diff --git "a/deepl_ner.ipynb" "b/deepl_ner.ipynb"
--- "a/deepl_ner.ipynb"
+++ "b/deepl_ner.ipynb"
@@ -21,11 +21,28 @@
     "Since the goal is to extract the locations from a user's query, the times in which random sentences might be inputted should be accounted for. Therefore, to stay rational with the frequency of that happening, the ratio will be $1:10$ for the lambda sentences which will be extracted from `data/french_text/1k_unlabeled_samples.txt`. In addition, more complex sentences where the **departure** and **arrival** locations are not in the same sentence. Their ratio will be the same as the unlabeled sentences.\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sentence Loading and Preprocessing\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 321,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[nltk_data] Downloading package punkt_tab to /Users/az-r-\n",
+      "[nltk_data]     ow/nltk_data...\n",
+      "[nltk_data]   Package punkt_tab is already up-to-date!\n"
+     ]
+    }
+   ],
    "source": [
     "from app.travel_resolver.libs.nlp import data_processing as dp\n",
     "\n",
@@ -36,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 322,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 324,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -123,12 +140,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -148,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,9 +177,171 @@
     "MAX_LEN = 100"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Stanza Pipeline for POS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-29 19:18:12 INFO: Checking for updates to resources.json in case models have been updated.  Note: this behavior can be turned off with download_method=None or download_method=DownloadMethod.REUSE_RESOURCES\n",
+      "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/main/resources_1.9.0.json: 392kB [00:00, 172MB/s]                     \n",
+      "2024-12-29 19:18:12 INFO: Downloaded file to /Users/az-r-ow/stanza_resources/resources.json\n",
+      "2024-12-29 19:18:12 WARNING: Language fr package default expects mwt, which has been added\n",
+      "2024-12-29 19:18:12 INFO: Loading these models for language: fr (French):\n",
+      "===============================\n",
+      "| Processor | Package         |\n",
+      "-------------------------------\n",
+      "| tokenize  | combined        |\n",
+      "| mwt       | combined        |\n",
+      "| pos       | combined_charlm |\n",
+      "===============================\n",
+      "\n",
+      "2024-12-29 19:18:12 WARNING: GPU requested, but is not available!\n",
+      "2024-12-29 19:18:12 INFO: Using device: cpu\n",
+      "2024-12-29 19:18:12 INFO: Loading: tokenize\n",
+      "/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/stanza/models/tokenization/trainer.py:82: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  checkpoint = torch.load(filename, lambda storage, loc: storage)\n",
+      "2024-12-29 19:18:12 INFO: Loading: mwt\n",
+      "/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/stanza/models/mwt/trainer.py:201: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  checkpoint = torch.load(filename, lambda storage, loc: storage)\n",
+      "2024-12-29 19:18:12 INFO: Loading: pos\n",
+      "/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/stanza/models/pos/trainer.py:139: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  checkpoint = torch.load(filename, lambda storage, loc: storage)\n",
+      "/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/stanza/models/common/pretrain.py:56: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  data = torch.load(self.filename, lambda storage, loc: storage)\n",
+      "/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/stanza/models/common/char_model.py:271: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  state = torch.load(filename, lambda storage, loc: storage)\n",
+      "2024-12-29 19:18:13 INFO: Done loading processors!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import stanza\n",
+    "\n",
+    "nlp = stanza.Pipeline(\n",
+    "    \"fr\", processors=\"tokenize,pos\", use_gpu=True, pos_batch_size=3000\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = [stanza.Document([], text=sentence) for sentence in sentences]\n",
+    "\n",
+    "pos_tags_docs = nlp(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_sentences_pos_tags(sentences: list[str]):\n",
+    "    \"\"\"\n",
+    "    Get the POS tags of the words in the sentences.\n",
+    "\n",
+    "    Args:\n",
+    "    sentences (list): List of sentences to get the POS tags from.\n",
+    "\n",
+    "    Returns:\n",
+    "    pos_tags (list): List of POS tags for each sentence.\n",
+    "    \"\"\"\n",
+    "    docs = [stanza.Document([], text=sentence) for sentence in sentences]\n",
+    "    pos_tags_docs = nlp(docs)\n",
+    "\n",
+    "    sentences_pos_tags = []\n",
+    "    for doc in pos_tags_docs:\n",
+    "        sentences_pos_tags.append([word.upos for word in doc.sentences[0].words])\n",
+    "\n",
+    "    return sentences_pos_tags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos_tags_sentences = []\n",
+    "\n",
+    "for doc in pos_tags_docs:\n",
+    "    pos_tags_sentences.append([word.upos for word in doc.sentences[0].words])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_pos_tags = []\n",
+    "\n",
+    "for doc in pos_tags_docs:\n",
+    "    pos_tags = [word.upos for sent in doc.sentences for word in sent.words]\n",
+    "    all_pos_tags.extend(pos_tags)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ADJ',\n",
+       " 'ADP',\n",
+       " 'ADV',\n",
+       " 'AUX',\n",
+       " 'CCONJ',\n",
+       " 'DET',\n",
+       " 'INTJ',\n",
+       " 'NOUN',\n",
+       " 'NUM',\n",
+       " 'PRON',\n",
+       " 'PROPN',\n",
+       " 'PUNCT',\n",
+       " 'SCONJ',\n",
+       " 'SYM',\n",
+       " 'VERB',\n",
+       " 'X']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "unique_pos_tags = sorted(set(all_pos_tags))\n",
+    "\n",
+    "unique_pos_tags"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating inputs\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 329,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +371,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 330,
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_and_pad_sentence_pos(\n",
+    "    sentence_pos: str, pos_tags: list[str] = unique_pos_tags, max_length: int = MAX_LEN\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Encode a sentence into a list of integers\n",
+    "\n",
+    "    Parameters:\n",
+    "    sentence (str): The sentence to encode\n",
+    "    pos_tags (list): The vocabulary\n",
+    "\n",
+    "    Returns:\n",
+    "    list: The list of integers\n",
+    "    \"\"\"\n",
+    "    encoded_sentence = [pos_tags.index(pos) for pos in sentence_pos]\n",
+    "\n",
+    "    return tf.keras.utils.pad_sequences(\n",
+    "        [encoded_sentence], maxlen=max_length, padding=\"post\", value=0\n",
+    "    )[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 331,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +428,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pos_tags_sentences = [\n",
+    "    encode_and_pad_sentence_pos(sentence_pos) for sentence_pos in pos_tags_sentences\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -234,9 +450,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 334,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-29 19:19:50.647172: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
+     ]
+    }
+   ],
    "source": [
     "dataset = tf.data.Dataset.from_tensor_slices((encoded_sentences, padded_labels))\n",
     "\n",
@@ -246,6 +470,32 @@
     "train_dataset, test_dataset = tf.keras.utils.split_dataset(dataset, left_size=0.8)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-29 19:19:51.603887: I tensorflow/core/framework/local_rendezvous.cc:405] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
+     ]
+    }
+   ],
+   "source": [
+    "pos_dataset = tf.data.Dataset.from_tensor_slices(\n",
+    "    ((encoded_sentences, pos_tags_sentences), padded_labels)\n",
+    ")\n",
+    "\n",
+    "\n",
+    "pos_dataset = pos_dataset.shuffle(len(encoded_sentences), seed=42)\n",
+    "\n",
+    "pos_train_dataset, pos_test_dataset = tf.keras.utils.split_dataset(\n",
+    "    pos_dataset, left_size=0.8\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -274,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 336,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 337,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -418,12 +668,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 338,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
     "eval_sentences, eval_labels, _, __ = dp.from_bio_file_to_examples(\n",
-    "    \"./data/bio/fr.bio/eval_small_samples.bio\"\n",
+    "    \"./data/bio/fr.bio/800_eval_small_samples.bio\"\n",
     ")\n",
     "\n",
     "eval_unlabeled, eval_unlabeled_labels, _, __ = dp.from_bio_file_to_examples(\n",
@@ -431,7 +681,7 @@
     ")\n",
     "\n",
     "eval_large, eval_large_labels, _, __ = dp.from_bio_file_to_examples(\n",
-    "    \"./data/bio/fr.bio/eval_large_samples.bio\"\n",
+    "    \"./data/bio/fr.bio/100_eval_large_samples.bio\"\n",
     ")\n",
     "\n",
     "eval_short_sentences, eval_short_labels = process_sentences_and_labels(\n",
@@ -447,7 +697,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 339,
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eval_short_sentences_pos = get_sentences_pos_tags(eval_sentences)\n",
+    "eval_unlabeled_sentences_pos = get_sentences_pos_tags(eval_unlabeled)\n",
+    "eval_large_sentences_pos = get_sentences_pos_tags(eval_large)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_eval_short_sentences_pos = [\n",
+    "    encode_and_pad_sentence_pos(sentence_pos)\n",
+    "    for sentence_pos in eval_short_sentences_pos\n",
+    "]\n",
+    "\n",
+    "encoded_eval_unlabeled_sentences_pos = [\n",
+    "    encode_and_pad_sentence_pos(sentence_pos)\n",
+    "    for sentence_pos in eval_unlabeled_sentences_pos\n",
+    "]\n",
+    "\n",
+    "encoded_eval_large_sentences_pos = [\n",
+    "    encode_and_pad_sentence_pos(sentence_pos)\n",
+    "    for sentence_pos in eval_large_sentences_pos\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -493,11 +776,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 340,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "\n",
     "def tf_round(x, decimals=0):\n",
@@ -533,11 +817,15 @@
     "    y_pred_class = tf.cast(y_pred_class, tf.float32)\n",
     "    # Create the mask, i.e., the values that will be ignored\n",
     "    mask = tf.not_equal(y_true, -1.0)\n",
+    "    mask2 = tf.not_equal(y_true, -100)\n",
+    "\n",
     "    mask = tf.cast(mask, tf.float32)\n",
+    "    mask2 = tf.cast(mask2, tf.float32)\n",
+    "\n",
     "    # Multiply the true values by the mask\n",
-    "    y_true *= mask\n",
+    "    y_true *= mask * mask2\n",
     "    # Multiply the predicted values by the mask\n",
-    "    y_pred_class *= mask\n",
+    "    y_pred_class *= mask * mask2\n",
     "\n",
     "    # Flattening to match the confusion matrix function signature\n",
     "    y_true_flat = tf.reshape(y_true, [-1])\n",
@@ -584,13 +872,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 341,
+   "execution_count": 243,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import tensorflow as tf\n",
     "from tqdm import tqdm\n",
+    "from sklearn.metrics import f1_score\n",
+    "import transformers\n",
     "\n",
     "bootstrap_eval_sentences = tf.concat(\n",
     "    [\n",
@@ -601,6 +891,15 @@
     "    axis=0,\n",
     ")\n",
     "\n",
+    "bootstrap_eval_sentences_pos = tf.concat(\n",
+    "    [\n",
+    "        encoded_eval_short_sentences_pos,\n",
+    "        encoded_eval_unlabeled_sentences_pos,\n",
+    "        encoded_eval_large_sentences_pos,\n",
+    "    ],\n",
+    "    axis=0,\n",
+    ")\n",
+    "\n",
     "bootstrap_eval_labels = tf.concat(\n",
     "    [padded_eval_short_labels, padded_eval_unlabeled_labels, padded_eval_large_labels],\n",
     "    axis=0,\n",
@@ -610,8 +909,11 @@
     "def bootstrap_evaluation(\n",
     "    model,\n",
     "    sentences=bootstrap_eval_sentences,\n",
+    "    pos_sentences=None,\n",
     "    labels=bootstrap_eval_labels,\n",
     "    num_bootstrap_samples=30,\n",
+    "    from_logits=False,\n",
+    "    has_mask=False,\n",
     "):\n",
     "    \"\"\"\n",
     "    Perform bootstrapping on the evaluation dataset and calculate both accuracy and entity accuracy.\n",
@@ -624,30 +926,76 @@
     "    unique_labels (dict): Dictionary of unique labels.\n",
     "    max_len (int): The maximum length of the sentences.\n",
     "    num_bootstrap_samples (int): Number of bootstrap samples to generate.\n",
+    "    from_logits (bool): If the predictions are logits\n",
+    "    has_mask (bool): Set to true if the inputs contains masked values (-100)\n",
     "\n",
     "    Returns:\n",
     "    dict: Dictionary containing the accuracy and entity accuracy for each bootstrap sample.\n",
     "    \"\"\"\n",
     "    accuracies = []\n",
     "    entity_accuracies = []\n",
-    "    num_sentences = tf.shape(sentences)[0]\n",
+    "    f1_scores = []\n",
+    "    num_sentences = None\n",
+    "\n",
+    "    if isinstance(sentences, transformers.tokenization_utils_base.BatchEncoding):\n",
+    "        num_sentences = sentences[\"input_ids\"].shape[0]\n",
+    "    else:\n",
+    "        num_sentences = tf.shape(sentences)[0]\n",
     "\n",
     "    for _ in tqdm(range(num_bootstrap_samples)):\n",
-    "        indicies = tf.random.uniform(\n",
+    "        indices = tf.random.uniform(\n",
     "            [num_sentences], maxval=num_sentences, dtype=tf.int32\n",
     "        )\n",
-    "        sampled_sentences = tf.gather(sentences, indicies)\n",
-    "        sampled_labels = tf.gather(labels, indicies)\n",
+    "\n",
+    "        if isinstance(sentences, transformers.tokenization_utils_base.BatchEncoding):\n",
+    "            sampled_input_ids = tf.gather(sentences[\"input_ids\"], indices)\n",
+    "            sampled_attention_mask = tf.gather(sentences[\"attention_mask\"], indices)\n",
+    "            sampled_sentences = dict()\n",
+    "            sampled_sentences[\"input_ids\"] = sampled_input_ids\n",
+    "            sampled_sentences[\"attention_mask\"] = sampled_attention_mask\n",
+    "        else:\n",
+    "            sampled_sentences = tf.gather(sentences, indices)\n",
+    "\n",
+    "        sampled_labels = tf.gather(labels, indices)\n",
+    "\n",
+    "        if pos_sentences is not None:\n",
+    "            sampled_pos_sentences = tf.gather(pos_sentences, indices)\n",
+    "            sampled_sentences = [sampled_sentences, sampled_pos_sentences]\n",
     "\n",
     "        predictions = model.predict(sampled_sentences, verbose=0)\n",
     "\n",
+    "        if from_logits:\n",
+    "            predictions = predictions.logits\n",
+    "\n",
     "        acc = masked_accuracy(sampled_labels, predictions).numpy()\n",
     "        entity_acc = entity_accuracy(sampled_labels, predictions).numpy()\n",
     "\n",
+    "        positive_labels = np.unique(sampled_labels.numpy().flatten())\n",
+    "        positive_labels = positive_labels[positive_labels >= 0]\n",
+    "\n",
+    "        predictions = tf.math.argmax(predictions, axis=-1)\n",
+    "\n",
+    "        if has_mask:\n",
+    "            mask = tf.not_equal(sampled_labels, -100)\n",
+    "            mask_value = tf.cast(-100, dtype=predictions.dtype)\n",
+    "            predictions = tf.where(mask, predictions, mask_value)\n",
+    "\n",
+    "        f_1 = f1_score(\n",
+    "            sampled_labels.numpy().flatten(),\n",
+    "            predictions.numpy().flatten(),\n",
+    "            average=\"micro\",\n",
+    "            labels=positive_labels,\n",
+    "        )\n",
+    "\n",
     "        accuracies.append(acc)\n",
     "        entity_accuracies.append(entity_acc)\n",
+    "        f1_scores.append(f_1)\n",
     "\n",
-    "    return {\"accuracies\": accuracies, \"entity_accuracies\": entity_accuracies}"
+    "    return {\n",
+    "        \"accuracies\": accuracies,\n",
+    "        \"entity_accuracies\": entity_accuracies,\n",
+    "        \"f1_scores\": f1_scores,\n",
+    "    }"
    ]
   },
   {
@@ -657,104 +1005,379 @@
     "## LSTM\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LSTM with POS\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 342,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "lstm = tf.keras.models.Sequential(\n",
-    "    layers=[\n",
-    "        tf.keras.layers.Embedding(len(vocab) + 1, MAX_LEN, mask_zero=True),\n",
-    "        tf.keras.layers.LSTM(MAX_LEN, return_sequences=True),\n",
-    "        tf.keras.layers.Dropout(0.2),\n",
-    "        tf.keras.layers.Dense(len(unique_labels), activation=tf.nn.log_softmax),\n",
-    "    ]\n",
-    ")"
+    "%load_ext tensorboard"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 343,
+   "execution_count": 161,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy TF-Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    }
+   ],
    "source": [
-    "lstm.compile(\n",
+    "word_input = tf.keras.layers.Input(shape=(MAX_LEN,), name=\"word_input\")\n",
+    "pos_input = tf.keras.layers.Input(shape=(MAX_LEN,), name=\"pos_input\")\n",
+    "\n",
+    "mask = tf.keras.layers.Masking(mask_value=0)(word_input)\n",
+    "\n",
+    "emb_size = 32\n",
+    "\n",
+    "word_embedding = tf.keras.layers.Embedding(len(vocab), emb_size, name=\"word_embedding\")(\n",
+    "    word_input\n",
+    ")\n",
+    "\n",
+    "pos_embedding = tf.keras.layers.Embedding(\n",
+    "    len(unique_pos_tags),\n",
+    "    emb_size,\n",
+    "    name=\"pos_embedding\",\n",
+    ")(pos_input)\n",
+    "\n",
+    "concatenated = tf.keras.layers.Concatenate()([word_embedding, pos_embedding])\n",
+    "\n",
+    "masked_cat = tf.keras.layers.Masking(mask_value=0)(concatenated)\n",
+    "\n",
+    "lstm_layer_with_pos = tf.keras.layers.LSTM(\n",
+    "    emb_size, return_sequences=True, name=\"lstm_layer\"\n",
+    ")(masked_cat)\n",
+    "\n",
+    "dropout = tf.keras.layers.Dropout(0.2)(lstm_layer_with_pos)\n",
+    "\n",
+    "output = tf.keras.layers.Dense(len(unique_labels), activation=tf.nn.log_softmax)(\n",
+    "    dropout\n",
+    ")\n",
+    "\n",
+    "lstm_with_pos = tf.keras.Model(inputs=[word_input, pos_input], outputs=output)\n",
+    "\n",
+    "lstm_with_pos.compile(\n",
     "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
     "    loss=masked_loss,\n",
-    "    metrics=[masked_accuracy],\n",
+    "    metrics=[entity_accuracy],\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 344,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 62ms/step - loss: 0.2007 - masked_accuracy: 0.9329 - val_loss: 0.0142 - val_masked_accuracy: 0.9966\n",
-      "Epoch 2/10\n",
-      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 64ms/step - loss: 0.0088 - masked_accuracy: 0.9973 - val_loss: 0.0086 - val_masked_accuracy: 0.9969\n"
+      "Epoch 1/10\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "<keras.src.callbacks.history.History at 0x2bab7cd40>"
-      ]
-     },
-     "execution_count": 344,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ValueError",
+     "evalue": "in user code:\n\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1398, in train_function  *\n        return step_function(self, iterator)\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1381, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1370, in run_step  **\n        outputs = model.train_step(data)\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1147, in train_step\n        y_pred = self(x, training=True)\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/input_spec.py\", line 219, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Layer \"model_1\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 2, 100) dtype=int32>]\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[162], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mlstm_with_pos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpos_train_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpos_test_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmonitor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mval_loss\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmin_delta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrestore_best_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      8\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[43m    \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+      "File \u001b[0;32m/var/folders/3h/5n6s9rcj3sx0gpncsxbq_99m0000gn/T/__autograph_generated_filengd_sbb1.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1398, in train_function  *\n        return step_function(self, iterator)\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1381, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1370, in run_step  **\n        outputs = model.train_step(data)\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/training.py\", line 1147, in train_step\n        y_pred = self(x, training=True)\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/utils/traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/Users/az-r-ow/Developer/TravelOrderResolver/venv/lib/python3.12/site-packages/tf_keras/src/engine/input_spec.py\", line 219, in assert_input_compatibility\n        raise ValueError(\n\n    ValueError: Layer \"model_1\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 2, 100) dtype=int32>]\n"
+     ]
     }
    ],
    "source": [
-    "lstm.fit(\n",
-    "    train_dataset.batch(64),\n",
-    "    validation_data=test_dataset.batch(64),\n",
+    "lstm_with_pos.fit(\n",
+    "    pos_train_dataset.batch(32),\n",
+    "    validation_data=pos_test_dataset.batch(32),\n",
     "    epochs=10,\n",
-    "    shuffle=True,\n",
     "    callbacks=[\n",
     "        tf.keras.callbacks.EarlyStopping(\n",
     "            monitor=\"val_loss\", min_delta=0.01, restore_best_weights=True\n",
-    "        )\n",
+    "        ),\n",
     "    ],\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
+   "execution_count": 106,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m2367/2367\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "test_predictions = lstm.predict(test_dataset.batch(1))"
+    "encoded_eval_short_sentences_pos_array = np.array(encoded_eval_short_sentences_pos)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 346,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
-      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n"
+      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
+      "(758, 100)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lstm_with_pos_eval_short_predictions = lstm_with_pos.predict(\n",
+    "    [encoded_eval_short_sentences, encoded_eval_short_sentences_pos_array]\n",
+    ")\n",
+    "\n",
+    "confusion_matrix(\n",
+    "    padded_eval_short_labels,\n",
+    "    lstm_with_pos_eval_short_predictions,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix on short sentences\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━���━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
+      "(100, 100)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "encoded_eval_unlabeled_sentences_pos_array = np.array(\n",
+    "    encoded_eval_unlabeled_sentences_pos\n",
+    ")\n",
+    "\n",
+    "lstm_with_pos_eval_unlabeled_predictions = lstm_with_pos.predict(\n",
+    "    [encoded_eval_unlabeled_sentences, encoded_eval_unlabeled_sentences_pos_array]\n",
+    ")\n",
+    "\n",
+    "confusion_matrix(\n",
+    "    padded_eval_unlabeled_labels,\n",
+    "    lstm_with_pos_eval_unlabeled_predictions,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix on unlabeled sentences\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
+      "(96, 100)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "encoded_eval_large_sentences_pos_array = np.array(encoded_eval_large_sentences_pos)\n",
+    "\n",
+    "lstm_with_pos_eval_large_predictions = lstm_with_pos.predict(\n",
+    "    [encoded_eval_large_sentences, encoded_eval_large_sentences_pos_array]\n",
+    ")\n",
+    "\n",
+    "confusion_matrix(\n",
+    "    padded_eval_large_labels,\n",
+    "    lstm_with_pos_eval_large_predictions,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix on large sentences\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 30/30 [00:05<00:00,  5.39it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "lstm_with_pos_results = bootstrap_evaluation(\n",
+    "    lstm_with_pos, pos_sentences=bootstrap_eval_sentences_pos\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(\"lstm_with_pos_results.pickle\", \"wb\") as f:\n",
+    "    pickle.dump(lstm_with_pos_results, f)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LSTM without POS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstm = tf.keras.models.Sequential(\n",
+    "    layers=[\n",
+    "        tf.keras.layers.Embedding(len(vocab) + 1, MAX_LEN, mask_zero=True),\n",
+    "        tf.keras.layers.LSTM(MAX_LEN, return_sequences=True),\n",
+    "        tf.keras.layers.Dropout(0.2),\n",
+    "        tf.keras.layers.Dense(len(unique_labels), activation=tf.nn.log_softmax),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lstm.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
+    "    loss=masked_loss,\n",
+    "    metrics=[entity_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m149/149\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 65ms/step - entity_accuracy: 0.9696 - loss: 0.0147 - val_entity_accuracy: 0.9146 - val_loss: 0.0274\n",
+      "Epoch 2/10\n",
+      "\u001b[1m149/149\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 68ms/step - entity_accuracy: 0.9857 - loss: 0.0062 - val_entity_accuracy: 0.9782 - val_loss: 0.0099\n",
+      "Epoch 3/10\n",
+      "\u001b[1m149/149\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 67ms/step - entity_accuracy: 0.9940 - loss: 0.0023 - val_entity_accuracy: 0.9816 - val_loss: 0.0120\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x30f2a1b20>"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lstm.fit(\n",
+    "    train_dataset.batch(64),\n",
+    "    validation_data=test_dataset.batch(64),\n",
+    "    epochs=10,\n",
+    "    shuffle=True,\n",
+    "    callbacks=[\n",
+    "        tf.keras.callbacks.EarlyStopping(\n",
+    "            monitor=\"val_loss\", min_delta=0.01, restore_best_weights=True\n",
+    "        )\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m2368/2368\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 5ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_predictions = lstm.predict(test_dataset.batch(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step\n",
+      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n"
      ]
     }
    ],
@@ -766,16 +1389,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 347,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eval short sentences accuracy:  0.923747\n",
-      "Eval unlabeled sentences accuracy:  0.9981966\n",
-      "Eval large sentences accuracy:  0.98186636\n"
+      "Eval short sentences accuracy:  0.93448687\n",
+      "Eval unlabeled sentences accuracy:  0.99549145\n",
+      "Eval large sentences accuracy:  0.9792176\n"
      ]
     }
    ],
@@ -803,15 +1426,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 348,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Eval short sentences tag accuracy:  0.73246133\n",
-      "Eval large sentences tag accuracy:  0.74429226\n"
+      "Eval short sentences tag accuracy:  0.7413793\n",
+      "Eval large sentences tag accuracy:  0.7351598\n"
      ]
     }
    ],
@@ -828,7 +1451,81 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 349,
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor: shape=(757, 100), dtype=int64, numpy=\n",
+       "array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       ...,\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0]])>"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.math.argmax(eval_short_predictions, axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  0,  0, ..., -1, -1, -1], dtype=int32)"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "padded_eval_short_labels.flatten()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7738132174992243"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score\n",
+    "\n",
+    "f1_score(\n",
+    "    padded_eval_short_labels.flatten(),\n",
+    "    tf.math.argmax(eval_short_predictions, axis=-1).numpy().flatten(),\n",
+    "    average=\"micro\",\n",
+    "    labels=[1, 2],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -842,7 +1539,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x3000 with 6 Axes>"
       ]
@@ -886,7 +1583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 350,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -930,14 +1627,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 351,
+   "execution_count": 148,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 30/30 [00:10<00:00,  2.93it/s]\n"
+      "100%|██████████| 30/30 [00:10<00:00,  2.73it/s]\n"
      ]
     }
    ],
@@ -947,21 +1644,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 352,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
-      "J'ai: O\n",
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(\"lstm_results.pickle\", \"wb\") as f:\n",
+    "    pickle.dump(lstm_results, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
+      "J'ai: O\n",
       "passé: O\n",
       "de: O\n",
       "belles: O\n",
       "vacances: O\n",
       "à: O\n",
-      "Paris: LOC-ARR\n",
+      "Paris: O\n",
       ".: O\n",
       "C'est: O\n",
       "l'heure: O\n",
@@ -974,369 +1683,2693 @@
     }
    ],
    "source": [
-    "test_sentence = (\n",
-    "    \"J'ai passé de belles vacances à Paris. C'est l'heure de rentrer à Montpellier.\"\n",
+    "test_sentence = (\n",
+    "    \"J'ai passé de belles vacances à Paris. C'est l'heure de rentrer à Montpellier.\"\n",
+    ")\n",
+    "\n",
+    "test_pred = predict(test_sentence, lstm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
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+            "zerolinecolor": "white"
+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "F1-scores for LSTM vs LSTM with POS"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "import pandas as pd\n",
+    "\n",
+    "df_lstm_results = pd.DataFrame(lstm_results)\n",
+    "df_lstm_with_pos_results = pd.DataFrame(lstm_with_pos_results)\n",
+    "\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "fig.add_trace(\n",
+    "    go.Box(\n",
+    "        y=df_lstm_results[\"f1_scores\"],\n",
+    "        name=\"LSTM\",\n",
+    "        boxmean=True,\n",
+    "        boxpoints=\"all\",\n",
+    "        marker=dict(color=\"blue\"),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig.add_trace(\n",
+    "    go.Box(\n",
+    "        y=df_lstm_with_pos_results[\"f1_scores\"],\n",
+    "        name=\"LSTM with POS\",\n",
+    "        boxmean=True,\n",
+    "        boxpoints=\"all\",\n",
+    "        marker=dict(color=\"red\"),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig.update_layout(title=\"F1-scores for LSTM vs LSTM with POS\")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conclusion of using LSTM\n",
+    "\n",
+    "The limitation of a simple LSTM is that the inputs are treated **sequentially**. Which means that it does not benefit from the context of the words that comes after, only what comes before. Therefore, we will try the **BiLSTM model** hoping that treating the sentence from both sides will leverage more context.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BiLSTM\n",
+    "\n",
+    "As mentioned in the previous section, we will be testing a **Bidirectional LSTM** model in order to improve performance.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](./assets/bi-lstm.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BiLSTM without POS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bilstm = tf.keras.models.Sequential(\n",
+    "    layers=[\n",
+    "        tf.keras.layers.Embedding(len(vocab) + 1, MAX_LEN, mask_zero=True),\n",
+    "        tf.keras.layers.Bidirectional(\n",
+    "            tf.keras.layers.LSTM(MAX_LEN, return_sequences=True)\n",
+    "        ),\n",
+    "        tf.keras.layers.Dropout(0.3),\n",
+    "        tf.keras.layers.Dense(len(unique_labels), activation=tf.nn.log_softmax),\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:absl:At this time, the v2.11+ optimizer `tf.keras.optimizers.Adam` runs slowly on M1/M2 Macs, please use the legacy TF-Keras optimizer instead, located at `tf.keras.optimizers.legacy.Adam`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "bilstm.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
+    "    loss=masked_loss,\n",
+    "    metrics=[masked_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " embedding (Embedding)       (None, None, 100)         499300    \n",
+      "                                                                 \n",
+      " bidirectional (Bidirection  (None, None, 200)         160800    \n",
+      " al)                                                             \n",
+      "                                                                 \n",
+      " dropout_160 (Dropout)       (None, None, 200)         0         \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, None, 3)           603       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 660703 (2.52 MB)\n",
+      "Trainable params: 660703 (2.52 MB)\n",
+      "Non-trainable params: 0 (0.00 Byte)\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "bilstm.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "149/149 [==============================] - 15s 80ms/step - loss: 0.0866 - masked_accuracy: 0.9744 - val_loss: 0.0119 - val_masked_accuracy: 0.9961\n",
+      "Epoch 2/10\n",
+      "149/149 [==============================] - 11s 73ms/step - loss: 0.0023 - masked_accuracy: 0.9994 - val_loss: 0.0023 - val_masked_accuracy: 0.9995\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tf_keras.src.callbacks.History at 0x2e143fbf0>"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bilstm.fit(\n",
+    "    train_dataset.batch(64),\n",
+    "    validation_data=test_dataset.batch(64),\n",
+    "    epochs=10,\n",
+    "    shuffle=True,\n",
+    "    callbacks=[\n",
+    "        tf.keras.callbacks.EarlyStopping(\n",
+    "            monitor=\"val_loss\", min_delta=0.01, restore_best_weights=True\n",
+    "        )\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24/24 [==============================] - 1s 13ms/step\n",
+      "4/4 [==============================] - 0s 10ms/step\n",
+      "3/3 [==============================] - 0s 13ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "blstm_eval_short_predictions = bilstm.predict(encoded_eval_short_sentences)\n",
+    "blstm_eval_unlabeled_predictions = bilstm.predict(encoded_eval_unlabeled_sentences)\n",
+    "blstm_eval_large_predictions = bilstm.predict(encoded_eval_large_sentences)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 30/30 [00:12<00:00,  2.41it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bilstm_results = bootstrap_evaluation(bilstm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "with open(\"bilstm_results.pickle\", \"wb\") as f:\n",
+    "    pickle.dump(bilstm_results, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(758, 100)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "confusion_matrix(\n",
+    "    padded_eval_short_labels,\n",
+    "    blstm_eval_short_predictions,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix for BiLSTM on evaluation (short sentences) dataset\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(100, 100)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "confusion_matrix(\n",
+    "    padded_eval_unlabeled_labels,\n",
+    "    blstm_eval_unlabeled_predictions,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix for BiLSTM on evaluation (unlabeled sentences) dataset\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(96, 100)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "confusion_matrix(\n",
+    "    padded_eval_large_labels,\n",
+    "    blstm_eval_large_predictions,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix for BiLSTM on evaluation (large sentences) dataset\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'predict' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[47], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m test_sentence \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mJe pensais partir de Bangkok. Mais finalement, je vais devoir trouver un voyage de Tokyo vers Osaka.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 4\u001b[0m p \u001b[38;5;241m=\u001b[39m \u001b[43mpredict\u001b[49m(test_sentence, bilstm)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'predict' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "test_sentence = \"Je pensais partir de Bangkok. Mais finalement, je vais devoir trouver un voyage de Tokyo vers Osaka.\"\n",
+    "\n",
+    "\n",
+    "p = predict(test_sentence, bilstm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### BiLSTM with POS\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bilstm_word_input = tf.keras.layers.Input(shape=(MAX_LEN,), name=\"bilstm_word_input\")\n",
+    "bilstm_pos_input = tf.keras.layers.Input(shape=(MAX_LEN,), name=\"bilstm_pos_input\")\n",
+    "\n",
+    "emb_size = 64\n",
+    "\n",
+    "bilstm_word_embedding = tf.keras.layers.Embedding(\n",
+    "    len(vocab), emb_size, name=\"bilstm_word_embedding\"\n",
+    ")(bilstm_word_input)\n",
+    "\n",
+    "bilstm_pos_embedding = tf.keras.layers.Embedding(\n",
+    "    len(unique_pos_tags),\n",
+    "    emb_size,\n",
+    "    name=\"bilstm_pos_embedding\",\n",
+    ")(bilstm_pos_input)\n",
+    "\n",
+    "bilstm_concatenated = tf.keras.layers.Concatenate()(\n",
+    "    [bilstm_word_embedding, bilstm_pos_embedding]\n",
+    ")\n",
+    "\n",
+    "bilstm_masked_cat = tf.keras.layers.Masking(mask_value=0)(bilstm_concatenated)\n",
+    "\n",
+    "bilstm_layer_with_pos = tf.keras.layers.Bidirectional(\n",
+    "    tf.keras.layers.LSTM(MAX_LEN, return_sequences=True), name=\"bilstm_layer\"\n",
+    ")(bilstm_masked_cat)\n",
+    "\n",
+    "bilstm_dropout = tf.keras.layers.Dropout(0.2)(bilstm_layer_with_pos)\n",
+    "\n",
+    "bilstm_output = tf.keras.layers.Dense(len(unique_labels), activation=tf.nn.log_softmax)(\n",
+    "    bilstm_dropout\n",
+    ")\n",
+    "\n",
+    "bilstm_with_pos = tf.keras.Model(\n",
+    "    inputs=[bilstm_word_input, bilstm_pos_input], outputs=bilstm_output\n",
+    ")\n",
+    "\n",
+    "bilstm_with_pos.compile(\n",
+    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
+    "    loss=masked_loss,\n",
+    "    metrics=[entity_accuracy],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "\u001b[1m297/297\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 85ms/step - entity_accuracy: 0.7609 - loss: 0.1435 - val_entity_accuracy: 0.9983 - val_loss: 0.0027\n",
+      "Epoch 2/10\n",
+      "\u001b[1m297/297\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 97ms/step - entity_accuracy: 0.9953 - loss: 0.0036 - val_entity_accuracy: 0.9990 - val_loss: 7.3262e-04\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.src.callbacks.history.History at 0x2e928c5f0>"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bilstm_with_pos.fit(\n",
+    "    pos_train_dataset.batch(32),\n",
+    "    validation_data=pos_test_dataset.batch(32),\n",
+    "    epochs=10,\n",
+    "    callbacks=[\n",
+    "        tf.keras.callbacks.EarlyStopping(\n",
+    "            monitor=\"val_loss\", min_delta=0.01, restore_best_weights=True\n",
+    "        )\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 30/30 [00:23<00:00,  1.25it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "bilstm_with_pos_results = bootstrap_evaluation(\n",
+    "    bilstm_with_pos, pos_sentences=bootstrap_eval_sentences_pos\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Comparison\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [
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+           }
+          },
+          "shapedefaults": {
+           "line": {
+            "color": "#2a3f5f"
+           }
+          },
+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "F1-scores for BiLSTM vs BiLSTM with POS"
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "import pandas as pd\n",
+    "\n",
+    "df_bilstm_results = pd.DataFrame(bilstm_results)\n",
+    "df_bilstm_with_pos_results = pd.DataFrame(bilstm_with_pos_results)\n",
+    "\n",
+    "fig = go.Figure()\n",
+    "\n",
+    "fig.add_trace(\n",
+    "    go.Box(\n",
+    "        y=df_bilstm_results[\"f1_scores\"],\n",
+    "        name=\"BiLSTM\",\n",
+    "        boxmean=True,\n",
+    "        boxpoints=\"all\",\n",
+    "        marker=dict(color=\"blue\"),\n",
+    "    )\n",
     ")\n",
     "\n",
-    "test_pred = predict(test_sentence, lstm)"
+    "fig.add_trace(\n",
+    "    go.Box(\n",
+    "        y=df_bilstm_with_pos_results[\"f1_scores\"],\n",
+    "        name=\"BiLSTM with POS\",\n",
+    "        boxmean=True,\n",
+    "        boxpoints=\"all\",\n",
+    "        marker=dict(color=\"red\"),\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig.update_layout(title=\"F1-scores for BiLSTM vs BiLSTM with POS\")\n",
+    "\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CamemBERT\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The model used will be the fine-tuned version of [CamemBERT](https://huggingface.co/almanach/camembert-base) which is a state-of-the-art language model for French text.\n",
+    "\n",
+    "The model has been fine-tuned for our task [in here](./camemBERT_finetuning.ipynb) and saved on Hugging Face for convenience.\n",
+    "\n",
+    "[Checkout the model on Huggingface Hub](https://huggingface.co/Az-r-ow/CamemBERT-NER-Travel)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 353,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_10\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential_10\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ embedding_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │       <span style=\"color: #00af00; text-decoration-color: #00af00\">500,000</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">80,400</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │           <span style=\"color: #00af00; text-decoration-color: #00af00\">303</span> │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ embedding_10 (\u001b[38;5;33mEmbedding\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │       \u001b[38;5;34m500,000\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ lstm_10 (\u001b[38;5;33mLSTM\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │        \u001b[38;5;34m80,400\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m100\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │           \u001b[38;5;34m303\u001b[0m │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some layers from the model checkpoint at ./models/distilcamembert-base-ner-cross-entropy-11 were not used when initializing TFCamembertForTokenClassification: ['dropout_39']\n",
+      "- This IS expected if you are initializing TFCamembertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing TFCamembertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "All the layers of TFCamembertForTokenClassification were initialized from the model checkpoint at ./models/distilcamembert-base-ner-cross-entropy-11.\n",
+      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFCamembertForTokenClassification for predictions without further training.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "from transformers import TFCamembertForTokenClassification, CamembertTokenizerFast\n",
+    "import numpy as np\n",
+    "\n",
+    "tokenizer = CamembertTokenizerFast.from_pretrained(\"cmarkea/distilcamembert-base\")\n",
+    "\n",
+    "camembert = TFCamembertForTokenClassification.from_pretrained(\n",
+    "    \"./models/distilcamembert-base-ner-cross-entropy-11\", num_labels=len(unique_labels)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tokenized_eval_short_sentences = tokenizer(\n",
+    "    eval_sentences,\n",
+    "    return_tensors=\"tf\",\n",
+    "    padding=\"max_length\",\n",
+    "    max_length=150,\n",
+    ")\n",
+    "\n",
+    "tokenized_eval_unlabeled_sentences = tokenizer(\n",
+    "    eval_unlabeled, return_tensors=\"tf\", padding=\"max_length\", max_length=150\n",
+    ")\n",
+    "\n",
+    "tokenized_eval_large_sentences = tokenizer(\n",
+    "    eval_large,\n",
+    "    return_tensors=\"tf\",\n",
+    "    truncation=True,\n",
+    "    padding=\"max_length\",\n",
+    "    max_length=150,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,742,111</span> (6.65 MB)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,742,111\u001b[0m (6.65 MB)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24/24 [==============================] - 34s 1s/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "camembert_short_sentences_predictions = camembert.predict(\n",
+    "    tokenized_eval_short_sentences\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">580,703</span> (2.22 MB)\n",
-       "</pre>\n"
-      ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m580,703\u001b[0m (2.22 MB)\n"
+       "<tf.Tensor: shape=(150,), dtype=int64, numpy=\n",
+       "array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 0,\n",
+       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])>"
       ]
      },
+     "execution_count": 100,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.argmax(camembert_short_sentences_predictions.logits, axis=-1)[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 51ms/step\n"
+     ]
     },
     {
      "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,161,408</span> (4.43 MB)\n",
-       "</pre>\n"
-      ],
       "text/plain": [
-       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m1,161,408\u001b[0m (4.43 MB)\n"
+       "<tf.Tensor: shape=(1, 100), dtype=int64, numpy=\n",
+       "array([[0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])>"
       ]
      },
+     "execution_count": 95,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "lstm.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Conclusion of using LSTM\n",
+    "test_sentence = \"Je veux partir de Paris à Montpellier.\"\n",
     "\n",
-    "The limitation of a simple LSTM is that the inputs are treated **sequentially**. Which means that it does not benefit from the context of the words that comes after, only what comes before. Therefore, we will try the **BiLSTM model** hoping that treating the sentence from both sides will leverage more context.\n"
+    "tokenized_test_sentence = tokenizer(\n",
+    "    test_sentence, return_tensors=\"tf\", padding=\"max_length\", max_length=MAX_LEN\n",
+    ")\n",
+    "\n",
+    "camembert_test_sentence_predictions = camembert.predict(tokenized_test_sentence)\n",
+    "\n",
+    "tf.math.argmax(camembert_test_sentence_predictions.logits, axis=-1)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 59,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## BiLSTM\n",
+    "def align_labels_with_tokens(encodings, labels):\n",
+    "    \"\"\"\n",
+    "    Aligns the labels to match the tokenized outputs.\n",
     "\n",
-    "As mentioned in the previous section, we will be testing a **Bidirectional LSTM** model in order to improve performance.\n"
+    "    Args:\n",
+    "        encodings (BatchEncoding): Tokenized outputs from the Hugging Face tokenizer (must use a fast tokenizer).\n",
+    "        labels (List[List[int]]): Original labels for each sentence before tokenization. Each inner list corresponds to one sentence.\n",
+    "\n",
+    "    Returns:\n",
+    "        List[List[int]]: Aligned labels, where each inner list corresponds to the aligned labels for the tokenized sentence.\n",
+    "                         Special tokens and padding are assigned a value of -100.\n",
+    "    \"\"\"\n",
+    "    adapted_labels = []\n",
+    "\n",
+    "    for i, label in enumerate(labels):\n",
+    "        word_ids = encodings.word_ids(\n",
+    "            batch_index=i\n",
+    "        )  # Get word IDs for the i-th sentence\n",
+    "        aligned_labels = []\n",
+    "        previous_word_id = None\n",
+    "\n",
+    "        for word_id in word_ids:\n",
+    "            if word_id is None:\n",
+    "                # Special tokens (e.g., [CLS], [SEP], or padding)\n",
+    "                aligned_labels.append(-100)\n",
+    "            elif word_id != previous_word_id:\n",
+    "                # New word\n",
+    "                aligned_labels.append(label[word_id])\n",
+    "            else:\n",
+    "                # Subword token (same word)\n",
+    "                aligned_labels.append(\n",
+    "                    label[word_id]\n",
+    "                )  # Or append -100 to ignore subwords\n",
+    "            previous_word_id = word_id\n",
+    "\n",
+    "        adapted_labels.append(aligned_labels)\n",
+    "\n",
+    "    return adapted_labels"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 101,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "![](./assets/bi-lstm.png)\n"
+    "aligned_eval_short_labels = align_labels_with_tokens(\n",
+    "    tokenized_eval_short_sentences, padded_eval_short_labels\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 354,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [],
    "source": [
-    "bilstm = tf.keras.models.Sequential(\n",
-    "    layers=[\n",
-    "        tf.keras.layers.Embedding(len(vocab) + 1, MAX_LEN, mask_zero=True),\n",
-    "        tf.keras.layers.Bidirectional(\n",
-    "            tf.keras.layers.LSTM(MAX_LEN, return_sequences=True)\n",
-    "        ),\n",
-    "        tf.keras.layers.Dropout(0.3),\n",
-    "        tf.keras.layers.Dense(len(unique_labels), activation=tf.nn.log_softmax),\n",
-    "    ]\n",
+    "aligned_eval_long_labels = align_labels_with_tokens(\n",
+    "    tokenized_eval_large_sentences, padded_eval_large_labels\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 355,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "bilstm.compile(\n",
-    "    optimizer=tf.keras.optimizers.Adam(0.01),\n",
-    "    loss=masked_loss,\n",
-    "    metrics=[masked_accuracy],\n",
+    "type(tokenized_eval_large_sentences)\n",
+    "\n",
+    "num_sentences = tokenized_eval_short_sentences[\"input_ids\"].shape[0]\n",
+    "\n",
+    "indicies = tf.random.uniform([num_sentences], maxval=num_sentences, dtype=tf.int32)\n",
+    "\n",
+    "sampled_input_ids = tf.gather(tokenized_eval_short_sentences[\"input_ids\"], indicies)\n",
+    "sampled_attention_mask = tf.gather(\n",
+    "    tokenized_eval_short_sentences[\"attention_mask\"], indicies\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 356,
+   "execution_count": 124,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_11\"</span>\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1mModel: \"sequential_11\"\u001b[0m\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ embedding_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)        │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
-       "├─────────────────────────────────┼────────────────────────┼��──────────────┤\n",
-       "│ bidirectional_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ ?                      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
-       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
-       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ embedding_11 (\u001b[38;5;33mEmbedding\u001b[0m)        │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ bidirectional_5 (\u001b[38;5;33mBidirectional\u001b[0m) │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)            │ ?                      │             \u001b[38;5;34m0\u001b[0m │\n",
-       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
-       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
-       "</pre>\n"
-      ],
-      "text/plain": [
-       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "bilstm.summary()"
+    "aligned_eval_unlabeled_labels = align_labels_with_tokens(\n",
+    "    tokenized_eval_unlabeled_sentences, padded_eval_unlabeled_labels\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 357,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/10\n",
-      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 86ms/step - loss: 0.1828 - masked_accuracy: 0.9319 - val_loss: 0.0045 - val_masked_accuracy: 0.9984\n",
-      "Epoch 2/10\n",
-      "\u001b[1m148/148\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 116ms/step - loss: 0.0030 - masked_accuracy: 0.9991 - val_loss: 0.0022 - val_masked_accuracy: 0.9994\n"
+      "(758, 150)\n"
      ]
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<keras.src.callbacks.history.History at 0x2ba90f590>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
-     "execution_count": 357,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "bilstm.fit(\n",
-    "    train_dataset.batch(64),\n",
-    "    validation_data=test_dataset.batch(64),\n",
-    "    epochs=10,\n",
-    "    shuffle=True,\n",
-    "    callbacks=[\n",
-    "        tf.keras.callbacks.EarlyStopping(\n",
-    "            monitor=\"val_loss\", min_delta=0.01, restore_best_weights=True\n",
-    "        )\n",
-    "    ],\n",
+    "confusion_matrix(\n",
+    "    aligned_eval_short_labels,\n",
+    "    camembert_short_sentences_predictions.logits,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix for CamemBERT on evaluation (short_sentences) dataset\",\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 358,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step\n",
-      "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n",
-      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "blstm_eval_short_predictions = bilstm.predict(encoded_eval_short_sentences)\n",
-    "blstm_eval_unlabeled_predictions = bilstm.predict(encoded_eval_unlabeled_sentences)\n",
-    "blstm_eval_large_predictions = bilstm.predict(encoded_eval_large_sentences)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 359,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 30/30 [00:18<00:00,  1.63it/s]\n"
+      "3/3 [==============================] - 4s 1s/step\n"
      ]
     }
    ],
    "source": [
-    "bilstm_results = bootstrap_evaluation(bilstm)"
+    "camembert_large_sentences_predictions = camembert.predict(\n",
+    "    tokenized_eval_large_sentences\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 360,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(757, 100)\n",
-      "(100, 100)\n",
-      "(95, 100)\n"
+      "(96, 150)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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QYDRq1MgYM2aMsWfPnnuu88EHHxiSjMOHD99zvxcvXjQ8PT2NuXPnGoZhGEuXLjUsFotx5MgR27lzr3PTMAyjYcOGhqenp3Hw4EHb2KlTp4zMmTMbzzzzjG1s1qxZhiSjRo0aRnx8vG28V69ehpubm3H58uV77qd69epGyZIljVu3btnG4uPjjUqVKhmFChWyjfXv39/w8PAwLl68aBuLjo42/P397V4Hjp6bCf+u/z03kzrvDOPO66VatWq2n2NjY43o6Gi7ZS5dumRky5bNLpbz588bkozBgwcn2ubd7xlp8T6WlAMHDhiSjEmTJtmNb9++3ZBkfP311/dcX5Lh6elpHDhwwDa2Y8eORNtM6flTpUoVIzY21m5f9zvfUxr/kSNHDDc3N2PEiBF247t27TLc3d3txhPeI/97zkRHRxvZs2c3XnrpJdvYH3/8kej90TDunMOFChUywsPD7V4XN27cMPLly2e88MILtjFH38/3799vWK1Wo1GjRrb3p//uzzDuvIf5+/sbHTt2tPv9mTNnDD8/P9v4pUuXkvyMS46np6fRuXPney7z7bffGpKM0aNH28ZiY2ONqlWrOvQZMn/+/ETn9b3OgaS2ER4ebuTPn9/28+LFiw1Jxh9//JFs3L/99pshyfjiiy/sxpcvX55ovHjx4nbvBUBqoHXtMZPQEpRQPbifZcuWSZIiIiLsxnv37i1JiVqRihUrZvvmTbrz7VDhwoV16NChB475bgnfCn733XeKj493aJ3Tp0/rzz//VJs2bZQlSxbbeKlSpfTCCy/YjvO/3njjDbufq1atqgsXLjjUVpUpUyY1a9bM9nPhwoXl7++vokWL2n3bmvDf/31+fHx8bP8dExOjCxcuqGDBgvL399e2bdscONo78uXLZ/sm/n5ef/11hYeHq1u3bnrttddUoEABjRw50uF93W3ZsmWqUKGCqlSpYhvLlCmTXn/9dR05ckR///233fKtW7e2O+6HNWvWLE2ePFn58uXT4sWL1adPHxUtWlTVq1fXyZMnH2rbAQEBqlWrlubPny9JmjdvnipVqmSrJN1PXFycVq5cqYYNGyp//vy28Rw5cqhFixb6/fffE51jr7/+ul0rVtWqVRUXF6ejR48mu5+LFy9q9erVatKkia5du6aoqChFRUXpwoULCg8P1/79+23PRdOmTRUTE2P3TezKlSt1+fJlNW3a1DaWWufmvbi5udkqevHx8bp48aJiY2NVrly5B97Ho3ofS2gNCggIsBtPqHisWLHivu2vNWrUUIECBWw/lypVSr6+vrZ9P8j507FjR7m5ud1zv/fiSPzffPON4uPj1aRJE9u5FhUVpezZs6tQoUJ2rYfSnfeDli1b2n729PRUhQoVHPqs+PPPP7V//361aNFCFy5csO3r+vXrql69un799ddEnw33ez//9ttvFR8fr0GDBiW6PjDhtffTTz/p8uXLat68ud0xurm5KTQ01HaMPj4+8vT01Nq1a+3as5ITEBBw35kaly1bJnd3d7tqoZubm7p165Zo2f++Tm/duqWoqChVrFhRkhx+Df13G1euXFFUVJSqVaumQ4cO2doVEz6LlyxZopiYmCS38/XXX8vPz08vvPCC3XNWtmxZZcqUKdF5AaQ2Ep3HjK+vryTp2rVrDi1/9OhRWa1WFSxY0G48e/bs8vf3T/SH1pNPPploGwEBAQ692TuqadOmqly5sjp06KBs2bKpWbNm+uqrr+6Z9CTEmVT7VtGiRW0fkv9197Ek/PHiyLHkypUr0TUCfn5+yp07d6Kxu7d58+ZNDRo0SLlz55aXl5eCgoKUNWtWXb58Ocl+/uTky5fP4WUlaebMmbpx44b279+vzz777KESj6NHjyb7XCf8/mFivR+r1aquXbtq69atioqK0nfffafatWtr9erVdgnog2rRooWtDeXbb79VixYtHF73/PnzunHjRrLPT3x8fKJrGh7kXDxw4IAMw9DAgQOVNWtWu8fgwYMlyXZdVunSpVWkSBG7VrgFCxYoKChIzz//vG0stc7N+5k9e7ZKlSolb29vBQYGKmvWrFq6dOkD7+NRv48ZhmH3c758+RQREaFPPvlEQUFBCg8P15QpU5I8nvvt+0HOn4d9fTkS//79+2UYhgoVKpTofNuzZ4/dNYBS0u+Rjj7H+/fvl3TnC5K79/XJJ58oOjo60XN7v9fQwYMHZbVaVaxYsfvu9/nnn0+035UrV9qO0cvLS6NGjdKPP/6obNmy6ZlnntHo0aOTnUbaMIz7TkRw9OhR5ciRI1Grd1LnwcWLF9WjRw9ly5ZNPj4+ypo1q+0ccPQ1tG7dOtWoUcN2PWvWrFlt18EmbKNatWp66aWXNHToUAUFBalBgwaaNWuW3XVv+/fv15UrVxQcHJzoOfv3338TnRdAauManceMr6+vnnjiCf31118pWs/R2WCS+9bw7g/+lOwjLi7O7mcfHx/9+uuvWrNmjZYuXarly5drwYIFev7557Vy5cqH+ubyvx7mWJJb15FtduvWTbNmzVLPnj0VFhZmu6lqs2bNHK5gSUpxorJ27VrbB9SuXbsUFhaWovUfRmpWc+4WGBio+vXrq379+rZrGI4ePepwBSYp9evXl5eXl1q3bq3o6Gg1adIkFSNO7EHOxYRzpU+fPslW9v77h3/Tpk01YsQIRUVFKXPmzPr+++/VvHlzu9nLHubcvNfr+7/H9/nnn6tNmzZq2LCh3nrrLQUHB8vNzU2RkZE6ePDgPfdxP2n9PpZwDU1Sf6yPHTtWbdq00XfffaeVK1eqe/fuioyM1MaNG5UrV66H3ve9pMbr637xx8fHy2Kx6Mcff0zyGO7+A/1hjjPhXPvggw+SnXY6Nfd3937nzp2r7NmzJ/r9f18rPXv2VL169fTtt99qxYoVGjhwoCIjI7V69epE1+JcvnzZbkr7h9WkSROtX79eb731lkJCQpQpUybFx8erVq1aDn2GHDx4UNWrV1eRIkU0btw45c6dW56enlq2bJk+/PBD2zYsFosWLlyojRs36ocfftCKFSvUrl07jR07Vhs3brTtNzg4WF988UWS+0ruujAgtZDoPIbq1q2rjz/+WBs2bLjvH7N58uRRfHy89u/fb3eh+9mzZ3X58uWH+mPxbgEBAUnOIJNUe47ValX16tVVvXp1jRs3TiNHjtS7776rNWvWqEaNGkkeh3TnAv277d27V0FBQelmytWFCxeqdevWGjt2rG3s1q1biZ6b1JyK9PTp0+rWrZtq1qxpmxQgPDz8gf998+TJk+xznfB7ZyhXrpx++eUXnT59+qFi8PHxUcOGDfX555+rdu3aKfojJWvWrMqQIUOyz4/Vak1U+XsQCW1NHh4eSb4m7ta0aVMNHTpUixYtUrZs2XT16tVE1S9Hz82k3Ov1/d8WrIULFyp//vz65ptv7M7xhCpUgpSc/4/qfezJJ5+Uj4+PDh8+nOTvS5YsqZIlS2rAgAFav369KleurI8++kjvvfeew/tIrfPnQd4/7hV/gQIFZBiG8uXLp6eeeirF205JjAmtfb6+vg6d244oUKCA4uPj9ffffyebPCXsNzg42KH9FihQQL1791bv3r21f/9+hYSEaOzYsfr8889ty5w8eVK3b9+2Oy+TkidPHq1atco2CUqCu8+DS5cuadWqVRo6dKgGDRpkG0+oRv1Xcs/vDz/8oOjoaH3//fd2lbDk2swqVqyoihUrasSIEZo3b55effVVffnll+rQoYMKFCign3/+WZUrV75vws302kgLtK49ht5++21lzJhRHTp00NmzZxP9/uDBg5owYYIk6cUXX5R05/4j/zVu3DhJUp06dVItrgIFCujKlSvauXOnbez06dOJZkS6ePFionUTPpjunio2QY4cORQSEqLZs2fb/bH1119/aeXKlbbjTA/c3NwSfcs4adKkRJWthMQsNe5q3bFjR8XHx2vmzJn6+OOP5e7urvbt2z/wt8gvvviiNm/erA0bNtjGrl+/ro8//lh58+a9Z3vIwzpz5kyia4CkOzNHrVq1KskWpgfRp08fDR48WAMHDkzRem5ubqpZs6a+++47u6lmz549q3nz5qlKlSq2FtOHERwcrGeffVbTp0/X6dOnE/3+7umpixYtqpIlS2rBggVasGCBcuTIoWeeeSZR7I6cm0kpUKCANm7cqNu3b9vGlixZkqjNKuGb9//uZ9OmTXbnkiTbDG+OnP+P6n3Mw8ND5cqV05YtW+zGr169qtjYWLuxkiVLymq1JvuelZzUOn9S8v7hSPyNGzeWm5ubhg4dmugcMQzDbmpjRyUXY9myZVWgQAGNGTPG7qa/CRydev2/GjZsKKvVqmHDhiWqeiQcT3h4uHx9fTVy5Mgkr0lJ2O+NGzcSzcBYoEABZc6cOdG/99atWyXpvrOgvvjii4qNjbWb4jkuLk6TJk2yWy6p14+U+NyXkn9+k9rGlStXNGvWLLvlLl26lGg/d38WN2nSRHFxcRo+fHii/cfGxtrtO2PGjKnyeQb8FxWdx1CBAgU0b948NW3aVEWLFlWrVq1UokQJ3b59W+vXr9fXX39tu99F6dKl1bp1a3388ce6fPmyqlWrps2bN2v27Nlq2LChnnvuuVSLq1mzZurbt68aNWqk7t2726btfOqpp+wuoBw2bJh+/fVX1alTR3ny5NG5c+c0depU5cqVy+7i97t98MEHql27tsLCwtS+fXvb9NJ+fn62+2+kB3Xr1tXcuXPl5+enYsWKacOGDfr5559tbTEJQkJC5ObmplGjRunKlSvy8vKy3fcgJWbNmqWlS5fqs88+s7XQTJo0SS1bttS0adPUpUuXFB9Dv379NH/+fNWuXVvdu3dXlixZNHv2bB0+fFiLFi164JuBJtiyZUuS34I/++yz8vb2VoUKFfT888+revXqyp49u86dO6f58+drx44d6tmzZ6q0iZQuXVqlS5d+oHXfe+89272gunTpInd3d02fPl3R0dF29xJ5WFOmTFGVKlVUsmRJdezYUfnz59fZs2e1YcMGnThxQjt27LBbvmnTpho0aJC8vb3Vvn37RP9Ojp6bSenQoYMWLlyoWrVqqUmTJjp48KA+//xzuwvvE/bxzTffqFGjRqpTp44OHz6sjz76SMWKFbP7o9bHx0fFihXTggUL9NRTTylLliwqUaJEkvcyepTvYw0aNNC7776rq1ev2hKO1atX680339Qrr7yip556SrGxsZo7d67c3Nz00ksvpXgfqXH+JExr/O6776pZs2by8PBQvXr1kqxsOxJ/gQIF9N5776l///46cuSIGjZsqMyZM+vw4cNavHixXn/9dfXp0ydFx1mgQAH5+/vro48+UubMmZUxY0aFhoYqX758+uSTT1S7dm0VL15cbdu2Vc6cOXXy5EmtWbNGvr6++uGHH1K0r4IFC+rdd9/V8OHDVbVqVTVu3FheXl76448/9MQTTygyMlK+vr6aNm2aXnvtNT399NNq1qyZsmbNqmPHjmnp0qWqXLmyJk+erH379ql69epq0qSJihUrJnd3dy1evFhnz55NVCX96aef9OSTT95zamlJqlevnipXrqx+/frpyJEjKlasmL755ptE19z4+vrargmKiYlRzpw5tXLlyiSrjMmdAwmV/Xr16qlTp076999/NWPGDAUHB9t9aTJ79mxNnTpVjRo1UoECBXTt2jXNmDFDvr6+ti8XqlWrpk6dOikyMlJ//vmnatasKQ8PD+3fv19ff/21JkyYoJdfftkWz7Rp0/Tee++pYMGCCg4OtrtGEHggj3CGN6Qz+/btMzp27GjkzZvX8PT0NDJnzmxUrlzZmDRpkt10tDExMcbQoUONfPnyGR4eHkbu3LmN/v372y1jGHemZa1Tp06i/dw9fWxy00sbhmGsXLnSKFGihOHp6WkULlzY+PzzzxNNFbtq1SqjQYMGxhNPPGF4enoaTzzxhNG8eXNj3759ifZx97SkP//8s1G5cmXDx8fH8PX1NerVq2f8/fffdsskN0VwwlSt95uOtVq1akbx4sUTjSf3/Oiu6ZIvXbpktG3b1ggKCjIyZcpkhIeHG3v37k1yet4ZM2YY+fPnN9zc3Oym801uXwm/S9jO8ePHDT8/P6NevXqJlmvUqJGRMWNG49ChQ/c83rvjT3Dw4EHj5ZdfNvz9/Q1vb2+jQoUKxpIlS+yWSZiG+H7T7t69v+Qew4cPN65evWpMmDDBCA8PN3LlymV4eHgYmTNnNsLCwowZM2bYTUf7X45OL30vjk4vbRiGsW3bNiM8PNzIlCmTkSFDBuO5554z1q9fb7dMwjl39/StSU3fnJyDBw8arVq1MrJnz254eHgYOXPmNOrWrWssXLgw0bL79++3PZe///57ot87em4mF9/YsWONnDlzGl5eXkblypWNLVu2JHp/iI+PN0aOHGnkyZPH8PLyMsqUKWMsWbIkyenn169fb5QtW9bw9PS0m2r67vcMw0j997HknD171nB3d7dNP24YhnHo0CGjXbt2RoECBQxvb28jS5YsxnPPPWf8/PPPdusmd44l9dp/mPMnwfDhw42cOXMaVqv1nue+o/EbhmEsWrTIqFKlipExY0YjY8aMRpEiRYyuXbsa//zzj22Z5N4jk/o3/u6774xixYoZ7u7uid7Tt2/fbjRu3NgIDAw0vLy8jDx58hhNmjQxVq1aZVsmpe/nn376qVGmTBnDy8vLCAgIMKpVq2b89NNPdsusWbPGCA8PN/z8/Axvb2+jQIECRps2bYwtW7YYhmEYUVFRRteuXY0iRYoYGTNmNPz8/IzQ0FDjq6++sttOXFyckSNHDmPAgAGJn/QkXLhwwXjttdcMX19fw8/Pz3jttddsU3//93k5ceKE0ahRI8Pf39/w8/MzXnnlFePUqVNJTsee3Dnw/fffG6VKlTK8vb2NvHnzGqNGjTI+/fRTu2W2bdtmNG/e3HjyyScNLy8vIzg42Khbt67tefivjz/+2Chbtqzh4+NjZM6c2ShZsqTx9ttvG6dOnbItc+bMGaNOnTpG5syZDUlMNY1UYTGMh7jCEQAA2Gnfvr327dun3377zdmhIB1LmLHx4MGDypEjh7PDAUyJRAcAgFR07NgxPfXUU1q1apUqV67s7HCQToWFhalq1aqp2q4KwB6JDgAAAADTYdY1AAAAAKZDogMAAADAdEh0AAAAAJgOiQ4AAAAA0yHRAQAAAGA67s4O4HHjU+ZNZ4cAuKRLf0x2dgiAS4qNY3JVIKUyeVmcHUKy0svfkje3p//PZSo6AAAAAEyHig4AAADgKizUKRzFMwUAAADAdEh0AAAAAJgOrWsAAACAq7Ck34kS0hsqOgAAAABMh4oOAAAA4CqYjMBhPFMAAAAATIdEBwAAAIDp0LoGAAAAuAomI3AYFR0AAAAApkOiAwAAAMB0aF0DAAAAXAWzrjmMZwoAAACA6VDRAQAAAFwFkxE4jIoOAAAAANMh0QEAAABgOrSuAQAAAK6CyQgcxjMFAAAAwHRIdAAAAACYDq1rAAAAgKtg1jWHUdEBAAAAYDpUdAAAAABXwWQEDuOZAgAAAGA6JDoAAAAATIfWNQAAAMBVMBmBw6joAAAAADAdEh0AAAAApkPrGgAAAOAqmHXNYTxTAAAAAEyHig4AAADgKpiMwGFUdAAAAACYDokOAAAAANOhdQ0AAABwFUxG4DCeKQAAAACmQ6IDAAAAwHRoXQMAAABcBa1rDuOZAgAAAGA6VHQAAAAAV2HlPjqOoqIDAAAAwHRIdAAAAACYDq1rAAAAgKtgMgKH8UwBAAAAMB0SHQAAAACmQ+saAAAA4CoszLrmKCo6AAAAAEyHig4AAADgKpiMwGE8UwAAAABMh0QHAAAAgOnQugYAAAC4CiYjcBgVHQAAAACmQ6IDAAAAwHRoXQMAAABcBbOuOYxnCgAAAIDpUNEBAAAAXAWTETiMig4AAAAA0yHRAQAAAGA6tK4BAAAAroLJCBzGMwUAAADAdEh0AAAAAJgOrWsAAACAq2DWNYdR0QEAAABgOlR0AAAAAFfBZAQO45kCAAAAYDokOgAAAABMh9Y1AAAAwFUwGYHDqOgAAAAAMB0SHQAAAACmQ+saAAAA4CqYdc1hPFMAAAAATIeKDgAAAOAqqOg4jGcKAAAAgOmQ6AAAAAAwHVrXAAAAAFfBfXQcRkUHAAAAgOlQ0QEAAABcBZMROIxnCgAAAIDpkOgAAAAAMB1a1wAAAABXwWQEDqOiAwAAAMB0SHQAAAAAmA6tawAAAICrYNY1h/FMAQAAADAdKjoAAACAq2AyAodR0QEAAABgOiQ6AAAAAEyH1jUAAADARVhoXXMYFR0AAAAApkOiAwAAAMB0aF0DAAAAXASta46jogMAAADAdKjoAAAAAK6Cgo7DqOgAAAAAMB0SHQAAAACmQ+saAAAA4CKYjMBxVHQAAAAAmA6JDgAAAADToXUNAAAAcBG0rjmOig4AAAAA06GiAwAAALgIKjqOo6IDAAAAwHRIdAAAAACYDq1rAAAAgIugdc1xVHQAAAAAmA6JDgAAAADToXXNAYZhUCYEAACA8/EnqcOo6NzDzZs3FR0drePHj+vWrVvODgcAAACAg0h0krFnzx61bNlS5cqVU4ECBRQWFqZ+/fo5OyykQKcmz2jv0qG6tPFD/Tqnj8oVz5Pssu7uVvV/vZZ2fz9YlzZ+qE0L+umFSkXtlrFaLRrUpY72LBmiixvGaff3g9WvY620PgwgXfty3heq/cLzKl+mpF5t9op27dzp7JAAp/nqyy9Ut9bzCitXSq1aNNFfu+79evhp5XI1rl9bYeVKqUnjevr9t19sv4uJidHED8eoSeN6qlyhjMKrV9Wgd/rq/LmzaX0YSOcsFku6eLgCEp0k7Nq1S2FhYcqRI4d69uypr776Snny5NH48eNVr149xcTEODtE3MfLNZ/WqN6NNGL6jwprMUo7953U91O7KmtApiSXH9Klnjq8VEURo79WmZfe0ycLf9eCsR1VunAu2zK927ygji9XVa/3v1ZI4/c0YOJ3imhdQ12aV3tUhwWkK8t/XKYxoyPVqUtXffn1YhUuXESdO7XXhQsXnB0a8MitXL5M4z54X6+/0VVfLPhGTxUurDff6KCLybwedvy5Te/27a2GjV7WvK8W69nna6h3jzd1YP8+SdKtW7e0d8/f6tCpi75YsEhjxk3SkSOH1at7l0d5WIBLsxiGYTg7iPTk/PnzCg8PV3h4uCIjI+3Gv/rqK/Xt21d169bVl19++UDb9ynzZmqFinv4dU4fbd19VL1GfS3pzrcfB5YP17Qvf9GYWT8lWv7QyhEa9ckKTf/qV9vY/DEddPPWbbUbMEeStGjCGzp38ao6D52X7DJIO5f+mOzsEHCXV5u9ouIlSuqdAYMkSfHx8apZvZqat3hN7Tu+7uTokCA2jo/5R6FViyYqXqKE+r7zv9fDizWfVdPmLdW2feLXQ7+3eunmzRuaMHm6baz1q01VuEgRvTNwaJL72P3XLrVq8YqWrFitHDmeSJsDgSQpk1f6rVj4v/q5s0OQJF3+oqWzQ7gvKjp3OXHihGJiYvTqq68qLi5O0p03q6xZs6ply5bq37+/fvjhB3377bfODRTJ8nB3U5miubV60z+2McMwtHrTP6pQKl+S63h6uOvWbftK3c1bt1WpTAHbzxt3HNJzFQqr4JPBkqSST+VUWEh+rVz3dxocBZC+xdy+rT1/71bFsEq2MavVqooVK2nnju1OjAx49GJibmvvnt2qUNH+9VAhNEy7dvyZ5Do7d/yp0NBKdmNhlSprZzLLS9K//16TxWJR5sy+qRE2XJSzW9ZcqXWNWdfusmPHDh04cEAlSpSQdOcPZKv1Tj7o5+enFi1a6IMPPtCBAwfuu63o6GhFR0fbjRnxcbJY3VI/cNgEBWSSu7ubzl28Zjd+7sJVFc6bLcl1ft6wR91bPq/ftx3QoeNReq5CYTV4PkRubv97IY+Z9ZN8M3lrx+IBiosz5OZm0eApS/Tlj1vS9HiA9OjS5UuKi4tTYGCg3XhgYKAOHz7kpKgA57h8KbnXQ5COHD6c5DoXoqKU5a7lswQG6UJUVJLLR0dHa+KHYxReu44yZUq6DRuAPSo6dylYsKAkadGiRZIS3302X758yp8/v06ePHnfbUVGRsrPz8/uEXt2a+oHjYfW54OFOnjsnHZ8M1BXN4/Xh/1e0ZzvNyo+/n8tHy/XfFrNapdXm3dmK6zFKHUYNFc9X6uuV+uFOjFyAIDZxcTEqF+fnjIMqf+AIc4OB3AZVHTukjdvXvn6+mrOnDkqV66c8uS5M1NXfHy8rFarLl26JB8fH5UtW/a+2+rfv78iIiLsxoKr9k2TuPE/UZf+VWxsnIKzZLYbDw701ZkLV5Ndp0nEDHl5uivQL6NOnb+i97o30OGT/7uIdGTPhhoz6yd9veJOsrr7wCk9mSOL3mr7gr74YVPaHRCQDgX4B8jNzS3RxAMXLlxQUFCQk6ICnMM/ILnXQ1Syr4fAoKBEExVcvBClwLuWj4mJUb+3eun06VP66JPPqObAZdrG0gMqOnfJlSuXpk2bpuXLl2vgwIHavXu3JNna18aNG6dTp06patWq992Wl5eXfH197R60raW9mNg4bd9zXM+FFraNWSwWPVfhKW3emXQLQYLo27E6df6K3N2talg9REvW/m9qUB9vT8Ub8XbLx8X/r7UReJx4eHqqaLHi2rRxg20sPj5emzZtUKnSZZwYGfDoeXh4qkjR4vpjk/3r4Y9NG1WydEiS65QqHaLN/1lekjZtXK9S/1k+Ick5fvSopn08S/7+AWkRPmBaVHSS0KBBA02cOFFvvvmmNm/erMqVKytHjhw6fPiwfvzxR61atcpW6UH6NPHz1Zox7DVt/fuYtvx1RG+2eE4ZfLw057uNkqRPhr+mU+euaNCk7yVJ5Uvk0RPB/trxzwnlDPbXu51elNVq0bjPfrZtc9mvu9S3fbiOn76kvw+eVkiRXOre8jnN+XajU44RcLbXWrfVwHf6qnjxEipRspQ+nztbN2/eVMNGjZ0dGvDItWzVRoMH9FPRYndeD/M+v/N6qN/wzuth0Dt9lTVbsLr16C1Jav7qa+rYrpXmzv5UVZ55Vit/XKq/d+/Wu4OGSbqT5PTt3UN79/yt8ZM/Ulx8nKKizku6c82wh4encw4UTkdFx3EkOklwc3NTp06dFBISotGjR2vTpk3y9/dX6dKltX79ehUpUsTZIeI+Fq7cpqCATBrUuY6yBWbWzn9OqkHXKbYJCnJnz2J3/Y2Xl4cGd62rfDmD9O+NaK1Yt1vtB87RlX9v2paJGPW1BnepqwnvNFXWgEw6ff6KZi5cp5Ef//jIjw9ID2rVflGXLl7U1MkTFRV1XoWLFNXU6Z8kar0BHgc1a72oS5cu6qOpk3Qh6ryeKlxUk6bNUGDgndfDmTOnZLH+7w/U0iFPa8T7YzRt0nhNmfihnnwyr8ZOmKyChZ6SJJ0/d1a/rF0tSWr+SkO7fU2fOVvlynN9KHA/3EfnPuLi4mS1WmWxWGzX6TwM7qMDPBjuowM8GO6jA6Rcer6PTmCr+c4OQZJ0YU5zZ4dwX1R07iMhyZEoFQIAAMDJ+HPUYVxFfR//TW5IdAAAAADXQKIDAAAAIM1NmTJFefPmlbe3t0JDQ7V58+Z7Lj9+/HgVLlxYPj4+yp07t3r16qVbt245vD9a1wAAAAAX4aodRgsWLFBERIQ++ugjhYaGavz48QoPD9c///yj4ODgRMvPmzdP/fr106effqpKlSpp3759atOmjSwWi8aNG+fQPqnoAAAAAEiR6OhoXb161e4RHR2d7PLjxo1Tx44d1bZtWxUrVkwfffSRMmTIoE8//TTJ5devX6/KlSurRYsWyps3r2rWrKnmzZvftwr0XyQ6AAAAgIuwWCzp4hEZGSk/Pz+7R2RkZJIx3759W1u3blWNGjVsY1arVTVq1NCGDRuSXKdSpUraunWrLbE5dOiQli1bphdffNHh54rWNQAAAAAp0r9/f0VERNiNeXl5JblsVFSU4uLilC1bNrvxbNmyae/evUmu06JFC0VFRalKlSoyDEOxsbF644039M477zgcIxUdAAAAACni5eUlX19fu0dyic6DWLt2rUaOHKmpU6dq27Zt+uabb7R06VINHz7c4W1Q0QEAAABchCtORhAUFCQ3NzedPXvWbvzs2bPKnj17kusMHDhQr732mjp06CBJKlmypK5fv67XX39d7777rqzW+9drqOgAAAAASDOenp4qW7asVq1aZRuLj4/XqlWrFBYWluQ6N27cSJTMuLm5SZIMw3Bov1R0AAAAAKSpiIgItW7dWuXKlVOFChU0fvx4Xb9+XW3btpUktWrVSjlz5rRNaFCvXj2NGzdOZcqUUWhoqA4cOKCBAweqXr16toTnfkh0AAAAAFfhep1rkqSmTZvq/PnzGjRokM6cOaOQkBAtX77cNkHBsWPH7Co4AwYMkMVi0YABA3Ty5EllzZpV9erV04gRIxzep8VwtPaDVOFT5k1nhwC4pEt/THZ2CIBLio3jYx5IqUxe6TebCG7/lbNDkCSdm9nE2SHcFxUdAAAAwEW44mQEzsJkBAAAAABMh0QHAAAAgOnQugYAAAC4CFrXHEdFBwAAAIDpkOgAAAAAMB1a1wAAAAAXQeua46joAAAAADAdKjoAAACAi6Ci4zgqOgAAAABMh0QHAAAAgOnQugYAAAC4CjrXHEZFBwAAAIDpUNEBAAAAXASTETiOig4AAAAA0yHRAQAAAGA6tK4BAAAALoLWNcdR0QEAAABgOiQ6AAAAAEyH1jUAAADARdC65jgqOgAAAABMh4oOAAAA4Coo6DiMig4AAAAA0yHRAQAAAGA6tK4BAAAALoLJCBxHRQcAAACA6ZDoAAAAADAdWtcAAAAAF0HrmuOo6AAAAAAwHSo6AAAAgIugouM4KjoAAAAATIdEBwAAAIDp0LoGAAAAuAha1xxHRQcAAACA6ZDoAAAAADAdWtcAAAAAV0HnmsOo6AAAAAAwHSo6AAAAgItgMgLHUdEBAAAAYDokOgAAAABMh9Y1AAAAwEXQuuY4KjoAAAAATIdEBwAAAIDp0LoGAAAAuAg61xxHRQcAAACA6VDRAQAAAFwEkxE4jooOAAAAANMh0QEAAABgOrSuAQAAAC6CzjXHUdEBAAAAYDokOgAAAABMh9Y1AAAAwEUw65rjqOgAAAAAMB0qOgAAAICLoKDjOCo6AAAAAEyHRAcAAACA6dC6BgAAALgIq5XeNUdR0QEAAABgOiQ6AAAAAEyH1jUAAADARTDrmuOo6AAAAAAwHSo6AAAAgIuwUNJxGBUdAAAAAKZDogMAAADAdGhdAwAAAFwEnWuOo6IDAAAAwHRIdAAAAACYDq1rAAAAgItg1jXHUdEBAAAAYDpUdAAAAAAXQUXHcVR0AAAAAJgOiQ4AAAAA06F1DQAAAHARdK45jooOAAAAANMh0QEAAABgOrSuAQAAAC6CWdccR0UHAAAAgOlQ0QEAAABcBAUdx1HRAQAAAGA6JDoAAAAATIfWNQAAAMBFMBmB46joAAAAADAdKjoAAACAi6Cg4zgqOgAAAABMh0QHAAAAgOnQugYAAAC4CCYjcBwVHQAAAACmQ6IDAAAAwHRoXQMAAABcBJ1rjqOiAwAAAMB0qOgAAAAALoLJCBxHRQcAAACA6ZDoAAAAADAdWtcAAAAAF0HnmuNIdB6xC5snOTsEwCUFVHvX2SEALunkymHODgFwPV5uzo4AqYDWNQAAAACmQ0UHAAAAcBHMuuY4KjoAAAAATIeKDgAAAOAiKOg4jooOAAAAANMh0QEAAABgOrSuAQAAAC6CyQgcR0UHAAAAgOmQ6AAAAAAwHVrXAAAAABdB55rjqOgAAAAAMB0qOgAAAICLYDICx1HRAQAAAGA6JDoAAAAATIfWNQAAAMBF0LrmOCo6AAAAAEyHRAcAAACA6dC6BgAAALgIOtccR0UHAAAAgOlQ0QEAAABcBJMROI6KDgAAAADTIdEBAAAAYDq0rgEAAAAugs41x1HRAQAAAGA6JDoAAAAATIfWNQAAAMBFMOua46joAAAAADAdKjoAAACAi6Cg4zgqOgAAAABMh0QHAAAAgOnQugYAAAC4CCu9aw6jogMAAADAdEh0AAAAAKS5KVOmKG/evPL29lZoaKg2b958z+UvX76srl27KkeOHPLy8tJTTz2lZcuWObw/WtcAAAAAF+GqnWsLFixQRESEPvroI4WGhmr8+PEKDw/XP//8o+Dg4ETL3759Wy+88IKCg4O1cOFC5cyZU0ePHpW/v7/D+yTRAQAAAJCmxo0bp44dO6pt27aSpI8++khLly7Vp59+qn79+iVa/tNPP9XFixe1fv16eXh4SJLy5s2bon3SugYAAAC4CIvFki4e0dHRunr1qt0jOjo6yZhv376trVu3qkaNGrYxq9WqGjVqaMOGDUmu8/333yssLExdu3ZVtmzZVKJECY0cOVJxcXEOP1ckOgAAAABSJDIyUn5+fnaPyMjIJJeNiopSXFycsmXLZjeeLVs2nTlzJsl1Dh06pIULFyouLk7Lli3TwIEDNXbsWL333nsOx0jrGgAAAIAU6d+/vyIiIuzGvLy8Um378fHxCg4O1scffyw3NzeVLVtWJ0+e1AcffKDBgwc7tA0SHQAAAMBFWNPJZAReXl4OJzZBQUFyc3PT2bNn7cbPnj2r7NmzJ7lOjhw55OHhITc3N9tY0aJFdebMGd2+fVuenp733S+tawAAAADSjKenp8qWLatVq1bZxuLj47Vq1SqFhYUluU7lypV14MABxcfH28b27dunHDlyOJTkSCQ6AAAAANJYRESEZsyYodmzZ2vPnj3q3Lmzrl+/bpuFrVWrVurfv79t+c6dO+vixYvq0aOH9u3bp6VLl2rkyJHq2rWrw/ukdQ0AAABwERYXvZFO06ZNdf78eQ0aNEhnzpxRSEiIli9fbpug4NixY7Ja/1eDyZ07t1asWKFevXqpVKlSypkzp3r06KG+ffs6vE+LYRhGqh8JknUjhqcbeBCBzw5wdgiASzq5cpizQwBcTpaMbvdfyEle/Gizs0OQJC17o4KzQ7gvKjoAAACAi3DRgo5TcI0OAAAAANMh0QEAAABgOrSuAQAAAC7CInrXHEVFBwAAAIDpkOgAAAAAMB1a1wAAAAAXYaVzzWFUdAAAAACYDhUdAAAAwEVYuJGOw6joAAAAADAdEh0AAAAApkPrGgAAAOAi6FxzHBUdAAAAAKZDRQcAAABwEVZKOg6jogMAAADAdEh0AAAAAJgOrWsAAACAi6BzzXFUdAAAAACYDokOAAAAANOhdQ0AAABwERZ61xxGRQcAAACA6VDRAQAAAFwEBR3HUdEBAAAAYDokOgAAAABMh9Y1AAAAwEVY6V1zGBUdAAAAAKZDogMAAADAdGhdAwAAAFwEjWuOo6IDAAAAwHSo6AAAAAAuwsJkBA6jogMAAADAdEh0AAAAAJgOrWsAAACAi7DSueawVKnoXL58OTU2AwAAAACpIsWJzqhRo7RgwQLbz02aNFFgYKBy5sypHTt2pGpwAAAAAPAgUpzofPTRR8qdO7ck6aefftJPP/2kH3/8UbVr19Zbb72V6gECAAAAuMNisaSLhytI8TU6Z86csSU6S5YsUZMmTVSzZk3lzZtXoaGhqR4gAAAAAKRUiis6AQEBOn78uCRp+fLlqlGjhiTJMAzFxcWlbnQAAAAAbCyW9PFwBSmu6DRu3FgtWrRQoUKFdOHCBdWuXVuStH37dhUsWDDVAwQAAACAlEpxovPhhx8qb968On78uEaPHq1MmTJJkk6fPq0uXbqkeoAAAAAAkFIpTnQ8PDzUp0+fROO9evVKlYAAAAAAJM1VJgJIDxxKdL7//nuHN1i/fv0HDgYAAAAAUoNDiU7Dhg0d2pjFYmFCAgAAAABO51CiEx8fn9ZxAAAAALgPK51rDkvx9NL/devWrdSKAwAAAABSTYoTnbi4OA0fPlw5c+ZUpkyZdOjQIUnSwIEDNXPmzFQPEAAAAMAdFoslXTxcQYoTnREjRuizzz7T6NGj5enpaRsvUaKEPvnkk1QNLr0xDMPZIQAAAABwQIoTnTlz5ujjjz/Wq6++Kjc3N9t46dKltXfv3lQNzpkOHTqkgQMHqnPnzpo4caKkOxk0yQ4AAACQ/qX4PjonT55UwYIFE43Hx8crJiYmVYJyth07dqhWrVoqWrSoTp06penTp2v37t2aPn26y5TqAAAAYD78Jeq4FFd0ihUrpt9++y3R+MKFC1WmTJlUCcqZdu3apUqVKql9+/b66aeftGbNGn344YeaMWOGvv76a2eHBwAAAMABKa7oDBo0SK1bt9bJkycVHx+vb775Rv/884/mzJmjJUuWpEWMj8ylS5f08ssvq1ChQnrvvfckSTly5FCtWrX0/vvvm6ZiBQAAAJhdiis6DRo00A8//KCff/5ZGTNm1KBBg7Rnzx798MMPeuGFF9IixkcmLi5OLVq00IkTJxQZGWk3fuXKFQUEBDgxOgAAADzurBZLuni4ghRXdCSpatWq+umnn1I7FqfZt2+frly5ovLly6tr167y9vZWZGSk/Pz81K5dO9WsWVMdO3ZU7dq1nR0qAAAAAAc88A1Dt2zZorlz52ru3LnaunVrasb0SO3YsUNFihTRxo0bJUlBQUFq3769+vXrp3feeUeZMmVS8+bNNWHCBBmGofj4eCdHDAAAgMeVxZI+Hq4gxRWdEydOqHnz5lq3bp38/f0lSZcvX1alSpX05ZdfKleuXKkdY5rZsWOHKlWqpHfeeUfdunWzjQcFBalVq1by8PDQyJEjbS1rFouFRAcAAABwASmu6HTo0EExMTHas2ePLl68qIsXL2rPnj2Kj49Xhw4d0iLGNLFz505VrlxZPXv2tE08IEnz58+XJD3xxBN67bXX9Pbbb2vUqFEaPXq0JNndOwgAAABA+pTiis4vv/yi9evXq3DhwraxwoULa9KkSapatWqqBpdWTp06pZCQEHXo0EEjRoywjY8aNUr9+/dXoUKFVK5cOQUHB6t9+/ayWq3q27evPD091bNnT+cFDgAAgMca93R0XIoTndy5cyc5zXJcXJyeeOKJVAkqrXl5eal06dL6448/tH//fhUqVEjvv/++xowZoxUrVqhcuXIyDEMWi0VBQUFq166dPDw8mIwAAAAAcBEpbl374IMP1K1bN23ZssU2tmXLFvXo0UNjxoxJ1eDSgmEYCgwM1M8//ywPDw81adJEffr00dixYzV//nzbFNkJ2fLu3bvl5+enHj162FWxAAAAAKRfDlV0AgIC7Mpk169fV2hoqNzd76weGxsrd3d3tWvXTg0bNkyTQFNbYGCgli9frkaNGmncuHGaOXNmovsA9e3bV8uWLdOvv/7KPXQAAADgdHSuOc6hRGf8+PFpHEbaOn36tDZu3KgVK1bIMAzVrl1b5cuXV86cObV48WLVrVtXY8eOVaVKlWxVm8GDB2vSpElau3YtSQ4AAADgYhxKdFq3bp3WcaSZ3bt3q02bNvLz89OZM2fk4+OjGTNmqEGDBoqIiFDVqlW1dOlShYeHq1GjRvr55581Y8YMjRo1SuvWrVPZsmWdfQgAAACAJMlKScdhKZ6M4L9u3bql27dv2435+vo+VECpaceOHapWrZpef/11derUSQUKFJAkzZo1S71799bNmzc1YsQIlS1bVj/++KPq1aunXLlyycfHhyQHAAAAcGEpnozg+vXrevPNNxUcHKyMGTMqICDA7pFe7N69W1WqVFGPHj00evRo5c2bV5IUHx+vtm3bavLkyVqzZo0WLFgg6c41O999952aNm1KkgMAAAC4uBQnOm+//bZWr16tadOmycvLS5988omGDh2qJ554QnPmzEmLGFPs1q1bat68ubJkyaJWrVpJunOjz4QpoyWpRYsW6tmzp6ZNm6aTJ09KkrJmzaovvvhCISEhzgodqWjB/C/0Ys3nFfp0Kb3WvIn+2rXznsv/tGK5GtWrrdCnS+mVRvX026+/2P1+1U8r1bljOz1bOVRlShTRP3v3pGX4gNN0ahyqvQv76NLqIfr14zdUrmiuZJd1d7Oqf9vntPurCF1aPUSbPntTL4QWSnb5Pi2f0c11I/RBjxfTInTAqRYumKdGdWqoWsUQtW/VVLv/uvfnzqqflqtp4zqqVjFErzZpoPW/23/uDB/8jsKeLmb36Nn19bQ8BLgAiyV9PFxBihOdH374QVOnTtVLL70kd3d3Va1aVQMGDNDIkSP1xRdfpEWMKebt7a1Ro0bJw8ND7733nnbs2CHpf1NGx8XFSZJq1Kghq9Wq8+fP29a1WlP8lCAdWvHjMo0d/b46de6qeV9/o6cKF1aXTh108cKFJJf/c/s29X+7txo2elnzv16sZ5+voYjub+rA/n22ZW7evKmQp8uqe68+j+owgEfu5eolNarbixrx6WqFtZuinQfO6PtxbZTVP2OSyw95/QV1aFBBER8uUZmWE/TJt5u1IPJVlS6UI9GyZYvkVPsG5bVz/+m0Pgzgkft5xY+aOG6U2r/eRZ/NW6hChYqoV9fXdfFi0p87O3ds1+B33lK9Bo01e94iPfNsdfWN6KaDB/bbLVexUhUtWfmL7TEs8oNHcTiAKaT4r/qLFy8qf/78ku5cj3Px4kVJUpUqVfTrr7+mbnQPKGFmtSlTpmj16tUaP368du3aJelOspOQ8Bw7dky5c+fWk08+6cxwkQY+n/OZGr/8iho0ekkFChTUu4OGytvbW98uXpTk8vM/n6tKlauodbv2yl+ggLp266GixYrpy3n/S97r1m+gTp27qmJY2KM6DOCR6960smb9sEVzl23T3iPn1e2D73QzOkat6ybdztuiVohGz1mrFRv26cipS5rx7Wat2PCPejSvYrdcRh9PzRrcRF1GfavL124+ikMBHqn5X3ym+o1eUd0GjZUvf0G9/e5geXl7a8l33yS5/Ffz5io0rIpatm6vvPkLqFOX7ipcpJgWLrD/0tjT01OBQVltD19fv0dxOIAppDjRyZ8/vw4fPixJKlKkiL766itJdyo9/v7+qRrcg7JYLDIMQ+Hh4fr444+1evVqjR07Vjt33ikhW61WxcTEaNOmTQoNDVXGjEl/UwnXFBNzW3v+3q3QipVsY1arVaEVw7Rzx59JrrNzx58KDatkNxZWqXKyywNm5OHupjKFn9DqPw7YxgzD0OotB1ShRNJfCHl6uOvW7Vi7sZvRsapUKo/d2Pje9bR8wz9as+Vg6gcOOFlMzG39s+dvlQ+taBuzWq0qHxqmv3b+meQ6f+36U+VD7b84Cw2rrL927rAb27blD71YvYqaNnpRo0cO1ZXLl1M7fLiYhC/tnf1wBSmeda1t27a22cz69eunevXqafLkyYqJidG4cePSIsYHEhsbKw8PD4WHh2v69Onq1KmTJCkiIkKlSpXSiBEjtGTJEq1evVpeXl5pEkN0dLSio6PtxuKsnmm2P9xx6dIlxcXFKUtgoN14YGCQjvx/kn63qKioxMsHBelCVFSaxQmkN0H+GeTu7qZzF/+1Gz938V8VfjJrkuv8vGm/ujerrN//PKJDJy/quXL51aBaMbn9pw34leolFfLUE6rSYVqaxg84y+XLl+987mQJshvPkiVQR48cSnKdC0l87mQJDNKFC//73KlYqYqefb6GcjyRSydPHNNHk8erV7dOmvHZPLm5uaX+gQAmk+JEp1evXrb/rlGjhvbu3autW7eqYMGCKlWqVKoG54j/TjCQIC4uTh4eHjpx4oSuXLmiWrVq6eOPP9brr78uHx8fxcbG6vPPP9e6detUpEiRNIstMjJSQ4cOtRt7Z8AgvTtoSJrtEwAepT4Tlmhq30baMa+nDMPQoVMXNWfpNlurW65gP33Qs67q9vxU0XdVfgDc2wvh/5u0o2Chp1SwUGG9XD9c27ZsTlQNwuODq8kd91D30ZGkPHnyKE+ePPdfMA0cOnRIixYt0p49ezRmzBhlyZJF8fHxcnNz09GjR1WsWDFFRERo2LBhCg8P14wZM9S4cWPFx8dr/fr1KlOmTJrG179/f0VERNiNxVk903SfkAICAuTm5pZo4oELF6IUGBSU5DpBQUGJl49KfnnAjKIu31BsbJyCs2SyGw/Okkln7qry/HedJv2/kJenuwJ9M+hU1FW91zlch0/duX6zTOEnlC1LJm34tKttHXd3N1UJyas3GleU33ODFR9vpN1BAY+Av7//nc+di/ZdABcvXlBgYNKfI4FJfO5cvBCV7PKSlDNXbvn7B+jE8WMkOoADHEp0Jk6c6PAGu3fv/sDBpMSuXbvUuHFjvfDCC/Lz81OGDBkk3emJPXfunCpWrKhWrVpp6NChtmt2atasqZUrVypr1qwqVCj56U9Ti5eXV6I2tRsxfKCnNQ8PTxUtVlybNm3Qc9VrSLpz/6TNmzaqafNXk1ynVOkQbd64Qa++1to2tnHDepUqHfIoQgbShZjYOG3/55SeK1dAP/x2Z/p0i8Wi58oW0EeLNt5z3ejbsToVdVXublY1fLa4Fq2+MwHMmq0HVbblBLtlP373Jf1z9LzGfv4rSQ5MwcPDU4WLFtOWzRtV7bn/fe5s2bxRLzdtkeQ6JUqGaMvmjWr2aivb2OZNG1SiVOlk93Pu7BlduXJZQVmTbiUFYM+hROfDDz90aGMWi+WRJDr79+9X9erV1a5dO40YMcLWp5rQxvbXX3+pf//+6tatW6K2tkqVKiW1SZhMy1ZtNOjdfipWvIRKlCileZ/P1s2bN9WgYWNJ0oD+fRUcHKzuvXpLkpq3fE0d27bSnM8+VdVnntWKH5fq7927NXDIMNs2r1y5rDOnT+vcuXOSZLveJzAoSEFBfOjAHCYuWKcZ776krXtPasvfJ/Rmk0rK4O2pOUu3SpI+GfCyTkVd1aCPVkqSyhfLpSey+mrH/tPKmdVX77arLqvFonFf/CZJ+vfGbf19+JzdPq7fvK2LV28kGgdcWfNX22j44P4qUqyEihcvqS/nzdGtmzdVt34jSdLQgf2UNThYXbrd6fRo0uI1denYWvPmzlKlKtX084pl2vv3X+o34E7L+40b1zVz+lQ9V72mAoOCdOL4MU2ZMFa5cj+p0LAqycYB83OViQDSA4cSncPJXMDtDHFxcZowYYJq1KihwYMH212Ml/AP//zzz+v555+3W4+T4vESXvtFXbp0UdMmT9KFqPMqXKSopnw0w9aKdub0KVmt/zsnQso8rZGjxmjKpPGaPOFDPZknr8ZNnKyChZ6yLfPLmtUaPOAd28/93rrzYdWpc1e90bXbIzoyIG0tXLVLQf4ZNahDdWXLklk7959Wg96f6dyl65Kk3Nn8FG/8rwrj5emuwR1fUL4nAvTvzdtasWGf2g//Wlf+veWsQwCcokZ4bV26dFGfTJukCxeiVKhwEX04ebqy/H8r2tkzp+3u1VeqdBkNHTFaH0+dqI8mj1fuJ/No1LhJKlDwTseJ1eqmg/v36ccl3+natasKyhqs0IqV9XqXbvL0pA0ecITFMAyX6xsoV66catasqZEjRyb6XVxcnNzc3BQfHy+r1Wr7//SC1jXgwQQ+O8DZIQAu6eTKYfdfCICdLBnT76x23b/d6+wQJEkTG6bdhF6pJf1kAA4wDEP//vuvTpw4ocD/n5Lx7jwtIcnp0aOHLl++nK6SHAAAAOBhWC3p4+EKXCoLMAxD3t7eCgkJ0eLFi3Xo0CFbS9p/E55Dhw5p69atOn36tLNCBQAAAOBELpXoWK1Wubu7q2HDhlq/fr1mzpypU6dOSbK/Bmfu3Lny8fFR9uzZnRUqAAAAkOqcXclxpYrOQ99HJy0dPHhQ8+bN06ZNm+Tl5aXcuXNrwIABeuONN3Tw4EFFRkbqypUratmypSpWrKidO3dq1qxZmj17tn755RcFBAQ4+xAAAAAAOMEDJTq//fabpk+froMHD2rhwoXKmTOn5s6dq3z58qlKldSZ8nDnzp2qWbOmypcvL39/f505c0arV6/WkiVLNGHCBI0aNUo+Pj6aMGGCZsyYoSxZssjf319eXl5avXq1SpYsmSpxAAAAAHA9KU50Fi1apNdee02vvvqqtm/frujoaEnSlStXNHLkSC1btuyhgzp69Kjq1Kmjdu3aafjw4XJzc1NsbKx27typ119/XZ06ddKiRYs0bNgwVa9eXWfPntWhQ4cUGhqqokWL0rIGAAAAU+KWKY5L8fTSZcqUUa9evdSqVStlzpxZO3bsUP78+bV9+3bVrl1bZ86ceeigJk2apKVLl2rx4sXy8fGx3QhUutPOVr9+ffn5+Wn9+vUPva9HjemlgQfD9NLAg2F6aSDl0vP00r1/+MfZIUiSxtYr7OwQ7ivFkxH8888/euaZZxKN+/n56fLly6kRk7Zv3664uLhESY4k5cuXTz169ND27du1bdu2VNkfAAAA4AqcPQmBK01GkOJEJ3v27Dpw4ECi8d9//1358+dPlaAsFovdbGr/LTpZrVbVqFFD0dHRunDhQqrsDwAAAIC5pDjR6dixo3r06KFNmzbZEpIvvvhCffr0UefOnR8qmISEpmrVqjpz5oymTp0q6U6yExsbK0mKj49XVFSUihUrpnz58j3U/gAAAACYU4onI+jXr5/i4+NVvXp13bhxQ88884y8vLzUp08fdevWLcUB3Lp1S97e3nZjderU0YcffqixY8fK399fLVq0kLv7nVCtVqsWL16sjBkzKkuWLCneHwAAAOCqmIvAcSmejCDB7du3deDAAf37778qVqyYMmXKlOJtnDx5Ur169VLnzp313HPPSZJiY2Pl7u6uw4cP65lnnpHFYlGdOnX05ptv6uDBg/r11181ffp0/fbbbwoJCXmQ0J2KyQiAB8NkBMCDYTICIOXS82QEby9NH5MRjK6T/icjeOAbhnp6eqpYsWIPtfPo6GidOHFCY8eOlaenpypXrix3d3fFxMQoX758Wrdunfr27atvv/1WM2bMUL58+fTkk09q3bp1KlWq1EPtGwAAAIB5pbii89xzz91z/u7Vq1enKID9+/ere/fuMgxDAwcOVOXKlSXdqRh5enrq5s2bio6O1ooVK1SvXj3FxMTIz88vRftIT6joAA+Gig7wYKjoACmXnis6/Zbtc3YIkqT3X3zK2SHcV4onIwgJCVHp0qVtj2LFiun27dvatm2bSpYsmeIAChUqpIkTJ8pisWj48OFat26dpDsVo7i4OFmtVo0cOVJff/21PDw8XDrJAQAAAPBopLh17cMPP0xyfMiQIfr3338fKIiEZKd79+4aPny4rbITFxenPn36aNq0afrjjz/k4eHxQNsHAAAAzCDFVYrHWKo9Vy1bttSnn376wOvfXdlZu3atBg4cqJkzZ+qPP/5QmTJlUitUAAAAACaXaonOhg0bEk0TnVIJyY6Hh4caN26s8ePH6/fffyfJAQAAAJAiKW5da9y4sd3PhmHo9OnT2rJliwYOHPjQARUqVEhjxozR22+/rZEjR6p48eIPvU0AAADADLiPjuNSnOjcPRmA1WpV4cKFNWzYMNWsWTNVgipcuLAWLlzINTkAAAAAHkiKEp24uDi1bdtWJUuWVEBAQFrFJEkkOQAAAAAeWIoSHTc3N9WsWVN79uxJ80QHAAAAgD0rvWsOS/FkBCVKlNChQ4fSIhYAAAAASBUpTnTee+899enTR0uWLNHp06d19epVuwcAAACAtGGxpI+HK3C4dW3YsGHq3bu3XnzxRUlS/fr1ZfnPURqGIYvFori4uNSPEgAAAABSwOFEZ+jQoXrjjTe0Zs2atIwHAAAAAB6aw4mOYRiSpGrVqqVZMAAAAACSZ3WRtrH0IEXX6FhcpSEPAAAAwGMtRdNLP/XUU/dNdi5evPhQAQEAAADAw0pRojN06FD5+fmlVSwAAAAA7oH76DguRYlOs2bNFBwcnFaxAAAAAECqcDjR4focAAAAwLn4k9xxDk9GkDDrGgAAAACkdw5XdOLj49MyDgAAAABINSm6RgcAAACA83AfHcel6D46AAAAAOAKSHQAAAAAmA6tawAAAICLsIjeNUdR0QEAAABgOlR0AAAAABfBZASOo6IDAAAAwHRIdAAAAACYDq1rAAAAgIugdc1xVHQAAAAAmA6JDgAAAADToXUNAAAAcBEWC71rjqKiAwAAAMB0qOgAAAAALoLJCBxHRQcAAACA6ZDoAAAAADAdWtcAAAAAF8FcBI6jogMAAADAdEh0AAAAAJgOiQ4AAADgIqwWS7p4PIgpU6Yob9688vb2VmhoqDZv3uzQel9++aUsFosaNmyYov2R6AAAAABIUwsWLFBERIQGDx6sbdu2qXTp0goPD9e5c+fuud6RI0fUp08fVa1aNcX7JNEBAAAAXITVkj4eKTVu3Dh17NhRbdu2VbFixfTRRx8pQ4YM+vTTT5NdJy4uTq+++qqGDh2q/Pnzp/y5SnmYAAAAAB5n0dHRunr1qt0jOjo6yWVv376trVu3qkaNGrYxq9WqGjVqaMOGDcnuY9iwYQoODlb79u0fKEYSHQAAAAApEhkZKT8/P7tHZGRkkstGRUUpLi5O2bJlsxvPli2bzpw5k+Q6v//+u2bOnKkZM2Y8cIzcRwcAAABwEenlPjr9+/dXRESE3ZiXl1eqbPvatWt67bXXNGPGDAUFBT3wdkh0AAAAAKSIl5eXw4lNUFCQ3NzcdPbsWbvxs2fPKnv27ImWP3jwoI4cOaJ69erZxuLj4yVJ7u7u+ueff1SgQIH77pfWNQAAAABpxtPTU2XLltWqVatsY/Hx8Vq1apXCwsISLV+kSBHt2rVLf/75p+1Rv359Pffcc/rzzz+VO3duh/ZLRQcAAABwEValk961FIqIiFDr1q1Vrlw5VahQQePHj9f169fVtm1bSVKrVq2UM2dORUZGytvbWyVKlLBb39/fX5ISjd8LiQ4AAACANNW0aVOdP39egwYN0pkzZxQSEqLly5fbJig4duyYrNbUbTazGIZhpOoWcU83Yni6gQcR+OwAZ4cAuKSTK4c5OwTA5WTJ6ObsEJI1df0RZ4cgSepSKa+zQ7gvrtEBAAAAYDokOgAAAABMh2t0AAAAABdhdc25CJyCig4AAAAA0yHRAQAAAGA6tK4BAAAALsJqoXfNUVR0AAAAAJgOFR0AAADARVDQcRwVHQAAAACmQ6IDAAAAwHRoXQMAAABcBJMROI6KDgAAAADToaIDAAAAuAgKOo6jogMAAADAdEh0AAAAAJgOrWsAAACAi6BK4TieKwAAAACmQ6IDAAAAwHRoXQMAAABchIVp1xxGRQcAAACA6VDRAQAAAFwE9RzHUdEBAAAAYDokOgAAAABMh9Y1AAAAwEVYmYzAYVR0AAAAAJgOiQ4AAAAA06F1DQAAAHARNK45jooOAAAAANOhogMAAAC4COYicBwVHQAAAACmQ6IDAAAAwHRoXQMAAABchIXeNYdR0QEAAABgOiQ6AAAAAEyH1jUAAADARVClcBzPFQAAAADToaIDAAAAuAgmI3AcFR0AAAAApkOiAwAAAMB0aF0DAAAAXASNa46jogMAAADAdEh0AAAAAJgOrWsAAACAi2DWNceR6Dxit27HOzsEwCXtWjzA2SEALqnwmwudHQLgcs7PaursEJAKSHQAAAAAF8F1J47juQIAAABgOiQ6AAAAAEyH1jUAAADARTAZgeOo6AAAAAAwHRIdAAAAAKZD6xoAAADgImhccxwVHQAAAACmQ0UHAAAAcBHMReA4KjoAAAAATIdEBwAAAIDp0LoGAAAAuAgr0xE4jIoOAAAAANMh0QEAAABgOrSuAQAAAC6CWdccR0UHAAAAgOlQ0QEAAABchIXJCBxGRQcAAACA6ZDoAAAAADAdWtcAAAAAF8FkBI6jogMAAADAdEh0AAAAAJgOrWsAAACAi7Ay65rDqOgAAAAAMB0qOgAAAICLYDICx1HRAQAAAGA6JDoAAAAATIfWNQAAAMBF0LrmOCo6AAAAAEyHRAcAAACA6dC6BgAAALgIC/fRcRgVHQAAAACmQ0UHAAAAcBFWCjoOo6IDAAAAwHRIdAAAAACYDq1rAAAAgItgMgLHUdEBAAAAYDpUdAAAAAAXYaGg4zAqOgAAAABMh0QHAAAAgOnQugYAAAC4CCYjcBwVHQAAAACmQ6IDAAAAwHRoXQMAAABchJXONYdR0QEAAABgOlR0AAAAABfBZASOo6IDAAAAwHRIdAAAAACYDq1rAAAAgIuw0LnmMCo6AAAAAEyHRAcAAACA6dC6BgAAALgIOtccR0UHAAAAgOlQ0QEAAABchJXZCBxGRQcAAACA6ZDoAAAAADAdWtcAAAAAF0HjmuOo6AAAAAAwHRIdAAAAAKZD6xoAAADgKuhdcxgVHQAAAACmQ0UHAAAAcBEWSjoOo6IDAAAAwHRIdAAAAACYDq1rAAAAgIuw0LnmMCo6AAAAAEyHRAcAAACA6dC6BgAAALgIOtccR0UHAAAAgOlQ0QEAAABcBSUdh1HRAQAAAGA6JDoAAAAATIfWNQAAAMBFWOhdcxgVHQAAAACmQ6IDAAAAwHRoXQMAAABchIXONYdR0QEAAABgOlR0AAAAABdBQcdxVHQAAAAAmA6JDgAAAADToXUNAAAAcBX0rjmMig4AAAAA0yHRAQAAAGA6tK4BAAAALsJC75rDqOgAAAAASHNTpkxR3rx55e3trdDQUG3evDnZZWfMmKGqVasqICBAAQEBqlGjxj2XTwqJDgAAAOAiLJb08UipBQsWKCIiQoMHD9a2bdtUunRphYeH69y5c0kuv3btWjVv3lxr1qzRhg0blDt3btWsWVMnT550/LkyDMNIeah4UBevxzk7BMAlXbx+29khAC4prO/3zg4BcDnnZzV1dgjJ+vPYNWeHIEkqms1T0dHRdmNeXl7y8vJKcvnQ0FCVL19ekydPliTFx8crd+7c6tatm/r163ff/cXFxSkgIECTJ09Wq1atHIqRig4AAACAFImMjJSfn5/dIzIyMsllb9++ra1bt6pGjRq2MavVqho1amjDhg0O7e/GjRuKiYlRlixZHI6RyQgAAAAAF5FepiLo37+/IiIi7MaSq+ZERUUpLi5O2bJlsxvPli2b9u7d69D++vbtqyeeeMIuWbofEh0AAAAAKXKvNrXU9v777+vLL7/U2rVr5e3t7fB6JDoAAAAA0kxQUJDc3Nx09uxZu/GzZ88qe/bs91x3zJgxev/99/Xzzz+rVKlSKdov1+gk4/z589q8ebO2bNmiGzduODscAAAA4E7vWnp4pICnp6fKli2rVatW2cbi4+O1atUqhYWFJbve6NGjNXz4cC1fvlzlypVL2U5FRSdJu3btUrNmzWQYhvbu3au2bduqT58+Klq0qLNDAwAAAFxORESEWrdurXLlyqlChQoaP368rl+/rrZt20qSWrVqpZw5c9omNBg1apQGDRqkefPmKW/evDpz5owkKVOmTMqUKZND+yTRucvOnTsVFham7t2767XXXtOvv/6qrl27qlSpUiQ6AAAAcCpLupmOIGWaNm2q8+fPa9CgQTpz5oxCQkK0fPly2wQFx44dk9X6v2azadOm6fbt23r55ZfttjN48GANGTLEoX1yH53/2LNnj0JCQtStWzeNGTNGknTlyhUVK1ZMJUqU0LJly+Tm5mZbPj4+3u4fxBHcRwd4MNxHB3gw3EcHSLn0fB+dncf/dXYIkqRSuR2rqjgT1+j8x9atWxUTE6Mnn3xSV69elSRNmTJFp0+f1o0bN9SnTx/NmjVLv/zyiyTJ8iC3hQUAAACQ5mhd+4+WLVsqKipKPXv2lJeXl86ePasPP/xQM2fOlL+/v/bt26dZs2bpyJEjCgoKUsuWLdWrVy8SHgAAADwS/NnpOBKdu/Ts2VNxcXHq3LmzLBaLFi9erPr169t+365dO504cULjx49XvXr1SHIAAACAdOixTnSOHz+un3/+WfHx8SpatKgqVaokSerdu7d8fX3VqVMnHTx4UFeuXJGfn58kKTAwUFmzZtXs2bOdGToAAACAe3hsE52dO3eqfv36Cg4O1qFDhxQQEKDRo0erUaNGkqSOHTvq2rVr6t27t2JiYvTGG2/I19fXNvmAYRhUcwAAAPBI8den4x7LyQgSppBu1qyZ1qxZo3nz5un69euaNWuWbt68qZiYGEl35vseM2aMBg4cqA8//FDXrl2zbYMkBwAAAEi/HruKzvHjx1W9enXVq1dP77//viSpZs2aeuKJJ7R//37FxsbKx8fHtnxERISuXbumCRMm6M0333RW2AAAAAAlnRR47Co6+/fvV968eXXr1i1t2LBBkhQZGalt27bJ19dXLVu2VIcOHTRt2jQdP35ccXFxGjx4sPbv36/AwEAnRw8AAADAEY/lDUOXLl2qSZMmycfHR8HBwVq8eLGmTJmiChUqaMuWLdq9e7cmT54sb29vlS5dWt9//70Mw0jxzUGTwg1DgQfDDUOBB8MNQ4GUS883DP3rZPq4YWiJnOn/hqGPVetabGys3N3dVadOHcXHx2vixIlavny5RowYoVdeeUWSlCdPHr300kvq3Lmz1qxZo6effloWi4VrcgAAAOB0FnrXHGb6ROf8+fPy8fFRpkyZ5O7urpiYGHl4eKhevXry8PCQxWLR6tWrFRYWprCwMElSTEyMsmbNqiZNmjg5egAAAAAPwtTX6Bw7dkx58uRRjx491KVLF/377792lZlatWrpzTffVGxsrIYNG6aNGzdKkjw8PJwVMgAAAJAsiyV9PFyBqROdo0eP6tatWypdurQuX76ssLAw9e/fX2vXrrUtU79+fb3xxhuyWq2KiIjQH3/84byAAQAAAKQKUyc6VatWVdeuXXX06FHNmzdPPXr0kJubm+rXr68uXbpozpw5kqSGDRuqW7duypYtm7Jly+bkqJFaFi6Yp0Z1aqhaxRC1b9VUu//aec/lV/20XE0b11G1iiF6tUkDrf/9F7vfDx/8jsKeLmb36Nn19bQ8BMAplnzzpdq+UlsNq1dQr9db6p+/dyW77NHDBzRiQG+1faW26lQN0bdffX7PbX/1+aeqUzVEH08cndphA+lOu+cLausHdXX845e1fEANlcmX5Z7Ld3rhKW0YWVvHpr+kP8fW0/BmIfJyN/WfakCaMu2rJ2EyudDQUG3fvl3Xrl1Thw4d9P7776tYsWJasmSJhgwZokqVKmnixImqWrWq5s+fryeffNLJkSM1/LziR00cN0rtX++iz+YtVKFCRdSr6+u6ePFCksvv3LFdg995S/UaNNbseYv0zLPV1Teimw4e2G+3XMVKVbRk5S+2x7DIDx7F4QCPzK+rVmjG5LFq0aaTJn4yX/kKPqWBvbvo8qWLSS4ffeuWsufIqTadeiggS9A9t71vz19a/v1C5SvwVFqEDqQrDSvk1rBmIRrz3W5VH7JSu49f1le9qykos1eSyzeu+KQGvFJKH3y/W5Xf+VE9P92shhWe1Lsvl3rEkSO9s6SThyswbaKTcC1Oy5YtdebMGdvNQVu3bq0TJ05owYIFWrNmjQIDAzV//nxdunRJ3t7ezgwZqWj+F5+pfqNXVLdBY+XLX1BvvztYXt7eWvLdN0ku/9W8uQoNq6KWrdsrb/4C6tSluwoXKaaFC76wW87T01OBQVltD19fv0dxOMAjs3jBXNWq11gv1GmoJ/MV0Jt9Bsjb21srl36b5PJPFS2h9l0jVK1GLXl4Jn99480bN/TBsHfU7e1BypQ5cxpFD6Qfb9QsrM9/PaT5vx/WvlNX1WfOFt28HasWVfMluXyFgkHavD9K32w8puMXbmjt7rP6ZtMxPX2fKhCA5Jk20ZGkuLg796wZMmSI9u3bp+rVq2vlypX67rvvFBYWpjx58uiHH37QsmXLlCtXLidHi9QSE3Nb/+z5W+VDK9rGrFaryoeG6a+dfya5zl+7/lT50DC7sdCwyvpr5w67sW1b/tCL1auoaaMXNXrkUF25fDm1wwecJiYmRgf27VFI2VDbmNVqVUi5UO3dfe/Wz/uZ9uFIlQ+rqjLlKt5/YcDFebhZVTpvgH7ZfdY2ZhjSr3+fVbmCSVc+Nx+IUum8Abb2tjxZM6pGqRz6eefpRxIzYEamnV46Li5Obm5ukqTixYvr7bff1r///qs///zTltTEx8fLarUqICAgTWKIjo5WdHS0/Visu7y8ki5bI3VcvnxZcXFxynJXG02WLIE6euRQkutciIpSlsBA++UDg3ThQpTt54qVqujZ52soxxO5dPLEMX00ebx6deukGZ/Ns51rgCu7euWS4uPi5J/F/rXgHxCo40ePPPB2f/l5uQ7s26vxH39x/4UBE8iS2VPubladv3rLbvzclVsqmN03yXW+2XhMgZm8tOSd52WRRR7uVs1afUDjl+55FCHDlbhK31g64PIVnYRrcf4rIck5efKkdu/ereLFi6tfv34KCAhQbGysbTmrNW0PPzIyUn5+fnaP8WPeT9N9Iu28EP6iqlZ7XgULPaVqz9XQmAnTtGf3Lm3bstnZoQHp1vmzZ/TxxNF6a+BIefIlD5CsSoWzqmfdouo7d5uqD12p1pN+1wulcyiiXjFnhwa4LJdOdA4dOqQxY8aoXbt2unjxzoWy8fHxcnNz09GjR/XUU0/pyy+/lCRVqFBBWbJk0S+//HKvTaaq/v3768qVK3aPnn36PbL9P678/f3l5uamixej7MYvXrygwMCkWwYCg4J08YL9RAUXL0Qlu7wk5cyVW/7+ATpx/NjDBw2kA75+AbK6uenyXZN2XL50QQH3eC3cy4F//tblSxfVvUNz1Xu2rOo9W1a7/tyq7xfOV71ny9pajAEzuXjttmLj4pXV1/7a32A/b527q8qToH/jkvpq/VF9/ush7TlxRcu2ndSIRbvUo05Rl7lnCR4NSzr5nytw2URn165dCg8P1+HDh+Xn56cMGTJIulOlOXfunCpWrKhWrVpp6NChkqSnn35aAQEBmjx5sl1VJy15eXnJ19fX7kHbWtrz8PBU4aLFtGXzRttYfHy8tmzeqBKlQpJcp0TJELvlJWnzpg0qUap0svs5d/aMrly5rKCsWVMlbsDZPDw8VPCpovpz6/+qlPHx8fpz62YVKf5gMz+VLheqKbMXatKnC2yPQkWK6dkXXtSkTxfQ9glTiomL144jl/RMsf/dssJikaoWzaYtB6KSXMfH003xd3WpxMXf+dlV/qgE0huXvEZn//79ql69utq1a6cRI0bYPigNw5DFYtFff/2l/v37q1u3brJYLLZWtqlTp8owDLm7u+RhIwWav9pGwwf3V5FiJVS8eEl9OW+Obt28qbr1G0mShg7sp6zBwerSLUKS1KTFa+rSsbXmzZ2lSlWq6ecVy7T377/Ub8CdRPnGjeuaOX2qnqteU4FBQTpx/JimTBirXLmfVGhYFacdJ5DaGjV9TeNGDlShIsX0VNES+u7rL3Tr5k298GIDSdLY9wYoMChYbd7oLunOBAbHjhyUJMXGxOrC+XM6uH+vfHwy6IlcTypDhozKm7+g3T68vX3k6+eXaBwwk49W/qNJHUL155GL2nbogjrVLKwMXu6a//thSdLkDqE6c/mG3lt45z5VK/48pc7hhbXr6CVtO3RR+YIzqX+jElq541SiBAiAY1zuL/64uDhNmDBBNWrU0ODBg+2+DUyYUvr555/X888/bxtPWCZ//vyPNlg4TY3w2rp06aI+mTZJFy5EqVDhIvpw8nRl+f/2m7NnTttdo1WqdBkNHTFaH0+dqI8mj1fuJ/No1LhJKlCwkCTJanXTwf379OOS73Tt2lUFZQ1WaMXKer1LN3l6ejrlGIG08Ez1cF25fEmfz5ymSxejlL9gYQ0bM1UB/z9Bwfmzp23vtZJ0MeqcurdrZvv5my/n6Jsv56hkSFm9P2nmI48fSC++3XxcgZm91LdhCQX7eeuvY5fVdNwvOn/1ziRFuQIz2F1nPO6Hv2VIeqdxSWUP8NGFa9Fa+ecpjViU/A178XiildFxFiOpq/nTuXLlyqlmzZoaOXJkot8lVG8SZlRL+P/04uJ1+tGBB3Hx+m1nhwC4pLC+3zs7BMDlnJ/V1NkhJOufMzecHYIkqXD2DM4O4b7STwbgAMMw9O+//+rEiRMK/P+pgO/O0xKSnB49eujy5cvpKskBAAAA8Gi4VBZgGIa8vb0VEhKixYsX69ChQ7YWiv8mPIcOHdLWrVt1+jQ32QIAAIB5WNLJwxW4VKJjtVrl7u6uhg0bav369Zo5c6ZOnTolSXY943PnzpWPj4+yZ8/urFABAAAAOFG6nozg4MGDmjdvnjZt2iQvLy/lzp1bAwYM0BtvvKGDBw8qMjJSV65cUcuWLVWxYkXt3LlTs2bN0uzZs/XLL78oICDA2YcAAAAApB5XKaekA+k20dm5c6dq1qyp8uXLy9/fX2fOnNHq1au1ZMkSTZgwQaNGjZKPj48mTJigGTNmKEuWLPL395eXl5dWr16tkiVLOvsQAAAAADhJukx0jh49qjp16qhdu3YaPny43NzcFBsbq507d+r1119Xp06dtGjRIg0bNkzVq1fX2bNndejQIYWGhqpo0aK0rAEAAACPuXQ5vfSkSZO0dOlSLV68WD4+PrYbgUp32tnq168vPz8/rV+/3smRphzTSwMPhumlgQfD9NJAyqXn6aX3n73p7BAkSYWy+Tg7hPtKl5MRbN++XXFxcYmSHEnKly+fevTooe3bt2vbtm1OjBIAAABAepUuEx2LxWI3m9p/i05Wq1U1atRQdHS0Lly44KwQAQAAAKRj6SrRSUhoqlatqjNnzmjq1KmS7iQ7sbGxkqT4+HhFRUWpWLFiypcvn9NiBQAAAB41iyV9PFyB0xOdW7duJRqrU6eOcuXKpbFjx2revHmSJHf3O/MmWK1WLV68WBkzZlSWLFkeaawAAAAAXINTE52TJ0+qVatWWrNmjaT/VW6yZs2qb7/9Vrdv31a/fv3UuXNn7d69W99//7369OmjyZMna/r06SQ6AAAAeKxY0snDFTg10YmOjtaJEyc0duxYrVu3TtKdyk1MTIzy5cundevWqXLlyvr2229VunRp9e7dW9u3b9e6desUEhLizNABAAAApGNOn156//796t69uwzD0MCBA1W5cmVJ0u3bt+Xp6ambN28qOjpaK1asUL169RQTEyM/Pz9nhvxQmF4aeDBMLw08GKaXBlIuPU8vffBc+pheukAw00vfV6FChTRx4kRZLBYNHz7cVtnx9PRUXFycrFarRo4cqa+//loeHh4uneQAAAAAD8XZPWsu1Lvm9ERHSj7ZiYuLU58+fTRu3Di9++678vDwcHKkAAAAAFxBukh0pMTJztq1azVw4EDNnDlTf/zxh8qUKePsEAEAAAC4CHdnB/BfCclORESEGjdurOvXr2vDhg0kOQAAAIAki6v0jaUD6aaik6BQoUIaM2aMqlatqm3btunpp592dkgAAAAAXEy6qugkKFy4sBYuXMg1OQAAAMB/WCjoOCzdVXQSkOQAAAAAeFDpNtEBAAAAgAeVLlvXAAAAACRG55rjqOgAAAAAMB0SHQAAAACmQ+saAAAA4CroXXMYFR0AAAAApkNFBwAAAHARFko6DqOiAwAAAMB0SHQAAAAAmA6tawAAAICLsNC55jAqOgAAAABMh0QHAAAAgOnQugYAAAC4CDrXHEdFBwAAAIDpUNEBAAAAXASTETiOig4AAAAA0yHRAQAAAGA6tK4BAAAALoPeNUdR0QEAAABgOiQ6AAAAAEyH1jUAAADARTDrmuOo6AAAAAAwHSo6AAAAgIugoOM4KjoAAAAATIdEBwAAAIDp0LoGAAAAuAgmI3AcFR0AAAAApkOiAwAAAMB0aF0DAAAAXISFedccRkUHAAAAgOlQ0QEAAABcBQUdh1HRAQAAAGA6JDoAAAAATIfWNQAAAMBF0LnmOCo6AAAAAEyHig4AAADgIiyUdBxGRQcAAACA6ZDoAAAAADAdWtcAAAAAF2FhOgKHUdEBAAAAYDokOgAAAABMh9Y1AAAAwFXQueYwKjoAAAAATIeKDgAAAOAiKOg4jooOAAAAANMh0QEAAABgOrSuAQAAAC7CQu+aw6joAAAAADAdEh0AAAAApkPrGgAAAOAiLMy75jAqOgAAAABMh4oOAAAA4CKYjMBxVHQAAAAAmA6JDgAAAADTIdEBAAAAYDokOgAAAABMh0QHAAAAgOkw6xoAAADgIph1zXFUdAAAAACYDhUdAAAAwEVYREnHUVR0AAAAAJgOiQ4AAAAA06F1DQAAAHARTEbgOCo6AAAAAEyHRAcAAACA6dC6BgAAALgIOtccR0UHAAAAgOlQ0QEAAABcBSUdh1HRAQAAAGA6JDoAAAAATIfWNQAAAMBFWOhdcxgVHQAAAACmQ6IDAAAAwHRoXQMAAABchIXONYdR0QEAAABgOlR0AAAAABdBQcdxVHQAAAAAmA6JDgAAAADToXUNAAAAcBX0rjmMig4AAAAA0yHRAQAAAGA6tK4BAAAALsJC75rDqOgAAAAASHNTpkxR3rx55e3trdDQUG3evPmey3/99dcqUqSIvL29VbJkSS1btixF+yPRAQAAAFyExZI+Him1YMECRUREaPDgwdq2bZtKly6t8PBwnTt3Lsnl169fr+bNm6t9+/bavn27GjZsqIYNG+qvv/5y/LkyDMNIeah4UBevxzk7BMAlXbx+29khAC4prO/3zg4BcDnnZzV1dgjJuhXr7Aju8E7hBTChoaEqX768Jk+eLEmKj49X7ty51a1bN/Xr1y/R8k2bNtX169e1ZMkS21jFihUVEhKijz76yKF9UtEBAAAAkCLR0dG6evWq3SM6OjrJZW/fvq2tW7eqRo0atjGr1aoaNWpow4YNSa6zYcMGu+UlKTw8PNnlk8JkBI9Yloxuzg4ByYiOjlZkZKT69+8vLy8vZ4eDu2TJ6OPsEJAEXjfpX3r+ZvpxxmsHDyqllZS0MuS9SA0dOtRubPDgwRoyZEiiZaOiohQXF6ds2bLZjWfLlk179+5NcvtnzpxJcvkzZ844HCMVHeD/RUdHa+jQocl+GwEgMV43wIPhtQNX179/f125csXu0b9/f2eHZSed5IQAAAAAXIWXl5fD1cigoCC5ubnp7NmzduNnz55V9uzZk1wne/bsKVo+KVR0AAAAAKQZT09PlS1bVqtWrbKNxcfHa9WqVQoLC0tynbCwMLvlJemnn35KdvmkUNEBAAAAkKYiIiLUunVrlStXThUqVND48eN1/fp1tW3bVpLUqlUr5cyZU5GRkZKkHj16qFq1aho7dqzq1KmjL7/8Ulu2bNHHH3/s8D5JdID/5+XlpcGDB3NRKJACvG6AB8NrB4+bpk2b6vz58xo0aJDOnDmjkJAQLV++3DbhwLFjx2S1/q/ZrFKlSpo3b54GDBigd955R4UKFdK3336rEiVKOLxP7qMDAAAAwHS4RgcAAACA6ZDoAAAAADAdEh0AAAAApkOiAwAAAMB0SHQAAAAAmA6JDvD/mIAQAADAPEh08Ni7efOmoqOjdfz4cd26dcvZ4QCPNb5wAACkFhIdPNb27Nmjli1bqly5cipQoIDCwsLUr18/Z4cFPBYOHTqkgQMHqnPnzpo4caIkyWKxkOwAAFIFNwzFY2vXrl2qWrWqWrZsqTJlyihLliyaPXu2li9frhdeeEHffPONPDw8nB0mYEo7duxQrVq1VLRoUZ06dUr79u1Tx44dNX36dGeHBqR758+f1+HDh2W1WlWsWDFlyJDB2SEB6RKJDh5L58+fV3h4uMLDwxUZGWk3/tVXX6lv376qW7euvvzySydGCZjTrl27VLFiRfXq1UtDhw7VuXPn9NVXX6lXr15asGCBXnnlFWeHCKRbu3btUrNmzWQYhvbu3au2bduqT58+Klq0qLNDA9IdWtfwWDpx4oRiYmL06quvKi4uTpIUHx+vrFmzqmXLlurfv79++OEHffvtt84NFDCZS5cu6eWXX1ahQoX03nvvyc3NTTly5FCtWrWULVs2xcTEODtEIN3auXOnKlasqPr162vhwoWaOnWqPvvsM61cudLZoQHpEokOHks7duzQgQMHVKJECbm5uckwDFmtd14Ofn5+atGihTw8PHTgwAEnRwqYS1xcnFq0aKETJ07YVVPj4uJ05coVBQQEODE6IP3as2ePypcvr86dOysyMlLFihVT8+bNlT17di1btsz2pV2C+Ph4J0UKpB/uzg4AcIaCBQtKkhYtWqSXXnpJFovF7vf58uVT/vz5dfLkSWeEB5jOvn37dOXKFZUvX15du3aVt7e3IiMj5efnp3bt2qlmzZrq2LGjateu7exQgXRp69atiomJ0ZNPPqmrV6/K19dXU6ZM0enTp5U/f3716dNHpUqVUv78+VWtWrVEn2vA44hEB4+lvHnzytfXV3PmzFG5cuWUJ08eSXe+AbNarbp06ZJ8fHxUtmxZJ0cKuL4dO3aoTJkymjBhgsqXL6+goCC1b99ehmHonXfeUffu3dWrVy998MEHMgzDrsIK4I6WLVsqKipKPXv2lJeXl86ePasPP/xQM2fOlL+/v/bt26dZs2bpyJEjCgoKUsuWLdWrVy8SHjzWSHTwWMqVK5emTZum5s2ba+DAgerbt6+KFy9u++Nq3LhxOnXqlKpWrerkSAHXtmPHDlWqVEnvvPOOunXrZhsPCgpSq1at5OHhoZEjR9pa1iwWCy03QDJ69uypuLg4de7cWRaLRYsXL1b9+vVtv2/Xrp1OnDih8ePHq169eiQ5eOyR6OCx1aBBA02cOFFvvvmmNm/erMqVKytHjhw6fPiwfvzxR61atcpW6QGQcjt37lTlypXVs2dPvffee7bx+fPnq3nz5nriiSf02muvKTY2ViNHjpS7u7vefvttubm5OTFqIH04fvy4fv75Z8XHx6to0aKqVKmSJKl3797y9fVVp06ddPDgQV25ckV+fn6SpMDAQGXNmlWzZ892ZuhAukGig8eWm5ubOnXqpJCQEI0ePVqbNm2Sv7+/SpcurfXr16tIkSLODhFwWadOnVJISIg6dOigESNG2MZHjRql/v37q1ChQipXrpyCg4PVvn17Wa1W9e3bV56enurZs6fzAgfSgZ07d6p+/foKDg7WoUOHFBAQoNGjR6tRo0aSpI4dO+ratWvq3bu3YmJi9MYbb8jX19fWlWAYBtUcQCQ6gEJDQ/XVV1/JarXa2ma4PgB4OF5eXipdurT++OMP7d+/X4UKFdL777+vMWPGaMWKFSpXrpztj7GgoCC1a9dOHh4eTEaAx97OnTsVFhambt26aeDAgVq3bp3atGmjWbNmqVatWnJ3d5eHh4ciIiIkSf3799fNmzcVERGhzJkzSxJJDvD/uGEoIPtvv/gmDHg4Ca+hCxcuqHbt2oqJiVH16tU1e/ZszZs3Ty+88ILd8rt371bhwoXl5ubGaw+PtePHj+vpp59W9erV7W5YXa5cOV2/fl2bN2+2JTMJhg4dqgkTJmj//v0KDAx81CED6RpfWwOy//aLP7SA1BEYGKjly5fL19dX48aN0+jRoxMlOX379lWzZs107do1Xnt47O3fv1958+bVrVu3tGHDBklSZGSktm3bJl9fX7Vs2VIdOnTQtGnTdPz4ccXFxWnw4MEkOUAyqOgAAB7a6dOntXHjRq1YsUKGYah27doqX768cubMqYsXL6pu3bq6evWqFi1apMKFC0uSBg8erA8++EBr165VhQoVnHwEQPqwdOlSTZo0ST4+PgoODtbixYs1ZcoUVahQQVu2bNHu3bs1efL/sXfncTbW///Hn2f2sY3BGGv2Nfs2hiSRIR9LKiSRrULIUKiELBNRZAsJkahEZQmhzVbZU8SMPWNfso1Z3r8//OZ8nWZwhhnnnMvj/rmd2yfvc53rel3nnOvMeZ3X63pfExUQEKCKFSvqm2++YUp24CZIdAAAd2XXrl16/vnnFRQUpNjYWAUGBmrz5s1q3ry5IiMjVadOHZ09e1YRERG6ePGivv/+e02fPl1RUVFat24d16sCJCUkJMjH5/qp099++60++OAD/fLLLxoxYoT9fJxkJ0+e1Nq1a1WlShX7BbABpMRkBACAO7Z9+3bVrVtXL7zwgl588UUVK1ZMkjRz5kz17dtXV65c0YgRI1S1alUtX75cTZs2VYECBRQYGEiSg/veyZMnFRgYqCxZssjHx0fx8fHy9fVV06ZN5evrK5vNpjVr1ig8PFzh4eGSpPj4eIWEhKhVq1Yujh5wf9Q5AQB3ZNeuXXrooYfUu3dvjR49WoULF5YkJSUlqWPHjpo4caLWrl2rBQsWSLp+zs7XX3+t1q1bk+Tgvnfo0CEVKlRIvXv3Vvfu3XXx4kWH89QaNWqkl19+WQkJCXr77be1ceNGSZKvr6+rQgY8Dq1rAIA0u3r1qmrUqKHz589rzZo19kpO8p+U5C9s/fv31+TJk7V7927lz59fkpjCHZD0888/q27duho3bpw2btyonTt3qlGjRmrSpIkeeeQR+3KLFy/W9OnTdfbsWY0fP17Vq1d3XdCAh+EvDQAgzQICAjRq1Cj5+vpq+PDh2r59u6T/S3ASExMlSQ0aNJCXl5dOnjxpfyxJDiDVqVNHPXr00MGDBzVv3jz17t1b3t7eatasmbp3765PPvlEktSiRQv17NlToaGhCg0NdXHUgGehogMASLPka+WsWLFCL7zwgh599FFFRkaqfPnykv6vajNjxgy9//77+umnn5QjRw4XRw24h+TjZ+7cufr444/19ddf26+PU7NmTf3zzz/y8fFRnjx51KZNG3Xu3Fne3t4KCAhwceSAZ+FnNQBAmtlsNhljFBERoWnTpmnNmjUaO3asduzYIel61SY+Pl6bNm1SWFiYMmfO7OKIAfeRXPls166dYmNj9c4770iSOnTooCNHjmjBggVau3atcubMqc8++0xnz54lyQHuABUdAMAdSZ4hSpK+++47vfjii6pXr54iIyNVoUIFDRkyxJ4ElS5d2sXRAu4lMTFR3t7e+vzzz/XFF1/ozJkz+vPPP7VkyRKHiTrOnj2r4OBgF0YKeC4SHQDALSW32dwo+UvakSNHdP78eT344IP2NrbHH39cCQkJmjt3rtatW6cqVaq4KHLAPSUfP9L12QubNGmiixcvatu2bSpQoIAkJu0A0gNHEADgpmJiYjRmzBh16tRJZ86ckXT9C5i3t7cOHjyoUqVKaf78+fY2tunTp2vOnDn69NNPtX79epIc3NdS+y05Ock5evSodu3apQcffFADBgxQcHCwEhIS7MuR5AB3j6MIAJCqnTt3KiIiQvv371dQUJAyZcok6foXsBMnTqhmzZpq3769hg4daj9np2HDhlq5cqW2b9+uypUru3gPANe53Y8EJUuW1Pz58yVJNWrUUI4cOfTjjz+6MmTAcmhdAwCksHfvXtWuXVudOnXSiBEj7G02yW1sa9as0R9//KGePXva29pSa3ED7kc7d+5Uy5Yt9dhjj8nf319RUVH2yQROnDihihUrqkWLFpo0aZK9ctOoUSOdPn1aGzZskI+PjyvDByyDRAcA4CAxMVG9e/fWmTNnNGPGDAUGBro6JMBjpPVHguRWtpiYGBlj7BffBXD3SHQAAClUq1ZNDRs21MiRI1Pcl/zFLPlkaU6aBq7jRwLAvfCXCQBgZ4zRxYsXdeTIEeXMmdM+dqPkJKd37946d+4cSQ7w/3l7e2vjxo0qXLhwqklOYmKipOvn6tz4/wAyBn+dAAB2xhgFBASoUqVKWrRokWJiYhzOwUkWExOjzZs369ixY64KFXAr/EgAuB+OMACAnZeXl3x8fNSiRQutX79eM2bM0D///CNJDhMNzJkzR4GBgcqTJ4+rQgXcCj8SAO6HaT0A4D4WHR2tefPmadOmTfL391fBggX15ptv6qWXXlJ0dLSioqJ0/vx5tWvXTjVr1tSOHTs0c+ZMzZ49Wz/++CNXbAf+Py8vL3l5ealFixbq3r27ZsyYoR49eihfvnz8SAC4CIkOANynduzYoYYNG6p69erKnj27YmNjtWbNGi1ZskTjx4/XqFGjFBgYqPHjx2v69OnKkSOHsmfPLn9/f61Zs0bly5d39S4ALsOPBID7Y9Y1ALgPHTx4UA899JCee+45DRs2TN7e3kpISNCOHTv0wgsvKDY2VgsXLlRYWJh+/PFHHT9+XDExMQoLC1OZMmX4NRr3tRt/JAgKClJsbKw2b96snDlzavz48WrcuLGGDBmi8ePH6+rVqw4/EsyaNUuVKlVy9S4A9wUSHQC4D02YMEFLly7VokWLFBgY6HCxz+joaDVr1kxBQUFav369iyMF3As/EgCeg0QHAO5DnTp10uHDh7Vq1SqHJEe6PuXtRx99pN69e2vdunWqUqWKCyMF3As/EgCeg1nXAOA+ZLPZHGZTu/E3Ly8vLzVo0EBxcXE6ffq0q0IE3NLWrVuVmJiYIsmRpCJFiqh3797aunWrtmzZ4sIoAUgkOgBwX0lOaOrUqaPY2FhNnjxZ0vVkJyEhQdL1is6pU6dUtmxZFSlSxGWxAu6IHwkAz0GiAwAWd/Xq1RRjTZo0UYECBTR27FjNmzdPkuTjc30iTi8vLy1atEiZM2dWjhw57mmsgLviRwLA85DoAICFHT16VO3bt9fatWsl/d+XspCQEC1evFjXrl3TgAED1K1bN+3atUvffPON+vXrp4kTJ2rq1KkkOriv8SMB4NmYjAAALCwmJkbt2rVTjhw5NHDgQNWuXVuSFB8fL19fXx06dEj9+/fXDz/8oJMnT6pIkSJ64IEH9P7776tChQoujh5wnaNHj6pPnz7q1q2b6tWrJ0lKSEiQj4+P9u/fr4cfflg2m01NmjTRyy+/rOjoaP3000+aOnWqfv75Z6aQBtwAiQ4AWNzevXvVq1cvGWM0aNAge7Jz7do1+fn56cqVK4qLi9OKFSvUtGlTxcfHKygoyMVRA67FjwSA5yPRAYD7wM2SncTERCUkJGjQoEGKiYnRZ599Jl9fXxdHC7gHfiQAPBuJDgDcJ1L70nbt2jX17dtXU6ZM0W+//abKlSu7OkzArfAjAeC5SHQA4D5y45e2AQMGaPny5ZowYYLWrVtHkgPcBD8SAJ6JRAcA7jN79+5VZGSk1q1bp0uXLmnDhg2qUqWKq8MC3Bo/EgCeh0QHAO5De/bs0WuvvaaRI0fqwQcfdHU4gEfgRwLAs5DoAMB9Knn2KADO40cCwHOQ6AAAAKQBPxIAnoFEBwAAAIDleLk6AAAAAABIbyQ6AAAAACyHRAcAAACA5ZDoAAAAALAcEh0AAAAAlkOiAwAW9Pzzz6tFixb2fz/yyCN65ZVX7nkcP/zwg2w2m86dO3fTZWw2mxYvXuz0OocMGaJKlSrdVVwHDhyQzWbTtm3b7mo9AAD3RaIDAPfI888/L5vNJpvNJj8/PxUvXlxvv/22EhISMnzbX331lYYNG+bUss4kJwAAuDsfVwcAAPeTRo0aaebMmYqLi9OyZcvUo0cP+fr6auDAgSmWvXbtmvz8/NJluzly5EiX9QAA4Cmo6ADAPeTv7688efKoUKFC6tatmxo0aKBvvvlG0v+1m40YMUL58uVTqVKlJEmHDx9Wq1atlD17duXIkUPNmzfXgQMH7OtMTExUZGSksmfPrpw5c+q1117Tf68F/d/Wtbi4OPXv318FCxaUv7+/ihcvrhkzZujAgQOqV6+eJCk4OFg2m03PP/+8JCkpKUlRUVEqUqSIAgMDVbFiRX355ZcO21m2bJlKliypwMBA1atXzyFOZ/Xv318lS5ZUpkyZVLRoUQ0aNEjx8fEplps6daoKFiyoTJkyqVWrVjp//rzD/R999JHKlCmjgIAAlS5dWpMnT77pNs+ePatnn31WISEhCgwMVIkSJTRz5sw0xw4AcB9UdADAhQIDA3X69Gn7v1evXq1s2bJp1apVkqT4+HhFREQoPDxcP//8s3x8fDR8+HA1atRIO3bskJ+fn8aOHatZs2bp448/VpkyZTR27FgtWrRIjz766E232759e23YsEEffPCBKlasqP379+vUqVMqWLCgFi5cqCeffFJ79uxRtmzZFBgYKEmKiorS3Llz9eGHH6pEiRL66aef1K5dO4WEhKhu3bo6fPiwWrZsqR49euiFF17Q77//rr59+6b5OcmaNatmzZqlfPnyaefOneratauyZs2q1157zb7Mvn379Pnnn+vbb7/VhQsX1LlzZ3Xv3l2ffvqpJOnTTz/VW2+9pYkTJ6py5craunWrunbtqsyZM6tDhw4ptjlo0CD9+eefWr58uXLlyqV9+/bpypUraY4dAOBGDADgnujQoYNp3ry5McaYpKQks2rVKuPv72/69etnvz80NNTExcXZHzNnzhxTqlQpk5SUZB+Li4szgYGBZsWKFcYYY/LmzWtGjx5tvz8+Pt4UKFDAvi1jjKlbt67p3bu3McaYPXv2GElm1apVqca5du1aI8mcPXvWPnb16lWTKVMms379eodlO3fubJ555hljjDEDBw40ZcuWdbi/f//+Kdb1X5LMokWLbnr/u+++a6pWrWr/9+DBg423t7c5cuSIfWz58uXGy8vLHDt2zBhjTLFixcy8efMc1jNs2DATHh5ujDFm//79RpLZunWrMcaYpk2bmo4dO940BgCA56GiAwD30JIlS5QlSxbFx8crKSlJbdu21ZAhQ+z3ly9f3uG8nO3bt2vfvn3KmjWrw3quXr2q6OhonT9/XseOHVNYWJj9Ph8fH1WrVi1F+1qybdu2ydvbW3Xr1nU67n379uny5ct67LHHHMavXbumypUrS5L++usvhzgkKTw83OltJFuwYIE++OADRUdH6+LFi0pISFC2bNkclnnggQeUP39+h+0kJSVpz549ypo1q6Kjo9W5c2d17drVvkxCQoKCgoJS3Wa3bt305JNPasuWLWrYsKFatGihWrVqpTl2AID7INEBgHuoXr16mjJlivz8/JQvXz75+Dh+DGfOnNnh3xcvXlTVqlXtLVk3CgkJuaMYklvR0uLixYuSpKVLlzokGNL1847Sy4YNG/Tss89q6NChioiIUFBQkObPn6+xY8emOdbp06enSLy8vb1TfUzjxo118OBBLVu2TKtWrVL9+vXVo0cPjRkz5s53BgDgUiQ6AHAPZc6cWcWLF3d6+SpVqmjBggXKnTt3iqpGsrx582rTpk16+OGHJV2vXGzevFlVqlRJdfny5csrKSlJP/74oxo0aJDi/uSKUmJion2sbNmy8vf316FDh25aCSpTpox9YoVkGzduvP1O3mD9+vUqVKiQ3njjDfvYwYMHUyx36NAh/fPPP8qXL599O15eXipVqpRCQ0OVL18+xcTE6Nlnn3V62yEhIerQoYM6dOigOnXq6NVXXyXRAQAPxqxrAODGnn32WeXKlUvNmzfXzz//rP379+uHH35Qr169dOTIEUlS79699c4772jx4sXavXu3unfvfstr4BQuXFgdOnRQp06dtHjxYvs6P//8c0lSoUKFZLPZtGTJEp08eVIXL15U1qxZ1a9fP/Xp00ezZ89WdHS0tmzZogkTJmj27NmSpJdeekl79+7Vq6++qj179mjevHmaNWtWmva3RIkSOnTokObPn6/o6Gh98MEHWrRoUYrlAgIC1KFDB23fvl0///yzevXqpVatWilPnjySpKFDhyoqKkoffPCB/v77b+3cuVMzZ87Ue++9l+p233rrLX399dfat2+fdu3apSVLlqhMmTJpih0A4F5IdADAjWXKlEk//fSTHnjgAbVs2VJlypRR586ddfXqVXuFp2/fvnruuefUoUMHhYeHK2vWrHriiSduud4pU6boqaeeUvfu3VW6dGl17dpVly5dkiTlz59fQ4cO1YABAxQaGqqXX35ZkjRs2DANGjRIUVFRKlOmjBo1aqSlS5eqSJEikq6fN7Nw4UItXrxYFStW1IcffqiRI0emaX+bNWumPn366OWXX1alSpW0fv16DRo0KMVyxYsXV8uWLfX444+rYcOGqlChgsP00V26dNFHH32kmTNnqnz58qpbt65mzZplj/W//Pz8NHDgQFWoUEEPP/ywvL29NX/+/DTFDgBwLzZzs7NVAQAAAMBDUdEBAAAAYDkkOgAAAAAsh0QHAAAAgOWQ6AAAAACwHBIdAAAAAJZDogMAAADAckh0AAAAAFgOiQ4AAAAAyyHRAQAAAGA5JDoAAAAALIdEBwAAAIDlkOgAAAAAsBwSHQAAAACWQ6IDAAAAwHJIdAAAAABYDokOAAAAAMsh0QEAAABgOSQ6AAAAACyHRAcAAACA5ZDoAAAAALAcEh0AAAAAlkOiAwAAAMBySHQAAAAAWA6JDgAAAADLIdEBAAAAYDkkOgAAAAAsh0QHAAAAgOWQ6AAAAACwHBIdAAAAAJZDogMAAADAckh0AAAAAFgOiQ4AAAAAyyHRAQAAAGA5JDpwyt69e9WwYUMFBQXJZrNp8eLF6br+AwcOyGazadasWem6XisoXLiwnn/++XRb3/Hjx/XUU08pZ86cstlsGjduXLqtG57phx9+kM1m0w8//OCS7Q8ZMkQ2m80l25akw4cPKyAgQOvWrcvQ7Tz//PMqXLjwHT3WZrPp5ZdfTrdYMuIzd9asWbLZbDpw4EC6rRPOadOmjVq1anXX67HZbBoyZMjdBwS4CRIdDxIdHa0XX3xRRYsWVUBAgLJly6batWtr/PjxunLlSoZuu0OHDtq5c6dGjBihOXPmqFq1ahm6PSv6888/NWTIEJd/CejTp49WrFihgQMHas6cOWrUqFGGbs+ZL2hJSUn65JNPFBYWphw5cihr1qwqWbKk2rdvr40bN0q6nvDZbLbb3pK/uCX/u0uXLqlu84033rAvc+rUqXTdZ6R0+fJlDRkyxGXJ1K28/fbbCgsLU+3atV0dCjKIO7//0kP//v21cOFCbd++3SXbd5e/b8nmzZvHj3iQJPm4OgA4Z+nSpXr66afl7++v9u3bq1y5crp27Zp++eUXvfrqq9q1a5emTZuWIdu+cuWKNmzYoDfeeCNdf1G8UaFChXTlyhX5+vpmyPrdwZ9//qmhQ4fqkUceSdOvunv27JGXV/r9JrFmzRo1b95c/fr1S7d13q1evXpp0qRJat68uZ599ln5+Phoz549Wr58uYoWLaqaNWtq3Lhxunjxov0xy5Yt02effab3339fuXLlso/XqlXL/t8BAQFauHChJk+eLD8/P4dtfvbZZwoICNDVq1czfgehy5cva+jQoZKkRx55xOG+N998UwMGDHBBVNLJkyc1e/ZszZ492yXbx71xq/efFVSuXFnVqlXT2LFj9cknn9zz7d/p37eMMm/ePP3xxx965ZVXXB0KXIxExwPs379fbdq0UaFChbRmzRrlzZvXfl+PHj20b98+LV26NMO2f/LkSUlS9uzZM2wbNptNAQEBGbZ+T2OM0dWrVxUYGCh/f/90XfeJEyfS9bW8evWq/Pz87jgZO378uCZPnqyuXbumSNbHjRtnf/+1aNHC4b7Y2Fh99tlnatGixU3/sDZq1EjffPONli9frubNm9vH169fr/379+vJJ5/UwoUL7yhupB8fHx/5+Ljmz9HcuXPl4+Ojpk2bumT7QHpp1aqVBg8erMmTJytLliyuDgdwC7SueYDRo0fr4sWLmjFjhkOSk6x48eLq3bu3/d8JCQkaNmyYihUrJn9/fxUuXFivv/664uLiHB5XuHBh/e9//9Mvv/yiGjVqKCAgQEWLFnX4NWjIkCEqVKiQJOnVV1+VzWazf6m8Wb95av32q1at0kMPPaTs2bMrS5YsKlWqlF5//XX7/TfrF1+zZo3q1KmjzJkzK3v27GrevLn++uuvVLe3b98+Pf/888qePbuCgoLUsWNHXb58+eZP7P/3yCOPqFy5ctqxY4fq1q2rTJkyqXjx4vryyy8lST/++KPCwsIUGBioUqVK6fvvv3d4/MGDB9W9e3eVKlVKgYGBypkzp55++mmHEv6sWbP09NNPS5Lq1atnb5lKbqNIfi1WrFihatWqKTAwUFOnTrXfl3yOjjFG9erVU0hIiE6cOGFf/7Vr11S+fHkVK1ZMly5dSnU/k/vnjTGaNGmSPYZkMTExevrpp5UjRw5lypRJNWvWTJFAJ5/LMX/+fL355pvKnz+/MmXKpAsXLtz2eb6Z/fv3yxiTatuQzWZT7ty573jd+fPn18MPP6x58+Y5jH/66acqX768ypUr5/S6tm7dqsaNGytbtmzKkiWL6tevb2+rS5b8HK9bt06RkZEKCQlR5syZ9cQTT9gTttvZvXu3nnrqKeXIkUMBAQGqVq2avvnmG/v9v//+u2w2W6oViBUrVshms2nJkiWSnHtv3szNzg175JFHHH4Rv3btmt566y1VrVpVQUFBypw5s+rUqaO1a9falzlw4IBCQkIkSUOHDrW/95LPBUjtMyM9P8duZfHixQoLC0vxxdDZ/U8+Jj7//HONGDFCBQoUUEBAgOrXr699+/bddvtjxoxRrVq1lDNnTgUGBqpq1ar2z57UfPrppypVqpQCAgJUtWpV/fTTTymWOXr0qDp16qTQ0FD5+/vrwQcf1Mcff3zbWKTbv/+S7dq1S48++qgCAwNVoEABDR8+XElJSU5tIzY2Vh07dlSBAgXk7++vvHnzqnnz5inel8uXL7d//mfNmlVNmjTRrl27HJZ5/vnnlSVLFh09elQtWrRQlixZFBISon79+ikxMVHS7d9/zu53Wo/v5cuXq27dusqaNauyZcum6tWrp/gs2rRpkxo1aqSgoCBlypRJdevWTXGu2L///qtXXnlFhQsXlr+/v3Lnzq3HHntMW7ZscVjuscce06VLl7Rq1arbvgZxcXHq06ePQkJClDVrVjVr1kxHjhxJsVx6/H37+uuv1aRJE+XLl0/+/v4qVqyYhg0bZn99ku3du1dPPvmk8uTJo4CAABUoUEBt2rTR+fPnHZabO3euqlatqsDAQOXIkUNt2rTR4cOH7fc/8sgjWrp0qQ4ePGiPxR2qTHARA7eXP39+U7RoUaeX79Chg5FknnrqKTNp0iTTvn17I8m0aNHCYblChQqZUqVKmdDQUPP666+biRMnmipVqhibzWb++OMPY4wx27dvN++//76RZJ555hkzZ84cs2jRIvt2ChUqlGL7gwcPNje+tf744w/j5+dnqlWrZsaPH28+/PBD069fP/Pwww/bl9m/f7+RZGbOnGkfW7VqlfHx8TElS5Y0o0ePNkOHDjW5cuUywcHBZv/+/Sm2V7lyZdOyZUszefJk06VLFyPJvPbaa7d9vurWrWvy5ctnChYsaF599VUzYcIEU7ZsWePt7W3mz59v8uTJY4YMGWLGjRtn8ufPb4KCgsyFCxfsj//iiy9MxYoVzVtvvWWmTZtmXn/9dRMcHGwKFSpkLl26ZIwxJjo62vTq1ctIMq+//rqZM2eOmTNnjomNjbW/FsWLFzfBwcFmwIAB5sMPPzRr166139ehQwf79mJiYkyWLFnME088YR8bMGCAsdls5scff7zpfkZHR5s5c+YYSeaxxx6zx2CMMbGxsSY0NNRkzZrVvPHGG+a9994zFStWNF5eXuarr76yr2Pt2rVGkilbtqypVKmSee+990xUVJR9P1MjyfTo0eOm9//zzz9GkmnSpMkt1/Nf7777rpHk8F5IbbvTpk0zgYGB5t9//zXGGBMfH29CQkJMVFSU/b1z8uTJW27rjz/+MJkzZzZ58+Y1w4YNM++8844pUqSI8ff3Nxs3brQvN3PmTPt78dFHHzUTJkwwffv2Nd7e3qZVq1a33ac//vjDBAUFmbJly5pRo0aZiRMnmocfftjYbDaH16Fo0aLm8ccfT/H4jh07muDgYHPt2jVjjHPvTWP+73VNfs8Zk/J9l6xu3bqmbt269n+fPHnS5M2b10RGRpopU6aY0aNHm1KlShlfX1+zdetWY4wxFy9eNFOmTDGSzBNPPGF/723fvt0Yk/Izw5j0/Ry7mWvXrpnAwEATGRmZ4j5n9z/5uatcubKpWrWqef/9982QIUNMpkyZTI0aNVLs038/MwsUKGC6d+9uJk6caN577z1To0YNI8ksWbLEYTlJply5ciZXrlzm7bffNqNGjTKFChUygYGBZufOnfblYmNjTYECBUzBggXN22+/baZMmWKaNWtmJJn333/fvlxqn7nOvv+OHTtmQkJCTHBwsBkyZIh59913TYkSJUyFChVueUwmq1WrlgkKCjJvvvmm+eijj8zIkSNNvXr1HD6/PvnkE2Oz2UyjRo3MhAkTzKhRo0zhwoVN9uzZHdbfoUMHExAQYB588EHTqVMnM2XKFPPkk08aSWby5MnGmNu//5zd77Qc3zNnzjQ2m82UK1fOjBgxwkyaNMl06dLFPPfcc/ZlVq9ebfz8/Ex4eLgZO3asef/9902FChWMn5+f2bRpk325tm3bGj8/PxMZGWk++ugjM2rUKNO0aVMzd+5ch23Gx8ebwMBA07dv31s+/8YY065dOyPJtG3b1kycONG0bNnS/voNHjzYvlx6/H1r0aKFadWqlXn33XfNlClTzNNPP20kmX79+tm3ExcXZ4oUKWLy5ctnhg8fbj766CMzdOhQU716dXPgwAH7csOHDzc2m820bt3aTJ482f69oHDhwubs2bPGGGNWrlxpKlWqZHLlymWPJfl7C+4/JDpu7vz580aSad68uVPLb9u2zUgyXbp0cRjv16+fkWTWrFljHytUqJCRZH766Sf72IkTJ4y/v7/DB2XyH8R3333XYZ3OJjrJidKtvkym9ke3UqVKJnfu3Ob06dP2se3btxsvLy/Tvn37FNvr1KmTwzqfeOIJkzNnzptuM1ndunWNJDNv3jz72O7du40k4+Xl5fBFdsWKFSnivHz5cop1btiwwUgyn3zyiX3siy++SPFlMlnya/Hdd9+let9/v3BNnTrVSDJz5841GzduNN7e3uaVV1657b4ak3ri8corrxhJ5ueff7aP/fvvv6ZIkSKmcOHCJjEx0Rjzf1/qihYtmup+O7u9/0r+EhscHGyeeOIJM2bMGPPXX3/d8jHOJjpnzpwxfn5+9qRu6dKlxmazmQMHDjid6LRo0cL4+fmZ6Oho+9g///xjsmbN6pCwJ38RatCggUlKSrKP9+nTx3h7e5tz587dcjv169c35cuXN1evXrWPJSUlmVq1apkSJUrYxwYOHGh8fX3NmTNn7GNxcXEme/bsDseBs+/Nu0l0EhISTFxcnMMyZ8+eNaGhoQ6xnDx5MsWXqGT//czIiM+x1Ozbt89IMhMmTEhxX1oTnTJlyjg8D+PHjzeSHJKQ1D4z//saXbt2zZQrV848+uijDuOSjCTz+++/28cOHjxoAgICHH706Ny5s8mbN685deqUw+PbtGljgoKC7NtL7TPX2fdf8ufFjV/GT5w4YYKCgm6b6Jw9ezbVvyc3+vfff0327NlN165dHcZjY2NNUFCQw3hyQvz22287LJuceCa71fvP2f129vg+d+6cyZo1qwkLCzNXrlxx2Fby45KSkkyJEiVMRESEw7ouX75sihQpYh577DH7WFBQ0G0/Q5OVLFnSNG7c+JbLJB9f3bt3dxhv27ZtiucoPf6+pbaOF1980WTKlMn+nG/dutVIMl988cVN4z5w4IDx9vY2I0aMcBjfuXOn8fHxcRhv0qRJqt9PcP+hdc3NJbcEZc2a1anlly1bJkmKjIx0GO/bt68kpWhFKlu2rOrUqWP/d0hIiEqVKqWYmJg7jvm/ks8H+frrr51ubTh27Ji2bdum559/Xjly5LCPV6hQQY899ph9P2/00ksvOfy7Tp06On36tFNtVVmyZFGbNm3s/y5VqpSyZ8+uMmXKKCwszD6e/N83Pj+BgYH2/46Pj9fp06dVvHhxZc+ePUVrwa0UKVJEERERTi37wgsvKCIiQj179tRzzz2nYsWKaeTIkU5v67+WLVumGjVq6KGHHrKPZcmSRS+88IIOHDigP//802H5Dh06OOz33Zo5c6YmTpyoIkWKaNGiRerXr5/KlCmj+vXr6+jRo3e17uDgYDVq1EifffaZpOsnqdaqVcveknk7iYmJWrlypVq0aKGiRYvax/Pmzau2bdvql19+SfEee+GFFxxaserUqaPExEQdPHjwpts5c+aM1qxZo1atWunff//VqVOndOrUKZ0+fVoRERHau3ev/blo3bq14uPj9dVXX9kfv3LlSp07d06tW7e2j6XXe/NWvL297RM9JCUl6cyZM0pISFC1atXueBv36nPs9OnTkq6/R+5Wx44dHSa8SI7ndjHc+BqdPXtW58+fV506dVJ97sLDw1W1alX7vx944AE1b95cK1asUGJioowxWrhwoZo2bSpjjP09dOrUKUVEROj8+fM3fU3S8v5btmyZatasqRo1atgfHxISomefffa2z1NgYKD8/Pz0ww8/6OzZs6kus2rVKp07d07PPPOMwz54e3srLCzMoS0yWWqf/878HUvLfie73fG9atUq/fvvvxowYECKc0+TH7dt2zbt3btXbdu21enTp+3bvXTpkurXr6+ffvrJ/vcye/bs2rRpk/7555/b7k9wcPBtZ5FMPr569erlMJ7aifvp8Rly4zqSn+M6dero8uXL2r17tyQpKChI0vX225u1nH/11VdKSkpSq1atHN4XefLkUYkSJVJ9XwAkOm4uW7Zskq5/ODjj4MGD8vLyUvHixR3G8+TJo+zZs6f4ovXAAw+kWEdwcPBN/wDdidatW6t27drq0qWLQkND1aZNG33++ee3THqS4yxVqlSK+8qUKWP/g3Cj/+5L8pcXZ/alQIECKc4RCAoKUsGCBVOM/XedV65c0VtvvaWCBQvK399fuXLlUkhIiM6dO5eit/hWihQp4vSykjRjxgxdvnxZe/fu1axZs+4q8Th48OBNn+vk++8m1tvx8vJSjx49tHnzZp06dUpff/21GjdurDVr1jgkoHeqbdu2WrVqlQ4dOqTFixerbdu2Tj/25MmTunz58k2fn6SkJIf+cOnO3ov79u2TMUaDBg1SSEiIw23w4MGSZD8vq2LFiipdurQWLFhgf/yCBQuUK1cuPfroo/ax9Hpv3s7s2bNVoUIFBQQEKGfOnAoJCdHSpUvveBv3+nPMGHNHcd4qBmc/f5YsWaKaNWsqICBAOXLkUEhIiKZMmZLqc1eiRIkUYyVLltTly5d18uRJnTx5UufOndO0adNSvIc6duwoSQ7n9t0oLe+/gwcPphpLasfIf/n7+2vUqFFavny5QkND9fDDD2v06NGKjY21L7N3715J0qOPPpoilpUrV6bYh4CAAPs5OMmcff3Tst/JbvdaR0dHS9ItzwFM3scOHTqk2O5HH32kuLg4+3tg9OjR+uOPP1SwYEHVqFFDQ4YMuWkSZ4y57TWpko+vYsWKOYyn9vqlx2fIrl279MQTTygoKEjZsmVTSEiI2rVrJ0n2dRQpUkSRkZH66KOPlCtXLkVERGjSpEkO29i7d6+MMSpRokSK5+yvv/666Xsb9zdmXXNz2bJlU758+fTHH3+k6XHOXnzP29s71XFn/vDfbBv/PcEwMDBQP/30k9auXaulS5fqu+++04IFC/Too49q5cqVN40hre5mX272WGfW2bNnT82cOVOvvPKKwsPD7RdVbdOmjdMVLElpTlR++OEH+4nZO3fuVHh4eJoefzfSs5rzXzlz5lSzZs3UrFkzPfLII/rxxx918OBBpyswqWnWrJn8/f3VoUMHxcXFpcuF9W7lTt6Lye+Vfv363bSyd+MX/9atW2vEiBE6deqUsmbNqm+++UbPPPOMw+xld/PevNXxfeP+zZ07V88//7xatGihV199Vblz55a3t7eioqLsX/juVEZ/juXMmVNS6smIs/t/NzH8/PPPatasmR5++GFNnjxZefPmla+vr2bOnJnipHVnJL+m7dq1U4cOHVJdpkKFCrd8rLPvv7vxyiuvqGnTplq8eLFWrFihQYMGKSoqSmvWrFHlypXtscyZM0d58uRJ8fj/ztB3N39D7mS/7+ZvzX+3++6776pSpUqpLpM8QUarVq1Up04dLVq0SCtXrtS7776rUaNG6auvvlLjxo0dHnP27NlUk9A7dbd/386dO6e6desqW7Zsevvtt1WsWDEFBARoy5Yt6t+/v8M6xo4dq+eff15ff/21Vq5cqV69eikqKkobN25UgQIFlJSUJJvNpuXLl6f6GjDTHFJDouMB/ve//2natGnasGHDbb/MFipUSElJSdq7d6/913jp+hS+586du6svi/8VHBysc+fOpRhPrT3Hy8tL9evXV/369fXee+9p5MiReuONN7R27Vo1aNAg1f2Qrl9D5r92796tXLlyKXPmzHe/E+ngyy+/VIcOHTR27Fj72NWrV1M8N+l55fdjx46pZ8+eatiwofz8/Ox/pO/09S1UqNBNn+vk+12hWrVq+vHHH3Xs2LG7iiEwMFAtWrTQ3Llz1bhxY4fr7txOSEiIMmXKdNPnx8vLK0Xl704kt8X5+vqmekz8V+vWrTV06FAtXLhQoaGhunDhQorql7PvzdTc6vi+sYXvyy+/VNGiRfXVV185vMeTfw1Plpb3/736HHvggQcUGBio/fv3p7jP2f2/GwsXLlRAQIBWrFjhMI38zJkzU10+uQpwo7///luZMmWyVzSyZs2qxMREp95DN0rL+69QoUKpxpLaMXIzxYoVU9++fdW3b1/t3btXlSpV0tixYzV37lx7pSF37txp3o+budn7L63HnTOS4//jjz9umhwmL5MtWzantps3b151795d3bt314kTJ1SlShWNGDHCIdFJSEjQ4cOH1axZs1uuK/n4io6OdqjipPb63e3ftx9++EGnT5/WV199pYcfftg+ntoxJ0nly5dX+fLl9eabb2r9+vWqXbu2PvzwQw0fPlzFihWTMUZFihRRyZIlb7mP6fn3Fp6N1jUP8Nprrylz5szq0qWLjh8/nuL+6OhojR8/XpL0+OOPS1KKKwK/9957kqQmTZqkW1zFihXT+fPntWPHDvvYsWPHtGjRIoflzpw5k+Kxyb9g/Xeq2GR58+ZVpUqVNHv2bIcP1D/++EMrV66076c78Pb2TvFL3oQJE1JUtpITM2e+ZN5O165dlZSUpBkzZmjatGny8fFR586d77gF5/HHH9evv/6qDRs22McuXbqkadOmqXDhwipbtuxdx3wzsbGxKc4Bkq5PW7x69epUW5juRL9+/TR48GANGjQoTY/z9vZWw4YN9fXXXztMqXr8+HHNmzdPDz30kL3F9G7kzp1bjzzyiKZOnapjx46luP+/09eWKVNG5cuX14IFC7RgwQLlzZvX4YtEcuzOvDdTU6xYMW3cuFHXrl2zjy1ZsiRFm17yL6s3bmfTpk0O7yVJypQpkyTn3v/36nPM19dX1apV0++//57iPmf3/254e3vLZrM5vB4HDhzQ4sWLU11+w4YNDudFHD58WF9//bUaNmwob29veXt7268NlVoXwK2mOE/L++/xxx/Xxo0b9euvvzrc/+mnn95yf6XrF+7870V6ixUrpqxZs9r/HkRERChbtmwaOXKk4uPj07QfN3Oz919ajztnNGzYUFmzZlVUVFSKfU0+TqpWrapixYppzJgxDhdC/u92ExMTU7SI5c6dW/ny5Uvx9/PPP//U1atXHS6anJrk5OiDDz5wGP/v8Sbd/d+31D4frl27psmTJzssd+HCBSUkJDiMlS9fXl5eXvb9bNmypby9vTV06NAUMRlj7OfcJceTnu258FxUdDxAsWLFNG/ePLVu3VplypRR+/btVa5cOV27dk3r16/XF198Yb/eQ8WKFdWhQwdNmzbNXjL+9ddfNXv2bLVo0UL16tVLt7jatGmj/v3764knnlCvXr10+fJlTZkyRSVLlnT4Y/z222/rp59+UpMmTVSoUCGdOHFCkydPVoECBRxOfv+vd999V40bN1Z4eLg6d+6sK1euaMKECQoKCnK4/oGr/e9//9OcOXMUFBSksmXLasOGDfr+++/tbTHJKlWqJG9vb40aNUrnz5+Xv7+/Hn300TRfJ2bmzJlaunSpZs2apQIFCki6/oenXbt2mjJlirp3757mfRgwYIA+++wzNW7cWL169VKOHDk0e/Zs7d+/XwsXLrzji4Em+/333zV8+PAU44888ogCAgJUo0YNPfroo6pfv77y5MmjEydO6LPPPtP27dv1yiuvpKkCczMVK1ZUxYoV7+ixw4cPt18Lqnv37vLx8dHUqVMVFxen0aNH33VsySZNmqSHHnpI5cuXV9euXVW0aFEdP35cGzZs0JEjR7R9+3aH5Vu3bq233npLAQEB6ty5c4rXydn3Zmq6dOmiL7/8Uo0aNVKrVq0UHR3t8Gv7jdv46quv9MQTT6hJkybav3+/PvzwQ5UtW9bhC1xgYKDKli2rBQsWqGTJksqRI4fKlSuX6nkM9/JzrHnz5nrjjTd04cIFh4TV2f2/G02aNNF7772nRo0aqW3btjpx4oQmTZqk4sWLO/yAlKxcuXKKiIhQr1695O/vb/+yOHToUPsy77zzjtauXauwsDB17dpVZcuW1ZkzZ7RlyxZ9//33qf7wlMzZ999rr72mOXPmqFGjRurdu7cyZ86sadOmqVChQqnGfaO///5b9evXV6tWrVS2bFn5+Pho0aJFOn78uL0imS1bNk2ZMkXPPfecqlSpojZt2igkJESHDh3S0qVLVbt2bU2cODFNz/Wt3n9pPe5uJ1u2bHr//ffVpUsXVa9eXW3btlVwcLC2b9+uy5cva/bs2fLy8tJHH32kxo0b68EHH1THjh2VP39+HT16VGvXrlW2bNn07bff6t9//1WBAgX01FNPqWLFisqSJYu+//57/fbbbw5VFun6JAiZMmXSY489dsv4KlWqpGeeeUaTJ0/W+fPnVatWLa1evTrV6z7d7d+3WrVqKTg4WB06dFCvXr1ks9k0Z86cFInKmjVr9PLLL+vpp59WyZIllZCQoDlz5tiTd+n6d6Hhw4dr4MCBOnDggFq0aKGsWbNq//79WrRokV544QX169dP0vVEcsGCBYqMjFT16tWVJUsWLgp8v7pX07vh7v3999+ma9eupnDhwsbPz89kzZrV1K5d20yYMMFhWsz4+HgzdOhQU6RIEePr62sKFixoBg4c6LCMMdenT23SpEmK7fx3+tSbTS9tzPX56suVK2f8/PxMqVKlzNy5c1NMFbt69WrTvHlzky9fPuPn52fy5ctnnnnmGfP333+n2MaNU50aY8z3339vateubQIDA022bNlM06ZNzZ9//umwzM2mCE6eCvR213SoW7euefDBB1OM3+z50X+mSz579qzp2LGjyZUrl8mSJYuJiIgwu3fvTnV62unTp5uiRYsab29vh6k4b7at5PuS13P48GETFBRkmjZtmmK5J554wmTOnNnExMTccn//G3+y6Oho89RTT5ns2bObgIAAU6NGjRTX8kieSvdWU4Cmtr2b3YYNG2YuXLhgxo8fbyIiIkyBAgWMr6+vyZo1qwkPDzfTp093mHr1Rs5OL30rzk4vbYwxW7ZsMRERESZLliwmU6ZMpl69emb9+vUOyyS/53777TeH8dSmb76Z6Oho0759e5MnTx7j6+tr8ufPb/73v/+ZL7/8MsWye/futT+Xv/zyS4r7nX1v3iy+sWPHmvz58xt/f39Tu3Zt8/vvv6f4fEhKSjIjR440hQoVMv7+/qZy5cpmyZIlqU6lvH79elO1alXj5+fnMI1tatfRSe/PsZs5fvy48fHxsU8/ntb9v9kxkdpnWmrPyYwZM0yJEiWMv7+/KV26tJk5c2aqz0fy+3nu3Ln25StXrpzqe+r48eOmR48epmDBgsbX19fkyZPH1K9f30ybNu2W8Rnj/Ptvx44dpm7duiYgIMDkz5/fDBs2zMyYMeO2n7mnTp0yPXr0MKVLlzaZM2c2QUFBJiwszHz++ecpll27dq2JiIgwQUFBJiAgwBQrVsw8//zzDlNsd+jQwWTOnDnFY1N7Dm/2/nN2v9N6fH/zzTemVq1a9r9fNWrUMJ999pnDMlu3bjUtW7Y0OXPmNP7+/qZQoUKmVatWZvXq1caY69PGv/rqq6ZixYoma9asJnPmzKZixYr2awTdKCwszLRr1y7FeGquXLlievXqZXLmzGkyZ85smjZtag4fPpzieUmPv2/r1q0zNWvWNIGBgSZfvnzmtddes1+qIXmZmJgY06lTJ1OsWDETEBBgcuTIYerVq2e+//77FLEvXLjQPPTQQyZz5swmc+bMpnTp0qZHjx5mz5499mUuXrxo2rZta7Jnz24kMdX0fcxmTDpMNwMAgIfq3Lmz/v77b/3888+uDgW4I9u2bVOVKlW0ZcuWm05uANyPSHQAAPe1Q4cOqWTJklq9erVq167t6nCANEueBe3zzz93dSiAWyHRAQAAAGA5zLoGAAAAwHJIdAAAAABkmJ9++klNmzZVvnz5ZLPZbjqN/o1++OEHValSRf7+/ipevLhmzZqV5u2S6AAAAADIMJcuXVLFihU1adIkp5bfv3+/mjRponr16mnbtm165ZVX1KVLF61YsSJN2+UcHQAAAAD3hM1m06JFi9SiRYubLtO/f38tXbrU4eLHbdq00blz5/Tdd985vS0qOgAAAADSJC4uThcuXHC4xcXFpcu6N2zYoAYNGjiMRUREaMOGDWlaj0+6RAOnBVZ+2dUhAB7p7G9puxI6AAB3KsCNvyG7y3fJ/s1zaejQoQ5jgwcP1pAhQ+563bGxsQoNDXUYCw0N1YULF3TlyhUFBgY6tR43fhkBAAAAuKOBAwcqMjLSYczf399F0aSORAcAAADwFDb3OPPE398/wxKbPHny6Pjx4w5jx48fV7Zs2Zyu5kicowMAAADAjYSHh2v16tUOY6tWrVJ4eHia1kOiAwAAACDDXLx4Udu2bdO2bdskXZ8+etu2bTp06JCk621w7du3ty//0ksvKSYmRq+99pp2796tyZMn6/PPP1efPn3StF1a1wAAAABPYbO5OoI0+/3331WvXj37v5PP7enQoYNmzZqlY8eO2ZMeSSpSpIiWLl2qPn36aPz48SpQoIA++ugjRUREpGm7XEfnHnOXmTIAT8OsawCAe8WtZ12r2tvVIUiSrmwe7+oQbovWNQAAAACW48b5KgAAAAAHbjLrmifgmQIAAABgOVR0AAAAAE/hgZMRuAoVHQAAAACWQ6IDAAAAwHJoXQMAAAA8BZMROI1nCgAAAIDlkOgAAAAAsBxa1wAAAABPwaxrTqOiAwAAAMByqOgAAAAAnoLJCJzGMwUAAADAckh0AAAAAFgOrWsAAACAp2AyAqdR0QEAAABgOSQ6AAAAACyH1jUAAADAUzDrmtN4pgAAAABYDhUdAAAAwFMwGYHTqOgAAAAAsBwSHQAAAACWQ+saAAAA4CmYjMBpPFMAAAAALIdEBwAAAIDl0LoGAAAAeApmXXMaFR0AAAAAlkNFBwAAAPAUTEbgNJ4pAAAAAJZDogMAAADAcmhdAwAAADwFrWtO45kCAAAAYDkkOgAAAAAsh9Y1AAAAwFN4cR0dZ1HRAQAAAGA5VHQAAAAAT8FkBE7jmQIAAABgOSQ6AAAAACyH1jUAAADAU9iYjMBZVHQAAAAAWA6JDgAAAADLoXUNAAAA8BTMuuY0nikAAAAAlkNFBwAAAPAUTEbgNCo6AAAAACyHRAcAAACA5dC6BgAAAHgKJiNwGs8UAAAAAMsh0QEAAABgObSuAQAAAJ6CWdecRkUHAAAAgOVQ0QEAAAA8BZMROI1nCgAAAIDlkOgAAAAAsBxa1wAAAABPwWQETqOiAwAAAMBySHQAAAAAWA6tawAAAICnYNY1p/FMAQAAALAcKjoAAACAp2AyAqdR0QEAAABgOSQ6AAAAACyH1jUAAADAUzAZgdN4pgAAAABYDhUdAAAAwFNQ0XEazxQAAAAAyyHRAQAAAGA5tK4BAAAAnoLr6DiNig4AAAAAyyHRAQAAAGA5tK4BAAAAnoJZ15zGMwUAAADAcqjoAAAAAJ6CyQicRkUHAAAAgOWQ6AAAAACwHFrXAAAAAE/BZARO45kCAAAAYDkkOgAAAAAsh9Y1AAAAwFMw65rTqOgAAAAAsBwqOgAAAICHsFHRcRoVHQAAAACWQ6IDAAAAwHJoXQMAAAA8BK1rzqOiAwAAAMBySHQAAAAAWA6tawAAAICnoHPNaVR0AAAAAFgOFR0AAADAQzAZgfOo6AAAAACwHBIdAAAAAJZD6xoAAADgIWhdcx4VHQAAAACWQ6IDAAAAwHJoXQMAAAA8BK1rzqOiAwAAAMByqOgAAAAAHoKKjvOo6AAAAACwHBIdAAAAAJZD6xoAAADgKehccxoVHScYY1wdAgAAAIA0ING5hStXriguLk6HDx/W1atXXR0OMkjtKsX05bgXFbNyhK5snaimj1RwdUiAx5g/71M1fuxRVa9cXs+2eVo7d+xwdUiAR+DYATIeic5N/PXXX2rXrp2qVaumYsWKKTw8XAMGDHB1WMgAmQP9tfPvo3olaoGrQwE8ynfLl2nM6Ci92L2H5n+xSKVKlVa3Fzvr9OnTrg4NcGscO7gbNpvNLW6egEQnFTt37lR4eLjy5s2rV155RZ9//rkKFSqkcePGqWnTpoqPj3d1iEhHK9f9qaGTl+ibtfyaBqTFnNkz1fKpVmrxxJMqVry43hw8VAEBAVr81UJXhwa4NY4d4N5gMoL/OHnypDp06KBu3bopKirKPv7QQw/p888/V//+/fXcc89p/vz5LowSAFwr/to1/fXnLnXu+qJ9zMvLSzVr1tKO7VtdGBng3jh2cLc8pZriDqjo/MeRI0cUHx+vZ599VomJiZKkpKQkhYSEqF27dho4cKC+/fZbLV68+LbriouL04ULFxxuJikxg/cAADLe2XNnlZiYqJw5czqM58yZU6dOnXJRVID749gB7h0Snf/Yvn279u3bp3Llysnb21vGGHl5XX+agoKC1LZtW/n6+mrfvn23XVdUVJSCgoIcbgnHN2f0LgAAAAD3PRKd/yhevLgkaeHC632y/y0PFilSREWLFtXRo0dvu66BAwfq/PnzDjef0KrpHzQA3GPB2YPl7e2d4uTp06dPK1euXC6KCnB/HDu4W66ehIDJCDxY4cKFlS1bNn3yySc6ePCgfTwpKUmSdPbsWQUGBqpq1dsnLP7+/sqWLZvDzeblnWGxA8C94uvnpzJlH9SmjRvsY0lJSdq0aYMqVKzswsgA98axA9w7JDr/UaBAAU2ZMkXfffedBg0apF27dkmSvX3tvffe0z///KM6deq4Mkyko8yBfqpQMr8qlMwvSSqcP6cqlMyvgnmCXRwZ4N6e69BRX335ub5ZvEgx0dEa/vYQXblyRS2eaOnq0AC3xrED3BvMupaK5s2b64MPPtDLL7+sX3/9VbVr11bevHm1f/9+LV++XKtXr1ahQoVcHSbSSZWyhbTyo972f4/u96Qkac43G/XC4LmuCgtwe40aP66zZ85o8sQPdOrUSZUqXUaTp36knLTfALfEsYO74SltY+7AZowxrg7CXW3atEmjR4/Wnj17lD17dlWsWFE9e/ZU6dKl73idgZVfTscIgfvH2d8mujoEAMB9IsCNSwE523/m6hAkSac/ecbVIdyWG7+MrhcWFqbPP/9cXl5estlsSkpKsrewAQAAAPccBR2n8a39NpKTHIlSIQAAAOApSHRu48bkhkQHAAAA8Ay0rgEAAAAegh/enUdFBwAAAIDlkOgAAAAAsBxa1wAAAAAPQeua86joAAAAALAcKjoAAACAh6Ci4zwqOgAAAAAsh0QHAAAAgOXQugYAAAB4CjrXnEZFBwAAAIDlkOgAAAAAyHCTJk1S4cKFFRAQoLCwMP3666+3XH7cuHEqVaqUAgMDVbBgQfXp00dXr151enu0rgEAAAAewlNnXVuwYIEiIyP14YcfKiwsTOPGjVNERIT27Nmj3Llzp1h+3rx5GjBggD7++GPVqlVLf//9t55//nnZbDa99957Tm2Tig4AAACADPXee++pa9eu6tixo8qWLasPP/xQmTJl0scff5zq8uvXr1ft2rXVtm1bFS5cWA0bNtQzzzxz2yrQjUh0AAAAAA9hs9nc4hYXF6cLFy443OLi4lKN+dq1a9q8ebMaNGhgH/Py8lKDBg20YcOGVB9Tq1Ytbd682Z7YxMTEaNmyZXr88cedfq5IdAAAAACkSVRUlIKCghxuUVFRqS576tQpJSYmKjQ01GE8NDRUsbGxqT6mbdu2evvtt/XQQw/J19dXxYoV0yOPPKLXX3/d6RhJdAAAAACkycCBA3X+/HmH28CBA9Nt/T/88INGjhypyZMna8uWLfrqq6+0dOlSDRs2zOl1MBkBAAAA4CHcZTICf39/+fv7O7Vsrly55O3trePHjzuMHz9+XHny5En1MYMGDdJzzz2nLl26SJLKly+vS5cu6YUXXtAbb7whL6/b12uo6AAAAADIMH5+fqpatapWr15tH0tKStLq1asVHh6e6mMuX76cIpnx9vaWJBljnNouFR0AAADAQ7hLRSetIiMj1aFDB1WrVk01atTQuHHjdOnSJXXs2FGS1L59e+XPn99+nk/Tpk313nvvqXLlygoLC9O+ffs0aNAgNW3a1J7w3A6JDgAAAIAM1bp1a508eVJvvfWWYmNjValSJX333Xf2CQoOHTrkUMF58803ZbPZ9Oabb+ro0aMKCQlR06ZNNWLECKe3aTPO1n6QLgIrv+zqEACPdPa3ia4OAQBwnwhw41JAvhe/cnUIkqR/prZ0dQi35cYvIwAAAAAHntm55hJMRgAAAADAckh0AAAAAFgOrWsAAACAh/DUWddcgYoOAAAAAMuhogMAAAB4CCo6zqOiAwAAAMBySHQAAAAAWA6tawAAAICHoHXNeVR0AAAAAFgOiQ4AAAAAy6F1DQAAAPAUdK45jYoOAAAAAMuhogMAAAB4CCYjcB4VHQAAAACWQ6IDAAAAwHJoXQMAAAA8BK1rzqOiAwAAAMBySHQAAAAAWA6tawAAAICHoHXNeVR0AAAAAFgOFR0AAADAQ1DRcR4VHQAAAACWQ6IDAAAAwHJoXQMAAAA8BZ1rTqOiAwAAAMBySHQAAAAAWA6tawAAAICHYNY151HRAQAAAGA5VHQAAAAAD0FFx3lUdAAAAABYDokOAAAAAMuhdQ0AAADwEHSuOY+KDgAAAADLIdEBAAAAYDm0rgEAAAAeglnXnEdFBwAAAIDlUNEBAAAAPAQFHedR0QEAAABgOSQ6AAAAACyH1jUAAADAQzAZgfOo6AAAAACwHBIdAAAAAJZD6xoAAADgIehccx4VHQAAAACWQ0UHAAAA8BBeXpR0nEVFBwAAAIDlkOgAAAAAsBxa1wAAAAAPwWQEzqOiAwAAAMBySHQAAAAAWA6tawAAAICHsNG75jQqOgAAAAAsh4oOAAAA4CEo6DiPig4AAAAAyyHRAQAAAGA5tK4BAAAAHoLJCJxHRQcAAACA5ZDoAAAAALAcWtcAAAAAD0HrmvOo6AAAAACwHCo6AAAAgIegoOM8KjoAAAAALIdEBwAAAIDl0LoGAAAAeAgmI3AeFR0AAAAAlkNFBwAAAPAQFHScR0UHAAAAgOWQ6AAAAACwHFrXAAAAAA/BZATOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIegc815VHQAAAAAWA4VHQAAAMBDMBmB86joAAAAALAcEh0AAAAAlkPrGgAAAOAh6FxzHhUdAAAAAJZDogMAAADAcmhdAwAAADwEs645j4oOAAAAAMuhogMAAAB4CAo6ziPRucfO/jbR1SEAAAAAlkfrGgAAAADLoaIDAAAAeAgmI3AeFR0AAAAAlkOiAwAAAMByaF0DAAAAPASda86jogMAAADAcqjoAAAAAB6CyQicR0UHAAAAgOWQ6AAAAACwHFrXAAAAAA9B55rzqOgAAAAAsBwSHQAAAACWQ+saAAAA4CGYdc15VHQAAAAAWA4VHQAAAMBDUNFxHhUdAAAAAJZDogMAAADAcmhdAwAAADwEnWvOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIdg1jXnUdEBAAAAYDlUdAAAAAAPQUHHeVR0AAAAAFgOiQ4AAAAAy6F1DQAAAPAQTEbgPCo6AAAAACyHRAcAAACA5dC6BgAAAHgIOtecR0UHAAAAgOVQ0QEAAAA8hBclHadR0QEAAABgOSQ6AAAAACyH1jUAAADAQ9C55jwqOgAAAAAsh0QHAAAAgOXQugYAAAB4CBu9a06jogMAAADAcqjoAAAAAB7Ci4KO06joAAAAALAcEh0AAAAAlkOiAwAAAHgIm83mFrc7MWnSJBUuXFgBAQEKCwvTr7/+esvlz507px49eihv3rzy9/dXyZIltWzZMqe3xzk6AAAAADLUggULFBkZqQ8//FBhYWEaN26cIiIitGfPHuXOnTvF8teuXdNjjz2m3Llz68svv1T+/Pl18OBBZc+e3elt2owxJh33AbdxNcHVEQAAAOBWAty4FPD4h7eugtwry16qkablw8LCVL16dU2cOFGSlJSUpIIFC6pnz54aMGBAiuU//PBDvfvuu9q9e7d8fX3vKEZa1wAAAAAPYbO5xy0uLk4XLlxwuMXFxaUa87Vr17R582Y1aNDAPubl5aUGDRpow4YNqT7mm2++UXh4uHr06KHQ0FCVK1dOI0eOVGJiotPPFYkOAAAAgDSJiopSUFCQwy0qKirVZU+dOqXExESFhoY6jIeGhio2NjbVx8TExOjLL79UYmKili1bpkGDBmns2LEaPny40zG6cWEOAAAAwI1sco8L6QwcOFCRkZEOY/7+/um2/qSkJOXOnVvTpk2Tt7e3qlatqqNHj+rdd9/V4MGDnVoHiQ4AAACANPH393c6scmVK5e8vb11/Phxh/Hjx48rT548qT4mb9688vX1lbe3t32sTJkyio2N1bVr1+Tn53fb7dK6BgAAACDD+Pn5qWrVqlq9erV9LCkpSatXr1Z4eHiqj6ldu7b27dunpKQk+9jff/+tvHnzOpXkSCQ6AAAAgMfwsrnHLa0iIyM1ffp0zZ49W3/99Ze6deumS5cuqWPHjpKk9u3ba+DAgfblu3XrpjNnzqh37976+++/tXTpUo0cOVI9evRwepu0rgEAAADIUK1bt9bJkyf11ltvKTY2VpUqVdJ3331nn6Dg0KFD8vL6vxpMwYIFtWLFCvXp00cVKlRQ/vz51bt3b/Xv39/pbXIdnXuM6+gAAAC4N3e+jk7z6b+7OgRJ0tddq7k6hNuidQ0AAACA5ZDoAAAAALAcNy7MAQAAALiRzT0uo+MRqOgAAAAAsBwSHQAAAACWQ+saAAAA4CG86F1zGhUdAAAAAJZDRQcAAADwEBR0nEdFBwAAAIDlkOgAAAAAsBxa1wAAAAAPYaN3zWlUdAAAAABYDokOAAAAAMuhdQ0AAADwEHSuOY+KDgAAAADLoaIDAAAAeAgvSjpOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIegcc15VHQAAAAAWA6JDgAAAADLoXUNAAAA8BA2Zl1zGhUdAAAAAJZDRQcAAADwEF4UdJxGRQcAAACA5aRLonPu3Ln0WA0AAAAApIs0JzqjRo3SggUL7P9u1aqVcubMqfz582v79u3pGhwAAACA/2Oz2dzi5gnSnOh8+OGHKliwoCRp1apVWrVqlZYvX67GjRvr1VdfTfcAAQAAACCt0jwZQWxsrD3RWbJkiVq1aqWGDRuqcOHCCgsLS/cAAQAAACCt0lzRCQ4O1uHDhyVJ3333nRo0aCBJMsYoMTExfaMDAAAAYGezucfNE6S5otOyZUu1bdtWJUqU0OnTp9W4cWNJ0tatW1W8ePF0DxAAAAAA0irNic7777+vwoUL6/Dhwxo9erSyZMkiSTp27Ji6d++e7gECAAAAuM5TJgJwBzZjjHF1EPeTqwmujgAAAAC3EpDmUsC9037eDleHIEn6pG0FV4dwW069jN98843TK2zWrNkdBwMAAAAA6cGpRKdFixZOrcxmszEhAQAAAJBBvOhcc5pTiU5SUlJGxwEAAAAA6SbN00vf6OrVq+kVBwAAAACkmzQnOomJiRo2bJjy58+vLFmyKCYmRpI0aNAgzZgxI90DBAAAAHCdzWZzi5snSHOiM2LECM2aNUujR4+Wn5+ffbxcuXL66KOP0jU4d8MEdQAAAIBnSPPkeZ988ommTZum+vXr66WXXrKPV6xYUbt3707X4FwpJiZGM2fO1KlTp1SmTBn16tVLNptNxhiPyWIBAABgLXwLdV6aE52jR4+qePHiKcaTkpIUHx+fLkG52vbt29WoUSOVKVNG//zzj6ZOnapdu3Zp6tSpJDkAAACAB0hz61rZsmX1888/pxj/8ssvVbly5XQJypV27typWrVqqXPnzlq1apXWrl2r999/X9OnT9cXX3zh6vAAAAAAOCHNFZ233npLHTp00NGjR5WUlKSvvvpKe/bs0SeffKIlS5ZkRIz3zNmzZ/XUU0+pRIkSGj58uCQpb968atSokd555x3LVKwAAADgmbzoLnJamis6zZs317fffqvvv/9emTNn1ltvvaW//vpL3377rR577LGMiPGeSUxMVNu2bXXkyBFFRUU5jJ8/f17BwcEujA4AAACAs9Jc0ZGkOnXqaNWqVekdi8v8/fffOn/+vKpXr64ePXooICBAUVFRCgoKUqdOndSwYUN17dpVjRs3dnWoAAAAAJxwxxcM/f333zVnzhzNmTNHmzdvTs+Y7qnt27erdOnS2rhxoyQpV65c6ty5swYMGKDXX39dWbJk0TPPPKPx48fLGKOkpCQXRwwAAID7lc3mHjdPkOaKzpEjR/TMM89o3bp1yp49uyTp3LlzqlWrlubPn68CBQqkd4wZZvv27apVq5Zef/119ezZ0z6eK1cutW/fXr6+vho5cqS9Zc1ms5HoAAAAAB4gzRWdLl26KD4+Xn/99ZfOnDmjM2fO6K+//lJSUpK6dOmSETFmiB07dqh27dp65ZVX7BMPSNJnn30mScqXL5+ee+45vfbaaxo1apRGjx4tSfL29nZJvAAAAIDNZnOLmydIc0Xnxx9/1Pr161WqVCn7WKlSpTRhwgTVqVMnXYPLKP/8848qVaqkLl26aMSIEfbxUaNGaeDAgSpRooSqVaum3Llzq3PnzvLy8lL//v3l5+enV155xXWBAwAAAHBKmhOdggULpjrNcmJiovLly5cuQWU0f39/VaxYUb/99pv27t2rEiVK6J133tGYMWO0YsUKVatWTcYY2Ww25cqVS506dZKvry+TEQAAAAAeIs2ta++++6569uyp33//3T72+++/q3fv3hozZky6BpcRjDHKmTOnvv/+e/n6+qpVq1bq16+fxo4dq88++8w+RXZySW7Xrl0KCgpS7969HapYAAAAwL3m6kkIPGkyApsxxtxuoeDgYIdevEuXLikhIUE+PtcLQsn/nTlzZp05cybjok0HyZUaSTpz5oyeeOIJ/fzzz5oxY4Y6duzosGz//v21bNky/fTTT+l2DZ2rCemyGgAAAGSQgDu6AMu98eKXu1wdgiRp6lMPujqE23LqZRw3blwGh5Gxjh07po0bN2rFihUyxqhx48aqXr268ufPr0WLFul///ufxo4dq1q1atmrNoMHD9aECRP0ww8/cKFQAAAAwMM4VdHxZLt27dLzzz+voKAgxcbGKjAwUJs3b1bz5s0VGRmpOnXq6OzZs4qIiNDFixf1/fffa/r06YqKitK6detUtWrVdI2Hig4AAIB7c+eKTreFf7o6BEnSlCfLujqE27qrl/Hq1au6du2aw1i2bNnuKqD0tH37dtWtW1cvvPCCXnzxRRUrVkySNHPmTPXt21dXrlzRiBEjVLVqVS1fvlxNmzZVgQIFFBgYmCFJDgAAAIB7I82TEVy6dEkvv/yycufOrcyZMys4ONjh5i527dqlhx56SL1799bo0aNVuHBhSVJSUpI6duyoiRMnau3atVqwYIEkKWfOnPr666/VunVrkpz70Px5n6rxY4+qeuXyerbN09q5Y4erQwI8AscOcGc4dnCnXD0JgSdNRpDmROe1117TmjVrNGXKFPn7++ujjz7S0KFDlS9fPn3yyScZEWOaXb16Vc8884xy5Mih9u3bS7p+oc8bJyJo27atXnnlFU2ZMkVHjx6VJIWEhOjTTz9VpUqVXBU6XOC75cs0ZnSUXuzeQ/O/WKRSpUqr24uddfr0aVeHBrg1jh3gznDsAPdGmhOdb7/9VpMnT9aTTz4pHx8f1alTR2+++aZGjhypTz/9NCNiTLOAgACNGjVKvr6+Gj58uLZv3y7p/6aMTkxMlCQ1aNBAXl5eOnnypP2xXl5pfkrg4ebMnqmWT7VSiyeeVLHixfXm4KEKCAjQ4q8Wujo0wK1x7AB3hmMHuDfS/K3+zJkzKlq0qKTr5+MkTyf90EMP6aeffkrf6O5Q8sxqkyZN0po1azRu3Djt3LlT0vVkJznhOXTokAoWLKgHHnjAleHCheKvXdNff+5SzfBa9jEvLy/VrFlLO7ZvdWFkgHvj2AHuDMcO7lbyd1lX3zxBmhOdokWLav/+/ZKk0qVL6/PPP5d0vdKTPXv2dA3uTtlsNhljFBERoWnTpmnNmjUaO3asdvz//lcvLy/Fx8dr06ZNCgsLU+bMmV0cMVzl7LmzSkxMVM6cOR3Gc+bMqVOnTrkoKsD9cewAd4ZjB7h30jzrWseOHe2zmQ0YMEBNmzbVxIkTFR8fr/feey8jYrwjCQkJ8vX1VUREhKZOnaoXX3xRkhQZGakKFSpoxIgRWrJkidasWSN/f/8MiSEuLk5xcXEOY8bbP8O2BwAAAOC6NCc6ffr0sf93gwYNtHv3bm3evFnFixdXhQoV0jU4Z9w4wUCyxMRE+fr66siRIzp//rwaNWqkadOm6YUXXlBgYKASEhI0d+5crVu3TqVLl86w2KKiojR06FCHsTcGDdabbw3JsG0ibYKzB8vb2zvFCaCnT59Wrly5XBQV4P44doA7w7GDu8XZ5M676+eqUKFCatmypUuSnJiYGI0ZM0adOnWynyuUlJQkb29vHTx4UKVKldL8+fPtbWzTp0/XnDlz9Omnn2r9+vWqUqVKhsY3cOBAnT9/3uH2av+BGbpNpI2vn5/KlH1QmzZusI8lJSVp06YNqlCxsgsjA9wbxw5wZzh2gHvHqYrOBx984PQKe/XqdcfBpMXOnTvVsmVLPfbYYwoKClKmTJkkXT//5sSJE6pZs6bat2+voUOH2s/ZadiwoVauXKmQkBCVKFEiw2P090/ZpnY1IcM3izR6rkNHDXq9vx58sJzKla+guXNm68qVK2rxREtXhwa4NY4d4M5w7OBueMpEAO7AZowxt1uoSJEizq3MZlNMTMxdB3U7e/fuVe3atdWpUyeNGDFC3t7ekv6vjW3NmjX6448/1LNnT/ubIbUWN1cg0XFPn306V7NnztCpUydVqnQZ9X/9TVWoUNHVYQFuj2MHuDMcO+4tIM0nd9w7vRbvdnUIkqQPWmTc6R/pxalEx50kJiaqd+/eOnPmjGbMmKHAwEBXh5QmJDoAAADujUTn9jwh0fG485m8vb21ceNGFS5cONUkJ/lioElJSQ7/DwAAAHg6L5t73DyBRyU6xhhdvHhRR44csc8//9+ClLe3t5KSktS7d2+dO3dOXl4etYsAAAAA0oFHZQHGGAUEBKhSpUpatGiRYmJiHM7BSRYTE6PNmzfr2LFjrgoVAAAASHeuruRQ0ckgXl5e8vHxUYsWLbR+/XrNmDFD//zzjyTHGSjmzJmjwMBA5cmTx1WhAgAAAHAhNz7VSoqOjta8efO0adMm+fv7q2DBgnrzzTf10ksvKTo6WlFRUTp//rzatWunmjVraseOHZo5c6Zmz56tH3/8UcHBwa7eBQAAAAAucEeJzs8//6ypU6cqOjpaX375pfLnz685c+aoSJEieuihh9IlsB07dqhhw4aqXr26smfPrtjYWK1Zs0ZLlizR+PHjNWrUKAUGBmr8+PGaPn26cuTIoezZs8vf319r1qxR+fLl0yUOAAAAwF24w+VSPEWaE52FCxfqueee07PPPqutW7cqLi5OknT+/HmNHDlSy5Ytu+ugDh48qCZNmqhTp04aNmyYvL29lZCQoB07duiFF17Qiy++qIULF+rtt99W/fr1dfz4ccXExCgsLExlypShZQ0AAAC4z6X5OjqVK1dWnz591L59e2XNmlXbt29X0aJFtXXrVjVu3FixsbF3HdSECRO0dOlSLVq0SIGBgQ4X+4yOjlazZs0UFBSk9evX3/W27jWuowMAAODe3Pk6On2/3ePqECRJY5uWcnUIt5XmyQj27Nmjhx9+OMV4UFCQzp07lx4xaevWrUpMTEyR5EhSkSJF1Lt3b23dulVbtmxJl+0BAAAAnsDVs61Zeta1PHnyaN++fSnGf/nlFxUtWjRdgrLZbA6zqd1YdPLy8lKDBg0UFxen06dPp8v2AAAAAFhLmhOdrl27qnfv3tq0aZM9Ifn000/Vr18/devW7a6CSU5o6tSpo9jYWE2ePFnS9WQnIeF6z1dSUpJOnTqlsmXLqkiRIne1PQAAAMCT2GzucfMEae5AHDBggJKSklS/fn1dvnxZDz/8sPz9/dWvXz/17NkzzQFcvXpVAQEBDmNNmjTR+++/r7Fjxyp79uxq27atfHyuh+rl5aVFixYpc+bMypEjR5q3BwAAAMD60jwZQbJr165p3759unjxosqWLassWbKkeR1Hjx5Vnz591K1bN9WrV0+SlJCQIB8fH+3fv18PP/ywbDabmjRpopdfflnR0dH66aefNHXqVP3888+qVKnSnYTuUkxGAAAA4N7ceTKC15a6x2QEo5u4/2QEd/wy+vn5qWzZsne18bi4OB05ckRjx46Vn5+fateuLR8fH8XHx6tIkSJat26d+vfvr8WLF2v69OkqUqSIHnjgAa1bt04VKlS4q20DAAAAnsbLU/rG3ECaKzr16tW75YWK1qxZk6YA9u7dq169eskYo0GDBql27dqSrleM/Pz8dOXKFcXFxWnFihVq2rSp4uPjFRQUlKZtuBMqOgAAAO7NnSs6A5b97eoQJEnvPF7S1SHcVponI6hUqZIqVqxov5UtW1bXrl3Tli1bVL58+TQHUKJECX3wwQey2WwaNmyY1q1bJ+l6xSgxMVFeXl4aOXKkvvjiC/n6+np0kgMAAADg3rjjc3T+a8iQIbp48aLGjBlzR49PrbJz7do19e3bV1OmTNFvv/2mypUrp0eoLkVFBwAAwL25c0XndTep6Iz0gIpOuiU6+/btU40aNXTmzJk7XseNyc6AAQO0fPlyTZgwQevWrbNEkiOR6AAAALg7Ep3b84REJ91exg0bNqSYJjqtktvYIiMj1bJlS126dEkbNmywTJIDAAAA3A3mInBemhOdli1bOvzbGKNjx47p999/16BBg+46oBIlSmjMmDF67bXXNHLkSD344IN3vU4AAAAA95c0Jzr/nQzAy8tLpUqV0ttvv62GDRumS1ClSpXSl19+KV9f33RZHwAAAID7S5oSncTERHXs2FHly5dXcHBwRsUkSSQ5AAAAwH9wHR3npWl6aW9vbzVs2FDnzp3LoHAAAAAA4O6l+To65cqVU0xMTEbEAgAAAADpIs2JzvDhw9WvXz8tWbJEx44d04ULFxxuAAAAADKGzeYeN0/g9Dk6b7/9tvr27avHH39cktSsWTPZbthLY4xsNpsSExPTP0oAAAAASAOnE52hQ4fqpZde0tq1azMyHgAAAAA34eUh1RR34HSiY4yRJNWtWzfDggEAAACA9JCmc3RsntKQBwAAAOC+lqbr6JQsWfK2yc6ZM2fuKiAAAAAAqeM6Os5LU6IzdOhQBQUFZVQsAAAAAJAu0pTotGnTRrlz586oWAAAAAAgXTid6HB+DgAAAOBafCV3ntOTESTPugYAAAAA7s7pik5SUlJGxgEAAADgNriOjvPSNL00AAAAAHgCEh0AAAAAlpOmWdcAAAAAuI5N9K45i4oOAAAAAMsh0QEAAABgObSuAQAAAB6CWdecR0UHAAAAgOVQ0QEAAAA8BBUd51HRAQAAAGA5JDoAAAAALIfWNQAAAMBD2Gz0rjmLig4AAAAAyyHRAQAAAGA5tK4BAAAAHoJZ15xHRQcAAACA5VDRAQAAADwEcxE4j4oOAAAAAMsh0QEAAABgObSuAQAAAB7Ci941p1HRAQAAAGA5JDoAAAAALIfWNQAAAMBDcB0d51HRAQAAAGA5VHQAAAAAD8FcBM6jogMAAAAgw02aNEmFCxdWQECAwsLC9Ouvvzr1uPnz58tms6lFixZp2h6JDgAAAIAMtWDBAkVGRmrw4MHasmWLKlasqIiICJ04ceKWjztw4ID69eunOnXqpHmbJDoAAACAh/CSzS1ucXFxunDhgsMtLi7upnG/99576tq1qzp27KiyZcvqww8/VKZMmfTxxx/f9DGJiYl69tlnNXToUBUtWvQOnisAAAAASIOoqCgFBQU53KKiolJd9tq1a9q8ebMaNGhgH/Py8lKDBg20YcOGm27j7bffVu7cudW5c+c7ipHJCAAAAACkycCBAxUZGekw5u/vn+qyp06dUmJiokJDQx3GQ0NDtXv37lQf88svv2jGjBnatm3bHcdIogMAAAB4CHeZdc3f3/+mic3d+vfff/Xcc89p+vTpypUr1x2vh0QHAAAAQIbJlSuXvL29dfz4cYfx48ePK0+ePCmWj46O1oEDB9S0aVP7WFJSkiTJx8dHe/bsUbFixW67Xc7RAQAAADyEl809bmnh5+enqlWravXq1faxpKQkrV69WuHh4SmWL126tHbu3Klt27bZb82aNVO9evW0bds2FSxY0KntUtEBAAAAkKEiIyPVoUMHVatWTTVq1NC4ceN06dIldezYUZLUvn175c+fX1FRUQoICFC5cuUcHp89e3ZJSjF+KyQ6AAAAADJU69atdfLkSb311luKjY1VpUqV9N1339knKDh06JC8vNK32cxmjDHpukbc0tUEV0cAAACAWwlw41LAtI0HXR2CJOmFmoVcHcJtcY4OAAAAAMtx43wVAAAAwI3cZXppT0BFBwAAAIDlkOgAAAAAsBxa1wAAAAAP4UXvmtOo6AAAAACwHBIdAAAAAJZD6xoAAADgIehccx4VHQAAAACWQ0UHAAAA8BBUKZzHcwUAAADAckh0AAAAAFgOrWsAAACAh7AxG4HTqOgAAAAAsBwSHQAAAACWQ+saAAAA4CFoXHMeFR0AAAAAlkNFBwAAAPAQXkxG4DQqOgAAAAAsh0QHAAAAgOXQugYAAAB4CBrXnEdFBwAAAIDlkOgAAAAAsBxa1wAAAAAPwaRrzqOiAwAAAMByqOgAAAAAHsJGScdpVHQAAAAAWA6JDgAAAADLoXUNAAAA8BBUKZzHcwUAAADAckh0AAAAAFgOrWsAAACAh2DWNedR0QEAAABgOVR0AAAAAA9BPcd5VHQAAAAAWA6JDgAAAADLoXUNAAAA8BBMRuA8KjoAAAAALIdEBwAAAIDl0LoGAAAAeAiqFM7juQIAAABgOVR0AAAAAA/BZATOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIegcc15VHQAAAAAWA6JDgAAAADLoXUNAAAA8BBMuuY8KjoAAAAALIeKDgAAAOAhvJiOwGlUdAAAAABYDokOAAAAAMuhdQ0AAADwEExG4DwqOgAAAAAsh0QHAAAAgOXQugYAAAB4CBuzrjmNig4AAAAAy6GiAwAAAHgIJiNwHhUdAAAAAJZDogMAAADAcmhdAwAAADyEF5MROI2KDgAAAADLIdEBAAAAYDm0rgEAAAAeglnXnEdFBwAAAIDlUNEBAAAAPAQVHedR0QEAAABgOSQ6AAAAACyH1jUAAADAQ9i4jo7TqOgAAAAAsBwqOgAAAICH8KKg4zQqOgAAAAAsh0QHAAAAgOXQugYAAAB4CCYjcB4VHQAAAACWQ6IDAAAAwHJoXQMAAAA8hI3ONadR0QEAAABgOVR0AAAAAA/BZATOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIfwonPNaVR0AAAAAFgOiQ4AAAAAy6F1DQAAAPAQzLrmPCo6AAAAACyHig4AAADgIWwUdJxGRQcAAACA5ZDoAAAAALAcWtcAAAAAD0HnmvOo6AAAAACwHBIdAAAAAJZD6xoAAADgIbyYds1pVHQAAAAAWA4VHQAAAMBDUM9xHhUdAAAAAJZDogMAAADAcmhdAwAAADwFvWtOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIew0bvmNCo6AAAAACyHig4AAADgIWwUdJxGRQcAAACA5ZDoAAAAALAcWtcAAAAAD0HnmvOo6AAAAACwHBIdAAAAAJZD6xoAAADgKehdcxoVHQAAAACWQ0UHAAAA8BA2SjpOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIew0bnmNCo6AAAAACyHRAcAAACA5dC6BgAAAHgIOtecR0UHAAAAgOVQ0QEAAAA8BSUdp1HRAQAAAGA5JDoAAAAALIfWNQAAAMBD2OhdcxoVHQAAAACWQ6IDAAAAwHJoXQMAAAA8hI3ONadR0QEAAACQ4SZNmqTChQsrICBAYWFh+vXXX2+67PTp01WnTh0FBwcrODhYDRo0uOXyqSHRAQAAADyEzU1uabVgwQJFRkZq8ODB2rJliypWrKiIiAidOHEi1eV/+OEHPfPMM1q7dq02bNigggULqmHDhjp69KjT27QZY8wdxIo7dDXB1REAAADgVgLc+OSO7Yf+dXUIkqTSoX6Ki4tzGPP395e/v3+qy4eFhal69eqaOHGiJCkpKUkFCxZUz549NWDAgNtuLzExUcHBwZo4caLat2/vVIxUdAAAAACkSVRUlIKCghxuUVFRqS577do1bd68WQ0aNLCPeXl5qUGDBtqwYYNT27t8+bLi4+OVI0cOp2N043zVtU6ePKn9+/fLy8tLZcuWVaZMmVwdEgAAAO53bjIZwcCBAxUZGekwdrNqzqlTp5SYmKjQ0FCH8dDQUO3evdup7fXv31/58uVzSJZuh0QnFTt37lSbNm1kjNHu3bvVsWNH9evXT2XKlHF1aAAAAIDL3apNLb298847mj9/vn744QcFBAQ4/Tha1/5jx44dqlmzppo1a6Yvv/xSkydP1qxZs7Ry5UpXhwYAAAB4nFy5csnb21vHjx93GD9+/Ljy5Mlzy8eOGTNG77zzjlauXKkKFSqkabskOjf466+/VL16dXXr1k1RUVEqW7asnnnmGeXJk0fLli1TYmKiw/JJSUkuihQAAAD3I5ub/C8t/Pz8VLVqVa1evdo+lpSUpNWrVys8PPymjxs9erSGDRum7777TtWqVUvzc0Wic4PNmzcrPj5eDzzwgC5cuCDp+nzfx44d0+XLl9WvXz/NnDlTP/74oyTJxhWbAAAAgNuKjIzU9OnTNXv2bP3111/q1q2bLl26pI4dO0qS2rdvr4EDB9qXHzVqlAYNGqSPP/5YhQsXVmxsrGJjY3Xx4kWnt8k5Ojdo166dTp06pVdeeUX+/v46fvy43n//fc2YMUPZs2fX33//rZkzZ+rAgQPKlSuX2rVrpz59+pDwAAAA4J7w1K+drVu31smTJ/XWW28pNjZWlSpV0nfffWefoODQoUPy8vq/GsyUKVN07do1PfXUUw7rGTx4sIYMGeLUNrmOTirGjh2rV199VTabTYsWLVKzZs3s9508eVJHjhzRuHHj9Oabb6pEiRJpWjfX0QEAAHBv7nwdnZ1HnK9oZKTyBbK4OoTbcuOXMeMdPnxY33//vZKSklSmTBnVqlVLktS3b19ly5ZNL774oqKjo3X+/HkFBQVJknLmzKmQkBDNnj3blaEDAAAAuIX7NtHZsWOHmjVrpty5cysmJkbBwcEaPXq0nnjiCUlS165d9e+//6pv376Kj4/XSy+9pGzZstlLasYYWtYAAABwT/Ht03n35WQEO3bsUHh4uNq0aaO1a9dq3rx5unTpkmbOnKkrV64oPj5e0vWTpsaMGaNBgwbp/fff17///mtfB0kOAAAA4L7uu3N0Dh8+rCpVqqh+/fqaP3++fbxatWq6dOmSfv31V2XNmtXhMUOHDtX48eO1d+9e5cyZ8662zzk6AAAA7s2dz9H546h7nKNTLr/7n6Nz31V09u7dq8KFC+vq1avasGGDJCkqKkpbtmxRtmzZ1K5dO3Xp0kVTpkzR4cOHlZiYqMGDB6dLkgMAAADg3rjvKjqStHTpUk2YMEGBgYHKnTu3Fi1apEmTJqlGjRr6/ffftWvXLk2cOFEBAQGqWLGivvnmGxljHKa8u1NUdAAAANwbFZ3b84SKzn2V6CQkJMjH5/o799tvv9UHH3ygX375RSNGjFBkZKTDsidPntTatWtVpUoVFS9ePN1iINEBAABwb+6c6Ow6esnVIUiSHsyf2dUh3JYbv4zp4+TJkwoMDFSWLFnk4+Oj+Ph4+fr6qmnTpvL19ZXNZtOaNWsUHh6u8PBwSVJ8fLxCQkLUqlUrF0cPAAAA4E5Y+hydQ4cOqVChQurdu7e6d++uixcvOsyW1qhRI7388stKSEjQ22+/rY0bN0qSfH19XRUyAAAAgHRg6UTn4MGDunr1qipWrKhz584pPDxcAwcO1A8//GBfplmzZnrppZfk5eWlyMhI/fbbb64LGAAAALgFm809bp7A0olOnTp11KNHDx08eFDz5s1T79695e3trWbNmql79+765JNPJEktWrRQz549FRoaqtDQUBdHDVeYP+9TNX7sUVWvXF7PtnlaO3fscHVIgEfg2AHuDMcOkPEsOxmBMUY2m01z587Vxx9/rK+//tp+fZyaNWvqn3/+kY+Pj/LkyaM2bdqoc+fO8vb2VkBAQIbGxWQE7ue75cv05sDX9ObgoSpfvqI+nTNbK1d+p6+XfMeU4sAtcOwAd4Zjx/2582QEf/3jHpMRlMnn/pMRWLaik3wuTrt27RQbG6t33nlHktShQwcdOXJECxYs0Nq1a5UzZ0599tlnOnv2bIYnOXBPc2bPVMunWqnFE0+qWPHienPwUAUEBGjxVwtdHRrg1jh2gDvDsQPcG5ZNdCQpMTFRkjRkyBD9/fffql+/vlauXKmvv/5a4eHhKlSokL799lstW7ZMBQoUcHG0cIX4a9f015+7VDO8ln3My8tLNWvW0o7tW10YGeDeOHaAO8OxA9w7blyYuzuJiYny9vaWJD344IN67bXXdPHiRW3bts2e1CQlJcnLy0vBwcEZEkNcXJzi4uIcxoy3v/z9/TNke0i7s+fOKjExMUWrQM6cObV/f4yLogLcH8cOcGc4dnDXPGQiAHfg8RWd1E4xSk5yjh49ql27dunBBx/UgAEDFBwcrISE/ztJxssrY3c/KipKQUFBDrd3R0Vl6DYBAAAAeHiiExMTozFjxqhTp046c+aMpOtVGm9vbx08eFAlS5bU/PnzJUk1atRQjhw59OOPP96z+AYOHKjz58873F7tP/CebR+3F5w9WN7e3jp9+rTD+OnTp5UrVy4XRQW4P44d4M5w7AD3jscmOjt37lRERIT279+voKAgZcqUSdL1Ks2JEydUs2ZNtW/fXkOHDpUkValSRcHBwZo4caJDVScj+fv7K1u2bA432tbci6+fn8qUfVCbNm6wjyUlJWnTpg2qULGyCyMD3BvHDnBnOHZwt2xu8j9P4JHn6Ozdu1f169dXp06dNGLECPu5OMlTSv/xxx8aOHCgevbsKZvNZm9lmzx5sowx8vHxyN1GBnmuQ0cNer2/HnywnMqVr6C5c2brypUravFES1eHBrg1jh3gznDsAPeGx11HJzExUb1799aZM2c0Y8YMBQYGujqkNOE6Ou7ps0/navbMGTp16qRKlS6j/q+/qQoVKro6LMDtcewAd4Zjx72583V09sRednUIkqRSeTK5OoTb8rhER5KqVaumhg0bauTIkSnuS67eJM+olvz/7oJEBwAAwL2R6NyeJyQ67pMBOMEYo4sXL+rIkSP2aRn/m6clJzm9e/fWuXPn3CrJAQAAAHBveFQWYIxRQECAKlWqpEWLFikmJkY2m81+X7KYmBht3rxZx44dc1WoAAAAQLqzucnNE3hUouPl5SUfHx+1aNFC69ev14wZM/TPP/9Ikj3hkaQ5c+YoMDBQefLkcVWoAAAAAFzIjTsQpejoaM2bN0+bNm2Sv7+/ChYsqDfffFMvvfSSoqOjFRUVpfPnz6tdu3aqWbOmduzYoZkzZ2r27Nn68ccfFRwc7OpdAAAAAOACbjsZwY4dO9SwYUNVr15dQUFBio2N1ebNm5UzZ06NHz9ejRs31pAhQzR+/HhdvXpVOXLkUPbs2eXv769Zs2apUqVKrt6FVDEZAQAAgHtz58kI/j7uHpMRlAx1/8kI3DLROXjwoB566CE999xzGjZsmLy9vZWQkKAdO3bohRdeUGxsrBYuXKiwsDD9+OOPOn78uGJiYhQWFqYyZcq4dcsaiQ4AAIB7I9G5PRKdOzRhwgQtXbpUixYtUmBgoP1CoNL1drZmzZopKChI69evd3GkaUeiAwAA4N7cOdHZe/yKq0OQJJUIdf9rWbrlZARbt25VYmJiiiRHkooUKaLevXtr69at2rJliwujBAAAAOCu3DLRsdlsDrOp3Vh08vLyUoMGDRQXF6fTp0+7KkQAAAAAbsytEp3khKZOnTqKjY3V5MmTJV1PdhISrvd8JSUl6dSpUypbtqyKFCnislgBAACAe81mc4+bJ3B5onP16tUUY02aNFGBAgU0duxYzZs3T5Lk43O9WdLLy0uLFi1S5syZlSNHjnsaKwAAAADP4NLJCI4ePao+ffqoW7duqlevniQpISFBPj4+2r9/vx5++GHZbDY1adJEL7/8sqKjo/XTTz9p6tSp+vnnn912CulbYTICAAAA9+bOkxHsO+EekxEUz81kBLcUFxenI0eOaOzYsVq3bp2k65Wb+Ph4FSlSROvWrVPt2rW1ePFiVaxYUX379tXWrVu1bt06j0xyAAAAgLthc5ObJ3D59NJ79+5Vr169ZIzRoEGDVLt2bUnStWvX5OfnpytXriguLk4rVqxQ06ZNFR8fr6CgIFeGfFeo6AAAALg3d67oRLtJRacYFZ3bK1GihD744APZbDYNGzbMXtnx8/NTYmKivLy8NHLkSH3xxRfy9fX16CQHAAAAuCuuLuV4UEnH5YmOdPNkJzExUf369dN7772nN954Q76+vi6OFAAAAIAncHnr2o1ubGMbMGCAli9frgkTJmjdunWqXLmyq8NLF7SuAQAAuDe3bl076SatayHu37rmVomOdD3ZiYyM1Lp163Tp0iVt2LBBVapUcXVY6YZEBwAAwL25c6ITczLlpVlcoWhIgKtDuC23aF27UYkSJTRmzBjVqVNHW7ZssVSSAwAAAODecLuKTrL4+HhLnpNDRQcAAMC9UdG5PU+o6Ljty2jFJAcAAAC4GzYPmfHMHbhd6xoAAAAA3C23regAAAAAcERBx3lUdAAAAABYDokOAAAAAMuhdQ0AAADwFPSuOY2KDgAAAADLIdEBAAAAYDm0rgEAAAAewkbvmtOo6AAAAACwHCo6AAAAgIewUdBxGhUdAAAAAJZDogMAAADAcmhdAwAAADwEnWvOo6IDAAAAwHJIdAAAAABYDq1rAAAAgIdg1jXnUdEBAAAAYDlUdAAAAACPQUnHWVR0AAAAAFgOiQ4AAAAAy6F1DQAAAPAQTEbgPCo6AAAAACyHRAcAAACA5dC6BgAAAHgIOtecR0UHAAAAgOVQ0QEAAAA8BJMROI+KDgAAAADLIdEBAAAAYDm0rgEAAAAewsZ0BE6jogMAAADAcqjoAAAAAJ6Cgo7TqOgAAAAAsBwSHQAAAACWQ+saAAAA4CHoXHMeFR0AAAAAlkOiAwAAAMByaF0DAAAAPISN3jWnUdEBAAAAYDlUdAAAAAAPYWM6AqdR0QEAAABgOSQ6AAAAACyH1jUAAADAU9C55jQqOgAAAAAsh0QHAAAAgOXQugYAAAB4CDrXnEdFBwAAAIDlUNEBAAAAPISNko7TqOgAAAAAsBwSHQAAAACWQ+saAAAA4CFsTEfgNCo6AAAAACyHRAcAAACA5dC6BgAAAHgIZl1zHhUdAAAAAJZDogMAAADAckh0AAAAAFgOiQ4AAAAAy2EyAgAAAMBDMBmB86joAAAAALAcEh0AAAAAlkPrGgAAAOAhbKJ3zVlUdAAAAABYDhUdAAAAwEMwGYHzqOgAAAAAsBwSHQAAAACWQ+saAAAA4CHoXHMeFR0AAAAAlkOiAwAAAMByaF0DAAAAPAW9a06jogMAAADAcqjoAAAAAB7CRknHaVR0AAAAAFgOiQ4AAAAAy6F1DQAAAPAQNjrXnEZFBwAAAIDlkOgAAAAAsBxa1wAAAAAPQeea86joAAAAALAcKjoAAACAp6Ck4zQqOgAAAAAsh0QHAAAAgOXQugYAAAB4CBu9a06jogMAAADAckh0AAAAAGS4SZMmqXDhwgoICFBYWJh+/fXXWy7/xRdfqHTp0goICFD58uW1bNmyNG2PRAcAAADwEDabe9zSasGCBYqMjNTgwYO1ZcsWVaxYURERETpx4kSqy69fv17PPPOMOnfurK1bt6pFixZq0aKF/vjjD+efK2OMSXuouFNXE1wdAQAAAG4lwI3PYneX75K2xDjFxcU5jPn7+8vf3z/V5cPCwlS9enVNnDhRkpSUlKSCBQuqZ8+eGjBgQIrlW7durUuXLmnJkiX2sZo1a6pSpUr68MMPnYrRjV9Ga3LnA+d+FxcXp6ioKA0cOPCmBykARxw3wJ3h2MGdcpfvkkOGR2no0KEOY4MHD9aQIUNSLHvt2jVt3rxZAwcOtI95eXmpQYMG2rBhQ6rr37BhgyIjIx3GIiIitHjxYqdjpHUN+P/i4uI0dOjQFL9OALg5jhvgznDswNMNHDhQ58+fd7jdmMjc6NSpU0pMTFRoaKjDeGhoqGJjY1N9TGxsbJqWT42b5IQAAAAAPMWt2tTcBRUdAAAAABkmV65c8vb21vHjxx3Gjx8/rjx58qT6mDx58qRp+dSQ6AAAAADIMH5+fqpatapWr15tH0tKStLq1asVHh6e6mPCw8MdlpekVatW3XT51NC6Bvx//v7+Gjx4sNuXYQF3wnED3BmOHdxvIiMj1aFDB1WrVk01atTQuHHjdOnSJXXs2FGS1L59e+XPn19RUVGSpN69e6tu3boaO3asmjRpovnz5+v333/XtGnTnN4m00sDAAAAyHATJ07Uu+++q9jYWFWqVEkffPCBwsLCJEmPPPKIChcurFmzZtmX/+KLL/Tmm2/qwIEDKlGihEaPHq3HH3/c6e2R6AAAAACwHM7RAQAAAGA5JDoAAAAALIdEBwAAAIDlkOgAAAAAsBwSHeD/Y14OAAAA6yDRwX3vypUriouL0+HDh3X16lVXhwPc1/jBAQCQXkh0cF/766+/1K5dO1WrVk3FihVTeHi4BgwY4OqwgPtCTEyMBg0apG7duumDDz6QJNlsNpIdAEC64Do6uG/t3LlTderUUbt27VS5cmXlyJFDs2fP1nfffafHHntMX331lXx9fV0dJmBJ27dvV6NGjVSmTBn9888/+vvvv9W1a1dNnTrV1aEBbu/kyZPav3+/vLy8VLZsWWXKlMnVIQFuiUQH96WTJ08qIiJCERERioqKchj//PPP1b9/f/3vf//T/PnzXRglYE07d+5UzZo11adPHw0dOlQnTpzQ559/rj59+mjBggV6+umnXR0i4LZ27typNm3ayBij3bt3q2PHjurXr5/KlCnj6tAAt0PrGu5LR44cUXx8vJ599lklJiZKkpKSkhQSEqJ27dpp4MCB+vbbb7V48WLXBgpYzNmzZ/XUU0+pRIkSGj58uLy9vZU3b141atRIoaGhio+Pd3WIgNvasWOHatasqWbNmunLL7/U5MmTNWvWLK1cudLVoQFuiUQH96Xt27dr3759KleunLy9vWWMkZfX9cMhKChIbdu2la+vr/bt2+fiSAFrSUxMVNu2bXXkyBGHampiYqLOnz+v4OBgF0YHuK+//vpL1atXV7du3RQVFaWyZcvqmWeeUZ48ebRs2TL7j3bJkpKSXBQp4D58XB0A4ArFixeXJC1cuFBPPvmkbDabw/1FihRR0aJFdfToUVeEB1jO3/+PvfsOj6L82jh+b3ooSSAJvYQOkQ4S6QhIEakqTXpTOgSUonQhgvReRKoUBUEFpEgRpSkdERBCLwFCr6nz/sGb/bEkgQ0Ekl2+n+vaS5l9dubMZracPWee+e8/3bp1S2+++aY6d+4sNzc3BQUFydPTU23atFG1atXUvn171axZM6lDBZKlPXv2KCIiQtmyZdPt27fl4eGhKVOm6NKlS8qZM6d69+6twoULK2fOnKpYsWKszzXgdUSig9eSn5+fPDw8NH/+fJUsWVLZs2eX9OgXMAcHB924cUPu7u4qUaJEEkcK2L4DBw6oWLFimjBhgt588035+Piobdu2MgxD/fv3V7du3dSzZ099/fXXMgzDosIK4JFmzZopNDRUPXr0kKurqy5fvqxx48Zp9uzZ8vLy0n///ac5c+bo9OnT8vHxUbNmzdSzZ08SHrzWSHTwWsqSJYumTZumJk2aaMCAAerTp4/eeOMN85ersWPH6uLFiypfvnwSRwrYtgMHDqhMmTLq37+/unbtal7u4+OjFi1ayNnZWSNGjDC3rJlMJlpugHj06NFDUVFR6tixo0wmk1asWKE6deqY72/Tpo3Onz+v8ePHq3bt2iQ5eO2R6OC1VbduXU2cOFFdunTRX3/9pbJlyypjxow6deqUfv31V23cuNFc6QGQcAcPHlTZsmXVo0cPffnll+blixcvVpMmTZQpUyY1b95ckZGRGjFihJycnPTZZ5/J0dExCaMGkodz587pt99+U3R0tAoUKKAyZcpIknr16iUPDw99/PHHCg4O1q1bt+Tp6SlJ8vb2lq+vr+bNm5eUoQPJBokOXluOjo76+OOPVbRoUY0aNUq7du2Sl5eXihQpou3btyt//vxJHSJgsy5evKiiRYuqXbt2Gj58uHn5yJEj1a9fP+XJk0clS5ZUunTp1LZtWzk4OKhPnz5ycXFRjx49ki5wIBk4ePCg6tSpo3Tp0unkyZNKkyaNRo0apfr160uS2rdvrzt37qhXr16KiIjQJ598Ig8PD3NXgmEYVHMAkegACggI0Pfffy8HBwdz2wznBwAvxtXVVUWKFNHff/+t48ePK0+ePPrqq680evRorVu3TiVLljR/GfPx8VGbNm3k7OzMZAR47R08eFClS5dW165dNWDAAG3btk2tWrXSnDlzVKNGDTk5OcnZ2VmBgYGSpH79+unBgwcKDAxU6tSpJYkkB/h/XDAUkOWvX/wSBryYmNfQtWvXVLNmTUVERKhKlSqaN2+eFi1apHfeecdi/OHDh5UvXz45Ojry2sNr7dy5cypevLiqVKliccHqkiVL6t69e/rrr7/MyUyMIUOGaMKECTp+/Li8vb1fdchAssbP1oAsf/3iixaQOLy9vbV27Vp5eHho7NixGjVqVKwkp0+fPmrcuLHu3LnDaw+vvePHj8vPz08PHz7Ujh07JElBQUHau3evPDw81KxZM7Vr107Tpk3TuXPnFBUVpUGDBpHkAPGgogMAeGGXLl3Szp07tW7dOhmGoZo1a+rNN99U5syZdf36db333nu6ffu2li9frnz58kmSBg0apK+//lpbtmxRqVKlkngPgORh9erVmjRpktzd3ZUuXTqtWLFCU6ZMUalSpbR7924dPnxYkydPlpubm4oUKaKff/6ZKdmBeJDoAABeyOHDh9WqVSt5enoqJCRE7u7u2rNnj+rWravAwECVL19eN27cUPXq1XX37l399ttvmjVrloKCgrRt2zauVwVIioyMlJPTo1Onf/nlF02cOFF//vmnhg8fbj4fJ8bVq1e1efNmFS9e3HwBbACxMRkBAOC5HThwQBUrVlSHDh308ccfK1euXJKkOXPmqFevXnrw4IGGDx+uEiVK6Ndff1Xt2rWVJUsWubu7k+TgtXf16lW5u7srVapUcnJyUkREhJydnVW7dm05OzvLZDJp06ZNKl26tEqXLi1JioiIkK+vrxo2bJjE0QPJH3VOAMBzOXz4sMqVK6fu3btr1KhR8vPzkyRFR0erdevWmjx5sjZv3qylS5dKenTOzk8//aRGjRqR5OC1d/bsWWXPnl3du3dXp06ddPfuXYvz1GrUqKEuXbooMjJSQ4cO1c6dOyVJzs7OSRUyYHNoXQMAJNjDhw9VqlQp3bp1S5s2bTJXcmI+UmK+sPXp00dTp07V0aNHlTlzZkliCndA0h9//KGKFStq/Pjx2rlzpw4dOqQaNWqoVq1aqlSpknncypUrNWvWLN24cUMTJkzQm2++mXRBAzaGTxoAQIK5ublp5MiRkGBrDgAAn61JREFUcnZ21pdffqkDBw5I+l+CExUVJUmqWrWqHBwcdPXqVfNjSXIAqXz58urcubPOnDmjRYsWqXv37nJ0dFSdOnXUqVMnzZ8/X5JUr149de3aVenTp1f69OmTOGrAtlDRAQAkWMy1ctatW6cOHTqocuXKCgwMVKFChST9r2oze/ZsjRs3Tlu3blXatGmTOGogeYh5/SxcuFDffvutfvrpJ/P1cd566y1dvHhRTk5OypAhgxo3bqy2bdvK0dFRbm5uSRw5YFv4WQ0AkGAmk0mGYah69eqaOXOmNm3apDFjxujgwYOSHlVtIiIitGvXLgUEBChlypRJHDGQfMRUPps1a6aQkBB99dVXkqSWLVvq/PnzWrp0qTZv3ixvb28tXrxYN27cIMkBngMVHQDAc4mZIUqS1q5dq48//lhvv/22AgMDVbhwYQ0ePNicBOXPnz+JowWSl6ioKDk6Our777/XDz/8oOvXr+vff//VqlWrLCbquHHjhtKkSZOEkQK2i0QHAPBUMW02j4v5knb+/HndunVLb7zxhrmN7d1331VkZKQWLlyobdu2qXjx4kkUOZA8xbx+pEezF9aqVUt3797V/v37lSVLFklM2gEkBl5BAIB4nTx5UqNHj1abNm10/fp1SY++gDk6OurMmTPKly+flixZYm5jmzVrlhYsWKDvvvtO27dvJ8nBay2u35JjkpwLFy7o8OHDeuONN9S3b1+lSZNGkZGR5nEkOcCL41UEAIjToUOHVL16dZ06dUqenp5KkSKFpEdfwK5cuaK33npLLVq00JAhQ8zn7FSrVk3r16/XgQMHVKxYsSTeAyDpPOtHgrx582rJkiWSpFKlSilt2rT6/fffkzJkwO7QugYAiOX48eMqW7as2rRpo+HDh5vbbGLa2DZt2qR//vlHXbt2Nbe1xdXiBryODh06pAYNGuidd96Rq6urgoKCzJMJXLlyRUWKFFG9evU0ZcoUc+WmRo0aunbtmnbs2CEnJ6ekDB+wGyQ6AAALUVFR6t69u65fv67Zs2fL3d09qUMCbEZCfySIaWU7efKkDMMwX3wXwIsj0QEAxFKyZElVq1ZNI0aMiHVfzBezmJOlOWkaeIQfCYDkhU8mAICZYRi6e/euzp8/L29vb/Oyx8UkOd27d9fNmzdJcoD/5+joqJ07d8rPzy/OJCcqKkrSo3N1Hv8vgJeDTycAgJlhGHJzc1PRokW1YsUKnTx50uIcnBgnT57Unj17dOnSpaQKFUhW+JEASH54hQEAzBwcHOTk5KR69epp+/btmj17ti5evChJFhMNLFiwQO7u7sqQIUNShQokK/xIACQ/TOsBAK+x4OBgLVq0SLt27ZKrq6uyZs2qL774Qp988omCg4MVFBSkW7duqVmzZnrrrbd08OBBzZkzR/PmzdPvv//OFduB/+fg4CAHBwfVq1dPnTp10uzZs9W5c2dlypSJHwmAJEKiAwCvqYMHD6patWp688035eXlpZCQEG3atEmrVq3ShAkTNHLkSLm7u2vChAmaNWuW0qZNKy8vL7m6umrTpk0qVKhQUu8CkGT4kQBI/ph1DQBeQ2fOnFG5cuXUvHlzDRs2TI6OjoqMjNTBgwfVoUMHhYSEaPny5QoICNDvv/+uy5cv6+TJkwoICFCBAgX4NRqvtcd/JPD09FRISIj27Nkjb29vTZgwQTVr1tTgwYM1YcIEPXz40OJHgrlz56po0aJJvQvAa4FEBwBeQ5MmTdLq1au1YsUKubu7W1zsMzg4WHXq1JGnp6e2b9+exJECyQs/EgC2g0QHAF5Dbdq00blz57RhwwaLJEd6NOXtN998o+7du2vbtm0qXrx4EkYKJC/8SADYDmZdA4DXkMlksphN7fHfvBwcHFS1alWFhYXp2rVrSRUikCzt27dPUVFRsZIcScqRI4e6d++uffv2ae/evUkYJQCJRAcAXisxCU358uUVEhKiqVOnSnqU7ERGRkp6VNEJDQ2Vv7+/cuTIkWSxAskRPxIAtoNEBwDs3MOHD2Mtq1WrlrJkyaIxY8Zo0aJFkiQnp0cTcTo4OGjFihVKmTKl0qZN+0pjBZIrfiQAbA+JDgDYsQsXLqhFixbavHmzpP99KfP19dXKlSsVHh6uvn37qmPHjjp8+LB+/vln9e7dW5MnT9aMGTNIdPBa40cCwLYxGQEA2LGTJ0+qWbNmSps2rfr166eyZctKkiIiIuTs7KyzZ8+qT58+2rJli65evaocOXIoW7ZsGjdunAoXLpzE0QNJ58KFC+rZs6c6duyot99+W5IUGRkpJycnnTp1ShUqVJDJZFKtWrXUpUsXBQcHa+vWrZoxY4b++OMPppAGkgESHQCwc8ePH1e3bt1kGIYGDBhgTnbCw8Pl4uKiBw8eKCwsTOvWrVPt2rUVEREhT0/PJI4aSFr8SADYPhIdAHgNxJfsREVFKTIyUgMGDNDJkye1ePFiOTs7J3G0QPLAjwSAbSPRAYDXRFxf2sLDw9WrVy9NmzZNf//9t4oVK5bUYQLJCj8SALaLRAcAXiOPf2nr27evfv31V02aNEnbtm0jyQHiwY8EgG0i0QGA18zx48cVGBiobdu26d69e9qxY4eKFy+e1GEByRo/EgC2h0QHAF5Dx44d02effaYRI0bojTfeSOpwAJvAjwSAbSHRAYDXVMzsUQCsx48EgO0g0QEAAEgAfiQAbAOJDgAAAAC745DUAQAAAABAYiPRAQAAAGB3SHQAAAAA2B0SHQAAAAB2h0QHAAAAgN0h0QEAAABgd0h0AMAOtWrVSvXq1TP/u1KlSurRo8crj2PLli0ymUy6efNmvGNMJpNWrlxp9ToHDx6sokWLvlBcp0+flslk0v79+19oPQCA5ItEBwBekVatWslkMslkMsnFxUW5c+fW0KFDFRkZ+dK3/eOPP2rYsGFWjbUmOQEAILlzSuoAAOB1UqNGDc2ZM0dhYWFas2aNOnfuLGdnZ/Xr1y/W2PDwcLm4uCTKdtOmTZso6wEAwFZQ0QGAV8jV1VUZMmRQ9uzZ1bFjR1WtWlU///yzpP+1mw0fPlyZMmVSvnz5JEnnzp1Tw4YN5eXlpbRp06pu3bo6ffq0eZ1RUVEKDAyUl5eXvL299dlnn8kwDIvtPtm6FhYWpj59+ihr1qxydXVV7ty5NXv2bJ0+fVpvv/22JClNmjQymUxq1aqVJCk6OlpBQUHKkSOH3N3dVaRIES1btsxiO2vWrFHevHnl7u6ut99+2yJOa/Xp00d58+ZVihQplDNnTg0YMEARERGxxs2YMUNZs2ZVihQp1LBhQ926dcvi/m+++UYFChSQm5ub8ufPr6lTp8a7zRs3buijjz6Sr6+v3N3dlSdPHs2ZMyfBsQMAkg8qOgCQhNzd3XXt2jXzvzdu3CgPDw9t2LBBkhQREaHq1aurdOnS+uOPP+Tk5KQvv/xSNWrU0MGDB+Xi4qIxY8Zo7ty5+vbbb1WgQAGNGTNGK1asUOXKlePdbosWLbRjxw5NnDhRRYoU0alTpxQaGqqsWbNq+fLlev/993Xs2DF5eHjI3d1dkhQUFKSFCxdq+vTpypMnj7Zu3apmzZrJ19dXFStW1Llz59SgQQN17txZHTp00O7du9WrV68EPyepU6fW3LlzlSlTJh06dEjt27dX6tSp9dlnn5nHnDhxQt9//71++eUX3b59W23btlWnTp303XffSZK+++47DRw4UJMnT1axYsW0b98+tW/fXilTplTLli1jbXPAgAH6999/9euvv8rHx0cnTpzQgwcPEhw7ACAZMQAAr0TLli2NunXrGoZhGNHR0caGDRsMV1dXo3fv3ub706dPb4SFhZkfs2DBAiNfvnxGdHS0eVlYWJjh7u5urFu3zjAMw8iYMaMxatQo8/0RERFGlixZzNsyDMOoWLGi0b17d8MwDOPYsWOGJGPDhg1xxrl582ZDknHjxg3zsocPHxopUqQwtm/fbjG2bdu2RpMmTQzDMIx+/foZ/v7+Fvf36dMn1rqeJMlYsWJFvPd//fXXRokSJcz/HjRokOHo6GicP3/evOzXX381HBwcjEuXLhmGYRi5cuUyFi1aZLGeYcOGGaVLlzYMwzBOnTplSDL27dtnGIZh1K5d22jdunW8MQAAbA8VHQB4hVatWqVUqVIpIiJC0dHRatq0qQYPHmy+v1ChQhbn5Rw4cEAnTpxQ6tSpLdbz8OFDBQcH69atW7p06ZICAgLM9zk5OalkyZKx2tdi7N+/X46OjqpYsaLVcZ84cUL379/XO++8Y7E8PDxcxYoVkyQdOXLEIg5JKl26tNXbiLF06VJNnDhRwcHBunv3riIjI+Xh4WExJlu2bMqcObPFdqKjo3Xs2DGlTp1awcHBatu2rdq3b28eExkZKU9Pzzi32bFjR73//vvau3evqlWrpnr16qlMmTIJjh0AkHyQ6ADAK/T2229r2rRpcnFxUaZMmeTkZPk2nDJlSot/3717VyVKlDC3ZD3O19f3uWKIaUVLiLt370qSVq9ebZFgSI/OO0osO3bs0EcffaQhQ4aoevXq8vT01JIlSzRmzJgExzpr1qxYiZejo2Ocj6lZs6bOnDmjNWvWaMOGDapSpYo6d+6s0aNHP//OAACSFIkOALxCKVOmVO7cua0eX7x4cS1dulTp0qWLVdWIkTFjRu3atUsVKlSQ9KhysWfPHhUvXjzO8YUKFVJ0dLR+//13Va1aNdb9MRWlqKgo8zJ/f3+5urrq7Nmz8VaCChQoYJ5YIcbOnTufvZOP2b59u7Jnz67PP//cvOzMmTOxxp09e1YXL15UpkyZzNtxcHBQvnz5lD59emXKlEknT57URx99ZPW2fX191bJlS7Vs2VLly5fXp59+SqIDADaMWdcAIBn76KOP5OPjo7p16+qPP/7QqVOntGXLFnXr1k3nz5+XJHXv3l1fffWVVq5cqaNHj6pTp05PvQaOn5+fWrZsqTZt2mjlypXmdX7//feSpOzZs8tkMmnVqlW6evWq7t69q9SpU6t3797q2bOn5s2bp+DgYO3du1eTJk3SvHnzJEmffPKJjh8/rk8//VTHjh3TokWLNHfu3ATtb548eXT27FktWbJEwcHBmjhxolasWBFrnJubm1q2bKkDBw7ojz/+ULdu3dSwYUNlyJBBkjRkyBAFBQVp4sSJ+u+//3To0CHNmTNHY8eOjXO7AwcO1E8//aQTJ07o8OHDWrVqlQoUKJCg2AEAyQuJDgAkYylSpNDWrVuVLVs2NWjQQAUKFFDbtm318OFDc4WnV69eat68uVq2bKnSpUsrderUql+//lPXO23aNH3wwQfq1KmT8ufPr/bt2+vevXuSpMyZM2vIkCHq27ev0qdPry5dukiShg0bpgEDBigoKEgFChRQjRo1tHr1auXIkUPSo/Nmli9frpUrV6pIkSKaPn26RowYkaD9rVOnjnr27KkuXbqoaNGi2r59uwYMGBBrXO7cudWgQQO9++67qlatmgoXLmwxfXS7du30zTffaM6cOSpUqJAqVqyouXPnmmN9kouLi/r166fChQurQoUKcnR01JIlSxIUOwAgeTEZ8Z2tCgAAAAA2iooOAAAAALtDogMAAADA7pDoAAAAALA7JDoAAAAA7A6JDgAAAAC7Q6IDAAAAwO6Q6AAAAACwOyQ6AAAAAOwOiQ4AAAAAu0OiAwAAAMDukOgAAAAAsDskOgAAAADsDokOAAAAALtDogMAAADA7pDoAAAAALA7JDoAAAAA7A6JDgAAAAC7Q6IDAAAAwO6Q6AAAAACwOyQ6AAAAAOwOiQ4AAAAAu0OiAwAAAMDukOgAAAAAsDskOgAAAADsDokOAAAAALtDogMAAADA7pDoAAAAALA7JDoAAAAA7A6JDgAAAAC7Q6IDAAAAwO6Q6AAAAACwOyQ6AAAAAOwOiQ4AAAAAu0OigyRx/PhxVatWTZ6enjKZTFq5cmWirv/06dMymUyaO3duoq7XHvj5+alVq1aJtr7Lly/rgw8+kLe3t0wmk8aPH59o64Zt2rJli0wmk7Zs2ZIk2x88eLBMJlOSbFuSzp07Jzc3N23bts28rFWrVvLz80uymJC8TJ8+XdmyZVNYWNgLradSpUqqVKlS4gQF2CESnddYcHCwPv74Y+XMmVNubm7y8PBQ2bJlNWHCBD148OClbrtly5Y6dOiQhg8frgULFqhkyZIvdXv26N9//9XgwYN1+vTpJI2jZ8+eWrdunfr166cFCxaoRo0aL3V7JpNJXbp0eeqY6OhozZ8/XwEBAUqbNq1Sp06tvHnzqkWLFtq5c6ekRwmfyWR65i0mWY75d7t27eLc5ueff24eExoamqj7jNju37+vwYMHJ1ky9TRDhw5VQECAypYtm9Sh2LSpU6fa7Y9VrVq1Unh4uGbMmJEk209ur5/t27dr8ODBunnzZlKHAjvjlNQBIGmsXr1aH374oVxdXdWiRQsVLFhQ4eHh+vPPP/Xpp5/q8OHDmjlz5kvZ9oMHD7Rjxw59/vnnz/zC+ryyZ8+uBw8eyNnZ+aWsPzn4999/NWTIEFWqVClBvxQfO3ZMDg6J9xvHpk2bVLduXfXu3TvR1vmiunXrpilTpqhu3br66KOP5OTkpGPHjunXX39Vzpw59dZbb2n8+PG6e/eu+TFr1qzR4sWLNW7cOPn4+JiXlylTxvz/bm5uWr58uaZOnSoXFxeLbS5evFhubm56+PDhy99B6P79+xoyZIgkxfpF+4svvlDfvn2TICrp6tWrmjdvnubNm5ck27cnU6dOlY+PT6JWoJMLNzc3tWzZUmPHjlXXrl1feQXyaa+fpLB9+3YNGTJErVq1kpeXV1KHAztCovMaOnXqlBo3bqzs2bNr06ZNypgxo/m+zp0768SJE1q9evVL2/7Vq1cl6aW+mZlMJrm5ub209dsawzD08OFDubu7y9XVNVHXfeXKlUT9Wz58+FAuLi7PnYxdvnxZU6dOVfv27WMl6+PHjzcff/Xq1bO4LyQkRIsXL1a9evXiTRxr1Kihn3/+Wb/++qvq1q1rXr59+3adOnVK77//vpYvX/5ccSPxODk5yckpaT7eFi5cKCcnJ9WuXfulbSM6Olrh4eG8x9m4hg0batSoUdq8ebMqV66c1OEAdonWtdfQqFGjdPfuXc2ePdsiyYmRO3dude/e3fzvyMhIDRs2TLly5ZKrq6v8/PzUv3//WL3Ffn5+eu+99/Tnn3+qVKlScnNzU86cOTV//nzzmMGDByt79uySpE8//VQmk8n8pTK+Hva4+u03bNigcuXKycvLS6lSpVK+fPnUv39/8/3xnaOzadMmlS9fXilTppSXl5fq1q2rI0eOxLm9EydOmH9d8vT0VOvWrXX//v34n9j/V6lSJRUsWFAHDx5UxYoVlSJFCuXOnVvLli2TJP3+++8KCAiQu7u78uXLp99++83i8WfOnFGnTp2UL18+ubu7y9vbWx9++KFFi9rcuXP14YcfSpLefvttc8tUTBtCzN9i3bp1KlmypNzd3c0tEo+fo2MYht5++235+vrqypUr5vWHh4erUKFCypUrl+7duxfnfs6dO1cmk0mGYWjKlCnmGGKcPHlSH374odKmTasUKVLorbfeipVAx5zLsWTJEn3xxRfKnDmzUqRIodu3bz/zeY7PqVOnZBhGnG1DJpNJ6dKle+51Z86cWRUqVNCiRYssln/33XcqVKiQChYsaPW69u3bp5o1a8rDw0OpUqVSlSpVzG11MWKe423btikwMFC+vr5KmTKl6tevb07YnuXo0aP64IMPlDZtWrm5ualkyZL6+eefzffv3r1bJpMpzgrEunXrZDKZtGrVKknWHZvxie/csCfPMQgPD9fAgQNVokQJeXp6KmXKlCpfvrw2b95sHnP69Gn5+vpKkoYMGWI+9gYPHiwp7veMxHwfe5qVK1cqICBAqVKleubY0aNHq0yZMvL29pa7u7tKlChhfp94XEy75nfffac33nhDrq6uWrt2rSSZ32fc3d2VJUsWffnll5ozZ45MJlOsv8uvv/5qfv9LnTq1atWqpcOHDz8zzoiICA0ZMkR58uSRm5ubvL29Va5cOW3YsMFi3LOONcn6Y9rPz0+HDx/W77//bv77Pn6c3Lx5Uz169FDWrFnl6uqq3Llza+TIkYqOjjaPifkcGD16tGbOnGn+27/55pv6+++/Y+3n0aNH1bBhQ/n6+prfnz///HOLMRcuXFCbNm2UPn16ubq66o033tC3334ba12TJk3SG2+8oRQpUihNmjQqWbJkrPeNEiVKKG3atPrpp5+e+TeQZN4Hd3d3lSpVSn/88UesMYnx+jl48KBatWplbmvPkCGD2rRpo2vXrlls686dO+rRo4f8/Pzk6uqqdOnS6Z133tHevXstxu3atUs1atSQp6enUqRIoYoVK1qcvzZ48GB9+umnkqQcOXKY40nqtmzYCQOvncyZMxs5c+a0enzLli0NScYHH3xgTJkyxWjRooUhyahXr57FuOzZsxv58uUz0qdPb/Tv39+YPHmyUbx4ccNkMhn//POPYRiGceDAAWPcuHGGJKNJkybGggULjBUrVpi3kz179ljbHzRokPH4ofrPP/8YLi4uRsmSJY0JEyYY06dPN3r37m1UqFDBPObUqVOGJGPOnDnmZRs2bDCcnJyMvHnzGqNGjTKGDBli+Pj4GGnSpDFOnToVa3vFihUzGjRoYEydOtVo166dIcn47LPPnvl8VaxY0ciUKZORNWtW49NPPzUmTZpk+Pv7G46OjsaSJUuMDBkyGIMHDzbGjx9vZM6c2fD09DRu375tfvwPP/xgFClSxBg4cKAxc+ZMo3///kaaNGmM7NmzG/fu3TMMwzCCg4ONbt26GZKM/v37GwsWLDAWLFhghISEmP8WuXPnNtKkSWP07dvXmD59urF582bzfS1btjRv7+TJk0aqVKmM+vXrm5f17dvXMJlMxu+//x7vfgYHBxsLFiwwJBnvvPOOOQbDMIyQkBAjffr0RurUqY3PP//cGDt2rFGkSBHDwcHB+PHHH83r2Lx5syHJ8Pf3N4oWLWqMHTvWCAoKMu9nXCQZnTt3jvf+ixcvGpKMWrVqPXU9T/r6668NSRbHQlzbnTlzpuHu7m7cuXPHMAzDiIiIMHx9fY2goCDzsXP16tWnbuuff/4xUqZMaWTMmNEYNmyY8dVXXxk5cuQwXF1djZ07d5rHzZkzx3wsVq5c2Zg0aZLRq1cvw9HR0WjYsOEz9+mff/4xPD09DX9/f2PkyJHG5MmTjQoVKhgmk8ni75AzZ07j3XffjfX41q1bG2nSpDHCw8MNw7Du2DSM//1dY445w4h93MWoWLGiUbFiRfO/r169amTMmNEIDAw0pk2bZowaNcrIly+f4ezsbOzbt88wDMO4e/euMW3aNEOSUb9+ffOxd+DAAcMwYr9nGEbivo/FJzw83HB3dzcCAwNj3RfX+1uWLFmMTp06GZMnTzbGjh1rlCpVypBkrFq1ymKcJKNAgQKGr6+vMWTIEGPKlCnGvn37jPPnzxtp06Y1vL29jSFDhhijR4828ufPbxQpUiTWsTx//nzDZDIZNWrUMCZNmmSMHDnS8PPzM7y8vOI95mP079/fMJlMRvv27Y1Zs2YZY8aMMZo0aWJ89dVX5jHWHmvWHtMrVqwwsmTJYuTPn9/8912/fr1hGIZx7949o3Dhwoa3t7fRv39/Y/r06UaLFi0Mk8lkdO/e3byOmM+BYsWKGblz5zZGjhxpjBo1yvDx8TGyZMliPq4N49Fnk4eHh+Ht7W3069fPmDFjhvHZZ58ZhQoVMo8JCQkxsmTJYmTNmtUYOnSoMW3aNKNOnTqGJGPcuHHmcTNnzjQfazNmzDAmTJhgtG3b1ujWrVus57Zq1apGiRIlnvr8G4ZhfPPNN4Yko0yZMsbEiRONHj16GF5eXkbOnDkT/fUzevRoo3z58sbQoUONmTNnGt27dzfc3d2NUqVKGdHR0eZtNW3a1HBxcTECAwONb775xhg5cqRRu3ZtY+HCheYxGzduNFxcXIzSpUsbY8aMMcaNG2cULlzYcHFxMXbt2mV+7ps0aWJ+HmPiuXv37jOfF+BZSHReM7du3TIkGXXr1rVq/P79+w1JRrt27SyW9+7d25BkbNq0ybwse/bshiRj69at5mVXrlwxXF1djV69epmXxXz4fP311xbrtDbRiUmUnvZlMq5Ep2jRoka6dOmMa9eumZcdOHDAcHBwMFq0aBFre23atLFYZ/369Q1vb+94txmjYsWKhiRj0aJF5mVHjx41JBkODg4WX2TXrVsXK8779+/HWueOHTsMScb8+fPNy3744YdYXyZjxPwt1q5dG+d9T37hnDFjhiHJWLhwobFz507D0dHR6NGjxzP31TDiTjx69OhhSDL++OMP87I7d+4YOXLkMPz8/IyoqCjDMP73hThnzpxx7re123tSzJfYNGnSGPXr1zdGjx5tHDly5KmPsTbRuX79uuHi4mJO6lavXm2YTCbj9OnTVic69erVM1xcXIzg4GDzsosXLxqpU6e2SNhjvhRWrVrV4gtGz549DUdHR+PmzZtP3U6VKlWMQoUKGQ8fPjQvi46ONsqUKWPkyZPHvKxfv36Gs7Ozcf36dfOysLAww8vLy+J1YO2x+SKJTmRkpBEWFmYx5saNG0b69OktYrl69aohyRg0aFCsdT75nvEy3sficuLECUOSMWnSpFj3xfX+9uTzGR4ebhQsWNCoXLmyxfKY947Dhw9bLO/atathMpnMX2ANwzCuXbtmpE2b1uJYvnPnjuHl5WW0b9/e4vEhISGGp6dnrOVPKlKkiFGrVq2njrH2WEvIMf3GG29YHBsxhg0bZqRMmdL477//LJb37dvXcHR0NM6ePWsYxv8+B7y9vS2O7Z9++smQZPzyyy/mZRUqVDBSp05tnDlzxmKdj8fYtm1bI2PGjEZoaKjFmMaNGxuenp7mv2fdunWNN954I/4n6zEdOnQw3N3dnzomPDzcSJcunVG0aFGL10ZMQpXYr5+4XueLFy+O9brw9PR86ntxdHS0kSdPHqN69eoWz+P9+/eNHDlyGO+884552bPef4HnRevaayamJSh16tRWjV+zZo0kKTAw0GJ5r169JClWK5K/v7/Kly9v/revr6/y5cunkydPPnfMT4o5H+Snn36yaFN4mkuXLmn//v1q1aqV0qZNa15euHBhvfPOO+b9fNwnn3xi8e/y5cvr2rVrVrVVpUqVSo0bNzb/O1++fPLy8lKBAgUUEBBgXh7z/48/P+7u7ub/j4iI0LVr15Q7d255eXnFagl4mhw5cqh69epWje3QoYOqV6+url27qnnz5sqVK5dGjBhh9baetGbNGpUqVUrlypUzL0uVKpU6dOig06dP699//7UY37JlS4v9flFz5szR5MmTlSNHDq1YsUK9e/dWgQIFVKVKFV24cOGF1p0mTRrVqFFDixcvliQtWrRIZcqUMbdkPktUVJTWr1+vevXqKWfOnOblGTNmVNOmTfXnn3/GOsY6dOhg0YpVvnx5RUVF6cyZM/Fu5/r169q0aZMaNmyoO3fuKDQ0VKGhobp27ZqqV6+u48ePm5+LRo0aKSIiQj/++KP58evXr9fNmzfVqFEj87LEOjafxtHR0TzRQ3R0tK5fv67IyEiVLFnyubfxqt7HYlp70qRJY1Vcjz+fN27c0K1bt1S+fPk497NixYry9/e3WLZ27VqVLl1aRYsWNS9LmzatPvroI4txGzZs0M2bN9WkSRPzcRAaGipHR0cFBARYtDXFxcvLS4cPH9bx48fjvD8hx1qM5zmmY/zwww8qX7680qRJY7E/VatWVVRUlLZu3WoxvlGjRhZ/k5i/bczf8+rVq9q6davatGmjbNmyWTw2JkbDMLR8+XLVrl1bhmFYbLd69eq6deuW+e/m5eWl8+fPx9ke96Q0adLowYMHT22L3r17t65cuaJPPvnEYhKUVq1aydPT02JsYrx+Hj8uHz58qNDQUL311luSZLEOLy8v7dq1SxcvXoxzPfv379fx48fVtGlTXbt2zfx83bt3T1WqVNHWrVut/gwHnheJzmvGw8ND0qPeWmucOXNGDg4Oyp07t8XyDBkyyMvLK9aH0pMfEtKjN/IbN248Z8SxNWrUSGXLllW7du2UPn16NW7cWN9///1T3zBj4syXL1+s+woUKGB+833ck/sS80Fpzb5kyZIl1jkCnp6eypo1a6xlT67zwYMHGjhwoLn33MfHR76+vrp586Zu3br1zG3HyJEjh9VjJWn27Nm6f/++jh8/rrlz575Q4nHmzJl4n+uY+18k1mdxcHBQ586dtWfPHoWGhuqnn35SzZo1tWnTJosE9Hk1bdpUGzZs0NmzZ7Vy5Uo1bdrU6sdevXpV9+/fj/f5iY6O1rlz5yyWP8+xeOLECRmGoQEDBsjX19fiNmjQIEkyn5dVpEgR5c+fX0uXLjU/funSpfLx8bE4STqxjs1nmTdvngoXLmw+H8TX11erV69+7m286vcxwzCsGrdq1Sq99dZbcnNzU9q0aeXr66tp06bFuZ9xvUbOnDkTa58kxVoWk6BUrlw51rGwfv16i/Pz4jJ06FDdvHlTefPmVaFChfTpp5/q4MGD5vsTcqzFeJH31+PHj2vt2rWxtlW1atXn2lZMwvO0c+yuXr2qmzdvaubMmbG227p1a4vt9unTR6lSpVKpUqWUJ08ede7c2eKclMfFHCtPm3Ut5vjMkyePxXJnZ2eLH0tivOjr5/r16+revbvSp08vd3d3+fr6mo+/x9cxatQo/fPPP8qaNatKlSqlwYMHW/wYEHPctWzZMtZz9s033ygsLCxR3zeAuDDr2mvGw8NDmTJl0j///JOgx1k79aWjo2Ocy6354I9vG1FRURb/dnd319atW7V582atXr1aa9eu1dKlS1W5cmWtX78+3hgS6kX2Jb7HWrPOrl27as6cOerRo4dKly5tvqhq48aNE/TrV0ITlS1btphPzD506JBKly6doMe/iMSs5jzJ29tbderUUZ06dVSpUiX9/vvvOnPmjNUVmLjUqVNHrq6uatmypcLCwtSwYcNEjDi25zkWY46V3r17x1vZe/wLcaNGjTR8+HCFhoYqderU+vnnn9WkSROL2cte5Nh82uv78f1buHChWrVqpXr16unTTz9VunTp5OjoqKCgIAUHBz91G8/yst/HvL29JVn3Zf2PP/5QnTp1VKFCBU2dOlUZM2aUs7Oz5syZE+ukdenFXiMxf5sFCxYoQ4YMse5/1gx1FSpUUHBwsH766SetX79e33zzjcaNG6fp06erXbt2CT7WpBd7f42OjtY777yjzz77LM778+bNm2jbenybktSsWTO1bNkyzjGFCxeW9OgHi2PHjmnVqlVau3ateUr6gQMHmqd0jnHjxg2lSJEi0d4DE+P107BhQ23fvl2ffvqpihYtqlSpUik6Olo1atSweJ03bNhQ5cuX14oVK7R+/Xp9/fXXGjlypH788UfVrFnTPPbrr7+2qDo+zppJO4AXQaLzGnrvvfc0c+ZM7dix45lfZrNnz67o6GgdP37c/Gu89GgK35s3b77Ql8UnpUmTJs6LhcXVyuDg4KAqVaqoSpUqGjt2rEaMGKHPP/9cmzdvNv+q9+R+SI+uIfOko0ePysfHRylTpnzxnUgEy5YtU8uWLTVmzBjzsocPH8Z6bhLzuguXLl1S165dVa1aNbm4uJi/sDzv3zd79uzxPtcx9yeFkiVL6vfff9elS5deKAZ3d3fVq1dPCxcuVM2aNS2uu/Msvr6+SpEiRbzPj4ODQ6zK3/OI+aXX2dk5ztfEkxo1aqQhQ4Zo+fLlSp8+vW7fvh2r+mXtsRmXp72+H/9VetmyZcqZM6d+/PFHi2M8pjIQIyHH/6t6H8uWLZvc3d116tSpZ45dvny53NzctG7dOosp3+fMmWP19rJnz64TJ07EWv7ksly5ckmS0qVLZ9WxEJe0adOqdevWat26te7evasKFSpo8ODBateuXYKPNWvF9zfOlSuX7t69m2jbion/aT8A+vr6KnXq1IqKirJquylTplSjRo3UqFEjhYeHq0GDBho+fLj69etnMS34qVOnLI7JuMQcn8ePH7eosEZEROjUqVMqUqSIedmLvn5u3LihjRs3asiQIRo4cKB5eXxtixkzZlSnTp3UqVMnXblyRcWLF9fw4cNVs2ZN83Hn4eHxzOfsVV9HCK8PWtdeQ5999plSpkypdu3a6fLly7HuDw4O1oQJEyRJ7777rqRH1x953NixYyVJtWrVSrS4cuXKpVu3blm0RFy6dEkrVqywGHf9+vVYj435tejJqWJjZMyYUUWLFtW8efMsvmz9888/Wr9+vXk/kwNHR8dYvzROmjQpVmUrJjFLjCtJt2/fXtHR0Zo9e7ZmzpwpJycntW3bNkG/eD7u3Xff1V9//aUdO3aYl927d08zZ86Un59frHMNElNISEisc4CkR9Oubty4Mc4WpufRu3dvDRo0SAMGDEjQ4xwdHVWtWjX99NNPFtOnXr58WYsWLVK5cuXMLaYvIl26dKpUqZJmzJihS5cuxbr/yempCxQooEKFCmnp0qVaunSpMmbMqAoVKsSK3ZpjMy65cuXSzp07FR4ebl62atWqWG16Mb++P76dXbt2WRxLkpQiRQpJ1h3/r+p9zNnZWSVLltTu3bufOdbR0VEmk8niuTt9+rRWrlxp9faqV6+uHTt2aP/+/eZl169f13fffRdrnIeHh0aMGKGIiIhY63nWVOVPTiucKlUq5c6d2/x+m9BjzVopU6aM8+/bsGFD7dixQ+vWrYt1382bNxUZGZmg7fj6+qpChQr69ttvdfbsWYv7Yo5DR0dH83Wy4kqIHt/HJ58vFxcX+fv7yzCMWM//3r17LS5KHJeSJUvK19dX06dPt3j9zJ07N9bz86Kvn7geL8V+7URFRcVqO0uXLp0yZcpkPi5KlCihXLlyafTo0RYXZ47x+HOWmJ9nwOOo6LyGcuXKpUWLFqlRo0YqUKCAWrRooYIFCyo8PFzbt2/XDz/8YL7eRZEiRdSyZUvNnDlTN2/eVMWKFfXXX39p3rx5qlevnt5+++1Ei6tx48bq06eP6tevr27duun+/fuaNm2a8ubNa3EC5NChQ7V161bVqlVL2bNn15UrVzR16lRlyZLF4uT3J3399deqWbOmSpcurbZt2+rBgweaNGmSPD09zdcPSA7ee+89LViwQJ6envL399eOHTv022+/mdtiYhQtWlSOjo4aOXKkbt26JVdXV1WuXDnB14mZM2eOVq9erblz5ypLliySHn15bdasmaZNm6ZOnToleB/69u2rxYsXq2bNmurWrZvSpk2refPm6dSpU1q+fPlzXww0xu7du/Xll1/GWl6pUiW5ubmpVKlSqly5sqpUqaIMGTLoypUrWrx4sQ4cOKAePXokqAITnyJFilj8kpoQX375pflaUJ06dZKTk5NmzJihsLAwjRo16oVjizFlyhSVK1dOhQoVUvv27ZUzZ05dvnxZO3bs0Pnz53XgwAGL8Y0aNdLAgQPl5uamtm3bxvo7WXtsxqVdu3ZatmyZatSooYYNGyo4OFgLFy40/+r7+DZ+/PFH1a9fX7Vq1dKpU6c0ffp0+fv7W3xZcnd3l7+/v5YuXaq8efMqbdq0KliwYJznWbzK97G6devq888/1+3bt5+asNaqVUtjx45VjRo11LRpU125ckVTpkxR7ty5LX7seZrPPvtMCxcu1DvvvKOuXbsqZcqU+uabb5QtWzZdv37d/Cu5h4eHpk2bpubNm6t48eJq3LixfH19dfbsWa1evVply5bV5MmT492Ov7+/KlWqZL7uy+7du7Vs2TJ16dLFPCahx5o1SpQooWnTpunLL79U7ty5lS5dOlWuXFmffvqpfv75Z7333ntq1aqVSpQooXv37unQoUNatmyZTp8+neDX+MSJE1WuXDkVL15cHTp0UI4cOXT69GmtXr3anEh+9dVX2rx5swICAtS+fXv5+/vr+vXr2rt3r3777Tfzj3DVqlVThgwZVLZsWaVPn15HjhzR5MmTVatWLYuJgPbs2aPr169bXHw4Ls7Ozvryyy/18ccfq3LlymrUqJFOnTqlOXPmxDpHJzFePxUqVNCoUaMUERGhzJkza/369bGqlHfu3FGWLFn0wQcfqEiRIkqVKpV+++03/f333+aKr4ODg7755hvVrFlTb7zxhlq3bq3MmTPrwoUL2rx5szw8PPTLL7+Y/9aS9Pnnn6tx48ZydnZW7dq1k02nBWzYK53jDcnKf//9Z7Rv397w8/MzXFxcjNSpUxtly5Y1Jk2aZDFFaEREhDFkyBAjR44chrOzs5E1a1ajX79+FmMM49G0rHFNQfrk9LHxTS9tGIaxfv16o2DBgoaLi4uRL18+Y+HChbGmit24caNRt25dI1OmTIaLi4uRKVMmo0mTJhZTjcY1vbRhGMZvv/1mlC1b1nB3dzc8PDyM2rVrG//++6/FmPimCI6ZFvVZ019WrFgxzqlF43t+9MR0yTdu3DBat25t+Pj4GKlSpTKqV69uHD16NM7peWfNmmXkzJnTcHR0tJjON75txdwXs55z584Znp6eRu3atWONq1+/vpEyZUrj5MmTT93fJ+OPERwcbHzwwQeGl5eX4ebmZpQqVSrW9UFipiH+4YcfnrqNJ7cX323YsGHG7du3jQkTJhjVq1c3smTJYjg7OxupU6c2SpcubcyaNctimtPHWTu99NNYO720YRjG3r17jerVqxupUqUyUqRIYbz99tvG9u3bLcbEHHN///23xfK4pm+OT3BwsNGiRQsjQ4YMhrOzs5E5c2bjvffeM5YtWxZr7PHjx83P5Z9//hnrfmuPzfjiGzNmjJE5c2bD1dXVKFu2rLF79+5Y7w/R0dHGiBEjjOzZsxuurq5GsWLFjFWrVsU5PfP27duNEiVKGC4uLhZT5cZ1HZ3Efh+Lz+XLlw0nJyfz9OMx4op/9uzZRp48eQxXV1cjf/78xpw5c+KM/WnH3r59+4zy5csbrq6uRpYsWYygoCBj4sSJhiTzdbVibN682ahevbrh6elpuLm5Gbly5TJatWpl7N69+6n79OWXXxqlSpUyvLy8DHd3dyN//vzG8OHDLa5DYxjWHWsJOaZDQkKMWrVqGalTp441jfKdO3eMfv36Gblz5zZcXFwMHx8fo0yZMsbo0aPNcT3ts+bx4yXGP//8Y9SvX9/8npUvXz5jwIABFmMuX75sdO7c2ciaNavh7OxsZMiQwahSpYoxc+ZM85gZM2YYFSpUMLy9vQ1XV1cjV65cxqeffmrcunXLYl19+vQxsmXLFu970pOmTp1qvt5WyZIlja1bt76U18/58+fNz4Onp6fx4Ycfmq9PFjMmLCzM+PTTT40iRYoYqVOnNlKmTGkUKVLEmDp1aqy49+3bZzRo0MD8fGTPnt1o2LChsXHjRotxw4YNMzJnzmw4ODgw1TQSjckwnrM3BQAAxNK2bVv9999/cV65/lXo0aOHZsyYobt37yba5CxIXGFhYfLz81Pfvn3VvXv3pA4HsFucowMAQCIaNGiQ/v7773inFE5MDx48sPj3tWvXtGDBApUrV44kJxmbM2eOnJ2dY12vDUDioqIDAICNKlq0qCpVqqQCBQro8uXLmj17ti5evKiNGzfGmkwCAF43TEYAAICNevfdd7Vs2TLNnDlTJpNJxYsX1+zZs0lyAEBUdAAAAADYIc7RAQAAAGB3SHQAAAAA2B3O0XnF3It1efYgALHc+Dv+CxoCiF80HepAgqVwNiV1CPFKLt8lH+xL/p/LVHQAAAAA2B0SHQAAAAB2h9Y1AAAAwFaYqFNYi2cKAAAAgN2hogMAAADYClPynSghuaGiAwAAAMDukOgAAAAAsDu0rgEAAAC2gskIrMYzBQAAAMDukOgAAAAAsDu0rgEAAAC2glnXrEZFBwAAAIDdoaIDAAAA2AomI7AazxQAAAAAu0OiAwAAAMDu0LoGAAAA2AomI7AaFR0AAAAAdodEBwAAAIDdoXUNAAAAsBXMumY1nikAAAAAdoeKDgAAAGArmIzAalR0AAAAANgdEh0AAAAAdofWNQAAAMBWMBmB1XimAAAAANgdEh0AAAAAdofWNQAAAMBWMOua1ajoAAAAALA7VHQAAAAAW8FkBFbjmQIAAABgd0h0AAAAANgdWtcAAAAAW8FkBFajogMAAADA7pDoAAAAALA7tK4BAAAAtoJZ16zGMwUAAADA7lDRAQAAAGwFFR2r8UwBAAAAsDskOgAAAADsDq1rAAAAgK1w4Do61qKiAwAAAMDukOgAAAAAsDu0rgEAAAC2glnXrMYzBQAAAMDuUNEBAAAAbIWJyQisRUUHAAAAgN0h0QEAAABgd2hdAwAAAGwFkxFYjWcKAAAAgN0h0QEAAABgd2hdAwAAAGwFs65ZjYoOAAAAALtDRQcAAACwFUxGYDWeKQAAAAB2h0QHAAAAgN2hdQ0AAACwFUxGYDUqOgAAAADsDhUdAAAAwFYwGYHVeKYAAAAA2B0SHQAAAAB2h9Y1AAAAwFYwGYHVqOgAAAAAsDskOgAAAADsDq1rAAAAgK1g1jWr8UwBAAAAsDtUdAAAAABbwWQEVqOiAwAAAMDukOgAAAAAsDu0rgEAAAC2gskIrMYzBQAAAMDukOgAAAAAsDu0rgEAAAC2gtY1q/FMAQAAALA7VHQAAAAAW8F1dKxGRQcAAACA3SHRAQAAAGB3aF0DAAAAbAWTEViNZwoAAACA3SHRAQAAAGB3aF0DAAAAbAWzrlmNig4AAAAAu0NFBwAAALAVTEZgNZ4pAAAAAHaHRAcAAACA3aF1DQAAALAVTEZgNSo6AAAAAOwOiQ4AAAAAu0PrGgAAAGAjTLSuWY2KDgAAAAC7Q0UHAAAAsBFUdKxHRQcAAACA3SHRAQAAAGB3aF0DAAAAbAWda1ajogMAAADA7pDoAAAAALA7tK4BAAAANoJZ16xHRQcAAACA3aGiAwAAANgIKjrWo6IDAAAAwO6Q6AAAAACwO7SuAQAAADaC1jXrUdEBAAAAYHdIdAAAAADYHVrXAAAAABtB65r1qOgAAAAAsDtUdAAAAABbQUHHalR0rGAYRlKHAAAAACABSHSe4sGDBwoLC9O5c+f08OHDpA4HCfRxwwo6unqIbuwcp63ze6vkG9njHevk5KB+HWro8M+DdGPnOO1a2lfvlClgMSZVCld93ft9HVszVNd3jNXmuYEq4Z/tZe8GkKwtWfSdar5TWW8WK6SPGn+oQwcPJnVIQJJZuvg7vVutsgKKF1bzJg31z6Gnvx42rFur+rVrKqB4YX1Yv7b+2Pq7xf0bN6xXx/ZtVKlsgIoVzK9jR4+8zPABu0OiE48jR46oWbNmKlmypHLlyqXSpUurb9++SR0WrPRBteIa2au+hs/4VaWbjtTB/y7o56md5ZsmVZzjB3eqrXbvl1PgqB9U7P0v9c2yP7V0THsVyZfFPGbawKaq/FZ+tflinko2HKHfdhzV6uldlcnX81XtFpCsrP11jUaPCtLHnTpryQ8rlC9ffnX8uK2uXbuW1KEBr9y6X9dozKiv9HHHzlr0w4/Kmy+fOn3cTtfjeT3s37dX/T7rpXr1P9DiH1aoUuWqCuzWRSeO/2ce8+DBAxUtXkLdevZ+VbsBG2AymZLFzRaQ6MTh0KFDKl26tDJmzKgePXro+++/V/bs2TV+/HjVrl1bERERSR0inqFbs8qa8+N2Lfh5p46eDFHX4Uv04GG4WtYrHef4pu+V0qjZ67Xuz391+sI1zfrhT63b9q+6N68sSXJzdVa9KkX1+fiV2rY3WCfPhWr4jDUKPndV7T8s/yp3DUg2FsybowYfNFS9+u8rV+7c+mLQELm5uWnlj8uTOjTglVs4f64afPCh6tZ/X7ly5dbnA///9bAi7tfD4oULVKZsObVs01Y5c+VS567dVcDfX0sWfWce816duvq4Y2e9VTruzy4AT0ei84SrV6+qZcuW6tixoyZPnqy2bduqfv36mjVrlsaMGaPNmzerefPmSR0mnsLZyVHFCmTVpl3HzMsMw9CmXcdUqnCOOB/j4uykh+GWCeyDh+EqUyyXJMnJ0UFOTo6xxjwMizCPAV4nEeHhOvLvYb1Vuox5mYODg956q4wOHtiXhJEBr15ExKPXQ8Bblq+HgLdK6+CB/XE+5uCB/Qp47PUjSaXLlI13PICEI9F5wvnz5xUREaGPPvpIUVFRkqTo6Gj5+vqqWbNm6tevn3755RetXLnymesKCwvT7du3LW5GdNRL3gP4pEklJydHXbl+x2L5lWu3lcHbI87H/LbjiLo1q6xc2XxlMplUOSC/6lYuqgw+j8bfvR+mnQdOql/7msro6ykHB5Mav/umAgrnMI8BXic3bt5QVFSUvL29LZZ7e3srNDQ0iaICksaNG49eD2ljvR58dC2e10NoaGjs8T7xjwdiJHXLGq1rNuzAgQM6ceKEChYsKEdHRxmGIQeHR0+Tp6enmjZtKmdnZ504ceKZ6woKCpKnp6fFLfLynpe9C3gOvb9epuCzV3TgxwG6/dd4jev7oeb/vFPR0f+bca/NF/NlMkkn1w/XrV3j1blJRX2/drfFGAAAACQPXEfnCblz55YkLV++XO+//36sjDVHjhzKmTOnLly48Mx19evXT4GBgRbL0pXvk3jBIk6hN+4qMjJK6dKmtliezttDIddux/uYhoGz5OriJG/PlLp49Za+7FZXpy787yTSU+dDVa3dBKVwc5FHKjeFhN7Wgq9a69QFfn3D6yeNVxo5OjrGmnjg2rVr8vHxSaKogKSRJs2j18OTEw9cuxYq73heDz4+PrHHh8Y/HohhK9WU5ICKzhP8/Pzk4eGh+fPn68yZM+bl0dHRkh6Vp93d3VWiRIlnrsvV1VUeHh4WN5OD40uLHY9EREZp35Fzejsgn3mZyWTS26Xy6q+Dp5762LDwSF28ektOTg6qV6WoVm2JPTXo/YfhCgm9La/U7qpapoBWbTmU6PsAJHfOLi4q4P+Gdu3cYV4WHR2tXbt2qHCRYkkYGfDqOTv//+thl+Xr4a9dO1W4SNE4H1O4SFH99djrR5J27tge73gACUei84QsWbJo2rRpWrt2rQYMGKDDhw9Lkrl9bezYsbp48aLKl2emreRs4sJNal2/jD6qHaB8OdJrYv9GSuHuqvk/7ZQkfTOsuYZ2rWMe/2bB7KpbuYj8MnurbLFc+nlyZzk4mDR27m/mMVVLF9A7ZQooeyZvVQ7Ir7Wzuuu/U5c1/+cdsbYPvA6at2ytH5d9r59XrtDJ4GB9OXSwHjx4oHr1GyR1aMAr16xFK61Y9oN+/unR62HEsEevh7r1Hr0evujXRxPHjTGPb9KsubZv+1Pz536rUydPavqUSfr38GE1bvqRecytWzd17OgRBQcHS5JOnzqlY0ePKDT06qvdOcBG0boWh7p162rixInq0qWL/vrrL5UtW1YZM2bUqVOn9Ouvv2rjxo3Knj3+i08i6S1bv1c+aVJpYMdaSu+dWgePXVDdzlPMExRkzZDW4twaV1dnDer8nnJk9tHd+2Fat+2w2g6Yr1t3H5jHeKZy09CudZQ5vZeu37qvnzbu16ApvygyMvqV7x+QHNSo+a5uXL+uqZMnKjT0qvLlL6CpM76h9Qavpeo139WNG9c1bfIkXfv/18OU6bPMr4eQSxfl4PC/lqOixYprxMjRmjJpvCZPGKds2f00duJk5c6T1zzm982bNOiL/uZ/9/30UTv8xx0765POXV/RniG5oXXNeibDMDiTOh67du3SqFGjdOzYMXl5ealIkSLq2rWr8ufP/9zrdC/WJREjBF4fN/6enNQhADYpmo95IMFSOCffZMK7xeKkDkGSdG1+k6QO4Zmo6DxFQECAvv/+ezk4OMhkMik6OtrcwgYAAAAg+SLReYaYJEeiVAgAAIAkxtdRq1GeeIbHkxsSHQAAAMA2UNEBAAAAbAQ/vFuPig4AAAAAu0OiAwAAAMDu0LoGAAAA2Aha16xHRQcAAACA3SHRAQAAAGyEyWRKFrfnMWXKFPn5+cnNzU0BAQH666+/njp+/Pjxypcvn9zd3ZU1a1b17NlTDx8+tHp7JDoAAAAAXqqlS5cqMDBQgwYN0t69e1WkSBFVr15dV65ciXP8okWL1LdvXw0aNEhHjhzR7NmztXTpUvXv39/qbZLoAAAAAEiQsLAw3b592+IWFhYW7/ixY8eqffv2at26tfz9/TV9+nSlSJFC3377bZzjt2/frrJly6pp06by8/NTtWrV1KRJk2dWgR5HogMAAADYClPyuAUFBcnT09PiFhQUFGfI4eHh2rNnj6pWrWpe5uDgoKpVq2rHjh1xPqZMmTLas2ePObE5efKk1qxZo3fffdfqp4pZ1wAAAAAkSL9+/RQYGGixzNXVNc6xoaGhioqKUvr06S2Wp0+fXkePHo3zMU2bNlVoaKjKlSsnwzAUGRmpTz75hNY1AAAAAC+Pq6urPDw8LG7xJTrPY8uWLRoxYoSmTp2qvXv36scff9Tq1as1bNgwq9dBRQcAAACwEbZ4HR0fHx85Ojrq8uXLFssvX76sDBkyxPmYAQMGqHnz5mrXrp0kqVChQrp37546dOigzz//XA4Oz67XUNEBAAAA8NK4uLioRIkS2rhxo3lZdHS0Nm7cqNKlS8f5mPv378dKZhwdHSVJhmFYtV0qOgAAAICNsMWKjiQFBgaqZcuWKlmypEqVKqXx48fr3r17at26tSSpRYsWypw5s3lCg9q1a2vs2LEqVqyYAgICdOLECQ0YMEC1a9c2JzzPQqIDAAAA4KVq1KiRrl69qoEDByokJERFixbV2rVrzRMUnD171qKC88UXX8hkMumLL77QhQsX5Ovrq9q1a2v48OFWb9NkWFv7QaJwL9YlqUMAbNKNvycndQiATYrmYx5IsBTOybdqkqH9sqQOQZIUMuuDpA7hmajoAAAAADbCVlvXkgKTEQAAAACwOyQ6AAAAAOwOrWsAAACAjaB1zXpUdAAAAADYHSo6AAAAgK2goGM1KjoAAAAA7A6JDgAAAAC7Q+saAAAAYCOYjMB6VHQAAAAA2B0SHQAAAAB2h9Y1AAAAwEbQumY9KjoAAAAA7A4VHQAAAMBGUNGxHhUdAAAAAHaHRAcAAACA3aF1DQAAALAVdK5ZjYoOAAAAALtDogMAAADA7tC6BgAAANgIZl2zHhUdAAAAAHaHig4AAABgI6joWI+KDgAAAAC7Q6IDAAAAwO7QugYAAADYCFrXrEdFBwAAAIDdIdEBAAAAYHdoXQMAAABsBK1r1qOiAwAAAMDuUNEBAAAAbAUFHatR0QEAAABgd0h0AAAAANgdWtcAAAAAG8FkBNajogMAAADA7pDoAAAAALA7tK4BAAAANoLWNetR0QEAAABgd6joAAAAADaCgo71qOgAAAAAsDskOgAAAADsDq1rAAAAgI1gMgLrUdEBAAAAYHdIdAAAAADYHVrXAAAAABtB55r1qOgAAAAAsDtUdAAAAAAbwWQE1qOiAwAAAMDukOgAAAAAsDu0rgEAAAA2gs4161HRAQAAAGB3SHQAAAAA2B1a1wAAAAAb4eBA75q1qOgAAAAAsDtUdAAAAAAbwWQE1qOiAwAAAMDukOgAAAAAsDu0rgEAAAA2wkTvmtWo6AAAAACwO1R0AAAAABtBQcd6VHQAAAAA2B0SHQAAAAB2h9Y1AAAAwEYwGYH1qOgAAAAAsDskOgAAAADsDq1rAAAAgI2gdc16VHQAAAAA2B0qOgAAAICNoKBjPSo6AAAAAOwOiQ4AAAAAu0PrGgAAAGAjmIzAelR0AAAAANgdEh0AAAAAdofWNQAAAMBG0LlmPSo6AAAAAOwOFR0AAADARjAZgfWo6AAAAACwOyQ6AAAAAOwOrWsAAACAjaBzzXpUdAAAAADYHRIdAAAAAHaH1jUAAADARjDrmvWo6AAAAACwO1R0AAAAABtBQcd6VHQAAAAA2B0SHQAAAAB2h9Y1AAAAwEYwGYH1qOgAAAAAsDskOgAAAADsDq1rAAAAgI2gc816JDqv2JUdE5M6BMAm+TSdm9QhADbpn6mNkzoEwOb4+bgldQhIBCQ6AAAAgI1gMgLrcY4OAAAAALtDogMAAADA7tC6BgAAANgIOtesR0UHAAAAgN0h0QEAAABgd2hdAwAAAGwEs65Zj4oOAAAAALtDRQcAAACwERR0rEdFBwAAAIDdIdEBAAAAYHdoXQMAAABsBJMRWI+KDgAAAAC7Q6IDAAAAwO7QugYAAADYCFrXrEdFBwAAAIDdoaIDAAAA2AgKOtajogMAAADA7pDoAAAAALA7tK4BAAAANoLJCKxHRQcAAACA3SHRAQAAAGB3aF0DAAAAbASda9ajogMAAADA7lDRAQAAAGwEkxFYj4oOAAAAALtDogMAAADA7tC6BgAAANgIOtesR0UHAAAAgN0h0QEAAABgd2hdAwAAAGyEA71rVqOiAwAAAMDuUNEBAAAAbAQFHetR0QEAAABgd0h0AAAAANgdWtcAAAAAG2Gid81qVHQAAAAAvHRTpkyRn5+f3NzcFBAQoL/++uup42/evKnOnTsrY8aMcnV1Vd68ebVmzRqrt0dFBwAAALARDjZa0Fm6dKkCAwM1ffp0BQQEaPz48apevbqOHTumdOnSxRofHh6ud955R+nSpdOyZcuUOXNmnTlzRl5eXlZvk0QHAAAAwEs1duxYtW/fXq1bt5YkTZ8+XatXr9a3336rvn37xhr/7bff6vr169q+fbucnZ0lSX5+fgnaJq1rAAAAABIkLCxMt2/ftriFhYXFOTY8PFx79uxR1apVzcscHBxUtWpV7dixI87H/PzzzypdurQ6d+6s9OnTq2DBghoxYoSioqKsjpFEBwAAALARJpMpWdyCgoLk6elpcQsKCooz5tDQUEVFRSl9+vQWy9OnT6+QkJA4H3Py5EktW7ZMUVFRWrNmjQYMGKAxY8boyy+/tPq5onUNAAAAQIL069dPgYGBFstcXV0Tbf3R0dFKly6dZs6cKUdHR5UoUUIXLlzQ119/rUGDBlm1DhIdAAAAAAni6upqdWLj4+MjR0dHXb582WL55cuXlSFDhjgfkzFjRjk7O8vR0dG8rECBAgoJCVF4eLhcXFyeuV1a1wAAAAAbYTIlj1tCuLi4qESJEtq4caN5WXR0tDZu3KjSpUvH+ZiyZcvqxIkTio6ONi/777//lDFjRquSHIlEBwAAAMBLFhgYqFmzZmnevHk6cuSIOnbsqHv37plnYWvRooX69etnHt+xY0ddv35d3bt313///afVq1drxIgR6ty5s9XbpHUNAAAAsBEm2eaFdBo1aqSrV69q4MCBCgkJUdGiRbV27VrzBAVnz56Vg8P/ajBZs2bVunXr1LNnTxUuXFiZM2dW9+7d1adPH6u3aTIMw0j0PUG87jyMfvYgALFkbDE/qUMAbNI/UxsndQiAzfHzcUvqEOL13oy/kzoESdKqj99M6hCeidY1AAAAAHaH1jUAAADARjjYZudakqCiAwAAAMDukOgAAAAAsDu0rgEAAAA2wpTQi9i8xqjoAAAAALA7VHQAAAAAG0FBx3pUdAAAAADYHRIdAAAAAHaH1jUAAADARjjQu2Y1KjoAAAAA7A6JDgAAAAC7Q+saAAAAYCPoXLMeFR0AAAAAdoeKDgAAAGAjTJR0rEZFBwAAAIDdIdEBAAAAYHdoXQMAAABsBJ1r1qOiAwAAAMDukOgAAAAAsDu0rgEAAAA2woHeNatR0QEAAABgd6joAAAAADaCeo71qOgAAAAAsDskOgAAAADsDq1rAAAAgI0wMRmB1ajoAAAAALA7JDoAAAAA7A6tawAAAICNcKBzzWqJUtG5efNmYqwGAAAAABJFghOdkSNHaunSpeZ/N2zYUN7e3sqcObMOHDiQqMEBAAAA+B+TyZQsbrYgwYnO9OnTlTVrVknShg0btGHDBv3666+qWbOmPv3000QPEAAAAAASKsHn6ISEhJgTnVWrVqlhw4aqVq2a/Pz8FBAQkOgBAgAAAEBCJbiikyZNGp07d06StHbtWlWtWlWSZBiGoqKiEjc6AAAAAGYmU/K42YIEV3QaNGigpk2bKk+ePLp27Zpq1qwpSdq3b59y586d6AECAAAAQEIlONEZN26c/Pz8dO7cOY0aNUqpUqWSJF26dEmdOnVK9AABAAAAIKESnOg4Ozurd+/esZb37NkzUQICAAAAEDdbmfEsObAq0fn555+tXmGdOnWeOxgAAAAASAxWJTr16tWzamUmk4kJCQAAAICXxIGCjtWsSnSio6NfdhwAAAAAkGgSPL304x4+fJhYcQAAAABAoklwohMVFaVhw4Ypc+bMSpUqlU6ePClJGjBggGbPnp3oAQIAAAB4xGQyJYubLUhwojN8+HDNnTtXo0aNkouLi3l5wYIF9c033yRqcMmNYRhJHQIAAAAAKyQ40Zk/f75mzpypjz76SI6OjublRYoU0dGjRxM1uKR08uRJDRgwQB07dtTEiRMlPcqgSXYAAACA5C/B19G5cOGCcufOHWt5dHS0IiIiEiWopHbgwAHVqFFDBQoU0MWLFzVjxgwdPnxYM2bMsJlSHQAAAOwP30Stl+CKjr+/v/74449Yy5ctW6ZixYolSlBJ6dChQypTpozatm2rDRs2aPPmzRo3bpxmzZqlH374IanDAwAAAGCFBFd0Bg4cqJYtW+rChQuKjo7Wjz/+qGPHjmn+/PlatWrVy4jxlblx44Y++OAD5cmTR19++aUkKWPGjKpRo4a++uoru6lYAQAAwDY50F1ktQRXdOrWratffvlFv/32m1KmTKmBAwfqyJEj+uWXX/TOO++8jBhfmaioKDVt2lTnz59XUFCQxfJbt24pTZo0SRgdAAAAAGsluKIjSeXLl9eGDRsSO5Yk899//+nWrVt688031blzZ7m5uSkoKEienp5q06aNqlWrpvbt26tmzZpJHSoAAAAAKzz3BUN3796tBQsWaMGCBdqzZ09ixvRKHThwQPnz59fOnTslST4+Pmrbtq369u2r/v37K1WqVGrSpIkmTJggwzAUHR2dxBEDAADgdWUyJY+bLUhwRef8+fNq0qSJtm3bJi8vL0nSzZs3VaZMGS1ZskRZsmRJ7BhfmgMHDqhMmTLq37+/unbtal7u4+OjFi1ayNnZWSNGjDC3rJlMJhIdAAAAwAYkuKLTrl07RURE6MiRI7p+/bquX7+uI0eOKDo6Wu3atXsZMb4UBw8eVNmyZdWjRw/zxAOStHjxYklSpkyZ1Lx5c3322WcaOXKkRo0aJUkW1w4CAAAAkDwluKLz+++/a/v27cqXL595Wb58+TRp0iSVL18+UYN7WS5evKiiRYuqXbt2Gj58uHn5yJEj1a9fP+XJk0clS5ZUunTp1LZtWzk4OKhPnz5ycXFRjx49ki5wAAAAvNa4pqP1EpzoZM2aNc5plqOiopQpU6ZECeplc3V1VZEiRfT333/r+PHjypMnj7766iuNHj1a69atU8mSJWUYhkwmk3x8fNSmTRs5OzszGQEAAABgIxLcuvb111+ra9eu2r17t3nZ7t271b17d40ePTpRg3sZDMOQt7e3fvvtNzk7O6thw4bq3bu3xowZo8WLF5unyI7Jlg8fPixPT091797doooFAAAAvGpJPQmB3U1GkCZNGosy2b179xQQECAnp0cPj4yMlJOTk9q0aaN69eq9lEATm7e3t9auXav69etr7Nixmj17dqzrAPXp00dr1qzR1q1buYYOAAAAYEOsSnTGjx//ksN4uS5duqSdO3dq3bp1MgxDNWvW1JtvvqnMmTNrxYoVeu+99zRmzBiVKVPGXLUZNGiQJk2apC1btpDkAAAAADbGqkSnZcuWLzuOl+bw4cNq1aqVPD09FRISInd3d82aNUt169ZVYGCgypcvr9WrV6t69eqqX7++fvvtN82aNUsjR47Utm3bVKJEiaTeBQAAAECS5GArfWPJQIInI3jcw4cPFR4ebrHMw8PjhQJKTAcOHFDFihXVoUMHffzxx8qVK5ckac6cOerVq5cePHig4cOHq0SJEvr1119Vu3ZtZcmSRe7u7iQ5AAAAgA1L8GQE9+7dU5cuXZQuXTqlTJlSadKksbglF4cPH1a5cuXUvXt3jRo1Sn5+fpKk6OhotW7dWpMnT9bmzZu1dOlSSY/O2fnpp5/UqFEjkhwAAAAkS0k9CYHdTUbwuM8++0ybN2/WtGnT1Lx5c02ZMkUXLlzQjBkz9NVXX72MGBPs4cOHatKkidKmTasWLVpIenShz5gpoyWpadOmOnDggKZOnaru3bsrc+bM8vX11XfffScHhwTnf0iGvl/ynRbM+1bXQkOVJ29+fdr3cxUsVDje8b+tX6tpUybq0sULypotu7r26KVy5Sua758xbbLWr12jyyEhcnZ2VgF/f3Xq0kMFCxd5FbsDvDIdqudX99oFld7LXYfOXFfvb3dpT3BovOM7veuvdtXyKatPSl27HaaVu05r0KK9CouIkiT1/7Co+n9Y1OIx/124peI9V7zM3QBeuZ+XL9GyRfN0/XqocubOq049+yq/f6E4x54+eULzv5mqE8eO6HLIRX3c7VM1aNTMYsyS+bO17feNOnfmlFxcXeVfqKjaduyhrNn9XsHeALYvwd/of/nlF02dOlXvv/++nJycVL58eX3xxRcaMWKEvvvuu5cRY4K5ublp5MiRcnZ21pdffqkDBw5I+t+U0VFRjz58q1atKgcHB129etX8WJIc+7B+7RqNGz1S7T/urIVLlitvvnzq2rG9rl+7Fuf4A/v36fO+vVW3/vv6bumPqvR2FfXu0VUnjv9nHpM9u58+6/eFliz/Sd/MXaiMmTKrc8d2unH9+qvaLeCle7+0n4JavKmgZftVrs/P+ufMda38/B35erjFOf7Dsjk0tGkJBf2wXyV6rlSn6dv0fukcGtykuMW4f8/eUM72S823dwaueRW7A7wyW35bq5mTRuujNh9ryrdLlDN3Pn0e2FE3b8T9uRMW9lAZM2VRm47dlNbbJ84xB/fvVu0GjTR+5gIFjZ+hqMhI9e/5iR4+uP8ydwWwGwn+Vn/9+nXlzJlT0qPzca7//5e8cuXKaevWrYkb3XOKmVltypQp2rRpk8aPH69Dhw5JepTsxCQ8Z8+eVdasWZUtW7akDBcvwXcL5qlegw9Vp14D5cyVW/2+GCw3Nzf9vPLHOMcv+W6+Spcppxat2ipHzlzq2KW78hcooO+XLDKPqfHuewp4q4yyZMmqXLnzqGfvvrp3966OHz/2qnYLeOm6vPeG5m78Twu3nNDRC7fUbdYOPQiPVPO388Q5/q186bTz2GX9sO2Uzl69q00HL+qHbSdVIrflF7fIaENXbj0w367dCXsVuwO8Mj8uXaAatRuoeq16yp4jl7p9+oVcXd20btXKOMfnK1BQ7bsEqlLVmnJ2dolzzIix01StVl355cytXHnyqdfnQ3Xl8iUdP3bkJe4JkruY77JJfbMFCU50cubMqVOnTkmS8ufPr++//17So0qPl5dXogb3vEwmkwzDUPXq1TVz5kxt2rRJY8aM0cGDByU9qtpERERo165dCggIUMqUKZM4YiSmiIhwHT1yWAFvlTYvc3BwUKm3Suvgwf1xPubgwQMq9dh4SSpdppwOxTM+IiJcK5Z/r1SpUytv3vyJFTqQpJwdHVQsp7c2H7pkXmYY0uZDl1Qqr2+cj9l57IqK5vRRiVyPEhu/dKlUvVgWrd933mJcrgypdXx6Qx2a9L5mdy2vLN6878J+RERE6PixIyr+5lvmZQ4ODipW8i39+8/BRNvOvXt3JUmpk9HET0ByluBzdFq3bm2ezaxv376qXbu2Jk+erIiICI0dO/ZlxPhcIiMj5ezsrOrVq2vGjBn6+OOPJUmBgYEqXLiwhg8frlWrVmnTpk1ydXV9KTGEhYUpLMzyV8tww/mlbQ+P3LxxU1FRUUrr7W2xPK23t07/f5L+pGuhobFaB9J6e+taqOV5CX/8vln9+/TWw4cP5OPjqynTZ8srGU3CAbwIbw9XOTk66MrNBxbLr9x8oLyZPON8zA/bTsnHw00bhtWUSSY5Oznom/VHNXrFIfOYv49f1SdT/9R/F28rQxp39fugqNYPralSvVbq7sPIl7pPwKtw++YNRUdFySut5edOmrTeOnc27s+dhIqOjtb0CaP0RuGi8ssZd4UVgKUEJzo9e/Y0/3/VqlV19OhR7dmzR7lz51bhwvGf6P2yPD7BQIyoqCg5Ozvr/PnzunXrlmrUqKGZM2eqQ4cOcnd3V2RkpBYuXKht27Ypf/6X92t8UFCQhgwZYrGs7+cD1f+LQS9tm3i5Sr4ZoEXf/6ibN29oxfIf1O/Tnpq7cGmspAp4XZT3z6De9Qur5zc7tfv4VeXM4KFRrUupz/uFNXL5o1+yN+y/YB5/+OwN7T4eqn+nfqAGpXNo/ubjSRU6YFMmjxmhMyeDNWba3KQOBUmMs8mt90LX0ZGk7NmzK3v27IkRS4KdPHlSy5cv15EjRzR69GilTZtW0dHRcnR01JkzZ+Tv76/AwEANHTpU1atX16xZs9SgQQNFR0dr+/btKlas2EuNr1+/fgoMDLRYFm44v9RtQvJK4yVHR8dYEw9cv3ZN3j5xn/Dp7eOj69dCnznePUUKZc2WXVmzZVehwkVVv3Z1/bRyuVq37ZC4OwEkgWu3wxQZFa10Xu4Wy9N5uevyE1WeGAMaFdPircGat+lRwnL43E2lcHPSpA5lNOrHgzKM2I+5dT9cJy7eVs4MqRN9H4Ck4OGVRg6Ojrp53fJz58b1a0qTNu7PnYSYPGaEdm3fqjFTvpVvuvQvvD7gdWFVojNx4kSrV9itW7fnDiYhDh06pAYNGuidd96Rp6enUqRIIelRT+yVK1f01ltvqUWLFhoyZIj5nJ1q1app/fr18vX1VZ48L7/s6+rqGqtN7c7D6Je+3deds7OL8hd4Q3/t2qlKlatKelTy/3vXTjVs/FGcjylcuIj+3rVTTZu1NC/btXO7ChUu+tRtRUcbsS6aC9iqiKho7Tt5TZUKZtSqv89KenSthEoFM2rG2qNxPsbd1VHRT2Qz0dGP/m2SSYZiZzopXZ2UI0NqLfkj7uQJsDXOzs7Kk6+A9u3epTIVKkt69Lmzf88u1Xm/8XOv1zAMTRkbpO1bN+nrybOVIVOWxAoZNsxWJgJIDqxKdMaNG2fVykwm0ytJdI4fP64qVaqoTZs2Gj58uBwdHSX9r43tn3/+Ub9+/dS1a9dYB0OZMmVeenxIeh81b6nBA/rJ/42CeqNgIS1aOF8PHjxQ7Xr1JUkDP++jdOnSq0v3RxW3xh+1UIe2LbRw3hyVq1BR69au0b+HD6v/gEethw/u39e338xQhUpvy8fHVzdv3tT3Sxbp6pXLqvpO9STbTyCxTV51WDM6l9fek6HacyJUnd/1VwpXJy3c8qhiM7NzOV28fl+DF++VJP2657y61PLXwVPX9ffxq8qZIbW+aFRMv+45Z06AhjcvqV93n9PZ0HvKmMZdnzcspuhoQz/8eTLJ9hNIbA0aNdfo4QOUN/8byudfUCu+X6iHDx+oWq16kqRRwz6Xj086tenYXdKjCQzOngo2//+1q1cU/N9RuaVIocxZHs0GO3nMCG3e8KsGfzVe7ilSmjsPUqZKJVfXuKd8B/A/ViU6p+I5gTspREVFacKECapataoGDRpkTnKk/2W4lStXVuXKlS0eR/b7eqlW413duHFD06dO1LXQUOXNV0CTps6U9/9POBAScsnimklFihbT8KCvNXXyBE2ZNE5Zs2XX6PGTlDtPXkmSg6OjTp86qVU/r9TNmzfk6eUl/zcKadachcqVm5NCYT+W7zgtHw83fdGwmNJ7uevg6euqP2KDrtx6KEnK6pNK0Y8VaUYuPyDDMDSgcTFlSptCobcf6tc95zRk8T7zmMxpU2pO94pKm9pVobcfasfRK3r789UKZYpp2JFKVWvo1s0bmv/NVN24HqqcefJp+JipSvP/ExRcvRwiB9P/PneuhV5Rp9aNzP9etnieli2ep8LFSurrybMlSatWPJrZ9tMubS221av/UFWrVfdl7xJg80yGEVcHdfJWsmRJVatWTSNGjIh1X1RUlBwdHRUdHS0HBwfzf5MLWteA55OxxfykDgGwSf9Mff7WKeB15eeTfCtmPX6Ku5X4VRtfN/lfXiP5ZABWMAxDd+/e1fnz5+X9/7NcPZmnxSQ53bt3182bN5NVkgMAAADg1bCpLMAwDLm5ualo0aJasWKFTp48aW5JezzhOXnypPbs2aNLly7FtyoAAAAAdsymEh0HBwc5OTmpXr162r59u2bPnq2LFy9KsjwHZ8GCBXJ3d1eGDBmSKlQAAAAg0TmYksfNFrzwdXRepuDgYC1atEi7du2Sq6ursmbNqi+++EKffPKJgoODFRQUpFu3bqlZs2Z66623dPDgQc2ZM0fz5s3T77//rjRcsR4AAAB4LT1XovPHH39oxowZCg4O1rJly5Q5c2YtWLBAOXLkULly5RIlsIMHD6patWp688035eXlpZCQEG3atEmrVq3ShAkTNHLkSLm7u2vChAmaNWuW0qZNKy8vL7m6umrTpk0qVKhQosQBAAAAJBfMJGy9BCc6y5cvV/PmzfXRRx9p3759Cgt7ND3orVu3NGLECK1Zs+aFgzpz5oxq1aqlNm3aaNiwYXJ0dFRkZKQOHjyoDh066OOPP9by5cs1dOhQValSRZcvX9bJkycVEBCgAgUK0LIGAAAAvOYSPL10sWLF1LNnT7Vo0UKpU6fWgQMHlDNnTu3bt081a9ZUSEjICwc1adIkrV69WitWrJC7u7v5QqDSo3a2OnXqyNPTU9u3b3/hbb1qTC8NPB+mlwaeD9NLAwmXnKeX7vXLsaQOQZI0pna+pA7hmRI8GcGxY8dUoUKFWMs9PT118+bNxIhJ+/btU1RUVKwkR5Jy5Mih7t27a9++fdq7d2+ibA8AAACwBUk9CYEtTUaQ4EQnQ4YMOnHiRKzlf/75p3LmzJkoQZlMJovZ1B4vOjk4OKhq1aoKCwvTtWvXEmV7AAAAAOxLghOd9u3bq3v37tq1a5c5Ifnuu+/Uu3dvdezY8YWCiUloypcvr5CQEE2dOlXSo2QnMjJSkhQdHa3Q0FD5+/srR44cL7Q9AAAAAPYpwZMR9O3bV9HR0apSpYru37+vChUqyNXVVb1791bXrl0THMDDhw/l5mbZB1mrVi2NGzdOY8aMkZeXl5o2bSonp0ehOjg4aMWKFUqZMqXSpk2b4O0BAAAAtopJ16yX4MkIYoSHh+vEiRO6e/eu/P39lSpVqgSv48KFC+rZs6c6duyot99+W5IUGRkpJycnnTp1ShUqVJDJZFKtWrXUpUsXBQcHa+vWrZoxY4b++OMPFS1a9HlCT1JMRgA8HyYjAJ4PkxEACZecJyP4bHXymIxgVK3kPxnBc18w1MXFRf7+/i+08bCwMJ0/f15jxoyRi4uLypYtKycnJ0VERChHjhzatm2b+vTpo5UrV2rWrFnKkSOHsmXLpm3btqlw4cIvtG0AAADA1jhQ0rFagis6b7/99lMvVLRp06YEBXD8+HF169ZNhmFowIABKlu2rKRHFSMXFxc9ePBAYWFhWrdunWrXrq2IiAh5enomaBvJCRUd4PlQ0QGeDxUdIOGSc0Wn75r/kjoESdJX7+ZN6hCeKcGTERQtWlRFihQx3/z9/RUeHq69e/eqUKFCCQ4gT548mjhxokwmk4YNG6Zt27ZJelQxioqKkoODg0aMGKEffvhBzs7ONp3kAAAAAHg1Ety6Nm7cuDiXDx48WHfv3n2uIGKSnW7dumnYsGHmyk5UVJR69+6tadOm6e+//5azs/NzrR8AAACwBwmuUrzGEu25atasmb799tvnfvyTlZ0tW7ZowIABmj17tv7++28VK1YssUIFAAAAYOcSLdHZsWNHrGmiEyom2XF2dlaDBg00fvx4/fnnnyQ5AAAAABIkwa1rDRo0sPi3YRi6dOmSdu/erQEDBrxwQHny5NHo0aP12WefacSIEXrjjTdeeJ0AAACAPWDSNeslONF5cjIABwcH5cuXT0OHDlW1atUSJah8+fJp2bJlnJMDAAAA4LkkKNGJiopS69atVahQIaVJk+ZlxSRJJDkAAADAE7iOjvUSdI6Oo6OjqlWrpps3b76kcAAAAADgxSV4MoKCBQvq5MmTLyMWAAAAAEgUCU50vvzyS/Xu3VurVq3SpUuXdPv2bYsbAAAAgJfDZEoeN1tg9Tk6Q4cOVa9evfTuu+9KkurUqSPTY3tpGIZMJpOioqISP0oAAAAASACrE50hQ4bok08+0ebNm19mPAAAAADwwqxOdAzDkCRVrFjxpQUDAAAAIH4ONtI2lhwk6Bwdk6005AEAAAB4rSXoOjp58+Z9ZrJz/fr1FwoIAAAAQNy4jo71EpToDBkyRJ6eni8rFgAAAABIFAlKdBo3bqx06dK9rFgAAAAAIFFYnehwfg4AAACQtPhKbj2rJyOImXUNAAAAAJI7qys60dHRLzMOAAAAAEg0CTpHBwAAAEDS4To61kvQdXQAAAAAwBZQ0QEAAABshEmUdKxFRQcAAACA3SHRAQAAAGB3aF0DAAAAbASTEViPig4AAAAAu0OiAwAAAMDu0LoGAAAA2Aha16xHRQcAAACA3aGiAwAAANgIk4mSjrWo6AAAAACwOyQ6AAAAAOwOrWsAAACAjWAyAutR0QEAAABgd0h0AAAAANgdWtcAAAAAG8Gka9ajogMAAADA7lDRAQAAAGyEAyUdq1HRAQAAAGB3SHQAAAAA2B0SHQAAAMBGOJiSx+15TJkyRX5+fnJzc1NAQID++usvqx63ZMkSmUwm1atXL0HbI9EBAAAA8FItXbpUgYGBGjRokPbu3asiRYqoevXqunLlylMfd/r0afXu3Vvly5dP8DZJdAAAAAC8VGPHjlX79u3VunVr+fv7a/r06UqRIoW+/fbbeB8TFRWljz76SEOGDFHOnDkTvE0SHQAAAMBGmEzJ4xYWFqbbt29b3MLCwuKMOTw8XHv27FHVqlXNyxwcHFS1alXt2LEj3n0dOnSo0qVLp7Zt2z7Xc0WiAwAAACBBgoKC5OnpaXELCgqKc2xoaKiioqKUPn16i+Xp06dXSEhInI/5888/NXv2bM2aNeu5Y+Q6OgAAAICNcFDyuI5Ov379FBgYaLHM1dU1UdZ9584dNW/eXLNmzZKPj89zr4dEBwAAAECCuLq6Wp3Y+Pj4yNHRUZcvX7ZYfvnyZWXIkCHW+ODgYJ0+fVq1a9c2L4uOjpYkOTk56dixY8qVK9czt0vrGgAAAICXxsXFRSVKlNDGjRvNy6Kjo7Vx40aVLl061vj8+fPr0KFD2r9/v/lWp04dvf3229q/f7+yZs1q1Xap6AAAAAA2wpQ8OtcSLDAwUC1btlTJkiVVqlQpjR8/Xvfu3VPr1q0lSS1atFDmzJkVFBQkNzc3FSxY0OLxXl5ekhRr+dOQ6AAAAAB4qRo1aqSrV69q4MCBCgkJUdGiRbV27VrzBAVnz56Vg0PiNpuZDMMwEnWNeKo7D6OTOgTAJmVsMT+pQwBs0j9TGyd1CIDN8fNxS+oQ4jV9x+mkDkGS9Elpv6QO4Zk4RwcAAACA3SHRAQAAAGB3OEcHAAAAsBEOtjobQRKgogMAAADA7pDoAAAAALA7tK4BAAAANoLONetR0QEAAABgd6joAAAAADaCyQisR0UHAAAAgN0h0QEAAABgd2hdAwAAAGwEnWvWo6IDAAAAwO6Q6AAAAACwO7SuAQAAADaCKoX1eK4AAAAA2B0qOgAAAICNMDEbgdWo6AAAAACwOyQ6AAAAAOwOrWsAAACAjaBxzXpUdAAAAADYHRIdAAAAAHaH1jUAAADARjgw65rVqOgAAAAAsDtUdAAAAAAbQT3HelR0AAAAANgdEh0AAAAAdofWNQAAAMBGMBeB9ajoAAAAALA7JDoAAAAA7A6tawAAAICNMNG7ZjUqOgAAAADsDhUdAAAAwEZQpbAezxUAAAAAu0OiAwAAAMDu0LoGAAAA2AgmI7AeFR0AAAAAdodEBwAAAIDdoXUNAAAAsBE0rlmPig4AAAAAu0NFBwAAALARTEZgPSo6AAAAAOwOFZ1XzNGRLBx4Hn+P/zCpQwBsUvGePyZ1CIDNub6gaVKHgERAogMAAADYCNqxrMdzBQAAAMDukOgAAAAAsDu0rgEAAAA2glnXrEdFBwAAAIDdoaIDAAAA2AjqOdajogMAAADA7pDoAAAAALA7tK4BAAAANoK5CKxHRQcAAACA3SHRAQAAAGB3aF0DAAAAbIQD865ZjYoOAAAAALtDRQcAAACwEUxGYD0qOgAAAADsDokOAAAAALtD6xoAAABgI0xMRmA1KjoAAAAA7A6JDgAAAAC7Q+saAAAAYCOYdc16VHQAAAAA2B0qOgAAAICNcGAyAqtR0QEAAABgd0h0AAAAANgdWtcAAAAAG8FkBNajogMAAADA7lDRAQAAAGwEFR3rUdEBAAAAYHdIdAAAAADYHVrXAAAAABth4jo6VqOiAwAAAMDukOgAAAAAsDu0rgEAAAA2woHONatR0QEAAABgd6joAAAAADaCyQisR0UHAAAAgN0h0QEAAABgd2hdAwAAAGyEic41q1HRAQAAAGB3SHQAAAAA2B1a1wAAAAAbwaxr1qOiAwAAAMDuUNEBAAAAbIQDBR2rUdEBAAAAYHdIdAAAAADYHVrXAAAAABvBZATWo6IDAAAAwO6Q6AAAAACwO7SuAQAAADbCROea1ajoAAAAALA7VHQAAAAAG0FBx3pUdAAAAADYHRIdAAAAAHaH1jUAAADARjgwG4HVqOgAAAAAsDskOgAAAADsDq1rAAAAgI2gcc16VHQAAAAA2B0qOgAAAICtoKRjNSo6AAAAAOwOiQ4AAAAAu0PrGgAAAGAjTPSuWY2KDgAAAAC7Q6IDAAAAwO7QugYAAADYCBOda1ajogMAAADA7lDRAQAAAGwEBR3rUdEBAAAAYHdIdAAAAADYHVrXAAAAAFtB75rVqOgAAAAAsDskOgAAAADsDq1rAAAAgI0w0btmNSo6AAAAAOwOFR0AAADARpgo6FiNig4AAAAAu0OiAwAAAMDu0LoGAAAA2Ag616xHRQcAAACA3SHRAQAAAGB3aF0DAAAAbAW9a1ajogMAAADA7lDRAQAAAGyEiZKO1ajoAAAAALA7JDoAAAAA7A6JDgAAAGAjTKbkcXseU6ZMkZ+fn9zc3BQQEKC//vor3rGzZs1S+fLllSZNGqVJk0ZVq1Z96vi4kOgAAAAAeKmWLl2qwMBADRo0SHv37lWRIkVUvXp1XblyJc7xW7ZsUZMmTbR582bt2LFDWbNmVbVq1XThwgWrt2kyDMNIrB3As92P4OkGnseZq/eTOgTAJpXt81NShwDYnOsLmiZ1CPHaf/ZOUocgSSqQ3kVhYWEWy1xdXeXq6hrn+ICAAL355puaPHmyJCk6OlpZs2ZV165d1bdv32duLyoqSmnSpNHkyZPVokULq2KkogMAAADYCFMyuQUFBcnT09PiFhQUFGfM4eHh2rNnj6pWrWpe5uDgoKpVq2rHjh1W7ff9+/cVERGhtGnTWjVeYnppAAAAAAnUr18/BQYGWiyLr5oTGhqqqKgopU+f3mJ5+vTpdfToUau216dPH2XKlMkiWXoWEh0AAADAViSTy+g8rU0tsX311VdasmSJtmzZIjc3N6sfR6ITj6tXr+rUqVNycHCQv7+/UqRIkdQhAQAAADbHx8dHjo6Ounz5ssXyy5cvK0OGDE997OjRo/XVV1/pt99+U+HChRO0Xc7RicOhQ4dUqVIltWrVSqVKlVLXrl115MiRpA4LAAAAsDkuLi4qUaKENm7caF4WHR2tjRs3qnTp0vE+btSoURo2bJjWrl2rkiVLJni7VHSecPDgQZUuXVrdunVT8+bNtXXrVnXu3FmFCxdWgQIFkjo8AAAAvMZMyaV3LYECAwPVsmVLlSxZUqVKldL48eN17949tW7dWpLUokULZc6c2TyhwciRIzVw4EAtWrRIfn5+CgkJkSSlSpVKqVKlsmqbVHQec+TIEb355pvq2LGjgoKC5O/vryZNmihDhgxas2aNoqKiLMZHR0cnUaQAAACA7WjUqJFGjx6tgQMHqmjRotq/f7/Wrl1rnqDg7NmzunTpknn8tGnTFB4erg8++EAZM2Y030aPHm31NqnoPGbPnj2KiIhQtmzZdPv2bXl4eGjKlCm6dOmScubMqd69e6tw4cLKmTOnKlasKNPzXhYWAAAAeA62/PWzS5cu6tKlS5z3bdmyxeLfp0+ffuHtkeg8plmzZgoNDVWPHj3k6uqqy5cva9y4cZo9e7a8vLz033//ac6cOTp9+rR8fHzUrFkz9ezZk4QHAAAASGZIdJ7Qo0cPRUVFqWPHjjKZTFqxYoXq1Kljvr9NmzY6f/68xo8fr9q1a5PkAAAAAMnQa53onDt3Tr/99puio6NVoEABlSlTRpLUq1cveXh46OOPP1ZwcLBu3bolT09PSZK3t7d8fX01b968pAwdAAAAryF+Yrfea5voHDx4UHXq1FG6dOl08uRJpUmTRqNGjVL9+vUlSe3bt9edO3fUq1cvRURE6JNPPpGHh4ccHB7N32AYBtUcAAAAIJl6LWddi5lCunHjxtq8ebMWLVqke/fuac6cOXrw4IEiIiIkPZoGb/To0RowYIDGjRunO3fumNdBkgMAAAAkX69dRefcuXOqUqWKateura+++kqSVK1aNWXKlEnHjx9XZGSk3N3dzeMDAwN1584dTZgwId5ZIgAAAIBXgt/arfbaVXSOHz8uPz8/PXz4UDt27JAkBQUFae/evfLw8FCzZs3Url07TZs2TefOnVNUVJQGDRqk48ePy9vbO4mjBwAAAGANk2EYRlIH8aqtXr1akyZNkru7u9KlS6cVK1ZoypQpKlWqlHbv3q3Dhw9r8uTJcnNzU5EiRfTzzz/LMAzz+Tkv4n7Ea/d0A4nizNX7SR0CYJPK9vkpqUMAbM71BU2TOoR4Hb5wL6lDkCS9kTllUofwTK9V61pkZKScnJxUq1YtRUdHa+LEiVq7dq2GDx+uDz/8UJKUPXt2vf/+++rYsaM2b96s4sWLy2QycU4OAAAAYEPsPtG5evWq3N3dlSpVKjk5OSkiIkLOzs6qXbu2nJ2dZTKZtGnTJpUuXVqlS5eWJEVERMjX11cNGzZM4ugBAAAAPA+7Pkfn7Nmzyp49u7p3765OnTrp7t27FpWZGjVqqEuXLoqMjNTQoUO1c+dOSZKzs3NShQwAAADEy2RKHjdbYNcVnTNnzujhw4cqUqSIdu7cqdKlS6tGjRqqVauWKlWqJEmqU6eOoqOjNWvWLAUGBmrChAl68803kzZwJIqli7/TvDmzdS00VHnz5Vef/l+oYKHC8Y7fsG6tpk6eoIsXLihb9uzq1rO3yleoaL5/44b1Wvb9Eh3597Bu3bqlJctWKF/+Aq9iV4BXas2KpVqxdL5uXr8mv1x51b7bZ8pboGCcY8+eCtaiOdMU/N8RXb18SW0691KdDz6yGNO+cS1dvXwp1mNr1v1QH/fo91L2AUgKbavmUdd3Cyidp7sOn7uhPvP3aO/Ja/GO/6R6PrWukkdZvFPo+p0w/fz3OQ39fr/CIqIlST1q++u9klmVJ6OHHkZE6a/jVzVkyX6dCLkT7zoB/I9dV3TKly+vzp0768yZM1q0aJG6d+8uR0dH1alTR506ddL8+fMlSfXq1VPXrl2VPn16pU+fPomjRmJY9+sajRn1lT7u2FmLfvhRefPlU6eP2+n6tbg/cPbv26t+n/VSvfofaPEPK1SpclUFduuiE8f/M4958OCBihYvoW49e7+q3QBeuT83rdO308aqccsOGjtzkfxy5dGQzzrr5o3rcY4PC3uoDJkyq0WHbkqT1ifOMaOnL9Sc5evNtyGjp0mSylR656XtB/Cq1Q/Ipi+bFteoFf/o7QG/6p+zN7Xss7fl4+Ea5/j3S2fXwIZFNWrFIb3VZ7W6fbNL9QKyacCHRc1jyuZPp9m//afqQ9arwchNcnZ00PI+lZXC1fEV7RVg2+w20YmZTC4gIED79u3TnTt31K5dO3311Vfy9/fXqlWrNHjwYJUpU0YTJ05U+fLltXjxYmXLli2JI0diWDh/rhp88KHq1n9fuXLl1ucDh8jNzU0rVyyPc/zihQtUpmw5tWzTVjlz5VLnrt1VwN9fSxZ9Zx7zXp26+rhjZ731/+dyAfbopx++U7Va9VWlZl1l9cupjoGfy9XNTRt/jXvmrjz531CrT3qqfOXqcoqn7dfTK43SpPUx3/7esVUZMmVRwSIlXuauAK9Up5r5NX9LsBb9cVLHLt5W4Jy/dD8sUh9VyBXn+FJ5fLXr+FUt33FG50LvafM/IfpxxxkVz5nWPObDr7do8R+ndPTCLR0+e1OdZ+5UVp+UKuKXNs514vVgSiY3W2C3iU7MuTjNmjVTSEiI+eKgLVu21Pnz57V06VJt3rxZ3t7eWrx4sW7cuCE3N7ekDBmJJCIiXEf+PayAt8qYlzk4OCjgrdI6eGB/nI85eGC/AkqXsVhWukzZeMcD9igiIkLB//1fe3ceVVW9/3/8dc5h1KsIzlNqiYqWII5kZl5N9HodGn6VaWJynTJnSylJvaY0SDmbmaHXMof8YuWQ15tj4JQamKkpOKFiiohpgnDO/v1BniLxXjTzDDwfLZaLfT5n7/dmrU/nvPf7vT/7gBo2bm7fZjabFRzaXIf2J9+xY2xev1ZtO3ZlNUu4DU+LWcE1A7R5f7p9m2FIm/enq2ntwiudOw+fU0jNAIXem/+MvhrlS+rR4Cpan3T6pscp7Zt/MeHilWt3MHrAfbn1PTpWq1UWi0Xjx4/X8uXL1bZtW33//fdatWqVGjfOv5L4xRdfKDMzU/7+/nf8+Dk5OcrJySkYk9lL3t6Fl7FxZ2RmZspqtSrgdw94LVu2nI4dPVroe86fP3/j+HLllHH+/J8WJ+Bsfsq6KJvNqjL+Ba8W+/kHKO3EsTtyjB1fb9SVyz+pbYcud2R/gDMoW8pbHhazzmVlF9h+7lK26lQpXeh7Vmw7rrKlvLUmup1MMsnTw6wPvzqsd7/4vtDxJpM0uWdjbT/0ow6kZd3xc4AL4RpRkbltRed6kiNJDRo00K5du5SUlKRdu3bZkxybLf9mvz8jyZGkmJgY+fn5FfiZ8mbMn3IsAHAF/1mzUqHNH1RAufKODgVwqJb1Kmh45wZ6acE3eiR6rZ6bukXtg6toVNfCF/54O6Kpgqr56R+zEu5ypIDrcvlE5/q9OL91Pck5deqU9u/frwYNGmjMmDHy9/dXXl6efZzZ/OeeflRUlLKysgr8jBrNCkN/Nn9/f1kslhsWHsjIOK+y5QpvIShXrtyN48/ffDzgjkr5lZHZbLlh4YGszAvyDyh7k3cV3Y/pp5W8Z6ce/dtjf3hfgDPJ+ClHeVabyvsVbIEvX9pHZy9mF/qeV55sqGUJR7Voc4oOpGVp9e40TVyepGGd69+wdO+bvZooPKSKusR8pdOZV/+s0wDcjksnOqmpqZoyZYr69OmjCxfyP5htNpssFouOHz+uOnXqaMmSJZKkZs2aKSAgQJs3b75r8Xl7e6t06dIFfmhb+/N5enopqH4D7dixzb7NZrNp547tahgcUuh7GgaHaOf2bQW2bd+WeNPxgDvy9PTUfXWClLxnp32bzWZT8p6dqtvg5kuzF9VXX34uvzIBahL20B/eF+BMcq02JR27oIfr/7pyq8kktW5QSbuOFN4C7evlIdvvrtVaf9lg+k1v0pu9mqhT42rqGrNBJ85dufPBw+WYnOQ/V+Cyic6+ffsUHh6uo0ePys/PTyVKlJCUX6X58ccf1aJFC/Xq1UsTJkyQJIWGhsrf318zZ84sUNWBe+rZq7fiP12uzz+LV2pKiiZPHK+rV6+qa7fHJUljo0Zr+rux9vHdez6nxISv9a8FH+poaqremzVD3+/fr2ee/fV5IFlZF3Xo4AGlpKRIko4dPapDBw/o/Plzd/fkgD9R1//XQ+tXxWvDl1/o5PFUvffuZGVnX7XfUzN1crQWzZthH5+bm6vUI4eUeuSQ8vJydeH8j0o9ckhnTp0osF+bzaYNX36uNuF/l8Xi1reHopiavfagej1SW888VEt1qpRWbO+mKuHtocVbUvNf7x+m6KeC7ePX7T2lPm0D9XiLGrqnfEk9cn8lvfJkQ63be0q2X7pV3o5ooqcerKl+cxJ1OTtXFfx8VMHPRz6eLC8NFIVLftocPnxYbdu2VZ8+fTRp0iT7vTiGYchkMum7775TVFSUBg8eLJPJZG9lmz17tgzDkIeHS542bkF4x78pM/OC5sycoYzz51S3XpBmvTfP3oqWfua0zOZfr0aENArV5DenaNaMqZo57V3dU6Om3pk+U7UD69jHbN64QePGvmL/fcxLIyRJ/QcO0oBBg+/SmQF/rof+Gq6srEx9smCOMi9kqNZ9dTXuzZkq80vr2rkf02X6TdvvhYxzGtG3u/33lUsXaeXSRWoQ3FiTps6zb0/avUPnzqarbceud+9kgLsofscJlS3lo6gnGqqCn4++O5Gp//f2Rp27lN+6Vq1sCXsCI0lTPvtOhgy98mRDVfb3VcalHH357Sm9vjzJPiayXf5n0KpX2xU41qD3t+mTrYUvrgPgVyajsJtcnJjVatXQoUN14cIFzZ8/X76+vo4O6Zb8nOtSf27AaRw/97OjQwBcUsvRhT8DCcDNXVj0rKNDuKlD6c7xeVi3UglHh/A/uVzrmsVi0fbt21WzZs1Ckxyr1Srp1xXVrv8LAAAAoPhwqUTHMAxdvnxZaWlpKvvLM09+X5CyWCyy2WwaOnSoLl68+KevrAYAAADcLSYn+XEFLpUFGIYhHx8fhYSEKD4+XqmpqfYna/824UlNTdXu3bt15swZR4UKAAAAwIFcKtExm83y8PBQt27dlJiYqPnz5+v06dOSZE94JGnRokXy9fVVpUqVHBUqAAAAAAdy6uXHUlJStHjxYu3YsUPe3t6qXr26xo4dqwEDBiglJUUxMTHKyspSz5491aJFCyUnJysuLk4LFy7U5s2b5e/v7+hTAAAAAO4cV+kbcwJOm+gkJyerffv2atq0qcqUKaP09HRt2LBBq1at0rRp0/Tmm2/K19dX06ZN07x58xQQEKAyZcrI29tbGzZs0AMPPODoUwAAAADgIE6Z6Bw/flydOnVSnz59NHHiRFksFuXl5Sk5OVn9+vVT//79tWLFCv3zn/9U27ZtdfbsWaWmpqp58+YKCgqiZQ0AAAAo5pzyOTozZszQ6tWrFR8fL19fX/uDQKX8drYuXbrIz89PiYmJDo701vEcHeD28Bwd4PbwHB3g1jnzc3QOn73q6BAkSYEVnf9Zlk65GMHevXtltVpvSHIkqVatWho6dKj27t2rPXv2ODBKAAAAAM7KKVvXTCZTgdXUfpvsmM1mtWvXTjk5OcrIyHBkmAAAAMBdZWIxgiJzqorO9S66Vq1aKT09XbNnz5aUn+zk5eVJkmw2m86fP6/69eurVq1aDosVAAAAgPNyeKKTnZ19w7ZOnTqpWrVqio2N1eLFiyVJHh75xSez2az4+HiVLFlSAQEBdzVWAAAAAK7BoYnOqVOn1KtXL23cuFHSr5Wb8uXLa+XKlbp27ZrGjBmjgQMHav/+/fr88881atQozZw5U3PnziXRAQAAQLFicpIfV+DQRCcnJ0dpaWmKjY1VQkKCpPzKTW5urmrVqqWEhAS1bNlSK1euVHBwsEaOHKm9e/cqISFBISEhjgwdAAAAgBNz+PLShw8f1pAhQ2QYhqKjo9WyZUtJ0rVr1+Tl5aWrV68qJydH69atU+fOnZWbmys/Pz9HhvyHsLw0cHtYXhq4PSwvDdw6Z15eOuVH51he+r4KLC/9PwUGBmr69OkymUyaOHGivbLj5eUlq9Uqs9msyZMna/ny5fL09HTpJAcAAAD4Qxzds+ZCvWsOT3Skmyc7VqtVo0aN0jvvvKNXX31Vnp6eDo4UAAAAgCtwikRHujHZ2bRpk6KjozV//nzt2rVLjRo1cnSIAAAAgEOZnOQ/V+A0iY70a7Lj6empxx9/XFOnTtXXX39NkgMAAADgljhVoiPlJztTpkxRq1attGfPHoWGhjo6JAAAAAAuxsPRARSmbt26+vTTT7knBwAAAPgNk2t0jTkFp6voXEeSAwAAAOB2OW2iAwAAAAC3yylb1wAAAADciM61oqOiAwAAAMDtUNEBAAAAXAUlnSKjogMAAADA7ZDoAAAAAHA7tK4BAAAALsJE71qRUdEBAAAA4HZIdAAAAAC4HVrXAAAAABdhonOtyKjoAAAAAHA7VHQAAAAAF0FBp+io6AAAAABwOyQ6AAAAANwOrWsAAACAi2AxgqKjogMAAADA7ZDoAAAAAHA7tK4BAAAALoPetaKiogMAAADA7VDRAQAAAFwEixEUHRUdAAAAAG6HRAcAAACA26F1DQAAAHARdK4VHRUdAAAAAG6Hig4AAADgIliMoOio6AAAAABwOyQ6AAAAANwOrWsAAACAizCxHEGRUdEBAAAA4HZIdAAAAAC4HVrXAAAAAFdB51qRUdEBAAAA4Hao6AAAAAAugoJO0VHRAQAAAOB2SHQAAAAAuB1a1wAAAAAXYaJ3rcio6AAAAABwOyQ6AAAAANwOrWsAAACAizCx7lqRUdEBAAAA4Hao6AAAAACugoJOkVHRAQAAAOB2SHQAAAAAuB1a1wAAAAAXQeda0VHRAQAAAOB2SHQAAAAAuB1a1wAAAAAXYaJ3rcio6AAAAABwO1R0AAAAABdhYjmCIqOiAwAAAMDtkOgAAAAAcDu0rgEAAAAugsUIio6KDgAAAAC3Q6IDAAAAwO2Q6AAAAABwOyQ6AAAAANwOixEAAAAALoLFCIqOig4AAAAAt0OiAwAAAMDt0LoGAAAAuAiT6F0rKio6AAAAANwOiQ4AAAAAt0PrGgAAAOAiWHWt6KjoAAAAAHA7VHQAAAAAF0FBp+io6AAAAABwOyQ6AAAAANwOrWsAAACAq6B3rcio6AAAAABwOyQ6AAAAANwOrWsAAACAizDRu1ZkVHQAAAAAuB0qOgAAAICLMFHQKTIqOgAAAADcDokOAAAAALdD6xoAAADgIuhcKzoqOgAAAADcDokOAAAAALdD6xoAAADgKuhdKzIqOgAAAADcDhUdAAAAwEWYKOkUGRUdAAAAAG6HRAcAAADAn27WrFmqWbOmfHx81Lx5c+3cufO/jl++fLnq1asnHx8fPfDAA1qzZs0tHY9EBwAAAHARJpNz/NyqpUuXasSIERo3bpz27Nmj4OBghYeH68cffyx0fGJiorp3767IyEjt3btX3bp1U7du3fTdd98V/W9lGIZx66Hidv2cy58buB3Hz/3s6BAAl9Ry9GeODgFwORcWPevoEG4qO8/REeTzucU7/Zs3b66mTZtq5syZkiSbzabq1atr8ODBGjNmzA3jn376aV25ckWrVq2yb2vRooVCQkL03nvvFemYVHQAAAAA3JKcnBxdunSpwE9OTk6hY69du6bdu3erXbt29m1ms1nt2rXTtm3bCn3Ptm3bCoyXpPDw8JuOLwyrrt1lJTxZKcNZ5eTkKCYmRlFRUfL29nZ0OPidoColHR0CCsG8cX7OfGW6OGPu4HbdaiXlzzL+9RhNmDChwLZx48Zp/PjxN4w9f/68rFarKlasWGB7xYoVdfDgwUL3n56eXuj49PT0IsdIRQf4RU5OjiZMmHDTqxEAbsS8AW4PcweuLioqSllZWQV+oqKiHB1WAU6SEwIAAABwFd7e3kWuRpYrV04Wi0Vnz54tsP3s2bOqVKlSoe+pVKnSLY0vDBUdAAAAAH8aLy8vNW7cWF999ZV9m81m01dffaWwsLBC3xMWFlZgvCStX7/+puMLQ0UHAAAAwJ9qxIgRioiIUJMmTdSsWTNNnTpVV65c0fPPPy9J6tWrl6pWraqYmBhJ0tChQ9W6dWvFxsaqU6dOWrJkib755hu9//77RT4miQ7wC29vb40bN46bQoFbwLwBbg9zB8XN008/rXPnzum1115Tenq6QkJC9OWXX9oXHDhx4oTM5l+bzR588EEtXrxYY8eO1SuvvKLAwECtXLlS999/f5GPyXN0AAAAALgd7tEBAAAA4HZIdAAAAAC4HRIdAAAAAG6HRAcAAACA2yHRAQAAAOB2SHSAX7AAIQAAgPsg0UGxd/XqVeXk5OjkyZPKzs52dDhAscYFBwDAncIDQ1GsHThwQGPHjtUPP/yggwcP6v7771d4eLjeeOMNR4cGuL3U1FTFxcXp/PnzCgoK0pAhQ2QymWQYhkwmk6PDA5zWuXPndPToUZnNZtWvX18lSpRwdEiAU6Kig2Jr3759CgsLU+XKlTVs2DAtW7ZMNWrU0NSpU9W5c2fl5uY6OkTAbSUlJally5ZKSEjQxo0bNWzYMPXv31+SSHKA/2Lfvn165JFH1Lt3bzVr1kyDBw/WgQMHHB0W4JRIdFAsnTt3ThERERo4cKBmzpypyMhIPfbYY5o3b55iY2O1ceNGPffcc44OE3BL+/bt04MPPqjIyEitX79eGzdu1Lvvvqt58+Zp+fLljg4PcFrJyclq0aKFunTpok8//VSzZ8/WggUL9O9//9vRoQFOiUQHxVJaWppyc3PVo0cPWa1WSZLNZlP58uXVs2dPRUVF6YsvvtDKlSsdGyjgZjIzM/Xkk08qMDBQr7/+uiwWiypXrqwOHTqoYsWKVFKBmzhw4ICaNm2qgQMHKiYmRvXr11f37t1VqVIlrVmzxv5Zdp3NZnNQpIDzINFBsZSUlKQjR47o/vvvl8VikWEYMpvzp4Ofn5+effZZeXp66siRIw6OFHAvVqtVzz77rNLS0hQTE1Nge1ZWlvz9/R0YHeC8du/erdzcXN1zzz26dOmSJGnWrFk6c+aMfv75Z40aNUpxcXHavHmzJFpAAYnFCFBM1a5dW5K0YsUKPfHEEzd8INSqVUv33nuvTp065YjwALfzww8/KCsrS02bNtWgQYPk4+OjmJgY+fn5qU+fPmrfvr369u2rjh07OjpUwCn17NlT58+f17Bhw+Tt7a2zZ8/q3Xff1fz581WmTBn98MMPiouL07Fjx1SuXDn17NlTw4cPJ+FBsUaig2KpZs2aKl26tP71r3+pSZMmqlGjhqT8Ur/ZbFZmZqZ8fX3VuHFjB0cKuL6kpCQ1atRI06ZNU9OmTVWuXDlFRkbKMAy98sorGjJkiIYPH663335bhmEUqLAC+NWwYcNktVo1cOBAmUwmxcfHq0uXLvbX+/Tpo7S0NPuiOiQ5KO74JEGxVK1aNc2ZM0dffvmloqOjtX//fkmyf7l65513dPr0abVq1cqRYQIuLykpSQ8++KBeeeUVDR482L69XLly6tWrl6Kjo+Xn52dvWbu+vDRQ3J08eVJxcXGaP3++EhMT7dtHjhypuXPnyjAMpaSkKCsry/5a2bJl1ahRIy1cuFCBgYGOCBtwKlR0UGx17dpV06dP14svvqidO3eqZcuWqly5so4ePaq1a9fqq6++sld6ANy65ORktWzZUsOGDdPrr79u3/7JJ5+oe/fuqlKlip577jnl5eVp8uTJ8vDw0MsvvyyLxeLAqAHHS05OVpcuXVShQgWlpqbK399fb731lh577DFJUt++ffXTTz9p5MiRys3N1YABA1S6dGn7xTqeRQXko6KDYstisah///76+uuv1aBBA+3YsUObNm1SmTJllJiYqEaNGjk6RMBlnT59WiEhIXr22Wc1adIk+/Y333xTPXr00DfffCNJqlChgiIjIzV27FiNGTNGU6dOdVDEgHNITk5WWFiYnnnmGW3cuFGLFy/WlStXFBcXp6tXr9pXJhwxYoSmTJmi6Ohovfvuu/rpp5/s+yDJAfKZDHoEAFmtVpnNZplMJvt9OgBuX0ZGhtq1aydJWrZsmQIDA/XGG29oypQp+uSTT/Too48WuOqckZGhRYsWqWPHjqpbt64jQwcc5uTJkwoNDVXbtm21ZMkS+/YmTZroypUr2rlzp0qVKlXgPRMmTNC0adN0+PBhlS1b9m6HDDg1Eh1ABcv8lPyBP+b6HMrIyFDHjh2Vm5urtm3bauHChVq8eLEeffTRAuP379+vunXrymKxMPdQrG3YsEGjR49W1apVNXr0aIWFhSkmJkavvvqqmjZtqkqVKql8+fJq3Lix/v73v6tKlSqyWCzKyMggyQEKQaIDALijfnux4MKFC3rssce0detWzZ8/X88//3yBsaNHj9aaNWu0ZcsWnqEDSFq9erVmzJghX19fVahQQfHx8Zo1a5aaNWumb775Rvv379fMmTPl4+Oj4OBgff7556xUCNwEiQ4A4A87c+aMtm/frnXr1skwDHXs2FFNmzZV1apVdeHCBf3973/XpUuXtGLFCntr2rhx4/T2229r06ZNatasmYPPAHCsvLw8eXjkrxH1xRdfaPr06fr66681adIkjRgxosDYc+fOaePGjQoNDbU/Fw7AjUh0AAB/yP79+9W7d2/5+fkpPT1dvr6+2r17t7p27aoRI0aoVatWyszMVHh4uC5fvqz//Oc/mjdvnmJiYpSQkMDzqlBsnTt3Tr6+vvrLX/4iScrNzZWnp6ck6csvv9Q777wjLy8vvfrqqwoLC7thDID/jkQHAHDbkpKS1Lp1a/Xr10/9+/fXfffdJ0mKi4vTyJEj1axZM02aNEmNGzdWRkaGOnfurO3bt8vX11dbtmwhyUGxdeLECdWrV0/du3eXt7e33nrrLfn4+NirOpL0+eefa/bs2TKZTBo3bpxatGjhwIgB10NDJwDgtuzfv18PPfSQhg4dqrfeeks1a9aUJNlsNj3//POaOXOmNm7cqKVLl0rKf5jhZ599pqeffppKDoq948ePKzs7W8HBwbp48aLCwsIUFRWlTZs22cd06dJFAwYMkNls1ogRI7Rr1y7HBQy4ICo6AIBblp2drWbNmikrK0sbNmywV3Kuf6RcX4xg9OjRmj17tg4ePKiqVatKEku4A78YPHiwvLy8FBsbqw8++EBHjhzR7Nmz1bNnT7Vo0UK9evWSlN/GNnfuXE2bNk333HOPg6MGXAeJDgDgtqxdu1aDBw9Wq1atNGzYMAUHB0vKT3ZsNpssFovWr1+vJ598Ups3b1ZISIhjAwacxPWVCT/66CN9+OGH+uyzz+zPx2nRooVOnz4tDw8PVapUSc8884wiIyNlsVjk4+Pj4MgB18IlNQDALbu+stqsWbO0YcMGTZ06Vfv27ZOUX825XtE5ceKEqlevzlVo4Deuz4+ePXsqPT1db7zxhiQpIiJCaWlpWrp0qTZu3KiyZcvqk08+UWZmJkkOcBuo6AAAbsv1q9Lr1q1Tv3791KZNG40YMUINGzaUlL861KBBg2S1WjV79mx5e3s7OGLAeVitVlksFi1btkzLly/XhQsX9P3332vVqlUF7l/LzMzkGVPAbSLRAQDclt8vhdu/f/8Cyc748eP1/vvva8OGDapXr56DowWcx/UkR8pf1KNTp066fPmyvv32W1WrVk0S97IBd4LH/x4CACjOrldufstqtcrT01NpaWnKyspShw4d9P7776tfv37y9fVVXl6ePvroIyUkJJDkoNi62dyxWCw6deqULl68qAYNGmjMmDGKjY1VXl6efRxJDvDHMYsAADeVmpqqKVOmqE+fPrpw4YIk2RcaOH78uOrWraslS5bIMAyFh4dr3rx5WrRokT7++GMlJiYqNDTUwWcAOMb/mjt16tTRkiVLJEnNmjVTQECANm/e7MiQAbdDogMAKNS+ffsUHh6uo0ePys/PTyVKlJCUf6X5xx9/tC9/O2HCBJlMJhmGofbt2+vf//63kpKS1KhRIwefAeAYtzJ3JCk0NFT+/v6aOXNmgaoOgD+Ge3QAADc4fPiwWrZsqT59+mjSpEn2+wmut+Js2LBB3333nQYPHmxvzSmsTQcobm517lxvZUtNTZVhGPZnUgH440h0AAAFWK1WDR06VBcuXND8+fPl6+vr6JAAl8DcAZwLrWsAgAIsFou2b9+umjVrFvpFzWq1Ssq/3+C3/wLFHXMHcC4kOgAAO8MwdPnyZaWlpals2bL2bb9lsVhks9k0dOhQXbx4kdWhADF3AGfEDAMA2BmGIR8fH4WEhCg+Pl6pqakF7sG5LjU1Vbt379aZM2ccFSrgVJg7gPMh0QEA2JnNZnl4eKhbt25KTEzU/Pnzdfr0aUkqsNDAokWL5Ovrq0qVKjkqVMCpMHcA58MDQwGgGEtJSdHixYu1Y8cOeXt7q3r16ho7dqwGDBiglJQUxcTEKCsrSz179lSLFi2UnJysuLg4LVy4UJs3b5a/v7+jTwFwCOYO4PxYdQ0Aiqnk5GS1b99eTZs2lZ+fn9LT07V7926VLVtW06ZNU8eOHTV+/HhNmzZN2dnZCggIUJkyZeTt7a0FCxYoJCTE0acAOARzB3ANJDoAUAwdP35cDz30kJ577jlNnDhRFotFeXl5Sk5OVr9+/ZSenq4VK1aoefPm2rx5s86ePavU1FQ1b95cQUFBtN2g2GLuAK6DRAcAiqEZM2Zo9erVio+Pl6+vb4GHfaakpKhLly7y8/NTYmKigyMFnAtzB3AdLEYAAMXQ3r17ZbVab/iiJkm1atXS0KFDtXfvXu3Zs8eBUQLOh7kDuA4SHQAohkwmU4EVoX5b3DebzWrXrp1ycnKUkZHhqBABp8TcAVwHiQ4AFCPXv5S1atVK6enpmj17tqT8L2x5eXmS8p/Wfv78edWvX1+1atVyWKyAM2HuAK6HRAcA3Fx2dvYN2zp16qRq1aopNjZWixcvliR5eOQ/ccBsNis+Pl4lS5ZUQEDAXY0VcCbMHcC1sRgBALixU6dOafjw4Ro4cKDatGkjScrLy5OHh4eOHj2qhx9+WCaTSZ06ddKLL76olJQUbdmyRXPnztXWrVtZBhfFFnMHcH1UdADAjeXk5CgtLU2xsbFKSEiQlH/1OTc3V7Vq1VJCQoJatmyplStXKjg4WCNHjtTevXuVkJDAFzUUa8wdwPVR0QEAN3f48GENGTJEhmEoOjpaLVu2lCRdu3ZNXl5eunr1qnJycrRu3Tp17txZubm58vPzc3DUgOMxdwDXRkUHANxcYGCgpk+fLpPJpIkTJ9qvTnt5eclqtcpsNmvy5Mlavny5PD09+aIG/IK5A7g2KjoAUEwUdnX62rVrGjlypObMmaNdu3apUaNGjg4TcDrMHcA1kegAQDHy2y9sY8aM0dq1azVjxgwlJCTwRQ34L5g7gOsh0QGAYubw4cMaMWKEEhISdOXKFW3btk2hoaGODgtweswdwLVwjw4AFDOBgYGaMmWKWrVqpT179vBFDSgi5g7gWqjoAEAxlZubK09PT0eHAbgc5g7gGkh0AAAAALgdWtcAAAAAuB0SHQAAAABuh0QHAAAAgNsh0QEAAADgdkh0AAAAALgdEh0AAAAAbodEBwDcUO/evdWtWzf774888oiGDRt21+PYtGmTTCaTLl68eNMxJpNJK1euLPI+x48fr5CQkD8U17Fjx2QymfTtt9/+of0AAJwXiQ4A3CW9e/eWyWSSyWSSl5eXateurX/+85/Ky8v704/9f//3f5o4cWKRxhYlOQEAwNl5ODoAAChOOnTooLi4OOXk5GjNmjUaNGiQPD09FRUVdcPYa9euycvL644cNyAg4I7sBwAAV0FFBwDuIm9vb1WqVEk1atTQwIED1a5dO33++eeSfm03mzRpkqpUqaK6detKkk6ePKmnnnpKZcqUUUBAgLp27apjx47Z92m1WjVixAiVKVNGZcuW1csvvyzDMAoc9/etazk5ORo9erSqV68ub29v1a5dW/Pnz9exY8fUpk0bSZK/v79MJpN69+4tSbLZbIqJiVGtWrXk6+ur4OBgffrppwWOs2bNGtWpU0e+vr5q06ZNgTiLavTo0apTp45KlCihe++9V9HR0crNzb1h3Ny5c1W9enWVKFFCTz31lLKysgq8/sEHHygoKEg+Pj6qV6+eZs+efdNjZmZmqkePHipfvrx8fX0VGBiouLi4W44dAOA8qOgAgAP5+voqIyPD/vtXX32l0qVLa/369ZKk3NxchYeHKywsTFu3bpWHh4def/11dejQQcnJyfLy8lJsbKwWLFigDz/8UEFBQYqNjVV8fLz++te/3vS4vXr10rZt2zR9+nQFBwfr6NGjOn/+vKpXr64VK1boiSee0KFDh1S6dGn5+vpKkmJiYvTRRx/pvffeU2BgoLZs2aKePXuqfPnyat26tU6ePKnHH39cgwYNUr9+/fTNN99o5MiRt/w3KVWqlBYsWKAqVapo37596tu3r0qVKqWXX37ZPubIkSNatmyZvvjiC126dEmRkZF64YUX9PHHH0uSPv74Y7322muaOXOmGjVqpL1796pv374qWbKkIiIibjhmdHS0vv/+e61du1blypXTkSNHdPXq1VuOHQDgRAwAwF0RERFhdO3a1TAMw7DZbMb69esNb29vY9SoUfbXK1asaOTk5Njfs2jRIqNu3bqGzWazb8vJyTF8fX2NdevWGYZhGJUrVzbeeust++u5ublGtWrV7McyDMNo3bq1MXToUMMwDOPQoUOGJGP9+vWFxrlx40ZDkpGZmWnflp2dbZQoUcJITEwsMDYyMtLo3r27YRiGERUVZdSvX7/A66NHj75hX78nyYiPj7/p62+//bbRuHFj++/jxo0zLBaLkZaWZt+2du1aw2w2G2fOnDEMwzDuu+8+Y/HixQX2M3HiRCMsLMwwDMM4evSoIcnYu3evYRiG0blzZ+P555+/aQwAANdDRQcA7qJVq1bpL3/5i3Jzc2Wz2fTss89q/Pjx9tcfeOCBAvflJCUl6ciRIypVqlSB/WRnZyslJUVZWVk6c+aMmjdvbn/Nw8NDTZo0uaF97bpvv/1WFotFrVu3LnLcR44c0c8//6xHH320wPZr166pUaNGkqQDBw4UiEOSwsLCinyM65YuXarp06crJSVFly9fVl5enkqXLl1gzD333KOqVasWOI7NZtOhQ4dUqlQppaSkKDIyUn379rWPycvLk5+fX6HHHDhwoJ544gnt2bNH7du3V7du3fTggw/ecuwAAOdBogMAd1GbNm00Z84ceXl5qUqVKvLwKPi/4ZIlSxb4/fLly2rcuLG9Jeu3ypcvf1sxXG9FuxWXL1+WJK1evbpAgiHl33d0p2zbtk09evTQhAkTFB4eLj8/Py1ZskSxsbG3HOu8efNuSLwsFkuh7+nYsaOOHz+uNWvWaP369Wrbtq0GDRqkKVOm3P7JAAAcikQHAO6ikiVLqnbt2kUeHxoaqqVLl6pChQo3VDWuq1y5snbs2KGHH35YUn7lYvfu3QoNDS10/AMPPCCbzabNmzerXbt2N7x+vaJktVrt2+rXry9vb2+dOHHippWgoKAg+8IK123fvv1/n+RvJCYmqkaNGnr11Vft244fP37DuBMnTuj06dOqUqWK/Thms1l169ZVxYoVVaVKFaWmpqpHjx5FPnb58uUVERGhiIgItWrVSi+99BKJDgC4MFZdAwAn1qNHD5UrV05du3bV1q1bdfToUW3atElDhgxRWlqaJGno0KF64403tHLlSh08eFAvvPDCf30GTs2aNRUREaE+ffpo5cqV9n0uW7ZMklSjRg2ZTCatWrVK586d0+XLl1WqVCmNGjVKw4cP18KFC5WSkqI9e/ZoxowZWrhwoSRpwIABOnz4sF566SUdOnRIixcv1oIFC27pfAMDA3XixAktWbJEKSkpmj59uuLj428Y5+Pjo4iICCUlJWnr1q0aMmSInnrqKVWqVEmSNGHCBMXExGj69On64YcftG/fPsXFxemdd94p9LivvfaaPvvsMx05ckT79+/XqlWrFBQUdEuxAwCcC4kOADixEiVKaMuWLbrnnnv0+OOPKygoSJGRkcrOzrZXeEaOHKnnnntOERERCgsLU6lSpfTYY4/91/3OmTNHTz75pF544QXVq1dPffv21ZUrVyRJVatW1YQJEzRmzBhVrFhRL774oiRp4sSJio6OVkxMjIKCgtShQwetXr1atWrVkpR/38yKFSu0cuVKBQcH67333tPkyZNv6Xy7dOmi4cOH68UXX1RISIgSExMVHR19w7jatWvr8ccf19/+9je1b99eDRs2LLB89D/+8Q998MEHiouL0wMPPKDWrVtrwYIF9lh/z8vLS1FRUWrYsKEefvhhWSwWLVmy5JZiBwA4F5Nxs7tVAQAAAMBFUdEBAAAA4HZIdAAAAAC4HRIdAAAAAG6HRAcAAACA2yHRAQAAAOB2SHQAAAAAuB0SHQAAAABuh0QHAAAAgNsh0QEAAADgdkh0AAAAALgdEh0AAAAAbuf/AzE6qnz6ckcjAAAAAElFTkSuQmCC",
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",
       "text/plain": [
-       "<Figure size 1000x3000 with 6 Axes>"
+       "<Figure size 640x480 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -1344,235 +4377,154 @@
     }
    ],
    "source": [
-    "fig, axs = plt.subplots(3, 1, figsize=(10, 30))\n",
-    "\n",
-    "confusion_matrix(\n",
-    "    padded_eval_short_labels,\n",
-    "    blstm_eval_short_predictions,\n",
-    "    unique_labels,\n",
-    "    title=\"Confusion matrix for LSTM on evaluation (short sentences) dataset\",\n",
-    "    return_ax=True,\n",
-    "    ax=axs[0],\n",
-    ")\n",
-    "\n",
-    "confusion_matrix(\n",
-    "    padded_eval_unlabeled_labels,\n",
-    "    blstm_eval_unlabeled_predictions,\n",
-    "    unique_labels,\n",
-    "    title=\"Confusion matrix for LSTM on evaluation (unlabeled sentences) dataset\",\n",
-    "    return_ax=True,\n",
-    "    ax=axs[1],\n",
-    ")\n",
-    "\n",
     "confusion_matrix(\n",
-    "    padded_eval_large_labels,\n",
-    "    blstm_eval_large_predictions,\n",
+    "    aligned_eval_long_labels,\n",
+    "    camembert_large_sentences_predictions.logits,\n",
     "    unique_labels,\n",
-    "    title=\"Confusion matrix for LSTM on evaluation (large sentences) dataset\",\n",
-    "    return_ax=True,\n",
-    "    ax=axs[2],\n",
-    ")\n",
-    "\n",
-    "plt.show()"
+    "    title=\"Confusion matrix for CamemBERT on evaluation (long sentences) dataset\",\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 361,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\n",
-      "Je: O\n",
-      "pensais: O\n",
-      "partir: O\n",
-      "de: O\n",
-      "Bangkok: LOC-DEP\n",
-      ".: O\n",
-      "Mais: O\n",
-      "finalement: O\n",
-      ",: O\n",
-      "je: O\n",
-      "vais: O\n",
-      "devoir: O\n",
-      "trouver: O\n",
-      "un: O\n",
-      "voyage: O\n",
-      "de: O\n",
-      "Tokyo: O\n",
-      "vers: O\n",
-      "Osaka: LOC-ARR\n",
-      ".: O\n"
+      "4/4 [==============================] - 4s 924ms/step\n"
      ]
     }
    ],
    "source": [
-    "test_sentence = \"Je pensais partir de Bangkok. Mais finalement, je vais devoir trouver un voyage de Tokyo vers Osaka.\"\n",
-    "\n",
-    "\n",
-    "p = predict(test_sentence, bilstm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## BERT\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The model used will be the fine-tuned version of [CamemBERT](https://huggingface.co/almanach/camembert-base) which is a state-of-the-art language model for French text.\n",
-    "\n",
-    "The model has been fine-tuned for our task [in here](./camemBERT_finetuning.ipynb) and saved on Hugging Face for convenience.\n",
-    "\n",
-    "[Checkout the model on Huggingface Hub](https://huggingface.co/Az-r-ow/CamemBERT-NER-Travel)\n"
+    "camembert_unlabeled_sentence_predictions = camembert.predict(\n",
+    "    tokenized_eval_unlabeled_sentences\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 409,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Some layers from the model checkpoint at Az-r-ow/CamemBERT-NER-Travel were not used when initializing TFCamembertForTokenClassification: ['dropout_379']\n",
-      "- This IS expected if you are initializing TFCamembertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing TFCamembertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "All the layers of TFCamembertForTokenClassification were initialized from the model checkpoint at Az-r-ow/CamemBERT-NER-Travel.\n",
-      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFCamembertForTokenClassification for predictions without further training.\n"
+      "(100, 150)\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "import tensorflow as tf\n",
-    "from transformers import TFCamembertForTokenClassification, CamembertTokenizer\n",
-    "import numpy as np\n",
-    "\n",
-    "tokenizer = CamembertTokenizer.from_pretrained(\"camembert-base\")\n",
-    "camembert = TFCamembertForTokenClassification.from_pretrained(\n",
-    "    \"Az-r-ow/CamemBERT-NER-Travel\"\n",
+    "confusion_matrix(\n",
+    "    aligned_eval_unlabeled_labels,\n",
+    "    camembert_unlabeled_sentence_predictions.logits,\n",
+    "    unique_labels,\n",
+    "    title=\"Confusion matrix for CamemBERT on evaluation (unlabeled sentences) dataset\",\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 410,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tokenized_eval_short_sentences = tokenizer(\n",
-    "    eval_sentences,\n",
-    "    return_tensors=\"tf\",\n",
-    "    padding=\"max_length\",\n",
-    "    max_length=MAX_LEN,\n",
-    ")\n",
-    "\n",
-    "tokenized_eval_large_sentences = tokenizer(\n",
-    "    eval_large_sentences,\n",
-    "    is_split_into_words=True,\n",
-    "    return_tensors=\"tf\",\n",
-    "    truncation=True,\n",
-    "    padding=\"max_length\",\n",
-    "    max_length=MAX_LEN,\n",
+    "camembert_eval_sentences = eval_sentences + eval_unlabeled + eval_large\n",
+    "camembert_eval_labels = np.concatenate(\n",
+    "    [padded_eval_short_labels, padded_eval_unlabeled_labels, padded_eval_large_labels]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 407,
+   "execution_count": 189,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "24/24 [==============================] - 39s 2s/step\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "camembert_short_sentences_predictions = camembert.predict(\n",
-    "    tokenized_eval_short_sentences\n",
+    "camembert_eval_sentences_tokenized = tokenizer(\n",
+    "    camembert_eval_sentences, return_tensors=\"tf\", padding=\"max_length\", max_length=150\n",
+    ")\n",
+    "\n",
+    "camembert_eval_labels_aligned = align_labels_with_tokens(\n",
+    "    camembert_eval_sentences_tokenized, camembert_eval_labels\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 411,
+   "execution_count": 248,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 1s 1s/step\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "<tf.Tensor: shape=(1, 100), dtype=int64, numpy=\n",
-       "array([[0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])>"
+       "954"
       ]
      },
-     "execution_count": 411,
+     "execution_count": 248,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "test_sentence = \"Je veux partir de Paris à Montpellier.\"\n",
-    "\n",
-    "tokenized_test_sentence = tokenizer(\n",
-    "    test_sentence, return_tensors=\"tf\", padding=\"max_length\", max_length=MAX_LEN\n",
-    ")\n",
-    "\n",
-    "camembert_test_sentence_predictions = camembert.predict(tokenized_test_sentence)\n",
-    "\n",
-    "tf.math.argmax(camembert_test_sentence_predictions.logits, axis=-1)"
+    "len(camembert_eval_labels_aligned)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 412,
+   "execution_count": 244,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "(757, 100)\n"
+      "100%|██████████| 1/1 [00:42<00:00, 42.16s/it]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "camembert_results = bootstrap_evaluation(\n",
+    "    camembert,\n",
+    "    sentences=camembert_eval_sentences_tokenized,\n",
+    "    labels=camembert_eval_labels_aligned,\n",
+    "    from_logits=True,\n",
+    "    has_mask=True\n",
+    "    num_bootstrap_samples=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "[0.2422770593488873]"
       ]
      },
+     "execution_count": 245,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "confusion_matrix(\n",
-    "    padded_eval_short_labels,\n",
-    "    camembert_short_sentences_predictions.logits,\n",
-    "    unique_labels,\n",
-    ")"
+    "camembert_results[\"f1_scores\"]"
    ]
   },
   {
@@ -1584,7 +4536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1620,6 +4572,25 @@
     "    return wrong_predictions\n",
     "\n",
     "\n",
+    "def plot_mistakes_positions(\n",
+    "    predictions, labels, title=\"Mistakes density in sentences\", max_len=None\n",
+    "):\n",
+    "    mistakes_positions = get_mistakes_positions(predictions, labels)\n",
+    "    mistakes_positions = np.where(mistakes_positions >= 0, 1, 0)\n",
+    "    mistakes_positions = np.sum(mistakes_positions, axis=0)\n",
+    "    mistakes_position_density = mistakes_positions / np.max(mistakes_positions)\n",
+    "\n",
+    "    plt.figure(figsize=(20, 5))\n",
+    "\n",
+    "    if max_len:\n",
+    "        mistakes_position_density = mistakes_position_density[:max_len]\n",
+    "\n",
+    "    sns.heatmap(mistakes_position_density.reshape(1, -1), cmap=\"Blues\")\n",
+    "\n",
+    "    plt.title(title)\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
     "def label_pos_freq_in_sentences(\n",
     "    labels,\n",
     "    unique_labels,\n",
@@ -1827,6 +4798,90 @@
     ")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_mistakes_positions(blstm_eval_large_predictions, padded_eval_large_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_mistakes_positions(lstm_with_pos_eval_large_predictions, padded_eval_large_labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_mistakes_positions(\n",
+    "    lstm_with_pos_eval_short_predictions, padded_eval_short_labels, max_len=25\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_mistakes_positions(\n",
+    "    blstm_eval_short_predictions, padded_eval_short_labels, max_len=25\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -3782,7 +6837,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.8"
   }
  },
  "nbformat": 4,