{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a0b670f6",
   "metadata": {},
   "source": [
    "Sage attention seems to have really significant error for the SM 9.0 version. This happens to the latest main branch, 2.2.0 version released as of Nov 2025."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef3f78cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/Qwen-Image-Edit-Angles\n"
     ]
    }
   ],
   "source": [
    "%cd /home/ubuntu/Qwen-Image-Edit-Angles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4b4fed92",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.local/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 82011.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running tests with 100 inputs...\n",
      "Config: batch_size=4, head_num=32, seq_len=64, head_dim=128, dtype=torch.float16\n",
      "--------------------------------------------------------------------------------\n",
      "Completed 20/100 inputs...\n",
      "Completed 40/100 inputs...\n",
      "Completed 60/100 inputs...\n",
      "Completed 80/100 inputs...\n",
      "Completed 100/100 inputs...\n",
      "\n",
      "Completed all 100 inputs!\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import time\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch.nn.attention import SDPBackend, sdpa_kernel\n",
    "from sageattention import (\n",
    "    sageattn_qk_int8_pv_fp16_cuda, \n",
    "    sageattn_qk_int8_pv_fp16_triton, \n",
    "    sageattn_qk_int8_pv_fp8_cuda, \n",
    "    sageattn_qk_int8_pv_fp8_cuda_sm90\n",
    ")\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "\n",
    "# Import Flash Attention 3\n",
    "try:\n",
    "    from kernels import get_kernel\n",
    "    _k = get_kernel(\"kernels-community/vllm-flash-attn3\")\n",
    "    _flash_attn_func = _k.flash_attn_func\n",
    "    FA3_AVAILABLE = True\n",
    "except Exception as e:\n",
    "    _flash_attn_func = None\n",
    "    FA3_AVAILABLE = False\n",
    "    print(f\"Flash Attention 3 not available: {e}\")\n",
    "\n",
    "\n",
    "def calculate_tolerance_metrics(actual, expect):\n",
    "    \"\"\"Calculate tolerance metrics between actual and expected outputs.\"\"\"\n",
    "    actual = actual.float()\n",
    "    expect = expect.float()\n",
    "    diff = (actual - expect).abs()\n",
    "    eps = torch.tensor(\n",
    "        torch.finfo(actual.dtype).eps, device=actual.device, dtype=actual.dtype\n",
    "    )\n",
    "    rdiff = diff / torch.maximum(torch.maximum(actual.abs(), expect.abs()), eps)\n",
    "    return {\n",
    "        \"mean_relative_tolerance\": rdiff.mean().item(),\n",
    "        \"max_relative_tolerance\": rdiff.max().item(),\n",
    "        \"mean_absolute_tolerance\": diff.mean().item(),\n",
    "        \"max_absolute_tolerance\": diff.max().item(),\n",
    "        \"mse\": F.mse_loss(actual, expect).item()\n",
    "    }\n",
    "\n",
    "\n",
    "def flash_attn3_wrapper(q, k, v):\n",
    "    \"\"\"\n",
    "    Wrapper for Flash Attention 3 that matches the SageAttention interface.\n",
    "    FA3 expects (batch, seq_len, num_heads, head_dim)\n",
    "    SageAttention provides (batch, num_heads, seq_len, head_dim)\n",
    "    \"\"\"\n",
    "    # Transpose from (B, H, S, D) to (B, S, H, D)\n",
    "    q = q.transpose(1, 2)\n",
    "    k = k.transpose(1, 2)\n",
    "    v = v.transpose(1, 2)\n",
    "    \n",
    "    # Call FA3\n",
    "    outputs, _ = _flash_attn_func(q, k, v, causal=False)\n",
    "    \n",
    "    # Transpose back to (B, H, S, D)\n",
    "    outputs = outputs.transpose(1, 2)\n",
    "    return outputs\n",
    "\n",
    "\n",
    "# Test configuration\n",
    "batch_size = 4\n",
    "head_num = 32\n",
    "seq_len = 64\n",
    "head_dim = 128\n",
    "dtype = torch.float16\n",
    "num_inputs = 100\n",
    "\n",
    "print(f\"Running tests with {num_inputs} inputs...\")\n",
    "print(f\"Config: batch_size={batch_size}, head_num={head_num}, seq_len={seq_len}, head_dim={head_dim}, dtype={dtype}\")\n",
    "print(\"-\" * 80)\n",
    "\n",
    "# Define attention implementations to test\n",
    "attention_types = {\n",
    "    \"SDPA (Flash)\": lambda q, k, v: F.scaled_dot_product_attention(q, k, v),\n",
    "    \"SageAttn QK-INT8 PV-FP16 CUDA\": sageattn_qk_int8_pv_fp16_cuda,\n",
    "    \"SageAttn QK-INT8 PV-FP16 Triton\": sageattn_qk_int8_pv_fp16_triton,\n",
    "    \"SageAttn QK-INT8 PV-FP8 CUDA\": sageattn_qk_int8_pv_fp8_cuda,\n",
    "    \"SageAttn QK-INT8 PV-FP8 CUDA SM90\": sageattn_qk_int8_pv_fp8_cuda_sm90,\n",
    "}\n",
    "\n",
    "# Add Flash Attention 3 if available\n",
    "if FA3_AVAILABLE:\n",
    "    attention_types[\"Flash Attention 3\"] = flash_attn3_wrapper\n",
    "\n",
    "# Storage for metrics and runtimes across all inputs\n",
    "all_metrics = {name: defaultdict(list) for name in attention_types.keys()}\n",
    "all_runtimes = {name: [] for name in attention_types.keys()}\n",
    "\n",
    "# Enable math backend for ground truth\n",
    "torch.backends.cuda.enable_math_sdp(True)\n",
    "\n",
    "# Run tests for 100 inputs\n",
    "for i in range(num_inputs):\n",
    "    # Generate random input\n",
    "    q = torch.randn(batch_size, head_num, seq_len, head_dim, device=\"cuda\", dtype=dtype)\n",
    "    k = torch.randn_like(q)\n",
    "    v = torch.randn_like(q)\n",
    "    \n",
    "    # Get ground truth using mathematically correct implementation\n",
    "    with sdpa_kernel(SDPBackend.MATH):\n",
    "        out_math = F.scaled_dot_product_attention(q, k, v)\n",
    "    \n",
    "    # Test each attention type\n",
    "    for name, attn_fn in attention_types.items():\n",
    "        try:\n",
    "            # Measure runtime\n",
    "            torch.cuda.synchronize()\n",
    "            start_time = time.perf_counter()\n",
    "            \n",
    "            if name == \"SDPA (Flash)\":\n",
    "                with sdpa_kernel(SDPBackend.FLASH_ATTENTION):\n",
    "                    out = attn_fn(q, k, v)\n",
    "            else:\n",
    "                out = attn_fn(q, k, v)\n",
    "            \n",
    "            torch.cuda.synchronize()\n",
    "            end_time = time.perf_counter()\n",
    "            runtime = (end_time - start_time) * 1000  # Convert to milliseconds\n",
    "            all_runtimes[name].append(runtime)\n",
    "            \n",
    "            # Calculate metrics\n",
    "            metrics = calculate_tolerance_metrics(out, out_math)\n",
    "            \n",
    "            # Store metrics\n",
    "            for metric_name, value in metrics.items():\n",
    "                all_metrics[name][metric_name].append(value)\n",
    "                \n",
    "        except Exception as e:\n",
    "            print(f\"Error with {name}: {e}\")\n",
    "    \n",
    "    if (i + 1) % 20 == 0:\n",
    "        print(f\"Completed {i + 1}/{num_inputs} inputs...\")\n",
    "\n",
    "print(f\"\\nCompleted all {num_inputs} inputs!\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cba055ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime Statistics (in milliseconds):\n",
      "================================================================================\n",
      "\n",
      "SDPA (Flash):\n",
      "  Mean: 0.1078 ms\n",
      "  Std:  0.3156 ms\n",
      "  Min:  0.0691 ms\n",
      "  Max:  3.2466 ms\n",
      "\n",
      "SageAttn QK-INT8 PV-FP16 CUDA:\n",
      "  Mean: 5.0245 ms\n",
      "  Std:  47.9926 ms\n",
      "  Min:  0.1753 ms\n",
      "  Max:  482.5450 ms\n",
      "\n",
      "SageAttn QK-INT8 PV-FP16 Triton:\n",
      "  Mean: 0.3310 ms\n",
      "  Std:  1.1954 ms\n",
      "  Min:  0.1947 ms\n",
      "  Max:  12.2249 ms\n",
      "\n",
      "SageAttn QK-INT8 PV-FP8 CUDA:\n",
      "  Mean: 0.2200 ms\n",
      "  Std:  0.3340 ms\n",
      "  Min:  0.1746 ms\n",
      "  Max:  3.5422 ms\n",
      "\n",
      "SageAttn QK-INT8 PV-FP8 CUDA SM90:\n",
      "  Mean: 0.3097 ms\n",
      "  Std:  1.0341 ms\n",
      "  Min:  0.1956 ms\n",
      "  Max:  10.5982 ms\n",
      "\n",
      "Flash Attention 3:\n",
      "  Mean: 0.1370 ms\n",
      "  Std:  0.2310 ms\n",
      "  Min:  0.1071 ms\n",
      "  Max:  2.4339 ms\n"
     ]
    }
   ],
   "source": [
    "# Calculate mean metrics and runtimes across all inputs\n",
    "mean_metrics = {}\n",
    "mean_runtimes = {}\n",
    "for name in attention_types.keys():\n",
    "    mean_metrics[name] = {\n",
    "        metric: np.mean(all_metrics[name][metric]) \n",
    "        for metric in all_metrics[name].keys()\n",
    "    }\n",
    "    if all_runtimes[name]:\n",
    "        mean_runtimes[name] = {\n",
    "            'mean_ms': np.mean(all_runtimes[name]),\n",
    "            'std_ms': np.std(all_runtimes[name]),\n",
    "            'min_ms': np.min(all_runtimes[name]),\n",
    "            'max_ms': np.max(all_runtimes[name]),\n",
    "        }\n",
    "\n",
    "# Display runtime statistics\n",
    "print(\"Runtime Statistics (in milliseconds):\")\n",
    "print(\"=\" * 80)\n",
    "for name, stats in mean_runtimes.items():\n",
    "    print(f\"\\n{name}:\")\n",
    "    print(f\"  Mean: {stats['mean_ms']:.4f} ms\")\n",
    "    print(f\"  Std:  {stats['std_ms']:.4f} ms\")\n",
    "    print(f\"  Min:  {stats['min_ms']:.4f} ms\")\n",
    "    print(f\"  Max:  {stats['max_ms']:.4f} ms\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cf0c0ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runtime plots generated successfully!\n"
     ]
    }
   ],
   "source": [
    "# Create runtime comparison plot\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 6))\n",
    "fig.suptitle(f'Attention Implementation Runtime Comparison (averaged over {num_inputs} inputs)', \n",
    "             fontsize=16, fontweight='bold')\n",
    "\n",
    "names = list(mean_runtimes.keys())\n",
    "colors = ['#485696', '#7a77a9', '#858a9d', '#f9c784', '#fc7a1e', '#f24c00']\n",
    "\n",
    "# Plot 1: Mean runtime with error bars (std)\n",
    "mean_times = [mean_runtimes[name]['mean_ms'] for name in names]\n",
    "std_times = [mean_runtimes[name]['std_ms'] for name in names]\n",
    "\n",
    "bars = ax1.bar(range(len(names)), mean_times, yerr=std_times, capsize=5, color=colors, alpha=0.7)\n",
    "ax1.set_xlabel('Attention Type', fontweight='bold', fontsize=12)\n",
    "ax1.set_ylabel('Runtime (ms)', fontweight='bold', fontsize=12)\n",
    "ax1.set_title('Mean Runtime with Standard Deviation', fontsize=14, fontweight='bold')\n",
    "ax1.set_xticks(range(len(names)))\n",
    "ax1.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax1.grid(axis='y', alpha=0.3)\n",
    "\n",
    "# Add value labels on bars\n",
    "for i, (bar, val, std) in enumerate(zip(bars, mean_times, std_times)):\n",
    "    height = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2., height + std,\n",
    "             f'{val:.3f}±{std:.3f}', ha='center', va='bottom', fontsize=9, fontweight='bold')\n",
    "\n",
    "# Plot 2: Box plot showing distribution\n",
    "runtime_data = [all_runtimes[name] for name in names]\n",
    "bp = ax2.boxplot(runtime_data, labels=[name.replace(' ', '\\n') for name in names], \n",
    "                  patch_artist=True, showmeans=True)\n",
    "\n",
    "# Color the box plots\n",
    "for patch, color in zip(bp['boxes'], colors):\n",
    "    patch.set_facecolor(color)\n",
    "    patch.set_alpha(0.7)\n",
    "\n",
    "ax2.set_xlabel('Attention Type', fontweight='bold', fontsize=12)\n",
    "ax2.set_ylabel('Runtime (ms)', fontweight='bold', fontsize=12)\n",
    "ax2.set_title('Runtime Distribution (Box Plot)', fontsize=14, fontweight='bold')\n",
    "ax2.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.setp(ax2.xaxis.get_majorticklabels(), fontsize=9, ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Runtime plots generated successfully!\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e290fb",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "74c352d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+N+jMDvO4pTJufJlW7Wl0WJ3lzq6Mb+3mN9ndsMT0XczrnEp6lGVbG7dmb/YrFH+ia2kf6+Hv+j/l6+PAZh3y1etd1HH4YL3vrTKPgyvpR5Rp1ePPiZW8b6ikL/G76cXvfCOKG+P+g7ZjVHX4YiXvr8q0F4BVKBo4aser0yvz68n3XusVMbOSVm9f2KP4KBq4a2knd5i+XTnRi+MsRaNr7Tf1BGVv4nI+f6rk7XQ77rC8J4FlK+NOqszjk2XaxpW0Y8q0Wm/DRcB6vd3P0r7udDdlz55ebitTK/Oo14Pnpcr4ajl9sk4891TSNqzknVRJ77IuR/c9eHpST6nO44BOtpvb6pThzEreztKr5TW1Q+xbVMZdQdHLci9gTCfr+u1K/rX6+nt3cBiug62WGql2pThrAEXPlfUiYlPaWu1h4O6e35OzIvWejvNYZlbPrvynfB1dJ29vvLryvnqN90OV9/VivqdOLFCcieuvxzOzdpb/hU5iWlC+1utNA5V1Kef1ZPnxVVG5h0EHa9B2prAr9b6fOZX39WJeUEmrLb+v28L9mbmofN/rso+IDSkqx3tRnLWq94TEFeuk9VTdbaqM+V/lx2Upyrujgdiu6t1k+Xed5L2pm3l1HN/Z7wW6/810t6yu/Kh8rZXZjzIzO8nb1+2qOz2Nv3p2+M5OcxWXDlxPcTb9EIobJV8D/CsidulmGdUeXfXKodabZgzFPdRqPR1eoXzqYWa+QrFff5jiD8VRFA0Pt5X30Xh9NzG0k8UNjmu9VVanaOT5VacrUBxjavdEeQhYrkyr9l7q73Hnyo6/BdrfpLk3qr/lpP39n9bq5X6lp9tIVe2eZPdTXI5V1ddtvrv9TXWahzt53ydR3DT9zxS9h19PcaKpo+p+uLZNr0BRxvvSdrz6afna014X9cqi3e+7l/FV7x34j07e1/TmOLs6bT3cHi1/szU9/Q6qy3ski146NUvsszPzTuC6Mm1KeS+dvcvPl2bmP6r5u1GvnG/pYt/dH49V3q9WeV+9h12t12Vv8vZVb+spnW03a3XM2EGvn/CcmX+j6KU5n6IR+5sUDdP/jOI+cR3nOSIetCJ1xgYejVTVy7O2Bm4rh+9W0t8dEfX+kNbT1cG7etA8qs4f0aB+xeHlHiyjL5WGJyrv1+vkfb0DfTWega6sLOxlej2L44/iZsW1itZzmflS/Ul4iuIMHcC9nXw3y2Tm73saW4fKZEfVcv1QJ8t7R53pFpd9FxXFrr6TPWirmJ8NrFEu68Q+zKueuttURIyiuMwCivJ6aokFZQ7mdlXPC70c39nvpePner+Z7pbVqcy8nuJeWVCcqf15F9mry962k+2qegPNnpZzT+Ov/nl4c2eZMvO+zHwHxX1odqa4XPRRigr9qZ1NV/p35f2r64w/l7Z4P0Tb+l6SmYv/GGbmBRTf23iK38XRFPuATal/qWx3qpfN/TwzX+wib7XxZn3ajju/qKRvXjb6DAfV33JQNJ7VzKV3+5UebSMdXFG+bgWc1+FS2do2/xwwupP9dr1LL7rb38ytvB/Tyfu+2oG2Y/1lwGvLWA/pJP8ltDVqTKHtksO7M7N2KVT1t//HLo5hd9SZf8ffd2/iq5bTupX39RpJe3OcfZqiURbgtR0uj+npd1Bd3uvK41BNZ/vs2v51Q4r9QG0f87NO8vdkP1vT5+NAN66vvN+48n6TOnl6k7evelJPqeqs/lnbtqr1tmoj0YadzK/LZWbmMRTHmi0oLtOq3dx9L9oa9Gpq9ce69Rap2dnAoxEniif07NFtxqKnSPVgXesRsn6UT4WpMw7gzR0qohfTduD7fBRP01opIl4bETtExI9ou79Bb1WXu1F5v5DuXFB5f2xEvCaKpxx9tZJer0FjuPtkRLyjPAv5LdrOPv6xswky8wWKiiwU5Xd8RKwdEatExOYR8bnK+IFQLftjImLbiFghIl4fEe+OiF/Qdr+V3upqG6w2Oj0PvBgRW1M+pamreVXuodGViyrL+EhEbFf+zr5K29m4y7v58ztcXU3xpwOKRt89IuJVEXEAxX0uAO7JzI49DAbCN4D/A76RmXO7yFfdrk4pt90VImLDKJ5sdQFt97+Btu93rYio/jnrq/Mr7z9XLvNVEbFuRHwmyqdTRcThEbEvRWX9TxRPe3mknK5j41k75Vn0WiPBZnXGzwPOKz/uT9GIBO3/oBERp1LcxPU5iu32t7T9kegyhk5cSNFA8390fo8kyj+q+3U2voNePzFokOwVEbtE8TSsw2n7g31TZj5J7/Yr1W3k2IjYqTwOjo2Ir3Sy/KNp66myC/DLyp/12jb/KorHQb++nN8mETGN9r1xe+OKyvuPRMTby+P9sfUyR/sn+szuZt7V8noR+E95z5fP1Mtc9l45s/y4M8W9caCyTWfmvRSXGwLsHMVTz1YrhwkR8VXanhDand7Ed3nl/Uci4m3lCbFv1lmPHh9nM/M/FJdbQnHs+GKZbx+K++J0q1xeLb41KO7RtmpEbEvbk6cW0v5eONUG4qPK16cobqpc09v9bJ9ExFpRPF31VZXkFWvplUavmZXxXy/rchMpenpB0Xh+cfm+dvsBgCPK7fYttN3z73m66H04SA6L4kmHa9J2n0poq7P9m7Z987YRsUZEvIrOG+KrdaBNo/1TzTaOiK8Dm1P0FjoPuLSSv+O+f/Py9cYOvcikkWEwr/9ycGjEQPunUPyyzvjqPTeurqRfUEmvDdPLcStQ/4kAreX4L9QZt8R8yrxzqFy/Xic9O6TfXmd+U8txMytpY8u0vj5Fa2ZlmdX02T0o8zkd4yjTa2lzKmlTOymX2R3Xv0Peevdk6MlTtN5M231O6g3VvNM7lnFn3w2dP23qB91sC1PrlM/sDuVZL7ZOt0GK+0e8UGfc3ztZ7ufr5J3dzXr16SlaHdZrdmWasdVxdbap6V0sL4GbK3lndjXfHoz/EH17ilaX69DFb+RVXeTr7Cla9fZP7baDyjxOqzN+Zp3f1NQ6y687np49RevSLvL8rgdlVPuT8gh17mlB0XBTnedcOtzfh67vBXJoN8uvrvsS98So5FviHjzAjpW0v9SZ5o2V8f+k/T01Ztf7HjvZ1yyxP+5u+i7m1eVTtOjFfoW+P0VrFMWfsVrameW8XkNx8+xOt/kebLPV9Oq+bGad+dV9Ih69OBZSXH70eDflNb3DNG+g/ZOJXgZe0yHPxE6+hyXioov9U2/jo/5TtKpPunqwkrc3x9mdqL+/rX4H3W3H3T1F64g605zVIc+pHcb3eD9LN/dH7Cb2rubf7nujaFyul+dlYLcO8/1mF/P8VA/iml6v/OttZx3S53Qyj3r7l8VP0Srz/7wy7kWKBp//VNJmVvKuS/v7Ei0uL4onzXW27ouALSrzWZW2+08d3V25ODg042APHo1E1bOjZ9YZfylt9w3ZprzPABRnsS6k7Wz+Yln0TNiXouHk+Trjj6d4+sQfKCo5CynOTlxLccCrF0dPfYTiCR/P9iRzFmfI3klxdvQuigPii8DNFH/s98jmPGPxNYqbpj5CsU7XAjtl5u1dTZSZd1E8BeaHFH8YFlA8WeJOigrsgZ1O3AeZeRDFNnhluZyXKbrhX0HREPiHPs63020wM++n6LV2E8V3/SDFk4t+QX3fp7gHTO2x1D1Z/nEUN+q+nGK9FlKs188oKk89ve/GsJOZ51D82byAtt/vYxRnPN+RmbMbFhxQ/l73pLhp7HUUvVNeorjfxEVl+t8qk0ynOKv/BAMkMz9P8TSjSyn2kS9T/BZ/R9uTWs6g2Ic+TLEdvkxxb5UT6dm9Z04vX19L/SdDXU77e2Odk5kLOuT5FsV9Rh6n+B6fpyibQyhu/DxYujzuZObfKRrvoOgpU2/9htpPKJ6C9iDFfvFWYM/M/CP0br+SmUnRI/YAil5xz5bz/Add9DDJ4p4eU2i7j9BHgNMy8zGKpw4eT3ET25cotvt7y+V39rTFnpgGnEDx+3iBYpvurGdG9X4hN3Q108x8muIplX+m2O4eofgtfquLae6n6O1Wc2G57tU8V1Lc7PpMit/WyxSNG7dSXPr4pa7i6kd8BwLfoa2cLqbYB9Qs7lHRm+NsZl4GvJfi5NUCin3ENNr3oOluXe6kuBTnpxQNpgvL5V1BcQP+euv0s64+92E/OxQ+SnFvs9vLWOaVsUzMDpeWZ+aXKJ40dQPF9/UcRd1xcmZ29iTIwfQpihNecyn2H5cA78zM6uW4h1IcN56g2BbOp3ha2RIy818U5XEn7S/vgmKb+yFFz76nKRp1nqY4Zrw7i3v01Lyfogf4K7T1IJRGlCiOyZI0/ETEVNqunf94Fo/DlDRCRcQ1FPdNOzcze3rJkzQoIuIoihMLt1E09jbjZah9EhFvBl7JzHvKzytRNIzVGmu+nZlHNCo+DT8RMZ3iZBzADo0+OVKPxxgtDezBI0mShovDytd9ImJcQyORih4vL1I8cnypadwp7QTcHRHPRsQ/KHpE1Bp37qboXSU1jYjYkaJx5yWKy86lEanXj6KTJEkaDFk8PchH2GpYyMztGh1DA/2N4rKstwLrUFxCcyfFZZknZub8hkUm9UFmXo7HFy0FvERLkiRJkiSpyXmJliRJkiRJUpOzgUeSJEmSJKnJ2cAjSZIkSZLU5GzgkSRJkiRJanI28EhqmIhojYgsh9aRtjxJkqTeiIg5ZT1l5gDOs1b3mT5Q85Q0PNnAIw1DETG7cjC+OyKiMm7liHi6Mn5mA0PtUkT8uBLnoxGxbKNj6qvBaByKiKmVeXYcZg/EMiRJGs6avc5T5/i9ICIeiIjvRcQKQxhHtU4xtsPo68rh4SGKZWYX9ZvpQxGDtLRq2j9b0lJkPPAu4KLy80eB1RoWTQ9FxIrAvpWkdYBdgQsaE9GwdxfwbOXznV1ljojlM3NBb8f1RNkQtygzs6/zkCSpD5qyzlOaCzwArAdsABwCjAIObmRQAJm5VQMXf12Hz102Mg1y/aZf00vNwB480vD2cvl6SCWtVlFY2DFzRCwfEV+JiHsi4qWIeDIifhERYyp53hYRl5U9al6KiP9ExPUR8eEO86qdafl2RJxWzuvx8oxUTxqH9wJagEXAzWXax7vIv25EzIqI5yPinxFxYCWWURFxTETcFxEvlGfzbo6Ib3XIc1hE3FGu17MRcXlE7NRVkBExvbaulbR2vXXKs01XVCa7onomMQqfjohbyvjmRcT5EbFxD8qp5qDM3KoyHFSJpxbLd8qzYvOA/4mIsZVxX4iI30XE88Dx5XTrRcSZEfHviHg5Iv4VETMiYu3KvGtn2eaUZ/8eBBZQfHeSJA2VZq7zAPw+M7ekaOB5oEyb2GE5b4yIX5bzXhAR90bE4RHR5X+yiDirzDu/nO6hiDglIlYtx88Efl6Z5MGo9JaprN/0iFgh2npFHVZZxmsiYmGZ/qEybZWIODEiHiyX+2hE/CgiVuthmdChbrNVZp5ezrta1zogIq6IiBeBg6J9b6S9I+KGiFgAvKecdruIuLisb71UbgNHRcRylfWpXep2VkScEBFzWbKxSRpxbOCRhrfbgHuAXSNio4iYBGwM/BGYVyf/b4GjgXHldAFMAa6OiNXLPBsArcBLwB3l6wTgrIjYrc48/7ucxwvAqykqXl011NTU8lwMnFy+nxwRa3WS/8fluj0PjAF+GBHvKccdBBwFjAX+DjwOvAn4QIfpv1vO458UjRQ7AJdExLt7EG9XHqboYVNzF0Ul4f7y8ynAacBmwIMUZTUZuCYiNuznsqsOAfYG/kHxvVV9g2J97wcWlo041wIfoTj7+XeK7+8A4M8R8aoO078O+ClFuT0+gDFLktQTzVznqcoyFiiO1wBExDiKusO+wHIUdYkNKU7KfK+bee4FrE5xjP8nRSPSZyiO25TpD1Ty30wnl2Rl5ovAr8qPUyqj9qHocfQs8L8RsTwwm6JMXlfGuwrwKeCyamPKADgN2LRch1c6jDsbeC3wEFC7TP4KYJcy7xzgjcAxwJl15r0PRUPho8BzAxizNCzZwCMNb0lx0AuKg1PtrNYpHTNGxDuB3cuP787MzSgqDnMpKgK1HiFXA6/LzLGZuQXFQfu+ctwH68TwcDmfccAjZVp3vWLWA3YsP55JUQl7nqJC86FOJjsvM98AvIG2CtEXy9c3lq9nZObmmTmeoqLz0XJ5GwL7l3m+n5njKCp191Ls547pKt7ulGebDqok1XrbfCOK69w/XaZ/KjM3BtanqEi2AEf2cDG1XkG14dA6eZ4FxmfmW1iyHB8A1i/HfbGM6XUU29D2mbkJsEeZdyOWrLAuV67XeIqKVL3KtCRJg6Up6zwVu0XEXygaYDYA7gYOq4z/Em0nXNbLzM0p6zEUvVZe38W8t8vMtTLzrWVd6dgy/b0RsUJmfoPiRE/NXtXeMnXMLF/fVjY8QVt5/E9mvlB+3oKi99QWZbybUPTM3oKi4aRbseQ9eN5aJ9s1wOvLOtT3O4z7TTluI4rL/L9OcZuRfwIblvWWb9fWISLeUmf+by/rR609iVlqZjbwSMPfTIo/258AdqM4S3NhnXxbVt5fHMUlR08DtR4zteuvXwFOiIhHImIhxVmq2sH9dXXme35mzivP+DxYpr2mm5g/RrF/mQf8X2Y+B/yuHDe1k2l+BZCZ82i79n7T8vUCiorf/mX34CspKje1e9ZMoO1s2S/K+cyn7X4/b42IUd3E3Fdvryz7x2W5v0hRCYK2cu9OrVdQbXi0Tp7fZubDAJm5qMO4MzLzmcq4t5fp92Xm9WX6RRTbBBRlVvUC8JMyX3r/HUlSA8yk+eo8NWuVca1Tfr6SojGnY8xvBJ4tYz67TFsGeEcX894pIm6P4jLwpOjVDEVDx6t7GN9imXktRa8nKBpF1gO2Lj/XLvWqxbsscHu53DkUvXyg5/Wb6zoM/6mT58dlmder35yama9UxtXqNxdlZq1O84tK/o71mysy89ZO5i2NON5kWRrmMvO5iPg5cGiZ9P3MfCXaHjJRU034K0WDSFWtV8zZwKRy/F3AfIou0KvQdtCueqbyvnYN/BIL7+Bj5eurgH+XsY4u094aEW/NzJu7mL7d/DPz4ojYguLypM2B/wLeCRwQS97nprcNE9V774wqD/69uf9MNdZbKBp3qh6hZw7KzNnd5Pl3H8b1tDwer1WgJElqhCat89ScQXH50ucpeg5/iqIn8Qkd5vMkbb2Iql6oN9PyfjjfLT8+StFzZS2KnkZQfz16Gu83KXrqvFTG9/ey8aca78vA3+pM/1hPFtLDGzwPZv2mq3lLI449eKTmcCrFWajngJ91kuevlfcn1m5mR3FG5gsU96iBtjMuPykv23kPA3hNctlt+g3lx1EUjSUtQPVRofWuZ/9AOf0qFNdVA9xepm1G0QBxVGbuXlmHV1Gc8bqRtgP9hyrzqXXfvrmLszbV+83U4t6rTr7nK+9Xrry/vrLsc6s3EqS4TOq7NMb15etGEfF2gIjYleLSNoAbGhKVJElda5o6T0eZ+RJFo8mNZdKXKve8q8X8H2ByJeZdgB9mZr2eStC2DvOBDcobOV9SJ19n9ZTOnElRzpsAnyvTqjdqrsW7LHBoJd7tKC6TOpvGqNVv3l2519J+lfHWb7RUs4FHagKZ+QDF2Zox5SVM9fLMBv5QfvxlRPw9Im6j6Op8JcX10gC3lq+fjIg7KLo/Vxtf+qvWePMUsFxmRm2g7WaAHypv3lf1/oi4n6JL9Ppl2vHl6z7AwxHxj4i4kbYzSYuAOzLzftoqgZ+OiPvK+WxEUXn5chfxXkHbDf0uj4jrKG5M3NH9tD3h48yI+EtEfCAzHwR+VKZ/K4onW9wSEU9RVDJ2qTOven5QzrM2nNfD6TrzfYozfQH8KSJuB84vx91H+0qcJEnDQpPVeerFlkDtKZ9rUPTkgaLhZx7FPYIeioibIuIBih49M7uYZW0dVgEeKKepd/+buyvvLy3rEtt2Eee/gEvLj+tQ1IXOqmQ5l+JmzUHx0Ig7IuKuch0upHjwRbc61G3+EhFf6cl0XfgaRe+q11OUxz203bPxl5l5Wz/nLzU1G3ikJpGZT3dW0anYi+LAdzdFI8kYipvvnkDxJAQo7oFzBcWlRCtRdIO+lQEQESvT9mSrWZnZ8bGmvy1f16R4ylTVpyi6T68E/As4ODN/X467kqIyERT35VmW4oZ878/MuyvTHw7cSXHQH035lIXMrFUCl1BOP43iuvI1Ka7hP6hOvicpbvj4T4peMNXr7Gs3g7wFWJvi5oqPAj+srHN33lzOszb8Vw+nqyszH6c463cWRZfz8cATwOnAtuV9kSRJGnaaoc7Tjf+l7f47h0XE6Mz8O8Xx/ZcUvXE2oairzKbtkrR6fgqcSHED6VXK/F/tmKm8z8w3KC6dWqdc1uod83VQPdnzx7LRpza/lyhuSnwiRR1pI4r7/dxBcQna7d3Mu2bLDsMbus7etbJxbweKXkzLUNS5/g58hbabVktLrfA+mpIkSZIkSc3NHjySJEmSJElNzgYeSZIkSZKkJmcDjyRJkiRJUpOzgUeSJEmSJKnJLdvoAAbDWmutlWPHjm10GJIkqQFuvPHGuZn56kbHMVis50iStHTrrK4zIht4xo4dyw033NDoMCRJUgNExEONjqE3ImJD4CigJTM/0F1+6zmSJC3dOqvreImWJElSH0XEzyLi8Yi4vUP6rhFxT0TcFxFHdDWPzHwgMz8xuJFKkqSRbkT24JEkSRoiM4HTgDNrCRExCvg+sDPwMHB9RJwPjAKO6zD9/pn5+NCEKkmSRjIbeCRJkvooM6+KiLEdkt8B3JeZDwBExC+BPTPzOGD3IQ5RkiQtJWzgkSRJGljrAv+sfH4Y2LKzzBGxJnAs8F8RcWTZENQxzzRgGsCYMWOYO3fuwEYsSZKang08ktQLc+bMYcstt+TNb34zyy+/PJdccsnicV//+te56KKLADjmmGPYaaeduPPOOznggAMA2HHHHfnGN77R7TLmz5/Pfvvtx1NPPcWnPvUpPvrRj3LJJZdw9NFHs2jRInbccUeOPfbYwVnBQdbb8vvHP/7B1KlTWbhwIQcffDD77LNPt8sYyeWnphF10rKzzJn5JHBgVzPMzBnADIAJEybkWmut1a8AJame559/nr333pv//Oc/tLS08Ktf/YrRo0cD8OKLL/LpT3+aBx98kE022YRTTz2VM844g5/+9Ke8+OKLTJ06lYMOOqjbZdQ7Ts+aNYtjjz2WiOADH/gAhx122GCv6qDpbRnW7LHHHmy22WYcc8wx3S5jpJeh+iEzR9zwtre9LSVpMDz44IP5oQ99qO64Bx54IDMzn3766dx+++0zM/Mzn/lMXnnllZmZOWnSpHz66ae7XcYJJ5yQ55xzTi5cuDC33377fOmll3LBggWLx7e2tubjjz/ezzVpjN6W30EHHZTXXHNNLly4MHfeeed8+eWXu13GSC4/9QxwQw5hvQMYC9xe+bw1cHHl85HAkQO1POs5kgbLb3/72/z617+emZnHHHNM/u53v1s87tvf/nZeeuml7fLXjq8LFy7MzTbbrEfLqHecfuihh3LhwoX5yiuv5Dvf+c585plnBmiNhl5vyzAz8+abb86dd945jzrqqB4tY6SXobrXWV1nRD1FKyImR8SMefPmNToUSSPYFVdcwfbbb89JJ53ULn2DDTYAYPTo0UQUJ/A32WQT5s2bx6JFixaPe+GFF5gyZQo77rgj++67Ly+//HK7+Vx77bVMmjSJUaNGsfnmm3PPPfew3HLLAbBo0SLWWWcdVl111cFezUHTm/J74IEH2GyzzRg1ahSvec1ruO+++5b68lNTuB7YKCI2iIjlgQ8C5zc4Jknq1hve8AZeeuklAJ555hnWXHPNxeNmz57N+eefT2trK+efX+zSasfXBQsW8OY3vxmgT8fp9dZbj1GjRhERjBo1imWWad6/qb0tQ4BTTjmlXe+npb0M1Xcj6lvPzFmZOa2lpaXRoUgaoV772tfy97//nSuuuIJLL72UW2+9dYk806dP51Of+hRQXJb1+c9/nvHjx7P11luz4oorcvrpp7PHHntw+eWX09raym9+85t20z/zzDOLGyBaWlp4+umnAZgxYwbjx49nzTXXXNzVt9n0tvzGjx/PlVdeyfPPP89f/vIXnn766aW6/DT8RMS5wLXA+Ih4OCI+kZkLgYOBi4G7gF9l5h2NjFOSemKjjTbiuuuuY5NNNuGGG25gm222WTzu/vvvZ7fdduP3v/893/jGN1i4cCEARx99NBtttBFve9vbAPp8nAb4wx/+wLhx41hllVUGe1UHTW/L8O6772bttddmtdVWW5xvaS9D9d2IauCRpME2evRoVl55ZZZddll23313br/99nbjzzvvPJ588kn2228/AL785S9z9tln8/e//53bbruNOXPmcNddd3HyySfT2trKGWecweOPP85hhx1Ga2srF110EautthrPPvssAM8+++ziA/60adO45557ePjhh7npppuGdL0HSm/L78gjj2TGjBnsvffevOlNb+I1r3nNUl1+Gn4yc0pmvjYzl8vMMZn50zL9wsx8Y2a+ITO96ZOkpnDGGWfwrne9izvuuIPddtuNs88+e/G4lpYWJk6cyMorr8y4ceN47LHHAPjqV7/K/fffz69//WuefPLJPh+nH3jgAY4//vglevg2m96W4YknnshnP/vZdvNY2stQfedNliWpF+bPn7/4jMjVV1/NZz7zmcXjbr31Vr7//e/z+9//fnFaZrLGGmuwzDLL0NLSwvz58xk/fjw77bQT73//+wF4+eWXF3dxBrjzzju57LLL2Geffbj55psZP348L730EqNHj2bUqFGsvPLKrLjiikO0xgOrt+X3mte8ht/97ne88MILfOQjH2GDDTZYqstPkqTBVKu3AKy11lpUb32xzTbbcOutt7LFFlswZ84cXv3qVy8+vi6//PKstNJKjB49uk/H6fnz5zN16lRmzpzJyiuvPLQrPcB6W4YPPfQQU6dO5amnnuLJJ59k5513XurLUP1Q78Y8zT5480FJg+X3v/99brHFFrn11lvn4YcfnpmZBx98cGZm7rLLLrnpppvmxIkTc4899sjMzBtuuCG33nrr3G677fKAAw7IzMznn38+P/ShD+WOO+6YO+ywQ/7lL39pt4x58+blbrvtlltvvXX+/Oc/z8zMH/7whzlx4sTcdttt88tf/vIQre3A6235XXDBBdna2po77bRT3nDDDZm5dJefeoYhvsnyUA3AZGDGuHHjBrS8JKnm6aefzl122SUnTpyYkyZNyieffHLxcfqRRx7JnXfeObfccss8/fTTMzPza1/7Wk6cODG33nrr/MEPfpCZfTtOf/Ob38wxY8bkxIkTc+LEiYsfvNCMeluGNVdcccXimywv7WWo7nVW14li3MgyYcKEvOGGGxodhiRJaoCIuDEzJzQ6jsFiPUeSpKVbZ3UdL9GSJPXaoqPWb3QIDTXq2IcaHYIkSRokS3s9B6zrNCsbeCQtdRb8xfudLr/VUY0OQZIkDZKlva5jPUdLK5+iJUmSJEmS1ORs4JEkSZIkSWpyI6qBJyImR8SM6qPoJEmSJEmSRroR1cCTmbMyc1pLS0ujQ5EkSRpQnsiSJEldGVENPJIkSSOVJ7IkSVJXbOCRJEmSJElqcjbwSJIkSZIkNTkbeCRJkiRJkpqcDTySJEmSJElNzgYeSZIkSZKkJmcDjyRJkiRJUpOzgUeSJEmSJKnJ2cAjSZLUBCJickTMmDdvXqNDkSRJw9CIauCx4iNJkkaqzJyVmdNaWloaHYokSRqGRlQDjxUfSZIkSZK0NBpRDTySJEmSJElLIxt4JEmSJEmSmpwNPJIkSZIkSU3OBh5JkiRJkqQmZwOPJEmSJElSk7OBR5IkSZIkqcnZwCNJkiRJktTkbOCRJElqAhExOSJmzJs3r9GhSJKkYcgGHkmSpCaQmbMyc1pLS0ujQ5EkScOQDTySJEmSJElNzgYeSZIkSZKkJmcDjyRJkiRJUpOzgUeSJEmSJKnJ2cAjSZIkSZLU5EZUA4+PD5UkSZIkSUujEdXA4+NDJUmSJEnS0mhENfBIkiRJkiQtjWzgkSRJkiRJanI28EiSJDUB7zUoSZK6YgOPJElSE/Beg5IkqSs28EiSJEmSJDU5G3gkSZIkSZKanA08kiRJkiRJTc4GHkmSJEmSpCZnA48kSZIkSVKTs4FHkiRJkiSpydnAI0mSJEmS1ORs4JEkSZIkSWpyNvBIkiRJGhFuv/12ttlmG7bffns+/vGPk5mLxx166KG0trbS2trK6quv3m66PfbYgy9/+cs9Wsb8+fOZPHky2267LWeeeSYAs2bNYquttmLrrbfmhBNOGLgVkqResIFHkiRJ0ogwfvx4rrnmGv70pz8BcMMNNywed/LJJzN79mxOOukkdtttt8Xpt9xyCy+++GKPl/GTn/yEKVOmcNVVV3H66aezYMECNt98c66++mquueYazj//fObNmzdwKyVJPWQDjyRJkqQRYbnlllv8fvTo0bz+9a9fIs95553H+973vsWfTznlFA466KDFn1944QWmTJnCjjvuyL777svLL7/cbvprr72WSZMmMWrUKDbffHPuuece1ltvPUaNGkVEMGrUKJZZxr9Zkoaeex5JkiRJI8b555/PpptuyuOPP86aa665xPiLLrqIXXfdFYC7776btddem9VWW23x+NNPP5099tiDyy+/nNbWVn7zm9+0m/6ZZ55h1VVXBaClpYWnn3568bg//OEPjBs3jlVWWWUQ1kySumYDj7SUeeSRR9hiiy1YYYUVWLhwYbtxV155JVtuuSVbbbUVP/rRj4CiovKmN72J7bbbrsfLqHdt+iWXXMJ2223H1ltvzVFHHTVwKyRJklSxxx57cPvtt7PuuutywQUXtBt37733su6667LSSisBcOKJJ/LZz362XZ677rqLk08+mdbWVs444wwef/xxDjvsMFpbW7noootYbbXVePbZZwF49tlnFzcOPfDAAxx//PGcdNJJg7+SklTHso0OQNLQWmONNbjsssvYa6+9lhh3wgkn8Otf/5oxY8aw9dZbc+CBB7LVVltxyy23sNNOO/V4GbVr0/fdd1922GEHPvjBD7LDDjuwyy67ALDDDjvwxBNP8OpXv3rA1kuSRrqImAxMHjduXKNDkYatl156idGjRwOw6qqrsuKKK7Ybf95557WrAz300ENMnTqVp556iieffJKdd96Z8ePHs9NOO/H+978fgJdffrndpV933nknl112Gfvssw8333wz48ePZ/78+UydOpWZM2ey8sorD8GaStKS7MEjLWVWWGGFJZ4cUbPJJpswb948XnrppcWVk9VXX31xRammL9em1ypGixYtYp111lnctVmS1DOZOSszp7W0tDQ6FGnYuuiii5g4cSITJ07kscceY5ddduEzn/nM4vEXXHABkydPXvz54osv5qKLLuL4449nypQpTJw4kWnTpnHeeeex0047seOOO/K3v/2t3TI++clPcs4557D99tuz//77M3r0aE477TQefPBB9t9/f1pbW3nwwQeHbJ0lqcYePJIWe+9738vuu+8OwFe/+tVO89WuTZ8yZQo//OEP+c1vfsOUKVMWj+/s2vQZM2Zw/PHHs+uuuy7RaCRJktRfe+65J3vuuWe7tFNPPXXx+6uuuqrudLXHpwOsuOKKnH322Z0uY9VVV13i0q8jjzySI488so9RS9LAGFE9eCJickTM8LGEUt8cfvjh/PnPf+bee+/lzDPP5Pnnn6+br6/Xpk+bNo177rmHhx9+mJtuummoVkuSpKZw++23s80227D99tvz8Y9/nMxcPO64447jne98J29/+9s577zzADjjjDN45zvfyTve8Q5+8IMf9GgZ9e6TN2vWLLbaaiu23nprTjjhhIFfMUnSkBhRPXgycxYwa8KECQc0OhapGY0aNYrVVluN5ZdfnmWWWWaJS69q+nJteu2a+FGjRrHyyisvcU28JElLu/Hjx3PNNdcA8PGPf5wbbriBt7/97QB8/vOf58gjj+S5555j0qRJ7LXXXuy333587GMfY9GiRWyxxRbtHvXdmXr3ydt88825+uqrWWaZZWhtbeWTn/wkjbwU8Jvf+VnDlj0cfOnw/RsdgqQmNaJ68Ejq3ssvv8ykSZO45ZZbeNe73sV11123+Nr0L37xi0yaNImtt96aHXbYgZaWFm644QYmTZrE7bffzqRJk3jxxRf7dG36z3/+c1pbW9luu+3YcMMNedOb3tSI1ZckadiqniwZPXo0r3/965cY98ILL7Dpppu2S1uwYAFvfvObF4/v7X3y1ltvPUaNGkVEMGrUKJZZxr8IktSMRlQPHkndW2655bj00kvbpW255ZYA7Lrrruy6667txk2YMGGJ/ECvr00/8MADOfDAA/satiRJS4Xzzz+fL33pS7zxjW9kzTXXbDfuoIMO4rzzzmt3GdXRRx/NjBkzFj/qu6/3yQP4wx/+wLhx41hllVUGcxUlSYPE5nk1lUceeYQtttiCFVZYgYULF7YbN336dDbffHNaW1s58cQTAfjWt761+KZ5K6+8Mk899VS3y6h3bfoll1zCdtttx9Zbb81RRx018CsmSZIE7LHHHtx+++2su+66S5ws+cEPfsDdd9/Nscceuzjtq1/9Kvfffz+//vWvefLJJ/t8n7wHHniA448/npNOOmnI1lWSNLDswaOmssYaa3DZZZex11571R1/wgknMGnSpMWfjzjiCI444gjmzp3LBz7wAdZYY41ul1Hv2vQddtiBXXbZBYAddtiBJ554gle/+tUDs1K9tLRflw5emy5JGplq96uDojds9X51tXErrrji4h44tbTll1+elVZaidGjR/fpPnnz589n6tSpzJw5k5VXXnkI11iSNJDswaOmssIKK7D66qt3Or52D5mbb765Xfr555/PHnvsAfTt2vRaxWjRokWss846iytWkiRJA+Wiiy5i4sSJTJw4kccee4xddtll8X3yPvvZzy7ulXz44YcDxZO1Wltb2Xbbbdl333151ate1af75J122mk8+OCD7L///rS2tvLggw8O+bpLkvrPHjwaMQ455BCmT5/Ovffey/7778+f/vSnxePOO+88TjnlFKDv16bPmDGD448/nl133XXx2TVJkqSBsueee7Lnnnu2Szv11FMB+NGPfrRE/unTpy+RtuKKK/b6PnlHHnkkRx55ZB8iliQNJzbwaMSoXX610UYbtUt/7rnnmDt3LhtssAEAd911FzfeeCM//vGPefHFF5kyZQqHHXYYN954I0ccccTia9NXWGGFdtemT5s2jU984hO8//3v56abbuK//uu/hnT9JEnS8HfUEb9qdAgNd+y39ml0CJK0VLKBRyPGs88+y6qrrsrcuXPb3YD5wgsv5N3vfvfiz325Nr12jfuoUaNYeeWV210TL0mSJElSo3kPHjWVl19+mUmTJnHLLbfwrne9i+uuu27xtemHH3442267LZMnT+Zb3/rW4mnOO+883ve+9y3+3Jdr03/+85/T2trKdtttx4Ybbsib3vSmoVlhSZIkSZJ6wB48airLLbccl156abu0LbfcEoAf//jHdac599xz233uy7XpBx54IAceeGBfQpYkSZIkadDZg0eSJEmSJKnJ2YNHQ25pv/mgNx6UJEmSJA00e/BIkiRJkiQ1ORt4JEmSmkBETI6IGfPmzWt0KJIkaRiygUeSJKkJZOaszJzW0tLS6FAkSdIwZAOPJEmSJElSk7OBR5IkSZIkqcnZwCNJkiRJktTkbOCRJEmSJElqcjbwSJIkSZIkNTkbeCRJkiRJkpqcDTySJEmSJElNzgYeSZIkSZKkJmcDjyRJkiRJUpOzgUeSJEmSJKnJ2cAjSZIkSZLU5GzgkSRJkiRJanI28EiSJEmSJDU5G3gkSZIkSZKanA08kiRJkiRJTc4GHkmSJEmSpCY37Bt4IuLNEfGjiPhNRPy/RscjSZIkSZI03HTZwBMRo8qGlT36MvOI+FlEPB4Rt3dI3zUi7omI+yLiiK7mkZl3ZeaBwD7AhL7EIUmSJEmSNJJ12cCTmYuANwHr9XH+M4FdqwkRMQr4PvBuYGNgSkRsHBFviYgLOgxrl9PsAfwZuKyPcUiSJNUVEatGxOhGxyFJktQfy/Ygz+3A0RGxPvBoLTEzT+xuwsy8KiLGdkh+B3BfZj4AEBG/BPbMzOOA3TuZz/nA+RHxe+AXPYhZkiSpWxERwCPA54AZDQ5HkiSpz3rSwLNP+XpYJS2Bbht4OrEu8M/K54eBLTvLHBGtwPuA0cCFXeSbBkwDGDNmDHPnzu1jeBpsq6wSjQ6hofq7ba60Yk9+tiNbf8tw4YKVBiiS5rVsP8vwlVXeMECRNKdlPMaMGJmZEXEhxWXgNvBIkqSm1ZN/ih8f4GXW+3efnWXOzNnA7O5mmpkzKCtmEyZMyLXWWquP4WmwzZ/f6de9VOjvtvn8CwsHKJLm1d8yXLD88wMUSfNavp9luGj+/QMUSXMa5TFmpHkr8IaIeC/w7zItM3PzhkUkSZLUS9028GTmGRGxOrBVmfSXzHy6H8t8GHh95fMYiq7RkiRJjTCufF2rHCRJkppOtw08EbEt8H/A6mXSUxGxR2Ze28dlXg9sFBEbAP8CPgjs18d5SZIk9UtmdvnQCUmSpGbQkwrNicDLwHHAt8r3J/Vk5hFxLnAtMD4iHo6IT2TmQuBg4GLgLuBXmXlHX4KXJEkaCBExNSL+JyJ+GREfbXQ89UTE5IiYMW/evEaHIkmShqGe3INnE+C/M/MnABHxEHBCT2aemVM6Sb+QLm6Y3FcRMRmYPG7cuG7zSpIkAUTEl4GjK0l7R8SYzPxmo2KqJzNnAbMmTJhwQKNjkSRJw09PevA8Anw0InaMiB2BjzBM75mTmbMyc1pLS0ujQ5EkSc3jk8As4I3AeOACyidzSpIkNYue9OD5DvBj4I/l5wA8cyRJkkaKNYA/ZuZ9ABHxR6C1oRFJkiT1Uk+eovWTiLgPeE+ZdGFmXjG4YUmSJA2Z64FvRsQ7ys97lmmSJElNo8sGnogYRfFY869k5uFDE5IkSdKQ+gzFJVofLj/fX6ZJkiQ1jS4beDJzUUTcDrxhiOKRJEkaMuXJrHEUvXayTL6nfOqnJElS0+jJPXhWAr4QETvTdnPlzMw9By8sSZKkwVeezPopcHhmzmx0PJIkSX3VkwaercvXLcoB2s5wDSs+Jl2SJPXBOcDUiLgeeLSWmJlPNS4kSZKk3ulJA88Ggx7FAMnMWcCsCRMm+JQvSZLUU4dQnLy6tZKW9KyeJEmSNCz05CbLfwG+nJk/HZqQJEmShtRVDNPeyZIkST3V05sse82TJEkaccqTWScCf8/MuxsdjyRJUl95k2VJkrTUqt5kGbCBR5IkNa0RdZNlSZKkPvAmy5IkqemNqJssS5Ik9YE3WZYkSU2v04pLRGwGzMnMhzqkvxZ4O/BQ3QkbyMekS5KkPvAmy5Ikqekt08W4m4B3RcSaEbEoInYs098JnDf4ofVeZs7KzGktLS2NDkWSJDWJzGzNzB06Do2OS5IkqTe6auCJcqDyKkmSNCJExIkRsXlELB8RH42I15Tp20bE/zY6PkmSpN7oqoEH2ndXtuuyJEkaSQ4FxgOrAD8HNinTxwA+LVSSJDWV7m4eeAawkKJx54KIWNSDaSRJkpqNvZUlSVJT66qx5h+09dp5cghikSRJGmrvAFYs328XEasBWzYuHEmSpL7ptIEnM8cOYRySJEmN8N+V918rXwMvTZckSU3Gy60kSdLS6uuNDkCSJGmgjKgGnoiYDEweN25co0ORJEnDXGbawCNJkkaM7p6i1VQyc1ZmTmtpaWl0KJIkSZIkSUOmRw08EbF8RLw1Imw5kSRJkiRJGma6beCJiP8C7gduAN4eEXdFxE8GPTJJkiRJkiT1SE968JwG/IfiiRKvAGcDkwYzKEmSpKEUEbtFxMkRsXFEfDgiNm90TJIkSb3RkwaezYGZlc+PAGsPSjSSJElDLCIOBWYBnwHWAd4HfKeRMUmSJPVWTxp4HgYmlu83Aw4E5gxWQJIkSUPsUODXlc+XAls0JhRJkqS+6UkDz/HAuygu0ToBeDvw7cEMSpIkaQitDtxS+bwSMKpBsUiSJPXJst1lyMyfRcT9wG4UjTwXZOaVgx6ZJEnS0LgO+H/l+88D2wFXNy4cSZKk3uvJU7QuByIzv5CZhwP/iYgZgx+aJEnSkDgEeIHiRNauwKMUl21JkiQ1jZ5cotUKvLry+e3AJwYlmn6KiMkRMWPevHmNDkWSJDWPucCbgU2BtwAbZ+Y9jQ1JkiSpdzpt4ImIr0XEIiCBX0bEovLzacDjQxVgb2TmrMyc1tLS0uhQJElS83gEOI+igef+zFzU4HgkSZJ6rat78DwO3AVsTPEkrXkUjT1PA98b/NAkSZKGxP9RXJq1OzA/Iv4XODszL2tsWJIkST3XaQNPZv4Q+GFE/Bz4QWZeP3RhSZIkDY3M3DsiVgDeA+wLfBT4CD14GIUkSdJw0ZOKyxXAmyPizdXEzDxzcEKSJEkaOhGxDMWTs3YBJlLcbPmFhgYlSZLUSz1p4JlJcWlWRzbwSJKkkeAxYA3gFeAy4GyKe/JIkiQ1jZ408HyBtgae1Sm6Lf950CKSJEkaWnOAbwC/zMxh+SAJSZKk7nTbwJOZ361+johbgK8MWkSSJElDICLWAJ4F3tUhDYDMfGqI4ngvsBuwNvD9zLxkKJYrSZJGlm4beCLi/A753wYsN2gRSZIkDY0ngCnAuXXGJT2rJ/2M4ulbj2fmppX0XSmeOjoKOD0zv9XZPDLzd8DvImJ14LuADTySJKnXenKJ1u4dPr8IHDEIsUiSJA2lqygaef5E/fsN9sRM4DQq9yaMiFHA94GdgYeB68sTZqOA4zpMv3/lsrAvl9NJkiT1Wk8aeDaovF8EPJaZLw9SPJIkSUMiM3cAiIj7gbmZ+Xwf5nFVRIztkPwO4L7MfKCc/y+BPTPzOJY8cUZEBPAt4A+Z+bfexiBJkgRdNPBExPu6GEdm/u/ghCRJkjSkHgQ+CPwaICL2Bs7OzNF9nN+6wD8rnx8Gtuwi/2eASUBLRIzLzB91zBAR04BpAGPGjGHu3Ll9DE2DbZVVotEhNFx/t8+VVuzJOeiRayB+3wsXrDQAkTSvZftZhq+s8oYBiqR5LeNxpil1tff8DfW7K0eZPmpQIuqHiJgMTB43blyjQ5EkScNcRGwGvJWibtMaESuWo95TpvV51nXSOr0ELDNPAU7paoaZOQOYATBhwoRca621+hGeBtP8+X292m/k6O/2+fwLCwcokuY0EL/vBcv3ukPiiLJ8P8tw0fz7ByiS5jXK40xT6qqB52j6fj16Q2TmLGDWhAkTDmh0LJIkadjbC/gaRX3nwHKAooHmhn7M92Hg9ZXPY4BH+jE/SZKkbnXawJOZ02vvy5sFvrH8+PfMXDTIcUmSJA22S4D/AMcD5wA3UzT2PA2c3/lk3boe2CgiNgD+RXH51379ilSSJKkbPXn858bA74DahYj3RcSemXn3YAYmSZI0mDLzWuDaiLgeuCMze33DgYg4F2gF1oqIh4GvZeZPI+Jg4GKKS9p/lpl3DGDokiRJS+jJHcxOA14LnEvRZXlymTZpEOOSJEkaKn8Hjo2ItwArlGmZmW/rbsLMnNJJ+oXAhQMXoiRJUtd60sAzATgyM08DKM9IfXNQo5IkSRo6pwPvojiRtRBYDnimkQHV48MkJElSV5bpQZ6ngEkRsWFEbAjsDDw5uGFJkiQNmW2A48r3uwM/ouitPKxk5qzMnNbS0tLoUCRJ0jDUkwaenwB7APeWw+QyTZIkaSRYHniQogfPO4D5wKcaGpEkSVIvdXuJVmYeGxGPAu8uk36fmTMHNSpJkqShMwdYE7gVOLpM82ESkiSpqfTkKVprAhdk5s8iYkfgdRGxQma+OPjhSZIkDbp9gAXAH4Avl2nHNC4cSZKk3uvJTZYvAG6OiF8ClwJJ0ZvnQ4MZmCRJ0mCKiDXKt49Wkg9qRCySJEn91ZMGno0pni6xK3A1cAew92AGJUmSNATmUpy4qifpWT1JkiRpWOhJxWUZYCywLUXX5YeBjwxiTJIkSUPhKjpv4JEkSWoqPWng+StwFEUF6DCKp2g9NJhBSZIkDbbMbG10DL0REZOByePGjWt0KJIkaRjqyWPSPwh8DtgjM68HbqFo6JEkSWp6EbFCRBwfETdFxLYRcUpE7NPouDrKzFmZOa2lpaXRoUiSpGGo2waezHwCuAzYICIOBv6emX8Y9Mj6ICImR8SMefPmNToUSZLUPL5HcTJrM2A0MAr4QkMjkiRJ6qVuG3gi4jDgZorKzynATRHx34McV594ZkuSJPXB+4DvVD7fCLyxQbFIkiT1SU8u0ToCuBM4AJgG3A0cOZhBSZIkDaFXgKh83hx4rkGxSJIk9UlPbrL8EPDjzPwZQEQE8KlBjUqSJGno/J7iEi2As4B1gNMbF44kSVLvddrAExG1is7twFcjYl2Ks1v7AxcOQWySJElD4VCKOs5uwHLAGcDnGxmQJElSb3XVg+e7FI9Gr3VZ/mpl3CexF48kSRoBMvNZ4OPVtIjYlOIklyRJUlPoqoHn412MkyRJanoR8X5gQ+CvmXllRLwFOBqYTM8uZR8yETEZmDxu3LhGhyJJkoahTisumXlGvfSI2ATYd9AikiRJGgIR8T3gYIreyhkRJwOfBpaneJLWsJKZs4BZEyZMOKDRsUiSpOGnJ0/RIiLeFBFfjYg7gFuBowY3LEmSpEG3L/AX4MPAz4D/Bh4B9szMtzcyMEmSpN7q6ibLGwH7lMOmlGe3KJ40cdaQRCdJkjR4Xg18LjN/ERGXAp8Ajih7ykiSJDWVrq4tv4eiQedR4PvAX4EzgdMz8/whiE2SJGkwBfC5iPggxdOzEjg0Ij4MZGbu2dDoJEmSeqG7mwe+AlwJXE7R4CNJkjSSbFEONVuVr9mAWCRJkvqsqwaeQ4C9Ka5P/yCwgKKy8/aIuDoznxyC+CRJkgbLBo0OQJIkaaB09RSt04DTIuK1FPfh2RfYkuIGy0dQdGWWJElqSpn5UKNjkCRJGijdPkUrMx/NzO9l5jbA+sDhDMNHh0qSJEmSJC2tevSY9JrMfDgzT8jMrbrPLUmSJEmSpKHQqwYeSZIkNUZETI6IGfPmzWt0KJIkaRiygUeSJC3VImJ8RPwkIv4YEZeXw2WNjqujzJyVmdNaWloaHYokSRqGuntMuiRJ0kj3O2B8hzQfky5JkppKtw08ETEe+DwwFhhVJmdm7jSIcUmSJA2VNYCTgOOBhQ2ORZIkqU960oPnd3hWS5IkjVw/Ad4CvAp4usGxSJIk9UlPGng8qyVJkkayL1GcvNq9kpZ4KbskSWoiPam4NM1ZrYiYDEweN25co0ORJEnN4yrsnSxJkppcTxp4muasVmbOAmZNmDDhgEbHIkmSmkNmtjY6BkmSpP7qSSONZ7UkSdKIFREBfJCix/IKZXJm5mGNi0qSJKl3um3g8ayWJEka4b4PHEhxQivKtARs4JEkSU2jJ49J96yWJEkayfYCfgFMAT4LvBf4UyMDkiRJ6q2eXKLlWS1JkjSSrU7RoLMf8BTwG+DzwPQGxiRJktQrPWng8ayWJEkayf5NUSd6lOLE1vLAsw2NqA6fFipJkrqyTA/y1M5qBW1ntT4ymEFJkiQNoS8D91P0Tn4RmAcc2siA6snMWZk5raWlpdGhSJKkYagnPXia4qyWJElSX2Tm2QARsRqwfma+1NiIJEmSeq8nPXia4qyWJElSX0TE2Ij4KzAX2D4iroyIoxsdlyRJUm/05DHpntWSJEkj2Y+AMRSXo78CXEXxBNGvNjIoSZKk3ui2B49ntSRJ0gi3DXBa5fP9FA0+kiRJTaMnl2h1dlZLkiRpJJgLbFq+X5uinvNI48KRJEnqvZ408HhWS5IkjWQ/oWjUCeAcYGfgxw2NSJIkqZd68hQtz2pJkqQRKzOPi4hHgN0oGnlmZeaZDQ5LkiSpV3rSwPMT4Njy/Tnl6xGDE44kSdLQy8wzgDMaHYckSVJf9eQpWp7VkiRJI05ELOpidGZmT06ESZIkDQs9qrh4VkuSJI1AASTFpefPNDYUSZKk/um0gcezWpIkaYSbCXwAWAv4E/DzzPxjQyOSJEnqo66eohXl6yPAnR2GuwY5LkmSpEGVmfsD6wAHAa8HLoqIORGxa2MjkyRJ6r2uGnhmAv+hOKt1G/C5zHxLbRiK4CRJkgZTZj4PPAA8CCygqPes0tCgOhERkyNixrx58xodiiRJGoY6beDxrJYkSRrJIuJLEXEvcDkwDvgM8NrM/HVjI6svM2dl5rSWlpZGhyJJkoahLu+jk5nPR0TtrNYEhvFZLUmSpF46huImyw8Ac4E9gD0iAor7De7ZwNgkSZJ6paubLH8J+DiwIXAdxVmt/8nM+UMUmyRJ0mAL4A3lUJUNiEWSJKnPuurB41ktSZI0km3Q6AAkSZIGSnePOvesliRJGpEy86FGxyBJkjRQumrg8ayWJEmSJElSE+i0gcezWpIkSZIkSc2h08ekS5IkSZIkqTnYwCNJkiRJktTkbOCRJEmSJElqcjbwSJIkSZIkNTkbeCRJkiRJkppcUzTwRMTKEXFjROze6FgkSZIkSZKGm0Ft4ImIn0XE4xFxe4f0XSPinoi4LyKO6MGsvgj8anCilCRJkiRJam7LDvL8ZwKnAWfWEiJiFPB9YGfgYeD6iDgfGAUc12H6/YHNgDuBFQY5VkmSJEmSpKY0qA08mXlVRIztkPwO4L7MfAAgIn4J7JmZxwFLXIIVETsAKwMbAy9ExIWZ+cpgxi1JkiRJktRMBrsHTz3rAv+sfH4Y2LKzzJl5FEBETAXmdta4ExHTgGkAY8aMYe7cuQMVrwbYKqtEo0NoqP5umyut2Iif7fDS3zJcuGClAYqkeS3bzzJ8ZZU3DFAkzWkZjzGSJEkaZhrxT7Hev/vsbqLMnNnN+BnADIAJEybkWmut1afgNPjmz+/26x7R+rttPv/CwgGKpHn1twwXLP/8AEXSvJbvZxkumn//AEXSnEZ5jFEDRMRkYPK4ceMaHYokSRqGGvEUrYeB11c+jwEeaUAckiRJTSMzZ2XmtJaWlkaHIkmShqFGNPBcD2wUERtExPLAB4HzGxCHJEmSJEnSiDDYj0k/F7gWGB8RD0fEJzJzIXAwcDFwF/CrzLxjMOOQJEmSJEkayQb7KVpTOkm/ELhwoJfntemSJEmSJGlp1IhLtAaN16ZLkiRJkqSl0Yhq4JEkSZIkSVoa2cAjSZIkSZLU5GzgkSRJkiRJanI28EiSJEmSJDU5G3gkSZIkSZKa3Ihq4ImIyRExY968eY0ORZIkSZIkaciMqAYeH5MuSZIkSZKWRiOqgUeSJEmSJGlpZAOPJEmSJElSk7OBR5IkSZIkqcnZwCNJkiRJktTkbOCRJEmSJElqciOqgcfHpEuSJEmSpKXRiGrg8THpkiRJkiRpaTSiGngkSZIkSZKWRjbwSJIkSZIkNTkbeCRJkiRJkpqcDTySJEmSJElNzgYeSZIkSZKkJmcDjyRJkiRJUpMbUQ08ETE5ImbMmzev0aFIkiQNKOs5kiSpKyOqgSczZ2XmtJaWlkaHIkmSNKCs50iSpK6MqAYeSZIkSZKkpZENPJIkSZIkSU3OBh5JkiRJkqQmZwOPJEmSJElSk7OBR5IkSZIkqcnZwCNJkiRJktTkbOCRJEmSJElqcjbwSJIkSZIkNbkR1cATEZMjYsa8efMaHYokSZIkSdKQGVENPJk5KzOntbS0NDoUSZIkSZKkITOiGngkSZIkSZKWRjbwSJIkSZIkNTkbeCRJkiRJkpqcDTySJEmSJElNzgYeSZIkSZKkJmcDjyRJkiRJUpOzgUeSJEmSJKnJ2cAjSZIkSZLU5GzgkSRJkiRJanI28EiSJEmSJDW5EdXAExGTI2LGvHnzGh2KJEmSJEnSkBlRDTyZOSszp7W0tDQ6FEmSJEmSpCEzohp4JEmSJEmSlkY28EiSJEmSJDU5G3gkSZIkSZKanA08kiRJkiRJTc4GHkmSJEmSpCZnA48kSZIkSVKTs4FHkiRJkiSpydnAI0mSJEmS1ORs4JEkSZIkSWpyNvBIkiRJkiQ1ORt4JEmSJEmSmpwNPJIkSZIkSU3OBh5JkiRJkqQmN6IaeCJickTMmDdvXqNDkSRJkiRJGjIjqoEnM2dl5rSWlpZGh1LXddddxzbbbMP222/Pf//3f7cbN336dDbffHNaW1s58cQT243bY489+PKXv9yjZcyfP5/Jkyez7bbbcuaZZwIwe/Zs1l9/fVpbW/noRz86MCsjSWoIjyUazhYuXMgHP/hBdthhB77whS+0G3fooYfS2tpKa2srq6++OgB/+MMfeNOb3sR2223X42XU2z5rDjnkED784Q/3f0UkSQ3hcaR/RlQDz3C3/vrrc/nll/OnP/2Jxx9/nNtuu63d+BNOOIHZs2fzuc99bnHaLbfcwosvvtjjZfzkJz9hypQpXHXVVZx++uksWLAAgI985CPMnj17iQ1YktRcPJZoODvvvPPYfPPNueKKK3jhhRe45ZZbFo87+eSTmT17NieddBK77bYbAFtttVW7PD3R2fb52GOPMWfOnAFbF0nS0PM40j828AyhddZZhxVWWAGAZZddllGjRrUb/8UvfpFJkyZx8803L0475ZRTOOiggxZ/fuGFF5gyZQo77rgj++67Ly+//HK7eVx77bVMmjSJUaNGsfnmm3PPPfcAcO6557L99ttz7rnnDtLaSZKGgscSDWcPPPAAm222GQBvfetbufbaa5fIc9555/G+970PgNVXX53Ro0e3G9/X7fOkk07iM5/5zGCsliRpiHgc6R8beBrg1ltvZe7cuWy88caL0w455BBuvPFGfvjDHy7eqO6++27WXnttVltttcX5Tj/9dPbYYw8uv/xyWltb+c1vftNu3s888wyrrroqAC0tLTz99NNMmDCBu+++m4suuohTTjmFJ554YvBXUpI0qDyWjBwR8eaI+FFE/CYi/l+j4+mP8ePHc+WVVwJwxRVX8PTTTy+R56KLLmLXXXftdB592T6feuopnnjiCTbaaKMBXBtJ0lDzONI/NvAMsaeeeoqDDz6Yn/70p+3S11hjDYB2G9SJJ57IZz/72Xb57rrrLk4++WRaW1s544wzePzxxznssMNobW3loosuYrXVVuPZZ58F4Nlnn2W11VbjVa96Fcsttxwrr7wy73znO7n33nsHeS0lSYPJY8nwERE/i4jHI+L2Dum7RsQ9EXFfRBzR1Twy867MPBDYB5gwmPEOtsmTJ/PCCy+w0047MXr0aF7zmte0G3/vvfey7rrrstJKK3U6j75sn9/73vc4+OCDB3XdJEmDz+NI/yzb6ACWJgsXLuTDH/4w3/nOd1hnnXXajXv22WdZddVVmTt3LgsXLgTgoYceYurUqTz11FM8+eST7LzzzowfP56ddtqJ97///QC8/PLLLLfccovnc+edd3LZZZexzz77cPPNNzN+/PjF8160aBHXX3/9EhV9SVLz8Fgy7MwETgMW35goIkYB3wd2Bh4Gro+I84FRwHEdpt8/Mx+PiD2AI8p5Na1Ro0Zx6qmnAjBt2jR22WWXduPPO+889tprry7n0Zft88EHH+TII4/khRde4N577+VXv/oV++yzzwCvnSRpsHkc6R8beIbQr3/9a66//nq++MUvAnDcccfxi1/8glNPPZXDDz+c22+/nVdeeYVvfetbAFx88cVA8eSSSy+9lIkTJ/KOd7yDAw44gB/84AdkJscddxxbbrnl4mV88pOfZL/99uPUU09l2rRpjB49mrPOOosZM2awzDLLMGXKFF73utcN/cpLkgaEx5LhJTOvioixHZLfAdyXmQ8ARMQvgT0z8zhg907mcz5wfkT8HvhFx/ERMQ2YBjBmzBjmzp07cCsxgB599FEOPPBAlllmGfbZZx9WWGEFPvnJTy7eHs877zzOOuusxfHffPPNfOMb3+C2225j4sSJnHPOObzvfe/jc5/7HN/73vfITL785S/ztre9bfEy9tprLz71qU9x0kkn8ZGPfIT58+cvfmrcP/7xD4477jh23HHHhpXRKqtEQ5Y7nPS37Fdacen+izIQ2+7CBZ33blgaLNvPMnxllTcMUCTNa5kG7UM9jvRPZGajYxhwEyZMyBtuuKHRYagTRx3xq0aH0FDHfqt/LcHf/M7PBiiS5vWlw/fv1/QL/nLsAEXSvJbf6qh+Tb/oqPUHKJLmNOrYhxodgroQETdm5pBd6lQ28FyQmZuWnz8A7JqZnyw/fwTYMjPr9v2OiFbgfcBo4NbM/H5Xy7OeM7wt7fUcsK7TX/2t54B1Hes5/WddZ3jrrK6zdDeP98G79/1ho0NouD/8T1Pf/1GSGm7+ZM/wrzJr5J1gqqj3BXe6wpk5G5g9WMH01tJe17GeI0n9t7TXdRpVz/Emy5IkSQPrYeD1lc9jgEcaFIskSVpK2MAjSZI0sK4HNoqIDSJieeCDwPkNjkmSJI1wNvBIkiT1UUScC1wLjI+IhyPiE5m5EDgYuBi4C/hVZt7RyDglSdLI5z14JEmS+igzp3SSfiFw4RCHI0mSlmL24JEkSWoCETE5ImbMmzev0aFIkqRhyAYeSZKkJpCZszJzWktLS6NDkSRJw5ANPJIkSZIkSU3OBh5JkiRJkqQmZwOPJEmSJElSk7OBR5IkSZIkqcnZwCNJkiRJktTkbOCRJEmSJElqciOqgSciJkfEjHnz5jU6FEmSpAFlPUeSJHUlMrPRMQy4iJgH3NvoOAbRWsDcRgfRxCy//rMM+8fy6z/LsH9GevltlJktjQ5isFjPUQ9Yhv1j+fWfZdg/ll//jfQyrFvXWbYRkQyBazNz10YHMVgi4obMnNDoOJqV5dd/lmH/WH79Zxn2z0gvv4i4qNExDDLrOeqSZdg/ll//WYb9Y/n130gvw87qOiPqEq2akVzpkSRJXRvp9YCRvn6SJKlrndUFRmQDjyRJkiRJ0tLEBp7mNKPRATQ5y6//LMP+sfz6zzLsH8tPw5nbZ/9Zhv1j+fWfZdg/ll//LZVlOCJvsixJkiRJkrQ0sQePJEmSJElSk7OBR5IkSZIkqcnZwDMMRMQyEfHjiLg6Iv4UEedExNSIeDAiZkfEdRHxo4hoKfNPj4i7ynFXRMR6Zfo2EfFKRGzY2DUaWPXKZwDnvUJEPBMRH6+ktUbEZpXPhwzU8hrB8us/y7D/LMOhERFjI+Lp8vgwOyIOj4j7ejmP1og4fbBi1NLJuk7n3D/2n2XYf5Zh/1h+Q8N6Tvds4BkedgWWzcxtM3N74DNl+k8zszUztwTuAb5XmebYzGwFfgJ8qUz7UJlnv6EJe8h0Vj4DYTJwFrBvJa0V2Kzyudl3mJZf/1mG/WcZDp0by2NHa2Z+p9HBSCXrOp1z/9h/lmH/WYb9Y/kNHes5XbCBZ3h4Dtg0It4MkJlPdcyQmScB20dEx+/sb8D6EbEc8F/AEcC7BzneobZE+UTEDuUZvT9FxP9FxAoAEfG5iLihPDN4fUSMLdOPi4grI+LaiNi9Mu8pwPHASxHxuohYA5gKHFW2Cn8IWLd8f1TZ4vuHiDg3Im6LiL2HsBz6yvLrP8uw/yzDBqtX3hGxUlkWV5bl88Yy+7pLW/lo0FnX6Zz7x/6zDPvPMuwfy6/B6pV3LI31nMx0GAYDsD9wBfAgcCjFj/bLHfL8BVgbmA58uEw7BPgusHstP/B94G2NXqdBLp+VK+O+DXy0LJubgOWAVYG5wFiKFvUflXlXAm4BAlgDuLBM3xs4rHy/uHzLz/dV3rcCNwKjgNcBNzS6bCw/y7DRZWMZDp+hLKungdnlMLG27p2U9xbALyrpy4zk8nFo7FBnHzAV6zqdlY37R8vQMmyyMrT8hqSMx2I9p8thWTQsZObPgJ9FxKrAVcApdbK9mmInAEWL7SeBf1FUfE6laIncjmJn8WGKDXdEqFM+10TEMcBo4DXAs8AGwO2Z+TLwckTcXU7+FmBiRMwuP48G1gQ+AIyJiIuAZYGVgRN6EM7NmbkIeCQiVhuI9Rtsll//WYb9ZxkOmRszc1LtQ0TU3m5Sp7zPAm6MiLOBJ4GvlXlHcvmoQazrdM79Y/9Zhv1nGfaP5TdkrOd0wQaeYSAiXgc8l5nPAvMpuvhFhzyHAFdn5ivlRnxsZp5djnsVMCYz31nJ/9eIGFVuuE2tk/L5CvC1zLw2Io6nKK85FD/sZYEVgfHlLO4ALsnMz5bzWz4zF0TEvsA7M/OZMv2siNgYWED738bCiFgmM18pP+cgru6As/z6zzLsP8twWDiKJct7NHBiZmZEfBn4CHAbS2f5aBBZ1+mc+8f+swz7zzLsH8tvWLCegw08w8UY4KSIeIXiO5kFLAI+ERGTKH78t9L5zbPeR9FKXHUrsBNwyaBEPLTqlc+DwE8j4h5gHvBsZj4WEb8ArgP+DjwMLMjMCyNi67JFPIGHyx/4qNrOsnQxxdnA84CTo7j2dR/gN8DvI+IPFOXabCy//rMM+88ybLxf0qG8gY2BUyJiIUW35Y8B6zcuRI1g1nU65/6x/yzD/rMM+8fyazzrOUBkjtjGKy2FImK5zHw5iq6RNwFvbPYze0PJ8us/y7D/LENJqs/9Y/9Zhv1nGfaP5afBZA8ejTRHRMROQAvwFXeWvWb59Z9l2H+WoSTV5/6x/yzD/rMM+8fy06CxB48kSZIkSVKTW6bRASzNImKriPh6RMyMiJsiYnY5vDoipkfEh3s5v9kRMaZD2vIRcX5EjJjvOiLeHhGXR8SV5evbI2JsRFxajl+lHPeBDtPdV75OjYjnI+K15efWiDg9Ij5bluHNEfFo+f6Csgx/FRF/iojrImLHoV/rgWP59Z9l2H+WYe9FxIwon64REatFxEcr41ojYrM+zrfdtBFxTr+DlUphXafX3D/2n2XYf5Zh/1h+fRPWdfovh8Gz2pfWgeLmWGsDM4HtOoybDny4l/ObTfGEiY7pRwJ7Nnp9B6jMWoA7gbHl57Hl57cClwKvKsthnzrT3le+TgVuBk4uP7cCp1fydfy8O/DzyvKua3Q5WH6WYaPLwTIc8jJbnuKGjLOA9cp1uLQyfjq9PGYMxLQODt0NWNfpbXm5f7QMGz5YhpZfg8rNus4ADCPiTEcziohVgLUy8/Ee5D2jbJ39W0TsUaZ9MIrHg14REcdVsh8cEZdExGURMbpMuxDYe+DXoiF2B36XmXMAytf/A3aj2FleAPwgM3/VzXx+C2xbaxXvxv3A6IgIYA2g2+9sGLP8+s8y7D/LsPd2A84HzgD2Az4HvK08NnyIoiJ4VPl5VETsXZ7F+3NEfBUWn736Q0ScGxG3lXnWqDNt7ezhShHx6/IM4xURMa5Mnx0R36pzrJHasa7TJ+4f+88y7D/LsH8sv76xrjMAvMly47wJeKjy+dSImAc8mJkf75D3oMz8T0SsCVxJseHvR9EK+fdo3yX5L5l5RETMAHam2IH8HXjLoK3J0BoD/KND2kPAahRl+iBF+XQnge8AR1CcXezKAxSPb727XM7kHkc7/Fh+/WcZ9p9l2HtTgM8Dj1E8EvpjwMaZOQkgIjaiOOt3dkSsDhwGbJ/FUzrOi4jaMWBtiorna4DzM/PXETGzNm05r9oypwG3ZebREfFO4HiKR1VD/WON1JF1nd5z/9h/lmH/WYb9Y/n1jXWdAWAPnuHjM5nZ2rHCU1ZovhYRf6ZoxV2/HHUk8Pkorh+s/oBvLF//AaxZSR8pd9P+F0WXvar1KLr03QCcA/xPRCwbEceUra/HdDKvXwNbA921in8M+Gdmjge2BH7U5+gbz/LrP8uw/yzDXoiIFmBbYAbFGcCxFN2/OzOO4ljxxyiuY9+AtmPHzZm5KDMfoagAdmU8cE35/hqKSmlNZ8caqSvWdbrn/rH/LMP+swz7x/LrJes6A8cePI1zN8WG253Ngc2A7Sk2rPvL9Aczc1rZXexeih8CtK/c1Jom3wjc0d+Ah4kLgC9FxI8z86GIWA/YE/gI8M7M/G7ZDe/nwEczs9PKXmZmRHwHOIpiZ9uZAOaW758GVhmIFWkQy6//LMP+swx75wPAcZl5GkAUj1b9NO2P4Qsqnx8A7gMmZebC8s9zUBxH6pVlddqqe4BtKO4XsE35uabesUbqyLpO77l/7D/LsP8sw/6x/HrPus4AsQdPg2TmfGBuRLymm6z3AMtR3IjrK8AzZfp3IuKqMv3H3cxjN4rW36aXmc8AHwdmRsQ1wF8odpZPVfJ8CXgOOKUHs/wN3f8OzgYmRMSVFD/+o3of+fBg+fWfZdh/lmGvfQi4qPL5zxQ3V3whIn5bVoL+CBwQEb+hqNidDFweEVeU0766i/kvnrbDZTA/ATYvjzXfAL44QOujpYR1nd5z/9h/lmH/WYb9Y/n1iXWdARJdNBhqkEXE1sC7M/Org7iM5Sm6O++Zma8M1nIaofxx/gy4JzOP6y6/2rP8+s8y7D/LUBrZrOv0nfvH/rMM+88y7B/LT0PNBh5JkiRJkqQm5yVakiRJkiRJTc4GHkmSJEmSpCZnA4+aSkSMjYiny8cJ3hARH4uI73bI84uI2LlD2oPlNLPLx61Svr82Iv5UTrNsRLwhIm6MiOciYrvK9KMi4rsRcWk53cZDs8YDy/IbWENQnqtHxCURcWVEXB0Rmw3l+g02y2/oRcSqEXFNWV5/LW9aKGmY8Djdf5bhwPE43X+W4dBb6us6meng0DQDxeNWLy3frwo8CTwILFOmrUzxyLxRHaa7r868ZgNjyvczgA8CKwFrADOB7Sp5/x8wrdHrb/kNr2EIyvNg4GtlWivwP41eZ8uvuQeKEzvLlu83BK5vdEwODg5tg8dpy3A4DR6nLcNmHJb2uo49eNS0MvNZ4F5gDjCxTN4TmJWZi3o5u78B62fm85n5VJ3xewPrR8QVEXFaFE/saGqW38AajPIE7qKoDEBRmXy8/5EOT5bf0MjMVzJzYflxVeDWRsYjqXMep/vPMhw4Hqf7zzIcGkt7XccGHjWtiFgXeDVwErBfmbwfcHad7KMq3Rw/22E+AUwC7uhicesCj2bmDsCLwP79jb/RLL+BNUjleSOwVUTcDpwCnDBY8Tea5Td0ImLdiPgzcAlwXqPjkVSfx+n+swwHjsfp/rMMh87SXNdZttEBSH3wtoi4AkhgGnAV8N2IeB2wXmbeGBEHAx+g6N74SWBRZrbWmdevgQXA1cDvu1jmU8BF5fuLgPcNyJo0huU3sAazPI8FfpuZJ0bE1sD3gd0GfY2GluU3xDLzX8B2ETGWorv3BQ0NSFJHHqf7zzIcOB6n+88yHGJLc13HBh41oxszc1I1ISL+CPwM+BVAZp4GnNaDee2dmQ/3IN9sYALFNbK112Zl+Q2sQSvP8gzN3PLj4xRdb0cay28IRcTozHyp/PgsML+R8Uiqy+N0/1mGA8fjdP9ZhkNoaa/r2MCjkeIcipbsg/ozk4hYFfhfYGNgk4i4MDO/BhwP/DwiDqQ4Q/ORfsY73Fh+A2tAyhM4FTgrIvYHVgS+2N/AmoTlN3g2jYiTgEXAcsChjQ1HUg95nO4/y3DgeJzuP8tw8CzVdZ3I4u7SkiRJkiRJalLeZFmSJEmSJKnJ2cAjSZIkSZLU5Gzg0YgQEZtFxB/KRwleExGfi4j7OuS5r3ydGhEPlnmvi4gfRURLJd+6EbEoInYY6vVohIhoqTyG8ZmIuLZ8/4Fy/K4R8ZHy/dTy2nN14DbYf5ahJNXn/rF/rOsMDLfD/rH8NBRs4FHTKw/CZwMHl48T3Ba4o5vJfpqZrZm5JXAP8L3KuP2AU4APDUK4w05mzivLohW4meLu/K2Z+ZuIGJWZF2XmWWX2qYCVng7cBvvPMpSk+tw/9p91nf5zO+wfy09DxadoaSTYHZiVmfcDZHHn8IsjokcTZ+ZJEXF/RCyTma8Ak4F3AX+M9o/ZW1qMjYjzgLuBlyPiKmAMcA3wVuDXEXEDcAjwI2ATisbiQzPzrxExE3gO2ABYE9gjMx8f8rUYWm6D/WcZSlJ97h8HnnWd3nM77B/LT0PCHjwaCV4P/LOf83gCWCsiNgX+npkvAP9HsTNeGo0FPp2Z+9cSMvNy2s56fQbYE1guM7cDPgycVpn+vszcDTgf2Geogm4gt8H+swwlqT73j4NjLNZ1esPtsH8sPw0Je/BoJPgnsGmd9OzFPF4NzAU+B7wlIi4CVijTftvvCJvP7Zn5bDd5xlOc6SIzH4iI1Svjbixf/wG8YRDiG27cBvvPMpSk+tw/Dg7rOr3jdtg/lp+GhD14NBJcAOweEYsPrhGxM/B4RGxUfn4T8Gi9iSPiEOBqih3sJGDrzNy1vD52tQ4H86XFok7SF9DWMHwPsA1ARGwIPFPJVz1Y9azvaXNzG+w/y1CS6nP/ODis6/SO22H/WH4aEvbgUdPLzGfLJx98PyJWAJYHfgN8GphRubb105XJPhERk4AVgVsprrHeHri7vK61ZjawNzBjUFeiefwv8NOIuAb4GrBbRPwZGAV8pqGRNZDbYP9ZhpJUn/vHIWddpw63w/6x/DRUori/kyRJkiRJkpqVl2hJkiRJkiQ1ORt4JEmSJEmSmpwNPJKkhouIZSLixxFxdUT8KSLOiYipEbEoItar5LsvImaW798YEbPL4cQoL2CPiC3K+VwTEVMbs0aSJEltrOtoKNjAI0kaDnYFls3MbTNze9puZPlXYApARGwFPF6Z5njgiPIJEitSPFUC4FTgw0ArcIhPlpAkScOAdR0NOht4JEnDwXPAphHxZoDMfKpMvxjYpXz/IeAXlWneCNxQvv8rsENEjAZWzswHM3MB8Cfg7YMdvCRJUjes62jQ2cAjSWq4zLwK+DHwg4h4MCIOLUctBO6JiLcCmwI3Vya7Ddi17K68K7AGsCbwTCXPM2WaJElSw1jX0VCwgUeSNCxk5s8ycwdgc2Aqbceoc4EZwGUdJjkM+ATwR+Bp4BHgKaClkqelTJMkSWoo6zoabDbwSJIaLiJeFxGrlh/nU3RjjvLznynOYJ1dnSYzH87MvYCdgZWB/83MF4HnI2K9iFgO2I6iS7MkSVLDWNfRUFi20QFIkgSMAU6KiFcojk2zgEUAmZkUZ6+IiDG1CSJiP+AAIIGzMvP2ctRnKc6EBfCDzHx6qFZCkiSpE9Z1NOii2JYkSZIkSZLUrLxES5IkSZIkqcnZwCNJkiRJktTkbOCRJEmSJElqcjbwSJIkSZIkNTkbeCRJkiRJkpqcDTySei0iDo6ILIfxlfSNI2J6RLRW0t5Tpo0dgOV+KSIOrXxuLWM4rb/zLuc3trJeHYeZA7EMSZI0/FnXkdSMbOCR1Bf7AK9U3tdsDHwNaK2kvadMGzsAy/0ScGjl853AFOBnAzBvgCfK+U0BbirTDik//3CAliFJkoY/6zqSmo4NPJJ6JSJeB2wL/Ap4hLLSU561+nWZ7WvlmaCpwKfLtCsiIsu8W0fEtRHxXET8PSKm1OZRTvfniPi/iHg2In4RhdnAysD6lbNMGwPnAvuX028SEZdFxPyIeCgivhIRUY7LiLg3In4eEfMi4pKIWKm6bpn5n8z8ZWb+slw3gFnAH4HZEfG7cl7LRsSTEfGXyrzviYjTI+KZiLgwIlYvx705Iv5YrstDEfHfA/NNSJKkwWBdx7qO1Kxs4JHUW3tT7Dt+DfwvsGlEbExxRujkMs9vKc4E3QVcUqZ9A5gSEWsAFwCrAccCc4CzIuKtlWVsDfwFuKecz3bA0cBLwFzqnGWKiOWA84EtgaOAW8tpPl7JNq6M81pgZ+D9PVnhzHyyXN/dImJtirN2awBnVrK9EXiMojL4buArEbEs8H8UlbPjgeuAEyNick+WK0mSGsK6jnUdqSnZwCOpt/YFFgB3UxzEAfbJzP8AV5efby/PDl0H3FumXV6eLdqaosLwJuCbFJWPUcCOlWVcl5nHUVSeAMZm5uXAQuA/lXlXjQc2BP4vM08BPlemv7uS59HM/AIwszbfXqz3D4FlgQ9TVJYWAL+sjH84M48CDqbo0t1axrQR8DqKSt/eZd6de7FcSZI0tKzrWNeRmtKyjQ5AUvOIiNcDWwEB3FEZtS8wHcg6k3VMi/L1TOCsSvqcyvunyteF5euoTuZVT1d5Optv9zPNvDYibqboIr0m8PvMfKpO1qjz/mLgu5X0f/d0uZIkaehY17GuIzUzG3gk9cY+FAfy44C/lmmfAHaPiLcAT5dp20fEBym67NbSPhARKwPXUFQ+dgWup9gP7U5x1uehbpb/NPDqiPhYOW3VPcD9wJ4R8RlgUpl+YW9Xsgs/BH5cvj+jw7gxEXEs8GqK3pFXUJz5u5ei2/VlwPNlXOcBtw9gXJIkaWBY17GuIzUtL9GS1Bv7UJw1Oikzf5eZv6PtzNS+wJ8pDu7bU9wQcE3gHIqD/0HA98ozQbsD9wHforiG/Hnan9XqzPEU3YVnAu+rjsjMl4E9KSpD3wT+C/gqbV2UB8I5wLMU18Z3rEzdA6xNUUYXAcdk5sIypquBL1NU7FYBbhvAmCRJ0sCxrmNdR2pakdmTXoCStHQrbzg4kaKS9/3MPKwyLoE7MnPTRsUnSZLUH9Z1pOZnDx5J6pn3UNxo8CaKs2aSJEkjiXUdqcnZg0eSJEmSJKnJ2YNHkiRJkiSpydnAI0mSJEmS1ORs4JEkSZIkSWpyNvBIkiRJkiQ1ORt4JEmSJEmSmpwNPJIkSZIkSU3OBh5JkiRJkqQmZwOPJEmSJElSk7OBR5IkSZIkqcnZwCNJkiRJktTkbOCRpAEUEXMiIiNiZqNjkSRpJIiInSPi5oh4sTzGntzomAZDREwt1y8jYuxIW95QiIjWyjq1NjoeaajZwCMNYxExu3KQujsiojJu5Yh4ujJ+ZgNDrSsi1omIH0bEg2Wl7KmIuD0izomI1RsdX6NExNjK97bE0Oj4JEnNodnrCT0REcsA/wNsDrwIXAfM6STv1A7H1G06jP9JI4+3EXFxZfnXD/XyB9JANw5FxMyu6kblMLv/kUsj27KNDkBSj40H3gVcVH7+KLBaw6LpRlkh+z2wBbAQuBNYARgHbAIcBTzdsADrKCvGozJz4RAu9l/Awz3NHBHLZ+aC3o4biPlLkoa1pqon9MJrgdpJoaMy8/u9mPYQ4BqAiFgD+NAAx9ZjETEGmFRJmhARm2bm7Y2KaZi5n6LxDmBV4M3l+weAJ8r3dw51UNaL1GzswSM1h5fL10MqaQeXr0s0RkTE8hHxlYi4JyJeiognI+IXZeWiludtEXFZRDxa5vlPRFwfER/uMK/aWZNvR8Rp5bwej4jvRURXjcRvpGjcAXhXZm6emeOBFmAylcadiBgTEb+PiBci4qGI+FTlrOTsOrFMr6TVy/ediLgjIp6JiJcj4pGIOCMiXlvJM70yv3dHxJ1lOW9ajt8lIi6PiGfLuK6LiMkdymaTiPhz2Tvp7oh4bxfl0ZnTM3Or6lBv3SLiixHxCPBYOa52KdhZEXFCRMylrBhFxIoRcWxE3BcRC6LoOTUrIraozLt65m3viLghIhYA7+nDOkiSGqsZ6wlExBrlNP8oj9ePR8S5EfGGcvxU2p8EOa1c1tQelsn7I+J15ecDgBVpK6tqHLtGxJ/K5S8oj/1XRcSulTwnlsueGxGvKdO+WqY9GxEbdhPPRyn+ez1F0WgB8PEu8m9cxvBiRNwbEXtVYlk5Ir5fltuLZZlfFxGfq+Tpti5QT7T1pJlTSWvXWyeK3mA/r0z2YLV+1pPtq6PM/EalHnRQZVQ1/TMRcVgUdbyXynK/PCJ26mqdypjeHhEXlOXwUkTcFhEf75Cnto7fKcthHkXvMcr61r0RMb8sz4ci4pSIWLVe2ZV1q7vL381VETG+w7ImRcQlUdRVa9/xpyrj3xgRv6xsk/dGxOFRnECVOpeZDg4Ow3QAZgMJ3AjcDbwCbERxBiiBS4C55fuZlelmlWmLgFspKhMJPASsXub5QDl+DvC3Sp4EdqvMq5a2AHiSoqJVSzugi9g3ruSbCbwTWKWTvNeV+V6hODvzH+C5Mm12nVim1ymjar7bgWeA24C7yvkm8NdKnumV+b1EUdn6B/DWsmxq0/wTuLcS3wfK6Vco8ydFZfEO4HmK7uPtvo866zu23rp08f2/RFsvqAfKcXMq414q1/VP5bg/VuZ/F/Bs+f554K1lnqkd1v9f5Xru2ejt3sHBwcGhZwPNXU9YoTx2ZXmMuwN4ofz8BDAG2A24qTK/+4G/VJffYZ7VY9svytdvAKPK9VgI/KqWpzLd58v47y/XdX7l+L55mWc0cEuZ/huKS8YWlJ8/1oPv6u9l3u8DXyvfPwYs20n8z5Xfae0YvhB4S5nvhMrx+29l3C8Dl1bm1du6wNgybWb5eU4ncY0FvlIus5Z2U/m9fLKn21c3ZdVamffUSvrplfT7aNu2FwHvrjNta5m2TVlWtTK/o5LnsDrb8ksUddHbgF+W454rl3dzh3X/dWX6Wtm9XG4b1Tro1ZV8e1fSXyiX8zTlb5Sit/vT5finKba7ReXnUxu933EY3kPDA3BwcOh8oK3idgPFmbgEvgecX77fnQ4VN4qGlNpBZ5cybTWKylJSdG+GosvzayrLWoG2hoyzKum1eT1A0ftmBYrGgKwd9DqJPYDLK9PXKid/oagoRJlvh8r4z5ZpbyoPjknfGng2A5apfP5kZdo3lGnTK2nfquQdVa5rAudU4vxJmXZv+fnjlen3LNMmVdJmdlE2YzuUS3X4XZ11S2DXWnzl6xzaKiGbVWKvlufny/R1aKso/LZMm1rJd06tvGrzd3BwcHAY/gPNXU+oHkdrJ082pagrJHBCmVY9Zk7tpjyqx7ZWij/PjwFTaGuY+W4tT2W6scBqlc+r09Yg8o1K+ia0NUL9u3z9nx58T9tV4toS2LDyec9O4j+mTBtD20mvM8q0WgPKVyrTrgq8vXzfl7rA2DJtJt008HSW1pvtq5vyau34nZdlVmsUOa1MW4W2hrMb60zbWqbV6qNXAsuVaUeVac8CK3TYlp8AxpRptXrXWzvEeAxtjTkrdCi7BCaXaSdW0lYs02r1zAeB15Vpy9LWgPezcvw9lCdHKS4vTIqGntc3et/jMHwHu3hJzWMmMA/4BMUZrfuBC+vk27Ly/uIobiL4NLBWmVa7BOgV4IQoLl9aSFFhGVeOex1LOj8z52XmixQHJIDXdBZsZiawK3Ao8GeKMxmjyvh+Dny6zPqWymS/Kqe9m+KMT19tDlwfEc+V6/+Tyrh663Zy5f0awAbl+/2AV8p5fLJMGxcRa1bifomiIk1mXkpxlqo3/kXRg6k23FMnzz2ZeVG5jEUdxl2RmbdWxr29Mu4XZfq/gSvKtAl15n9qZr7SyfwlSc1hJk1UT6DteLUA+C1AFvejqR3/6x2vemMuxXFwbeBHZdqpneRdHphZXg6ziOJYvko5bvG6ZuYdwBHlx9dQHMM/RfdqlwLdk5nXZeYDlPcGomgoqed/ymU+DFxdpm1avs4qX48uL9O6FPgCbfeq6UtdYKD0dPvqrQkUJw+hbZ3mAxeUaW+NiFHdxPROYEEZ0zFl2ioUDXdVvy3LvVov2imKB4W8UE5/VJm+LPDqDtPPy8zad1S9b9DaEfFq2uqZMzPzkXI5CzPztg7xvhF4tlze2WXaMsA7OllPyZssS80iM5+LiJ9TNJgAfD8zX4m2B2bUVBP+StHaX/WP8vVs2nqc3EXRHXljigNdvQPkM5X3tev5l1h4h5gXUJxJ/F5EjAZ2ojgotwB7Aqd1MXlX867G19JuoojtgDPK6Z+kOLC+irab9S2xbmWlp95yHwQer7P85bqIrcsyqeP0zJzeTZ5/93Fcx+++L/OQJDWBZqwn1ELvQZ6+OgXYn6J3yy2ZeWV0uJ9e6QKKS9sWUlwu8yLwXxQNPx3XdWzl/WoUDRfPdBZARKxEcUkOwIYRUcu7Yvm6W0S8OjOfWGLiymyqHzJzRkTcDexBccLpbRR1rI9HxBs7TNvb8q3l77Su1Y2ebl/90ddt5hGKS+87eqXD53b1ooj4EEXPL4BHy3msRdGrCJbcRp6pvK/eA6undcRavicpLkXr6IUezkdLIXvwSM3lVIqD0HMU3Tfr+Wvl/YnZdmO6rSnO7vy4HFc7g/KTzNyE4ua6zw1UoBHx+og4JspHZ2bmSxRdZJ8sszxbvt5WmWzvctrxtO/ZU1NrbKndfHEcbWezarak7cD4lsx8B3BmT+POzMdpe/zq7cD2lTLcBziubBCqPfViNMVNo4mIHWl70kejVB+7+iEoHldP0V0bim78kqSRqWnqCbQdr0YD7weIiE0pLrOGATheZeYtFJflQCe9d8peuRuVH7+amW8FPkidRoSI2Bn4LEUZ3wqsDJwdXd9M+gO09QZajqKxpIWi8aiW9uE609XqRK+j+G6grHtExDuAOzLz85n5LqB2A+bXUVzm3p+6QK2utXZErFLe1HfPOvmer7xfufK+p9tXb91I23dSW6dVKC5DBLi5i17ItfJ4BNipEtNk4OTMvKmbZdd+C/OBDTJzS4r7W/Va2ZBX6+H2sfJ7ISJGlds/tJXhfygu9arFuwvww8ys1zNPAmzgkZpK2aV3LYrrgud1kmc28Ify4y8j4u8RcRtFt+0raXuyVa0L9Ccj4g6KrtwrDGC4K1N0X30wIp6IiL9RdGWune04t4z3CtoOZCeXsfyNost2R5eVr1Mi4kqK+/l03I9VL+26LSLuAg7vZey17teTgUcj4qYonmA1B/jvctwvaHuyx28j4naKx8Iv8XSObnwyIv7SYXht95PVV5bnpeXH75Trfw/FWcYXKW42KUkagZqsnnAubSdLflku468UvSHmAicN0HLeQ3EJzcxOxj9F2/H862VZ/I3iXieLlQ1BMylOIn2P4jK4ZyhOLH2li+XXLs+6LTOjOtBWr6n3NK3Plcfwuyh6Ir9CcXNlKJ6W9u//3969x1lV1osf/3wFQUEcy0uW5KUwyrx0lFK8MYKWpWidSgTLzAudU1odLfOXZWaaZaWWlwrJ8JaVlgZesEQRL1Rqh8gbpWgnNTW8AKXG7fv7Yy1wwJlhZvbsvWfPfN6v136x17Mu+7seNns9fNezniciHo2Ie3nlMaV/AY9U2BZYGdMAinqYA4xqZbuHWry/uWy/7NGJ71enZOYjvJK0/FREPEyRKNmWom6+1M7uX6Jon43glXbd/1H01PlGBz5+5b+FIcD8iJhPcdOvq75AkazapjzeXIrE2ufK9V+nqK8tgb+W8c6nuEk6pYLPVR9ggkdqMJn5fFuNthY+QDFDw0PAVhQD9M2naBjMLLc5guJZ7JeBQRRduisZ92ZNTwCnlJ/3b4rnm9ejuGv08cy8usW2H6RoDPybohv151j97tNKx1MkUf5JcVH8JsX4Pqtk5m8oLpxPUnR/fgj4784Enpk/A95L0eNoAMXjXS8DV1F20S3HGHgfxTP0K8rPOrL83M7YgqJx2PI1sJPHWNNBFI2D+RS9nVZQNP72yMw5FR5bktSDNUo7obyOjqKYVervFOON/Iti7JndVo6B0g2f82JmLmird0c5ZuAHKdodyykSTIfxyng2K02i6CHzZ4qBgh8HjivXnRwRI9fYnrIX88rkyC9b+fhflH/uEBG7rLHukDKGgRTJtXErx9yjaAvdVq7bgXIGLYqZpF4ot+lSWyAzf02REHmSYqDth2gleVLG8jWKQaw3p2i/rOzF3JHvV1d8guKm3QPAGynO/1aKwZxvbGunzLwD2Ivi/JdRPGoIRT22l5xb6UcUgyUvoEjyzKRo43ZJZl5F0RvnZoq/u+EUicbfluv/TFGfP6XoNfR2inOdySuPYEqtWjk7jCT1KBExk6JRdFtmNtc3GkmSJEnq2ezBI0mSJEmS1OBM8EiSJEmSJDU4H9GSJEmSJElqcPbgkSRJkiRJanAmeCRJkiRJkhpc/3oHUA2bbLJJbr311vUOQ5Ik1cG99967IDM3rXcc1WI7R5Kkvq2ttk6vTPBsvfXW3HPPPfUOQ5Ik1UFE/LXeMVST7RxJkvq2tto6veoRrYgYGxGTFi5cWO9QJEmSJEmSaqZXJXgyc1pmTmxqaqp3KJIkSZIkSTXTqxI8kiRJkiRJfZEJHkmSJEmSpAbXqxI8jsEjqVbOPvts9txzz9XKXn75ZY466ihGjx7NcccdB8DixYsZO3Yse+yxB5deemmHjt3aPjNnzmSrrbaiubmZww8/vHtPpg46Wn833ngjb33rW1+1bXv6Qv2pb7KdI6napk+fTnNzM83Nzbz+9a/n2muvXbXuySefZPTo0ey+++7cfPPNAFxyySXsvffevOtd7+LCCy/s0Ge0dp2eNm0au+22GyNHjuQ73/lOt5+X1FdEZtY7hm43YsSIdHYJSdXy73//m4kTJ/LII49wxx13rCo/66yz2GWXXRgzZsyqsrPPPpvNN9+ccePGsc8++3DzzTczYMCAdo/f2j533XUXN998M6effnrVzqtWOlN/zz//PIMGDWLMmDGrbdue3l5/WruIuDczR9Q7jmqxnSOpFnbddVdmzJjBBhtsAMCnP/1pDj30UHbccUcOPPBAZs6cydKlS1l33XVZvnw5O++8M3/84x/XetzWrtNPPfUUW2yxBeussw7Nzc1MnToVx1WV2tZWW6dX9eCRpFqYPHkyH/vYx15VPnPmTKZOnbqqYQIwe/Zs9t13X/r168dOO+3EvHnzeOmllxg/fjyjR49m3LhxLF26dLXjtLYPwJVXXslee+3FlVdeWf2TrKLO1N9rXvMaBg4cuNp2fb3+JEmqtvnz5/O6171uVXIHYO7cuYwcOZINNtiAIUOGsHjxYtZdd10AlixZwtve9jaga9fpLbfckn79+hER9OvXj3XW8b+pUlf4L0eSOmHp0qXcdtttjB49+lXrHnnkEQ444ACuv/56vva1r7Fs2TJeeOEFNtxwQwCampp4/vnnmTx5MgcddBC33HILzc3NXH311asdp7V9RowYwUMPPcT06dP53ve+xz/+8Y/qn2wVdLb+WtOX60+SpFr45S9/yQc+8IHVypYvX05EAK9cXwFOO+00tt12W3bZZRega9fplW688UaGDRvGkCFDqnZuUm9mgkeSOuGyyy5jwoQJra5rampi1KhRDB48mGHDhvH000+z0UYbsWjRIgAWLVrERhttxIMPPsi5555Lc3Mzl1xyCc888wwnnHACzc3NTJ8+vdV9NthgA9Zdd10GDx7M3nvvzV/+8peanXN36mz9taYv158kSbUwbdo0DjrooNXK+vXrt+r9yusrwCmnnMIjjzzCVVddxbPPPtul6zQUvYbOOusszjnnnJqco9Qb9a93AJLUSObNm8ecOXP4wQ9+wP3338955523akDg3Xffnblz57Lzzjvz2GOPsemmmzJy5EhmzJjBIYccwpw5cxg+fDjDhw9nzJgxfPCDHwRY9fz6Sg888MCr9lm0aBEbbrghy5cv5+677+Yzn/lMXc6/Up2tv9b05fqTJKnannrqKQYMGMDGG2+8WvmOO+7I7Nmz2XHHHVddV//9738zcOBABgwYwKBBgxg4cGCXrtOLFy/miCOOYMqUKQwePLim5yv1KpnZa17AWGDSsGHDUpKqbY899sjMzGOPPTYzM5988sncb7/9ctddd83JkydnZubChQvzgAMOyJEjR+aPf/zjzMx88cUX87DDDsvRo0fnPvvsk7/97W9XO25r+1x00UX5zne+M3fdddc899xza3OCVdaR+rv77rtzzJgx2dTUlGPGjMmXXnrJ+tNaAfdkD2iXVOu1yy67dGNtSdLqfvCDH+R55523annldfpvf/tb7rPPPrnbbrvlTTfdlJmZX/nKV3LUqFE5cuTIvPDCCzOza+2cr3/96zl06NAcNWpUjho1KufPn1+DM5UaV1ttHWfRkiRJvYqzaEmSpN6srbaOj2hJ6nOW/PaMeodQdwN2O7mi/ZefvFU3RdKY+p3x13qHoD4oIsYCY4cNG1bvUCT1cH29rVNpO6dSl156KZdccgnLly/niiuuYIsttgDgq1/9KtOnTwfg9NNPZ8yYMSxevJgJEybw3HPP8YlPfILDDz98rcdvbZ9p06ZxxhlnEBF86EMf4oQTTqjqOapncpBlSZKkBpCZ0zJzYlNTU71DkSS14YknnuC2225jxowZzJw5c1VyB+Dwww9n9uzZ3HjjjXz1q18F4KKLLmL8+PHMmjWLyZMns2TJkrV+Rmv77LTTTtx5553cddddTJ06lYULF1btHNVzmeCRJEmSJKkb3HTTTSxfvpwxY8Zw3HHHsXz58lXrttlmGwAGDhy4asr52bNns++++9KvXz922mkn5s2bx0svvcT48eMZPXo048aNY+nSpat9Rmv7bLnllvTr14+IoF+/fqyzjv/V74v8W5ckSZIkqRs8/fTTLFmyhBkzZjBo0CB+9atfvWqbU089lU984hMAvPDCC2y44YYANDU18fzzzzN58mQOOuggbrnlFpqbm7n66qtX27+1fVa68cYbGTZsGEOGDKnWKaoH61Vj8PhsuiRJkiSpXpqamhg1ahQAo0ePZs1B8a+55hqeffZZJkyYAMBGG23EokWLWG+99Vi0aBEbbbQRDz74IPfeey8//OEPefnllxk/fjwnnHAC9957LyeddFKr+wDMnz+fs846i+uuu66m56yeo1cleDJzGjBtxIgRx9Q7FkmSJElS37L77rtz0UUXATBnzpxVj2UBzJ07lwsuuIDrr79+VdnIkSOZMWMGhxxyCHPmzGH48OEMHz6cMWPG8MEPfhCApUuXsu66667a54EHHnjVPosXL+aII45gypQpDB48uEZnq57GR7QkSZIkSeoG73jHO1h//fVpbm7m7rvv5kMf+hDHHXccAJ///Od5+umnec973sPBBx8MwNFHH80VV1zBXnvtxZFHHsnAgQOZOHEi11xzDWPGjGH06NH84Q9/WO0zWtvn/PPP59FHH+XII4+kubmZRx99tObnrvqLzKx3DN1uxIgRuWZXOElaqa9PHQpOk14pp0nv2SLi3swcUe84qsV2jqS16ettnXpPky5VW1ttnV71iJYkSZIkSZXo6zeywJtZjcpHtCRJkiRJkhqcCR5JkiRJkqQGZ4JHkiSpAUTE2IiYtHDhwnqHIkmSeiATPJIkSQ0gM6dl5sSmpqZ6hyJJknqgXpXg8c6WJEmSJEnqi3pVgsc7W5IkSZIkqS/qVQkeSZIkSZKkvsgEjyRJkiRJUoMzwSNJkiRJktTgTPBIkiRJkiQ1OBM8kiRJkiRJDc4EjyRJkiRJUoMzwSNJkiRJktTgTPBIkiRJkiQ1OBM8kiRJDSAixkbEpIULF9Y7FEmS1AOZ4JEkSWoAmTktMyc2NTXVOxRJktQD9aoEj3e2JEmSJElSX9SrEjze2ZIkSZIkSX1Rr0rwSJIkSZIk9UUmeCRJkiRJkhqcCR5JkiRJkqQGZ4JHkiRJkiSpwZngkSRJkiRJanAmeCRJkiRJkhqcCR5JkiRJkqQGZ4JHkiRJkiSpwZngkSRJkiRJanAmeCRJkiRJkhqcCR5JkiRJkqQGZ4JHkiSpAUTE2IiYtHDhwnqHIkmSeiATPJIkSQ0gM6dl5sSmpqZ6hyJJknogEzySJEmSJEkNzgSPJEmSJElSg+vxCZ6IaI6I2yPiBxHRXO94JEmSJEmSepqqJngi4uKIeCYi7lujfP+ImBcRD0fESWs5TAL/BNYDHq9WrJIkSZIkSY2qf5WPPwU4H7h0ZUFE9AMuAPajSNjcHRFTgX7AmWvsfyRwe2beFhGvA84GDqtyzJIkSZIkSQ2lqgmezJwVEVuvUfwu4OHMnA8QET8FDs7MM4ED2znc88DAqgQqSZIkSZLUwKrdg6c1WwB/a7H8OLBrWxtHxH8C7wE2ougN1NZ2E4GJAEOHDmXBggXdEaukXmjZkkH1DqHu+lf4G7liyJu7KZLGtI7XGEmSJPUw9UjwRCtl2dbGmflL4JdrO2hmTgImAYwYMSI32WSTLgcoqXdbMuDFeodQdwMq/I1cvviRboqkMfXzGiNJkqQeph6zaD0OvLHF8lDgyTrEIUmSJEmS1CvUI8FzN7BtRGwTEQOAQ4Gp3XHgiBgbEZMWLlzYHYeTJEmSJElqCNWeJv1KYDYwPCIej4ijMnMZcCxwE/Ag8PPMvL87Pi8zp2XmxKampu44nCRJkiRJUkOo9ixa49sovwG4oZqfLUmSJEmS1FfU4xGtqvERLUmSJEmS1Bf1qgSPj2hJkiRJkqS+qFcleCS177HHHuN1r3sdzc3NvPvd715t3csvv8xRRx3F6NGjOe644wBYvHgxY8eOZY899uDSSy/t0Ge0tc83v/lN9t13X5qbm1mxYkX3nZQkSZIkqbpj8Ejqefbbbz8uv/zyV5V/73vfY8KECYwZM2ZV2UUXXcT48eMZN24c++yzD4ceeigDBgxo9/it7fPHP/6Rf/7zn9x8883dfj6SJEmSpF7Wg8cxeKS1u/XWW9lrr70455xzViufOXMmU6dOpbm5malTpwIwe/Zs9t13X/r168dOO+3EvHnzeOmllxg/fjyjR49m3LhxLF26dLXjtLbPtGnTePbZZ9lnn3047bTTanaukrQ2ETE4IsZFxPkRcV1ETIuICyLikIgYXO/4JHXO7373O3bffXf22msv/ud//me1da31Vp4yZQrDhw+nubmZE088sUOf0Vpv5WnTprHbbrsxcuRIvvOd73TvSUlSB/WqBI9j8Ejte/3rX8+f//xnbr31Vm6++Wbmzp27at0jjzzCAQccwPXXX8/XvvY1li1bxgsvvMCGG24IQFNTE88//zyTJ0/moIMO4pZbbqG5uZmrr756tc9obZ+nn36ajTbaiFtvvZUHHniAP/zhD7U7aUlqQ0ScDTwFXAlMBHYB3gkcA/wU+HtE9Jj/qXkjS1q7rbbailtuuYXbb7+dZ555hj/96U+r1q3srXzLLbdw3nnnrSr//Oc/z8yZMznrrLM69BkreyvPmjWLyZMns2TJEnbaaSfuvPNO7rrrLqZOnYr/TiXVQ69K8Ehq38CBAxk8eDD9+/fnwAMP5L777lu1rqmpiVGjRjF48GCGDRu2KimzaNEiABYtWsRGG23Egw8+yLnnnktzczOXXHIJzzzzDCeccALNzc1Mnz691X1WHhtgn3324cEHH6z9yUvSqx0CnAvsBgzOzNdn5ubABmXZ94Bx9Qtvdd7IktZu8803Z7311gOgf//+9OvXb9W61norA5x77rnsvffezJgxA6BLvZW33HJL+vXrR0TQr18/1lnH/2ZJqj1/eaQ+ZPHixave33nnnbz5zW9etbz77rszd+5cli9fzmOPPcamm27KyJEjmTFjBsuXL2fOnDkMHz6c4cOHc+KJJzJz5kx++9vf8slPfpLvfOc7zJw5k/3337/VfVYeG2DOnDlss802NT93SWrFVpn55cz8fWau+h9cZi4py74EbFXH+CR10dy5c1mwYAHbbbfdqrLWeiu///3vZ+7cufziF7/gc5/7HMuXL+9Sb+WVbrzxRoYNG8aQIUNqc6KS1IIJHqkPuf3229lll13YfffdecMb3sCuu+666hn0L3zhC5x88snsscceHH300QwYMICjjz6aK664gr322osjjzySgQMHMnHiRK655hrGjBnD6NGjX/W4VWv7HHjggTzwwAOMGjWKFStWsPvuu9fj9CVpNZm5fOX7iNgjIg6LiMNXvtbcRlJjeO655zj22GP50Y9+tFp5W72V11lnHTbddFPe8pa38PTTT3eptzLA/PnzOeuss141zqEk1UqvmkUrIsYCY4cNG1bvUKQe6X3vex/ve9/7Vitb+Qz661//en7961+vtm7DDTfkuuuuW61s/fXXb3UWrvb26d+/Pz/+8Y8rCV2SqiYiLgfGtywCEri0PhFJ6qply5bxkY98hG9961tsvvnmq61b2aN45513XtVbedGiRWy44Ya89NJL/OUvf2HTTTdl+PDhjBkzhg9+8IMALF26lHXXXXfVcR544AFmzJjBIYccsqq38uLFizniiCOYMmUKgwc7Pruk+uhVPXh8Nr1vOPvss9lzzz1XK/vqV7/KyJEjVz0eBEUX2be+9a2v2rY9rc2KAPDNb36Tfffdl+bmZlasWNE9JyJJ6inGAvcCXwROBD5f/inVVHszQAFkJjvttBOTJ08G4JJLLmHvvffmXe96FxdeeGGHPqO3zwB11VVXcffdd/OFL3yB5uZmZs+e3W5v5XPOOYeRI0fS3NzMSSedxLrrrtul3srnn38+jz76KEceeSTNzc08+uij9Th9SX1cZGa9Y+h2I0aMyHvuuafeYagK/v3vfzNx4kQeeeQR7rjjjlXljz76KNtssw0vvPACBx10ELNmzeL5559n0KBBjBkzZrVt23P22Wez+eabM27cOPbZZx9uvvlm/vjHPzJ16lS+9rWvVeu0OuXr37q43iHU3Rc/f2RF+y/57RndFEnjGrDbyRXtv/zkvj0sSb8z/lrvENSOiLg3M0d0YvtrgdmZ+c3qRdV9bOf0Xk899RQbbbQR6623HocddhgnnXQSO+yww6r1v/rVrzj//PMZN24cRx999KqeJcuXL2fnnXfmj3/841o/o7W2zlNPPcUWW2zBOuuss2oAYm+YNra+3taxnVM52zo9W1ttnV7Vg0e93+TJk/nYxz72qvKVg/YOHDiQiADgNa95DQMHDlxtu67MijBt2jSeffZZ9tlnH0477bQqnZkkqY42Bk6PiNsjYmr5+lW9g1Lf094MUABXXnkl48a9MrHbyseGlixZwtve9jbAGaAkqS/rVWPwqHdbunQpt912G5/61Kc45ZRTWt3m1FNP5ROf+ESbx1g5K8L48eP5/ve/z9VXX8348a8Mu9DarAhPP/00G2+8MbfeeiuHHnoof/jDH9h555279+QkSfW0xxp/QjEGj1QXrc0AddNNNzFq1Cj69+/PsmXLVpWfdtppTJo0ic985jNA19o6K/WUGaD6em/lSnsqS+q7elV6PiLGRsSkhQsX1jsUVcFll13GhAkT2lx/zTXX8Oyzz7a7TVdmRVg54wLAPvvsw4MPPti9JyZJqrdtWnm9qa4Rqc9qawaoyZMn8/GPf/xV259yyik88sgjXHXVVTz77LPOACVJfViv6sGTmdOAaSNGjDim3rGo+82bN485c+bwgx/8gPvvv5/zzjtv1aB5c+fO5YILLuD6669v9xhdmRVh5YwL73nPe5gzZw4f/ehHq3eSkqSay8y/RsQAYGtgvTqHoz6svRmg/vKXv/D+97+fJ554gsxkzz33ZJtttmHgwIEMGDCAQYMGMXDgQGeAkqQ+rFf14FHv9s1vfpObbrqJ6dOn8/a3v53jjjtuVYLn85//PE8//TTvec97OPjggwG455572HfffbnvvvvYd999efnll7s0K8KBBx7IAw88wKhRo1ixYgW77757zc9dklQ9EfF+4B/Ag8D/tnhJNdXeDFBz5sxh+vTpnHDCCXz2s5/lrW99K2eeeSbNzc3ssccejBs3jg022MAZoCSpD3MWLanB9PXn0sFZtLqDs0tUxpklerYuzKJ1f/n2bcD1FGPxTM/Mtp/5rSPbOert+npbpzvG4OnrbR3bOZWzrdOzdWkWrYjYJiK+EBHXRcR9EfGniLg+Ik6MiG2qF64kSVLNvAm4iGJg5e8DXwY2q2tEkiRJndTmGDwRcQ0wliIJ9DfgSSCAHYD3Al+PiF9l5gdrEah6j5NP+nm9Q6irM75xSL1DkCSt7iVgMbAU+CwwiKK9I3VaX2/ngG0dSaqX9gZZfgPwCWBaZj7TckVEbAYcBDiYsSRJanQ3A68FfgocXpb9tH7hSJIkdV6bCZ7M3LWddc8Ak8tXjxERY4Gxw4YNq3cokiSpQWTmIQARsQ5wJUWP5V/XNShJkqROanMMnoj4dEQMi4j+EbF3RDSV5e+IiLNrF2LHZea0zJzY1NRU71AkSVIDiYhPAr8EFgDvAJrrGY8kSVJntTfI8rnAzkATcCuwS1k+HPhMdcOSJEmqjYg4EzifYuzBJorZtL5a16AkSZI6qd1ZtFqIqkYhSZJUP4cDP2yxfAcOsixJkhpMe4MsA2wD7Fi+3zYiXqCYSlSSJKm3WB/4e4vlLShm1JIkSWoYa0vwfL3F+wvLPwPI6oQjSZJUc7cCx5fvv03Re+eX9QtHkiSp89pL8FyKiRxJktT7HQdcAewN7ATcBvxPXSOSJEnqpPamST+ihnFIkiTVRWY+CewTEYPL5X/VOSRJkqROazPBExFvB5oy866IGAicCmwPzAW+kZmLaxNix0XEWGDssGHD6h2KJElqEBGxJfBZirEH+0UEQGbmwfWMS5IkqTPae0Trx8CfgbuAU4AvlOXvA7YEPlrd0DovM6cB00aMGHFMvWORJEkN41cUj2a15GPqkiSpobQ3Tfq2wO3l+0OBFcBo4CLgPVWOS5IkqVa2Ab4LDCvfb4OzhkqSpAbTXoJnAEU35a0oGjpzM3Mm8DtgcA1ikyRJqoXTgLdT9FDeuMWrR4mIsRExaeHChfUORZIk9UDtJXjmUEyTfjtFN+WpZfn2wBPVDUuSJKlm+gNjgBnA3S1ePUpmTsvMiU1NTfUORZIk9UDtjcHz38AUYDjwG+DbEbEuMBaYXv3QJEmSauJLwP8BdwJL6xyLJElSl7Q3TfpcYOdWVr2leuFIkiTV3B3ArzPz3HoHIkmS1FXtTZN+eDv7ZWZeVoV4JEmSam0w8M2I+ACwcoAbp0mXJEkNpb1HtKbwyhShUf6Z5fsETPBIkqTeYK81/gSnSZckSQ2mvQQPFMmch4GfAS9UPRpJkqTa26beAUiSJFWqvQTPKOAo4IPA8cC1wI8z8+YaxCVJklRVEbFRZr6QmX9d2zY1DEuSJKlL2pwmPTNvz8wjgNcDJwP/CdwUESfUKDZJkqRqeiIiLomID0XEVhGxbkQMiIity7JLgcfrHaQkSVJHtPuIVkTsBRwJfBhYAfwEuKEGcXVJRIwFxg4bNqzeoUiSpJ7vi8D/AB/l1WPuBPBX4P/VOihJkqSuaG8WrXnAMGA+8HXgSsqZJSLitZn5XE0i7ITMnAZMGzFixDH1jkWSJPVsmfld4LvlDa09gC3LVf8H3JGZd9QtOEmSpE5qrwfPtuWfbwK+Vr5WyrXsK0mS1BAy83bg9nrHIUmSVIn2kjSzcIpQSZIkSZKkHq/NBE9mNtcwDkmSJEmSJHVRm7NoRcQnImL9dtYPiohPVCcsSZIkSZIkdVR7j2idDZwdEbcCdwNPUswo8QZgBLAPxSNcP6x2kJIkSdUUETsCo4GpwBbAY5n5t/pGJUmS1HHtJXi2AY4DxgPvW2PdfODbwAVVikuSJKkmIuJQ4DKKns1zKaZG/yfwgXrGJUmS1BntjcHzDPBl4MsRsTktpg7NzKdqEZwkSVINfBW4Bdi3XL4O+GL9wpEkSeq8Dk11XiZ0TOpIkqTe6A3AxbyS4FkGtDkOoSRJUk/UoQSPJElSL/Yn4PDy/UeB/YE/1i8cSZKkzmtzFi1JkqQ+4gRgc4rJJD4GrAt8rq4RSZIkdVK7PXgioh9wADAvM+fVJiRJkqTayczZETEMGEmR5LkrM5+vc1iSJEmd0m4PnsxcDvyIosEjSZLU60TEJ4H3Z+YNmXk98P6I+FS945IkSeqMjjyidQVwRES8PSJeu/JV7cAkSZJq5HRgYIvlgcBpdYpFkiSpSzoyyPKngQTmtijLDu4rSZLU0wWwWYvl15VlkiRJDaMjSZpZFAkdSZKk3mg2cHJEbEeR2Hk/cHNdI5IkSeqktSZ4MrO5BnG0KSLWAb4GbAjck5mX1DMeSZLU63wGuA44pFz+M/DZukUjSZLUBWsdgycimiJiSkQ8Xb4ujoimjhy83PaZiLhvjfL9I2JeRDwcESet5TAHA1sAS4HHO/K5kiRJHZWZfwG2A7YvX28vyyRJkhpGRx7R+h7wUeCJcvkIiu7LH+/AvlOA84FLVxaUU69fAOxHkbC5OyKmAv2AM9fY/0hgODA7M38YEVcDMzrwuZIkSR0SEZsBxwBbU7RHiIjMzKPqGZckSVJndCTB817grMw8CSAivknHkjtk5qyI2HqN4ncBD2fm/PJ4PwUOzswzgQPXPEZEPA4sKReXd+RzJUmSOuE6YMQaZQmY4JEkSQ2jKzNhVTrg8hbA31osPw7s2s72vwTOi4i9KAZ8blVETAQmAgwdOpQFCxZUGKaqZciQvj0xSaXfzUHrO4FdpXW4bMmgboqkcfWvsA5XDHlzN0XSmNbxGtPbvBm4DLgQWFbnWCRJkrqkI/9TvAH4fERMKJe3oMUjV13Q2v/u20waZeaLdOAOWmZOAiYBjBgxIjfZZJMuB6jqWry4b0/KVul388WX/L9HpXW4ZMCL3RRJ4xpQYR0uX/xIN0XSmPp5jeltLgI2Bf6QmUvrHYwkSVJXdCTB81mKwZjfWy5fBvxPBZ/5OPDGFstDgScrOJ4kSVIljgXWBw6PiJfKsszMDk0qIUmS1BO0m+ApB0T+MvDjzDy8mz7zbmDbiNiGYuDmQ4EJ7e/SMRExFhg7bNiw7jicJEnqGxZQ+SPokiRJddXuNOmZuRx4P8Wz6Z0WEVcCs4HhEfF4RByVmcso7pTdBDwI/Dwz7+/K8VuJd1pmTmxq8oabJEnqmMzcOjO3WfNV77gkSZI6oyOPaM0ETomIgcDfVxZm5i/XtmNmjm+j/AaKsX0kSZLqLiJGAjsA65VFmZnn1TEkSZKkTulIgmfllOjfK/8Mim7M/aoSUQV8REuSJHVWRJwCfKWVVSZ4JElSw+hIguerVY+im2TmNGDaiBEjjql3LJIkqWEcA0wH9gfOBA6keJRckiSpYXRkkOUNgesy89bahCRJklRTmwHXUSR45gD/BxwHfKGOMUmSJHVKuwmezFweEe+nGAzZBI8kSeqNFgArgBeAbwMDy5ckSVLDqOogy7XmGDySJKkLzqVI7nwVOIci2fP5OsYjSZLUab1qkGXH4JEkSZ1RPo7+b2B+Zv4sIiYDkZn/qnNokiRJndKRBM9pFAkdSZKkXqV8HP1E4MvA3Zn5Yq1jKB+HP4BiLKALMvPXtY5BkiQ1vrUmeDLz1BrEIUmSVC9Tgf+JiGdY/XH0P6xtx4i4mGLWrWcyc/sW5fsD36Xo8Tw5M7/R1jEy81rg2oh4DcUYQCZ4JElSp63T1oqI+ENE7BcRgyPi4oh4a1n+gYh4rnYhSpIkVdV/AW+nSPTc3eLVEVMoZt9apXzs6wLgvcB2wPiI2C4idoiI69Z4bdZi1y+V+0mSJHVaez143gG8BlgPOAK4HHgIGAA0VTuwrnCQZUmS1AWX0sXH0TNzVkRsvUbxu4CHM3M+QET8FDg4M8+k6O2zmogI4BvAjR3pNSRJktSatT2i1VBj7zjIsiRJ6qzMPKKbD7kF8LcWy48Du7az/XHAvkBTRAzLzB+suUFETAQmAgwdOpQFCxZ0Y7jqTkOGRL1DqLtKv5+D1u/IMKG9V3f8+162ZFA3RNK4+ldYhyuGvLmbImlc63idaUhr+/U8EtiPItFzbDkI4LbVDkqSJKlWImJj4PsUbZ4PA58AZmZmVx+Xau1/+G3eNMvM7/HKbKVtbTMJmAQwYsSI3GSTTboYmqpt8eKGuj9aFZV+P198aVk3RdKYuuPf95IBNR8vvkcZUGEdLl/8SDdF0rj6eZ1pSGtL8Lynxfv3t3jvlUuSJPUW36cYL2cQsAJ4jGJcnq4meB4H3thieSjwZAXxSZIkrVV7CZ59ahaFJElS/exHMXvVKeXyA8B/V3C8u4FtI2Ib4AngUGBCRRFKkiStRZsJnsy8rZaBdAcHWZYkSV3wL+B15ft+FOPhPNuRHSPiSqAZ2CQiHge+kpk/iohjgZvK412cmfd3e9SSJEkt9KoRzBxkWZIkdcFPgeMpHkG/jqJ99K2O7JiZ49sovwG4obsClCRJWpteleCRJEnqgv8HLAYOoBggeRpwZl0jkiRJ6iQTPJIkqU/LzKXAV8tXj+Wj6JIkqT1tJngi4pS21gFk5mndH44kSVJtRMSidlZnZjbVLJgO8FF0SZLUnvZ68Jza4n1SdFle+R7ABI8kSWpkz/FKu0aSJKmhtZfg+XD55z7AKOAcYB3gM8CtVY5LkiSpqjJz63rHIEmS1F3amyb9FwAR8TXg7My8uFwO4MTahNc5PpsuSZI6KyLWBU4G3kvRo+cG4MxybB5JkqSG0JFBljcCvhIRQyke0/o40K+aQXWVz6ZLkqQuOIuih/KKcvmdFO2f4+sVkCRJUmet04FtPgdsDJwCfBnYBDihmkFJkiTV0CHAj4FBwGBgCjCungFJkiR11lp78GTmTyLiZmC3sui3mflMdcOSJEmqmfWBeZm5BCAi/gx8oL4hvZqPokuSpPZ0pAcPFF2VRwMPA++OiJ2qF5IkSVJNzQLOiIjbI2IW8DVgZn1DerXMnJaZE5uaetTs7ZIkqYdYaw+eiPgscDbFoINTgf8EDgfeXdXIJEmSqigibgFOA44FXgvsWa6aBRxXr7gkSZK6oiM9eD4LXNVi+WZg56pEI0mSVDvNwGaZ+Xhm7g0MATbMzObMfKK+oUmSJHVOR2bReg3wR+BD5fIgeugsWpIkSZ30tojYu2VBRACQmbPqEpEkSVIXdCTB83vgv8v3n6Povnxn1SKSJEmqnVPKV2u8oSVJkhpGRxI8x1GMvRPA/sBfKB7b6nGcXUKSJHXSbyjaNpIkSQ2t3QRPRPQD3kIxsPIKiiTPQ5m5vAaxdVpmTgOmjRgx4ph6xyJJkhrCxZn583oHIUmSVKl2B1kuEzk/AkZk5gOZeX9PTe5IkiRJkiT1VR15ROsK4IiIuBv4+8rCzHyualFJkiRVWWZ2ZDbRHsNH0SVJUns60rD5NLAXMBf4R/l6pppBSZIkaXWZOS0zJzY1NdU7FEmS1AN1pAfPLCCrHYgkSZIkSZK6Zq0JnsxsrkEckiRJkiRJ6qK1JngiIoBDgR2A9crizMwTqhmYJElSLUTEesBpwLuBTwHjgDsz82d1DUySJKkTOvKI1gXAf1E8phVlWQImeCRJUm/wXeAoinbOQKAf8HnABI8kSWoYHRlk+QPAT8r3nwFuBb5WtYgkSZJq6z+Bb7VYvhd4S51ikSRJ6pKOJHheA9xOcVfrOeBq4KPVDEqSJKmGVvBKL2WAnYB/1ikWSZKkLunII1pPldv9neJxrQHAomoGJUmSVEPXA8eX7y8DNgcm1y8cSZKkzutID54vAY9QjLnzMrAQ+GwVY+qyiBgbEZMWLlxY71AkSVLj+CxwBfAssC5wCfC5egYkSZLUWWtN8GTm5Zk5PTN/mpmbZ+brM/OntQiuszJzWmZObGpqqncokiSpcbwfODUzN8vMzSgGWN6hviG9mjeyJElSe9aa4ImIW1p5zahFcJIkSTXwY+BdLZb3oxh/sEfxRpYkSWpPR8bgaW6lLLs5DkmSpJqKiIMoeu8E8MmIeG+56j8oHkuXJElqGB1J8Gza4v1rgFMpBlyWJElqZP8BHEFx42pU+VrpinoEJEmS1FUdGWQ5W7wWAfOAj1UzKEmSpBqYRPFoVgAnA+8ERgBvzsyP1jMwSZKkzupID54FvPqRrHlViEWSJKlmMvPvFL2SO3LDS5IkqUfrSIJnFq8keJYDjwHfrlZAkiRJtRQRy1spzszsSDtJkiSpR1hrwyUzm2sQhyRJUr08yCs3szYCtgDm1y0aSZKkLlhrgiciLm5ndWbmUd0YjyRJUk1l5vYtlyPiC8AOdQpHkiSpSzrS9fgIirtaUS6v+d4EjyRJalgR8Z8tFvtTDLS8X53CkSRJ6pKOJHi+TTHDxGkUgxB+CbgduLZ6YUmSJNXM1aw+oUQAv65TLJIkSV3SkQTPh4GzMvMWgIh4C/CFzPxyVSOTJEmqjdN49YQSv6hbNJIkSV3Q0dkhzoyIXSnuaB0MPFu9kCRJkmonM0+tdwySJEmV6kiC52jgcuDwcvmpskySJKlhRcSidlZnZjbVLJgOiIixwNhhw4bVOxRJktQDdWSa9BkRsRXw1rLoocxcUt2wJEmSqu45Vh97p0fLzGnAtBEjRhxT71gkSVLP026CJyIiC0si4vXA9sDrgN/UJLoihr2Awyhi3S4zd6/VZ0uSpN4rM7eudwySJEndZZ22VkTEDMpETkQcBdwAnAVMj4gvdeTgEXFxRDwTEfetUb5/RMyLiIcj4qT2jpGZt2fmfwHXAZd05HMlSZI6KiLWjYhTI+J3EfHbiDglItatd1ySJEmd0V4Pnu2Bb5Tv/6v882vA3sAxwOkdOP4U4Hzg0pUFEdEPuADYD3gcuDsipgL9gDPX2P/IzHymfD8Bx/6RJEnd7yzgM8CKcvmdwEbA8fUKSJIkqbPa7MEDNAHPRkQT8B/A/5WzTFwCbNaRg2fmLIrn21t6F/BwZs4vx/L5KXBwZv4pMw9c4/UMQERsCSzMzPYGQ5QkSeqKQ4AfA4OAwRQ3qMbVMyBJkqTOaq8Hz2PACcCHKRJB08vyLalsmvQtgL+1WH4c2HUt+xxF0fBqU0RMBCYCDB06lAULFlQQoqppyJCodwh1Vel3c9D6HZn8rnertA6XLRnUTZE0rv4V1uGKIW/upkga0zpeY3qb9YF5KyeRiIg/Ax+ob0iSJEmd097/FL8MXAbsQJHQ+U5Zfijw2wo+s7X/3bc7g0VmfmVtB83MScAkgBEjRuQmm2zStehUdYsXN8yEJVVR6XfzxZeWdVMkjavSOlwy4MVuiqRxDaiwDpcvfqSbImlM/bzG9DazgDPKacgT2I1i7D9JkqSG0WaCJzOviohbgDcBD2bmPyOiP8VYOE9V8JmPA29ssTwUeLKC40mSJFXiWOC1wJ7l8izg0/ULR5IkqfPafdYjM5+lxeNYmbkM+GOFn3k3sG1EbAM8QdEjaEKFxwSgvPM2dtiwYd1xOEmS1Adk5uPA3hExmKKn8YuZuWItu0mSJPUo7Q2yXLGIuBKYDQyPiMcj4qgySXQscBPwIPDzzLy/Oz4vM6dl5sSmpqbuOJwkSeoDIuJbEXEqxQQTDwD/jIgj6xuVJElS51R1tNbMHN9G+Q3ADdX8bEmSpA6aAHwLGA8MAB4GvgRcXM+gJEmSOqOqPXgkSZIawMbAQuA/KCaY+BbwhrpGJEmS1Elr7cETEcOBzwFbA/3K4szMMVWMq0scg0eSJHXB/wFfpRho+SjgdcBzdY1IkiSpkzrSg+daisbOGKC5xavHcQweSZLUBScCzwM3AtcAWwG/qGtErYiIsRExaeHChfUORZIk9UAdSfC8FjgHeD2wafnarJpBSZIk1UpmXpuZOwFHAgMy84TMPK7eca3JG1mSJKk9HUnwXAQMAzYAssVLkiSp4UXE9hFxD/ACsDAifh8R29c5LEmSpE7pyCxaX6RI6BzYoiw7uG9NOQaPJEnqgiuBtwJ3AgHsBvwE2LGeQUmSJHVGR5I0s2iQHjuZOQ2YNmLEiGPqHYskSWoYGwMnZeZ3ACLicxQTTEiSJDWMtSZ4MrO5BnFIkiTVVES8tnz7I2BkRGzNKz14zq9XXJIkSV3RkWnSAzgU2AFYryzOzDyhmoFJkiRV2QJW76X8gfLPKN+fXvOIJEmSuqgjj2hdAPwXRQMoyrIEelyCxzF4JElSJzTMY+iSJElr05EEzwcoBhocD3wGeD9wexVj6jLH4JEkSR3V3mPoEfGmGoYiSZJUsY5Mk/4aioROAM8BVwMfrWZQkiRJtRYRW0XE5yPibuDP9Y5HkiSpMzrSg+epcru/UzyuNQBYVM2gJEmSaiEihgIfBsYB7yyLXwRuqltQkiRJXdCRHjxfAh6hGHPnZWAh8NkqxiRJklR1EXEH8BjwHWBbYGq56rDMPKBecUmSJHVFR6ZJvxwgIjYCtsrMf1c7KEmSpBrYHVhOkeD5ErA5cHBdI5IkSeqitfbgiYitI+L3FFOJ7hURt0XEadUPrfMiYmxETFq4cGG9Q5EkST3fH4B+wPHA48DZFLNqObOWJElqOB15ROsHwFCKQZZXUEwpemg1g+qqzJyWmRObmprqHYokSerhMnME8GaK3juPU8wcGsAVEXFtHUOTJEnqtI4keHYHzm+x/AhFwkeSJKmhZeajmXlmZv4H8Bbgy8BfgbH1jUySJKlzOpLgWQBsX77fjKL3zpNVi0iSJKkOMvPhzDwjM3cAtqt3PJIkSZ3RkWnSLwLOKN9fUf55UnXCkSRJqr/MnFfvGCRJkjqjI7NonRkRTwIHUDyXPi0zL616ZJIkSZIkSeqQjvTgITMvAS6pciwVi4ixwNhhw4bVOxRJkqRuZTtHkiS1p80xeCJieTuvZbUMsqOcRUuSJHVWRJwcEeu0WB4UEee3t0892M6RJEntaa8HTwBJMaDyCzWJRpIkqfa+BhwUER+jmFDix8BWwLF1jUqSJKkT2ptFawrwL2AT4E/A8Zm5w8pXLYKTJEmqgUOBLYH/BW4BlgP71jUiSZKkTmozwZOZRwKbA58E3ghMj4jHImL/WgUnSZJUAzOBu4GBFG2j24F76hmQJElSZ7U7yHJmvhgR84FHgREUvXmG1CIwSZKkGnkIGACcALweOJ6iB89W9QxKkiSpM9pM8ETEF4GPA28CfgccB/wsMxfXKDZJkqRaeAj4WGb+BSAirqEYh0eSJKlhtNeD53SKQZbnAwuAgygGIATIzDy4+uFJkiRV3Z6ZuWLlQmbOjoh31DEeSZKkTmv3ES2KmbTeXL5ayuqEI0mSVHOvi4hTgR2A9cqyBHapW0SSJEmd1F6CZ5uaRdFNImIsMHbYsGH1DkWSJDWOycB7KG5sLQPWBV6oZ0CSJEmd1WaCJzP/WstAukNmTgOmjRgx4ph6xyJJkhrG7sCZwBeBA4EPUDyeLkmS1DDanCZdkiSpjxhAMWNoAO8CFgOfqGtEkiRJnbS2MXgkSZJ6u8eAjYG5wGll2UN1i0aSJKkLTPBIkqS+7hBgCXAj8KWy7PT6hSNJktR5JngkSVKflpn3t1g8tG6BSJIkVcAEjyRJ6pMiYnk7qzMzbSdJkqSGYcNFkiT1VQEk8CROiy5Jkhqcs2hJkqS+agrwL2AT4E/A8Zm5w8pXXSOTJEnqJBM8kiSpT8rMI4HNgU8CbwSmR8RjEbF/fSOTJEnqPBM8kiSpz8rMF4H5wKMUM2ltAgypa1CSJEldYIJHkiT1SRHxxYj4C3ALMAw4Dnh9Zl5V38gkSZI6r1cNshwRY4Gxw4YNq3cokiSp5zudYpDl+cAC4CDgoIiAYhatg+sYmyRJUqf0qgRPZk4Dpo0YMeKYesciSZIaQgBvLl8tZR1ikSRJ6rJeleCRJEnqhG3qHYAkSVJ3McEjSZL6pMz8a71jkCRJ6i4OsixJkiRJktTgTPBIkiRJkiQ1OBM8kiRJDSAixkbEpIULF9Y7FEmS1AOZ4JEkSWoAmTktMyc2NTXVOxRJktQDmeCRJEmSJElqcCZ4JEmSJEmSGpwJHkmSJEmSpAZngkeSJEmSJKnBmeCRJEmSJElqcCZ4JEmSJEmSGpwJHkmSJEmSpAZngkeSJEmSJKnBmeCRJEmSJElqcP3rHcDaRMSWwPnAAuDPmfmNOockSZIkSZLUo1S1B09EXBwRz0TEfWuU7x8R8yLi4Yg4aS2HeQtwfWYeCWxXtWAlSZIkSZIaVLUf0ZoC7N+yICL6ARcA76VI2IyPiO0iYoeIuG6N12bA/wKHRsQtwK1VjleSJEmSJKnhVPURrcycFRFbr1H8LuDhzJwPEBE/BQ7OzDOBA9c8RkR8DvhKeayrgR+39lkRMRGYCDB06FAWLFjQfSeibjVkSNQ7hLqq9Ls5aP0e/2Rl1VVah8uWDOqmSBpX/wrrcMWQN3dTJI1pHa8xkiRJ6mHq8T/FLYC/tVh+HNi1ne2nA6dGxATgsbY2ysxJwCSAESNG5CabbFJ5pKqKxYuz3iHUVaXfzRdfWtZNkTSuSutwyYAXuymSxjWgwjpcvviRboqkMfXzGiNJkqQeph4Jnta6b7T5P/7MvA/4UPXCkSRJkiRJamz1mCb9ceCNLZaHAk/WIQ5JkiRJkqReoR4JnruBbSNim4gYABwKTO2OA0fE2IiYtHDhwu44nCRJkiRJUkOo9jTpVwKzgeER8XhEHJWZy4BjgZuAB4GfZ+b93fF5mTktMyc2NTV1x+EkSZIkSZIaQrVn0RrfRvkNwA3V/GxJkiRJkqS+oh6PaFWNj2hJkiRJkqS+qFcleHxES5IkSZIk9UW9KsEjSZIkSZLUF5ngkSRJkiRJanC9KsHjGDySJEmSJKkv6lUJHsfgkSRJkiRJfVGvSvBIkiRJkiT1RSZ4JEmSJEmSGpwJHkmSJEmSpAbXqxI8DrIsSZIkSZL6ol6V4HGQZUmSJEmS1Bf1qgSPJEmSJElSX2SCR5IkSZIkqcGZ4JEkSZIkSWpwvSrB4yDLkiRJkiSpL+pVCR4HWZYkSZIkSX1Rr0rwSJIkSZIk9UUmeCRJkiRJkhqcCR5JkiRJkqQGZ4JHkiRJkiSpwZngqaH77ruP3Xffnb322ouPf/zjZOaqdU8++SSjR49m99135+abbwbgkksuYe+99+Zd73oXF154YYc+Y/HixYwdO5Y99tiDSy+9FICFCxdy4IEH0tzczLnnntvt5yVJqh2vJerJli1bxqGHHso+++zDiSee+Kr1mclOO+3E5MmTAfjGN75Bc3Mzzc3NDB48mOeee26tn9Ha93OlT3/603zkIx/pnpORJNWc15HK9KoET0+fJn348OHcdddd3H777QDcc889q9Z94xvf4PTTT+fXv/41p59+OgATJkxg1qxZzJ49mx/+8Icd+oyLLrqI8ePHM2vWLCZPnsySJUuYNGkShx12GDNnzmTWrFksWLCg+09OklQTXkvUk11zzTXstNNO3Hrrrbz00kv88Y9/XG391KlT2WyzzVYtn3TSScycOZOrr76ad77znbz2ta9d62e09v0EePrpp3nssce69XwkSbXldaQyvSrB09OnSV933XVXvR84cCBvfOMbVy3PnTuXkSNHssEGGzBkyBAWL168avslS5bwtre9DYCXXnqJ8ePHM3r0aMaNG8fSpUtX+4zZs2ez77770q9fP3baaSfmzZvH/Pnz2XHHHQF4+9vfvtp/BiRJjcVrSe8TEW+LiB9ExNUR8d/1jqcSLb8n73jHO5g9e/Zq66+88krGjRv3qv2mTp3KQQcdBHTt+wlwzjnncNxxx1XjtCRJNeJ1pDK9KsHTCKZOncr222/PM888w8Ybb7yqfPny5UQEAE1NTTz//PMAnHbaaWy77bbssssuAEyePJmDDjqIW265hebmZq6++urVjv/CCy+w4YYbrnac4cOHc9ttt7F8+XJmzZq16tiSpMbktaTniIiLI+KZiLhvjfL9I2JeRDwcESe1d4zMfDAz/ws4BBhRzXirbeX3BODWW29d7Xty0003MWrUKPr37/+q/a655ho+8IEPAF37fj733HP84x//YNttt63WqUmSasDrSGVM8NTYQQcdxH333ccWW2zBddddt6q8X79+q94vWrSIjTbaCIBTTjmFRx55hKuuuopnn32WBx98kHPPPZfm5mYuueQSnnnmGU444QSam5uZPn06G220EYsWLVrtOMcccwx33XUX733ve3nDG97A6173upqesySpe3kt6VGmAPu3LIiIfsAFwHuB7YDxEbFdROwQEdet8dqs3Ocg4A5gRm3D715jx47lpZdeYsyYMQwcOHC178nkyZP5+Mc//qp9/vnPf7JgwQK22WYbgC59P7/73e9y7LHH1uYkJUlV43WkMq9Ofalq/v3vfzNw4EAANtxwQ9Zff/1V63bccUdmz57NjjvuyKJFi9hwww1XbT9gwAAGDRrEwIEDGT58OGPGjOGDH/wgAEuXLl2tu/4DDzzAjBkzOOSQQ5gzZw7Dhw9n4MCBXH755SxfvpwJEyaw22671fbEJUndxmtJz5KZsyJi6zWK3wU8nJnzASLip8DBmXkmcGAbx5kKTI2I64GfrLk+IiYCEwGGDh3ao8dA+spXvgLA8ccfz4gRI1bF+uCDD/K+972Pp556iszk7W9/O9tuuy3XXnsto0aNWrXdFltswTvf+U7Gjh0LvPr7effdd3Pttddy8MEHc/fdd/OFL3yBhx56iBNOOIGXX36Z+fPnM3nyZN7//vfX9sRLQ4ZEXT63J6n0+zlo/b79X5Tu+Pe9bMmgboikcfWvsA5XDHlzN0XSuNap43Wmr19HKtG3fz1rbPr06Zx99tkAbLvttrz73e/muOOO47zzzuPEE0/k8MMP56WXXuKrX/0qAGeeeSYzZ85kyZIlfPSjH2WDDTZg4sSJHHPMMVx44YVkJmeeeSa77rrrqs84+uijmTBhAueddx4TJ05k4MCB3HvvvXzuc58jIjjxxBMZNKhv/+BLUiPzWtIQtgD+1mL5cWDXNrYlIpqB/wQGAje0tk1mTgImAYwYMSI32WSTbgq1ez3xxBMcdthhrLPOOhx++OHsuOOOq76f991XPMU2ZcoUli1bxsiRIwGYMWMGJ598MivP6fjjj+eYY47h8ssvb/X7+ZnPfIYJEyYwZcoUPvGJT7DFFlvws5/9DIDHHnuML33pSxx99NE1PvNXLF6ca9+ol6v0+/niS8u6KZLG1B3/vpcMeLEbImlcAyqsw+WLH+mmSBpXvzpdZ7yOVCZaTq/aW4wYMSId/LHnOvmkn9c7hLo64xuHVLT/1791cTdF0ri++PkjK9p/yW/P6KZIGteA3U6uaP/lJ2/VTZE0pn5n/LXeIagdEXFvZtZsLJuyB891mbl9ufxh4D2ZeXS5/FHgXZnZLSM32s7p2fp6Owds61Sq0nYO2NaxnVM52zo9W1ttHcfgkSRJ6l6PA29ssTwUeLJOsUiSpD6iVz2iFRFjgbHDhg2r2me8d9z3q3bsRnHjzxp6BldJqrvFYx2jY8i03teDuIW7gW0jYhvgCeBQYEJ9Q+q4vt7WsZ0jSZXr622derVzelUPnsyclpkTm5qa6h2KJEnqAyLiSmA2MDwiHo+IozJzGXAscBPwIPDzzLy/nnFKkqTer1f14JEkSaqlzBzfRvkNtDFgsiRJUjX0qh48kiRJvVVEjI2ISQsXLqx3KJIkqQcywSNJktQAfBRdkiS1xwSPJEmSJElSgzPBI0mSJEmS1OBM8EiSJEmSJDU4EzySJEmSJEkNzgSPJEmSJElSgzPBI0mSJEmS1OBM8EiSJDWAiBgbEZMWLlxY71AkSVIP1KsSPDZ8JElSb5WZ0zJzYlNTU71DkSRJPVCvSvDY8JEkSZIkSX1RZGa9Y+h2EfEP4K/1jqOKNgEW1DuIBmb9Vc46rIz1VznrsDK9vf62ysxN6x1EtdjOUQdYh5Wx/ipnHVbG+qtcb6/DVts6vTLB09tFxD2ZOaLecTQq669y1mFlrL/KWYeVsf7Uk/n9rJx1WBnrr3LWYWWsv8r11TrsVY9oSZIkSZIk9UUmeCRJkiRJkhqcCZ7GNKneATQ4669y1mFlrL/KWYeVsf7Uk/n9rJx1WBnrr3LWYWWsv8r1yTp0DB5JkiRJkqQGZw8eSZIkSZKkBmeCR5IkSZIkqcGZ4OkBImKdiPhhRNwZEbdHxBURcUREPBoRMyPidxHxg4hoKrc/NSIeLNfdGhFbluW7R8SKiHhTfc+oe7VWP9147PUi4oWI+HiLsuaI2LHF8qe76/PqwfqrnHVYOeuwNiJi64h4vrw+zIyIz0fEw508RnNETK5WjOqbbOu0zd/HylmHlbMOK2P91YbtnLUzwdMz7A/0z8w9MnMv4Liy/EeZ2ZyZuwLzgO+22OeMzGwGLgK+WJYdVm4zoTZh10xb9dMdxgKXAeNalDUDO7ZYbvQfTOuvctZh5azD2rm3vHY0Z+a36h2MVLKt0zZ/HytnHVbOOqyM9Vc7tnPaYYKnZ/gnsH1EvA0gM59bc4PMPAfYKyLW/Dv7A7BVRKwL/AdwEvDeKsdba6+qn4jYp7yjd3tE/Coi1gOIiOMj4p7yzuDdEbF1WX5mRNwWEbMj4sAWxx4PnAX8OyLeEBGvBY4ATi6zwocBW5TvTy4zvjdGxJUR8aeI+HAN66GrrL/KWYeVsw7rrLX6johBZV3cVtbPW8rNt+hr9aOqs63TNn8fK2cdVs46rIz1V2et1Xf0xXZOZvrqAS/gSOBW4FHgsxT/aL+0xja/BTYDTgU+UpZ9Gvg2cODK7YELgF3qfU5Vrp/BLdZ9Ezi8rJv/BdYFNgQWAFtTZNR/UG47CPgjEMBrgRvK8g8DJ5TvV9Vvufxwi/fNwL1AP+ANwD31rhvrzzqsd91Yhz3nVdbV88DM8jVq5bm3Ud87Az9pUb5Ob64fX/V9tfIbcAS2ddqqG38frUPrsMHq0PqrSR1vje2cdl/9UY+QmRcDF0fEhsAs4HutbLYpxY8AFBnbo4EnKBo+51FkIvek+LH4CMUXt1dopX7uiojTgYHA64BFwDbAfZm5FFgaEQ+Vu+8AjIqImeXyQGBj4EPA0IiYDvQHBgPf6UA4czJzOfBkRGzUHedXbdZf5azDylmHNXNvZu67ciEiVr59eyv1fRlwb0RcDjwLfKXctjfXj+rEtk7b/H2snHVYOeuwMtZfzdjOaYcJnh4gIt4A/DMzFwGLKbr4xRrbfBq4MzNXlF/iMzLz8nLdBsDQzNy7xfa/j4h+5Re3obVRP18GvpKZsyPiLIr6eoziH3Z/YH1geHmI+4FfZ+ZnyuMNyMwlETEO2DszXyjLL4uI7YAlrP5vY1lErJOZK8rlrOLpdjvrr3LWYeWswx7hZF5d3wOBszMzI+JLwEeBP9E360dVZFunbf4+Vs46rJx1WBnrr0ewnYMJnp5iKHBORKyg+DuZBiwHjoqIfSn+8c+l7cGz/pMiS9zSXGAM8OuqRFxbrdXPo8CPImIesBBYlJlPR8RPgN8BfwYeB5Zk5g0RMbLMiCfwePkPvN/KH8vSTRR3A68Bzo3i2ddDgKuB6yPiRop6bTTWX+Wsw8pZh/X3U9aob2A74HsRsYyi2/LHgK3qF6J6Mds6bfP3sXLWYeWsw8pYf/VnOweIzF6bvFIfFBHrZubSKLpG/i/wlka/s1dL1l/lrMPKWYeS1Dp/HytnHVbOOqyM9adqsgePepuTImIM0AR82R/LTrP+KmcdVs46lKTW+ftYOeuwctZhZaw/VY09eCRJkiRJkhrcOvUOoC+LiN0i4qsRMSUi/jciZpavTSPi1Ij4SCePNzMihq5RNiAipkZEr/m7joh3RsQtEXFb+ec7I2LriLi5XD+kXPehNfZ7uPzziIh4MSJeXy43R8TkiPhMWYdzIuLv5fvryjr8eUTcHhG/i4jRtT/r7mP9Vc46rJx12HkRMSnK2TUiYqOIOLzFuuaI2LGLx11t34i4ouJgpVLY1uk0fx8rZx1WzjqsjPXXNWFbp3LZA+Zq76svisGxNgOmAHuuse5U4COdPN5Mihkm1iz/f8DB9T7fbqqzJuABYOtyeety+R3AzcAGZT0c0sq+D5d/HgHMAc4tl5uByS22W3P5QODHLT7vd/WuB+vPOqx3PViHNa+zARQDMk4DtizP4eYW60+lk9eM7tjXl6+1vbCt09n68vfROqz7yzq0/upUb7Z1uuHVK+50NKKIGAJskpnPdGDbS8rs7B8i4qCy7NAopge9NSLObLH5sRHx64iYEREDy7IbgA93/1nUxYHAtZn5GED556+AAyh+LK8DLszMn6/lOL8A9liZFV+LR4CBERHAa4G1/p31YNZf5azDylmHnXcAMBW4BJgAHA/sUl4bDqNoCJ5cLveLiA+Xd/HuiIhTYNXdqxsj4sqI+FO5zWtb2Xfl3cNBEXFVeYfx1ogYVpbPjIhvtHKtkVZjW6dL/H2snHVYOeuwMtZf19jW6QYOslw/bwX+2mL5vIhYCDyamR9fY9tPZua/ImJj4DaKL/4Eiizkn2P1Lsm/zcyTImISsB/FD8ifgR2qdia1NRT4vzXK/gpsRFGnj1LUz9ok8C3gJIq7i+2ZTzF960Pl54ztcLQ9j/VXOeuwctZh540HPgc8TTEl9MeA7TJzX4CI2Jbirt/lEfEa4ARgryxm6bgmIlZeAzajaHi+DpiamVdFxJSV+5bHWvmZE4E/ZeZpEbE3cBbFVNXQ+rVGWpNtnc7z97Fy1mHlrMPKWH9dY1unG9iDp+c4LjOb12zwlA2ar0TEHRRZ3K3KVf8P+FwUzw+2/Ad8b/nn/wEbtyjvLaNpP0HRZa+lLSm69N0DXAH8LCL6R8TpZfb19DaOdRUwElhbVvxjwN8ycziwK/CDLkdff9Zf5azDylmHnRARTcAewCSKO4BbU3T/bsswimvFb6J4jn0bXrl2zMnM5Zn5JEUDsD3DgbvK93dRNEpXautaI7XHts7a+ftYOeuwctZhZay/TrKt033swVM/D1F8cddmJ2BHYC+KL9YjZfmjmTmx7C72F4p/CLB642ZlavItwP2VBtxDXAd8MSJ+mJl/jYgtgYOBjwJ7Z+a3y254PwYOz8w2G3uZmRHxLeBkih/btgSwoHz/PDCkO06kTqy/ylmHlbMOO+dDwJmZeT5AFFOrforVr+FLWizPBx4G9s3MZeV/noPiOtJaXbbct6V5wO4U4wXsXi6v1Nq1RlqTbZ3O8/exctZh5azDylh/nWdbp5vYg6dOMnMxsCAiXreWTecB61IMxPVl4IWy/FsRMass/+FajnEARfa34WXmC8DHgSkRcRfwW4ofy+dabPNF4J/A9zpwyKtZ+7+Dy4EREXEbxT/+kzsfec9g/VXOOqycddhphwHTWyzfQTG44ksR8YuyEfQb4JiIuJqiYXcucEtE3Fruu2k7x1+17xqPwVwE7FRea74GfKGbzkd9hG2dzvP3sXLWYeWsw8pYf11iW6ebRDsJQ1VZRIwE3puZp1TxMwZQdHc+ODNXVOtz6qH8x3kxMC8zz1zb9lqd9Vc567By1qHUu9nW6Tp/HytnHVbOOqyM9adaM8EjSZIkSZLU4HxES5IkSZIkqcGZ4FFDiYitI+L5crT5eyLiYxHx7TW2+UlE7LdG2aPlPjPL2Tgo38+OiNvLffpHxJsj4t6I+GdE7Nli/34R8e2IuLncb7vanHH3sv66Vw3q8zUR8euIuC0i7oyIHWt5ftVm/dVeRGwYEXeV9fX78pl2ST2E1+nKWYfdx+t05azD2uvzbZ3M9OWrYV4Us3HcXL7fEHgWeBRYpywbTDGier819nu4lWPNBIaW7ycBhwKDgNcCU4A9W2z738DEep+/9dezXjWoz2OBr5RlzcDP6n3O1l9jvyhu7PQv378JuLveMfny5euVl9dp67AnvbxOW4eN+OrrbR178KhhZeYiimlTHwNGlcUHA9Myc3knD/cHYKvMfDEzn2tl/YeBrSLi1og4P4oBHRua9de9qlGfwIMUjQEoGpPPVB5pz2T91UZmrsjMZeXihsDcesYjqW1epytnHXYfr9OVsw5ro6+3dUzwqGFFxBYU0+GdA0woiydQTBO4pn4tujl+Zo3jBLAvcH87H7cF8PfM3Ad4GTiy0vjrzfrrXlWqz3uB3SLiPoppNL9TrfjrzfqrnYjYIiLuAH4NXFPveCS1zut05azD7uN1unLWYe305bZO/3oHIHXBLhFxK5DARGAW8O2IeAOwZWbeGxHHAh+i6N54NLA8M5tbOdZVwBLgTuD6dj7zOWB6+X468J/dcib1Yf11r2rW5xnALzLz7CimGr4AOKDqZ1Rb1l+NZeYTwJ4RsTVFd+/r6hqQpDV5na6cddh9vE5Xzjqssb7c1jHBo0Z0b2bu27IgIn4DXAz8HCAzzwfO78CxPpyZj3dgu5nACIpnZFf+2aisv+5Vtfos79AsKBefoeh629tYfzUUEQMz89/l4iJgcT3jkdQqr9OVsw67j9fpylmHNdTX2zomeNRbXEGRyf5kJQeJiA2BXwLbAW+PiBsy8yvAWcCPI+K/KO7QfLTCeHsa6697dUt9AucBl0XEkcD6wBcqDaxBWH/Vs31EnAMsB9YFPlvfcCR1kNfpylmH3cfrdOWsw+rp022dyGJ0aUmSJEmSJDUoB1mWJEmSJElqcCZ4JEmSJEmSGpwJHkmSJEmSpAZngke9QkTsGBE3RsTMiLgrIo6PiIfX2Obh8s8jIuLRctvfRcQPIqKpxXZbRMTyiNin1udRDxHRVNbFzIh4ISJml+8/VK7fPyI+Wr4/ohxcUGvwO1g561CSWufvY2Vs63QPv4eVsf5UCyZ41PDKi/DlwLGZ2QzsAdy/lt1+lJnNmbkrMA/4bot1E4DvAYdVIdweJzMXlnXRDMyhmH6xOTOvjoh+mTk9My8rNz8CsNGzBr+DlbMOJal1/j5WzrZO5fweVsb6U604Tbp6gwOBaZn5CEAWU8PdFBEd2jkzz4mIRyJincxcAYwF3gP8JiIGZua/qxV4D7V1RFwDPAQsjYhZwFDgLuAdwFURcQ/waeAHwNspksWfzczfR8QU4J/ANsDGwEGZ+UzNz6K2/A5WzjqUpNb5+9j9bOt0nt/Dylh/qgl78Kg3eCPwtwqP8Q9gk4jYHvhzZr4E/Irix7gv2hr4VGYeubIgM2/hlbtexwEHA+tm5p7AR4DzW+z/cGYeAEwFDqlV0HXkd7By1qEktc7fx+rYGts6neH3sDLWn2rCHjzqDf4GbN9KeXbiGJsCC4DjgR0iYjqwXln2i4ojbDz3ZeaitWwznOJOF5k5PyJe02LdveWf/we8uQrx9TR+BytnHUpS6/x9rA7bOp3j97Ay1p9qwh486g2uAw6MiFUX14jYD3gmIrYtl98K/L21nSPi08CdFD+w+wIjM3P/8vnYjda4mPcVy9soX8IrieF5wO4AEfEm4IUW27W8WHWs72lj8ztYOetQklrn72N12NbpHL+HlbH+VBP24FHDy8xF5cwHF0TEesAA4GrgU8CkFs+2fqrFbkdFxL7A+sBcimes9wIeKp9rXWkm8GFgUlVPonH8EvhRRNwFfAU4ICLuAPoBx9U1sjryO1g561CSWufvY83Z1mmF38PKWH+qlSjGd5IkSZIkSVKj8hEtSZIkSZKkBmeCR5IkSZIkqcGZ4JEk1V1ErBMRP4yIOyPi9oi4IiKOiIjlEbFli+0ejogp5fu3RMTM8nV2lA+wR8TO5XHuiogj6nNGkiRJr7Cto1owwSNJ6gn2B/pn5h6ZuRevDGT5e2A8QETsBjzTYp+zgJPKGSTWp5hVAuA84CNAM/BpZ5aQJEk9gG0dVZ0JHklST/BPYPuIeBtAZj5Xlt8EvLt8fxjwkxb7vAW4p3z/e2CfiBgIDM7MRzNzCXA78M5qBy9JkrQWtnVUdSZ4JEl1l5mzgB8CF0bEoxHx2XLVMmBeRLwD2B6Y02K3PwH7l92V9wdeC2wMvNBimxfKMkmSpLqxraNaMMEjSeoRMvPizNwH2Ak4gleuUVcCk4AZa+xyAnAU8BvgeeBJ4DmgqcU2TWWZJElSXdnWUbWZ4JEk1V1EvCEiNiwXF1N0Y45y+Q6KO1iXt9wnMx/PzA8A+wGDgV9m5svAixGxZUSsC+xJ0aVZkiSpbmzrqBb61zsASZKAocA5EbGC4to0DVgOkJlJcfeKiBi6coeImAAcAyRwWWbeV676DMWdsAAuzMzna3USkiRJbbCto6qL4rskSZIkSZKkRuUjWpIkSZIkSQ3OBI8kSZIkSVKDM8EjSZIkSZLU4EzwSJIkSZIkNTgTPJIkSZIkSQ3OBI+kTouIYyMiy9fwFuXbRcSpEdHcoux9ZdnW3fC5X4yIz7ZYbi5jOL/SY5fH27rFea35mtIdnyFJkno+2zqSGpEJHkldcQiwosX7lbYDvgI0tyh7X1m2dTd87heBz7ZYfgAYD1zcDccG+Ed5vPHA/5Zlny6Xv99NnyFJkno+2zqSGo4JHkmdEhFvAPYAfg48SdnoKe9aXVVu9pXyTtARwKfKslsjIsttR0bE7Ij4Z0T8OSLGrzxGud8dEfGriFgUET+JwkxgMLBVi7tM2wFXAkeW+789ImZExOKI+GtEfDkiolyXEfGXiPhxRCyMiF9HxKCW55aZ/8rMn2bmT8tzA5gG/AaYGRHXlsfqHxHPRsRvWxx7XkRMjogXIuKGiHhNue5tEfGb8lz+GhH/0z1/E5IkqRps69jWkRqVCR5JnfVhit+Oq4BfAttHxHYUd4TOLbf5BcWdoAeBX5dlXwPGR8RrgeuAjYAzgMeAyyLiHS0+YyTwW2BeeZw9gdOAfwMLaOUuU0SsC0wFdgVOBuaW+3y8xWbDyjhnA/sBH+zICWfms+X5HhARm1HctXstcGmLzd4CPE3RGHwv8OWI6A/8iqJxdhbwO+DsiBjbkc+VJEl1YVvHto7UkEzwSOqsccAS4CGKizjAIZn5L+DOcvm+8u7Q74C/lGW3lHeLRlI0GN4KfJ2i8dEPGN3iM36XmWdSNJ4Ats7MW4BlwL9aHLul4cCbgF9l5veA48vy97bY5u+ZeSIwZeVxO3He3wf6Ax+haCwtAX7aYv3jmXkycCxFl+7mMqZtgTdQNPo+XG67Xyc+V5Ik1ZZtHds6UkPqX+8AJDWOiHgjsBsQwP0tVo0DTgWyld3WLIvyz0uBy1qUP9bi/XPln8vKP/u1cazWtLdNW8dd+0EzZ0fEHIou0hsD12fmc61sGq28vwn4dovypzr6uZIkqXZs69jWkRqZCR5JnXEIxYX8TOD3ZdlRwIERsQPwfFm2V0QcStFld2XZhyJiMHAXReNjf+Buit+hAynu+vx1LZ//PLBpRHys3LelecAjwMERcRywb1l+Q2dPsh3fB35Yvr9kjXVDI+IMYFOK3pG3Utz5+wtFt+sZwItlXNcA93VjXJIkqXvY1rGtIzUsH9GS1BmHUNw1Oiczr83Ma3nlztQ44A6Ki/teFAMCbgxcQXHx/yTw3fJO0IHAw8A3KJ4hf5HV72q15SyK7sJTgP9suSIzlwIHUzSGvg78B3AKr3RR7g5XAIsono1fszE1D9iMoo6mA6dn5rIypjuBL1E07IYAf+rGmCRJUvexrWNbR2pYkdmRXoCS1LeVAw6OomjkXZCZJ7RYl8D9mbl9veKTJEmqhG0dqfHZg0eSOuZ9FAMN/i/FXTNJkqTexLaO1ODswSNJkiRJktTg7MEjSZIkSZLU4EzwSJIkSZIkNTgTPJIkSZIkSQ3OBI8kSZIkSVKDM8EjSZIkSZLU4EzwSJIkSZIkNbj/D6HXZJUJhq2hAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barplots generated successfully!\n"
     ]
    }
   ],
   "source": [
    "# Create barplots for key metrics\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "fig.suptitle('Attention Implementation Error Metrics (vs MATH backend, averaged over 100 inputs)', \n",
    "             fontsize=16, fontweight='bold')\n",
    "\n",
    "# Prepare data for plotting\n",
    "names = list(mean_metrics.keys())\n",
    "colors = ['#485696', '#7a77a9', '#858a9d', '#f9c784', '#fc7a1e', '#f24c00']\n",
    "\n",
    "# Metric 1: Mean Absolute Error\n",
    "ax1 = axes[0, 0]\n",
    "mean_abs_errors = [mean_metrics[name]['mean_absolute_tolerance'] for name in names]\n",
    "bars1 = ax1.bar(range(len(names)), mean_abs_errors, color=colors)\n",
    "ax1.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax1.set_ylabel('Mean Absolute Error', fontweight='bold')\n",
    "ax1.set_title('Mean Absolute Error', fontsize=14, fontweight='bold')\n",
    "ax1.set_xticks(range(len(names)))\n",
    "ax1.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax1.grid(axis='y', alpha=0.3)\n",
    "ax1.set_yscale('log')\n",
    "# Add value labels on bars\n",
    "for i, (bar, val) in enumerate(zip(bars1, mean_abs_errors)):\n",
    "    height = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Metric 2: Mean Relative Error\n",
    "ax2 = axes[0, 1]\n",
    "mean_rel_errors = [mean_metrics[name]['mean_relative_tolerance'] for name in names]\n",
    "bars2 = ax2.bar(range(len(names)), mean_rel_errors, color=colors)\n",
    "ax2.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax2.set_ylabel('Mean Relative Error', fontweight='bold')\n",
    "ax2.set_title('Mean Relative Error', fontsize=14, fontweight='bold')\n",
    "ax2.set_xticks(range(len(names)))\n",
    "ax2.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax2.grid(axis='y', alpha=0.3)\n",
    "ax2.set_yscale('log')\n",
    "for i, (bar, val) in enumerate(zip(bars2, mean_rel_errors)):\n",
    "    height = bar.get_height()\n",
    "    ax2.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Metric 3: MSE\n",
    "ax3 = axes[1, 0]\n",
    "mse_values = [mean_metrics[name]['mse'] for name in names]\n",
    "bars3 = ax3.bar(range(len(names)), mse_values, color=colors)\n",
    "ax3.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax3.set_ylabel('Mean Squared Error (MSE)', fontweight='bold')\n",
    "ax3.set_title('Mean Squared Error', fontsize=14, fontweight='bold')\n",
    "ax3.set_xticks(range(len(names)))\n",
    "ax3.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax3.grid(axis='y', alpha=0.3)\n",
    "ax3.set_yscale('log')\n",
    "for i, (bar, val) in enumerate(zip(bars3, mse_values)):\n",
    "    height = bar.get_height()\n",
    "    ax3.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Metric 4: Max Absolute Tolerance\n",
    "ax4 = axes[1, 1]\n",
    "max_abs_tolerance = [mean_metrics[name]['max_absolute_tolerance'] for name in names]\n",
    "bars4 = ax4.bar(range(len(names)), max_abs_tolerance, color=colors)\n",
    "ax4.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax4.set_ylabel('Max Absolute Tolerance (mean)', fontweight='bold')\n",
    "ax4.set_title('Mean of Max Absolute Tolerance', fontsize=14, fontweight='bold')\n",
    "ax4.set_xticks(range(len(names)))\n",
    "ax4.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax4.grid(axis='y', alpha=0.3)\n",
    "ax4.set_yscale('log')\n",
    "for i, (bar, val) in enumerate(zip(bars4, max_abs_tolerance)):\n",
    "    height = bar.get_height()\n",
    "    ax4.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Barplots generated successfully!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e0607c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barplots generated successfully!\n"
     ]
    }
   ],
   "source": [
    "# Create barplots for key metrics\n",
    "fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "fig.suptitle('Attention Implementation Error Metrics (vs MATH backend, averaged over 100 inputs)', \n",
    "             fontsize=16, fontweight='bold')\n",
    "\n",
    "# Prepare data for plotting\n",
    "names = list(mean_metrics.keys())\n",
    "colors = ['#485696', '#7a77a9', '#858a9d', '#f9c784', '#fc7a1e', '#f24c00']\n",
    "\n",
    "# Metric 1: Mean Absolute Error\n",
    "ax1 = axes[0, 0]\n",
    "mean_abs_errors = [mean_metrics[name]['mean_absolute_tolerance'] for name in names]\n",
    "bars1 = ax1.bar(range(len(names)), mean_abs_errors, color=colors)\n",
    "ax1.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax1.set_ylabel('Mean Absolute Error', fontweight='bold')\n",
    "ax1.set_title('Mean Absolute Error', fontsize=14, fontweight='bold')\n",
    "ax1.set_xticks(range(len(names)))\n",
    "ax1.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax1.grid(axis='y', alpha=0.3)\n",
    "# ax1.set_yscale('log')\n",
    "# Add value labels on bars\n",
    "for i, (bar, val) in enumerate(zip(bars1, mean_abs_errors)):\n",
    "    height = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Metric 2: Mean Relative Error\n",
    "ax2 = axes[0, 1]\n",
    "mean_rel_errors = [mean_metrics[name]['mean_relative_tolerance'] for name in names]\n",
    "bars2 = ax2.bar(range(len(names)), mean_rel_errors, color=colors)\n",
    "ax2.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax2.set_ylabel('Mean Relative Error', fontweight='bold')\n",
    "ax2.set_title('Mean Relative Error', fontsize=14, fontweight='bold')\n",
    "ax2.set_xticks(range(len(names)))\n",
    "ax2.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax2.grid(axis='y', alpha=0.3)\n",
    "# ax2.set_yscale('log')\n",
    "for i, (bar, val) in enumerate(zip(bars2, mean_rel_errors)):\n",
    "    height = bar.get_height()\n",
    "    ax2.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Metric 3: MSE\n",
    "ax3 = axes[1, 0]\n",
    "mse_values = [mean_metrics[name]['mse'] for name in names]\n",
    "bars3 = ax3.bar(range(len(names)), mse_values, color=colors)\n",
    "ax3.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax3.set_ylabel('Mean Squared Error (MSE)', fontweight='bold')\n",
    "ax3.set_title('Mean Squared Error', fontsize=14, fontweight='bold')\n",
    "ax3.set_xticks(range(len(names)))\n",
    "ax3.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax3.grid(axis='y', alpha=0.3)\n",
    "# ax3.set_yscale('log')\n",
    "for i, (bar, val) in enumerate(zip(bars3, mse_values)):\n",
    "    height = bar.get_height()\n",
    "    ax3.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "# Metric 4: Max Absolute Tolerance\n",
    "ax4 = axes[1, 1]\n",
    "max_abs_tolerance = [mean_metrics[name]['max_absolute_tolerance'] for name in names]\n",
    "bars4 = ax4.bar(range(len(names)), max_abs_tolerance, color=colors)\n",
    "ax4.set_xlabel('Attention Type', fontweight='bold')\n",
    "ax4.set_ylabel('Max Absolute Tolerance (mean)', fontweight='bold')\n",
    "ax4.set_title('Mean of Max Absolute Tolerance', fontsize=14, fontweight='bold')\n",
    "ax4.set_xticks(range(len(names)))\n",
    "ax4.set_xticklabels([name.replace(' ', '\\n') for name in names], rotation=0, ha='center', fontsize=9)\n",
    "ax4.grid(axis='y', alpha=0.3)\n",
    "# ax4.set_yscale('log')\n",
    "for i, (bar, val) in enumerate(zip(bars4, max_abs_tolerance)):\n",
    "    height = bar.get_height()\n",
    "    ax4.text(bar.get_x() + bar.get_width()/2., height,\n",
    "             f'{val:.2e}', ha='center', va='bottom', fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Barplots generated successfully!\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2d3c4ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Attention Type</th>\n",
       "      <th>Mean Runtime (ms)</th>\n",
       "      <th>Std Runtime (ms)</th>\n",
       "      <th>Mean Abs Error</th>\n",
       "      <th>Mean Rel Error</th>\n",
       "      <th>MSE</th>\n",
       "      <th>Max Abs Tolerance</th>\n",
       "      <th>Max Rel Tolerance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SDPA (Flash)</td>\n",
       "      <td>0.1078</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>2.536206e-05</td>\n",
       "      <td>9.707840e-04</td>\n",
       "      <td>3.027220e-09</td>\n",
       "      <td>9.472656e-04</td>\n",
       "      <td>1.958950e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SageAttn QK-INT8 PV-FP16 CUDA</td>\n",
       "      <td>5.0245</td>\n",
       "      <td>47.9926</td>\n",
       "      <td>1.568080e-03</td>\n",
       "      <td>3.326230e-02</td>\n",
       "      <td>4.213332e-06</td>\n",
       "      <td>2.425812e-02</td>\n",
       "      <td>1.999416e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SageAttn QK-INT8 PV-FP16 Triton</td>\n",
       "      <td>0.3310</td>\n",
       "      <td>1.1954</td>\n",
       "      <td>1.810164e-03</td>\n",
       "      <td>3.736821e-02</td>\n",
       "      <td>5.599509e-06</td>\n",
       "      <td>2.653595e-02</td>\n",
       "      <td>1.999536e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SageAttn QK-INT8 PV-FP8 CUDA</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.3340</td>\n",
       "      <td>5.282955e-03</td>\n",
       "      <td>8.630530e-02</td>\n",
       "      <td>4.595685e-05</td>\n",
       "      <td>7.033081e-02</td>\n",
       "      <td>1.999910e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SageAttn QK-INT8 PV-FP8 CUDA SM90</td>\n",
       "      <td>0.3097</td>\n",
       "      <td>1.0341</td>\n",
       "      <td>5.290417e-03</td>\n",
       "      <td>8.640017e-02</td>\n",
       "      <td>4.605384e-05</td>\n",
       "      <td>6.204712e-02</td>\n",
       "      <td>1.999901e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Flash Attention 3</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>0.2310</td>\n",
       "      <td>2.536210e-05</td>\n",
       "      <td>9.707830e-04</td>\n",
       "      <td>3.027246e-09</td>\n",
       "      <td>9.472656e-04</td>\n",
       "      <td>1.958820e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Attention Type Mean Runtime (ms) Std Runtime (ms)  \\\n",
       "0                       SDPA (Flash)            0.1078           0.3156   \n",
       "1      SageAttn QK-INT8 PV-FP16 CUDA            5.0245          47.9926   \n",
       "2    SageAttn QK-INT8 PV-FP16 Triton            0.3310           1.1954   \n",
       "3       SageAttn QK-INT8 PV-FP8 CUDA            0.2200           0.3340   \n",
       "4  SageAttn QK-INT8 PV-FP8 CUDA SM90            0.3097           1.0341   \n",
       "5                  Flash Attention 3            0.1370           0.2310   \n",
       "\n",
       "  Mean Abs Error Mean Rel Error           MSE Max Abs Tolerance  \\\n",
       "0   2.536206e-05   9.707840e-04  3.027220e-09      9.472656e-04   \n",
       "1   1.568080e-03   3.326230e-02  4.213332e-06      2.425812e-02   \n",
       "2   1.810164e-03   3.736821e-02  5.599509e-06      2.653595e-02   \n",
       "3   5.282955e-03   8.630530e-02  4.595685e-05      7.033081e-02   \n",
       "4   5.290417e-03   8.640017e-02  4.605384e-05      6.204712e-02   \n",
       "5   2.536210e-05   9.707830e-04  3.027246e-09      9.472656e-04   \n",
       "\n",
       "  Max Rel Tolerance  \n",
       "0      1.958950e+00  \n",
       "1      1.999416e+00  \n",
       "2      1.999536e+00  \n",
       "3      1.999910e+00  \n",
       "4      1.999901e+00  \n",
       "5      1.958820e+00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a summary comparison table\n",
    "import pandas as pd\n",
    "\n",
    "summary_data = []\n",
    "for name, metrics in mean_metrics.items():\n",
    "    row = {\n",
    "        'Attention Type': name,\n",
    "        'Mean Runtime (ms)': f\"{mean_runtimes[name]['mean_ms']:.4f}\" if name in mean_runtimes else 'N/A',\n",
    "        'Std Runtime (ms)': f\"{mean_runtimes[name]['std_ms']:.4f}\" if name in mean_runtimes else 'N/A',\n",
    "        'Mean Abs Error': f\"{metrics['mean_absolute_tolerance']:.6e}\",\n",
    "        'Mean Rel Error': f\"{metrics['mean_relative_tolerance']:.6e}\",\n",
    "        'MSE': f\"{metrics['mse']:.6e}\",\n",
    "        'Max Abs Tolerance': f\"{metrics['max_absolute_tolerance']:.6e}\",\n",
    "        'Max Rel Tolerance': f\"{metrics['max_relative_tolerance']:.6e}\",\n",
    "    }\n",
    "    summary_data.append(row)\n",
    "\n",
    "pd.DataFrame(summary_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d17553b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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