{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Subset — PHASE 1 (LOCAL, máy bạn)\n",
    "\n",
    "Chạy trên **Windows local** (data đã nằm sẵn ổ D, nhanh, không timeout).\n",
    "\n",
    "**Việc:** lọc → stratified sampling 40k/5k/5k → xuất **bundle** gồm:\n",
    "- `manifest_{train,val,test}.json` + `.csv` (đường dẫn PhysioNet + ảnh đã chọn)\n",
    "- `reports/files/pXX/pSUBJ/sSTUDY.txt` (giữ **cấu trúc + tên gốc** PhysioNet)\n",
    "- `vqa/{vqa.json,vqa_val.json,vqa_test.json}` (đã lọc, `image_path` trỏ path gốc)\n",
    "\n",
    "→ Zip bundle, **upload lên Google Drive**, sang PHASE 2 (Colab) tải ảnh + upload HF.\n",
    "\n",
    "> Giữ **nguyên cấu trúc gốc** `files/pXX/pSUBJ/sSTUDY/<dicom>.jpg` → tải đứt vẫn đối chiếu được manifest. Không cần GPU. Không tải ảnh ở bước này."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Config (local paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  OK   SPLIT: D:\\USTH\\KLTN\\cxr-vlm-data\\mimic-cxr-2.0.0-split.csv\n",
      "  OK   META: D:\\USTH\\KLTN\\cxr-vlm-data\\mimic-cxr-2.0.0-metadata.csv\n",
      "  OK   CHEX: D:\\USTH\\KLTN\\cxr-vlm-data\\mimic-cxr-2.0.0-chexpert.csv\n",
      "  OK   REPORTS: D:\\USTH\\KLTN\\cxr-vlm-data\\mimic-cxr-reports\n",
      "  OK   VQA_TRAIN: D:\\USTH\\KLTN\\cxr-vlm-data\\mimic-ext-mimic-cxr-vqa-a-complex-diverse-and-large-scale-visual-question-answering-dataset-for-chest-x-ray-images-1.0.0\\MIMIC-Ext-MIMIC-CXR-VQA\\dataset\\train.json\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "DATA_DIR = Path(r\"D:\\USTH\\KLTN\\cxr-vlm-data\")\n",
    "CXR_ROOT = DATA_DIR / \"mimic-cxr-reports\"          # files/p10../sXXXX.txt\n",
    "\n",
    "SPLIT_CSV    = DATA_DIR / \"mimic-cxr-2.0.0-split.csv\"\n",
    "META_CSV     = DATA_DIR / \"mimic-cxr-2.0.0-metadata.csv\"\n",
    "CHEXPERT_CSV = DATA_DIR / \"mimic-cxr-2.0.0-chexpert.csv\"\n",
    "\n",
    "_VQA_DIR = (DATA_DIR\n",
    "    / \"mimic-ext-mimic-cxr-vqa-a-complex-diverse-and-large-scale-visual-question-answering-dataset-for-chest-x-ray-images-1.0.0\"\n",
    "    / \"MIMIC-Ext-MIMIC-CXR-VQA\" / \"dataset\")\n",
    "VQA_TRAIN, VQA_VALID, VQA_TEST = _VQA_DIR/\"train.json\", _VQA_DIR/\"valid.json\", _VQA_DIR/\"test.json\"\n",
    "\n",
    "# Bundle output (sẽ zip cái này rồi upload lên Drive)\n",
    "BUNDLE = Path(r\"D:\\USTH\\KLTN\\subset_bundle\")\n",
    "BUNDLE.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "N_TRAIN, N_VAL, N_TEST = 40_000, 5_000, 5_000\n",
    "SEED = 42\n",
    "JPG_BASE_URL = \"https://physionet.org/files/mimic-cxr-jpg/2.1.0\"\n",
    "\n",
    "for n, p in [(\"SPLIT\",SPLIT_CSV),(\"META\",META_CSV),(\"CHEX\",CHEXPERT_CSV),\n",
    "             (\"REPORTS\",CXR_ROOT),(\"VQA_TRAIN\",VQA_TRAIN)]:\n",
    "    print(f\"  {'OK ' if p.exists() else 'MISSING'}  {n}: {p}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ready\n"
     ]
    }
   ],
   "source": [
    "import re, json, shutil\n",
    "import numpy as np, pandas as pd\n",
    "import matplotlib.pyplot as plt, seaborn as sns\n",
    "sns.set_theme(style=\"whitegrid\"); plt.rcParams[\"figure.dpi\"]=110\n",
    "np.random.seed(SEED)\n",
    "\n",
    "CHEXPERT_LABELS = [\"Atelectasis\",\"Cardiomegaly\",\"Consolidation\",\"Edema\",\n",
    "  \"Enlarged Cardiomediastinum\",\"Fracture\",\"Lung Lesion\",\"Lung Opacity\",\n",
    "  \"No Finding\",\"Pleural Effusion\",\"Pleural Other\",\"Pneumonia\",\n",
    "  \"Pneumothorax\",\"Support Devices\"]\n",
    "print(\"ready\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load metadata + parser ALL-CAPS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images: 377110 | studies: 227835 | split: {'train': 368960, 'test': 5159, 'validate': 2991}\n"
     ]
    }
   ],
   "source": [
    "split_df = pd.read_csv(SPLIT_CSV); meta_df = pd.read_csv(META_CSV); chex_df = pd.read_csv(CHEXPERT_CSV)\n",
    "for d in (split_df, meta_df, chex_df):\n",
    "    d[\"subset\"] = \"p\" + d[\"subject_id\"].astype(str).str[:2]\n",
    "img = split_df.merge(meta_df[[\"dicom_id\",\"ViewPosition\"]], on=\"dicom_id\", how=\"left\")\n",
    "print(\"images:\", len(img), \"| studies:\", img.study_id.nunique(),\n",
    "      \"| split:\", img.split.value_counts().to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b0c846e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "parser ok — exact-header FINDINGS / IMPRESSION only\n"
     ]
    }
   ],
   "source": [
    "# Parser: CHỈ lấy đúng header \"FINDINGS\" và \"IMPRESSION\".\n",
    "# Không gom CONCLUSION / WET READ / view-desc / FINDINGS AND IMPRESSION.\n",
    "SECTION_RE = re.compile(r'^[ \\t]*([A-Z][A-Z ,/()\\.\\-]{1,70}?):\\s*', re.MULTILINE)\n",
    "\n",
    "def parse_report(txt):\n",
    "    \"\"\"Trả về (findings, impression). Chỉ section có header == đúng\n",
    "    'FINDINGS' / 'IMPRESSION' (sau strip+upper) mới được tính.\"\"\"\n",
    "    m = list(SECTION_RE.finditer(txt))\n",
    "    if not m:\n",
    "        return None, None\n",
    "    f = i_ = None\n",
    "    for idx, mm in enumerate(m):\n",
    "        hdr = mm.group(1).strip().upper()\n",
    "        s   = mm.end()\n",
    "        e   = m[idx+1].start() if idx+1 < len(m) else len(txt)\n",
    "        body = txt[s:e].strip()\n",
    "        if hdr == \"FINDINGS\" and f is None:\n",
    "            f = body or None\n",
    "        elif hdr == \"IMPRESSION\" and i_ is None:\n",
    "            i_ = body or None\n",
    "    return f, i_\n",
    "\n",
    "print(\"parser ok — exact-header FINDINGS / IMPRESSION only\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Filter: (a) frontal PA>AP 1 ảnh/study → (b) cả findings+impression → (c) IQR outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(a) frontal studies: 218,139  {'AP': 132267, 'PA': 85872}\n"
     ]
    }
   ],
   "source": [
    "# (a) frontal, ưu tiên PA\n",
    "frontal = img[img.ViewPosition.isin([\"PA\",\"AP\"])].copy()\n",
    "frontal[\"vr\"] = (frontal.ViewPosition==\"AP\").astype(int)   # PA=0 ưu tiên\n",
    "frontal = (frontal.sort_values([\"study_id\",\"vr\"])\n",
    "                   .drop_duplicates(\"study_id\", keep=\"first\").reset_index(drop=True))\n",
    "print(f\"(a) frontal studies: {len(frontal):,}  {frontal.ViewPosition.value_counts().to_dict()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing report files: 1,053\n",
      "(b) có cả 2 phần: 116,641 / 218,139\n"
     ]
    }
   ],
   "source": [
    "# (b) parse report (local SSD -> nhanh, vài phút cho ~150k study)\n",
    "recs=[]; miss=0\n",
    "for r in frontal.itertuples(index=False):\n",
    "    p = CXR_ROOT/\"files\"/r.subset/f\"p{r.subject_id}\"/f\"s{r.study_id}.txt\"\n",
    "    try: f,i_ = parse_report(p.read_text(encoding=\"utf-8\",errors=\"ignore\"))\n",
    "    except FileNotFoundError: f=i_=None; miss+=1\n",
    "    recs.append((r.study_id,f,i_))\n",
    "rep = pd.DataFrame(recs, columns=[\"study_id\",\"findings\",\"impression\"])\n",
    "rep[\"findings_len\"]=rep.findings.str.split().str.len()\n",
    "rep[\"impression_len\"]=rep.impression.str.split().str.len()\n",
    "pool = frontal.merge(rep, on=\"study_id\")\n",
    "both = pool[pool.findings.notna() & pool.impression.notna()].copy()\n",
    "print(f\"missing report files: {miss:,}\")\n",
    "print(f\"(b) có cả 2 phần: {len(both):,} / {len(pool):,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cutoff findings=102w  impression=42w\n",
      "(c) eligible pool: 108,783  {'train': 106756, 'test': 1187, 'validate': 840}\n"
     ]
    }
   ],
   "source": [
    "# (c) IQR outlier (Q3+1.5*IQR) cho cả findings & impression\n",
    "def iqr_hi(s):\n",
    "    q1,q3=s.quantile(.25),s.quantile(.75); return q3+1.5*(q3-q1)\n",
    "f_hi,i_hi = iqr_hi(both.findings_len), iqr_hi(both.impression_len)\n",
    "print(f\"cutoff findings={f_hi:.0f}w  impression={i_hi:.0f}w\")\n",
    "elig = both[(both.findings_len<=f_hi)&(both.impression_len<=i_hi)\n",
    "            &(both.findings_len>=2)&(both.impression_len>=1)].copy()\n",
    "print(f\"(c) eligible pool: {len(elig):,}  {elig.split.value_counts().to_dict()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98ce6f5d",
   "metadata": {},
   "source": [
    "## 3. CheXpert label + stratum + cờ has_vqa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VQA total: 377391 | eligible có VQA: 76,939/108,783 (70.7%)\n",
      "stratum\n",
      "No Finding                    51726\n",
      "Atelectasis                    7302\n",
      "Support Devices                7043\n",
      "Cardiomegaly                   6558\n",
      "None                           6533\n",
      "Lung Opacity                   6050\n",
      "Edema                          5758\n",
      "Pneumonia                      5362\n",
      "Pleural Effusion               3981\n",
      "Pneumothorax                   2165\n",
      "Consolidation                  1966\n",
      "Lung Lesion                    1953\n",
      "Fracture                       1380\n",
      "Enlarged Cardiomediastinum      604\n",
      "Pleural Other                   402\n"
     ]
    }
   ],
   "source": [
    "label_cols=[c for c in chex_df.columns if c in CHEXPERT_LABELS]\n",
    "elig = elig.merge(chex_df[[\"study_id\"]+label_cols], on=\"study_id\", how=\"left\")\n",
    "\n",
    "# tầng = nhãn positive HIẾM nhất của study (bảo toàn nhãn hiếm)\n",
    "rare_order = list((elig[label_cols]==1).mean().sort_values().index)\n",
    "def stratum(row):\n",
    "    for lab in rare_order:\n",
    "        if row.get(lab)==1: return lab\n",
    "    return \"No Finding\" if row.get(\"No Finding\")==1 else \"None\"\n",
    "elig[\"stratum\"]=elig.apply(stratum,axis=1)\n",
    "\n",
    "# VQA\n",
    "vqa_all = pd.concat([\n",
    "    pd.DataFrame(json.load(open(VQA_TRAIN,encoding=\"utf-8\"))).assign(vqa_split=\"train\"),\n",
    "    pd.DataFrame(json.load(open(VQA_VALID,encoding=\"utf-8\"))).assign(vqa_split=\"valid\"),\n",
    "    pd.DataFrame(json.load(open(VQA_TEST ,encoding=\"utf-8\"))).assign(vqa_split=\"test\"),\n",
    "], ignore_index=True)\n",
    "vqa_ids = set(vqa_all.image_id.astype(str))\n",
    "elig[\"has_vqa\"] = elig.dicom_id.astype(str).isin(vqa_ids)\n",
    "print(\"VQA total:\", len(vqa_all), \"| eligible có VQA:\",\n",
    "      f\"{elig.has_vqa.sum():,}/{len(elig):,} ({elig.has_vqa.mean()*100:.1f}%)\")\n",
    "print(elig.stratum.value_counts().to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Stratified sampling + carve split (patient-disjoint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  + bù 4,160? val từ train pool\n",
      "  + bù test từ train pool\n",
      "train=40,000 val=5,000 test=5,000\n",
      "patient-disjoint OK\n"
     ]
    }
   ],
   "source": [
    "def stratified_pick(df, n, seed=SEED):\n",
    "    if n>=len(df): return df.copy()\n",
    "    prop=df.stratum.value_counts(normalize=True)\n",
    "    alloc=(prop*n).round().astype(int)\n",
    "    while alloc.sum()!=n:\n",
    "        diff=n-alloc.sum()\n",
    "        k=alloc.idxmax() if diff<0 else alloc.idxmin()\n",
    "        alloc[k]+=int(np.sign(diff))\n",
    "    picks=[]\n",
    "    for s,k in alloc.items():\n",
    "        g=df[df.stratum==s]; k=min(k,len(g))\n",
    "        gv,gn=g[g.has_vqa],g[~g.has_vqa]\n",
    "        tv=min(k,len(gv))\n",
    "        sel=gv.sample(tv,random_state=seed)\n",
    "        if k-tv>0: sel=pd.concat([sel,gn.sample(k-tv,random_state=seed)])\n",
    "        picks.append(sel)\n",
    "    out=pd.concat(picks)\n",
    "    if len(out)<n:\n",
    "        out=pd.concat([out, df.drop(out.index).sample(n-len(out),random_state=seed)])\n",
    "    return out.sample(frac=1,random_state=seed).reset_index(drop=True)\n",
    "\n",
    "def carve_split(pool,nv,nt,ntr,seed=SEED):\n",
    "    val =stratified_pick(pool[pool.split==\"validate\"],nv,seed)\n",
    "    test=stratified_pick(pool[pool.split==\"test\"],nt,seed+1)\n",
    "    used=set(val.subject_id)|set(test.subject_id)\n",
    "    tp=pool[(pool.split==\"train\")&(~pool.subject_id.isin(used))]\n",
    "    if len(val)<nv:\n",
    "        e=stratified_pick(tp,nv-len(val),seed+2); val=pd.concat([val,e])\n",
    "        used|=set(e.subject_id); tp=tp[~tp.subject_id.isin(used)]\n",
    "        print(f\"  + bù {nv-len(val)+len(e):,}? val từ train pool\")\n",
    "    if len(test)<nt:\n",
    "        e=stratified_pick(tp,nt-len(test),seed+3); test=pd.concat([test,e])\n",
    "        used|=set(e.subject_id); tp=tp[~tp.subject_id.isin(used)]\n",
    "        print(f\"  + bù test từ train pool\")\n",
    "    train=stratified_pick(tp,ntr,seed+4)\n",
    "    return (train.assign(final_split=\"train\"),\n",
    "            val.assign(final_split=\"val\"),\n",
    "            test.assign(final_split=\"test\"))\n",
    "\n",
    "tr,va,te = carve_split(elig,N_VAL,N_TEST,N_TRAIN)\n",
    "print(f\"train={len(tr):,} val={len(va):,} test={len(te):,}\")\n",
    "assert not(set(tr.subject_id)&set(va.subject_id)), \"leak tr/va\"\n",
    "assert not(set(tr.subject_id)&set(te.subject_id)), \"leak tr/te\"\n",
    "assert not(set(va.subject_id)&set(te.subject_id)), \"leak va/te\"\n",
    "print(\"patient-disjoint OK\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Verify phân phối (subset vs eligible vs raw full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            raw_full(%)  eligible(%)  subset(%)  |Δ| subset-elig  |Δ| elig-raw\n",
      "Atelectasis                       20.11        10.99      10.71             0.29          9.11\n",
      "Cardiomegaly                      19.68         6.64       6.34             0.30         13.04\n",
      "Consolidation                      4.73         1.92       1.86             0.07          2.81\n",
      "Edema                             11.86         8.03       7.56             0.46          3.83\n",
      "Enlarged Cardiomediastinum         3.15         0.56       0.55             0.00          2.59\n",
      "Fracture                           1.93         1.28       1.35             0.07          0.64\n",
      "Lung Lesion                        2.76         1.85       1.86             0.01          0.91\n",
      "Lung Opacity                      22.62        14.85      14.51             0.34          7.77\n",
      "No Finding                        33.12        51.55      52.80             1.25         18.43\n",
      "Pleural Effusion                  23.83        12.28      11.63             0.65         11.55\n",
      "Pleural Other                      0.88         0.37       0.38             0.01          0.51\n",
      "Pneumonia                          7.27         5.56       5.55             0.01          1.71\n",
      "Pneumothorax                       4.55         2.17       1.97             0.20          2.38\n",
      "Support Devices                   29.21         8.70       8.46             0.24         20.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1540x440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "subset=pd.concat([tr,va,te],ignore_index=True)\n",
    "def prev(d): return (d[label_cols]==1).mean()*100\n",
    "cmp=pd.DataFrame({\"raw_full(%)\":prev(chex_df),\"eligible(%)\":prev(elig),\"subset(%)\":prev(subset)})\n",
    "cmp[\"|Δ| subset-elig\"]=(cmp[\"subset(%)\"]-cmp[\"eligible(%)\"]).abs().round(2)\n",
    "cmp[\"|Δ| elig-raw\"]=(cmp[\"eligible(%)\"]-cmp[\"raw_full(%)\"]).abs().round(2)\n",
    "print(cmp.round(2).to_string())\n",
    "ax=cmp[[\"raw_full(%)\",\"eligible(%)\",\"subset(%)\"]].plot(kind=\"bar\",figsize=(14,4),\n",
    "      color=[\"#bbb\",\"#888\",\"#4C72B0\"])\n",
    "ax.set_title(\"CheXpert prevalence\"); ax.set_ylabel(\"% positive\")\n",
    "plt.xticks(rotation=40,ha=\"right\"); plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SqClPIIYsu3POOSd+P3feeWf8YR8l4EhGYBKwSz4jgeTVZcex3YorJUiwtSSBRtpFRZUzvfvuu8Ndd90VtyOflwB4cSibyWdljTXaRGkkxwYCkUVJgtTlCb7zGqy7x3MlGWypktdOzaZMJG2oNMdtSZIkqbIZOJMkSVJGInOGYMFbb70VB8bJKiETjAH5okrxka3BoO2MGTPiIGzhYFhxJemw/fbb59/26aefxjXI8N///rfYv/3qq69igCPJfipKkoFBtkxRigqqFIV1q8guI1hCMI9BfrKT+CEThXJvbJ81pbJKQ5JVRuCMgCmBM9ayIwuMz56azUKQjDXBPv744/gY1tYiyEKQlZ/TTz89XHrppasN1LFWGutFsV3JOOO7pzQkPwTx7rjjjtW+58LZiqmKCiKkSg2cErDjdf/5z3+GsWPHhnfffTdmUw0dOjQG9fi8BIySbU+5z1UFi1aX/bMqZO+t7nsuz/OvDtl1BK8p30cGItuC/Y21zChvedVVV8XMvGTbs6+nrnVXlKKCNgn+tqjgdmEErsqLbcv75/PRBlm3sKisxFRJ1u2qgmvFSdYwS8qBFpYEb4sLKpb0NQickdVXVJbiqtZAS46Nq9qPJEmSpDXNwJkkSZIyEpkYRx11VLj//vvD6NGjY6YJgTOCTdxeVOYCZRR5LIPrZ5xxRoH7J02aFAdnt9122/ySd5RwIwiTms2UZJtdcMEFMeulKJT2I5hBwGpVgTMGlHmN77//vsA6V5g3b16pytkx6EywgB8G3wkqsMYVwRWCTYU/b2WVsQSD5JWBbbnJJpvE4BEBs6LKNKYi646fJEuK8pm33HJLGDZsWMwGLEkwhAAUP+edd17McCOIxnZ95ZVXYmCVjD/aYnHbgu+xOMWVB/zuu+/i78Lvj8wdsgj5SdoswT0CaQTxKNeZBB8IoiVreFU01lbDzz//HNKFbU4wMckeJNuUfe7BBx+M3/Gxxx6bvy0IinFbWRE8XRNo0+eee27MqKQsKWUoU489xeHxKEsW5JZbbhm35ZQpU4q8/+uvv46/2QfKipKYBDZ5jcKBM/YpgnOUuWXNyOKyG5M2J0mSJGUC1ziTJElSxkoyywhmMMBLJhjl+IoagEWvXr1iYO2+++6LA7mpHnrooVj+kXWxCEgQHAHZSUk5Mwa2KYW4uuyOI488Mv4mSLeq4EKSBVN4nbbUwfDVufnmm2P2XVK+D3zGPfbYI34WEJir6EygJEOnqEwVMr0qA++d7DLWtuM7J9OodevWcWA+dSCedsHjUjEwT2YejydTikBLcchyoVQj2zV1PTWCr7SRZE26ZLsmZeyKCoQlWYtF4b0Wta1oN+jQoUP8nWTYESROxbpdF198cYESjUmgljKVqe89QWlLsuWuueaaMreJzTbbrEA20ppEoJnvln05FUFGtkXDhg1j+yB4S5CGMp5kmRYVGCLz9KCDDorf9YIFC0K6/eUvf4n7PQF11rErSdCMNsRnI6BckkBwYbTddu3axW1BycfCXnvttfi7PGv67bnnngWeKxVZlASci3v+pI0lbU6SJEnKBAbOJEmSlLGaN28e2rdvH7OrKNOGoso0Jii52Lt37ziwftJJJ+UHKMDtPXv2jIPHBL4Iwu26667xtgQDv7/++mvMSmvRosUqX4fSkATayHIqDmtTUY6PzBLKACamT58eA2IlQWCAgA1rYKUO/vPaBJeS91O4POCqMqFKIslAIQOPwfvUbUQ2VmVJssvYPn/88Ud+kDI1uEXgi+/v8ccfL3AfAVECpgQLWAevOASSyPhJtmtqSULWkpowYULM0tluu+0KbAva4eTJk/MfSzD33nvvXeXnoSxfklWTBFHJEORzJG2ZtjRt2rT4eVi7LRWlJ1O/Y/YJ1voiWNS/f/8CWYvcdu2118YsxNS1sErbJshmIzhL0K+4MqOVhcxMthclWgsHYiinSaCaIFJSipXMQr4/1r1LDSCzXfr16xcDcQSBV1WqcU0YMWJE3F/53vmeS5rh9fnnn8fvIGmLq8J2I8iWuh2S4xBoG6ltceTIkbHMKce7JIhbFgQn+T6ee+65GLhNPc7deuutcX8jeFkUys2i8JqVkiRJUjpZqlGSJEkZjeACZRUZbKY02+oyI84888xY9otyigTLyNJo06ZNDGAR6CAYk2DAncHdJMiSlGksyVpCBHSGDBkSB59PO+20Ih9DptRll10WS+odf/zxMVDH+yCTivtWlRWVoCTd3/72t7gN9ttvv1iasEaNGjEQR+CHDKTU7Ksk4McgOaUtyXJZVRCwOJQLJOBHEIaBcQa2GZD/7LPP4md/4YUXQmUgiESQgNfhcxb1XZBNRWCU7UoQiu+PdfAIeFHGkoASAYpVIdBCIOyRRx6J66SR3UWwhefgNxlPBKmSbBiCVZSQ7Nq1a8wkJHDJWnN8p/x/UWivBKvYfmQX8R7JHKQNDB48OD/4QxCFwMKjjz4aPy8ZQrThpPwdGZbnn39+/vPStk8++eT8QAVBNbJ6eD+8F7LX2D6J5Psn++ess86Kn4XSp8UhyMT7JajCe0jN+KtsfOe0XUpnUtaQtkCgjLZOkIX7CUYmCJxxO0Fy2izbgu+eTECCRHx3PF860SaTACttgv2qOJdffnnMnkwNhqK4LNtUZLHdfffd8bujrGWC7ULQjsxGshG5n2PPJ598EgPItMXyYHtff/318XjL8ZfJDrQhsiLZly688MK4fxVGQJDvjrZOm5ckSZIyhRlnkiRJymgM9DIIm2QfkQmzOgSpGCQmGMHAMAEAMlgILrA+GgEKAidk1BB0IgDDQDIZLWRHJGtMrS4ziqwkAksEYIpDgOOBBx6IgScGifnh+SkdWZISerVq1YqPJZBDmTqCbvwQdCEoxnpePCbBwDVrvRGw4fOQcVPWwXCyZNheBBgJ0DDQzVpSvEZlSrLMCEpR1q6ojKgnn3wyrvlEBhJZXGSCEdBieyQZNqvLbGKNOl6LtkVQjPZA4IV1xZISiQluI+hE4IO2RGYY/yYgQjCnuG1IMIPPwXf25ZdfxrJ2Tz31VDjggAMKPJYAKwE/Sk0S0CDIRSlGAmBkNaauHUUQZdSoUTG4RDsgQEGgkb8lqEYWXep7ImhBW+GxtImiSvYVRoAQSVbjmsS2oc2zrQgc8f2Skcf3TaCUwF+CfZDPO3DgwPj5ydBie7CNWLuMx5ck6FSZaJtJSUKOFwTCi/shWzZVkiHGWozlwX5LBh77E/syxzuCtLSjigiM7r///jFYRwlZ2jmB/m222SZ+N+wnReF7IpjMsTT1GCZJkiSlW7W8NV17Q5IkScoQBD8IajVt2jRmt0hagWApgRWCd5RMJEClzEcGJYH8oUOHhkzXp0+fGBQl47NJkybpfjuSJElSPq9+JEmSVGVRwo5sM4NmUkEEytgvvvvuu5j9puwwbty4mHmX6ch4IyBLZqNBM0mSJGUaM84kSZIkSSuhOEnPnj3DzJkz43p5JSmTmor1/yjZVxqUV2SdO5UeZRcpl8h6kOutt15Gb8K+ffvGErqUp2SNM0mSJCmTGDiTJEmSJBWbGcQ6gBdccEE48cQTSx0cee6550r1NwR9OnTo4LdRBr///ntcN3GttdbK6O33xRdfhD//+c/hwQcfjGuiSZIkSZnGwJkkSZIkSZIkSZLkGmeSJEmSJEmSJEnSCtX//29JkiRJkiRJkiSpSjNwJkmSJEmSJEmSJBk4kyRJkiRJkiRJklYw40ySJEmSJEmSJEkycCZJkiRJkiRJkiStYMaZJEmSJEmSJEmSZOBMkiRJkiRJkiRJWsGMM0mSJEmSJEmSJMnAmSRJkiRJkiRJkrSCGWeSJEmSJEmSJEmSgTNJkiRJkiRJkiRpBTPOJEmSJEmSJEmSJANnkiRJkiRJkiRJ0gpmnEmSJEmSJEmSJEkGziRJkiRJkiRJkqQVzDiTJEmSJEmSJEmSDJxJkiRJkiRJkiRJK5hxJkmSJEmSJEmSJBk4kyRJkiRJkiRJklYw40ySJEmSJEmSJEkycCZJkiRJkiRJkiStYMaZJEmSJEmSJEmSZOBMkiRJkiRJkiRJWsGMM0mSJEmSJEmSJMnAmSRJkiRJkiRJkrSCGWeSJEmSJEmSJEmSgTNJkiRJkiRJkiRpBTPOJEmSJEmSJEmSJANnkiRJkiRJkiRJ0gpmnEnKan/88UeYOXNm/r+fffbZsM0224R//etflfaac+fODfPnz6+055ckSVXDtGnT1nhfKRPfoyRJUmVyHEdSaRk4k5S1vvvuu3D44YeHsWPH5t/Wvn37cNNNN4VWrVpVymu++eaboXPnzmHWrFmV8vySJKlquO+++8JJJ520xvtKpXH11VeHSy65pMLflyRJ0priOI6ksqhZpr+SpAwwY8aM8M033xS4rVmzZvGnsnzyySfh119/rbTnlyRJVcM777wTli1btsb7SqXx1ltvhQ033LBC35MkSdKa5DiOpLIw40ySJEmSJEmSJEkycCZVDazHdcUVV4R99903bLfddvH3ddddF37++ef8x1B6sF+/fmH33XePjznssMPC8OHDCzxPsn7Yp59+Gv7yl7+Edu3ahT/96U+hb9++YcGCBeHdd98NxxxzTNhxxx1jOcOXXnqpwN/zmNtvvz0ceuih8TH8HHHEEeHpp58u8LiTTz45/rz33nvhuOOOCzvssEPYY489wg033BAWL16c/166d+8e//+aa66J76u4Nc6WLl0ahg4dGg4++OD4XPvtt1+4+eab4/spDT7n3XffHf//kEMOie9x5MiR8fVGjx690uOTbbRkyZL89zVx4sRwwQUXhJ133jnstttu4fLLL4+1tlPl5eWFxx57LG6n7bffPn52vr8ff/yxVO9XkiSV3Oeffx7OPvvs0KFDh9i/4Tz//vvvF9tXol/x4IMPFsgaGz9+fDzfUxJo4MCBoWPHjrHvQX+G+xL0RXhuzu08/q677sq/jz7MCSecEHbaaafQtm3b0KtXr/jeKqqvVFI8nlKPH3/8cfx/nu/444+P24d10wr3NXkPV111Vf7769atW8xYo6/HfWyvESNGrPQ6JemDSpKkzDdq1KjYV6D/wjl9//33D4MHD45jIqC/Q5+CjPjzzjsvjpfwWP6f21K9/vrroWvXrvF+xk/oV7z22mulfk+O40gqKzPOpCqgT58+4eWXX45rXPTv3z8ceOCBMeBD5wRz5swJxx57bFz/gk4OgxfNmzePwbUBAwas9Hz83cKFC8Oll14aB2mee+65cO6558aAEANE3M4gEr+nTJmS/3dnnXVWeOSRR8Kee+4ZrrzyynDOOefE4BWDLK+++mqB16CsEPcTOOKx2267bXj88cfDnXfemb+WGc+Ho48+Oq5rVpzzzz8/DBkyJGy11Vax08Rg1bBhw+J2KQ0Gpth2YL0PXp8AYa1ateL2TbVo0aLwxhtvhE6dOoXatWvn337RRReF6dOnx9cmMPb888/HwaWkIwm2x6BBg0KbNm1iYK1Lly7h73//e3z91GCnJEmqGB9++GEckGFyEMGmCy+8MPzyyy+hR48e4d///nf4/vvv4+QgzsdHHnlk7Cttttlm4ZZbbonn9sKuvfba+HdnnHFG7CPRr+H/k/M45/fNN9881K9fP/Zhkv4F/QIeV61atfi8/P+3334b3xvvsbL6SkXh8Q0bNox9Qv6f56MvyXZ5++23CzyWgSz6MgTJEvR3CETyvugTbrDBBjGAl0xCKksfVJIkZSaCYvRHNt5443jeZ8yE/2cMiInLqehr0dfhMfRRCJKljs/Qh+rdu3dYa6214oTkiy++OI5BMRbFhO3ScBxHUpnlScppP/30U97WW2+dd+211xa4/bbbbsvr0qVL3m+//ZbXt2/fvLZt2+ZNnz69wGNuuOGG+Ldffvll/Pdf//rX+O+ePXvmP2bp0qV5u+++e7x99OjR+be/9dZb8bbHH388/vvjjz+O/37kkUcKvMaUKVPi7VdeeWX+bSeddFK87eWXX86/bdmyZXn7779/XseOHfNve++99+Ljnnrqqfzbkvf45ptvxn/zm38PGTKkwOvecccd8XbeV2nceeed8e+++uqr/NvOO++8vB122CFv/vz5+be99NJL8XHvvPNOgfd12GGH5S1atCj/cbx3bn/00Ufjv99///3472HDhhV43c8++yyvdevWeYMGDSrV+5UkSavXtWvXvF122SVv1qxZ+bf9/PPPee3bt8/r1atX3kUXXRTPzx988EGBv7vmmmvi7WPGjCnQN+F8//vvv+c/LukHjBw5skB/hz5UYt68ebE/dtZZZxV4DW7fd999Y7+tIvpKpcHrsm0Sc+fOzdt2223zLr744gKP69GjR94+++yTt3z58gLv75577sl/zB9//JF3/PHH52233XZ5P/74Y7ytpH1QSZKUuejztGvXLu/MM88scDvn/r322iuvU6dOBcZTrr766gKPu+yyy+Lt33zzTYH+FeNZqX0QnqfwWElJOI4jqSzMOJNy3DrrrBN/mCFNiZ3ffvst3s5sHv5dr169MGbMmJj6Xrdu3Vg2MPkhWwrMAk6V3I4aNWrEmcH8JpMr0axZs/yZxKBED7OGTjzxxAIlCSmjCGYPpWJmUTL7GtWrV48p/aUtV/jPf/4z/j7llFMK3H7aaaeFF154odRli4rC7GrKIjFLKkGZysaNG8dyRoVft06dOvn/pvRAgwYNYnYakpKPbMvU74KZWmTMJZ9HkiRVjJ9++imWI6QMc6NGjfJvX2+99WLJwOuvvz6ep3fZZZdYwjEVGV8oXDqIvhJ9mQRZ5Kn9oqK88847seThQQcdVKAP8Pvvv4e99947lmukrGFF95VKgww0KgfQ50my5XmPlIykxCKzxxP0d+j3JGrWrBn7Y3yecePGheXLl5e6DypJkjIPfRKy0cnEL9zHYryj8DIZ9LlStW7dOv5O+jBNmjSJv+mDffbZZ/l9EMZLTj311Ap5z47jSFqdmqt9hKSsRhlBOhuskUX5G8oAsrbYAQccEFPiKak4b968OIDBmltFoTxRqg033LDAvxkIYXCJ36mDN2BQJLUz9cwzz8TBlWnTpoWpU6fmB8xSHwdKF6UOOCWfpfDjVoe1Oeiorb/++is9f6tWrUJFYDCLz09wks7Xr7/+GrcnJRiT7ZDYcsstC/ybbbbpppvG9wm2CVIHwlIV3iaSJKl8knMwpRcLY9IKgzj0VyitWNhGG20U+xnJcyQK9zuS8/eq+jFJH+Cyyy4r9jH0yZiYU5F9pdKiVCWBRIJaBPn+8Y9/xIlQqWUaQf9m7bXXLnBbso3ZXpStLG0fVJIkZSb6IKxtyqQYykkz5pOs5154DInyzYX/Fsm6sSeddFKcUMQYCz/0t/baa6/YByk8ObmsHMeRtDoGzqQqgNk8yexgFpynA8ICrdSaJussyXAi0FOU1NnXILussNQZxkWhw8TaFQyAMDjCWminn356nLm9zz77rPT4wgGnsqLjtbr3Vl508hg4YlsSNGO9tj/++GOlAaTiAl+8x2SbMtjFmmj3339/pb5nSZK0QhJoKq6/QIZ86u+i/r7w+b0s/ZjkfVx99dWhZcuWRT4mNXhXUX2l0qLPSNCOgSz6P2TZMxmJIGOq1AlVhT8j9yWDY6Xpg0qSpMxDH4m15QmaMVGb9VeZqE1WOeu+sl5rqtWN0VAZ6bHHHotrzzKORUDuueeeC3/961/jOme8Vnk5jiNpdQycSTlu0aJFYdKkSaFp06bhqKOOij8MWgwbNiwu9P7iiy/G2cCUzdl9991XCnZ98MEHoUWLFuV+H0899VScST106NACgbLUkkOVYZNNNomdLGY1k9qfoFQSi84TzCtulnNpECQbOXJkePPNN2PHjsGjpNxAKmZdpd5OgI1Z10npp+T9kplWeLCI2d1ktkmSpIpDHyk5RxdGf2nGjBmxlCCzpwubPXt2LK+YlBQqD/oAWHfddVfqk3300UfxdVLLPadLMtBE4OyHH36I7+3iiy9e6XH0b1InByEZOKNvSVbemuiDSpKkysWyHATNevTosVLmfFlKSE+fPj32sdq1axeDcCw1MnPmzFimkQngBM8qYoK04ziSVsU1zqQcR2CK4NBDDz1UYIYynQ8wmEGKOlloDHykuvPOO0Pv3r3DV199Ve738csvv8TfW2yxRYHbH3300fg7mXVcGqlZWsVJgnQjRowocDuzlV555ZVSD0Als7sLzzqnQ0dJIp6TUpSHH354kX//5JNPFni/BNsYCOvcuXP89/777x9/33vvvQX+ju+GdVSYdSVJkioOE1W23XbbWHKQiTYJ1oWl/0Swh/7E+++/HweGUiUZ4qnrvJamT5HaJ9hjjz1iv+Thhx+OwaTUPhT9MUpuF5X1X96+UmneY4JSSZSvvPnmm+O/Wd+sMMowJpUNQDlH+jGsvUvlAbLO1kQfVJIkVa5kvKfw0hSUdaYflaxtX1K33357XCeV4FmCdd/ps9E3KW3QzHEcSWVhxpmU41hLglnBTzzxRFyQdaeddoqdGgI4ZGAx0MF6Z+PHj4+zd7p16xb/huAPM4kZKKLMY3nxPLwHgj/HHXdc7OiQQcUCspQ3KrxYbEkkGWQvv/xynP3cpUuXlR6z7777xp877rgjfP311zHA9Z///Cc8/fTT4dBDD42lA0ojWbOEGegMkiWBLj4PwbL77rsv//+L8vHHH4dTTjklBsr++9//xsBZ+/bt4wAUGEDq1KlTDPQxo4o63iyoy/fFGioXXHBBqbeTJElatcsvvzwO0BxzzDFxwhEBrFGjRsXgz1/+8pd4DqZvRJnpE044IU6WoQ9Dljl9gaQ/UNo+BYE6gnP0BShtxGvdcMMN8X1QJYDA1//93//FgaMhQ4YUWf6wtH2l0jwH75H+yvDhw+OaIsmAGO+XTD3KNO666675666l4nVYZ5d+F5ljPJYAGbcRPAOZapXdB5UkSZWrbdu2sa/EhBr6LKxhxtjH888/H5eiYLJNcSWvi0KfjCUwWOvs2GOPze+HMYnprLPOKvX7cxxHUlmYcSZVAaxnRudiwoQJsTwhM5np2BCcYZHVZs2axcGhAw88MLzwwgvxMV988UWsG03AqSLW0GBm8aBBg+KsZUpEEmBKSkYSgPrwww9jWcnSIHuNNTEoRTlw4MAiF5AniMWs5XPPPTcO1vA4ZjaT2n/jjTeW+nMQbKOcECUub7nllgL3JcEvgnNJ2afCrrvuulivm21A4JBSBgyYpW7j2267LQ6cUZ6AbUaQj0Epvq/UtU0kSVLFoGQywSHWFqOPcvfdd8dZzZx727RpEwNlzzzzTJzcQtY6fQhKO/bt2zfcddddZSoX1LNnz3heZ1Y1a3age/fu8bXpK/C899xzTxx8otQ1a9aWVkn6SqtCX5DgG/0RSjAlUicJFTdZiPLSfJZx48bFfg/lqfk3A2CJNdEHlSRJlYu+ygMPPBD7HQ8++GAcK/nyyy/DlVdeGS699NKYSc94VEltt912cayI/hfjV0y6YSIPz0fZxtJyHEdSWVTLK03IX5JULNZwY0CNQZ+uXbsWuI9SRZRYohNJFpkkSVI2Y6IPJbfJvEsyyBIE68j05z5JkqRM5TiOpOI4hU+SKgiz0uvWrRsOPvhgt6kkScpZlFwi+57S04WDZpIkSdnCcRxJxXGNM0lVGmurMfhTEuuuu25cH6Sw/v37hxkzZoS33norll1yAEmSJGWiuXPnhmXLlq32caytlqwHkmry5Mnh/vvvj+UUWcOEktOSJElrkuM4ktYEA2eSqrRHHnkkrrdREo8//njo0KHDSrf//PPPsV73EUccEXr37l0J71KSJKn8/vznP4fvvvtutY/bZJNN4lqshTE56L333ouBNdZM22abbfxaJEnSGuU4jqQ1wTXOJFVp06dPjz8lse2228asM0mSpGzERJ8lS5as9nG1a9cO7dq1WyPvSZIkqTQcx5G0Jhg4kyRJkiRJkiRJkkII1d0KkiRJkiRJkiRJkoEzSZIkSZIkSZIkKaoZsqwmP6pXN1FOkiSt3vLly+PvXFqrx/6QJEkqDftDkiSpqlteyvEhI1Aq0HiSBiRlG9uvspVtV7nCtuy2sm2ln/uh28p2lX7uh7K9yeOPMoHnI9keqlDGWZJptvPOO6f7reSkSZMmxd+tWrVK91uRSs32q2xl261cEydODLkmU/tDtmW3lW0r/dwP3Va2q/TLxP3Q/lDuysT2pvSxPcj2II8PFdcfMuNMkiRJkiRJkiRJMnAmSZIkSZIkSZIkrWDGmSRJkiRJkiRJkmTgTJIkSZIkSZIkSVrBjDNJkiRJkiRJkiQphFDTrSBJynR5eXnhxx9/DIsXLw7Lli0LuWTJkiXx97fffpvut5JVatSoEerUqRM23HDDUK1atXS/nYy0pvcb23Jmb6vq1auHtdZaKzRo0CDUq1dvjb2uJEmSJFWUXB4fqky5fL1eo5LGhwycSZIyvlP03XffhXnz5oVatWrFE2IuWWedddL9FrLS77//HubPnx87f5tssonBswzYb2zLmb2tli5dGhYtWhR++eWXUL9+/dC0adMYTJMkSZKkbJDr40OVKZev13+vpPEhA2eSpIzGTCI6RY0aNQobbLBByDUMZGPttddO91vJOj/99FOYPXt2bCMbbbRRut9OqOr7jW0587fV8uXLY9tg3yGAtv7666/R15ckSZKkssr18aHKlOvX6z9VwviQgTPla9y4cahdu7ZbRFJGIf2emUR2ilQYbYLBf9qI3G+0emSYcRHx22+/xRl5Bs4klYTXiVLmcv+UVJU4PqQ1OT5k4Ez5CJrVqRHC4ulfZsVWqdWoRaheu26634akSkbNatPvVRzahnXN3W9UcpStqFmzZsw+k6Q1fZ3oNZyUe+M47tdSZmrevHn+ula5wvEhrcnxIQNnKuD32VPD949fmRVbpWn3AaFOs9bpfhuSJEmSlNMq6jrRazgp98Zx3K+lohG0mjNnTto2D5UmrCwmlZ2BM0mSJEmSJEmSKghBs+HDh6dte5544omWZpfKwcCZJEmSyo21CCVJkiRJkrKdgTNJUtZKd+mDiih/MGvWrHDVVVeFjz/+OD7PqFGjwmabbbbKvzn55JPD0qVLw4gRI+K/99tvv9C2bdtwyy23FHjcwIEDw1prrRUuueSS+G9mu91///1xbaPu3buHM888s8DjzzvvvFgH/dJLLy1w+9tvvx1uvfXWMHLkyPh8ym65st/QTidOnOh+I0mVjOP1H25lSZKU4dJ9rZvJ17k333xzHM/p169f/LfjQ6tn4Ez5vCCSlG3SXfogKX+w6aablvnvef8TJkwIN954Y2jSpEm5nivV+++/H/7+97+H0aNHx39Pnjw5DBgwIFx++eVh3XXXDVdeeWVo06ZN2HPPPeP9dMw++OCDcMMNN6z0XHvssUd44oknwr333hsuuOCCCnl/Sp9c2G8effTR2F7dbySp8geQmjZt6maWJEkZL93Xupl8ncvY0Isvvhj/7fhQyRg4y/FId2l4QSRJa94vv/wS644fccQRFfq8ZJuddNJJoV69evHf7733Xthyyy3jbCT84x//CO+8805+4Oymm24KvXr1ikG1opxzzjnhhBNOCMcff3xo3Lhxhb5XqSz7zQYbbOB+I0lrYACpT58+bmdJkqQsvs5lLKdu3brx344PlYyBsxyPdJeGF0SStGaRQv/dd9/F/99mm21Cly5dwnPPPRczw7p27Zr/uIsvvjh8+OGH4Y033ijR87755pth0qRJMUMsUa1atQIlA0jRp2QjXnvttTBz5sz8oFpRdthhh7DJJpuERx55JD+1X0oH9xtJkiRJUi6p7OvcIUOG5N/m+FDJVC/h4yRJUgW7++67Q8eOHUPDhg3j+mFkdVWEF154Iey0004FMon5N+n4n3zySfjmm29iqn67du3CsmXL4vpllGBcXS3uzp07h7/97W8hLy+vQt6nVNb9Zu+9946Zmu43kiRJkqRsV9nXuRtvvHH+bY4PlYyBM0mS0oQ1xgiakf1Fx6V69Yo5LZN2v912262UMXbWWWfFmtuHH354OOyww0KnTp3CM888E2rWrBmOPPLImE126KGHhlNOOSXOSCps++23Dz/99FP4z3/+UyHvUyrrfsPFhPuNJEmSJCkXeJ2beSzVKElSDlm4cGEMblFWsbBzzz03nHHGGTFjrFatWvGxd911V7jhhhti+v6DDz4Yhg4dGhejJcj26quvxsclkoVpZ8yYEUsHSLnC/UaSJEmSlEu8zi0fM84kScoh8+bNi7/r1KlT5P1k6STBsGHDhoWWLVvGcgCvvPJKOPDAA2Nm2mmnnRZ+/fXX8NFHHxX427XXXrvAa0i5wv1GkiRJkpRLvM4tHwNnkiRlCBZoBeuOFZ4lVFKUfixJcGvu3LmxNOMll1wS//3jjz/m/y0lI9dZZ514WyqCaamvIWUC9xtJkiRJUi7xOjf9DJxJkpQhCFbhhx9+yL/t999/D59++mmJn4Nsso022ijMnDlztQvPduzYMWaYYcMNNwxz5szJf81ffvklbLDBBgX+ZtasWfF3UWUgpXRxv5EkSZIk5RKvc9PPNc4kScoQ6667bth5553DU089FTbffPOw3nrrhcceeyz88ccfscRiSe25555hwoQJxd4/derU8Oyzz4YXXngh/7Z99903XHHFFfFvP/zww/hedtxxxwJ/x3M2bdo0bLnllmX8hFLFc7+RJEmSJOUSr3PTz8CZJClrkVl14oknpv09VKQbb7wxXH/99eHKK68MDRo0CF27do3BNAJdJdW5c+fw3HPPxayzjTfeeKX7hwwZErp06RJatGiRf9tBBx0UPvvss3DNNdfEz3TnnXeutE7av/71r/g4ZTf3m6K530iSJElS9kr3tW4mjw9R2ahJkyYr3e/4UPEMnEmSslbt2rXDpptuGrIZnaBUm222WXj44YdXetx5552X//9PPPFEgfveeOONAv/ea6+9QuvWrcPTTz8dLrjggpWe64477iiyfvbFF18cf4rywQcfhBkzZoTTTjutBJ9KVXG/Sdbmq1GjRqhsXECkcr+RJCl96HM++uijsa/IpK0TTjghdO/ePX99FtbWvemmm8Kbb74ZFi9eHDp06BAuv/zy0Lx5c782SVKFyfYxosq8ziXYds4556z0XI4PFc81ziRJyjEMUlxyySVh5MiRYf78+RXynA888EA45ZRTQuPGjSvk+ZR7WBuPn2zlfiNJUuk9+eST4eqrrw4HHnhgGDp0aDjqqKPC4MGDw7333ps/saZnz55h/PjxsSz4wIEDw7Rp02JgraL6qZIkadXXuc8884zjQ6Vk4EySpBy0++67h4MPPjjcc8895X6ucePGhdmzZ4fzzz+/Qt6blKncbyRJKrnly5fHYNnhhx8eLrzwwrDbbruFs88+OxxxxBH5M+BfeeWV8Pnnn8dA2mGHHRb7p2SnkYU2YsQIN7ckSWvgOrdTp05xQnR5jatC40OWapQkKUddddVVFfI8e+65Z/yRqgL3G0mSSj6LfdiwYaFu3boFbl9rrbXys9AZYGvWrFksE5Vo1KhRaNeuXRg7dmzo1auXm1uSpErWt2/fCnmePavQ+JCBM0mSJEmSJJU6cLblllvG/8/Lywu//vprGDNmTHj++edjKUZMmTIltGzZcqW/bdGiRcxGkyRJykQGziRJkiRJklRmH3zwQTj55JPj/2+77bahR48e8f/nzZsXNt1005UeX69evXKvtUKpyEmTJoWqjG2bCWuwLFy4MK5dp/RatGhR/F3V94tM0Lx585AJcu04uWTJkrDOOuvkt3WVHBNckMvbbtmyZbFvUVybZ3+oXr165QfO2rdvH3777beVbn/rrbfCRhttFKZPnx5uvPHG8P7778fb99lnn5gSuMEGG5T1JSVJkiRJkpRhyCBjXbMffvgh3H333eGYY44JzzzzTP5AXXEZa5IkSZmoTIGzGTNmxKDZlVdeGbbffvsC96233npxRtEpp5wS6tevHwYOHBgWLFgQbrnllli7etSoUaFGjRoV9f4lSZIkSZKURo0bN44/2HHHHUOnTp3i+A/jQowJFcaMcO4rD2aNt2rVKlRlZHplAta5q+rfRSZIsiz8LjLD3Llz0/0Wcu44+e2338bfa6+9drrfStZJMs1yedvVqFEjrLvuumGzzTYr8v6JEydWfuAsORAfdNBBcVHXwlgcds6cOWHkyJEx+wxbb7116NKlSxg9enQ45JBDyvKykiRJkiRJygBMmn7jjTdC27ZtQ7NmzQpkn1FKa+bMmXF9s08++WSlv6Ws3xZbbLGG37EkSVLJlKkU8pdffhlLLhYVNMO4ceNixykJmqFNmzax8zR27NiyvKQkSZIkSZIyBKUWr7jiivDII48UuP3DDz+MGWWtW7cOHTt2jBkCkydPzr9/9uzZYcKECfE+SZKkTFSzrIEzUurPPvvsMH78+FizmjXM+vXrF4NpU6ZMiWn5hRE44z5JkiRJkiRlL7LKevToER588MHQoEGDsOuuu4avv/46rnHG5Omjjz46BteGDh0al+646KKLQp06dcKdd94Z1l9//dCtW7d0fwRJylksp1S7du10vw0pa5W5VOPPP/8cunbtGk4//fTw1VdfhbvuuiucfPLJ4dlnn43p+nSgCqtXr15Mxy+P5cuX55eKzHTNmzdP91uoEvW8y9umlFu1erPl+KCSW7JkSTynJN9xYq211sqYBcWZQPLHH3+U+W9R+PNlMt5zpmz7ZcuWxRnNxe379Buo667KV6tWLTdzluw3kiRVlD59+sS1zUaMGBGX7WBtkUMPPTTengzYcvvAgQPD9ddfH8+F7du3jxOvCbZJkipHHDNZuiQs/n5q2jZxrUYtQvXaddP2+qp8eTl8nVumwNlNN90UBzGTxQX/9Kc/ha222iqccMIJ4bnnnssfBCxKrm5ISdKaxfmETuDvs9PXCUw6gqFm+mZxfffdd3Fw4uqrr46zeivbM888E6ZOnRr+8pe/FLid4NXxxx8fbr/99rDlllvGf19zzTXhvffeC5tuumks47P99tvnP561UI866qjw6KOPxj5EKgZSKO3TvXv3Sv88VdHyJQsrdb/5IwsuoGbMmBH233//MGDAgDgRrLKx7u8333wT+vbtW+B29hP2g3vvvTeuB8y/L7/88vD222/HCVjXXntt2GGHHQqUturcuXMcnNxmm20KPBf75Lbbbhtn/kuStKYwQenEE0+MP8UhsHbHHXf4pUjSGsZ13/ePX5m27d60+4BQp1nrnLnWXR2vc3PrOrdMgTMCZYW1a9culm9kxje/FyxYsNJj2EjcV95OWRKwywZz585N91vIaXXr1s2q9qDKk2Sb2B5yD2siYO21117pPmZOpbMTmNoRLOr9lUSSaVbWv6fcTZLtU9bnKA1K8ey+++4rvRYziPfcc8/84BgleMhIHzJkSBgzZky47LLLwujRo/Ozkh566KFw0EEHFegspQbODj/88BjYIAi3KjVq1IgzmzfbbLMi7584cWI5Pm1uSvfFUzouoNLtnnvuiftNYcy+Z30XLiaSx7EGTLLfXHDBBQX2G0pfsd8UvpjApZdeGvebvfbaa7X7jSRJkiTlmnRf63qdm1vXuaWuXUSJxlGjRsW61YVLIVGmqmHDhqFly5ZFls/jti222KJ871iSJGUU1j59/vnnw5lnnpl/27vvvhuOO+64sPfee4dLLrkkfP/99zFTDax3+re//S307t272FnJZNGR4S7l8oQP9xtJkiRJUq74MofGh6qXpT4qpZcefvjhAre/8cYbYfHixaFDhw4xojhhwoTw448/5t//xRdfxA3CfZIk6X+D5z179oxrPey0005xkfRx48bF+1g/lBk3S5cuzd9c/D+3cV8qUtvPOuusmMHFTBxKJqb+3fTp02PHhUXbeUyXLl3Ciy++WOA5fvjhh5gGz7l8xx13DCeddFL46KOP8u/fb7/9wqxZs2JZZt4D5e5w//33x/e/8cYbFyilmaxrQd8hWY8Mt956a/ycqY8v7Igjjghvvvmm6xaqSPQrWVs3m/cb+tLuN5IkSZKkXLnOvT+HxodKHThjbbPTTjst/PWvfw0333xzjBiyPgklmPbZZ58YGOPDUkLv1FNPDa+88kp+lLFNmzaxXqUkSVpRwvjss88O6623XrjttttiZ4fSi3Rw6MiUBqnvPA+///znP4cHHnggDBo0KD8rnPPwwoULw4033hiGDh0aNt988zjT54MPPsjPKGeNMjpC1J4mhZ6ODeuMffbZZ/np8+uvv36cJcSaTY0aNYqlmV9//fVwyCGHFHg/dPLoA1CymD7DBhtsEDPSmVjz73//u8Dso6LsvPPOoUmTJit13iT2m9NPPz1WOcjW/Ybn/Oc//+l+I0mSJEmq9OvcW265pdKvcxfk2PhQmdY4u/DCC+PGYKM88cQT8QslWHbeeefF+/k3t1PPkkXQ2bBEN/n/mjXL9JKSJOUcUtLpkLCYerL+0XbbbRdn6CxZsqRUz7XbbrvFTg9Ya4xO1/Dhw8O5554bO0a8Fp0RJrmAGUAbbbRRXCMMTIIhU/zvf/97XKwVPJYFXem0kR3DBBhmB9E5ouMDJtBQqjlZ2yxx/vnnx7rVvC9eh8k29Af4fcYZZ8T3RCo+da732GOPWLc6Wa8twbZ47733yrx9lZtoy3S46bQn6+5m237z4Ycfxhl/7jeSJEmSpDVxnctYDOMyjg+VTJmiWAwW8CXyUxzWMitczlGSJP3PVlttFWfaEEAiI5sAEp2afv36lXozFZ7R06lTp/DYY4+FiRMnxhR6FmW9+uqrY5o/2eHJhJYEASoe07Rp0wIp/MweIjjw+++/5y/cmipJx+fvUhEkYBINs5jIQserr74a0/0pPcBspurVq4d77703vi9mU3Fbqk022SR/xpOUut9suOGGMVuTBYTdb9xvJEmSJCmbrYnr3E8++SSOPTk+VDKmf0mSlCYElIYNGxYeeuihMGbMmDBq1KiYmUInqX///qV6LjpYhQNX+O2332I96UceeSTOVCJ4xcKrTIKhE8brEPQi8421SLfddtsin5/7WZS1sHnz5sXfa6+9drGfEQTjqF1NFhoBs9deey089dRTcaINGXeUDSgcOOM5k+eXUtsUbYf2nK37DTP+kjZeFPcbSZIkSao6cuE6d16OjQ8ZOJMkKY0o73bdddfFdPnPP/881n2mE9OgQYOYjQZKxiXI4CrKr7/+WuDflI9L7SBRXu6qq66KP//9739j3en77rsvXHPNNbHedf369UO7du2Knc1EGeZV3U4HjNcoDp0+MtaOPPLI8NNPP8WFYKm5jXXXXTf//abiOYt7XVVtLVq0iGuRsW9k436TtH33G0mSJElSLlznNsyx8aHqa/TVJElSvn/84x9h3333jZ0CZtmw3hGzaphl8/3334d11lknPo7yhgnWRirKm2++WeDfL7/8cgzGsaYSf8MaaqTlJyUAWGB21113ja+DXXbZJXzzzTdhs802i+8j+WEG0pNPPhlnOiFZ2ym1nCJmzpxZ7DfLArEsSnvxxRfHz0lnh+dJOkOzZ89eaUZU8rkLl4CU2G9ou3PmzMna/SZp1+43kiSpPOi3SJKy35q4zm3btq3jQ6VgxpkkKWvVatQiNO0+IO3voazotJCiTno6A/LMImINMmb89OzZM+y4445xthHp8r169YqD7ASgkvT2VMwQuummm2J6/VtvvRVGjhwZzjvvvDhbp02bNqFOnTqx08VtjRo1Ch9//HF83Lnnnhv//tRTTw0vvPBCOOWUU+JrE9ziOVmnjDXYSOcH7/GLL74I77//fthhhx3iorWkzNNh499FoRxly5Yt43ppqFmzZnyfrGt2+umnxxlUBxxwQIG/ycvLi895wgknlHn7Knf3G2akUfudxY2zcb/hM/Dc7jeSJGW3JUuWxEHOdHGSmSRlzrXuqq5zyRRLXU++MAJl3M/YEOMkXENy7cl17mmnnRbvB1liPXr0iAE0yi1yncv1MevS//HHH/ExXJPeeOONcZ20t99+O17ncg1LVle9evUcHyohA2eSpKxVvXbdUKdZ65CtqAlNR4dBfTo/pNkTYLrhhhvCUUcdFR8zePDgmDJPgICMF+67+uqrV3ouUuhHjx4dB+xJie/bt28c1AcD9ASnhgwZEjtPpO2TKdanT5/YIUveC50p6kxfe+21YfHixbGMJMGH1OAVwQGCEvwdATECZ/vss0+c0ZS8XirS7nntRx99tMDtlAC49NJLw4UXXhiDFgQZUpHl88svv8R63sqO/YbOelHZVRWNtkqbuv3227N2v6FW/F577eV+I0lSliNoNnz48LS9Pv0SSVLmjxERFGN8pDhcR998883h4YcfjteTixYtiteWjJtQCQWXX355vHYlCEbVk7/85S/xWpRrYp6btcdwzjnnxGtNqqBQnpF/n3nmmfE+x4dKrloeU7qzxMSJE+PvnXfeOWSLGTNmpLUTVdoOV97sr8P3j18ZsgEzCDL1YKg1b9KkSfF3q1at3Pw55ttvv42/6RTkIjpDq1o8NRt8+eWX4eijj44BCDp2FYGABp0+Aoursrr2kY19h9UpyWdKx36TC215TW4rzlsE1zJxv8k0nuPdVrar9F6PVuR1YlW4hsvEY1ZV7Q9VhTGfTBjHqQr7dbbIxONPVebxoeKPD9l2HZOKjLBVBc4qG2ukEZyr7ImuX+bQ+JBrnEmSpHJp3bp1OOyww8JDDz1UIVuS+t2sEUU2mpSrGNBwv5EkSZIk5YrWOTQ+ZOBMkiSVGyXzqL89efLkcj/XLbfcEut6b7PNNn4zymnuN5IkSZKkXHJVjowPucaZJEkqNxaufeONNypkS7KmVLZ4+umn4/ptlOHYeOONY+m97t27h2rVqsX7586dG2666aZYX5z1rzp06BDrkldUyQJlt6q630iSJEmSclODHLnONXAmSZJUBiy0O2DAgLjI7q677ho++uijMHjw4DB//vy4WO+yZctCz549Yy3uK664ItYSv+uuu2Jg7aWXXgrrrLOO212SJEmSJCnDGDiTJEkqpeXLl4ehQ4eGww8/PL/W9m677RamTp0annjiiRg4e+WVV8Lnn38enn/++VjnG+3atQsHHHBAGDFiROjVq5fbXZIkSZIkKcO4xpkkKaORpUPmjlQU2gZtZE2jFOOwYcNWWqB2rbXWCr///nv8/3HjxoVmzZrlB83QqFGjGDwbO3Zspb4/9xsVJy8vLyxdujRUr+5lgCRJkqTs4XWu1uT4kFfMkqSMVqdOnRiI+Omnn9L9VpRhaBO0DdpIOgJnW265ZWjatGkMRPzyyy9h1KhRMbusW7du8TFTpkwJLVu2XOlvW7RoEe+rTO43Ki5Tcs6cOeGPP/6wVKgkSZKkrOJ1rtbk+JClGiVJGW3DDTcMS5YsCbNnz47BiXRkF1WmJJsu1z7XmthudIrq168f20g6ffDBB+Hkk0+O/7/tttuGHj16xP+fN29e2HTTTVd6fL169eI6aOUNgEyaNKnY+wnmsX2+++678OOPP4aaNSu/y8drJkFFZd62Yp8hYEa2Ga87a9aseFzNBosWLYq/V9Xm5bayXa2sefPmGblrLFy4MEybNi3kqkw8ZtFvMNNYkpTtsnl8iHMx7z2d/ROuA3Pxen1ZJY0PGTiTJGU0TuqbbLJJHPxfvHhxzpVtTAIo6667brrfSlapVatWaNCgQewUpbvjRwYZ65r98MMP4e677w7HHHNMeOaZZ/KDI0Wp7PfM87ONCJKw36wJvBbWRJAu26VrW9EueM1susCUJEmSpGwfHyJwVdmVZ1Zliy22CLVr187J6/ValTQ+lHtbSpKUczjxbbTRRiEXJbORN9tss3S/FZVR48aN4w923HHH0KlTp1i2kdlOCxYsKDJYyn3lwazxVq1aZWRbzrT3lYncVm4v21b6VZX9cO7cuSHT1K1bN6e3eya2rYkTJ6b7LUiSVKXHh2bMmBG+/PLLtL1+27Ztw/rrrx/7YSoZ1ziTJEkqJcowvvDCC2H69OkrZZ+ts846YebMmXF9s6lTp670t5SnYraXJElSLmRRP/roo+HQQw8NO+20UzjggAPCoEGDCpSlPuecc8I222yz0s/LL7+c1vcuSZJUHDPOJEmSyjDL7Yorrghdu3YN/fv3z7/9ww8/jANFrVu3jjO5XnzxxTB58uQ4OARqsU+YMCGce+65bnNJkpT1hgwZEh5//PFwxhlnhPbt24evv/463HnnneGjjz4KI0aMiFnyZOEdffTR4bjjjltpwpEkSVImMnAmSZJUSmSV9ejRIzz44IOxlvauu+4aB4pY46xNmzZxcIjg2tChQ0OvXr3CRRddFOrUqRMHkiiP0K1bN7e5JEnKaqzXQtCMPlHv3r3jbbvttlto2LBhuPDCC8P48ePDtttuG7777ruwxx57xIw0SZKkbGDgTJIkqQz69OkT1zZjNvWwYcPCuuuuG8sUcTuL7oLbBw4cGK6//voYSGMmdr9+/WKwTZIkKZv99ttvMfu+c+fOBW7ffPPN8zPtyTgD2fiSJEnZwsCZJElSGTAQdOKJJ8af4hBYu+OOO9y+kiQp59DPSS1ZnXjttdfi76233jpmndFnIjON23/99dewww47hMsuuyzsuOOOaXjXkiRJq2fgTJIkSZIkSeU2ceLE8MADD4R99903Zpk99thjYfny5THz/rbbbgs///xzLGXdvXv3MHLkyNCqVasyvxbPy/pp6dK8efO0vXamWbhwYZg2bVq630aVR/lUpHO/0AoeH/7H40PmtId0nzcz4fMnmfAlYeBMkiRJkiRJ5UJ22TnnnBM23XTTMGjQoHhbz549w1FHHRXXg02wDlqnTp3CvffeG9d/lSRJyjQGziRJkiRJklRmzz77bLj66qvDVlttFR588MHQsGHDePuWW24Zf1Kx1mvbtm3LPeudWePlyVirCHPnzk3r62eKunXrpv270P8yzfwuMoPHhxU8PmROe8iE82a6s+JLo+S5aZIkSZIkSVIK1nPt169f6NChQ3jyySfDhhtumH/fiy++GN59992VtteSJUvyg2uSJEmZxsCZJEmSJEmSSo31zCi5ePTRR8e1y+rVq1fg/ieeeCJcd911YenSpfm3zZo1K3z44Ycx0CZJkpSJLNUoSZIkSZKkUvn2229jttnmm28ejj322PDZZ58VuL958+bhvPPOC2eeeWb8fcIJJ4Rffvkl3HPPPbFc4+mnn+4WlyRJGcnAmSRJkiRJkkplzJgxMZPs66+/Dscff/xK9w8YMCB07do1rnlGsKxPnz6hZs2aoWPHjuHiiy8O6667rltckiTlZuCMjhCp959//nnsAGH69OnhxhtvDO+//3789z777BP69u0bNthgg/K/Y0mSJEmSJKVVr1694s/q7LnnnvFHkiSpSqxxxgKvLPyaat68eeGUU04JM2bMCAMHDgxXXHFFfBydqWXLlpX3/UqSJEmSJEmSJEmZlXH222+/xSyyJk2ahJkzZ+bfPmLEiDBnzpwwcuTIsNFGG8Xbtt5669ClS5cwevTocMghh1TMO5ckSZIkSZIkSVKx1ltvvVC7dm230JoInF177bWhWbNmoX379uHee+/Nv33cuHGhbdu2+UEztGnTJrRo0SKMHTvWwJkkSZIkSZIkSdIasNZaa4VqS5eExd9PTcv2rtWoRaheu27I+cDZ3//+9/DGG2+EF198MTz//PMF7psyZUro1KnTSn9D4Iz7JEmSJEmSJEmStGb8Pntq+P7xK9OyuZt2HxDqNGsdcjpwNmvWrJhtdumll8aMs8JY42ydddZZ6fZ69eqFadOmhfJavnx5mDRpUsgGzZs3T/dbyHkLFy6skHal7Ldo0aL4O1uOD1LCtlu56DdUr16uJV0lSZIkSZJUhZR6JOnyyy8P2223XejWrVuR9+fl5RX7t9WqVSvty0mSJEmSJEmSJEmZl3E2fPjw8NFHH4UXXnghLF26NH8md/Kbn/r164cFCxas9Lfz58+P95UXs8ZbtWoVssXcuXPT/RZyWt26dbOqPajyJJlmtgdlG9tu5Zo4cWIlv4IkSZIkSZKqbODslVdeiQGw/ffff6X7tt9++3DeeeeFli1bFlk6j9vatm1bvncrSZIkSZIkSZIkZULgjLXNCmeTPf300/k/TZo0CTVq1AgPPPBA+PHHH8OGG24YH/PFF1+EqVOnht69e1fsu5ckSZIkSZIkSZLSETjbfPPNV7pt7Nix8fe2224batasGdc+e/LJJ8Opp54aM9AWL14cbr311tCmTZvQuXPninrfkiRJkiRJkiRJUoWqXrFPF0LDhg3DE088ERo3bhz69u0bBg0aFHbdddfw0EMPxcCaJEmSJEmSJEmSlInKHck6//zz40+qLbbYIjz88MPlfWpJkiRJkiRJkiQpezPOJEmSJEmSJEmSpGxk4EySJEmSJEmSJEkycCZJkiRJkiRJkiStYMaZJEmSJEmSJEmSZOBMkiRJkiRJkiRJWsGMM0mSJEmSJEmSJMnAmSRJkiRJkiRJkrSCGWeSJEmSJEmSJEmSgTNJkiRJkiRJkiRpBTPOJEmSJEmSJEmSJANnkiRJkiRJKoulS5eGRx99NBx66KFhp512CgcccEAYNGhQmD9/fv5jpk+fHs4999zQvn37+HPJJZeEn376yQ0uSZIyVs10vwFJkiRJkiRlnyFDhoTHH388nHHGGTEo9vXXX4c777wzfPTRR2HEiBFhwYIF4ZRTTgn169cPAwcOjP++5ZZbQq9evcKoUaNCjRo10v0RJEmSVmLgTJIkSZIkSaWyaNGiGDTr0aNH6N27d7xtt912Cw0bNgwXXnhhGD9+fPj000/DnDlzwsiRI8NGG20UH7P11luHLl26hNGjR4dDDjnErS5JkjKOa5xJkiRJkiSpVH777bfQtWvX0Llz5wK3b7755vH37Nmzw7hx40Lbtm3zg2Zo06ZNaNGiRRg7dqxbXJIkZSQzziTlhObNm4clS5ak+21IkiRJUpXQuHHj0L9//5Vuf+211/Izy6ZMmRI6deq00mMInHGfJElSJjJwJqlYBKIoq5ENmMFYu3btdL8NSZIkSaqyJk6cGB544IGw7777htatW4d58+aFddZZZ6XH1atXL0ybNq1cr7V8+fIwadKkkM7Jm1ph4cKF5f4+VTHlU5HO/UIreHz4H48PtodMaQv0G6pXL3kBRgNnkopF0Gz48OFZsYVOPPHEsP7666f7bUiSJElSlcSaZuecc07YdNNNw6BBg+JteXl5xT6+WrVqa/DdSZIklZyBM0mSJEmSJJXZs88+G66++uqw1VZbhQcffDA0bNgw3l6/fv2wYMGClR4/f/78eF95MGu8VatWIZ3mzp2b1tfPFHXr1k37d6H/ZZr5XWQGjw8reHywPWRKWyArvjRKnpsmSZIkSZIkpbjjjjtCv379QocOHcKTTz4ZNtxww/z7WrZsWWRZJm7bYost3I6SJCkjGTiTlBPWW2891ziTJEmSpDWI9czuvffecPTRR4ehQ4fGtctSdezYMUyYMCH8+OOP+bd98cUXYerUqfE+SZKkTGSpRkk5Ya211grVli4Ji7+fGrJBrUYtQvXaddP9NiRJkiSpTL799tuYbbb55puHY489Nnz22WcF7m/evHno1q1bzEI79dRTw3nnnRcWL14cbr311tCmTZvQuXNnt7wkScpIBs4k5YzfZ08N3z9+ZcgGTbsPCHWatU7325AkSZKkMhkzZkxYunRp+Prrr8Pxxx+/0v0DBgwIXbt2DU888UQYOHBg6Nu3b6wSstdee8X/r1nTISlJkpSZ7KVIkiRJkiSpVHr16hV/Voe1zB5++GG3riRJyhqucSZJkiRJkiRJkiSZcSZJkqSK0Lhx41h+SZIkSZIkKZtZqlGSJEnl1qBBg1Bt6ZKwePqX5X6uWo1ahOq16/qtSJIkSZKkNc7AmSRJUhW2ZMmSMGfOnHI/T9OmTcPvs6eG7x+/svzP1X1AqNOsdbmfR5IkSZIkqbQMnEmSJFVhBM2GDx9e7ufp06dPhbwfSZIkSZKkdKpe1j98+umnwyGHHBJ22GGHcNBBB4XHHnss5OXl5d8/d+7c0Ldv37DbbruFnXfeOZx11llh2rRpFfW+JUmS0mrp0qXh0UcfDYceemjYaaedwgEHHBAGDRoU5s+fn/+Y6dOnh3PPPTe0b98+/lxyySXhp59+Suv7liRJkiRJUgVnnD355JNhwIAB4cwzzwy77rpr+Oijj8LgwYPjQBGDQ8uWLQs9e/YMP//8c7jiiitCjRo1wl133RW6d+8eXnrppbDOOuuU5WUlSZIyxpAhQ8Ljjz8ezjjjjBgU+/rrr8Odd94Z+0UjRowICxYsCKecckqoX79+GDhwYPz3LbfcEnr16hVGjRoV+0eSJEmSJEnK8sDZ8uXLw9ChQ8Phhx8eLrzwwngbWWVTp04NTzzxRAycvfLKK+Hzzz8Pzz//fGjdesX6FO3atYszsRlIYsBIkiQpWy1atCgGzXr06BF69+6d3x9q2LBh7B+NHz8+fPrpp7EM4siRI8NGG20UH7P11luHLl26hNGjR8fMfUmSJEmSJGV5qcZq1aqFYcOG5QfNEmuttVb4/fff4/+PGzcuNGvWLD9ohkaNGsXg2dixYyvifUuSJKXNb7/9Frp27Ro6d+5c4PbNN988/p49e3bsD7Vt2zY/aIY2bdqEFi1a2B+SJEmSJEnKlYwzAmdbbrll/H/WNPv111/DmDFjYnYZpRgxZcqU0LJly5X+loEistHKg4y3SZMmhWzQvHnzdL+FnLdw4ULXzqsktt/KZ/tVkrmEbDm3ZRv6DdWrl3lJ12I1btw49O/ff6XbX3vttfzMMvpDnTp1KrI/xH2Z0B/K5GN9Lh8j3e/dXrat9KsK+2GmHuNz+fieqW2rsvpDkiRJuapMa5wlPvjgg3DyySfH/992221juSLMmzcvbLrppis9vl69enEdNEmSpFwzceLE8MADD4R99903Zt3THypqXVf6Q7k8YChJkiRJklRlA2fMmGZdsx9++CHcfffd4ZhjjgnPPPNMzERbVcZaeTBLqlWrViFbzJ07N91vIafVrVs3q9pDtrH9Vi7br1JnI3ssq7xg1prAmmbnnHNOnDg0aNCgeFu29Icy9Vify8dI93u3l20r/arKfpiJx/hcPr5nattaU/0hSZKkXFGzvGWK+MGOO+4YyxGNGjUq1K9fPyxYsGClx5Ntxn2SJEm54tlnnw1XX3112GqrrcKDDz4YGjZsGG+3PyRJkiRJkpR9Sl3kmrJDL7zwQpg+ffpK2WeUI5o5c2Zc32zq1Kkr/S1libbYYovyvWNJkqQMcccdd4R+/fqFDh06hCeffDJsuOGG+ffRHyqqJKP9IUmSJEmSpBwKnFFa6IorrgiPPPJIgds//PDDmFHGmh4dO3YM3377bZg8eXL+/bNnzw4TJkyI90mSJGU71jO79957w9FHHx2GDh0a1y5LRZ+Hvs+PP/6Yf9sXX3wRJxfZH5IkSZIkScqRUo1klfXo0SOWImrQoEHYddddw9dffx3XOGvTpk0cPCK4xgBSr169wkUXXRTq1KkT7rzzzrD++uuHbt26Vc4nkSRJWkOYIES22eabbx6OPfbY8NlnnxW4v3nz5rHPQxbaqaeeGs4777ywePHicOutt8b+UufOnf2uJEmSJEmScmWNsz59+sS1zUaMGBGGDRsW1l133XDooYfG22vXrh0fw+0DBw4M119/fQyktW/fPpYyItgmSZKUzcaMGROWLl0aJw8df/zxK90/YMCA0LVr1/DEE0/E/lDfvn1jH2mvvfaK/1+zZrmWmZUkSZIkSVIlKdOoTfXq1cOJJ54Yf4pDYI2Z2JIkSbmGrHp+Voe1XR9++OE18p4kSZIkSZKUhjXOJEmSJEmSJEmSpFxk4EySJEmSJEmSJEkycCZJkiRJkiRJkiStYMaZJEmSJEmSJEmSZOBMkiRJkiRJ5TVr1qywyy67hHfeeafA7TfccEPYZpttVvp54IEH3OiSJCkj1Uz3G5AkSZIkSVL2mjlzZjj99NPDr7/+utJ9kyZNCh07dgznn39+gds33njjNfgOJUmSSs7AmSRJkiRJkkpt+fLl4fnnnw+DBw8u9jEEznr27Bl22mknt7AkScoKrnEmSZIkKWM1b948NG7cON1vQ5JUhMmTJ4f+/fuHo446Ktx0000r3T9jxozw22+/hVatWrn9JElS1jDjTJIkSVKFW7JkSZgzZ065n2ejjTYKtWvXrpD3JEmqWJRbHDNmTGjSpEkYP358kdlm4DEE2DgvbLXVVuHCCy8Me++9d7mz3ZLnT9fEDq2wcOHCMG3aNDdHmi1atCj+Tud+oRU8PvyPxwfbQ6a0BfoN1auXPI/MwJkkSZKkCsfg6PDhw8v9PCeeeGJYf/31K+Q9SZIq1nrrrbfK+7/88sv4e/78+WHgwIFxUsUTTzwRzjrrrDB06NCw1157+ZVIkqSMY+BMkiRJkiRJFY4SjjvssEOB7LKOHTuGI488Mtx+++3lCpwxazzdJSDnzp2b1tfPFHXr1k37d6H/ZZr5XWQGjw8reHywPWRKW5g4cWKpHu8aZ5IkSZIkSapwzZo1W6kk41prrRX22GOPuD6aJElSJjJwJkmSJCmjy4C5xpkkZafXXnst/hRGycaGDRum5T1JkiStjqUaJUmSJGUsMhOqLV0SFn8/tVzPU6tRi1C9dt0Ke1+SpNV76aWXwrvvvht23XXXsM4668TbFi5cGMaOHRs6dOjgJpQkSRnJwJkkSZKkjPb77Knh+8evLNdzNO0+INRp1rrC3pMkafXOPPPM8MYbb4SePXuGM844IyxdujQ88MADMXh2wQUXuAklSVJGslSjJEmSJEmSKlzr1q3Dk08+GerWrRv69u0bfzbYYIPw1FNPhebNm7vFJUlSRjLjTJIkSZIkSeVC6cXJkyevdPsOO+wQHnnkEbeuJEnKGgbOJEmSJEmSJCmLNW7cONSuXTvdb0OScoKBM0mSJEmSJEnKYgTN6tQIYfH0L9P2Hmo1ahGq166btteXpIpi4EySJEmSJEmSstzvs6eG7x+/Mm2v37T7gFCnWeu0vb4kVZTqFfZMkiRJkiRJkiRJUhYzcCZJkiRJkiRJkiQZOJMkSZIkSZIkSZJWMONMkiRJkiRJkiRJMnAmSZIkSZIkSZIkrWDGmSRJkiRJkiRJkmTgTJIkSZIkSZIkSSpHxtnSpUvDo48+Gg499NCw0047hQMOOCAMGjQozJ8/P/8x06dPD+eee25o3759/LnkkkvCTz/9VJaXkyRJkiRJkiRJkipdzbL80ZAhQ8Ljjz8ezjjjjBgU+/rrr8Odd94ZPvroozBixIiwYMGCcMopp4T69euHgQMHxn/fcsstoVevXmHUqFGhRo0aFf9JJEmSJEmSJEmSpDUZOFu0aFEMmvXo0SP07t073rbbbruFhg0bhgsvvDCMHz8+fPrpp2HOnDlh5MiRYaONNoqP2XrrrUOXLl3C6NGjwyGHHFKe9yxJkiRJkiRJkiSlv1Tjb7/9Frp27Ro6d+5c4PbNN988/p49e3YYN25caNu2bX7QDG3atAktWrQIY8eOrYj3LUmSJEmSJEmSJKU346xx48ahf//+K93+2muv5WeWTZkyJXTq1GmlxxA44z5JkiRJkiRJkiQpJ9Y4K2zixInhgQceCPvuu29o3bp1mDdvXlhnnXVWely9evXCtGnTyvVay5cvD5MmTQrZoHnz5ul+Czlv4cKF5W5TKprtt/LZfpWUQEa2nNuyDf2G6tVLnWAvVfo5dsmSJW5lSZIkVZjatWuHP9yekpQZgTPWNDvnnHPCpptuGgYNGhRvy8vLK/bx1apVK+9LSpIkSWscwS7W8S0vypkzsCFJkqTcUVF9xbJq2rRp2l5bknJNuQJnzz77bLj66qvDVlttFR588MHQsGHDeHv9+vXDggULVnr8/Pnz433lwazxVq1ahWwxd+7cdL+FnFa3bt2sag/ZxvZbuWy/Ss0081hWOciKlyoKAyHDhw8v9/OceOKJYf3116+Q9yRJkqTc6iuWVZ8+fdL22pKUa8pcu+iOO+4I/fr1Cx06dAhPPvlk2HDDDfPva9myZZHl87htiy22KPu7lSRJkrLceuutZ8aZJEmSJEm5lHHGemb33ntvOProo8P1118fatYs+DQdO3aMj/nxxx/zA2pffPFFmDp1aujdu3fFvHNJkiQpC6211lqh2tIlYfH3U8v9XLUatQjVa9etkPclSZIkSZLKEDj79ttvY7bZ5ptvHo499tjw2WefrbTYebdu3WIW2qmnnhrOO++8sHjx4nDrrbeGNm3ahM6dO7vdJUmSVKX9Pntq+P7xK8v9PE27Dwh1mrWukPckSZIkSZLKEDgbM2ZMWLp0afj666/D8ccfv9L9AwYMCF27dg1PPPFEGDhwYOjbt28sRbPXXnvF/y+cnSZJkiRJkiRJkiRlglJHsXr16hV/Voe1zB5++OGyvi9JkiRJkiRliVmzZoXDDz883H777WH33XfPv33u3LnhpptuCm+++WasSNShQ4dw+eWXx4pFkiRJmah6ut+AJEmSJEmSstfMmTPDaaedFn799dcCty9btiz07NkzjB8/PlxxxRWxMtG0adNC9+7dw/z589P2fiVJklbFuomSJEmSJEkqteXLl4fnn38+DB48uMj7X3nllfD555/Hx7RuvWJNznbt2oUDDjggjBgxokQVjSRJktY0M84kSZIkSZJUapMnTw79+/cPRx11VCzHWNi4ceNCs2bN8oNmaNSoUQyejR071i0uSZIykhlnkiRJkiRJKrWNN944jBkzJjRp0iSWYyxsypQpoWXLlivd3qJFi5iNVt5st0mTJoV0cY22/1m4cGEswVmV2R7+x/Zge0hle7A9ZEpboN9QvXrJ88gMnEmSJEmSJKnU1ltvvVXeP2/evLDpppuudHu9evVc40ySJGUsA2eSJEmSJEmqcHl5ecXeV61atXI9N7PGW7VqFdJp7ty5aX39TFG3bt20fxeZwPawgu3B9pDK9mB7yJS2MHHixFI93jXOJEmSJEmSVOHq168fFixYsNLt8+fPj/dJkiRlIgNnkiRJkiRJqnCsbzZ16tSVbmeNky222MItLkmSMpKBM0mSpHKaNWtW2GWXXcI777yzUrmWvn37ht122y3svPPO4ayzzqryC6dLkqSqo2PHjuHbb78NkydPzr9t9uzZYcKECfE+SZKkTGTgTJIkqRxmzpwZTjvttPDrr78WuH3ZsmWhZ8+eYfz48eGKK64IAwcOjEGz7t27x/JEkiRJue7ggw+OmWW9evUKzz//fHjllVfCqaeeGtZff/3QrVu3dL89SZKkItUs+mZJkiStyvLly+MA0ODBg4u8n4Ghzz//PD6mdevW8bZ27dqFAw44IIwYMSIOIEmSJOWyWrVqhWHDhsUJRNdff32oVq1aaN++fejXr19o0KBBut+eJElSkQycSZIklQElh/r37x9OOOGEsPvuu4czzjijwP3jxo0LzZo1yw+aoVGjRjF4NnbsWANnkiQpp3To0KFAScZE48aNwx133JGW9yRJklQWBs4kSZLKYOONNw5jxowJTZo0ieUYC5syZUpo2bLlSre3aNEiZqOVN9tt0qRJobyaN28eMtXChQszaj04t1VubK9Ma1cVbdGiRfF3RRwfcl1V2Fbuh+mRiW2LfkP16q7UIUmSVFIGziRJkspgvfXWW+X98+bNC5tuuulKt9erV881ziRJkiRJkjKUgTNJkqRKkJeXV+x9rO9RHswab9WqVagIc+fODZmobt26FfYZK4rbKvu3Vya2q4qUZLjk8mesKFVlW7kfrnmZ2LYmTpyY7rcgSZKUVczVlyRJqgT169cPCxYsWOn2+fPnx/skSZIkSZKUeQycSZIkVQLWN5s6depKt7O+0hZbbOE2lyRJkiRJykAGziRJkipBx44dw7fffhsmT56cf9vs2bPDhAkT4n2SJEmSJEnKPAbOJEmSKsHBBx8cM8t69eoVnn/++fDKK6+EU089Nay//vqhW7dubnNJkiRJkqQMVDPdb0CSJCkX1apVKwwbNiwMHDgwXH/99aFatWqhffv2oV+/fqFBgwbpfnuSJEmSJEkqgoEzSZKkcurQoUOBkoyJxo0bhzvuuMPtK0mSJEmSlCUs1ShJkiRJkiRJkiQZOJMkSZIkSZIkSZJWMONMkiRJkiRJkiRJMnAmSZIkSZIkSZIkrWDGmSRJkiRJkiRJkmTgTJIkSZIkSZIkSVrBjDNJkiRJkiRJkiSpIgJns2bNCrvsskt45513Ctw+d+7c0Ldv37DbbruFnXfeOZx11llh2rRpbnRJkiRJkiRJkiTlXuBs5syZ4bTTTgu//vprgduXLVsWevbsGcaPHx+uuOKKMHDgwBg06969e5g/f35537MkSZIkSZIkSZJU4WqW5Y+WL18enn/++TB48OAi73/llVfC559/Hh/TunXreFu7du3CAQccEEaMGBF69epVvnctSZIkSZIkSZIkZULG2eTJk0P//v3DUUcdFW666aaV7h83blxo1qxZftAMjRo1isGzsWPHlu8dS5IkSZIkSZIkSZmScbbxxhuHMWPGhCZNmsRyjIVNmTIltGzZcqXbW7RoEbPRyoNst0mTJoVs0Lx583S/hZy3cOFC186rJLbfymf7rTyNGzcOtWvXDtlg0003DYsWLcqac1u2od9QvXq5l3SVJEmSJElSFVGmwNl66623yvvnzZsXBwILq1evnmucSZIqHUGzGjVqhDlz5mT81mYyytprrx3PkdliyZIlYdasWel+G5IkScoS7du3D7/99ttKt7/11ltho402Sst7kiRJqtDA2erk5eUVe1+1atXK9dzMGm/VqlXIFnPnzk33W8hpdevWzar2kG1sv5XL9lu5ZsyYEYYPHx4yXZ8+fcJaYVmo/tPUkA1qNWoR6jZsGBo2bBiywcSJE9P9FiRJkkJV75cTNLvyyivD9ttvX6qJ2ZIkSTkTOKtfv35YsGDBSrfPnz8/3idJkv7n99lTw/ePX5kVm6Rp9wGhTrP/rWEqSZIkrUpSkvyggw4KjRo1cmNJkqSMVymLfrC+2dSpK8+cnzZtWthiiy0q4yUlSZIkSZKUYb788suwwQYbGDSTJElVO3DWsWPH8O2334bJkyfn3zZ79uwwYcKEeJ8kSZIkSZKqRuCM6kNnn312aNu2bdh5553DhRdeGMeJJEmSqkypxoMPPjgMHTo09OrVK1x00UWhTp064c477wzrr79+6NatW2W8pCRJkiRJkjKwVOPPP/8cunbtGk4//fTw1VdfhbvuuiucfPLJ4dlnnw316tUr0/MuX748vwxkOjRv3jxtr51pFi5cGKtMVWW2h/+xPdgeUtkebA+Z0hboN1SvXj29gbNatWqFYcOGhYEDB4brr78+VKtWLbRv3z7069cvNGjQoDJeUpIkSZIkSRnmpptuCuuss05o1apV/Pef/vSnsNVWW4UTTjghPPfcc+Gkk05K91uUJEmq2MBZhw4dCpRkTDRu3Djccccd5X16SZIkSZKkrMC67kwe1v8QKCusXbt2sXxjeTLGmDWeBOPSZe7cuWl9/UxRt27dtH8XmcD2sILtwfaQyvZge8iUtjBx4sRSPb5SMs4kSZIkSZKyxZIlS8KcOXPK/TxNmzYN4Y/FYfHM/5b7uWo1ahGq164bshklGl977bUYKNt8880LlEv6448/QsOGDdP6/iRJkopi4EySJEmSJFVpBM2GDx9e7ufp06dPyJs9NXz/+JXlfq6m3QeEOs1ah2y21lprhWuuuSYcddRR4YYbbsi//Y033giLFy+OVYwkSZIyjYEzSZIkSZIkVTjWNjvttNPCQw89FNZbb73QsWPHuNzHXXfdFfbZZ5/4b0mSpExj4EySJEmSJEmV4sILLwyNGjUKI0eODE888UQsz9itW7dw3nnnucUlSVJGMnAmSZIkSZKkSlGjRo3QvXv3+CNJkpQNqqf7DUiSJEmSJEmSJEmZwMCZJEmSJEmSJEmSZOBMkiRJkiRJkiRJWsGMM0mSJEmSJEmSJMnAmSRJkiRJkiRJkrSCGWeSJEmSJEmSJEmSgTNJkiRJkiRJkiRpBTPOJEmSJEmSJEmSJANnkiRJkiRJkiRJ0go1//9vSZIkSVIWa9y4cahdu3a630ZWcFtJkiRJKo6BM0mSJEnKAQ0aNAjVli4Ji6d/We7nqtWoRaheu27IVW4rSZIkScUxcCZJkiRJabRkyZIwZ86ccj9P06ZNw++zp4bvH7+y/M/VfUCo06x1yDRuK0mSJEmVzcCZJEmSJKURQbPhw4eX+3n69OkTcp3bSpIkSVJlq17pryBJkiRJkiRJkiRlAQNnkiRJkiRJkiRJkoEzSZIkSZIkSZIkaQUzziRJkiRJkiRJkiQDZ5IkSZIkSZIkSdIKZpxJkiRJkiRJkiRJBs4kSZIkSZIkSZKkFcw4kyRJkiRJkiRJkgycSZIkSZIkSZIkSWsg4+y9994Lxx13XNhpp53C3nvvHe64446wdOnSynxJSZKkjGJ/SJIkVXX2hyRJUjaptMDZxx9/HHr16hWaNGkS7rzzznDiiSeGBx98MAwePLiyXlKSJCmj2B+SJElVnf0hSZKUbWpW1hMTLNtiiy3C7bffHqpVqxb22muvUKtWrXDTTTeFnj17hsaNG1fWS0uSJGUE+0OSJKmqsz8kSZKyTaVknP3+++9h/Pjx4cADD4xBs8TBBx8cli1bFsaNG1cZLytJkpQx7A9JkqSqzv6QJEnKRpUSOJs+fXr4448/QsuWLQvcTpZZnTp1wpQpUyrjZSVJkjKG/SFJklTV2R+SJEnZqFpeXl5eRT/pRx99FI477ri4phklGlPtvvvu4YADDgjXXXddqZ93woQJIZuQbcdPJWziSrEiOzAvhCx5vyFuW95ulrzfLGP7rfQNbPut1M2bPcdfj71rRrt27cKalg39oYrcVyq0LWfgMdJtlQPbKwPbVcZuqwzdXm6r9GyvXG9XValt2R/KvWuOjLiWyND9ek2zPeRvCNuD7SF1x7A92B4yri2UtD9UKWucLV++PFSm6tUrJVGu0qSWq8x81WJDzha81ezavtknu7av7VfZ2n5tu5WlsvskudIfqrh9peLacqae491W2b29MrVdZeK2yuTt5bZKx/bK/XaVy23L/lDlSn97Tv+1RCbv12ta+reD7SGT2B48PtgeMudcUdr+UKUEzho0aBB/L1iwYKX7uK1+/fpZMztKkiSpLOwPSZKkqs7+kCRJykaVkrrVvHnzUKNGjTBt2rQCt8+aNSssXrw4bLHFFpXxspIkSRnD/pAkSarq7A9JkqRsVCmBs1q1aoVddtklvPrqqwVS4P7xj3+EmjVrhl133bUyXlaSJClj2B+SJElVnf0hSZKUjSptsbBzzjknfPHFF+H8888Pb775ZnjggQfCLbfcErp16xaaNm1aWS8rSZKUMewPSZKkqs7+kCRJyjbV8vLy8irryd94441w5513hq+++ipsuOGG4eijjw7nnntuLOMoSZJUFdgfkiRJVZ39IUmSlE0qNXAmSZIkSZIkSZIkhapeqlGSJEmSJEmSJEnKJgbOJEmSJEmSJEmSJANnkiRJkiRJkiRJ0gpmnEmSJEmSJEmSJEkGziRJkiRJkiRJkqQVzDiTJEmSJEmSJEmSDJxJkiRJkiRJkiRJK5hxJkmSJEmSJEmSJBk4kyRJkiRJkiRJklYw40ySJEk5Y9myZaGqy8vLS/dbUJZbvnx5ut+CJEmSJKWNgTNJ+v8caFQ2s/1KK9SoUSMsXbq0Sm+OatWqpfstZCwDQiXbRtWrr7hM/P333+NvzzFS+bkfScplc+fODZ9//nm634YkVRgDZyq3f/7zn+H555+P/+/FgLLRiBEjwvfff+9Ao7J2gJNjrwPl0grvvPNOOOGEE8IXX3xRpTfJsGHDwltvvRX/3/7Zim1ANmISEEoNoLl9CmZrso2+++67cPHFF4fRo0fH26ryOYa2UjjgapupPLm4bZPjT1XejyTltkcffTTst99+YezYsWHJkiXpfjsqg6o+8VC52w8rj5rl+mtVCV9//XWcvd2iRYsiZ5TcddddYdNNNw1HHnmkFwPKOm+88UYYOHBgaNKkSWjatGm6345U5qwAggTTpk0LG220Udhmm23COuus49ZUlejQFx6I/PXXX8Nnn30W1l9//VAVFBU4/+GHH8KTTz4Z9tprr9CxY0f7Z/+/ndCfnTFjRrjnnnviIHaDBg3CiSeeGJo3bx7vq+qTENgGmDJlSrj66qvDH3/8ETbbbLMqvV1oJ8l2mTVrVtwmjRs3DmuttVa631pOWbx4cTx2b7jhhvnbO7WPk82Sz8Hnmj17dnj//fdDo0aNwpZbblllzlNacz7++ONQq1ateB3QrFmzeFtVPoar8r322mth0qRJ4dNPPw29e/cOhx9+eKhdu7abPss8+OCD4fXXXw+bbLJJ2HPPPcNRRx3lsaOKYBzpxx9/jH2wNm3aeL4oxMCZikVplkGDBsVsnCFDhhQZOKOz37Bhw/wDaq5c4KhqzKapWbNm2HnnnWP7TQZhbcPKJhxv582bF6666qqY/UsAeOrUqaFdu3bxwqVDhw7pfotShWH26rPPPhsn6tStW7fA7VygJ8fv7bbbLtSvXz/8+9//DocddlhOX/QV/mzJNuBYsN566+XfV9XObRMmTIgXfmuvvXaBbTRq1Kg4WWb77bePQbNx48aFMWPGhJNPPjn07Nkzp9tKUYr6vGeddVbMVOQ8Qv+fyRhVaZsURrBjwYIF4brrrgvvvfde3I8InB133HGhS5cu6X57OeG+++6L+2a9evXifnnooYfGrOFcOWYln+O2226LGRm0HyY6bb311uH444+Pn1Uqr2+++SZceeWV4auvvor/XrRoUejevXs4/fTTC4zXSBWNNnf33XfHvueNN94Y25uyx08//RSuvfba8Mknn8Sxgw8++CC8/PLLMYDWvn37dL89VSIm8zBRbuLEiXFCGONK999/f9htt93c7ilyozeqSsFsgxdeeCFcf/314ZBDDom3cVHDATXV7rvvHj766KM4cJUrFzjKTVxAPP3003H2MEEzLiA4QbRq1Sr861//io+xDSvb3HHHHTEz+N57740/ZFIQRKDEFrdLuYL2zDH8r3/9a34wiAt1BoW+/PLL/OM3x3iCRmRdIZcHivhsZMBwHJg+fXqBc1jbtm3D+PHjq9y5jeDGnXfeGT788MMCt8+fPz8888wzcSY099N23n333RgguuWWW2L/tqpsp1WVjevVq1e8naoSderUidukKq8LR5bZueeeG/773/+Gv/zlLzHIuvHGG4d+/fqFkSNHhoULF6b7LWZtG2Tb9e3bN25HAkjMbmfSA9eeDz/8cAxYZnPJ01Scu/72t7/FwUn6amQEM8mDQH5yDSKVFf0djk9MaqYaEOc3Avu0M/YnqTLL+THhZttttw0///xznBSAqtxvyJZzMEESxgu4ruL/OW4MHjw4nqM4P910003569wqN4NmnDfoazGBiaA3k8SYMKeCqsbVoUosWQ+EkyEDDFzQ7LDDDnGBzwEDBoSHHnoonHHGGXF2bpKhw2xefpLZTVImIkvhnHPOiScDThDJmmYMCtF+Cfwy+Chl8jpmhQdmGCh/6aWXYim2XXbZJWyxxRZh//33j8HgOXPmxAFk27WyGYONBIORzJimzd96661xsJXjOG39zDPPjNlDoLQcZYqSi/eiBjGzFZ+l8LGAbFOyGHr06BEnPCWYKcr+zyz0qoTy4QQMWWOD9bkoIQQmeVHC6s9//nMMrIJ/M7OWWdLZOkhfWknWAZlUBMf+/ve/x/JKv/32W7yfQOIRRxwR+0mUPEVVCCimVh9I/p20G66PLrroojiRkP2MzKGWLVvGgQbKU6n0aIMco9lPzz777HDqqafGSRDMdD7ggAPiMZ79MxsD0kmpySSoyjU1k09pM/TRKNH4pz/9Kc7k55qbYxSPkcqKc93MmTNjkJ9qKrQtgvsnnXRSPMYzSSSpDiSVxwMPPBCP1X369AnDhw/P72tfeOGFcTyFYzrBFvoNrpOUuTgeMFGFdaGpLkDJcoJlIBv6lFNOiWPAyWRF5R6+X0qzM3mQyZaMJ1HVhX2XMSb9T+5fBalEOKk9/vjj4eijj46RZ7JxWBeD8ozdunWLnS4GZAmc0elntlwykEXHnxNmcpHgCVKZhLJ1DHIwc/Wggw6KJwYuUC+77LKYkkxbp5PAzHTXq1AmYhCGDkySAZAMrnDMZU2QX375JQ7m0X4pTUeHhwGaK664IpYj4zFSNqLWOgEQLuwYXCS4QekIBrGpw8/EBwJm/D812fv37x//H3T+GfTn75L+Sbajf8Vn4VjAZ3v77bfjwGynTp3CP/7xjxhYJHuBgQxsvvnmMahIELEqYECQ4yWBM8rokV12wQUXxOMi2Fa0GbYHx1EGfshy2XvvvWP/lgDj5MmTQ64pPLEtyTIj6LPffvuFm2++OW4HfghUg8FXUKqH806u9+85TrBd+Elm0ifbiUEl9q1dd9019hkJlLGtyPBgQhbZZ4Vn36tohQftybRin91nn33ifknwmsltrD9MiTmuQzO13RV+X0kb4hhNv4xMuvPPPz/ex3Hnu+++i1VaOJ/x/2Ro0E877bTT4vGKTI2inlcqChnSTPqgLYE+AccpJs5xPUB7pN1xfbDVVluFRx55pMpMglDloK1RVvb//u//YpCFYxYBFwJo/D/9btbFItCSZPzncsWHbEX/N/U8QwUGMHkqmWzLcYI+z4EHHhizz7geU/YrXCGB75n+yptvvhknYLL0BxOXGEti/WeuqZ3Us4JrnCn/pLbHHnvEC2QWK04ydAg64LzzzosBNDDLkgEGUnnZ+ZiVSlYapR3prHmCVCYgWJCUsOPigXZLJ4+LCNCGuaAlBZ12y4x9Bsy22WabdL91qQAGYei0MCDOzCAGnpjkwMAdHV8CBo899lh83PPPPx/XdKLtMzhz6aWXxkkQzBqTsgEDp0zOIQMhmajDAvfsA2QHczFOm6ezz6L3DGRzbKfNMwuWLAVKlXIOYBCJyUBNmzYN2SzJYKB/9euvv8YBWbJJuZ11vJhVvuOOO8bKAGThUZZpxowZcdIT6wWxzcg+y/X1TZIBQWY6M3mAYyXbhUFpJGtusL043zPxiyAjsyzJuKJfy3E2V/oBzPy+5pprYlCVbEzOCYkXX3wxPPXUU3E9HMorse0YAGMAhfUDOY8w6Yjsn3333TcOnuRi20n2CY4jDBgNHTo0BpsJ0NMuuCai3TCwQDYHbYSsTgYW2M84XjEhi32P51Dx2Ne4VmR7MpudSQ8EHSkPBQZtaINM1OR74LhNeyTTjzXBMnH/4pqCdkOgIvn++QxM1uP4TFCe36wfw3mKfZHPy2AU7euJJ56Ik/dYU5zH0aZycT9TxeH4RGk1MmE5btMGhw0bFsdkaENMlCCjMTkf0kdgX+LvGBinTyCVBYPrTBhhTIVgLH0FJnBxLKdNctyjz0FWLZUhGF8huz/X+57ZgqxTyrhyPcH5iskp9HXoCzN5hUA8E82Sta0o18dYA9cbfLdMOld2YgIck+S4NuRagHVk6dtzfujcuXNc44xzAxOY6N9SuYV9m/ElJl+ss846oapzyonyZ0lyocIPs0Q4aDJrhA48B08urhPM+OZCm1m8XAARmWYHTMpTSJmACwk6d8xm5UTARXoy654TxO233x5npTPIyiDIBhtsYPkKZSQ6Lsz8IQjM2jx0ZrgAJjDAYDiDLkx6IKhGhgX1qXkM67HQ2U06O85iVjYgm4yyQgQwUK9evTjozwAkWcIEhgh8EBQZMWJE/t8RRKMULxfw9GPIEqHMF8G2bG3/SXZGasYcmRj02wgSMqmJAVnOY+A8R5+MTCoyq3v37h0Ha7lQQq4PXFAeKGkbHDfpq9IGaAt8/5SuoqQt6+GxjheVFhi8pu/K4xloZHvlCgLIZJTRfrj4TR3wp19Pe6E/1Lp16xgsJHORWcVJ1hntiAFX9rOkDeWaZJ/guodjzHPPPRfPmwRrCJKBQSXaCMFDshLJ1COgw8DhW2+9Ffc1Z2MXj2NUz549Y7CI60wyE9h+DMIyMMeaTAy0khFzySWXxAAb16AEBjgXZFqpWSZjJBUskFSrIBjNJFQGjBmE4thCwI/9j98E8f/zn//EcxiDl2wPyqKyPV599dX8axRL6akotAsmfNBu6BcxkM0kGgLRBDLYZ5jwzLEsWXc+GZehz8APfyeVFO2HYzXrfIJjF+c9jmUEzVInKTEBh4ldXJcSkGGSAH2vqtD3zGTJtU8yYZwsbhIemAxEf5kxA9Bfph/DOpxJ9jOoXMPaowTnXZYnO7EfkuhChTj6+kyguPzyy+MPE1HpyzDxmj4Y11EEvymd3bVr19gmXPJjBQNnVVhSyz+ZIUdnnoEDZr8xGMOBkh2GtSCY6U2UOjXQxmxwUrPpnPF3XNhwcWCHX+ksIcCMu6SNUmqIDgLlLDhJpF5EEDSjM8ggGoOxEyZMyC93YRtWOju3Ra1fROYNg7wMPjHgQiYZAWEugjlOM6GBmfEE0QiosTYNAWH+zcU0vHBRNuCCnD4IwTJ+knZL556sMjDATwkRBogYWE0tk8UaC2TKsG/Qd2EQPNvaf3IOSmaMExDnM7FgN/9PMIQgEGvOElSnRBODFwkujilbyWAHM0iTMhu5NLmpqM/CZAECHwxgM5hIxh1BIQZ76AeAGbMM1FMClIk1ZOMRKGKwnokJBGBzQbKYO8EgPheDJkkVCc4xXAwz05SJFQQ2mAyXlI1j5jjbkglI7E9cdDMIlosXz2QaJuuUsS8RJOTcyQxbJgcS0CC4wXYksEFmGbN02TcJJnL9k0vtpjJwDGbAjW3HQA2DMxy32aYc7/nN9SPBppNPPjkOyDIAy7bdaaed4rE+k7DP8DnYZxJcI9MvO/bYY+PxhKoArBtCxkWiS5cuMROIDAzaFG2IAUoqvDABlTYHS+mpKASVCejTLyIwQUCfbGraHbcxHsOAOAPcBKWTftH7778fx2j4m1wpW601g3McZfqSABj96qQvzeQt2iBtiyBuhw4d8tfCIqBLNjbn09QgjNY8vi+uAejXcF6in8exhL4ywRTKbnK8YFyM6wwCZ2SmpZ7vCJwRWOP8pezBWBDYL5lQTYCM88WTTz4Zx/BfeeWVeBt9DvoqxAT44fqBv+XaieoTtA0ZOKvSklr+dLS4MKaOKYM1RJgJPjA4CwZnuCjkQocyJUk5Exx88MHxcVwAMEuc2Qt2+LWmcYLngpT1AvhNmRQuujnBH3PMMfEEkGRNJu2TgSMCDZxAGEjjopUBlNTHSGtKMuhfeICfYyoXLHR4GNykrTNAzsQFgmjMMmWmNlk2DAxTaoGgA6UZGRimY5SJJY6konBcZnCaNs3kHQZawZoJ9EPoZyTrd9Fn4SKejj0XhfRNkkkTDIDfcMMN8bie9FeyKeMsOQcx4YNywsmFDcFzJnkwOJZgsJWZvgRGktr1ZEAQMCfQyMAGA9DIpUEzPksSHEq+d7LIyKKiTZCBCwYJCAKRFUQ7IeOFrDzaDutT0QdgO9H3pS+QK2udJtkrZETRPujfM7iVutYb2QsEYwlcEGSlVB4DKgykcKHNvkNGGtcBbONsL0VYONjKZ+I8yfdP1ibnT/qFbBvaEpUI2Oc4dvBvygpyTqVNEWzjHExfk2CJikdGPMdijklJ/4ZJbbRJMoI5XhMgY5YzASgmcJItS9+H4HcmHb95zwT2kkAq+wz4DGSaEUxjkgeDyQwcs85kMlGVgSnaEcdyshfpp9GGOHYTsOZaWioO56rp06fHyXGpwWSOz5Td4rjNZBrOi6wTyDmNYxX7Eu2QNiuVJKM26VvRH6J9kaUPJojQB2WtRrLYaZP0t6hORfujr8m1Ksc4yu3S/pIS2UofKi/QDyZwllShYYIHE/F23nnn/O8omZRLUC2ZaAUmoCXnYmU++in0USmzyTgS1z/0b+nD0m+hLdDHp7/LuYG+MeNL9EO4/iZIyiQmgmdMuMj2vn9FcXS4iqMMCSn/dMIYZGK2CBc2zObmhMfJj52KgRkuBCjdiGQH4qKTix9mLLCTJhcHUmVL2hmpxczA46TOwZ2Lb2abk23GLCdOGpQV4MKd2Z9ctNNWU4MUyWMYpMy1WfnKDsmEBAY2mYzAhQgDmcxM5rjMBQnHWQbtyCThwobOEMdpZv0x65SLGNaipC2zL1DSLalTLmWqJOiB5LhMGyaTkgu3ZEF7BvAJADBQSQCEbErKZX377bf5JRtTFzDmnECmZpJplE0ZZ2wTyqfcc8898f1TWpjBVfppDNoysJzgoodBMwbwmRmceo5kIJY+HYEA7s8lDN4QFGTCSzJDEpSeIWhGpu6iRYviY2gnDARQ+i2Z9MXaXcysZZtSmoSyhLmE7cI6BbQjZhqzfRgo4eKYjDsGvsi4Y7Cfvj3nDwZjCaqx7Xh8EkTkvMRkjWzah1IlfbokcExfLwkEkj3GdmK/SmbV0keknA3thBLJBOsJenC9RB+T4xIztPk7MqdSA9lVHfsSWXoEZcHkTLYz2z5Zyyzpv3O8Zm2vZIIAfR3aJ/sqfR/69+y7SHfbS9pQ6sQ6Bp6S/YuAPeebRFLBgrJlyURV0KY4fhPgoJQjg1nsh1yHSKvCJDiOw/T9R48enX87k0AY8KSEO5mzTARgAt26664bH8tkCPoPlEWVVnWM4/qTY1RSyYFSzQRaOA+CCWycO2lLHP8IpjAQz0QBKleRxc6EExCoTc241ZrBMYC+LdUUEvRp6M9xPCAIyvUU51wmnHMOI7ueCh5JxQoyWKlkUZhjvJkr9buhn0L/irF7JpxyLqAN0K9nHIn9mGtHrq/Zn+nzU4GLawEC5fxtUm7V/m2KPFUJy5Yty1u+fPlKt1944YV522yzTd7JJ59c4PZ//etfebvuumtenz594r8XLFiQd+edd8bHvvvuu/G2P/74I//xY8aMifd9//33lf5ZpFTdu3fPO+ecc/J+/vnnldrjbbfdFv/9wgsv5O255555V199dbEb7/zzz4/PJaXDP/7xj3jMPeSQQ+LPdtttl3fQQQflvf/++3lLly7Ne/rpp/PuuuuuvAcffDBvxowZ8Xg+a9asvK5du+ZdcMEFeUuWLMl/rqKO9VIm9ktSjR49Om/cuHF5U6dOjf+mnZ977rl5O+20U978+fPjbU899VRehw4d8gYOHBj/PWfOnLxu3brl7bLLLnlHHnlk3uWXX57366+/xvs+//zzvAMOOCDuN5mM/bsoZ599djyPHX/88QVuv+iii/L23Xff2E9L/PTTT/F2jiH/+c9/Cjz+/vvvj9tn4cKFedmEfic4thVuK+DYuM8+++SddNJJ+bcljzvzzDPjOf/tt9/O3z5t2rTJO+6442K/9oEHHsjLZWwH+jQ9evTI++qrr/J++eWXvFdeeSWvU6dOeV26dImPYZ/abbfdYvv65ptv4m3sO/fdd1/egQcemPfhhx8WeE7OK0V9D9m0Xz3zzDPx8x977LF5/fv3z/9cf//73/O23XbbvEceeaTA39KvpH0dccQReZMnTy7w3En7LHw9VFVNmTIl7/DDD4/Ha37owyTHqCFDhsRjEH3zwn0Ujlk33nhj/P+kfXFcT93W6e7TpLb7jz76KO+NN96I+xXnqNNPPz2vY8eO+ff//vvv8TfnHY7TqX9Ln+2dd97Jf4xUWosXL87bY4894vF97ty5BY5BtLkdd9wx9qOk0uJYNWjQoNjv5Fg+cuTIePuLL74Yz4+0PTCWQhu8/vrrYx9r7NixcRzx4IMPjv1upQfnlcsuuyyef3fffff4PdIX/uyzz/KmT58eb+OnVatWeVdccUXet99+G//ugw8+yGvfvn38nhOvvfaaX2OWSa6Tkz4H40rt2rWL/ZTDDjss9vd32GGHOG6UXBtx3uA66vbbb8//O/pbqf1n+7f/Y8ZZFZlBQuSZ2W5JGZ8EZWnIKCPT4Ycffsi/nVnKrG1GpJn1n5iJyZoazKYj/R/MIOS5WT+DcmHUqU/Sf6XKwoyYZJFaMsiYjU8ZL2angpnBzDinPTKTk5kUZOow65yyL8yGTZ2ZwQxYZk1xOzOwpTWNVHiyaKghzsx+ZiOTGUDbpFQds4XIvmEGKWV9mMFM++X+uXPnxnablOXKhJnZUkkkM/cpIUH/gjVjmE1NeSFmTtPOKb3L4wYOHBgfSzY8WZTM8Gd2K7PpmB3J7ElmuVKfnzIjrKfDfkRZI0o+ZiL2YWYAJlkwrCXBPk2GBljvhxnmlMhOznkgM4GZo5zrkiwIMogo/cVsQrIXwH3MOuVx9OmyYaYo75nyZmSRU54yKd1JG2Ab8J0n62VQXoZSZ/RBU9e5A30A2hB9WLJz2T6UZ2Tb0qel9G2uZWumIguTdaXo91CaktmmZEexj7ENaRNkKfBvZpZy7mHf4YcsZmaRs31TcV7J9DLW9Amvvfba+P+pay6zbzF7lhnWZJJRlogsbta5BZl2ZDaRvU07S/6WfiVlGDlHs10SbAeui5JKG1W5jA3ZYuxTlLQk24C+C+U/Oe6Szcn+SjYnxyb62hyTk8oPlI9iWxee0cxxPfX4mO4+Dd83GWTsH3wWyhhxbqIqCyUayeblOhjJeyUDg2tmPgfX3lTC4G84lyWZd1Jp1a5dO57LOO+xPk2CYxBtkXGYpEyxtDpknHONmRznyIIFS11w/UlmP8ex5s2bx/4k6HNzLUo/nFKM/NCfIGudc4DWPKoqcY5hLJdqYqxzSOYplQc4XnB9RPUwzlVkmfHd8p1yXqavSB8x+e5BuT5kw3VDVUcfnrKKXBuSbZj00+n78/3RJqjCxXgR3yt9NMaN6F8x9kllDqq5JH9HHybpA1f1/m1hbokqgMbPzkLdeC4e6VSx0B+DTwzKsLPR4SelN1mYmAtCLiLZoSijwU7GTsWJsnAqKAMdHIgZ8KKGvVRZSCXn4M+iswyuMvhB++agT1kY1hbgQpY1BLhApXwXAymsc0ZdZ0rrUAaGgFpycUvQmFq/DNIy8ChVFo6VqWsMJaWiKGtE6QQGyps0aRLvY6CXARbaNCVZKHdBCQ3KZZBOz0UKpVcYYKK8kZRN6EdwDGawlVIRBL7ofxDwYtCV9YYoH0Jb51jOQsb0VdgPKEvIhT59Gkqn0ZchMJQaPGZCEAP/HNMzdV2PpHwXFzpcxNJPI7DDIDJlvCg1yWem/CIXxQzagjIalJcjWPTss8/GoDr4rGyfJCjEsYYfBtp4Pvp1mSw5HnJsmzZtWvyOGRykDXDhz3EyuYBjEgGfm3YzZsyY2FZoP7QBjrOUEOfC8P33348TZggMcVG51157xdJDuSD1gpYJRZwLOH9QlpPBEAbnk/MJj6UtMDDCNqNMJdcB/BBcoyQeAUsexz6VrHeR7KfZgoEj+n3sO6nrjnEbwWYG9thHKFdEcJb1QukPss/RPlgPjnUhWGsr+dyUtKHfyAStRHJfpgcS1wT65ATDOM5w7KbMLj8MxNEXZ1IQ62gz6ZJgOEFt+u9sO9ZXoe+dDNilbs/U8obp9uOPP8a+GPsF5ysGlAhOs89R3pNzEvvNCSecEK+ruZ9gK22NPhvBXK6xuYam3UnlkSynQR+A/k9yzmf9KYIZSak8aVUI7jN5hMkPyVqn9COZMEK/i2M4QTHOjbQpJkHQT+O4xzGc4x6D9vQTKPum9GCslmsmxr/op/Dd0d+hfDB9HQJo9PnoN1OunAksjDHwPdPP5jzMJDTOXYX7fJlyDlbxuGZif6SfQVle+mT087keYlyecVD6w1w7MsmJsSQmNjG5kMliXCtzHVCY/dsipGSfKUck6ZVJaQvSMUmpPuGEE2Ja9YknnhhT+Xv37p2flklJMNKsp02blv88/P3jjz8eU31HjRq1ytfMpvItyj6pZVoox0WpHcpRLVq0KJZJad26dUw9v+qqq/JTz0lL32uvvfKuueaa/L/9+OOPi3z+3377bQ18ClVVhUtGpZZXAe2WknJJmn1qe+e4ffTRR8c2+tJLL+Xtt99+sfwPP0l5Iynb+iVJCT5KRtCOk/s5RnOMp99BSStQMo79g75LUjaCciSU+Erts6S+TibhM1PyhtJehUtnv/7663Gfptzq//3f/8XycXvvvXfcvykNx+dhe/z5z3/O+/LLL/Ofk8/N7fvvv3/ejz/+uNJrZuJ2WBXKn1FGhLLKSTugJCelNykdyDHw5ZdfjtuRkpS0D25PypHQB7jjjjvyn49yn7QZygtRqmb27Nl5uSL1u6XETufOnWMZPPaHvn375s2bNy+/X5/081P3u4cffjhuv3vuuWeVr5Hu8nilkVyDfP3113mnnnpqLDWZnE9nzpwZS1Rynk2OH3/7299iGUvax8UXXxxLUNGfvPXWW2Mpm6SvmG37UTpQMnXnnXcuUK4QbNNrr702709/+lP+sYuS03w3XJOyf59yyimxjFCme/XVV+MxuXAZXD4X5Twp3cjxhmMNfvjhh3g843PSxs4444wij9NSWdE/oCTbDTfc4EZUiXH9SZ/p3//+dzzncfxiTOWoo46K5ZxBP4Ifzn+9evXKH2ehvB88L6YfS+NQFjPp53AdRTnypNx0UlaT+ynreuihh8ZxBI4b9Ks5N9EvooQf1yHKfuzT9Gspk/3cc8/F2yjF3q9fv/j/XIOyhA39Nb572gBltFVyBs5yGBeLYJCBAScOssk6OBxYuXAePHhw/kUB/37ssccKrJXDugesbfbdd9+l6VOoKmMdkk8++aRAJ4CL9NT1ywjqJvXeEwyMPPvss3GNk6KCZXb6lA6p66vQuWGgHH/9619jm07WUKF9JmtgPPTQQ3HgKVnDj0FROj+pa/pJmYrB99QB+NRj74QJE+JgP2ubgfZPf+Wss86KASH2EwbEOZ6zH7CPJIEVAiPJBWOmY59l4OG8885bqWY865hxH2twpQaRUs9pbJ+2bduuFChnm3DRs6r689kS/CAQSGAs9f1ycc85nAF5gmSpE7TYZtxHcJXBH4If22+/fZxcwGA2a72wvZg4VniwOxewZhlthn2EgBBrjLANuCC+6aab4mOGDh0aJxT985//LPC33M++xA/rUmX7RLjCgcERI0bEoExyfcN2InBBkBHsM0yqog9J8JDBpmTtrf/+979x32O7FvU6VR3H6EcffTSuCcc+m2wT2iCD+M8//3yBx9NXYf+95JJL8vvwDNzyt2zrbNm27EPsLwT+WDOSiRscfxhMJmDG50/WAR8/fnz8rJzD6O9xnpMqA+t1su6xVBL0i+gjsKYRxyomioA+E2vFMwFn0qRJcb0j+qb0Xek/saYwj6eP5bVnetHfJRBCP4VzK2tY0dejD8NExNS+SzKeO3z48HgNwYSOBGseF+7/Zfp5WCW7NrjwwgvjxB0SB5hQyDrgqWP7rCFL3yw1acBx0ZKpxn+KykRT9iKNmjRMUvhJv6U0TVKPGJSlo2wG5RX79OkT138ipZeSGpSWoORNUWuCZFvJFmUv1hKg1BDlASg1ROkJUP6E1GHSzSl7cs899+SXsKN8CqnIlE1hjQpKD1Hi6+KLL44lZGy7SgeOm4sXL45rq1BSjNIWtGHqxrM+HyXIqB9Pij3lxUibT7AP0NZZ44hSU5Tgsta0stHbb78da+5T+oVyMPQ7QHtnzRjK6FKCl/URWHeJEn3XX3997I9QZo+SMpSfoAQYZUeKK3+aiSgHTMlr1ofgHEV5DPZrymhQcvG6666LZQdT+1iUlKP0ysiRI2PZuTPPPDOWQ6MMB3+TS5ISjaBcJWVnKK3Mebxbt26xX8r5nHM9pSw5n1P+jPP7cccdF9sLa7pQ2pbjKuXI6dOyvhJr5+UazgeU6mKtro033jieOyi7Q8lqtgVlBTlfUFqJfYYyLbQ3SvqyTSnNxLZjG7Gds1nq/s95lnMkJSo5r7JuB/sR24k2RIlOygVyrGF/pCTVZ599Fvc99qnevXvHNsa1E8epPfbYI90fL2PQF2H/osQTZeHY/yjrRBlLrifZvuyrbD+OZ9yXfD+sNclxnH466+wV7otnwzGcNcpoO//85z9j26A0EscW2hZrBFHCjFJJtDdKY9HGKMHL9pAqS3JNLK3OkCFDYslvSsVyzcm6Rxx3KfVJX5SlXCjLmJQLZ31YSqRzXcrxjb4n/TT6pZT9dkxlzePcwndEH5AxXc7LnGcoh0yZYL4fypOfeOKJ+SXcwXI7lJ9mDILSyIVlwzlYJcc+TF/k4Ycfjt/tTjvtFJc2oO9S+HzB/dzm/lwynm1zZGHwZAFscODkJNegQYNYu5Ydhfs5wBIo40KHWsQMYnE7J0dwMuUC+7XXXosDPakMmmlNXZwyaMH6enTkCPiydlkSOEvaOwtzE/hlUJGDPh26yy67LLZ3FktmMIRABY9jIMUTgipL4bknhf9N22OQiQEXgrj80FapN/7nP/85Bn8Z8OP///Wvf8U1QJjcwAAnndypU6fGwWEGgw2aKRskx+kEQY9LL700BjwIiDFozW1g7a3Zs2fH9f240OMCkLr8PBZcpLPGHxf6XASkBs2Q6Rd7nJ8IULDeFv0qPsNGG20UB5bpk7FmDgGx5LHJMYRBe/pnDMCCNWQ5jnBRnDwukdr/yybJsTI5rvH5CHYR/GEwgG3UvXv3/MAraBdsR/qwDPiwXgNYg5c1X1gUnQECgkfZHjTjey3qu6VPz3oU9HOYKETQDGuvvXY48sgj46AW6xZzDUCwYsstt4yBC/Y1ArBMRmICRzYHzZLtwv5Pv5F2QwCH755BJQKDbKfkeEHQjP4h+08SNAPHI7zxxhtxLUGei7XyDJoV9Nxzz8X132hXydpKrI/H/7POItuXgTv2R+5L8P0cfPDBccID30tRffFMP4aDfer222+P18ccw2kvXF9wzqK9MOjMOptM9mOiBwyaqbIZNFNJ+llMSOIalMkNnPvpYyeBf/pdTLqhL8H6nlxrstYwE7yYxAT6qdzHdSuTBhxTSQ8mjLOeLettct7hXMOEw5NOOimek+hL8z3Sl+F75XsnQErAlL5hUUGzbDkHq+T7e7KuNf1ixkCZRFbc+YLv3v255AycZblkwCEZqEoCXizyx+LqzBTh4pnBKBYFZFYgmTnMOGXWIAdXFjhmIIZZzVxknnXWWXGgJ5U7lSoTnTYuQhnU4aKb9syJnkXbGRxjpgQIpjHYyMAPgQY6CnQGaZ+nnXZazLAkg4GA2v7775/Vg4rKDrTH1N9FHStpl7RpZlsnx1YuROj80mlhQIoBFzo6zM4mK4V9gQAbM+G52JGyLRBC0IfFqglikFVGkJjjMwP5XIQzSA2O+fRdTj755PhvgsYEkbnQY/FqZlmC7BoUDhxlcuCQ/ZvtMn78+NjnYgCWhdW5sOGChoELzmEMUrDd+FuOIfw/xwoudPh7Zgwy2YljQuGL3GwZPFuwYEGB7y85VjJBgMWpGRBgkJ1+K31UkMVBFiLbj4ldqe2LAR4WtWfiQfJvjrEM6OdKJgE/DGKxD5F1CdrQMcccEwdBuC3ZP0CFCbYB5xQG+dl/GPBnxjETjJ566qn4b7bV/y/XH7JR0uaZZELAhrZDe+IzE1TkGELfkQzE119/PT72P//5T9zP2CYce8hSpA9JII2Z9QQX2bb6H45H9M/pU3P82nXXXWOQjH20UaNG8TEsNs/xmxnwHKOZ6EDgO0EA/P/+7//yg+DZimM2g5LJcWv+/Plx/6MN0U9r3rx57KsRwJWkdKFPxDGYvhbHKybX/Pjjj3GSFn0AJqQxhkJfgaALGelMLOH8x+RO+qXJxJKkP1t4XFBrHoFMxnTpLyP5bujb8f9cbzH+y7mJyUNMPqc/RBUnJuAq96WOQbF/M5mMfhl9YZVfdlxtq1gMGnDSY1CKmQXJiW2LLbaIJ0wCZZwQuY9AAhfPzHzmopwDMBdEDEokAzFk+jBr1WCD1hRmw3DB/emnn8Y2SmYBB3lmjnPyJ6DAwBAdvSR4lmDwg8wFBpYSDEqmDs5ly6CisgvHVGbsE/xKbZcM+DPAzUAn7Rf169ePqfPJgG9yMUMpLTo2lPrhuEvWL4EFykxRyo6AArPJnA2mTJealU7WL2XhaM/0KdgXCIIls/CZ8coxnUAaGMgmg5hAGzPjHnzwwfDll1/GADLH98IXfJm6P9BvYjsk+znZLQzOs79TXoVzGAGw4cOHx0EMBp4pW8mxJAkU8bcE4d955524Xfbcc8/87UogIHmdbMI2oUwl2bSp3x/Z5A899FAYN25c/K4ZDKAPwDahHTAAj6R/wHZLMADEQA/tiwBkrqHfQslBBuHpAzEwwgxjAqfsG/STCDRzvkm9IObvqCpBcJpsMzBoxn7XuXPnWIad7yM5B2XrpDgCYwzw0X6YAc+xhIAgxx6COkxAYYCJmfXJcYZrH/qHDCaRwcl1E+dd9isGmxg0zPSg/JrA8Yj9i23B8YhtxG3snwRcyXTkmExVB9onWVfsy+zXlPznmJe6r4LviHaXbceuwtjXCFoT4OeYzoQ9rrP53Nm6L0nKHVxDcl1Jv4mfpGoBY4GMAZI1TAYxExr69esXj98c0+lzgr4C/QxKpTNp2UonmYNrBoJjyXk0uV5gUgfXVWR8c24im5AxCTLrCZwwoYhrD1UdSRuhb0uboe+m8ltxda+sxYAsA1Kc3BhgpfNOZhkDCdzHxQy1bnfZZZc4K5ALaS6cmXXJyZULIXaqwgw2aE3hIpuZUQTKmM3KxScne0p3kQFJqjEDQQQjSEtv1qxZLO1F0JjZnVzQJ8GybBhcVW6go9q2bds4U502zAw9ZnYxwMsgEeVyWV+GDF7KijEIxaxrMijo0NA+Oc7ym+diRhgDfgQcpGyQDIQmpR4oK0oGO6W7GFBkn2DQmoty1vNL1uZifSYCI6yXwwAs/6ZkNJkf7BNk0jAom8yq5DWyYZA/6TcRKOS8RT+L/Z59evDgwXFCExe1nNfYLv3794+zfslaIKODjCxKmjFbmGMF5z0CJIVLZWdb/4z3zjGSLCg+K5+JwCnbiWAaJfWSjBbwuckgogwcgTECRrQPZkqTNURQKMlySUru5dpaNbQb1pRiUhBBVSbFESxiv6DkHeuVsO8QkCTQzJoklAAFffyOHTvG6hJsx9Q+ftKWsr1/RJ+PwDQDAuecc07MgmIbMljEvkawkfs4htC2WO+B8zPb8tVXX43BDvY/9sdU2b5dyoP9ieMWgWzaCduOwBgBfPomL774YixJyKArA7MDBgyI+zIDcxy7GJBl8I5qEbTHwrLhGL469Pm4jmYbce1BANaZ/JLSjYnEHKPpV9P/ZmI81U7oRxA0YSwwmXzFGCHnTHDspm+VTP5M1rtKXSNLmYHxXfrFfMdM/OE8nXxvXDcw7kB/mj4z11z0sRlzSM7vZg1WHVxPME5KxQXaCZO3VX4GzrIcFyGc3Jg5woU0FzkcVFnvgds+/vjjeLLkApELbIIPXFxzomTWnCUllAk1m5kJQXAhuajebrvtYtukXB0nf9o4vwkSMyBEySYGVymtk6yFI60plEQgOEZWGLP0rrvuunihQgbZiBEj4n3Mfn/88cdj8IDZXwyEcx//T0kfkHVC9gATGxiYkrJF6gA8mZVcpHOcpgQpGcP0O5i8wz5BZgiD12RPMUOSDBiCIxzTCSKRicVgJEEROvfsD8lrZFOgiPdLsII1cMicSkrmMdBMoIxZvAQJKTtIIIhSKgwwM/BPUI0gPJNI+LxkGBFUy1bMcubczGQYkO1ESUYGmgmuUj48CaASWE1K3SaBHwIa9GUJCjEhjNnSBEoYGODin4AZpW1TM9CzURIUTto4mT5MDmJfIOOYtQEZLEkGPMioYvYwPwSH2J69e/eOwSD+nQR+qERBCcPCk4qyPXCR4HOy3gpZ30ykAtuQQT/Opex7ZAcxaMg5lmsfgjoEPggIpW6HZKCwKmOSZVIGliwFgv70T9gv6Z+zzdhn6e/Qj+G4xWMJ4lJqloG6BJPgsuWYXVrJxD5+XPtbUrpxHKJ/yTUmS64wWZNrU47PjAMyoYH7WTuWcRYmjdAv5zqWsUCO55wzkwk2Vf1cmMl23HHH2LfhHJv0ZcCENPrRTC4nKErJbspskmFIMDVpD6pa2NcZn+KawjVXK0Zu9myrIA6SZJ0xY5fySJxAGbjh4psLHwZl+vTpEy+uWdeMQZskaGZpEqUTQQRmBjMgluCim+AvZUTp8NF++c1gK+2ZABoX8UnQzDasNSFpZ0npCgaPGKzkOMvxlcwS2i3HYSYrEDhgJh/tmUFeggtkoTGIx6AopTAYKGUAHdm63oyqnmTgmcx1LriZfc8APllnBI5p+8k+QrCYizpmSSYox8ftZCcw8Eo5MC4Kk6BZstZXpg70F1VyjHKT9K0IWJAxzWdkQJkZoGThMTjN/zOAz++khBzHB9b/oX9G4I3SrUnQLFvPbQQMyV5JcGxjkIbjHRfxBNQoO0OwlbbDgHzq2nBsI2ZJ05dlEJ8JCKx5Sl+XviuBtGwPmvHd8t3Txgk+v/DCCzGYSMCCjDxmCLOdCJqRYcdkt7fffjueN9g2lHJkmxCUTBaDTxCcJmiW7aXxVoX+IccQArHJ+nfJ/sLtSRCStkT/km2H5JiSbJuqPKCU9DnIlGd7cEyikgPHcgZcknUl6eewvWmvZKCxL5MBSUlMjvWpmXvJuoy53p/J1HOTpKqD4xCTa0CpPvoGyTmNAD9ZZvQtkjXPmMhGwIWSxVyPMkGJUtkc35XZCIrR9yXQyaQpgqQERvl/xsYYS0gmdHDtwbmZaw/k6mQWFY9g2bXXXhuvvQmiq/zci3IIJX646GHglhMhA1LMKuEiPLmA4UKaIAQHU06g3F6VLxqVfgyUMqOVWfmpaKsMjHFBz4mfYAQX8swypuOQOkhiG1ZlSz1WUv6AGV/M+qczQgeVYNoee+wR76cDS+YNpaHI+qVcBgN5lCXjcWRR8P/MlCfwQBYlHIhRtmAfoOwZx2fWeuEYTsCIi+///ve/MVCWIPuMIAmdd4IgYD9gsJUyImQYF5ap6yowuFy4rF6CNbf43MwIJdhBoGzUqFGxJA7/TtYxo2QlxwOCRQzqM4CRbK8mTZpk/bmN7cN5nUAOGT9ksjDrlXbARBlmQRIs4ztmwIdyjGTnTps2Lf852GZMlCGAlATgKA3Hc7EWVS7gu2UyxZVXXhmz6xnc4jzDtkr652+88UaclEEwgz4Q/SSCaWwT2gzbkO3KuYOyLIXl+mAJE1QoZ0nAuvBaowR9uNZhf+R6KFlnsapsm1VJrgmTPgeBbvYrJrFRhpD9kVJQBPAZXCWIy2QAKpawX9Lm6N+wBiXrvDL7PVUmT3qQpGzGZDMmXzJpBATLyDaiXB/rxieTQpjkwEQlzoNcs9LnYJI9fXWO92So0ccgQ13ZgTHcpIw5E1ioYMH1BFXF+D6T8y7XYlTzYJK5qi6OC1bmqjjV8nJ9SlgVkgzmECjjIjIZbKBkjpFmZSpmmTNjhpmrrHHDQCIo48WgGwNDzJTiAn3TTTddaaa2tKYwa/2qq66KA950SgmKscYHbZYAGGtdUFortZY4A0+0V2b9kH1DZgH/5jhNB1jKdIUDRQyu0p7JkGLAlLafLDxM34N9hEwighwJ1hgi05IgM/tItilclovzE5+VgCAD82S2EOR56KGH4oxe+l0EFJnQxOfl/MYEELYVWVNkw1B7nmMJZV6TtSeyWWrJO4JjZKkwOYBAIesg0Q/l8zMoTyCIAflkW7INaDNkkyUXeRwrWdOLwCozonOtNBptgLaSZNETVGYwjKAiky3YFgSFmAzXr1+/ODhGf4igEBnMTMCg/Cl9KLZNVV2/goA024KsTY5HDCYxiZDzMplTSbspHCyqqjhGEWTlmEVZVGYlM3hKAIxtxIAq6+WQAdygQYNYIpXHDB8+PPZtaKNJyVQCaIlc2z8lKZMQJKEvRXCMYzHHZ85/VH1g4hGTGcgmY5Jx0ien/06wjIlr/G1SVtc1r7If3y3B0mQyWdIH91wsVQ4DZzmMgRpKubB+hgdRZTIGHP/yl7/EckSUZKLjx4wqZlhzYd63b984o4bOoJQulMNijRlqxRMEY8Y/nVb+f/DgwTFjgHbLADGDocx+ZwCdjAFmhzGZIRszSFQ1pQZCCgfPyLakzZNBxJpeqY9lUJ+SjQTQkmxKBrNPPvnk/IFbSsklisvgykS8VzKfCGyQFcWMX4LoBAkp1QomejCIT9kUAh5Jf4z9n8FqgojJwuyJbO6jFV4jin8zEE+AjIxEBmqYBIOkT8o5n8EcBuTJuuLYSllGZtKmZq9kU9soLbKgCEDz2du1axe/fyZjkIFG34fAD+cOyvAkQQ0CkfSDOO+wPmAyOFaV1+riXEsAkVKpBBcJuJKVx3ZUwSxhgviUxeX4S2YwfRTaERmiDMoy+MqAKiVAk2MUx3H65hzni5qESeA2UzOEJSmbJX1DJmoxkYgS4Ey04bzHteiWW24Zs4FZ9z2ZgEwwjQldCa5VmUTCRE+O48qttpGslZut1xBStsjNq9EqLknRJvWaAQp4MFUmo5zAXXfdFQeJKHHHYBuz8u+777448MrMdRY5ZbF3KR0YlGPtIS5SGJRjhheDTbRT2i/rzFAal4FQJANJXOQwG5B2ncvrzSj3JIPwBHo4NnNxzpp9tGPaPZMZWDeIjCEey4U8KD2Y7C8MYoN1zwiesBB5atAMmRoYKVyQgbKLBAzJFuXcxGehDAoLc3NfUiqPAQ5mAhM0Y1uRfUV23u677x630XvvvVfgeQl4ZGsfLbWELZlQrLnArOZk/aOknBsBIVAyiMxc2geBMjCQT7YZmWZkn5PtkultoyLQDshYJHiYfP9k17H/jBkzJp4z2C60LdY+IzuNfj1ralLaMTVohqoYNAPBH4LSbEuOSbS3JGjmOXcFjsNMWKDPwm9+yByjvV1yySVxgg+Bf9ob+ydBM/ZtsiCZHMB9RQXNeIxBM0mqHEm2NJO1OA4zkZjJxQTKmIjFhAj6XvQjKaNLFjHH96QsOjjOU7bPoFluSfqNVmCS1ozcvSKtwpKBhuSAajVOZQMCEczSZ/Y1ZXeSmfpg5lSPHj3ibCkpHZjhz2BwanlFjq1crJBFwextakmPHTs2ljbiOLxkyZL4OMpGkZFSVUtpKTtNmTIlHH300TFwxqA8WQdku1x00UUxY4gBVi7cmeGaDGAzUM2F+2GHHRYDSNTdTxAgIDiSrN+VqZJ1zFKDWZ9//nkMkpH5Q4YFa3MR2CCIfswxx8TBi9GjR8fH8vkopfPvf/87HhcocUbGHcEOMlbZprkS8GAbEUgkYEbAh+MkmStk5bFWEsdEjpkM2iRBDNoGkwkYrCcoROCVYyXBDta8y5U1zFaH0tO0k6SPngQhKc3I4u+sSUK5RmaZs34m+xeBDYJqBED0P/vvv39cF5cBRLYpyJzK5cDr6lDyk8wD2hVZsARqaUdkCRN0pT9CQI1jE9uJ4xblvNg/CUQS2O7Zs2fMcmRAtijZGvCXpGzB5Cv6WfQZ6tSpE8s8M1bCpE364PQx6Wvyb/piZJixHip91QTBNklS2VXdK4oqxAsbZRMu8ukYMsiWDLAy4EqJmcKZCtKawmzrrbfeOg6GJ5k1HFsZ6CTjhGAvg08szstgJ5K1ehjwlLINpUcJYnCBTllGgmBkvTA4TUm9zTffPJbSJaBG9gKSC3XKNVLClDKGhWVyoChZP5MfMi3YBgTNCGawLhfnJvZx9ukkMM5aXWwntg/rv7GmF6X22AZMBiHbjO3GbGGyqzi35dKEJiYGcEykNCPr2PFvAj9kka+77roxS4rJBEkbAbcReKQMHNuVQX6ydgmAVBWUZ6QNkU2PJLDITHLOHR988EEMtJKp2L9//xjMoE0hdUBMK87FN954Y9xXaYeoyhNVqM7AfkWG7HfffRezYTnmEDgDwViqOZAtRt+F/ZeBWW4jc4+JAGSMEuAmc5gSmJKkNa9JkyZxohoZ/B9++GGswsNkCMo4U+Xk66+/jhNqmAhBNQgm1nAOyPRJapKUTSxKLiljU8+lTEJdecqQsX4PpbOSNsrsP2a3s5bPxhtvnD/jPZvXLVLVUNz6NAy2UhqGTAMmLiTIQCBDiLX8yLRiHSYGZ2+55ZbY/gkosS8w+MrAa7YhqMeaWwxGELRg2zAYwcQNPiv7PpkYBMsIbvBZCQAxoHH77bfHMnGnn356LIlDhgePISCU4JiQS+c21rJ7/fXXw7HHHht23HHH2J4oJcg2+eijj+I6Xnx+BnVYS4lgEZ+f2c8E1piIwPpUVXFSDJmZrH1JZhkZjASaQcCM/Yjb2GYnnHBCvJ3tlgRcLY+3Mia2sDYXx56qir4I5U4ZSGV/pHIDZWM5ThFIpP9C2VQmABBo5JhGf4VSvFdccUXs1xBI4xhIIJf9uCqvnydJ6ZRkotMPoIw1x3TOc0ykwVtvvRWP1f+vvTsB13rO/z/+8TO2LCMKyVJkl2xNoVWLolKJ7KUsWYqRNXuUxlIqFJEYjNRIiOwaaoqiBql0MRRFTGQty/yv53v+33Pd5+7EOUhneT6u61ync597+d53uX3vz+vzfr859+Q7XREGDhwY1WeSpN9O+fn0LknSasQiJ4u8tKtjQZgFJ9pjsJhONQktydgJyCIUDM1U2mUL8FQYUK2xdOnS+Jn2Xcyhoq1XJqtyoZKBtqUEaFWrVo2dsLSS4cN6fqVHWdvxysLzKaecEtVmhIGEOwSELEbzehAQ8Z1AIxcVVDvttFNUuxAGUaFK0J6FZtlrV97eE3g+zC1r0aJF/Ew7SyrOCIF4v+TPtG884YQToqqMVoy8hizyUFnFa1QRQzMQqvL/CuZmnnXWWVGpTCUnswSp2OO1YtGMlpYZB8D/NFqCEu5XNFkVPLMkWVydO3duvB8RfLHoSkUs/z1mGx6ojm3Xrl38juCb97QscCS0pVI0m8vKl6GZJK0enH/TGp1zpExWgZ57zsi5AK1zOW8C79t0gKCSmHNxOhrA0EySfntWnEmSVAwsJPXv3z96yPfp0ydtvfXWUY3ChxRa2VkFoLIgt3qANovsYKXFIB/Uaa1I2EH1BvMUmFdFUEa1S3YbqoaoWMhCNtrCsMuVXbD5ytqCK6EXgThVQFTa8VrRZpCKjdtvvz0Gs1OFR1UGO4AJyFi0ZrGZSrOHHnooQo985fW9gWCVSjsW6Xv37h3Vh7Rf7NSpU2wuYO4bc5No6UmwyiwOwsQOHTqs6UMvFfhvjFaoBGZUAlGtx+YM/v/CvEz+bW2++eZr+jDLjPJUzVkchGSE+gsXLoz3It6TCKlnz54dC6psAgC/IzijCnb+/PmxQYA5Z2z8IcQm+KYdbUV/PSXp98L5Ep8p+f88IRjv02zCYtNM7ntv1r2EDQ1UA0+ePDk2Y2Wzdak+4zzcsQCStPqs9d/yNGhBkqTVbMmSJbHgRJsyPsjQok4qSwi9WHS99NJLY7g41Qcs1BNsNG/ePF1//fWxmE8oMmzYsEKhGLOqCJCoxmrcuHGh+y3r7UnPP//8CM6onCL04/VgYYJ2Zyxy8JpQ1UF1EO0GhwwZYhuz/99e8Nxzz432b9kcJcJHvsBsLn6vVWO3OVVo/PtatGhRuvbaa+NyNmVkO8mlDOcgtJAFm3gIXgn6eW966623oqqMWWYHH3xwXIfwf8qUKVHR+MUXX8T7PucwBNnM0PPfmCT9PmixyCYjNmKxWYYgjE1rvI/TNpfuJUW1Uu/Vq1fclvNTqvXZZJN/Hi5J+u0ZnEmSJJVT+WEWVZJ80GbgODtcWaDnOx/SqV5gPheVU1TDnHHGGdFChu9UmhG4DR8+PCrOuC5VC6t6nLKIhWVeB8IfFqCp2iBMa9CgQVSaEpQTGN5///0RKDLHjVlUuSriPCCq8Vj0oe0bFbgs6l999dXpyCOPjH8XvJ78G1PRCLFHjhyZZsyYES3z2EnOvzVCM6pApQxtctnYwHsMlWb8uUqVKlG1QJtK2uw2bdo0qon5M+/loKKYKgba0NJaly/Cf1pM5/5ekrT6cI7J+SWdHqg45zyagIxzbTY70PmAqjLOs7Pz6uz9mfd5ZhCzweuAAw7wr0mSfieeIUuSJJUTfPgGH7D5sJ0fZvEhnV2rXI+QIws0+ODeunXrWEi98soro8qKcGyHHXaID/PMYerZs2dUxBC25YZmKOuhGQ488MDUqFGjmJXEDDfamNFmkPaLvJYzZ86MqjNaWTKfinaO+SpaaIbatWtHYMi/ESpazjzzzKieon1Qx44dDc1+BjOomAtHaEYrJl4/KoYMzZSPkIz2sY899li8X1Ftxns378cErZ988kksyBKKscBK9WzuvMkaNWrE7Winynt9NsfM0EySVo/ceb/Lli2LqmDa43IOzvs3ARnvx9n8Mubl5p5XZ+/PtGOsWbOmoZkk/c7K59AFSZKkCraLlWow2uOxWzWbjfTmm29G4EOFGVVjfFBnbhmzp6g+ePfdd+ODOPgg37lz55i7QKUCocfgwYNjMZZF2GrVqhUs5pfXxVZa5k2cODFa6LDITAD53HPPRbhBBdUbb7yRTjvttAgZy0ul3a9Vr169qDCjjSdzuk466aSYtaTiI7Dly39P+ikE87zP89788ccfx2W8D7MwS8BPq10qF3kPZ3GW4Iz3sqJm5fFvrTy+h0tSaUALdDoTcN5dp06ddOKJJ0ZQxnkl59XMNqO6nPdvLq9fv360zaWy2HMBSSo9PFuWJEkq4/jQvdtuu8XcA9p3gRlmzEqg/SJVLN27d49KBXatUh00e/bs9OKLL8aHeBAA0eaLVo7Mq+KDOxUOVFY1a9YsQjMu40N+eV1wpXKDqjoCR14b2g/SinCnnXaKWUAPPPBAqlq1alzXhY3/4d9Chw4dYubb6NGjDc1+hYoewurn8T7M7DI2PmQVxllFA5cvWLAgZpgddthh0V6XmThF8d+aJP32li9fHt0JevfuHS0X58yZE5sZqCSjmwMbHGjJzOXIZplx/kmLXSr2fX+WpNKjfK56SJIkVRDZoikzD9q3bx8tBseMGRO7VqlAY8frvffeGwFZ3759YzZOy5YtY/bUfffdFzOpMiy4tmrVKn3xxRcx4ytXFhSV53aE66yzTjr99NPTq6++mi644IIYvk5FFe0qs8UNqvvgwkZh7JQuz/82pNKCEH/+/PnpiSeeiJ/ZDAHes6kM3njjjWNxdsKECQXtvyRJq9cdd9wRM0vZsNCvX784B+eyNm3apBEjRqT33nsvnXrqqem7776Ln7PND7RpfvDBB1PlypV9z5akUmat/7IKIkmSpDKFdonIrf5iNhczyqgmY8YUH9izRdVJkyZFFRotGWn3RSsv2g926dIlwqJsbtmXX34ZIRvVaxUVLSw//PDDqDRj9lQWUBoMSSoNqPBk4ZX2X/vuu29UKtxyyy1RacYcy2yjQ/ZR36BfklYv2lTTLpe5pXQoyNDFgDbWhx56aJyjP/LII/Gd83c2vXF+SZt1bs8M3WyjliRpzfMdWZIkqYzJDXFo2TVr1qxUq1at+LBO1dk777wTfyY0o20MrV+Yn8Cu17Fjx0aLGFo7HnfccdE+pm7duqlJkyZxf1mARjDHYmtFXHBl7ls2+y1rTWloJqm06NGjR1QM04qXhVo2PDRs2DBCM2Tv2xXx/VuS1gTmvdIinXm5vCdn59M777xzhGJsdiA8a9euXdpuu+1invCyZcvifJtKNWYRS5JKF4MzSZKkMoYQh9YuV111VZo4cWLsTuVDOu0FqThgKPmjjz6azjvvvAjNaAvD93322ScWW6lOwMknnxxzvIqaWVZe55iVlIGZpNKGTRG0AqP9LrPNunXrVlClwCKs79+S9PuqUaNG6ty5c1SbMVOYEC1rA96xY8dor0tAtssuu6S99947vjg/5/eSpNLJFRFJkqQyZsGCBemUU06JarMbbrghDR06NB1xxBFp0KBB6fPPP48WjMziuv322+P62Ydy2nbRhrFSpUrxc9WqVSN4a9So0Rp9PpKkkmnWrFmqUqVKzDZbvHhxXMYirKGZJK0ZnJszq4zgjLbfma233jrmm7Gxja+sja6hmSSVbgZnkiRJZcz06dNjoZQZCc2bN495ZsziYtGUsGz//fePMIw5OFOnTk2LFi1KH330UbRppCUjs7syVCnQjlCSVHbQhnHAgAExP4eWu3ARVpLWHNozUgE8d+7c6PyQ+37duHHjdPbZZ6cGDRrYRleSygiDM0llGovELAhn6BVO+wN2364uWd9ySVpTpkyZkqpVq1YQgNEWhjk3hGjscp09e3ZUoFWvXj1179499e7dO3Xt2jXNmzcvBpTnDx63HaEklT3MzmEzBBsnJElr3lFHHRVzhJ977rk0bdq0gsupRDv99NPT5ptvvkaPT5JUfAZnksqsDz74ILVt2zbaHWTq1q2brrvuurTrrruulsecNGlSatWqVVRuSNKa0qFDh2gHg169eqXrr78+HXLIIen8889PNWvWTMOGDYvB48w7o5qM98pLLrkk3sPq1avnX5wklRM33nhjzKuUJJUOBGRz5syJOcLMnZQklU2FtxtLUhmycOHCQr3Dse2228bX6vKvf/0r5gdJ0pp04IEHxnc2CvC+9PDDD8dQcvABfebMmdGysU6dOunCCy+M4Iz2MWD2WX7FmSSpbHKmmSSVLgcccEDMIG7ZsqXv0ZJUhrlqIkmSVAbRNnbixIkRihGarVixItrCVKpUKR155JHpjTfeSKeddlqqWrVqXJ9B5MxYMDSTJEmSVp82bdr48kpSGWerRqkCYB4XLbqaNm2a9txzz/jet2/ftHTp0oLr0Hrw4osvjioGrsOJ3n333VfofrL5Ya+//nrMy9lvv/3S/vvvny666KL01VdfpX/+858xU4cKB9oZMmcnF9e56aabonUY1+GrXbt26cEHHyx0vRNOOCG+pk6dmjp37pz22muvdNBBB6V+/fqlb7/9tuBYTjzxxPjzlVdeGce1qhlnVFfcdtttqXXr1nFfBx98cLQ143hKgud58803x58PPfTQOMbRo0fH4z355JMrXT97jZYvX15wXK+99loMBd5nn31iJ1qfPn1i8TsXi9t33313vE61a9eO587f3yeffFKi45VUvlFBtuGGG6Y333wzvfjii9EOhioz5p5dccUVMfcsPzSTJEmSJEnST7PiTKoAzjnnnDR9+vQImmhj+Pbbb0coNnfu3Pi+ZMmSGGJLtcIxxxwTA2snT54c4RqtEC+99NJC93fWWWel3XffPV1wwQVpypQpady4cWnx4sVp9uzZcfuOHTumUaNGxe8ZjLvjjjvG7Xr06JFmzZqVjj322LiMwIjQ7LLLLkubbrpptDLI8LhnnHFG3Bdh3DPPPJPuueeetM4668T9MsuM+xs+fHhcp379+qt8/j179owqDOb/EHa988476a677krz5s1LI0aMKPbrSIhHCPn000/HHCGeGyHjNddckyZMmBD3n/nmm2/iMQkQ11tvvYLLzz333BgMzN/JggUL0v333x+vCcFadj1ej7Fjx0YVyfHHHx+z3Ph7Ikjkcm4vSbwfMkOBAP6tt96KjQVHH310tGbMZG0ZDc0kSZIkSZKKx+BMKucIp6hEOO644yK0ydDKi6qsL774Ig0cODACofHjx6dtttkmfs/1+/fvH5VPnTp1SrvuumvBbXfeeec0bNiw+DO/I5Sj2mzo0KEF4Rdtw7p16xbBGiEZM3hefvnlqNo66aSTCu6rRYsWUQnGMeYGZ4R5gwYNisqu7HH4/aOPPhrBGQEg1XEEZ1SRHX744UU+f54jARYh25///OeCyzfZZJN0yy23xHFx++KgSoyqMYIzqvayQLBJkyZp0qRJUcFG9Qd4zK+//joq6vIrRAjL1l9//fiZ+6BijsqQLl26pFdeeSWNGTMmqv+6du1acDteI1qvUTnHayhJhGG8RxLif/jhh1FptsUWW8QL88MPP6S1117btoySJEmSJEklZKtGqZwjqOHr8ccfj6qmZcuWxeVUPPEzQQ9BEKEQYRpBW/aVBVkvvPBCofvMDbhYmN1uu+3iOy0QMwRbWQAGwikCNgK5DK3DqIYAIVN+JQWhWu7gc0KrkrYrfP755+M7oVQuwjuCwqzF469BOEalB23SMrSp3HLLLVO9evVWetwsNANhGCEeQRuylo+8lrl/F9WqVYtF8ez5SFKmZs2a0dKV0IzAjPdW3pMlSZIkSZJUclacSeXcuuuum66++uqYkUUVE20AmS3WvHnzaHHIIitVZ1R8MXOrKFQy5KpSpUqhn2kDRqtFvucGXfjxxx8LhWG0GqTl4Pvvv5/ee++9gsAs93rYeOON4/r5zyX/ej+HNocEU5ttttlK959bRfdrNG7cOJ4/4SQh2ueffx6vJ20hs9chU6tWrUI/85pR5cdxgtcEuaFhrvzXRJJyGZhJkiRJkiT9OgZnUgVAK6+GDRtGRRStC2mf+Je//CWNHDkyqs6yCieCnqJkrb9+amH25+bnUDXF7B1COAK6Bg0apO7du6f9998/Wh3myw+cfimCwdU924dAj/lmvJaEZk899VT67rvvVmrTuKrgK2upBoJBZp3RglKSJEmSJEmS9PsyOJPKuW+++SbNmTMnbb311ql9+/bxRThz1113peuuuy498sgjaYMNNkgrVqyImWH5YRczt7bffvtffRzM9aKaihlduUHZRx99lFan6tWrp5deeiktXbo0Va5cueByWkhec801EeatqtKuJAjJRo8eHbPOCChpq8jcoXxU2uVeTsBGtRkBYu7xUpmWH1jSzpHKNkmSJEmSJEnS6uGMM6mcI5giHLrjjjsKVXPVrl07/kylE60GqUKbOXNmodsOGTIk9erVK82fP/9XH8dnn30W33fcccdCl48aNaqg6qqkcqu0ViUL6f72t78VunzcuHFp4sSJheaNFUdWCccMoVz77bdftFzkPmlF2bZt2yJvf++99xY6XsK2L7/8MrVq1Sp+btasWXy/9dZbC92Ov5szzjgj3X333SU6XkmSJEmSJElS8VlxJpVzNWrUiDaCf/3rX9NXX32V9t577wixCHCowGrTpk3MO5s2bVrq2rVrOuaYY+I2hD/M7CJ4os3jr8X9cAyEP507d472iVRQTZ48OdoXcmwllVWQTZgwIdoldujQYaXrNG3aNL4GDx6c3nnnnQi45s2blx588MF02GGHpX322adEj5nNSqNij/aWWdDF8yEsGzZsWMGfizJr1qzUpUuXCMrefvvtCM7q1q2bDj/88Pg9IWbLli0j6Fu0aFFq1KhR+vTTT+Pvi1ltZ599dolfJ0mSJEmSpIqKjkqsG2200UZr+lAklRFWnEkVAPPMevTokWbMmBHtCe+888607777RjhTtWrVtO2226YxY8akFi1apPHjx8d1Zs+enXr27BmB028xb4yZZtdee21UW9EikoApaxlJAPXqq69GW8mSoHqNuWy0ouzfv3/MT8tHiEXl3JlnnhlVW1yP6rqzzjorDRgwoMTPg7CNlpa0uLzhhhsK/S4LvwjnaI1ZlL59+6YNN9wwXgOCw27dukU1YO5rPGjQoNS7d++0YMGCeM0I+erXrx9/XzvssEOJj1mSJEmSJKkiYqQGm5dX96gQSeXLWv/N7zcmSfpFmOFGtRjB45FHHlnodw899FC6+OKL04gRI6KKTJIkSZIkSavX0KFD08033xxdlfLHh0jSqlhxJkm/ESrCKlWqlFq3bu1rKkmSJEmSJEllkMGZpAqN2WpLliwp1teKFSuKvI8rrrgide/ePdpOHnvssfbMliRJxfL++++v9lfqu+++i7mppfkYJUlS2ccIkKOPPjpGg+y5554xE57RIcuXLy+o/Npll13SwoULY3wGYy64Ln/mslzPPvtsdPLh98ymP+aYY9IzzzxT4mO66KKLotoMhx56aIz7YNY8x/Hkk0+udH3GZnBcHDOdg7jea6+9FvPmOY4DDjgg9enTJ2am5aKh29133x3jPWrXrp0OOuigdMkll6RPPvmkxMcsqXT4w5o+AElak0aOHFlwEvVz7rnnnlSvXr2VLl+6dGnMj2vXrl3q1avXajhKSZJU3jDvlWr1f/zjH6vtMT744IPY3NOlS5dYcCqpyy+/PM2dOzcWmCRJkn6uHSLhVPv27SN4evrpp2PNhU08l156acF1TzzxxLTHHnuk888/P82fPz/dd999afHixWns2LHx++nTp8faSp06dSLIArPfCdjYsEx4VVydO3dOX375ZRwLj7fbbrtFqMeIjQkTJqRDDjmk4LrffPNNzKJnHtp6661XcPm5556bKleunM4555yYRX///fenWbNmRbCWXe+yyy6L42/btm06/vjj4xyM5zV16tS4nNtLKlsMziRVaJzQsZuoOHbdddciLx8yZMjP3rZjx47xJUmShClTpqQffvhhtb4Y7N5+9913f/HtX3rppVSlSpXf9JgkSVL5QjBGtVXTpk3ToEGDCi4/7rjjoursxRdfLHT9hg0bpquuuqrgZ4KtcePGpX//+9+pRo0aEWh9//33EcRtttlmcR0COarZ2NBTkuCMKjGqxgjOOL5sxlmTJk3SpEmTogvRhhtuGJcRmn399dexKTrXRhttFGHZ+uuvHz9zH1deeWV64IEHYnPSK6+8EtV2zLXv2rVrwe0Y40HV3G233RaVb5LKFoMzSRXatttuG1+SJEmSJEkqmXXWWSdNnjw5ArRcn376adpkk02iS08uQrBcVIERnNHWkOBsq622isuvvvrqqJynQoyKraJaK/5ShGNPPfVUtITMgrLHHnssbbnllit1GjrppJMKQjMQhg0cODCCNoKz7LgOPvjgQi0cq1Wrlnbaaaf0/PPPG5xJZZAzziRJkiRVeG+++WY6/fTTY7Fk//33jxkYL7/8csHr8tFHH8VO4gMPPDAWcNhFPGLEiEJVY9OmTYtdzexg7t+/f2rQoEHaa6+9ok0Qv8uwsMJ9s0DE9WlvlKF1IzNT995775jrccopp8Sx5eLY+KL9D/fNYzBLo1+/funbb7+N69A+iFZIYFc0j1MSXJ82Q7Qi4s/cHzu9eX3yF8bYKc4x0KYoOz5aQ1KxxmIUv+P1ojVlvvzXtU2bNtHaSJIklR3rrrtunOtQWcW5CVVhjRo1SvPmzYv5X7k233zzlW6L7JyKVof169dPjz/+eDriiCPifIq5YrnnUr9W48aN06abbhqPgc8//zwq45hR9n//V3i5vFatWoV+/sMf/pC22WabOE/Ce++9F99btGgRzzv3a86cOQXXk1S2WHEmSZIkqUJ79dVXo7UOu6IJm1hIof1Ot27d0qhRo9LWW2+djjrqqPTFF19EqMViCaHQDTfckN544400ePDgQvdH+yHu49RTT415GXfeeWf8+YUXXogd0yz+3HjjjWnJkiURNmWh1sMPPxwLTrSRZp4G7YL+/ve/RwjFcRCkZWjBeMYZZ0QraBaVnnnmmZjHyq7vCy64INWtWzf16NEjDR8+PK7DAlRJXHfddenaa69NG2+8ccwU4bF5Ln379o1d5bQ4yvDYzDLJbW3EDBCCSIIwXruJEydGgMfuc+4PPH9+t2LFiniOLKRx3zwGzy93HookSSqdCMZ69uwZ7RCZS1a7du0496BNIudEtGDMtdZaa/3k/dE6kdaPr7/+elSEcc5FRRrnRJxD8Fi/FmEd883YGERoRvUZG4Py2zSCc6t8hHxrr712/PnHH3+MWWecc0kqPwzOJEmSJFVoAwYMSBtssEEsnmyxxRZxGTuOW7ZsmW6//fYIjwh5qISiGi2b28FiEDMvCI6aN29eaMFn9OjRBQstVatWjaoqFpQIirguC0LLli1Lhx9+eEHVFi2JmL8xbNiwgvti1zWLOAyx5/gyHA9zRLJ2R506dYrjffTRRyM4oxU1VVws4lDxlT1OcXF9AkGCvuy2PBZhGrNHcoMzHpNwMXttsuM7++yzI9wD1WpUojHnIwvJaHPE8x4/fnyEkdnrSrUerw/PaVUzZiVJUukwffr0OMdhw9GFF15Y6HdU15cUm28+/vjj2EhECHfOOeekRYsWxSankSNHRnj2c+FbcXB+xfkanQII6GirSNvIfO+//36hywnYqCLLznuqV68e4R6Vadl5ZIZ2jmymklT22KpRkiRJUoVFBRTtCAmFchc7WOQgKCPMYtHjT3/6U6FgCFkoRHCWiwArd3fy7rvvXhAmrcqUKVMiRGL3M/Mxsi+qsWgnRLtG2hpmuH9aAmVoK0Tl2i9ZoCouQrSGDRvG4hIVZuAYaRlJZVnuIhazQJgJktvWiDkgPB9aIbE7m0U2dqNXqlSp0HPm9QMVepIkqXT77LPPimxpyP/HqTb7/vvvS3R/N910U5xDEJ7lzgvjPI3znZKGZlnrxfyWkQRzbNyhKp5zmbZt2xZ5+3vvvTfOWzKEbZyztWrVKn5u1qxZfL/11lsL3W7mzJlxrshmIElljxVnkiRJkiqsbO4Ew+jzsfOYIIqWiTvssMNKv6eSjPaO+bMrNttss0I/ZyFa7qJLvmw+Rv5O7VwffvhhDK0HVXD5rYNoO/RTj/FboPqMIJHFMEK+J554IhbE8lsbsRBFFV+u7DXm9Vq6dGm0viREYwbIqp6vJEkq3WjnzPnQ9ddfH2EXVeVsSqIFNS0MOY/KD61+CqEZrROpuqdSn/sm2GI+LG2oSyo7L7vrrrtizmwWdBHAEZZR6Z/9uSg8Fzb/EJS9/fbbEZzREjuryGeDE5t+mOVKZRyz3diYReDGsVOBL6nsMTiTJEmSVGFlQdOqdi9nCz2rWvDh9vkBVv5Q+ZIcx+WXX55q1qxZ5HVyw7tf8hi/BRacCO0ef/zxCM4ee+yxaKdIyJiLCrNVPUd+x2yQ7P5o4ViU/HZHkiSp9CEoo7U1s19HjBgR/59nAw2zSvn/PdX7M2bMKPb97bnnnhFyUcHFnFiqu9h8w/0RppUU7bcJ4h555JGYa5sFZyD8Ijij+oy200Vh9ipVacx//eMf/xgtKZmzlnsuRvts2kgSFtLWms4FzJclNCtq85Wk0s/gTJIkSVKFlS2SML8iH4s2CxcujFaC77777kq/Z1c1izlbbbXVrz4O5mOABRlmk+W3+uFxaH+4plHVRmBGcLZ48eI4tvPOO2+l61FVxmLZ2muvXXAZ7Zqw/fbbx+5vKtJo3Zj/fGnX+Morr8T1JElS6UfrZVpcFyULu2h5TeCUj9mnfOXiugRRvwWqvjinK0oWfrVv336Vt+echZmxP4Ww8NRTT40vSeWDM84kSZIkVVhUNe2xxx7RcpD2gZlly5alO+64I8KeJk2aRHug6dOnF7pttohC1VRJsVCT21bxoIMOimCMndWESblzQ3r16pUuvvjiQiFUcWTX/6XtG/OPMXd3Nm2XaMkE5pvlow3jQw89VPAz7RyZ8bHRRhulBg0axAITrY2Y7Ub4lmvIkCHxnOfPn/+LjluSJKk4aK/IBqnWrVv7gkkqxIozSZIkSRVanz59Yp7GEUcckY4++ugIsMaMGRPhT+/evQtma3Tv3j0de+yx0X5o8uTJ6dlnn412P7ktf4qL3csEdYRzzMmoU6dOPFa/fv3iONj5TPD1wAMPRGXbwIEDi2x/+FMqV64c3ydMmBCVYh06dCjRfXCMzPJgB3m9evVSrVq14nKOl0o92jTShiibu5aLx6E107x586JyjOsSkHEZ4RmoVJs2bVrq2rVr7DSnDROvM9VshJUNGzYs0fOVJEnl31dffRUbeIqDSn7OgfJdccUV0VXgpZdeSieffHLBuYkkZQzOJEmSJFVotAMiHBo8eHDMuWBmWe3atdOAAQPS7rvvHtcZO3Zsuummm9K4ceNiwYYw6KKLLkonnnjiKuej/RQWaebOnRv32bFjxwjOuK9q1apF1dnQoUPjOHbeeeeoNqM6q6R23HHHmB9G5Vf//v0j/Npuu+2KfXvaKbGwxKyOM888syA44/m2bds23XbbbfG9KMz24HY87ujRo2MG2s0335xatGhRcJ1tt902AkoqzMaPHx9BJYEcj8vrs6bmuEmSpNKLFo6cUxTHPffcE+c/+di8xNy1du3aRZW7JOVb67+rmnItSZIkSVIRBg0alEaNGhWVd/m7tAnr3nnnnfidJEnSb2nBggXxVRy046bqTJJKyoozSZIkSVKx0R7pkUceSa1atbK1kSRJ+l1Rsc6XJK1OBmeSJEmSVAH85z//ST/88MPPXo/Zasw3y0dryeHDh6fZs2fH3LVu3bqtpiOVJEmSpDXH4EySJEmSKoBOnTqlDz744GevV7169fTcc8+tdDktGadOnRrBGrPLdtlll9V0pJIkSZK05jjjTJIkSZIqgBkzZqTly5f/7PXWW2+9tN9++/0uxyRJkiRJpY3BmSRJkiRJkiRJkpRS+j9fBUmSJEmSJEmSJMngTJIkSZIkSZIkSQpWnEmSJEmSJEmSJEkGZ5IkSZIkSZIkSdL/WHEmSZIkSZIkSZIkGZxJkiRJkiRJkiRJ/2PFmSRJkiRJkiRJkmRwJkmSJEmSJEmSJP2PFWeSJEmSJEmSJElKSun/Ab3OngkdAxMWAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1760x440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sel_imgs=set(subset.dicom_id.astype(str))\n",
    "vqa_sel=vqa_all[vqa_all.image_id.astype(str).isin(sel_imgs)].copy()\n",
    "def ans_type(a):\n",
    "    if not isinstance(a,list) or not a: return \"no answer\"\n",
    "    x=str(a[0]).strip().lower(); return x if x in (\"yes\",\"no\") else \"open\"\n",
    "vqa_all[\"ans_type\"]=vqa_all.answer.apply(ans_type)\n",
    "vqa_sel[\"ans_type\"]=vqa_sel.answer.apply(ans_type)\n",
    "fig,axes=plt.subplots(1,3,figsize=(16,4))\n",
    "for ax,col in zip(axes,[\"semantic_type\",\"content_type\",\"ans_type\"]):\n",
    "    a=vqa_all[col].value_counts(normalize=True)*100\n",
    "    b=vqa_sel[col].value_counts(normalize=True)*100\n",
    "    pd.DataFrame({\"full(%)\":a,\"subset(%)\":b}).fillna(0).plot(kind=\"bar\",ax=ax,color=[\"#888\",\"#DD8452\"])\n",
    "    ax.set_title(col); ax.tick_params(axis=\"x\",rotation=30)\n",
    "plt.suptitle(f\"VQA dist full vs subset (n_sel={len(vqa_sel):,})\"); plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Xuất bundle: manifest + reports + vqa (giữ cấu trúc gốc PhysioNet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: 40,000 manifest rows\n",
      "val: 5,000 manifest rows\n",
      "test: 5,000 manifest rows\n"
     ]
    }
   ],
   "source": [
    "chex_lookup = chex_df.set_index(\"study_id\")[label_cols].to_dict(orient=\"index\")\n",
    "\n",
    "def build_manifest(df, start, split):\n",
    "    rows=[]\n",
    "    for k,r in enumerate(df.itertuples(index=False), start=start):\n",
    "        sp=f\"files/{r.subset}/p{r.subject_id}/s{r.study_id}\"   # path gốc PhysioNet\n",
    "        labs=chex_lookup.get(r.study_id,{})\n",
    "        rows.append({\n",
    "            \"study_name\":f\"Study_{k}\",\"split\":split,\n",
    "            \"subject_id\":int(r.subject_id),\"study_id\":int(r.study_id),\"subset\":r.subset,\n",
    "            \"physionet_study_path\":sp,\"dicom_id\":r.dicom_id,\n",
    "            \"image_filename\":f\"{r.dicom_id}.jpg\",\"view\":r.ViewPosition,\n",
    "            \"image_relpath\":f\"{sp}/{r.dicom_id}.jpg\",   # path ảnh trong package (giữ gốc)\n",
    "            \"report_relpath\":f\"{sp}.txt\",               # path report trong package (giữ gốc)\n",
    "            \"jpg_url\":f\"{JPG_BASE_URL}/{sp}/{r.dicom_id}.jpg\",\n",
    "            \"has_vqa\":bool(r.has_vqa),\n",
    "            \"chexpert\":{l:(None if pd.isna(labs.get(l)) else int(labs.get(l))) for l in label_cols},\n",
    "        })\n",
    "    return rows\n",
    "\n",
    "manifests={\"train\":build_manifest(tr,1,\"train\"),\n",
    "           \"val\":  build_manifest(va,N_TRAIN+1,\"val\"),\n",
    "           \"test\": build_manifest(te,N_TRAIN+N_VAL+1,\"test\")}\n",
    "\n",
    "for sp,rows in manifests.items():\n",
    "    json.dump(rows, open(BUNDLE/f\"manifest_{sp}.json\",\"w\"), indent=2)\n",
    "    flat=[]\n",
    "    for x in rows:\n",
    "        d={k:v for k,v in x.items() if k!=\"chexpert\"}\n",
    "        d.update({f\"chex_{k}\":v for k,v in x[\"chexpert\"].items()}); flat.append(d)\n",
    "    pd.DataFrame(flat).to_csv(BUNDLE/f\"manifest_{sp}.csv\", index=False)\n",
    "    print(f\"{sp}: {len(rows):,} manifest rows\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reports copied: 50,000 | missing: 0\n",
      "→ cấu trúc: bundle/reports/files/pXX/pSUBJ/sSTUDY.txt\n"
     ]
    }
   ],
   "source": [
    "# Copy report đã chọn -> bundle/reports/<path gốc>.txt  (giữ nguyên cấu trúc + tên)\n",
    "n_ok=n_miss=0\n",
    "for sp,rows in manifests.items():\n",
    "    for x in rows:\n",
    "        src=CXR_ROOT/x[\"report_relpath\"]                      # files/pXX/.../sSTUDY.txt\n",
    "        dst=BUNDLE/\"reports\"/x[\"report_relpath\"]\n",
    "        dst.parent.mkdir(parents=True, exist_ok=True)\n",
    "        if src.exists():\n",
    "            shutil.copy(src, dst); n_ok+=1\n",
    "        else: n_miss+=1\n",
    "print(f\"reports copied: {n_ok:,} | missing: {n_miss:,}\")\n",
    "print(\"→ cấu trúc: bundle/reports/files/pXX/pSUBJ/sSTUDY.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: vqa 102,941 mẫu -> vqa.json\n",
      "val: vqa 12,189 mẫu -> vqa_val.json\n",
      "test: vqa 11,880 mẫu -> vqa_test.json\n"
     ]
    }
   ],
   "source": [
    "# Lọc + remap VQA theo ảnh đã chọn -> bundle/vqa/{vqa.json,vqa_val.json,vqa_test.json}\n",
    "# image_path trỏ thẳng path gốc trong package: files/pXX/pSUBJ/sSTUDY/<dicom>.jpg\n",
    "VQA_OUT={\"train\":\"vqa.json\",\"val\":\"vqa_val.json\",\"test\":\"vqa_test.json\"}\n",
    "(BUNDLE/\"vqa\").mkdir(parents=True,exist_ok=True)\n",
    "for sp,rows in manifests.items():\n",
    "    d2path={str(x[\"dicom_id\"]): x[\"image_relpath\"] for x in rows}\n",
    "    d2name={str(x[\"dicom_id\"]): x[\"study_name\"]    for x in rows}\n",
    "    vq=vqa_all[vqa_all.image_id.astype(str).isin(d2path)].copy()\n",
    "    vq[\"study_name\"]=vq.image_id.astype(str).map(d2name)\n",
    "    vq[\"image_path\"]=vq.image_id.astype(str).map(d2path)   # path gốc\n",
    "    cols=[c for c in [\"study_name\",\"image_path\",\"question\",\"answer\",\"semantic_type\",\n",
    "          \"content_type\",\"subject_id\",\"study_id\",\"image_id\",\"template\",\n",
    "          \"template_program\",\"template_arguments\"] if c in vq.columns]\n",
    "    json.dump(vq[cols].to_dict(orient=\"records\"),\n",
    "              open(BUNDLE/\"vqa\"/VQA_OUT[sp],\"w\"), indent=2, default=str)\n",
    "    print(f\"{sp}: vqa {len(vq):,} mẫu -> {VQA_OUT[sp]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BUNDLE zip: D:\\USTH\\KLTN\\subset_bundle.zip  (49.7 MB)\n",
      "\n",
      "→ Upload file zip này lên Google Drive, rồi chạy build_subset_colab.ipynb\n"
     ]
    }
   ],
   "source": [
    "# Zip toàn bộ bundle -> 1 file để upload Drive\n",
    "zip_path = shutil.make_archive(str(BUNDLE.parent/\"subset_bundle\"), \"zip\", BUNDLE)\n",
    "sz = Path(zip_path).stat().st_size/1e6\n",
    "print(f\"BUNDLE zip: {zip_path}  ({sz:,.1f} MB)\")\n",
    "print(\"\\n→ Upload file zip này lên Google Drive, rồi chạy build_subset_colab.ipynb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======================================================\n",
      "  PHASE 1 (LOCAL) DONE\n",
      "======================================================\n",
      "  eligible pool : 108,783\n",
      "  train/val/test: 40,000/5,000/5,000\n",
      "  VQA mẫu subset: 127,010\n",
      "  bundle dir    : D:\\USTH\\KLTN\\subset_bundle\n",
      "  Next: upload subset_bundle.zip -> Drive -> PHASE 2 (Colab)\n",
      "======================================================\n"
     ]
    }
   ],
   "source": [
    "print(\"=\"*54)\n",
    "print(\"  PHASE 1 (LOCAL) DONE\")\n",
    "print(\"=\"*54)\n",
    "print(f\"  eligible pool : {len(elig):,}\")\n",
    "print(f\"  train/val/test: {len(tr):,}/{len(va):,}/{len(te):,}\")\n",
    "print(f\"  VQA mẫu subset: {len(vqa_sel):,}\")\n",
    "print(f\"  bundle dir    : {BUNDLE}\")\n",
    "print(\"  Next: upload subset_bundle.zip -> Drive -> PHASE 2 (Colab)\")\n",
    "print(\"=\"*54)"
   ]
  }
 ],
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