{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2953dfb9",
   "metadata": {},
   "source": [
    "# Encoder, Decoder, and Encoder-Decoder Models\n",
    "\n",
    "| Part | Topic | Time |\n",
    "|------|-------|------|\n",
    "| 1 | Architecture intuition: encoder vs decoder vs encoder-decoder | ~20 min |\n",
    "| 2 | Explore an encoder with ESM-2 | ~15 min |\n",
    "| 3 | Use the embeddings for a secondary structure prediction task | ~15 min |\n",
    "| 4 | Explore a decoder with Progen2 | ~10 min |\n",
    "| 5 | Explore an encoder-decoder with ProstT5 | On your own |\n",
    "| 6 | Postion Embedding | On your own |\n",
    "\n",
    "**Learning objectives:**\n",
    "1. Explain the differences and applications between encoder-only, decoder-only, and encoder-decoder transformer models\n",
    "2. Interpret what each model returns: embeddings, generated residues, or translated structure-like tokens\n",
    "3. Connect model architecture to downstream biological tasks such as classification, design, and sequence-to-structure translation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266996da",
   "metadata": {},
   "source": [
    "Models used in this section\n",
    "\n",
    "### 1. ESM-2 `t6_8M`\n",
    "\n",
    "Why use it:\n",
    "- truly small for a modern protein encoder,\n",
    "- official and widely used,\n",
    "- clean masked-language-model story,\n",
    "- good for embeddings and fine-tuning discussion.\n",
    "\n",
    "### 2. ProGen2-Small\n",
    "\n",
    "Why use it:\n",
    "- compact decoder-only model,\n",
    "- next-token / generation\n",
    "\n",
    "### 3. ProstT5\n",
    "\n",
    "Why use it:\n",
    "- genuine encoder-decoder protein model,\n",
    "- maps amino acid sequence to structure-like 3Di tokens,\n",
    "- makes the seq2seq idea concrete in biology.\n",
    "- ProstT5 is the least lightweight of the three\n",
    "\n",
    "## Architecture cheat sheet\n",
    "\n",
    "| Model | # Params |Architecture | Pretraining flavor | Best intuition |\n",
    "|---|---|---|---|---|\n",
    "| ESM-2 `t6_8M` | 7.51M |Encoder-only | Masked token prediction | \"What representation does this sequence have?\" |\n",
    "| ProGen2-small | 151.15M |Decoder-only | Next-token prediction | \"What residue comes next?\" |\n",
    "| ProstT5 | 2818.85M |Encoder-decoder | Seq2seq translation | \"How do I map one protein representation to another?\" (ie amino-acid vocabulary to 3Di character vocabulary) |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3580518d",
   "metadata": {},
   "source": [
    "## Encoder-only model: ESM-2 `t6_8M`\n",
    "\n",
    "An encoder reads the **whole sequence at once** and returns a contextual representation for every position.\n",
    "\n",
    "That makes encoder models ideal for:\n",
    "\n",
    "- embeddings,\n",
    "- classification,\n",
    "- residue-level labeling,\n",
    "- contact prediction,\n",
    "- and fine-tuning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53d3bff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n"
     ]
    }
   ],
   "source": [
    "#@title Loading python modules\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "import json\n",
    "\n",
    "from dataclasses import dataclass\n",
    "import json\n",
    "from pathlib import Path\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "# Install missing packages\n",
    "try:\n",
    "    import esm\n",
    "except ImportError:\n",
    "    !pip install -q fair-esm\n",
    "    import esm\n",
    "\n",
    "DEVICE = (\n",
    "    torch.device('cuda') if torch.cuda.is_available()\n",
    "    else torch.device('mps') if torch.backends.mps.is_available()\n",
    "    else torch.device('cpu')\n",
    ")\n",
    "\n",
    "print(f'Using device: {DEVICE}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15927780",
   "metadata": {},
   "source": [
    "## Building a toy encoder to understand masked pretraining\n",
    "\n",
    "### Why start with a toy encoder?\n",
    "\n",
    "Before we use a large pretrained model, we want to make the encoder training objective concrete.\n",
    "\n",
    "In this section we will build the smallest useful example of encoder pretraining:\n",
    "\n",
    "1. take a sequence,\n",
    "2. hide a few tokens,\n",
    "3. ask the model to reconstruct the missing tokens from context.\n",
    "\n",
    "This is the core idea behind masked language modeling. The toy model is small enough to understand line by line, and later we will connect the same idea to ESM-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d19daf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Code from Lecture\n",
    "\n",
    "# multi‑head self-attention\n",
    "class MultiHeadAttention(nn.Module):\n",
    "    def __init__(self, d_model, num_heads):\n",
    "        super().__init__()\n",
    "        assert d_model % num_heads == 0\n",
    "        self.d_model   = d_model\n",
    "        self.num_heads = num_heads\n",
    "        self.d_head    = self.d_model // self.num_heads # H = D/n_heads\n",
    "\n",
    "        # The weights\n",
    "        # uses all dimensions equal to d_model\n",
    "        self.Wq = nn.Linear(d_model, d_model)\n",
    "        self.Wk = nn.Linear(d_model, d_model)\n",
    "        self.Wv = nn.Linear(d_model, d_model)\n",
    "        self.Wo = nn.Linear(d_model, d_model)\n",
    "\n",
    "    def forward(self,\n",
    "                x,         # [B, L, D]\n",
    "                return_attn = False,\n",
    "                mask=None  # [L, L] with -inf on masked\n",
    "    ):\n",
    "        # x: (B, len, d_model)\n",
    "        B, L, D = x.shape\n",
    "\n",
    "        # H x d_head = d_model\n",
    "        #\n",
    "        # q[B, H, L, d_head]\n",
    "        # k[B, H, L, d_head]\n",
    "        # v[B, H, L, d_head]\n",
    "        q = self.Wq(x).reshape(B, L, self.num_heads, self.d_head).transpose(1, 2)\n",
    "        k = self.Wk(x).reshape(B, L, self.num_heads, self.d_head).transpose(1, 2)\n",
    "        v = self.Wv(x).reshape(B, L, self.num_heads, self.d_head).transpose(1, 2)\n",
    "\n",
    "        # q[B, H, L, d_head]\n",
    "        # k.transpose(-2,-1)[B, H, d_head, L]\n",
    "        # attn[B, H, L, L]\n",
    "        # v   [B, H, L, d_head]\n",
    "        # out [B, H, L, d_head]\n",
    "        scores = q @ k.transpose(-2, -1) / (self.d_head ** 0.5)\n",
    "        if mask is not None:\n",
    "            scores = scores.masked_fill(mask == 0, float('-inf'))\n",
    "            \n",
    "        attn = torch.softmax(scores, dim=-1)\n",
    "\n",
    "        out = attn @ v  # (B, heads, L, d_head)\n",
    "\n",
    "        # concatenate all heads together\n",
    "        # out.transpose(1, 2)[B, L, H, d_head]\n",
    "        # out[B, L, H*d_head]\n",
    "        out = out.transpose(1, 2).reshape(B, L, D)\n",
    "\n",
    "        # apply one last FC layer\n",
    "        out = self.Wo(out)\n",
    "        if return_attn:\n",
    "            return out, attn\n",
    "        return out\n",
    "    \n",
    "# simple Transformer block = attention -> FF\n",
    "class TransformerBlock(nn.Module):\n",
    "    def __init__(self, d_model, num_heads, d_ff):\n",
    "        super().__init__()\n",
    "\n",
    "        self.attn = MultiHeadAttention(d_model, num_heads)\n",
    "\n",
    "        self.ff = nn.Sequential(\n",
    "            nn.Linear(d_model, d_ff),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(d_ff, d_model),\n",
    "        )\n",
    "\n",
    "        self.norm_att = nn.LayerNorm(d_model)\n",
    "        self.norm_ff  = nn.LayerNorm(d_model)\n",
    "\n",
    "    def forward(self, x, return_attn = False, mask=None):\n",
    "        if return_attn:\n",
    "            attn_out, attn_weights = self.attn(self.norm_att(x), return_attn = return_attn, mask=mask)\n",
    "            x = x + attn_out\n",
    "            x = x + self.ff(self.norm_ff(x))\n",
    "            return x, attn_weights\n",
    "        \n",
    "        x = x + self.attn(self.norm_att(x), mask=mask)\n",
    "        x = x + self.ff(self.norm_ff(x))\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4104991b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO fill in the Encoder\n",
    "\n",
    "# ENCODER\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, abc_size, d_model, K, n_heads, d_diff, max_len):\n",
    "        super().__init__()\n",
    "\n",
    "        # define the token embedding\n",
    "        self.token_emb = nn.Embedding(abc_size, d_model)\n",
    "        # define the position embedding\n",
    "        self.pos_emb   = nn.Embedding(max_len,  d_model)\n",
    "\n",
    "        # define the transformer block layers\n",
    "        self.layers = nn.ModuleList([\n",
    "            TransformerBlock(d_model=d_model, num_heads=n_heads, d_ff=d_diff)\n",
    "            for _ in range(K)\n",
    "        ])\n",
    "\n",
    "        # the prediction layer used for Masked‑Language‑Modeling (MLM)\n",
    "        # each embedded vector is projected back predict a token in the input vocabulary\n",
    "        self.mlm_head = nn.Linear(d_model, abc_size)\n",
    "\n",
    "    def forward(self, tokens, return_attn = False):\n",
    "        B, L = tokens.shape\n",
    "\n",
    "        # define the positions\n",
    "        pos = torch.arange(L, device=tokens.device)\n",
    "\n",
    "        # define the embedding\n",
    "        x = self.token_emb(tokens) + self.pos_emb(pos)\n",
    "        \n",
    "        if return_attn:\n",
    "            all_attn  = {}\n",
    "            for i, layer in enumerate(self.layers):\n",
    "                x, attn_weights = layer(x, return_attn = return_attn)\n",
    "                all_attn[i]  = attn_weights\n",
    "\n",
    "            logits = self.mlm_head(x)\n",
    "            return logits, all_attn\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "\n",
    "        return self.mlm_head(x)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60caa1fb",
   "metadata": {},
   "source": [
    "In masking, not every masked position gets replaced with `[MASK]`. We use an 80/10/10 split:\n",
    "- 80% → replace with `[MASK]`\n",
    "- 10% → replace with a random token\n",
    "- 10% → leave unchanged\n",
    "\n",
    "Why might replacing some tokens randomly (rather than always masking) make the representations more robust?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba1b94aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_tokens(tokens, abc_size, mask_id, pad_id, mask_prob=0.15):\n",
    "    labels = tokens.clone()\n",
    "    \n",
    "    # making sure padding isn't masked\n",
    "    non_pad = (tokens != pad_id)\n",
    "    mask = (torch.rand(tokens.shape) < mask_prob) & non_pad\n",
    "    \n",
    "    probs = torch.rand(tokens.shape)\n",
    "    # for 80% of masks, actually replace given token with mask token\n",
    "    replace_mask = (probs < 0.8) & mask\n",
    "    tokens[replace_mask] = mask_id\n",
    "\n",
    "    # for 10% of masks not above, actually replace given token with a random (not mask) token\n",
    "    random_mask = (probs >= 0.8) & (probs < 0.9) & mask & (~replace_mask)\n",
    "    tokens[random_mask] = torch.randint(0, abc_size, (random_mask.sum(),))\n",
    "\n",
    "    # for the remaining 10% of masks, do nothing.\n",
    "\n",
    "    # padding mask from tokens\n",
    "    pad_mask = (tokens != pad_id) # True where not PAD\n",
    "\n",
    "    # Combine both:\n",
    "    #    - must not be PAD\n",
    "    #    - must be a masked position\n",
    "    total_mask = mask & pad_mask\n",
    "\n",
    "    # labels is a copy of the tokens that keeps the masked tokens, and replaces unmasked tokens with -100\n",
    "    # labels of masked residues are the original tokens\n",
    "    # labels of not masked residues are assigned to -100\n",
    "    labels[~total_mask] = -100\n",
    "\n",
    "    return tokens, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8ee6534e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tokens    3,    3,    3,    3,    0,    0,    1,    3,    0,    1\n",
      "masked    3,    4,    3,    3,    0,    0,    4,    3,    0,    4\n",
      "labels -100,    3, -100, -100, -100, -100,    1, -100, -100,    1\n"
     ]
    }
   ],
   "source": [
    "# generate masked inputs and labels with random nucleotide sequences\n",
    "\n",
    "abc_size = 4\n",
    "MASK_ID = 4\n",
    "PAD_ID = None\n",
    "mask_prob = 0.5\n",
    "tokens = torch.randint(0, abc_size, (1, 10))\n",
    "\n",
    "masked, labels = mask_tokens(\n",
    "        tokens.clone(),\n",
    "        abc_size=abc_size,\n",
    "        mask_id=MASK_ID,\n",
    "        pad_id=PAD_ID,\n",
    "        mask_prob=mask_prob,\n",
    "    )\n",
    "\n",
    "print(\"tokens\", \", \".join([f\"{int(token):4}\" for token in tokens[0]]))\n",
    "print(\"masked\",\", \".join([f\"{int(m):4}\" for m in masked[0]]))\n",
    "print(\"labels\",\", \".join([f\"{int(label):4}\" for label in labels[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b0b45afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 10, 6])\n",
      "tokens    0,    3,    3,    0,    0,    1,    3,    1,    2,    1\n",
      "masked    0,    3,    3,    0,    0,    1,    3,    1,    2,    4\n",
      "labels -100, -100, -100, -100, -100, -100, -100, -100, -100,    1\n",
      "logits    3,    5,    3,    3,    1,    0,    3,    0,    2,    4\n"
     ]
    }
   ],
   "source": [
    "# define/initialize the Encoder and run the masked tokens through the forward pass\n",
    "\n",
    "model = TransformerEncoder(\n",
    "    abc_size=abc_size+1,\n",
    "    d_model=64,\n",
    "    K=2,\n",
    "    n_heads=4,\n",
    "    d_diff=128,\n",
    "    max_len=10,\n",
    ")\n",
    "\n",
    "logits = model(masked)\n",
    "print(logits.shape)\n",
    "\n",
    "print(\"tokens\", \", \".join([f\"{int(token):4}\" for token in tokens[0]]))\n",
    "print(\"masked\",\", \".join([f\"{int(m):4}\" for m in masked[0]]))\n",
    "print(\"labels\",\", \".join([f\"{int(label):4}\" for label in labels[0]]))\n",
    "print(\"logits\", \", \".join([f\"{int(label):4}\" for label in torch.argmax(logits, dim=-1)[0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "60d6da76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss (mean cross-entropy loss) 1.769417405128479\n",
      "perplexity 5.8674340284624344\n",
      "mean entropy 4.585736151281308\n",
      "pytorch cross entropy 1.769417405128479\n"
     ]
    }
   ],
   "source": [
    "tx = logits.view(-1, logits.shape[-1]).detach().numpy() # dim (B*L, abc_size)\n",
    "ty = labels.view(-1).detach().numpy() # dim (B*L)\n",
    "\n",
    "# cross entropy\n",
    "ce = []\n",
    "# model entropy\n",
    "me = []\n",
    "for i in range(len(tx)):\n",
    "    if ty[i] != -100:\n",
    "        probs = np.exp(tx[i]) / np.sum(np.exp(tx[i]))\n",
    "        correct_class = ty[i]\n",
    "\n",
    "        # sum the negative log probability of the correct class (although in this case there is only 1 correct class so nothing to sum)\n",
    "        ce.append(-np.sum(np.log(probs[correct_class])))\n",
    "        me.append(-np.sum(probs*np.log(probs)))\n",
    "\n",
    "# take the mean loss (sum then divide by length)\n",
    "loss = np.mean(ce)\n",
    "print(f\"loss (mean cross-entropy loss) {loss}\")\n",
    "print(f\"perplexity {math.exp(np.mean(ce))}\")\n",
    "print(f\"mean entropy {math.exp(np.mean(me))}\")\n",
    "\n",
    "loss = F.cross_entropy(\n",
    "        torch.tensor(tx),   \n",
    "        torch.tensor(ty),                 \n",
    "        ignore_index=-100,\n",
    "    ) # default reduction is mean\n",
    "print(f\"pytorch cross entropy {loss}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1b8a2895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a toy encoder\n",
    "abc_size = 5 # 4 nucleotides and the mask\n",
    "MASK_ID = 4\n",
    "PAD_ID = None\n",
    "B, L = 8, 10\n",
    "tokens = torch.randint(0, abc_size-1, (B, L))\n",
    "\n",
    "model = TransformerEncoder(\n",
    "    abc_size=abc_size,\n",
    "    d_model=64,\n",
    "    K=2,\n",
    "    n_heads=4,\n",
    "    d_diff=128,\n",
    "    max_len=24,\n",
    ")\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "mask_prob = 0.15\n",
    "\n",
    "accs = np.zeros(500)\n",
    "perplexity = np.zeros(500)\n",
    "\n",
    "for step in range(500):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 1. Create masked inputs and MLM labels\n",
    "    masked, labels = mask_tokens(\n",
    "        tokens.clone(),\n",
    "        abc_size=abc_size,\n",
    "        mask_id=MASK_ID,\n",
    "        pad_id=PAD_ID,\n",
    "        mask_prob=mask_prob,\n",
    "    )\n",
    "\n",
    "    # 2. Forward pass through encoder + MLM head\n",
    "    logits = model(masked)\n",
    "\n",
    "    # 3. Compute loss ONLY at masked positions\n",
    "    loss = F.cross_entropy(\n",
    "        logits.view(-1, logits.shape[-1]),   \n",
    "        labels.view(-1),                 \n",
    "        ignore_index=-100,\n",
    "    ) # default reduction is mean\n",
    "    perplexity[step] = math.exp(loss.item())\n",
    "\n",
    "    # 4. Backprop + update\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        preds = logits.argmax(dim=-1)\n",
    "        masked_positions = labels != -100\n",
    "        accs[step] = (preds[masked_positions] == labels[masked_positions]).float().mean().item()\n",
    "\n",
    "f, ax = plt.subplots(1,2, figsize=(6,2))\n",
    "ax[0].plot(accs)\n",
    "ax[0].set_ylabel(\"accuracy\")\n",
    "ax[0].set_xlabel(\"training steps\")\n",
    "ax[0].set_ylim([0,1.1])\n",
    "\n",
    "ax[1].plot(perplexity)\n",
    "ax[1].set_ylabel(\"perplexity\")\n",
    "ax[1].set_xlabel(\"training steps\")\n",
    "ax[1].set_ylim([0,2*abc_size])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86ae8e37",
   "metadata": {},
   "source": [
    "### Cross-Entropy Loss and Perplexity\n",
    "\n",
    "In language modeling (and masked language modeling), **cross-entropy loss** and **perplexity** are directly related.\n",
    "\n",
    "Remember cross-entropy measures how “surprised” the model is about the correct answer:\n",
    "\n",
    "Across C classes for one token, the cross entropy is defined as:\n",
    "$$\n",
    "\\text{Cross-Entropy} = -\\sum_{c}^{C} p_{\\text{true}}(c)\\log p_{\\text{model}}(c)\n",
    "$$\n",
    "\n",
    "For one-hot labels, $p_{\\text{true}}(c)$ is 1 for the correct class and 0 for all others, so this simplifies to:\n",
    "\n",
    "$$\n",
    "\\text{Cross-Entropy} = -\\log p(\\text{correct class})\n",
    "$$\n",
    "\n",
    "- if the model has probability 1 of predicting the correct token then -log(1) = 0\n",
    "- if the model has probability 0 of preficting the correct token then -log(0) = inf\n",
    "\n",
    "\n",
    "For a sequence of tokens $x_1, x_2, \\dots, x_L$:\n",
    "\n",
    "$$\n",
    "\\text{Cross-Entropy} = \\frac{1}{L} \\sum_{i=1}^{L} -\\log p(x_i)\n",
    "$$\n",
    "\n",
    "Here, $x_i$ are the **true tokens (labels)**.\n",
    "\n",
    "### From Cross-Entropy → Perplexity\n",
    "\n",
    "Perplexity is defined as:\n",
    "$$\n",
    "\\begin{align}\n",
    "\\text{Perplexity} &= e^{-\\frac{1}{L}\\sum_i^L{\\log{p(x_i)}}} \\\\\n",
    "&= e^{\\text{mean}\\left(-\\log{p(x_i)}\\right)} \\\\\n",
    "&= e^{\\text{mean}\\left(\\text{Cross-Entropy}\\right)}\n",
    "\\end{align}\n",
    "$$\n",
    "So if your loss is computed using `F.cross_entropy`, you can directly convert it:\n",
    "\n",
    "```python\n",
    "perplexity = math.exp(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "759c2728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss (mean cross-entropy loss) 2.0917553901672363\n",
      "perplexity 8.099119808586362\n",
      "mean entropy 4.704603727856539\n"
     ]
    }
   ],
   "source": [
    "tx = logits.view(-1, logits.shape[-1]).detach().numpy() # dim (B*L, abc_size)\n",
    "ty = labels.view(-1).detach().numpy() # dim (B*L)\n",
    "\n",
    "# cross entropy\n",
    "ce = []\n",
    "# model entropy\n",
    "me = []\n",
    "for i in range(len(tx)):\n",
    "    if ty[i] != -100:\n",
    "        shifted = tx[i] - np.max(tx[i])\n",
    "        probs = np.exp(shifted) / np.sum(np.exp(shifted))\n",
    "        correct_class = ty[i]\n",
    "\n",
    "        # sum the negative log probability of the correct class (although in this case there is only 1 correct class so nothing to sum)\n",
    "        ce.append(-np.sum(np.log(probs[correct_class])))\n",
    "        me.append(-np.sum(probs*np.log(probs)))\n",
    "\n",
    "# take the mean loss (sum then divide by length)\n",
    "loss = np.mean(ce)\n",
    "print(f\"loss (mean cross-entropy loss) {loss}\")\n",
    "print(f\"perplexity {math.exp(np.mean(ce))}\")\n",
    "print(f\"mean entropy {math.exp(np.mean(me))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6fe1a4e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average probability assigned to 0: 0.2096632421016693\n",
      "Average probability assigned to 1: 0.28879567980766296\n",
      "Average probability assigned to 2: 0.30874332785606384\n",
      "Average probability assigned to 3: 0.19222427904605865\n",
      "Average probability assigned to 4: 0.0005734485457651317\n"
     ]
    }
   ],
   "source": [
    "probs = F.softmax(logits, dim=-1)\n",
    "\n",
    "for i in range(abc_size):\n",
    "    mask_probs = probs[..., i]\n",
    "    print(f\"Average probability assigned to {i}:\", mask_probs.mean().item())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300afcf7",
   "metadata": {},
   "source": [
    "In this training loop, the encoder never sees a class label.\n",
    "It only learns to reconstruct masked tokens from context.\n",
    "\n",
    "That is why this is called **self-supervised pretraining**:\n",
    "\n",
    "- the input sequence provides its own supervision,\n",
    "- the model learns a representation before we define a downstream task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "842284cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tokens    3,    2,    3,    3,    2,    3,    0,    3,    2,    3\n",
      "masked    3,    2,    3,    3,    2,    3,    0,    3,    4,    3\n",
      "labels -100, -100, -100, -100, -100, -100, -100, -100,    2, -100\n",
      "logits    3,    2,    3,    3,    2,    3,    0,    3,    2,    3\n"
     ]
    }
   ],
   "source": [
    "# look at the trained outputs\n",
    "print(\"tokens\", \", \".join([f\"{int(token):4}\" for token in tokens[0]]))\n",
    "print(\"masked\",\", \".join([f\"{int(m):4}\" for m in masked[0]]))\n",
    "print(\"labels\",\", \".join([f\"{int(label):4}\" for label in labels[0]]))\n",
    "print(\"logits\", \", \".join([f\"{int(label):4}\" for label in torch.argmax(logits, dim=-1)[0]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28224faf",
   "metadata": {},
   "source": [
    "We trained a simple encoder model to unmask our random training set. What is this model good for?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9ea47e3",
   "metadata": {},
   "source": [
    "## From the toy encoder to ESM-2\n",
    "\n",
    "Our toy encoder and ESM-2 solve the same style of learning problem: predict hidden tokens from surrounding context.\n",
    "\n",
    "The difference is scale:\n",
    "\n",
    "- the toy encoder is small and trained on synthetic or tiny examples,\n",
    "- ESM-2 is a real protein language model trained on a massive protein corpus (~250 million protein sequences).\n",
    "\n",
    "So the key question becomes:\n",
    "\n",
    "> If masked pretraining teaches useful representations, what information is already present in ESM embeddings?\n",
    "\n",
    "That is what we will test next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c8abe2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded ESM-2 small with 7.51M parameters\n",
      "Layers: 6\n",
      "Embedding dimension: 320\n",
      "Attention heads: 20\n",
      "Alphabet size: 33\n"
     ]
    }
   ],
   "source": [
    "# Load the smallest esm protein model t6_8M trained on uniref50\n",
    "esm_model, esm_alphabet = esm.pretrained.esm2_t6_8M_UR50D()\n",
    "esm_model = esm_model.eval().to(DEVICE)\n",
    "esm_batch_converter = esm_alphabet.get_batch_converter()\n",
    "\n",
    "total_params = sum(p.numel() for p in esm_model.parameters())\n",
    "print(f'Loaded ESM-2 small with {total_params / 1e6:.2f}M parameters')\n",
    "print(f'Layers: {esm_model.num_layers}')\n",
    "print(f'Embedding dimension: {esm_model.embed_dim}')\n",
    "print(f'Attention heads: {esm_model.attention_heads}')\n",
    "print(f'Alphabet size: {len(esm_alphabet.all_toks)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0558f15e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['logits', 'representations'])\n",
      "torch.Size([8, 10, 33])\n",
      "dict_keys([0])\n",
      "torch.Size([8, 10, 320])\n"
     ]
    }
   ],
   "source": [
    "# you can input the masked sequence into the esm_model\n",
    "# to retreive the representation layer you will need to call an optional argument \"repr_layers\" and indicate which layer(s) should be returned in the \"representations\" key list\n",
    "outputs = esm_model(masked.to(DEVICE), repr_layers=[0])\n",
    "print(outputs.keys())\n",
    "\n",
    "print(outputs[\"logits\"].shape)\n",
    "print(outputs[\"representations\"].keys())\n",
    "\n",
    "for key in outputs[\"representations\"].keys():\n",
    "    print(outputs[\"representations\"][key].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3abdb819",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: torch.Size([8, 10])\n",
      "Keys in output dictionary: dict_keys(['logits', 'representations'])\n",
      "Output logits shape torch.Size([8, 10, 33])\n",
      "Keys in output representations dict_keys([0, 1])\n",
      "0 torch.Size([8, 10, 320])\n",
      "1 torch.Size([8, 10, 320])\n"
     ]
    }
   ],
   "source": [
    "# you can request multiple layer representations up to the esm_model.num_layers\n",
    "output_repr_layers = [0,1]\n",
    "\n",
    "outputs = esm_model(masked.to(DEVICE), repr_layers=output_repr_layers)\n",
    "print(f\"Input shape: {masked.shape}\")\n",
    "print(f\"Keys in output dictionary: {outputs.keys()}\")\n",
    "\n",
    "print(f\"Output logits shape {outputs[\"logits\"].shape}\")\n",
    "\n",
    "# now representations has 2 keys in the the dictionary each with [B, L, D] representations (shape is the same as input)\n",
    "print(f\"Keys in output representations {outputs[\"representations\"].keys()}\")\n",
    "for key, temp_repr in outputs[\"representations\"].items():\n",
    "    print(key, temp_repr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0525af85",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO \n",
    "# Modify the Encoder class to output the repr_layers like esm\n",
    "\n",
    "# ENCODER\n",
    "class TransformerEncoder(nn.Module):\n",
    "    def __init__(self, abc_size, d_model, K, n_heads, d_diff, max_len):\n",
    "        super().__init__()\n",
    "\n",
    "        self.token_emb = nn.Embedding(abc_size, d_model)\n",
    "        self.pos_emb   = nn.Embedding(max_len,  d_model)\n",
    "\n",
    "        self.layers = nn.ModuleList([\n",
    "            TransformerBlock(d_model=d_model, num_heads=n_heads, d_ff=d_diff)\n",
    "            for _ in range(K)\n",
    "        ])\n",
    "\n",
    "        # the prediction layer used for Masked‑Language‑Modeling (MLM)\n",
    "        # each embedded vector is projected back predict a token in the input vocabulary\n",
    "        self.mlm_head = nn.Linear(d_model, abc_size)\n",
    "\n",
    "    def forward(self, tokens, mask=None, repr_layers=None):\n",
    "        B, L = tokens.shape\n",
    "        pos = torch.arange(L, device=tokens.device)\n",
    "        x = self.token_emb(tokens) + self.pos_emb(pos)\n",
    "        \n",
    "        if repr_layers != None:\n",
    "            representation_layers = {i: None for i in repr_layers}\n",
    "            for repi, layer in enumerate(self.layers):\n",
    "                x = layer(x, mask)\n",
    "                if repi in repr_layers:\n",
    "                    representation_layers[repi] = x.clone()\n",
    "            return self.mlm_head(x), representation_layers\n",
    "\n",
    "        for layer in self.layers:\n",
    "            x = layer(x, mask)\n",
    "\n",
    "        return self.mlm_head(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c3626956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([1, 4, 1, 1, 4, 1, 0, 1, 1, 4])\n",
      "torch.Size([8, 10, 64])\n"
     ]
    }
   ],
   "source": [
    "# check that your implementation works\n",
    "\n",
    "model = TransformerEncoder(\n",
    "    abc_size=abc_size,\n",
    "    d_model=64,\n",
    "    K=2,\n",
    "    n_heads=4,\n",
    "    d_diff=128,\n",
    "    max_len=24,\n",
    ")\n",
    "logits, representations = model(masked, repr_layers = [0,1])\n",
    "print(torch.argmax(logits[0], dim=-1))\n",
    "\n",
    "representation = representations[1]\n",
    "print(representation.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633da603",
   "metadata": {},
   "source": [
    "Let's now use the esm pretrained encoder for a downstream task head to show why encoders are useful."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93513117",
   "metadata": {},
   "source": [
    "## Frozen encoder + learned head setup for Protein Secondary Structure Prediction\n",
    "\n",
    "Previously in block 1 we trained an MLP to predict protein secondary structure for a given protein sequence. We got a test accuracy around ~60% and a train accuracy around ~65%. Let's see if we improve our results by using the esm embedding as an input. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6a543e02",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Reload MLP model from block 1\n",
    "\n",
    "# Define the model from the Block 1 section to get a baseline\n",
    "# Same model from Block 1\n",
    "class ProteinMLP(nn.Module):\n",
    "    # MLP definition\n",
    "    def __init__(self, I, H, O):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(I, H)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(H, O)\n",
    "\n",
    "    # Definition of the forward pass\n",
    "    # inputs are X outputs are Y in logits (not normalized)\n",
    "    def forward(self, x):\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "def accuracy_from_logits(logits, y):\n",
    "    preds = logits.argmax(dim=1)\n",
    "    return (preds == y).float().mean().item()\n",
    "\n",
    "def train_model(model,X_train, Y_train,X_test, Y_test,lr=1e-3,epochs=200,\n",
    "    batch_size=None,          # None => full-batch, 1 => SGD, >1 => mini-batch\n",
    "    shuffle=True,\n",
    "    optimizer_cls=torch.optim.Adam,\n",
    "    criterion=None,\n",
    "    device=None,\n",
    "    print_every=10\n",
    "):\n",
    "    # If there is a GPU use it\n",
    "    if device is None:\n",
    "        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "    model = model.to(device)\n",
    "\n",
    "    # Default to Cross Entropy Loss\n",
    "    if criterion is None:\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "    # Optimizer Defaultis Adam\n",
    "    optimizer = optimizer_cls(model.parameters(), lr=lr)\n",
    "    def lr_lambda(step, warmup=200):\n",
    "        if step < warmup:\n",
    "            return step / warmup\n",
    "        return 1.0 / np.sqrt(max(step - warmup + 1, 1))\n",
    "\n",
    "    scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)\n",
    "\n",
    "    # Create tensors\n",
    "    X_train_t = torch.tensor(X_train, dtype=torch.float32)\n",
    "    Y_train_t = torch.tensor(Y_train, dtype=torch.long)\n",
    "\n",
    "    X_test_t  = torch.tensor(X_test,  dtype=torch.float32).to(device)\n",
    "    Y_test_t  = torch.tensor(Y_test,  dtype=torch.long).to(device)\n",
    "\n",
    "    # Build a loader if using batches; otherwise one full batch\n",
    "    if batch_size is None:\n",
    "        train_loader = [(X_train_t, Y_train_t)]  # full-batch\n",
    "    else:\n",
    "        train_ds = TensorDataset(X_train_t, Y_train_t)\n",
    "        train_loader = DataLoader(train_ds, batch_size=batch_size, shuffle=shuffle)\n",
    "\n",
    "    history = {\"loss\": [], \"train_acc\": [], \"test_acc\": []}\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        running_acc = 0.0\n",
    "        n_batches = 0\n",
    "\n",
    "        for xb, yb in train_loader:\n",
    "            xb = xb.to(device)\n",
    "            yb = yb.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            logits = model(xb)\n",
    "            loss = criterion(logits, yb)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            scheduler.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "            running_acc += accuracy_from_logits(logits, yb)\n",
    "            n_batches += 1\n",
    "\n",
    "        train_loss = running_loss / n_batches\n",
    "        train_acc = running_acc / n_batches\n",
    "\n",
    "        # --- evaluation (full test set) ---\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            test_logits = model(X_test_t)\n",
    "            test_acc = accuracy_from_logits(test_logits, Y_test_t)\n",
    "\n",
    "        history[\"loss\"].append(train_loss)\n",
    "        history[\"train_acc\"].append(train_acc)\n",
    "        history[\"test_acc\"].append(test_acc)\n",
    "\n",
    "        if (epoch == 0) or ((epoch + 1) % print_every == 0):\n",
    "            bs_str = \"full-batch\" if batch_size is None else f\"batch_size={batch_size}\"\n",
    "            print(f\"Epoch {epoch+1}/{epochs} ({bs_str}) \"\n",
    "                  f\"Loss: {train_loss:.4f} \"\n",
    "                  f\"Train Acc: {train_acc:.3f} \"\n",
    "                  f\"Test Acc: {test_acc:.3f} \"\n",
    "                  f\"LR: {scheduler.get_last_lr()[0]:.6f}\")\n",
    "\n",
    "    return model, history\n",
    "\n",
    "# Amino acid and secondary structure mapping\n",
    "aas = 'ACDEFGHIKLMNPQRSTVWY'\n",
    "# returns dictionary of Amino acid and Corresponding index\n",
    "aas_idx = {aa: i for i, aa in enumerate(aas)}\n",
    "# Map for the secondary structure to the index it corresponds to\n",
    "ss_map = {'H':0, 'E':1, 'C':2}\n",
    "# Inverse Map from Index to Secondary Structure\n",
    "inv_ss_map = {0: 'H', 1: 'E', 2: 'C'}\n",
    "\n",
    "def onehot(aa):\n",
    "  # make vector of 20 dimenions\n",
    "    aa_hot = np.zeros(20)\n",
    "    # if amino acid letter in dictionary\n",
    "    if aa in aas_idx:\n",
    "        aa_hot[aas_idx[aa]] = 1. #update the 20 dimenional vector by adding at 1 corresponding to the index # TODO\n",
    "    return aa_hot # return one hot encoding\n",
    "\n",
    "def window_encode(seq, pos, w, show=False):\n",
    "    half = w // 2\n",
    "    pad = '-' * half\n",
    "    # pad the entire sequence so that N terminal and C Terminus have a window\n",
    "    padded = pad + seq + pad\n",
    "    window = []\n",
    "    for aa in padded[pos:pos+w]:\n",
    "        window.extend(onehot(aa) if aa in aas_idx else np.zeros(20))\n",
    "    if show:\n",
    "      print(f'Padded Sequence: {padded}')\n",
    "      print(f'Window of Interest: {padded[pos:pos+w]}')\n",
    "      print(f'Window Vector: {np.array(window)}')\n",
    "    return np.array(window)\n",
    "\n",
    "def encode_dataset(seqs, sst3, w):\n",
    "    half = w//2\n",
    "    X, Y = [], [] # X will store input vectors each length 260 each, Y will store labels either (0,1,2)\n",
    "    for sq, ss in zip(seqs, sst3):\n",
    "        for i in range(len(sq)-half):\n",
    "            # Encode the each amino acid \"window\"\n",
    "            X.append(window_encode(sq, i, w))\n",
    "            # Map the secondary structure to the index it corresponds to (HINT: Map Made earlier)\n",
    "            Y.append(ss_map[ss[i]])\n",
    "    return np.stack(X), np.array(Y) # converts list of (260,) vectors into a 2D array and converts labels into shapes (N,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ae2c4ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Amino Acid Sequences:  (513,)\n",
      "Length of Sequence of Interest 131\n"
     ]
    }
   ],
   "source": [
    "# load in the same data from block 1\n",
    "#import kagglehub\n",
    "#dir_path = kagglehub.dataset_download(\"tamzidhasan/protein-secondary-structure-casp12-cb513-ts115\")\n",
    "# Retrieve the CASP-12 file\n",
    "#casp12_path = os.path.join(dir_path, 'test_secondary_structure_casp12.csv')\n",
    "#cb515_path = os.path.join(dir_path, 'test_secondary_structure_cb513.csv')\n",
    "\n",
    "dir_path = \"/Users/louiscolson/Desktop/MCB128/B1/Protein Secondary Structure casp12\"\n",
    "casp12_path = os.path.join(dir_path, 'test_secondary_structure_casp12.csv')\n",
    "cb515_path = os.path.join(dir_path, 'test_secondary_structure_cb513.csv')\n",
    "# Retrieve the\n",
    "# Read in the path as a dataframe\n",
    "df_train =pd.read_csv(cb515_path)\n",
    "df_test = pd.read_csv(casp12_path)\n",
    "\n",
    "seqs = df_train['seq'].to_numpy()\n",
    "sst3 = df_train['sst3'].to_numpy()\n",
    "#print(sst3.shape)\n",
    "seqs_test = df_test['seq'].to_numpy()\n",
    "sst3_test = df_test['sst3'].to_numpy()\n",
    "#print(sst3_test.shape)\n",
    "print(\"Number of Unique Amino Acid Sequences: \", seqs.shape)\n",
    "print(\"Length of Sequence of Interest\", len(seqs[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce28e37f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (141282, 260) [N_windows, window_size*alphabet_size]\n",
      "Y_train shape (141282,) [N_windows] -> only predicting secondary structure at the center of the window\n"
     ]
    }
   ],
   "source": [
    "# Define the train and test sets\n",
    "X_train, Y_train = encode_dataset(seqs, sst3, 13)\n",
    "print(f\"X_train shape: {X_train.shape} [N_windows, window_size*alphabet_size]\")\n",
    "print(f\"Y_train shape {Y_train.shape} [N_windows] -> only predicting secondary structure at the center of the window\")\n",
    "X_test, Y_test = encode_dataset(seqs_test, sst3_test, 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2dde0dc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300 (full-batch) Loss: 1.0997 Train Acc: 0.371 Test Acc: 0.393 LR: 0.000025\n",
      "Epoch 50/300 (full-batch) Loss: 0.9984 Train Acc: 0.541 Test Acc: 0.553 LR: 0.001250\n",
      "Epoch 100/300 (full-batch) Loss: 0.8419 Train Acc: 0.624 Test Acc: 0.606 LR: 0.002500\n",
      "Epoch 150/300 (full-batch) Loss: 0.8262 Train Acc: 0.632 Test Acc: 0.607 LR: 0.003750\n",
      "Epoch 200/300 (full-batch) Loss: 0.7900 Train Acc: 0.653 Test Acc: 0.621 LR: 0.005000\n",
      "Epoch 250/300 (full-batch) Loss: 0.7726 Train Acc: 0.662 Test Acc: 0.623 LR: 0.000700\n",
      "Epoch 300/300 (full-batch) Loss: 0.7648 Train Acc: 0.667 Test Acc: 0.624 LR: 0.000498\n",
      "Number of trainable parameters: 10563\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define Model Paramters\n",
    "#  Qian & Sejnowski parameters\n",
    "w = 13\n",
    "I = w*20\n",
    "H = 40\n",
    "O = 3 # 3 classes [C, H, E]\n",
    "\n",
    "MLP_model = ProteinMLP(I, H, O)\n",
    "\n",
    "model, hist = train_model(MLP_model, X_train, Y_train, X_test, Y_test, lr=0.005, epochs=300, batch_size=None, print_every=50)\n",
    "print(f\"Number of trainable parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")\n",
    "\n",
    "# Plot Train vs Test Accuracy\n",
    "plt.figure()\n",
    "plt.plot(hist[\"train_acc\"], label=\"Train Accuracy\")\n",
    "plt.plot(hist[\"test_acc\"], label=\"Test Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim([0,1.0])\n",
    "plt.title(\"Baseline Train/Test Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52b096c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "874\n",
      "1494\n",
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      "Embedded 500/513\n",
      "Embedded 513/513\n",
      "Done — shape: torch.Size([164, 320])  (L × 320)\n",
      "Embedding test sequences with ESM-2...\n",
      "Embedded 4/21\n",
      "Embedded 20/21\n",
      "Done — shape: torch.Size([455, 320])  (L × 320)\n"
     ]
    }
   ],
   "source": [
    "@torch.inference_mode()\n",
    "def get_esm_embeddings(sequences, max_len=256, batch_size=4, pool='none', repr_layer_list=None, verbose=True):\n",
    "    if repr_layer_list is None:\n",
    "        repr_layer_list = [esm_model.num_layers]\n",
    "\n",
    "    esm_model.eval()\n",
    "    out = {tl: [] for tl in repr_layer_list}\n",
    "\n",
    "    for i in range(0, len(sequences), batch_size):\n",
    "        chunk = [s[:max_len] for s in sequences[i:i + batch_size]]\n",
    "        data = [(f'seq_{j}', s) for j, s in enumerate(chunk)]\n",
    "        _, _, tokens = esm_batch_converter(data)\n",
    "        tokens = tokens.to(DEVICE)\n",
    "\n",
    "        result = esm_model(tokens, repr_layers=repr_layer_list, return_contacts=False)\n",
    "        tok_reprs = result['representations']\n",
    "\n",
    "        for temp_layer, tok_repr in tok_reprs.items():\n",
    "            for k, seq in enumerate(chunk):\n",
    "                L = len(seq)\n",
    "                emb = tok_repr[k, 1:L+1, :].cpu()\n",
    "                if pool == 'mean':\n",
    "                    emb = emb.mean(dim=0)\n",
    "                out[temp_layer].append(emb)\n",
    "\n",
    "        del result, tok_reprs, tokens\n",
    "        if DEVICE.type == \"mps\":\n",
    "            torch.mps.empty_cache()\n",
    "\n",
    "        if (i // batch_size) % 4 == 0 and verbose:\n",
    "            print(f'Embedded {min(i + batch_size, len(sequences))}/{len(sequences)}')\n",
    "\n",
    "    return out\n",
    "\n",
    "seq_lens = [len(seq) for seq in seqs]\n",
    "print(max(seq_lens))\n",
    "\n",
    "seq_lens = [max(seq_lens)]+[len(seq) for seq in seqs_test]\n",
    "\n",
    "max_seq_len = max(seq_lens)\n",
    "print(max_seq_len)\n",
    "\n",
    "# Pre-compute per-residue embeddings once.\n",
    "# Training then only updates the small head — very fast on CPU.\n",
    "print('Embedding training sequences with ESM-2...')\n",
    "train_seq_embeddings = get_esm_embeddings(seqs, max_len=max_seq_len, pool='none', repr_layer_list = list(range(esm_model.num_layers+1)))\n",
    "print(f'Done — shape: {train_seq_embeddings[esm_model.num_layers][0].shape}  (L × 320)')\n",
    "\n",
    "print('Embedding test sequences with ESM-2...')\n",
    "test_seq_embeddings = get_esm_embeddings(seqs_test, max_len=max_seq_len, pool='none', repr_layer_list = list(range(esm_model.num_layers+1)))\n",
    "print(f'Done — shape: {test_seq_embeddings[esm_model.num_layers][0].shape}  (L × 320)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "da8d9b2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "874\n",
      "1494\n",
      "Embedding training sequences with ESM-2...\n",
      "  Embedded 16/513\n",
      "  Embedded 80/513\n",
      "  Embedded 144/513\n",
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      "  Embedded 272/513\n",
      "  Embedded 336/513\n",
      "  Embedded 400/513\n",
      "  Embedded 464/513\n",
      "  Embedded 513/513\n",
      "Done — shape: torch.Size([164, 320])  (L × 320)\n",
      "Embedding test sequences with ESM-2...\n",
      "  Embedded 16/21\n",
      "Done — shape: torch.Size([455, 320])  (L × 320)\n"
     ]
    }
   ],
   "source": [
    "@torch.no_grad()\n",
    "def get_esm_embeddings(sequences, max_len = 256, batch_size = 16, pool = 'none', repr_layer_list = [esm_model.num_layers], verbose=True):\n",
    "    # Produces per-residue (L, D) tensors when pool='none',\n",
    "    # or mean-pooled (D,) tensors when pool='mean'.\n",
    "    #\n",
    "    # ESM-2 prepends a BOS token (position 0) and appends an EOS token;\n",
    "    # we strip both so each returned tensor has exactly one row per residue.\n",
    "    esm_model.eval()\n",
    "    out = {tl: [] for tl in repr_layer_list}\n",
    "    for i in range(0, len(sequences), batch_size):\n",
    "        chunk = [s[:max_len] for s in sequences[i : i + batch_size]]\n",
    "        data  = [(f'seq_{j}', s) for j, s in enumerate(chunk)]\n",
    "        _, _, tokens = esm_batch_converter(data)\n",
    "        tokens = tokens.to(DEVICE)\n",
    "\n",
    "        #result   = esm_model(tokens, repr_layers=[esm_model.num_layers], return_contacts=False)\n",
    "        result   = esm_model(tokens, repr_layers=repr_layer_list, return_contacts=False)\n",
    "        tok_reprs = result['representations']   # (B, L+2, D)\n",
    "\n",
    "        for temp_layer, tok_repr in tok_reprs.items():\n",
    "            for k, seq in enumerate(chunk):\n",
    "                L   = len(seq)\n",
    "                emb = tok_repr[k, 1 : L + 1, :].cpu()   # strip BOS and EOS → (L, D)\n",
    "                if pool == 'mean':\n",
    "                    emb = emb.mean(dim=0)                # (D,)\n",
    "                out[temp_layer].append(emb)\n",
    "\n",
    "        if (i // batch_size) % 4 == 0 and verbose:\n",
    "            print(f'  Embedded {min(i + batch_size, len(sequences))}/{len(sequences)}')\n",
    "    return out\n",
    "\n",
    "seq_lens = [len(seq) for seq in seqs]\n",
    "print(max(seq_lens))\n",
    "\n",
    "seq_lens = [max(seq_lens)]+[len(seq) for seq in seqs_test]\n",
    "\n",
    "max_seq_len = max(seq_lens)\n",
    "print(max_seq_len)\n",
    "\n",
    "# Pre-compute per-residue embeddings once.\n",
    "# Training then only updates the small head — very fast on CPU.\n",
    "print('Embedding training sequences with ESM-2...')\n",
    "train_seq_embeddings = get_esm_embeddings(seqs, max_len=max_seq_len, pool='none', repr_layer_list = list(range(esm_model.num_layers+1)))\n",
    "print(f'Done — shape: {train_seq_embeddings[0][0].shape}  (L × 320)')\n",
    "\n",
    "print('Embedding test sequences with ESM-2...')\n",
    "test_seq_embeddings = get_esm_embeddings(seqs_test, max_len=max_seq_len, pool='none', repr_layer_list = list(range(esm_model.num_layers+1)))\n",
    "print(f'Done — shape: {test_seq_embeddings[0][0].shape}  (L × 320)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8587f286",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = \"train_seq_embeddings.pt\"\n",
    "test_path = \"test_seq_embeddings.pt\"\n",
    "\n",
    "torch.save(train_seq_embeddings, train_path)\n",
    "torch.save(test_seq_embeddings, test_path)\n",
    "\n",
    "#train_seq_embeddings = torch.load(train_path, map_location=\"cpu\")\n",
    "#test_seq_embeddings = torch.load(test_path, map_location=\"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96350b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_embedding_dataset(seq_embeddings, ss_labels, window_size, pad_value=0.0, return_torch=False):\n",
    "    \"\"\"\n",
    "    Convert per-residue embeddings into a windowed dataset for residue-level prediction.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    seq_embeddings : list of torch.Tensor\n",
    "        Each element has shape (L, D), one tensor per sequence.\n",
    "    ss_labels : list\n",
    "        Each element is a sequence of residue labels of length L.\n",
    "        Labels should already be integer encoded, e.g. 0/1/2 for 3-state SS.\n",
    "    window_size : int\n",
    "        Must be odd so there is a center residue.\n",
    "    pad_value : float\n",
    "        Value used to pad residues beyond sequence ends.\n",
    "    return_torch : bool\n",
    "        If True, return torch tensors. Otherwise return numpy arrays.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    X : np.ndarray or torch.Tensor\n",
    "        Shape (N_windows, window_size * D)\n",
    "    Y : np.ndarray or torch.Tensor\n",
    "        Shape (N_windows,)\n",
    "    \"\"\"\n",
    "    assert window_size % 2 == 1, \"window_size must be odd\"\n",
    "    half = window_size // 2\n",
    "\n",
    "    X_all = []\n",
    "    Y_all = []\n",
    "\n",
    "    for emb, labels in zip(seq_embeddings, ss_labels):\n",
    "        # emb: (L, D)\n",
    "        if isinstance(emb, np.ndarray):\n",
    "            emb = torch.tensor(emb, dtype=torch.float32)\n",
    "        else:\n",
    "            emb = emb.float()\n",
    "\n",
    "        L, D = emb.shape\n",
    "        assert L == len(labels), f\"Embedding length {L} != label length {len(labels)}\"\n",
    "\n",
    "        # Pad along residue dimension: (L + 2*half, D)\n",
    "        padded = torch.full((L + 2 * half, D), pad_value, dtype=emb.dtype)\n",
    "        padded[half:half + L] = emb\n",
    "\n",
    "        for i in range(L):\n",
    "            window = padded[i:i + window_size]      # (w, D)\n",
    "            X_all.append(window.reshape(-1))        # (w*D,)\n",
    "            Y_all.append(ss_map[labels[i]])                 # center residue label\n",
    "\n",
    "    X = torch.stack(X_all)                          # (N_windows, w*D)\n",
    "    Y = torch.tensor(Y_all, dtype=torch.long)      # (N_windows,)\n",
    "\n",
    "    if return_torch:\n",
    "        return X, Y\n",
    "    return X.numpy(), Y.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "857b1099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (144360, 4160)\n",
      "Y_train: (144360,)\n",
      "X_test: (7256, 4160)\n",
      "Y_test: (7256,)\n"
     ]
    }
   ],
   "source": [
    "w = 13\n",
    "\n",
    "X_train_esm, Y_train_esm = encode_embedding_dataset(train_seq_embeddings[esm_model.num_layers], sst3, w)\n",
    "X_test_esm, Y_test_esm = encode_embedding_dataset(test_seq_embeddings[esm_model.num_layers], sst3_test, w)\n",
    "\n",
    "print(\"X_train:\", X_train_esm.shape)   # (N_windows, 13*320)\n",
    "print(\"Y_train:\", Y_train_esm.shape)\n",
    "print(\"X_test:\", X_test_esm.shape)\n",
    "print(\"Y_test:\", Y_test_esm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "131ab5d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300 (full-batch) Loss: 1.1107 Train Acc: 0.274 Test Acc: 0.310 LR: 0.000025\n",
      "Epoch 50/300 (full-batch) Loss: 0.5879 Train Acc: 0.757 Test Acc: 0.719 LR: 0.001250\n",
      "Epoch 100/300 (full-batch) Loss: 0.5427 Train Acc: 0.775 Test Acc: 0.717 LR: 0.002500\n",
      "Epoch 150/300 (full-batch) Loss: 0.5141 Train Acc: 0.787 Test Acc: 0.717 LR: 0.003750\n",
      "Epoch 200/300 (full-batch) Loss: 0.4929 Train Acc: 0.797 Test Acc: 0.704 LR: 0.005000\n",
      "Epoch 250/300 (full-batch) Loss: 0.4591 Train Acc: 0.812 Test Acc: 0.707 LR: 0.000700\n",
      "Epoch 300/300 (full-batch) Loss: 0.4514 Train Acc: 0.816 Test Acc: 0.705 LR: 0.000498\n",
      "Number of trainable parameters: 166563\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = 13\n",
    "D = train_seq_embeddings[0][0].shape[1]\n",
    "I = w * D\n",
    "H = 40\n",
    "O = 3\n",
    "\n",
    "MLP_model = ProteinMLP(I, H, O)\n",
    "\n",
    "model, esm_hist = train_model(MLP_model, X_train_esm, Y_train_esm, X_test_esm, Y_test_esm, lr=0.005, epochs=200, batch_size=None, print_every=50)\n",
    "print(f\"Number of trainable parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")\n",
    "\n",
    "# Plot Train vs Test Accuracy\n",
    "plt.figure()\n",
    "plt.plot(esm_hist[\"train_acc\"], label=\"Train Accuracy\")\n",
    "plt.plot(esm_hist[\"test_acc\"], label=\"Test Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim([0,1.0])\n",
    "plt.title(\"Secondary Structure Head Train/Test Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89fd5dcd",
   "metadata": {},
   "source": [
    "**Prediction:** Which layer do you expect to give the best secondary structure accuracy — early, middle, or late?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "956c7abb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200 (full-batch) Loss: 1.1138 Train Acc: 0.224 Test Acc: 0.207 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.8115 Train Acc: 0.640 Test Acc: 0.620 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.7096 Train Acc: 0.697 Test Acc: 0.636 LR: 0.005000\n",
      "Epoch 1/200 (full-batch) Loss: 1.0724 Train Acc: 0.434 Test Acc: 0.486 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.6878 Train Acc: 0.702 Test Acc: 0.658 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.6233 Train Acc: 0.733 Test Acc: 0.647 LR: 0.005000\n",
      "Epoch 1/200 (full-batch) Loss: 1.0848 Train Acc: 0.426 Test Acc: 0.465 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.6150 Train Acc: 0.738 Test Acc: 0.676 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.5318 Train Acc: 0.782 Test Acc: 0.662 LR: 0.005000\n",
      "Epoch 1/200 (full-batch) Loss: 1.1015 Train Acc: 0.337 Test Acc: 0.346 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.5938 Train Acc: 0.751 Test Acc: 0.700 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.5100 Train Acc: 0.791 Test Acc: 0.678 LR: 0.005000\n",
      "Epoch 1/200 (full-batch) Loss: 1.0729 Train Acc: 0.446 Test Acc: 0.455 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.6014 Train Acc: 0.746 Test Acc: 0.707 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.4625 Train Acc: 0.809 Test Acc: 0.689 LR: 0.005000\n",
      "Epoch 1/200 (full-batch) Loss: 1.0910 Train Acc: 0.401 Test Acc: 0.417 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.4605 Train Acc: 0.811 Test Acc: 0.701 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.2751 Train Acc: 0.899 Test Acc: 0.684 LR: 0.005000\n",
      "Epoch 1/200 (full-batch) Loss: 1.0916 Train Acc: 0.371 Test Acc: 0.374 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.5417 Train Acc: 0.776 Test Acc: 0.717 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.4788 Train Acc: 0.803 Test Acc: 0.706 LR: 0.005000\n"
     ]
    },
    {
     "data": {
      "image/png": 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eeGOy7KabbkIIwZ/92Z/t12NRLDxeuJEKiiOaU089lVNPPRWIfU8/+MEP8i//8i/ccMMN3HDDDYmLwGmnnTbn+g0/uqGhIYBdBpuMjY0hpZzTZ7Svrw9g1tR7e3t709+NAKxqtdrUvqenp6mdYRiz1v3nf/5nrrvuOq666ir+7u/+jo6ODnRd52//9m/nFADz+ba+973v5aUvfSl33nknF110ETfeeCNnnnlmkxvAruju7ubtb387b3/724HYl+3iiy/mve99L2984xv3aBvz9W1PGBsbIwzD5x0YtDfM7G/jurr88svnXWd0dJRMJrPL7fb39yfX73Rmuo3szf6uvfZabrzxRj74wQ9y3nnn0draiqZpXHnllcl1B/G1t3z58lnbmXktPh/mOs+lUolzzz0Xx3H45Cc/yZo1a0in02zZsoU//uM/burjfMy8NyC+t6avu3PnTn7961/zV3/1V3vc34GBAX70ox/N+ghs0BCtb3nLWwiCgH//93/nda97HVEUcdppp/HJT36Sl7/85Xu8v+k8++yz3HPPPbzuda9DSpmktbv88sv50pe+xC233JL4kg4NDe32+h8aGmoS/PuDdDpNPp9vWhZFERdddBHbt2/nb//2bzn++OPJZDJEUcQZZ5yRnJO9uW+vueYarrzySp5++mlWrFjBv//7v3P55Zfv12tTsTBRYlZxxGOaJh/96Ef5l3/5Fx5//HEAOjo6gDiiuWEpmIuGpWDr1q3ztmmIgrn8zRpBXY397SmNl/LOnTtZtGhRsjwIglnC+Ktf/Srnn38+n//855uWzxewM1++zAsvvJDjjjuOz372s2SzWR566CG++tWv7lW/p/OSl7yEiy66iB/84AcMDg7S1dW123Xm6lvDIu66btPymePQ1taGruu7PFe7w3GcWfuBWKzMdQ5n9rfR5t/+7d/mjYTv7u7e5/7NZG/299WvfpW3vvWtic9mg+Hh4aY0ae3t7ezcuXPWduZatq/MdZ5/8YtfsH37dn75y18m1lhgv+ck/v73v08mk9krcdnR0cEJJ5zA//2//3fO3xsfrUDyQVcul7nnnnv46Ec/yqWXXsq6det2+ayZj1tuuQUpJd/5znf4zne+M+v3L3/5y3zyk59E13U6Ozt3e/3vSZv57rn5LM1znc/HH3+cRx99lFtvvZUrrrgiWd4IkmywN/ftm970Jj74wQ9y4403csYZZ7Bz507+9//+37tdT3Hko8Ss4ohix44dc1p9GhbKxkvnFa94BYZh8Nxzz+0yCGTNmjWsXLmSW265hWuvvXbOFFaZTIYXv/jFfO973+PTn/50kkoqiiK++tWv0t/fv9dT543I+dtuu41TTjklWf6tb31rVvS0EGJWvx577DHuu+8+Fi9evFf7veaaa7jqqquYmJigu7s7ibzeFQMDA0kKpumEYcgzzzxDOp1OxNJMC/Se0N3djeM4TcUXAH74wx82/Z1KpTjvvPP49re/zf/9v/933g+IXfVh2bJls/azbt06nn766T36IDn77LMpFAo8+eST+zVgan/sb67r5Mc//jHbtm1j1apVybILLriAG264gUcffbTJ1eBrX/va/u38HP0DZvXxC1/4wn7dz3e/+10uvfTSvUpHd+mll3L77bezcuVKWltb92idTCbDxRdfjOd5/NEf/RFPPPEES5cu3at7IAxDvvzlL7Ny5Uq++MUvzvr9v/7rv/inf/on7rjjDi699FIuvvhivvKVr/D0009z1FFHzbnNiy++mI985CP84he/mNPVA0jy3z722GO84hWvSJb/53/+52773GBPz+ee3rcQi+w/+7M/47Of/Sz33nsvJ510EmefffYe90lx5KLErOKI4hWveAX9/f1cdtllrF27liiKeOSRR/inf/onstls4nO4bNkyPvGJT/A3f/M3rF+/nle+8pW0trYyMDDAb3/7WzKZTBKRe+ONN3LZZZdxxhln8P73v58lS5awefNmfvrTn3LbbbcBcP311/Pyl7+cCy64gL/8y7/Esiw+97nP8fjjj/P1r399r6sHHX300bz5zW/mM5/5DKZp8rKXvYzHH3+cT3/607Om8y699FL+7u/+jo9+9KOcd955PP3003ziE59g+fLls4Tv7njzm9/Mhz/8Ye655x7+z//5P7OSyc/FV77yFb7whS/wpje9idNOO42Wlha2bt3KF7/4RZ544gk+8pGPJNs5/vjjAfj//r//jyuuuALTNDnqqKPI5XLzbl8IwZvf/GZuueUWVq5cyYknnshvf/vbOcXVP//zP3POOefw4he/mA996EOsWrWKgYEB/vM//5MvfOEL5HK5pHrS//t//49cLofjOCxfvpz29nbe8pa38OY3v5mrr76a173udWzatIkbbrhhj31ws9ks//Zv/8YVV1zB6Ogol19+OV1dXQwNDfHoo48yNDQ0y4L+fNib/V166aXceuutrF27lhNOOIHf/e53fOpTn5o1vfu+972PW265hUsuuYRPfvKTdHd3c9tttyVpmw4UZ511Fq2trVx11VV89KMfxTRNbrvtNh599NH9to+RkRHuvvtuvvGNb+zVep/4xCe48847Oeuss7jmmms46qijqNVqbNy4kdtvv52bbrqJ/v5+3vWud5FKpTj77LPp7e1l586dXH/99bS0tCQuTbu6/mZyxx13sH37dv7xH/+xKTVcg8ZMys0338yll16aZAJ4yUtewl//9V9z/PHHMz4+zk9+8hOuvfZa1q5dy/ve9z6++c1v8prXvIYPfehDnH766VSrVe6++24uvfRSLrjgAnp6enjZy17G9ddfT2trK0uXLuXnP/853/ve9/Z4zNauXcvKlSv50Ic+hJSStrY2fvSjH3HnnXfOarsn922Dq6++mhtuuIHf/e53cwp8xQuUQxp+plDsZ775zW/KN73pTXL16tUym81K0zTlkiVL5Fve8pY5I3x/8IMfyAsuuEDm83lp27ZcunSpvPzyy+XPfvazpnb33XefvPjii2VLS4u0bVuuXLlyVqaBX/3qV/LCCy+UmUxGplIpecYZZ8gf/ehHTW0a2QweeOCBpuVzRQ+7riuvu+462dXVJR3HkWeccYa87777ZkXcu64r//Iv/1IuWrRIOo4jTz75ZPmDH/xAXnHFFXLp0qVJu0Yk8qc+9aldjuHb3vY2aRiG3Lp16y7bNXjyySflddddJ0899VTZ2dkpDcOQra2t8rzzzpNf+cpXZrX/8Ic/LPv6+qSmaU3HPDOCfDoTExPyyiuvlN3d3TKTycjLLrtMbty4cVY2g0Z/Xv/618v29nZpWZZcsmSJfNvb3iZrtVrS5jOf+Yxcvny51HW9KTo7iiJ5ww03yBUrVkjHceSpp54qf/GLX8ybzeDb3/72nP29++675SWXXCLb2tqkaZpy0aJF8pJLLpm3fYPdnaP5skHsyf7GxsbkO9/5TtnV1SXT6bQ855xz5K9+9atZx9YYw5e//OXScRzZ1tYm3/nOd8of/vCH+y2bwbHHHjtn+3vvvVeeeeaZMp1Oy87OTnnllVfKhx56aFYE/XzZDOa6fqYf3xe/+EWZTqdluVzeZb/n2tbQ0JC85ppr5PLly6VpmrKtrU2ecsop8m/+5m9kqVSSUkr55S9/WV5wwQWyu7tbWpYl+/r65Bve8Ab52GOPNW1rvutvJn/0R38kLcvaZZT/n/zJn0jDMOTOnTullFJu2bJFvuMd75A9PT3SNM2kDwMDA8k6Y2Nj8r3vfa9csmSJNE1TdnV1yUsuuUQ+9dRTSZsdO3bIyy+/XLa1tcmWlhb55je/WT744INzZjOYeY4bNK6jXC4nW1tb5etf/3q5efPmfb5vG5x//vmyra1NViqVecdF8cJCSFkPXVYoFC94GjkdzznnHL71rW8d6u4oFPuVV73qVaRSKb773e8e6q4o9pHBwUGWLl3Ke97zHlXVUZGg3AwUCgVDQ0M8/fTTfOlLX2JgYIAPfehDh7pLCsV+5/bbbz/UXVDsI1u3bmX9+vV86lOfQtO0WWnqFC9sVJ5ZhULBj3/8Y84991zuuOMOPve5z+1xOi6FQqE4GHzxi1/k/PPP54knnuC2225ryvKiUCg3A4VCoVAoFArFguWQWmbvueceLrvsMvr6+hBC8IMf/GC369x9992ccsopOI7DihUruOmmmw58RxUKhUKhUCgUhyWHVMyWy2VOPPFEPvvZz+5R+w0bNvCqV72Kc889l4cffpi//uu/5pprrlHO/AqFQqFQKBQvUA4bNwMhBN///vf5oz/6o3nbfPCDH+Q///M/m0p0XnXVVTz66KPcd999B6GXCoVCoVAoFIrDiQWVzeC+++7joosualr2ile8gptvvhnf9+esm+26blNJviiKGB0dpb29fa8T2SsUCoVCoVAoDjxSSorFIn19fbMqTM5kQYnZnTt3zqpr3t3dTRAEDA8Pz1nG9Prrr08qOSkUCoVCoVAoFg5btmyZValwJgtKzAKzrKkNL4n5rKwf/vCHufbaa5O/JyYmWLJkCRs2bNhlCc39ie/73HXXXVxwwQVzWo9fqKhxmR81NvOjxmZ+1NjMjxqbuVHjMj9qbObnYIxNsVhk+fLle6TVFpSY7enpYefOnU3LBgcHMQxjzrrWALZtY9v2rOVtbW2zatwfKHzfJ51O097erm6IaahxmR81NvOjxmZ+1NjMjxqbuVHjMj9qbObnYIxNY7t74hK6oIomnHnmmdx5551Ny/77v/+bU089VV1oCoVCoVAoFC9ADqmYLZVKPPLIIzzyyCNAnHrrkUceYfPmzUDsIvDWt741aX/VVVexadMmrr32Wv7whz9wyy23cPPNN/OXf/mXh6L7CoVCoVAoFIpDzCF1M3jwwQe54IILkr8bvq1XXHEFt956Kzt27EiELcDy5cu5/fbbef/738+NN95IX18f//qv/8rrXve6g953hUKhUCgUCsWh55CK2fPPP59dpbm99dZbZy0777zzeOihhw5grxQKhUKhUCgUC4UF5TOrUCgUCoVCoVBMR4lZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUCsWCRYlZhUKhUCgUihc4UkZIKZO/I3cCd+d9hJWdh7BXe4YSswqFQqFQKBRHKDIKdrvMn3iG6ubbiaoDybKwOkhYHSQobz3gfXy+KDGrUCgUCoVCsYCRMiLyS0ResWm5O/Q7alv/u0m8+pPrqW6+g8gvT60fVCEKCSY3JMv0TC9m27FoVuGA9//5YhzqDigUCoVCoVAo9h1/7CmCiWfQM73YXacnyyN3FBn6RLVh9HQPAGFpC8iIsLQFrXUtAGbhKIzsEoSZTdbVzCxay6qDeyD7iBKzCoVCoVAoFAuEoLSVsLoTI7cC3WkDwMguIiiun9XWbFmDZrehWbmpZa3HggDNbk+WCd1G6PaB7/wBQolZhUKhUCgUisMMKSXuzl8jNBOr40UI3QIgrO4kLG1DM3OJmNWsFlJLXoUQzd6jRm7prO3qqY4D3/mDjBKzCoVCoVAoFAcRKaNZwlNKCWENYaTiBVFAVBuJ/39aWyPTHwvZVHfT+jO390JCiVmFQqFQKBSKg4SUktqWn4BmYXWchO50EPklvKGHkJGPs+iCWJgKDbvrdGTkIbQpuaanexL/V0WMErMKhUKhUCgUBwgpJdIvJX6rQgiEkSHyxhFa7KcqNBsZVJAyRHoTCLsVoenomd5D2fUFgxKzCoVCoVAoFAcAKSP80d8TFDdj95yJ7sT+qnbX6UTexJTA1U2szlPRzMyUm4Fij3nhOlgoFAqFQqFQ7CeklPhjT+EO3I8M3anlQQ2IkH4pWSaM1CxXAT3VoYTsPqIsswqFQqFQKBR7gQw9wtowQneSjAJCCILyNqRfIvIm0FNdCKFhdZ5C5I6ip7oOca+PXJSYVSgUCoVCodgFkTcBmoVWt5zKoII3+ADCzOAseilCCADM/AokEmFMFR8QmqGE7AFGiVmFQqFQKBSKeQgmnsEvPYNZOBqtsCZeqFloThtCGBB5UC84YOSXH8KevnBRYlahUCgUCoViGlJGyf8LI4WUICMvWaaZaZzecw9F1xRzoMSsQqFQKBRHMGF1EKE7CDM7b2J9KSOC4kb07JIXdPL9oLwdf+QxjNwyyK4EQEsvwnTy6E77rldWHDJeuFesQqFQKBRHOFJKvKHfUdt2V+z3OQ/h+B/whh/FG/pdXInqBYIMfcJGlS1AehPI0CVyR5NlQgglZA9zlGVWoVAoFIojFRkgzBwwiWbmk8WRXwIEUsYyQNhtyOpW9ExfEsz0QiCYfBZ/fB1GfgVW+/EYueVoqW40K0cQHureKfYUJWYVCoVCoTjCkKGH0C2EZuL0noOUMhGpUkZ4gw8QeZPonWcAoKd7sTNdCMNBygjpTaLZhUN4BAcOKSVhZQd6uhcpQxACrW55FYaDbjhxw9A/hL1U7A1KzCoUCoVCcQQRVgbwhh7EbDsOI7cUoMnaKoMKIEBozSmkDAcZVKnt/B9k6JLqfzmiHqW/0Im8SYSZBQT+yKMExU2YhTVYbcdh5FaoYgULHCVmFQqFQqE4jJBRgAzKaFbLPq3vTz6LjAIib3LO3zUzi7Po/LjUqh80/6g7CM2EKCDyiuiphS9mo6CCu/PXCCOD3XU6wsqDEHX3izgzgWJho8SsQqFQKBQHEBmFBMX16OlFuxVOUVDFG7gPGXrYfeclSfqbticlwfhT6OneOV0B7O4zCSs7d5uoXwhtln+sEAKr89Q4+4F2ZEgE6VdASiACzcTMr0BPdaGZ2d2uq1gYqGwGCoVCoVDsJTLadXRQUNpSD7ICb+QR/NEn8cef3GV7GQWxVbT+apZhbe629aAld+A3yMhHRiEyqCa/C6FhZPr2WYxqZnZBC9nIL+FPrkeGLgB6qgO77zysztMRmg6ghOwRxsK9WhUKhUKhOAQExU14I49i5Fdith49Ky9rWB3CG3oIzcph974EM7+SqDaCnuqec3thbTRub7dg95yL1XU6QojEj3N68BaAkVtGWN6OkV9B5I7jDT+EZrVgdZ4Cwtiv2Qhigb2wpEJQ3Egw8RyamU/cJDQzc4h7pTiQKMusQqFQKBR7gZ7uBSkJKzthDgut0Cw0K4dmt4LQ0ewCTv/LMLKLYxeB0lYidyxprxmx64Ge6kFoOpqZbgpICsafxh24P6lKJTQTu/dcjOziuDpVWCPyJnAH7sfdfjdhbZTnS+SXqW2/h9q2Xxz2eWellPgTzySWWM3Mo1kt+GNPHvZ9V+wfFtbnlkKhUCgUBxBv5PdoZg49tzSxcEbuGGFlALN1LQBCt7A6T0azCgjdBBrlTwVCiNjC2vuSuG19Gw3rbTDxLP7Yk2h2Abv3JfHvuo3ddTpaumdWfxqVuWToElUG0DO9TdvTzCx291loZo7qtp9D4qrw/BC6Q+RPQhQi/RLCyj3vbR4owvJW/NEnCSY3xB8NuSUYuSWzLNqKIxclZhUKhULxgqYhesLaKMHkehDg2AWEXUAGNWo7/gdkhOZ0oKc6ADCyi5u2EUxuICxvxeo8LbaszjM1b2T7CSafQ0/3AJKGAG6I1JkIoWH3nE1Q2gLzCDM91QlAqv9lhNUhtP0gPIWmY3eehrDycwahHUoiv4I39FuM/CqMbD/CyKBZOfTM4iaXDyVkXzgoMatQKBSKg0rkTRD5JYzMokPdlSR7gJFfiZ5djNl2HDKsJlkChOFg5JbGvqPzZCKQUhKWNiGDGpE7usuMBcJI4fS/bK/8UDUrh9V2zG7bCd3GyPbv8XZ3h56e28f3UOOPPELkTkBduOpOG1rf+Ye2U4pDihKzCoVCoThoRH4Zd+e9mK1HTy1zJwgr2xFGOknyvz9p+JrORTC5nsgrEpS2YOSWYrasnNXGbDt+l1Y+IQR2zzkExU1Iv7Tb6e2FFlB1sAmKmwkr2zHbjpsz64DZdjxy+CE0c8oCPTMIT/HCQt1RCoVCoThohOVtyNAj8suJ6Iv8SfzxdeipzgMiZr2B+wnCAFPEqa5CdwwhDISZxSwchdBtNKsw7/p7Ml0tdAuzsHp/dfmwIawOE5a3oKW6MTJ9B2Wf/sQzSL8EQsfuOm3W75qVw+k776D0RbEwUGJWoVAoFM8bKSVRbZiguBGr4+Qkn2dQ2kpY3oaeWYSR7ccsrInFY6orEYmamcXIL6+XG92XfUdIb3LOAgIy9AhrQ0RhQCQ1ZFDBG7wXKQNSSy5BaAZmy6p9Pu4jnficbkaPgoMmZp2es3GHf4fV8aK4D0GFYHI9eroH3ek4KH1QLCyUmFUoFArFHhFWdhJ5E5iFo+b4VeINP4QMaoROB0Z+OQCRN0lY2YnQHaj7c860vmp2K5bd2rTMH38mbptfttvofHfgPqLaMKnFr0TozeVXhW7hLLoQrzRAyOPIoIwwswgZqun+PUDP9CIjP05HdpAQhoPTc/bUAhkRTDxHUNyE1X7ifvULVhwZqDtZoVAoFLsl8su4gw+AjBBmDiPTN5X3VGgIoWEWjiLyivVI/RgjuxihGWhO+x7vK6zsxB97EoTAyC3ZZVsZ+fVypRD5RTTNJCxvJ6oNY3WcBMSWXz1rA4+jOZ2YuT6Vf3QP0awWrPbjD8i2pYxiq32qG6Fb87YTRgYjvwLNakkyNygU01FiVqFQKBQAU9Wk7DbM9hObfEU1M4PZupbIm0BP9yAjn9r2u9GdjkQ0Grlls7apWbm9ThWlpbox249H+uVZltaZCM3E6X9ZnIHASMfZCYYfBhmhZ/vnnZZWaZsOPcHkevzRJ9DT3djdZyBDH3fg3viDo3VtEtQlhDhgglpxZKDErEKhUChiNIvIKxL5RYyW1YgZJUDNltVTOVmrQ8igTFiVyNDdrejcG4QQmPkVTctkUAPdmjNqPS79GqfD0owURn4lQtOJ3PG47Gt2KWjzp8tS7BopozggC7Ffctg20NO9+KNPELljyCiMfZvdcWQU7FEqMoWigRKzCoVCoQBAM9OYbcegp3rQzAxRUCWYfA6z9ZgmKxnEOUjtrhcT+UUQB/ZV4g78hrAygN1zJnqqa7ftG0Kotv1uInc8TuGUUmJ2XwkmnsEfewojuxir8+R520XuBLXtvwTAbDsGs2XX2R00M4Oz+KKkKIPutO9y+wrFfKjEbAqFQrGACKtDRH5pl7lTd8d0f9GwNkLkl5K/zZbVaFYOKSXe4G8JJp7DH3lszu3o6R7MltVJ5oIDhdBif8rIm2xaHrnjVLf+PPavnQOzsBYjuxj9IEXhH6kIMxcHy+3GNSNyR5P/Dys7520npUyuwenVxeKiD4tnVVdTKHaHsswqFArFISZOazUCkY+W6pw3yl7KCHfgPpASZ/HLk6n13W8/SiyrcSnQB7HaTwDdxht8AGSI3XMW2rSMAkIIzMJavNHfY7Ssef4H+TwwCmsx246d5coQVoeQfonIK865np7unqpiFfkHuptHLHq6B33Jq3brZ6ynezFlSFjehpFvLj4howCIC0ZEtWH80ccxWlYlwlVGPmFlQGUqUOwTSswqFArFISaqDePuvBeA1JJX0ng0R36JyB2bslSFLpqZR4ZVhJ6aZ2vNyNDFHbg/Fg6ZPvzxPxC5Y3gjj2J3nR77xUYhwpztC6mnu3FSnYe8utJ85WGN3FKEmdlt6i7F82NPz78wHMyWVbPy9srIp7btF+hOJ1bnyQTFjUTeZGzJzS5GRgHVTbfH2zAz6DPStCl2TxQFSVaRFyJKzCoUCsUBREqZRNrP9ZsQAs3pwMgtIfImmqyPwcQzBMXNIDSMzCKEkcJZdP5uy6VOJ5h8jsgdwx/9PXqqC6vtBDwpMduOQRgp7J6zIfTmtQYfzi9HoVsHLZG/Yt+JaqPIoEYUVACw2k8ksFuTFG5CMzDyywgmNyK9Iigxu1eEQZXi2LNYToF07oXpoqHErEKhUBwgZFDFHXoQ6Zdw+l+WWBC94YcJq0PYXach7NY49VC92lHzBmQsbkOvaXFDyMoowB97Ej3Tj+60zdkHo7AWAD3TnwhWu+vUadvSwHCe97EeaCJ3HH/yOYRuY7Udd6i784IjKG4iKG/FyCyaMwVb5BWJ3DE0pw2hp5BBGYSOZmbiXLXTrm+hW7Ost2bb8RjZZQgrf6AP5YjD90qAxKuNkcr2vyDTzikxq1AoFAcK3UaGHjIKiNyxJBJfBlVkUCVyJ5r8VGeyu8huf3wdweQGZFBFd15cXyqRfhmpx9PvQmiYrQs/zZGMfMLSVoThIAtH448/jWZm4oT7C0CML3RkUCWqDhMZaZhDzIaVHfhjf0DP9CGMFMHEcxj5FVjtxyMMZ7fFL4TQEHbLAer9wUXKECH2PigyigLcyiCW04a+F9e0nerATrUDez5jc6ShxKxCoVDsI2FlADQzsYpKGRFWdmBkFgHxC9rqPBmhO01R20bhKIyWNWh24Xnt32xZTVQdaKq4pePj7vgFFFZjth13xLzcNLsNs7AazelEBhWCyWdAgt19xl69+BX7hp7pRRjpea9ZoTtoTjua044Q+hFRKjjOuLD3leJK4xtARqRy/Rjz+HvPhVsZIPCrGFYOnT2/puN7/Mi4z/eVQ+4M9bnPfY7ly5fjOA6nnHIKv/rVr3bZ/rbbbuPEE08knU7T29vL29/+dkZGRg5SbxUKxQuVoLQlFq91wsoA7sBv8IYfSiK1g+JGvMEHCUpbk3a63dokZCHOp6mnOp73C1/oJnbfeeiZqQhwTUQIYRBMricsb3te2z+cEJqO2XoMeqoTzcphtqzB6jhpKluB4oCiWS0YuSVo87gBGLklOL3nYOZXoGcX4yx5FVb78URBhaC8HRlUD3KPnz+18na6Wn2i0N3jdaIoIAyqhGENTd+7wETfKxEGFXg+afei8HmXapYywquNEflVInfseW3rYHFIxew3v/lN3ve+9/E3f/M3PPzww5x77rlcfPHFbN68ec72//M//8Nb3/pW3vnOd/LEE0/w7W9/mwceeIArr7zyIPdcoVAcSQTl7YS10aZlUVBJUj5F3iTe8CO4g78hrMUfz5rTjtBN9NQ0MRXWQAjC6vw5Nvc3QmhNeV596WAvvpjUssuO6DRHZuvRGLmlh7obijmIo+pjS2FUGcAbfCAuMbyAiCIf3x0HQOxFURBNM8i3H00mvwxNM/cqH3S2ZTmp7CIMK7vH67jVUYpjz+JWRyhPbGRi5EnC+oeDlJLIm9gjcett/jHe5juIasOEQZXqxHOUfv8ZvGe+iqwH7sUpBEd3s6VDwyEVs//8z//MO9/5Tq688kqOPvpoPvOZz7B48WI+//nPz9n+N7/5DcuWLeOaa65h+fLlnHPOObz73e/mwQcfPMg9VygURwoyCvBHHsPd8avE8iqjEH/4kXhZbRRhZDCyS9BT3Wh27FIgNAOn74LYJ7BuYTVbjyG15BLszlPn3d/B4nDOQqBYmER+hbC8o6nIBsT30LyCSehoVgua034Qerj/EGg4mR5qnrbXRUE0zcCwslSK25gcfYqoPnPTIAyq1MoDSBk2r6db2Kl2otCnUtxGtAe5kQM/tubKKAChAZLQLwPgb/whtcc+g/fct5L20pskGHqQYPTxqWVSEg4/TDD4WxBGLFqjkNCbjH3+vUkCv4y3816Cw/Sj5JA5tXiex+9+9zs+9KEPNS2/6KKLuPfee+dc56yzzuJv/uZvuP3227n44osZHBzkO9/5Dpdccsm8+3FdF9edmiKYnIwryPi+j+8fnCTajf0crP0tFNS4zI8am/mZb2zmS1clI59g9FFk6GF2nTFL5MnQQ1rtSG+M0CgQ1bcbYhJJCKVBFEaIlmNARgTB9BeTAXOdo3DfpwmfD+q6mR81NnOzN+PiDz9GWNmOUTgWI79iavno74kq29Dza5LlQXEDUXUnenYZetfZSCkPq7H33QkCv1QPtpo7Z7PQ80yWDXzfT8S6ECIWf36JKPKx5skiIqXEc4uEgUe1PIrlTAV6Via3EfglfL+GM0dqufLEJsKgSiQFdqpz3mOQfhk9EohUH+gOup5BN9vQDDvuc+EEoqGHEXZXMvZRcTv+hh8hrDxRdjVCaPGxdZ2DkJJQyyClxEgvQl/zTjSrhdAsUJncSDDyFPbiixHT9NOBPKd7s20hn69zxT6yfft2Fi1axK9//WvOOuusZPnf//3f8+Uvf5mnn356zvW+853v8Pa3v51arUYQBLz61a/mO9/5DqY5t2/Kxz72MT7+8Y/PWv61r32NdFrV6lYojgwi+qwNVKIc40HXjN8ki+11CCRb3VVEGOh4tJkDjAXdBNJKtjF9siqjTeBKh0DaKBQKyOsjpPUixaBAOSrUl0q6zC04WoUhv59qFE+Rtxo7yenjTATtTITzC7JDRVvexzIlxbJOuaYzFeg1+4PYsSKyqYDa5CiR7xHaPbQWQEqYGCnieFvwtRb89FJSdkTN1XB9DcuMQIIXTA/QktiWJJcKGSsahJFAE5JsOsT14vUcK8KxI8pVDdxJQOLrhfo2JI4lseU4ufFf4updDDnnxD2XHl21eyhax2C2dFEs64iwSiRMpIg1kiZrdNR+Q03vpGgehZzhQmGZEZ4/M6BM0pINsc2IkQmTMDo4wWaVSoU3velNTExMkM/vOmXbIQ83nGlJ2VUy8CeffJJrrrmGj3zkI7ziFa9gx44dfOADH+Cqq67i5ptvnnOdD3/4w1x77bXJ35OTkyxevJiLLrpot4Ozv/B9nzvvvJOXv/zl84ruFyJqXObnhTI2M+/3sLIdohDN6UDMYy2Za2ykjAhGHyUsb8XsPKXZjxUIS8eBZnKs04XQDMLyNoKxJxBWC1bXi+fazYLkhXLd7AtqbOZmf4yLlJKoOsCxqe4pX1l3FOkXEXY7mrnnPqAHmsYzJ/DL+O44RmkDAghSfUjdwsksioM6hx+EzHJ+8XjA6aefRORPoD13G6aZxlx7ObWgjK476OUtBFvWE6VMRP8J+F4xtvbuvBstvwqt/UWJm8L0513Djhj6JXxvAt+dQDccMi1TZYCj2jD+M18m9IqEfS8n03167IIwuQFR2oQ5uhzN6cBce3F8TNt/QTjQhh9tJ1p6OoZdIJ1bQhR6gJgWkPbHc45NGFQpT6xH000yLavn1GcQ67aDcT81ZtL3hEMmZjs6OtB1nZ07mwMlBgcH6e6eOzr1+uuv5+yzz+YDH/gAACeccAKZTIZzzz2XT37yk/T29s5ax7ZtbHu2ZcU0zYP+QDsU+1wIqHGZnyNhbGQUIIMqwkglvqVhdRB/9AmElcOa5l8alp4j8ooYXadhmLv5Etckhj7lzybsPIa5GjPbi/QnibxJ9OwShBCYrc0J2oWhIzWB3fUitAU+vnNxJFw3Bwo1NnOzt+MSV7arTWXqsGZUnjK78UaHCUd+i95yFEZ+2fPu43QxBeBVBoiqI1jpTrRp0/He1jsJqiOkll2KMLNIKaltv5toxy8x247HXPE6TLNAKl2guvk7RN4kQe9L4wImskZY2Uo4/iSm5gCdpLM9yDALHcchkJimgZVbiRCCUAvwW1YTGVkMIUnnetAjj6C0HlneiNG+Fs1sJQo9ysUtpLOLklRybnUUtxJnPklnO9F0u+kcSK0N0t2ESISVIXSHMUvPYacXY+TOgPa1sT+/IdA0E2Pxhfgiws6voiYF6dwifG+cWnknltOK7TQHhEoZUSvvBEScp1aTGKaNptn4tRE0TUNoAstuRdNNwsBF060mkXsg76e92e4hE7OWZXHKKadw55138trXvjZZfuedd/Ka17xmznUqlQqG0dxlXZ/64lEoFIcOKSXSLyEjD31asIe74x4ir4jdc2ZSNAAEkTeJkM2BEZrTAbrdtL4MaqAZs9JYBeNPEvqjWG0noGd6MQpH1f3ZIrzhR4i8SczQxSysmdVX3elEX3TBvNZfheJQEwcHxdO9UxXfQmrVIWQUYjmte5XDdH/jjz5OMLme1JJXIeZJQRW5Y/H92/DLpHk2VsoozgCip5Ll4eR6wtEnEKkuguxSTCuHbqRxn/gs0h3HPvZqhNNO4Jcpb/kZcuBe6D0HZ/UbE8unu+Ne/OoAFNaS7jwJrzZCrTIE1VH0oNzUR6P9BGToYhRWgmZjWBnMtmPASGG2HA3P/R5NMzDtTjjqilnHqOeWkV7ZSa28EyfTi2GmkZGPtuRiZFBOiqJUyzsI/TKBX0bT7bjqn92CWxnAtAs4me5Z/vxCt7FW/ynCHQPNIdr0ffziJqzO07CWvoqiNxkHqJpZLKeA0EysJRfHx1Ufi/g6kkgZzpoJi0IPzx1HRgGWU8C0W8hbOSrFgXqasCK6bgAC0y5QnNiCk27HSR1+5YYPqZvBtddey1ve8hZOPfVUzjzzTP7f//t/bN68mauuugqIXQS2bdvGf/zHfwBw2WWX8a53vYvPf/7ziZvB+973Pk4//XT6+lR9boXiYCFlhPRLCN2Oy60CUXUAd+B+NCuHvujCpK0w0oigipwWmatZLdjdZyBmTD9a7Sc0/R15k7gDv4nLYXadNu1hL4ncEbTIhfr+pz+k9exi5OT6eVM3qYpRigNNGNSQMkI3UvO6zk1vG4Uepj01G+FWR2KBlO7GydRnKwW4lUGE0LHT++6HGkUBtfIOLKeDfZEBMgqJvPE4yGn00absHd62u4j8ElbvS7C7zyDyxonGnqL27NfQ8iuJus9BN9OYVp7aIzcgQxfr2KvByKDrNrI2RDD8EFF6EYGWIvCKZAurIApj8VvPAKAbaQynE5nqQrNy+F6RWmkH6fxStM5TENXR5KPYslupFdZgdZyI7rRTqwwm1kaz/6JZx5duOwbZenQ92PP3ux0Pw8yQaVmRnGehmRhdpze1cdLdyNAn8Ir1YDCB0HRybUc1iVgZ1Igq29HrgXRCt7DquZTDnnPw3XH0tmPiIC0zCwgMMzOrT42+6EaaTMsKAr+G7xYx7dy03xwy+aXUKoNJEJwQGulcN7WKgYwyRGGVWnkHtfIgaDbWXqQNO5gcUjH7v/7X/2JkZIRPfOIT7Nixg+OOO47bb7+dpUvjF9COHTuacs6+7W1vo1gs8tnPfpbrrruOQqHAhRdeyD/+4z8eqkNQKI5opIyI3HFkWE2qWgF4gw8QVnZitZ+YTB9qVgE0HTQLKaPkAW11ngbT8k5C/IDek2T3MvKRoRunAgo9SESowOq9AD0YS6pvJdsWGmbLKozc8r1OqaNQ7CtR5KNpUxbKWnknvjeJaeXJtCwD4vupWtqOlBGZ/FR512ppO4FfIltYlVhbG4U4pqdnEkLHTnWgm2l03WJfqZa24W37BZ6RId17QbI8HHuSYPRxtFQ3Zt95yfJg8LdElZ2Y/S8HzcKtDiM0G4buByKoi9nAL1PefAf4FYyOkzDsFjS7HWlmkaGLROJWhxA1A6ujBXQLQhd3YgO+7pDK9mNml2AuugCcLiQ6TjqezTFX/QmV8iCRkUUjFmvZJS9DLL0IKSXlyY2EYY1aZYDMsktxghpa40NX08l3HI8QGpXiVrzaKFHokc7Nn4d5byvn7ap9OLkBf/N/YbWsxlr8yhnrTROyUYC3/ltExY2Yy16D0X5ikzVVb1mFOPZ/J76vqexs10opJb5XxjAdNM2oW2fBrY6BEJjuGELTk2M3zAzZluVNz2whNFKZ+GPJ90oEE+vRdJ1c64rDNuXfIQ8Au/rqq7n66qvn/O3WW2+dtew973kP73nPew5wrxSKFx6hO0ZUG0az2xKLhgxruDt+BUJDT/dOPezMDEIzkNPcBIThxFOOM6fKnoeg1J127O4Xo1kFxIyXtxBaUxnXmSghqzhYSBlSHHsG3UiRzvWjaSap7CKCsRL6NKtZXFkpTjofhj1NglTTLWTkE048g/QmsFuPx053IRDI0COceBo9t5xUNp6FlH6ZsLgBkV+J75Vw0p2EkxviFFnZZWj1ghlRdQj3qS+CbuMc/774Y8/KE/hFtIl1ROm+puMIx/4AM/KbBoO/JaqNoLefgIuBVxtFqwxi6A7SnarAKYQOLWsxzTS63YqUEeXJTZhON86xf4400kivRFAvRuIcczVoJrXKAJo7GU/PZ3rR0rFIM6YJOS+ShIBXG0sE/5T4EqSyfXjVEZz6R/LMEseNtoaZJQrdplRZBxwZxsUGoqeQi16KmPbRE449icgsQrNaAIGwCqBZCKeLankYhCCVjp/Hbm2CwK+SznbPK56r5SG82gS2UyCVjQWpphvYTgFhGNSKW+LzVBezZc8lDH2i6iCO04Kdamv2ibWy5NuOJoq8w1bIwmEgZhUKxcFFSokMyki/hJbqSh5QYXEzQXEjRn5FImaF7iCMFJqZi19wdUuHWViL2XrsrAfqgXjYTfnZxoSlzWgE87RWKKaIoqBejWr316XvTuB7JTTdTKyB0PArnMBOdSTXe+BXcKtDpHOLEUIj8Cvx1HvoJdWiNN0k334sUymf4vvDTnViWBmE0Kk9dTNaZhGZ7rMRVo6oOkTtma8B4KR60LL9RNVB3Ke/DJGHfmIc/Cz9ErXf/ysydHF7X4awWzCtLOHo7+Ok9osuSMSssPLI0ENIOTVb4rSiL3klwcB9kFsGxIJUz6/CWvJKhNUs9PS2YxFhgOfXMDN9hH4Fq/tUjJ4XJz6hEAvI3Oo/QTcchNBwqyMEXpFQVDDbjkLXDFJmFjLxR2jD3SeV7UtE+nSmP1/sdCdhUJ3T9xNA1+05tzETyylgOYXdttuf6C2rsJa9Br316CYh6xa34a37GrqRwjnmz9BSnZhLL8PoORsfA7c0GPfZyiI0PRa3MsI301jO3AGylp3Fd4tNH/OG4WBkHWQUIjK9CE3H9yv4tUnGI5Mdo1vJ4rJIM7BT8UzXs8M7yNkpOrN5NN1EaAYVzyVtHZ6pCpWYVSiOYKKgQlQbRWhm07R+bdtdICOc/pcmfqt6ugcZeU0vJyE0Uotn+5TNDMY6WIS1UfzRR+m1Nsa13lVU+kFBRiHSmwAZNkWNR7VhpDeBsNuS60ZGAdHEutinr+3Yg9K/wC8ThR6GlSfY+H3Qbcwll8ZT+pFPpmX5rHWi0ENCYh0VWt3iqBlNYrZa3onvjqPpFpbdAoBXG8V3J6gKg3RuEYaZIdd2FDLy4xRJI48hNAOtcHQiIIOh3xGOPYnVeQp6fXrY6Dodb/33EELH7H85WqoTo+tUZHV4Sow6HbFveroH6vedMLNo+eVIv4RpOgi7EFfayi3FkBHa9A9AzcI57j2zrK16YQ16YU1TYnphpDC6XkwUeowP/R7dSJFtWY7ZdwG1yhBueQdhdZhsa5y2KYoCKsWtOJmexBI6PSjNctqIIh/DSKM9z2eGphlkCyt23/AwYC6xbXSc1PR3FAVUizuQRguanUU4HUAs4IXTjiUlAgFCoBuxgMzkegn8Mqadq28jjAO1Qp/IyGDpJpaZJte6dM7xFpqOleogiAIqE1uQUUhrqpVBK4eh55osviW3xlB5ElPXaUvnkMATA1s4tnvxYSlolZhVKBYoMx+Y3shjRN4EVscpaPUXSlQZxBt5FD3dnYhZIUQ8pSXDphfc9DaHK0K30Ow2fLktsRIrppChhxWOEJW3QWFZsjwYfpioOoDeegx6NvbVjNwx/A0/AM3EXvPmpK238UeEo49h9r8Mo56DV/qT1B7/N4Rmkjr5r6e2O3AfwdBDmH3nozV8LEMX97lvA6C3HpNco/7Oe4nGn8Jc/Eq0Oaoe7fMxS0mtPEDgl7BlABPPYnSeHO9ft/GCKrXyIIbdXE61UtyG75fI5BZjOQUMM0Mqu4jIL+I+9SWkDDAXvQwZSXQjTTT+NF5lG3r7CehmC6bdgiEDao99BuG0Y695S+wDCgTbf4n0xnFOuBbqH4vh+FOEk+vR8ivQ6/0WRgY9vxKje6pwkLn4Vc3+5ULDWv1mhF1osjBby/8YNAubaRbM9hPjf9OIxdGUX7mM4gCqMKzh1caQzP4glDIitihLoihA13R0w8a08hhWPtlftbQd35skigKyhZVzzNQIUpn5XYH2lSgKCQMXw7B36U4UBm7dV1QjnZ1ZTOXAIGVEcXwzhpkmlW6f1b8o9InGn0J646TaTsDL9JJKt805djOtr6aVxrSmZbCQkmppkOFKkaEoTU++leVt3WiaQSQlZa+GY5gYmh5neQh8NowOEErJmtZuvNoEqXQbJ6XbCWWEPq2vPflWxipFWlPx9asJQWcmT9k/PK2zSswqFAuMsDZMML4uFiFdpyXLo9owkVdE+kVo+JRZeTSnve6PNYXde+5eBzgcDmhmFqv7bAb9icPaf2s6SQS20JM+xxHL20AzE3EJEIw+gfTG0POr0eofFlFthGDnr0EzsJa8KmnrbfoR0cRzGP0vxWg7Pt6uO0JP9WcEG0awX/SBpG048Szh2JNodhs09iclYWlzko1iWo/j3MDTA480M/ZZ1qymjyhh5tHS3Yjp0dSagZ5bSqNaUaOSUFTaQljaghh6ECvz6l2PWVDFffpWtPwKrMWvmLuNV0RYcWS2abcQ+CVkdRBRDxoUQqDpdpx5Y/038NP9ZC2fqDaGNNqJRh6BiXVoa9+ebNNOtSHtHJ6RJhp/CjSDbD7On+o++3WC8XVomX7s/ErsVAdRbYSaN4HwJ5F+GWFmkFGI1rKaqLSZsLgpsU6biy9Gz69DKxwVj50Q6PkVSdR6MtZz3JfaHCVTZ5+3PcP3JqgUt05tR4uj2MPQRUZlDDOLpttkCyvrmRjia9a08phWs7hKZfuQUUAq23dQnydh4FKe3IbQDPKFpQhNIwhq1MrDpDJd6Eb8USGJS8o+X6vw3uB7ZaLQJ6AConNWGdzS4GNEz92GaWVxMouw8subCimUJrYyGBjknBTt6Ty6Nv9zTmg6pp3FjnT0mk+LM3Uf1nyPx3duxtA0Tu1flWx/slZBAh46mfzUR6UhmkV3T65AT67QtGxZ28H5INgXlJhVKA5jgsn1RMEYRssq9Po0FBLC6tCstFZG4SiQskm46k4beu85s7a7EIXsoUBGYZwHUwiEMWURCcefRnrjaC1HodkFAKLyNvxtP0eYeazlf5S09db9B2FxE/aq/4VeWBu3rQ3irvsqmtOOftxfTG135BHCiWcRy7KJmCXyCIYfRlg5mCZmZVAl8iYgqCXLhG4TiAzMKDihtx6N5rQh0lPRz8LKYa98PWjNljlz0YUYfech9KkcvMLMknrRh2eNj9l3XlPUe6MP9lFvi48nqFKe2Iid7sLsfylath+j+8xZ25lJVB3EnXgWbXIjeudp6E4bYeihaWY8ve2VqP3+X9Ayi7BXvwXLacMwM+idJxD2noVXGULKCNNuQfPG8LwJvNIOemRI5dHfYp50HWbxWfSgBKVNSKcVf9vPMXvOiXN2Lns1wVAvWmYq2t3oOBkt1Y02Ld2bsFpwjr4SYeYSQS80Pcn1OR3NaUNzztjtse8rURTgVUcw7ZZZwU9N7cL4I8W08miGA9Stye44gTeKaRfI5JfEmQCmvkWQUhIGLrphJ8+PgzX1H1tiq5j1tFBCCIRmYNlZRF3sudUxAr9KrTKciDRdM3EyHU1iNooCqqVBnHRHInr3J6aVJduyKP6IigKGxzZTDSK6O5ZhEiHtVmg/GSPdipZb3vQsdqtjVN0SxXKZ4XKO1lQWndli1gsCaoFH3kmTyfWSykr6aX6uB1GIbRiY9YwGAI5psbK9l5RpHZbW1eeDErMKxSGgkXan4XsaeUW84YcBiTNNHEhvlLA2iOZ0JGJWs1sx249Hd5rzTE5PnaWIkUE1ttKZmSTwIqqNEI7/AWGkMTpOTtp6G35AVNmOufQy9GxsjYuK63Gf+Rpath9n7TuTtv7O/yEqbcVemUdaLUSRR+RXCCc3xIUfiKOuAWRdDcigmqwvNAst3YOYYTHX8itiYTTNEiesAuaiC2d9vJiLLsTsOTuOfm60tdvYnrmUk456VVNbo+044DhqlUFKI38g27Ic3XDQW4+ZNWbCzM5RnX7fqJS2E0U+gV/Gzi9B6+nYo/VEpg/ReTqBVSCQEhEFlMafQ9dt7FQ71Z33gVfGTAVxQQ0hEgHneUVqY3/A3/Yzssv/CD23FPuoK4gmNhON/gDdSBGWt2Af9TaCoQfRO0/F3/A9wsnnEGYuHlMjhdn7kqY+6YWj0OtW1aSfmoE4TO67anErvjeJ0PRdilkn042d6kAi0TQj8ZkVmoGuO5hWjjBwKU1uI53txjDTVEtbkQh8t4phpcnm998xSykJ/CqBXyGVmX19SCmplgbwvXKcsD/dhmGmyLcuY3pwnZNuR6DhpKfcSYSmz0rwXy0Px9ZTGZHNL9rvH/ZCiMRv2PfKRIFLyQvYsXU9Jy1aTq6wlDDbg2GmEmvt9slRunMFNN3EstJ0GnlCzcLUZ0s0KSUbxgYYrZRY2tpJX74NbY5jyDtpTl60clZBqfZMbr8e7+GCErMKxQEiTiweV8AxslMWHm/4EYLSprisYiMxtmYQuXEewNhfLUbLLMbI9KI5zQ9oM78wAiH2FOlNxnkOzWwi8KVfIqrsjCuCZadKZQY77qa99htk7cVgxv544fhTeBv/s26p+9OkrbvuP4gqO7HXvBU9HwcBydow/tafo2UWNYlZ6Y4RVYfAL011rDH1Frpx/kZ3nMCvoGf6MawWMDPUKgO4lUFMI4W94nUII7bQxflEQ7LL/jgO4NCM+ku7hOV04Bzz7lnjYM5htYyF1bmzljdE83QCv4ymzV0Ncap0JU3X2PNB1hPZa/NUgHLS3fi1MaxU8zR55BUJxp5Cz3Q3uVk00DQTu//leLVRLKeVwC+DDKdyr6b7YMXrCaKAyY13YNs57N5z4nRtZgYxsQ7NHSEcewIzfSF6bhm2s4hN/hbWrD0bs+5PnFRL6juPcOIZiPw5g3cWArqZQSKbouUDv0KtMgpCI5PrnZbUX5/1wWI57Zi5HqSUVEo7kXW/VBl5eLUxAr+GbrWi72dfdSlDysXtICWmncWYQ4hrugWiimlNTaPHxyKo+h5V38UxLNK53fv8pzIdSBmSynTu03mWMqJc3IGUEZadx3Zapv3WfO2YVoaWll5KY0NoRhrHjC3B2jS/160TI2ydGGG8WuaY7sUU7ByFGfsMwpBQRtiGiQRMTUcTosmtYD4W4rW8Lygxq1DsB6KgQuSOoxnpZNo5ztH6axCgZ/qmfDw1EyTIoDK1Ad3B6joNzWh+OOmpbozDKGJf+mVkWENYhSSwIaoMEE4+h2a3orcenbT1t96JDKoYvS9JxiQsbiTY8T9o6R7M/pclbWtPfRHpFbHXvj0RN+HkerwN34/9Cte8JWkbjT9FJtiE9CeBRnCJVrfCTk25SxnFLxdNh8iLtxnUkEYWo+MkhNMcEGQsuhBDhmjTctdqueWkTvlIErndEKi53pckFXN0dzwuCmGkMHKrp7Zn5ZAyRDOcpNynWx3Cd8fx3Yk5A2aeD2FQo1rcSHuLP69YTWX7CLxS0ve9RUZhct6ljCiNP0cYeWRbVsxZWtW0ssnU8HTKG3+Mv/MejLbjyB171ZQv8TQxYKfasJzW2CfWypEtrAYkWj0Fk2HlKW79JdGW2wnT3dQGf4OeX4m55FVkl76KqLQxcetIxkjLos1hSdWcDuxjrgK/dFBf/kFQAxnF2Ubm+SDYU5x0J9A8WxOFAYFfxXJamo4rDLzkAyQMapjG1PUihCCd7aYmjGQ9y6/gpA2sVAfTraFRFH9gaLq1z+OmaQaWHQeVaWLq2opCP9luKtOBnSpQ9n3K1TG6s4Vkf8PlSbZOjNCezrGmM3YvCKOIh7atRxeCNZ19ZO1U0/6er2XZsvNx4JU7jFuusbjQji5gcnQDqUxHXFq23r9UqpWVToFomoVUSkkoIwxNp5DKMFiaoDPbMucY1gKfpwe3EkQRJy9agSYEK9p76GtpxzEOn3fDoUaJWYViL5BRSFjZjvTLGIU1U2l3Jp4jmFyPkV+BVRduwkijWTmE7kAUgl4PpGhZhdmyCrQpfy0hBEZThHd4sA4prrLlTSCs1kSohONPEQw9hJZd3GQVrD3xWWRQwznufyepZKLyFvytd6IX1jaJ2XD0CSJvAqPzFGgIfG+ScPI5oFlsCTMPQQ0xzT9MmLl4Kn5aqjAZhcjC0UzYpabpdS23FOfYq+OxpuGruQnZcz75tqOSl0RclalIqvvcxFrYiNzWskuSdlEU4FYGCUOXTKPCmWbgZLrjXKLTfPBMK09L+zGzAtKmV3iaahsL3HR276c3w6CG701iOW1zBrTE9d5NdC0+fmb4xMU5TjuwU/NP9Xu1MbzaKIaZjRP2TxuPamk7oV8h19a47kUs0sO4FCtziFmo5zWuB5Np9Wh/p+sUgvGnkU5H07hNjv4B22mHjd8HoWEtu4xgcgPRxDqsNW9FCA0pZXIM+cUvpVp8Fl0ziCo7CIsbMXUbo3UttK7F3/lrwuIGjLYTQOy6hLFmF5Lr9EARRUHTuXOr4/huETvdliTG35+Ydg4n8jGteGo58MvUykO4tQksp4BppikXt9GSDQlDH7P+4SyEliTcB0jnZou/hgU38Cqksl2JhTLwa/heCcNwMO09K3063Uoqo4gdw88xVhrFzPazpivet6YZrBvahBcG5Ow0mfr1nTZtspZDbrpgFYIwihCaRsrctSU5DFyE0GbNLtR8j7FykQjww7BpbAwzjZlqZcfQAD7jZG2H9lS8f88rYada2Tw2RMZyaEtnYzeYaUFe60cHqHgux3QvJmenOLFvGcY8WRkEsTgPo+ZnphKyzSgxq1DMg4z8uGqL0NEbAkAIvOFHQEbo2cVJ0Idmt6LZBcQ0i5cQAmfRhbO2u69RyHvX9wCCKpiZqaIIE88SDD+MlunD7Dk7bicltUc/jQw9nOP+IrFWSr8UT7vOrObVCAqaHunudGK0n5BU7Wlg9JyFjLxYqNbRMoswl15GpNlN5T+do6+cdQwiuxi55NWJVRcgCMq4Vjt210qY5m8q0ZBmrh7QUq+mRIRAEIUuuuHEoqouXKdbEX13gkpxS1PZUSE03NooyIgo8pKp1bmE4N5kVbCc1lmVh2YKnPmolncQeEUMMzN3DkkhSGX7GRx7Zs5a7Q1iH8UyUehip9pn/BYS+OWmqep42xqhXyGKPAKvhFm3pKVzi5FRmOTBnL4PrzaKaedxqyO4lUEspz0RRWZhNS2n/DVy2kdNI2CmWt6BVd4ef1jpDsHOXyODCtH4U2iFtbhPfA4t0x/nZTUzpI/983j9ynakNznNyhsR7LwPGZRja3tq9sfFwSJO17SFKPTIty1Pzp9lZQkDF23aNRR/SI3hpNv2uopdGLhUK6Oks51JKdPpPqSBX8GtDROFEUIzMKwsQmg4lmR84EEKHcfgzOG3OvcxhUkeVMOceu5FkRcHY5kpdCudHFsYBtTCACkhY9m41TFMO0vR89k4OshRXX2J8BRSIqUkmuZnDpCuBy5N9wNtz+Rm+YEKITixbymRpCkbQM33kql+AN+rUCnuQNNNsi39TffycKXIptEBJlKCR3ds5PSla5JtaZpOOt3G6m6HodIEnZk8UeRjWlnsdCtlr8a2ybjS24m9y5qCrdwwYKxSIohCJmsVWtPZeYUsgG2YHNuzhLJXmx6Pp5iBErMKBbGbgAxqaGYmEZtBcRP+6BNx/tVUI6G1hpFdTPxImXqsGNn+Jr/YA9LH8jZkUEHLLk1Ku4aT6wmHH0akexKBClB77F+QQWWGQJ0kHHsyFqL1tkKIuticjMVvHS23HGvZZYgZQWb2ce+ZXXkntxQ9txTPnWBy9Gksu4Cd7sToOn3WMWhOO5XaeOwD6RaTajPT09ckx1DeiVcbJQxrZBtJ7yXohk2lNpXmyquNUyluxjCzSWS1EDqZ/PJ65PVUyctsYUV9+nJKrEV1YS6mpaYRQiOV7q6n0zpwZXEDv0x5chO5wup5/U6TPiXX2/yvM91ME0Wzfw/8ClKGGGaWMKhRnlgfWz7tQpypIUm/1ELouEmqJa82RhhUcTLdpHL9CKE3CRdNM5JE/s37K1EtbaNWGSSV6Y37PMP1YfbxSpxMD5puYxz/F0h3LAl+Qwi0ljVExU1EtRFkUMVcelk8Gg0/0MwimO5CICOCwvFEk8+iWx0IObcvMcTT8WHooulmU4nZfUFGIbVq7KfasLbGVchi4RcGHpoVj5lpZ2dZL2uVUbzaROy+UZ8Ol1LWP3r0eT+epJSMDT1B4BWJwir51tl+9YaZIZNbjKaZGHY+vk9aVoBYF4v/0Nutz7AXBOiahq4ZZPJ9sa/7tD7puo000mwuTpIOh1je1h2nhBrfxKaJMWpahpP7FlOrjOBWx5gQWaqBx/bJMVa29yA0jY6OFeQL/Vgzsg0c3b14ZnfmZbpFVkrJtokRtk6MclRXX5I7VdfN2EVI6PXjnlrf0vVEhEbEOVuzloXvlTHNNJpu0uKkaXHShFHE9uIkZS/iqJyNrUX0t7Tjh8GsrAGOYXJU1yL8MKA1vWeWa9swsZUldpcoMat4QSFDj7A6CDLEmJZixxt8kMgdw+46HT0TWxh1p5PATCcBPQ2sGZVc9mr/03wOAcLiJoj8OEVLfXkw8hje1rtodUeAqah095mvxQL12D9H1Cv8SG+cYPRx9NBNBCrEQUOE1TjnbF3MatklcalKZypXYBi6aKveiGm3Nk0pSzOH3v6iORN5z4dupOqlP8ex053ztjPMbL3M6NSyhnXUSXfhZOIgjrh8Za3JemjaedLCoVR9emq/ZgoQdR/ZqRfrdNE1nZkiyk51xvuYoXV2dQz7i1p5ABkFuLWR3SaXz7Qsa7JIRVEQV70y0/jeJL47gdCmXo5h6Cbi360M4nuTOOlu7HRXbN3VbbzaKJ47Tjq3GN1w0HQzsZ5KGeF7kwReCa2eRaCxvDy5CdvpqFv2BGHoUSsP4bljZHJ9sQ+okY4t+0In13ZUIhK92hhC0+vWYw1Z3obILEIIvanyFvVr3Og8JVmk5ZbhrH0nkTdOELiEQewPOne1I4P04gsoja/C90rYqbndIAB8v0y1NIjQdHKFpWhzWMo8t0StMoxuOGRy85+rIKjhVsdBCGy7Ba0ekZ7O9sTW0l3kDQWw7ByBX8WZdt1XSgP4bhHDypCtp52KU2XV0DQTTa9bYVOtVIIKhjH37I9hpmf5Nmu6zdYBgzVHH0Um1zPvPR5GEetHdjJcKXJcz5JkWn+wNMnm8WFydoq1XYviD0i7wOREiWqlyOKWjvgDWkaYAiLDBHQMK4OumziRyZJCBz25qRkLUzfmjOTfV4QQeGGIRDJZq04VAtBNsi2LkpRv0+nKFmi1M6x/6HFe1LuctOPg1iaolAbRDJuWwpSl3w8Dtk+OEknJpFulxUmzuDC/hXu6S4Ri/6DErOKIJSzvIHRH0dO96PVUR5E3gTf0O4ThoE/zkRRmGhG5yOl11K08qf6X7/V+o+oQ0ptAyy1NpmuD4Yfxt/wUvXAU1vLXJm39Dd8n8iZif89GmVAZIt1RjKjctF0t3Y0MajBN0GiZxViLX5H4r0opiUIXbeX/wrDbkpeyWx3FrQxjpPqb/N9K488ho4CcmU3Ehu9OUC1ta5p2n/dYp1k5NS2uaa9Pc22IooDK5CacTE8y9W1PE6yNPtcqA4BkusLVdZtc66pZ+2xEMSdjoFmxz+peTsk2b08/JPN3droLI8jGfqJ7QFJNyytSntyEppnkWtfgu5N4tTEaOt13x/CqAwihkW9bGwfSaAamHQeZZFpiq11xbB1R6FIpbsd0WjHMVBJNLiUIYSCEgTUtVVhxbD21SiyuCp3HAYIw8GMfZUJMKxOXQLVyFMe3UCnuJJ3rQdfjgguV4hbc6gR2phd7++1IdwznmD9ryoG7q+MX2X4iv4PyxBaE0JISs1EU4NUmiKIwqfYkhI6dio+rcddIGVGc2EI605W4R2iaEQv5bM+cQra+JlHoz0p75dYm0DQzqcxkmGlsp4BhZRIhC+xxTlPDTJErLGkSV7pu4QO2U0iWNQSuk+lI0k+lcotwMr2z3D52hRcGuLrA1dLkGpW9fI91Q9twg4CTFi3H0g00IagF8SyGNUNoBlGIMU2kF1IZVrR1x1Poug66TkvrcnItfvKRaVkppJSkDlLA3bK2TgqpNG3pZpeEmZb4mQYHQ4Kpx38LoVMOIkbKk/SbxcS9wTEtlhQ6sQ2DvBKqhwQlZhULnsgv4Y8+gYx8nGkFAoLyNsLyNoRuJ2K24duq2W3xtGd9CtnuPHXWdqdXZYmKG5H+JHrLmsQvNpx4hmDH/yAyvViLX5ms5z7zVaQ32fyC1kxk6CLd8eZ9OO1ohtMUha/nV2Gufgtj2+5PlkkpMVe9CSH0pF9ebRzfK6NnlzeJw9L4c3HEvZkDrfEQjn3Z5Iz67ELoaLoRFwfQG/uKAJH4nzYIQ6/pwV+rDOFWBsm0LMMwM7FlKNOcGicMKgR+mcrkZrKtq+a0gAghyLWujl8i+1DVK56+PXCuAAeS+aL9d4eupxCI+vRoUI/610GLBZVuZIiiECEg8Kuksn04mTg1UxDUiEIfy86RbVmBWx0mDKN69aTORMxGoUe1Mpqc2wZSxi98w25JzpduWGRbVxP6pbpbQb2fhg31fKYxEZqRBjGJpse5dqOgQlQbTXyuw4lnYz/X7BK0acF/0zFMB9PKJFbJuF9RPQ2VwEnHQXKapuOk43u/kU/V94qEfo1KeZBsvr+eLSHTlPZJyohadQxkhJPuiCP67RxI2SQUoyigWh5C03QMc1kstoVoCp7aF2beI3aqgJ1qbVpumCkCv9L0cavrVnIfQ2xNbfh5Niz5uuE03WcDxXEqlmDLxDAdufjDwDYMvDAkkhIzeYYIlrd1I4RomvJuS2fJ2alE8DXonlE9StONJnE/13EeSDShzRKy05FS4tbGcatjWJluHGuu7BwZRqVNBQhnuM305ue+VhUHByVmFQsKGfmEpa0II4VeT6EkNJOwUs+fGXqJP6me7kbodlNFLKEZTUUJZhLVhgnHn64n1H9RvI4QeBu+h/RLcbUfoz4NG7qEpc3oMwSY5rQjdRsZeskyPb8qzgAws6zstJRTEE8NB6FHaLUSTJsyLo4+RRT55FpXT0urJPG9yab1G8njG2mpGhhWnmxhVRJw1SDf1pwEHuIUP3aqvSm9k++VKE9swMl046S74iAir1gPGKrMG3Ck604ionbleyqEhtAXRnnaA0EyZaxbc1oGK8VthGEN3cjEHzBGilzr6jjQTUp8f5JICsy6v7emW6Syi6lVhgn8KqYduwPE5TK3AfGLWdNNUtnepORnk0VPxOdPzDBZZ3J9OOl2rGlWQl03SWdnp4ZqWEh9r0h5YmPc75bl9WwOGlrmVaCnQAiqpSHsdCvB0AOE4+uwll6CNu0jc6YvZ3pa3tS4DxZ2qjBLrM1ECAPTzmE5+V2KKbcSB/DEAVRxO8tprqwmpcS0ssgoIIr85+1vO3+fZx+PZeeSlFZzEUQhv9+xiY5MnkUt7QRekUpxS+w327IiWW9RSxtGKClMy1mqCY21XYswNK1p+1l7dkaI/e0ScDAIo6heIav5eRh4ZaIoZN2O5wiMDGva4nfMpFtlZHyIrOVwXM8ShstFOjP5uTatOEQsrCtQ8YJDRkHdQT9+mPtjTxFMrkfPLp4Ss7qN1XESwsw1BaMY2cVQT7YvQw8ZVpuEbTD4AFFpE0bf+UkC+qiyE3/rz9CzixMxC6DnV8b+p9MDHbJLsFe+fg6B+tbmY5ARkRBg5prEZLW0nSj0sNPdydRb6JeplrYl9dIbxELQJ4rCxPBimFlS2f4k3VGDbGHlrHGMrVR7fruLaWMOcUAPyKnKZUKQaVmG707Mis5v2q9u1fNfHjnMFaw2tVzuUkhJGSW+mXaqFU0ziKKA4tgm3OoQudblpOrWbc+dxK1OkM52EQYVwqCKYWSRUYSMwuS8CyHwa5NEUYA+zb/bcvIgaPKflDJCEFv1ZBQmHxCxMGq2WhmGQ0v7qlnHaVhpDOb3P53zuKMA35tEyhAn042RXN9x3yqlQbzaBEFQxU71okd+4hfeoFoeIopCrHQb1rSSqkW3StGtkjZtCpmpa23bxAhCCNpS2aYIdtPKYJqF3fRYYKcK7M73RNfNXfrP7gslt0rRrdHipJPgISklI5UiGcueivgXGkEUMlYuJRHxUkpcdwIZVJnw4xylw+VJ+vJtSBkSobFpoki5+Cyn9cfnVhMaOQ/6W5pdXY5Uv86h0gTrRwcopDIc1TnlchVn5+ihVJnALY+AjLDqYtcLfEbLE9R8l76WNrqyLfNtXnGIUGJWcciRMiTyakDUJDZrO39NVB3G7jk7ySZg5JYQ1YaS0q4NGsFccYDXTjSnc8odYPwp3Ge/iZ5djL32Hck64ejjhKXNaIW1iZjV0r0YbcchmnK+grX8j2b1W1h5dKu5HKjvFYlCN64J3/CXrfs36ka6yQc08MtxAMu0QA/dSGFYORDNPm+ZwopZAlPTzSQbwIEmlemp56qcerkLoe1SyC40pIzw3CK6bs0bPAb19EbVMZx0e9KuVh3DrY5jOy3JtLaUErc6im44yRS2lBLPLdanwhuR7jpR5BKFLn5tLBGzgV8lDGp4Xol0bjFhUEM30lip1iS5PMTuJpquxwnup32w6Lo5K3eppum0tM/+2JmP/TUNbJgZUpneWR9eDexUAd8rkcp0YBSW4AY+E56L5dbI2g5RGOC5k2wbH6asj3DykqOTdEYTtQpbxofpyrZQSE2J+W0TI4Sy2eK4p8SJ+g/NR9j2yTFGKkWWtnYmYnbSrfLM8A4AXrxkTVK+1A0Ctk2OsqM4xtFd/YxVS+wYeoZ2U9LRupQ1HX1YhhFnH0h1YNptPFtZRxhF1AJvtzlYj0RSpkUkJTV/drU3TTPIZ9s5NdNK1feTcc7baXqtiJTh1Wei9u5jTnHgUWJWcVAJq8NE3njdqlq3Orjj1IbvR7NyTXlZhWiUNS1CXcxqVgt23wXT/Fmb08K46/6DqLwNa8XlGG3HxtupJ9eXM3IW6m3HohWOaq745LRjrXjdbo8jCn18bwIpZZPlsVYZJKzn6WwEpjTSO8202MXT9SG6PjV1pxspsi3LE/++pF97YVU9UOwqd+mBIE5H5COjCMOcGqMwqJGyBWHgJonM94RaZSSOEp8mQmf+7lbHMcwU2Zb+eh+iOCWS0JOgkFpltF7AoJxsRyCQURBXdEr66VKrjKIbNoaZnvKnzEyleYv/K8i1LMNJtWJM8591Um0YZrqex9WZFXjU2EeluBmAVKaLSB4evsNBGFINPDKWgyYEmm4RaA5jQ3/ANFN0dR2X3MMbtz9BtO2/6c5kMdreBcBYtcSmkR20pTOs7lqKphtkW/pxi2VCYeIFAYYVH2vGtOnM5MlNmwKXUtKZbUHKWLw08LRYGLYfRlX1prNjcoxQRhScNPb0rBv12YAWJ50ILIjTPDmGyUStghcGjJSLeJhgOehmmnar2dquaRpru5dgG+asIK4XChnL4aS+ZTjG/FXLNKGRNk3KpREEYOoabZmWOCfwAXIlUTw/XphXs+KAI6MgToEVuhj55cnyYPI5wsrOeNo8Vc/Lqlv1cp+i6UvZbDs+dh+YUWRACEHkjuFv+AHSLzblPtXSPXGZ0+lJ/VNdpE76q6aCBsCceVDnIwr9ONdlXVRGkU+1tB2hGdipjqk+m9l6kNOUcDWtLC0dx87apmm/8Kaq4mwLHlEUNAXceG6RwK/UA6Li5VHoURzfjNA0WtqmrIm+V6QlpxEEFWDPg6cMM43nFilPbiffunxWiiTbKeDVJjGm9SsMPUrjWzDtLOlsnLYok+vFrY5hp6alErKz6IbVVCZWRkGcCmlaIElchWu2NduwmttBbHm3dpN7VtOt+nUk0HSbKAj2aCz2lSAM0af5UXphQLFWRde0xCoqpeR3254jkpKT+pYl1r9idZyxWhkn0uieJiLG/IjM5Eb8KI/0iggrhykEBVHDdD2kjCP7DcPhhCXHIRBNifBb09lZ+TobwUrTKbpVyhasG97OCZadVJA6EFQ8F02IJveG3RFGEZvHh4iknJVovyWV4cylU/7tjeekrmksbe0kiCIylsParn6Gy3naM7mmQgzTyTsvbKuiEGKPLNLl4k7cWpFsJs7DnMn1xqkMDwPDgmI26qwonjdhZSdhZSAuLtCwckYB3uADILS4Ulb9AaA57TTq2DfCkzQzh71kKp9qw9qq1adyguGHCAYfRG9di9n7EgCEmSWqbIvLm3rjSclTc/HFWEsvbeqfEBrsQS36OAinQhT69QpH8cugWtqGWx3ByfQkeTDjqeN8XbxM1WWZGc1/JDLTZ1TKqJ6UXzb5XdYqI3huKY7Erpe6lFEYC1ShkW+bCkIJ/CpebbKe4qheVU03EZqGEEbTR46mWbiebPI/9r0KnjtBOtM9bx5PXbfrqadSc7bRdLOpT3GHG9WvavW0TBaabsyKVp/LJ3muhPj7gu8V4+wBZmZWjlwhBJn80nnW3D2RlHiBj6kbiUD0woDJWgVdaE0i8Ymdm5l0q5zQu5SMFVtBi7Uq64a30+KkEzErhCBjOXihTzCtBKfjtGHlV5KaEXSzqH0J6H9Cqm014cQ6/G0/J92ylt7CCUnFtsb9tatKSbsjbdqYERScDOk9FJlxyqld73Pm/eAGPk8NbkUCR3f1N/m9TtYq5J30nBbBSEZ0ZVuo+l6TNXl2O8mj2zfQk2ulN9/aJMyEEHTO4c9ZLe8k8ErY6c6pGSPFLNzAZ8PoAFnLoTvdEj9X6nYRTdPRtBf2h8DhjBKzir1mpp9RWB0iKG6Mo58bYla30Jw2NDMLUZAEZpktUz6j0YypdBn5BDvuIRh+JM67WhegMqgRVXYkfq0QZzCwVrwekepqSt+zp7lGG24CmmZh2o2oVElpfD0gyZtrk6wImm4DIq4/39iP0Habg3WhEIU+kQybBFkUBdTKI4AkPS3ApRGok8p0JBZGGUWUJ3eAEJj1JPqN5Q0rbIIQCE2Pp+rkVF5Z04or6hjTptKFaLbINrCcFsYmoySlVRxUNRCnSRIa6ezcHxRCm/1bENSoVUbIZHsQmj5LZBims1c+ps8HKSPCoApSNrkbuNVhAq9IKts/p490EIWzrJVu4CM0Lc7xSXzPbp8cJW3ZtDiZZKr6sR0bqfoex3QvpqVusat4Ls8M7yBrOXNWKKr4XiJmdU0jb6dIz7B0HdO9uGk6HOLco9N9Wht0ZVsgeyYA/ugjyKCKoRukWpftdsz2Bl3TyHiwop5eandIKXlqcCuGprO8rRvbMCl7NQZLEyxr7Uq2sbM4ztaJYbqzBZa0dtbPhQ7Ipqn8sWqJp4di4X90VzwrNb0fpm40WZOjeklXIeJ8Eo22o5UitcBn8/gQndn8vGI7DFyiyMO0cgReiTCozKrC9kJFSsmmsSEmahWO7VmcjGHZqzFWLeOFAf2FDjL5xbjeE4e4t4o9QYlZxR4jZUQwuZ6guAG75+w4XySgZxaBEOipqQexEBpO77l7twNhEI4+gfRLRJXt6PlYROj1AC3hNL/I9UJzWqnAL1MtbQehkZsW0e+7k4Shi2nlEr9DtxbXizftlkTMCqHN6Rdq2a1Ydus+J+U/lEz/8IhCn2plGBmFiU8oQK06ilebbEq+jpR47iQIQWraNhppmqan7RJCQzdnp0OynDymnWnyMdM0nZa22WU2TSvLvnoxCqGRzvXie8WmOvQNfK9MENSw7XyTZVNKSa0cp65y3Ulsp3BQ8142+hBJia5p+N4klcnNcaCgNfXRpxuNzAPNgjGSEY9u30gt8Dm2e3EyfVw24dEdG1nZ2ZtUVXIDn83jw2hCcNriVTQsnbZu4AY+QRhO7U/TaHHSs6yDy9u6MXW9KQ3TfAJ1ppDdU4y+89DbjgftwPi0Cpr7tn1ylKzlzDn1XvZcyp477ZqXbBkfZqxapi2dS8S/HwZNFmjLMDi2ZzFhFKFpIqkMZWo6uhBkLYfxWplNY0Os7uib093huZGdDJUmWdHejaFpPDu8k/ZMjpXtPWQth/Z0ju5cYV4h20ilJzSDfNtRZPJLCPxy00fSCxkhBOO1MlXfY7JWSfLPpi2H5W1diYvGvuS9VhwalJhV7AWCsLID6VcIixvRWuNIft1pS4oS7CkyqBHsvJfO6q+Q8uJ460JgLHppnC4muyxpqzltMGP7UoZ4tQkMM5X4KWqaSRhUZ+UzdWsjBF4jj2YsZi27ldAvzxKv2cJsoXU4i1gpJWHoIqOgKfl+tTyM507ipNrqKYYAIfDdEohm3+RG4YTp+USFpuNkOmY9zO10G3a6tWmMhaaRa5ldM31vqhA9XwzTaQoSm45bHY+Ty0NTdH9c5KEDtzqW/L2vzJeua1dt/DDg0e0bCWXE6o5eWqwUmmbiy1hk5e00WdshlenBswMe2b6e3nxbUiYzeeFCYikF0CUwrVoTxBP1nZk8UsomX8rVnX3oojmXaM5OcUz37PM5s8b8/iYqbSUYeRgtuxSj/YQDui+AofIkm8aGMDSNFy1aMUsYZm2H43uWUgs8bMPEDXwiKenOtiSFBAAWtbRTSGWSNE4Qu0MYms54NRatmhCc1LecQmo5hqbz7MgOqr7HtokR1nT24YUBURQlPra6piGRVH0XTWhNCfod02JNZ3O2lZkYZrr+vEsladwsFbjURH9LOwLR9CHjGGZTWV3FwkGJWcUuibxJhJmtp4USWG0nxNkIMv27X7mO9IpE5a2gmegNNwOhEQ7cSyrcjixvhrolqpGBYDqV4jakDEhl+hLLWrW0A682iuW0ks7FL15Nt8i0LEPTzCaxZpgZNGHU3QVidMOeMx/r4UzgVwn8at1fN34ASxlSGt8CQtDStrJJlMgoJGxyjdBJZTpn+V2mMh1JhP1UW23KSjuN+Ut9Hl74XgndSKFpccoqBNj27CTnhuFg5OLKU7JeVz1j2XvlmzlRq/DM0HYWFzqSqkeRlJS9WlOuzoHSOAPFCfryrXRmWzB1g0Iqw1B5krTloBsm+faj2TYxwuaxITozeVbZcd8m3AqhlAyVJuKXcP08H9W5CGuavyuAFcCLepeRdqb2nTItVnXMLhf7fHxQ9zdhaRPB0EPo7vhBEbNtqSx5J03eTiXjIKXEDfxYjOo6actORLxtmHOKfF3T5g2qanHSdGby5J00lm4k521ZaxeOYbGoJf5IHypNsHk8dlVY0d5Nb66V3lxr4qbQns6xN99aQmjkWtcc1h/ih5qOw6joQRBFFD2PVmfqo/TuTZt5cmSE03t7OaU3dvXyw5AvPvoYpqZx5UknJm0rvo+pabOqsL2QUGJWMS/+2B/wJ9Zhth2PmY8tlprdgraLAAIZuoTjT9fLvtbLYpa34D73bfTskkTMCt1C7zmXkY05RDq2MsgoxPdLIGVTdaHAmySKfOzUlBCznFYCv9wUPQ5gWrMfUI2grcOBmf7GnlvC94r1eu5T41op7qCtRWuq4uV75TiK3ikkYja2qppomoGUYZLOzHYKWE4eTUzd4kKIKSvtAkcSi0jhaWRsB2eaVaxaHsKtjmPaOTK5Hiw7i2Vnkyn9+abAi26VPwxsxTFMju1ZPGdVo4rn8uzIDnpzrXRk8kgkzw7viNMp1afb/TDgsR2b8MOQk/tXJIJkpFyk4rv40dSU/vK2bha1tDX1P2VatKYy5OxUEgzZkc6R7rVxA6+pP3NZSzVYcBWZAPTWY5DuKHrr7A/aA7I/TePorkVNMxJPD21nrFpiZXvPfkmML4SY8yPCNkyWtk4FEbanc2weH05cDmZWptoXq3hDyLrV4bjqmZVT4nYXBFFIya2Rsez9cv9IKakGAY5hJM+cB3fs5KmREVa1FjhjUVywYbBc4YuPPArAh886YypTSBQxUqlS9Kbu+VoQMlKJU0xOf5b9ctNmnhwe5sJlyzi558gPQp6LhffEUxwwZD39VCPzgNAdkCBnlEydd30Z4T75BSJ3DGv5axPrirBa0DKLEDNEpd51JmVzDFH3j/P9IpXJzWi63SRm7XQ3IJssinEBgjUH3cdxX4mikPJkXPEr37osealEkY/vlhBo0Ij4lzIupmCKesWteHrQMFNxXlpzepCUID9HoMzMGuiHG0EU4ocButCxjHrQmYzYNjFKEIVNATbbJkbYURyjK9PCkmkC4OmhbWi6zvE9S2H6FK+ZwnNLTdkFGsE8k26VJYXOOeuo5+wUGcum5NUIoghTj4NtDE1PLG9xEJDLhrFBCqkMpm6wprOPgeJ4YhUxdQPbMOrTxF4iZo/qWsRopUghNeUOomsaKa1ZqLSlc4kPX2n8OcLQJZXpI+MUDmg6qUONZrdiLb3s4O5zhhtN2rSYqAm88MCmOJuLvnwbndn9ay2M/cIHkDKMS2FrR2ZVr30liEJGK0XCSGIbJk8PbSNt2pzYt2zO9r/espUNExO8ZPFilrTE58oNAn4/NExnOsXSlpZk2WceeBA/jHj3ySfRmY6fH4Ym2Dg+QdaacvnoSKewdI1IQjUISNdzIJ/U3cXq1lbaUlPP+5Rp8ObjjiWaZuSQUrKtWKIWhLQ5c7ta7Q92lspsKxZZ1dZKi334PYcO7zee4qARFDfjjz2O2XYCRjZ2IdBzS7GtPLozO6hmLoTQMLrPJBj8DdPnxLTMIpyjr2xqWy1to1oeImVP+YKZZg5ddzCsbFMxhLkiuA8XERtFIVJGaNpUUYQw9OpppvQk4l8IDSnjtmHoJ9OapplGZDpn+ZemMt2MTT49I19tpik36+FG2atR9mqkTDuZXg+ikHVD2wmikON7libnbevECDsmx+jLt02zUAm2TowAsLiloykS3w/DJotmw08066Sa6sXXfA/LzNDSlm2yaksgZdmM1ypNgU1hFKHVixnEVrSeRGCPlIusG96OWRfMtmEihGBJIc4r3LDe5OzUrNKfqzr6sHSjyQpsaDpd2cIuxzDwSlTLO9E0g1SunzD0krRcigNPX76N/kL7vDlaDxSOaTVZagFGKyW2jA9jaBr9hY4k4GzviJ+jcQaRAyd0Fiplt8ajO7YwUq3RksrTm7aSD0YvDNlZKjU9R3aUSmwcn2BNW2siZoeqVX7y3HpylsV7Tz8VANsw6M1m2TwxSWVa1p7VrW1csoomdwJNCP785JNJm0bTe60znaZzxik3NI1lheYZAyEEV550AluLRRbnpz6GKr7fZBXeE6SUbJ6cZEk+P+sd+/uhIe7ftp0TS11ctnrVPFs4dCgxqwAg8iaQoU/kjkG2kTZG26WQlaFHsONu9Lbjkypaeucp6B0nJ5ZHGYVUyzsJ/TLZ1lXTxJmGlGCZUw8Koenk2tYcmAN8niTVqGTUlD6qNL6Z0JvErG4iKj6HDH1k5BJiYHSdidV/XiKU0tnu+KWiTbcw27OErBACw8rgevKgTgvOrKYGMF4tJ7kxG9PoYRTx+M7NhFHIixZN5WUdKk2yoxgL1Ia404TGRK2SrNcQqHGAjMb0x6UmBD25ArrQplfNpStboDWdw5rhD3Zs9+KmCmBu4PPEwBYc0+Kojr5kX41tLy100upkmvwbB0rjbBob4pRFK7EMoylnZyGVIWPZib9jYzuLWnb/cecY+xiNL7Q417EWu47k244iDKqz/JwVBwbjMPI5HK3EbimN/98XMSuEHscG1J9BLxTCKGKsViOIJD3ZqQ/Bn23YyFClwqtWraTFju9tKXTWT1RYLFJcvPoopJQEUcSnf/NbgjDgqGli9uSeHta0tzWJRg3B6rZWUmaznHrD0WsxNa3Jnz1jmbxoDjeAjPX87m8hRFOfthaLfO+ppzmhq4vzly7Zo21IKfn3Rx5lsFzhihOOa9oeQEcqxeJ8jtVth2eAnBKzCgDM1qMRZgYjt2yP1/G3/jfB0O8IS5uxj3rHVAqoKKjnWQREnHJIRj6BX8asl1e0051oZoGJ0sb9fCTzE7njBKUtIMO4xKxmIIwUwsgAkmByA8Hks0ReES3di5HtR7PySL+CXxvFLW5FhGUMQkAgdJvQnSCaeApXhrP2FxSfZXLoXuzel6BZOYSRQRgZpJkGPU1YGyAYeohw4lmM9hMwu89KCkVEfom0Nhl/ZOiFxPVj9jGN4e/4H4Kd9xJVtiMr25BeMS7V234yXnopIRo520aGLhKo+C7exHrM8gaM8iaiyiDSG0MGFaTuoFktCLsVzWnHNVqQkYFHhVF/AtAwsv2kAwfX7qYWHo3htCJrI2RH1tE/sREn1UYt24Ewc4hUJ6tFFs1pQ5Y2EgoNodv0pQv0t6wG6lH+oYsMqyx1IqLqAMETX6O69WeEk89htJ+A0X0mtJ9I4LQTaTmMqIh0x4hCA8IaMihTLQ1jjmzCkC5+tRWt42S07FSgohCClhlppCaqZSB2WTiuZ0nTC1/XNI7tXtL0MjrQ6IZDOr8U3XDq/tVzp4tTHPm0pbOUvLg88lzpz/aUI7386hNDw4zVahzb2ZFYPNeNjvHdp55mUS7L20+cCibcWiyydbLIxvEJTuyOXZlO6FkKWorldYunEAJDCAqOTdXTqE5Lu7aitTBr/325LP/rmKNnLXeMQyevxms1Jl2Pp0ZGOLt/0R4Fhgkh6MtmmXBdxmq1WWL2RT3dcwrxwwUlZl+ghJUBwtowZusxseVQM5Igrz3F7H0JUXkrZs+5CCEI/AqVyU0IzSDXGguVuBZ9nJB++ks5TtA/VdlnfyJlhIwiNN0gKG+nuum/8MefQnoTz3/bgDfHcj27NM69a+VB6ATjT1PbcTfB+NMEO+6Bynao7ITQndpSUEXU66A1lmLFpUklkpM1m9KP/xHsdoSVBykRMoh9mN0xcIfj/85BOPw74D+AeIRLM35vvN6mS3DB1NmQ9d/M+r/pBEDDNln9/dRyDWic4QrNywGKMzupxSWMieYa0Sm8iXV467/TtOxMoPS12W0b0rVa/0duOeX0KnKUobwtvgZ0CzQTgUaPDOmOAiBiXIbxGNttaJletHQfIt2LlulDS3WBZiN0Cxl6yOoAUXUQZIiw8ggzj7Ba4v+38ugtqxHpvr22hgmhYdktRFHwgrKkKWYz3Xf6UOGFIbqYKsgxUq3ysw0bWdPWxkndXQfsGh0sV3hmdJSUac4KaKr6PqlpMzIPDwywcXyCgmMnYrbVcTB1DWPGh+hZixZR7Q7oy035rfdkM/RklzOTK086ERFF3H77zv15aAeF4zo7CSPJ2va2PRKyWyYnuX/bdpYVCly0YvmsWbCFgBKzL0DC2iju4P0gJZrVkvjI7glRbTTO+woIK4999LunlRk14mT6URAnea9bZy3nwExLRFFAGLj1fIp2smxydAMyKGGOP4K789eQFM4VCKcT3cwiZQiRT+gVIawCEXp2CWZ+JaFmExQ3gzeKiAKEmY6FTBSgRR4iKCO9idglI6xhdp+NveK1aC1rkNUBwvGn0MpbMCfWEQzcB5E/3yGAZoPdinTHEJELdcGdvCJqg03NJbORZhZSPURmPrY0a+aU2PXL0xqGiMhHhi4YacgsxsivQMsuRsv0UzVaMPxJdHcQWdmJrA0hayPIoBLnlRUayCj+WAhryMiHyIXQjwWikYnLBtevASIPwhoE1eYxkGEs4ucYF4kAzUArHI2z+k/R244lHH6EYOA+wol1SHcM6Y7F1vUGQkMYWTAzCCMNwkBKH1ncCMUNZIobmKvu0cyxbPwtvXGi4vo51tg7hN2G1rIa/BJRdQDpl9HSvWjZfoSRIaoOIqsDgECku+uC2USGHqE7BjL2543H0kdGHiBioZ1ZHJeGblwpmhmXeNYc+ipP4v3+aQIRxUGcZiZOr2dkEGYGNAsZVCCoxNbthjAPqvE5FhqgTf2/EHX3Ey1e38ggNIPIHUd644BAzy1Dy6+Iy1S7Y0TuOMJIo6W6EOle9MLa3brMSCkp3/NnAGTOuTGpwLenyCiYNYMhvWJ8/NPcZ8KxJ/E3fhNr1Z82pQL0t99NVNqMueilaJld53GdTlTeRljciNF2AsKaX3yGpS0EO+4hHH4YvfUYzMWvRMv0EU5uwN9yB+HoE3HVQzMbzwz1nINeNzZMZ9J1eXz7JtZW78Pc9G2Cwd/iHH8NzkkfTIJpAX63YydyYh0rJ+/Cxuf/Z++/wyQ5z/Ne+Fe5c5ruyXFnNkcssIuciEgwihIpkpKsZFuydWxLDrL8Hck6si1bnxyOZVmSZcmKFEUqEiAJEAQBECAyNmHz7M7sTg49nVPlqvNH9fbu7OwCYBBIUbivay9guiv3W1X3+zz3cz9S1x6E1E5mvByJcJTeWBTfc3FWXsIofIbXn3qG3T3dKOEu1E0f40jZ4tnZWd43Mc62qI89/yXs6c9x+9LztNReGrt+iMj4RxHjowiCEFgBFo9hLz+H11hASowjpbYipbdvmNj5Tgu3NkO5eI6EWwrGSHo7YnQAr7nI3PlXmF8+j6po7DK3I0b6kbI38NzcAodXVviHe3YQrp0EJc54IhpIBq4ojuyJRvjZW27ecO22dF3bD72iNzmTXyAZinTs11RJwvb+dnZM8x2dvT1v38XnTKHI2WIJVZa4qa93w/eFVotMOPwNN0N5JyD4V6qb/w6gVquRTCapVqskEu+Mz5xt2zz++OM88sgj6zR+3y74vo/bXMAziihde952lxOneBx79jHUiU8itaO4nmuv0/M5dgtJ3tgNasMxuCZWq8RzX32aex76KOo3UKV9yYJJC6c7Pqmu3aQ6+Wn84iHwg4pkJXsDcu5WHDmBGrnc5cr3vYD44pNIjSG2Z6OWWcOoXcRefgE3/xpC6Q3E2vmAbL4ZJO2KyOtliNEh5MH7UQbup6X1UTeaRGSJdPcuxEgfPvDaxZNEG2cYFdeQtTS+nOS1Q88wECuSbJ1F9QwELY2gJqj6YfxQD6nMJkK9NyNn9tDyRFq2QViWibh1rPJp7PJpfLuBkt6O0rUHOT7aLkTzv+mIiu97+FYNzyzjWRUEJYYUyiHIUZzaNHb5FE5zCVGJIqhJ8Byc+ixOYwY8F1HLBJMK18A11vDtZkDI1BS+XUVQk6Rv/S+dceQ5Or7dQArnsCyLJx7/Ag8/eB/u6nM4zXl8s4JrFtdF3wUljh3fgdlcJZMZQ7DKmIWjAQHzPcAnNPgQWt8dIEhYhcPoM48Fv6HTCiYqsdFgkmas4bsGTnUa33dACgXEXZBQM7uCCZxdw9fXcBtz7Yj536lH65tCSu8ifPMvoww9ct2x59YuUP1MoJlXJz5J9N4/2PAcsea/hH7o/yG065+ibf4kEDRbqD/1ffh6nsSHvoYYDSyPnNIJao/ehRjKEb37tyF3B6/+1S+wo/lb4DRBkAnt/qeom74P/fAvYc8/efl4u29G7r4ZoT1B8u16IMcxiwhKPJh4CBL24tO4xWPBSoKIlNmNnLsJMbUNKbUF3yhiLz2Hs/w8Xv3ihnMWIv34raXrXjchlEPO7UeMjyPGR2hUZlibf4Gsfg7ZX/+sMZK7yd353/GtCk7hDZbO/jmZ1pkN23QEFSM6QTq7FWf1RXw9v2EZ5Ahrfd/LabubPcaLpKuvXb8VriDiihEEXERXv/YySgwptQ0Arz6Lb6xd95yvByHczWzkILZVZ0w/jOi0802iipTejpTcHEyoQjm82kXc6ll8o4wYH26/r0TcyiRudRIcA5QYghJFDHfTkLqoi3Ei9hoJcx6vsRh0nNRyLJZ8Rva9H633YNDMpz2xFOQIQij3N1bX4ORfw5z6U7RtP46c2bXhe99p4eRfBc9BjA0haBnsuccxJ38fZ+VFQnt+hvDNvwrAarNFTzRy3XtvtdnkbKHISDLZKTArGwZJTcP3ff7ba68jAD++b28n+v1O8Jqvh6+9S2bfAXynkdlvFOb5T+NWz6P03Yncfw9GcwVDLxD2LbzGXKDJdM3AQzY6iBQbxHcM3MYMTn2uTXxqeHZ1HekTlCRq7gbU7A0oqW2d6IpnN7Dyh3CtCo6t43s24exe1K7dCIKI2VimNf8kfm0KBAEPAYxCh3TKyc1ENn0fStvb1nUMasUzuG6gQ8P38TwLz7XwrDqUTyAWjyJVziA2ZhCuiuf5gowXyuGHcvhqEl+O4gsSUn0aqTaN4Nv4iAjRAcT0DkrxPTTTBxge3I/gmbiOTt32WLFkMvHcOv/JUytziMBYOoPg6hhGlTdOHGPbjr1EIykUScK2Gjh2A1GQkbVE4Knre7iuieeawX+dYD+2VWtrlZ2gHW84g6zGEUUZQVSQ5QiyGjTD8D0XQ1/DMWuE4wMdOYjv+8G18WxESUUUlU4EprRyCNczUZQYkhIhEh9BVt7a9sd1DDzX6uz7Ejy71W7X6lN+6Z/jO03ie/45amYnvu9RPfzvcRtzSJk9tMIj1Mpr9PizePryVXsQEMPd+J4VRLulMKGR92EsPotvBk4JgppESW3Dyr+KIIVJHPwP4DlUD/1bcE1ELR2MZacFgkzypl9EjvbTOP8ZzMWnQAohhHvAbuCbRcTYKNHd/wTB92m88Z/x9FXw3CDKbtdAjhDf88+R4sM45Umap34D366BFAYphBQbIjxwH75VBs/FF0Qss44SSiOH0gHJF9Ug+u05eM0lvOY8vlm6PDZdG99p4ll1lpeW6RscCbSSbT2xbzfwnSbYzcsvYjmYaAjhHsRId0c7fin6zqV/eJ1ou9+eVOBZweRKS4Pn4NYv4tUugGcjaKlgUuK08PU8bv1iMDkA5L47ib3nUx3CeSWs+S/ReOL9nb9De/8VkZv/0+VztGpUPrsDXw/SvqF9P4s68UkaT3wArzkPgLb9HxK98zcBqH/pQ9hzX+ysL/Xdg7v8VSCYYF5apwNRQUrvwi0efctxfPWYEyK9+K2rx+LVi0lI2RuQszdirh3BLxwKMhSChNhzG2eEzRi2wQ3ZOGL9PM7Ky+2s0bUhJsZRJz6JE+qh8cq/IeRtEPLgIaEMPYgcHcApncAunkB0W+sXUpLMs41U7xjZSASncBi3+MaGbUld+1CGH0EZvB+3fBbrwp8FEqorSK6vxFH77kJKbsatXcCtTOLVptZnUtpwpBgVuYdYcoiovYZbPR9kc0QFMT4WFBX7bse7HHv9+QlaF75ngn21iOqdhIAQynYyHoKaILzv51DHPvxNbdWaf5LGl78v+P0FmdDef0l438/ilE7gLD2LvfhsO+v35hKt0I2/yKfED5BvtvixvXvWySveDJ+bPMfJtQIf2DzBQDzGH544iQD8zMEDlzsYvktmv734u0xmPasaFOV8nbYznudgttZw7CZhT0dMbcXV87QWn8UpHN7wkHnbEGRcz0cSrnjQSSGk5BbwfdzKmU50dR2UOIT78evTCNf43pWi2NFRtMQYseQoihzC0tcoL7+G6zRB0vDlKCAgl48jFw8jlU8j+FelvcO9iN0HUXrvRO2/FyG1jZrRoG7U6U1kkEQF33dZyZ/Gqk2jGCv4WgbEt06PinIUWQ4FDgmejefaQcTvHYQgSMhqHMeqB7ILAEFEDfWAEMK2GviXXnyCCoKIokYRRRezuXj1GRGKjRFLDiErkcClQdIQ2po1x9aplU7TKE+19yUgK1GiyTGS2V2dB2S1cJLW9J+h6ou4kSH8njuR9GWElWc7e/IFKdC24iEoCUJDD9JoFTCtBp4UBVEGzyJcOYFkVzrreYKCFduEmNlFz/AD1I7+Mm5jHjsyjOSZiMYqcmKC+L5/RXHxZbz5zyNbRQj3oA5/AHPy/yDgo6f24WpdCK5BpPgKgu9ixLchOg1UfQFPVDG7DhKKDyHlX8ZrzCAnJkjs+Wmqx34VtzEXyCIEAb9972j99xDb8kMAWKWTNM7+HlKkj1Dv7ai5G9tE/61xvWfN1RmUdxKeWcY49qsYJ38dXAN100eJ3f+nG5YzTv5PWi/9NEKkr0MMI7f+V0K7/xkArVf+Ncbx/4qgJi9H4AUJfBcxOhh0GRRkkh87iW8UqD0aRNzV8Y9jTf1JZz/Kzn9C7Nb/jL3wJK0XfxqvfhFl7CNEbv5PSIlxvNYy1uwX8OoX8e0WvtNEkIMInhDKYhplvNYqqm8g99yGMvQQYjiH11rGXH6Rk5PPItbPMcIqaiiBlb2VN9jC6OaHGe8ewvN9/ujESYqlOR7qMtix5T4ENcnvvXGCgt7iI1u3sDmTodKq87mXPkuXdZH7uny8+gxCpAcvvY9wz4Egfd++b/78yLPsmfn/k6u+ipSYQOrajdx9EHXsexHDly2/LMdGaM7il07i1qaQu/bi527niS891Rkzvu/jLD2DcfJ/4hsFlJH3o276aCcbdyV8p8ViaZHJlXkO9nUT69q+IVLpuxZebRq3MsmpQpFXqgrd3Vv5nt0HmalUO0VVrmvz8vRx9o/sJKKFNmzDWXkBe+EpBDmMMvQwUi6wwfLqs7ilk8E+ahfwjTxifBQptR0hlMGrz+HWpsH3AtlDaiuCEm9P8up4rRW8xjy+nkeMDgRR9dgQnlXBqS9z7uiTjKUbeIXDQURZVIJ/js41sy+CSPTeP0Sb+MSb3BWX4ZZP03z+JxHCPaijHwJBoPnc3wfPvjyugw1v2J8QHUBUU8Hx2zXE5Ba0rT8Mvo/++s8DMLXpX3HWH+KOWIUevwDtyS2ShhQfQ0xsAt/FayzgtZbA95hv6Cy0XHbf/I/pGbwFz/epGAaZ8OWAxbtk9tuMv6tk1ndNjMVnEKQQas/NiPLbs3nxfQ+rcJTW2hv4ThMJD7d+IbgZ2hDkCGr2RgQ1jtDW47nNBZzGAoKkIcdHkeMjiKEcoppAbBfJtFoN3jjyIjvGYviV4GEkXBWJECP9+KEcjtXAc1rI5to6AuvKMZzoGLIoI9cmoXoOr3YBoTmHcI1owJtBiPQh978HZfB+lP67EaJD2K7T6cbj2CYnF88ieBZDmX5SiR5EUabUqrNcLZGUXRKiidHK41h1XM/D92wEQUBqR0Jdu4FlvHkKWpIjSHKUtUKJbFcC1wmuiaxGkZUYvudgWzUcqwGCgCSFEKXA4kuUNCQphKLGkbUEoiBhGiX0xnJ7Oz6+7+HYjavShiLBw/LSNbvqwSmEQQwHERbvEpFoEyzfhktRbDEWrOvbgTZWlBBFqb1v75rbTmZ3kcrtoVmbpbD4IqJVIVI+jC9INLN3EC4fQnKa2KE+RLeJZAdNPBy1C/ruwRMkLL0YEHEtiesYuI4BvotWO4ts5nGio0jdt6HreXzPIZocQ/Ut7PN/1DkOHxGr7/0IahjLKLTJ6qtB0R0iAh5OqA9x8CEg8BMWyqeQK2/gC1JnvOmpG3C1tq7c1dvbcPHlKILTRFBiJPf/AlI4i1U6Tf34fwUgvvunEeQwtTf+67qIiyCFUNI7UNI7EUNd2KUTWIWjgE9i388ihnIboiUP33cLzvLTSLFh6q5Pqz5PpvdApzATwDOr+J6JFH5nOuQ5hSPU/uogCCLJj51CSm5e933zxZ/GPPU/Ce39lwhKAv3QvwUgfNMvcUS7jW0vP4Lg2cQefgzfLAf6WtdE6r6Z2EOP0nz2h3EWnkSd+CSevoKz+Azq1h8ldvfvoM8+QfPof2JJ2M+29/4qqqriey7lldcwSmcI524gmhy9pr7fcx0Wmy2GEgmWGw3+/MxZoorKD+/ZhSQIHM+vkW+1uH90hJZe4unpSc40BD62fTtjqRTPzs7y4vwi2UiYn7hhH67v8/TMLCfyef7+vr2k2mnblUaTuKp2bJoKrRZPXZzBcBw+tn37N23fdD28nfeT3lxBklTU0HrNqe97wNu3/Tq0vMLLC4vcMzLM7u71vrorjSa/e+wNutrX6TtBn3n1tblSouV7Dr5RCPTmro7vWpiTf4h17g8CQnvP76Nt/oE33b5TfIP6Fx/CNwobvlPGvpfYe/4Ye+5xmi/+U/zWEoLWhTJwL3L/vSj99yImN18+HqcFUrjzt37436Ef/nff5BUQ0Lb+KOEDv4QYWd/J7juNzL5bAPZ3BJ5V5RKBEN7EPNt1DIxWvu1C0Efj7O9hLX8tmIkKAh0aKaooyQm0nltRcze9abGG69oYzVUMo4xgGzitAq5jYJt1+rt1ai0d1B7o6ka0q8hmAfBxQr14SrugQgk0sZYgoZglJH0JRQDVbeLN/hlu/vXO+V2KO/uCHETpBOny/0shBNdEsKvg2UjZGzH7HkDP3Uk0OY7rtGi5Jm6lQGl1CU2SSMhgmw1c1+TSq66eL1LPn0QLpxAllW4EBFfCE8NEkhPYZoN6ZRZfUPEJanjsjsYrCZ0rGXgISHKYUCRLKJJFVsJ4Prxx9gU2bb0RQXCRJA0tnF5fRPEWLxJTr9CoLtBqrOJ5dmdfQXFPEgSPgLxKwWeijCwJOFaxkxaUlTiOXQdfJ5PbTb0yjW0AyAhyinhqiEi0m9LKa1hGAbz1KT/fs3E7nFkOCLGoEY5kEAWfZvU81cJJmrVFHKsCQCizF5rTCFaFiD6H6DRBVNFGP0Q4sYn63FcolwtoqS4wgnVEUSU3dFendbHve7iOgWM1aFTncYw6pmEAYaBBs3qRJhDSsu3xBlZsDNtrgtEEBLqG78eJdOHMPYaAhy9qWJnb8HWD7sGbCIXT+MP3Uj38H3AbswBo/feh9tyFgIttlNCbK5jWVkK100HhIALhLT+K5QuEfB81s4PQwP0Yi1+hcfb3gkyEZ6FkdiEnJjBXXsAzCliFI1iFIxt+4/qp38LoOgiCTG7oLkAkJpVpvvErgZQBQAyhRIeoLjTQ5BCC28JYfAZr7Qjg4Yf7sWJjSPFRugfv7kTW9OYKhYUXiCZHsZoreJWzdI0+hNa1G4Dj+TxnC0U2ZzLc0NuDU5/FR+S3Ts1xZ2yF4dw4XT37AFhrtahJY3QPPYI9/zj6G/+FyM2/uq41tlc9F9wRsRFCO34S3zMxjvwy+qFfZKuaQfBshMGHUYcfwfd9ynoVL/8a4sQneOz8Ik7oY3yIJ7Gm2jYXokJ4//+N73tUPAFr60+SBMorL5Pu3k1x+TUsowRSCLt0hlrpDIqWIp7eghYfRvQdasXTVMtTVByZfGIHE73jiL7NhLTG0tQ0SnSQp+dMDFdgTFhG0WfYK/vszSXISQ18P8kt/f1UTYs7Bgc6tk8PbRrjtoF+IvLle/dKP1SAbCTCJ3buuHx9PAdBkN5Rlwvf96msHaNWPIMgSPSPv78jQ2rV51lbeAFZjRGJDxKJD6GFs6y1WiRUFe0a1lQ39fVet93qXK2GKAjcOTT4HUFkr4Urr70gygiR3o7HOoDcezuCKGOe/V2az/4IghILoq3XgJN/nfoTj+CbZaTsjSjDD2NffBS3fBJt648RufM3EUQZdezDKEMP47UWEeNj182sClcFqEL7fwHfrmMc/++I8TGkrt2By4qSQFCi+HYTqzrN3OJxPEFiuG87WnwIQVSC53b5NPbM5zAnfw9z6tPI2X1ImT1IXXtQhh8BbWOb5m8n3o3MvgP4TojMQkAsfNdEVK6vm3Fdi3rpLL5rIa48i1M+DZVJpOQEcv97kKIDyIlR5PjYhsphz7PRGytYRhnPNfA8C9usYps13iwSKYgq4WgvipbEtVvYdh3PNfE9F8+qIVbPI1bPITUuIOiriGYBwTM2bii1Ha/rAGZ4CDc2ga9lCTrgOO0uLlqQ2gUUOYwsKziui20337ZBmKxEUNQoltnoREzfDGooSTiawzLrWEYQ0ZQkFVFS8Ty7HUV8i8Kyzr6jJNLDyEoE22pgWy2C6yoiiiKKFkcLpQCfSuE8rcZqZ11RUlHUKI7dwnVMBEFCDSVQQ0k0LfivrAQFAq5jYBklFC2NrIQprRymXp7suBmAQN/owyih1OWogO9RWj1Mo3weSQqhRXtRtRSOY2JbTVzXwfMItKxXRoQ9Hfwrr6MCYgy1fgq1cUXxSvYWlP77qRSnOpKIaLyPVvUskhyie+geRDnc0fVewtrS0XXXAUBRFWw9+EyVwyhLT+IpCYz0fvx29FgJ9dM3ejcA9VO/iVU4gpG+BTccVDqLokLv8M0oagynPkv16K8ghnMYuQewrCYIIqnMOInMGL7vUz/5G9il45iJbTiRTSCE0SIZMrntKEqI6uF/h9tOrYvxMbz+9xGK9aEoYepLzwdjXl/GNyso6W0o6R00z38a32liRYaw4lvQQlmikoQx/RlEwcOTIuDbiG/ipnGlOZ4rJ3BjYyxoO+kKhQhXD4NVRtGX1st5UrvQQ4PMqDv56lKFg3293Kmco9W2T1sI7SCV6EEQFfoHb8NceZEjrTgv1eI8FJpk+MhPBhPMsY8QHv844dEPIggC5U+P4zdmae35BVK7f4pQOIN96tfRX/4XwbEKEvn7vsr2TbfSqF6kcuFR1NY8nqjSEuNMe/3sXfld5LVXguMc+yip+/6Y6uyTGPnXkOwaZmQYL3yZTImiSjM0Qr68yLB22SrP9iUU0d9Q9BSODaA3V9fJnzwkXEFFaY9jQZA6Y1SSw8hKtJ1tCSFKKoIgYRpFzNYanmuihbPE01uIJIbxPQfHbiGIIrISRxAEPM+hWjhFrXgGNZSme+guJPnrb0vr+z718jm0cBYtfLnxx/XeT77vUlx6lWZtpvNZJDFCbuB2PNdiafoLl+sP2qjKfXw+r3L38Ah3DA1imzU8z0ILZ9/WMdqu+7aspN4pfCPvbt/3aH3tH2Oe/V0ENUniI69vkGh4jQWqf7EX36oi99xC7L1fRFTb7cytWmDF+C3Clc5Cnu+vmyiUdJ1nZmbRHYcf2r2xyMxeeYnWK/8SN//aus9jD30Oof+hdyOz7+LbA0FU1lm3XAuiqKA4Taz5x3FaS+A6aLmbkBKjhLb8AIKo4Ng61eJpjOYqgigHxMwN0uvXq3gVRRVJiaJoSRQ1gShreL7I0aPH2b//IIJv4bgmSFFkNOzyK0izf4m89iLCdUTubmQYN7ENN7kNN7O/TV4DeKJGUwiRTg3SnxnEcy0qpRnq1XkE38FxdJw2GRWCi4OiJYlEMshKBMcxsOxW0No0FEdV4yhaDLFN4H3fx7FbmEYF33Pxfb/9ItKx7RaCIBBPjRCO5t4ykuJ5DkarhNEqYBk1XNfGcy0s2yIUiiLJGpZZw7GblPIbq5PfDNF4P7HkAFo4c0V6zOPNOgJJcohw7LI1UbrnBmyritEMCm8SXdtQw+vTsYIg0tV7gFR2N6KkXXfbvu9jGRWa9RX0Zh7fDyNg4JhFRCmEEh7AdQyE1C5ok1lfkGhK3VCYBECUNDzXRG+V6Bt/P7Icollbprj6MrHkEF09gd2S61q0GkGldqZnF4oaZXX+VWzTJtV9A4IgUassYfW8n1T3djLxXsr5Y7QaJWzbQG/mkSSNWng7Xk8/vhQmnhzGNKtYRpX84mF6h25BjA4Q2ftzFPPncK0gqovvUSmep1lfJtk1TnzXT1EvnqZZOAG+AYiYepnluZeIxPsIjXwPzrnfx5XjGNG9UF+lUV9FFCw8pwFoEB5FjIUJJ0ZYMmX06AEGq19Fbc0j+D5S4UUs10AUwNGyGImdgeex2cKpT5MRGp17yQn1YocHMAmRsFeR6ueRnBpS5Q02sbH4x5UiNOVuEuYMVE6iSdNsii2QGLiTvuaztOZf6iw7aJzCpgiIVFe+DPjsAIalNKqfwg93I+h5KJ9Bn30MV88T3vRRvMY8AuCFcyzPfZXnWsM82Hs30tafQLv4WazBR+iLB3ZSlbXjqM2LSE4DCUiSZz/T6P0PIBVeB0Gi1XUQ76WfQXBaHV9lqXKestxHUvFQQxlyg3fywlKRo/UKKUWiR3UpmBYxyQEftHCOaHKMpeI8IXsZvRFoxRUtTTw9QaMyjWWUEH0dUQrR1XsALdJNrXSGemkS19HfctJr6gVMvQBLL3PlpF9W44Rj/ej1xUAWBFhGkeWZL9MzdA+ymsAyyrh2k3C8P7DPexPojUXKq4cRRDmYjGqJzj2pKh6WUcK1wHFamK0CRmsVx6oBAomuHdSKp2jVZjEzW2lWZ3FdA1mNk8rtoVWfp1WbI+ksc0skRlXPUs4HEV3wURMTtMLjjGfevHvedwKR9TwH26ygvs027ldDEEQid/w6bvkUzurLNL7yCRIfen6d7r31ys/iW1Wk3E3E3/vEOku3byWRBRBEibJh8OT0RWqWyT/Yt7fzfM6Ew3zf9m3XXVfpvY3Eh17Eq5zBKZ7ALR3HLR5H7trH1yfi+5vHu5HZdwDfzsisZ7fAsxC11HWXsYwKoqThN+dozTyKUwlIg6DESez+aaRoH75ZwREVKoVT6PVFuKZzZ6D3FOUIshxBDSXwPB/HdbDNZqfP/JtaFtkNwqd+GblyhRt/ZACl7w6crgNU1AEiyTG6e3Zg2iaV4hRGq4grKMQjabRQknCsG9OX8fGJqBryFQUJlyr0A2cAHUkOoagxJPn6BOzbgavHjOc5NGuLNKqL+L6HosaQ1UjwAvM9PM/BMqqYZhV8n3A0Ryq7BVX71piuu67F6uzTgE/v6APrWvJ+swgIbhFFS3UmCwCVw/8Bt34RufduWuEJHFsnld2CFs4xc/5ZNFUg2TVBKJxhdaEtMxEEBjfdiySpNKoLFFdPoqgx+kfvAGBl7hVMo0KyazOKGqWwfAxRVBjYdHdn3+XCOWqlC4hicN3BR1bCdPXuIRRO4zomK/Ov4Ng6V+t/FTVGrv8GTKNKee0sntsmj0goooDv1DuRaBcVSZDaxXVSoEURJBAEtFAK0yiDe6kphsxlaQoghMC3UOtnUVuXq/J9QcKODGNFx5C1NI4DCCK6rxDyigg44AfXCYRA8iKICK6OZMwhmWvtojkBTwqhRAfRkXCUdFCNbxbQaqcRr7Kp8wE3fRDP01GqJzsRTgA5uQWnPnNZB9yYR1h6Bl8K4499BEGU8V0bcfrT+KKCs+ffYMTGKbsqaTm4/pHYIK3GAiAQSoxiVKeI5p9DwEcbfAh99WVEu4aQu5lI1x6mV6dJ23NojalAYhQfQ9Tz+HaVl5W7cROb+eCug1hrR2ld/MtA99hGa+zHaMo5xjIZRKtK7fh/RdK6UMc/QdMoE472EU1etrnTGwtYRpl4egvSFa2ug+dMDauxiH3xLyE6jN+1F9+1UbQkoUg3khKhUblAo3y+E+UUJS2QBV0RGJDkMMnsLmrFMzh2IwhMCCJe2xUm3XMjiczWa99gbZRWDlEvB1IORUvRO/og+D5riy9iNK9tDyYIMrnBOwjH+iksvUKzegFZTeBYdcCne+hewrEg3dyoXqS49ArgB7+pt76gNW9rePHt3Dg4guB5CEoE8Rq2Vr7vUi2cwmytoUW6icQHg6ydY+A6raDBj5r4uguZ3w4cu0l+7qvYVpV0937CifHOc9i1SriuSTTx9trDuo15an95E75ZRNv5U0Rv/zUA7KVnqX/hARBEEt/zGnJ233WOpUWrPk8sNb7uufiNwHQc/t/XDuF4Hj+5fx/ZyNffGvlqvKuZfRfvKOzKWdzGfFBAktq84XvHbtBYfBq/cBjaqVcEGa3/bkJDDyOqCfTmCpW149hmpbOeKIVRQl34PsiyhhZKIWtJPC8gx0ZrDb18ff/E9o5wHI9wJImiRZBcHV76WaicxhM1Gr0P07P/XxDquxVBEFiqlVgpr5HV4vTIEUJyhN7BA5i2jSrL68jo9RS8QTGW1mmy8LcFoigTT40QT4286XK+5+F59rf8/CRJpW/s4b8Rwi8IwjXTkLGtP4K1dpjw8MMkJa1TfGHbNst5h9FBhVrpInVhlg6h9H2atSUS6VGa9SCSHI1f1nbFUsOYKxUa1Xmkts47nhpe97JIZjbRrC0FRWRAXYyjqyMMtKPRTRdO2FnG/UVUwWufg0hLTPB0WWO3VuNAXz/haJbF1fM0qwtEJBffAzXciyJDs3YRCSvg31iISh+u08LwREpyH7cMBcSlslYGRDwhjqhGkf0mjlVuR3fBjG1B8EDwLbzoKJYcxvIkYl030Nu9jVZjhcLKCcLYIEbAqwVEVhBJ5fbjeT6GXsIyBJzIFpxwf7ugDxDCGGI4+NurAx6ulsEeeD/UppCMRSSrDAgY6ZtxtRx4dVxxH6HaJJ6oYid346i9COEdKLXDyGYBb/ARxMokUmsR3fUJSQqCPhfsU4kjt2aRtQxpNSg2iiZG6eq/lcLiC7Tq8xi1i4hOEwEfQY4SHf8ocrSf5uTvIzTnKacOUBJ7ietH0YBGdDMvyvfzYPdFxMUvcLsyRXjbJ/BaeRpnfqctGRAQtRSeWSa2/BiDB/49CCLVk/8DXBO3tYRx+jdIbvtx1NTllLEgCIRjA2hqDJwWrtMKvJUlLZD2yCFaF/4ssBPTV0lv+XhH5uU05tHPf45o/10kN3+oM7kWRRnPtdGbS+iN5YDIdm1HlFQi8SHyC89h6cV1IQFTLwCXyaxVPI5VOIpn1fCtGnJyAp1LBEbANisUZh7H8cC2G8FzXAlkOqKkoYa70MLZgHC3CXoqt4dWbbYdrYVIfLhDZAFiyTFEQWZt6UV8z0GSw2R6D+D7PquLL9GtmKAfIX/sr1GbF3FC3VjpfYiiSjjWRzS5CUnWKCy+FGiZAaO1SrVwRWCjc90l1FCGULSnTXbT3/SzyTJK5Oef60TSq8XThGKjwW9lN8nPPwu+hyjeTTi20VrONmtU1o6T6NqGFs4ixYaI3vsHNL70AcxTv4Fv14nc8qu0XvxpALTtP3FdIgtQWHoJs5XHsRtkem78ps5Nk2U+sHmC7mikQ2Qdz8Pz/bfV7cvznG+aUP9N4zv76N7FN4VO0F0A6Sqy4PseVv41WjOPXjbNFmS03tuR++6lWLxAcfZV8FtwpTm3oIIQwkPGNALbJsts0WpW2BhxFdBCSbRIhlA40F8KgowoSgiihOO4fPHxx7n13u24xhrqCz+CWzmNEMpRu/1PaEbG6cn2dR5SMTXESDpHVF1fwKb9Lfbu/VZDEEUk8W+GqL8TkWvf9ykbBrrj0BcbINLuTteybUzXJSLLiECl7qFoCWyzhu+7qFqCaLyPcmGSRnWBaKIfoxX4yrakBAtrBXZku4jGeimLZzqOB4IgdiYI0+Uy50pl7h8dIdu7m3LhHCUSfG6uwpZMnQPt95cmSRwr1HnDj/JDO7fQG08jiBJfmJpmqZlni9PWS0oq6a7NzNhxkkKL0ahCPDWCKMmctXNgrDLEAr7v0t2/B0kOI0pKJ+JktAIyHstsJZPb27E5a9XmKS6/iuc5dA/dhc97ECWFUDiD3izy7Fdf4MGtEwiCQDTehyyHqVfniSb6aVbO06xeJNt/K9HE5YmR146iOVaD1bmvoGhpRKULvbGCGsqiav00yudIdu0g1b0Xx26xOP0YeA5aZIBwKIPemMX1wNX6aOaGwG8SFBeCL4AV34IV3wZSBq37TqSZz6A0LtLa/GOorVVUQExM4AHh1hx1NYMaypDpO4jvNEn33IjRXMXzrI5PtRQbRhAE1Ow+mpMiXmsJuzrNiCQQdcv4CBzydrJm2ITG7kRfeAJai5TP/wVq/TT4Dkp6J7GdP4mASOX1n8czCujzTwACbnMRQYkjRXpxquepn/oNIhOfJDx4X2e8Nk7/b6y11y8PYlElNPAewoP3Uz/923htX1x8Byv/GkrvHQiCTOPs7+M2ZrHyrxIefiTQDrcJgygpRBMj634jCCRAPcP30arNBe4ojk5x9ils43Jba8+sUj/5G+t0vU79An5qH2hZcoN3UDn3p0irk3ihHsTMAc5c8Ln3vve/aYRNViIkunZQLZxAEGXSPfs3LBNJDNGr3I/RzKPER3lpKc+dQ4MMjb+PtdmnkPMvdOzyZCOP6Ri4okOjMk2jMn35Ekoqicw2TL2I0Vzp2PlJchjPtfB9B1Nfw9TXqBZOIilRYskxYqnxdW3TL8H33SDA0ljEqC+jhJJ09d/aIWh6c4W1hefxPQdFS+K5Fq6j06oHhZ310qlOpLy0coi+TT3ryJ3nOawtPN/29653Jv7q8HsJ3/wr6K/+G6xzfxS05XZaCFoX4Zt+qTOGjOYSipbpeHUbzVXMVvBerpfPk8hsR1Y2RlMto4woKsjqW/vHbkvHcOwmEGG1UOCrZ87yQqHIvdu38ZFtwUTo9eMnmJ6d47Ybb2C4P5CarS4eoVU9y/RqiPvv/chb7ufbhXfJ7HcxBEFAy+3HS29bZ8Xl1GZonP0/uO3OM4ISQ+u/h/DAfbhIrM6/huvoSKVXEawybmICIsMo4W4SmXEkUcFoFTH1cru4xwR8BEFEVqOoWpxwNEcokkW6ytvS93389rLg4ogwtbbE6JEfxS2fRIj0kXjfl0mnt284n0QoQiL05ukR3/eZq6xxfHmWst5AFEQkQSQbjbOjZ4j+ROY7Sk7w3QbDcSjoOiFJWpfKen5unqpp8r6J8U4BwovzC7yytMSe7m4eGBsFgunQbx4OTOt/5uCBjh3RifwaT12cYVcuy/s2BX3Uk5ktFJYPI8kauYH9iIJEpXge22pQXpsEfBQtzpNzKyzU6pSNYe4YGiQU76dVDV5ShLuR5CBCe2h5hfOlMn2xKPt6eugbvhW3XudmN8pQ4rJcQ5NlHh4PDOb7YrHOeLpjaJDtXV2kQpcnE3FN5a6RjWnJO0YngAlWZr6MqRfaGr1U53vX0TGaQaYkkZ7oEFkICEMo2oPn2Rte3IqWxHHXj28tnEILB9sOhTNkevYHDRWuwKUXsxpKMbj5I1xyyXBdq1NUl+7e21lOViIkMlupFc9gGquY7eYVgiiT6b2ZWuUidqsJ+AyM3U1l7QTNah1BCuHj4ST3oPIZlPoUWs8BzIXHAZgL72VI0fDNIrlYltDAfegzn8OYexy6DqIkd2LpK4Ta0WNPSVFem8SxW3hqFtnKo9lrnWiiq2Y5kBI4EBKQJZWWOkLcvoCy9hz4Nr6goox8pPN8jIx/P43T/wt99vHOtYlOfAI1dyOt6T/DWHya1vRnEWPDWEIYuXnxMpEV2q9Tz8KY/xLG/JOAjyCFUboPYC0/T332S7SaFopbQ2vMckmmos99EatwJNBLei6imkDrvb3TodEzK9jVKeT4CFI4Ryy1KSjoOv2/iBYPYZqbcez7sMw6/sqz4DtI0UFCA/diV89jrb6CWj+Pm5hAUyJozaBds2KsEotk0a9owPFmSHbtwPc9QpHcNclVMN6yaOEsf3ziJLPVGqbr8t7hbkKrzwaevVI4cJax6+QymxDi4zRrM7Rqc/i+ixbppqvvVmRZRWhLfTzXbkvBRHzfw7bqWHqBVmMRo74I9Qs0mgtU80cJJ0ZIZLajRboRBIFWfZHSyuuBRMFpEq4cA6BQOEJm6w9iOTpriy+A7xGK9JAbvJNG5QLl/BEalUlCqoveJrWipOHYTaqFU6S793bOubx6BLsdsbbNMmYrTygaFBqG9/5L5J7baD7343jV88FnB38ZMZTB91wKSy/Tqs8hyRH6xh5GkkNUCifbWw7099XiKbp6D3T2t5zPs7I8SVqdR5bDDIx/EMt2OXvhAvVGgzsO3NRZ9qsvv8qxM2d4380JVNGgZ+R+bNvjqUNHWJYkHtoV1Bj4nku1NM3C4iLLQ4MM9/cHRLs+gyjApm4do1UgFHl7xXzvNN4ls38HcCWRdY0StRO/hm/XEKQwYu4gbnwTSnYXriC3iWwdvBZicxbRbZEY+xBa9wEcW0dRYyhqlFDkst+g59p4ntNuYxu8SF3Po2EZRAWxo1ldqVdYqBbIRROMpAMLJckDtfAi4fIRXFGlcMunCMXGeTu1uoE9T4NCs07d1KmbOrPlPGW9uWHZ1UaFU6vzpMJR+hMZ4mqIeChCdyxJNhpHvI7+yvU8Zsp5zuYXaFmXI9TJcJSeWJLuWIruWBJJfGv9ltd2AriymrRpGaw1a0QUlUQoSkh+8yiz6dhIorhOB/xO4UqPRc/3ObqyStO2uXWgv1O48bX5BV5dXOJgfx8Ptkln8Pk8vg/3DA8T1wIiJQig2w6We7mUQBQEEpqKgIDluURROvtTJHGd3Y+gxHi8HCcWivBBVyChyqy4YXrEJs1aUKgTjvYwIYYo6Tp72r6W6cwozTaZ7eq6nDLekc2iSdK632cwHmcwvlF3vK9no71QOhTqtHp8u1C0FKZewDKrXElLm7VAOqGGu1DUjfsX244YXy8EQXhTG71gmctjWbpi2avTjInMDurlqbaXskQkMUwisx01lCKaGGDh3F/geTaeq+O2belS2R2ooSy1UAL/uIJgrKG5OnbbZ/OC38PE8J0Y05/GmHsctz6DlX8VAK98AlMdBiGG35ZEtRwPpxy0iZXDAwGZdUrg+LiAnGkTDqNAfqGAmN6Kn7/QaZBiJvfSzJ8loldJdU2g5m5CSW3HrgTFh3J6FyXdJVScIjn+cTyrgrV2mPqp36KVuoVI8VkEIDL2EUJD7yW/eAi7fAqtfgrRLuMjYmRuo+5HiSIiWkVEu4JUC8iKHZ1A69qJv/TlwM3iii5iVuEIopZGkCO47SYlghwhvuv/QkltRZ/9PPbaIQDUxjT5i09hexBdfQYBCI1+EC27HzV3AHPtKJLbhNYC9ckz4NkgauCZWDN/Tki+nSvLZzzXxmiVAuIa7eqMA0GU1pG4S/A9F6cx22mZDXDX8BCPT02zO5fDWPpq4FMe6SO+52cwFp7CWHgKr3qeWN8dhGN9eD03YZsVZEmhcfo3cSrnUNI7ULsPoGZv6GxXaPtJq1qSWGqcxvk/xayevHzspRCF4lak5FYkJYxeD5oOSJ5NqPIGwiUHhspxKq/+a6zYOERHiMSHyPbfhiBKxNLjVAsnce0GmwaC50EkPkw0OcLawteoFc8EvsRakmZtjkZlKtiHHMV1mtRKZztkFoJCquT3HkY/9p/Bs3CHP87swiyKNYndtgZ0nRbTk0/iKUOE3VVApKvvIMXlV6iVzmOLffR2B5mq6Qtn6I0uICAEEeTGAq7QxaNf/gqiKHBw7x5UNfjNKvUaoq+jtpv5nJ9/AyO2kw/s2oGFwO1Dg1hGhcLSS2zuaTGa6yfZG7i22FatI2mSRIHC4gsdwv2dhnfJ7HcpPCsgq8IVkVHfs2mc+q2gU0hkADN7C6ZZgdocjdo8ghTB92zwLfB9vL57SMe6CQ3cgSBpqKFrC7BFSVnXXcjzfQ4vTuN6Hnv6RpHVgOhIgoDtutQNnflKgdMrc8wnYdu5XwdgMvkgh5ZriCtfpi+eJhOJk47EUCUJy3WxXQfdtmjZJg3ToNCsYbkbu2apksyOniE2ZXrw8bFdl4ulVc6tLVHRm1SuIruKKJGLJQnJCookIwoCnu/jeh5LtSIte6ObwmKtxOnVoPBGEkV6YykGkl30JzL0JdJocmCwbTo2C9Ui59aWuFBaxfd90uEoyXCUUqu+gXiHZIVkKEpcC1EMC7w8dw5FlqmbOsu1EmW9iSyK9Ce6GEnnSIWjaLJCWFZJhaPrSLXlOJT0OhW9RcMygheTrBJSFEDC8kVSoRADV5C1v548h+W6fGDzBJF2yvG1pSWen5tnZy7He8cD8icAT12cwfE8dnfnSLfJbDYcJqGpKNJ6cn9DTw9JTUMSLxPFfT09bM5kCF/lR/lPr4gqXMKtgwPcOjgQuEg4wW++2mpRdVxcyyamBNHDeHIQ6pOd9WKJfu7IRjjY39fRhslKmFDXDjzfIxa+PKZ3d+fWGbm7nve2JinfDJS2z6ptVtd9folsRxOjf6P7/2YgyRo9w+/BNiuE40PriK8gCKjhLozmCqZexGwFL+xQpBs1lEIbupViZg9C8TCVyU8TalzAB96z+z4i/bdiLT2Np69i5V/Fb5uHiZ5OMp5GDPdiL/81AGpyM+FIL7ISQu7ein7sKG79wqWjIL3pfcRcj+LqSWyrgelHiad24VVOIqd3o+VuxWks06ov06qvEEsOkJj4OPbhf48gKrRiu7CsGpZVQ5Q04lt+GKtyDsGuEil8BcF3ESKDaEMPUcyfwtBLEOqjpfUimav4ooqnBBN/LzKE1JolYS/gmMv4gBUdx3QUxNyDRGWPSKwXQZRxatOYyy/imWUwA12yoMTw7Tq1N/4bcvctOKsvAOBLEQS3hVR8GUHJIvgOrpxktZSH8pOAiBIdQaufQyy+juu7IEgkb/g5Gmd/D7c5z76uUyzPpFC0GAIi1rrxKBAKp4mnR4nErt1gI4haf4XYth9H670NgJFkkp/YfwOC51A++QwA4ZEPIIW6ULv2YCw8hVU8ju97CIIYvEfsKtVjv4Xf9py2yyexyydpnv8T1OEPEBl6EEm6/Lxw6rOYi08H40PL4JklRNcgVHkD0zWwIoOAQDyShYUv4rs6UnQIufsWWgtPIdkV1MYUatduugZu7xBmUVSIpTdTK55CVQIDu1RuT9thYgC9scjKxS8h4iPVJpHUNG54BNdryxYai9hmjcW1Oi8fOUpvNsnBXSOYg+/DtuosnPxropoAmhhINnL7KK4cQRObONZZkCAWH0CTZFZKNr0ZBbd1ERjEdQwGUwUEX8D3RQTBo1GZpmd4hK3jY+RSUcqrh0jndqCGUtx/+20UV8NgBrr0iFvg9fwKP/bA/QDUSpMs5492ZBSKqJOKSZ3zgMDVw3UNHKvO2uIL9Ay/502eDN8evEtmvwvh+z5W4Qie3UDLHUCKBDPE5vlP49QvgBSmGduCa1YAAS2Sw2zl8d1LxEpAi/YRSW4ilBzekJZ3PJelaom1Zg3Hc2laBqIgMpDMEJJVIopGSFKw221qfd+nZZusNWtU9CYnV+ZoWsFsr8c4TY9+Bk9QCO35F2SbHoVmjcVaicXaW6e+ZFEkG02SCIWJa2Ey4Ribs/2oVxGkiWwfd4/v4mJplYrepG7q1IwWK/UKluuw9Cb7iigq23uG6I2lQAjIeqnVYLVeYbVeQXesdccrABE1hGFbuNewKiu06hRal1sAp8NRTMemZVsYjo3RqLDaAMICR5YubFjf8TzmKmvMVdbWfS4KIpKkkonEcF2LYrP2Zr4R+IAmq0x09TCQ7GIwkeFcsYjt+YE+tU1mfd/HtnUWS4s8YVcQBQFJFOkPQ1iN4XnBRKNlW/RHNX587y4iyuVUu+f73DM8iG5blFo16qJEJhIjoihEFAXX8yg261wsrTJdXGGtWWUs08utI1vIROKBcbvRxPN8uqLxzrEvVVcJeRWiRDvR1G29o5ytniMi+lRsn5QHSVhX5NC0DP7i7ImAiPfWuGvTDhRp/Xg5k1/gycmjDCQy7B8cZ1Om57rylLqps1qvkIslSb6FDOZqqG2XkSuLK22rjmUUAeFtV05/u6CFu9Z5ll4JNZTBaK7QqEzj+w6CqHTIuyAIREbfj1E8jLT2Mn4reGkalolfniGy6XtpnPpNEBWM1C1o+kUEfQHNqaCow1R9B0SN7Mjd6yLJdmITTi24Z+TU1iBdD/QN30ajtsJzL7zO8Ht+GK90BK33NkQ5QsIYo1I8j95co1FdwDYbZPb/PPXqIlar0vFXrhQC6UorcSPh4leDrm6CRDO+B3/tXDsbIJDr24ukhPEcK6hXkDREWcWtDtA48d9x2lFfNXsD4cE7qJamsa0GdRd8omSyO9FyN6INPULp4uOIgkR69H5EKUz99G9jF491iKw2cD+Gksaf+QtkMw9WoBN30/su/xC+jR0eQGnNI7Y7LArZm5Hjw2jjn6B5/L8gG4tEVh/HVTO4ahYim5DVQEJjWw0MvYShl+gduqUjWfE8F72xiqbFMJa/Flz/6lSHzEKQaTHyrwYBFC2NmgsKmaT4BEghfLuGW59FToxhrh2mcfq3wXeRIn1EJj5OfflVvPIJRKeONfOXtJZfQuh7gFh2O6FIhsbkHwI+avdB4jt+As/RaU19BnPlBUL1STy3hYyLtxpI6qTYMIm9/wJRiaF0H6R24n8gtuaRyydg9JHgcjkGduUsYWzqZhnkEEu1OMNq4Pub7rmRVmMFwa6jVY4hujp+ax5DTOCGegEFfJPqyqvoeo7hVIHBdI3C4mXXkWwieB4JokrvyHtQQxmqDR2vdgTNrqDUVyH/VWq+y5jWh+GM4OjzzE3+Gbg2Aj6ymiA7cAcrM1/CaK5gWw0+9r5HWJ17GqOxRMGu0Df2XqKRCBW30LbSEpAFn7tyckerW149DEA41t9uXZ+nWZ0h1b0XvRFct0himHC0l+WZJzFbeerlc4Tj49d/MHwb8C6Z/W7EJVsX3+9Ycpmrr2AuPw8IWOl9uIKIKIVIZLaRzO5gZfYFzNZSoD8cvBNFjXfM7WdKefKNCnXToGI0WaqVcL2NJO3Y0sV1f0dVjaOLF2jZJt5VDnCqJDPR1cvYK+2Wldt/nJu23slNQLnVYKVRodxqUGo18HwPWZJQRJmwohJRVSJKiGw0TiYSu65E4GqokszW3PoqVN/3KbbqFJt1LNfB9hxcz0cSBSRBJBGKMJzKXTdCF0gdgmuyWC2yVCtRNVodsg4Q18JsyfazOddHWFYp6Q0qepNEKMJAIkNIUfF8n/lqhdlqhb5oiEqrzsmzZwl358jrOtlIlFuHRumNp6gaOn947DASNv3RCLbn0LRMLNfBcwxWa5f3HVVD1CwHD5GtXV34vofhWBSadWzXwXIsTq/Od6LMGhCTZD534iXAx/M9mpZJGJ+GAZPtjltX4o9Lsxs+k0WJqKphOjaGc23T/lQ4iuf71A0d/yrafb6wxFRhicFUllKrTrMt8ehPpNnXO8ZaVGBmIUjtVY0mhWaNXCxJyzY5Vbc5kJSZarksXDzN+7avj/SeWpnHaY/fEyuzLFaLPLL9RrLRy76br88F2rbFWonF0yUy4Rjfs/sW4tplAczhhWmOL89QbRdCSqLIA5v3sq2dCrwSZb3BUrXEWKaHiHqZ6F8id47d4Mj8OWwfsn4JGQhFer4hc/xvFlcbq3+j0No+nZcq07Vwdt2EQBl4D8bhX0IqBZ3NfDmO4TgYxSmsaDexnf+EwtoFfDlCKJbGmlvALp3sPNPk2NAGeyY1u79DZrXuyxpDQRQJRXO4LohKHG3w/svrhBJ0D9yI3ipSWDqKaVTIrxkdN4tc3z70Rp5GbYFK4RxoOfyumxGKr+Hn7sSXojSqQdQrndtKJN7LtSBldiJq6SDaCoSHHkZJ9BGJ99KsLVJcPUmjuoCsRInG+1hdOoojBGNSqi6Tzm1DGf8ELb2G0rqAE+rHUoYQBBc/OoLanAHfRQz3IMWHkKwamZ4bMfUytWIVP3sAVp/Hk2LoSh8JW6farOIl9qDWjiG6DUS9gaLPEe7eTWTgTgBsq0V57Sx6M09h5Th9I7cjILC2dASjVUQzF1HaVm3uJUecNnzfx1j4MgChgfuD7lh6hdWF19GULLK7QHX2KUKD92FO/gH4Lmr2RsKbf4ji2iSGMgC5fiL2EkLhFWRzGX/mU1TzY1SlMGpjFl8MoY18mFZ9Fb25Rl0cRo3vDKQerfm2iaSAktlFbPvfR1RiuK7Lo8+8wljPfWwS/xSnNoW+9CIzRY9c/YuIdjApiNJ2srP3dMaurER55pVZHhqeRpQcfITAIq70Mk8u7OCuPWlCtcMI+QYZtYtoZlv76Sa1ddUyogAxYxrBrNA6eZqWIBLR1/AvdYnsDFwJyVwmYq7ihLoRnSaiUwdBJrzlB9HCGULR3s6kUVFjmM0VBNfA9n1atVkkJRLohQWJeHqCWmkSv7XAl6bC7PBOojTn0JKbyAzeHfgIt/K0iscRVr6K7augJgnHBlDUGJnUBGZjgXhqM467kQN8O/Eumf0uhCCpaP33tsX2Gp7Tojn1WQDE7luwhAiiFGJg/P1BSsvWMc0GSAl6Ru5AFiWcleeRkls5WjV5/sLpDfuIqhr9iS5EQaBu6oHZeTut3rAMLNfpEJBL6IrEGU5lGU7nGEplcZZepGWeAEEmtPdfdZZLR2KkI29dnfmtgCAIZKOJDpH5RtbPRGJkIjF29QZRtKZlUDcNIkoQpZavsj5JhgN1pON5yG2SbDoOnzp1Bt+Hnz54E6PJLEtHz5BJ9TPVWGQs0sVYpl1QoGgMpHtRRZFHJsaJqSq+73O+uMaFch58l4lMN32JNFE1hNlOyytX6EEvRcvzjSqL1RIL1SL5RgXP97Fdh+pV8o24FmYknSMTieH5Po7rUWzVWa6VaLSJuyQEmtaWbeF4bofkXcIlOYTp2ui2tU7uIYkiA4kM4119dEXjHF28wHRxhflKobNtgKVamaVaGTShra+NUDGanC8sk4slmSnlWTB8ar5I3fTxjWWWqiX6k+1Ur+9zYiUg33v6RpkqLFPSG3zu5Kv8yIH3IIsSy/UyJb2BLIrs7R/jxPIsJb3BK7OTPLBlHwCL1SJfuxjcFwIQ08LUTZ0vTR5lrVHjttFtSKKI47kcmp/m9fnzuL6HKAhMZPvY1zdGfzLT7goVNIE4vnCaiiNxa6JFrwozLZfcFcRyqVbizOpCRzOdjSbYmuvvvGSrRosvnH6delzgfGGZrT2DGyZhtutQaNbpiaeuSVjzjSqPnnqVXDTJ+7fftGHsfj1Qw5l1f4ciuXV/y7kDIEcRnGAciMkJ0rltVArn0Jt59JYIcoRQJEskM4g19xh29TxiOEh1y/GNUWs1uz/oRCZIqNmN1fZvhnCki56hm8kvHuoQ2XhqhEism3AkG8gUjAqSpJHZ8SOI/DCeL2DOvojrmsQSg29qnScIIlrv7eizX0BOjKMkJ9qfC8SSg3ieQ3ntLJXCJPXyDK5rIkoKnmtTK8+gaAmqxSmc1I2I3bdhOS6e1Qis06KjyEYe0W2h9t9LrRFob9cWvtaZEGndBwj33UqpvIhvm6wuvB50G4tv5dRKD3fctAVn7RWstdex8y/BwF0AKGqETM8uVmZfxLFblNcm201yAsIn1i43cnH1VYqrpzD1Ml09OxH0pUDvK2pofXfhuQ6FlTfwfRcn1IdsLODWJmmcnUN2WvihHkqhXTAbTKYFQSLbv4dHn3Fxmgd4ZHwZoTWH2rrsfGDGd7K8ePTydQbs+A58OY5kruCF+shOfAi1Pf583+f01BRnpy4wNSPxkw8+iL/4eVrTn6XLMRBFH0FNImoZfLuJZ+SZUI/TmP5LlJ7bsZef5QOj5xAFH1dJY6RvJVI7imgs896R81AyuOTsI1tFpOIrmPHdONEJ1HAK3zGQlx/Hs6+VDRQRw1nU7A1oPbciyFGa5/4Iu3QCxVi5vJjvYEz+AYK+RjS5Hbt0CnP+SWyrSNQqI/gujtpFRQ6hxQYQXJNIYxIv/1WkxHZUDWTjDKpzMpgENaawu3YQTm1D8EEpvorj6oTEEHbfQyhqLNBFL3wR6rOYkQxS9ua3vKfeSbxLZr9LIQiBxgpAn3ksSPOEuqmLMfA90t37OgUkjWogkNfCQfcrrzqFvfwClZWjvKAH3UEmunqRJQnTcUiHY9zefllfC77vYzg2VaPVTrlrhBV1Q/OC5pHAmkTZ/ENI8Tf3T/3bhKga6tiHlXSdsmHQH4sRbqftp8tlnpi+wFAiwYe2BN6/YUVhOJFAFsUgin0pbd7VxXA6RVJbb7f1g+0K1EsQBIEt2W62ZDdq2q7VI10QBKJqiLFMqEOSPd9Dt21atolpW4hi4AQRUlQSWvi6afaWZSKJIqoUeP06nkvd1GlZJiFZJawEOt0rI+hNy6TYrCGJIslQlKi6vmnFYLKL1XqFxVqJXDRBXzyN4dgcXbrA8aUZXMfhQ7sOors2X5o8yvnCMreNbuNiKbCz2d4zQtMyOLkyx3MXT/HxvXcgCAKz5Tx1U0eTFe4a28Etw1v402PPUzcNTizPcsPAJk6tBFHqzdl+7hzbwXhXL3/2xoucyS9w68hWYlqY1+eDqPDW3ADvmdiNIsm8PHuW1+enOLw4zdGlC6RCURzfo9Ym9XEtRN00OLe2xLm1Je4Y286NA+NB21nXJCF5dCX7yBFEFk+Vm7SmT3LP+C6miys8cfbIBtnKar3MXZt2YrkOj516jWKrDorAl6fe4GuzZ7h7085OpNh2Hf78+EvkG1XS4RgHhybY2j3Q+V1alsnnT79O0zJpWnmemDzC+7bfuCHzcWp1ngvFFe4c20EqvNEG6RIkOZg0e+2CGy28nswKkorSezt2O3KnZHYSS4+ihpKsLR7u2IWls1sQtThiKItnFDBXXw62H9tIZqVID7Ed/whBUhC/gU5Kqhand+gWCisnEEWZdC6wLBJEkVz/fuqVWSLx3o4+WAJ6hm/G1MtE433XvUcuITz8CIKoonYf3PBdIj2KY7eoV+ZwXRNZCdMzeIBaeZZ6ZZbiyvFgn3KI7NDtOLbO6sJreK4HgoSevoHu7A4MKQaNtU7jAtcJxl8o0osazpAO9bAy9zKOHXweS43Rsk8jp3eiJkew1g7hVM/jNpeRon14doPG0V8hqiSoRfd0otAIAolwFM8ud6KTvlmmUZkBQWJ14RDxRkAy69pOKksFkmql46c7WRlgj/9626qrgo+IHt8LXnBcihqlq3cPWihJvlikWrNo3fUP6I3UaM1+AadyhgZdmMomNAKHjVA0y/OHzrKwUubHPv5JamvHsc0qa6vnkWI+YalFtTRFLhzj7psPMNjXR3qon2r5EG5rGVmEvN3D1tv/f4hKDMuyOPnMrzESOos5/zjG/JcQ8IIue6EBjNRB4plxkhMPUjv6Kx0bNiG1i5aUQK2dQbLLhGrHwJwnuukjGIVXcO0SvqBipG4MGqX4Hr4UJZrbTTK3FVGUcV0Lx9bRtvwYcvkEXmMOJTGGnJgICugWv4I+90Xgi9csmJatIkL+axitccK1Mwjt6HmoehI9tY/t5AMiC4BP48zvkLzxFwm3ZjpyFNEz0IxAAqTPP4Fbn0GQIyiZnddpm/Ttw7tk9rsMfruL0KWHqtNcxFgIBPJu1358x0YNZYi0C0t83+uQ2Wiij1p5BtluQXIbr87N46k+E129nVTtTDlPVyT+poUxgiAQVgIScz3Yc1/EXXkODwV1z7/+Vpz6txW6bTNfq+Phs63rsobws6fPUNQNPrlzB5vSKQAEBCqGietV1zkEXNkb27aD1HwqpJF7h3x0RUEkqmpEVe2tF74CkauWl0WJdDhGOnz96Hqwn9x1vwfoiafoiac6f8ckiTvHdnBj3yaefPJJ+hMZPCHQ5ZX1BvlGtaMjHsv0ENdCTK4tslqv8MrcOW4d2cqJ5SAqu6N7EFmSkCWJA0ObeWbqBK/PT7E1N8C5QvDw3tmOtPcnMgwkMizWShxdvMi27gFmynkE4NaRrWhtB4rbR7eTiyZ5Zuo4hmNT0oOUYURRuXt8F1uy/aw1axxZnOZsfpEXLp6hZrSQGzojGowlomwf3sTq7DS+oFBzRd5YnqFlm0wVVvDxGU130x1L0rRMTq3OcXTpIpIoUWjWKLbqRBQNpWbgJEI0bZMvTR7F8322dw/y5ORR8o2gsKesN3jy3DFenjvHgcEJtnUP8PjZw9RNnbgWpmWZTBdXeGbqBPdNXE6xHl6Y7kSkV+sVvm/PbdcktJ7vISCghTNt3V1QEHY15P57O2RWakcqQ+E0PUM3U8qfJhzJdgpPlcwuzKWvdjqJybFrT4C17o0FhF8PZCVM79BGsinJKqnsxsYzihJBuY5N1dUQJI3wyPuu+306tw3f93Bsna6e3chKiHRua9Ddry3x6erZhSgpqJJCz9DNNGtLNCunA01kYhP6aiDbSHcHkenS6iFkOYwaCpp+aKEk0Xg/zfoSshollhwGgt9U0tIoXXuwi29gLD9PdOL7qZ//88BpgWVi3e+h0SzgA+cWHDYpr5IGnNAgsrmC4NtYrSqh5AieXcGtnkMAXpz2GLBPIncH741s317+6vnP05OL0BsNyKvcdw+2OMiZC0sooQwP3HVHZ9w9cu/dSKJELtuFovWTTG/DNUpk1HjbvsvtdBN7/wObWVkrEI1EUfv3sjz7Eo5do7H6Oma7GNk0KhzYtb9T0KZu+hjNc3+CmruJbRPfe4VzgsC8uYWh4T6E/LMIeLhyAiuxFzfcT1fPTmKJQLqW2PvPMea/jJrdh5LeToZAV2zMP4Ux/0V8s0jzzO+0x0EYcez70aSgBsB1LEy9RL06T7OxGrgUuOszm4KYIxebQApniW7+BHJijMbkH4FngpLEFjU8NYUf2YTnWoSLLyA59Y4VmRjpRdIy2OXThCtvcCl6rPXfi1Obxm3MUTn87xDtOj5gR4ZQW/P4xSNUZ5/CmXkMAL/7bnzxnZc/vRXeJbPfZbCLx3GNIkpmJ1Kkj+b5TwMeUnon1bZ2UQvnqJfPEkuOYRq1djpLRZbDmLaBF8rxYtljSo0Q10Lcv/lyL+dLUbxvBr7n0Hr15wBYjLyPHd/hUdkrCSfAXLXGaqvJaDJJru2lOl+r82dnzpKNhNeR2b5YDFEU12lC++Mxfmj3Tnqj0Xc9b78BKJLEpamUJiuMpHNcLOV57sIpLNchomj0xJIIgsAdozt4dvoEr86dQ7dNLpYCTd/uvstjbmfPMK/PT1E3dR49/Rq265IKRxlIXE6T3zQ0weKp1zixMktJD4r3Nmf7NxC5Lbl+Nmf7qJs6Zb2BbluMprsJtSd23bEkD2/dTy6a5GsXT3N8eZZRzWdEg76wgtFsdy2L9XFXvJ/nL5zifCFIGe/oGeL+zXs6kdLuWJJnp09wqK0dlkSR923dz6HnX+The+7mpblzvLE8w5fPHWNybZHZ8hqSIPKBHQdYa1Y5sniBmtHi6anjPHfhFI7nokgSH955M2W9wRfPHOLkyhwVvcnmbD9Ny+C1+bZPpqzSsAz+8sRLfGT3rYiCSFVvslQrM1fJs1KvEFVDHMxIJABBSXBseY6a0WI4lWM0040oCIh9d3eunZCY6Px/ECFdn8ZUM7sDMguBljDa/3WNm78NEASRrp7Lk9rAlxuyffsorhwnFM0Sjl72+VS1OGpuK7a+jO7o6I3FtkZZIBIfRJJDROIDCFcEOABcuQdftknnxjvE7cLcPCcmzzHRNcgQb2CuvISauxF79YVLiSJikSiSlmRutcKhky+xd9e5oCtyei9eoYFklzl6epZb772HiHAiiNaKKnfcONHJNgUtqNPcd9utROpA/Tmk2AjJzR8nJUps2jhfYPPo6IbPpNDl+/PKtriKojDU39f+/wjp3A5Kq8cJqRI+IqFwClMvUSvPEIl14/s+1WYDK3svrpYieVUmIhISaMlZxK57SKcG0HpuxrFbQUtx5TKpk0JdRDd/Yt26oigRGXmY0MBdAald+DIgEN/z0x2ZySW0GnlK+TOdDmQQFA/6+Hieg+85rC0dpXvwpqDdtdpHo/f9CL5PNDmM25zFdcFHRg6F8aIfRVj4a0S3haN2kdj2jwhFc9Te+G84tfYzIzEeeCsXT+Oc+U0EO3i2WbFx7MgIot1Atss4Fz8DgBPqx/CjRKwGsprc+EN9G/Eumf0ugu/7uEYB39ERRAWrcBSnchZEBTe1B1p5IolhJDmE77mI7f71ALHEAKFIF1Xb4YWLZ1mo1xGAff2byDeq9CXSGyq+v1GYZ/8PXuUsgtbFfPQj7PiWbPWbg+k4lA2TmKoQa/vzrTSa/NXkJKIg8JP7b+gs+/ryMmcKRR4e39Qhs4OJOLlohL5YdB35/fDWLRv2FZJlRpLfWQ+Cv82YyPZzsZRnsRpo+EYz3Z3rv7d/FMdzO8QRYCDZRSZy2Y5MEkVuHt7MV84fZ7VeAWBnz9C6l/9oupuuSJxiq96RMtw0tP5ldAmCILxlg48bB8eJqhpfPneMpq8AJo5VxWgn70LRXvanN9GyTA4vTLGvf4y7Nu1cd0x7+0dxPZfn25HSh7bcQHcsGFeiIHLP+C58fI4vzzJbDiLW92/Zy2imm9FMN/v6xzi5MsehhelOweLDW/fTFY3TFY3znok9PDN1nIVqkYX2tQW4bWQrO3uH+YvjL1PWG/zhoWeveY51U+elVY+b4hJTNZNl6xQQFIrGtTB9iTTzpTU+KMXQ3AZfXG6yM7G4TgcMgfxhqVYCN0VKkBB8Fyk60OmWdSV02+TVufOEZIWbh7d0tjNTyvPU+WOErko65BtVZsp5bhocf9uFpH8TMBybUqtO/xUTKMdz+ePDXyUkq3z/vjvouSJa7Ps+nuchtTXNSiiF3lyiWpxEALRIDkkOcWZqmleOHmNkoJ/33HZrZ/0/+qvH0A2Tf/SDY6TaCbRqvc7p81M4zjAjuaBQrX7i17hyvu21VkkN3IMjlvnIrTPIjeOI4W5SE++nbi7glso8cttuon19GMvnaQG+lkVWo3iujRZOk8wE9n47t2zGd0cxV4ZQc/sR/oa8s+PJfnzPwbFbJDOb8H2XxYvPY+olLKOGbTWwzKDhgWlUcF2rIyPxfY/BvmCchbN7ifXtCa63en15zbUgyhEiYx8iPPQQvu8hXiOSH4l1E4p0YbSKSJKKosU63s6+75FfPILRKpBfPIwWSmG0CoCIL0CjtsglOicrEXoGDyIrIYzsFsqzX8aSu7Hyk/QOZ4hs/wnqp34Dz25idd3F8txLgfQjdYBQ+VWkxDha33uwK1PBZHPuUcDHFzXEgYdJhLLI7/rMvou/SQiCQGjgPXhGAUFLoZ/4NQBCgw9QavsGxlIThCLdeK6J7/vobf/HWGKAQwtTvHLhBD4+mhrlzrEdNC2DxVqJVDj6LSGzvlVDPxxoZdV9/zfuzNf3UPhW4BJx7Y1d3vdj56eYLJZ4aHyMA33BrD6syJR0A0kU1lV497XXS2qXZRQRReEnbtj3zp3Eu+hgPNPD021vYICxzHrd8I2D4wgCnULGPb0bMwHbu4d4bX6KmtFCQGBHz9C67wVB4MbBcb587hhAJ93/zWBb9yB9iQy+Z1Od/SKuo3cKjy4Zrt8xtp2Dw5tRr3Pv7R8cJx0JLJRG090decqlY753fDcAx5dnuXl4C9uvcFpQJJkbBjaxu2+E84VlQrKyLvOyu2+EoVSWqeIy08UV8vUqt49tY/9AYMnzfXtu5a9OvEKxVUcUBJKhCNloguFUjoFkF/lGldOrc3ytUmhvO0NMDXG+sBQ0OVkLIlAvj/wCYWuFebqZnzzCcxdO0hWJkwpHKTTrrNQvt2p9wE/TS4FpXUK/eIZt3QMkQhEUUeJ8YZlnp0+gt32hLdfhrk07KTRrPH72MJbrYGnrMyFPnTvGWrNGTA1t+M2/VWhaJn998hU2ZXq4bXTbNZd59OSrLNfLPDC6m51DowAUm3WqRosqLS6sLTPRHUSiX3vjOE8+9zV2bd3M9zz0IHDZFUNoT4Yi8eBcdMNgfmkZTV0v+cqkUhimhetdblgy2NfLg3fdQTadRvN99NnH8J0WrqAwSw+b/AXKpSnCA/eQzaQJremYjaDoTpIU1PgQeukYWME7pVaaQgaWhRxbBm/pZCeuhCAphAbu/cYu7NeBRHq9vjoS76FVX6Favoh1lUOL3iwQSwTXullbIBK65AW79S3307QMIop23YybIId4s1ycKErX9PIVBJFc/w3kFw9h6uU2kRVI57YiKxGqxSkssxYQ2aGDHbIZig/Ss+OHWZ1/FcussTz7YtAaOHEgsGkwAymUIMokhu8nuv2jiEoC33OQlAix5CYswUWfe5z49h9Ha1urAeueNd8JeJfMfpdBEGWkSC/G8gu4rWUEOYqYPYC3+AKiqBCKdCMIIpIcptXIg+8jK2FMJF6cOUvGnGOvP8nYwH3Ee4Yo6w0qemudJdE3Ct/RaTz9A/h6HjG5GWXb34eZp74FZ31tGI5Dw7JQJKlTQFUxDP7noSPIosjP3npzh6CmQiEiioznXZYDxFWVv7d7F6mQtu4BdNvgRuuld/HtQ0hRGUxmmausIQoCw6mNWtz9A+NElBDFVo3Nub4N30uiyK0jW3ly8igT2b5OAd+V2Job4JW5c9SMFgeHrpEL/QZwyZe2IUfahTo+khxBVi7rja9HZC/hzaQ/giDwnok93D66vaPtvRqyKK0juVciFY5y0+AENw1ujEJH1RA/uP9umpZJRNU2uCNkIjG2dQ/guC6SKHZe8HeP7+R8YZlSq94mvu/HcV26li5waGEa3bY2RIOzkTiqLLNg7CGuv8JZf5C1hamOxEIWxY7dWiIUoWa0OLJ4AVmUOJtf6DRXsWWBpmWQUhQaZtB9D2ChWrwmmTUdm8m1RXrj6etOXs6tLbFUK3HL8JYNhM3zPN5YvEihWaOsN7hpcIKXDx2hUq/x0F13EtI0VuplltuE/fVzpztktnSFF/WJ5dkOmZUlKSiyNS83c1k13HUv81As+D3Hhgb5yHsfIp1c30Xux7//owDotsVrC1MsxQWagsMt7Qm5a8TQZz8P+BxhKzYKm1igXrmI53uIgohZC6wYG3KWKCCFg3Go15f4whsvsLdylm7goi5z4sTLfGT3rW9aR/FOIpEapVVfoVUPJDySpBGJ91KvzKI314gl+vE9NyhmAxLpcZ65cJZ8o8Ku3mF29AxtCO5M5hd5YvIIvfE0D2+94U2LI78RiKJEd/+NrC0fxXUtunp2oYWCMRmO5rDMGooa3dCpTxQlcgP7WZl7pTNZVrUkWjiFokaRlTBqKLm+6YmkkMoGchdp9EOER96HIL4ztRvfKN4ls9+F8F0bfeZRAMIj78PQg5lyODawzpdRbwapx3A0x2z+Al2KQJ/nsjnaixJOt22n4utSsteC11rFLb2BoCQQtDRidBDhqp7xvlWj/uSHcZafBylM9M7/Bd/Cm8P3fUqGQVf4Mul+fWmZ5+bmuaG3h/dNBNGkpKahShKaJNGwLBJtknvf6AgPjI2u26YoCAwnvzHLrr+N0G2bmmXRtGxc36M3Guu0nvV9n4ppIglC55pdiblqjU+dPMXX5ufZ293NB7dMcHN/kC6umiam4xJVFCKK/A111SobBk3bvmZrWYCt3QPMVdYYTuWuS9q2dQ8AA9f8DmB79yBdkTjp67yEJFHko3tuo2mZ9F5RmPatgKIlL1edR3u/5Vrq612TbxaCIBDT3jzleLW917XIsyrLHBzewg0Dmyi1GpRadSp6k5gWZizTTawzmb4Dx/s4B0urnFqZZ6FawPE8HC+wPTswtJmDQ5s5tnSRr1083dH4JkMRJEGkpDeYrxZJRePMXtF0ZPEK4gxg2BZHly5ydPECluugSBIf33vn5aYdvo/n+zx34WRHvnJxeYmP33QX4XZL40sR1MzeERCDjnKLtSKHTpyg2dK5ed9eenM5ji1e9ud25cu/+6UCQoCFegnTsdFkhZ2bN7N5dJRQ+96smzpfuXCeB5MgClC0RQr5FW4cHKfh27xYukC4rvKxTKZDti3H4ZW5SU4sz2J7LigCX5w8woFWjVtHtiKFunCHPsT5hZNMCpu4oycOy0eJuBVOrcwzksrgNheQgC8tFPhArk683ZzHaCyxIpa5h2Ci0FJyrDVr/OWJl7l1ZCszpTzz1QLJUITdvSOMZXpYqBY4tjTDUq1ETyzJaKaHwWQXiiQhCSINy2C5Vu5E6dPhwA4xF02SjsSuaTN3bm2JmXKee8d3bSCeWjgV6E7bUdlk1wSiHKZemaVeX+X1yiEO5jJ4no1l+3haF6dWg5a5z06f5OXZSW4f3b5Oe3+07bO+Ui/zqSPPcfemnezqXd906MjiBVqWye2j2zbc477vUzNalPRG+9w30jNRkukZPLDhc0EQOsT2Shi2xXylwHi2l96hW7DMGlo4tY64vhUEQQDhO5vIwrtk9rsGvmtiFY4ihXuwq+eDtn5qGq3vHkozTwKgRXpoVC4gq3G0cLZDZkORLNXCKXKqQKb/fYT73l7k0a1NYxz7L5jn/rBTYQyAFEId+QDq5k8iaGnctcOY5/4Yt3gMQUkQe/hRlL47v2VpCtfz+N9Hj1HUDX7qpv2k2y8TTZYJyRLSFQ8NQRD4Zwdu3GBX9a0wiX+n4Xoex/NrvDC/wNlikXtGhvnwls0o1/EGtVyXyWKJU2sF6lbwe9meyxv5PK8uLnGuVN6wTkJViaoqhVYLux356gqHGEtdtgurWxavLy13Styem5vnfxw6TEiWMR1nQxcySRA6D/ItmTTv3zzBBzdvZm/3xuYUb6zm+a0jR/mzM2fxfJ+nPvn97MtmO+f/u0eOsjmd5r7REVRJWqc5vHTOy43Gm2qUbddFbkcO30o6ENfC35IsxdVQtRRGM4gSXdnT/e8aFEne4GJxNWRRYnO2n83Z/qA7nevSsk00WSbc7jx34+A4TcvgyOIFVEnmQzsPcmp5jpLeYK5SYHf/KLNt7TMEHr1VvUkyHMV2Hf702Nc6PsmyKGK7Lo+dfo3dkV6+9vJrDI0O4GYjnYiq7/tUMTk8P8Udm4OIVkjTEEIKtnj5DrhQXOXg3sAdIhIK07QMzhWWOt+HE5cnUqXWZTLr+h7n1pbY3TeCpqlobSLr+R5PnD2M7jjovkxUcFiyFC7OnMV2XV6bP4fn++i2xeNnj/DhXTfjei6Pnnq107EwG4mjl2s0NYHX56eYKixjOg4tGxB3sSnTww2bdlFe/hMiGHxl5gRTosFdeJgolF2NR0+9xvs2b0cCIuhsi4soVQcEmQf3PsBfnniNQrPG50+/3jmnit4MihJFcV0TnrlKgbm2v/TbgSxK9MSS3D66veMnXWo1eHLyaDApj6fY0ze6Yb1EepS15WPIapQaER49/ioPZARU0aNYXaEsVVCBtZKLlQl+55gaQhJFqkaLZ6aOM5zOkQxFKOsNVuplBAL3k8VaiaenjiMKQscVpdxq8PyFQDc+kMx0MiqOG2j6p4srHc/uvX2j3Dux+22df6nV4MjidGd83Dl2uQrlmakTnCsscdPgBHeMbUdW3p7WtWkZvD4/xd6+0XfM8/2bxbtk9rsErp7Hba3iWfW29xyERz+I4+g4dgMEEVmNYraCB7gohXAdI4jUylHOV5uEJLg5OwhylMm1RboiLl3RxDWJnn70V9AP/dtOP2cxMQG+g2cUwa5jXfhzrAt/vm4dQesi/sjjyFfobr5e5JstXloMrJM+3PZolUSRmKpSNS3yzWaHzB7s7+Ng/8aU8rV8V78T4fg+Z4tFpmt1lut1yoZJ2TBYqNe5UKlwoVyhecWE4PePn6AvFuP7t2/Dx6eoGxRaOkVdp6C3mK/VO6nYN0NMURAFgZpldf5diaJuUNRXNqx378gw758Y5/DKKo9PTVMxL1vLCNAhta7vB3ot4HShyOlCkV99+VVkUWQkkaA/HqOo6yzWG1TN9fY0P/PU0zz98Y8B8OuHj/KLL7yIAPz2Iw+v8971fZ/PT03xc888x0y1yr+85SC/dOcdG6Ihj50/zz964sts7crwl9/7PZ2xcyUWajVeXFjkw1s2X3PseL7Pp0+dZksmc83xdul4mrbdKS68Gpc0jxB0/noXbw+CIKDK8ob21QC3j2yjO5KgJ5kmHY4xlMpyeOkCF4orNJrNTmRWQsDF59EXnuPvPfAIF4qrVI0WvuOhzxX5xAMP8eziWapGi5PuMnY2xIJqQN1ElWQe3rqfl08eZ00wOLo6y46BETKRONs2jZEXdE7m54mpIRqWwUx5lR87cH9nHL48O4nn+8TbTTdKrXqngPQSmR1OZZmrFDiTn+9EAkutOufWljm3tkhJb6BKMrne/WDkcV1w9SKvzE0CgYZ8vlJkrrLGCxdPs9ZuF37p2AfjaZ544gk279zHsxdPUr6imclgsosHt+xDVFQELYNvllDtAoofSCDU+AgJN0LVaPHpE4f5GAoaNrclTOwqSLFBUtEU37fnNj538hUcz2O8q5fRTDfLtTKnVufQbQtVktnRM8TmbB+rjQoXS3kKjRqu7+H5Hqqk0JdI0xdPI4pCJ3q/1qxhuy6LtRKfO/UqH993B+lwjK+cf6PjyTxdXLkmmY3Ee+kWb0LRYry6cBHLdSnYCv0a7IqJqDgIgkSpYtJoE/8tuX7uGNvBX598hflKgaOLF7hnfBdn88E7aTid48M7b+ZrF09zZPEChxam2NEuJr3UrAXg6OLFDpl9fWGKN5ZngvFM8Jw8V1ji7vFdnXfv2fwCM+U892za1Ymuu57Hk+eOcm7t8mTo2OJFbhqcIKyoGLbFdDF4Th9ZnGZn79AGu8RSq8F0cZlsNLFOrvS1i6c5m1+k1Krzkd2XCweniysYjs327utnuL5d+NvxVn8XbwlRy6Ckd2AVjuDbDcRwN1rv7VSLQdFLONqLFsogigqiKNNqBjPfUKSLi9USZccnqyVIR2JU9CZlvUnDNAKJwVUEQD/2n9Ff/3kAlKGHCe371yh9QetD3/dxC0cwz/8J1sW/BEDO7kfO7kfd8kNI8dG3fU6e7zNTqZIMaR35gOt7nMyvEZKlda4B7988QVRRUL+JjkVvB67n8fTMLK7vMxiP0x+PEVUUFFFEFARc38dqR/rezrFMlcr82ZmzTJXLXKhUWG40MF0Xy3GpmSbeuek3XT+hqtw6OMB4OsVfnJ1kudHgv79+6LrLpzSNnbks2bYLgwBszmS4ub+PA/19dIXDnQdow7JYrNdpWDbd0Qi5SATLdZmpVrlYqdJqE2lRELi5v5/R1GVCZrsuc7UaMVUlHQqhiGKgYbZt7HYbRNf3eHF+kcfOn+epizM0bZvpSoXpSqWzHVkU+fCWzXz/ju382Bce58jKKn988hS6afLLLwfm+T7wE49/CVEQ+ODmCZ6ZneV3jr7B0zOXXx7/5ZXXKOkGv/bAfUjtxhT/8cWX+Y8vBdt4ZXGJ9332z/nCx76PzBVSlVcWF/noX32Oom7wh8PDfOZ7PrhOZmG7Lv/w8S/x2TNnUUSRT33oA3xg83p9ad20+MHHPs9XZ+f4fx+4jx/bu2fD76JFcoCAEgoal7yLt49CqUyt0WBkoL9T3X/oxEmeePY5dmyeYNt7HwKgN5bCdVyQJY4uXuik7XNKlAW9QtMPxvNk22c46Sn0pruJqBof3HGAz77xAhVbR+0KCMFoupt7xneRCkcZu+t+/vrkK8xVCjx+9ggf3XMbiiIHHfmAuzbt5MvnjlI3DQqtOrloAsdzOdEmMbePbuepc8dwvKDJRkwLUzUCUnnb6Dbmj73AUq3MTDnPqZW5jl0bBJP5B7fsI9fVB0zwnqzOp448h+nYHBzazK0jWzm3tsQTk0c4shg05FAkiQ/vupn+RKaTIduc7WMwnWWpViIZitIVja/Ta8uRfmyzRNKv0yPWwYVwaoIP9h7kz954Mej6KCTQ/CJeMWiWIMcCHXImEuNHD9y3bjI53tXLrSNbWWsGTTwuSWEGkl2dIsO3guf7VPQGT58/zmKtxKOnXmNHzxBLtVIn4jtfKXR+66txyeasbgTFiLF4D1irZFSxfc45XG+2E8UeTGYRBYGbBseZrxQ4tTrHLcNbOJsPXIG2dw8iCAK3DG/l5MocZb3JTDnPUDLbaRkOMFdZo9SqE1aClu8A92zaxfaeQX7v9afRbYvlWomBZBe26/DM1Aks18H34b3bAv/gV+fOdYjspkwPZb1BWW9yNr/ADQObmFxb6hB6z/d5/sJpPrTzIL7vcya/wBtLM6w2KsEYEkR++KZ7SYQiNEy9s925SoFyq0E6EsPzPZ6/cIqq0cJ2HXbmvrNqR94ls98lEJUoQmKMxpnfBoK+34IooTeCmywSH0KUVLS2cbmeD2bt4WiW6cXgwbhTLmLNPEYouZ3hVBYBYUPa1zj56+iv/Ztg3QP/gfANP7fue0EQkHM3IuduJHrbf/umzumx81OczK9xx9Ag94wEqZreaJS7hocYTqzXsl4rovatxmtLy/yzL3+FN/L5t1xWEgR25LLs7+lhNJVEaJeQxVSF7kgETZb51MlTfOH81IY0/JWIKQpbujKMJJOkQyHSIY2+WIyxVIqxVJLN6XTnN/qP99zNX509x9fm50lqGl2RMF3h4F82HGYwEWcwHn/besyYqrK1a73RfUiW2dPdzZ7ujRW3V0KRJMbT6XWfhRWl0wXtEj6+M8HHd27H832W6nUuVKos1RtkI8HxDsUTRNVgnZ+//Tb+9bNf5f954SVivoflerx3fBMD8Ti/e+wN/uHjX+IfiyKWG1Roq5LEPztwE72xKP/q6Wf5vTeOc7pQIB0KMV+rcXItmND9vd27eGJ6mmOreR757J/z6w89wHAiwcsLi/zYF5/AaLcD/urcHA/+6Wf56+/7CH2xGIbj8IOPfp7Hp4OXke15/MCjn+ePPvj+TtZgtdnke/7irzi2GoyZ/+vJpxAFgR/ZE6QQT66t8bnJ83z+/BT1VolUuMXvJYrrvIrfDJ7v86enTiMKAt+/Y/vbWudvK5bzeU5Onicei3YKlQA+9blHqdUb/INPfIy+9rjU2i2em/oVnp2iiFfXkdIxThXbkbRUlk2pbhamKkQyCQzHZrYURGw/eMsd69pcP7LtRp6cPEpfIsPNw1vW6aYFQeChrTfwqcPPUWjW+KsTr3DDwCZatklIVhjv6mU4leNCaZWLxVVy0QRn8wu0bIuYGmJzto9DC1Od5hdBRNJHESV6YilG0t3MlPN87uSrwf6AkXQ3W3L9bOrqJXQFUYtrYT55w10YttWRa2ztHmC1UWkXxYl8aOfNGyQ5wJtayknRPuzySW7qiqIaZfwGyLFhstEEH9p5kBPLs2TsMSgW200Wgu+vvEYbtimK9MbTGz5/uxDbdR3v33ETf3rsBapGi5dng3fbnaM7OL4yQ6nV4GJplW3dgziey5fPHSMdinLrFc4SNTMYJ2o4A1bgRe37PkUvjCPQ6WZ5ScYwnMqRjcQptOo8df4NqkYLRZQY7+oNtiPL7Ood5sjiBY4uXsCwLQzHJq6FyUUTXCitcmzpIookY7kOuWiCvf2jCILApkwPZ/ILTBWWGUh2MVVY7hQwTq4tsjnbt64L4SPbbmRLrp9jSxf56vRJTq3Os69/jDNtgr27d4RTq3NcLK1yanWeqcJSx15QQCCsKLRsi5dnJ3lo6w0cW5rpOMMAHF+Z5e5NOzmzukDVaBGW1aBY0nuzN9c7j3fJ7HcRrPxreGYZQUmg9dyGYzexjDIgEI5dTgt4ro2pBxogUU1RqJ1EBIbEMk5hATXSw0D3xmpta+YxWi/9DACh/T+/gch+M6ibFqcLBfb2dBNqpwy3ZjJMl8v4vsfnz0/x1MUZZFFgMB4n32yiSCID1ykIertwPI+VRoOTawWevHCRL124QKGlc9/oCB/cspnhRIKTa2u8vLDIX5ydxCcoIhtJJlis1ynqxjW36/o+J/JrnMivXfP7K/HgpjHuGBxgUyrFYCJBWJYRfY/XXniBH/jAB1Cvk5q+Gqok8fGd2/n4zr99pEYUBAYTCQYT1y+4+8n9+/iD4yc4UyxSAjKhEL/x0IN0RyO4nsfvHz+B5bpsSqV4eHyMf7x/f6frWm80yo9+4XFeWbycklMliV9/8H5+aPcuThcKPPKZP+d4fo27//jT6/b7yPgmfubgAX7g0c9zPL/G3t/5PdLhEKbrkm+2CMkyf/SB9/EXZyf5szNn+aFHP897xzcRV1VeWlxktlojFwlz3+gonzl9hp/60pc5srLKK4uLHULdQbPCPX/8af7og++nOxrhT0+d4Wvz82QjETalkmzJZHjP6AhbMxlmqlV+4vEneWEheGn976PH+O/3vQdoazhNE7tN7K909AA4tLzCLz7/NQq6zl985MMMta/7dLnMJz/3eXblsvzPhx7YMPn4m0C90UQ3DbqvIPCf+fwXWVxZ4eMfeD8DvUH6s1Sp8vKRowz29a4js7lMBlVRaF1xL24dG+Wf//0fJRJer2/uUSOUCHxdIYiujmR6YCroinZqZQ7X9+iKxNcRWQhcI37y1oevex5RNcSHd93MX598hdVGhS9NBp24tuYGkESRsUxPQGZLq4xlunluOtBP7u0fQxJFuiLxNpltdMjEJdu1HT1DzLSjvMOpLHdt2rnh+K5EMhTpOGVcwh1j2+mKJuiOJsh9A7ZyUjR4h4SdIk4zmAzI7UzbQLKLgWQXrZkF9OLlzJAU+5uxO7saYUXjQzsO8tk3XsByHfoSafb0j9K0DUqtKaaLK2zrHuTY0kXOrS0hAAeHt3QCAfU2mU2EEwhmUBi2YvqsOVWM9i2QiyU7kwZBELhhcJynzh3rpPInsn3rirb29o9xdPECc5UClbb+enfvMH2JDBdKq5xeXeCS+Oq2KwrCxrt6AzJbXOGuTTs52Y7oXpKiPD11nJCs4uOzJdfPllzgcrEtN8DXLpym0KwxubbY1vAK3DKyBVWSObw4zVNta0FJCLy1d/WOUDN1PnPsa5zJL7Cnb7Qjh9jVO8zJlTlOr85zy/AWXm0XU940NIEqydjeu9Zc7+JbDM9u4Dk6+twTAIQHH0CQFPRaMCi1cBee5+CZNWQ1it4qAj6yGmWxVqJfAyUaIjl4FzTnEOObNuzD9z30138h2N7OnyJ84y9+y47f933++ORJSrpBWAkif03L5tTaGkdXVvmVl15Zpw29Enu6czw4Nsbu7hxbMhnGUkniqnrNKIDpOJwrlXlpcZEX5uZ5bXmZxXpj3Sz0Eh47P8Vj56c2fP6Du3byH+6+i+5o8KKwXBfTcbE8F9v1UKVAXlAx/j/2zju8rSLrw+9Vlyz33luKY8exkzi9QCCNBEILLfTAsvRlYfmALbDLUpbOsktdIPQWOiQEUkjvvRf33ossq0v3+0OyYsdOMTiVeZ+Hh+hq7ty5I/nqzJnfOcfG5poaNlXVUGex+O+z1eGgts1Co83KiLg47ho2tFsvnNPpJF+lEhXCOqBWKnlu4jlM+9SrxX7+3An+XMH/mTKJSzP6kxAYSN+w0C7zdnH/fqSHhvBzcSlBWq/0YXBMtD8wLDMiggVXXc79i39md30D1WavXvH3Q3J55pwJKBUKllx9FRd9/iX5TU2Yfd9Ho1rN55dezPikRKamp6GQJD7ZvYfv8w/KQ1JDgvn2sktJCwkhRKfltc1beXPrNsBrUE9KTeHCfn0ZHhvLHT/+5NXnfv7lEeciJTiYOouFNqeTALUaCVhXWcW4Dz8mSqXkpv++isXnUW4nLSSEsYkJ2FwuPtuz13/8si+/ZvGsq3DJHi778mv2NjSyo66OUpOJzy+9uJMRfDhKfIZ1gEbNBzPO9xvBFa2tPLl6LdcOzCI3IoKG5mZCg4P8Ef/b9+7j6x8XkhAbw+zLZ/L9gXyWlJSwuriEepsN09Zt/GOqN5dqTGQEGVlZrLV4tdTt47r6ohldxqPRaLpdBOoPeYwk+2QEYQYjjRYz60r3A7Chpomvv/iK92ecj6Ebg76wqZm3tm3j94MHd8p4Eh0YwmWDxvDlzjW0Obx67/4+fWF7/uOq1ia+2bUep8dNYkgEQ+K9z9twX6BNu24WvNvz4JUATOqXi1Gj8+6a/YLngkJSkPUrcukqDV49uKt5P+DxlmTVd06D156ey4uEKuDEbUWHBwRyYdZwtlUWMyYlA4Uk0Sc8lg1l+RQ31tJqt7K+1GuQyYDJZvFvn5vt3oVQoFaPMrwPdbX72FXfiJ0GNGrvXCcEd35O94+MY1XRHixO7+eccYiONFhnoE9ELAfqqzDZLP5gMINa6/fqAsQEhpISenCnKzk0EpVCQavdyoH6Kn+mjUuzR/Hd7g00WFqxOh0Y1Fp/HmnwpijsExHLvroKFudv9/cVoNExPKkve2rLsTjtRAQEMbX/YP9iyKDR0tc3zq93rcPuchKiC2BCejalzfWYbBa+370Rk82CQa1hUIcMDqcSwpg9A3C1lmCvWoXbUomk1KGNOxvAH+ylM0Rjt9bhcrSiN8Z1Ssm1u74GhwciA0JRh/SlRhmJTtIQdEgJV2fR17ibdiFpgtEPe/SYH6ayLPN9fgGvb97iD4DRq1Q4PR40CgV1NTWsWbEKhUJBo83Kq5u2UN7ayuryCqwdfowTAgO5wKeLLW9t5UBjE5urq9leW8f2Q7yfCkkiSKMhUKshSKMlUKuhrs1CUUtLt4arWqEgKSiIs5OTmJqeRnRAAD8UFPB9fgEmu52siAgGRkUyLT2N4XGdS2hqlMputbFGjYaEoCBm9O2dfKQCL2cnJ/HfSeeyZft2Lu53cG4VksQ5KUd+yB5NHpERHs53l88EvFpYq8vVSR+bFhrCptnXs6ehAYfbg8PtIiM83K+xVSkU/G/aVC4fkEGZqZU2pwOVQsEVmQP8leKeO/ccgrU6NlVXc1G/vlzcv18nicy8Ky7j7p8W8d6OnWiVSs7vk86Mfn1pczopbG5ma3UNy8vKKW7xFkEZl5jAa+dNQa1Uct+iJXx3IJ9KR/dBfoXNzRR20CNfMSCDJSUlbK+t45YfFmBxOtnb0EhMQAAWp5NV5RVM/vhTRsfHs7aikqLmZsYmJnBVVibT0tP8Buvq8nKu/Opb6n1b+vcsXMzjI4dT02Ji9rLl7KitY3N1NTfr9ZRXVXPpeVNY2dbGnG3beW3C2UiShFKppLLVzJVff9vpb/S1vXv5y6Rz0SiVhIeGsshh54O9+/Ho9bw4aeIRP+/uUHvA4QGNAqotdr7an8+12QOJDwqn0WL2b+d+mV9Go83JDd/N4+OLZnSRWz2xeg0f7drN1/sPsGjWlcQaDwbWhAcEcvmgMXy9az2BWh2xvm10o2+Lua7NhNlhI1QfwPSMof6+21MgNnTILxvmC9iRJOlXGaK9gdLQ/uzzfr+UgUmdUj1CZ2NWoY9CUvV+5o8j0e4hbifKGOwPvvtm13r/5wvQbGsj1GDEbLch4y2KE6DRIml1JKSE46z+EYfLidO3JkoIjuh0LZVCSW5cKqtL9hKg0ZLYTX7rwfFpfn1zeniMP391bnwqiw54Dc5D03SplSqSQ6MoaKjuZJSG6AOY0j+XT7auxCPLnNt3UJe8vVnRieyrq/DvyAyI9i4mtCo1MweNprq1iX6RcagOqbY2OjmD/Ppq7L4di8Hx3t2C7JhkVhXvoazFu4OUl9in1yqB9jan5qgEPUKSlLhavbo9bdxZ/lJ5Np8xqw2IBtmDR2lHqQrAZvGu9DS6MPY3FuL0eBjWLx2Xx01xUy0eWWZQbAoBGu8PuSzLWLc84e1r4J0ojqEmsyzLLCgs4p8rV/n1godj7abN3R5PCwnhon59ubB/X/JiuuberLNYWFhUzIrSMvY3NrKvoZFGm80bFGC3+6LpWzudE6jRkBcbw9jEBMYmJNAnLJTogIAuGRvyYmP429gxR71PwYnn2oFZhJeWHL3hr0CtVHab4kytVB7RIFYqFExN77qz0Y4kSTwy7vDfK41SyatTJ3P70MEkBwd36xVtczhZWlqKQpKYkpbq/+5+evGFrCsrZ/HKFVwycSJJISF+yU6rw8Ga8gpWlpVTb7Xyu9wchsbGsKa8gqmffMZX+7weSZ1KxdxLLvLqKud+0UUqM7+gkPkFhehUKtJCgokxGFheVo5LlukfFsaBpibe37mL2r37KJUk9vg82FtrarEN6I8xwECr3c7fl6+kxW5nQUUFD9z6OzQaDZ/s2oNHlkkJDuYPw/N4fOVq6q1W1lVUMi4pEY8s82Oh9zn3+Z59PH3OhB4HfEpAQYuFAaEG9jaaeWnbj2ysqubW3Az/9qpSoaHR5h339/kFPPDzUp71yTfaWeWTdhQ1t3Dh3C/48aorOi1KlEo1/9qUj8nuwCQbuT57oF8PWddmQqtSc/6AYZSazMQavfmcw33GbMeUXAtLKvi5opH7R4446ekDFWoDCk0oHodXoqYydl08KvQH/zZUJ0hicCQkSSI9PIZtVcXU+wpktBu37anX2iUGgVq9/zemvfjK/vpKZEnqpJftyOD4VCxOOymhUd1+PrGBocQHh1PZ0kBuXKr/eEZkAgUNNQRp9SSGRHQ5r094DAUNB43LgdFe7XGUMYSLskZgdzv9+tyOJIZE+IuGaJQq0sMOtgkzGP2e/kMJNRgZGJPEjuoStCq1v4BIVnQia0v24ZY9GNRaBsWkdHv+qYAwZs8AlIZoPLY6QEIX7/VWuBxmXxJ2Ca0+AoVChZ44HPZWbxUQSUGN3YXT4yZQqyNKacdhriTCEIDV5cbQYcXnLP0ed8NWUBvRDfzDEcciyzKLi0v458rVbKjyrkgD1GpuGzqYvNgYzHYH6yurKG9tpV9oCA0lpQTEx1NsasXidJIZGc7AyEiGxcYyMPLI22mRBgOzsjKZlXUwr16bw4nJYafV7ksrZbdjcjgI1mroHxZOrDFAbN0LTmkkSTqiwRygUfuLgBzKkJhoqg0G0kNCUHfYHg/WapmantbF0B6VEM9Lkydy24KfAHjjvCkMjfX+AC6++kr+sWIVMQEBDImKpLmqihXVNWy0eNO87a5vYDfehfG4yAi+vHoWr27ezMPLV/Kjb1dFq1TSNyyUnXX11AQH848LzueDnbv8Kdd+LinlwdHe1D/Ly7zawAv79eH3g3NZX1HJx7v3sLComHFJiWyqrqbO4jU8Gm02FhQW9njno8Xl4vOSSgZHBDEquQ8Lyxr439ZtjEs4mFKt1u71PA6OjmZLTQ2vbNpCemgotw0ZDEBlq5mSFhMKSSLSYGBnXT2XfP4lP1x5uX/x8HNJKSUtXuPp9gU/8fGu3bx23hQGx6dhd7sYEJXAG9t28s+VqwGvFGVEXCy5IRIuj5tas9fz/sbWndRZHRQ0NfPq1MldPMQLCgr5x4pVPHvuBMYkdr+lb3e5vCnMeiHTizIg9qAx201mGoXagKQ2IjvNKDsEf51M+kTE+lNfpYZFEao3srmikBZfCrKOxmxHksOi/DmAIwKCOgXZtaNWqjg7feBhry1JEjMyh2Fx2Dvla1UplVyYNfyw56WGRaPwlejWqzSkdTBck0K7eoA7Xm9QTDIri/cwICqhS8GSIzE6pT82l6OT9teg0TIgOoGd1aWMSOrHz6VllJlM/gDWUwlhzJ4BOBq9gQSqwBSUOu/qsd0rq9GHdSpv1+6V1elD2eoTrqeHx+KqXo7ctIekhEmookf5Db6dtbV4lvwfCcDXnM3bn80nMyKC0QnxjI6P76RPXFZSyj9XrWZ1uTc4QK9SceuQXP4wLI9Ig8Hf7orMAZidTgwKBfNb5zNtwlmdfnh/DQEaNQEaNbGnR55ngeCk4y3ZrEOjVHJeehor1m9g6569DBmYxXszzgfAZrfz9JKl9AfeuvV3VFgsFDa3sGTHDlrq6rl+yGACNGruGzGc9ZVVfs3wq1OnoFJIXPfdPD7bs5dHxo3l7W3b/ddeW1FJm8NJgEbN8lKvMTs+yWsETUpN5ePde/ixqIhHzxrHAl/WiPZcnB/v2tNjYzbfZsfq8tDkVPLPs86izenitc1bWV5exdjoMBotrawt9aZhunfEMEpaWvjrshU8uWoNt+TmoFQoWOvLc50VGcFb089j8kefsq6yio927fanXVtUVAxAdlQkBU1NrCgr5/Ivv2H9jddxdvpAPLLMW1sPzkNRszfdXfboTCS8CwFZhgabN8fzBzt34XC7+d+0qZ12DJ5YvYZttbVc8+33rLvhOr+Wv51mm41hc94lVKdj2TWzug3oqzSb+bqhkTF2OxGHeQ4vKyklWKelryEOZ5M33aMqsHtZj8qYgrNpJ+rgriWQTyS1bRYONDYyKiGOQK0Oi8PB2JRMf5nkds+s6XDGbAfZQHw3mR+OFa1K3eMKfB1LdA+ITuhR1cTs2BS21TUxMDb16I07oFdrmdRvcJdFz9npA8mOSSZQF8CVb79DSYsJm8vF704xg7bndSUFpxzORm+ZPXXYwRWirYNe1uN2djju/UPW6sMwt9aSplfQJyQESaFGUulQGLzb+WUmE7fMX8AfP3iEBOcBrLKaR+oHs7m6hg927uL2BT+R+9YcUl5+lSu/+oapH3/GeZ/OZXV5BVqlkjuHDmHXLTdzx9ChfJ9fwA8Fhf6gBqVCcUxBJQKBoHew2x3UNjTQ2EEz63a7mTP3C559402mpaZwns9rK8syTc0tNDYdbKvTahmRm8O5Y0cjIZEeGsqk1BSenHEBr9x0IyN8RpwkSfxv+nnMzhnEq1Mnc2XWAKb1SSdQo6GkxcTb27aztqISpSQRZTDg9HhYWV5OuclEYXMzSkliTII3kObc1GQkYEdtHZWtZn4s9JYLvTPPW3Rlfn4BjR1Sb3Wk3GTi4eUryHz9Tf788zL/8XxfxoNhvuIWZ/kM51Vl5VySPZKrh5zNhmrvs3NwdDR35Q0lWKul3mr17zSt8WXEGBUfx8DISP44wltetF2qIcsyC33G7CNjx7DxxhswqFXsqq9njc8QXllWTqXZTLBWy4HbbuFhn6SpqOWgxKDJ7sQjw6nO0wIAAOKWSURBVM25OagVCj7bs5d7Fi72v1/U3MzGKq9Doqatjd/N/6FLTMCne/ZS0erN1vLSxk1d5kmWZWbPW8Cn9Y38edmKbufylU2bOe/TuUz7ZC6y3ushlJQ6Nja6iPv3f3ln+45O7Y0ZswkcdB+q4H7d9ncsuD2eLgVTOuKRZT7dvYf9DY3dvu/yeJj+6VwmffwpC4tKuCJnLNcMPYvwgECC9V6Dv8VmodFqxeQzaoN8xuzX+/Yz7O132VXfSJQvSCopuKsU4HhzdvpA8hL6MCKp+3lsstkY/OYcrvjqm07H/71hE3ctWs4zazd0e97h2FxdQ8JLL/N/i3/udFylUBIdGMIza9dT0mIiITCQ67IP740+WQhj9jTH0bgLR4M3MrqjMdse/KXRhmJq3ENr4348Hhc2i/ePv9mjRI2bALWSSGMIrvhpKLLuRRGYSmmLiRFz3uODnbu4VOPVs9bFXMh/LpzFxxfN4P6RIxiTEI9WqaTOYuXbA/ksLytDrVDw+8G57LzlJp72RZqrlQqqzGa219ZRZW5DIBAcP9xuN3uLivlm4WJcHQIoN2zfzmsffMyKDgU1lEol9Y2NWG12ajsYBYMGZHD9zIuZMGpEp76nnDWOMUOH+EupHo5grZb/TpnE9T7PjUGt5kJfsN79vh/K6X3SmeaTSvxcXMLyMq8GdXBMtD/oLtJgYEiM13j6aNcuNld783/+cfgwsqMicXo8fLF3P41WKzd9P59+r75B7ptvM+Kd9xjw+ps8u3Y9xS0t/HfTZr/Re8DmNWbbK7W1G857GhpostkpaG7B4XYTotWSGhKMWqlkSprXwzU/3+sZbjdIR8V7z72kv9fYWFpSSr3FQkFTM8UtLWiUSsYnJZISEsylGf0BmLPNa/h9unsP4K1iGB8YyJ15QzCq1RQ0HzRmqyx2QrRanj13Ah/5Mja8u2Mnxc1eCcLne735VDMjwtGpVCwsKubljQfjD2RZZk4HL/gza9dR0do5hmBefgFrKr3G+ce791DYYQED8NrmLfzJ95k12+1ss4WBQoMmcij/27aDZrud93fs7HSOQhuMJizzF8u5PLLM5V99Q/xLL/PgkqX+4iwdeWPLVm78fj7nfTqXZlvX9Ij/27qNXfXeoKWXN23GqNX7q1/Z3V6Dv8ZsIuE/r7CqtBSAQJ0eWZb5x4pV7Kqv56Z5PzA+dSBRrZ5OulZZlvlm/wHWV1Z1uuaW6hrO/uCjTjsPHZFlmc9272HOtu1+x047Jru9y0IkzGBkbOoAtCo1b2zZynXfft/JwH93+w72NTby3YH8Tp/r4uJiAHbXH3tJYIDHVq7G7HR2ysTSzv6GRp5ftx6AZ86dcNhKhicTITM4zXG1HACPA0mpQxXofei6nBZvCVskVBojTocJFEocNhOy7Eah1LCvqZliq4fcoBg0GgN7aisw2Sykh8fw56XLabbbGRoRyEx5L7gha8Rd5MZ4f5Daf5jsLhebq2tYVV6O1eVialoaZSYT+xsb/flfIw0GZvTtS1pIiD/5vUAg6BkejweT2UxgQIC/ytWe/ALWbN5CYmwsk3xBZQqFgj2FxVhkGDtsKDGR3q1SY0AABr0O5SFRzBdOnkhIUBARHQpchAQFEXKEfL+/hKuyBvDBzl3YfVHWN+YMotVu553tO/i5pJQmX2qk8Ymdg4Ymp6WwqbqaZ9Z6f0gHR0cTYwxgVmYmD9Uu45VNm3lm7TrKDzHS2vsqazVR1NzC1/sPMCujPwXWzsZshMFAZkQ4u+sbWF1e4Td6B8dE+42xaelpfLZnL/PyC3hg1Ei2+QJa243Z9NBQBkVFsr22ju8O5PuLbIyOj/f/6M8eNIj3d+ziy337efzss/h6vzdFVHuhC6NGw8wBGWwoL/KPv85iZ0a/vmiUSqb3SWdiagqLior576ZNPHvuOcz1pVe7M28oTreHPyxcxF+XLWdsYgKDY6LZXF3D9to6tEolAyLC2VpTyyPLV/Lm9PMAr/fy4eVeb6xaknDKMv9as5Y3pnlz6b6xZSv3LloCQKRBT53FyoKKFkaPfRGPpGThojcAb3Cfy+NBdYxb4ZWtZm7/8Sd0SiUj4uIYlRDH8Lg4fwDV/7Zu4wefpOSljZv4Pr+AV6dOZmxiAqs2bsKt0fDoilUAVJnN3PPDj4y22bhkymQiwkJptFp53KdFBq/ko6CpifTQUIqamznno7ncl5uESiERqFZic9kJ1mgJ1BrYWF3Nvkbvwq6wuZnpL/wX67LlDO2fTf/0NGRZ5qGfl/HSxk1olUq23XQj2zdtJjdzAPcuXsL6yirWV1bRYLVy/0jvYvCV9z9k/qo1aM85mx98VQkbbTbuG+HVzL6zaTN3LlpClEbNO5de7JfZtPPSho086NtdiNXreWrSubg9Ht7Yss3f5sttO/js3//hvHMnsL7N62ku9mm229mxbz8bt+/guksu8j9D/O/V1rHAF1xZ0tKCzeXy679lWeaPixbj9HiYkpbKjL4nVz5yOIQxe5oj+xIXq0IHIPl+qPxeWV0IWn04Gm0IHo8TU7N3BaozhFNYUo0HSI5MBCRkWUYGttc18OW+/SgkiTmDFSg2tKEITEEVPbrTdctMJnbV1ZMbHc0on3ejpKWFHwuL0KmUjE1I8Gu7sqMOL1gXCH7LtHto2g2nxuZmisorMBoM9E87qHl78e13MLdZ+P3VVxId4fUSOZxOyquqUakOPsYlSaJvciKjRwz353IFyM0cQG43FcL6pfZMV/dLGZ+YSKzRSJXZTGJQIBNTkmn0edR21NVR3eb1SI5POsSYTU3lydVraXV4taPtXtLLMzP4y7LlfsMjPSSE5yaeg16los3pJCUkmIzwcJ5du46Hl6/k8z37GBIViU2WMarVDOiQ23lMQgK76xtYVVbuTwc4OPpgiqlJaamoFAr2NDQwd+9e3LJMnNFIYtDBgi2X9O/P9to6vtq3369vnJSa4n9/eFys32j+3fwfaLLZiDUaGdchaOuGQdl8v/9g/t9aq4O7RvX3v/7DsKEsKirm3e07ubR/f3bW1aNWKJjRtw+hOh1LSkr4Zv8Bbpo3n1XXXePf/r+oX1/uzBvK+Pc/5KNdu/ldbg4j4uN4f8dO9jY0EqrTcWtEGE+WV/LRrt3838gRfHPgAH/zyQ7uGZZHdlQkN837gUVFJfzzrPFsrToYjGd1udhT39DlOW9qNVNWVYVHlumTnIRep8PmcnHV19/6JRvtubzPTkri3RnTMdnsPLRkKQCZskyNRkNhczPnffIZ6RUV7J73A8pxY1EMyCDWoKfKYuWzA/l8NO8Hvl+8hG/ffJ0nV6+l0WZDaTLhbGlBkZjI/7Zs48kJZ3HPT4tostlpttoJM+gINZsI0XqdLIFaPS9sXAtAjFpNtdPJfoMBt93BbX99hIUfvMODS5fzsi/7jt3t5oL/vsLud94jYmAWLaNH+YO2Hlm+knqLlQBzK49/8RWKgQORiktQ+kqeP7xsBX3DQpFluGPREmRJotrpYuonc7k8oz+zc3PIjY7io127/YYswH83buLytBSqZfzp+QBe/3kp+7ZsZV11DaoLpgPe32i3x4NSocDc1sa0G26iuq6e9du288o//97Jc/6cz+sKXj36gcZGsn1BqF/u28/PJaXoVCr+PnI4H3z9DddcdCGnGsKYPc1xNXu3mjThuf5j/pRcBu+XUVIoUSqUfr2sVdJhcTrQqtQkBEfgqlhIn7ZKnOF5TPjW+8f8u9wcomtewglo+szqsmX0c0kppS0mQnRaf+L6WKORCclJJIcEH/MqXSA407FYrVTV1qFUKklJOJhY/X+ffEZtfT03XjaTuGjv32pFTS3zFv9MSmJCJ2M20GjEarNhbrMQ7dvxTEmI59LzphAR1rkc6MA+6YwdltdrQZW9gVKhYHZONo+vWsPtQ4agVCiINBj8Hs06ixWlJDE6oXPi+bzYGMJ0Or/h256NIdZo5OJ+ffli336uGJDBS5MnEdiN/OGyARk8vHwly0pL/dunQ2KiOwXUjE1M4H9bt7GirBylQvK3aSdUp2NMQjzLSst4fJXX4zcqPq7TM/GS/v34+4qV/FxS6g+gmZSW4n9fkiRuHDSI+5f87NfTXpbRv9M4hsXGEG0M8ns5XbLCr+kFOCc5mezISHbU1XHdd98DMDE1xZ/n+D+TJ7K2opK9DY38afHPfhnCjTmDGBobw7XZWby3YxfnfvQJ6Ro15TY7SBLqXbt4ZfMWQqdMpikoiPFz3qXZ50GfGBiIZ8MGPi2vhIR4ttXWcu8zz5Ov71w+/B8ffERYXR0V1TWUV1VRVlVNa9tBWZkkSaQkJNA6OJeWmGgkhwPdgXzcoaE4oyJZWlpK2jPPg9WGOywUT2Ul276fD2o1mvHj8KSncSA+HvX4ccg+WUfZx58hpaehHJiFcvxYFvy8jGeX/MzrW7YCYF+xChQSisRE3t+5i9zoaBYWlyC73TRV1BDWN5nhIQFold7PQJYUzN3jnbPyr79F0b8fij7pqM6dwOrqagb/9xXy7d5F1W1Dcnl181b2q1QQEkJTWhoK4Pe5OSQFB/PQ0mX8x6dRVvqydcgmEy9PnsgWu4PXt2xl9vfzcbjcyJKE50A+OJ1IAzL4bO8+PvN9du24t2xFio1FERPNxKefZ9Bwr067PeNGgdO7CJPiDmblcHo8VJrNJAYF8ez/3qLaV23wzU8+IyYigkfuuQvwFgD53Oflly0WJIOBtxYs5MXrrgbg/Z3eAPP+FgtnTbuQNouFvikpDB2YxamEMGZPYzyOVlytxQBowg5+sToWS2jH7XbgsHlXckUmC/FaiajgMBSShLO1FE9bBd/WB7Crvp4wnY6/5fXF+fmPAGj7zqLJZiNEq/U/wEfGxRGq03UqJ6tRKg+bHkYgOF2xWK04nE4C9Hq/gdjU0kJhWTl6rZbMDttuPy1fSUNzMxPHjCYy3BsBfaC4hG9+WkRKYkInY9bj8eB2e2izWvzHwoKD6ZuaQnRE52pD11w0A12Hvz+A4MBAgn9lOecTyYOjRjK9Tzo5HdKOnZOc7C96MjQ2posWT6lQcG5KMnP37iNCr2doByPzf9PP429jx3Rb8a2d5OBghsfFsr6yin/79KTDYjvn5xyT4H1mba+t9S/CB8dEd2ozvU86y0rLqGj1epBzwsMoq6wiPDQEg15Pn7BQsqMi2VFbh9XlItZoJCsigpbWVnbuP8De/AKiwsPRKBU43N7UX1d08JTb7Q7qGhs5NyqSn0priDJoyIuN6+QUkCSJu4YN5Zb5C/zjuHxABo3NzSxdu55Fq1YTXt9ATWoKc3xe2QCXi4cfeIjqmjoqW014xo2DmGgO2B0gScitrVT8vAw8HqRFS1BdcpHfkHWvWccPO3bwg+/6qksuQoqI4L+Ll6DIzEQRE+03fr7bvgPPylVd5j80OBiPx0OLw0FJeBjKmGhkjwfXwsU4fNpjQkJQTZ6IOyQE9HpwOhljtZF+2aUsWrmassVLULS2oszNgYz+SECsyURpTQ0RsowiN4fGwEBUM87nYd9nHNjYSGNFhfceTa00Abf8sAAAz7btJFzulVpMHJZLo9OC2enihq++pclmQza3IVdWcu/Mi3lPhkZAERxMvt2BBDw5djRVK1fhKSpBkZpC9BWX0SjLyC4XLatW84e//41wvY77vvqGlqZmgpRK4vU6dn7xFa/s2s3yuR+T39TEYp/kwFNQyOXBQVx/8YVc8fd/Yk5LQxMfh8u3s6LYsxfnho3c8cCfeKOpGVtqChsaGpCA/02fyvC33sFjDGDcuedwID7elyzPS3FzC5jbeP7NOaBScf60qXz/7fc89t9X0Ot0/O6qy3li+Qo8gKe0DL0sY09O4r3FS/j7RRdgNBhYUVAIksSm+T8gWyxk9e2D5TCBlycTYcyexthrVgMySkMcCq3XO+N22bwaWUChUNNmKkWtDcbp8P5gKtUGCqoridNKpBgNeGQPquQLcLdV8ugnXqH/I+PGYqj8HovsQhkxhPXmQJbu3sLUtDT/Q75feBj9wn95uhKB4ETh8XjweDydtuOLyytobWvzb38CFJSUsnz9BmIiIzjv7LP8bd+e+wWNTc1cP/Nikn06yaraOuYt/pnEuNhOxmxxRQXVtXUMGZjlN2YjQkOJCAslLLhzsZHLpk1FqVRiNBxMpxQfE81VvnRYHekoGTjVkGWZvQWFrNiwkV37D1BUVkZxWQU6nZaEmBgS42IZMjCLs0YMJzchHqvNxr7CInbtP8CBXbvAl9O6YO16xn/7PXFRUWRn9CO7f39GDM7h8swBzN27j8szM/yeTFmWsVutBDidVPo0rM0mE4WlZRSWleFxe4iKCCcqPJwBCgXrwV8SO8Rmp6W11b8QiAs0kh4SQkFzM06Ph2CNhj1btrKwuoam5haaTC3sqa6B2INer4f+9AAP1XvNBmOAgbDgYEzp6TAgA4D6HTuIGjqSZlNn3aJm0kRITSHA4eDOO/9AQ2MTtQ0NB72YWi2qa2YhKZW4v5/D+lffpE9yEmq1CpVKhcvjQRcRgU2pRHK7ue+W26jxBca1oxg7BqXPUDZt2syqjgFJ335HZHoaYYOykSMjmZaezthX/8OuHTuJiI3l5cIi9ksS/WtqiAwyoptwNqlJiaQmJvCTxcoSs5m0s8ZT6vsc4mprqUpJIbJfX24dOpiE2FgSY2OIiY6m0Onk8wP5rCgrx9LBS/v7fn25/oLpKBQK3G43bo8Hk93BUzt3saa+nv9MO48bBuf4P+fdB/JRq1XMr6vnz0uXE6zVsurB/6Nl9g3ER0extbGJq7/8mtraWiS7nVH9+rH8w4/RabWkJiSwb/dulCNH4PJ4kFtb8WzZSu5f7mFPcxVmj9fT2mx3sdiXUtKTn88tV13OE3fdwQWlZbz4449s3rGb0qIigmw27nvjTe+NhISgTE2h0ScV8uzZw7tr1vHTTwuxOxw0Nreg02pZ8tVnRISGkvXdPDbt2Mk7c7/gnRnnM33Ou2zZsAnDtu089dM8YqOiWPfm60y46hqqFi9h3LgxxCYn89mKlQzNHsgLN89m78efstw3Tqm8nC0rV+OurkaKjWXotKlsyS8AtxvZ1IoUFMgnS5dh3rYdq81G1OUzWRIWxu/uuI3/vfwqf3n2ef768quoZl0JSiXxNTVcd9vveWrjJiwaDQ8+9SyljU1YU1OQZZlpuTncc+01nDVyOJIk4TxMifmThTBmT2Mcdd5VqLJDvr92iYFaG4LH48Bpb0ah0NDS6BV3N7tVWN0eXEojUaFxNFhaKWlqpdFuoNQqE6bTcWNONm3f3u7tp88sTA4Hbo9MSUtLF4+FQHCicTqdmMxtNDQ1E9NBp7dr/wHqGhoZlpNNgM9A3LJrN98tWkJaUiLXXHxQ5/XtosU0t5i44bJLSPKVKHY4nZRVVnWJNNao1ajVKtzug2ViQ4OD6JeWStQhC7qxeUOx2e2dPKvxMdHcfu3VXe4jLCTkl0/CSaKmvp5vFy1h/dZtNLW00NRiYm9BIbUNDd2237Jrd6fXYSHBNLWYDs6xUonqhuuQlEpqduyk2uet+2LBj/5zsjP6c+WI4dT/tIixr75BQUkpjS0teDzdl+3tgl7vNRB93tsH//QAD9psBAcGkhAbQ2RYGKb4OPDpaJsKi7jwv6906UY181KksFBkpxMaGr3GpcuFuc2Cuc0CbW2ofcastaAQi8+QTYiJISM9jT35BVRs2IgywEDL1u2s90Wdt6NUKokMCkS7Zy+tQGNlJdsqK9m2Z0+ndopB2ShHjsBdWOQ3ZAekp3Pu2NEMzsqktqWFl+rqMcsy906bSv/ZNxAbHUVcVBSxUZFdFkZOpxO3uZVp06YxW6XC7nb7g386MqiklCWfzqXUJ6PIiojg89/fzIDX38Sk0fCn236PTqViXn4BFy9c3CUoLy0khKuyBvDn0aO69aSfO2woFqcTQwd5jCRJZPkCjvulpnJ2chLBWq+0LcboleGMDQig+O47GHnxZWzeuYuNej1Yrcy6fCYZ6en83/Mvoho+DFmhwL1qNeOGDCYtNo49zVX+8rbNtoOGWbLFwjMPPQB4ZS6zoiJ5+M5bGXHRZTT7tNuDszK59eqr2BAYyLs7dqJTqbh1xHCeWbOOKt9Og0ql4t+P/JWB/byyiIf/cCd/evxf3PG3v3PH3/7uv97DD/4fsb7divTkJL7532ucM+taVqxYBb5At8fuuwdJkvjnhLM46/2PAHBs28EN8xegGJyLMjaWrysrsbvdROn1yPX11BHIm/N+wOPT+NrCw3G63RgH5/LE/ffy7hdfcUCrBaUSqaGR7574J/nIsHEThATz1qdzkRISUKWmEK/T8fUr/+nymZ1KCGP2NMZt8wV6hR1MXnxQYhCJRheGQqnDbjPhtLciKVSsrTfhlKF/XH/0hjAqG6pxedx+Qf65KclIlgrctetAUqDrcyVTDDEMiYkmQn9i62wLTh+cTicutxutRoPC57Uxt7XR0NyMTqv1By2B18Cx2e3kDMjA4PtOlVZWsmXXbiLDwxntq7QE8NE331FT38DVF11AlM/Y2FtYxA+rVoPewLWXXORvu2zdeuobm0hJTPAbs+0/3O5DDJ/E2BhCgoI6RfcnxMRw6XlTukTy/+7Ky7v8+MZGRXGlL9CiI5mnaKTvseDxeKioruFAcTEHiks4UFRMcXk5Dp8Hpr6pmY3bd3Qx9sGbh3bk4FzyBg0kLSmJtMQEbHY75dU1FJaWsXrTZjbu2EmjL61UWEgwA/r0YcjATEwxMbRq1Nz4z79jsVgoKa9g+759bN29h5379rNj7z52HKIhbEelUvk/mwC9ntTEBNISE1GrVdQ2NFDb0IhOq6XUaqXJYEBtsRBmNFJjs9HS2kqLz+CS+vZFNcHnja+vZ0B6On1SkgkNCSY0KIj4mBjWq5R8U1PLmKRE5u3cikajptXcRk1DPc2mVhQKBQ9v2Uqx2cycF58jWKcjMizUv2iRZZnte/exYsNGtHlDiQwLJSIsjMiwMCLDQgkJCvL/7QBUVNewdstWahsacDidOJ1OlAolSrWKfLebUYMG0u8Pd5IcF9dFN32Xy4VbljsZhseCJEndGrLg1Qkb1CosPn3m5LQUkoKCiNDrqbda2V5bx8DICH4/fwGNNhuhOh1XZGYwM6M/g6Kijimd09HGmxvdvTNFkiTumX0D1917v38L/K4briUkMIj/e/IpnPN+IDQ6isbSMq6/4zaCdQGdzreZvN8Dua6Ojx/7h/+51E6/1FQWffguB4qLOXf0aL/GfZq5jQqzmRl9+3Bzbg43z7iAltZWNGo1EWGhnZ57t18ziy9++JE1m7f4jw3OyuTO66/pdK3BWZl8/NILXHTL7bjdbs4aMZxzx3gDsIfFxvLMOROoNplYtmkzayoqkH0pwtqlJ2enJDNg6GD+sXI1km/34aJLL2aeTz7y9f4D7Lv1d9z/+98x8f0PWV1VzZ3jx5DZtw8K36JUHR6OGwhIjMcOjE07MYGivwZhzJ6meJxmZJ+cQN3BmD0Y/BWNSh2AQqGlrsqrn2pVhmB2VhGqDyA9wrtllqwyE6pu5Z/F3qjSSWmpXkMWUIYPRmHw6ssiO2yFCs4sGpubqalvIMhoJL6D572wtAxJkoiLivLnFi2pqGBvfiHRkRGdouOff3MOdoeDO667mnBfmqfd+QUsWLqcAX3SucyXDghg6Zp1tLa1kZIQ7//RaGoxsW33XtKTkzoZs40tLbSazbRZrOBzdqqUStQqVZeqOKmJCSTFxaHXHSzI0Sc5iT/dchPqQ36gL54yucs8BBoD/F6gjpwJ5Y+dTifrtm5j0arVbNu9F6vdhtVmx2a3Y7PZsNrtVNXWYTtCovp28gZlM2X8WGIiIwkLCSY+Joa8gQOPmn+2zWJhX2ERibGxfgnG0ahraGTJmjWs3bKNiNAQ+qel0Tc1hajwMEKDg9EdY/GVT3fv4cbv5zMhLpa5K5Zgszsoq6qiorqG+qYmWmSZe31FD95+4E/M8hWB6DQWi4XApcu5OTfHf69BgUaCAg+WG5x3hKAYSZLIGZBBjs97ezTiY6K59Lwpx9T2ULSHMUh/DVqVinGJif7iFZNTU5EkiSGxMfxUWMSm6mq21dTSaLOREhzMptnXd1tx7Hgx87wpPPTUs1TU1HDO6JF+j+jYYUNZuWETjVVVBBgMXDp1MppDvjdnJ6fy7daF3DIw87CBTSMH5zJycG6nYzHGAL697FL/67RDsnF0RK1Ws+zTDzG3WXC6nDicTiLDwrqkyQKYetZ43n76Sf7z7vu88PBfOj2D7sgbAoBpWB53PvIo6SlJvOz2YPYtOsclJhLk+37mDB/G+YOy6XPWWOb9uAiA8tZWNlZXkxIczFqfZ/+2Md7UfmkhId6MCwoFj/z5ATbpDfxYVsag0yAjkTBmT1PaA78U+igUaq+h6XbbcdqbAa9nFqClsQCP24FSbWB1TQPBKhgW562sA+CuXY+nfjfNtWYgkXNTknHtfBeAMk1ftB3yzQl6n45ervYHlizL1DY00HDIVmpVbS0VNbVEhIZ2CiSat+RnHE4nU88a30n/uWPfPvqkJPsf6gD/nvMuVquNu2641u+93FdYxMIVqxjYvx+XTD1o5H3908KD6aC0Xg9DdV0967ZuI6NPWidjVqVSYneAy7f6B6+nLDw0FOMhJTb7p6didzjQdvDUxEZFcs6YUV10pTMmnoNSoSSyg+dpQJ90Ljl3AtOmndepbUed68FxqTppZc9EZFmmzWKhvqmZqtpalm3czMpde3zb/400t5iorq8/pqANlUpFWmICfVKS6ZOcTHpykn/BoVGrGT98GAmHBFAdKwEGA0N6GAEdGR7GFedP54rzu3rBe8IVmQNICwqiaIM3BVGgMYDMvn38nnRZlnnvvQ/Ib2xigi9jQpexGAz+HKy/VSampPBjYRFGtdqfknFoTDQ/FRaxsbLKX5HszrwhJ9SQBa+x+PSf/49Hnv83j957j//4FedPZ+UGb2aBS8+bgjHA65XVqzVYnV7ZwNkDszGd3XWB29tIkkSgMeDoDYFZF17ArAsvOOz7QYFG3nv+aQC2fv6lf5ExPimBJpt3UdrodvG3u+/wZ+Fo55t9B0gNCcEjywyOjiYlxPvc1SiVpIWEcKCpibHnnsNnP3kN4JzoKE51zuyn/BlMuzGrMh7Uy9otPq2OJgiQsLbVYGryfsHrCcTqbCErSEOM2o3TaUWt1qPQR7DXqqXGI5EdFUms0Uhjjdczu5cU1C0t9A/vHFktODqyLON0OtH4DDaPx8NPK1bSbGrlivOn+Q3XlRs2snTtekbk5jB5/Fj/uW98/CkHDhzg0osuQuvzIuQXl/DzmnUMzsrsZMxu37sPp9PF2SNH+I3ZmoYGtu/Zh8cjdzJmHQ4HDqcTk9nsN2aDjEYS42IJP0TDGR4ail6n6+T9SoyNZdSQwZ08uAB3XX8tKpWq0zZpVr++3Xo6p004u8uxqPBwv4ygI+161jMVh8OByVcZT5ZlbHY7bVYrFqsVjUaN0RCAx+NhzeYtLFu3nj35Bf4FkM3uoKGpifqmpmPyqEaEhnLumFGMHjqEkKAg9DotOq0WvVaHTqf1L5LOVOM/NzqKysOkDJQkiXlXXIbF6STWaOy2jQBmDujP+zt3MqNvX38KsqG+Km1f7NuPw+0mWKvl2oEnp9zp5dOncfn0aZ2OXTp1Cvc8+jhut5vrO8iSgnUBfmM2UHt6S+jOTkrix8IiYgIC6BMaSq3FG/Bd2WrG7nKx05eWa0RcLOsqq/hq/35SfY6DSzI6l8vtGxbKgaYmNlVXk9/UBMCgKGHMCo4TziZvYIWkOvhHeFAvG0VbazlNtbsABbImjKXl5agkiA9PQK3WU9FqwmSvJTl8DO9a3VTJu7kqJQXZ44aGrQCk9TlXGLJHQJZlisrKMZnNZPfv598uWrd1Gz+vXsvQ7IH+ykySJLFpx07cbg/NJhOhvgeJw+n0Gr6ugwEICoWCIKORAL2+k9YzIiyMjD5pnYKeAM7yVZrpaHQmx8Vx7tjRBB3yw3zjZTNRKKROxw9ndF5/6cVdjsVFR/n1Yh3RnILlDU8FzG1tKBQKNGo1TpeLorJyCkvL2LJrN8vXb2Dtlq3HZIgeC1qNhoiwUIIMes4aNYpBAzKIj44iJDiYiNAQ+iQnd1psCDoTqtMRegpnjTgViA4IYO0N13U6NtTnqXf4dmVm52R3m/P3ZBEZHsbbTz9JdV0d43z5WQFCdAaqW73G2uluzF6ZNYCv9u/niswBSJJElMHg1zeXt7ay0xeUdu+I4dzw3TyKmlso8unXL+7X2ZjtHx7G/IJCvvDp1OOMxtNCZiiM2dMUd5u3nrbScDBdTLteVqUOorn+AMgeJLWRBdXeL21mTDIZCVm4PR4aK4twuF3IyCz0RdVOSk3B3bwHXG2gNjK43/gTe1OnMDv372f7nn0kxccxNm+o//gn332Py+UmOT7Ob6AqFAocTid1jQfr3UuSxPjhwzAYDJ2218cPH8bwnBw0h2zJ3X3DdcyfP5/AgINbUgP6pDPAV8++Ix01pu3Ex0R38Z4CXQJFTleaTCbm/byUHXv3U9vQQJvVSlubhTarFbPFgtPppH96GkMGZpHpmzNvEI0Lh9OrVwsMCCA1MYHUxAS0Gg1tFiutbW3UNTZSW99Ak6kFo8FASLA3CCgsJJjQoOBO2tDi8gq+WbiIBUuXY7XbCQ0KwmgMoKyyin0FhdT7PBvHikqlIjAgAINOh8Pp9N6Ly8XgrEzGD88jLzvb//3RqNVEhIcRERpCRGgoBr0el8vF/PnzmTZt2ilVNEFw5hIdEEBCYCDlra0oJYnbunkenWy6264P1nufrUqFAr361DG+fwnRAQEsvWaW/7UkSSQHBbOnoYHd9Q0UNjcDMCIujslpqXzjK6ecExVFWmhIp776hXn17Ft9Ke9OB4kBCGP2tMTjNCM7vdGXmsg87zG3E4fN+8Npai5HliVUukiW1FuxutwkBIdzVpp360epUJATl0Jjm4mSljZq2ywE+DRQ7gOLAVBFDPWXx/0t4Xa7+fyHBdTUN3DjzEv9+iab3UF+cQlKZeck5snx8ciy3EnbmtknndSEBMJCOus/O3oF2lGr1b9Zo8PUauZASQl78wvILy6hyWSizWLBZrej1WoJ0OvRabW4XF4DtNlkoqC0jMKSUhp8D+cjsX7bdt7/8uteH7dGrUajUaNWqWnqUFLyWAgyGklLSiQjPY2xw/IYP3wY/VJT/LKT7jynsiyfEUFogjOXvNgYyltbuaR/PxIOyQZyqhKs88mstPoz8u8rOdhrzM7PL0AGogIMRAUYuLhfX78xe6jEAA4as+3knAYSAxDG7GnJweCvaJQ67xfPbq0DZCSFGo/HhUodwJpmDw1WG0E6A1P7ZCLhAbw/liqFklDTFjZv/pYhSg8xSRPQKJWUla0gAHCHnXqr696m1dzG7vx8JEliuC96WalU0tDUTHOLiZr6er8xm56UyPnnTuii67z6ohld+g0wGPx6VIGX2oYGFixbwY/LlrNr/wHKqqoxmc2/uD+FJJGRnsaQ7IEkx8dhNBgw6PUYAwL8RQh27tvPpp07KSgpRaVSefPFqlR+Q7Q9yX5Lh3yYCoWCiNBQoiLCCQsOxmyx0NxioslkotnkzY/a7tltbz82bygXTp5IQkwMjc3NmMxm4qKj6Z+WSnpSEgqFhMPpRKFQEBwY2OMfzjPxh1ZwZvHQ6FEEajX8beyYkz2UYyY5NJJgnYGMqDOzamVKiHdR8UOBN8d8dqRXnjY1PQ2DWoXN5eaS/t0Ys4dkGhkkPLOC44Wr1VsGT9VNsQRZlkCS2G1RUt7agFal5sLMYbht9TSaa5B14UQGeSPTPbZ6SuorgFgm+fLIOWrWEwBU6zI4MzakvXg8HuqbmjDodP5o1pr6en5ctoKgQCPDBmX7jYapZ433p6RqJzQ42C8jOBotra3UNTQSFx3VJV/hb4mmlhY+/+FHPvrmW1Zt3NxtftLoiAj6p6XSLy2VyLBQAvQGdDotdrsDs6UNm93hNUDVagKNAf70W/l79nDJxRcd0avdMTPD0cbpdnswBnglIIczHj0eDyazGZPZjNPpwu5wEB0R7k9FJhD8VsmOiuT1806vTA8BGh03Djv3ZA/juJHi+71qDwbL8uW8DdJqmX/FZZgdTtK7eXaF6/X+3MEAucIzKzheuEwFACg0If5jtrZq37/UmGQlbZYqQtRqJg8YTqhOj9mppt7STJNFxi0piQkMpTbsbJ6u30GrrGNaehoep4UQqzffbGLqhBN8V8eXLxf8xO4D+UwaN4ZRPk1XUlws6clJJCfE4/F4/AFcR8oV6PF4WLBsOeu3bWfH3v3kFxcTaDQSHRGBRq1m2549HPDV3AZvcviM9HTOGTWSc8eOJiYigiaTiRaTqVNFqXZ0Wi15gwZ20dCearjdblwub8CH1W5j2br1zP95GWs2baHNasHhdNLQ3NKp5GFu5gCmTTiLsXl5JMbFkhAT7V9Y9ASn00l5YUGv3cuxLlIUCgUhQUFdiioIBALBqUbyIc+1gR0Ch4cfJUtM37BQ6iusBGu1JAefHs+7k27MvvLKKzzzzDNUVVWRlZXFiy++yLhx4w7b3m638+ijj/LBBx9QXV1NQkICf/nLX5g9e/YJHPXJpd0z244su/16WVQB5Dea0CtlxiZkEBfs3TIIDE5A79HQZjETqvdGsn+2v5gqOYRxiQkkBgXhrFmDJLuQ9FEEhHafa/F0QJZl9hcVd9IixkRGkF9cgsNx0LjSaDTdygQOx/6iIn7/57/5cxYeCb1Oh9Vmo7G5hdWbNrN602Ye66ZEZneo1WpGDc4lMshIYV0DEeFhJMXGMWRg1jHnKDwUWZY7eUYPF9Xeam5j6+7dHCgu8bc3mdsoKiujsLSM8upqausbqG9q6tbTeiiDBmRw9YUXcPn0ab84P6lAIBAIekbKocZsZMRhWnalX1gYayoqGRQVedrInE6qMfvpp59yzz338MorrzBmzBhef/11zjvvPHbv3k1SUlK351x++eXU1NTw1ltv0adPH2pra3G5XCd45CcPj6MV2dEMgDo0E4C21kpABiR2WJRUOlTkxsSRFnWwBJ0kSSSHRRMfHIHK54H8dLe35veVvuT37rqNAKgih502X2C3282aLVtZu3mLr/Sjt2a6QadlxqSJDMroT1hIMFl9+zJ66BAUCgUej4e9BYVs2L6DPfn57CsooqyqGpXKW1lKlmWaTSYam1vQajT0S0shLjqaz+cvwGa3E2AwcNm0qQwakEFGWhptVgs19Q1YLFYG9u/HkIGZhIWEYDKbKa2sYsO27SxauYqf16zD5rATGhRMcFBgl6pUAPWNTZRVVbF8/QYAvlj0s/89SZLI7JPOsJxBDMsZxPCcQfRLTekkZbDabJRXV1NeVU15dTX7CorYvHMXm3YeLCUKEBIURGpiAikJ8bjdHhpbmqmtb+hkxPaE9KQkzptwFpPHjSEyPBy1SkVIUCDJ8fFHP1kgEAgEvUpHj6pSksjoQZrNc1OSeXfHTqamnT5OrZNqzD7//PPcdNNN3HzzzQC8+OKL/Pjjj7z66qs8+eSTXdovWLCAZcuWUVhYSJgv4i4lJeVEDvmk4zJ7vbIKfTSacG8ZW1ODtwyjrNCS32JGpVCRmzQQhVKN02lBpdIhSb7AL58hu6t0J/rGDaSowri4fz9MdjvmspXoAFVU16j7U5Gflq/k/558il0H8rt9/8flKzu9jgoPJyE2hsLSMppNpmO+Tnl1tf/fE8eM5tXHH+1UtOBwBAcGkt0/kOz+/Zh9+cxjupYsyxSUlPLj8hXMX7iIoJBQmk0m9hcVUVpZxa4D+ew6kM87n3/pPycsJJjo8AjqGhuPORVUs8nEll272bJrd5f3EmNjyezbxy910Ou0pCYm+vWq0ZERRIWHYdB5jWhJkjAGGE6bBZBAIBCc6YTodIRotTTb7fQNC+1RJc9LM/qTFxtLYlDgcRxh73LSjFmHw8GmTZt48MEHOx2fPHkyq1ev7vacb7/9lry8PJ5++mnef/99AgICmDFjBv/85z/RHybQxm63Y++QlNzkM2KcTmcnPd/xpP06vXE9R7M3MlERkITT6cRubcRp93rcah1eg3VQTBIaSYndZqGuoQCry0VsZD+0HXLpLdn+M+PU+QRE5hKgVLKmopKE6nXoAEIHn5C5+aXzUlJRwd3/eIyFK1YBYDQYGD9iGOOG5REXFcWOffvZsW8/VXV13kj0lhZa29qobWigtqEBAINex9CBAxnYvx/9UlNISYhHlvFHqYcGBRESHITFYmVfURH5JSXkZGRwydTJSJJ0XOcnOT6OG2deQkJwIJMmTfIHOVXX1bNxxw42bN/Bpu072bxrF82mVhqbWzp5XQ16HQkxMSTExJCcEM/grEyGDMwiKS4WCQkZmdr6BorKyymtqEStVvvzqGb27Ut0RM8LZZzI3ZHe/Hs60xBzc3jE3HSPmJfDc7rPTVJwEM21dWSGh/f4HuIDDHjcbjwdSpR35ETMTU/6luRfsqfYC1RWVhIfH8+qVasYPXq0//gTTzzBu+++y759+7qcM3XqVJYuXcrEiRN5+OGHqa+v5/bbb+ecc87h7bff7vY6f//73/nHP/7R5fhHH32E4TRMn5Rh2ECEuooiayYVjj70TVEToDEDLra0aqm2K0lu8+BxKZAUErpQJXYPmJrd6Hz2hkeWea5oPTFUMj68L8nBAylvq+Eq8+0ArIl8B5fi1FyRrd+xi5c/nUub1YZKqWRoZgYxEeGkxsUxZnDOYb2DbVYbNQ0N1Dc1ExEaQnJsjD/g63RFlmUsNhv1zS20tJoJMgYQHhKMUX9m5k0UCAQCwbHzYmU161rNXBERxkWHpNw6HbBYLMyaNYuWlhaCjhJ4e9IDwA790T1SgnCPx4MkSXz44YcE+8TNzz//PDNnzuTll1/u1jv70EMPce+99/pfm0wmEhMTmTx58lEnp7dwOp0sXLiwk5ftl9K6aQ2yDfolh9A3Jpfm+j3gi4pvcSvJi40j1ahBowtFFxCH2W6lprWJ1PAYFD6pwbLSMjbvLyBYG8c7l92EVqXCWfQFtqUghQ5k8vlX/NpbPiZ6Mi8Oh5O/Pf8i/3n3fQCG5wziraeewKDX8+n38xg2KJvRQ4ecMUZcb35nzjTE3BweMTeHR8xN94h5OTyn+9zE19byzo5d/HnUiF4vSXsi5sbUAzngSTNmIyIiUCqVVHfQIwLU1tYSHd21DCdAbGws8fHxfkMWYMCAAciyTHl5OX37dq0vr9Vq0XaoWd/Oyai89GuvKbtsyLZ6AFS6UFpM5SB7AA+yDFaPmoyYFHCZUWuN3u1jtZpQY2ej/T2fTvLSjP4YfQsAR613y14TP+GUm5eSigpm3X0v67dtB+CPN93I43/6o/+cP9x4PRrN6V2O8HD8liuEHQ0xN4dHzM3hEXPTPWJeDs/pOjd58fHkHecg3OM5Nz3pt/v8PCcAjUbD0KFDWbhwYafjCxcu7CQ76MiYMWOorKzE3KFy0P79+1EoFCQknJlVPDriaqsAZCRNEMqo0TgcrYDXK9vmkUiNiMNojMIYkoZaG9JtH9tqavly7x7UuPhdbg4ATTYbrqplAKhjzzoBd3LszFvyM8NmXMr6bdsJCQriy9de5pKpkzH7EkEDZ6whKxAIBAKB4OicNGMW4N577+XNN9/k7bffZs+ePfzxj3+ktLSUW2+9FfBKBK677jp/+1mzZhEeHs6NN97I7t27Wb58Offffz+zZ88+bADYmYS7rRwAVUAiFmuz999qHQAtLiWZ0d5k/7IsU1a1h7KqPdgdlk59/H3FShIUTbwav52MlgU02Wy8vXYx7iavt1YVe/gcvyealRs2csmtd9LU0sKwnEFs+PZLkuLj+Gn5SubM/QKrzXayhygQCAQCgeAkc1I1s1dccQUNDQ08+uijVFVVMXDgQObPn09ysrdMa1VVFaWlpf72RqORhQsXctddd5GXl0d4eDiXX345jz322Mm6hROKy1wGgDIggRZTJQAOjwsFYENDQrA3Ct3utNJqb0OWISRUpl1ksaqsnB8LixissnFWchIotFSZzcRbtnn7DRuEQtfzSPbjganVzI33P4jH4+Hy6ecx55l/odFoMLWaiY6MICMtDb1Od7KHKRAIBAKB4CRz0gPAbr/9dm6//fZu33vnnXe6HMvIyOgiTfit0O6ZdbnseGhFoTZisTdhkCA4MBrZ48TUlI+kUBMR3oc2uxmj1iv6lmWZh5evAGBI1nnEjRyD7HaQqQ0h3liBG1DFnToSg3sff5Li8gpSExN47fF/+qUEQYFGZl92Kaoe5MwTCAQCgUBw5nJSZQaCY0eWZdxmrzHrtNQjeZxo9FFocQCQHJGKx+1All2Ah5jgcNKjkv3R/T8WFrGmohKdSsWDo0ciqQwo2nW1NV4j91TRy37900Le/fxLJEni7Wf+5ZVNVFb531er1WdM1gKBQCAQCAS/DuHeOk3w2BqQ3VaQFNiUQcgKLXUOB4ESuGWJ8KBoQCYwtB+y3DnJsSzLPLl6LQC3Ds4lPtCbQ9bt8SDZ608pvezaLVu5+YG/AHD/LTczLDubd774koamJmZOm0q/1NSj9CAQCAQCgeC3hDBmTxPaJQaSNhK3Ph61xkiVqY5ANcgqo89TKdHU2oDb7SA4MBKd1gjAzyWlbKiqQqdS8YehA3GUfAfacN4o05BjW00Wp4ZedsnqNVxy6520WSyMyRvKI3+4E0mSCAkKpM1iIaoHtaUFAoFAIBD8NhDG7GlCe/CXW+3Nsas1RkNDNaghwBABeD2wza112Jw2XJKWeJ8x+9Qar1d2dk42kQortrrNNLs1NDvPQlm7Eji5elmPx8MHX33DrX95GLvDwcQxo/n81f/4dbKXTTuPltZWQk5QkQuBQCAQCASnD8KYPU1wt3mNWafkS8XlVhOk8soJAgK8xqzD1oRao8cuKwgKCAW8GQxWlJWjVii4Z/gwULtQx44jQlJyU9AglN96CxGcDL2sLMts3bufx956l6279wBw0eSJvP/8s9Q01BPgq1iiUCgI7VAoQyAQCAQCgaAdYcyeJrh8wV8KRzNqezUl5gSilN6CCe0FEuzWWoKVDuLj0lGpvXl3n1q7DoBrsweS4NPKKuLPASDKVECLOR8kFar4CSfydli7ZSt/eeZ5lq/fAEBgQAD33nwjD972e1Zs2MiK9Rs5Z8woxuYNPaHjEggEAoFAcHohjNnTANltx2Ot9f5boUOtD6OqpZZkvQyAxmfMqtSBeBR2FArv9vzWmhoWFRWjlCTuGzGsS7/O0h+858WMQaE5vp5PWZY5UFzMms1b+frHhXy/5GcA1CoVt159FQ/dfiuR4WHeY74SdlpR2UsgEAgEAsFREMbsaUB7GVtZqccRnI02pB/O+h2gB4XKgEKpxu2y45HU6ALCUSi9xuAHO3cBcFG/vqSGhADerAhfF1UTajCSWzIfAHXSeb06Xo/Hwxc//MjjL79KaYW3uIPL7e5UsUuhUHDtxRcyakB/rrt6VqcazGOGDiEhJoaUhONbU1ogEAgEAsHpjzBmTwPc7cFfqiCQFDS61QT7JAZandeb6bC3UlpbhEeppU98FkpJwdw9ewG4emAWALLHRePWFzFU1bAl7EJyqpYBoEma1mtjXbZ2PQ889Qybduzs8p5Wo2Fo9kBGDcnlhpmXkp6UyPz586lrbGTj9p1MP+dslEolkiQJQ1YgEAgEAsExIYzZ0wB3WwUAHlUwKrWBInMrQSqvMavRhQDglBU4UONGjVapYkFhEXUWK5EGPRNTUwCQHSb0GgMDomKJ1tSCx47CmIwiZMCvHmN9YxP/96+nef/LrwEwBhi47+bZXHHBdBSSAkmSSIiJ9mcoAHA6nbjdbj765jssVhuhwUGMG95VDiEQCAQCgUBwOHpszKakpDB79mxuuOEGkpKSjseYBIfgtviqX8ludHIb5U0uBmq8mQzag780kpOUQC1ogpAkiU92eQshXDYgA5XCW+hNoQvDMPhBktxWItb+H3a8EoNfW03r8/kLuPORf9DQ1IwkSfzuqst55A93HVNeWKVSycQxo9l1IJ/BWZm/ahwCgUAgEAh+e/S4nO19993HN998Q1paGpMmTeKTTz7Bbrcfj7EJfLjavLpTSXYjORqot3T1zHo8TsCNTq3FZLfzXX4BAFdldjYQJUkCpR5n6a/Xy7rdbh56+lmuuvuPNDQ1M7B/P5Z/9hEvP/r3Ixqy+cUlVNfV+V9n9evLrAsvwBgQ8IvHIhAIBAKB4LdJj43Zu+66i02bNrFp0yYyMzO5++67iY2N5c4772Tz5s3HY4y/aTxOC7KjGQCnLoFmTSJGlYxSAklSolQF4LC3otNHExjaF402lG/2H8DmctE/LIwhMdH+vlaXl7OtphZbw0485lJQ6lDH/bKUXM0mExf+7jaefeMtwFt6dv3XnzNycO4Rz9uyazcfffMdXy74CafT6T/+a73DAoFAIBAIfpv02JhtJycnh3//+99UVFTwyCOP8OabbzJs2DBycnJ4++23kWW5N8f5m8Vt8XplPQo9qpD+FHtC/cFfam0IbpcdU0sFRVW7qWmzolCq+XiXtwDBlVkD/EaizeWiZNcnbNv8Pqb9n3rPjzsbSWXo8ZhcLheX3nonPy5fgV6n48N/P8cT/3dfp4wEh6NvSjKBRqMI8BIIBAKBQNAr/OIAMKfTyVdffcWcOXNYuHAhI0eO5KabbqKyspK//OUvLFq0iI8++qg3x/qbpF0v61EHoTOEU1ZSSUwniYEHhyzR4vRgtZpxSyqWlZYCcGXmwcAuWfYwWFNHq82Crno9bkD9C7MYPPLCSyxfvwFjgIHFH77HEF+2hMNhsVox6L1FHIwBAdx69ZXodbpOnlmBQCAQCASCX0KPjdnNmzczZ84cPv74Y5RKJddeey0vvPACGRkZ/jaTJ09m/PjxvTrQ3yousy+TgdKIrNLTZGmlf9DB4C+1xkhwcDweKlFpFLy6dTsycHZSEskdSsDqlEr6ZF+Fu3EnbUtfBUmBJvWSHo/nu0VLePr1/wHwvycfP6ohu2XXbn5ctoIrZ5zv98bqdboeX1cgEAgEAoGgO3pszA4bNoxJkybx6quvctFFF3W7tZyZmcmVV17ZKwP8rePy5ZiV8GCuWEooEqFqb3orjS7U+3+FjFFhQ3Z7eGf7dgBuGZLbqR9JoUQVkYuz/CcAVLFnoTDE9GgsRWXlzP6/hwC464ZrmTlt6hHby7JMcXk5DqeTHXv3CWmBQCAQCASCXqfHxmxhYSHJyclHbBMQEMCcOXN+8aAEB2nXzCo1IZjsVmQpCJ3C65lVKvXIsowkKdHowlhWVkmdxUqc0cj5fdL9fTTZbLQ5ncQZjTgKPgNAk35Fz8bhdnPDnx6g2WRi5OBc/vV/fzrqOZIkcf45E0iIjSUve2CPricQCAQCgUBwLPQ4AKy2tpZ169Z1Ob5u3To2btzYK4MSePG4rOA0AaCJn8gmdxpKpRYApcpAm7mG5qZi2lwetAFxPLPFm47rptxB/tyyAJurq5m7cQkrN3+Bu2ErSCo0qRf3aCwvvv0Oqzdtxhhg4P0Xnu1U/OBQGpqa/P9Wq9UMG5QtshUIBAKBQCA4LvTYmL3jjjsoKyvrcryiooI77rijVwb1W8PlMOOwNXU9bi4HwKPQIesjabLbCFZ7s0SoNIFISFhdbnbXVrAwfw9rKypRKRTcMCi7S19921bTt+TfAKgTJqLQHb2gQTs79+/n4ee95z73l4eOKBfYW1DIK+9/xMoNG0VGC4FAIBAIBMedHhuzu3fvZsiQIV2ODx48mN27d/fKoH5LyB43VcU/UVX0A61NBzq9Z2vO97bRhFBlbgMgVq8EQKuPICg0FYU2BK1CweJi7wLjwr59iDUaO/VzTnIy56T1RWf29t8TiYHD4eCG+x7E4XQybcJZ3HjZpUdsX1BSgizLtLS2HvM1BAKBQCAQCH4pPdbMarVaampqSEtL63S8qqoKleoXZ/r6zWIxl+Nx2wBorN6A22UjOGIgkiThbC0GQKkNx1SzgUjaiFB6t/f1xlgkhYLooHAkazluSykGpcQt3RQtkCQJTfQwbLZ6UGpRp8w45vF9+M13bNuzh7CQYF57/NGjygWmnzOB7Iz+JMTECGmBQCAQCASC406PPbOTJk3ioYceoqWlxX+submZP//5z0yaNKlXB/dbwNxcCIBa402j1VK/g+barQC4LdUAqHThuFpLSVGZUOBBpQlCq48EQPY42VVXhyTLJIeEMjYxoVP/Lo83J2174Jc68TwUmmCOBVmWefUDb67g+2+5mdioqGM6LykuDoXiF9fjEAgEAoFAIDhmeuxKfe655xg/fjzJyckMHjwYgK1btxIdHc3777/f6wM8k3E5LdjavAZrZOI4rOYqmmo2YWrcg0YXhuRoBMAdmEZJnYfcAG8WA5UmBIu5BoMxGoVSwz+21JDfaOGBMUM7eUPdHg//3rCRSL2e8/O9Vb96IjFYv207W3btRqvRcMPMw8sLbHY7qzdtZszQoWi1hw8MEwgEAoFAIOhtemzMxsfHs337dj788EO2bduGXq/nxhtv5KqrrjqmcqaCg7S1FAEyWn0kak0Q6rAgnHYT5uYDNFVvQO+2AFAlReJSNxCgs3iLHWjDkSQFdW0tbKgoo9rcgtkFs7IyO/VfaTZjdbqIq34VzMWgMqDpQdWv1z78GIDLpp9HRFjoYdstWb2Gjdt3UllTyzUXX9jjeRAIBAKBQCD4pfwikWtAQAC33HJLb4/lN4Usy5hbigAICDmoPw6JysHSWopsq/MeUBrY19hEqs7hbRuUQlB4OhISVc0NrK8oR62QmNG3DxEGQ6drJAQGcvvQwbjme3PCqhOmIKkDjml89Y1NzJ33AwC3XTPriG2T4+Mpraxi7LC8Y+pbIBAIBAKBoLf4xRFbu3fvprS0FIfD0en4jBnHHlz0W8ZhrcflMCFJSgICk/zHlUoNoVGDaSn8CgBZE4ajtYT4YCegJDC0D0pfEFi4IZgdVeXEaWVuGjSgyzUkSSJEq6HFUoIMaPpdc8zje+fzL7A7HAwZmMWwblJ9dSSrX18y+/YRAV8CgUAgEAhOOL+oAtjFF1/Mjh07kCTJn0u03ZBxu929O8IzlHavrCEoCYWyszzDEJhEqy/DgUv2cJY6Hywgh+Wg6ZAf9vuCQkJULoaGGxkV131wlrtmFbK1BkkTjCbxyOVn/ee43bz+0ScA3Hr1VcdkpApDViAQCAQCwcmgxyHnf/jDH0hNTaWmpgaDwcCuXbtYvnw5eXl5LF269DgM8czEZqkBICCoa2lgp6MVyW0HwKXU4ZCVeBRaNIYE7NZGf7t3t+9gr8lJSkQiapWuUx/V5jYWFBRSt+s9ANQpFyP5qocdje8WL6G4vILQ4GCuOP/wGtvNO3exJ79AFEcQCAQCgUBw0uixZ3bNmjUsWbKEyMhIFAoFCoWCsWPH8uSTT3L33XezZcuW4zHOMwpZ9uBymAFQa0O6vG+3taBwed9fY42iWhnHtf1ywGXF4/F6vnfX1VDYWE+Lw83Z/YehUHbOIlDU3MymynIGl34J0KPytc+/OQeA38+6EoNe320bu93BopWrsdntXHHBdPqnpR5z/wKBQCAQCAS9RY+NWbfbjdFXYSoiIoLKykr69+9PcnIy+/bt6/UBnom4nGZARpKUKFVdjUW7tRmF22vMmjCSHh5DcHA8LqfFL0n4dt9eMsOMhBkCiQ8M7NJHQlAgEzX7UbtMoDKgDM06prGt3ryFNZu3oFGruf3awwd+ycgMy8mmsLSMvildvcsCgUAgEAgEJ4IeG7MDBw5k+/btpKWlMWLECJ5++mk0Gg1vvPFGl6pggu5x2r2lXlWawG61po62SnSyGw8SZgwMiEpAkiTUGm8mAlmW+bGwGNnj5PKs7j2iCYGBBFW+iRtQRY9CERB3TGN74c23AZh14QVHLJKg02qZMGokZ48cIfSyAoFAIBAITho91sz+9a9/xeOrKvXYY49RUlLCuHHjmD9/Pi+99FKvD/BMxOX0GrNqTVePqsNuQrZ69bStGElUtRLrLsdtO6iV3VBVxbrqegwKJ1OiJazmqi79OMt/xF23EZR6jBPeQ1IevZjBgeJivlm4GIB7Zt9wTPciDFmBQCAQCAQnkx57ZqdMmeL/d1paGrt376axsZHQ0FBh2BwjBz2zQZ2Ou112TA17UDibAGjByIBANdb6XciKAPS6MAA+3LkbgLMT4tAolV2yIdS1taHc8A8AdJm/R2GIPqZx/fvtd5FlmfPOHk9Wv76Hbbdu6zZSExOICg8/bBuBQCAQCASCE0GPPLMulwuVSsXOnTs7HQ8LCxOGbA9wOUxAV8+sQqnG5bQh+fSyFmUQ8dFZYIjHJXkNVofbzZd7vdrkoWm5BIdnodF2rs61Y9snUL8Bj0KLLudPxzSmwtIy3vncGyx23803HbZdQ1MTPy5bwesffkKrue2Y+hYIBAKBQCA4XvTIM6tSqUhOTha5ZH8lTkf3MgNJUoBCh8JlBSAsLB1jRAZOe4Lf+7qgsJAgjcSo2Bj6BgcgKZSd+pBlmZTS17z/DhmIo/hrtBm/69LuUO795xPYHQ7OGT2S8SOGHbadLMOAPul4ZA+BxmOrJiYQCAQCgUBwvPhFmtmHHnqIxsbGozcWdMHjceF2WYCuMgNZlrHZTCjcXmM3JW4gCoUKrT4EtSYAs8PBoytWYVQryYmKQK3suhaxbX2KENMWUGgwxI0HSXVUQ/b7xT8z7+elqNVq/v3I347oZY8IC+Wy6edx+fTD558VCAQCgUAgOFH0WDP70ksvkZ+fT1xcHMnJyQQEdPbObd68udcGdybSnl9WodCgVB0sYmA1V2J1OpFcNhRur2dWUmiwmkrQBcSBQsXvf/iR3fUNxAYEcHX/FAI8LbhdOn96L3vBXKwb/gqAYdRzaPvfgOwLNjscVpuNP/7zcQD+OPsGMtKPLSOFkJUIBAKBQCA4FeixMXvRRRcdh2H8dnD69LKqDhIDt8tGQ3M55Y2VxCu8hiwqA631u1BYKyA8m1dKVXy1bz9qhYKPLryAUFULHqcJWY4EwFWzlralNwCgHXg3uqzbAJC6yWPbkadee4Pi8goSYmL48x23HrHtnvwCUhLi0et0R2wnEAgEAoFAcKLosTH7yCOPHI9x/GZwtetltQeN2QMNNWwuryJBCwrZ67lV6mNweVxYXR7m7tzPI1u9so7nJp7D8Pg4XM5gXE4zkrOVto0PY9/9GrjtVIaMY1fwbKba7QRrj1y+dsnqNTz5yusAPPuXBwkwGA7bttlkYu68H1CplPzxphuFQSsQCAQCgeCUoMfGrODX0R781a6XLWuu58f925FRMCAkAKmtEIA9bUrmlFoobJZYU1ePjMRNOYO4pE8qFWVrMbZsRVW/npaiL8HlyyoQO4Hvgx8gvHoDau0e3FFDUQZ1Lxsoq6zi6j/ch8fj4fqZl3DJ1MlHHLe5zUJ0ZAR6nU4YsgKBQCAQCE4ZemzMKhSKI+olRaaDI9MxLVer3cr8vZuQgRBDCC5XGwqX19j9vKSFb6sL6a9q5J8peiZFQorjNeyfX43B0YgHcPj6VEbmYRj2KIq4iVzXZsGybx80lyIHJUE3xqzd7uCKu+6hvqmJ3MwB/OfvRw76AkiIjeH3s67E4XAcsZ1AIBAIBALBiaTHxuxXX33V6bXT6WTLli28++67/OMf/+i1gZ2ptHtmW1wqvty5AoXHTqvdzmPrN7BqWobfmB1gcLIt7FnUbjM0A83gxJt+QlZokSKGoos/G3Xc2ajiJviN0bhAI+6MGXhM+SiC+3W5/vpt27nvsSfZsG07ocHBfPbySz3ytGo0R68kJhAIBILTB0mSsNvtwhl1CE6nE5VKhc1mE3NzCL09N2q1GqXyyJmXjkSPjdkLL7ywy7GZM2eSlZXFp59+yk03HT7h/m8Vu8tFpcPBosJ8Mj12AP60eBHDY8IIVjhx2lq4IjUEnULyGrMeJ5favkB2m0EbCgEJoI9FF382ivAhWDWhKNVGDGH9u72e0piI0pjof+12u1m9aTNvfPIZn3z7PQB6nY4PXniW1MSEo46/qaWFkKAgkcFAIBAIzjDa2tqIjo6mtLRUPOMPQZZlYmJiKCsrE3NzCL09N5IkkZCQgNFo/EXn95pmdsSIEfzud7/rre7OGP61ei3/XLkKGchuquKzcRHU2twMifJW7ZKVAVyRlUBCUDgttTuQZBfUbURuKwNtONox/8YdNhSlSosuIAaP24HGWo9C2Tm4y+xwsKmqmsTAQMy1tezJz6ewtIy9BYX8tHwl9U1N/rbXXnIR/7z3HuJjjl7m1uPxMGfuF0iSgmsvvpCIsNCjniMQCASCUx+3201VVRVhYWHExcX9Ks/YmYjH48FsNmM0GlEoepyW/4ymN+dGlmXq6uooLy+nb9++v+h72CvGrNVq5T//+Q8JCUf38v3WCNfrkQGtJDE2JgQAhUqPSiGREBzOzEGjkWWZloYCFM5maCtHatkPgDrvCTRRw1EFpiJJEm0OG2qljoDgFABaWltZvGoN1XV17KqrZ2dDEUEN21m3spwmW+cvQ2hwMNMmnMVdN1zH0IFZxzz+hqZmXC43CoWH0OCgo58gEAgEgtMCp9OJLMsEBQWh1+uFwXYIHo8Hh8OBTqcTc3MIvT03kZGRFBcX43Q6T4wxGxoa2smlLMsyra2tGAwGPvjggx4P4Ezn8swMpqelsP7nnxk3PBlz0x7MLu97wxP7Al73utPZhsJWDdWrAdAOvAv9gBtQdKjelV9fjcVpJyMqnqa6JiZfewMlFZXeNwMCmJIjkx3YREBfiWrtcNKSEklLTGD00CGMG5aHWq3u8fgjw8O49+YbqW9qEqt2gUAgOAMRW+iCk82v/Q722Jh94YUXOl1UoVAQGRnJiBEjCA0VW9CHEqzVYvBlgHA5WgBodUnEBIaSGBLhb+d0mFHXrkRyW5H00RiGP9GpDK1HlpEkkIDKimqmX38zFTU1xEdHMywnm+iICHIStYxJk+gz9DI0IcdWyetYUKlUxERG9lp/AoFAIBAIBL1Fj43ZG2644TgM47eAjN1aD0CjS8m49L7YLDW4HGY0+gic9la09esA0PS/EY+zDcnjRFIbkSQFCkliUGwKuwsKmHzNDdTU15PZJ50F771NbFTUcRmx2+0W3liBQCAQnBByc3MBcDgc7N+/n4EDBwLQv39/Pv3002Pq47XXXsNqtfLHP/7xF41h/PjxVFVVsX///l90/qnGO++8w/fff8/nn39+Uq5fW1vLddddR0FBAVqtltdee42xY8f2+nV6bMzOmTMHo9HIZZdd1un43LlzsVgsXH/99b02uDMJrVpG9jhwy6DShpASGkVbSyFulwW304Kyfh2SoxlZocaTfBnW6tUokNHGjkOpC/P38/hLL1NTX8+gARkseOctIsPDqG9rQ6/WEKDpuYzgSHw27wccTieTxo4hLvr4GMwCgUAgEABs3boVgOLiYvLy8vyvO+JyuVCpDm+63HrrkcuyH4kDBw5w4MABQkNDWb58OePGjfvFfR0rR7uf04nu7uXBBx9k5MiRLFiwgA0bNjBz5kwKCgp6/Z57rNr917/+RURERJfjUVFRPPHEE70yqDMRncGbh63JpWR4Un8kSUIfmIghMAmPR0Zb/jUAUmgWLoUOWVIjKdQo1AfL3sqyzIoNGwF48W9/JjI8DFmW2brpbT5d+j921NT22nitNhsFJSWUlFeg7WUjWSAQCASnJg6HA4fDgSzL/mNutxuHw4HL5frFbX8NKSkpPP7440yYMIHrr7+e6upqJkyYwNChQ8nKyuLuu+/2j+Hvf/87f/rTnwCvV3LKlClcddVVZGdnk5eXR2Fh4WGv89Zbb3HNNddw880389Zbb3V676mnniI7O5ucnBxGjhyJxWIBvA6+3NxccnJyyMvLo7i4mOLi4k52ktls7iTPlCSJ5557jrPPPpuHHnqIHTt2MG7cOIYMGUJmZiZPPvmkv21LSws333yz/9qzZ8/GZrP502K189BDD/HAAw8ccR4PN29H6+/AgQNMnz6dYcOGkZOTwyuvvOJvp1QqO93LoXz22WfccccdAAwbNozo6GhWrlx5xHH+EnpsGpeUlJCamtrleHJyMqWlpb0yqDMRdaAHAKfSSHp4DABKpQalUkNb9UaUzTuQAWXidPTGKFShKSiUGiRJwu3xsL2qGLPJTE1DAxq1mmE5gwBwmQoxtO4hzuEiTj2t18ar1+m464brKCwtI1xooQUCgeA3wb9efQOA+343mwCDAYDVmzbz85p1DM7K5IKJ5/jbPvfm2zidLu6+8TpCgrzZbjZs38FPy1cysH+/o5ZJ7wmlpaUsWbIESZKw2Wx89913GI1G3G43F154IV988QUzZ87sct66devYtm0bycnJPPjggzz11FO8/vrrXdq5XC7ee+89Fi9eTEREBI8++igtLS1IksS7777L119/zapVqwgKCqKpqQmtVsvSpUt5/PHHWbFiBbGxsX4Dt7b26I4lu93O0qVLAWhtbWXRokVotVqsViujR49m0qRJ5OXlcc8992A0Gtm2bRsKhYK6ujp0Oh033XQTr7/+Oo899hh2u505c+awdu3aI14zJCTksPN2uP7cbjezZs3i/fffJyMjA4vFwsiRIxk+fDh9+vTpci8daWhowOPxENkh5iYlJeW42Io99sxGRUWxffv2Lse3bdtGeHh4rwzqTKOkqY4QrdczmxbTv0vUnnv/295/BCSgjR6OVh+KUqX1tzM7bNhcTnYXF+FxexiaPRCd1ptnVh2czvDhNzFq+A2Eh6X06riDAwMZnJXZq30KBAKBQNBTbrzxRv9vosfj4YEHHiAnJ4fBgwezcePGbiUJAGPHjiU5ORmAUaNGUVBQ0G27+fPnk5yczIABA4iMjGTixIl8/PHHAMybN4/bbruNIJ/BHhoailKpZN68eVx33XXExsYCYDAYMPgWAEdj9uzZ/n9brVa/93XkyJGUlJT47+f777/n/vvv96e/ajcMb7/9dt555x0cDgeffPIJI0aMICUl5YjXPNK8Ha6/ffv2sWvXLq688kpyc3MZPXo0ra2t7N69u9t7OZRD7Z2OXvzepMee2SuvvJK7776bwMBAxo8fD8CyZcv4wx/+wJVXXtnrAzzdcXncrC3dybgAGRkID0kCwGlvQZY9KFUGpPJ53sYhA1AGJnfpw6jRkRmdyEcfegXcY/IGI7tsSCpvGVpV5FCCe3HMsiyLVC0CgUDwG+TB224B6JTKcfTQIYzIzemST/S+m2d3aTtsUDZDsjJ7PS9rx8pQzz//PA0NDaxbtw6dTse9996LzWbr9jxdh3LtSqXysPKHt956iwMHDvgNQqvVSklJCbNmzerxWFUqVacSr92NreP9/PnPfyY6OpotW7agUqm45JJLDns/7cTHxzNu3Dg+//xzXn75ZR5//PGjjutI83a4/mRZJiIiostiwePxYDKZutxLR9odnHV1dX4jvKSkhKSkpKOOtaf0+Nv22GOPMWLECM4991z0ej16vZ7JkydzzjnnCM1sN2ypKETjNgOgUgehVGoAsFnqsLSW4WjcieRqQ5YUYIhB1kZiq1mPrXoVblsjAEqFgmCdgaXLVmNQe7ioXxOOgk/weNzHZZUz/+elfLdoCU0tLb3et0AgEAhOXTQaDRqNppNDQ6lUotFougTt9KRtb9LU1ERMTAw6nY6amhrmzp37q/qrrq5m8eLF5Ofn+zWvlZWVlJWVsXPnTi644AJeffVVv/HW3NyM2+3mggsu4L333qO6uhoAi8WCxWIhJiYGl8vFvn37AHjvvfeOej8JCQmoVCr27dvHwoUL/e/NmDGDZ555Bo/HK1Wsq6vzv/eHP/yBBx54AJPJxMSJE496n0ebt+7669+/PwaDodM95Ofn09jYeNTrAVx22WW8/PLLAGzYsIHq6upTI5uBRqPh008/5bHHHmPr1q3o9Xqys7P9bnxBZ0L1Rqxa7x+6Vu9dmciyjFoTCLIHuX6Xt6EmBEkTgtVqQmotQ61UQcjBfuoaGtlbUEiABvpGa/C0VVJas5+vy8wMiorinJTemf82i4Wtu/fgdnsYnJVJaHBv+nwFAoFAIPh13H333Vx22WXk5uYSHx9/TIbckXj33XeZMmUKISEh/mNKpZIrr7yS999/n1dffZXq6mpGjRqFWq3GYDCwaNEixo8fz1//+lcmT56MJEloNBo+//xzkpOTeemllzjvvPNISEjgvPPOO+L1//rXv3Lttdfy4YcfkpKSwjnnHNQlv/DCC/zxj39k4MCBaDQahg0bxv/+9z8ARo4cSUhICLfccssx7aYebd6660+lUvHdd9/xxz/+kWeffRa3201kZCTvv/8+gYGB3V2mE0899RTXXnstffv2RaPR8P777x+XhY4kHy8BwymKyWQiODiYlpYWv/7leFNR8AMuRxMh0SMIDkvv9F7r2j/j3P40clA6yoxbUCTPRCV50CiVKANiKWlpQg2s37iNy2+7m6y+fdj44b9Q6CNZXG1hXUUlOdFRXNC3T6+MVZZlyqqqyC8u4ZzRo3qlz8PhdDqZP38+06ZN+0XVyc5kxNwcHjE3h0fMzeERc9MVm81GYWEhERERREREiJKth9C+lR4UFHRKzk1ZWRnDhw9n//79x2RY9mZ/vT03NpuNoqIiUlNT/dKQnthrPTaPZ86cSV5eHg8++GCn48888wzr16//1e7+Mw2Px4XL0QyARtc1pZm7cYf3H9pQNMHpGEIPelidbhf1NXsIqF3Izu3ej2p03lBUYVkAnJPsIT0kBEMvPpglSSIpLo6kuLhe61MgEAgEAkHv8fDDD/P222/zr3/9q1cM2d7u70TTY3N62bJlTJ8+vcvxqVOnsnz58l4Z1JmEt+qXjMMpoVIbkD1uXE6LX+vqafZFBGrDUB0S/KWQFCQ79xMs2Wkr/AmAMUOH+N9XKhSkhYYQYww4IfciEAgEAoHg5PPoo49SXl7ea4Wqeru/E02PjVmz2YxGo+lyXK1W+8XRgoPYLV6xdpvVO9UOewvm5nwsphJkpxnZ7Mu3pglBGZiC21qHu60K2WVDqVAQMeBajDFjeOVn79yOHTYUp9uNp5fVIQ6Hgzlzv2D15i04nc5e7VsgEAgEAoHgeNFjY3bgwIHd1kj+5JNPyMwUOUkPRW+MxRiSQXOrVyYgyy4kSYFSbcDdtAsJGVmpQwpKx2Jtpa1mI7badbgsVQBIKh2b62OxOtwkxMSQFBfHqvIKXt+ylYKmpl4b5+ZduymrrGL91m291qdAIBAIBALB8abHmtm//e1vXHrppRQUFPgj7hYvXsxHH33E559/3usDPN3R6iNQqIJpafOWidMZotDqI0CWsZd+52sUiiI0B7fLDpIanK0cKN1KbFokoQYjm3buBGDUkFxkYGddHc02O05fqo7eYERuDpIkERkWJoIjBAKBQCAQnDb02JidMWMGX3/9NU888QSff/45er2enJwclixZcsKyA5xuuJxtBAa4cNiaUKujkCQFSOCqWe9toA1FFz0cpS4cOTCWmt3vomrcRx12QrKuoLLGWxovOSEehSTxu9wcdtTV0z8srNfGKEkSI3Jzeq0/gUAgEAgEghPBL8qnMH36dFatWkVbWxv5+flccskl3HPPPQwdOrS3x3dG4HHbCdB5cDvNnY47a9d53zckoTHGo9YEoNYEEBWZgdEQQlLqOUiSRG1DAyiVRIV7syFoVSryYmN+dZUul8vFjn37/cmYBQKBQCAQCE43fnFysCVLlnDNNdcQFxfHf//7X6ZNm8bGjRt7c2xnDEqVHrNFiUpz0HMtyzKyKR8Ad2guFqcDj9uJJEloE84ldsTD6AKiAChqbUWRlYm1l7MWfDbvB75a8BPvf/WNMGgFAoFAcFLJzc0lNzeXzMxMVCqV//UVV1zRo36WLl3KTz/9dNR21113HUFBQVgsll865FOKpUuXkpeXd9Kub7FYuOqqq+jTpw/9+vXjyy+/PGHX7pHMoLy8nHfeeYe3336btrY2Lr/8cpxOJ1988YUI/joCSpUes1WJWnuwmparfiu4bchI2MNHUVpVQJKrBK0SmvT9CQ9NJFjr/XgamltAraZRpcIjyyh+hUdWlmW/R3dEbg6VNTWMG5Z3SiaEFggEAsGJQZZl2o6zUWfQ64+4o7h161YAiouLycvL87/uKUuXLsVsNjN58uTDtjGZTHz33XdkZ2czd+7cE5KSyuVyHdcyvycSl8vV5dizzz6LVqslPz+foqIiRo0axYQJEwgNDT3u4zlmC2batGlkZmaye/du/vOf/1BZWcl//vOf4zm2Mxpb8Tfef2jDkXQR6NxteFr2U1y5hy/3l5DfeDBTQVNpGXJpGbMyB3QxZN1ud5cH0I/LVvDFDz/S2NzsP1ZQUsrrH33CT8tX+o+lJydx9w3XkZaU2Ps3KBAIBILTBovVSsigocf1P4vV+ovG9uOPPzJ27FiGDh3KiBEj/DntDxw4wJgxY8jJySE7O5u//vWvbN26lddee4333nuP3NxcHn300W77/Oijj5g4cSL33Xcfb731Vqf35syZQ25uLoMHD2bChAkUFxcDMG/ePIYNG0ZOTg65ubmsW+eVCkqShNl8UEYYERHhPyclJYXHH3+cCRMmcP3111NdXc2ECRMYOnQoWVlZ3H333f688w6Hg/vvv5/s7GxycnKYOnUqANnZ2axZs8bf/+uvv35Ub7XL5WLKlCnk5eWRlZXF1Vdf7fdAH6m/6upqLr/8coYPH86gQYN4+OGH/e063ssNN9zQ5Zqffvopd9xxBwCpqamMHz+eb7755ojj7C2OeYnw008/cffdd3PbbbfRt2/f4zmmMw6Xx4PD48HhdqNWq5FlD86qZQC4A5Ix6oMJ0hjBEoCxYTOpmiCUioHec10uGpuakGWZhKjITv2u2byFZWvX0yclmZnTpvqP7ysqornFxIjcHMJCvMesdhs1dfU4XS6mMM7ftrucwQKBQCAQnAoUFhbyj3/8gwULFhAUFER+fj5nnXUWxcXF/Pe//2X69On8+c9/BqCxsZGwsDBuvfVWzGYzzz777GH7feutt3j00UeZOHEit912G/v376dfv34sXbqUxx9/nBUrVhAdHU11dTVBQUHs37+fm266ieXLl9OvXz+cTucxyxNKS0tZsmQJkiRhs9n47rvvMBqNuN1uLrzwQr744gtmzpzJk08+SUFBARs3bkSr1VJX581Tf/fdd/Pyyy8zapS3xPzLL7/Myy+/fMRrKpVKPvroI8LDw5Flmdtvv51XXnmFP/3pT0fs7/rrr+cvf/kL48ePx+Vycf755/PVV19x8cUXd7oXWZa71BYoLS0lOflg8aeUlBRKS0uPaY5+LcdszK5YsYK3336bvLw8MjIyuPbaa3usY/mtsq22jm8bm/DkF3DFwCycjTuQ28qRAHdQJnaPgsSQBBzN0cQ7k0hLn4AyOBqAusZGZFlGoVAQHhLSqV9JktBqtZRXV3c6Pm5YHg6nk+AOJemSYuO44oLpxEVFHe/bFQgEAsFphkGvp3n7puN+jZ6yYMEC8vPzGT9+fKfjZWVljB8/nvvvv5+2tjbOOussJk6ceEx97tixg6qqKiZPnoxSqeTaa6/1l3KdN28e1113HbGxsXg8HgwGAwaDgYULFzJt2jT69esHeAtFBQcHH+VKXm688Ua/vMLj8fDAAw+wcuVKZFmmtraW3NxcZs6cyffff89zzz2HVqsFIDLS68C65ppreOSRR6itrWXPnj1IksS4ceMOez3wykZeeOEF5s2bh8vloqWlxT+Hh+uvra2NJUuWUFNT4+/HbDazd+/eLvciH6ZwU0cZyeHaHA+O2ZgdNWoUo0aN4t///jeffPIJb7/9Nvfeey8ej4eFCxeSmJh4WtbzPRlYyxeBvRkAtzGVcGMIAJqUC1Ennd+pbU19AwARoaEolcpO740cnMuQrMwuGpzBWV31y0GBRoICjb10BwKBQCA4k5AkiQCD4WQPowuyLDN16lTee++9Lu+lpaUxevRoFi5cyH//+19efPFF5s+ff9Q+33zzTcxmM+np6QA4nU48Hg+PPfbYLxqjUqnE7Xb7X9tstk7vG40Hf3uff/55GhoaWLduHTqdjnvvvbdL+0PR6/Vcf/31vPnmm2zZsoU777zzqGP66KOPWLZsGcuXLycwMJCXXnrJL884XH8ejwdJktiwYcNh8813vJdDSUpKori42G+El5SUMG3atKOOtTfocdSPwWBg9uzZrFy5kh07dnDffffxr3/9i6ioKGbMmHE8xnjaMzg6iovDw7iwbx9cbZW4GneCswUAT0ASSpUWe+0GnC3e7AaS4qDRWuszZqMiwrvtW6PRiOAtgUAgEJyRTJ48mQULFrDTVzwIYP16b472AwcOEBUVxXXXXcfTTz/N2rVrAQgKCqKlpaXb/ux2Ox9++CFr166luLiY4uJiKioqiI+PZ/78+VxwwQW89957VPt2PC0WCxaLhSlTpvDDDz+wf/9+wGsAt18jPT3dr5/98ssvaWtrO+z9NDU1ERMTg06no6amhrlz5/rfmzFjBi+++CJ2ux3ALzMAuOOOO3j11VdZtmwZV1999VHnrampifDwcAIDA2ltbeWdd97p9H53/QUGBjJu3Dj+9a9/+dtVVlZSXl5+1OsBXHbZZX65QlFREcuWLTthduGvsoL69+/P008/TXl5OR9//HFvjemMwyN7QCHhlj3YKhaDsw1J9iBLamRdJG5rA5aGPVjrt4HU+SOpaagHILqDMWtqNVNaWXlCXfgCgUAgEJxo+vbtywcffMDNN99MTk4OAwYM4N///jcAc+fOZdCgQQwePJgrr7yS1157DYCLL76YjRs3dhsA9vXXX5OcnExGRkan49deey1vvvkm48eP569//SuTJ09m8ODBnH/++dTV1dGnTx/eeustrrrqKgYNGsTw4cPZt28fAC+++CJ33HEHY8aMYfPmzYSHd+98Aq/+dfXq1eTm5jJ79uxO0ogHHniA9PR0Bg8eTG5ubqcMCwkJCeTm5nLttddiOAYP+nXXXYfZbCYzM5NLLrmkiyzhcP19+OGH7Nmzh+zsbLKzs7n00ktpaGg46vUA7r//fqxWK3369GHKlCm8/PLLhPVicacjIcm/MYvIZDIRHBxMS0vLCatYVt5Uz6INqxmR1Z+oghfBVIhUuRh3QArWEa8REZWBbefLKNU6AgfehkJ/UNf63P/e4sGnnuWqGRfw3vNPA/DzmrWsWL+RwVmZXDDxnBNyD8cDp9PJ/PnzmTZtmiihewhibg6PmJvDI+bm8Ii56YrNZqOwsJCIiAgiIiLELt8heDweTCYTQUFBJ31uzGYzGRkZrFixgtTU1JPeX2/Pjc1mo6ioiNTUVHQ6HdAze018c08AEiDJMsamdeBx+NNreQwJqNR6tLpQNCo1KtkNSm2nc9s1sx09s7Iso1GrRUotgUAgEAjOcF577TUyMjK4/fbbe8WQ7e3+TgXOjOy9pzhRxmD6ussw1G8BQKnU4QI8hkRUKj0KtQHDwNvxWCpRaDpHR9bUd5UZnDN6FGPzhnYJCBMIBAKBQHBmceutt3Lrrbeesv2dCgjP7AnA2bidfvqtAOjiJ4LdWxDBY0hA6WnDY61F0kejihjS5dyDAWARnY5rNBphzAoEAoFAIPjNI4zZ44yzeR/WvW8gSTLqyJEY+lyBu8UrGvfo41FYK7FULMPekt9tQFeNT3gdfQRBuUAgEAgEAsFvFWHMHmcc9VtBdtHgjEHX91pkeyOyzSsd8BjikHQROOu3Y20uQpY9Xc6va+icmuvVDz7i1Q8+or5DuVuBQCAQCASC3ypCM3ucMaRfDtpI9m1uJlVS4m72eWV1UaAyoDFE4fBYkWqWIyVP6nSux+Ohzme0RkdE4Ha7qW/0lrbVijK0AoFAIBAIBMIze7yRJAlNzDg8ePWtnuaDEgMAtS4UQ/x49LGjOpWBA2hoavZXFYkMC0WhUHD7tbOYdeEFGANOvUotAoFAIBD8UnJzc8nNzSUz01vZsv31FVdcccx9vPbaa7zwwgu/eAzjx4+nb9++Z0we93feeYeZM2eetOs/8cQT9O/fH4VCwffff3/criM8sycYd7O3xrGsi0TCjSogHsmY0G3b9oIJ4aEh/ryI4aGhhIeGnpjBCgQCgUBwgti6dSsAxcXF5OXl+V93xOVydSnh3pFfE6V/4MABDhw4QGhoKMuXL+9SaOB4cLT7OZ3o7l7OPfdcrrjiCm666abjeu2T7pl95ZVX/Elyhw4dyooVK47pvFWrVvlXbqcT7TIDVAa0lmJkl/WwbdszGYjgL4FAIBCcCBxuNw63u5Nn0u3x4HC7cXk8v7jtryElJYXHH3+cCRMmcP3111NdXc2ECRMYOnQoWVlZ3H333f4x/P3vf+dPf/oT4PVKTpkyhauuuors7Gzy8vIoLCw87HXeeustrrnmGm6++WbeeuutTu899dRTZGdnk5OTw8iRI7FYLADMmTOH3NxccnJyyMvL85fIjeiQgchsNnfaeZUkieeee46zzz6bhx56iB07djBu3DiGDBlCZmYmTz75pL9tS0sLN998s//as2fPxmazERMTQ1lZmb/dQw89xAMPPHDEeTzcvB2tvwMHDjB9+nSGDRtGTk4Or7zyir+dUqnsdC+HMmLECNLT0484rt7gpC4HPv30U+655x5eeeUVxowZw+uvv855553H7t27SUpKOux5LS0tXHfddZx77rnU1NScwBH/evya2YAklIYELK1VyAo1OkMEak1n6UDNIWm5CkvLaDaZSIqLIyJMeGcFAoFA0Ls8vWYdAH8cPowAjXdHcE1FJUtLSsmNjuL8vn38bV9YvwGn28OdeUMI8VVt2lRdzU+FxQyMjOCi/v16bVylpaUsWbIESZKw2Wx89913GI1G3G43F154IV988UW32+nr1q1j27ZtJCcn8+CDD/LUU0/x+uuvd2nncrl47733WLx4MRERETz66KO0tLQgSRLvvvsuX3/9NatWrSIoKIimpia0Wi1Lly7l8ccfZ8WKFcTGxvoN3Nra2qPej91uZ+nSpQC0trayaNEitFotVquV0aNHM2nSJPLy8rjnnnswGo1s27YNhUJBXV0dOp2Om266iddff53HHnsMu93OnDlzWLt27RGvGRIScth5O1x/brebWbNm8f7775ORkYHFYmHkyJEMHz6cPn36dLmXk8VJ9cw+//zz3HTTTdx8880MGDCAF198kcTERF599dUjnvf73/+eWbNmMWrUqBM00l+Hx95EkLIeV2sRnlbvqtAZmocSF67dL+Mq+a7b8w4tmLBtzx6+X/wz+4qKTszABQLB/7d353FVlvnj/1+Hc1hEOKiggFBoLilKHBUXTDAawi21zFJTwW3mZ9pQmX7RdEqdHG1xyY8LlqRiOhVZlkEW6iBWo2Vpo+YoLrizqMgB4cBZ7t8f5D0SHEBTQH0/Hw8ennPf130tb2/gzXWu+76FEPXA2LFj1dlNm81GXFwcwcHBdOrUib1791a6JAGgV69eBAQEABAaGsrx48crLZeSkkJAQADt27enadOmREZG8s9//hOA5ORknnvuOfWRqo0bN0ar1ZKcnEx0dDS+vr4AuLq64upas+tZxo0bp74uLi5WZ1979OjBqVOn1PF8+eWXTJs2TX1kbNOmTQGYNGkSa9eupbS0lA8//JDu3bvTokWLKtusKm726jty5AiHDh1i+PDhGAwGevbsSUFBAb/++mulY6krdTYzW1payk8//cT06dPLbY+KiuL777+3e9yaNWs4fvw4H3zwAa+//nq17ZSUlFBSUqK+NxqNQNlzus1m8032/saYiy7RSHeR0qzdoNhQdG4oTo1xsF5C69gQjdt9KIpDhf5k5eQC4NW4MWazmaaNm9DC3w9PD49a6/vtdG0Md8NYbjWJjX0SG/skNvZJbCoym83qx/OKomCz2ZjavSsAjg4O2H5bKtDd14cQH28cNBp1G8ALIV0qlO3UrBkPNW1aoeyNuHbc9ce7urqq7xcuXMjFixf597//jYuLCy+//DLFxcXYbDYURVHHYrPZcHZ2Vo/TaDRYLJZK+7V69WoyMjLUhLC4uJhTp07x7LPPqn35/XHXt3U9BwcHrFaruv3ajK298cyYMYNmzZrx008/odPpeOqpp9Tx2Gvb19eXXr168fHHH7N8+XL+/ve/VzquazGx2WxVxs1efVarFS8vL37++ecKYy8oKKgwlqpUNo7f99NsNqsPhLqR79U6S2YvXryI1WrF29u73HZvb2+ysrIqPSYjI4Pp06eza9euGi+Ynj9/PnPmzKmw/ZtvvqnxX1B/lLOmCFdtIzIO/sgDgMWlOWg07DvnwdXCILhQjO3g1xWO+/mXXwDIu3SRlJQUADx0Dhw5dJAjhw7WSt9rQ2pqal13od6S2NgnsbFPYmOfxOZ/dDodPj4+AGpico3pBuq5kbI1UVhYiKIo6uSTzWajsLBQfZ+dnU2TJk0oLS3l7NmzfPzxxwwZMgSj0UhJSQmlpaUYjUZMJhMWi0U9rqioqNz7a7Kzs9mxYweHDh3Cw6PskfJWq5XAwEAOHjxIZGQkK1as4NFHH0Wv15Ofn4+bmxsRERH89a9/Zfjw4Xh7e6tJq6urKxaLhZ9++ok2bdrw3nvvAZRr12g0qoldbm4urVu3pqioiIyMDLZt20ZoaChGo5G+ffvyj3/8gwULFuDg4MDFixfV9bjjx49nzJgxNGzYkG7dulUYF1AuBlXFzV59vr6+uLi48O677zJ8+HAATpw4QePGjWn828Xo14/FHovFQlFRUaV9hLIJzuLiYtLT07FYLOr/V03V+SV0v78dlaIoFbYB6rqNOXPm0LZtzdfhzJgxgylTpqjvjUYj9913H1FRUepHBreb2WwmNTUVQ8AFLFcAt7L1wD1Cw3B0amj3uHc3l93GIqxnT/r3718LPa1d1+Ly2GOPqXdrEGUkNvZJbOyT2NgnsanIZDJx+vRpANzd3Sv93VsX3Nzc0Gg06u9oBwcH3Nzc1Pcvv/wyw4YN45FHHqF58+Y89thjODk5odfrcXZ2xmw2o9frcXFxQafTqce5urqWe39NfHw8UVFR3HfffeW2Dx8+nPXr17NixQquXLlCv379cHR0xNXVlW+++YZ+/foxa9Ysnn766bLbcDo58fHHH+Pj48OSJUsYNmwY/v7+9O3bF6Bcu3q9Hjc3NwBee+01YmJi+PTTTwkICCAiIoIGDRqg1+v5v//7P6ZMmUKvXr1wcnIiJCSEd999Fyi7U0CTJk3485//rCbhv3d9DKqKW1X1bdmyhSlTprBixQqsVitNmzYlMTGx0rH83oIFC1ixYgW5ubk8//zzuLi48NNPP6nLJa4xmUw0aNCA8PBwXH5bf20v8a2UUkdKSkoUrVarfPrpp+W2x8bGKuHh4RXK5+XlKYCi1WrVL41Go27bvn17jdrNz89XACU/P/+WjKMmSktLlc2bNyv5Xz2pXFqlVc6njlcyj3ylmEuLlJLifMVcWlzpcV0HDVF0rdopyTv+pdhsNsVms9Van2vDtbiUlpbWdVfqHYmNfRIb+yQ29klsKiouLlYOHTqkZGdnK1arta67U+9YrVYlLy+v3sbm9OnTio+Pj2I0Gmu9vlsdm+LiYuXXX39Viov/lw/dSL5WZxeAOTk50aVLlwof+aSmptKzZ88K5fV6PQcOHGD//v3q18SJE3nwwQfZv38/3bt3r62u3xSNYsZyfgcAlkbBOBqPUHLiE67m/ExJ8eVKj8m57m4GJ06f4Y2V7/Jxckqt9VkIIYQQ9c+rr75KaGgoCxYswN3dvd7VV9vqdJnBlClTGD16NCEhIYSGhvLuu+9y+vRp9abHM2bM4Ny5cyQmJuLg4EDHjh3LHd+sWTNcXFwqbK+PPEp/BUshuDTD5t4K59w0FGsO2gZ+aHUuFcorikLO5bIkt5lnE3Iv51FqNmO13rp79wkhhBDizjN37lzmzp1bb+urbXWazA4bNoxLly4xd+5cLly4QMeOHdXbYwBcuHBBXc9zp2tS+hMAGp9w0Djg0PwxnJ1A5xOKRtegQvm8/Hz1Sr5mnp40b9aMgObN75pH7AkhhBBC3Ap1fgHYpEmTmDRpUqX71q5dW+Wxs2fPZvbs2be+U7eYoig0KdkLgK3ZwwDo9C1w9Gpj95hrD0zwcHfHxdkZQB6UIIQQQgjxO3X+ONt7gS3/KA2s2eDghKXRQwDoHCvOxl5PfZStlzzKVgghhBDCHklma4H1TNlFW1rf3lhKC9Hm/QcHSyHGyycpuHIam81S4Zjc39bLNvUsS2a//XEv+w79Smlpae11XAghhBCinqvzZQb3AsvZrwDQ+vdFyT+C9soBlOzG2Jo9AooVjUZb4ZiLeXkAeDZqhNlsZsf3Zc9cbtfqgVrrtxBCCCFEfSczs7eZrSQPa3bZ43kdfP+EzbERNld/HJt2wb3R/TR0b17pjaovX7kCgGfjRlisVroEdeDBVi1p4FLxzgdCCCHEnc5gMGAwGAgMDESn06nvhw0bdkP1pKWl8c0331RbLjo6Gr1ef0NPmqrP0tLSCAkJqbP233//fYKCgtDpdCxbtqxW25aZ2dvMfOYbUKxc1d6Hk0szFNfm4NEap6adAag4J1vmUt4VoCyZbeDiwoBHI2qnw0IIIe45iqJwtdR8W9twddRV+ZSx/fv3A5CZmUlISIj6/kalpaVRWFhIVFSU3TJGo5EtW7YQFBREUlISMTExN9XWjbBYLOh0d0fade2Rs9fr0qULH3/8MfPnz6/1/twdUa3HrJd/AaDUoRGWMyng6FPtxV8Al67NzDZqdBt7J4QQQkCR2YL30ts7m5b7YiwNnW78UcJff/01f//73ykuLkan0/HWW28RHh5ORkYGY8aMobCwEJvNxuDBgxk6dCjx8fHYbDa2bdvGkCFDePXVVyvUuXHjRiIjIxkxYgRLliwpl8yuWbOGd955B0VRcHBwYNOmTTzwwAMkJycze/ZsSktL0Wg0rFq1iu7du6PRaCgoKFAf6erl5cXevXtp0aIFLVq04M9//jPbtm2jefPmLFy4kBEjRmA0GjGZTPzpT3/inXfeQaPRUFpaysyZM9m6dSsODg74+vqydetWgoKCePfddwkNDQVg1apV7Nixg48++shuzCwWCwMGDODSpUsUFxdjMBh47733cHV1rbK+rKwsYmNjyczMxGQy8cQTT6j3n71+LL6+vqxYsaJcm8HBwUDZ44drmySzt5lrt3+AZ3eufr+WRqX5ajJrLi1CsVnQOrqg1TpVOO7StTWzjRujKEq9eWa2EEIIUVtOnDjBnDlz2Lp1K3q9nmPHjtG7d28yMzNZtmwZAwYM4JVXXgHg8uXLNGnShIkTJ1JYWMjbb79tt96EhATmzp1LZGQkzz33HEePHqVt27akpaUxb948du3ahbe3N1lZWej1eo4ePcr48eNJT0+nbdu2mM3mGi9POH36NDt27ECj0WAymdiyZQtubm5YrVYGDx7Mpk2bGDp0KPPnz+f48ePs3bsXZ2dncnNzAYiNjWX58uVq8rl8+XKWL19eZZtarZaNGzfi6emJoihMmjSJFStWMHXq1Crri4mJYebMmYSHh2OxWHj88cf57LPPePLJJ8uNRVEUjEZjjcZfGySZrQU6396ca3gJ70YtwVyCztGV0hIj5pICGjRsirZBJcnslXygbGY2eUcah45m8Ehod7obgmu7+0IIIe5yro46cl+Mve1t3KitW7dy7NgxwsPDy20/c+YM4eHhTJs2jatXr9K7d28iIyNrVOeBAwe4cOECUVFRaLVaRo8ezfvvv8+CBQtITk4mOjoaX19fbDYbrq6uuLq6kpqaSv/+/Wnbti0Ajo6OeHh41Ki9sWPHqhNSNpuNuLg4vv3227InfebkYDAYGDp0KF9++SULFy7E+bd7yzdt2hSAUaNG8dprr5GTk8Phw4fRaDSEhYVV2aaiKCxevJjk5GQsFgv5+flqDO3Vd/XqVXbs2EF2drZaT2FhIf/9738rjKW+PcBJktlaoHFsSKnWkxKrFQDnBo1BUVCcXHGoZFYW4PJva2abNG7EuawsSkpL0WrtrbAVQgghbp5Go7mpJQC3m6Io9O3bl8TExAr7HnjgAXr27ElqairLli1jyZIlpKSkVFvn6tWrKSwspFWrVgCYzWZsNhuvv/76TfVRq9Vi/e33O4DJZCq3/9ryA4BFixZx6dIl9uzZg4uLC1OmTKlQ/vcaNGhATEwMq1evZt++fTz//PPV9mnjxo3s3LmT9PR03N3dWbp0Kenp6VXWZ7PZ0Gg0/Pjjjzg6Vn4uXD+W+kTuZlBLGrhoUGwWNA46nF08cHFtgpveD0cn10rLX79m9onHHmNy9EgCW7eqxR4LIYQQdSsqKoqtW7dy8OBBddsPP/wAQEZGBs2aNSM6Opo333yT3bvLbmGp1+vJz8+vtL6SkhI2bNjA7t27yczMJDMzk3PnzuHn50dKSgoDBw4kMTGRrKwsAIqKiigqKqJPnz589dVXHD16FChLgK+10apVK/bs2QPAp59+ytWrV+2OJy8vDx8fH1xcXMjOziYpKUndN2jQIJYsWUJJSQmAuswAYPLkyaxcuZKdO3cycuTIauOWl5eHp6cn7u7uFBQUVHiiamX1ubu7ExYWxoIFC9Ry58+f5+zZs9W2V9ckma0l+oZloXZx9USjqTrsFouFK7+tRfFq3BhnZyc8GzfGtUH1F44JIYQQd4s2bdrwwQcfMGHCBIKDg2nfvj3vvPMOAElJSTz00EN06tSJ4cOHEx8fD8CTTz7J3r17MRgM6sVL12zevJmAgADatWtXbvvo0aNZvXo14eHhzJo1i6ioKDp16sTjjz9Obm4urVu3JiEhgREjRvDQQw/RrVs3jhw5AsCSJUuYPHkyDz/8MD///DOenvaf3BkbG8v333+PwWBg3Lhx5ZZGxMXF0apVKzp16oTBYCh3UZq/vz8Gg4HRo0fj6lr5JNj1oqOjKSwsJDAwkCFDhlRYlmCvvg0bNnD48GGCgoIICgriqaee4tKlS9W2B/DBBx/g7+9PUlISf/vb3/D392ffvn01OvaP0ij1beHDbWY0GvHw8CA/Px+9Xl8rbZrNZo4eTMWtoQNNmgXipvcHjcbuRV05ly7h170XAMX/PXDX3Mrj98xmMykpKfTv39/uRxr3KomNfRIb+yQ29klsKjKZTJw4cQIvLy+8vLzq5Cr0+sxms2E0GtHr9XUem8LCQtq1a8euXbto2bJlndd3q2NjMpk4efIkLVu2xOW3++nfSL4mZ24tsNksNHQtS1xdXL0oLsrFmHeCElPlH4Ncu8dsI70ejUZD+p4f2HvgIDabrba6LIQQQoh6ID4+nnbt2jFp0qRbksje6vrqg7tzyq+eKS3OQ6PRoNU1wNHJFVPRJRSbDQeHyi/ounTdo2yvFhWTtvsHHBw0dOnYoTa7LYQQQog6NnHiRCZOnFhv66sPJJmtBabiywA4N2gCgJuHPzZrKQ4OlX/Udem6R9lqNBo6d+yA1WaVe80KIYQQQvyOJLO1oKS4bPG0c4OyReFls7TOdstf/yhbd7eGPP4neZStEEIIIURlZM3sbWYuLcJqKUZRlLL7y9bA5d9mZpvIo2yFEEIIIaokM7O3manoIgBXixUcHHSYii6joODk7F7pY2wBLl63ZlYeZSuEEEIIYZ/MzN5miqLgoHWioLDsTgSlJUZKii6j2Cx2j7m2zMCrSWO2f/c9C1asIn3PD7XRXSGEEKJOGAwGDAYDgYGB6HQ69f2wYcNqXEd8fDyLFy++6T6Eh4fTpk2beve41pu1du1ahg4dWmftjxs3jgcffBCDwUB4eDj79++/Le3IzOxtpm8cgEtDX/Yd+kpdamC1mNBqXewec/lK2S27mjRqxNXiYkrNZhzkUbZCCCHuYtcSnczMTEJCQipNfCwWS5X3Xv8jV+lnZGSQkZFB48aNSU9Pr/CggduhuvHcSSobyxNPPMG7776LTqfjyy+/5JlnnlGfonYrycxsLdBoNChK2b/OLh64unmjqeImw9c/yrZf73CejxlFp8D2tdRbIYQQ9yqrzYbVZis3M2lTFKw2GzbFdtNl/4gWLVowb948IiIiiImJISsri4iICLp06UKHDh2IjY1V+zB79mymTp0KlM1K9unThxEjRhAUFERISAgnTpyw205CQgKjRo1iwoQJJCQklNv3xhtvEBQURHBwMD169KCoqAiANWvWYDAYCA4OJiQkRH1ErpeXl3psYWFhueWCGo2GhQsX8sgjjzBjxgwOHDhAWFgYnTt3JjAwkPnz56tl8/PzmTBhgtr2uHHjMJlM+Pj4cObMGbXcjBkziIuLqzKO9uJWXX0ZGRkMGDCArl27EhwczIoVK9RyWq223Fh+b9CgQWqC26NHD06dOnVb7pl/d/w5UM+ZSwpo2kSL6Woujo2aV1tevc9s40Y4OTnRxKnytbVCCCHErfTDmQwAQvxb4agtSxHOGy9z5spFmrl50MrTRy3709ljWBWFTn4P4KIru9VkdsEVMvNy8GroThuv6n/f1dTp06fZsWMHGo0Gk8nEli1bcHNzw2q1MnjwYDZt2lTpx+l79uzhl19+ISAggOnTp/PGG2+watWqCuUsFguJiYls374dLy8v5s6dS35+PhqNhnXr1rF582a+++479Ho9eXl5ODs7k5aWxrx589i1axe+vr5qgpuTk1PteEpKSkhLSwOgoKCAbdu24ezsTHFxMT179uSxxx4jJCSEF198ETc3N3755RccHBzIzc3FxcWF8ePHs2rVKl5//XVKSkpYs2YNu3fvrrLNRo0a2Y2bvfqsVivPPvss69evp127dhQVFdGjRw+6detG69atK4ylKu+88w79+/e/LU9Tk5nZWqCgoHUApYZ/qf7v1lw1u/uBEEIIcTcbO3asOrtps9mIi4sjODiYTp06sXfvXrtrMXv16kVAQAAAoaGhHD9+vNJyKSkpBAQE0L59e5o2bUpkZCT//Oc/AUhOTua5555TH6nauHFjtFotycnJREdH4+vrC4Crqyuurq41Gs+4cePU18XFxers67XZy2vj+fLLL5k2bZqaADZt2hSASZMmsXbtWkpLS/nwww/p3r07LVq0qLLNquJmr74jR45w6NAhhg8fjsFgoGfPnhQUFPDrr79WOhZ7PvjgAz7++ONK/5C4FWRmthboHBtyMc+qPjShKoqicDn/tzWzHh7s+uFHXFxcMLRvJ88TF0IIcVt1u68NAA7XfSzeXN8EX/fG/P7GOl38W1co6+3eiGZuHhXK/lFubm7q60WLFnHp0iX27NmDi4sLU6ZMwWQyVXqci8v/rk/RarVYLJVffJ2QkEBGRoaaEBYXF3Pq1CmeffbZG+6rTqfDarWq7yvr2/XjeeWVV/D29mbfvn3odDqGDBlidzzX+Pn5ERYWxieffMLy5cuZN29etf2qKm726lMUBS8vrwp/LNhsNoxGY4WxVOajjz5izpw5bN++nWbNmlXbz5shM7O1wMFBi8UKDtrqk9H8ggL1m0Dv1pB//XsPX/1r511zZaUQQoj6S+vggNbBodwaTweNBq2DAw4ah5sueyvl5eXh4+ODi4sL2dnZJCUl/aH6srKy2L59O8eOHVPXvJ4/f54zZ85w8OBBBg4cyMqVK9Xk7cqVK1itVgYOHEhiYiJZWVkAFBUVUVRUhI+PDxaLhSNHjgCQmJhY7Xj8/f3R6XQcOXKE1NRUdd+gQYN466231HWmubm56r4XXniBuLg4jEYjkZGR1Y6zurhVVt+DDz6Iq6truTEcO3aMy5cvV9sewMcff8ysWbPYtm0b999/f42OuRkyM1vPXLxctl62oasrTk5OdO7YAVNJCU6yblYIIYQgNjaWp59+GoPBgJ+fX40SuaqsW7eOPn360Oi6BxVptVqGDx/O+vXrWblyJVlZWYSGhuLo6Iirqyvbtm0jPDycWbNmERUVhUajwcnJiU8++YSAgACWLl1Kv3798Pf3p1+/flW2P2vWLEaPHs2GDRto0aIFjz76qLpv8eLFvPTSS3Ts2BEnJye6du3Ke++9B5RdUNWoUSP+8pe/1Oh+9NXFrbL6dDodW7Zs4aWXXuLtt9/GarXStGlT1q9fj7u7e7Vtjhw5Eh8fHwYPHqxu2759O56entUeeyM0yj025Wc0GvHw8CA/P19d/3K7mc1mUlJS6N+/f7VLBfbs/4VeQ4dzf3NfjqfvqJX+1ZUbicu9RmJjn8TGPomNfRKbikwmEydOnMDLywsvL6/bcmHOnezaR+l6vb5exubMmTN069aNo0eP1iixvJX13erYmEwmTp48ScuWLdWlITeSr9W//517nDzKVgghhBBVefXVVwkNDWXBggW3JJG91fXVNklm65mL192W6x6bNBdCCCFEDcydO5ezZ88SExNTL+urbbJmtp5RH2XbuDHf7f2Jb3/8iS5BHXks7OG67ZgQQgghRD0kM7P1zPXLDK4W/fYoW4dbfI8TIYQQQoi7hMzM1jPXP8o2IrQ7XYODcNTJxQpCCCGEEJWRZLaeuXT5CgBN5FG2QgghhBDVkmUG9cy1mVkveZStEEIIIUS1JJmtZ65fZvD9z/v48T8HMJWU1G2nhBBCiNvMYDBgMBgIDAxEp9Op74cNG3ZD9aSlpfHNN99UWy46Ohq9Xk9RUdHNdrleSUtLIyQkpM7af+WVV2jfvj3BwcF069aNHTtq7175ssygnrn8290MmjTyYOvOdGw2hQdbtsTF2bluOyaEEOKupSgKZqvltrahc9BW+aSq/fv3A5CZmUlISIj6/kalpaVRWFhIVFSU3TJGo5EtW7YQFBREUlJSrdySymKxoNPdHWmXxVLxXAkLC+Nvf/sbDRo04JdffuGRRx7hwoUL6kMQbqe7I6p3CUVR1PvMNtK706lDIFeLi3FtcPtPBCGEEPcui83Kyt1f39Y2Jvfsh6P2xtOOr7/+mr///e8UFxej0+l46623CA8PJyMjgzFjxlBYWIjNZmPw4MEMHTqU+Ph4bDYb27ZtY8iQIbz66qsV6ty4cSORkZGMGDGCJUuWlEtm16xZwzvvvIOiKDg4OLBp0yYeeOABkpOTmT17NqWlpWg0GlatWkX37t3RaDQUFBTg5uYGgJeXF3v37qVFixa0aNGCP//5z2zbto3mzZuzcOFCRowYgdFoxGQy8ac//Yl33nkHjUZDaWkpM2fOZOvWrTg4OODr68vWrVsJCgri3XffJTQ0FIBVq1axY8cOPvroI7sxs1gsDBgwgEuXLlFcXIzBYOC9997D1dW1yvqysrKIjY0lMzMTk8nEE088wdy5cwHKjcXX15cVK1aUa/P6x/YGBQVhtVq5ePEi/v7+N/x/fqMkma1HioqLKSktBcDbqymtW7So2w4JIYQQdejEiRPMmTOHrVu3otfrOXbsGL179yYzM5Nly5YxYMAAXnnlFQAuX75MkyZNmDhxIoWFhbz99tt2601ISGDu3LlERkby3HPPcfToUdq2bUtaWhrz5s1j165deHt7k5WVhV6v5+jRo4wfP5709HTatm2L2Wyu8fKE06dPs2PHDjQaDSaTiS1btuDm5obVamXw4MFs2rSJoUOHMn/+fI4fP87evXtxdnYmNzcXgNjYWJYvX64mn8uXL2f58uVVtqnVatm4cSOenp4oisKkSZNYsWIFU6dOrbK+mJgYZs6cSXh4OBaLhccff5zPPvuMJ598stxYFEXBaDTabX/NmjW0atWqVhJZkGS2Xrm2XtbR0RG3hq512xkhhBD3DJ2Dlsk9+1Vf8A+2caO2bt3KsWPHCA8PL7f9zJkzhIeHM23aNK5evUrv3r2JjIysUZ0HDhzgwoULREVFodVqGT16NO+//z4LFiwgOTmZ6OhofH19sdlsuLq64urqSmpqKv3796dt27ZA2e9pDw+PGrU3duxYdXmFzWYjLi6Ob7/9FkVRyMnJwWAwMHToUL788ksWLlyI82/LCps2bQrAqFGjeO2118jJyeHw4cNoNBrCwsKqbFNRFBYvXkxycjIWi4X8/Hw1hvbqu3r1Kjt27CA7O1utp7CwkP/+978VxlLVE0q3b9/OnDlzSE1NrVF8bgVJZuuRCzllf4X5eHlhs9lwcHCocn2REEIIcStoNBocbyLZvN0URaFv374kJiZW2PfAAw/Qs2dPUlNTWbZsGUuWLCElJaXaOlevXk1hYSGtWrUCwGw2Y7PZeP3112+qj1qtFqvVqr43mUzl9l9bfgCwaNEiLl26xJ49e3BxcWHKlCkVyv9egwYNiImJYfXq1ezbt4/nn3++2j5t3LiRnTt3kp6ejru7O0uXLiU9Pb3K+mw2GxqNhh9//BFHx8rvb3/9WCqzc+dOxo4dy5YtW3jwwQer7eetInczqEfO//bXUHPvZvz7530sWLmK7d99X8e9EkIIIepGVFQUW7du5eDBg+q2H374AYCMjAyaNWtGdHQ0b775Jrt37wZAr9eTn59faX0lJSVs2LCB3bt3k5mZSWZmJufOncPPz4+UlBQGDhxIYmIiWVlZABQVFVFUVESfPn346quvOHr0KFCWAF9ro1WrVuzZsweATz/9lKtXr9odT15eHj4+Pri4uJCdnU1SUpK6b9CgQSxZsoSS3+5gdG2ZAcDkyZNZuXIlO3fuZOTIkdXGLS8vD09PT9zd3SkoKGDt2rXl9ldWn7u7O2FhYSxYsEAtd/78ec6ePVttewDp6emMHj2azz//nODg4Bodc6vIzGw9cj677MRt7t2MvHwjZrMF3U0slhdCCCHuBm3atOGDDz5gwoQJFBcXU1paSufOndmwYQNJSUls2LABJycnFEUhPj4egCeffJL169djMBgqXAC2efNmAgICaNeuXbl2Ro8ezerVq/niiy+YNWsWUVFRaDQatFotmzZtonXr1iQkJDBixAjMZjNarZZVq1bRrVs3lixZwuTJk2nWrBkRERF4enraHU9sbCxPP/00BoMBPz+/cksj4uLimDlzJp06dcLJyYnmzZurM83+/v4YDAbatm2Lq2v1yxCjo6P5/PPPCQwMxM/Pj7CwMM6dO6fut1ffhg0bmDJlCkFBQUDZTGx8fHyN1r6OHz+ekpISxo4dq25bv369WtftpFGqWvhwFzIajXh4eJCfn49er6+VNs1mMykpKfTv39/u1D3ArLcX80b8u0waPZJFs2aQX1CAo84Rd7eGtdLP2lbTuNyLJDb2SWzsk9jYJ7GpyGQyceLECby8vPDy8sLBQT6svZ7NZsNoNKLX6+s8NoWFhbRr145du3bRsmXLOq/vVsfGZDJx8uRJWrZsqd7K60byNTlz65HzOTkA+Hl7o9VqadKo0V2byAohhBCievHx8bRr145JkybdkkT2VtdXH8hn2PXItTWzvt5N67gnQgghhKgPJk6cyMSJE+ttffWBzMzWIxeyy2ZmG+v1bP/ue/b/eriOeySEEEIIUb9JMluPnP/t1lxOjk58t/dnvtv7cx33SAghhBCifpNlBvVEUXExV357mkbL+/2x2qw0cJbH2AohhBBCVEWS2Xri/G9LDFwbNKB1QABt5FG2QgghhBDVkmUG9cSF3+5k0LxZU3nqlxBCiHuOwWDAYDAQGBiITqdT3w8bNqzGdcTHx7N48eKb7kN4eDht2rSp8nGtd5K1a9cydOjQOmt/7NixPPTQQxgMBrp27cr27dtvSzsyM1tPXLstV3Nvb4pNJlycnSWpFUIIcc/Yv38/AJmZmYSEhKjvr2exWNDp7Kcuf+Qq/YyMDDIyMmjcuDHp6emEhYXddF01Vd147iSVjWXx4sU0atQIKPv/jYyMJDc395bnNzIzW0+cz7qWzDZjxfoNLFi5itxLl+u4V0IIIe4limL77Uu5bpuibr/Zsn9EixYtmDdvHhEREcTExJCVlUVERARdunShQ4cOxMbGqn2YPXs2U6dOBcpmJfv06cOIESMICgoiJCSEEydO2G0nISGBUaNGMWHCBBISEsrte+ONNwgKCiI4OJgePXpQVFQEwJo1azAYDAQHBxMSEqI+ItfLy0s9trCwsFzyptFoWLhwIY888ggzZszgwIEDhIWF0blzZwIDA5k/f75aNj8/nwkTJqhtjxs3DpPJhI+PD2fOnFHLzZgxg7i4uCrjaC9u1dWXkZHBgAED6Nq1K8HBwaxYsUItp9Vqy43l964lsgBXrly5bZN0d8efA3eBazOz3l6eFBWbUBRFHpgghBCiVuVfOg6AvklLNJqyFKGkOA9T0SWcXDxwdWumljVePomi2NA3boFGW/ZUtVJTPsVXc3F0dqehu88t69fp06fZsWMHGo0Gk8nEli1bcHNzw2q1MnjwYDZt2lTpx+l79uzhl19+ISAggOnTp/PGG2+watWqCuUsFguJiYls374dLy8v5s6dS35+PhqNhnXr1rF582a+++479Ho9eXl5ODs7k5aWxrx589i1axe+vr5qgpvz2+/zqpSUlJCWlgZAQUEB27Ztw9nZmeLiYnr27Mljjz1GSEgIL774Im5ubvzyyy84ODiQm5uLi4sL48ePZ9WqVbz++uuUlJSwZs0adu/eXWWbjRo1shs3e/VZrVaeffZZ1q9fT7t27SgqKqJHjx5069aN1q1bVxhLZaZPn05SUhJ5eXl8+umntyWhlZnZeuLamll/X19emTyR52NG4eLsXMe9EkIIIere2LFj1STIZrMRFxdHcHAwnTp1Yu/evZUuSQDo1asXAQEBAISGhnL8+PFKy6WkpBAQEED79u1p2rQpkZGR/POf/wQgOTmZ5557Tn2kauPGjdFqtSQnJxMdHY2vry8Arq6uuLq61mg848aNU18XFxers689evTg1KlT6ni+/PJLpk2bpj4ytmnTsocqTZo0ibVr11JaWsqHH35I9+7daVHNheNVxc1efUeOHOHQoUMMHz4cg8FAz549KSgo4Ndff610LJVZsGABx48f5+OPP2batGmUlpbWKEY3QmZm64lrdzNo7t1MfZStEEIIUZs8PFv99up/s2fODRrj3KBRhbL6Ji0rlHVy8cDJRX/L++Xm5qa+XrRoEZcuXWLPnj24uLgwZcoUTCZTpce5uPzvFpdarRaLxVJpuYSEBDIyMtSEsLi4mFOnTvHss8/ecF91Oh1Wq1V9X1nfrh/PK6+8gre3N/v27UOn0zFkyBC747nGz8+PsLAwPvnkE5YvX868efOq7VdVcbNXn6IoeHl5VfhjwWazYfztdqLXj6UqkZGRPP/88xw4cIAuXbrU6JiakpnZekJNZps1q6akEEIIcXtoNA6/fZVf43lt+82WvZXy8vLw8fHBxcWF7OxskpKS/lB9WVlZbN++nWPHjqlrXs+fP8+ZM2c4ePAgAwcOZOXKlWryduXKFaxWKwMHDiQxMZGsrCwAioqKKCoqwsfHB4vFwpEjRwBITEysdjz+/v7odDqOHDlCamqqum/QoEG89dZb2Gxla5Bzc3PVfS+88AJxcXEYjUYiIyOrHWd1causvgcffBBXV9dyYzh27BiXL1d/TY/FYiEjI0N9/8MPP5CTk8MDDzxQ7bE3SmZm6wFFUdQ1s/mFhez4/t88+MAD+Pl413HPhBBCiPolNjaWp59+GoPBgJ+fX40SuaqsW7eOPn36lLtYSavVMnz4cNavX8/KlSvJysoiNDQUR0dHXF1d2bZtG+Hh4cyaNYuoqCg0Gg1OTk588sknBAQEsHTpUvr164e/vz/9+vWrsv1Zs2YxevRoNmzYQIsWLXj00UfVfYsXL+all16iY8eOODk50bVrV9577z0AevToQaNGjfjLX/5So3Wo1cWtsvp0Oh1btmzhpZde4u2338ZqtdK0aVPWr1+Pu7t7le1ZrVbGjBlDfn4+Wq2Whg0b8sknn9C4ceNq+3qjNMrdcjO1GjIajXh4eJCfn6+uf7ndzGYzKSkp9O/fH0dHxwr7rxiNNO3cHYDVC/7BqXPn6P/oI4QEdayV/tWV6uJyL5PY2CexsU9iY5/EpiKTycSJEyfw8vLCy8tLXZcpylz7KF2v19fL2Jw5c4Zu3bpx9OjRahPLW13frY6NyWTi5MmTtGzZUl0aciP5Wv3737kHncvKBqCxhwfBge0Ieagjft6y3EAIIYQQFb366quEhoayYMGCW5LI3ur6apssM6gH1Kd/eTfDENgeQ2D7Ou6REEIIIeqruXPnMnfu3HpbX22Tmdl64HxO2YJuufhLCCGEEOLGSDJbD1z47U4GjT08yC8oqOPeCCGEEELcOSSZrQfOZZetmS0pLeGd99fx438O1HGPhBBCCCHuDJLM1gPX1szq3dzRaDTc53vrHgEohBBCCHE3kwvA6oFrD0wYHPUnHuv1MA2ue2KJEEIIIYSwT2Zm64EL1y4A8/bGtUGDGt38WAghhLibGAwGDAYDgYGB6HQ69f2wYcNuqJ60tDS++eabastFR0ej1+spKiq62S7XK2lpaYSEhNRZ+zNnziQoKEj9f/voo49qrW2Zma1j+QUF6prZ+5v71nFvhBBCiLqxf/9+ADIzMwkJCVHf36i0tDQKCwuJioqyW8ZoNLJlyxaCgoJISkoiJibmptq6ERaLBZ3u7ki7LBZLhW3Tpk1j3rx5AJw/f5527doRFRV1W5749XsyM1vHvtv7EzabDc9GjfjpwMG67o4QQoh7kKIo2GyW2/p1sw8c/frrr+nVqxddunShe/fupKenA5CRkcHDDz9McHAwQUFBzJo1i/379xMfH09iYiIGg8HuvVM3btxIZGQkL7/8MgkJCeX2rVmzBoPBQKdOnYiIiCAzMxOA5ORkunbtSnBwMAaDgT179gCg0WgoLCxUj/fy8lKPadGiBfPmzSMiIoKYmBiysrKIiIigS5cudOjQgdjYWDUupaWlTJs2jaCgIIKDg+nbty8AQUFB/Pvf/1brX7VqVbWz1RaLhT59+hASEkKHDh0YOXKkOgNdVX1ZWVk888wzdOvWjYceeohXX31VLXf9WMaMGVOhzesfB1xQUIBGo8Fms1XZz1vl7vgT4Q62c8+PANzfvDklpaV13BshhBD3IkWxcvb4jtvaxn2tI9FobiztOHHiBHPmzGHr1q3o9XqOHTtG7969yczMZNmyZQwYMIBXXnkFgMuXL9OkSRMmTpxIYWEhb7/9tt16ExISmDt3LpGRkTz33HMcPXqUtm3bkpaWxrx589i1axfe3t5kZWWh1+s5evQo48ePJz09nbZt22I2m2u8POH06dPs2LEDjUaDyWRiy5YtuLm5YbVaGTx4MJs2bWLo0KHMnz+f48ePs3fvXpydncnNLVuCGBsby/LlywkNDQVg+fLlLF++vMo2tVotGzduxNPTE0VRmDRpEitWrGDq1KlV1hcTE8PMmTMJDw/HYrHw+OOP89lnn/Hkk0+WG4uiKBiNxgrtLl26lOXLl3P27Fnef/99PD09axSjP0qS2Tq2c88PAIwe8gQ9u3Su494IIYQQ9cfWrVs5duwY4eHh5bafOXOG8PBwpk2bxtWrV+nduzeRkZE1qvPAgQNcuHCBqKgotFoto0eP5v3332fBggUkJycTHR2Nr68vNpsNV1dXXF1dSU1NpX///rRt2xYAR0dHPDw8atTe2LFj1WthbDYbcXFxfPvttyiKQk5ODgaDgaFDh/Lll1+ycOFCnJ2dAWjatCkAo0aN4rXXXiMnJ4fDhw+j0WgICwursk1FUVi8eDHJyclYLBby8/PVGNqr7+rVq+zYsYPs35Y+AhQWFvLf//63wljszbLHxsYSGxvLL7/8wqhRo4iMjKyVhFaS2TqUX1DAvkO/AvBkn8fw9vKq4x4JIYS4F2k0Wu5rXbNk8I+0caMURaFv374kJiZW2PfAAw/Qs2dPUlNTWbZsGUuWLCElJaXaOlevXk1hYSGtWrUCwGw2Y7PZeP3112+4f1A2C2q1WtX3JpOp3H43Nzf19aJFi7h06RJ79uzBxcWFKVOmVCj/ew0aNCAmJobVq1ezb98+nn/++Wr7tHHjRnbu3El6ejru7u4sXbpUXZ5hrz6bzYZGo+HHH3/E0dGx0nqvH0tVgoOD8fPzIy0tjaeeeqpGx/wRsma2jiiKQsJHSdhsNloH3I+/3FtWCCFEHdFoNDg46G7r183cqScqKoqtW7dy8OD/rin54YeyTzQzMjJo1qwZ0dHRvPnmm+zevRsAvV5Pfn5+pfWVlJSwYcMGdu/eTWZmJpmZmZw7dw4/Pz9SUlIYOHAgiYmJZGVlAVBUVERRURF9+vThq6++4ujRo0BZAnytjVatWqnrZz/99FOuXr1qdzx5eXn4+Pjg4uJCdnY2SUlJ6r5BgwaxZMkSSkpKANRlBgCTJ09m5cqV7Ny5k5EjR1Ybt7y8PDw9PXF3d6egoIC1a9eW219Zfe7u7oSFhbFgwQK13Pnz5zl79my17QEcPnxYfX38+HH27dtHYGBgjY79o2Rmto6cOH2GzV+nAtCra93dSkMIIYSor9q0acMHH3zAhAkTKC4uprS0lM6dO7NhwwaSkpLYsGEDTk5OKIpCfHw8AE8++STr16/HYDAwZMiQchcxbd68mYCAANq1a1eundGjR7N69Wq++OILZs2aRVRUFBqNBq1Wy6ZNm2jdujUJCQmMGDECs9mMVqtl1apVdOvWjSVLljB58mSaNWtGRERElR+rx8bG8vTTT2MwGPDz8yu3NCIuLo6ZM2fSqVMnnJycaN68uTrT7O/vj8FgoG3btri6ulYbt+joaD7//HMCAwPx8/MjLCyMc+fOqfvt1bdhwwamTJlCUFAQUDYTGx8fj7+/f7VtTp8+nWPHjuHo6IhOp2PZsmW0b9++2uNuBY1ys5cX3qGMRiMeHh7k5+ej1+trpc2DR47yXuJ6BvbrS2SvhwEoKSml7aOPcT47h3UL3+TZwQNrpS/1idlsJiUlhf79+9v9SONeJbGxT2Jjn8TGPolNRSaTiRMnTuDl5YWXlxcODvJh7fVsNhtGoxG9Xl/nsSksLKRdu3bs2rWLli1b1nl9tzo2JpOJkydP0rJlS1x+e3DUjeRrcubWgoKrV8nNy+Pi5Tx1m6m0hKzciwCEd+taV10TQgghRD0WHx9Pu3btmDRp0i1JZG91ffWBLDOoBQ/cdx89gx+iuyFY3Xbt/rKyXlYIIYQQ9kycOJGJEyfW2/rqA5mZrQVNGnlwn493uaT12v1lw7t3q6tuCSGEEELc8SSZrQPns3PY+PkXAPSWZFYIIYQQ4qZJMlvLik0mnnruebJyLxLYuhVPRN3e+/oJIYQQQtzNJJmtRYqi8JdX/sbe/xygSSMPPnt3Ja4NGtR1t4QQQggh7lhyAVgtUBSFg8eO8/7kWFL+tROtVstH//cOD9x/X113TQghhBDijlbnM7MrVqxQ7yvWpUsXdu3aZbfsp59+ymOPPUbTpk3R6/WEhoby9ddf12Jvb9ymr76m+xNDmb3yPVL+tROApbP/xiOh3eu4Z0IIIUT9YTAYMBgMBAYGotPp1PfDhg2rcR3x8fEsXrz4pvsQHh5OmzZtuFtuwb927VqGDh1a191g3bp1aDQavvzyy9tSf53OzH700Ue8+OKLrFixgocffphVq1bRr18/fv31V+6///4K5dPT03nsscf4xz/+QaNGjVizZg0DBw5kz549dOrUqQ5GUL3Ms2c5eDQDZydHRj/5BM/HjKZD2zZ13S0hhBCiAkWx/vbKQX38rKLYAAXQoNE43FTZmti/fz8AmZmZhISEqO+vZ7FY0Onspy5/5JZTGRkZZGRk0LhxY9LT0wkLC7vpumqquvHcSeyN5ezZs6xatYoePXrctrbrdGZ20aJFjB8/ngkTJtC+fXuWLFnCfffdx8qVKystv2TJEv7f//t/dO3alTZt2vCPf/yDNm3asGXLllruec2NffopFsRNZdXfZrB09t8kkRVCCFFv5V88RP7FQ9clqlBSfJH8i4coLjxfrqzx0uGysjazuq3UdJn8i4coKjh7y/rUokUL5s2bR0REBDExMWRlZREREUGXLl3o0KEDsbGx6kzq7NmzmTp1KlA2K9mnTx9GjBhBUFAQISEhnDhxwm47CQkJjBo1igkTJpCQkFBu3xtvvEFQUBDBwcH06NGDoqIiANasWYPBYCA4OJiQkBAyMzPJzMzEy8tLPbawsFBN9gE0Gg0LFy7kkUceYcaMGRw4cICwsDA6d+5MYGAg8+fPV8vm5+czYcIEte1x48ZhMpnw8fHhzJkzarkZM2YQFxdXZRztxa26+jIyMhgwYABdu3YlODiYFStWqOW0Wm25sVTmL3/5C4sXL8bZ2bnK/v0RdfbnQGlpKT/99BPTp08vtz0qKorvv/++RnXYbDYKCgpo0qSJ3TIlJSWUlJSo741GI1D2aEOz2WzvsFvGvWFDnhs5gtTU1Fpp705yLR4Sl4okNvZJbOyT2NgnsanIbDarSaCiKNhsNvW9zWYDbOprRVHUMteU26apuuyNuHbc9cefOnWKbdu2odFoMJlMfP7557i5uWG1WnniiSdISkpi6NCh5dq22Wzs2bOHffv2ERAQwIwZM1iwYAHx8fEV2rRYLCQmJpKamoqXlxdz587lypUrODg4sG7dOjZv3syuXbvQ6/Xk5eXh6OjIjh07mDdvHjt37sTX11dNcHNyciodx/XjMZlM7NixA4CCggK++eYbnJ2dKS4uplevXvzpT38iJCSEF154ATc3N/bt24eDgwO5ubk4OTkxbtw44uPj+fvf/05JSQlr1qzh+++/rxDza/8fNpsNvV5vN2726jObzTz77LOsW7eOdu3aUVRURM+ePdVJxd+P5fftr1y5ksDAQLp27arur+y8uNZPs9mMVqsFbux7tc6S2YsXL2K1WvH29i633dvbm6ysrBrVsXDhQq5evcozzzxjt8z8+fOZM2dOhe3ffPMNrq6uN9bpPyg1NbVW27tTSFzsk9jYJ7GxT2Jjn8Tmf3Q6HT4+ZQ/zKSgoKNvoeN9v769et3TAGRzvo9QK5t8mhAAUnV9Z2cJiNBrTb2UdwfE+zLb/TR7dqMLCQhRFUY+32Ww8/fTTah+Liop49dVX2b17N4qicPHiRdq3b09UVBQlJSWUlpZiNBoxmUx0796dxo0bYzQaCQ4O5t133620XykpKfj7++PnVzam3r17s3btWsaNG8fnn39OTEwMUDYmrVbL1atX+eyzz3jmmWdo2LBhuTp/3//CwkL12GuGDh2qvs/NzWXWrFkcOHAABwcHzp07x+7du2nbti1ffvkl//rXv9Q6nJ2dMRqNjBo1iscee4wXXniBTZs20blzZ5o0aVJhbCaTCYvFgtForDJu9ur76aefOHToULl1y0ajkZ9//llNZq8fy/VOnTrFu+++y9atWzEajVgsFoqKiiotW1paSnFxMenp6VgsFvX/uabqfKHG9VPvUPaX3u+3Veaf//wns2fP5vPPP6dZs2Z2y82YMYMpU6ao741GI/fddx9RUVHo9fqb7/gNMJvNpKam8thjj+Ho6Fgrbd4JJC72SWzsk9jYJ7GxT2JTkclk4vTp0wC4u7vX6HdvbXBzc0Oj0ai/ox0cHPD29lbf/9///R8FBQX88MMPuLi48PLLL6MoCnq9HmdnZ8xmM3q9HhcXF9zc3NTj3NzcACr93f/hhx9y4sQJDAYDAMXFxZw/f55x48ah0+lwcXGpcJyzszPOzs4Vtjdq1EidCYWyRO337fr6+qr9efnll/H392f9+vXodDqeeuopdfwajQZ3d/cKbej1esLCwvjmm29Ys2YNf//73ysdl4uLCzqdDr1eX2Xc7NXXsGFDvLy8+OWXX8rVqyiK+sfF9WO53oEDB8jOziY0NBQoW+bwwgsvMHfuXP785z+XK2symWjQoAHh4eG4uLgAN/bHUJ0ls15eXmi12gqzsDk5ORVma3/vo48+Yvz48SQlJREZWfVDB66dbL/n6OhY6z/Q6qLNO4HExT6JjX0SG/skNvZJbP7HarWqCaxGo8HBoc5vcASg9uP6/jg4OKjvr1y5gq+vL66urmRnZ/PJJ58wbNgwHBzKLkS7NpZr739f3+/HmZWVxY4dOzh79iyNGjUCymLj5+fHwYMHGThwIPHx8QwZMgS9Xs+VK1dwd3dn0KBBjBs3jv/v//v/8PHxUWcSmzdvjsViISMjgwcffJAPPvig2vEEBQXh5OTEkSNH2LZtG3/6059wcHBg0KBBLFy4kHfeeUddZtC0aVMAXnzxRZ5++mkaNmxIVFRUpX+MXB+DquJmr7727dvj6urKBx98QHR0NADHjh2jUaNG6sVe14/leqNGjWLUqFHq+0ceeYSpU6fy+OOP2+3n9d+fN/J9WmdnrpOTE126dKnwkU9qaio9e/a0e9w///lPxowZw8aNGxkwYMDt7qYQQggh6pHY2Fi+//57DAYD48aNq3ZSqzrr1q2jT58+aiILZRc2DR8+nPXr1zN69GieeOIJQkNDMRgM9O/fn5KSEsLDw5k1axZRUVEEBwfTu3dvcnNz0el0LF26lH79+hEeHl7uup3KzJo1i9WrV9O1a1dmzZrFo48+qu5bvHgxRUVFdOzYEYPBwCuvvKLu69GjB40aNWLy5Mk1mlWvLm6V1afT6diyZQsff/wxDz30EB06dGDChAkUFxfXJLS1R6lDH374oeLo6KgkJCQov/76q/Liiy8qDRs2VDIzMxVFUZTp06cro0ePVstv3LhR0el0yvLly5ULFy6oX1euXKlxm/n5+Qqg5Ofn3/Lx2FNaWqps3rxZKS0trbU27wQSF/skNvZJbOyT2NgnsamouLhYOXTokJKdna1Yrda67k69Y7Valby8vHobm9OnTys+Pj6K0Wis9fpudWyKi4uVX3/9VSkuLla33Ui+VqefKQwbNowlS5Ywd+5cDAYD6enppKSkEBAQAMCFCxfU9TwAq1atwmKxMHnyZHx9fdWvF154oa6GIIQQQghRq1599VVCQ0NZsGAB7u7u9a6+2lbnF4BNmjSJSZMmVbpv7dq15d6npaXd/g4JIYQQQtRjc+fOZe7cufW2vtpWP1Z7CyGEEEIIcRMkmRVCCCGEEHcsSWaFEEIIIcQdS5JZIYQQQghxx5JkVgghhBB1zmAwYDAYCAwMRKfTqe+vf5RqTaSlpfHNN99UWy46Ohq9Xn9Dj02tz9LS0ggJCanrbpCWloZWq2XZsmW11mad381ACCGEEGL//v0AZGZmEhISor6/UWlpaRQWFhIVFWW3jNFoZMuWLQQFBZGUlERMTMxNtXUjLBaL+tSsO53FYql0e0FBAXFxcfTr169W+yMzs0IIIcQ9TlEUbDbLbf1SFOWm+vb111/Tq1cvunTpQvfu3UlPTwcgIyODhx9+mODgYIKCgpg1axb79+8nPj6exMREDAaD3dtNbdy4kcjISF5++WUSEhLK7VuzZg0Gg4FOnToRERFBZmYmAMnJyXTt2pXg4GAMBgN79uwByh4FXFhYqB7v5eWlHtOiRQvmzZtHREQEMTExZGVlERERQZcuXejQoQOxsbFqXEpLS5k2bRpBQUEEBwfTt29fAIKCgvj3v/+t1r9q1apqZ6stFgt9+vQhJCSEDh06MHLkSHUGuqr6srKyeOaZZ+jWrRsPPfQQr776qlru+rGMGTOm0nanTJnCtGnT8PLyqrJ/t9rd8SeCEEIIIW6aolg5e/ST29rGfQ8+g0ZzY2nHiRMnmDNnDlu3bkWv13Ps2DF69+5NZmYmy5YtY8CAAeojXi9fvkyTJk2YOHEihYWFvP3223brTUhIYO7cuURGRvLcc89x9OhR2rZtS1paGvPmzWPXrl14e3uTlZWFXq/n6NGjjB8/nvT0dNq2bYvZbK7x8oTTp0+zY8cONBoNJpOJLVu24ObmhtVqZfDgwWzatImhQ4cyf/58jh8/zt69e3F2diY3Nxcoewzt8uXLCQ0NBWD58uUsX768yja1Wi0bN27E09MTRVGYNGkSK1asYOrUqVXWFxMTw8yZMwkPD8disfD444/z2Wef8eSTT5Ybi6IoGI3Gcm1+9dVXXLlyhaFDh/Lll1/WKDa3iiSzQgghhKiXtm7dyrFjxwgPDy+3/cyZM4SHhzNt2jSuXr1K7969iYyMrFGdBw4c4MKFC0RFRaHVahk9ejTvv/8+CxYsIDk5mejoaHx9fbHZbLi6uuLq6kpqair9+/enbdu2ADg6OuLh4VGj9saOHYtGowHAZrMRFxfHt99+i6Io5OTkYDAY1ARw4cKFODs7A9C0aVMARo0axWuvvUZOTg6HDx9Go9EQFhZWZZuKorB48WKSk5OxWCzk5+erMbRX39WrV9mxYwfZ2dlqPYWFhfz3v/+tMJbfz7JfuXKF6dOnk5qaWqOY3GqSzAohhBD3OI1Gy30PPnPb27hRiqLQt29fEhMTK+x74IEH6NmzJ6mpqSxbtowlS5aQkpJSbZ2rV6+msLCQVq1aAWA2m7HZbLz++us33D8omwW1Wq3qe5PJVG6/m5ub+nrRokVcunSJPXv24OLiwpQpUyqU/70GDRoQExPD6tWr2bdvH88//3y1fdq4cSM7d+4kPT0dd3d3li5dqi7PsFefzWZDo9Hw448/4ujoWGm914/legcPHuTChQt069YNgIsXL7JlyxZyc3OZM2dOtf39o2TNrBBCCHGP02g0ODjobuvXtdnJGxEVFcXWrVs5ePCguu2HH34AytbMNmvWjOjoaN588012794NgF6vJz8/v9L6SkpK2LBhA7t37yYzM5PMzEzOnTuHn58fKSkpDBw4kMTERLKysgAoKiqiqKiIPn368NVXX3H06FGgLAG+1karVq3U9bOffvopV69etTuevLw8fHx8cHFxITs7m6SkJHXfoEGDWLJkCSUlJQDqMgOAyZMns3LlSnbu3MnIkSOrjVteXh6enp64u7tTUFDA2rVry+2vrD53d3fCwsJYsGCBWu78+fOcPXu22vZ69epFTk6OGtOhQ4cyZ86cWklkQWZmhRBCCFFPtWnThg8++IAJEyZQXFxMaWkpnTt3ZsOGDSQlJbFhwwacnJxQFIX4+HgAnnzySdavX4/BYGDIkCHlLmLavHkzAQEBtGvXrlw7o0ePZvXq1XzxxRfMmjWLqKgoNBoNWq2WTZs20bp1axISEhgxYgRmsxmtVsuqVavo1q0bS5YsYfLkyTRr1oyIiAg8PT3tjic2Npann34ag8GAn59fuaURcXFxzJw5k06dOuHk5ETz5s3VmWZ/f38MBgNt27bF1dW12rhFR0fz+eefExgYiJ+fH2FhYZw7d07db6++DRs2MGXKFIKCgoCymdj4+Hj8/f2rbbMuaZSbvbzwDmU0GvHw8CA/Px+9Xl8rbZrNZlJSUujfv7/dqft7kcTFPomNfRIb+yQ29klsKjKZTJw4cQIvLy+8vLxwcJAPa69ns9kwGo3o9fo6j01hYSHt2rVj165dtGzZss7ru9WxMZlMnDx5kpYtW+Li4gLcWL4mZ64QQgghRD0VHx9Pu3btmDRp0i1JZG91ffWBLDMQQgghhKinJk6cyMSJE+ttffWBzMwKIYQQQog7liSzQgghhBDijiXJrBBCCCGEuGNJMiuEEEIIIe5YkswKIYQQQog7liSzQgghhKhzBoMBg8FAYGAgOp1OfT9s2LAa1xEfH8/ixYtvug/h4eG0adOGu+UW/GvXrmXo0KF11v4jjzzCAw88oP5f/pH/m6rIrbmEEEIIAYBis5S90GjVx88qig0UG6BB46C9qbI1sX//fgAyMzMJCQlR31/PYrGg09lPXf7ILacyMjLIyMigcePGpKenExYWdtN11VR147mT2BvL0qVLefzxx29r2zIzK4QQQggAik8lU3wqGWyl6jZLfgbFp5IxXz5QvuyZrRSfSkaxFP+vrPEkxaeSKb2075b1qUWLFsybN4+IiAhiYmLIysoiIiKCLl260KFDB2JjY9WZ1NmzZzN16lSgbFayT58+jBgxgqCgIEJCQjhx4oTddhISEhg1ahQTJkwgISGh3L433niDoKAggoOD6dGjB0VFRQCsWbMGg8FAcHAwISEhZGZmkpmZiZeXl3psYWGhmuwDaDQaFi5cyCOPPMKMGTM4cOAAYWFhdO7cmcDAQObPn6+Wzc/PZ8KECWrb48aNw2Qy4ePjw5kzZ9RyM2bMIC4urso42otbdfVlZGQwYMAAunbtSnBwMCtWrFDLabXacmOpK3fHnwNCCCGEuGudPn2aHTt2oNFoMJlMbNmyBTc3N6xWK4MHD2bTpk2Vfpy+Z88efvnlFwICApg+fTpvvPEGq1atqlDOYrGQmJjI9u3b8fLyYu7cueTn56PRaFi3bh2bN2/mu+++Q6/Xk5eXh7OzM2lpacybN49du3bh6+urJrg5OTnVjqekpIS0tDQACgoK2LZtG87OzhQXF9OzZ08ee+wxQkJCePHFF3Fzc+OXX37BwcGB3NxcXFxcGD9+PKtWreL111+npKSENWvWsHv37irbbNSokd242avParXy7LPPsn79etq1a0dRURE9evSgW7dutG7dusJYKjNt2jRmzJihJuoPPPBAtfG5UTIzK4QQQggAGgQMoEHAAHBwUrfpPNrQIGAAjk2Cype9ry8NAgag0TX4X1l9SxoEDMDJs9Mt7dfYsWPV2U2bzUZcXBzBwcF06tSJvXv3VrokAaBXr14EBAQAEBoayvHjxystl5KSQkBAAO3bt6dp06ZERkbyz3/+E4Dk5GSee+459Ho9AI0bN0ar1ZKcnEx0dDS+vr4AuLq64urqWqPxjBs3Tn1dXFyszr726NGDU6dOqeP58ssvmTZtGg4OZela06ZNAZg0aRJr166ltLSUDz/8kO7du9OiRYsq26wqbvbqO3LkCIcOHWL48OEYDAZ69uxJQUEBv/76a6Vj+b3169dz+PBh/vOf/xAWFnbblhvIzKwQQgghANA4VEwLNBoH0FSc+7qRsn+Um5ub+nrRokVcunSJPXv24OLiwpQpUzCZTJUe5+Lior7WarVYLJZKyyUkJJCRkaEmhMXFxZw6dYpnn332hvuq0+mwWq3q+8r6dv14XnnlFby9vdm3bx86nY4hQ4bYHc81fn5+hIWF8cknn7B8+XLmzZtXbb+qipu9+hRFwcvLq8IfCzabDaPRWGEsv3ffffcBZUsrnn/+eaZOncqlS5fw9PSstr83QmZmhRBCCHHHyMvLw8fHBxcXF7Kzs0lKSvpD9WVlZbF9+3aOHTumrnk9f/48Z86c4eDBgwwcOJCVK1eqyduVK1ewWq0MHDiQxMREsrKyACgqKqKoqAgfHx8sFgtHjhwBIDExsdrx+Pv7o9PpOHLkCKmpqeq+QYMG8dZbb2Gz2QDIzc1V973wwgvExcVhNBqJjIysdpzVxa2y+h588EFcXV3LjeHYsWNcvny52vYsFgvZ2dnq+02bNuHt7X3LE1mQmVkhhBBC3EFiY2N5+umnMRgM+Pn51SiRq8q6devo06cPjRo1UrdptVqGDx/O+vXrWblyJVlZWYSGhuLo6Iirqyvbtm0jPDycWbNmERUVhUajwcnJiU8++YSAgACWLl1Kv3798Pf3p1+/flW2P2vWLEaPHs2GDRto0aIFjz76qLpv8eLFvPTSS3Ts2BEnJye6du3Ke++9B0CPHj1o1KgRf/nLX8pdYGZPdXGrrD6dTseWLVt46aWXePvtt7FarTRt2pT169fj7u5eZXslJSUMGDCAkpISHBwc8PLy4osvvqi2nzdDo9wtN1OrIaPRiIeHB/n5+er6l9vNbDaTkpJC//79cXR0rJU27wQSF/skNvZJbOyT2NgnsanIZDJx4sQJvLy88PLyUtdlijLXPkrX6/X1MjZnzpyhW7duHD16tNrE8lbXd6tjYzKZOHnyJC1btlSXhtxIvlb//neEEEIIIYRdr776KqGhoSxYsOCWJLK3ur7aJsmsEEIIIcQdZO7cuZw9e5aYmJh6WV9tk2RWCCGEEELcsSSZFUIIIYQQdyxJZoUQQgghxB1LklkhhBBCCHHHkmRWCCGEEHXOYDBgMBgIDAxEp9Op74cNG3ZD9aSlpfHNN99UWy46Ohq9Xk9RUdHNdrleSUtLIyQkpM7aHzNmDP7+/ur/27Rp02qtbXloghBCCCHq3LVHpmZmZhISElLhEao1lZaWRmFhIVFRUXbLGI1GtmzZQlBQEElJSbVyFb/FYkGnuzvSLnuPBZ4+fTrPP/98LfdGZmaFEEKIe56iKCjWktv7dZPPaPr666/p1asXXbp0oXv37qSnpwOQkZHBww8/THBwMEFBQcyaNYv9+/cTHx9PYmIiBoOBuXPnVlrnxo0biYyM5OWXXyYhIaHcvjVr1mAwGOjUqRMRERFkZmYCkJycTNeuXQkODsZgMLBnzx4ANBoNhYWF6vFeXl7qMS1atGDevHlEREQQExNDVlYWERERdOnShQ4dOhAbG6vGpbS0lGnTphEUFERwcDB9+/YFICgoiH//+99q/atWrap2ttpisdCnTx9CQkLo0KEDI0eOVGegq6ovKyuLZ555hm7duvHQQw/x6quvquWuH8uYMWOqbL+23R1/IgghhBDi5tlKufzd7Z1RaxK2ArTON3TMiRMnmDNnDlu3bkWv13Ps2DF69+5NZmYmy5YtY8CAAbzyyisAXL58mSZNmjBx4kQKCwt5++237dabkJDA3LlziYyM5LnnnuPo0aO0bduWtLQ05s2bx65du/D29iYrKwu9Xs/Ro0cZP3486enptG3bFrPZXOPlCadPn2bHjh1oNBpMJhNbtmzBzc0Nq9XK4MGD2bRpE0OHDmX+/PkcP36cvXv34uzsTG5uLlD2GNrly5cTGhoKwPLly1m+fHmVbWq1WjZu3IinpyeKojBp0iRWrFjB1KlTq6wvJiaGmTNnEh4ejsVi4fHHH+ezzz7jySefLDcWRVEwGo0V2l20aBHvvvsu999/P6+//joGg6FGMfqjJJkVQgghRL20detWjh07Rnh4eLntZ86cITw8nGnTpnH16lV69+5NZGRkjeo8cOAAFy5cICoqCq1Wy+jRo3n//fdZsGABycnJREdH4+vri81mw9XVFVdXV1JTU+nfvz9t27YFwNHREQ8Pjxq1N3bsWDQaDVD2GNi4uDi+/fZbFEUhJycHg8HA0KFD+fLLL1m4cCHOzmUJf9OmTQEYNWoUr732Gjk5ORw+fBiNRkNYWFiVbSqKwuLFi0lOTsZisZCfn6/G0F59V69eZceOHWRnZ6v1FBYW8t///rfCWCqbZZ83bx6+vr44ODjw2Wef0a9fPzIyMnBzc6tRnP4ISWaFEEKIe52DU9nM6W1u40YpikLfvn1JTEyssO+BBx6gZ8+epKamsmzZMpYsWUJKSkq1da5evZrCwkJatWoFgNlsxmaz8frrr99w/6BsFtRqtarvTSZTuf3XJ3OLFi3i0qVL7NmzBxcXF6ZMmVKh/O81aNCAmJgYVq9ezb59+2q0JnXjxo3s3LmT9PR03N3dWbp0qbo8w159NpsNjUbDjz/+iKOjY6X1VpWY+vn5qa+ffPJJpk+fzpEjR+jSpUu1/f2jZM2sEEIIcY/TaDRotM639+u32ckbERUVxdatWzl48KC67YcffgDK1sw2a9aM6Oho3nzzTXbv3g2AXq8nPz+/0vpKSkrYsGEDu3fvJjMzk8zMTM6dO4efnx8pKSkMHDiQxMREsrKyACgqKqKoqIg+ffrw1VdfcfToUaAsAb7WRqtWrdT1s59++ilXr161O568vDx8fHxwcXEhOzubpKQkdd+gQYNYsmQJJSUlAOoyA4DJkyezcuVKdu7cyciRI6uNW15eHp6enri7u1NQUMDatWvL7a+sPnd3d8LCwliwYIFa7vz585w9e7ba9oBy5Xbv3s2lS5do3bp1jY79o2RmVgghhBD1Ups2bfjggw+YMGECxcXFlJaW0rlzZzZs2EBSUhIbNmzAyckJRVGIj48HymYF169fj8FgYMiQIeUuYtq8eTMBAQG0a9euXDujR49m9erVfPHFF8yaNYuoqCg0Gg1arZZNmzbRunVrEhISGDFiBGazGa1Wy6pVq+jWrRtLlixh8uTJNGvWjIiICDw9Pe2OJzY2lqeffhqDwYCfn1+5pRFxcXHMnDmTTp064eTkRPPmzdWZ5mu3vGrbti2urq7Vxi06OprPP/+cwMBA/Pz8CAsL49y5c+p+e/Vt2LCBKVOmEBQUBJTNxMbHx+Pv719tm2PGjCE7OxutVkuDBg1ISkqq8VKMP0qj3OzlhXcoo9GIh4cH+fn56PX6WmnTbDaTkpJC//797U7d34skLvZJbOyT2NgnsbFPYlORyWTixIkTeHl54eXlhYODfFh7PZvNhtFoRK/X13lsCgsLadeuHbt27aJly5Z1Xt+tjo3JZOLkyZO0bNkSFxcX4MbyNTlzhRBCCCHqqfj4eNq1a8ekSZNuSSJ7q+urD2SZgRBCCCFEPTVx4kQmTpxYb+urD2RmVgghhBBC3LEkmRVCCCGEEHcsSWaFEEIIIcQdS5JZIYQQQghxx5JkVgghhBBC3LEkmRVCCCFEnTMYDBgMBgIDA9HpdOr7YcOG1biO+Ph4Fi9efNN9CA8Pp02bNtwtt+Bfu3YtQ4cOrbP2FUVh9uzZtG3blo4dO/LII4/clnbk1lxCCCGEAECxlpa9cHBUHz+r2KygWEHjgMZBd1Nla2L//v0AZGZmEhISor6/nsViQaezX+8fueVURkYGGRkZNG7cmPT0dMLCwm66rpqqbjx3ksrGsnTpUg4cOMDBgwdxcnLiwoULt6VtmZkVQgghBADF++ZTvG8+WIrUbZbs7yjeNx/z6a/KlTX98jbF++ajlOar26y5P5aVzfzilvWpRYsWzJs3j4iICGJiYsjKyiIiIoIuXbrQoUMHYmNj1ZnU2bNnM3XqVKBsVrJPnz6MGDGCoKAgQkJCOHHihN12EhISGDVqFBMmTCAhIaHcvjfeeIOgoCCCg4Pp0aMHRUVl8VmzZg0Gg4Hg4GBCQkLIzMwkMzMTLy8v9djCwkI12QfQaDQsXLiQRx55hBkzZnDgwAHCwsLo3LkzgYGBzJ8/Xy2bn5/PhAkT1LbHjRuHyWTCx8eHM2fOqOVmzJhBXFxclXG0F7fq6svIyGDAgAF07dqV4OBgVqxYoZbTarXlxvJ7b731Fm+88QZOTk4A+Pr6VtnHm3V3/DkghBBCiLvW6dOn2bFjBxqNBpPJxJYtW3Bzc8NqtTJ48GA2bdpU6cfpe/bs4ZdffiEgIIDp06fzxhtvsGrVqgrlLBYLiYmJbN++HS8vL+bOnUt+fj4ajYZ169axefNmvvvuO/R6PXl5eTg7O5OWlsa8efPYtWsXvr6+aoKbk5NT7XhKSkpIS0sDoKCggG3btuHs7ExxcTE9e/bkscceIyQkhBdffBE3Nzd++eUXHBwcyM3NxcXFhfHjx7Nq1Spef/11SkpKWLNmDbt3766yzUaNGtmNm736rFYrzz77LOvXr6ddu3YUFRXRo0cPunXrRuvWrSuM5XpGo5Hc3Fw+++wzNm3aBMBLL710Q8tGakqSWSGEEEIA0KDTb7NrDo7qNp33w+ia9QBN+Q9zXYKnViirbdqVBl6dK5T9o8aOHavObtpsNuLi4vj2229RFIWcnBwMBkOlyWyvXr0ICAgAIDQ0lP/7v/+rtP6UlBQCAgJo3749AJGRkfzzn//k2WefJTk5meeeew69Xg9A48aNAUhOTiY6OlqdbXR1da3xeMaNG6e+Li4uZtKkSezfvx8HBwfOnDnD/v37CQkJ4csvv+Snn37CwaEsnk2bNgVg0qRJdO/enVdffZUPP/yQ7t2706JFiyrbrCpu9ur79ddfOXToEMOHD1frKSgo4Ndff1WT2evHcj2z2UxpaSnFxcXs3r2b06dPExoaSocOHejYsWONY1UTkswKIYQQAgCN1qniNgctoP1DZf8oNzc39fWiRYu4dOkSe/bswcXFhSlTpmAymSo9zsXFRX2t1WqxWCyVlktISCAjI0NNCIuLizl16hTPPvvsDfdVp9NhtVrV95X17frxvPLKK3h7e7Nv3z50Oh1DhgyxO55r/Pz8CAsL45NPPmH58uXMmzev2n5VFTd79SmKgpeXV4X1yzabDaPRWGEs1/P09MTNzY1Ro0YBcP/99/Pwww+zd+/eW57MyppZIYQQQtwx8vLy8PHxwcXFhezsbJKSkv5QfVlZWWzfvp1jx46pa17Pnz/PmTNnOHjwIAMHDmTlypVq8nblyhWsVisDBw4kMTGRrKwsAIqKiigqKsLHxweLxcKRI0cASExMrHY8/v7+6HQ6jhw5Qmpqqrpv0KBBvPXWW9hsNgByc3PVfS+88AJxcXEYjUYiIyOrHWd1causvgcffBBXV9dyYzh27BiXL1+utj2AESNGsHXrVrX9H374gYceeqhGx94ImZkVQgghxB0jNjaWp59+GoPBgJ+fX40SuaqsW7eOPn360KhRI3WbVqtl+PDhrF+/npUrV5KVlUVoaCiOjo64urqybds2wsPDmTVrFlFRUWg0GpycnPjkk08ICAhg6dKl9OvXD39/f/r161dl+7NmzWL06NFs2LCBFi1a8Oijj6r7Fi9ezEsvvUTHjh1xcnKia9euvPfeewD06NGDRo0a8Ze//KXcBWb2VBe3yurT6XRs2bKFl156ibfffhur1UrTpk1Zv3497u7u1bb5j3/8g7Fjx6oXjc2YMYPOnTtXe9yN0ih3y83UashoNOLh4UF+fr66/uV2M5vNpKSk0L9/fxwdHas/4B4hcbFPYmOfxMY+iY19EpuKTCYTJ06cwMvLCy8vL3Vdpihz7aN0vV5fL2Nz5swZunXrxtGjR2uUWN7K+m51bEwmEydPnqRly5bq0pAbydfq3/+OEEIIIYSw69VXXyU0NJQFCxbckkT2VtdX2ySZFUIIIYS4g8ydO5ezZ88SExNTL+urbZLMCiGEEEKIO5Yks0IIIYQQ4o4lyawQQgghhLhjSTIrhBBCCCHuWJLMCiGEEKLOGQwGDAYDgYGB6HQ69f2wYcNuqJ60tDS++eabastFR0ej1+spKiq62S7XK2lpaYSEhNRpH1asWEH79u3p2LEjDz30ULVPMrtV5KEJQgghhKhz1x6ZmpmZSUhISIVHqNZUWloahYWFREVF2S1jNBrZsmULQUFBJCUl1cpV/BaLBZ3u7ki7Knss8Oeff86GDRvYvXs3Hh4e5OTk1No9nWVmVgghhLjHKYqCYr56e79u8hlNX3/9Nb169aJLly50796d9PR0ADIyMnj44YcJDg4mKCiIWbNmsX//fuLj40lMTMRgMDB37txK69y4cSORkZG8/PLLJCQklNu3Zs0aDAYDnTp1IiIigszMTACSk5Pp2rUrwcHBGAwG9uzZA4BGo6GwsFA93svLSz2mRYsWzJs3j4iICGJiYsjKyiIiIoIuXbrQoUMHYmNj1biUlpYybdo0goKCCA4Opm/fvgAEBQXx73//W61/1apV1c5WWywW+vTpQ0hICB06dGDkyJHqDHRV9WVlZfHMM8/QrVs3HnroIV599VW13PVjGTNmTIU233rrLebMmYOHhwcAzZo1Q6vVVtnPW+Xu+BNBCCGEEDfPUkTeusa3tYnGY/PBseENHXPixAnmzJnD1q1b0ev1HDt2jN69e5OZmcmyZcsYMGAAr7zyCgCXL1+mSZMmTJw4kcLCQt5++2279SYkJDB37lwiIyN57rnnOHr0KG3btiUtLY158+axa9cuvL29ycrKQq/Xc/ToUcaPH096ejpt27bFbDbXeHnC6dOn2bFjBxqNBpPJxJYtW3Bzc8NqtTJ48GA2bdrE0KFDmT9/PsePH2fv3r04OzuTm5sLlD2Gdvny5YSGhgKwfPlyli9fXmWbWq2WjRs34unpiaIoTJo0iRUrVjB16tQq64uJiWHmzJmEh4djsVh4/PHH+eyzz3jyySfLjUVRFIxGY7k2f/31V/bu3ctrr71GSUkJ0dHRxMbG1ihGf5Qks0IIIYSol7Zu3cqxY8cIDw8vt/3MmTOEh4czbdo0rl69Su/evYmMjKxRnQcOHODChQtERUWh1WoZPXo077//PgsWLCA5OZno6Gh8fX2x2Wy4urri6upKamoq/fv3p23btgA4OjqqM5DVGTt2LBqNBih7DGxcXBzffvstiqKQk5ODwWBg6NChfPnllyxcuBBnZ2cAmjZtCsCoUaN47bXXyMnJ4fDhw2g0GsLCwqpsU1EUFi9eTHJyMhaLhfz8fDWG9uq7evUqO3bsIDs7W62nsLCQ//73vxXGUtksu8Vi4fjx46Snp5Ofn0/v3r1p3bo1/fv3r1Gc/ghJZoUQQoh7nc61bOb0NrdxoxRFoW/fviQmJlbY98ADD9CzZ09SU1NZtmwZS5YsISUlpdo6V69eTWFhIa1atQLAbDZjs9l4/fXXb7h/UDYLarVa1fe/v+jJzc1Nfb1o0SIuXbrEnj17cHFxYcqUKdVeJNWgQQNiYmJYvXo1+/bt4/nnn6+2Txs3bmTnzp2kp6fj7u7O0qVL1eUZ9uqz2WxoNBp+/PFHu2tdrx/L791///2MGDECrVZLkyZN6NevHz/88EOtJLOyZlYIIYS4x2k0GjSODW/v12+zkzciKiqKrVu3cvDgQXXbDz/8AJStmW3WrBnR0dG8+eab7N69GwC9Xk9+fuWJeUlJiXqRUmZmJpmZmZw7dw4/Pz9SUlIYOHAgiYmJZGVlAVBUVERRURF9+vThq6++4ujRo0BZAnytjVatWqnrZz/99FOuXr1qdzx5eXn4+Pjg4uJCdnY2SUlJ6r5BgwaxZMkSSkpKANRlBgCTJ09m5cqV7Ny5k5EjR1Ybt7y8PDw9PXF3d6egoIC1a9eW219Zfe7u7oSFhbFgwQK13Pnz5zl79my17QE8++yzbN26FShL6Hfu3ElwcHCNjv2jJJkVQgghRL3Upk0bPvjgAyZMmEBwcDDt27fnnXfeASApKYmHHnqITp06MXz4cOLj4wF48skn2bt3b6UXgG3evJmAgADatWtXbvvo0aNZvXo14eHhzJo1i6ioKDp16sTjjz9Obm4urVu3JiEhgREjRvDQQw/RrVs3jhw5AsCSJUuYPHkyDz/8MD///DOenp52xxMbG8v333+PwWBg3Lhx5ZZGxMXF0apVKzp16oTBYCh3hwV/f38MBgOjR4/G1bX6Ge7o6GgKCwsJDAxkyJAhFZYl2Ktvw4YNHD58mKCgIIKCgnjqqae4dOlSte0BvPTSS2RlZREYGEiXLl3o16+futb2dtMoN3t54R3KaDTi4eFBfn4+er2+Vto0m82kpKTQv3//WrtNxZ1A4mKfxMY+iY19Ehv7JDYVmUwmTpw4gZeXF15eXjg4yPzW9Ww2G0ajEb1eX+exKSwspF27duzatYuWLVvWeX23OjYmk4mTJ0/SsmVLXFxcgBvL1+TMFUIIIYSop+Lj42nXrh2TJk26JYnsra6vPpALwIQQQggh6qmJEycyceLEeltffSAzs0IIIYQQ4o4lyawQQghxD7vHLp0R9dAfPQdlmYEQQghxD3J0dESj0WA0GnFzc6u1R4/eKWw2G6WlpZhMpjq/AKy+uZWxURSF3NxcNBrNTV+cKcmsEEIIcQ/SarX4+vpy5MgRzGbzTd0H9m6mKArFxcU0aNBAYvM7tzo2Go0Gf3//m/6DSpJZIYQQ4h7VsGFDsrOz6dChAzqdpATXM5vNpKenEx4eLrdz+51bHRtHR8c/9MlAnZ+5K1as4K233uLChQt06NCBJUuWVPnM4Z07dzJlyhQOHTpE8+bN+X//7//ddVflCSGEELVFURScnZ0lYfsdrVaLxWLBxcVFYvM79S02dboI5KOPPuLFF19k5syZ7Nu3j7CwMPr168fp06crLX/y5En69+9PWFgY+/bt45VXXiE2NpZNmzbVcs+FEEIIIUR9UKfJ7KJFixg/fjwTJkygffv2LFmyhPvuu4+VK1dWWj4+Pp7777+fJUuW0L59eyZMmMC4ceN4++23a7nnQgghhBCiPqizZQalpaX89NNPTJ8+vdz2qKgovv/++0qP+fe//01UVFS5bX369CEhIQGz2VzpVHdJSQklJSXq+/z8fAAuX76M2Wz+o8OoEbPZTFFREZcuXaoX0/H1hcTFPomNfRIb+yQ29klsKidxsU9iY19txKagoACo2W276iyZvXjxIlarFW9v73Lbvb29ycrKqvSYrKysSstbLBYuXryIr69vhWPmz5/PnDlzKmy/Wx7hJoQQQghxtyooKMDDw6PKMnV+Adjvb+mgKEqVt3morHxl26+ZMWMGU6ZMUd/bbDYuX76Mp6dnrd1qw2g0ct9993HmzBn0en2ttHknkLjYJ7GxT2Jjn8TGPolN5SQu9kls7KuN2CiKQkFBAc2bN6+2bJ0ls15eXmi12gqzsDk5ORVmX6/x8fGptLxOp8PT07PSY5ydnXF2di63rVGjRjff8T9Ar9fLN0QlJC72SWzsk9jYJ7GxT2JTOYmLfRIb+253bKqbkb2mzi4Ac3JyokuXLqSmppbbnpqaSs+ePSs9JjQ0tEL5b775hpCQEFnPIoQQQghxD6rTuxlMmTKF1atX8/7773P48GFeeuklTp8+rd43dsaMGURHR6vlJ06cyKlTp5gyZQqHDx/m/fffJyEhgalTp9bVEIQQQgghRB2q0zWzw4YN49KlS8ydO5cLFy7QsWNHUlJSCAgIAODChQvl7jnbsmVLUlJSeOmll1i+fDnNmzdn6dKlPPXUU3U1hBpxdnbmtddeq7Dc4V4ncbFPYmOfxMY+iY19EpvKSVzsk9jYV99io1Fqcs8DIYQQQggh6qE6XWYghBBCCCHEHyHJrBBCCCGEuGNJMiuEEEIIIe5YkswKIYQQQog7liSzt9mKFSto2bIlLi4udOnShV27dtV1l2rd/Pnz6dq1K+7u7jRr1ownnniCI0eOlCszZswYNBpNua8ePXrUUY9rx+zZsyuM2cfHR92vKAqzZ8+mefPmNGjQgEceeYRDhw7VYY9rT4sWLSrERqPRMHnyZODeOl/S09MZOHAgzZs3R6PRsHnz5nL7a3KelJSU8Ne//hUvLy8aNmzIoEGDOHv2bC2O4vaoKjZms5m4uDiCgoJo2LAhzZs3Jzo6mvPnz5er45FHHqlwLg0fPryWR3LrVXfe1OR76G48b6qLS2U/dzQaDW+99ZZa5m49Z2ryu7q+/ryRZPY2+uijj3jxxReZOXMm+/btIywsjH79+pW73di9YOfOnUyePJndu3eTmpqKxWIhKiqKq1evlivXt29fLly4oH6lpKTUUY9rT4cOHcqN+cCBA+q+N998k0WLFrFs2TJ+/PFHfHx8eOyxxygoKKjDHteOH3/8sVxcrj0s5emnn1bL3Cvny9WrVwkODmbZsmWV7q/JefLiiy/y2Wef8eGHH/Ltt99SWFjI448/jtVqra1h3BZVxaaoqIiff/6Zv/3tb/z88898+umnHD16lEGDBlUo++c//7ncubRq1ara6P5tVd15A9V/D92N5011cbk+HhcuXOD9999Ho9FUuAXo3XjO1OR3db39eaOI26Zbt27KxIkTy21r166dMn369DrqUf2Qk5OjAMrOnTvVbTExMcrgwYPrrlN14LXXXlOCg4Mr3Wez2RQfHx9lwYIF6jaTyaR4eHgo8fHxtdTD+uOFF15QWrVqpdhsNkVR7s3zRVEUBVA+++wz9X1NzpMrV64ojo6OyocffqiWOXfunOLg4KBs3bq11vp+u/0+NpX54YcfFEA5deqUuq13797KCy+8cHs7V8cqi01130P3wnlTk3Nm8ODByqOPPlpu271wzihKxd/V9fnnjczM3ialpaX89NNPREVFldseFRXF999/X0e9qh/y8/MBaNKkSbntaWlpNGvWjLZt2/LnP/+ZnJycuuhercrIyKB58+a0bNmS4cOHc+LECQBOnjxJVlZWufPH2dmZ3r1733PnT2lpKR988AHjxo1Do9Go2+/F8+X3anKe/PTTT5jN5nJlmjdvTseOHe+5cyk/Px+NRkOjRo3Kbd+wYQNeXl506NCBqVOn3hOffkDV30Ny3kB2djbJycmMHz++wr574Zz5/e/q+vzzpk6fAHY3u3jxIlarFW9v73Lbvb29ycrKqqNe1T1FUZgyZQq9evWiY8eO6vZ+/frx9NNPExAQwMmTJ/nb3/7Go48+yk8//VRvnjByq3Xv3p3ExETatm1LdnY2r7/+Oj179uTQoUPqOVLZ+XPq1Km66G6d2bx5M1euXGHMmDHqtnvxfKlMTc6TrKwsnJycaNy4cYUy99LPIpPJxPTp03n22WfR6/Xq9pEjR9KyZUt8fHw4ePAgM2bM4JdfflGXttytqvsekvMG1q1bh7u7O0OGDCm3/V44Zyr7XV2ff95IMnubXT+TBGUnyO+33Uuef/55/vOf//Dtt9+W2z5s2DD1dceOHQkJCSEgIIDk5OQKP0juFv369VNfBwUFERoaSqtWrVi3bp16IYacP5CQkEC/fv1o3ry5uu1ePF+qcjPnyb10LpnNZoYPH47NZmPFihXl9v35z39WX3fs2JE2bdoQEhLCzz//TOfOnWu7q7XmZr+H7qXz5v3332fkyJG4uLiU234vnDP2fldD/fx5I8sMbhMvLy+0Wm2Fv0RycnIq/FVzr/jrX//KF198wb/+9S/8/f2rLOvr60tAQAAZGRm11Lu617BhQ4KCgsjIyFDvanCvnz+nTp1i27ZtTJgwocpy9+L5AtToPPHx8aG0tJS8vDy7Ze5mZrOZZ555hpMnT5KamlpuVrYynTt3xtHR8Z47l37/PXSvnze7du3iyJEj1f7sgbvvnLH3u7o+/7yRZPY2cXJyokuXLhU+dkhNTaVnz5511Ku6oSgKzz//PJ9++ik7duygZcuW1R5z6dIlzpw5g6+vby30sH4oKSnh8OHD+Pr6qh9hXX/+lJaWsnPnznvq/FmzZg3NmjVjwIABVZa7F88XoEbnSZcuXXB0dCxX5sKFCxw8ePCuP5euJbIZGRls27YNT0/Pao85dOgQZrP5njuXfv89dC+fN1D2iVCXLl0IDg6utuzdcs5U97u6Xv+8uW2Xlgnlww8/VBwdHZWEhATl119/VV588UWlYcOGSmZmZl13rVY999xzioeHh5KWlqZcuHBB/SoqKlIURVEKCgqUl19+Wfn++++VkydPKv/617+U0NBQxc/PTzEajXXc+9vn5ZdfVtLS0pQTJ04ou3fvVh5//HHF3d1dPT8WLFigeHh4KJ9++qly4MABZcSIEYqvr+9dHZPrWa1W5f7771fi4uLKbb/XzpeCggJl3759yr59+xRAWbRokbJv3z71ivyanCcTJ05U/P39lW3btik///yz8uijjyrBwcGKxWKpq2HdElXFxmw2K4MGDVL8/f2V/fv3l/vZU1JSoiiKohw7dkyZM2eO8uOPPyonT55UkpOTlXbt2imdOnW6q2NT0++hu/G8qe77SVEUJT8/X3F1dVVWrlxZ4fi7+Zyp7ne1otTfnzeSzN5my5cvVwICAhQnJyelc+fO5W5Hda8AKv1as2aNoiiKUlRUpERFRSlNmzZVHB0dlfvvv1+JiYlRTp8+Xbcdv82GDRum+Pr6Ko6Ojkrz5s2VIUOGKIcOHVL322w25bXXXlN8fHwUZ2dnJTw8XDlw4EAd9rh2ff311wqgHDlypNz2e+18+de//lXp909MTIyiKDU7T4qLi5Xnn39eadKkidKgQQPl8ccfvyviVVVsTp48afdnz7/+9S9FURTl9OnTSnh4uNKkSRPFyclJadWqlRIbG6tcunSpbgd2C1QVm5p+D92N501130+KoiirVq1SGjRooFy5cqXC8XfzOVPd72pFqb8/bzS/DUAIIYQQQog7jqyZFUIIIYQQdyxJZoUQQgghxB1LklkhhBBCCHHHkmRWCCGEEELcsSSZFUIIIYQQdyxJZoUQQgghxB1LklkhhBBCCHHHkmRWCCGEEELcsSSZFUKIe5hGo2Hz5s113Q0hhLhpkswKIUQdGTNmDBqNpsJX375967prQghxx9DVdQeEEOJe1rdvX9asWVNum7Ozcx31Rggh7jwyMyuEEHXI2dkZHx+fcl+NGzcGypYArFy5kn79+tGgQQNatmxJUlJSueMPHDjAo48+SoMGDfD09OQvf/kLhYWF5cq8//77dOjQAWdnZ3x9fXn++efL7b948SJPPvkkrq6utGnThi+++OL2DloIIW4hSWaFEKIe+9vf/sZTTz3FL7/8wqhRoxgxYgSHDx8GoKioiL59+9K4cWN+/PFHkpKS2LZtW7lkdeXKlUyePJm//OUvHDhwgC+++ILWrVuXa2POnDk888wz/Oc//6F///6MHDmSy5cv1+o4hRDiZmkURVHquhNCCHEvGjNmDB988AEuLi7ltsfFxfG3v/0NjUbDxIkTWblypbqvR48edO7cmRUrVvDee+8RFxfHmTNnaNiwIQApKSkMHDiQ8+fP4+3tjZ+fH2PHjuX111+vtA8ajYZZs2bx97//HYCrV6/i7u5OSkqKrN0VQtwRZM2sEELUoYiIiHLJKkCTJk3U16GhoeX2hYaGsn//fgAOHz5McHCwmsgCPPzww9hsNo4cOYJGo+H8+fP86U9/qrIPDz30kPq6YcOGuLu7k5OTc7NDEkKIWiXJrBBC1KGGDRtW+Ni/OhqNBgBFUdTXlZVp0KBBjepzdHSscKzNZruhPgkhRF2RNbNCCFGP7d69u8L7du3aARAYGMj+/fu5evWquv+7777DwcGBtm3b4u7uTosWLdi+fXut9lkIIWqTzMwKIUQdKikpISsrq9w2nU6Hl5cXAElJSYSEhNCrVy82bNjADz/8QEJCAgAjR47ktddeIyYmhtmzZ5Obm8tf//pXRo8ejbe3NwCzZ89m4sSJNGvWjH79+lFQUMB3333HX//619odqBBC3CaSzAohRB3aunUrvr6+5bY9+OCD/Pe//wXK7jTw4YcfMmnSJHx8fNiwYQOBgYEAuLq68vXXX/PCCy/QtWtXXF1deeqpp1i0aJFaV0xMDCaTicWLFzN16lS8vLwYOnRo7Q1QCCFuM7mbgRBC1FMajYbPPvuMJ554oq67IoQQ9ZasmRVCCCGEEHcsSWaFEEIIIcQdS9bMCiFEPSWrwIQQonoyMyuEEEIIIe5YkswKIYQQQog7liSzQgghhBDijiXJrBBCCCGEuGNJMiuEEEIIIe5YkswKIYQQQog7liSzQgghhBDijiXJrBBCCCGEuGP9/6nYfqmcWkUVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Which layer representation provides the best performance on the secondary structure fine tuning task? ie which layer is most useful for structure?\n",
    "w = 13\n",
    "D = train_seq_embeddings[0][0].shape[1]\n",
    "I = w * D\n",
    "H = 40\n",
    "O = 3\n",
    "# Plot Train vs Test Accuracy\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "custom_colors = [\"#042328\",\"#09898c\",\"#8fc2b2\",\"#e0d9b7\",\"#e0d6a5\",\"#ecbf62\",\"#ed9903\",\"#c55211\",\"#b3371b\",\"#9b2227\"]\n",
    "\n",
    "final_test_acc = []\n",
    "final_train_acc = []\n",
    "\n",
    "for i in range(7):\n",
    "    X_train_esm, Y_train_esm = encode_embedding_dataset(train_seq_embeddings[i], sst3, w, return_torch=False)\n",
    "    X_test_esm, Y_test_esm   = encode_embedding_dataset(test_seq_embeddings[i], sst3_test, w, return_torch=False)\n",
    "\n",
    "    MLP_model = ProteinMLP(I, H, O)\n",
    "\n",
    "    model, esm_hist = train_model(MLP_model, X_train_esm, Y_train_esm, X_test_esm, Y_test_esm, lr=0.005, epochs=200, batch_size=None, print_every=100)\n",
    "    plt.plot(esm_hist[\"train_acc\"], label=f\"Train Accuracy layer {i}\", color = custom_colors[i], linestyle = \":\", alpha = 0.5)\n",
    "    plt.plot(esm_hist[\"test_acc\"], label=f\"Test Accuracy layer {i}\", color = custom_colors[i])\n",
    "    final_test_acc.append(esm_hist[\"test_acc\"][-1])\n",
    "    final_train_acc.append(esm_hist[\"train_acc\"][-1])\n",
    "\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim([0,1.0])\n",
    "plt.title(\"Secondary Structure Head Train/Test Accuracy\")\n",
    "plt.grid(True)\n",
    "plt.legend(fontsize=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843d9393",
   "metadata": {},
   "source": [
    "Using the final layer might not always give you the highest protein secondary structure accuracy. The final layer is optimised for the MLM pretraining objective (predicting masked tokens), which can pull representations back toward token-level predictions rather than structural features. The best representation layer might depend on the model and the downstream task."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143ae596",
   "metadata": {},
   "source": [
    "Does this perform better than the original MLP just because it has more parameters to train?\n",
    "\n",
    "The ESM-based MLP is more accurate — but it also has far more input dimensions (13 × 320 = 4160) than the baseline (13 × 20 = 260). More input dimensions → more parameters → potentially just more capacity, not better representations.\n",
    "\n",
    "We need to test: does a baseline MLP with the *same number of parameters* match ESM performance? If yes, pretraining doesn't help. If no, the representations carry real information beyond what parameter count explains."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5973418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10563\n"
     ]
    }
   ],
   "source": [
    "#TODO How many parameters does the original model have?\n",
    "w = 13\n",
    "I = w*20\n",
    "H = 40\n",
    "O = 3 # 3 classes [C, H, E]\n",
    "\n",
    "params = I * H + H + H * O + O\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47c0b9d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "166587\n"
     ]
    }
   ],
   "source": [
    "#TODO Find a configuration so that the number of parameters is similar between the 2 models\n",
    "w = 13\n",
    "I = w*20\n",
    "H = 631\n",
    "O = 3 # 3 classes [C, H, E]\n",
    "\n",
    "params = I * H + H + H * O + O\n",
    "print(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "3e871625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200 (full-batch) Loss: 1.1071 Train Acc: 0.257 Test Acc: 0.241 LR: 0.000025\n",
      "Epoch 100/200 (full-batch) Loss: 0.7694 Train Acc: 0.667 Test Acc: 0.625 LR: 0.002500\n",
      "Epoch 200/200 (full-batch) Loss: 0.4771 Train Acc: 0.825 Test Acc: 0.614 LR: 0.005000\n",
      "Number of trainable parameters: 166587\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MLP_model = ProteinMLP(I, H, O)\n",
    "\n",
    "model, hist = train_model(MLP_model, X_train, Y_train, X_test, Y_test, lr=0.005, epochs=200, batch_size=None, print_every=100)\n",
    "print(f\"Number of trainable parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")\n",
    "\n",
    "# Plot Train vs Test Accuracy\n",
    "plt.figure()\n",
    "plt.plot(esm_hist[\"train_acc\"], label=\"ESM MLP head Train Accuracy\")\n",
    "plt.plot(esm_hist[\"test_acc\"], label=\"ESM MLP head Test Accuracy\")\n",
    "plt.plot(hist[\"train_acc\"], label=\"MLP Train Accuracy\")\n",
    "plt.plot(hist[\"test_acc\"], label=\"MLP Test Accuracy\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.ylim([0,1.0])\n",
    "plt.title(\"Train/Test Accuracy Comparison\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46da2775",
   "metadata": {},
   "source": [
    "The ESM MLP (even with a similar number of model parameters) performs ~10% better on the test set than the one-hot encoded MLP. This suggests that the ESM embeddings contain some additional information that helps with the task."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb99730",
   "metadata": {},
   "source": [
    "## Switching from encoders to decoders\n",
    "\n",
    "Encoders read the whole sequence and return a representation.\n",
    "Decoders work differently: they predict the next token from the tokens that came before.\n",
    "\n",
    "That makes decoder-only models natural for generation, sampling, and sequence design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "4c86f3e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, d_model: int, max_len: int = 2048):\n",
    "        super().__init__()\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0, max_len).unsqueeze(1)\n",
    "        div_term = torch.exp(\n",
    "            torch.arange(0, d_model, 2) * (-np.log(10000.0) / d_model)\n",
    "        )\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "        pe = pe.unsqueeze(0)  # [1, max_len, d_model]\n",
    "        self.register_buffer(\"pe\", pe)\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        # x: [B, L, D]\n",
    "        L = x.size(1)\n",
    "        return x + self.pe[:, :L]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "e360007b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decoder class init\n",
    "abc_size = 4\n",
    "d_model = 256\n",
    "max_len = 32\n",
    "K=2\n",
    "n_heads=8\n",
    "d_diff=512\n",
    "\n",
    "token_emb = nn.Embedding(abc_size, d_model)\n",
    "pos_enc   = PositionalEncoding(d_model, max_len=max_len)\n",
    "layers = nn.ModuleList([\n",
    "            TransformerBlock(d_model=d_model, num_heads=n_heads, d_ff=d_diff)\n",
    "            for _ in range(K)\n",
    "        ])\n",
    "ln = nn.LayerNorm(d_model)\n",
    "ff = nn.Linear(d_model, abc_size, bias=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "6927c48c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if i <= j: mask[ij] = -infy,  attn_score + mask = -inf      , attn = 0\n",
    "# if i >  j: mask[ij] = 0    ,  attn_score + mask = attn_score, attn unchanged\n",
    "#\n",
    "def causal_mask(seq_len: int, device):\n",
    "    # upper triangular (1 above diag) then convert mask to value -inf\n",
    "    mask = torch.triu(torch.ones(seq_len, seq_len, device=device), diagonal=1)\n",
    "    mask = mask.masked_fill(mask == 1, float(\"-inf\"))\n",
    "\n",
    "    print(\"mask[0,1] = \", mask[0,1])\n",
    "    print(\"mask[1,0] = \", mask[1,0])\n",
    "    return mask  # [L, L]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "ea6a90b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inputs\n",
      "B L maxL  1 5 32\n",
      "inputs embedded  torch.Size([1, 5, 256])\n",
      "inputs embedded + pos encoding torch.Size([1, 5, 256])\n",
      "mask[0,1] =  tensor(-inf)\n",
      "mask[1,0] =  tensor(0.)\n"
     ]
    }
   ],
   "source": [
    "# Decoder forward pass\n",
    "inputs = torch.tensor([[0, 3, 3, 1, 2]])\n",
    "B, L = inputs.shape\n",
    "print(\"inputs\\nB L maxL \", B, L, max_len)\n",
    "device = inputs.device\n",
    "\n",
    "# inputs embedding\n",
    "x = token_emb(inputs) * np.sqrt(d_model)  # [B, L, D]\n",
    "print(\"inputs embedded \", x.shape)\n",
    "x = pos_enc(x)\n",
    "print(\"inputs embedded + pos encoding\", x.shape)\n",
    "\n",
    "attn_mask = causal_mask(L, device=device)\n",
    "\n",
    "for layer in layers:\n",
    "    x = layer(x, mask=attn_mask)\n",
    "\n",
    "x = ln(x)\n",
    "logits = ff(x)  # [B, L, V=abc_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "670800d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next token: torch.Size([1]) 0\n",
      "Extended: [[0, 3, 3, 1, 2, 0]]\n"
     ]
    }
   ],
   "source": [
    "# get next token\n",
    "last_logits = logits[:, -1, :]            # [B, V]\n",
    "next_token = last_logits.argmax(dim=-1)   # [B]\n",
    "print(\"next token:\", next_token.shape, next_token.item())\n",
    "\n",
    "extended = torch.cat([inputs, next_token.unsqueeze(1)], dim=1)\n",
    "print(\"Extended:\", extended.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab97e37c",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Decoder-only model: ProGen2\n",
    "\n",
    "A decoder-only model solves a different problem. It does not primarily give us one embedding for the whole sequence. Instead, it learns to predict the **next token** from the tokens that came before.\n",
    "\n",
    "That makes decoder models natural for:\n",
    "\n",
    "- sequence continuation,\n",
    "- design-style sampling,\n",
    "- autoregressive likelihoods,\n",
    "- and \"what would the model write next?\" experiments.\n",
    "\n",
    "Using ubiquitin is helpful here because we already know what the true continuation should look like. That gives us a simple teaching question: if we feed the first part of ubiquitin into a decoder, what kind of continuation does it generate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "743a3d81",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mkdir: progen2-checkpoints: File exists\n",
      "--2026-03-31 15:30:39--  https://storage.googleapis.com/sfr-progen-research/checkpoints/progen2-small.tar.gz\n",
      "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.188.27, 142.251.211.91, 142.250.65.251, ...\n",
      "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.188.27|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 561673660 (536M) [application/x-tar]\n",
      "Saving to: ‘progen2-checkpoints/progen2-small/progen2-small.tar.gz.1’\n",
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      " 10200K .......... .......... .......... .......... ..........  1% 9.98M 76s\n",
      " 10250K .......... .......... .......... .......... ..........  1% 20.6M 76s\n",
      " 10300K .......... .......... .......... .......... ..........  1% 21.4M 76s\n",
      " 10350K .......... .......... .......... .......... ..........  1% 1.14M 77s\n",
      " 10400K .......... .......... .......... .......... ..........  1% 14.4M 77s\n",
      " 10450K .......... .......... .......... .......... ..........  1% 9.29M 77s\n",
      " 10500K .......... .......... .......... .......... ..........  1% 73.5M 77s\n",
      " 10550K .......... .......... .......... .......... ..........  1% 22.5M 77s\n",
      " 10600K .......... .......... .......... .......... ..........  1% 7.10M 77s\n",
      " 10650K .......... .......... .......... .......... ..........  1% 5.40M 77s\n",
      " 10700K .......... .......... .......... .......... ..........  1%  126M 76s\n",
      " 10750K .......... .......... .......... .......... ..........  1% 47.5M 76s\n",
      " 10800K .......... .......... .......... .......... ..........  1% 11.1M 76s\n",
      " 10850K .......... .......... .......... .......... ..........  1% 9.46M 76s\n",
      " 10900K .......... .......... .......... .......... ..........  1%  164M 75s\n",
      " 10950K .......... .......... .......... .......... ..........  2% 9.35M 75s\n",
      " 11000K .......... .......... .......... .......... ..........  2% 9.49M 75s\n",
      " 11050K .......... .......... .......... .......... ..........  2% 13.3M 75s\n",
      " 11100K .......... .......... .......... .......... ..........  2% 23.4M 75s\n",
      " 11150K .......... .......... .......... .......... ..........  2% 14.6M 75s\n",
      " 11200K .......... .......... .......... .......... ..........  2% 25.1M 74s\n",
      " 11250K .......... .......... .......... .......... ..........  2% 8.64M 74s\n",
      " 11300K .......... .......... .......... .......... ..........  2% 17.2M 74s\n",
      " 11350K .......... .......... .......... .......... ..........  2% 6.51M 74s\n",
      " 11400K .......... .......... .......... .......... ..........  2% 9.79M 74s\n",
      " 11450K .......... .......... .......... .......... ..........  2%  211M 74s\n",
      " 11500K .......... .......... .......... .......... ..........  2% 16.9M 74s\n",
      " 11550K .......... .......... .......... .......... ..........  2% 10.3M 73s\n",
      " 11600K .......... .......... .......... .......... ..........  2% 14.3M 73s\n",
      " 11650K .......... .......... .......... .......... ..........  2% 7.41M 73s\n",
      " 11700K .......... .......... .......... .......... ..........  2% 19.1M 73s\n",
      " 11750K .......... .......... .......... .......... ..........  2% 18.2M 73s\n",
      " 11800K .......... .......... .......... .......... ..........  2% 31.1M 73s\n",
      " 11850K .......... .......... .......... .......... ..........  2% 13.5M 72s\n",
      " 11900K .......... .......... .......... .......... ..........  2% 16.1M 72s\n",
      " 11950K .......... .......... .......... .......... ..........  2% 7.29M 72s\n",
      " 12000K .......... .......... .......... .......... ..........  2% 26.2M 72s\n",
      " 12050K .......... .......... .......... .......... ..........  2% 11.2M 72s\n",
      " 12100K .......... .......... .......... .......... ..........  2% 10.2M 72s\n",
      " 12150K .......... .......... .......... .......... ..........  2% 10.9M 72s\n",
      " 12200K .......... .......... .......... .......... ..........  2% 17.8M 72s\n",
      " 12250K .......... .......... .......... .......... ..........  2% 11.3M 71s\n",
      " 12300K .......... .......... .......... .......... ..........  2% 14.4M 71s\n",
      " 12350K .......... .......... .......... .......... ..........  2% 11.3M 71s\n",
      " 12400K .......... .......... .......... .......... ..........  2% 55.4M 71s\n",
      " 12450K .......... .......... .......... .......... ..........  2% 5.51M 71s\n",
      " 12500K .......... .......... .......... .......... ..........  2% 39.1M 71s\n",
      " 12550K .......... .......... .......... .......... ..........  2% 1.40M 72s\n",
      " 12600K .......... .......... .......... .......... ..........  2% 45.9M 72s\n",
      " 12650K .......... .......... .......... .......... ..........  2% 10.2M 72s\n",
      " 12700K .......... .......... .......... .......... ..........  2% 10.9M 72s\n",
      " 12750K .......... .......... .......... .......... ..........  2% 13.4M 71s\n",
      " 12800K .......... .......... .......... .......... ..........  2% 16.5M 71s\n",
      " 12850K .......... .......... .......... .......... ..........  2% 18.9M 71s\n",
      " 12900K .......... .......... .......... .......... ..........  2% 10.3M 71s\n",
      " 12950K .......... .......... .......... .......... ..........  2% 6.24M 71s\n",
      " 13000K .......... .......... .......... .......... ..........  2%  603M 71s\n",
      " 13050K .......... .......... .......... .......... ..........  2% 10.2M 71s\n",
      " 13100K .......... .......... .......... .......... ..........  2%  568M 70s\n",
      " 13150K .......... .......... .......... .......... ..........  2% 4.75M 71s\n",
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      " 13300K .......... .......... .......... .......... ..........  2% 11.2M 70s\n",
      " 13350K .......... .......... .......... .......... ..........  2% 15.1M 70s\n",
      " 13400K .......... .......... .......... .......... ..........  2% 17.1M 70s\n",
      " 13450K .......... .......... .......... .......... ..........  2% 47.7M 70s\n",
      " 13500K .......... .......... .......... .......... ..........  2% 4.64M 70s\n",
      " 13550K .......... .......... .......... .......... ..........  2% 3.97M 70s\n",
      " 13600K .......... .......... .......... .......... ..........  2% 45.0M 70s\n",
      " 13650K .......... .......... .......... .......... ..........  2% 8.51M 70s\n",
      " 13700K .......... .......... .......... .......... ..........  2% 37.8M 70s\n",
      " 13750K .......... .......... .......... .......... ..........  2% 7.67M 70s\n",
      " 13800K .......... .......... .......... .......... ..........  2% 10.6M 69s\n",
      " 13850K .......... .......... .......... .......... ..........  2% 4.64M 70s\n",
      " 13900K .......... .......... .......... .......... ..........  2% 10.3M 70s\n",
      " 13950K .......... .......... .......... .......... ..........  2% 10.5M 69s\n",
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      " 57950K .......... .......... .......... .......... .......... 10% 17.9M 55s\n",
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      " 58050K .......... .......... .......... .......... .......... 10% 23.1M 54s\n",
      " 58100K .......... .......... .......... .......... .......... 10% 15.8M 54s\n",
      " 58150K .......... .......... .......... .......... .......... 10% 10.2M 54s\n",
      " 58200K .......... .......... .......... .......... .......... 10% 21.1M 54s\n",
      " 58250K .......... .......... .......... .......... .......... 10% 16.5M 54s\n",
      " 58300K .......... .......... .......... .......... .......... 10%  527K 55s\n",
      " 58350K .......... .......... .......... .......... .......... 10% 10.3M 55s\n",
      " 58400K .......... .......... .......... .......... .......... 10% 52.7M 55s\n",
      " 58450K .......... .......... .......... .......... .......... 10% 13.0M 55s\n",
      " 58500K .......... .......... .......... .......... .......... 10% 18.9M 55s\n",
      " 58550K .......... .......... .......... .......... .......... 10% 4.96M 55s\n",
      " 58600K .......... .......... .......... .......... .......... 10% 15.4M 55s\n",
      " 58650K .......... .......... .......... .......... .......... 10% 5.88M 55s\n",
      " 58700K .......... .......... .......... .......... .......... 10% 31.0M 55s\n",
      " 58750K .......... .......... .......... .......... .......... 10% 32.9M 55s\n",
      " 58800K .......... .......... .......... .......... .......... 10% 9.66M 55s\n",
      " 58850K .......... .......... .......... .......... .......... 10% 15.0M 55s\n",
      " 58900K .......... .......... .......... .......... .......... 10% 12.0M 55s\n",
      " 58950K .......... .......... .......... .......... .......... 10% 20.4M 55s\n",
      " 59000K .......... .......... .......... .......... .......... 10% 25.9M 55s\n",
      " 59050K .......... .......... .......... .......... .......... 10% 14.4M 55s\n",
      " 59100K .......... .......... .......... .......... .......... 10% 21.5M 55s\n",
      " 59150K .......... .......... .......... .......... .......... 10% 6.68M 55s\n",
      " 59200K .......... .......... .......... .......... .......... 10% 12.9M 55s\n",
      " 59250K .......... .......... .......... .......... .......... 10% 19.5M 55s\n",
      " 59300K .......... .......... .......... .......... .......... 10% 15.6M 55s\n",
      " 59350K .......... .......... .......... .......... .......... 10% 12.1M 55s\n",
      " 59400K .......... .......... .......... .......... .......... 10% 22.3M 55s\n",
      " 59450K .......... .......... .......... .......... .......... 10% 12.7M 55s\n",
      " 59500K .......... .......... .......... .......... .......... 10% 18.6M 55s\n",
      " 59550K .......... .......... .......... .......... .......... 10% 7.06M 55s\n",
      " 59600K .......... .......... .......... .......... .......... 10% 24.3M 55s\n",
      " 59650K .......... .......... .......... .......... .......... 10% 13.4M 55s\n",
      " 59700K .......... .......... .......... .......... .......... 10% 17.2M 55s\n",
      " 59750K .......... .......... .......... .......... .......... 10% 5.49M 55s\n",
      " 59800K .......... .......... .......... .......... .......... 10% 1.76M 55s\n",
      " 59850K .......... .......... .......... .......... .......... 10% 23.6M 55s\n",
      " 59900K .......... .......... .......... .......... .......... 10% 7.41M 55s\n",
      " 59950K .......... .......... .......... .......... .......... 10% 10.4M 55s\n",
      " 60000K .......... .......... .......... .......... .......... 10% 22.5M 55s\n",
      " 60050K .......... .......... .......... .......... .......... 10% 9.89M 55s\n",
      " 60100K .......... .......... .......... .......... .......... 10% 10.1M 55s\n",
      " 60150K .......... .......... .......... .......... .......... 10% 14.6M 55s\n",
      " 60200K .......... .......... .......... .......... .......... 10% 93.4M 55s\n",
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      " 60300K .......... .......... .......... .......... .......... 11% 16.5M 55s\n",
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      " 60400K .......... .......... .......... .......... .......... 11% 60.3M 54s\n",
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      " 60600K .......... .......... .......... .......... .......... 11%  110M 54s\n",
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      " 60700K .......... .......... .......... .......... .......... 11% 47.9M 54s\n",
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      " 60850K .......... .......... .......... .......... .......... 11% 4.01M 54s\n",
      " 60900K .......... .......... .......... .......... .......... 11% 17.3M 54s\n",
      " 60950K .......... .......... .......... .......... .......... 11%  265M 54s\n",
      " 61000K .......... .......... .......... .......... .......... 11% 8.33M 54s\n",
      " 61050K .......... .......... .......... .......... .......... 11% 20.4M 54s\n",
      " 61100K .......... .......... .......... .......... .......... 11% 9.90M 54s\n",
      " 61150K .......... .......... .......... .......... .......... 11% 13.6M 54s\n",
      " 61200K .......... .......... .......... .......... .......... 11% 15.1M 54s\n",
      " 61250K .......... .......... .......... .......... .......... 11% 30.5M 54s\n",
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      " 61350K .......... .......... .......... .......... .......... 11%  173M 54s\n",
      " 61400K .......... .......... .......... .......... .......... 11% 7.32M 54s\n",
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      " 61500K .......... .......... .......... .......... .......... 11% 8.43M 54s\n",
      " 61550K .......... .......... .......... .......... .......... 11% 10.6M 54s\n",
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      " 61650K .......... .......... .......... .......... .......... 11% 76.4M 54s\n",
      " 61700K .......... .......... .......... .......... .......... 11% 11.9M 54s\n",
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      " 61850K .......... .......... .......... .......... .......... 11% 8.72M 54s\n",
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      " 62050K .......... .......... .......... .......... .......... 11% 4.91M 54s\n",
      " 62100K .......... .......... .......... .......... .......... 11% 1.25M 54s\n",
      " 62150K .......... .......... .......... .......... .......... 11% 46.7M 54s\n",
      " 62200K .......... .......... .......... .......... .......... 11% 14.7M 54s\n",
      " 62250K .......... .......... .......... .......... .......... 11% 10.7M 54s\n",
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      " 70100K .......... .......... .......... .......... .......... 12% 8.11M 53s\n",
      " 70150K .......... .......... .......... .......... .......... 12% 44.7M 53s\n",
      " 70200K .......... .......... .......... .......... .......... 12% 6.66M 53s\n",
      " 70250K .......... .......... .......... .......... .......... 12% 6.01M 53s\n",
      " 70300K .......... .......... .......... .......... .......... 12% 21.0M 53s\n",
      " 70350K .......... .......... .......... .......... .......... 12% 17.5M 53s\n",
      " 70400K .......... .......... .......... .......... .......... 12% 9.04M 53s\n",
      " 70450K .......... .......... .......... .......... .......... 12% 20.0M 53s\n",
      " 70500K .......... .......... .......... .......... .......... 12% 40.0M 53s\n",
      " 70550K .......... .......... .......... .......... .......... 12% 8.89M 53s\n",
      " 70600K .......... .......... .......... .......... .......... 12% 6.96M 53s\n",
      " 70650K .......... .......... .......... .......... .......... 12% 1.46M 53s\n",
      " 70700K .......... .......... .......... .......... .......... 12% 8.88M 53s\n",
      " 70750K .......... .......... .......... .......... .......... 12%  292M 53s\n",
      " 70800K .......... .......... .......... .......... .......... 12% 10.3M 53s\n",
      " 70850K .......... .......... .......... .......... .......... 12% 13.1M 53s\n",
      " 70900K .......... .......... .......... .......... .......... 12% 16.4M 53s\n",
      " 70950K .......... .......... .......... .......... .......... 12% 5.77M 53s\n",
      " 71000K .......... .......... .......... .......... .......... 12% 7.07M 53s\n",
      " 71050K .......... .......... .......... .......... .......... 12% 6.46M 53s\n",
      " 71100K .......... .......... .......... .......... .......... 12% 52.2M 53s\n",
      " 71150K .......... .......... .......... .......... .......... 12% 27.1M 53s\n",
      " 71200K .......... .......... .......... .......... .......... 12% 15.6M 53s\n",
      " 71250K .......... .......... .......... .......... .......... 12% 69.6M 53s\n",
      " 71300K .......... .......... .......... .......... .......... 13% 4.43M 53s\n",
      " 71350K .......... .......... .......... .......... .......... 13%  190M 53s\n",
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      " 71500K .......... .......... .......... .......... .......... 13% 13.1M 53s\n",
      " 71550K .......... .......... .......... .......... .......... 13% 15.3M 53s\n",
      " 71600K .......... .......... .......... .......... .......... 13% 20.1M 53s\n",
      " 71650K .......... .......... .......... .......... .......... 13% 10.5M 53s\n",
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      " 71850K .......... .......... .......... .......... .......... 13% 15.5M 53s\n",
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      " 72000K .......... .......... .......... .......... .......... 13% 31.0M 53s\n",
      " 72050K .......... .......... .......... .......... .......... 13% 3.70M 53s\n",
      " 72100K .......... .......... .......... .......... .......... 13% 21.6M 53s\n",
      " 72150K .......... .......... .......... .......... .......... 13% 9.75M 53s\n",
      " 72200K .......... .......... .......... .......... .......... 13% 10.1M 53s\n",
      " 72250K .......... .......... .......... .......... .......... 13% 14.5M 52s\n",
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      " 82200K .......... .......... .......... .......... .......... 14% 15.9M 52s\n",
      " 82250K .......... .......... .......... .......... .......... 15% 45.1M 52s\n",
      " 82300K .......... .......... .......... .......... .......... 15% 8.89M 52s\n",
      " 82350K .......... .......... .......... .......... .......... 15% 19.6M 52s\n",
      " 82400K .......... .......... .......... .......... .......... 15% 21.0M 52s\n",
      " 82450K .......... .......... .......... .......... .......... 15% 10.7M 52s\n",
      " 82500K .......... .......... .......... .......... .......... 15% 8.22M 52s\n",
      " 82550K .......... .......... .......... .......... .......... 15% 20.2M 51s\n",
      " 82600K .......... .......... .......... .......... .......... 15% 8.01M 51s\n",
      " 82650K .......... .......... .......... .......... .......... 15% 37.4M 51s\n",
      " 82700K .......... .......... .......... .......... .......... 15% 12.5M 51s\n",
      " 82750K .......... .......... .......... .......... .......... 15% 4.13M 51s\n",
      " 82800K .......... .......... .......... .......... .......... 15%  444M 51s\n",
      " 82850K .......... .......... .......... .......... .......... 15% 5.54M 51s\n",
      " 82900K .......... .......... .......... .......... .......... 15%  214M 51s\n",
      " 82950K .......... .......... .......... .......... .......... 15% 1.64M 52s\n",
      " 83000K .......... .......... .......... .......... .......... 15% 10.1M 52s\n",
      " 83050K .......... .......... .......... .......... .......... 15% 20.0M 52s\n",
      " 83100K .......... .......... .......... .......... .......... 15% 18.6M 51s\n",
      " 83150K .......... .......... .......... .......... .......... 15% 8.89M 51s\n",
      " 83200K .......... .......... .......... .......... .......... 15% 15.6M 51s\n",
      " 83250K .......... .......... .......... .......... .......... 15% 10.2M 51s\n",
      " 83300K .......... .......... .......... .......... .......... 15% 19.4M 51s\n",
      " 83350K .......... .......... .......... .......... .......... 15% 15.8M 51s\n",
      " 83400K .......... .......... .......... .......... .......... 15% 11.2M 51s\n",
      " 83450K .......... .......... .......... .......... .......... 15% 43.4M 51s\n",
      " 83500K .......... .......... .......... .......... .......... 15% 7.71M 51s\n",
      " 83550K .......... .......... .......... .......... .......... 15% 13.0M 51s\n",
      " 83600K .......... .......... .......... .......... .......... 15% 14.1M 51s\n",
      " 83650K .......... .......... .......... .......... .......... 15% 40.9M 51s\n",
      " 83700K .......... .......... .......... .......... .......... 15% 5.23M 51s\n",
      " 83750K .......... .......... .......... .......... .......... 15% 11.8M 51s\n",
      " 83800K .......... .......... .......... .......... .......... 15% 25.5M 51s\n",
      " 83850K .......... .......... .......... .......... .......... 15% 11.3M 51s\n",
      " 83900K .......... .......... .......... .......... .......... 15% 25.1M 51s\n",
      " 83950K .......... .......... .......... .......... .......... 15% 6.68M 51s\n",
      " 84000K .......... .......... .......... .......... .......... 15% 34.1M 51s\n",
      " 84050K .......... .......... .......... .......... .......... 15% 9.77M 51s\n",
      " 84100K .......... .......... .......... .......... .......... 15% 13.8M 51s\n",
      " 84150K .......... .......... .......... .......... .......... 15% 12.6M 51s\n",
      " 84200K .......... .......... .......... .......... .......... 15% 20.3M 51s\n",
      " 84250K .......... .......... .......... .......... .......... 15% 17.6M 51s\n",
      " 84300K .......... .......... .......... .......... .......... 15% 15.5M 51s\n",
      " 84350K .......... .......... .......... .......... .......... 15% 5.01M 51s\n",
      " 84400K .......... .......... .......... .......... .......... 15% 13.1M 51s\n",
      " 84450K .......... .......... .......... .......... .......... 15% 6.82M 51s\n",
      " 84500K .......... .......... .......... .......... .......... 15% 18.4M 51s\n",
      " 84550K .......... .......... .......... .......... .......... 15% 31.4M 51s\n",
      " 84600K .......... .......... .......... .......... .......... 15% 8.35M 51s\n",
      " 84650K .......... .......... .......... .......... .......... 15% 20.8M 51s\n",
      " 84700K .......... .......... .......... .......... .......... 15% 6.43M 51s\n",
      " 84750K .......... .......... .......... .......... .......... 15% 9.01M 51s\n",
      " 84800K .......... .......... .......... .......... .......... 15% 15.4M 51s\n",
      " 84850K .......... .......... .......... .......... .......... 15% 1.69M 51s\n",
      " 84900K .......... .......... .......... .......... .......... 15% 8.25M 51s\n",
      " 84950K .......... .......... .......... .......... .......... 15% 82.1M 51s\n",
      " 85000K .......... .......... .......... .......... .......... 15% 13.7M 51s\n",
      " 85050K .......... .......... .......... .......... .......... 15% 17.4M 51s\n",
      " 85100K .......... .......... .......... .......... .......... 15% 23.8M 51s\n",
      " 85150K .......... .......... .......... .......... .......... 15% 4.84M 51s\n",
      " 85200K .......... .......... .......... .......... .......... 15% 75.4M 51s\n",
      " 85250K .......... .......... .......... .......... .......... 15% 12.4M 51s\n",
      " 85300K .......... .......... .......... .......... .......... 15% 8.81M 51s\n",
      " 85350K .......... .......... .......... .......... .......... 15% 59.5M 51s\n",
      " 85400K .......... .......... .......... .......... .......... 15% 8.03M 51s\n",
      " 85450K .......... .......... .......... .......... .......... 15% 15.8M 51s\n",
      " 85500K .......... .......... .......... .......... .......... 15% 9.26M 51s\n",
      " 85550K .......... .......... .......... .......... .......... 15% 72.0M 51s\n",
      " 85600K .......... .......... .......... .......... .......... 15% 7.63M 51s\n",
      " 85650K .......... .......... .......... .......... .......... 15% 20.8M 51s\n",
      " 85700K .......... .......... .......... .......... .......... 15% 8.73M 51s\n",
      " 85750K .......... .......... .......... .......... .......... 15% 17.2M 51s\n",
      " 85800K .......... .......... .......... .......... .......... 15% 9.65M 51s\n",
      " 85850K .......... .......... .......... .......... .......... 15% 28.5M 51s\n",
      " 85900K .......... .......... .......... .......... .......... 15% 5.95M 51s\n",
      " 85950K .......... .......... .......... .......... .......... 15% 7.74M 51s\n",
      " 86000K .......... .......... .......... .......... .......... 15% 9.64M 51s\n",
      " 86050K .......... .......... .......... .......... .......... 15%  493M 51s\n",
      " 86100K .......... .......... .......... .......... .......... 15% 8.73M 51s\n",
      " 86150K .......... .......... .......... .......... .......... 15%  465M 51s\n",
      " 86200K .......... .......... .......... .......... .......... 15% 11.0M 51s\n",
      " 86250K .......... .......... .......... .......... .......... 15% 3.62M 51s\n",
      " 86300K .......... .......... .......... .......... .......... 15%  110M 51s\n",
      " 86350K .......... .......... .......... .......... .......... 15% 7.20M 51s\n",
      " 86400K .......... .......... .......... .......... .......... 15% 29.5M 51s\n",
      " 86450K .......... .......... .......... .......... .......... 15% 10.3M 51s\n",
      " 86500K .......... .......... .......... .......... .......... 15% 15.7M 51s\n",
      " 86550K .......... .......... .......... .......... .......... 15% 9.71M 51s\n",
      " 86600K .......... .......... .......... .......... .......... 15% 9.62M 51s\n",
      " 86650K .......... .......... .......... .......... .......... 15%  152M 51s\n",
      " 86700K .......... .......... .......... .......... .......... 15% 10.7M 51s\n",
      " 86750K .......... .......... .......... .......... .......... 15%  183M 51s\n",
      " 86800K .......... .......... .......... .......... .......... 15% 8.68M 51s\n",
      " 86850K .......... .......... .......... .......... .......... 15% 24.2M 51s\n",
      " 86900K .......... .......... .......... .......... .......... 15% 10.5M 51s\n",
      " 86950K .......... .......... .......... .......... .......... 15% 16.5M 51s\n",
      " 87000K .......... .......... .......... .......... .......... 15% 7.04M 51s\n",
      " 87050K .......... .......... .......... .......... .......... 15% 19.0M 51s\n",
      " 87100K .......... .......... .......... .......... .......... 15% 10.5M 51s\n",
      " 87150K .......... .......... .......... .......... .......... 15% 4.39M 51s\n",
      " 87200K .......... .......... .......... .......... .......... 15%  132M 51s\n",
      " 87250K .......... .......... .......... .......... .......... 15% 20.9M 51s\n",
      " 87300K .......... .......... .......... .......... .......... 15% 7.06M 51s\n",
      " 87350K .......... .......... .......... .......... .......... 15% 70.4M 50s\n",
      " 87400K .......... .......... .......... .......... .......... 15% 7.20M 50s\n",
      " 87450K .......... .......... .......... .......... .......... 15% 19.6M 50s\n",
      " 87500K .......... .......... .......... .......... .......... 15% 23.4M 50s\n",
      " 87550K .......... .......... .......... .......... .......... 15% 1.75M 51s\n",
      " 87600K .......... .......... .......... .......... .......... 15% 9.93M 51s\n",
      " 87650K .......... .......... .......... .......... .......... 15%  511K 51s\n",
      " 87700K .......... .......... .......... .......... .......... 15%  488M 51s\n",
      " 87750K .......... .......... .......... .......... .......... 16% 8.65M 51s\n",
      " 87800K .......... .......... .......... .......... .......... 16%  136M 51s\n",
      " 87850K .......... .......... .......... .......... .......... 16% 7.62M 51s\n",
      " 87900K .......... .......... .......... .......... .......... 16% 11.0M 51s\n",
      " 87950K .......... .......... .......... .......... .......... 16%  364M 51s\n",
      " 88000K .......... .......... .......... .......... .......... 16% 5.10M 51s\n",
      " 88050K .......... .......... .......... .......... .......... 16% 10.6M 51s\n",
      " 88100K .......... .......... .......... .......... .......... 16%  173M 51s\n",
      " 88150K .......... .......... .......... .......... .......... 16% 8.66M 51s\n",
      " 88200K .......... .......... .......... .......... .......... 16% 8.26M 51s\n",
      " 88250K .......... .......... .......... .......... .......... 16% 83.9M 51s\n",
      " 88300K .......... .......... .......... .......... .......... 16% 7.58M 51s\n",
      " 88350K .......... .......... .......... .......... .......... 16% 15.5M 51s\n",
      " 88400K .......... .......... .......... .......... .......... 16% 9.64M 51s\n",
      " 88450K .......... .......... .......... .......... .......... 16% 4.84M 51s\n",
      " 88500K .......... .......... .......... .......... .......... 16% 15.1M 51s\n",
      " 88550K .......... .......... .......... .......... .......... 16% 9.14M 51s\n",
      " 88600K .......... .......... .......... .......... .......... 16% 9.26M 51s\n",
      " 88650K .......... .......... .......... .......... .......... 16% 9.50M 51s\n",
      " 88700K .......... .......... .......... .......... .......... 16%  543M 51s\n",
      " 88750K .......... .......... .......... .......... .......... 16% 46.2M 51s\n",
      " 88800K .......... .......... .......... .......... .......... 16% 12.3M 51s\n",
      " 88850K .......... .......... .......... .......... .......... 16% 11.9M 51s\n",
      " 88900K .......... .......... .......... .......... .......... 16% 16.7M 51s\n",
      " 88950K .......... .......... .......... .......... .......... 16% 8.93M 51s\n",
      " 89000K .......... .......... .......... .......... .......... 16% 8.59M 51s\n",
      " 89050K .......... .......... .......... .......... .......... 16% 64.3M 51s\n",
      " 89100K .......... .......... .......... .......... .......... 16% 8.96M 51s\n",
      " 89150K .......... .......... .......... .......... .......... 16% 11.8M 51s\n",
      " 89200K .......... .......... .......... .......... .......... 16% 10.4M 51s\n",
      " 89250K .......... .......... .......... .......... .......... 16% 5.86M 51s\n",
      " 89300K .......... .......... .......... .......... .......... 16% 16.9M 51s\n",
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      " 89400K .......... .......... .......... .......... .......... 16% 1.20M 51s\n",
      " 89450K .......... .......... .......... .......... .......... 16% 6.93M 51s\n",
      " 89500K .......... .......... .......... .......... .......... 16% 19.8M 51s\n",
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      " 89600K .......... .......... .......... .......... .......... 16% 11.0M 51s\n",
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      " 89700K .......... .......... .......... .......... .......... 16%  212M 51s\n",
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      " 89850K .......... .......... .......... .......... .......... 16% 17.2M 51s\n",
      " 89900K .......... .......... .......... .......... .......... 16% 7.04M 51s\n",
      " 89950K .......... .......... .......... .......... .......... 16% 7.25M 51s\n",
      " 90000K .......... .......... .......... .......... .......... 16% 9.57M 51s\n",
      " 90050K .......... .......... .......... .......... .......... 16% 69.4M 51s\n",
      " 90100K .......... .......... .......... .......... .......... 16% 8.99M 51s\n",
      " 90150K .......... .......... .......... .......... .......... 16% 12.2M 51s\n",
      " 90200K .......... .......... .......... .......... .......... 16%  102M 51s\n",
      " 90250K .......... .......... .......... .......... .......... 16% 2.25M 51s\n",
      " 90300K .......... .......... .......... .......... .......... 16% 6.68M 51s\n",
      " 90350K .......... .......... .......... .......... .......... 16% 3.38M 51s\n",
      " 90400K .......... .......... .......... .......... .......... 16% 7.14M 51s\n",
      " 90450K .......... .......... .......... .......... .......... 16% 13.9M 51s\n",
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      " 90550K .......... .......... .......... .......... .......... 16% 19.1M 51s\n",
      " 90600K .......... .......... .......... .......... .......... 16% 8.60M 51s\n",
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      " 90750K .......... .......... .......... .......... .......... 16% 19.2M 51s\n",
      " 90800K .......... .......... .......... .......... .......... 16% 10.5M 51s\n",
      " 90850K .......... .......... .......... .......... .......... 16% 16.3M 51s\n",
      " 90900K .......... .......... .......... .......... .......... 16% 7.88M 51s\n",
      " 90950K .......... .......... .......... .......... .......... 16% 19.9M 51s\n",
      " 91000K .......... .......... .......... .......... .......... 16% 10.5M 51s\n",
      " 91050K .......... .......... .......... .......... .......... 16% 80.3M 50s\n",
      " 91100K .......... .......... .......... .......... .......... 16% 1.51M 51s\n",
      " 91150K .......... .......... .......... .......... .......... 16% 13.4M 51s\n",
      " 91200K .......... .......... .......... .......... .......... 16% 13.3M 51s\n",
      " 91250K .......... .......... .......... .......... .......... 16% 9.83M 51s\n",
      " 91300K .......... .......... .......... .......... .......... 16% 48.1M 51s\n",
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      " 91500K .......... .......... .......... .......... .......... 16%  407M 50s\n",
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      " 91650K .......... .......... .......... .......... .......... 16% 16.0M 50s\n",
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      " 91750K .......... .......... .......... .......... .......... 16%  211M 50s\n",
      " 91800K .......... .......... .......... .......... .......... 16% 17.5M 50s\n",
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      " 91900K .......... .......... .......... .......... .......... 16% 61.2M 50s\n",
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      " 92000K .......... .......... .......... .......... .......... 16% 61.5M 50s\n",
      " 92050K .......... .......... .......... .......... .......... 16% 17.8M 50s\n",
      " 92100K .......... .......... .......... .......... .......... 16% 5.20M 50s\n",
      " 92150K .......... .......... .......... .......... .......... 16% 14.5M 50s\n",
      " 92200K .......... .......... .......... .......... .......... 16% 18.8M 50s\n",
      " 92250K .......... .......... .......... .......... .......... 16% 7.91M 50s\n",
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      " 92400K .......... .......... .......... .......... .......... 16% 8.33M 50s\n",
      " 92450K .......... .......... .......... .......... .......... 16% 14.3M 50s\n",
      " 92500K .......... .......... .......... .......... .......... 16% 13.3M 50s\n",
      " 92550K .......... .......... .......... .......... .......... 16% 7.17M 50s\n",
      " 92600K .......... .......... .......... .......... .......... 16% 11.1M 50s\n",
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      " 92800K .......... .......... .......... .......... .......... 16% 10.1M 50s\n",
      " 92850K .......... .......... .......... .......... .......... 16% 15.6M 50s\n",
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      " 93000K .......... .......... .......... .......... .......... 16% 8.88M 50s\n",
      " 93050K .......... .......... .......... .......... .......... 16% 20.0M 50s\n",
      " 93100K .......... .......... .......... .......... .......... 16% 10.5M 50s\n",
      " 93150K .......... .......... .......... .......... .......... 16% 13.8M 50s\n",
      " 93200K .......... .......... .......... .......... .......... 17% 21.5M 50s\n",
      " 93250K .......... .......... .......... .......... .......... 17% 1.44M 50s\n",
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      " 93350K .......... .......... .......... .......... .......... 17% 11.6M 50s\n",
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      " 93450K .......... .......... .......... .......... .......... 17% 16.8M 50s\n",
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      " 93700K .......... .......... .......... .......... .......... 17% 74.4M 50s\n",
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      " 93800K .......... .......... .......... .......... .......... 17% 29.0M 50s\n",
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      " 94000K .......... .......... .......... .......... .......... 17%  414M 50s\n",
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      " 94150K .......... .......... .......... .......... .......... 17% 16.5M 50s\n",
      " 94200K .......... .......... .......... .......... .......... 17% 4.24M 50s\n",
      " 94250K .......... .......... .......... .......... .......... 17% 6.27M 50s\n",
      " 94300K .......... .......... .......... .......... .......... 17% 2.60M 50s\n",
      " 94350K .......... .......... .......... .......... .......... 17% 3.76M 50s\n",
      " 94400K .......... .......... .......... .......... .......... 17% 12.6M 50s\n",
      " 94450K .......... .......... .......... .......... .......... 17% 61.6M 50s\n",
      " 94500K .......... .......... .......... .......... .......... 17% 12.0M 50s\n",
      " 94550K .......... .......... .......... .......... .......... 17% 16.8M 50s\n",
      " 94600K .......... .......... .......... .......... .......... 17% 7.63M 50s\n",
      " 94650K .......... .......... .......... .......... .......... 17%  465M 50s\n",
      " 94700K .......... .......... .......... .......... .......... 17% 13.6M 50s\n",
      " 94750K .......... .......... .......... .......... .......... 17% 7.30M 50s\n",
      " 94800K .......... .......... .......... .......... .......... 17% 9.94M 50s\n",
      " 94850K .......... .......... .......... .......... .......... 17%  543M 50s\n",
      " 94900K .......... .......... .......... .......... .......... 17% 7.09M 50s\n",
      " 94950K .......... .......... .......... .......... .......... 17% 3.67M 50s\n",
      " 95000K .......... .......... .......... .......... .......... 17% 1.24M 50s\n",
      " 95050K .......... .......... .......... .......... .......... 17% 4.03M 50s\n",
      " 95100K .......... .......... .......... .......... .......... 17% 3.73M 50s\n",
      " 95150K .......... .......... .......... .......... .......... 17% 4.48M 50s\n",
      " 95200K .......... .......... .......... .......... .......... 17% 3.35M 50s\n",
      " 95250K .......... .......... .......... .......... .......... 17% 6.26M 50s\n",
      " 95300K .......... .......... .......... .......... .......... 17% 11.1M 50s\n",
      " 95350K .......... .......... .......... .......... .......... 17% 3.63M 50s\n",
      " 95400K .......... .......... .......... .......... .......... 17% 3.99M 50s\n",
      " 95450K .......... .......... .......... .......... .......... 17% 4.06M 50s\n",
      " 95500K .......... .......... .......... .......... .......... 17% 12.2M 50s\n",
      " 95550K .......... .......... .......... .......... .......... 17% 4.05M 50s\n",
      " 95600K .......... .......... .......... .......... .......... 17% 3.75M 50s\n",
      " 95650K .......... .......... .......... .......... .......... 17% 4.53M 50s\n",
      " 95700K .......... .......... .......... .......... .......... 17% 4.11M 50s\n",
      " 95750K .......... .......... .......... .......... .......... 17%  484K 51s\n",
      " 95800K .......... .......... .......... .......... .......... 17% 23.2M 51s\n",
      " 95850K .......... .......... .......... .......... .......... 17%  436M 51s\n",
      " 95900K .......... .......... .......... .......... .......... 17% 6.91M 51s\n",
      " 95950K .......... .......... .......... .......... .......... 17% 50.5M 51s\n",
      " 96000K .......... .......... .......... .......... .......... 17% 9.58M 51s\n",
      " 96050K .......... .......... .......... .......... .......... 17% 2.98M 51s\n",
      " 96100K .......... .......... .......... .......... .......... 17% 6.93M 51s\n",
      " 96150K .......... .......... .......... .......... .......... 17%  574M 51s\n",
      " 96200K .......... .......... .......... .......... .......... 17% 10.5M 51s\n",
      " 96250K .......... .......... .......... .......... .......... 17% 9.12M 51s\n",
      " 96300K .......... .......... .......... .......... .......... 17% 9.15M 51s\n",
      " 96350K .......... .......... .......... .......... .......... 17% 11.8M 51s\n",
      " 96400K .......... .......... .......... .......... .......... 17% 17.1M 51s\n",
      " 96450K .......... .......... .......... .......... .......... 17% 10.6M 51s\n",
      " 96500K .......... .......... .......... .......... .......... 17% 36.5M 51s\n",
      " 96550K .......... .......... .......... .......... .......... 17% 9.96M 51s\n",
      " 96600K .......... .......... .......... .......... .......... 17% 18.6M 51s\n",
      " 96650K .......... .......... .......... .......... .......... 17% 17.1M 51s\n",
      " 96700K .......... .......... .......... .......... .......... 17% 8.01M 51s\n",
      " 96750K .......... .......... .......... .......... .......... 17% 3.56M 51s\n",
      " 96800K .......... .......... .......... .......... .......... 17% 22.7M 51s\n",
      " 96850K .......... .......... .......... .......... .......... 17% 5.98M 51s\n",
      " 96900K .......... .......... .......... .......... .......... 17% 6.63M 51s\n",
      " 96950K .......... .......... .......... .......... .......... 17% 10.2M 51s\n",
      " 97000K .......... .......... .......... .......... .......... 17% 13.4M 51s\n",
      " 97050K .......... .......... .......... .......... .......... 17% 5.63M 51s\n",
      " 97100K .......... .......... .......... .......... .......... 17% 19.6M 51s\n",
      " 97150K .......... .......... .......... .......... .......... 17% 10.5M 51s\n",
      " 97200K .......... .......... .......... .......... .......... 17%  140M 51s\n",
      " 97250K .......... .......... .......... .......... .......... 17% 10.0M 51s\n",
      " 97300K .......... .......... .......... .......... .......... 17% 10.2M 51s\n",
      " 97350K .......... .......... .......... .......... .......... 17% 65.8M 51s\n",
      " 97400K .......... .......... .......... .......... .......... 17% 9.77M 51s\n",
      " 97450K .......... .......... .......... .......... .......... 17% 4.17M 51s\n",
      " 97500K .......... .......... .......... .......... .......... 17%  221M 51s\n",
      " 97550K .......... .......... .......... .......... .......... 17% 7.14M 51s\n",
      " 97600K .......... .......... .......... .......... .......... 17% 21.1M 50s\n",
      " 97650K .......... .......... .......... .......... .......... 17% 11.2M 50s\n",
      " 97700K .......... .......... .......... .......... .......... 17% 21.6M 50s\n",
      " 97750K .......... .......... .......... .......... .......... 17% 6.83M 50s\n",
      " 97800K .......... .......... .......... .......... .......... 17%  425M 50s\n",
      " 97850K .......... .......... .......... .......... .......... 17% 14.0M 50s\n",
      " 97900K .......... .......... .......... .......... .......... 17% 41.1M 50s\n",
      " 97950K .......... .......... .......... .......... .......... 17% 5.38M 50s\n",
      " 98000K .......... .......... .......... .......... .......... 17% 72.4M 50s\n",
      " 98050K .......... .......... .......... .......... .......... 17% 40.1M 50s\n",
      " 98100K .......... .......... .......... .......... .......... 17% 10.5M 50s\n",
      " 98150K .......... .......... .......... .......... .......... 17% 18.4M 50s\n",
      " 98200K .......... .......... .......... .......... .......... 17% 1.50M 50s\n",
      " 98250K .......... .......... .......... .......... .......... 17% 10.2M 50s\n",
      " 98300K .......... .......... .......... .......... .......... 17%  313M 50s\n",
      " 98350K .......... .......... .......... .......... .......... 17% 10.6M 50s\n",
      " 98400K .......... .......... .......... .......... .......... 17% 16.3M 50s\n",
      " 98450K .......... .......... .......... .......... .......... 17% 8.72M 50s\n",
      " 98500K .......... .......... .......... .......... .......... 17% 31.1M 50s\n",
      " 98550K .......... .......... .......... .......... .......... 17% 9.80M 50s\n",
      " 98600K .......... .......... .......... .......... .......... 17%  603M 50s\n",
      " 98650K .......... .......... .......... .......... .......... 17% 9.99M 50s\n",
      " 98700K .......... .......... .......... .......... .......... 18% 10.1M 50s\n",
      " 98750K .......... .......... .......... .......... .......... 18% 7.65M 50s\n",
      " 98800K .......... .......... .......... .......... .......... 18% 6.50M 50s\n",
      " 98850K .......... .......... .......... .......... .......... 18% 17.9M 50s\n",
      " 98900K .......... .......... .......... .......... .......... 18% 6.28M 50s\n",
      " 98950K .......... .......... .......... .......... .......... 18% 9.08M 50s\n",
      " 99000K .......... .......... .......... .......... .......... 18% 9.17M 50s\n",
      " 99050K .......... .......... .......... .......... .......... 18%  244M 50s\n",
      " 99100K .......... .......... .......... .......... .......... 18% 4.38M 50s\n",
      " 99150K .......... .......... .......... .......... .......... 18% 2.65M 50s\n",
      " 99200K .......... .......... .......... .......... .......... 18% 4.60M 50s\n",
      " 99250K .......... .......... .......... .......... .......... 18% 5.12M 50s\n",
      " 99300K .......... .......... .......... .......... .......... 18% 9.51M 50s\n",
      " 99350K .......... .......... .......... .......... .......... 18% 9.09M 50s\n",
      " 99400K .......... .......... .......... .......... .......... 18% 10.4M 50s\n",
      " 99450K .......... .......... .......... .......... .......... 18% 11.2M 50s\n",
      " 99500K .......... .......... .......... .......... .......... 18% 9.10M 50s\n",
      " 99550K .......... .......... .......... .......... .......... 18% 8.99M 50s\n",
      " 99600K .......... .......... .......... .......... .......... 18% 6.68M 50s\n",
      " 99650K .......... .......... .......... .......... .......... 18% 12.8M 50s\n",
      " 99700K .......... .......... .......... .......... .......... 18% 8.13M 50s\n",
      " 99750K .......... .......... .......... .......... .......... 18% 37.3M 50s\n",
      " 99800K .......... .......... .......... .......... .......... 18% 8.61M 50s\n",
      " 99850K .......... .......... .......... .......... .......... 18% 18.5M 50s\n",
      " 99900K .......... .......... .......... .......... .......... 18% 8.42M 50s\n",
      " 99950K .......... .......... .......... .......... .......... 18% 41.3M 50s\n",
      "100000K .......... .......... .......... .......... .......... 18% 4.55M 50s\n",
      "100050K .......... .......... .......... .......... .......... 18% 13.4M 50s\n",
      "100100K .......... .......... .......... .......... .......... 18% 10.1M 50s\n",
      "100150K .......... .......... .......... .......... .......... 18% 9.66M 50s\n",
      "100200K .......... .......... .......... .......... .......... 18% 16.9M 50s\n",
      "100250K .......... .......... .......... .......... .......... 18% 13.3M 50s\n",
      "100300K .......... .......... .......... .......... .......... 18% 46.6M 50s\n",
      "100350K .......... .......... .......... .......... .......... 18% 7.31M 50s\n",
      "100400K .......... .......... .......... .......... .......... 18% 8.76M 50s\n",
      "100450K .......... .......... .......... .......... .......... 18% 7.47M 50s\n",
      "100500K .......... .......... .......... .......... .......... 18%  456M 50s\n",
      "100550K .......... .......... .......... .......... .......... 18% 4.80M 50s\n",
      "100600K .......... .......... .......... .......... .......... 18% 9.05M 50s\n",
      "100650K .......... .......... .......... .......... .......... 18%  509M 50s\n",
      "100700K .......... .......... .......... .......... .......... 18% 19.8M 50s\n",
      "100750K .......... .......... .......... .......... .......... 18% 1.18M 50s\n",
      "100800K .......... .......... .......... .......... .......... 18% 11.5M 50s\n",
      "100850K .......... .......... .......... .......... .......... 18% 7.77M 50s\n",
      "100900K .......... .......... .......... .......... .......... 18%  174M 50s\n",
      "100950K .......... .......... .......... .......... .......... 18% 9.59M 50s\n",
      "101000K .......... .......... .......... .......... .......... 18% 67.3M 50s\n",
      "101050K .......... .......... .......... .......... .......... 18% 10.2M 50s\n",
      "101100K .......... .......... .......... .......... .......... 18% 20.7M 50s\n",
      "101150K .......... .......... .......... .......... .......... 18% 7.38M 50s\n",
      "101200K .......... .......... .......... .......... .......... 18% 29.2M 50s\n",
      "101250K .......... .......... .......... .......... .......... 18% 14.4M 50s\n",
      "101300K .......... .......... .......... .......... .......... 18% 15.3M 50s\n",
      "101350K .......... .......... .......... .......... .......... 18% 20.9M 50s\n",
      "101400K .......... .......... .......... .......... .......... 18% 4.23M 50s\n",
      "101450K .......... .......... .......... .......... .......... 18% 24.0M 50s\n",
      "101500K .......... .......... .......... .......... .......... 18% 11.5M 50s\n",
      "101550K .......... .......... .......... .......... .......... 18% 4.42M 50s\n",
      "101600K .......... .......... .......... .......... .......... 18% 9.68M 50s\n",
      "101650K .......... .......... .......... .......... .......... 18% 22.2M 50s\n",
      "101700K .......... .......... .......... .......... .......... 18% 8.06M 50s\n",
      "101750K .......... .......... .......... .......... .......... 18% 12.1M 50s\n",
      "101800K .......... .......... .......... .......... .......... 18% 1.54M 50s\n",
      "101850K .......... .......... .......... .......... .......... 18% 4.85M 50s\n",
      "101900K .......... .......... .......... .......... .......... 18% 53.9M 50s\n",
      "101950K .......... .......... .......... .......... .......... 18% 5.99M 50s\n",
      "102000K .......... .......... .......... .......... .......... 18% 8.60M 50s\n",
      "102050K .......... .......... .......... .......... .......... 18% 17.2M 50s\n",
      "102100K .......... .......... .......... .......... .......... 18% 23.8M 50s\n",
      "102150K .......... .......... .......... .......... .......... 18% 13.0M 50s\n",
      "102200K .......... .......... .......... .......... .......... 18% 29.2M 50s\n",
      "102250K .......... .......... .......... .......... .......... 18% 8.04M 50s\n",
      "102300K .......... .......... .......... .......... .......... 18% 53.5M 50s\n",
      "102350K .......... .......... .......... .......... .......... 18% 9.43M 50s\n",
      "102400K .......... .......... .......... .......... .......... 18% 17.0M 50s\n",
      "102450K .......... .......... .......... .......... .......... 18% 9.46M 50s\n",
      "102500K .......... .......... .......... .......... .......... 18% 8.11M 50s\n",
      "102550K .......... .......... .......... .......... .......... 18%  425M 50s\n",
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      "102700K .......... .......... .......... .......... .......... 18% 33.6M 50s\n",
      "102750K .......... .......... .......... .......... .......... 18% 7.71M 50s\n",
      "102800K .......... .......... .......... .......... .......... 18% 6.26M 50s\n",
      "102850K .......... .......... .......... .......... .......... 18% 16.1M 50s\n",
      "102900K .......... .......... .......... .......... .......... 18% 11.2M 50s\n",
      "102950K .......... .......... .......... .......... .......... 18% 2.02M 50s\n",
      "103000K .......... .......... .......... .......... .......... 18% 17.2M 50s\n",
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      "103100K .......... .......... .......... .......... .......... 18%  537M 50s\n",
      "103150K .......... .......... .......... .......... .......... 18% 7.58M 50s\n",
      "103200K .......... .......... .......... .......... .......... 18%  264M 50s\n",
      "103250K .......... .......... .......... .......... .......... 18% 7.07M 50s\n",
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      "103350K .......... .......... .......... .......... .......... 18% 16.7M 50s\n",
      "103400K .......... .......... .......... .......... .......... 18% 14.3M 50s\n",
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      "103500K .......... .......... .......... .......... .......... 18% 9.76M 50s\n",
      "103550K .......... .......... .......... .......... .......... 18% 16.6M 50s\n",
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      "103800K .......... .......... .......... .......... .......... 18%  160M 50s\n",
      "103850K .......... .......... .......... .......... .......... 18% 10.5M 50s\n",
      "103900K .......... .......... .......... .......... .......... 18% 9.77M 50s\n",
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      "104000K .......... .......... .......... .......... .......... 18% 1.63M 50s\n",
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      "104100K .......... .......... .......... .......... .......... 18% 36.8M 50s\n",
      "104150K .......... .......... .......... .......... .......... 18% 10.7M 50s\n",
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      "104450K .......... .......... .......... .......... .......... 19% 16.1M 50s\n",
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      "104650K .......... .......... .......... .......... .......... 19% 91.3M 50s\n",
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      "104750K .......... .......... .......... .......... .......... 19% 20.8M 50s\n",
      "104800K .......... .......... .......... .......... .......... 19% 12.0M 49s\n",
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      "104900K .......... .......... .......... .......... .......... 19% 6.74M 49s\n",
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      "105000K .......... .......... .......... .......... .......... 19% 8.55M 49s\n",
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      "105150K .......... .......... .......... .......... .......... 19% 3.58M 50s\n",
      "105200K .......... .......... .......... .......... .......... 19% 12.1M 50s\n",
      "105250K .......... .......... .......... .......... .......... 19% 22.5M 50s\n",
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      "105450K .......... .......... .......... .......... .......... 19%  626M 50s\n",
      "105500K .......... .......... .......... .......... .......... 19% 9.60M 50s\n",
      "105550K .......... .......... .......... .......... .......... 19% 6.25M 50s\n",
      "105600K .......... .......... .......... .......... .......... 19% 13.9M 50s\n",
      "105650K .......... .......... .......... .......... .......... 19% 16.5M 50s\n",
      "105700K .......... .......... .......... .......... .......... 19% 16.7M 50s\n",
      "105750K .......... .......... .......... .......... .......... 19% 18.1M 50s\n",
      "105800K .......... .......... .......... .......... .......... 19% 13.7M 50s\n",
      "105850K .......... .......... .......... .......... .......... 19% 18.4M 50s\n",
      "105900K .......... .......... .......... .......... .......... 19% 8.95M 50s\n",
      "105950K .......... .......... .......... .......... .......... 19% 13.9M 50s\n",
      "106000K .......... .......... .......... .......... .......... 19% 11.6M 50s\n",
      "106050K .......... .......... .......... .......... .......... 19% 18.4M 50s\n",
      "106100K .......... .......... .......... .......... .......... 19% 7.72M 50s\n",
      "106150K .......... .......... .......... .......... .......... 19% 1.24M 50s\n",
      "106200K .......... .......... .......... .......... .......... 19% 11.4M 50s\n",
      "106250K .......... .......... .......... .......... .......... 19% 45.9M 50s\n",
      "106300K .......... .......... .......... .......... .......... 19% 16.0M 50s\n",
      "106350K .......... .......... .......... .......... .......... 19% 10.3M 50s\n",
      "106400K .......... .......... .......... .......... .......... 19% 19.7M 50s\n",
      "106450K .......... .......... .......... .......... .......... 19% 8.95M 50s\n",
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      "106950K .......... .......... .......... .......... .......... 19%  574M 49s\n",
      "107000K .......... .......... .......... .......... .......... 19% 15.1M 49s\n",
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      "130500K .......... .......... .......... .......... .......... 23% 11.0M 47s\n",
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      "130600K .......... .......... .......... .......... .......... 23% 12.8M 47s\n",
      "130650K .......... .......... .......... .......... .......... 23% 17.3M 47s\n",
      "130700K .......... .......... .......... .......... .......... 23% 40.4M 47s\n",
      "130750K .......... .......... .......... .......... .......... 23% 6.99M 47s\n",
      "130800K .......... .......... .......... .......... .......... 23% 12.1M 47s\n",
      "130850K .......... .......... .......... .......... .......... 23% 9.55M 47s\n",
      "130900K .......... .......... .......... .......... .......... 23% 11.7M 47s\n",
      "130950K .......... .......... .......... .......... .......... 23%  165M 47s\n",
      "131000K .......... .......... .......... .......... .......... 23% 10.0M 47s\n",
      "131050K .......... .......... .......... .......... .......... 23% 9.14M 47s\n",
      "131100K .......... .......... .......... .......... .......... 23%  276M 47s\n",
      "131150K .......... .......... .......... .......... .......... 23% 7.22M 47s\n",
      "131200K .......... .......... .......... .......... .......... 23% 7.78M 47s\n",
      "131250K .......... .......... .......... .......... .......... 23% 19.0M 47s\n",
      "131300K .......... .......... .......... .......... .......... 23% 6.77M 47s\n",
      "131350K .......... .......... .......... .......... .......... 23%  222M 47s\n",
      "131400K .......... .......... .......... .......... .......... 23% 9.70M 47s\n",
      "131450K .......... .......... .......... .......... .......... 23% 9.58M 47s\n",
      "131500K .......... .......... .......... .......... .......... 23% 18.4M 47s\n",
      "131550K .......... .......... .......... .......... .......... 23% 19.6M 47s\n",
      "131600K .......... .......... .......... .......... .......... 24% 18.7M 47s\n",
      "131650K .......... .......... .......... .......... .......... 24% 14.0M 47s\n",
      "131700K .......... .......... .......... .......... .......... 24% 9.58M 47s\n",
      "131750K .......... .......... .......... .......... .......... 24%  108M 47s\n",
      "131800K .......... .......... .......... .......... .......... 24% 7.76M 47s\n",
      "131850K .......... .......... .......... .......... .......... 24% 71.8M 47s\n",
      "131900K .......... .......... .......... .......... .......... 24% 9.69M 47s\n",
      "131950K .......... .......... .......... .......... .......... 24% 18.1M 47s\n",
      "132000K .......... .......... .......... .......... .......... 24% 7.70M 47s\n",
      "132050K .......... .......... .......... .......... .......... 24%  148M 47s\n",
      "132100K .......... .......... .......... .......... .......... 24% 1.20M 47s\n",
      "132150K .......... .......... .......... .......... .......... 24% 20.9M 47s\n",
      "132200K .......... .......... .......... .......... .......... 24% 16.7M 47s\n",
      "132250K .......... .......... .......... .......... .......... 24% 77.0M 47s\n",
      "132300K .......... .......... .......... .......... .......... 24% 6.36M 47s\n",
      "132350K .......... .......... .......... .......... .......... 24% 60.9M 47s\n",
      "132400K .......... .......... .......... .......... .......... 24% 8.88M 47s\n",
      "132450K .......... .......... .......... .......... .......... 24% 11.6M 47s\n",
      "132500K .......... .......... .......... .......... .......... 24% 18.4M 47s\n",
      "132550K .......... .......... .......... .......... .......... 24% 15.3M 47s\n",
      "132600K .......... .......... .......... .......... .......... 24% 7.24M 47s\n",
      "132650K .......... .......... .......... .......... .......... 24% 42.3M 47s\n",
      "132700K .......... .......... .......... .......... .......... 24% 5.94M 47s\n",
      "132750K .......... .......... .......... .......... .......... 24% 10.1M 47s\n",
      "132800K .......... .......... .......... .......... .......... 24%  524K 47s\n",
      "132850K .......... .......... .......... .......... .......... 24% 16.3M 47s\n",
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      "133000K .......... .......... .......... .......... .......... 24% 8.52M 47s\n",
      "133050K .......... .......... .......... .......... .......... 24% 19.6M 47s\n",
      "133100K .......... .......... .......... .......... .......... 24% 70.5M 47s\n",
      "133150K .......... .......... .......... .......... .......... 24% 8.06M 47s\n",
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      "133250K .......... .......... .......... .......... .......... 24% 17.9M 47s\n",
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      "133400K .......... .......... .......... .......... .......... 24% 10.8M 47s\n",
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      "133500K .......... .......... .......... .......... .......... 24% 20.9M 47s\n",
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      "134100K .......... .......... .......... .......... .......... 24% 16.0M 47s\n",
      "134150K .......... .......... .......... .......... .......... 24% 9.14M 47s\n",
      "134200K .......... .......... .......... .......... .......... 24% 24.6M 47s\n",
      "134250K .......... .......... .......... .......... .......... 24% 10.2M 47s\n",
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      "142800K .......... .......... .......... .......... .......... 26% 3.40M 46s\n",
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      "143000K .......... .......... .......... .......... .......... 26% 25.4M 46s\n",
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      "144000K .......... .......... .......... .......... .......... 26% 7.46M 45s\n",
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      "154600K .......... .......... .......... .......... .......... 28% 13.4M 44s\n",
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      "154700K .......... .......... .......... .......... .......... 28%  167M 44s\n",
      "154750K .......... .......... .......... .......... .......... 28% 13.4M 44s\n",
      "154800K .......... .......... .......... .......... .......... 28% 19.2M 44s\n",
      "154850K .......... .......... .......... .......... .......... 28% 10.7M 44s\n",
      "154900K .......... .......... .......... .......... .......... 28% 14.2M 44s\n",
      "154950K .......... .......... .......... .......... .......... 28% 9.16M 44s\n",
      "155000K .......... .......... .......... .......... .......... 28% 22.7M 44s\n",
      "155050K .......... .......... .......... .......... .......... 28% 47.8M 44s\n",
      "155100K .......... .......... .......... .......... .......... 28% 9.78M 44s\n",
      "155150K .......... .......... .......... .......... .......... 28% 8.70M 44s\n",
      "155200K .......... .......... .......... .......... .......... 28%  121M 44s\n",
      "155250K .......... .......... .......... .......... .......... 28% 9.10M 44s\n",
      "155300K .......... .......... .......... .......... .......... 28% 8.10M 44s\n",
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      "155450K .......... .......... .......... .......... .......... 28% 10.4M 44s\n",
      "155500K .......... .......... .......... .......... .......... 28% 15.9M 44s\n",
      "155550K .......... .......... .......... .......... .......... 28% 7.46M 44s\n",
      "155600K .......... .......... .......... .......... .......... 28% 1.63M 44s\n",
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      "155800K .......... .......... .......... .......... .......... 28% 9.78M 44s\n",
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      "155950K .......... .......... .......... .......... .......... 28% 5.75M 44s\n",
      "156000K .......... .......... .......... .......... .......... 28%  461M 44s\n",
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      "156100K .......... .......... .......... .......... .......... 28% 21.1M 44s\n",
      "156150K .......... .......... .......... .......... .......... 28% 13.6M 44s\n",
      "156200K .......... .......... .......... .......... .......... 28% 10.4M 44s\n",
      "156250K .......... .......... .......... .......... .......... 28% 13.0M 44s\n",
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      "156350K .......... .......... .......... .......... .......... 28% 71.5M 44s\n",
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      "166650K .......... .......... .......... .......... .......... 30% 10.7M 42s\n",
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      "166750K .......... .......... .......... .......... .......... 30% 4.41M 42s\n",
      "166800K .......... .......... .......... .......... .......... 30% 9.92M 42s\n",
      "166850K .......... .......... .......... .......... .......... 30% 24.8M 42s\n",
      "166900K .......... .......... .......... .......... .......... 30%  126M 42s\n",
      "166950K .......... .......... .......... .......... .......... 30% 16.3M 42s\n",
      "167000K .......... .......... .......... .......... .......... 30% 7.60M 42s\n",
      "167050K .......... .......... .......... .......... .......... 30% 14.3M 42s\n",
      "167100K .......... .......... .......... .......... .......... 30% 4.59M 42s\n",
      "167150K .......... .......... .......... .......... .......... 30% 6.15M 42s\n",
      "167200K .......... .......... .......... .......... .......... 30% 11.6M 42s\n",
      "167250K .......... .......... .......... .......... .......... 30% 18.8M 42s\n",
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      "167350K .......... .......... .......... .......... .......... 30% 18.5M 42s\n",
      "167400K .......... .......... .......... .......... .......... 30% 11.7M 42s\n",
      "167450K .......... .......... .......... .......... .......... 30% 23.6M 42s\n",
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      "167850K .......... .......... .......... .......... .......... 30% 41.1M 42s\n",
      "167900K .......... .......... .......... .......... .......... 30% 19.9M 42s\n",
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      "168000K .......... .......... .......... .......... .......... 30% 9.09M 42s\n",
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      "168100K .......... .......... .......... .......... .......... 30% 30.6M 42s\n",
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      "168500K .......... .......... .......... .......... .......... 30% 10.5M 42s\n",
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      "169000K .......... .......... .......... .......... .......... 30% 12.0M 42s\n",
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      "170000K .......... .......... .......... .......... .......... 31% 16.2M 42s\n",
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      "190750K .......... .......... .......... .......... .......... 34% 11.4M 39s\n",
      "190800K .......... .......... .......... .......... .......... 34% 7.81M 39s\n",
      "190850K .......... .......... .......... .......... .......... 34% 23.2M 39s\n",
      "190900K .......... .......... .......... .......... .......... 34% 13.0M 39s\n",
      "190950K .......... .......... .......... .......... .......... 34% 6.15M 39s\n",
      "191000K .......... .......... .......... .......... .......... 34% 6.23M 39s\n",
      "191050K .......... .......... .......... .......... .......... 34% 6.36M 39s\n",
      "191100K .......... .......... .......... .......... .......... 34% 7.65M 39s\n",
      "191150K .......... .......... .......... .......... .......... 34% 6.34M 39s\n",
      "191200K .......... .......... .......... .......... .......... 34% 15.9M 39s\n",
      "191250K .......... .......... .......... .......... .......... 34% 7.39M 39s\n",
      "191300K .......... .......... .......... .......... .......... 34% 17.4M 39s\n",
      "191350K .......... .......... .......... .......... .......... 34% 15.6M 39s\n",
      "191400K .......... .......... .......... .......... .......... 34% 15.5M 39s\n",
      "191450K .......... .......... .......... .......... .......... 34% 10.2M 39s\n",
      "191500K .......... .......... .......... .......... .......... 34%  618M 39s\n",
      "191550K .......... .......... .......... .......... .......... 34% 5.65M 39s\n",
      "191600K .......... .......... .......... .......... .......... 34%  113M 39s\n",
      "191650K .......... .......... .......... .......... .......... 34% 10.1M 39s\n",
      "191700K .......... .......... .......... .......... .......... 34% 8.70M 39s\n",
      "191750K .......... .......... .......... .......... .......... 34% 19.2M 39s\n",
      "191800K .......... .......... .......... .......... .......... 34% 11.8M 39s\n",
      "191850K .......... .......... .......... .......... .......... 34% 9.80M 39s\n",
      "191900K .......... .......... .......... .......... .......... 34%  660M 39s\n",
      "191950K .......... .......... .......... .......... .......... 35% 8.32M 39s\n",
      "192000K .......... .......... .......... .......... .......... 35% 12.9M 39s\n",
      "192050K .......... .......... .......... .......... .......... 35% 11.0M 39s\n",
      "192100K .......... .......... .......... .......... .......... 35%  618M 39s\n",
      "192150K .......... .......... .......... .......... .......... 35% 10.9M 39s\n",
      "192200K .......... .......... .......... .......... .......... 35% 8.05M 39s\n",
      "192250K .......... .......... .......... .......... .......... 35% 10.4M 39s\n",
      "192300K .......... .......... .......... .......... .......... 35% 10.4M 39s\n",
      "192350K .......... .......... .......... .......... .......... 35% 15.3M 39s\n",
      "192400K .......... .......... .......... .......... .......... 35%  136M 39s\n",
      "192450K .......... .......... .......... .......... .......... 35% 1.57M 39s\n",
      "192500K .......... .......... .......... .......... .......... 35% 25.5M 39s\n",
      "192550K .......... .......... .......... .......... .......... 35%  436K 39s\n",
      "192600K .......... .......... .......... .......... .......... 35% 8.77M 39s\n",
      "192650K .......... .......... .......... .......... .......... 35% 4.24M 39s\n",
      "192700K .......... .......... .......... .......... .......... 35% 7.30M 39s\n",
      "192750K .......... .......... .......... .......... .......... 35% 5.88M 39s\n",
      "192800K .......... .......... .......... .......... .......... 35% 4.46M 39s\n",
      "192850K .......... .......... .......... .......... .......... 35% 5.03M 39s\n",
      "192900K .......... .......... .......... .......... .......... 35% 1.30M 39s\n",
      "192950K .......... .......... .......... .......... .......... 35% 5.72M 39s\n",
      "193000K .......... .......... .......... .......... .......... 35% 3.81M 39s\n",
      "193050K .......... .......... .......... .......... .......... 35% 13.0M 39s\n",
      "193100K .......... .......... .......... .......... .......... 35% 5.11M 39s\n",
      "193150K .......... .......... .......... .......... .......... 35% 2.86M 39s\n",
      "193200K .......... .......... .......... .......... .......... 35% 4.38M 39s\n",
      "193250K .......... .......... .......... .......... .......... 35% 3.59M 39s\n",
      "193300K .......... .......... .......... .......... .......... 35% 4.95M 39s\n",
      "193350K .......... .......... .......... .......... .......... 35% 17.0M 39s\n",
      "193400K .......... .......... .......... .......... .......... 35% 4.15M 39s\n",
      "193450K .......... .......... .......... .......... .......... 35% 3.28M 39s\n",
      "193500K .......... .......... .......... .......... .......... 35% 3.29M 39s\n",
      "193550K .......... .......... .......... .......... .......... 35% 16.6M 39s\n",
      "193600K .......... .......... .......... .......... .......... 35% 19.9M 39s\n",
      "193650K .......... .......... .......... .......... .......... 35% 3.92M 39s\n",
      "193700K .......... .......... .......... .......... .......... 35%  122M 39s\n",
      "193750K .......... .......... .......... .......... .......... 35%  634M 39s\n",
      "193800K .......... .......... .......... .......... .......... 35% 3.59M 39s\n",
      "193850K .......... .......... .......... .......... .......... 35% 23.4M 39s\n",
      "193900K .......... .......... .......... .......... .......... 35% 16.4M 39s\n",
      "193950K .......... .......... .......... .......... .......... 35% 6.16M 39s\n",
      "194000K .......... .......... .......... .......... .......... 35% 6.08M 39s\n",
      "194050K .......... .......... .......... .......... .......... 35% 1.92M 39s\n",
      "194100K .......... .......... .......... .......... .......... 35%  332M 39s\n",
      "194150K .......... .......... .......... .......... .......... 35%  595M 39s\n",
      "194200K .......... .......... .......... .......... .......... 35% 13.9M 39s\n",
      "194250K .......... .......... .......... .......... .......... 35% 20.4M 39s\n",
      "194300K .......... .......... .......... .......... .......... 35% 11.5M 39s\n",
      "194350K .......... .......... .......... .......... .......... 35% 3.19M 39s\n",
      "194400K .......... .......... .......... .......... .......... 35% 59.0M 39s\n",
      "194450K .......... .......... .......... .......... .......... 35% 15.8M 39s\n",
      "194500K .......... .......... .......... .......... .......... 35% 10.2M 39s\n",
      "194550K .......... .......... .......... .......... .......... 35% 14.0M 39s\n",
      "194600K .......... .......... .......... .......... .......... 35% 20.5M 39s\n",
      "194650K .......... .......... .......... .......... .......... 35% 9.07M 39s\n",
      "194700K .......... .......... .......... .......... .......... 35% 18.0M 39s\n",
      "194750K .......... .......... .......... .......... .......... 35% 10.7M 39s\n",
      "194800K .......... .......... .......... .......... .......... 35% 13.6M 39s\n",
      "194850K .......... .......... .......... .......... .......... 35% 10.0M 39s\n",
      "194900K .......... .......... .......... .......... .......... 35% 48.0M 39s\n",
      "194950K .......... .......... .......... .......... .......... 35% 14.2M 39s\n",
      "195000K .......... .......... .......... .......... .......... 35% 1.56M 39s\n",
      "195050K .......... .......... .......... .......... .......... 35% 10.3M 39s\n",
      "195100K .......... .......... .......... .......... .......... 35%  448M 39s\n",
      "195150K .......... .......... .......... .......... .......... 35% 8.89M 39s\n",
      "195200K .......... .......... .......... .......... .......... 35% 14.7M 39s\n",
      "195250K .......... .......... .......... .......... .......... 35% 16.5M 39s\n",
      "195300K .......... .......... .......... .......... .......... 35% 51.1M 39s\n",
      "195350K .......... .......... .......... .......... .......... 35% 14.7M 39s\n",
      "195400K .......... .......... .......... .......... .......... 35% 15.3M 39s\n",
      "195450K .......... .......... .......... .......... .......... 35% 5.30M 39s\n",
      "195500K .......... .......... .......... .......... .......... 35% 8.94M 39s\n",
      "195550K .......... .......... .......... .......... .......... 35% 21.4M 39s\n",
      "195600K .......... .......... .......... .......... .......... 35% 10.7M 39s\n",
      "195650K .......... .......... .......... .......... .......... 35% 18.1M 39s\n",
      "195700K .......... .......... .......... .......... .......... 35% 16.8M 39s\n",
      "195750K .......... .......... .......... .......... .......... 35% 10.9M 39s\n",
      "195800K .......... .......... .......... .......... .......... 35% 62.9M 39s\n",
      "195850K .......... .......... .......... .......... .......... 35% 7.26M 39s\n",
      "195900K .......... .......... .......... .......... .......... 35% 7.35M 39s\n",
      "195950K .......... .......... .......... .......... .......... 35% 9.54M 39s\n",
      "196000K .......... .......... .......... .......... .......... 35%  245M 39s\n",
      "196050K .......... .......... .......... .......... .......... 35% 11.5M 39s\n",
      "196100K .......... .......... .......... .......... .......... 35% 9.51M 39s\n",
      "196150K .......... .......... .......... .......... .......... 35% 21.9M 39s\n",
      "196200K .......... .......... .......... .......... .......... 35% 8.02M 39s\n",
      "196250K .......... .......... .......... .......... .......... 35% 9.86M 39s\n",
      "196300K .......... .......... .......... .......... .......... 35%  127M 39s\n",
      "196350K .......... .......... .......... .......... .......... 35% 8.48M 39s\n",
      "196400K .......... .......... .......... .......... .......... 35% 17.4M 39s\n",
      "196450K .......... .......... .......... .......... .......... 35% 7.50M 39s\n",
      "196500K .......... .......... .......... .......... .......... 35%  440M 39s\n",
      "196550K .......... .......... .......... .......... .......... 35% 41.8M 39s\n",
      "196600K .......... .......... .......... .......... .......... 35% 8.74M 39s\n",
      "196650K .......... .......... .......... .......... .......... 35% 14.0M 39s\n",
      "196700K .......... .......... .......... .......... .......... 35%  514M 39s\n",
      "196750K .......... .......... .......... .......... .......... 35% 10.1M 39s\n",
      "196800K .......... .......... .......... .......... .......... 35% 7.53M 39s\n",
      "196850K .......... .......... .......... .......... .......... 35% 37.1M 39s\n",
      "196900K .......... .......... .......... .......... .......... 35% 7.88M 39s\n",
      "196950K .......... .......... .......... .......... .......... 35% 13.0M 39s\n",
      "197000K .......... .......... .......... .......... .......... 35% 19.2M 39s\n",
      "197050K .......... .......... .......... .......... .......... 35% 9.85M 39s\n",
      "197100K .......... .......... .......... .......... .......... 35% 9.10M 39s\n",
      "197150K .......... .......... .......... .......... .......... 35% 82.3M 39s\n",
      "197200K .......... .......... .......... .......... .......... 35% 10.7M 39s\n",
      "197250K .......... .......... .......... .......... .......... 35% 1.43M 39s\n",
      "197300K .......... .......... .......... .......... .......... 35% 10.5M 39s\n",
      "197350K .......... .......... .......... .......... .......... 35% 41.4M 39s\n",
      "197400K .......... .......... .......... .......... .......... 35% 7.64M 39s\n",
      "197450K .......... .......... .......... .......... .......... 36% 19.4M 39s\n",
      "197500K .......... .......... .......... .......... .......... 36% 8.21M 39s\n",
      "197550K .......... .......... .......... .......... .......... 36% 8.94M 39s\n",
      "197600K .......... .......... .......... .......... .......... 36% 15.8M 39s\n",
      "197650K .......... .......... .......... .......... .......... 36% 8.79M 39s\n",
      "197700K .......... .......... .......... .......... .......... 36% 46.5M 39s\n",
      "197750K .......... .......... .......... .......... .......... 36% 17.3M 39s\n",
      "197800K .......... .......... .......... .......... .......... 36% 8.03M 39s\n",
      "197850K .......... .......... .......... .......... .......... 36% 8.49M 39s\n",
      "197900K .......... .......... .......... .......... .......... 36% 7.54M 39s\n",
      "197950K .......... .......... .......... .......... .......... 36% 9.87M 39s\n",
      "198000K .......... .......... .......... .......... .......... 36% 12.7M 39s\n",
      "198050K .......... .......... .......... .......... .......... 36% 11.4M 39s\n",
      "198100K .......... .......... .......... .......... .......... 36% 19.2M 39s\n",
      "198150K .......... .......... .......... .......... .......... 36% 20.6M 39s\n",
      "198200K .......... .......... .......... .......... .......... 36% 12.2M 39s\n",
      "198250K .......... .......... .......... .......... .......... 36% 7.98M 39s\n",
      "198300K .......... .......... .......... .......... .......... 36% 12.7M 39s\n",
      "198350K .......... .......... .......... .......... .......... 36% 7.43M 39s\n",
      "198400K .......... .......... .......... .......... .......... 36% 8.81M 39s\n",
      "198450K .......... .......... .......... .......... .......... 36% 14.2M 39s\n",
      "198500K .......... .......... .......... .......... .......... 36% 34.9M 39s\n",
      "198550K .......... .......... .......... .......... .......... 36% 10.6M 39s\n",
      "198600K .......... .......... .......... .......... .......... 36% 10.3M 39s\n",
      "198650K .......... .......... .......... .......... .......... 36% 16.8M 39s\n",
      "198700K .......... .......... .......... .......... .......... 36% 46.8M 39s\n",
      "198750K .......... .......... .......... .......... .......... 36% 8.99M 39s\n",
      "198800K .......... .......... .......... .......... .......... 36% 8.72M 39s\n",
      "198850K .......... .......... .......... .......... .......... 36% 19.9M 39s\n",
      "198900K .......... .......... .......... .......... .......... 36% 22.8M 39s\n",
      "198950K .......... .......... .......... .......... .......... 36% 8.33M 39s\n",
      "199000K .......... .......... .......... .......... .......... 36% 75.7M 39s\n",
      "199050K .......... .......... .......... .......... .......... 36% 8.24M 39s\n",
      "199100K .......... .......... .......... .......... .......... 36% 7.51M 39s\n",
      "199150K .......... .......... .......... .......... .......... 36% 17.6M 39s\n",
      "199200K .......... .......... .......... .......... .......... 36% 49.8M 39s\n",
      "199250K .......... .......... .......... .......... .......... 36% 9.20M 39s\n",
      "199300K .......... .......... .......... .......... .......... 36% 6.04M 39s\n",
      "199350K .......... .......... .......... .......... .......... 36% 84.3M 39s\n",
      "199400K .......... .......... .......... .......... .......... 36% 8.06M 39s\n",
      "199450K .......... .......... .......... .......... .......... 36% 29.0M 39s\n",
      "199500K .......... .......... .......... .......... .......... 36% 1.58M 39s\n",
      "199550K .......... .......... .......... .......... .......... 36% 9.54M 39s\n",
      "199600K .......... .......... .......... .......... .......... 36% 25.9M 39s\n",
      "199650K .......... .......... .......... .......... .......... 36% 7.64M 39s\n",
      "199700K .......... .......... .......... .......... .......... 36%  525M 39s\n",
      "199750K .......... .......... .......... .......... .......... 36% 17.0M 39s\n",
      "199800K .......... .......... .......... .......... .......... 36% 9.76M 39s\n",
      "199850K .......... .......... .......... .......... .......... 36% 6.31M 39s\n",
      "199900K .......... .......... .......... .......... .......... 36%  359M 39s\n",
      "199950K .......... .......... .......... .......... .......... 36% 6.01M 39s\n",
      "200000K .......... .......... .......... .......... .......... 36% 18.4M 39s\n",
      "200050K .......... .......... .......... .......... .......... 36% 23.9M 38s\n",
      "200100K .......... .......... .......... .......... .......... 36% 31.3M 38s\n",
      "200150K .......... .......... .......... .......... .......... 36% 7.64M 38s\n",
      "200200K .......... .......... .......... .......... .......... 36%  307M 38s\n",
      "200250K .......... .......... .......... .......... .......... 36% 4.96M 38s\n",
      "200300K .......... .......... .......... .......... .......... 36% 92.7M 38s\n",
      "200350K .......... .......... .......... .......... .......... 36% 2.75M 38s\n",
      "200400K .......... .......... .......... .......... .......... 36% 3.82M 38s\n",
      "200450K .......... .......... .......... .......... .......... 36% 8.03M 38s\n",
      "200500K .......... .......... .......... .......... .......... 36% 5.29M 38s\n",
      "200550K .......... .......... .......... .......... .......... 36% 8.86M 38s\n",
      "200600K .......... .......... .......... .......... .......... 36% 24.6M 38s\n",
      "200650K .......... .......... .......... .......... .......... 36% 19.6M 38s\n",
      "200700K .......... .......... .......... .......... .......... 36% 7.71M 38s\n",
      "200750K .......... .......... .......... .......... .......... 36%  263M 38s\n",
      "200800K .......... .......... .......... .......... .......... 36% 16.0M 38s\n",
      "200850K .......... .......... .......... .......... .......... 36%  513K 39s\n",
      "200900K .......... .......... .......... .......... .......... 36% 21.5M 39s\n",
      "200950K .......... .......... .......... .......... .......... 36% 20.2M 39s\n",
      "201000K .......... .......... .......... .......... .......... 36% 22.0M 39s\n",
      "201050K .......... .......... .......... .......... .......... 36% 5.25M 39s\n",
      "201100K .......... .......... .......... .......... .......... 36% 21.8M 39s\n",
      "201150K .......... .......... .......... .......... .......... 36% 19.5M 39s\n",
      "201200K .......... .......... .......... .......... .......... 36% 36.9M 38s\n",
      "201250K .......... .......... .......... .......... .......... 36% 4.74M 39s\n",
      "201300K .......... .......... .......... .......... .......... 36% 16.1M 38s\n",
      "201350K .......... .......... .......... .......... .......... 36% 21.4M 38s\n",
      "201400K .......... .......... .......... .......... .......... 36% 11.3M 38s\n",
      "201450K .......... .......... .......... .......... .......... 36% 14.5M 38s\n",
      "201500K .......... .......... .......... .......... .......... 36% 9.48M 38s\n",
      "201550K .......... .......... .......... .......... .......... 36% 35.3M 38s\n",
      "201600K .......... .......... .......... .......... .......... 36% 22.6M 38s\n",
      "201650K .......... .......... .......... .......... .......... 36% 10.3M 38s\n",
      "201700K .......... .......... .......... .......... .......... 36% 12.4M 38s\n",
      "201750K .......... .......... .......... .......... .......... 36% 29.2M 38s\n",
      "201800K .......... .......... .......... .......... .......... 36% 10.6M 38s\n",
      "201850K .......... .......... .......... .......... .......... 36% 10.2M 38s\n",
      "201900K .......... .......... .......... .......... .......... 36% 83.2M 38s\n",
      "201950K .......... .......... .......... .......... .......... 36% 10.0M 38s\n",
      "202000K .......... .......... .......... .......... .......... 36% 28.5M 38s\n",
      "202050K .......... .......... .......... .......... .......... 36% 5.97M 38s\n",
      "202100K .......... .......... .......... .......... .......... 36% 14.2M 38s\n",
      "202150K .......... .......... .......... .......... .......... 36%  356M 38s\n",
      "202200K .......... .......... .......... .......... .......... 36% 6.19M 38s\n",
      "202250K .......... .......... .......... .......... .......... 36%  531M 38s\n",
      "202300K .......... .......... .......... .......... .......... 36% 13.7M 38s\n",
      "202350K .......... .......... .......... .......... .......... 36% 7.93M 38s\n",
      "202400K .......... .......... .......... .......... .......... 36% 59.0M 38s\n",
      "202450K .......... .......... .......... .......... .......... 36% 16.2M 38s\n",
      "202500K .......... .......... .......... .......... .......... 36% 7.16M 38s\n",
      "202550K .......... .......... .......... .......... .......... 36% 7.71M 38s\n",
      "202600K .......... .......... .......... .......... .......... 36% 22.2M 38s\n",
      "202650K .......... .......... .......... .......... .......... 36% 7.13M 38s\n",
      "202700K .......... .......... .......... .......... .......... 36% 20.9M 38s\n",
      "202750K .......... .......... .......... .......... .......... 36% 9.26M 38s\n",
      "202800K .......... .......... .......... .......... .......... 36% 4.68M 38s\n",
      "202850K .......... .......... .......... .......... .......... 36%  456M 38s\n",
      "202900K .......... .......... .......... .......... .......... 37% 17.3M 38s\n",
      "202950K .......... .......... .......... .......... .......... 37% 8.79M 38s\n",
      "203000K .......... .......... .......... .......... .......... 37% 5.19M 38s\n",
      "203050K .......... .......... .......... .......... .......... 37% 1.59M 38s\n",
      "203100K .......... .......... .......... .......... .......... 37% 21.5M 38s\n",
      "203150K .......... .......... .......... .......... .......... 37% 8.84M 38s\n",
      "203200K .......... .......... .......... .......... .......... 37% 24.1M 38s\n",
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      "215100K .......... .......... .......... .......... .......... 39% 10.9M 37s\n",
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      "215200K .......... .......... .......... .......... .......... 39% 34.5M 37s\n",
      "215250K .......... .......... .......... .......... .......... 39% 10.8M 37s\n",
      "215300K .......... .......... .......... .......... .......... 39% 13.2M 37s\n",
      "215350K .......... .......... .......... .......... .......... 39% 10.3M 37s\n",
      "215400K .......... .......... .......... .......... .......... 39% 22.3M 37s\n",
      "215450K .......... .......... .......... .......... .......... 39% 15.7M 37s\n",
      "215500K .......... .......... .......... .......... .......... 39% 8.25M 37s\n",
      "215550K .......... .......... .......... .......... .......... 39% 9.14M 37s\n",
      "215600K .......... .......... .......... .......... .......... 39% 42.2M 37s\n",
      "215650K .......... .......... .......... .......... .......... 39% 16.0M 37s\n",
      "215700K .......... .......... .......... .......... .......... 39% 8.33M 37s\n",
      "215750K .......... .......... .......... .......... .......... 39% 22.4M 37s\n",
      "215800K .......... .......... .......... .......... .......... 39% 5.81M 37s\n",
      "215850K .......... .......... .......... .......... .......... 39%  284M 37s\n",
      "215900K .......... .......... .......... .......... .......... 39%  425M 37s\n",
      "215950K .......... .......... .......... .......... .......... 39% 7.56M 37s\n",
      "216000K .......... .......... .......... .......... .......... 39% 25.1M 37s\n",
      "216050K .......... .......... .......... .......... .......... 39% 5.40M 37s\n",
      "216100K .......... .......... .......... .......... .......... 39% 24.8M 37s\n",
      "216150K .......... .......... .......... .......... .......... 39% 15.3M 37s\n",
      "216200K .......... .......... .......... .......... .......... 39% 9.66M 37s\n",
      "216250K .......... .......... .......... .......... .......... 39% 22.2M 37s\n",
      "216300K .......... .......... .......... .......... .......... 39% 13.5M 37s\n",
      "216350K .......... .......... .......... .......... .......... 39% 5.23M 37s\n",
      "216400K .......... .......... .......... .......... .......... 39%  328M 37s\n",
      "216450K .......... .......... .......... .......... .......... 39% 1.94M 37s\n",
      "216500K .......... .......... .......... .......... .......... 39% 12.5M 37s\n",
      "216550K .......... .......... .......... .......... .......... 39% 14.8M 37s\n",
      "216600K .......... .......... .......... .......... .......... 39% 22.5M 37s\n",
      "216650K .......... .......... .......... .......... .......... 39% 8.07M 37s\n",
      "216700K .......... .......... .......... .......... .......... 39% 21.7M 37s\n",
      "216750K .......... .......... .......... .......... .......... 39% 6.34M 37s\n",
      "216800K .......... .......... .......... .......... .......... 39% 10.1M 37s\n",
      "216850K .......... .......... .......... .......... .......... 39%  618M 37s\n",
      "216900K .......... .......... .......... .......... .......... 39% 15.5M 37s\n",
      "216950K .......... .......... .......... .......... .......... 39% 5.86M 37s\n",
      "217000K .......... .......... .......... .......... .......... 39% 9.07M 37s\n",
      "217050K .......... .......... .......... .......... .......... 39% 8.45M 37s\n",
      "217100K .......... .......... .......... .......... .......... 39% 93.9M 37s\n",
      "217150K .......... .......... .......... .......... .......... 39% 8.75M 37s\n",
      "217200K .......... .......... .......... .......... .......... 39% 25.0M 36s\n",
      "217250K .......... .......... .......... .......... .......... 39% 17.0M 36s\n",
      "217300K .......... .......... .......... .......... .......... 39% 6.62M 36s\n",
      "217350K .......... .......... .......... .......... .......... 39% 8.59M 36s\n",
      "217400K .......... .......... .......... .......... .......... 39% 16.5M 36s\n",
      "217450K .......... .......... .......... .......... .......... 39% 13.5M 36s\n",
      "217500K .......... .......... .......... .......... .......... 39% 18.1M 36s\n",
      "217550K .......... .......... .......... .......... .......... 39% 11.2M 36s\n",
      "217600K .......... .......... .......... .......... .......... 39% 10.6M 36s\n",
      "217650K .......... .......... .......... .......... .......... 39% 10.5M 36s\n",
      "217700K .......... .......... .......... .......... .......... 39% 15.7M 36s\n",
      "217750K .......... .......... .......... .......... .......... 39% 16.1M 36s\n",
      "217800K .......... .......... .......... .......... .......... 39% 11.4M 36s\n",
      "217850K .......... .......... .......... .......... .......... 39% 16.0M 36s\n",
      "217900K .......... .......... .......... .......... .......... 39% 12.0M 36s\n",
      "217950K .......... .......... .......... .......... .......... 39% 6.68M 36s\n",
      "218000K .......... .......... .......... .......... .......... 39% 10.9M 36s\n",
      "218050K .......... .......... .......... .......... .......... 39% 19.6M 36s\n",
      "218100K .......... .......... .......... .......... .......... 39% 9.74M 36s\n",
      "218150K .......... .......... .......... .......... .......... 39% 17.4M 36s\n",
      "218200K .......... .......... .......... .......... .......... 39% 9.25M 36s\n",
      "218250K .......... .......... .......... .......... .......... 39% 20.7M 36s\n",
      "218300K .......... .......... .......... .......... .......... 39% 18.8M 36s\n",
      "218350K .......... .......... .......... .......... .......... 39% 8.38M 36s\n",
      "218400K .......... .......... .......... .......... .......... 39% 14.1M 36s\n",
      "218450K .......... .......... .......... .......... .......... 39% 10.2M 36s\n",
      "218500K .......... .......... .......... .......... .......... 39% 47.9M 36s\n",
      "218550K .......... .......... .......... .......... .......... 39% 11.4M 36s\n",
      "218600K .......... .......... .......... .......... .......... 39% 10.3M 36s\n",
      "218650K .......... .......... .......... .......... .......... 39%  351M 36s\n",
      "218700K .......... .......... .......... .......... .......... 39% 6.25M 36s\n",
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      "218800K .......... .......... .......... .......... .......... 39% 9.44M 36s\n",
      "218850K .......... .......... .......... .......... .......... 39% 23.3M 36s\n",
      "218900K .......... .......... .......... .......... .......... 39% 6.41M 36s\n",
      "218950K .......... .......... .......... .......... .......... 39%  388M 36s\n",
      "219000K .......... .......... .......... .......... .......... 39% 11.8M 36s\n",
      "219050K .......... .......... .......... .......... .......... 39% 15.1M 36s\n",
      "219100K .......... .......... .......... .......... .......... 39% 1.56M 36s\n",
      "219150K .......... .......... .......... .......... .......... 39% 9.65M 36s\n",
      "219200K .......... .......... .......... .......... .......... 39% 7.42M 36s\n",
      "219250K .......... .......... .......... .......... .......... 39% 11.3M 36s\n",
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      "219350K .......... .......... .......... .......... .......... 39% 6.19M 36s\n",
      "219400K .......... .......... .......... .......... .......... 40%  166M 36s\n",
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      "219600K .......... .......... .......... .......... .......... 40%  425M 36s\n",
      "219650K .......... .......... .......... .......... .......... 40% 15.3M 36s\n",
      "219700K .......... .......... .......... .......... .......... 40% 17.8M 36s\n",
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      "219800K .......... .......... .......... .......... .......... 40%  452M 36s\n",
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      "220000K .......... .......... .......... .......... .......... 40% 3.86M 36s\n",
      "220050K .......... .......... .......... .......... .......... 40% 27.1M 36s\n",
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      "220500K .......... .......... .......... .......... .......... 40% 10.3M 36s\n",
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      "221000K .......... .......... .......... .......... .......... 40% 17.2M 36s\n",
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      "221100K .......... .......... .......... .......... .......... 40% 47.0M 36s\n",
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      "221300K .......... .......... .......... .......... .......... 40%  527K 36s\n",
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      "223000K .......... .......... .......... .......... .......... 40% 23.7M 36s\n",
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      "227250K .......... .......... .......... .......... .......... 41% 8.16M 35s\n",
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      "227350K .......... .......... .......... .......... .......... 41% 22.4M 35s\n",
      "227400K .......... .......... .......... .......... .......... 41% 53.4M 35s\n",
      "227450K .......... .......... .......... .......... .......... 41% 5.50M 35s\n",
      "227500K .......... .......... .......... .......... .......... 41% 15.3M 35s\n",
      "227550K .......... .......... .......... .......... .......... 41% 14.1M 35s\n",
      "227600K .......... .......... .......... .......... .......... 41% 18.4M 35s\n",
      "227650K .......... .......... .......... .......... .......... 41% 19.1M 35s\n",
      "227700K .......... .......... .......... .......... .......... 41% 51.8M 35s\n",
      "227750K .......... .......... .......... .......... .......... 41% 7.35M 35s\n",
      "227800K .......... .......... .......... .......... .......... 41% 17.1M 35s\n",
      "227850K .......... .......... .......... .......... .......... 41% 12.2M 35s\n",
      "227900K .......... .......... .......... .......... .......... 41% 29.7M 35s\n",
      "227950K .......... .......... .......... .......... .......... 41% 3.23M 35s\n",
      "228000K .......... .......... .......... .......... .......... 41% 7.55M 35s\n",
      "228050K .......... .......... .......... .......... .......... 41% 17.5M 35s\n",
      "228100K .......... .......... .......... .......... .......... 41% 1.16M 35s\n",
      "228150K .......... .......... .......... .......... .......... 41% 16.7M 35s\n",
      "228200K .......... .......... .......... .......... .......... 41% 12.5M 35s\n",
      "228250K .......... .......... .......... .......... .......... 41% 15.9M 35s\n",
      "228300K .......... .......... .......... .......... .......... 41% 7.00M 35s\n",
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      "228450K .......... .......... .......... .......... .......... 41% 40.1M 35s\n",
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      "229050K .......... .......... .......... .......... .......... 41% 11.3M 35s\n",
      "229100K .......... .......... .......... .......... .......... 41% 8.55M 35s\n",
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      "229200K .......... .......... .......... .......... .......... 41% 45.0M 35s\n",
      "229250K .......... .......... .......... .......... .......... 41% 10.5M 35s\n",
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      "229600K .......... .......... .......... .......... .......... 41% 89.9M 35s\n",
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      "230000K .......... .......... .......... .......... .......... 41% 9.35M 35s\n",
      "230050K .......... .......... .......... .......... .......... 41% 16.2M 35s\n",
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      "263550K .......... .......... .......... .......... .......... 48% 8.81M 31s\n",
      "263600K .......... .......... .......... .......... .......... 48% 5.31M 31s\n",
      "263650K .......... .......... .......... .......... .......... 48% 16.1M 31s\n",
      "263700K .......... .......... .......... .......... .......... 48% 17.6M 31s\n",
      "263750K .......... .......... .......... .......... .......... 48% 6.43M 31s\n",
      "263800K .......... .......... .......... .......... .......... 48%  137M 31s\n",
      "263850K .......... .......... .......... .......... .......... 48% 12.6M 31s\n",
      "263900K .......... .......... .......... .......... .......... 48% 10.5M 31s\n",
      "263950K .......... .......... .......... .......... .......... 48% 9.26M 31s\n",
      "264000K .......... .......... .......... .......... .......... 48% 59.6M 31s\n",
      "264050K .......... .......... .......... .......... .......... 48% 6.56M 31s\n",
      "264100K .......... .......... .......... .......... .......... 48% 10.9M 31s\n",
      "264150K .......... .......... .......... .......... .......... 48% 14.5M 31s\n",
      "264200K .......... .......... .......... .......... .......... 48% 6.92M 31s\n",
      "264250K .......... .......... .......... .......... .......... 48% 47.5M 31s\n",
      "264300K .......... .......... .......... .......... .......... 48% 7.37M 31s\n",
      "264350K .......... .......... .......... .......... .......... 48% 8.86M 31s\n",
      "264400K .......... .......... .......... .......... .......... 48% 23.7M 31s\n",
      "264450K .......... .......... .......... .......... .......... 48% 12.3M 31s\n",
      "264500K .......... .......... .......... .......... .......... 48% 15.4M 31s\n",
      "264550K .......... .......... .......... .......... .......... 48% 9.30M 31s\n",
      "264600K .......... .......... .......... .......... .......... 48% 10.0M 31s\n",
      "264650K .......... .......... .......... .......... .......... 48% 8.71M 31s\n",
      "264700K .......... .......... .......... .......... .......... 48% 7.96M 31s\n",
      "264750K .......... .......... .......... .......... .......... 48% 47.2M 31s\n",
      "264800K .......... .......... .......... .......... .......... 48% 8.43M 31s\n",
      "264850K .......... .......... .......... .......... .......... 48% 19.9M 31s\n",
      "264900K .......... .......... .......... .......... .......... 48% 14.4M 31s\n",
      "264950K .......... .......... .......... .......... .......... 48% 9.30M 31s\n",
      "265000K .......... .......... .......... .......... .......... 48%  417M 31s\n",
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      "265100K .......... .......... .......... .......... .......... 48% 30.6M 31s\n",
      "265150K .......... .......... .......... .......... .......... 48% 6.07M 31s\n",
      "265200K .......... .......... .......... .......... .......... 48% 11.3M 31s\n",
      "265250K .......... .......... .......... .......... .......... 48% 12.2M 31s\n",
      "265300K .......... .......... .......... .......... .......... 48% 14.2M 31s\n",
      "265350K .......... .......... .......... .......... .......... 48% 22.6M 31s\n",
      "265400K .......... .......... .......... .......... .......... 48% 9.66M 31s\n",
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      "265550K .......... .......... .......... .......... .......... 48% 1.30M 31s\n",
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      "266350K .......... .......... .......... .......... .......... 48% 3.39M 31s\n",
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      "266550K .......... .......... .......... .......... .......... 48% 10.0M 31s\n",
      "266600K .......... .......... .......... .......... .......... 48% 17.2M 31s\n",
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      "299750K .......... .......... .......... .......... .......... 54% 7.62M 27s\n",
      "299800K .......... .......... .......... .......... .......... 54% 2.40M 27s\n",
      "299850K .......... .......... .......... .......... .......... 54% 3.73M 27s\n",
      "299900K .......... .......... .......... .......... .......... 54% 9.65M 27s\n",
      "299950K .......... .......... .......... .......... .......... 54%  370M 27s\n",
      "300000K .......... .......... .......... .......... .......... 54% 6.50M 27s\n",
      "300050K .......... .......... .......... .......... .......... 54% 40.1M 27s\n",
      "300100K .......... .......... .......... .......... .......... 54% 9.13M 27s\n",
      "300150K .......... .......... .......... .......... .......... 54% 10.8M 27s\n",
      "300200K .......... .......... .......... .......... .......... 54% 22.6M 27s\n",
      "300250K .......... .......... .......... .......... .......... 54% 18.9M 27s\n",
      "300300K .......... .......... .......... .......... .......... 54% 8.03M 27s\n",
      "300350K .......... .......... .......... .......... .......... 54%  101M 27s\n",
      "300400K .......... .......... .......... .......... .......... 54% 9.14M 27s\n",
      "300450K .......... .......... .......... .......... .......... 54% 18.9M 27s\n",
      "300500K .......... .......... .......... .......... .......... 54% 19.3M 27s\n",
      "300550K .......... .......... .......... .......... .......... 54% 17.9M 27s\n",
      "300600K .......... .......... .......... .......... .......... 54% 1.14M 27s\n",
      "300650K .......... .......... .......... .......... .......... 54%  207M 27s\n",
      "300700K .......... .......... .......... .......... .......... 54% 14.8M 27s\n",
      "300750K .......... .......... .......... .......... .......... 54% 6.06M 27s\n",
      "300800K .......... .......... .......... .......... .......... 54%  461M 27s\n",
      "300850K .......... .......... .......... .......... .......... 54% 8.33M 27s\n",
      "300900K .......... .......... .......... .......... .......... 54% 34.6M 27s\n",
      "300950K .......... .......... .......... .......... .......... 54% 9.58M 27s\n",
      "301000K .......... .......... .......... .......... .......... 54% 19.3M 27s\n",
      "301050K .......... .......... .......... .......... .......... 54% 41.4M 27s\n",
      "301100K .......... .......... .......... .......... .......... 54% 10.1M 27s\n",
      "301150K .......... .......... .......... .......... .......... 54% 11.1M 27s\n",
      "301200K .......... .......... .......... .......... .......... 54% 10.4M 27s\n",
      "301250K .......... .......... .......... .......... .......... 54% 76.9M 27s\n",
      "301300K .......... .......... .......... .......... .......... 54% 9.06M 27s\n",
      "301350K .......... .......... .......... .......... .......... 54% 19.6M 27s\n",
      "301400K .......... .......... .......... .......... .......... 54% 11.0M 27s\n",
      "301450K .......... .......... .......... .......... .......... 54% 23.3M 27s\n",
      "301500K .......... .......... .......... .......... .......... 54% 14.0M 27s\n",
      "301550K .......... .......... .......... .......... .......... 54% 10.8M 27s\n",
      "301600K .......... .......... .......... .......... .......... 54% 4.81M 27s\n",
      "301650K .......... .......... .......... .......... .......... 55% 22.5M 27s\n",
      "301700K .......... .......... .......... .......... .......... 55% 4.84M 27s\n",
      "301750K .......... .......... .......... .......... .......... 55% 15.1M 27s\n",
      "301800K .......... .......... .......... .......... .......... 55% 9.86M 27s\n",
      "301850K .......... .......... .......... .......... .......... 55% 18.7M 27s\n",
      "301900K .......... .......... .......... .......... .......... 55% 6.22M 27s\n",
      "301950K .......... .......... .......... .......... .......... 55% 14.1M 27s\n",
      "302000K .......... .......... .......... .......... .......... 55% 11.2M 27s\n",
      "302050K .......... .......... .......... .......... .......... 55% 19.3M 27s\n",
      "302100K .......... .......... .......... .......... .......... 55% 7.96M 27s\n",
      "302150K .......... .......... .......... .......... .......... 55% 10.2M 27s\n",
      "302200K .......... .......... .......... .......... .......... 55%  549M 27s\n",
      "302250K .......... .......... .......... .......... .......... 55% 4.71M 27s\n",
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      "302350K .......... .......... .......... .......... .......... 55% 8.41M 27s\n",
      "302400K .......... .......... .......... .......... .......... 55% 16.2M 27s\n",
      "302450K .......... .......... .......... .......... .......... 55% 11.3M 27s\n",
      "302500K .......... .......... .......... .......... .......... 55% 39.9M 27s\n",
      "302550K .......... .......... .......... .......... .......... 55% 15.0M 27s\n",
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      "302650K .......... .......... .......... .......... .......... 55%  188M 27s\n",
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      "302800K .......... .......... .......... .......... .......... 55%  198M 27s\n",
      "302850K .......... .......... .......... .......... .......... 55% 1.50M 27s\n",
      "302900K .......... .......... .......... .......... .......... 55% 99.0M 27s\n",
      "302950K .......... .......... .......... .......... .......... 55% 14.7M 27s\n",
      "303000K .......... .......... .......... .......... .......... 55% 8.66M 27s\n",
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      "303100K .......... .......... .......... .......... .......... 55% 15.8M 27s\n",
      "303150K .......... .......... .......... .......... .......... 55% 4.75M 27s\n",
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      "303250K .......... .......... .......... .......... .......... 55% 49.3M 27s\n",
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      "304100K .......... .......... .......... .......... .......... 55%  400M 27s\n",
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      "311900K .......... .......... .......... .......... .......... 56% 5.81M 26s\n",
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      "360150K .......... .......... .......... .......... .......... 65% 5.22M 21s\n",
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      "360250K .......... .......... .......... .......... .......... 65% 15.0M 21s\n",
      "360300K .......... .......... .......... .......... .......... 65%  219M 21s\n",
      "360350K .......... .......... .......... .......... .......... 65% 12.3M 21s\n",
      "360400K .......... .......... .......... .......... .......... 65% 8.97M 21s\n",
      "360450K .......... .......... .......... .......... .......... 65% 40.8M 21s\n",
      "360500K .......... .......... .......... .......... .......... 65% 8.74M 21s\n",
      "360550K .......... .......... .......... .......... .......... 65% 6.49M 21s\n",
      "360600K .......... .......... .......... .......... .......... 65% 37.0M 21s\n",
      "360650K .......... .......... .......... .......... .......... 65% 22.3M 21s\n",
      "360700K .......... .......... .......... .......... .......... 65% 22.3M 21s\n",
      "360750K .......... .......... .......... .......... .......... 65% 13.4M 21s\n",
      "360800K .......... .......... .......... .......... .......... 65% 8.47M 21s\n",
      "360850K .......... .......... .......... .......... .......... 65% 6.99M 21s\n",
      "360900K .......... .......... .......... .......... .......... 65%  428M 21s\n",
      "360950K .......... .......... .......... .......... .......... 65% 3.60M 21s\n",
      "361000K .......... .......... .......... .......... .......... 65% 20.2M 21s\n",
      "361050K .......... .......... .......... .......... .......... 65% 8.82M 21s\n",
      "361100K .......... .......... .......... .......... .......... 65% 20.4M 21s\n",
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      "361200K .......... .......... .......... .......... .......... 65%  175M 21s\n",
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      "361300K .......... .......... .......... .......... .......... 65% 55.6M 21s\n",
      "361350K .......... .......... .......... .......... .......... 65% 1.53M 21s\n",
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      "361450K .......... .......... .......... .......... .......... 65% 9.50M 21s\n",
      "361500K .......... .......... .......... .......... .......... 65% 18.5M 21s\n",
      "361550K .......... .......... .......... .......... .......... 65% 4.02M 21s\n",
      "361600K .......... .......... .......... .......... .......... 65% 19.6M 21s\n",
      "361650K .......... .......... .......... .......... .......... 65% 18.9M 21s\n",
      "361700K .......... .......... .......... .......... .......... 65% 33.1M 21s\n",
      "361750K .......... .......... .......... .......... .......... 65% 10.6M 21s\n",
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      "361900K .......... .......... .......... .......... .......... 65% 31.8M 21s\n",
      "361950K .......... .......... .......... .......... .......... 65% 9.91M 21s\n",
      "362000K .......... .......... .......... .......... .......... 66% 10.7M 21s\n",
      "362050K .......... .......... .......... .......... .......... 66% 11.5M 21s\n",
      "362100K .......... .......... .......... .......... .......... 66%  187M 21s\n",
      "362150K .......... .......... .......... .......... .......... 66% 8.78M 21s\n",
      "362200K .......... .......... .......... .......... .......... 66% 20.2M 21s\n",
      "362250K .......... .......... .......... .......... .......... 66% 10.2M 21s\n",
      "362300K .......... .......... .......... .......... .......... 66% 83.8M 21s\n",
      "362350K .......... .......... .......... .......... .......... 66% 12.1M 21s\n",
      "362400K .......... .......... .......... .......... .......... 66% 21.7M 21s\n",
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      "362500K .......... .......... .......... .......... .......... 66% 6.13M 21s\n",
      "362550K .......... .......... .......... .......... .......... 66% 23.1M 21s\n",
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      "362800K .......... .......... .......... .......... .......... 66% 10.6M 21s\n",
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      "363000K .......... .......... .......... .......... .......... 66% 19.1M 21s\n",
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      "363100K .......... .......... .......... .......... .......... 66% 9.48M 21s\n",
      "363150K .......... .......... .......... .......... .......... 66%  410M 21s\n",
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      "364000K .......... .......... .......... .......... .......... 66% 42.7M 21s\n",
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      "364200K .......... .......... .......... .......... .......... 66% 1.78M 21s\n",
      "364250K .......... .......... .......... .......... .......... 66% 4.55M 21s\n",
      "364300K .......... .......... .......... .......... .......... 66%  626M 21s\n",
      "364350K .......... .......... .......... .......... .......... 66% 7.41M 21s\n",
      "364400K .......... .......... .......... .......... .......... 66% 11.0M 21s\n",
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      "364500K .......... .......... .......... .......... .......... 66% 18.6M 21s\n",
      "364550K .......... .......... .......... .......... .......... 66% 6.10M 21s\n",
      "364600K .......... .......... .......... .......... .......... 66% 10.2M 21s\n",
      "364650K .......... .......... .......... .......... .......... 66% 15.0M 21s\n",
      "364700K .......... .......... .......... .......... .......... 66% 15.9M 21s\n",
      "364750K .......... .......... .......... .......... .......... 66% 4.77M 20s\n",
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      "372250K .......... .......... .......... .......... .......... 67% 6.39M 20s\n",
      "372300K .......... .......... .......... .......... .......... 67% 1.09M 20s\n",
      "372350K .......... .......... .......... .......... .......... 67% 6.57M 20s\n",
      "372400K .......... .......... .......... .......... .......... 67% 19.1M 20s\n",
      "372450K .......... .......... .......... .......... .......... 67% 15.7M 20s\n",
      "372500K .......... .......... .......... .......... .......... 67% 48.6M 20s\n",
      "372550K .......... .......... .......... .......... .......... 67% 7.32M 20s\n",
      "372600K .......... .......... .......... .......... .......... 67% 36.9M 20s\n",
      "372650K .......... .......... .......... .......... .......... 67% 7.00M 20s\n",
      "372700K .......... .......... .......... .......... .......... 67% 69.5M 20s\n",
      "372750K .......... .......... .......... .......... .......... 67% 5.17M 20s\n",
      "372800K .......... .......... .......... .......... .......... 67%  332M 20s\n",
      "372850K .......... .......... .......... .......... .......... 67% 19.4M 20s\n",
      "372900K .......... .......... .......... .......... .......... 67% 6.94M 20s\n",
      "372950K .......... .......... .......... .......... .......... 68% 6.81M 20s\n",
      "373000K .......... .......... .......... .......... .......... 68% 15.5M 20s\n",
      "373050K .......... .......... .......... .......... .......... 68% 8.62M 20s\n",
      "373100K .......... .......... .......... .......... .......... 68% 13.6M 20s\n",
      "373150K .......... .......... .......... .......... .......... 68% 7.18M 20s\n",
      "373200K .......... .......... .......... .......... .......... 68% 19.1M 20s\n",
      "373250K .......... .......... .......... .......... .......... 68% 8.60M 20s\n",
      "373300K .......... .......... .......... .......... .......... 68% 20.4M 20s\n",
      "373350K .......... .......... .......... .......... .......... 68% 14.1M 20s\n",
      "373400K .......... .......... .......... .......... .......... 68% 11.5M 20s\n",
      "373450K .......... .......... .......... .......... .......... 68% 14.2M 20s\n",
      "373500K .......... .......... .......... .......... .......... 68% 8.65M 20s\n",
      "373550K .......... .......... .......... .......... .......... 68% 10.3M 20s\n",
      "373600K .......... .......... .......... .......... .......... 68% 21.3M 19s\n",
      "373650K .......... .......... .......... .......... .......... 68% 29.5M 19s\n",
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      "374000K .......... .......... .......... .......... .......... 68% 15.2M 19s\n",
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      "374100K .......... .......... .......... .......... .......... 68% 10.5M 19s\n",
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      "375000K .......... .......... .......... .......... .......... 68% 60.1M 19s\n",
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      "384300K .......... .......... .......... .......... .......... 70%  394M 18s\n",
      "384350K .......... .......... .......... .......... .......... 70% 8.34M 18s\n",
      "384400K .......... .......... .......... .......... .......... 70% 14.5M 18s\n",
      "384450K .......... .......... .......... .......... .......... 70% 6.64M 18s\n",
      "384500K .......... .......... .......... .......... .......... 70%  112M 18s\n",
      "384550K .......... .......... .......... .......... .......... 70% 7.18M 18s\n",
      "384600K .......... .......... .......... .......... .......... 70% 19.7M 18s\n",
      "384650K .......... .......... .......... .......... .......... 70% 15.0M 18s\n",
      "384700K .......... .......... .......... .......... .......... 70% 26.4M 18s\n",
      "384750K .......... .......... .......... .......... .......... 70% 3.67M 18s\n",
      "384800K .......... .......... .......... .......... .......... 70%  185M 18s\n",
      "384850K .......... .......... .......... .......... .......... 70% 76.7M 18s\n",
      "384900K .......... .......... .......... .......... .......... 70% 8.39M 18s\n",
      "384950K .......... .......... .......... .......... .......... 70% 6.98M 18s\n",
      "385000K .......... .......... .......... .......... .......... 70% 5.28M 18s\n",
      "385050K .......... .......... .......... .......... .......... 70% 19.7M 18s\n",
      "385100K .......... .......... .......... .......... .......... 70% 8.73M 18s\n",
      "385150K .......... .......... .......... .......... .......... 70% 12.1M 18s\n",
      "385200K .......... .......... .......... .......... .......... 70% 25.2M 18s\n",
      "385250K .......... .......... .......... .......... .......... 70% 22.1M 18s\n",
      "385300K .......... .......... .......... .......... .......... 70% 10.3M 18s\n",
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      "385450K .......... .......... .......... .......... .......... 70% 37.7M 18s\n",
      "385500K .......... .......... .......... .......... .......... 70% 10.6M 18s\n",
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      "385700K .......... .......... .......... .......... .......... 70% 12.7M 18s\n",
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      "385850K .......... .......... .......... .......... .......... 70% 12.8M 18s\n",
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      "386000K .......... .......... .......... .......... .......... 70% 4.37M 18s\n",
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      "386100K .......... .......... .......... .......... .......... 70% 78.1M 18s\n",
      "386150K .......... .......... .......... .......... .......... 70% 10.2M 18s\n",
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      "386350K .......... .......... .......... .......... .......... 70% 11.3M 18s\n",
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      "386700K .......... .......... .......... .......... .......... 70% 99.4M 18s\n",
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      "386800K .......... .......... .......... .......... .......... 70%  561K 18s\n",
      "386850K .......... .......... .......... .......... .......... 70% 14.2M 18s\n",
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      "387000K .......... .......... .......... .......... .......... 70% 1.24M 18s\n",
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      "387100K .......... .......... .......... .......... .......... 70% 10.7M 18s\n",
      "387150K .......... .......... .......... .......... .......... 70% 13.9M 18s\n",
      "387200K .......... .......... .......... .......... .......... 70% 18.4M 18s\n",
      "387250K .......... .......... .......... .......... .......... 70% 26.9M 18s\n",
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      "388000K .......... .......... .......... .......... .......... 70% 46.7M 18s\n",
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      "388150K .......... .......... .......... .......... .......... 70%  315M 18s\n",
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      "396350K .......... .......... .......... .......... .......... 72% 9.72M 17s\n",
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      "396450K .......... .......... .......... .......... .......... 72% 6.65M 17s\n",
      "396500K .......... .......... .......... .......... .......... 72% 8.20M 17s\n",
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      "396600K .......... .......... .......... .......... .......... 72% 22.5M 17s\n",
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      "397000K .......... .......... .......... .......... .......... 72% 27.1M 17s\n",
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      "408500K .......... .......... .......... .......... .......... 74% 8.66M 16s\n",
      "408550K .......... .......... .......... .......... .......... 74% 5.60M 16s\n",
      "408600K .......... .......... .......... .......... .......... 74% 10.8M 16s\n",
      "408650K .......... .......... .......... .......... .......... 74% 19.9M 16s\n",
      "408700K .......... .......... .......... .......... .......... 74% 7.89M 16s\n",
      "408750K .......... .......... .......... .......... .......... 74% 3.76M 16s\n",
      "408800K .......... .......... .......... .......... .......... 74%  356M 16s\n",
      "408850K .......... .......... .......... .......... .......... 74% 12.5M 16s\n",
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      "408950K .......... .......... .......... .......... .......... 74% 6.12M 16s\n",
      "409000K .......... .......... .......... .......... .......... 74% 8.36M 16s\n",
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      "409100K .......... .......... .......... .......... .......... 74% 12.1M 16s\n",
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      "409200K .......... .......... .......... .......... .......... 74% 36.7M 16s\n",
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      "409400K .......... .......... .......... .......... .......... 74% 21.5M 16s\n",
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      "409650K .......... .......... .......... .......... .......... 74% 69.5M 16s\n",
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      "410000K .......... .......... .......... .......... .......... 74%  561M 16s\n",
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      "410100K .......... .......... .......... .......... .......... 74% 16.3M 15s\n",
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      "420550K .......... .......... .......... .......... .......... 76% 23.5M 14s\n",
      "420600K .......... .......... .......... .......... .......... 76% 3.85M 14s\n",
      "420650K .......... .......... .......... .......... .......... 76% 7.42M 14s\n",
      "420700K .......... .......... .......... .......... .......... 76% 5.07M 14s\n",
      "420750K .......... .......... .......... .......... .......... 76% 11.3M 14s\n",
      "420800K .......... .......... .......... .......... .......... 76% 2.84M 14s\n",
      "420850K .......... .......... .......... .......... .......... 76% 8.93M 14s\n",
      "420900K .......... .......... .......... .......... .......... 76% 9.90M 14s\n",
      "420950K .......... .......... .......... .......... .......... 76% 9.74M 14s\n",
      "421000K .......... .......... .......... .......... .......... 76% 11.5M 14s\n",
      "421050K .......... .......... .......... .......... .......... 76% 3.40M 14s\n",
      "421100K .......... .......... .......... .......... .......... 76% 3.75M 14s\n",
      "421150K .......... .......... .......... .......... .......... 76% 3.92M 14s\n",
      "421200K .......... .......... .......... .......... .......... 76% 2.13M 14s\n",
      "421250K .......... .......... .......... .......... .......... 76% 5.84M 14s\n",
      "421300K .......... .......... .......... .......... .......... 76% 1.30M 14s\n",
      "421350K .......... .......... .......... .......... .......... 76% 2.45M 14s\n",
      "421400K .......... .......... .......... .......... .......... 76% 5.07M 14s\n",
      "421450K .......... .......... .......... .......... .......... 76% 2.19M 14s\n",
      "421500K .......... .......... .......... .......... .......... 76% 6.81M 14s\n",
      "421550K .......... .......... .......... .......... .......... 76% 7.72M 14s\n",
      "421600K .......... .......... .......... .......... .......... 76% 2.90M 14s\n",
      "421650K .......... .......... .......... .......... .......... 76% 21.8M 14s\n",
      "421700K .......... .......... .......... .......... .......... 76% 4.58M 14s\n",
      "421750K .......... .......... .......... .......... .......... 76% 15.2M 14s\n",
      "421800K .......... .......... .......... .......... .......... 76% 6.21M 14s\n",
      "421850K .......... .......... .......... .......... .......... 76% 6.86M 14s\n",
      "421900K .......... .......... .......... .......... .......... 76% 9.45M 14s\n",
      "421950K .......... .......... .......... .......... .......... 76% 3.32M 14s\n",
      "422000K .......... .......... .......... .......... .......... 76%  417M 14s\n",
      "422050K .......... .......... .......... .......... .......... 76% 7.29M 14s\n",
      "422100K .......... .......... .......... .......... .......... 76% 7.49M 14s\n",
      "422150K .......... .......... .......... .......... .......... 76% 8.82M 14s\n",
      "422200K .......... .......... .......... .......... .......... 76% 11.1M 14s\n",
      "422250K .......... .......... .......... .......... .......... 76% 5.64M 14s\n",
      "422300K .......... .......... .......... .......... .......... 76% 5.44M 14s\n",
      "422350K .......... .......... .......... .......... .......... 77% 1015K 14s\n",
      "422400K .......... .......... .......... .......... .......... 77% 3.77M 14s\n",
      "422450K .......... .......... .......... .......... .......... 77% 9.53M 14s\n",
      "422500K .......... .......... .......... .......... .......... 77% 3.26M 14s\n",
      "422550K .......... .......... .......... .......... .......... 77% 1.86M 14s\n",
      "422600K .......... .......... .......... .......... .......... 77% 2.54M 14s\n",
      "422650K .......... .......... .......... .......... .......... 77% 5.24M 14s\n",
      "422700K .......... .......... .......... .......... .......... 77% 1.22M 14s\n",
      "422750K .......... .......... .......... .......... .......... 77%  548K 14s\n",
      "422800K .......... .......... .......... .......... .......... 77% 3.73M 14s\n",
      "422850K .......... .......... .......... .......... .......... 77% 1.98M 14s\n",
      "422900K .......... .......... .......... .......... .......... 77% 5.39M 14s\n",
      "422950K .......... .......... .......... .......... .......... 77% 4.43M 14s\n",
      "423000K .......... .......... .......... .......... .......... 77% 5.61M 14s\n",
      "423050K .......... .......... .......... .......... .......... 77% 21.1M 14s\n",
      "423100K .......... .......... .......... .......... .......... 77% 7.56M 14s\n",
      "423150K .......... .......... .......... .......... .......... 77% 10.2M 14s\n",
      "423200K .......... .......... .......... .......... .......... 77% 11.2M 14s\n",
      "423250K .......... .......... .......... .......... .......... 77% 9.25M 14s\n",
      "423300K .......... .......... .......... .......... .......... 77% 4.59M 14s\n",
      "423350K .......... .......... .......... .......... .......... 77% 2.76M 14s\n",
      "423400K .......... .......... .......... .......... .......... 77% 2.63M 14s\n",
      "423450K .......... .......... .......... .......... .......... 77% 1.13M 14s\n",
      "423500K .......... .......... .......... .......... .......... 77% 5.39M 14s\n",
      "423550K .......... .......... .......... .......... .......... 77% 7.36M 14s\n",
      "423600K .......... .......... .......... .......... .......... 77% 4.36M 14s\n",
      "423650K .......... .......... .......... .......... .......... 77% 2.42M 14s\n",
      "423700K .......... .......... .......... .......... .......... 77% 4.73M 14s\n",
      "423750K .......... .......... .......... .......... .......... 77% 1.58M 14s\n",
      "423800K .......... .......... .......... .......... .......... 77% 19.7M 14s\n",
      "423850K .......... .......... .......... .......... .......... 77% 2.37M 14s\n",
      "423900K .......... .......... .......... .......... .......... 77% 6.67M 14s\n",
      "423950K .......... .......... .......... .......... .......... 77%  426K 14s\n",
      "424000K .......... .......... .......... .......... .......... 77% 11.7M 14s\n",
      "424050K .......... .......... .......... .......... .......... 77% 3.96M 14s\n",
      "424100K .......... .......... .......... .......... .......... 77% 8.34M 14s\n",
      "424150K .......... .......... .......... .......... .......... 77% 3.77M 14s\n",
      "424200K .......... .......... .......... .......... .......... 77% 8.94M 14s\n",
      "424250K .......... .......... .......... .......... .......... 77% 1.88M 14s\n",
      "424300K .......... .......... .......... .......... .......... 77% 1.85M 14s\n",
      "424350K .......... .......... .......... .......... .......... 77% 4.15M 14s\n",
      "424400K .......... .......... .......... .......... .......... 77% 6.22M 14s\n",
      "424450K .......... .......... .......... .......... .......... 77% 7.12M 14s\n",
      "424500K .......... .......... .......... .......... .......... 77% 5.11M 14s\n",
      "424550K .......... .......... .......... .......... .......... 77% 4.35M 14s\n",
      "424600K .......... .......... .......... .......... .......... 77% 2.53M 14s\n",
      "424650K .......... .......... .......... .......... .......... 77% 2.42M 14s\n",
      "424700K .......... .......... .......... .......... .......... 77% 2.59M 14s\n",
      "424750K .......... .......... .......... .......... .......... 77% 1.55M 14s\n",
      "424800K .......... .......... .......... .......... .......... 77% 1.21M 14s\n",
      "424850K .......... .......... .......... .......... .......... 77% 3.29M 14s\n",
      "424900K .......... .......... .......... .......... .......... 77% 2.71M 14s\n",
      "424950K .......... .......... .......... .......... .......... 77% 3.79M 14s\n",
      "425000K .......... .......... .......... .......... .......... 77% 1.66M 14s\n",
      "425050K .......... .......... .......... .......... .......... 77% 4.99M 14s\n",
      "425100K .......... .......... .......... .......... .......... 77% 68.2M 14s\n",
      "425150K .......... .......... .......... .......... .......... 77% 2.77M 14s\n",
      "425200K .......... .......... .......... .......... .......... 77% 2.24M 14s\n",
      "425250K .......... .......... .......... .......... .......... 77% 1006K 14s\n",
      "425300K .......... .......... .......... .......... .......... 77% 4.11M 14s\n",
      "425350K .......... .......... .......... .......... .......... 77% 2.88M 14s\n",
      "425400K .......... .......... .......... .......... .......... 77% 6.78M 14s\n",
      "425450K .......... .......... .......... .......... .......... 77% 21.6M 14s\n",
      "425500K .......... .......... .......... .......... .......... 77% 6.44M 14s\n",
      "425550K .......... .......... .......... .......... .......... 77% 4.47M 14s\n",
      "425600K .......... .......... .......... .......... .......... 77% 5.42M 14s\n",
      "425650K .......... .......... .......... .......... .......... 77% 1.42M 14s\n",
      "425700K .......... .......... .......... .......... .......... 77% 4.23M 14s\n",
      "425750K .......... .......... .......... .......... .......... 77% 7.36M 14s\n",
      "425800K .......... .......... .......... .......... .......... 77% 7.74M 14s\n",
      "425850K .......... .......... .......... .......... .......... 77% 14.8M 14s\n",
      "425900K .......... .......... .......... .......... .......... 77% 3.24M 14s\n",
      "425950K .......... .......... .......... .......... .......... 77% 3.96M 14s\n",
      "426000K .......... .......... .......... .......... .......... 77% 68.1M 14s\n",
      "426050K .......... .......... .......... .......... .......... 77% 4.83M 14s\n",
      "426100K .......... .......... .......... .......... .......... 77% 4.60M 14s\n",
      "426150K .......... .......... .......... .......... .......... 77% 11.4M 14s\n",
      "426200K .......... .......... .......... .......... .......... 77% 5.39M 14s\n",
      "426250K .......... .......... .......... .......... .......... 77% 10.1M 14s\n",
      "426300K .......... .......... .......... .......... .......... 77%  987K 14s\n",
      "426350K .......... .......... .......... .......... .......... 77% 7.39M 14s\n",
      "426400K .......... .......... .......... .......... .......... 77% 8.97M 14s\n",
      "426450K .......... .......... .......... .......... .......... 77% 6.65M 14s\n",
      "426500K .......... .......... .......... .......... .......... 77% 2.55M 14s\n",
      "426550K .......... .......... .......... .......... .......... 77% 5.31M 14s\n",
      "426600K .......... .......... .......... .......... .......... 77% 5.86M 14s\n",
      "426650K .......... .......... .......... .......... .......... 77% 1.74M 14s\n",
      "426700K .......... .......... .......... .......... .......... 77% 5.71M 14s\n",
      "426750K .......... .......... .......... .......... .......... 77% 2.36M 14s\n",
      "426800K .......... .......... .......... .......... .......... 77% 5.62M 14s\n",
      "426850K .......... .......... .......... .......... .......... 77% 4.50M 14s\n",
      "426900K .......... .......... .......... .......... .......... 77% 18.4M 14s\n",
      "426950K .......... .......... .......... .......... .......... 77% 2.82M 14s\n",
      "427000K .......... .......... .......... .......... .......... 77%  983K 14s\n",
      "427050K .......... .......... .......... .......... .......... 77% 7.59M 14s\n",
      "427100K .......... .......... .......... .......... .......... 77% 7.06M 14s\n",
      "427150K .......... .......... .......... .......... .......... 77% 2.79M 14s\n",
      "427200K .......... .......... .......... .......... .......... 77%  515K 14s\n",
      "427250K .......... .......... .......... .......... .......... 77% 5.04M 14s\n",
      "427300K .......... .......... .......... .......... .......... 77% 40.6M 14s\n",
      "427350K .......... .......... .......... .......... .......... 77% 15.8M 14s\n",
      "427400K .......... .......... .......... .......... .......... 77% 6.34M 14s\n",
      "427450K .......... .......... .......... .......... .......... 77% 24.3M 14s\n",
      "427500K .......... .......... .......... .......... .......... 77% 14.3M 14s\n",
      "427550K .......... .......... .......... .......... .......... 77% 2.57M 14s\n",
      "427600K .......... .......... .......... .......... .......... 77% 1.87M 14s\n",
      "427650K .......... .......... .......... .......... .......... 77% 50.4M 14s\n",
      "427700K .......... .......... .......... .......... .......... 77% 5.55M 14s\n",
      "427750K .......... .......... .......... .......... .......... 77% 11.3M 14s\n",
      "427800K .......... .......... .......... .......... .......... 78% 23.9M 14s\n",
      "427850K .......... .......... .......... .......... .......... 78% 8.37M 14s\n",
      "427900K .......... .......... .......... .......... .......... 78% 21.9M 14s\n",
      "427950K .......... .......... .......... .......... .......... 78% 10.9M 14s\n",
      "428000K .......... .......... .......... .......... .......... 78% 9.90M 14s\n",
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      "428100K .......... .......... .......... .......... .......... 78% 1.21M 14s\n",
      "428150K .......... .......... .......... .......... .......... 78% 20.2M 14s\n",
      "428200K .......... .......... .......... .......... .......... 78%  182M 14s\n",
      "428250K .......... .......... .......... .......... .......... 78% 77.5M 14s\n",
      "428300K .......... .......... .......... .......... .......... 78%  581M 14s\n",
      "428350K .......... .......... .......... .......... .......... 78%  173M 14s\n",
      "428400K .......... .......... .......... .......... .......... 78% 27.2M 14s\n",
      "428450K .......... .......... .......... .......... .......... 78% 4.02M 14s\n",
      "428500K .......... .......... .......... .......... .......... 78%  483M 14s\n",
      "428550K .......... .......... .......... .......... .......... 78%  651M 14s\n",
      "428600K .......... .......... .......... .......... .......... 78% 13.5M 14s\n",
      "428650K .......... .......... .......... .......... .......... 78% 12.1M 14s\n",
      "428700K .......... .......... .......... .......... .......... 78% 24.5M 14s\n",
      "428750K .......... .......... .......... .......... .......... 78% 3.16M 14s\n",
      "428800K .......... .......... .......... .......... .......... 78% 13.4M 14s\n",
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      "428900K .......... .......... .......... .......... .......... 78% 8.39M 14s\n",
      "428950K .......... .......... .......... .......... .......... 78% 12.9M 14s\n",
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      "429150K .......... .......... .......... .......... .......... 78% 11.5M 14s\n",
      "429200K .......... .......... .......... .......... .......... 78% 7.65M 14s\n",
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      "429600K .......... .......... .......... .......... .......... 78% 10.9M 14s\n",
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      "429700K .......... .......... .......... .......... .......... 78% 8.25M 14s\n",
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      "430000K .......... .......... .......... .......... .......... 78% 12.7M 14s\n",
      "430050K .......... .......... .......... .......... .......... 78% 9.06M 14s\n",
      "430100K .......... .......... .......... .......... .......... 78% 22.0M 14s\n",
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      "430200K .......... .......... .......... .......... .......... 78% 21.8M 14s\n",
      "430250K .......... .......... .......... .......... .......... 78% 18.3M 14s\n",
      "430300K .......... .......... .......... .......... .......... 78% 4.64M 14s\n",
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      "430800K .......... .......... .......... .......... .......... 78% 1.32M 14s\n",
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      "430900K .......... .......... .......... .......... .......... 78% 3.93M 14s\n",
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      "431800K .......... .......... .......... .......... .......... 78% 4.01M 14s\n",
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      "432350K .......... .......... .......... .......... .......... 78% 16.6M 13s\n",
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      "432600K .......... .......... .......... .......... .......... 78% 13.6M 13s\n",
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      "432700K .......... .......... .......... .......... .......... 78% 7.10M 13s\n",
      "432750K .......... .......... .......... .......... .......... 78% 19.9M 13s\n",
      "432800K .......... .......... .......... .......... .......... 78% 8.57M 13s\n",
      "432850K .......... .......... .......... .......... .......... 78% 17.4M 13s\n",
      "432900K .......... .......... .......... .......... .......... 78% 78.4M 13s\n",
      "432950K .......... .......... .......... .......... .......... 78% 5.96M 13s\n",
      "433000K .......... .......... .......... .......... .......... 78% 13.0M 13s\n",
      "433050K .......... .......... .......... .......... .......... 78% 21.4M 13s\n",
      "433100K .......... .......... .......... .......... .......... 78% 5.95M 13s\n",
      "433150K .......... .......... .......... .......... .......... 78% 18.7M 13s\n",
      "433200K .......... .......... .......... .......... .......... 78% 13.7M 13s\n",
      "433250K .......... .......... .......... .......... .......... 78% 7.56M 13s\n",
      "433300K .......... .......... .......... .......... .......... 79% 9.16M 13s\n",
      "433350K .......... .......... .......... .......... .......... 79% 3.92M 13s\n",
      "433400K .......... .......... .......... .......... .......... 79% 4.75M 13s\n",
      "433450K .......... .......... .......... .......... .......... 79% 10.6M 13s\n",
      "433500K .......... .......... .......... .......... .......... 79% 4.80M 13s\n",
      "433550K .......... .......... .......... .......... .......... 79% 4.09M 13s\n",
      "433600K .......... .......... .......... .......... .......... 79% 3.99M 13s\n",
      "433650K .......... .......... .......... .......... .......... 79% 16.0M 13s\n",
      "433700K .......... .......... .......... .......... .......... 79% 3.23M 13s\n",
      "433750K .......... .......... .......... .......... .......... 79% 6.14M 13s\n",
      "433800K .......... .......... .......... .......... .......... 79%  456K 13s\n",
      "433850K .......... .......... .......... .......... .......... 79% 26.1M 13s\n",
      "433900K .......... .......... .......... .......... .......... 79% 6.20M 13s\n",
      "433950K .......... .......... .......... .......... .......... 79% 9.01M 13s\n",
      "434000K .......... .......... .......... .......... .......... 79% 14.0M 13s\n",
      "434050K .......... .......... .......... .......... .......... 79% 12.5M 13s\n",
      "434100K .......... .......... .......... .......... .......... 79% 15.5M 13s\n",
      "434150K .......... .......... .......... .......... .......... 79% 8.51M 13s\n",
      "434200K .......... .......... .......... .......... .......... 79% 13.1M 13s\n",
      "434250K .......... .......... .......... .......... .......... 79% 9.72M 13s\n",
      "434300K .......... .......... .......... .......... .......... 79% 38.5M 13s\n",
      "434350K .......... .......... .......... .......... .......... 79% 8.60M 13s\n",
      "434400K .......... .......... .......... .......... .......... 79% 10.8M 13s\n",
      "434450K .......... .......... .......... .......... .......... 79% 4.58M 13s\n",
      "434500K .......... .......... .......... .......... .......... 79%  189M 13s\n",
      "434550K .......... .......... .......... .......... .......... 79% 9.74M 13s\n",
      "434600K .......... .......... .......... .......... .......... 79% 35.7M 13s\n",
      "434650K .......... .......... .......... .......... .......... 79% 7.60M 13s\n",
      "434700K .......... .......... .......... .......... .......... 79% 18.6M 13s\n",
      "434750K .......... .......... .......... .......... .......... 79% 7.56M 13s\n",
      "434800K .......... .......... .......... .......... .......... 79% 9.82M 13s\n",
      "434850K .......... .......... .......... .......... .......... 79% 30.0M 13s\n",
      "434900K .......... .......... .......... .......... .......... 79% 8.20M 13s\n",
      "434950K .......... .......... .......... .......... .......... 79% 1.01M 13s\n",
      "435000K .......... .......... .......... .......... .......... 79%  237M 13s\n",
      "435050K .......... .......... .......... .......... .......... 79%  351M 13s\n",
      "435100K .......... .......... .......... .......... .......... 79%  603M 13s\n",
      "435150K .......... .......... .......... .......... .......... 79%  483M 13s\n",
      "435200K .......... .......... .......... .......... .......... 79%  281M 13s\n",
      "435250K .......... .......... .......... .......... .......... 79% 41.9M 13s\n",
      "435300K .......... .......... .......... .......... .......... 79% 1.68M 13s\n",
      "435350K .......... .......... .......... .......... .......... 79% 8.57M 13s\n",
      "435400K .......... .......... .......... .......... .......... 79%  238M 13s\n",
      "435450K .......... .......... .......... .......... .......... 79% 15.2M 13s\n",
      "435500K .......... .......... .......... .......... .......... 79% 15.6M 13s\n",
      "435550K .......... .......... .......... .......... .......... 79% 6.36M 13s\n",
      "435600K .......... .......... .......... .......... .......... 79% 1.31M 13s\n",
      "435650K .......... .......... .......... .......... .......... 79% 6.04M 13s\n",
      "435700K .......... .......... .......... .......... .......... 79% 12.8M 13s\n",
      "435750K .......... .......... .......... .......... .......... 79% 19.9M 13s\n",
      "435800K .......... .......... .......... .......... .......... 79% 7.38M 13s\n",
      "435850K .......... .......... .......... .......... .......... 79% 35.4M 13s\n",
      "435900K .......... .......... .......... .......... .......... 79% 3.64M 13s\n",
      "435950K .......... .......... .......... .......... .......... 79% 12.4M 13s\n",
      "436000K .......... .......... .......... .......... .......... 79% 30.1M 13s\n",
      "436050K .......... .......... .......... .......... .......... 79% 6.62M 13s\n",
      "436100K .......... .......... .......... .......... .......... 79% 11.1M 13s\n",
      "436150K .......... .......... .......... .......... .......... 79%  140M 13s\n",
      "436200K .......... .......... .......... .......... .......... 79% 10.9M 13s\n",
      "436250K .......... .......... .......... .......... .......... 79%  574M 13s\n",
      "436300K .......... .......... .......... .......... .......... 79% 9.12M 13s\n",
      "436350K .......... .......... .......... .......... .......... 79% 10.1M 13s\n",
      "436400K .......... .......... .......... .......... .......... 79% 8.03M 13s\n",
      "436450K .......... .......... .......... .......... .......... 79% 9.69M 13s\n",
      "436500K .......... .......... .......... .......... .......... 79% 7.97M 13s\n",
      "436550K .......... .......... .......... .......... .......... 79% 2.74M 13s\n",
      "436600K .......... .......... .......... .......... .......... 79% 3.41M 13s\n",
      "436650K .......... .......... .......... .......... .......... 79% 4.05M 13s\n",
      "436700K .......... .......... .......... .......... .......... 79% 4.69M 13s\n",
      "436750K .......... .......... .......... .......... .......... 79%  160M 13s\n",
      "436800K .......... .......... .......... .......... .......... 79% 7.55M 13s\n",
      "436850K .......... .......... .......... .......... .......... 79% 29.5M 13s\n",
      "436900K .......... .......... .......... .......... .......... 79% 5.47M 13s\n",
      "436950K .......... .......... .......... .......... .......... 79% 17.2M 13s\n",
      "437000K .......... .......... .......... .......... .......... 79% 9.07M 13s\n",
      "437050K .......... .......... .......... .......... .......... 79% 9.76M 13s\n",
      "437100K .......... .......... .......... .......... .......... 79% 1.19M 13s\n",
      "437150K .......... .......... .......... .......... .......... 79% 10.7M 13s\n",
      "437200K .......... .......... .......... .......... .......... 79% 11.3M 13s\n",
      "437250K .......... .......... .......... .......... .......... 79%  561M 13s\n",
      "437300K .......... .......... .......... .......... .......... 79% 10.1M 13s\n",
      "437350K .......... .......... .......... .......... .......... 79% 6.43M 13s\n",
      "437400K .......... .......... .......... .......... .......... 79% 12.2M 13s\n",
      "437450K .......... .......... .......... .......... .......... 79% 9.35M 13s\n",
      "437500K .......... .......... .......... .......... .......... 79% 8.16M 13s\n",
      "437550K .......... .......... .......... .......... .......... 79% 18.5M 13s\n",
      "437600K .......... .......... .......... .......... .......... 79% 10.1M 13s\n",
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      "437700K .......... .......... .......... .......... .......... 79% 8.35M 13s\n",
      "437750K .......... .......... .......... .......... .......... 79% 7.71M 13s\n",
      "437800K .......... .......... .......... .......... .......... 79% 13.6M 13s\n",
      "437850K .......... .......... .......... .......... .......... 79% 14.5M 13s\n",
      "437900K .......... .......... .......... .......... .......... 79% 14.6M 13s\n",
      "437950K .......... .......... .......... .......... .......... 79% 5.23M 13s\n",
      "438000K .......... .......... .......... .......... .......... 79% 2.26M 13s\n",
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      "438200K .......... .......... .......... .......... .......... 79%  436M 13s\n",
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      "438300K .......... .......... .......... .......... .......... 79% 20.7M 13s\n",
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      "438700K .......... .......... .......... .......... .......... 79% 26.2M 13s\n",
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      "438800K .......... .......... .......... .......... .......... 80% 1.29M 13s\n",
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      "438900K .......... .......... .......... .......... .......... 80% 43.2M 13s\n",
      "438950K .......... .......... .......... .......... .......... 80% 4.45M 13s\n",
      "439000K .......... .......... .......... .......... .......... 80% 12.9M 13s\n",
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      "439100K .......... .......... .......... .......... .......... 80% 19.0M 13s\n",
      "439150K .......... .......... .......... .......... .......... 80% 7.46M 13s\n",
      "439200K .......... .......... .......... .......... .......... 80% 9.78M 13s\n",
      "439250K .......... .......... .......... .......... .......... 80% 16.7M 13s\n",
      "439300K .......... .......... .......... .......... .......... 80% 70.8M 13s\n",
      "439350K .......... .......... .......... .......... .......... 80% 5.02M 13s\n",
      "439400K .......... .......... .......... .......... .......... 80% 11.6M 13s\n",
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      "441650K .......... .......... .......... .......... .......... 80% 17.1M 12s\n",
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      "444000K .......... .......... .......... .......... .......... 80% 5.34M 12s\n",
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      "444100K .......... .......... .......... .......... .......... 80% 1.28M 12s\n",
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      "444900K .......... .......... .......... .......... .......... 81%  247M 12s\n",
      "444950K .......... .......... .......... .......... .......... 81% 7.33M 12s\n",
      "445000K .......... .......... .......... .......... .......... 81% 8.69M 12s\n",
      "445050K .......... .......... .......... .......... .......... 81% 8.60M 12s\n",
      "445100K .......... .......... .......... .......... .......... 81% 3.85M 12s\n",
      "445150K .......... .......... .......... .......... .......... 81% 2.79M 12s\n",
      "445200K .......... .......... .......... .......... .......... 81% 6.75M 12s\n",
      "445250K .......... .......... .......... .......... .......... 81% 6.85M 12s\n",
      "445300K .......... .......... .......... .......... .......... 81% 18.3M 12s\n",
      "445350K .......... .......... .......... .......... .......... 81% 18.8M 12s\n",
      "445400K .......... .......... .......... .......... .......... 81% 14.5M 12s\n",
      "445450K .......... .......... .......... .......... .......... 81% 13.7M 12s\n",
      "445500K .......... .......... .......... .......... .......... 81% 5.81M 12s\n",
      "445550K .......... .......... .......... .......... .......... 81% 2.95M 12s\n",
      "445600K .......... .......... .......... .......... .......... 81% 6.52M 12s\n",
      "445650K .......... .......... .......... .......... .......... 81%  795K 12s\n",
      "445700K .......... .......... .......... .......... .......... 81% 2.67M 12s\n",
      "445750K .......... .......... .......... .......... .......... 81% 8.64M 12s\n",
      "445800K .......... .......... .......... .......... .......... 81% 9.37M 12s\n",
      "445850K .......... .......... .......... .......... .......... 81% 12.8M 12s\n",
      "445900K .......... .......... .......... .......... .......... 81% 4.07M 12s\n",
      "445950K .......... .......... .......... .......... .......... 81% 56.1M 12s\n",
      "446000K .......... .......... .......... .......... .......... 81% 9.35M 12s\n",
      "446050K .......... .......... .......... .......... .......... 81% 18.4M 12s\n",
      "446100K .......... .......... .......... .......... .......... 81% 48.5M 12s\n",
      "446150K .......... .......... .......... .......... .......... 81% 6.34M 12s\n",
      "446200K .......... .......... .......... .......... .......... 81% 7.84M 12s\n",
      "446250K .......... .......... .......... .......... .......... 81% 13.6M 12s\n",
      "446300K .......... .......... .......... .......... .......... 81% 12.1M 12s\n",
      "446350K .......... .......... .......... .......... .......... 81% 7.66M 12s\n",
      "446400K .......... .......... .......... .......... .......... 81% 9.33M 12s\n",
      "446450K .......... .......... .......... .......... .......... 81% 11.2M 12s\n",
      "446500K .......... .......... .......... .......... .......... 81% 11.4M 12s\n",
      "446550K .......... .......... .......... .......... .......... 81% 17.3M 12s\n",
      "446600K .......... .......... .......... .......... .......... 81% 10.4M 12s\n",
      "446650K .......... .......... .......... .......... .......... 81% 18.4M 12s\n",
      "446700K .......... .......... .......... .......... .......... 81% 17.6M 12s\n",
      "446750K .......... .......... .......... .......... .......... 81% 14.0M 12s\n",
      "446800K .......... .......... .......... .......... .......... 81% 9.11M 12s\n",
      "446850K .......... .......... .......... .......... .......... 81% 7.23M 12s\n",
      "446900K .......... .......... .......... .......... .......... 81% 15.2M 12s\n",
      "446950K .......... .......... .......... .......... .......... 81% 21.5M 12s\n",
      "447000K .......... .......... .......... .......... .......... 81% 38.9M 12s\n",
      "447050K .......... .......... .......... .......... .......... 81% 1.10M 12s\n",
      "447100K .......... .......... .......... .......... .......... 81% 7.22M 12s\n",
      "447150K .......... .......... .......... .......... .......... 81% 13.7M 12s\n",
      "447200K .......... .......... .......... .......... .......... 81% 9.99M 12s\n",
      "447250K .......... .......... .......... .......... .......... 81% 26.9M 12s\n",
      "447300K .......... .......... .......... .......... .......... 81% 13.4M 12s\n",
      "447350K .......... .......... .......... .......... .......... 81% 46.5M 12s\n",
      "447400K .......... .......... .......... .......... .......... 81% 9.99M 12s\n",
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      "447500K .......... .......... .......... .......... .......... 81% 8.98M 12s\n",
      "447550K .......... .......... .......... .......... .......... 81% 47.8M 12s\n",
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      "447700K .......... .......... .......... .......... .......... 81% 18.8M 12s\n",
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      "447800K .......... .......... .......... .......... .......... 81% 36.3M 12s\n",
      "447850K .......... .......... .......... .......... .......... 81% 5.75M 12s\n",
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      "448000K .......... .......... .......... .......... .......... 81% 5.50M 12s\n",
      "448050K .......... .......... .......... .......... .......... 81% 65.8M 12s\n",
      "448100K .......... .......... .......... .......... .......... 81% 22.1M 12s\n",
      "448150K .......... .......... .......... .......... .......... 81% 8.07M 12s\n",
      "448200K .......... .......... .......... .......... .......... 81% 27.8M 12s\n",
      "448250K .......... .......... .......... .......... .......... 81% 9.24M 12s\n",
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      "448400K .......... .......... .......... .......... .......... 81% 10.8M 12s\n",
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      "448600K .......... .......... .......... .......... .......... 81%  231M 12s\n",
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      "449100K .......... .......... .......... .......... .......... 81%  127M 12s\n",
      "449150K .......... .......... .......... .......... .......... 81%  643K 12s\n",
      "449200K .......... .......... .......... .......... .......... 81% 10.4M 12s\n",
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      "450550K .......... .......... .......... .......... .......... 82% 3.68M 11s\n",
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      "457000K .......... .......... .......... .......... .......... 83% 7.53M 11s\n",
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      "457100K .......... .......... .......... .......... .......... 83% 4.07M 11s\n",
      "457150K .......... .......... .......... .......... .......... 83% 4.73M 11s\n",
      "457200K .......... .......... .......... .......... .......... 83% 5.47M 11s\n",
      "457250K .......... .......... .......... .......... .......... 83% 7.78M 11s\n",
      "457300K .......... .......... .......... .......... .......... 83% 8.25M 11s\n",
      "457350K .......... .......... .......... .......... .......... 83% 4.69M 11s\n",
      "457400K .......... .......... .......... .......... .......... 83% 3.12M 11s\n",
      "457450K .......... .......... .......... .......... .......... 83% 8.09M 11s\n",
      "457500K .......... .......... .......... .......... .......... 83% 9.70M 11s\n",
      "457550K .......... .......... .......... .......... .......... 83% 2.07M 11s\n",
      "457600K .......... .......... .......... .......... .......... 83% 11.5M 11s\n",
      "457650K .......... .......... .......... .......... .......... 83% 11.4M 11s\n",
      "457700K .......... .......... .......... .......... .......... 83% 9.73M 11s\n",
      "457750K .......... .......... .......... .......... .......... 83% 3.55M 11s\n",
      "457800K .......... .......... .......... .......... .......... 83% 4.08M 11s\n",
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      "457900K .......... .......... .......... .......... .......... 83% 11.2M 11s\n",
      "457950K .......... .......... .......... .......... .......... 83% 7.79M 11s\n",
      "458000K .......... .......... .......... .......... .......... 83% 1.39M 11s\n",
      "458050K .......... .......... .......... .......... .......... 83% 12.8M 11s\n",
      "458100K .......... .......... .......... .......... .......... 83%  537M 11s\n",
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      "458300K .......... .......... .......... .......... .......... 83% 4.34M 11s\n",
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      "458400K .......... .......... .......... .......... .......... 83% 15.2M 11s\n",
      "458450K .......... .......... .......... .......... .......... 83% 3.07M 11s\n",
      "458500K .......... .......... .......... .......... .......... 83% 23.5M 11s\n",
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      "458800K .......... .......... .......... .......... .......... 83% 23.6M 11s\n",
      "458850K .......... .......... .......... .......... .......... 83% 1.63M 11s\n",
      "458900K .......... .......... .......... .......... .......... 83%  928K 11s\n",
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      "459100K .......... .......... .......... .......... .......... 83% 33.3M 11s\n",
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      "459850K .......... .......... .......... .......... .......... 83% 5.45M 10s\n",
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      "481400K .......... .......... .......... .......... .......... 87% 5.15M 8s\n",
      "481450K .......... .......... .......... .......... .......... 87% 17.8M 8s\n",
      "481500K .......... .......... .......... .......... .......... 87% 6.06M 8s\n",
      "481550K .......... .......... .......... .......... .......... 87% 7.87M 8s\n",
      "481600K .......... .......... .......... .......... .......... 87%  521K 8s\n",
      "481650K .......... .......... .......... .......... .......... 87% 9.84M 8s\n",
      "481700K .......... .......... .......... .......... .......... 87% 16.3M 8s\n",
      "481750K .......... .......... .......... .......... .......... 87% 9.02M 8s\n",
      "481800K .......... .......... .......... .......... .......... 87% 10.6M 8s\n",
      "481850K .......... .......... .......... .......... .......... 87% 15.9M 8s\n",
      "481900K .......... .......... .......... .......... .......... 87% 56.8M 8s\n",
      "481950K .......... .......... .......... .......... .......... 87% 8.22M 8s\n",
      "482000K .......... .......... .......... .......... .......... 87%  939K 8s\n",
      "482050K .......... .......... .......... .......... .......... 87% 20.1M 8s\n",
      "482100K .......... .......... .......... .......... .......... 87% 5.30M 8s\n",
      "482150K .......... .......... .......... .......... .......... 87% 14.8M 8s\n",
      "482200K .......... .......... .......... .......... .......... 87% 18.7M 8s\n",
      "482250K .......... .......... .......... .......... .......... 87% 10.5M 8s\n",
      "482300K .......... .......... .......... .......... .......... 87% 2.00M 8s\n",
      "482350K .......... .......... .......... .......... .......... 87% 7.46M 8s\n",
      "482400K .......... .......... .......... .......... .......... 87% 3.41M 8s\n",
      "482450K .......... .......... .......... .......... .......... 87% 24.5M 8s\n",
      "482500K .......... .......... .......... .......... .......... 87% 9.51M 8s\n",
      "482550K .......... .......... .......... .......... .......... 87%  470M 8s\n",
      "482600K .......... .......... .......... .......... .......... 87% 6.60M 8s\n",
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      "482800K .......... .......... .......... .......... .......... 88% 8.52M 8s\n",
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      "483000K .......... .......... .......... .......... .......... 88% 63.7M 8s\n",
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      "483100K .......... .......... .......... .......... .......... 88% 10.3M 8s\n",
      "483150K .......... .......... .......... .......... .......... 88% 42.4M 8s\n",
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      "485500K .......... .......... .......... .......... .......... 88% 26.1M 8s\n",
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      "485900K .......... .......... .......... .......... .......... 88% 51.3M 8s\n",
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      "486100K .......... .......... .......... .......... .......... 88% 9.10M 8s\n",
      "486150K .......... .......... .......... .......... .......... 88% 11.3M 7s\n",
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      "493400K .......... .......... .......... .......... .......... 89% 23.5M 7s\n",
      "493450K .......... .......... .......... .......... .......... 89% 8.38M 7s\n",
      "493500K .......... .......... .......... .......... .......... 89% 18.0M 7s\n",
      "493550K .......... .......... .......... .......... .......... 89% 7.57M 7s\n",
      "493600K .......... .......... .......... .......... .......... 89% 6.41M 7s\n",
      "493650K .......... .......... .......... .......... .......... 90% 5.39M 7s\n",
      "493700K .......... .......... .......... .......... .......... 90% 18.2M 7s\n",
      "493750K .......... .......... .......... .......... .......... 90% 11.6M 7s\n",
      "493800K .......... .......... .......... .......... .......... 90% 19.8M 7s\n",
      "493850K .......... .......... .......... .......... .......... 90% 10.4M 7s\n",
      "493900K .......... .......... .......... .......... .......... 90% 16.6M 7s\n",
      "493950K .......... .......... .......... .......... .......... 90% 7.12M 7s\n",
      "494000K .......... .......... .......... .......... .......... 90%  574M 7s\n",
      "494050K .......... .......... .......... .......... .......... 90% 15.1M 7s\n",
      "494100K .......... .......... .......... .......... .......... 90% 6.88M 7s\n",
      "494150K .......... .......... .......... .......... .......... 90% 14.2M 7s\n",
      "494200K .......... .......... .......... .......... .......... 90% 6.91M 7s\n",
      "494250K .......... .......... .......... .......... .......... 90%  222M 7s\n",
      "494300K .......... .......... .......... .......... .......... 90% 52.2M 7s\n",
      "494350K .......... .......... .......... .......... .......... 90% 10.1M 7s\n",
      "494400K .......... .......... .......... .......... .......... 90% 9.33M 7s\n",
      "494450K .......... .......... .......... .......... .......... 90% 39.3M 6s\n",
      "494500K .......... .......... .......... .......... .......... 90% 4.10M 6s\n",
      "494550K .......... .......... .......... .......... .......... 90% 56.8M 6s\n",
      "494600K .......... .......... .......... .......... .......... 90% 21.2M 6s\n",
      "494650K .......... .......... .......... .......... .......... 90% 8.13M 6s\n",
      "494700K .......... .......... .......... .......... .......... 90% 42.9M 6s\n",
      "494750K .......... .......... .......... .......... .......... 90% 6.67M 6s\n",
      "494800K .......... .......... .......... .......... .......... 90% 9.34M 6s\n",
      "494850K .......... .......... .......... .......... .......... 90% 9.38M 6s\n",
      "494900K .......... .......... .......... .......... .......... 90%  198M 6s\n",
      "494950K .......... .......... .......... .......... .......... 90% 9.70M 6s\n",
      "495000K .......... .......... .......... .......... .......... 90% 11.8M 6s\n",
      "495050K .......... .......... .......... .......... .......... 90% 7.01M 6s\n",
      "495100K .......... .......... .......... .......... .......... 90% 21.9M 6s\n",
      "495150K .......... .......... .......... .......... .......... 90% 8.96M 6s\n",
      "495200K .......... .......... .......... .......... .......... 90% 37.6M 6s\n",
      "495250K .......... .......... .......... .......... .......... 90% 10.2M 6s\n",
      "495300K .......... .......... .......... .......... .......... 90% 7.26M 6s\n",
      "495350K .......... .......... .......... .......... .......... 90% 7.00M 6s\n",
      "495400K .......... .......... .......... .......... .......... 90% 14.1M 6s\n",
      "495450K .......... .......... .......... .......... .......... 90% 16.3M 6s\n",
      "495500K .......... .......... .......... .......... .......... 90% 11.1M 6s\n",
      "495550K .......... .......... .......... .......... .......... 90% 6.00M 6s\n",
      "495600K .......... .......... .......... .......... .......... 90% 14.4M 6s\n",
      "495650K .......... .......... .......... .......... .......... 90% 11.3M 6s\n",
      "495700K .......... .......... .......... .......... .......... 90% 1.17M 6s\n",
      "495750K .......... .......... .......... .......... .......... 90% 16.2M 6s\n",
      "495800K .......... .......... .......... .......... .......... 90%  452M 6s\n",
      "495850K .......... .......... .......... .......... .......... 90% 15.5M 6s\n",
      "495900K .......... .......... .......... .......... .......... 90% 10.3M 6s\n",
      "495950K .......... .......... .......... .......... .......... 90% 20.7M 6s\n",
      "496000K .......... .......... .......... .......... .......... 90% 12.0M 6s\n",
      "496050K .......... .......... .......... .......... .......... 90% 16.4M 6s\n",
      "496100K .......... .......... .......... .......... .......... 90% 8.49M 6s\n",
      "496150K .......... .......... .......... .......... .......... 90% 10.5M 6s\n",
      "496200K .......... .......... .......... .......... .......... 90% 16.8M 6s\n",
      "496250K .......... .......... .......... .......... .......... 90% 9.98M 6s\n",
      "496300K .......... .......... .......... .......... .......... 90% 9.22M 6s\n",
      "496350K .......... .......... .......... .......... .......... 90% 13.6M 6s\n",
      "496400K .......... .......... .......... .......... .......... 90% 6.77M 6s\n",
      "496450K .......... .......... .......... .......... .......... 90%  286M 6s\n",
      "496500K .......... .......... .......... .......... .......... 90% 10.4M 6s\n",
      "496550K .......... .......... .......... .......... .......... 90% 5.50M 6s\n",
      "496600K .......... .......... .......... .......... .......... 90% 7.96M 6s\n",
      "496650K .......... .......... .......... .......... .......... 90% 7.20M 6s\n",
      "496700K .......... .......... .......... .......... .......... 90% 94.1M 6s\n",
      "496750K .......... .......... .......... .......... .......... 90% 6.52M 6s\n",
      "496800K .......... .......... .......... .......... .......... 90% 15.1M 6s\n",
      "496850K .......... .......... .......... .......... .......... 90% 9.67M 6s\n",
      "496900K .......... .......... .......... .......... .......... 90% 15.3M 6s\n",
      "496950K .......... .......... .......... .......... .......... 90% 8.17M 6s\n",
      "497000K .......... .......... .......... .......... .......... 90% 7.61M 6s\n",
      "497050K .......... .......... .......... .......... .......... 90% 4.76M 6s\n",
      "497100K .......... .......... .......... .......... .......... 90%  319M 6s\n",
      "497150K .......... .......... .......... .......... .......... 90% 5.69M 6s\n",
      "497200K .......... .......... .......... .......... .......... 90%  129M 6s\n",
      "497250K .......... .......... .......... .......... .......... 90% 9.68M 6s\n",
      "497300K .......... .......... .......... .......... .......... 90% 11.5M 6s\n",
      "497350K .......... .......... .......... .......... .......... 90% 8.31M 6s\n",
      "497400K .......... .......... .......... .......... .......... 90% 7.14M 6s\n",
      "497450K .......... .......... .......... .......... .......... 90% 73.6M 6s\n",
      "497500K .......... .......... .......... .......... .......... 90% 12.9M 6s\n",
      "497550K .......... .......... .......... .......... .......... 90% 5.62M 6s\n",
      "497600K .......... .......... .......... .......... .......... 90% 4.70M 6s\n",
      "497650K .......... .......... .......... .......... .......... 90% 1.67M 6s\n",
      "497700K .......... .......... .......... .......... .......... 90%  332M 6s\n",
      "497750K .......... .......... .......... .......... .......... 90% 9.53M 6s\n",
      "497800K .......... .......... .......... .......... .......... 90% 56.2M 6s\n",
      "497850K .......... .......... .......... .......... .......... 90% 8.68M 6s\n",
      "497900K .......... .......... .......... .......... .......... 90% 20.9M 6s\n",
      "497950K .......... .......... .......... .......... .......... 90% 6.64M 6s\n",
      "498000K .......... .......... .......... .......... .......... 90% 45.7M 6s\n",
      "498050K .......... .......... .......... .......... .......... 90% 9.62M 6s\n",
      "498100K .......... .......... .......... .......... .......... 90% 26.9M 6s\n",
      "498150K .......... .......... .......... .......... .......... 90% 16.7M 6s\n",
      "498200K .......... .......... .......... .......... .......... 90% 7.05M 6s\n",
      "498250K .......... .......... .......... .......... .......... 90% 19.1M 6s\n",
      "498300K .......... .......... .......... .......... .......... 90% 15.9M 6s\n",
      "498350K .......... .......... .......... .......... .......... 90% 6.50M 6s\n",
      "498400K .......... .......... .......... .......... .......... 90%  531M 6s\n",
      "498450K .......... .......... .......... .......... .......... 90% 8.38M 6s\n",
      "498500K .......... .......... .......... .......... .......... 90% 7.82M 6s\n",
      "498550K .......... .......... .......... .......... .......... 90% 22.1M 6s\n",
      "498600K .......... .......... .......... .......... .......... 90% 8.86M 6s\n",
      "498650K .......... .......... .......... .......... .......... 90% 12.7M 6s\n",
      "498700K .......... .......... .......... .......... .......... 90% 9.46M 6s\n",
      "498750K .......... .......... .......... .......... .......... 90% 56.3M 6s\n",
      "498800K .......... .......... .......... .......... .......... 90%  445K 6s\n",
      "498850K .......... .......... .......... .......... .......... 90% 31.5M 6s\n",
      "498900K .......... .......... .......... .......... .......... 90% 3.66M 6s\n",
      "498950K .......... .......... .......... .......... .......... 90% 11.2M 6s\n",
      "499000K .......... .......... .......... .......... .......... 90% 11.2M 6s\n",
      "499050K .......... .......... .......... .......... .......... 90% 7.85M 6s\n",
      "499100K .......... .......... .......... .......... .......... 91% 9.02M 6s\n",
      "499150K .......... .......... .......... .......... .......... 91% 11.7M 6s\n",
      "499200K .......... .......... .......... .......... .......... 91% 16.0M 6s\n",
      "499250K .......... .......... .......... .......... .......... 91% 3.83M 6s\n",
      "499300K .......... .......... .......... .......... .......... 91% 9.33M 6s\n",
      "499350K .......... .......... .......... .......... .......... 91% 3.86M 6s\n",
      "499400K .......... .......... .......... .......... .......... 91% 3.67M 6s\n",
      "499450K .......... .......... .......... .......... .......... 91% 5.34M 6s\n",
      "499500K .......... .......... .......... .......... .......... 91% 13.7M 6s\n",
      "499550K .......... .......... .......... .......... .......... 91% 16.7M 6s\n",
      "499600K .......... .......... .......... .......... .......... 91% 8.00M 6s\n",
      "499650K .......... .......... .......... .......... .......... 91% 10.9M 6s\n",
      "499700K .......... .......... .......... .......... .......... 91% 4.60M 6s\n",
      "499750K .......... .......... .......... .......... .......... 91%  404M 6s\n",
      "499800K .......... .......... .......... .......... .......... 91% 6.38M 6s\n",
      "499850K .......... .......... .......... .......... .......... 91% 26.5M 6s\n",
      "499900K .......... .......... .......... .......... .......... 91%  610M 6s\n",
      "499950K .......... .......... .......... .......... .......... 91% 7.40M 6s\n",
      "500000K .......... .......... .......... .......... .......... 91% 12.8M 6s\n",
      "500050K .......... .......... .......... .......... .......... 91% 4.09M 6s\n",
      "500100K .......... .......... .......... .......... .......... 91%  379M 6s\n",
      "500150K .......... .......... .......... .......... .......... 91% 15.3M 6s\n",
      "500200K .......... .......... .......... .......... .......... 91% 8.69M 6s\n",
      "500250K .......... .......... .......... .......... .......... 91% 50.4M 6s\n",
      "500300K .......... .......... .......... .......... .......... 91% 5.61M 6s\n",
      "500350K .......... .......... .......... .......... .......... 91% 1.12M 6s\n",
      "500400K .......... .......... .......... .......... .......... 91% 18.3M 6s\n",
      "500450K .......... .......... .......... .......... .......... 91% 6.39M 6s\n",
      "500500K .......... .......... .......... .......... .......... 91% 7.96M 6s\n",
      "500550K .......... .......... .......... .......... .......... 91% 6.55M 6s\n",
      "500600K .......... .......... .......... .......... .......... 91% 13.3M 6s\n",
      "500650K .......... .......... .......... .......... .......... 91% 9.99M 6s\n",
      "500700K .......... .......... .......... .......... .......... 91% 9.39M 6s\n",
      "500750K .......... .......... .......... .......... .......... 91% 7.75M 6s\n",
      "500800K .......... .......... .......... .......... .......... 91% 55.2M 6s\n",
      "500850K .......... .......... .......... .......... .......... 91% 4.25M 6s\n",
      "500900K .......... .......... .......... .......... .......... 91% 31.7M 6s\n",
      "500950K .......... .......... .......... .......... .......... 91% 9.25M 6s\n",
      "501000K .......... .......... .......... .......... .......... 91% 12.2M 6s\n",
      "501050K .......... .......... .......... .......... .......... 91% 9.91M 6s\n",
      "501100K .......... .......... .......... .......... .......... 91% 8.90M 6s\n",
      "501150K .......... .......... .......... .......... .......... 91% 7.84M 6s\n",
      "501200K .......... .......... .......... .......... .......... 91% 9.93M 6s\n",
      "501250K .......... .......... .......... .......... .......... 91%  257M 6s\n",
      "501300K .......... .......... .......... .......... .......... 91% 19.6M 6s\n",
      "501350K .......... .......... .......... .......... .......... 91% 7.41M 6s\n",
      "501400K .......... .......... .......... .......... .......... 91% 15.5M 6s\n",
      "501450K .......... .......... .......... .......... .......... 91% 4.85M 6s\n",
      "501500K .......... .......... .......... .......... .......... 91% 3.85M 6s\n",
      "501550K .......... .......... .......... .......... .......... 91% 84.9M 6s\n",
      "501600K .......... .......... .......... .......... .......... 91%  337M 6s\n",
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      "501750K .......... .......... .......... .......... .......... 91% 15.8M 6s\n",
      "501800K .......... .......... .......... .......... .......... 91% 5.40M 6s\n",
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      "501950K .......... .......... .......... .......... .......... 91% 8.24M 6s\n",
      "502000K .......... .......... .......... .......... .......... 91% 6.51M 6s\n",
      "502050K .......... .......... .......... .......... .......... 91%  186M 6s\n",
      "502100K .......... .......... .......... .......... .......... 91% 5.89M 6s\n",
      "502150K .......... .......... .......... .......... .......... 91%  215M 6s\n",
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      "502250K .......... .......... .......... .......... .......... 91%  202M 6s\n",
      "502300K .......... .......... .......... .......... .......... 91% 15.3M 6s\n",
      "502350K .......... .......... .......... .......... .......... 91% 38.9M 6s\n",
      "502400K .......... .......... .......... .......... .......... 91% 7.03M 6s\n",
      "502450K .......... .......... .......... .......... .......... 91% 9.47M 6s\n",
      "502500K .......... .......... .......... .......... .......... 91%  394M 6s\n",
      "502550K .......... .......... .......... .......... .......... 91% 4.89M 6s\n",
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      "502650K .......... .......... .......... .......... .......... 91% 13.3M 6s\n",
      "502700K .......... .......... .......... .......... .......... 91% 2.78M 6s\n",
      "502750K .......... .......... .......... .......... .......... 91%  294M 5s\n",
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      "502850K .......... .......... .......... .......... .......... 91% 27.8M 5s\n",
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      "502950K .......... .......... .......... .......... .......... 91% 42.4M 5s\n",
      "503000K .......... .......... .......... .......... .......... 91% 18.1M 5s\n",
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      "503100K .......... .......... .......... .......... .......... 91% 8.25M 5s\n",
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      "503200K .......... .......... .......... .......... .......... 91% 37.7M 5s\n",
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      "503550K .......... .......... .......... .......... .......... 91% 19.9M 5s\n",
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      "503750K .......... .......... .......... .......... .......... 91% 6.00M 5s\n",
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      "503850K .......... .......... .......... .......... .......... 91% 10.1M 5s\n",
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      "503950K .......... .......... .......... .......... .......... 91% 18.6M 5s\n",
      "504000K .......... .......... .......... .......... .......... 91% 81.7M 5s\n",
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      "504100K .......... .......... .......... .......... .......... 91% 17.8M 5s\n",
      "504150K .......... .......... .......... .......... .......... 91% 12.6M 5s\n",
      "504200K .......... .......... .......... .......... .......... 91% 10.9M 5s\n",
      "504250K .......... .......... .......... .......... .......... 91% 8.14M 5s\n",
      "504300K .......... .......... .......... .......... .......... 91% 13.1M 5s\n",
      "504350K .......... .......... .......... .......... .......... 91% 14.2M 5s\n",
      "504400K .......... .......... .......... .......... .......... 91% 10.1M 5s\n",
      "504450K .......... .......... .......... .......... .......... 91% 16.2M 5s\n",
      "504500K .......... .......... .......... .......... .......... 91% 8.79M 5s\n",
      "504550K .......... .......... .......... .......... .......... 91% 72.2M 5s\n",
      "504600K .......... .......... .......... .......... .......... 92% 6.67M 5s\n",
      "504650K .......... .......... .......... .......... .......... 92%  131M 5s\n",
      "504700K .......... .......... .......... .......... .......... 92% 7.80M 5s\n",
      "504750K .......... .......... .......... .......... .......... 92% 24.2M 5s\n",
      "504800K .......... .......... .......... .......... .......... 92% 14.0M 5s\n",
      "504850K .......... .......... .......... .......... .......... 92% 5.73M 5s\n",
      "504900K .......... .......... .......... .......... .......... 92% 17.1M 5s\n",
      "504950K .......... .......... .......... .......... .......... 92% 9.50M 5s\n",
      "505000K .......... .......... .......... .......... .......... 92% 14.5M 5s\n",
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      "505100K .......... .......... .......... .......... .......... 92% 55.0M 5s\n",
      "505150K .......... .......... .......... .......... .......... 92% 10.0M 5s\n",
      "505200K .......... .......... .......... .......... .......... 92% 17.2M 5s\n",
      "505250K .......... .......... .......... .......... .......... 92% 5.52M 5s\n",
      "505300K .......... .......... .......... .......... .......... 92% 27.2M 5s\n",
      "505350K .......... .......... .......... .......... .......... 92% 6.55M 5s\n",
      "505400K .......... .......... .......... .......... .......... 92% 21.8M 5s\n",
      "505450K .......... .......... .......... .......... .......... 92% 7.57M 5s\n",
      "505500K .......... .......... .......... .......... .......... 92%  951K 5s\n",
      "505550K .......... .......... .......... .......... .......... 92% 9.33M 5s\n",
      "505600K .......... .......... .......... .......... .......... 92% 9.22M 5s\n",
      "505650K .......... .......... .......... .......... .......... 92% 17.8M 5s\n",
      "505700K .......... .......... .......... .......... .......... 92% 19.7M 5s\n",
      "505750K .......... .......... .......... .......... .......... 92% 16.5M 5s\n",
      "505800K .......... .......... .......... .......... .......... 92% 8.71M 5s\n",
      "505850K .......... .......... .......... .......... .......... 92%  498M 5s\n",
      "505900K .......... .......... .......... .......... .......... 92% 10.4M 5s\n",
      "505950K .......... .......... .......... .......... .......... 92% 9.44M 5s\n",
      "506000K .......... .......... .......... .......... .......... 92% 14.4M 5s\n",
      "506050K .......... .......... .......... .......... .......... 92% 9.96M 5s\n",
      "506100K .......... .......... .......... .......... .......... 92% 22.2M 5s\n",
      "506150K .......... .......... .......... .......... .......... 92% 10.3M 5s\n",
      "506200K .......... .......... .......... .......... .......... 92% 9.82M 5s\n",
      "506250K .......... .......... .......... .......... .......... 92% 55.0M 5s\n",
      "506300K .......... .......... .......... .......... .......... 92% 14.5M 5s\n",
      "506350K .......... .......... .......... .......... .......... 92% 3.16M 5s\n",
      "506400K .......... .......... .......... .......... .......... 92% 21.3M 5s\n",
      "506450K .......... .......... .......... .......... .......... 92% 22.8M 5s\n",
      "506500K .......... .......... .......... .......... .......... 92% 6.48M 5s\n",
      "506550K .......... .......... .......... .......... .......... 92% 42.7M 5s\n",
      "506600K .......... .......... .......... .......... .......... 92% 10.8M 5s\n",
      "506650K .......... .......... .......... .......... .......... 92% 9.35M 5s\n",
      "506700K .......... .......... .......... .......... .......... 92% 15.8M 5s\n",
      "506750K .......... .......... .......... .......... .......... 92% 7.35M 5s\n",
      "506800K .......... .......... .......... .......... .......... 92% 14.9M 5s\n",
      "506850K .......... .......... .......... .......... .......... 92% 18.9M 5s\n",
      "506900K .......... .......... .......... .......... .......... 92% 14.6M 5s\n",
      "506950K .......... .......... .......... .......... .......... 92% 27.3M 5s\n",
      "507000K .......... .......... .......... .......... .......... 92% 8.35M 5s\n",
      "507050K .......... .......... .......... .......... .......... 92%  131M 5s\n",
      "507100K .......... .......... .......... .......... .......... 92% 8.24M 5s\n",
      "507150K .......... .......... .......... .......... .......... 92% 19.6M 5s\n",
      "507200K .......... .......... .......... .......... .......... 92% 9.24M 5s\n",
      "507250K .......... .......... .......... .......... .......... 92% 9.98M 5s\n",
      "507300K .......... .......... .......... .......... .......... 92%  354M 5s\n",
      "507350K .......... .......... .......... .......... .......... 92% 3.86M 5s\n",
      "507400K .......... .......... .......... .......... .......... 92%  561M 5s\n",
      "507450K .......... .......... .......... .......... .......... 92%  660M 5s\n",
      "507500K .......... .......... .......... .......... .......... 92% 18.5M 5s\n",
      "507550K .......... .......... .......... .......... .......... 92% 1.58M 5s\n",
      "507600K .......... .......... .......... .......... .......... 92% 22.1M 5s\n",
      "507650K .......... .......... .......... .......... .......... 92% 9.81M 5s\n",
      "507700K .......... .......... .......... .......... .......... 92% 13.1M 5s\n",
      "507750K .......... .......... .......... .......... .......... 92% 35.7M 5s\n",
      "507800K .......... .......... .......... .......... .......... 92%  491K 5s\n",
      "507850K .......... .......... .......... .......... .......... 92% 11.0M 5s\n",
      "507900K .......... .......... .......... .......... .......... 92% 16.6M 5s\n",
      "507950K .......... .......... .......... .......... .......... 92% 8.29M 5s\n",
      "508000K .......... .......... .......... .......... .......... 92% 12.8M 5s\n",
      "508050K .......... .......... .......... .......... .......... 92% 23.8M 5s\n",
      "508100K .......... .......... .......... .......... .......... 92% 13.8M 5s\n",
      "508150K .......... .......... .......... .......... .......... 92% 43.9M 5s\n",
      "508200K .......... .......... .......... .......... .......... 92% 5.27M 5s\n",
      "508250K .......... .......... .......... .......... .......... 92% 16.0M 5s\n",
      "508300K .......... .......... .......... .......... .......... 92% 12.0M 5s\n",
      "508350K .......... .......... .......... .......... .......... 92% 16.8M 5s\n",
      "508400K .......... .......... .......... .......... .......... 92% 11.0M 5s\n",
      "508450K .......... .......... .......... .......... .......... 92% 2.07M 5s\n",
      "508500K .......... .......... .......... .......... .......... 92% 11.5M 5s\n",
      "508550K .......... .......... .......... .......... .......... 92% 17.8M 5s\n",
      "508600K .......... .......... .......... .......... .......... 92% 17.6M 5s\n",
      "508650K .......... .......... .......... .......... .......... 92% 16.5M 5s\n",
      "508700K .......... .......... .......... .......... .......... 92% 7.08M 5s\n",
      "508750K .......... .......... .......... .......... .......... 92% 13.2M 5s\n",
      "508800K .......... .......... .......... .......... .......... 92% 14.8M 5s\n",
      "508850K .......... .......... .......... .......... .......... 92% 25.5M 5s\n",
      "508900K .......... .......... .......... .......... .......... 92% 7.97M 5s\n",
      "508950K .......... .......... .......... .......... .......... 92% 17.7M 5s\n",
      "509000K .......... .......... .......... .......... .......... 92% 3.76M 5s\n",
      "509050K .......... .......... .......... .......... .......... 92% 10.4M 5s\n",
      "509100K .......... .......... .......... .......... .......... 92% 21.9M 5s\n",
      "509150K .......... .......... .......... .......... .......... 92% 4.08M 5s\n",
      "509200K .......... .......... .......... .......... .......... 92%  456M 5s\n",
      "509250K .......... .......... .......... .......... .......... 92%  708M 5s\n",
      "509300K .......... .......... .......... .......... .......... 92% 4.77M 5s\n",
      "509350K .......... .......... .......... .......... .......... 92% 62.1M 5s\n",
      "509400K .......... .......... .......... .......... .......... 92% 13.4M 5s\n",
      "509450K .......... .......... .......... .......... .......... 92% 13.0M 5s\n",
      "509500K .......... .......... .......... .......... .......... 92% 44.2M 5s\n",
      "509550K .......... .......... .......... .......... .......... 92% 3.80M 5s\n",
      "509600K .......... .......... .......... .......... .......... 92% 12.9M 5s\n",
      "509650K .......... .......... .......... .......... .......... 92% 16.7M 5s\n",
      "509700K .......... .......... .......... .......... .......... 92% 10.4M 5s\n",
      "509750K .......... .......... .......... .......... .......... 92% 81.1M 5s\n",
      "509800K .......... .......... .......... .......... .......... 92% 2.44M 5s\n",
      "509850K .......... .......... .......... .......... .......... 92% 7.75M 5s\n",
      "509900K .......... .......... .......... .......... .......... 92%  305M 5s\n",
      "509950K .......... .......... .......... .......... .......... 92%  294M 5s\n",
      "510000K .......... .......... .......... .......... .......... 92%  121M 5s\n",
      "510050K .......... .......... .......... .......... .......... 92% 8.98M 5s\n",
      "510100K .......... .......... .......... .......... .......... 93% 19.4M 5s\n",
      "510150K .......... .......... .......... .......... .......... 93% 5.42M 5s\n",
      "510200K .......... .......... .......... .......... .......... 93% 11.0M 5s\n",
      "510250K .......... .......... .......... .......... .......... 93% 20.7M 5s\n",
      "510300K .......... .......... .......... .......... .......... 93% 4.71M 5s\n",
      "510350K .......... .......... .......... .......... .......... 93% 19.0M 5s\n",
      "510400K .......... .......... .......... .......... .......... 93% 5.95M 5s\n",
      "510450K .......... .......... .......... .......... .......... 93% 77.6M 5s\n",
      "510500K .......... .......... .......... .......... .......... 93% 2.72M 5s\n",
      "510550K .......... .......... .......... .......... .......... 93% 2.21M 5s\n",
      "510600K .......... .......... .......... .......... .......... 93% 3.09M 5s\n",
      "510650K .......... .......... .......... .......... .......... 93% 13.7M 5s\n",
      "510700K .......... .......... .......... .......... .......... 93% 20.3M 5s\n",
      "510750K .......... .......... .......... .......... .......... 93% 2.63M 5s\n",
      "510800K .......... .......... .......... .......... .......... 93%  503M 5s\n",
      "510850K .......... .......... .......... .......... .......... 93% 5.35M 5s\n",
      "510900K .......... .......... .......... .......... .......... 93%  176M 5s\n",
      "510950K .......... .......... .......... .......... .......... 93%  688M 5s\n",
      "511000K .......... .......... .......... .......... .......... 93%  718M 5s\n",
      "511050K .......... .......... .......... .......... .......... 93%  763M 4s\n",
      "511100K .......... .......... .......... .......... .......... 93% 15.5M 4s\n",
      "511150K .......... .......... .......... .......... .......... 93% 1.93M 4s\n",
      "511200K .......... .......... .......... .......... .......... 93% 7.34M 4s\n",
      "511250K .......... .......... .......... .......... .......... 93% 1.99M 4s\n",
      "511300K .......... .......... .......... .......... .......... 93% 5.13M 4s\n",
      "511350K .......... .......... .......... .......... .......... 93% 4.00M 4s\n",
      "511400K .......... .......... .......... .......... .......... 93% 8.48M 4s\n",
      "511450K .......... .......... .......... .......... .......... 93% 4.07M 4s\n",
      "511500K .......... .......... .......... .......... .......... 93% 67.4M 4s\n",
      "511550K .......... .......... .......... .......... .......... 93% 9.15M 4s\n",
      "511600K .......... .......... .......... .......... .......... 93% 15.9M 4s\n",
      "511650K .......... .......... .......... .......... .......... 93% 58.8M 4s\n",
      "511700K .......... .......... .......... .......... .......... 93% 10.6M 4s\n",
      "511750K .......... .......... .......... .......... .......... 93% 6.91M 4s\n",
      "511800K .......... .......... .......... .......... .......... 93% 4.22M 4s\n",
      "511850K .......... .......... .......... .......... .......... 93% 48.0M 4s\n",
      "511900K .......... .......... .......... .......... .......... 93% 4.31M 4s\n",
      "511950K .......... .......... .......... .......... .......... 93% 1.35M 4s\n",
      "512000K .......... .......... .......... .......... .......... 93% 8.11M 4s\n",
      "512050K .......... .......... .......... .......... .......... 93% 7.63M 4s\n",
      "512100K .......... .......... .......... .......... .......... 93% 9.72M 4s\n",
      "512150K .......... .......... .......... .......... .......... 93% 8.80M 4s\n",
      "512200K .......... .......... .......... .......... .......... 93% 12.7M 4s\n",
      "512250K .......... .......... .......... .......... .......... 93% 8.14M 4s\n",
      "512300K .......... .......... .......... .......... .......... 93% 8.51M 4s\n",
      "512350K .......... .......... .......... .......... .......... 93% 10.3M 4s\n",
      "512400K .......... .......... .......... .......... .......... 93% 4.15M 4s\n",
      "512450K .......... .......... .......... .......... .......... 93% 28.9M 4s\n",
      "512500K .......... .......... .......... .......... .......... 93% 18.1M 4s\n",
      "512550K .......... .......... .......... .......... .......... 93% 8.68M 4s\n",
      "512600K .......... .......... .......... .......... .......... 93% 19.8M 4s\n",
      "512650K .......... .......... .......... .......... .......... 93% 6.98M 4s\n",
      "512700K .......... .......... .......... .......... .......... 93% 12.2M 4s\n",
      "512750K .......... .......... .......... .......... .......... 93% 6.85M 4s\n",
      "512800K .......... .......... .......... .......... .......... 93% 11.3M 4s\n",
      "512850K .......... .......... .......... .......... .......... 93% 25.3M 4s\n",
      "512900K .......... .......... .......... .......... .......... 93% 9.03M 4s\n",
      "512950K .......... .......... .......... .......... .......... 93% 6.43M 4s\n",
      "513000K .......... .......... .......... .......... .......... 93% 12.2M 4s\n",
      "513050K .......... .......... .......... .......... .......... 93% 18.5M 4s\n",
      "513100K .......... .......... .......... .......... .......... 93% 10.3M 4s\n",
      "513150K .......... .......... .......... .......... .......... 93% 4.92M 4s\n",
      "513200K .......... .......... .......... .......... .......... 93% 17.5M 4s\n",
      "513250K .......... .......... .......... .......... .......... 93% 11.4M 4s\n",
      "513300K .......... .......... .......... .......... .......... 93% 11.1M 4s\n",
      "513350K .......... .......... .......... .......... .......... 93% 8.32M 4s\n",
      "513400K .......... .......... .......... .......... .......... 93% 19.2M 4s\n",
      "513450K .......... .......... .......... .......... .......... 93% 3.35M 4s\n",
      "513500K .......... .......... .......... .......... .......... 93%  865K 4s\n",
      "513550K .......... .......... .......... .......... .......... 93% 56.6M 4s\n",
      "513600K .......... .......... .......... .......... .......... 93% 84.8M 4s\n",
      "513650K .......... .......... .......... .......... .......... 93%  301M 4s\n",
      "513700K .......... .......... .......... .......... .......... 93%  425M 4s\n",
      "513750K .......... .......... .......... .......... .......... 93%  219M 4s\n",
      "513800K .......... .......... .......... .......... .......... 93%  432M 4s\n",
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      "513900K .......... .......... .......... .......... .......... 93%  444M 4s\n",
      "513950K .......... .......... .......... .......... .......... 93% 14.9M 4s\n",
      "514000K .......... .......... .......... .......... .......... 93% 2.75M 4s\n",
      "514050K .......... .......... .......... .......... .......... 93% 8.11M 4s\n",
      "514100K .......... .......... .......... .......... .......... 93% 1.28M 4s\n",
      "514150K .......... .......... .......... .......... .......... 93% 10.8M 4s\n",
      "514200K .......... .......... .......... .......... .......... 93% 8.90M 4s\n",
      "514250K .......... .......... .......... .......... .......... 93% 21.4M 4s\n",
      "514300K .......... .......... .......... .......... .......... 93% 7.02M 4s\n",
      "514350K .......... .......... .......... .......... .......... 93% 5.78M 4s\n",
      "514400K .......... .......... .......... .......... .......... 93% 14.0M 4s\n",
      "514450K .......... .......... .......... .......... .......... 93% 4.14M 4s\n",
      "514500K .......... .......... .......... .......... .......... 93% 9.92M 4s\n",
      "514550K .......... .......... .......... .......... .......... 93% 10.6M 4s\n",
      "514600K .......... .......... .......... .......... .......... 93% 11.1M 4s\n",
      "514650K .......... .......... .......... .......... .......... 93% 21.8M 4s\n",
      "514700K .......... .......... .......... .......... .......... 93% 4.56M 4s\n",
      "514750K .......... .......... .......... .......... .......... 93% 4.16M 4s\n",
      "514800K .......... .......... .......... .......... .......... 93%  229M 4s\n",
      "514850K .......... .......... .......... .......... .......... 93% 3.11M 4s\n",
      "514900K .......... .......... .......... .......... .......... 93% 16.3M 4s\n",
      "514950K .......... .......... .......... .......... .......... 93% 5.95M 4s\n",
      "515000K .......... .......... .......... .......... .......... 93% 4.63M 4s\n",
      "515050K .......... .......... .......... .......... .......... 93% 18.8M 4s\n",
      "515100K .......... .......... .......... .......... .......... 93% 1.54M 4s\n",
      "515150K .......... .......... .......... .......... .......... 93%  167M 4s\n",
      "515200K .......... .......... .......... .......... .......... 93%  341M 4s\n",
      "515250K .......... .......... .......... .......... .......... 93%  589K 4s\n",
      "515300K .......... .......... .......... .......... .......... 93% 3.66M 4s\n",
      "515350K .......... .......... .......... .......... .......... 93% 15.4M 4s\n",
      "515400K .......... .......... .......... .......... .......... 93% 18.0M 4s\n",
      "515450K .......... .......... .......... .......... .......... 93% 15.1M 4s\n",
      "515500K .......... .......... .......... .......... .......... 93% 9.33M 4s\n",
      "515550K .......... .......... .......... .......... .......... 94% 13.0M 4s\n",
      "515600K .......... .......... .......... .......... .......... 94% 7.60M 4s\n",
      "515650K .......... .......... .......... .......... .......... 94% 5.98M 4s\n",
      "515700K .......... .......... .......... .......... .......... 94% 13.7M 4s\n",
      "515750K .......... .......... .......... .......... .......... 94% 12.4M 4s\n",
      "515800K .......... .......... .......... .......... .......... 94% 12.4M 4s\n",
      "515850K .......... .......... .......... .......... .......... 94% 29.0M 4s\n",
      "515900K .......... .......... .......... .......... .......... 94% 10.9M 4s\n",
      "515950K .......... .......... .......... .......... .......... 94% 14.6M 4s\n",
      "516000K .......... .......... .......... .......... .......... 94% 9.84M 4s\n",
      "516050K .......... .......... .......... .......... .......... 94% 21.8M 4s\n",
      "516100K .......... .......... .......... .......... .......... 94% 4.38M 4s\n",
      "516150K .......... .......... .......... .......... .......... 94% 6.27M 4s\n",
      "516200K .......... .......... .......... .......... .......... 94%  182M 4s\n",
      "516250K .......... .......... .......... .......... .......... 94%  483M 4s\n",
      "516300K .......... .......... .......... .......... .......... 94% 8.68M 4s\n",
      "516350K .......... .......... .......... .......... .......... 94% 3.25M 4s\n",
      "516400K .......... .......... .......... .......... .......... 94%  196M 4s\n",
      "516450K .......... .......... .......... .......... .......... 94% 6.15M 4s\n",
      "516500K .......... .......... .......... .......... .......... 94% 32.6M 4s\n",
      "516550K .......... .......... .......... .......... .......... 94% 8.87M 4s\n",
      "516600K .......... .......... .......... .......... .......... 94% 1010K 4s\n",
      "516650K .......... .......... .......... .......... .......... 94% 14.2M 4s\n",
      "516700K .......... .......... .......... .......... .......... 94% 2.68M 4s\n",
      "516750K .......... .......... .......... .......... .......... 94%  223M 4s\n",
      "516800K .......... .......... .......... .......... .......... 94% 28.0M 4s\n",
      "516850K .......... .......... .......... .......... .......... 94% 10.0M 4s\n",
      "516900K .......... .......... .......... .......... .......... 94% 9.23M 4s\n",
      "516950K .......... .......... .......... .......... .......... 94%  163M 4s\n",
      "517000K .......... .......... .......... .......... .......... 94% 11.1M 4s\n",
      "517050K .......... .......... .......... .......... .......... 94% 4.08M 4s\n",
      "517100K .......... .......... .......... .......... .......... 94% 19.1M 4s\n",
      "517150K .......... .......... .......... .......... .......... 94% 5.84M 4s\n",
      "517200K .......... .......... .......... .......... .......... 94% 9.72M 4s\n",
      "517250K .......... .......... .......... .......... .......... 94% 58.5M 4s\n",
      "517300K .......... .......... .......... .......... .......... 94% 9.22M 4s\n",
      "517350K .......... .......... .......... .......... .......... 94% 16.6M 4s\n",
      "517400K .......... .......... .......... .......... .......... 94% 3.61M 4s\n",
      "517450K .......... .......... .......... .......... .......... 94% 7.52M 4s\n",
      "517500K .......... .......... .......... .......... .......... 94% 1.24M 4s\n",
      "517550K .......... .......... .......... .......... .......... 94% 48.2M 4s\n",
      "517600K .......... .......... .......... .......... .......... 94% 7.32M 4s\n",
      "517650K .......... .......... .......... .......... .......... 94% 2.76M 4s\n",
      "517700K .......... .......... .......... .......... .......... 94% 3.30M 4s\n",
      "517750K .......... .......... .......... .......... .......... 94% 1.55M 4s\n",
      "517800K .......... .......... .......... .......... .......... 94% 20.8M 4s\n",
      "517850K .......... .......... .......... .......... .......... 94% 52.1M 4s\n",
      "517900K .......... .......... .......... .......... .......... 94% 7.15M 4s\n",
      "517950K .......... .......... .......... .......... .......... 94% 9.28M 4s\n",
      "518000K .......... .......... .......... .......... .......... 94% 20.6M 4s\n",
      "518050K .......... .......... .......... .......... .......... 94% 9.48M 4s\n",
      "518100K .......... .......... .......... .......... .......... 94%  543M 4s\n",
      "518150K .......... .......... .......... .......... .......... 94% 6.13M 4s\n",
      "518200K .......... .......... .......... .......... .......... 94% 4.73M 4s\n",
      "518250K .......... .......... .......... .......... .......... 94%  227M 4s\n",
      "518300K .......... .......... .......... .......... .......... 94% 42.5M 4s\n",
      "518350K .......... .......... .......... .......... .......... 94% 10.1M 4s\n",
      "518400K .......... .......... .......... .......... .......... 94% 10.8M 4s\n",
      "518450K .......... .......... .......... .......... .......... 94% 20.0M 4s\n",
      "518500K .......... .......... .......... .......... .......... 94% 13.8M 4s\n",
      "518550K .......... .......... .......... .......... .......... 94% 10.6M 4s\n",
      "518600K .......... .......... .......... .......... .......... 94% 8.62M 4s\n",
      "518650K .......... .......... .......... .......... .......... 94% 18.7M 4s\n",
      "518700K .......... .......... .......... .......... .......... 94% 19.4M 4s\n",
      "518750K .......... .......... .......... .......... .......... 94% 4.59M 4s\n",
      "518800K .......... .......... .......... .......... .......... 94% 3.65M 4s\n",
      "518850K .......... .......... .......... .......... .......... 94% 12.7M 4s\n",
      "518900K .......... .......... .......... .......... .......... 94% 1.48M 4s\n",
      "518950K .......... .......... .......... .......... .......... 94% 26.3M 4s\n",
      "519000K .......... .......... .......... .......... .......... 94%  198M 4s\n",
      "519050K .......... .......... .......... .......... .......... 94% 9.37M 4s\n",
      "519100K .......... .......... .......... .......... .......... 94% 16.5M 4s\n",
      "519150K .......... .......... .......... .......... .......... 94% 6.38M 4s\n",
      "519200K .......... .......... .......... .......... .......... 94% 22.3M 4s\n",
      "519250K .......... .......... .......... .......... .......... 94% 14.5M 4s\n",
      "519300K .......... .......... .......... .......... .......... 94% 4.58M 4s\n",
      "519350K .......... .......... .......... .......... .......... 94%  210M 4s\n",
      "519400K .......... .......... .......... .......... .......... 94% 6.12M 4s\n",
      "519450K .......... .......... .......... .......... .......... 94% 73.3M 4s\n",
      "519500K .......... .......... .......... .......... .......... 94% 12.8M 3s\n",
      "519550K .......... .......... .......... .......... .......... 94% 7.47M 3s\n",
      "519600K .......... .......... .......... .......... .......... 94% 11.8M 3s\n",
      "519650K .......... .......... .......... .......... .......... 94% 7.79M 3s\n",
      "519700K .......... .......... .......... .......... .......... 94% 8.26M 3s\n",
      "519750K .......... .......... .......... .......... .......... 94% 5.74M 3s\n",
      "519800K .......... .......... .......... .......... .......... 94% 58.5M 3s\n",
      "519850K .......... .......... .......... .......... .......... 94% 10.5M 3s\n",
      "519900K .......... .......... .......... .......... .......... 94% 3.22M 3s\n",
      "519950K .......... .......... .......... .......... .......... 94% 15.9M 3s\n",
      "520000K .......... .......... .......... .......... .......... 94% 7.57M 3s\n",
      "520050K .......... .......... .......... .......... .......... 94% 26.7M 3s\n",
      "520100K .......... .......... .......... .......... .......... 94% 6.64M 3s\n",
      "520150K .......... .......... .......... .......... .......... 94% 1.40M 3s\n",
      "520200K .......... .......... .......... .......... .......... 94%  351M 3s\n",
      "520250K .......... .......... .......... .......... .......... 94% 14.3M 3s\n",
      "520300K .......... .......... .......... .......... .......... 94% 2.80M 3s\n",
      "520350K .......... .......... .......... .......... .......... 94% 10.2M 3s\n",
      "520400K .......... .......... .......... .......... .......... 94% 9.42M 3s\n",
      "520450K .......... .......... .......... .......... .......... 94%  106M 3s\n",
      "520500K .......... .......... .......... .......... .......... 94% 10.2M 3s\n",
      "520550K .......... .......... .......... .......... .......... 94% 9.95M 3s\n",
      "520600K .......... .......... .......... .......... .......... 94%  260M 3s\n",
      "520650K .......... .......... .......... .......... .......... 94% 6.70M 3s\n",
      "520700K .......... .......... .......... .......... .......... 94% 13.4M 3s\n",
      "520750K .......... .......... .......... .......... .......... 94% 23.9M 3s\n",
      "520800K .......... .......... .......... .......... .......... 94% 7.23M 3s\n",
      "520850K .......... .......... .......... .......... .......... 94%  436M 3s\n",
      "520900K .......... .......... .......... .......... .......... 94% 8.56M 3s\n",
      "520950K .......... .......... .......... .......... .......... 94% 21.8M 3s\n",
      "521000K .......... .......... .......... .......... .......... 94% 10.5M 3s\n",
      "521050K .......... .......... .......... .......... .......... 95% 17.9M 3s\n",
      "521100K .......... .......... .......... .......... .......... 95% 11.4M 3s\n",
      "521150K .......... .......... .......... .......... .......... 95% 11.5M 3s\n",
      "521200K .......... .......... .......... .......... .......... 95% 5.53M 3s\n",
      "521250K .......... .......... .......... .......... .......... 95% 3.54M 3s\n",
      "521300K .......... .......... .......... .......... .......... 95% 89.9M 3s\n",
      "521350K .......... .......... .......... .......... .......... 95% 9.82M 3s\n",
      "521400K .......... .......... .......... .......... .......... 95% 18.5M 3s\n",
      "521450K .......... .......... .......... .......... .......... 95% 9.80M 3s\n",
      "521500K .......... .......... .......... .......... .......... 95% 68.8M 3s\n",
      "521550K .......... .......... .......... .......... .......... 95% 11.5M 3s\n",
      "521600K .......... .......... .......... .......... .......... 95% 9.73M 3s\n",
      "521650K .......... .......... .......... .......... .......... 95% 15.5M 3s\n",
      "521700K .......... .......... .......... .......... .......... 95% 6.79M 3s\n",
      "521750K .......... .......... .......... .......... .......... 95% 11.4M 3s\n",
      "521800K .......... .......... .......... .......... .......... 95% 16.3M 3s\n",
      "521850K .......... .......... .......... .......... .......... 95% 85.4M 3s\n",
      "521900K .......... .......... .......... .......... .......... 95% 10.5M 3s\n",
      "521950K .......... .......... .......... .......... .......... 95% 6.56M 3s\n",
      "522000K .......... .......... .......... .......... .......... 95% 14.2M 3s\n",
      "522050K .......... .......... .......... .......... .......... 95% 14.1M 3s\n",
      "522100K .......... .......... .......... .......... .......... 95% 5.74M 3s\n",
      "522150K .......... .......... .......... .......... .......... 95% 1.09M 3s\n",
      "522200K .......... .......... .......... .......... .......... 95%  205M 3s\n",
      "522250K .......... .......... .......... .......... .......... 95% 9.69M 3s\n",
      "522300K .......... .......... .......... .......... .......... 95%  122M 3s\n",
      "522350K .......... .......... .......... .......... .......... 95% 7.77M 3s\n",
      "522400K .......... .......... .......... .......... .......... 95% 15.0M 3s\n",
      "522450K .......... .......... .......... .......... .......... 95% 3.76M 3s\n",
      "522500K .......... .......... .......... .......... .......... 95% 22.2M 3s\n",
      "522550K .......... .......... .......... .......... .......... 95% 7.27M 3s\n",
      "522600K .......... .......... .......... .......... .......... 95% 15.0M 3s\n",
      "522650K .......... .......... .......... .......... .......... 95% 15.9M 3s\n",
      "522700K .......... .......... .......... .......... .......... 95% 5.80M 3s\n",
      "522750K .......... .......... .......... .......... .......... 95%  367M 3s\n",
      "522800K .......... .......... .......... .......... .......... 95%  390K 3s\n",
      "522850K .......... .......... .......... .......... .......... 95% 5.51M 3s\n",
      "522900K .......... .......... .......... .......... .......... 95% 18.4M 3s\n",
      "522950K .......... .......... .......... .......... .......... 95% 38.7M 3s\n",
      "523000K .......... .......... .......... .......... .......... 95% 6.67M 3s\n",
      "523050K .......... .......... .......... .......... .......... 95% 6.09M 3s\n",
      "523100K .......... .......... .......... .......... .......... 95% 4.84M 3s\n",
      "523150K .......... .......... .......... .......... .......... 95%  286M 3s\n",
      "523200K .......... .......... .......... .......... .......... 95% 8.28M 3s\n",
      "523250K .......... .......... .......... .......... .......... 95% 16.0M 3s\n",
      "523300K .......... .......... .......... .......... .......... 95% 18.9M 3s\n",
      "523350K .......... .......... .......... .......... .......... 95% 6.20M 3s\n",
      "523400K .......... .......... .......... .......... .......... 95% 32.8M 3s\n",
      "523450K .......... .......... .......... .......... .......... 95% 8.05M 3s\n",
      "523500K .......... .......... .......... .......... .......... 95% 12.2M 3s\n",
      "523550K .......... .......... .......... .......... .......... 95% 20.8M 3s\n",
      "523600K .......... .......... .......... .......... .......... 95% 9.95M 3s\n",
      "523650K .......... .......... .......... .......... .......... 95% 6.87M 3s\n",
      "523700K .......... .......... .......... .......... .......... 95% 4.50M 3s\n",
      "523750K .......... .......... .......... .......... .......... 95% 21.3M 3s\n",
      "523800K .......... .......... .......... .......... .......... 95% 8.82M 3s\n",
      "523850K .......... .......... .......... .......... .......... 95% 4.32M 3s\n",
      "523900K .......... .......... .......... .......... .......... 95% 15.7M 3s\n",
      "523950K .......... .......... .......... .......... .......... 95% 6.70M 3s\n",
      "524000K .......... .......... .......... .......... .......... 95% 2.41M 3s\n",
      "524050K .......... .......... .......... .......... .......... 95% 76.5M 3s\n",
      "524100K .......... .......... .......... .......... .......... 95%  588M 3s\n",
      "524150K .......... .......... .......... .......... .......... 95%  483M 3s\n",
      "524200K .......... .......... .......... .......... .......... 95%  305M 3s\n",
      "524250K .......... .......... .......... .......... .......... 95%  220M 3s\n",
      "524300K .......... .......... .......... .......... .......... 95%  436M 3s\n",
      "524350K .......... .......... .......... .......... .......... 95% 37.9M 3s\n",
      "524400K .......... .......... .......... .......... .......... 95% 12.1M 3s\n",
      "524450K .......... .......... .......... .......... .......... 95% 7.80M 3s\n",
      "524500K .......... .......... .......... .......... .......... 95% 20.1M 3s\n",
      "524550K .......... .......... .......... .......... .......... 95% 49.1M 3s\n",
      "524600K .......... .......... .......... .......... .......... 95% 9.22M 3s\n",
      "524650K .......... .......... .......... .......... .......... 95% 19.2M 3s\n",
      "524700K .......... .......... .......... .......... .......... 95% 11.7M 3s\n",
      "524750K .......... .......... .......... .......... .......... 95% 13.7M 3s\n",
      "524800K .......... .......... .......... .......... .......... 95% 13.4M 3s\n",
      "524850K .......... .......... .......... .......... .......... 95% 12.2M 3s\n",
      "524900K .......... .......... .......... .......... .......... 95%  174M 3s\n",
      "524950K .......... .......... .......... .......... .......... 95% 1.60M 3s\n",
      "525000K .......... .......... .......... .......... .......... 95% 8.28M 3s\n",
      "525050K .......... .......... .......... .......... .......... 95%  230M 3s\n",
      "525100K .......... .......... .......... .......... .......... 95% 7.90M 3s\n",
      "525150K .......... .......... .......... .......... .......... 95% 9.22M 3s\n",
      "525200K .......... .......... .......... .......... .......... 95% 13.7M 3s\n",
      "525250K .......... .......... .......... .......... .......... 95%  142M 3s\n",
      "525300K .......... .......... .......... .......... .......... 95% 10.0M 3s\n",
      "525350K .......... .......... .......... .......... .......... 95% 7.57M 3s\n",
      "525400K .......... .......... .......... .......... .......... 95% 8.03M 3s\n",
      "525450K .......... .......... .......... .......... .......... 95% 59.8M 3s\n",
      "525500K .......... .......... .......... .......... .......... 95% 10.0M 3s\n",
      "525550K .......... .......... .......... .......... .......... 95% 5.53M 3s\n",
      "525600K .......... .......... .......... .......... .......... 95%  568M 3s\n",
      "525650K .......... .......... .......... .......... .......... 95% 21.0M 3s\n",
      "525700K .......... .......... .......... .......... .......... 95% 11.9M 3s\n",
      "525750K .......... .......... .......... .......... .......... 95% 30.6M 3s\n",
      "525800K .......... .......... .......... .......... .......... 95% 14.5M 3s\n",
      "525850K .......... .......... .......... .......... .......... 95% 14.4M 3s\n",
      "525900K .......... .......... .......... .......... .......... 95% 19.1M 3s\n",
      "525950K .......... .......... .......... .......... .......... 95% 4.34M 3s\n",
      "526000K .......... .......... .......... .......... .......... 95% 6.99M 3s\n",
      "526050K .......... .......... .......... .......... .......... 95% 17.0M 3s\n",
      "526100K .......... .......... .......... .......... .......... 95% 9.11M 3s\n",
      "526150K .......... .......... .......... .......... .......... 95% 34.0M 3s\n",
      "526200K .......... .......... .......... .......... .......... 95% 3.58M 3s\n",
      "526250K .......... .......... .......... .......... .......... 95%  250M 3s\n",
      "526300K .......... .......... .......... .......... .......... 95% 66.8M 3s\n",
      "526350K .......... .......... .......... .......... .......... 95% 7.60M 3s\n",
      "526400K .......... .......... .......... .......... .......... 95% 10.4M 3s\n",
      "526450K .......... .......... .......... .......... .......... 95% 35.8M 3s\n",
      "526500K .......... .......... .......... .......... .......... 95% 20.4M 3s\n",
      "526550K .......... .......... .......... .......... .......... 96% 7.24M 3s\n",
      "526600K .......... .......... .......... .......... .......... 96% 26.3M 3s\n",
      "526650K .......... .......... .......... .......... .......... 96% 9.88M 3s\n",
      "526700K .......... .......... .......... .......... .......... 96%  525M 3s\n",
      "526750K .......... .......... .......... .......... .......... 96% 9.75M 3s\n",
      "526800K .......... .......... .......... .......... .......... 96% 13.1M 3s\n",
      "526850K .......... .......... .......... .......... .......... 96% 8.57M 3s\n",
      "526900K .......... .......... .......... .......... .......... 96% 16.8M 3s\n",
      "526950K .......... .......... .......... .......... .......... 96% 63.7M 3s\n",
      "527000K .......... .......... .......... .......... .......... 96% 5.26M 3s\n",
      "527050K .......... .......... .......... .......... .......... 96% 40.4M 3s\n",
      "527100K .......... .......... .......... .......... .......... 96% 18.6M 3s\n",
      "527150K .......... .......... .......... .......... .......... 96% 9.24M 3s\n",
      "527200K .......... .......... .......... .......... .......... 96% 15.1M 3s\n",
      "527250K .......... .......... .......... .......... .......... 96% 8.57M 3s\n",
      "527300K .......... .......... .......... .......... .......... 96% 8.17M 3s\n",
      "527350K .......... .......... .......... .......... .......... 96% 32.6M 3s\n",
      "527400K .......... .......... .......... .......... .......... 96% 11.3M 3s\n",
      "527450K .......... .......... .......... .......... .......... 96% 78.4M 3s\n",
      "527500K .......... .......... .......... .......... .......... 96% 10.7M 3s\n",
      "527550K .......... .......... .......... .......... .......... 96% 4.38M 3s\n",
      "527600K .......... .......... .......... .......... .......... 96% 45.0M 3s\n",
      "527650K .......... .......... .......... .......... .......... 96% 10.1M 3s\n",
      "527700K .......... .......... .......... .......... .......... 96% 14.4M 3s\n",
      "527750K .......... .......... .......... .......... .......... 96% 8.51M 2s\n",
      "527800K .......... .......... .......... .......... .......... 96% 41.1M 2s\n",
      "527850K .......... .......... .......... .......... .......... 96% 15.0M 2s\n",
      "527900K .......... .......... .......... .......... .......... 96% 11.9M 2s\n",
      "527950K .......... .......... .......... .......... .......... 96% 8.86M 2s\n",
      "528000K .......... .......... .......... .......... .......... 96% 28.6M 2s\n",
      "528050K .......... .......... .......... .......... .......... 96% 12.6M 2s\n",
      "528100K .......... .......... .......... .......... .......... 96% 13.5M 2s\n",
      "528150K .......... .......... .......... .......... .......... 96% 1.43M 2s\n",
      "528200K .......... .......... .......... .......... .......... 96% 12.2M 2s\n",
      "528250K .......... .......... .......... .......... .......... 96%  323M 2s\n",
      "528300K .......... .......... .......... .......... .......... 96% 20.5M 2s\n",
      "528350K .......... .......... .......... .......... .......... 96% 6.69M 2s\n",
      "528400K .......... .......... .......... .......... .......... 96% 9.32M 2s\n",
      "528450K .......... .......... .......... .......... .......... 96% 15.2M 2s\n",
      "528500K .......... .......... .......... .......... .......... 96% 6.88M 2s\n",
      "528550K .......... .......... .......... .......... .......... 96% 8.02M 2s\n",
      "528600K .......... .......... .......... .......... .......... 96%  354M 2s\n",
      "528650K .......... .......... .......... .......... .......... 96%  142M 2s\n",
      "528700K .......... .......... .......... .......... .......... 96% 12.6M 2s\n",
      "528750K .......... .......... .......... .......... .......... 96% 3.16M 2s\n",
      "528800K .......... .......... .......... .......... .......... 96% 3.10M 2s\n",
      "528850K .......... .......... .......... .......... .......... 96% 15.3M 2s\n",
      "528900K .......... .......... .......... .......... .......... 96% 2.76M 2s\n",
      "528950K .......... .......... .......... .......... .......... 96% 8.29M 2s\n",
      "529000K .......... .......... .......... .......... .......... 96% 6.70M 2s\n",
      "529050K .......... .......... .......... .......... .......... 96% 2.70M 2s\n",
      "529100K .......... .......... .......... .......... .......... 96% 3.19M 2s\n",
      "529150K .......... .......... .......... .......... .......... 96% 3.58M 2s\n",
      "529200K .......... .......... .......... .......... .......... 96% 2.02M 2s\n",
      "529250K .......... .......... .......... .......... .......... 96% 40.3M 2s\n",
      "529300K .......... .......... .......... .......... .......... 96% 4.27M 2s\n",
      "529350K .......... .......... .......... .......... .......... 96% 1.43M 2s\n",
      "529400K .......... .......... .......... .......... .......... 96% 3.42M 2s\n",
      "529450K .......... .......... .......... .......... .......... 96% 23.3M 2s\n",
      "529500K .......... .......... .......... .......... .......... 96% 3.98M 2s\n",
      "529550K .......... .......... .......... .......... .......... 96% 2.13M 2s\n",
      "529600K .......... .......... .......... .......... .......... 96% 15.7M 2s\n",
      "529650K .......... .......... .......... .......... .......... 96% 6.62M 2s\n",
      "529700K .......... .......... .......... .......... .......... 96% 23.8M 2s\n",
      "529750K .......... .......... .......... .......... .......... 96% 17.3M 2s\n",
      "529800K .......... .......... .......... .......... .......... 96% 22.5M 2s\n",
      "529850K .......... .......... .......... .......... .......... 96% 5.68M 2s\n",
      "529900K .......... .......... .......... .......... .......... 96% 21.3M 2s\n",
      "529950K .......... .......... .......... .......... .......... 96% 3.60M 2s\n",
      "530000K .......... .......... .......... .......... .......... 96% 49.7M 2s\n",
      "530050K .......... .......... .......... .......... .......... 96% 16.9M 2s\n",
      "530100K .......... .......... .......... .......... .......... 96% 10.9M 2s\n",
      "530150K .......... .......... .......... .......... .......... 96% 8.52M 2s\n",
      "530200K .......... .......... .......... .......... .......... 96% 16.6M 2s\n",
      "530250K .......... .......... .......... .......... .......... 96% 10.7M 2s\n",
      "530300K .......... .......... .......... .......... .......... 96% 11.2M 2s\n",
      "530350K .......... .......... .......... .......... .......... 96% 3.26M 2s\n",
      "530400K .......... .......... .......... .......... .......... 96% 17.2M 2s\n",
      "530450K .......... .......... .......... .......... .......... 96% 10.4M 2s\n",
      "530500K .......... .......... .......... .......... .......... 96% 7.06M 2s\n",
      "530550K .......... .......... .......... .......... .......... 96% 4.56M 2s\n",
      "530600K .......... .......... .......... .......... .......... 96% 5.19M 2s\n",
      "530650K .......... .......... .......... .......... .......... 96% 17.0M 2s\n",
      "530700K .......... .......... .......... .......... .......... 96% 14.1M 2s\n",
      "530750K .......... .......... .......... .......... .......... 96%  439K 2s\n",
      "530800K .......... .......... .......... .......... .......... 96% 5.77M 2s\n",
      "530850K .......... .......... .......... .......... .......... 96%  483M 2s\n",
      "530900K .......... .......... .......... .......... .......... 96% 3.77M 2s\n",
      "530950K .......... .......... .......... .......... .......... 96% 10.9M 2s\n",
      "531000K .......... .......... .......... .......... .......... 96% 18.9M 2s\n",
      "531050K .......... .......... .......... .......... .......... 96% 7.36M 2s\n",
      "531100K .......... .......... .......... .......... .......... 96% 8.80M 2s\n",
      "531150K .......... .......... .......... .......... .......... 96% 10.6M 2s\n",
      "531200K .......... .......... .......... .......... .......... 96% 71.5M 2s\n",
      "531250K .......... .......... .......... .......... .......... 96% 6.62M 2s\n",
      "531300K .......... .......... .......... .......... .......... 96% 29.6M 2s\n",
      "531350K .......... .......... .......... .......... .......... 96% 2.68M 2s\n",
      "531400K .......... .......... .......... .......... .......... 96% 15.2M 2s\n",
      "531450K .......... .......... .......... .......... .......... 96% 17.1M 2s\n",
      "531500K .......... .......... .......... .......... .......... 96% 4.21M 2s\n",
      "531550K .......... .......... .......... .......... .......... 96% 1.63M 2s\n",
      "531600K .......... .......... .......... .......... .......... 96% 8.49M 2s\n",
      "531650K .......... .......... .......... .......... .......... 96% 19.1M 2s\n",
      "531700K .......... .......... .......... .......... .......... 96% 59.4M 2s\n",
      "531750K .......... .......... .......... .......... .......... 96% 6.32M 2s\n",
      "531800K .......... .......... .......... .......... .......... 96% 4.85M 2s\n",
      "531850K .......... .......... .......... .......... .......... 96%  170M 2s\n",
      "531900K .......... .......... .......... .......... .......... 96% 6.00M 2s\n",
      "531950K .......... .......... .......... .......... .......... 96% 13.3M 2s\n",
      "532000K .......... .......... .......... .......... .......... 96% 20.1M 2s\n",
      "532050K .......... .......... .......... .......... .......... 97% 4.63M 2s\n",
      "532100K .......... .......... .......... .......... .......... 97% 4.65M 2s\n",
      "532150K .......... .......... .......... .......... .......... 97% 7.07M 2s\n",
      "532200K .......... .......... .......... .......... .......... 97% 7.47M 2s\n",
      "532250K .......... .......... .......... .......... .......... 97% 15.9M 2s\n",
      "532300K .......... .......... .......... .......... .......... 97% 13.3M 2s\n",
      "532350K .......... .......... .......... .......... .......... 97% 5.91M 2s\n",
      "532400K .......... .......... .......... .......... .......... 97%  173M 2s\n",
      "532450K .......... .......... .......... .......... .......... 97% 14.8M 2s\n",
      "532500K .......... .......... .......... .......... .......... 97% 2.56M 2s\n",
      "532550K .......... .......... .......... .......... .......... 97%  307M 2s\n",
      "532600K .......... .......... .......... .......... .......... 97% 14.2M 2s\n",
      "532650K .......... .......... .......... .......... .......... 97% 24.5M 2s\n",
      "532700K .......... .......... .......... .......... .......... 97% 48.2M 2s\n",
      "532750K .......... .......... .......... .......... .......... 97% 3.97M 2s\n",
      "532800K .......... .......... .......... .......... .......... 97% 7.99M 2s\n",
      "532850K .......... .......... .......... .......... .......... 97% 9.80M 2s\n",
      "532900K .......... .......... .......... .......... .......... 97% 5.70M 2s\n",
      "532950K .......... .......... .......... .......... .......... 97%  197M 2s\n",
      "533000K .......... .......... .......... .......... .......... 97% 8.29M 2s\n",
      "533050K .......... .......... .......... .......... .......... 97% 41.2M 2s\n",
      "533100K .......... .......... .......... .......... .......... 97% 10.2M 2s\n",
      "533150K .......... .......... .......... .......... .......... 97% 7.77M 2s\n",
      "533200K .......... .......... .......... .......... .......... 97% 1.39M 2s\n",
      "533250K .......... .......... .......... .......... .......... 97% 3.26M 2s\n",
      "533300K .......... .......... .......... .......... .......... 97%  298M 2s\n",
      "533350K .......... .......... .......... .......... .......... 97% 9.78M 2s\n",
      "533400K .......... .......... .......... .......... .......... 97% 11.3M 2s\n",
      "533450K .......... .......... .......... .......... .......... 97% 12.4M 2s\n",
      "533500K .......... .......... .......... .......... .......... 97% 23.1M 2s\n",
      "533550K .......... .......... .......... .......... .......... 97% 2.76M 2s\n",
      "533600K .......... .......... .......... .......... .......... 97% 4.88M 2s\n",
      "533650K .......... .......... .......... .......... .......... 97%  146M 2s\n",
      "533700K .......... .......... .......... .......... .......... 97% 8.33M 2s\n",
      "533750K .......... .......... .......... .......... .......... 97% 10.4M 2s\n",
      "533800K .......... .......... .......... .......... .......... 97% 38.9M 2s\n",
      "533850K .......... .......... .......... .......... .......... 97% 8.53M 2s\n",
      "533900K .......... .......... .......... .......... .......... 97% 3.78M 2s\n",
      "533950K .......... .......... .......... .......... .......... 97% 9.67M 2s\n",
      "534000K .......... .......... .......... .......... .......... 97% 8.70M 2s\n",
      "534050K .......... .......... .......... .......... .......... 97% 15.1M 2s\n",
      "534100K .......... .......... .......... .......... .......... 97% 6.92M 2s\n",
      "534150K .......... .......... .......... .......... .......... 97% 17.6M 2s\n",
      "534200K .......... .......... .......... .......... .......... 97% 20.7M 2s\n",
      "534250K .......... .......... .......... .......... .......... 97% 31.7M 2s\n",
      "534300K .......... .......... .......... .......... .......... 97% 6.07M 2s\n",
      "534350K .......... .......... .......... .......... .......... 97% 10.8M 2s\n",
      "534400K .......... .......... .......... .......... .......... 97% 14.9M 2s\n",
      "534450K .......... .......... .......... .......... .......... 97% 9.51M 2s\n",
      "534500K .......... .......... .......... .......... .......... 97% 14.3M 2s\n",
      "534550K .......... .......... .......... .......... .......... 97% 13.4M 2s\n",
      "534600K .......... .......... .......... .......... .......... 97% 12.7M 2s\n",
      "534650K .......... .......... .......... .......... .......... 97% 13.5M 2s\n",
      "534700K .......... .......... .......... .......... .......... 97% 15.3M 2s\n",
      "534750K .......... .......... .......... .......... .......... 97% 10.3M 2s\n",
      "534800K .......... .......... .......... .......... .......... 97% 12.7M 2s\n",
      "534850K .......... .......... .......... .......... .......... 97% 16.4M 2s\n",
      "534900K .......... .......... .......... .......... .......... 97% 6.89M 2s\n",
      "534950K .......... .......... .......... .......... .......... 97% 9.56M 2s\n",
      "535000K .......... .......... .......... .......... .......... 97% 5.25M 2s\n",
      "535050K .......... .......... .......... .......... .......... 97%  362M 2s\n",
      "535100K .......... .......... .......... .......... .......... 97% 5.68M 2s\n",
      "535150K .......... .......... .......... .......... .......... 97% 2.76M 2s\n",
      "535200K .......... .......... .......... .......... .......... 97%  178M 2s\n",
      "535250K .......... .......... .......... .......... .......... 97% 1.41M 2s\n",
      "535300K .......... .......... .......... .......... .......... 97%  394M 2s\n",
      "535350K .......... .......... .......... .......... .......... 97% 10.8M 2s\n",
      "535400K .......... .......... .......... .......... .......... 97% 9.27M 2s\n",
      "535450K .......... .......... .......... .......... .......... 97% 42.9M 2s\n",
      "535500K .......... .......... .......... .......... .......... 97% 10.8M 2s\n",
      "535550K .......... .......... .......... .......... .......... 97% 16.2M 2s\n",
      "535600K .......... .......... .......... .......... .......... 97% 9.09M 2s\n",
      "535650K .......... .......... .......... .......... .......... 97% 5.00M 2s\n",
      "535700K .......... .......... .......... .......... .......... 97% 7.64M 2s\n",
      "535750K .......... .......... .......... .......... .......... 97% 45.5M 2s\n",
      "535800K .......... .......... .......... .......... .......... 97% 7.73M 2s\n",
      "535850K .......... .......... .......... .......... .......... 97% 29.1M 2s\n",
      "535900K .......... .......... .......... .......... .......... 97% 6.46M 2s\n",
      "535950K .......... .......... .......... .......... .......... 97% 5.65M 2s\n",
      "536000K .......... .......... .......... .......... .......... 97% 20.5M 2s\n",
      "536050K .......... .......... .......... .......... .......... 97% 12.2M 2s\n",
      "536100K .......... .......... .......... .......... .......... 97% 4.96M 1s\n",
      "536150K .......... .......... .......... .......... .......... 97% 19.7M 1s\n",
      "536200K .......... .......... .......... .......... .......... 97% 7.44M 1s\n",
      "536250K .......... .......... .......... .......... .......... 97% 18.8M 1s\n",
      "536300K .......... .......... .......... .......... .......... 97% 47.5M 1s\n",
      "536350K .......... .......... .......... .......... .......... 97% 6.67M 1s\n",
      "536400K .......... .......... .......... .......... .......... 97% 8.85M 1s\n",
      "536450K .......... .......... .......... .......... .......... 97% 12.8M 1s\n",
      "536500K .......... .......... .......... .......... .......... 97% 12.0M 1s\n",
      "536550K .......... .......... .......... .......... .......... 97% 9.31M 1s\n",
      "536600K .......... .......... .......... .......... .......... 97% 19.4M 1s\n",
      "536650K .......... .......... .......... .......... .......... 97% 8.73M 1s\n",
      "536700K .......... .......... .......... .......... .......... 97% 23.0M 1s\n",
      "536750K .......... .......... .......... .......... .......... 97% 3.65M 1s\n",
      "536800K .......... .......... .......... .......... .......... 97%  391M 1s\n",
      "536850K .......... .......... .......... .......... .......... 97% 16.8M 1s\n",
      "536900K .......... .......... .......... .......... .......... 97% 6.28M 1s\n",
      "536950K .......... .......... .......... .......... .......... 97% 9.95M 1s\n",
      "537000K .......... .......... .......... .......... .......... 97% 13.2M 1s\n",
      "537050K .......... .......... .......... .......... .......... 97% 10.9M 1s\n",
      "537100K .......... .......... .......... .......... .......... 97% 9.78M 1s\n",
      "537150K .......... .......... .......... .......... .......... 97% 16.5M 1s\n",
      "537200K .......... .......... .......... .......... .......... 97% 7.33M 1s\n",
      "537250K .......... .......... .......... .......... .......... 97% 15.0M 1s\n",
      "537300K .......... .......... .......... .......... .......... 97% 15.5M 1s\n",
      "537350K .......... .......... .......... .......... .......... 97% 16.6M 1s\n",
      "537400K .......... .......... .......... .......... .......... 97% 4.02M 1s\n",
      "537450K .......... .......... .......... .......... .......... 97%  191M 1s\n",
      "537500K .......... .......... .......... .......... .......... 98% 54.1M 1s\n",
      "537550K .......... .......... .......... .......... .......... 98% 4.87M 1s\n",
      "537600K .......... .......... .......... .......... .......... 98% 8.82M 1s\n",
      "537650K .......... .......... .......... .......... .......... 98% 57.0M 1s\n",
      "537700K .......... .......... .......... .......... .......... 98% 8.63M 1s\n",
      "537750K .......... .......... .......... .......... .......... 98% 18.6M 1s\n",
      "537800K .......... .......... .......... .......... .......... 98% 83.8M 1s\n",
      "537850K .......... .......... .......... .......... .......... 98% 1.35M 1s\n",
      "537900K .......... .......... .......... .......... .......... 98% 11.1M 1s\n",
      "537950K .......... .......... .......... .......... .......... 98% 11.1M 1s\n",
      "538000K .......... .......... .......... .......... .......... 98% 9.56M 1s\n",
      "538050K .......... .......... .......... .......... .......... 98% 17.9M 1s\n",
      "538100K .......... .......... .......... .......... .......... 98% 5.60M 1s\n",
      "538150K .......... .......... .......... .......... .......... 98% 16.4M 1s\n",
      "538200K .......... .......... .......... .......... .......... 98% 13.4M 1s\n",
      "538250K .......... .......... .......... .......... .......... 98% 12.9M 1s\n",
      "538300K .......... .......... .......... .......... .......... 98% 10.9M 1s\n",
      "538350K .......... .......... .......... .......... .......... 98% 8.11M 1s\n",
      "538400K .......... .......... .......... .......... .......... 98% 10.1M 1s\n",
      "538450K .......... .......... .......... .......... .......... 98% 9.57M 1s\n",
      "538500K .......... .......... .......... .......... .......... 98% 9.39M 1s\n",
      "538550K .......... .......... .......... .......... .......... 98%  618M 1s\n",
      "538600K .......... .......... .......... .......... .......... 98% 8.75M 1s\n",
      "538650K .......... .......... .......... .......... .......... 98% 19.7M 1s\n",
      "538700K .......... .......... .......... .......... .......... 98% 2.81M 1s\n",
      "538750K .......... .......... .......... .......... .......... 98% 25.6M 1s\n",
      "538800K .......... .......... .......... .......... .......... 98% 43.9M 1s\n",
      "538850K .......... .......... .......... .......... .......... 98% 7.61M 1s\n",
      "538900K .......... .......... .......... .......... .......... 98% 14.7M 1s\n",
      "538950K .......... .......... .......... .......... .......... 98% 9.64M 1s\n",
      "539000K .......... .......... .......... .......... .......... 98% 5.44M 1s\n",
      "539050K .......... .......... .......... .......... .......... 98%  538K 1s\n",
      "539100K .......... .......... .......... .......... .......... 98% 9.62M 1s\n",
      "539150K .......... .......... .......... .......... .......... 98% 9.63M 1s\n",
      "539200K .......... .......... .......... .......... .......... 98% 70.8M 1s\n",
      "539250K .......... .......... .......... .......... .......... 98% 9.92M 1s\n",
      "539300K .......... .......... .......... .......... .......... 98% 7.71M 1s\n",
      "539350K .......... .......... .......... .......... .......... 98% 16.4M 1s\n",
      "539400K .......... .......... .......... .......... .......... 98% 20.7M 1s\n",
      "539450K .......... .......... .......... .......... .......... 98% 6.77M 1s\n",
      "539500K .......... .......... .......... .......... .......... 98% 9.38M 1s\n",
      "539550K .......... .......... .......... .......... .......... 98% 28.4M 1s\n",
      "539600K .......... .......... .......... .......... .......... 98% 11.5M 1s\n",
      "539650K .......... .......... .......... .......... .......... 98% 11.9M 1s\n",
      "539700K .......... .......... .......... .......... .......... 98% 11.9M 1s\n",
      "539750K .......... .......... .......... .......... .......... 98% 28.1M 1s\n",
      "539800K .......... .......... .......... .......... .......... 98% 9.98M 1s\n",
      "539850K .......... .......... .......... .......... .......... 98% 27.6M 1s\n",
      "539900K .......... .......... .......... .......... .......... 98% 9.41M 1s\n",
      "539950K .......... .......... .......... .......... .......... 98% 4.45M 1s\n",
      "540000K .......... .......... .......... .......... .......... 98% 5.42M 1s\n",
      "540050K .......... .......... .......... .......... .......... 98%  394M 1s\n",
      "540100K .......... .......... .......... .......... .......... 98% 9.74M 1s\n",
      "540150K .......... .......... .......... .......... .......... 98% 7.54M 1s\n",
      "540200K .......... .......... .......... .......... .......... 98% 8.96M 1s\n",
      "540250K .......... .......... .......... .......... .......... 98% 29.7M 1s\n",
      "540300K .......... .......... .......... .......... .......... 98% 4.06M 1s\n",
      "540350K .......... .......... .......... .......... .......... 98% 6.24M 1s\n",
      "540400K .......... .......... .......... .......... .......... 98% 21.9M 1s\n",
      "540450K .......... .......... .......... .......... .......... 98% 86.9M 1s\n",
      "540500K .......... .......... .......... .......... .......... 98% 9.38M 1s\n",
      "540550K .......... .......... .......... .......... .......... 98% 8.95M 1s\n",
      "540600K .......... .......... .......... .......... .......... 98%  465M 1s\n",
      "540650K .......... .......... .......... .......... .......... 98% 7.19M 1s\n",
      "540700K .......... .......... .......... .......... .......... 98%  130M 1s\n",
      "540750K .......... .......... .......... .......... .......... 98% 4.53M 1s\n",
      "540800K .......... .......... .......... .......... .......... 98% 8.81M 1s\n",
      "540850K .......... .......... .......... .......... .......... 98% 18.6M 1s\n",
      "540900K .......... .......... .......... .......... .......... 98% 1.35M 1s\n",
      "540950K .......... .......... .......... .......... .......... 98% 12.4M 1s\n",
      "541000K .......... .......... .......... .......... .......... 98%  421M 1s\n",
      "541050K .......... .......... .......... .......... .......... 98% 5.44M 1s\n",
      "541100K .......... .......... .......... .......... .......... 98% 48.0M 1s\n",
      "541150K .......... .......... .......... .......... .......... 98% 5.36M 1s\n",
      "541200K .......... .......... .......... .......... .......... 98% 15.3M 1s\n",
      "541250K .......... .......... .......... .......... .......... 98% 6.07M 1s\n",
      "541300K .......... .......... .......... .......... .......... 98%  217M 1s\n",
      "541350K .......... .......... .......... .......... .......... 98% 10.1M 1s\n",
      "541400K .......... .......... .......... .......... .......... 98% 44.1M 1s\n",
      "541450K .......... .......... .......... .......... .......... 98% 3.91M 1s\n",
      "541500K .......... .......... .......... .......... .......... 98% 12.2M 1s\n",
      "541550K .......... .......... .......... .......... .......... 98% 8.71M 1s\n",
      "541600K .......... .......... .......... .......... .......... 98%  135M 1s\n",
      "541650K .......... .......... .......... .......... .......... 98% 9.01M 1s\n",
      "541700K .......... .......... .......... .......... .......... 98% 26.3M 1s\n",
      "541750K .......... .......... .......... .......... .......... 98% 9.71M 1s\n",
      "541800K .......... .......... .......... .......... .......... 98% 7.53M 1s\n",
      "541850K .......... .......... .......... .......... .......... 98% 13.4M 1s\n",
      "541900K .......... .......... .......... .......... .......... 98% 14.3M 1s\n",
      "541950K .......... .......... .......... .......... .......... 98% 3.39M 1s\n",
      "542000K .......... .......... .......... .......... .......... 98% 9.65M 1s\n",
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      "\n",
      "2026-03-31 15:31:45 (8.08 MB/s) - ‘progen2-checkpoints/progen2-small/progen2-small.tar.gz.1’ saved [561673660/561673660]\n",
      "\n",
      "x ./\n",
      "x ./config.json\n",
      "x ./pytorch_model.bin\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# checkpoint\n",
    "model=progen2-small\n",
    "mkdir progen2-checkpoints\n",
    "wget -P progen2-checkpoints/${model} https://storage.googleapis.com/sfr-progen-research/checkpoints/${model}.tar.gz\n",
    "tar -xvf progen2-checkpoints/${model}/${model}.tar.gz -C progen2-checkpoints/${model}/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "3e5ca98e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#@title Define ProGen2 model\n",
    "\n",
    "# -----------------------------\n",
    "# Config\n",
    "# -----------------------------\n",
    "class ProGenConfig:\n",
    "    def __init__(\n",
    "        self,\n",
    "        vocab_size_emb=32,\n",
    "        vocab_size_lm_head=32,\n",
    "        n_positions=1024,\n",
    "        embed_dim=1024,\n",
    "        n_layer=12,\n",
    "        n_head=16,\n",
    "        rotary_dim=32,\n",
    "        n_inner=None,\n",
    "        layer_norm_epsilon=1e-5,\n",
    "    ):\n",
    "        self.vocab_size_emb = vocab_size_emb\n",
    "        self.vocab_size_lm_head = vocab_size_lm_head\n",
    "        self.n_positions = n_positions\n",
    "        self.embed_dim = embed_dim\n",
    "        self.n_layer = n_layer\n",
    "        self.n_head = n_head\n",
    "        self.rotary_dim = rotary_dim\n",
    "        self.n_inner = n_inner if n_inner is not None else 4 * embed_dim\n",
    "        self.layer_norm_epsilon = layer_norm_epsilon\n",
    "\n",
    "    @classmethod\n",
    "    def from_json(cls, path):\n",
    "        data = json.loads(Path(path).read_text())\n",
    "        return cls(\n",
    "            vocab_size_emb=data.get(\"vocab_size_emb\", data.get(\"vocab_size\", 32)),\n",
    "            vocab_size_lm_head=data.get(\"vocab_size_lm_head\", data.get(\"vocab_size\", 32)),\n",
    "            n_positions=data.get(\"n_positions\", 1024),\n",
    "            embed_dim=data.get(\"embed_dim\", data.get(\"n_embd\", 1024)),\n",
    "            n_layer=data.get(\"n_layer\", 12),\n",
    "            n_head=data.get(\"n_head\", 16),\n",
    "            rotary_dim=data.get(\"rotary_dim\", 32),\n",
    "            n_inner=data.get(\"n_inner\", None),\n",
    "            layer_norm_epsilon=data.get(\"layer_norm_epsilon\", 1e-5),\n",
    "        )\n",
    "    \n",
    "# Minimal PyTorch implementation of ProGen2-small-compatible architecture\n",
    "# Keeps the exact forward math needed for checkpoint compatibility.\n",
    "\n",
    "# -----------------------------\n",
    "# Config\n",
    "# -----------------------------\n",
    "@dataclass\n",
    "class ProGenConfig:\n",
    "    vocab_size: int\n",
    "    n_positions: int\n",
    "    n_ctx: int\n",
    "    n_embd: int\n",
    "    n_layer: int\n",
    "    n_head: int\n",
    "    rotary_dim: int | None\n",
    "    n_inner: int | None\n",
    "    activation_function: str\n",
    "    resid_pdrop: float\n",
    "    embd_pdrop: float\n",
    "    attn_pdrop: float\n",
    "    layer_norm_epsilon: float\n",
    "    initializer_range: float = 0.02\n",
    "\n",
    "    @property\n",
    "    def hidden_size(self):\n",
    "        return self.n_embd\n",
    "\n",
    "    @property\n",
    "    def num_attention_heads(self):\n",
    "        return self.n_head\n",
    "\n",
    "    @property\n",
    "    def max_position_embeddings(self):\n",
    "        return self.n_positions\n",
    "\n",
    "    @classmethod\n",
    "    def from_json(cls, path):\n",
    "        d = json.loads(Path(path).read_text())\n",
    "\n",
    "        vocab_size = d[\"vocab_size\"]\n",
    "        n_embd = d.get(\"n_embd\") or d.get(\"hidden_size\") or d.get(\"embed_dim\")\n",
    "        n_layer = d[\"n_layer\"]\n",
    "        n_head = d.get(\"n_head\") or d.get(\"num_attention_heads\")\n",
    "\n",
    "        n_positions = d.get(\"n_positions\") or d.get(\"max_position_embeddings\") or 1024\n",
    "        n_ctx = d.get(\"n_ctx\") or n_positions\n",
    "\n",
    "        return cls(\n",
    "            vocab_size=vocab_size,\n",
    "            n_positions=n_positions,\n",
    "            n_ctx=n_ctx,\n",
    "            n_embd=n_embd,\n",
    "            n_layer=n_layer,\n",
    "            n_head=n_head,\n",
    "            rotary_dim=d.get(\"rotary_dim\"),\n",
    "            n_inner=d.get(\"n_inner\"),\n",
    "            activation_function=d.get(\"activation_function\", \"gelu_new\"),\n",
    "            resid_pdrop=d.get(\"resid_pdrop\", 0.0),\n",
    "            embd_pdrop=d.get(\"embd_pdrop\", 0.0),\n",
    "            attn_pdrop=d.get(\"attn_pdrop\", 0.0),\n",
    "            layer_norm_epsilon=d.get(\"layer_norm_epsilon\", 1e-5),\n",
    "            initializer_range=d.get(\"initializer_range\", 0.02),\n",
    "        )\n",
    "\n",
    "\n",
    "\n",
    "def get_activation(name):\n",
    "    if name == \"gelu_new\":\n",
    "        return lambda x: F.gelu(x, approximate=\"tanh\")\n",
    "    if name == \"gelu\":\n",
    "        return F.gelu\n",
    "    if name == \"relu\":\n",
    "        return F.relu\n",
    "    if name == \"silu\":\n",
    "        return F.silu\n",
    "    raise ValueError(f\"Unsupported activation: {name}\")\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# Rotary embeddings\n",
    "# -----------------------------\n",
    "def fixed_pos_embedding(x, seq_dim=1, seq_len=None):\n",
    "    dim = x.shape[-1]\n",
    "    if seq_len is None:\n",
    "        seq_len = x.shape[seq_dim]\n",
    "    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=x.device).float() / dim))\n",
    "    sinusoid_inp = torch.einsum(\"i,j->ij\", torch.arange(seq_len, device=x.device), inv_freq).float()\n",
    "    return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)\n",
    "\n",
    "\n",
    "def rotate_every_two(x):\n",
    "    x1 = x[:, :, :, ::2]\n",
    "    x2 = x[:, :, :, 1::2]\n",
    "    return torch.stack((-x2, x1), dim=-1).flatten(-2)\n",
    "\n",
    "\n",
    "def apply_rotary_pos_emb(x, sincos, offset=0):\n",
    "    sin, cos = [\n",
    "        t[None, offset : x.shape[1] + offset, None, :].repeat_interleave(2, dim=3)\n",
    "        for t in sincos\n",
    "    ]\n",
    "    return (x * cos) + (rotate_every_two(x) * sin)\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# Attention\n",
    "# -----------------------------\n",
    "class ProGenAttention(nn.Module):\n",
    "    def __init__(self, config: ProGenConfig):\n",
    "        super().__init__()\n",
    "        self.embed_dim = config.hidden_size\n",
    "        self.num_attention_heads = config.num_attention_heads\n",
    "        self.head_dim = self.embed_dim // self.num_attention_heads\n",
    "        self.scale_attn = self.head_dim ** 0.5\n",
    "        self.rotary_dim = config.rotary_dim\n",
    "\n",
    "        max_positions = config.max_position_embeddings\n",
    "        self.register_buffer(\n",
    "            \"bias\",\n",
    "            torch.tril(torch.ones(max_positions, max_positions, dtype=torch.bool)).view(\n",
    "                1, 1, max_positions, max_positions\n",
    "            ),\n",
    "        )\n",
    "        self.register_buffer(\"masked_bias\", torch.tensor(-1e9))\n",
    "\n",
    "        self.qkv_proj = nn.Linear(self.embed_dim, 3 * self.embed_dim, bias=False)\n",
    "        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)\n",
    "        self.attn_dropout = nn.Dropout(config.attn_pdrop)\n",
    "        self.resid_dropout = nn.Dropout(config.resid_pdrop)\n",
    "\n",
    "    def _split_heads(self, x, mp_num=8):\n",
    "        reshaped = x.reshape(x.shape[:-1] + (self.num_attention_heads // mp_num, self.head_dim))\n",
    "        return reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])\n",
    "\n",
    "    def _merge_heads(self, x):\n",
    "        # x: [B, H, T, Dh]\n",
    "        x = x.permute(0, 2, 1, 3).contiguous()\n",
    "        return x.reshape(x.shape[:-2] + (self.num_attention_heads * self.head_dim,))\n",
    "\n",
    "    def _attn(self, query, key, value, attention_mask=None):\n",
    "        # query,key,value: [B, H, T, Dh]\n",
    "        q_len, k_len = query.size(-2), key.size(-2)\n",
    "        causal_mask = self.bias[:, :, k_len - q_len : k_len, :k_len]\n",
    "\n",
    "        attn_weights = torch.matmul(query.to(torch.float32), key.to(torch.float32).transpose(-1, -2))\n",
    "        attn_weights = attn_weights / self.scale_attn\n",
    "        attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))\n",
    "\n",
    "        if attention_mask is not None:\n",
    "            attn_weights = attn_weights + attention_mask\n",
    "\n",
    "        attn_weights = F.softmax(attn_weights, dim=-1).to(value.dtype)\n",
    "        attn_weights = self.attn_dropout(attn_weights)\n",
    "        attn_output = torch.matmul(attn_weights, value)\n",
    "        return attn_output\n",
    "\n",
    "    def forward(self, hidden_states, attention_mask=None):\n",
    "        qkv = self.qkv_proj(hidden_states)\n",
    "\n",
    "        mp_num = 8\n",
    "        qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))\n",
    "        local_dim = self.head_dim * self.num_attention_heads // mp_num\n",
    "\n",
    "        # Official order: query, value, key\n",
    "        query, value, key = torch.split(qkv_split, local_dim, dim=-1)\n",
    "\n",
    "        query = self._split_heads(query, mp_num=mp_num)\n",
    "        key = self._split_heads(key, mp_num=mp_num)\n",
    "        value = self._split_heads(value, mp_num=mp_num)\n",
    "\n",
    "        # query/key: [B, T, H, Dh], value -> [B, H, T, Dh]\n",
    "        value = value.permute(0, 2, 1, 3)\n",
    "\n",
    "        seq_len = key.shape[1]\n",
    "        if self.rotary_dim is not None:\n",
    "            k_rot = key[:, :, :, : self.rotary_dim]\n",
    "            k_pass = key[:, :, :, self.rotary_dim :]\n",
    "            q_rot = query[:, :, :, : self.rotary_dim]\n",
    "            q_pass = query[:, :, :, self.rotary_dim :]\n",
    "\n",
    "            sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len)\n",
    "            k_rot = apply_rotary_pos_emb(k_rot, sincos)\n",
    "            q_rot = apply_rotary_pos_emb(q_rot, sincos)\n",
    "\n",
    "            key = torch.cat([k_rot, k_pass], dim=-1)\n",
    "            query = torch.cat([q_rot, q_pass], dim=-1)\n",
    "        else:\n",
    "            sincos = fixed_pos_embedding(key, 1, seq_len=seq_len)\n",
    "            key = apply_rotary_pos_emb(key, sincos)\n",
    "            query = apply_rotary_pos_emb(query, sincos)\n",
    "\n",
    "        key = key.permute(0, 2, 1, 3)    # [B, H, T, Dh]\n",
    "        query = query.permute(0, 2, 1, 3)\n",
    "\n",
    "        attn_output = self._attn(query, key, value, attention_mask=attention_mask)\n",
    "        attn_output = self._merge_heads(attn_output)\n",
    "        attn_output = self.out_proj(attn_output)\n",
    "        attn_output = self.resid_dropout(attn_output)\n",
    "        return attn_output\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# MLP / Block\n",
    "# -----------------------------\n",
    "class ProGenMLP(nn.Module):\n",
    "    def __init__(self, config: ProGenConfig):\n",
    "        super().__init__()\n",
    "        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd\n",
    "        self.fc_in = nn.Linear(config.n_embd, inner_dim)\n",
    "        self.fc_out = nn.Linear(inner_dim, config.n_embd)\n",
    "        self.act = get_activation(config.activation_function)\n",
    "        self.dropout = nn.Dropout(config.resid_pdrop)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.fc_in(x)\n",
    "        x = self.act(x)\n",
    "        x = self.fc_out(x)\n",
    "        x = self.dropout(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class ProGenBlock(nn.Module):\n",
    "    def __init__(self, config: ProGenConfig):\n",
    "        super().__init__()\n",
    "        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)\n",
    "        self.attn = ProGenAttention(config)\n",
    "        self.mlp = ProGenMLP(config)\n",
    "\n",
    "    def forward(self, x, attention_mask=None):\n",
    "        h = self.ln_1(x)\n",
    "        return x + self.attn(h, attention_mask=attention_mask) + self.mlp(h)\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# Full model\n",
    "# -----------------------------\n",
    "class ProGenForCausalLM(nn.Module):\n",
    "    def __init__(self, config: ProGenConfig):\n",
    "        super().__init__()\n",
    "        self.config = config\n",
    "        self.transformer = nn.Module()\n",
    "        self.transformer.wte = nn.Embedding(config.vocab_size, config.n_embd)\n",
    "        self.transformer.drop = nn.Dropout(config.embd_pdrop)\n",
    "        self.transformer.h = nn.ModuleList([ProGenBlock(config) for _ in range(config.n_layer)])\n",
    "        self.transformer.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)\n",
    "        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)\n",
    "\n",
    "        self.apply(self._init_weights)\n",
    "\n",
    "    def _init_weights(self, module):\n",
    "        if isinstance(module, nn.Linear):\n",
    "            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n",
    "            if module.bias is not None:\n",
    "                module.bias.data.zero_()\n",
    "        elif isinstance(module, nn.Embedding):\n",
    "            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n",
    "        elif isinstance(module, nn.LayerNorm):\n",
    "            module.bias.data.zero_()\n",
    "            module.weight.data.fill_(1.0)\n",
    "\n",
    "    def forward(self, input_ids, attention_mask=None, labels=None):\n",
    "        x = self.transformer.wte(input_ids)\n",
    "        x = self.transformer.drop(x)\n",
    "\n",
    "        if attention_mask is not None:\n",
    "            attention_mask = attention_mask[:, None, None, :].to(dtype=x.dtype)\n",
    "            attention_mask = (1.0 - attention_mask) * -10000.0\n",
    "\n",
    "        for block in self.transformer.h:\n",
    "            x = block(x, attention_mask=attention_mask)\n",
    "\n",
    "        x = self.transformer.ln_f(x)\n",
    "        logits = self.lm_head(x).to(torch.float32)\n",
    "\n",
    "        if labels is None:\n",
    "            return logits\n",
    "\n",
    "        shift_logits = logits[..., :-1, :].contiguous()\n",
    "        shift_labels = labels[..., 1:].contiguous()\n",
    "        loss = F.cross_entropy(\n",
    "            shift_logits.view(-1, shift_logits.size(-1)),\n",
    "            shift_labels.view(-1),\n",
    "        )\n",
    "        return logits, loss\n",
    "\n",
    "\n",
    "# -----------------------------\n",
    "# Weight loading\n",
    "# -----------------------------\n",
    "def load_progen2_small(ckpt_dir, device=\"cpu\"):\n",
    "    ckpt_dir = Path(ckpt_dir)\n",
    "    config = ProGenConfig.from_json(ckpt_dir / \"config.json\")\n",
    "    model = ProGenForCausalLM(config)\n",
    "\n",
    "    state_path = None\n",
    "    for name in [\"pytorch_model.bin\", \"model.pt\", \"model.bin\"]:\n",
    "        p = ckpt_dir / name\n",
    "        if p.exists():\n",
    "            state_path = p\n",
    "            break\n",
    "    if state_path is None:\n",
    "        raise FileNotFoundError(f\"No checkpoint file found in {ckpt_dir}\")\n",
    "\n",
    "    state = torch.load(state_path, map_location=\"cpu\")\n",
    "    if \"state_dict\" in state:\n",
    "        state = state[\"state_dict\"]\n",
    "\n",
    "    # Drop wrapper prefixes if present\n",
    "    cleaned = {}\n",
    "    for k, v in state.items():\n",
    "        if k.startswith(\"module.\"):\n",
    "            k = k[len(\"module.\"):]\n",
    "        cleaned[k] = v\n",
    "\n",
    "    missing, unexpected = model.load_state_dict(cleaned, strict=False)\n",
    "    print(\"missing keys:\", missing)\n",
    "    print(\"unexpected keys:\", unexpected)\n",
    "\n",
    "    model.eval().to(device)\n",
    "    return model, config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "e81e10e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "missing keys: []\n",
      "unexpected keys: []\n",
      "torch.Size([1, 16, 32])\n",
      "Loaded ProGen2-small with 151.15M parameters\n",
      "vocab_size 32\n",
      "layers: 12\n",
      "heads: 16\n",
      "embed_dim: 1024\n",
      "logits shape: torch.Size([1, 16, 32])\n"
     ]
    }
   ],
   "source": [
    "# Load in the ProGen2 model\n",
    "model, config = load_progen2_small(\"progen2-checkpoints/progen2-small\", device=DEVICE)\n",
    "\n",
    "x = torch.randint(0, config.vocab_size, (1, 16), device=DEVICE)\n",
    "with torch.no_grad():\n",
    "    logits = model(x)\n",
    "print(logits.shape)\n",
    "\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "print(f'Loaded ProGen2-small with {total_params / 1e6:.2f}M parameters')\n",
    "\n",
    "\n",
    "print(\"vocab_size\",config.vocab_size)\n",
    "print(\"layers:\", config.n_layer)\n",
    "print(\"heads:\", config.n_head)\n",
    "print(\"embed_dim:\", config.n_embd)\n",
    "\n",
    "# Dummy test\n",
    "x = torch.randint(0, config.vocab_size, (1, 16), device=DEVICE)\n",
    "with torch.no_grad():\n",
    "    logits = model(x)\n",
    "print(\"logits shape:\", logits.shape)  # (1, 16, 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "95325fd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'<|pad|>': 0, '<|bos|>': 1, '<|eos|>': 2, '1': 3, '2': 4, 'A': 5, 'B': 6, 'C': 7, 'D': 8, 'E': 9, 'F': 10, 'G': 11, 'H': 12, 'I': 13, 'K': 14, 'L': 15, 'M': 16, 'N': 17, 'O': 18, 'P': 19, 'Q': 20, 'R': 21, 'S': 22, 'T': 23, 'U': 24, 'V': 25, 'W': 26, 'X': 27, 'Y': 28, 'Z': 29}\n"
     ]
    }
   ],
   "source": [
    "# Load in the tokenizer used for ProGen2\n",
    "vocab = {\"<|pad|>\": 0,\n",
    "         \"<|bos|>\": 1, # beginning of sequence\n",
    "         \"<|eos|>\": 2, # end of sequence\n",
    "      \"1\": 3,\"2\": 4}\n",
    "\n",
    "# add the whole alphabet starting at id 5\n",
    "j = 0\n",
    "for i in range(25):\n",
    "    vocab[chr(65+j)] = i+5\n",
    "    j+=1\n",
    "    # skip 'J' not sure why progen skips J and only J\n",
    "    if j == 9:\n",
    "        j+=1  \n",
    "print(vocab)\n",
    "\n",
    "stoi = vocab\n",
    "itos = {v: k for k, v in vocab.items()}\n",
    "\n",
    "def encode(seq, add_special_tokens=False):\n",
    "        ids = [stoi[c] for c in seq]\n",
    "        if add_special_tokens:\n",
    "            ids = [1] + ids\n",
    "        return ids\n",
    "\n",
    "def decode(ids):\n",
    "    return \"\".join(itos[i] for i in ids.cpu().detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "id": "ea6f2cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prompt: MQIFVKTLTGKTITLEVEPSDTIENVKAKI\n",
      "input_ids: tensor([[16, 20, 13, 10, 25, 14, 23, 15, 23, 11, 14, 23, 13, 23, 15,  9, 25,  9,\n",
      "         19, 22,  8, 23, 13,  9, 17, 25, 14,  5, 14, 13]])\n"
     ]
    }
   ],
   "source": [
    "# encode the human ubiquitin sequence\n",
    "wt_sequence = \"MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG\"\n",
    "prompt = wt_sequence[:30]\n",
    "input_ids = torch.tensor(\n",
    "    [encode(prompt)],\n",
    "    dtype=torch.long\n",
    ")\n",
    "print(\"prompt:\", prompt)\n",
    "print(\"input_ids:\", input_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "id": "b39eb009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logits shape: torch.Size([1, 30, 32])\n",
      "argmax_logits shape: torch.Size([1, 30])\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    logits = model(input_ids.to(DEVICE))\n",
    "print(\"logits shape:\", logits.shape)  # (1, L, alphabet_size)\n",
    "\n",
    "argmax_logits = torch.argmax(logits, dim=-1)\n",
    "print(\"argmax_logits shape:\", argmax_logits.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "5e5aa570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the input sequence 30\n",
      "Length of the output sequence 30\n",
      "MQIFVKTLTGKTITLEVEPSDTIENVKAKI\n",
      " LALLLGLTGKTITLDVEPSDTIENVKAKIQ\n"
     ]
    }
   ],
   "source": [
    "decoded = decode(torch.argmax(logits, dim=-1)[0])\n",
    "print(\"Length of the input sequence\",len(prompt))\n",
    "print(\"Length of the output sequence\",len(decoded))\n",
    "print(prompt)\n",
    "print(\" \"+decoded)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fea2d54",
   "metadata": {},
   "source": [
    "Why does the output also have length 30? Why doesn't the output have the same sequence as the input prompt sequence?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f00af236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the last token of the output\n",
    "next_token_id = logits[:, -1, :].argmax(dim=-1, keepdim=True)\n",
    "next_token_aa = decode(next_token_id[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "693e1eb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQ\n"
     ]
    }
   ],
   "source": [
    "# append the new token to the input prompt to predict the next amino acid given all of the ones before\n",
    "with torch.no_grad():\n",
    "    logits = model(input_ids.to(DEVICE))\n",
    "\n",
    "next_token_id = logits[:, -1, :].argmax(dim=-1, keepdim=True)\n",
    "new_ids = torch.cat([input_ids.cpu(), next_token_id.cpu()], dim=1)\n",
    "print(decode(new_ids[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "b8df3eed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQD\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    logits = model(new_ids.to(DEVICE))\n",
    "\n",
    "next_token_id = logits[:, -1, :].argmax(dim=-1, keepdim=True)\n",
    "new_ids = torch.cat([new_ids.cpu(), next_token_id.cpu()], dim=1)\n",
    "print(decode(new_ids[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "583aa0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The function will generate a sequence for us\n",
    "def generate(model, prompt, max_new_tokens=40, device=\"cpu\"):\n",
    "    model.eval()\n",
    "\n",
    "    input_ids = torch.tensor(\n",
    "        [encode(prompt)],\n",
    "        dtype=torch.long,\n",
    "        device = device\n",
    "    )\n",
    "\n",
    "    for _ in range(max_new_tokens):\n",
    "        with torch.no_grad():\n",
    "            logits = model(input_ids)                 # (B, T, vocab)\n",
    "        next_token_id = logits[:, -1, :].argmax(dim=-1, keepdim=True)\n",
    "\n",
    "        probs = torch.softmax(logits[:, -1, :] / 0.8, dim=-1)\n",
    "        next_token_id = torch.multinomial(probs, num_samples=1)\n",
    "\n",
    "        input_ids = torch.cat([input_ids, next_token_id], dim=1)\n",
    "\n",
    "        # optional early stop\n",
    "        if vocab[\"<|eos|>\"] is not None and next_token_id.item() == vocab[\"<|eos|>\"]:\n",
    "            break\n",
    "\n",
    "    decoded = decode(input_ids[0])\n",
    "    decoded = \"\".join(decoded.split())  # remove whitespace/newlines\n",
    "    return decoded\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b951348",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIYAGKQLEDGRTLADYNIQKESTLHLVLRLRGGMQ\n"
     ]
    }
   ],
   "source": [
    "prompt = \"1MQIFVKTLTGKTITLEVEPSDTIENVKAKI\"   # first 30 aa of ubiquitin with control token\n",
    "out = generate(model, prompt, max_new_tokens=48, device=DEVICE)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de34693c",
   "metadata": {},
   "source": [
    "This loop shows the defining behavior of a decoder.\n",
    "\n",
    "At each step:\n",
    "\n",
    "1. the model reads the current prefix,\n",
    "2. predicts a probability distribution over the next token,\n",
    "3. chooses one token,\n",
    "4. appends it to the sequence,\n",
    "5. repeats.\n",
    "\n",
    "That is why decoder generation is sequential and why causal masking is needed during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "e15fb9c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input prefix:          1MQIFVKTLTGKTITLEVEPSDTIENVKAKI\n",
      "True WT continuation:                                 QDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG\n",
      "Generated sequence:    1MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIYAGKQLEDGRTLADYNIQKESTLHLVLRLRGGMQ\n"
     ]
    }
   ],
   "source": [
    "print('Input prefix:         ', prompt)\n",
    "print(f'True WT continuation: {wt_sequence[30:]:>78}')\n",
    "print('Generated sequence:   ', out)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73082c57",
   "metadata": {},
   "source": [
    "### Interpreting the decoder section\n",
    "\n",
    "- The encoder answers: **\"What kind of sequence is this?\"**\n",
    "- The decoder answers: **\"What token should come next?\"**\n",
    "\n",
    "The decoder is the right architecture when the task is **generation**.\n",
    "\n",
    "As always the case with generative AI, a generated continuation can look protein-like without being biologically correct."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b11aacf",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Encoder-decoder model: ProstT5\n",
    "\n",
    "The encoder-decoder architecture combines both ideas.\n",
    "\n",
    "1. The encoder reads the input sequence.\n",
    "2. The decoder generates a new output sequence conditioned on that encoded representation.\n",
    "\n",
    "This is most natural when the output is **not the same symbolic object** as the input.\n",
    "\n",
    "ProstT5 translates between an amino-acid sequence and a structure-like 3Di token sequence. The 3Di tokens encode 3D structural motifs into a 20-state alphabet. The 3Di alphabet was created as a way to speed up protein structure similarity searches in large protein structure databases by transforming the structure alignment/similarity search into a sequence alignment/search task.\n",
    "\n",
    "For more on 3Di see:\n",
    "\n",
    "  van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., & Steinegger, M. (2024). Fast and accurate protein structure search with Foldseek. Nature Biotechnology, 42(2), 243–246. https://doi.org/10.1038/s41587-023-01773-0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "id": "a7dd8b3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping ProstT5 load. Set RUN_PROSTT5 = True to run the encoder-decoder example.\n",
      "\n",
      "Cached result:\n",
      "Loaded ProstT5 with 2818.85M parameters\n",
      "Model type: t5  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# DO NOT RUN this cell\n",
    "# Requires 11.3 Gb of data and will take ~20min to download and ~30min to run inference\n",
    "# This code is here to show one of the only encoder-decoder models. And if you are really interested in encoder-decoder models this gives you a start.\n",
    "\n",
    "# ------------------------------------------------------------------\n",
    "# Load ProstT5 as an encoder-decoder model\n",
    "# ------------------------------------------------------------------\n",
    "RUN_PROSTT5 = False\n",
    "\n",
    "if RUN_PROSTT5:\n",
    "    from transformers import AutoModelForSeq2SeqLM, T5EncoderModel, T5Tokenizer\n",
    "    if T5Tokenizer is None or T5EncoderModel is None or AutoModelForSeq2SeqLM is None:\n",
    "        raise ImportError('transformers/sentencepiece are not installed. Run the install cell first.')\n",
    "\n",
    "    PROSTT5_NAME = 'Rostlab/ProstT5'\n",
    "    prost_tokenizer = T5Tokenizer.from_pretrained(PROSTT5_NAME, do_lower_case=False)\n",
    "    prost_encoder = T5EncoderModel.from_pretrained(PROSTT5_NAME).to(DEVICE).eval()\n",
    "    prost_seq2seq = AutoModelForSeq2SeqLM.from_pretrained(PROSTT5_NAME).to(DEVICE).eval()\n",
    "\n",
    "    total_params = sum(p.numel() for p in prost_seq2seq.parameters())\n",
    "    print(f'Loaded ProstT5 with {total_params / 1e6:.2f}M parameters')\n",
    "    print('Model type:', prost_seq2seq.config.model_type)\n",
    "else:\n",
    "    print('Skipping ProstT5 load. Set RUN_PROSTT5 = True to run the encoder-decoder example.')\n",
    "    print(\"\"\"\n",
    "Cached result:\n",
    "Loaded ProstT5 with 2818.85M parameters\n",
    "Model type: t5  \n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "73e94454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProstT5 translation skipped because RUN_PROSTT5 = False.\n",
      "\n",
      "            Cached result:\n",
      "            Encoder hidden-state shape: (1, 78, 1024)\n",
      "            Predicted 3Di-style outputs\n",
      "            ---------------------------\n",
      "            ubiquitin prot  AA seq        MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG\n",
      "            ubiquitin pred 3Di seq        dweweaepvrdididdddqfdfllnvlvsccvvpvahsvqwwkdfpndtgdrpdgnvvvvndhhyyiyidtddddd\n",
      "            \n"
     ]
    }
   ],
   "source": [
    "# ------------------------------------------------------------------\n",
    "# Encode ubiquitin and generate structure-like 3Di tokens\n",
    "# ------------------------------------------------------------------\n",
    "if RUN_PROSTT5:\n",
    "    import re\n",
    "    def prost_prepare_aa(sequence: str) -> str:\n",
    "        cleaned = re.sub(r'[UZOB]', 'X', sequence)\n",
    "        return '<AA2fold> ' + ' '.join(list(cleaned))\n",
    "\n",
    "    prepared_inputs = [prost_prepare_aa(seq) for seq in [wt_sequence]]\n",
    "    tokenized = prost_tokenizer(\n",
    "        prepared_inputs,\n",
    "        add_special_tokens=True,\n",
    "        padding='longest',\n",
    "        return_tensors='pt',\n",
    "    )\n",
    "    tokenized = {k: v.to(DEVICE) for k, v in tokenized.items()}\n",
    "\n",
    "    with torch.no_grad():\n",
    "        encoder_outputs = prost_encoder(**tokenized)\n",
    "\n",
    "    print('Encoder hidden-state shape:', tuple(encoder_outputs.last_hidden_state.shape))\n",
    "\n",
    "    with torch.no_grad():\n",
    "        generated = prost_seq2seq.generate(\n",
    "            input_ids=tokenized['input_ids'],\n",
    "            attention_mask=tokenized['attention_mask'],\n",
    "            max_length=len(wt_sequence) + 8,\n",
    "            do_sample=True,\n",
    "            top_p=0.95,\n",
    "            top_k=6,\n",
    "            temperature=1.1,\n",
    "            repetition_penalty=1.2,\n",
    "        )\n",
    "\n",
    "    decoded = prost_tokenizer.batch_decode(generated, skip_special_tokens=True)\n",
    "    decoded = [''.join(text.split()) for text in decoded]\n",
    "\n",
    "    print('Predicted 3Di-style outputs')\n",
    "    print('---------------------------')\n",
    "    print(f\"ubiquitin prot  AA seq        {wt_sequence}\")\n",
    "    for label, seq in zip(['ubiquitin pred 3Di seq'], decoded):\n",
    "        print(f'{label:18s} {seq}')\n",
    "else:\n",
    "    print('ProstT5 translation skipped because RUN_PROSTT5 = False.')\n",
    "    print(\"\"\"\n",
    "            Cached result:\n",
    "            Encoder hidden-state shape: (1, 78, 1024)\n",
    "            Predicted 3Di-style outputs\n",
    "            ---------------------------\n",
    "            ubiquitin prot  AA seq        MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG\n",
    "            ubiquitin pred 3Di seq        dweweaepvrdididdddqfdfllnvlvsccvvpvahsvqwwkdfpndtgdrpdgnvvvvndhhyyiyidtddddd\n",
    "            \"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1512a7df",
   "metadata": {},
   "source": [
    "### Interpreting the encoder-decoder section\n",
    "\n",
    "The encoder-decoder model is ideal when:\n",
    "\n",
    "- input and output play different roles,\n",
    "- the output sequence should be generated conditionally from the input,\n",
    "- and the task looks like a biological translation problem.\n",
    "\n",
    "Encoder-Decoder models are not that popular. Popular tasks such as embedding/annotating/clasification/structure-prediction are not necessarily a sequence-to-sequence problem. It is also more complicated to train an encoder-decoder than an encoder-only or decoder-only model. The encoder-decoder model will have twice as many parameters and slower inference compared to encoder-only (ProstT5 is much slower than esmfold or openfold).\n",
    "\n",
    "Overall summary:\n",
    "\n",
    "- ESM-2 gives us a **representation**,\n",
    "- Progen2 gives us a **continuation**,\n",
    "- ProstT5 gives us a **mapping between representations**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ab80fee",
   "metadata": {},
   "source": [
    "### Appendix: Positional Embeddings\n",
    "\n",
    "What happens without positional information?\n",
    "\n",
    "If position encodings carry real information, then shuffling the tokens in a sequence should produce a DIFFERENT representation at each position. Without position encodings, the model is permutation-equivariant: if the tokens are shuffled, the output representations shuffle in the same way\n",
    "\n",
    "Let's test this with our toy encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "e59c5c34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0, 0, 1, 0, 3, 2, 3, 2, 1, 1])\n",
      "tensor([3, 1, 0, 3, 2, 0, 2, 1, 0, 1])\n"
     ]
    }
   ],
   "source": [
    "# First create a batch with an original unshuffled and shuffled tokens\n",
    "unshuffled = tokens[0]\n",
    "perm = torch.randperm(len(unshuffled))\n",
    "shuffled = unshuffled[perm]\n",
    "\n",
    "print(unshuffled)\n",
    "print(shuffled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b289676a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a simple model\n",
    "max_len = len(unshuffled)\n",
    "d_model = 16\n",
    "n_heads = 4\n",
    "K = 2\n",
    "d_diff = 32\n",
    "\n",
    "token_emb = nn.Embedding(abc_size, d_model)\n",
    "layers = nn.ModuleList([\n",
    "    TransformerBlock(d_model=d_model, num_heads=n_heads, d_ff=d_diff)\n",
    "    for _ in range(K)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "7e3f618b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hidden unshuffled shape torch.Size([10, 16])\n",
      "hidden shuffled shape torch.Size([10, 16])\n"
     ]
    }
   ],
   "source": [
    "# TODO: run both sequences through the encoder and extract hidden states.\n",
    "# Then compute the cosine similarity between the representation at position 0\n",
    "# for the original vs shuffled sequence.\n",
    "#\n",
    "# What do you expect? What do you get?\n",
    "\n",
    "x = torch.stack([unshuffled, shuffled])\n",
    "B, L = x.shape\n",
    "x = token_emb(x)\n",
    "\n",
    "all_attn = {}\n",
    "hidden_states = [x.detach().clone()]   # save embedding layer output\n",
    "\n",
    "for i, layer in enumerate(layers):\n",
    "    x, attn_weights = layer(x, return_attn=True)\n",
    "    all_attn[i] = attn_weights\n",
    "    hidden_states.append(x.detach().clone())  # save each layer output\n",
    "\n",
    "# Final hidden states\n",
    "h_unshuf = x[0]   # shape (L, d_model)\n",
    "h_shuf   = x[1]   # shape (L, d_model)\n",
    "\n",
    "print(\"hidden unshuffled shape\", h_unshuf.shape)\n",
    "print(\"hidden shuffled shape\", h_shuf.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "f9383852",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cos_sim = F.cosine_similarity(\n",
    "    h_unshuf.unsqueeze(0),\n",
    "    h_shuf.unsqueeze(0),\n",
    "    dim=-1\n",
    ")\n",
    "\n",
    "inv_perm = torch.argsort(perm)\n",
    "h_shuf_unpermuted = h_shuf[inv_perm]\n",
    "\n",
    "tokenwise_cos = F.cosine_similarity(h_unshuf.unsqueeze(0), h_shuf_unpermuted.unsqueeze(0), dim=-1)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(torch.stack([cos_sim[0],tokenwise_cos[0]]).detach().numpy())\n",
    "plt.yticks(list(range(2)), [\"permutated hidden emb\", \"unpermutated hidden emb\"])\n",
    "plt.xlabel(\"position\")\n",
    "plt.xticks(list(range(10)), list(range(10)))\n",
    "plt.colorbar(orientation='horizontal', pad=0.15)\n",
    "plt.title(\"Tokenwise Cosine Similarity to unshuffled sequence without pos embedding\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1a538ff",
   "metadata": {},
   "source": [
    "Using an analogy to language might be helpful. Consider the statement \"friendly dog\"\n",
    "\n",
    "If we encoded it into a vector where the first position represents friendliness and the second position represents dog-like. You could imagine that the statement could be encoded as\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\text{}\\\\\n",
    "\\text{friendliness}\\\\\n",
    "\\text{dog-like}\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\text{friendly} & \\text{dog} \\\\\n",
    "1 & 0\\\\\n",
    "0 & 1\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Now consider the phrase “dog friendly”. If we ignore order and use the same encoding scheme, we would also get\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\text{}\\\\\n",
    "\\text{friendliness}\\\\\n",
    "\\text{dog-like}\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\text{dog} & \\text{friendly} \\\\\n",
    "0 & 1\\\\\n",
    "1 & 0\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Even though these phrases mean very different things, their representations are permutation equivarent and the tokens individual tokens represent the same values.\n",
    "\n",
    "This is exactly the problem in transformers without positional encoding:\n",
    "the model knows what tokens are present, but not the order in which they appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "ba7c34a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# with position embedding\n",
    "x = torch.stack([unshuffled, shuffled])\n",
    "\n",
    "# define the position embedding layer\n",
    "pos_emb   = nn.Embedding(max_len,  d_model)\n",
    "\n",
    "B, L = x.shape\n",
    "pos = torch.arange(L, device=x.device)\n",
    "\n",
    "f, ax = plt.subplots(1,2,figsize=(8,4))\n",
    "cb = ax[0].imshow(pos_emb(pos).detach().numpy(),  vmax = 2, vmin = -2)\n",
    "ax[0].set_ylabel(\"position\")\n",
    "ax[0].set_xlabel(\"embedding dim\")\n",
    "ax[0].set_title(\"Pos embedding\")\n",
    "#plt.colorbar(cb)\n",
    "\n",
    "cb = ax[1].imshow(token_emb(x[0]).detach().numpy(), vmax = 2, vmin = -2)\n",
    "ax[1].set_ylabel(\"position\")\n",
    "ax[1].set_xlabel(\"embedding dim\")\n",
    "ax[1].set_title(\"Token embedding\")\n",
    "plt.colorbar(cb)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "cafe0aff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = torch.stack([unshuffled, shuffled])\n",
    "x = token_emb(x) + pos_emb(pos)\n",
    "\n",
    "all_attn  = {}\n",
    "for i, layer in enumerate(layers):\n",
    "    x, attn_weights = layer(x, return_attn = True)\n",
    "    all_attn[i]  = attn_weights\n",
    "\n",
    "# Final hidden states\n",
    "h_unshuf = x[0]   # shape (L, d_model)\n",
    "h_shuf   = x[1]   # shape (L, d_model)\n",
    "\n",
    "cos_sim = F.cosine_similarity(\n",
    "    h_unshuf.unsqueeze(0),\n",
    "    h_shuf.unsqueeze(0),\n",
    "    dim=-1\n",
    ")\n",
    "\n",
    "inv_perm = torch.argsort(perm)\n",
    "h_shuf_unpermuted = h_shuf[inv_perm]\n",
    "\n",
    "tokenwise_cos = F.cosine_similarity(h_unshuf.unsqueeze(0), h_shuf_unpermuted.unsqueeze(0), dim=-1)\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(torch.stack([cos_sim[0],tokenwise_cos[0]]).detach().numpy())\n",
    "plt.yticks(list(range(2)), [\"permutated hidden emb\", \"unpermutated hidden emb\"])\n",
    "plt.xlabel(\"position\")\n",
    "plt.xticks(list(range(10)), list(range(10)))\n",
    "plt.colorbar(orientation='horizontal', pad=0.15)\n",
    "plt.title(\"Tokenwise Cosine Similarity with pos embedding\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "6ff711a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 6, 20, 10, 10])\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    outputs = esm_model(\n",
    "        masked.to(DEVICE),\n",
    "        repr_layers=[esm_model.num_layers],\n",
    "        need_head_weights=True,   # ← this is the key argument\n",
    "        return_contacts=False,\n",
    "    )\n",
    "\n",
    "# outputs['attentions'] shape: (n_layers, B, n_heads, L, L)\n",
    "attn_weights = outputs['attentions']\n",
    "print(attn_weights.shape)\n",
    "\n",
    "# Extract one layer, one head\n",
    "layer_idx = 5   # last layer of the 6-layer 8M model\n",
    "head_idx  = 0\n",
    "single_head_attn = attn_weights[layer_idx, 0, head_idx].cpu().numpy()  # (L, L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "42e17ce4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toy encoder positional embedding matrix: (10, 10)\n"
     ]
    },
    {
     "data": {
      "image/png": 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UopycHPXs2bPW+/3888/lOI5KSkpOes6bb74px3H07bffSpJWrVqlZs2a1XotNXHifZg4caKuvPJKV2oJR8eOHZWXl+d2GQAA4AxTk7GYJA0ePFizZ88+I2oBUL+sWbNGs2fP1rx581RcXKyBAwdq+PDhKi0tDXn+Z599phEjRmjgwIEqLi7Wb3/7W82cOVPPPvtsWNdl0ghRaeLEiXIcJ6gNGzbM7dJqzdixY7Vjxw63y5AkPfDAA1q1apXbZfygzZs361e/+pXbZQB1xivJayw1t785AFHn++O7hg0bqlOnTvrNb36jQ4cOnXbfqampKi8vV48ePSQFv1l33HPPPae77rrrtK8XTi0A7HAcuy1cS5cu1aRJkzR58mSlp6crLy9Pqampys/PD3n+ww8/rPbt2ysvL0/p6emaPHmybrjhBi1ZsiSs67IQNqLWsGHDtHLlyoBjcXFxLlVT+xISEpSQkOB2GZKk5ORkt0uokbPOOsvtEoA6dUS+iOwbAE7m+Pju6NGj2rBhgyZPnqxDhw6d9JeqmoqNjVWbNm1+8LwWLVqc1nVqsxYAp8fj8Vjv+8RrxMXFhfyd9MiRIyoqKtLcuXMDjg8dOlTvvvtuyGts3LhRQ4cODTh2+eWX65FHHtHRo0fVsGHDGtVK0ghRKy4uTm3atAlozZs397/uOI6WL1+ukSNHKjExUenp6dq4caN27typwYMHq3HjxsrKytKuXbuC+l6+fLlSU1OVmJio0aNHB70LtXLlSqWnpys+Pl5du3bVsmXLAl7ftGmTevXqpfj4eGVmZqq4uDjoGuvWrdN5552nhIQEDRkyRJ9//nnA6yc+nnb8kbHHH39cHTt2VHJysq6++modOHDAf86BAwc0btw4NW7cWG3bttX9999fo5j13XffrZSUFDVt2lSTJk3Sd999F/D6iY+nDR48WDfffLNmz56t5s2bKyUlRQUFBTp06JCuv/56NW3aVOeee65efvnlgH62bt2qESNGqEmTJkpJSdH48eO1b9++gH5nzpypW2+9VS1atFCbNm2Uk5MT0EdOTo7at2+vuLg4tWvXTjNnzvS/duLjaaWlpRo1apSaNGmipKQkjRkzRv/617/CuqfAmahRo0Zq06aNntRurVSZlfakdqtNmzZq1KiR298ugChyfHyXmpqqa6+9VuPGjdMLL7wgSaqsrNTMmTPVunVrxcfHa8CAAdq8ebP/a/fv369x48bprLPOUkJCgrp06eJ/g/H7j4R9/vnnGjJkiCSpefPmchxHEydOlBT8eNr+/ft13XXXqXnz5kpMTNTw4cP1ySef+F8/Pl579dVXlZ6eriZNmmjYsGEqLy8/6ffI42mAXcfHSV3SUpXSMtlK65KWqiZNmig1NVXJycn+lpubG7Kmffv2yev1KiUlJeB4SkqK9uzZE/Jr9uzZE/L8qqqqgN+hfgiTRsAp3HXXXbruuutUUlKirl276tprr9WUKVOUnZ2tLVu2SJJmzJgR8DU7d+7UM888o5deekmvvPKKSkpKNH36dP/rK1as0Lx587Ro0SJt27ZNixcv1h133KFHH31UknTo0CGNHDlS559/voqKipSTk6Pf/OY3AdcoKyvTf//3f2vEiBEqKSnR5MmTg2adQ9m1a5deeOEFrV27VmvXrtX69et19913+1+fM2eO3nnnHb344osqLCzUhg0b9MEHH5yyz2eeeUYLFizQokWLtGXLFrVt2zZoEiyURx99VK1atdKmTZt0880366abbtLo0aPVr18/ffDBB7r88ss1fvx4HT58WJJUXl6uQYMGqWfPntqyZYteeeUV/etf/9KYMWOC+m3cuLHef/993XvvvVq4cKEKCwslSX/5y190//33a/ny5frkk0/0wgsv6IILLghZnzFGV155pb755hutX79ehYWF2rVrl8aOHRvWPQXORPHx8frss89UUVFhtX322WeKj493+9sFEMUSEhJ09OhRSdKtt96qZ599Vo8++qg++OADde7cWZdffrm++eYbSdIdd9yhrVu36uWXX9a2bduUn5+vVq1aBfWZmprqXxNk+/btKi8v1wMPPBDy+hMnTtSWLVv04osvauPGjTLGaMSIEf6aJOnw4cNasmSJHn/8cb311lsqLS0NGvsBqDt1NU765z//GXQsOzv7lLU5JzzXZowJOvZD54c6fkoGiEITJkwwsbGxpnHjxgFt4cKF/nMkmdtvv93/+caNG40k88gjj/iPrV692sTHx/s/X7BggYmNjTVlZWX+Yy+//LKJiYkx5eXlxhhjUlNTzVNPPRVQz1133WWysrKMMcYsX77ctGjRwhw6dMj/en5+vpFkiouLjTHGZGdnm/T0dOPz+fzn3HbbbUaS2b9/vzHGmJUrV5rk5OSA2hITE43H4/Efu+WWW0zfvn2NMcZ4PB7TsGFD8+c//9n/+rfffmsSExPNrFmzTnovs7KyzNSpUwOO9e3b11x00UX+zydMmGBGjRrl/3zQoEFmwIAB/s+rqqpM48aNzfjx4/3HysvLjSSzceNGY4wxd9xxhxk6dGjAdcrKyowks3379pD9GmNMnz59zG233WaMMea+++4z5513njly5EjI76VDhw7m/vvvN8YY89prr5nY2FhTWlrqf/3jjz82ksymTZuMMT98TwEAQN05cbzx/vvvm5YtW5oxY8aYgwcPmoYNG5onn3zS//qRI0dMu3btzL333muMMeaKK64w119/fci+P/vss4Cx2BtvvBEw7jpu0KBB/nHTjh07jCTzzjvv+F/ft2+fSUhIMM8884wxpnq8Jsns3LnTf84f//hHk5KSctLv88RaANR/lZWVJjY21jz33HMBx2fOnGkuueSSkF8zcOBAM3PmzIBjzz33nGnQoMFJfx8KhaQRotaQIUNUUlIS0L6fCJKkCy+80P/fx6N930+mpKSk6Lvvvgt4FrV9+/Y655xz/J9nZWXJ5/Np+/bt+uqrr1RWVqZJkyapSZMm/va73/3O/5jbtm3bdNFFFykxMTGgj+/btm2bfvKTnwTMEJ94TigdO3ZU06ZN/Z+3bdtWe/fulSR9+umnOnr0qC6++GL/68nJyTr//PNP2ee2bduCrl2TWr5/b2NjY9WyZcugeyvJX19RUZHeeOONgPvWtWtXSQp4RPD7/Z74PY4ePVr//ve/1alTJ9144416/vnnVVVVddLvKzU1Vampqf5j3bp1U7NmzbRt2zb/sVPdUwAAULfWrl2rJk2aKD4+XllZWbrkkkv04IMPateuXTp69Kj69+/vP7dhw4a6+OKL/T/Xb7rpJj399NPq2bOnbr311pOuE1JT27ZtU4MGDdS3b1//sZYtW+r8888PGEskJibq3HPP9X/OWALAiRo1aqSMjAz/ExTHFRYWql+/fiG/JisrK+j81157TZmZmTVez0hiIWxEscaNG6tz586nPOf7f5mOT9CEOubznXzB1+PnOI7jP2/FihUBAwipeuJE+k9k8FRqck4oJ/7j8P2azEmiij/2Wj+mllPdW5/PpyuuuEL33HNPUF9t27Y9Zb/H+0hNTdX27dtVWFiov/3tb5o2bZp+//vfa/369UFfZ04S9Tzx+KmuBwAA6taQIUOUn5+vhg0bql27dv6f08fXCDrVox3Dhw/XF198ob/+9a/629/+pksvvVTTp08Pe6eh7/d9suM/NJawNf4CELnmzJmj8ePHKzMzU1lZWSooKFBpaammTp0qScrOztbu3bv12GOPSZKmTp2qhx56SHPmzNGNN96ojRs36pFHHtHq1avDui5JI6CWlZaW6ssvv/R/vnHjRsXExOi8885TSkqKzj77bH366afq3LlzQEtLS5NUnWb58MMP9e9//9vfx3vvvRdwjW7dugUdO/HzcJ177rlq2LChNm3a5D/m8XgCFmsMJT09vdZrCaV37976+OOP1bFjx6B717hx4xr3k5CQoF/84hf6wx/+oDfffFMbN27URx99FHRet27dVFpaqrKyMv+xrVu3qqKiQunp6bXyPQEAgNp1/E3BDh06BEzGdO7cWY0aNdLbb7/tP3b06FFt2bIl4Of6WWedpYkTJ+qJJ55QXl6eCgoKQl7n+CL/Xq/3pLV069ZNVVVVev/99/3Hvv76a+3YsYOxBICwjR07Vnl5eVq4cKF69uypt956S+vWrVOHDh0kVU+Ol5aW+s9PS0vTunXr9Oabb6pnz56666679Ic//EFXXXVVWNclaYSoVVlZGbTSfIMGDUIueBiO+Ph4TZgwQUuWLJHH49HMmTM1ZswY/9aoOTk5mjlzppKSkjR8+HBVVlZqy5Yt2r9/v+bMmaNrr71W8+bN06RJk3T77bfr888/D3qHa+rUqbrvvvs0Z84cTZkyRUVFRVq1atVp1d20aVNNmDBBt9xyi1q0aKHWrVtrwYIFiomJOeVCabNmzdKECROUmZmpAQMG6Mknn9THH3+sTp06nVY9J5o+fbpWrFiha665RrfccotatWqlnTt36umnn9aKFSv8Sa1TWbVqlbxer/r27avExEQ9/vjjSkhI8P9D+32XXXaZLrzwQo0bN055eXmqqqrStGnTNGjQIGVmZtbq9wYAAOxq3LixbrrpJv84p3379rr33nt1+PBhTZo0SZI0f/58ZWRkqHv37qqsrNTatWtPOrnToUMHOY6jtWvXasSIEUpISFCTJk0CzunSpYtGjRqlG2+8UcuXL1fTpk01d+5cnX322Ro1apT17xlA/TNt2jRNmzYt5Guhfh8cNGjQD25s9ENIGiFqvfLKK2rbtm1AGzBgwGn327lzZ//OZkOHDlWPHj0CdhObPHmy/vSnP2nVqlW64IILNGjQIK1atcqfNGrSpIleeuklbd26Vb169dK8efOCHslq3769nn32Wb300ku66KKL9PDDD2vx4sWnXfvSpUuVlZWlkSNH6rLLLlP//v2Vnp5+yt2Pxo4dq/nz5+u2225TRkaGvvjiC910002nXcuJ2rVrp3feeUder1e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      "text/plain": [
       "<Figure size 1200x900 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# === Learned positional embeddings from the toy encoder ===\n",
    "#\n",
    "# learns its positional embeddings from the MLM training objective.\n",
    "# The weights live in model.pos_emb.weight — shape (max_len, d_model).\n",
    "abc_size = 5 # 4 nucleotides and the mask\n",
    "MASK_ID = 4\n",
    "PAD_ID = None\n",
    "B, L = 8, 10\n",
    "tokens = torch.randint(0, abc_size-1, (B, L))\n",
    "\n",
    "model = TransformerEncoder(\n",
    "    abc_size=abc_size,\n",
    "    d_model=10,\n",
    "    K=2,\n",
    "    n_heads=1,\n",
    "    d_diff=32,\n",
    "    max_len=10,\n",
    ")\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n",
    "mask_prob = 0.15\n",
    "\n",
    "accs = np.zeros(500)\n",
    "perplexity = np.zeros(500)\n",
    "for step in range(500):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    # 1. Create masked inputs and MLM labels\n",
    "    masked, labels = mask_tokens(\n",
    "        tokens.clone(),\n",
    "        abc_size=abc_size,\n",
    "        mask_id=MASK_ID,\n",
    "        pad_id=PAD_ID,\n",
    "        mask_prob=mask_prob,\n",
    "    )\n",
    "\n",
    "    # 2. Forward pass through encoder + MLM head\n",
    "    logits = model(masked)\n",
    "\n",
    "    # 3. Compute loss ONLY at masked positions\n",
    "    loss = F.cross_entropy(\n",
    "        logits.view(-1, logits.shape[-1]),   \n",
    "        labels.view(-1),                 \n",
    "        ignore_index=-100,\n",
    "    ) # default reduction is mean\n",
    "    perplexity[step] = math.exp(loss.item())\n",
    "\n",
    "    # 4. Backprop + update\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "toy_pos_weights = model.pos_emb.weight.detach().cpu()   # (max_len, d_model)\n",
    "max_len_toy     = toy_pos_weights.shape[0]               # however many positions the model was built with\n",
    "print(f'Toy encoder positional embedding matrix: {tuple(toy_pos_weights.shape)}')\n",
    "\n",
    "sim_toy = F.cosine_similarity(\n",
    "    toy_pos_weights.unsqueeze(1),\n",
    "    toy_pos_weights.unsqueeze(0), dim=-1\n",
    ").numpy()   # (max_len, max_len)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 9))\n",
    "\n",
    "# ── Learned toy encoder (bottom row) ─────────────────────────────────────────\n",
    "ax = axes[0]\n",
    "im1 = ax.imshow(toy_pos_weights.numpy(), cmap='RdBu')\n",
    "ax.set_xlabel('Embedding dimension'); ax.set_ylabel('Position')\n",
    "ax.set_title('Learned (toy encoder after MLM training)\\nembedding matrix')\n",
    "plt.colorbar(im1, ax=ax, shrink=0.6)\n",
    "\n",
    "ax = axes[1]\n",
    "im2 = ax.imshow(sim_toy, cmap='Blues', vmin=0, vmax=1)\n",
    "ax.set_xlabel('Position j'); ax.set_ylabel('Position i')\n",
    "ax.set_title('Learned — cosine similarity\\n(what structure did training find?)')\n",
    "plt.colorbar(im2, ax=ax, shrink=0.6)\n",
    "\n",
    "plt.suptitle('Sinusoidal vs Learned Positional Embeddings', fontsize=13, y=1.01)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "a3436949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ── ESM-2 uses Rotary Position Embeddings (RoPE), not a learned table ────────\n",
    "#\n",
    "# In ESM-1 (and BERT), position information is added as a learned vector\n",
    "# before the first layer:\n",
    "#\n",
    "#     x = token_embedding(tokens) + position_embedding(positions)\n",
    "#\n",
    "# ESM-2 uses RoPE instead. Rather than adding position to the input,\n",
    "# RoPE rotates the Query and Key vectors inside every attention layer\n",
    "# by an angle proportional to position. This means:\n",
    "#\n",
    "#   - there is no single embed_positions weight matrix to inspect,\n",
    "#   - position information is woven into attention at every layer,\n",
    "#   - the model generalises better to sequences longer than those seen in training.\n",
    "#\n",
    "# We can still visualise *what RoPE does* by building the rotation matrix\n",
    "# for a short sequence and plotting it.\n",
    "\n",
    "d_model  = esm_model.embed_dim          # 320\n",
    "n_heads  = esm_model.attention_heads    # 20\n",
    "d_head   = d_model // n_heads           # 16\n",
    "max_pos  = 50\n",
    "\n",
    "# RoPE computes sin/cos at frequencies determined by dimension index\n",
    "# The rotary_dim for ESM-2 8M is typically d_head (all dims rotated)\n",
    "rotary_dim = d_head   # 16\n",
    "\n",
    "half = rotary_dim // 2\n",
    "theta = 1.0 / (10000 ** (torch.arange(0, half).float() / half))   # (half,)\n",
    "\n",
    "positions  = torch.arange(max_pos).float()                          # (max_pos,)\n",
    "angles     = torch.outer(positions, theta)                          # (max_pos, half)\n",
    "rot_matrix = torch.cat([torch.cos(angles), torch.sin(angles)], dim=-1)  # (max_pos, rotary_dim)\n",
    "\n",
    "# Cosine similarity between position vectors — same plot as before\n",
    "sim_rope = F.cosine_similarity(\n",
    "    rot_matrix.unsqueeze(1),\n",
    "    rot_matrix.unsqueeze(0), dim=-1\n",
    ").numpy()   # (max_pos, max_pos)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n",
    "\n",
    "ax = axes[0]\n",
    "im = ax.imshow(rot_matrix.numpy(), aspect='auto', cmap='RdBu', vmin=-1, vmax=1)\n",
    "ax.set_xlabel('Rotary dimension')\n",
    "ax.set_ylabel('Sequence position')\n",
    "ax.set_title('RoPE rotation vectors\\n(each row encodes one position)')\n",
    "plt.colorbar(im, ax=ax)\n",
    "\n",
    "ax = axes[1]\n",
    "im2 = ax.imshow(sim_rope, cmap='Blues', vmin=0, vmax=1)\n",
    "ax.set_xlabel('Position j')\n",
    "ax.set_ylabel('Position i')\n",
    "ax.set_title('Cosine similarity between RoPE vectors\\n(nearby positions stay similar)')\n",
    "plt.colorbar(im2, ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5410c4de",
   "metadata": {},
   "source": [
    "**ESM-2 vs ESM-1: why switch from learned embeddings to RoPE?**\n",
    "\n",
    "| Property | Learned positional embedding (ESM-1) | RoPE (ESM-2) |\n",
    "|---|---|---|\n",
    "| Where applied | Added to input once, before layer 1 | Inside every attention layer |\n",
    "| Parameters | Yes — a (max_len × D) weight matrix | No — computed from a fixed formula |\n",
    "| Extrapolation beyond training length | Poor | Better |\n",
    "| Encodes relative vs absolute position | Absolute | Relative (dot product of Q and K depends only on their distance) |\n",
    "\n",
    "The key insight of RoPE is that it encodes **relative** position rather\n",
    "than absolute position. When you compute the attention score Q·K^T,\n",
    "the rotation cancels in a way that makes the result depend only on\n",
    "*how far apart* positions i and j are — not on what i and j are\n",
    "individually. This is why the cosine similarity matrix above has a\n",
    "banded structure: position 5 relates to position 10 the same way\n",
    "that position 15 relates to position 20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c865c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The toy encoder uses *learned* positional embeddings (nn.Embedding).\n",
    "# ESM also uses learned embeddings (rotary in ESM2).\n",
    "# The decoder used *sinusoidal* positional encodings\n",
    "\n",
    "# Sinusoidal (fixed, not learned)\n",
    "d_model  = 32\n",
    "max_len  = 16\n",
    "position = torch.arange(max_len).unsqueeze(1).float()\n",
    "div_term = torch.exp(torch.arange(0, d_model, 2) * (-np.log(10000.0) / d_model))\n",
    "pe = torch.zeros(max_len, d_model)\n",
    "pe[:, 0::2] = torch.sin(position * div_term)\n",
    "pe[:, 1::2] = torch.cos(position * div_term)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "ax = axes[0]\n",
    "im = ax.imshow(pe.numpy(), aspect='auto', cmap='RdBu', vmin=-1, vmax=1)\n",
    "ax.set_xlabel('Embedding dimension')\n",
    "ax.set_ylabel('Position')\n",
    "ax.set_title('Sinusoidal positional encodings\\n(each row is one position vector)')\n",
    "plt.colorbar(im, ax=ax)\n",
    "\n",
    "# Cosine similarity between position pairs — nearby positions should be more similar\n",
    "sim = F.cosine_similarity(pe.unsqueeze(1), pe.unsqueeze(0), dim=-1).numpy()\n",
    "ax = axes[1]\n",
    "im2 = ax.imshow(sim, cmap='Blues', vmin=0, vmax=1)\n",
    "ax.set_xlabel('Position j')\n",
    "ax.set_ylabel('Position i')\n",
    "ax.set_title('Cosine similarity between position vectors\\n(diagonal = 1.0, nearby positions stay similar)')\n",
    "plt.colorbar(im2, ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# What pattern do you see in the similarity matrix?\n",
    "# Why does it matter that nearby positions have higher cosine similarity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "5b8cbbc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0000, 0.8415],\n",
       "        [1.0000, 0.5403]])"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The 2x2 case\n",
    "d_model = 2\n",
    "seq_len = 2\n",
    "\n",
    "div_term = torch.exp(torch.arange(0, d_model, 2) * (-np.log(10000.0) / d_model))\n",
    "\n",
    "pe = torch.zeros(seq_len, d_model)\n",
    "\n",
    "position = torch.arange(seq_len).unsqueeze(1)  # shape: (2,1)\n",
    "\n",
    "pe[:, 0::2] = torch.sin(position * div_term)\n",
    "pe[:, 1::2] = torch.cos(position * div_term)\n",
    "\n",
    "pe.T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9533ae1",
   "metadata": {},
   "source": [
    "Consider again the statement \"friendly dog\"\n",
    "\n",
    "If we encoded it into a vector where the first position represents friendliness and the second position represents dog-like. You could imagine that the statement could be encoded as\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\text{}\\\\\n",
    "\\text{friendliness}\\\\\n",
    "\\text{dog-like}\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\text{friendly} & \\text{dog} \\\\\n",
    "1 & 0\\\\\n",
    "0 & 1\n",
    "\\end{bmatrix} +\n",
    "\\begin{bmatrix}\n",
    "\\text{pos 0} & \\text{pos 1} \\\\\n",
    "0 & 0.8415\\\\\n",
    "1 & 0.5403\n",
    "\\end{bmatrix} =\n",
    "\\begin{bmatrix}\n",
    " \\\\\n",
    "1 & 0.8415\\\\\n",
    "1 & 1.5403\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Now consider the phrase “dog friendly”. If we ignore order and use the same encoding scheme, we would also get\n",
    "\n",
    "$$\n",
    "\\begin{bmatrix}\n",
    "\\text{}\\\\\n",
    "\\text{friendliness}\\\\\n",
    "\\text{dog-like}\n",
    "\\end{bmatrix}\n",
    "\\begin{bmatrix}\n",
    "\\text{dog} & \\text{friendly} \\\\\n",
    "0 & 1\\\\\n",
    "1 & 0\n",
    "\\end{bmatrix} +\n",
    "\\begin{bmatrix}\n",
    "\\text{pos 0} & \\text{pos 1} \\\\\n",
    "0 & 0.8415\\\\\n",
    "1 & 0.5403\n",
    "\\end{bmatrix} =\n",
    "\\begin{bmatrix}\n",
    "\\text{dog} & \\text{friendly} \\\\\n",
    "0 & 1.8415\\\\\n",
    "2 & 0.5403\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "\n",
    "Now these phrases mean different things and their encoded permutations are different"
   ]
  }
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