transformer/transformer-scaling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transformer Scaling\n",
    "\n",
    "**References**\n",
    "- *Transformer Sizing: [Code](https://github.com/karpathy/nanoGPT/blob/master/transformer_sizing.ipynb)*\n",
    "- *Transformer Scaling Laws: [Code](https://github.com/karpathy/nanoGPT/blob/master/scaling_laws.ipynb)*\n",
    "- *Scaling Laws of Neural Language Models: [Code](https://github.com/shehper/scaling_laws)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer Sizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate the Number of Parameters in the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# configs\n",
    "block_size = 1024\n",
    "vocab_size = 50257\n",
    "n_layer = 12\n",
    "n_head = 12\n",
    "n_embd = 768\n",
    "bias = False\n",
    "assert not bias, \"this notebook assumes bias=False just for simplicity.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def params():\n",
    "    \"Estimates the number of parameters in the Model.\"\n",
    "    out = OrderedDict()\n",
    "\n",
    "    # token and position embeddings\n",
    "    out[\"embedding/position\"] = n_embd * block_size\n",
    "    out[\"embedding/token\"] = n_embd * vocab_size\n",
    "    out[\"embedding\"] = out[\"embedding/position\"] + out[\"embedding/token\"]\n",
    "\n",
    "    # attention blocks\n",
    "    out[\"attention/ln\"] = n_embd  # note, bias=False in our LN\n",
    "    out[\"attention/kqv\"] = n_embd * n_embd * 3\n",
    "    out[\"attention/proj\"] = n_embd**2\n",
    "    out[\"attention\"] = (\n",
    "        out[\"attention/ln\"] + out[\"attention/kqv\"] + out[\"attention/proj\"]\n",
    "    )\n",
    "\n",
    "    # MLP blocks\n",
    "    ffw_size = 4 * n_embd  # feed forward size\n",
    "    out[\"mlp/ln\"] = n_embd\n",
    "    out[\"mlp/ffw\"] = n_embd * ffw_size\n",
    "    out[\"mlp/proj\"] = ffw_size * n_embd\n",
    "    out[\"mlp\"] = out[\"mlp/ln\"] + out[\"mlp/ffw\"] + out[\"mlp/proj\"]\n",
    "\n",
    "    # the transformer and the rest of it\n",
    "    out[\"block\"] = out[\"attention\"] + out[\"mlp\"]\n",
    "    out[\"transformer\"] = n_layer * out[\"block\"]\n",
    "    out[\"ln_f\"] = n_embd  # final layernorm\n",
    "    out[\"dense\"] = (\n",
    "        0  # 0 because of parameter sharing. This layer uses the weights from the embedding layer\n",
    "    )\n",
    "\n",
    "    # total\n",
    "    out[\"total\"] = out[\"embedding\"] + out[\"transformer\"] + out[\"ln_f\"] + out[\"dense\"]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we see: 124337664, expected 124337664, match: True\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>params</th>\n",
       "      <th>ratio (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>embedding/position</th>\n",
       "      <td>786432</td>\n",
       "      <td>0.6325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embedding/token</th>\n",
       "      <td>38597376</td>\n",
       "      <td>31.0424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>embedding</th>\n",
       "      <td>39383808</td>\n",
       "      <td>31.6749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention/ln</th>\n",
       "      <td>768</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention/kqv</th>\n",
       "      <td>1769472</td>\n",
       "      <td>1.4231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention/proj</th>\n",
       "      <td>589824</td>\n",
       "      <td>0.4744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>2360064</td>\n",
       "      <td>1.8981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp/ln</th>\n",
       "      <td>768</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp/ffw</th>\n",
       "      <td>2359296</td>\n",
       "      <td>1.8975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp/proj</th>\n",
       "      <td>2359296</td>\n",
       "      <td>1.8975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>4719360</td>\n",
       "      <td>3.7956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block</th>\n",
       "      <td>7079424</td>\n",
       "      <td>5.6937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transformer</th>\n",
       "      <td>84953088</td>\n",
       "      <td>68.3245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ln_f</th>\n",
       "      <td>768</td>\n",
       "      <td>0.0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dense</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>124337664</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       params ratio (%)\n",
       "embedding/position     786432    0.6325\n",
       "embedding/token      38597376   31.0424\n",
       "embedding            39383808   31.6749\n",
       "attention/ln              768    0.0006\n",
       "attention/kqv         1769472    1.4231\n",
       "attention/proj         589824    0.4744\n",
       "attention             2360064    1.8981\n",
       "mlp/ln                    768    0.0006\n",
       "mlp/ffw               2359296    1.8975\n",
       "mlp/proj              2359296    1.8975\n",
       "mlp                   4719360    3.7956\n",
       "block                 7079424    5.6937\n",
       "transformer          84953088   68.3245\n",
       "ln_f                      768    0.0006\n",
       "dense                       0    0.0000\n",
       "total               124337664  100.0000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare our param count to that reported by PyTorch\n",
    "p = params()\n",
    "params_total = p[\"total\"]\n",
    "print(\n",
    "    f\"we see: {params_total}, expected {124337664}, match: {params_total == 124337664}\"\n",
    ")\n",
    "\n",
    "# create a header\n",
    "# Create an empty dictionary to store data for the dataframe\n",
    "data = {}\n",
    "\n",
    "# Loop through the dictionary and populate the data dictionary\n",
    "for k, v in p.items():\n",
    "    data[k] = {\"params\": v}  # Add 'params' key with value from 'p'\n",
    "\n",
    "# Create DataFrame from the data dictionary\n",
    "df = pd.DataFrame.from_dict(data, orient=\"index\")\n",
    "\n",
    "# Add 'ratio (%)' column with calculation\n",
    "df[\"ratio (%)\"] = df[\"params\"] / params_total * 100\n",
    "\n",
    "# Set column formatting (optional)\n",
    "df[\"params\"] = df[\"params\"].astype(int)  # Convert params to integer\n",
    "df[\"ratio (%)\"] = df[\"ratio (%)\"].apply(\"{:.4f}\".format)  # Format ratio with 4 decimals\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the size of each checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "est checkpoint size: 1.49 GB\n",
      "measured with wc -c ckpt.pt: 1542470366\n",
      "fluff ratio: 103.38%\n"
     ]
    }
   ],
   "source": [
    "# params are stored in fp32, and the AdamW optimizer has 2 additional buffers per param for statistics\n",
    "params_bytes = params_total * 4\n",
    "params_and_buffers_bytes = params_bytes + 2 * params_bytes\n",
    "print(f\"est checkpoint size: {params_and_buffers_bytes/1e9:.2f} GB\")\n",
    "\n",
    "measured_bytes = 1542470366  # from wc -c ckpt.pt\n",
    "print(f\"measured with wc -c ckpt.pt: {measured_bytes}\")\n",
    "print(f\"fluff ratio: {measured_bytes/params_and_buffers_bytes*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate the ratio of GPU memory \n",
    "\n",
    "Memory that will be taken up just by the weights and the buffers inside the AdamW optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "memory ratio taken up just for parameters: 3.73%\n"
     ]
    }
   ],
   "source": [
    "gpu_memory = 40e9  # 40GB A100 GPU, roughly\n",
    "print(\n",
    "    f\"memory ratio taken up just for parameters: {params_and_buffers_bytes/gpu_memory*100:.2f}%\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "i.e. not that much of the memory for this tiny model, most of the memory is activations (forward and backward). This of course changes dramatically for larger and larger models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eestimate FLOPs for a single forward pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flops():\n",
    "    \"\"\"\n",
    "    We only count Weight FLOPs, all other layers (LayerNorm, Softmax, etc) are irrelevant.\n",
    "    We count actual FLOPs, not multiply-adds. Hence 2* all over the place.\n",
    "    Basically for any matrix multiply A (BxC) @ B (CxD) -> (BxD) flops are 2*B*C*D.\n",
    "    \"\"\"\n",
    "    out = OrderedDict()\n",
    "    head_size = n_embd // n_head\n",
    "\n",
    "    # attention blocks\n",
    "    # 1. the projection to key, query, values\n",
    "    out[\"attention/kqv\"] = 2 * block_size * (n_embd * 3 * n_embd)\n",
    "    # 2. calculating the attention scores\n",
    "    out[\"attention/scores\"] = 2 * block_size * block_size * n_embd\n",
    "    # 3. the reduction of the values (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)\n",
    "    out[\"attention/reduce\"] = 2 * n_head * (block_size * block_size * head_size)\n",
    "    # 4. the final linear projection\n",
    "    out[\"attention/proj\"] = 2 * block_size * (n_embd * n_embd)\n",
    "    out[\"attention\"] = sum(\n",
    "        out[\"attention/\" + k] for k in [\"kqv\", \"scores\", \"reduce\", \"proj\"]\n",
    "    )\n",
    "\n",
    "    # MLP blocks\n",
    "    ffw_size = 4 * n_embd  # feed forward size\n",
    "    out[\"mlp/ffw1\"] = 2 * block_size * (n_embd * ffw_size)\n",
    "    out[\"mlp/ffw2\"] = 2 * block_size * (ffw_size * n_embd)\n",
    "    out[\"mlp\"] = out[\"mlp/ffw1\"] + out[\"mlp/ffw2\"]\n",
    "\n",
    "    # the transformer and the rest of it\n",
    "    out[\"block\"] = out[\"attention\"] + out[\"mlp\"]\n",
    "    out[\"transformer\"] = n_layer * out[\"block\"]\n",
    "    out[\"dense\"] = 2 * block_size * (n_embd * vocab_size)\n",
    "\n",
    "    # forward, backward, total\n",
    "    out[\"forward_total\"] = out[\"transformer\"] + out[\"dense\"]\n",
    "    out[\"backward_total\"] = (\n",
    "        2 * out[\"forward_total\"]\n",
    "    )  # use common estimate of bwd = 2*fwd\n",
    "    out[\"total\"] = out[\"forward_total\"] + out[\"backward_total\"]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>flops</th>\n",
       "      <th>ratio (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>attention/kqv</th>\n",
       "      <td>3623878656</td>\n",
       "      <td>1.2426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention/scores</th>\n",
       "      <td>1610612736</td>\n",
       "      <td>0.5522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention/reduce</th>\n",
       "      <td>1610612736</td>\n",
       "      <td>0.5522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention/proj</th>\n",
       "      <td>1207959552</td>\n",
       "      <td>0.4142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attention</th>\n",
       "      <td>8053063680</td>\n",
       "      <td>2.7612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp/ffw1</th>\n",
       "      <td>4831838208</td>\n",
       "      <td>1.6567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp/ffw2</th>\n",
       "      <td>4831838208</td>\n",
       "      <td>1.6567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mlp</th>\n",
       "      <td>9663676416</td>\n",
       "      <td>3.3135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>block</th>\n",
       "      <td>17716740096</td>\n",
       "      <td>6.0747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transformer</th>\n",
       "      <td>212600881152</td>\n",
       "      <td>72.8963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dense</th>\n",
       "      <td>79047426048</td>\n",
       "      <td>27.1037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forward_total</th>\n",
       "      <td>291648307200</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>backward_total</th>\n",
       "      <td>583296614400</td>\n",
       "      <td>200.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>874944921600</td>\n",
       "      <td>300.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         flops ratio (%)\n",
       "attention/kqv       3623878656    1.2426\n",
       "attention/scores    1610612736    0.5522\n",
       "attention/reduce    1610612736    0.5522\n",
       "attention/proj      1207959552    0.4142\n",
       "attention           8053063680    2.7612\n",
       "mlp/ffw1            4831838208    1.6567\n",
       "mlp/ffw2            4831838208    1.6567\n",
       "mlp                 9663676416    3.3135\n",
       "block              17716740096    6.0747\n",
       "transformer       212600881152   72.8963\n",
       "dense              79047426048   27.1037\n",
       "forward_total     291648307200  100.0000\n",
       "backward_total    583296614400  200.0000\n",
       "total             874944921600  300.0000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compare our param count to that reported by PyTorch\n",
    "f = flops()\n",
    "flops_total = f[\"forward_total\"]\n",
    "\n",
    "# Create an empty dictionary to store data for the dataframe\n",
    "data = {}\n",
    "\n",
    "# Loop through the dictionary and populate the data dictionary\n",
    "for k, v in f.items():\n",
    "    data[k] = {\"flops\": v}\n",
    "\n",
    "# Create DataFrame from the data dictionary\n",
    "df = pd.DataFrame.from_dict(data, orient=\"index\")\n",
    "\n",
    "# Add 'ratio (%)' column with calculation\n",
    "df[\"ratio (%)\"] = df[\"flops\"] / flops_total * 100\n",
    "\n",
    "# Set column formatting (optional)\n",
    "df[\"ratio (%)\"] = df[\"ratio (%)\"].apply(\"{:.4f}\".format)  # Format ratio with 4 decimals\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate from the PaLM paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "palm_flops: 875062886400, flops: 874944921600, ratio: 1.0001\n"
     ]
    }
   ],
   "source": [
    "# this formula is often used to calculate MFU (model flops utilization)\n",
    "def palm_flops():\n",
    "    \"\"\"Estimate of the model flops following PaLM paper formula.\"\"\"\n",
    "    # non-embedding model parameters. note that we do not subtract the\n",
    "    # embedding/token params because those are tied and get used in the last layer.\n",
    "    N = params()[\"total\"] - params()[\"embedding/position\"]\n",
    "    L, H, Q, T = n_layer, n_head, n_embd // n_head, block_size\n",
    "    mf_per_token = 6 * N + 12 * L * H * Q * T\n",
    "    mf = mf_per_token * block_size\n",
    "    return mf\n",
    "\n",
    "\n",
    "print(\n",
    "    f\"palm_flops: {palm_flops():d}, flops: {flops()['total']:d}, ratio: {palm_flops()/flops()['total']:.4f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How to calculate our model flops utilization (MFU)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of A100 used: 37.14%\n"
     ]
    }
   ],
   "source": [
    "# here is what we currently roughly measure\n",
    "batch_size = (\n",
    "    20 * 5\n",
    ")  # assuming 5 is grad_accum with a batch_size of 20, so total batch size is 100\n",
    "measured_time = 0.755  # in seconds per iteration\n",
    "measured_throughput = batch_size / measured_time\n",
    "flops_achieved = f[\"total\"] * measured_throughput\n",
    "\n",
    "# A100 is cited to be 312 TFLOPS of bloat16 running on tensor cores\n",
    "a100_flops_promised = 312e12\n",
    "\n",
    "# the fraction of the A100 that we are using:\n",
    "print(f\"Fraction of A100 used: {flops_achieved / a100_flops_promised * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example for reference, we'd prefer to be somewhere around 50%+, and not just for a single GPU but for an entire DDP run. So we still have some work to do, but at least we're within a factor of ~2X of what is achievable with this GPU.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time needed to train the model: 3.46 days\n"
     ]
    }
   ],
   "source": [
    "# Finally let's check out the 6ND approximation as total cost of training in FLOPs\n",
    "# The 6ND comes from the cost to train a transformer model.\n",
    "model_size = params()[\"total\"]  # this is number of parameters, N\n",
    "tokens_num = 300e9  # 300B tokens, this is dataset size in tokens, D\n",
    "a100_flops = 312e12  # 312 TFLOPS\n",
    "assumed_mfu = 0.3  # assume this model flops utilization (take the current 37% from above and add some DDP overhead)\n",
    "flops_throughput = (\n",
    "    a100_flops * 8 * assumed_mfu\n",
    ")  # assume an 8XA100 node at 30% utilization\n",
    "flops_needed = 6 * model_size * tokens_num  # 6ND\n",
    "time_needed_s = flops_needed / flops_throughput  # in seconds\n",
    "print(f\"Time needed to train the model: {time_needed_s/3600/24:.2f} days\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer Scaling Laws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gpt_params(seq_len, vocab_size, d_model, num_heads, num_layers):\n",
    "    \"\"\"Given GPT config calculate total number of parameters\"\"\"\n",
    "    ffw_size = (\n",
    "        4 * d_model\n",
    "    )  # in GPT the number of intermediate features is always 4*d_model\n",
    "    # token and position embeddings\n",
    "    embeddings = d_model * vocab_size + d_model * seq_len\n",
    "    # transformer blocks\n",
    "    attention = 3 * d_model**2 + 3 * d_model  # weights and biases\n",
    "    attproj = d_model**2 + d_model\n",
    "    ffw = d_model * (ffw_size) + ffw_size\n",
    "    ffwproj = ffw_size * d_model + d_model\n",
    "    layernorms = 2 * 2 * d_model\n",
    "    # dense\n",
    "    ln_f = 2 * d_model\n",
    "    dense = d_model * vocab_size  # note: no bias here\n",
    "    # note: embeddings are not included in the param count!\n",
    "    total_params = (\n",
    "        num_layers * (attention + attproj + ffw + ffwproj + layernorms) + ln_f + dense\n",
    "    )\n",
    "    return total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123.653376 M parameters\n"
     ]
    }
   ],
   "source": [
    "gpt2 = dict(seq_len=1024, vocab_size=50257, d_model=768, num_heads=12, num_layers=12)\n",
    "print(f\"{gpt_params(**gpt2) / 1e6} M parameters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OpenAI reports gpt2 (small) as having 124M params, so this is a match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chinchilla_params(seq_len, vocab_size, d_model, num_heads, num_layers, ffw_size):\n",
    "    \"\"\"Parameters in the Chinchilla models. Unlike GPT they use relative positional embeddings.\"\"\"\n",
    "    # token embeddings only\n",
    "    embeddings = d_model * vocab_size\n",
    "    # transformer blocks\n",
    "    attention = 3 * d_model**2 + 3 * d_model  # weights and biases\n",
    "    relative_pos = (\n",
    "        d_model**2 + 2 * d_model\n",
    "    )  # relative keys, content bias, relative bias\n",
    "    attproj = d_model**2 + d_model\n",
    "    ffw = d_model * ffw_size + ffw_size\n",
    "    ffwproj = ffw_size * d_model + d_model\n",
    "    layernorms = 2 * 2 * d_model\n",
    "    # dense\n",
    "    ln_f = 2 * d_model\n",
    "    dense = d_model * vocab_size  # note: no bias here\n",
    "    # note: embeddings are not included in the param count!\n",
    "    total_params = (\n",
    "        num_layers * (attention + relative_pos + attproj + ffw + ffwproj + layernorms)\n",
    "        + ln_f\n",
    "        + dense\n",
    "    )\n",
    "    return total_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "our estimated params: 12296.1623M, chinchilla params: 12295.0000M, d_model: 4608, n_heads: 36, n_layers: 44\n",
      "our estimated params: 13124.4826M, chinchilla params: 12569.0000M, d_model: 4608, n_heads: 32, n_layers: 47\n",
      "our estimated params: 14614.4279M, chinchilla params: 13735.0000M, d_model: 4864, n_heads: 32, n_layers: 47\n",
      "our estimated params: 16037.5039M, chinchilla params: 14940.0000M, d_model: 4992, n_heads: 32, n_layers: 49\n",
      "our estimated params: 16184.4582M, chinchilla params: 16183.0000M, d_model: 5120, n_heads: 40, n_layers: 47\n"
     ]
    }
   ],
   "source": [
    "# Load in all the 50 Chinchilla models on the last page of the paper\n",
    "chinchilla_models_txt = \"[[44000000.0, 512, 2048, 64, 8, 8], [57000000.0, 576, 2304, 64, 9, 9], [74000000.0, 640, 2560, 64, 10, 10], [90000000.0, 640, 2560, 64, 10, 13], [106000000.0, 640, 2560, 64, 10, 16], [117000000.0, 768, 3072, 64, 12, 12], [140000000.0, 768, 3072, 64, 12, 15], [163000000.0, 768, 3072, 64, 12, 18], [175000000.0, 896, 3584, 64, 14, 14], [196000000.0, 896, 3584, 64, 14, 16], [217000000.0, 896, 3584, 64, 14, 18], [251000000.0, 1024, 4096, 64, 16, 16], [278000000.0, 1024, 4096, 64, 16, 18], [306000000.0, 1024, 4096, 64, 16, 20], [425000000.0, 1280, 5120, 128, 10, 18], [489000000.0, 1280, 5120, 128, 10, 21], [509000000.0, 1408, 5632, 128, 11, 18], [552000000.0, 1280, 5120, 128, 10, 24], [587000000.0, 1408, 5632, 128, 11, 21], [632000000.0, 1536, 6144, 128, 12, 19], [664000000.0, 1408, 5632, 128, 11, 24], [724000000.0, 1536, 6144, 128, 12, 22], [816000000.0, 1536, 6144, 128, 12, 25], [893000000.0, 1792, 7168, 128, 14, 20], [1018000000.0, 1792, 7168, 128, 14, 23], [1143000000.0, 1792, 7168, 128, 14, 26], [1266000000.0, 2048, 8192, 128, 16, 22], [1424000000.0, 2176, 8704, 128, 17, 22], [1429000000.0, 2048, 8192, 128, 16, 25], [1593000000.0, 2048, 8192, 128, 16, 28], [1609000000.0, 2176, 8704, 128, 17, 25], [1731000000.0, 2304, 9216, 128, 18, 24], [1794000000.0, 2176, 8704, 128, 17, 28], [2007000000.0, 2304, 9216, 128, 18, 28], [2283000000.0, 2304, 9216, 128, 18, 32], [2298000000.0, 2560, 10240, 128, 20, 26], [2639000000.0, 2560, 10240, 128, 20, 30], [2980000000.0, 2560, 10240, 128, 20, 34], [3530000000.0, 2688, 10752, 128, 22, 36], [3802000000.0, 2816, 11264, 128, 22, 36], [4084000000.0, 2944, 11776, 128, 22, 36], [4516000000.0, 3072, 12288, 128, 24, 36], [6796000000.0, 3584, 14336, 128, 28, 40], [9293000000.0, 4096, 16384, 128, 32, 42], [11452000000.0, 4352, 17408, 128, 32, 47], [12295000000.0, 4608, 18432, 128, 36, 44], [12569000000.0, 4608, 18432, 128, 32, 47], [13735000000.0, 4864, 19456, 128, 32, 47], [14940000000.0, 4992, 19968, 128, 32, 49], [16183000000.0, 5120, 20480, 128, 40, 47]]\"\n",
    "chilchilla_models = json.loads(chinchilla_models_txt)  # all 50 models\n",
    "chilchilla_models[\n",
    "    0\n",
    "]  # tuples of params, d_model, ffw_size, kv_size, n_heads, n_layers from Table A9\n",
    "\n",
    "for m in chilchilla_models[-5:]:  # only print last 5 models of the table\n",
    "    p, d, f, k, h, l = m\n",
    "    nparams = chinchilla_params(\n",
    "        seq_len=1024, vocab_size=32000, d_model=d, num_heads=h, num_layers=l, ffw_size=f\n",
    "    )\n",
    "    print(\n",
    "        f\"our estimated params: {nparams/1e6:.4f}M, chinchilla params: {p/1e6:.4f}M, d_model: {d}, n_heads: {h}, n_layers: {l}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FLOPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chinchilla_flops(seq_len, vocab_size, d_model, num_heads, num_layers, ffw_size):\n",
    "    \"\"\"\n",
    "    Calculate total number of FLOPs, see Chinchilla\n",
    "    paper Appendix F as reference: https://arxiv.org/pdf/2203.15556.pdf\n",
    "    \"\"\"\n",
    "    key_size = d_model // num_heads\n",
    "\n",
    "    # embeddings\n",
    "    embeddings = 2 * seq_len * vocab_size * d_model\n",
    "\n",
    "    # attention\n",
    "    # key, query, value projections\n",
    "    attention = 2 * 3 * seq_len * d_model * (key_size * num_heads)\n",
    "    # key @ query logits\n",
    "    attlogits = 2 * seq_len * seq_len * (key_size * num_heads)\n",
    "    # softmax\n",
    "    attsoftmax = (\n",
    "        3 * num_heads * seq_len * seq_len\n",
    "    )  # 3* is for subtract (max), exp, divide (?)\n",
    "    # softmax @ value reductions\n",
    "    attvalue = 2 * seq_len * seq_len * (key_size * num_heads)\n",
    "    # final linear\n",
    "    attlinear = 2 * seq_len * (key_size * num_heads) * d_model\n",
    "    att = attention + attlogits + attsoftmax + attvalue + attlinear\n",
    "    # feed forward\n",
    "    dense = 2 * seq_len * (d_model * ffw_size + d_model * ffw_size)\n",
    "\n",
    "    # logits\n",
    "    logits = 2 * seq_len * d_model * vocab_size\n",
    "\n",
    "    # this is what you'd expect:\n",
    "    # forward_flops = embeddings + num_layers * (att + dense) + logits\n",
    "    # but:\n",
    "    # per author correspondence apparently there is typo in the paper,\n",
    "    # they do not count embeddings and logits to repro table 4. So instead:\n",
    "    forward_flops = num_layers * (att + dense)\n",
    "    backward_flops = 2 * forward_flops  # as in Kaplan et al. 2020\n",
    "    total_flops = forward_flops + backward_flops\n",
    "\n",
    "    return total_flops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>seq_len</th>\n",
       "      <th>vocab_size</th>\n",
       "      <th>d_model</th>\n",
       "      <th>num_heads</th>\n",
       "      <th>num_layers</th>\n",
       "      <th>ffw_size</th>\n",
       "      <th>N</th>\n",
       "      <th>F</th>\n",
       "      <th>approx_flops</th>\n",
       "      <th>chinch_flops</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2048</td>\n",
       "      <td>32000</td>\n",
       "      <td>640</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2560</td>\n",
       "      <td>73825280</td>\n",
       "      <td>929877196800</td>\n",
       "      <td>907165040640</td>\n",
       "      <td>9.298772e+11</td>\n",
       "      <td>1.025036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2048</td>\n",
       "      <td>32000</td>\n",
       "      <td>1024</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>4096</td>\n",
       "      <td>305707008</td>\n",
       "      <td>4135248199680</td>\n",
       "      <td>3756527714304</td>\n",
       "      <td>4.135248e+12</td>\n",
       "      <td>1.100817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2048</td>\n",
       "      <td>32000</td>\n",
       "      <td>1280</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>5120</td>\n",
       "      <td>552604160</td>\n",
       "      <td>7353453772800</td>\n",
       "      <td>6790399918080</td>\n",
       "      <td>7.353454e+12</td>\n",
       "      <td>1.082919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2048</td>\n",
       "      <td>32000</td>\n",
       "      <td>1792</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>7168</td>\n",
       "      <td>1143453696</td>\n",
       "      <td>14670316437504</td>\n",
       "      <td>14050759016448</td>\n",
       "      <td>1.467032e+13</td>\n",
       "      <td>1.044094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2048</td>\n",
       "      <td>32000</td>\n",
       "      <td>2048</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>8192</td>\n",
       "      <td>1593126912</td>\n",
       "      <td>20220437594112</td>\n",
       "      <td>19576343494656</td>\n",
       "      <td>2.022044e+13</td>\n",
       "      <td>1.032902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2048</td>\n",
       "      <td>32000</td>\n",
       "      <td>3584</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>14336</td>\n",
       "      <td>6796274688</td>\n",
       "      <td>83021046743040</td>\n",
       "      <td>83512623366144</td>\n",
       "      <td>8.302105e+13</td>\n",
       "      <td>0.994114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_len  vocab_size  d_model  num_heads  num_layers  ffw_size           N  \\\n",
       "0     2048       32000      640         10          10      2560    73825280   \n",
       "1     2048       32000     1024         16          20      4096   305707008   \n",
       "2     2048       32000     1280         10          24      5120   552604160   \n",
       "3     2048       32000     1792         14          26      7168  1143453696   \n",
       "4     2048       32000     2048         16          28      8192  1593126912   \n",
       "5     2048       32000     3584         28          40     14336  6796274688   \n",
       "\n",
       "                F    approx_flops  chinch_flops     ratio  \n",
       "0    929877196800    907165040640  9.298772e+11  1.025036  \n",
       "1   4135248199680   3756527714304  4.135248e+12  1.100817  \n",
       "2   7353453772800   6790399918080  7.353454e+12  1.082919  \n",
       "3  14670316437504  14050759016448  1.467032e+13  1.044094  \n",
       "4  20220437594112  19576343494656  2.022044e+13  1.032902  \n",
       "5  83021046743040  83512623366144  8.302105e+13  0.994114  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now try reproduce Table A4 from Chinchilla paper Appendix,\n",
    "# comparing accurate flops above to approximate flops F = 6*N*D\n",
    "# note Chinchilla mentions using vocab_size = 32K\n",
    "\n",
    "chilchilla_models_table4 = [\n",
    "    [10, 640, 2560, 10, 64],\n",
    "    [20, 1024, 4096, 16, 64],\n",
    "    [24, 1280, 5120, 10, 128],\n",
    "    [26, 1792, 7168, 14, 128],\n",
    "    [28, 2048, 8192, 16, 128],\n",
    "    [40, 3584, 14336, 28, 128],\n",
    "]\n",
    "\n",
    "rows = []\n",
    "for num_layers, d_model, ffw_size, num_heads, _ in chilchilla_models_table4:\n",
    "    args = dict(\n",
    "        seq_len=2048,\n",
    "        vocab_size=32000,\n",
    "        d_model=d_model,\n",
    "        num_heads=num_heads,\n",
    "        num_layers=num_layers,\n",
    "        ffw_size=ffw_size,\n",
    "    )\n",
    "\n",
    "    D = args[\n",
    "        \"seq_len\"\n",
    "    ]  # dataset size (cancels anyway, for the purposes of the ratio calculation below)\n",
    "    N = chinchilla_params(**args)\n",
    "    F = chinchilla_flops(**args)\n",
    "\n",
    "    approx_flops = 6 * D * N  # approximate flops\n",
    "    chinch_flops = F * (\n",
    "        float(D) / args[\"seq_len\"]\n",
    "    )  # exact flops according to Chinchilla paper calculations\n",
    "\n",
    "    # first copy all keyvalues from args into out\n",
    "    out = {k: v for k, v in args.items()}\n",
    "    # then add the calculated values\n",
    "    out[\"N\"] = N\n",
    "    out[\"F\"] = F\n",
    "    out[\"approx_flops\"] = approx_flops\n",
    "    out[\"chinch_flops\"] = chinch_flops\n",
    "    out[\"ratio\"] = chinch_flops / approx_flops\n",
    "    rows.append(out)\n",
    "\n",
    "# make a pandas dataframe from rows\n",
    "df = pd.DataFrame(rows)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Laws: Approach 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In their \"Aproach 3\", Chinchilla paper fits a function L(N,D) to approximate the final loss gives the model size and the data size. Here is the final fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2312bd02290>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m8UCrVKAgvJ/APSLqlAVDIQw5XHD7J1BbmCfKOR11uOFw+1Bk0EGnmtJuYAY7drcXY04PDFo1cnM0op3iCX8AIzYXQiEW5UWmmO3FOaFOlw9jNjdytCrkGrUx24t3iicmghgdcyIYDKGsJDclO3yO6diYC9ZRFyor8qBQTPmTkcApttvcGB5yoKIiFyqVYsbtJ48t3mkNBkMYGbTD551A5aJCbq5inT5B+8k5s37vBHrbh5BbqIepgOeGPctOcTAQRFfLALQ5apirCmbFKY6QiZROo6kYBoMhLRvnS30RZe4i+VqfE7/Wj/nd+Ht3K7ZVLYZBOfkgKJVT7Ar4sLv3DLaWLkauKmbtk8gp9gR9eK//FLYU1aFQnRNjh3+aMkacUzwR8mPPYDPW59WgWG2atCPoFIubf4j14+Ph41huqEKpJn/G7cU6xSwCOGg9ikptGco0JZPbCzqU4pziEBvCKfsh5KuKUK6pivtOvFM8fU4sy6LddRA6mQFl2iXx20vgFLMsiwHPYTBEgVJt/RT7AnZEOsVj3sMIsj4Uai6MW+ukcoo9viZMBIeRq7k8zr5UTnHAfxoTgXboNFeDkMnUZ2GnWDg9eq6u9cD5td7Pqvp0Jti8eTP27NkT99n777+PzZs3Z/zYpwaH8ObJMzgxMDjzxmGCIRavHj2Jv548C39AXKrDR6fa8e6xFlhsTrFTRVN7P/7a2IJz/SOi9wWAEZsL73x+Cp+d6Ehp/wjHW/rwt8/OoK07tXnEMjRix18/OIkDRzvTthXLnj2n8Oln52C1uiS1G+Gj90/h870tGB60Z8R+LMMDNvz9zaM4/MlZYWcvwxzeewaf/60ZbSf7ZuX4M3HyYBs+/0sTjn92dranQqFQBMj0Wr+npxV9Ljt2tTbD7vdKYjOWvQNt6Hfb8WrnCYz5pBXFAYB9w+3o99jwZs8xDHvFPyPMxJGxDvR5rPhrfxMGveOS2z9l70KfZwQfDh1Fv2dUcvvtzi70eyw4MHoEfZ4Bye0PeLvR7+3BCVsjej1dktu3+nsx4GlFq/MI+tzSr1XOwCAs3mb0e46i331McvvewDCGvQdh9R3DsGe/5M8jgeA4xr0fwTXRjDHPHsnth0JOuLzvwR84DZfnbbBCL/Mp84pZdYqdTieampqiPQk7OjrQ1NSE7u5uAIDVakVTUxNOnToFAGhpaUFTU1NczdBtt92Ghx56KPrv73znO9i9ezd+9rOf4cyZM3j00Udx+PBh3HvvvRk/H42Ci/J5JgJJ76OUy6AIF9e7/ROijheJDrt8flH7AUCOhntb7vT6Zthyhv09vrRuNgYd9wbe7kr/ocNo4N5mOZxehMRGDxOQm8tFEKxjmXGKC4q4aOnwkCMj9mMpNBsgkzHwuP2wj0n/IJYMFXVcHWB369CsOeaJqFtdAcIQDPVaYR20zfZ0KJR5z3xc67dVLoFRqYbd7ws7xqmtlUJ8qbQO+SotXAF/RhzjLUV1KFLr4QsFMuIYb8yrRYk6FxNsMCOO8SpjDco1hQiyoYw4xnU5NajQlIIFmxHHuFRdhUpNDQBkxDHOU5ajQrscADLiGOsVZpRp1wFARhxjtbwQxdovAEBGHGO5zIQ8zTYAJCOOMcPkIEdzDQAG/kALdYwXCLPqFB8+fBgNDQ1oaGgAAOzcuRMNDQ14+OGHAQBvvfUWGhoacPXVVwMAbrnlFjQ0NMS1W+ju7sbAwOTN7KKLLsIf//hHPPvss6ivr8euXbvwxhtvYNWqVRk/n0mnWJxzq1Vy+7n94pxbnToNpziyr1f8vgCQo1GCgIt0e3zizjcWQ07YKXam7xTrtErIZQxCIRYOp3QPMHl5nFM8ljGnmEtvGRnOvFMsk8tQWGIEAAz0jmX8eHyUVOVDrpDB7fRiNAvRcbFo9RpULuHS6VqOdM7uZCiUBcB8XOv1ShVurFsV4xifkNQx1sqV+Fr16ow5xiqZHNeWr8mYYyxnZNheUp8xx1hGGFxSVJ8xx5gQgo15DRlzjAkhWGFYG+cY90noGBNCUKNbk1HHuESzJqOOca5qZUYdY51yWUYdY6ViMXI012HSMX6HOsbznFmtKb700ksT/oLefvvtuP322xPa+Oijj6Z9dtNNN+Gmm25Kc3bi0YTrTd2xTnESZQJapRI2ry9xpJjHjjZcf+pOwSmOONROzwz7Cgk8MAx0GiWcHj8cbh+0aiX/hmGEfsqxTjGfYIEYCCEwGjQYHXNh3O6B0aBJ2VYsEadYsvTpKacYiRSPDDsQCrFgEil3pGB/KuaKXFh6x2Dps2JZfXnCmlZx9gW+mPI3LpfLUFZTiK6zFnS3DqLAbEzyAEI1e8kdV8z2y9bXoOtMPzpO9WHtJcug1k6vQxRkDka/KZTZZM6v9Sx4bi8EeoUaN9auxq62E7D5vdjVegI31q2GQcl3P+C/rwjdzUJgoZYp8dWq1Xi96wRGfW682nkCN1SvjqsxngmhR3AGgJJR4JqyerzddwxDXgfe7DmG6yrq42qMIwRD/DESoVrjEEvAEDmuMK/F3yxNGPCO4a/9TbiydG1cjXHUjsBMhV0IBgCDLYUN+GT4KPo8w/hw6CguK2qIqzGeGaHYTwgAwfrcdWBB0Ovpw4HRI0D+urga4yisUMVpgrI3IsNS/TqEQNDracdxWyNCLIMKbQXv5nLBpU9IBEWOSm0DWJag13MKrc4jCIFBhbZOwI7AtRA4rpwNolhdD5Yl6Pc0ot9zFCyAsik1xpP2BeYvJK7DMjAqV4NlCYY8H8PqOwYWBEWaTfzPgoLXgf/jAACVcjlMAMY978E10YwQgPwpNcYz2RF+lghBpqiFFtfC7XkL/sAZsB4WOZpr4mqME80/SJ8X5hQLrqZ4NolEir0i0qcBQBOJFIuMME+mT4uP1OaoJ9OnU31zpg87Cg5P6m/Pc8Lp0xOBILx+cdeNj4gjbLNL98Y9kj49Nu5GKJGAVoqYcnWQyxlMTARht2U+pbmkPA8AMNg3jlBwdt5qVi7mUqh7WgfnZAp1QakJ+WYjQsEQWo91z/Z0KBTKLKFXqnBj7eqMRYw1cs4xzmTE+Jqy+oxGjK8wr81oxPgLhQ0oy2DEeENuA8o1ZRmLGC/XN6BcswgA0Gw/JHnEuEq3FuWaFQCAdudhySPGZs0alGrWAwAGMhAxNqlWoUjzRQDAmK9J8oixRrkcpnDE2JOBiLFCUQet5loADCZoxHheQ51iCYk6xYEAgiJqWnURp1ikc6sLR4qdaaRPB0Nsys5opK7Y4U79AUEu4yLOACdNny6mcF3xuN2Ttq0Ier0acjmXlm2T0G4EhiHIL8xeXXFuQQ6UKjkCE0GMDM1O+nJZdQFkcgZOmwdjWUgbFwshBEvXc2lvZ492IjhLLw8oFMrsQx3jxFDHeGb7sY7x8QykUsc6xplIpY51jDORSh3rGGcilTrWMc5EKnWsY+wPnKGO8TyFOsUSoo5p1+MNJO9opltTnEr6tEzGRNOvUxXb0mu5KK/DnZ4zO5lCnb7DaTJykeJxCSOuhBDkRcS2rNKreAJAYTFXVzycBSeVEIKSCi5aPNBjzfjx+JAr5Cir5lpCdZ21zLD17FC1rBSaHBU8Th+6TvfP9nQoFMosslAc4+IYx3gkA45xqSazjnEma4w35DagQksdYyGoY5wY6hjPf6hTLCEMIZN1xSKUpKNp0CmqT/sCQUwExbVzAiajxTPWFQtgiKRPu9J7MDDqwynPEohtRXodj9s9kqY65+VxNViZassUcYqHsiQ8FXWKu2fHKQaAqiVcCnXXWcucTKGWyRgsC0eLTx9qm5NzpFAo2SPT4luaLIhvXRMjvvVGBhzjbeZ5Lr6Vuzaj4lvL9Q1R8a1MOcaZFN8yZ1h8y6RalVHxLY1yeUbFtxSKuhjxrTPnnSr1k08+iY0bN0Kv16OoqAjXX389Wlpa4rb59re/jdraWmg0GhQWFuK6667DmTNnEtq9/fbbQQiJG9u3b4/bxmq14tZbb4XBYIDJZMKdd94Jp1Pc/Y06xRKjTUGBOtVIsVIug1zG/QhTUZGOpj+nWBOs10kTKTZKqECdo1NDJmMQDIbgkKDNU4T8fC5SPJqpSHFYgdpu88DrTV3NO1lKKjmneHTIAW+KL0XSpbS6EHKFDC6HF6OWudn6aHF9FeQKGcaHHRjoHJ7t6VAolFlmoalSZ8IxpqrUie3HqlJnwjGmqtSJOV9UqUMht2QjWfbu3YsdO3Zg//79eP/99zExMYErrrgCLtdkQGn9+vV44YUXcPr0abz33ntgWRZXXHEFgjME97Zv346BgYHo+NOf/hT3/a233oqTJ0/i/fffxzvvvIOPP/4Yd999t6hrNqvq0/MRQXG98P9ydcWemUWzYlTuUo0UE0KQo1Ji3O2F0++HMUcT893Mx9XH9ioWKfgLxApt+RFiWTCEzHh9+DBEIsWOmPRpoRMgAkcIf8wwBKaIArXNDVPY4U52QkL3xdhIcboq2Xyo1AoYTRrYxj0YHrKjojJGXTNNtWc+tDoVTPk6jI+6MNA7hpqw8FV6iPuZyZUylC8qRGeLBZ1nLSgoNXFfiP0lkmot4/mZKjVK1NVX4szhDpw+2I6ymqKZDys4TxHXR9pfLwqFkogQ4UYsjPCNaKGpUr/RcwzXV9SjgKpSQzpVakBQmZqqUoftU1XqkGQPMNMZtqyF15V+7NPh4K6v3R6fyahSqaBSxd/zdu/eHffvF198EUVFRWhsbMQXv8ilxsc6qtXV1Xj88cdRX1+Pzs5O1NbWCs5DpVLBbDbzfnf69Gns3r0bhw4dwoYNGwAA//Ef/4GrrroKP/3pT1FaWprUudJIscRMRn3FR4o9/gmERL6tirZWSitSnFqkUKdWgmEIWJaFK41oo3FKW6Z0iaRQj41L93bdZNSAYQgmJoJwSCAIxkckWpz1FOpZqisGgKql3A2u66wlI8reUrBsfQ0IIbB0jcA6ODcj2mLI9ptjCmUhslBqjDMZMabiW4ntU1XqxFBV6rlFRUUFjEZjdDz55JMz7mOzcc9MeXl5vN+7XC688MILqKmpQUUF/0uhCB999BGKioqwdOlS3HPPPRgdnfx737dvH0wmU9QhBoCtW7eCYRgcOHAgmdMDQCPFkhNJnxbTXkmjVIAQ7qWTxz8RjRwnw2RbplSc4khNcWoLOSEEeo0KNpcXDrc3GjkWi16nAiEEgWAIbo8fuhTtRDCZwk6xhGJbDMMgN1eH0VEnRq1OGCTqgRxLUbEBrWcHMZwlp7i0Mh+nm3rQ32PNSPQ7GUqqCqBUK+B1+zHYM4qSqoKsz2EmcoxaVC0rQefpfpw62IYt16yb7SmlhRRvjyNvjimU85mIY5xcxFg8Ecc4nYhxIiKOcWzEWKiPcSpEHONkIsapEHGM04sYCxNxjAFEI8abhCLGKdpfro/Y5yLGLIByTZVk9qt0a8P2uYgxAJRpl0hiH+AcYwDRiDEAlApFjFPApFoFANGIMQAUai6U7HlFo+TSzCMRYwDIFYoYp4BCURcXMYYH0Gm+wt/HWGIKzU0wGAxp21Hr7ABK0NPTE2dvapR4KqFQCA888AAuvvhirFq1Ku67X/7yl/jud78Ll8uFpUuX4v3334dSKez7bN++HV/72tdQU1ODtrY2fO9738OVV16Jffv2QSaTwWKxoKioKG4fuVyOvLw8WCzJi7nSSLHEaMM/VDGRYoaQqDPtEllXnBN2ilNpyxRJn3akqD4NAIZwXbEtjfpdhmFg0HFzsUkQhc0zSu8UA0B+PvegMDqaGbGtomIjAGBk2IFgIPNOR2GJEXKFDF7PBKwjmamVngmZjEHVEi5a3H5aurfwUrPiAi6lp+tMP+wZqiunUCjzj4USMaaq1PxEVakzLL4ViRifoKrU06Cq1KnBMFrJBgAYDIa4MZNTvGPHDjQ3N+Oll16a9t2tt96Ko0ePYu/evViyZAm+/vWvw+sVfv6/5ZZbcO2112L16tW4/vrr8c477+DQoUP46KOP0rpGU6FOscSkEikGUq8rzkkjfTriFHv9AUwExKtXA5NOsT1NUauIAvW4I/3FPtfEiWLZHR4EUjwvPgqiTnFm+urqDWpoNAqEQixGstC7VyZjUFKeCwDo65LuIUIstSu4Wo+e1kH4RfbqzhZ5xUaU1RaBZYHmfa2zPZ20KDQ3oaikNa1RaG6a7dOgUOYMVJU6MQtClTrD4ltUlToxC02V2u19XzLbc5F7770X77zzDj788EOUl5dP+95oNGLx4sX44he/iF27duHMmTN4/fXXk7a/aNEiFBQUoLWVex4zm80YGhqK2yYQCMBqtQrWIfNBnWKJ0aXaczjFiG+OmnNsU1GfVinkUCk4kQh7iinU0jvF6fcq1qgVUKvkYFmuNZNURCPFYbEtqSGEoMjMRYsHs6TGXFrFpZnNplOcV2yAIU+HYCCE7nODszaPmVhzEZdy1nGqD46xzGQLZAMp3xxTKBQOqkqdGKpKPbN9qkqdmIWiSk2IFirlGsnsziVYlsW9996L119/HR988AFqamqS2odlWfh8yd8ve3t7MTo6ipISrpRh8+bNGB8fR2NjY3SbDz74AKFQCJs2bUraLnWKE0F4xgxE0qdF9xyO7ucXddzJSHGKbZVmaMvEzjBi06cT3jr4zinmvEzhGl0pnFhCSDRabJVKbIsQmEw6yGQMJiaCsDu8CSS+IXy+CXZhY53iQRtYQsCmUtdCCP/gmUxZuIZ3dNgBt2ciekxWyEaiIXaa4f8YwqB2RRkAoP1Uv3j7Um0/g538EhNKFxWBZVmc2HdOvB2Rvw8UCiVL8C1uEUXqqUNwQZxUpY51jG0+H1gWPIPwDgiMEIuoKnWsY2z1egRt8Y2QwGBZElWljnWMhz1O3vkHQwzvELo8sarUsY5xv8eOIMtMG2LnH2QZAHJsmSK+1eu2Cu4jZIdvhFgCFgzW566LE9/q9lgQBOEfrIx3hFjwD3Cq1JFU6uO2RvS4exBgGd4RAv8IsoR3hMCpUsemUve4WxPMn/9aBCDjHSGWoFhdH5dK3ec+hiAY3hFgZbxDaD6hsCp1bCr1kOcAJliCAJhpQ/g68I8ACKdKHeMYj3r2IBBiEWQxfUBgsCz/AKdKrc/5JxBZMYIIIZhAZ30+smPHDvz+97/HH//4R+j1elgsFlgsFng83LN9e3s7nnzySTQ2NqK7uxuff/45brrpJmg0Glx11VVRO8uWLYtGjp1OJ/7P//k/2L9/Pzo7O7Fnzx5cd911qKurw7Zt2wAAy5cvx/bt23HXXXfh4MGD+Oyzz3DvvffilltuSVp5GqBOseREIsXeQADBUPK/7DpVejXFnokAAkHxf1wzOcUzERspTueNmomvLVMa5IXFtqzj0kX0GIYgL49ztkdGM1NXWlxiAsApUIdE/P6kikanQl6hHgDQ3z170eKaZSUgBBjuH4dDQtVwqVlz8WIAQMfJ+R0tplAomWGh1BhTVWp+qCr1zFBV6sQQkryY7nzjV7/6FWw2Gy699FKUlJREx8svvwwAUKvV+OSTT3DVVVehrq4ON998M/R6PT7//PM4oayWlpaocrVMJsPx48dx7bXXYsmSJbjzzjuxfv16fPLJJ3F1zX/4wx+wbNkyXH755bjqqquwZcsWPPvss6LmT53iGTjQ24u/njuHUXdyi45aLgcTji55RNQVp5o+rVLIIWe4H2MqCtTpOsV6rQoE4JSj06gHjUSK3d4J+CcCKduJEI0USyy2VVDAOZAjI5mp+TXlaqFSKxAMhLJSVwwAZeGeyH2ds+cUa3PUKKnkotZtp/pmbR4zUVCSi9KaQrAsi+Z952Z7OhQKZQ6yEB3jYeoYR8m2Y3zc1oheKr4Vx3wX31qoRFKhp47bb78dAFBaWop3330Xg4OD8Pv96OnpwR/+8AcsXbp0mp3IPhqNBu+99x6Ghobg9/vR2dmJZ599FsXFxXH75OXl4Y9//CMcDgdsNhuef/555OSIU9KnTvEMdIyN4ezoKMYTqKLFQgiBJqokLcIpjk2fFgEhJK0Uar02HOn1pFYTLGMY5IRbKNmdqUd5lQo5tOEWUWMSpFDnRZziMWnrfyNO8XCGHFZCCIpLuBRqS/94Ro4xlbJqzike6LEiKKEwmVhqV06mUGcjSp4qay4O1xaf7IU9QxkDFAplfrNQHGOqSs0PVaWeGeoYU+Yb1CmegVTUpCMp1GIc3Khjm0q/YXWktZL4fQ1hh9bhTn2xNuWEU5/TFNvKjdYVp7+wmwwaMAyBfyIIZxrnNpWicKqx1eqSVNk6lpJSEwDA0jeeEftTySvUQ6NTIRAIZu2YfJQvKoJKo4DH5UN/58iszWMmCkpzUV5XDJYFjn3aMtvToVAoEsGyLLyB9DOVIlBV6sRQVeqZ7VNV6sTMd1VqytyCOsUzEO07LMopFi+2FakN9gWCmAiKc7b0aUSKDdFIsS/lP3RjTlhsK41IMQCYDJH+wulHimUyJmpvVMLaT51OBY1GAZZlMZqhKKG5lGuTNDxkz5jjHQshBOXhaHHvLDqjMjmDRcu5aHHbybmbQg0A9Vu4VJ/ulgFYB7OjFE6hUDIHy7L4sKcDf245AdeE+BfMQlBV6sRQVeqZ7VNV6sTMd1VqytyBOsUzkHSkOEZNNhopFrGwKmUyKGTcj4MvWiyk6AikFymO1BT7A0H4UqzlNepiIsVpKOxGIsVjCSLFQuKcfMfMz42kULunfSl4PWeYPyEEhYUGAOEU6lTOdwaVY71BDa1OhVCIxZDFJpnac6K5VtQUAgB6O0YQCqV4sxeyL2L+das4p7ivYwTuqb9PQgeQ4LiJz2u6jdxiI6qXc4qGxz9pSVNMOo0/GgqFIgnuwATax62wej149exJLtOLqlJTVeoUVal73VSVmqpSJ6dKTZk7UKd4BnQppU+LjxQTQqJiW2IFsyKObSqRYrmMgTasfJ1qr+LJSHG66dNcZFeK9GkAUaXoUQkVqAGgMFJXnCGxLULIZAp1luqKi0tNUCrl8Hr8GJnFyKcxLweFpSawLMu1Z5rDrLl4CQgh6GsfwnDf2GxPZ17w9NNPo7q6Gmq1Gps2bcLBgwcTbj8+Po4dO3agpKQEKpUKS5Yswbvvvsu77Y9+9CMQQvDAAw/EfX7ppZeCEBI3/uf//J9SnRJlgaBTKHHjkpXIUSgnHWOpI8YLoMaYqlLzM1V866C1kapST4GqUmeXYMgt2ThfoE7xDEQixS4RTrE2EikW6dxGUqgdYp3icPp0ym2VIinU7tSc2kik2O7yph5lRLwCtc+ffl1XRGxLyvRpACgM1xUPDdkzdkM0h53igSzV+DIyBmXVnPpzT/twVo4pRCRa3NbcN6cXHENeDhatKgcAHN17ek7PdS7w8ssvY+fOnXjkkUdw5MgR1NfXY9u2bRgaGuLd3u/348tf/jI6Ozuxa9cutLS04LnnnkNZWdm0bQ8dOoT/+q//wpo1a3ht3XXXXRgYGIiOH//4x5KeG2VhYFJrqGOcAKpKnRiqSj0zVHwre5zqW4/m3qVpj1N962f7VLIGdYpnIJo+LUI0KxLxFbMPMOkUixXbmkyfTq0uOCK2ZUtRkEqnUUIuYxBiWThSdKwBToFap40oUKe/kEfSp90ePzwppJYLUVCgB8MQeL0TcKQZHReipIyrK7aOOuH1SDf3RFQu4lKou9qGZ/UmX7nYDKVKDqfdM6cFtwAuWiyTMxjqtaLnnGW2p5MUs/Xm+Oc//znuuusu3HHHHVixYgWeeeYZaLVaPP/887zbP//887BarXjjjTdw8cUXo7q6Gpdccgnq6+vjtnM6nbj11lvx3HPPITc3l9eWVquF2WyODoPBIHr+lPMD6hgnhqpSJybbjjFVpZ4OdYwpqUKd4hnQpSC0lRPexykifRoAcsJNqJ0+cQtkjloJAiAYYuEReUwAMKYZKSaEwBhWoB5P00nMC6dQS9FfWKGQwajn5jUiYbRYJmOQn8/1PhsaykwKtUarRG44/bs/SynUJZV5UChl8Lh8GLbMXgq1XCHDohVcve7Z4z2zNo9k0Bk0WL6xFgBw5KPTs9rSKllO9G3Asd5laY0TfRsAAHa7PW74BO5dfr8fjY2N2Lp1a/QzhmGwdetW7Nu3j3eft956C5s3b8aOHTtQXFyMVatW4YknnkBwihDhjh07cPXVV8fZnsof/vAHFBQUYNWqVXjooYfgTrLvPOX8JCuO8TTxLelesFJV6sRkQ3wrG+2aqCq1MFSVGlhR1ohV5S1pjxVljbN9KlmDOsUzEIkUT4RC8CepCh1xij0TEwiK6LeaalsmGcNAF0mhTkOB2pZGlNcUriseT1OBOtconVMMAAWRumKrtErRxUVcpGloyC6p3Vgi0eKBLNWrymQMyqu5aHF32+ymUC9eUwEA6O8YgWN8bjswKzfVQqNTwTnuxpnGztmeTlapqKiA0WiMjieffJJ3u5GREQSDQRQXF8d9XlxcDIuFP8Le3t6OXbt2IRgM4t1338W//uu/4mc/+xkef/zx6DYvvfQSjhw5InhcAPiHf/gH/P73v8eHH36Ihx56CL/73e/wzW9+M4WzpZxPZN8xbpbUMaaq1ImhqtQz26eq1ImZ66rUMkYr2ThfkM/2BOYyLACFTAYFw2AiFILL74dSJRPYeFItVq2QgyEEIZaFOzABfTgCPBP6mJriqX9WM2nR6tUqOL1+2D0+FBv1SR0vgkGXIFIsdOApn5vCEdkxp0dgJ4EbxZRNI3XAVonEtvJzc9DWNSIiUixwwiR+/kXFBuBkn6BTzCZQOSZCN80pu5RW5OLUiV70942DZVmQVJSTkyHGbFVdITrOWtDdNoT1m2vBMFIcM7lrGoUFDLk6lFYXoL9zBGeP9WD9JUuFlaMFr6dE2wsRtqNQyrH2kmXY9+4xNO87h0WryqHWJfc3HzbEMxdxUxHD6rLDaacP2+12ACXo6emJs6VK8l6XDKFQCEVFRXj22Wchk8mwfv169PX14Sc/+QkeeeQR9PT04Dvf+Q7ef/99qNVqQTt333139P+vXr0aJSUluPzyy9HW1oba2lrJ5kuZx0QUpGNh2KhjvOvsyahjfMOSldAplPw2+GCEHpInValfbTuBcb8Xu1qbcWPdKhiUfL/P/PaFbhUhsFFV6te7TmDU58arnSdwQ/Vq5KqSf8gVeq3PAFFV6rf7jmHI68AbPcdwfUU9CtQ507YPhvhjMDKG/wixqtR/szRhwDuGv/Y34crStShWm6bbEZipcFiCAcBgS2EDPhk+ij7PMD4cOorLihpQqskX3IvfDj8yAqzPXQeAoMfThwOjR7Axbz0qtGb+HViB50sIBGQIp0odAkGvpx3HbY0IsQwqtBW8m8uFfllYgatEOFVqliXo9ZxCq/MIQmBQoa0TsCNwLQSOK2eDKFbXg2UJ+j2N6PccBQugTFvPv4Pgn5LA9QmrUrMswZDnY1h9x8CCoEizif95SvA68H8cADhVagDjnvfgmmhGCEC+5nJ++zwfzf3csvMLGilOgqiadJIp1ISQaFsmMVHfVNOngUkF6lTEtozhmmKXb0J0j+SojXD6dLq9iiOR4jGbW5I3bgW5mYkUR9oy2eweeL3iU9aTochshEzGwOP2Y3wsO9FSc0UeFEo5PG7/rKZQA8DStZUAuJ7FExIIr2WSRSvLkVdsxIQ/gGOftsz2dBIi5Ztjg8EQN4Sc4oKCAshkMgwODsZ9Pjg4CLOZ/wGxpKQES5YsgUw2+aC4fPlyWCyWaDr20NAQ1q1bB7lcDrlcjr179+IXv/gF5HL5tDTrCJs2bQIAtLa2ir52lPOPbESMb4irMZY2YkxVqRPDL74lnZYFIQTrY2qMD1FV6mlQVWrKXIE6xUkQVaAWIZwVFc0SsU9ERdoXCCadqh3BEBbbSqWtkkohh1LOPXjaUxTbMuVMRorTuRmY9GowhGAiEIQzxbnEkp/LvbF2SSy2pVYrYDRy55ypFGqZjEFxqREA0NebvRTqikWcCnVXK78qcLYoqcqHPleLCX8A7afndnsmQgg2fIl7aGg73g3rLLa1mosolUqsX78ee/bsiX4WCoWwZ88ebN68mXefiy++GK2trQjFlKCcPXsWJSUlUCqVuPzyy3HixAk0NTVFx4YNG3DrrbeiqakpzpmOpampCQDndFMoyZB98a357xjPb1XqJskdY6pKnRgqvkWZC1CnOAnERorj9hHhFCvlcijDD3Jiew5HFKRTcYoJITDp0qsrNuaoQQD4J4Lw+FKPnDIME23NJJXYVsTeiNR1xcWcwzqYwbrisvI8AEB/rzVjx5hKVV0RAKCrfRihYPI18VJDCIlGi88c6Uqr3Vc2KKrIR9XSErAscPjvzXRBnMLOnTvx3HPP4Te/+Q1Onz6Ne+65By6XC3fccQcA4LbbbsNDDz0U3f6ee+6B1WrFd77zHZw9exZ/+ctf8MQTT2DHjh0AAL1ej1WrVsUNnU6H/Px8rFq1CgDQ1taGH/zgB2hsbERnZyfeeust3HbbbfjiF78o2L6JQuGDOsaJWXiq1PPbMaaq1NOhjjFlJqhTnAS6lCLF4XRm0T2HU9vPoAnXBXtSdGojYluu1NKf5TIZ9GHHeizNFOpof2GbNIrRBXlctHg4Q06xJYNRwdIKziketNjhz1IKsbk8F2qNAj7vBAayFKEWonZFKdeeyeZBX/vsRq6TYd2XVkCukGG4bwwdJ/tmezpziptvvhk//elP8fDDD2Pt2rVoamrC7t27o+Jb3d3dGBiYfEisqKjAe++9h0OHDmHNmjW4//778Z3vfAf//M//nPQxlUol/v73v+OKK67AsmXL8L//9//GDTfcgLffflvy86MsfGZHfEvadk1UlVqY6eJbTVSVeop9qkqdmPmgSk0RhjrFSZCTQqQ4lfRpIEZsS2ykOFxT7PT6U4qoGXXhlkppKFDnRsS2HOktsvkmrl5xVCLV4cKIUzwqrVNsNnN1xWNjrow5rAajBgajBizLYqBvPCPHmArDMNFocce5wRm2zixyhTyqRH3qiHSLe6bQ6TVYddFiAMDRvafhTyNrYiFy7733oqurCz6fDwcOHIjW9wLARx99hBdffDFu+82bN2P//v3wer1oa2vD9773PcG06IiNp556KvrviooK7N27F6Ojo/B6vTh37hx+/OMf0z7FlJSZnXZN0jnGVJU6MVSVemb7VJU6MXNdlZoiDHWKk0CbQip0tFexUMSX8I9opHiqUyywfWTo1ErIGE7x2unzzbj91GGMpE+7vGAJokMMEQXqcQdPpFjo2DxEFahtrri5iJ0PwO1TUMCpcY9YnQiB5eyImA/AiR5OHRqtCnq9Giyb2dZMZeVca6a+qSnUgudBxA0eI9VLOPGjns4RTARCYAmJDvH2BRDYfup/S9dWgZERjAyMY8QyzvMzE/nDFDtPkXaWb1gEfa4OXpcPxz9tiZmmRNeNQqFIC98NPqJIPXVAwDH2+8XZ4duWBcBOqlLHOsY2nw8sC55BeMe0xTM8QiyiqtSxjrHV6xG0xTdCAoNlSVSVOq7G2OPknX8wxPAOocsTq0od6xj3e+wIssy0IXb+QZYBIMeWKeJbvW6r4D6JbPHOCQzW566LS6XudVsQBOEfrIx3hFjwD3Cq1LE1xj3uHgRYhneEwD+CLOEdIXCq1LGp1D3u1gTz578OAch4R4glKFbXx6VS97mPIQiGdwRYGe8Qmk8orEodm0o95DmACZYgAGbaEL4O/CMAwqlSxzjGo549CIRYBFlMG5S5A3WKkyCV9Gl9NFIs7g1vqunThJCoAnUqdcXGNGuKASBXH1aOTjd9OqxA7XD54J9IPwKba9SCYQh8/gAcTuneuAOAuZiLOGUyhbosnELd12PN2tvG/CIDcgwaBAMh9HRIV1eVClqdCjVLSwEAp+dBtFgmY7BxK1fTevZIJ0ZnWcWbQqFIT/ZrjKWNGFNV6sTwq1JLn0odcYwPUlXqaVBVakq2oU5xEkSjvimoT7v8EwiJ+CNINX0amKwrtqVQVxypKXb7JlJ2RE0SpU+rVQroNNx1sEqQQi2TMdHo8/CoI217sUzWFWcuUlxsNkIuZ+DxTMA6Kk2d9UwQQlCzmKv17DxnycoxE7F8XRUAoKd1EA6J0uozSWlNYVR068B7x+MUlCkUysKAOsaJWXiq1Jl1jKkq9XSo+BYlm1CnOAkiStIToVDSrZK0SiUIgBDLwu1Pvq5wMlKcSr/hsNhWCtFelUIOTbi3cqp1xbnhtkxefyAtBWoAyI+IbY1L4wQWhlOopXaKzWGn2Gp1ZqyuWCZjUFLGpVD3dEu3IM9E9RLOKR7oGYPHJW2EXSymAj1KqwvAssCpw52zOpdkWX/5SihVClgHbTh9qH22p0OhUDIAdYwTs/BUqTPjGFfMe1XqlQCoY8zHbDnGgZBbsnG+QJ3iJFDKZJOtkpKMFjOEQBeJ+opYwCL9hp0+P4Iio0tRBelUew2HU6jHnak5xXK5DIawDas9TbGtXGmd4qKwUzw0Iq1TrNOpYDBwdcWZTKGuqORSqHu6sucUG0xaFBQbwLLsrAtuAcDKjTUAgPbTfXCn+DuaTbQ5aqwP9y4+9ulZ2CUWeqNQKHOD+S6+RVWpE5MN8a31816Vup6qUidgqir1uHevZLaF+Kj7Euzp2pD2+Kj7kozPda5AneIk0aWQQq1XTTq4yaJRKCBnGNH7AYAxXFOcal2wKRzpTbUtEwDkhVOorWmmUBeEI8UjY9IsnEX5nFNsHXdhYiK5aH+ymM0mAIAlg7Wj5ZX5IAQYs7rgcGTPIVy0rAQA0H5mYNZTforKclFYakIoyOLMPKgtBoBFq8pRUl2IUDCE/e8dn/VrSKFQMsN8d4ypKnViqCr1zPapKnViIqrUBEpoFHWS2aVIh3y2JzDnCYvA5iiVGPN4RDrFSgxAXCo0YQj0ahXG3B7Yfb5o9BdJ/F1ORoq9YFkWRKSCrSnclmksHafYoEWnZYyrK449vMD8hU4rEiket3sQCAQhl4fbsAidE0l8gBydCjqtEi63H8NWJ8qKBFqyCF0yoYkSghKzCWfPWjBgsU3OL9GNVOQxWEKg0ihRZDZicMCG7q5RrFhdLqyvLPYmnuDXpKquCI2fnYNtzI3RITsKzMakfheTsi94TYW3X7mxBh+9eRRnj/dgxYYaqLXKBErNIg8gdN3SUIImhGDT9jV45/mPMNxrxdmjnVi6riZle1LApUOld+s/n9KpKOcJyd7XQgL3A4aNOsa7zp6MOsY3LFkJnUIpyg4/k6rUu9pOwOb3YlfrCdxYtxoGpYp3e34r/ITARlWpX+86gVGfG692nsAN1auRq9IK7MVnhx8GiKpSv913DENeB97oOYbrK+pRoM6Ztn0wxB+zkTH8R4hVpf6bpQkD3jH8tb8JV5auRbHaNN2OwEyF8/MYAAy2FDbgk+Gj6PMM48Oho7isqAGlmnzBvYRtTUdGgPW568CCoNfThwOjR3BB3nqUa838Zlih9nQCL/4Jp0odAkGvpx3HbY0IsQwqtBW8m8sFl1aBq0Q4VWqWJej1nEKr8whCYFChFXAAWYG4nMBx5WwQxep6sCxBv6cR/Z6jYAGUaesF7AvMnwhcn7AqNcsSDHk+htV3DCwIijSb+J+nBa8D/8cBACrlchTJq8EwGgQABBM9gKXJpZV7JWk/aLfbAZSkP6F5AHWKkyQqtiXizW+0V7HIiK8h4hR7fEBu8vvpNSoQAgRCIbj9E9H07WQxxbRlSpWIAnW6kWKtWgm1SgGvbwJWmzsa6U2HogI9OrpHMTRiF3aKU8Bs5uqKx8fd8Hj80GjEXfdkqagqwOCADT1dI1ixujwjx5iKUiVHZW0ROlosaD09wDnFs0hpdQHyigywDtlx+kgXGrYsntX5JEOOUYuGS5bj0PvNaNp7BmW1xcgxJv+QKTXvd10OrV64128yuB3SZltQKAsFUY5xCkRqjJNzjMUTqTFOxzFORKTGOBnHOBUiNcbJOMapEKkxTt8x5idSYwwAvZ4+HLQ2gpB1KNNI45REUqk5++1oth8CQ0Io01RJZr9KtzZs/xTanYfBIIQy7RJJ7ANcKjUA9HsaMeA5CgKgVMgxTgGTiuseMeT5GGO+JhCwKNRcKDrQJATDaCSxMxNyRgs5k/7frZzJjF7OXGRW06c//vhjXHPNNSgtLQUhBG+88Ubc9yzL4uGHH0ZJSQk0Gg22bt2Kc+fOJbT56KOPghASN5YtW5b2XHNS6FUcSZ8WK5oVEduye8U5pzKGie6bilhWJFI87vKIUsyOJc8Qbstkd6eVdkIImUyhlqyumHOEpa4rVqsVyA1HtgcymEJdWc0tukMWOzwe6dLyZqJ2ObcYd7UOYSJDYmLJQgjB6k2cYMjZY93wZvE6pMOShmoUluchMBHEgd00jZpy/jGf1vt0oeJbiaGq1ImhqtQzM9/Ftyhzk1l1il0uF+rr6/H000/zfv/jH/8Yv/jFL/DMM8/gwIED0Ol02LZtG7wzOIsrV67EwMBAdHz66adpzzWVtkyTTrH4SDGQWlum2BRqsei1KsgYgmCIhTOFXscAYNRpwDAEE8EQHCkKfkWIim1JVVccI7YVCkl7cyspMQEA+vvHJLUbiy5HjfwC7m16T2f2egcXlRihN2kQmAii89xQ1o4rRNmiQuQV6RGYCM4bJWpCCC7cvgYyOQNL1wjOHumctbl8uWoPrqzen9b4ctWeWZs/ZX4yn9Z7KaCOcWKoKnViFo4qNXWMKfOHWXWKr7zySjz++OP46le/Ou07lmXx1FNP4V/+5V9w3XXXYc2aNfjtb3+L/v7+aW+YpyKXy2E2m6OjoKAg7bnmpKAkrVdz+9hFRooNEcGsFJziaLTXLb4umCFksq7YmVpdMcOQaAr1qD29CG9hHucADo9JEynOM+mgkMswMRHEmE3amsiyUi7Pvb9/PKM3zaqaQgBAV0f2nGJCCBavKAUAnDvZN+uLAiEEazZzNUpnj3XPeruoZDHk5aDhUu4B4eje0xiXOGMhWSIpVekOCkUM82G9Z1kWgZB0pQHzXXyLqlInhqpSz2y/SreWqlJT5g1zVn26o6MDFosFW7dujX5mNBqxadMm7Nu3L+G+586dQ2lpKRYtWoRbb70V3d3dCbf3+Xyw2+1xYyrpRIq9gQAmkuxvDABGdbjfcArRWlM4Upxur2Frik4xAOSHU6hH03Q8CyJiWzY3JgLpP6gwDEFRIRcttgxP/xmnQ3GxAXI5A4/Hj7HxzIkQVS7iHvgGB8azmkK9aFkJGBmDsREnRodmx5mLpbS6AAUlRgQDITQfnD89gJc0VKGkphDBQAifvX0EQQl+rymU+U621vtEaz3LstjX24Ndp07CG5CuTGS+O8ZUlToxVJV6ZvtUlZoyX5izTrHFYgEAFBcXx31eXFwc/Y6PTZs24cUXX8Tu3bvxq1/9Ch0dHfjCF74Ah0P4Qf7JJ5+E0WiMjoqK6Up8+rCIhScQQIANcepysYMHlVwe7W88NVrMCgxgMlLsmZiAX+RDs3EGpzjRcYFJp3jMOcOiN/X8Y65DvoFzZq0xkWLB4yawo9OooFUrwIJrpSQF5kKurnhQwClOZZ4AIJMxMBdzIlT9fWPC2yfSaSBEYEzuqzdokFeQA5bNbs9ilVqBqroiAMDZ0/1gCZk2hOcvMIQukMD2sf8xhMHaiziRrdbmXjgdHp7rLGRf6CuheQogcnsCLhvjoivrodIqMT7sQNPHZ8Qfl0JZYGRrvRda6wkLePwTODk0iCGXC2+cOQXvREB4QeAbIcI/IOAY+/3i7Agee1KVOtYxtvl8YFnwDMI7IDBCLKKq1LGOsdXrEbTFN0ICg2VJVJU6rsbY4+SdfzDE8A7BH0uMKnWsY9zvsSPIMtOG2PkHWQaAHFum1Bj3uq2C+ySyxTsnMFifuy6uxrjXbUEQhH+wMt4RYsE/wKlSx9YY97h7EGAZ3hEC/wiyhHeEwKlSx6ZS97hbE8yf/zoEIOMdIZagWF0fl0rd5z6GIBjeEWBlvENoPqGwKnVsKvWQ5wAmWIIAmGlD+DrwD8rcYc46xaly5ZVX4qabbsKaNWuwbds2vPvuuxgfH8ef//xnwX0eeugh2Gy26Ojp6Zm2jUoum+wfLCJabFCJrw9WyeVQyeWi9wNiFKTd3pTEsnJzwkJZ6USKjZH06fTfJhfkhlOordK8OS4OO8WWYbvkb/rKyrgU6r6+zNUVA5Mp1J1ZTKEGgMUruRTqrtYh+HwTWT02H+aKfJgr8hAKsWg+MH+ixZocNTZv55QyzxzuwEDH8CzPiEKZn4hd7xOt9TqlEl9btgIauXzSMZ5vEeMFUGOcyYhxNsW39g4fyaj41kFrY0ZrjJvthzJaY9zuPJzRGuOBDNcYj/maaMR4ATJnnWKzmevLNjg4GPf54OBg9LtkMJlMWLJkCVpbWwW3UalUMBgMcWMqhBDowynUYtSkIynUYuuKjZG6Yo+4NOgcNSeWFWJZOFJIv87LmawpTvWPPaJA7XD74J9I76GiIFpXLM3iWJCXAxlD4PUFYHOk3nqKj9KwUzw4ZIc/gyrNVbOUQl1QbIApX4dgIISOM8LRm2xSv5mLFref6ofdKk02QTYoryvGkgauBcbn7zbB45T2d5FCmU9ka72faa0v0OqoY5wAqkqdGKpKPbN9Kr5FmcvMWae4pqYGZrMZe/ZMqpza7XYcOHAAmzdvTtqO0+lEW1sbSkrS7/GWE4n6iokUq1NryxSpKx4X6RQzhMSkUIuP9hq0asgYgkAwBHuK6tFqpQK6sMhYutHiorBTPDQqzcIokzEoDKtQW4ZsktiMYNBrYNCrwbJsRlsz6Q0a5IdTqLMuuLWyDABwdg4IbgFAYakJZTUFYFkWTZ8lbt8y11h36QqYCvTwunz4/C9NkiuiUyjzhbm03kccYzV1jHmhqtSJyZZjTFWphaGO8ezx5JNPYuPGjdDr9SgqKsL111+PlpaW6PdWqxX33Xcfli5dCo1Gg8rKStx///2w2RI/M09tvRcZP/nJT6LbVFdXT/v+Rz/6kaj5z6pT7HQ60dTUhKamJgCc2EZTUxO6u7tBCMEDDzyAxx9/HG+99RZOnDiB2267DaWlpbj++uujNi6//HL853/+Z/Tf/9//9/9h79696OzsxOeff46vfvWrkMlk+MY3vpH2fKORYhELTCR9WqyStFETToMW6RQDQG5UgVr8vgwzqUBtnamuOAH5xnCPYZs0CtROtw9uiaKiJUVc7e/AkLRiWwBQXp4HAOjttUpuO5bqWq6+t6M9u6m3NUuLoVDK4bB50N+d2XNMlrVbloAQoKdtCMP947M9naSRK2T4wnXrIVfIYOkawcl988upp1DEMJ/W+wKtDl9bPs8dY6pKLUg2VKkz3a6JqlInhqpSzw579+7Fjh07sH//frz//vuYmJjAFVdcAZeL8wX6+/vR39+Pn/70p2hubo5qQtx5550J7ca23RsYGMDzzz8PQghuuOGGuO0ee+yxuO3uu+8+UfOfVaf48OHDaGhoQENDAwBg586daGhowMMPPwwA+O53v4v77rsPd999NzZu3Ain04ndu3dDHY6iAkBbWxtGRiajZb29vfjGN76BpUuX4utf/zry8/Oxf/9+FBYWpj3fVPoOpxwp1qbuFEfbKrlSqwvOD7dUsjpSX+QKIk5xmgJZSoUcueEa5SGrNKrH5iIuZW5gSPq64qhT3DeW0RtkdS33+zw8aIdT4jTwRCgUctQu56IwLSd6s3bcRJjyc7BoBRfBPvLJ2Xm1MBnzc3DBFasBAMc/OwtLV3brxCmUbDHf1nvqGCeGqlInhqpSz2yfqlJnlomQR7KRLLt378btt9+OlStXor6+Hi+++CK6u7vR2NgIAFi1ahVeffVVXHPNNaitrcWXvvQl/PCHP8Tbb7+NQIL7a2zbPbPZjDfffBOXXXYZFi1aFLedXq+P206n04m6ZnJRW0vMpZdemvAXiBCCxx57DI899pjgNp2dnXH/fumll6Sa3jT0KfUqnowUszEiczPpzZk0SaZP8xiKiG2NuT1xxwQSiNrGfJ5n0AL94UixwPYz/d0XmCKRYifYRMdN4v5RlJeDMZsbQ6MO1JTm85sRsM933MICPWQyBl7fBMbt3qjTnXhCQgeI376o2ACFQgavdwIjo85oqnb8XPltERE3U61OheISIwYHbOhsH8aq+hjFdMFfLpEqhwLTWbKqDGeO9WCgxwr7mBuG3BR71or9nUiw/ZrNtehsGcDIwDi6zw2iaolZ+JdO6DqLVXyWyM6ileUY7B5F99kBBCeCM4qUUyjzkTm93k9twwDub7BQo8MNy1bgtTOnoo7x9eHU6qQJCfw1M2zUMd519mTUMb5hyUroFEpRdviZVKXe1XYCNr8Xu1pP4Ma61TCEu2lM3Z7fCj8hsFFV6te7TmDU58arnSdwQ/Vq5KqSXxNCAp8zQFSV+u2+YxjyOvBGzzFcX1GPAnXOtO2DIf4Yj4zhP0KsKvXfLE0Y8I7hr/1NuLJ0LYrVpul2BGYqNH8AYAinSv3J8FH0eYbx4dBRXFK4DuXavAR78Vri/VRGgPW568CCoNfThwOjR3BB3nqUawXq8FmZgH2BLieEU6UOgaDX047jtkaEWAYV2ukdWgBALrhGC1wlwqlSsyxBr+cUWp1HEAKDCm2dgB2BOJ7AceVsEMXqerAsQb+nEf2eo2ABlGnrBewLzJ8IXJ+wKjXLEgx5PobVdwwsCIo0m0D4ngN4rkNQ6OFVAl7puAaanPTdPI+Tc1antqtVqVRQqfjuJZNE0qLz8oR/5202GwwGA+RJ3lcHBwfxl7/8Bb/5zW+mffejH/0IP/jBD1BZWYl/+Id/wIMPPpi0XWAO1xTPRSYjxSLSp9WTvYr9YnoVh51ip8+PQDDRbXc6udFIcWoRxIjYltWRugJ1JFI87vAgkGYv1uJ8zrGUsq64KOysDkhcVyyTMSgt5QS3Mp1CXbOIi4ZkO4Vab9CgrIp7OdHSPDeixdocNVZs4N5oH/307Lzr/7tx6ypc/a0voryueOaNKRRK1qDiW4mhqtSJoarUM9unqtTzg4qKiriWdk8++WTC7UOhEB544AFcfPHFWLVqFe82IyMj+MEPfoC777476Xn85je/gV6vx9e+9rW4z++//3689NJL+PDDD/Htb38bTzzxBL773e8mbReY5UjxfCPSq9jh94NlWf43QVPg2ivJ4AsEYff6UKBL7g2qRsH1OPYHg7B5vSjQJP/mNZI+7fFPwDsRgFoh7secF06fHnd5EAyFIGPEvzvRqpXQqBTw+CYwanfDnDv9zW6yFIWd4pExZ8rzmUpJkREDgzZYhmxYsTh9EbZYKipy0dU1gt7eMTSsrZLUdixVNQU4uK8NY1YXxsdcMOWKSxNJh6VrytHXNYr2MxasuaAGKpUia8cWYsX6KrQ298Jl9+L0kS6s2rho5p3mCHKFDDmmFCPuFAolo0Qc47QixgkQFTFOgYhjnFzEWDwRxzidiHEiIo5xbMT4uop6FPJEjFMh4hgnEzFOhYhjHBsxvqyoAaUa/sw3sUQcYwDRiPGm/HUo00jzbBNxjDn7XMSYBVCukeb5JuIYc/a5iDEAlGmXSGIf4BxjANGIMQCUCkWMU8Ck4py+SMQYAAo1FyblJ2SKm2re5u2mIxa73Y57YEZPT0+cvZmixDt27EBzczM+/fRTQbtXX301VqxYgUcffTTp+Tz//PO49dZb40prAK4kJ8KaNWugVCrx7W9/G08++eSMc41AI8UiyAmnTwdCIXhEvCmOKEnbvclHbgkhMEVVpMVFfJVyGXThuaZSV6xTK6FSyMCygDXFfsWEkGgK9fB4em91DTlqqJVyBEMsRsekabtTGiO2JbXqb1lZHggBrGMuODPYakelUqC0nItKt50bythx+DCX5cKUr0MgEMS55r6sHlsIuUKOhou5Fk3NB9vhzmKt9Xwi2zVGFMpCgKpSJ2ahqFLP93ZNVJVamPNNlVrBaCQbAKa1s0vkaN57771455138OGHH6K8vHza9w6HA9u3b4der8frr78OhSK5wMonn3yClpYW/NM//dOM227atAmBQGBa2U0iqFMsAjnDQBf+waWSQi1WgXqyrlj8A2hsv2GxEEKQb+Ac2lF76k5ooSncYzhNp5gQguJIG6URaRSjC/JyoFDI4PMHYE1TDGwqarUCRWExr56ezKZQ1y7m0m3b24ay2tKHEIIVaysBAGeO96adIi8V1ctKUFhqQjAQwpFPpV1QFwq/a/sanj+3Pa3xu7avzXwgCmWBQcW3ErMQVKkzLb5FVakT26eq1PMblmVx77334vXXX8cHH3yAmpqaadvY7XZcccUVUCqVeOutt6ZFfBPx61//GuvXr0d9/cxR/qamJjAMg6KioqTtU6dYJJEWS3YRTnEkUmwTESkGAJM29dZKk3XFqS1IEQXqkTQUqKVyigGguIBzMi2j0ihQMwyJtmbqHxyXxGYslRVcWlR3hp3i8oo8qNRyeNx+9PeOZfRYU6mqK4JOr4bPO4H2M5asHlsIQgg2XLoMhABdZy2wZPj6UyiU8wvqGCeGqlInhqpSz2yfqlLPX3bs2IHf//73+OMf/wi9Xg+LxQKLxQJPOLgXcYhdLhd+/etfw263R7cJxuguLVu2DK+//nqcbbvdjldeeYU3Srxv3z489dRTOHbsGNrb2/GHP/wBDz74IL75zW8iNzc36fnTmmKR6FUqDDid0xWoEyjjRiLF9thIcRIq0NH06RTaMuVFeg2n2pbJwDnFo2Kd4pj5F4briG1OL/wTASjF1DZPuT6RSPHgqAMhTK/nJiLvNywBSoqN6O6zom/QhtUruPQOsSrZQoetKM/DocMdGByyweubgDqZmluxxyYEjFyGmrpinGnuQ+u5QZRV5QsrF4u9Kc9QCsPIGCxfW4HDn5zD6WPdWLzcDIav3lu0qrO4+Uz92ecXGbF4TQXOHuvB4Y9O46pbN4ORxcxLUKpc5IGlUrHOlA0B/kfta2nXGdntdjwIAYVTCmWeQVgCMuW+wArciKgqdaJPqSp1UudAVanDx6Wq1IEFFpv81a9+BYDrNhDLCy+8gNtvvx1HjhzBgQMHAAB1dfE/z46ODlRXVwMAWlpaosrVEV566SWwLMvbh16lUuGll17Co48+Cp/Ph5qaGjz44INxdcbJsLB+GlkgsmhkI1KcG44Uj7nFO7a5Odzik2qv4gJ9OH3a4Ur5DZdGpUCOlrtew2mmKOfn6iCTMfD5A7CloYodS6mZixQPDdslT//V69XIzdWBZYG+DEdw68Ip1L3do/B6JzJ6rKnULiuBSq2A0+5Fd5ZVsBNRv3kxVBoFbFYXWo71zPZ05hRS1hhRKOcrVJU6MVSVOjELUZW6l6pSx3G+qFLHwrIs77j99tsBTLbm4xsRhzhiJ7JPhLvvvhtutxtGo3HacdetW4f9+/djfHwcHo8Hp06dwkMPPZS0wFYE6hSLJLX06clIcUjEH0SkLZN3IgDvhLjFNpI+7fT64U/B4cvN0YAhBL6JIJze1BdiqVKoZQyDojzOlmVEmhRqo14DrUaJYIjFoEQ2Y6ms4N76dvVIt9DxkZufg7yCHIRCLDpaBzN6rKnIFTIsXV0GADh5tHvO3PBVagXWhkW3ju9vhTuDgmcUCuX8hDrGiZkNx3h4HjvGmRLfKs+i+FYmHWMqvkXJNNQpFkkqTnGOSgWGEIRYFk5f8guaUi6LKl6PiRTbUivk0Cq5lN1U6oplMga5YbGukTTEtorCKdRDEtQVmwvDdcUSiW0RQlBmNgEA+gakj+ZG6or7+8cxMZFZIaq6JVxa1LkzlqzfcJesLodcIcPYqAt9XZl9ASCG2pVlyDcbEfAHcfijM7M9HQqFsgBZKKrUpnnuGM93VepyTdGCUaWmjnE81DGeP1CnWCRRJWmfL+lfaoaQ6H7jWUyhzsuJ1AWnmEIdVqAelsApHhxzpn0TiDjFA8N2yW4oZSUmAEDvwLgk9mLJzdVCr1cjGAyhty+zgk81dUWQyRnYxt0YGpTmpUGyqNQKLFnFRYuPN3bNmZs9IQSbLl8BQgh6WofQneUoOoVCOT9YCOJbN1BVakGyIb61pXDtglGlzpRjvJBUqUe8ByW1T5EG6hSLRB+uKQ6EQnAHkq/fNEXqikWKZqXjFOeHnWJrigrUBUbOKR6xp754FZh0IITA45uA05PeIl6UlwOGELg9fjhc0izYpWYTCAFsdg8cEqfYEkJQVclFizszHEFVKuWoWcTJzp+bBSXo5WsrIJMzsA470D+HFJ9zC/VYsaEaAHDog9PwZbnmmkKhnB8sBMeYqlILQ1WpZ7Yfq0qdiT7GC0WVmkAGjZwKVc5FqFOcCDJ9yBkGOUoupTlWTZoVGBFS7Tmcq+P2s7o9vPNJdNxIr2KrM0Wn2BBxil1gCeIG31z4lP7kMhnyjZxzPjTmnL6DgB2+c5LJZSgM1xUPDMer0gkywzxVSnm03VPvwJjw9qIHAQhBdXUhAKCvbwwTgWD48+Su3eQ5EIERv//i5dxNtrNzGD4fj/M3w1yTHjxG1BoVlqwMR4sPdyEETh2bTUVFWfD6CHwh+DH335pNtTDk6uB1+3H0k7OiziuRfWE7Qucl9jpTKJSswLPgRBSppw6hRZewk6rUcTXGEwHhhZpvhAj/gIBj7PeLsyN47ElV6ljHmMuKA88gvGPaw0J4hFhEValjHWOr1yNoi2+EBAbLkqgqdVyNscfJO/9giOEdgj+WGFXqWMe432NHkGV4h9hzADhV6tga4163VXB7oSE4HzBYn7sursa4121BEIR/sDLeEWLBP8CpUkdSqY/bGtHj7kGAZXhHCPwjyBLeEQKnSh2bSt3jbk0wf/7rEICMd4RYgmJ1fVwqdZ/7GIJgeEeAlfEOofmEwqrU1YZ/gFZegxDLhH/umcEf8ko2zhdoS6YUMKpUcPr9sPt8KNHrk9pn0imehfRpZ6rp09z+Lp8fbp8fWhVPO4gkKMrVY2TchcExJ2rLClKyEcFcaMDgqAOWYTuW1hSnZStCeWkuLMN29PaPYUWdtG/v8vJ0MOjVsDu86OmxYtGi5JuIiyW/UI/cfB3GRl1oOzeEFeGU5myxfG0Fzjb3YXTIDkvvGEoqxLaXyAwyuQwXfnkl/vbng2g72YeqxWaUVOXP9rQoFMoCJFJjnFa7pgSIateUApEa4+TaNYknUmOcTrumRERqjJNp15QKkRrjZNo1pUKkxjjSrmnv8BFcVtSAUo00a1akxhgAej19OGhtBCHrUKYpkcz+cn3Efjua7YfAkBDKNFWS2a/SrQ3bP4V252EwCKFMu0QS+wCXSg0A/Z5GDHiOggAoFWrXlAIKJjm/IV1+efZWqHPSv+94ndJlvMx1aKQ4BSJiWzYxCtQazrlNtabY7vUhEBLuh8dHJFLs8U/A7RefNqqQy2AKR6rTEdsqjohtjaWv8FySgbri8nBd8cCQ9K2ZSEy0uKMzsy2LCCFYvIxb2M6dGch6ba9Gq0LdilIAwPHDHXOmthgAikpzsbS+EgBwYM9JTPjPn5s8hULJLlSVOjFUlToxC02V+ngGUqnnu/gWZW5CI8UpYFSF64N9yTu4ueFIsd3rQzAUgjzJ9xFapQIquQy+QBDjbg8KcnRJH1Mhk8GoUcPm8cLqcEObP72310wUGHQYd3kxbHOhsjBX9P4AUJTHvRUbsbkQCAYhlwk1ik/CVr4eDCFwefxwuLww5KTfL9Vk1EKnVcLl9qN/yI7K0tTOU4iamkIcP9GDvr5xeL0TUKsy92dXU1eEIwc7YLN5YBmwoaTUlLFj8bGioRKtp/oxYrGjr2sU5dXpZQZIydqLF6O3YxguuweNH7fgwq0rZ3tKFAplgRJxjF+d5xHjV9tOYHweR4zf6TuGQa8Db87TiPGnw03o9Qzhw6GjGYkYEwA9nj4cGD2CTfmZixgftzUCQMYixq3OI5z9DEWM+z1HAUgbMc40/2vJH2AwGNK2Y7fb8Qikycyc69BIcQoYI6JZIqK+OUol5AyDEMuKaudECIlGi62piG3puQVmJMW64kJjuM+wLfVIsV6rgkalQIhlMTyeuh2Ai14X5nNz6h+SrjVTZRmX6tudAZVok0mLvFwdWJZFV9eI5PZjUSrlqK3jUrRbTvVn9Fh8aHUqLF1TDgBo2t+OUGjuRIsVSjk2f3kVAKDtZB/6OjIbuadM5+mnn0Z1dTXUajU2bdqEgwcTK3COj49jx44dKCkpgUqlwpIlS/Duu++Ksun1erFjxw7k5+cjJycHN9xwAwYHqRI5hZ+QhBkuC0F8i6pSC0NVqWe2H6tKfZyqUmcVJaOWbJwvUKc4BUyRtkxecc5tpK5YbH1wni5cG+wS7xQXRJxiR2rOaFFYgTodp5gQAnM4WjxoTT+FurSIi3j3D9nSthWhIuwU9/SPZSTtd9EiLoW6vT3zjtjS5dyb3p7uUTglVtROhhUNlVAq5bCNudB5NvtK2IkwV+Rh2dpIGvUpqkadRV5++WXs3LkTjzzyCI4cOYL6+nps27YNQ0NDvNv7/X58+ctfRmdnJ3bt2oWWlhY899xzKCsrE2XzwQcfxNtvv41XXnkFe/fuRX9/P772ta9l/Hwp84/Gnn68ceIUJoLSldEsBMeYqlILQ1WpZ7ZPVakp8wXqFIuFTEaKnRN+BNjQDMrBkyM3xbrifF0kUuyerjKdQDQXJEZB2uGecVuh/Qk4sS2XN/WFtjjsFFvGHHH2Z1Lt5iPiFA8M2SYdWNFKwPHDXGSAQi6DxzuBEatr2gbC80xOsbgm7BQPDdvhcPD//CNqzVOHWIx5OTCXmsCyQMsZy6QdqVSOZ/idUakVWLGeczyPHepAUKhOW6was+AxxW1fv2UxDLk6eFw+HPrwNFiwie0I/oxFDsHrKdHPJUlmS43y5z//Oe666y7ccccdWLFiBZ555hlotVo8//zzvNs///zzsFqteOONN3DxxRejuroal1xyCerr65O2abPZ8Otf/xo///nP8aUvfQnr16/HCy+8gM8//xz79+9P7QJSFh4st8Yd7ulDn82Bt5rPcN0CqCo1VaVOUZV6wGOjqtRUlTopVWrK3IE6xSmglsuhDNfFikmhNmlTixTnRpziFCLFkfTpcZcHgaA4oS4gLLYVrttNJ1ocGylONxJbmJcDuYyB1x/AmE2aN8oyGYOycP1td/+YJDZj0WpVKCnhnPlMC24BwNKw4FXrmQHJxcOSOv7qcmh0KridPrSczH4adyLkchku2rYKhBB0nbWg44x0b8XnAz87czueOHVzWuNnZ24HwNUaxQ6fQGmI3+9HY2Mjtm7dGv2MYRhs3boV+/bt493nrbfewubNm7Fjxw4UFxdj1apVeOKJJxAMR/GSsdnY2IiJiYm4bZYtW4bKykrB41LOT3RKJa5btQxKmQz99rBjLHXEmIpvCTIb4ltSR4xjHeP3Bo5mVHxr7/CRjIpvHbQ2ZlR8q9l+KKPiW+3OwxkV3xqgEeMFCXWKU4AQAlMKdcWRSPGYyLZMkfRpm8crWoE6R6WESiFHiGVhdaVaVxxJoU59Ack36iBjCHwTAYyn2CIqgoxhYA73Fu6TMIU6k3XFAKLtmNrbhzOuzFxemQ9djgo+XwDt57JfPymXy7BmYzUAoPlIF/x8fZNnkXyzEasvrAUAHPrwNBzj0j18nU9UVFTAaDRGx5NPPsm73cjICILBIIqL48U6iouLYbHwp9i3t7dj165dCAaDePfdd/Gv//qv+NnPfobHH388aZsWiwVKpRImkynp41LOX8x6PXWME7AQHWOqSj0JVaWeGapKvbCh6tMpYlKrMeRyYVyEaFaqPYd1MQrUY24PCkUoUBNCUKjXotdqx4jdjeIc8cqLRcYcnO0bwVAaTrGMYVCUq8fAqB2WUQdy9ekpTJYWG9E7OI6+wXGsXlKalq0I5aW5IIRgzOaGzeGBUZ++snUslZX52L+/DTa7ByMjThQWZq5XHcMQLF9VhsP723G6uQ91S0sgy3KWzqJlZpw+1gP7mBsnjnRj/eba7E5gBlZurMFA9wiG+8bx6bvHcMVNmyCTL/z3hP972YtpK1La7Xb8CMXo6emJs6VSSaNMCwChUAhFRUV49tlnIZPJsH79evT19eEnP/kJHnnkEcmOQ6HEEnGM32w+E3WMr121DIo0uibEQlWpE0NVqRNDValntk9VqSmpsvCfADNEJFI85knewY20ZfIGAnBPJB85I4QgPyq2Jf6taaSueDjFXsNFpnCfYZszrQhnSX64x/Bo+qrRFWYTAMAyLF1vYZVSjpIibo5dvdJHi5VKOaqquIWrtZ1fXEhK6paWQKmUw27zoLdburfJycIwDNZdVAcAaDnRC4ctvQwBqWEYgi3b10ClVsA65MCRT1tme0pZQUo1SoPBEDeEnOKCggLIZLJpqs+Dg4Mwm828+5SUlGDJkiWQxTgjy5cvh8Vigd/vT8qm2WyG3+/H+Ph40selUPgixn4qvhWFqlInJhsRY6pKndg+VaWmpAJ1ilMkN+wUixHNUshkMIQfGsVGi6NOcQqtlQqjTnFqN/18vTac+hyEzZ26mnGsU5xu+rBRr4FOq0QwxGJgWJrWTABQXc45rV29mXEi6+q4VM+OjuGM1/oqFDIsCStRnzzWk/GUbT5KK/NQUp6LUIjF0QPtWT/+TGj1amzexrVpOnusB92zkGp+PqBUKrF+/Xrs2bMn+lkoFMKePXuwefNm3n0uvvhitLa2IhRTMnL27FmUlJRAqVQmZXP9+vVQKBRx27S0tKC7u1vwuBQKAJgN8Y7x25lIpZ7njjFVpRaGqlLPbJ+qUlPmGtQpFklEcTCiQD3m9YZVoFn+MYVoz2GvZ0YV39gRdYrdU1Skk6BQzznFo043ggI1yYlUoGUME402D46HFw0Rc4+oTBfm5oAQApfXD4fHl1hlWshW5GtCUF5sAgD0Do4LmhESvRSiopyrKx62OuH0+Ca3n2E+044rMIrNRuh0KkxMBNHTO5akOrHQEFI6ntxm2aoyMDKCkWEHhgYFXh6Iti+kljx9EMJFiwkButuHMWixpayqLZU69NT/ymuKsGJ9NQBg/99PwmGfqtQ+83VO77qdH+zcuRPPPfccfvOb3+D06dO455574HK5cMcddwAAbrvtNjz00EPR7e+55x5YrVZ85zvfwdmzZ/GXv/wFTzzxBHbs2JG0TaPRiDvvvBM7d+7Ehx9+iMbGRtxxxx3YvHkzLrzwwuxeAMrchW/pZgVqjKkqNVWlTkKVmgWoKjVVpZ5RlTrISlOWQZEG6hSniCksmuXy+0WlVaVaV5yfE4kUi09BNWrVUMplCIZYjKWgYA0AxaawevR46m9SFXIZCk2ccz0wkn50t9ycCwDotYynbSuCVqNEUQF3rplIoSaEoK6WE9xqbc18VFKjVWLRYi46ffJEb8aPx4cpPwe1y7m678bPWhEKZT9iPRNrL1qMghITJvwBfPbucQRTUGqnJObmm2/GT3/6Uzz88MNYu3YtmpqasHv37qhQVnd3NwYGJiMRFRUVeO+993Do0CGsWbMG999/P77zne/gn//5n5O2CQD//u//jq985Su44YYb8MUvfhFmsxmvvfZa9k6cMq+h4luJoeJbiaGq1DPbp6rUlLkCdYpTRC2XR4UxxKRQ54Wd4lERtcgAkKebdMK9E+IWTEIICsLCVsOO1OqKi8N1xYNjjpT2jyBlXXFpkQGEENidXtidqad1T6UqwynUtbXcA3v/wDhcLukeHoRYsbocANDbbcX4WOpttdKhfmMNFEo5rMMOtM3BFkiMjMGWK1dDqZJjdNCOIx+fH/XF2ebee+9FV1cXfD4fDhw4gE2bNkW/++ijj/Diiy/Gbb9582bs378fXq8XbW1t+N73vhdXYzyTTQBQq9V4+umnYbVa4XK58Nprr9F6YoooqGOcmIXmGL9JVanjoKrUMzMXVal9Qa9k43yBqk+nQa5GgwGHA+MeD4o1ySlCR5xiq8hIsUouh16lhMPnx6jLjTKTOPXYQkMO+sccGLK7sDwFsebiXC56anW44Q8EoUpRibOkwIhjrf3oG7GBZdm02pYrFXKYC/QYGLaj2zKGVXXSqCNWlefhUFMnBoftcHv80GqkUe2MoNerYS42wjJow7nWQaytr5TU/lSMJi0qqwvQ3TmCE8d68IVLl2X0eHyotUqs2VCNxs9b0bS/DRU1BdCoFVmfRyJ0Bg02b1uNvW8dxdljPcgrMqB2ZdlsT4tCocwBqCp1YhaKKvXbfccwFFalvq6iHoVUlRrApGNMQFWphZhrqtSPnfqfUOWk/5zlc86tlpqZhEaK0yA3pq44WSJOscvvh0/km+CCcCumEaf4aF9RuCZ4KEWxrRy1EnqNCiyAwfHUo8XmPD0YQuDy+GF3pf/2qaKES6HuGRhL21YEfY4ahfk5YNnMRYsXL+EiVefOWbKSTryqoQIA0Nk+DLt9dlSgl6wuQ25+Dvy+AI7ua5uVOcxE+aJCrAn3Lz74wSkM94/P7oQoFMqcgapSJ2YhqFLHim/Nx4gxVaVObJ+qUlMSQSPFaZAbrisW05ZJJZcjR6mE0+/HqNuDUkPyvWoLcrToGB3DSAoK1EVG7m3niIMT25Ix4t+HmHP1cHh8sIw5UJlvEr0/wNUVF+dx/Yr7RmwwVapTshOhsiQXB493YWDYDv9EAEqFNL/SNZUFGB51or1rBMsXS/MmNJaqynwcUivgdvvR22tFZaU0b3OFyC/Qo6w8F329Y2g+1ouLvrA4o8fjg2EYXPDFJXjv9SNob7GgdqkZxaWmrM9jJlZtWoSxEQd6Wofw8TtNuPKWC6HVp/d7SqFQFgYRVepIxPjtSMRYLmHEePkKvHZ6SsRYorVNMGKslDBiXLcKu1qbYYuJGBsl6mEeUaV+rTM+Ypyn1khiP+IYv9V7PBoxvr6iHoUa6SLG20vqsXvgWMYixpcU1WPv0DH0eoYzEjHemNcAWIEeT39GIsYrDGsBAN2eDpywNYJA2ohxjY6L6Pa4T2ckYlwSjhj3uY/MasT44RXPwGAQl1XKh91ux3/idQlmNPehkeJEzKAmHXGKrSnWB1vd4pzbyUixeKfYoFFBpZAjxLL8bZ0SiPhGRkke58BbEtQVC6pYx9gpLTQCAPpHbMLHE2LKdkaDBoYcNUIsi94hW9Iq0zNRU1kAABgaccDp8iZ1fcQMmZyJtmdqOTuQcNuIWvPUIZbVDdyi0t46CKfLN2lHKlXkJM67oMSIuhVc/v6BT84iEGKnn5fY+Qj+Dgl8Ifgx9x9DGFx0xWqYCnLgdfux950mBILBtO1TVWoKZY4ioA7N+whAVampKnUSqtSJlKmpKjVVpY4dmUIlU0s2zheoU5wGeTG9isX0gM3XRtoriXOmCyIK1C7h1kpCEELSTqE2504qUKeT8lsWcYqH0+9XDACVpbkApE2h1mqUMBdxb9jau0cksxvLknAKdX//eFZSmguLDTCXmhAKsTh5fHaUqAFg7YWLoNIoYB9z48yxnlmbRyIUSjkuvaYBKrUC1iE7Drx/clb6PFMolLkJFd9KzEIT38pEH2OqSp3YPlWlpmQb6hSngUGtBkMIAqEQHCJu9vnhSPGoyEixQa2CQiZDiGUx7hZfj1tkCCtI21JTIM7N0UAplyEQDGEkRRVrACg06aCQy+CbCGAkxbnEEq0rtowhJKHjsqiqEADQ3pUZp1ivV6OsjJt7S4slI8eYyuq1nKjXuTMDcEmo2C0GlVqBdRfVAQBONHbCMUs1zjORY9TiC1evBWEIOlssOHmoY7anRKFQ5hDUMU4MdYwTQ1WpZ7ZPVakp2YQ6xWnAEAJTOFosJoU6LxwpHnG5RUWfCCHRaPFwKmJbRi5SPJhipJgQEk2hHrCmLrbFMAxKCrgobN+wLWU7EcwFeigVMnh9AQyPptcyKpbq8jwwDMHYuBvW8cy0Mlq2jKvDaW21YGJCuocpIcylJhSXGBEKsTh+pDvjxxOiZkkxiktNCAZCOPhRy5yNwpor8rDhEk6t+9jnreg6m52XF5mEtmigUKQjW47xfBbfurF2NUzUMeYlW45xuaaIOsYC9qljTIlAhbbSJE+jgdXjgdXjRbUpyX20GhAA3kAA7okJUQIXRTk6DNgcGHK4sMxcKGquxWGxrTGXB75AAKoUWj2U5BnQNTSO/jE76mtSF1YoLzSh2zKGnmEb1i5Or+0NwzCoKMlFW/cIuvqtKC5IX1gAAFQqBSpKc9HVa0Vr5zAuWJtc2y0xlJXlQq9Xw+Hwor1jCEuXSC/qNZWGDTXY/XYT2s5ZsGJNOUxGaQRKxEAIwaZLl+IvLx+CpW8MbWcGUJdKr7AssKS+AvYxF1qauvH5e83Q6FQoCkf45yP/9/h9UOakJ6rjd0r30EyhzHf42jVdE3aUpUBQfGsetWu6gUd8S8p2TXziW1K2a4oV33pjHrZr2lK4Fp8OH82Y+NaG3AYQAvS4M9euiQGLbk8HjtsawQIol7hdEyFsxsS3zJo1IISNim8REJRo10hmnyINNFKcJnkRsS1v8m8mFTIZTBouwjziEvdGs1DPOWbDKaQva1VKGDTcIjSUYtpySR7ncA6MpVcPXF7E1RUPWh2cQEmaVJXmAQA6+6ySRh1rq4sAAG1dIxlpnUQIwfKwM3j6zEBWIqaFxQaUV+aBZYFjjZ0ZP54QeqMWazbWAACOfN4K9yylcyfDui8uRfmiQoSCIex96yhso9JFCigUyvwnokodiRi/nYmI8Txv13RjBts1aWehXZPUEePtJfUZjRhfUlSf0XZNG3PXZrRd0wrD2mi7phMZiBjX6NZktF1TSbRdE4FKlnznGUr2oJFisUwRhY2kQotVoM7XajHm8WLE7UZlninpwxZFnGKnCyGWhUxIqlng42JjDuweHyx2B8oLjJObJ+mLFRp0kMsY+CaCGHW4UWCYEj0VEs2dYt+Yo4Feq4LD7UP/iB1V5rwpdvgnJOQzlptNkDEEDpcPY3YP8ozhN8RCKr5JnnB5qQkqpRwe7wT6B20oL4mPELJTTyxiPoGE9tR9FtUW4ejRLthsHvQPjKOsNMkopOAhZv4hrN1Yg95uK7o6RjA64kR+Ac8bb9H2ZzzsFDMslq0tR3f7EEaHHNi/twWXXb0mwWHFKjILHljU9oQlkDEEW66qx55XD2N4YBwfvHEE275+AX+rJiENPMHfOZ75ZFB9+t/W/EfabRrsdjtewJ8lmhGFMruQEDdiYQVCBonuHnwR42tXLYOCJ2IstEYIrymTqtSvnUkjYhwSOAOGFRcxTmCHn0lV6l1tJ5KIGPPbF7r+IbBRVerXu1KPGAvdvhkgqkr9dt+xaMT4+op6FAhEjIMh/l8iGcN/lIgqdWzEeFtJA0o0Rt7tZQKzFTwHwqlSfzJ8FH3hiPElhetQrs0T2EMIgfMiwPrcdWBB0OvhIsYX5K1HudbMb4YVyqQQeJlEOFXqEAh6Pe04bmtEiGVQoa3g3Vwu+McqcIUIp0rNsgS9nlNodR5BCAwqtHUCdsTdJORsEMXqehgVVVDLTGGlbdppYi5BI8Vpkq8Ni2Z5POIUqHURBWpxbzJNWg0UDINAKIRxkerVwGQK9aAttTecDEOiKtT9VntKNiKUF5kAAD3D42nZAbj+x6XFnL2ufmva9iLIZAwWVXHtmVo7hyWzG4tSKUdtHReRPnNGujericjN06EmfMyjhzuzckw+GIbB5i8tByNjMNBtRdvp7Jx/KsjlMlx6bQMMuTq4HV588MYR+L0Tsz0t0dAWDRRK5qDiW4mh4luJoarUM9uf76rUaplJUnsU6ZhVp/jjjz/GNddcg9LSUhBC8MYbb8R9z7IsHn74YZSUlECj0WDr1q04d+7cjHaffvppVFdXQ61WY9OmTTh48GCGzgAwqtUgAPzBIFwTyT8gF+gmxbbEwBCCgnC0eCiFFOpiY7itks2ZcqpuaTiFuj9Bv+JkiDjFvUPjadmJUBWOsHb1SecUA0BtTWHUrn9CuoePWJYt41Koe/vGYMuSGnP9+ioQQtDfNwZL/3hWjsmHMU+HtZu4lKjGz1rhnKNq1ACg0ijxpa+uh0angm3Uib1vNyEgQfo/hbLQWQjrfbJQxzgx1DFODFWlntn+fBffWqg8+eST2LhxI/R6PYqKinD99dejpaUl+r3VasV9992HpUuXQqPRoLKyEvfffz9stsSiu8msD1arFbfeeisMBgNMJhPuvPNOOJ3i/i5n1Sl2uVyor6/H008/zfv9j3/8Y/ziF7/AM888gwMHDkCn02Hbtm3weoVrD19++WXs3LkTjzzyCI4cOYL6+nps27YNQ0NDGTkHOcNEFahHPcnfdCORYqvbI7rncFEaTnGBXgsZQ+CdCKTU1gkASvPDTrE1vbri0gIjGEJgd/tgc6bvCFWW5oEAGB13weGSrj61IC8HJoMGwWAoYz2LDQYNysPiTadO92fkGFPRGzRYvJxLa2o81DGrCtBL11Sg0GxEYCKIzz44k5H6banIMWhw2XXroFDKMdQ3hk/+cgyhoLi/YQrlfGMhrPdioKrUiaGq1ImhqtQz26eO8dxj79692LFjB/bv34/3338fExMTuOKKK+Bycf5Kf38/+vv78dOf/hTNzc148cUXsXv3btx5550J7SazPtx66604efIk3n//fbzzzjv4+OOPcffdd4ua/6w6xVdeeSUef/xxfPWrX532HcuyeOqpp/Av//IvuO6667BmzRr89re/RX9//7Q3zLH8/Oc/x1133YU77rgDK1aswDPPPAOtVovnn38+Y+eRr42kQifv2BlUKijDPYfHRNYjF+vDKdAptFaSMUy0X7FlPLVIb6FRB4WMgW8igFFH6ouMUiGDOZ+LXHdLEC3WqBQwF3IOe0evtDf3xTVcqvHZ9sw9bK1cyalwt7UNwZultNw166qgUMgwOuJER1tm0sOTgWEILtq6HHKFDMMWG04enb12UcmQV2TApdc1QCZj0N8xgs/fa57TjjyFMtvMl/VeypeDfI6xn4pvRYmoUmcyYpxN8a03eo5heJ45xlsK12ZUfGtDbgMqtJl1jCPiW5lyjDMpvpVJfEGfZCNZdu/ejdtvvx0rV65EfX09XnzxRXR3d6OxsREAsGrVKrz66qu45pprUFtbiy996Uv44Q9/iLfffhsBgXtXMuvD6dOnsXv3bvz3f/83Nm3ahC1btuA//uM/8NJLL6G/P/lA05ytKe7o6IDFYsHWrVujnxmNRmzatAn79u3j3cfv96OxsTFuH4ZhsHXrVsF9AMDn88Fut8cNMUSdYhGRYkJINIV62CnuJl0cdmqHnS7RUWYAMJs4RzRVp1jGMFEV6r7R9OqKK4u46Gj34FhadiLUlHMtBqR0igGgrroQhBCMWJ0Yy1DP4uIiA/LzcxAMhnCmJTu1tRqNEqvqOZGKI4c7ZzUVOMegwcYvcC0Qjjd2YmQovd+tTFNUlosvfKUehCHoOmvB/veb52y/ZQplLpOt9X6mtb65fxB/aW5JaV0VgqpSJ4aqUieGqlLPbD9WlToTjnGmVakzxf1Hd+Luxv+V9rj/6E4AmHbv9Plm/juNpEXn5QmLudlsNhgMBsgFBAOTWR/27dsHk8mEDRs2RLfZunUrGIbBgQMHZr5YYeasU2yxWAAAxcXFcZ8XFxdHv5vKyMgIgsGgqH0ALgfeaDRGR0VFWMmO8AweJsW23Pz7CNgpzOHSoEdcMU5WEvsaNCqo5HKEWBYjbv5jsuAfAFASdooHbEk4xQJzKAunUPdabWAJokMslWbOKR6w2rkHhZnmn+B6AkB1eT4IAUbGXLAnSMmOnXMy81drlKgMpze3dAxNbi/hIAyDVSvLAXCCWxOBEKdCLLA9SwjvEMuyVWXQ6lRwu3w4dbJ/0o7QEAtD+AfPSdUsNaOytghsiMVne07DHwimfF6C8xc9eKcKAoLymiJ84ap6EELQcXoAB/ecCv+e8l03QUNJ32solIVIttZ7wbWeBVw+Pz5t7ULn6DjebT6LUCAUVaWOHUKLExEYYAVSqQNBATuEdyQ6bkSVOq7GeCIgvJDyjRDhHxBwjP1+cXYEjz2pSh3rGNt8PrAseAbhHUKLeohFVJU61jG2ej2CtvhGSGCwLImqUsdHjF0C8+dUqfmG0CWKqFLHOsYDHhuCLMM7xJ4DwKlSx9YY97qtgtsLDcH5gMH63HVxqdS9bguCIPyDlfGOEAv+AU6VOjaVusfdgwDL8I4Q+EeQJbwjBE6VOjaVusfdmmD+/NchABnvmC9UVFTE3T+ffPLJhNuHQiE88MADuPjii7Fq1SrebUZGRvCDH/wgYZpzMuuDxWJBUVFR3PdyuRx5eXkJ/b+p0JZMAB566CHs3Lkz+m+73T65WCbBZKSYU6AmST7AF6YotkUIQbFBh26rDUMOZzSdOlkiCtTjLi+8/gmolQpR+wNAWR7XImDAakcwFIKMSe39ilGnjrZm6hu2obpEbGuAeDQqBUqLjOgbtKG9ZxQNy8rTshfL4kXF6Oq1oq1zGBvqqyCTSf9OqbIyH3q9Gg6HF2fPWbByRZnkx5iKXC5Dw4ZqfLa3Bc3HelC7pBhaLV+LjMxDCMEFlyzBiMUGh82Dxs9aceGly2ZlLslSWVeMi7evxme7j6O1uY97M37Z8qTvAxQKJTskWut1SiW+smop3mluQZeVc4yvWrUk5bVtKmLaNaVCpMY4rXZNCRDVrikFIjXGybVrEk+kxjiddk2JiNQYR9o1vdnTlLBdk1giqdSRdk3vDRzFlaVrUaw2SWI/UmMcade0d/gILitqQKkmXxL7kVRqAOj19OGgtRGErEOZpkQy+8v1EfvtaLYfAiEhlGuqJLNfpVsbtn8K7c7DYBBCmXaJJPYzwS8afp52+0WAu0++hN+jp6cnzp5Klfhvc8eOHWhubsann34qaPfqq6/GihUr8Oijj6Y9TymYs5Fis5kTABocHIz7fHBwMPrdVAoKCiCTyUTtA3A/WIPBEDfEYFSrISMEgVAItiTSCaLz1YV7DrtcotMuI46wJYW6Yo1SAZOWEwezpNiaKd+ghUohx0QwhGFb6unEhBBUFnMRWKlSqBeVcy2U2nukFcUqM5ug1Sjh8wfQLbHCdQSGIdFo8alTfQhmScCppq4IBYV6BCaCOHqoIyvHFEKlUmDz5VyqUtvpAXSeG5xhj9mnemkJNl+xGgBw7kQvDu89Q1OpKZQkydZ6P9NaX55rxFdWLYWcYaKOsaSp1FSVOiFUlToxVJV6Zvux4lsnbI3oPY/Ft1QylWQDwLR7ZyKn+N5778U777yDDz/8EOXl04NTDocD27dvh16vx+uvvw6FQjg4l8z6YDabpwksBgIBWK3WhP7fVOasU1xTUwOz2Yw9e/ZEP7Pb7Thw4AA2b97Mu49SqcT69evj9gmFQtizZ4/gPlLAEII8DZdCPSKi73CeVgOGEPgCQdhFONMAJsWyUnCKAaAkN9JWKbW6TULIZAr1iC0lGxGqwk5x1+CYJI5EVVkeGIZgzO7BaBoO+1QYhmDxonA/4XPJp2OIpba2CFqtEm63H62t2XEICSHYsLkWANB+bghDlvR+puliLsvFqvXcG979H53BuDUzddxSsmh5KS788koAwNljPTj4wWnqGFMoSTCX1vuF4hjP6xpjqkotCFWlntk+dYxnD5Zlce+99+L111/HBx98gJqammnb2O12XHHFFVAqlXjrrbegDnfwESKZ9WHz5s0YHx+PCnoBwAcffIBQKIRNmzYlPf9ZdYqdTieamprQ1NQEgCumbmpqQnd3NwgheOCBB/D444/jrbfewokTJ3DbbbehtLQU119/fdTG5Zdfjv/8z/+M/nvnzp147rnn8Jvf/AanT5/GPffcA5fLhTvuuCOj51KgDdcHi3CKZQwTFdsacop76DeHneJxj5erHRJJaS5XV9yXolMMAOX5XAp172h6DlRJgQEKuQwe3wSGx9NfPFRKOSrCtcptErdQWlpbDEIAy7Ad1gwJbslkk7XFJ5p7sxYtLiwyoG4p90btwGfnEJLwQTAVVm+ogbksF8FACJ+814wJf2Z6REtJ3cryqGPc2tyL/X8/SVWpKRTMr/W+PNeIr6zOrmNMVaknWQiq1NNrjOeXY5wVVeoMim/FqlJnyjGej+JbmWbHjh34/e9/jz/+8Y/Q6/WwWCywWCzwhLvsRBxil8uFX//617Db7dFtgjH3wGXLluH1118HgKTWh+XLl2P79u246667cPDgQXz22We49957ccstt6C0tDTp+c+qU3z48GE0NDSgoYGrA9i5cycaGhrw8MMPAwC++93v4r777sPdd9+NjRs3wul0Yvfu3XFvFdra2jAyMun43HzzzfjpT3+Khx9+GGvXrkVTUxN27949rUBbagrCdcUjbnGOUlFYbEusU6xRKmDUhFOg7eJVpEvDkeIRuwu+FJxqACgv4JziwXEH/CnaALiXAxVFJgBA54A0acl1VVwKdWvPiKTROp1Whcoyru45k9HiJUvM0GiUcLl8aGvLXs/NdRtroFLJMT7mxpmT2emXLATDEFy8dQU0OhXs427s39syLyKvtSvKcNG21SAEaD/Vj892H8/ai41k8AZ9kgwKRQzzbb3PuGNMVakTQlWpE7MgVKnzMusYx6pSn6Cq1FnhV7/6FWw2Gy699FKUlJREx8svvwwAOHLkCA4cOIATJ06grq4ubpuenp6onZaWlqhyNZDc+vCHP/wBy5Ytw+WXX46rrroKW7ZswbPPPitq/oSdD0+ZWcZut3PKah+9B3XYaY0S4hfP6bXa8NqZ0zCqVLh9bcOM25Pw2nrSMoQPWttRbjTgq6tXRD8X2j6W90+1omVwBBsry7CpJl4YjAj8VGPt/P6To7B7fLi6YSmq83P5d5hhPn/c2wS724tt65agpjgvqePy2W/rG8EHjedg1Klx05fWCr6tEbw+U44bCIbwp7cPwz8RxJVfXIHSIuMUO/wTFZp/7HUYGLRh94cnIZcxuPna9VApeYRMEjw7CV6jKX+Kp0714dChDuh0Knz1unXThb2StDPT9piy/bkzA9j/yTnIFTJcf8N6aHWqhNuLtS/WzrDFhvffOAI2xGL9xXVYtmbq77pI+0IIRnRTm3936yA+/esxhIIsSqsK8IWv1EOu4BHV4Tmux+vG3Q/fHG1VIAWR+9p1f/tHKHTpieNMuPx484rnJZ0fhZJtIn8TP37rfWh0U9b68NLdO2bDO80tCIRCqMozCYpvsYKLFv/HkW4HFocDb4YjxaUGvbD4lqAdoYWA+58RtwuvnTkFTyCAIp1OWHxLSBdQ6HOGO+6414NdZ0/COeFHnlojLL41gx2h7R1+X1R8y6BUCYpvCekaEuEFFwDgCfij4ls6uVJQfEvIjqB9AAxh4QsGouJbKkaO6yrWolCt491e6BxkDP8DBQEQCAWj4lsKIsO2kgaUaIy828sEHqCEzoEhLIJsKCq+JSMMLilch3ItvyAqk8CO0HxYlsXhsaPo9fSBgOCCvPUo1/LXgMoE1mIZ4X+ZxBAunfe04yh6Pe0AgFWGjajQ8gvpygWuDyPwQCcjLFiWRZerCb2eUwCARTkbUKGtS3r+TocHly36XxlZ66WyKbW9ucycrSmeb0QixTafT1QaVGykWOz7CXNYRdriSO3tY1mk13AaKdQV4WhxT5p1xRVFJjAMgc3lxXiCVkrJIpcx0Z7Fbd3DaduLxVxkgMmoQSAYQmuntLZj4aLFCi5a3J69aHHdUjMKijjRrcMH2rN2XCEKzUasv4hbZI7sa8PQwPjsTihJKuuKcdm16yCTy9DfNYIPXm+E3zsx29OiUCgiWCg1xlR8i59s1xi/2dOU0Rrj9waOZlR8a+/wkYzWGB+0Nma0xrjZfkjyiHFsjXG78zCNGM9jqFMsERqFArqweppYsS0ZIfAHg7B5vaKOaTZwdcGDdidCKQT8IynU/dbUneLKQhMAoGd4PK3UVqVCjrKwg90hUQp1bVUhZ6/XioCEDxmEECyv49oInD5nyVhKr1wuw8pIbfGJ7NUWE0Kw6eLFIATo7BhBf580quDpsGR1OarquP7Fn75/Eh7X/EjfLakqwNavrYdSJcdw/zj+vuvwrM/92Q0/wu82PZXWeHbDj2b1HCiUbEId48RQxzgxC0V8a6GoUh/PQCo1Fd9aGFCnWEIKIy2WRNQVyxgGBeFo8aBDXF1xvk4LhYyBPxiEVWSvY2AyUjycRl1xaZ4BMobA4fFh3CXOqZ9KpEdxR780N1tzgR45WhUmAkF0SdxCqba6EEqFDHanFz0ZjFwuXWqGWq2A0+VDa1v2WhPlFeRg6XJOnGD/Z60IBKR7QEsFQgg2XboUxlwtPC4/Pn6vGcFZnlOyFJbm4ss3boRaq8TYiAPvvXQAtllU01bLVJIMCuV8YqE4xvO6xpiqUgtCValntk8dY8pMUKdYQgrDKdTDIh1Ucw6XBj0oMg2aYQjMei5aPJCC2FaOWgWTVg0WqadQK+QylIad666h9CKK1SV5IASw2t2wSZBCTQjB4nC0+KzEac4KhQxLajkxl5MtmROkkstlWLOaq385drwnq85pw/oqaHUqOB1eNB2RbvFIFYVCji9uXw2lSo6RQTv2fTh/egHnFhpwxc0XQG/SwuXw4m9/PojB3sz0uqZQKJmBqlInhqpSJ2YhOMYLSZX6OFWlpkyBOsUSUqgVHykGgGJ92Cl2ir85lhjDTrFNvFMMAGV54bZKadQVVxWF+wwPp+cUq5WKaAp1u0Qp1IurOae4f8gGp1vatNXli0tACDAwZMfoWOYif0sWF0OnU8HjmcCZFukWiJlQKOW4MFzLe7q5D8NDqf+OSIXBpMUXrlgFwhB0tQ6hubFztqeUNHqjFld8/QLkm43weyfwwWuNaD89uwrfFApFHFSVOjFUlToxU1WpM+EYU1XqxPapKjVFCOoUSwUBCsNp0KNuN4JsiJMGJKzAQHQUh3sODztdMfslN0pMMU5x7HdJUp7PRXl7rQJCWUnMoTLcTsky5hBMw2YFxlRbNWWcOFZ7/yj/wQTmIGRfr1OjpJA7x3NdMdFiQngHS8A7+I6Zk6NCdQXX+qn57ABYQqJDzM8weg4CQyaXYW19JQDgRHMffP5geL5CdgjvEAtLCMqq8lFTVwSWBT7/5BwCQVbw2gmfm9D2Yu1ww1yRiwu+uAQAcPxQJzpah/jPV2gIwRD+ITQRkfMnINBoVfjyjRtRubgYoRCLfe814/iBNu53bNpxKRRKNuBbogUXFRYoN01PpQ4FQiAhTBtCNoQeDcAKpFIHggJ2CO9IdNxCjQ43TK0xnggkPOdpI0T4BwQcY79fnB3BYxPoFeppNcY2nw8sC55BeIfQghtiAbVseiq11esRtCU0QgJDySh4+hi7BOYPBEMM7xC6RAyRT4sYD3hsCLIM7xA7f0COLVNqjHvdVsHthYbgfMBgfe66uFTqXrcFQRD+wcp4R4gF/4AMS/Xr4lKpe9w9CLAM7wiBfwRZwjtCkKNS2xCXSt3jbuWde0jMA7tIpGq/eD61YOTR5aekilGlglIm42p8PZ5ojfGM+6lVUMvl8AYCGHG5o5HjZCjW54AAcPj8cHh90KvF1fpFIsVjLg9cPj90KvGtWgxaNXJzNBhzetAzMo66kgLRNiJUl+Th02PtGA2nUBtzNCnbirC4qhADw3ac6xzC2mVlICk4iEKsXFqCju4RdHSPYEN9FbSa9FrdCLFoURFOnurD+Lgbzc09WL++JiPH4WPjhbUY6BuDbdyNY0e7sH5DddaOLUTdilLYx9043dSDfR+eQY5ejQIzfxuKuYZcLsMXrqpH02fncPJwB04caIfD5sGFW1dCJqfvKSmU+UCkxvid5paoYyzUrikVIo7xm81noo6xYLumFIjUGL925lTUMRZs15QCEcd419mTUcdYsF1TCkRqjCPtmna1nhBs15QKkRrjSLumVztPCLZrSoVIKnWkXdObPU24vqIeBerkn/8SEUmljrRrem/gKK4sXYtitUkS+5Ea40i7pr3DR3BZ0VqUalJ//oslkkoNAL2ePhy0NoKQdSjTlEhmf7k+Yr8dzfZDYEgIZZoqyexX6daG7Z9Cu/MwGIRQpl0iif1k+Ob+f027/SLAtWA8X6BPYBJCCImpK04+nZYQEnWELSJTqJVyWVSoK5UUarVCjkIDt79gtDgJqsLR4s6h8ZRtAOEU6kLOuWmTSHCrujwfCjkDh8sHy4i0KcCF+XoUFegRCrE4fS5zqc0MQ7BuXTUA4NTpfjid6YmaiUGlVmDTxYu5Yx/vwchwaqn6UrP2wlqUVxcgFAxh7+4TcNjTr0PPFoQQNGxZgk1bV4IwBJ1nBrDntdlXpqZQKMmzUMS3qCo1P7MhvjU8z2qM41Wpm9DvGZHMPhXfomQb6hRLTFFENEuEUwwAZn3qPYdLwynU/bbUHL7ycLS4Jy2nOBcA0D0ylvZDwaJS7k1jW780N1eFXIaacs5mS4f0/X5XLeNUms+cs2BiInNCWOVluSguNiAUYnHkqHQ37mSorC5AdW0hWBb45KOWjJ5nsjAMwUVfXo7cghx4PRP48J1j8Hrm1xvNxavKcdn166BQyjFqscFpmz+OPYVCWTiO8byuMV4AqtTF0T7G8098izrGie3PlmP8+wt/gFcv/kna4/cX/iAr850LUKdYYorDKdNiRbPM4briAYf4KFypkauZ7RtPLYJXkR92ikdtKav5FufqoVbK4ZsIwjKWXiSxujQPDEMw5vDAapdGwGp5WCm6o3cUHt+EJDYjVJTmwaBXwz8RREubRVLbsRBCsHEDd+Pu6BjGcJYjthdcVAetVgmH3YPGgx1ZPbYQCoUcl161BtocFRw2Dz569wQmUmwvNluUVOZj+y2bcNH21SgsNc32dCgUikioKnViqCp1YlQyOb4yz1WpuXZNsY4xVaWOtT8bqtRStV88n1owUqdYYorDkeIRt1vUohiJFNu9Pk6QQgSlYQXqMbcHbr94h68kVw85w8Dtn8CoM7UbPUNINFrckWZrJpVCjsqwrXN90rxxLMjNQUGuDqEQi7MSR4sZhmD1sjIAQPOZfgSC0j0MTSU/Pwd1dZyDf/Bwe1ZbEqlUClx0yVIAwNkzA+jrmRsthbQ6Fb50dT2UKjlGh+z45L1mBDP4M8gEhjwdqpaYZ3saFAolRagqdWKoKnViqCp1YhaaKjVDpNEGoEgLdYrFIiAZGVH9M6g40awgy2LEnfwNUymXIz9cj8yXQi2o3gxAo1QgT8cJUvWPz5BCzaOKK5MxKAv3Gu622uJVl0VQUxx2igetCIGdUb05keheXTjdua1vBCwm1bqTVbHms7+8lnM6WjoGEZwyv1TON5ba6kLotEp4vBNobR9KqJ4s/LPk32HqHNc2VEEul2FkxImOThEvDQTnlLyKckl5Lpat5F4AfP7JOXh8gdTVnkWrUgvP05ivw2VXr4FMzmCgZwz7PjiNIMuvxC2ZKrVEqtqE57+EfxzzmKeffhrV1dVQq9XYtGkTDh48KLjtiy++CEJI3FCr1XHb3H777dO22b59e9w21dXV07b50Y9+lJHzo8xTRKpDU1XqBIOqUlNVaqpKnVCVut60DUXqJdH5UOYO1CmWGEIIiiIp1CLriksiKdR28WmxZaZICnVqdcWTKdTjKe0PAOUFJshlDJxeP0Yd6b1BrSw2QSGXwenxY9AqTZrwovJ8KBUyOFw+9FnGJbEZQSZjsHo55yweP92LkIQRgqlotUqsXl0OAGg80olAILv1vQ0bq2E0aeHx+LH/k7NZjVYnosBsxCXbV4MJ9zA+PIfmRgFefvll7Ny5E4888giOHDmC+vp6bNu2DUNDwpkbBoMBAwMD0dHV1TVtm+3bt8dt86c//WnaNo899ljcNvfdd5+k50ahRFgoNcZUfIufbItvvdnTlNFU6vcGjma0xnjv8JGMpFJHHOOD1saM1hg32w9JHjHWK6RR6KZID3WKZ+DYsAXvtJ3BoCv5m1IkhVp8XXFYMCuFuuJyE+fUpu4Um7hjjzlSXmDlMgblBdw8OgbTS62Vy2SoKckDAJzrlSaFWi6XYXF1EQDgdAZqfxfXFEGjVsDl9qNVTAQ3BZavKIVOp4Lb7Ufzyb6MHmsqcrkMWy5dBoYh6OkaxdnTmVPdFktJZR4u2soJWpw71Y9jB7KbYj4f8AR9kgyx/PznP8ddd92FO+64AytWrMAzzzwDrVaL559/XnAfQgjMZnN0FBcXT9tGpVLFbZObmzttG71eH7eNLsl2eRRKKlDHODHUMU7MwlOllj6Vej6Lb1HmLtQpnoEu2xhax63odybvbKYqtlUSdoqHnC4ERC6gEQVqq5vrNywWk1YNvVqFEMuiz5p626KaYs6R7bCkX29aV8a9TWvvH5GsRnT5Iu6huscyDrtTWqVfuVwWVaJuOpXZaLFcLsOGcIum5pN9cDiy16IJAPIKctCwsQYAcHh/G0bnSJsmAKiqK8IFl3C9AE8e7UZzY+fsTmiOceOn38dVe7+X1rjx0+8DAOx2e9zw+fgfPP1+PxobG7F169boZwzDYOvWrdi3b5/gXJ1OJ6qqqlBRUYHrrrsOJ0+enLbNRx99hKKiIixduhT33HMPRkenP3z96Ec/Qn5+PhoaGvCTn/wEAQkf8CkUPhaKYzyva4ypKrUg1DGe2T51jM8/qFM8A0XacNTXnXwqtDkcKR71eOALJr+IGNUqaBUKhFgWgyJbM2kUChSG+xX3jtlE7QtwN4CqAhMAoGskdaGs6qJcMITA6vRgLE2ns7TQCJ1aCd9EEF2D6Yl3RTDqNSg3mwAAJ1uljxYvqzNDo1bA6fLhXMew5PZjqarKh9lsRDAYwsFD2Y+ILl9VhoqqfIRCLPbuOQWfxKre6bB4ZRnWXVQHADh+qBMnj9DFLBNUVFTAaDRGx5NPPsm73cjICILB4LRIb3FxMSwW/r/DpUuX4vnnn8ebb76J3//+9wiFQrjooovQ29sb3Wb79u347W9/iz179uDf/u3fsHfvXlx55ZUIxjgH999/P1566SV8+OGH+Pa3v40nnngC3/3udyU4ewolMVSVOjFUlToxC0+VOjOO8XxVpabMPeSzPYG5TrGOc3CH3MnfiLRKJQwqFew+HwadLlQajUntRwhBqUGP1lEr+u0OlIVbLSVLRa4Rw04XesZsWFZUKGpfAKgsMKG5dxBdI+NgWRYkkfiQACqFHGUFRvQMj6PdYsX6ujLRNiIwhGBxeQGaWvtxtmcIi0rzU7YVy8q6EvRaxnGucxjrV1ZAqZDuz0Aul2H18jIcPNqJplO9qKsuhEyWmXdPhBBsumAR3n6nCb19Y+jusaKqUpprlOzxL/riUvzljSNwOrz47OOzuGzripR+bzLB8voKBIMhHDvQjqYD7WABrFpXNdvTmnV2bXkEBoO4e8tU7HY7zHgaPT09cbZUKulaN2zevBmbN2+O/vuiiy7C8uXL8V//9V/4wQ+4vom33HJL9PvVq1djzZo1qK2txUcffYTLL78cALBz587oNmvWrIFSqcS3v/1tPPnkk5LOlzL/OTcwgs6RcVy+shaMTJr7WMQxfudES9QxvmrVEjCMNOtCRJX6zeYzUVXqa1ctg0IujYBPxDF+7fSpqGN8/bIVUEu0bkYc411nT0Yd4xuWrIROqZTEfkSVeldrM2x+L3a1nsCNdathlOhvP6JK/VrnCYz63Hi18wRuqF6NXJVWEvsRVeq3eo9jyOvAGz3HcH1FPQrUOZLYj6hS7x44hgHvGP7a34QrS9eiWG2SxH5ElXrv0DH0eobx4dBRXFbUgFKNNM8qEVVqWIEeTz8OjB7Bpvx1KNOUSGZ/hWEtAKDb04ETtkYQAGUa+iyxEKGR4kQQREWzrF4P/KEEb2CnKMpGosUWEbXIwGR7pT6HPTn15phRkRcWyxqzxSk2J6d8DJTlGSBjCJxeP6wuD+95zTgA1IZTqNstMW8ERdqJzGtxJVcD3DM4DqfXn/C4fPAJSZaajTDqNZgIBHG2c0o0V0A5mGXAO/jmsnSxGVqNEi63Hy0dQzzqxylcV77BEJhydVi5khPdOnionUufE7qmPErMiefDfy1it1Gq5fji1uVgZAS93VacPMFT3yyk3iwWQdVogUGAVeursOaCGgDAsQPtaG7sTHAdhOyLnI/gYASGwClkCI1MJckAOCGs2CHkZBYUFEAmk2FwcDDu88HBQZjNybWiUigUaGhoQGtrq+A2ixYtQkFBQcJtNm3ahEAggM7OzqSOS1n4EBZwe/344FQbzllG8PfmVoSCLFWlpqrUKatSj3rdVJWaqlInoUotxR2MIhXUKZ4BnUKJHAX3xnJYTAp1uO+wRWRdcWk46mKxOxESmQ5bYtRDxhC4/BMYc4uvMVXIZCjL5RzrruE0UqiLc0EIMOpww+ZKr9bVlKNBcZ4eLIBzPdKkIxNCsLKOexA/1Tog+jrPhFzGYM2KsBL1qb6Mq0OvWVMOvV4Nt9uPo0e7M3osPvIL9Ni4uRYAcPRwBwYttqzPIRGrN1SjflPYMT7YgROHO2Z5RucfSqUS69evx549e6KfhUIh7NmzJy4anIhgMIgTJ06gpEQ4AtDb24vR0dGE2zQ1NYFhGBQVFSV/ApQFj06lxLbVi8EQgtbBUc4xDkm3NiyUGmMqvsXP1Brj17uoKnUsVJU6+3iCfsnG+QJNn06CIp0OznE/Bt1OlOmSSzuMKFBbnA5Rqcj5Oi0UMhn8wSBGXe5onXAyyGUMSo0G9IzZ0DM2Hu1dLIbqQhO6R8fROTyGdTWppT6rlQqU5RnRO2pDm2UU62pTT6EGgKWVRRi0OtDSPYS1tSWSpOfWVRXicHM3HC4fegfGUFmal7bNWJYsKsaJ031ctLhtECuXlkpqPxa5XIZNm2rx97+fxJkz/VhUU4CCfH3GjsfH4mUlGBqwoaNtGB9/eAZfua4BGq006W9SsGp9NQghaNrfjuOHOhEKsVizsWbOpHqfD+zcuRPf+ta3sGHDBlxwwQV46qmn4HK5cMcddwAAbrvtNpSVlUXrkh977DFceOGFqKurw/j4OH7yk5+gq6sL//RP/wSAE+H6/ve/jxtuuAFmsxltbW347ne/i7q6Omzbtg0AsG/fPhw4cACXXXYZ9Ho99u3bhwcffBDf/OY3eVWqKec3NYV52L5mMXYfP4fWQe6BfeuqOjCpZLbwEHGM32mOT6WWSZVKrY9PpX4rkkotkzCVetkKvHZmSiq1PMOp1AoJU6lrV2NX24m4VGqDUppU6ohj/HpX5lKprymrx9t9x6Kp1NdV1KNQwlTqK8xr8TdLU8ZSqb9Q2IBPho+iL0Op1BtyGwAAvZ6+jKRSL9dH7LfjuK0RwNxNpb527+OQ69L/3Q64pHt5NNehkeIkKI6IbYlIhS7U6cAQAk8gAJuAIisfDCHRfsV9NvEq0BXhSG93CmJbAFBdyD0oWmxOuFNQsY5QW8Ld5Fr7029NVFOaD4WMgd3lxcBo6srYsSjkMiyt4UR/TpyVvqWQTMagfmUFAODYqV74JzKrdltW9v+z999xUlxX/jf+rs6Tc86BicwMzBAFAgQIkFCWg/xdry05rb3S7k+2v4/T4yCv5dWuH3vtdVh7vQ5aW84ICQkJEBJJZBgYJuecc+5J3f37o6aHCV09Xd09wwD1fr3uS6j61qlb1T1169Q593MCSEgIwWKB8+er3abW7SiCILBpyyr8/D0wjk5w6r2yZR/DYmTmxrF2OqJdnN9A/rlqpVzTMvLhD3+Y73//+3zzm99kzZo1FBQUcPTo0RnxrcbGRtrabv4t9vX18elPf5r09HQefPBBBgcHOX/+PBkZYskttVpNYWEhjzzyCCkpKXzyk58kLy+P999/fyaNW6/X8+c//5nt27eTmZnJd7/7XT7/+c/zy1/+cvkvgMJtgdUxViLGtlFUqe2jqFLbR1GlVljJCBblqXABg4ODopLq6WMYvL1oGOjntapS/PUGns7Mtb2TeeGb5L8VF9M2NMz9CUlkhMwVvhJs9AfAAvnNLZyvbyIhMICHMlKn+9vuPn979/AIf75ShEal4tNb1qGZ/wbaATt/u1RE1+AIO9ITyIxaWBfU3nis9scnp/jf9/IxWyx8cEsWwT62I96L2bHy/o0ayhtEsa3deasctyPx6xYsMDI6zl+OXMdisfDIzixCAr3FNVo27dj+wN5xzWYLrx25zuDQGDmZ0eRmxU7vI2FL7rHnbR4bm+T11/MZH59iTU4MOdmxC8Zk247EB7KvhYXBgVHeeqOAyQkTicmhbNmWIh2NlXrQlHtLknxgtb29oqiZq+9XAZCYGs7GHamoVCo710HmeGRfz4U/otGxUT7z/36QgYEBl0WxrFjva+6w6U5bCgq3Cuvv+PuvHcdjXu3q2q5ejhVWYbZYSA4Luhkxlpq6pYLJEtub+wc4XFTBlNlMXKD/TMTYIhWqkHnc9qEhDk2rUUf6+tyMGEvakZogbG/uMo5wsEx0iEO9vG5GjGVeB1S2j9s/ZuRAZQnDkxMEGjxuRoxl2pHqPzQ5NiO+5avTz0SMpaYrQfL62N5unE6h7hkfxUujmxMxlrIltV1lY/u4aWomYqxXaeaIb0mdg1pl+4HFVvcps2kmYqwV1HMixmqJBx854zdZzJztuk6zsQu1oJoTMbbV3952W+OxWCzk912jydiKgDAnYqyWmIzVgu2XR7YSRSwWCxVD12g0isuxsvzyiJ6OGGskro/KxgP4yNAoDyR/Zknm+vbeLrfYHBwcJDww5K6Y75VIsQNY6w73j48xNuV42ZkIHzGFtW1YXg1Xq+p06+Cg7EhWkJcnnjotU2YzrQPO1Y5NDJ0Wyup0fl2xXqshLtQfgCo3RIvT48U1wPVtvRjdVPrHy1NPUqxYC7mwwoZAlIuoVAJ52eJNsri8lZHRpU1BMRi0bNwoRkILi5rp63N8Dby78PXzZPvOdAQBaqs7KSlqXnynZSZ1dTSbrWOsaOfM0WKmJpd23beCgsLtRUJoIHuXOmK8lOWafJchYnybl2v6wG1erumR6OwlLde0LyJnSSPG20NzlrRc0/rApS3XlOG7ZqZcU9EKjBh7qHVua3cLilPsAAaNFn+9AYB2GaWZ5jjFdkRy5yvOhnp7o1WrGJ8y0T0i7yYqCAKxgf4ANPT2L1SZdmAMCdNOcXPvABMSk5yUiuFsO6siRYezqq1H0rmXEnWcP6Zgfy9C/L0wWyxUNHUt6OConfnKvtkp4lrf+pZe+gddq6tsi7joQEKDfTCZzFwvahI3SilcI3FNJU7CVt+4+GBiogMxmy2cu1AtBlFtqEY7pEotE4tKwKISiIgJZN10mvK1K/U0NUhMdJLfj+3rI4mkurVEEyAxLZxt+7JQq1W0NPTw3psFjI1PLbEqtdSQbClSy7/+CgoKTmLjZipYINFWKrWiSj1zXEWVWuLBY7opqtSKKrUjqtQKKwfl23CQcC/564qtTnGP0SjrDapKEGZUqJudWFccN12aqbGvX/a+AAFeBvw9DZgtFhq6nbMBEBsagE6jZmRsgtZe19cCp8WLqdzlDR1uWwsa4OdJbEQAAIWV7o8WC4LA+jXxAFTVddLbv7TRW0EQ2LQpCa1WTU/PMKWl7j8nR0jNiCQlXUxVev90BX29yx+1XozohGB2PpKDTq+hu2OQd16/xsiwa2rpCgoKdxbKGmP7KKrU9lFUqe2jqFIrrCQUp9hBwrxEB1dO3WFPrRZ/gxhhlptCHT2dQt3cPyBrP4DoQD8EoHfEyNCY/Ju7IAgz0eJaF0ozadQqEsNFO1Vtrt/kkqKC0WrUDI6O09It/7pIkZMmqmNXN3QzvAQpzqHBPiTEimtlrlyvX3JxJ09PPevXizfo6wUNtySNWhAE1t+TRHikP1OTJk4cL8E4uvJk/UMj/Ln/8Vw8vPQM9o3yzsFr9PW474FCQUHh9udOdIwnFMd4BtuOsftekC6X+NbciLEivmVFEd9ScBTFKXYQa6S4fXRIllMTOR0tbh2U6RT7i9HelsEh2ZOjh1ZL6HSd5Ibefln7WkkME53Z+u4+plxQEV4VJaZQ17T3uGQHRMXoVdGivZK6dpdszSY0yIeIEF8sFgs3lmBtMUBedhwqlUBrxwCNrc6/aHCU5KRQoqMCMJstnHm/4pYoQatUKrbtSsfH18DI8DgnjpcwuQLX7voHerH3iVx8/T0ZHRnnndeu0drovglZQUHh9udOc4zfVFSp57BQlbr4tnSMFVVq2yiOsYIjKE6xg4R4eqGeKbHk+I0yatopbhmSlz4c4uWJQaNh0mSiY0j+jS0hSEwJrnfSKQ719cLboGPKZKaxxzkbAJGBvvh46JmYMlHX0eu0HSuZ04JbjR19DI26b8JamxENQEV955IIYvl4G1idJq5fvnS9zuUXBIshCAL33LMKg0FLf/8o+fl1S3o8KfQGLbv2rkZv0NDTPcyZk+WY3fig5y68fAzseTyX0OnI9qm3CykvbFZKNikoKMyQEHKbi28tRyr1bS6+9eQc8a3i265c00PzIsa3o2O8lOJb6wKWVnwr3WftjPhW4UA+zYpjfFuhOMUOolGpCJ2uV9w24njUN8pHTIPuGBmRNfkIgkDMdLS40YkU6rggfwCa+waccsAEQSApbLrWcIfzNyVBEEiZjhZXtHQ5bcdKgI8nUcF+WIDS+g6X7VmJCPEjPNgXs3nposXZ6dF4eugYHhmnpKJ1SY4xGw8PHVu2iOWrysrbaG5Z+gi1LXx9Pdi5O1MUtWrq5cLZqhXpbOoNWnY+lENiajgWC+Sfq+Ly6VsTZV9KjFMTbmkKCncjiiq1fe4Ex1hRpZZGUaVe3P5KV6VWkEbjys7j4+Po9Xp3jWXlMSPjKBLu7U3byBBto0OkB4fO62vbhK9Bj7dOx/DEBO0jw8T4iY6u1BQ6W3Q2NsCPqu4emvoH2BwbI7GD7c3B3p546XSMTEzQMjBA3HTkWPrACzclhwdyo6GN+u4+Js1mNOqb71AkxXFt2E+NDiG/uoWmngGGxsbx9rj5m5GszSth3yJAZmI4Ld0DlDd2kpsWjUatlixdKOd812ZGc+R0KeX1nWSnR+E1e5wSR7BI1Ea0VaZOq1Ozfk0cpy9UcaO0maSEELw8Zx1D6hWVC89D0dGBpKdHUFbWxrnzVTzy0Bo8PByT17fYKs6H7XMTd7B9LSyCQHC4H9t2pXPqeAk1VZ3oDTrWbUiQMiSxXepHIdFfYvyS5rGg1qjYtDMN/yAvrp2vobqsjcH+Ue7duxrDvOvmNn1oW4akxu4G9pz4d9Sert23TUtcXkxhZXGnz/XzpnrA/l3Iqkp9tLBq5qXxTB1jG/1tYc+NtqpSHy6umHGMrXWMF9iRmDfsHdcaMT5UXD7jGM/UMV5gR2LukzgDgZuq1AfLS2cc45k6xo5iljgDlWXGMT5QWTLjGM/UMZZhxzY3VakP1BQxMDHGgeqimTrGtvpLIfWJVZXaWsf41foiMYJs8JS0ZQupqdiqSm2tY/x60w0ejVlDiMHLZn+T2faPSKqusVWV2lrH+EhrAXsj1hLh4WfbjsRIpcavEkRV6ve7rtNi7OJk53W2h+QS7RkosYcUEuclQF5ALhYEmo0tXOq5xobAPKI9w22bsSz8uxCReJkkiKrUZgSajbUUDuRjtqiI8Vz4HD8lWZhc4VYg69s4cuQIH//4x0lMTESr1eLp6Ymvry/bt2/nu9/9Lq2tSx/9upVEeFtLLDn+1k0QhJvriofkrSuOCRBvMB1Dw4zLfNMqCALx09HieifTn0P9vPE26Jh0MYXa19NARIB4DSrdULM4JiwAb08945NTVDe7bs9KRIgv4cE+mM0WCsuX5recEBdMWLAPUyYzVwuW5+1hXl4C/v6ejI1Ncu5C9S2L0kbHBrH53hQASouaV2QNY5hOgVoTy4792Wh1ajrbBjj2aj79igCXwl3C3T7XO8KdtsZYUaWei6JKbR9FlXpx+3ejKvVLL73E+vXr8fHxITQ0lMcee4yKioo5fX75y1+yY8cOfH19EQSB/v5+t9jdsWMHgiDMaZ/97Gdljd8hp/i1114jJSWFT3ziE2g0Gr785S9z8OBBjh07xq9+9Su2b9/Ou+++S2JiIp/97Gfp6nI9TXYlEuEtpk93G0dkKTda1xU3D8pbV+yj1+PvYcACNDlRmineuq64p88pR2h2CnWNCynUIEaLASqau1x2ylSCMLO2uLi2zW1OniAIrM0Q3+RV1HUwYnR/NEwQBDbmiRHS2sZu2jvdp6IthVqtYtu2VFQqgZaWPsrK3Xfjl0tSSji568Xzz79cR3Wl+1Lg3U1UXBB7n8jD29eD4aExjr12jaba2//e9s7OL3Nuzzddau/s/PKtPg2FJUCZ6+VxJzrGiir1TZbbMVZUqeeiiG/dfpw+fZpnn32Wixcvcvz4cSYnJ9mzZw8jIzeroIyOjrJv3z6+9rWvudUuwKc//Wna2tpm2ve+9z1Z4xcsDngUmzdv5utf/zoPPPAAKhvpO1ZaWlr4yU9+QlhYGJ///OdlDWQlMTg4iJ+fHy+dOYrBe266ya8L8hmanODJVZnE+M5KFZFI0RHM0DM6yiuFhagFgc+uX49GpQKTRP9538bpmnoKW9vJDAtlV3KiTfs2McOkycSvz+UzZTbz1Losgr29JPtLpTF39A3z6uViNGoVz2zPm0mvkrQjsX1i0sTvTuQzZTLzyMYMIgN97duR+lVO9x+fnOJP7+QzaTKzb2MasaH+ssYjZd9itvDW6RI6uodISwxjS26i/fFI/PlIfi8AFjh/pYaKmg78fD14dG8OarUKQa4tqf4SYy0rbeXylVpUKoEH9mYRHOwzMx45CFIPfVK3kvlpiRYL+ZdqKStuQRDg3h1pxCeGyLazaH8pJO3b3j5unOT9Y8V0tPYDkJYTw9qNiajlpjjLGOfo2Aif+cqTDAwM4Dtds9xVrPc1d9h0py2FlcPdOtf/4NXjeHjNnestUn/eNrbXdfVytLAKs8VCcljQ3FRqO0uBHLXf3DfA4eIKpsxm4gL956RSS2Zfyjhu+9AQh6Yd4khfn7mp1JJ2JO5nNvp3j47w6rRDHOrlNTeVWsZ1AGymPfePGTlQWcLw5ASBBo+5qdQy7Ej1H5oYn0ml9tXp56RSSy4lAwTJazR3u3E6UtwzPoqXRseT8VkE6G+mUkvZkdqumrd93DTF4ZYbdIwNoVdpeCwmh2CD9yw7tocplT49v/uU2TSTSq0V1DwQuYYwg/9NOxIPMY6O32Qxz6RSqwUV94WuJdIjSLL/Ytvnj8disXC17zrNxhYEBDYG5RLlEXGzv8TDh1qw/QJp/qOBxWKhbOg6zcZaALL98ojyiANgZGiUR1d9Yknm+raeLrfYHBwcJCIoxKkxdnV1ERoayunTp9m2bducz06dOsV9991HX18f/v7+LtvdsWMHa9as4Uc/+pEsW7NxaIHHhQsXHDIWFRXFv/3bvzk9mNuBSG9fKvq6aRkenOsU2yHQwwNPrZbRyUk6hoeJkvGjigvwo7C1nYa+fiwWC4K9O/A8tGo1MQF+1PX0UdvdR7C37fUk9gj19cLXQ8+gcZz6rj5WhQfLtgGg06hJjgimvLmTsqbOGafYWfRaDWlxYRTVtnGjulXSKZaLIAisy4zlrdMlVNR1kpUSia+3wS22Z5OXE0djSy8Dg0YKS5tZmxXr9mPMJy01nPb2fhqbejl1poKH9udg0GuX/LjzEQSBvI2JTE6YqK5s5/1TFag1KmJigxbf+RZgFeAquFRL2Y0mym800d0+wL33Z+C1BL8NBYVbhTLXO4dVlfqYA2uMncEqvnW4aPE1xs4gZ42xM1jFtw6WubDG2A6y1hg7gVV860B1sQNrjOVjjRjPXmM83zF2Basq9ew1xvMdY1ewRoyPdxTQahTXGM93jF3BGjE+23Wd5uk1xvMdY1ewRowFLDQZW7nUc22BY+yq/XSftaiw0Giso3AgH2DGMV4qdh3//1zWD4GbGiKD87Jd9Xr9oloTAwNiNmRgoNz14PaRsvuHP/yBV155hfDwcB5++GG+8Y1v4Onp+N+R03fUiYkJKioqmHJjKsztQOT0uuLWYcfTmQVBIHraEW4ckJcuG+3nh1olMDwxQe+oUda+AEkh4g+mpsu5ckiCIMw4wlVtrqWtpMeI4mS17T2MTbj+u1mdFIEgCLT1DNLZ576UoPAQX6LD/bFYLFwraXKb3dnodRo25k7L9pe10DfgvpQpKQRBYMs9q/DxMTAyMs77ZyvdmuondyybtiSTkBSCxWLh9HtlNDe6XrJrqVCpVeTek8y2favR6jR0dwzy9oGrtCj1jBXucO7WuV4uiiq1fRRVavvcCarUe8MVVWp79merUk+Yb78KDjExMfj5+c20l156yW5/s9nM888/z5YtW1i9erXbxiFl9//8n//DK6+8wsmTJ/nqV7/K73//ez760Y/Ksu1Q+vRsRkdH+ad/+if+93//F4DKykoSExP5p3/6J6KiovjKV74iawArEXvp013Do/yh7AZalYrPrdmIyhq5lVI4nJ6zijs7OVFbS4SPDx/MzLSTfrzQzqHiMhr7BtgSF0tedKRN+wvtiP8dm5ziN+fzMVssfHRDDgEeHnbHactO7/Aof75QiEoQeHp7HgatZtH0Zlt2LBYLB84V0TM0yj3pcWTHR8i3M6//qWvVVDV1kRARyP3rUm30l5eKa71uPf0jvP5uIQCP7soiOMD2G1WpVGLJ84KZc7NYLLz3fjlNrX2EBvuwf+dq25kAMtOkF+vf2zvC22/fwGQyk50dw9ociSi1ZOaXm9KbLWA2Wzh7soyGum5UKoEd92cSHR0g244sZKZPz7c/NGDk7DvF9HaJwnmZuXFkr4+fSTeVvD4yxqOkTyvcau6muf4HBxamTzub9mw3ldoN9u2lUs+x42Ratd1Uaofs2E9L7h4d4WB5KUZbqdQO2F8sHdpuKrUMO1L97aVSL9hF4hj20qrnp1LbU6V2Jq163DQ1EzHWqzR2VamdSauen0ptV5XaibTq+anU9lSpnUmrnp9KbU+V2pm0aovFQu9EJ0H6MEBMn34i5enbIn26qalpjr3FIsWf+9znOHLkCGfPniU6OnrB586mTy9m18qJEyfYtWsX1dXVJCUlOWRbdqT4q1/9Kjdu3ODUqVMYDDdTB3fv3s1f/vIXueZuO4I8PNGp1UyazXQbRxbfYZqY6R9Sx/CwbCGL+AB/ABocUGibj0GrIcpfPHZtt3N1agO9PQny9sRssbgkuCUIwky0uKyx0y0CWdnJ4kuCurZeBkbkR9KlCPL3IilWjJBfKWpcEsVmQRDYlJeIRqOis3uI8up2tx/DFoGBXmzenAxAYWETTc23LkKrUglsvS+N2PhgzGYLp46X0Ny0ciPGAD5+Hux5Io+U1VEAlFxr4L03ChgdHrvFI1NQcB93+1zvLHei+JaiSn0TRZXaPooq9eL2rQ7xUuOp0bmtAfj6+s5p9hzi5557jsOHD3Py5Em7jqtc5NjduHEjANXV1Q7bl+0Uv/766/z0pz9l69atc6JamZmZ1NTUyDW3KENDQzz//PPExcXh4eHBPffcw5UrVyT7nzp1aoEktyAItLe7x+FQCQKRXmIKdcuQ4ynUfgYDvno9ZouFFpkq1HGBYuSsdXBIdmkmgKRgawq18zeOlAjRQaxsc60EUkpkMBq1ir4RI6298hW15xPo60lMmD8AN6rdWyYkLzMGlUqgtXOAlo5+t9q24u2lZ12OuK7kamEjw8tU/zUpKZTUVHG9zPtnKxkcdN8LBbmoVCru3TnLMX63lOYVnpasVqtYf28KW+7PQKMVyza99dcrNNR03uqhKSi4heWe62HlzffOcic6xooq9U0UVWr7KKrUdy8Wi4XnnnuO1157jRMnTpCQkHDL7BYUFAAQEeH42nDZTrFV8Ws+IyMjskSgHOVTn/oUx48f5/e//z1FRUXs2bOH3bt309LSYne/ioqKObLctsbsLJHeYuS1Rca6YoAYPzGFpEnmumJ/DwP+BgNmi4WmfvklfBKCRae6Y2iEwTHnbtyrwoMRgLb+IfpdiMjqtBpSIkUHu6TBPeV4claJEbvK5i63llHy8TKQniSmzVwqbFiy9bdpyeGEBvswOWXi3JWaZasjvH59AiEhPkxOmjhxsowJN6zzdpb5jvHJd8uor1v55V7ik8N48IPrCAzxYWJ8irPvlHDuvTImxidv9dAUFFxiued6WJnzvbPcaY7xm0sUMb6t1xjfAY5x2EzE2P1rjBXH+O7j2Wef5ZVXXuGPf/wjPj4+tLe3097ejtF4029ob2+noKBgJoJbVFREQUEBvb03swR37drFT3/6U4ft1tTU8J3vfIf8/Hzq6+t54403+NjHPsa2bdvIzs52ePyyneJ169bx1ltvzfy/dXL81a9+xebNm+Was4vRaOTVV1/le9/7Htu2bSM5OZkXXniB5ORkfv7zn9vdNzQ0lPDw8Jlmr7yEXKJ9bjrFchyYuGmn2Jk06PhAfwDq+uSnQHvpdUT6idHtaiejxd4GHTFB4vjL21xzVlbHiY5mXUcvQ25wYiOCfAkP9MFstnCjxr3R4rXp0eh1GvoHjZTXLU1NXUEQ2LohGbVaRUt7P5W1yxNtVKtV7NiRjqenjoFBI2duofAW3HSM4xNF8a33T5ZTU7Vy6xhb8fHzZM/juazOi0MQoK6qg8N/u0p7i3PLFRQUVgLLOdfDyp3vXcGqSn3bim8tRyq1Ir4lyXI4xg/Nixjfjo7xUopvrQtYOvGtO5Gf//znDAwMsGPHDiIiImba7CU3v/jFL1i7di2f/vSnAdi2bRtr167ljTfemOlTU1NDd3e3w3Z1Oh3vvvsue/bsIS0tjS9+8Ys8+eSTvPnmm7LGL3vm+Nd//Ve+9rWv8bnPfY6pqSn+8z//kz179vDb3/6W7373u3LN2WVqagqTyTRnPROAh4cHZ8+etbvvmjVriIiI4P777+fcuXN2+46PjzM4ODin2SPM0xuNoMI4NUXvmONR02g/PwSgb2yMwXF5N86EADHaW9fbj9mJSOKqUFG6vsqFFOq0KPHte0Vrt1NjsBLo40lUkC8WoLjRPWluuSni2oKyhg5Gx9036el1GnIzYwC4VtLE+BJFU/18PcibLst0qaCO4ZHlWZ/q6anjvh1pokPe0se16/XLclwpVCoVW7enkpwShsUC585UUlpsP0q0ElCrVeRsSOT+x3Lx8fVgdHicd9+8wdVzVUxNuu8hUkFhuVjOuR6WZ75fbK5v6Ozj/ZI6t2brKKrU9lEcY/vcCarU+yIUVeq7BYvFYrM9/fTTM31eeOGFRfvU19fzwgsvOGw3JiaG06dP09PTw9jYGFVVVXzve9+TLTQm2yneunUrBQUFTE1NkZWVxTvvvENoaCgXLlwgLy9Prjm7+Pj4sHnzZr7zne/Q2tqKyWTilVde4cKFC7S12f5RRkRE8Itf/IJXX32VV199lZiYGHbs2MG1a9ckj/PSSy/NkRmPiRGdIATbTa1WETFdmqlpeGB6u0Wi3dzPoNUQ5i2qGDcODti2L0GEnw86tZqxqSnah4exCNNqkQ62pNBABKBzaIR+4xgWmNMcsZEQGoBeq2FkfIKmHok0bgfHkxUvRovLmjuZMJtmzmfR85IgMsSP0ABvTGYLhTVtC+wtsC+Brb6piWH4+3owPjHF9dLmeecr2Gx2jy3R0lMiCAv2YWrKzJlL1Zix2O0//zuc+S4ldpDqHxziy5YtqwAoKW2lsrpDlEmUOq4g2GzS37ftayTVX1Cr2HRvCunTQlZXL9Vy7Wr99O/Uhh2VRJNCcjzyxmmrb0iEPw98eB3JGaIAXHlRC28duEpHx4Cd6ybRFBRuIcs518PyzPdSc71gAaNxguPXqyhp7OBMcR0Ws8XmDVNqqpe6wQoWSLSVSm1yj30sEO2/MJXaPGVGMLOg2Run1HFtRoynTBJ2BJvN3nFDPLx4cv4a48kpu+e8oJkF2w0Jx3hiQp4dyWML+GgNC1Opx8exWJBogs0m9dBgUC+MGPeMjUrakWpmiaZTaW2sMR6RHL/JrLLZpC6RStAsiBi3GQcwWVQ2m9zxg4at81Kpm0d7JftLNcnxoCIvIHdOKnXzaDsmBNvNorbZzBZsNoWVg+ySTMtNTU0Nn/jEJzhz5gxqtZrc3FxSUlLIz8+nrKzMIRvbt28nNjaW3//+9zY/Hx8fZ3xW5HZwcJCYmBheen9hSSbrTfZyazPnWxtJ9g/koeQ0yRJC80s1XWxq4nJzC8mBgexflbKgu2CSeBi2wNHyKqq6e8iLjuSeeDGqKF3aaeG21wtKae4bZHNCDHmxUXP7S/wK5tt5v7yeosZ2ksMC2Zu1cPyLlTqyYrZY+NOpAoaM42xbnUBGTJjd/ottxwIN7X28c7kcjVrFR+7PxaDTSpbHsWfHFi1t/Rw7W4YgCDyxJwd/H7G0ldyySIsde3BojEPHCpiaMrN+TRyr06KkS/w4WLZqsTFZf3EFBY3cuNGIIMDu3auJDPeTHKft40p8IPcaWawfWyi+0UTB1XoAVqWGs/EeifImtpA727hYqmn+By0NPVw6VYFxRLy3pGZFs2ZjIhrt3PImtq7b6NgIn/nyE0pJJoW7iqWe76Xm+v/423E8PL2obO3mRFE1FiA9JpRtmQkL109LTdFSt6VZ2x0q1+SCfUfKNTlbqgkcLNckaUdq4rj5T4fKNTlwHeYwq8SSQ+WaHLAj1d/Rck3OlGoCFpRrejI+iwD9wnJNzpRqAhaUa3osJodgw8JylM6UaoKF5ZoeiFxDmMF/oR0nSjUBC8o13Re6lkiPIMn+jm63jmd+uaaNQblEeSwUcZJTqmlkaJQPpC5NSSZ32byb5nvZkeKdO3fy7W9/e8H2vr4+du7c6ZZBzSYpKYnTp08zPDxMU1MTly9fZnJyksTERIdtbNiwwa4kt16vXyA1vhjRvqLD0Dwkc13xdC2upoEB2SnICdMq1HW9fbL2s5LsjhTqyBAAajv7GJtwXkxIJQgza4uL6tvdkq4WG+ZPkK8nUyYzRTXuTW+JDvcnJiIAi8XCpRv1brU9G18fAxvWJgCQX9hIb5/jZb9cJScnhsTEECwWOHWqjL5lPLYtBEEga00sm7auQhCgqqKdMyfKmJq6PdKRo+KCeOip9SSliZNmRVEzb/31Cu23sAQWwOjUhFuawp3Ncs/1sPTz/WJzfUpkMDuzkhGAsqZOzrg7lfoOE99SVKnncqeIbymq1LZRxLfufGQ7xadOneKnP/0pjz32GCMjNx+aJyYmOH36tFsHNxsvLy8iIiLo6+vj2LFjPProow7vW1BQIEuS2xHCPL3QqlSMmaboklGvOMzbG71azbjJRPuwvJtNXKA/AtA7aqTfKH/NaVJwICpBoHt4lL5R5xSkQ3y9CPYRaxZXtLtWniktJkQszzRspLl7wCVbIN6wclPFdLji2jZGx9z74L4hOw6VINDc3k9D69I5NimJocREBYjlic5Ximlqy4AgCNxzzypCQ32ZnDTx7olSRkaWp0SUPValRbBtZwYqlUBjQw/HjxQxNnZ7qDvr9Fo27Uzjvoey8fTSMzxo5L03b3DhRBljo7fGsdzy1g9Zc+jfXWpb3vrhLRm7wvJxq+Z6uLXzveIY20dRpbbPneIYK6rUtlEc4zsbpyQa3333Xdrb29m0aRP19fVuHtJcjh07xtGjR6mrq+P48ePcd999pKWl8cwzzwDw1a9+lY997GMz/X/0ox9x6NAhqqurKS4u5vnnn+fEiRM8++yzbh2XWqUi2keMFjcOOu7QqQSB2Olocb1MFWqDRkO0v3jMmh75TpmHTktMgLh/RYfzDm1GtJjqXNLc4dLDgl6rIT1aFO+6Ueeem0pceAAh/t5Mmcxcr3KvQJO/jwerU8SHrQsFdUvmrFrVqD09dAwMGbl4rW5JjmMLtVrFzp3p+Pl5MDo6wfH3ShhfAeWFYhOCuX9fFjqdhq7OIY68UcBAv/seBJaayNgg9n94A6syxWULtRXtvPmnS1SWtNxSxW8FBXss51wPK2e+T4kM5r7sm47x6WL3O8a3tfiWokptlzvBMVZUqaVRVKnvXJxyiiMiIjh9+jRZWVmsX7+eU6dOuXlYNxkYGODZZ58lLS2Nj33sY2zdupVjx46h1WoBaGtro7Gxcab/xMQEX/ziF8nKymL79u3cuHGDd999l127drl9bLG+8p1igPgAfwDq++WnQScFBQJQ3e3cH3hqmFgjuLKz2+lJPiUiSIzwjo7R1j/klA0rWfHhCEBzzwDdg66n6wqCwIYMcb11eX0Hg25WcV6THo23p56R0QmulTS51fZsDHot2zdPpw3XdVFTv3w1e/V6Lbt3Z4qlmgaMnDi5MlKWwyL82PdwDt7eeoaGxnj7zQJab6OyRzq9hg3bUtj7eC4Bwd5MTExx5Uwlx16/Rk+Xa39Hcji3//MUPPpll9q5/Z9ftvEq3DqWc66HlTXfp0TddIzLm5cgYqyoUtvljnCMFVVqSRRV6qXHXUul7qblUrKFttRqNW1tbYSGihG+F198kRdffJEvf/nLvPjii5jceFO8VVgXlb901obQ1iwhrB7jKL8vKUAtqPhczgY0tmojmhcqEoxOTvKrq/kAfHJtLt46nd3+cFM8aXRigl9fEpU1P75+LX42RBxAWsxpctLEb87nM2ky8+TaTCKm6xdLCltJ/DpOFtdS1tLJqvAg7s9atehxJcWfzHC8oIqath4xbS0nedH+jth/+3wpLV0DJEcFszN31cIdpESn7OtcANDU1sc7Z8sRBHh0VzZB/l52+y/4TOrBx8bm60WNFBQ3o9GoeHRvDr7TAl/iMZZWgKu/d4QjRwuZnDQRHR3IfTvSxPqfMoWn3CbANY3ROMHp46V0dQwiCLB+UxJp02rPDuHm8Thj32w2U1Xcwo3LtUxOmBAEWJUZRc7GBHR6LUbjCJ/50uMrVnzjbhLeuFu5m+b6//irKLQ1h+mpeI74VnQo21bbEN+a1X8+iwlkOSS+5YJ9R8S3wHkBLofEt+zasS9g5ZD4lh37iwlnOSS+5YAdqWMvEN9KysJXL/Hc5oQA13zxrcfjsggyLBTfsmfHnoDVfPGtR2PWEGJY+Mxjb/z2BLjmi2/tjVhLhIdtkU9nBLjmi29tD8kl2jNQsr+c7WrBvEB8a0NgHtGe4bb723jIGB0a5UNpf78kc33iy/8vKk/D4jssgnl0jNqnv3tXzPeyI8Xzfeivf/3r/OEPf+AHP/iB2wZ1uxBo8MBLq8VkMdM24nikx1OrJcxLvKnITaH21OmI9BUdWWdSqLVqNYnB4g2h0oUU6szpmsU1Hb0uCW4B5CSIKcnVbT0MG93zJnX9dLS4uqWbXjdEoGcTExFAfHSgWEc3v9alms2LkZMZQ1iIL1NTZk6dr8Rkct+b/sUICPBi5/Ra3ubmXs6dr3JrpMRZPDx03L8/m8TkUCwWuHyhhssXam6rNGSVSkVqdgwPf2QT8avEmszVpa0Yb9E6YwWF+ShzvcicNcZLETG+w9YYL0nE+E4S36pZ2lTq1xqWVnzrUFPBkqZSH2u7vqRrjE93XVvSNcaXe/NXXMRYwXFkR4obGhqIiYkRI0azKC4uJj8/n49//ONuHeCtwNFIMcCxuirKerpYFxbF1qi4hcYkIr+XG5u52NJMUkAAD6WkLtp/9ouqGy3tnKmtJ8LXmw9mrbbdX7JEFDT09vNmYTkGjYZn7slFrVI5FeH928UiuoZGuCcljjVxEXaPu1jk941LpbT2DpIdH8E96XEuR4oB3rtSSW1rD7FhAezbkDb3QxcixQAjxglePVrA5JSJzWviyUiOsNt/zmcyIsUAIyPjHDp6g/GJKdJTItiUmzB9jCUu1TS9uamph5OnyrBYIDU1go3rJSIlyxQpvtltumTTlXoAIqL82XZfGnq9drEdl2Q8TtvHQntzHwN9I6RmRQMokWKFW87dNNfbixRbWbRckwullMCBiLGL9heLGLtSqgkWRowfnnaUF7ezeKkjcCBi7EKpJnAgYuxkpNiKI+WanC3VBI6Va3K2VBMsLNf0aEwOIfPKNTlbqgkcK9fkbKkmcKxck7OlmsCxck3LHSlu6+lyW0mmiKCQu2K+lx0pjouLWzBJAqxevfqOmCTlcnNdcb+s/azrihsHBpiS+dY2aTrS2zY4zNC4/DeOMf5+eGq1jE1N0dDbL3t/KxnTIlklTa4JbgGsSRRvHqVNHRjdJOyUlx6DIEBjRx+tblC3no2Xh451WWI0+kpRI0NuXrs851ieeu7dKKaVl1W2UV3XuWTHskVMTBBbt4g1qSsq2rhy1b2REmexlmzavisdjUZFW0s/bx0qoKfbfW+xl4vw6IAZh1hBYSWgzPVzUVSp7aOoUtvnThDfUlSppVmJqtSeGp3b2t2CQ07xE088weDg4My/7bW7jVhffwA6jSOybqChnl54abVMms00yRTq8tbfTKGuckJwS6USSA0XBbdK25wXcEqJCEanUTNgHKOxp99pOwAxwf6E+HkxZTJzo949NxJ/bw/S40Sl7AslDW535NKTwggL8mHKZOZsfu2SOooxUYHkZIpO0/mrtXT3Lq/jl5gYyj2bpx3z8jbyr9WvCMcYIC4+mH0P5eDtY2B4aIwjhwuoLG9bMeNTULhdUOZ6+yiq1PZRVKntcyc4xooqtTSKKvXtj0NOsZ+f30yakJ+fn912t+Gl1RE6nXYlJ1osCAKJAQEA1PbJV9BNCRHTPiqdVKHOCBejvA09fYyMO3fT16rVpEeGAFDY2O6UDSuCIJCXJDp9xQ3tGF1cp2wlLyUGrUZNz+AIVc3uVXAWBIF71yWhVgm0dg5QtcQK0WtXxxATGYDJZObE2XKMy1yrd9WqcDZtSgKgpLSV6wWNK8bxDAzyZv+ja4iODcRssnDxXDXn369cEarZCgq3C8pcvziKKrV9FFVq+yiq1PZRVKkVbiWy1xTfDdxcU3xk4ZpiG2t+zzU3cqWthdTAYB5ISFm0P4hrb+v7+zlUXo6XVssnc3PFhxGp9aDz7IxOTPKbS/lYgI/lrsHfY57CnAPrSl+9VkLbwBCbE2NYFxNle4dF7AyOjvGHswVYgKfuySbI07bqoeR601n2LRYLr54rontwlNzESDamxDpux84a5BtVLVwua8TLoONDO9eg0agl17k6s5a5sKKFK0WN6LRqntiTg5eH3u6aYrnrmWef88TEFG++U8jg0BjhIb7svS9jQYrjUq81rihr5dLlWgCysmJYuyZW/O3eYlVqsYuFkhtNFFytx2KBgEAvtu3KwNfPAdVu2WuBZY5Thn1xTfFjyppiBYUlxPo7/uFfFq4pXmytrqJKbf+4d70q9SxbtvZRVKkVVWoQ1xR/JO2jK3auXwp7KxnZa4qNRiOjozffOjU0NPCjH/2Id955x60Du52I9/MHoGGgX5YScbSvL1qVipHJSTpG5Ckke+q0xPiLNwhnUqgB0iPEKG9pW5fTb7p9PQ3Eh4oRb7dEi5PFaHFRQwdjE+55+5uZGIG3h56RsQlu1LS6xeZsVq+KJDjAi4lJ05KnUet0Gnbdm4ZWo6a9a5DLBQ1Ldiwp0tIiWb9OFPsqKmri2jX3p6Y7iyAIrF4Ty+4HszEYtPT1jvD269doqFu+Os8KCncCylxvH0WV2j6KKrV9FFVq+yiq1Aq3AtlO8aOPPsrvfvc7APr7+9mwYQM/+MEPePTRR/n5z3/u9gHeDoR7eaNTqRkzTdE56vhNQaNSEefvD0BNr/zySqumU6grup0rrZQcEoRWrWLAOEbrgOMlpeaTEyuKZFW2drtcnik+LIAgH08mTSYKG9xzA9GoVWyYLtF0o7qV4VH3TTwgrtHetk58i9/c3k/FEgth+ft5sm2zWHu5rLKNqtqOJT2eLTIyomYc4+KS5hUjvmUlPNKf/Y/nEhruy+SkiTPvlXHpbJWSTq2g4CDKXL84iviWfWw5xhOKYzzDnbDG+OF5a4y7bsM1xneT+JaCfWQ7xdeuXePee+8F4MCBA4SHh9PQ0MDvfvc7fvzjH7t9gLcDapVqRoW6fqBf1r7JgWK6RnVvr+zJNCk4EJUg0DtqpEtmpBlAp1GzKlQU3Cpuc96xigjwIdjHkymzmaJm1xw0MVospnIX1re5bW1xYmQQ4UE+mExmLpTUu8XmbAL8PFmXKTrel27U0z9kdPsxZhMbHcia1TGAKLzV3uledW1HyMiIYuOGRADKylo5f8G9D2yu4uml5/4Hs8nMFq9TZXkbb712je4u518AKbjGz372M+Lj4zEYDGzcuJHLly9L9n355ZcRBGFOMxjmLhM5ePAge/bsISgoCEEQKCgoWGBnbGyMZ599lqCgILy9vXnyySfp6Fj+F0m3G8pc7xiKY2wfRZXaPlbH2P8OcYwPKY7xHBTH+PZCtlM8OjqKj4+ofPzOO+/wxBNPoFKp2LRpEw0Ny5/KuVJI8BNTiOsG5EV8EwICUAsC/WNjdI/Ku1HpNRoSAsXjlnc6Fy1eHSUKblV39TrtgAqCwNr4SACKmtqZMrk2oSaEBxLs68mkycz1WvekOwuCwJasRAQB6tt6aeyQL262GKtTIogI8WXKZObU5Sq3PljYYk1mNPExQZjNFt47W0H/oPsmOkdJS4tkyz2rEASorunk9PsVmFz8/t2JSqUid0MCux/IwtNTx+CAkSNv3qCooHFFOfB3A3/5y1/4whe+wLe+9S2uXbtGTk4Oe/fupbNTOrPC19eXtra2mTZ/jhkZGWHr1q38+7//u6SNz3/+87z55pv87W9/4/Tp07S2tt616slyUOZ6x1FUqe2jqFLbx0en58k7IGIcdps7xooqtYJspzg5OZnXX3+dpqYmjh07xp49ewDo7Oy84xdg2yN+2inuGB1heMLxm6VOrSZ+OoW62okU6rQQMdJb2dUtaz2zlVAfb0J9vDBbLJS2O7/uMiksCB+DDuPkFOUulHkC8QayYZUY3StubGdkzD2TT6CvJ1nT9ZDPFde77LzPRxAEtm9IRq/T0N0/Qn5Jk1vt2zrevRuTCQnyZmJiiuOnyzC66VrJITk5jO3b01CpBBobe3jvZBmTkysrTTkiKoCHnsgjNiEYi8XC9fwG3nm7kKGhpasvvVIZnZxwS5PLf/zHf/DpT3+aZ555hoyMDH7xi1/g6enJb37zG8l9BEEgPDx8poWFhc35/O///u/55je/ye7du23uPzAwwK9//Wv+4z/+g507d5KXl8dvf/tbzp8/z8WLF2Wfw92EMtfLQ1Glto+iSm2fO0GV+uFZqtRLUcf4TlKlHpgYdJttBfch2yn+5je/yf/9v/+X+Ph4Nm7cyObNmwHxTfLatWvdPsBbimCjSeCl1RHm6Q1A/eCsKKRgsdksMNOSg6bLK/X0yJ5E4wL9MWg0jExO0jQwgEWYVn+UajZYHSU+aBa3dWC2zB2bpJ15TaUWyIkXHc7rDa2YsMyMRVIF0479mBB/wvy9MZktXK1tWfS8Zo/Z3vhz02LwMugYGh2noLpFlEWc1Ry1I9W8PPVsXSemFBdWttLc2T/nOojnINhs8/steu0AjUbN7nvT8fE2MDwyzrtnypicMtsenNS1kzqujHHGxgWze1cmGo2KtrZ+3nm3mLHxqel9pI4r2G4q201qPNJt7vH0Hlq27Urnnu2paLVqOjsGefO1a1RUtIvC3FJ2VBJN8ncgc5xS9peIDa/+lIw//4dLbcOrPwVEVcrZbXzc9kPcxMQE+fn5c5xXlUrF7t27uXDhguRYh4eHiYuLIyYmhkcffZSSkhJZ55qfn8/k5OSc46alpREbG2v3uAp32Vxv46YvMXVLTjaCGVIj5qVSF9dhMVvcY98CibZSqU3usY8Fov0XplKbp8wIZhY0e+OUOq7NiPGUScKOYLPZO26IhxdPzl9jPDll95wXNLNguyHhGE9MyLcl0d9Ha1i4xnh8HIsFiSbYbFKTukG9MGLcMzYqaUeqmSWaTqW1scZ4RHL8JrPKZpO6nCpBsyBi3GYcwGRR2Wxyxw8ats5LpW4e7ZXsL9Ukx4OKvIBcNgTmkeqTOuu4CisF2U7xBz7wARobG7l69SpHjx6d2b5r1y5++MMfunVwtxuJ/gEA1Pb3ydpvTgq1Ud6bO7VKxapg0al2NoV6VWgQOrWawbFxGvsGnLIBkB4Vil6rYdA4Tl2n/Kj3bARBYEOKGC0ub+pkcNQ9ET2tRs3mrHgACqpb6R92/9rf+Kgg0hLFFw1nrlQzNr609YQNBi33b09Hr9fQ3TvCqQuVtyQ1OCLCnz33Z4mR8u5hjhy5seIisYIgkLQqjP1PiCJcU5MmLp2t4r2jxYwMu1eA7W4gJiZmTu3al156yWa/7u5uTCbTgkhvWFgY7e22VetTU1P5zW9+w6FDh3jllVcwm83cc889NDc3Ozy+9vZ2dDod/tPZOI4cV0FEmeudQ1Glto+iSm0fRZXaPneCKnWUR6Tt8m1uxl1ZYc5kht2u2CjqtjjWVLbZbNiwwS0Dup1J8AvgQmsTjUP9TJnNaGzU/LOFTq0mzt+f2r4+qnt7CfG0XetNitTQYIraO6jt6WXCZEJnqxagHbRqNWnhIRS2tFPc2kFcoL+s/WfsaNRkRYdxta6F6/WtJIYGuvSHHxXkR3SQH809A1ypamZXTrLTtmYTHxFITKg/TZ39nCus48HN6W6/QW3MjqOta5CBISPvX61h9z2pS3oT9PPxYPe96Rw9WUJTax8X8mu5Z13istx4ZxMS4sMDD2Rz/N0SBofGePvIDXbtTCc4yGdZx7EYPj4e3P9gDuUlLRRcraetpY83DuazbmMiySlhy37dlpPLTz7nljrFEf/wbZqamubY0kvU2XSGzZs3z0QnAe655x7S09P57//+b77zne+47TgK0ihzvXOkRIrLmk4UVVPWJK6Z35YpUcfYCayO8dHCKqo7xAd2yTrGTmB1jA8XV8w4xlJ1jJ3B6hgfKi6fcYwfnnaU3YHVMT5YXjrjGEvWMXYCq2N8oLJkxjGWrGPsBFbH2FrH+EB1ER9IzsJX5577q9UxttYxfrW+iCfjswjQ265jLBerY2ytY/x60w0ei8kh2ODtFvtWx9hax/hIawEPRK4hzODvFvtWx9hax/hk53XuC11LpEeQW+wvFxsO/BSVh2HxjotgNq6s4MZS4p47nAIgpu54a3VMmc00DfXL2nfVdAp1hRMp1OE+3vgZDEyazdQ4WbN4dYQYwanv6WNwzPk/gNWx4ahVAp2DIzT3ur5mYmOqGC2uau2ms989bxtF0a0E1CqBlu4Bqpqdi7DbQ6NRc9/GVeI627Y+iquWXlQhNNiH7dOlmipqO7hWvLRrmqXw8/PkwQeyCQjwYmxskqPHimlodN+bVnehUglkZEWz//FcgkN8mJw0ceFsFcePFDE4uLTq4bcST63OLQ1EIazZTcopDg4ORq1WL1B97ujoWOB0SaHValm7di3V1dUOn2t4eDgTExP09/c7fVwFBWdQVKnto6hS2+dOU6V+fQnWGN/OqtQKKxPFKXYjgiCQ5D9dYqlPvgq1RqViYHyMDpnllQRBICMsBICSdudq5AZ6eRAT4IcFKGpxvlyJp05LxvQa5fw6x9McpQjx8555636+vMFtDxW+Xgbyph3uC8X1GJcgxTnI34uNOfEAXClqoK3L+dR0R4mLDuKePHFN843SZoor3KPeLRdPTz379mYRFRmAyWTm1OlyikuaV1QtYyt+/p7sfXgN6zYkoFaraG8b4I2D+RReb1xRStq3Mzqdjry8PN57772ZbWazmffee29ONNgeJpOJoqIiIiIiHD5uXl4eWq12znErKipobGx0+LgKCs6iqFLbR1Glts+dokp9u9cxXirxraXm8geeo/QjX3C5Xf7Ac7f6VJYNxSl2M8kBYsS3pr9Xlhq0Tq0mMUBck1zZIz9ymR4WggC0DQ3TM+LcTS07SoyclLZ3ujQxrY2PQCUItPYN0drnerR4Q0oMGpWK9r4hajtcW6s8m+ykCIJ8PRmfnOJcUZ3b7M4mPTGMpNhgLBY4cbGKEePSr1tNSw4nL0usmXy5oJ7ymluzdlKn07BzZwapKeLvKv9aAxcuVq9IR9MaNX7kiVwiIv0xmywUXGvgzdeu0d7af6uHd0fwhS98gf/5n//hf//3fykrK+Nzn/scIyMjPPPMMwB87GMf46tf/epM/3/5l3/hnXfeoba2lmvXrvHRj36UhoYGPvWpT8306e3tpaCggNLSUkB0eAsKCmbWC/v5+fHJT36SL3zhC5w8eZL8/HyeeeYZNm/ezKZNm5bx7BVuB1q7B7hS3uRWx1VRpbaPokptnztBlfoRRZX6luCurDBrZtjdgOIU28GWsOxi6sNRPr4YNBrGTFO0DNtxCG3smzItmFXZ24MZi4MKwWLz0uuIDxKd6tLOTieUhiE+yB8/g57xKRMVHd12z1NKHRABvD30pEWJkeurtc327SxyXgjg7aknZ7qU0oWKRibNZrv9HWkWAQS1im1rkxAEqG3toaatx45KsHPjF1QCW/MSCfTzZGx8khMXK5myLBy/XbVnFTabvTFlZUSxOk2sHX3+ai1V9Z1uPDeJcdroK6gFNm5KYsMGsUZ0VXUn77xbIkbm5X5nkqrUUvvIPF+VgLe/J7sezGLrzjQ8PMS6xu8cKeLs6QpGxybnqGTLtS9blfoO48Mf/jDf//73+eY3v8maNWsoKCjg6NGjM+JbjY2NtLXdXGbQ19fHpz/9adLT03nwwQcZHBzk/PnzZGRkzPR54403WLt2Lfv37wfgqaeeYu3atfziF7+Y6fPDH/6Qhx56iCeffJJt27YRHh7OwYMHl+msFW4HBAsYjRMcvVzB9aoWLpU2uk81WlGlXvS4d6Qq9aSiSq2oUjumSq2wchAsDryyfOONNxw2+Mgjj7g0oJXA4OAgfn5+/Nu5Ixi854peWcwSD6uzth+vr6aku5OckHDui0lctL8Vk9nMr67mM24y8URaOjF+fnb7w/SkMk1dbx+HSyowaDR8Yn2ubaEviRe8wvT2G81tvF/dgL+Hgb/bkIMKqfOVsDM9niHjOH84W4DZYuGx9RlE+UkI+0j8+oR59ienTPzpdAGj45NsTIlhbVKU3f7zx2PvuFfLGrle2YJBp+GDO9bgodfasCNhSGr887YPDhs59G4RE5MmMpLC2bw2QcZYbX8gdc7WMVksFi5dq6Osqh1BgB2bU0iICXbYvqPntug4p//b0tLH6dPlTE6a8PLSc9+ONIICZQhvSI5H3vglz3ceE+NTFFytp6JUTEHX6TSsXR9PcmoEKpVg57gyx2MDo3GEz3zxEQYGBtxWE9Z6X3OHTXfaUlg53K1z/Y/+dBwPTy9K6ts5W1wPiJlEG9NjF4pjSUyJkpVVZm2vbO3mRFE1FiA9JtS2+JYL9uu6ejlaWIXZYiE5LMi2+JYL9pv7BjhcXMGU2UxcoL9N8S3JZ3wHjts+NMSh4nImTCYifX14ZHUa2vniW5J2pCaIm//sHh3hYHkpxqkpQr28bItvOXAd5qC6edz+MSMHKksYnpwg0OAhLb7lgC1b/Yem1agHJsbw1eklxbek3qkKktdI3G6cVqPuGR/FS6OTFN+SsiO1XTW9fdw0NSO+pVdpJMW3pMavVtl+6LF2nzKbZsS3tIJaUnxLLfHwtNj4TRbzjPiWWlBJim+pFrEzm9GhUT6W8ZEVO9cvhb2VjENOscpBxUFBEDC5Me3lVjHfKTZbLFgsFtQqlUNOcV1/H4eqy/DSavnU6nW2FScl7JyorqW4q5PMkBB2JyYt2n/235jZYuHly9cZmZhgX0oyKSE2HKBFnOKJKRMvX7jGhMnE/tWpJE5Hnx22M2s8p0prKW3uJCrAl8fyMmzv4KBTDFDR3MXJwhq0ajUf2Z6Dp15nt//88Ugd12Qy89rpQvqGjCREBLI7L2XBd+aqUwzQ2NrH8XPlAGxbn8yquBAHx+qcUyzuauHc5Rqq6joRBIFdW1KJjQp0yL67nWKAgYFRTpwoY3DQiFqt4p7NySQmhNjcz/HxLI1TbKW7c4hLZ6vo7RFTvgKDvFm3KZHwcD/bOyhOscJtyt0611udYmBxx9gFpxIccIxdtL+oY+yi/cUcY1ecYljoGC9QpXbBKQYHHGMXnGJw0DF20ikGxxxjZ51icMwxdtYpBsccY2edYnDMMXbWKQbHHGPFKb59cWgGNJvNDrU7YZKcz/n2Rv675DKVA46v843x9UOnVjMyOUnryJCs46VOp1BX9fYyJXPdjkoQyAgXHYyidufEsnQaNZmRoQBcb3JNpCkvIQqVINDSN0hT74BLtkBcmxXi58WkycTFikaX7VlRq1Vsz01GEATq2nqpbnG/GjVAbGQAa9LFCPe5/Bo6e+T9NpxBEATuWZ9EQmwwFouFE+craGxx37psufj5ebJ/fw5RUaIA1/tnK7l8pXZFrjO2EhziwwOPrmX95iS0OjW9PcO881Yhp94rZegOVqlWuPu4m+d6K5nx4WxdHQ9AYU0bl8oa3bvGWFGltouiSm0fRZXaPooqtYIruJTMPuZC6Z7bBYvFwphpipoBxx0JjUo1o0Jd2SfPwYry8cVHp2PCZKJWpoI1wOrwUASgZXDIacGtnOhpoayBIdoGnHfcfDz0ZMaI6wUvVrv+YCEIAlsz4gGobOmmvc99TmWIvze5qdEAnC2qY2h0aQSxcjNjiI0IwGS28O6FCkaW6DizUakEtm1aRUJMEGaz6Bg3NN+6m7hVgCtrtXi9y8rbOHa8mOGRpb8WzqJSCaRlRvHoB9eTkhaBIEBjfQ+HXs3n6uVaJsbd91CloLDSuBvm+tlkxoezNSsemHaMS93vGCuq1NIoqtT2UVSp7aOoUis4i2yn2GQy8Z3vfIeoqCi8vb2pra0F4Bvf+Aa//vWv3T7AW02Sn+jc1g/1yYrcpkyrUFf19chSoRYEgbRgMdpb2iU/Yumt15MYJI650MlosbdeR1qYmHqd74ZosUatonNwhNquPpdsAYT5+5AWLV6f90vqZF3bxVizKoqwAG8mp0ycul7lVttWBEFg+4ZVBPh6YByb5Pj5ClFQZIlRqQS2b0ohITZ4xjGuaeha8uPaG0/u2jju25GGVqumq2uINw8X0NR866LYjuDhoWPj1lU89EQekVEBmM0WSotaeO1vVygtblnREW8FBTncbXP9fDJmO8a1SxAxVlSp7aKoUttHUaW2j6JKreAMsp3i7373u7z88st873vfQ6e7uU5i9erV/OpXv3Lr4G45AoR5euOl1TFpNtM0MmBHWdYyp8X6+aFXqxmdmqRlZNDOfgtb+vRa4MaBfvEmasP+TenGhWRFiNHZ8q4uxk0mp1Sa18aJysV1PX10j4zaVJl2pHkatOTEicrRl2qaMGGZOx47195mAzamxKLTqOkZGqWkocNuf0kFZRuoVAI7clehVato6x2isKZVcv/Frqe9fXRaNfdvScOg19DTP8KpS6IDLlvtWaYqtaAWI8ZJ8SFYLHD6YhUVtR2S9lFhs7lDldoiACqB2LhgHn54LUFB3kxMTHHiZBlX8+vFFxIOq2QLNpvbVKBt2PAP9GLXg1ns3LcaP39PxsenuHqpltcPXKW2pmv6b2T+9ZRodn7rCgq3irtqrrchaStYIDNuXiq1oko9pymq1EgrSZsFRZVaUaV2qCmsHGQ7xb/73e/45S9/yd/93d+hniV+kJOTQ3l5uVsHtxIQBIEkXzHyWjPg+FsgtUpFcsB0CnWvvIhvgIcH4d7eWIDybvnR4mh/X/w9DEyazFR0ORcNDPD0IDFEHL+ra4vXxEeg12roGzFS2eZ6dNJDr2VDSgwAlyubGBlz39tXXy8Dm7MSALha3kT3wIjbbM/Gx8vA7nvSUKsEGtv6uHyjfkmOMx+VSuDejcmkJosvTs5dqaGk0rXv11V8fAw88EA26enii5iS0haOHi1ieHjlp2xGxQTy0JN5bLp3FR6eOkaGxzl7uoLDr1+ntbnvVg/PJqOTE25pCnc2d9tcL8WcNcZLETGevcZ4KSLGd9ga4yWJGM93jG+3iPFsx7hmaVOpX2tY2lTqQ00FS7rG+Fjb9SVdY3y669pdHTF+6aWXWL9+PT4+PoSGhvLYY49RUVExp8/Y2BjPPvssQUFBeHt78+STT9LRYT+z9emnn0YQhDlt3759c/r09vbyd3/3d/j6+uLv788nP/lJhofl/ZZkO8UtLS0kJycv2G42m5mcnJRr7rYg2c/qFPfKmqxSAsWIb3W/vBRqgMwQawp1l+wJUhAEsqejxYVtHU5PsHmxopNS2dnD0JjzN1m9VkNuvGjrck0zU25IMc2IDSN0WnTrXFm9y/ZmkxITQnx4IGaLhRP5lUuW3hwW5MO29eLfUkl1OyXVbYvs4R4EQWBzXuJMHeNL1+spKGly64OYXNRqFRs2JLLDmk7dLaZTNzQsjeiZO1GpBFalRfDYh9ezZn08Wq2avt4R3j1WzDtvF9LRPnCrhziHDa/8nIzf/qdLbcMrP7/Vp6GwxNyNc70UiviWfW6FYzyhOMYzLHCM74A1xor4luu46wW4nJfgp0+f5tlnn+XixYscP36cyclJ9uzZw8jIzQDT5z//ed58803+9re/cfr0aVpbW3niiScWtb1v3z7a2tpm2p/+9Kc5n//d3/0dJSUlHD9+nMOHD3PmzBk+85nPOH7BAM3iXeaSkZHB+++/T1xc3JztBw4cYO3atXLN3RZEe/uhV4mp0G2jQ0R6OSZJHuPrh4dGg3FqisbBfuL9Ahw+5qqgIE43NNBrNNI2PEykl4+sMaeFhnChvoneUSPNA4PE+PvJ2h8gzNebaH9fmvsHudbUyvZVCbJtWMmKCaewsZ3hsQmKmtpZO+0kO4tKENi2OpFXzxdR295LXUcvCWGBi+/oAIIgcO+aRLpODTMwPMb7N2rYuTbZdmktF0mMCWZoZJyrxY1cLKjH06AjIXph3Tt3IwgC63Li0GjUFBQ3ca24CePYJJtybdTNXEbi4oIJDPDm9JlyenqGOXW6nMTEEDZuSEKnVS9u4Bai0ajJWhNLSko4RQWNVJS10d42QPtbhYRH+JGzNo6wCPl/hwoKt4K7ca63R2Z8OABni+sprBFfYNqsY+wkKZHiS/QTRdWUNXUC2K5j7CSiY5zC0cJKqjvEB3abdYydxOoYHy6umHGMbdUxdharY3youHxGldpmHWMnsTrGr5aXzjjGNusYO4nVMT5QWTLjGEvWMXYCq2P8ak0R/RNjHKgukqxj7AxWx9harunV+iLJOsbOYHWMreWaXm+6IVnH2BmsjrG1XNOR1gLJOsbOYHWMreWaTnZel6xjvFxseOXnqDwMLtsxG8WsvcHBwTnb9Xo9ev3c39fRo0fn/P/LL79MaGgo+fn5bNu2jYGBAX7961/zxz/+kZ07dwLw29/+lvT0dC5evMimTZskx6HX6wkPD7f5WVlZGUePHuXKlSusW7cOgJ/85Cc8+OCDfP/73ycy0jGfQ/Zf+ze/+U0+/vGP09LSgtls5uDBg1RUVPC73/2Ow4cPyzV3W6BWqUj0C6Ssr4uqgW6HnWKVIJASEMyNrnbKe7tlOcV6jYaUoCBKu7oo6uggMlGeU6zXaEgLC6GorYMbre1OOcUA6+KiaO4fpKStk7zYKLz1zt3ANWoVG5OjOVFSS35dC2mRIXjotE7ZshLs68WahEiu17ZyprSOyEBf9Fr3TGAGnZadeas4fL6UmpYewgN9Zh6K3E12aiTDo+OU13Zw6nIVBp2GiNCld54EQWDt6hgMWg0Xr9dRVt2OcWySbZtWoVG750HGGXx8DDywL5sbhY0UFzdTW9tFe/sAWzavIjLS/5aNy1EMBi3rNyWRnhlF0Y0maqo6ROe4rZCwCD9y1sYSHuF/y8Z3+aOfc0ud4ojnv+mmESmsRO7GuX4xMuPDQYCzRdOOsQU2ZrjXMbYIcLJQdIwtFti+2p2OcQB7s1dxrLBq6RzjrFQOFy2PY/zGUjjG6RkcLLt9HeMnk7Jm6hjfCY7xozE5hNxmjvHZrus0rxDH2J3ExMTM+f9vfetbvPDCC3b3GRgQM+UCA8WgVX5+PpOTk+zevXumT1paGrGxsVy4cMGuU3zq1ClCQ0MJCAhg586dvPjiiwQFidf2woUL+Pv7zzjEALt370alUnHp0iUef/xxh85RsDiRo/P+++/zL//yL9y4cYPh4WFyc3P55je/yZ49e+SaWpFYC1X/2/kjGLy9AHE98Rt15XhrdXwybf3CSUoiU6htcJi/VBShVan4TPb6mzdvs+1JaHZN8fahIf5SUoJaEPjU2jybN2ZBwg4W6DMaeSX/BgB/n5eDv4cHEjXLJbdbTBZeu1FK68AQ2VHhbEuOF/tL/GqktmMGs8XCgUtFdA+NsjomjG1pCdLjsWNnNlMmMwfOFdI/MkZaVAj3ZSXNOwEJ+1KZXfP6F9W0crGkAZUg8MiWTEID5hWZlxqnnb8qW8c2WyycvFhJfUsvWo2a/TsyCfL3WuQYtj9w9Nxm+lugtrGbM5eqMJsthAb7sHurKARmE7nfmcSBHbl2nV2DnDtbyeCQ+KYyNTWcvLViivIi5hGkbm1y73hyly/MS0scHhqjuLCJ6sqOmZTFsHBfVufEEhnlb1NTy2gc4TNfeISBgQGXHVgr1vuaO2y605bCyuVumet/9MfjeHh6zf1QamoVoLS+nbNF9QBkJ0WIEWMJx1JSR0fKvkosOXiysBoLkB4TKkaM3WVfgLrOXo4VigKPyWFBomOsdo99BGjuG+BwUQVTZjNxgf48uDoFlUbCMbYzTqn+7YNDHJpOoY709REdY41tx9gi+bAifdzu0REOlokp1KFeXqJjLPXC3d77BJXtY/ePGTlQWcLw5ASBBo+bjrGULQk7Uv2HJsc4UF3MwMQYvjr9jGMs9W5FkPlAN2aa4GC96Bh7aXQzjrGUHantKontE+ZJ3mgupHNsCL1KMxMxlhq/WmX7oUTqcpotUxxtu0HbWB9aQT3jGKslHp7kjh+mON15g2ZjF2pBNeMY2+o/OjTK05lPLclc39bd5Rabg4ODRASH0NTUNMeerUjxbMxmM4888gj9/f2cPXsWgD/+8Y8888wzjI/PTe/fsGED9913H//+7/9u09af//xnPD09SUhIoKamhq997Wt4e3tz4cIF1Go1//qv/8r//u//Lli/HBoayre//W0+97nPOXSuTr2+u/feezl+/DidnZ2Mjo5y9uzZO2aSnMMsKcQ4X3+0KhXDkxO0G4dsSCXaJtzLGz+9gUmzeW6tYwnJxdnif6E+3gR7emKyWCjr6bKjpmujIQp2xQf4A1DQ2j59XHlNUAmsjxPryZa0dTAyMWG3v5QKIIK49nJLqpiKV9LUQc/wqLQdiTa/n0ajYvu0I1ze0kVjT79L6tDz+61OiiA+Qlxf/G5+pahM6YiyssxjCyqxVFN4sA+TUyaOvV/GwJARsDdWwWaTq0ptESAhLpg92zPQadV0dg9x+L0iBobHbas6L7Eq9ewWEurL/ofXkpoqKphXVLRz+K0COruHZtmSOC+VIKu5S616vl0vPw823pvCYx9eT0p6BCqVQEf7IO8dK+atNwqob+jBLAhYVKo5TUHhVnO3zPVy1ZIVVWpp+4oq9aymqFIrqtQOqFIvFZ5andsagK+v75xmzyEGePbZZykuLubPf/6zy+fy1FNP8cgjj5CVlcVjjz3G4cOHuXLlCqdOnXLZ9myUJy8H0UynUANUDTgu/iMIAmnTglvlPfKUlwVBYHVYKADFHZ1OCW+sjRKdibKOLoxOiqNEB/gS7uuNyWzhWqNrSsVRgX4khgZiAc5V1LtFTCQi0IfVcWJq8+niWrcKYwmCwPa1Sfh66hk2TnDyWvWSCVJp1Cp235NGoJ8nxvFJjrxfysio+8QyFiMizI/9u7Pw9tQzODTG4XcLRefzFqPVqtm4KYn7d2fi6aljcGiMI0cKuXK1lqllqPHsLry8DWzcuorHPryB9NVRqDUqeruHOfNeGW8cuEpNZTtmN4rTKCgoLB2KKrV9FFVq+yiq1PZRVKlvb5577jkOHz7MyZMniY6OntkeHh7OxMQE/f39c/p3dHRIrhe2RWJiIsHBwVRXV8/Y7ezsnNNnamqK3t5eWXYdcooDAgIIDAx0qN3JrPIXc9er+ntkTU5pgaKSdMNgv+ybXmpwMBqVit4xI61D8h2UKD9fQry8mDKbKWqzL3kuhSAIbIgXf9TFrR0Mu6BEDbA5JRa1SqC5d5C6rj6XbFnZmBKDj4fouF6qbHSLTSs6rYbd61NRqwSaOvu5XtXiVvuz0es07Ls3HV9vAyOjExx5vwyjG0tOLYa/nyf7788iKMCL8fEpjp4sob5pZShAR0YG8MjDuSQliS+KSktbeePN67S19d/agcnEy1vPus1JPPGRjWStjUWn0zA4YOTC+5WMDC/fSxAFhfkoc708FFVq+yiq1PZRVKnto6hS335YLBaee+45XnvtNU6cOEFCQsKcz/Py8tBqtbz33nsz2yoqKmhsbGTz5s0OH6e5uZmenh4iIsTA3+bNm+nv7yc/P3+mz4kTJzCbzWzcuNFhuw45xT/60Y/44Q9/yA9/+EO+/vWvA7B3715eeOEFXnjhBfbu3QvAN77xDYcPfDsSP51CPTQ5Tvuo43+YAQYPwjzFusMVMmsW6zUaUoJFZ7ywU75TKwgCudHij+ZGa7vTb1JjAvyI9PPBZLFwucE1p9DP00BOnDims5X1bnm7q9Wo2bZa/OMrbuiguXvAZZuzCfbzYkt2IiDWL27scI8zbwsPg44HtmXg5aFjYMjIkTOlGMeXrwSKp4eOB3auJjoyAJPJzMlzlVwvcu/DnrPo9Rq2bklh164MPD11DA2N8c67JZw9V8nY2O1VJsZg0LJmXTyPP7WB3PUJpGZG4ePrcauHpXAXo8z18rkzHOOUO8YxfnOJIsaG29wx9lccY5sojrF7efbZZ3nllVf44x//iI+PD+3t7bS3t2M0issB/fz8+OQnP8kXvvAFTp48SX5+Ps888wybN2+eI7KVlpbGa6+9BsDw8DD/z//z/3Dx4kXq6+t57733ePTRR0lOTp6Zk9LT09m3bx+f/vSnuXz5MufOneO5557jqaeeclh5GpAvtPXkk09y33338dxzz83Z/tOf/pR3332X119/XY65FcmM0NaFt2eEtqy8XVdJRX83a4Ij2BGVePMDk8S6gGkhrMKudk401hJo8ODvM9YgSEkA2BDO6hwZ4c+FRagEgWfWrMVbd1OpUFJoa9acY7ZY+H1+AYNj42xLiGNNZMSC7pLiTLO2tw0M8er1EgTg/6zPIdDTxgO8gyJMk1Mm/nT+BsNjE+TFR7IpOXZuB4lf5WIiUmeKaylt6sRLr+ODW7Pw0EooXMsUi7Juf/9GLWUNHWg1ah7duppAH9tOjJ1l5g4fe2DIyNunSxgdmyTQz5MHtmVg0Gsl+8/gJgEus9nC1Rv1lFSI5UdiIgPYtmkVeqnSSFLnJXFYSQGrRb4DKxMTU1y/3kD59Pj0eg15ufEkJ4XaV2uVtC9vPNL2Ze5go7vROMI/PP+QIrSlcMu4m+b6/3xlodCWRSpkIHFrKWmYJb6VGHFTlVqiv1wBq4q2m+JbadGhN1Wp3WS/tsuG+JbKffab+xeKb6lVKtnXWeq47UM2xLfUajt2pCYC25u7jDbEtzQaOxOctC1bwlmS4lsy7Uj1H5pOoZ4vvgW4RYDLOJ1CPV98y54dOQJW46apGVVqvUozR5XaHQJcU2bTjCr1bPEtwC0CXCaLeUaVerb41ujQKJ9Y/eEVO9fLtSf17PXb3/6Wp59+GoCxsTG++MUv8qc//Ynx8XH27t3Lf/3Xf81JcxYEYWYfo9HIY489xvXr1+nv7ycyMpI9e/bwne98h7CwsJl9ent7ee6553jzzTdRqVQ8+eST/PjHP8bb23H1ctlrio8dO8a+ffsWbN+3bx/vvvuuXHO3Han+Yip0VX83ZhkPv6mBwWgEMQ26bUTeW65QLy8ivH0wWywUOxEtVgkCedHim5JrLW1Ov6WN8PMhISgAC3ChzrUUZa1Gzda0eACuN7TRN2J0yZ6VzWlx+HkZGBmfEMVJ3BzdvCcrnoggX1EM63L5kkZw/Xw8eGB7Jh56Lb0Doxx5v5TxieWLhqpUAhvWJrB1Y7KYOt7ax5vHC+kfdN9bYFfQ6TRs3JjEg/uyCQjwZHx8ivMXqjlyrIieXve9SVZQuBu52+d6uWTEh7M1Kx5YojXGUcHcly1GjMuXYo1xaCB7lzqVOmsJI8a+y7DGOP02jxgnr17SVOon4pc2YvxIdPasNcbujxjvi8hZ0ojx9tAcoqcjxq3GlbEszd1YLBabzeoQAxgMBn72s5/R29vLyMgIBw8eXLDud/Y+Hh4eHDt2jM7OTiYmJqivr+eXv/zlHIcYxLJPf/zjHxkaGmJgYIDf/OY3shxicMIpDgoK4tChQwu2Hzp0aKZe1J2CLSHaeB9/9GoNI1OTtI4M3BR7llSuFZteo2FVoHh9Sno6Fu0/v+WEi19+UWcnJot5ZrtFsNhs8/dPDwvBS6dleGKC8q5uh5R/bakob06KQQBqu/toHRiyqTLtaEsIDSA22B+zxcKZijrMWCSP66hqtFarZveaZFSCQG1HL+UtXbLUoRe7Hiq1it0bUvD1MjA0Os47VysxmS3IUVaWc2w/Xw8e2C5GiHv7RzlypoyxaUfcbarUUm16XKsSQ3lwdxZe0yJXbx4vpr6ld8lUqeWqVYeE+fLQQ2tYty4ejUZNV9cQb719g0uXa5mYNNmwJXX9FyptW2yoSbtbrVrSjoLCLeRumuttSdraUkpWVKmdt6+oUs9qiir1Xa1KvTEwkzX+aTN2FFYOsr+Nb3/723z5y1/m4Ycf5sUXX+TFF1/k4Ycf5itf+Qrf/va3l2KMKwq1SsUqP/GBoLxP3pue1cGiY1vZ2y1bDCI5MBBPrZbRyUmqe3sX32EeapWKtVFitDi/uVVWlHs2gV6epIWL0fILda69CRcEgXtT42dEt6o73LPOIsTPm/UpYpHxs2X19LspCm3FoNOyd2MaOo2ajt4hztyoXdL1tgG+N1One/pHePtMKcZlXj8bHOTNw3uzCQ8Vo+QnzlZwbYWsMwZQqVRkZkbz2GO5xMcHY7FAeUUbr72eT3V1x4oZp4LC7cLdPtc7i6JKbR9Fldo+iiq1fe4EVepknxj7S7wUbhmyneKnn36ac+fO4evry8GDBzl48CC+vr6cPXt2TnjcXQwNDfH8888TFxeHh4cH99xzD1euXLG7z6lTp8jNzUWv15OcnMzLL7/s1jGl+osllqoGemTdzCO9fQiYrllcKdOhVqtUZE2nCtxob5e1r5XMiFAMGg39Y2NUdzvvgG5IiEYtCLQODFHf2++0HRBFt3LjowA4V9HAhJsmlzWJEUQG+TJlMvPejWq3TroA/j4e7FqfgiBAVXMXBdWulapajEA/T/Zvz8DDoKVvYJS3T5cwYlw+VWoQBcD23pdBRsq0cFtJM8fPLK869mJ4eenZvj2NPXtW4+fnwdjYJOfOV3HkSCGdXYO3engKCrcNyz3Xw8qc753hzhDfurMcY0WV+iaKKrV9llp8S2Hl4lTcfuPGjfzhD3/g2rVrXLt2jT/84Q+yJK/l8KlPfYrjx4/z+9//nqKiIvbs2cPu3btpabGtgFxXV8f+/fu57777KCgo4Pnnn+dTn/oUx44dc9uYor398NJoGTdNUTfU5/B+giCQOR0tLuqSvzY4KzQUlSDQNjxM+7D8G4BOrSYnUszbv9LU4vQk6mPQkx0l2jlf2+jyZLY2PhI/DwOjE5Ocd1M5JUEQ2JmdhF6rpmtghEsVTW6xO5voUH/umVa8vlLWSHWzvDrUcvH39WT/9kw8PXT0Dxk5fLKYweGxJT3mfFQqFRtzE9i2aRVqtYqWtn4OHb1Ba3v/so5jMSIi/Hn4obXk5caj0ajo6h7iyJFCTp0qY3DQvZkDCgp3Kss518PKnO+d5c5wjBVVaikUVWr7KI6xwu2IbPVpAJPJxOuvv05ZWRkAmZmZPPLII6jVEqq0TmI0GvHx8eHQoUPs379/ZnteXh4PPPAAL7744oJ9vvzlL/PWW29RXFw8s+2pp56iv7+fo0ePOnRcq9Lav9tQn7ZMqz2faa0nv6uFJN9AHklIn9m+gHnbRycn+FVhPmaLhY+kZRPm5W23vxWr+N071dWUdXeTHBjI/pQUacVfCTvjk1O8fOU6EyYT+9JWsWq63JOUMrHU9vGJKX5/qYCxqSm2JcfPOMkyxApFpu239A5wKF/8PT2Sm05MoJ/d/o7ar2/v5ei1SgD25qaQECbW15SrxGzvvC4U11NU24ZKEHhwczqRwX7YUyB29dhDI2McOVPK0Mg4HgYt+7amE+jvJdn/pn3bH0j3t7+9t3+EU+crGZh2MrPTo8hdHY1KpbLZ3/XjSoxforvVzujoOAUFjVTXdGCxiC9M0lLDyc6KwWDQLujvKEupVm00jvAP/7+lUZ9u6+52i/p0RHCwoj59h7Nccz3cmvl+Rn369+8sUJ+WkrRVVKkVVerZSKpS29lHUaVWVKmtjA6N8umsD67Yud5q726Z7zVyd6iurmb//v00NzeTmpoKwEsvvURMTAxvvfUWSUlJbhvc1NQUJpMJg8EwZ7uHhwdnz561uc+FCxfYvXv3nG179+7l+eeflzzO+Pg44+M335ANDi6eZpkRGEp+Vwt1g32MTk3iodItug+Ap1bHqoAgKnq7KepuJ8wr2aH9rORGRlLW3U11by/9Y2P46wyL7zQLvUbDmqgILjc2c7mxmeSgQKfWNug1GjbER3Omup7L9c2khAZj0Mr+Oc0QFejH6ugwips7OFlay1ObstFpXH/wig8LJDs+gsL6Nk4W1hC0xRNfT3nXbDE2ZcYxMjZBbWsPxy5X8PCWTIJ9Pd16jNn4eBl46L7VHHu/jN6BUd46XcKeLemEBfss2TFtEejvxSN7srl0rZ7K2g4Ky1po7xxg++ZV+Hi59xq7gqennnvuWUV6eiT51+ppaemjrLyN6ppOslZHk54WgcYNv7XbhU2/+m9UBte+H/PY8mYoKCw/yznXw/LM94vN9Z29w7T3DJK9yvG6louRES++MD5bVE9hbRsIsDE91m1rClOixOVcJwurKW/uRBBgW2aC2+xbVamPFVbN6H7sXp2MSu0e+1ZV6sNFFTMR4wdXpyx8ueokVlXqQ8XlM6nUj6xOQ+ume75VlfpgWelMxHiOY+wi1ojxgcqSmYjxHMfYRayq1AeqixmYGONAddEcx9hVrKrUB+tFx/jV+qI5jrGrWFWp32gupHNsiENNN3gsJodggzzVYSmsqtRH227QNtbHkdaCOY7xSmbTr3/h8lwPd9d8L/uu88///M8kJibS1NQ0k1LV2NhIQkIC//zP/+zWwfn4+LB582a+853v0Nraislk4pVXXuHChQu0tbXZ3Ke9vX2BTHdYWBiDg4MzxaPn89JLL+Hn5zfTYmJiFh1bsMGTMA9vzFgo75OXNpsdIk6S5b3djJvkpdsEe3oS7+8PwDWJa7AYayLD0anV9I4aqXJhbfHqyDACPD0Ym5riSkOz03asbF4Vi49Bx9DYOBer3ZNGDbAxNYZQPy8mpkwcv17FlMm964sFQWDH2uSZUk1HLpQxMLy0KbqeBh0Pbs8kNMiHiUkTR86U0NAqX4DNVTQaNVs2JLHjnhS0WjWdPcMcOlZIXdPKK04fEODF7l2Z7NmdSWCAF5OTJq5db+Dg6/mUV7RhcvPvQkHhdmY553pYnvne3lw/OjbB0fNlXC5u5Hq56/PZbOaUa1qKVOpZ5ZqWJJVaKddkF6Vck308l7lc01KkUi9luSaFlYPs9GkvLy8uXrxIVlbWnO03btxgy5YtDDux1tUeNTU1fOITn+DMmTOo1Wpyc3NJSUkhPz9/JqVrNikpKTzzzDN89atfndn29ttvs3//fkZHR/Hw8Fiwj623xzExMfz7xbdspE/ffI9wo7uNE821hHh48Xer1tg+ARtpzBaLhVeKb9AzZmRHTAJrQiPs9he33/xn88AAB8vKUAsCn1ibi6dWu6C7VPq01c7lxmYuNTbj72Hg73JzUEvk4Uim+k5vb+zt543CcgTgI+uzCfKUePsnlW0zz35zzwBvTKdRP5qXTtS8NGrJNO9FUnGHjOMcOFvE+OQU6TGh7MhMlDdOB1J9JyanOHyulJ6BEXw89TyydTVeBhtvcyUeJuSmEwsWmJwycfJSFU1tfQjAPbmJpCWGSfa3bd894xkaGuP0hUq6esR7QFJ8CJtyE9BLZhBIGJL6jiWsSKaq28kgs1gs1NZ2UVDQwPCw+Lfv7W1gzZpYEuJDxPRAJ+1LISfd2mgc4R/+ef+KTam6m9Kp7laWe66HpZ/vpeb6H/9OTJ8uqGzhSqmoP5GXHs3atOiFg3Qhrbqkvp2zxfUAZCdF2I4Yu5CWXNnazYkiMZU6PSbUdsTYBft1Xb0ctZVK7Sb7zX0DHC5emEo9x44LadWSqdQO2Vk8Jbl7dISD5aUYbaVSO3CMxdKh7aZSy7Aj1d9eKvWc7k6m+CXsuAABAABJREFUVdtLpXbEzmJpyfNTqaUixs6mVdtLpZ5jR0ZatXFolM9kf2DFzvVWe3fLfC87UqzX6xkaGlqwfXh4GJ3OPekcs0lKSuL06dMMDw/T1NTE5cuXmZycJDHRtlMTHh5OR8dcEauOjg58fX1tOsQgnpOvr++c5gip/sGoBYEu4widRscfEARBmIkWF3a1y36jG+XrS5iXFyaLxWkl6jWREaIStXGM8g7nBaJiA/1JCArAApypqnf57XR0kB8Z0aEAnCytFWsMugEfDz2714ip6mVNnZQ1d7rF7mx0Wg37NqXh66VnaHScIxfKGJtY2tJJWo2a3ZtTSYkPxQKcu1bL1eJbUyrJx9vAg7tWk5MRjSBATX0Xh47eoK1jYNnHshiCIJCUFMpjj+WxcWMiBoOW4eExzp6t5PDh6zQ0dN+xZZw8tVq3NIU7m+We62Hp5/vF5vo1KVGszxCjx/llzW6PGN8Z4luKKrUUiiq1fRTxreXFXXP93TTfy3aKH3roIT7zmc9w6dIlLBYLFouFixcv8tnPfpZHHnlkKcYIiG+tIyIi6Ovr49ixYzz66KM2+23evJn33ntvzrbjx4+zefNmt4/JoNGS5CcKN5X0ynOy0oJC0KpU9I4ZaR6SVypGEARyI8U1Tzc62hl34qar06jJixZtXGpsdimleGtyHGpBoLl/kKou19Nm70mJxdugY9A4zrnKBpftWYkJ8Wd9ivjm/0xJHe19Cx/4XMXToOOBzRl46LX0Do1y5GIZE5PumxRtoVIJbM1LZG26eG43yls4eamKKTdO9o6PRUVudiwP7FqNj5ee4dFxjpwq4Xx+LZOTyz+exVCrVaSlRfLEE+vIXRuHVqumr3+UU6fLOfTGdWprO9360KegcLtwq+Z6uLXzveIY20dRpbbPneIYK6rUtrndHGMFech2in/84x+TlJTE5s2bMRgMGAwGtmzZQnJyMv/5n//p9gEeO3aMo0ePUldXx/Hjx7nvvvtIS0vjmWeeAeCrX/0qH/vYx2b6f/azn6W2tpYvfelLlJeX81//9V/89a9/5fOf/7zbxwawOkiM+Jb1dTJpdvzGqldryAgSI6LXO+WvDU4KDCTAYGDcZKKwQ355J4DsyHB89DqGJyYocHJ9MoCfh4F1cWKt4fdrGpxy0mej02jYmSmKuJS2dFLf5XjZq8XITYoiISwAs8XC0euVDBvdd7O34utlYP/mDPQ6DV39Ixy9VO62iLcUgiCQmxnDveuSEASBuuYe3j5destqCIcF+/LovjWkJoup3OXV7bx2rGBFRo0BtFo1WVkxPPnEerKzY9Bq1QwMjPL+2Upef+MaVdUdyppjhbuK5Z7rYeXM98viGM9eY1zqfsd4SdcYhwTc3muMbUSMlXJNN/HR6XnyDosYdymOsYIDyHaK/f39OXToEBUVFRw4cIADBw5QUVHBa6+9hp+fRBkdFxgYGODZZ58lLS2Nj33sY2zdupVjx46hnQ7nt7W10dh4U5QpISGBt956i+PHj5OTk8MPfvADfvWrX7F37163jw0g1tsPX52eCbOJqgF5UdKcUNGhrh3oZWBcnrqbShBYFyU6otfb25y6oWtUKjbFiRP/1eZWjJPOp/rmxkbiP11r+EKd6zWBowP9yIkV11qfLK1l1E1pyIIgsDMnmUBvT4wTkxy9XunWydBKoK8n+zelo9Ooae8d4tjlpXeMAVLiQ9l3bzo6rZqu3mEOvVdEd9/Ikh/XFlqtmnvWJbFvRwZenjqGR8So8dnL1YxPLG303Fn0eg1r18TxgSfXs3ZtHHq9hqGhMc5fqOa1Q9cor2hjahm+RwWFW81yz/Wwsub7nNSldYzniG/VKuJb81HEt+xzu4tveSyz+NYhRXxLwQGcqlN8pzNTp3gRoS0rlzuaONfWSKSnDx9Kzp774SL1i1+rKqVhsJ81oRHsiElwSGhrZpPFwu8LChgYH+fe2DhyI24Kdi0mtDVzPhYLfy4oontklDUR4WxLjJ/z+WJCW7Np7hvg9RuiGMqH1q4mzHeWwIGDQluz+0+ZzBy4XETvsJG4YH8eXJOKSkKtQ7Yo1PAYr14oZmxyiuTwIHbnJIuCJG6qqWvt39E7xNsXy5icMhEZ7MveDWloJUpNOHsMW/QPGXn3XDkDw2Oo1Sq2rUsiKTpYwr576xfbsjM5aeLKjXrKa8SsBg+Dlk25CcRH2ygJtgxCW472n5w0UVnZRklpC0aj+GJGr9eQnhZBakoEBr3ja21WitCWO2y605aCwq3C+ju2Cm3NxjrV3KiYJ76Vbrs6hbMCUKX1s+oYJ82qYyzR32H70+OpbLlZxzg9JpRtq22Xa5Jtf3p7Xee8OsZZNsS3nLBv3d7cN7eO8QPZC8W3HBmnVP/2wXniW1k2xLeQX7/YeuzuURt1jKXEJ+UKZCEhviW15l9G/WJxu0UU35ou12QV3/LT2y7XJKd+MYBKgNGpiZlyTVbxrUCDbf0fOfV/rf3HTVMz5Zqs4lshHrbLNcmpX2w97pTZNFOuySq+Felhe05cbqEtd9m8m+Z72U6xxWLhwIEDnDx5ks7OTszz3twdPHjQrQO8Fcw4xZfewmOBU7zwz2N4coL/Kb6KBfj71LUEGWap6S3iFDcM9PNaVSlalYpPZq/DoJK4WUrYKWnv5L26Wry0Wp5esxaNdbKQdEIX2mns6+dQcTkqQeCjuTn4eRhm9ZeyY3v78dJqKjq6CfH25IO5WTOToz3n16b96e09Q6McuFSEyWxha2ocOTERtneQ6ziaobVnkMNXyjBbLKxfFUNecpT7HNNZ2zt6hzhyQXSMI4J92bdBokailHMq8+W4dUzjE1OculRFc3s/ADlpUeRlxix4IJKrSi15XAe+446uQc5dqWFgUCyXEhsVyKa8BLw8b06ysscj+6WBfCfaZDJTVdVBSUkLw8NiVodGo2JVchgZGVF4e8+qBSj3PaOEU/zZ5x5csRPl3TRJ3q3cTXP9T/5X2ikGFFXqRewrqtT2j62oUiuq1GBbldo4NMI/5ChO8UpBdvr0888/z9///d9TV1eHt7f3nJp/S5VStdLx1upI9BUFt4p65KlBx/r6EeThyaTZTHGX/LXB6cHBeOt0jExOUtLlnKJybIA/sf5+mC0Wzje4Vh94S1Iseo2aruFRilqdU8aeTZCPJ/ekxAFwvrKRrkH3pQJHBvmyJSMegCtVTVS3dbvN9mzCAn14YHM6Wo2atu7BZVljDKDXabh/axpZqdOibOUtHD9fcUtTl8NCfHlkbw45mdGoVAKNLb28dqSA0sq2FS1oJQpyRfD443ls25ZKYKAXU1NmysrbOPjaVc6cqaCza/COVaxWuPtQ5vqb3GniW6eLFVXq2Siq1PZRVKnto6wxvnOQHSkODAzklVde4cEHH1yqMd1y5EaKAWr6+zlUV4pereZTGevRqqbfMi4SKQYo6e7geH0NXlodz2Tm3oz2SvSfjWCGG+3tnGqox1ur4+Nr1oj7y4gUA3QNjfDngiIAnszOJNLXZ8a+1HFtYoHi1g5OVdahVan4yPpsfD0MTkeKQYxYHCmopL6rD18PPR/amIV+/ltWJyLFVs6V1VNU345KEHhoXRpRQTYe+NyQVj07YhwW4M2+Telza/i6OVI8m+qGLs5ercFktuDjpWfX5lSC/L0WOQf3R4pn09c/wrkrNTN1jQP8PNmYm0BkqMQD9y2MFC8cioW21n6KS1poa+uf2R4U5E1aagQJ8cGo1Q6+c1QixQorkLtprl8sUmzFbsTYhUixFbsRYxcirVbsRozdYL+uq4+jhZW2I8ZusG8vYuxKpNjK/Ijxw9OOsn07i0eKrSwaMXYyUmxl0Yixk5FiK0MT47xaU0S/RMTY2UixlcUixs5Giq0sFjF2NlJsZbGIsRIpXvnIjhT7+flJ1gy8m4nz8cdPJ6pBV/TJq/ubGhiCt1bHyOQE5b3yawZnhobirdMxPDlBkZNK1CHeXmSEhQDwfq1r9YYzI0KJ9PNh0mzmREWty2+kBUFgZ2biTJmmEyWu25zN5rQ4EsMDZxSpe4aWRpgqLNCH/fdkoNeq6egb5vC5EozjS1vH2EpyXAgP3bcab089QyPjvHmiiPLajlsa2Qzw9+LBXVlsXpeIXqehb2CUoydLOHm+guER96uCuxNBEIiMDGDP/at5aP8akpNCUakEenqGOXe+igOvXuXa9QZGVvh5KChIocz1C1FUqe1zp6lSv7lEEePbWXxLUaWWRokY3/7IdopfeOEFvv3tb2M0GpdiPLctKkEge7o8043udlkTjUalIjdMTHHN72jBLHOS0qhUbJhWor7S2uL0TXxzXAxatZrO4RHKOuQ751YEQWBnahIalYrm/kGKWp1z1Gdj0GnZmyPWRqzt6qWwyfXUbCsqQWBndjIRAT5MTJl462o5Q0tQqgkgJMCbh+7JxEOnpWdwlDfOFjM0ujyOU3CAN4/uyiIm3B+T2cK5a7Wculy15HWU7aFSCaQlh/Pk/rWkJYcjCFDX1MOrR65TUNLkUv3s5SIoyJstW1L44Ac2kLs2Dk9PHWPjkxQVN/Pqa1c5cbKMltY+JbUa+NnPfkZ8fDwGg4GNGzdy+fJlh/b785//jCAIPPbYYws+Kysr45FHHsHPzw8vLy/Wr18/R6F4x44dCIIwp332s5911yndsShzvW0UVWr73Gmq1EtSx1hRpZZEUaVWuJXIdoo/9KEP0dfXR2hoKFlZWeTm5s5pdzOZgaGoBRVdYyO0jQ7J2nd1SBh6tYa+8TFq+ntlHzsjOAR/gwHj1BTXnaw57KnTsSFGdK7PNzS6dKP29zRwT2KsaKumkQGjvJJTtgjz82bLqun1xVWNtPfLu8b20KhV7MtLJcDbg5HxSQ5fLWPMTWWg5hPk58XDWzPx9tAxMDLGobNF9A6676ZvD4Ney/1b0tiQFYcgCNQ29fD6e0V099+ask1W9Hotm9cl8sieHMJDfDGZzFwrbuLg29epbey+LRxKg0Er1jp+fB07tqUSFuaLxQJNzb28+14pB1+/RlFxM2Njy5MdsNL4y1/+whe+8AW+9a1vce3aNXJycti7dy+dnfa1EOrr6/m///f/cu+99y74rKamhq1bt5KWlsapU6coLCzkG9/4BgaDYU6/T3/607S1tc20733ve249tzsRZa6XZlkd46VYY6w4xnZRyjXZZ6kdY89ldoyXYo3xSnCMRycm3dbuFmSvKf7Qhz7EyZMn+cAHPkBYWNgCBcNvfetbbh3grcCaP/+9y4cXrCk2m2y/R7BML1B5p7GKkt5OUvyD2R+fKrkG2VbZmQstjVxqbSHM04un0rLnXlsH7FR2d3O0uhqtWs0zOWvw0C4sFyNZqmn6V2Aym/nTtSL6jEayI8LYkZjg8Phh4Vrg1wvKaOkfJNLPh8dzMhxWPpbabjFZeKeoipqOXrz0Oj60KQsPnVa+arTE+IdHx3ntYgkjYxOE+Xvz8IYMNGqVW8slWfcZMY5z5EIZfUNGdFo1+zakER5kY72Gm0omze/f0TPEyYuVjIxOoFIJbMyJJz1x4d+048d1zzgtZgt1jT1cKahn1ChO5CFB3mxYm0BosI8N+/LWCMsv1eT8GuT+/lEqK9uoru5kclJ8qFKpBGJjg0hZFU54uJ/N4xqNI3z22aVZU9zW1e2WNcURIcGyxrdx40bWr1/PT3/6UwDMZjMxMTH80z/9E1/5ylds7mMymdi2bRuf+MQneP/99+nv7+f111+f+fypp55Cq9Xy+9//XvK4O3bsYM2aNfzoRz9y+PwU7q65/icvL1xT7MhaVEWV2r79u1mVWrRlf5JQVKkVVWrj0Aj/uOaJJZnrE7/1r6jmvSB2BvPYGLXf/tpdsaZYtlPs5eXFsWPH2Lp161KN6ZbjilPcZRzhlYoCBOCTGevw1kj8IG38LY1OTvKbG9eYsph5YlUGsb7+s/ov7hRbLBb+XFRE1+goa8Mj2BYXt6D7Yk4xQFP/AK8XlSEAT+VkETLvGkiNHxY6QIPGMf50pZBJk5mtSXGsiY6w23+x7ZhhYsrEgUtF9I+OER3ox0Nr01BL3bVkOsVYoHdolNcvljAxZSIuxJ89uSloBImZ1QWnGGBsYpJjF8vp7BtGrVaxO28VceGB8/ovjVMMMD4xyZnLNTS29QEQFxnIlrxEPOzU311qp9i6fWrKRHFFK0VlLUxNiV9YfEwQedmx+PrcrGO4kp1iK1NTJupqu6iobKen5+YbaW9vA8lJoSQlhswp67SUTrE7JkrrJNnU1DRnfHq9Hr2NGpYTExN4enpy4MCBOSnQH//4x+nv7+fQoUM2j/Otb32LwsJCXnvtNZ5++uk5TrHZbMbPz48vfelLnD17luvXr5OQkMBXv/rVOcfYsWMHJSUlWCwWwsPDefjhh/nGN76Bp+fChyuFm9xNc72zTjE44Bi7KMC1qGPsooDVbMc4LTqU7fPrGLtof1HH2EX7iznGrgpwLeoYu+AUg4OOsQsCXA45xi4IcC10jFfjq5s7v7giwOWIY+yKAJcjjrErAlyLOcaKU7yykO0Up6Wl8de//pXs7OylGtMtxxWnGOBvVUU0jwyyLjSKreHyIq2nGuop6GwjytuXD6auntXfsYhzQ38/h8rLUQsCf5+dg9+8PwhHnGKAI2WVVHf3EubtxQezV6Oaf1dw0CkGKG4R1ajVgsAHc7MI9va029/edutxe4ZHefVyMVMmM1kx4WxLjbfd3wmnGKCtV6xhbDJbSAgL5P6c5AVvoO3ad9ApBtFhevdqJU0d/QjAPVkJZCaEz+q/dE4xiJHZkqo2rhQ1YrZYMOi1bM1LJC4y0Gb/5XKKrYwaJ7hW1EhVrZhmKwgCqUmhZGdE4+Wpvy2c4tn9e3qGqapqp7auayZ6DBAe5se2e1Pw8NDdNk7xfL71rW/xwgsvLNje2tpKVFQU58+fZ/PmzTPbv/SlL3H69GkuXbq0YJ+zZ8/y1FNPUVBQQHBw8AKnuL29nYiICDw9PXnxxRe57777OHr0KF/72tc4efIk27dvB+CXv/wlcXFxREZGUlhYyJe//GU2bNhwR9TZXUruprneFacYFFXqxezfaarUcxxjF51iuDNUqe05xooqtbRjvJROsTuywqz25GaG3a7IdorfeustfvKTn/CLX/yC+Pj4JRrWrcVVp7hmoJc36srQqdR8Mn09erWNdBgJp2xobIKXi69hslj4QEom0T5+0/0dc4otFguvl5fTNDBASlAQDySvmvO5o07x8PgEf8i/wYTJxPbEeHIiwud2kOEUWywWDhdW0NDbT5CXJx/MXT1TdspZpxigpqOXY4WVAGxPS2B1dJiNg0vYd6B8UFNXP0fyK8SJPDyIXdk2Ur/c4BSDGPU6e6OOikbR8ctKjGBjZpz4MmKJnWJr/+6+EU5fqaJ/UBTWWRUXwqY18ei0c3+/y+0UW+ntG+FqYQMt0yWQ1CqBtFXhZKdF4WGwEdleoU6xlakpEw0N3VTXdNLePoCHh44PPLEOlUq4bdKnHY0Uy3WKh4aGyM7O5r/+67944IEHABY4xVabH/nIR/jjH/84s+8jjzyCl5cXf/rTn2yO/cSJE+zatYvq6mqSkpKcvgZ3OnfTXO+qUwx2HGM3OMUAJQ3tnC2qByA7MYKNGdOOsRucSoCKtm5OFtpwjN1kv7arl2O2IsZust/cP8DhooWOsTucYrBTrskNTjGIjvGr02uLFzjGLjrFsIhj7KJTDPZTqV11isG+Y+yqUwwLHeNHY3IImXaMXXWKQdoxXkqnWCnJJB/ZQlsf/ehHOXnyJElJSfj4+BAYGDin3YkMTIxhnHJ8oXmibwCBeg8mzCZKeuUpL/vo9GQGhwJwsa1J1r4gRtG2xooCV5U9PbQPOydG5a3XsTleFBI539DE8LjzIg2CILArNREPrYaekVEu1DYuvpMDJIUFsiFJHOP7FfU09w64xa6VmBB/9uaKitfV7T2cLK6RrQzuKCqVim05iaxLE8+nqLaNY5fKl1UZOjjAi0d3ZZOVIiqhVzV0cfD4DVo73XtdnSUwwIs92zN4YGcmYcE+mMwWSira+Ntb17ha2Mj4MpW3chcajZqkpDD23r+aJx/P494tqxa+dFkCPHVatzQAX1/fOc2WQwwQHByMWq2mY17JuI6ODsLDwxf0r6mpob6+nocffhiNRoNGo+F3v/sdb7zxBhqNhpqaGoKDg9FoNGRkZMzZNz09fY769Hw2btwIQHV1tazrdrdxN871rqCoUtvnThPfUlSp56KoUttnvvhW9ZD7KqgouA8bIUz73G1iJec66sjvaWZTSBwbQmId2kcQBHJDI3m3qYbr3a3kBEegllqTaoN14dEUd3fSPDRI89DAzWixg4R4eZEREkJpVxdnGhr4YEbmouJJtsiKCKOis5v2oWFO19axPz1Vtg0rnjodu1KTOFxcwY2WdmID/YkL9HfanpW8hEj6Rkapau/haGElH9iQhb+n62sorMSFBnD/2lUcv15FZWs3giBw3+pEp67nYgiCQG5KNH5eBk5dr6aps59DZ4vZuz4VXy/3nZM9NGoVG7LjiI0M4MyVaoZGxjlyppT0pDDWZ8Wh1agXN7LEhIf68cCu1bS2D3CtqJHu3mEKy1ooq24nMyWCzJQI9DrZt7Zbire3Yc6a4jsNnU5HXl4e77333sx6X7PZzHvvvcdzzz23oH9aWhpFRUVztn39619naGiI//zP/yQmJgadTsf69eupqKiY06+yspI4G3oKVgoKCgCIiIiQ7KNw98317iAndbo0YmkT+WWiU7w2PcZt9jPixRdIZ4vqKawRq0zMRIzdQEpUMAAnC6spaxKzlrbNX2PsAlbH+FhhFdUdPQDszrKRgeUkVsf4cFHFjGP8QPZC8S1nsTrGh4rLZ1SpH8myIb7lJFbH+GBZ6Yxj/FhaBgate+Yzq2N8oLJkxjF+MiUTL50N8S0nsDrGB6qLGZgY40B1ER9IzsJP4mWpXKyq1AfrxYjxq/VFPBmfRaDBY/GdHcDqGL/RXEjn2BCvN93gsZgcQjwWim85g9UxLh1sZrWfY/6EwvIiO336bsCaKvD/XTlMw9QIx1oq8NboeCZlA5ht31zN83Jupsxmfl2Sz+jUJPtiU0gLCJm3g1Q6tLj9vYYairo6iPL25QOpmQhSOT0SdkbGJ/jfggKmzGYeXLWKVUFB0/1tm5FKq+4eHuUvBUWYLRb2p6eQGBQ43V/CziJpz6cr6yhq7cBDq+Gpddl4S92MZaQAT5nMHLpSSsfAMP5eBp5cvxr99CTiLlXqmtYe3r1RhcUyN7VMdmqwg2Pq6hvmnUvljI5PYtBpuH99KhHzlamXOF15atLE5cIGymvF6J63p56teYlEhfnLPK68W4yc8VssFppa+rhW1Ehfv/jWWKtVk2F1jm0Ihi39GmSZH9jYbDSO8LnPPbBiU6qcsfWXv/yFj3/84/z3f/83GzZs4Ec/+hF//etfKS8vJywsjI997GNERUXx0ksv2dx/fvo0wGuvvcaHP/xhfvazn82sKX7++ec5deoUW7dupaamhj/+8Y88+OCDBAUFUVhYyOc//3mio6M5ffq0S9dA4fbH+jv+6W8Xpk/LTd9VVKkdt39XqFLbtWV/UlFUqe0/AN7uqtSzMQ6N8Nzax1fsXL8U9lYyDr0+GxmRV8NUbv+VTLJvMB5qLcNTE9QM9Ti8n0alYk2IGInI72yRnYa0ISIatSDQMjxI05D89FVvnY686UjIucZGppxMIwr28mRtlGjnVE09Ey6m82xJiiPIyxPj5BTHy6rdko6sUat4ICcFL72O/pEx3imqcmvaFEBSRBA7lzC1bD4hAd48tj2LYD8vxiameOt8KeUN8lLxXUWrUbMlN5F996bj7alneHSco++X8f7VasZXSN06QRCIjQrk0b057LgnBX8/TyYnTdwoaeZvb17j6o0GjGPuSxFTcJ4Pf/jDfP/73+eb3/wma9asoaCggKNHjxIWJmoBNDY20iazxvrjjz/OL37xC773ve+RlZXFr371K1599dUZxWSdTse7777Lnj17SEtL44tf/CJPPvkkb775ptvP707gbp7r3cmalKVNpc6MD2fr6nhgieoYRwazM+vmfHe62M2p1CGB7FvqVOrVS5hK7bMMdYzTMvC4nVOpk7KWNJX68bilTaV+OCpnyeoYK6xcHHKKk5OT+bd/+ze7DywWi4Xjx4/zwAMP8OMf/9htA7zVaFQqVgeIKUs3elpk7ZsdHIZGpaJrbISGoX5Z+/ro9GSFiA+L55qdm/ByIyPx0moZGB/nmsyHzdlsiInGz6BnZGKC9+tcWw+sUavYl7EKjUpFc/8gl+rd87Dgqdfx4BpxEmzqGeB0mfud1lWRwezIFoV5ypo6ee9Gtdud79l4eeh5eGsmCRGBmC0Wztyo5cyNGqZMS3dMW0SF+fPEnhwyksS/g8r6Lg4cK6C6sWtJXwzIQRAEEmKDeWxfDvdtSSXA35PJKRNFZS387c1rnL9aw8CQ8VYP867nueeeo6GhgfHxcS5dujSzvhfg1KlTvPzyy5L7vvzyy3OixFY+8YlPUFVVhdFopKCggEcffXTms5iYGE6fPk1PTw9jY2NUVVXxve99745/2+0sd/Nc727uJMe4vHkJ1hgrjrFdFMfYPopjrLAUOOQUnzp1iitXrpCQkMDGjRt59tln+e53v8sPfvADvv71r/PEE08QGRnJJz7xCR5++GG+9KUvLfW4l5XswAhUCLSMDtI55rhwlYdGS1ag6Ehc6ZQ/Ia6PiEarUtExOkx1v+NRais6tZqt02vrrrS0MDg2JtsGiI7srlWiM1ja0Uldb59TdqwEeHlwX2oiAFcbW6jrcc2elRBfL/Zkr0IQoLy1i4vV8oXKFiM1KoTda5LFibyth2PXK5fUSdVo1OxelzIjwFXe0MkbZ4sZHHHuu3QWrUbN5rUJ7N+Rib+vB2PjU5y+XM3R98voH3TfROQqgiAQHxPEo3tz2Lk1jZAgb0wmMxXVHRx86zrvnS2no9s58TkFhTudu32udzfL4hhbxbdq2rhUugSOcc4Sim+FBLIvJ+X2Fd+y4RhPuN0xzry9xbfuMMe4S3GM72gccopTU1N59dVXqays5EMf+hAtLS0cOHCA//mf/+HUqVNERUXxP//zP9TX1/OP//iPqN0kOrBS8NbqWeUnClAU9MqLFueFRopp0CODtAzLS4P20urIDROVgM+1NmKyyL+ZpwYFEe3ry5TZzOmGBtn7W4ny82VNpJhGfaKqFuOka+mzqWHBZEWJkfDj5dUMGN3j5MWHBLAjXXS4r9e3UtDgfIRciuSIYPbmpqBWCTR09fPW1TKX08rtYRXgenBTOnqdhu6BEQ6eLqS+vXfJjilFeLAvj+3OJi8zBrVKoLVzgIPHC7lc2MDklPseBlxFEATiogPZvzuLffdlEhMZAEBjcy9vvVfM4XeLqG/ucesDmILC7c7dPtf39Y9QUePeZSq3uyr1qsilVaWODwlQVKntEOzpqahS2+F2V6VWWFnIkuSLjY3li1/8Iq+//jrXr1+nvLycs2fP8pOf/ISHHnrojpsgZ7M2SFSVrBrsYnjS8T9ob62e9ACxxNJlJ6LFeeFReGg09I+PUdLdKXt/QRDYER+PShCo7eujts/5qOzm+BgCPDwYnZzkZK3rE+PWpDjCfbwZnzLxdqn7Iq7pUaFsTBYfQs5VNVDR1u0Wu7OJCw3gofXp6DRqWvuGeONyGcYlXmcbHerPk9uzCQvwZmLKxDtXKrlU2rDsjp1apWJNejRP7FlDbEQAFouFospW/vZOAbXN3SsmpRrE339EmB+7t6Xz+ANrWJUYikol0NkzzIlzlbz69nWKK1qZmFi+0lcKCiudu3GuHzVOcORUCeeu1lBa5d6XqcvqGC9FKrVSrsku8x3jJUmlVhxjSTyX2TFWUqntc+bMGR5++GEiIyMRBGHBsqeOjg6efvppIiMj8fT0ZN++fVRVVdm1uWPHDlHcdl7bv3//TJ+nn356wef79u2TNXb36NTfoQiCZaaFe3oT6emLGQvF/a0IAnMbEk2A9WHRCEDDUD8dxiFRsU6yWeY0nUbFxkhRufJiWxMTZpOdfW82y6wW6OXJ2ggxjft0Yz2TNmxYJNrsPhq1ij2pSeLE1dNLZW8PFhVzmwN2rE2tVrEvcxUeWg3dw6Ocqq7DjEWyv6PjRIDchEiyY8VzPlFaQ0NP/6LjQWDh+Uw3W30jgnx5eINYLqFrcIRDl0oZGhu3a9/eOTjSvDz17N+ayepEMWp/o6aNwxdKGR6fxCIIizTHvxtHroWvj4H7t6Zx/5Y0fLz0jBonOHGpirffL6V3aPTmMVSCzbbwj0hskucvNR6pNu98/P092boxmQ8+nEd2RjQ6nYahkXEuFzTw5zfzuXC9jv7hsYXjlGhS45FsEt+LlH0FBYVlwmLBQ68hJUF8gX3xWh1llW3zp2NR/Fai2eo7uy1IpS5rEpX55zXBjM222HEz4+atMS5txGK2ODxOSfvTx0+NmCu+daa4zj32p1vi/DXGRdWYTa7bt7Zo/7lrjI8UVmKeMsu+zlLHtbnGeMokYUuw2ewdO8TDiyfnrzGenFr0vOc0s2C7IeEYT0zIsyN5bAEfrWFBKvXA+LitPwEsFsFmk3o4MFvAoF6YSt07ZpS0ZauZJZrFIqBTaRemUhuHbY7fZFbZbFKX505jZGSEnJwcfvazny34zGKx8Nhjj1FbW8uhQ4e4fv06cXFx7N69265w48GDB2lra5tpxcXFqNVqPvjBD87pt2/fvjn9/vSnP8kauyynuLS0lH/8x39k7dq1REREEBERwdq1a/nHf/xHSktLZR34dmRtkJjKXNTfJjqWDuKvN5A6XZLpUrv8da5ZIWH46Q2MTk1yraNV9v4A66Oj8dbpGBwf50qrczYAQn28WRcjRs1P1dQxPO7a20Rvg569GavEiba9i5JW+dFwWwiCwJbUOFaFB2G2WDh6o5L2fvevJQ3x8+LRTZl4GXT0jRh5/WIJvUNLu8ZWrVKxOSue3etS0GrUtPcOcfDUDRpuQTo1QGxkAE/sXUNuRjRqlUBb1yCvHb/Bhet1GMdXhkr1bDw9dORlx/KhR/LYvC4Rf18PpqbMlFW1c/Dt6xw/XUpTa6+SWq1w13I3zvWCIJCXFUt2mjjPX7xW5/aI8Z0kvqWoUi9EEd+yz524xnglR4xHJybd1uTwwAMP8OKLL/L4448v+KyqqoqLFy/y85//nPXr15OamsrPf/5zjEajXQc2MDCQ8PDwmXb8+HE8PT0XOMV6vX5Ov4CAAFljd7hO8ZEjR3jsscfIzc1l7969M2U0Ojo6OH78OPn5+Rw6dIi9e/fKGsBKxFqT6/tX38TD+2btQrPFwsuVVxmcHGdHWDLZgZE3P5Oo82uZLm7XOzbK78qvYwE+siqbMA8J9VOJ+3NFdw9H6irRqlQ8vTr3Zk05qXrHNr7V6p5e3q6sRCUIfGR1FsGes+q6SdixVc7NZDZz4EYJncMjRPv58ujqdFTTBdsk6xdLzTvT9vMbW7hQ24RKEHg4O41Yfz+Hz8uefZPJzNsFFTT1DKDXqHkkL4MQXy+31S+29h8yjvPWlTL6R8bQadTsWbOKmGB/WceQU5vZ2n9g2Mh7VyvpmRa7SosLZXNGPFqNjfRGuXWNJbA3nsHhMS4V1tPY2geIAl05aVFkrgpHMy/l8lbUNba13WKx0NYxQGllG03T4wbw8tSxKjGMlIRQvD0X1liUPIDc79fGNqNxhM/9w74VW7vwbqpbeLdxN871P/3NsZk6xRaLhfyiRgrLxZfHm3ITyFgVMbOPK/WLrditY+xC/WIrdusYyxinVH+7dYzdYN9uHWM32LdXx9iV+sVWFq1jLGnLsUli0TrGMn+j8+sOL1rH2EE7Uv0Xq2PsbP1iK4vVMXa2frGVxeoYy6lfbBwa4Z+WqE5x8lf+FbXe4LI90/gY1f/2NZqamuaMUa/Xo9fbeja6iSAIvPbaazz22GMAFBUVkZ2dTXV1NUlJSTP9YmJi2LVrl90KFLPJyspi8+bN/PKXv5zZ9vTTT/P666+j0+kICAhg586dvPjiiwQFBTl8rg47xTk5OTz66KP8y7/8i83PX3jhBQ4ePEhhYaHDB1+pSDnFANe72jjdUYOv1sDHktbPOIOLOcUAxxqqKO3rJNbHnycSVts+uITzZTHBXyqKaB8ZJjMolPvjk6f7O+4UWywW3iqvpLa/j3Avbz6YmTkzfjlOMUDfqJE/Xy9iymxmc1zMTPTYWafYYrFwvKyGys5udGo1H8xdTaCnh0PntZj9SZOJN6+V094/hF6r4dG8dELmfa+u2LcyNjHJsWuVtPWJKfLbMhLIiAlz+BjOOMUgOv5XyhspqhGjGr5eBnauTSY0wGde/6V3iq20dg5wubCBnn4xHcbLQ0deZgzJcSEzD08rxSmezcCQkYrqdqrruhifXmcsCBAdEUBqYhjREQE3H85uU6e4ravbLU5xREiw4hTfgdyNc/1spximHePiJgrLRGHNTWsTyEgRHWN3OMVgxzF2g1MMUNLQztmiemCeY+wGpxKgsr2bEzdsOMZusl/X3cfRG5ULHWM32W/uH+Bw0ULH2B1OMSx0jB+ejiDbt+X4JNE9Osqr5SWM2XKMXXSKYRHH2EWnGOw7xq46xWDfMXbVKQb7jvGd6hTP51vf+hYvvPCC3X3nO8WTk5MkJyezceNG/vu//xsvLy9++MMf8pWvfIU9e/Zw7NixRcdz+fJlNm7cyKVLl9iwYcPM9j//+c94enqSkJBATU0NX/va1/D29ubChQsO62A47BR7eHhQUFBAamqqzc8rKipYs2YNRuPtXwvUnlM8Pmnht9WXGDNNsS8qjRRfcQ2SI07xwPgYL5dfw2yx8GTiamK8/RfuIOV8mQVah4f4a0URAP8nPZtQT29ZTjHAsHGcVwoLmTCb2B4Xz5rw8Bn7trDncJS2d/JedS0qQeDJrAzCfX2cdooBpkxmXr9RSvvgMH4GPR/MXY2HVuvQeS1mf2JqijevldMxMCw6xrnphPjYcIxdcIpBdFBPFddS1SqKe+XER7A5ddZbejvHcNYpttLSNfD/Z++9w9u6rnTv30EhwAoSBHvvVRLVbclFsuReYztxycR24kzKpFwnM8mN5xt7Zm48cfpkJsmdJL6ZiRPXuNuyLRfZkq1eKYq9dwLsBSABopzvDxAQCyoJSpR03ufhI/tgn7U3QBAb737Xehf7TjVjMk8jCLC2IJ11BelnSdw5JMXO6URaOgc5XtOJadKZghWniWBDWQYZKXHIvH4zOH+k2AWb3UFn9xANLQb0/ePu6xHhYeRnJ1CQk4gm2stms8JJcf7/XvpGabeYaf7JP0qk+CLEpbjXzyfF4PxzPXGmcwExDhUpFgU43eCBGIeIFIsC1LZ7IMZe/AqCfl4yaOwZ5OOqecQ4VPEFaOsf5r35irE8NPERZhTjecRYpvDyQgdLxvFBjENAigEGpky8Wle7kBiHgBSDD2IcAlIMMGE183Jz9QJiHApSDGC2T/Nq+0JiHApSDDDtsPJmd9UCYrxSSHEoDsBd8VISdCFRigFOnDjBww8/zOnTp5HL5ezcuROZTIYoirz77rt+1/PVr36VQ4cO+T2YbW1tJS8vjw8//JAdO3b4jQtB1BRnZ2fz9ttve3387bffJmumJ+7FDKVMzpo4pyp6YrA7qHoajUrNqnincnhQ3xF0LU5qVDRFcc7WUB93Lq6WJ1qlYmums6bpYFcn45bF13OUJCVQoJup2W1ocpo+LAEKuYybyouIVqsYM1t4pyZ09T5hCgW3rC0mSROFxWrjjZN1DE54L+pfLORyGdeszmNjvvPU/3R7H+9VNoa0rsgb0hI03LVtDXlp8YginGzs5o39ZxhZ5hpnbxAEgfysBO6+fi0bV2USppQzMjbJBwcb2LW3hr6BsfOyrkCgkMvIzUrgxmvKuevGtZQXpaJWKZicmqaqrodX3jnF23uqaWztx2pdOa2oJEhYKqS93gl3jXGJc78/fKqN2kbJlXo2JFdq3/DUrinkfYwlV2qvuNRdqSPClCH7AYiJiZnz448Qe8P69euprKxkdHSUvr4+du/ezdDQELm5uX7vNZlMvPDCCzz88MN+x+bm5qLT6Whubg54bQErxS+99BL3338/N954Izt37pxTZ7Rnzx52797Nc889x1133RXw5CsVrlOWXx5/c4FSbHfImLJZ+e+mo9hEB3dklpMVFedVKXbMO0Y0Wqf575qT2EUHt+eUkBOjnXeDlyOmmesT0xaerj6FzeHg+pwCSrQJPscvvO5U8F6praV3YoJMjYbbi4uReTnuFLweczv/sdhsvHjqDGNmCznaOG4pLpyris4bvyC+h/1n2DTJyydqmLbbKUrSsbM472zKbZD71fzxFquNt07U0T9uQq1UcNv6EnSzFOOga429jXdAU+8gH1e14BBFdDGR3LCukKhwVciUTV8Kb3P3IAeqWpm22pHJBNYXZ7AmN3VW6u/s+EEqyItUQi3TNqoaeqhp0mOfab+VlqRhQ3kmurgozzcHtJ5lVpZnYLc76OodoanVQE/fqHtahUJGdkY8+dmJJCfGeFfAg0i3npoy8XdfkdKnJZx7XIp7/W//nweleEYyWFBjPCuVes74JSjIPmuM3XEWryD7rDEOYp3exvusMQ5BfJ81xiGI76vG2B0nSKV+9tw+U6kDeg6+N1e/Ncb+1upH+fVbYxxgHG/j/dUYu4cvUkH2V2PsL44/Bdlit7Gr5zQGLzXGvtY/ZTTxyLrbV6x/yFLjeVKK56OpqYni4mLeffddrrvuOp/x/vSnP/G1r32Nnp4ev7XC3d3dZGZm8vrrr3PbbbcFtN6AleLPfvaz7Nu3j4iICH7xi1/wwAMP8MADD/CLX/yC8PBw9u7de1FskoEgXKGkPM6ZdnxssDOoe6OUYVQkOO892Bf8qW10mIpNKc4Nc393+6JOHQVBYEduLnJBoHNsjJqBgaBjuKBSKLihpBCZINA2PMKpXv2iY7mgjYzg+jKnI3WDYZATnYt3y54PlVLBretLSIyJxGy18eaJ5VGMAQpSddy6qQR1mILBcROvHKymb3jc/40hQH66jru3ryEjKRaHQ+RYbSdvHqhmePz8qMYAqjAFG1dl8bkb11KSl4QgCPQYxnhjzxn2HGpgcGR5fg+hglzuJL/XXl3KZ29bz/rVmcREq7HZHDS3DbD74xpeeusEx053MDxqWlH9mmcjlCfHEi4+SHv9XCxwpV4Gxficu1LXSq7Us3GuXamXRTGWXKm94ly4Ut9yAblSLzeMRiOVlZVUVlYC0NbWRmVlJZ2dTr700ksvsXfvXndbpmuvvZY77rhjDiF+4IEHePTRRxfE/uMf/8gdd9yxgBAbjUa+973vcfjwYdrb29mzZw+33347+fn5QZlCBqwUX0rwpxQDGK0W/tR8DLsocmfWKtLC4zzGmq8UA06lueYk0w47N2QWUByXOOsG30oxgM3h4C81lYxZzKxNTOHqjByf4+deP/ufJ3t72d/ZiVIm4/Plq9GoF9YZ+lOKXajq1bOvpR0B+Ex5KemaGJ/j3fF91Oqe6dGzr6kdgB1FuZSkJC5ZKXbBMm3jrZNOxVilkHPLumKSNNEhVYpdmJiysPtEA0MTk8gEgcuLMlmVlbzwJD2ESrH7VlGkqWuAg2fasdrsyASBioI01hakIZe7pZDg4oegZhZgfMLMydouWjoH3dcykmNZU5JOUnz0gvHnWyn2NF4URfoHJ2huH6C9c5DpWanUsTHh5GbqyMnUoYkO9z7BOVaKJfdpCRKcCEQpdv+/5ErtN77kSu17bsmV2ve8l6Ir9cWoFO/du5ft27cvuP7ggw/ypz/9if/8z//kZz/7GQaDgZSUFB544AEee+wxwsLOvpe2bdtGdnb2HDfqhoYGiouLef/997n22mvnxJ6amuKOO+7g1KlTjI6OkpqaynXXXccPf/hDd7ZTIJBIsQcEQooBPu5rpmqkj9SIGO7MWOMxbdgTKQY40tfNQX0n0UoVDxavQ+FK1QmAFAO0j43welMdAnCfy3TLx/iz12evTeTVmTTqlKho7i4pPetGPYNASbEoirzf0EzjwBDhSiX3rVlFlCrM63h3fD8GVgdaOjjV1YcA3FheSJ5W6+WG4ONbrDZ2nXKabynkMm5YU0iWNtbnehbED4AUA1htdvZVt9LcNwRAQUo8V5flzm2dtAyk2AXjlIUDp9voNIwAoIlSc8WqXNISNOeNFLvGj4xPcrq+h9bOQXeIlIQYKkrSSUmIOf9u1d4wb7zN7qCrZ5i2jkG6+kbmKBna2EhyMuLJydASEx3uMw5IpFiChHOBYEgxeHClnkWMQ+ZK3dTDsZrldKU2sP9MGyC5UntCqFypvc2tnzDyRnWdZ2IcAgMuyZV69gMLr59rV+rbM9aQ4MOV+mIkxRcyAk6f9oe6urqAiqQvJmzUZSAXBHonx+meHA3q3rUJqUQpw5iwWqgcDD49OFsTR0FcPCKwp6MVxyLONmSCwHV5eSjlcvqMExzvW3yasiAIXFOQiy4igimrlXcaGrGFIP1oS24mJckJiMB7NU10j4bOnEmlVHDbuhLStRpsdgfvnGqgyTAUsvizoVTI2bEmny0lWQgCNPUN8erhGkZN58bBNSpcxfWbiti5oZBwlZIxo5m3D9Xy0YkmJs2hS4NaDOJiIti2qYC7b6igMDsRQRDoGxjn3U9qeevjatp7hlZsKvJsKOQycjJ17LiimPtu38gVG/NIS45FEGB41MSJM528/E4lb7xfxem6HsYmLnz3XgmXHi7Fvd6FBeZbJ9uobQqx+dYyp1KXZictq/lWQerymm9lJ8Rd2OZb0VFzUqnfrK4PqRGnLiJCMt/ygfBzbL71xiWeSn2hIWSkeHp6mo6OjlCFuyAQpVRRHuc8JT4yGJybtFIm5/LkTACOGrqZslmDnn9bRg5hMjmGSSNnBhZXyxujVrMtOxuAIz3dGEyL/+NVyuXcVFyISi5HP2Hk07b2RcdyQRAEthfmkquLwy6K7KpppH8idB8wSoWcm9cWkZekxSGKvH+miTNdS6+L9gRBEFidncJtG0sJD1MybJzk5YPVNPcN+r85RPPnpsbzuWsqKM12ppM09wzy149PU9OmX9TBSigRExXOlRvy3DXHcpnAwLCRPYcaeeW9SurbDG6DrpUOVZiCwtwkrr+6lPtu38jWDXmkJmkQBBgaMXGiqpNX3qnktd2VnKzuYmgF1yBLkDAbl+JePxvnhBhLrtQ+cbG5UoeeGEuu1L5wqbtSS/COgNOnv/vd7/p8fGBggOeeew77OWg9s9xwpQr8+4k3fKZPg7O2+H+aTjjdpDPKyYqam97rq3+xQxR5tqGSQfMkFboUtqfnIgaQ9jwbp/V6Pu5qI0wm54GytUS5cvK9pvUujC+KIu82NtE8PEycWs19q1adTefx2nfYe1p1+/AIb9U2AHBNfi5lyYkBpxm743tITX2rqp6e0XHUSgV3VZQRF3k2DXWptcYOUeTTujZquvsB2JibzobctABSdwNb//x7TOZpPqxsom94AoDSzES2FGejkC88pwrZ3POuD4wY2X+6lcExp8GVLjaSK1bnkhAb5ec5nJt06ynzNDVNeupa9O5a3XC1krL8FIpzk1CFeXDXDGQ93hBsGnaw35FEMJutdHQP0d49TJ9hbM4XxegoNbfsXIVarWRq0sQ3/vb6FZtSdSmlU11quBT3+t8+tTB92ld/XvBfY+weL7lSX5qu1AHM7bfG2G8cyZUaVrYrta8aY3CmT39nvZQ+vVIQMCmWy+VUVFR4fUGMRiMnT568qDbKQEgxwL6+Vk4N95IcHs1nsyrmfOj7IsUAnROjvNJSgwB8oXgt2rBIj+O9kUeHHV6sP4Nh0khBXDw35xbNTODlyXlZj3nayrNVVZisVlYnJbE9J8fnvL5IMcCxzm4Od3YjEwTuKC8h3dsfUoCkGGDaZuO1U3UMGE1EqcK4c20ZMeqZWpMQGHCJosixlm6OtzrrxVZlJLG1KBuZIIScFAM4HCLHm7o52eKcLz46gmsrCoiNmltzulykGJyHAXVtBo7VdWK1Of92izIT2VCcQaTKw+bmY+LlqkGettppaDNQ09iHacp5Gq1UyCjITqQkL5nY+TW6/tbjDeeAFM+GZdpGV88wHd3D9OhHiYwI486b1iIIgkSKJZw3XIp7/WJIMQTWrmmptcZ+ifESa43nEOPcFDaXziPGS6zVnU2Mi9MTubo8J6Tx/RLjJcb3R4yXQoohwHZNS6g1DogYL6HWOCBivIRa40CI8VJqjQMhxkupNfZHjCVSvLIQMCkuKiriscce42/+5m88Pl5ZWcn69esvqo0yUFJssk7zP03HsYkObkkvIzf6rFW4P1IM8EZrHa3jw2RGafhMTrnnPr/evoA7BPonjTxfV4UI3JpXTF6sNmhSLIjQMTrK6/X1ANxcWEi+VrtoUiyKIrsbmmgeHEatUPC51eXEhi90tw6GFANMWay8cqqG0SkzMWoVd64tI0oVFjJXakSo6tSzv6EdgGxdHNeuyvfcU3BmvMf4AZBiF7oGRtlzuhnztA2FXMbWkmyK0xNCr1L7uD5pnuZIbQfN3c5UbqVcRkVBGqtyU1AseO7nx5jLbnfQ2jXEmYYeRsbP1uOmJWkozUshIyV2zt/OSifFs2G12jGazMTFOj9vJFIs4XzhUtzrF0uKQXKlDiT+JelKHcTckiu173kvZlfq5STFfQODISPFKQm6S2K/D5gUf/7znycxMZF///d/9/j46dOnWbt2LY4Q1l2cLwRLigE+1XdwYqgLrSqC+3PWu12cAyHFoxYzf64/iV0UuTmzmIJY3cIbfJBigP3dHRw39BChUPKFsgrC5V76iPogxQCfdnRwsq+PMLmc+1c5+8Z5HO+HFANY7XZePVNLv9FErFrNZ1eXEa6ct64gSTEiGM0WXq2sZdxsQaNWcUdFqceUGl/w53rdpB/ko5oW7A6R+KgIbq4oIlrtYY4QkGJwplN/dLqZniFnH+PsxDiuLs8lXKU8J6TYBcPwBIeq2xkYdda/RKrD2FySSV6abhbhPD+kePaBS2//GLXNejr7RtwPR0eqKMlNpiA7AbWv181r/PNHiudDIsUSzhcuxb1+KaQYJFfqQOJfcq7UPmJJrtTe43gbf7G5UruI8XKS4qL/9SPkKs/f4YOB3WKm4T/+8ZLY7wM22vrFL37BI4884vXxNWvWXBSb5GKxPj4dlUzBsGWShrH+oO6NVanZmOjc4Pb1tS6qqftlqRlo1eFM2qx83Nka9P0ubMnIICUqimm7nbcbl+YgrZTLuaW0iGhVGKNmM2/Xh8aROspFhNUqxswWXqusZcIcOhMGgIJkHbevdxpiDRkneeloNfqxiZDOMRuR6jBu2VjCZUWZyASB9v4RXtx/mjb98LLN6QlJ2mhuv7Kc7evyiVSHOcn6qWZe//QMPYNj53Qt3iAIAmlJsVy7tZjP3rCW8oIUwpRyJkwWjp7p4IW3T7D3aBP6wXHJvEqChCAh7fXBQ3Kl9g/Jldo3JFdq37jYXKmrRntCFltC6CD1KfaAxSjFDoeME0NdHOhvI1qh4gt5G1HIZAEpxQA2h52n608xPm1hQ0I6V6Rkz5vAy2JnxTeYjLxQ70yjvimnkMI4T4qzb6UYYMJi4fkzZ5iy2ShPSGRHzsL2G4EoxS4MmSZ5uaqGabudIl081xXmn1UdF6EUu9c5Q4jHzRZiVCo+s6aEGHVgp2L+lGL3HFMW3qlsYMg4iVwmsKM0j4Jkndfxftfv4x7X9cFxEx9VtTA84fxALkzVcUVJNiqlwuP4gOcO8rrdZudMax+VTT1YZ1yf0xM0bCrJRKdZWPt+rpRiT7Da7LR0DlLfamBo1OS+HhcTQUluEvmZOsLmv34L4ktK8fmIJUHC+UKolGLneOFsKvUsxbikcKH5ls/4fq6fbpiXSl2SEeQ6fcevbdez/0w7MKMYz68x9rNOf69bY88gH1fNUozn1xgvNv7M9bb+Yd6bnUq9yoP51iLiu653j8xVjG9cvdB8K9C1ehqvH/eQSq3wXL4VjFLsmndw0sSrdbVzFWNve2OwCi9eFOMwL/4kQSjFzuuiUzFurp6jGGtUnjMFg1GKAWQCTNqmebV9rmKsVXvxLQlCKXaNt9htnBruYqMuC7kgk9KnVxgkUuwBrjfUr04uJMXeSK7dIXMS2+bjGG3TXJmUw7r4dK/jHR4+FVvGhnmztR6ZIPCFwgq06rM1Dd5dqedeP9jTydG+btRyBV8oq1hoeBAgme0YHeWNOmd98fV5+RTrdD7Hu+CNLHeNjPFmTT0OUWRjRhqXZTk38qW6UhvNFl6rrGNsyky0Kow7KkrRzKpdDrrW2MN6pm12PjjdRMfgKAAbctPYmJuOsAgDLl+PzY5ltzs41tRNZauzTi1KHca2VXmk6zQ+1+pz7kWS0CmLlVMN3dS1G9xtm/LSdGwoziAmctZr7fU5hyjd2gtmxxFFkcERI3UtBlq7htwtnORyGbnp8RTmJJIUH+25bj+A+AEhWHLtYfjUpIlvPLw8pDgUG+WltElKuHjh+pv4v3/YvYAUi15zMj1fllypF67T2/hLwZUaFn8wIblS+17/he5KPRtTRhPf3XDbij0AX454KxlBk+K4uDiPXygFQUCtVpOfn89DDz3EF7/4xSUvzm638y//8i8888wz6PV6UlNTeeihh/inf/onr19q9+7dy/bt2xdc7+vrIzk5OaB5F0uKAWpG9HzY14RKpuChgg2ECZ5PyDyRYlEUeaOlnraJETKjNNyZW+Z+noGSYrvDwfN1VQxOTZKr0XJrXtHc1yoIhfdwVxdHu3tQyGTcW1ZOfESEz/HgW0GuNfSzp8mZ2h2qVk0ARss0r5+qZXTKTJQqjDvWlBIboZ5Zj5f4XuBtPQ67yOGmTio7nClxeYlatpfloQrSgMvXY57m7hse5+PTLYxPOdOEyjKT2FyYQZhScc6V2XGTmeN1nbT0DAEgEwSKsxJZU5BGVLhqRZDi2bBYbDR3DFDfamB04qwxlyZaTWF2InmZCUSGe3PYDmCd3rDCSXHxt5ZeZ2S3mKn/9aVRY3Sp4lzu9XB+9vvlIMXggRhLrtQLxl/srtSweFIMkiu1v3kvdFdq9zwSKV5RCLim2IXHH38cmUzGzTffzL/+67/yr//6r9x8883IZDK+8Y1vUFhYyNe//nWeeuqpJS/uJz/5Cf/1X//Fb37zG+rq6vjJT37CT3/6U37961/7vbehoYG+vj73T2Ji4pLXEwhKYpOIV0Vgcdg4MtAZ1L2CILAtLQe5INBpHKNhdDDo+eUyGdfnOOtqWseGqRkKrr55Njalp5MRo8HmcLCrqRHLEutPSpMS2ZjhrLn6uLmVpsGhJcVzIUoVxmcqSokNV2O0TPPqqRoGjSb/NwYBmSCwpTCLbaW5yASBlv5hXj5yhiFj6GpOPCFFG8Nnr1hNaYbz/VvTaeDFT6vOea0xQEykmms2FPKZq1aRlqDBIYrUtht4cc8pDlS1YpwKbV33UqEKU1BWkMKd163hlm3lFGYnoJDLGJswc+xMJy++fYLdn9bS1DHgbkclQYIEJ87lXg8X5n7vDe4a4+JUAA6fCn2NccUy1xiXZSdzRXk2AFWtoa8xLkzVcc0qZ41xfXfoa4xzErTcsNw1xuXLWWMcPafG+K2Q1xhHcmdxKeEXco1x3qplrTH+TNby1RhLWJkIWim+6667uPbaa/na17425/rvf/973n//fV555RV+/etf84c//IEzZ84saXG33HILSUlJ/PGPf5wzf3h4OM8884zHe1wnxyMjI8TGxi5q3qUoxQCdxhFe66xGAO7LWU+8amH9pSel2PmAwBFDFwf1nYQrlDxUtBa1QhmwUuzC8b4e9vd0oJDJ+HzJGuJcNRFBKMXgTJ19ofoME9PTZGliua2oyOmsvQilGJwn6B83t1Fj6EcmCNxSUkh2XJyH5+Ulvg91cXJ6mjdO1zNkmiRMLufmVUXe+yN7QSDKtX50gveqmjBZplHIZGwryaUoJbD0cl+P+VNOuwfH+KSmjfFJM+B0qL6yNJuo+a7Yy13DO3O9Z2CMk41d6IecBmQyQaAoM5GK/DSiI1QLb5g/7zIrxZ7iTFvttHUP0tQ+gGHorHGaQi4jK1VLXqaOtCQNslkn/hebUiylT0sIBOdyr4fzs98vl1Lsvia5UvuNf7G6UsPSlGIXJFdq3/Ne6K7UklK8shC0Uvzee++xc+fOBdd37NjBe++9B8BNN91Ea+viHZBd2LJlC3v27KGxsRFwtoLYv38/N954o997KyoqSElJ4dprr+XAgQM+x1osFsbHx+f8zIYoikGdYGZGxZEXHY8IfGpoCfr0c0NCGvHqCKZsVvb2tgV1rwvrk1JJj47B5nCwu23xJ5gRSiW3FM6cho6NcrArOPV7PgRBYFt+DgW6eByiyNv1jfSMjfu/MZC1hjkV41RNNNN2O2+erqN5MPSKanJsNJ+9bBXpWufr+2FNM/vq20J6SuwJ6ToNn7tiNWvzUt0O1S98epqq9j53re+5RFqChlu3lnPzllJSdDE4RJG6DgMvfHSKfZXNjBqn/Ac5xwhTyinKSeKW7eV89oYK1pamExOlxmZ30NI1yPsH6nn+7RMcPNV60bpXR4QpQ/Ij4eLGudzr4dzs9/72+vGJKdraB5b2RGZBcqX2D8mV2jckV2rfuNBdqSWsLARNirVaLW+99daC62+99RZarRYAk8lEdHT0khf3gx/8gHvvvZfi4mKUSiVr167lkUce4fOf/7zXe1JSUvjd737HK6+8wiuvvEJGRgbbtm3j5MmTXu958skn0Wg07p+MjLNujv3mcd7qqaRxQh/U2q9McqZBd02O0moMLk1YLpNxbXo+AHUjA7SNB0/sBEHg+uwCVHIFhkkTB3sXT2YTIyPZOeNAfaKvj9qBpX1pkAkC1xbmkR0Xi90h8mZdA4YJ45JiuqBWKrhtdQm5ujjsosi7tY2c6TWEJPZsRIQpuWVdCRtynF92qrsNvHq8xl37u1xQyGVsLszk7q2rSIqNwmp3cKC+g1cPVWMYDc1rGCxSdRpu2VLGrVvKSNNpEEWRhq4B/vpxJR8cb2BwNLSp7KFCTFQ460ozuPv6Cm67ppzS/GTUKgVmi426FgNv763hxXdOcqSqnYER40VJkCVI8IZzudfDudnvfe31U1PT7H6/mn2fNtLcsviyo/k4J8S4aLmJcfKyEuPCtOUlxjmJ2gubGMdELzMxjpSIsQ9ESMT4kkHQ6dNPPfUUX//617npppvYtGkTAMeOHeOdd97hd7/7HQ8//DC/+MUvOHr0KC+++OKSFvfCCy/wve99j5/97GeUlZVRWVnJI488wi9/+UsefPDBgONcffXVZGZm8pe//MXj4xaLBYvl7B/Q+Pg4GRkZ/OfJ16m3DnFosJUohYrPZ28GPBsr2T3kyRwwtHNsoJsYpZq/yVuPYlZKptdWTbPSsPf1tHFyoJcoZRhfKFyLSu7BHdBHGjZA88gQu1oaALi9oIScGA+pyrPGL7x+9j8PdXVxrKcHmSBwV0kpqZ6+DAXQ8skFm93BWzX1dI+No1YouHNVKfGREV7Hz1+Pr/gOUWRfYxs1vc4vNxuz0tiUne428gg2ddfX+I6BET6sbsFitaFSyNlelkduotbLDaGbW3SI1Hb2c6Shk+mZmtiSjEQ2F2ag9qDknStjLsPwBJVNPXTqR9wPpSVoqChIIyU+xv/vwNvEwf5uggwvAA6Hgx7DGK1dQ3T0DM+pNY6OVJGbriM7XUt8bGRQDtbBrnNq0sQ3v3jdik2pupTSqS5VnMu9Hs7Nfu9tr/+/v9+NWh3B4WOtNDQ5D8Cv2FJAft7C2mTJlbodkFypPY2XXKm9XJdcqb1enzKa+IeNt67YvX454q1kLKol04EDB/jNb35DQ4OTbBUVFfGtb32LLVu2hHRxGRkZ/OAHP+Ab3/iG+9oTTzzBM888Q319fcBxvve977F//34OHToU0HjXG+A/T76OMlLNs+1HMNmm2ZqQR7km0+M9nkjxtN3O000nMNmmuTwhi40JZ+8NhBRbHXb+Ul/J2LSZcm0SO2fU47k3+K81/rijldMDesIVCj5fsoYoDx8SgZBiURR5p6mJluFhwhUK7ikrRzO/L3AQpBicNZ6vV9diMJqIDFNy56oyYsPVIXGlFkWRo23dHOtw1nOVpiSyrSAHmSz4Vkr+xo9PmfmgqhnDmFOtLc9IYktBFgr5wvdFqOeetExzuL6Txh6nMZtKqWBzYQbFGYnO+u9FzrvUGuThcROnm3pp6Rl0l9kmxkVRnptCTooWueDtm8H5I8WzYbM76NaP0No5RGffiLu9EzgJcnaalqzUeBLjowIiyBIplnCh4Vzt9XB+9nt3TfHvdxMeHokoin6JseRKLblS+4ovuVJ7gORK7fW6RIpXFoJOnwbYunUrzz//PCdPnuTkyZM8//zzy7JJTk5OzjG8AZDL5TiCTEuprKwkJWXhiWwgUMjkbIzPBuD4UAcWuzXge8PkcrYm5QBwbLCL8WlzUHMrZXKuzXAS4ephA+0TI37u8IwrM7JJCI9kymbjndZG7OLi0noEQeC6vDwSIp2x3mioX3KKTZhCzm1lJcRHhGOatvLqmRqGJ0NTiyoIAptzMthW4Pwd1Pb189aZeszW0KUFuRATruaOjaVUZDvfZ9VdBl46cobBieVPHY5QhXHNmnxuv6wUbXQEFquNT2raeOXAGboHx5Z9fm/QxkSyfX0Bn7tmLSXZSchlAv0jRj460cQLH56isqkH83Tgf0/nGgq5jOy0eK65rJDP37qBbZsLyE7TIpfLmDBZONPYx6691Ty/6wT7T7TQ2TuCLYQpbRIknG+cq70eVsZ+LwgCl23MpajA2c5p/8Gm5UmlllypvUJypfYNyZXaNyRXaglLwaJIsd1u55VXXuGJJ57giSee4LXXXsO+DF8Gb731Vv7t3/6Nt99+m/b2dl577TV++ctf8pnPfMY95tFHH+WBBx5w//+vfvUr3njjDZqbm6muruaRRx7ho48+mnP6HCyKY5LRhjnbLJ0Ybg/q3qKYBFIjYrCJDj4xBG9IkhGtoULn3OA/6GrCbAueRChkMm7OK3R+iJom+LS7I+gYLijlcm4tKiIqLIwRs5m3Gp0nokuBWqngjvLZxLiWIVPoPmTK05K4qbwQpUxG18gYL52sDhnxng25TMaWwixuWVdMRJiSEdMULx+p5lR77zmpR03RxnD31lVsLckiTCFnaGKSXcfqeOd4PcMT5+9DOyZSzRWrc7l35zrWFaYTHqbEZJ7maH0nz35wkk9OtzA8vrI3FaVCTl6Gjh2XF/H5WzdwzWWF5GXoUCrkTFmsNLT188HBep558zgfHKinoc3ApDl0G70ECecD52qvh5Wz37uJceEyE+NlrDGuKExjY9kyE+NVzsPmZakxTtVxzZplrDFO0HLDmsILnBiXLG+NcXHZhV1jvIAYBydK+YJEjC9eBJ0+3dzczE033URPTw9FRUWAs0dgRkYGb7/9Nnl5eSFb3MTEBI899hivvfYa/f39pKamct999/H4448TFuZMt3jooYdob29n7969APz0pz/lD3/4Az09PURERLB69Woef/xxtm/fHvC8s9Onw6OdbRo6TcPs6qlChsDnMjehCZubLuEpfRqcadJDZhPPt57CgcgtGaXkRscHlD7tgtVh59mG04xYpijU6Lgpq2jWDYG3amoZHeatZmca2o05hRRpdT7HO697vjxkmuSl2hqm7XYKtfHckJ/vTHEKMn16dirrlNXK69V1DJomUSsU3FFWQuK8lliLadXkwqDRxNtnGpiwTBMml3NDaQFZ2liv44ONPxtTFit7a1tpG3Cq+ymx0VxTlocmwkdqeIjmRoSpaSsnmrup7ezHIYoIOFPRNuanE6kOWzA+qPV4GR5oHLvdQUvPINWteobGzyrpKdpoSrOTyU6J81iHda7Tp/2NB7A7HPT1j9PZN0xn7wimqbkbuy4ukozkODJSYkmIDSzNGqT0aQnnH+dyr4fzs9/PT5+eDVEQOXy0lYbGuanUoWjVBM6PlRNnOhe0awpVqyZRgNMNHlKpQ9SqSRSgtl3P/jPtwKwa4/k1un7W6et1a+wZ5OOqeTXGoYovQFv/MO/NT6WWhyY+eG/XFIpWTeCjxngJ6dOzMTBl4tW62oXtmpaQPj0bXlOpl5A+PRsTVjMvN1cvSKUORasmALN9mlfbF6ZSr5T06b7+pbdfdMVLSbw0WjAGTYpvuukmRFHk2WefdTtQDg0N8Td/8zfIZDLefvvtZVnouYQnUgywq7uKzslhsiN1XJ+yas49vkgxwAFDGyeGuolShPH5vPUoBc8tTTyRYoA+4wQvNlchAjdmFlIUmzBzQ+CkGOBAdwfH9D0oZTLuLV5NfHiEz/G+SGjX2BivN9TjEEUqkpO5KjMLwcvrECixM9tsvFFdR7/RhEou5/ayEpKjowJaTyDxJ6etvFvTSN/YBAKwNS+LirTks2QlhMRUFEXqegc40NCO1e5AIZOxpTCT8rQkz+QohHO7MGqa4khDF20Gp4O5Qi6jIieViuwUlAp5wHHmrMfL8GDj4BDRD09Q3dZHu37YXXccrlJSkpVISWYSkeGzCPwKJMVzhokiw2OTdPaN0Nk7zODI3NR5tUpBelIsGclxpCVpPJqhuSCRYgnnG5fSXu+JFCNz/k3PJ8Z5+UmegwVJihGEszXGs4hxSaGX1O9FkGLwQIxLMjyPXyRRW0CM59cY+1mnv9dtATGeX2O82Pgz1xcQ41UezLcWEd/1WPfIQmIsU3h5sRdRz6wf90CMFZ4NYYMlxaIwU2M8nxgrPZhv+YjjlczihRiHeTDf8hXH6+sjOmuM5xFjjcqDrw7Bk2KZAJO2hcRYqw4POP5ykuKyr/4IuUrt/wY/sFvM1Pz+Hy+J/T5oUhwZGcnhw4dZtWouKTx9+jRbt27FaDw/bWFCCdcb6tenXptDioctJl5oP4GIyG1pFaRGxLkf86b8usiyzWHnmZaTjE2bWaNN5aokD6ZZgOhNQRYFDvV1ctjQhUou54GitUSFqbyO90YeHQ54rbGWrokx4tTh3Fuyyulq7S2zxyvpdv5TPzjI+83NAGzJyGBjapqX8YEryNM2G2/WNNA3PoFSLue2smJSY6K9jp+9nkDi2x0O9ja0Uad3tpYqSU5gW2EOcpksdGZUs66PT5n5qLqV3hFnT8w0bQzbS3OJCVd7vSdUc7vQNzzOofpO+mfaNoWHKVmfn0ZJZqJXw6tzacxlmrJQ39FPXbuBKYuzREAQICtJS3F2ImkJsci9He96XY+X09oA1hMQ/DzfyalpuvWjdOlH6NGPzXGyFgCdNor0pFjSkmJJ0EbN+SI2NWniWw9JpFjC+cOltNf/1+88KcUz/wZgvuUcvzgFWXKl9h//gnSlnnWP5Ert5brkSs2U0cT3Nt0ikeIVgqBJsVarZdeuXQvMNg4cOMCtt97K8HDwPXVXGryRYoCP9U3UjvWiU0VxV8YG9wezP1IM0Gkc4bWOagA+m11BcvjCN5cvUmwXHbzYeAbDlJGMKA135pV5VWZ9kdxJq5Xnak9jtE6TGxvHrXnFCP6Om33EP9XXx6cdzjrlHTm5lCcu/MIQtCu1zc6u2gZ6xsZRymTcXFpERqwmJKQYQHSInO7Wc6ClAxFIionixtICor2cIC6VmIqiSHWXgUONndgcDpRyZ/1xaVriottEBd3CSRRp1Q9zpKGT8Umn8UR0uIoN+ekUpOoWfAk4127V4DywaO8dprZdj35own09KjyMosxEijISiYqY9ztaoaR4NhwOB4bBCbr6nCR5dHxuTXuYUk5qooYrN+QRplRctKT4t7/9LT/72c/Q6/WsWbOGX//61+52P77wwgsvcN9993H77bfz+uuvA2C1Wvmnf/on3nnnHVpbW9FoNOzcuZMf//jHpKamuu8dHh7mW9/6Fm+99RYymYy77rqL//iP/yAqKsrLbBLg0trrfZFiCIwYS67Ukiu1rzkkV2oPkFypl5UUS+nTwSNoo61bbrmFr3zlKxw5cgRRFJ2bxeHDfO1rX+O2225bjjWuKGzU5hAmkzNoMVI/Hpw5RmZUHCUa50b6YW/wBlVyQcaNWYUoZDK6jGMcNSzOPCNCqeSW/CLkgkDr6Aj7ezoWFceFtSkprJ/5ErqnrZWGwcElxQOnc/etpUVkxmqwOhy8WVNPQ//S47ogCAIVGSncsqoYlUKOYdzICyfO0DE8GrI55s+3KjOZe7asJjk2Gqvdwb66Nl4/Xsuw8dwYNAiCQF5KPPdctYYry7KJUCmZmLLw8ZkWXvz0NI09AzjOgSGYL8hlMvLSdNy6tZy7tq2hPDcZlVKOcWqaEw3dPPfhSd45VEtz9yA224Xj9CyTyUhJ0LBpdRZ3XVfBvTet48r1eeSkxxOmlDNttdM/NOE17S2UmLJYQ/ITLF588UW++93v8s///M+cPHmSNWvWcP3119Pf79vEqL29nX/4h3/gyiuvnHN9cnKSkydP8thjj3Hy5EleffVVGhoaFuxDn//856mpqeGDDz5g165dfPLJJ3zlK18Jev2XGi71vX42JFdq/5BcqX1DcqX2DcmVOvSIUClD9nOpIGileHR0lAcffJC33noLpdL5QtlsNm677Tb+9Kc/odFolmWh5xK+lGK7Q8bpkS4ODTajliu5L2szKrkyIKUYYMpm5ZmWE0zarKyLT2drYu6cx30pxS7UDPfzfmcTAnBnbjkZUbELbwggHbp+aIDdbU0A7MzMo1znoVYqAKXYuT6Rj9vaqO7vRwBuLigkb6YOzTk+OKXYpcLZHQ7eb2ymedCpSmzNymRdWsrC1Kkl1BqPTZnZXdPIwAw53ZiZxqbs9Dk9fkOl1gI4HCJnOvUcaenCZncgEwTWZKWwMSfNc8pTCOeeDavdTnW7gdOtve42VZoINWvzUilI1aHw1kf4HNcg2+x22nqHaezqp3dw3H1dqZCTm6qlIC2BlPiYhe+JFaQU+xrvEEUGh41Mmq1kpzn/ZpZTKS7/2x8hD1taSpV92kz1U/9IV1fXnPWpVCpUXrItNm/ezMaNG/nNb34DONXzjIwMvvWtb/GDH/zA8zx2O1dddRVf+tKX+PTTTxkdHXUrxZ5w7NgxNm3aREdHB5mZmdTV1VFaWsqxY8fYsGEDALt37+amm26iu7t7jqIsYS4upb3en1LsviaKHD6+0HzLOX7xSvHs+CequxaYb3lbj8/4Xq5XNvVwrMaDYhwiA66aDgP7z7QB81Kpl6jkutCoH+Sj0x5SqUMUv21whN2nGxcqxktUil3wpRiHwoBLP2Hkjeo6z6nUS1CKXRicnOSV+pqF5ls+4gej8PpUjJegFLuwUDEuJ2ZmPwyFAZcvxfhcK8VSn+LgEbRSHBsbyxtvvEFDQwMvv/wyL7/8Mg0NDbz22msXxSYZCMpj04gLi8Rst3JkKLg2S+EKJdekFABwaqgb/dS4nzsWokybSJk2ERF4t7Nh0adpxfEJbE5xbogfdbbSNTG2qDjgPOnenpNDsU7nXFdzE+2jo4uO54JcJuOGogIqUp0n9Ac6OvmkrSOkiqYmXM1da8spT3UeChzr7OH103WYLMvTUsdFgu+7fA05CXE4RJFT7b08f6iK9sGRZZnTE5RyOWvzUrl/21o2FWagUioYmzSz90wrz39SSU2nAZs9dCfZi4VCLqcgI4FbtpRx7461rCtMJzpChdVmp6FzgF2Hanl+zymO1XcyOhH6VlvLDZkgkBgf7SbEFxIyMjLQaDTunyeffNLjuOnpaU6cOMHOnTvd12QyGTt37uTQoUNe4/+f//N/SExM5OGHHw5oPWNjYwiCQGxsLACHDh0iNjbWTYgBdu7ciUwm48iRIwHFvFQh7fULIQgCl226sNs1rVlmxbg0O4krVmUDy9OuqSBVx/bVy9euKTshjuuXWzFetZyKcdQcxTj07ZoiuLOk9MJu15RfPksxrg65Ynxn9spSjCUEjqCV4ksB/pRigN6pUd7sPgXAZzLWkRAW6zGWN1fq3V0NNIz3ExcWzr0561G4TgoDUIrB2abp+cYqhsyTZERp+ExO+VxlM0DjLFEU2d3WRMPwICq5nHuKVqFVR3gd7y++6BB5t7mJ5uFh5ILA7UXFZGg0S2rV5MKpnj72tzlTvfPitVxfmO9+3ZbqSu1Ck2GQjxpasTocRCiVXFuST2acJrRq7bzH2vqH+bShHeNMX9u8RC1XFGYRpVb5nCPUbsxWm52aTgOn2/qYmnamx0aqlKzJSaU0PfG8u1XPjiOKTufqpq4BWnqH5phYxcdEkp+uIy9FS1S4BzONYNcT5PhQxF9OpVgfgjqj8fFxkhN1ASvFvb29pKWlcfDgQS6//HL39e9///vs27fPI0Hdv38/9957L5WVleh0Oh566CGfSrHZbGbr1q0UFxfz7LPPAvCjH/2Ip59+moaGhjljExMT+dd//Ve+/vWvL+bpS7hIEKxSDEiu1AHOe8m7UvuYQ3KlnoHkSi0pxSsMXt7Bc/Hd73434IC//OUvF72YlQa5ICKf/6aXOdlXRmQMxTFJ1I8b+LS/kTsz1iPzkG4qejlh3JaSS9fkCCPTUxwdbOeKpBzAe2ek+QiTy7glp5DnGqqc9cX9nVyenHl2Xi+9/Ji3HkGAa3PyGLdY6DNN8EZLPfcWryJc4aoh8PJh4CW+gMD1+fnYGhtpHx3lzcYGbi0qIjPGs7Lg7RDA09W16SlEhin5oLGFlqFhXqup45aSItRKBV4zfYMkjgVJOnRRkbxb28iwaYo3qupYl5HK5ux0j26R3n5dvjbK+XPnJGtJ12k41tLN6Y4+WvqH6RgcZX1uGhXZKZ579vp4Dl6PufzsG0qlnIq8VMqzk6nr6qeytReTeZqD9R2cbOmhPDuZ8qwk1ErP9SVBr8frixfAdUEgWRdDsi6Gy1dn06EfoalrgO7+MYbGTQzVmjhS20FyfDR5aTpyU+JR+6uL8bo/e3tivsMtZbzXL6IhQLhKSfgSa4SsM/fHxMQsyyY5MTHBF77wBZ566il0Op3f8Varlc997nOIosh//dd/hXw9lwou1b1eEEWEBR9UXg6oHc597rINuSBCQ5Oe/QedZUgeXam9xBG8HSzLnHesL88AUaSqvpfDJ53pyB5dqT2H8dkpCJw1xgDHars4UedUiz25UgveDur98LeyrGQQYX91O1UtTrXbkyt1sNuA63UrStEhiPDRmWbqupxqvSdX6qDjz/ybO1NjvLuqiWbDEIBHV2pfr7Ovj/z0WGeN8a7qBrdiHKwrta/n5qoxfqO63q0Ye3OlFry91708AwFICI/kruJSXq2vdSvGXl2pvcHbF16Z6FaMX26scSvGXl2pfcTxDIFopZq781a5U6lfbj7j1ZXa2yvt7fV3IKKWO2uMXanUr7Sf8Wi+NV/wknB+EdC799SpUwEF89h79SLGloRc2oxDDFqMVI/2sDrO80mrJ6jlSnakFPBWVy0nh7rJjY4nNSK4L5fx6gh2pOexu7OJw4YuUiNjyIqODfJZgEIm49a8Yl6or2LMYuatlnruLCg7q8IGCblMxk2Fhbzd2EjH6ChvNTRwW2GRUzFeIgoTdEQolbxd10jf+AQvna7mlrIir33hFoO4yHA+u66c/c0d1PT1c7Krl+7RMa4rzic2InTzzIZSIWdLYRaFKTo+qWtHPzrBkeYu6nsH2FqYRZYu9pz9fSnkMlZlJ1OakUhDzwCVrb2MT1o43tRNZWsvxemJrM5OJiZi6Vb/oYBCLicvTUdemg6zxUpr7xAtPYPohybcPwfPtJGm05CTGk92snbJpFBCcNDpdMjlcgwGw5zrBoOB5OTkBeNbWlpob2/n1ltvdV9zzKQYKhQKGhoayMvLA84S4o6ODj766KM5JD05OXmBkZfNZmN4eNjjvJc6pL0+cLjMt8A/MV5s/PWrnAfdbmIsssCVeikIlBgvFmXZM2nmLmIs4lUxXgwKU50HZi5iLIosdKVeAnICJMaLhct8KxBivBh4IsYeXakXCZf51pKIsQ8ERYwXAZf5VmDEOHi4zLf8EWMJKwdS+rQHuFIF/m/lqwvTp+ed6tSM9rLX0IRSkPO5rE1EK+cSBVe69Xy4jLne62mgfqyfGKWK+3PXoRQ8f1n3dpokOgQ+6GqmethAuFzB/YVriAlTez998pZW7RAYmprkxfozTDvs5MVquTm3CFmweTuz4tscDjcxlgsCNxcWkhMbt2BeT/CX9jxkmuSt2nomLNOEyeXcUFRAdlys1/EBx5/3+rQMDPNRQwsWmx2FTMYVeVmUpQTQRskH/D03URRp7BvkUGMnkzNpzOnaGLYUZqGb9X5c7hZOLjgcIq36ISpbexkcd9bGCEB2kpbV2ckkx0UjCELw6/GGEKWFGycttPQM0tIzxNCY6WwcIDk+hpwULdmzU6xDpBSHKn362w9cXC2ZNm/ezKZNm/j1r38NOEluZmYm3/zmNxcYbZnNZppn+p+78E//9E9MTEzwH//xHxQWFhIWFuYmxE1NTXz88cckJCTMucdltHX8+HHWr18PwPvvv88NN9wgGW1JcL+Pf/df73pIn/ay5y7w8/PdrmmpBlz++hiHwoDLZ7umEBhw+exjHAKDLJ99jEMQ32e7Jh/8ONA5/LVrWqoBl98+xks04PLbrmmJBlx+2zUt0YDLX7umpRpw+TLfmjKa+P7mm1fsXr8c8VYyljFJ79JAqSaFlPAYrKKdT/obgjZ82JacR4xSzbjVwkd9TYsyjNielkNieCRTdhtvtjk/+BaD+PAIbs0vRi4ItIwO82FHy5IMLBQymZMIx8VhF0V2NTbSNDy06Hhz1hoZwWfXlJMSHcW03c5btfWc6O4NqeEGQF6Clns3rCY9Ngabw8HepjZ2VTdgXCYTLnAqBEWpCdx/xRrWZqciEwS6h8d56fAZPq5pcdcenyvIZAL5qTru2rqKmzcWk6HTIAJthmHeOFLLywfOUNfVvyJMuWYjKlzFmvw07rx6NZ+7poKNJZnoNJGIQN/QOAer23nug5O8uq+Kkw3dDI2bQv7+kXAW3/3ud3nqqad4+umnqaur4+tf/zomk4kvfvGLADzwwAM8+uijAKjVasrLy+f8xMbGEh0dTXl5uZsQ33333Rw/fpxnn30Wu92OXq9Hr9czPe38GykpKeGGG27gb//2bzl69CgHDhzgm9/8Jvfee69EiCWEBO52TRew+VZFYRoby5a5XdMqZ4nYcphvFabquGbN8plv5SRouWFN4QXerqlkGc23nIrxBW2+taBdkzlk8VdiuyYJniGR4iVCEAS2JxchF2R0TQ7TOGHwf9MsqOQKbkxzKrKN44PUjumDXoNCJufW7BIiFEoGzCbe61ocuQbIiNZwY24hAlA71M+nPe1LJsY3FRRQqI3HIYq829RE3cDAouPNRmRYGJ9ZVUppUgIiTmfq95taQk7OotUqbl9dwhV5WcgFgY7hUZ4/fpo6/cCykqgwhYLLCzO5f+sa8pK0iEBd7wDPHajkcFMnlhBuOoFAEAQyEmK5eWMJn7tiNSUZiShkMoYmJtlX3cqf957kcEMnE1Ohc3IMFTRR4VQUpPGZq1dz3851XFaWRZI2GoDBMRPHG7p4ZV8VL+w5xcHqNnoGx0L6pUQC3HPPPfz85z/n8ccfp6KigsrKSnbv3k1SktOgqLOzk76+wL/s9/T08Oabb9Ld3U1FRQUpKSnun4MHD7rHPfvssxQXF7Njxw5uuukmrrjiCv7whz+E/PlJuHQhuVL7h+RK7RsXvit1pORK7QPzXamPDnSFLPZKwyeffMKtt95KamoqgiAsMMc0GAw89NBDpKamEhERwQ033EBTU5PfuC+99BLFxcWo1WpWrVrFO++8M+dxURR5/PHHSUlJITw8nJ07dwYUdzak9GkPCCZ92oXjg10cGWpFJVNwT9ZmIhTO1A5/6dOz7z/Q345CkHFP9jq0ARbjzzaq6jWN81JLNQ5R5LKkTC5Lylx4g4/06dmoGezngw5n+uKW1Ew2Jc+rM1qEK/WetlZqZwjxtuxs1iQlh8SVWhRFzvQZ+KS1HRFIjIzk5pJColWqJadPz5932DTJhw0t9E8403GztLFcU5BDlBfXwqDm9nO9b3SCQ02d6EcnAFApFWzITmNVRtLcGqRz5FYNYJ62Ud/dT3WnHuOUc4MSgOzEOEozksjQabyaeAQSP1TrnBNn1n9PWax0GEbo0A/T3T+KfdaXHKVCTkZCLJlJsWQkxi6sQ5bSpy+JdCoJFy9CkT7thuRKHdC8l4or9WLmkFypZ3CRu1IfGejkyqQcFDL5RZk+/e6773LgwAHWr1/PnXfeyWuvvcYdd9wBOD8jt2zZglKp5Be/+AUxMTH88pe/ZPfu3dTW1hIZGekx5sGDB7nqqqt48sknueWWW3juuef4yU9+wsmTJykvLwfgJz/5CU8++SRPP/00OTk5PPbYY5w5c4ba2lrU6sA8cCRS7AHujfL0KwtIsc0LyZ12iLzaeZJBi5G8qASuSy0DfJBiD62RXmuvodM0ik4VyedyKuYYXc0n0Wfvm0dmhwy839kCwM1ZRRTEznNu9ebS5+FdcFLfxydd7QBsz8xlTUKyz/HO697JsiiKfNLRwWm98wvDprQ0LktL92yK4e2zxoc9d/foGO/WNWG22VArFFxfXECWN3OvJRBHh0PkVFcvR9q6cYgiYXI5V+RnUZKcEFit8RLmFkWR9oERDjd1MWJy9uSNVqvYmJ9OYYrO2ZYrWFK5SLfq2XCIIh2GEarb9fQMne29HROhoiQjieK0hAWkMlQk1xuCfb5Wm52egTE6+obp6h9lymKd83hiXBQZiXFkJMWi00TObYEW0DoDr02emjTxv74gkWIJEpYT7r3+t+8sIMXeOiz4I8v+aoz9xfFH+vzVGM9fT8DxZ133WWPsjrP4WmOfNcZBrNPbeJ81xiGI77PGOARz+KsxdsdZ5MGE3xpjv3F8fznwW2PsJ74/suy3xjjAON7G+6sxdg9fYq0xgNlo4n9ftjykOBTtF13xkhN1i1qjIAhzSHFjYyNFRUVUV1dTVubkSQ6Hg+TkZH70ox/x5S9/2WOce+65B5PJxK5du9zXLrvsMioqKvjd736HKIqkpqby93//9/zDP/wDAGNjYyQlJfGnP/2Je++9N6D1hsYiTgJyQca2pCJe6TxBi3GA1okBcqMT/N84A0EQuD69kGeaTzFoMbHf0Mq2lPyg11EWn8Tg1CQnB/p4r6sJTZiaxIiooOMArEtOwWy1cVTfzcedrajkcoq1gT+n+RAEgauyslArFBzp7uZoTw9TVhvbsrO9E4wgkB6r4Z615bxT28iAaZI3q+u4LDODDempIXVLlckE1melkaOLY09dC4YJEx81tNLcP8S2whxiwpfPlVkQBHIStWTp4qjvHeBYSxcTZgsfVbdwqq2XjXnp5CVqz7k7rEwQyEnSkpOkZXhiktqufhq7BxiftHCkoZNjjV3kJmspyUgkVRuzIt1rlQo52TPmW6IoMjBqpFM/QqdhlKExE/0jRvpHjJxo6EIdpiA9MZb0hFjSEzREqEPjhilBgoQLG5IrtX9IrtS+IblS+8aF7kp9rnD99/6APGzp30ftM/XV4+Pjc66rVCpUQWRJAlgszpT02cqtTCZDpVKxf/9+r6T40KFDC1oGXn/99e7U7La2NvR6PTt37nQ/rtFo2Lx5M4cOHQqYFEs1xSFEgjqatVrnZrWvv5FJW3D1CJHKMK5NLQSgaqSPpvHF1d5emZZNVnQsNoeDN9vrmFhCXcTlqRmsnlGI32tronFkcNGxwLmhb05P5+rsbADO9Bt4u6kxZLUtMWo1d68pd9cZH+rs4o3aereLcyihjYzgrrXlbMnNRC4IdI6M8dyxKo539Cx7PapMJlCansj9V1RwWUEGKoWcEdMU71c18eKhKpr0gzjOUxKINjqCK0qz+cI169i2KpcETSQOUaS5b4i3jtbx7L5THGvqYnwydEYWoYYgCCTGRbOhJJM7r17N/deu44rVuWQna1Eq5JinbTR3D7L3VDPPvH+Clz6u5GB1Gx36Yaat57bWW4IECSsLbvOtgmWuMS52msUdPrVM5lvLWGNclp3MFeXZAFS1LpP51ipnjXF99zKZby13jfGym2+drTF+a5nMt8Iv5BrjBeZbK88v5VwiIyMDjUbj/nnyySeDjlFcXExmZiaPPvooIyMjTE9P85Of/ITu7m6fniJ6vd7tP+JCUlIS+pnMU9e/vsYEAkkpDjE2aLPpNA0zaDHykb6BG1LWBHU6mR2tZX18OieGuvmwt4l4VeSC+mJ/kAkCN2YV8demKoYtU7zRVstn81ehkgf/6xYEge0ZOVgdduqGBni3tRExB4ridP5v9oE1yclEKJW839xM68gIr9TVcltRMRHKpfePVchl7CjMIzkmmk9a2ukcHeP5yiquL8onPQS9kmdDJhNYl5lKTnwce5va6Bkd53BbF/X6AbYV5JARF9r55kMpl7MuJ42y9CROd/RR1aln2DTFB2eaOd7aw/qcNPKT4kN2gh3U2hRyijMSKU5PpH/MSH1XP819QxinpjnR3MOJ5h5S4qIpTksgNzmeMC91TSsBkeEqSrKTKMlOwu5w0D88QXf/KN0DYwyOmRiZmGJkYorqVj2CAAmxUaTGa0hL0JAUF4UiRCfwEiRIuDDgVowFaGhcRsVYEKiq63EqxuAxlXqxqChMAwGO1SyjYiwI7D/T5lSMwXMq9SJRmKoDGXx02qkYA55TqRcJlyv17tONF7BiXMIb1XVuxdhrKvUi4CLGr1xUinE5MSFQX88F3vvZV0KXPv30P9LV1TUnXrAqMYBSqeTVV1/l4YcfRqvVIpfL2blzJzfeeOOK6P4hKcUhhlwmY0dyiduNumYs+NPVyxOzSYvQYHXYeburFos9+NM1tVzB7bmlRCiUDJon2dVev+hTRkEQuDYrn9J4p/q6u62R+uGlO0gXxMe73QoNJhMv1lQzPDW15LgulCUncs/qcrTh4ZisVl6rruNwR9eyqLhxkeHcsaaEa0vyiVAqGZ0y83pVHe/VNWFaxvZNLqiUCjblZ/CFK9eyMTedsBnl+MPqZp47WElNt+G8uiknaqK4qjyXB65Zz841+WTonIcFfSMTfFzdytMfneCD0010DIyseNdnuUxGik7DptIs7rx6NV+4fgM71hdQkpVETKQaUYT+ESOVzT28faiWP+0+xpsHqjle30XP4NiKa10lQYIEJyZNFrq7hkMWT3Kl9g/Jldo3JFdq37jQXamXE+EqZch+AGJiYub8LIYUA6xfv57KykpGR0fp6+tj9+7dDA0NkZub6/We5ORkDIa53X0MBgPJycnux13XvI0JBBIpXgZoVZFcrnP+co8MtTBsMQZ1v0wQuDG9mEhFGCPTU3zQ27ioD3FNmJrbc0pRymR0Gcd4fwmtmmQzxLhcl+gkxu1N1A4tfXNPjY7mc2VlaFQqxi0WXqyppmN0dMlxXYiPjOCeNeWUJjoJ/dHuHl4+U8uoOfSpu4IgUJSk4/Ob17A6LRkBaOwf4i/HTnOqq++ckD2VUsHGvHS+cMVaNuWlo1YqGJ+ysK+ujb8cOEVlRx/TttBteMFCIZeRn6rj5o0lfGHbWjYXZKCJUGNzOGjuG+KdEw38ee9J9te2Yxg1roiTQ38IVynJS9Nx5Zpc7t2xlvt2rOPqNXnkp+mIUCtxOET0wxOcbOrh7UN1MyS5hqN1nXT1j57zdOspizUkPxIkXEwwm6d5790qPt5TS2fH0sqEZuOiIMZFy02Mk5eVGBemLS8xzknUXtjEOCZaIsY+sJAYS6nUoYBGoyEhIYGmpiaOHz/O7bff7nXs5Zdfzp49e+Zc++CDD7j88ssByMnJITk5ec6Y8fFxjhw54h4TCCT3aQ9wObf9oeplhAgFR4YaSY/QkReVjN2L3d98V2pRFNnVU02naZj4sEjuzNgwJ+XFm5v0bFdq/eQEL7VXYRdFLkvIYpMuy8N4z89BnLWejvERXm+tw4HIWl0qV6VmL0wfCtCVWhRFPupo48yA8zTm2qw8ynRJXsefve67hdOk1co7jY30TkwgAFdnZbPG0+mOV1M//+tvHBjk46Y2pu12lHIZV+fmUJyom/NaBOtY7Gt8/4SJfQ2tGGbaN8VGqLkyP4ssbVzQsRa7JqvNTm1PP5XtfW7FWqVUsCojiVWZyYSHKUPnVu0F/uKIosjAmImm3kGae4eYmlX/HROhIj9FR35qPNooz2UEy+1ivZTWS6IoMm4y0zc0Tt/gOL2DY0ya5xJKAYjXRJIcH83G4kwUCjlTkyYe+fy1y+JIufbz/7Zk8w37tJlTz/5/kvu0hAsabvfp37yDShXBgf0NtLUOIAgCV28vJjPLQ5mQ5EoNSK7UnsZLrtSSKzUE50ptNpr435fftGI7TSwmntFopLnZ2dJ17dq1/PKXv2T79u1otVoyMzN56aWXSEhIIDMzkzNnzvC//tf/Yv369bzyyivuGA888ABpaWnuuuWDBw9y9dVX8+Mf/5ibb76ZF154gR/96EcLWjL9+Mc/ntOSqaqqKqiWTJJS7AcNE700G/UcHmxgyh74yZMgCFyTXIRarmRo2sTRodag506OiOaalDwADg900DYxFHQMgKyYOK7LdDpZnxrs5cRAz6LiwMzzyspxt2f6oKOFMwOBF7F7Q4RSyR0lJZTodIjA3o52PmprC+lpaGGCjvvXrSY1Jhqr3cGHTS3sbmjCvExKXWJ0JHevL+ea4lzClUpGJ828VdXAW1X17nZKyw2lQs6arBT+5soKtpXmoolQY7HaON7aw58/Ocm+2tZzthZvEASBxNgots6Yc920sZiCVB0KuYzxSQsnW3r466dVvPjpaU40dzNiPL/rDQaCIKCJCqc4K4nt6wu4/7r1fG5HBVdV5FGYkUB0hAoRGBwz0dw9iFwufSRLkHA+IJMJbL2iiJzcBERRZN/H9XS0h1gxPpfmWyfbqG28gM23Wvo4Urt85lt1Xf3sq5bMt2ZjvvnWmzMEOVSQzLcuDRw/fpy1a9eydu1aAL773e+ydu1aHn/8cQD6+vr4whe+QHFxMd/+9rf5whe+wPPPPz8nRmdn5xzjrS1btvDcc8/xhz/8gTVr1vDyyy/z+uuvuwkxwPe//32+9a1v8ZWvfIWNGzdiNBrZvXt3wIQYJKXYI2YrxWFRat7oOcrItJHsyESuTvBsnOWtf3HL+DC7+84AcFPqajIj44HAlGIXPuptpmpEj1Im53PZFcSrImeN9/wcRA/rOd7fw6e97QDsSM9jVfwsJTaI/sUAoh32dbdT2e98025JzWRjchpCsEeg8z7PRVHkZF8fBzo7AWd69U0FBWdP+5agFLunFEVOdPVytNPZZzgyTMn2vFxy4uNCqhTPhsVm41h7D1XdehyiiACUpyaxMTudiDBlULEWvSacz73VMMyp9l4Gxk3u65nxsazOSiZDq/Hfa3mZ+wW7YLXZ6egfoblviM6B0TlfLuKiwmdaQMWREB0ZnHHKOVSKA4Fpahr90DjTVjsl2c6si+VUikPRu3ApfQslSFgpmK0Uu/oUOxyib8V4kUqx+//9KMaLVYpnx/elGC9FKXbBp2K8BKXYBZ+K8RKUXBd8KsYhiO9XMV7iHP4U48UqxS74VYwXqRS74FcxXqRS7IJfxXiRSrEL/hTjS10pvpAhkWIPmE2Kw6MjGLSM82bPMURErkpYRW7UwpQkb6TY7pDxaX8jNWM9qOVK7s7YQJRSHRQptjscvNJRQ+/kGDFKNZ/LriBCETYz3vNz8ESKAT7paXcrxTdmFlIUN9N3OEhSjENAFEUO9HZyXO+MV5GQzNXpXpwdAyTFLrQNj/BeSzPTdjtRyjBuLiwgOSo6JKTYdd0wYeSDxmZGppz1xYUJ8VyVk+3ZATtEKcYjk1McaO6kfWgEAKVcRkV6CmszUlF5c3wMISl23yqK9I6MU9Whp21gxH09NlJNeXoyxak6727l54gUz4bFaqNNP0yrfpjuwbE57aai1GFOgpwYR0pcjH/nzxVGij2NX05SHIqYl9ImKeHihSdSDH6I8RJJMcwQ4+OtNDQuJMZLJcWu+Cequ6iqc+7Ns4lxKEgxQGVTD8dqPBDjEJBigJoOA/vPOB215xDjEJBWgEb9IB+d9kCMQxS/bWCE3VWNnolxCObwRYyXSooB9BNG3qiu80yMl0iKwUmMX5lRihcQ4yWSYvBDjJdIisE3MZZI8YULKVcvAOhUMayNywHg8FAdJltwJk2X6/LQqaIw2618oK/BLgaX7iKXybg5rZQYpZpxq5l3umuxLTJl5oqULLdC/F5nE81ji0vJBme61hVpWVyVng1A5YCed9oaF7222ciNi+PesnLi1GqM1mleqq2lUq8PaapTUnQU91asZl1aitMUa2CIZ06epnFgcNkMnuIiwrllVRF3rCkhISoSq93BsY4e/nzkFJXd58aMC5y/uzSthhvXFvH5rRWszkxGKZcxajKzv6Gdpz85yb76NoaMk+dkPf6gUiooTk/kpg3FPLhjPTvW5JObpEUhl2E0T3OmQ8+bx+r408cn2FPVTIt+iOkQpmVJkCDh0oFMJrD1yrmp1JL51lxIrtS+kSO5UvuEZL4lYSVCIsUBYk1sNjpVDNMOGwcGa4L68FTI5FyXUk6YTIHBPM7hwZag5w9XKLkto5wwmZzeqXH29DUs6gNcEASuSculOC4BByLvdDTQOr60FhTrklK5MacQmSDQNDrE682LayM1H3Hh4dxTVk6BVotDFNnX0c67LU0hrXFRyGVszcnis2vKiY+IwGyzsbuxmV31jRiXsZVSepyGz60v54bSAjThasxWG5+2dPCXo5XU6QfmKKHLDU2EmiuKsnnwqnVcWZxNXGQ4VruD6m4DLxyu4vUTtTQZhlZMqySVUkFBqo7r1hXy0I4N3LCukKK0BFRKBRarjcbeQd6vbOJ/9pxg17E6znToGZ8Mvdu4BAkSLl5IxNg/JFdq35BcqX1DIsYSVhqk9GkPcKUK/L8zLxERfdbxdnTaxCvdR7GLDrboiimOyXA/ZveWPj0rL6XNOMg7PTUA7EwuIz96ofOkt7Rql+t1h3GENztqcCCyUZfB5Qk5Hsd7iyPOrMchirzb0Ujj6CByQeCW7BJyYjy4IgeRVt05PsaupgamHXZ04RF8pqDUby2w13yheQ6+p/V69nd04hBF4tRqbiosRBcxy43Y2z7gJb6nLB+7w8Hxrl6Od/XgEEXC5HK25mRSlpSIzGu+kLd5vVz3MLfd4aBOP8DRtm4mZ5yXtRHhbMpOJy9B67VOO+j03QAzg0RRpHd4nDOdBtoGhnF9QoQrFRSnJVCaloQmYqFxQchcoBeZruxwiOhHJ+gwjNDeP8KYaS4RjosKJzMhlsyEWJLjoj26dnqc9zy4W09NmvjOfVL6tAQJywnX+/j3//n2nPRpAHEm1dVvjbELkis1ILlSe7pHcqWWXKnB85+A2WjiB1tuXLF7/XLEW8mQSLEHeCPFAKdHuzky1IBCkHFH+uXEKJ2PB0KKAQ4NtHJyuAulIOfOzPXEhc3diP2RYoCaET0f9jYBcE1yAeVxCzc4f6QYnMT4nfYGmsaGkAsCt+WUkBU9jxgHWWvcbzTxelMdkzYrMWEq7igoQauOWBIpdqFvYoJ3G5swTk+jkMnYnpNDaYKrJjq4+L7qXIdMk3zY1EK/0WlElRIdxfa8HHSRkR7HB7p+f3NbbXaqevSc7OzFMtNLOD4ygk3Z6eTGxy3YpJeLFM8ebzRbqO3up66nH9Os3rTpWg0laQnkJDhTmOH8k+L5GDVO0d4/QodhBP3oBLM/6ZRyOek6DRk6Dek6DTEeSL57XokUL2ssCRLOFwIhxbCQGF+1rZis7HnEeAm1xoEQ46XUGi8gxmtzKC2c+71hqbXGfonxEmuN5xDj3BQ2l84jxkus051NjIvTE7m6PGdp8efdExAxXsJzCIQYL6XWeD4xvnVGQQ4sjv9NPSBivIRa44CI8RJqjQMhxhIpXvmQSLEH+CLFVofA7r4T9JlH0KliuCV1IzJBFjApdogib3ZX0TM5ikYZzp0Z61HJzxo7BUKKAQ71t3N0oAsBuCm9lLzouRt0IKTYGdfB2+0NtIwNIxcEbs4uJjdGOytQkAqpQ2DMYua1plpGLWbC5HJuyikk25MKDUGRYgDztJXdzc10jo0BUBgfz/acHNQyb6ZQwZNicP6eqnr1HO7swmp3IABrUpLZnJmOavYHdQhJsesei81GZVcfp7v17lRxbWQ4GzOdyrHM5Q7tbYJlIJsOh0j74Ai13f10Do66r6sUcvKS4ilKTSAlJio0LtDLYGxlsdroGhyjs3+EroGxOb2QwdkPOT3eSZDT4jWolGd/xxIpXt5YEiScLwRKikFypfa3fpBcqf3NIblS+573UnSlXk5SrDcsvdOEK15y0qXRbUIixR7gixTbRBlG2xSvdx9m2mFjlSaLjfGFAZNigEnbNC91nMRos5ARoeXG1NVuohMoKRZFkQ97m6gdNSAXBG7PWEV6ZKz78UBJMTjTd9/ucBJjmSBwU2YR+bHxM4GCJ8UAk1Yru1rr6TVOIABXpmWzNjFlIWkKkhQLopOwHu/p4XB3NyIQFRbGdbl5ZMRoPMRZHCl2YcJi4dPWDlqGnHXXEUolW7MzKU7QOZ/LMpBiF8xWJzmu6jlLjmPD1azLSKUoSYfCW/rvMpPN8Ukz9T0D1PcNYDSfrc/RhKspStFRlKIjJjyAvnDnkBTPGe4QGRgz0TkwSvfQGIZ5KrIA6DSRpGpjSIvXkBobjVLhxRk8BOuRSLEECecHwZBikFyp/a0fJFdqf3NIrtS+573UXKmXkxSv/9y/IQ8LvEevN9inzZz46/93Sez3Ein2AH+kGKDd1M9HhtMAXJtcQao6yWMsT6QYQD9p4vXuk9hFBxVxmVymywMCJ8XgJIe7OutoMw4RJpNzV9YaEtRRPuN4IsUANoeD9zqbaBwdRACuyyigRJu4aFIMTrL9UWcrNUNO85BSbQLXZObNJXOLIMUu6Ccm2N3SwpjZWTu6LjmFy9MzAoofbJugzuFR9rW2MzozV3J0FFflZDnbRAURZzFzm6dtVPXoOd3T506rjgxTUpGeQnlKEmHzCdtyk82Z66Io0jMyTkPvAC39w9jsZ3PYU2KjKUzWkZ+oRR3mocXVuVhngOOnrTZ6hyfoHhqjZ3CMEdPUnMdlgkCiJtJJkLUxJMVGea6nCtF6QCLFEiScCwRLisG57x741AMxDgEpdl4XOXx0ITEOBSkG58fNiTOdC4hxqEixKMDpBg+KcYhIsShAbbue/WfagVnE2OvrH2R8GTT2DPJx1TxiHGx8H3O0DgzznifFOETEvnt0jF1nFhLjUJBi8KEYh4AUAwxMmXi1zgMxDgEpBh/EOASkGGDCaubl5uoFxFgixSsfEin2gEBIMcDhwXpqx7tQyZTcmno5kYrwBbG8kWK7Q0bThIE9+loArkkqoTAmOShSDGC1OXi9q5reyTEi5Eruzq4gNiw8aFIsziiwH3Y1UzviJLHbUnOoiE/zOD4QUuyMK1LZ38cn3e2IQEpkNLfkFs0y4Fo8KQaYttv5tKOD6n7nmnXhEVyfl3/WhCtEpFgQnQcHlb19HOvqwTrj8FiUoGNrZiZRqnm1KSEkxa7r0zY7NX39VHb3YppJ/1Up5KxOTWZ1WjIRLvJ5jkjxbFhtdloNw9T3DdAzMu6+LhMEMuM1FCbryE6Im0smVwgpng+jeZreoTF6hsbpGR7DODXXrdJFklO0MaTGxZASN09JlkixBAkXBBZDihFmFOP5xDgnwePwYEkxshnFeB4xzsv3fPAeLClGEM6mUs8ixiWFC71JfMb3c30BMS7J8Dx+kURtATGeX2PsZ53+XrcFxHh+jbG/+L7mEKCt3wMxloeG2CPMKMbziLFM4eXFXgQZ1497IMZeMqqCJcWiMJNKPZ8YK72UyQVLZvFCjMM8mG/5iuP19RGdivE8YqxRLTTfktKnVxYkUuwBrjfU/9S8uJAUz0qTtosO3uw5zqBlgkRVLDelrEcmzP3QsXn5xHelWx8aaOXUSCcyQeD29AqS1LE+x8+HQxSw2G283F7FoNlEtFLF3dmrifZA0MG/giyKInt72qgcdLZuuCwpg81JGQs3gyAV5I7RMd5pbcBitxOlDOPW/GKSIqOCTz/2Mm/r0DAftrZittmQCwKXZ2RQkZKCPEjXaCEAkm60THOovZP6fmd7DoVMxvr0VNampbhJn8861EUQ8tmwOxw0GAY52dHL6JRTuZYLAkXJCVSkp6CN9Py7D5qMe4OfdU6YLTT3DdGkH2Rw4myfY4VcRk5CHPnJ8WTqYpELnt/T56OGF7zvb+Mms5MgD43RNzyByTyXJAsC6GIiSYmLJlkbQ0psNOEqL+q4p3m9keJ7JFIsQcJywk2K/2PXAlLsXdl0Xpdcqb3En3X9UnalDmQOyZXa92Z8KbhSm40mHt0qGW2tFEik2AMCJcUA49ZJXu0+itVhZ5Umm43agrnj/ZBiURTZ3VtDm2mQcLmSz2SsJ0bpQXH2QYoBTLZpXmmrYmR6Ck2Ymrsz1xCpXHgqFYiCLIoiRwxdHNI7N7MKXQpXp87bDIJNqxYFRsxTvNlcz4h5CrkgcE1WHmXxCzd5n3F8zGuanmZPayvto6MApERFcW1uHnHhHkjiEkixC4YJI5+2dtA3PgFAlCqMy7MyKEzQeSfjPuYOlBS74HCItA2OcLKzB8OEyX09SxvLmvRkMuI0c35n54oUz8awcZKmGYI8PnW2f1+YQk5OQhx5SfGkazVuB2uf6wxyPcGOD0SJFkWRiSkLvUPj9A6P0zc8wcTUwr6Emkg1ybHRJMdFkxQbRWxUuNs3YMG8EimWIOG8YCmkGCRXan/rh0vXlTrQOSRXat/zXuyu1BIpXlmQSLEHBEOKAVqNBvYYqgHYkbiGrMizm5Y/Ugxgddh5vesUAxYjscoI7shYh1qu9Dp+NhyzPp0mrBZebjvNuNVCXFg4d2auXkCMg0mrPjXQy94ep6FFgSae6zMLz9brLoIUA5htNt5ra6JtbASA0vhEtmfmoJQFWBfrZ15RFKkZGODTjg6sdjtyQWBTWjrrU1LmbgQhIMUAokOkaXCIg+2dTFicCqI2IpzLMzPI1S5so+QrVrCkePZz7huboLK7j9bBEffDcRHhrEpLojhJR5hCcV5IsfsWUcQwZqRZP0SLYRiT5azaqpTLyNTFkpugJVMX6/kkeBHrCXb8YtOzJ6Ys6Icn6BuZQD8ywfAsddwFpVxOYmwkibFRJGmiSIyNImIm5f5ck+JQpFRdSulUEi5eLJUUg+RK7W/9cGm6Ugczx8XmSr2AGC+BFMPF7UotkeKVBYkUe0CwpBjgwEATteOdKAUFt6VtQqN0brCBkGIAk83CK51OR+okdQy3plWgmEUUAyHFAGPTZl5pr2LCCzEOtta4bniA97ucH9ZpkTHcml2MWqFcNCl2ziVytK+bw71diEC8OoKb8wqd/Yz9xQlw3nGLhY9aW92tm3QREezIySU5Kspn/GBJseu6ze6sNz7Z3Ytlxik6KSqKLVkZZMRqgoq1cE2Bjx+dNFPVo6dOP4B1Zh1KmYzCJB2rUpNIiAqi17I3LIIUz7ldFNGPOglya/9cgiwTBNLiYshJiCMnQUuU2kuNTwDrCXb8YknxfFimbehnCLJh1MjAmBHrLBMyF6LUYSRookjSRJKgiSIhJtLdCmo5SfHGzzyBQrk08w2b1cyx1/7pktgkJVy8CAUpBsmV2t/64dJzpQ52jovNlXoOMV4iKYaL15VaIsUrCxIp9oDFkGKrHd7tO4HBMkqsMpJbUzehlCkCJsUAwxYTr3adYtphIztSx3Up5e6Uy0BJMcDY9BSvtJ1hwraQGC/GgKtrYpS32uuZdtiJU6m5PaeUWGWEx/GBkGIXusbHeLe1kUmbFaVMxo7MPIrjE3zHCYKMi6JI4+Ag+zo6MNtsCMCapGQuS09HJfesRi6WFLtgsdk42d1LZa8e24wZV4ZGw2VZ6aRERwcV6+yaghsPMG2zUacfpLrXwMjkWTfllJgoylOTyE+IP6v6n2NSPDuOS0FuGxihbWCYUZN5zpCE6EiyE+LI1sWSEB0ZnPLuY15PCBUpnj/eIYqMGKfoHzViGHUS5RHj1ILbZILAwzs3opDLJFIsQcI5QKhIMUiu1P7Xf2m5Ui9mjovNldpNjENAiuHicqUuitVxY3aRRIpXGCRS7AGLIcV2h4xJm4U3eg8zZZ8mMyKBHYlrsOPZjc8bye2eHGNXTyUOUaRMk8YVCQUIghAUKQYYNZt5taPKTYw/k7maKKVqUaQYYHDKxBtttUxYp1HLFdyaVUJapMbDDR7DeP0UNU1P825bI90TTsfi0vhEtmXkEDY/ndqFIBVqQXT2TP6ko52GoSEAIpVKrsrMpkCrXUCylkqKXZi0THO8u5czegOOmRcxK1bD5sz0oNs4LSXtWRRFekbHqe410Do44l6LWqGgOElHWUoi2ggvBxxegwa5ziDijJimaO93EmT9mHHOYxFhSjdBTtdqzhp6rHBS7AnTNjuDYyb6x5xKcv+YiTCFnM9tXQ1I6dMSJJwLhJIUS67Uvtd/qblSL2oOQXKl9jfvxeBKfaCvg+3puajkCokUrzBIpNgDXG+AP9c+HzAptonOP/p+8xi7eo7jQGRtXA4VsQWex/sguc0T/bzf52zVtCk+mw3x2di9kEGHl53DIQpOxbj9DBNWC5owNXdmrfLuSu2tZnbWp5/JOs0brfUYJo3IBIFr0/Mp1SZ6HT93As+XEQUcosiR3i6O9HUDEKdSc2NuIYkRUR7Ge4/j+frZ/+wYHWVvW7u7r3GmRsP2nBxi1WqP4+eu33N8f0Rw3GzmWGcPdYYBd+isuFg2ZaaTHD33+S22VdOCNXkZbjJPU9vXT01vP8ZZKcvJMVGUpSSSnxgfUMuk5SaPrtdh0jJNx+AoHQOjdA2NzklBlgkCqXExZOpiyYyPJS5S7fWLibf4S11nsOP9vW42u8NtODY1aeK7n5WMtiRIWE64SfGvPJFiLzdJrtRz1hNw/FnXLwlX6iXMIblS+95EL3RX6tkwG008esUNK3avX454KxkSKfaApZBigMbxXj4ZcJLabYmryYlMXjjej/JbNdLN/oFmAK5KLKAkxsOmgW9SDDA+bebV9jOMWc1EKVXcmbma2LCFxDgQUgxOU7D3OpppGnWqrpsS09mSfHbDWQwpdqF7YozdrU0YrdPIBIGtaZmsS0ydu9ksgRSDs9fw8Z4eTvT0YhdF5ILAxrQ01qemOtOJQ0yKXRibMnOsq4f6eeR4Y0YaKTHRvmOFiBS7xjtEkc7hUWp7+2kbGnFnA4TJ5RQkxlOcnEByTBSCl0jnihTPht3hoGd4nI7BUdr7R5gwz3V8jlaHkamLJV0bS5o2xvvJsZf4i1lnsOODed0kUixBwvJjOUgxSK7U/tYPl4Ar9RLnuNhcqT0S4yWkVV/ortQuSKR4ZcHbW35FwG6389hjj5GTk0N4eDh5eXn88Ic/xB+P37t3L+vWrUOlUpGfn8+f/vSnc7PgGRTGpFKuyQTg04FqBixjQcdYHZfOBm0WAJ/0N9Ew3reotcSEqbk7ZzVxYeEYrRZeaq+kf2piUbEAlDI5N2cXsinRuYEd7e/mzfZ6LHbbomO6kB6t4W/K1pAXq8Uhinza3cErjTWMW8z+bw4QCpmMyzIy+Pzq1WRqNNhFkcPd3fy5spKGwUG/763FQhOuZmdhHl/YUEFJYgIC0DEyystVNbxcVTNDTs/N+ZRMEMiOj+OmVUU8dPk6LsvJIEatYtpup6avn1dO1fDMkUqOtHe5+yCfb8hlTofqK4uz+ZsrK7hv6xq2FmWRHq9BJghMmKep6e7nvapG/mfvcV46coZDTZ10DY1h82ByJUGChJWFC3W/9wSZTGDrFUXk5CYgiiKf7K2ns2MwZPEFQeCyjbkUFTgP3PcfbKK5pT+k8devymR1cSoAh0+1Udu0uO8g3lBRmMbGUmfq9Im6bk7Vd4c0fll2MleUZwNQ1drHkbrOkO6xhak6rlmVjwDUd/fzSU1bSOPnJGi5YXUBMkGg2TDEh9XNOLypF4tAepyGW8qLUMhkdAyP8k51I3ZH6PbK5Ohobp+pKe4dn+DN6nq3+WcooIuI5M7iUsIVCvpNJl6fMeEKFWLV4dxdWEaUMoxh8xSvNNZgsk77v1HCBY0VrRT/6Ec/4pe//CVPP/00ZWVlHD9+nC9+8Yv827/9G9/+9rc93tPW1kZ5eTlf+9rX+PKXv8yePXt45JFHePvtt7n++usDmne+UjwyPY5aHka4XB2QUgzgEB18oD9N1+QQalkYt6RuJnpW/2F/SjE4T2v3DzRzZrQHAbgmqZT86KR54wOrNTbZpnmjo5oBswmlTM4t6WVkRMbOGu8xjA/lV6B2uJ8Pu5uxiyJxKjW3ZJcQr/LgbgwBKcVn5xSpHjDwSXc7VoeDMJmcqzKyKYtP9KpeBqoUuyDMmDw1DQ3xaWcnxmnnh11yVBRXZWadNcVyr39pSvH8NY1OmTnR3UN9/6C7zlcbHs66tBSKEnQBtY8KWXqzeLb2uE4/QOvAMNZZm2NyTBSFiTryE+KJCFOeF6XYF6w2Oz3D43QNjdI9PM6Iaa6JlVwmkKSJJi0uhjRtDMkxUR5TxYJdZ7DjJaVYggTvOB/7/XIpxS5IrtS+1w8XsSu1j3suLVdqH4rxEpRiFy50V+rlVIr1hoGQKcXJSQmXxH6/oknxLbfcQlJSEn/84x/d1+666y7Cw8N55plnPN7zv//3/+btt9+murrafe3ee+9ldHSU3bt3e7zHYrFgsZxNxxwfHycjI4M/1z7PiHyCg0OVxIVp2J64CVH0nJY5nxQDTDts7Oo5wfD0BLHKSG5K2YRqpv9wIKQYnJvSvv5Gasf6EBDYmVxKblTirPGBG3BZ7Dbe6qylZ3IMmSBwQ2oJ+TG6mfEew/gkxQD6yQl2tdczYZ1GIZOxM62A4jgPxiJBkGLndeeH0XvtzfSZnMp2ZkwsOzPz5jQ+DySOJ8z+zLXa7Zzq6+N4b6+bDBbGx7M1I5MY1cxcISbFLhgt05zu01PdZ2B65hQ1MiyMNSlJlCcnOT/czwEpng2r3U7r4AgN+gG6RsbcDws4T5cLE+PJ02lRzU9VOo8u1rNhNE/TMzxG98yPyWKd87hCJiM5Noq0OA2pcdEk+SPJEile1lgSJMC52e+97fUuUmyemmZsZJKk1NiQkGKQXKn9r/8idaUOIfGWXKl9z3shu1IvJynefOsPl9xpApzdJo689dglsd+v6PTpLVu2sGfPHhobGwE4ffo0+/fv58Ybb/R6z6FDh9i5c+eca9dffz2HDh3yes+TTz6JRqNx/2RknHVDjFZGIUPGoGWEypG6oNYfJlOwM2ktEXIVo1YTH/efxi4Gl54iCAJXJxZSGJ2MiMgefS3tpsWlYankCm7PWEVetA6HKPJuTy2nh3sXFcuF5Iho7i+sIDNKg83hYHdXA3t7WkKShhOrDuezReVcmZ6FXJDROT7KM7WVVA3oQ5qmpJTL2ZSezoMVFZQmOAl949AQfz5dyf7OjpCm5MxHlCqMrdmZPLRxLVuzMolUKjFNT3Owo4v/PnaSj1vaGJla2L5nOaGUyylK0nHb6hIeumwdV+RlkRgdiQh0jYyxp6GV/3fwBG9XN9DYP+gm8ysFUeowilIT2FGezwNXruO+LWu4qjiHvCQt4WFKbA4H3cPjHGnp4rXjtTy19xivHa/hSEsXXUOjTNtW1vORIOFSwLnY733t9RazlQ93VfLRO6fp6x4OwTNyQiYT2Hrl2VTqfR8vQyr1plyKCpc5lbokDYDDJ0OfSr2maHlTqUuzk7liVTYAVS3LkEqdpmP7amcqdV3XMqRSJ2q5frlTqVctYyp1zNxU6reWI5W6xEmEpVRqCUvBilaKHQ4H//iP/8hPf/pT5HI5drudf/u3f+PRRx/1ek9hYSFf/OIX54x55513uPnmm5mcnCQ8fKHJlLfT42frniUiOoLuyX72DZwCYJN2NTmRC02vvPUjtjlkDFkm2NV7HJtoJz8qmasSyhC9tGrymp7tgD36Opom+pEJAjemlJMVFb8oV2qHKPJxXzPVI86T5fXx6WxJ8OyeGKiC7BBFDvV1cdTg3MxSIqK5JbuIqBlV1+tZQIAK74h5ig/aWug1OlXjjGgNO7Py0Kj8nIIFaZyFCP0mE592dNAz7mwTpZLL2Ziaxprk5LO9ff3F93pU61sxtDscNA4McqpHz5Bp0v1QdlwsFWkppGti3L+nkBlGBajYjk6aaeofpMkwxPCs3sdymUCWNpb8hHiy4+MIm99+4Xy5WHsaOtMzuHd4nJ6RcXpHJpianqskCwLooiNJiY0mOTaaFE00kWovLRk8zRuC9U+ZTPz93ZJSLOHSwbnY773t9X/45ZuEhYXzyYe1dHcOIZfL2HbDKlLStQsnXaSCLLlSe4k/6/pF5Uqd7qNd0yLnkFypLz5XarPRxKNXSunTKwXeLVpXAP7617/y7LPP8txzz1FWVkZlZSWPPPIIqampPPjggyGbR6VSoVJ5SMmdQXpEIqs0eZwZa+HESDUaZTTaME3A8eNV0VyTtIoP9KdpNuqJkKtYry0Kao0yQWBHcjF20UGrcZB3+6q5IaWMjAjPPRD9xbomJZ9opYpD/R2cGOpm3Grh2pSihaQviJhbUzNJjojmvY5G+iYneLbxNDdkFZIVHbuomLMRpw7n7uIyTuv1HOjppGtijGdqK7k8NZOKxBRkAbbjCQSJkZHcWVJC++goBzs7GZqaYn9XJ5UGPZelpVOSkBDS+WZDLpNRkpRIcWIC3WPjVPb00T48SvuI8yc+IpxVKckUJehQedpMlhGxEWo2ZqezISuNIdMUTYZBmgeGGZsy0zo4QuvgiJsg5+q0ZMfH+XSBPh8QBAFtVATaqAjKM5MRRZGxSTO9IxP0jozTNzLOhHmagXETA+MmqjqdXzBjwlUkx0aTpIkiKSaK+OiI4OqSJUiQ4BPnYr/3tdfL5TKu2lnqJsZ7d5/xTowXAZf5FkBb6wD7Pq737Eq9SLjMtwAamvTsP9gE4JEYLzb++lVOA9Gq+l4On2wDkQWu1EtBRaFTjT5W28WJOucBu0divEiUZc+o6dXtVLX0gYjXPsaLQWGq83f50Zlm6rr7EcGzK/Ui4TLf2l3VRLPB2QHEKzFeBFzmW7uqG9yKsTdivBi4zLfeqK53m295JcaLgMt869X6Wrdi7JUYLwIuxfjlxhq3YuyVGK8AhKvDCA/iQN8brNMr8/ktB1a0UpyRkcEPfvADvvGNb7ivPfHEEzzzzDPU19d7vOeqq65i3bp1/OpXv3Jf+5//+R8eeeQRxsbGAprXdcriUophprZ34BQ9UwOEy9Vcm7SFcPlZldKXUuxC40Qvn860atoQV8Cq2Byf42fDVSNsFx180FdLq3EQGQLbk0oWmG85xwdWa1w/2s8HvU4ThyR1NLdklBGpCJs13mMY77XGosCoZYpdbQ0MmE0ArEtIZUtSlmfCHWQtMA6BUYuZD9qb6TE6lVxdeATXZOaSGuXhBGsRSvGcYaJIw+Agh7q63GZcsWo1m9PSKIzXIQv6SDZ4hXRkcorTvXrqDAPYZlKalHIZxQk6ypOSSIjyYm4WYPyl1PaKosigcZLmgSGa+50E2X0/kKKJJlenJSc+Dk34XFX/fCjFgcSfmLKgH52gb+ZneGJywS1ymYAuOtJNkhM1UcSEqxAEQVKKJUhYBM7Hfu96H//hl2+6jbbsdodvxXiJtcZ+FeMl1hr7U4yXWmvsTzEORa2xT8U4BLXGPhXjENTp+u1jvMQ5/CrGS4zvTzFeaq2xX8V4ibXGfhXjJdYa+1WMV4hSLLVkCh4rWuqYnJxENo9IyeVyHD5qHS6//HL27Nkz59oHH3zA5ZdfvqS1CILAFt0qohWRTNnN7B88iV0MriaiMDqVjdp8AI6PNFE/3hX0OuSCjGtTSimITsSByB5DLfXji68LLo5N5I6sclQyBQbzBC+2nWLAbFx0PIBYVTj3Fq5idbzzVPbkQC8vNlcxbJ70c2eg8dXcXVjGjsw8VHIFg1OT/LWhmg/am5myWf0HCAIyQaAkIYEH11RwZWYm4QoFo2Yz77W08Jeq09QNDrjdo5cLceHhbMvL4Uub1nFlThax4Wqsdgdn9P08f/oML56upsbQf15qewVBICE6kstzM/mbTWu4d8MqNmSlER8ZgQj0jk2wv6WDvxyt5LljpznY2knv6Piyv2ZLQXS4ioIUHVeV5HDP5at5ePsGbllXzIbcNDLjNagUcuwOEcOYkapOPR9UN/PsgUr+e+9x3jxRx6HmTlr6h5kwW85Zm60LBb/97W/Jzs5GrVazefNmjh496nXsq6++yoYNG4iNjSUyMpKKigr+8pe/zBkjCILHn5/97GfuMdnZ2Qse//GPf7xsz1HC4rBS9nuXYpyepcNud7B395nQ1xhfscw1xhsv7BrjisI0NpYtc7umVU5RYllqjGe1a1qWGuMELTesLpTaNXmBSzGWaowlBIsVrRQ/9NBDfPjhh/z+97+nrKyMU6dO8ZWvfIUvfelL/OQnPwHg0Ucfpaenhz//+c/A2RYN3/jGN/jSl77ERx99xLe//e1FtWSarRS7MGIx82H/QaYdVjIjUrlMuwZBEAJSil04PtzM6dF2AK5OWEVuVIrP8bBQ4XWIIp/MuFIDXK7LZ3VsxqzHA3elBhgxm3mzq5rR6SmUgoxrU4vJj9EtSimejZaxYT7obGLKbkMuyLgqNZvV2uSzp6aLUIpnY9Jq5UBPBzVDzk1fLVewNS2Tcl2Sc44lKsUuuA4op+12Tuv1nNT3uT9kNSo1m1JTKdbNSqsOoVI8/7ooivSMjVOt76dlaNhNMJUyGYUJOkqTEkiOilqYsrUMSrGvOONTZtqGRmgbHKFndHzOwyqFnCxtLNnaODK1GsKVSv/rDHBeF5ZLiXalXPePmzCMGTGMTTA4MemR6IcrFSTERKKLjiQxOpKEmEii1aq5v5tLRCl+8cUXeeCBB/jd737H5s2b+dWvfsVLL71EQ0MDiYkLUzz37t3LyMgIxcXFhIWFsWvXLv7+7/9+zme5Xq+fc8+7777Lww8/THNzM7m5zlTS7OxsHn74Yf72b//WPS46OprIyAAzLCScE5yP/d6TUuyCTYRPPqimp2OeYrxEpdgFyZXa3/ovbFdqgIY+yZXa17ySK7WkFK80rGhSPDExwWOPPcZrr71Gf38/qamp3HfffTz++OOEhTlTFR566CHa29vZu3ev+769e/fyne98h9raWtLT03nsscd46KGHAp7XFym2OeQYzEPsGziKiEhZTD7lmsKgSLEoihwcbKR+ogsBgR1JFe7a4EBJsSvOgYFWqkZnNg1tNuvjnKpIsKRYdAiY7Vbe7amjyzQKwMb4DDbpsj3WwwRKigGM1mne62ii0+iMmxsdx86MfCIUYUsmxS70Gsf5qLOVwSmnGp0YEcm2jBxSI738AS+SFLswbbdTZdBzsq+PKTc5VrEhxUmOFYL3GpmlkuLZa5qcnqa2f4AawwBj5rOpy9qIcMoSEylK1BHhIpznmBTPvm622ugYHqVjeISO4TEss05tBSApJoqsuFgytbEkRkUGV7N9noy8ZsPucDBsnKJ/3MjAuIn+MSNDpkk8fbqqFHJ00ZHooiPQRUWii4ogLjJ8TnracpLiUJhvLMZ4Y/PmzWzcuJHf/OY3gNNYKSMjg29961v84Ac/CCjGunXruPnmm/nhD3/o8fE77riDiYmJOephdnY2jzzyCI888khAc0g4Pzgf+70vUizKZM5U6vnEOMNLjXGQpBhhJpV6PjHO8ewTEiwpRjaTSj2PGOflLyy3cq3H8/q9xBeEs6nUs4hxibca40WQYvBAjEsyPI9fJFFbQIy91BgvhhR7bdcUqnZQArT1eyDG8hARe2EmlXoeMZYpvLzYiyDj+nEPqdTzzTrdcYLb7EVhJpV6PjH25nUSJCkGL8Q4zEsNrkSKVzxWNCk+X3C9AV6s/8sCUmyd6UfcPNHF0eEaAC6LX0VmhOcParuXT2qrXWDfQA0tRr0zJTppDWkR8V7JrDeybHfAieEOjg61A7AqNo0rEvIRvWTGO7y6VQsz/4p8amjj1JAzJTs7Ko7r04pRyed+iIhe4ngjyw4HnBroY39fO3ZRJFyhZGd6HnmaeI/jvX6K+nCxdogip/v7ONTb5U4lLtLquCIti+j5vY2DdY32Mn7aZueMwcDJ3l43OY5UKlmXkkp5YqLz1DNQeCWn/tckiiK94xPU6PtpHhzCPiPxywSn8VVJYgLZcbEeDTO8kt9l6jvscIgYxo20DY3QMTQ6x2UbQK1QkKHVkKmNJTNOQ6TKj8lDqEh9kPG9wfXbstkdDE1MMjBhYnDGuGvIi6IsEwTioyK4c3MZcpmMKZOJf/jM8pDiy2/4P0vuXWizmjm0+3G6urrmrM+bkdH09DQRERG8/PLL3HHHHe7rDz74IKOjo7zxxhs+5xNFkY8++ojbbruN119/nWuvvXbBGIPBQHp6Ok8//TT333+/+3p2djZmsxmr1UpmZib3338/3/nOd1CEyHxFwoULNyn+xUJS7PpD9ltjPG/8wuu+ybLkSu0l/qzrF6Qr9ax7JFdq3/Neyq7UZqOJR6++XiLFKwQruqZ4JSM/OoPSGGd63pGhagzm4GqCBEHgqoRSsiISnOZZhtP0TQVftyQIAhvis7kiwVmrfGa0h/f7ahdd/yETBK5OzuX6tELkgox24wgvtlUyNGOatVgIgsC6xFTuK1yDTh3BlM3KW+317O5sxByiOmCZILA2KZUHy9dSrnNu+g3Dgzxdc4ojfV1YHaGvuQ2Ty1mfmspDa9dyZVYWkWFhmKxWPu3s4H8qT3G4u4tJa2jrnD1BEATSNDFcV5TPw5vXsy0vm8SoSByiSNvQCO/UNfLfR0+yr6UN/YTxvNa6ymQCKbHRbMnL5L5Nq3nw8rVsL8olL0FLmFyO2WajqX+IPfUt/M+hkzx39DSfNrfTNjjC9DL2jA41FHIZSbFRlGcksa0sl89etoq/3bGRz162iu1luazKTCY1LpowhRyHKGKx2S44R+uMjIw5fV+ffPJJj+MGBwex2+0kJc1VqZKSkhakQM/G2NgYUVFRhIWFcfPNN/PrX//aIyEGePrpp4mOjubOO++cc/3b3/42L7zwAh9//DFf/epX+dGPfsT3v//9IJ+phEsV7hrjzPhzVmPc0b4MNcYFy1xjXJwKzNQYNy5DjfEy9jEuy07mivJsYKbGuHYZa4y7+9lXvRw1xsvcx/giqDEOl2qMJfiBpBR7QCBKMcykLw+epnNSj1JQsC3xMuLC5p6ieFOKXcqvXXTwof403VNDyAUZO5PWkhq+UD0NJK26adzAHn09DkRS1BquT12FWq6cO96PUjwb/VNG3uqqY8JqQSHIuCa1gGKNk2wGqxTPvm5zODis7+J4fzciEKFQck1aHvmxs573IpTiBes3Gdnb2UavydnbOEoZxmWpGZTGJwbvGh2gmmpzOGgYHOR4Ty9jFmc6s3zGrKsiKZn4iAgPQXzPEYhS7A1Dpknq+wdo6B/ENKsfr0atokAXT0GCDl14uOdT62VSin3B7nBgGDPSOTxGx/AoA8a5hzECkBgTRVpsDGmxMaTERHtV48+3UhxoHFEUmZiyMDltJTk2GmBZleJQpk8HqhT39vaSlpbGwYMH55ggff/732ffvn0cOXLE4zwOh4PW1laMRiN79uzhhz/8Ia+//jrbtm1bMLa4uJhrr72WX//61z7X/t///d989atfxWg0+mzFJ+HiRyBKsQuSK/X89S+ML7lS+55DcqX2Pe+l6EotKcUrCxeWLLHCIAgCl+tWkaiKwyra+HTgKEZbcIqqXJCxI2k16eHxToJsOEX35OJOiQtikrg5bRVhMjl95jFe6zrB2PTiHZ8Tw6O4J6eCjMhYbKKD93sa2NPbtGTFVSGTcUVqFvcUrEarCmfSZmVXRz272utDerqWGBnFZ4vKuTGnkOgwFUbrNB92tPCX2kqaR4aWRS1VyGSUJSbywJo13JhfQGJkJHZRpLq/n2fOVPFaXR1toyPnTKmNj4xga04WD21ax21lxRQmxKOQyRgzWzje3cvzp6p45lQVRzq7GZmcOidr8gW5TEZqbAyX5WZwz4ZVPLx1PTeUFlCakogmXI0IGMaNnOzs5a2qep7af4yXTlZzsLWTjuHRC0pJdkEQBGIi1G5CvNxw9S5c6g9ATEzMnB9vJFOn0yGXyzEYDHOuGwwGkpOTva5VJpORn59PRUUFf//3f8/dd9/tUY3+9NNPaWho4Mtf/rLf579582ZsNhvt7e1+x0qQ4ILkSu0/vuRK7RuSK7VvSK7UFwc++eQTbr31VlJTUxEEgddff33O40ajkW9+85ukp6cTHh5OaWkpv/vd73zGfOqpp7jyyiuJi4sjLi6OnTt3Luhe8dBDDy3oNHHDDTcEtXaJFC8RckHOVYnriFVGY3ZMs6//GFN2S1AxFDI5O5PXkBmhwy462GM4Refk4jarjEgtd2asI0qhYsw6xWvdJ9FPjS0qFkCEIozbM8vZpHNuRDWjel5oPcXgEtOpAVIio7m/sIJNiekIQPPYEE/Xn+T0YF/INgpBECjS6niwbC1XpWejlisYMTv7KL/QcIauicW/Nr4gEwQK4+O5t6ycu0tKyY/TIgCd42O82dDAn6tOc1qvP2dtlGSCQFZcLNcXFfDlzeu5viifXG0cMkFgZGqKI13d/OXUaZ47VcXRrm6GVwBBBghXKslPjOeaoly+sLmCBy9by46iXIqTE4hWq5wkecLIya5e3jpTz1MHjvPiiSo+aW6neWCIyelLb0NaiQgLC2P9+vVzDLAcDgd79uwJqn2Ow+HAYln4+frHP/6R9evXs2bNGr8xKisrkclkHh2vJUjwBblcxpXXlpGWtYyp1FcuMzHedGET4zXLnEpdmp3EFauygeUjxttXLycxjuP65U6lXnVuiXEovyfpIiK5s0QixssJk8nEmjVr+O1vf+vx8e9+97vs3r2bZ555hrq6Oh555BG++c1v8uabb3qNuXfvXu677z4+/vhjDh06REZGBtdddx09PT1zxt1www309fW5f55//vmg1i6lT3tAoOnTszFhtfKR4RAm+ySxyhi2JW4mTKb0mz49G3bRwceGajom+xEQ2Ja4muzIJK/jwbubtNE6zbu9ZxiwTCAXZGxLLKYgJimo9On517tMo7zf04DJNo1ckHFlYi7lsSlz0n8CSZ+ee935b/+UkT1dLRimnD2SkyOi2ZGWT8L8lLYg0qed1+f+r8Vu46Shl5OGXqwzH+RZMbFsSckkKTJqyenTLnjK5hm3mDmtN1A9cLancJhMTnGCjtWJScSHe06tXkr6tL/xFpuNtqERGgeH6Bodm2MCpQ0PJz9eS358PPERXlKsA1hPyFysPWDcbKF3ZJyesXF6RscZNy8kTLHhalJiomd+oogL5Lkscj0QGtfr5UyfPp8tmR588EF+//vfs2nTJn71q1/x17/+lfr6epKSknjggQdIS0tzK8FPPvkkGzZsIC8vD4vFwjvvvMMPfvAD/uu//muOIjw+Pk5KSgq/+MUv+NrXvjZnzkOHDnHkyBG2b99OdHQ0hw4d4jvf+Q433ngjTz/99JJeAwkXPoJJn3ZBcqV2rd9LfMmV2v8ckiu133kvFVfq5UyfDkWplCtesN0mXBAEgddee22OwWZ5eTn33HMPjz32mPva+vXrufHGG3niiScCimu324mLi+M3v/kNDzzwAOBUikdHRxco08FAst/0AbngQC7MZWEOL38d0Uol1yRt4AP9YUat4xwYPMb2xA3IBW/W7AsvKYBrksrZ119Dq8nA3v4qrkooJT86BW+fNXYvnypRyjDuyKjgw75a2kxD7DHUMmI1slGb65kQeCObs4ZmRWv4fH4F73c30W4cYa+hme7JEXamFrrdqb0eSHo7e5lZf1JkJPcWreL0oJ6DvZ3oJyd4rukU6xPTuCwpw11X4vUjKEDSqpLJuTw9g9WJyRzt7ebMoIGO8VE6xkfJjY3jspRMEiM89C8N0q3a0/DoCDVX5GaxKTOduoEBqvr0jJjNVBkMVBkMpMfEsDopidy4uLl1Ol5fu+DW5OnXrgpTUJycQHFyAlNWK21DIzQPDtM1Osbw1BRHu3s42t2DRq0mTxdHbrzWcw9kvJPfpf7O3PE9XIuJUBETnkBxqvMLo9EyTd/oOL1jE/SNTTBonGR0yszolJk6w4DzOSvkJMdEk6KJJjkmisToKMK8bLbBrge8v9V9te5YEOMizOG55557GBgY4PHHH0ev11NRUcHu3bvd5ludnZ3IZr3vTSYTf/d3f0d3dzfh4eEUFxfzzDPPcM8998yJ+8ILLyCKIvfdd9+COVUqFS+88AL/8i//gsViIScnh+985zt897vfXd4nK+HCgih6+MP18jnqcKAQ4Oodpe4a4727z3h3pfbyASJ42XNFmYBMcKZSA7S1DrDv43quBo+u1ILXzwove5MDBAQu25ALIjQ06dl/sAnAsyu119fB2/qdd6wvzwBRpKq+l8Mn2wA8u1IHtfqzqCh0qtHHars4UedUiz25Ugve/E/88LeyrGQQYX91O1UtTrXbkyu1r3V6fW4OKErRIYjw0Zlm6rr6QcSrK7XXvcZbfCB3xnxrd1UTzYYhAK+u1MHGB0iPdaZS76pucCvGwbpS+5rXpRi/UV3vVoy9uVIL3t7r3v72gITwSO4qLuXV+lq3YuzVldobvLX2lIluxfjlxhq3YuzRlXoZZcm77v8tCsXSOk0A2GxOf5zx8fE51715iPjDli1bePPNN/nSl75Eamoqe/fupbGxkX//938POMbk5CRWqxWtdu5n7t69e0lMTCQuLo5rrrmGJ554gvh4L11uPEBSij3AdcrycsPTASvFLkV4ZHqcDw1HsTpsJKvj2arbgNxDz1pfCrJDFNk/UEuT0flBfHl8EcUxWV7m9fxHaZ9RlkVR5PBgG6dGOgHIjtRxTXIJYbK5f/heFWQPHzaiKHJysJcDhnYciEQrVVyfWkRapMZ7H2SvJkMLxxunLXzc3U7zmPODPCZMxba0HHJjtP6Pjxdc93zZNX7MYuZwbxf1QwPuofmxWjanZJAwmxwH28LJF2ZiiaJI19g4Zwx6WodH3FNEKpWUJSZSlpBAjFodNHn0Sor9rGc2XApy8+AwnSOj2Gf9AiOUSnK0ceTEx5GuiXFvUqHsa+wJi1FgLVYbfeMT6MeM9I1NYBg3YvOQ6qWNDCc5Jpqk6EiSYqLQRkSc/fKw3H2QPWDKZOJ7t19cSrEECSsNbqX45294UIr9G08F1K7JqxrmX0Geb7511bZisrLnEeMlGHAF0q5pKQZcC8y31uZQOk8xXqoBl992TUs04JpjvpXrQTH28RUgkOc223yrOD2Rqz0R4yUYZAXUrmkJ8eebb91YVohCPvfFXYoBV0DtmrzG8b95B9SuKVglfZaC7M98y2w08ei25VGKt+74l5CR4gN7/mXB9X/+53/mX/5l4fXZ8KQUWywWvvKVr/DnP/8ZhUKBTCbjqaeeciu+geDv/u7veO+996ipqUGtdj7HF154gYiICHJycmhpaeEf//EfiYqK4tChQ8gDbI8qkWIPWAopBhiwjPCx4Tg20U6KOoEtunULiLG/tGpRFDk81EjtuPPDfrUmh3Vx+Qs+LP2RYhcaxvXsNTRgF0XiwiK4PmUVsWFnn1swpBhAdAjopybY3V3PmNV5irRWm8ZlCdkoZAtfo2BIsfMBgdaxYT7qbmXC6kyJzYzScFVqDjq1JyV3caTYheGpSQ73ddM4fLZ+K1ejZVNKGsmR0ctCimdjwmKh2tBPdX8/U7NaOGXExFCakEi+Voti/gnsMpLi2Zi22ekYGaV1aJj24dE59T1ymUC6RkNWXCw5cbFo1EF8AJ8DUjwfdoeDIeMkfWNG9GMT6CeMTHhIuVbIZMRHRZAYHUliVBSJ0ZHERYQjm/X3J5FiiRRLuLCxVFIMkiv1wvUvjH8pulJ7W6uney42V+r5xFhypfZOjJeTFIc6fTrQbhOz4YkU//znP+epp57i5z//OVlZWXzyySc8+uijvPbaa+zcudPven784x/z05/+lL1797J69Wqv41pbW8nLy+PDDz9kx44d/p8oEin2iKWSYgCDeYi9/Sewiw6PxDiQWmNRFKkcbePkSCsAeVEpXKErQzYrXypQUgxgmBpnd18NJpsFpUzONUkl5EQ5004XQ4rBWaP7iaGV2lGnq2xsWDjXphaRHD73D3ExpBjAardzxNDNyYEe7KIzLWtVfDKXJ2USrlAuGL8wjufL3sYPTpo40tdN08iQ+1pGtIYNSWlkRmsWnuCGiBSDU2m1Oxw0Dw9TMzBA19iY+7EwuZyieB2lCQkkRUY613GOSPFs2B0OekbHaR0eoW14BKNlroFEXLiarLhYsmJjSdPELCTygcy73MrsvPEmyzSGCSP6cSP940b6J0wejT0UMhm6qAgSoiJJiIokMSoSbWT4wnQxiRRLkHBBIBSkGGaI8Z46ujsGFxLjJZJi8EOMl0iKYYYYH59bY+wixkslxa74J6q75tQYu4hxKEgxQGVTD8dqPBDjEJBigJoOA/vPONPA5xDjEJBi8EOMl0haAdoGRthd1eiZGIcgvi9ivFRSDH6I8RJJMTi/+71SP6/G2EWMl0iKwTsxXk5SvBJaMs0nxVNTU2g0Gl577TVuvvlm97gvf/nLdHd3s3v3bp/xfv7zn/PEE0/w4YcfsmHDBr/zJyQk8MQTT/DVr341sPVKpHgh5pNig9lAhDyCaGV0wKQYQD81xL4Bz8Q4GAOuhvEeDgzWIyKSFh7P9sQ1KGfSn4MhxQBGq5UP9NX0zThSr4nLYFN8LoKX5+WPFLvQNjHMnr4mTLZpBGBtfDqbddluUrRYUuzCmMXMJ71nU6pVcjmbEzNZE5/sJCUhIsWu8cP/P3vvHSbJVd77f6pzTpPzbM7aVdhd5bhCEjJIGNtCcI1A4AhcC2yC7r1GwuCfCDbIBmywL5Z8wQIkm2ALLEBIi0A57Gpznp2cejqH6li/P2q6Z3q6emZ6pmd3dvd8n6efnjl96q23e3rq9KfPOd83meDVkUGOBvzkJxsbrHa2N7ex2lM3NWNYYyierogsc8jv59DYGNFpLspei4X19Q2sr6vHpfUt3RJC8fT+iqIQSCQ5HQzRGwgyFImWhNLrJNpcLro8Hjq9bnwz6yEvEygue1hRCCVlxqJxxqNxxqIxxmNxMrnyZdc6ScJnt9LgsFNvt1HvsFFvs1c27JinBBQLCS29agXFAFmFcvOtdl9NoBggr2iYb3XV1wSK1fZy863VqxprAsWgXnZnmm9tXNNSMyhWJA3zrfXtNYNiRdIw39rQWdEca7ZcK53j6LCG+dYs4F1t/FPjGuZbutrFHwiVm2/pdbqaQDGUg/HbJl2qawHFAONJDfMtg6EmUAylYLzWW89bV6694KC4EOunP/0pt912W7HfH/3RH9HT08PPf/7zirG++MUv8td//df87Gc/4/LLL5/z3AMDA3R2dvKjH/2It7/97fPLV0BxuaZDccQY4ZXAK5h1Zq6pvwajXruWaCXIHUwE+I3/NXJKnlZLI1fUX4xe0lcFxQC98Ql2j71JVslTZ3Kyq+kSbAZz1VCcVyRySp6X/CfZH1LNKZotbm5s2oRDY+/BfKEYQM5l2D18iqMRtcSDz2Tjpta1NFtdi4bigvojYX411MP4ZEkot8nCVc1drHHVaxuILRCKC4qkU7wxOsQB/2hxL6rHbOHixlY21jVglBYAP1W6NCt5hf5IhEPjY5wMBkv2xLY7XWyor2eVz1c0OztTUDxT6UyWvlCY3mCI3lCIeDpT8rjDZKLT46bD46bD7cI203BijvhnCoq1+k8HZX8szvjkLZXVLhXhNJuot9upd9ios6s3j9VSsvx6NgkoFhJaetUSioUrdSH/CvEvMFfq2XIVrtRz51mp//ngSv2rgdO8pXs1VoPxvITiWCzGiRMnALj44ov58pe/zA033IDP56Ozs5Prr78ev9/P1772Nbq6uvjVr37Fn/zJn/DlL3+ZP/mTPwEoq0bxhS98gU9/+tM89thjXHXVVcVzORwOHA4HsViMz3zmM7zzne+kubmZkydP8olPfIJoNMr+/fvnbQgmoFhDhTfAj449gsFmYLf/eaLZGE6DnavqrsGsL39xZ5tBLp0xrueahosBY8X+ldrH5TA/G96LnM9gN5i5pfli3EbtN2glWJ5uhHUyOs4zI0dJ53NY9EZuatpAh73Upa3SuyNfIc+8InEy4ueXQydI5lQo2uZr5fLGbkxae40rOUPOUtoprygcDIzywnAfiax6jmabg2tauml3uOeVf7Uzy8lMlr2jw7w5NoKcU2vaWfQGLmpoZmtjc7mj4GxaRE6pbJaTgQCHx/0MTnMC1EsSK7xe1tXX0+3xaC9bPoOGXYVZ5N5giL5gmKFwpMSsC6DOZqPD46LD46bV5ZrdAZoaGnlV2b/S81XyClE5xXgsgT8Wxx9L4I8lNPcog/o38tmt+OwFUFZ/dphNZR+qkvE4n3ibgGIhoaVUEYq/+MNyKK609WMOWJ6X+da0/vONX4DlOfcYF/Nf2AzyfMy3ZoszFyzPtcd4Zj7zjj+tfU7zLVjUDPKse4yrzFWrf8lS6vbGiq7UC40/L/OtRcSfuZS6Wlfquc47L/OtWePMPtjPy3xrlvjVwPL5CMW7d+/mhhtuKGu/5557ePTRRxkZGeH+++/n5z//OYFAgK6uLv7wD/+Qj370o8X3+fXXX093dzePPvooAN3d3fT29pbFLJh9JZNJ7rzzTvbs2UMoFKK1tZW3vOUtfPazny1WuJiPBBRraDoU2502Etkkz/p/QzIn4zF6uLLuSoy6Uqida1m1CsZvkFNyNJl9XFl/WXEJtFb/Su2RTIKfDe8lnElglPRc33gR7bbyb4rnA8UAoXSSnw0dwp9S6wNf4u3isrru4r7lhUAxQDKb4bmRUxwJq7PGTqOZG1pW0+0o/XCwECguKJ3L8cb4IK+NDRZrDq90+bi6pYs6i23W/Be63Dqdy3HIP8aesSHCKRV+dJLEOl89Fze2apdzqvIc8+0fkVMc9fs5Mj5OUJaL7Sa9ntU+H2vr6mh3uaYGo7PgYl1QJpdjKBylPxSmPxTGH0+UxWxyOmhzu2hzu2hxOssgeblBcaX+qUwWf1wF5YlYkol4golYQtP1GsCo1+OzW6mzWbl+7Up0OklAsZDQGdBSQDEIV2rt/Evjn/eu1FXkqtX/fHOl1gLjC9mVuqDzEYrPZQko1tBMKAaIZKLs9j9POp+hzlTH5b7LMUyD2vnsNR6TA+wee52sksNncnNN/XbMelPF/pXaU7kMT4/sY1gOIgGX+tay2dVVcsGcLxQDZPN5fj16kkMRdYBqtri5sXkDLqN1wVBc0OlogGeGTxQdpFe76rm2aSUOozrbvhgoLiieSfPSSD/7J0aKnLLeU8/Opg68ZltZfzXQwqC4oLyicCoU4I2RIYbi0WJ7i93J1oZmVnvrKptM1QzUJ+8UhfFEgmN+P0cnJohP239sMRhY6fWyyuej0+Wucga5dlA8U8l0hoFQpAjJkVTp7KoENDjsKiC7nLS6nNgM2qsrKuosQbFmV0UhkkwxEUvgjycIxBME4klCSZn85D+ZzWTk3isvBZZ2pnhkZPGOlJFIhObmhgtikBQ6f7VUUAzClbo8//L457UrdZW5avU/31ypZ4Lxhe5KDQKKl5sEFGtIC4oBAukQz/lfJKtkaTA3sNO3s2icNV8DLn8qxO6x10nnM7gMDq5t2I7NYK3Yv1J7TsnzwvgRjkbVAWWVvYUr6zcWyyFVA8Wg7kE+Hh3lubGjZPI5jDo9VzWsYa2jWXPZznyhGNTZ1RfHTvNmYAgFMOr07GzoZKuvFV2F160aKFYfgICc4PmRvqIZlwSs8zRMwrF1Rv/FQfH0/sPxKHtGhzkRnCiaclkNBjbVNbGloQm32VJ2TLXnmG+7oigMR6Mc8fs5GQiQzGaLj5l0erq9Hlb7fHS7PVODx1mA4pn9I3KKwXCYgXCEoXC0DJIBPBYLrZOA3OJy4rFYKu7lWmw+07UUz7egXD5PKCETSCTI5vJsaFE/dC4lFF97zacXXbswm5V57td/dUEMkkLnr5YSikG4Upfmrx3/vHWlXkCuF4Ir9XQwFq7UAoqXmwQUa6gSFAOMyEFeDLxITsnRbG5mu287OklXlSt1OBPjmdHXSOZkbHoL1zbswGV0VOxfqV1RFA6GB3l54igKCvUmFzc0bcNhsCwIigEimSTPjBxmRA4D0G2v59rGdVhnzGhXA8VqrjAux3hm+ASjSXVm1Wuycm3TajrtXo3+1UNxQWOJGC+O9nMqEgDU69N6bwM7GqfBcY3dqkGdsT7gH2X/+CixzNRsbbfLw5aGZla4varR0hJCMUxdj/OKwmAkwslAgBOBAPFp9Y/1kkSn281Kr48VHo/2nuizZNgFat3moXCUoUiE4UiUiUSyrI/FYKDZ6aDF6aTF5aDJ4Sj9lvgcgOJKElAsJLT0mgnF6VSWWDiBr9FVEygG4Uo9lX+Fds5TV+oaukYLV+rZz3suu1ILKF5eElCsodmgOKPoGE+N89LES+TJ02xpZrt3O7kqjbPC6RTPjb9CNBvHrDNxdf1l1Jk9VUExqDA7nAzw7NibpPIZzDoj1zdeRJNFw3iDuaFY7aPwZrCPVyd6yKNg1Ru5rnE9Xfb6aX2qh2L1XuFQaJQXxk4XjbhWOeq5unElLpNlWv+FQ3FBo4kYL4700RMNAlMzx9sb26kzV9j7WwNgzSsKp8IB9o2P0hcJFdvtRiMb6xrZXKcxe1zlOWZr14qiKAoj0Rgngiogz5yJbXE4WOn1sdLjxVuYgT2LUDxTcibLSCTK0ORtLBYrM+6SgDq7jWaHg2anCsllZaAWkM/5BsVi+bSQkKrpUGzQm3nmx3sITcS58Y5tNLRV5xotXKmn8tHOv0L889WVepZyTcKVWrhSFySgeHlJQLGGCm+A/zr+rTIoLhpnyWO8MPEqefK0WJq4zLujaE41XbPNIMu5NLvHXiOQjqCXdFxVv40WS7Nm/+wcsBzNJHl6ZB8T6ai6z9i7mi3u7jIgqAzd5f/F43KMp4ePEEir5Y82uFq4qnEVJp2hIvzmK+wRnlnaSc5leWmslzcnhlEAvaTjsvp2Lq1rx6jTzwLFms2zQvRIIspLI/30RILF9tXuOnY0ttNkc8wr/kJnloNykgPjoxyaGCtZytzhdLO5oYlVHt/UPt9qjbAqaY5cFUVhIpHkVDDAqUCQsXi8pJvLbGaF18sKj4c2l6t8H/IS5zkfCM3l84zH4oxEYwxHooxEYsSm7aUuyKTX0+i00+R00Ohw0OS04zCWuz3PprNh8JWMx/nk7cJoS0hoKVWE4i/8AJPewrM/eZPRwRBGo54b7thGQ4un/CDhSg0IV2qt887LfGueuWr1F67Us5/3XHSllmNx7r/xLct2rF+KeMtZAoo1NB8ohlIwbjY3c5nvsuIe44LmWladyWf5zfhehmU/EnCxZxNrnF1l/eeCYoBsPsfz/iMcjw4D0GVr5JqGjZimOWVXA8UAmVyelyd6eDOo1jR2GMxc37SOdludZv/5QnFB44kEvxo5yUAiPBnfxFWNK1jratS82C8EigsaSUR5dXSAE+FAsa3b6WF7YzttdheSJNUcigvK5fOcCgXY7x8rmT026w2s99Wzoa6BJpujulrLlVRlrlE5RU8wyKlgkIFIpGj8BGDU6eh0u+n2eul0u3GZzcsCirUUS6UYicYYjcYYicQYi8U1HZ9tRiONDhWUGyZrCTtMlUFZQPHSxhISOluaDsU2i51sJlcEY4NRr84YzwRj4UotXKlnOa9wpZ49vnClLtVSQnEtVoUV4l0oK8MEFGtovlAMMCqP8/zEK+TJ02huZLt3e9Wu1Hklz6uBQ5yMqeC5xtHNNs8Gdf/ppOYDxaAOKkejg7wwfpQ8Ck6DlRsat1Bvdmv2n4ozO1QOJoI8O3qUSEYt/bPW2cQVDavL9xpXCcVKXl06dTzi5zdjPUWX6nqznasaV9Bp95Zc8BcDxQX5kwleHR3gaGi8yCNNVgeXNLSy2l2HXmPGv2Z7kIFwSuaQf4yD/rGSvcdes4X1dQ2s9zWULq9eYiie3p7O5egPh+kJBjkdCpGYtg8ZwGux0On20Ol20+5yqft2apznQqF4pvKKwkQ8wWg0xmgszlg0xkQ8oRnGYjDQ4LDTYLdRb7fT4LDhsVrRSZKA4iWOJSR0tjQTioG5wVi4UgtX6jnOK1ypZ48vXKmntJRQfN0V/2fR/iGgeoj86sXPXRDjvYBiDVUDxaCC8QuBV8kpOepMdez07SzWMZ6vAZeiKByMnGJf6DgALZYGrqjbVowzXygu5pSM8OzYPmJZGR0Sl/pWs9nVRZ5K+cwNlZl8lpf8PewPqft+LHojV9avZo2zqXhBXggUF5TN59gTGOI1fz/pfA6ADpuHqxpX0Gh1TuajGWZBe5BDqSSvjQ9yKDBW3J/qMJrYVtfCZl9z6UWuhlBcUF5R6I+EOTQxxslQoGRWs8XuZJ2vnjXeOuwGDROs2VSjvckoCmPxOD2hEH2hECOxWElXnSTR7HDQ4XLT4XbRbHNoLoWqNs9aQbGWsjl12fVYLM5oNIY/HieQSGqG1usk6mw26m026u2TN5u98j6iheYpoFhI6KxIC4phEox/uo/RgWA5GAtXauFKPY/zHuod5dfClbpif+FKrUpA8fKSgGINVQvFAKOpEC9OqOWaPEYPl/sux6w3V+VKDXA6NsrLgTfJKXlcBgfXNFyGw2CrGorzeYlULsNv/IfoTYwB0GrxcVX9RdgMZo0484fK0WSEZ0ePFvcat9u8XNOwFrfJtigoLiiZzfCKv599waHiUt7Vznp2NnThM2kbZC3GmCuRSbNvYoQ3J0ZIZNWZUaNOx0ZvE1vrmvFZbEsCxdOVzuU4EZzgyMQ4fdFwsV0C2p1u1nnrWe2p096/Mt9zL9KwK5XN0h+J0BcK0Rsury1s1OlodbrodLlod7mpt9lKVjvMN8+lhGKtONlcnolEgvF4HH9MvZ+IJ8hoLL0GsJuM1NlsKjDb1Xuf1YpBP8fe8HnmAwKKhYTOhCpBMahbh3Y/+WY5GAtX6sl24Uo923kBDvYKV+rZ4gtXarF8erlJQLGGFgLFGUVHKB3ixcCLpPNpHAYHV/iuwKh3avafzU06kA7x6/HXkfMpTDojV9RdTL253NBitjgFOC0sp345cJScksesM3Jl/WY6baXxqoFigExedah+PdBLTsmjl3Rc7O3kIndXsVZyST5VQHHhvJG0zIvjpzkaUaFeAta6GtlZ34XbZC3rX03+WiCSzec5Ehxnj38Iv5wotnc6PGyra6Hb6SuHvBpB8fRYsXSaY0E/x4J+RuKx4sM6SaLL6WGNt46Vbl9lQF5CF+vp/UOyTH8kTH84TH8kgjzNSAzArNfT5nTR5nLR7nSVQ/IygWLNLopCWE7hj8eZiCXwJxL44wnN+skFuS1mfDYbdVYrvklQLoHlKvIRUCwktPSaDYoVnUQ2kysHY+FKrUq4Us963sI5hCt15fjClVoYbS03CSjWUOEN8NTJf8LuLIWvTF77zV6A00gmyq8nXiKZk7HqLFxedwUuY/mbqBLMFmaEE1mZ58b3MpEOIwFbPetZ6yg3VKgEszPjB9Nxnh09wERahaz1rnZ2+NYWAbYSPM5V7ziUTvDc6An6E6qzs8to4erGNXTZS424Ks0gVzrvdHdrvxznxbFeTkZVgywdEhs9Texo6MRpNM8ap+rl1qgDfX8szJ6xYU5Nc6x2mcxsrW9mk68Jq8E4a/xZobgKOA3LMkcDExwN+JlIToG6TpLocs0DkOejGpiLKYqCP5GgPxxhIBxmMBolk8uV9DHr9bQ4nbS5XLQ6nTTZ7drLrZd8D+/CYTydzTKRSDIRTzCRSBKIq7A88wuB6XJZzPhsVryTkOy1qbfZ/mZyPM4nbxVQLCS0lCpC8ef/owyKCxA6fY+xcKWe3j55L1ypK2vyEOFKPXv8C9mVWo7Fuf8mAcXLRQKKNbQYKAZI5JL8xv8SkWwMo2Rkp28ndea6iv2na/oy6ZyS45WJQ5yKq4NBl62Vy7xbSmZi5wvFalueVyZOcSDcC4DHaOfaxk3Um90LhmJQB62TMT+/GTtBPKsaR61w1HNVw2qcRnU/w2KguKDRZJQXRvvojaugqpckNnmaubS+HafBWtZfja/ZPCsUT1c4JfOmf4QDE2OkctnJ8+pY56lnS30TzVZnBcfo2kDx9FgTyQTHAn6OBycIyMniwzpJosPpZpXHx0q3D4ep2j3Is5933v2nKa8ojMfjDIQjDEYimpBs0OlodjhoczppcTppdjgwGwzLGoo1H1YUkpksgcQUKAcSSQKJ5KywbDUa8FqteKxWvFYLHquFFT7VWE5AsZDQ0ms+UAzzMN8C4UotXKlnPbdwpZ49/oXqSi2geHlJQLGGFgvFAOl8mt9MvEIgHUSHjku9l9Jqba3Yv6CZe4fV5c99vBE8ioKC2+jkqrpLcBrtk3HmD8WF9sHEBM+NHyCZSyMhscXTxVb3Ks1lz/OB4qnnnOUVfy/7ggPF2sNbve1c7OvEgLE8CNVBcaH/YCLMi2O9DE6WcdIhsd7dyKV1HXjNthn9NcPMG4oLyuTyHA362esfZjw5VdfXZ7ayua6J9d5GbIZpz3EJoHi6JpIJjgcmOB6cYGLaUm+AJpuDlR4vqzw+6iy2uWvyLgEUz+xfgOTBSIShaJShaFQTGOusVpodDpodTlocDnxW68Lzr9i/tlBc8bBJWA4mkgSSSQLxJMGkCsvxCjWV//DyywQUCwmdIc0XikG4Upe3z/hduFLPeW7hSj17/AvRlVpA8fKSgGINzYTiwWQPNr0Dr6lh3lAMkM1neSmwh5GUOkhsdG1ktX01kiTNG4oLGkqEeGFiD6l8GqNkYLtvCx22lgVBMYCcS/Oi/wg98VEA3EYbV9VvpsnimdG/OmjNKxITqRi/GTvOUFKFVqveyGW+lax3NaOTZkJ/9VCs3isMJMK8Mt5XrHEMqiHXZfWdNFock/00w1QNxUw773Aiyn7/KMdCfrKKasakkyRWuXxs9DXS5fSiY5aRsgZQPL1/QE5yMjTByVCgZA8ygNNkZoXbwwq3l3anG6PGFx9nAorLmhSFYDLJ4CQgj0SjhDX265r0eprsdpodDprsDpodDuwzZ8KXKRTPFiedzRGSkwQTMsGkCst6Scdb1q0GxPJpIaEzoWqgGIQrdWm7RptwpZ7z3MKVevb4F5ortYDi5SUBxRqaDsWyKcye0K/RoeMiz5W4jdomDJUgNJ2X2B/ZT09cvQh22jrZ6t6KgjZcV4LibF5PIivz4sQe/Gl1+fAqewcXeTahl7RmeOfnVn06PsaL/sMkc+rM1UZXJ5d4V2OcrLW8ECgGdfDqifl5yX+KcEZd6us12bm8bhWd0/YbLxSKp2s4EeFV/wA9sYliW4fdw6W+DtptHs2ZxoVC8XSlclmOBP0cnBhlNDkFozaDkQ2eRjb4Gqmf+UFrtpPUAE7jmTQ94SCnQgF6I2FyypSDsl7S0eF0scLtpdvtnaqFfBagGMrHh3g6zUgsxnA0xkgsymg8XlKmqiCHyUST3UGT3U6Tw0GjzV7dnuplAMVzaSmhuBaOlBeSG6XQ+atqoRiEK/VUu3Y6wpV67nMLV+rZ419IrtQCipeXBBRraDoUWxwm9odexJ8eRkJivfNKGi2dZcfM5kqtKAqn4qc4EDkAQJ2pjku9OzHpyksjzQbFAHklz4HwcQ5HTwLgNjrZ6bsYl7HU5Xq+UAyQymV4eeIYJ2LqsiSnwcqV9RtptdYtGIqnzpfnYGiI1yZOk8qry2XbrV521q+i3uysCRQX2v1ynNcn+jkWGSvyR53ZziW+dta4GtBPm6WuBRSrcdT78WScg4FRjgbHSeamlgU3Wh1s9Day1tMwtbx6CaF4ujL5HAPRMD3hID3hINEZS3a9ZgtdLg9dLi/tTlf5LPIZhuKZ/XP5PIFkkpF4jJFYjNFYjIlkUvMQt9lMk91Bo91Og81Og82G1ai9ZP9Ch+IbLrkfg35xtQuzOZln33joghgkhc5fLQSKhSt1oV07vHClnt+5hSt15fgXkiu1gOLlJQHFGiq8AX556h+xO63klTz7wq8xLA8AsMF1GW3WFSXH5CrUI87kp9qH5VFeDrxBVsli09u43LejzJm6UpyZsDyc9PO8fz9yPo1e0nOpdwMr7W3FWdFqoLjQ3p/w85vxw8Sz6jLW1Y5mtvvWYdWXGzdVKrGUy2vHT+YyvDbRx/7gIPlJOljjbOQy38qy8kpQ/bLn6RAdTsvsmRjiYGikWGvWYTCxzdfGZm8zZr2hZm7VM5XL5+mJhDgYGKMnHCw+VwnodnlZ721gldOn6XxYLZBXA9eKojCRTNITCnI6HGI4Hi3WgAbVtKzN4aLT7aHT5aHBakOardZENflU7F99/HQux1gsxlg8zmgszlgsprnsGsBpMqmQPHmrt9lwmEyVayefJYMvLcnxOJ96i4BiIaGlVBGK/79/L4fiilCptgtX6tkB6EJ3pZ6ea6VzC1fq2eNfCK7UclxA8XKSgGINzYRiUC/AByN76E+eBmCNYytd9rXFY+YDxQDhTJQXJl4hnktgkAxc5r2UZsvUN6jzhWKAZC7F8/79jMjqsuEuWwuX+TZi0hkXBMWgmmW9HjjJwbA6EJh1Rrb71rDG0VpyMa0WigtvsnA6ycv+Ho5HxwHVJGuDu5VLfF3YDVMz54uB4oLkXJZ9gRH2BgZJZDMAGCUdGz1NXORtKzPlWsh5K0lRJJLZDEeC4xwOjJcsrzbodKx2+VjvbaDT4ZnaL1Mx2OKheGacVDZLfzTM6XCI3kj5LLLVYKDD6aHT5abD6Z5aaj2bzgAUa0nOZFVIjscYj8cZj8UrgrLFYKDeZlNB2WajbrKesEGnu2CgWCyfFhJStRgoBuFKrd0+7ecL2JV6Zq6Vzi1cqWePf767UgsoXl4SUKwhLSgG9QJ8OHqQ3sQxALps61jt2DJpnDU/KAZI5VK8EHidibQKsxuc61nrWDNrnMrLquFwpId94eMoKNj0FnbWbabBXP6NLMx/BnlcDvOb8cPFusaNZjdX1K2nzqz+QywUiqfiR3nJ30NfsbySjk3uNrZ5O7EZTDWB4kL/bD7P0cgYeyYGmUhNOTV32D1c5G1lhaOuOHtYSyieroCc4EhwnCNBP+G0XGw36/WsctWxxlNHh8OjwllZsNpDcWmuCkE5yelIiP5ImIFouDjDXpDTZKbd6aLd4aLd6cZlMpcPrGcJirWUymYZj8UZTyQYi8fxx+MEkknNEBLgsVios9qos1kn7224zWbtOsoLyEftvzygWBhtCQmpWiwUg3ClLm+f8fsF6kqtlWulc59PrtSrmnzcvHmNcKWeptnAWEDx8pKAYg1VgmKAdF7H6cQRTsbU/cFN5g42urcD2rVhtaBYjaOwL7yf04neyThNXOq9GL2kXW+3EhQX9vz6U0FenNhPLKtC3yp7Jxd51hcNs6b6z38GOa/kORAa4I3gSbJKDglY62zjEu9qzPry/dAwfyguqC8W5pWJU4zKEUCF443uVrZ6OktmjotxFrEHWVEU+uMh9gaG6IkFiu0Og5nNnmY2epo1zznbeSup8vJshZF4jCPBcY6F/cUZbACTTs9Kl4/Vnjq6nZ6pEllLDMUzlcvnGY5H6QuH6Y+GGY3HisvAC3IaTbQ53bQ5XLQ5nXjN1gUst146KNbqn53co+yPq7Dsj8eZSFauJayTJLwWCz6rFZ/VRp3Vis9qxW2xLHBmWUCxkNByUi2gGIQrdWm7RtsF6EpdKddK5z6fXKnLwLgG8c9XV+qlhOLRGqwKK8RrukBWhgko1tBsUJyZnMkdSp7mcOS1ydrBdWx2X4NJV77EtBIUZyf/K0/He9kX3k+ePFa9lUs9O/CayvcNzQXFoJaA2hs6xvFYHwB2vY0ddRfRYPZN61/dsup8XiKelXklcKxYvsmkM7DVs5L1rs4S8yqoHopzedWIrD8R4LXAacamwfE6VwvbPJ04jVOva62MucKpFPtDwxwMDSNPGmNJQLejjs2eFjrt3pK9p7WCYvXBQs4KQ/EIx8MTnAhPEMtMLWE26HR0OT2sctWx0unDYtAwjVoiKJ4ZJ53LMRyPMhANMxCNaEKy1WCg1e6izeGi1eGkwWYve2/UKp/F9p9+VkVRSGQy+BMJJhJJJpIJJhIJAslk2Wz59OPdFgteixWf1YLPYsVrseK1Wmd3wRZQLCS0rFQrKAbhSj3Vrp3OheZKPVuuF4IrdQkY1yj++eRKvbaujltXr11SKL5x6ycxVJjAqkbZXIpn3vzCBTHeCyjW0HygGCCQHmNf6AWySgaLzs5Wz3XYDKVvmLmgGCCUCfNq4FXiuQQSEptcW+i2rSxZ3jIfKC5oRJ7g5Yn9JHLqMt21jm42u9dh0OkXBMVTcYO8PHGUiXQUAJfRxnbfOtqt9VMGXwuA4mIfRWEgEeT1wGlG5DCg7jle62pmm7cLt9FaU7dqUGcPT0THORAcZigZKT7uNJjZ5Glmg7sZh9G8JFBc2l9hKBHlRHiC46EJopmpPbES0O5wq4Ds8uEyzVVKqVJ7bSA0U4TkCIOxCCPxWEnpJ1ChvtnmoMXupNXhpNnuxDoT7JcBFFfqrygK0XSaQFIF5IlkkkAiSUBOks7lKp7SajDgsVjxWix4LRY8FiseiwWPxYJBo3RaJQkoFhJaetUSioUrdaFdO/yF5ko9e66VzyFcqSvHP19cqZ/t6eG2NWtxmEwCipeZBBRrqPCGeq7nazhmLp+esec3lo3wWvAFkrkEBsnINs9V+ExTg0hmnnuEM/kMrwbfZDCpDhZt1lYu9mybVi+4wp7iCu1yLs8bwSOcjKmO2Q6DjZ11m6g3aQ9wlUsv6Wb8rnAsOswrgZPIk7WN26x17Khbg8/kqAiDFaFbo7+iKAwmw7w60ctgIgSo15hVzga2ebqotzg1jtEMXxmiNa5aE3KC/YERDofHSE2bPe50eNnoaWKFo658z2+VLtaz5TT1uMJ4Ms7xcICToQB+OVHyeL3Fxkq3j5VOH802R9ne3sqGXZXaFwen2XyesUScoUiUwViEoVi0+PpNl9dipcXuoHkSkusttspO0IvIZ+7+C3++iqIQz2QIJpMEkkmCicl7WSY+w6xsppwmUxGQ3VYrHrMZdwGYZ7yv5HicTy3jfUYCioXOBxWh+K+fwGaZYbxYaaWLcKWezH9hM8gXjCs1zPJaz35u4Uo9e/xz3ZVaUZTi6y3H43xql1g+vVwkoFhD1UAxQCon83roJcKZwORM72W0WruB+UMxqP8oR2OnORA+hIKCw2Bnu/cyPCZ31VBc6D+UHOeViYPFWeOV9g62etZj0hln9J8fFBeUzOXYG+zhYLiPPAoSsMbZysWeVdgN5cvIq4FimOKQoUSYNwJ99Man9gB32Hxs83bSavUULyy1gOJC/2w+x4nIBPuDwwwlpmaPLXoD69yNbHA30WCxq+deAiieqaAsczIc4GQ4wFA8UsKANoORFU4v3S4vnQ4PFoPhjEPxzDgF466hWJShWJTheJSgXF5f2KDT0WRz0Gx30Gx30mizaxt4LTSfiv2XJn46lyMky4SSSYJJmWAyqf4uy7POLgM4JoH5tzdsQJIkAcVCQmdASwHFIFyptdun/XwhuFLDgqEYhCv1XPHPdVfqgpYSioXRVvVa9lDc3d1Nb29vWfuf/umf8vWvf72s/dFHH+X9739/SZvZbEaW5bK+lVQtFAOk83Ag/AqjKXVmttO2hrWOi8ihsReUWdykFR0TqQCvBl8jmZORkNjoWs9K+1pNWJgLigEy+Sx7g1N7jS06E9s8G+i0TZVZqhaKC/0jmQSvBk5wOj4GqHuBNzg7uMjTjWVafeOFQnFBfjnGG4F+TkTHio/Vmx1s8bSz2tGErsIHmIVA8XQFU0kOh0Y5FBolnp2aCfSarKx3N7LW1VhVneXZcppP/2Q2w+lIkFORAKcjIdL5KdiSgGabky6nhy6nlyabo3Qm9gxBsZaSmQzD8Sgj8RgjsSgjiZgmKFr0BhrtDppsdppsDhptdpxzgfIygeKKhysKyWyWUFIF5pAsE5ZlQqkU4WnA7DSZuPeSSwAxUyx04elsjvW1hmIQrtTl7TN+P99dqWFRUAznlyu1JhgvMv657koNAoqXm5Y9FI+Pj5Ob9uH5wIED3HzzzTz77LNcf/31Zf0fffRR/uzP/oyjR48W2yRJoqmpwr4VDS0EinOKHkVROBE/SE/8MABeYz0bXFdh1pdD02xQDJDKpdkT2suwrA4YdaY6LvZcgs1g1+xfnk95+5gc4OWJg0SzcQCazHVc4t2Ey+hYMBQXNCqHeDVwglE5BIBR0rPZ3cUmdxcmnWHRUFxQMJXizWA/RyPDZCf3sVr1Rja4Wtnobitzj14sFBeUyyv0xYIcCo/SE50gN+3fptnqZJ2rkTWuBmwG02ScCk9glnNU2z+XzzMYi9ATCXI6GiSQKp2NtegNdDjcar1hhxu30aINl2cAisu6KgqBZJKReJTheIyxRAx/MkFe44Uz6w002uw02Ow02uw0Wu14LNYp4F/mUDxbHEVRSGWzxdnkLo8HEFAsdOHpbI71SwHFIFypS9s12s5nV2pYNBTD+eBKPQsY1yD+ue5KLaB4eWnZQ/FM3XfffTz55JMcP35c8wP+o48+yn333UcoFFrwORYKxQWNyoMciLxCTsli0lnZ7L4Kt7F06dFcUAzqBb030c/+8H6ySg6DZGCL+yLarR3F514NFAOkcwpHoz0cipwgp+RVIyvnCtY515SVb4L5Q3Eh3/5EgNeDJwhMmnGZdUY2u7tY5+yqEL86KC4Yc8m5DIfCQxwMDxLPqqZUOiRWOhrZ7GmjyeKezKk2UDz9vySVy3Iy6udoeJyBeKiYqwS02z2scTaw0lmPVa+9SqBmpl0zXqRoOsXpaJDeaIi+WIjUjJlYl9FMp9OjgrLDXQT4swHFWnGy+TwTyQSjCRWSR+NxJmRtUDZIOuqsNhpsdhqsNhqsduqtdtUpcs7zLh8oBu1xVUCx0IWuMznWF6A4m8kRjyZx+xw1gWIQrtRT7drpnLeu1FATKAbhSj1X/HPZlVpA8fLSHDVTlpfS6TTf+c53uPfee2ddThmLxejq6qKjo4M77riDgwcPzho3lUoRiURKbotRk6WNy303Ydc7SeeT7Ak+w2DiONV+/yBJEt32Tm5svB6v0UdWybIn9AavBV8llUvNHUBDeknPRtdqbm2+hlZLI3kUjkRP8bOR3fQlBqvOcWa+HbZ67mjdyfUNW3AbbaTyGV4PnuA/Bp5jf/gUmbx2TdhqZdEbucTXxXu6L+fm5k00W9zkUTgRG+VHA2/wH/2vcSQyRDY/+17OhcisN7DR08w7urZw7+qdXNO0kiaLEwXoj4d4ZuQ4/3L8JX7cv59DoRHkXGbOmLWQ02RmS10zt3ev54827eT3Vm/hiuZO2uwudJJEJJPiQGCU/+47xj8depVvH93Ds4OnOBH2k8yemRxnk0Gno8nu4KKGZnZ1reY9G7fyp9t28u4NF7GraxVbG5ppsTsx6HRklTyjiRgH/KM829/D48cO8A9vvsy/HHid/zx5mBeG+jga8DNRYfZZ6Ozo61//Ot3d3VgsFnbu3Mkrr7xSse8PfvADLrvsMjweD3a7nW3btvHtb3+7pM+DDz7I+vXrsdvteL1edu3axcsvv1zSJxAI8J73vAeXy4XH4+EDH/gAsVhsSZ6fUG10Nsb6bCbH7h+/wc++9zLB8cV9Bpgug1HP9b+1laZ2L9lMjmd+vJfx4VDN4uv1Oq69eTNtXXXkcnl2P7Wf4YHA3AfOUzqdxFXXrGPFygYUReFXzx6hr9dfs/iSJHH5jpWsW9sMwG9eOM6Jk2M1jX/plk4u2tAGwEtv9HDo+HDN4gNsXdfG9o2qC/XrhwfYc2SgpvE3djdz9ZZuAPadHOblw32L+qw2U2vb6rnhotVIwOH+MZ472FPT+Csafdxy0Rp0ksTJ0QBPHzhBPl+7+O1eN7+1ZR0GnY7eQIifHjhGrkJJxYWo2eXkjkkQHopE+a8DR8jM4RMidG7qnJopfvzxx3n3u99NX18fra2tmn1efPFFjh8/zkUXXUQ4HOZv/uZveO655zh48CDt7doOgQ8++CCf+cxnymP1PIzDZSWRjWDSWTDoTGQrzBRrGWpl8xn2hvcwmlK/oWy1dLHRdQl6SV9xhreSMVcmr+No7DiHI0dRUDDrTGzzXESzpU2z/3xLLw0kxngteIRYVl1622D2cql3I16Tc9Y4lWZ4p/fPKwonYyO8EewhklHjW3RGtni62OBqx6gz1MStGtRvg8flKPuCgxyLjhVByKTTs87VzCZPK17T1NLzmpV2mvZzKJXkWNjPsYgfvxwvtuskiQ67h9WuelY6fVin7bUuDTb3LHWlc88VJ53LMRCL0BcN0RcNlzlag+pq3e5w0+5w0eZwY6s0013xvJUemN9M99xxVOUVhXBKZjwRZzyRwJ9IMJ6Il9R5ni69JOG1WKmz2qZuFhtu84y9yks8I1yN5Fic+2+4Zdl+e7yQWN///vd573vfyze+8Q127tzJww8/zBNPPMHRo0dpbCx3n929ezfBYJD169djMpl48skn+fM//3N+8pOfcMsttwDw2GOP0djYyMqVK0kmk3zlK1/hiSee4MSJEzQ0qE77t912G8PDw3zzm98kk8nw/ve/n+3bt/PYY48t6jUQWjqd6bH+nz73OHrJyDM/eJ2JkTAmi5Gbf3c73gaN97ZwpRau1BX6C1fq2eMLV+ryDwdyPM6nbhYzxctF5xQU33LLLZhMJv7rv/5r3sdkMhk2bNjA3XffzWc/+1nNPqlUilRqauY1EonQ0dHBiz0PY7LDm6Gn0EtGNrqvx6Bza59nFpg9nTjGsdh+ABwGN9vcl2PWVxenAOPBdIjXg3uIZNXlyW3WNra4tmCeUYusmnrEOSXHwXAvByMnySnqwuJVjg62uFdj1JXvh4b5QfFU3zzHo6PsCfYQnYRvs87IJncHG5ydmDXgazF7kJPZDIcjIxwMDRHJTJmutFo9bHS3sMJRj14qX8o9a/wql3kH5CTHI36OhceZSE0BqAS02txFQHYapzl1LyEUz+yfzGYYiEXoj4bpj4UJaLhD+8xW2hwu2u1uWuxOXEYVIs82FJf3V+Mnsxn8iQQTyQT+ZBx/Uv05U+EbY4NOh89ixWex4bNa8Zmt+CxW3GaL5kA6/3wWfmhBSwnFo8OLL9MQiURoaqmuRMPOnTvZvn07X/va1wDI5/N0dHTwkY98hE996lPzinHJJZdw++23V7yWF57j008/zU033cThw4fZuHEjr776KpdddhkATz31FG9961sZGBioCFxCZ1dneqz/p889jtViI53KlIDxrt/Zjq9xxvtbuFILV+pZ+gtX6tnjC1fqUgkoXl46Z6C4t7eXlStX8oMf/IA77rijqmN/93d/F4PBwHe/+9159S+8AV7seRjJJnMo/CzpfBKDZGaN8zpcpuayY+aC2YnUKPsir5DOp9CjZ63zYlos5RebueKACrFHIsc4FjuBgoJJZ2KzazPt1vZpbtLV7TXOKRLxbJI9wSP0J0cBMEh61jlXss65AsOM/cDVQHGhf17JcyI2wpvBHiKTcGyQ9Kx3trPJ3VlSyqkWxlyKotAbD3IwNERvfKLYx6QzsMbZxDpXMw1mZ8nfoFZQPL1/IJXgRMTPycgEY3Lpss16i50VDh8rHD6aLC7Nb2WXAopn9o9n0gzEwgzGIwzGIpozyXaDkRa7ixabkxa7k0aro7S+7lmGYs2HFIVIOjUJygkCyQQTySQBOVFiljYzW7fZgtdixTt577GowGwzGOcuGbXMoXjX2o9hmPElWrXK5lI8fezL9Pf3l+RnNpsxm8tjp9NpbDYb//7v/86dd95ZbL/nnnsIhUL8+Mc/nvV8iqLwzDPP8Pa3v50f/ehH3HzzzZrn+Pu//3s+97nPceLECerr6/mXf/kX/vzP/5xgMDiVezaLxWLhiSee4B3veMcCnr3QUupsjPUFKAbKwLhsxli4UgtX6lnyB+FKPVd84Uo9JQHFy0vnzJ7iRx55hMbGRm6//faqjsvlcuzfv5+WlvJlMvOR3eDlIs+t2A0+skqKw5FfMJI8UvV+izpzE1f4duEzNpAjx+HoaxyIvEQmr73kczbpJT2b3Bu4ruFqXAYX6XyaN0Jv8FLgJeLZ+NwBKshusHJ1w8Xc1LgDn8lFVslxMHKcnw7/ipOxPvLK4vZo6CQda52tvLPjCq5v3IzX5CCr5DgQ6eWJ/t/w6/GDBNO12+snSRKd9jpua9vCe1ZczqW+LhwGM+l8loPhQX7Q/zpP9L3Km8E+Etnq/w7zlc9sY0dDJ3evupj3r9nO1U0raLWqFxa/HOdVfz+Pn36T/3v8JZ4eOsqJiJ9UrjZ7r+cru9HEOm8DN7av4vfXX8wfb97B27vXc0lDK802Bzok4tkMJ8IT/Hr4NI+f2M8/HniJ7x1/k92DpzgSHCeckmu6D6kWkiQJt9nCSo+PHS3t3LpyLe/ZtJUPXXI592y+mN9atY4r2zpZ72ugyWbHqNOhAKGUTE84yBtjw/yy7xT/cewg/7zvNf5h78v826E3+cmpozw/2MtB/xhDsQjxTHrZPfczoY6ODtxud/H20EMPafbz+/3kcrkyZ+CmpiZGRkYqxg+HwzgcDkwmE7fffjtf/epXy4D4ySefxOFwYLFY+MpXvsIvfvEL6uvVD+kjIyNlS7MNBgM+n2/W8wqdPZ2tsb4gk9nIjb99KfXNbtJyhl888WrN9xjfcPsS7zHetZH2rvql22N89RLvMd5+bu8x3rZ2afcYb+pu5potK4Al2mPcWs+NW5Zwj3GDj1sn9xifGJ1Ymj3Gm5dwj7GzdI/xf4o9xiV67rnneNvb3kZrq1r29Uc/+lHJ45Ikad6+9KUvVYz54IMPlvVfv359SR9ZlvnQhz5EXV0dDoeDd77znYyOjlaVu/Ya0mWmfD7PI488wj333IPBUJrye9/7Xtra2oofxv7qr/6Kyy+/nNWrVxMKhfjSl75Eb28vH/zgBxd8frPexhbPzZyIvow/dZre+CsksgG6HTvRSfNwu52URW/lMu+19CSOciJ2kLFUP5HMBJtcO/GYGqrOy2fycl3DdZyIneBo9ChjqTGeGXuGtc61rLCvRV9FbtPVaPHxlqYr6EuM8GboOPFcgteDBzgW7WGLey1t1mYqf908t3SSjlWOZlbam+hPBNgfPs2IHOR4bIjjsSHarHVscHbRaq2fe1ZunnIaLeyoX8Fldd0MJoIcCY/QE/cTSMd50X+Sl/ynaLd5WeNsptvegEG3sNduLrlMFi6pa+eSunYS2TS9sSA9sQB9sSDJXIZD4VEOhUfRIdFic9Ht8NFp81FnttXstZiPrAYjq9x1rHLXAZDN5xhJxBhORBmKRxlORElmM4wkYowkYoD6ocKqN9Bsc9Jkc9BkddBkc065XC8j6Sb3GXst07YHKOosQiyTJiTLBFJJQnKSoJwkmJKJpGQy+TzjyTjjyfIvn4w6HW6zBY/ZgttkmfrZbMFhMqGvNMN0hvVvv7q/RsuntWeKaymn08nevXuJxWL88pe/5GMf+xgrV64sKdFzww03sHfvXvx+P//8z//M7/3e7/Hyyy9r7lMWWt4622N9QSazkRvfeRm//I/XmBgJ84snXq28x3gBKphvFVypn/nxXu2l1AuUXq/jmps3FV2pdz+1v/Ie4wWoYL4F0HNqnF89e6TyHuMFqGC+BXD02Ai/eeE4gOYe44XGv3RLJwD7Dg/y0htqySOtPcYL1ba1KnS/eqif1w+rUFxxj/ECtLGrCQWF3+w/zb6T6vhbNmO8CK1trUeR4Nl9Jzjcr34pUTZjvAitaFDNt3627zgnRidQUKZcqWuggvnWk/uPFsG40h7jhagAxj8+cKQIxkVX6mUiOZHGZFj8hI+cqC5GPB5n69at3Hvvvfz2b/922ePDw6VfQv33f/83H/jAB3jnO985a9xNmzbx9NNPF3+fOUZ89KMf5Sc/+QlPPPEEbrebD3/4w/z2b/82zz///LxzPyeg+Omnn6avr49777237LG+vj50097kwWCQP/iDP2BkZASv18ull17KCy+8wMaNGxeVg14ysNZ5JVa9j/7EG4ynTpDMhVnjug6TzjZ3gElJksRK+3rcxiYOhl8imY/zemg33bb1rLBvBKr7h1JnX9fSYm1hX2gf/rSfI9Ej9CcG2OzeSoN5YYOIJEl02VtosbZwMtbH4cgJotk4L0zswWt0sdG1jmZLw6IukAW36g5bPWNyiAORPnrjowwmJxhMTuA22tno6mKlvbVmkKqaXvlot9WRymU4ER3jaHSEMTlCfyJAfyKAUdKzwtHAGmczLVbvVD3cGstmMLHB08QGTxM5Jc9QPEJPLEBPdIJwRmYwEWYwEeZ5enAYTHTafXTZvbTbPVgqmGAtlQw6/aQJlxsFFR7DaZmRRLQIy+PJOMlclp5okJ7o1HJVp9FMs81Bo9VJk9VBo9WOxXBm85+vJEnCaTLjNJnpoHTffzafJ5KWCckyoZRMUE4SSsmEUkmi6TSZfB7/5DLtsrio7uDuSWB2mc3Fe5fJPL9l2TWSxWbCYlvcFxXprHq8y+WaF2DX19ej1+vLvrUdHR2lubl8O0pBOp2O1atXA7Bt2zYOHz7MQw89VALFdrud1atXs3r1ai6//HLWrFnDt771Le6//36am5sZGyudZcpmswQCgVnPK3R2tBzG+oJMZiM3aYFxo6cm8SuCcVttwLXgSl0Gxh1LDMYrqv+CX0uVwHjV6vnXoZ4rvhYYb1hbOzDeum6Jwbh7cjZ9qcC4Tf2SY8nAuHEKjE+OBpA4oS6l1i8tGOtqBcauUjD+rwNH1KXUhuUBxu+5+q8XvVUK1O1SQFllnkrbpW677TZuu+22ivFmjr0//vGPueGGG1i5cuWseRgMhorjdjgc5lvf+haPPfYYN954I6CuOtqwYQMvvfQSl19++ayxCzpn9hSfSRXWz792+m9xuEqNprKKnmB6iCOR35BTMhglC+tdV2M3al9IM4r29w5ZRUc2n+FQdC9Dch8AToObTe6dOAzlJly5inuNS+sa9yeHeDN0CDmvvonbrW1scW/CordMxqlur24hfjqf4Uikl8OR02QVdZlIvdnDVvdaGi1Tg2y18WfWQY5mkhwI93MkMkRm8jwWnZF1rjbWu9qx6ysYf1WYua583tL2UDrB0cgoxyJjJeZcNr2J1c4GVjkbNff8VutirR6k3Tz9OYRSSU7HgpyOBumPh8lNW7ouAU1WJ512D512L802Z8ks5Hz2Oc8nn2r3LBdmUUfi6uzxaCJGMFVu4AXgMplptDposqmQ3Gi1YzMW6iZXm0+lB6rcy1x1/Cll83kiqRThlArM4UlwDqdSRFJyxf3LBeklHV6Lhfds3IokSeqe4uvOL/fpnTt3smPHDr761a8C6qxgZ2cnH/7wh+dttHXvvfdy6tQpdu/eXbHPqlWr+P3f/30efPDBotHWa6+9xqWXXgrAz3/+c2699VZhtCU0taf4r75X3FNc1OQH5zn3GBckXKmFK3WFY4Qr9ezxL2RXajke55NvuXlp/ENW3VczKH765MNl7Q888AAPPvjgrMdKksQPf/jDEi+R6RodHaW9vZ1//dd/5d3vfnfFOA8++CBf+tKXcLvdWCwWrrjiCh566CE6O9Uvtp555hluuukmgsEgHo+neFxXVxf33XcfH/3oR+d6mmq+AorLNRcUAySzEQ5HniORCwMSXfZLaLZs0DDOqgzFBY3IAxyM7CGjpJHQsdqxmU7b2pJY84Hi4jnzGfaFj3Eqrn7zaZAMrHeuY5VjBUqFxQFzQXFBci7NoUgPx6J9RVBrttSx2b2aBrN30VBcUDKX41hkkIORfmJZFVIlJLpsDWxwddBs8ZYaZC0SiovteRiRwxyNjHIyOk5qWl1lp9HCamcjqx0N1JkdqhPzEkHxdGVzOQYSEU5Hg/TFQwRSpTORRp2eNpubDruHDrubOrO9gmHX0kKxVv9ULstYPF6E5LFknHBa1uip7mtutNppsNhpsKo3t8lSnKlfzlA86+GKQjyTJiynCadkwimZSDpFJJUikpaJptWlSS6TmXsvUsHtfITi73//+9xzzz1885vfZMeOHTz88MM8/vjjHDlyhKamprLlsQ899BCXXXYZq1atIpVK8dOf/pRPfepT/OM//iMf/OAHicfj/PVf/zVvf/vbaWlpwe/38/Wvf53HHnuM119/nU2bNgHqt9ajo6N84xvfKJZkuuyyy0RJJqF5QTGUg7FwpRau1HPFn3mMcKWePf6F6kq9lFBci0oThXhNLQ3zNtacrrmg+Itf/CKf//znGRoawmKxaPYBdYl1LBZj3bp1DA8P85nPfIbBwUEOHDiA0+nkscce4/3vf39JdQGAHTt2cMMNN/CFL3xhXs/1nFg+vRxlNbjY6r2VE9GXGU+dpjf+OpHMKKscV2LQVffNTLOlHa+xngOR1xlPj3A8to+x1CAbXZdhN1T/hjbqjGz1bKHL1sHe0D6CmRAHIgfpiZ9mo2sTzZaWBS9/sehNXOJdx1pHN4cipzgZ62dEnmBEnqDR7GW9azVN5sXvBTbpDGz2dLHR3UFfws+hcD/DcpDTiTFOJ8ZwG22sd7azytGCpVLd3wVIkiRarB5arB6uaVxDfzzA8egYPTE/0YzMnkAfewJ9uIwWVjoaWGFvoLGCa3StZNDp6XZ46XZ4AYhmUvTFQvTGQvTHgyRzWU7HApyOqWYqZp2BNrubdpubtklIXqol4HPJrDfQ4fTQ4fQU2+RslrGkCshjiRijyRihlEw8k6Ynk6YnMrX02iDpqLPYqLcU6gvbqbfYzuhy48VKkiQcJjMOo4U2Z/n/cy6fJ5pOkz7D5mpnWnfddRfj4+N8+tOfZmRkhG3btvHUU08VzbdmLo+Nx+P86Z/+KQMDA1itVtavX893vvMd7rrrLgD0ej1HjhzhX//1X/H7/dTV1bF9+3Z+/etfF4EY4N/+7d/48Ic/zE033YROp+Od73wnf//3f39mn7zQOa2C+VYBjJ/+99rvMb7h9q1FMF6KPcbX7tpYBOMl2WN89dRS6ud2L8Ee4+2TS6mPL/Ee4yNDvLSnB6Rza4/xpsJS6gOn2XdqGKTa7zEGeGb/CY4MjCFJtd9jfOtFa3hqco8xUHnGeAEqmG89eeDM7TGuOGN8hlSLrVJQ/XapavQv//IvvOc975kViIGS5dgXXXQRO3fupKuri8cff5wPfOADNctHzBRraD4zxQUpisKwfIye2Bso5DHp7KxxXoPTqO6rmc9M8fRYfclejsXeJKdk0aGj276Bbvt6FLT3YGrFmd6uKAq9iX4ORQ6TmlxSXWeqY5NrCx6Tp9h/vjPFM/vHsgkOhU/REx8kPzml5jO52eBcRau1qXjBrHamOKfRP5COcTg8wInYcHEJt17S0WVrZI2zjWaLr+wCXe1McaXZ1HQ+R298ghPRMfrigZLlzDa9iRWOBlY6GmixutFJuprOFFecyVUkFEVhXI7THw/RnwgxlIiQyZe6IJp1BlpsLlptbtpsbhosjlLTpzNQ8mkupXNZ/HKCsUS8aGTllxMlr/N0WfQG6iw29Wa24Zv82VayV3l5zBRPxZn/4H4+zhQLCS03zXemuKB0KsOzP3gdv9ZS6kWWaoLJGeOf7mN0IFg+Y7zIUk0wOWP8y8MM9PrLZ4wXMVNc0KxLqRdZqgkmZ4xfO8XRY+Uzxost1VSI//qBfvYdHgRKl1Ivdqa4oFlnjBdZqgngUO8ov96vrhIsK9e0iJnWgmYt11SD+LPOGNcg/mwzxost1QRzzBif4Zni5VCSabaZ4l//+tdce+217N27l61bt1ad1/bt29m1axcPPfSQWD69lKoGigsKZ4Ici/yaVD4KSHTYttJq3UQW7W9pKsIsOuRcgsOR15lIqxd+u97FOtcO3Mbyb13nguKCMvksx2PHOR49SR4VNNqtHax3bsBmsC0YigtKZGUOR3s4GesvgozL4GC9ayUdtlakCgZi1UAxqCCYzmc5FRvmSHSAwLQSTk6DlTXONlY7WrFN1jyuFRRP/yfJ5LP0xQOcivnpjU+UQKhZZ6DTXkenrZ4Oex0mncaXIjWE4pnKKXnGkjEGEmEG4iGGk9EySDZIOpqsTlqsLlpsblosLsz68jzPJBRrBcpPmnn5JwHZLyfwJ+OEKiy/BtX92mex4TVb8ZlVUPaZbTiMpqmBW0CxgGIhIaqHYoBMOls03yoB4xpAMUAmly+ab5WAcQ2gGCCrUDTfKgHjGkAxqNft53+tAcY1gGK1XeGlV8rBuBZQDOrl/vX9fWVgXCsohlnAuAZQDHCwd4Tf7D8NzADjGkAlwNFhP8/u0wDjGsU/NR7gZ5NgvKrJN+VKXaP4A6EwT+4vB+NaQDGUg3HRlVpAcYne9773ceDAAV577bWqc4rFYnR2dvLggw/yP//n/yQcDtPQ0MB3v/vdoov10aNHWb9+PS+++KIw2lqMFgLFqnFWmp7Yy0ykTwPgNDTS5bgOs96h2V8zDlMzvKNyP0eje8ko6gxvq3U1q+wXYdCZ5o5ToT2WlTkcOcRAUl2+o0PHCvtKVjnWYtJY9j1fKC4ons1wLHqak7FeMoq6HNSqt7Da0c1KeydGXemM90KgeOpnhYl0lGPRQU7GRornk4A2az2rHK20WxvRazhXLwaKpyubUxhIBumJjXM65kfOZ4qP6ZBotXrpstfTaa/DabTOGqwWUDyze15RGJdjDCUiDMbDDCXDyBrLdOvMNlqsbpqtLpqtTjxGa+UPW2cIiis1Z/I5gnKSCTkxdUsliKRT2geilkvymKwqMJuseM1TN+NczuYCipc0lpDQ2dJCoFiSJNKpTDkYV3KlrhKKFZ1ENpMrB+NKrtRVw2hnMssAAE5KSURBVKBOnTGeCcaVXKmrhGKkyRnjmWBcwZW6WihGNzljPAOMK7pSVwnFSNLUHuNpYFzRlXoBUKxI8OZRDTCuERQrEhw6rQHGFb+YqDK+Do4NaoBxreJL0DM2BcbFGeMKrtRVf2EhTc4YzwBjnaHSF0+V86zUfySiMWOs4Up9PkJxLBbjxIkTAFx88cV8+ctf5oYbbsDn8xWNsSKRCC0tLfzt3/4tf/zHf1wW46abbuId73gHH/7whwH4i7/4C972trfR1dXF0NAQDzzwAHv37uXQoUM0NKjXlj/5kz/hpz/9KY8++igul4uPfOQjALzwwgvzfq4CijVUeAPs6/0CzhlQnKkIxWq7oiiMpXo4EXuNnJJFLxlZ5dhJvbm7tH+FmdOZEJrOpzgcPchAshcAs87CBufFNJrVotiVoVg7fsEIK5AOsT98iPG0unfDIBlY61zNKvtKDNNmNxfjVn082s+RaC9yLj15Dj2rHR2sc3ZPm8mtEL/CVSiX1+6fyufpiY1yNDrEqBwqtpt0Blbam1jjbKXBPLX3t6JB1iIMu/KKwkgywumYn57YBKFMqfOyz2Sjy1FHp62OJqurrG5tRcitCInVg72iKARSCYYSUYYSEYYSEU3jK4veQPPkbHKz1UmT1VmcTa7dDHJVzXNCZSaXI5BKEpDV24ScIDBZNik/C9k6jCa8Zises2Xy3orXbMFlsmDQ6aqG4loYgsmxOPdffeuyHSgFFAudDypC8YPf1YDiCtcb4Uo9a3zhSj2Pc0y2C1fq2eNfCK7UcjzOJ285v6B49+7d3HDDDWXt99xzD48++igA//RP/8R9993H8PAwbnd5xZ3u7m7e9773Fd2t3/Wud/Hcc88xMTFBQ0MDV199NX/913/NqlWrisfIssyf//mf893vfpdUKsUtt9zCP/zDP1RVflFAsYYWA8UFJXNRjkReIJpVobPBvIIV9u3FWd75QnFBo6kJDkXeIJFTlwvXm5pZ79yGWa/9Bp0LimFyNjo1zv7IYcIZtf6YRWdmvWsdXbZOdJJuwVA8db48p+NDHAqfJpKNA6qLdKetmXXObrwmr3b8KqF4ev9wOs7x6DDHY8PEs1Ozh26jjVWOZlY5mnEa7FWdt9pl2ADBdIKe2ASnY35GkpESJjLp9HTYfHTafbTbfDiM5jMCxVqKp9MMJ1VAHk5EGZNjmiWEfCYrTZOA3GR1Ume2q9BYPMHZheJKcXJKnnAqRVBOEkglS+61Zs2ny2U04zFb8JiteEyWqTrDJrPmkvPZ86/0gIBiIaGzocVAMQhXai0JV+p5nGNau3Clnj3++e5KfT5C8bksAcUamgnF6VwUg86KTjLMG4oB8kqe0/GDDCQPAAomnZ1Vjp14Ta1VQ3FW0ZNTcpyKH6EnfhSF/CRcrqXbthHDjGXJ84HighRFoTc5xKHIERI5tdyPVW9lnXMNbdYu9NL8lx9Xyj+XhyF5nCORHsZSU+7CPpOb1Y4uOm0tJedZDBRPf15DySDHo0Ocjo+RnWbaVG9ysdLRzAp7E3aDZdY4sDAoni45l6E/HuR0fIK+WAA5XwpjXpONdpsKyK1WT8mS3qWG4pkP5PJ5xuU4w4kow8koI8kIkUz50mQdEnUWGw0WB40WB40WJ/VmO4YZy5HPNhTPFieZzRBKyQRTSUKpJMFUkmBKJpRKkslrm3wVZNEbcJssuE1mXJOg7DZZcE7+bpj5YVVAsZDQstJioRjmMWO8yL3Gc4LxIvcazwnGi4BimMeM8SL3Gs8FxovdazzXjPFioRjmAcaLXFZdAsYzzbeqyLNS/znBeJHx5wTjRcafC4wXu9d4NjAWULy8JKBYQ9Oh2O40cCL8IyRJR4fjRvQ67RIDs+01jmTGOB59gVReneVtNK+i3b5ds3TTfJZDx7NRDkf3MpEeBcCks7DafhHNlq7ihagaKAbIIZFX8vTET3M0erzoVG3RWVnjXEOnrRSOF2PMFUiHORrppS8xXFzWataZWGnvYJWjA7vBVhMohimgTOeznI6PcTI2wnAyUMInTWYPKxxNdNsasRismnEWC8XTlc3DmByhLx6gPxFgTI6WPK5Dosnqos3qpc3mpcFcvtQalg6KteIksmlGkzFGklFGk1FG5ajmLKsE+MwqKNebHTRY7NRbHFj0Gu7pywCKK3ZRFBKTwBxKyYRTSYLpQo1hmeQ8yifZDUack7DsNJpxGc0qME/eT800CygWEjobqgUUg3Clni7hSj2Pc2i0n0+u1GVgXIP456srtYDi5SUBxRqaDsUGW4LT0f8mqySRMNBkuxq3aV3Z8pDZoBggp2Toi+9lWD4KgFGy0u24HK+5U7N/eZzyUlDj6WGORPeRnFxS7TL4WOPYisfUsCAonuqT43S8l2PRE8h5db+pRWdhtUOFY4POsGi3agA5l+JEbJCTsT4Sual9rc2WBlbaO2mxNqKb8YFioVA8XclsilPxcXpiI4ymQiWPNZo9dNub6Jo5g1xDKJ55jJzLMJgI0h8PMZAIEM2W7vE1SDqarR7arB5arV7qzQ50ku6MQvHM7oqiEM2kGJdjjBVuyRjJXEbzGIfBrAKy2aHWGjbbcRutmvWTlwMUzxUoncsSTqcIp2XCaZlIOkVk8j6cluecZQZ1Cb1zEpAdRjMuk5ntDe1IkrSkUDw6PFYTKG5qabwgBkmh81e1gmIQrtQFCVfqeZyjQvv55EpdAsY1in8+uVKva6jnLetXCyheZhJQrKGZy6cz+QQDsd3EsurF0GVaTZPtWvTSdBfo+S2HjmTGOBl7iWRO3cPrM3XT5diBUWfV7D9X/HReoT9xnJ7EIXKT7ssN5ja6bRdjMzjL+s8Hiqf65uiJ93M8ehw5r5pGmXQmVtpX0WVbiVFXXm6qWrfqvKIjr+QZSo5xItbLaGqi+JhFb2aFvYOV9k5skzO4tYBitV3tH8vK9MRH6I2PMZYKl/RpMLvpsjXSaW/EYSh3EJ/tec2m2ZyvFUUhkpEZTAYZTAQZTAaRZ4CmUdLTbHXTYvXQYvHSYHGWfHlwJqBYsz0P8WyaMTmGX44xnoozLseIZLRLKBkkHT6zjXqznTqzHd/kvVVv1DbrWEZQPJvyioKcyxJJy0TTaSIZFZaj6ZR6n0lpzrLbDEb+cONOYGlnine1/pHmKpVqlM2neHromxfEICl0/qqWUCxcqafyrHTeC8qVerZzzNIuXKlnz/98cKV+5vgpbt+4DrfVIqB4mUlAsYYKb4DDvQ/hdKmzhWqJpH0MJd9A3R/sotNxIzaDugSocomlcpjNKTl6YgcZSB4GFAySmRWOS2g0ryBXca9xhfbJEk6pnMzx2CH6k6cB1cyqw7qKlY6NJaWWKu75naU9p+Q4nejnaPQEiZwKxwbJwAp7F6sdq7Do555RnS3+dEUzcY7HBjgZGyKVTxfbWyz1rHK002JtrGqP82wwPlOxrMyp2Dg98bESB2sAj9FOl72BbnsjdSbngl2sZz1Go10tO5VgIBFkIBFiOBEmNWM/sjqT7FIh2eqm0eLSLDNUKyOvysuetfvLuSx+Oc64HC/eT8iJkj3e02XRG6gz26kz2/BZbOq92YZNr13z+6y5YVd53unK5HJEMyki6fTkfQoJuKJFXTkix+J86oq3CigWElpCFaH4gcfKobgSVApX6pL+841/oblST89p3ueYbBeu1LPHP9ddqRVFKb4ecjzOJ29dGiiuxaqwQrwLZWWYgGINaUFxQeHMGH3xZ8nk40joaLJeRoNlMzm0nWgrG2rpiWUmOBZ9iUQuBIDb2ES3fSdWQ7k9+VxQXFA0E+FobD/jKXW/sQqvG+i0rUYvGRYExQXllTz9ySGORk8Qyar7YHXo6LR1sNqxCqfRsWgoLiijKAwkRjkR62dUDhTbTToj3fZWVtjb8Zqm/jlrAcUwBbPxbIre+Bin4+MMJ4Mo09DIrjfTaW+g01ZPk9lXZiw1PY6WqoFiKIWyvKIwkYozlAwxmAgzlAiVQbIOiXqLQ607bHHTZHVhN9TO3bpaKNbqnlcUwikZf0oF5UKt4VA6WRFCrXoDvklA9plseM1qzWGH0VzVzPJygOK54i8lFIvl00JCqpYCikG4UmvpQnOlnpnTvM4xrf18cqUuzhgLV+oyLSUU72r5o2LFm8Uom0/z9PCF8SW4gGINzQbFWXRk8zID8V8TyfQCYDM00Wq7HpNeA2bnmPlVlw4fpi++nzw5JHS0WDfQZtuCXjKW9S+Pr/1fOSb7ORbbRzQbAlQzrhX29TRbVmvOtFYDrYqiMCiPcSx2nEB6ykm6ydzISvsqGswNZRe/aqF4OlRGM3FOxQY5FR8kmZtyQfYYnXTZW+mytWDR27TCLBiKpyuVy9AX99ObGGcg4S+Z4dRLOlotPjps9bTb6nFMLvNeKiieGUdRFALpOMPJMMPJMEPJcEkZqoIcBjNNFnUWucniot7sKML82YDiSg9k8zkmUjITKRWUAyn1Fq6wBBvUv4HHZMFrsql1hk2TN6MNq6Hc4OtCh2JhtCUkpGqpoBiEK/VMXWiu1Fo5zXmOGe3nkyu1JhgvMv657koNAoqXmwQUa2guKAb1ghhIHWU48TJ5MqoJl3UHXvOmkn/6uaC4IDkX42TsVYJp9QJr0tnosl+Gz6RepKqF4pyiR1EUhuVeTsQOIufVUktmnZUu20ZarSvQTYPjaqE1hzS5tDfA8dgJRuTR4mMOg4OV9pV0WDsw6AwLiq8FlXlFYSg5QU9sgMHkaNG5GqDB7KPL1ka7rRnTtPJUtYBimALHbD7HUDJAX8JPf8JPIlcKoB6jnTZrHa3WeposXs1Z5FpCsZYiGZnhZIjhZJhROUIgFS+LoUPCZ7bTaHHRYHbRaHbhMdlKjK/OBhRXipPJ5ybrC6szysF0gmA6STid1KypXJBZZ5iCZJN1soSSFbfRikVvKB2gBRSflVhCQmdLSwnFIFypp+tCc6WulNOs59BoP59cqcvAuAbxz3VXarF8enlJQLGG5gPFBaVzUfrjzxHPDgNgM7TSarsOk9452X/+e4QVRWE8NURv/FVS+TgATkMT3Y7LMOu1zShmg+KC8kqewWQPp+KHSU0aZll0Nrrtm2i2dKOTdAuC4umKZWOcivXQm+gnO2n4ZZSMdNo66bZ3Y9Vr/yNVA8Xqc1HbU7k0/YkRehNDjE+re6xDR4u1gU5bKy2WBnSSRikgFg7FpW3qUuaBpArI46lQCeDoJR1NFi9t1nparT48RgeSJC05FM/sn8lnGZOjjCSjjMkRRuRwmXkXqAZedWYHDWYnDRYn9WYXbqOtfMnTWYDiSv3zikI4o9YZDqbV5dehVJJQOkksmy4/YJrMOrXOsMtowWWy4jZaJm/qkuzCFwQCipc2lpDQ2dJMKM7l8iRjMg63rSZQDMKVuqALzZV6tpyEK7VwpS5oKaFYGG1VLwHFGqoGimESZuXDjCZfQSGLDiON1h14zRvJSRX2Gs/iVp1XsgwmDjKUPIhCDoB68xrabJcUXapnywdKobigvJKjL3Ga04lDpIullmx02jbQZFlVMnM8FWf+btUAqVyWvkQfPfEe4rl4sb3B1EiXfQWN5uYSp+SFQvF0xbNJTseH6UsMEs7Eiu16SU+LpZEOWwvNlsYZdZZr62IN6jLrITnAYMLPQHKibBbZqjfRYqlTb9a6kpJPlZ4bLB6KZ/ZXFIVYNsWYHCnexlMxskqu7BijpKfe7KTe7KTO7KDe7MRjtJWVylLPe+aheLb4mVyecDpZnFEOZ2RCkz/PBcw6JLVMktFSvDmNFrXGsNGC3WDS3scMAoqFhM4RTYdik9HCr3/4KhPDQW5+99W46surNwDClVq4Us/or+1KPWu5piqHDuFKPXf+56ortYDi5SUBxRoqvAFO9X1uXlAMkFUkUrkIvfHniGfHALAbGmm1X4dF7ynrn5lHHWE5F+dUfC/jqT4A9JKBdttmWq3riwBbGa4rxEcip+ToS5ziVPwY6bwKbmadhW7bOtptK9FPA/lcxXrE2vELUKkoCiPyGCfivYzIY8XHrXorK+xddNk6seot5Cu8+7SgvpC/5nkn8wymo5OAPEIsmyw+bpD0tNsa6bA102KpR1fhy4pqDbsqAVwuD8FMnIFEgMHEBMNyiNwMt2W30Uar1UeL1UuL1Yu5kiNwhdeoMsBXB6d5RSKvKATTccbkGONylDE5ij8VL8sZ1BnwOrNac7hwqzPbi0vl55tPJZ2JPc7ZfI5wWiZUrDM87eeMPOuSbAC9NB2aVVB2mszqzwa17rB+xhcH1Vxok7E4n9jxW8t2oLyQBkmh81dFKP4/30anGPjFd18k5I9itZu5+T1X46rTKMUnXKmFK3VJ/lPxS823utm4tnXWnOZ9jsl24Uo9e/xz0ZU6GY/zydsEFC8XCSjW0EKhGNQLoz91mKHEa+TJIqGj0Xox9ZatJTOx84HigsKZcU7E9hDLqjV8LToHXfZt1Jk7K7tezwMqc0qOgcRpTsWPFesQGyUz3fa1tFtXYdQZFwzF0xXLxjkZ6+d0oo/0ZJklCYlmSxNdti6azI0axlwLg+KCVAOqCL2JEXrjIyRyU0ZNeklPq6WBdlsTrdYGjPPag1wdFM/sn1PyjMphBhMBBpMB/KlIGSR5jQ5arF6arT6aLR4shRJEZwCKtdung3IMfyqKX46T0ZhRBnAbrZOllBzUmR34THZcRgtU+J+ppLNt/KUoCvFsehKQU0VQLvwey6TmBbh2gwmHwYzTaMIxCc4Oo9rmMJqxGYxl4FyQgGIhoaXXdCi2WmzIiRRPf+8lFYwdZna9+yrcdTNmjIUrtXClLsm/NL5wpZ49vnClLpWA4uUlAcUaWgwUF5TOxehPvEAkMwCAWe+lzXYVdqN6gawGitX+EuOpHnrje0lPAqzd4KPddjFuY2vZRWU+UDx1zhwDiQF6EkdITi551ksG2qwraLeuxaq3azzf+UNx4XnllBwDySF64qdLXKstOgudtg46bR04DI7J/ouD4oKUoiFYmN74MP2J0RJA1iHRaKmjzdpIq7URa5Uu1vOF4pnHpHIZhuUgw8kgQ8kgwXSsrJ/baKfJ4qHJ7KHJ4sFpsJb8nZcairVjK4TSMuOpGH45ykQqjj8VI5HTXo5skHR4TXZ8Jjs+sx2vSb05DBXKKHH2oXgu5RWFWCZFOJOarDEsEy38PHmvNcOuJZvBiMNgxm4w4TSaua55FZIkCSgWEjoDmgnFQBkY3/zuGTPGwpVauFKX5F8eX7hSzx5fuFJPSUDx8pKAYg3NhOJsPoZesiJJ+nlDMUzOGqdPM5x4kZyigpjHtJpm206QtPcrVYLiwnlzSpbBxGGGkofITRpaOQ1NdNgvxmmcurBXA8VqXD15Jc+w3Mfp+FHiucjkIxKN5nY6bWtxG+vmjD8bFE9XJBOhJ95Hf3KgOHsM4DN66bB10GzpwKRhJb8QKC75fXIGuS8xymBilEg2XvK42+ik1dpIq6UJn8ldvFDXGopnKplLM5QIMSwHGEkGCWXiZX2sehNNZg+NFg+NZg9es0tzpnEpobhS/GQ2jT8Vw5+KM5GKEUjHCabjFZchGyS9WkLJZC/ee0w2XEYLUoX/seUCxXPGzyskc5kiKMcyKaLZws9pYtkU8Uy6xD0d1JnlD6zdCYiZYiGhMyEtKIZJMP7+y4TGI+VgLFyphSt1Sf7a8WcDY+FKLVypCxJQvLwkoFhD06HY4TQyEvs+oFBnews6vbY5gxYUg+o+nc3LjCZfJZA6AoAOI3XWy/CZNyPNMLeaC4oLyuRlBhIHGU4eQ0GdlfIY22m3b8VuqFsQFBekzqyO0ps4xkR6qtSS21hPh3UNDeY28lRwdZ4nFE89jxwj8ih9iT5GU1N7j3XoaLI0027tKDHnWiwUz+wfycQYTI4ymBxnIhUswRSLzkSzpYFmawON5saSUk/zjb+QnADkXJoxOcyoHGRUDuFPRcogSifpqDe5aLR4aDC7qTe7sRssZwWKteLnlTzhjExAjhNIxydBOUE4nSh7LgXpkHAZrXhMNvVmVO/dRhtmfYV6e8sMiudjCKYoKjhPh2QFhYt86h40AcVCQkuvSlAMIKeyPP3Y84TGZ8wYC1dqQLhST+VfoX2Wck3ClVqVcKUWULzcJKBYQ9Oh2GyPMRb/D/KKDOhwmHfgNO8og9nZoLigRHacofjzJHPjAJh0HppsV2E3dhT7zBeKC4pnZQYTbzKeOknhY7fX1EGz9WJshrqy/vOB4ukKpcP0JY8yKvcX4duss9JqXUOLZTXGGeZQ1ULxdKMtOSfTnxygPzFAJBspthslE63WNtqs7biN9ZrLbmsxk5vKpRlM+hlKjjIi+4ulpQqqM3lVSLY04jG61PJKSwTFMw4gm88xngozlgozlgoxJodJ5cvLKtn0ZhrMHuonIbnO5MI4aYB1pqG4Uv+ckieSSRJKJ1RQTsUJZRKE0gmysyw7tuiMuCcB2W20Tv5sxWWwFp9jaT7LF4rnkoBiIaGl12xQjF6vzhjPBGPhSj0ZX7hSq/lXiA8VyzUJV+qpfC50V2oBxctLAoo1VHgD9Pd/DpfLQjYfZzz5LPHMSQCMunrqbW8pqR2crQiDpe3qLOwJBhKvkZ1cUu02dtNq245Z76rsJl1x2bbaP5GN0Jc4wFiql8LHb5+pnQ77FhwGX1n/SnHK8p98XnIuSV/iFP3J00XHah06mi0ddNlW4zJ6J+NUKnVUqXSU9usWSEfpTQzQnxhEzk+VNrLqrbRZW+mwtuOeBFOYzSyq0lJcbRXgPafkGZeDDMp+hpLjhGcsZ7boTDRb62gy19NsqcNWVl6p8khZLZxqwZeiKATTsjqbnAoxJkcIpmNlsSXAY7RTb3FRb3LRYHbhMztLll1XhMczVCJq6ne1VFQgnSCYThBMJQhNwnN8jhJKNr0Jj8mKy2hVQdk0eW+0YNZrr2qYb/6V+1fVvSqITsbifOyyty/bgfJCGiSFzl8V3sff/N//rwyKC2NLyR5j4Uo9rV29E67U2s3qc9BYSi1cqcv6X8iu1Ml4nE++VUDxcpGAYg3NhGKY/MCeOc548tnirLHbfBke83YkyTBvKC5IzmcYSe7BnzqE+nFZR4NlE3WWS9FL5ctE54LighLZML2JA4yneottPlM7HbbNOIyVl1XPd7l1TskxIg9wOn6KSHbKLMtt9NFhXUm9ubOkpFNB1UJxASrzSp6xlJ/+5BCDyeGS2VuHwUG7tZVWaysOvavCDPLCoHimolmZoeQ4w0k/I/JEWU1ft9FBs0UF5AazV/NvONe5Fzvbncln8aeijMoRdVZZjpTVSgZ1ibLX5KDO7KTe7KLO5MJrcmDQlb4HzjQUz9Y/nc8RTicIpVVIDmUKtYeTyLmsxhFTMusMuI3WyTrD1ml1h60lpZMuFCgeHRqrCRQ3tTZeEIOk0Pmr+UAxlJtvCVdqZnWlvu6WLbTOnDFeIBTDuelKrT6HqfjClXr2+BeqK7WA4uUlAcUa0oLiglK5BBPJZ0lkTwBg0Hmps16P0dClGatiSaNJSExmAwwlXiaaVS+WeslMg+USfOaNJSWc5gvFBUWzEfoT+/FPg2O3sZlm62bcxpZFuVUD5PIS4UyA3uQJRuUBlMlP9gbJSLOli1bLSpxGT7H/QqG4tG+OQXmMgcQgI/IoeaaW2tr1dlqsLbRaWvEYPdNmkGsDxdOXheeUPBOpEMNJP8PyBIF0uKSvhITX5KbJ7KPRUke9yVtSw3epoFirfyKbYjwVYTwVwS+r91rLriUk3EYbPrMTn8mJz+TAa3RhM5TXTj4bUDybktlsEZDDmSSRacCczJU/15myG9Tawk6jFYfBotYcNphxGC04DBaMOu3/jXMVim9yvgfDLF/azEdZJc0vo/92QQySQuev5gvFIFypy9tLfxWu1FrPoTS+cKWePf6F6Eq9lFBciy/AC/EulC/BBRRraDYozirqxS2ROU5A/hU5JQGA1bgOj+Va9LrS8kVzQTGo8SKZfoYSr5LKhwAw6pw0WbfjNqolWqqF4gLMJrJhBoozx+qf2qb30mLbTJ2pC2lyoK0WiqfDZionMyj3MJA8XSzpBOAy+GixdtNk7kSvs2iFqQqKp+eTyWcYlkcYTA4zJo+VALJVb6XZ0kyzpRmfsbFo0jVdi4HikjiKuhd5VJ5gWJ5gLDVBLJss6SMh4TO5aTD7aDD7qDN7NU27lgKKZ+aqLlGW8aciTKSj6n0qiqwByqAuE/eZnHhNjsmbE7fRXjarrMY/O1A8W5x0PkskIxPNJIlkZCJp9T6aVWsPz6d0kkVnxGE04zBYJssnqcBs16ttNoOpYr3h0nwEFAsJLSdVA8UgXKlL28ubhCv1zOdQHl+4Us8e/0JzpV5KKK7FWA8X1ngvoFhDc0FxQXklRVB+kWj6TQAkTLgtV2I3XVSEzflAcUGKkmc8dZKx5GtkJ2HbrPfRZLkMq1F7j8V8YVbOxRhKHmE0eZI86nJTs85Bk3U9DebVIFnnFacgLWhVFIWx9DhDyVOMp4aKxlw6dNSb22m2rMBnaiq+NrBwKJ6uTD7LiDzGUHKI0dQouWlLmw2SkSZzE02WFhrNTRgnYbSWUDxT8WySETnI2CQkT6+LDOp11G100WD2UW/2Um/2YtVbzggUa8eBZC7FRDpKIBUjkI4SSEcJZxKa/SXAabThNaqw7DHZ8RgdOA32qr6AOBNQPPtxhdJJk5CclollU0QzSfU+K5PJ5+aIosqmN2GfBGa7wYzDYMZmMGPXF9pMGDS+CKmUqFg+LSS09KoWikG4Uk+1azcLV+rpz0GjTbhSzxn/QnKlFlC8vCSgWEPzheKCUrlR/MlnyeTU8kVGXSMe6/WYDa1VQbEaX09eyeCX9+NP7SOvqAZDFn0DDdYd2AztJYN1tTO8yVyGUfkoo8kjZJVJwyzJSL15LY2WDZj1pfukqoFiNZ9J85C8zIjcy3DyNLFcuPi4WWelydJNs6Ubu8FdEyhW+6vtOSXHeGqcYXmYUXmU1DSTLgmJOlM9TZZmGszNOAzlZim1gOLp+SuKQjyXZDwVKN5i2XLYtOtt1Ju91JnUmWSXwVH8Oy81FFfqn8nlCWZiBFJRgpkYwXSUYDqmufwaCuWUVED2mOy4jQ7cRjtOo70m9ZQraSnjpHJZYlmZWFYmmkkRz6rgrJZRUn+uVF5qpow6fRGSbQYzNr2pCM6Fn11G9cspYbQlJLT0WggUC1fqQrt2s3Clnv4cKrQjXKlnjX8BuVKL5dPLSwKKNVR4Q40N/H8aUKy93DKj5Imk9xNIvkAeFWQdxvW4LVdh0JXDVyVjrunuzdl8ilF5P+PyoeLsrt3QTLP1MuzGZmAWOJ1juXVOyTIq9zCYPEoyVyh/JFFnaqfVth6noQFJkiqXiKoA4zPhUVEUItkwfYk+huV+MsqUi7DL4KHF0kWzpQOzvnSmuiIsV7E3Oa8o+FNhhuQRhuURotlSB2mHwU6zuYlmSxP15jp0kq6kRNR8zlsJyCrBO0A8k2IsFWRMDjKeChHMRMv6GHUG6k0e6szuyXtP2ZLrswHLiqKQyGYIpGOTtzjBdJxQOk5G0Z5VlZisPWy04zbZivdug13TGbr6UkfVQXEtIbpYbzibIpZJEc+miGZTxLOT9YcnaxBXem2my6TT8wdrrgYgGY3zkYvfIaBYSGgJVYTi+/+1vCRThQ/gwpW6NE55u3p3wbtSw6zQJ1yp535eF4IrdTIe5xO/JYy2losEFGtoIVBc8L/N5uME5BeIpg8BIGHEbdmBy7QNaZor83yguKBMPslwch8TqcPFJckOQytN1kuwGrQvovPdg6yW9hlmIHmEUGak2G43eGmxrsNrWqHpJj1fKC4op+jIKznGUsMMJnvxp0eL5lwAdaYmWixdNJpbMeiMNYFiKP3SIJqJMZwaZUQeYzw1UXJ+vaSn0dwweWvEbijdG15LKJ55TDqfwZ9SSyv55SD+dEhzr6vLYKfO7KHO5KbO7MZldGnOwC71DLLWc1YUhWhWJliA5IwKyqFMgnS+sju0RWdU6w6b7LiMag1ip9GGy2Ar37e8DKF4vnHS+SzxTFqF5OzkfS5NIjvVZtYZ+L3uSwEBxUJCZ0KLgWIQrtTa7VM/XtCu1DArFBfiC1fq2eOf767UAoqXlwQUa2gxUFyQnB3Bn/wVqZx6MTVILjzWq7EZVk8aZ80fitX4OtK5GGPyHgKpYxQ+etsMLTRYtuGYuax6AcZc8WyIoeQR/PJp8qgzW3rJRIN5JU2WtVgN7jnjzAbF05XOpxiRBxiU+wlnJortOnTUmZtpNHdQb2ot24dZCxdrgFQ+y1hqnBFZheTpy6wBbHobjeYGGswNNJjrMVQwCqsFFBdznTwmr+QJpaP402H8qRD+VEhzybUOHR6TE5/Jjc/kwmty4zY6gApuyUsIxWp/rb4K8VxmEpDjhCdBOZyOE9coFzVddr1ZBWSjFafBhtNgxWVU76fPMJ8LUFytBBQLCS29FgvFIFypy9tLf71gXalhTiguxD+vXKm1wHgRUAnntyu1gOLlJQHFGpoJxYqSLc7yzheKQb3YhdNHCMnPk1PUpbsmfRNey9UYDB3aceaxHDqdizIm7yWYOl6cObbo62mwbMNl7EaSdAt2qwbI5FOMyicYSZ4glY8V252GJposq/GZO8lTXqoH5g/FU/11JLIxhuU+huU+ErmppcQ6dPhMzTRaOqg3tWDUmWoGxdPzVBSFcCbCaGqMEXmMQDpQMosM4Da6qTepkOwz1RVLLC0FFJf3BzmXZiIVYiIdZiIdIpCOkNbY21sAZY9RhWSvyYXb6EQv6c8KFM8WJ53LEs4kirfItPvZZpcBzDrjZPkkFZIdBmvxZ7vBgl7SCSieIQHFQkKlqgUUg3ClLm0vb7ogXalhXlBciH9euVLPBONFQiWcv67UAoqXlwQUa2gmFMcSPwAM2Cw3kJfsmsdU+gifVSCvZIikXieSegMFFWTMhm7clqsw6htm9J//HuF0Ps548gCB1GGUyQxMOjf1li04TOvRVbHsWQviFEUhkB5hVD5GMD1I4eO+XjLhM62kwbIGm6Gu5JiFQPH088WyEUZT/YzIAyWALKHDa2qk3txOvalt/nuQqzTOyiuqm/VE2s9YapzxlJ9otnTPr1qH2EudqZ46UwNeo6+kDjHUHorL2xSi2SSBdJhAOkwwHSGYjpBRyt+JEhJOgx2PyY3H6MJjcuExujDrTRXjw9JD8Wxu2Kl8hkgmQSSTJJqdvM8kiGaTJHNp7QOnyaY345iEZcdk/WHHJDDbdVb0WiWl5ow6M38BxRfCICl0/qpWUAzClXqqXbv5gnOlhnlDMXD+uVJPB+MaQCWcX67U65vq2bVhtYDiZSYBxRqaDsV2e4xI/FHUj7pGzOYrMBovQZJKP1TPBsUF5fJxwqlXiKYPwOQMr9W4HpflCgw692T/6oyz8oqObD7JROoAgdQhcpOO0nrJgte8GY95EwbdFEBW61ZdgNlULq6Wi5JPkM5PGVbZ9HXUW9bgM63AoDMvCoqnK5uHeC7CqNzPWGqARNEMTJXbWF8EZJvBWVMonik5JzOWmsCfHmc8NU4yV16H2GMsQHI9XpMPna6yDX4toBjKYVP9UiFBIB0lmA4TTIcJZaKk8toQadGZcZtcuA1O3EYnbqMLl9FRLKt0tqB4LqOtTD476QatQnIkKxOb/DmaTc6z9rBpCpINFuwGKza9pfi7RWeq7D5bzF9A8YUwSAqdv6olFAtX6kK7dvMF50o9mZP2c6jQznnmSl0A4yVypS6C8RK5UhfBuMau1L88eorf2rKOOrtNQPEyk4BiDRXeAKHBL+ByWcnkRokmf04mNwCAXleH3bILo6GreEwW7Q/iOY2XN50LMS6/SDxzbLJFh8u0CY9lB5Kk4V7J/Iy5ckqGidQxxuSDpCeXPUvoqTOvpcGyCbPeXTUUaxpzZUYYlk8xkRooLt+W0FFnbqfevBKvsbWkFrEav9Ky5/lBaywbZUQeYkQeJpwNljxm1ztpMLfSYG7FY6wr+dBSCZYrgdd88oll45PllSYYT02QmAHJUFhu7aPOXEedyYdFP7UnuVoYrAT2lf5xZy4NT+ZSxdrDwXSUQDpCLFueMxScogvllJx4Ju/tBiu6OUpEnTVYLjlWQc5l1LrDGZloJkk0KxPLyESzSWJZueIWiOnSIU0Cslm916v3xRJKBgtWvan4mswrzyqutMlonD/a+jvLdqC8kAZJofNXRSj+1KPlUFzlDK9wpS6NU96u3l0ortSzxRKu1PPLs1L/kqXUbY1cu2UpXal97CrMGNcofi6fL91T/DYBxctFAoo1NBOKYfLDdmY/UflZFEU1PTIZNmC33oxOslQFxaDOLKeyowTkF0hm+wAVYB2mLXgsV5Utfa7GmEtR8gTSpxmT95PMTZlYuYztNFh2YDWUDxDzheKp/joyeZkxuYdR+RSJXKj4mFGyUG9eQbttM0adudhfM06VM7k5RUcyl2AsNcRoaohg2l+y/9comWgwt9BmXYnXVL8kUFwSR5GIZxMqIKf9+FMB4jmtOsR2fCYvjZYGOqza+8mXAopn5lpQJp8llIkRSkcJTN6H0lHN5dcAekmHy+DAZbTjMjrpsDXhMpZ+0FsOUDxXPoqiIOczRVCeqjus1iKOZ1Mk5jAAK0hCwqo3qeCsL9QeNmMzWFntLHcQFVAsJLS8tBRQDMKVWrt96sd5gfECoRiWhyv1bLHmmkEWrtRzx58OxiVLqWsUfzoYlyylrlH8ggQULy9VemsvG3V3dyNJUtntQx/6UMVjnnjiCdavX4/FYmHLli389Kc/XXQekiRhNV2Ex/FBzMaLAcjmhpEo37c7X5kNTbQ43kGL451Y9K0o5EhlB5EquAfPP1cdXvMq1rruYJXzNlxGFcIimYFiveNayKiz0GbbwMXet7LNcxut1nUYJDMZRWY0dRydtLjnUUlWvY0u22p2eK/lxobfYqt7B82WDgySkYySZkjuJZoNLcm5tWQ32Oi2d7DdezG3Nd/E7c03s917KSvt3bgM6gUknovTnxygN95/xvKaTUadgQazhzXODrb7NnJz805+p+Mm7mi7jusbL2WbZx3dtla8Rhc6dOSUPMFMhN7EMPvDxwhp1FY+FyRJEha9iQazixWORjZ7Orm8fg27mrdwR/sO3t19De9feSN3dV7Fb7Vexg2Nm9nuW81GVwddtgYazC5sejMSoKCQyKUYT0U4nRjnUGSA14IneS1w/Gw/zWWrr3/963R3d2OxWNi5cyevvPJKxb7//M//zDXXXIPX68Xr9bJr166y/rFYjA9/+MO0t7djtVrZuHEj3/jGN0r6XH/99WXjxx//8R8vyfMTWriWy1hfC1lsZna963I89U50eh16fW3HQpPZyI2/fSl1zW50Ogm9vrYf5QxGPTfcvpWmNs+SxNfrdVy7ayPtnerKrlrH1+kkrrp6HStWNiDpwGCobXxJkrh8+0rWrWlGkpYm/qVbOrlovTpDXOv4ANvWtrF9Y8dk/Np/VtvU3czVm7sn4+vm3I5Urda21nPjltVIgLHG7x+AFQ0+br1oDTpJwqjXV/yuR6hczz33HG9729tobW1FkiR+9KMflfU5fPgwb3/723G73djtdrZv305fX1/FmFrjuCRJ3H777cU+73vf+8oev/XWW6vKfeFEd4b06quvksvlir8fOHCAm2++md/93d/V7P/CCy9w991389BDD/Fbv/VbPPbYY9x555288cYbbN68edH56CQLDuvNWExbUJRMSe3hhcpqaMfi+B2S2T4UDDW7eEiShNPYitPYSioXJpQ+jU0/y/6XRZzHYfThMProtF1KKD1EKh/XrG9caxl1JlosHTRZutRSRhk/46khGswaM3VnSFa9hXZbG+22NkCtQxxMB5lIB7DrbXMcffYkSRJ2gxW7wUqrtaE4Y5tXFOLZBOFMbPIWx2s8f78t1Es6nEYrTmNhlUh5n7yikMylSWRl4rkUickZ5kQ2VWa6JqTq+9//Ph/72Mf4xje+wc6dO3n44Ye55ZZbOHr0KI2N5bMsu3fv5u677+bKK6/EYrHwhS98gbe85S0cPHiQtjb1f+tjH/sYzzzzDN/5znfo7u7m5z//OX/6p39Ka2srb3/724ux/uAP/oC/+qu/Kv5usy3f/8MLVcttrF+sCmCczYHDU/v3WwGMU8kMLq+2AehiVADjaEzGo7X8e5EqgHE4IuNdgvgFMN64uZ26JYhfAOM1a5qoX6L4l27ppLu9jvqZqwxqpG1r22hrcFPvq33+oIJxo8dBvXdp4q9trcdjt1DvsdccukEF49/Zvpk6l21J4p+visfjbN26lXvvvZff/u3fLnv85MmTXH311XzgAx/gM5/5DC6Xi4MHD2KxaJc+BfjBD35AOj3ljzMxMcHWrVvLxodbb72VRx55pPi72axdKaeSzrnl0/fddx9PPvkkx48f13yT3nXXXcTjcZ588sli2+WXX862bdvKZhAqSWv5dEFZJad5zEKWT2v3126vtq5xpSW0i91TPNW/kvFXhWXJNVw+rd2/ulJNtVw+rRlnVvfpCu1ncPn0/Pprx1/Oe4rPZD4Vz7vM9xSPDI3WZPl0c2tTVfnt3LmT7du387WvfQ2AfD5PR0cHH/nIR/jUpz415/G5XA6v18vXvvY13vve9wKwefNm7rrrLv7yL/+y2O/SSy/ltttu43Of+xygfsO8bds2Hn744SqfpdDZ1Jkc62u9fLpElWaJa+RKXfG8NXKlrmhsVSNXaqViHO3mhRhwaapGrtSzrbdc6PLp8v5V5lTlst7KcRa+fHp+8WvUf7nls0yWT9dirC/Eq3a8L0iSJH74wx9y5513Ftve9a53YTQa+fa3v73gnB5++GE+/elPMzw8jN2ufin4vve9j1AopDkzPV+dU1Ma6XSa73znO3zsYx+rOBC8+OKLfOxjHytpu+WWW2Z9kVKpFKnU1D7CcDgMQCQql/WtXKe4NlCs5X6s9q8VFFeAx4qQWCUUV4pTIyiu7CZdKb5m87KE4kpXWAHFs/evpFrlU62ddMXu1UBxTN2XvhTfWf5O6/sxYFxUjOxkablIpNQV3mw2a34zm06nef3117n//vuLbTqdjl27dvHiiy/O65yJRIJMJoPPN7X38Morr+Q///M/uffee2ltbWX37t0cO3aMr3zlKyXH/tu//Rvf+c53aG5u5m1vext/+Zd/KWaLl7HO9FifTGkYD54jUFwZQquMX3UcAcWzxp8lloBi0b8gOaFWc1muYz1UP97Ppnw+z09+8hM+8YlPcMstt7Bnzx5WrFjB/fffXwLOc+lb3/oW73rXu4pAXNDu3btpbGzE6/Vy44038rnPfY66uroKUTSknEP6/ve/r+j1emVwcLBiH6PRqDz22GMlbV//+teVxsbGisc88MADCupHVnETN3ETt2VzO3nyZM2un8lkUjFhqVluDoejrO2BBx7QPPfg4KACKC+88EJJ+8c//nFlx44d88r/T/7kT5SVK1cqyWSy2CbLsvLe975XARSDwaCYTCblX//1X0uO++Y3v6k89dRTyr59+5TvfOc7Sltbm/KOd7yjuhdP6IxKjPXiJm7idiHdlvNYD9WN99MFKD/84Q+Lvw8PDyuAYrPZlC9/+cvKnj17lIceekiRJEnZvXv3vJ7fyy+/rADKyy+/XNL+3e9+V/nxj3+s7Nu3T/nhD3+obNiwQdm+fbuSzWbn/dqdUzPF3/rWt7jttttobdW2p1+o7r///pJvnEOhEF1dXfT19eF2u2t6rjOhSCRCR0cH/f3956RT3LmeP5z7z0Hkf3YVDofp7OwsmRVdrCwWCyP+4ZJ9OYuKZzOXzY5V+63xfPX5z3+e733ve+zevbtk39FXv/pVXnrpJf7zP/+Trq4unnvuOT70oQ/R2trKrl27APjDP/zDYv8tW7bQ0tLCTTfdxMmTJ1m1atWS5Cu0OImxfv461691Iv+zK5H/2dW5MNZD7cb7fF5dVXvHHXfw0Y9+FIBt27bxwgsv8I1vfIPrrrtuzhjf+ta32LJlCzt27Chpf9e73lX8ecuWLVx00UWsWrWK3bt3c9NNN80rv3MGint7e3n66af5wQ9+MGu/5uZmRkdHS9pGR0dpbm6ueEylJQBut/uc/CcryOVyifzPss715yDyP7vSVVpauEB56zw1jTdf1dfXo9frq742A/zN3/wNn//853n66ae56KKLiu3JZJL/9b/+Fz/84Q+LDpQXXXQRe/fu5W/+5m+KUDxTO3fuBODEiRMCipehxFi/MJ3r1zqR/9mVyP/s6nwZ6+dSfX09BoOBjRs3lrRv2LCB3/zmN3MeH4/H+d73vldinFlJK1eupL6+nhMnTswbipd9SaaCHnnkERobG0vst7V0xRVX8Mtf/rKk7Re/+AVXXHHFUqYnJCQkJFRBJpOJSy+9tOTanM/n+eUvfznrtfmLX/win/3sZ3nqqae47LLLSh7LZDJkMpmyDxN6vb74bbSW9u7dC0BLy9lzqBeqLDHWCwkJCZ2fMplMbN++naNHj5a0Hzt2jK6urjmPf+KJJ0ilUvyP//E/5uw7MDDAxMREVWP9OTFTnM/neeSRR7jnnnswGEpTfu9730tbWxsPPfQQAH/2Z3/Gddddx9/+7d9y++23873vfY/XXnuNf/qnfzobqQsJCQkJoZZPuueee7jsssvYsWMHDz/8MPF4nPe///1A+bX8C1/4Ap/+9Kd57LHH6O7uZmRkBACHw4HD4cDlcnHdddfx8Y9/HKvVSldXF7/61a/4f//v//HlL38ZUEs/PPbYY7z1rW+lrq6Offv28dGPfpRrr722ZNZZaHlIjPVCQkJC57ZisRgnTpwo/t7T08PevXvx+Xx0dnby8Y9/nLvuuotrr72WG264gaeeeor/+q//Yvfu3cVjZl7vC/rWt77FnXfeWWaeFYvF+MxnPsM73/lOmpubOXnyJJ/4xCdYvXo1t9xyy/yTn/fu47Oon/3sZwqgHD16tOyx6667TrnnnntK2h5//HFl7dq1islkUjZt2qT85Cc/qep8siwrDzzwgCLL8mLSPmsS+Z99nevPQeR/dnWu519JX/3qV5XOzk7FZDIpO3bsUF566aXiYzOv5V1dXZqGH9PNPYaHh5X3ve99Smtrq2KxWJR169Ypf/u3f6vk83lFURSlr69PufbaaxWfz6eYzWZl9erVysc//nElHA6fqacsVIXEWF+9zvXnIPI/uxL5n12d6/lr6dlnn9Ucu6dfv7/1rW8pq1evViwWi7J161blRz/6UUkMrev9kSNHFED5+c9/XnbORCKhvOUtb1EaGhoUo9GodHV1KX/wB3+gjIyMVJX7OVenWEhISEhISEhISEhISEioVjpn9hQLCQkJCQkJCQkJCQkJCdVaAoqFhISEhISEhISEhISELlgJKBYSEhISEhISEhISEhK6YCWgWEhISEhISEhISEhISOiC1QUJxdFolPvuu4+uri6sVitXXnklr7766qzH7N69m0suuQSz2czq1at59NFHz0yyGqo2/927dyNJUtmtUOJkqfXcc8/xtre9jdbWViRJ4kc/+lHJ44qi8OlPf5qWlhasViu7du3i+PHjc8b9+te/Tnd3NxaLhZ07d/LKK6+cM/k/+OCDZX+P9evXn5X8f/CDH/CWt7yFuro6JEkq1nGdS0888QTr16/HYrGwZcsWfvrTn9Y+eZYm/0cffbTs9bdYLGc8/0wmwyc/+Um2bNmC3W6ntbWV9773vQwNDc0Z90y9/4WEzlWJsV6M9Wc7fzHWz19irNeWGOvPnC5IKP7gBz/IL37xC7797W+zf/9+3vKWt7Br1y4GBwc1+/f09HD77bdzww03sHfvXu677z4++MEP8rOf/ewMZ66q2vwLOnr0KMPDw8VbY2PjGck3Ho+zdetWvv71r2s+/sUvfpG///u/5xvf+AYvv/wydrudW265BVmWK8b8/ve/z8c+9jEeeOAB3njjDbZu3cott9zC2NjYOZE/wKZNm0r+Hr/5zW9qnjvMnX88Hufqq6/mC1/4wrxjvvDCC9x999184AMfYM+ePdx5553ceeedHDhwoFZpl+RX6/wBXC5Xyevf29tbi3Q186uUfyKR4I033uAv//IveeONN/jBD37A0aNHefvb3z5rzDP5/hcSOlclxnox1p/t/EGM9fOVGOvLJcb6M6yqCjidB0okEoper1eefPLJkvZLLrlE+d//+39rHvOJT3xC2bRpU0nbXXfdpdxyyy1LlmclLST/Qs2wYDB4BjKcXYDywx/+sPh7Pp9XmpublS996UvFtlAopJjNZuW73/1uxTg7duxQPvShDxV/z+VySmtrq/LQQw8tSd4F1Sr/Bx54QNm6desSZqqtmflPV09PjwIoe/bsmTPO7/3e7ym33357SdvOnTuVP/qjP6pBlpVVq/wfeeQRxe121zS3+Wi2/At65ZVXFEDp7e2t2Odsvf+FhM4VibH+7EqM9arEWL8wibFelRjrz6wuuJnibDZLLpcrWz5htVorfnv34osvsmvXrpK2W265hRdffHHJ8qykheRf0LZt22hpaeHmm2/m+eefX8o0562enh5GRkZKXl+3283OnTsrvr7pdJrXX3+95BidTseuXbvO+N9kIfkXdPz4cVpbW1m5ciXvec976OvrW+p0a6bl9D+xUMViMbq6uujo6OCOO+7g4MGDZzslAMLhMJIk4fF4NB9fTu9/IaHlKjHWi7G+lhJj/ZTEWF8bibF++emCg2Kn08kVV1zBZz/7WYaGhsjlcnznO9/hxRdfZHh4WPOYkZERmpqaStqampqIRCIkk8kzkXZRC8m/paWFb3zjG/zHf/wH//Ef/0FHRwfXX389b7zxxhnNXUuFvU5ar2+lfVB+v59cLlfVMUulheQPsHPnTh599FGeeuop/vEf/5Genh6uueYaotHokuZbK1X6nzjTr/9CtW7dOv7lX/6FH//4x3znO98hn89z5ZVXMjAwcFbzkmWZT37yk9x99924XC7NPsvp/S8ktFwlxnox1tdSYqyf0rk01oixXqgaGc52AmdD3/72t7n33ntpa2tDr9dzySWXcPfdd/P666+f7dTmpWrzX7duHevWrSv+fuWVV3Ly5Em+8pWv8O1vf/tMpS00Tbfddlvx54suuoidO3fS1dXF448/zgc+8IGzmNmFoSuuuIIrrrii+PuVV17Jhg0b+OY3v8lnP/vZs5JTJpPh937v91AUhX/8x388KzkICZ1PEmO9GOvPtsRYf3YlxnqhanTBzRQDrFq1il/96lfEYjH6+/t55ZVXyGQyrFy5UrN/c3Mzo6OjJW2jo6O4XC6sVuuZSLlE1eavpR07dnDixIklzHJ+am5uBtB8fQuPzVR9fT16vb6qY5ZKC8lfSx6Ph7Vr1y6Lv8l8VOl/4ky//rWS0Wjk4osvPmuvf2GQ7O3t5Re/+EXFb45heb3/hYSWs8RYL8b6WkmM9VM6l8caMdYLzaYLEooLstvttLS0EAwG+dnPfsYdd9yh2e+KK67gl7/8ZUnbL37xi5Jvn86G5pu/lvbu3UtLS8sSZjc/rVixgubm5pLXNxKJ8PLLL1d8fU0mE5deemnJMfl8nl/+8pdn/G+ykPy1FIvFOHny5LL4m8xHy/V/YqHK5XLs37//rLz+hUHy+PHjPP3009TV1c3afzm9/4WEzgWJsf7sjytirFclxvqzKzHWC82qs+30dTb01FNPKf/93/+tnDp1Svn5z3+ubN26Vdm5c6eSTqcVRVGUT33qU8rv//7vF/ufOnVKsdlsysc//nHl8OHDyte//nVFr9crTz311DmR/1e+8hXlRz/6kXL8+HFl//79yp/92Z8pOp1Oefrpp89IvtFoVNmzZ4+yZ88eBVC+/OUvK3v27Ck67n3+859XPB6P8uMf/1jZt2+fcscddygrVqxQkslkMcaNN96ofPWrXy3+/r3vfU8xm83Ko48+qhw6dEj5wz/8Q8Xj8SgjIyPnRP5//ud/ruzevVvp6elRnn/+eWXXrl1KfX29MjY2dsbzn5iYUPbs2aP85Cc/UQDle9/7nrJnzx5leHi4GOP3f//3lU996lPF359//nnFYDAof/M3f6McPnxYeeCBBxSj0ajs37//nMj/M5/5jPKzn/1MOXnypPL6668r73rXuxSLxaIcPHjwjOafTqeVt7/97Up7e7uyd+9eZXh4uHhLpVLFGGfz/S8kdK5KjPVirD/b+Yux/uzmL8Z6oWp0QULx97//fWXlypWKyWRSmpublQ996ENKKBQqPn7PPfco1113Xckxzz77rLJt2zbFZDIpK1euVB555JEzm/Q0VZv/F77wBWXVqlWKxWJRfD6fcv311yvPPPPMGcu3UCZi5u2ee+5RFEUtdfCXf/mXSlNTk2I2m5WbbrpJOXr0aEmMrq4u5YEHHihp++pXv6p0dnYqJpNJ2bFjh/LSSy+dM/nfddddSktLi2IymZS2tjblrrvuUk6cOHFW8n/kkUc0H5+e73XXXVfsX9Djjz+urF27VjGZTMqmTZuUn/zkJ+dM/vfdd1/xvdPU1KS89a1vVd54440znn+htITW7dlnny3GOJvvfyGhc1VirBdj/dnOX4z1Zzd/MdYLVSNJURRl7vlkISEhISEhISEhISEhIaHzTxf0nmIhISEhISEhISEhISGhC1sCioWEhISEhISEhISEhIQuWAkoFhISEhISEhISEhISErpgJaBYSEhISEhISEhISEhI6IKVgGIhISEhISEhISEhISGhC1YCioWEhISEhISEhISEhIQuWAkoFhISEhISEhISEhISErpgJaBYSEhISEhISEhISEhI6IKVgGKhmuv666/nvvvuO9tpVNTRo0dpbm4mGo1W7PPoo4/i8XjOXFJnUadPn0aSJPbu3VuTeE899RTbtm0jn8/XJJ6QkJCQ0PKTGOvPLYmxXkhodgkoFjonNDw8zLvf/W7Wrl2LTqerOBA/8cQTrF+/HovFwpYtW/jpT39a1uf+++/nIx/5CE6ns6Y5dnd38/DDD9c05lyqxSDX0dHB8PAwmzdvrklOt956K0ajkX/7t3+rSTwhISEhoQtDYqzXlhjrhYSWXgKKhc4JpVIpGhoa+D//5/+wdetWzT4vvPACd999Nx/4wAfYs2cPd955J3feeScHDhwo9unr6+PJJ5/kfe973xnKfPlLr9fT3NyMwWCoWcz3ve99/P3f/33N4gkJCQkJnf8SY/3SSYz1QkKzS0Cx0JIrGAzy3ve+F6/Xi81m47bbbuP48eMlff75n/+Zjo4ObDYb73jHO/jyl79csqSpu7ubv/u7v+O9730vbrdb8zx/93d/x6233srHP/5xNmzYwGc/+1kuueQSvva1rxX7PP7442zdupW2traSYx999FE6OzuL55+YmCh5/OTJk9xxxx00NTXhcDjYvn07Tz/9dPHx66+/nt7eXj760Y8iSRKSJAEwMTHB3XffTVtbGzabjS1btvDd7363JPa///u/s2XLFqxWK3V1dezatYt4PF58/P/+3//Lhg0bsFgsrF+/nn/4h38oPrZixQoALr74YiRJ4vrrr6/4N3jPe95DQ0MDVquVNWvW8MgjjwDl30C/733vKz6H6bfdu3cD6oeWv/iLv6CtrQ273c7OnTuLjxX0tre9jddee42TJ09q5iMkJCT0/7d39zE1xX8cwN+JW4drkbqFyDyUtModY61Ns1Bq18U8jjyFmJLRjFFD8Y8rzNMwf9iwZmQzVHI913XJaliXNipmSXnYhDzcfX5/+Dlz3OTnN0b1fm33j+/3fL/n+zn37u5zv/ec8z3UtjDXM9cTtWacFNMfN2/ePJSWluL06dOw2WwQEcTHx+PTp08AgOLiYixZsgRpaWkoLy/H2LFjsXnz5l8ex2azYcyYMZq62NhY2Gw2tXzt2jUMHz5c08ZutyMpKQkpKSkoLy/H6NGjkZ2drWnT2NiI+Ph4WK1WlJWVIS4uDiaTCY8fPwYA5OXlISAgAJs2bUJtbS1qa2sBAE1NTRg2bBjOnj2Le/fuYfHixUhMTMTNmzcBfLlUbObMmViwYAEcDgcuX76MyZMnQ0QAAEePHkVmZiY2b94Mh8OBLVu2ICMjA4cPHwYAdT8XLlxAbW0t8vLymn1vMjIyUFFRgfz8fDgcDuzbtw8+Pj7Ntt25c6d6DLW1tUhLS4PBYMDgwYMBACkpKbDZbMjNzcWdO3cwdepUxMXFaX789O3bF35+frh27dqPPi4iImpDmOuZ64laNSH6zaKjoyUtLU1ERCorKwWAFBcXq9sbGhpEURQ5fvy4iIhMnz5dEhISNPuYNWuWeHl5/XT/3+rUqZMcO3ZMU7dnzx4xGAxqOSIiQjZt2qRpM3PmTImPj9fUTZ8+/YfjfxUaGiq7du1Sy4GBgbJ9+/YW+4iIJCQkyKpVq0RE5Pbt2wJAqqurm207YMAAl2PKysqSyMhIERGpqqoSAFJWVtbimCaTSebPn9/stpb2cfLkSfH09JTr16+LiEhNTY24u7vL06dPNe1iYmJk7dq1mjqj0SgbNmxoMS4iImqdmOtbxlxP1LrwTDH9UQ6HAx07dsTIkSPVuh49eiA4OBgOhwPAlxUiR4wYoen3ffl3ef/+PTw9PV1i/DY+AIiMjNSUGxsbkZ6ejpCQEHTr1g16vR4Oh0P99/hHnE4nsrKyEBYWBm9vb+j1ehQWFqr9IiIiEBMTg7CwMEydOhUHDx7Eq1evAABv377Fw4cPkZSUBL1er76ys7N/+VKlpUuXIjc3F0OHDsXq1atRUlLy0z5lZWVITEzE7t27ERUVBQC4e/cunE4ngoKCNDFduXLFJSZFUfDu3btfipOIiFof5nrmeqLW7vfdbU/0l/n7+6Ourk5TV1dXB39/f7Xs4+OjJqJfkZ6ejqKiIlgsFgwcOBCKomDKlCn4+PFji/22bt2KnTt3YseOHQgLC0OXLl2wYsUKtZ+7uzuKiopQUlKC8+fPY9euXVi3bh3sdjs6d+4M4Ms9WN8ncnd391+Kf/z48aipqcG5c+dQVFSEmJgYLFu2DBaLpdn2z549w4QJE7Bw4UIkJSWp9Y2NjXB3d8ft27ddYtDr9Zryy5cv4evr+0txEhERtYS5/seY64n+fzxTTH9USEgIPn/+DLvdrta9ePECDx48wJAhQwAAwcHBuHXrlqbf9+X/RWRkJKxWq6auqKhI80+w0WhERUWFS4zfxgcAN27c0JSLi4sxb948TJo0CWFhYfD390d1dbWmjU6ng9PpdOlnNpsxe/ZsREREoH///qisrNS0cXNzQ1RUFDZu3IiysjLodDqcOnUKfn5+6NWrFx49eoSBAwdqXl8X3dDpdADgMm5zfH19MXfuXBw5cgQ7duzAgQMHmm3X1NQEs9mMwYMHIycnR7PNaDTC6XTi+fPnLjF9+4OkqakJDx8+hNFo/GlcRETUujHXM9cTtXY8U0x/1KBBg2A2m7Fo0SLs378fXbt2xZo1a9C7d2+YzWYAQGpqKkaNGoWcnByYTCZcvHgR+fn56qqOX31dMbGxsRH19fUoLy+HTqdTE25aWhqio6Oxbds2JCQkIDc3F6WlpZqEEBsbi4ULF8LpdKr/fi5fvhxRUVGwWCwwm80oLCxEQUGBy3Hk5eXBZDLBzc0NGRkZLg+s79evH65evYoZM2bAw8MDPj4+GDRoEE6cOIGSkhJ0794dOTk5qKurU2O22+2wWq0YN24cDAYD7HY76uvrERISAgDYuHEjli9fDi8vL8TFxeHDhw8oLS3Fq1evsHLlShgMBiiKgoKCAgQEBMDT07PZFTszMzMxbNgwhIaG4sOHDzhz5ow6xveSk5Px5MkTWK1W1NfXq/Xe3t4ICgrCrFmzMGfOHGzbtg1GoxH19fWwWq0IDw9HQkICgC8/NDw8PFwuTSMioraHuZ65nqjV+9s3NVPb8/3iGC9fvpTExETx8vISRVEkNjZWKisrNX0OHDggvXv3FkVRZOLEiZKdnS3+/v6aNgBcXoGBgZo2x48fl6CgINHpdBIaGipnz57VbP/06ZP06tVLCgoKNPWHDh2SgIAAURRFTCaTWCwWzeIbVVVVMnr0aFEURfr06SO7d+92OU6bzSbh4eHi4eEhX79aL168ELPZLHq9XgwGg6xfv17mzJkjZrNZREQqKiokNjZWfH19xcPDQ4KCgjQLeoiIHD16VIYOHSo6nU66d+8uo0aNkry8PHX7wYMHpU+fPtKhQweJjo5u9jPJysqSkJAQURRFvL29xWw2y6NHj9RjwzeLbwQGBjb7Xl+6dElERD5+/CiZmZnSr18/6dSpk/Ts2VMmTZokd+7cUcdbvHixJCcnNxsLERG1fsz1zPXM9dSWuIn8dz14on/IokWLcP/+/T+yzP+ePXtw+vRpFBYW/vZ9E9DQ0IDg4GCUlpaql34RERF9j7m+9WKup7aGl0/TP8FisWDs2LHo0qUL8vPzcfjwYc2D63+n5ORkvH79Gm/evEHXrl3/yBjtWXV1Nfbu3cskSUREGsz1bQdzPbU1PFNM/4Rp06bh8uXLePPmDfr374/U1FQsWbLkb4dFREREvwlzPRH9qzgpJiIiIiIionaLj2QiIiIiIiKidouTYiIiIiIiImq3OCkmIiIiIiKidouTYiIiIiIiImq3OCkmIiIiIiKidouTYiIiIiIiImq3OCkmIiIiIiKidouTYiIiIiIiImq3/gOG9SaDLikyzQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def L(N, D):\n",
    "    \"\"\"\n",
    "    Approximates loss given N parameters and D dataset size (in tokens),\n",
    "    per Chinchilla paper.\n",
    "    \"\"\"\n",
    "    E = 1.69  # entropy of natural language, limit of infinite model on infinite data\n",
    "    A = 406.4\n",
    "    B = 410.7\n",
    "    alpha = 0.34\n",
    "    beta = 0.28\n",
    "    return A / (N**alpha) + B / (D**beta) + E\n",
    "\n",
    "\n",
    "ns = 10 ** np.arange(7, 11, step=2**-4)  # model sizes from 10M to 100B\n",
    "ds = 10 ** np.arange(9, 12, step=2**-4)  # dataset sizes from 1B to 1T\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(121)\n",
    "# create a 2D countour plot of loss L as a function of model size and dataset size in ns,ds\n",
    "loss2d = np.log10(np.array([[L(n, d) for d in ds] for n in ns]))\n",
    "plt.imshow(loss2d, extent=[9, 12, 7, 11], origin=\"lower\", alpha=0.5)\n",
    "plt.contour(loss2d, levels=30, extent=[9, 12, 7, 11], origin=\"lower\")\n",
    "plt.xlabel(\"log10(dataset size)\")\n",
    "plt.ylabel(\"log10(model size)\")\n",
    "plt.title(\"loss\")\n",
    "plt.colorbar()\n",
    "# plot the compute for each point, which is a deterministic function: flops = 6*N*D\n",
    "plt.subplot(122)\n",
    "compute2d = np.log10(np.array([[6 * n * d for d in ds] for n in ns]))\n",
    "plt.imshow(compute2d, extent=[9, 12, 7, 11], origin=\"lower\", alpha=0.5)\n",
    "plt.contour(compute2d, levels=30, extent=[9, 12, 7, 11], origin=\"lower\")\n",
    "plt.xlabel(\"log10(dataset size)\")\n",
    "plt.ylabel(\"log10(model size)\")\n",
    "plt.title(\"log10 flops\")\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok so given any N,D we can estimate both:\n",
    "1. the loss, and \n",
    "2. the total flops. \n",
    "\n",
    "Now we want to solve the following problem: \n",
    "- Given a specific budget of flops C, \n",
    "- find: N_opt, D_opt = argmin_{FLOPs(N,D) = C} L(N, D). \n",
    "\n",
    "i.e. how big of a model should we train and for how many tokens?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model size: 316.23M\n",
      "best dataset size: 11.65B\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'loss')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = 2.21e19  # target compute budget (usually know this because we know how many GPU for how long go brrr)\n",
    "# (I got this flop number from row 1 of Table A3)\n",
    "# sweep model sizes from 10M to 100B\n",
    "ns = 10 ** np.arange(7, 11, step=2**-4)\n",
    "# using C = 6*N*D, solve for D that maintains the compute budget c\n",
    "ds = c / (6 * ns)\n",
    "# evaluate the loss in each case\n",
    "losses = L(ns, ds)\n",
    "# find the argmin\n",
    "best = np.argmin(losses)\n",
    "print(f\"best model size: {ns[best]/1e6:.2f}M\")\n",
    "print(f\"best dataset size: {ds[best]/1e9:.2f}B\")\n",
    "# plot the loss\n",
    "plt.figure(figsize=(3, 3))\n",
    "plt.plot(ns, losses)\n",
    "plt.xscale(\"log\")\n",
    "# plot a vertical bar at the best model size\n",
    "plt.axvline(ns[best], color=\"red\")\n",
    "plt.xlabel(\"model size\")\n",
    "plt.ylabel(\"loss\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot above, basically the models on the left of best are too small and trained for too long. The models on the right of best are way too large and trained for too little. The model at the red line is just right.\n",
    "\n",
    "Now, the Chinchilla paper says that best model size for this flop budget is 400M params and 9.2B tokens (instead of 316M params and 11.65B tokens) so there is some unresolved disagreement here too..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2304"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the Chinchilla optimal models for a range of compute budgets\n",
    "\n",
    "# sweep over compute budgets from 1e17 to 1e26\n",
    "cs = 10 ** np.arange(17, 26, step=2**-8)\n",
    "models = []\n",
    "for c in cs:\n",
    "    # sweep over model sizes\n",
    "    ns = 10 ** np.arange(7, 14, step=2**-8)\n",
    "    # the dataset sizes that would maintain the given compute budget\n",
    "    ds = c / (6 * ns)\n",
    "    # losses at each point\n",
    "    losses = L(ns, ds)\n",
    "    # n,d for the best model\n",
    "    best = np.argmin(losses)\n",
    "    models.append((c, ns[best], ds[best]))  # c, n, d tuple log\n",
    "\n",
    "len(models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "closest model found:\n",
      "\n",
      "model size: 399.54M\n",
      "dataset size: 14.43B\n",
      "flops: 3.459892e+19\n",
      "loss: 2.76\n"
     ]
    }
   ],
   "source": [
    "query_model_size = 400e6\n",
    "ns = np.array([n for c, n, d in models])\n",
    "ds = np.array([d for c, n, d in models])\n",
    "# find the index of the closest model size in ns\n",
    "ix = np.argmin(np.abs(ns - query_model_size))\n",
    "# retrieve the corresponding params, flops, and data size\n",
    "print(\"closest model found:\\n\")\n",
    "print(f\"model size: {ns[ix]/1e6:.2f}M\")\n",
    "print(f\"dataset size: {ds[ix]/1e9:.2f}B\")\n",
    "print(f\"flops: {6*ns[ix]*ds[ix]:e}\")\n",
    "print(f\"loss: {L(ns[ix], ds[ix]):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This should have come out as 9.2B according to Table A3 in Chinchilla paper, per my understanding of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling Laws: Approach 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approach 2 is probably my favorite one because it fixes a flop budget and runs a number of model/dataset sizes, measures the loss, fits a parabolla, and gets the minimum. So it's a fairly direct measurement of what we're after. The best way to then calculate the compute-optimal number of tokens for any given model size, as an example, is via simple interpolation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1.0409573169995876x + 0.9353887152390992\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Approach 1 numbers\n",
    "# # parameters, tokens\n",
    "# raw = [\n",
    "#     [400e6, 8e9],\n",
    "#     [1e9, 20.2e9],\n",
    "#     [10e9, 205.1e9],\n",
    "#     [67e9, 1.5e12],\n",
    "#     [175e9, 3.7e12],\n",
    "#     [280e9, 5.9e12],\n",
    "#     [520e9, 11e12],\n",
    "#     [1e12, 21.2e12],\n",
    "#     [10e12, 216.2e12],\n",
    "# ]\n",
    "\n",
    "# Approach 2 numbers\n",
    "# parameters, tokens\n",
    "raw = [\n",
    "    [400e6, 7.7e9],\n",
    "    [1e9, 20.0e9],\n",
    "    [10e9, 219.5e9],\n",
    "    [67e9, 1.7e12],\n",
    "    [175e9, 4.3e12],\n",
    "    [280e9, 7.1e12],\n",
    "    [520e9, 13.4e12],\n",
    "    [1e12, 26.5e12],\n",
    "    [10e12, 292.0e12],\n",
    "]\n",
    "\n",
    "# fit a line by linear regression to the raw data\n",
    "x = np.array([np.log10(x[0]) for x in raw])\n",
    "y = np.array([np.log10(x[1]) for x in raw])\n",
    "A = np.vstack([x, np.ones(len(x))]).T\n",
    "m, c = np.linalg.lstsq(A, y, rcond=None)[0]\n",
    "print(f\"y = {m}x + {c}\")\n",
    "\n",
    "plt.figure(figsize=(3, 3))\n",
    "# plot the line\n",
    "plt.plot(\n",
    "    [q[0] for q in raw],\n",
    "    [10 ** (m * np.log10(q[0]) + c) for q in raw],\n",
    "    label=\"linear regression\",\n",
    "    color=\"r\",\n",
    ")\n",
    "# plot the raw data\n",
    "plt.scatter([q[0] for q in raw], [q[1] for q in raw], label=\"raw data\")\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"parameters\")\n",
    "plt.ylabel(\"tokens\")\n",
    "plt.title(\"compute optimal models\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted parameters for 1.240000e+08 tokens: 2.292426e+09\n"
     ]
    }
   ],
   "source": [
    "xquery = 124e6  # query model size here (e.g. GPT-2 small is 124M)\n",
    "yquery = 10 ** (m * np.log10(xquery) + c)\n",
    "print(f\"predicted parameters for {xquery:e} tokens: {yquery:e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Commonly used Torch Operations Explained"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Einsum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`torch.einsum(equation, *operands) -> Tensor`\n",
    "\n",
    "equation (str): The equation string specifies the subscripts for each dimension of the input operands in the same order as the dimensions. It uses letters in [a-zA-Z] to represent subscripts. The subscripts for each operand are separated by a comma (','). For example, 'ij,jk->ik' specifies subscripts for two 2D operands. The subscripts that appear exactly once in the equation will be part of the output, sorted in increasing alphabetical order. Optionally, you can define the output subscripts by adding an arrow ('->') at the end of the equation followed by the subscripts for the output.\n",
    "\n",
    "Note that for example for Q, if you want bhkn instead of bhnk, you can do that since the operation is always the sum, its just the output dimension is different so essentially something like a reshape.\n",
    "\n",
    "The torch.einsum() function in PyTorch is a powerful tool that allows you to perform various multi-dimensional, linear algebraic operations in a concise way. Here are some common use cases:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Element-wise multiplication**: You can use `einsum` to perform element-wise multiplication of two tensors, similar to the `*` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = torch.rand(3, 3)\n",
    "B = torch.rand(3, 3)\n",
    "result = torch.einsum(\"ij,ij->ij\", A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Matrix multiplication**: einsum can be used to perform matrix multiplication, similar to `torch.matmul()` or the `@` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = torch.rand(3, 4)\n",
    "B = torch.rand(4, 5)\n",
    "result = torch.einsum(\"ij,jk->ik\", A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Batch matrix multiplication**: If you have a batch of matrices and you want to multiply them, you can use `einsum` to do this in a single operation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = torch.rand(10, 3, 4)\n",
    "B = torch.rand(10, 4, 5)\n",
    "result = torch.einsum(\"bij,bjk->bik\", A, B)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. **Dot product**: `einsum` can be used to compute the dot product of two vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.rand(5)\n",
    "b = torch.rand(5)\n",
    "result = torch.einsum(\"i,i->\", a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "5. **Sum along a dimension**: You can use `einsum` to sum the elements of a tensor along a specific dimension, similar to `torch.sum()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = torch.rand(3, 4, 5)\n",
    "sum_along_dim1 = torch.einsum(\"ijk->ik\", A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. **Complex operations**: `einsum` really shines when you want to perform more complex operations that involve multiple steps and intermediate tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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