Text Generation
Transformers
TensorBoard
Safetensors
biology
genomics
rna
sequence-generation
regression
reinforcement-learning
git-lfs
Instructions to use JoyXiangLab/rnaseek-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoyXiangLab/rnaseek-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JoyXiangLab/rnaseek-full")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JoyXiangLab/rnaseek-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JoyXiangLab/rnaseek-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JoyXiangLab/rnaseek-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JoyXiangLab/rnaseek-full
- SGLang
How to use JoyXiangLab/rnaseek-full with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "JoyXiangLab/rnaseek-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "JoyXiangLab/rnaseek-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JoyXiangLab/rnaseek-full with Docker Model Runner:
docker model run hf.co/JoyXiangLab/rnaseek-full
File size: 239,771 Bytes
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"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "c31f2351-17b5-4ddf-858e-7b6cefcb23de",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open('evenBetterDataFolded-tr.json', 'r') as f:\n",
" # Load the JSON data from the file object into a Python dictionary\n",
" data0 = json.load(f)\n",
"\n",
"with open('evenBetterDataFolded-vl.json', 'r') as f:\n",
" # Load the JSON data from the file object into a Python dictionary\n",
" data1 = json.load(f)\n",
"\n",
"data = {}\n",
"\n",
"for stuff in data0:\n",
" data[stuff]=data0[stuff]\n",
"\n",
"for stuff in data1:\n",
" data[stuff]=data1[stuff]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8b709430-e987-4c2a-a617-70bcac516e57",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample mean = -0.0001, sample std = 0.9991\n",
"Shapiro–Wilk: W = 0.9667, p = 6.428e-74\n",
"D’Agostino’s K²: χ² = 313.2887, p = 9.337e-69\n",
"Kolmogorov–Smirnov: D = 0.0875, p = 0\n",
"Anderson–Darling: A² = 775.1348\n",
" 15.0% critical value = 0.576\n",
" 10.0% critical value = 0.656\n",
" 5.0% critical value = 0.787\n",
" 2.5% critical value = 0.918\n",
" 1.0% critical value = 1.092\n",
"Skewness = -0.1820, Excess kurtosis = -0.0160\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/rhome/schen647/.local/lib/python3.11/site-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 57560.\n",
" res = hypotest_fun_out(*samples, **kwds)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import (\n",
" shapiro,\n",
" normaltest,\n",
" kstest,\n",
" anderson,\n",
" skew,\n",
" kurtosis,\n",
" probplot\n",
")\n",
"from tqdm import tqdm\n",
"values = np.array(list(data.values()))\n",
"\n",
"# 2) Plot histogram\n",
"plt.figure(figsize=(8,4))\n",
"plt.hist(values, bins=50, edgecolor='black', density=True, alpha=0.6)\n",
"plt.title('Target-Value Distribution')\n",
"plt.xlabel('Normalized target value')\n",
"plt.ylabel('Density')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"# 3) Summary stats\n",
"mean, std = values.mean(), values.std(ddof=1)\n",
"print(f\"Sample mean = {mean:.4f}, sample std = {std:.4f}\")\n",
"\n",
"# 4) Normality tests\n",
"sw_stat, sw_p = shapiro(values)\n",
"print(f\"Shapiro–Wilk: W = {sw_stat:.4f}, p = {sw_p:.4g}\")\n",
"\n",
"k2_stat, k2_p = normaltest(values)\n",
"print(f\"D’Agostino’s K²: χ² = {k2_stat:.4f}, p = {k2_p:.4g}\")\n",
"\n",
"ks_stat, ks_p = kstest(values, 'norm', args=(0, 1))\n",
"print(f\"Kolmogorov–Smirnov: D = {ks_stat:.4f}, p = {ks_p:.4g}\")\n",
"\n",
"ad = anderson(values, dist='norm')\n",
"print(f\"Anderson–Darling: A² = {ad.statistic:.4f}\")\n",
"for sl, cv in zip(ad.significance_level, ad.critical_values):\n",
" print(f\" {sl}% critical value = {cv:.3f}\")\n",
"\n",
"# 5) Skewness & excess kurtosis\n",
"s = skew(values)\n",
"k = kurtosis(values, fisher=True)\n",
"print(f\"Skewness = {s:.4f}, Excess kurtosis = {k:.4f}\")\n",
"\n",
"# 6) Q–Q plot\n",
"plt.figure(figsize=(6,6))\n",
"probplot(values, dist=\"norm\", plot=plt)\n",
"plt.title(\"Q–Q Plot vs. N(0,1)\")\n",
"plt.xlabel(\"Theoretical quantiles\")\n",
"plt.ylabel(\"Sample quantiles\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "42f70b73-ddca-4156-ac71-d5ea8da484a4",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.stats import norm\n",
"def sample_normal_fast_dict(\n",
" x_dict: dict,\n",
" m: int,\n",
" mu: float = None,\n",
" sigma: float = None,\n",
" nbins: int = 100,\n",
" oversample: bool = True\n",
") -> list[dict]:\n",
" \"\"\"\n",
" Faster importance‐sampling by using a histogram to approximate the empirical density.\n",
"\n",
" Args:\n",
" x_dict: {key -> numeric output}\n",
" m: number of samples\n",
" mu,sigma: target Normal parameters (default = empirical)\n",
" nbins: number of bins for histogram\n",
" oversample: sample with replacement if True\n",
"\n",
" Returns:\n",
" list of {\"input\": key, \"output\": value}\n",
" \"\"\"\n",
" # 1) unpack\n",
" inputs = list(x_dict.keys())\n",
" outputs = np.array([x_dict[k] for k in inputs], dtype=float)\n",
"\n",
" # 2) target Normal\n",
" if mu is None: mu = outputs.mean()\n",
" if sigma is None: sigma = outputs.std(ddof=0)\n",
"\n",
" # 3) empirical density via histogram\n",
" counts, edges = np.histogram(outputs, bins=nbins, density=True)\n",
" # assign each point to a bin\n",
" inds = np.digitize(outputs, edges[1:-1], right=True)\n",
" f_emp = counts[inds]\n",
"\n",
" # 4) normal density\n",
" f_norm = norm.pdf(outputs, loc=mu, scale=sigma)\n",
"\n",
" # 5) importance weights\n",
" w = f_norm / f_emp\n",
" p = w / w.sum()\n",
"\n",
" # 6) sample indices\n",
" if not oversample and m > len(inputs):\n",
" raise ValueError(f\"Can't draw {m} without replacement from {len(inputs)} items.\")\n",
" idx = np.random.choice(len(inputs), size=m, replace=oversample, p=p)\n",
"\n",
" # 7) build result\n",
" return [{\"input\": inputs[i], \"output\": outputs[i]} for i in idx]\n",
"\n",
"val = sample_normal_fast_dict(data, 5000, 0,1, oversample=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "7492037c-f07f-4423-8ac3-abdacb3edf7e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample mean = -0.0256, sample std = 0.9894\n",
"Shapiro–Wilk: W = 0.9962, p = 4.553e-10\n",
"D’Agostino’s K²: χ² = 30.2875, p = 2.649e-07\n",
"Kolmogorov–Smirnov: D = 0.0120, p = 0.4598\n",
"Anderson–Darling: A² = 1.7382\n",
" 15.0% critical value = 0.576\n",
" 10.0% critical value = 0.655\n",
" 5.0% critical value = 0.786\n",
" 2.5% critical value = 0.917\n",
" 1.0% critical value = 1.091\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skewness = -0.1622, Excess kurtosis = -0.1866\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import gaussian_kde, shapiro, normaltest, kstest, anderson\n",
"\n",
"# Extract outputs from the sampled list\n",
"outputs = np.array([item[\"output\"] for item in val])\n",
"\n",
"# Compute and print sample mean & std\n",
"mean, std = outputs.mean(), outputs.std(ddof=1)\n",
"print(f\"Sample mean = {mean:.4f}, sample std = {std:.4f}\")\n",
"\n",
"# 1) Shapiro–Wilk test (good for n < 5000)\n",
"sw_stat, sw_p = shapiro(outputs)\n",
"print(f\"Shapiro–Wilk: W = {sw_stat:.4f}, p = {sw_p:.4g}\")\n",
"\n",
"# 2) D’Agostino’s K² test\n",
"k2_stat, k2_p = normaltest(outputs)\n",
"print(f\"D’Agostino’s K²: χ² = {k2_stat:.4f}, p = {k2_p:.4g}\")\n",
"\n",
"# 3) Kolmogorov–Smirnov against N(0,1)\n",
"ks_stat, ks_p = kstest(outputs, 'norm', args=(0, 1))\n",
"print(f\"Kolmogorov–Smirnov: D = {ks_stat:.4f}, p = {ks_p:.4g}\")\n",
"\n",
"# 4) Anderson–Darling test\n",
"ad = anderson(outputs, dist='norm')\n",
"print(f\"Anderson–Darling: A² = {ad.statistic:.4f}\")\n",
"for sl, cv in zip(ad.significance_level, ad.critical_values):\n",
" print(f\" {sl}% critical value = {cv:.3f}\")\n",
"\n",
"# Plot density\n",
"kde = gaussian_kde(outputs)\n",
"x_grid = np.linspace(outputs.min(), outputs.max(), 1000)\n",
"y = kde(x_grid)\n",
"\n",
"plt.figure(figsize=(6,4))\n",
"plt.hist(outputs, bins=50, density=True, alpha=0.6, edgecolor='k')\n",
"plt.plot(x_grid, y, lw=2)\n",
"plt.axvline(0, color='gray', linestyle='--')\n",
"plt.title('Density Plot of Resampled Outputs')\n",
"plt.xlabel('Output Value')\n",
"plt.ylabel('Density')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"from scipy.stats import skew, kurtosis, probplot\n",
"\n",
"# 5) Skewness & excess kurtosis\n",
"s = skew(outputs)\n",
"k = kurtosis(outputs, fisher=True) # Fisher’s definition: 0 for a normal\n",
"print(f\"Skewness = {s:.4f}, Excess kurtosis = {k:.4f}\")\n",
"\n",
"# 6) Q–Q plot\n",
"plt.figure(figsize=(6,6))\n",
"probplot(outputs, dist=\"norm\", plot=plt)\n",
"plt.title(\"Q–Q Plot vs. N(0,1)\")\n",
"plt.xlabel(\"Theoretical quantiles\")\n",
"plt.ylabel(\"Sample quantiles\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ca692dd4-3b18-4419-a570-5e740ff0f1bc",
"metadata": {},
"outputs": [],
"source": [
"used = set()\n",
"for stuff in val:\n",
" used.add(stuff['input'])\n",
"rest ={}\n",
"\n",
"for stuff in data:\n",
" if stuff in used: continue\n",
" rest[stuff]=data[stuff]\n",
"\n",
"\n",
"train =sample_normal_fast_dict(rest, 50_000*24, 0,1, oversample=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "4912b845-3693-42b6-a3f7-c78ae2dcd2cb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sample mean = -0.0228, sample std = 0.9719\n",
"Shapiro–Wilk: W = 0.9978, p = 2.901e-56\n",
"D’Agostino’s K²: χ² = 5336.5953, p = 0\n",
"Kolmogorov–Smirnov: D = 0.0098, p = 1.789e-100\n",
"Anderson–Darling: A² = 220.9994\n",
" 15.0% critical value = 0.576\n",
" 10.0% critical value = 0.656\n",
" 5.0% critical value = 0.787\n",
" 2.5% critical value = 0.918\n",
" 1.0% critical value = 1.092\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/rhome/schen647/.local/lib/python3.11/site-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 1200000.\n",
" res = hypotest_fun_out(*samples, **kwds)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 600x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skewness = -0.1199, Excess kurtosis = -0.2002\n"
]
},
{
"data": {
"image/png": 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KDANGjDDHEsDw4TB6tEtEE0AeZw8gIiIiApijaehQGDPG/HjMmP8CykUonERERMT5DAMGDjQfkgPzn2mH6lyIwklEREScKzUV+vaFyZPNjz//HF580bkzZUHhJCIiIs6TkgK9e8OXX5rPY5o+3Xz5ARelcBIRERHnSE6GHj3M12fy8YE5c6BzZ2dPlS2Fk4iIiOS+69fNkfTtt5AnD8yfD+3bO3uqHCmcREREJHclJkKHDrB8Ofj5meOpVStnT2UVhZOIiIjknqtXoW1bWLMG8ueHpUshMtLZU1lN4SQiIiK5IyHBvGcpKgoCAuC77yA83NlT2UThJCIiIo4XGwvNm8O2bRAYCKtWQb16zp7KZgonERERcazLl82H4/73PwgOhrVroW5dZ091SxROIiIi4jgXLkDjxrBzJxQpAuvXQ61azp7qlimcRERExDHOnoWICPjtNyheHDZuhOrVnT3VbVE4iYiIiP2dPGk+8fvAAShd2hxN99zj7Klum8JJRERE7Ov4cQgLg0OHICTE/C66ypWdPZVd+Dh7ABEREfEgR45A/frmaKpQAbZs8ZhoAoWTiIiI2MvBg+ZoOnoUqlSB6GgoX97ZU9mVwklERERu3/795mj691/zuUzR0ebDdB5G4SQiIiK3Z88eCA2F06ehRg3YvNl8QrgHUjiJiIjIrdu+HRo2hPPn4cEHYdMmKFHC2VM5jMJJREREbs1PP5kvOXDpEjzyiPmSA0WKOHsqh1I4iYiIiO22bDFfETw21nxu09q15tupeDiFk4iIiNhmwwbzvefi481XBl+92nzjXi+gcBIRERHrff89tGgBV69Cs2awYgUEBDh7qlyjcBIRERHrLF8OrVtDYqL5zyVLIH9+Z0+VqxROIiIikrNvvoEnn4Tr1+Gpp8yP8+Vz9lS5zm3D6f3338dkMvHKK684exQRERHP9tVX8PTTkJwMzzwD8+ZB3rzOnsop3DKcfv31V6ZOnUqNGjWcPYqIiIhnmz4dunaF1FR47jmYORPy5HH2VE7jduEUHx9P586d+eKLLyhcuLCzxxEREfFcn30GvXqBYUCfPjB1Kvj6Onsqp3K7cOrTpw/NmzcnIiIix2UTExOJjY3N8CEiIiJW+Ogj6NvX/PnAgTBpEvi4XTbYnVvta1uwYAE7d+7k119/tWr5MWPGMGLECAdPJSIi4mFGj4ahQ82fDx0KI0eCyeTcmVyE26Tj8ePH6d+/P/PmzSO/lW99HDJkCDExMekfx48fd/CUIiIibsww4J13/oumkSPhvfcUTTcwGYZhOHsIayxbtow2bdrge8Ox1ZSUFEwmEz4+PiQmJmZ4zZLY2FiCg4OJiYkhKCjI0SOLiIi4D8OAwYPhww/Njz/8EF5/3bkz5RJb+sBtDtWFh4fz22+/ZXiuR48eVK1alTfeeCPHaBIREZEsGAa88gp88on58SefwMsvO3UkV+U24RQYGMh9992X4bkCBQpQtGjRTM+LiIiIlVJT4aWXzO+YM5lgyhR4/nlnT+Wy3CacRERExM5SUszXZpo1y/yOuRkzoFs3Z0/l0tw6nDZv3uzsEURERNxTcrL5wpZff22+NtPcuearg0u23DqcRERE5BYkJZkjackS861TFiyAtm2dPZVbUDiJiIh4k2vXzDfrXbUK/Pxg8WJo0cLZU7kNhZOIiIi3uHIF2rSBdevA3x+WLYPGjZ09lVtROImIiHiD+Hho2RI2b4YCBWDlSmjQwNlTuR2Fk4iIiKeLiYFmzeDHHyEoCFavhkcfdfZUbknhJCIi4skuXYImTeDXX6FQIfNhujp1nD2V21I4iYiIeKrz56FRI9i9G4oWhQ0boGZNZ0/l1hROIiIinujMGQgPh337oGRJczTpThu3TeEkIiLiaU6cMEfTn39CmTIQFQV33+3sqTyCwklERMST/PMPhIXB4cNw553maKpUydlTeQwfZw8gIiIidvL331C/vjmaKlaELVsUTXamcBIREfEEf/5pjqZjx+Cuu8zRVK6cs6fyOAonERERd/f77xAaCidPQrVqEB0Nd9zh7Kk8ksJJRETEne3aZb4C+Jkz5ksNbN4MpUo5eSjPpXASERFxV7/+aj4R/MIF80Uto6KgWDFnT+XRFE4iIiLu6IcfzJccuHwZHnvMfJ2mwoWdPZXHUziJiIi4m82bzbdRiYszH6Zbs8Z8DzpxOIWTiIiIO1m3Dpo2hYQEaNwYVq2CggWdPZXXUDiJiIi4i5UroWVLuHYNWrSA5cshIMDZU3kVhZOIiIg7WLIE2raFpCTzn4sXQ/78zp7K6yicREREXN3XX8NTT8H16/D007BwIfj5OXsqr6RwEhERcWWzZkHnzpCSAt27w9y5kEe3mnUWhZOIiIirmjYNevQAw4Dnn4cvvwRfX2dP5dUUTiIiIq7ok0+gd2/z5/36wZQp4KN/tp1NPwERERFXM3Ys9O9v/nzQIJg4EUwmp44kZgonERERVzJypDmWAN55B95/X9HkQnR2mYiIiCswDHj7bRg1yvx41Ch4803nziSZKJxERESczTDg9ddh/Hjz4/HjYcAA584kFimcREREnCk11Xzy92efmR9/+in06ePcmSRLCicRERFnSU01v3Nu+nTzeUzTpsFzzzl7KsmGwklERMQZkpPh2WfNF7T08TFf6PKZZ5w9leRA4SQiIpLbrl+HLl3gm2/MF7ScP998SxVxeQonERGR3JSYCB06wPLlkDevOZ5at3b2VGIlhZOIiEhuuXoV2rWD1ashXz5YsgSaNXP2VGIDhZOIiEhuSEgw71nasAH8/WHFCggPd/ZUYiOFk4iIiKPFxUGLFrBlCxQsCKtWQf36zp5KboHCSURExJEuX4amTeHnnyE4GNasgYcfdvZUcosUTiIiIo5y8SI0bgw7dkDhwrB+PTz4oLOnktugcBIREXGEc+cgIgL27oXixc3RdP/9zp5KbpPCSURExN5OnTJH0/79UKoUbNwI997r7KnEDhROIiIi9nT8uPndcgcPQtmyEBUFVao4eyqxE4WTiIiIvRw5AmFhcPQolC9vjqYKFZw9ldiRj7MHEBER8QgHD0JoqDmaKleG6GhFkwdSOImIiNyuP/4wR9Px41C1qjma7rzT2VOJAyicREREbsfeveZoOnUKqlc3R1OZMs6eShxE4SQiInKrdu6Ehg3Nlx6oVQs2bYISJZw9lTiQwklERORW/O9/5hPBL16EunXNlxwoWtTZU4mDKZxERERstXWr+TpNMTFQr5754paFCjl7KskFCicRERFbREVBZCTEx5v3OK1eDYGBzp5KconCSURExFpr1kDz5nDlijmeVq6EAgWcPZXkIoWTiIiINZYvhyeegGvXoFUrWLYM/P2dPZXkMoWTiIhITr79Fp58EpKSoH17WLQI8uVz9lTiBAonERGR7Hz1FXTsCMnJ0LkzzJ8PefM6eypxEoWTiIhIVmbMgK5dITUVevaE2bMhj27z6s0UTiIiIpZMnmyOJcOAl16CadPA19fZU4mTKZxERERuNmGCOZYAXn0VPv0UfPRPpiicREREMnr/fRgwwPz5kCEwfjyYTM6dSVyGwklERATMh+SGDzfHEsCIETBqlKJJMtAZbiIiIoYBb75p3tsE5j/feMO5M4lLUjiJiIh3MwzzobmJE82PJ06E/v2dOZG4MIWTiIh4r9RU6NMHpkwxP548GV54wbkziUtTOImIiHdKSYFevWDmTPN5TF9+CT16OHsqcXEKJxER8T7JydCtm/kq4L6+MGcOdOrk7KnEDSicRETEuyQlmSNp8WLzVcAXLIB27Zw9lbgJhZOIiHiPxETzTXpXrAA/P/PNelu2dPZU4kYUTiIi4h2uXoU2bWDtWsifH5YtgyZNnD2VuBmFk4iIeL6EBPOepU2bICAAVq6Ehg1z5UvHx0PVqnDiRK58OZuk3UXG19e8Ay5PHvPnvr7mtvTxMX+78uc3fwQEQPHima8J6uMDISFQpAhcvmx+rkgRKFXK/AFw9iyUKPHf56VLQ7165sdbt8KpU/89d+MtAVNSsn89tymcRETEs8XGQvPmsG0bBAbC99/D44/f9maHDoXRo+0wnxOlpv735/Xruf/1ixY1/3nhwn/PlS0LH38MbdvCkiXmS2r9+6/l153BZBiG4ZwvnftiY2MJDg4mJiaGoKAgZ48jIiKOdukSREbCL79AoULmw3QPPXRbm3zkEfj5Z/uMJ5ml7c167TUYN858fVJLry9aZL94sqUPFE4iIuKZLlyARo1g1y7zro1166BWrdvapG5bl3t8fc2H6Swxmcx7no4csc9hO1v6QDf5FRERz3PmDDRoYI6mEiXM5zbdYjS1b2/+h1rRlLuyiiYw74U6ftx87lNu0zlOIiLiWU6cgPBw+PNP89nEUVHms7Nz0KwZrF6dC/OJ3Zw6lftfU+EkIiKe49gxCAuDv/82v80rKgoqV7a46KpV0KJFLs8ndlW6dO5/TYWTiIh4hsOHzdH0zz9QoYI5msqXt7ioDru5Pl9f87v9LJ2JnXaOU9rlDHKTznESERH399dfUL++OZruugu2bMkUTWnnKSmaXFvaz2jAgP8e3/w6wMSJzrmek8JJRETc27595mg6cQLuvReio827I/6fYsl1FS3637Wc0pQta77UwIcfmv+84w7LrzvrOk46VCciIu5rzx6IiIDz5+H++2H9eihenObNzde5lOy5+pXD27aFJ57QlcNFRERu3/bt0Lix+SKXtWvD2rXMW12ELl2cPVj2Bg+GMWOcPYVradAg69d8fbN/PbcpnERExP389JP5iuCxsfDIIwT/tJrYosHOnsriicziWXSOk4iIuJfoaPMVwWNj2UwogT+tJRZFk+QOhZOIiLiP9euhaVNISGAdjWjG98QT6NSRNm5UNHkThZOIiLiHVaugZUu4epVVNKMV33GVgFwfIzLSHEppH2FhuT6COJHOcRIREde3dClJbTvgx3WW0IaOLOA6fg79kkWKmO8TLHIjhZOIiLi2hQtJ7tgZP1JYQAeeYS7J5HXIl9IhN8mJDtWJiIjLGl11DikdO5GHFGbTlc7Mc0g0NW2qaBLraI+TiIi4pF6mL5hKb3wwmEYvXmAKhh3//37btrB4sd02J17CbfY4jRkzhjp16hAYGEiJEiVo3bo1f/75p7PHEhERB+hr+pQveB4fDD7hZXoz1W7RdOGCee+SokluhduEU3R0NH369OHnn39m/fr1XL9+ncaNG5OQkODs0URExE5MJhhoGs+nvAzAWF6jPx8Dt3+zubR3wRUpctubEi9mMgz3PKp77tw5SpQoQXR0NPXr17dqndjYWIKDg4mJiSEoKMjBE4qIiC1MJniTUYziLQBG8hbv8C63G03u+a+c5CZb+sBtz3GKiYkBoEg2/9chMTGRxMTE9MexsbEOn0tERGyTJw+kpBi8yzu8zXsAvMXI9IC6VQomcQS3OVR3o9TUVF555RUee+wx7rvvviyXGzNmDMHBwekfISEhuTiliIjkxGQyR9OHDEqPptcYe1vRlHZITsQR3PJQ3Ysvvsjq1avZtm0bZcuWzXI5S3ucQkJCdKhORMTJatSA334DE6l8TH9e5lMA+jKJz+h7y9t1v3/RxBV49KG6vn37snLlSrZs2ZJtNAHky5ePfPny5dJkIiJiDdP/n7JkIpUpvMDzfEEqJl5gCl/w/C1tU8EkucVtwskwDF5++WWWLl3K5s2bqVChgrNHEhERG6VFkw8pzOBZujGHFHx4lhnMoZvN21MwSW5zm3Oc+vTpw1dffcX8+fMJDAzk9OnTnD59mqtXrzp7NBERycGIEf9FUx6u8xVd6MYckvGlE/MVTeI23OYcJ5PJ8ttRZ86cSffu3a3ahi5HICKS+2789Z2XJBbQkbYsJYm8dGAhy2hj0/bc418tcSceeY6Tm/SdiIjc4MZoysc1FvEkLVjFNfLRjsV8T3Obtqd/CsTZ3OZQnYiIuJcbo8mfK3xHK1qwiiv405IVNkWTbsIrrsJt9jiJiIj7uDGaChDPSlrQgGjiKUBzVrGFUKu3pWASV6I9TiIiYjejRmWMpiBiWEsTGhBNDEE0Zp2iSdya9jiJiIhd3PwensJcZC1NqMN2LlKYxqxjB7Wt2paCSVyVwklERG7bzdFUjHOspxE12cM5itGI9eyhplXbUjSJK1M4iYjIbbk5mkpymo2EU439nKYk4WxkP9Vy3I6CSdyBwklERG7ZzdF0B/+ykXDu5i/+5Q7CiOIgd+W4HUWTuAuFk4iI3JKbo6kcR4kijIoc4SjlCCOKI1TMcTuKJnEnelediIjY5NixzNFUiUNEE0pFjnCIStRnS47RZBiKJnE/2uMkIiJWs3T3q7s5wEbCuYOT/EFVwtnIKcpkux0Fk7gr7XESERGrWIqmavxONKHcwUl+4z4asFnRJB5N4SQiIjmyFE012cVmGlCSs+zkARqyibOUzHY7iiZxdwonERHJlqVoqsMvRBFGMS7wPx4inI1coFi221E0iSdQOImISJYsRdNjbGMDERTmMtt4jEas5zKFs9yGTgIXT6JwEhERiyxFU0OiWEsTgogjioZEsoY4grLchoJJPI3CSUREMrEUTY1ZyyqaU4ArrKEJzVlFAgWz3IaiSTyRwklERNJVqmQ5mlqwgu9ohT/X+I6WtGYZ1/DPcjuKJvFUuo6TiIgAloMJoC2LWUBH8pLMItrRiflcxy/L7SiaxJNpj5OIiGQZTU8zn4V0IC/JzKMTHVmgaBKvpnASEfFyWUVTd2byFV3IQwoz6EFX5pCSxYEKvXNOvIXCSUTEi2UVTc8zlZk8iw8Gk3mB55hOKr4Wl1UwiTdROImIeKmsoqkfHzOVFwCYSH9e4nOMLP65UDSJt1E4iYh4oayi6XU+5GNeAeB93uBVJgCWF1Y0iTdSOImIeBnL0WTwNu/yIW8AMJxhDGEMiiaRjHQ5AhERL5JVNI1iKG8yBoAhjOZ9hmS5DUWTeDOFk4iIl8gqmsYzkAFMAOBVPmIir2a5DUWTeDuFk4iIF7AUTSZS+ZS+vMRkAF7iMybzUpbbUDSJKJxERDyepWjyIYWp9OY5viQVE734ghn0tLi+gknkPwonEREPZimafElmJj14hq9IwYduzGYeXSyur2gSyUjhJCLioSxFUx6uM4/OPMW3XCcPnZjPItpbXF/RJJKZLkcgIuJhhg61HE1+JPIt7XmKb0nEj3YsVjSJ2Eh7nEREPEhWF7bMz1UW045mrOYq+WnDUtYSaXFZRZNI1hROIiIeIqtoCiCB5TxBBBtJIIBWfEcU4RaXVTSJZM/mQ3U7d+7kt99+S3+8fPlyWrduzZtvvklSUpJdhxMREetkFU2BxLKapkSwkTgKEskaRZPIbbA5nHr37s1ff/0FwOHDh+nYsSMBAQF8++23DBo0yO4DiohI9rKKpmAus47G1GcrlwmmEevZRj2LyyqaRKxjczj99ddf1KxZE4Bvv/2W+vXrM3/+fGbNmsXixYvtPZ+IiGQjq2gqwgU2Es7D/I8LFCGcjfyPhy0uq2gSsZ7N5zgZhkFqaioAGzZsoEWLFgCEhIRw/vx5+04nIiJZyiqainOWDURQg984S3Ei2MBv1LC4rKJJxDY273GqXbs27733HnPnziU6OprmzZsDcOTIEUqWLGn3AUVEJLOsoqk0J4kmlBr8xklKE0q0oknEjmwOp4kTJ7Jz50769u3L0KFDqVy5MgCLFi3i0UcftfuAIiKSUVbRVJbjRBPKPRzgGCGEEs0B7rG4rKJJ5NaYDMM+//lcu3YNX19f8ubNa4/NOURsbCzBwcHExMQQFBTk7HFERGyWVTSV5whRhFGBoxymAmFE8Q/lLS6raBLJyJY+uKUrh1++fJnp06czZMgQLl68CMD+/fs5e/bsrWxORESskFU0VeYgW6hPBY7yF1UIJVrRJOIgNp8cvnfvXsLDwylUqBBHjx6lV69eFClShCVLlnDs2DHmzJnjiDlFRLxaVtF0D/vZSDilOc1+7iGcjZymdKbl/PwgMdHBQ4p4AZv3OA0YMIAePXpw8OBB8ufPn/58s2bN2LJli12HExGRrKOpOnvZTANKc5o91KABmxVNIg5mczj9+uuv9O7dO9Pzd9xxB6dPn7bLUCIiYpZVNNViB5toSAnOsZ0HCSOKc5TItNy//yqaROzJ5kN1+fLlIzY2NtPzf/31F8WLF7fLUCIiknU0PcxPrCGSYGL5iYdpympiKJRpOZ3PJGJ/Nu9xatWqFe+++y7Xr18HwGQycezYMd544w3atWtn9wFFRLxRVtFUjy2sozHBxLKFejRmnaJJJBfZHE7jx48nPj6eEiVKcPXqVUJDQ6lcuTKBgYGMGjXKETOKiHiVrKIpnA2sIZJA4tlAOE1ZTTyBmZZTNIk4js2H6oKDg1m/fj3btm1j7969xMfHU6tWLSIiIhwxn4iIV8kqmpryPUtoS34S+Z6mtGMx1/DPtJyiScSx7HYBTHegC2CKiCvLKppasZxvaY8f11nGE3RgIUnky7Sc9/w2F7EvW/rAqj1On3zyidVfvF+/flYvKyIiZllFU3u+YR6dyUsyC3mKLnxFMpnv0KBoEskdVu1xqlChgnUbM5k4fPjwbQ/lKNrjJCKuKKto6sxXzKYbvqQyh2d4lhmkWPj/u4omkdtj9z1OR44csctgIiKSUVbR9Cxf8gW98MHgC57jBaaQim+m5RRNIrnrlu5VJyIity+raHqRz/mS5/DB4FP60JupiiYRF2HVHqcBAwYwcuRIChQowIABA7Jd9qOPPrLLYCIiniyraHqVj/iIgQCMZwCvMQ7IvLCiScQ5rAqnXbt2pV/wcteuXQ4dSETE02UVTUMYzWiGAjCKN3mL91A0ibgWXY5ARCQXWY4mgxEM4x1GAvA27/Ieb1tc33t+Y4vkHlv6wOZznJ599lni4uIyPZ+QkMCzzz5r6+ZERLxGVtH0PoPTo2kQHyiaRFyYzXucfH19OXXqFCVKZLwL9/nz5ylVqhTJycl2HdCetMdJRJwlq2iayCv0x3ytvH58zCQsXwtP0STiOHa/HEHaRg3DwDAM4uLiyJ8/f/prKSkpfP/995liSkRELEeTiVQ+5yVeYCoAvZnCNHpbXF/RJOI6rA6nQoUKYTKZMJlM3HXXXZleN5lMjBgxwq7DiYi4O0vR5EMK03mOHswiBR+eZQZz6GZxfUWTiGuxOpw2bdqEYRiEhYWxePFiihQpkv6an58f5cqVo0yZMg4ZUkTEHVmKJl+SmUNXOvE1yfjyDHNZwNMW11c0ibgeq8MpNDQUMF9FPCQkBB8fXTtTRCQrlqIpL0nMpxNPspgk8tKRBSylrcX1FU0irsnqcEpTrlw5Ll++zC+//MLZs2dJTU3N8HrXrl3tNpyIiDuyFE35uMa3tKclK0nEj3YsZhUtLK6vaBJxXTaH04oVK+jcuTPx8fEEBQVhuuE3hMlkUjiJiFezFE3+XGEpbWjCOq6SnydYznoaW1xf0STi2mw+3jZw4ECeffZZ4uPjuXz5MpcuXUr/uHjxoiNmFBFxefnyWY6mAsSziuY0YR3xFKApqxVNIm7M5j1OJ06coF+/fgQEBDhiHhERt5PVLVSCiOF7mvEYPxJLIE1ZzY88ZnFZRZOIe7B5j1OTJk3Yvn27I2YREXE7WUVTIS6xnkY8xo9cohARbFA0iXgAm/c4NW/enNdff539+/dTvXp18ubNm+H1Vq1a2W04ERFXllU0FeU862nEA+zmPEVpxHp284DFZRVNIu7F5luuZHcZApPJREpKym0P5Si65YqI2EtW0VSCM2wknPvYx2lKEsEG9nGfxWUVTSKuwSG3XElz8+UHRES8TVbRVIYTbCScqvzJCcoQRhR/cbfFZRVNIu7J5nASEfFmWUXTnfxDFGFU4jD/cCdhRHGYShaXVTSJuK9bCqeEhASio6M5duwYSUlJGV7r18/ynb1FRNxdVtFUkb+JIoxyHONvKhJGFMcoZ3FZRZOIe7M5nHbt2kWzZs24cuUKCQkJFClShPPnzxMQEECJEiUUTiLikbKKprv4k42EU5YT/MldhBHFSe6wuKyiScT92Xw5gldffZWWLVty6dIl/P39+fnnn/nnn3948MEHGTdunCNmFBFxqqyiqRq/E00oZTnB71QjlGhFk4iHszmcdu/ezcCBA/Hx8cHX15fExERCQkL48MMPefPNNx0xo4iI02QVTTXZxWYaUIoz7KImDdjMGUpZXFbRJOI5bA6nvHnzpl+SoESJEhw7dgyA4OBgjh8/bt/pREScKKtoqs2vRBFGMS7wC3UIZyMXKJZpOcNQNIl4GpvPcXrggQf49ddfqVKlCqGhobzzzjucP3+euXPnct99lq9VIiLibrKKpkf5gdU0JYg4fuBRmvE9sQRnWk7BJOKZbN7jNHr0aEqXLg3AqFGjKFy4MC+++CLnzp1j2rRpdh9QRCS3ZRVNoWxmLU0IIo5NNKAJaxVNIl7G5iuHuzNdOVxEcpJVNDViHct5An+usY5GtGYZV8l8s3Pv+Y0q4jls6QOb9ziJiHiqrKKpOStZQUv8ucYKWtCK7xRNIl7K5nOcKlSogCmr3y7A4cOHb2sgERFnyOrXWhuWsICO+HGdxbTlab7mOn6ZllM0iXgHm8PplVdeyfD4+vXr7Nq1izVr1vD666/bay4RkVyTVTR1YAFf0YU8pDCfp+nKHFIs/NpUNIl4D5vDqX///haf/+yzz9i+ffttDyQikpuyiqauzGYGz+JLKjPpznNMJxXfTMspmkS8i93OcWratCmLFy+21+ZERBwuq2jqxTRm0x1fUpnK8/TkS0WTiAB2DKdFixZRpEgRe21ORMShsoqml/mEafQG4GP68QJTMCz8qlQ0iXinW7oA5o0nhxuGwenTpzl37hyff/65XYcTEXGErKLpNcYylkEAfMjrvMEHQOaFFU0i3svmcGrdunWGxz4+PhQvXpwGDRpQtWpVe82Vpc8++4yxY8dy+vRp7r//fiZNmsRDDz3k8K8rIp4hq2h6i5GM5B0A3uVthjECRZOI3MytLoC5cOFCunbtypQpU6hbty4TJ07k22+/5c8//6REiRI5rq8LYIp4N8vRZDCSt3mLUQAM5T1GM9Ti+u7z21JEbGFLH9gcTrGxsVYva+84qVu3LnXq1OHTTz8FIDU1lZCQEF5++WUGDx6c4/oKJxHvlVU0jeV1XmM8AAMZx0cMtLi+oknEc9nSBzYfqitUqFC2F8AE83lPJpOJlJQUWzefpaSkJHbs2MGQIUPSn/Px8SEiIoKffvrJ4jqJiYkkJiamP7Yl+kTEc1j6lWUilU/oR18+A6APn/I5fSyur2gSkTQ2h9PMmTMZPHgw3bt355FHHgHgp59+Yvbs2YwZM4by5cvbe0YAzp8/T0pKCiVLlszwfMmSJTlw4IDFdcaMGcOIESMcMo+IuIesomkqvenFdFIx8TzT+JLnLK6vaBKRG9kcTnPmzOGjjz7i6aefTn+uVatWVK9enWnTprF582Z7zndbhgwZwoABA9Ifx8bGEhIS4sSJRCQ3WYomX5KZwbN0ZS4p+NCdWXzFMxbXVzSJyM1svo7TTz/9RO3atTM9X7t2bX755Re7DGVJsWLF8PX15cyZMxmeP3PmDKVKlbK4Tr58+QgKCsrwISLewVI05eE6X9GFrswlGV+e5mtFk4jYxOZwCgkJ4Ysvvsj0/PTp0x26N8fPz48HH3yQjRs3pj+XmprKxo0b0w8ZioiA5WjyI5FveIqOLCSJvDzJIr7lKYvrK5pEJCs2H6qbMGEC7dq1Y/Xq1dStWxeAX375hYMHDzr8lisDBgygW7du1K5dm4ceeoiJEyeSkJBAjx49HPp1RcR9WIqm/FxlMe1oxmqukY+2LGE1zSyur2gSkezYHE7NmjXj4MGDTJ48mT/++AOAli1b8sILLzj8/KEOHTpw7tw53nnnHU6fPk3NmjVZs2ZNphPGRcQ7WYqmABJYRmsasYEr+NOK79hIhMX1FU0ikhO3ugDm7dJ1nEQ8l6VoKkgcK2lBKFuIoyDNWcVW6ltc33t+E4rIzRx6HScREVdjKZqCucxqmvIIPxNDEJGs4Wcsnw+paBIRaymcRMStWYqmwlxkHY2pzQ4uUpjGrGMHmd8NLCJiK4WTiLgtS9FUjHNsIIL72ctZitOI9ezl/iy3ob1NImILhZOIuCVL0VSKU2wggmrs5xSlCGcjf3BvlttQNImIrWy+jhNAcnIyGzZsYOrUqcTFxQFw8uRJ4uPj7TqciIgllqLpDv4lmlCqsZ/jlKU+WxRNImJ3Nu9x+ueff4iMjOTYsWMkJibSqFEjAgMD+eCDD0hMTGTKlCmOmFNEBLAcTeU4ShRhVOQIRyhPGFEcpYLF9RVMInI7bN7j1L9/f2rXrs2lS5fw9/dPf75NmzYZruotImJvlqKpMgfZQn0qcoSDVCaUaEWTiDiMzXuctm7dyo8//oifn1+G58uXL8+JEyfsNpiIyI0sRVNV/mAj4ZThFH9QlXA2cooyFtdXNImIPdi8xyk1NZWUlJRMz//7778EBgbaZSgRkRtZiqbq7CWaUMpwir1UJ5RoRZOIOJzN4dS4cWMmTpyY/thkMhEfH8+wYcNo1szyvZ9ERG6VpWh6gJ1soiElOMcOatGQTZyjhMX1FU0iYk8233Ll33//pUmTJhiGwcGDB6lduzYHDx6kWLFibNmyhRIlLP/ycgW65YqIe7EUTQ/xP9bShELE8DN1iWQNMRSyuL6iSUSsYUsf3NK96pKTk1mwYAF79+4lPj6eWrVq0blz5wwni7sihZOI+7AUTY+zle9pRiDxbOVxmrOKOCz/t6xoEhFrOfxedXny5KFLly63NJyISE4sRVNDolhBSwpwhY2E0YrvuEIBi+srmkTEUawKp++++87qDbZq1eqWhxERsRRNTVjDUtrgzzVWE0lblnANy3u4FU0i4khWhVPr1q2t2pjJZLL4jjsREWtYiqaWfMe3tCcfSSynFU/xDUnks7i+oklEHM2qcEpNTXX0HCLi5SxFUzsW8TVPk5dkvqE9nZlHMnktrq9oEpHccEv3qhMRsSdL0dSJeSykA3lJ5is604n5iiYRcbpbCqeNGzfSokULKlWqRKVKlWjRogUbNmyw92wi4gUsRVMPZjCXZ/AllS95lm7MJiWLHeSKJhHJTTaH0+eff05kZCSBgYH079+f/v37ExQURLNmzfjss88cMaOIeChL0fQCk5lBT3ww+JwX6cUXpOJrcX1Fk4jkNpuv41S2bFkGDx5M3759Mzz/2WefMXr0aJe+X52u4yTiOixF0ytMYAIDAJjAKwzgI8DCgiiaRMR+bOkDm/c4Xb58mcjIyEzPN27cmJiYGFs3JyJeyFI0vcH76dE0hsGKJhFxSTaHU6tWrVi6dGmm55cvX06LFi3sMpSIeK7M0WQwjOG8zxAAhjGcNxmNoklEXJHNVw6/9957GTVqFJs3b+aRRx4B4Oeff+aHH35g4MCBfPLJJ+nL9uvXz36TiojbsxRNo3mTIbwPmPc6fcgbWa6vaBIRZ7P5HKcKFSpYt2GTicOHD9/SUI6ic5xEnMdSNH3EAF5lIgD9mcgn9M9yfUWTiDiKQ+9Vd+TIkVseTES8083RZCKVT+nLS0wGzO+km8oLWa6vaBIRV3FLN/kVEbHWzdHkQwrTeJ6ezCAVEz35kln0yHJ9RZOIuBKbw8kwDBYtWsSmTZs4e/ZsptuxLFmyxG7DiYh7uzmafElmNt3ozHyS8aUrc/iaTlmur2gSEVdjczi98sorTJ06lYYNG1KyZElMlt5XLCJe7+ZfDXlJYj6deJLFXCcPT/M1i3kyy/UVTSLiimwOp7lz57JkyRKaNWvmiHlExAPcHE1+JPIt7WnFChLx40kWsZKWWa6vaBIRV2VzOAUHB1OxYkVHzCIiHuDmaMrPVZbShkjWcpX8tGEpa8l8Ed00iiYRcWU2XwBz+PDhjBgxgqtXrzpiHhFxYzdHUwAJrKI5kawlgQCas0rRJCJuzeY9Tk899RRff/01JUqUoHz58uTNmzfD6zt37rTbcCLiPm6OpkBiWUVz6rGNWAJpxvf8wONZrq9oEhF3YHM4devWjR07dtClSxedHC4iQOZoKsQl1hBJXX7hMsE0YS2/UDfL9RVNIuIubA6nVatWsXbtWh5/POv/5ygi3uPmaCrCBdbTiFrs4jxFacw6dlEry/UVTSLiTmwOp5CQEN2uRESAzNFUgjOspxE1+I0zlCCCDfxO9SzXVzSJiLux+eTw8ePHM2jQII4ePeqAcUTEXdwcTaU5yWYaUIPfOElpQolWNImIx7F5j1OXLl24cuUKlSpVIiAgINPJ4RcvXrTbcCLimm6OphCOEUUYlfmbY4QQRhR/UznL9RVNIuKubA6niRMnOmAMEXEXN0dTBQ4TRRjl+YfDVCCMKP6hfJbrK5pExJ3d0rvqRMQ73RxNVfiLKMIoywn+ogphRHGCslmur2gSEXdnczjd6Nq1ayQlJWV4TieOi3imm6PpXvaxkXBKcYZ93EsEGzhN6SzXVzSJiCew+eTwhIQE+vbtS4kSJShQoACFCxfO8CEinufmaKrBHjbTgFKcYTf304DNiiYR8Qo2h9OgQYOIiopi8uTJ5MuXj+nTpzNixAjKlCnDnDlzHDGjiDjRzdH0INvZREOKc57tPEgYUZyneJbrK5pExJPYfKhuxYoVzJkzhwYNGtCjRw/q1atH5cqVKVeuHPPmzaNz586OmFNEnODmaHqYn1hDJMHE8iOP0JTVxBKc5fqKJhHxNDbvcbp48SIVK1YEzOczpV1+4PHHH2fLli32nU5EnObmaKpPNOtpRDCxRFOfJqxVNImI17E5nCpWrMiRI0cAqFq1Kt988w1g3hNVqFAhuw4nIs5xczSFs4HVNKUgCayjEU1ZTTyBWa6vaBIRT2VzOPXo0YM9e/YAMHjwYD777DPy58/Pq6++yuuvv273AUUkd90cTU35npW0IICrrKIZrfiOqwRkub6iSUQ8mckwbu/X3NGjR9m5cyeVK1emRo0a9prLIWJjYwkODiYmJkaXTRCx4OZoas1SFtIBP66zhDZ0ZAHX8ctyfUWTiLgjW/rgtq7jBFC+fHnKly9/u5sRESe7OZqeYiHz6EweUlhAB55hLsnktbwyiiYR8Q5WH6r76aefWLlyZYbn5syZQ4UKFShRogTPP/88iYmJdh9QRBzv5mh6hjnMpxN5SGE2XenMPEWTiAg2hNO7777Lvn370h//9ttv9OzZk4iICAYPHsyKFSsYM2aMQ4YUEce5OZqe4wtm0R1fUvmC5+jBTFLxzXJ9RZOIeBOrw2n37t2Eh4enP16wYAF169bliy++YMCAAXzyySfp77ATEfdwczT14VO+4Hl8MJhEX3ozFSObXxOKJhHxNlaH06VLlyhZsmT64+joaJo2bZr+uE6dOhw/fty+04mIw9wcTQMYz6e8DMA4BtKPTxRNIiI3sTqcSpYsmX79pqSkJHbu3MnDDz+c/npcXBx582Z9DoSIuI6bo+lNRjGe1wB4j6G8zljAlHnF/6doEhFvZXU4NWvWjMGDB7N161aGDBlCQEAA9erVS3997969VKpUySFDioj9ZIwmg3d5m1G8BcBbjORt3kPRJCJimdWXIxg5ciRt27YlNDSUggULMnv2bPz8/ruey4wZM2jcuLFDhhQR+7g5mj7gDQYxFoDXGJu+1ykriiYR8XY2XwAzJiaGggUL4uub8V02Fy9epGDBghliytXoApjizW6Opo/pTz8mAdCXSXxG32zXVzSJiKdy6AUwg4Mt39SzSJEitm5KRHLJjdFkIpXJvEhvppGKiReYwhc8n+36iiYREbPbvnK4iLi2G6PJhxRm8CzdmEMKPjzLDObQLdv1FU0iIv9ROIl4sBujKQ/XmUNXnmYByfjSha9YSMds11c0iYhkpHAS8VA3RlNeklhAR9qylCTy0oGFLKNNtusrmkREMlM4iXigG6MpH9dYxJO0YBXXyEc7FvM9zbNdX9EkImKZwknEw9wYTf5cYRmtacx6ruBPa5axnuwvG6JoEhHJmsJJxIPcGE0FiGclLWhANPEUoAUriaZBtusrmkREsqdwEvEQN0ZTEDF8TzMe40diCKIpq/mJR7NdX9EkIpIzhZOIB7gxmgpzkbU0oQ7buUhhGrOOHdTOdn1Fk4iIdRROIm7uxmgqxjnW0ZgH2M05itGI9eyhZrbrK5pERKyncBJxYzdGU0lOs5FwqrGf05QknI3sp1q26yuaRERso3AScVM3RtMd/MtGwrmbv/iXOwgjioPcle36iiYREdspnETc0I3RVI6jRBFGRY5wlHKEEcURKma7vqJJROTW+Dh7ABGxzY3RVIlDRBNKRY5wiEqEEq1oEhFxIIWTiBu5MZru5gDRhFKOYxzgbkKJ5hjlsl1f0SQicnsUTiJu4sZoqsbvRBPKHZzkN+4jlGhOcke26yuaRERun8JJxA3cGE012cVmGlCSs+yiJg3ZxFlKZru+oklExD4UTiIu7sZoqsMvRBFGMS7wPx4ijCguUCzb9RVNIiL2o3AScWE3RtOj/MAGIijMZbbxGI1Yz2UKZ7u+oklExL4UTiIu6sZoasAm1tKEIOKIoiGRrCGOoGzXVzSJiNifwknEBd0YTY1Zy/c0oyAJrKEJzVlFAgWzXV/RJCLiGAonERdzYzS1YAXf0Qp/rvEdLWnNMq7hn+36iiYREcdROIm4kBujqS2LWUJb8pHEItrxJItIJH+26yuaREQcS+Ek4iJujKanmc9COpCXZObRiY4s4Dp+2a6vaBIRcTyFk4gLuDGaujOTr+hCHlKYSXe6MoeUHG4rqWgSEckdCicRJ7sxmp5nKjN5Fh8MptCbnnxJKr7Zrq9oEhHJPQonESe6MZr68TFTeQGAifTnRSZj5PCfqKJJRCR3KZxEnOTGaHqdD/mYVwD4gEG8ygTAZHG9NIomEZHcp3AScYL/osngbd7lQ94AYDjDGMz7KJpERFxT9meciojd3RhN7/EWQxkNwBBG8z5Dclxf0SQi4jxuscfp6NGj9OzZkwoVKuDv70+lSpUYNmwYSUlJzh5NxCY3RtN4BqZH06t8pGgSEXEDbrHH6cCBA6SmpjJ16lQqV67M77//Tq9evUhISGDcuHHOHk/EKmnRZCKVT+nLS0wG4CU+YzIv5bi+oklExPlMhuGev47Hjh3L5MmTOXz4sNXrxMbGEhwcTExMDEFB2d8gVcSe0qLJhxSm0pvn+JJUTPTiC2bQM8f13fO/UhER92BLH7jFHidLYmJiKFKkiLPHEMlRWjT5ksxMevAMX5GCD92YzTy65Li+oklExHW4ZTgdOnSISZMm5XiYLjExkcTExPTHsbGxjh5NJIO0aMrDdebRmaf4luvkoRPzWUT7HNdXNImIuBannhw+ePBgTCZTth8HDhzIsM6JEyeIjIykffv29OrVK9vtjxkzhuDg4PSPkJAQR/51RDJIiyY/EvmW9jzFtySRlydZpGgSEXFTTj3H6dy5c1y4cCHbZSpWrIifn/nmpidPnqRBgwY8/PDDzJo1Cx+f7LvP0h6nkJAQneMkDpcWTfm5ymLa0YzVXCMfbVjKGprmuL6iSUQk97jNOU7FixenePHiVi174sQJGjZsyIMPPsjMmTNzjCaAfPnykS9fvtsdU8QmadEUQALLeYIINnIFf1qygijCc1xf0SQi4rrc4hynEydO0KBBA8qVK8e4ceM4d+5c+mulSpVy4mQiGaVFU0HiWEVz6rOVOArSjO/ZRr0c11c0iYi4NrcIp/Xr13Po0CEOHTpE2bJlM7zmpldTEA+UFk3BXGY1TXmEn7lMMJGs4X88nOP6+p+yiIjrc4srh3fv3h3DMCx+iLiCtGgqwgU2Es4j/MwFihDORkWTiIgHcYs9TiKuLC2ainOWDURQg984S3Ei2MBv1MhxfUWTiIj7UDiJ3Ia0aCrNSTYSzj0c4CSlCWcjB7gnx/UVTSIi7kXhJHKL0qKpLMeJIowqHOI4ZQkjikNUyXF9RZOIiPtxi3OcRFxNWjSV5whbqE8VDnGE8tRni6JJRMSDKZxEbJQWTZU5yBbqU4GjHKQy9dnCUSrkuL6iSUTEfSmcRGyQFk33sJ8t1CeEf9nPPdRnC/+S8y19FE0iIu5N4SRihZEj/4um6uxlMw0ozWn2Up0GbOY0pXPchqJJRMT96eRwkRykBRNALXawjsYU5SLbeZAmrOUiRXPchqJJRMQzaI+TSDZujKaH+YmNhFOUi/zEw0SwQdEkIuJlFE4iWRg58r/P67GFdTSmEDFsoR6NWUcMhXLchqJJRMSzKJxEsvDOO+Y/w9nAGiIJJJ4NhNOU1cQTmOP6iiYREc+jcBKxIO0QXVO+ZyUtCOAq39OUlqzgCgVyXF/RJCLimRROIjdJi6ZWLGcZrclPIst4gjYs5Rr+Oa6vaBIR8VwKJ5H/l5LyXzS15xsW8SR+XOcb2tOeb0kiX47bUDSJiHg2hZMIMHMm5Pn/i3N05iu+5mnyksxcutCJ+SSTN8dtKJpERDyfruMkXq9AAbhyxfz5s3zJF/TCB4Pp9KQ3U0nFN8dtKJpERLyD9jiJV8uX779oepHP+ZLn8MHgM17ieaYpmkREJAOFk3gtkwmSksyfv8IEPqcPAOMZQF8+xbDiPw9Fk4iId1E4iVe68YrgQxjNBAYAMIo3eY1xgMnyijdQNImIeB+Fk3id/6LJYATvMJqhALzNu7zFKBRNIiKSFZ0cLl7lxmh6n8G8wYcADOIDxjLIqm0omkREvJfCSbzGjdE0kVfozycA9ONjJtHPqm0omkREvJvCSbxCWjSZSOVzXuIFpgLQmylMo7dV21A0iYiIznESj5cWTT6k8CU9eYGppGKiOzOtiqZixRRNIiJipj1O4tHSosmXZObQlU58TTK+dGUOX9PJqm2cO+fAAUVExK0onMRjpUVTXpKYTyeeZDHXyUNHFrCEdlZtQ3uaRETkRjpUJx4pLZrycY3FtONJFpOIH21ZYlU0tWihaBIRkcy0x0k8Ttu25j/9ucJS2tCEdVwlP61Zxjqa5Lj+2bNQvLiDhxQREbekcBKPsns3LF0KBYhnBS1pyGbiKUALVhJNgxzXDwpSNImISNYUTuIx0g7PBRHD9zTjMX4klkCaspofeSzH9X18ICbGwUOKiIhb0zlO4hHSoqkQl1hPIx7jRy5RiAg2WBVNhQtDSoqDhxQREbenPU7i9tKiqSjnWU8jHmA35ylKI9azmwes2sbFiw4cUEREPIbCSdxaWjSV4AwbCec+9nGGEoSzkX3cZ9U29O45ERGxlg7VidtKi6YynCCaUO5jHycoQyjRVkXTSy8pmkRExDba4yRuKS2a7uQfogijEof5hzsJI4rDVMpx/aAg+OwzBw8pIiIeR3ucxO2kRVNF/mYL9anEYf6mIvXZYlU0gd49JyIit0bhJG4lLZru4k+iCaUcx/iTuwglmmOUs2obOjwnIiK3SuEkbiMtmu5lH9GEUpYT7ONeQonmBGWt2oaiSUREbofCSdxCWjTdz24204BSnGEXNWnAZs5QKsf1589XNImIyO3TyeHi8tKiqTa/so7GFOYyv1CHSNZwiSI5rq9gEhERe9EeJ3FpadH0KD+wkXAKc5kfeJRGrFc0iYhIrlM4ictKi6ZQNrOWJgQRxyYa0IS1xBKc4/qKJhERsTeFk7iktGhqxDpW05SCJLCORjRnFQkUzHF9RZOIiDiCwklcSkzMf9HUnJWsoCX+XGMlzWnFd1wlIMdtKJpERMRRFE7iMipXhkKFzJ+3YQlLaEs+klhCG9qyhETy57gNRZOIiDiSwklcQuXK8Pff5s87sIBveAo/rvM1HenAQq7jl+M2FE0iIuJoCidxurlz/4umrsxmHp3JQwqz6EYXviKZvDluQ9EkIiK5QeEkTrVkCXTtav68F9OYSQ98SWUavXiWGaTim+M2FE0iIpJbFE7iNCkp0K6d+fO+TGIavfHB4BNepjdTMaz4n6eiSUREcpPCSZwmz/9ft/41xjKJfgB8yOv052PAlOP6iiYREcltCidxirRLDrzFSMYyCIB3eZs3+ABFk4iIuCrdq05ynTmaDEbyNm8xCoChvMdohlq1vqJJREScReEkuSotmsbyOq8xHoCBjOMjBlq1vqJJREScSeEkucZkAhOpfEI/+vIZAH34lM/pY9W6qamOnlBERCR7OsdJHG7Hjv+iaSq96ctnpGKiF9OsiqYaNRRNIiLiGrTHSRwq7SRwX5KZwbN0ZS4p+NCDmcylq1Xb2LPHgQOKiIjYQHucxCGOHfsvmvJwna/oQlfmkowvnZhvdTTpnCYREXEl2uMkdpc3LyQnmz/3I5EFdKQNy0giLx1YyDLaWLUdRZOIiLgahZPY1Y3RlI9rLKYdzfmea+SjLUtYTTOrtqNoEhERV6RwErvZseO/aAoggWW0phEbuII/rfiOjURYtR1Fk4iIuCqFk9y2+HgoVMh87zmAgsSxkhaEsoU4CtKcVWylvlXbUjSJiIgr08nhclsefBACA/+LpmAus47GhLKFGIJozDpFk4iIeAztcZJb5u8P167997gwF1lHY2qzg4sUpjHr2EFtq7alaBIREXegPU5is7RLDdwYTcU4xyYaUpsdnKMYDdlkVTSVLKloEhER96E9TmK1mBjzuUw3K8UpNhBBNfZzilKEs5E/uDfH7cXFQcGC9p9TRETEUbTHSaxSoYLlaLqDf4kmlGrs51/uIJToHKOpcGHzXiZFk4iIuBvtcZIcpV0B/GblOEoUYVTkCEcpRxhRHKFittvKmxcuXnTAkCIiIrlAe5wkW1lFUyUOsYX6VOQIh6hEfbbkGE1+fpCU5IAhRUREconCSTJJSYGFC7OOpqr8wRbqcyfH+YOq1GcLx7kz221Onw6JiQ4YVkREJBfpUJ1kMHcudM3m/rvV2csGIijBOfZSnQg2cI4S2W4zORl8fe08qIiIiBNoj5OXu3oVunc3H0YzmbKPpgfYySYaUoJz7KAWDdmUbTTt22c+CVzRJCIinkJ7nLxYy5awcqV1yz7E/1hLEwoRw8/UJZI1xFAoy+V1bSYREfFE2uPkha5eNV/129poepytbCCCQsSwlcdpzLoso+mZZxRNIiLiuRROXqZ1awgIyHjV7+w0JIo1RBJIPBsJI5I1xBGUablKlcwnf8+ZY995RUREXIkO1XmR1q1h+XLrl2/CGpbSBn+usZpI2rKEa/hnWu7KFfMeLBEREU+nPU5e4upV26KpJd+xnCfw5xrf0ZLWLMsUTTVqmA/LKZpERMRbKJy8xOuvW79sOxaxmHbkI4lveZInWUQS+TIs06IF7Nlj5yFFRERcnMLJSxw8aN1ynZjHQjqQl2Tm0Ymn+Zrr+GVYZuBAWLHCAUOKiIi4OIWTl6hSJedlejCDuTyDL6l8ybN0ZQ4pN5wGN2KE+QTwceMcOKiIiIgL08nhHiglBTZuhJkzzYfTzp+HuLjs13mByUzmJQA+50X68inG/3f1iy/C5587emoRERHXp3DyMEuWQLduEB9v/TqvMIEJDABgAq8wgI8oVMjEa6+Zz43y88thAyIiIl5C4eRBliyBdu1sW+cN3ud9hgAwhsGUnzcao1MWd/cVERHxcjrHyUOkpMDLL9uyhsEwhqdH0zCG8yajKV1G0SQiIpIV7XHyEFu3wsmT1i5tMJo3GcL7AAxmDB8wGIBTpxwzn4iIiCdQOHkI64PH4CMG8CoTAfP5TR/zSvqrpUvbezIRERHPoXDyENYEj4lUPqUvLzEZgBf5nCm8mP562bJQr56jJhQREXF/bneOU2JiIjVr1sRkMrF7925nj+MSUlIgKQkKFMh6GR9S+IJevMRkUjHxLF9miCaAjz8GX18HDysiIuLG3C6cBg0aRJkyZZw9hstYsgRKloQmTSAhwfIyviQzm270ZAYp+PAMc5nJs+mv+/jA4sXQtm0uDS0iIuKm3OpQ3erVq1m3bh2LFy9m9erVzh4n16WkwObNEBUFR4/CsWOwbVv26+QliXl0pj2LuE4eOjGfRbRPf/3hh83b0J4mERGRnLlNOJ05c4ZevXqxbNkyAgICnD1OrluyBJ5/Hi5csH4dPxL5lva0YgWJ+NGeb4kOakXZIGjWDCZOBH9/h40sIiLicdwinAzDoHv37rzwwgvUrl2bo0ePWrVeYmIiiYmJ6Y9jY2MdNKFj3cqFLfNzlaW0IZK1XCU/bVjKWiLZtBwaNHDImCIiIh7Pqec4DR48GJPJlO3HgQMHmDRpEnFxcQwZMsSm7Y8ZM4bg4OD0j5CQEAf9TRwnJQX69bNtnQASWEVzIllLAgE0ZxVriQR0nSYREZHbYTIMw3DWFz937hwXcjj2VLFiRZ566ilWrFiByfTfVa1TUlLw9fWlc+fOzJ492+K6lvY4hYSEEBMTQ1BQkH3+EpjjZutWc5SULm1+S//N5wxZWgb+e65ECfPj06fh3DkoWtR8WO7sWRgzxvpZAollFc2pxzZiCaQZ3/MDj6e/vmmT9jiJiIjcKDY2luDgYKv6wKnhZK1jx45lOMx28uRJmjRpwqJFi6hbty5ly5a1aju2fGOstWQJ9O8P//7733Nly5rf2p/2LjVLyxQtav7TlnOWclKIS6whkrr8wmWCacJafqFuhrmOHtWJ4CIiIjeypQ/c4hynO++8M8PjggULAlCpUiWro8kRliyBJ5+Em9PzxAnz84sWmR9bWsaewQRQhAuspxG12MUFitCI9eyiVoZldJ0mERGR2+MW4eSKUlLMe5Es7a8zDDCZzK+nPXakEpxhPY2owW+cpTjhbOR3qqe/HhgIs2bpOk0iIiK3yy3DqXz58jj7COPWrRkPvd3MMLJ/3V5Kc5KNhHMPBzhJacLZyAHuAeDxx+GddyAsTHuaRERE7MEtw8kVuMK700I4RhRhVOZvjhFCGFH8TWWKFoVp07SHSURExN4UTrfImpvqOlIFDhNFGOX5h1P5K/BRkyg6VCtPWJj5XXPawyQiImJ/CqdbVK+e+V1qJ05YPofJZII77jB/ntUyt6oKfxFFGGU5weE8VSh3IIqJ5Zx3kryIiIi3cLub/LoKX1/zu9TAHEk3Snv88cdZL3Or7mUfW6hPWU6wj3s5MCUaX0WTiIhIrlA43Ya2bc2XHEjbs5SmbFnz823bZr1M0aL/XcvJWjXYw2YaUIoz7Mt7P0dmbKZZTycfMxQREfEibnEBTHtxxAUwwbFXDk/7s0rMdsI/bEz+K5eIu+tBAratw7d4Ebv9HURERLyVx1053F4cFU4O99NPEBkJsbHwyCOwejUEBzt7KhEREY9gSx/oUJ2ri46GRo3M0VS/Pqxdq2gSERFxEoWTK9uwAZo2hYQEiIgw72kKDHT2VCIiIl5L4eSqvv8eWrSAq1ehWTNYsQICApw9lYiIiFdTOLmiZcugdWtITDT/uWQJ5M/v5KFERERE4eRqFi6EJ5+E69fhqafgm28gXz5nTyUiIiIonFzLnDnQqZP52gXPPAPz5kHevM6eSkRERP6fwslVfPEFdO8Oqanw3HMwaxbk0R1xREREXInCyRV8+ik8/7z5hnZ9+8LUqeCjH42IiIir0b/OzjZ+PLz8svnzgQPhk08UTSIiIi5K/0I706hR8Npr5s+HDoWxY+13N2ARERGxO4WTMxgGvP02vPWW+fHIkfDee4omERERF6ezj3ObYcAbb5j3LgF8+CG8/rpzZxIRERGrKJxyk2FA//4waZL58Sef/Hd+k4iIiLg8hVNuSU2FF1+EadPMh+SmTDG/k05ERETchsIpN6SkQM+eMHu2+R1zM2ZAt27OnkpERERspHBytOvXoWtXWLAAfH3hq6+gY0dnTyUiIiK3QOHkSElJ5khautR865SFC6FNG2dPJSIiIrdI4eQo166Zb9a7apX5Jr2LF0Pz5s6eSkRERG6DwskRrlyB1q1h/Xrw94dly6BxY2dPJSIiIrdJ4WRv8fHQogVER0OBArByJTRo4OypRERExA4UTvYUEwPNmsGPP0JQEKxeDY8+6uypRERExE4UTvZy8SI0aQLbt0OhQrBuHdSp4+ypRERExI4UTvZw7RqEh8Pu3VC0KGzYADVrOnsqERERsTPd5Nce8uc3X3agZEnYvFnRJCIi4qEUTvbyxhuwbx/cd5+zJxEREREHUTjZU9Gizp5AREREHEjhJCIiImIlhZOIiIiIlRROIiIiIlZSOImIiIhYSeEkIiIiYiWFk4iIiIiVFE4iIiIiVlI4iYiIiFhJ4SQiIiJiJYWTiIiIiJUUTiIiIiJWUjiJiIiIWEnhJCIiImIlhZOIiIiIlRROIiIiIlZSOImIiIhYSeEkIiIiYiWFk4iIiIiVFE4iIiIiVsrj7AFyk2EYAMTGxjp5EhEREXEVaV2Q1gnZ8apwiouLAyAkJMTJk4iIiIiriYuLIzg4ONtlTIY1eeUhUlNTOXnyJIGBgZhMJmeP4xSxsbGEhIRw/PhxgoKCnD2O19LPwfn0M3A+/QycTz8DM8MwiIuLo0yZMvj4ZH8Wk1ftcfLx8aFs2bLOHsMlBAUFefV/JK5CPwfn08/A+fQzcD79DMhxT1ManRwuIiIiYiWFk4iIiIiVFE5eJl++fAwbNox8+fI5exSvpp+D8+ln4Hz6GTiffga286qTw0VERERuh/Y4iYiIiFhJ4SQiIiJiJYWTiIiIiJUUTgJAYmIiNWvWxGQysXv3bmeP4zWOHj1Kz549qVChAv7+/lSqVIlhw4aRlJTk7NE82meffUb58uXJnz8/devW5ZdffnH2SF5lzJgx1KlTh8DAQEqUKEHr1q35888/nT2WV3v//fcxmUy88sorzh7F5SmcBIBBgwZRpkwZZ4/hdQ4cOEBqaipTp05l3759TJgwgSlTpvDmm286ezSPtXDhQgYMGMCwYcPYuXMn999/P02aNOHs2bPOHs1rREdH06dPH37++WfWr1/P9evXady4MQkJCc4ezSv9+uuvTJ06lRo1ajh7FLegd9UJq1evZsCAASxevJhq1aqxa9cuatas6eyxvNbYsWOZPHkyhw8fdvYoHqlu3brUqVOHTz/9FDDfiikkJISXX36ZwYMHO3k673Tu3DlKlChBdHQ09evXd/Y4XiU+Pp5atWrx+eef895771GzZk0mTpzo7LFcmvY4ebkzZ87Qq1cv5s6dS0BAgLPHESAmJoYiRYo4ewyPlJSUxI4dO4iIiEh/zsfHh4iICH766ScnTubdYmJiAPS/eyfo06cPzZs3z/DfhGTPq+5VJxkZhkH37t154YUXqF27NkePHnX2SF7v0KFDTJo0iXHjxjl7FI90/vx5UlJSKFmyZIbnS5YsyYEDB5w0lXdLTU3llVde4bHHHuO+++5z9jheZcGCBezcuZNff/3V2aO4Fe1x8kCDBw/GZDJl+3HgwAEmTZpEXFwcQ4YMcfbIHsfan8GNTpw4QWRkJO3bt6dXr15Omlwkd/Xp04fff/+dBQsWOHsUr3L8+HH69+/PvHnzyJ8/v7PHcSs6x8kDnTt3jgsXLmS7TMWKFXnqqadYsWIFJpMp/fmUlBR8fX3p3Lkzs2fPdvSoHsvan4Gfnx8AJ0+epEGDBjz88MPMmjULHx/9fxpHSEpKIiAggEWLFtG6dev057t168bly5dZvny584bzQn379mX58uVs2bKFChUqOHscr7Js2TLatGmDr69v+nMpKSmYTCZ8fHxITEzM8Jr8R+HkxY4dO0ZsbGz645MnT9KkSRMWLVpE3bp1KVu2rBOn8x4nTpygYcOGPPjgg3z11Vf6ZeVgdevW5aGHHmLSpEmA+VDRnXfeSd++fXVyeC4xDIOXX36ZpUuXsnnzZqpUqeLskbxOXFwc//zzT4bnevToQdWqVXnjjTd02DQbOsfJi915550ZHhcsWBCASpUqKZpyyYkTJ2jQoAHlypVj3LhxnDt3Lv21UqVKOXEyzzVgwAC6detG7dq1eeihh5g4cSIJCQn06NHD2aN5jT59+jB//nyWL19OYGAgp0+fBiA4OBh/f38nT+cdAgMDM8VRgQIFKFq0qKIpBwonESdav349hw4d4tChQ5liVTuDHaNDhw6cO3eOd955h9OnT1OzZk3WrFmT6YRxcZzJkycD0KBBgwzPz5w5k+7du+f+QCI20KE6ERERESvpDFQRERERKymcRERERKykcBIRERGxksJJRERExEoKJxERERErKZxERERErKRwEhEREbGSwklERETESgonES+2efNmTCYTly9fdvYoNjGZTCxbtsxu2ytfvjwTJ0602/Zy29GjRzGZTOzevRtw35+riDtQOIl4KJPJlO3H8OHDnT1ijoYPH07NmjUzPX/q1CmaNm2a+wO5gO7du9O6desMz4WEhHDq1CndY0wkF+hedSIe6tSpU+mfL1y4kHfeeYc///wz/bmCBQuyfft2Z4xGUlISfn5+t7y+boCcka+vr74nIrlEe5xEPFSpUqXSP4KDgzGZTBmeK1iwYPqyO3bsoHbt2gQEBPDoo49mCCyA5cuXU6tWLfLnz0/FihUZMWIEycnJ6a8fO3aMJ554goIFCxIUFMRTTz3FmTNn0l9P23M0ffp0KlSoQP78+QG4fPkyzz33HMWLFycoKIiwsDD27NkDwKxZsxgxYgR79uxJ30s2a9YsIPOhun///Zenn36aIkWKUKBAAWrXrs3//vc/AP7++2+eeOIJSpYsScGCBalTpw4bNmyw6XuZkpLCgAEDKFSoEEWLFmXQoEF069Ytw54fS4f7atasmWHP3kcffUT16tUpUKAAISEhvPTSS8THx6e/PmvWLAoVKsTatWu55557KFiwIJGRkekRPHz4cGbPns3y5cvTvyebN2/OdKjOkm3btlGvXj38/f0JCQmhX79+JCQkpL/++eefU6VKFfLnz0/JkiV58sknbfoeiXgLhZOIMHToUMaPH8/27dvJkycPzz77bPprW7dupWvXrvTv35/9+/czdepUZs2axahRowBITU3liSee4OLFi0RHR7N+/XoOHz5Mhw4dMnyNQ4cOsXjxYpYsWZL+D3z79u05e/Ysq1evZseOHdSqVYvw8HAuXrxIhw4dGDhwINWqVePUqVOcOnUq0zYB4uPjCQ0N5cSJE3z33Xfs2bOHQYMGkZqamv56s2bN2LhxI7t27SIyMpKWLVty7Ngxq78/48ePZ9asWcyYMYNt27Zx8eJFli5dauu3GR8fHz755BP27dvH7NmziYqKYtCgQRmWuXLlCuPGjWPu3Lls2bKFY8eO8dprrwHw2muv8dRTT6XH1KlTp3j00Udz/Lp///03kZGRtGvXjr1797Jw4UK2bdtG3759Adi+fTv9+vXj3Xff5c8//2TNmjXUr1/f5r+fiFcwRMTjzZw50wgODs70/KZNmwzA2LBhQ/pzq1atMgDj6tWrhmEYRnh4uDF69OgM682dO9coXbq0YRiGsW7dOsPX19c4duxY+uv79u0zAOOXX34xDMMwhg0bZuTNm9c4e/Zs+jJbt241goKCjGvXrmXYdqVKlYypU6emr3f//fdnmhswli5dahiGYUydOtUIDAw0Lly4YOV3wzCqVatmTJo0Kf1xuXLljAkTJmS5fOnSpY0PP/ww/fH169eNsmXLGk888US227j//vuNYcOGZbndb7/91ihatGj645kzZxqAcejQofTnPvvsM6NkyZLpj7t165bh6xqGYRw5csQAjF27dhmG8d/P9dKlS4ZhGEbPnj2N559/PsM6W7duNXx8fIyrV68aixcvNoKCgozY2NgsZxURM53jJCLUqFEj/fPSpUsDcPbsWe6880727NnDDz/8kL6HCcyHrq5du8aVK1f4448/CAkJISQkJP31e++9l0KFCvHHH39Qp04dAMqVK0fx4sXTl9mzZw/x8fEULVo0wyxXr17l77//tnr23bt388ADD1CkSBGLr8fHxzN8+HBWrVrFqVOnSE5O5urVq1bvcYqJieHUqVPUrVs3/bk8efJQu3ZtDMOwek6ADRs2MGbMGA4cOEBsbCzJycnp38eAgAAAAgICqFSpUvo6pUuX5uzZszZ9nZvt2bOHvXv3Mm/evPTnDMMgNTWVI0eO0KhRI8qVK0fFihWJjIwkMjKSNm3apM8kIv9ROIkIefPmTf/cZDIBZDjUNWLECNq2bZtpvbRzlaxRoECBDI/j4+MpXbo0mzdvzrRsoUKFrN6uv79/tq+/9tprrF+/nnHjxlG5cmX8/f158sknSUpKsvprWMPHxydTSF2/fj3986NHj9KiRQtefPFFRo0aRZEiRdi2bRs9e/YkKSkpPVJu/FmA+edha6DdLD4+nt69e9OvX79Mr9155534+fmxc+dONm/ezLp163jnnXcYPnw4v/76q00/CxFvoHASkWzVqlWLP//8k8qVK1t8/Z577uH48eMcP348fa/T/v37uXz5Mvfee2+22z19+jR58uShfPnyFpfx8/MjJSUl2/lq1KjB9OnTuXjxosW9Tj/88APdu3enTZs2gDkijh49mu02bxQcHEzp0qX53//+l37eT3Jycvo5WWmKFy+e4Z2MsbGxHDlyJP3xjh07SE1NZfz48fj4mE8v/eabb6yeI40135Ob1apVi/3792f5MwTzXrSIiAgiIiIYNmwYhQoVIioqymIwi3gznRwuItl65513mDNnDiNGjGDfvn388ccfLFiwgLfeeguAiIgIqlevTufOndm5cye//PILXbt2JTQ0lNq1a2e53YiICB555BFat27NunXrOHr0KD/++CNDhw5Nv0xC+fLlOXLkCLt37+b8+fMkJiZm2s7TTz9NqVKlaN26NT/88AOHDx9m8eLF/PTTTwBUqVIl/YT0PXv20KlTp/S9adbq378/77//PsuWLePAgQO89NJLmS4uGRYWxty5c9m6dSu//fYb3bp1w9fXN/31ypUrc/36dSZNmsThw4eZO3cuU6ZMsWmOtO/J3r17+fPPPzl//nyGvVpZeeONN/jxxx/p27cvu3fv5uDBgyxfvjz95PCVK1fyySefsHv3bv755x/mzJlDamoqd999t83ziXg6hZOIZKtJkyasXLmSdevWUadOHR5++GEmTJhAuXLlAPOhpOXLl1O4cGHq169PREQEFStWZOHChdlu12Qy8f3331O/fn169OjBXXfdRceOHfnnn38oWbIkAO3atSMyMpKGDRtSvHhxvv7660zb8fPzY926dZQoUYJmzZpRvXp13n///fRo+eijjyhcuDCPPvooLVu2pEmTJhn2FFlj4MCBPPPMM3Tr1o1HHnmEwMDA9D1YaYYMGUJoaCgtWrSgefPmtG7dOsO5Svfffz8fffQRH3zwAffddx/z5s1jzJgxNs0B0KtXL+6++25q165N8eLF+eGHH3Jcp0aNGkRHR/PXX39Rr149HnjgAd555x3KlCkDmA+NLlmyhLCwMO655x6mTJnC119/TbVq1WyeT8TTmYzbPXguIuKFunfvzuXLl+166xcRcX3a4yQiIiJiJYWTiIiIiJV0qE5ERETEStrjJCIiImIlhZOIiIiIlRROIiIiIlZSOImIiIhYSeEkIiIiYiWFk4iIiIiVFE4iIiIiVlI4iYiIiFhJ4SQiIiJipf8DsOUuYTNgDusAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import gaussian_kde, shapiro, normaltest, kstest, anderson\n",
"\n",
"# Extract outputs from the sampled list\n",
"outputs = np.array([item[\"output\"] for item in train])\n",
"\n",
"# Compute and print sample mean & std\n",
"mean, std = outputs.mean(), outputs.std(ddof=1)\n",
"print(f\"Sample mean = {mean:.4f}, sample std = {std:.4f}\")\n",
"\n",
"# 1) Shapiro–Wilk test (good for n < 5000)\n",
"sw_stat, sw_p = shapiro(outputs)\n",
"print(f\"Shapiro–Wilk: W = {sw_stat:.4f}, p = {sw_p:.4g}\")\n",
"\n",
"# 2) D’Agostino’s K² test\n",
"k2_stat, k2_p = normaltest(outputs)\n",
"print(f\"D’Agostino’s K²: χ² = {k2_stat:.4f}, p = {k2_p:.4g}\")\n",
"\n",
"# 3) Kolmogorov–Smirnov against N(0,1)\n",
"ks_stat, ks_p = kstest(outputs, 'norm', args=(0, 1))\n",
"print(f\"Kolmogorov–Smirnov: D = {ks_stat:.4f}, p = {ks_p:.4g}\")\n",
"\n",
"# 4) Anderson–Darling test\n",
"ad = anderson(outputs, dist='norm')\n",
"print(f\"Anderson–Darling: A² = {ad.statistic:.4f}\")\n",
"for sl, cv in zip(ad.significance_level, ad.critical_values):\n",
" print(f\" {sl}% critical value = {cv:.3f}\")\n",
"\n",
"# Plot density\n",
"kde = gaussian_kde(outputs)\n",
"x_grid = np.linspace(outputs.min(), outputs.max(), 1000)\n",
"y = kde(x_grid)\n",
"\n",
"plt.figure(figsize=(6,4))\n",
"plt.hist(outputs, bins=50, density=True, alpha=0.6, edgecolor='k')\n",
"plt.plot(x_grid, y, lw=2)\n",
"plt.axvline(0, color='gray', linestyle='--')\n",
"plt.title('Density Plot of Resampled Outputs')\n",
"plt.xlabel('Output Value')\n",
"plt.ylabel('Density')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"from scipy.stats import skew, kurtosis, probplot\n",
"\n",
"# 5) Skewness & excess kurtosis\n",
"s = skew(outputs)\n",
"k = kurtosis(outputs, fisher=True) # Fisher’s definition: 0 for a normal\n",
"print(f\"Skewness = {s:.4f}, Excess kurtosis = {k:.4f}\")\n",
"\n",
"# 6) Q–Q plot\n",
"plt.figure(figsize=(6,6))\n",
"probplot(outputs, dist=\"norm\", plot=plt)\n",
"plt.title(\"Q–Q Plot vs. N(0,1)\")\n",
"plt.xlabel(\"Theoretical quantiles\")\n",
"plt.ylabel(\"Sample quantiles\")\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "16168a08-edcb-49bb-bedc-0b120bcf4b8d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'input': '(G(C(T(G(TC(A(C(C(G(G(A(T(A(A(A(A(C(T(T(G(GAAGCACC)C)A)A)G)T)T)T)T)GTAAG)T)C)C)G)G)T)CTGATGA(G(T(C(CCGATCTAAG)G)A)C)GAAA)C)A)G)C)~$predict_ribozyme_efficiency\\n',\n",
" 'output': np.float64(-0.9650336184219599)},\n",
" {'input': '(G(C(T(G(TC(A(C(C(G(G(ATGCTTCT)C)C)G)G)T)CTGATGA(G(T(C(CCATTCC(G(C(G(TG(AGTCTCCCGT)GA)T)G)C)CTTAG)G)A)C)GAAA)C)A)G)C)~$predict_ribozyme_efficiency\\n',\n",
" 'output': np.float64(-0.7394037902020928)}]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train[:2]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fedf39c3-9322-49c6-b8a8-f58fbab9c6af",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'input': '(G(C(T(G(TCA(C(C(GGATAC)G)G)G(C(G(T(C(C(G(GT(C(TGATGA)G)TC)C)CACGAATACC(T(T(C(A(G(T(TTTACCA)A)C)T)G)G)G)AG)G)A)C)G)AAA)C)A)G)C)~$predict_ribozyme_efficiency\\n',\n",
" 'output': np.float64(-0.16327956260463686)},\n",
" {'input': '(G(C(T(G(TC(A(C(C(G(G(A(T(AAGTT)G)T)C)C)G)G)T)CTGATGA(G(T(C(CAA(G(C(GAAAATCCCAC)G)C)CTAAGCACTATGAG)G)A)C)GAAA)C)A)G)C)~$predict_ribozyme_efficiency\\n',\n",
" 'output': np.float64(-0.5493558904409944)}]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val[:2]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "8d9e5112-60bd-485d-b070-2cbd98172944",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from typing import Any, Iterable\n",
"import random\n",
"def write_list_to_jsonl(data: Iterable[Any], filename: str, *,\n",
" ensure_ascii: bool = False,\n",
" indent: int = None) -> None:\n",
" \"\"\"\n",
" Write a list of JSON-serializable items to a JSON Lines file.\n",
"\n",
" Args:\n",
" data: An iterable of items (e.g. dicts, lists, primitives) that can be serialized by json.dumps.\n",
" filename: Path to the output .jsonl file.\n",
" ensure_ascii: If True, the output will have all non-ASCII characters escaped.\n",
" Defaults to False (better for human readability).\n",
" indent: If not None, pretty-prints each JSON object with this indent level.\n",
" Usually left as None for compactness in jsonl.\n",
"\n",
" Example:\n",
" >>> items = [\n",
" ... {\"id\": 1, \"text\": \"hello\"},\n",
" ... {\"id\": 2, \"text\": \"world\"},\n",
" ... ]\n",
" >>> write_list_to_jsonl(items, \"output.jsonl\")\n",
" \"\"\"\n",
" random.shuffle(data)\n",
" with open(filename, 'w', encoding='utf-8') as f:\n",
" for item in data:\n",
" line = json.dumps(item, ensure_ascii=ensure_ascii, indent=indent)\n",
" # indent adds newlines, but JSONL expects one object per line:\n",
" if indent is not None:\n",
" line = line.replace('\\n', '')\n",
" f.write(line)\n",
" f.write('\\n')\n",
"\n",
"write_list_to_jsonl(train,'train.jsonl')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "ee5a5a29-537f-4174-b7d1-1ce2bbf72280",
"metadata": {},
"outputs": [],
"source": [
"write_list_to_jsonl(val,'val.jsonl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8f516d0e-0a10-4178-8b61-37be5cc2f645",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|