notebooks/sound-generation-audioldm2/sound-generation-audioldm2.ipynb

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    "# Sound Generation with AudioLDM2 and OpenVINO™\n",
    "\n",
    "[AudioLDM 2](https://huggingface.co/cvssp/audioldm2) is a latent text-to-audio diffusion model capable of generating realistic audio samples given any text input.\n",
    "\n",
    "AudioLDM 2 was proposed in the paper [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://arxiv.org/abs/2308.05734) by `Haohe Liu` et al.\n",
    "\n",
    "The model takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music.\n",
    "\n",
    "![](https://github.com/openvinotoolkit/openvino_notebooks/assets/76463150/c93a0f86-d9cf-4bd1-93b9-e27532170d75)\n",
    "\n",
    "In this tutorial we will try out the pipeline, convert the models backing it one by one and will run an interactive app with Gradio!\n",
    "\n",
    "\n",
    "#### Table of contents:\n",
    "\n",
    "- [Prerequisites](#Prerequisites)\n",
    "- [Instantiating Generation Pipeline](#Instantiating-Generation-Pipeline)\n",
    "- [Convert models to OpenVINO Intermediate representation (IR) format](#Convert-models-to-OpenVINO-Intermediate-representation-(IR)-format)\n",
    "    - [CLAP Text Encoder Conversion](#CLAP-Text-Encoder-Conversion)\n",
    "    - [T5 Text Encoder Conversion](#T5-Text-Encoder-Conversion)\n",
    "    - [Projection model conversion](#Projection-model-conversion)\n",
    "    - [GPT-2 conversion](#GPT-2-conversion)\n",
    "    - [Vocoder conversion](#Vocoder-conversion)\n",
    "    - [UNet conversion](#UNet-conversion)\n",
    "    - [VAE Decoder conversion](#VAE-Decoder-conversion)\n",
    "- [Select inference device for AudioLDM2 pipeline](#Select-inference-device-for-AudioLDM2-pipeline)\n",
    "- [Adapt OpenVINO models to the original pipeline](#Adapt-OpenVINO-models-to-the-original-pipeline)\n",
    "- [Try out the converted pipeline](#Try-out-the-converted-pipeline)\n",
    "\n",
    "\n",
    "### Installation Instructions\n",
    "\n",
    "This is a self-contained example that relies solely on its own code.\n",
    "\n",
    "We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.\n",
    "For details, please refer to [Installation Guide](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md#-installation-guide).\n",
    "\n",
    "<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/sound-generation-audioldm2/sound-generation-audioldm2.ipynb\" />\n"
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   "source": [
    "## Prerequisites\n",
    "[back to top ⬆️](#Table-of-contents:)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%pip install -qU accelerate \"diffusers>=0.30.0\" \"transformers>=4.43\" \"torch>=2.1\" \"gradio>=4.19\" \"peft>=0.6.2\" --extra-index-url https://download.pytorch.org/whl/cpu\n",
    "%pip install -q \"openvino>=2024.0.0\""
   ]
  },
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   "source": [
    "## Instantiating Generation Pipeline \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "To work with [AudioLDM 2](https://huggingface.co/cvssp/audioldm2) by [`Centre for Vision, Speech and Signal Processing - University of Surrey`](https://www.surrey.ac.uk/centre-vision-speech-signal-processing), we will use [Hugging Face Diffusers package](https://github.com/huggingface/diffusers). Diffusers package exposes the `AudioLDM2Pipeline` class, simplifying the model instantiation and weights loading. The code below demonstrates how to create a `AudioLDM2Pipeline` and generate a text-conditioned sound sample."
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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      "2024-08-14 08:47:45.295561: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-08-14 08:47:45.297388: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-08-14 08:47:45.331810: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-08-14 08:47:45.955688: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
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AwoDifSF+k38Bw6RC7rwVwE3/toCYAXw+dX5xvyqBHb+lgJ6AOT9JQNFAoQEAvyX+hUGxQfw/+z6G/6p/WwFCAgG+iX4xwNXBF8CygBk+ZcBBRBZAVL9mfkYBjIOg/1U/KP5NgzoByIB+vcY98YGzAijBx/1n/qaDcwJ7fx89pb5kARXEaL2JPD3/W8BKA8n/ITwhfp4/qANNASm8oX2XP7YDE4Hs/nM9z78MA0a/2n5NvvB/ZYMEAHh8Yn+XAEnA9ICK/kV/DcD4QUUAFT5ZvXF+eQFtAjgAO71bvUMBngIL/54/vT8nP5o/E7+vf2bBAATHAG57LvrpgFiHuYTBvN64iHwHRUdD0j88fMz/tsLy/wb7/7tkgSPGDMN3eak3TP97hcOHOv9H+XO5yD5KRVXFF/ztPLh9NAImQew+5r9ZgQwCcjzjvgQ/Y8LWBHYALTyqfQQBzkP0AXGBJL6Xf0+//765QMBB4gHHfuy72IBUAfdCPn9rf1V/msHOAOi+WH/yP/CEUIHOPZX9VPyHgYrEIIJ+vqi7qDoIv+dDjMP3wXW69npD/uqDk0TOg5G+Mbswf34AeMG2A3pBEn/vPGa9U4F9g1hEc/4vezx8ycEpBWmDaEA2Osp81QFhROmCTL/VfHt6bMIsQ/bB6IGh/MU6sj/4QfFF+MKhPDK50DvsQkRHKcMA/UA6GjyFQKIEokVLwi19iDrXvUdCqwQ6BB6/Erp6PLX/iAPlxcbBXvy/+YJ9UAFdRUwFIry4eqX7NX/ixWxDr36DvHu+GT4qwGoDqsK5/vk75DxLgigCUkJEgIH+h3+ovvHAP0DIAc0BJ72e/Vp+4MHVxB7BGX2y/zj+HgA6gu8+5v9cQJ+9PD+cwcfAW8GwP4k82b91P3WBUMEev9U9ub4C//j/FYO8we6/BD3JPTPC0QJeQJv9Ob5sQSWBU0ANvcN/ogF0QCc+oT8/v2zBn0HNfaM+CYDawgMBLMCC/kl/gcIDATJCGkFcP0sAhUF8AMsA1MDtAbSBEECg/3QAa8El/srC2H9VQhvAefxwgq9Bov7WgQs/eX7IhK6/lb5Tg9O+iz5HQBP/AUGSgbG9zv1SQhvCcX2Uf969M4AThD0+Yn3fv2OAMH+4/+R/t3+DwKy+30CKf5RA3AHkQCtCRn74/TS/YIKvwsK+p7wqPtLAkoHPguC9fH6LgM4+xAAwQYj/xX92gHT8Cb30gzS+1/5oPfO+GsBrgVp8rb+rgzJ+eT99gBa/3wE1gQ3+5T8aQWO/en/9wTy+koD+wCF+zMELf6+ALYEyfZlBY8J5QXxCMH9yffv/9oG8QJFAgP6/fliBnkCcQD0/0X8mPv4A9wGf/6O+3b2+v6aCr0I8P2Y9x/5bAJ0BUP87QO//h73iwfyAL//MwPt93L7sQXPCL756v9S+gcAcwqt/B0FJP8B+VUHZwN0AOb/ivri/WcGiAUE/nf8JP8T/csEawOz+8n/CQW29jv/jASS+Y//Ev4W+ej/Hwq/9xb5JwJS/tsBDAQ+AaT4mgHIAfMDLwhjAJT4vfz3A0wEaP7qALkBM/moDqkG6fwDCXL5r/z0DtoBEPw0AR7/QP5OCYICE/xxBcUCbP5J/uvzxPjUB/cP/AX27Iz6WvwNAo8EDve++gH+L/04+dcCeAXk9Qz3FvTL+0cLdQYo9qHz4AC9B5IE1fkL7UABsQdE/ZX5jvRIAxEEAf70A9b7v/to/Dz+ogVvBYwCY/iP/dAFf/+uBToAO/ebAQgK7fobAAkBnPdDDcwByPm0BmP8P/mMALQHsvlyApX4f/7HB3H/vv3t+8cF7vz3+6YG5PqU+2X9EPlPBwoByPi4/LYCVQaFBssAiADcALUDZgM6/eYEWv76/p0CbvpcEDgG6/O7/dMByBYHBrHw4fYfAd0SCwfI89D4OQQZAxQETfuj/1UE+wVF+2D5Hgu9BKABMAJS9+4AJAtJ/PsBrARmA4IEVAM1+9sAbwq7B9P+X/yrCFQFuAUZATX5YAf1CTX8Wv1i/1v9/f1MAmgERwC4/fgCLfngAn8FIvXeB3r9EvwzBo/76QeM/r3+YvuQ/NIDbgVVBRf7Nfw/94YEKAEN+roB+vq1/+UFT/xw/GcAmQIx/HIFxv8m918Hxvr6CmIOyPhU/tD49wcPA6P8SgtI/sX+rgGp+JENbQd/+Ev44P5eC64F3wBO8ib6cAwJBxv9V+4m/IMLkQK+/H/16PzeDiYEyfq19v39rQVxAaH/zPaz/44CS/+T/IH5Lgf8Ajn7Mfgt9YAFvwKG+bn6UPoQATn/fADxARn/mQNW9a4B/AGB/AAA4vVu/44CtgLj+Tj/hP2H+14DDAHcCpH6swK3+wTzDQzt/uL/MgQd9jcHGQOm+6b4Pf0UChD7u/jw/RYCtwbk/pz2nftdBnAKe/3W9235qv3EBkAIg/kk/pQCCP1cBQb/5v7HABYDGf21+vn5WgXqCNwBGvtv8xP/MwfyBxz6RfkRAtH+TguJ/qH41P4qCGsJAvop/O39Zw9TBGX8wgdiAo4GtP3p+UUFGQieBe/4jfmoACYFEw3f/Vb7n/+uBEkMG/nlA+EFb/yrDVz+Hvi7Beb/WgOWA3T2WwT6BjEFfwNN+Lj+6QGDBRYFqvz++soB9gVUArX+u/N1ARIMHgDK/SD53PxtB1390QBN/DH4cQUi/msEx/xD+U8BCPxoA1v7cv+4/fwAQAbE96QEwv8o9/EBz/zVAcICyPr7AWv8I/ktBN8AZ/3Z/wH9LvlpAygFoQTl+az2UwH3AtYDX/2a8iL4GwibAuUCcP3W+BX8TP0lAEAD6QWm+OH6LPzwAJ3/k/+b+nT4nvw0AwoGXfmIAD79VfpPCeb9Jf/sAAH5U/7XAV0BpwYWASz5LgcbA/H1KvixAzUAWQND/lf2v/r1/mcK7vpM+jf8ffcgB4EAWvi6/IT9V/75/GwBsQHA/SUFTQX+A6cCDfxG9/AEXAiy+rb9SgAB/TUFMQV1AxoFvf9SCen8YANaC5z8tAWNAkH9C/63BXAJwwIZAwAAIP7G/dgK0AUlAakDD/+bDUoB9/rUBEX+N/3RBuH92v8JCqf61P8oBhAE1AO0/JP+Hgpz+wQBNvvH/l0KZAETBBX7Y/v4B0QKpP9O+fsJGwXwALYAuvsE/7MM/AC8/t0GbfcGCa0E9PwSBJX4AQQ8DNz+pP4J/4L9SQi8+r3/mgRLAL0HAPdk/GkHnP43AMn+Rfsj/en+gAaB9u798gnV9xICAwJ49qj6yQStBm/7AvwmBKD8gP+t+6H8HATMBFz/5fURAKECpgB2/tX0Gf7GAef+mwWK+gH+PQCuA6QDzfYO/Nf/UAcoBJUARvt79+gBTQPfBb/4RwCs+8P2rAbcASz54vkgBIYGgPbn/sX+4vuLAjL8zf7XAf4FevrW/IoFBvwlBaX67PwQCBIB3wXw/Kb5eAB/A0gGBvzP9ov+EQCrAPcBx/59/QL//fzh/qoAnQD+BUL6zPtLARX9wQY3ASL+avzh+QkDhwQEA7P5WvY7Ag4GGP0y/Pn7HvvOBSAAJ/y2A2cAvgBWAh/6oPvkDJ8B3Pbu/Ln6mQaGCNz8UfvS+CcGzQUR9nAKCAKm/KMFev3/AVYE4v1jAzf7cP62BM/+tv5KARj9z/sOCsj6xASfBE/22wrpASAEQPvq/cD+6f0CBvn2qv1TBrQALQfg+CP4UAPs9bkPzwPV8qIHgPn2CAP90fDTChH9iwRAAxn1if9eC4X8Wv1nBhH9TwC7Abn8rfzhAAYDdwGk+a75I/yXBkIJ1P7b/MX5D/vYB0P5t/Vt/5oDGgPU+Jb1VP+7BmEGp/ec98b0sP/CCGX+fflN/XL79gcZBBf5ngHE/XgCSAMu9jkGZv6a+9ADcvGK/m78evrZCwH1HP5MBeAE4vpc9yX/Df3eDkD96fQR/xgFeAn//7T5J/0IB+IGyfj29jsI4wAzAq35zvq8COQIxvr09fYCQwOxBKoDdfZM/aQFsQH9Anr/IgPUASD3vQIoADkJnPqp/24NkfqcANL5hPxhBwEBIgW19z33Dgdh/WEIkADA980ALgPgAg79zvoI+6ILaf8n+Vr8zQPZBl79Pfmt//4HqQpB/E36+AJ1A60IqvtJBN8GmP2PBY7+fwOVCwn7/QHRBCH9bAtBBDf7lQAwAukDnQtT/X/8ewfT9X4KFARd/0QHJfmYDKUEQv55B/gA+gClCpb8IgI2DWH+tAWj/Fj7gASAAJEFFQXjAxMExP0uAMsEBASH/yQBFAJf+eQHG/6f/9gDrfNlDJEFRvmrCKwBr/91BZD/6wZvAP33UwHv/KMGHgIT+Cj+dv5U//sB5v1E9tQDtgO7+SoFoPnM/QAJG/hE98H2JQDM++n49ADP+iECe/+9+N8A+PwnA6gE4PxB+VH5UASOAhP9BgF5+dQDmATJ/5D7Gv8/CMIC+/xH+tECNQX4/WL6BP/2CgQDg/yy9wr/Hg15/wD5Xv+5+ocHQACI+sYAXPoHAxkD/v+W/eH3OPxg/UABRf3DACP+aADq+Sbz2/1e/E8I3gGM8Nz65wJqBPH9qvz89zsAywDr+XsAWvr0BBgD4PjP+2MEkgLoAk74Uf00/7H/4gVfAGf/aPz5At8CKfs+A/8Db/yXAnwD0Pd6A7cIYQES/sP4gwf1Bdv6HACEAAUB6v58+V8AQ/wgAMsIEPa6/XEEu/zG/KYBZgctAbwCPv6j+foKZALF/ZX/aPWtBlgGaftmBPUCMQVM/Yv3swV5BQEAKP+J/TQCZAjY/Ej7FAfsASgDxQAj/HMIxgGp/aAHmPu6/IgCPP17BIH/7vkUBygGw/vk+KQDHgFOAzQEc/xE/h76MAQU/+z1lf79A4QGz/fe+4II+vnLAeABSPefBw8Dif99/QwCMAtt/zsF9ACc+3sFOP0GBdv3u/anCBcE6P6j/Rb14/oXDIwGgQYX85X7ewm1AngFBfYu9ZADcgLsAQ7+TfgkBrcCv/6a9NECBgwM+RkBzfdE+ekFbAKyADL4sfoPAwT/1gCs/wT5Gv2NB4oBVgAsA5EDGQGW8lIIOgPb+UsLLPch/wYAYvSWCib71AIcCAzzWQTMAs364Aj3/hv6+gGT+uD93wLT+6YCpAnu75QAp//t91MOTu1CAakH1/SbBojybf9YCCr+xwPN92v+YP+rAqIBWf0gAyv9OgXRBZX90P1d/A8LTwB/+KYBIP6ICCkGXPnvAhEIHAUUDcX3iwCuB8oBfAfR+KMNVwESAg8IXPdXCPX/+AGSB1UAoge/ArT31ge+A4P+xAlM8nP/LgbU9rkNQ/yp+y0O7vV9BYkGYPhaAiv+vwtq/jj5eQOc+EEH7/47AiYFm/qWCTcAv/1RCeD1cQXCByb7qf9o/8UK4wMl+nIEVwILBEAC1QObAiT/2QVX/u8C+v/c/gMJKgGYBab3+fezBd/7oACJA9r4qAdq/y7+fgFk9bAJAv8u/xoEo/gSAtsB9//F/pr4BP6DB0f9ufmc92T4lQSFAV36tP3D8rYAQQM9/L4D7fe2Bnz9rvfCAAwAUAX4+lIDv/2y/YsAnf0tBRP48/wa/vP4pg70+QT8Zv199iwLO/sP/4H/VfHDAhj/QPxO+zL/wflUAMb+I/tSCdT3RwNN/2z3Hw6J+CT+VABt8LsEXwQI/88CIPMBCFsG5ffgACP90ANrA1j51/qq/D8EhftV+9YAUP/0CQT9vvqcAgEAHRAD+A76RgQl//IFBPnm/B4EGQNU/v35RAKTBXMDk/uR/m0LofaA/s79gv6rDFryMvjj+OP+JQmR9Cb67wymA9/73vMeBUwLygJY+uHzzgpQBjr54fvl9YMRnASh8WMIHf6aCasDoO+4ClcAsgCxA+35gApQAjX+C/91/nwGjwQ6/dr6ogT0CWX+E/+0/LMArQxz9coGKwCUAScTwvdlAlAApf7mCJf7AgFMB/z+gAGtAXv/FQyrBCf7EgHb/GMCSwPM918NmgFt96EDIvh9CR8GAPnfBlD7UgZUCGD7BAEY/M8EnAFP/MQEW/oBBBoAwPwuB7/5of91A5b//gjv8J33XgEEAUgDHvHoAHL+hfw0BB31LQeL+0oAmwSa9WcDTPlf/wkEGvczBy0C5PxM+xb/tAnA/lECy/o8BQsDMPEwA7n6CA/IAzTw9v48/hEF2fs19Rv8Lg1PAsj1X/y4+CISZftX8lMNKv5YDqXztev0E8AJMgTE6z7yQRLlDCn+GeyH/G0M4wiz813xrwsYCbYEWfSS/WgMFANzCkTyPAHWCLj/4gTp9e79xAbQ/WcA7v74BSYFS+9jB9cBuAEwBPL8QguT+Ef4lQlhBsgIIP2K+9j8uwTLAR8EGQGC+0EFhv8qA+795f5pAkEAcQQX9ioA0QQZ/HwGNvo6Ae8JiPJdBev74Pu+EmHzvQSZBfr33RI09pT5tgZT+ykHzP2j+NcL6v4V/MoCUPe3ARkDNf1DBVn9Hf0uAAH6NQHLDnP9MQCQAsH6EgsJ/nD4pgJh96wH0PtX+YEFcf3uApT0lgPrBVD/QgXf8x8CNv/aAxQEEu+SAKb/BgChA+P1HAExAAAAzAp7+UD6pvzzAeMH4/fEBHX+LPykBR/64wME/+L4VQJ7+XcHYQIR+kn/wP/XBasEfffz9RAG1QX+AmL73fbw/aYFev66/QIDTvk6/zP8YwSbDgf9nvWN+VcCMhObAqT05vWj9bANTA7b+LX+rPWE+OL97QO8CVD5A/Po8scETgxEBhrtVu0nAgb/Zhbv+e7mBwIv+MAHHQ508wr7YPBZ+XgR7gaPBF7wxeG5/u8S2BXl/6zhqer3B4YapxHh/BTdtfO0EBMMXhd27W/uqgSTBlgYjAGi+cj5/f9qDrUFqACJ9Wr8lgACDR4I/AZQBRXyjf1QD5YGrgmW9xTh2wQLF0ACDf5A6v70MRh0ELcHRu/L87cJHwLAG0L39P5F/3vyIwhBD3MNcPql8joDaQKFEiMJXPAc+0sFJe91GLMC8/VzBGjqzv06EKsJbwzi+MH0BPw0A7QZMQ3h7sT6J+veC7UbNAdc+efkPvPXC98ZHxXt5tDqe/a7BBomngrc5vzr8vNhCoES4gyq99bpYvdW+tcRwx6E7+LjBukSAq8nzR4c7NjL3++5F8sj4w4Z1MXbBgckIn8ZN/uS6DLdPgTgGb8QTgbo5fTgSAG1HN0PcgBa58PmCw7dEzML7vj367HyEABuGlwLX/Pt8WnkoQbZGv8MN/I95QXu4AaXK7IG4Okr6f/shR4sHbQGa+wA4TQEABKGGy4B4Nwh7nMAZxTtGr73n+O68Yf/ChbDGBn1eO3z7TH9rh0RDIf3juce8TIQqhInBIDtYOtUBK0LHQbwAPDu//KQCdYGagNT+KDxWf6iEZcLIP5G/dD07AXbFav/s/dn9w38UBO+CXH9L/lD84QI4gD+A8INfvmL9NzwdwV8GzcJC/Os6dL37RWiFv/+OO6g+9wAsRMiCBz5fQRr9Mz9SAcxFH4Nf/GX8S/3WxOxGq3zOO+i/bUJMhcZ/NH5hf8OAMYKffkLC+AN+AAB+q/rHw3nF1cIPvOX8m4NRhKaC2TvrvuVEx8IRwKW96QCgxGi/zL2DPrNDJYQyvYU9x79KBNcFBbuIvBpAjwLjwvU7vvwhQmzCicAoPPi+dYSIgnq+innkPinF2kIefVz8KTxdxTiBSTw0QER990HVf/D7RoP4Pss/vL2XuqFC5f5vvmG94j1VAW4Ae76BOoyCX8KXfpt96rp4wz/FRv64OpD8UYO1RWH+fjtWOhcBWcX0vT39nz+OPxRART6FPXCEuYLHfNt7Sr7cxJIFBb3Ou0z/swOxhR28ZnwTAKlAasOxOtRBngVA/R+9DTnrxGMJEf4VO3D3ocNviDc+JH4meZwAtUNTf2HAw7/+/hN9Z7+GgrjFo72hOb+9HcJQSBR/8DjuPl6AxoXKgfF6pUDfv/XBzYDgPajDz0HcPMq93v+iBW/EcLv1e6bBsoGYQ8U/77uBxNO9ooAMgzF7fUVy/E/8BgQagbdHaL1pdy7AMcRdhF/BN3fWPZ+GdsGUAj59v30/AVK+uADNhOlDHP9zulb+TUYQiSM+Ijm4vPJ/8stwvkS6dECvvU2HHX/Lut+FIcAdv6F+9X41CMPA2XmlvmJAScjQQbC5RX0bQaUDnMH6/5H9ecIIgOR+W8OxPtoDLkA8+jFBF38QxIC+M/k+QZYAM8Rgfvp7YgGOQQGChv7Fv7LBdz2dQLy8MEBCg3C/mr87esuBUUQ+AjC+nPu4QEOCpIJgPUj+JkBsAWWBzfyqAISBVT+jAHO7coOSAz+8fv49u9WDSEH5+6S+NX3fApy/hXwrvaEAkIJuvhE9On6bRDAAafvv/b1/n4aIwBJ6ezxMQZcEPr4mvFh9w0MGwEp9LT36AAhDTT6P/Ju/u4D/AmV/cjsBgqLA5kEVfcY9SkQaAclAcz1k/msEWwK6/i/9fr8wwgAC2H77/nTAOEBvgMk/LD+qgeBAk7/1fFy+RkP2/7A+4n4Pfj4DigDc/sP+HL+5AyQAPQExQVK+i8LYvoz9h4NIwSmAGn2iPpZEucK/wIY9kjzFwu5EN/+jf09An//swnN+jUDSRXJ/IH9X/ctB80azPqy+Dv7VQKlFsT19/D9AR0H7w/L9bT4ewW3/g4Ji/Hi/w8QvAbN9tTpiQFrDhkGZ/Cw8AEH0gPDB6n6EPZLCAj8lgbc/fX6MhCZ8fv9XAVaBucS3uyt9qECKAcOEm3yu/KOAmsIAwrVADb0PP1d/HcFvwpu+VH+mPc5/BoLtQGn/Rn7Ru4DB5wLpwo4+pTnWwMD/+kPEQZz73r5hvynBWT9agHQA2j6xfl+/ZAB8AUoAI/vBPd3Cr0J5AiO8Sr0rwan91AQjPzV80sIQvGxBR4EBvtZBwvxnP2nClYCrAln8Nzquw1LC6AGVPbQ7Ij8uQiaAjD86/6N9P3/mfSEAn8OYezj/7PxLvz2DqT2JwKj9Ln6+Qm8+gz/svdW/oj/uv2UB6YA1AAT/CT5nwKuBw4LgPHD84//yAOxDAoBD/gLABEA+gR7CAf5jwRW+04EOga+94MT7/Ig/h8IivtfD0b8x/lRAnwGwAly/dv3nggZAIv6Pgdw8+gH1AXX7mUHXgRZBAYHTPGlAXkCDwbbCCn3GQln+Uv6uQym+ogHpviE+pYGHPk1A4n/I/w2BHz3qQn8Cg3/VACl94sIJBCH/2z5Mvcr/uwIpQz8/vDy2QAPB1ECW/yb/MEKnwkv/kzoN/X+Gen7G+mC/ub91xajBentS/iR/RQYzwXH9KoAkv5DCTMC5OnCAQIIfQAxDbP0afM2BAgAtgw4A5XwQwBt91UMqgy285kC8Pz1BeUDsPfM+XYK4QOl/S0EovVkDnsIpPlO/tfzrAkvDZj5YQpr9G77OQ3I99kHlPwZ7x8CDvtIBrkGpvHI+Y8InfkZCwv6mPLHD7bzrQQLAnb0dw+f9Mj5OAah+JwKGAGC8BsJH/3oB80MxPB4+VcHnAthBqT+rvpz/bwF0wUzBPsBg/z1AXkAagf/B8ny7wX//n395wwZ+3T6gQJv+sAC5g+L+3b53fV9BO8QCf04/Pf7m/OKDyEEkPb+Az/3fQaf/7f3jAEyA/0D2QLz/RH76/3QCTQEhf6vAo4BngWy+Vj+iQOC9nQMQAH59SkJvvzSArAKWfiyBW/7vf0kCDUC/wMt9Q4DMgvT/En44wAVBkEFVwNm//L5iAvPCBv7AQhq92ULTwrE+d8Laf9RBREEj/N7A78CTQJKByftB/35D8j8vwUg+3H6mggL9mMGjAfz828JLPwx/dML5Pm+AcsBu/xfCir83gAfAZT/RQNC9sQB+gQVAO34zfSZARAANgJQ+QL3bACBBuT8dfpI+/33cwIYAMf/hvyd+2z/6fzV//j+m/29/Gb/PvvFAtD8BP9J/wP7zAIP/4EL5/0484IDHwXnBdYFOe9XArkJA/R7BJPxrASoA1Hu6QeF+ND4QwOt75EIFA/W8vYDj/4u9vIKwfNe/GkGI/snBB7u5frBDo72QgIQ/BP+3w7K+k3+3gMiAnH/Kv9OAeIEufsMALUI0/z1CcL+bPv/GAn6V/0a/Of8qhEP9zn01ABU/v4OXPqU+6wRlvkS/vP9bv0iE9gC+fvt9FT26g+CCc0BkPcIAHsGdv1//REGbAPI/ov8SwEWBhv6ggMc/AH7bAxQ9+f/Yggk9L4GHf/JAoIOde1k/xgJwP+jEZT0CwGXDZn2Cg0F+SD5cgqs+e4FUwmaANv+R//EAtf/L/xHBW0KrP7a/aD8jwLBB38ApflSAa7/wQFLA4/7+gJZAZ38+ALz/tkAjPoe/cwBSgTRBSj0A/+E+jMIZxCa9j4EHfTLAT8WEu57BYwBNPloEGnvEgclAs33TAuq8yH3TwBnBXICbv2S9poJD//GArgJ4OjOBpYC4P8z+J70NA6Q/eT4RQdc90j9zQPY+44CffZRBW4HAu2lAbAEwvvc/mD0VvxhCPoBiwJn8Fz32xaFBJD+I/pX+uIKUfh5/9/7KQQAB5L1IPlG/bj/4Aja99L9hQjF/NAEXfQGBAQAyfQaDKDxNP54AQsA5wVu9aEC+PgY+mADcgifBYX00fay/IQIrwXa9gP/ivbzBfwANvTxD/nyi/3YAsf7IBBg8jUD9QIu+PwUqgLg+pX5YvkUEEEFQPdAAV34XwHZA87/EAa4/YL/uQM6/54DrwMR+nr9kP8C/8QDNwLl/+X4o/bdBEsIbvrx/3/64P/ZBkH/2wQW/FwEt/1q/ncLpgI2Cl7wSQY5Cdj02xRV8DP9WQcV+pwTIelKBWMRSO2EDcv67QQ5DIzzLQyz87wH3BCR8vMBRgLjBmAG7/VsCT/9LAGlCOv2xQPeBfsN9/wD+2oG0gTuDRj09QIE/sn/RRFP8MgEkAXq/sEKA/KIBhcLQPfXBdD57f/7CQr0ZAfG+HH+xBTr8xcAewAvCZ0Nhuy8+8EAOP9VE3fvufT6BsX+eQhy8jIF2QH5/VoKlfxWBAD/UASJ9YD14gnE/8L/lfbuANoKK/hY+N7zdQEiEmr74vO09GECCP+2BooBpPIzAVb4eQXr+Vr6tBF2+ekIK/2m87sPnviaAqX90PqwEQPzf/20A+n+VgeW/D8BC/c0Ak8GTvuT/5ADCAno/0D6tP6/Ad8JR/uJ7W0IxgdoA4nvt/EkDMoCyADU80vztggPBjUAjvO5+ecJKQHPAAT7Evw/A9T/1/4Z840KYQLh+c8Bi/iaEPz4wPmSAEP4MQ0W+Mj+uAjU/Mn+XPY/Aw0N8fr9/IT4ywCdB40DNPZA+SEFngTeAgf3nwKT/+b8kgFM+UAIoQWu+TUCLvvJCOMJvfpBCIT2ewKGCNn5rQYB+iD57AXNAVEDyPys9CcBvAoOAor8Ge3d+F4f5wI87qTtjve/KQgKOPEq7OL14yFUD7Pm3/CRDeES9gru3gzs4hAGD7QOFuXB6WEVMRR6B6XqKO8nD+kOefw77aQFcAVWBy78V+MzCfgF5A4PDXbkrALoBO4N/RJM4LX2fxAgBlYR0+jq63wOiQq+DUTwluthCjsPbv/J+CX9wwYfE+r0OPLJ/uoCZhIK9C7yugSc+zAS4f658o4A1/utCiQCofoz+4/3tAcGBdwB5/p6+pYGnv6zAWD6XgB8EWz+8v2185D8Uw7XAqP+//aZ+08IPQh9/BH7+/99BEEMa/ig+JgAKQGDC1T7X/rPBYD/ygvc+pj3FAQ7AocI9Pqs+K38IwQCCwD+XPh3+iYEOQeKAOH6//pyAfMFDQGk9+X6OQKVBUAErPYI+WT/7gViBSD3G/ipAykHCwRv91z5d/6XCHIGIvuqAA31KAXaBmn3uwfX7xkGawel+WEJ3PHGBhwHwfmRAzb9UPtlBR0DWwJAAlP1pwP3Brb3DgaC/ED6gw2x7+4I5gSe8n0Sju4XB+8HaPEiFAzwoPxvBUzyVxDZ+PD1kwWZ98UDgABh94cABALwA+QE5vCL+IUEOAJWCkz6V/n4/q7+/Qab/rn+MAHg/qX3Dvjp/PAEPwp6+qr6//ni+mAEWQE0CNn9mO9T41j+WRkzH5wQxcAb4WQGxBVFPxvk0PXp64Tg0TLq/EMURf503OEF+wPAFL4GEva2BY8OmO8X8boMAhGcGIXpStRnAIUI2iZXAnDkqv1G8XUc7Q27C+4JY+d4+T79TRffHf73N/I98PD5jh5RC1AA5fl753gARgtzDwcMgPL69BIHsAD6C0EITvSUC234g/nd/vz6DBdgA7T14/Zp+DELKQ27AdTsefSZ/AEOGQ788jb4ZfMLAP8SL/YCB/f3EPV0DYz44Qy+/0r4hwem9b4JOwJV/mAF2/oACoL91AC5APP4cwto/NcIS/6XALcKuflQD6L5MwFXChD4zgt69BUFVwXy+VQJTQFk+R/9kftuBacG8Pa//Xz/RwHWC6L1jf86CXD+tQrD8qYCHQdr/wcOYPQ0/Zr7mQVVB0X4cgBn+/kELwNG+pf/nQLsAuP/gPlm+M4HL/7fAxP7n/FXCXv+LAbo/dntug2B/pf/4wAR9OAMd/c1/xIDHfWCBTn31gDvBSr6vPpJ+0D+pQfo+FT9MwDh+BAI1P+T/gAAbvzzCRv9x/n0AfL85gGC/bT6IP0MBVL9KvyEB1YBiwlE9Y32OAF1+IAMy/4h9TUIXvpSBBABT/HDBYv86Aj6Bv3z3AF9ASkGGQAU+Cb65gXVA+D8y/wj+VgEgABu/gEDXfUoAdwBNwE9CEH8fQGy95z9YAhnBGoHTPOh/UwCygclCw34tQA5+xgBTwvo/V8GoPfL++sETftcB5P+K/4d/wL5ywf6A5YCrP50/ygDpgF+AkQBdgQC/z4AqQHvAGMEOf1hAMIDo/zQ/2X/L/uRAUgAcQcK/gj0lwR3BMULbAFQ8wwCvAToDLb+vvdmA5X0sQa2CCb8AgKM9kEESADkAFsG1vbB/lcAggH9A5MDRftA/KoAmf2WBDMBxv8C+/Xxzf5hAaf3Mf1e9dX7nAJ1+1kE0Pzl/i0Hcf4fA0v8GvlZArH+PwAQ+3kE7QXC72AEIP+0A0cOcO6z/s77oQDDDn/pAwPp+LH1JSBV7Bb5NwKf+OMNvOmR/AQDfwGNFYjwsP1gAzX11wXG+28MnP0P+JwFxvVBEgoFvPaD/Tz7IgolAgX5qwTlAJT8egfO+ar+pQ2Y7l8D7hDs+BYNUe/k/aASk/YnGBHv/frWHpPmwR0y93zxayeU5L0G3P4S/7EUzeT7BKX9xAouElnzHvg1Av4GjQa9Bcj9oPbW/88KlPv49cYB7P+qA20HQfIF+rkOnvkxA4AES/3sAZwB5QVp+GoCwgjL+/cHpvcg/T8MWgGABTHtXgyjBpvtISe+5Z0COBdn2TwbXQgw9IIUZ/SL+owKoPqbFd77aOmoG6jiHRlWFEzSjijA81TtASPf4DwQlPkWBFIQ1OF5JLHzafcrKBre7QCyEknsFwatDSz7AwAB9PYNQAj+62AVu+Nq/Bgx4dXXBD0GB9x/IUn6cfjkCT7sLRJh66IAnRT17mMbsPZ/52EV3vqA/2/+j/xP+wUGlgDf7R4JhvhF+8sGK/WwBaL8o/jxByr+R/v4CSHzxftUGhrikBQxCcPWTSzn5Fb45yqcxSggwQXQ3mQ6Q88AA5QbfM9cMnv2+9k+LzvfOgdGGzjSah3XBYbkrCO04nkBzx/N1jYS+guU434iYe5R7ykYkum6FMz/VOSNGEbwrf52GI3muQDpC4DuJgWID3T3kPfsDMTxjwQvDX3rAgqf9kH5Shb06RIWffQl5+Qmr+bL/+EXzOG4EF0DcuvGEKT9H/cmCjP3PQDADOf2JgEM/9P2xAlY/v8BRvluA4ML2egNEaQDe/OrGN3l7wMnEl3xGRg/6tT+2BlR4K8cifkP4Vsvj+mb9LInfuDEClILu+JzD7ACN/8jB0vxbAtbACzwGhN2/Vf1/RpD8IzwIB9b8pMA7AuF6hIH5QXZB6MCmf4IFVnnjv/TEZ3kCRg8AW7l5w69+03/wP+1/V4SvO4MBDQGXtTfJ84I+Nj8KBfX2vA8Mv7SYxbKBrDgqyVq6dcJRADO6Pgak+HnBAsQBNifEzQBK9wVHc8D/N5hHEnuU+SfKzzuA/GKCk7yTg9w+mv6vPBQ7gAiAeyN9bYOzN0wGrwHDOHdGDT9bvYaE8PtIPoIE/gBzO6u/0gHP/6JA2kNMe3L6Nwybeh36jRH8LztCSk+H8boIpAFEudDCrn/ZzPgx68H8S5vtPAy5Blzrmoo3BmMzFIYdRSF08sR7hrn70vrsR//FfrGyjSzANHCKVlg6zHStUJ645T3vB7i+RH32PO4K1borN8JPrbTGOyHS7jFq/TaRALFfgVELJi/1ydgCMXucg7J1Bs34dwr+b9G+Kd6JyEmpbX2Kt0IBtLFK3AEQ9uQD8IAmQwE9wT5HBslyfMitRdz0FMq+f133BwSFhYE3AYLAR/Wyp8pQvZT5mkhStrtK+bpte/DMZC7ny8/ELzB4z6P49Pn4y6P2KwbZ/iV8VEdZNgWIl8EqtwJGXX3HfTFEPMPutn3B6sJmuzmG0zkyQSQ9DEDiigRxvkoXPqC2Ug7P9SUAGMTivH0El3d0hnV/jvuDR/628kAKRR7+HX78QFpArn2qBOD9A4BFwhZ8rANhvWkC+n9d/kjDyzoJRaH/ZX0vQg2+WwHCvaGFhDyGPH9IR7d+Qw/CenzOwQI9lUQFOfzC74Zy9rBFvAB/N0wDF8Pm/VI+4oR0PMT6wAdRwCh4WwZNAcH3L8YIww932IKbBN+7aP0WCOk9mPiDSjj7Z3gbyv+AArlXQt/ECTi+gK4MNTNdf5MMOTb+fsjJcHrSeiEKVH/Ydk2H28FiM5bJO0l971JEksgz8jkG6khDNPG/qkpYfHi1BkyHf/szA9Gz/JaujdPs/6zzBkr+QAa4ecMFCiZ2v3pOEdu227fXjiP4B7kqDzp6HTcky9tAH3kzBmHEMThswIAJNXXO/m8LxjehvNEIo3dpPzSIGTxn/MyCTYMweqEAMgYLtgYBoIY5Ol8A0kGxPZP/P4Hc/04+OAIovoP+gP/Jwp6/8H0hBS38SL/IB9i5TkKuAZ78bELLPYyEMzykPGEGsjaZgAuIW/nw/uDFnjoL/dHFDgD1fe2BA4OVt+gBmgu8dip9hwaweQGBQMjdugs53kgKQXs4aYZ8Qyc2XASCx++3uz+9itm6fzkACtI90HecCCgBpLeVgzAFPPrSfT4Ie8EE9NAE7AZ8NLrEVAbaN8OAlYY9v1Q530RJR2Q3v0MzhbC0YUZVCwHyR4AoCQX3oX1GCd090TVwyiMIYuz+gLlQf3RY+oRQU/rctJiIi0qaNYe3tZP6vjcucUsAR6juQ4Aq1Z62UW4tFOjDgepuTFXL8evrwncTb7DWtxGUuHmec6NO8v8e9cIM8IBb8vkIn8ZhdDqDz4jkcqRGrIoi7f+BYk1Td8G7ZglgPr84iMWWxfI5eL51h9T9cXz0fpXDygYiMuLFLo1i7jW/s9EJtcO1bo/cAejtTUivTJUtsH6DTdd43jmniGvBp3is/xRHM0BU91L/Msm/fh70R8hESGPuwMFJ0R50tfTTDw1DanK9wf1KtbnXNXULToXcMdtBi0tZetw0nEcKyaLyPn5L0IJ0ZPXD0I2+CPO5BuHEwrhEv2DHiTvAOv5HfL25OiHFKP08PhoELbqyP15EdT5N/uq/0sDHv/2AlIRVvUh8DYULAht6gEHQA/w7n/zvRE7D5zoavwfG+rnhvXlIDn3FPLTBxADefps+gILEQWf7qP+Hwfc9P8AdQYq/8r20PaUFpX5Qe56FxH1ZvjvEL/p9QjZFUzosPbfE5D5gu2rHZYJK8rDC8osxtWw7WUyOug/4Ywui/2j1NoalRZn4A3+Gx4l+bbmRxMCDk7rIwn4A7f2mgjT86kFgBMf6JACXhjc43L9aRgJ9ebzWA73DZnqx/12IJDpuvCbJoj3U+qMCkEA3gCQ+Mv7rhRh97jtLRJJ/ufruhKrDMTrSvaLA9MKGf9E9AH5jwZ/HnLr2+eOMmrjRdXvPjv7IbYRFv08PODQyEo8mAjHxa8gxBO84cT89CNj/mPEERqoNJ/S9OnUGhMMNdgl+Y1LPt/QuwUuPBrM05vwoC89A4+0dCBFPzSuGfRkTpHtWsaVC1w7ytkZ1KxQNevuuA0/oiPYz3bUDCzpNs2v3/3nSDLUv97jOesEG84KBMw23+kIxNszVh4fwskVqhYw6mYFjgTQ/6z1AwQfHdXUJOHmRa0BYMnu/BoYfBZj2jfxnCXk5y79vhp27H7w6hWJCRz73eKQAb8uIvlC5/DtWvzcH8gFrPAJ9EPtaB3+BOTUIP18EzURZuDF4xwt/O+c+FghA90Z+kIaKASJ9c/uLCXK+SfU9iO9GhziQvPCF9IKU+Xk9/kmaPcn5SIkSQXg2Aoa+yV39/vgF/9sMxPrZOZzHdTy8xF9Ak7fGAjaBAEYWfbM3mYqzAOg5eEQnwlCAzbiavfxJHj4FQpL/3/Y8Qj0JfsMMNPM6noxwhae5bneFwr+JiQDbO/X27kBFjUZ/jHaeehZGtcnP+a/67n+YfyFGdb6Je9dAnjzTgtN9LL1cyKG7XrmR/4gDBwbAulu9WwKovKVBT/zpQVhG1r1teVu/vsBbB5SD0jUOuwJIvER1uJm6jAbTQRm89v7iOrrDaUvie9u2NvwmxoeJ4Twr9rLAS8MLAm//07zzgCDBw4HX/O+6NIM8RKb/InzxenbAukP8ABSBZDzBeAzCrcWuQPM71DsAgcwAyIK+vuC1GwPuCQY8fvjCvPCHPEQSPHs7KPsqQ9mID/+dOOt6QUCuiWgCQ/ia/XD/7gJIg338VT6kgRKBKb9kOwuCrsOq/S9BKn6Oe6QBp0TYhGD8WviywCpCE4JjhG89Anp2w6AAxMAlATA+TYKoPX/AB/1mvBuKCMYAuZ830L2KhhHJCf4gejX87sNeBTu8gL5vQlUEXIIs+E58foftif3/1rlOuwW988rlyfu4YXljfSiFckSTPQb/7MHtASf+973oQK2AO4WSyHL3/vIQBL3M6gcn+tD3pP1URDKHWgOXPQm72MJ5QR/+EgX4Q6V+wf6p/2U83f4GynqGPD1QfEU440ECiJ3Gfn4dN+J7qwLKCKBC6DpG/26Arb9Yv8GARkNbg8oAsjnwfUNHiUgA/3L4BP/TxUWCnnsBPu1G+wRgPAe2Vb9/ySmDyD8N+Wc5WYEFhqaDUvzzf7t/wX3vwWoBCoEbvsR+ssCUfps6GXxbxI9Er0DEOkT2hoGjCNZBjvlbPr2AYX36Pjw/a0QAgyd+b/rR+8DAqgK8wvDAQvzr+xR/8MG4Ay/BGz6tO4OAawLEPZNBO0GbfV8AGn/v+lpAugkgfgI5cIIQw5A/WL3zvvbB/8OjvGz6yQJMwbu+dMBsf2n9MoFMwZK8D/6pRBW+yX1JQI487IDXw/z9Wj3dQl7Apnypv+2Di7+F/tw/Kn4egUlBgbzTvtKDPr50u8kCccJCP6u/DgBivx2AfIFof5i+vkIkfnp8TsIBQ2U+vL33vjLARcPhAOR7pwDoArY9877IgHd+3UEVwxR937z9wixD4X8oeYy+ogZLAiR69/35g81Cnv6AP3n/UH8cgYu/xwCOAFVArIC9fnBBTEKVgotAan8vfwnBZsH//45BswHLwJT9lX+nA35C8f5NfEBCsgNKP1180UDRg3rAWTzeP78CuUKQQM19gj8FAveCRf3nfqDBCkGdQWV/Gj40fuLCXEQz//O86j80wyOBKzzRATkC/4EDfK89HkO4A5EApfzi/u/BXP6GgNzEdj8tO+jAn8G7vsvBbsD0PkJ+Qn/3QIxAxQEdf+P/kwJzPlB7RMYOwuA68f56v5kAtUHGvlS7XwdkQR81ZsMICIW8+7sxRIwC4vwnPWmDnYMoe1p6vkcjSDc1NLhjSnhG2nXquj5ITMVyNqS4FclHCc26jXZSg31GREBd/KDAWcRKu/f57UTHCD05zzfJyGNDkvPy/fOQUD/DcJn8K8dexwc4GzaqR7DF4PZ2+nZJpsKCNaa8d8jNQl032/43xdX/ivw3fdBBUkGWPVZ/mQN/fY9+CsU7/5C5eUB0RK6C+f1seK895EXLQtf8svsDf2IEdwBV+yz+zsKnwY6+K73eOwmAj0mEwIt0C3kIRzBHXz59t449V4k2wNXz3EHwjPq8H/JGQlgMPb2ot3eEgEXDtkV5LYgCxe/5MTgIxInGNPfL/EpMrj+3LuL9YA+cQ8wxlboPDD+IW/ONtrzLLMyYtcHw+UdWzaO9pXTHf3XGzgOWfFE8ZUEWAq5AUn6me/XB+YbiPE214ITtjE78ZvSYAuHIyX+Bdq2EakrsuvR0kUPO0BOBIHL1/46KJ3/iu9bEa8DEvdLA1sH4f2T/RQGYhmg/2bWpwGZNDUK492R9DEQ+ACkAK8U3QEr8WwEzABI8JYPfB7T7ErmUwkKBcn4DP5pCp4P3//K2tT5RShrEVP04PHJ7eQJgBcVALTqCABjGIv8U+In+0shhhkv+HvfUfObHdQOQOaP/48WJwLk62/uNxIcGsPrsfoIFOzshOhfEq0USQKe/LnwGe5BCH0YtwZ77DH3sRgr/wrmuffTHqEoZvpA0K7kJxbjK58TauV941H7iA5KA2gHPxUmApnllfUbCsHxYgTbKx8EI8n81w0DeTDtNy/y+LTmyo8qUmAk7ayihhOZPfffW8wXBs4p2icr6a66vemdI3gw3hpb4X/JO+vQELQ0KSkb2fKvp/xIVBEVIKwi2fBM2TMK02e73e5DOmdQCufIjaPxq2jhIZbHD+VcBg8QryNP82jBhgRtTXcGvKoi7OpE1hmX1Df0cQ6B/2YRmQyV2XbtFTHoHdnbFdEPCWEp3iGV6ja3j/wBWMIZ2K0z1ZksTzSm+5bZtuS3B8o7EyugwBm4FT5VVFDnCM1v+vMJaiC6GLThD92FGqEl/vod7YbjY/q9OFwmjsmdyG0ebysyBIH0sODmANEqvAPF22ACISArBHbxxfKF7lQPdzXv9ArFqwo/KCDxmvw7DfLmbgf1JWPdQuZnMSQLNdsuEPYMqebH+rIguQs76Jr2PgRHCj0XNwF83R787yPY/lDs3QYx9t4D1iRa88rReghNKHr/wORp+UEAHQlAFu/yAM+oCKU7Uvddysz/5hNgCCUJUvLy2BYINzbb/Z3Eg/YBJvQG7fNr/E3kQ/W4ObIRA7wW6So3hRSg4nrzx+4F+7guzP8Ox60MCygD4hPpkS329kvSVxpJER3gcwZcHFnhUevHKhT5C91HHIb4lc4HJKIs3Moe7SczquSY3T1BEQYsql4P8El02vzZlzFy8F7I7D8/JoOyrOxYOEr4r+8FH0XfYtSjQEIpXL2Y400mJP0E8n4kZPBjz+0cCh7A4z33ghY+8u7x2Bu1BiPmA/kAASIVKgsa1ZXkoDLCKlvbgO0xCE307hz3Iy/iM9zaEAoM3O/8HHwCD8vIBK8oCvnf8esUyu0c5JYzvA/fyD8G0SKc9YsBlR8z4QzcdTZUF1LdHweNBcnaGAUCNtvp789FGg4L2vyzGhnvKtgeGSMuReoO9ZwGVNc8Am061PmLz5kKphGZ7y8RgBES2a/ijibkF3zxP/DS7CH6OxdqHfTYDOdJLR36qeB9FZALV+p2+cYm7f8X7eX9p+gaFeIox/ci3J4QwhZT4coMCSq432vr3jCbC4TYof8+A7r57yEsFYHZAuaxMkEIBde2EeMOYu2XD7sQvd3y9CgsHfUd7hUqie+I2wUZ5B2Q5/70KyIN9przaxtR8OnmCBctEH3q+QemGMnagfLJJOf4zO7MEy4FgO7ZDKQLzNsFCf4iG+qO/HwP4Oco7IIcYxn55yf4GQJU9cYctAwA1IjwvCr2F7HdsvWUBRnyDhN0FiPw6eoDDKfxy/aeKqcArdfi/Ksj4Ps95P4FdfyECskdfuwa4lINuh3k7z/9SRJQ57wKyxK67nru6QehEjH5wg3Q/eDg1hC6CO75JwqiBoLvT/UbGtj2E+zqC04D0Ak4BcT3t+mdBlgYYPmWBmT9l+xb+yUN0Qc4+YwAhfPJBNAdl+4M6TMLP/8PASQKmP4q5qn4+Aqo91cTDAfO4aYATBIW+1PruA1vBk/xhg6tADvunwRQBb7wUgJJGzfubu4kHScDCezg8WIVeQd6BxcQl8879gorQxcv7hD0Xg167pEE8ivY6Cvstx5U+hnpritTEK6w6fV2SMEAVOTMBObdFvG0Pe8Q0M3N/aQUs+ExDco3/tSgz8kn/BIj/ZQEBuAH2UsvDDN12hbzxAQK5PIKASrh/nzM1fv5FXD37Rp7/6rJIAp/IcHzKe0gHCAB1tjDDBMEPv4FEm4DWuYt4/wfahYx78v9uuzw884NphDO9uDnBQAJAMUR0wwt8kT3AekoFWMbSezR/UT8wvY7/E4FrhEx++/pK/9mAHQAQhfLAFnpjfiY+CEKeRWgBcLs1OF3DW8cZAKP/GPt5ulRBXcYcAek6tntzv1UCTsX5QZ142nyrwj6ANMJwQb14bLxMRqRA/z/pv/93SoE4CAi/vvfTOtOH0sEW/ERFG7eRvBvMR37kuEGADL+z/rVDuoPPulL23AcKiFu3tIDihIJ1TESLiVm24n5Gg2y+lMLahaX7hLgIBXGEcn5ZvkWCgP4XuVwLFkAsNN2LSX0dueTKHnhEQiwDVXl/h9d9+D+fRk31S0EYzKT7OP66idt4mLusiIb9CwEEQfW6OUO1AccAkL+X+xHG4UAK/E7GQrw4fgHFDsM2Pj72ywUpA+D8Z8Xi/aP8UgG9vwSE3IFOQij8+/ZDxnfDNX0ERZK6/zyWA2kEw8Gwt8LEPUQlPfuFQ7w7/XvA2z2yyQWAJrnxwiS6/kJkSrk5wn3eR7G50gLZh+24zMHZgY//EYQYO1OGbgINtuDHaD2V/0vKzPZrvbqFwbwQQu0E97ttP3kCssPEQyH7sH8HQK6/xQOHwEqBe3rQv3uGPL3SCKo3zPejzzY0oQXTCyIuYoO2fgREPgwJb5SCfcEFe3NO9fX9d8sJrXg7QiqMp7cmc6ZFqoT3Ay7AengMOmKBp4gEAX44njwNANUDEsJvAdg39bwCCyE5Wf8ACue1LjzxRqf/6QIzfC69xwjE98mBvgm/MY8KIUAtbiQV4T0t8z3MLjZ0xX/+AfwKEX8t2UP0SmMt9Y3z/+6yi424t75A/gUQeSUJZzYavXBNW7hug08//zkjgrWFqQQMtvH+ZX7nAhGLBzUnAju63X1jUZwyj0Muwf8zrlAl+IKAj4RqsMBOUb94OTTFnHWmSBNByjjYRAxAGr8L/vOACP1rApTDFXu8Al39L8GWv67A3MZP80PG40Fd+LUMK3VBgydCyHmii6h0/AEHhn25p0iYOMDAloYuOurDbX6j/zFD1X49gHn+4EEkxEH75z5Sw2o/wUS3erH9CcVf+9mHTXzKO+kGcvRIixZCW/aKRso0DAlrRxxzLwfut2LCUIY8N0rKqnf4OSRKNLl3Reh/rfeZxEv3owHtBio49kKbeZx/tIY5O+ZA8vhIgbXEi/dHCFX6qbjWAwSAxAoWdlh8g8dWdhJMUfuiMw8IR/sjBsX/hrZmhYa4rIaVgwFz8MpDfA25v8dUO0eHevogOCGOy/kIPf/CHbtlx5G2M0aaBVZu7g5Vudm7XlGR8hmEuztM/A4Mg3HM0Cu3cG6cGpT0V8ErBCcyx0uaehEIMEAKsCZP5LsbvJ4IE/fuv9BDnwJswMa7zsNTgYj8asV/AjA0logChJC3jUEbv/QIBTnDuftQq/D5gitJznQ2iKn7qz8sgSe9QsvesHZIagYh7XHOq8ay+SRzfopLzeVmGo22xnvuhsgRhEJJ0O8uQZ+RXStMUt2Axm4CEZj59z+Pgd59yAffrYnK4IlcLl7PvT13sYNPpnyg/r7B40EawcL0UU5QhMQu/Q1pvop1MUnUhT33kzg3jKCB/fMyCq4Ao7RqBmoJv3Vo+HNRb74L8wyJukN4cwGB609ydhe4DojNgMM8hkGOhx7zXX3LkC+xXINKxVV05MPbARBEX3cTPUaOrnKjgwAFxTRDRSnDngDHvzX7iAChADPHy3kcunlMWzynN7HFc4siLZb9jpuEKq80btOaRE7sjf/xHYmpD3G/3962Ra4xSGtSdvJ08o7aDHO4cKLcs7pna9ZJYAqeNai4NJPMeZ5v6hc8PK50TUjpQTb9OLs2Ceq63PlFDPj14Aa9wv830EPXQeiFTLaIRCNJXjGexDjKQXuZ9nQMLUQQ8J1Ddkt5PeVxro8lylWljknz0B32T3PkC2UOEWYih55UsCo3/QTN7cPJ7xRDqJYBrCX5wRn8N+Gv2kf6jh7zmXYL1ye7gm20TWPJSrD2fsBRJDkHNb8LSoKTdJX+CJIJugKvipPnwV7ulYHsC/mCemrWSEtWcWnSNOUT4cQfsR37JVPrP4do1Io3kZkxebQljzbFmfDSODwSpcF9qLqKKo6V88D3xcU3yyszmri5Esy8vvN2f3BOp7+6sZnGAsdmPvjzNMm4jjUt+7uhyLpJvfLW9LdYYbj6bcEJ6g5yuHlqZZFDzriqozXZFzODZWU8ByfNu/ve7oiHldN3qCT+Yk3vuzI3sb0JDWS58vCqi8wFmXbcAS4AwP9Ne35BXgoXdmn40kegxIo4XHnQDkP8RTM4ib5ItbsSbyGPVMrIK10EKwog/5SwjkQMDn71tnpZxsmJ+3FV/TUNX/2UfKu71Encv1Ey78pQR9O4dzXrxxyN4XN4OAwLr4Xh9Ag6pxQCAEFphwbAkL28z3RTwrMOf/VXtLxSKQHS9Jo91kklSG3vGz/q0QV1cbibw9/JnDmlbuyV7sfg6xx9kM+jinzmUTvZm816JOu6f5aYzLtCIprQGdG492tpB8iiHqDroapDjjnSQ3QNqDfQhVPLqUvt8Jg1Ci0smfPmTvdQmmZ+NtWZEHu9sqp8hYzuCPFniIS20KP3ATgAgPYML35ILoTF9c2HOEY42ANERb//MvNSxiSI9PYq/NnDp8Vzeya5tIS+wry7kLugwlx/3MAZf9L+iP/9PbaDC30EvjDEhP7mOCi+JU2SgCVut/+4E8P9sylGhZ8VLbmCZwdHh1fztVLpGA6lj5fxiu8+C9fTs6hm8FbW7gbzL1a1ZBGbTLyo9LsClgX+3fIYfwdMVUKdcbGD9Ar0+Ly63sgRgyP4xb11iHrBBTcCQOBNJ70YdjFHRUexOtf0OgwmDuMxk3SdyxPSQjKZLSuQwA+Ntdwu600qFMIw9C3ZDuTRoDX6K2pLHNMLdVCyIQk3jq65z/DZSQGMBTywdvfAnguEPwl26cIJBanGdviDN2zLkkbOuze4Y8F9T5l5QvF9SdpJc7vCtMHBHpFGuVjyBwjCxg49+Lj7QPcKPTdye4jJjX+dudQ/S8RKwiH55/97SQa6wXqRx7kBw/0MfGFFPgZp99E9SAkhgCb6fEDHRFDBv7jQgk2J/no1OvlEGoJtvwj7RkNGBRr54gI4Qka8mj8cgvBGWvo9dlXJr8b8eaV4AsIUTWf7SHJahsKHQf33dky+zkvfvHZ3FwSsQqR+D3kjgGMJjj3POKT+tgUBhOx3Zbj6SdHGcrho9uKDvo14+AmyC0gRymY8P3HTQU1Sabjh7iaGnM4efuVs0D5C1fx6uK5ohNFMKID58UJ/mRHfOVExpERQx9gCQjVvOrtPR0DeNoY8ZH+wDM8/tXNcP0CFYoe8ee7yl4gsjLo3ubVdBCEK5f1c9MuGcsg5dN72A81wilk30LNlw+9UEL4dLL9B5IzSyXu2j6zXi3jOLXtwd2v6Bs8Oh1Nv1YBKCXAAdXxVfIhLiMHuKvhFchObvjmulDehlHyMKmg79uzQlwrXeG2wegeczs30zzqkhN0Ci/0OdzrKBUiEsRJ6IgscCyR2Hi/tiU9PXbjAchvFQ0vZO6U2t8PFyN+5dncrDRyIMzN6d/ELKc+O9OLvI42Ezjc3h7QyAVsOkf7+8SnG/wYRe069PsAoCQ/5grVwiltFcHtRONn9gUwJ/0C2q4BsQcfFIT1uN7SCvAJYgGy/ePksgGICOMCWAkr6zD3eQIyDykN6t/C8b4X0RlJ6+fWYBm4GnTvYeCACz0fPeSF68MqgwM017jk+SzWKt+41t1LMK8fDOX10eIWFB/44nP5khFy9APvxgYNJb/048W8EX0uTgWi1lvoASqNClvpLfqt6u0C1QYQDowLZ8DEAQUokwAM9orPDhqGLsblDfhs5Rz9/icJANn5zdV77mBFuQuz03zmxAXPKdAF2uYF9dnrpxQ/MS7yz8AY61A74zztzKO8RhXZKwUO0eVJ5i3zhvkoMKAay8q9v5EeKla89WyrtepKPW0pbeFK7LvwH/xLGzkZSPzitezuRV8LEDDPqc6OHXlD19z07+wDWezKF9UDlQ9K6NjYuT/tFRLdcOQHCH1BEu8H0X0RmRN4DAHz7fH1Cl/r/hQaKW7lENsN7pU+LipSw7ndJxJTMBYOj9BT8+kI/xElJwnq79JN9+4vJzMY2KTJnAXnLR8qXO4EymXyZh7YNaELjrsO4kgk8zllD7KwNOhDLS0dtxonzu3XqhKBHH84ldOQse4OmDCBO6LTiqpgF5I2FhyZ43TJPgTRHn4i+/wBx/PcLzfTOJfhYcLN5uVBtCc90HnZ2uqKM0gfLtut4uDfBTP8NjLl7Mj71Jw/PDr54LXDSNpdN1A8dfIuzFHQ7jGoQlnyyMOnzLgw2kXK9LTLw83AIc9DBgYu34C5MBG2T6gP2eOcrssLAkugDMvoZsLp+ls8Gh2S+UPHVdKQODI9KfFJvTTMAz4wQMrp4czu2KskVzZY/z/n7cJ+CF5IwRWQ2IywQg8zVysHA9Cvw/QYQkKlA8rmBdVG/bU0QRLs6yrIFf7nQzIQhNiIzYMLeTzmBSrfyuVRD1omtgmI66XfEAKYIi4cPuj6yVgO8TJUFQ3gucwSFpoyywUh8CXdKBA/JQf+3QDl5AsCRzMTEoPoaMpJ96JJjiYG0KDPSgG5NiUa/N2x8GcBSwYEF7f38PO2+NcJtC45/2/UI+XxMHQ4pvI4y3jhBTyNKNr7tObb3p4aWRqNDvv2es2zHDsrsPsw28PaLzONMyvlv9BY59YhfjDR+pfY5utN/XIy3Akw0sz6UQVwJivxw8oUGZkeFwqv41zXhBqkJ+MDtvMa1Aj6+CdSGVP1SM7f7EAlEScE/ojOVt8FHVYw0gbE2ovneALdHpIYM+Lu4SP0vxpHLA/y58hF6YwvvSzW9ZvLFe/kGRgaTgRL2IntbR0HB6T4S/Sd+TkbcvxR8TX0Mv9dFMcBHOzO7LEE+h9aAnzkzttbDI0+pA/d0m28XwRnUlMNAdinzcT0AjU2FNbxsO//3hsNghIZCQLt/OC6FUQReP5p8wHqKAUfFHAJje4g8Sz/DPgRDPgQqf70+UXdgwdXDhwNxRiz5kHpr/B0Dlc3L/Eiy1fxXhMSLNgDos9V9AEN/B2bECLSEOqCEqQRuyHV4D/RahQIINEcguhuzLYMqxtiG4z2Stsp8RQNMSUTCNjjdOQjB6AgVwB9+MjuQ/upCPAOlwvk9dvuifaFEUAPzvv79CT8PAY1+7kH0BZkAWvitO0bE/4sLvqy0R/vERgQLBQGAeqP4178wTgQJIvm99Vg6UE6lDDc2src5eVtLk4lvOau7Tnnyg2KJ5bwmuj+8rr/tSqeAdLWfP6mBu8TIfv98LEQXfV6+pYE0/2FFdnsY+zhAeEGCBQK/7LmUO34Bz4fBg9C6HPlff08DpAlIu0f1ecIdATOG9795NkeAnsE6xb3BT/YUP7QCKwVZxT+1NnxJwtMCCoTsOug8Q320wAtIa8FsO5e4V7+XiVBEdvqf+ar+u4UDw8G/4PvO+dSD/wLqAgWDZLh2vJ8CZMGESGt+CLg2PWg+qMwLBRX2RTrseqXMiE2O+EC2ULbgx2aQ+j/79KK0WII0D+pHkbnaNGR60wkzS06/yLiL+M3A3cewRnL/BTv/ufb+M4fdBs/BPXdedo0EZEr3hmd7PTMLe/RLXovNQhazG3Kxhn4OuAteuIorxUBUSuNMwEZ464a2XcbFS12PiXMcL6+AosncD6R9DbCVe24CSg4JC5Syn3gsvCqHlBOLOM22jTaKwjERFAJZfrW21LaTygjH+ATx/Kxvwz/ZSAaIVoQ9tDTzWoB2Tj+PFLmqrIb72YnG0RsBxG6f9/QCP0vmCAa20zKTeyeOo41M9vxyfTuNh8xPQnwiM725mcOLThuBUrk5czp/u496QYj4GbkLQUrGMwIdvHH9ND64fg8DgkPfgXR6EPzsBD2AUMQMfzg3SsCJQH0IMwO8cQC+ekMUTB2Dz68UfNBBwIrASvFy9LUcvsOJhJAnuLTvDL1axg2QYL6jciG7dX1Ji27ImzlRONr2zINizJKC8fk0c8e9rYoviAn/Y7YFtcoE5QzPBDN64nIKPJiKccpRf2/yT/qngVWL1Me++P+5i3mLRm+LowGNuf/yo//yjJlGooDq8ZU3P8mzyV3HUbUJcmADLwXgSjt6j3Qmf00CUYrhQrx3o/ztu5tG/Ad4fzv5AbboA/DKm0fUN9Izzv7TSLqNtPuZsex47cR10J4EUzM1dSs9T885igP57re0tc1HhctDgkB/EXGAOzbIVUqbxwN2jjVM/nRFCk3GwBL0Gbd6PexQc4j49ryxh3qEzGkKN3/qtBG1HQXEyufFS3jCsvT/bEekSLZ79TZR/8qABUYUAmx807+VO5C/GAN5gQ8+Vr1qPPGChEOiALI+bLlMRDMFqsAvvzM3BcPORez9aL6gefKEEwRSPjj84nqqBKpEK4KYvj/4VIAaBfiDlz+5+pI7QsMqw83DlP1R+oa9WwKyihxATXbAOrOA+MooRoI4kjl9/V+H8Mpa/Nh26jheRHoM3UEZOLe3672yjFvFqTtI+uL1vAYsSbPBb75tNIP+8AX7BY+FcDYV+OX/qkSSDD/9HjVyuTNDaQ2Lw+u5H7P3PLwL8AnKvVx1ELUnxJONuQOWN0h1pH+yCfTH9b459wv4OsPvx/JEwH4SNBU8cYaPCA+DWHlF9l5AXglUhW0+eDfy+E4GMYcOxVU7BDYBBBTBYseqwIc26EKOP2CEysSfuq27kv8zAvrHf/8+O209DD7ZComCHfu3PUE7kMeWx04AHTpYOPEFcoYchY48OrNRBFxEYAfovzZ1Jz6CQMALK8Qk+Ce7UrwtBrbLBjxJewT7OPzYzBMEoD+U9/N5KIbByD1GEffV9iA/VMhLSw7+zvkMtPzEdUzCw2G+cfJQu/2JnMZQRA52eTeewUeGjooCPHu1dbszAhDL04R++XI2d3xeSX1JIAD4t4y3bETri7gFqHqcdbx9D0l1CL0AGTX1t0kEzcfHx6V7U3R9PzPETgq3ArH2OLejv6UL5AjLuh42mrhTxm4MLQEheEH0Hn5yy5uI3Ty285P4gsTdyduCf7jPdVmA7YghRuI/uPT/+qECuIlIA+M4R3kaPF3HD0d6/3s5/joeAHsIsAKhvB/3GbwNx6pE8wHTOTN32sNwxNvDmz6HuGj828QoREfCZLpfu/WBH0OBhh+7fbr1wNpBdobNPdg3XL7MghWGQUKp+NY+Nn9fxdvD1Xgp/cm/cURPR4a5pfszvTZBzEhw//x7f3lY/ZxJdUSfvfu5zzf4RGxIW4KHu901UD4ASQ8IQv+sNd139YUfSpSF9Lo/s2q9aUhaiUl/1vT1tooDK4xmxmw5iPPFO1GHl0vOg5Y2YrQxQUkJiol8/bwxiHr1g/ILe0fJ9eG04v6oSY7OMr1Nde/29QEwkFMF8flhtQQ5q80ry7a/87bJMsjEHMzEB9X9NTEpe3rIo0omxPMz7XTkA39Jb0s9ulyyK7sfBjMN0UP3c+s1k75XjMDLrHmA9j44YMVvjkXCbHaXs+f+/otoiIq+cvWXthNFEIpGBQD8fPOJPXmG58eARUl2EjeiwepETQlP/O71JP1owekJp0Ir9un6KL8dyQ8HUrm9eeb9OcPyShA+gPm3/XT++IdRAtx6pj70PqaC1IXOvIy/IT65vrYFBD7Rf119x71FhWKBQ/+xP9s868KAgmI+V/8ePgeA/sOPf4s82kBmAKDCo0TQuiO81P9owA8GDX37vHv+1D9HBkkBqDymvvW8LsYjhD28H0CcOVxCrkY4va4Bfznqfd6GqD7zQpP+JrnrRvsAMYGOAbB3nYNcALaAe8S6ur1AUYAYPyoFUz5PP7a+8j1bRfWAW/84vnj6owQywiXCtAGd+fiA6UNiAjYDj/ucPHXBwQMkAg1+ob0rv/7BwsGKgLG//75agK3Bt8AOQQ9/80AV/U6AIQKAgHoCA7owe/uEsD8LwX2+3borA59ATsD8wOp5i0FtgbiBJ4H7+pj9mX/kPkkEPH1b/G/AULy2wiZ/lTztAWi9kP0lQQo9u8ACQeE9AABZQKu/aQAO/a48V0MswNSAHMB5PB3BbEF2AN5ApTxpv/fCMr79Q1E9WHy6Ako+MQPCwao6Y0EkPnqBC4ZXPKM+zYDY/acFnv7jfKBC17zqw5SBrjudwyP/iP+KQhr9NQCUQh3+GkLtfrS+eYKh/vm/LwIoQK3/lP+//nCAtQF/QCcAWYBsAGeBqj9gvtQBD/6mQjU/tD5TQG8+6n88wTu+Q4C2gUG9HUJ2fXD+msPUPlH+q8Bku4sCRwCePiZAWTwkAE8Bln8TP8EAiP/BwGmA876O/i1B7YB+gN4Agf3HAz2/ET8WApx9CIERgZg/F0Jp/tr/qMH5vm9C/P4hPhTC8wDAwrt9TL5TghjCwkDUvoC/eADKQBvB7D7CP+2BaX1HAzk9x3/0gej+X8OjgS++n8Aee+UAvgKNvnVBpnv+flmEW38eQjw+cbyIxBc9xj5vQhk8RgILgN/+S8Gt/Zx/wMF9gAqBJn3AvyuAEcEzwNt99sBNQL0/p0FsfYs/nv+of46CsEAFfqC/7b79gSvAI/2GQNm/MUCbwVo/C4Hcf70+wcGOgTD/0H+1fO/BGT+mwTVBMfzCQfq/i4Lmwx39pj+NwkTBrMRYvJo+jQNMPbGEf356ftpCAT1vwpQB8r0egBT+zoD2hJ+9c//yP+S+s4LKPurBBgIgPrCBDb9ugBTCFr3RgGnA1EApgQw9nMCSgmA+u3/tv0RAzIHjPfv/PcO2/3k/g/7NfxQGzjy9/a8AMsGJxFj8Nb2bAnX/9EL3fu4+JYK/PUwBpIBFPjZBE/1KglVBuLs3fr7AzcNjgVG74393gar/8/9zveMB7cG4PG3/0P+DAy2AFjmYAJYBwoAq/yr53INDwtU7a0DZvfqA6b7ueT1GIcGvPPa+HbzPhd5/NzmUwdsA3cFKfn98BwQ4QEp7DECHgX2CLfw0+tGFigS5POv7oIDuRPxAz7yLf50CgD6efgrBOAFBQD38SD9Rw9HCSb2I/mBAuH5JQ1r/Gv8+/w2+fAfe/fO78sBtfnOFJL1/fDkDs30zALH+IkEmiBA5GjtDg0NFRwGsM+F/ywt5PZO5R38jCZBBKbHWg0NM14AkMSB7m08SCIgy2bPnzjXIF/VTeP/H480TMuzxqRBADEw2Iy/kQx4XBji/LYYFeQv8A5Sw9L1/z9g9D3V+/HPIeEeZNTB7pgd9RSw+G7Xpv7JIrURWuyp5owJxwxaFLLp/+WyGHv9SQgo8Fn2GiGu74b4uwWX/AYMgu53CMocFe4D6/YLRBglARPlC/PHKOgIzNMXAUIL9hlC76PZCjLrBXnmv/rs/4E0bOP/3X8k8QId++jkwgiwK6ztuNUVDL8fY/wP6qLsvyCDDyfVIv4GGXMIIO7H5AoQjh/N52jtCQm3Dx8PkNmjBdUhyerkAlvuugyrHs3Y+BAdBpz3cwmc4F8cmhMj4skNqPSVDlkV1NlNFbMFC+7BGRPk+A0ICvfikhmt9RgGBxDQ4rgRHwcn82wQPehdCrQTpuBUD4rwewdaF63cNBVmAPrxrxMw5igYCgD65RYTvf6TDOvuQunXH3z9me6Q/tgL6B598UzY3RlsHkXxoups+EAkxAoJ0BUKMh78AZj1keDoJz0ZOcnhCUMRAhnV8ETBbTpuKczlXuSA8BxC3AdSv9YHXSyFDxHozNyRH0YopdTh9LIakxLH+fXZRxeDFr3wW/N3AYMhmecv66YXtwpDCPLY+xQMGFbkJQ2D7VEjEgrz1BAh9fdS9x8Hvu3gGp3sJeatIw7/W/p87lr4pyEi9mPlKxI1BDHzUvUI/6smjfDHzuEUiRleBZHa5uEuNd8Lmdq79iwK9STi5GffNyQZDDX7zuCIEC0tht6/87YAKxlqEPPKLQnUIIkCs/Au5qQqNBOF3Ub6NAu4HOX01eCJE1YW+PUK8vABRxrNA/7ZAALLGN8ILO0n5AgOLhvM+qn1N/UVBAobW/MG+yD+XfXCExvqyQAY/2D0xBgY5KYG4w7N6ucIEO43CpoTzuHJA4j/rgv7C+XWgQeQD/0EqPuA54wYsgv66Wz20v/dFZ786uf0AAkLcgcq75z7uwsc/mn17/GKHZgKl+06/DX5ch0Z+w/rtw7h/3gCyvgRAjcPCfSP9kYGPAqzA6jtj/UAEVILBvqf/LwCdfv//o4FEgwBAFXmVQabECcFpPL57YUYywxy+o7ubgADGx3uNvuUAtcNcwkU4J8HphDFAFD6netwDvwMNu44/58A0wlm/yTxZA9zBNbxdvmHAB8L7wFx768EvAY1+hz+0PgzB0wFvO/5BmcJcPfPAlfxfggvE3zkYA8A9bcBuRTT4+8RE/4p/eYJ/fIjDtkFWe/WBKICZP5DETLt4geMBCTxahYK9EMIcwyq6JQIJvr/AsIJx+UsDbMFtvgWCSvpDwQmBmn3DgmW8a0EYQI69pAIgvViBcoHDPSO/+L/ggYEAlXs8f9NBmAKaPxJ6kT//RE/BL/zwvh9918LAQOJADkFTer7C6sA+AKtBifuPQnSAJQCHv5r8z4AtgVW/bUBfwXQ9fsDxPh5Ae4HpPMLCyH4VveWC9nybRIc/YHmmxLh9hcRHAG15xMX4fR8AKsEVO73C273EPTsFb/2iAOn/bjuIBSe/M397Qkc9O4H6gFQ/8cR0fKC9FcKdAHeDjz0Ce9xBxAJBgoN+hj1uQZYBXn9iQdAA+4A8fWLAUEPwv68CSfuTwYYA+z3+Rqs7qj+0wIC+nMcEfkr8RAHcvQAE1r+a+ksF274egWoAXn26hW9+Fn3vw7U/7sLwvtA8l0Izf1sCyr9EfeEADoPSw54BJj6NvbeF13zkRFK9qbwDRQI5hgWlA6j8TQKtuTJBzEfsOfSEn3xduyGKE301Roq7jPoIR56+KoMmv//8/kKjPVC9e8U1vxxA3joiPQlGswBY/474tD6ZwtYDbb+8etk/Ur9/AvhA4znpv2s+I3/Bw3E8vcH2vV57QoIxPjgEzoCtuMh+ef7YRRrDeHeXvZZAksFmBBO7ePzFP6j+i0a0QGn6+vyn+z1HYATlO0E78TmihZ7FNb68e/c8MQAWAojBnbwMv/l+jIFdwn1/QAAmPpu9zoGcAS5/HgB4e24CCkE1/x4Cg/2j/pEDKD4YQqbAi/thhAy+Dj9lA798GEQx/sd71kYsvp+BVz9BOYnGOD8ogUiC4zpGwbf+nsB7BMm7F4C5v0/+2gXSe+8/s3++vY2E0L42AKK/PbwJAoeDLAAW/3q6vEHDhZU+vP4wPMkAaQVGAFd+nr+bvCZDiwPU/2ZANTriANLGVX+HgAv+bv41xld+QAAwwzr7t0LQQe59tkLhfEpCwYia+XYACj/Mf7VJrPqbwUaCHHq+hBi/CL/SRmq5koAtwlJ9jIbRfAr+xAJTvMNFXwEyP2mAq34xwzWAVf7xfuJAVASYvHMApn9awLfDi/opgw/BuH4RxEB9BEDeQQn8OIXuAQl81QDkui4Ek8QZPkTBS7n1v0QEUgBtAu/873nxwezCrUGcQDS5ef/pgPi8hEISfQ3/4QHu+V9AeoCawSGCNri8/SGCXD8mwiN9/v4bATV6+8Gsgp3/xMCFu0m+rgIwwVjBY34X/EFAxgDRgfcAyz2R/ur+UYHFAbBAmL5PfRLBLT/twA2BH4AJf4h8EP4fQ6HA00DCPI78MEEu/vuCfv+D/Y4ALXxGvv6CdX4Jvyc/d74GgCp8/4G6g0C72j7Yu0DAR8N1/yZC2HulfXE+aL9Hhd/CCTnzPVt/xYKNQEI8IgQ6geaBj/ye+6JD1oC9AFD+gMBLQzX7Drx1RvKIGYPqd5+2eAcaQW2CZ4HBv9qBVLPofxyKGQaIhJayxDhuyuAFA4QeuK87Iouf+7M6hwA7RYFM2PZp9ypDRkR4RWQ6rkCXBpU7MfuNgK1IYkZPNKK7hYkxBfW+5Xc4RKaMtD9zt7o9iYmnR5o4lT0tyUkDlzjj9/KFjQwbvgc24/0/xnqFinzcfSbE0cAIO2s/1AOfQfL+rX8rwrXAP3vVwLPA0sK0w3f4ZnxPBAECMoBbu0rBJ4WCut6/mD6oQJsGWrlWQBiBK3vlgeJ8A8RLAcy464EIAPY+vQNQOq9Cl4SI+Rm+AH7SiJ6CMHZ5QBRCPP+IwLn9IILMwI256cEsgckE/XqqeceH70Lkuoc7HUS9BmU8T/nlgYWFBgJqt9S8P4nnQcW40XvwRc3I07h5N/WHUMR+gW/0Y3uuEH4+zXYbuk/FOYutt5W4RAXoBGsAx/h8fWaLj/74dmK+TAeixox3bHfeR/1HqzwEOIC/x0maQBG0zsIRhg8DFzmber8KLQM2elC77MJrSjN8KHasQyjGiERseJd78YbvQrkAqrlRQewGZv0GvPg/XIWZwb962kHoAzm+lkCkPVbEDEPwfJH/JD0HCHkAVTh3AvDBtUP0/b/5iwWLBR2+Znzc/B1E3QGB+YfFZICjvd3+mHvGR4kCanvUQBx+tIMvPGX9gsedPhG9w3zxwGPHyruye9jCy8KuQRJ3T4DKyjL5YbomQnZHAQLSc1a8Q0vwiN84g/ExwzqOCL/lM+Z+r0mdwNi007lniyaL0nhENqTADMZ7f2z2ZoVlB6a2+Xb9QOWOxIDuK/Q/G860BMkzgvNXC+XSlXMVqiTA8RMyhYxpH/cYT4qHz7a482tEQo7MeWT1MkUeCPZ89vJqBGXNxj3csX3/T0tfhOF3CnVyDsDKgrDo8mfH4xZi+/0nNUJt0D8IMvPycJxO8E1rdDxz5YVAkBS+BDCLgc0OBX/xt6J4vkpfjLQwbji/ysXJFz4ucqfEvAv/e3h31z7DCRbF1LQ+fn+KF3+g+NMAKQcLQXc4LHzwBosGXvtleG9FdgpD/St0y8PNi+4/Evl4/BlFHoYUfXx7eAGCxCl+8H2lw8nC+LqZ/rHGnAGIuwM+c0FRhSh/8zqbgDeE88J8+SF+T4aAQuI9br1/wKNBFoBFwPk/CgNXv+w6w4VBBZw9Wrqa/YeHvELde1P+ecBIhpvAMvb8g4fE/f5hPGp+p4VuAHh+D/2dwGpETb6cPUsBr8M2ftk66cR8BJe9nPz7eo6IrAW+t2z8yoJWhwz9mjaaQtAFv39QusG5lMZ+SEj3Zriww8tGXwAedbp/9MlnQCQ9ATkXg3CIB/eIe+SEYILs/gY39EIaRo4/3PiaOfaIokQvd0J40gTjB5d6UvkC/9yFIgLquI89UMaIATv32TtNx27E1Xi0uUrC94dUP+/2q3v6CJtFdDWNesDE+EdQPbh3NEH8xHJDGPtBukiI6cS5+TH8LUEHR7V/q3oLQxc/p4KkPIY7eodyAfG6hj6jwgZBy7yK/MzGnYS1+9l6OTtITF3H63MDunC/2ImaA1d1PH6LAnsGwH4buBgHL4Q2e/N88kF+Aty/ob2WfqSFDkGs/MO8+QM8yBa5x75xg8pA9QTSd9I7m0ZzAGbCd73JQPsCMLnCRRZD3X3TgAc8+cPxhME+kP5PgFCE/sATvrW+SAGgg1PAhr90/v3BNsGeP0cAR4K7flg+0gLEvlzDrgFLe2ZEEb/MAqLCT/icxMwAej3Dw4T5hcUWg6T5+YP9/hGBsoFAe1WEbgCg/MXBucAtAZM/UnuAgFSFgcEVfSG95791hUD/mXzngxd8r0FoPnk/TgWweyx/Gr/zgGnC/npTgf7AlUASQEB6kEL6/huBy4Fu+UACDb6VAgiAqTq9gcOAWcB1v6k8tcI4wIH9a37p/+A+yUBGvqK/o8MifTE++r6qAyEDYzkg/0oA4oDGQCI7ZcFyAcu/N78zPKvAnkBUfiaAcD0BvmCDGoBs/9/+ZzxywzgCAn94vxC7q4IzAmT/DIBm/OtCfIFx/tBBPz2FQZj/9wB4fbA97URevY29c8H0fzIDY/vn/zQE938vQTO55cBChdB9Vf9xvuKCcIJGPnf98f7fA6HAyb8DgLxCS34N/PIDe776AC/ArHygBB69+X5iwrr+dMd6emt7ugUw/VlG+r4UOjeGvn3ZwW9/Ur15Bui9YP20wfU9ZYQR/+z7IMM2QKB+cMC3Pj3C+cFW+9mBCYE8P3HBEXxaA44DELmx/79A3YLPAcn5FYCsxAoBqj6vu9JDWsHSfuV+ef+qhBm/eP5xfUNDVwRMOqY+m78YAwXDp/pdQWz/nsAywkA8d0SQ/rW8zwL5u05F9j/KPP8Di3vkAaVBVf33BhY6b3yuApSB8sRDObC8VEZkgrN9sHs+QRPGk8AOuL69msaRQpS7UXulAU1F7H0x/GrBkwJIgDH3+kG2SIy7evhWfFGFbgfl+S95cYGohjQCITcSvhUGWX84vYS+vkAHAEW8zD0IAy9Dbnirfb0DzUU+/8023cDyBe7BX73dN9AEWIf2+2w8vv7mgn4C6jvY/jeDfsDKvRf/IQMXQy/9avuwBfrFF7xUOub9dAqixWa3UTzmwYCHngJlOqPAeYNMgEy93YG+QfhBxD2qQFbGnT3u/ROAkkRHg+x69fp+RvuHRb0duRl9hwmIhK85ynoTQYpI80Ep9ZR/zQVZBc1+/jYXQ2jF0gBzO+W8oQjiQqJ5yj87waLFij9BNliFY4YZvOM+qzyeR8yCFPg1gQ9FuIQ0ep559wedR0q5HnxewhlKhEI3MwxBE4p6RAO42jb2B7sIpTuZuRK87UoIBF734H72AwaByX1RfJ7EskGPvQS9+cFJxbu7pbp5xPdGRf5S909+X0kfxIR5SjbkAgKJBb6xuE9/IsYmAe+6GjuHha1FafuFOz+A7UgX/fF4Qv/qRW8EBzhcvR6FsIOPu1Z5pESHyBI+fXXOPanIaESbeGK5rsWLxMm+pftmfqAHF78sOLmB58HxAcB9cf2HB3l+Zvs2/17DkwWtuUo3yYXdhdl+0LiCO3hI0kMi9888jgKKR65AM7ZBgGQFToBNfiP74UM+Ath7Tb5EQlfB5b6y+z0BFETJPQf8QkBJA03C+TvZ/gnERsPcPLF7N0AJg1ECp7rv+yGCyUMJAJS9sT4ug6m/xj3GP4L+YkPkgGX9A0BO/+hBkQIxPu2AJ75vvsfDoQFlvsl9rf9FA0ZCzH6Ofx+B08EAwAg90MKff+O/U37s/feD83/OfiZBJQBsgjB+k/w8hN6/br5jQTX80gYVf4M8kIEPvvxERQFYfZTBNIEzgHrCGz1OAFjAbL+qgSe9kn7IvrYB8YGfP2h74b7vhDQBaACEPL2+KwUO/ao9dv2GAGhGrnxbPPP/zv/ORLb8/756gZC+j8DsPbz+dcI0fszB98AQ/CB/57+8An1A6vuWAUhAJsBdQN+9BsGffwI+vH/0PewBMj7BAEaBaP75/e+9+8QVwDn+vX4Av3JEdb/HvT2/9wCQw8E+1rvTgh1/b0Nufzo74MIjwH9/VUF7/mdAnkBbvTYEoACWfMdBW39ehGgBPDmxAZPBzUEJAaf49QHdQ2Z/7oOvecT/QsOB/vNBDruDgIPFKTzqPxE88kCqBEK9I/9IfNHA/AGbvhx/Gr/cANm/Tn64APdCdv9+vlK/zMD+AZR/a36cwd0+40BQv8z+qMNcfYy/3cEO/0HDlv+3PrMBE/+3gaLBzf67v6ZAt0BhQx8+Hr07RpC9lj/LgOh8/keA/EwBLIJPfEhEyj8S/ddErP8QP1fCILzvw9K+ovzJxKX86gGogDk/iUW3viJACv75QChEML1wQgh8lAJhQv48agJU+3aEosQ+uwpBv33KQ5jDQ3nxAeYBWv/LhJ27CsGiwZU/XUQs+/WADYCB/3jB8z3R/wrALoMoPzt8cX8YgXpDYHu3fdm/cH73BUE88X8JwGP+qcPzvHTA/YHs/A/CGP8uAS9/0Hy2wgm/z//ZP/B+2sE1gNmAEz1xf5LC70Ryvpu6x4FcBNGE6fv8ewFCkkQ3wpG5T/3wA3pAV8EOejJAA8OWfhJC+jptP1pB438CBJh7cT0G/kaBAkTIvQ46JT2uwlTC83+NOww9hIRcg64+dXYiAAHHzAKEvBl2DUH3RtID9HphNQKDUcn2gVv4n3cUxKUHHkCGeiB3WwWfx+H//7d/+IUG2IZ1/qU4nPvLRJ1HjUCBdqh634RdiWR7WTUef74F+0ba+yf32b9xQ3jGqfxkOL7CxwKvhGa6oPlMRHwBxQPlu6f66YRmfsPDEsAPfJYCOTxigYyDln6Wv/H8TULwBfx9pLyDvgIFiUYJPVV6Cb7Tx8ODR3vKuu8AsYgqgcu8uLuBgKAHnoCMO986pkAdiL/Bivp9eadARYlOP8Q71zu5P/MG7QGcPrz4Zj5piGRDhLxhdUkBKEhWQmC8+/Zgg2CItYDpPOE26cSTBsU/5TzLd7lERQjqv3R60DfuBNTJZv++eZA6/sJsyOWAOXmxfZj/68dVgXi7O322PxSHFgLFeg/8pX5EBxSFYrkzeqP9wsjKhF17CzvxfPUGvIQDPuC5wfzRBZOC3j8He7q9ygNtQxn/i7vmwFPCh4LrgGk7wkHMv0iCzYIO+ePBYb8DgGaDd3qOASLA+f+Eg3R7v3/TQ3V/D4EC+989vcIPgqb//L4uffd+xcZF/uV/Xz6DOpDI0f8zgGo94jfNh+YDFUP8u+I1kkPWRedFvLuEeNmANITCh3v+GXi5fP0DjYhmAJe4Qno2AVoJD8UleZv4zL98SOIHdnr8+2t8EMeEhrF+HHzw+fbGsoPbv0t/czm/wsgCmkHbwQ17pj4hwWEDtYHkvbf5k0E6RH2BzICjuTK/jsRnwVtDkLpxvLQBK4MKxio5/nw0ABVEEEUVuUT7x0DaRDgEefuBO4uAa4IaxgU9fXbcfzgGN0hSfE91Lz7lhopHZT5sdnx7ysXECGq/zjgO9ZpEDozcxAj5HDDoP4YNCgbZ+mjwPfnwjq5J27vWMPB2FsvyS0LAcXXqdQvDVErIw1Y6DjTk/C5KNgbAPrT2+TcChzJKKn/1eTQ0S8Ubi5gB8nuk9hXBlggQxOO9ZvmT/f8CJQem/zn/s3w0vIJHAAC2AX89UTkPRyoCEwQ1vzu3M4I7BZkFXn+Sdxc8ZAeDh/G/NLvP9uWGxIcKQJwApHWgwf6EtAFVRI54LPxrwi3CNgVQwNq64/8EAKaGwYOc+GV8m8L7hQoDJTtqfLVCLIWJwaJ+wP+6gOeFCMAOAGs/xUBthXg82QASf/OAFMNJQOW+Uj3GQtwCU4FNfZP9wMOkwi7CbH3rfB6DYwH8gS99in0nQbjAHIDyvxx/XMBIwGRBPj8NgB6B9PzMgIKCgH/uwKb8fn3HgrA/iANCveq8RcIafrVD+IN5+ex+pPx4AzkG435t+oX5mYbvyZ39NjioufbFnEkofxi5bDlxRIPGzIAlPwG290DjQ8IDNgSrePw7lsABQ7bGR3youkf/dUEUA4BBY/lE/8LAvn/Nw0586IDPALO7VgMtfsnBXIIk+Sn/vwJvQcDDKPlRPJDCiwUCwWh7zHt2gcDEpEAcvbf7xcBgQyF+XUB/PgF/xb/+v3m/owEUPrc9WT/VfybCgb87vTxCbL3t/yo9VUCvRDWADrtVesZCg8TSwjB7HDuQvsPCA4V6ftL70/zWfYTFmAUOfXV8uroLQPYDp0Hygf/3KPzWgVq/4caxPv+44X8hf7OGl0NquIY7jj+8h+bEinjUfP0/tsO0BN78zry6wI1BOcD9v5aAiT9ZQRt+DIDCw+iA3H77usQEHcMEPzGBanxixF4ChnxOQXiAvELuf0388wMiAiPChj64On5D8QOowLr/uLxfAtoEaAE1vYs+CkNRQifCw0EdvrX/PP8bwLzEGUEpfq88Y77VhO2Bx0DKfy+7FoN1Qa5AZEJQui7A2sMZf3TDwHsd/SMCKQB/A429yzxMQ8bCioO/PFd7BMIwg6ZBU33R+0l93oasgLM+fsAkO0zDn8RGQcQANj0TfBRA6MSggrO+C/oBPpVBOEayg6A5FL1XAkyBkMSifUq8xsJzQIn/3D5cQnRA3f6Mvjb9wYLEhIC+QHwvv5l/GEPTgN48qoFd/fA/GT4hggQFNPsjenx8QIMvSNx9zPX+vQrFEIdsf+94vfmcgY4GJoR/OV55jr67w0wFgPrVOr6BeUAMQmY+Hv3owMv98MAJQYAANoFwO9g9ukLTvss/AkDH/JSCO0BePZYBPP2d/sjAl7vLAeqBRAALwjY5Sf9BQ1b/FcLv/LF+b39Xfz6CDsIz/ny7mYBBQP+Cwj90/MXAzYCJgn5AQn1WwAHC48C1wF7AgL43QVFCTPzUBNC/ur2Dwue79cUyQIZ9s4OO/d+Eo4G9OhVDdgGlgPUDRXukwJLDCj1FAfyBIUCWfyq+bT91QliAUL5hwK38fgJ3gpP/B4FKOM2BzUYKgB+Ay7iff/vEnkAdgil7SD+cv9CBLkOfPd2/lj0kgbZCgn3/QCG8gwH8Axh/bAEK/YIACoJBANX/vz59AnMD4AFNeiE/awN8wyFCdjamfw3Em4RHg5K37v0ZAvPE0IOJuxw9Ur+hwQSDt3/Avnc8Jr8swrQCxT8Xe7r/lcBbw4ABkP07vYc+mUN8AKH/gv17PXRBfcAvQEJ85b96fzl+fUHl/0KATf5q/Q5BwkBFwBNA0Xxhgep9YUGQAnt7oz/K/mPCKQC3fXH+zj+ChAvAo77vfj//uYIRwEWAEr1swHxAfz72g1T/TD6Bv2h9YQR6AG3+bb7d/D8ExkCSPZS+x3u9BLIDPn1LABd9uUH5Qg89n0Bjv3IA4EJf+9D+XIErQMZDM3/EvDf+kgGwgylEE3tBuy4//ID6iGS/snn2fgj95IlAguP5oP0JO1MHC0O3/UP+47rHwsLBc4E9wMs7pf50fyrFfQEuv0I9EXsRxbvA60LAfVe4HoYkgIpDOX3Ce98GSH37gFf/OL0bxSLAfv1i/9z/rUNUAOA9gkEX/wdCMYN4fvB/136Kv01D6n82gVp//n0Wguj/wEKPPpa9fgFlP2uD+ABh/LC+xP89g78CEr3nvzh8xMLLgjH+4X+z/HICU4JSQCP/FsAbwWA9fr+MALgDPMDQ/Mg/iLxIxi+CznsHwhw7+oNrQn18N8JfPSBBzUB0uxuEoQEbP/L+1L0MxBqCq73nPK2/xoJxwZD/e7xvASsAYb8ogqM92QJfPkI7vURH/z5DPT4VuWvA6gAxxFC/lnoJ/mXC2UOJP9Y81H1eQgqA5gIsgCG808Bv/0SA4oJYv7I9Tb6BgTTBxcCEPnGAlkDbwRDD57xEAPXBx3/PxJ66AAGjQs++p4Ms+sH/zUNrAr9Dfby8PMZEa8IUgKA/+rsdRHHDE/yCfcN8q8W+wxL8PAAB/ZZE7EM5OfaBgT9/g3SDDPjSAPj/KwMbQxj608B/v3IB58Lnfhs//ECSAk+BvIC1/jN+XIG/PsvBKYCTvoYBSj5QAYkCe767f1x8xgQQhIL9y4AjfR+B30T7/009nj35Q6DDLT6uP0T8eoEmx6V9x/uzPyIEJQOqvUo9iP6/QfnBhsBTf76BM0CEfV//4X/0vsC/zzyZgwREdrr5+zD8wgY7xXL8lHiue4dElIYmwKJ5An7lQWmDpAFeOt5/nP+XAEQEoHxwPd1AXX3pQcv8Sj9tQd29ocEC/fe9C4GUQop+5/6SfR6AxQPOPZnAN7vTQngEQb1m/zJ+KYCfAud+yb2JAKrAccFU/yE/gv+OP+mBYj+5QarAxj8YP3SBO4HJvnd/7gEGvhTC6wCL/kQBuf04Qxs/3X8qgtd+LwAbfnD/QEOkwRc/rr+c+7ZA6IUA/4g9cXx0v3LE0AH/fM+9o36jRniA/z2FfNN/bkShvxh+B8FkfmNApv/qv1KByP6EQ2B/Uzwog+W9kECqQrv8JkGtQRJ/dMIdvACAHkI6QDzCw/5SfjuCjj+CQI2BJXp2QgXDp4AhQJC8gECJglZ/135RQMO+4EInAFe92AIxfxaCMkDXPQRAAsIrwR/DZ/9l+6oBi4Eyv9MBqvzPfvvCfj8NQ7X9j738g4C+4cRJgLw7AYDAwF+Bn4KUPBO/ZQKu/cPBLb+oP/hBPf72QKaBH36ePo4/8L+YQ5qAfv1av8P9FgGdwVt9ZUE8/bX+3UHdvqh/tj3CQKbB6sAnPuF997/RwUmCSX6L/LeBGkIDQNu/A/xRwoHCID8GQT39mMFcAJ5/c0G5v9k9wUFWQRwA6IHd+kmAxQKEwOjC+Hw/gA8CIT52ANtAOv/igmo9f8FKgom9qP6sfyvCqoHEfWz9bMJMgVo/eL34fLTBsAAsv///hX9WPxL9UQFXgVWCAL54Pyb/mb8PBFq+H32O/5zBYIHUfks8+IJxAFRAKP78fXyDk0EkQEN+/PytgepCR79qAA7Agn9uQPK/A0B2gJ0/yUIEfrk/scHjPwPAj3+yP1KBGT1UQEyAzv4WARO/wUDnfo2+I4FgvdoBiEJk/iQAtr5UP8LBg762QFK++740QUq+/QASwSr9oQEsf/I9zoFlvxh/XwFxfgrAjAAu/TVCo3+O/6YCML2vQNP/1L3Wwx2+Dv6kf8i95wNrfyG90X+u/vqBPX0pwAfD7oFlPOk+lkJzfvF/5/50/7oA3jyzgjA+1P2/wwb938DYwFkAnYMYuHkA+QXEf6fA5juHvq1ELD92gr09iP4qQff9W4K4RQtAD7t/PQ5B90ZB/2F86/2N/8TFAn+GvM79kANZwoR9z/53P9BBOoFl/us/lX6LP+aDP36nPxl97oCwAxW/aEC3/9u+Pv+ZQG8/pz/q/2L/cgCuflmAc8DyfdrC2TyxvV3DD311wal/mT2ngbX7qIHeQqC+VcIIPh5BiIIcf3t/xQAHgXHAt4CuvZS/XwKGv9pD2n4LveBDtz5lxPo8P31PRls+fP/0u6vApEX1vouAPf5y/9tBm0DsPyC+x4EHwh4BHj3HP+9Ax4DTQIg/2D4XAQZCDz7hgERAFEFtQW59xQPAv0g+oECWvdEGBzv+ASvBZPxjxlE9pQBvvpk/wkdG/hg+0MEgPouBT4ILvbJBQsAHv2CCMzzRQ0SBfLrWxL5AgQCDgOt7lESb/ojASwKc+w+E83/tPPlArv+2w5g++z24AgB9G0FQgWD9wUJLPd0/nH/pwAqCq/prf4vEJIB4f3f8RMGoQiY/f4AEverA68GQQJt+FD9BQpQ/cv7EvS3+SgMMAPqBLzy5/iUEgb5qQEe/N76xAgH/JAFtffN+xMA6gAg/4oCiAVH7McBmACh/x0E3/ORDWz/wPcBBoXyHAyZ/q76k/5+98oLafptBoz3GQBACDf3vwgo9mQOyga886AJwfIzBx0FG/d0BPPyNQF4CMP9oQAI+UD8lwfbBkP3hwD5/UgB9AuQ8KYCMgH2AyIOueszAC0H7//IBrnwnPl2C5kAifuq8hYBQRLR9ofxbPoXB9UQVf1b9cv1oQg8EE72f/a9+kMG4w0Y+5/95vZlBF4LRu6FASIJawSj+h3tqAt+DYIB2Pmh77IFMQpB/xT7HPV4C4r/mvwH+xj+iAnw9RcDIgAABxMI1fKwAeH9bAneAFHqXAVkCUMGfP6L7E0I/wRtAEMDyu0XDyoKI/4BADzvZA9QAez/OgFw9esQtgEpAFL0df0kF2oCYgMK88v4hwqqBLsGvO9V+OQJ7QGpCaz2KPnoBpQC3gzb9unwMAAVBM0SHPwm6Av6pwlSFbgFUeHc8u8F/xXjCp/hnumbDCwUwgzM4bTdhht5F1YIsdz51bQkxR2ZBl7iGNEJD64X0w+L5Y/U0AndHIIT1N3W4dQElRyqE9jeN+szCmccjgUX4FruSxcRHcj8g9Zn7+8ohhsc6jfVfAEVMucZytmm1TwGGzCbGzrdQdgNB0UqLRYt4dzbPxNpJSIJIufu4IwUqRejEWr4Sd4pAdkLgBcqBXXfXPm3BqEaVQo13ur0EQndF1sGHOj08/gHJRFWCAjzTurHCtANBwY59WrtvAqfDBsEHv5z7wYFhw1P/n0Dsfp5A+4LQvk7A58Bcv27C43/Rv3XA3L4hgYvCMv0Nv7OBG0DChEK77/8lQ0l9QcSK/jW+s4N+fjlEO79bPRVBOsCdQO5BLbzeAIyEsf/gPyv8Ur92BGfA2v3afrEBVwNyAhC9LD0VQvCBmEMHvwv5nYRvAc2Cev9K+JUFGIVW/uU+mrm6wijHgL87foF6in4dyOEBdj5ou9i9psXfgo59Oz0lQAPDrEJJvAY9WoNzQPGBEzu4vTDEpUB1P5j63j0lRodB4z7K+7080IQqAcOAvv6ePeVAVb7wgNqAzP7Cf+V/SMIG/3E+ZgAXwhWFIT3R+mE9lwUyxl++CPi4fhnE3cE1f6J9usNrgy44bb0rgYSIqwWGNd72XDzAxw0MCr2mN3q3NH4riXtEXj7a+jw7rIOYAhx8E/xWwkbGPAEMdkL4YITJzGSFHjR+c+T+lolwiO8917igN+d/DURtRhKBCzxue3E/HYC9AYZ9qf7pw79AOAAT+fwAH4YkQP0BUXqavl0DOEDjwFlAG//d//99xH3oxAsANT47vhc+bATzvhS8DcFQvvQFaUImOrbAg3uywqLDln3SwvY6E/8KQ4r/5UWo+8X8HYMmvubCof6YvTbDiP5bv96/h71ugps+5/85QYW/BcC//06/VcK0AEL7+X//QiwC6wAyujB/kAKjwU2BFXp7f3wBBf4gwol/4gCbfWH44YTJQwnBHXy2eC9C7MTfwsy9Jbl3/2kEJoM4vLO++MA6vh5A776pAjlAGPx7g5c/pEIhPwR7gwX5QVhA77/z+nhDx4JkwH9Ajnz9P3+ATz9nQsUBFH6O/+C8UIOUhLD/bL+Bu/mApITKv9DAQ33dvzcCn75cAda+lIFkA419uEAYfljBVYM3wO1ACXzVP4oBYoDGQSv/Kv+1gMiBfz7UgPsAS8BIAx+8f4C5ATx+/0Qa/JGArsHg/EUDXP4IAX7DXfxUAVCAab+kA7M+l4BTASM/ZgCqv4DANcDnwkk+mIDH/mG+pgVme1gEBb9+vBWFPHrQRG4B8P1bg0N7L0K/QN4AUcFVPQwBHMEDv/PAoD/TvsmBcEDdvp2/yL90QP+A+QBEfri+/MGFADOA+/zrQCnBBcDVgZB7WoKyPyLAuQFsu2wC1D9zADiBUX4OwSC/LgBzANb+y7/rAKx+mUBCQLg9wsIHv1V+a0GIvZZB/kC0vjnBtP3bwTM/8P3Dwkb9t0CcwHb9SwIA/sI/MwFDvlKB//32/k+Cg36XgPP9//8swjq+cQAp/jb/a8D/QAx/cj1WQSBCbf+GfJw72UajBVc8/fiVe4YHhUDNvH/8zHuZR1dChbvjPTw6bgOOwtu/w0CYPDh9wUCjwiB/fD3GfSICaEF7PjQAFHu+gOIDfH4oQ1g9jz4XgVp9tEZSv4J97b2i/aEFZUKqgZY8fD75QKeDrAOovQbA4P5fP1VEtL+QA7I+Gz8Zw09+CEJQABb950GH/4XA98ABf+KA8YKSPthCTwHIfTfERb9rwbRC/Tz6wdY82EELhNh94H4vvt9+h0PGwSO/mj/h+3uBOwHzvspAP/3AgLBBUL/igA/BPkC9vnWA3kErf5wAVz1egwECrv7nfsS8xMQKwep8Nv/sAlSC67+dPAL+eAH8BCfBEHyyPcTBosQSPTV+pv7ZQKbDD37o/6I/PgCXg3V+CL4BwHcA1sBkQJIBgkAD/pUAFv7kwKgBdwK6AL67QQGrAn1/2kBifEdBbIAMwGRCELt3fo+CFkLqQCF8vL8kPsgCvsIBf0D9Cf0BAw/BuL6rgUy8BIAgQIMApsLcexX/XAGj/rxCGoAXvZG+8n5oAqoBwvuLwDj+H37ng4z/UX5Yvqq/s8TO/il+t0JH/C/AVn+yv82EEDzn/6o/rb3Pw5E+/f7FPw9BLcEmfRoAa3/MQaj9X/66/9T/7gLJvsc8QP94gj2ByL7U/oxBer9IP+kAXr8VP7PA6AFJ/2Q+x75+QiMAzT6vgKV9M8EDAl6+oj67v3YBLcLoPG2/uIG8fsdEFfz0f/y+7/6ABXO9FL9UAHF8kIGcgLBCvYAUutHCbwKmvt7/X7/jv5PCify1AC6BX/3Pg848/b+MAs/9rMJCvxwCccN+e44/MsHawD5CcAFQQBvAXr1qP5BEYcAIguF+k72JhpN/t7yfANA+acgQAkn7uMIKPAXDgUT2e2oE0D/FQHjAVbzXB1YEDD46vdz+G8BMxFaBSEF/AXT86v4Cf66ClAQaP386rX80g15BmAJ9flp+JAIA++9CM0L/vAWFFrvAfL8CuLwZRQTBDLqxAOX7pAFIQ+o82kHpfcY8BQLN/qqBNYBY/FX++kDs/+hAqsB+fNHBAH9wfwTCF7uUwdO/8zzGgan8oUHBgGV7hUI/PqWAEf8BPVpCFIBKAOl9R/3BggQ+lwCr/l5/FgB1/NBAjL8fv/LBnf3zf2N/xcAWwWW/Vn4igai/sD6QQOD8zIM5QI3+XIFa+6YC8oMKfUQ/274tgYiBqrrVf3x/hYIzAEj5tzwSgQxGiQNR+z04zUDKRLZCID9+eiy/lcMFQX7/dL3lw4gADv4IPcN/XUR2AOY/j7vlPs6BOkFdgOm9h4DIQe68aP7i/i0DqkVx/CZ+5Du2glsFJLtrQCe/s0JcgnT7/f3hAlEEcL/k/PB9FMPswue++H5wPKOEtwIwutkAlH4PQsOFfnnpgEj96kFrBMw8rUQB/cW90cVauysDv8FvO6LBx/yAwuqByvyOQ4d+3n6fwC99LMPZQug+Rr4gPQQCOUTFvye6kH/7gTHEMb+iOeRAQsDkRGDAh3ecP7wBO8Nuwgw5Pf5fvzaDLcMCOJv/3YI1xCJCdjhvPTUAZAY9QZa5pPxU/5oIf8EAeZ8+ID9Ehx0Av3t/fvI85UfDAQ+73cD9/LCEHUJ0/UYDE/wBwHMAw39/hO494j4KP/YAV8Obv1w8asBGwclCMT/eu1+/nUHpgjY/mnxKv5tBsAIAgKq8BUD3Qf8/6IAu/YkBycDnAEp/Gz3qg5+BwL8f/b6+bsM7A2E/1rzY/40BkwKeAO48LEGSv9nBJwFdewTEFwFm/u5CWjs3Q1IC3/5/Qtw8+0JeQTT8YUFhQWkBqr7fPQ5A/sQWwjD9Br96AJ+E7sMweIUAiMCBhTlE+LeVgBFAnwUIxUF5Uf++AT2DZcJBfSi9vUEbg6+BiH9WvEWBc8L6gLhAcDxPfz9D7EIRAFM+Cr5vgDCBCEDOQL0AhL88AcE9/EHeANyBWQJMOvbD9T8iv4eBr7vfxKtAxMBqv1o7q0J7wvA+qMHc/e/8fYW9/61Bvb7GuUYE1QIwwNOBIfspw2GDDX1SQo8+E0OOQYh6L0Ik/6gD8IHnuT1/RMEWQkWA/vpbQcDB3gErARu6Tn+GgDtBF8JNO9N/Lv63/xfCHL40QTw9Zf2FwILALkC4vQY9zoDpgZj+HPn8fzMCpMIU/rG6DMA2AEgAW774upnC5cE/ALC9wz18wKy/bb/dQHG/irxTgUU///5JAGA99UIiPw49tEJ0v0dBin9I/U6Ddz+TwS2/YQDgAKR/wr9CfS5CmMCbwlnBVLyEAoV/O4FSgjQ95kEFfom+fcNMPkp//IBQP2MCDn8tfiLCs4BJvzvAD7zaAnnCTX68Qhf8hP8DAc6/j4JtfU39IMIYAEu/fX94viRCcoHlPjL+un5Dw5TDkT0KfO/ALMOHgur/P3t5v5VFpMJefbi80T/XRN+AWQA1AW+898GDQAbCAAPoffH9wMEEQXeCw73YfoVBnMDLgO+/Vz9UgKeCI/8kwaW/mX9bgohAX4IsPmU/FMGcgBBBb8EHQBvCSL5tPqNB+gCOgsi8XT4fA5ACAwQaO8R9FcGbgqeDKT0RfWHAJIJxAeBAEj2v/K1Az8H9Afv+LHuF//SDTMLafv/6R39ug95CKcCx/Fg9PEH1f7cCBH7w/ZxARn8EwKgASX4Ugkc+Cv7kf7E/TMJvfXq9ocHiwRYBJzzLPWlEUsDxwAi9xf2hhGB/37+ZAIJ9PsC3P45//sGGvjwBFwAkAMqBXj7Wfs5AToJPgCF/Z/5SgFRCrkBPvcOAjME8ASK+1j5ugfx/HcEKv04+uEBIAPE+8QCzvsN9+MIqf2nAkb/OfZICQn+lvcuBKf3Dwb9CIbw5gO6+18IlQoU8WEAxPtgBM0OYfN5++/6UgPwEf/16/1n9xj/qAll/DkAyPfi9wsHgQjH/In6QfPdCU4QNf47+Xz2GwM7DO/+3/++BI/3PgdbAvcDTgh+8dD9VQjhBekHQfvA/HUI3/h9/IoK0P3OBWICpfOQD0z7a/9/DIvr3RRN+n30VRZo8eUJLPoO9n8SfQAq+Ln8PfLOC0MQpvvGBm3tkPiRCK3/bgnB9kn9qAbYC4AHot6I+K4E8xWyGN7WV/+4+2sKgRvh3sgK9/+U830bIu0gATgJG/W/Ex3wkPnnEd757BHp8rf03wwOA2cFIuu4+qURGBBbBMjnUPb5AroZogSk6GsAEftCD3r2xu6KEYX4GBbn9mjztQ0H6F8TMQeb+PEIR+wlDNkDyfNs/j34TBSWC5Hxrex8AuoQqQNa9uro7xPZEdz9KvBG5dgUkhDJAuP16uumCogF5gPD8Cz9kxGL+ikJ7O6r9l8IEvAWEAXzhPyeCln0Lwu77Mr13xCYD8sDid8m5nwR7yAK/GfoSegXB84dovlE9T/joQH/Gvv6afzA5s8CcA3e8M0AffazAfkXNuz++mD1aQF2DDLuFQiE+5v8zf+F+QkCqvgJB6MAx/YN8ioM5hbv+bDttO7KC2QGHv7E+5n3VAshAJP4mvfpADAV7/Rs9tUEOfnZEvPuzPcdDDj+nw5o5hL1QBlNBR4QDOp58FUYT/9dBsb90vkFDyzzmPzwA+gDaRCD98UChwXFAuwBWPucC+z8wwSrBpsDsAhC9KQJKgpV/loAS/EHD7cFEP3EB2v0nga0BZMAhgfs7pkM+AFP82EBiQByGOT9n+/r/RYFyBPw+GD4iPm7AsIQHPejApr7/P++DJbxfA2qALoCiwnC7LsGxgecBO0Aduv8BYMMvAHy+FL1Gv6dDZIDZftY+4r3vghrA67/5wRb+UQBXP/P/f4I7/llBGX8qfOVBjr/3QkS9Rz6jgZI/IMHU/vrAbP+vQA2Air25QEOAzcFwPvO+tgB/PulCkQADgGxAHr1wgdV/MYMTwdY8BECxPoWAWwHJPqxBQr9y/mPBrT8bwGMA7r8YwE6+Nj/bwSx/xr4DgKcBPb5yQXs8TgHNAo576EHXepNDCYOHe7XBMvyvA+iANTrkAXA/1QO9/lK9737wvufC8r5x/nTAhgDUgHT8z/+uAeoAyj8KfeU/fYNHAUJ/jT1t/vMEz77+PeU9XwH2RSM9UP+/fN0CsET2/LT/Mj7vgyjCJHyyvnXCZMUP/q37Vn5eBWuDj3v0vK5/W4ZPAsS5/D+PAS4DPv8QfUVBwAACwja8/r6dQ7K/CwAgPlq/JQGiwC/Awv9uvKsCNYGFfxh/kH+ewgb+mD2agmZAAf7Nf9WA+kBXPi6+P77vwfh+e/+CQ1+/oP45ehnA4QM5/lp/iX1pPp2EtL8NvMv+FP0Fw7D/i72LQaVA4T7t+5f8LMP2wv9+Yz6bPAdBOcOPPTu//L+v/PgDu36OAwgBl3o5v6D+RINJAlwAKr9cvpj+h8GYRES+BgJUPfM7uETswN4C3n3eOTnHHwHdP2B+CX0RxhPBDv0ZgCt/m0GfARO8jj3+xWt+0wR4/YK8YohdvcMA9P5WgWTIHHxee1XAv4DnA65AsDwWgnU/W8LcAot68j6Xwy2A5YLDgSf9zr1Q/46D7gUXQp7727zR/16FUgSP/qJAqDpFwMqEuYAb/7bAc36Df7vDiQC9QM/9av7KxCXBa8E3PqA+TwLMfW+AVkGQgcGChD43PYqAKgJ9gEm/NcEMPgKBA4RoPrpBBwH9vcu/rf87gzfCeHw/v+P/vcIKRmU7NTxbwTi978OPvUm9R8OdxGrANXgt/3BEwMFgvYq9l0F3Atu/Y70m/SdDxkHbvQ8ATLu1xMpCirv3gqD+GMBFv8N9I4HXfx5+w4NRflK+JkAv+ySEJwL2vX4Aq3s7wUGFJzxyP9n+NACHhi57Rf0IP6f+4oVQ/pD9u0CTf4TAXcDcvV4AVMGZv0cBKr0c/p1B7/7/wrE/sj0JPwBAc4AlQT+9IPyqArXB8kHzO+g6KkJpQyfD6v4regu/WAN8wSG/DP71/wqC6P9ngJ/+OT1QQmZChgBkvdL+E0EVweq9JMEqvzR/NoGuvRVC0D0g/ppDBLtBQGRCs0CrwGC5/b/YxIn+rD/If+UAKwC6PU5/wENXwDl/N4AffzTBbEKLfq08hn+7wedHUv8qOzrBHYCnxXa9Tn6/AdO+o0MyQB0Axj+jPZpBgwFQg2P/vb7nQVO++IHxgH1+h0Ex/7hCtoAWfkcB6UDywM7+2X7bgxNAoUAAgW1/XH/5PZn/GMRsf7GAgf5kvU5C7/+6gYgBePqAwJ4/Hr87Qxv8toErf7p9TgYjfZP8tD3ZPtvGrYBEPCb+9H6cA/wA6P2kvX9A9MGIQLp+ADxNhFy+E38JQ3m9VoKQvUL/V4LPf6yAP37bP35Be4BL/sa/UP2eg4qDWDxVvw+AUX/twZI9RgJqg6b9xr37PLJDNEIn/tD+2743QUHB5L3IPivAcAEEQwL+Jv4iw/z+ef2Yu+RA3QZCPv09GjrxvoAHC76Ku/t/HkF7hAm+KD14fMA+qkQqxA6933xjfvqCMYKt/Mp/PcJivpoBugBEvrD/FbuqwZlEr4FzgPd5872Rw32AUcEeP4n9lcFYAfq/C3/H/MO96ELOfYiAJsFGPsGFSzs6fS+C9n10Q6i8aD8KggQ8WsI5gWb+Wz6fgIQ/vkDWfkO/TAJ0flfBcz6+fcMBagBGQWw+wgCJgDe/AQH2PgEFT/4eu/NEFf1CBhW+87xFwz58k8XPAir6scHpP9eDA4JBu+QCpoAxfYqDtAJDghV+I72lfngDooNsvoD/zXsAgwuDLn3cQxh6mIGKwk4+coNq+RdA1cIhveQEyT5+AKM8Or1wxaTBo0Moe/8+qIL5QBSCtjuXAD9CgUGQQUf7+b+5wpvCXv9LvYEBqkJTQds6qv1ZArIDXANV+Hd+cgKQwx1FHjmCvt6BqQH4g4H9OcECQIX+xUFgwDSDV7/Fvkp+Z8DKBCB/7X3IvGYCmIJ0/+3/jPxUQt3B1n+De6P/eARWf6g+9HwTgk7BOP6JgDY8vIJSwFb9gsBo/tUDb4Ez+3oAJT01wsECin4r/3N8+sJxAel+PH3w/7fBpT/qQJp+6v7M/9CAuP/kPRqBXUH/f3r/Cr6aAmj+rvzvgYf/e0LkAEP8j3+DPWcD1P81vB/EL/9xwE3/HLwswqHBBkBzP8l7vkFnAwW+b36ePM3Ax8MqPxV+8b9/PnBAEwP7PzL/Rj2WvoMETz7uAZb+iPumg6f/akDV/1u7zsEnO+KAEIKcQSMBPPoVfePAOMAcQf289X56fhoCoACfPGVAO75wQua9Sv1qwvF7J0BQ/ooBmwR8umAAAj4hvw/Gz3tgPl6BIYIphYH5j704waqCWEXhO/f6P8D2w+3DuH3H+cFDbwLewzFAwno9QBiAM0QtAo48zUA5/69AnkLT/ywBt3+l/nuDAwAJgxL+h36eP5/+HEOVQHC/kAKHwGI9+v8KAaLDv8HrfZtBhMB3QAgEwPpmv19C9H22RSX8RH7FQZP9IUVi/Pn8tEX9vjbCJ0C1esZBSwH6wGw/b7yxAgMCA/2pvRFBLINDQoJ+c7qZhCDBnsE2flt4jsV/BKLAD/mGeZ5FyMWxwDe65LwCBCdEPr/lPAA+DMLnwnWAcPvLP8cCpz+5w4G9PXzegF2BwwNP/P0+lwEvv+RCyEEL/E3DUr+KwhSCP7puw0Z/UH+Kgb5+qIJ5fnN+J0O7P0Y+jUNi/RUAowBrvCcDBv9hQAZANb3nwc5+PD4AAUt9dwOYgTU7WcDgvpmC0MBTu9mA+IBvwgx/bX5Rgh7AFf9zQGS+7oB5gEi/0sAPP7m/oABsANuAmf3dvlbBFoKcwai9eP1/AQfBQT7SQBG+X39eQpH/ij5qvbDA1QLdQDD+az6TQCqEP/6NvFDCCX96APsBgT5hAHxAWT+uwHr9yX7ugQJ/XIIKP5i9NQAYfm8ALQG/+3OAxgDB/wgBiv8N//7AVcAXfsl++D9IRBmA1D1aASg+YcJ/AAy+YYKB/09+gwLqPs/A2EEN/lCAaj2swJKDk/4uvmL+zn5UQgBCD73QP+M8JIBLw8293AGcPTn+eMJIv2iByv/lfMr/ygD9wH/Bfb5Rf4o/5X/dAU9Acj7LwAGCaH9Mf+M/l8ALQZ3/D4DqQiU+IMFDwL29rULVPu9/oMGB/2iBegEmf4e/xD+0QndDu7w7f1kAXT9Qg5q//j0JAB9/jgHPAaz+eoDFAD7CMgAa/dZCLoDXvxD/xj+UgMt/bj/RASM/6cDQwMnByD+J/W8AGcCbAcvD3z+OfHH+YoG/AhU/GUAHv0rBYkNKAOH/drwSQZiB/QAqv6H/bIRaPuY7M4HlAAUA10Je/dQDfj2nfxSAZ7zXR/LC1j0SfoP7mYDMwYJ+xISDP668A0Jj/iHAOQCGfSnBsX2kwK0EvrrufiSBXf9ZwkN/8kFlAB97kgJZgCg/LD///uC/mTzDgg4A4v6l/qR9wwLqv83/031N/YWDBsOzPcu7skApAdvBPvyp/yaAcD/wgf+91/20we3Bjr8MPqyAOsT6/po+nYGC/4TC8r4LQdpDCj2pQQtBuj8CgrK9vMDBBQ483AKoffrCJMP4OzLBs8CLv1wDGH79vbHB0z60RGgBLrmPQmQAIEKLgb17bwLSv9d+w8Qh/FsA6kCX/7iCT/vS/6IA6v7ewHP/2wEPwRV+/L9gAcN9fQC2QKK+20S3PKL9y8C5P2pDYf1r/uaCi/3jQfy/ET4UxKT+Rj/p/2v9ycNNP6/BBn5X+2PBwMHEgXB9eABHAkd99r1gQDnBbf9e/zZAHIG+/tq/uj/IflvBzX8Ff1oEQf1kvkeAM3+WhQH7toBlAui8ZsMT/TOAmYNbvJ7BkrzYgAhE0/24fgr/5UHtQd7/BYALv1C+a7+5Qon/yD7av7C+CgBJAnFAQT9PfspBvYHYvDR/XcM/ALz/Gv2SQJ/Ae0Dx/0g/A8Guf47Bybu9fsdEbT50wqX8Kv+8BMK61IDEPlrBMMWg+fZB23/cAHxEE3uCv9qAAMIVQQv8nj6vxDA/5n+zgTr8pwPzfvzCTX9HPC2FQb5P/zXBin/5QNC9xv/2gc3+PEB3v7c8RkFUggb/lP79vfI+sH9ZgBnCz35BO50CdYC9AEp+tzxjgi3+EYHsQOj+ykIe/VQ/pgC5/qZCwP5OgNkB2b1iwx69fAA3v2B9w4a3PNB+0L5aQJnEIPvTQBr+dMAsgfu+CMAiPy4BOb82ve9/5UDFAky8af72//LAlQQeegGAg8HUQN3CJfiQwuLCIT63QnU744HYwHo94wEC/Z+//EHtviN+838S/4HCF79hvm/A1D5hgEWAz33gQWF+LT+8wVcAHsCyfIE+94JCwgm91P7LQIlCYUCa/JDAEIEbwvf+tHyUQCBBAMNivjL/q78i/9SF6T1Yfnw/ZgDmBS87n8EZgZF9FYSh/SP/hoJcvzeENvo3/vFDg//3BI1+jf1vQOJ/pIXZ/jP9iQFWf+GDff8gQO2+4UE+wrx+vMIlPcbB9T9sfcOEc/0KQjjCFv2pQW68loI/gzW8bcEKfqh/yoQuvdN/fTyHQNlGsb2F/pu/EAJgxgR9GzslPkPEGohaO4R5tb6UQ7pIjb6Terv5yIMeyPU/cDsT+8fEdwRoPtD8LHqhxKqF/wBBPS66dQINxOzB4332erWCj0ThQCH6MP7ZAfECTQMJ+08/qP23v+3Ffz6qgrv7E3wrRwSAsn5W+vB/bMbfP2P9e7pgAFAHQQHtufU5z8Y8Rfn+3vjkPPmHEIQ1/EY6yMCsh8dAVDayvWrCzgd4/4E4Iz3hgjPE57+Z+qaAEAMHAZU913yHvrLBOcH4wNrAXb1LwRXB0r31gO+7s8IAxFMAGMKVd6K+w4T6gvvBXreFv7YFw8Fj/vH7iYGcxP5+2jwiPa1DUIM6/uC7jgG7gliAVT8ovLJCAoDZ/0ZAAv6sAnH/fLwnQP8/hEPdAY/8oEAffOCBXoJAfwrBxb73/mQAhb/bgMZAwP6Lv6aBkT+IwOF83HyNRPJ/aILfQQY7ywKEfHYBqAGPwFCEWr+Ke488yn+SgbcHOMEe/1v9B3rVAmrBEALBg+0ApD/HvWm8yn5fg2MEEn/ugOd59ME6wtm8M4Jg+8fF1AgX+IO7hjy+BFdKuPqd+ogA8D+XRpG7fPsexi9CbEH1uSk6hYShQ0J/VL2fgKqAnwHC/LY+6YQJPiPASn4owTpEl7qYfZj+wYBuwtz9SYF8waB+BTttOK5C/If3xQx9M/aVvXr/NIH6AmFBOQTQfr56qPpHvugGNsRPQQZ/Dz8I/W/9a722gd6IQkKI/436MzrAwWc+18J5RAuCAEKV+wB6jr3IP1KHI8VKQdT8BfjyfG4/4oQMA3mBvX4bPdm9HHyIP9+/zcQ0w1f/xf22OtCADL9OPp6CGkQCAfq9tDnKPdUB4MOWRBa/ev0ZPwH92r2RAW999cS7w+G94YFuttYAJ8Fx/24HEn0agF6/nf6wgGg9mv2lRHdCx/+zgQM6+4CLPzG9jMNIvbeBrYOaPVDASv46PgfEgDyoQQ3Dan5HxsL6aj25v+l7/Ef1/scBzQA1OcFALz8/gilDvYCpPwACXz56wFNAED1FxOw/M8EtAfH+kkA0vJZ9CILwBFrB6/+AesX/TMV/P+R+WDpfP2TJcH5LPoh7Jj9fRUm7s3+2ffDDQoSK/e66+znFgmzCvQJqe0d+2IGqvTfBorvigO5B374nxDs8e3/nAWs9L0NaO9J/D0TsgQaDEnly+RgB4oY3BKp+QXXtvxbFTELmAoY2EwBRgw6BkIQ+O/PCsYAe/ZA/lwETwtRA3fxwfeWBLsKPwnm70v4m/U4BZAQiBP7CLPfXfYr/hYUcxCc5v33fwVgGTYIMucn6M0F+RrhBlb7pugJAr8FUfM2Dk36bwEG/yXgihf6EfUOQwKV0bYCJhWYGZQLMN/17e4DUhYBG2UARegv69oBzRZ5JE/6kNs69jMAUC6fCIbobvJg83Ur8Q0gAmDw3ektDgEGcxZMBk75ZfOeAXoEAA7UC/rt7fYJ9LUPthzzCA36jt/+ApUXMw+0AezaZvzLGWQgLgce4jnxffoPKJMLmu+F9oz5jhdXB9D+AfO7/DMG0P/8BTYDmwJ79NMEqPphA0wPuvWD/ocDogb7ELgAme9T8Xn/hxSMCAr8s/qz8XQPhwEi/Ln9lPKIAkwKrArH+Uzv7P93A4YLugB29BD8HAtm/c/+vQTk+A4EAv4E9EoDww+1CcTusujxBIsZABgM9RvB7fHJHhwXTgm53Y7eTA/uHhIJIOsT4hL1Ogq7FwILgPbC9LvnwewIEnQYDgUh8hDVF+y7JaojsAHo1W7IXgSoKFwvU//Ex9zalfqsMIovKfMV1DDXAQeGMKAn0esO1iriSgRdL2UNCPqp6ffh0xFvCp4W3gKA357plfNuJhgqk/rw0QDTCAMjMaAzz+TM0ivgiwxaQxQWMueMw+jrtST3J+UdE+bzzHTz3RWTHJwlWPMezTzllAS/I6IqugK4xxbOuhPeM/Uro/oHxsvY6Rz9OsAZYgCaze7JFiEIM9kn0fNiwqTYSB4XSxEUa+SgzmbeIiEqQkAf19yb0T3ZPhfwTgEb9+Ltxe3hnB2OPZIcCM6K1uT5ChYINBwOINoY4RX06wx/LwMkNuYVxCLnNhxZM3YleNvhu8sA2Si6JQoYj9Zzye38AB/7KO8SuedExTPq3i78KpQIqeH/zM78MCrCIRH1senT5x37zybwCyv+TvC+6ZACvAc0GdcDFO1z86X0mBFdIWoCItwP6S//Wh4DJDHwjeA973cLqSIDAlfyzvDQ87QSowU0AlsKI+6c8+j9qA/cHVQA3dog73sRNR0DDqLgte0RCacKzwtV8oP8WgNt98z3IQiCCdkG7e9v3zAJxBTBDKr07dqQBNoLewBv/e73egJhAKnzA/F4C3kRdQBd5iDyPAjfDbYFIu6j6sgEFgzZ994Cq/4J+70CQ+7r96oEGBVV+kvcA/ue/RYGZBHQ7lH0fP41BngHVv3H/tP2S/fp/+kD+QxTElzxDNvxBtMTBRQ9/13XOuUVIusdjgvf7sTgeAOxCFQUz/3Q9NMAJuqG/OEUhxYM+GrfVeJ3DFoxMw+94x7twvjqC5wOIAO6ABb8IPF8+LAMbiJfCETgRtsRCvcsJB/N8DHW3fjqGe4PzP+H+2T8KgtS8qj3IRIZDen75+F97UMg/BxS93bk69+5E8Muffa59OXtBvjxFc/9Pv82Cob7XQCo84YFvRP+APn2yfOh+ksPnxNM5tcLiQG785cbU/TLBYAKsP1q+33/PA0wBFMGo/N2/S0H/xifAPvuBfx9BD8MVhOB85T3tgFOAcwJFA2TEi30SviO8DAHuSI3B2zr7+siBbUkyhiv49bioQbcE34f9/RV6Q0FvgONAav7Awj9INL50eDg68oONTOQEALcV854FsgltxMw7mrUrPrcD/wROghk/u7mp+zkAHEa+h+d/6bdd+e9ADYimBhI4cr3kfOHAY8R6fzoAcUFt+5i9H8NVhTUCX/nK9u4BU0e0Qh1+kLnmwUxD2z1b/0u9gETNe2M7CcBnQauHgD1iNca7ZkYJxXXCDXoteOmEcoCBAztC8jogPv6AZTzyiQ+DB/os+689xURDysB9KLVlAhX+78cMQWf27gPhP/i9b0N1f+3CNsO/9In+c0a3BXNC2PVIdiNG9ctBvlO7pvc7PpBKlrwGfxI/tP2cwuU9HT4NRFpDMT2K+fz8XAKDB54+9vbs/KSAXsfrgpc4sz4pPXRASQNnQe7/4D4g+7N8EIdyxAu+DT4JeXcEY4h1/fD+hj4iP+uFmEDt+ydEgAJe+14AwTtkhHoKO3llu9m/78EByGH/Avl1AkKEZkE5wKM92oANxKM7nT6mQ0pCR0Eyu/E9doIShYZCi7r4fbRBUEUKwo4+qjwbv8HDeXzZQuu/wX5Yww18HT/MBsC+irs/wqw8/EWwwtf69z/Aw4y7bEJNger7ioesfWg6o0NkfnCCekW7+hZ8wQVqPUPDXwGbOmVEogMeOyDEXj9Gvv5FzPpGPjcGgH36v+D/4LnHxFdGHbrCwPP+j3vFBl6AIPzvR6d8uTrUwMm7kYTNg/M59j1+/sTANsRAPm98l0FyP0V/moDPALsC1XwYufQA+oOEhrr+H3U6vwnFGMVyAHy3zLwhRGQCA76jvhi9ov6UgS4930B9A4F5dvjPwIEEF4ci/i81/bucxfKCin/yO5y7AMZzhCN5iED1v+J+yILC+29+Yoa2vog95kBgvEHEQ8QgecLAFYFBfW6ERr2CvM6FpL3L/nCCRPx5QaaEzflTgQXBYD+Exhv7jvo4hVQBGH5BwYZ5PIIOxXe8ev9uwdh7tAGQv8N+OgfLuvx5VoNowOiECcFmNj0/5IgLgZjAdzoo+oDHX4M5vGfBMj2PAipEP7upvueCYf5EgOHCzLz4wBiBA3nfhKIFvPyMQqV7kzw8iPfAPXu8gc0740OehqV5rIJUBAJ6YkLVAG6+p8gtPHc64gPpQPiDGUIVOx3A4EHTAF6CUoA6/P2+kv6WQdGD/ENW/jL7GX+5g7bEfkKJOmj5gwQWwz+EE32NeAYAsMTCgccDFP0v+2pDrEA/hD1DGDwKPC6BHgCNhYQBErbnABNEpAJ5xEc7K3qRBsG+Bb5CBN/7GcHdRGb21oOmQkS6W0dafMw7l4f5eph/6QgROUIAjMEWO2iGVcG2txVBMwAGAFQH1TxUOS+DZj64wl2F+fmsPEQDCf36RJuBF7j0AhGCdj1ghJ9+P/tAxbH9cL26hE36y8JJg0R570HN/q488ULIvFs/dwMDfBY/4IIPf5WCr/0KOXeDQwOPgWeAo7cDvudEl36mAeqAFDoHgvwBSv3GRgK6v3rahoP+7QKUAfj3OYSTRM38pECXPKN/PIejPj56WMSPwDhDpsHdObJD1YKVfnIEYH3APtTCxH+rAs0CIDxD/YkBysO3wi0+Lbs/wQzFFkLR/JW9LD7oAwuHpTyO/RCATH1+BwzA13m7w0d9577EBlj7KH+vxYu9IsGegCQ50oZyBHJ5hMCEfcsABYjJ/LQ7AcFQvvCFTYUHO+E9YL12QRzG+sFSfIc53L8aBsLE3/+0++Q6TYatRj+96UFiOY8+LoiWfLc/FkOTOJDD9kEyeetGrT9RO19EhbqJ/2fIfvzJPxjAcrfoxX5HlTuyASm7Jjt6io+AEb1HgqB5OcH+xij+soGT/O+6ysKkAuZBKwF/fbq7FkTYQqv8B7+MfPVCzkRHuxf+HT7EAXxDu/1TPYh/lAGGP6WBqoGAfPt/VX55QkWCETmmgP0/pHxRhFs+bDvnxAn9Kj4nxjQ5wQJ7hhA0tsPxAY16FowOum43CEWQvVyF4kP5daIAqEVXO9zDrEJT993EOb/5/FJGz3sO/r7CKDukxA4DYzv9PBWB+gPJfaL/af1Kv1LGFLzxvczChH6oQr6/CDsiwumEWfwYvrqCVP0gglNBNj3rQrL74cIWArh7T4IafDnD7EMheSuHSjtV/YkIK7bdBH/DALk7xxr7fTxxSSG8uT20QrH+bgLPwV65pf9rxxa9ZP38g+T7g0MnQKk9u0cQejo/nATRviQ968J3RuN2SgBoB859uoactI0Duwp8cXQITwL6OKbF6PlkQzACkTn7A0mEtryGPD2IPH9l/JdFdHk5BB8CMndFCjO9jTrbBmb2LkApiNy65oE6ggj380GfxJP9yD+VO1R93Mf2fqg5ysfDvuW1ZgfEQMg/p4GO8FBK7sTk9eMMErHz+sSLAfhHi2450zeby0K33MZZQza2gQELgYKDzvqV/CyF1wBq/fc+cQOW/fZ6hsQnAyw7ZTtVxO7EbDs7eZTD1Igi+Pq4hMc0Q4A90HswgivGYvfzvgIJWPzk+IJ+6kjBAdWy8sS9SEX0sryXCq+BoXybt63C1k4u8Q89hZFmM41ARwMcffHI7PFeQTaRHHVGee+FCv/PAMU+BLugR0wA/zbPRYTDK3rOgIl/+oCGw2L8af3+hd67on4PiFb8A3+4RDv8VX+iPSqBoAjXt0E5q83o/bz47sWTf7ZCy/nW/U8UcDXtMzXK3D9cQ1V7er4PTn4wU34nTy25bbxhfQIKuQe6q4wDicu8PTy+Pjr/BfrF7rO/BRiHEvhqA97AdD6ABNF4jsL2CbQ5s3xLh4C9RUE9xAu3Gsg8Akr1xs2NeUh6g8sddZnKnQSjcnsLdHw3PX+JbPkagWb9y3wRB+I/aLzsfVgAK4HH/6sCrT63efnAxAVKPcR+dcA4/ISD3TwFQQTGM3JUQhXGbP3YAU42+QUTgxU4DwQlfl5AODyvvv0Hsbnu+/oD6kTJ/J34IQgOw516r7xwP5lLjvvX9ttKADw9+RdJvX0zQDn9JPXtEe8+UPHIyaM7IoNagwO0m4eTfKS738o5/Yj8nbqgQraH4Lri+yH+qwNtwsa6q4JCfyZ5sML0AjzAHTgXPO5QibueNTnBCwFtybo4Png6Cfi4OACEyUB4prmRAo7F8sJ8+ku3UgVER9J0zUNSQSK3pwxCe+E5pkjutUdFI0gR9IRGsIBItqIJCv44O1dFunnWgvkF6zeqwMxCWX2zfzOB00BNfJeA7r1DBX3DirOuSQl/DnkeSCw4IYsYAFLxn1FYOoW7isSoOBPPevjWNDATDPn1vFJByHs5DY57c7ZBTYL5rztNimq9a/6OOsy8zVHzvZJx84PyAqKDOUJfvJ7+HzqtA1OKz726sqLAYc/xQMIzCz5iifSBPLdEhTWAPj2ZADc7m0p0N5y4AxF19809d0Hl+4zJazj3P9mGUvbYw/hD8oCYuVb+/klz+R2C6v/VO7pIx7KPRCSMU7MFgjw8l4DWTwBvVrjAj1O/kj0q+p0+Kot4vBZ1uYhuxOh3VEAwQNJCd0ZT9NK8mUkPPiqEZbyKueOHEj4T/hLDK33Rv/MA5jzb/tPGmAChOzB8oMGsRSB8q3+iAm28Avy3QRYNTrl1cytKmr+qwow9+3TQyy//I4B+iCuwjABXjVY5uT7ogqV62QP1vYmAIw83MsR5so0NfUfDOXhne+MPqDtaOmY9fsgoB1DwA/6wSzWFWLc7uCwNdUNPNh6A9kqevzg2W0JPRcoBmznwPbUFWr7ZfwVDsDuXe+kGywGfPpn9V7/Sh/94AIEtxe64xsKX/aoIkQJhb4lJ+cdoPhI7GPijUWC+t/LyB017ogbwQYE0hsxjOOFACJAwrxPBP0swNy8J5L2fubHL3DRkQ53RjvEtfMpEjMJ2iBb0nf4zyVp/OHwGAPoBCj6MB+F7WjfNzE3/dT0Awo35jgYfwSh4lUh+/ey55kt/+ME8McqA+VLA475/gSIM522mPdRQPXmhugo5ywrrxmAuHEdRRiw4znp/gAMOirwesVdC+gVawso7qXo5AuYDXTzrQtV9zHt+yKD7+f2hQYr638rI+ru5PEpT/Nu8yUCUv32ArMPrO3J9RsmstG6GAMMr83QRRjSY+10QR3BUiTk8DzfslDAx+vgnjNG6gcPrOuI3TxAEPad4m8c496JFHMObOD9IXDj9v7HIn3NSCPP/mnShSV/5k8JtxM0y9AnG//g3KIbqf777MsEYA2s9dUBMfwH/Xsoy8ZVAKpEZMyq+g8AmvkVM4bRjP3sEUjhvRrTA6XzivZl/TkS4/fuBL8AG/hR9KoKgxk35ukFW/kv7jwn/OwiFbPuE8UUTTwEtuXLAVzprDaF9v3dHxeUAkn62PHDIgD8f+i1ED7vOCwm5kHoiEKxzHEIKCD+738oscRt/xoxxPIaCozxZP+N9tEUhTAy22Xu/PImG0orqtQn/TkFce8/HG8KqfZz86j7SBDeDiD7tO9J//YAFfyNEsv1vucSFUL9H/2uDt/oLRAV9aTpMC226QD7aQRW3bo13+Nx5qIwEMqOG+z/h96LR4m/1/b8Nm3bnBZu54rsHirR6Q/4bRPL7ojuZSnk/87iiP39/fcu8+pmyZQsWRfF85XonvlMHRv6VfNgCvP18/h9D68KaPV67Q8NZR4K8/zhbxGoHQvpGuu3EikUsvWV68gPQQ1X7tDnyR7uF6rLThNFAdH+kR6jv3EnRg300QBBVdI87IIdZ+43K4DkQtjvKxUDlPCYArEEQvkr7ZoEjCU//NHYIvjUHmgNbemABucErOhDDOgTyQT/5LXhezFiHQ/GEfY/H/MMo/pu1+wSGjLByaIMkR3oyL0fO/1z/+0sXqeYMpkzf6xTIqHo4wmZOzCvgiOiFwnPeyre7mLwwR4o2QQSVRfy2TUZaPqD5Q8bVvRwCaEP2ubBAKIZVfCYBv0QLtgNIh/6kdmmOYjWuw2pEfO1U0WTBZ7a+BzvyYYu0xl9wqwsNPe055wRZPRHKbT1j8s+HnccVfWd8SUBWBSu9/Df5B7sHVLRdvpTC4MUIPYa0igzfPbh3W8hW/5fDjnE2/8QVmrTy9gv/9kmnxUdu4wOAxuW8XwCiP3sBtz6ifEnGSMJMNJcDconUOS981n3NRXDEkPR+AhaJr/sm8hXG/4pdeLB7znbNinfHwPE2xu/6KTyai5W5FAGve0p8NclV+Xg6uwWSgMz/LnexfF+PODoftb5IxT5vvtE8hn7iRzR+MHimfzVJQjoQ+8sKJravgcx/7Pw9ygKzesOzzb4xZ36AgPYGYYNE8b7HncVUuHn8wIKjTBo2gDL3i97D0zkDP6TB6gUju4b5cQt8f9F0N8VtBTq8F/qYwaeE7P0/+mxDD0mhdGI5vJAZupl+APmjf6KSwC7RPABN1fU0C5w5S7SRD+N3UQd0B6YpiMwER5d2VQU896DGNAlC7xJGJ8kCNG7DWsb8AASAh3FgyMkTki9ZeYIJqAQxwEPx/kfxCUuzOgBUBnJDH3ZnvolTRbTINVzHqUjAAAcvuYVH0HY11fSYSeRG+nrEext+8Qz0uKx28Iz1N/aE0L8YeU3OaK8TBeYL1/NUheu2uANZztLtzQM5SWY3P0NwuwFDhM78a/AAlMupfLkDo3ORQbnNsjiX/wq7CQHsCV540D6MApVAYX99vWIFjv7Dff296sPDwTH5RIgOPPV9TIae/rXAJbu4wVfGtz6kd7oFJsOp+aFJQHaBQWKGgLLmVD/6C7AtSMD45hIDvAGqLlHPfN/70sgp9M7GoMRx+UtFineYwc7MOjiRtq4JqsCeu70/efxaC8J1afdlT9n3vgG8+Q8EzEpNryQDuUCERpfA1PTyCTq+bYA8uuCAtUssuT49gkDHBFpC5LT9xgTGZviZwDf/zoMbvni5JFGx+cm54giqdZcMkrl+AhJJiaeWUNqFhrg2Akf0QFTHf8FtGUjKhe2AHIAD95/CagrVsmvEgoHa+F+NFjXbAyhCrToRDPXxgYQVBMj96EN1cpsOoUDK8rhGN725SfB8erThz/W7rbv2h5p3MIbc/6T/j8VedB8HvMUr+g09rgI5B4D79n6TPtyGvX55dBORSXtG+ysA3PwRlONvx/QzDap9eMrydPa2d0/xeF0Hhnt4NKCOrACcPoE4mXwUCpHBPzZEgAnJkbOjfY/Cuocxxjnm8kX8DVb6mfy4sY5NqYjk8/N8OUFBh4x/An5AuqhDrUJy+7bIo++8SmNHSfL/yFixqQ+WAueqvxGyOUUBQD3PuNJQ+Poj9a6GO0KsfFh/27zjgoOF9y37zWA/7HUuTwGxVQrDPxIxmlePMdl+PMlrNrnKFnT8wIkPi/BPP1OFtYDBPb48Hgd7gOE16UAeyCFEmPPRwKOE8v/+g1K1bAXlAgn328zm+GT/e37x+9TLR3cQ/ySDEr0jBrX5GX2Uf66EUcaEtbd+yICrB1wDYHVYgJDB80Ssvvn8K8NQQPTAlb6EwI4AnTpqyN4/4Hy5A7T5a8jvvdZ6pMu+dTfFT8Ne9luLvna7RWSJsq++Slk5p4RUhyZzMoz393NBjElytryGXHlPx9eDn3Yzydr3uMWgRNH1ck2w8eTHfkZd9UqNFjNGRudJlTLfxCl9sIaESEpwJDzsTil91v/vOgs8dRB3czG+jws48UVOxPhP+jTLhjGZj3o7VzTm0MF0MUTvOzHAqw36bKG/ZUg9BxS53fJ5SRUHgz+4MJFFC0brf7A8DzlhzAN6k712xKz7v8KnvzPEpv+PclHGSQpPvYc43P0cSne4kT2tCGyFKbg+7kcR58zJtBb3i7+/DpsENSl9x//GqzjUDF9vpsm9euk5P9Qy8BkD7ABmdqxJzHfHADpN4LW2eX/En7rfjNi4ob8fh4OqLhDahkt1iMSdbB/WG8a9bRjDlHqHSrQ9rftXhZL9rPzwwDDFTXoNS900ETgFldQzZ0FwOiW9upscrbvx8AAwhXvcd3CEMHBJe3bhT9pE8/MJg8s564K+wyz6MQY4Rja30TsjBiSApoJO9fHK4srv7rq44oVVixiCLfsTt6mAokbBOBeJgINReFDA8THNTnRKqHO0iB7xXga3TA6vSMuI/H09is2B7zaECoQhvHkI3bfBAAYDpbZWRg5FO4DJv5k29AFfiVl/OUJj/he6DwEuAzpCEIM9OhTA+UTgOJxHur/rP6F/DnTvE0+8xbj2/Sv7tVQ5eqL3ogCOPFgHgb6nB01An+9nhQyEWwo0+cvz+QTzu9SLPLmVv3LANHYoCEUEOvnS+3eBZkDvRA95eMCHgiZ5uwjt97dG1D9ecF4MLQJyOzdBm/dxhZRAIr1hBA342MOSPuP8ioaZO8R6Ggg3/zi6vAZrtzIB/8AzAOwJQLGCfvy/goHjTyNz8jamwiR+qAzQAOIzqb3s/vjL1T8W+Iu8wP06ymGDffcy9qhGlYXP/eNDDPccf5OC/3+tRwD9u3eJ/76FggRlO/s/JkBo/p0/8ML4A9J26gJCwTDA18hucVFCXYPtfASKnTjIO++C4jvHSs59K7c6xzE69UHXhu31jcOkQR869oZCf0n8agDYO71FRYU+NhMAbYE+/gFKpniyvdgGujT3iyeCCPfShh+2H8i+vlv/cId+NfsBckHvhaKCnfk0d29FpE7duYo7ofyLwUGMLfucwE254PkiEMq8PUWkOqn1osyP+3GGDoWX9TY+GcAbSWrHqbDWv4QEG3+hSx+2Tz4fhEm8WEVfQID9FgJV/CyAwcE2ALxCuTbEvLXFr4StPxi69HnTQtBHg3uZ/789g314xEm++QFCPcO7P0X0Pe7+/MF2epJGewCo/HpBc3s/gGzCswSv+vF4FsSgPzyF0fw0ORhCXb+SB0B77Pgkwm/+08ZofuTzwQMhQR8A8YNq+xs8tX7AApJB7oBOvXr+Cj5KQA8Hv3uUewWFdzoIiP/+nnilyso4fYEOgik/IwU9dMl/7weMg+4/aDpWOsbJAwXbOZFCWHm6AlfG1PwB/dzAoIDQgZc+Ej6Zw2699wCG/E5+10zHN7i4EEfZv1qHiLlWtJoNfz0DhWK6SnaCCP06v0gVQ2ZznQbTf4O9o8l1OWt+SYMl+gVLDTqy/drBLnyzjac3f7hyiOw/AwZN/fE1/kdOAk3CmkSrc93Furzpg03Mz7cuwMT6kn0uk6h9lbnyuw6+e01ev/D+xz3guA7Ijn9XwC8KHHhi+cODu0MkBan/ebs8PE/CAgcuQ197qbxUPW5EQwm3+i58ZgZQ+fX+/0a3QA8E4rk4PmaCp0AKRrE9K4Hbuox4bQkFSjZBs7K7ehcCdgyRhfdzdrktQERGXo29u2aw6jtQSjTHw/8oubY8KUCUezGIMIi2/Ja0Z3JVUq9LkTde+mK2MIXNiAD734JSOXo5g8SOAIiEsz8tuYMAibvOQH4KYb8vuGc7zH84Sr39KjnExC9+1sBW+rMCSMesPB06Df4gwt3DfkEovqY9kHwiQdsDRAMafv77bUGQfUlE20L9PXdBEvgfgyzGsXzRQpm7j774hLm61cTCAZr8en9DfF2HOP6yO9eFszyNexwDT0GFxQF8CfpvvmjE/wdCuzG6ij5pgpiE2YN6+Aa77sdEvLYD5H1OOs1FfPz8AME/5EEQAfJ3nENlRBH7UALbe8oCSIXsN6rCl//mvxzFkf5gf707/8i9yA31jPtpgtcFjUiyeSF3GISzgqYCosDL/f18fP71RKTCzH9EPb8BDIG6Acx+wn+DRGS7WcJXRzH6DHxUAmrEoQXE+Ea7fQVNwkAB8P8dfrs9MH4wCc2C4rfyvJDA68e1haO24XqcgjpDk4ade9/52b1vA16JKrt+uYm7r8XLSI+2KL3ZQmg/aEHNP4V+Ib9qfYKA54ISAXd8LbvSB4M/LPvhgkq7GD6TyBq+EDw3PXJ/eceeAO54RnmBxE+Lark5u1zBW3v+iEkBO3ehBSM5vgILRSZ7esKFOehC/39qvdYKiPbEu0XIobxKAExBu/ndhLL/6vuHvkBCEQSzeyX8NUT2+jOEmEHb9jtF4n5KepZFmIInPnq5ogBGRVC8VEZI/es2VwlrP+h8jYZOuQ4/yYbsfWk/MnzTwgvCb/vowUc+Dj8Hiju6kHgVx327+oIaBgW5H0GCPZrCy4TeeLW/UAD+A2aElXmEgL5DAXaDSZTH8Pe8PN4/cMUQxFy+uXiIfMUL7n9iOgZAVby+yLDEGjlqukC/bE6tfQu5jEQCuv+IfICW+IXIoLrFe3hJHX92g6h9OXioAlnC/omTu4S0GYJ6xbIGCIFiNfJ6aMwoQ4WAaXyeNo9He0UlPg7ABHu/wzZBkT9YgMT63EVIgN+8NASUPjNAa8ELQjl/rPvQQ7G/uoDZg3B63YAk/9dEdYi2uVQ5FkEOhyjHvrvCd9g/ngQjieeAozHPgEVFfwZmhH61V3vVg3qDuAWSfFJ8xYA4/KFGHYF1fVM/Vfg5xLUFUbwle8i+hYO6ADj8mIH9O+W/zoSLudvBBQGZvQtBAbx8Q8Z+WP8NPnU9TkTDQoe/UT3/uR8AsgacQif65Di0QNWBtgRHghc1abtewo1FUoMu+Zz6cIMwBSP9vvu7f95B9X+cACGAU/5pA7m9V7iQBRjD+P+XPnM5BwO7RD1+3v3te9aGk0FouY/Czj7WxTWCwLZHwMcBygJOA247HkApQCPBWUP4O+oAesJsfPJBtMFvgI5B6bqUf69ETsF3v3z9Qr+GgcLACkJGf1p7YASDwV6+jcEDfOUCqwNje2N/o8IwQo2+R3zsgd8BEACrg3h/Dv08wjg91kF2Q7X66QXXfiy5TYarAA/DJbwbfDaFin0dxvtAYjfegzNAfoQzRAx3i4F8w4DBOILCejYAzoITwF1DtrqSgFkDY/jvg6lDIH3uw2q5RL72A79DQUDIeO+BRoPWfpAC/HygQMFCEbpZQreES0CWO/H9SQK0AOTEhT8RubQBHkF+QPeAa8HRf2R8XH/ewE4FPgK+fIz5YAEnBm6/5z95ec5BrENMe2GCub8XvS6/8sEkRcb8b/wnv61/wQLDv7d97f67vnrBCwTO/4E6IIAOQsAACwDEOzpBikQ3fQb+Y3xyAzYCyP0NQDL7QYBJRTu+an50/OtBDoSyvVB+fsK4/fU/TcAiAJeCW74pAEj+9z53wxIALD/SfqN+9AN9vyl+mr/swO/ANP7SfncAWUQv/n87z0ABwfnCggEqfYpAND+wv7KBWH7xQq6+Sfy8g/+9KIFaQov6fsJHv7JA+gDffGVDnUAbP9xBN/tUBLkAiD2kQly9A0FOwF4AwMKtPf+AOD5dwoYB5X1Qvv0A6MF9PtbAVr8pf8vECL3P/1qAAjzuhBpCC72t/zdBuERQfWzAvUCEPJ6Fc4AsPPICCwCbAb5AY4ATv1K/SYMRPQp96oTN/4jAoT4ufXBBuMDkfzHAA0IKviT+G//w/i5/hADtgiO9cXvjf9PAUwOSfwg8RwBevFiCHULHvkM9Cz7O/tfA2EFa/uF/H70GQFKDkP9qfoI7RID9xdT8g/xB+7nBHgbtO8J9Or9jQHfCn3+4/Mk+osHJwPzBrD33/apBFcEmgO8+F7+bful8jMNBwgE8Nz8GfapB7AT/fCe78cEywmj/jj+wAN1+13/uf90/8wG2P7m/Mf+JANpBRr6FP868hkL7Bjz8D7zA/VKBm4WSv/x7hT10Qc4CJT6KQXn+6f5QgJF+hIHqAgB+Jry4QqBBVr6b/ur/t0CwAmu/8fvbv8XFXX9MfqQ/l/35hIKBtXwqv66BD8KrPv1+Y0CavtlDhoAYPDrC3wD0fWNBZwEJv/8BvX4LvrdDB3+wAFK/A0AGQgCAkgObfZN+9sOlgAb/hIM3/MyAIwToPthBMIDlv5w9z7/bw9oE+sGxu1g8CkVchwa+A7nSwO1FH8N7fzx2yEHJSXR/hj23eio/BYkXRHp837svQVvHP/8nfO8/g0DAhDw92XzMApZB37+XPzZ+1oDPAXv+aH5fwSk/zMF8P6b99T8DvwTB1H5+f48Bv/5qAYv+Pj23Qf4ATsBlgAA9mv7jAfcCST9cfWb/sb/kAtEB1T4+/XU/tUHegUmB4ny0PhVAx4E9QDxAOH9f/0cAjT6xwEdAyb/cv/p96H96wIZBIsHtPO+9fUFcgNSCU33CPDA+/ID2Qvh+lbwxPzZCKkF0PkI7jT5zxHJBGr39vCL/+4MzQPD+4TtIAQuDP3+B/QG9wsFSQLvAbX1JPviCQ8FHvEZAc3+mAHfAqv/BQGsAMYCEP2H/Y4JFv0b+6wGwP+c/IAJGf3s/67/SPrz/h0DiwCf+5n/eQiP9dX/6wNN93cBzAF1+z4Gmv4N+b74VwjPCVH8/AEY9k339BpiAg/vDfk0/jwMnwNa9cv3Sf8oERb9SuzqAJIAA/1nBHL+YQEtCHvv7PmjCM8IpwDu7zz8nwaLBUQG9vI79kgMgQGz+TT/zf3WBJD+ZfyN+0n/JgnL+sD8XQV2/lr6fP5jBm4IaPxM8eQCKAy/AhL5PvciAGYLeAUIAOT1JfgBDLoFRwRpAD/7GgcvAKQCggW7/D8Bof+7/lgGeQIeAXH/DfofBMYDSQXXA+f3IP38AbUHjgLr+hT9QQP7/17/uf70AvAFDvwE/u79MgkFB2D4LvqnAIYHSwHY/SkCbQOM/UD/eQA/AFYD5gFEA97/nfsTCvkEHv3wAD/8ZAe8Akv6iAZlAB0DgAF7+UYJCQb+/vAEgP1XBecBqPsUD4YGV/bY/8oDwQmr/aL6qwuFCuPvGAI5CmIA5/zS84sVPQS+6mME3wNQA/z9fgEdCbP+tPIT/fwKZweX/PnxmATZDfn60fCP/b8NigB47fX9rg797p/0uBAzB0Hy0QHxEJ74eOb2FnUXddrI7lgg9hzz1ebpvjRa/p7VagNgKWb9uddFDbUchu4Z7K0V5QWK9YnpiwvzIm7o8u0NEXcYGgLn3bYCWisH/Vfh2wA/FAEKWO0bBMEHxv2aAyH2lwQxG5AEjtgu+NlB7gHVtAsTtFE35OWuxiLSTvjbvLcpKQAuM9kV8hYbEPW635gfGSKzykba6jqPH3i+hOsTON0DQboFCz5FwNwwxPQriDJMzrzHXzXKJRrNFvIbIksDet7q+ioWNv7D6YsBLwiUAH/xj+RxENUjh+MY1kcRSSdH75LOLB3rHdXnAev4DZMMRPQABIX+Kf7KA2sC0fdk/pcVw/KX53QUtBIK9ajsGxPbCDXj4AX/GLL4NunV/s4FUwCyCxn2x+piASYaI/dR3l8aHg5Y4zfwSxpYEnna/vKyHJP8qObpDGoPwuyh72wWyxEQ33X0fBqXABvupgYgBtTtrAmdEwTsI+4CGVoX7+Fl9lMgGvqE6hQP9BIZ6rLvzRcz/xfpEw0XEwznhv41Hq70fuWXGdcc8d4956oqNwnT0wwQxyJC50Dk2x90Evzgn/9CIgTw1ec+HMcRveFd8/cYgQiD+TP8VfgJ+9IBeg3nC8f0+vLsBfkMGwbK8QX7ovYz9cQpHBEFz+LlhUIvEvi4OgrZMuzvo+TeJakI88OiGe02rNBi4xc23RZIwa3/cD+o7svofCd/9qnK4yTtOdXPksv3Nn0kEMzw+xArQenO34omKxpG1XfxODho/IrLEhsBIaPn3u5wExQLIOEqD9shYeix5WEM1xvW8hDwy/74+ggXzwC06ubyIw8LDTzoUQIXETruWud5ExIdt93p3ColGSIe4Y/Y6BuAG5rbBPAaITkEedNW/oUrl/HF2WYMnxpy9NbpCgtkDZEDt/wM5ygMESXI6C/ifhpqH7zZhOocP6j/zMDVGdY9tswp0xFAPx3axsHwUz076//WFDekCWXMTwcGMQrtfNi7K34AXN0dHl0Tq95w8tU52vsvvwMetTfT5BjLFSlIKNXQJPMpJHUPFNzp+MAraPXC5+4P9w4a+lfpdAl7E5H4QABB9hsF/xDN9uH0wAcFJML2StH2FgskE/KG4eAXJRJk3M4Bfikv8mrMNSLLKRrVC+emKSESysyK/TMtD/Ug5EcDHhKK+C/3fhAD7y79IRf87Hj4lROnBCHr7fyhHMT29ORLC8ITm/VI75sFgQxgBPj2SPOxB8USkPdU6KAOBxv27/zoWhGyELvxEPfwDMH4pPN+E8L/u+yjAZULcAXZ5gf6cSWTATLWVuk9NO8YecYo61kv0STvvpXawldFAyu4bv5bMeQHUs9UDboOU+9AD/Xs//FxLXcILbva59BjrQcZjKIOPGcp6FqVjyU3ZcTHUrYzFc42JQi40ZDylA5dIdT7IsuqFJAsqeoozeUWBTXY1PrnziN99vrt+A9gFpjc/eG3RtUGrLfSB0g7bfdJuqAYoDif31jgjQuLH9cF0erJ7CnwBzN4Hr64i/KXRVIBZLLNDwFUpNk7s4chzTyz8rLjNBLO7wPz2yrE+gnZuhrlF1DFZ+ugT0kB1amFC6xKgNvQySw3DxP+wPb5hz4G+mbGKhHsETH1Qglk+03olQ2FN0jUH6wRUXNKQ51/vjFWHDBaqL/j3URyDSzYPvCyC+cM7w6M8JvTYRUyPsHbHb/dK7UxydIy2z4oNRVB11r2qh0K/d7wYQj8EQT2RfQUFtT5mepcHCsAnuE9CvMp0P4QvcMO3T/P53zYUgyFI6zvgeJxIdv/EfZ5C+fwhfi3EVAegull1XQgkxcG8LL2TQOCEuH1oemMC84c6PdJ1qMSiCie3DTtjTTM9+HQjRrbHebhX/ttKg3d/dW5ShMQ+7LvEfk/X9RI0MdVKxJ7o1cSh0ar2E/S4EIABsSzris4KpPC3guSN+3VAMi2TPMr3ZiNDpNcd9WeyIE3/BiivU4J0UoM03jXh0mi8VvHLzePH2m1+/EjXdzlEbLyQAUscbd17q1LTPiZtdQyJiY1vsgGWzbx4c3IUzzKKtyw8QXRP4zfydmBKqYRHs2OCg8pSNTu8t0qLvg33UMSxxqq2U/9tCb84XXnRBqJFhflvOhpGcX4MPNhBXv2OQVJ+l/4pwCtAN7+vu6iBmIFierICQQIguo083gUqwvT4u36rhDX9sH+IQ4D9TftdBFTFbbpTO2NGwMDMef+DeIPF/K38KoUrAoP3qsLORqV6IrxMw+KASXrbA2GHpbcJuujKfAC0dgMDfoiNeGT4HYq1RFp0KMACih77ujifhe3B2PiKBD2G3/X7uxtLdgD8dTRCmsf+dh79402I++XxyQdEi063Z7f+SuSAwnRTRe6F3PayvtPJ0n0mdZkH9IZ/s7r/S8yn/IQ2XsUCxrv6NT0JR6w77DxASah9e3cGBEyJ3zmMtgqMO0Iu8iSFT0rwOne0eQavCXM1OX8TyYr7f/q3BcaEs3iO/VBIpXuCe2gIdn9GuFyCU8dNPCX5roZmQSK4SkTrxR44kP3HRo7BCrqpArWD0/jcgSRHc/u+etRDr0LROzU/swa0OyI8Uka9P6p85kCnQXO8mv82yHC67Henh1IEgPxPOgXBYYIFvkEHqH1Z9mWER8SEfrm9PoQ+Pq6zCMnPTaY1b3VQxepIwbrUPccHYPedu42JU36AedBBoEF7uh0ApQnq93l1Fs3hRRAyqT9Nyuw8dTWCSINCarTlRaSIwnZMOPuLkoKZ878Fy4mytKn8q4wnPgL09AfbiwRzRTpMy0C5xbq50HuDd6uNuLKPLQZQ/aCE8rWFsEUOzY/59cs24gnyAWBw2sjwzwhw7LihELiF4O68eKnMKQopA1x3e654wf3Xa4hlbNV8M4z7+Qg6dM2WCTHzd/FWzJ2I9j0v/gN4ksPUiZeC4ffYM4YIIQ7LRJw0AK1uxILXM4oo8cZu04JaTLJJjPxOMuoCXMa2PNYACQUXftIzywXQFO8zxy2lSTKOtYCutd4AEX24gi4RXnbRrI9Lqs3SOHl3QQwgguXuYYh2DuRxePtyiJm/XvyfxkgFiLAEAFrUL3ej9ZaLwkU/9JQ/e829+gx4vA87fJyvTcg6TWe3XjiKzON9r3HljDYJEDMz/BWLwT6fdSBKSQZCMlCB74wxuJY2t4xaAxmw0QTMi/l2wPgJiqaCeTSTRmCGFzWHwTPFqzrLvLFF1sMHtbUAFgjpOLf+W0PiO10BAYNXvQO6lUOoRY929n1NibS6RLq/RUQ+FPyjRBBCD7U7PYbP7f0Ss/eF1cVOulT9wIZOfQC8v4nvewp02EbhCdK7IjdFSB6BSLX8xujFMLnFP6dCLrxh+uYJHwWd8/W904WJfVQBdUS1/F18AAFdgzu8874rhso9snvpAev99IMSAcq9Cj4AQPsB7rwSv+OEKXsHv/6CzPsPPRDGAEPceGW/GcZ5uuF9VweOQF45GYFuxXK7lX5ayMv8xXcHhnUGXfi0fiDIL/1WODmFwodVt/0+jglNep5698fZgqx4r79QCJc7OXjMTAkAWjXMhRGFdLvJ/BIH0oGjNxEHoYJi9WrEbUl8fVI4qYTXg4f4BAZ/g/72kQFziRA/krcTRFNGmXcKglXGezkmPVEGk0LkduhDXYiQNcn+poiIvlt60gQRA/s4WP8AC2t5SfpGiTrAVLgSQNuJrbuut9sIkgAjtsQFqYMpOXx98EV2wf22dQJvhCb4JILkhW58hXuiP/9FgjyTfvrFEPrg/xcAqILWxvu3lzwiRLrA74PzvLT9lfygQM0JXzhfuibIqgUbduB7hghlP4Z7WYd/wsZziEEnCH+8OL1hBmd8yvRfRhjLajbVtdPMZcNysqHAFcl6uy36/E2vO1DwO0d1hyu8A36qgs47nnPwij1KdW+MwMQJvjuF+h7EH0Uz898AcYut9bA5ccaCgBu8KP/OiFo4sDd6ifb/X71tQ1/+gndnvqfHA0CgfQiGczvxePHEaIQ3wT284kBVAExANwI3/ZK9moZhxI64ZbzER0P9/L6DRxO+sD6p/Yq+lYA3QpzKKXiE9FlKbcUyfgDBojy7vqJ+7sltA55zlQTIRVo7OYHfBUC7YbiwBLlL3nrFup+C930JRJpEqbv5d18Et8sI+ke7LIZNQhQ6H388SPbA6DjyxBo9k8BexeS8pUGFfZ1AH0NdvC2GF8N+N4zBbkLUAWyBSkCvAHo6GcIWhKYBfT4zfqdBj37AQZ/Ei3zDv+mBs/8OgVo/G4FxQOOANr1VvpyDJgGRwRS6gMKzRPS7V352wW0CGUFFvGy/KMA+gKlDMvmYwUrFj/4O/Vz9+8IhghVAaH4b/vo9LkHWwuA9LX97gVp9y/ynQt9+wr8N/+X/jEKsPZP8AQFuP+YCi4HLtzM9vMLdgtR/JDu0AOG/HbvAwgpDwT7k/K+84kCdArp/vr5mPib/+YFtvlY+L76QwgK/HYBlQ394wL79AWT9H8SUAeJ7Gv8re00EioWX+pW9wYFIQZr+rP6tf14ELoPyOEk7KgSMwk0AwIGk+6ACKz5me8CHvn8XvZQC8/wlwGSDTjuqwMIFH35O/EC+xL/Yw7mDdzsCggF+Ovr8CSJ+cr3gBlR5qYACgvn7loZCQLV8N4LLPIQ+xIM0/0sB+QGHt6E9A4gvQjr+3/3R/gZDC30CPeuGlkKmvK67Jr1KxWGD+vvfvLxCb8B8POO9MEGvxcd/b/t1fPM9vkXNg5r7Yr44QNfBTruCvnUGW4UbPb+4Y4EYhDJBEALz/dc+y4K2eqK/P4h3Q5F+aXkue/OGvcSY/VVCRb6YefREJn8CQMIHJb0JvCb+pT1UxtkDfnm9BLZA4juuv/EAKINRRWh85jsGgJE/H0dZQsN5TYHswDz5RgWqgxF+ocACuRZC84R/vXuBe/7xO8nFogDyduLDOET7gBbDQTuMfkjD0Lp/QxiEWD1aArk6urqmx/CEpjyZvq+95wAkRK2+DD9ZxKc8+76jf+M7SYW0hyP62rzpf41AP0WHfsy82oMDe3r9EgWzfiaErsEcN/kA5n+ABFtD7zsGv0n/k/xIQoUFPf/4PpZ+8z9JP/LA5UF6A1t9Vf/OQYp5cMTfQ809kwB/PB2/swOtPDCFNMRcN5B/PH+SgfbExcDxO4w8/T1wAEvEYf1QwZfD/PjxvfDBQr4bijSBBHQpQN1+2YOqSmc3E/0mg0/6LsT1A5t6C0KdgNV9XMHvfvD/uMRsvL96wERY+3LBsoXbt4KBlsJp+N8GEUEduijGAL2If60DIbjlQuUHmvw4fO1ASPtThqvGc/cuPs2AS30Bx+e92jyPg8T7ywKVf+s7UoU8AII9bj/9/aFBnQRDPUY7cwJMQ1nCwb9Gt9hD5ESXvU9CyzxvutBIRsD3v0PApDfQBaPHBLvWQid8mHxYi0GAQH1ifzB6ngbVSTo6YrlGgnGA80hfQMD19gEOhGuCl0I5eENCB4b6vi3BG/u1PkEJ3YJ1d8f68YKOhypDX7lMO0V+gQRLCFG+sXXuf3OG/EBzwtw9d7dyhKwHRv58uo25iMRCCAV8a/2tvQN7r4iSRbr2ljxywmcCQUVYfFz3wIJbhQsC835D+PQ/eQdhQIB9RrrA/o+FpsFiPyX613xexZxEhHtwfWS+YIKHA1b6DT+Nw3w8uUC7vZr8NYc/vzU8Zr8BfLHD34RQueJ+r4FNfyfEyf5adr2DCgYTgPR+oTdcvlEJc0Ax/np6oDnAyC4G6PtNu738soKbBmk9MHqXP+hBGwOIQzS4uPyng8TBPoNIPqN5FEHuw2hBb4HXeDM9/sbWwVP9rr0VPbGHrkQTOOb9hwADA55GXzrpeeQEPcKYRFr/yTeyAvlFp/9oQhY6Kb0cyqXAR342f1Z510VQR4s7sn9tvA1/EoybP9s288BJgLRF4UUTddB8O4Yjgm0FaTqht0uEz4SDwhMBnzeE/NIIScLovqM+tjnTw1ZGHb4efnl837ySx9GB1XlSvscADcNFhHF6/fyMQ2HCCEFXvKz9bMVMQVc9Sz6RPt2C6kbiugY858HjP3eFhkCuuctBDYFKwQJDEPqxQAbDcv6VBBSACfrLwP8BuIJLg0k5z7xGA25BeoM0/+q2ZUF7iF6/R8BO/ZI44UVwhUO67n8jf1eAKwXEPuC86AIXvd5Bf4Jqfi7/A34k/leChv/dPguCdr98vmhD+b5cO3nBAMDFhH4DxTjxu3iDWcEOhR98SXk0AvJED3/nwI/7Cv9cA9j9zoEGPlW7Q8SEAe/+5ICq+hD+Q0cyvni+4cH5+iuCYMRkfISCrjy9uUcIZYFb/GEDQ3oHgTdFQ72+gdM/RfqERZyBJX1Wgd87ZcA3B6n9uX6qQAQ+gMOVA689fX8C/+MBTAO6PV39QIMaAGy+gMFNfuf+zUJXvWR+8IWhv7L82z+wv3uDEQQnOyA8OMJywI5EI4DweKn97UTogw+BJLvHPS6C8j9RALmBbn0e/34/Vj9Ow0IADzugfkW+J0QgA/Z6O/yuQZoAiwQ2fSi6RkOJgRX/LcBAPc8/x0LEvNfAMIOb/bY/tICdv/3Dun6++0FEUv+qQE3A//k+g1+EXTsjgMr/tXxmg8s8V/wih4//cromv8n+0wQeQ0m3xT2fBBfARQH9u6L85YU2/ZA6wIFOgUaEL/7e+FWBKkSe/+e9Y3v4wmCFsDyA+gZAfgPTg849xHm0gSXDBIAevwt+BwHjQp27q35Qw3bDgYIROME9Dsciwx1/DD3gfEKFl0NI/Ll8uT/mw9ZB2rwlwr1BPjwOQrO9zn+fiUW7mXgexWlCOgDPRAA5dL+bB0Z/dj15QDLBKMLcfol9+wLQwGhCpEEQerYBB0Xj/xeANT6/PQbDeoJZf+WAKz3SQphCpPyqwe//ubxLSB4CsrmegU0/Tj9pRwQ/dXp7v+yB1kO+fsC9FcCugaRCa7/+uShBV4Z1e/c/tgDn++rC+AUVfHB+yYCfvrMCsECEP7LAA371wQAAOT5BhWV/zrujw3M/gP7RBPe+C/yLw7dAND/qAUr9sDxOATcFBcJDOva94oJ4foVEN4EI+ds/kUWLQVU9Uf/a/Y4AfAWVfqV48sF4QzlC9sKNuZO7XIMmA4MAx70kPkbDx8EQ/sj/3ryaAcbHG/wpOSGBtX+tRMLBXnbPQGzEF8AVwLj9mD8Dwmr/rEHYPaT7pYaFAoV7kz/sfhMAhIYy/IT7HwM0gZJ/i72vvW/Cw4FEgDJ+z7uZQdqEwv16fKC/9H3gw8LDlDlNPTyDp0EFwDF/Gv3vv8TBikIDPVD81UM1ASl+0IEPvrK9lENdQau9Sj/Ov/FBEMJPv2G8kEENRJnCB71Ye97B8EOIwOgAXTrLvayILUJSe2Z+D//3w1IDPnzze03AlsS3Qqa88P0WQRjC3YPsfZN5/T85hOEE8/3Peuv9n4NiRUAAPXpf/ZOFakGoPv29pfrawcNElL10/F9B/8BjAXvAN34LPnEB6YGZf5Z/3ACKQG/9PP/DQxQ+wn+fgPQ9F8BDxMbAB8E6PzB68oI8xBa+Qn4CvsRAFII7Abn+TnwJgD8El4BdvVj++f3+wUSGo/1tOxLC7D6IwH7FWXwZ/MsCDj9FgSTBBj7mP2y/68I7AAW8BL8IAT9/cUHygT38t/9eP3RANAMegEP86b5EAI/CjwFJ/rK+EX4WAFwCTL11fNtADr9XAfVAy/qF/aRCmMEwQF6967y2PhzBjsM+vu47vHxxgUSDTj9kPO/9/3+QQp2BGD4N/JG+r0IJwbw/L/3YABOBzcAbgGD++b9iwSSAGb/fwO5+pj89wFyASEGrwAt+bX83wJ0BJAD4v6R91QAfgfMBHf77PZs/F8KUQi3/AL3ffcDCBEOAgAb9QP22gE1D4kI0fbX9Qv9fwhfCdL7OPlq/n4Ejwf9/7/7nv/9AmEEYgIC/k3+BgM9AUEAov9L/y8CmAGSARgGNwHC/sIAIABvBeoGF/2nAPL+2vpyCX8MfPjn/Dj5WQTNGIL0ovoCCDLuCBjiDEXnPgTEB/r73BWn/YDpLwqBA84HWA/z7Jn2jBAeARwFiPom9d4N/QIkAPL8TvhoAfQJQQJN/KQAl/7pAIAGRgbm+e/7OQMfBu3/kfrMAToEIATr/hP91wDAAoYDmADQ/xr9KvxLAjUFZPzM/j0BX/veA5YARPvfAykA7f6NAvf9ev58Adr9tQCuA2j87f2qACf/4QMc/Uf5ogYq/xL8pwRY+FT74AhH+778UgRW+uEAIwVbAIEAv+1v+mobV/vb8osO+dshA/AtBOlA7hD+WOGEK405zcMjvmgKDEjgDXW+dNuXE8E41hVswyfB7CrKQiL7z8bP3dcmHTWG46+2+upiPnc7H8z+sKIOlCugLqDvpai7AUg68hPo+XDXlOF4LuEpIfxM0szMdSAAUbX8lL4g3hkUGVQMG2+wGMomG8tKEB0audzP8yHOLjcaEeIAylkTkCua/wz89ufO7OgmxxMg8aDyV/A4AogmIA5V6BnqNfhHJ1AQyeJ6/bP3CwVGIJnxIeUQG578G/D7GM77e/PbFU35ludFEZoeSPgO6JX9aw6pFd8PMO2o20EPqCzJ/jDs3PIOB1wS9wn28ufhJg3bK/j6i+I7AAn0sxmUK0fVdtudGwkXaA0a50rgUx6SExz3TP6n5c0D6y786cnnUBf397AEaxjD3QT5PCL3/BD52vHh8mcgr/0d6pEHIPv5BkoSN+h39ocYHveq7ngOnfpYAPkL3elw/NUR5wA679T3VBIXDUXuA/js/8IIDwyU6i70eQs7A4n39fkGAnIRBgCh4EAA7g9LB6sFo99D7qYjNwTH7UP6TOzjE1Ydvd9q7zkLsv1/FXb/Vd8wCtEHHfwDEzTxhOnzD90C4wUQDkDdLvPrIx0H9v3L9dPjYwwuIlvzpuybBfj0nBA0DLzk3AV/DRvuiggOBcnu/hgn+ZPdqh0CBaPugBDA7cTtviRK/BrrwQ3k6ZIETCKO8lf2ufim9EkhLBTP3DbrMQrdFfcLKu395UsG3RL7BCn99+hp9xETxAKH9sP9AgUGAjQEl/8X+u8M3gAd9q0QzgN49lUHfAOPAH4J3P6//zMO4fwz9+0G+f2RBAwXXvTp+gwOy/eBES/6ze9GFQ0GTf7jA+35R/wmDWcAo/n4CRH63weMEJDyhgFpBXgBWhHH8dHy9hMVAB4D/P8I7jIHawtOATH+5/vbA18OgPyB6f0FxhTmAfcC++Vl8j8kMAdb+c/2gOwfElgMJvcv9kH5AQwtBvP5LPiY9pwNLv6/9ZwNY+8dAE0NhPRIC1jyx/hyD+D3Ivc/AUn7Af7AHObv39xaEu8LpQWfADbsKfWyGe0Keul19oEH8g45+5f8rPTo/8sfN/3X8BoANABPBXLylgLBFgn4xfM08AX9qB79D1Tpy+XDA/ITVAYPBufrBuKcEiMO6AX9A7ra7/aJEXwVggqq11PugBAuDmQJduYm6kAKFx0EC7Hi+fH7/f0G2hVY+UvsRfjKA6sOugXV8LHsdQFJFPYDtvXG9zTxmQF2FDkHqvPt8sn9ZwVrDFP+Xuys/VwKKwnf/KT1kQbl/Cf9Agko5yMAyxQS9LERDvQ17RkLb/oPFgP1jfe9AXDoWBthDyHvevRD9NsThRAN7kn6zfUMBiEer/CY8jT5NQc+GQ76IAZP7tb/ZRx+7esWg/R97U8dxvJ3DLbw0/YBHT30CglU+pfvoBbi98AAkAsp7eMQqf/i8OsJ5vc0E+z8Yvl9BH70wAbUAXUSVvZG8qH9LQGzIiHuj/UM8Df/zCYU6E39dPMp8X0lZ/gSA4f29uiwFr/+9QvW/XPoIRCH9NsIPwo59DMGH/ElC8YO/fiPAfTzTf6wCmwBVwmR+5/phAXJAuoO1Akn3hAEXP1XEfUbkeG3/b/xXBCdG43rfgX364EB9xxb+qMJIep17xkdCgO//Lz52urNDUoRtvsX/arn4Qs7FG37nAnX2WUGKBep+8kMduB1Ae4JTwIoEr7sMvdk/l8CVR1u/vblXP7pBRAhM/Vf7AYFPPZEIA4EounuADb7bAoaEa31WPlTAJ4EIg8L9xAGtvWC8kwe/PgnBRb9hO33FlQA8viF/VT1QxLMAFr74g7A5jEJRgjg8OIfg+pp/tMM1u6bGij08QK8/yPniB4cAurzUAMe6rkLexPR+0f+COdb/ukUiArnCQzaeezhEy8RchEm6ormUgCFGPIbze494kjygQQEHZIIhuJo8Vv7iQzXER70+OY07DQUixE49un9gt79A0gbWvfUATHdwvEMHskAffrS3UcDaSE+6x0KY+ng39IrK/adENz1CNLfG1cCZR4o9afVqg9U+yIiWhEyzxP25wHlDLsv6NmM7Ob7Q/6KJ3XhwQ479ZHk9Bi395QUjP/B2KkCrxOBCIsJAuXt9p4CORAtE23a4/7U+qwLNSXV8vrwOvSSAt0SggaYAMD+yuGf/9MT4wfbFefRKu3LHucEVR2E5jXk0wud9ZEbCAcb7hcEze5DCbQIRPlAEzn1CumsF8z/JANQEtbmlPW1EBQL6gJaAfDnkvlUDfwLmwft9AsKxNZhDWIosOtaD0/l5fFJK/P/+vpt93X7CwRr/e4gTuwY89gUpuWKKD8H9eHg/7X2Gw14D+EE5O0m5Zwa1xmj90AIh9b0DwUtY+JwC8jsd//VE+D/Ighp5AUB9hj2/qoKivZG4rQpW/8y7jIIm+NYHIwKefs78BHpnya4DQzyqfln7LYUIhRv+DMFD+mgA1sMlv46E4vgNAmEF2jsMRD7+4T/wQn18nz9lgFPEn3/su9UDyb4YfarFET6KwleCCXoIvsMGnIL6PCVAXzqigYWI6bvr/eB7Ub8xCSCABPs+O3mBeojh+yf7r0FWvunEL3xn/ZJEcbzDADi/yzuGwew/tf67wME71kCJAoA82MCPAo583r6R/Z8B88TR/sz7a79kA2BAosH9/UU/MoDDQMsBPX3fwtM9oIGUglv5xsPbg9q8A0Gm/r1+msT2fWz/EIC0vvICr3/2wTMACD1QQ2gC4/rnw3N98H4BSSO71YJWvqe8RIdgwQBAQ30IPUzFhIKLv2MAqrr7gYBDXMFHwAB87sEOwICB9/+7fwGCZr4i/8uDhL8Vv4w/vAALQdq+9IJ1fvH/NkDLvajAZ8KS/ag/CUN0ez6BjIKbu+TBaoE4v2A/yryqAQ1BOj62QLI858KGwMB+iECVfsZARL9IwgeA6ftQgv9/ov6ZxgF69b7Ywen9eYSpAI49pv1BQaPED75+/7Q9QX4HCHg8F71kQNI+mcZr/Zx+Sj91QGpDszyyAJkByjzVAiy+hT/rwGB7SkRsPsD/dr9O/rrDl/3vAPP/VXufgaJ+JD/oxCu4RX5EQvZ78wQsARV57cGCPzc//cSSO0I8tgFLAuU/qfrdwTjBO8DQgCR78cMyv5d+RoDjfYtB7EFiP6H/Wz20PyqBqoEx/JyBN8GzPoG+3H+QA4vBYH7ke4DDPoTpe90/fr7Hf8aFkr8iuq9CuX/AQyl/FvniQvpA4ERa/Pl8QQWL/08Cev7efaqEN7w1/5iDDfomB60+Cz0jAji4+ciSA2w3aX05gf9GGEA5ehcB64Dtgbw/QDswiRq+QnsMQsmA78LKf769aoAHwlvB20Bb/oe9xcLCAmQ9jkJNvtvBCUKN97yEucI/ADR/0vmqBJ5CzIGU/JJ5j4OBQTmFhYCH99IBnoEmhMZDiLXawMuFNAHGgVJ2B8O8iPU5mzvQws1FLUBJ+aS/KcufwZoxU/ksERaIEXZoddb8zRDfRETyQD1FQSdGgoMsNXPCa/86fc6G4fv6whG+WLkgRCjDQISueu02XgcQxLFAZbkxOzjHd4LnPmy5wMJUAzu9cMC7gCh95QBAgCO/VoM5fEBAqAEM/y6/+b4qQufAKMArQo6/XzvrgP6DW4KQ/c96DAFRCAWCrbdwPtZC1UJ+AVO9AQIlgMA5ygKURBI/7IEIOA1AFkZuuuhCyD+y/MdAPEDtQ4//Ervo/ewCnUNifde+PsIm/jeAM7++wRtCjfoaf58EWX+SfF0AtMBLA6s7jLzRxLkACv6s/TF+iYOi/kE9QkRovdJ/9IDWP3L/Az44wHyCEwCPvcE//36uAmUAqn85ARd9j/6IwdiCB8CdvYH/pkF4PDkEhcJEvCF/ED4WgqgC3v0ovE3DoP+9/4B+gkC5RGh6TDybhVYFTzp/fQ7BkMLyA257xzxyR5N9sfxqhPe8MkR/u8c8cQdu+28+qkJ6v08DTn8IenfB6kE2wQOBqP6Nu0l/V8OIwfoCYbiZ/huDB4DjwuC6h74eQWCATEEDPiC+2j93gUWARHwFQJZCXwBBAIj5X0HLAzA/9kJ3PN+/VgAvQTGAJ0PePlA7sgLkfy8Ds4Myejx/roRjvo0FXvqg+xHJ3wBoPYbAHLodxr2HhnaYAlcAMsCCh207KL9eAOW/lsMyv7+9RAHCxTYA9bspAIB/skTewjx6lMG4P2y+mgN+AbH8AXz5/gjGpEQ3e3C9AgHGg38BQLnBgaYFWvyEgI/8dYIjw/F/DXwHwqP/4P+Vgfn73oICwnt87UMFfqo6nkhZvPC74sL8PoLCCoEofHs+wIVGvErBOAAxPJBFjf0BfqHFgT89v8e/q7wwQnSBCf+Rv6ZAEfzYP8WF6fz2AaC9+Hzmh7A7Ub2TBmH9DsPsvkY6GIPv/s8ByADeOz4BKQS7Ptg/XLzFP4iD+b+Ggnw8k32BgceAI0HYAL46OgRbwb9++wK0N5BCK4QTPBFBuv/Fu2IEBT/QfxiCK/stvy5D0IDZ/Kl/7/u3x7NC+bhpQak9IYJFxq64TX9Wwhw9DEpyekH6L4R0guZDNLwVfXGA5sR7vNOCL8CVffnDJz+w/kUFSIBIfN1EdXw9w72CHj9qQMD++T6qBE0Fd3mKQUx/wUA5hD97Pn2oCT+6SH8sAsb58oh9vem5QoaiPZ4BGEEB+ZTDEoN4vU9CHYDpvVDE3IHquQj/hMK7gbVCsfor/5OB4T8AwZZ8t0Lavt4+ewA2/uJD+/5Wu3bCp34QwpZAenjoxSoBBb+Dfkc86wBSBQO/O7qkwDr/zAN+/gA8BkLzAI9/7EKT+7L+UoDnf++DU/vpfT5/gIJ8gwd7Y/tKAzyC7j+gvJk6pASYwqN90z+QOztCSIPnPB1+lcCkvd0D8H1q/alCYD5LgOd+Zn9sQQXBOfuEv9RA5wE0ALO9jH/dQT7ADD33AGv+TYBugcC9t0DOAif7a8ChQAb90odkeHI9hcZM+5EEiv2+uK8IzH9UvjtAZXmcB0xCf/oawVQ9BMNzgUB9VH7Bv3GA/QEk/62/xMAq/nF/+/9Pwx4Ae3xRgIvCEf/OQXs9jz9UBG/9DAISPg//EIMjv8C/b0GjPpeBO0FtPQRDOL5AwetBRT7qQGYAv4HMgEe+zELMwZ392AEAveGGhIA4fxsCETr1Q8SDev0mAje/8n3KxrT8pQBWQMq8jEbJgYI71gA+AelCCsUsO2g9TsPJwJ5AqgOCO9TBJ4NYultGRT3dvhXHyDvhw9UCHjhpxFsBt3+JRKp1k39XRrvB5v7nuCB9gUdARTn65rsFfSBFEIYD/F+8c7svAQ1EcwFTfuU6LPsKxFEDqcCnutX6PAINQViDYn1XOkZAvoHGgjA/MnwpO7CBE8CzxDOBHjpevkoDGoEwA+K8FviZwrbBz0dzvpF1qMAyBrECTMOQOe25YkRbv13G7cCmdEp98ILahFgG97QouZ2B5kH9Si+6ujZbwEHBl4O0Qja3Xb1wgCmD28QK+sj6Mz+TA3sDecIfeU17rgC1xFcCEj82OOp/VoOswMTDWPxt96q/ewhuhCc9fDa0ehEGb0aUgVY65Dk2A1rKDTySuwi/jf/GxvMCbvtC/vR+44AhRuN9Fv7Rwcl8N0S1A0i7fL7EwBMCLINNOz//FUO+xHuA7LyCuvAAmIjVQTV9mD3HP3NHZ4Fxeu0/1X7lRTZFRbmlf5YAwcJeRJw7nP6+gft+jISSf9v9b8PLgHTBBsM4PCYDV8JdvNJDvf0Df20FC75NQBfAR/1yhHPA5L3dQKG+GcMNQtY/I/3mPtYCM4OlwAU89n86wEbBDAKKv0H/rIBz/zcDGr+d/WxC44AyPzbBoX7gQRFAoPx1wvuAyT6RQRU+F4AuA5H+KH4QgRf+dQOZAFr8wf8v/p3AW0MOffX9jkDDveXA+ECIgNO+6X1Fvd5DKoJlvrT+Ff09f4TDUsGv/RJ9g77lwxIBsH4LAOE/kn8nQHN+kb7gwsH/i736P11/PUF6gI4/r79I/XC/loKKPxV+48A+/+mATL3nQHICCn7Ifs5/+cE/QQd+O33vAFX/gIMjvvI68oL4/87AN4NgvNh/Wr/Ce9PC+kAEflsDlXtS/WAD88BYf749un7igk/AyP40vyl99EG2gT3ALr5WPpwDZb0Lv3WCY778P4PDIvypgKhASP/ZAuD6cwD6w0D+p/7lwJ9+lUJdPmECHECVfw3Djby/PVlAkUNtw4x8dnxBwXHCIccd+qU5cAa/P/XFQTy+NKGKDcLgPFKCGDmmho2FYneSwvUBd/+ZRbP4s/4UxdeB8n71fH+9ecG2RW991TrYP79Ai4PSQim5DEI2w5E6f/9pvwsBl0Z7fCs3k0O8QhqDqAN6tOO6+ci1QZY/93wtecrE7QGEfxw9C/rFg9ZBkfrev1YBi8SdADK3YbnyQn2OLgLB8Cs1g0fDC6LAznO3trYDLwoEw1/1I3apQSvOEsLMM1f5JcLOCeRFuzPXePPE6gRNRX47zLo5AXbFPoCP/if7Mn9nReOB0r4ZPdh+TEMZQ3v9+39NfHt+2sW3/3v9IXy/QC6FSf3d/QVCNL3Vw+hAk/rvgaDAcEMwAwb5ln8VwehBR4XpuqY6dEWzwXyBfL6outOEykNdfNqA9rzqAJ5HCbzj/ATDRb68BArCdPcMgS+GWMGnweN6CP0HCG1B8X+Vfu18KALdA71/lsEZPpE/UAKvAZ8DEb18PrJCj4D4Qr4+8LxzhR1Bir+8wYr5xIMIyEU9530Fv+1ByQac/2c5P4JyhriEMP73OAW/Q0xMgVb4134PQD/I/oY7d0k6kQSeBuTD3fl3uYmF3Acm/9E8q7n7gpXLbv0uN+T/uAPcR1V/dHevv8jD8IRdQNA4GT2gRY1DkP8QerJ81IUPxHe9uvtgPRoCJwU/wKP8NHr5fUlHq0Wqe926zrrPxWTK7joY+Ig788FyDng75rMLgGyB0UxXBgRtZ7bwh93K5EZa8VQvsIfCjyXFf3gbrEdASFLVSAu5NC8ZugoQEU0IOeNxMLlfCYzKM75ht415J4MzxvUD8P5v94h8KAXAxRKB/Ty9dzQBuIf7AUb963h+/k9JTkLxOqT69f8ahNuChwD2u5L8qUHWvyYC3ECPOzd/sD4dwWqGl/ieOiHFp3/YAPf+tviQQ3GFEvvfPzP8KEA3B8N95/mQgiM/IAAJRLI8sLxZA0n+pwC5ggy7u8FKQZb+B8S//qF7a0FQf4jEEcNsN5m9/8NjQn6DXDkpO3HFrcL7REo6+bVtxViHgsOPvMk1ocJrSDVEHH4LNjL7TQfgxSoBCDv/uYKCJkT8Q479fzUavXmLgsi9vMJ2V3lxyXqLa/2kOVD4A4GPTLWAVb0KudS5Kow7RZU62b6Xt28CT8o7AFK/7TiwukgJkcayvbq50DmwRI6KE/02eui/DIC7hQi/YLwrAqs+d7+fwQt/QUDO//b+SfyuQiGE2j6e/oL8SYEUg1n9IH/g/87A7j8f/MbDu8IKvyy8Wf1vRVRHTL7D9hf7/4bsCPj/rPQvO4xHbYdJgGT51TnkQaAHtEGU/pG5TPmXyAyGMv/4+mF1tYS3Sf9CRPkPshWB/ozURjK3wDHsvxGMFsfL/Va3aPjiRfiHHAA+fvZ2CbzGipPGAD5J9n55oEYSSu/Ao7Wiu3LA3sqoRIA0m3upAyoF+4Rbebo7jr5LQplEwsKJgNf4LX2hRX8GWYPztRE5ZEimh1gAzHrK+eqDtcd+QOL9zzl9Pc1HzoQn/9U7U7w9gz+Df4Tn/YF6lgGtgVbG5z4U+pwBnr9SCAv/ZLtEhAf8UgS0gPx9PgSm+yZCMkKYvRmFRn4a/In/vn9gBQeBxj7yvey+tYDMQ60EJfnAgK6Cd3+ohaX5skCKA889jUJ++eqC/0NaPSx/+bzhB2q+VrxLA/75/oUfPiT/FcHlfKoHMTilvavD50O+RCxyKsH0BC7AvETQdeIBMcAqACNFwbpmQz67NDuaB54Ax8IXuSz5hMeOwFcBnb5qN4cGG8QkOwN/gnq/RiwFxbb6ARB/C4DkhEr8ysIevQ2+vD/sQdmGGjuIvav6DkFNjJ08Gr/zuaB7RMaUf3pIgbtdt7C+qQAdzWg/GboLNX7+Ys74AyE+f7QQvpEFLH/lxtR254I5v8o5j8rXOcpB4X9P9KZK5kAqvyhDtnXVwlQCqz+ogld9tf3t/iICCjrChWQFGzeRxR80+QPAyoa2qoY/9fJ/2cau/VvIvjeG/5O+Jr4cCwz8df5Ctvu9vstIBCi+uHa/eeVFEwoKg362lbTngzcF7wVMQcDyI0GagL4F8AlOcdXDKzyuQSOI8ri8xtz3TsNlAiM6nMnSd/+F6DyNef2Kbzz7BA28nfaihFGHJT8PvtZ9SbqUy3v8x33fR0a2o4XzvPZ7ycnZvhZB4LnU/7uFBMBTA5Q0skFIQtz8Ygnm+BS9XMQE9+2KVQJdeSdA0vpJx+nE4LqBvxr9LYSjQPzAHf2GPLqGRz22Qk19/3lKB/q+ioM8Paw4akTcfWeBsQBGO/gCbn1tAtOAJr0wfow6DgWHwat+8IAFt+HDO8Eo/CZCckCRQC6AInuMPrgJUn5X+twCFjm8ibzBzzYCReB9NUU0QTz448RLgBCBt7qyghUDLH8AQrS2XobLAi48ygUieFBG9L7k/E7Egf6OQ/t6vn9YgTOBgsO6ufNBin8gAm8C0D10/vm+IcMVP9zDnP71uv3DOjuKRJUD3HiqwGJ/5j/jxdZ9Tv8qAgj8CEF/gYjCQoF9fcq+cj6QiK7+8z1RP/f79MmbvvD+J/+7/MrFhnyHxBN/jT9hg116UsQaQPbDGj8qO/vBrf9zyDZ6DX6IRNw5p0YWPv591Ya7OzT86oTYgSc/4UJTudGB2wX7PA1CG794/X7Et8AJuuWD7UHDfKeEMPqmAFLIhXrigqf/mvsah30+Fb+cAEW9SoJowI7DaP3dfvcBvnu5hzk9Fv8DxgQ5HsURfUn+rwe0Of9C7fxKvx0DnADIxdc7MP6WvMDCmAUeujrEDftzQOJEvfslg7K95z6jwQiBE77ZQL2EPTgyhBO/Gn5bCpQ2xX8jAW/85sZkwDO/Z39lvnS7n0SFREX9WD8N9mNB/ckOQ7n6ljhuwOHChsd6e5I9sv+Wu1JCvYGJg819DbziN+GCKExOO65BHTHBfIAMTwKJAqZ2bn48PcrFmkiFuDN+X/OICA4MVPYOvhO6UIMORwk6jMGg/2Y5zAHoPvWDdsDPtfGD6AAuheqBHjISwirCUcyuu9Ct+8Xww8CN6PczcSkMtr3fBYQ/H7g/CJi4UwKaQvT/+IN/NfTDdn/RiDeF1q7VhDj/e0uPhr2qrET0AV9E5wbsM+oEZzr2PnIGAoDYxbd2kv4wwu2BMkoXtxP7FT/QfgtPOjxy9nUDY/t3BbrAJ/wHCOt51/23hBA/c0P8euPAMkP6/cCAMvv2wvKEkT5o/jc6KgJzSMK943spOtQ/4o5cuYD9HMDFOG6PPjdmf8yHHPQ7jQV0jMHuiwy12kaSMtDDSwrHuf7CwzecBmgCzjrcwnp9Z0Zkf9H67QC9voiGFwKaOi99kQKohVU/8ziRAtCChv92/1I9Wwa2fNw+JcJcfrIFRnsdwSD+5X+2BDX6u0XOfElEaQAstqQIxnrZCAR5D7m7TNV4KIjS9AT+mc2qNEgKybMawULI4HS5jKn1pwKmhNt0pQpTuzlBTD9tORqGikFSPwl9VbyxRNVECruFvFNCMAIKAuk8qztkgadDKEPWPN85fIHWRD5EOzwTvXx/TwEnRkM6RUSSu696WYrXd9NMJ3l/+deMw/FuCyS8F3iPS/fzfIq1/wJ4SojFOM/BLUPz+qoDWn/Q/JbHXfjrADYDfzlSitW5EcRMQKL5XIuVdUgJZ30+urrL+PJPijz54n4Zyvry4grv/Ve5Foqv9pHHkcGMd0DLljf9AW6HXjYvibU3XgH3RtY2uUgiOAYFBIMhefnFM3inCOA6lP+sAis7F0kZ9t4DTP5vPIOMIXXshPv8+vw/Ctq1T4aXgNl6l4jOd19FEcHJuwUEoLw7ApNC0H0/v6XBmT+bwes/RHv+xub6jEFMgah6dQkmeYxCfb+Tup8InHcyxEa+cn0FBoU13oYAveK+TQYSdT7GLEAYPLTClHh4xUFCrbqOxEp6o/9ZRIA5NMLwAWE+UwUNNtlCMIN/u6BFhvdlQNkGXjhgBeI4EYFfhpv1DofjfDc+xwgzs8LFZn2ngLXHb/M7xClCP799hAM2BgMcAkA754Pq+wZ9/cR6PwtEZ/vW/HAGnjnUQTXBdj+ghVIz2MFsBEK9PEp39G39C4aEOxqLAPV9e40JvLbWSgJ8KDnNTPe0T0R5PmU6hA8yM7W9UIY+PESL5DdKOv8DZvjyS7c/d3kZA9S5kMSdgZwBCURcdsY+ZkSyQ1GBU3pwPMWC+kGZ/tl+Yv4twge/7/6CwNAA00H6gIu7Fj0LRSnDWruMPC1BJ8LRw4p7ir+VwuJ40wlcfKA/bobS8rUJlLvN/crLDXEpjT75Y7s7TufzuMgrfH43T48qtBYGTIRdMScL8f5XPV7F/HECjd1DUvOEhpQ3eMvThhkyLALdeVOI2Ym9c0gBJz69wBiKR7e/Qzw9uD1cx8A4UsShvtI9TUNo+97KzP6x9o+EEX9LxQ+CuTScRVgCJL1LiXwypIA6iz103UuxNjK7sk3IsrKGUv1TuPdRKrayOzLFkrihyFcCEDfKiF62xYNGx/Q358IPPZYAdQPbfjH+3z/4vSCBAgQT+kYDEzs/Qu6Ej/a1yRx6R0BdxPk0fg1mNzhCdETP862IKjqABT1GLPUxBIj6/ILFRIw4PwIiwJ68o0W8eutEFMFEOAvJvzi6RMS+r3m9yaT1sgpNP9K3HAg1eH3HdgEFte8H5YAPOnUKinawQzLFBnKgDNc6D33YCJ7z+0rs/q67BAggNrTE8sFCfQKFUPeZRvP//vm0Rhp6fUSige+5ici0OR7CZsfv9UpEdfyHwzwG4DcXg60/WD0chmI8VsPSP+K9OgRy/8VAqzzBBEW+Z8BsAt28kIdmtlJA2wijOAcEg3/Q/TCKYbYrwWLGnHcDikk8b/2Uxzv2ygUWwuE4yYeEe9D9dgR0u1mFoAAVeNADV4L0/W/CPrmAAHGHIrtHQp2+9D1RAdzBuz41QaP9g/vGyNi51UPxQ6K2G4gDuc5DDEeYsE6KkPtMf/FGwfX6BcRA/7koR6u7SruxyNc6mX7hhIEyrM69+hG4TY0bc3eJ8n4S9SYOxrYBv+eFMrWbRQkDk7xvhngzh//HiNG7f8KafpC8u0P8fSKCaP/P/vq/r0LpghP9fIRp+f6CywRtOtJJI7dPwoNDYruuQPZBfD/OQQZCZvmCBLl8/n6/y1V2xYB0AOZ6ggpIun1AbP3BvRHH3X7MP+59Qf84Q2m9KQSFwMb1bokUfF6EyESyM3BIqfum/CqO6jL2hTj/PflikPi1Nz9ZRhV2SQ1k9bT8MUoruGPLqvl1+FGLw7sZBxZ7QHpVyx96EkWjPKM8rMs59UxGFDt/f79Jm7YmR2oA0HgDCFl6a8J1xCQ34wdD/aw3Y02ruAQA94UjdmSNIDmxt+UPd/QuxdQCBnX9D402qr75SGf1zUrI+7m7aIfdurbBicRl+MFGwT7E/O+GInemRcADcvcSyPM5egc6vZl7M4cv/RxBxLwh/+gEn71GAceAIr0AAQhAnkEmhPT1gQPFgCY8eImtNUlDYv0LANhKh7JXw92Dk76XAis0G8a5x8N1OscJt8zC98djskuLk3mAOyQLp3B4iGwBTPnjSyszL0HciXS2m4Wt+4ABWAVl9x7BqsWzfk/+938tgIR91oD4fnFB7r/T9sNLr79A/MAD5fLSzA68Yf8dxixznQgEurKEBML4dX1I6reyxgMCD/nYiW30XUDNyTJ3/4c5t1z9zIuYtI6G3wAlOqkI5LPoh5fClHlqiLM2T0TEgsZ3JozANrcD44QhM5LNtDqb/0NHjfB8i05/4fcgitKz/4k+Q5Ow5M7rN/Q/k4ZO8iuP8nrxNsbMC/PIio+77Dn5Cl06F4ER+/oAxYfGvno6Mr18R7GDGjuiuHJFNci+AEb05r3lSU7Dtn/XsvdDMkpywbP+i3RRw1+L8Hxq/UP45AZVTAn0jn+RfFpGnIdZs1DDk8G2vnFHqjSJg26F1jb4yTC5VjxmzLexs0iQPug5GMta84nFk8Ui9bNHU3XiRCZKWXY4Amb60YTsRuFybsGfxif+Vr5H+oqBcQfqN4hAg4E0/NRE6X2J+ksENTwRwWiBi3tnB758bfc0RhH7q4QVBaPzP0HVxB6B9gXWbi49cBE7+nL+7HhBuXMZp7bCdhS+EbslmPj1TPBkB+r++U2TtwNwIQoAxnkA4nqFsieMPUfd+SX8zjmdB/rCn/yKAsz4OAYzvlJDd8UTcLRITMCtA/5Fsmk8iY6FrMAaBIBrQ0mXznU20QF9806GKZDTsT9/D/4WvK9RBDWlwB7/qbqKSfJ+nHxOfv3+G4FJCEc7hjiJQ06+zg2Ft6GzY4hPfuNLkjlUMGbMY4IgR/y3+HHVDl/CkwFUe/s2v4v+fodAUb6hOrqIX//nwYFAWXn8w1HDBUMRv8w4YoIlB7p+Yv9sOQWCtgrh+N0ABnsqgyIMrDSd/6b9wcPFyj9ztQD5AkQ/yohWtx7/ZgQff+uGXng9ej9H/j97A8O9FLZ6BsIBHsSlv59zkkeCgYWCyMCjdBjGMEJXBDE+knR4RmKEowJmu8E3bQacRXt+EsDQNkeGaYJ3PUjFB3Z6wpyCwX6uRQD4A/4WxVEBVUJSuNN57soLgppA2DoXNksLS8SHPwc6nHYfzIdDWnqifW71fkrjRZZ6W0JocrUH+kcNdxgFmHVDhQ6LWPSPwl/3v8HLz3B0tT0UfUV+ZQ6cNu44KkLzQHfLZXdo9ALHSgRVBt933fLRRUYJHsWbvFdz/YCXSLLDzr/u9Bn6HUa4RbRGKvRztc7EAAoXhwf0LzGlAGVPzcsDswDxbb6nD33IQfPUdw99h4nxjDlyi3wXAAYDVkr2MtFB28KLeOLKzbaORJNEnHceSL04I8IaiB14fkXzewO+K0lYPhKBUTrOtyHND8NegWf6K3JYjIGJMz9Sd7/y/UxEzb795faJsrtIZlHXfgP4fDIBQwbTN4Nxee7vHryPEh0LNT09cAT2PY5Y0Rz+6jHscLBKvxPp/7e1Dy+thJmVrMDEuipxT36SEVIDOb5gte64RUsWxc5DYf2fcpkBKQd1RaGEJ3IGvIpGU8Y9hXZ1w/ddxCiIZcK7/Y52zf/EB9U9aUUA+IK8U0QvuaDLuP2LuxZ/ZznVDVw/MvrauTo8SourgzL9/TTSvSJJDcQgAlx1/TkVRH6EmsS+ep/1vsKkQ/AGikFzrq2CEQM0yQUGVCylwI/Bg0hJB+Wx4f2RwYrEeMoZuGz6GEOZvHRKsv+8d7MDR/ZryJ2F/LpzgaTz+MUbjDa9bsCOc3T/VY5dv6OA/DateiGNCQA/wpl6ZHoPiKV++8V0vSk3vgOBwQzHiECjd+9+rX/uR4aDuPbkPTxDBogVxB232nxFwu4E+oF6fC98a0AxxsjAoD7O/Wk6Q8XTAhEB2wFOdriC48DsxH7Hu3N2/ptCe0JYyfN5i3wIfxZ9FIb7AibAn79q+EvBzgVggseAzDisvQ6InISMPk16JH4/R9aEysAaN3a/gUa7wv9Elnb7AHQCFMIpSS43IkDjfY7/5dI4ugT8iXmiOtcSYj/w/Hl6lToEy2rFZrz1Pjh73gCTR0H78MTMfsi4PMhOu2sHxAHMNPTFe3v2QzvD6bkDBK4Azny3Qj/4xkWUxIY3MQEme6lFuwbFd7VBIb53vmRCMnyZRHY+iv33PU37wIffgmy7f/ojP2pEo8TLPCV3QIEchMyFN/mEffb+/DzdSBe5+P8LAit4r4Rg/1dAKMMRt8QCET+MgBGFPjVtwYgCrTqRhuZ5TsEgw0s5LIOL/UQGn4JzNPCDDrvFhqaIE7Po/7f/AwIHh374/UE//SLAZYaouIyFhj7VOhXGALvnwVGAAf/Tg0l6uEHzvXn+wAVX+3cC6HwGvtQFZj5lhA85lL5JxkD9GME9fe19hkeSP3k6/r+WvilG/4BbeTxB1H8mhacCAbX/gubByIB9A9w4lYJ+Qt/9VEQhOzSD94ITenQCE36XwuzEOnxnPcQCRX/bgQP/mj1khYq9MoCngMp+8EbgOsu+PoHxgabDkz15O59AQwQ5Q0w/gXrgQQOBb0MXAT/6aQFT/mrFRQItfaFDOrkWBFEFyzzDg1T5QT88Rxb9kQX0Ofx9WEVDezOKOTw99p6Frf0nxkACXjeIwlF/goPNxCb4BgNOv9ECU0V5+DDCSsO2P2LA8jyrQnICMv5XPQV9yUTEQkeAJb0H/y3CS37IQ/Q5WAI0A7l63cWw+jYB0kGhek3FHXxRAqWBXLqOxv0/rbtgQYs8AsWzQUo6FACCvNgG+cC9udfAZvwJw/+CUnwiAOo/K38ifxY9OEMPf9y+tb7gexzFVkDRusS/rDwfRJxB6HnxPpi9LwPPhCT6vj5GvNg/5oYr/JB/jH32vEyHNjwRADQ9j7xZRlg/HT6UfRw+loI2gcDAZv0TwBQBqQFz/OKBgAA2AR7BZjs/ArcBF8Fevv0BEoLj/3tBtr3pgGdDaT3Qv1RCJ0E7Ql784b64ArUB+QLnfEs96cUlQB7Ajf0JvbQF7/4KABXBgH1Fw/O8i4Avxfv+rn/yOew9VMnFwIA+STnFu84J2QRCPwD6O3sVhTnDPECb/cW7SgDIRMABWkDfu3k8BoOXwgeA6H6AvNiAK8DgggABp7xFP4sBOAA/ASy8fb4rwtLAjAKAf/W8OT9IvqmAvIOzvvn+7oC3/f4EpwAUvQlB4TwqhD2AoP91Qr47gAIsgH79RQGGgHrBE8D9O8bAS0DzfwJChT0LP52Au0DfQsF91ADTf1z/EcVwu3T/Q79gPAaK6j5lfHi8IjuJy05BfbwM/g/7RYeBhQL76UCveOzCc8eWvKYCGPoVfcoD879Dw+++S/u8vjhBT4JCQmb+S/uUgTJ+mcM7Qb37HUFs/OeBNwQrfcXBc/8Nf8TBsD6RwpMA0n39f+B//gOrAXz/Pr2mvkHEiUBTwKY/aH7RQ2B+R4CJAXzAyUJe/Jn8lwQqAvNAuL71+TBD9ATQQSP/urkywvrDHT+hwNe8MoNAg2n7pcFCfzBCzwGc+hYBckDpv2ODI/07PfiDEX46wTA+5T1Qw2tAIkEwfOH95YQW/wWCOz8y/9lCEr0VAilAtD+6gNX//T7Vf/L/dwM9fyTAEr5xvdTFkj7iAKL/Q/v6w+gAIz63Ake+UYBGwO6+HcMlf0i/cgJFvRCC0L+HPqmCZv5DRYo913jpg8Z+s8MmwuW5KUORvU7/0UOPuFCD2IFKPcTD+3qOwpP/eTv6xbD59QDGwg86LgP9fZf/esPw+JdBpwEJvEdDR/2yv+SAuTuigNJ+3382BDB/Pn4JPGf+YQOPwD395v2x/D6BiUNpABA8LXzaQEoClcE8uru/Y4Gnv0SEPTrdfRtEs/39A7Q8MfrhhvG9jYL+gA+56kV+vWWBGkHKOR/EIP5Zv4ZGLXujQNt+Fr9+hsq8IQCPv1Z8oka3wOc+//5KfC6FRkDQPkqBxPzXwxN/fT6jgui95MMVvox8SQOof63BtEBQOtGDK4Ew/xICDXw2v5eCH4ATQa48oUFUguA9EwDO/qNAE0O8PR1CPr5ufIKFF/4uAMSBmPqpQsQASj+Qga49DsQfviD79MTNP+OAm4CuvDXDYQBYPG5DN/79wPpB8jmmgN6Diz/jAdi79n7+xJb+u8Bs/Ih9r4k//p89fLg0e9/RQz4w+F6BnD3FhXs9zLkQgyIA/gC9AVE7gr61RLvAe7zAwp9/E36xATd8eYClv0ACQsJ2uKdDl8XC++F9yXw0hlCElvfKA6B9HH+KB/A4tD7/PvdBpgnWuD39IcD1/c4LOHiZuBPHIX/2BtX38HeOjQx/fL8k/f/6UAieAw3/s/2wPB/HfgDeO77/L4BDB5W88Hssg+m+OMRDAbD5LYHXQf2CcEBh+rdCIYLeASP+83vkAbADdMHGfYV9OkYcg0q7af38AM1DWwRiO0m5HoWgBHeAOL0MdQkFAgzWvzZ7mLS4QOkTCj7utca5+MDITllCWPbL94x9kEwpAwh5dT4FvEzGsEPU+h++kLylA/aInzvbecF/1EOdRtLBabnv+5AD74TEgll9zDrYhN9EhH4Ovly/iURzwk+9e/3sP+xHOQIhOo4+mD6iR3eEPLjjQIg+ccEghvw6135pRHG9voFVPNx/OYYEvT7AYn9GPIZFJ/72fRMBmb9wQI4+BH2/w2W/fn0a/9e/SID4wSx+kTzAQJ6DqP8E+8E+F0KYhAu+dLsJ/4HDXAFmfTW9Lb7aA7WBgfvKvoRAJwAxwj/9nT1FwtA+dH6QgVx7koGEA3p8+j+8/dW+z0KZvhU/4gClPW3/uUF9wD9+w/+vvwO/Q8H2fkq+/QCjQHuCfXz+PUdDIj5IQLNACj2+AQqAOIGgP7W7x0L8gJf+tj+TPi+Cg/+fvryCPX4N/+yAj//tQBQ/XIDaP61/dgC8Pt3AR77cwG6B2b3IQQqAxv58QRt/a79aQPv+fIA2fpQB0QNP/O0+Sj+aA8eB7foJwWwBE/5xw3z+Vb4FgfK91cJTgWv9C4JQfopArYJg/Ru/4IC6QUXAWD36f/v/wYFTAVu9Yz5rwiEB7UIq/cz9L0GOgObCI/+pPehAn4EmQLx+4cE6ASd+tMB9//rA/EHUPYWBUsE7PxiBin5hgV1B+v99AFE/v4EUgg8/OMGJwF3+/0NlvL5CeMLfO6kCYn8k/+WEjL1HfvUCz3wAwZVEivvUg7vBwTpcgLQ9TIJVSZS9y7qG+OK8tYl9ids+nLezOptAWUQugwf+ev2dhQY/FnjPPJ1/m1EyyDa2ezQBb9SIqRtwR9NwbeB7OCganFLQfZrurTJ7SpMIC36d+dy/Ckz/v271Zje5AysUBgQJ8ug0mP0fj2qKpXm8+wz5aoG2Brd+WMKPfwL85fxQ/VwDokN7A9l737ik/o6/+gmPw0i21X3QPA0E5MfLOFL/u36xQAqF4rfIACXDTgA+Am34GH37REHCJcIIOY/8tcLMgb0DlryxO4GC4766wcC/KL15gpp80T+av9R/esT1voJ8Iz32v0kFdgCZezo+mT6Qg00B4fxzQI49pwEuQbT9m0OwPLt+5kM7Pf0C1jsIvYNGj72+AqU9JTyoBMt9QAJegCP7iQKVPsMBXoNq/USBP/46vixEpoBQgFc8QPwdw6oEYAB3vZa7iT9jRFs/5gBVf0x+DwMuPh6+0gJqvSnEHMFkOxdCMbrBw72EjzxWw0i5a//XBa9/fwM9unD+MMPnP53EEbzBfJgHPf3qAuy+3/o+h2B+QQGJQJc5/AQlgUA+v0IzPlaAVYGTPd/DKL/ZvzT/Gf66RdL+yP9u/4b9K0SO/ni/l0IROzlEsUAhfYPE97huwgYDGvwRBnV6ez/vxXp8fYPZOwD+gwZqfqJCILyRvS4EYMDrwbx9Sj1ywha/nACcPwK+S8H0fd6BVr9e/oeB1b23QWj+YwAbAgz98P8C/btAdcLOv0w+FD/zv97DSH82vDUANH9RhIRAMrrNgP4AusH6gpj6+cBlAag9QsUW+39BC0EGvofFzrrtQB4ASr3vxMT/Jv3Hg+r9IoJHgHt694Uj/SIDO8Gd+5fDwT8Cv+vCXD3PgMMCBv5rv/0+QMCtQhc/wv+U/mXACwGtgkK/7f+PvqH+pQSNf/ZAKgAQ/duDFD2BAI+++j/rxbL8Ib69P3z+/ETcQHV7vMDhvg5COUBLfIYFM/sTwYUB4npVwz7BlX4AgYh9NjuRBnS+FT96P/D/5gM8/i3+o3vbwLjDv3+v/oA6m/3Zxva+joKjuyG6p8c6/BiFzgAUOHYFnf3KAPzGE3cXgUO/3rzPx6V6rgFkPar+8QHr/CeBSMD9AXXBLL2OvmrDxbz/AL6/ar4ChuM4kIBCwwt/bIEt/j3+9cSqAG/21sElwO6E7kDWOxQ8q0MAx815DUQRtV0CMQ1/Oc6CYzsD+BqHWkRFvuGHDvGZv3sDCIVgSFHzon2NfrJH4QSzv6r1zkAuxdlDJwLHPLi6pEI9h/73+cZfvHy+dMaS/QKBdUFF/8z604U7/QBCUz/WOlGDP0D7QjhBSDhPvv7FUzw8hsL6tflkSsY82gJ/fhx3Xr/vBRPDTr5IQF+4t/3ahtfCowEyPjF2o8LWRdA+vv/ROEGAjYX/wOY/XPzy+8YFV8NafHU+0D0QgJPFDECB/XYCojul/66BvkFTA3S98700/hdFOEBoQp7+RfvswY6BwYN4AOq9CD+5Q+MAZANWOxI/wAJ4vndF0f0xvuABFwIjgFdB43qjgRHGtf0WQfu9BP+Ew8ICg3o3hD/9wb0ryNY4JQLagGL9EwUAgYL6t0Lqvaz8kInhu08CUH5G+cRFa8NFf6U8I0PWe+6GBz8iezKDWAF5fccDYz6BurKJkLiyBYZ/0zhRiTv+y/8QRRc4ir9byp93ewq9vJY418hK/zN+8oShe8c+HskX+0u+af9xAPk71cg2u/77uMh9+RzBXoBNe9uEHIU8ObTENTqtgEQEpUCcvZtBY/0DQQtHJngKgZt/HMAOQxJAxzlcwlXCrEAHA196UP79gbiBUUALggR6pUReP+C6hsKTv/cByUFXu4B7UgS7/otEZf59uFgCjgFigVZCq/tA+uEB0EEjgmTBlDxU+5YCCgGrwLlAvfkiwS1ClH48Rdu7SvsLAe19sMULQe85IX6r/7nAF8Vuu3Y8SoEHPrGB536cvQwATcFWvw2+8v0VQpuCRP8WfRq6L0UuQq7Bp35E+KDARAZiQDs/F78MuKlFv4LPv3U/anyNfatGf8FS+n8EoTrnwXUE7Lr1QJhBYfzfB0Z77LssxhY4/slpv+a6BwAjgGUBHsTlO+v720UJfHAIPz7jug2BYUIc/cgG83xVvh+IPvlYBii9joFDfqGD7D20AF2BgrxYhI2AgD20AvI8yf7qC6q1BgGgQfg4KI7suio8GwMcuqUIOsH0uB6D6EM2fg6DQTm/P7VEHUMdvVD/fD75wREDiTxZP/z/PT6hAb4BJv7ghO+6awBYfj/+18O6fOKAfQCnQfr+EvnCwD7Dq0SR/kB3n/1sAUcG+wHN9U9+lAF6w61D/Db7v9GAgoRIhx55yzuyQfM/xwR6f9h4iAEqPb8E2QE+fCc/8MFHgVi+wsHm+oYDVUC/vTO/vQHKffrCzD1WfwrCt3s8hNP9ewCV/L3AJj92BdW/fvzafl8+xUZPA1W69jrGQpID7MTzvRM8EICwxIBC7fx8Pr37gYjsu6dBHkWNuvNBUzqev2KGCMA/vDyAPHoTyFYBDzoEP+G+ZMYK/vn+1boMRA8/+AKAvtK7LAFRQrnAMj3h/zE9eMRmP7nBPn4hP7J+V8JCgPf+0YZT+FKD6gANv1IC2z8w/MsE1/48ADmETfsTRYN88j3FhFuAdv/5wxu6Mz9WxVy8skjSOUEDWgG+u1XE7YAbvVrFhf0Vfq2HYLnnxgH8b/3ahRg+1YGYQh4+GQCgA5a9g4NDPsh9TcAQxr8/ugDeOpK7RQw3e88Djr1l+OOHKEQsvCVCWXtQw2CEsrbMxMbArv/KgHx9ZX9JRgdBf/qh/xA9JkgBQ/65nH6XPkDCQESdv/M7kD4aATWEc/vAP87AkD/2QQY8HYH4vstAyAB4/ML+twMbwOa/mT07vduCx0ITAX8+rz76vS8E8oA2/uh/P/7Ff3MCBADb/9f//bn8RQkCgXxafrS9IUE5x6i7Jrw6gHg/50jb+qv5PIECwyjCa8EkN6lAZkJ9wLdA53pCgLZDJ7+Ieqy+TD9URVH9Zv0VP7D+zX6AQqU/4L02wMz71cSyfRZ/Gj2kwDJ/ekS2fDg9xIEkADX+mH9EwmY8AIHW/WcCoz+0usxB3sHvPRoA8AAJe0FE6H+Dfvx/uLzLxEXAa7n+wgGCv7ydAgr9BX+dQRr92QJ5vjr/58F/vzV7LMMmARf//b9TP3d9gENUAIi+Sf+4O67F7sIOu6GBBAFm/v7EvHyKfYNA7f28RZrAqXsow1UAXP6TxWm8O4EMAry78oTGfVI9XIUav8V92QIK/GKB4wFHf3UEYrvU/jkBwgEa/1eB6X9UwQO/kn/Xgy792n7IgBHAvcRugAG9A0DMfcxC3sLY/a8ANX+n/kPEoQH/PmfBVn6CwBlAmsKIf2Z/nbnCQyJHM/3yAZz+NLwxAYTFuj9TBJv34gJJg+A/dgIC/it7uoHQxWh+KcER+oqEKsNMABV9EH+Av7TFNkMIuOZBIf7dhPhBAf/lPiq/i33MRe1Bx7rDQTD/74QRgkF/VD9a/PP91oc/+4IAHUFgPGXDYv4E/MPAz31CBDvD0TZRAlSBPf/ABLO9V/pzwbj9aUP2Abu3AsWlQ0x8BcD+vXn8gAVFvC8Auv/1unGDh39N/S5DhMGbPBFBxT1aQP6/yz9WxV07+z7NP9WCsX9lQSb9gwLz/VXBRUD2eujFMMBGf+X+uj+AQUkDd71NgQ0+eT8QhIR9q4GAPj2Cpn/6PkPBPECgvaADSf7dwXnAWDu7BUF9SEGfQjo31wKrReh7EMHI/EfA9IabuTEBgkFM+pLDEgM+PXdAyPx2AMRBUj0JwCvCy/+cf5uAjz1hA1VC2zys/dGBXsBWf7EAVD+EAbf8pEAkwxmAA75LAlF/yL29guAAcv60PgF+1gQFAav+p3zMADB/qMHFv1b+V/9WA37/+7zsP9g+GIONv32/O38j/5t/30EKPg6/bP8+fqQCcL1YwRS8Wj88g3f/6D0DPoK8W8P4A+U7pADoPY4AcoQ5PqC7gwItPY2ClUIWu6RC98ANPnVCLv8CfjfCbrybA9wAIryRf5o9SQK4BQK7kfzyfd7/3EeRvaV+BH7dvDhGqkEM+yCCen3IP+fCHHzxhTr/43sxgzF+Z/3DRpW+LT90fl+9JwTpQd6744EBP//+BAVk/mU9CgBnvlSBnwSY/En/B4EqvoUEG76DffKAmMFw/zx/tj8MwZKAEr/IQG4+TQKTfatBhIGv/UsBcoGg+r/E24Gcu8gB9r2DwD7DVn2ov+0DLL2cg6RAyjlGQs+D/b53gZI+xL7qxMt+Gf+wghZ9M4NRAQM8nALcwrv/IQEXfkBC+oC0/fEAgcEOwv1AEYAWPvWBdMFpQlm+dj7TgqGBzoAQf4s/DoCfBCe9FsBwgHXAscR4vqe8p0EWP6WBRAEQPvJB875ZAcUANLzQA/2/dr7HAaD6xMPJxBI4iQRewfh6hMPiPAB9aEeHvKY8VwIk/khFDcHXtdOAfoZw/YZ/rjxR/cYHV37ResZ+VkFehI6/XnlvgAmDdz/p/Cg67wIcAurA0v1AOToEAQY5fNK7Nj3oQjNFn7nRegGDugKfwEP8CD25QjbGjj5P+/YCfwHIQWNDP/nhPnDHDPnJgmR/uLo3htl+LjovB4J8bnuUxLo6usPshLf2nv9bw579BgPQPlY6wIQdQEZ/K8GOPGb9ioNzPu+/PcA+PymBcIFl/jq8XkBKAlqDm720+KvD5IIDPucDTHmFAd0Imr03fcX57D2/iaj/yfx3gDXA3QOnPxS7Xv4GxPEC9P4nf4G7Av/giN9+Qf1+fn3+7gguvy32OIPxhGQDrYDMNTL+BorVg6X7qzuD/OhGM4hF+Vt7WUMZwCLImP16dD1GuEKkgP3DpfaxQmQHcX0YgBI8f/7GRcQBnHwv/mFAmoJaxBc+V3mvQHrEjwHRvnw8AsAFBlBCF3mkPnUEaYJbwZx54b8uBjwA4n+PPNR+O8QGAaX+t/wkgKrCJj6kgLF9ykB2ALZAjIICu9VAj0DzvwPFGnyJPCOABgCABkq8Lzl2xHgEDz7YfCx8aUItxmq9ZTmhwPLCh8TQfV73fwMwBLTAHL6sugDBVwTHQEF8qDw6QRcDl0D4fBk9G8JjgV5AzX73ux1C/4JwALW/zfkqgOwFTwFLPmO5YX7ThsIDIj0ZOWHAH4UMxLh91TfsgdiCYYIlwah4+YB/gq+B00FfOQL//wRbwug9/jprQPPCNMWiPU43eMJEg1JD5r+pt7NBCoYQQbe/ITrovR1F6gUwfDF8rjyeQe/Jl3yZvLm8LX2ay5u/djvye7j8Zsp/Qdq+NHkmObkKOocof/K4wrhGA+LIaMTp+Yf4pj7Bxp4Ijjwxt6N/n0FZSeVCDLOp/vjAw4aRhkC4MH5SvI/CG8pNvM0++/jGAKxH+wIARZLzSrtrQ8IHlwx7s2nz/4UxCU1MLTnfrCGC7gsshrcCXa9Pu5MLhsXHw1j2F/krx58E9AShefh7G4GRAakI7Hphu+HCg3v1ShF8qro4x2H7vkNJve65T4usPhe9azxJf+aMFX7S/An0kMHHT1Q/rkCscffAiZAGgQbBtrHRufxLoEWtxXUxq7MXxYeMngqLr8OyEsJXjvRPaC0QsziBmwgvEQE2BrT7PMPAYI8WAHhycHWcAwMOLIfa9mIuF0MoCTmGqH2+bhTGDYW6v8LBVbekB0z/G/5egMs+ZEYa9pEDYMB5Q6LDC/PMSAI+TITkvwz3TQsWew+DYX0GvHjGZXiiwpw+x8M2gaf2ykCGQanHMT9mOer7vgJShXD8jELCOKmBLUW4eiYIWjr0e9fD8fwCB0gBrDlxPx6/ZURTw9z6pn57AMiDlYDzOiuDTf7PgeL/JDuxBvJ6EgKs/rO/AsbGdUDEB72CghzH4jbIQEu9qsK5BjC/ajtse+8BnkMlRtU96vanv6tBbcK6yE46trosfx56781TQ693OL52+YfIIcS9PVe70Lv7xXa+0cWzPsu8b4d9dPYB/AVFvPwJTnWVe+5Jwf2uxRb3z39CBT6+8wLptuJIXX8NvgrImfKUh28/uLvIywT20IKlf1M8cAU2/x4Bxjq4wbFAyYG4Rha13sPXAFk8K0qsdToAcQMwu+qLuLojfGeApcBbxNt9uX8q/AjH2T28/EFHujejCQB7snmQi7p66oSvudY9W4Vrg/fAe/PyRv3+1EMaAExzUA0B/yd6yL8KviNH0AGR+hP7TYXkgB9A6n9fOUdFCYAbPOOCRsHW/is/vvpXgbpJf/iJf9+88sGvhwd44ESA/xb/ljyN/McLiwAkQhvyKn+4y5z9VAekrrQAn4jCPBzG7vc+f09CB7/qvgcBwsKd9zxGBPzeRHgFKnIYwaJEdUThQO+7FPlVxQNJUHaBgoQ8DQEYCfv0OwI5guTAJgBWO4iCWYGfBf73Ln9+xLS7zoojeO68BgjO+5NDDb19vQhGbv89P37+KgT0QLK9JcSbuCFEYUCzezxL97fAfttBt3sPiM8ACP8v+vHAbUF5RQlFR3LbBFB/KEGYiQ8z9oHpQqLA+gO/u9m/WMB3hHu7esC5Re44qIa8u5V7/owvtzXCiYSyd1LIofpdf52Esz0vQYk73UIzvPZEiYLdeOPG8jjnQ8LHdDcsBEo9CT17CBM8AUF+PYl8wMTiwbVCOf01AXK/rL0GyI86oQMCfJZ5MU1n+AGEs32CujrLn/fVhtp9/T6PgJJ7i4n4ulCHJzfK/PaLOHh/SoD2Ar4Uh3e6jgQW+hGE3L0VQnD9zv3bDVjy2ISSu2x+JExlc9R/yLxSiCxDWrn5/GO5kU3EOBK8wgEZu3XLLLZgfxUEf3xdAQl59gKSBAoBIH1dOVyENMKIBOW73rNbim6CQsD3fNy0QMqa/vPAFcDA+d8GbrjGxA6+OrzKRR94ZwoJueM8i0RjORIH2nyYQel/AX74QXW8ewW7Nx6Gs8KQu/0GYzOlxeaD2v8qQf611EXxf2OFDT8juQAHhbh7x7BBxHkZx3I2+EUoBxH59AW7+GG+4EWEPfKG4zonO54DiP2/CKu69n3EwaF2/ItDf57/PIDPNg2Inv1gAxdBx7okwh64gIjjflC/08O4c+FK5noyRU3ESLJvSdW3M0iXgSl2QctV9U5JLjn8AmJIhTJuidC2BISwxtH3DomBtAqD1kbn/AlCIfvvQ05+0j7g/12EioIYOct+3wIGRRPAmPzfObMD7ILcwCjFB3WnRGg/4XxzRu87h8RreMm8xEQQBFQDv7NuQExBzMcVxP9x48ACQSHC3whpNnA/PMHjPR7JvblzQBYAJDfUSs/+0gV9fKby3ckC/coKMXytM8iFh3pNDFq+cvXbQPX7U4pmfFV8QwEK/g4E6PpJwdv+L70Nhnn9EQD/vIV/LkSd/XcAib1fAI3BUD7YweC6ZEKYwFWCT0Q0dEyFJb+aAWkGJ/ZVSTF5lXwmiQB69AequEk9ScWYvePFPXqCvjZ/rUQ0Aha73D5C/oJFPP6r/i4/SYOx/oQ82kOVPElHt7logBdEkTgFywg2479ORWe+TcfBtSyBjwVHgag9hfpsgwRDroJ0O4VB8TuEwzBCMTk+y3h0+wThf082gNI+dfjBBH18elySZvhY/Yo/iEA4CGL55b83ARP+oYSbe+f90AL+fvNG8zuA+83HHnwHROq+vDzwgfw52ITWgj0/sD3s/ERDY4LsA616oH6KAV5+aEe7PDX7wILXvVEIQf4meLVKB3jnf6bFfbkFCwQ267z7y603FUdZeqd5V8yaPJREKLvX92eGecV0fxt9oj2lQEMHtbiw/PiD+7p8itJ9m7rPA8A570d+AMV9zP/3+z+AHkJDSLG6+r5ZPea759GC+J+8sMBh+eXUHXUje88GAHcmjjB5bfoHQsS8BIsgvB15bQGsRVEAtP8u/F487cbu/O2Eoj3FeoWEf3wrRXW90XrJwQy/QQEbwY2AJ70/fTH/XEhkPE674wFb/woGbTnZxSm+SXvLhv46kMawuicEWEIKNysK6Tg1hL18bjsfT2U4CgAsfmk+F4duv+e7DcCTQkA8C8k++PP+8Ibtc/iJ/31evrfH1TIAhU1CoX+YQ954WwJHg98+bT7TvYnENoL8uy89nACVx3GA/7n7QR29q8YQhP94L758QEDE4kcSNok9bMhHuVCDqgAFex9LajZywktDD/XNC5z+V70tgqM6wETmgDm9zQECAME8xkVTQix5jIf3ufzCfMTseGpFPXhwgGKKUz1dPJs9jAEnQpRDeveFAJGGGTqSiBM0qz1ijbF2uAaO/LV3ck9Z9izASQSf9uQKlXv69zfLU7kQwnLA4fWXiY+/5jsJBEz2pYUVRFP4+oD9gFm/VkJj/mX8bAS5Oph+8UbT+RkF7XsEPHuGGrhCxyr81ruCQgG9zUgztxc/+QBUQSlEH/YbiT/8FryGxeJ4mAgPudrADoak9lOCq4MZwD9+SzyT/rkHUr/kuyzEcjWYhrpE0LmyxMz3B4ENRwo6/oVjvqH61sFPfklCCkN0urz93oFvATXGQXw/uk8BlYCuxSP/PfrpwZ6A3wF//yM9EcPkQGT+pb63Pj0C6j5IQSgAdT1zgxM9t3/HP+PA0kEGPEA980DciOQ8h3w6vnF6Qw10+979M8Gsd2AMcP2qehXF+3dFiD0CuPb3hl17IUIRRet0ooUNQ7x5I0Q2/BmAY0lld8RBcoQrdWCJ5f3093GO8/QDyBO9W/PFUkU3XQPvQSw0sc1feNDBCYJKu75CigCfPiEBYQFkfHTFh701uwbG6vyxwGXBfvxvf3CCArvxSNl+K7Z2ji53ID+CAiy6NdAW9BL9LsYN/LSFGn3dvC9CCX0yAqsIBTMZAu4Cznh4zP2yJMhdwXyu7lNfuJf/UcMjsZuP+X7l+hCHFHjWQK/F8PhJhF3+ePtch3T3GIJYQ4d7l8Sh80eGFUPzPNXD+rXExBWBWsC4w6v3hD5egMNGgEA5OaHBeYJew9w9EH+/Qga62kMUANrAcQAV+gKI2PoL+bUIS3vDR8c4ZvySiiw1LERcwJ58FgX6uf/Dij8ae6iB+QFkQIrAOQHve6CCV38c/f9FtDnfxMOAfTzVBNs77AGUAftBDPvWAZVCgTwRCTL2qAbOwWKzZRE5dVbDMwlpcTgQHzQtwuPMmi8XizN6kDn8jMc2SEWQv9T3Ksk9wUW7CkcA9n7Bq0fPdsfKu3qQexPKkzmGxl68kvfLCu6AlHoBwSM9XgSUwvg3vAVfgCdAzQNZvBb/pv6pSIx+i7zLveYBfsyN8jHChkSdvijI+i9fCBnCcDpXix5yTcY6gzw5SAi5eZcAq0MLfBeCNANv+yMApIH3fbMBqb5ofTPFm0G1ula/ob9Rw0eEsHlbAIDAZP85AqNAj/6PO+UEp0ATQOkB2jeDyBC6Yj2GjG+0UMOUANt8IItKsY4CaUdttf4I8nfU/2cJ3jgoR3Q3+PuBS567kz/QPE7AKAebvBV9RH8Rw14BLEAu/ZZ8KEcwPwe/ggEM+ZnHjj3NfF2GWzrTgonBjv26gdK/OryQAv0DkjvehN84uUCXyduxZYf3QBS8aMzSrZlKnoM19b9P1nOPgxaBrjj7zJh4U30wQdBDc0Kpf6P5EgBWBz85/8Ulesq+xwbyd0rG1/rsfjlG4DxagSu9KoEQgbE+c/9bAZUASP3oQZS9p4AJwW3AIb7TAcR/f4COQQ89mMjntyF+pMZAuiTLu7P9gAhLJbcTRVU7V//gCJS8MLkcg+uBvAGtSDixTQVLRPQ8GQkhcXcGHEPZuyLHdPOfB+w9Rj9thKj19ci4vNn9twc1eA9ByEAx+8cI/7xROsoGK7rUBk/C57SoRxtBjf7qg232OAcug0JzFUYigK/CRAGqs74JzIAreGyC5f6SC8N6yDYpw+OCFge5tgE9TMRJwwsARXXyRt0BKP5ivsJ6nAkY/C49A4I6wafBzHsyfbk+IUkWOvI8AYYa+GALfffzvkYLZvHKiM++pnrqztXyd0FgRQ+7oEoWeMv3CU3CvMK9r4As98gOcX4WNVZH0bzZx0C+LbW9SPTCBPtEQh5+zb/ZgwI4WAXKhJ5340IRfDEF8YSttry+sEQfhZk65H0IAIpEscZYso2B8wOQwOjHRnH2v4fMPXtCwF26PH7UDQ90THwYhnG+XslKc8c+YAmSfrbCLzahwuREQr9rvx+8owaA+68AOT9UQhWG+LfFwXv+BUZFA/d23kO0+6FGnUDA9wAJ+P0tAF6Da3Xhi1TAE3bVxoL5F4j9g+OzGUiwPiqA0wEXN2QJLAHCfZj+hz6+BeC/Hf50fKyDmoTe+e6BXv4XA5TEBrWigqSAgEK4hSz3KQOdwal97oMYOULDwoJiu3+Bhnxyg7uAWznXQ/TAlD8A/Wo5bYU3RK46vMA/uFtEwkhNNWaCYHqyxU4JsLBlw9E/JgRQiKUuZcGlA8ID18OgcIGC7sfSQjx7pfd1gwZHhoGqdvS9IsTahEpAK7W8ApUH9L/cfbB3ZoNpiPh9VTtafKYCcAdcfZN5hQK6QpqBjD2F/JXD2MItO5WAg4H6ALSCh7jRwS3F6zvww4Q4HEU9CPY14kPCup4FEgbQ8/7FwzyoBNaE6zNPRdZBSgL0P4S2GMaFgvh9zT4ZfhvDAQHAvRx/tgTGwAK9zjubwE2K3n09uiQ+6MBnyig9NvhugkUBDYb2vPl4eoPIhK9AUTzq+0EBWgoFO/p6m8EZvQ6O3LmpOEfHabjZjq78uTTQCiU6KUqqgH5zJkcqfssC04TRM9sCqgfI/n3FiTXzvVRJ3jwqhjX4kwEVR1w+bYWvtEdDkkKmgUgKa3AyxSh92YEUzOVv/ESGgCQ73I9nchOCKgQMt8xOEzdDfZTIdXYwyQI7Un6nx1w21INDPhXCjYPe+3v+Qz2RB4Z+Gr7vex58x0nvunrBdrbxOgUOfLzKxVf1fPVfTBSAHEdO96IxwkjRBCvI8jmorywCGQjzyle8pXBJvPHLCYmh/N+y43isjcAJVnyes4y3hg58SdU+CXNXtu3M10k4wUiyKzYICjJIQkeTdF506ISyw2yKWvlc9a7/1sCCCvs/afcavp5/5Yb2Qsr3+Lv/QDbEnIYPeaE8O35EwR4IErsyvLe9570aSjl8yz52/S05YEoXQTt+U/59dT4G7QN/wBBArLZNBNPExICqwKo3IYCpxKmCGkJXeRx8YQQtBE3Dc3mxON5Dc4U5BUO7evXfwv7DpYfLfo6x7QDJAzfJloU0cLV9DEAGCIjKinSGenC804bJ0Mt3ULcgOgCCfpL3/IH1NLjsQFrS7EJKNEY2DH3IUIgIDHcpNXz5qcuVzgn98vP9drVEypHzxCgzyrWUfmvP/Qns9Nc0/jlUCj1PUz1LtsD028GO0LcCP/xGtFn5Fww1B+DCvrTCs3kENw11Csd0vzQF++CIs1CneYg3LPWBQeaR/oAhP23yUblzzdsE3gaedzwvQAWjRkyJy77+LvG9/wVHyM4D0jGhe95EekUOyBV29ji9g4BB9se8/VC1rQANwTDJgILb9i071jwuSyxHdPjMeX+4RwbQSbVBeDkq9sjDIMVphTM+Pbb0PfsEL0RsgQT9T3gJwpKFncB9gPx15v/pRjOBlsLE9nE8vYSTAS/GYXm+t4vFCUDNhpRB2nar/wkADcb4A0C7vjusOyQHQEhR/vm4ILx2wAEG6Elcs8b9UAAaQEmNPPqjvbP77cDxR8m6y8Z9uMH+Pwjiuw2Do8AjuvbByUPAAex/OX1g/SxANIfqwmI360IzPc8CyopeuBo9zT/SAqIC5YExwf03r0WBgG5/IUd/+zV+pgBeAbwA+sGZv69+AwL6wF1++z0JxZy/dj4lgvi44Ic7w+e6/oH8vHEBFcJSgO3CRjtBgYA/4HwLiOG63r16hLx67YV6/1S/lUGPOy/FMP0fQGDGefoHgi0DEPqAhO3Ehvlvv3TFp//IAgCD1faVAHRGKHujxN6DkX0JOhUBz4HIPGIMXzmfdp8L+vi9gn9ITTTiwXREGbzrQ+6CbLsJfz8B3P+Bwia/fvzSvgaA2kTBPxfBKvoueJOJ1P2wxwMBwG78QyXAREdHCJ/3OTYafTaEqoH+ST/5Dnjbwwz1w4t3wOx96EA3dCIGknxNxuZ/pvfsh2X6PP5Qf719v8Lsh6w3KrjiQ4K5mJHQu/11O0UA9O1JDsN4fNvBSLhwAW6/AoF7hMR7Z/rfRlT4xcLYB8PxiYaagLo6r8V9vK9BmL3HgNf7ksNRB6a7ooHI+D0FooMJvRSHsffnAsPAlXouQ0mAdUPTvvR9owJJOwWCywVzO9FCK8GluW+/F4VpwTxDo8LJcxY/TkZ2v9LGaX/sfBo/IoCev2kBnslhPcK8xv8au3jHU0SFPkADLbbHQGiFwf3VDEK6z7sJRgs07orKgHM6vcmheGjFsbr7gIoI6Piayjz4fn57hKJ7CMrofMWA7P2ceulG5r2wBuQ+1PgLB5K9/f3sxbO5AEW/Aud0bUVg+74CWgsGdU9BlH97PiCDbHs8yM2/5nn2x0I0DwYgTAwxNMl++lw34ZMbc32Ez3+zOhFL37Nmh978ob4kyKZ2yQTWev/CLMH+gBNDPbxF/hk+IIZTfH6ClsIBtxUHUP3jPvEBGH9GwtYARQGb99oAbwe6ebvHHz/FNRKHHP1jQAoPXTKxQBnCoDRO1NJ48f5VRCUtl5PStxeCecKF72OV/nQxwe9CUjAsEna1MUP0RjYz70NWvqC/ZUcDvgC5Z0RQuRsJoHzPd1xNXW/tTi87sXX70NCwlsm+e7G7jYgdO1NISPSiBZI8YAMbRNt0Lwr+eIgED8OVdUjMpvmkgLVFpbC5Cmt/BXn9UBJvIAY2BRDyA9CYtGHCHAc69LBMR3iOQfGC0boLRan8S/7MAze+bAJe/2r7XkPLvu0CYMKJuHuEFjs1RZPDc7ilRlj29gWGQwP4YEmo+FtCWMODt6fIhjyGfLsHNLinxxo9IznByjN3AMXZfr0584tjNZGE9D5MfScIw3e4hZp77r/Ygqd64AhT+ceEDbws/t0If3aOx3O5MX5SyzI1gYi4t0P8boxitEBKN7i7/HqLQHEIyzz5yL+8ho1zywiDuuRDyMB3epSER7tvxN+7qEGtgGX9doBWPdvEQ/1Iwg462v/8hgx6L8T5eHACNYLIvD4GOHdngq4AdnzWBY35/gHgf4A8mUFFwNE/rv/aPd19dQOp/5dANj2NfznDeXppA2j93L3phjx4OwMqAsV36EegO3k6aYhc9YbGm0EfOTyIYLMHhp/6pEHIi3ix4Egysib/F87heSyE/ntIOl0BikS/O5RFKz+AunOIZPiHxwU7B/rzx8V7CE+DtCy6LoijNA/SLfkuN/3QxfAiCTc7q3GXVmr17cfUge/u3FJptCoFI0Pi8FCUW/hFfuKIOy8+CPp9YX1SjGV3xMDqQIV71scGO/j+pYE6AP2GCzxHvBK+vDzURjrD8Lv6wiT9IzkwC3i5+4KxBvhvY4qrPAm7tg3d8sFJarumP2BJEvaohqG0TYW3xau5EIzycTIFqUK2ONPMynPGCQlBNPf8S0L3nsnEvvE3gMqpenBEF8Ko877JSv9FvC6IJTaGxnODJHSPR5c6W0ZrxMQyToTmO7mFhIcSMxfDnf0PgbQIbffGBUEBKLhGhe3+BIXHAaX6Ab7kfh0FgUCCPw6A5D+aAeo9gv5ogh+Ev7xEAPC+WD2Pi6x2lr4WP/a5do/1OBH/gAGhdalKb/jhvOnHoHeqBRuCtveIw6X/gnqVRVg/9H1ABbr4qrwPR124/QM/hDh2JwmlOMd/FwZNM6gNbTQnBULFtW560dZzaEB5TRQtxo6rufl6n4pwNg4D4H5uQW4/2ABdQlT5FMeFPQT8JkXIursC18SbdwWF1L53uqqLDzXixpa+wTsqTBZyvwttOYT8iozBsyMLWPuA95dO3/eEgTEGGLHJjda+OTPMkQHxLQZwBhfvVhHe9ln/hsl6LwROKnvyQHOHCrIbjM78Nrx9yoqv0Mwrf8I6fM4uMbHD4QQD9+RMZbgngcvDBblXB6W7E8MSAZq6jMVMfXXCyYCtvVCDOUCIwQ98sAMWAHq+6UY3dc4GC3+TurwNOvWaQJtE8LjnDUr227zZiUJ5EAl1O6d57Yg4eXHHO8DsuCNDGfwZAkuHY7s1fruBcz3Ew1Z+I3vhxcn9MH/5P5V96oeIOucAcr69PrNG1Tj/Bi3973/nQPT25AmT/sjECXx+9cIPw7q/wwW9tHZR0KB3+X6LQ9B2BEyXOYH7LMW+O0RFiX/qeTlEWn76uzIIDDnBAmhDEnXSSys7ILz0RwA2kUjMAA64OYjmN3pF7wLINltHKbvGA9kDovbZxlr7zgDvA+V67EW9/EvAKEG5PVQDiPsnAevCKz1Vwzh708JnQQC9PkEef3VBOgDNvmb+CYCPv43AwIIAOyaA38F1PIcESDrmAjmCL/yoBJ/6fQCxw0t8b8OmOdbCPsOiO+lFHTsIwYADlDieBsm7cUHWRRF3eAfyu/H9lQZE98IIur3jeT8IJronhzO/DPhhB4S8K4NKAUE8DQSLfD2C0QBUfU6E//t8A0D90YE8RrM5t0NWvVOAoMSaOx3B1oFLvvKCKj2RP/0GaD34vcOCd71CxWY/SHp1S/j2EQGbBGS5KM6xdFn8jMwPN/dNFPT7/F4QvHSGBlX2WoAnT89zaIS2eJXEvQkdslO/LwBAhj2FH/c6vxjDyH5f/o/+BkQOwWW71nnYA0jJcXzKOqm0s0ZSzFp8X7ymuccFOQX4uR1+//4FRQrBKvpQAvD5HYpsPbd7LsaPsdwQwLxowA2COOnoyqQBmMbVhGpwA4Jrw339QULhfst9Z4jkd1P/8MTRuXkKXnoh/HKHiXAYSmA8xIHmx6CtOUVhvenCKcoJ9XnAHn5s+vlESgHRv66KsrhHN64FPvcAjE0CYnjZSNQvwH/IyGS7n4toPMQ2hYilcmfHDgZV+LXMC7aQPV7B7jhszs1AjL0t/0s6dYAXxpsCLsQ3P1j1FsI8P0cJPAWDe1g/LPn4BQP+L0L0Rai7HkJnd1g/xwsb+kcG+TxvuWwKdfXrB8MFhLj9igTyNb71hwO8QAsNuvn2g8ew+6IFIwE9+EFHHX2Cv3uCW3pABqyA7nwCwBG/OwFoAcO+ov/tQeJ+pcC2v6n+nYXB/V89fgCY/gwHoruIfvLCxb1SSG33zT+EweACowjWNIHAUcCIgBCIGLPExZ3+14AOyafz4QR9fSkBGMZ/t1LEEcJmfHEEC3x7f3MFFPi8xhE/mb3shDW5J0GAwq/9zoOmPCH8fgU/u+KCqL7FvEeFbHwCgZJ/Hj1Cg/99tv6ff30/XkO8/rH9s//L/6RCqn5/vRMAUMCUQ9p/bD2P/7l/wcDo/52/GUGJgTu9A//nfu2CEcK+uxgAZn7RQL0DL3xrATa/ur32wP49HEEog41ACMA2PNN9pQMUwF1/sICdvsqAgb5xfX5BXUFoAApAxTzt/i1CVL69Qmb9pf3yg9E9KgAOP2S++sNXvii/wsBN/5NAh/9nf1rAAEF6f2sAQQDKvgiAlT5XQUUCuD20gUu/s33UQQ4/ccDxAyb9noEa/1+8aURMPohDWADhPIoB0r2j/wXCw0CbALeBrPzWw1q/rLyWg8K9bgMIwEe+N8J4+sr/0QJ7P+WDV38NfTYBEX7GQWGCNb5Kwub+sUACQM+8XAJLf+aCYUDvveH+JMHygcZ+jcOjvdEEDj2xwJQ/CPlJSk28vH+gRYU2ZogqQCS1yQps/FmCXgf9tIdIbrpIf2SG5DJ1S47ELHXrifjzBb5rlJwv54Iqgt0BCEitbj1CHIE2RGLHNzdMe/EDWPSiR2sIJje5yf70DLw5wW48lsTxO33FisUX9EB+Hnpy/gpRA/O5hl0AO/hFgIYAZAJf/rNCO3trAzzGAHoB+41/k0iMwmQ/qoBGtX4K5/wPsQlM7rarD7ZBUS+2QKH+tUo7+1F+Qj86RO7+6T5aupOEbEec8uaN6fkb/IGIXLfpv5TI7faOw9M/urv/Rwl8FUE/e+HBqMfGtgZFRYCt+wsEafw6fzBFiH/+/ef9bkWBed9A1EOCeFhLDrv0gTd7136xhvW5XkgjvNm6WgkXO2X7d8i6O3ZBsX++/Yt/pYUI/N37xMftvYJ7vsDQQUgCRP/kPvb7fMRYRE/33UZ0/kG9xkV7fOfEBzoWxR4A63tkSp91/j/BC/22XoRTgKa5sYiOPnm+c8Ea/3cCfIGjuviDiH/igHJFrPWOCEYBdPqDhdU8lD0cx2j9yL3HxXQ5UoTegXH9KIN4ej0DkUL5vdXE2Xyef2XC2X6OgCBBrkCFu7tGdT3+/cTER3n9xtyBXDwHQdS894VpgDT8DkZs+xrBbYQvt5iH3z3+/JTFrPvjAP8/WwI/P/e8sgJl/pSCZwBqOwfAZYL3gG77O4G2/G5+3Eh9OY2CQj8PeYCGg73mv66BDz6LwQo7uQHDgYB/5UIAumNAHoK+gTR/y/48QAzAAcJffNk/F8L/P6r+7b1rQI+A6oJXvhd8wgKOvgbCf0G8vmC/X/xdw0UA5z8XAtb7WwJkv0D+DkM9u/CCnUJBfgQ/Fr3If+xEW7+nO8fADT06A8wCYb5xQYw5FcL5QSd/okLFOkHF4P78vOyAh7uJxZMEVTyewXM5rz4ZhUfAWQL8vVT+mXyggAPHP7t/hV99JTvahGY60P4FCN8FYntF/Ko1ZEacChwAA0CDOiBAAzaww0UMAHoXhH456bjFTK934MKVwoI7VIm/dFnBNQHYvn9TCDZMd+n8jzwS04g/vfhcfcC5dIiiRCq+o0FTuzqCgUD1+wnBYUEpw81Egn2qeNK/GYNDiSmFezeB/Aa/48LTByS8ED7t/Zf+LAOqPV2HF4AIuUDHA/iwApWE7nyJhaE66r8zPzA+akGdgE5GEDpIPZcBhnshg4NAqUSKAZX0MAEEvxvLG0RVMg2/HPudxsBKNvYc/Wf5VkHgSmC82/sHPDo/CQco/ZR6QT+2euvHqIHp/DQ8ErZtxTSITz7/efw330LfyXA/ePzZNbK/9QxdAcg8tHRh/Z+I4kgKPuL10ToIg83JgsGouzP2vj3jicfDKMDI+Fp5nMaPPxmE+rz49kxGQwB0gsYA0bOuQc6Ex4RhQyL0Vn23hA2Cu8hyuPZ4ZAFmgb3Fvv9xuO+/yMPL/obF6HoEu50ITHWdCu1703iNCg20wQrS/To6v0jz9H8JBH2rf1kEiTdWw2cA0ECjQwy95z3ev1NFOj8oQLq8WfykBOtC1P4kfYj/bQDCBdI7r8EOvV6AxgaxuOMJ93WZAjWGQbuoyM848X7pAq+ANcT0wFU65sHNAPcA1QZRtBHD/4Qq/MVFZroYwQLETXprSMn8vj19gqG7loxce936qMTZuuxJAzwFvssGJLoqA6pBmn1bBXN4V4LyQ4Q6lERDfjAAfcMIu8YERv8KvNGCskIlQ+f9mbuPguiCvD9uwZF+4n1qhE7+WQDCQGv62ACPBrl8h0BI/fb9okWUej7D8/6PfGuE2P1zgSV+znvngecBnnwUwqQ+v3zCgMj+R4N/QMh4ksLiAOxBUgHx+HzFbsCXf7l/f724/9MBNAK1usvDpbxggUVDkjwPQZN9+P97xIP6iwP2AWM8EgNGefEDaoPsedtD7nulgJmDafw2gpPENvqKP55As73OCSn6mL62AEW7bId4/aBCDj+HuPMCHgETf2DHDHw5etmAl7u5SKjCpvowvTc+ucTmwCp+BoAp/2dBUT8a/eDE9ENIe1N5koOfROeBYYAEOM0+nkO0AiW9xf2dQ0sDbLyBffi96sJdhd967352gyYDePmXPYTAmMSpBHZ7PD01voYFSwFGwXBBYni/vlqC80TNRMN6S7suwP6Ak0VJ/46+w4GI/DUEwEMjwBVCtjbvBBMEQb7tQP97tMI3Ar1C38BIvwHAKP/+wpKBdcAbf26+xH4HP53AX8WRPit/i/wW/m8K9H3tvMQ/Oj1hxtQ+ArtrxQq+wT7pwJ5/30QVeS68mQRHgiJDYHY5eVxM63/KQmd9BrPVAdMC8UVSiB70P/TtwG5JxEuiM9iz7f6hhhEJ+/3i9Wy79sVIwVzCN32YfS0Bm0CrPgv9/AIUAFI/ZIBFgkg6nv75wgZ/ikR3/QZ6WwTCg9B71r/TvgTCCET2/GX88/5hwspEwb6F/d5+eEE5xT29v33SA2R8vMKv/UW+OYLfPeWBf3+/fgDBmDn6AkjESzuZAx2+5n2KgZe/YMB8Qt+7hP6eARKD04OJ9sK+KIMFQl7GVHpMu/HFOj50QA6Ak/9Kg6M+S74mQEd/NERBwPb7BQGg/dUEDsdS+de9PfzHPk2KVYKMfhk933cdBY+IoDzUgmn4OIHbRJN8tUQ+gGg/ZoL/esU+XYQjATGCDH5VPF/B6kKQgt2+Pn4QvkgDUEOH/yB/EXsnQ5mI+L5ZwIk5BLtUDlpCdz4Ze7v3ZAl4RRk8KP/MPFGDewG9e/uD3oAcvlpCKPv0wToCS74BQqF/yXxRgvADMH9+v7v818F5A2Z+MoFWvztBAf9tuyNGeEDTP2N/bnsaxNGCDz6wvvE7y8HPAvnA1H9TOeYCdcMeACOAbHxHAzzBpr5Bf6u/jkKwwpm+tTwsfgvCbkVwPjy+Oj0afZOGNH98gGJ98PkJQ0gD60Ew/E845gKpRGv/8P0OefLCjoRov2t8ZzfiAiWIKj96OP75QkAHSjjBjrdK+mtACgl8P6k7JfnEvocJzT9o/3Z4hHnvyC6ETwBKeEQ40USzhuXDAb2reDYBLQGRRxEBvTXtv0SAlobQws/43H5dwckDaYNZ/PW6CMIIxd+GqvrlNx5AYcLESi+77XbzBFl9n8S6Qrs2VwMRwMCCX8UW9TtBXr9IP/gItHfkQXFANLreCDL81T/XvaU7VcqFP7y76T7RupwH5kR4+sv9aPoIhM+I3f35+wO6RsJvCHmCSz2ze0J9NQTWRDPAhT4S+y5ClACI/W7E4oA9f0m+APvDhQWCPr1Kf297kICKhBTHS32TNfO/PsKRCp38crjVw+L6vEbJg2EBMf91s8mI54Ss/azDU/YcBgGDXTzzx2W4SQFnww66j4WE+xV/KAhTPTH/YPu+/LHNqrzo/7r+2jhXyDl+UYZwvmA0E0NxA8ZI/r7UtAsAe8OYRS/FR/gl/Zv/KoQwA5N5X39Ru6mDPoZ3PNm+HP41fxqBOICQAlrDnTkm+/3FOP8BBV975jedxlN95gZSv5g2JAIPe7qDR4N1eVGER30BOghD0MGLA21+tLjFgOGDLwHA/y38UToRQ5rF9T1mAQQ38kIRRHG43giz++6BOD069fqNiv79Pwa/M/eHCFP/D4GFgIA6bQLz/2EBw4GDvvJAJHxChZpAyfxOgHd8/4MAwgz+uII4ezHChMXafAoC7rlbg1OGWTnnxTM5yIA+RqT9wsOJ+2b7YUeZf6vA2T/AuFTIXsCEur+CpfoTB0CB3TlnQiE7hYWzws/43gO/fNvCxMMaepGF+jylvW2DoPzDBbp8Lr92RUA7PYFBf/UAAYQi+8FA0kAWQKXDaX0QQg7AtjyzwVTCv8M3vgP89QAyw34B2T0owKm9JEFdBNH8gQFPfGv/HwaR/RD/gr+R/5RG1HpovrBFN71uw02+eX6aQ1o8o4HYAQ58jn/vPkHBGMT5PiH9if1af0bEwMBV+sk953+/AofBt/4R/5XACMD3vp2+5r9Zg9e/gDuwwh2/X0JcvnK890VD/dZ+xMJ//FAEkD74O67CvL0ng3JASXlrwbO/X/+QAku9R8Erwh+6uMHkPyN/MkYmeTsAN8HtPdVF6PxUPw3BW7s/xAKBCv6nwmT6HkC4g19AIYB6fBZB+YKhvyvASb8MQGuBB8DtQC2/J4FJvzL9+8ErQEvB37/3ffmAowAGv62/U38eAYdBPDu5wBNAa0BKwrt7qr6rwn9+0QJafBW+EgPB/ZDCrn1kfduEGz8EwI89un6cRII8RAEAwWT+dsKR/iuCgkAFPI0Dsfz8QikDTPyNA3X+mv+NQ5L9KIPCvmr9E0Ra/ufDir+ffIDDcH8f//nA7wB3gls908CrwdX/dEFuvUeDGIB7fmaDf734xAlCdPt8QNd+UAEbBQ97ZQK7AOc8k0cbu8HAsYVY+ooEKb5De/WH9bqzQ7mDA7pEhPi77QIWReR5i4GPP2hDKATJujt9zn6sBgvFqXi0fS6/joaBw3w4En8hgbwEFUOW+Gc/9oKfAUvB5fi8vthD74L8wtZ5JXrzxKvDcT/xvVX8mIVsgXz+OP6YfCGEWcIEPy0C7X1YP9fC/vr7QgKB/kF0BKT2vwEtgpq/AgR4OWiAU0UpvHtAXrwi/LlGyb5qAM1/qXcKhOGAhv+jA2e5RAAlAuS/OkMyulL8kQQM/1fCJf3BurZENMHG/LY/NnrDwomF6DwWAHN5UYJ4RuT6VkGzuywApAf5/XyAPzrPPxEFb/3UQQn/vX2kB/S7+L2AABI8/gjW/ZE+7wBvuqyGU0BTvYKCKX2iBJpAKjjwAzcBJsO+QBh4t8VEP65BYIAbeAJGdP+NgKPBHzdcxHnBCn+OhTU1RgKvwwz5uEfQunbB08TldvDGJjvuv77Farc6gzGBzfx6g4c6TYC/A+k7+AN0vpa/JYZnfT7/wj4Wfg+EcX+xQFF+Bv29AnmBcADNPnU+aQLcgXPAdYH8/RvB1oCDfZ/CP/xIgx+/oD4QB1F9er9TPsx+I8UD/1tBfjxBucXHvcCdAC3+w73tBoC8gcEyAGl5bQdLQJn/mMKnuweDxL0UPnjHO/qdgXtAMryow8JAtUCZftI8rUUNwJX+3EG5u93/bP8xwiMAgHxewaJ+3H8FPwQ/ncBTPzI/8f7OveOAIICWvmq9mr2jwZzA9/9ggKY8Nj8s/8p+pwBN//GAkj6KvFH/2sH1fWx/on/GPRMCbL4ivgQ/7j87hH09Uzt+gkfBjoGKv117kkC4QonAnL/4fG3AnIMf/zi/c79Gv4JA4YD7/8pCNz/TfssByL3nAnIAsD3CAGX80gQSgXa+w0CiPn4B/734QHhAaIAbwhj9gP/uvuYAcMKVPavA43/n/ljB4n/mgPM//z3vwv3/0j5xQtv80ADuP6Y+j0Ye++jC1EB2u2UFOX86AUyACf1CxUuAVb2jQwN89wCqhN08gwMdvRGBn0Lneg9FdD8VftlEIP4xwfN+R7/mg3w8O4ETQtV9qwO6P6Q/o8HjPeHDQP4O/zYEED2MgRiAQj/PgUA+NYCav/6AMYGHfdh/OAJzQOL/Tv65/2qB84DywMZ+oP56QW0Ahn4kP4fDKYDWQBh+PT2Ug77B5D/L+5/9lQhHQHl7wz8uP9jFCj+B/je86r07hHzC4D77v+p+hbzGvl6/pYFVP8GB8UNyPUg55f3XwxRDKD77vRc+3oJ9wkk76L5yf2NCAQBA+kgDyQIA//t/UjrXQ8MA1j9xAbn6KX/qQDR9JwMyfqZ/qb9WvWRDIQEyPF8AcL5e/kIB8/02AyqARn6cAOG6Q4I4wux/KAAZPahABQJ1Pqb+vf1Nf/qFn7/W/lW8tv7HxXTA0wFZvBo+igVUv2H+3Xx0fzkFaEFVgHR89X2uA2J/AkC3wpb+vP8y/xMCBEOFfLB9ecBEgJEE1X7bu/CAf4FnwQY/ML2QQizBsP4xwEQ9AEKSRH18CP9fPfBD6UI2OXAAyYHMw4ECejmTvj/ArYMgQx56B36uwwnDmkMxuca9fcGdhNOFdbzk/TN/acH5gumAdMAQv3t/iUMSQU8/yn+NANDCL77MwI4/RgF+w4BA7cI6ffB/fcMyv2MCmL/6frzC5T5PgV7C6D8chKn9xr53AOy+pkTfQEa9X8Eef7gDvP/Z+5VBsMEBQysAersxAyGBkEGZwcQ6g36svnAClEO2ex0/T72yRBKBg3ss/te9hAPRAo+7rr3Cf0p/3kI8vap/oL+nPmH/7btCAfWCZb5Afr072kCBRRI72r2b/y8/6Ibofbo6dX5uP0YHEUHSudnAD77RwP7CM7maggkAPcI5g9N5wL5IPncC2Yc3vDj9qP33vn0GQT2jvWxAqn7iQ9V/+z3mfqE9O8KvhB8+A4DGPKtAm4M/fF3Bkn62QLQFoL13fCr+Pj94A6cB8T58Pvc+4kEcwo590j/VAD4+9AMbvtX/RwB6/NpB0IKM/xhEp7yfPWBDXroyQywBcX6nA+b8XoO+gCE8AEFp/9ACm8F6e60AwoGjQUTCRrlnv8+C9MHnQrZ8bAA/QE9Bu4GvfTyBvH+u/23DZPuAQxvA8nzRBQK78UHfAlA8FAOifrRAtwJifG+Df75OeorFMUA5A7GAPLm7QrIAHsF6gQ083EFWQuQAnAAW/bV8wAPhgYRBs37tOgWDZoG3PoYBxT1CAbTA/X2GQOW9w0BGQdc9NIGaPxn/kgIyfN8/qQAEwV0BenzUfcw/isEnwFiArf+O/sB+hEBVgQ/A0n/wPdfBoIDwgDf+570ywfvBcT+R/nX964HKwh2+n3+J/Di/XkJVf4a+5/qEf2xCmr/FwCt/4v2gPl19xj4Dg4hBRz+HfNs9igIxvwN/fD6VwHCAvn+aQPv8w7+VP8hBm7/VP3N/4/75PPO/FL+pAO3Cbj1tgGj/xUAhQT78skFTQj/+1H/1QBO/yAFZfgD/vAHOP4gDXz29/dJC9D/+goT8HvuiQ8S/2UHOQRW71IQffod96IDTfIDFIgEbvM+DgP6xgxJ/FTpHw35/1oP7wNb5r0BTQF7C8sKPPIR/3f6LwJRFNf9hf3J+HT53QkmC+MHsfmg9DoJyw2dBiz8X/nFBKEDNwIj+QX+0QtzApcCXvnWAecE3vwcBS8B9v2ZD1380/yKBOj0CgdP+EAHgRn176/6a/nx+bMcqf/c9wj0g/LxJ5QHZvhr+trqYh4XE0j7LffI31wP0g9jASkFZu/nAV4N5/qQCd31KP/KDcz1UhNr/AD5gv55+cAXcgNo7iv7XP8kGvINkOvc9lj3TBaPFU705/x08Nn7RwkMCiAQbPR06coALQcgGyQDgtsS8/cGtRqVDm3h9PSK+WP/uBOL8aD7RQCs9GcMuPkM+tz6OezuCpUNyPpy99vsof7fFskEm/hA8l729Q6uAV7xdwVbAfoFNALJ6WQHXwgD+cYFBu9zCtkIYO8ZBiL7xgpnCuTv8wLU/TEGqAON9G4IBgZDAl397vWUCCkFJAyg9unwJgKe/kYLQv1r+WUHyPhLBAYFY/OpDJb5yv9iCL/xTRAr8Rf6vhUU+pIK6vaP8IoWHPypBLn7femrIbwFM/Ii/J7m1RgBByv2fwVN7DsR8QhT8PsIRvd0AkICWPfKEQ/72PYFANPyyw/MAhv8zvzA9VsLuwLMACcGWPHaAU0EegHrAF75uQJoAiAAFQfn/Qj81P8FByoIQ/z++r37SAWr//L7HAToAkUKL/RP9koIcfwQByEBsvUGByP9iARjAofzHwZJ/XwBkwk4/ZwACPnS+lQIqf9MAeX6hvNOCCAB5QbO/fT3RwIl+jUE/f5l/vEHYPovAmH/V/hDDvz3zgDiAXXuJxAu/C38Ngk78XUErfvi8yYTWPX/BBD8ueiqEy/9eAXDAYnkPQyTAB8ClRDx52v3QQQD9xwbe/dh8RMBHu7sGCEAJ/EdA7fxtwTdCZf32gHh/h/5+gVl9ogJLQmw6AntUgR1E0QW2PgV4h375gekGIMJ//AB/acBAwSCC5MEBP9ABPfvVQTsBQsFPwop8bH+MwOp/aQH8wfU+o0FlPrI+48MgwB6AlL6h/EoD3gKBwOu/1nmUgKMB6sNLA6R7AAACgWn90MVtvYL+TcKp/bDDSj7vfVAEKfxcQ++Edz0dgeD7u4GcBP88C4FNu0qANMiJQJO/1vmDPWiIEMHvv9i+TL6qRNw+ZP8kQWP/okSOPI5ANMEMP2PFe78ogKcBM7qUgbbBksCohGp7JYABwqQ+fUS4Owh/kUSXPpIEH75NveSEfLzswnCDNbyyA6l81H0ZA/g+IYUEflo7hULkftbCWgCbe4LCUz89AHEBsju/wbbAcD37g0s8iYBPghc+9EKEPQY9k8C0P9+Eb0BO+4x8v8EDBNCBezwPO8k+4gTnQp98bLt1u+xFE4OPQFa+U7pqwgCDMIBnfhl5P8F+hOn/QcIsOkt70MLZPqQEuv6pOnSA4/4zgnxB0ftK/xi+YoGWREU7xnzwfXtARMV4f+C9Cv/g/6VBm4CdvJU/5j+9P5DCGL+yQLOAiTy1QDiAef/Zgvc894A4ADe/PUI3/T+BzMGUfFl/hz4Qg0sD7vxtfuu+okDZA3G9KL73/r4A+0N5PbNBN77ivkfBZz1ngh3Bef+hwl38vX76gSc+oIQ3/0b/zsCqOsiDCoDWfhaBkLuCAmXCz7yugCM7+0AGRdQ+rMAGPCu9HITVABJBsf7tfPlFKH+0/wV+/XyABCPCK78c/p39KQEwwnCAiv4tPYbBQgKpAM1+9LzFATPBVUAAvsD+gUHGQVXBNL7Pfk5CO8DsACDASnyZAVtBzj8Fgo89msB5ASb/WMQA/rM8RQAcv6gFI4CffFmBhX5BQpjBwTtgQot/U0DoAgn7+IEy/xL/B8QVfk5/ZcC9ve+BI7/iQPSA4vzCQMeAFn+bQZO+Jf3kgDVAqAKDvjA8qQCAADZAO8Cb/0z/gf7gfweA0n6DgciBO7uMwXyBrX8dQWi9Er+BQSI+KsLjfLg6gYNOfko/5gCefgnGS705u7eCN3ygBSn+VHo7RmSBD0CBOp85OokUhfK7l7w3fNBGBIKO+QoAv0I9QTH8Tf+7xOB928BlfcQCJkYfdvEBv0SzwD5EPLVExijGDnXiRjh9l0a1hZ60JAiLP/l5tEh+fmtFST33NOiJu8WZ/va2/XsjDxvEr/SI+vSEUoVmQRv9UsCLwAb68IgHQvE00rzngVcOywFEr5s+5ASjDFW+PHMxhV4CtP9GAgx/JP9IOFSEzMt//Z55DnnDSTYH7rf7/c+BaURmggz578OiwYQ5aYOAQ8OBUfwrNxCLWwRmdygAvADPCbu6KbULyXEDkQIZ+fw7dY3Wu9m5DgLogUxF8bab/F4M2z5keyM8VoNAxrB05P5liNE/Zb93OccBdcSguvBCXkFyfSl+1T9oSQV8TLa/g1jHvUMHsoP6ltBUgUL4SbguQ2hLdLegfXkGGXyUu/m+Pwd7xTaz+btxyxHEXXd3t01GIY1kt503JkaIxZN/ejFUAhLOnfpquSJ6/8frx2PxrwIQA9EAyv1Js+eO5cMh85wCnH/Risw5q7DIzlcAyzwLfYM71c0muva1gUkewg5AZfdbuinQr8BvNRQ/zgNbBkj6P3dpikuDpLko/DaBukjcuv112UdfhbU+u/Y6f0NNxvvjuGT9PMY0htl2P4E6wrUCwr3yNSMLOMKvOr19C76Gj2k24/Y3x8XChMX2MZn+RZGGtyn9jntoBYdN/W/l/2WFLgGlRo8yHQMTBt65HUSm/GdFQMLJdZwEP8H1xNb+bviyhsz/AT69fUu/n4m7eYI8fYFQAnyFI7qeQgRCgDuSgVeBecZd/dc0wUY0R0g/mzv4wBZHbP0Jd/mEDUnn/2I3PX30Bo7E2LOmQDQN8roIOT+4Xs2HUjIn27aaCTbNF4Sf4I8DepdhfZz3KbH8i/mNEO1bPoFIdoHm93zyLtGRT3KuL63uyjpWo/riZg3609uEA9mwEDeXw6JOljNae9XL1Dz+OjL2aI2IiOdvODqciyBKELVQM7YKjAsUdu/zNYVYTw18vPDJw6SKRj8ntI+DH4yC+To238BaDT2EXetggTbKAcayvHFv8IoNRqn4x/9LP/sLPz13LwiGfcmPQWl8gK9HCGtMrftvwfw08AIuhaZ9IArkufX40IG/ANxNE7uQs6YDOgIVyV572jaxxEPA+sOKf607ScOzvH/AlsTVfh+/53uc/9/Htn5uPLbAQD9UBU++rrs0RdY/dgEZflk7sUah/9j74QLnv3DBur8P+3RDy4Qs/PU/1b7+AkKG+XbRAoHDK/yphHF4wAhQhvo2Hjxif4lGnYejN218IgXLgGlArXgzQMiMSj3heWr6VsMfDvW98DgwOyrApEqhwJrAlLhp/viBdv9/zFQ7lPqs99hBVFUBPoC3Qfbm/tgUB74sdGI+9b33yWI9AD3lx7l7zvqQPoTDmghhviZzrgNNAxrE7T3vtXMG3MDhvTz/i7xpgx/BJXb2gnyD/4LvPhzxjUQpx8UAPnyst0UFO4b2exm9/XviwwZEQv0/wTy9JADnfoaAiD9cfxSEaT9afxG61QIaxDVCwDv6eiZDYIIWQwT39f/tA/C+lgEKfU1ChEG8d8bAQcM9QZmCTTYqALGFMv+dwbp43oQvQvW5CYDEP0WFBYL+dzqABsETgVhEIff1AzMC9D2mBA93A0RFgRB8MAOOfU3CO//e/gFDfTx7O1TF2YJCwvu7uXqnxo//hoMrOhl/loc1wmgAZHskwY0BAIAlAJ+Csvuevc4B04Jihiv4swEOAX++kEUD+iyBEwWZPNLDizy4PWsIYLuvxSH+f7juSSc+AoVw/pD3xge7/sJAOIJ8+hSC6EAdfl/Fo7z0ftSCVj5vxkh+53yiBGy8vcHVQc3+YkKJPAK9g4VqgO5/s32mezgGg8GXvbaAOnxfQpJBM/9qgZu9rf83gLS+ucCWAtvAqjyPPWOBCUWYQnI46P9jv9zBk4OZO78BkT+LvRjDg37hQMCBoPnFg31/rL8rhFl4ZEGUf0hACYPQO9G/IcGFgCF/1v3+PijFAv4bPo//OL5gxEz91L92fQkAVcG0APh/qH6RAI6+P8I7P74BVn88PQaBZr3gAkMBEf9nP7y9YQFlAXK+OIBZveL/c4HNPOXC2z/nPtzCDjpjg49Bev0lwaY7mkM1A6T71cGDvVU/LAWWu2ZBXcEd+5LGCHuRgCIDtbl8hRh+jHw9haG76MMCgTT4VkYCQHT+NkSHeAMFlAEJuzOFkbs4Q+gBcnoGA7g++4Csg0S7/T9FwEt/3YVu+3N9awFkgoxBk/1I/KrBMITqf0W+RjyaAP2FO3/JvdP+nD3Lg/YDCD5SA5y56ACcw60+ekTY+3H/HwOTPIRC8cFGPMIFpHvJPmJEW3sfCLs9yfurwvZ5ycfhgyK7oEDQuiXE84YIOzTBXT0VwPEGs/t+gs481XzoRF9+CEFrADB8aERk/KM6xcILwsCMz7wdcga24AWe0kjFI/dTbpT7GgfAkDEKrjO+7Q41C4sb2dkBoCx3MIzAftTNgfLy4vxN/dZMsULCs8s38fqlULHODTYYr8w0KsqSVUl99LHcMkt+fk83w479P3h2eatFaEKugCM7QLznhIKE2/90uD38AsZshRRDoroA+OoCov3DiU8BFPuAgNW2FEQcRMk/PofQNre9uUNAPE/KlvyEvBbE5LckhMF/tb5/BoP5h0BIBYN6v8KAe8V/5Iwq+ci9UjutvdcMV/4je+3+l/xFhPPCDHyjwZg9UcOMguH40r7JQ98CEMJMfJZ9M8Ilvo7DZQJzviB/hX0tgGmEjD+E/jpB9P2EQZn/ecCLxPk9sD27fxfDmsL6PdH8p8MNg4R9q4BNvl7AXYKTgDQDLX8PO0dDhEAag/TAtjmyA+uANwD7w6Q6JUQMf2r/CMae+anBfgGVPcAHsDhLvmjFZf1lBqn9fjxgBcD6BsTiAIZ6gocF/QuAywE0PF1HPz2IPcSBpT2Oxab/cfvWQchBCESKvKv84wF/gScCcL5TwA7Affz7ga8BLD/7wK17o0JxQpY+lwN7u+K/YwLwfTREFP5LPXREZXq6A/8CCnt6BHy6ucCvxFx7VocLO1U7yQQhu4qG08A4uv1/6fnfBD8Eu74j/go7oP7XxEY+a8FCgaw8YIAtvUOAvEO+Pf48VD9zQOrBBDx3/BFEQsF9v8w9q7qVP+QEo0HtfVE+Ej3+PyQB4UOjAL37i76mw6a+5IIoAPq67EXMfds+PgAYv54Ff/jP/t/FcX2B/+a81zvqiHJEKvt5usZ+rMiOfK86QEdMwHd8XXupu6EMFwc2d2b1/P8KT3LBfXPVwakDpn4HPxp7qkh4Rzazu32wwqGJcQJ/bfmEH0nYQhS/Y6/8xU5MObe1v2yBJsFtvoU5bsrWwZ51ML+VAHUMEEPQcZ17AIYjSkI8TLbagzQC9L6PgccA9oAZvg179YJigutDe7nSezqJaj6Fvb8+VH9rBTa7v8Bxgiq7+EdofBy6zUhMvfmCbzxrvQdKmfsXA9WAWXqWhjc6+oM/Ru76yn1ou6LEXEYq+1XAoUOa/cd/ZTy0A7EJ8rtBt8Y/ZgdtR663djmyCVmB9H2PfBN/r0gKOf57iofcwxG9ZXTiAAiO4/7hd4u7MkI4B9z8I3uwxdLEWPcWtaKI/MxUvmAwM32My9lE0fqGdCIGOg0ddwk0nQbaR/v9rjY2gCTL130q+H35dsiskWsxaa2biRBL58Irc4B/FooBu6C7vMAxyb0IfG3ZNB2QLwvPuByxtgDyz39BCvWEu0CGfYuEt2C0K4uMhrq4eDetgs5NuH0L8ML+QsygDEsyby7SDLDOmP0f8UC9XI53BDk0X7U0imCNY7gHsqxFPosUu8Z1tsO/zGc+MbDC/N/RZQrOLvPwms1rDwB7Ti+0wBYRyL8YNV17aIhfCoqxtHnBTzNFavjb8VnHZdLaOXRzpbyxCusMbLKg9fLLwMbEunI258NAj3k6LjNngZ5JkwjV9A5zwQ0HiH35XrYLQLgNaT42cpaATIlHRu705nVsDJDJ/vlztUDDoAx5fKo0N79tja8FAbFc+AAJWEjVORd3D4mTRvr1jHVpBPHTlgAzKbC5bpE+TrnzQLC6SxcLurgY9FzEf0/DfU1tHzzfUqFIWe7PclPQmpA88l7vrENaEhhDvClf/FHUZAYob+U0JRIzjjJvKXR8htFO0n/56guA6ZO2wewup7aMEUCIj6+U+R4J4ApcNs/sDYmwmNb8UaSLdwAXcEu48f906cVuSMp4brNjRYRSwbzKaj2+WNCoRMoxmfbuzTyKqbUvbt6ECNM1vh1vPoEOyrR97vQ5P6cQoEYcbGqyW8zeVCA9darAfwZMFIROPGl57AigBhU0l309SoXGf7Kxdp7PQgzXtkir0UGR14gKGezpLd9NnBEOPBR0i/1LCDKDqfmPPMMDgsP4uzh9rkn3P0px8n6YDWIJtvoh7+6+78yuRr07AvikwalB+LxdxMgDz/p7vP5Ap0Zogwh0uHpeCKCOGH83LCV94kwUSe5/qfKT/AmG4oTgQI5/D0CX+wn79wfChjE7ZffXfqzJrUhgNeGzJsY4UMBCde7aOUPKlgilwHD3PLxuiEV/t31aQpdCa70odtQGyAxBeew0A/4nC5NLifTY8SjH5IxrPyf07P41y+49QzjvQyWCCMYgeXs3EcnkBb598behvofKIP9RutyBO7zmgX+CPX8TQ9V64ProRLaGyADn9Ms9WUrJhr18NfTaP02KMkPKuuZ3k8a8Q9v7p8AU/fdEFP9iOuwGEEC1vV88oMFritn6APWBxnDHvYJH9dX4cIkViNU8g3XNfw6KdINxuPr+owQP/g3/mUCZwvaAvfb8gPVFrcCp/Iu56AkvBPC1gDt0hARNO343bfy/vo2vyTp0qLBwB5GNo3/BNCL4kcpOCeK7IPfngSPF5gEfPc3ByP+RvHj/acQOhaZ8XLcDAYLJoINDtxn7A4diBCE9H3sPAUWF9/vDuzBBuUSTQGC417/SRQTAIb1ffUKCVYSpOoV6UgRoht9/ffVSgGEJ4H2xuNU99UaJRdx3NbnHw9fJhD51MgD/5kqZw3q5V/eSBB6HPf2TOj0+gEfyf5o2sUBxBTcB5zrbfJ4GMsHwOoE8yIPiheE8kjk/ArmBD0GxQSC85kGi/77/SgDje8S//L3hg/UK0bgGdp59CYc8Dv23efBawwtOs4wHbtazFYztC3cAuu8xN6XQ8ofleRv208E5itI/CzmUwFSDrESaOuw7lwU8f0e9BEPDxBc/MPeeP5PJLIHsO407UgHzRRo6s74yBDDBVzzUOw7D8MWvuqs6pL+4xQcC1by3vZI87//nA5nA8z6eu/n+uf+lQqSFSjvp+tM+1AKBR4i7nDtvPceBx4X4u6Z81YCDfjFEoLwtPNZADn42hNR9pHxXAtA6ogLbhNC7DL+W/6JEt78JejjFNQQiAZL6kPekBgZKEv0/9809GARYQlXCPL6KO57BHkLPw1J/U72n/UX/c4YUwgt7qjx0fISDGgVd/4x8wr0kgGpC8AEW/NA/OXunwIEHBH1Z+Y16+ETsSPT6a3b2/BRHmQtXuEF2K0QKCDHAIbXh/G0IskReu0Y8CoACQrC+dn1XRPgB2/rRPutClYe3O6R5Lcnuw1L9qPoxvzDM3gBpuAH+bgM1iRX7ynaRhjbDUAGS/pa33oU7xdX8EUB0wCS/OsIevI/CqILDQDcBNDx0ghKAonw8B05AQnkzgvd/3IQ4gho3LgCtyRHDtzs8OWCEm4dsgGd4+7/yhFjB7kDLvtHCmz4nfM=\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import namedtuple\n",
    "from functools import partial\n",
    "import gc\n",
    "from pathlib import Path\n",
    "\n",
    "from diffusers import AudioLDM2Pipeline\n",
    "from IPython.display import Audio\n",
    "import numpy as np\n",
    "import openvino as ov\n",
    "import torch\n",
    "\n",
    "MODEL_ID = \"cvssp/audioldm2\"\n",
    "pipe = AudioLDM2Pipeline.from_pretrained(MODEL_ID)\n",
    "\n",
    "prompt = \"birds singing in the forest\"\n",
    "negative_prompt = \"Low quality\"\n",
    "audio = pipe(\n",
    "    prompt,\n",
    "    negative_prompt=negative_prompt,\n",
    "    num_inference_steps=150,\n",
    "    audio_length_in_s=3.0,\n",
    ").audios[0]\n",
    "\n",
    "sampling_rate = 16000\n",
    "Audio(audio, rate=sampling_rate)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert models to OpenVINO Intermediate representation (IR) format \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "[Model conversion API](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html) enables direct conversion of PyTorch models backing the pipeline. We need to provide a model object, input data for model tracing to `ov.convert_model` function to obtain OpenVINO `ov.Model` object instance. Model can be saved on disk for next deployment using `ov.save_model` function.\n",
    "\n",
    "The pipeline consists of seven important parts:\n",
    "\n",
    "* T5 and CLAP Text Encoders for creation condition to generate an sound from a text prompt.\n",
    "* Projection model to merge outputs from the two text encoders.\n",
    "* GPT-2 language model to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders.\n",
    "* Vocoder to convert the mel-spectrogram latents to the final audio waveform.\n",
    "* Unet for step-by-step denoising latent image representation.\n",
    "* Autoencoder (VAE) for decoding latent space to image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_base_folder = Path(\"models\")\n",
    "\n",
    "\n",
    "def cleanup_torchscript_cache():\n",
    "    \"\"\"\n",
    "    Helper for removing cached model representation\n",
    "    \"\"\"\n",
    "    torch._C._jit_clear_class_registry()\n",
    "    torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()\n",
    "    torch.jit._state._clear_class_state()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLAP Text Encoder Conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "First frozen text-encoder. AudioLDM2 uses the joint audio-text embedding model\n",
    "[CLAP](https://huggingface.co/docs/transformers/model_doc/clap#transformers.CLAPTextModelWithProjection),\n",
    "specifically the [`laion/clap-htsat-unfused`](https://huggingface.co/laion/clap-htsat-unfused) variant. The\n",
    "text branch is used to encode the text prompt to a prompt embedding. The full audio-text model is used to\n",
    "rank generated waveforms against the text prompt by computing similarity scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/transformers/modeling_utils.py:4664: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Encoder successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "class ClapEncoderWrapper(torch.nn.Module):\n",
    "    def __init__(self, encoder):\n",
    "        super().__init__()\n",
    "        encoder.eval()\n",
    "        self.encoder = encoder\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        return self.encoder.get_text_features(input_ids, attention_mask)\n",
    "\n",
    "\n",
    "clap_text_encoder_ir_path = models_base_folder / \"clap_text_encoder.xml\"\n",
    "\n",
    "if not clap_text_encoder_ir_path.exists():\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            ClapEncoderWrapper(pipe.text_encoder),  # model instance\n",
    "            example_input={\n",
    "                \"input_ids\": torch.ones((1, 512), dtype=torch.long),\n",
    "                \"attention_mask\": torch.ones((1, 512), dtype=torch.long),\n",
    "            },  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, clap_text_encoder_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"Text Encoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"Text Encoder will be loaded from {clap_text_encoder_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T5 Text Encoder Conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "As second frozen text-encoder, AudioLDM2 uses the [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the\n",
    "            [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) variant.\n",
    "\n",
    "The text-encoder is responsible for transforming the input prompt, for example, \"birds singing in the forest\" into an embedding space that can be understood by the U-Net. It is usually a simple transformer-based encoder that maps a sequence of input tokens to a sequence of latent text embeddings.\n",
    "\n",
    "The input of the text encoder is tensor `input_ids`, which contains indexes of tokens from text processed by the tokenizer and padded to the maximum length accepted by the model. Model outputs are two tensors: `last_hidden_state` - hidden state from the last MultiHeadAttention layer in the model and `pooler_out` - pooled output for whole model hidden states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text Encoder successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "t5_text_encoder_ir_path = models_base_folder / \"t5_text_encoder.xml\"\n",
    "\n",
    "if not t5_text_encoder_ir_path.exists():\n",
    "    pipe.text_encoder_2.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.text_encoder_2,  # model instance\n",
    "            example_input=torch.ones((1, 7), dtype=torch.long),  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, t5_text_encoder_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"Text Encoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"Text Encoder will be loaded from {t5_text_encoder_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Projection model conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "A trained model used to linearly project the hidden-states from the first and second text encoder models and insert learned Start Of Sequence and End Of Sequence token embeddings. The projected hidden-states from the two text encoders are concatenated to give the input to the language model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Projection Model successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "projection_model_ir_path = models_base_folder / \"projection_model.xml\"\n",
    "\n",
    "projection_model_inputs = {\n",
    "    \"hidden_states\": torch.randn((1, 1, 512), dtype=torch.float32),\n",
    "    \"hidden_states_1\": torch.randn((1, 7, 1024), dtype=torch.float32),\n",
    "    \"attention_mask\": torch.ones((1, 1), dtype=torch.int64),\n",
    "    \"attention_mask_1\": torch.ones((1, 7), dtype=torch.int64),\n",
    "}\n",
    "\n",
    "if not projection_model_ir_path.exists():\n",
    "    pipe.projection_model.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.projection_model,  # model instance\n",
    "            example_input=projection_model_inputs,  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, projection_model_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"The Projection Model successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"The Projection Model will be loaded from {projection_model_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPT-2 conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "[GPT-2](https://huggingface.co/gpt2) is an auto-regressive language model used to generate a sequence of hidden-states conditioned on the projected outputs from the two text encoders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:114: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if (input_shape[-1] > 1 or self.sliding_window is not None) and self.is_causal:\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/transformers/modeling_attn_mask_utils.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if past_key_values_length > 0:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Projection Model successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "language_model_ir_path = models_base_folder / \"language_model.xml\"\n",
    "\n",
    "language_model_inputs = {\n",
    "    \"inputs_embeds\": torch.randn((1, 12, 768), dtype=torch.float32),\n",
    "    \"attention_mask\": torch.ones((1, 12), dtype=torch.int64),\n",
    "}\n",
    "\n",
    "if not language_model_ir_path.exists():\n",
    "    pipe.language_model.config.torchscript = True\n",
    "    pipe.language_model.eval()\n",
    "    pipe.language_model.__call__ = partial(\n",
    "        pipe.language_model.__call__,\n",
    "        kwargs={\"past_key_values\": None, \"use_cache\": False, \"return_dict\": False},\n",
    "    )\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.language_model,  # model instance\n",
    "            example_input=language_model_inputs,  # inputs for model tracing\n",
    "        )\n",
    "\n",
    "    ov_model.inputs[0].get_node().set_partial_shape(ov.PartialShape([1, -1]))\n",
    "    ov_model.inputs[0].get_node().set_element_type(ov.Type.i64)\n",
    "    ov_model.inputs[1].get_node().set_partial_shape(ov.PartialShape([1, -1, 768]))\n",
    "    ov_model.inputs[1].get_node().set_element_type(ov.Type.f32)\n",
    "\n",
    "    ov_model.validate_nodes_and_infer_types()\n",
    "\n",
    "    ov.save_model(ov_model, language_model_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"The Projection Model successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"The Projection Model will be loaded from {language_model_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vocoder conversion\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "[`SpeechT5 HiFi-GAN Vocoder`](https://huggingface.co/microsoft/speecht5_hifigan) is used to convert the mel-spectrogram latents to the final audio waveform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Vocoder successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "vocoder_ir_path = models_base_folder / \"vocoder.xml\"\n",
    "\n",
    "if not vocoder_ir_path.exists():\n",
    "    pipe.vocoder.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(\n",
    "            pipe.vocoder,  # model instance\n",
    "            example_input=torch.ones((1, 700, 64), dtype=torch.float32),  # inputs for model tracing\n",
    "        )\n",
    "    ov.save_model(ov_model, vocoder_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"The Vocoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"The Vocoder will be loaded from {vocoder_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UNet conversion \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "The UNet model is used to denoise the encoded audio latents. The process of UNet model conversion remains the same, like for original Stable Diffusion model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/pipelines/audioldm2/modeling_audioldm2.py:736: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/models/downsampling.py:136: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  assert hidden_states.shape[1] == self.channels\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/models/downsampling.py:145: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  assert hidden_states.shape[1] == self.channels\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/models/attention_processor.py:613: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if current_length != target_length:\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/models/attention_processor.py:628: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if attention_mask.shape[0] < batch_size * head_size:\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  assert hidden_states.shape[1] == self.channels\n",
      "/home/ea/work/my_optimum_intel/optimum_env/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if hidden_states.shape[0] >= 64:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unet successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "unet_ir_path = models_base_folder / \"unet.xml\"\n",
    "\n",
    "pipe.unet.eval()\n",
    "unet_inputs = {\n",
    "    \"sample\": torch.randn((2, 8, 75, 16), dtype=torch.float32),\n",
    "    \"timestep\": torch.tensor(1, dtype=torch.int64),\n",
    "    \"encoder_hidden_states\": torch.randn((2, 8, 768), dtype=torch.float32),\n",
    "    \"encoder_hidden_states_1\": torch.randn((2, 7, 1024), dtype=torch.float32),\n",
    "    \"encoder_attention_mask_1\": torch.ones((2, 7), dtype=torch.int64),\n",
    "}\n",
    "\n",
    "if not unet_ir_path.exists():\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(pipe.unet, example_input=unet_inputs)\n",
    "\n",
    "    ov_model.inputs[0].get_node().set_partial_shape(ov.PartialShape((2, 8, -1, 16)))\n",
    "    ov_model.inputs[2].get_node().set_partial_shape(ov.PartialShape((2, 8, 768)))\n",
    "    ov_model.inputs[3].get_node().set_partial_shape(ov.PartialShape((2, -1, 1024)))\n",
    "    ov_model.inputs[4].get_node().set_partial_shape(ov.PartialShape((2, -1)))\n",
    "    ov_model.validate_nodes_and_infer_types()\n",
    "\n",
    "    ov.save_model(ov_model, unet_ir_path)\n",
    "\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"Unet successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"Unet will be loaded from {unet_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAE Decoder conversion \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "The VAE model has two parts, an encoder, and a decoder. The encoder is used to convert the image into a low-dimensional latent representation, which will serve as the input to the U-Net model. The decoder, conversely, transforms the latent representation back into an image.\n",
    "\n",
    "During latent diffusion training, the encoder is used to get the latent representations (latents) of the images for the forward diffusion process, which applies more and more noise at each step. During inference, the denoised latents generated by the reverse diffusion process are converted back into images using the VAE decoder. During inference, we will see that we **only need the VAE decoder**. You can find instructions on how to convert the encoder part in a stable diffusion [notebook](../stable-diffusion-text-to-image/stable-diffusion-text-to-image.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE decoder successfully converted to IR\n"
     ]
    }
   ],
   "source": [
    "vae_ir_path = models_base_folder / \"vae.xml\"\n",
    "\n",
    "\n",
    "class VAEDecoderWrapper(torch.nn.Module):\n",
    "    def __init__(self, vae):\n",
    "        super().__init__()\n",
    "        vae.eval()\n",
    "        self.vae = vae\n",
    "\n",
    "    def forward(self, latents):\n",
    "        return self.vae.decode(latents)\n",
    "\n",
    "\n",
    "if not vae_ir_path.exists():\n",
    "    vae_decoder = VAEDecoderWrapper(pipe.vae)\n",
    "    latents = torch.zeros((1, 8, 175, 16))\n",
    "\n",
    "    vae_decoder.eval()\n",
    "    with torch.no_grad():\n",
    "        ov_model = ov.convert_model(vae_decoder, example_input=latents)\n",
    "        ov.save_model(ov_model, vae_ir_path)\n",
    "    del ov_model\n",
    "    cleanup_torchscript_cache()\n",
    "    gc.collect()\n",
    "    print(\"VAE decoder successfully converted to IR\")\n",
    "else:\n",
    "    print(f\"VAE decoder will be loaded from {vae_ir_path}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select inference device for AudioLDM2 pipeline \n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "\n",
    "select device from dropdown list for running inference using OpenVINO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7747edfa958b454fadff32ec425ed43b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Device:', options=('CPU', 'AUTO'), value='CPU')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "core = ov.Core()\n",
    "\n",
    "device = widgets.Dropdown(\n",
    "    options=core.available_devices + [\"AUTO\"],\n",
    "    value=\"CPU\",\n",
    "    description=\"Device:\",\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "device"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adapt OpenVINO models to the original pipeline\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Here we create wrapper classes for all three OpenVINO models that we want to embed in the original inference pipeline.\n",
    "Here are some of the things to consider when adapting an OV model:\n",
    " - Make sure that parameters passed by the original pipeline are forwarded to the compiled OV model properly; sometimes the OV model uses only a portion of the input arguments and some are ignored, sometimes you need to convert the argument to another data type or unwrap some data structures such as tuples or dictionaries.\n",
    " - Do guarantee that the wrapper class returns results to the pipeline in an expected format. In the example below you can see how we pack OV model outputs into special named tuples to adapt them for the pipeline.\n",
    " - Pay attention to the model method used in the original pipeline for calling the model - it may be not the `forward` method! Refer to the `OVClapEncoderWrapper` to see how we wrap OV model inference into the `get_text_features` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OVClapEncoderWrapper:\n",
    "    def __init__(self, encoder_ir, config):\n",
    "        self.encoder = core.compile_model(encoder_ir, device.value)\n",
    "        self.config = config\n",
    "\n",
    "    def get_text_features(self, input_ids, attention_mask, **_):\n",
    "        last_hidden_state = self.encoder([input_ids, attention_mask])[0]\n",
    "        return torch.from_numpy(last_hidden_state)\n",
    "\n",
    "\n",
    "class OVT5EncoderWrapper:\n",
    "    def __init__(self, encoder_ir, config):\n",
    "        self.encoder = core.compile_model(encoder_ir, device.value)\n",
    "        self.config = config\n",
    "        self.dtype = self.config.torch_dtype\n",
    "\n",
    "    def __call__(self, input_ids, **_):\n",
    "        last_hidden_state = self.encoder(input_ids)[0]\n",
    "        return torch.from_numpy(last_hidden_state)[None, ...]\n",
    "\n",
    "\n",
    "class OVVocoderWrapper:\n",
    "    def __init__(self, vocoder_ir, config):\n",
    "        self.vocoder = core.compile_model(vocoder_ir, device.value)\n",
    "        self.config = config\n",
    "\n",
    "    def __call__(self, mel_spectrogram, **_):\n",
    "        waveform = self.vocoder(mel_spectrogram)[0]\n",
    "        return torch.from_numpy(waveform)\n",
    "\n",
    "\n",
    "class OVProjectionModelWrapper:\n",
    "    def __init__(self, proj_model_ir, config):\n",
    "        self.proj_model = core.compile_model(proj_model_ir, device.value)\n",
    "        self.config = config\n",
    "        self.output_type = namedtuple(\"ProjectionOutput\", [\"hidden_states\", \"attention_mask\"])\n",
    "\n",
    "    def __call__(self, hidden_states, hidden_states_1, attention_mask, attention_mask_1, **_):\n",
    "        output = self.proj_model(\n",
    "            {\n",
    "                \"hidden_states\": hidden_states,\n",
    "                \"hidden_states_1\": hidden_states_1,\n",
    "                \"attention_mask\": attention_mask,\n",
    "                \"attention_mask_1\": attention_mask_1,\n",
    "            }\n",
    "        )\n",
    "        return self.output_type(torch.from_numpy(output[0]), torch.from_numpy(output[1]))\n",
    "\n",
    "\n",
    "class OVUnetWrapper:\n",
    "    def __init__(self, unet_ir, config):\n",
    "        self.unet = core.compile_model(unet_ir, device.value)\n",
    "        self.config = config\n",
    "\n",
    "    def __call__(self, sample, timestep, encoder_hidden_states, encoder_hidden_states_1, encoder_attention_mask_1, **_):\n",
    "        output = self.unet(\n",
    "            {\n",
    "                \"sample\": sample,\n",
    "                \"timestep\": timestep,\n",
    "                \"encoder_hidden_states\": encoder_hidden_states,\n",
    "                \"encoder_hidden_states_1\": encoder_hidden_states_1,\n",
    "                \"encoder_attention_mask_1\": encoder_attention_mask_1,\n",
    "            }\n",
    "        )\n",
    "        return (torch.from_numpy(output[0]),)\n",
    "\n",
    "\n",
    "class OVVaeDecoderWrapper:\n",
    "    def __init__(self, vae_ir, config):\n",
    "        self.vae = core.compile_model(vae_ir, device.value)\n",
    "        self.config = config\n",
    "        self.output_type = namedtuple(\"VaeOutput\", [\"sample\"])\n",
    "\n",
    "    def decode(self, latents, **_):\n",
    "        last_hidden_state = self.vae(latents)[0]\n",
    "        return self.output_type(torch.from_numpy(last_hidden_state))\n",
    "\n",
    "\n",
    "def generate_language_model(gpt_2: ov.CompiledModel, inputs_embeds: torch.Tensor, attention_mask: torch.Tensor, max_new_tokens: int = 8, **_) -> torch.Tensor:\n",
    "    \"\"\"\n",
    "    Generates a sequence of hidden-states from the language model, conditioned on the embedding inputs.\n",
    "    \"\"\"\n",
    "    if not max_new_tokens:\n",
    "        max_new_tokens = 8\n",
    "    inputs_embeds = inputs_embeds.cpu().numpy()\n",
    "    attention_mask = attention_mask.cpu().numpy()\n",
    "    for _ in range(max_new_tokens):\n",
    "        # forward pass to get next hidden states\n",
    "        output = gpt_2({\"inputs_embeds\": inputs_embeds, \"attention_mask\": attention_mask})\n",
    "\n",
    "        next_hidden_states = output[0]\n",
    "\n",
    "        # Update the model input\n",
    "        inputs_embeds = np.concatenate([inputs_embeds, next_hidden_states[:, -1:, :]], axis=1)\n",
    "        attention_mask = np.concatenate([attention_mask, np.ones((attention_mask.shape[0], 1))], axis=1)\n",
    "    return torch.from_numpy(inputs_embeds[:, -max_new_tokens:, :])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we initialize the wrapper objects and load them to the HF pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b6f35accb974f06a044879ddb789c19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/11 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9df0c5fef7f459dbe067e298ed7ce11",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = AudioLDM2Pipeline.from_pretrained(MODEL_ID)\n",
    "pipe.config.torchscript = True\n",
    "pipe.config.return_dict = False\n",
    "\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0)\n",
    "\n",
    "pipe.text_encoder = OVClapEncoderWrapper(clap_text_encoder_ir_path, pipe.text_encoder.config)\n",
    "pipe.text_encoder_2 = OVT5EncoderWrapper(t5_text_encoder_ir_path, pipe.text_encoder_2.config)\n",
    "pipe.projection_model = OVProjectionModelWrapper(projection_model_ir_path, pipe.projection_model.config)\n",
    "pipe.vocoder = OVVocoderWrapper(vocoder_ir_path, pipe.vocoder.config)\n",
    "pipe.unet = OVUnetWrapper(unet_ir_path, pipe.unet.config)\n",
    "pipe.vae = OVVaeDecoderWrapper(vae_ir_path, pipe.vae.config)\n",
    "\n",
    "pipe.generate_language_model = partial(generate_language_model, core.compile_model(language_model_ir_path, device.value))\n",
    "\n",
    "gc.collect()\n",
    "\n",
    "prompt = \"birds singing in the forest\"\n",
    "negative_prompt = \"Low quality\"\n",
    "audio = pipe(\n",
    "    prompt,\n",
    "    negative_prompt=negative_prompt,\n",
    "    num_inference_steps=150,\n",
    "    audio_length_in_s=3.0,\n",
    ").audios[0]\n",
    "\n",
    "sampling_rate = 16000\n",
    "Audio(audio, rate=sampling_rate)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try out the converted pipeline\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Now, we are ready to start generation. For improving the generation process, we also introduce an opportunity to provide a `negative prompt`. Technically, positive prompt steers the diffusion toward the output associated with it, while negative prompt steers the diffusion away from it.\n",
    "The demo app below is created using [Gradio package](https://www.gradio.app/docs/interface)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "\n",
    "\n",
    "def _generate(\n",
    "    prompt,\n",
    "    negative_prompt,\n",
    "    audio_length_in_s,\n",
    "    num_inference_steps,\n",
    "    _=gr.Progress(track_tqdm=True),\n",
    "):\n",
    "    \"\"\"Gradio backing function.\"\"\"\n",
    "    audio_values = pipe(\n",
    "        prompt,\n",
    "        negative_prompt=negative_prompt,\n",
    "        num_inference_steps=num_inference_steps,\n",
    "        audio_length_in_s=audio_length_in_s,\n",
    "    )\n",
    "    waveform = audio_values[0].squeeze() * 2**15\n",
    "    return (sampling_rate, waveform.astype(np.int16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "if not Path(\"gradio_helper.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/sound-generation-audioldm2/gradio_helper.py\")\n",
    "    open(\"gradio_helper.py\", \"w\").write(r.text)\n",
    "\n",
    "from gradio_helper import make_demo\n",
    "\n",
    "demo = make_demo(fn=_generate)\n",
    "\n",
    "try:\n",
    "    demo.queue().launch(debug=True)\n",
    "except Exception:\n",
    "    demo.queue().launch(share=True, debug=True)\n",
    "# If you are launching remotely, specify server_name and server_port\n",
    "# EXAMPLE: `demo.launch(server_name=\"your server name\", server_port=\"server port in int\")`\n",
    "# To learn more please refer to the Gradio docs: https://gradio.app/docs/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# please uncomment and run this cell for stopping gradio interface\n",
    "# demo.close()"
   ]
  }
 ],
 "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": "3.8.10"
  },
  "openvino_notebooks": {
   "imageUrl": "https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/sound-generation-audioldm2/sound-generation-audioldm2.png?raw=true",
   "tags": {
    "categories": [
     "Model Demos",
     "AI Trends"
    ],
    "libraries": [],
    "other": [],
    "tasks": [
     "Text-to-Audio"
    ]
   }
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0140908e06944b239300a2362b4c3932": {
      "model_module": "@jupyter-widgets/controls",
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