{ "cells": [ { "cell_type": "markdown", "id": "linear-indianapolis", "metadata": {}, "source": [ "# 第7章 WaveNet: 深層学習に基づく音声波形の生成モデル\n", "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/r9y9/ttslearn/blob/master/notebooks/ch07_WaveNet.ipynb)" ] }, { "cell_type": "markdown", "id": "systematic-alexander", "metadata": {}, "source": [ "## 準備" ] }, { "cell_type": "markdown", "id": "fundamental-raise", "metadata": { "tags": [] }, "source": [ "### Python version" ] }, { "cell_type": "code", "execution_count": 1, "id": "characteristic-adobe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 3.9.1 (default, Feb 5 2021, 16:06:04) \n", "[GCC 9.3.0]\n" ] } ], "source": [ "!python -VV" ] }, { "cell_type": "markdown", "id": "intelligent-stretch", "metadata": {}, "source": [ "### ttslearn のインストール" ] }, { "cell_type": "code", "execution_count": 2, "id": "collected-technical", "metadata": {}, "outputs": [], "source": [ "%%capture\n", "try:\n", " import ttslearn\n", "except ImportError:\n", " !pip install ttslearn" ] }, { "cell_type": "code", "execution_count": 3, "id": "sealed-singing", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.2.2'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ttslearn\n", "ttslearn.__version__" ] }, { "cell_type": "markdown", "id": "adult-convenience", "metadata": {}, "source": [ "### パッケージのインポート" ] }, { "cell_type": "code", "execution_count": 4, "id": "static-hygiene", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "%load_ext autoreload\n", "%load_ext tensorboard\n", "%autoreload\n", "import IPython\n", "from IPython.display import Audio\n", "import tensorboard as tb\n", "import os" ] }, { "cell_type": "code", "execution_count": 5, "id": "correct-shade", "metadata": {}, "outputs": [], "source": [ "# 数値演算\n", "import numpy as np\n", "import torch\n", "from torch import nn\n", "# 音声波形の読み込み\n", "from scipy.io import wavfile\n", "# 音声分析、可視化\n", "import librosa\n", "import librosa.display\n", "# Pythonで学ぶ音声合成\n", "import ttslearn" ] }, { "cell_type": "code", "execution_count": 6, "id": "hungry-membership", "metadata": {}, "outputs": [], "source": [ "# シードの固定\n", "from ttslearn.util import init_seed\n", "init_seed(773)" ] }, { "cell_type": "code", "execution_count": 7, "id": "exotic-gateway", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.9.0'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "torch.__version__" ] }, { "cell_type": "markdown", "id": "honey-consistency", "metadata": {}, "source": [ "### 描画周りの設定" ] }, { "cell_type": "code", "execution_count": 8, "id": "pretty-wrapping", "metadata": {}, "outputs": [], "source": [ "from ttslearn.notebook import get_cmap, init_plot_style, savefig\n", "cmap = get_cmap()\n", "init_plot_style()" ] }, { "cell_type": "markdown", "id": "rocky-albert", "metadata": {}, "source": [ "## 7.3 WaveNetにおける音声波形の扱い" ] }, { "cell_type": "markdown", "id": "further-michael", "metadata": {}, "source": [ "### $\\mu$-law アルゴリズム" ] }, { "cell_type": "code", "execution_count": 9, "id": "secure-electronics", "metadata": {}, "outputs": [], "source": [ "def mulaw(x, mu=255):\n", " return np.sign(x) * np.log1p(mu * np.abs(x)) / np.log1p(mu)\n", "\n", "def quantize(y, mu=255, offset=1):\n", " # [-1, 1] -> [0, 2] -> [0, 1] -> [0, mu]\n", " return ((y + offset) / 2 * mu).astype(np.int64) \n", "\n", "def mulaw_quantize(x, mu=255):\n", " return quantize(mulaw(x, mu), mu)" ] }, { "cell_type": "markdown", "id": "composite-visibility", "metadata": {}, "source": [ "#### $\\mu$-law アルゴリズム適用前" ] }, { "cell_type": "code", "execution_count": 10, "id": "hazardous-israeli", "metadata": {}, "outputs": [ { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sr, x = wavfile.read(ttslearn.util.example_audio_file())\n", "x = (x / 32768.0).astype(np.float32)\n", "\n", "mu = 2**8-1 # 8-bit\n", "\n", "fig, ax = plt.subplots(2, 1, figsize=(6,4))\n", "ax[0].set_title(\"Waveform\")\n", "ax[1].set_title(\"Histrogram\")\n", "\n", "ax[0].set_ylim(-0.9, 0.9)\n", "librosa.display.waveplot(x, ax=ax[0], sr=16000)\n", "\n", "ax[1].set_xlim(-0.9, 0.9)\n", "ax[1].hist(x, bins=mu)\n", "\n", "ax[0].set_xlabel(\"Time [sec]\")\n", "ax[0].set_ylabel(\"Amplitude\")\n", "ax[1].set_xlabel(\"Amplitude\")\n", "ax[1].set_ylabel(\"Count\")\n", "\n", "plt.tight_layout()\n", "\n", "# 図7-6 (a)\n", "savefig(\"./fig/wavenet_mulaw_a\")" ] }, { "cell_type": "markdown", "id": "banned-collect", "metadata": {}, "source": [ "#### $\\mu$-law アルゴリズム適用後" ] }, { "cell_type": "code", "execution_count": 11, "id": "provincial-subscription", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(2, 1, figsize=(6,4))\n", "ax[0].set_title(\"Waveform\")\n", "ax[1].set_title(\"Histrogram\")\n", "\n", "ax[0].set_ylim(-0.9, 0.9)\n", "librosa.display.waveplot(mulaw(x), ax=ax[0], sr=16000)\n", "\n", "ax[1].set_xlim(-0.9, 0.9)\n", "ax[1].hist(mulaw(x), bins=mu)\n", "\n", "ax[0].set_xlabel(\"Time [sec]\")\n", "ax[0].set_ylabel(\"Amplitude\")\n", "ax[1].set_xlabel(\"Amplitude\")\n", "ax[1].set_ylabel(\"Count\")\n", "\n", "plt.tight_layout()\n", "\n", "# 図7-6 (b)\n", "savefig(\"./fig/wavenet_mulaw_b\")" ] }, { "cell_type": "markdown", "id": "behavioral-baking", "metadata": {}, "source": [ "### $\\mu$-law アルゴリズムによる逆変換" ] }, { "cell_type": "code", "execution_count": 12, "id": "veterinary-reliance", "metadata": {}, "outputs": [], "source": [ "def inv_mulaw(y, mu=255):\n", " return np.sign(y) * (1.0 / mu) * ((1.0 + mu)**np.abs(y) - 1.0)\n", "\n", "def inv_quantize(y, mu):\n", " # [0, mu] -> [-1, 1]\n", " return 2 * y.astype(np.float32) / mu - 1\n", "\n", "def inv_mulaw_quantize(y, mu=255):\n", " return inv_mulaw(inv_quantize(y, mu), mu)" ] }, { "cell_type": "markdown", "id": "drawn-heritage", "metadata": {}, "source": [ "#### $\\mu$-law なし" ] }, { "cell_type": "code", "execution_count": 13, "id": "structural-digest", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sr, x = wavfile.read(ttslearn.util.example_audio_file())\n", "x = (x / 32768.0).astype(np.float32) \n", "x = librosa.resample(x, sr, 16000)\n", "sr = 16000\n", "\n", "bits = [8, 4]\n", "\n", "fig, ax = plt.subplots(len(bits)+1, 1, figsize=(6,2*(len(bits)+1)), sharey=True)\n", "ax[0].set_title(\"Input waveform\")\n", "librosa.display.waveplot(x, sr, x_axis=\"time\", ax=ax[0])\n", "IPython.display.display(Audio(x, rate=sr))\n", "\n", "for idx, bit in enumerate(bits):\n", " mu = 2**bit - 1\n", " x_hat = inv_quantize(quantize(x, mu), mu)\n", " librosa.display.waveplot(x_hat, sr, x_axis=\"time\", ax=ax[idx+1])\n", " ax[idx+1].set_title(f\"{bit}-bit waveform\")\n", " IPython.display.display(Audio(x_hat, rate=sr))\n", "\n", "for a in ax:\n", " a.set_xlabel(\"Time [sec]\")\n", " a.set_ylabel(\"Amplitude\")\n", " a.set_xticks(np.arange(0, 3.5, 0.5))\n", " a.set_ylim(-0.5, 0.5)\n", "plt.tight_layout()\n", "\n", "# 図7-7 (a)\n", "savefig(\"./fig/wavenet_inv_mulaw_waveform_a\")" ] }, { "cell_type": "markdown", "id": "noble-clinton", "metadata": {}, "source": [ "#### $\\mu$-law あり" ] }, { "cell_type": "code", "execution_count": 14, "id": "sound-obligation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sr, x = wavfile.read(ttslearn.util.example_audio_file())\n", "x = (x / 32768.0).astype(np.float32) \n", "x = librosa.resample(x, sr, 16000)\n", "sr = 16000\n", "\n", "bits = [8, 4]\n", "\n", "fig, ax = plt.subplots(len(bits)+1, 1, figsize=(6,2*(len(bits)+1)), sharey=True)\n", "ax[0].set_title(\"Input waveform\")\n", "librosa.display.waveplot(x, sr, x_axis=\"time\", ax=ax[0])\n", "IPython.display.display(Audio(x, rate=sr))\n", "\n", "for idx, bit in enumerate(bits):\n", " mu = 2**bit - 1\n", " x_hat = inv_mulaw_quantize(mulaw_quantize(x, mu), mu)\n", " librosa.display.waveplot(x_hat, sr, x_axis=\"time\", ax=ax[idx+1])\n", " ax[idx+1].set_title(f\"{bit}-bit waveform\")\n", " IPython.display.display(Audio(x_hat, rate=sr))\n", "\n", "for a in ax:\n", " a.set_xlabel(\"Time [sec]\")\n", " a.set_ylabel(\"Amplitude\")\n", " a.set_xticks(np.arange(0, 3.5, 0.5))\n", " a.set_ylim(-0.5, 0.5)\n", "plt.tight_layout()\n", "\n", "# 図7-7 (b)\n", "savefig(\"./fig/wavenet_inv_mulaw_waveform_b\")" ] }, { "cell_type": "markdown", "id": "differential-cutting", "metadata": {}, "source": [ "## 7.4 因果的な膨張畳み込み" ] }, { "cell_type": "markdown", "id": "reverse-research", "metadata": {}, "source": [ "### 1次元の畳み込み" ] }, { "cell_type": "code", "execution_count": 15, "id": "interesting-clearance", "metadata": {}, "outputs": [], "source": [ "def _toy_1d_input():\n", " # (B, C, T) where B and C = 1\n", " return torch.tensor([1,2,3,0,1,2,4],dtype=torch.float).view(1,1,-1)" ] }, { "cell_type": "markdown", "id": "conservative-folks", "metadata": {}, "source": [ "#### パディングを行わない場合" ] }, { "cell_type": "code", "execution_count": 16, "id": "anticipated-render", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力: [1, 2, 3, 0, 1, 2, 4]\n", "出力: [17, 8, 7, 10, 21]\n" ] } ], "source": [ "conv = nn.Conv1d(1,1,3,bias=False, padding=0)\n", "conv.weight.data[0,0,:] = torch.tensor([1,2,4],dtype=torch.float)\n", "\n", "x = _toy_1d_input()\n", "with torch.no_grad():\n", " y= conv(x)\n", "print(\"入力:\", x.long().view(-1).tolist())\n", "print(\"出力:\", y.long().view(-1).tolist())" ] }, { "cell_type": "markdown", "id": "selective-thousand", "metadata": {}, "source": [ "#### パディングを行う場合" ] }, { "cell_type": "code", "execution_count": 17, "id": "respected-genealogy", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力: [1, 2, 3, 0, 1, 2, 4]\n", "出力: [10, 17, 8, 7, 10, 21, 10]\n" ] } ], "source": [ "conv = nn.Conv1d(1,1,3,bias=False, padding=1)\n", "conv.weight.data[0,0,:] = torch.tensor([1,2,4],dtype=torch.float)\n", "\n", "x = _toy_1d_input()\n", "with torch.no_grad():\n", " y= conv(x)\n", "print(\"入力:\", x.long().view(-1).tolist())\n", "print(\"出力:\", y.long().view(-1).tolist())" ] }, { "cell_type": "markdown", "id": "reasonable-speed", "metadata": {}, "source": [ "#### 2層の1次元畳み込み" ] }, { "cell_type": "code", "execution_count": 18, "id": "peripheral-softball", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力: [1, 2, 3, 0, 1, 2, 4]\n", "出力: [88, 76, 61, 62, 111, 92, 41]\n" ] } ], "source": [ "conv = nn.Conv1d(1,1,3,bias=False, padding=1)\n", "conv.weight.data[0,0,:] = torch.tensor([1,2,4],dtype=torch.float)\n", "\n", "x = _toy_1d_input()\n", "with torch.no_grad():\n", " y= conv(conv(x))\n", "print(\"入力:\", x.long().view(-1).tolist())\n", "print(\"出力:\", y.long().view(-1).tolist())" ] }, { "cell_type": "markdown", "id": "enabling-exemption", "metadata": {}, "source": [ "### 因果的な畳み込み" ] }, { "cell_type": "code", "execution_count": 19, "id": "patient-remains", "metadata": {}, "outputs": [], "source": [ "class CausalConv1d(nn.Module):\n", " def __init__(self, in_channels, out_channels, kernel_size, **kwargs):\n", " super().__init__()\n", " self.padding = (kernel_size - 1)\n", " self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=self.padding, **kwargs)\n", "\n", " def forward(self, x):\n", " # 1 次元畳み込み\n", " y = self.conv(x)\n", " # 因果性を担保するために、順方向にシフトする\n", " if self.padding > 0:\n", " y = y[:, :, :-self.padding]\n", " return y" ] }, { "cell_type": "code", "execution_count": 20, "id": "attended-volume", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力: [1, 2, 3, 0, 1, 2, 4]\n", "出力: [4, 10, 17, 8, 7, 10, 21]\n" ] } ], "source": [ "conv = CausalConv1d(1,1,3,bias=False)\n", "# テスト用に、畳み込みカーネルを手動で設定\n", "conv.conv.weight.data[0,0,:] = torch.tensor([1,2,4],dtype=torch.float)\n", "\n", "x = _toy_1d_input()\n", "y= conv(x)\n", "print(\"入力:\", x.long().view(-1).tolist())\n", "print(\"出力:\", y.long().view(-1).tolist())" ] }, { "cell_type": "markdown", "id": "convertible-bumper", "metadata": {}, "source": [ "### 1次元膨張畳み込み" ] }, { "cell_type": "code", "execution_count": 21, "id": "mechanical-survey", "metadata": {}, "outputs": [], "source": [ "class DilatedCausalConv1d(nn.Module):\n", " def __init__(self, in_channels, out_channels, kernel_size, dilation=1, **kwargs):\n", " super().__init__()\n", " # パディングの幅を計算する際に、 dilation factor を考慮する必要があることに注意 \n", " self.padding = (kernel_size - 1) * dilation\n", " self.conv = nn.Conv1d(in_channels, out_channels, kernel_size, padding=self.padding, dilation=dilation, **kwargs)\n", "\n", " def forward(self, x):\n", " # 1 次元畳み込み \n", " y = self.conv(x)\n", " # 因果性を担保するために、順方向にシフトする\n", " if self.padding > 0:\n", " y = y[:, :, :-self.padding]\n", " return y" ] }, { "cell_type": "code", "execution_count": 22, "id": "commercial-receipt", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力: [1, 2, 3, 0, 1, 2, 4]\n", "出力: [4, 8, 14, 4, 11, 10, 21]\n" ] } ], "source": [ "conv = DilatedCausalConv1d(1,1,3,dilation=2, bias=False)\n", "# テスト用に、畳み込みカーネルを手動で設定\n", "conv.conv.weight.data[0,0,:] = torch.tensor([1,2,4],dtype=torch.float)\n", "\n", "x = _toy_1d_input()\n", "y= conv(x)\n", "print(\"入力:\", x.long().view(-1).tolist())\n", "print(\"出力:\", y.long().view(-1).tolist())" ] }, { "cell_type": "markdown", "id": "framed-covering", "metadata": {}, "source": [ "## 7.5 ゲート付き活性化関数を用いた一次元畳み込み" ] }, { "cell_type": "code", "execution_count": 23, "id": "revolutionary-gross", "metadata": {}, "outputs": [], "source": [ "class GatedDilatedCausalConv1d(nn.Module):\n", " def __init__(self, in_channels, out_channels, kernel_size, dilation=1):\n", " super().__init__()\n", " self.padding = (kernel_size - 1) * dilation\n", " self.conv = nn.Conv1d(in_channels, out_channels*2, kernel_size, padding=self.padding, dilation=dilation)\n", " \n", " def forward(self, x):\n", " # 1 次元畳み込み \n", " y = self.conv(x)\n", " \n", " # 因果性を担保するために、順方向にシフトする\n", " if self.padding > 0:\n", " y = y[:, :, :-self.padding]\n", "\n", " # チャネル方向に分割\n", " a, b = y.split(y.size(1) // 2, dim=1)\n", " \n", " # ゲート付き活性化関数の適用\n", " y = torch.tanh(a) * torch.sigmoid(b)\n", "\n", " return y" ] }, { "cell_type": "code", "execution_count": 24, "id": "flexible-public", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力のサイズ: (32, 128, 100)\n", "出力のサイズ: (32, 16, 100)\n" ] } ], "source": [ "conv = GatedDilatedCausalConv1d(128, 16, 3, dilation=2)\n", "x = torch.ones(32, 128, 100)\n", "print(\"入力のサイズ:\", tuple(x.shape))\n", "print(\"出力のサイズ:\", tuple(conv(x).shape))" ] }, { "cell_type": "markdown", "id": "refined-victory", "metadata": {}, "source": [ "## 7.6 条件付け特徴量のアップサンプリング" ] }, { "cell_type": "markdown", "id": "rural-harvard", "metadata": {}, "source": [ "### 繰り返しに基づくアップサンプリング" ] }, { "cell_type": "code", "execution_count": 25, "id": "excellent-greek", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[[1., 2., 3.],\n", " [1., 2., 3.],\n", " [1., 2., 3.]]])\n", "tensor([[[1., 1., 1., 2., 2., 2., 3., 3., 3.],\n", " [1., 1., 1., 2., 2., 2., 3., 3., 3.],\n", " [1., 1., 1., 2., 2., 2., 3., 3., 3.]]])\n" ] } ], "source": [ "x = torch.tensor([[1, 2, 3],[1, 2, 3],[1,2,3]]).view(1,3,-1).float()\n", "y = nn.Upsample(scale_factor=3, mode=\"nearest\")(x)\n", "print(x)\n", "print(y)" ] }, { "cell_type": "code", "execution_count": 26, "id": "chemical-pregnancy", "metadata": {}, "outputs": [], "source": [ "class RepeatUpsampling(nn.Module):\n", " def __init__(self, upsample_scales):\n", " super().__init__()\n", " self.upsample = nn.Upsample(scale_factor=np.prod(upsample_scales), mode=\"nearest\")\n", "\n", " def forward(self, c):\n", " return self.upsample(c)" ] }, { "cell_type": "code", "execution_count": 27, "id": "parallel-browse", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力のサイズ: (32, 80, 10)\n", "出力サイズ: (32, 80, 1000)\n" ] } ], "source": [ "c = torch.ones(32, 80, 10)\n", "# 例として、100倍にアップサンプリング\n", "c_up = RepeatUpsampling([100])(c)\n", "\n", "print(\"入力のサイズ:\", tuple(c.shape))\n", "print(\"出力サイズ:\", tuple(c_up.shape))" ] }, { "cell_type": "markdown", "id": "worst-garlic", "metadata": {}, "source": [ "### 最近傍補間と畳み込みの併用に基づくアップサンプリング" ] }, { "cell_type": "code", "execution_count": 28, "id": "secure-terror", "metadata": {}, "outputs": [], "source": [ "from torch.nn import functional as F\n", "\n", "class UpsampleNetwork(nn.Module):\n", " def __init__(self, upsample_scales):\n", " super().__init__()\n", " self.upsample_scales = upsample_scales\n", " self.conv_layers = nn.ModuleList()\n", " for scale in upsample_scales:\n", " kernel_size = (1, scale * 2 + 1)\n", " conv = nn.Conv2d(\n", " 1, 1, kernel_size=kernel_size, padding=(0, scale), bias=False\n", " )\n", " conv.weight.data.fill_(1.0 / np.prod(kernel_size))\n", " self.conv_layers.append(conv)\n", "\n", " def forward(self, c):\n", " # (B, 1, C, T)\n", " c = c.unsqueeze(1)\n", " # 最近傍補完と畳み込みの繰り返し\n", " for idx, scale in enumerate(self.upsample_scales):\n", " # 時間方向にのみアップサンプリング\n", " # (B, 1, C, T) -> (B, 1, C, T*scale)\n", " c = F.interpolate(c, scale_factor=(1, scale), mode=\"nearest\")\n", " c = self.conv_layers[idx](c)\n", " # B x C x T\n", " return c.squeeze(1)" ] }, { "cell_type": "code", "execution_count": 29, "id": "patient-algebra", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力のサイズ: (32, 80, 10)\n", "出力サイズ: (32, 80, 800)\n" ] } ], "source": [ "c = torch.ones(32, 80, 10)\n", "c_up = UpsampleNetwork([10, 8])(c)\n", "\n", "print(\"入力のサイズ:\", tuple(c.shape))\n", "print(\"出力サイズ:\", tuple(c_up.shape))" ] }, { "cell_type": "markdown", "id": "aggregate-reducing", "metadata": {}, "source": [ "#### 実データ (mel-spectrogram) のアップサンプリング (bonus)\n", "\n", "書籍では解説しませんでしたが、二次元畳み込みの重みを適切に初期化することで、畳み込みの前後でスケールが保持されることを示します。" ] }, { "cell_type": "code", "execution_count": 30, "id": "eastern-pierce", "metadata": {}, "outputs": [], "source": [ "# 初期化の影響を確認するため、畳み込みのパラメータを乱数で初期化\n", "class RandomInitUpsampleNetwork(UpsampleNetwork):\n", " def __init__(self, upsample_scales):\n", " super().__init__(upsample_scales)\n", " for conv in self.conv_layers:\n", " nn.init.normal_(conv.weight.data, 0, 1.0)" ] }, { "cell_type": "code", "execution_count": 31, "id": "reduced-keeping", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from ttslearn.dsp import logmelspectrogram\n", "\n", "_sr, x = wavfile.read(ttslearn.util.example_audio_file())\n", "x = (x / 32768.0).astype(np.float32)\n", "sr = 16000\n", "x = librosa.resample(x, _sr, sr)\n", "hop_length = int(0.0125 * sr)\n", "sp = logmelspectrogram(x, sr, hop_length=hop_length)\n", "\n", "fig, ax = plt.subplots(figsize=(8,4))\n", "mesh = librosa.display.specshow(sp.T, sr=sr, hop_length=hop_length, cmap=cmap, x_axis=\"time\", y_axis=\"frames\")\n", "fig.colorbar(mesh, ax=ax)\n", "ax.set_xlabel(\"Time [sec]\")\n", "ax.set_ylabel(\"Frequency [Hz]\")\n", "plt.tight_layout()\n", "\n", "Audio(x, rate=sr)" ] }, { "cell_type": "code", "execution_count": 32, "id": "outside-saying", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "UpsampleNetwork(\n", " (conv_layers): ModuleList(\n", " (0): Conv2d(1, 1, kernel_size=(1, 21), stride=(1, 1), padding=(0, 10), bias=False)\n", " (1): Conv2d(1, 1, kernel_size=(1, 17), stride=(1, 1), padding=(0, 8), bias=False)\n", " )\n", ")" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "upsample_net = UpsampleNetwork([10, 8])\n", "upsample_net" ] }, { "cell_type": "code", "execution_count": 33, "id": "alleged-transparency", "metadata": {}, "outputs": [ { "data": { "image/png": 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Y/lUAl+Uu/Vj55BOo5Qgy/FJd3PBdV8PRTpBbobp3KAkQcPHCcNkBhD9512CZts1g2Vnk5/S17zxomkcfC/z6Tz/n0Mv2z/ijew6e6mxxnPE5fe6z9Blf+3dOoJaAyuKBUPONSm8u229YhotsJMvT3so9KwpEkCCoV1DBtdavTpEgiEKc6sIRNgr49FAsiuZLjmi3rVLHpYq7OiICsV1snEYHMweVdtNqEFBb51l/OH7n3/zKE4tNOH/xee7vnZmRe+iBaDx4msMgDnGCeG/1cgIiiHPgPXhnZXlYBLxbvFxK09uyUr+qdSFCDBAVYoQQuvEaQjec+zUENPavW8e8rcfMSd474ezG50m6VXwKlgXmYQAR+REsF/kVEanSf8DPYjmjTKFwaigQOdsXqmOkxGfhTDB5zjM/+qBpSmyW2CycXdYxPk9Sc/wu4M+lbDMCfDJmfP+LwOemaV6GGWUXCmcAJWg8dHdOKfFZOEccLTbPaXyW2CycM9YvPk9Sc/xrIvJa4E1AC/wW1tTzE8APiMg3pXE3Zbs5LqpnjMgNgPa59w2WxY1hra6KDJb53WH/8Ood7xuvz0PvHZ73RS8YLNt97p3Dy9wa///HzYfbWKv7hxP2TB58Ynidf/S20XWOIX5kv8eRut5z1/iC33/wuu2/3zPW5nxC3Lb4VDE5bSNopRAFF9JoB24uSIC4YXo8iWl4kobVytSBRJsfBK0iWaebwzFr+cRlqQKdhEB6Wl1XB6o60Mzs8ufrgAi0c4+rIr6KzLfrTovh60iYe7QRJAo00skkpBVirUhr9ZYotJeC9Te2vqwGUQH12tUXAbLsYml80j8o4BQRRdN2x9bZsIptJ+AngSCKOKWaBGJwxCDU0xbvIzvXp4hTXBURUULju2XEWYq3KIhXNElD9pWz9LlJo9I73m2Si2iSmaTjIUGQCLHWxbGJBwnD02IpsclJ3DuT3KD7TZICcQ6qyr5nqapOekBVQeXRyoFzi18vqDO5gvp0zi9pd5fvm5J1t3t0uhIVaRWJEQkmU5CgEIL9Ni20rUkZQoC2RZt2oevN63CCZFlHr97UtdW/ti56j9a5/gJOiJVDHTYstqzu2Ow5XUUxOUXaFkkaY4kgQW17oqZtUUQVaaN1ISBNgNa2gzZA05gco20hZlmG/WoI4xKMg6Sgqbzr70tLJB37vN98b9wK7RPrGJ8nmgREVb+Om3Orvx34mJNcb6FwVNataWiMEp+F80SJzRKbhbPLusVnyZBXKCQUJZyzL4kLhXPPfm+m905SYrNQOLOsY3yWh+NCoce6NQ0VCutCic1C4eyybvG51g/Hete4v+O1528OloXJ8NuMjavDzQd+Z1hzzJgf8QHMR/S/cTqsNRrTFMO4JnlsuX736BZxY7iLFwfLpBrZf5sbt7xuBcKaBfipkvTGRNMWRwGJ4HYdWilaKX5XcC00k6RHbsBlfTKYJlUwPayAJC2s1FlfHK3cmeZWo2lxnVOcN+2wqxRfBVSF0DqqOnR6XdPzOsSbHrezUEuaXOejrQOHRAfBLNoIgmsEP4N207SG2cZNa9uWOE3a2kqJXk0/7NI6XNJPi5r8Nkinn9aQtIFe0SDmwJYt3xRUXadHzoho1yGa5IKRrY05s90accp0avaLc8B521dNY+vSvDyVzpau029mXbDTnp9y1hXLzR7LTrtladYmC6ioTer7NlarxVuJzZOhszfz3q6v3ttw1udOJ1BX6KQiTip04okTR6wd0Qtai/16IVYk7W5aeD70PV2xxUnW4yaNcQRpFReS3tgn68PWtPxItFM/hoX1mmr6aMEtvlNxsr8Vm3e2DUlnrJVDq7QdlemNodeA0Z3HctO4xbB0m6h7tNXdvo0s9McKdBpkcE3EzQPSRKQJSNsi8wBNDU2DtH6hRXbJHlYjGrhZO9zv36sj7n57GuxsS9fps93ycJq+v50HsY7xedIZ8gqFQqFQGGbo475CoVA4Jdb6zXGhcFjWrWmoUFgXSmwWCmeXdYvPtX44lvl4trqNR4clEFqPSRVGZBXXdwfL2ne9Z7Q+Y0z+ZNiLrLr3ymBZc2U6uly5PnxCb7z3xmBZ+J23ji73qIymrI4jX8M+49aT7FjL13oF+KmTLNXUpSZJAXXaDWulREnNl16JWFP8UrY4eq3vkuQF6VRQdbbMADkFnLho61DMriyA80KMyVbOKZWPNC7JD6qA95HQOlSh2a3QxqMR4qwmVsniLFm5RW911UqJtckLlFSnZO8GZuumTiGAa0wKodO4JDfBL3aTVun2kiQXqiBVlknYDnC1bXhsXWdB53xk3taIV6aTlllT0UZP03ge37lA2KkQr9y4NkHqiAbp6tg/TARZZMx1/YKFNGIp095esoSmccRJtKbxZOWmLrUORyFu9OI4HKLZtsTmiaIhLB9e58xaTM2CzIWANhUy97jKJdszWdi3udyNrSRJKfJvZ3FGsjfLMoO4sDlrmiULN23bLtNcP+NcZ0mXLck6eYVH6gpxLklH3JI9Xd+ObtSWTpKEwi2kFKJqmTE1SY50ZFvyNvS3JdvRpe2ITXtwxjyNIC5ZmwbESZJbqMnOAN0joRDn0JwJMF90nEsylJ78JA13+6Jy8I4Vzh3WLz7X+uG4UDgs62VGUyisDyU2C4Wzy7rFZ9EcFwoJRQlH6AqFwq2wopVbic9C4UxykvEpIp8mIn8oIn8sIl+9T/lURH4wlf+aiDzvOLapvDkuFDIKodxLC4WzR4nNQuHsckLxKSIe+E7gU4H3AL8hIq9X1d/vTfZy4HFVfaGIvBT4FuALbnXda/1wHN/z4Gj5RjWcqri9e2uwLGwO77ZwadhSzE3H7cbibFivzO5sdN4h1I2/lXHN8Bktjz5xpHXeCnFn50jz+QdXyA99AFkGWjgmsguBSzpTZ9rhUC00rAGQlsXLQwH1ye0smH43VCltc2vph9UrBG8HrErpl6PZq+HUdJBg9mkC2gpNXFiOxeDAR3MpEsX7iEv6Y+ftDGiCQ5L+2WzexNaVTxJNsVUtLNZEzKaKSLKnAmrTPVtK5lQFzdOY9RtZY53dlHL6a0B8xPmF7rqqTRfaJrs2ETWtdOuIAruzmtA6YuuIjQm7JSY7OE2WcQelbBZdfpk7fJlcnlaTHjPv91zvtJ9VpDum+fgeLn104bjp0hL3LcGsJ1m8uYXVm/eIT1Zp3ifdrrN0zM5BtU86aWGRmrmn25W+PtXZ+anOI9Eh3pnFWeXM0qyukKbtrM0k6XSXNLwhLNc/2rtM0rrMDjGdyNo7oTUFLhHE06Vup/+dxP7nqKhppnNHTKmho6WHJgT77mlPfTX/xpguiKaZRuPBeuOu3ovptPu06oD3sHts3/r2b/kY5+PdpQ/3q4kLTjA+Pwb4Y1V9O4CI/ADwEqD/cPwS4OtT/2uB7xARUb01EfRaPxwXCodDCCs08RYKhdtNic1C4exyS/F5j4i8sTf8KlV9Vep/JvDuXtl7gI/dM383jaq2IvIEcDfwyFErBOXhuFDoUCCWpttC4fayQsyV2CwUzi63GJ+PqOqLj682x8NaPxwf1ETv3v6uwbKqet7InCPSidmwPZy749JofRjJvMelkcxxs2awbPq+cTs72R6Wa4T3vm903tuODDfxyMj+AeDRY67LIRGRTwO+HWuk/m5VfeWe8inwfcBHY7X9AlV9x+2u54kgumia3/NyQSdYRrmcgaonudAoSZ5g9lB+Zpnb4jRJFlSIRMRjNmStNd+KWBwpLDLSZfs3gdA4ZloRGoeIMtcaRImtWLa8no2cOLrMe+rMfiq1DSc7KlnIB5yiE6A2mUTIWe0cxGTBpl67z6AV7WQJ4rMVVKqr14V8I9q+0Ci0UiEuEluHJkmCBgeNQzVlvFMxt6csWVBM1gGdvOOWSZkKu/68w9O6stRFktQly7viJGUQTP2rWrkVTpjUTL/URN+m+0rvuptlC/tm1fPJKi01yVP1rNJ8ykznXM8mcJ9jr1miEO03t4z7pMmi6jLhibPMd0xCikuGM8EN2ZZlGUZQRFu7jsyH69b/laAQolmMhggxWc+FADEmyUrs+rN1m4awunziuEgyCrO3s/0gvWyC2f6u2097s+adHg8Az+4NPyuN22+a94hIBdzBMdzxi1tFodAjpOahw3QH0fuo4K8AHwp8oYh86J7Juo8KgH+FfVRQKBQSR4nNIsUoFG4PJxSfvwG8SESeLyIT4KXA6/dM83rgZan/c4FfuFW9Maz5m+NC4TAkS/WTWPSpfVRQKJx5VkgffYKxWSgUbpGTis+kIf5bwE9j7Y//TlXfIiLfCLxRVV8PfA/wH0Tkj4HHsAfoW6Y8HBcKPaIeKcDHPiiAU/yooFBYF44Ym4VC4TZwUvGpqj8J/OSecf+4178LfN5xr/cp/XAs9cjmj7ywG7NHC1v1YJnbGraHA4jvf3h43hHtT7wwnCJaD7BicX54ue7SsEY6XL06utyj4i+OaKsvDO+/ePfl8QW/4+B138J/v2fyg4JzQfYAyqdpluL10xdnKzAnC22ygPTFs1mqrHRa3JwKWSNdymZxZuU2JF8Xr9R1IAZBEVwd8VWk0WQ/lfXNulx3iZLSyEKYmK2ca1Pq5CrplLOWOFVUolmadaEtvQ3pvOEW+0CcIi5b0CWdMoBEYhDEm+2cZq2v164/S4SzpHlUd5xt73K98jGRPfN5WfZwciys2Vx2yVpekbRi+0OARjot9EGUN8enxB77r64/64yzbndJ37tneO+9S8TSL/ds0szKUExzHDFbtBiT5WFEgw13KaVbs2kjpbxGFNW40BtD+kJMF7rirKPN9nPZdq5a6Gu79NDJhm6J3qls6aKTPjrZt3U66dY0x5JSRNO2tg2N2dBp21rdNC4s3GCpf+Vjst8xyimj8/FJx6Qb7n6X+5f2R7bj84dIH71m8fmUfjguFPooQjgZGf6pfVRQKKwDJxibhULhFlnH+CwPx4VCjxNqGuo+KsAegl8KfNGeafJHBb/CMX5UUCiceVaMuSKrKBTOLusWn+XhuFBIrONHBYXCOrCOzbaFwrqwjvF57h+O/ZUrg2WytTk+8+awX3F7eXhe1w6/0PPXhlNAj2mKDyI+8NDwOtunDZbpiB4ZGPUxlJH9w9XxxR6VcP36YFk1Hd6WuDGs9T4LnNZHBbcHWehU85ikc8UpRNPo6iTadH2Nata2uqyfTctxammk64U3cOcnnLyQFfNIFm+63k5rXMXFMqNYVlgX0aQ79nXEV4HYZt2j4pyarXAQ5rFCWwdRiMFZGuoglnoZ0/pqqnv27NWone74pjyqPZ9hXNpXJI2yz37BknSXajm11SWRsEspbG32ME+LjGIezp3s0PSV5uucv4uwFNOEvuBYUWTcIMLtKey3lmZNcZCkm0zbZnJRXAA3E2KVUkXPxf4drBTJWu28PX70047CcZFTBff7+9rUTovrFz7BPmtV9/jddpriZa9gdc406LKnXwT8zRpjNPt6q6VhbrXTF0ub9Ltt8g8OOQVzSsPc8w0mBEvHnPW7Q7sgb1vy9V1opmXh99vpp/f4IGdddNIjd/shb0dcaJC7TBh9vXVKWy11tZDyq6IhmCZfxa4fLvZ8pkfYx5Ma0uV36fi2nR4Z76CuzYe6X/+kwe68qL2gPuuxLY6fqpz7h+NC4fgQgq6XbqpQWA9KbBYKZ5f1i8/ycFwoJOzD+/UK8EJhHSixWSicXdYxPs/9w7FMRprTJ5PxmZvhtMtjhE0/WKZuWIpQX7ljdLlxe3tkpcPtLXppWAISt8b3gXtyWAai12+Mznu7GbOPczfuO551rJlu6raS2wxzGmGSnVfcM00fh0koOjuz2H2gJb7XdNi3aoNOqoG3eSVKkg/QNeF2TfgRFNctQ3vWZCK66JziKrNumwdBxSEu4upImPnUXAnMXCf9yMsh2DhpxFoyFdwcYp22pU0WUSzWbdupqFek0oXUIS9XBamipa8Olj5avC4N2/aY5EMEYmvpoy0ts9j+BxYpng+QVBxEf5vTgtQvjk9EcLLYlpxeWxSkBdcIcZKs5SIrW7lBic1bZj9JRUoDvVdy0MkpMp0sQ5aH1YJMVDuJTNefbdGSFEN9klrdZO2WfpPMwrXaWbl1qaSz1GJuEgtp24XMom2haW29MS7kDXulFnnbvFvYmlVVsnbLaa9NWpAlBoOywyyniElHFeilkg7LcpCQZSBhIQfZz8btONAszQjLl1pxuLZFs5ykqtJvTgPuk+zCo7U3aUXtcXH1B951i89z/3BcKBwXquvXNFQorAMlNguFs8s6xmd5OC4UesQ1+++3UDjzrPjSrMRmoXB2Wbf4LA/HhULC7GjW67/fQmEdKLFZKJxd1jE+T+zhWEQ+CPjB3qgPAP4x8H1p/POwxISfr6qPH3U97VjK5c1xKzd337AFmoThNI7148NaZdkZLmsfHLZjO4g4nw+WVY89OVjmHxtfrj4xPO+YrdpJ4aYj9nEjqTVl3h7D2tevaWiI441PNS1qb9dluzVImtScwnmv3LARJAoxTS/BpXnM+ss1QvC6PF8U0xprmi5ZvVHZOOmmNyu3bt69LzbUMjt1g1GIrSPMPdp4O91iRWgU3auP7Qt3e9rnOAF1Zs8Ukw1dZ0HnWNiYObWrb8+ODU0a4TxNFLR1qCgEh0ZLfU22a8u2alFSyt09G9hbdKcBXuUtbbK82zd9dCbXW9NCs4baK7GW3j5Q2zcOqCHk/SF2XqwebiU2Oeq9c0iHurTSm1MS5zTR+2qT+3pdZ9pVUWc2bqY8R2NEWhAXOlszTRrezvosC5WzHVoISymY6aVgvsnCrafj1ZFvcgCzTfPeliMOvUljnSzssrWbc0hfb6ymfe76k65Z1ezmstZZQ7hZ77yn/1jopcnujtlB47PWOm3vkm48RPsoQBUJEamipbl2q8bc+sXniW2Nqv6hqn6Eqn4E8NHANvA64KuBn1fVFwE/n4YLhcJtpMRnoXA2KbFZKJw+t+tR/5OBt6nqO4GXAK9O418NfPZtqkOhMEq2ozlstwaU+CycHitIFY8am2sQnyU2C2eedYzP26U5finw/an/PlXN+oL3AsfjwVUoHANhzfLDr0iJz8KZp8Rmic3C2WXd4vPEH45FZAJ8FvA1e8tUVUX2V8GJyCuAVwBssHWkdcedndFyN6LjlXZE3zpSxvuOniL6qLQPPDhY5i9dGp33NHTFY4z5VsuIb/VBfs6roMjafVRwEMcRn/6uKwesRC2FMCy/JdSFNjUP752t0yp32kRLm6zJSznrY7vhvA5RfB0JcwcCfhI6TbE4CK1Hg5W1wSE+osGhbapXTnWsdGmjR0ma64XWWom9U1ld2pa8rKbnOxxkSaOd015rsG0TSfWJQCClht7jH01v+2+V/r7shqXnv0ynL+70yT1P6aXjkBeRddYplTSYNlv8ajfUEpvLHPu9c5+UxCoOCWGRijhrdFO6ZalC55NL9OCSX250dnJmn+C40BuLwzTFScPb9fc9grPeOATTvfY1xn097x6v4IO2T9s4qrm+SW+9Ijf5FR+XrniIIX14LtubFjz9SvJ47nyss148p8r2rkv3jev1r8A6xufteHP8V4A3qer70vD7ROQZqvqQiDwDeP9+M6nqq4BXAVyWu47rsl8ojBLX7KOCFbjl+Jw+91klPgtHZ8WsJCU2y72zcHZZt/i8HVvzhSyahQBeD7ws9b8M+NHbUIdC4UCyHc1hu3NOic/CmeeosXnO47PEZuFcsI7xeaJvjkXkAvCpwN/sjX4l8EMi8nLgncDnn9T6D7JyG0sf7Z4cl2QMEXeG0zGfBuHatdOuwqGQu+8aLhxp6joOKzdF1k43NcZti08Va25PaZX3NrurS2Ukyy+14Vip2ZplyUKepmftJrkVs3/cooITYhC6FM0xpVvWPJ4l6zPFdTKGvNzMLaVbzuyxutt7T1iSSMxTsymLOsrQtMdNOlaulc5mTdokXendLbJFnQQWFn3eZBPRR0sJrorWskgzHWUhu5BkjbfXfm6oWiU24STvnUNN9bmZ3iq1aKoXQTVa2vZsbSYB2l6zfq+JP22T9WdbtKgQk1Qip33up1tOw51l237plo9LwtDZ3cGo5d1pk45LZ8kmPWu9bNW2NwV4llLI4lggsnRcNaX6XpoGFnK2A1jH+DzRh2NVvQHcvWfco9gXuIXCmeMsfz173JT4LJwnSmyW2CycXdYtPtdrawqFQqFQKBQKhVugpI8uFBKqrF2Wn0JhHTjJ2BSRZ2PZ5+7DRB+vUtVvF5G7OMZsroXCurKO9861fjg+yMpttPzhR465NmcTqYat07Qd1mSfGCP2elR+sEi2Z8ewciGukpGgcDiGrNyg0xrvy5I1GlDrzVPqnrTO0Gl2tdcwFnd9b5Z9LuKNDB/52yFAzHo96Vnb5WFY6LMXEt4987O8b49aZ9GkQexVyS3XodNlJw13t25dLKPLFN6vrMti4/76Vq7YScZmC/x9VX2TiFwCflNEfhb4Uiwj3StF5KuxjHRfdVKVOFX2s3I7tcqcYeQID4DHae2mEfBoNPM0xKUM7qbb1vS9hYRoWmMRs9lrw006Y/vtac37emRJ18OVLe3W79651g/HhcJhUNbvv99CYR04ydhMiTUeSv3XROStwDOxjHR/MU32auANrOvDcaFwC6zjvbM8HBcKPc6ytUyh8FTmFmLzHhF5Y2/4VckL+CZE5HnARwK/RslIVyiszLrdO8vD8Rrg77pzsEyu3DE+sww3hej7h6Ult2IRJ35YHhHvG9mWMNzQJzsjcowVUYS4ZnY0hdtEtquLi0xyku3QnJodGqC1Luzs+lnnkhWanX5qdm2CZZNzi/Pe+WxllTzvkpxBG4f4NK2aRVrOGihROjlGJ8vI64ebpBzSCm4uaG31cbNkgTdZWOipV6tbm+qpYtZu3izrJKQVZWu+bNnW9la7oo1b2iO3EpuPqOqLD5pIRC4C/xn4u6r6pPSujWMZ6QrngP0y4CWruWX7s541Wu72yhH69nbZXk419fe6bEuncZHlL6b+Pdn9VrKmE7Nqy5nupK7tt6oW1m05Y2HOeOfcUsY7yXWDm3+79Rw+ztbx3lkejguFHuv232+hsC6cZGyKSI09GL9GVX8kjV4pI12hUFi/e2d5OC4UEsr6pcAsFNaBk4xNsVfE3wO8VVX/Za8oZ6R7JSUjXaEwyDreO8vDcaHQIYQ1++K2UFgPTjQ2PwH4EuB3ReTNadw/4DZmcy0Uzjfrd+889w/H/o5hTa3ccenoCx7T3ewO24aFR4dtME/MGu2+eweLZveP74Pq2rBWV9/+ziNXaQwNYbDMP7E9PONk2HZu/swDtNX/46BaFY5OSgPcs/SSIKhLGtUoSASdKMRUltNCg+lSU78Knd52VMK2x+IMeiErab2iEFyykstaXDodrLY9na1bTkPd6XL76xhSnKpAMA2tmwtxYtP6HUecKOpNtytRaKtgetykBY51So1dR8SB7Kmr32zxVSQGQaNjujHHO2XWVMQgiEA787b/q4V2WPsWd9rbXf1t2C9HtppOOuuNEYiVWUWp196+ETTYNpFkktI6aEAaWRzrPbprCULcTDPEfWz4TgFV/W8Mm8qd/4x0S3Zde/q9s5TQWXe7V4MLi5TCOcD6tl9D9HWsUZfH9zW5Savb6XJTGmmNfR1v7O4ZurSs1S3S+imXu9+qMruzqoK66n61cmjtUe/Bp9TK3bWll1Y9goSItBFpLMW1zO2Xtk37qIU264ojtC2aU2IflpziWiOiHlVNx6ntbZdbHDvnkJw62nvEOzTpjxFBK9dpka1fUN/rnBC9wO8evqrrwLl/OC4Ujot1bBoqFNaBEpuFwtllHeOzPBwXCj3WrWmoUDj7rBZzJTYLhbPLusVneTguFBKqsnb//RYK60CJzULh7LKO8XnuH45la3O4cHNjdN747gcHy9wdlwfLdHs47fRppFwObx0W1E7bF4zPvNfjsEd7nGkvV6R9+zsGy6p77xksk3svHsv61y3Lz21D+51pjLN+VWK20jVRcqclduaXK5PQaWVdFZMe2CGC6VqTRpcgC31ySCmiq7j04jHL8AxBY9JXplNZo1hd4rLPrmZd7lBq5jFEoQJFCZPF6HaaVypEAWkWemvXSOcHLC1o402n6xQNavpdpwRXEeaLCsykRkQJje803hqc7bOk35WcyvkorryiUCuxJ+/X3jYRxRYbBGmk05cjmO46CG5XaF3EBZC5bWesbZvdXIi1eSBLlM7/eRVKbB4d8X7J2xfZo8F1SYva88gFFrrf3K+mJe/uG1kb3A0n/XB/3XtSLqtGW2bSHN+kL+55/q7k/7sCYzrfzjs4a7DTsNtHw2vDbjE+xs7PuNsX2d84hMU2pv4j6427DTHd8eAyeh7ON3k5VxWStdVZc115tK4g66wrh1aeWAnU7lCWx+sWn+f+4bhQOC4U1i4/fKFw5lnhIb7EZqFwdlnH+CwPx4VCh6zdf7+FwnpQYrNQOLusX3ye/4fjONLcMhuXOMTtYdswccMHOo5YuZ019L3jSZ3k4oXbVJNjYDodLJLm1iUg9sXtev33e1pkG7ClYVhYrGWy3VuSCIgommcccIzqUiBDlxqZvIheCy+Syu21Rjdznm6xwFTVk0oOLIpWmLVduqzEabab60k8UpplaZM1XgXMnG2vN91HmCd7rSwxgUVq5n422JM6j7v9nm33TPaSbd5Qk8rgFY0Cddp+B3GiloJ6ElO1U5rsFVdbYvMW0IhGh7gIpHMoSQAs9DwL7ZEu7qu5v5NU6EIW0SvbK41YWrXscy8dSJt8VKu2W0FD6NYrTrr67itD2ZtOGpZlJ739tp8d3dL2nQBLkopkySedrVsvLbbvdZmgiAYkpHT3M7NzW4V1jM/1etQvFAqFQqFQKKw9InKXiPysiPxR+r1zYLogIm9O3etXWfb5f3NcKBwj65YfvlA488hqb9NKbBYKZ5dTis+vBn5eVV8pIl+dhr9qn+l2VPUjDrPg8nBcKCQUWbumoUJhHSixWSicXU4xPl8C/MXU/2rgDez/cHxozv3D8fyDnjlY1twxnG4Y4MJ0uDy8bTh1stTDu+00rNzcZDJYJnfeMTqv3jFsgVaN6K7bB4Zt8G4FqUaOmR+uT7hwPKdyLG+nDo9ammBpJel8obohhImlIXYzs3RrN6JZgCVrt+gU8YqfhM6GrN5o0Sg0VEw2GzYmDdeubRJbRzW1VMrtvCI0DhElznxnJabOtK25Hup0WevcaZFN13zTC8v+NEfaD9Jpp/N+6ZYbBBewdNG91M6d1V1Mut0st+5rp/OIlF5aNe1nZzZ2UilRvO3LOqWaDjlHtsDcoU4X60jpvEe3Y2m4tx1LdUq64/4+Jm1bTikdQJ0gnXUbhCrVP503q1Ji82i4yaSz8OpbeTGpLV1y7Ym1T1Zenlg7Yu264y2qSNBkD6hItGFpLG2ytC1uHqBpLW1y05j2NurNdmb7aJIBFmmtF2Uas53cMWqP9+ifO41usrNbSp3tejrjvpVbX3u8H0mfLSFC7Nm8pdTRBLN/0571m+5Jk32U7elSZO+TFnzpuHdpsr1ZuHXpsl1KHe3QylJHq78t8XmPiLyxN/wqVX3VivPep6oPpf73AvcNTLeR1tECr1TV/3LQgs/9w3GhcFyoQrjN//2KyF3ADwLPA94BfL6qPr7PdIFFlvt3qepn3a46FgqnzWnEZqFQWI1bjM9HVPXFQ4Ui8nPA0/cp+trlOqiKDGq0nquqD4jIBwC/ICK/q6pvG6tUeTguFHqcQtPQiWmmCoVzwYoxV2QVhcLZ5aTiU1U/ZahMRN4nIs9Q1YdE5BnAvvZcqvpA+n27iLwB+Ehg9OG4tFMVCgnTTblDd7fISzCtFOn3s291gYXCunHU2Fy3lLaFwlnkFOPz9cDLUv/LgB/dO4GI3Cki09R/D/AJwO8ftOBz/+a4vro7WBYnfnRerYfLx7TDbmPYb3evpml5oSfj2xjn88EyVx1wiEd8ouPjV49Yo6Mjk2HNsV59crBs+rbjuQmGo2X5OZOaqduGmHaWKpvgLvTG6kGrpH1NqZu7tM1RYe4Iks9RpZlVaDTNbAzO9LWY1tZ5xYn5/YpTuha0uNCwqusNs+elZB7Ipsa94c47ea9e+FD7YaEZXqynt97Y1+KKeTzntNV9HKimZVVpO5MftKssL3RoXOcLHYOzNNsCYe5sxUFs32U75b6P8irbFvfsC7V65XFapzo5iNnjWOxYayXgQUWJSfuNAC245OOsAq5d7I9VOGJsPuXJOlYhnVchWOrgEKCtkMa0yJY6uEJqj6scg7tbQWK0bm86aT3g5NqbWvomH13f8xzO3sfHqD3e68EcQHz6xaMSTaur/YvB2PJ0yQMajaYpzp7H/VTSneZ4oTG2Kh0hRXbfi7k33K+XabxtezQEcE3n0SxdeumerrqydOKktNlaJU/kFTml+Hwl8EMi8nLgncDnA4jIi4EvV9W/AXwI8F0iErGr2CtVdf0fjguFM8CZ1EwVCueCk82LUCgU1hRVfRT45H3GvxH4G6n/vwN/+rDLLg/HhULipLL8nJZmqlBYF9YxA1ehsC6sY3ye+4fj8DtvHSybfsgHjs6rm8NN+NVznz0836M3mQks5rvnrsGy9uFHRutzVKqn3TtYtvsB94zOG6fDzSZbV68PzzeSevtWcCP7jxHLOt0akbqsjJyGRjFrpl7JiGYK2FbVWU8z9c9vay1HUahj9wJQgbhJZ20Wa/A7DmnMQszNBK0UcYLbdei2J05zSmGgFfBKE4Rmp+pkCc1ORQNmU5aa6qVW8AGc4qpIDA6qZJMWUmpjB9K4RYrjKEgVFzZkXk1qELM2Q2zeJCfIp8So/VmSLUgrSUZisgF1Vn0/E/yu0FyyOrgGVAQmukj/7IBkPyfRZBdSB8QvZCTTaYsTZTavcGlcCI55mOAmgaoOhOAIc5/qLzCXzsoNZ2mdB9snVCCwsMfL8oecFjpP5my/2cHGWqCdNUVrtZCBdOEkmhdtkgxy+uhVb6inEptrgbaNyQRnN5eJ99asnn7Fe5PiZcuy1MROVYFzZv/l+/59gBfUVTCtIE5NapHsy/p2ZhLjcipq6FIv91NSCyynr4abUzHv+T2QTkbgltJCS7I2k0ndWZx1Vme1J1YO9T5JThY2Z+qkk2JJm+ztoiJt7H5pI9K2yDwgjVncSdOg8wZp226bpJNZyMHb09+Ofezn8rh97edEFl0nxxC7Dnmbp+uXZOM2ZFd3c8XWLj5PdGtE5IqIvFZE/kBE3ioiH7dqur9C4TSIyKG7W+SVwKeKyB8Bn5KGEZEXi8h3p2k+BHijiPw28IusqJk6iBKfhfPEUWLzGOLzVCixWThvrFt8nvSj/rcDP6WqHwx8OPBWFtZVLwJ+Pg0XCqdO9mo8bHdr69RHVfWTVfVFqvopqvpYGv/G9DEBqvrfVfVPq+qHp9/vOYbNhRKfhbPACneho8bmOfZGLrFZODesY3yemKxCRO4A/ifgSwFUdQ7MReQlnFC6v0LhVlm3pqEhSnwWzhslNktsFs4u6xafJ6k5fj7wMPC9IvLhwG8Cf4cVratE5BXAKwA22DpSBZr7hlMjA+zePaxhrZ42vM7pI1eGFzqSbtE37Wh9wtWrI/UZ1hU3HzicQjtsjp+wepOVTo8xy7qTwo3Y642k7Y6TWz+VTzE//GlwbPHp77oyvJZk3eZ3Ba1AWqh2IExsP/ttQb3pkiUmi6+5ECdJMyyS3iwmDWuyaXM+Zo8yS6vsLHWy6WSTj5sTVBziFLzYL4BTqmlLaOxcqyZmddXOPa6KOKemdc7rqiLaOrRJ9knZgkxNQ92lgA6CmwvthYgL4HadpYuukt52Q9GppdBuL6rpoSuFbFlVKbnqKmruTMKybd1Nx0FxyVJNnOJ9RFWIaVs1JMn2QmKILnnW7U+SXne6676jRKfFJpcJS9Z4/Xn3ngvKkcwpSmyewL0z61arCumlmO5SS09qSys8qVJ6aUecONPhVrJ0Xu09NGbZCISsx8VSTmddsWK/2QZOSVplNW1y/o3R5uulYKYN3e+SRdpiByzXJWuqR1IoD2mLY9bfCqatTtvc6Y0j9s1CVFxQxIvV16VrV7c/kpZYFVG1LPM5rTYgBPQQmaOlpxk2DbFLWuMBvXHWIve1xJK3TZJu3KVttOWol/Hngx7rGJ8n+ahfAR8F/B+q+pHADfY0A6nqnsvuUtmrVPXFqvrimlN4SCs8JVknzdQBHFt8+osXTryyhcK6aRpHKPfOwrlj3eLzJB+O3wO8R1V/LQ2/Fgv49yXLKsasqwqFwolS4rNwNig+x3spsVkonDInJqtQ1feKyLtF5INU9Q8xo+bfT92oddVhcFvDzUbhABuSOOzkRnNpuHm/2h3J4jYiq5g8ayj5WWJEVhGfMSKruDhcHzcfv/P43WGpx5hl3UkR7xh+C6kbw6fr/MqtvyFZR6/GIY43PqWzbevouxGlVnx1Ck6I3qQI6kA9Sy38XVO8pCbXxkEdrfkvLOQM6qTLEKcI4qxfsqVYaunPWdzQ1KaP1TO2ziQaQGjdop7AxrQhBkcMQj1t8T6yuz1ZLHPuU3Y3wTUQp2o2dSHtg8rs0tTbutVrkk8AtaJeEa/EYPXGW92kO/cUqWw7nTfZhLXKuqXEXTEKUT2h8WgrBPHMoti6W2fyhmTblre1s6gbO5rBstjljIdunvZZ7xhprWYPF3rt6tnrrh9D2eYtdbFWtErTVSxs7A6gxOYt3jv3yagmTpDJxGQVdQV1bVnSelZmWi+kBrFyRC9JbkAXgyq9/r7toWKSiiw/0F5/zFKK/ng7yYakFxIi0gSzS2sCtAFpGvvtZ3rde98Xse2qa5PmpW2Kk6qTUGQ5lkkOetuUF6FAa5KIPGyWbalebbJxC2Fh49b0JCAhwLxB2xZtmpRFL96cKW8FW7q+9R5VlYaT3V4+finjnSa5CJVLMomU+TBtq3byM5OvZYs6xGwoWVlWsX7xedI+x38beI2ITIC3A1+G3SJuSvdXKJwF1u2jggMo8Vk4N5TYLLFZOLusW3ye6MOxqr4Z2C+t7k3p/gqFU0fX76OCMUp8Fs4EB73KhhKbC0psFs4eaxif5z5DXqFwXCic6Q8ECoWnKiU2C4WzyzrG57l/OHYjX8pX1+aj88o9I9rhkRaC7fuGLeBcM/wWpHpieD6A6u67B8vakVTXYy9e/M64fdzkwSeG1/nEk6PzngRZ07Uv8+Ft8bPjOZXX7b/f24ZdHTuyFlWdnZ9hknS3QNjKemMlTkECnU5VYtYhCrQkfbFpdIlJdywKwQI0H64QIMz73wkk3XFn37YY7+uIrwPtPGVdrQIIhMbj/bLmL0ZZ6DSzP0DSSyNK1KTNq9X009g6tUryaAd4Op0xbqGLlqTDFR+pJoF27tEo+DoiLtr2iKWODjNPDI6Z1HgfCa0ntkkf2TrTCIsjJv20RrPGy9eGvb+jjIVAlpZnHXHev1nOnR2ibrJxEwiC3xFCmlZaWWiWV6DE5hE5KHVytv3qa3WjmnVacIhERARHQNQRI4g3a0bNOlXVTqPbfUuAWXz1KnJz3foWgTF3uvSb7eBcUKSJuCb29McT0x+HFLd7NbJpm7RyKf116nz+TXrbPHm2lcPWS1ePpIPOdnTB0j5nDbRpi3v64rZFm9a+dQjJci6lidYQVtIWH4lo60PE9omI2cQBomLfaCR9sQTpbC/zvgCSNpkuTfbqq16v+Dz3D8eFwnGxjh8VFArrQInNQuHsso7xWR6OC4Ue6xbghcKZZ8WYK7FZKJxd1i0+y8NxoZBYxyw/hcI6UGKzUDi7rGN8nv+H482NwSKtxq1FZpeHy+d3DM9X7QyXTZ8YFvU1d4578fr3Dm9L9ci1wTJph3XXoxreA6he+PzBsvaP3nbk5Y4hD494K+/sDha1H/eiE6hNYWW6NMLJA7QV8++NLLxsk0610/Pn0a2Yjg86v2CZKNI6pIFQm/emNG6xXG95kaUOi7TQYn6+roqWVtkr7cwjznTFqkIMDl8HXE8Uq0mLl3HONL+iDl9FKh9p0zrFRVrte4SadljnLukLZVmX7JKHcfJo1irtqyidTpfoaKJ0+yk0DhFBgyNER5iBtjZ9O3e0Sdeb9b6uDkQxoWS90RIa0x67KhKDQ2ce8Woex1W03yGtr5h2Ok6SLzWgm9G8jV0ap2Le07rYxs7XNgjVNU/YjNa/LaY3ry1VuJ9DnErv2B/yPCscmk5vnFIIi/dJbL8npXLPK7fzOK49Wnli7ayrBK2FWKWuFmKFdUmHHLN3ee8U6z83Sfo+IXseLzyRSZpewYWe/ri1X9cqrnFIUFxj/sKd/rhdTjltWuGkBVZFGkx/601bnVMmd2mUodPi9sdZgemN8zcVkodTZ97IFVJ5YJKuAzHVRSEGS3+d9MfEmDTIcalfszZ5TI+skdi0SfccoGmTx7QgWTfustm02PEWZ9vd+R8nP+Sku8bbcY4T68SZ1lidEOr1euA9DOf/4bhQOEbW7YvbQuHMs+L/7yU2C4Wzy7rFZ3k4LhQyun66qUJhLSixWSicXdYwPs//w/FIU/tBTG6MNF/IsOSi3Rye7fr9wyfI7pVxK7enPzgsjxhjdu+wHMPvjlvG+OnwKeCvz45Un4Nwk+H9EJ8xbGcn7fC2+PmtW+Os4xe3tw2h8+9SYXFlydIDleRtZtZt6uis0dQvLN+0AlBbhtfl5lmn4O1Xsyyg8dbUn23enKJzb2mWFaS25tA2Vp0UIcxNcgCptVSks4bzPnLX1jYxOprWc/elG2xUDQ/GO6irQF0FHpt7oppUQfJyGvMxk2jSDiK4JtldCSa3qM2WTaMglaKtmCTE68JiKe8TsvTA5qFKPmkuTauC8xEEqjrQRMFVSl233c4XrzgiQdLOzcuUdJB6qaiXLLVawc0FrdMqZ4JEIU7ULosqRCe2b9t8HFO6bB9pL2EptBXmk9xUnSyidtJ0ycpNV7wDldg8OhoVcRENZuelUcE7RHXpFKAnwZM90gKXykUdUV0ngXCtmJzCWxzH9HtTSmnJ4+jGqTM5xrI9oLDckrAYdhFck7pWcQ34eZZbpK61VM6uiUjbs1rLqaGd6yQV6nK66H7KZDtfNadPJltLJjlFX1oRb05xnafrUkiHPVZvMaW9FgGJaNvacVC7RmrYc/B6zyGSJTHeI0kKI3VPKuEduCSPcc5kEylldLayg2X5yF4rt1i5hX2bKm5vfYbOMdYvPs//w3GhcIysW4AXCutCic1C4eyybvFZHo4LhcQ6fnFbKJx5Vgi5EpuFwtllHeOzPBwXCj10zQK8UFgXSmwWCmeXdYvP8/9wHIa1phKPrkOtR/TI9Y3h+XbvGtYqH6Svu/6iOwfLYjV84s3uGF5nvT2+D6aPD8+79d6rg2VSjaTebpvRdcb5cFrv6v3D6wzPHNYj7941XJ/DsG5f3N5W9upXk85wdI92er5Ff55fJeuRk+7WLVLUSr3H4iCnZ+6WISk9alp+48CraZuDIziP1Ck2WkwT7COzWPOQ3kHbOjQ6Hru+hYgyn1e0rWfXmd4328dZOmssNbaqpa+tFY1Zf9lLGe2TrZtTs1kTh4jV21eR0GQtsWlEY+ss1bWPtK0jBsdk2uJ9pGn9QjOsoMGhLtAGT4yC5i7fsFSSqDsN+mSdl3djf1mdlVsqqkFROw5Z/zl3xKQblibpNL1pL91MaC9Hs+VrTScdvS07ToB07DTprlelxOYxoorGaHrXEBYa45C81Vw0q7No1mmavhvQrK2NSZeM4rAHI03pnrvvAfbYkHTPTnv1x5J/ZUmTvHe6bB0Ya9OvhxraTVmkmc52cJ1FnHbfONy8TFmsO9Vpv7rmZdmylUWK66S5Doq0umwv16ZU0yr2LYRI+nwg2v5t2866jT3PKOL9zbrjZO2mIenHQ0Cb1qadJRu3rEX23mzakl1bHs/Ed5ZteEF9tuVzSTMuaJX04y59H+DNnm9V1i0+x42AC4VCoVAoFAqFpxDn/81xoXBM6Bra0RQKZx7RAycpsVkonF3WMT7Lw3Gh0GPddFOFwrpQYrNQOLusW3ye+4djnQ178bob4z69N55+abBsLH305InhMjcit9UDRCw6ou+5/AfDaZWbe4b9keeXxw+xjmiZr334fYNlW/deHl7or/3O6DqPirs+4mmtW8ewhvX74va2Icv9mvWGkvSIfs+0aXo/F6obMKvMI9jvCK6FuQNpUv/dahrIRnAzZ3rZ2oR5LqWPFlGcU0JwuJ4XcDv3nT5YxOR72kvV3FUoee8uXeB7bzRFlMmkZVq3XAdC66jrwIXNGTuzmtnMTIHD3MPMIVUSUot5MDNz1rsRkQhRq6TvVaJAkOQhLEoIpBy8EBtP0+mChZ2d5Ncs2ksfHTu/5U5v7ZSNrTkXpnMee+Ii4iKTieVqns8rXNJnz29MkmY5eS+HpBMeQPO6wTTgSUtsms6UWloFgo13jX0vIaK4xvyT4yQJ0SO4lTXHJTaPjEY0OiAstOfBfHNVNelcLQ0z3lla4rZFdLrwB04+1XHiCFMhTBxxIoSJpXePtWmBY2W/N8lP+z7a+TuDrOXN6aT70+1zqM1DnIUYNJ1DKXBxYeF/7Nqe/3EbkZC/R+h5E+dxadvM41i6fnXS6aHJ/Xl1PV9knVhB27sFydK60nDQpGHOPswp/fW8NT/mWQtNC01juuQ2pZNuW7Rp0bYxj+rB46yoRiSKaZlb8itd05bX2Z/ZUka76FAF8WKpwUnW1iK2q9ye6/Yo6xef5/7huFA4Ttbtv99C4cyzYsyV2CwUzi7rFp/l4bhQSKxjlp9CYR0osVkonF3WMT7P/cOx3HfvYFlz13g65mpnuGx7WFHA7K5he7TJ1RFbtSdHqzP6AmX2jGEJyPX7R2zVDmoWGWml8cOOa7gnhyUOK2ac3H/e975/uD46fKzr6yM6mFVJblyFIxCw5jyw5vR5Sp3srDldAtYcmlNH59k2zJ4tTrWzEZMAcWIWUbRiTaet4HYd0ghxCrptVmZxE5NEREEbB05x04CImg1akl9UVTSLMxWqKuCc2bOJmJzCiRKj4JxyYXPG9Z0pMUk06irQtmaR1rSe0HhiKzQqXFMhBiHMUs7cmbNm1yBIYyl6cdauqh6kipY+2kWbxmGyj6U2ZZaajiWn5VarX97HfhIQgcmkZTar8FXkjq1dbswmNK1nY9J2UhJEaFuP99bEHnITdZSUalu69N5kC7pEzP25GVvTsQ6yqHIaJzEda7V+++1b7PU2M0swVqHE5pER37P0GrH9oq6h8pZKuPbopDLLrzp1laC12G+VrAqzzdqeLlZ0aeFzamlNVorZlg32WAmSpBZRlizTsgTDtXZt6Lpov65NtnKinRQgBpPsyERxIadNzjtkIYsAulTW1snScJZ3qAyfp6KLmMzb1NUtmMxDglr25iZJLWKygGtNXiFNMEnFfA5NgzatySSaprN906gmkQkAwSxT96SXRpz95mPtTEKBd5Zyuq6RdJypzNotd7F2i+Odj/WqVm5rGJ/n/uG4UDhO1s2rsVBYF0psFgpnl3WLz+JzXCgUCoVCoVAoJMqb40IhYR8Yr9d/v4XCOlBis1A4u6xjfJ7/h+N6eBOkGU+dPHYsNx4dmfGR4Rfu7YjMOU5GqzOqD95+2rCuuLk4vCGTJ8eFQJuPtINl1c6Ievg9D40u96iMpp6uhnfQ5oPXj2Ht62dHc9vQnv4UlrWlJP1el/7ZdHedrq9ndySBZLNm+sSwoVBZFzcj1GLtXdN0bmbbNgcysXE5bXKc+2R3JrRBkvYXGmpyGmcwza/pgJWqDoRo8S2ieBdxsrB5C9E0xhocISqxTbZkwaHJjkpzGmuvi/TRlUIVbZ1R8HUkpjS7vg5UVaSZV2gEV0WcV7OhExAXCY1f6H3T8jU6VMy+LiZd5Tx42uAIrWN7d0Ls7YeglWmeg4OQtMZROt2nhtS/NymH7DPQm1das8LLoRM20/ESNRu3ZFnHRAkXsFTbAGI68NU42dgUkU8Dvh3wwHer6itPbGW3GxO2p14HzlmacyfWbyfZ8jw5TfRe+nraPItq0paDtKYBVuj0x50dGj1rtKXxvWEWw90qpffbu170qyop5bFEczHLdZT++e0W67R+6WmKB3Zdtl4LupQ2erH8PF671NWktNKuMQs5ac0mT5qAtBHaiDRm1WappAPMG7RtTWPctgvLNo1L/Tcfj8U4DSDergsiutAFxMgiNbhpmWkdVJV9F+EceIeTdG3NqcRFRrXWe/bU2t07z//DcaFwjKzbRwWFwrpwUrEpIh74TuBTgfcAvyEir1fV3z+ZNRYK68e63TsHH45F5KPGZlTVNx1/dQqF02XdmoYKhTPPCumj4URj82OAP1bVtwOIyA8ALwHKw3GhsCLrdu8ce3P8v42UKfBJx1yXIzH22t/NRprogSt/POxV9tgHD2sg2pFkbNPHh8vq6+M3gTAZ3pZ2a7hsLPNeuzl+ws4vD0sVNh68MVgWrl0bXe5RqZ733OHC6Yi05MrGLa9bdf0C/DTIieE6hyPBGqsFa353Jn3AK9ErsRaoo0kh6iRJqEx34Xcdce7QqPhth4QktZjbpcukFhFtzToNp7BhTY1Sxy5TnpuadRqAT+PbxnfZ5nwVCa0jtJ5r1zZN8qDQ7Fad7EKT3ILckhkdMbkpddvqk4QiAi52VnGIyT9Qk2v0W0g1OmLUxTJUunJfByofaFP2v42NBu8i86bCuYgkG7rZdo2rbQFOrJm7bR2xdSaXALRNllYpC97eM12ypVuywcrXFWltX3RyCFKz9DSaPCPby3mbRpq0ZLGm52yJJa0gQYi5/TmavGMVTjg2nwm8uzf8HuBjT2plp0avuVzEdVZmgDXPq4PQ8xyYgTTBslKKgE/N7C7ZudWO6NNvJUQPMVu9+ZQtz/ds3/rWbz3yYe3+R0rypL6NWyfn6NkBLjLt9e6rvWXHanEe7ifnWPrNs++9RWeLwm5dkO0JXQAJkiQUYnZtQZOVIWbX1kRcEywL3jxlwJvNb86AFwKEQGza/eUTXQXNqk28twuPd9bv9vb7nnwmWfZ5hzpn0okkp1Fv0hrLCuhuzhC4opXbad07ReTzgK8HPgT4GFV948B0h5ZNDT4cq+onHqm2yxV6B3ANs75tVfXFInIX8IPA84B3AJ+vqiOPlIXC7WPddFNjlPgsnCduITbvEZH+TfNVqvqqY6jSiVFis3DeOKV75+8BnwN819AER5VNHWjlJiJbIvIPReRVafhFIvIZh6j8J6rqR6jqi9PwVwM/r6ovAn4+DRcKZwLVw3fnnBKfhVPmMG+njhSfj6jqi3vd3gfjB4Bn94aflcadNiU2C+eGW4jPW1invlVV//CAyTrZlKrOgSybGmUVn+PvBebAx6fhB4BvWmG+IV4CvDr1vxr47FtYVqFwrhGRzxORt4hIFJEXj0z3aSLyhyLyxyJykjfFEp+Fpxq/AbxIRJ4vIhPgpcDrT7lO+1Fis7CO3CMib+x1rzjm5e8nm3rmQTOt4lbxAlX9AhH5QgBV3RZZ2d9DgZ8Ry3/6Xek/9vtUNfuAvRfYN1Fz2kGvANhgROQ7polx48/+7YVhve3k2vC/NFuPDJfNL46s84C9dvkdIymZp8N1jdORlNVPjOSABqqrwzm05fpwfcZN8o5OfN9w+mj33GcNl7XH8wr3FHRTJ9YstALHEp/+riv7LFksrWpPf6yyTwgkHZ9G07VKa1No0iiHTUUnEfVK2MJOPA9aZV2xrUAmCpOYBIQKCDr3yVJKaXcqs1RzSkg64kWKZiWomPa4jmxszpnPK2IUptOGykdubE/xvgWB+Y2JaZwF0xcL0EjaZtA2b5cs9LauVzUExNJI264yPbEGMUs5VdQpsXU0QWikMvu1CDdmVdqhdDvTpZTQ852aGIWYtMbixXTWScNsaWuFsZTxiIIXoltMpP300cnuzQWIc9MQu0bQynTUEgRi0iOrbX93/JO1Xbc8H5FV09NycrGpqq2I/C3gpzFN4r9T1becyMoOUS1O4t7Zt3ETWQz3dKtaOdR7qEa0qFX/N+t3zUrNBTsXXCuo9M4dWLJvG7Vu66bpXTSUhaVaz0bNtTdbqhHU+nua5Jus28Ss5ZZ0xulVZlftaN8P2HIVCXYyS9S0jmjDaTwx2vRtsP4QLZd1iKYzjjHpi+OiXxVC0h8fREofne3dxEm6pJgWWfupwvdqkCuPZP1x5c36Do8SEZzV14tZNOb9c4g0cbcQn4/0WkduQkR+Dnj6PkVfq6o/etSVHsQqD8dzEdkkX9ZFXgDMVlz+n1fVB0TkacDPisgf9AtVVVPw30S6GLwK4LLcdf4brwtnHkVu+8Oxqr4V4ID/N0/qa/pjic/pc59d4rNwopx0bKrqTwI/eWIrODzl3lk4N5xkfKrqp9ziIo4km1rl/4KvA34KeLaIvAbTOn3lKjVS1QfS7/uB12E3+feJyDMA0u/wq8JC4TajR+g4o81CB1His3A2WO357SixeV6fDEtsFs4bZzg+jySbOvDhWFV/Fmv2/VLg+4EXq+obDppPRC6IyKXcD/wlrAn59cDL0mQvA07stXihcCjUmoYO23HABz8i8nMi8nv7dAd+FHBSlPgsnCuOGJvn0ZqxxGbh3HFK8Skif01E3gN8HPATIvLTafz9IvKTYLIpIMum3gr80CqyqVUz5G0Aj6fpP1REUNVfPmCe+4DXpebiCvhPqvpTIvIbwA+JyMuBdwKfv2Id9iVcGva3bS8O++IC1NeHUyc/+ezpYNn1Z414K49IfC+8d7Q6o+ms263h/2NmdwzrkUM9vB0AfmQfbTw8sv/eMbrYI+OeM/xCdPdZdwyWrerHeCAn8K/saTULHcDxxWfSDUtImr5ocRBTulhpzNMYSGlpTZuoHrQ2TXHOXqvJo9cW62zeIEgU/LYDhbAZcXNvVrnJ55jGwdzZejaSds8r4qNpFSfBNL4Ck2lLVQWaxi5/VRXM97j1eB/ZmDQ0jYmeQ3T4lKI6tH6xDTFp/VJaammtbuZvrEgUaMXSufqcnppOLyyi6H45cqHTIgNJr5wW7MBVirhIDA5fmY+y95HdbcHXgYsXZuzMatp5hfPJu7lxSymyRy80avV2jXTaYDdPx6OyanSN+RM1o7GkZc6psrVWqDXpraOVObW0133duaSTZ1XO62vgw3Os905xYvrTukaqKmlO029dm/609qY1rj3qHbFOOuMs9+ri0w7gkt7YLXTH6s3bOFapv0r9ldlaa9XTFO+jQ+6uJanL55sE07lrm3yFAcki/vRtQ9YLq2B6+yyvTx87mGa6V1+Xt2XvDuv1Kwuf5axf7vV3+uZuOGmUQ7T00U1OG91a6uimReaNpYzOqaObFpXkRR6Hn0nSwUQ6XfEej+OqsuNaV4PHNXsbL/kY7/F6PjKnEJ+q+jqsZWXv+AeBT+8NH1o2deDDsYh8C/AFwFtYfIelwOjDcdJHfvg+4x8FPvkwlSwUbhdn9E1T1yyEPRS/FPiiW1lgic/CmWHFj37OaGweOyU2C+eRdYvPVd4cfzbwQaq66kd4hcK55Xb7FovIXwP+DXAv1iz0ZlX9yyJyP5bJ59PP6Nf0hcJtZQ08xQuFtWXd4nOVh+O3AzWrO1TcVtzucIpoNxmWGwDMrwzLBrYeGTYrq3aHX3XsPG14fTfuH//Pqt4elkDsXjmEp0p/mdfH7WEmD28PlrmHHx8suxUrN7e5Obzci8MyGdcMr/XGvcPpvs8yJ9ksdFsRrEldQLCUsdbMbuPpWSZ1zfKaJBfRJRs2rD8CyaItbsROdtFW1nypAiqxW69oaravo1mHNc4sy1ohbgYkCDp3XZP//EbFrLLpUWi9R4NDXESngRBNhuAxiUXlI7N5Bd7kF2HubZvypnvtrJVwilTJUi6dkiKKn0TqSUuT0l7Xk5aoQgyCryLeRWa7E2IwGUVVB9q5BzGrthA8Gh0aA2ASjnbuTQ6CRxtHG4Vr0RGDoMERcEnCYhIQUczaLaZ21MhSc3a3P5NVFj37uS4duOTmc+3SSefFkezcXCPElCpaGmfHxCvsmpwmbi7iWFZMH104OhrVbBVD6JrvrUC7X1GF6JCgaO2RqKh3iyZ4xGI5Syr6tyPVlGLZUrVLUFxLZ2Oo0ut31kVvy7N+uv4upXMeX/dkFz35loQstxBcWJQt0kzrkhwCesvufiWd77o8b88SzrUs0kK3imvtV0K0/hA7SzdpY7JvU7NySymh6WzdAtq2ZuMWApps4KxfuSltdNKaiZNuuJNT1PWylMK5hVSmMikFLtnyVW5ZKnOTJZ8d0+jTsfUs5Cfjj1BrzSoPx9vAm0Xk5+k9IKvqV5xYrQqFU8Bka+VmXSicNUpsFgpnl3WMz1Uejl/P2cwWVCgcL8oxfZlQKBRWZpVmqBKbhcLZZQ3j88CHY1V99e2oSKFwFlg33VShsC6U2CwUzi7rFp+ruFV8AvD1wHPT9AKoqn7AyVZtNdwDDw+WTR4fSTsNhI1hgfCTzxnWI4cRd7TJ1eGyenv87JndMfyf1+zK8HxxxHEtTMe1uBfuGD4FLl+9NjrvkRnJBrf7tGE9Mm54vlifXSu3tUfo0gcjoKo9m6TUD+SUx9qzE5NWcHMhqulopTVdalDF7wrVtjC/ImitVNsOaYW4EYk+uYRtBah6y1Ngajp7V/W0rZe1S1HtqogIhDZp+gSkbojJ9uyJx7esiVBhvj2hOymC67YRsDSrbbKpy9vUt2GLSWgtEIPQtq67gYTgiDl9tAreKc5HwOHrgHeRWAmTScvGpGVnVhOC4+LWjIkP3JhNCNHma1rPbuPxk0BdB0JwtHOrtkZZtsrqo73xvfNeWsHvCgHAgU9aYdSOnSgEj2mrozOLN8VSegtEcTYd6YbpzdZNyHpP7TSkqzpV7K1jYXUsRXFExSFujorr0kd3qYZd6s/jnFtYvjmHVimdtHNQOeLEE+v0Wwlxkmzb6oV9W/RJv+pNP5y1rMMVTfEbwTWL4SXtcO5Y/nahryWONeTU0H2t8376Zzv/ZXFudRpl2aNBJqWsXvz6xrTVbm5aZN8o0kT8POLmEZkHs3Gbt0jTwnyONB7mTRd6GqNpiZ2lhe7YR28M2HGqa2RSm11bvbBv07pn3ZY0xvabdMZV3ic9+7aUIlo9XVnWhe9rczd6oh1i2nPAKrKK7wH+HvCbmKtlobCmnM+kAYXC+lNis1A4u6xffK7ycPyEqv7XE69JoXAWWLP/fguFtaHEZqFwdlmz+Fzl4fgXReRfAD/CslvFm06sVocgPHZ1sEyeNeKrBrj58JcgFx4aLtu+b7itobkwsr5m/D+rp/3asHXa/O5hiYiE4bpO3vnI6DqpRzQZ7ck0FOh82H7P7w5vS9ga9pXxs2OITF2/L25vD0lW0JPLaLY6S+2HfkcI4pAg+B0h1orWIPOcvcoyT6lXs/dySthS4kTQqVm0hU3LtmYyCmsGlGBvLKQVJGd1ixUoRAW2QmdfJgrUkdg6/GRxnjkfUTW7scmk5d7L13nk2gXa4Llja4dp3fLIkxdtWhfZfmITZq6Tk0gVkVk6N51aRrgsr1CxTF4+IILVBWvGJFm5aRDm2zUEmy+E2vaDQHNtwg1sH4rAY9cn3XJz1kFxZkkXZhUahdi6haSjb8O2l65ZmWV5Q2pm7bKYdV3Pvi4KMZpNnLRpITlDYs8yS6pFG7jWydIvWftZNsUV47bE5tHR3GQfTFKRsMx5Jp0Q7y1UfTqPRawpXwS8Ay+oc71m+yTLyNnqouKcwCxZgiW7MMuUJ501m2a7sNyU73pdbuqHzkJsYTtncgbf2K/JGMA1al2yWlvYsOVhRcLiHDO5gHSSgnwOmi2cXTQkxtQfLdtdZ7mWLfHScOzdq/aexxqXpwsBQkTz+L6Vm8aFFWR/fkBjPl7BjlcIaNPYvoc0v3XSrauCOim9agDXffMqTmxdWUIhts805P2S47W3v1ZhDeNzlYfjj02/L+6NU+CTjr86hcIps2b//RYKa0OJzULh7LJm8bmKW8Un3o6KFAqFQuEpyHq9cCoUCmvAKm+OEZG/CnwY0KUvU9VvPKlKFQqnR7lTFwpnkxKbhcLZZb3icxUrt38LbAGfCHw38LnAr59wvVbGXRwW+e48bdzK7cYzhjd/zFZt+uRw+8HdbxnOsh2m47kY28vDHnFhOiz+GUuD/ciHP3t8nSPOadXOcNm9b75vsEz+z98aXad/4fMGy558xrD1nIzoE4+aXvsm1qxp6PaQLMz6dmBZW5t0wW4mhA01vWBj8wS30OGHTdOxgo0Ljk7jqjkFdQ4fp1Cbik7qiHOaJJKRGBxVHXBOmU4art/YwFeRzemcNnjm8wpxSjv3xMYWGHYqsyVD2Nmuede1KflC/8iOXSM0um77NEin0ZUI2jpETGtMY1pLSemrc/ps3a1p2wlxKyBRaK/XtrxqYTFnabKTHjcIVBGpk67YK+KsQwWNUG+0CLAxbdjemVBVgbsubnN9NmU2r7jr4jYXJzPe/v578D4yTamwr21voFFork+gMaGn23VdSmg3E/wM4sQ04LGSdMxMw6hAnEakUpSIVgs9NwLMnemrnUIrnS2UNA5pLKU36bzgMOmjS2weDXHLKYhd0hOnNMSdLVjlO3swrT06qfaxbJPFr19oh/v9nU51wD4w26H5XcWFpCEOirR0aZsl64AjSeubrv97zgEZMNdVEbRn79lpjR1JL73QPneWc26hj+7GOyDrpVP/UprrgdM327+5NqegNp20DSt+V/GzZPs2C7jdgJs1yG6zSDut6cOJnq4YEbNvm0zQaZWOUUWcOLR2xMrStWuVjo9f1n33bR3727CcNnp5W1dmzeJzlU3/eFX9X4DHVfUbgI8DPvBkq1UonBJ6hK5QKJw8R4nNEp+Fwu1hzeJzFVlFfn+4LSL3A48Czzi5KhUKp4Qy/CqgUCicDKvcIEtsFgpnlzWMz1Uejn9cRK4A/wJ4E7YbvvskK1UonBbrlgKzUFgXSmwWCmeXdYvPVdwq/knq/c8i8uPAhqo+cbLVWh19/v2DZdtPH9+85uLwfzrVsHR4VI/82AdvDJZdfLAdrc/mm94zWFZfuWO47OmXB8uqnfH00fNLw8qasX8E42R4vnFlNWg1PK+bD0eYjASfHJcl85oF+G1B6dI+q5h22O8IsVLUg58Jrl3o11RsHkv1atNQ5/TTSsSZfrVWS0Oc0g3LdKHP7XRzwaFtWp5W4JR50hLPfI2I0rQVofHZOpRqGtjYnDP3FSLmc+xEaVtPXbfcc+kGDz95kbb1bG3O2KxbHru+ZdvplNkTG8nTN+mDncLMJ52h+bwSwLULHaa0krxUs8gP0yin1LgESZ7NtgwJguKSSbHpj9Xl7bTtmKcAbVtHDI7Qeh6JrktNfX02wTvb6JhSVIeY09JqCqi8I3vHU3QpjaxWimoeXvZB7aYn+TljulDtmk0F0cVxy/tIpbf9hzjPCodHvEdq8zLOnsZ4Z/ri6QQmtWlXN7LG2BOmrtMXqxdinVJC99JCL2lu9+hY++fH8H1k6STq0kN3aZq7VM2mTXat+dm7VnHziEvpmq0/QIxIG+03aOcnTFRLl+1MZ63OfJstLbYNa2UptdUvupi8mul0yHt+PZ1f8k1aXuilvlaW0lBH2wYJqevHgHeLp8yYFpC1xzHaNuT033nPqWmyNebvciyuHJquJwuP6aW00Vlz7EBVif002qTew7wMXrP4XNWt4uOB5+XpRQRV/b4TrFehcDqsWdNQobA2lNgsFM4uaxafq7hV/AfgBcCbgfx+ToHycFwoFAqFW2S9bqqFQuH8s8qb4xcDH6p6NhUlO8+6OFg2H5FNANx45nCZG85wzKV3Dpf1U1XuZfve8d0tH/sBg2XX7x+etx1xrHPz0VXiR+QjF94/LAOprg/voINOlNkzLg2W7dwzIrkYUaVsPXw8uoox6UZhGAmCNCkjaxDq6xCmQpimcyzblrVCnJrtmXprApTAwgqudUlSgFmmKUitOJ9swhQUYbLZgIATRYFJFdicNOw2FU3rcU4JwbF7Y4KvF+mhc6rmufbtD/N1Qmlnnve2nrbxoLAjE3ZmE9qZt/SoufkyW7cFQepodm2CbdNGsO2srPlVvMlDskREVSxldbKuM4s2W66qdDISjYLztu0xpaSuarNBi0FsX6cTNswlWaulLYnCbF5zw0dC6xCn7M4rVIXQeGIQ3JNV97KnuiHghPZixM3Nyq25iG1XbsLVtH2Q0lsr4tOxRcErropUF+e0c29uYVUgBIemNN/xRrWQZrSHs18ssXkERExSMZksLNvqCurarMCmNXFaEaeeMPUWsxNH2BDCBLNtq0yxFD2L/gqz+uo1zXe/LCQRWSbhApaKOSzKsuxg77jl+oN6IXgINbRTEBUkWSuK1kuyhH1fXmbpQM+ibcmebY805Cay3KMnaeokEz2phMT+NpltW9ef5BOS5BRuHnBNRJqINAFpW2QeoGnTBSZJJ6Syiyp0khDdqIjTmjhx6bg5wmSPvV7XydI2SpJxLVnlpWPkoy7JZPpSkVVYt/hc5eH494CnAw+dcF0KhdPljFvLFApPWUpsFgpnlzWMz8GHYxH5MWxzLwG/LyK/DnTvGVX1s06+eoXC7WTE1b1QKJwQq9xVS2wWCmeX9YvPsTfH33rbalEonBXW7L/fQmFtKLFZKJxd1iw+Bx+OVfWXAETk+cBDqrqbhjeB4dzBt5kb9w0/38dxF7NOP7cf8/uGNbWPXhle5x3/Y1hHd/md41ZuY7h2uK46IgyaD7u82bwjvmvzO4a38/JkWOi89Wvj66xuDO8HCcMHLQ5nyebJ5x5kILciaxbgt4WsK2wsLau0UN1IRQ5cTkVcR3QuhA3TwquzcdIKWpkPnDl8qWlaW4fsOPSiWZHpPOmR69hphv004H1kNq+YNRUahcmkpa4Cd17Y5jG3ReUjV7Z2mLcV2/Oay5u7OFHe/8QlnIt4p2zUDdd2Noidrtg0vvNtjyR9sDbOdMbQ2VVJmjbroVFJ+umkRybp+EK6LkwiKMSQfKBcsrBTOms4zZZK2ektuk77F9Jy+vrjygfixPTJm9OGpvW03nH3pRs8/cI1tncnOKfcffEGUYXHncXujdbBrmmr48Rs9WKlXZpZSXZzOf2t+KQtV4gOmAiaLOiMSIye+W4F3izdwq7v9pe2gps5dDOJM50u9ueK51nhcAjJym1Sw2QCG1N0UqMblno4bFSmWd1w9jsV2vStQJz0tKvJCixbgpnFn91DtRtncYtnIUBdsnszW0JLG57OpSCWNjrYeeaalG65Sf0p3byNT+mms8a30ypr53jYpYmWnnVZz/bMrCZTeEmvflmXDJ2WutPq7tXf7nlJKtnzrHd+SrIpdHtt6bIl3Vzxs9TtRkslvdviZm1nR2dps5MvYojQBkQiOHAiQE+PjLPLR9KDqxdzSUw2i3lbYpW3o2c/l7ddWEohnXXZK7Nm8bnKFxE/zLIbZUjjCoX1Q4/QFQqFk+cosVnis1C4PaxZfK7yQV6lqp3ngarOReSAd7KFwjkkv/krFApnixKbhcLZZQ3jc5U3xw+LSPfxnYi8BHjk5KpUKBQKhUKhUCicDqu8Of5y4DUi8h1p+D3Al6y6AhHxwBuBB1T1M5KG+QeAu4HfBL6k/2Z6YBm46f5pmWdXhufbefr4O/sxva1/ZFjgGi4Pe+o+8WHD+VB37xnf3U//lWGdc5gM/1fmR/ZefWN0lcupK/cwplce85C+OHCsMteeOVw+u3NkxpHDGUa8ng/Dunk1jnEcsdktq01+xc50dn4OYQIyMb1gJ0HUNG2bRLpeiZNoXsGAbCgaHSKK22oXkjqAadu9ncipoMPcE/CdxhevNDsWZ0+4LVyl7Ea4fn3DtNEOZk3FpAqWtTk6vAvsNpZq+tLmjOdcvsq7nrzCvKm4+8INLk1mvPPqnYTocKJce3zLtLokzeUkIrMk2vPa6W11ahstXsFFS9mcz6+sUc790Pkeg3kfqwraupSKOW131vc6Jc6tDu0kIAJtC9s66XTJTfBEFZxTnCi1CzTR4/LBmDkTyan5GksQtEraz7yrKyXWdmxVsFTSmFbcuQiVM+1wqtPi8wc1PWTSTiO2H+JmRCbp4PmwtM0H8VSKTTim+BRBplOYTk1vvDkxj9zNirDhaTeddRtC2BDaDQhTCJtJu9rpT3XZy9jtORgqlvY8CrSK9gJXOs3uQoeMS7Ej8WYNb6SXWl0W/sGt4OaSNMiCm/f0yGHAdziy0CPvd6ql7yVytWCf80z3jO+X9zXL/d+BZalL387kFPJgO0MAV6GVM81xt86kOca2DS+EqTd/4w1HmKRjNyH5HPc04nk/L6W63lPvfr/bZ1sOYUW+bvF54Kar6ttU9c8BH4olA/l4VX3bIdbxd4C39oa/BfhXqvpC4HHg5YepcKFwoqyRZmoFSmwWTp9Vn4/XTNO4AiU+C+eHNYvPlf8vUNXrqnr9MAsXkWcBfxX47jQswCcBr02TvBr47MMss1Ao3DolNguFs0uJz0LhdFlFVnErfBvwlVgiEbDmoKuqmr283gPsm8RZRF4BvAJgw1/CPf3efVdwmNf+e4l3juSIvj6subjwx8O7baxpoRnOdA3AI396WMpx47nDUo7qnt3BMjdiVwd0TdD7cnX4u8uth4df98gHPX90ndv3Dh+0Gy8Ytnkbs96bvG/E5+0QrFvT0AjfxhFjE5bjs7pypzW7e2t2R80KSmtrQgwbVia5mc5jTYWiZnfWmKUZKsRozftMIhGHq0yOIKL4yizVQnBsbDW2PAVVYVK3XJzOuTGfWPpoUdrg2N2e4JJNnKsslfLs+oRZlO7VwCxLi5yyc23K449f6LZzZ7c2eUMQYrB2Rw0CVTSruVbALazWpBU0aQlk7mx/eNtOVcFPgklCxGQU4iw9tPMLyYU4M2eMUfA+dqmwFah8xIkSorAxaU0KMpnx6I0LbNQNz7njKo/ubHFtPuXuLbNua4Oj8pFHty8w8YGm9TTzCpk5iylRYj3QlBoXTdfqkg2XApXgKiXMk25CwFURV0U2NxpmTYUTZToxa7kYHZO6ZaNueWJ7w/aFi1Q+8t4VT9inUGzCcd073UW4sAmbG8SNCXGzJm5Yquh209JEm3WbEGp6tmy5X2/qx+uShGbJqi3JhcyuLVm1xZ5tW5BFKukkacjyB3oSiKHm/z5xsrBr7UsIbFgWlmy57vm3iuAVrRSpFHEm7bA07natcWn7cnp27Wsl0nmoKl1a9779IypoK0hj1wdpsk2d2db5udlb+kbwM8HNlWrmzNZtHpFWwckeiYMNx8qOVbshxIml044TOuu9WOdtxKz1KoVKF/fOKJ1FI8qydCVKZzdnx8rkVauybvE5+mgpIk5EPv4oCxaRzwDer6q/eZT5VfVVqvpiVX3xxG8eZRGFwuHJ4rTDdOeMW41NWI5Pf+HCwTMUCrfKUWLzKRifS/dOV+6dhdvEmsXn6JtjVY0i8p3ARx5h2Z8AfJaIfDqwAVwGvh24IiJV+g/4WcADR1h2oXD8nHEN1DFSYrNwZnDVyFfBmadObEKJz8J5Yw3jcxVRws+LyP+cNE8ro6pfo6rPUtXnAS8FfkFVvxj4ReBz02QvA370MMstFE6UNfqgYIgSm4VzyZp98DNEic/CuWTN4nMVzfHfBP4/QBCRHZKqSFUPSEw8yFcBPyAi3wT8FvA9B84RInpt4FvAkUf26vr483x7x3D5/S8ctnJunz/8P8XD77xrsOzy/xjPxVhfGy4LW8PzNrvDPmZNPX72LVK/7lc2PF+zNTLf+x4dXeeF9w6fOjeeNXxKtheG3zAdZFlXWInDxyZ0qVZdA5C0anOQ6SJlaqxJdlCmf3M40wRWisyls/SS1nU6OG542HW0l1ukUtodD62g00jYrUDBbwR8Fdhup1y/YRaB02mDOOWOrV3T6LrIHZu7zNuK3bbizs1ttqqG9zx5B94p06plo2p4fGcLJ4qXyBPbm8QUG5NJS9N40xz3N9o2dyGUFF1uJcx2VzkEFVtmHp10f1EdsRdr+TWE6iJddF5w2xP2hWhlbXA0rSdE4cHrl9lpakshvel42sZ1JpWl2L73gl1DZ61nOml4YubNzk0lWUzZ8ck3LQkL271OG9omfWUjxNas5qQxO7coZj83T9rwKNAGT4gOjULTeuZNRTu3GG+jZ37ANxGFJQ4fn6owm4PziHM4L6Zn9YJv7Lj7rPEV6TSunXa3lS6NsHqSBZskXWvW7ZqG11URX8Wkk7e07PnXi/2KKCE6QtLCx+gIUQjREYMjNI7YerRJmt3Gzj/XmIVb1sHauZks3LKNW9+CjLxNaXu8JM2xLGuqO0u5xfQxZ4nfG88jZLm1ANmmra/dlZCm6d/CUpzZNqilx56rpcMO0WzcoqXGRs2aTpOVW0733W7Y9x1hklJ+16ZL1qr/a9fZLiV8jm/t1TWPi9w8/BTlwIdjVb100DQrLOMNwBtS/9uBj7nVZRYKJ8G6fVRwECU2C+eFp1psQonPwvlh3eLzQFmFGH9dRP5RGn62iJQALawna9QsVCicB+KqiUDWrNm2UFgrTiE+ReTzROQtIhJF5MUj071DRH5XRN4sIm9cZdmryCr+d6yh85OAfwJcB74T+LOrrOBYcCnLzz6EkWRs8zvH2wSqq8Ob//4Lw03/z3nasGzgwouGjYneeenu0fpc/o3hjYkjigzLNrY/d711/MYzuT68j2I1Ip2II/v2wni6unZr+H+yMemETkeyD957TF+9lpvpoZHcNKiL3Wd2QYumRNeSmulSM14LTLEm2anZtKEgF3RhjVQLoaosw1xeaK1IHdHGIdGS4oXGQetgZtZpYeYB4ZqL1BstIThubE/R6HA+0rSercmcGB2qihfH9TBFFTYnDc+7/Bhv93czayvuvXCdy/WMdzxxJ+Giw0vk0ccvEmcVVBFN2xBnOQUVZgNVKTg1iYRT6o2WC1szdmZmDXdxc2bSh2AWZyJwY3vaSTeqSUvbeGJr2QJjm32p6Npvw65dv2YyJRdsP5muIQLvbDyPbl1gd1Z3Sbaa1jNPsga2excVTVnxaumG83KWMoP1bLWy9VW2g5NkfRVa2wZVk1JoFFu/2LaIi6iaFdyhvmQpsXl4RKCuYVqjk4o4qaxZfuJop2J2blMWWdamdk+1JvocdyzJDrpATzZtNN6kF6IEh8WrVyRZFPok66l8pK4CXiK1i0x8MDs/iYgolYtENYlFq87kOCqEKDTRmwVh45nNvUkv5g6ZCy7ZpXWWcUuZ8hbSgC7bX5KNZMmEa2TpWtXNm+3MOnuz1N+CC9pJJehdnmxF/beoN0szzMJOkzzE5BTSqi2zVUQtw6CKgBdbfLplxsqZ7V6SVGRZRWfnluz4TF6RLN2SBKZ7Fbo3jvrxrbL/Nq3C6cTn7wGfA3zXCtN+oqoO62X3sMrD8ceq6keJyG8BqOrjIjJsgFsonFNED3kxOI51inwe8PXAhwAfo6r7/lcrIu8ArmEJf1tVHfwvuVBYN04jNguFwmqcVnyq6lsBDukXsRKrPBw3Kce7pkrci71JLhTWj9vvu3hi//kWCueBle9rZ9gTtVB4ynP0+Lxnj9ThVar6qmOoUR8FfkYsq8t3rbL8VR6O/zXwOuBpIvLNmJXMP7qlahYKZ5Xb/N/vSf7nWyisFeXNcaFwdjl6fD4y1hIqIj8HPH2foq9V1VXtDP+8qj4gIk8DflZE/kBVf3lshlXcKl4jIr8JfDKmOPrsfEO/bTgPFweycI0ckINe84dLw15l7r3D+t/3vONZg2XN/bPBMj8Zf+H+xIcO52p0F4dTXft6eLmPXtlfq52Zvm/4FLgwYjF/8aERn7cb26PrnI1Y6OnGyHJHmDx+PA+XZ7jp9tD/+d5WlEXq2WTdZnq3rH9LkzlFgiysolKa2ZiWQYv5KHlLq1xfXsSTu2AHJwaxfrF+VcFttMhl0yuHxqzD4twzz1Zoux6tIlIpzdUp1/0WbmLn2jUBDQ5XBa77DR69doGY9L272Xas7elzBWQSbJujpOHYaXJdpWgMqErS2JoWdzavujS0s6ay1LMqxOjYnDTM65bgHZMqUFeBHamZXGiZ+MCN2YQ2eDanc6qkmw7R4dIJe+PGlGoS2Jg0tMHRtp6NacO0anlSTQcdo6VsVoXQeKprnji1+esboCLEyiz5fLrcqNduHJiOURS0tu3CKyKRnD66qgNbm3N2ZjXeRTYmrWlFW49zkfm8IramJdVkAbcqZzg2zy4iMJ2gtUdrT5w4Yi2LroLoUzz2dLjdx1JZp+t0YfHWSx3dTRsh2zjSJG18rIhqRW0UZr1ldjZpnZ45aWKddusQp92v5HVmLayPyGaEDSxWD9wNNn/3m2zlJFU1p6FHkz4+DWsUiCllfEzp0zuLNksHLUEW9nLZaq61X9cfFzC7ttbiSWKyb2sUN4+4NiJNupcnuz11qZNstyjEuqc3TvrwhU6c5VTZXiH1L6WN7n//0R/X+1DusPF2UvGpqp9yDMt4IP2+X0Reh7m+3NrDsYj8B1X9EuAP9hlXKKwXRwvw0Wah0/rPt1BYK8rDcaFwdjmj8SkiFwCnqtdS/18CvvGg+VaRVXzYnhV54KOPVMtCYT0ZbRY6rf98C4XzgBYtcaFQOAIi8teAfwPcC/yEiLxZVf+yiNwPfLeqfjpwH/C6JF2sgP+kqj910LIHH45F5GuAfwBsisiTLBpS5sDZatItFI6DM/pF/FH/8y0U1oYzGpuFQoFTi09VfR32Tdze8Q8Cn5763w58+GGXPfhwrKr/DPhnIvLPVPVrDrvgYyUEeOLJfYv87n2Ds/nt8Rwnm88dztd8eXN3sOyhh+4cLNv6g2GN70GpGGM9XNZeOFpa5Wp3/K2MDMucR+tTXxuZ8QCmV4ej6MLbR7ZzxD55evXI1Vnm9lu5ndh/vrcLS8/KIsVw1iBmr+MA4nsT0/NBnjnczBEvBtP0zQWJQrgQiG1FjMDFFldHwtzSFeNgsjUHtdTO3inem2/qPHgmPjCpWi5NZrzn6hUmdctdm9vMQsWTuxvULjBrK25sT81DODjqjTmhdcTGsTtf6IvnmoJAUt0jyS9UTZ8YBKJ226ZqXnuoaanV2V0jtkLrk8YxaZVjcFaWUiuH4EyHHIV56wmteb3OXezKZvOaxkVCdHgXiYh5x1aRygcuTOdsz2tidEwri1GNDpXIvKnw3jyGNYillE2a3zAx7TReCRtCo6AT86sNm9F0j97KNfnBhrmD4GwfiBLxzJOeOEYhiKUHbluH5lTX25XpRwF2R8zb9z3RDjd5AYgRdnYxu23z0JWoSKyS/7ij3TBtbEgewaFLIWyaZCpdeARXatr92vyqXWU+xs6pnYc+4p3dj1SFmM6Ffn+bzm1tnKUfbwUaZynnk395l3Y5Xz9C7/65zy2t0y9nH2NZTKdCp53W5Due9dOx/4iw1484j+pd03LK5c4/Ofsj57rm6x70tL3pN2jaHl10reJa0xu7eUCaYBcIEdRZbm+VRcpvnUfc3OHnjjBJnse1xW+cpFTRklNky+I7kLSd+3oxa2+fcXP/yqxZfI69Of6o1PvDvf4OVX3TidWqUDgtbr9bxYn951sorBVrdvMtFNaKNYvPMc3x/zZSpljGvEJhrShNt4XCbWZF1/zTiE0R+RfAZ2JywrcBX6aqV1PZ1wAvxxoLvkJVf/r217BQOBus271zTFbxibezImNo29K+/+F9yzYee9HgfH4+vtwnr1wcLLv8AcOyihc8532DZQ9cujJYNntgwI4u8bRfH27DuPbs4bLpY8Pykft/eVg6AuB2hi3i5MbwPqAe/r8qPPr46Dr9fDiKmosjNm9jKpmSluZUiRPFzezYZZkFqYmza+7MqUn7zXbdAmTJSkgiuLngtx1NZdIDblS4VojTyKydQhTarQZXRWJwXarlerNFRHmi2iREYWdW83C8aE390TGZhk42JaJMfKDykZ2mZrNueM6lq/zJE3ex21TcfeEGlycz3vnEnYRgy3/y8QswM0mARIFp6IapoqVUDgLeTkpxytalGfdevM77nrwEwN2XbjBvK3bmNZO6RVV44sktYiuEmafaaImto931tlOSpKPtLK2kO+ez3dRcJtx45EJ3l9p9coqbBGLrCeoJk2ByitakEH5HFil0c3rvVvBzqG5Ac0mI3uG3HdUuhAloSimvXhGvqKrNJ3TN7HXd0jQmn6grs8sLwfZ1qPJJwcK262zzs8DXqGorIt8CfA3wVSLyocBLsQ/W7wd+TkQ+UFWP5kV5QmiM6JPXkKZBdifIxhTZrvEbFTqpCBsVYeqIU0c7Tc30EwgpnbT6ZPVWmcTCmum92TNOlFgpoVa0juzWEV9HfJ3SQvuI95Fp1VrqaG+poyFJLjCpRZZcqApNdGZFGCy1esxdK2jjIaWMllbs+jBf2KZJoLt2dFKGLGtI1xyTVcjClk5YSgvtm7w8s1zz82S31ipuniQQc7Ncc00w2UoEUbV+1cWwbejiYMTeOFXTr2jSseRxquAcVB6qCpxDKw+VQ51DNyrQCkSIXtO2iclGfD5eqT8fr1RmdVi+xna/SyfN+j3oHoUxWcUnqeoviMjn7Feuqj9yctUqFE6JclEoFM4mp/PBz8/0Bn8VS4IF8BLgB1R1BvyJiPwx5iDzK7e5ioXC2WDN7p1jsor/CfgFrElpLwqUh+PCelH+Yy4Uzia3FpvHlZ72fwV+MPU/E3tYzrwnjSsUnnqs4b1z7OE4t41/j6r+t9tRmUKhUCgUjplbTk8rIl+L5W98zclUsVAonCXGHo6/DPh24F8DN7lVnBU2Hh8Wm+7edYCV2wPDVkIPPzFsEdc+YzhF9B133Rgs02cOlwE8/iGXBsvmV0akbCN2K+/aHF4mmNZqiHp/9zwA7vv1YS2ztsM6ZoAwHa7wmH3c2L+mEo8pkcCa/fd7W0j6Pa3MIsmR0ppOFjo4P8P0go3gZyldqTftX5xG0+2qEDcVbRzilThtCRcEqZNIbisQFMQrEgUVJc4q4lzNEmrm0Doy2zWbqlkU5GKDBseNNlnI1ZEbsoGvIy5pgnecpZ0Wp4TgeJ+/yDx4VIV5qHhyDjFaKmjnFFcHYu9EcVU0S6hOe5t8n5K+WpzStJ5Hb1wgJO+oJ7ZNDx2iIySrK9PsOtPxYtvpvemnc6IM7y1Vs0ahrltEYGsy54ntTaZ1yz0Xr/PkbIOdWc2dF3bYqBre8f67qWrTVVc+cGN7SrtbLel9XXZm7NlSWSV0+fqSU/imVLwqts15OnGRugq0weNTf1Sxw+0iuim0c29OeBMWtm6rcErpaUXkS4HPAD5ZtRORPgA8uzfZs9K4M4eGAE2LiB0sSZZuGrIG1u6DXgCc2YilW2P0JoGNDbiefjVbhdkJDyp+8S2Bg8ZD47Q3rS6s4JJeHW92cJJSxTuvOMlpnZXKR/CxS5EeVZZ0yCHYMEHQmO4BORVyHu690bwpXXX2alPMRjJrcfNwzOOlG17SM+/RNi/peVPK6K5rdNE/V6pZxM0ifhZxs4CbNchui8ybhe7Ym844642zrZs604CrF2JlXagh1AudcdyjN1avnQUduUt6a3UsWXDu1SSvfqId9sw824w9HL9VRP4IuF9Efqc3XgBV1T9zslUrFE6BNQvwQmFtOB23ik8DvhL4C6q63St6PfCfRORfYh/kvQj49dtfw0LhjLBm984xt4ovFJGnAz8NfNbtq1KhcDrkFwqFQuE2soJjxSnG5ncAU+BnUxKeX1XVL1fVt4jIDwG/j8kt/t9nzamiULhdrOO9c+zNMar6XkrygcJTiTUL8EJhbTgdt4oXjpR9M/DNt7E6hcLZZc3unaMPx+eBandYc9xcGn8l0VwentePpF2+/FvDKaJvPH24rL18wIuFu4fL3aVh02ZfD2/H7PJ4ilYNw/vIPTosAL78rs3Bso3RNcLFdw9rttWN7L8RrXIzbFm9Omv4xe1tw6mlOc1pWSuW/Iz9Lkhj3qTVjaSLmwi+EWKttNNoOmKHaYenEakifjMgDhDFuTalXnZUk7aTwipQ+cjGpGF3XtM0phdudirzRvWKtA4m0TSJO54AhM0UbyrQCkwic5lw7eoW4iMiyo0bU/MRprcyTA+c/YVj65ZOnKzB1Z6Zc2gdu0lQrwozrbpyVUk6S5DsjSyWptklzaUm3e6kCogobXBcmM4RgTs2dpi1FVuThqdvXcOJ4lDuv/Akd062eeiJy0zqwOWNXWoXbN/Uge17PdJYHZqL3jxrN5R2bschTgAHcaq09NJNJw1mbDwaBAkm3lQVmpnniSemUEdQYTtsgSjiFpavUiVRo0vi5VUosXk0+n67qmY43WatsCC1RyaLe0T2yY3V4puBWPe6yr4tyPHd0aUbXnhyS0gxP8PSQa+iYxVL6ZzrwR7d8r6a4aTrVwGqmPTwoJKn104jL07zppveeYXTz3adxV9OEQ+m+9escU4xoVmv3Ipte7rmuUZwDdbNhGrXUe2qdduRaqemutHidhtk3i77Jc9bJHkkOyf46x6d1sSJT50jThyhzjrkpDXO6aNd8qre4zs/lHJb6U236qc8axif5/7huFA4VtYswAuFM8+qMVdis1A4u6xZfJaH40Khz5oFeKGwNpTYLBTOLmsWn2MZ8n6Mkc1V1TPxkd70fTuDZdX2uI0ZH7Q9WHTl4vByH37G8HIv/tawqGDrLeMyD9cOn127dw7LGMZOymp3/IydXRlJ1zwyn7pbsE7T4SXv3DNSn5FV3pQCs3BbUQfUmJzAQ9jM6WaVWMuSXZBrTd0grUksXCtsT7w1vbZQ7QjzK8COgyiEKw1SRUJwaCOdzAKgTqlqnSghOpwod1zYYVq33P2Mbd5x9U4265b7Llxju625urPJ5Y1dQnS894nLSFrO1sac6ztTYpIZhcZ1cgrxlg5agzMLsiyziCYpUK+L5mSnBFg6WTXJJTSnlQZisjDTZOOmjVvYmjXO5qmiTa+C1BGcMtPFsmfTCQBPTjZoW8furGanqZg3FSE6HqwCrTpidDStWWHttrXZX5H2ocgi0CWNCiaDcXOL8+qG4OZmwaje9kF0ahKQ6LtNFa84F5ft3rqdkEZnz7s9+6hwgoSAzueW3rgOSNuatVvTIDFCG5Gmxs89bu7xM4efC+2m0G7Y+ZHjO8tt4jSidUQqOw9cHfGVWQXmlOF92RDY+RejEIKjbTyxcWbB2JjFY04JneUH1XaSZbRJjtD27dR0yU4th9+SFKAvFegs51iWDPRPwSz70UV/Xg4sL7cb7q8jyxNYLKdv/5Zt1FxQ/Az8TPHziN9NKalzGukhNAWVc2jliLW7KfV3rJOEomfn1qWT7oX9vovfs3039T/FGHtz/K23rRaFwhlh3XRThcK6UGKzUDi7rFt8jlm5/VLuF5FN4Dmq+oe3pVaFwmmxZgFeKKwNJTYLhbPLmsXngQ6TIvKZwJuBn0rDHyEirz/hehUKtx89YlcoFE6Wo8Zmic9C4eRZw/hc5YO8rwc+BngDgKq+WUSef9BMIrIB/DJmoF4Br1XVr0vz/gBwN/CbwJeo6rBP2QHceO6FwbJ6OMMxADsPbQ2WhRfuDpY97/5HBsse3LpjsOzGO8f9xu5907DAZ+ee4fnGmjPu/e1x+7h7fuWx4cIbw7pr5sOHrB0sMWZ3TQbLdu4b3pix1NLTx45HHLVuTUNDHHd8Zr2gOtMG1teg3TS7NmmhvQBxM5rj0USQAGFDiRumZ9Xa0so6J7TOht1c8NtC3DT9r9zwuFaIG5E2TBCFuNniatPmxtYDyu5mjYjyWLVlKaCbit22IgRL1SwCW/WcSd3inDLxgdoHwtRxYTLn/gtP8o4n7mTWVlzZ2uGOyS7veuIKTWuWV9tXN2FuemiCQBWRuWlxqUAmC22x7Rxlutlw5cIOV29sosAdW7u00TFrKrYmcy5M5rz3icu0raOqommgt6eoCs5H5tv1wpauMoF92LWUvWE3Wao5mF9PsaXCA9cnPODuhGQ1d+OJDUBMt904Nt7vaS+YGNjvQHVDmN8Bfg7VLsxDEg9H04mrhxB7MbKkTTTdtXilnraE1uGc4n0kRDt+zkc0Cm1j+zGn7F75nH2KxCYcY3yKQFUhkwkynUBdw3QCdYVOKuJGRZxWhKknTh1hIoSpECaWlhjABWCWtOgziLtCrJKFWA1aKWESCbUyryK+jvgqdMe/rgK1s/Tl+fuAqLKkRQYI0dFGRxty54nBrBJj67oU8Z02uadPdi3LqY97D19ZAo9jkU7ZLTpJtnOupUudnvtdA75R/Fxxc8U36XcWOp2wtEm3HQIERdpglnlRQZOOOOpCTxyjdaoQIqrRykO6V3uP1BVUFeRf79FphU5rwlZN2KxoN511G0LYENqNvt1eT3OcjhGw9EAq+fuF3v5aSofNYtqVztk1i88VchPRqOoTe8atshtmwCep6ocDHwF8moj8OeBbgH+VzNUfB15+iPoWCifLGv3newAlPgvnizV7M3UAJT4L54s1i89VHo7fIiJfBHgReZGI/Bvgvx80kxrX02CdOgU+CXhtGv9q4LMPXetC4YRIL8AO1Z1HSnwWzgwrBtFRYrPEZ6Fwe1i3+FxFVvG3ga/F/pP9fuCngX+yysJFxGNNPy8EvhN4G3BVVXPL+3uAZw7M+wrgFQAbDMsf4tgWHPDov/m+4QmuXx3WMTz6nOFWrEt3DtvDzZ82nBkO4MbTh23g5neOyA02h33MHj7gED/57KcNlo3Zo11+RzNYNvmph0fXGethCYSbjZUNL/PYXKHOcLAeN8cVn/7OOxfj89uAXvN7Z7OU6NspxTpas98kSQUUs0erlDiFcCEgtV1F9ZJljSM1hWoUdO5NbtCIZdabKM2uhyjMFeRSg7aO7SRzkDqyc32Kn1izL8AsNf1nW7ft6UK/4yUS1aQYdRUQgZ0qEmNqrFWsflFNZtHDpA62zKbxXNudWhOywm5T0QazWZu1FRHLwJWd1bKEA7AMel6BQM4GBuA2AohS1YG28TinbEwbmtYTguPKxW3u2trmTx65GyfK5a1dVOHJnQ1C62hmW6jTdJwkNb/a8bEsZWrWXTXEsMiOpmDyDrGMeZ0DlNj+aOeVtSQHCMFScalCjNLtcwDnD3l3fArFJhw9Pve9d2brTWfp4TR1iFgmtU5msBiGdHiC3UolgjbZ4s+mNQszAXELK7O0rOBN0bPrzd4Rp5b1zqt1lf2Ki7hKcSkrZV+u4yuziAM6SU5sHaF1hFagTdnootg1J8sCUua6vs1bzrJndUn9/e3Mp6Mu5jVJkVk2ZvmFSTBqJLCYrv+w17dvC3TWc3mcCwsbt06iMQu4Wdtlx7OQlEUHdsycS8dvz0HP60/HSlO9YpWPm/T0JYt5um3fs5yu/DAxt2bxeeDDsapuYw/HX3vYhatqAD5CRK4ArwM++BDzvgp4FcBluWvNdnuhcPocV3xOn/PsEp+FwjFz1Phcune6u0tsFgpHYCwJyKgjxWGSgKjqVRH5ReDjgCsiUqX/fp8FPLDqcgqFE+WMa6BOihKfhTPPUzQ2ocRn4RywhvE59ub444B3Y1KKX4PD5UoRkXuxj/muJp/kT8U+JvhF4HOxL25fBvzoEepdKBw7exMmrTMlPgvniadSbEKJz8L5Yh3jc+zh+OlYQH4h8EXATwDfr6pvWXHZzwBenXRTDvghVf1xEfl94AdE5JuA3wK+58i1h9EjMrtzuAyguTj8r87GI8ML3vq1YSuy688eLnOT8X+tmsvDZZ0Vy35Mh+3adp4/nld5Z8SUz13zg2Uy4qt29+gaYeuhYZu8Oy6MpMkeIUyPKTTX7L/fEY4/PhX2pmPtdHZzIAiuEfxO0tDVSV/sksYuz5/0vDIJTKZt0vdaqmgw/W5VmT6xbT0xOPylQFUF2uBpdivTI888OjO7M7fj0UlEcTBztL6GzdCtU1tBqsh8p+ba9Y1OJL2za/EcgzO9s5r2USqFpHs2CzdFNWmQsWVK744R5p7t1uG86aZvtN50gWLb0ydG39mdOWfLrOoAtQ1LssKaVAERZaNuubYzZVK33HPhBjfmU3aaimddfoIXXHyEh29cpHKRZ1++CsC75QpOlPcHR7xRIxHai0KcKrFWwoatO+Z04LUSVIi1aTSzJjG2ZmeX9wvBodGOM1lbHCWJIa2/VUw/nfWPT2FN4wEcT3wOpSN21ql3aCVo7QiTZAs2hXZTCHXS6fZswbouf66zV6OaxyUbNNPcyuI3clPa5b0pmDXX7SbbNdO3e/aXqi+ljU7nWJYQZ42xrUPT9ifNsailaxfQ7kMJFrE7uG/prYBFHKhplMnprltLgb34hWpH8LuOaheqnYpqJ1JtR6qdgJuHziJOWrN9kzall8auZX5SEae12elNPLEWwtShXnr7zfpjPl5iuuVu+/bss6G02CuzZvE5liEvYIk/fkpEpthD8htE5BtU9TsOWrCq/g7wkfuMfzvmm1wonDnO8tezx0mJz8J546kSm1Dis3D+WLf4HP0gLz0U/1Xswfh5wL/GPgwoFNaTNQvwQuHME1d8PVVis1A4u6xZfI59kPd9wJ8CfhL4BlX9vdtWq0LhtFizAC8U1oYSm4XC2WXN4nPszfFfB24Afwf4CpHuv3sBVFVHFLK3j8nVYb2taw4wOh45mLv3DBe2myN65IdG1ifjb0g2HxnWB6sbnrfdHNY5z8czVjMf0WW7YStjXHv0SKh+908Gyy5XLxgsu/7M6WBZHJZHr84hbVcLPZyiFUmjpsSpEJNWMVbgW0yLmlMRO3ppiYUYpfMjlVZMB9k45s3E0iULhMabxhfQmGJFzCdVVWiDldcbLU6U+o7Azm6N80p9dyBGMd9dgRiE0Hjz6o2C2zRtr6pYyuNOE5x0iq35C4soMaRU0U6T/zBJa2xexdomb9+I6Q+jWDrpCGESF2WAONAI0jh0EhGnliY3ArUS0k6ROnTryjrnODVv5qb1NI0nRMcjmEdyiI5Hd7e4XF80P2UV5sEzj56gwqRqufPKDR5tL4GmlO8bAYIjbLnueuNaQRrpPM/9TjpOc08QkJnDz8XS1E7iQmO8FWybVRA1XblEwe26zpddGjnUW+MSm0ckpSnuciQDooqEfO7WEIGoSHQLj+Acw8nbN1ZJh54XmzXCQqfbXfoyq6cp3+sf3O86X+BcvX4a6L6HcK7XnvHL0+m+02j3IUDS3Up/G7JfMwvdcx52y8NL4xzs1el2Wl24yes4b6dLabj9TPEzqHYtPbVvUjrqJuJmDdIEmDcwm8N8js4bSzHtPW5S41JaaSa1pZeeeLSq0KlHK0+sXeqEWItdZ730dNyypOuOXpKhdV/nveo5tn7xOaY5XnW3FArrw5oFeKGwNpTYLBTOLmsWn+UBuFAoFAqnx5rdVAuFwvlnlfTRZ5r5HcPt6RuPjs+7/ZxhScbTn/vYYJmMtB889NCwTmHjHcPyB4CLDw6XPfns4eZHN+LWds9bhlNdA2z8yfB26nTk9HDD/1cN79VEHK7w7M5hi7ixVOGbjx241pVYt6ah20ZM9kWiZlk0S8116VC3F0A3AlE9s6niGkFrSyfrtx0yc4iC35XO3s0/WeN3hPm9LUwibHtc44jTSJhGVMFvtrhKidFkGAhMpi3eRyZ1S1TBu8iF6Zx58DSt5/LmLlPf8vD1izin1C4wrVuuz6ZMfODereu858k7mDcVlzZnbNVzHr5u8gSAdltgntpaIyaHaJPUojILOg0OyZcmUaaXG7Y25tzYNmnQ1uaMNnja4JjWLRt1y9Xrm7SNp7o4p64Du7t1SkENYdcTd20fqVfEKc2OBURu1kaFGRudddq7bkx4aOMO2plHVdjemRCCIzYOVymhcWjaDokCM0+24fJzUppdwc9snDpLWysKsVKzy/LapZzWWsFFpE5pgL3gqriQVzhFN0xmAYDvWb6tQInNI5DTD3tnze91BZMJOq3QiXWxNju3nGI6SxI6G0ZzE+vGa5IpxIkSJ2opxSd23P0k4uuAd5HKR5yLeKdULuJTpyqE6AhRCGq/MQ03wRNaR2wd2jqzWGydSXtawc0F15j8ws3p+qVdSDeWU0Gnbq9soieL6KeFzraTLmiSeSh+br+u/b/be/MwW5KzPvP9IiLznKq69/bt26uWllo7CAkJ1BJggw0WCBljy4uM8SqMZxjZ4BnP2IOl0QzG9jAPiz0MNrZlYWske7CBwWBpPAgtbF4GgRYktEutllqo1evtvlvVOSczIr75IyJPZVVXnlt3qapTp+N9nnxOZkaezDiZ+WXGyfjl71NMEzFtxDTZWi0oEtNOkaDkPOlJptJheseio/teyDs3hJ3jMUJUNIQ0rWlcrE3H0pgkqRjVUDm0sulY1llOUVuiE2ItWVrxeGlml256fsPWuKMszbuCU23F4vPYN44LhevKigV4obAylNgsFJaXFYvP0jguFHqs2r/fQmHp2ae4r8RmobC8rFp8lsZxodBxpRm7CoXC4VBis1BYXlYwPo9F41iMwayt71kWq2Etrp0tPlpma4Fe2Q37mL3ozH2DZedOPzxY9psn7lxYn0tnh33XLi1IAx3XF6SPvuUyOudn3j5YdvILw/tg/KsfWbjeRcjaeLBsdHZYI71x73CZPzVs83ZFrFiAHwqdbRLM07B2lkadti/UIDbpE0lS3aRVVJBKkxWcAkZSCtfO6q0BWoMasDODnUnSvqrBBCHaZN0UWwtTi7pIk4/hbOaoqkDrq7m9mRFl5hxOkiZSRBlVHieR2gZuGE945omzbPmKzWbEU06e56bRJrPgaILFoDx8qZ5b1kHSBJPT4xIExiSLMi9JH+yU0FpmxiE5HXSMJtnXkd5hqGxKfy0m1WfkPCEY6nVP5QKb9QjvDXWdpltvsdlabuQ8j11cZzTy3HryIuena2xOa0aVp2kdbVuDizSXaqSKaDCELQOtSfUh2efRpvqqTcdL62TP5TdAQk4fndPY6yim4zmKxHG2o+tS9s7sPIzilkvHVoCsG9VRPlmi7NRm7uM8K1wZIiB1jVQDeuPaopVJFl+dLRvb+mIT0mEykKzIetrczlpNFaKYpEM2ismWYMZERi4wrlrWXMu6a1l3DU4iRiJWFINiJGJEsSitGmbR4aOliY4m2rkF4VZbc2lWM20q2sYRGguz/L5CTtVMrtfcxi3rptV0KbA1p8TOqaN3pzLfrVuOkvTMwSRtc9i2nJNO+xyS5tn4pE1Omn1NVm2tYmYR2ySdspl5pPFIDND6ZNUWkmWbZrs2DQHp9MFi5lrwuX68QxX8tjUfMWIbg5Vk24YIaky2njPbFnu7LGWT9lq2Le1svsZmq7d9s2LxWdwqCoVM917TlQ6FQuFgudrYLPFZKBw8RxWfIvJjIvJJEfldEflFETk9sNwrReRTInK3iLxuP+sujeNCoY9exXANHGRwFwrHgitJH33I8VkoFPbJ0cTnu4EXqOpXAp8GXr97ARGxwD8B/jDwfODPisjzL7fiYyGrAJKFyR60C7LVXa7pf+qzw2Vf4MmDZZvPG5Yq3H7i4mCZs4vtxjafMly2/rTh9T7/lgcHyy49Z7Gs4tP33zpYNvudvaUsAHf85rA0gtl04Tbjpc3BsvruBwbL9ObTg2XNDcMWcEvOu4HXq6oXkR8hBfff7i/QC+5vAb4IvE9E3q6qHz/02g7RZbvK1kjzLsjOEiopEBKi2V5KYRQJ62FeFk6EufVSsIrWBqrU965OCSZ193dZ23RiCV6QqcVtScrM5w3k7uB4ukmSiy5bXRXw3rA1qpOMAwjRZAemdMG4x9zEpdmIJlgenmyw5SsmbUXTJks0yBnfQrKdizH3VOqu3+m7TFxCaA0zrXB1QFWSzCMYjI1JYqFC5QI2KuPKY02yoju9PmGjajhnAzPvuGXjEuuu5dxszKl6hjORG+stPqhP5bYTF/m6M5/jU5u3ce/FG7lpvMWFZsTnNmuqscfPLOP1htm0IgRBJM5vTkqut0Akpi7VLIEJJnXbqtF5v7s0BlXJ3cyCCPN9YmdCOBFTV7fvumpTmWnAu7xfYvpu4SCRdO90DpxDK5eyqGVJRRjZlEGtNoQqWX+FCkKdMqnNrc66c7xJ87Yzq+XudwtqLWoqYgXeQVMrFytNcpxKkSpiqoCtItbGdL7bJGeqnacyAWciMZ9j3aeqEHNQORsZVR4BjI2EyhDHZp45c2AXIDZiXMRVYb6OkfOMrKeyASOKEc2SD50PUQUfDV5N+oyGNiaJVhMs06Zi5i2htYTGoK1BWoOZGeyWwU3ATsBNwE2UakuptiJuK2AnIckspg0yaZDJFLYm0DQQu8CMO7xR1ScphpppOq4iydZNZNvmzVqkm5+nMQatHDiDumQFp86gNg8uZ8+rDNEyz6YXl/y2qqrv6k2+F3j1Hou9DLhbVe8BEJGfAV4FLLx/lifHhUIPUb3i4VpQ1Xepqs+T7wWeusdi8+BW1QbogrtQeMJwNbF5rfFZKBT2xzXE580i8v7e8D1XWYXvBt6xx/ynAL/Xm/5inreQY/PkuFA4cI6+G/a7gZ/dY/5ewf01h1KjQuGg2U/MHX1sFgqFIa4tPh9R1buGCkXkPcBezgFvUNW35WXeAHjgp6+6FrsojeNCocdVviBws4i8vzf9JlV903ydRxTchcIqUV6uKxSWl4OKT1X95oXbFfku4NuBl6vu2VV0H3BHb/qped5CjkfjWCTZ0OxBWCB9vfDsxT5Bc0uhPRg9OLxrzn/wlsGyh24/PVhmJsPWcQBuwckV9/vSyi5O15OF5bfeOKxlfvBJa4NlF1/+ZYNl67/w2wu3aZ7ypMGyc189vG9npw5BBXR1Ab7wn+9RBfdh0tk6Jf82kg1bgBiSHZudQnuTwUySHZtEaE8CM5O0reud1lhT6mWnuBtm1Ld4fEhxM74tWQvOWje3QGtmFTEIsuaJZzpdZNYYt4bYpJTIzGxKd2wMftPiZw7jUvy3bSTm1NCz1nFpWuN90hdPm4pHgBBMSsMcBfU5FrNllLTb56VocljS3W9jx6SFVhdzLJv5Nltgq6nwwaJKsoyLJukavcOgWesMPmsd22jZ8hXORHxMqa0vNiM+u3UL52Zr+GCpTeB5NzzEQxdPMqo8p85MqWzgoUsn4CRc2hzjNytQMDOT0j+LYrcsbiK0J5J1m91KNlaxVqJLvylWmlJ6N0nfrZKt31DihiIupQnWziZLQEWTTV+nW77Su2lpHF85XepoZ9M9tLLoyCa98dji10waxkIYC34MYZTuq7HKuuLO/swmXbl29meQYqD31FA0zev099IIbiY7lxPwBlqj2WaMrGPuVpLHswZ+boUAdCnbO5s1elWZf8+Q7APzpxhFJXlLqkp6vyCPd1rmTm/sTOyNBwxKRIgqRE3vBkSSDrmJlunI0QRH4y0z7+bvEvjG0jaWdmYxM5kPthHs1OAmDjsFN00aZDuJuC2PnXrM1EMIiI/gc1pp71NKacjHsUqWfM6hI4s6S6xNShXthFglO7duPDq2rfqEtD9ke3oHVxtnRxCfIvJK4PuBP6iqWwOLvQ94jog8g3Tf/E7gz11u3UVzXCj0OAIrmi64/9h+gltEalJwv/3atlwoHC+KlVuhsLwcUXz+JHASeLeIfEhE3gggIk8WkV8CyO/0fB/wTuATwM+p6scut+Lj8eS4UDgsDv9m+pPAiBTcAO9V1deKyJOBf6Gq35adLLrgtsCb9xPchcKxwO4z6EpDt1BYXo4gPlX12QPzvwR8W2/6l4BfupJ1l8ZxodBxBE+aDjK4C4WVoTwFLhSWlxWMz2PfOA4LsgbLTbOF3/3Kp31psOz8s4bFzJ//1IKUy58cNgZsTy6sDvW54bKLJ4dTS7/v/J3DX9xcfIjNAuvltS8N65xjNRwJZrw4lbO/afi3TM8MK33EDxZxakGq68IhoOz0Oc56Y4lgcppVYvLFtTksxUuaj9BWMWkIY/bJ3Qh4KkJjcaOUVrlpHTEKMQob6zOsUdbHDarCWtVyejThYjsiqrDmWmoTuOfsTayPGzbqhqjChck4aYlnFb5JWuZ2q0Ky/jg0FaG2cw9kyV6nwe88LyW/A9DX7KVU2ZokkkaTH7NNQktT5XTL3XqNIhrn+sdpUxF88g5uGocxSoyCDwZrRsRoUMAHgzFK09p5+msR2JrU+GCYtSnNddM6rDmF15SmetY64kiY+KRtNqLE1sy1oJJ11Fpl32Kf9dPz35t/rI1JWl4ruGRgHausIa4itorUo5ZmVmFsxLlAiIbQpn3tt9z2Sqd29e6oy4gISPLCVSvZ2zbpUUNtCKOsN14jaY7XwK8rYZx15VVEqoirU4rzUe2pbaCyAcn6XNtpdU2Ko04X33hHGw2tt/hg8N7iW0tsDeoN0pjkWe4FOzNzb/R5Y0vZkQoa9j5l5lpa0/tkezpm7bR30Fplq0sdbXVb6zxEL510/52KueY5p0GXnEJeIrh+mmm/PW5Cuh7amWKb9ClBsz96OjaxdkgQ1IHECDEioU7aYyPbqb9HLvlTj2z2p85aY9P5FCedeMzpvLfTaSsm6LYPvQLF8vBxHPvGcaFwXSnXiEJhOSmxWSgsLysWn6VxXChk+i9FFwqFQ2KxqRBQYrNQWGZWMT6PfeNYF/htyGW8OO7cODtYdsdNjw6WfWDjwmDZb248a7Cs/uLiVM7T4UzOjJ40nHJ5EdXHF2/z5o8MyxHajeGz/cS9w/WJk8X2ceqG5Rqy4EbpZsP1md50nU7l0r109fQtnWJvfJceTaXXnRclp5oWCKl7VRpBxmlag8UL2CrgQ+qOFaNMXYUAdeUxRpl6x9m4QRsNY+fx0XCqnrE+bhg7z03jLabBoSqcGqX05l+6cCo7XUU26oZzW2tJAhAMbWtBhXbmMDZJF2JrU0poo9vpo6OgUZMPo8nWZ1UkQpJqCBgXueHkhNtOXOT+i6cAuO3ERWbesdXW3Li2xc3jTT756K1Mm4r1uuH02oQHL56k9RYRmG1VxMYyVZBKERPRbAVn6mSDN/U1Ptgkz4jCObOWLKamFWKUh2OSM3X2d5yrU4+7gp0J2gqBmLp+O7mVkH6XZNlF7p5Vm9LrqhHE6LyBqwqzaUX0hugFP7N5frbzapOdHULaZ1cSbiU2r4nuGM7HzbY0qFMLkO2+NMsO1GpKu1wHqsqzNmrZqBs2qoax9Yxty9i2rNmWdduyZhrGJt1TQr45R2Q+3qpl04+YxIpNXzMNFVu+YuYdU18xaR1N67L8whC9RX06b/Cy47qxW26xw26uL5uwWd4k6VOMYm3Mca1oFIK3RC9oMGi+DtEaTNtZsGVLyhlZDgGmUUwnTQg6/0ySBe3VVXsyEc1Ss4hpI9IEpA2I98jMQ9vS03Tlz9yQMQLY7fLd8dA7vv39YjTFKZ2tYj7m2H77aae12w6rt32fYKsVn8e+cVwoXE9W7d9vobAqlNgsFJaXVYvP0jguFDp6Tz4LhcISUWKzUFheVjA+S+O4UOixSNZRKBSOjhKbhcLysmrxeWCNYxG5A/hXwG2k/xRvUtWfEJEzwM8CdwKfB75DVR+7zMqQam+LNLdA3mrvHk5/DPCBm+8YLLv1ScNplb/lzMcHy573kgcHy/7L05+5sD6f/dJw6uSvv+Nzg2WX2mHbuQ/ePmybBvBYM2w954bytQH1heFtXu6kslvDOudFGnI/GhZAhVOX2eh+WbF/v0Nc1/iEpE9zOdWsCqGGWEN04NezzVkViSOhdcneKNaKGDBesp4wp1n1QgiCmRncljCrIxoFJhaZGXQcmUUDCu3YYl0kBiG0FjFKNfII8IjLGtvWMWkrfNbohmgYuxZn09V87JJH4Mh5ahc4Uc94ZGuD1lvGlWdctZyfjJnWFRqFZlIlPTQxZXQdJ0s2NOmMXRXwgMkWUcZG1qqWU9WMx/K21l2Lk4hXQ20CsSfua6OlDTbZZNmItRE3CgSTNJOuCikNbhREkn6ymVVYFxjXLa1JKWzHdcup8ZTz1RrSs70L0WArmK3bpAHWZM04P37z1MCdFZ9gAmjMlm/Z+k0168K7324g+mQPh3Q6Y+inAZaNdp6tVsYLvBn3osTm1cWmSee9qKJzna4ibcRaQMz8OIcGXA1hU/BrOS1x5Qg1tFVkq1LO1hFTB1ydzrdx5Vmvkg75RNVwQzVhzTaMjGdkPGPTcsJOGUtLJQE73tmKCr2LfquWqVbMYpXGY8UsOmbR0UTHpq9pomPqHdPg5nZxXfwYSTri/qchjdc24CT2Pj0GpYmOLV8xDY6Jr2i8Y+aTHWLbWkKT7OeYJds50yS7SdMma8o0bI+bmK5vpiuLyTYtTSvGg20U00RsYzFNxMws4hzSOMSHlCo6+TymwNOs64+KGAFjEBHECsYKaky+3qRU0WQNeWfrprv1xVyhnng/rFh8HmT6aA/8TVV9PvC1wPeKyPOB1wG/oqrPAX4lTxcKhcOlxGehsJyU2CwUjpgDaxyr6v2q+sE8fpGU0/opwKuAt+bF3gr88YOqQ6FwpRxBbvgjocRnYWnYZ/roq4nN4xifJTYLx5FVi89D0RyLyJ3AVwG/BdymqvfnogdIXUeFwtHzBM0UVOKzsPSU2CyxWVheVjA+D7xxLCIngH8H/A1VvSB9r0VVFdn7v4OIfA/wPQBjM6ybXZSS2Z9cfLDu/93hNND/8uHTg2UvefrvDZa99PTnB8u+7NRDC+vz6GR9sOyW+tJg2XM3hnXO61/fLNzml7aGxbqfe+imwbLZ+zcGy570noWbRD5972CZe+ELh7d5elgkVV+8PoG5zP9kD4LrEZ/u9I3baV/JKaKbpDmWOqVLja5nw9mlW7ZKXA/EM4pk/a8axQdJvsJRmJ2wiNNsjgw6iiml7SxpW30QglVoDDI1xEqZVg7xhqmCPd0QmpSqFsCMApfMGFcHbE5127qU2lYEIi21TVpYY5TKhqxZBGcjasA7SyQtr0TEbae+RrMWV4XQytyi9NFL61ya1cRoiCo04UZCTMtuNTXORFqfPIFjFC7MxsRosDZSmcD62ow4MoyrFmt07ucsomxUDQ9eOslG3fC0k4/x6GydC7MxLzxzP19x4j5+pr2LkfU894aHAfjsxZs4UTVcuHHEvQ/chEbBO4ddb6G1xC1DtEn7rVFwm0k7qSZpjUVTGuswMjAx2MaAUcJ6BKOYOqX7VhXMKKIh/U7jIsboPG23avZI3u+5WmJzXnZF905jwFm0sujI5pTDhjA2+DWDH0tKIT1ixxAr5rEqHmww6AwwFjWOVqARuGA09UFL8hcWq2BjOt4u4lykcoHKBUbOM3YtY+cf55PcaZQBLCkd9di0jE2LSZH2OA3yLLiUIn3Xyyqm93aYySmuKxOo8zY6LXQlYb5cUEPMPzhoitOAwatNKbDV4qOlVYOPliY6mmjx0dBESxMsQQ0hGqY++TXPWkdo7fwaJK1JvslbFrdlcVvpvalqU6k2FbcZcFseO/WYqUdmLUxnMGvQ6Sylkq5rZFRj6xo7qtBRRawdWlvCyBJGhjgyhCof11pR+/h7Z3f2qHQezJ0vc3/6iRufB6k5RkQqUnD/tKr+Qp79oIg8KZc/Cdizxaiqb1LVu1T1rtosfrGuULhu6FUMx5TrFZ92Y/iPUqFw3bia2Dym8Xn97p3DL04XCteVFYvPA2scS/qb+y+BT6jq/94rejvwmjz+GuBtB1WHQuFK6FJgropmahElPgvHiauNzeMYnyU2C8eNVYzPg5RV/H7gLwIfEZEP5Xn/E/DDwM+JyF8B7gW+47JrEiF5zjyeRXZjetNs8Wrd8JEx9w4/rf7gfc8ZLLvw0tFg2a3jYWkEwNZ0ONXz//v5rxgse/4tw7KK28fDqa4BzpweTgPtzLBx4SfvGN4/5quev3Cb3Hv/4vIB7ILDeerexfKRfaG6crqpBVy3+Jynm5VsBRaylVtnKSTgNoFZ6lKsNiV9xxmYJSmEnmzTwkRiYxEXGZ1oOHHzjMmsQlU4ccsMEdic1XOrpq1JnezD6oC5ISarsq0KRLGblmAr8EJ93hLGShwbZGJoK6U93aBRmFolzix27PHBYFBab1EVogpttMQojCqPlZhSTNtkndTJAtqZS/IBb2inDpT8O5RohHarYgK4tRaNhumkJoYkNeh6x01+Mc2LYTpLMhIXA8EYJP/eiBCjJFu6fPU2oljTW0+e7rqWrYlzCYmRiDORE9WMW8aXeOjiSUIw6HrLqbUpj11cx5+0qM32XwGiE6ggjHVu5aYOMJpS0Lpk/yZWwUVG6y1ta7EmMh61zFpHDJIt91J6XgCCQff5Mt5Rx6aI/E3gHwC3qOojuQH7E8C3AVvAd3Uv0V0Hrt+9s0M1dckHxXhFW0WrdHzn3esmDy7bMI5SmnR1mlJ9u225hAjJXjF2nmF5PJJSrDeOGJNEKERolO1lrRLz+tQp4pKsymQbxLonwRhZz1rVMrYeJzGd/zkufUwyCJ+lSiEafJY1JMtCmcsjuuuFzbFhTcRJ+uys3ZyJ1CbgTJoeGY8zAYtiJFJJqpMVxeR86RFD6K4TanPdDLPo5qmxt3zFLCSJxcw7pk1Fs1XRTi1mYrBbBjsV3JbgJga35agmipsodhKw04Dd8pjJDPERNQac2f50BrUmxanZPt6igkTFBIE2pYCXmNNaZ+lEGtckoehs5/K8rnzf59eK3TsPrHGsqv+ZuWLpcbz8oLZbKFwLy/xP9npS4rNw3Diq2My+w68AvtCb/YeB5+Tha4B/lj+vmRKbhePIqt07D1RzXCgUCoXCQq57NoLrzo8D389OheSrgH+lifcCpzs9cKFQOP6U9NGFQp8V+/dbKKwMVx+bN4vI+3vTb1LVN+3niyLyKuA+Vf1w3y2C5Dvcty36Yp53dZqxQuG4s2L3zuPROBag2ruqi47HyRsW5JYG7rjh3GDZZ6rhVM7j/zJsLXf3A8Pfu3RmWI8MMJsOp3KOF4fLPnDvswfLFqVjBqhuXyDaXvRAZ0HZ5KmLU1ZXp54xWDY9s2DFC4pCdX06QVata+gw6L9Yob1Uwf1y44GQ7MHsFNSC98xTsIYNyRpGg8wMCMRgmOb05p3eVmNKV3xibUZlAyfHM9pguWE84eknHuO+rRu4/+KpZIf26AZMLTqKRGeI44g6xbRpHaG1SSYXSDZw1tICF9l+w3/SVsQoNK1DpE3pWkVRm7SLdeWJ0aSfnHWNbesQo9iNhqhdnZNWOGbLONWkMdYoBG/RmUXqlNtXW5Os4UaBJtagghl5jEua6k6zOx2nfXPereGDYTKrmLWOJlh8sHzW3cwsptTZjXc8WJ0karKJg6RN9sGgMdVtq6mTDV3WmapJmkMf0/biSCG/FhFHEbERrbfT1YqL2DowHrXEKBijaf+o4MVQuwAuENqkCVWJ2Gq/osZris1HVPWuwfWKvAfYy9fzDSSt7yuuestHjoA1yc7NmJxKWFAnqEl2bdGR3hGoejZu40gcJ9tEqSK2ClRVYFQnDXBlkwVaP+255vHGW2Y+aWx94wiNIbYGaUzSrENKt+x3XtAVaAzMKkWrbrt52y7gbJjbQe6Wt3Zp0ZPGOZ/TwaAxXUfSSw5Avn4QJduWyXYjYvdn3n19mzq69wzyPJE0LQbEJKtCJF0LVLevVxpNzgKd4lcbi7TZ+tHkYzAmHxPBrwm2AdMabOOwzQjbrCGx04bn73WpoW03na6t0aX50SYNuUr6XSbK3HZznvo6bqe6NoF56muCLmwCPO5MW7F75/FoHBcKh4GSctkXCoXl4gBjU1W/ea/5IvJC4BlA99T4qcAHReRlwH3AHb3Fn5rnFQpPPFbw3lkax4VCn9WK70Jh+dnvI6dDjk1V/QhwazctIp8H7spuFW8Hvk9Efob0It75Xva6QuGJx4rdO49H41gVmnbPorAgP4jExV3tT1oftjkztw4f6Y89bUHSgweGK3Rfs7cd3ZwFL6aYE3v/foD6/uFt3vZ+v3CTsxuGf8ui92QWyR/sdPE221PDp50u2EXVsOsc1aXh/XMlrFrX0KHR2QJJ6rYzDZgKYkzddABYnXflApCtoyTIti1U7m6NCn5mCd7g6oARZdpUhGDmVmaqssOmrI3p5Dk1nmIlMq48Z8+doKo98ZQwcgHvLU1dQTBgcz6sYNBxskKL3jLdNHO7Ku9N6hJVaNt8cubA8GqZTSrEgHUBY+DEeMaWJMnCjRtbbDY1rbfcdvIiZ0YTPv7wbVgbOTGa4STy2GQNVWE6q2inDo1ZUlHnjIHeIAoRR2zzyZllDrNZiqNpEMQqGmGrPZHssazymQtjvrBxIyH/hklT0fi0T89Va4RgaCZVuqkFg5/m/tc2HQOtk3xEnULMPdMuyyBGMW1TUyazLgOeqwK1DWyqID0rOR/StbjLCtjZf+n+VRXLFpu/RLJxu5tk5faXj7Y6Cwhxhw4hJ3BEjYBI+uxs3GyyY4zZok+szrPc1ZVPme1cy7prcSYd+y4DnZE0HVWYhoppSJKeqa9ogmXWOtp8/mmQZMGYrf3Ey9wmEC9IMNAYgkAQZZZlDAhJ1jCXbvXm9egnDxSj+TzrNBn5XEorx7SCaQTTpMyeJg+2TXKwJD/QbflBzNM5uZ5a5paViMylZWrYNS7z5ZKVWrZQy7Zp82toN6+3XclPZTtJRXRJzqRWtqUUWVoxP5ZddsOY6iAx/x4PJuj8txnfjSsmKOJ1x/h+WbL4vGaOR+O4UDgsVsyrsVBYGY44NlX1zt64At97dLUpFJaMFbt3lsZxodBj1f79FgqrQonNQmF5WbX4LI3jQqEjd20VCoUlo8RmobC8rGB8HpPGcdJG7YVfIP+1cbERyd3nbx4su2VtWOD61BcOv3fx8IVhG7PJ+fFgGcCddzw8WLZWDWtqPz59ymDZxQeGLeCgpwfdAzcbPtvXzg6Xucvof9uN4f0QF5yRYYETXpfutnA0dClJO0sg26Tj1c1Xl9MMOyWMJM/TlDraJBuwJMZLktoutXtsLJ6kG2ymDo1JX3vu0joA1kaMiZzfGvPAhVOEKFijGBOxRqlHLcYo41FANV1G1m9qsCYymaXYsEaxNjKdZdu2zZSSWkTRyQgVMGNPnKYU0XajnadCjiF5JFkbcTZSu0ATAtYolUn6W1VhbD1j2yZLKhNZy5rNrTalwh45z3nWkq2bC7hRIAYhWJ3bQqHssImaT2tK5SuOZL2Vj4mt0njMet/G22QtFQ1NI4SZQztrrZmBrFm0Pmkw1en8HQCJ+ZhZTVrh7maooMiOG6M1ETGKMUplYkp/ncva1mUrOkmpw69Ac1y4SqwBZ1OaYWeJtSWODKFOad67VO+xzqmjKyDrjUXS8e7sDFWFNhqmvtqRgtnl8U6HDORz3gPblqrz1M9q8DENmqdDNPhgCGoIUYh5urNDBObnf986USSdcybHnDWKyymhK5veV/DRzNNL+2DnqaVbb7PlnMW3FmZJ65w0yMlOLelyJWl1/fa08cxjYK4Z7qQFPXtLOitLdD5/nqI59Mb7qZz77g99q0wDuO472zaK23pxttOAd7rj+bsdef8ZwCdtdLKeS7ZvEiAGQSrdUacnKsekcVwoHDxC7+JWKBSWhhKbhcLysorxWRrHhUKfJ/A/5ULhSNhv+ugSm4XC8rJi8Vkax4VCj1X791sorAolNguF5WXV4vN4NI6TyegVf21ycXG65s/fPyxY/txo+G/Q2o3TwbKbT10aLGvWZgvr86SNYd/l2oTBsumdDw6WPXbb+sJthjCs1T17ftg/eXz38L7duG+xn7MsyKQjwz9zYVn1yPB+3zcr+FLBoZHTk5IyIM+1xt0+jY6UlthCGCnGZ3/OtYCpAyanEbYuEkZJLGddIGaNbJeCWUzSGLbTdOlqyRrcaNBWklZZUj00CnbNp3StIaWkFpu2Y6xi8rg3is62n16KjVlvCWEcUgZZp0QJiKY6ilGsBMSk76km7+WL0xE+GLzAWd1Ivswq/N6F09wnNzCZVYgom7OkNe58f7s0tLZKJ7kRBQvOtRiT00arULlUHqJQ52XHznNhMqKuAjeubTFpayat4/aTF7l5vMlHHnoSzkZuXEup4h+brONMZHNWs3VpNE9xK1VEEHRqkrdxLw24mr6Icu/jL9nfeb1qeEzWcTawUadrXgiGcdVibeR8Y0EVrcDUC4K6T4nNq0STz3GISFCI2vPO7fSzEAOYNsWpaXKKabWoWIIoQars2avsfgVo3ibqHR/pfIm7NMsm64Nt0qKL0blOuP9pRalNmL9zYLKu2IhiyMt203lef9plr2VnAob0aUUJKkQ183TXEcn6ZzPXPw9poTXrpLc/0/e76ZhjuIvlLlV8CIboTbr2BAGffdyzr7KdGUwLZrbtr5xSRiffYZN9jk2r83HIqaM7CbYy1//P9cG99wHE5MMSwcR0jE2bvY1bsI1mn2PFNNnfuI2YNiI+IO0TNz6PR+O4UDgUlFXzaiwUVoMSm4XC8rJ68Vkax4VCj1XzaiwUlp59Bl2JzUJheVm1+DwejWNVdCB99MnPD3/t7OnFP8/44RdBnvrLw2Wz08N2bV/8Q8Nyg6fd8cjC+nz6sVsGyx65/4bBMjMa7vroummHOLExLPW44cZhO7vzTxret2G8WFZh2uEoqraGvzd+bPh7Oq4XbnPfrNi/38NADfgNxcwENYo6YXpjSu0eRmDWwE6TXZi0UG1mG6FWwDvi2BDWfXr4EIXYWBDlhlNbPOv0WT5//kaaYHnmjY9Sm8C9F26cW7Y9+Mgp4tQmGUSVuv7lXIVpheqiMDtjMa1QPyaEUapndSltv7nVz1Mxm4khnghQRcT1rMhqP5cera8nWUAnlahcYK1qmfmUFhfItlAOYyPTSY1kScSWHyVLJ6c7u6GzHCUGk2QeQrZbS+mYu67Krjs6BDP/vvZeZBNJ0oWpr2hjqm9tAreOLrJe34w1kVvXLuGjpY2Wk9WMkyPHF5ozqQsYqNdafGOJtSGqSbZtkiz30CSDwQKauschWeyl1NW5u1mFxybrtI0jqvBQTLZcbeu2reTCdr2jvwILxhKbV4FkWaJHjGBjxDQOO3G4kcWtWcKaoV03+LEQxilm/ZpJ1m79lNKOlAI+Hz7TZolATrNs2pyauN22dDS+n544SeqiE0ItxCrZx4UKfDc+glgrsVZ0FGEUMVWyNqxcwNkwt26zWUrRpY8P+VzrrOB8sMS4LYnozlHN+bM1bseQ9OUiuZUnotnqbG/7ODMfz9cKt/29rj4xyvyzs6QL3hIaS5gZZGqyxEKSvGIGNqeyNm1KYW2toI1iydIK1ZRiHk2qlQgm5Gtvl6561yf0ZTQ6Py4pTTTzlNFz6YbN/nADFrp7smLxWcxhC4VCoVAoFAqFzPF4clwoHAb9lxoKhcLhsB8rtxKbhcLysoLxWRrHhUKfFesaKhRWhhKbhcLysmLxeSwaxxojcXNvMepsWIoLJxbkRmY7repebN06vGtOf2bYyu3cF4btz77AcLpqADaHt3nzh4frWl8a1vhe7qHMuWcP29lNbx/WK5t2eMV+fbFaZ+2B4f1XLdgHiyzgzCPnF25z36xWfB8OAtEpdktSmtUAbppTRhuwk6RBxGTrIemnQ1XINk99zZ8YJarQxO1z20eDkTR/rW6pTODEySnTumI8arlhfcJWU/OYJAs1NSk9dKySDjqMU/pqiZIEZUHmmmMJQKeHNUkbGLN1nCpoNBgTqW2gIaVuHTvPmfEW52ZrWEnpo30wnJ2eQCplVDUpBW6XihohZD11Csycirk12E1LHEWwipkYJAhxPSKNYFohbERCHWFmkDaldvbj9Khm4iK2DkRv2NxM7zyIUe4z6eI4aVNMPVqvM/OOzaamMoF1lzTUxoKrAmujlk0dEUcxVS8K5P2S/Ow02VEB6rPdVzBoZ9mXU2o7E5FsubVWt0xbR4iRtVFLbQNnvU3fEXD7tXKDEptXgwjUNYwqtHZoZYm1S7ry2hBHSWvsx4JfBz+GsA5+DeJIk47VJltBtVlrblKKY+8N4pPuWIJkzbFki7BsUdYCM0GiznXJxiu21WRJllMbR5ssymIFYSTESgijpHuOFfiR0rqUan7bIi6Na76u4CXVJ+RUz63Mtc7Wg3Ta6NDTRvtsa9dPAw3b55p0dUs63E6DHbtUzTZppmNOu602jeu8riQLy7klYvo0gbTffE+z7dlxjndpppPGOA8+6Z3pUkNLpzOW+fpVst3bPJV0vsZF3bZx82ldydItYpqAtBHTeGgD0rTJPrdd3IbawRHEp4j8GPBHgQb4LPCXVfXcHst9HrhIuqJ5Vb3rcusumuNCoYeoXvFwTdsT+TER+aSI/K6I/KKInB5Y7vMi8hER+ZCIvP+aNlooHEOuJjZXLTFBobCsHFF8vht4gap+JfBp4PULlv0mVX3xfhrGUBrHhcJOVK98uDYOLLgLheOAmH3G0NXEZmkcFwqHwxHEp6q+S1W7x9vvBZ56zb8jUxrHhUKHkjK8XelwLZs8wOAuFFaGq43NFXtJqFBYSq4tPm8Wkff3hu+5ylp8N/COBTV8l4h8YL/rPxaaY1RRv7fPsVkgiak3FqecXhs1g2VnX3R6sKzdGNYVn/zCAi9es9iLt7o4XKYLrIPrc8O6vbUvDqekBrjhk8MrPvviYUF3e3KBmFkvoyP8zQ8PFq3f8NLh7y3YZHxosYf0fhCuupvn5l1Shzep6puuYj3fDfzsQFkX3Ar886tc/8GgyTNcso4UttNHS9bx2Rngk37WTcg+x0Cb07qOUp7ToAozi9aR6aziSxdPMW0rogqPTJI+fpo1tMEarI1UlaeuPGuuJUTDqdNbWKPEm4UL59dwdSA+GSpR2tYyPeVgYpN/L4qZGsJa0lZKELBJe6wCxgoxpPTVk2nNzCjeGzQaJtOac5tr85S01kTqys89gCsXaFpBBNY2GpwNXLywNvcsNjbiG4fWAR178EkwGNbjfB3qUz2lEWgsdmKQCGGsMEv7TJ0STgpMLXhBraJ15NHGcnFznOqvMGuqeapbgNvPXGRUt8RouGljk42q4Z7mJuLIEEThYoWdCm4raUHVCHbLIAohQLQKTU6NK6BjCKI0wSZfWdKDoZDTf4dgmARDaHNO2yi0cX8+qtcQm09sYoTNTfAjxNdQVxhVoAIRZGSQKGnfaj6OpvM1VnCKZu9vW0VcFZJOPWvxY067rHE7jbL3gnoDrYE2v4fQptg3rcz9jyV7IYtPqY0l3zYkZs2yB90k62vTCwna19vOdbe56nF7oH/9idvbMr6b3k7RnNI16zxV8/ZnxHhNKbfzU87OZzjlWxfUGLQyqDOoFWL3WQnR5aEil0F0WQvcvXfRaZ0jeRvZ23iq2CZiZ4qdBcwsYGYe8XHnE9fus/sT6HJdKos6S6wMsUp16jTLxkfERyREpA1pvA1JX+x9+mxbtGnRdnEbquMa4/ORRb2hIvIe4PY9it6gqm/Ly7wB8MBPD6zm61X1PhG5FXi3iHxSVf/jokodj8ZxobDcLGVwFwqFQqFwnFHVb15ULiLfBXw78HLVvVvoqnpf/nxIRH4ReBlQGseFwr45gKdTRxXchcJxYN8hV54cFwrLyxHEp4i8Evh+4A+q6p6WZiKyARhVvZjHXwH8vcut+9g3jm/81PBj/y9+zeI0xk+79dxgmfmqxwbLzn75+mDZY5vDZc358cL6zBakUt3aHP4tF59eDZaZ9qaF25QFspRFUo7xAhVDtXkF9ky767MgvhaFnrnpzOIVL0hLvXMjhxvgBxnch4ZR4nrAq83WQzC5JXUnIskWqr6g2GlKizo6ly2j1gR3CWItTGqbugYNuIsGf1KYMmJ6cYQde4xRHmlOzO0XZ3U6cU0+YSazigfaU4SYZAwxarJeG3uMSTZrqpLG12cYo8wah4his5Vc6+08NWzr7fyEq0YezXZmCqlLWVKK6S6trOYUtU2b1olC0zpCTuXcepvWn0M8xtwvnLcnnWVWRnLWX0Y5QDVtPNzYLZBTNws4F9L61lvq2uNDStO8vjZjvW45e2EDMZokH6I0rcOgNMGlZRW22hrJNnkxd4vveE+us7mSgTjUVMcYDNNZhQbBY9k0Nd5bfJukFn5mwSeZhzSmpI8+aLIcgRhh1iDeI43DmBlaW+x0jJtUuInFbRjaicFPoZ0Ifj3ZH8aRENckuR5WKY3zet3ibMBJxJqIM5HaBFy28OvoJEdRZXsgffosyQiaP6NJQx5PUg3zOPlGZ62YPtMJqbHTV5AsGeeSBUnnbezK8ry5fVuyc5QcX/PzPMre9m55fNF9qlv28bKJ3nhnKZelHvg0z7YkKcVekopZuy3r6I5tf5Pzi8ZAlYQkGVNBxSBWEGOQKiK1A60hZHlETPINicDHL/Nb5xs4kvj8SWBE6k0FeK+qvlZEngz8C1X9NuA24BdzuQP+jar+8uVWfGCNYxF5M+lp2EOq+oI87wxJU3kn8HngO1R1uBVaKBwm3cXzcDmw4F5Eic/CseJoYvPIKPFZOFYcUXyq6rMH5n8J+LY8fg/woitd90G6VbwFeOWuea8DfkVVnwP8Sp4uFJaGw/ZpVNVnq+od2aLtxar62jz/S7lhjKreo6ovysNXqOoPXYef+hZKfBaWgf2+lPfE8jl+CyU+C8eIVYvPA2sc55eFHt01+1XAW/P4W4E/flDbLxSuiieIj2qJz8Kx4wnkc1zis3DsWLH4PGzN8W2qen8ef4DUXbwn2YvuewDGDOt43WRY3xrOjhZW5mPxSYNlN964OVh2Zm1YwHrLzQ8PlrlbFvc7ODP8W3wcFgA3C8p8XPz/x+tw+SNbw6mlz37uxsGymz+6+HfaG24YLNvaGK7PQj3ymVMLt8nvLS7Oa1nqYD0Erio+7U2nkTWPzjpbLyWMBK0UIikV7FhyKloIdU6xaiHWEEaklLSktMixJlnCKdAagrFEo8keKgrYSGhrUDAu2Z5pEKI3SYdrda4NtFWkbS2zrH0UE1P6ZyFpg8kZkbvD3o1nCSMw1xtD0haLUSR/odNUdjpLVcG6OHcdNKLQ1xLX2yJ/AYzdudl+mebvi+jcfs3ZFFshCnWVrhdj59mc1VQucMN4wqStaYLlGacf5TknHuLX9Tk4G3jGyUeJCF/aPMXt6xe5fXyBz7ibCcHQBMtGt91gkM5urbPm6/SZWXjcWWiRpdPdQmJ0x+9ImtGc2lbJx0pTBu1q27Lu8jzhYxP2GZ877p3uFKyvg9n1dD5GpAGZeqyRpFelsz5LKZhtk1I5h5EQNw1hZGmrimYUuTBKKcK7eDMmHXub9f3GRKxRrORPk7TJVpIm2YhS24BB59PzIZ9BXUwZiTumgfkye2H2uFF0cTqf7vmCbuuizZ7L77VsN75bR90NIRraaGmDxUeDD4bW26Sp9oYwczA1yMxgp5KGmWCn4CaShqnitgQ3MVgrWIC287vLx8ww15Wn9NbJWi46g86t5FJZ0jsrEg0SctrsnJaa/vjcsg4k7lcrsXrxeWRJQPJb+YN7U1XfpKp3qepdFYsbuYXCdUFZqX++18KVxKc9NfwnqlC4LlxtbD4B47Mfm7Ud9uQvFK4bKxifh904flBEngSQPx865O0XCoVhSnwWDp/9SY4LJT4LhUPjsGUVbwdeA/xw/nzbta7w3LOH7dGqxcnhqL8w/K/aPji83gduvnmw7NLTh7sh3O2ThfWRBbqBuOClFVlwc1m0Tkh2UENYM/xbtB7+3vSmxZkAzfPvHCyb3DQsEXGz4W3KZDjb4RXxBHojfg+uLj4j6MThJtu2SKNHwW+krFB2Bs0JiBuBFkt0ySapPRlpziisB4xL3YWuDvgNi0RhvN4wqj1bk5oQDPV6g7ORWesIbTpPwsyi07zdmLK2SWsQL4gXmpMe8QYzNUnWUedsc4CeCGhn7dQKjLuUWmCqkGzXJEk2EDBZ0iALAm7oQUgnLejHo6rskEx05ap7r19EaZrtS7YPaR+0zhJC6rqNmjLRRRXOzcY8Up8gRCGqYyvU+GhoguNSO+JLepqmtcSYunwvmHG6zmj+3RUEQ+oxNRBdksWgSlyLGBvRXqiLi1Qjzw3rUx4L6zgXOLk2Y9o6Zq2jdoHGW2atSRct38k09skTOzbhauIzKmgEU4GzOZObBWeIlSWOLX7N4tdMGsZCGCepU6wATfFrG3BbuStfDHNLsrArA122ckQhkAbYPswqSU6lFqIF7eRV3WdFyvDous80iIuQpRtpSCvt5ByXu8/tZneMDX1/r0jsL7tzfPc2sqwob2/bhi5ffwxglVhlO7qcnTBaCLXg1wS7LtjGYmcOO6vplJfdsvOsgdLNyxn4zK5x6VvH6TwroGnzPDTb3KUsehJzFj1/BUG3YvF5kFZu/xb4RlJq3S8Cf4cU1D8nIn8FuBf4joPafqFwNSzz27PXkxKfhePGEyU2ocRn4fixavF5YI1jVf2zA0UvP6htFgrXzIoF+BAlPgvHjidIbEKJz8IxZMXi89hnyCsUrhtK6oYsFArLRYnNQmF5WcH4PPaNYz/s8kYYLz5YYX1YO3jmdx4b/uICe5NLzxm2OGtPLKgsMDo/bOVmZgsEPQs0kBIW74PpzcOnwLlnDb+vOVqQCTuMFm/Tbwynu24XGB+E0QKt5+h6nMrL/fbs0hIFc8limqTNlQBukrWFayk96tz6i6SBk+5UN3u/dC+inFyb8eVnHuQTj97GzFuee+YRxrblngs3EaKhdp77Hr6RMHEwCtg6rTScr1EHEgRpkv2beKAC9cmmCqNpvNu0ggZBLIgLKU11SFZwCPM0xyLgWzO3NROTLI80ZnEukrR3fUu4bhuyva3+uEZBWpPSR4vObdS0UszUIEGITsF10xArJWfSpnEK6x5tDZOQLPOkUr7QOM5ubTCZVagKQWWexrrxllG1nWpaTEorHbqV7tZk9vbTjune75Gs1x5XLcbGlGa4alAFHwx1lVJ5N7ZKceaStnt/lNi8KkJAL15CxmOoa2Rco9aiVoh1smcLI0MYG8KIud7YrzFP/9637evGJbCtV/VgmqRLto2mz5li2ohpFDuLmDZgmoDM8vG2kvTPxoDpbMgEdekz2jxu87h1DLmOSndr1GxHlvXQaG8cdjbe9jqP92D+281ObS8iSTNtk1VazLrp6AR1PT111hBrfzD51Yas16av3Y5ZG5zTSXda5OgANcSoSWdsmA/zadlef7TJ5q3b9vyYeRAv2FbTceu0x16RVjHBYNqIeMF0qcf3xerF57FvHBcK15UVC/BCYWUosVkoLC8rFp+lcVwo9FmxAC8Ulp79Og2U2CwUlpcVi8/SOC4UOlZQN1UorAQlNguF5WUF4/PYN44XaVTjTYu9b6drwz//4a85M1h24r52sGz8yGywzK8tEOqyWFfstvxgmb04/DvNbLiuAPWjw57E9blhjfTFpw/rhusLi3WE488/OvzdO28fLFuQ6bpwlBglbgSYuG2vy6yVk5h0bp336VyrGFOaWg3Jo3iHNjd/NsHy0PQETUhevI9M16ltYOodVjTpjkctjShVFTi5NqUJlomNcw3sbLPGdB6pgLQm6Yendlt32JgsIVZoDCqa0yPpdn00Z2k1kX7uJBFFyfpjUcREoreAIk6Tp6lul6s323pj2V6vdmmUVbIOO086RY3ONxmdIpakK86exDITolikNdiZECtFK6WdGc5NHcZGYjBcVPDZH1pEUyrqvClnA8bEba1xzJptL9iJzPWS0qYHvUEMOhK0TZpoRImSfJEnTYVGwQfDLDhm3hGiYdpUTLZqtNNsTwyxKUF9oDiH3HQGrS1aO7SyxDr5HYc6exuvG9p1wa8nrXH6TJ7gcz9dq0mnn/2FUZl7iafzJGlZTStZyyqYxuTxpEc2rWLb9Jm8kVO6YhOAPC7KdpZyn9IYGyMpJmH7vYV5DMl8PKU/zt/trU/idjpkVOfLdePJkDjHv+4x3aenwVURsDtTN/f107Eyadz19Mkux5I8fn3zTcTkP7ytBY5pn4U43z9JZ5x0xZr10J1mO87TRpPH07bTfsga8aBI99ntm7yOWBkka8JXrcF7JRz7xnGhcP3IhvmFQmHJKLFZKCwvqxefpXFcKPRZMd1UobD0LMj+uYMSm4XC8rJi8XnsG8dhgVLhllsW54++/cTFwbLHnj2cWvrcdDRYNpkNyxSazWFpBIB5bFiqoHbBodLhnXCFWTV3rnZBj6e7NFx26vOXsXK7+57Bsvoltw6WtQus9/zp4eO1b1ZQN3UoGEU2PPExh53ni2UuGzBNllZEwXjBzpKswnuwwRC9EE3qL2xCljyMIhcvjdma1HOJxAPnT6EqRBWc3ZbuVFVARJm2FSEK1sYkcRDQ9RYj2+lltRKMicmSLduWdeUxGoyJ1FVgOqvm2+lSVnc/SzXMU0ont6OYpvMCtg5oJ5fIEgkRUjC64fNrz3tLf15PeiKwI4WuydszNqbUuAo3bEy4/cRF7j2X7CVvO3mRNlg2m5pbNy7x3FMP8Rv+2YQonBg1nBlv8Vlv2WrGYBVpDKYR3DTZesUa7DTZ36mBEFPabdOmtMLRKQHLhcmY4C0xChdkTNtafGvxRomXtq9xpjHg9/syHiU2rwJ1lnBmA7Umd+nnrv5sQRZGgh8nKUW7niUVG0o8EWAcsFXEuoBzgboKjJ2ndp6R9ZjezcXsutHEHKfdEDSlNFcVQuzGISLzmI7x8eMKOe1yntcbksdZKk9p68nSiS6Xcm+6z9BNcY+07Vdy/9xzM7prGtJ1sE3SE8myE9NuXyfdVHATxU3BNElSYSceu9VAG5LESuYXnyTv6CzdKksYJelMOr4mfdbbdUhSkl6dbZZ61L0fsG8Lt+7Hs3Lxeewbx4XCdWXF/v0WCitDic1CYXlZsfgsjeNCoc+KBXihsPTs8cRu7+VKbBYKS8uKxWdpHBcKc1Yvy0+hsBqU2CwUlpfVi89j3ziuF8iKLy3QBgM061uDZU/ZGF5xfXJYO7xbe9XHX8aLzEe7YL3Db4Iu2ma8zFOZuKBOF9rh/ffZh24eLDv7yImF27z9nQsKF1R3kf5rdtPiY70vlIWpwQt7IwbqtRZfjZIu0CRLqO59gFhtp0nt7NygmycYhRBM0ulGgVbACaGxBHXYOiAm4qNBY9pezBpfYzSdM1HwPtuUmW1rIhFNMZDjQLIFXC+bddI25vIYDbMmrzvrkJu8fCchdFVA3a4TtXclneubVebjl92H7JQX9+f312dMnI+7rK2uXGDaVDgbWK9bmmDxwXB6bcKpekrlAlaU2gRqE4gIZ0ZbfMPJT/HpE7fSRMtzTj3MHeNHeWy2hjXKrHE0D69hZjalo60gjhQ72dZWd5/zFLt5B7VTl3SgYpmqEL2Za0RlatC690vDfp8aU2LzajAk67Zs/5XszTSlSo9dGmIztwNLCF6SZtxXkVAZQmUJ0ROCIcSkV3YSsSbiTMSguDzuJGIkzu9LBsWIzu9htq9V3vOsHyb2bhBhwb1tr/vaonvovre/a73d/bWrVzfto8WrwUez/RkNQQ2Nd0y9Y9Y4vLc0jU32ho1BGoOdCG5TcFuC2zJUW0p1qaLarLFNRPy2BV1nh0dIx3U3Oy4/nRQ764nF5pkwt+zbTkmdreH267S4gvFZTCYLhUKhUCgUCoXMsX9yXChcV1asa6hQWHr2G3IlNguF5WXF4rM0jguFPisW4IXCylBis1BYXlYsPo9947g+P1zWfPTUwu9+8dJw+RcWSFjbBZJaf3JB6uS1xWmVscMnl5gFZddiZrxA8mfdsIaonQ6fOtVlzir3lCcPb3O2QD+9W+fZI9T71C4uRFfOq/Ew0JjPB0kphhFNnqpZv+azfyqVEtYUdSk1cRgrOgowiohLqj3jIrE2yR/ZKTEI0QuqLotbFTGKhuyXK0DOVa1e5t6/qCR9ch0hp6kWIcVYPq3FxbnWGJWcGjpre3MK6vT7ZEf8zT2L+5rH/vS1xOOeO7iXsrbzORZo8nasTfVuGse0qea/6azZwEdD0zpElPPNmBANPhjONWvc09zKZlvj1fBYs04lAR9sSi3tArNRJPjkpRrGShxHwnryj41Vl+eXrE/M+zxAxM73QVSDRrMt2Bbm6bF1wfVuj51QYvMqkKC481O0S3NszLYnrhFMazGtxU2VasvgRxDGgh8J6iwqNqUozsc5CFwycHGvU323vrXTrkrvc4euVbfHTT6H5svp/Fzplp3H9vyFgW3Tb+nms+t+eDnr3muI1f52RB4/X/fQRKeM1Ol6FL1J1yZvEJ92rgSZx5XapPUPtSDrgFhiY5LO2Hept3P655im1WQvawMYybrk7DMvOy9ZXaXn7wz0XsTQLi33vm+rqxefx75xXChcNxR0xVJgFgorQYnNQmF5WcH4LI3jQqHPiv37LRSWnv0+nSqxWSgsLysWn8e+cWwWpB9de2jxVff03e1g2eiBzeFtbs0Gy3Q8nAL6WpDHhlNdc2J9uOwyOqB4Yjj19KMvODlYNrl5eN9Ww7suVemmG4a/e3GB9GTBT5meuU7GKyummzoUvEHPjrCdBZqXeUrUMILoUuphtYo6Jdhk56a1Iuueer0l+HT8RiNPjELTOEajlnHtOX9xjegNrvZYG/HeEH3qRo1t6prEgBmF1JXZswfTWeril5i7CSPzce3biGlOw5pnhfbx55MY3dmQkyzx6MkG5hZZkqQjnYSAKKkb2XZyhJ2yjG7Z+fcgpeWepS5XtWnbZprWHzs7NAVvlXAiIDODaYVogTpy1o541J1ArKJR2NwcE0OSiFyajtjyFQ9fOpEsu4LlPncD5zbX5mEmTtFRxDtB64g4JawlWQUmhYqYJJMh/yS8wMSi45D2Q06zjdHUhWx1LqfQjbD9W/dDic0rZ9Yg9z2E1BXUFVQVWjm0dlBZxMo8bpPNomA8mCbJoiSCqCbbRe3SDye5hvHp/mt86uY3XjFtzN3+cYfdGDHOP+csOp6dTmGnXmF4edXtAVKcd9O7G21D2+3Wb6QnOZBtT69+nURyGm6LOoNWKS13dDlNdyVEl4dK0jXQkeexbZEmuz5z3W1LSi3t0362Tdq/u+veXbMEnddPomJa0BgRA8YI2HRdUCM9mzZNMrgdNn7XwIrF57FvHBcK1w3VlfNqLBRWghKbhcLysoLxWXyOC4VCoXB0XO8XGK8zIvLXReSTIvIxEfnR3vzXi8jdIvIpEfnWo6xjoVC4vpQnx4VCnxXrGioUVoYjiE0R+SbgVcCLVHUmIrfm+c8HvhP4CuDJwHtE5LmqehlLokJhRVmxe+exaByLc7gze6crjtWwWKZZ7OTGxTuG9cF2OqzFrS9Nhld634ODRTIeXmdacT1ctujEe2yBn51Z3DlgLlwaLLv5wvDvbG8b1iOH8eLTShdox7ZuGz4mfjz8vemZhZvcN7piXUOHgoJpO/shTVZFtpeO1LFt5UXS/HbppLW1tD37vtZbtGe9VtuAdSlN8njUUrnA1rQGFzEmMjMVsTWYKjIatfhg8Y1FjCKi+K0KbNIGG1GitymWgiQdMElDmdIf53mRZPkmuc4xa4c7HeJce5x/s/bkiAaUXG41ha2Svt9PYx1lh/Nb39Zsh8VZHdHeNS50ZVk/Pd+fvmfLBMkWqjGAJY4iohCmFqIQrTKZOu7Zque669k0xV1sbbLEmyUdtUTBzAQmhlh32lMhOp1rmTtXLc36aok63w+S950a3d5l/d9wJZLjo4nNvwr8sKrOAFT1oTz/VcDP5PmfE5G7gZcBv3kUlRzECIzqdG8ZVWid9MZx5Ii1IYwsYSSEkSHUQqghjCS9IzC/dTz+IKVzDkzoaWND/mw6/fEuHXIbMW0AH5EQki7Yh9QdHyJ4v1M3PNcP9457dz/Lut8dOuH9EHvr3n1P3Sv98oJVSX/7Itt1EEkXAmtSfW3SI2PMXKuMSfPUmHzdlKwH7r8HodudKj0Px2R1J3M9tDohVjbpiF1af7RkDXTSOauFaGXbNk/YZaOXL8+9srmF3z5ZtXtnkVUUCnN6L3VcyVAoFA6Yq4zNFJ83i8j7e8P3XMGGnwt8g4j8loj8hoi8NM9/CvB7veW+mOcVCk9Arik+l5Jj8eS4UDgUlJWzoykUVoJri81HVPWuoUIReQ9w+x5FbyDdI88AXwu8FPg5EXnm1VakUFhJVvDeeSwax9On1nzi79y5d2Hrh7/YLH4w3p4YLj//rGEJRBztdR3NZeu3DJbpZZ7TSzvch2G3hr9cXRj+nlympyMuUHIsYtF6ZcEhAXCbCza6YB8t2ma4jGJl36yYkflhIHXE3LGV5AWk62RrkryCrGAQo1RBkFPbGaRMzkRnTcR7m+aJpmx5orSt5WyzkWzeFDYvjXI2LIh+5zkfGsvWzG73g+Ztd1nw1Jtkk2ZikgJUmrLgQerWzFnwpFsm9yeaLBWIUVImurysaiqT7neSfpc1kaZxGJOyzIVoiMEkGzgF31qkk2KYJEvQ2JN4SJoHbK9bd9nIdT2s/ax9vZF5V2zeV/NTepcrlnWBkPe7mLjdS10D6xBDqlvI9ULSfqA3LV0mwe7Ya7a+sp20IiI27ddOjTvPLCaAHT6vHscBxaaqfvNQmYj8VeAXVFWB3xaRCNwM3Afc0Vv0qXneUjF96ohP/N0uI+m2Zdv8U9OIasjT+fh2ciIlyXdU5tffHWXddB6kl1Zt3iXfk890WfIQ3VG+IyvefL5ul+310mbfCnFeB9n+XXm6K5MsmZL8m+byLu3N6+2b+Tp7u27XZvfUXVxOirDn+6d77std9dCd9dvxnd469trO4/b1XtO98R3ZDffLit07j0RWISKvzG/43i0irzuKOhQKu1FAo17xsGqU+CwsG1cbm9chPv898E0AIvJc0l+HR4C3A98pIiMReQbwHOC3r3Vj+6HEZ2HZOML4PDAO/cmxiFjgnwDfQtJpvU9E3q6qHz/suhQKO1BduX+/V0qJz8Khs5+nU0cXm28G3iwiHwUa4DX5KfLHROTngI8DHvjew3CqKPFZWEpW8N55FE+OXwbcrar3qGoD/Azpzd9C4QmHiPx9EfldEfmQiLxLRJ48sNxrROQzeXjNAVapxGehkFHVRlX/gqq+QFW/WlV/tVf2Q6r6LFV9nqq+45CqVOKzUMgc5P3zKDTHe73l+zW7F8pvFH8PgL3p9M6XGvdI7Tr/3g7d7s5H9tWF3d/bLr+cHniervXxqyVsLBLgDn9P/OKN2s3rrzN+nMZ4V50WaYyk91xkd2eIWaAzdlvDZbB43y/aZtt3k9v9O65Ey9j/3uF38/yYqv4vACLy3wI/ALy2v4CInAH+DnAX6Zd+ID8teuwA6nPF8eluvgHVrCvNmtTk6qOgkiy9NI+nj7kWVgUissO9yWQbtqRvVYyJhKzb1ZyGWWzWHask3euOyuVN57TSc+3iXD/Y1avvnJRt17Lm10jS+6Z0yzE5p+XUy8ZEgjfE2D1dSHU12f5IRIlBCGLSspq1jpJEgzEmmzry9nbUuVeHTg9K3j+S69R95qLH/fS5vVzenztkk7J9TII3iAnEkOzzjImIRGKnO+1SROf6dDpjtFe1VLu8zVQnNXGuoUYUbSVZ4+UvaBCk6pbZf7wtcxfsIXLZ+Nx97+zQeZD1tMddQPanyccSzRriVKgk/a7OUzOzHUf58D7OfKB/vki37u3iuZ54XoVePHR1GjpHeitS3fEFpLcCzXpp7azRerHVhdD2+xKy47fMs7xf5tTb1vRe/hzVXtDu1gt329yx/Z2XiPk4O7/6uA6YBdLmx033d3M3fqXRdkTxeWD3z6W1clPVN6nqXap6lz2xcdTVKTxR0Hjlw7VsTvVCb3KDva9J3wq8W1UfzQH9buCV17Tha2RHfJ4q8Vk4BK4mNlesq3c/7IjNkyU2C4fEEcTnQd4/j+LJ8RW/5dvce9+lL3z36z51oLW6Mm4mvZSxLJT6XJ7nXW6Bizz2zvfoz++dbWYxYxF5f2/6Tar6pv1+WUR+CPhLwHnyyz+7OExP1SuOz9k9X7r02T/zA8sUn/thGc/Ry7GqdX765VZyDbHJPrZ/nLii+Gw+f9+le1/z+uMWm7C65/qysQzxuZT3z6NoHL8PeE5+w/c+UgrOP3eZ73xqkU/lYSMi7y/1GWbZ6gOpTpdbRlUP5GnsIh9VVX2bqr4BeIOIvB74PlIX0FFx7ONzPyzjOXo5nsh1PqjYPIZcaXweu9iEJ/a5fpgch/g8qvvnoTeOVdWLyPcB7yS5XL5ZVT922PUoFA6LRT6qu/hp4Jd4fHDfB3xjb/qpwK9fc8X2oMRnobC8lPgsPNE4qvvnkSQBUdVfIv2IQuEJjYg8R1U/kydfBXxyj8XeCfxvInJjnn4F8PqDqlOJz0JheSnxWSgkDvL+eSwy5AH71p8cEqU+i1m2+sBy1gngh0XkeaQ8VPeS37QVkbuA16rqf6Wqj4rI3yd1qQL8PVV99GiquyfLum8XUep8OBzHOq8Sx3X/H8d6lzofPgd2/xR9nPdKoVAoFAqFQqHwxGRprdwKhUKhUCgUCoXDpjSOC4VCoVAoFAqFzFI0jkXkzSLyUM5f3807IyLvzun+3t2JqSXxj0Tk7pw28KuPuD7fKCLnc/rCD4nIDxxSff60iHxMRGLW1/SXf33eP58SkW89yvqIyJ0iMuntnzceUn1+TEQ+mc+RXxSR072yA90/q4CI/Pf5eH5URP6tiIxF5Bki8lt53/2siNR52VGevjuX39lbz6Ht64E6v0VEPtc7/16clx28jsjhpepGRP67XN+PicjfyPOu+Nq3BHX+QRG5r7efv623/J7ngIi8Ms+7W0Red5B1XiWOY2wuqHeJz8Opc4nPK0VVj3wA/gDw1cBHe/N+FHhdHn8d8CN5/NuAd5CyJX4t8FtHXJ9vBP7DEeyfLycltvh14K7e/OcDHwZGwDOAzwL2COtzZ3+5Q9w/rwBcHv+R3vE68P1z3AeSQfrngLU8/XPAd+XP78zz3gj81Tz+14A35vHvBH72sPf1gjq/BXj1HsvveR0BzgD35M8b8/iNB1TnFwAfBdZJL0e/B3j2gmvNMtf5B4G/tcfye54Defgs8Eygzss8/6jP/WUfjmNsXqbeJT4Pp84lPq9wWIonx6r6H4Hdbw++CnhrHn8r8Md78/+VJt4LnBaRJx1hfQ6cveqjqp9Q1b0yH70K+BlVnanq54C7gZcdYX0OnIH6vEtVfZ58L8nbEA5h/6wIDlgTEUe60N4P/CHg53P57pjsYuPngZeLiHD4+3p3nb+0YNmh68hhpur+ctINdCufq78B/Emu/Nq3DHUeYugceBlwt6reo6oN8DN52cLlOY6xuVe9S3weXp2HKPE5wFI0jge4TVXvz+MPALfl8cNMpbuf+gB8nYh8WETeISJfcQh1WcRR7Z9FPENEfkdEfkNEvuEItv/dpH/0sJz7Z6lQ1fuAfwB8gXTjPQ98ADjX+8PR32/zfZrLzwM3cYj7eq86q+q7cvEP5W7OHxeR0e4676rbYZ4fHwW+QURuEpF10pOnO7jya98y1Bng+/J+frNse4ouQ51XhuMYm0P1LvF5qHWGEp9XxDI3jueoqgJL4zm3qz4fBJ6uqi8C/jHw74+qXkvK/cDTVPWrgP8B+DcicuqwNi4ibwA8KXtOYR/kC+erSN1sTwY2OLgnHdeFveosIn+BZPb+ZcBLSd2af/vIKrkLVf0ESfLzLuCXgQ8BYdcyy3btG6rzPwOeBbyYFPP/8GhquNocx9iEEp+HRYnP68cyN44f7OQS+fOhPP8+tv8JQeouv++o6qOqF1T1Uh7/JaASkZsPoT5DHNX+2ZPcXXM2j3+ApGN67mFsW0S+C/h24M/nixgs2f5ZUr4Z+JyqPqyqLfALwO8ndRN2iYP6+22+T3P5DcBZDndf71Xn36eq9+duzhnwf7LddTxUt0M9P1T1X6rqS1T1DwCPAZ/myq99R15nVX1QVYOqRuCnWLL9vEIcx9gcqneJz0Oqc4nPK2eZG8dvB7q3Ol8DvK03/y/lN0O/ltQ9c/9eKziM+ojI7VnDhYi8jLRPzx5CfYZ4O/Cdkt5SfgbwHOC3j6oyInKLiNg8/sxcn3sOYbuvBL4f+GOqutUrWqr9s6R8AfhaEVnP5/bLgY8Dvwa8Oi+zOya72Hg18Kv5z8hh7uu96vyJ3k1MSNrAztFk6DryTuAVInJjftr1ijzvQBCRW/Pn00jawH/DlV/7jrzOu977+BPs3M97nQPvA54jyWWhJr0s9vaDqvMKcRxjc6jeJT4Pqc4lPq8CXYK3AoF/S3rU35K0LX+FpIv6FeAzpDcuz+RlBfgnpCeQH6HnjHBE9fk+4GOktznfS/o3fBj1+RN5fAY8CLyzt/wb8v75FPCHj7I+wJ/K++dDJAnKHz2k+txN0kx9KA9vPKz9swoD8HdJeeo/Cvxr0tvMzyRdOO8G/m9glJcd5+m7c/kzj2JfD9T5V/N14qPA/wWcyMsOXkdIGvW78/CXD7jO/4nUuPkw8PI874qvfUtQ53+d6/S7pJvoky53DpD0kJ/OZW846nP+uAzHMTYX1LvE5+HUucTnFQ4lfXShUCgUCoVCoZBZZllFoVAoFAqFQqFwqJTGcaFQKBQKhUKhkCmN40KhUCgUCoVCIVMax4VCoVAoFAqFQqY0jguFQqFQKBQKhUxpHBcKhZUip079UB4eEJH78vglEfmnB7C9t4jI50Tktdd73fvY9jeKyH+4zDLfICIfF5GPLlquUDgMSnw+bpkSn0tIaRxfB4462HOijd8Skd8RkW+43ttbUI9fy7/xrsPaZqFwOVT1rKq+WFVfDLwR+PE8fUJV/9oBbfZ/VNU3HtC6rwlV/U8kz9JC4cgp8bmTEp/LSWkcXweWINhfDnxEVb8qB9ocydnpDgJV/Sbg/Qe1/kLhetJ/iiMiPygibxWR/yQi94rInxSRHxWRj4jIL4tIlZd7iYj8hoh8QETeuSvT1NB2/rSIfFREPiwi/zHPuzNv64N5+H29Ov2GiLxNRO4RkR8WkT8vIr+d6/KsvNxbROSNIvJ+Efm0iHz7HtvdEJE35+/+joi86nruv0LhICnxWVgmSuP4ADmMYBeRFwM/CrxK0tPqtfw09x+KyIeBrxORHxCR9+ULwptE5umuf11EfjwH9CdE5KUi8gsi8hkR+V972/gLOaA/JCL/XA6wwV0oHCLPAv4Q8MdI2bl+TVVfCEyAP5Jj8h8Dr1bVlwBvBn5oH+v9AeBbVfVFed0ADwHfoqpfDfwZ4B/1ln8R8Frgy4G/CDxXVV8G/Avgr/eWuxN4GfBHgDeKyHjXdt9ASg/8MuCbgB8TkY191LdQWEZKfBaOjNI4Plyue7Cr6odIwf6z+Wn1BNgAfktVX6Sq/xn4SVV9qaq+AFgD+v9qG1W9i/TE+23A9wIvAL5Lklzky0kXi9+fn4wH4M9fh31RKBw171DVlpRW1QK/nOd/hHSjex4pFt4tIh8C/mfgqftY738B3iIi/3VeL0AF/JSIfISUzvf5veXfp6r3q+qMlKr1Xbvq0fFzqhpV9TPAPcCX7druK4DX5br+Oil18NP2Ud9CYRkp8Vk4MtxRV+AJxjtUtc0BeLlgJy9z/1VsJwD/rjf9TSLy/cA6cAb4GPD/5LK39+rwMVW9H0BE7gHuAL4eeAnwvlynNdK/7ELhuDMDUNUoIq2qap4fSddGIcXE113JSlX1tSLyNaQnSB8QkZeQnjA9SHoKZYDp7nr0tj3rjfev0cpOdk8L8KdU9VNXUt9CYUkp8Vk4MsqT48NlHuzAomB/cR5eqKqvuIrtTFU1AOSunX9Kehr9QuCnSP9Yd9SJnUG/u05v7dXpear6g1dRp0LhuPEp4BYR+ToAEalE5Csu9yUReZaq/paq/gDwMOlP5g3A/Tn2/yLbT6yuhD8tIibrHJ+Z69fnncBf78mmvuoqtlEoHBdKfBYOjNI4Xi6uKtgvQ9cQfkRETgCvvsLv/wrwahG5NdfpjIg8/RrrVCgsParakOLlR7J+/0PA79vHV38sv0vwUeD/Az5M+oP6mryeLwM2r6JKXwB+G3gH8FpVne4q//uk7uHfFZGP5elCYSUp8Vk4SIqsYolQ1UZEXg38IxG5gXR8/g+SDOJq13lORH4K+CjwAPC+K/z+x0XkfwbeJSIGaEm65Huvtk6FwmHR7+VQ1V8naf3Y3fuhqicGvvMh4A9c4Tb/5B6zPwN8ZW/6b++uU57+xr3qm3mPqu7wat31mybAf3MldS0UjpISn4VlRbZ79gvHBRF5C/AfVPXnl6Auvw78LVUtlm6FJyQi8hOkl21+4qC8VK8l5iV5n/9T4Gz/5l4oPBEo8Vm4Gkrj+BhyGMG+z3r8GsmB44+q6oePqh6FQqFQKBQK14vSOC4UCoVCoVAoFDLlhbxCoVAoFAqFQiFTGseFQqFQKBQKhUKmNI4LhUKhUCgUCoVMaRwXCoVCoVAoFAqZ/x9isCy73ewSOAAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "tsp = torch.from_numpy(sp.T).view(1, 80, -1)\n", "\n", "# 畳み込みのカーネルを適切に初期化した場合\n", "tsp_up = upsample_net(tsp)\n", "\n", "# ランダムに初期化した場合\n", "torch.manual_seed(0)\n", "upsample_net_rand_init = RandomInitUpsampleNetwork([10, 8])\n", "\n", "tsp_up_rand_init = upsample_net_rand_init(tsp)\n", "\n", "A = tsp.squeeze(0).numpy()\n", "B = tsp_up_rand_init.squeeze(0).detach().numpy()\n", "C = tsp_up.squeeze(0).detach().numpy()\n", "\n", "s, e = 100, 120\n", "\n", "fig, ax = plt.subplots(1, 3, figsize=(10,5))\n", "ax[0].set_title(\"Mel-spectrogram\")\n", "ax[1].set_title(\"Upsample (random init)\")\n", "ax[2].set_title(\"Upsample (proper init)\")\n", "\n", "ax[0].set_xlim(s, e)\n", "ax[0].imshow(A, aspect=\"auto\", interpolation=\"nearest\", origin=\"lower\", cmap=cmap)\n", "fig.colorbar(ax[0].pcolormesh(A, cmap=cmap, rasterized=True), ax=ax[0])\n", "\n", "ax[1].set_xlim(s*80, e*80)\n", "ax[1].imshow(B, aspect=\"auto\", interpolation=\"nearest\", origin=\"lower\", cmap=cmap)\n", "fig.colorbar(ax[1].pcolormesh(B, cmap=cmap, rasterized=True), ax=ax[1])\n", "\n", "ax[2].set_xlim(s*80, e*80)\n", "ax[2].imshow(C, aspect=\"auto\", interpolation=\"nearest\", origin=\"lower\", cmap=cmap)\n", "fig.colorbar(ax[2].pcolormesh(C, cmap=cmap, rasterized=True), ax=ax[2])\n", "\n", "for a in ax:\n", " # あとでラベルを付け直すので、ここでは消しておく\n", " a.set_ylabel(\"\")\n", "\n", "ax[0].set_ylabel(\"Mel filter channel\")\n", "ax[0].set_xlabel(\"Time [frame]\")\n", "for a in ax[1:]:\n", " a.set_xlabel(\"Time [sample]\")\n", " \n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "id": "prompt-stanley", "metadata": {}, "source": [ "### 近傍の条件付け特徴量を考慮するアップサンプリング" ] }, { "cell_type": "code", "execution_count": 34, "id": "romantic-tractor", "metadata": {}, "outputs": [], "source": [ "class ConvInUpsampleNetwork(nn.Module):\n", " def __init__(self, upsample_scales, cin_channels, aux_context_window):\n", " super(ConvInUpsampleNetwork, self).__init__()\n", " # 条件付き特徴量の時間方向の近傍情報を、1 次元畳み込みによって考慮する\n", " kernel_size = 2 * aux_context_window + 1\n", " self.conv_in = nn.Conv1d(cin_channels, cin_channels, kernel_size, bias=False)\n", " # アップサンプリング\n", " self.upsample = UpsampleNetwork(upsample_scales)\n", "\n", " def forward(self, c):\n", " c_up = self.upsample(self.conv_in(c))\n", " return c_up" ] }, { "cell_type": "code", "execution_count": 35, "id": "collected-summary", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力のサイズ: (32, 80, 10)\n", "出力サイズ: (32, 80, 480)\n" ] } ], "source": [ "c = torch.ones(32, 80, 10)\n", "\n", "c_up = ConvInUpsampleNetwork([10, 8], 80, 2)(c)\n", "print(\"入力のサイズ:\", tuple(c.shape))\n", "print(\"出力サイズ:\", tuple(c_up.shape))" ] }, { "cell_type": "markdown", "id": "structural-density", "metadata": {}, "source": [ "## 7.7 WaveNetの実装" ] }, { "cell_type": "markdown", "id": "blond-shareware", "metadata": {}, "source": [ "### 1 x 1 畳み込み" ] }, { "cell_type": "code", "execution_count": 36, "id": "designed-banks", "metadata": {}, "outputs": [], "source": [ "def Conv1d1x1(in_channels, out_channels, bias=True):\n", " return nn.Conv1d(\n", " in_channels, out_channels, kernel_size=1, padding=0, dilation=1, bias=bias\n", " )" ] }, { "cell_type": "markdown", "id": "seasonal-australian", "metadata": {}, "source": [ "\n", "### 畳み込みブロック" ] }, { "cell_type": "code", "execution_count": 37, "id": "blessed-studio", "metadata": {}, "outputs": [], "source": [ "class ResSkipBlock(nn.Module):\n", " def __init__(\n", " self,\n", " residual_channels, # 残差結合のチャネル数\n", " gate_channels, # ゲートのチャネル数\n", " kernel_size, # カーネルサイズ\n", " skip_out_channels, # スキップ結合のチャネル数\n", " dilation=1, # dilation factor\n", " cin_channels=80, # 条件付特徴量のチャネル数\n", " *args,\n", " **kwargs,\n", " ):\n", " super().__init__()\n", " self.padding = (kernel_size - 1) * dilation\n", "\n", " # 1 次元膨張畳み込み (dilation == 1 のときは、通常の 1 次元畳み込み)\n", " self.conv = nn.Conv1d(\n", " residual_channels,\n", " gate_channels,\n", " kernel_size,\n", " padding=self.padding,\n", " dilation=dilation,\n", " *args,\n", " **kwargs,\n", " )\n", "\n", " # local conditioning 用の 1x1 convolution\n", " self.conv1x1c = Conv1d1x1(cin_channels, gate_channels, bias=False)\n", "\n", " # ゲート付き活性化関数のために、1 次元畳み込みの出力は 2 分割されることに注意\n", " gate_out_channels = gate_channels // 2\n", " self.conv1x1_out = Conv1d1x1(gate_out_channels, residual_channels)\n", " self.conv1x1_skip = Conv1d1x1(gate_out_channels, skip_out_channels)\n", "\n", " def forward(self, x, c):\n", " # 残差接続用に入力を保持\n", " residual = x\n", "\n", " # 1 次元畳み込み\n", " splitdim = 1 # (B, C, T)\n", " x = self.conv(x)\n", " # 因果性を保証するために、出力をシフトする\n", " x = x[:, :, : -self.padding]\n", "\n", " # チャネル方向で出力を分割\n", " a, b = x.split(x.size(1) // 2, dim=1)\n", "\n", " # local conditioning\n", " c = self.conv1x1c(c)\n", " ca, cb = c.split(c.size(1) // 2, dim=1)\n", " a, b = a + ca, b + cb\n", "\n", " # ゲート付き活性化関数\n", " x = torch.tanh(a) * torch.sigmoid(b)\n", "\n", " # スキップ接続用の出力を計算\n", " s = self.conv1x1_skip(x)\n", "\n", " # 残差接続の要素和を行う前に、次元数を合わせる\n", " x = self.conv1x1_out(x)\n", "\n", " x = x + residual\n", "\n", " return x, s" ] }, { "cell_type": "code", "execution_count": 38, "id": "flush-approach", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(torch.Size([32, 128, 100]), torch.Size([32, 64, 100]))" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_size = 3\n", "conv = ResSkipBlock(128,16,kernel_size, 64, dilation=4)\n", "x = torch.ones(32, 128, 100)\n", "c = torch.ones(32, 80, 100)\n", "out, skip = conv(x, c)\n", "out.shape, skip.shape" ] }, { "cell_type": "markdown", "id": "advance-hartford", "metadata": {}, "source": [ "### WaveNet全体の実装" ] }, { "cell_type": "code", "execution_count": 39, "id": "textile-impact", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "3070" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 受容野の大きさを数式通り愚直に計算\n", "(2 - 1) * sum([1,2,4,8,16,32,64,128,256,512]) * 3 + 1" ] }, { "cell_type": "code", "execution_count": 40, "id": "based-apparatus", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Layers: 30, Dilation cycles: 3, kernel size: 2]: recepive field (ミリ秒):\n", "3070 samples (191.875 ミリ秒)\n" ] } ], "source": [ "# 受容野の大きさを計算する関数\n", "from ttslearn.wavenet import receptive_field_size\n", "\n", "for layers, stacks, kernel_size in [\n", " (30, 3, 2), # WaveNetの論文の設定\n", "]:\n", " print(f\"[Layers: {layers}, Dilation cycles: {stacks}, kernel size: {kernel_size}]: recepive field (ミリ秒):\")\n", " size = receptive_field_size(layers, stacks, kernel_size)\n", " print(f\"{size} samples ({size / 16000 * 1000} ミリ秒)\")" ] }, { "cell_type": "code", "execution_count": 41, "id": "virtual-central", "metadata": { "tags": [] }, "outputs": [], "source": [ "class WaveNet(nn.Module):\n", " def __init__(\n", " self,\n", " out_channels=256, # 出力のチャネル数\n", " layers=30, # レイヤー数\n", " stacks=3, # 畳み込みブロックの数\n", " residual_channels=64, # 残差結合のチャネル数\n", " gate_channels=128, # ゲートのチャネル数\n", " skip_out_channels=64, # スキップ接続のチャネル数\n", " kernel_size=2, # 1 次元畳み込みのカーネルサイズ\n", " cin_channels=80, # 条件付け特徴量のチャネル数\n", " upsample_scales=None, # アップサンプリングのスケール\n", " aux_context_window=0, # アップサンプリング時に参照する近傍フレーム数\n", " ):\n", " super().__init__()\n", " self.out_channels = out_channels\n", " self.cin_channels = cin_channels\n", " self.aux_context_window = aux_context_window\n", " if upsample_scales is None:\n", " upsample_scales = [10, 8]\n", " self.upsample_scales = upsample_scales\n", "\n", " self.first_conv = Conv1d1x1(out_channels, residual_channels)\n", "\n", " # メインとなる畳み込み層\n", " self.main_conv_layers = nn.ModuleList()\n", " layers_per_stack = layers // stacks\n", " for layer in range(layers):\n", " dilation = 2 ** (layer % layers_per_stack)\n", " conv = ResSkipBlock(\n", " residual_channels,\n", " gate_channels,\n", " kernel_size,\n", " skip_out_channels,\n", " dilation=dilation,\n", " cin_channels=cin_channels,\n", " )\n", " self.main_conv_layers.append(conv)\n", "\n", " # スキップ接続の和から波形への変換\n", " self.last_conv_layers = nn.ModuleList(\n", " [\n", " nn.ReLU(),\n", " Conv1d1x1(skip_out_channels, skip_out_channels),\n", " nn.ReLU(),\n", " Conv1d1x1(skip_out_channels, out_channels),\n", " ]\n", " )\n", "\n", " # フレーム単位の特徴量をサンプル単位にアップサンプリング\n", " self.upsample_net = ConvInUpsampleNetwork(\n", " upsample_scales, cin_channels, aux_context_window\n", " )\n", "\n", " def forward(self, x, c):\n", " # 量子化された離散値列から One-hot ベクトルに変換\n", " # (B, T) -> (B, T, out_channels) -> (B, out_channels, T)\n", " x = F.one_hot(x, self.out_channels).transpose(1, 2).float()\n", "\n", " # 条件付け特徴量のアップサンプリング\n", " c = self.upsample_net(c)\n", "\n", " # One-hot ベクトルの次元から隠れ層の次元に変換\n", " x = self.first_conv(x)\n", "\n", " # メインの畳み込み層の処理\n", " # 各層におけるスキップ接続の出力を加算して保持\n", " skips = 0\n", " for f in self.main_conv_layers:\n", " x, h = f(x, c)\n", " skips += h\n", "\n", " # スキップ接続の和を入力として、出力を計算\n", " x = skips\n", " for f in self.last_conv_layers:\n", " x = f(x)\n", "\n", " # NOTE: 出力を確率値として解釈する場合には softmax が必要ですが、\n", " # 学習時には nn.CrossEntropyLoss の計算に置いて softmax の計算が行われるので、\n", " # ここでは明示的に softmax を計算する必要はありません\n", " return x" ] }, { "cell_type": "markdown", "id": "advisory-tiger", "metadata": {}, "source": [ "### トイモデルを利用したWaveNetの動作確認" ] }, { "cell_type": "code", "execution_count": 42, "id": "harmful-today", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WaveNet(\n", " (first_conv): Conv1d(256, 64, kernel_size=(1,), stride=(1,))\n", " (main_conv_layers): ModuleList(\n", " (0): ResSkipBlock(\n", " (conv): Conv1d(64, 128, kernel_size=(2,), stride=(1,), padding=(1,))\n", " (conv1x1c): Conv1d(64, 128, kernel_size=(1,), stride=(1,), bias=False)\n", " (conv1x1_out): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", " (conv1x1_skip): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", " )\n", " (1): ResSkipBlock(\n", " (conv): Conv1d(64, 128, kernel_size=(2,), stride=(1,), padding=(2,), dilation=(2,))\n", " (conv1x1c): Conv1d(64, 128, kernel_size=(1,), stride=(1,), bias=False)\n", " (conv1x1_out): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", " (conv1x1_skip): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", " )\n", " )\n", " (last_conv_layers): ModuleList(\n", " (0): ReLU()\n", " (1): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n", " (2): ReLU()\n", " (3): Conv1d(64, 256, kernel_size=(1,), stride=(1,))\n", " )\n", " (upsample_net): ConvInUpsampleNetwork(\n", " (conv_in): Conv1d(64, 64, kernel_size=(1,), stride=(1,), bias=False)\n", " (upsample): UpsampleNetwork(\n", " (conv_layers): ModuleList(\n", " (0): Conv2d(1, 1, kernel_size=(1, 21), stride=(1, 1), padding=(0, 10), bias=False)\n", " (1): Conv2d(1, 1, kernel_size=(1, 17), stride=(1, 1), padding=(0, 8), bias=False)\n", " )\n", " )\n", " )\n", ")" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# NOTE: inferenceに対応したWaveNetを利用するには、次の行をコメントアウトしてください\n", "# from ttslearn.wavenet import WaveNet\n", "\n", "# ここでは、inference関数の実装を省略します\n", "\n", "wavenet = WaveNet(out_channels=256, layers=2, stacks=1, kernel_size=2, cin_channels=64)\n", "wavenet" ] }, { "cell_type": "code", "execution_count": 43, "id": "verified-closer", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "入力のサイズ: (16, 16000)\n", "条件付け特徴量のサイズ: (16, 64, 200)\n", "アップサンプリングされた条件付け特徴量のサイズ: (16, 64, 16000)\n", "WaveNet の出力のサイズ: (16, 256, 16000)\n" ] } ], "source": [ "# 0 から 255 までの値を持つ適当な入力信号\n", "x = torch.randint(0, 255, (16, 16000))\n", "# フレームシフトを 80 サンプルとして、64 次元の条件付け特徴量を生成\n", "c = torch.rand(16, 64, 16000//80)\n", "\n", "print(\"入力のサイズ:\", tuple(x.shape))\n", "print(\"条件付け特徴量のサイズ:\", tuple(c.shape))\n", "\n", "x_hat = wavenet(x, c)\n", "\n", "# アップサンプリングの動作確認のために、条件付け特徴量のアップサンプリングのみ実行\n", "c_up = wavenet.upsample_net(c)\n", "\n", "print(\"アップサンプリングされた条件付け特徴量のサイズ:\", tuple(c_up.shape))\n", "print(\"WaveNet の出力のサイズ:\", tuple(x_hat.shape))" ] }, { "cell_type": "markdown", "id": "favorite-armstrong", "metadata": {}, "source": [ "### 負の対数尤度の最小化の実装" ] }, { "cell_type": "code", "execution_count": 44, "id": "referenced-chorus", "metadata": {}, "outputs": [], "source": [ "log_prob = F.log_softmax(x_hat, dim=1)\n", "# 自己回帰性を保つため、出力を時間方向に1つシフトする\n", "nll = nn.NLLLoss()(log_prob[:, :, :-1], x[:, 1:])" ] }, { "cell_type": "code", "execution_count": 45, "id": "seeing-trinity", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "nll: 5.548838138580322\n", "ce_loss 5.548838138580322\n" ] } ], "source": [ "ce_loss = nn.CrossEntropyLoss()(x_hat[:, :, :-1], x[:, 1:])\n", "print(\"nll:\", nll.item())\n", "print(\"ce_loss\", ce_loss.item())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 5 }