gIDXVwlI4hl8tR89UlDB8+AhGhoThTXt63zzPdtFPUESG94u7hgVcXLcIfjh1D5YUL4DgOEo5DQEAAduzahatXr8LEmwC0zTEY5e6O2upqnD93DocOHoSPry8A4MZ3N/Ddje/w3X/+02M1BrlcjsJDh/Dqyy+75IiIZTG+VxYuRENjAzw87kJ1VRWSEhKcUomiJ+4eHtiQkoK83NxeTZLzmzQJiclJSE5cj/g1a6COjOzzMSdPmYJ9Wi2+/voy1savtum8unt44OVXXsHvjx7FpYsX+/zcQxZ1RJzC1dsp/4AAFB46iMoLlQibNQvNLS0wieimCCFiJ5VKMT0wEGEREcjJykJZaWmXN1oVSiWUSiU8FQpwHIeiwkLwApTDtyeZzA2RUZGYFRqKUe7uqK6qQt6BA92+DlY0WZ0MlEqlAs/z1kXlLL3g0LAw5GRn29wdaF914Uf33IO7774bQcHB4DgOYRERuK7Toa6urtNRjvYVkyKjovD41McRpVY7tUKXvbi5ueGVhQuxPDYWp0pKoFAosHvvXnj7+AheqUat0cDT0xM6nQ7Xrl3r1WMqzp9H2MxZ4DgO5WVliIuPh8FgwM709F7dFQkKDsaO3bvw1qZNHaqfWeI5eeIEdUIIcQCJRAI3NznWJyTg1q1bQodDiMsxGAwwGAxQx8QgNCwMD3p5Qd/a2um+6pgYrIiLQ0lJMWY9+5yTI3UMvb4VmVotMrVaTA8MxOGjRxExZ063r0Nv9NgRYYwpAewHMBoAD+B9nud3MsbuAZALwAtAHYA5PM//mzHGAOwE8AyAWwAW8Dx/tt8RDgIymQxAW77d+uRkbEpLA2PMaVWJBoLjOAwfPhyhYWFINw/jW4b4k1JSMNHbG2/f0UloX3XBYuuWLWCM4S+ffdZhbolMJoM2OxuFBQWIWx2PW7du4/zZs1i+ciUS1q7DSy//Bn/8wx86rKwtdjKZDFeuXLGONjQ2NuLEF1/AZDIJFpNcLofBYMDzM2ciLSWlX6+p0WjEh++/D28fH6SkpeFH99yDhDVrIJPJYDAaIWGs046Jf0AAdqbvQE67RSntEQ8h1E51TyaTITk1Bc3NzYJefwhxdVWVlcjOykJYeBjcZDLrB3DL5yKZTIYPP/4YY728sOKNN1BWViZwxI5RVVkJbUYGwufMQdqmTUgwz3lpaWnp83P1mJrFGLsfwP08z59ljLkD+BLALAALAPyL5/nNjLF4AD/ieX4VY+wZAEvRdoH3B7CT53n/bo/h4kPePSk8dAjV1VVIXLsOUqkUDz38MD7KyMDSxYtxxsmLxfWFJXVnwkMPISI0DM3NtwG0faDcsWsXXn7pJVysrbXLUKNM5gajyYjhw4Zhn3Y/oiLmQCLhYDDoIZXJkH3gAFI3bEDF+fMDPpazHMjPx4vz59vcfbTMgXB2B1ShVGL8+PHYvXcvUpKTcaioyC7vm2V1c4PBgMSkJBwqKsJ///KX+PD99zukn9157jKZDBmZmSgqLLRbPEMSpWZRO9UDuVyOrAMHOiwSSgjpu7j4eLwZtxIn/voFwmfPBvBD6mNLSwse8/FFq14/6P/WOI7DiBEjUH3pEiQShpaWFkyc8DD0+k5GR7ppp3ose8Tz/LeWO0U8zzcBqAXgCWAmgAzzbhlou+jDvH0/3+Y0gLvNjcSQdSAnB0mJ6wG0fWCrranBwcJCRKvV4DhO4Oi6plKpMNbLCwtfftnaCeE4DkHBwVgeG9uh0tFA6PWtMBmNaG5uRklxCd5cuRL33XcveJ6HvrUVRQUFmOjtbZdjOcuwYcMQGma7MKDRaBTkwqSOicHssFBsTE1FQV6e3d43y1A1AGxISsKFCxcgk8mQsnEjotVqm33bn3tQcDDuu+8+qCMj7RoPGZqonepaUHAwlq9YASZh0LvAKDwhYnfyxAlsf3srmIQBaPtcFBUdDTc3OYoKi3C7uXlI/K0ZjUbcunUL6du2obysHMOHj0BkVN/njvap/ipjzAvAZAClAEbzPP+t+Ud/Q9uQONB28W9o97BG87Yhy2+SHySMddgeERkJiUg7IiNGjEBoWBiWLF5sU9FIJpPh8alTHbYCqCXt5/jnn+PW7dvW7QX5+Zjo7S3qjtudTCYT5kRFWWNmjEGt0eCRiRNt9mOMQS6XQy6X2/X4HMdBLpfDx9cXra2tSFiz1imrkmdptcjOzERraysizBPaLedniWdrejruf+ABh8dChh5ntFPt/2aF+sckEpvvLdfn9tsmT5mCPXv34rWlS5GUkDjwF5cQgpLiYuzYvh3e3j44cuxTPPrYY3jwoQl49umnkZyYOKSKQFhS8ZcsWoSI0FDErV6NI8c+hZ+fX6+fo9eT1RljowAUAojlef4Ga/fBmud5njHWp64fY+wVAK+0fTe4lzOZONEbzAlrbtiLQqnE67GxSFi7zjoS0r6i0XyNxqHHb2pqQllpKXILC7AxJRUV589Dr9fjL3/+M7Zs2yaaqlM9iYmOxqFPPsGy2Fjs2L4doeFhMBqNHVYHn+jtjeTUFBgNRryzezeMRiOqKisHfI6vL38DP/3ZzxA2czYuXrzotIujTqeDTqdDeVkZmEQCmUyGj/fvx7jx4/HPf/4DYTNnIyQoCIZ+lv8lpCvOaqem+fuj8NBBO0TcPy0tLVgwdx5y8nKt27a9vRUhM2bgiWlPWLcx1vb319zcjItUBIIQu/LwcIe/fwDc3NwQ+vzMIT2yb2n3X3v1VRQeOoSDR47gkQcndJ6mdYdedUQYYzK0XdyzeJ4vMm/+O2Psfp7nvzUPaf/DEg8AZbuHK8zbbPA8/z6A9wFz7u0QlHvggKjWlAB+WEhuY2qqtRMCAHd5eOBUSYlTKxodKizCRG9v67yQuqtXMX78eFFUneqN27du4aUFC6AcMwYSiQSMSTodkaitqcGc0DBrtbGQGTNw7do1lJ46hZzs7D4fl+M4vL78DXASDq++/EqvLgSOwPM8eKMRJqMR6shIxMXH4/jnnwsWDxncnNlO6RobsWN7ugPOoncMBgPqr12zieH0qVP427ffovjkSQBt14HY5W8IFSIhg45ao7GuLM5xnDXbITIqCl+eOSNkaKJh6YxJpVKgYyJQp3pTNYsB+B2AWp7n269+9AmA+QA2m/8/3G77EsbYAbRNAvyu3dD4kJWYlISkhARIpVJM9PbG7LAwLF28WFRDeCNGjMBvP/gANTU1qKmuBvBD1awXf/MbbEhKcmo8OdnZYIyB4zgYjUZr1amUtDTMev553HaBEpSNjY3WNVAK8vK63dcyxJl34AC8xo3D3vfeQ1hEBPR6PeZrNLDc3TWZTJ0uJsgYg0QiQUpaGiZPmYKXXnhBVAtBGgwGqthDHMLZ7VRjY6MoSorfGUP7lFm5XI6f/fznuHjxYoe5aoSQ3pHJZMgrLIRUJoWPjy+u1dUhPi4OAPCXzz4DAKSkpUFi/pxC2qxbvbrXWQ+9GREJAjAXQCVj7Lx52xq0XdjzGGMvAbgGYI75Z5+irRLJ12gri/hCryMfpKIiIgAAYeHhCI+cg4u1taJLTVEolfjtBx9gTlgYbjc3w2Q0dqiaJYRp/v6YExVlTcfamZ6On9x/P/ZnagWLydEsQ5xTJ09pu6PAAxMemoDk1BTrYpHJiYkd0rcmenvDV6VC4rp1ACC6YeKd6enUESGOQu1UOwqlEl5eXigrK0VtdQ2USiW8vb1xoaJC6NAIcRkKpRK3bt2C0WgEkzBUVl5AdVV1h4wMV0gVd7aLFy/2erI+raxOAACBQUGY5u+PXTt3WkdppgcGYuWqVTYT1p3NsgjgprQ06x8/x3GYHRbW4wiDEDiOQ1h4OPJyc3veuZ/Pf2f6Vn19PequXrUuBEmGGCrf6xSu1E7deT2Xy+U4fvIkDhYW2iwaSwjpyJKCFRgUhI2pqT2mgh86cgQFeXnI1GqdFKF4KRQKRKnVyM7Ksv3c2E07RR0R0qkRI0di4+bN2LJ5s+DpPctXrMD/PPWUTTqWr0qFxx57rF9zKBxp4+bNmPDQBMwJC3focTw9PeGpUGDM2LFYu24dFr78sujnzag1GoRHRGBOWJjoRmtcGnVEnMJV2inLwmrAD4uLyeVy1F6+jFGjRuHZp58W/bWCECFYKlcWHT6Mx/weA88DoTNn9rjeW9HhwzAajYgIDXVGmK6pm3aq11WzSN+5e3jgLg8P6xwBV7J6zRp8f/Om4J0QAJ2nY/GAp0IBd3d30QyLKpRKvL1lC5pu3HD4sSzpWxzHwdM8eU7seJMJj0yc6FIV5AhxJb4qFfwDAvDyK6/gypUrmK/RwGAwoKWlBbOffx7JqSlQKpUoLysb9GscENITX5UKHh4eaKivx9///nd8vH8/pDIpvv/++7aKTwZDr1LooyIiXG6dM0dgjGGitzdqa2r69jgxXIxc5U5TX00PDERQcLAoJjW6us7SseLi4zF69GjErVgh+CQxhVKJaLUaWZmZTu28TQ8MxOGjR13mLmfBwYOIjoyEvpUqZ9kNjYg4hSu0U0eOfYoH7n8ASxYv7nKtp0NHjuD06dP44vhxlBQXOzlCQoSnUCqhjolBtFoNk8nU7d9Lb1DqY9vCqSEzZmDqtCcQNnNWxx0GsrI66Z8RI0ciOTVVdCkoHMdh4+bNKDh4EIeOHIG3j4/QIfWK0WhEbU2NzWrdWVotVI8+io2bNwsYWZvx48eD53lRjCARQoYejuNgMBhw5cqVbj9UmUwmvPHmcgQGBTkxOkKEZ1nkd/z48Yhdvhz/9eMf9/j30lujR4/Girg4l8lQsDf/gAAseu01mIx9L0hDHREH0WZl4eyZM9i1c6fQoVi5e3hg6/btOPLJJ4iOjERBXh4YYy7TGbGkY/kHBIAxBp1Oh1nPPw83NzdrbW8h+E2ahNeWLkVDQ0PPOxNCiAO8vvwNbNm0GdFz5nS731y1GvpWPRRKJdzd3Z0UHSHO5+7hgemBgbjrrrvgHxCArdu34/DRo1gWG4sVy5fjQS+vHv9eeqOlpcXhCz2LXWFBAWKioqCOiurzY2mOiIMcyMlBYUGBqNYJUalUGOvlhWvXrkHf2opMrRZyuRzanGzMcYFSuNXVVbjr7ruwY/cuhAQFQ9/aitu3biEvNxdRarUgKXBBwcF4Yto0REdGiuq9JoQMLVJOCqPR2OMofNvPeahjYpCdmekSKZ2E9IdKpcLho0cRNmsWcvJy4eYmd1gas9iyX5wtMioKS5Ytw4NeXn1+LI2IOIBao0FVZaUoP5iWFBe7dPpQSXExdI06pKalWbdVVVYiLzfXuuCfs/iqVNixexdOnjghyvdajNzc3GzeO0LIwMhkMhz85BOYTCbrQrTdaW1txaznnkd2VhZkMhlkMpkToiRDkVqjQdHhw22rbHdCLpdj3vz5HbIyGGOQy+Wd/rOsZt6Tt7ZuRXJKCpqbm9HS0oJZzz2PZ59+uld/I/1hMpnQ3NyMlLS0Ls93sPJVqRAROaetSl8/pp1TR8SOGGPwDwiAp6enaCuSDIbh+LkxMWhubrZ+39TUhICAAMwKDXVaZ8SS0pa+bTu+PHPGKcccDGpra/HwI48IHQYhg4ZEIsET054AANy8ebPH/Xmex5nyclzX6ZBXWIgcEa7HRAYHhUKBqU880WmlxLaS0l/hra1vd/hMMs3fH99cq+v037LY2F4d+8EHH8TsmTPxoJcXysvKcKa8HGWlpb36G+mPstJSPOjlhZvf3xxylSE9PDxQkJcPn4cfgV7f90I0Q+vVcjA3Nzfs2NV2h/xiba3Q4XRKHRPjOnNCunD71i188P77NpMt83JzEaPRQOqku3tz581DdIwa2ZmZou10ilHi2rXQG3ouh0gI6Z2wiAjs2J6O4pMn+/S4kydO4O9//zvGjR9PE9eJINzc3MBxtqMHao0GMRoNykrLsGN7uvXf7p27wHEcQmbMgKenZ4/PnXvgAG7fvg19a6tT2mie59uOZRqanweMRmO/OiEAzRGxq9RNm9DQ0GCXCgyOUFVZCW1GBqRSqXXhnqSUFGxYnyRsYP3g6emJwKAgnCopsV5kktevx7qEBGxISnJoOV+1RoMVq1Yhph+TsgghxJ5Cw8L6VRK7pLgYMVFRmD59OgKDgqiULxGcWqNBSloartXVQaNW26SRM8bwl88+g2bePHys1eKZX/2q23kZ+bm5zgi5g+T165FfWIjcnBzRLbjsCDKZDClpafjDsWP9fg4aEbEThVIJPz8/LJg7V+hQutTU1IS4FSvwzLPPQpuTDW1ONh58cLxoR2+6U1ZaCiZhmDd/vjUdq/LCBfz5T3/q9dBtf7h7eCAgIACvvvwyKi9ccNhxCCGkO5aKQA0NDeBNfS+ZCQC1NTWora0dFCm7xPUpFApcq6vDqVOnOsxltaQUlp46BU9PT9GmP3116RKmPjF1yJTxlUgkeMDzgQFVDaURETsZM2YMPv3979Hc0iJ0KN0yGo1Yt3q10GEMGM/zKD5xEjt374bRaESmVgsAqLt6FdMDA+Hp6Wn3RYU4jsPLr7yC+vp61NXV2fW5hxKlcgzdgSVkADiOw4aUFMTMnYtnn356QBV7Gurr0djQAHcPDzQ1NdkxSkIAqVSKyMhIZGq1UGs01lL7HMd1OvH8woULqOrmJl9OdjYMRqPgixiTH1RVVSE3J6ffj6eOiB34qlQYO3YsdmzfLnQoQ0pJcTF+88IL0ObkAIzBb9IkjBs3Dp4KBU6eOIFtO3bYtXqFRCKBwaDHnLBwm+2pmzYiKSFxyJfv6y2lUkEdEUIGQCqVwtvHxy5VgBobG3H9+nX89sMPUFVZhdVxcXaKkgx1WVotik+exO/2fYxIdTR8fHx7HHk7d/Yszp071+0+TzzxBOYtmI9sbaZo05/UGg1+f/QoamtqhA7FYSzzeVr7mBZ6J+qIDJClUtY999wjdChDUkVFBV579VXkFRbg/LnzCJs1C3qDAQa9HmfKzwB2LqLlPmoUFAoFGhsbf9jIAz6+vrhQUWHfgw1CbSVGB++FmRBn8PH1xc2bN+22HgJvMsHb24fuMhO7YhIJGGOorqoGJ+VQWfnDSIeESTDN3x+SdilWDfX1OHfuXI/p4kaTCUajESYRFooxmUw4feo0xo0fP+jTHRUKBR6fOhUf/+53A3oe6ogM0Nx58/DqokV4felSoUMZsgwGA06VnMKSxYtx69Yt6/b+VnDozogRI6AcM8amI7I+IQHLYmMxatQousvfA71ej/UJCQgKDhY6FEJc1vrkZOTZcTKu5a6yGD/YEdeljomxLnJ3ZzEFuVyOb67Voay0zNqe5mRnQ63RYPLkyd2OdJw/exbrVq8WZcdZr9cjfPZsxMXHCx2KU7S2tiIxIWFAzyHO2T4uIjIqCstXrsRLL7wg2kpZQ4FMJnPaQo2NjY0YM2YMcvLyrGlfRqMRH77/PhobG52+qCIhZOiwLPTW3NyM/Fz7rv+Rk52N3JwccBxnrapIiKOVnj6Nb69fB9A2byRGo0GkOrrL/TmOQ2R0NCS9XNhQSIN5wVDGWK8Xl+wJdUQGoCA/H09MnkLVkwQkl8uxe+/eAVVs6KuC/HwsmDsXDz38sHVbU1MTwudE2FTxIoQQe2GMYcGLL+Kba3XYmZ7ukBFfAHh9+Ruo+eoSZDI3hzw/Ie3FLn8Dj0+dCgAICw/Hw488Ag+Pu6yT2u/Uts/Dnf5MTBrq6/G7fR8P2gVDp/n7Y1ns63Z5LuqIDMBAFnAh9hEWEYGNqakDqtjQV0ajEQaDAb4qlc320yWncO999yEquuu7OaTtAs0Y69WiVISQNm5ubkhJS4Wbm9yhKSlSTtrWCaH7KcQJOE5qnSeSl5uLixdroVKpEKVWd7p/Xm4uLtZedGaI/ZKTnY3Lly9DKhucMyAkEkmHxSj7/Vx2eRZCnIyxtjVEbt++jbwDB5x+fKPRiJqqKhw6csS6Un1JcTF279yJ+LVrEd3FRZQAd919N9QxMUOmzjohA2VJl6qqrLJLpazuZGm1iIqIwPrkZIcdgwwNBoMBLS0tQBdTj1paWpCxb5/N73PcmysQt2IFIqOiUHT4MEaOHAm5XI6Nmzej4OBBRERGYu3q1TDo9U46i4GRMIndUpjEwpJylpWpRejMmQN+L6gjQlzSNH9/LFm2DLU1NdaV1Z2ttrYWBXl58PHxsaZjtbS0YF5MDE367A4PXKu/hhs3bggdCSEu4fXlb6Dq4kWcO3sWZaWluHnzpsOOpdPp8OWZM5j6xBOYHhg46Cv/EMfZmZ4On4cf6TRzpKWlBRMnPIxVK1fa/D7X1tQg4+OPsTM9HRKJBPu0+6HNyUZ2VhaiIyNRWFCASxcvCtbu90VVZRVGjhzp0EWWhZBXWIicvDzoGnU4U14+4PdicI4ZkUFPIpGYh2iFWxWe53lkarU4dOQIhg8fbl1UseL8eYwaNcohiyoOBtXVVZj17HNCh0GIy5CaUyAGWp2mL1QqXxw+ehTPPv203coEk6HF2MPCg12ltlvaVkub6qpWx8VhemDgoKsSKZVJ7ToJn0ZEiMsZMXIkIqOjkZ2VJXQoAIDVq1ahuaXFJh2r7upV3L59GylpaQJGRghxdYwx8OC7TXGxt9bWVqyNX43m5makpKXZdWFYQoaSqspK5OXmDqpKdOtWr8Hs55+322cw6ogQl6PNysLtW7ecUq63N2pralBbXQNPhcKaxqDT6dDU1IRR5gUQCSGkP6b5+4M38V2muDgCz/PYn5GBB728cPP7m2AS+qhASH9YKmoOpkp0SRs24I0337TbZzC6uhCXEhQcjAc8PZ2aotAb1dVVkEgkSNqwwToxTa/XIy83t8vqH4QQ0h21RoOQGTNQfPKk0ys08jwPfWsrOAmHyMhIpx6bkMFCoVQiJGTGoKlEFxQcDC8vL7tWA6OOCHEZvioVpgcGYunixaKsmJGl1UL16KPYkJZq7YxUVVaCMQY/Pz+7HstSRYfjODQ3N8NkMtn1+QkhwlJrNEhJS0PQk8GCLpi7PiEBs0JDBTs+Ia7su//8BxtTU+1SXUoMgoKDsSM9HfEr4+z2nNQRIa6DBxoaGuxSpcERdDodfv3UUzj2+0+tVTKamprQ2NAAbU4O/CZNGvAxGGPwDwjA1u3bcfjoUfzqqf/Bg15e+PLMmQE/NyFEPBQKBa7V1aG6ynGlenvjYm0tRrm7UwUtQvqhqakJ169fh0wmg4+Pr9Dh2EVtTY1dCwXRDDTiMqqrq1BdXSV0GN0yGAyou3rVplJITnY2OKkUiclJCJs5q1/Pq1AqoY6JAcdxmB0airzcXLy9ZYto5skQQuxHoVQiMCgI8XFxglesMhgMqK6qogpahPSTOiYGK+LiUFJSTBUjO0EdEULsrLGxEUHBwXhz5UpERUTAYDAgS6tFeVlZj49ljMHNzQ0ymQwZmZltedp6PRYvfBXHP/8cAHCwqEjQssWEEMdSKpWY5u8vipRLo9GIgrw8zJ03z64lOwkZKtoWdmx2WtU7R5HL5eDB2/26RKlZhDhAQX4+OI6Dr0oFoG3i56WLF7t9jK9KhRdeegnHT5zAx/v346tLlxAdGYl5MRr8+9//QllpKcpKS6kTQsgg5qtSwVelws70dNGkXN747gZKSoqx9733Bk3lH0KcxbKw4/Dhw122iqZcLkft5a/Am3i7X5d6HBFhjCkB7AcwGm39ufd5nt/JGEsC8DKAf5p3XcPz/Kfmx6wG8BIAI4BlPM//0a5REyJyRqMReQcO4Je/+hVGjhyJkuLiTvfjOA6vL38DUk5qvcuwPDZW0MmphLiawdJOBQUHY8/evTCZTFiyeLFo5sJZFiGNVqsR++YbKD5xsstrGiHEltFoRGtrK3xVvohSq7F1yxahQ+oXN7e2mxD2vi71JjXLAOBNnufPMsbcAXzJGPuz+WfpPM9vbb8zY8wHQBQAXwAPAPiMMfYwz/NdL69JSDsymQx6c3UJS6pSa2uraBrl3srJzoZ/QACeDAmxdiwsf8gtLS2IjIpCZHQ0hg8fjvUJCaiprsbNmzeFDJkQVzUo2qmg4GDce999OFVSIsqbEUWFhfjmWh14E08dEUL6qLXVdatmyWQytLS0wGAw2P25e0zN4nn+W57nz5q/bgJQC8Czm4fMBHCA5/kWnuevAvgawDR7BEsGP7lcjozMTOv3E729cfzkSTw+daqAUfVfWWkpmIRh/gsvWNOuPsrIgFQqRUF+PiIjIvDcM8+grLSUOiGE9JOrt1OWangKhQJzwsIQPWeOUKF0y2Qy4fSp0wBAFbQI6YOWlhbMfv55NDQ0CB1Kv3z48cd4zMcXu3butPtz92mOCGPMC8BkAJayGUsYYxcYYx8xxn5k3uYJoP0r3YhOGgTG2CuMsTOMsTNwsTvdxLHaL5TT1NSEv//tb5C46Mq+PM+j+MRJ/PjHP8Y999yD5bGxiImKgsFggNFohL611SF3GAgZqlyxnXJzc0PhoYOIUqthMBhEe03Q6/UInz0bEokE3j4+QodDiEupOH8euTk5QofRL1KpFK16PUxG+w8a97pqFmNsFIBCALE8z99gjL0LIAVt+bgpALYBeLG3z8fz/PsA3gcAJpFST4RY+fr4ouDgQQDAXXfdZZf1N4RUUlxMaQyEOIGrtlOtra2Y9dzzkEgkqKkWdt2Q3sjSavHdd98JHQYhxMF8VSpsfnsLlMoxDqv61auOCGNMhraLexbP80UAwPP839v9/AMAR83f6gAo2z1cYd5GSK/U1tYiOjISABAQEIAdu3bhxo0bAkdFCBEzV26neJ7HmfJyoQ7fZzpav4iQIcHDwwP+/gGY9dxz0OtbHXKMHvNdGGMMwO8A1PI8v73d9vvb7TYbgGWluU8ARDHG5IyxcQAeAtDzAgqEoK3e9oGcHOhbW61pS/X19WiijgghpAvUThFCXEFQcDDi4uMRrVYLHUqfODJdtDcjIkEA5gKoZIydN29bAyCaMTYJbYM1dQAWAgDP89WMsTwANWirZPKa0JVIiOuQSCTw8/NDQV6edVtQcDA8FQq6C0cI6Qq1U4QQ0au7ehUA8Lt9H0M9VyNwND3zcPdw+DGYGEqiMsb+CeB7AP8ndCx3+DHEFxMgzrgopt4TY1xijAkQZ1xii2ksz/P3Ch3EYEftVJ+JMS4xxgSIMy4xxgSIMy6KqWddtlOi6IgAAGPsDM/zoqrRKsaYAHHGRTH1nhjjEmNMgDjjEmNMxDnE+N6LMSZAnHGJMSZAnHGJMSZAnHFRTAPjmjVRCSGEEEIIIS6NOiKEEEIIIYQQpxNTR+R9oQPohBhjAsQZF8XUe2KMS4wxAeKMS4wxEecQ43svxpgAccYlxpgAccYlxpgAccZFMQ2AaOaIEEIIIYQQQoYOMY2IEEIIIYQQQoYIwTsijLGnGGOXGGNfM8biBY6ljjFWyRg7zxg7Y952D2Psz4yxy+b/f+TgGD5ijP2DMVbVblunMbA2u8yv3QXG2BQnx5XEGNOZX6/zjLFn2v1stTmuS4yx/3FQTErG2OeMsRrGWDVj7HXzdsFer25iEvq1GsYYK2OMVZjjSjZvH8cYKzUfP5cx5mbeLjd//7X5515OjGkfY+xqu9dqknm7M3/fOcbYOcbYUfP3gr1ORHjUTnWIgdqp3sdE7VTv46J2qu/xuX5bxfO8YP8AcAC+ATAegBuACgA+AsZTB+DHd2zbAiDe/HU8gLccHEMIgCkAqnqKAcAzAI4BYAACAJQ6Oa4kACs62dfH/F7KAYwzv8ecA2K6H8AU89fuAL4yH1uw16ubmIR+rRiAUeavZQBKza9BHoAo8/b3ACwyf70YwHvmr6MA5Doxpn0AwjvZ35m/78sBZAM4av5esNeJ/gn7D9ROdRYDtVO9j4naqd7HRe1U3+Nz+bZK6BGRaQC+5nn+Cs/zrQAOAJgpcEx3mgkgw/x1BoBZjjwYz/NfAPhXL2OYCWA/3+Y0gLsZY/c7Ma6uzARwgOf5Fp7nrwL4Gm3vtb1j+pbn+bPmr5sA1ALwhICvVzcxdcVZrxXP8/xN87cy8z8ewM8BFJi33/laWV7DAgD/zRhjToqpK075fWeMKQD8GsCH5u8ZBHydiOConboDtVN9ionaqd7HRe1UHwyWtkrojogngIZ23zei+z8GR+MB/Ikx9iVj7BXzttE8z39r/vpvAEYLEFdXMYjh9VtiHn78iP2QDuD0uMzDjJPRdrdCFK/XHTEBAr9W5iHc8wD+AeDPaLur9R+e5w2dHNsal/nn3wH4L0fHxPO85bVKM79W6Ywx+Z0xdRKvPe0AEAfAZP7+vyDw60QEJYbrbHvUTvUdtVO9iwmgdqrHmETSTgGDpK0SuiMiNsE8z08B8DSA1xhjIe1/yPM8j+57wg4nhhjaeRfAgwAmAfgWwDYhgmCMjQJQCCCW5/kb7X8m1OvVSUyCv1Y8zxt5np8EQIG2u1kTnR3Dne6MiTGmArAabbE9AeAeAKucFQ9j7FkA/+B5/ktnHZOQPqJ2qm8Ev/YC1E71FrVTvTOY2iqhOyI6AMp23yvM2wTB87zO/P8/ABxE2x/B3y3Daub//yFAaF3FIOjrx/P8381/oCYAH+CHoVqnxcUYk6HtQprF83yRebOgr1dnMYnhtbLgef4/AD4HMB1tw8bSTo5tjcv887sA/D8nxPSUOW2A53m+BcDHcO5rFQTgecZYHdpScH4OYCdE8joRQVA71TvUTnWB2qm+o3aqR4OmrRK6I1IO4CHzLH83tE2g+USIQBhjIxlj7pavAfwKQJU5nvnm3eYDOCxAeF3F8AmAeeYqDQEAvms31Otwd+Q9zkbb62WJK8pcpWEcgIcAlDng+AzA7wDU8jy/vd2PBHu9uopJBK/VvYyxu81fDwfwS7TlBX8OINy8252vleU1DAfwv+a7do6O6WK7xpmhLb+1/Wvl0PeP5/nVPM8reJ73Qtv16H95no+BgK8TERy1U71D7VTnx6d2qvdxUTvVS4OqreIFni2PtgoDX6EtD3CtgHGMR1tViAoA1ZZY0JZD9xcAlwF8BuAeB8eRg7YhUT3a8vte6ioGtFVleMf82lUCmOrkuLTm415A2y/5/e32X2uO6xKApx0UUzDahrMvADhv/veMkK9XNzEJ/Vo9BuCc+fhVABLb/d6XoW3yYT4AuXn7MPP3X5t/Pt6JMf2v+bWqApCJHyqWOO333Xy8n+KHSiSCvU70T/h/oHbqzjionep9TNRO9T4uaqf6F+NP4cJtFa2sTgghhBBCCHE6oVOzCCGEEEIIIUMQdUQIIYQQQgghTkcdEUIIIYQQQojTUUeEEEIIIYQQ4nTUESGEEEIIIYQ4HXVECCGEEEIIIU5HHRFCCCGEEEKI01FHhBBCCCGEEOJ01BEhhBBCCCGEOB11RAghhBBCCCFORx0RQgghhBBCiNNRR4QQQgghhBDidNQRIYQQQgghhDgddUQIIYQQQgghTkcdEUIIIYQQQojTUUeEEEIIIYQQ4nTUESGEEEIIIYQ4HXVECCGEEEIIIU5HHRFCCCGEEEKI0zmsI8IYe4oxdokx9jVjLN5RxyGEEEL6g9opQggRFuN53v5PyhgH4CsAvwTQCKAcQDTP8zV2PxghhBDSR9ROEUKI8Bw1IjINwNc8z1/heb4VwAEAMx10LEIIIaSvqJ0ihBCBSR30vJ4AGtp93wjAv/0OjLFXALxi/vZxMM5BoRAyOE146CHwPI9vvv7aum30T36Cn/zkJ7h586bNdlczcuQoTHhoAkwmHlevXMGDEx7E5cuXcev77wEAjDGMGz8eV775xuZxnb0mgx5v/D+e5+8VOgwXRO0UIYQ4QzftlKM6Ij3ief59AO8DAJNIeUg9hAqFEJeUvvdjPPCAJ5YuXoyS4mIAwPzfLMOKuDiUlBRj1rPPCRxh/3Ach4VL34BEIoHBYMCZ8nIUHT6MZ59+GmWlpQAAN7kcm9Pfw5zQMJvHpu/9GEaj0WXPvV/0/74mdAiDFbVThBBiB920U47qiOgAKNt9rzBvI4TYib5VD4VSgcCgIGtHxNVxHIctW7dCOWYM5oS1dTLc3d3x7NNPI2auBrPDwrA6Lg5A2/kTMgDUThFCiMAcNUekHMBDjLFxjDE3AFEAPnHQsQgZkjalpeFf/+9faGho6HlnF/H68jcQPmcOFsyda93W1NSEstJSxMetwjC5HAqFAq16Pd7auFHASMkgQO0UIYQIzCEjIjzPGxhjSwD8EQAH4COe56sdcSxChqq1CQlISU5Gbk6O0KHYjZSTgjEGvcHQ4WetLS0AgCi1Grt37sRvf/c7HCwstNlH4anAtXrKVCI9o3aKEEKE57B1RHie/5Tn+Yd5nn+Q5/k0Rx2HkL5I3bQJb739NjjO9SedGgwG5OflAWg7r+EjRggckWNJpVJERM6xfv/df/6D459/bvNvyeLFMOg7dmII6Qy1U4QQIizBJqsTIoS0lBTsz9RiWWwsSoqLUVZaCkespeMMNdXV4E0mAIBMKsX+TC3Ky8phMplQWz04l0K4ceMGGhoaYDKZUHr6tHXyuoVcLoeJNwkUHSGEEOJY7h4eUKlUqKmuxkRvb0gkDhtTwI3vbuC7G99BqVTixnc3UF1dZfdjUEeEDCm3b91CRGgYAoOCsGfvXhQWFsJkNCJLq4VO51rzVBPXrbP5+vjJk5DJ3GAymVBdZf+LhTOcPHECs8NCO/1ZZFQUamtrkZuTA47jOj3HyKioQTHaRQghhNyJ4zhsSEnBWC8vxC5bhsCgIIe2eQ0NDWior8eTISGYFuCPsJmz7H4Mh6ys3ucgqCwiEYC3jw/c3d0RGRUF1aOP4vy5c1gTHw+j0Sh0aH3GGMO8+fORsnEjGAO+PPMlZj3nmiVsCw4exJUrV5CwZg0AwMfXFxvSUjF2rBdioqJQeeEC5HI5tDnZHcr3Fh0+DKPRiIjQzjszg5L+31/yPD9V6DAGO2qnCCFCGzVqFAoOHsSLCxbguhNvnjLGEBoejuHDhyNLq+17Jkk37RSNiJAhq7amLX3pyzNnMGzYMBQdPowt27Yh78ABUaVs+fj4oLa2ttt4eJ7H/owMZGdnw00mQ1x8vM3PFUolPD09XeK85sbEYH+mFtqcbACAhElgNBqtnRAAXaafWUr+EkIIIYOJQqnERxkZ+LK83KmdEKDtM8bFmlp8rN0PTiLB/owMu32WoBERQswUCgWi1GpERkWhsLAQXxw/Luj6HAqlEuqYGMwOC0NIUBD0ra39fq7AoCDs3L170J0XAY2IOAm1U4QQIcXFx+Pe++7DqpUrYRIocyMwKAjbdqQjJCi4b213N+0UdUQIuYO3jw/8AwKwcNGr0DXqMDcmBrdv3XLa8TmOw+a334afnx8qKiqQm5ODL8+cGfDdh/bnVXe1DvM1GhiNRqelonEch+HDh2N9cjLy8/JgMpnscl5DHnVEnILaKUKIUHxVKuzT7seiVxbiTHm5oLEUHDyIb77+GqtWruz9g6gjQkjfMMYglcmQmpYGNzc35OXmoqqyEk1NTQ47pq9KBQ8PD8yJjMREb2+EzZqF5pYWu975sJwXeGDK41MQGBSE9G3b7Pb8XR1zmr8/oqKjMeGhhxARGobm5tsOPeaQQh0Rp6B2ihAiBMYYXnjpJdx7773Ysnmz4DfvRo4ciaLDh/HSggVobGzs3YOoI0JI/ykUCuzeuxdXvvkGcStW2H0EwZKqFK1Ww2QyISc7G9lZWQ7PAVUoFIiOiYHRaMTO9HSHnRfHcZgdGoq83FynnNeQQx0Rp6B2ihAiBLlcjuMnTmB5bCxOlZQIHQ44jsOWbdvw7fXr2LplS+8eRB0RQgbG09MTY8aORezy5ZDL5Vi/bh0qKioG9Jx3pmDl5+bixo0buFhba6eo2xY6lEmlSFy3Dq2trR3upDjqvIYPH46iTz5BxfnzDjkv0g51RJyC2ilCiLNxHIfUTZuQpdWiqrJS6HCs/AMCsGfvXvzmhRd695mBOiKuSaFUQqlUWr83mUyiqno0FMlkboiMikTc6tV47dVXcfXKld4PTZq1T1VyVAqWxfARI7A/UwuFcgx+++67KC8t63RBIst5Pfv889izaxfqrl4d0Hl5+/jg3NmzWLtmjWCT6oYM6og4BbVThBBnW75yBX75y1/huWeegcFgEDocK8YYYjQarFy1ClMnT4Fe38PEdeqIuB6FUok9e/ciMCgIDfX1yMluK2XKJAzFJ04KWvWIANFqNfynT8fYsWNxqqTE5j3qTlBwMEJmzHB6qlJgUBCeDAlBWEQECvLyulzA0TLkOn78eJc4LwLqiDgJtVOEEGeyfA7cmJqKstJSocPp1KEjR1CQl4dMrbb7Hakj4lpkMhkOHTmCmpoam7QWxhgenzoV169fpw95IuDu7g5vHx+kbtoET09PJCUm4lBREfR6fYd9ZTIZfHx98d4HH+DdPXtQVlbm9FQly+9PtFoN1aOPInTmTDQ3N3eYG2I5L5lMhr3vvYerV692mbIlhvMa8qgj4hTUThFCnClkxgzMDgtFwpq1uHnzptDhdMrPzw+r161DTFRU9/NMqSPiWlQqFWQyGSouXKC0FhcglUrBJG2L7uXk5WLPzl3WlC13Dw/c5eGBnXv2oLamBslJSTDo9YKm13Ech42bN2P8g+NRUlyCkuLiLlP+ukpFc/fwgEqlQnJqCspOl4rivIYs6og4BbVThBBnmTxlCvZnZSJ1Qwpyc3KEDqdbjz32GB6eOBEFeXld79RNOyVxVGCk/za9vQXrUzZQJ8RFGAwG6FtbYTIaMWLECBQeOoQotRpAW6cySq0Gz/NITkqCvpMJ485mNBqxauVKRISGQSaTIb+oqK2kbyf0+lZkarW4dq2uw3kdPnoUEyd6i+a8CCGEkMFgfXIyNqVtFH0nBAAuXLiAb69fh6enZ78eL7VzPMQO9K16SCTUR3RFcW+usC4a+OSMEAwfNhyf/v73WLt6NQydpGwJLUurxX//4hfIKyhAYUEBsrTaTjsU7c8rZMYMuLu7I2PfPuTm5IjyvAghhBBXxHEcmpubkZ/bzQiDyPgHBCAxKQm/fuqpPk+qp9QsEXpi2jSsWr0a4bNnCx0K6QfLooG5+fltq7Lfvi3q0S2pVApflQoffPQR9u7Z02VlLct5FR06hOrqKqyJXy3q8xoyKDXLKaidIoQ4mruHBzakpCAvN1cUa4b0FsdxKDh4EEmJiag4f77jDpSa5VoemTgRTMKEDoP0E8/zbalaJhNazSlbYmYwGFBx/jxily7F6NGj8bF2P+Li47F8xQpwHGfdz3JeLS0tSFi7TvTnRQghhLiSuzw8oNPpcO3aNaFD6ROj0YjCggIkJif1+bHUERGh/NxcTJjwEDTz5kEul9v8TC6XWz8cyrrI6yfi0Fk6FsdxYEycncyS4mJs2bwZixcuxOjRo/H6G7FISkkROixCCCFkSPivH/8Y165dc8nKqFlaLbIzs6CZN69Pn3OoIyJCLS0tmBcTg2Wxsai9fBlPhoRgemAgQmbMQNnZs5gdFga5XI6MzEyhQyXduHTxos18C3cPD2zdvh2PTxVvFg3P8zhTXo64FSswYdx4JCcmCh0SIYQQMuj5TZqEWaGzUVhQIHQo/cLzPC7W1GJZbGyfPufQZHWRsqTKBAYFwT8gwLp9Y2oqCvLywHEcigoLBYyQ9JVlyPX69etCh9Ijo9HYfU1wQgghhNhFUHAwtqanIyQoyKXTnqurq1CQl4dotRrnzp7t1ecI6oiIWElxcZcrqBuNxu5rNhPByWQym8UN77r7bsz42U/x+6NHXXLYlRBCCCH2FxQcjHf37BkUVSiztFp8lJGBYcOG4fvvv+9xf0rNIsRBtNnZkMncrN97eHjA3z8A7u7uAkZFCCGEEDFpqK9HWVnZoFiPS6fT4fy5c9in3d+r/akjQoiDSKVSQJzz0gkhhJAhT6FUIjAoSPAYrl27hou1tYLGYU+J69ZBoRzTq9eWOiIuQq3RoOjw4bYPt4QQQgghpN9GjByJfdr9eDIkxKbKkzMrkspkMnyUsQ/T/P2ddkxnaG1txXvvvIOdu3fD28en232pI+IiFAoFpj7xBBituE4IIYQQMiBZOTkYO9YLYRERNlWe7kyrdiTVo4+i6UYTdu3c6ZTjOQvP89ifkYGd6enwVam63Zc+1RJCCCGEkCFFKpOiuroKsUuX4smQEOsabc5Mq16fnIzCggKXrpTVFZ7nkanVYv4LC7rdj/J8CCGEEELIkFRSXIzlK1Zgz+7dHcrNymQy5BUWIu/AAUyeMgXevt2nGXXGaDCCSRj0rXoU5ufjyCef4KOMDAwfMRxK5Rjk5w7tCqg0IkIIIYQQQoasi7W18Pf3h0Kh6PAzo9EIE8/DaDJZ19jqy7+83FxUVlzAvJgYAMBvP/wQb23aBKPRiE1paTAYXL9k70AMaESEMVYHoAmAEYCB5/mpjLF7AOQC8AJQB2AOz/P/HliYhBBCSN9RO0UI6UlKcjK+uVaHHdvTbbbr9XqEz54NAMjNyRnwcXKys5GTnQ0AmPXsczh05AhkUikytdoBP7cYBQUHQ+HZsXPXnj1GRH7G8/wknuctM33iAfyF5/mHAPzF/D0hhBAiFGqnCCFdam1txbo1a8FxHCROLAq0PiEBs0JDnXY8Z2KMIWTGDOxIT+92P0e82jMBZJi/zgAwywHHIIQQQvqL2ilChria6hrwprYFBHmeR3lpGaYF+OPhRx52YgzVaKiv7zQlzNVN8/fH7NBQlJ4+3e1+A+2I8AD+xBj7kjH2innbaJ7nvzV//TcAowd4DEIIIaS/qJ0ihHSwZtUqZGVmWr+vrq5C2MxZSF6f1GHSuqPo9Xrk5eYiSq12yvGcheM4REVHIy83t8eFGgdaNSuY53kdY+w+AH9mjF1s/0Oe53nGWKfr1ZsbBHOjQHPme5Kl1eK/f/ELocMghBBXQ+0UIQQcx8FkMoHn2/7cjUYjCvI6VqzKz811alxVlZUIfvJJ+Pn5oaKiwqnHdpSUtDTk5ebi2rVrPe47oCsrz/M68///AHAQwDQAf2eM3Q8A5v//0cVj3+d5firP81PBnFSw2YUxicRpda0JIWSwoHaKEOLu4YHX33gDDz/yiNChdNDU1ITGhgbsz8py2kKKjqRQKjF8xAiUl5Xhuk7X4/797ogwxkYyxtwtXwP4FYAqAJ8AmG/ebT6Aw/09BvnBmDFj4OHhIXQYhBDiMqidIoRwHIcNKSmQSCS4dPFizw8QQE52Nq5evYrIqEihQxkQhVKJPXv3IicrCwaDoVePGciIyGgAJxljFQDKAPye5/k/ANgM4JeMscsAfmH+ngxQ6enT+Pb6tz3vSAghxILaKUKGOJOJR35eHrKzsmy2y2QygSLq3PqEBISGh1tXeHdFXl5euHz5Mmqqq3v9mH53RHiev8LzvJ/5ny/P82nm7f+P5/n/5nn+IZ7nf8Hz/L/6ewzyg9eXv4HHp07teUdCHKymuhq8ySR0GIT0iNopQshjfo/hF7/8ZYc0IW12tqhSoWqqqzFi5Ehs2bYN7u7uQofTZ5OnTMHS11/HqpUrcfPmzV4/jmbfuQgpJwWjHGUiAonr1vV6yJUQQggRSlBwMN774ANs2rixw/bTp07BaHJOdaze0Ov1+M2CBfj2+nW4u2Aq/sSJE1FUWAhTHyuOUUeEEEIIIYQMKr4qFXbs3oV3du+GQa+3bvfx8cHW9HScPHGizx+aHa2xsREN9fXYlp4OqXSghW2dR63RYObs2SjMz+/zY13nLAkhhBBCCOkBYwz+AQEoyMtHllZrLdkLADqdDjGRUbh69YqAEXatID8fFy9edNpaJgPl7uGBgIAA7Nqxo1/ZEjQiQgghhBBCBg2JRIK77roLX/z1rzadELVGg1defRWjfzLaZruYGI1G+Pj4ICo6WuhQemSpSKZQKlFXV9ev56COCBHEW1u3InXjxg7VIVI3bXSp4cihYsvWrThy7FNE37H6q+X9Ums0KDp82Oa981WpcOjIEeTk5dF7SgghxGk2vvUWfvSjH+FUSYnN9vCICPzh2LEO28XmQE4OWvV6aObNE/X84OHDh8PbxwdLFi/u1ZohnaGOCBHEQw89hG+//RbLYmOt2xRKJUaOHCVcUA5247sbKCkpxo0bN4QOpc+8fX3g7e0NT4XCpprHxIneYBIJeJMJj0yc2LbwppmHhwcaGhqwd88elxliJoQQ4toUSiUmT56MzXdMUFcolbh27Rpqa2oEiqz3eJ7HxZpaLIuNFXXF1IzMTJw7e7bfnRCAOiJEIDI3GV586SVIpVJ4enoCANQxMTAZjaIdLh2o6uoqzHr2OVysrRU6lH757rsbCH7ySXj7+HT4WX19fYcOVkN9PRQKBbIOHIBUZPXaCSHEVSiUSsTFx2P5ihUuvcaEM1gW1KuoqEBzS4vNz8aMGYO333rLZao+VldXIXbpUjwZEiLK9z0oOBgPeHoiMSFhQM9D+RJEMA2NDWCMwWvcOOjMvemIyEjEr1pFd9BF6Lv//AfffP01UtLS8OunnrK5mNddvYob332HYXI5TEYjJBIJ7r3vPnh5eQkXMCGEuLi3tm7F5MmTMdHbGzxvwn/9+L+wYX0SWltbB+1Nu4EYP348rl69guTERJuKWH6TJsFToUBJcbGA0fVdSXExmpqaYBLZ2l2MMYTMmIF39+yxqUjWHzQi4gLcPTygUCggkUjg08ndaFfEm3jUVtegob4ee997T1SLCtmDQqnEXXfdNWjeLwC40XQDcStW4NatW/BVqWx+xiQSyIcNQ+3ly9iybRuO/elPOHrsGDwVCoGiJYQQ16ZQKuHm5oZZzz2HB728MGHceOTmHMDxkydFna4jFLlcjt179+L0qdM2C+pNnjIFa9atQ2FBgYDR9V9oeDiCgoOhEEl7yhjDghdfxOzQUJSVlQ24Q0wjIiIWFBxs/eVTKJXYvXMnPvjoIxTk5SFLq7WOIrgik8mE6qoq5GRnIzI6GpFRkUKHZBccx+H15W+Ak3D47M9/HjTvl4XRaERhQQESk5MQNnOWdbs6JgYPP/wwdqanw2g04tvr11FVVYUxY8bgi7/+lUa4CCGkDywpRhtTU3Hr1i3r9gsVFThYWIhotRrnzp6la+sdLl26iNycHJtt65OTkZebK7o1Q3orNTkZW7Ztw7fXr2Prli1ChwM3Nze8snAhXl+61C6p5tQRESGZTAbVo49ix+5duOee/8JctRp1dXX49vp1/OWzzxCtVuOjjAzMev553G53gXIlJpMJ4XPmIFOrxfqEBKxPTkZ5WVmX+8vMcwz0AxwCdASZTAaJRILIqCjMDg/DMPkwvPTCCx3er/PnziFx3TqXH1LP0mpx+/ZtaObNg6Td5HSDwYDt27ZB39oKAJgeGNg2dPvOOy7bABBCiBA8PT0xzd+/y59TGrMtjuOwNiEBGxLX22xXazTI2LcPh4qKBIps4IxGI/IOHMCO3btw5JNPcOniRUHjWZ+cjIaGertVHqPULBFx9/DA9MBA/P6Pf8C6xESkb9uOiRMeQklxMa7rdOB5HmfKyxG3YgXOnzuHjZs2YXpgIPwDAkRd3q0zm9LSrHMMaqqrYTAaMDssrMv9kzZswJFjxzA9MNCmapPQFEoljv3pTzhXeQEzQ2cjce06PPfMMx3er9CZMzH+wfE4fvIk5s2f73LvV3s8z+O6Toe3t23tMT0gdvkbqPnq0qBLvSOEEEe6WFuLXTt2dFllsaamGrzI5g0Ixd3DA1u3b8exTz9FVVWVzfaAgADoGhtd+uYfAJSVliImKho/+tGPBP38oFAq8fvf/x4x0eqed+4l6oiIhGVRmJWrVuHTo79HRFgYsjMzode3dtjXaDRi1cqV2Pb22wgKDsaevXuxas0aBAYFCRB5/yQmJ0EqaxuQ0+v1WP56LPJzc6HTNXa+/7p1+OOxY4iLj0fShg2CV5DgOA7LV67Anr17UVl5Acnr21KVKi9c6FCRw2g04vvvv0dEaBjejI3FvffdhxWr4lzq/bpTQ309Tp863eMFkeOkbZ0Q1+13EUKI0/n4+sI/IABNXXREqiurXP7Dtb2oVCqM9fLCtWvXrNvaL7TX2Nj55wpXwvM8fvKTnyB9105Bq1CqY2Iwa/Zsu47EMTH8IjOJlIfUQ+gwBCGXyxEZFYUvv/wSw4cPx/Xr1/tcj9nbxwf+AQF4bckS/ObFF1FTXS14CpPlvMrLyzut2e3t49NWJ9ucX+ju7g53Dw+4u7vjq0uXurzAenp6YszYsYhdvhyHiopQVFgIk8nktPO1nJclBWt9QgJqqqttJsb1hDGGefPn49XXFkPXqENURITg5QQZY5g7bx6eefZZaKKjO8Rz5/sFtL0X9z/wAL48cwYPPPCA9WvLe+fu7g5vHx+YTCab7cTO9P/+kud5mrnqYEO5nSLO5+7ujqQNG1CQn2+TAsMYw4pVcfj2+rfI0mqH/HVVJpNh4+bNSN++3eaz06hRo1Bw8CBeXLBgQGtciE3BwYO4cuUKVsfFOT0tz2/SJLz3wQdYungxzpSX9+3B3bRT1BERkFwuR+3lrzB8+HDMfPY5lJWW9vu5GGNYtWYNli5bhlMlJQifPduOkfaNPc+rKzKZG2LffAOvx8bi9KnTTjnf9ucVNms2ykpL+92BYIxBKpPhQH4eoiLmWOdVCMU/IACHjnyCsFmzXa684ZBHHRGnGOV+N//Y48F9flxNdTUmentDIpHYfN0bVZWVaGpq6vMxXQljDNP8/Xv9mtiLyWRCWWmpqD/IcxyHA/n52LVjBwwGA2qqqxEaHo7/+q//wra33xZt7IwxTPT2RlNTE5RKpUOPZTKZcPbLs9DrW+Hj44Pa2lrwPI/CQ4fw1aVLWL1qlUOP72zDR4zA/kwtcg/koramBh4ezvnsLJPJ8NrSpdCo1TDo9X3/3eumnaLJ6gIJCg5GyIwZ+L9//h/ycnO7HTpUKJVQx8R0W3mJ53l8cfy4tZ5zXHy8oJWa/v63v6OwoMBhQ6J6fSuKT5yEhEkQFhHhlPONjIqyvl91dXUDGsXgeR761lYcyM7BAw88gMioKMHeL7VGg4CAAJwqOYW6ujqnH58QVzBq1CgEBfe9I6LT6RAYFASO42y+7klQcDCufPMN4lasGNQTkufOm4cly5YhLzfXqceNjIrCzvR0ZGq1Tj1uXxiNRhQVFCAsIgLqmBhkZ2WhsaEBxSdPirYTArS9pzNDZ2PblrcdnoJsMBhQbr47v/Gtt1CQl4erV6/iAU9PRM2Z49BjC+H2rVuICG2bTxutVju1RH5RYaFDbprSiIiTWSpivf+7D7F7x06UlZV1Wv7MkgIUERkJuVyOiooKJCcm9ioFyDKkq3r0UadVarKc1/rkZKxetQomk2nAlR1kMhkyD+TgyOFPkJ+bi5Y7VkkF2u68PD51KqLVaqgefRShM2eiubnZbg13+/PyGjcOc9VqVF64YJfntrCkMMVoNFCOGYOiwsIuz9deZDIZfHx9kbB+PSY89BAWL1yIurq6QTWEPWTQiIhTOLud8vT0tFbbW7VypdOO60wcx6Hg0EEkrl1n9+tqT/wmTcL65GREhIaKvqPnSmmuHMfh0cceQ0tzM2rtUNq1L/wmTUJuQT5u37qN9G3boN2/X9Sv1ZBCqVnCc/fwgEqlQnJqCppuNKGwoAA5WVkd/kgUSiXGjx+P3Xv34uuvL8NkNGG+RoPmlpY+lUDlOA7Dhg1D0eHD8FX5Yt2atSgvLUN1dVXPD3bAefWX36RJSExOwoQJD2HJokW4euVKp6MsHMdh4+bNmDR5MqqqqpCUkDDglAaFUomPMvZZzys/N6/T4gH2IpVK4atSWc93XkwMKs6ft/txLOdVdroUyUlJAA+HnhdxMOqIOIUQ7dTIkSNRdPgwXlqwYFBMuL3T8pUr8Mtf/grPPfOM0+fJyWQyHDl2DNV2ai9ImzmRkZg5ezbmazSCvKe//8MfMGnyZDz79NMOSQsn/UQdEWFZUl8USiW++OtfsWvnzk47FZYFjK5evYLTp053WJSnPxQKBaLUanAch7CICLsursdxHLZu346xXl7dnpc9RKvV8J8+HWPHjsWSRYu6jN9yvnm5uQifE4HTJaf6POeh/aKEer3eoefVFc28eVgWG2v3xRAtv2OXL1/GqpUraX2PwYA6Ik4hRDvFcZyoFjKzt7j4eBz//HPBPjAqFArs3rsXm9LS6EOrHSiUSowZM0bQeYb0nooUdUSEYalEtGLVKrz68sudpr4wxuDm5obUTZvg5+fXpxSsvsZirxSmOytHvfTCC05J6Wmfctba2orcAwe6rBpiqUy1ZNmyXlcSs7xfzj6vrmJp/34NJMXO8ju2PjkZw0eMQE5WVp8rfRERo46IUwjVTvkHBODJkBBRT07uDx8fH/zqqafw4fvv21yLZDKZQ6sg3vn8y1eswP889RR+/dRTglcv7Myd8fqqVJg8ZYroKmbJZDL8/o9/wKdHf48d27cLGovY39MhiToizmNJVQIAT4UC0TFqJCUkdpr/yhjDghdfxCsLF6KhoQEL5s7tcwpWX/U3hclyXjKZzJo2lpSQiNqaGqf+oVtSzjIyM8EkDOtWrwGATksEM8YQo9EgNDwcI0aORHJiYpdVaHxVKnj7+FjfL2efV1cs57tPux8K5Ri898472J+R0esGyMfHB4xJsD4lGRvWJ+Fiba0ozovYEXVEnEKodspSrvUff/t7n/72xYwxBh8fX9RerLVp7yZPmYJVq+MRFeGYScZyuRwf798PdWSkdRvHcSg4eBBJiYkOSYUdqAP5+ZirjrGmz/r6qvCxdj8WL1zY9xKqDjR5yhSsS0xERFiY4CPtYn9PhyTqiDhH+xSskuJiNDQ0dJleFRQcjIaGBoSFh+P0qVM2dcKdwZLCxBgDYwwnT5zocjjVkoIVM3cusjK1dksbsweO47AsNhYymazLFCbLPiEzZthUobGkYEk5KSIi5yB923ZkZ2YKcBa9ExgUhJ27d6OwsBBfHD/e7fC3pdLa7LAwhAQFCV4emDgQdUScQsh2yvK3L/YqT70ll8uhzcnGHHP1H4tDR44gLzfXYddhuVyO4ydP4s3YWJvrpyUVNnbpUtGVLy84eBDRkZHWa7glvXZjaqqoUo8c/d71lWbePMwOC0XYzFlCh0IA6og4miVVqbsULKDtA/Hw4cORkZmJBzw9+7cojJ15+/jA3d0dM376U/zpj3+0SWGyx+J9ztDbKmGenp4Y6+WFuPh4FOTn4+GHH8axTz+F0Wh0iWokwA+LVy5c9Cp0jTrMjYnB7Vu3APyQWta+0lpuTo5LnBcZAOqIOIXQ7ZQrVXnqydvbt2Ps2LGYE9bWEbFcu5pu3sShoiKHXa8YY5j/wgv48Y9/bDPnxrJ99OjR2LJ5s6iul3d2RKYHBuLw0aOimoyt1mgQFx+PJyZPEU3hE8YYQsPD4SaTISc7W+hwSDftlHNXEBqEJk+ZgrKzZzEzdDZioqJQUlzcaSfE3cMDW7dvR8HBg/jq0iWEBAXhyzNnBIjYVm1NDcpKS9HY0ID1yck4cuwYpgcGImTGDOt5Ja5dh+eeeQZlpaWi64QAQFNTE+JWrEDozJkY/+B4HD95EvPmzwdjzGY/nU6H27dv45GJEzE7LBR5Bw7gVEkJykpLcaa8XFSNT1dqa2qQ8fHHCAkKxjdff42NmzZhemAg3N3dMdHbGzNDZ6O5uRmznnsOq1audJnzIoR0r6a6GiNGjsSy2FihQxkQhVIJPz8/LJg717ptmr8/lixbhtqaGoder3ieR3lpGYC2G1h3bg+LiMDjU6lP3xfuHh4ICAjA4oULRdMJAdre04s1tfBUKGzeayI+tKDhAAQFB2N6YCA2pqZ2m6rUPmXrxQULRLlWQ052NvJyc7EsNhZx8fHwDwjAiuXLRTPM2hOj0Yjvv/8eEaFh1jSG+z09bVKYgoKDsWP3LqQkJ7vMeXXGshjiqpUrO1QIoWFoQgYnvV6P5MRExMXHI+/AAcEWqx0odUwMKioq0GxeJ4njOERFRyMvN7fTNbXsrbq6CnfdfRd+88or2LVjh3V0qbq6CgV5eYhWq3Hu7FmXH3VyBo7jsCElBQqlUpSL4VZXV+HXzz2LpA0bBv3CoK6MRkQGICg4GF/89a9ddkImT5mCQ0eOIC4+HgdycrBk8WJRdkIsjEYj0rdtw6mSEsSvXImcrCyhQ+qXkuJizNNo8L+ffQb/gAAUHDyIkSNHImTGDLy3912XPa/ONDY24lRJCVI3bYJUSvcVCBnMqiorceWbbzBm7FihQ+mXyVOmICJyDnJzcqwTmqVSKbx9fJDtxOtySXExpvn7Y9iwYTbbs7Ra5GRng+M4p8XiyoYPHw5vHx9Rf7bJ0mqhevTRDu81EQ/qiNiZu4cHpgcGIvjJJxG/Zg3ycnPxxOQpXaZsidXFixddOqXHknK2a8cOfPP11yg6fBjLYl/H2oQESKUyocOzu4kTJ4JJ6M+ZkMHMkoa6fMUK+E2aJHQ4fSKXy7FPq0X6tu02ackTvb1RVlbq9PbxNy+8gNyCAigUCus2nU6HygsXUHT4sM120rmMzEycO3tW1J9tdDodzp87h33a/UKHQrpAn1zsTKVSYeWqVZj6xBOIjoxEdmamqPIm76TWaBCtVgsdhsMYjUasWrkSLy1YgNOnTqOwoABGEw3PEkJck9FoxOFDh5CYnCR0KH0SGRWFr7++jOzMTOtNrqDgYPzs5z9HUuJ6p8fT3NyMS5cuIeqO9s9gMHS6ndgKCg7GA56eSExIEDqUHq2Jj0dJcQkCg4KEDoV0gjoidlZVWYklixdjx/btgtfS7o3oGDUi1dFCh+FwlhQmHx8fSO6YxE4IIa4kPzcXEyY85DI3kTiOw+zwMCQlJFq3+apU2LF7F06eOCFIW2k0GpF34AAio6Lg5+fX6XZvHx+nx+UqgoKD8e6ePTA4cPFJezEajfjw/fcRFBxM76kI9dgRYYx9xBj7B2Osqt22exhjf2aMXTb//yPzdsYY28UY+5oxdoExNsWRwYtRU1OTqIcph7qDRUU0YY2QQWgotVUtLS1YsmgR/KdPd4mKQGHh4dC3ttosPOvh4YGCvHxBq0eWlZZiZ3o69mdlQSZzs9leWFjoEq+tENw9PMDzPMrKylwmhbupqQmMMfgHBHSoqEmE1ZsRkX0AnrpjWzyAv/A8/xCAv5i/B4CnATxk/vcKgHftEyYh9pGYlASpbPDNEck9cIA6WGSo24ch1FZdvXIFY8eORdKGDaKeXK1QKtHY2Ig5YeEwGAzW7Q319fjir38V9IMsz/PI1Gpx9epVREZF2mz/4vhxBAUHi/q1FYpKpWorj+uEKmf2dPLECSxc9Oqg/AzgynrsiPA8/wWAf92xeSaADPPXGQBmtdu+n29zGsDdjLH77RQrIaQTBoMBE70nUsoZGdKGWlvV2NiIJYsWISc7GyaTSehwOiWTyfBRxj5M8/e32T5i5EgsXLQIp0pKBIrM1upVq1BVWWnT6SgpLkZQcDA2bdkius6IwWAQ7D2XyWRITk2x6VS6ipLiYugadUhNSxM6FNJOf+eIjOZ5/lvz138DMNr8tSeAhnb7NZq3dcAYe4UxdoYxdgYuMrRHiBjtTE/HutVraESEkI4G1FaJvZ3S6XSiXrTUx9cXZadLsWvnTpvt2qwsUZUb//ryZcStXo3Z5pXeLebGxMDPz09Ui0je+O4GVixfLlhKm0QiwY3vbnR4T13F3JgYNDc3Cx0GaWfAk9X5titgn6+CPM+/z/P8VJ7np4Lu5BLSbwHTp+O9Dz6g4WZCutGftoraqYFJWL8eyUlJNpPRxVhtSa/XQx0ZiW+vX4en5w/90du3biEpMREhM2bYbHemO9Nuq6urbCqPOZvBYMCBduvAuJrbt25hvYh+90j/OyJ/twxjm///h3m7DoCy3X4K8zZCiIMEBQcL1kgSInLUVgmAMQa1RgPt/v02VZV8fHywNT0d7+zeLcpqS3VXryIuPt5mtMayiORHGRmCjOLk5+aK6kO/0WhEQV6e0GGQQaS/HZFPAMw3fz0fwOF22+eZK5IEAPiu3bA4IYQQ4kxDrq2yLKorVHUgxhgWvPgiHnjgAXxy6JDNnXudToeYyChkabWiTCfT6XRoamqCj6+vdZtlEckzZ8pttjuT0O/pUOCrUsHXV2WzTaFUdqic5u7hQYtd2llvyvfmADgF4BHGWCNj7CUAmwH8kjF2GcAvzN8DwKcArgD4GsAHABY7JGpCiA2qmkWGOmqr2tbr2JCSgsNHj+LgJ59g1Zo1Tl/Ezc3NDa8sXIjikydtOhtqjQavvPoqRv9ktCg7IRYpycn47Ycf2rxuRqMRG9YnddjuDGqNBmkbN2LlqlXYs3evU97ToOBgLF+xwmaSvkKpHLQLAgYFByMzOxv7s7MQFx9vzTBQx8R0WHdEpVJh9969lIVgR72pmhXN8/z9PM/LeJ5X8Dz/O57n/x/P8//N8/xDPM//guf5f5n35Xmef43n+Qd5nn+U53nhCoQTMkRkabVwk8moahYZ0oZ6W6XWaFBw6CDycnPx7NNPY9Zzz+Fv336Lnbt3O3URt9RNm9DQ0GBTEYvjOMyaPRt/OHZMNJWyutLa2or33nmnw+vW2tqK3777Lp4MCXHqqER4RAQO5ORgyeLFmKfR4H8/+wx1dXUOPWZDQwOenzkTGzdvtm5TDsKOSOqmjdiQmoq6q1ex8OWXseiVV/CApyc+1mq7TMOrqa6GTqeDJ42K2I14ylYQQvrFclFkkgHXniCEuCiFQoHhw4ejvKzMWlq1vKwMBr0ePj4+uFhb6/CRCIVSCT8/P8x67jmb7WHh4diwfj2qqqq6eKR48DyP/RkZuN/T0yYth+d5lJeW4WPtfvzls89wprzcKfGYTCaUl5dD39rqtMWSw+dEwGA0YMTIkVAoFGhsbHTKcZ1JoVTi2KfHUFZaCn1rK3Tm1/bBBx/E/zz1P/BVqTp93ERvb4SFh2H/vn1OjHZwo08uhLi4oOBgujtDCEHi2nU26zvwPI+rV69ixapVDq+qp1AqsWfvXlRUVKC5pcVmu9e4cfj3v//t0OPbU1cLGlZXV6EgLw/RarXT1haRSqV4c+VKm5Qhhx+Tk2LNqnhkZ2YiSq12yjGdTR0Tg5zc3A7bc7Kz8fXXXyM5NaVtP40GcfHxiIuPx/IVKyCTycBxdA/fnujVJMTFNTQ0YMUbb4iyCg0hxPF8VSpEq9U4/vnnHX7WWVU9Hx8fbLDzom7uHu549NHHsDE11abK03f/+Q+Of/45vvvuO7sez9FKiovx//7f/+t04cCIyEho9+/HusREux1v7erVuHTxYoft69etw8EjRyCVSvHfv/gFzp87hzXx8TZzAlM3bcTEid79PnZNdTUS163rsN1kMiEyKgq/P3q0388tVllaLZ6cEdJtQe8DOTn4aN8+hJrXl+F5E7669BWam5tFu4ioK6KOCCEuLnxOBF5bsgQTJzwMvb5V6HAIIU7m4eHRp1HR2tpaREdG2j2ON1euhI+vL8rLyqxpYE1NTSgrLbX7sZyhs44BANTUVKO6utqur2FXN5IqKirwyIMTAAa4yWQoOnwYW7ZtQ1JCApqamgAASQmJA0rN5bv4UF1WWorCwkL4BwTgq0uX+v38YsQkEmxO2wiDoesbeOFzIpCwdi3OtFs80sPDAyqVCl9d+soZYQ4JlJpFiIs7XXIK//jHPwCaq07IkNRQX4/ikycRGRXVIWXo5IkT2L51q80ddJ7noW9ttfu/L44fx8JFrw7axVUtlaMS166z+2vX3fwdvb5tn++//x5JiYmYO2+ezUR6g8EwoGO3T+drz5KitnDRq5ANsvd0zJgxeGLatA4duKDgYPzkJz9B3oFcSDkpjEajzWv18MMPI7+oCBO9JwoU+eBDHRFCXJxSqYS7u4fQYRBCBNLY2IgTX3yB8DlzMGLECMjlcsjlckyeMgVx8fE49umnTlkUr6S4GK+89BusT052+LGcTSaT4aOMfbh8+TJqqqudelzL+ymXyyGTyZCVqXVaDJb3dPXatU45nrOUFBfjx/fei02bN0Mul4MxCeRyOQKDg3D33T9Cfm4esrTOe52HMkrNIsTFnT59Gt9evy50GIQQAe1MT8fo0aNRcPAgPDw84OHhgYsXLyIvN9epaTVfXbqEhx952GnHcxbVo4+i6UYTVq1c6dSVzpM2bMDM2bPR1NQET09PlJeVIS83Fzdv3nTYMRvq63Hjxg3r95UXLiAtJQX+AQEOO6YQ0lJSsD9Ti+MnT+LjDz/Ea8uWYWNqKnwefgR6/Q+VtNq78d0NlJQU27w+ZGCoI0KIi4uMisIjE2mYmJChzGg0YtXKlVAoFFCOGQOFUonCggKnfmgG2tKEThWXIC4+Hllabacf5lzR+uRk5OXmOv31TFy3DufOncN1nQ7T/P2xa+dOh8eQk53dYdvVK1e6TOFyVbdv3UJEaBgCg4IQGBSEjampyM3J6fYx1dVVmPXsc93uQ/qGOiJkyDAYDGhpaem2SgYhhLiyxsZGQdd9MBqN+PD995GycSO8xo0bFB0RtUYDr3HjkJ+b5/RjG41GFOS1HbekuNjpx7cQ+vfKkUqKiwV9bYc6miNChoyd6enWIVdCCCGO0dTUhNJTp7D3vfcgk7kJHc6AuHt4ICAgAIsXLqS2gxAHoI4IGTKMRiM1JIQQ4gQ52dm4evUqIqPsXybYWTiOw8uvvIL6+nrU1dUJEsOdldAslbuEwnEc5jig9DMZuqgjQgghhBC7W79uHZ59/nmnrUJubyYTjz/98Y/YumULrguUYhYRGQlJu9ev6cYNzPjpT23K9zqTVCrFnE7KRBPSX9QRIS7Nx8cHjHW/gAZjDP4BAZgeGIjpgYHw9VU5KTrSX+4eHtb3y/LP3d1d6LAIIX1QUVGBPbt2Ycu2bS759/uY32PWVbXFYqK3N5bFvi7Y62kymTDK3R3LYmMFOT4ZfKgjQlzahrS0ThfPaj+c7ebmhh27diEoOBghM2bgY+1+QYe2Sfc4jsOGlBSsXLUKQcHBiIyOxuGjR5G0YQPdhSPExdRdvYpvr1+Hu4drrXUUFByMn/3850hNSRE6FBsSiQQcJ1ydIb1ej98sWIDTp04JFgMZXKgjYme+KhWOHPsURYcPQyoVf1GydavXwKAfXCX51BoN1m9IhoTjoNZocKAgHy+/9BK2btmCLZs3Y++ePdjz7ruCDW2T7m1++2086vcYlixejK1btiBhzRo8+/TTmPz4FGzcvFno8AghfdDY2IiG+nrs2rPHJdpEoG0UPWTGDJQUFzu9XK8raGxsxLjx46kNJXZBHZEBuHPRHwAA3zYpmud5+Pj6ChNYH9RUV6O6qspmW6fn5UIUCgVGjXK3fv3441NxybygF8/zKC8tQ339tV6ldbmChvp6NDU1CR2GXSiUSkydOhXlpWXWnOympiaUlZaivKwcDz/yiMAREkL6qiA/HxzHwVcl/rRYxhgWvPgiZoeGunQ76GgKhQL+AQGDog0lwqKOyADU19ej6Y4LlWWxG3VkJH774YeIi4+Hp6enQBH2TK/XY31Cgs22nOxsXKytFSgi+8s9cADGdne1qqur8Pbmt7Bi1apO07pcTX19PT747W9tztFVqWNi8OCECUi843cSABLXroXeoBcgKkLIQBiNRhQWFOCDjz4SfVqsRCLB3XffjdeXLh1U7aC9nTxxAgsXvToo2lAiLOqIDEBQcDC8xo3r9Getra1Yungxjn/+OeJWx+Ott9+GXC4X/d0DxhjmzZ/vMkOuMjcZcvPzUXDwIHLy8vCYnx8izKUFVSoVotVq5HeyGm5QcLCoO4i9ZUkh+OX//ApyN9eu198bKtWjiFarhQ6DENJHWVot9u7ZgydDQkTdDsrlcri7u+NUSYnQoYhaSXExdI06pG3aRHP3yIBQR2QAGurr8e7778Nv0qQOP+N5HmfKy1FWWor4uFUY/+B4HD95EvPmzxftRdhXpcILL72EJcuWgeddY/lxfasec2NiEB0ZiXkxGlReuIDCggIAgIeHBzwVCoEjdBxflQphERGYOu0JrF+XgPyiIkwPDBzUw+UeHu6D+j0lZLCypMWGRUTg8alThQ6nUwqlEvlFRdi6ZYvQobiEuTEx8PPzowpaZECoIzIAOdnZ2JSail/+6lcIDArqsPCQxe1btxARGoY3Y2Nx7333YcWqOFENTyuUSsTFx2Ofdj/uvfdelxuSbm1thb61FXp9K3iet45+NNQ3oPjkSYGjc4yg4GDs0+6Hm5sbwmbOwrmzZ/GXzz5DXHw8Dn7yCWI0GqFDdIjB/J4SMthVV1ehIC8P0Wq1KO+iq2NiUFNTg+aWFqFDcQm3b91CUmIiQmbMGBQZBkQY1BEZoJzsbPz23XdRV1eHiMhIFBw8CM28eZDL5R32LSkuxra338Y//vZ3bNuRjoKDBzFy5EjBLsgcx2HUqFH4KCMD9953Hxa9shBbNm8eFEPSyevX4+rVKzjxxRdISUvD8BEjrD9jjIHjOKxbvRoGvevMObC8X4WHDmFrejp2pu9ATlYWgLYc7PRt27Bk0SLMeu453L59G4eOHIHfpEmQuVAOb5ZWi6iICJsRHcYY5HI5Nm7ejIaGhkHx+0nIUJWl1eKRiRMxbNgwoUOx4TdpElpbW5GcmEiVsvqgqrIS165d6zJNnZCeUEfEDpqamnBdp0NURASSEhMxOywU+7RaBD/5JBR3pJHwPI/9GRkICQrGN19/jaLDh52+2JNlgb+t27ej4OBBfFlejlUrV+JMebnLpGT1ZHZoKDiOw870dFRUVGB/phYKpRLTAwPxwksvYXZoKMrKylzmfN09PKzv11eXLiEkKAhZWm2H+HU6HcpKS1FUWIiCvDysT07GkWPHXGZBQJ1Oh18/+yw++f3vrQsZvvDSS/jmWh18fH0xb5CO9BAyVOh0OkSGh+ODjz4SOhQruVyO1WvXYNfOnbh586bQ4biUpqYmlJ46hb3vvQeZbPDPUyT2x8TwQYxJpDykrrXYUU84jsOy2FhIpVIYjUbsTE/vtKqRQqHA7r17ce3aNZSeOoWc7GyHxhUUHIzA4CDwprb3PTsry1om1RVFREaisKDA5g5WYFAQ6urqrOfFcRxmh4Xhuk5nTYkrPnnSZe6sR0ZF4fSp04iInNPn98vyexgyYwaufPMN4lasEE11rcioKBTk53eIxxLznSOFXZ07x3EICw9HXm6uQ+MVLf2/v+R5XpxJ94PIYGynhGK5Jhfk5QkdCgBg3vz5mBk6G2EzZwkdSgcFBw8iOjIS+tZWAMD0wEAcPnoUzz79NMpKSwWO7geHjhxBQV4eMrVaoUMhYtRNO0UdEQfz9PTERxkZOHOmHBvWJ6G1tbXDXWxPT094KhRQKpWYO38+1q9bh4qKCrvGIZPJoHr0Ubz/uw+xM31Hp3fTibhMnjIF65OTMXbsWEx7fCr0+tYu97WkX+m7SDWz/B7eunULhQUFyM/NRYtAedB9Oa/ekMvlOH3mDLZs2oSiwkLBzksw1BFxisHcTgnBV6XCY489ZnPzTSaTQZud7bCFD/V6Peaq1TAYbBfxLTp8GOsTElB54YJDjjsQOXl5mBejsV4n/QMCkF9UhNCZM3GmvFzg6H7g5+cHbU4ONqWmOvyGKnFB1BERllQqhY+vL3774Yf47bvvory0DNXVVR32Y4whRqPBs88/jz27dqHu6lU0Njb2+7iMMUzz94dEIkFyagqabjShsKAAOVlZ1AkRMYVSCS8vLyx9/XUcPnQI+bl5PX5YT0lLw6hRo5CXmwuTyYSy0tIO77FUKoWvSoXE5CRMmPAQlixahKtXrgzod6wv+nNevSWTuSE8Ihzxa9c6/bwERx0Rpxjs7ZSz+fqq8PSvn8Fv333XZkFWmcwNcFTRPx421xx3Dw+oVCrMiYrCyuXLO3RQxEAqldrExRiDVCaDQa8XXTv+ZEhIW9o3VR0jd6KOiDgEBgXhyZAQhEVEIHbpUpQUF3e6H8dx2LJtG8aPH49TJSVoqK/v8x2GoOBghMyYgdmhocjLzYXBYMCunTtpEp6IqTUaKBQKBAYF4fLly1i1cmWf3i9Lmp9/QAB27dyJL44f7/J3LFqthv/06Rg7diyWLFoEnQPT8ziOw+vL30BIyIx+nVdfOPO8RIM6Ik4xVNopZ4qLj8fxzz8XLMVIoVAgSq12+RRlsVAoFFCOGeMyac/EiagjIh6MMTw+dSqeDAnBZ3/+M766dAkGg6FDnry7uzu8fXwgk8mw9733cPXqVaxetQq1NTXdPn/7FKzdO3airKzMpUrxDjWW92t9cjIefuRh1NfXI3HtOtRUV/dr0qQlzc/H1xcLF70KXaMOc2NicPvWrQ77uru7I2nDBqgefRTnz51D4rp1naYO9pclBctkMmH48OFYn5DQ7/PqC8vfTsxcDZpvN9v9vESHOiJOMZTaKWfx9PTE7du30dTU1GVaqaPIZDIcyM/HgZwc5A/V+WUOEBkVhYjIyC7bHTJEUUdEfDiOQ3JqKqY+8QT+eOwY0rdt63JfmcwNkVGROHPmTJedCssQM6VguQ6FUomPMvZZ36/8vDzwJpNd0gMsw/dpmzbBz88PLy1YkhRD3wAAIgBJREFU0GmqEsdxGDZsGPZp90OhHIP33nkH+zMyBvR7o1AqMX78eLzz3rvYlLbRrufVF8NHjDBXS7PPeYkWdUScYii2U86QtmkTHn/iCSQnJtpsr6muxkRvb+gaG3HX3XfDw6Pvr31DfT0aGxut7WN7MpkMr7/xBiLCwkSfKWCJv6u0WzHhOA6btmzBuHFeeOP1WCiVSuvPTCYTLtbWwsfX1yHHdoXXZ8gaSEeEMfYRgGcB/IPneZV5WxKAlwH807zbGp7nPzX/bDWAlwAYASzjef6PPcU3lC/w7YcyFUolxowZ02U6TVc4jsPW7dsx1ssLX/z1r5SC5QIUSiX27N3r8FSl9tXbGGPdpvkFBgUhMCgITMJQfOJkv34PLSlYV69ewelTp5Gbk2OP0xiQ9ufFwJCl1Q6ulC3qiFA75cLkcjm+uVYHNzfbtbfCZs1CTl4uysvK4eXlBc87SuH3xpbNm3H9+nUEBAQgSq22+ZnBYMCK5cuRnZk5oPgdTa3RWOO3pFh3l3YrBnK5HNqcbJwpK8eKuDjrdoPBgINFhYiYE+mQ4xoMBsS9+SZV7hKjAXZEQgDcBLD/jgv8TZ7nt96xrw+AHADTADwA4DMAD/M83+2nLLrAt/EPCMCTISHYvXMnTCZTj0PVjDHMnTcPzz7/PNK3bcO1a9coz1XEGGNwc3PD+uRkDB8xAjlZWU5JVQLaUiC8xo2zpvl1VZmNMYZ58+fj1dcWQ9eowzdff91jatOdv4dGo9Fp59VblvNK2bgRtTU1OHv2LNatXg2TyeT6d8+oI0LtlAuzpCtLJLbLml2srcXDjzzSYXtfNDY24q677up0DSWTyYQvz5wR9d+/WqNB/Jo1ePXll60jyj6+vvjxj3+MbW+/LcrYOY7D+uRk5B04gH//+9/96kD2l0wmw5srVyIiNFQ0ZeqJ2UBTsxhjXgCO9uICvxoAeJ7fZP7+jwCSeJ4/1e3z0wUeQNsFOSo6GmERERjl7o7fdJFOo1AqoVQq4alQIDpGjaSERFGWHSQ/8FWp4O3jgzlRkdiwPgkXa2sFqdBiSfOLW70ar736aqfVpSxpXW4yWY8pW74qFfwDAvDqokV46YUXRP172P684uLj8d2N7/Cv//evLqvYuQzqiACgdooMLu4eHnh10SL84dgxm+uq5XNCc0sLDhUVia4zsnzlCpwqLhFkwrpMJsORY8d6THcnAuimnRrIyupLGGMXGGMfMcZ+ZN7mCaCh3T6N5m2kF3ieR052NiLDw/HHY8cQHROD5StW2CzsZknpmRMV2baQ28xZov7wN9QplErExcdjn3Y/3NzcMCc0DFWVlYKVidTrW5Gp1WJTairCIiKwe+/eDr9jPM9D39qK77//HhGhYXgzNhZLli3DqjVrrAtCtj+ve++9F68vXSr638P257U+IQGnS05h9OjR+Fi7H3Hx8fD0pEvVIETtFHFJKpUKPM93uK5aPifEaDSQmtePEhMpJxVsNEKv1yM5MREhM2bQ9dyF9HfVoHcBpADgzf9vA/BiX56AMfYKgFfavhtIf2jwMRqNSN+2DZ6enrj/gQcw0dsbaZs2obm5Gbt37sTly5eRnJgoqtQX0tFbW7di8uTJqKiowKJXFuLLM2eEDskqJzsbR48cgbePDziOw4H8fBw+dKjThS5LiosxT6OBf0AAtu1Ix2uvLsLmt99Gxfnz1vMS21253igpLsapkhL87dtvkbJxI/73L38ZXHNHCLVTxGXVVFfjpz/7Gbx9fDpUy+Q4bkApa44yecoUmEwm1FRXCxZDVWUlrnzzDT7KyMCvn3pKlGvDEFv96ojwPP93y9eMsQ8AHDV/qwOgbLerwryts+d4H8D7gHnIm3Sg0+mg0+nAGENBXh4qKipQVVmJU6dO0WR0F/Dggw9i9syZuH37tijfr6amJmv9/tQNG/DBRx9BKpV2mqpUW1ODi7W1yMrMRF5BAc6eOYM1q1eL8rz6gud57M/IQHZ2NgxOLh9KHIvaKeLKJnp7Y1ns6/jLZ591+FlYeDgMBj14k0mAyDrnN2kS4tesQVFhoc1NUl+VCuDhsPTXO5+/qakJcStWoODgQfiqVKg4f94hxyX2068uNWPs/nbfzgZg+Q37BEAUY0zOGBsH4CEAZQMLkfA8j0ytFpUXLoDneZf/8DeUtLa2usT7VXH+PAry8rBl61akvbWp030sqU1GoxEJ69a5xHn1huW8XHFUh3SN2iniynSNjTh96jQio6I6pGd7jRuHN5a9Lpq7/UHBwfjZz3+O6MhIm0qJQcHB2KfdD7/Jkxx27MceewxP//oZa9ow0JZVUlhQgA8++shmOxGnHkdEGGM5AH4K4MeMsUYA6wH8lDE2CW1D3nUAFgIAz/PVjLE8ADUADABe66kSCSFEHLK0WhSfPIm9772HQ0eOwGQyISY6mhalIqJH7RQZbBobG7Fk0SJ8lJGBvKJCcBIOHMdBLpejoqIC3333ndAhAmibPB8yYwb+8tlnNjenfFUq7Ni9CzvTdyAnK8thx8/JzoZ/QAD27N2LeRqNNY0tS6uFVCrFkyEhOFVSQjeaRIwWNCTEQQoOHkR0ZCT0ra0AfliUqqqyEk1NTQJH1zVLZa3wOXNQcf481ick2Pz8zvMiAqOqWU5B7RQRglQqBZNIEBkZiZmzZ2O+RoPmlhbRjEj7BwRg9zvvYJ5GY11wmTGGF156Cffeey+2bN7s8E4AYwwxGg1u376NosJC6/F8fVX4WLsfixcuxJnycofGQHrgoKpZhJA+uMvDA3Hx8fD28RE6lG5ZKmvNeu65Dp0QQgghzmMwGKBvbbsmR4SG4tatW6LphHAch6DgYLy+dKm1EwIAbm5uePmVV/DFX//qlJEIS/p6dEyMTSWx6uoqxC5diidDQmzS24i4UEeEECe56+674eXlJXQYhBBCyIClpKXhT3/8Y4c1Q9YnJ+Pll14SZC2R1LQ0m+9LiosRFByMpJQUp8dCeqe/5XsJIX3k4eHh1FVmCSGEEEdQKJUYPmKEzUhId9udYW5MDOJXr+6wPSoiwumxkN6jERFCCCGEENJr6pgY5GRl2VTusiy4fOd2Z7l96xY+eP/9DgvUGgwG0VQYIx1RR4QQQgghhPRallbbYeHC8ePH4+rVK4IuaPjdf/6DBzw94TVunM12Hx8fRKvVkMvlAkVGukIdEUIIIYQQ0ivuHh5gjNksXCiXy7F7716cPnXaZruzNTU1ofTUKSxZthRS6Q+zD2rNqWKnz5yBTOYmVHikE9QRIYQQQgghPeI4DhtSUhClVttsj4yKwtdfX7ZZ0FAoOdnZiJ4TaZOOxfM8crKzsWXTJoRHhAsYHbkTdUQIIYQQQkiPhg8fDm8fH2S3W6RQrdFgxapVSEpIFDCyjhhjHcr2FhUWIiwigsr5igh1RAghhBBCSI8yMjNx7uxZXNfpALSlaQUEBODVl19G5YULAkdna5q/P7Zs2wZ3d3frNpPJhFHu7lgWGytcYMQGdUQIIYQQQki3goKD8YCnJxLbLXSrUqmgUCpRV1cnXGBd0DU2Yvz48TaLCOv1evxmwQJIpVKbylpEONQRIYT0iV6vBxy/WC4hhBCR8FWpsGP3LryzezcMej0AQCaTITk1BV/89a/WERIxaWxsxIkvvkBKWhqGjxhhs91oNOK9Dz+wmdBOhEEdEUJIn8xVq6HXtwodBiGEECdgjMHbxwfp27YjS6sFz7fdiVI9+iiabjRh186dAkfYtZ3p6aioqMD+TG2H7Ylr1+GRiRMFioxYUEeEENIntDAUIYQMHW5ubpgTFYnszExrJwQA1icno7CgACajUcDoumc0GpGwZg0UyjEIDAqy2e6rUmFDWqqA0RGAOiKEkD6SyWRCh0AIIcRJUjdt6pCOq9ZokLFvH3LaVc8Sq9bWVvz23XfxZEgIGGPW7fm5uZgw4SFo5s2z2U6cizoihJA+0WZn04JQhBAyBCiUSvj5+WHB3Lk223mTCbU1NTYjJGLF8zzKS8sQFhGBx6dOtW5vaWnBvJgYLIuNtdlOnIs6IoSQPpFKpQDdPCKEkEFPHRODiooKNLe02GzPyc7GRfNq5a6guroKBXl5iFarbdYQqTh/vtPtxHmoI0IIIYQQQmz4qlSIVquRn5sr6nkgvZWl1eLhiY9AIrHtcBzIycFEb+8O24lzUN0yQgghhBBiiwdMLpB61Vs6nQ6zn3u+Q8GV8DkRSN2wgapBCoRGRAghhBBCiI3q6irk5uQgMipq0KQt3dkJUSiVCAmZAeMgGPFxVdQRIcRJaqqrkZebK3QYhBBCSK9kabVQPfooNm7eLHQoDuHl5YXLly+jprpa6FCGLOqIEOIkE729ERYeJnQYhBBCSK/odDrMev55uLm5QaFQCB2OXU2eMgVLX38dq1auxM2bN4UOZ8iijgghTiKRSMBxNC2LEEKI67h96xbycnMRpVYLHYpdrU9OxuFDhwbFRHxXRh0RQgghhBDSJZPJhMioKPj5+Qkdil2oNRp4jRuH/Nw8oUMZ8qgjQgghhBBCulRWWoqd6enYn5Xl8gvaunt4ICAgAIsXLqRKWSJAHRFCnKShvh7FJ08KHUavBAUHIy4+HtGDbCieEEJI3/E8j0ytFlevXkVkVKTQ4fQbx3HYkJIChVKJuro6ocMhoI4IIU7T2NiIJYsWibo6h0wmw+QpU7Bj9y4sXLQIVVVVQodECCFEJNYnJGBWaKjQYfSbVCrFgw9NwJLFi3FdpxM6HAJa0JAQp9KJ9MLn7uEBlUqF5NQUNN1oQvq27cjPzaNha0IIIVY11dVoqK+HQqFAY2Oj0OH02YMTJiBs5mxq20SERkQIcRCpVIrISPEPYas1Gry6aBGCgoPx6dHfIyIsDNmZmZ1eqIOCgzHWywtvrlyJwKAgAaIlhBAiFL1e77IVtIKCg/G7ffsAJnQkpD0aESHEQdavW4f9WVkInzMH6xMSUFNdDb1eL3RYVnK5HJFRUVixahVioqJQeeFCp/txHIfhw4cjIzMTD3h6YveOHZBwHLbtSMcrL/0GF2trRXVehBBCHKeqshI6nQ6MMfA8L3Q4vcIYQ8iMGXh3zx4YqL0SlR5HRBhjSsbY54yxGsZYNWPsdfP2exhjf2aMXTb//yPzdsYY28UY+5oxdoExNsXRJ0GIGFVUVGDq5CkoyMvD+uRkHDl2DNMDA+Hu7i50aJg8ZQrKzp7FzNDZ3XZC3D08sHX7dhQcPIivLl1CSFAQtPv3I+PjjxESFIxotRpZuQegUCpFcV5kaKJ2ihDnaWpqQkBAAGaFhoIx8Q8vMMaw4MUXMTs0FGVlZS7TeRoqWE9vCGPsfgD38zx/ljHmDuBLALMALADwL57nNzPG4gH8iOf5VYyxZwAsBfAMAH8AO3me9+/2GBIpD6nHgE+GELHiOA7LYmMRMmMGrnzzDeJWrIBRgEWUFEol1DExMJlMaGhoQG5OTqf7BQUHIyg4GAqFAgqlssuJfRzHYXZYGMaPH4/Ro0cLdl5Dmv7fX/I8P1XoMIRE7RQhzldw8CCiIyOhbxX3fAu5XI7jJ05geWwsTpWUCB3O0NRNO9VjR6TDAxg7DGCP+d9PeZ7/1twIHOd5/hHG2G/NX+eY979k2a/L56QLPBkiPD098VFGBm7duoXCggJkabUOvzvDGINEIsHGt96Cm5sbcrKyUFNdjZs3b3bYd8TIkdBmZeEBT0/cd999mKtWo66ursfqIp6enhgzdiyWr1iBw4cOOeW8iBl1RDqgdooQx3v0sccQGR2NdatXCx1Kt97evh1jx47FnLAwoUMZurppp/o0R4Qx5gVgMoBSAKPbXbT/BmC0+WtPAA3tHtZo3mZzgWeMvQLglbbvaM48GRp0Oh1+/dRT8FWpkJichNu3b+NiTS2qqx1TJtcyJH333XejpbkZ6xMS0Hz7dof93D08cJeHBxYuWoSvLl1C1Jw5AI9eVxbR6XTQ6XRI3bDBel7XdTo01Ne7ZGUV4rqonSLEOb66dAlTn3hC1BW0FEol/Pz8MOu554QOhXSh1x0RxtgoAIUAYnmev9E+L5DneZ4x1qfbnzzPvw/gfcB8p4mQIcJgMKDi/HmEzZyFaLUaT//6Gfz6uWeRpdXatbxvUHAwAoODwJt4FJ882eWQtFqjQUBAAOrr65Gwdu2Ajtn+vCKjozF27Fic+OIL7ExPp5Qt4nDUThHiPAaDAdVVVdi9dy+WLFokyvL06pgYVFRUoLmlRehQSBd6dYuHMSZD28U9i+f5IvPmv5uHui35uf8wb9cBULZ7uMK8jRByh5zsbPz23XcxevRofJSRgaLDh6GZN69fEwAZY5DL5Rg1ahQKDx3C1vR0fHv9W2x7++1OOyGTp0zBoSNHcPv2bWRqtcjOyrLHKQFoO6+ENWtw5Ztv8PobsSg4eBCaefMgl8vtdgxC2qN2ihDnMhqNSEpIwLVr1+A1bpzQ4XQwecoUmEwmJCcmwkQ3wkSrN1WzGIDfAajleX57ux99AmC++ev5AA632z7PXJUkAMB33eXdEjLUNTU1IW7FCjz7zDNI37YNb2/bisen9j3lf5q/P765VoeLX38NmUyGkKCgLudqyOVyFB0+jFMlJThUVISy0lK7rzJrOa8J48Zj+9ateHvbVtR8dQkymZtdj0MItVOECKOpqQmlp07h3fffh9+kSUKHYzV5yhTsz8pEQ0NDp/MhiXj0pmpWMIATACoBmMyb16At/zYPwBgA1wDM4Xn+X+YGYQ+ApwDcAvACz/Nnuj0GTQIkBACgUCgQpVaDMYbGhgbkZGf36nGW9CqFUomS4uIeK2L1JmXLniznFRkVhcLCQnxx/DhKiosdftwhgSarUztFiMA08+ZhdlgowmbOstnOcRxeX/4GpJz9l607eeJEl+3IoSNHkJebi+zMTLsfl/SDPatmOQJd4Amx5efnh/1ZWbh69WqvFkM8kJ+PXTt2dFnh6s5FCd/ZvVuQylbePj7wDwjAa0uWYJ5Gg9qaGqcef1CijohTUDtFSNcYY5gVGophcjkO5ORY2xbGGB6fOhUSif2LPTQ2Nnba3qk1GsTFx+OJyVN6XXCFOBh1RAhxPTKZGyKjIhEaHo4RI0ciOTERVZWVaGpq6rCvVCqFwWDo9HncPTywISUF3j4+OHf2LBITEmDQ6wUrr8sYQ4xGgzNnzuBibW2n+7h7eEClUtls6+rchzzqiDgFtVOEdI8xhhWr4vD5X/4XZ8rLBYnB3cMDry5ahD8cO9blQr1EANQRIcR1DWQxRLVGA09PT/A8j+ysLLvPA3EEjuOwdft2jPXyglKpxOnTpxEZFQVtRgYtmNgZ6og4BbVThPQsMCgI/gEB2LVjh9Ov1Za2Q6fTYeuWLU49NukBdUQIcX13LoaYn5uLli5KEvqqVEjbtAle48ZhrlrtMneGOI7Dlq1b4ePri5deeAHu7u749vq3mOg9ESlpaSgrK0PCmjVChyku1BFxCmqnCOmdgoMHMV+jwffff+/U444aNQoFBw/ixQULXOKm25BCHRFCBgepVGpdDHHChIcwLyYGFefPd9jP11cFv8mTkJ+b51I5snMiI5GYnIRpUx7HrVu3bH7m5uaGaf7+qLt6VbSLZwmCOiJOQe0UIb3z1ttvY9LkyXhpwQLcdffd8PAY+N9NTXU1Jnp7QyKR2HzdXsrGNNxsukmLF4qRvVZWJ4QI687FEH/5q1/hf556yvpzSxWR6uoqh63W7kg6nQ5NTTeh72S+C2MMB/LzsGN7Og27E0KISK2Jj8ey2Fgox4yBl5cXPBWKAT+nTqdDYFAQOI6z+bq9P//xT5gW4D/gYxHnoo4IIS4qJzsb7u7uSNqwAbNCQzFXrUZdXZ3QYQ1IUHAwPD09u/y5ycR3OSmfEEKI8IxGI9K3bQMAu5aHtzznnV9byOVylJ09i2i1utel74nw7F9PjRDiNJZFAydOeAglxcWDOi/WZDJh38cfYdfOnUKHQgghRGRaWlqwZNEi+E+fDnd3d6HDIb1EHRFCXJzRaHSpeSDdOXniBHS6zud/RERGora6BiaqmkUIIaQTV69cwdixY+Ht4yN0KKSXqCNCCBGNkuJi6Bp1SNu0CXK5HHK5HCqVCkeOfYr4NWtQVeV6814IIYQ4R2NjI0588QVS0tIgldLsA1dAHRFCiKjMjYnBuHFe0OZk46OMDHBSKYxGI2KiolymDDEhhBBh7ExPx5kz5fDx9RU6FNILVL6XEEJcGZXvdQpqpwhxHXK5HMdPnsSbsbEoKS4WOhzSTTtFIyKEEEIIIWTQaG1txW/ffRdPhoSAMSZ0OKQb1BEhhBBCCCGDBs/zKC8tQ1hEBB6fSgPGYkYdEUIIIYQQMqhUV1chdulSPBkS0mHxQyIe1BEhhBBCCCGDTklxMYKCg7Fx82ahQyFdoI4IIUS0fHx8KL+XEEJIv83XaDD+wfFCh0G6QB0RQohobUhLg1QmEzoMQgghLqq5uRklxSUIDAoSOhTSCeqIEEIIIYSQQcloNOLD999HUHAwrbguQtQRIYQQQgghg1ZTUxMYY/APCKB0X5GhjgghhBBCCBnUTp44gdeWLEGMRiN0KKQd6ogQQgghhJBBraS4GL958UWEhodTOV8RoY4IIYQQQggZ9GqqqzFi5Egsi40VOhRiRh0RQgghhBAy6On1eiQnJiLk/7d3vzFSVXcYx7+PgNgUUkWNIUAAlbZB0iChFqJRY9OqtBFMfIE2rS+a2FBINK2xUhODJn3RJkpt0mq0UmlrKwo2NaZNagsJfVMoKn8WKboCiRAq/ROVlgR1+fXFPQvTcXeZgZ17zjLPJ7nZe8+dzX32N7Pzy9l75+411zBp0qTccQxPRMzMzMysS/Ts2MGeN99k1erVjB49OnecrueJiJmZmZl1hcOHD3PP3Xdz5MgRLps1K3ecrueJiJmZmZl1jb6+PtatXcv9D6zIHaXreSJiZmZmZl3luTVruPTSGdx62225o3Q1T0TMzMzMrKscPXqUZUuW8Ln58xk/fnzuOF3rpBMRSVMkbZD0mqSdku5M4yskHZC0NS0LGr5nuaReSbslXd/JH8DMzLqb+5SZnYq9e/YwdepUVjz4oP+3SCatnBH5EPh2RMwE5gFLJc1M+1ZGxOy0/A4g7VsMXAbcAPxEkp9dMzPrFPcpM2vb/v37+fPGjdy0aBFnneW3gBxOOhGJiIMR8UpaPwzsAoa6+fJC4JmIOBoRe4Fe4IrhCGtmZtbMfcrMTtUjK1cy85Of4oMP3s8dpSu19RkRSdOAy4FNaWiZpO2SVkk6L41NAt5q+Lb9DN0QzMzMhoX7lJm1o6+vz5OQjFqeiEgaB6wD7oqI94BHgUuA2cBB4KF2DizpDklbJG0hop1vNTMz+wj3KTOzkaWliYikMVRv7k9HxPMAEfF2RPRFxDHgCU6c1j4ATGn49slp7P9ExOMRMTci5iKdzs9gZmZdzn3KzGzkaeWuWQKeBHZFxMMN4xMbHnYz0JPWXwAWSxoraTowA9g8fJHNzMxOcJ8yMxuZRrfwmCuBrwI7JG1NY98FbpU0GwhgH/ANgIjYKelZ4DWqO5ksjYi+4Y1tZmZ2nPuUmdkIpCjguldJ/wD+C/wzd5YmF1BeJigzlzO1rsRcJWaCMnOVlmlqRFyYO8SZzn2qbSXmKjETlJmrxExQZi5nOrlB+1QRExEASVsiYm7uHI1KzARl5nKm1pWYq8RMUGauEjNZPUp87kvMBGXmKjETlJmrxExQZi5nOj1t3b7XzMzMzMxsOHgiYmZmZmZmtStpIvJ47gADKDETlJnLmVpXYq4SM0GZuUrMZPUo8bkvMROUmavETFBmrhIzQZm5nOk0FPMZETMzMzMz6x4lnRExMzMzM7MukX0iIukGSbsl9Uq6N3OWfZJ2SNoqaUsamyDpJUlvpK/ndTjDKkmHJPU0jA2YQZUfpdptlzSn5lwrJB1I9doqaUHDvuUp125J13co0xRJGyS9JmmnpDvTeLZ6DZEpd63OkbRZ0raU64E0Pl3SpnT8NZLOTuNj03Zv2j+txkxPSdrbUKvZabzO1/soSa9KejFtZ6uT5ec+9ZEM7lOtZ3Kfaj2X+1T7+UZ+r4qIbAswCngTuBg4G9gGzMyYZx9wQdPYD4B70/q9wPc7nOFqYA7Qc7IMwALg94CAecCmmnOtAO4e4LEz03M5FpienuNRHcg0EZiT1scDr6djZ6vXEJly10rAuLQ+BtiUavAssDiNPwYsSevfBB5L64uBNTVmegq4ZYDH1/l6/xbwK+DFtJ2tTl7yLrhPDZTBfar1TO5Tredyn2o/34jvVbnPiFwB9EbEnoh4H3gGWJg5U7OFwOq0vhpY1MmDRcRG4N8tZlgI/DwqfwHOlTSxxlyDWQg8ExFHI2Iv0Ev1XA93poMR8UpaPwzsAiaRsV5DZBpMXbWKiPhP2hyTlgCuA9am8eZa9ddwLfB5Saop02Bqeb1Lmgx8Cfhp2hYZ62TZuU81cZ9qK5P7VOu53KfacKb0qtwTkUnAWw3b+xn6l6HTAviDpJcl3ZHGLoqIg2n978BFGXINlqGE+i1Lpx9X6cTlALXnSqcZL6f6a0UR9WrKBJlrlU7hbgUOAS9R/VXrnYj4cIBjH8+V9r8LnN/pTBHRX6vvpVqtlDS2OdMAeYfTD4F7gGNp+3wy18myKuF9tpH7VPvcp1rLBO5TJ81USJ+CM6RX5Z6IlOaqiJgD3AgslXR1486ICIaeCXdcCRkaPApcAswGDgIP5QghaRywDrgrIt5r3JerXgNkyl6riOiLiNnAZKq/Zn267gzNmjNJmgUsp8r2WWAC8J268kj6MnAoIl6u65hmbXKfak/2915wn2qV+1RrzqRelXsicgCY0rA9OY1lEREH0tdDwG+ofgne7j+tlr4eyhBtsAxZ6xcRb6df0GPAE5w4VVtbLkljqN5In46I59Nw1noNlKmEWvWLiHeADcB8qtPGowc49vFcaf8ngH/VkOmGdNlARMRR4GfUW6srgZsk7aO6BOc64BEKqZNl4T7VGvepQbhPtc996qTOmF6VeyLyV2BG+pT/2VQfoHkhRxBJH5c0vn8d+CLQk/Lcnh52O/DbDPEGy/AC8LV0l4Z5wLsNp3o7rum6x5up6tWfa3G6S8N0YAawuQPHF/AksCsiHm7Yla1eg2UqoFYXSjo3rX8M+ALVdcEbgFvSw5pr1V/DW4D16a92nc70t4bmLKrrWxtr1dHnLyKWR8TkiJhG9X60PiK+QsY6WXbuU61xnxr4+O5Tredyn2rRGdWrIvOn5anuMPA61XWA92XMcTHVXSG2ATv7s1BdQ/cn4A3gj8CEDuf4NdUp0Q+oru/7+mAZqO7K8ONUux3A3Jpz/SIddzvVi3xiw+PvS7l2Azd2KNNVVKeztwNb07IgZ72GyJS7Vp8BXk3H7wHub3jdb6b68OFzwNg0fk7a7k37L64x0/pUqx7gl5y4Y0ltr/d0vGs5cSeSbHXykn/Bfao5h/tU65ncp1rP5T51ahmvZQT3Kv9ndTMzMzMzq13uS7PMzMzMzKwLeSJiZmZmZma180TEzMzMzMxq54mImZmZmZnVzhMRMzMzMzOrnSciZmZmZmZWO09EzMzMzMysdp6ImJmZmZlZ7f4HkcTSuInOUgUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x864 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_batch(val_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "straight-chaos",
   "metadata": {
    "papermill": {
     "duration": 0.044797,
     "end_time": "2021-04-17T13:08:08.087540",
     "exception": false,
     "start_time": "2021-04-17T13:08:08.042743",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "sensitive-tissue",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:08.189201Z",
     "iopub.status.busy": "2021-04-17T13:08:08.188424Z",
     "iopub.status.idle": "2021-04-17T13:08:08.192713Z",
     "shell.execute_reply": "2021-04-17T13:08:08.192163Z"
    },
    "papermill": {
     "duration": 0.059699,
     "end_time": "2021-04-17T13:08:08.192860",
     "exception": false,
     "start_time": "2021-04-17T13:08:08.133161",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Encoder(tf.keras.Model):\n",
    "    def __init__(self):\n",
    "        super(Encoder, self).__init__()\n",
    "        \n",
    "        # output: (bs, 1280, 14, 8)\n",
    "        self.feature_maps = efn.EfficientNetB4(include_top=False, weights='noisy-student')\n",
    "        # set global encoder dimension variable\n",
    "        global ENCODER_DIM\n",
    "        ENCODER_DIM = self.feature_maps.layers[-1].output_shape[-1]\n",
    "        \n",
    "        # output: (bs, 1280, 112)\n",
    "        self.reshape = tf.keras.layers.Reshape([-1, ENCODER_DIM], name='reshape_featuere_maps')\n",
    "        # output: (bs, 112, 1280)\n",
    "        self.permuate = tf.keras.layers.Permute([2, 1], name='permutate_features_last')\n",
    "\n",
    "    def call(self, x, training):\n",
    "        x = self.feature_maps(x, training=training)\n",
    "        x = self.reshape(x, training=training)\n",
    "        x = self.permuate(x, training=training)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "subject-latex",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:08.287061Z",
     "iopub.status.busy": "2021-04-17T13:08:08.286359Z",
     "iopub.status.idle": "2021-04-17T13:08:34.157454Z",
     "shell.execute_reply": "2021-04-17T13:08:34.158435Z"
    },
    "papermill": {
     "duration": 25.921843,
     "end_time": "2021-04-17T13:08:34.158696",
     "exception": false,
     "start_time": "2021-04-17T13:08:08.236853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/qubvel/efficientnet/releases/download/v0.0.1/efficientnet-b4_noisy-student_notop.h5\n",
      "71680000/71678424 [==============================] - 1s 0us/step\n",
      "Encoder output shape: (batch size, sequence length, units) (2, 1792, 112)\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/CPU:0'):\n",
    "    encoder = Encoder()\n",
    "    encoder_res = encoder(imgs[:BATCH_SIZE_DEBUG])\n",
    "\n",
    "print ('Encoder output shape: (batch size, sequence length, units) {}'.format(encoder_res.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "invalid-driving",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:34.277389Z",
     "iopub.status.busy": "2021-04-17T13:08:34.276685Z",
     "iopub.status.idle": "2021-04-17T13:08:34.279457Z",
     "shell.execute_reply": "2021-04-17T13:08:34.279993Z"
    },
    "papermill": {
     "duration": 0.066856,
     "end_time": "2021-04-17T13:08:34.280168",
     "exception": false,
     "start_time": "2021-04-17T13:08:34.213312",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class BahdanauAttention(tf.keras.layers.Layer):\n",
    "    def __init__(self, units):\n",
    "        super(BahdanauAttention, self).__init__()\n",
    "        self.W1 = tf.keras.layers.Dense(units, name='hidden_to_attention_units')\n",
    "        self.W2 = tf.keras.layers.Dense(units, name='encoder_res_to_attention_units')\n",
    "        self.V = tf.keras.layers.Dense(1, name='score_to_alpha')\n",
    "\n",
    "    def call(self, h, encoder_res, training):\n",
    "        # dense hidden state to attention units size and expand dimension\n",
    "        hidden_with_time_axis = tf.expand_dims(h, 1)\n",
    "        hidden_with_time_axis = self.W2(hidden_with_time_axis, training=training)\n",
    "        # dense features to units size\n",
    "        encoder_res_dense = self.W1(encoder_res, training=training)\n",
    "\n",
    "        # add vectors\n",
    "        score = tf.math.tanh(hidden_with_time_axis + encoder_res_dense)\n",
    "        score = self.V(score, training=training)\n",
    "        \n",
    "        # create alpha vector size (bs, layers)        \n",
    "        attention_weights = tf.nn.softmax(score, axis=1)\n",
    "        \n",
    "        # create attention weights (bs, layers)\n",
    "        context_vector = encoder_res * attention_weights #  tf.expand_dims(attention_weights, axis=2)\n",
    "        context_vector = tf.reduce_sum(context_vector  , axis=1)\n",
    "        \n",
    "        return context_vector, attention_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bright-tribune",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:34.397899Z",
     "iopub.status.busy": "2021-04-17T13:08:34.397123Z",
     "iopub.status.idle": "2021-04-17T13:08:34.482637Z",
     "shell.execute_reply": "2021-04-17T13:08:34.483119Z"
    },
    "papermill": {
     "duration": 0.150113,
     "end_time": "2021-04-17T13:08:34.483304",
     "exception": false,
     "start_time": "2021-04-17T13:08:34.333191",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "context_vector shape: (batch size, units) (2, 112)\n",
      "attention_weights shape: (batch_size, sequence_length, 1) (2, 1792, 1)\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/CPU:0'):\n",
    "    attention_layer = BahdanauAttention(ATTENTION_UNITS)\n",
    "    context_vector, attention_weights = attention_layer(tf.zeros([BATCH_SIZE_DEBUG, DECODER_DIM]), encoder_res)\n",
    "\n",
    "print('context_vector shape: (batch size, units) {}'.format(context_vector.shape))\n",
    "print('attention_weights shape: (batch_size, sequence_length, 1) {}'.format(attention_weights.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dated-keeping",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:34.601393Z",
     "iopub.status.busy": "2021-04-17T13:08:34.595229Z",
     "iopub.status.idle": "2021-04-17T13:08:34.604748Z",
     "shell.execute_reply": "2021-04-17T13:08:34.604107Z"
    },
    "papermill": {
     "duration": 0.06957,
     "end_time": "2021-04-17T13:08:34.604895",
     "exception": false,
     "start_time": "2021-04-17T13:08:34.535325",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Decoder(tf.keras.Model):\n",
    "    def __init__(self, vocab_size, attention_units, encoder_dim, decoder_dim):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.vocab_size = vocab_size\n",
    "        self.attention_units = attention_units\n",
    "        self.encoder_dim = encoder_dim\n",
    "        self.decoder_dim = decoder_dim\n",
    "        \n",
    "        # LSTM hidden and carry state initialization\n",
    "        self.init_h = tf.keras.layers.Dense(units=decoder_dim, input_shape=[encoder_dim], name='encoder_res_to_hidden_init')\n",
    "        self.init_c = tf.keras.layers.Dense(units=decoder_dim, input_shape=[encoder_dim], name='encoder_res_to_inp_act_init')\n",
    "        # The LSTM cell\n",
    "        self.lstm_cell = tf.keras.layers.LSTMCell(decoder_dim, name='lstm_char_predictor')\n",
    "        # dropout before prediction\n",
    "        self.do = tf.keras.layers.Dropout(0.30, name='prediction_dropout')\n",
    "        # fully connected prediction layer\n",
    "        self.fcn = tf.keras.layers.Dense(units=vocab_size, input_shape=[decoder_dim], dtype=tf.float32, name='lstm_output_to_char_probs')\n",
    "        # character embedding layer\n",
    "        self.embedding = tf.keras.layers.Embedding(vocab_size, decoder_dim)\n",
    "\n",
    "        # used for attention\n",
    "        self.attention = BahdanauAttention(self.attention_units)\n",
    "\n",
    "    def call(self, char, h, c, enc_output, training):\n",
    "        # embed previous character\n",
    "        char = self.embedding(char, training=training)\n",
    "        char = tf.squeeze(char, axis=1)\n",
    "        # get attention alpha and context vector\n",
    "        context, _ = self.attention(h, enc_output, training=training)\n",
    "\n",
    "        # concat context and char to create lstm input\n",
    "        lstm_input = tf.concat((context, char), axis=-1)\n",
    "        \n",
    "        # LSTM call, get new h, c\n",
    "        _, (h_new, c_new) = self.lstm_cell(lstm_input, (h, c), training=training)\n",
    "        \n",
    "        # compute predictions with dropout\n",
    "        output = self.do(h_new, training=training)\n",
    "        output = self.fcn(output, training=training)\n",
    "\n",
    "        return output, h_new, c_new\n",
    "    \n",
    "    def init_hidden_state(self, encoder_out, training):\n",
    "        mean_encoder_out = tf.math.reduce_mean(encoder_out, axis=1)\n",
    "        h = self.init_h(mean_encoder_out, training=training)  # (batch_size, decoder_dim)\n",
    "        c = self.init_c(mean_encoder_out, training=training)\n",
    "        return h, c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bacterial-signature",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:34.717085Z",
     "iopub.status.busy": "2021-04-17T13:08:34.716398Z",
     "iopub.status.idle": "2021-04-17T13:08:35.627689Z",
     "shell.execute_reply": "2021-04-17T13:08:35.626332Z"
    },
    "papermill": {
     "duration": 0.970118,
     "end_time": "2021-04-17T13:08:35.627863",
     "exception": false,
     "start_time": "2021-04-17T13:08:34.657745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decoder output shape: (batch_size, vocab size) (2, 41)\n"
     ]
    }
   ],
   "source": [
    "with tf.device('/CPU:0'):\n",
    "    decoder = Decoder(VOCAB_SIZE, ATTENTION_UNITS, ENCODER_DIM, DECODER_DIM)\n",
    "    h, c = decoder.init_hidden_state(encoder_res[:BATCH_SIZE_DEBUG], training=False)\n",
    "    preds, h, c = decoder(tf.random.uniform((BATCH_SIZE_DEBUG, 1)), h, c, encoder_res)\n",
    "\n",
    "print ('Decoder output shape: (batch_size, vocab size) {}'.format(preds.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "appropriate-recorder",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:35.740386Z",
     "iopub.status.busy": "2021-04-17T13:08:35.739346Z",
     "iopub.status.idle": "2021-04-17T13:08:35.742993Z",
     "shell.execute_reply": "2021-04-17T13:08:35.742456Z"
    },
    "papermill": {
     "duration": 0.062101,
     "end_time": "2021-04-17T13:08:35.743140",
     "exception": false,
     "start_time": "2021-04-17T13:08:35.681039",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# The start/end/pad tokens will be removed from the string when computing the Levenshtein distance\n",
    "START_TOKEN = tf.constant(vocabulary_to_int.get('<start>'), dtype=tf.int64)\n",
    "END_TOKEN = tf.constant(vocabulary_to_int.get('<end>'), dtype=tf.int64)\n",
    "PAD_TOKEN = tf.constant(vocabulary_to_int.get('<pad>'), dtype=tf.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eight-hampshire",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:08:35.864508Z",
     "iopub.status.busy": "2021-04-17T13:08:35.863360Z",
     "iopub.status.idle": "2021-04-17T13:09:14.718596Z",
     "shell.execute_reply": "2021-04-17T13:09:14.717837Z"
    },
    "papermill": {
     "duration": 38.924217,
     "end_time": "2021-04-17T13:09:14.718770",
     "exception": false,
     "start_time": "2021-04-17T13:08:35.794553",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# initialize the model, a dummy call to the encoder and deocder is made to allow the summaries to be printed\n",
    "with strategy.scope():\n",
    "    # Sparse categorical cross entropy loss is used\n",
    "    loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction=tf.keras.losses.Reduction.NONE)\n",
    "\n",
    "    def loss_function(real, pred):\n",
    "        per_example_loss = loss_object(real, pred)\n",
    "\n",
    "        return tf.nn.compute_average_loss(per_example_loss, global_batch_size=BATCH_SIZE)\n",
    "    \n",
    "    # Metrics\n",
    "    train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy()\n",
    "    train_loss = tf.keras.metrics.Sum()\n",
    "    val_loss = tf.keras.metrics.Sum()\n",
    "\n",
    "\n",
    "    # Encoder\n",
    "    encoder = Encoder()\n",
    "    encoder.build(input_shape=[BATCH_SIZE, IMG_HEIGHT, IMG_WIDTH, N_CHANNELS])\n",
    "    encoder_res = encoder(imgs[:2], training=False)\n",
    "    \n",
    "    # Decoder\n",
    "    decoder = Decoder(VOCAB_SIZE, ATTENTION_UNITS, ENCODER_DIM, DECODER_DIM)\n",
    "    h, c = decoder.init_hidden_state(encoder_res, training=False)\n",
    "    preds, h, c = decoder(lbls[:2, :1], h, c, encoder_res, training=False)\n",
    "    \n",
    "    # Adam Optimizer\n",
    "    optimizer = tf.keras.optimizers.Adam()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "stock-portal",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:14.845252Z",
     "iopub.status.busy": "2021-04-17T13:09:14.840074Z",
     "iopub.status.idle": "2021-04-17T13:09:14.875854Z",
     "shell.execute_reply": "2021-04-17T13:09:14.875052Z"
    },
    "papermill": {
     "duration": 0.102802,
     "end_time": "2021-04-17T13:09:14.876054",
     "exception": false,
     "start_time": "2021-04-17T13:09:14.773252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "efficientnet-b4 (Functional) (None, None, None, 1792)  17673816  \n",
      "_________________________________________________________________\n",
      "reshape_featuere_maps (Resha multiple                  0         \n",
      "_________________________________________________________________\n",
      "permutate_features_last (Per multiple                  0         \n",
      "=================================================================\n",
      "Total params: 17,673,816\n",
      "Trainable params: 17,548,616\n",
      "Non-trainable params: 125,200\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "extensive-immunology",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:14.989349Z",
     "iopub.status.busy": "2021-04-17T13:09:14.988293Z",
     "iopub.status.idle": "2021-04-17T13:09:14.995073Z",
     "shell.execute_reply": "2021-04-17T13:09:14.995811Z"
    },
    "papermill": {
     "duration": 0.066196,
     "end_time": "2021-04-17T13:09:14.996022",
     "exception": false,
     "start_time": "2021-04-17T13:09:14.929826",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"decoder_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "encoder_res_to_hidden_init ( multiple                  115712    \n",
      "_________________________________________________________________\n",
      "encoder_res_to_inp_act_init  multiple                  115712    \n",
      "_________________________________________________________________\n",
      "lstm_char_predictor (LSTMCel multiple                  8851456   \n",
      "_________________________________________________________________\n",
      "prediction_dropout (Dropout) multiple                  0         \n",
      "_________________________________________________________________\n",
      "lstm_output_to_char_probs (D multiple                  42025     \n",
      "_________________________________________________________________\n",
      "embedding_1 (Embedding)      multiple                  41984     \n",
      "_________________________________________________________________\n",
      "bahdanau_attention_2 (Bahdan multiple                  583169    \n",
      "=================================================================\n",
      "Total params: 9,750,058\n",
      "Trainable params: 9,750,058\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "naked-coordinator",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:15.108928Z",
     "iopub.status.busy": "2021-04-17T13:09:15.108068Z",
     "iopub.status.idle": "2021-04-17T13:09:15.111920Z",
     "shell.execute_reply": "2021-04-17T13:09:15.111249Z"
    },
    "papermill": {
     "duration": 0.062884,
     "end_time": "2021-04-17T13:09:15.112060",
     "exception": false,
     "start_time": "2021-04-17T13:09:15.049176",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Training configuration\n",
    "EPOCHS = 28\n",
    "WARMUP_STEPS = 500 \n",
    "TRAIN_STEPS = 1000 \n",
    "VERBOSE_FREQ = 100 \n",
    "STEPS_PER_EPOCH = TRAIN_STEPS // VERBOSE_FREQ\n",
    "TOTAL_STEPS = EPOCHS * TRAIN_STEPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "gross-devon",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:15.224104Z",
     "iopub.status.busy": "2021-04-17T13:09:15.223105Z",
     "iopub.status.idle": "2021-04-17T13:09:15.226650Z",
     "shell.execute_reply": "2021-04-17T13:09:15.226104Z"
    },
    "papermill": {
     "duration": 0.062493,
     "end_time": "2021-04-17T13:09:15.226793",
     "exception": false,
     "start_time": "2021-04-17T13:09:15.164300",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def lrfn(step, WARMUP_LR_START, LR_START, LR_FINAL, DECAYS):\n",
    "    # exponential warmup\n",
    "    if step < WARMUP_STEPS:\n",
    "        warmup_factor = (step / WARMUP_STEPS) ** 2\n",
    "        lr = WARMUP_LR_START + (LR_START - WARMUP_LR_START) * warmup_factor\n",
    "    # staircase decay\n",
    "    else:\n",
    "        power = (step - WARMUP_STEPS) // ((TOTAL_STEPS - WARMUP_STEPS) / (DECAYS + 1))\n",
    "        decay_factor =  ((LR_START / LR_FINAL) ** (1 / DECAYS)) ** power\n",
    "        lr = LR_START / decay_factor\n",
    "\n",
    "    return round(lr, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "associate-membership",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:15.372192Z",
     "iopub.status.busy": "2021-04-17T13:09:15.340789Z",
     "iopub.status.idle": "2021-04-17T13:09:15.580678Z",
     "shell.execute_reply": "2021-04-17T13:09:15.580092Z"
    },
    "papermill": {
     "duration": 0.301544,
     "end_time": "2021-04-17T13:09:15.580820",
     "exception": false,
     "start_time": "2021-04-17T13:09:15.279276",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the learning rate schedule\n",
    "def plot_lr_schedule(lr_schedule, name):\n",
    "    plt.figure(figsize=(15,8))\n",
    "    plt.plot(lr_schedule)\n",
    "    schedule_info = f'start: {lr_schedule[0]}, max: {max(lr_schedule)}, final: {lr_schedule[-1]}'\n",
    "    plt.title(f'Step Learning Rate Schedule {name}, {schedule_info}', size=16)\n",
    "    plt.grid()\n",
    "    plt.show()\n",
    "\n",
    "# Learning rate for encoder\n",
    "LR_SCHEDULE = [lrfn(step, 1e-6, 5e-3, 5e-4 ,EPOCHS) for step in range(TOTAL_STEPS)]\n",
    "plot_lr_schedule(LR_SCHEDULE, 'Encoder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "minus-press",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:15.701226Z",
     "iopub.status.busy": "2021-04-17T13:09:15.700516Z",
     "iopub.status.idle": "2021-04-17T13:09:15.703970Z",
     "shell.execute_reply": "2021-04-17T13:09:15.703378Z"
    },
    "papermill": {
     "duration": 0.06959,
     "end_time": "2021-04-17T13:09:15.704116",
     "exception": false,
     "start_time": "2021-04-17T13:09:15.634526",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# converts a dense to a sparse tensor\n",
    "# sparse tensors are required to compute the Levenshtein distance\n",
    "def dense_to_sparse(dense):\n",
    "    ones = tf.ones(dense.shape)\n",
    "    indices = tf.where(ones)\n",
    "    values = tf.gather_nd(dense, indices)\n",
    "    sparse = tf.SparseTensor(indices, values, dense.shape)\n",
    "    \n",
    "    return sparse\n",
    "\n",
    "# computes the levenshtein distance between the predictions and labels\n",
    "def get_levenshtein_distance(preds, lbls):\n",
    "    preds = tf.cast(preds, tf.int64)\n",
    "\n",
    "    preds = tf.where(tf.not_equal(preds, START_TOKEN) & tf.not_equal(preds, END_TOKEN) & tf.not_equal(preds, PAD_TOKEN), preds, y=0)\n",
    "    \n",
    "    lbls = strategy.gather(lbls, axis=0)\n",
    "    lbls = tf.cast(lbls, tf.int64)\n",
    "    lbls = tf.where(tf.not_equal(lbls, START_TOKEN) & tf.not_equal(lbls, END_TOKEN) & tf.not_equal(lbls, PAD_TOKEN), lbls, y=0)\n",
    "    \n",
    "    preds_sparse = dense_to_sparse(preds)\n",
    "    lbls_sparse = dense_to_sparse(lbls)\n",
    "\n",
    "    batch_distance = tf.edit_distance(preds_sparse, lbls_sparse, normalize=False)\n",
    "    mean_distance = tf.math.reduce_mean(batch_distance)\n",
    "    \n",
    "    return mean_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "detailed-orchestra",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:15.824977Z",
     "iopub.status.busy": "2021-04-17T13:09:15.824236Z",
     "iopub.status.idle": "2021-04-17T13:09:15.828289Z",
     "shell.execute_reply": "2021-04-17T13:09:15.827689Z"
    },
    "papermill": {
     "duration": 0.070232,
     "end_time": "2021-04-17T13:09:15.828445",
     "exception": false,
     "start_time": "2021-04-17T13:09:15.758213",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@tf.function()\n",
    "def distributed_train_step(dataset):\n",
    "    # Step function\n",
    "    def train_step(inp, targ):\n",
    "        total_loss = 0.0\n",
    "\n",
    "        with tf.GradientTape() as tape:\n",
    "            enc_output = encoder(inp, training=True)\n",
    "            h, c = decoder.init_hidden_state(enc_output, training=True)\n",
    "            dec_input = tf.expand_dims(targ[:, 0], 1)\n",
    "\n",
    "            # Teacher forcing - feeding the target as the next input\n",
    "            for idx in range(1, SEQ_LEN_OUT):\n",
    "                t = targ[:, idx]\n",
    "                t = tf.reshape(t, [BATCH_SIZE_BASE])\n",
    "                # passing enc_output to the decoder\n",
    "                predictions, h, c = decoder(dec_input, h, c, enc_output, training=True)\n",
    "\n",
    "                # update loss and train metrics\n",
    "                total_loss += loss_function(t, predictions)\n",
    "                train_accuracy.update_state(t, predictions)\n",
    "                # using teacher forcing\n",
    "                dec_input = tf.expand_dims(t, 1)\n",
    "\n",
    "        variables = encoder.trainable_variables + decoder.trainable_variables\n",
    "        gradients = tape.gradient(total_loss, variables)\n",
    "        gradients, _ = tf.clip_by_global_norm(gradients, 10.0)\n",
    "        optimizer.apply_gradients(zip(gradients, variables))\n",
    "\n",
    "        batch_loss = total_loss / (SEQ_LEN_OUT - 1)\n",
    "        train_loss.update_state(batch_loss)\n",
    "    \n",
    "    # reset metrics\n",
    "    train_loss.reset_states()\n",
    "    train_accuracy.reset_states()\n",
    "    # perform VERBOSE_FREQ train steps\n",
    "    for _ in tf.range(tf.convert_to_tensor(VERBOSE_FREQ)):\n",
    "        strategy.run(train_step, args=next(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "liquid-columbia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:15.949318Z",
     "iopub.status.busy": "2021-04-17T13:09:15.948578Z",
     "iopub.status.idle": "2021-04-17T13:09:15.952070Z",
     "shell.execute_reply": "2021-04-17T13:09:15.951499Z"
    },
    "papermill": {
     "duration": 0.067963,
     "end_time": "2021-04-17T13:09:15.952233",
     "exception": false,
     "start_time": "2021-04-17T13:09:15.884270",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def validation_step(inp, targ):\n",
    "    total_loss = 0.0\n",
    "    enc_output = encoder(inp, training=False)\n",
    "    h, c = decoder.init_hidden_state(enc_output, training=False)\n",
    "    dec_input = tf.expand_dims(targ[:, 0], 1)\n",
    "\n",
    "    predictions_seq = tf.expand_dims(targ[:, 0], 1)\n",
    "\n",
    "    # Teacher forcing - feeding the target as the next input\n",
    "    for t in range(1, SEQ_LEN_OUT):\n",
    "        # passing enc_output to the decoder\n",
    "        predictions, h, c = decoder(dec_input, h, c, enc_output, training=False)\n",
    "\n",
    "        # add loss \n",
    "        # update loss and train metrics\n",
    "        total_loss += loss_function(targ[:, t], predictions)\n",
    "        \n",
    "        # add predictions to pred_seq\n",
    "        dec_input = tf.math.argmax(predictions, axis=1, output_type=tf.int32)\n",
    "        dec_input = tf.expand_dims(dec_input, axis=1)\n",
    "        dec_input = tf.cast(dec_input, LABEL_DTYPE)\n",
    "        predictions_seq = tf.concat([predictions_seq, dec_input], axis=1)\n",
    "        \n",
    "    batch_loss = total_loss / (SEQ_LEN_OUT - 1)\n",
    "    val_loss.update_state(batch_loss)\n",
    "    \n",
    "    return predictions_seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "regulated-trunk",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:16.067315Z",
     "iopub.status.busy": "2021-04-17T13:09:16.066582Z",
     "iopub.status.idle": "2021-04-17T13:09:16.069659Z",
     "shell.execute_reply": "2021-04-17T13:09:16.070218Z"
    },
    "papermill": {
     "duration": 0.063291,
     "end_time": "2021-04-17T13:09:16.070388",
     "exception": false,
     "start_time": "2021-04-17T13:09:16.007097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def distributed_val_step(dataset):\n",
    "    inp_val, targ_val = next(dataset)\n",
    "    per_replica_predictions_seq = strategy.run(validation_step, args=(inp_val, targ_val))\n",
    "    predictions_seq = strategy.gather(per_replica_predictions_seq, axis=0)\n",
    "    \n",
    "    return predictions_seq, targ_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "competent-panic",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:16.184136Z",
     "iopub.status.busy": "2021-04-17T13:09:16.183319Z",
     "iopub.status.idle": "2021-04-17T13:09:16.189021Z",
     "shell.execute_reply": "2021-04-17T13:09:16.188484Z"
    },
    "papermill": {
     "duration": 0.06346,
     "end_time": "2021-04-17T13:09:16.189165",
     "exception": false,
     "start_time": "2021-04-17T13:09:16.125705",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_val_metrics(val_dist_dataset):\n",
    "    # reset metrics\n",
    "    val_loss.reset_states()\n",
    "    total_ls_distance = 0.0\n",
    "    \n",
    "    for step in range(VAL_STEPS):\n",
    "        predictions_seq, targ = distributed_val_step(val_dist_dataset)\n",
    "        levenshtein_distance = get_levenshtein_distance(predictions_seq, targ)\n",
    "        total_ls_distance += levenshtein_distance\n",
    "    \n",
    "    return total_ls_distance / VAL_STEPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "limiting-testimony",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:16.309969Z",
     "iopub.status.busy": "2021-04-17T13:09:16.307946Z",
     "iopub.status.idle": "2021-04-17T13:09:16.310737Z",
     "shell.execute_reply": "2021-04-17T13:09:16.311224Z"
    },
    "papermill": {
     "duration": 0.067052,
     "end_time": "2021-04-17T13:09:16.311418",
     "exception": false,
     "start_time": "2021-04-17T13:09:16.244366",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def log(batch, t_start_batch, val_ls_distance=False):\n",
    "    print(\n",
    "        f'Step %s|' % f'{batch * VERBOSE_FREQ}/{TRAIN_STEPS}'.ljust(10, ' '),\n",
    "        f'loss: %.3f,' % (train_loss.result() / VERBOSE_FREQ),\n",
    "        f'acc: %.3f, ' % train_accuracy.result(),\n",
    "    end='')\n",
    "    \n",
    "    if val_ls_distance:\n",
    "        print(\n",
    "            f'val_loss: %.3f, ' % (val_loss.result() / VERBOSE_FREQ),\n",
    "            f'val lsd: %s,' % ('%.1f' % val_ls_distance).ljust(5, ' '),\n",
    "        end='')\n",
    "    # always end with batch duration and line break\n",
    "    print(\n",
    "        f'lr: %s,' % ('%.1E' % LRREDUCE.get_lr()).ljust(7),\n",
    "        f't: %s sec' % int(time.time() - t_start_batch),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "historical-intention",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:16.433665Z",
     "iopub.status.busy": "2021-04-17T13:09:16.432668Z",
     "iopub.status.idle": "2021-04-17T13:09:16.436207Z",
     "shell.execute_reply": "2021-04-17T13:09:16.435571Z"
    },
    "papermill": {
     "duration": 0.067267,
     "end_time": "2021-04-17T13:09:16.436341",
     "exception": false,
     "start_time": "2021-04-17T13:09:16.369074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Stats():\n",
    "    def __init__(self):\n",
    "        self.stats = {\n",
    "            'train_loss': [],\n",
    "            'train_acc': [],\n",
    "        }\n",
    "        \n",
    "    def update_stats(self):\n",
    "        self.stats['train_loss'].append(train_loss.result() / VERBOSE_FREQ)\n",
    "        self.stats['train_acc'].append(train_accuracy.result())\n",
    "        \n",
    "    def get_stats(self, metric):\n",
    "        return self.stats[metric]\n",
    "        \n",
    "    def plot_stat(self, metric):\n",
    "        plt.figure(figsize=(15,8))\n",
    "        plt.xticks(fontsize=16)\n",
    "        plt.yticks(fontsize=16)\n",
    "        plt.plot(self.stats[metric])\n",
    "        plt.grid()\n",
    "        plt.title(f'{metric} stats', size=24)\n",
    "        plt.show()\n",
    "        \n",
    "STATS = Stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "horizontal-capacity",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:16.556511Z",
     "iopub.status.busy": "2021-04-17T13:09:16.555271Z",
     "iopub.status.idle": "2021-04-17T13:09:16.589063Z",
     "shell.execute_reply": "2021-04-17T13:09:16.588342Z"
    },
    "papermill": {
     "duration": 0.096899,
     "end_time": "2021-04-17T13:09:16.589210",
     "exception": false,
     "start_time": "2021-04-17T13:09:16.492311",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# custom learning rate scheduler\n",
    "class LRReduce():\n",
    "    def __init__(self, optimizer, lr_schedule):\n",
    "        self.opt = optimizer\n",
    "        self.lr_schedule = lr_schedule\n",
    "        # assign initial learning rate\n",
    "        self.lr = lr_schedule[0]\n",
    "        self.opt.learning_rate.assign(self.lr)\n",
    "        \n",
    "    def step(self, step):\n",
    "        self.lr = self.lr_schedule[step]\n",
    "        # assign learning rate to optimizer\n",
    "        self.opt.learning_rate.assign(self.lr)\n",
    "        \n",
    "    def get_counter(self):\n",
    "        return self.c\n",
    "    \n",
    "    def get_lr(self):\n",
    "        return self.lr\n",
    "        \n",
    "LRREDUCE = LRReduce(optimizer, LR_SCHEDULE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "obvious-force",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T13:09:16.714308Z",
     "iopub.status.busy": "2021-04-17T13:09:16.713246Z",
     "iopub.status.idle": "2021-04-17T20:50:48.812877Z",
     "shell.execute_reply": "2021-04-17T20:50:48.813408Z"
    },
    "papermill": {
     "duration": 27692.168723,
     "end_time": "2021-04-17T20:50:48.813589",
     "exception": false,
     "start_time": "2021-04-17T13:09:16.644866",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** EPOCH 1 *****\n",
      "Step 100/1000  | loss: 3.696, acc: 0.047, lr: 1.0E-06, t: 440 sec\n",
      "Step 200/1000  | loss: 1.557, acc: 0.566, lr: 2.0E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.822, acc: 0.707, lr: 7.9E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.640, acc: 0.758, lr: 1.8E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.537, acc: 0.795, lr: 3.2E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.447, acc: 0.828, lr: 5.0E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.369, acc: 0.858, lr: 5.0E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.328, acc: 0.873, lr: 5.0E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.301, acc: 0.883, lr: 5.0E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.278, acc: 0.892, val_loss: 0.997,  val lsd: 66.4 ,lr: 5.0E-03, t: 152 sec\n",
      "Epoch 0 Loss 0.897, time: 1366 sec\n",
      "\n",
      "***** EPOCH 2 *****\n",
      "Step 100/1000  | loss: 0.261, acc: 0.899, lr: 5.0E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.243, acc: 0.906, lr: 5.0E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.230, acc: 0.911, lr: 5.0E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.218, acc: 0.915, lr: 5.0E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.208, acc: 0.919, lr: 5.0E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.196, acc: 0.924, lr: 4.6E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.189, acc: 0.927, lr: 4.6E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.182, acc: 0.929, lr: 4.6E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.175, acc: 0.932, lr: 4.6E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.169, acc: 0.935, val_loss: 0.954,  val lsd: 49.4 ,lr: 4.6E-03, t: 102 sec\n",
      "Epoch 1 Loss 0.207, time: 975 sec\n",
      "\n",
      "***** EPOCH 3 *****\n",
      "Step 100/1000  | loss: 0.164, acc: 0.936, lr: 4.6E-03, t: 97 sec\n",
      "Step 200/1000  | loss: 0.158, acc: 0.939, lr: 4.6E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.156, acc: 0.940, lr: 4.6E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.151, acc: 0.942, lr: 4.6E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.143, acc: 0.945, lr: 4.2E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.139, acc: 0.946, lr: 4.2E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.138, acc: 0.947, lr: 4.2E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.134, acc: 0.948, lr: 4.2E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.130, acc: 0.950, lr: 4.2E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.128, acc: 0.951, val_loss: 0.846,  val lsd: 40.5 ,lr: 4.2E-03, t: 102 sec\n",
      "Epoch 2 Loss 0.144, time: 974 sec\n",
      "\n",
      "***** EPOCH 4 *****\n",
      "Step 100/1000  | loss: 0.126, acc: 0.952, lr: 4.2E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.123, acc: 0.953, lr: 4.2E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.119, acc: 0.954, lr: 4.2E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.118, acc: 0.955, lr: 4.2E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.114, acc: 0.956, lr: 3.9E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.111, acc: 0.957, lr: 3.9E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.110, acc: 0.958, lr: 3.9E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.108, acc: 0.958, lr: 3.9E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.105, acc: 0.960, lr: 3.9E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.103, acc: 0.960, val_loss: 0.856,  val lsd: 37.3 ,lr: 3.9E-03, t: 103 sec\n",
      "Epoch 3 Loss 0.114, time: 976 sec\n",
      "\n",
      "***** EPOCH 5 *****\n",
      "Step 100/1000  | loss: 0.104, acc: 0.960, lr: 3.9E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.101, acc: 0.961, lr: 3.9E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.101, acc: 0.961, lr: 3.9E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.096, acc: 0.963, lr: 3.6E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.094, acc: 0.964, lr: 3.6E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.094, acc: 0.964, lr: 3.6E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.093, acc: 0.964, lr: 3.6E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.091, acc: 0.965, lr: 3.6E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.088, acc: 0.966, lr: 3.6E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.090, acc: 0.966, val_loss: 0.677,  val lsd: 29.4 ,lr: 3.6E-03, t: 102 sec\n",
      "Epoch 4 Loss 0.095, time: 974 sec\n",
      "\n",
      "***** EPOCH 6 *****\n",
      "Step 100/1000  | loss: 0.089, acc: 0.966, lr: 3.6E-03, t: 97 sec\n",
      "Step 200/1000  | loss: 0.085, acc: 0.967, lr: 3.6E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.087, acc: 0.967, lr: 3.6E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.083, acc: 0.968, lr: 3.3E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.081, acc: 0.969, lr: 3.3E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.079, acc: 0.970, lr: 3.3E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.080, acc: 0.969, lr: 3.3E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.078, acc: 0.970, lr: 3.3E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.076, acc: 0.971, lr: 3.3E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.115, acc: 0.958, val_loss: 0.740,  val lsd: 30.3 ,lr: 3.3E-03, t: 102 sec\n",
      "Epoch 5 Loss 0.085, time: 974 sec\n",
      "\n",
      "***** EPOCH 7 *****\n",
      "Step 100/1000  | loss: 0.081, acc: 0.969, lr: 3.3E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.078, acc: 0.970, lr: 3.3E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.075, acc: 0.971, lr: 3.1E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.073, acc: 0.972, lr: 3.1E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.073, acc: 0.972, lr: 3.1E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.069, acc: 0.974, lr: 3.1E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.070, acc: 0.973, lr: 3.1E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.069, acc: 0.974, lr: 3.1E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.068, acc: 0.974, lr: 3.1E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.068, acc: 0.974, val_loss: 0.642,  val lsd: 25.0 ,lr: 3.1E-03, t: 102 sec\n",
      "Epoch 6 Loss 0.072, time: 975 sec\n",
      "\n",
      "***** EPOCH 8 *****\n",
      "Step 100/1000  | loss: 0.068, acc: 0.974, lr: 3.1E-03, t: 99 sec\n",
      "Step 200/1000  | loss: 0.065, acc: 0.975, lr: 3.1E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.064, acc: 0.976, lr: 2.8E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.064, acc: 0.976, lr: 2.8E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.065, acc: 0.976, lr: 2.8E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.063, acc: 0.976, lr: 2.8E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.064, acc: 0.976, lr: 2.8E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.065, acc: 0.976, lr: 2.8E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.061, acc: 0.977, lr: 2.8E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.061, acc: 0.977, val_loss: 0.630,  val lsd: 23.5 ,lr: 2.8E-03, t: 103 sec\n",
      "Epoch 7 Loss 0.064, time: 977 sec\n",
      "\n",
      "***** EPOCH 9 *****\n",
      "Step 100/1000  | loss: 0.062, acc: 0.977, lr: 2.8E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.059, acc: 0.978, lr: 2.6E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.058, acc: 0.978, lr: 2.6E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.059, acc: 0.978, lr: 2.6E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.057, acc: 0.978, lr: 2.6E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.056, acc: 0.979, lr: 2.6E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.056, acc: 0.979, lr: 2.6E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.054, acc: 0.980, lr: 2.6E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.059, acc: 0.978, lr: 2.6E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.057, acc: 0.979, val_loss: 0.750,  val lsd: 27.6 ,lr: 2.6E-03, t: 102 sec\n",
      "Epoch 8 Loss 0.058, time: 975 sec\n",
      "\n",
      "***** EPOCH 10 *****\n",
      "Step 100/1000  | loss: 0.055, acc: 0.979, lr: 2.6E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.052, acc: 0.980, lr: 2.4E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.053, acc: 0.980, lr: 2.4E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.051, acc: 0.981, lr: 2.4E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.051, acc: 0.981, lr: 2.4E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.051, acc: 0.981, lr: 2.4E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.051, acc: 0.981, lr: 2.4E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.049, acc: 0.981, lr: 2.4E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.050, acc: 0.981, lr: 2.4E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.052, acc: 0.981, val_loss: 0.500,  val lsd: 18.7 ,lr: 2.4E-03, t: 102 sec\n",
      "Epoch 9 Loss 0.052, time: 974 sec\n",
      "\n",
      "***** EPOCH 11 *****\n",
      "Step 100/1000  | loss: 0.049, acc: 0.982, lr: 2.2E-03, t: 97 sec\n",
      "Step 200/1000  | loss: 0.047, acc: 0.982, lr: 2.2E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.047, acc: 0.982, lr: 2.2E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.048, acc: 0.982, lr: 2.2E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.047, acc: 0.983, lr: 2.2E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.047, acc: 0.983, lr: 2.2E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.049, acc: 0.982, lr: 2.2E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.046, acc: 0.983, lr: 2.2E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.045, acc: 0.983, lr: 2.2E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.045, acc: 0.983, val_loss: 0.481,  val lsd: 17.9 ,lr: 2.2E-03, t: 102 sec\n",
      "Epoch 10 Loss 0.047, time: 974 sec\n",
      "\n",
      "***** EPOCH 12 *****\n",
      "Step 100/1000  | loss: 0.044, acc: 0.983, lr: 2.0E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.043, acc: 0.984, lr: 2.0E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.043, acc: 0.984, lr: 2.0E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.042, acc: 0.984, lr: 2.0E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.042, acc: 0.984, lr: 2.0E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.042, acc: 0.984, lr: 2.0E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.042, acc: 0.984, lr: 2.0E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.041, acc: 0.985, lr: 2.0E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.041, acc: 0.985, lr: 2.0E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.041, acc: 0.985, val_loss: 0.438,  val lsd: 15.7 ,lr: 1.9E-03, t: 103 sec\n",
      "Epoch 11 Loss 0.042, time: 975 sec\n",
      "\n",
      "***** EPOCH 13 *****\n",
      "Step 100/1000  | loss: 0.042, acc: 0.984, lr: 1.9E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.039, acc: 0.985, lr: 1.9E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.040, acc: 0.985, lr: 1.9E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.039, acc: 0.985, lr: 1.9E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.039, acc: 0.985, lr: 1.9E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.038, acc: 0.986, lr: 1.9E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.038, acc: 0.986, lr: 1.9E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.038, acc: 0.986, lr: 1.9E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.037, acc: 0.986, lr: 1.9E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.038, acc: 0.986, val_loss: 0.453,  val lsd: 15.6 ,lr: 1.7E-03, t: 102 sec\n",
      "Epoch 12 Loss 0.039, time: 974 sec\n",
      "\n",
      "***** EPOCH 14 *****\n",
      "Step 100/1000  | loss: 0.037, acc: 0.986, lr: 1.7E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.036, acc: 0.987, lr: 1.7E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.040, acc: 0.985, lr: 1.7E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.036, acc: 0.987, lr: 1.7E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.035, acc: 0.987, lr: 1.7E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.035, acc: 0.987, lr: 1.7E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.034, acc: 0.987, lr: 1.7E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.034, acc: 0.987, lr: 1.7E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.034, acc: 0.987, lr: 1.6E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.034, acc: 0.987, val_loss: 0.432,  val lsd: 14.9 ,lr: 1.6E-03, t: 102 sec\n",
      "Epoch 13 Loss 0.036, time: 974 sec\n",
      "\n",
      "***** EPOCH 15 *****\n",
      "Step 100/1000  | loss: 0.034, acc: 0.987, lr: 1.6E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.033, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.033, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.033, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.033, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.032, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.033, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.032, acc: 0.988, lr: 1.6E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.032, acc: 0.988, lr: 1.5E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.031, acc: 0.988, val_loss: 0.425,  val lsd: 14.5 ,lr: 1.5E-03, t: 102 sec\n",
      "Epoch 14 Loss 0.033, time: 974 sec\n",
      "\n",
      "***** EPOCH 16 *****\n",
      "Step 100/1000  | loss: 0.032, acc: 0.988, lr: 1.5E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.031, acc: 0.988, lr: 1.5E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.031, acc: 0.988, lr: 1.5E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.031, acc: 0.988, lr: 1.5E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.031, acc: 0.989, lr: 1.5E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.030, acc: 0.989, lr: 1.5E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.031, acc: 0.988, lr: 1.5E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.032, acc: 0.988, lr: 1.3E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.029, acc: 0.989, lr: 1.3E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.030, acc: 0.989, val_loss: 0.402,  val lsd: 13.2 ,lr: 1.3E-03, t: 103 sec\n",
      "Epoch 15 Loss 0.031, time: 975 sec\n",
      "\n",
      "***** EPOCH 17 *****\n",
      "Step 100/1000  | loss: 0.029, acc: 0.989, lr: 1.3E-03, t: 97 sec\n",
      "Step 200/1000  | loss: 0.028, acc: 0.989, lr: 1.3E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.029, acc: 0.989, lr: 1.3E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.028, acc: 0.989, lr: 1.3E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.028, acc: 0.989, lr: 1.3E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.027, acc: 0.990, lr: 1.3E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.031, acc: 0.989, lr: 1.3E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.027, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.027, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.028, acc: 0.990, val_loss: 0.393,  val lsd: 12.8 ,lr: 1.2E-03, t: 102 sec\n",
      "Epoch 16 Loss 0.028, time: 974 sec\n",
      "\n",
      "***** EPOCH 18 *****\n",
      "Step 100/1000  | loss: 0.027, acc: 0.990, lr: 1.2E-03, t: 97 sec\n",
      "Step 200/1000  | loss: 0.027, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.026, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.026, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.026, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.025, acc: 0.990, lr: 1.2E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.026, acc: 0.990, lr: 1.1E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.025, acc: 0.991, lr: 1.1E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.025, acc: 0.990, lr: 1.1E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.026, acc: 0.990, val_loss: 0.421,  val lsd: 13.5 ,lr: 1.1E-03, t: 102 sec\n",
      "Epoch 17 Loss 0.026, time: 974 sec\n",
      "\n",
      "***** EPOCH 19 *****\n",
      "Step 100/1000  | loss: 0.025, acc: 0.990, lr: 1.1E-03, t: 98 sec\n",
      "Step 200/1000  | loss: 0.024, acc: 0.991, lr: 1.1E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.025, acc: 0.991, lr: 1.1E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.026, acc: 0.990, lr: 1.1E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.025, acc: 0.991, lr: 1.1E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.024, acc: 0.991, lr: 1.1E-03, t: 96 sec\n",
      "Step 700/1000  | loss: 0.024, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 800/1000  | loss: 0.024, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 900/1000  | loss: 0.024, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.025, acc: 0.991, val_loss: 0.392,  val lsd: 12.5 ,lr: 1.0E-03, t: 102 sec\n",
      "Epoch 18 Loss 0.025, time: 974 sec\n",
      "\n",
      "***** EPOCH 20 *****\n",
      "Step 100/1000  | loss: 0.024, acc: 0.991, lr: 1.0E-03, t: 97 sec\n",
      "Step 200/1000  | loss: 0.023, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 300/1000  | loss: 0.023, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 400/1000  | loss: 0.023, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 500/1000  | loss: 0.023, acc: 0.991, lr: 1.0E-03, t: 96 sec\n",
      "Step 600/1000  | loss: 0.022, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.022, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.022, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.022, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.023, acc: 0.991, val_loss: 0.377,  val lsd: 11.8 ,lr: 9.7E-04, t: 103 sec\n",
      "Epoch 19 Loss 0.023, time: 975 sec\n",
      "\n",
      "***** EPOCH 21 *****\n",
      "Step 100/1000  | loss: 0.022, acc: 0.992, lr: 9.7E-04, t: 98 sec\n",
      "Step 200/1000  | loss: 0.022, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.021, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.021, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.021, acc: 0.992, lr: 9.7E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.021, acc: 0.992, lr: 8.9E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.021, acc: 0.992, lr: 8.9E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.021, acc: 0.992, lr: 8.9E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.020, acc: 0.992, lr: 8.9E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.023, acc: 0.992, val_loss: 0.396,  val lsd: 11.9 ,lr: 8.9E-04, t: 101 sec\n",
      "Epoch 20 Loss 0.021, time: 974 sec\n",
      "\n",
      "***** EPOCH 22 *****\n",
      "Step 100/1000  | loss: 0.021, acc: 0.992, lr: 8.9E-04, t: 97 sec\n",
      "Step 200/1000  | loss: 0.021, acc: 0.992, lr: 8.9E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.020, acc: 0.992, lr: 8.9E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.020, acc: 0.993, lr: 8.9E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.020, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.020, acc: 0.992, val_loss: 0.375,  val lsd: 11.2 ,lr: 8.2E-04, t: 102 sec\n",
      "Epoch 21 Loss 0.02, time: 974 sec\n",
      "\n",
      "***** EPOCH 23 *****\n",
      "Step 100/1000  | loss: 0.020, acc: 0.992, lr: 8.2E-04, t: 97 sec\n",
      "Step 200/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.019, acc: 0.993, lr: 8.2E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.019, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.019, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.019, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.018, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.018, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.019, acc: 0.993, val_loss: 0.373,  val lsd: 10.9 ,lr: 7.5E-04, t: 102 sec\n",
      "Epoch 22 Loss 0.019, time: 974 sec\n",
      "\n",
      "***** EPOCH 24 *****\n",
      "Step 100/1000  | loss: 0.019, acc: 0.993, lr: 7.5E-04, t: 97 sec\n",
      "Step 200/1000  | loss: 0.018, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.018, acc: 0.993, lr: 7.5E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.018, acc: 0.993, lr: 6.9E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.018, acc: 0.993, lr: 6.9E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.017, acc: 0.994, lr: 6.9E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.017, acc: 0.993, lr: 6.9E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.017, acc: 0.993, lr: 6.9E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.017, acc: 0.993, lr: 6.9E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.018, acc: 0.993, val_loss: 0.407,  val lsd: 12.1 ,lr: 6.9E-04, t: 103 sec\n",
      "Epoch 23 Loss 0.018, time: 975 sec\n",
      "\n",
      "***** EPOCH 25 *****\n",
      "Step 100/1000  | loss: 0.018, acc: 0.993, lr: 6.9E-04, t: 98 sec\n",
      "Step 200/1000  | loss: 0.017, acc: 0.994, lr: 6.9E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.017, acc: 0.994, lr: 6.9E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.017, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.016, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.016, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.017, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.016, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.016, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.017, acc: 0.994, val_loss: 0.382,  val lsd: 11.0 ,lr: 6.4E-04, t: 102 sec\n",
      "Epoch 24 Loss 0.017, time: 974 sec\n",
      "\n",
      "***** EPOCH 26 *****\n",
      "Step 100/1000  | loss: 0.017, acc: 0.994, lr: 6.4E-04, t: 98 sec\n",
      "Step 200/1000  | loss: 0.016, acc: 0.994, lr: 6.4E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.016, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.016, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.016, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.015, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.016, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.015, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.015, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.016, acc: 0.994, val_loss: 0.372,  val lsd: 10.5 ,lr: 5.9E-04, t: 102 sec\n",
      "Epoch 25 Loss 0.016, time: 974 sec\n",
      "\n",
      "***** EPOCH 27 *****\n",
      "Step 100/1000  | loss: 0.016, acc: 0.994, lr: 5.9E-04, t: 98 sec\n",
      "Step 200/1000  | loss: 0.015, acc: 0.994, lr: 5.9E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.014, acc: 0.994, lr: 5.4E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.016, acc: 0.994, val_loss: 0.379,  val lsd: 10.3 ,lr: 5.4E-04, t: 101 sec\n",
      "Epoch 26 Loss 0.015, time: 974 sec\n",
      "\n",
      "***** EPOCH 28 *****\n",
      "Step 100/1000  | loss: 0.015, acc: 0.994, lr: 5.4E-04, t: 97 sec\n",
      "Step 200/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 300/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 400/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 500/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 600/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 700/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 800/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 900/1000  | loss: 0.014, acc: 0.995, lr: 5.0E-04, t: 96 sec\n",
      "Step 1000/1000 | loss: 0.014, acc: 0.995, val_loss: 0.372,  val lsd: 10.1 ,lr: 5.0E-04, t: 102 sec\n",
      "Epoch 27 Loss 0.014, time: 975 sec\n",
      "\n"
     ]
    }
   ],
   "source": [
    "step_total = 0\n",
    "for epoch in range(EPOCHS):\n",
    "    print(f'***** EPOCH {epoch + 1} *****')\n",
    "    \n",
    "    t_start = time.time()\n",
    "    t_start_batch = time.time()\n",
    "    total_loss = 0\n",
    "    \n",
    "    # create distributed versions of dataset\n",
    "    train_dist_dataset = iter(strategy.experimental_distribute_dataset(train_dataset))\n",
    "    val_dist_dataset = iter(strategy.experimental_distribute_dataset(val_dataset))\n",
    "\n",
    "    for step in range(1, STEPS_PER_EPOCH + 1):\n",
    "        # train step\n",
    "        distributed_train_step(train_dist_dataset)\n",
    "        STATS.update_stats()\n",
    "        \n",
    "        # end of epoch validation\n",
    "        if step == STEPS_PER_EPOCH:\n",
    "            val_ls_distance = get_val_metrics(val_dist_dataset)\n",
    "            # log with validation\n",
    "            log(step, t_start_batch, val_ls_distance)\n",
    "        else:\n",
    "            # normal log\n",
    "            log(step, t_start_batch)\n",
    "            # reset start time batch\n",
    "            t_start_batch = time.time()\n",
    "            \n",
    "        total_loss += train_loss.result()\n",
    "        \n",
    "        # learning rate step\n",
    "        LRREDUCE.step(epoch * TRAIN_STEPS + step * VERBOSE_FREQ - 1)\n",
    "        \n",
    "        # stop training when NaN loss is detected, this can be caused by exploding gradients\n",
    "        if np.isnan(total_loss):\n",
    "            break\n",
    "            \n",
    "    # stop training when NaN loss is detected\n",
    "    if np.isnan(total_loss):\n",
    "        break\n",
    "\n",
    "    print(f'Epoch {epoch} Loss {round(total_loss.numpy() / TRAIN_STEPS, 3)}, time: {int(time.time() - t_start)} sec\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "extensive-consumer",
   "metadata": {
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     "status": "completed"
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   },
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "Unable to create file (unable to open file: name = '/content/drive/MyDrive/Kaggle/encoder.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 242)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-c262ba4d17cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# save weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Kaggle/encoder.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/Kaggle/decoder.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36msave_weights\u001b[0;34m(self, filepath, overwrite, save_format, options)\u001b[0m\n\u001b[1;32m   2105\u001b[0m         \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2106\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msave_format\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'h5'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2107\u001b[0;31m       \u001b[0;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2108\u001b[0m         \u001b[0mhdf5_format\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_weights_to_hdf5_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2109\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)\u001b[0m\n\u001b[1;32m    406\u001b[0m                 fid = make_fid(name, mode, userblock_size,\n\u001b[1;32m    407\u001b[0m                                \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m                                swmr=swmr)\n\u001b[0m\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_EXCL\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'w'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_TRUNC\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;31m# Open in append mode (read/write).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.create\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: Unable to create file (unable to open file: name = '/content/drive/MyDrive/Kaggle/encoder.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 242)"
     ]
    }
   ],
   "source": [
    "# save weights\n",
    "encoder.save_weights('/content/drive/MyDrive/Kaggle/encoder.h5')\n",
    "decoder.save_weights('/content/drive/MyDrive/Kaggle/decoder.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "located-speech",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "STATS.plot_stat('train_loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "opened-dispute",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T20:50:51.336358Z",
     "iopub.status.busy": "2021-04-17T20:50:51.335633Z",
     "iopub.status.idle": "2021-04-17T20:50:52.328770Z",
     "shell.execute_reply": "2021-04-17T20:50:52.328113Z"
    },
    "papermill": {
     "duration": 1.186693,
     "end_time": "2021-04-17T20:50:52.328923",
     "exception": false,
     "start_time": "2021-04-17T20:50:51.142230",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "STATS.plot_stat('train_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "apparent-discretion",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T20:50:52.625107Z",
     "iopub.status.busy": "2021-04-17T20:50:52.624343Z",
     "iopub.status.idle": "2021-04-17T20:50:52.630737Z",
     "shell.execute_reply": "2021-04-17T20:50:52.629987Z"
    },
    "papermill": {
     "duration": 0.154584,
     "end_time": "2021-04-17T20:50:52.630887",
     "exception": false,
     "start_time": "2021-04-17T20:50:52.476303",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# convert the integer encoded predictions to a string\n",
    "def int2char(i_str):\n",
    "    res = ''\n",
    "    for i in i_str.numpy():\n",
    "        c = int_to_vocabulary.get(i)\n",
    "        if c not in ['<start>', '<end>', '<pad>']:\n",
    "            res += c\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "integral-gazette",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-17T20:50:52.938212Z",
     "iopub.status.busy": "2021-04-17T20:50:52.937513Z",
     "iopub.status.idle": "2021-04-17T20:51:47.482325Z",
     "shell.execute_reply": "2021-04-17T20:51:47.482840Z"
    },
    "papermill": {
     "duration": 54.704761,
     "end_time": "2021-04-17T20:51:47.483049",
     "exception": false,
     "start_time": "2021-04-17T20:50:52.778288",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1bfa3e72e49402f9c324b362fc4f852",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: \tD,,cOm/,9,nS,46Ol6OO6O76O4676O2BO7PO86,8B,7P,2BOlP,l6,O6l6OsB,tPO,6,sB,mbOtP,,6t6Om646O6,6s628B,OP,2BO4P,l6,,nrO6,bm6O,b,l628cb2c,\n",
      "actual: \tDO2c4/49,nSOm67626sB46OlPO6,6O8B7P,86O26OOBO7PtBO4Pm6lBOsPO,BO2POtnrO6mb,8c\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fd4f38da5d44e20848d9f7f209960af",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: \tD2Oc289m(,1,nSO62t6,s6O,64626t6,8B,sP,lB27P2m62OB2tP2s6Ot6,,6,O6l6m6s6O462,B,Obm86,t6Om676O76m86,tP,76,46OO6,6O86,m6,2B2OPO26Os628B24P2lB,mP,snr2b4b7blbOO6O7b,,b28cb,bmbsbtbO8bOt6Olc,bOc2bBcb2mb27P\n",
      "actual: \tD,sc,492(,1,nSO62O6,O6OO6m626l6OlB,OP,76,sB28P,l6Os6Ot6O76t6,646O,6,,BO7P226,4BOtP,t6Om6s6O86,8B,tP,mB,lP,26O2676O462,6,2nr26mbs67bl6OObO26O4b,mcb,b4btbO,bOsc,bOc2bBcb,7b28P\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d69966c98ad1479aadabc54b7a02dc2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted: \tD2mc28D592(71nSO676m462mBm,P,l6OtB,P2762462tB2OB,lP,s6Os6,8B2l62Pt6OO6,4B,sP2sP22Bm8P,t6O46,,6l6O,6,7Bms6,mP,O6O86O26,2B2,BmOPmm6sPOlBmP,tB,OP28B,,Pm76O7B4Pm8nrt6Osb2,cb7bO7c,bO6sc2n+,l6Os6n02,6b246nH\n",
      "actual: \tD2Oc,7D59,(71nSO6s6m8628B27P,s6O7B2P2262O62mB,7B,sP,,6Om6OlB2,Pt6OO6,2B,,P2t6mP,tB24P,4Bm,62OPO46,86l6O,6,mBmO6,8P,O6O8676OtBO26OsB,OP,P,lB2sP2l64nr76O4b,7cbsc,bO64c2n+,46O46n0,76nH8nhO\n"
     ]
    }
   ],
   "source": [
    "def evaluate(img, actual=None):\n",
    "    # get encoder output and initiate LSTM hidden and carry state\n",
    "    enc_out = encoder(tf.expand_dims(img, axis=0), training=False)\n",
    "    h, c = decoder.init_hidden_state(enc_out, training=False)\n",
    "    \n",
    "    # the \"<start>\" token is used as first character when predicting\n",
    "    dec_input = tf.expand_dims([vocabulary_to_int.get('<start>')], 0)\n",
    "    result = ''\n",
    "    \n",
    "    for t in tqdm(range(SEQ_LEN_OUT)):\n",
    "        predictions, h, c = decoder(dec_input, h, c, enc_out, training=False)\n",
    "        predicted_id = tf.argmax(predictions[0]).numpy()\n",
    "        predicted_char = int_to_vocabulary.get(predicted_id)\n",
    "\n",
    "        # stop predicting when \"<end>\" token is predicted\n",
    "        if predicted_char == '<end>':\n",
    "            break\n",
    "        \n",
    "        # add every character except \"<start>\"\n",
    "        if result != '<start>':\n",
    "            result += predicted_char\n",
    "\n",
    "        # predicted charachter is used as input to the decoder to predict the next character\n",
    "        dec_input = tf.expand_dims([predicted_id], 0)\n",
    "    \n",
    "    # plot the molecule image\n",
    "    plt.figure(figsize=(7, 4))\n",
    "    plt.imshow(img.numpy().astype(np.float32))\n",
    "    plt.show()\n",
    "    print(f'predicted: \\t{result}')\n",
    "    print(f'actual: \\t{int2char(actual)}')\n",
    "\n",
    "for n in range(3):\n",
    "    evaluate(val_imgs[n], actual=val_lbls[n])"
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