Copy of Day_1__Cat_Dog_Classification_Final_gt5og.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"z_5otFpgWF3N"},"source":["classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters"]},{"cell_type":"code","source":["!pip install kaggle"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"heHCaA4_NLke","executionInfo":{"status":"ok","timestamp":1671467165835,"user_tz":-330,"elapsed":4036,"user":{"displayName":"Himanshu Gupta","userId":"10164527740482211978"}},"outputId":"cbd02ae7-4252-4792-86fe-98ddb1876418"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: kaggle in /usr/local/lib/python3.8/dist-packages (1.5.12)\n","Requirement already satisfied: urllib3 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.24.3)\n","Requirement already satisfied: python-dateutil in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.8.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from kaggle) (2.23.0)\n","Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.8/dist-packages (from kaggle) (1.15.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.8/dist-packages (from kaggle) (2022.12.7)\n","Requirement already satisfied: python-slugify in /usr/local/lib/python3.8/dist-packages (from kaggle) (7.0.0)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from kaggle) (4.64.1)\n","Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.8/dist-packages (from python-slugify->kaggle) (1.3)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->kaggle) (3.0.4)\n"]}]},{"cell_type":"code","source":["! mkdir ~/.kaggle"],"metadata":{"id":"zpxDYIHKNS9H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["! cp kaggle.json ~/.kaggle/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"251xX4yENTqt","executionInfo":{"status":"ok","timestamp":1671467215721,"user_tz":-330,"elapsed":572,"user":{"displayName":"Himanshu Gupta","userId":"10164527740482211978"}},"outputId":"48075ed2-be0b-4747-f683-bd3e2eede87d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["cp: cannot stat 'kaggle.json': No such file or directory\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"EAQ5a-q6NXf9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2_ucnxSBWe85"},"source":["![1_ATIx1SmkEH0FaL_5fMvX2w.jpeg](data:image/jpeg;base64,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CSnHQOMAnAG5RnJevvFlDdRSkAjpxWlLNJ0o0oxj8F+u6ZnVyeFaVaUpfHZrTZo8T/Za+Ng+LPggWuozhvEmkqsN2CfmnTHyTj13Yw2P4geACBXth/SkCgdsUuM15NacalRzhHlT6dj2qEJ06UYTlzNde58lftSfstf2sLvxj4OtP8ATuZdQ0uFf9f6yxj+/wByo+91HOQ3zd8G/g1rXxl8TjTtPVrexgIa91CRcpbp/VjghVBycHoASP1GIDdelZ+j6Bp2gLcJptjb2KXEzXEwt4wnmSscs7YHLHuT6V7dDOq9Gg6Nrvo+39dDwMTkVDEYhVk7Lqu5kfD74faN8MvDNtoeh2wt7SEZZzzJM56yOe7H19gAAAAOnHNGOKTH5V4MpObcpPVn0cIRpxUYqyQ+iiipLCiiigAooooAKKKKAPgT4s/8nwWv/Yb0f/0C2rtP+ChHXwD9L/8A9t64v4s/8nwWv/Yb0f8A9Atq7T/goR18A/S//wDbevtKf+9YT/B+jPz+f+643/H/AO3I9g/a8/5N38Uf9un/AKVw1xv7Bv8AySPXP+w3L/6Igrsv2vP+Td/FH/bp/wClcNcb+wb/AMkj1z/sNy/+iIK8iH/Isl/j/Q9qp/yNo/4P1PHv2Dv+Sxan/wBgSb/0fBR+3iMfGLTP+wJD/wCj56P2Dv8AksWp/wDYEm/9HwUv7eP/ACWLS/8AsCQ/+j569r/mcfL/ANtPB/5k3/b36n1H+0x4k1Pwp8DPE2p6ReSaffxxwxpcwnDoHnjjfaeoO12AYcjOQQQDXxL8Jf2a/FHxl8PXOsaJeaVb2tvdNZul9NIj7wiPwFjbjDgZODkHjvX2T+13/wAm7eKf+3T/ANK4a479gnP/AAqjWv8AsNy/+iIK8zCV54TL5VaVuZyte19LHq4zDwxmZQo1W+Xkva9up47/AMMHfEE/8xPw7/4FT/8Axmg/sH/EEf8AMT8O/wDgVP8A/Ga6r49/Hv4k6R8drzwd4R1NLKJJLSztLaO1gYzSyxxsCzyq2CWlC5yqgAdDkmcf8NZcYz/5Sa9BVsfyRnOtCPMrpOyf5HnOhl3tJU4UakuV2bWqv95x3/DB/wAQf+gn4d/8Cp//AIzR/wAMH/EH/oJ+Hf8AwKn/APjNdl/xll7/APlJo/4yy9//ACk0fWMZ/wBBFP71/kH1bBf9A9X7v+Ccb/wwf8Qf+gn4d/8AAqf/AOM0f8MH/EH/AKCfh3/wKn/+M12X/GWXv/5SaP8AjLL3/wDKTR9Yxn/QRT+9f5B9WwX/AED1fu/4Jxv/AAwf8Qf+gn4d/wDAqf8A+M0f8MH/ABB/6Cfh3/wKn/8AjNdl/wAZZe//AJSaP+Msvf8A8pNH1jGf9BFP71/kH1bBf9A9X7v+Ccb/AMMH/EH/AKCfh3/wKn/+M0f8MH/EH/oJ+Hf/AAKn/wDjNdl/xll7/wDlJo/4yy9//KTR9Yxn/QRT+9f5B9WwX/QPV+7/AIJxv/DB/wAQf+gn4d/8Cp//AIzR/wAMH/EH/oJ+Hf8AwKn/APjNdl/xll7/APlJo/4yy9//ACk0fWMZ/wBBFP71/kH1bBf9A9X7v+Ccb/wwf8Qf+gn4d/8AAqf/AOM0f8MH/EH/AKCfh3/wKn/+M12X/GWXv/5SaP8AjLL3/wDKTR9Yxn/QRT+9f5B9WwX/AED1fu/4Jxv/AAwf8Qf+gn4d/wDAqf8A+M0f8MH/ABB/6Cfh3/wKn/8AjNdl/wAZZe//AJSaP+Msvf8A8pNH1jGf9BFP71/kH1bBf9A9X7v+Ccb/AMMH/EH/AKCfh3/wKn/+M0f8MH/EH/oJ+Hf/AAKn/wDjNdl/xll7/wDlJo/4yy9//KTR9Yxn/QRT+9f5B9WwX/QPV+7/AIJxv/DB/wAQf+gn4d/8Cp//AIzR/wAMH/EH/oJ+Hf8AwKn/APjNdl/xll7/APlJo/4yy9//ACk0/rGM/wCgin96/wAhfVcE/wDmHq/d/wAE8F+MPwI8QfBQ6T/b11p1z/aXm+T9glkfb5ezdu3IuM+YMYz3r9EPhDe3GpfCrwddXc8t1dT6PaSSzzOXeRzCpZmY8kk8knk5r44+IPwj/aF+KjWH/CU6SdU+wb/s/wDpOnxbN+3d/q2XOdi9c9OMc56PRtH/AGpfDuj2WladCbexsYEt4It2ltsjRQqrkkk4AHUk+9Z42H1yhTjKtDnV762326GmAm8DiKk4UJ8jtbS78z7Uz70HpXyH+yV8dfHHxM+JN/pfiTXP7SsYtLkuUhNrBFiQSxKGzGinozcZxz044+vPwr5jE4aeEqulNq67H1+DxcMbS9rTTS8xRS0UVyncFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVDd3C2lvJMwyqLkgVNVHXP+QTc/7hoA55/Fl4WJVIlHpgn9c03/hK77+7F/3yf8ayKKx5mUa//CV3392L/vk/40f8JXff3Yv++T/jWI9xDHNHE8qJLJkohYBmxycDqcZGcdKbc3kFmqNcTxwKzBVaRwoJOcAEnknB49qOZgbv/CV3392L/vk/40f8JXff3Yv++T/jWK80aSIjuqvISFUkAtgZOB3wOeKfRzMDX/4Su+/uxf8AfJ/xo/4Su+/uxf8AfJ/xrG4qJby2e5e3WeM3CAM0QbLAHGCRnIHI5PrRzMDe/wCErvv7sX/fJ/xo/wCErvv7sX/fJ/xrIoo5mBr/APCV3392L/vk/wCNH/CV3392L/vk/wCNZFRT3kFtJEks8cTynbGrsAXPHABPJ5HA9aOZgbn/AAld9/di/wC+T/jR/wAJXff3Yv8Avk/41jGRA4QsA5BIXPJAwCQPQZH5j1p1HMwNf/hK77+7F/3yf8aP+Ervv7sX/fJ/xrGjkSZSUcOASpKnIBBII+oIII7EEU6jmYGv/wAJXff3Yv8Avk/40f8ACV3392L/AL5P+NY0bpLGsiMHRgCrKcgg8gg9wadRzMDX/wCErvv7sX/fJ/xo/wCErvv7sX/fJ/xrIoo5mBr/APCV3392L/vk/wCNH/CV3392L/vk/wCNYxlQSLGXUSMCwUnkgEAkDrgEjJ7ZHrTqOZga/wDwld9/di/75P8AjR/wld9/di/75P8AjWMJELlAwLgAlc8gHIBI9Dg/kfSnUczA1/8AhK77+7F/3yf8aP8AhK77+7F/3yf8ayKKOZga/wDwld9/di/75P8AjR/wld9/di/75P8AjWNHKk8aSRuskbgMrKcggjIII4II71G15Alylu08YuHBZYiwDEDOSBnJHB5HpRzMDd/4Su+/uxf98n/Gj/hK77+7F/3yf8ayKKOZga//AAld9/di/wC+T/jR/wAJXff3Yv8Avk/41izTJbxPLK6xxIpdncgBQBkkk8AAc5NMe7gjWIvNGglYLGxYAOSMgD1JA4A60czA3f8AhK77+7F/3yf8aP8AhK77+7F/3yf8axRKjStEHUyqoZkBGQCSASOoBIIB74PoaHmjiKB3VC7bVDEDccE4HqcAnA7A0czA2v8AhK77+7F/3yf8aP8AhK77+7F/3yf8axncRoWYhVUElicAAdSTSRTJcRJLE6yROodXQghgRkEEcEEc5FHMwNr/AISu+9Iv++D/AI10Gi6r/alsXK7JEOGA6exFcOOldN4P4S6+q/yqot3sJo6OiiitBBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfmt+1x/ycL4s+tr/AOkkNdN+w1/yWmb/ALBM/wD6HFXM/tcf8nC+LPra/wDpJDXTfsNf8lpm/wCwTP8A+hxV+k1P+RQv8C/I/KqX/I5f+N/mfoHRRRX5sfqoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUmKWigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPgT4s/8nwWv/Yb0f/0C2rtP+ChHXwD9L/8A9t64v4s/8nwWv/Yb0f8A9Atq7T/goR18A/S//wDbevtKf+9YT/B+jPz+f+643/H/AO3I9g/a8/5N38Uf9un/AKVw1xv7Bv8AySPXP+w3L/6Igrsv2vP+Td/FH/bp/wClcNcb+wb/AMkj1z/sNy/+iIK8iH/Isl/j/Q9qp/yNo/4P1PHv2Dv+Sxan/wBgSb/0fBS/t4/8li0v/sCQ/wDo+ek/YO/5LFqf/YEm/wDR8FL+3j/yWLS/+wJD/wCj569r/mcfL/208Ff8ib/t79T6S/a8/wCTd/FH/bp/6Vw1x/7BP/JJ9a/7Dkv/AKIgrsP2vP8Ak3fxR/26f+lcNcf+wT/ySfWv+w5L/wCiIK8SP/Irl/j/AEPoJf8AI3h/g/U8b+LX/J79r/2G9H/9Atq+3vGviM+D/Buua75H2s6ZYz3gg37PM8tGfbuwcZxjODj0r4h+LXP7b9r/ANhzR/8A0C2r7D+NXPwb8b4/6Al7/wCiHrTMFdYZP+SJll0nF4uS/mkfId7+3l45ku53tdH0GC1Z2MUcsM0jomTtDMJVDEDGSFXJB4GcCH/hvD4g8f8AEs8Oj/t1n/8Aj1dn/wAE9wC/jzIzxYf+3FfYhjB6gV04yrgsJXlQ+rp2trzPsc2CoY3G0I1/rLV76WXf1Pgj/hvH4g/9Azw7/wCAs/8A8eo/4bx+IP8A0DPDv/gLP/8AHq+9/LX0H5Unlr6D8q4vruD/AOgZf+BP/I7v7Ox3/QU/u/4J8E/8N4/EH/oGeHf/AAFn/wDj1H/DePxB/wCgZ4d/8BZ//j1fe3lr6D8qPLX0H5UfXcH/ANAy/wDAn/kH9nY7/oKf/gP/AAT4J/4bx+IP/QM8O/8AgLP/APHqP+G8fiD/ANAzw7/4Cz//AB6vvby19B+VHlr6D8qPruD/AOgZf+BP/IP7Ox3/AEFP/wAB/wCCfBP/AA3j8Qf+gZ4d/wDAWf8A+PUf8N4/EH/oGeHf/AWf/wCPV97eWvoPyo8tfQflR9dwf/QMv/An/kH9nY7/AKCn/wCA/wDBPgn/AIbx+IP/AEDPDv8A4Cz/APx6j/hvH4g/9Azw7/4Cz/8Ax6vvby19B+VHlr6D8qPruD/6Bl/4E/8AIP7Ox3/QU/8AwH/gnwT/AMN4/EH/AKBnh3/wFn/+PUf8N4/EH/oGeHf/AAFn/wDj1fe3lr6D8qPLX0H5UfXcH/0DL/wJ/wCQf2djv+gp/wDgP/BPgn/hvH4g/wDQM8O/+As//wAeo/4bx+IP/QM8O/8AgLP/APHq+9vLX0H5UeWvoPyo+u4P/oGX/gT/AMg/s7Hf9BT/APAf+CfBP/DePxB/6Bnh3/wFn/8Aj1H/AA3j8Qf+gZ4d/wDAWf8A+PV97eWvoPyo8tfQflR9dwf/AEDL/wACf+Qf2djv+gp/+A/8E+Cf+G8fiD/0DPDv/gLP/wDHqD+3f8QSCDpnh3/wFn/+PV97eWvoPypfLX0H5UfXcH/0DL/wJ/5B/Z2O/wCgp/d/wT8rPhR8V9X+DviSfWtFt7K4u5rV7Rkvo3dNjOrkjaynOUHOfwr1z/hvH4g/9Azw7/4Cz/8Ax6vvbylBztGa+av29EVfhRopAAP9txf+iJ67447DY/ERjUoaydr3/wCAebUy7FZbhpTpYh2jd2t/wT2T4P8AxDPxV+HeleKDYf2Yb7zf9F87zdmyV4/vbVznZnoMZx2zXZg4rxv9kL/k3nwrz0N3/wClc1eyCvnMTBU684R2TaX3n1eEnKrh6c5u7aT/AAHUUUVznWFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVHXP+QTc/wC4avVR1z/kE3P+4aAOFooornLPnvwB4W8EeL/D/ijWfHkFhe+J49Qul1qfVnCzaYEldYY43JDQRiIIVKEAg5yT00tH8PeGPHPxQ8eJ45tLLVdZ0+4WKx07VlWWG300wxsksMb5HzOZCzgZBGMjAz6hrHw68K+IdYi1bVPDelalqcQAju7qyjklAHQbiCeO2Tx2xTvFPw98MeN2gbxB4f03WngP7p761SVkGc4BIJAJ6jOD3Bp3A+a7TSZfFc3hbR9B127sNItvGWpxeHdUibzvKgi095ECFifNhWcSJgkhkUrnGDXQxXM/xb+Kd34P8V6cdK1uHwhqWn6isaloJN9zaeXdW7HG5GwXUEgqVIPIyfoSPQNLgXThHptpGNOz9iCQKBa5UofLwPkypK/LjgkdCRUr6XaSapFqLW0Rv4oXt0uSo8xY2ZWZQeuCUQkdMqKLgfL+keIvFfj7RfEPiEW0yeIPAvhy50aIMCzNrBBF1Mg4ywSOPAOeZSMHNa3jHwl8OND+CkHiXwo9rFr4hSbQ9esyG1K8viRsVnzvlaRztdGJADNkADj6KsdKtNMa6a0t47c3UxuJzGuPMkIALH1JCgE+1Ydj8MvCOm+IX1208MaTba07FjfRWcazbjnLBgMgnJyRgnuTRcDyHXrnzNG/aKt5zGl/LYljbqwLZ/sWHO0dSAcjIFaV3Jo3jvx58LbKI2OvWQ0PUJ7y3yk8f2doreMGRDkbWYhQCMEgjscesjwb4fTxDJr40PTRrki7JNUFnGLl12hcGTG4jaAME9AB0GKTQPBXh/wrPcz6NoenaRLdHdO9jaxwmQ8feKgZ6Z5789TRcD5V1Xwtouh6V4Qn07SLDT52+MQtjLa2yRMYo7y6WOMlQDtUDCr0A4AAr0TwV4U8D+ObbxpqHjy007UfEkGqXkGqHWGXdptusrC3WIscwxeT5bB1IyWJ3Ejj2qXwloc6QpJounyJDenUola1jIjuyxY3CgjiXczHePmyxOck1Q8QfDPwl4s1ODUda8M6Tq1/CAEuLyzjlfA6AkgkgdgcgHkc0XA+dNMg8U+Lm+F02ia9c2WqW1rr8ukX2oAt9utY7i2W3W6XqyyRbQSMnlWGSBnpfD3xbFh4j+LHinUNIudM1HS9I0mC40i4GXW8DXipErDh1d3jCuvDB1PGSB9AHSbJru0uzZ25ubWN4recxKXhRtu9UOMqDsTIBAO0ZzgYq3fhbRtQvXu7rSrO5uXaFmlmgV2LQszQtkg8ozuVPUEkgii4HzD4B1vWNA8DfEjwn4jttYtbvUdBvNct5NagETyztARfrGQSCgmZXUcECQ5HGT9HfDW5huvh/wCHDBLHKF062RjGwYBhEuQcdCPStTU/D2l61JDJqGmWd/JFHJFG11AspRJF2yqCwOAy8MBwRwcim+H/AAzo/hOxNnomlWOjWZcyG30+3SCMuQAWKqAMkAAnGSAPShu4HypoWqeIfAPwH1u9u5bjXPBmuWmqQEBd8ujXJkniUgDkwSELn+4xJxgk1taH4cg1/wCIJM+l+EdSSDw1oJJ8UW4leMFbjP2fIOCcc+pC+lfS1voWmWmlPpcGn2kGmOrq1lHAqwsrklwUA2kMWJIxySc5yaxtQ+Fvg3V5beXUPCWiahPbxJBFNd6fFM8caDCKGZSQAOgzxRcDxnxB4h07S/A37Q2n3d5FBfyXd2I7WRwJH87TbeOHCnk7mBAxnJFcVq+mOP2hYrKfTbTT5f7V0VR4lkY+baSRWkbm0QqpGZgrKNzbTgjBOK+p9S8E+HtZ1i11a/0HTb3VLbHkXtxaRvNHjptcgkY7YPFS33hPQ9TF59s0bT7v7a8cl159rG/nvHjy2fIO4rgbSckYGMYouB82/Ef4i6ovxibxfYWGs3Ph7wXcrp0s9pb77J42DDUmkOQQyZiAwCMwEk4Ir1T4e39tJ8YviaEuYnM8umvEFkBMi/Y1OV55GOcjIxXolt4f0uz0qXTINNs4NNlEgks4oFWFxISZMoBg7izE5HJJJzk1StfAfhmw1mHV7bw7pNvq0MYhivorGJZ44wgjCK4UMFCAKADgAY6cUXA8k+JmleJNQ+POnXPhPUlstZ0/w1Jdx2s5/wBGvwLpVa3mHZWDHDDlWAI6V5v4m8SRfEDUfGtyNOS0ll1vwva3Wma9F+7hmWdleKdcYZAcgkZDKcjIIr62OmWZ1IaibSD+0BCbcXXljzfLJDFN+M7SQDjOMgHGazdT8D+Hdbe/bUdD07UPt4iF2t3apKtx5WfLLqwIYruOCQSBgZwBguB4r8RvAOhr8GF+1+HPCIuLfWbERf2FYx/Z4xLfWiSbcrkM6AK+MbgADkcVD8VIbn4P+J5LfwdpsVhF4305NDs7exhEUVrqSSBIZcABVHkzSHjA/cDI4zXuOn+BvDmlaQ+lWPh/S7PS3mW4axt7KNIGlVlZZCgAUsGRCDjIKgg5Axo3mmWmoTWctzbxzyWk3n27OuTFJsZNw9Dtdxn0Y0XA+bfAWn6rceMdG+GF2kl3bfDy4n1H7RddLuMKBpYLAEAgTOSAAAbccZGBY8E+F/hz4h+DNx4n8XvaS6+0ckuua5ekJqNlegkOivkvC0bDbGikZCrhTnB+iotMtINRuL+O3jS8uI44pZ1XDOkZYoCe4Bkcj03GsK/+GHg/VdfTXLzwvpF1rCMGF9LZRtKWGMMWIySMDBOSMcEUXA+TPGLap4j1vwcfENhb2+q3/hfQo7nxLfFll0aZ7q5InwoLBpCApJZVDMAxAOB2HiGO3v8A9oLxXpvix54/h/datZiTyyFtptQFhaGGO7bOREQDtH3WfAbgCvpbUvC+jaybo6hpNjfG7gW1uDc2ySGaFWLLG+QdyhmYhTkAsSBkmmXHhDQry0v7S40XT57a/wBn2uCS1jaO42qqp5ikYbaqIBkHAUAYAGHcDiv2if3HwV8Q28X7mOdILJhGduI5Z44nAx0BVyMdMGuV8OeFfCnjL4iePI/GtlY6hqumXAt7HTtUVWhs9LEMZjlgjb5VDMZCzryGGMjAB9S8eeCLXxx4F1Xwy7/Ybe9tTbxyQoD5BABRgvAIUhSBkA4xkU/XPh/4d8XfY5PEWh6ZrtzageXNfWUcpUjrjcDgE84Bx9aXQDxjU9C1E/GGDUvh/fxudI8Haa1lYPcFrPVLQz3YELyEk5KhSkhJwwBOQSa4vU9ai+KDeILuyk1HQjqPjrRbfcVMN3ZTLaQxOMEcPG6sO4JXuDX1hbaLp9ldC5t7C2guRAlqJooVVxChJSPIGdilmIXOAWJAGTVeTwvo811LcyaXZyTy3Ed28jwKxaaMARy8j76gABuoAAB4FFwPBT4h1fWvjB4C8IeM7Af23YNqSy3cceLXVLZ7R1WePHCk4IdP4WPHBAHo37O91LdfBLwgZnMjxWQt1Zuu2NjGmfcKgH4V22saHBq6xylI49Qt1kNpemMM9q7xshZM9DhiCMgEcGqvgbwnb+BPB2i+HrRzLBptpHbCVhgyFVALkZOCxySM4BNFwNs9K6bwf9y6+q/yrmT0rpvB/wBy6+q/ypx3Ezo6KKK2JCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA/Nb9rj/k4XxZ9bX/ANJIa6b9hr/ktM3/AGCZ/wD0OKuZ/a4/5OF8WfW1/wDSSGum/Ya/5LTN/wBgmf8A9Dir9Jqf8ihf4F+R+VUv+Ry/8b/M/QOiiivzY/VQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD4E+LP/J8Fr/2G9H/APQLau0/4KEdfAP0v/8A23ri/iz/AMnwWv8A2G9H/wDQLau0/wCChHXwD9L/AP8AbevtKf8AvWE/wfoz8/n/ALrjf8f/ALcj2D9rz/k3fxR/26f+lcNcb+wb/wAkj1z/ALDcv/oiCuy/a8/5N38Uf9un/pXDXG/sG/8AJI9c/wCw3L/6IgryIf8AIsl/j/Q9qp/yNo/4P1PHv2Dv+Sxan/2BJv8A0fBS/t4/8li0v/sCQ/8Ao+ek/YO/5LFqf/YEm/8AR8FL+3j/AMli0v8A7AkP/o+eva/5nHy/9tPBX/Im/wC3v1PpL9rz/k3fxR/26f8ApXDXH/sE/wDJJ9a/7Dkv/oiCuw/a8/5N38Uf9un/AKVw1x/7BP8AySfWv+w5L/6IgrxI/wDIrl/j/Q+gl/yN4f4P1PG/iz/ye/a/9hzR/wD0C2r7F+Nf/JHPHH/YEvf/AEQ9fHXxZ/5Pftf+w5o//oFtX2L8a/8Akjnjj/sCXv8A6IetMf8A8w3+GJll+2L/AMUj5v8A+Cev3/Hv0sP/AG4r7FPSvjr/AIJ6/f8AHv0sP/bivsU9Kwzj/fqny/JHbkn+4U/n+bFooorxj3AooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACvmj9vb/kk2if8AYbi/9J56+l6+aP29v+STaJ/2G4v/AEnnr0cu/wB7peqPKzT/AHKr6HZ/sgf8m8+Ffrd/+lc1eyV43+yB/wAm8+Ffrd/+lc1eyVljP95q/wCJ/mbYD/dKX+FfkFFFFcZ3hRRRQAUUV5Fqv7Tfg6LWbzStBi1rxxqNm225i8K6ZLfrCcgENKo8sEdxu46HnigD12ivHf8AhpFP+iY/En/wnG/+Lo/4aRT/AKJj8Sf/AAnG/wDi6APYqK8d/wCGkU/6Jj8Sf/Ccb/4uj/hpFP8AomPxJ/8ACcb/AOLoA9iorx3/AIaRT/omPxJ/8Jxv/i6P+GkU/wCiY/En/wAJxv8A4ugD2KivHf8AhpFP+iY/En/wnG/+Lo/4aRT/AKJj8Sf/AAnG/wDi6APYqK8d/wCGkU/6Jj8Sf/Ccb/4uj/hpFP8AomPxJ/8ACcb/AOLoA9iqC8theW0kLEgOpGRXkn/DSKf9Ex+JP/hON/8AF0f8NIp/0TH4k/8AhON/8XQB2L+Fr4MQvlsoPBzjNN/4Re//ALsf/fdch/w0in/RMfiT/wCE43/xdH/DSKf9Ex+JP/hON/8AF1PKh3Ov/wCEXv8A+7H/AN90f8Ivf/3Y/wDvuuQ/4aRT/omPxJ/8Jxv/AIuj/hpFP+iY/En/AMJxv/i6XKgudf8A8Ivf/wB2P/vuj/hF7/8Aux/991yH/DSKf9Ex+JP/AITjf/F0f8NIp/0TH4k/+E43/wAXRyoLnX/8Ivf/AN2P/vuj/hF7/wDux/8Afdch/wANIp/0TH4k/wDhON/8XR/w0in/AETH4k/+E43/AMXRyoLnX/8ACL3/APdj/wC+6P8AhF7/APux/wDfdch/w0in/RMfiT/4Tjf/ABdH/DSKf9Ex+JP/AITjf/F0cqC51/8Awi9//dj/AO+6P+EXv/7sf/fdch/w0in/AETH4k/+E43/AMXR/wANIp/0TH4k/wDhON/8XRyoLnX/APCL3/8Adj/77o/4Re//ALsf/fdch/w0in/RMfiT/wCE43/xdH/DSKf9Ex+JP/hON/8AF0cqC51//CL3/wDdj/77o/4Re/8A7sf/AH3XIf8ADSKf9Ex+JP8A4Tjf/F0f8NIp/wBEx+JP/hON/wDF0cqC51//AAi9/wD3Y/8Avuj/AIRe/wD7sf8A33XIf8NIp/0TH4k/+E43/wAXR/w0in/RMfiT/wCE43/xdHKgudf/AMIvf/3Y/wDvuj/hF7/+7H/33XIf8NIp/wBEx+JP/hON/wDF0f8ADSKf9Ex+JP8A4Tjf/F0cqC51/wDwi9//AHY/++6P+EXv/wC7H/33XIf8NIp/0TH4k/8AhON/8XR/w0in/RMfiT/4Tjf/ABdHKgudf/wi9/8A3Y/++6P+EXv/AO7H/wB91yH/AA0in/RMfiT/AOE43/xdH/DSKf8ARMfiT/4Tjf8AxdHKgudf/wAIvf8A92P/AL7o/wCEXv8A+7H/AN91yH/DSKf9Ex+JP/hON/8AF0f8NIp/0TH4k/8AhON/8XRyoLnX/wDCL3/92P8A77o/4Re//ux/991yH/DSKf8ARMfiT/4Tjf8AxdH/AA0in/RMfiT/AOE43/xdHKgudf8A8Ivf/wB2P/vuj/hF7/8Aux/991yH/DSKf9Ex+JP/AITjf/F0f8NIp/0TH4k/+E43/wAXRyoLnX/8Ivf/AN2P/vuj/hF7/wDux/8Afdch/wANIp/0TH4k/wDhON/8XR/w0in/AETH4k/+E43/AMXRyoLnX/8ACL3/APdj/wC+6P8AhF7/APux/wDfdch/w0in/RMfiT/4Tjf/ABdH/DSKf9Ex+JP/AITjf/F0cqC51/8Awi9//dj/AO+6P+EXv/7sf/fdch/w0in/AETH4k/+E43/AMXR/wANIp/0TH4k/wDhON/8XRyoLnX/APCLX/8Adj/77roNE0o6XbMrsHkc5Yjp7AV5h/w0in/RMfiT/wCE43/xdH/DSKf9Ex+JP/hON/8AF1SikI9iorx3/hpFP+iY/En/AMJxv/i6P+GkU/6Jj8Sf/Ccb/wCLpgexUV47/wANIp/0TH4k/wDhON/8XR/w0in/AETH4k/+E43/AMXQB7FRXjv/AA0in/RMfiT/AOE43/xdH/DSKf8ARMfiT/4Tjf8AxdAHsVFeO/8ADSKf9Ex+JP8A4Tjf/F0f8NIp/wBEx+JP/hON/wDF0AexUV47/wANIp/0TH4k/wDhON/8XR/w0in/AETH4k/+E43/AMXQB7FRXjv/AA0in/RMfiT/AOE43/xdbfgf4++D/HevNoFvdXej+I1UyHRNcspbC7KjqVSVRv6EkKTgDJwKAPR6KKKACiiigAooooA/Nb9rj/k4XxZ9bX/0khrpv2Gv+S0zf9gmf/0OKuZ/a4/5OF8WfW1/9JIa6b9hr/ktM3/YJn/9Dir9Jqf8ihf4F+R+VUv+Ry/8b/M/QOiiivzY/VQooooAKKKKAE7e1JmvnH44/tA3/wAFvjLocMqte+G7zTlN5Zrjch81x5sZP8QGOM4YcHHBHvXhzxHpvi3RLTVtIu477TrtBJDPEchl/mCDkEHkEEEAiuidCpThGo17stjkpYqnVqTpRfvR3RrUUUVznWFFct4m+KHg3wVfx2PiLxboWg3skYnS21PUobeRoySAwV2BKkqwzjGVI7Gsr/hf/wAL/wDopHhH/wAHtr/8coA76iucT4ieFH8LP4mXxNo58NodrawNQiNmDv2YM27YPmIXr1OOtYsXx7+GU0ixx/EXwnJIxwqrrlsST6AB6AO9oqC3uYry3jnt5UmhkAZJYmDKwPQgjgj6VzmvfFTwX4W1b+y9a8YaDo+pkK32K/1OCCbDfdOx2Dc9uOaAOqoorldB+KngvxTq39l6L4w0HWNTAZvsVhqcE82F+8diMW478cUAdVRRXl3if9pz4WeD9SbT9U8c6RDeI+ySGKbzzG2TkP5YbaRg5zjHfGaAPUaK57wd498OfEHTP7Q8Na5Ya5ZghXlsZ1l2EjhWAOVOOxwaq2vxT8FX3iNvD9t4v0G418TPbnS4tTha6EqZ3xmIPu3LtbIxkYOelAHV0Vk+I/FOjeD9NOoa9q9jolgGEZu9RuUt4gxzgb3IGTg8Z7VV8LePfDXjlLl/DfiPSfEC220TNpV9FciItnbu2McZwcZ64PpQB0FFFFABRRRQAUUUUAFFFFAHwJ8Wf+T4LX/sN6P/AOgW1dp/wUI6+Afpf/8AtvXF/Fn/AJPgtf8AsN6P/wCgW1dp/wAFCOvgH6X/AP7b19pT/wB6wn+D9Gfn8/8Adcb/AI//AG5HsH7Xn/Ju/ij/ALdP/SuGuN/YN/5JHrn/AGG5f/REFdl+15/ybv4o/wC3T/0rhrjf2Df+SR65/wBhuX/0RBXkQ/5Fkv8AH+h7VT/kbR/wfqePfsHf8li1P/sCTf8Ao+Cl/bx/5LFpf/YEh/8AR89J+wd/yWLU/wDsCTf+j4KX9vH/AJLFpf8A2BIf/R89e1/zOPl/7aeCv+RN/wBvfqfSX7Xn/Ju/ij/t0/8ASuGuP/YJ/wCST61/2HJf/REFdh+15/ybv4o/7dP/AErhrj/2Cf8Akk+tf9hyX/0RBXiR/wCRXL/H+h9BL/kbw/wfqeN/Fn/k9+1/7Dmj/wDoFtX2L8a/+SOeOP8AsCXv/oh6+Oviz/ye/a/9hzR//QLavsX41/8AJHPHH/YEvf8A0Q9aY/8A5hv8MTLL9sX/AIpHzf8A8E9fv+PfpYf+3FfYp6V8df8ABPX7/j36WH/txX2KelYZx/v1T5fkjtyT/cKfz/Ni0UUV4x7gUUUUAJQaQkAckCsPVPG/h3RGZdR1zTbFl+8Lm7jjI57hiPQ/lTSb2JclHdm79KTpXnl7+0F8N9PbEvjTR3Of+WFyJR0/2M/54rGuP2sfhVbDLeLIzzj93aXD/wAozXRHC15axpt/JnNLGYePxVEvmj12lrxWb9sH4UQxF18SvMR/Amn3IP6xgfrSwftgfCiWIM/iV4GPVH0+5JH1xGR+tX9SxVr+yl9zM/r+E/5+x+9HtFGDXkdr+1f8KrsfJ4tiX5tv7y0uE/8AQox+dbVj+0B8N9R/1XjXRk6/6+7WH/0Miolhq8NZU2vkzSOLw8vhqJ/NHoXXtS1i6V4y0HW2CadrWn3zHtbXSSH8lNbOQRXO01udKkpbMWiiikUFFFFABXzR+3t/ySbRP+w3F/6Tz19L180ft7f8km0T/sNxf+k89ejl3+90vVHlZp/uVX0Oz/ZA/wCTefCv1u//AErmr2SvG/2QP+TefCv1u/8A0rmr2SssZ/vNX/E/zNsB/ulL/CvyCiiiuM7wooooA8a/aPuNS1yz8KeANKu5dPm8Z6p/Z95dwNtkisI4nmutjHgMyJsGQchiMdK9N8LeFNH8EaFZ6JoOnW+k6VaIEhtrZNqqB1PqSepY5JJJJJJNea/E/wD5OF+Cn+/rX/pEK9ioAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACivBP27P8Ak1Xxv/24/wDpdb18MfCf9hnx58YvAGleL9G1bw7baZqPm+VFf3M6TL5crxNuCQsoy0ZIwx4I6HIAB+sVFfMn7GH7M3ij9nT/AITD/hJL7SL0az9j+z/2VNLJt8rz92/zIkx/rVxjPQ5xxnxjU7fxb+19+0/418H3HjG88LeGvCslzDDa2DH5hDOIdwQMAzMxLF2JIBAGBgAA/QKiviz9jnx/4o8KfGD4gfCTxL4gk8Q6X4dgnubbULuQs0IhmjjYAsSQrLKCVJIUoQMZOfE/hd4w1z46+LNdk1b9orW/h1d3mpO2naY1zdNFKjsWAQieOOIAkKqg+wAAAIB+n9FfA3xtuobX41xeDfiv498QaN4Q0fwrC2k6hZPJH/aN6saK87gby7lhMcMScqBuGTug+HXxC8b/ABp+HHwy+FNxqOox6vqt0+p6trAlZbmPRoJiY2Mmdwd2UqrHBISPOQ+SDP0AoqONAiKoyQABliSfxJ61+aH/AA7L+KH/AEHvCP8A4GXX/wAjUCP0zrgPjF8JdN+LXhWSynAs9btc3GkaxFlJ9Pul5jkRwNwAYDIHUfgR+X3hj4Zap8HP2uPBnhDWZ7O51PTvEukebLYOzwnzJYJV2llVjhZADlRyD1GCf2AoA8/+A/jy7+Jfwi8M+ItQUJqd1bGK8AUKPtETtFMcDgZeNjjtnHavQK8b/ZE/5IHof/X9qv8A6crmvZKACiiigAooooA/Nb9rj/k4bxZn1tP/AEkhrC+B3xaPwY8Zya+NK/tjdaSWv2f7R5GNzKd27a3TZjGOc9eK3f2uP+ThvFnOebX/ANJIa8gr9XwtOFbA04VNU4q/3H41i6s6GYVKlPdSduvU+wf+HhL/APQhj/wcf/aKP+HhL/8AQhj/AMHH/wBor4+orl/sbL/5fxf+Z2f25mP8/wCC/wAj7B/4eEv/ANCGP/Bx/wDaKP8Ah4S//Qhj/wAHH/2ivj6in/Y2X/y/i/8AMP7czH+f8F/kfYP/AA8Jf/oQx/4OP/tFH/Dwl/8AoQx/4OP/ALRXx9RR/Y2X/wAv4v8AzD+3Mx/n/Bf5Hpvx6+NP/C7/ABLYasdH/sb7Lai28n7V5+75y27OxcdcYxWl+z3+0HqPwX1ryJ/MvvDF3IDdWQPMZPHmx56MBjI4DAYOMAjyCrGn6fc6tfW9lZW8l3d3EgiighUs7sTgAAckknGBXbLB4f6v7Br3F+HzPOjjMR9Z+sJvnb+/yP1q8OeI9O8W6JaavpF3HfaddoJIZ4jkMP55zwQcEEEHkVqZrxn9mH4Naj8H/Bc0Or38s2o6i63E1ism6C0OMbU7FiMbmHUgAZAyfZq/Kq8IQqSjTd0tmfsOGnUqUYzqxtJ7o+ef2hv2N9G/aF8a2XiPUPEN/pM1rp6aeILWFHVlWSV9xLc5JlIx04Ffn3+y78EbL4/fEuXwzqGp3GlQJYS3gntkV2yjRgDB4wd559q/Yyvy9/4Ju/8AJwlz/wBgS5/9GQ1idJ9FfHz4TWfwR/YR8UeErK/n1K2tJLaRbm4QK7F9Shc5A44JxXzf+zX+xzY/tC/CDW/ESa9daTr1pqU1jbRGNXtnCwQyLvHDAlpCCQeAAcE9fs39uz/k1Xxv/wBuP/pdb1+c/gD9or4g/Cn4ZX/hjw1dppWjareTyvfJb5nMpiiSRY5DkKQgjPygMu/IIJBAB77/AME0/iVrEPjvWfBEt1LPoVxYSahDbu2VgnSSNSUB6Blc5A6lVPYmuS/bx/5Ovi/69bCvoL9gj9m6X4c6TL4/1S+tL2/12xWKxjspPNSG2Yq5LOOC7FE4H3duCckgfPv7eP8AydfF/wBethQB+odfl5+wd/ydfL/1639fqHX5efsHf8nXy/8AXrf0Aesf8FCf2jtU0G9h+Gfhq9ksnmt1n1m5tnKyMj/ct8jlQV+ZgOWDKM4LA4vwm/4JrvrvhS21Txx4gu9H1K7iEiaXp0SFrYEZUSO2QWweVAGDxuPbzL42vHF+3xI/iHZ9iXxNphuPOzt+zD7ORnPbysZ7Y6cYr9UqAPnH9lD9mfUv2bvEXjy3n1KHWNH1ZbB7C8RPLkPl/aPMSSPJ2lfMTkEgggjByB8j/DWeO2/4KHajNNIkUUfivWXeR22qig3JJJPAAwSSeBX6jV+M3xZ0nVtc/aP+I1locU8+oy69rO2K3JDvGJZ2kUAHJygcEDkgkAHOCAev/Hf4i+IP20/jhp/grwUrS+HLGZ4rIsSsT4OJb2X0UDO3IJC4AG5iD9/fBn4P6J8EfAlj4Z0OPMcQ8y5u2UCS7nIG6V8dzjAHOAABwBXxn/wTU+IXhXStU13wpd2cNn4r1JhPa6g5+a7hRRm3GehUgvgfeBJP3RX6EUAVNRluINPupbOBbq6SJ2igeTyxI4BKqWwdoJwM4OM5wa/MXRf2yvif8M/j1rOp+NY7mWKWcWuqeGpfkS2jUnaLdSSFZQSQQSHBJJO4MP1Fr5L/AG/vhN4H1z4cz+MtXv4NA8UaeoisrrbltQPJW1ZRyxPJDDJTkn5cigD2DXv2lPAWhfCOP4itrUdzoE6f6KsJBnnm/wCeCoSCJAQQQcbcEkgAmvhnwH+1l8XviX+0hYap4dgfUBeP9lj8KpIfsa2mcnecfKyjLGYjII6bfkr5SNzK1slu0rmBGMixFjtDEAEgdASFUE9SAPQV+sH7F3wm8D+BfhZY634Wv4PEOo6vEr32touGLjkwBTzGqHgqcEkZPYAA+hAWKjIAOOQOcU+iigAooooA+BPiz/yfBa/9hvR//QLau0/4KEdfAP0v/wD23ri/iz/yfBa/9hvR/wD0C2rtP+ChHXwD9L//ANt6+0p/71hP8H6M/P5/7rjf8f8A7cj2D9rz/k3fxR/26f8ApXDXG/sG/wDJI9c/7Dcv/oiCuy/a8/5N38Uf9un/AKVw1xv7Bv8AySPXP+w3L/6IgryIf8iyX+P9D2qn/I2j/g/U8e/YO/5LFqf/AGBJv/R8FL+3j/yWLS/+wJD/AOj56T9g7/ksWp/9gSb/ANHwUv7eP/JYtL/7AkP/AKPnr2v+Zx8v/bTwV/yJv+3v1PpL9rz/AJN38Uf9un/pXDXH/sE/8kn1r/sOS/8AoiCuw/a8/wCTd/FH/bp/6Vw1x/7BP/JJ9a/7Dkv/AKIgrxI/8iuX+P8AQ+gl/wAjeH+D9Txv4s/8nv2v/Yc0f/0C2r7F+Nf/ACRzxx/2BL3/ANEPXx18Wf8Ak9+1/wCw5o//AKBbV9i/Gv8A5I544/7Al7/6IetMf/zDf4YmWX7Yv/FI+b/+Cev3/Hv0sP8A24r7FPSvjr/gnr9/x79LD/24r7FPSsM4/wB+qfL8kduSf7hT+f5sWiiivGPcGY7+lea/HD446P8ABXw8Lu8xearc5FlpqPtaYjqxODtQZ5bB7YBPFejzzR20Mk0riOKNSzOxwFAHJPpX5yumqftWftASR+fJFZXUzbXIz9ksY84wDxkrjjGC75OOa9bL8JDETlOq7Qgrv/I8XM8ZPDQjToq85uy/zJJfEXxd/ae1meGya8u7MH5rW0k+zWMA54YkhScZ+8S3p6V1OmfsG+N7kBr3V9Fss/wpJJKw69cIB+p/pX2x4U8J6V4I0K10fRbKOx0+2UKkUY/Mk9Sx6knk962a655xUh7mGioRW2mvzOKlkdOaU8XJzk99T45sf+CfT4BvPGwB5ysGm/kdxl/TFbdt+wD4fQj7T4q1OUY5EcEac+vOePavqvBpea5nm+Nf/Lz8F/kdkclwEdqf4v8AzPl3/hgPwn/0MetflD/8RR/wwJ4T/wChj1r8of8A4ivqLJ9KMn0qP7Uxv/Px/gX/AGRgf+fSPlG6/YA0Fy32bxXqMQI+US28b4PvjGaxNQ/4J9yhSbHxqrHslxp2AOP7wkPftjp+v2TSYzVLN8av+Xn4L/IiWTYGX/Lv8WfBerfsIeOrRXNjqujX6gcKZZI3b6AoR69SK5yw8dfFr9mbXYrPU/tkdoTxY6g3n2c6jk+U4JAPIJ2EHkZHav0Y61znjvwJo/xG8NXWia3ard2dwOvR4m7Oh/hYdj/QkV1U83nP3MVFTi/LVehx1MkhTXPhJuEl56GF8GvjHo3xm8MDU9MJgu4SI7ywlYGS3kxn8VPO1uM4PQggegd6/OXwVqWp/swftCGwvpWNlFcCzvCeFuLSTBWX04BV+vBUgnrX6Mj5hmuLMMJHC1E6bvCSumd+WYyWKpuNVWnF2Y6iiivLPYCvmj9vb/kk2if9huL/ANJ56+l6+aP29v8Akk2if9huL/0nnr0cu/3ul6o8rNP9yq+h2f7IH/JvPhX63f8A6VzV7JXjf7IH/JvPhX63f/pXNXslZYz/AHmr/if5m2A/3Sl/hX5BRRRXGd4g6UmOaXNUrTVrO+u721guYpriydYrmJGBaJ2RXUMOxKspHsRRYTdjyv4n/wDJwvwU/wB/Wv8A0iFexV478T/+Thfgp/v61/6RCvYqBhRRRQAUUUUAFFFFABRRRQAUUUUAFFFZPimR4vDGryRsySJZzMrqcFSEOCD2+tAGtRXx3+y/4k1fU/2JPGmpXmqXt3qMUGrmO7nuHeZNtvlcOSSMHkYPB5FeY/sR+JfD/iD4h+ETf+PPGV/4123jS6NdSmTTHAjmAyxYk4jIYZHDDHagD9EqK/Pf9p341+JE/aLbxH4ce4uPDPwzns7fUUgkYRSSSyEyhx0+YgwnPQxkj1r0P9t7xVaXWo/BiT/hKdR0DwprF7M17qWk3TwN9kYW58wEA5wrEjKnGehzQOx9i0V+d+g+OPFGrfsmfHFZPEereIPDmlajb23h/X79pFnnhF0gbDsd2NoiO0n5d5HHQX9D/aP1zU/2W/FvgzxVd3OleOtN0K2vtLvjKUm1Gwk8p45EcHJdUYAkHJXk5IcgCx+gNFfmR8WfG91DpnwHGveLvEekaLd+GEl1K60m6kNwx3uA+CwDsSFGTk4rqfinfJN8F/glbeEfGvim40fXdfurf+1b65aG+dGnETBtp5CsG2gkjGD3xQFj9DqK+RPhnf8AjH4B/tO6P8KdU8W3/jXwt4j02S9sJtWcvcWbRpK2NxJOP3DjAO35lIAIIPa/t36xqGhfs6avd6ZfXOn3S3lqontJmicAygEblIPPpQI+hqK/PLQPEuix/st/GDUvB/jjxfreoxQ6WLiXW5ijWbm4IxAwYkBtzgnI4Va7r9hbX/C2seKbsaZ418X+IfEH9hrJfWGusWtIWLxeY0RJOSHO0EgfKTQOx9p0V+d1h8e9af8Aayi+JL3M3/CubzXZPCMcokb7MI1jVQ+M4GSyzE9cZ4HSuh+LXwa1GX9rXw34Ti+IXiq007xl/aGqzmC/ZfsZAnlEUK5wEGwKARwKAsfeFFfKPwfvNS0/9t/4jeG5dY1C/wBM03w5YpDDdXDOu5YLEGTaTtDNliSACSx9TXgGp+OPEa/BH48XI8QaoLi08X28VvKLyTfChnkBVTnKggAEDA4oEfpbRXwt+x74z1af46S+H/DnjHW/Gngj+wI7vUG1iZ5VsrwqhdY2YDkSEqMYBBbO4rurzb9j7xRoviPx94Uj17x/41fxe2osYdLE7S6fMqqWUSMzE4IDZHTgetA7H6ZUV+bmrazI/wC0z4mi+LPjvxd4C1NNXX+w7uwZo7FLQSthd2flRo9oVgCoLMzknIP6QqwcAggg9CKBDqKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA8E/bs/5NV8b/wDbj/6XW9H7Cf8Ayar4I/7fv/S64o/bs/5NV8b/APbj/wCl1vR+wn/yar4I/wC37/0uuKAPe6+Ev2yPg3p/wf1rUPjD4X8b3ng7XtRlMf8AZlohMl5cuDvMbh1KAgbnBDDqRztU/dtcD8Uvgj4P+Mh0Y+KtKGoPpNyLm2cOVI5UtG2OGRtoDKeoHY80AfMH7L3w2034M/Avxd8U/ia9yx8TWZ+07zI1wLGVsDJBDGSZpAxOcgbDkHOOM/a60j4BWfwP0eX4fnw+uufaovsJ0aVXuXhIJk+0clyAMcy/MGAAxkivvvxF4a0rxdoN5oms2EGo6VeRmGe0nXcjp6Y7YIBBGCCARggV4loP7C3wd8P+Ihq8fhuW7ZJBJFZXl5JNbREHshPzD2csPagDW8FXWkL+zH4T8Q/EjT7XUItL8PwX1y2q2yTuNsIIbEgP7xgF9CWOOtfLvgrxb8SLj4maO+lT6b4V8b/FRW1carqcImWw0uIOLW1hRgR8yxMehLAw/dIJr7h+JXw00T4seD7jwzr8c76VO8bvHaztCSUYMoO3qMgcEEd8ZAIw/ir+z54H+Mmm6faeIdJ+bTlKWNzZSG3mtVOPlQrgbeB8pBAwMAGgdzkP2U/jH4h+KOjeKtM8VGzu9e8L6tJpc2paaoFvehcgSLjjJKtyAAQVIAya94rkvhp8LfDPwi8Mx6D4V01NM05XMrqGZ3lkIALuzElmIA5J4wAMAAV1tAj8zPjZ/wApItN/7GXw/wD+i7Ov0zr8zPjZ/wApItN/7GXw/wD+i7Ov0zoA8b/ZE/5IHof/AF/ar/6crmvZK8b/AGRP+SB6H/1/ar/6crmvZKACiiigAooooA/Nb9rj/k4XxZ9bX/0khrpv2GwD8aJx/wBQmf8A9Dirmf2uP+ThfFn1tf8A0khrpv2Gv+S0zf8AYJn/APQ4q/San/IoX+BfkflNL/kcv/G/zP0B8tf7oo8tf7op1Ffm9z9Vshvlr/dFHlr/AHRTqKLhZDfLX+6KTy1/uin0UXCyPhn9uXTrnVPiv4ZsbG3kuru405Y4oIU3O7GZwAAOTk17N+zT+zVbfCqxj1zXI47rxZcJ7MlipHKIehYg/M34DjJb16bwNo114zh8UT2iz61b2v2OCeTnyY8sTtHZjuIJ644HU56DrxXq1MwqPCxwsNEt/PU8OlldOOLni6mrey7DqKKK8k90K/L3/gm7/wAnCXP/AGBLn/0ZDX6hVk6b4V0TRrg3Fho9hYTlSvm21skbYOMjIAOOBQB41+3Z/wAmq+N/+3H/ANLrevlf9n/4Ex/Hf9jLxTYW0Sf8JFpviW5vNJkPBMgtLXdET6Oo28nAYKT92v0YvtPttVtJLW9tobu2fG+GeMSI2CCMgjBwQD+FM0zR7DRYGg0+ytrCBmLmO1iWJSxABJCgDOABn2oA+AP+Cff7QsvhnXJPhX4lmeG0u5mOktcEqbe5yd9uQeQHIJA4w4IwS/Fz/gpP8H9Uk13R/iLp1rJcaaLRdO1F4VLfZ3V2aOR8DhWDlc9AVUHBYZ+5z4N0Brw3Z0LTTdmTzfPNpHvL5zu3YznPOeua1JoI7mKSKVFkidSrI4BVgeCCD1B9KAPgRf8AgpnKPhqLT/hF5j41+y+R9u89fsnm7dvn4xuzn5vLxjPG7HNR/wDBNv4Q6ofEesfEXUrWSHTltGsNPknUj7RI7qZJEz1ChNuehMhAJIOPsM/s+/DE6kb8/D7wybvdvMh0mDG7+9jbjOec4znnrXeQwR20UcUSLHEihVRAAqgcAADoB6UAfFf7ff7MOqeNpoviH4UspL/ULa3FvqthboWlmjX7kyKBlioJVhydoUgcGvOfhT/wUg13wZ4Xt9E8WeGh4nuLKMQw6jHeG3mcKMATAowZscFgQeOQSSa/SCuI8SfBT4f+MNQe/wBb8FaDql+53PdXOnRNK5/2nK5PU8EmgDxn9jz9o3xD+0V4g+IF9q9rBp2maeLCPT9PtwWWHf8AaS5LkAux2Jk8DCjAHOflr4X/APKRW9/7G7WP/Qrmv0w0Pw9pXhfTo9P0bTLPSbCP7lrYwJDEv0RQAOg7VHH4V0WDUjqEej2Ed+XMhultUEpY5y24DOTk85zyaAPzi/bU+Al58DPiJZ/ETwcsmn6LfXYuFe1G3+zr4EthcDAViNy9gQy4AAz9mfst/tB2X7QHw8ivmMdv4ksNsGrWSH7smDiVR12PgkehDLk7cn1zUNOtdVtXtr22hvLZ8FoZ4w6Ng5GQQQecH8KraX4a0nQ5Hk07SrLT5HG1ntbdIiwznBKgZHtQBZv7mWzsLmeC1kvZooneO2iZVeZgCQgLEKCSAASQOeSBX5JfFHxR8Sf2rPjcNHuNLuY9XSd7Sy8P8qmmoDhw+QMEYzJIwBJHYBQP14rEtPCGiWHiS/8AENtpVpBrl9EkF1qEcQE00aZ2KzYyQP1woOcDAB8ga/8A8E3tGHwbg0/S9QL/ABCt83LanIzC3upCBm3Kfwx8YVsbgck5BK183/s2eP8A4j/AT41r4Y03RL2+vbu7FlqXhZxtMxHVhnhWUZYSfdC5JJUk1+tVYqeENETxS/iVdKtB4ge2Fm2pCIeeYQdwjL4zjPOPp7UAa6ElASCpI6HtT6KKACiiigD4E+LP/J8Fr/2G9H/9Atq7T/goR18A/S//APbeuL+LP/J8Fr/2G9H/APQLau0/4KEdfAP0v/8A23r7Sn/vWE/wfoz8/n/uuN/x/wDtyPYP2vP+Td/FH/bp/wClcNcb+wb/AMkj1z/sNy/+iIK7L9rz/k3fxR/26f8ApXDXG/sG/wDJI9c/7Dcv/oiCvIh/yLJf4/0Paqf8jaP+D9Tx79g7/ksWp/8AYEm/9HwUv7eP/JYtL/7AkP8A6PnpP2Dv+Sxan/2BJv8A0fBS/t4/8li0v/sCQ/8Ao+eva/5nHy/9tPBX/Im/7e/U+kv2vP8Ak3fxR/26f+lcNcf+wT/ySfWv+w5L/wCiIK7D9rz/AJN38Uf9un/pXDXH/sE/8kn1r/sOS/8AoiCvEj/yK5f4/wBD6CX/ACN4f4P1PG/iz/ye/a/9hzR//QLavsX41/8AJHPHH/YEvf8A0Q9fHXxZ/wCT37X/ALDmj/8AoFtX2L8a/wDkjnjj/sCXv/oh60x//MN/hiZZfti/8Uj5v/4J6/f8e/Sw/wDbivsU9K+Ov+Cev3/Hv0sP/bivsU9Kwzj/AH6p8vyR25J/uFP5/mxaQ0tITgV4x7h5F+1P42/4Qn4La7JG+y71FP7Ot8cHdLw2D2IjDkH1FePfsD+CjFY+IvFk0eGlddOtmP8AdX55PfkmMfVT+HPft4eOo9T8SaF4XtplkTT4mu7kRtn96/yqpHYqqk464eu5+FH7Qnww+D/ws0HQW1h7zUYIPMu4rK0kf9+5LyDeVCnDMVBB6AV9RGhVp5ao04NyqO706Lb+vM+PniKVXNHOrNKNNaa9WfUwoNfLuq/t8+E4Nw0/w9rF4w7zmKFT+O5j6dR3rk9T/wCCgd4+Rp/gyGLrh7nUC+emOBGuO/c/hXnQynGz2pv56fmerPOcBDep+bPs7r2oIFfCF5+3r41kz9l0PQ4ec/vY5pOPTiReff8ASsub9uT4jysxWLRIg3QLaMQv0zIf610RyPGy3SXz/wAjllxDgY7Nv5H6CUV+d3/DanxO/wCf7T//AACSj/htT4nf8/2n/wDgEla/2Bi+6+//AIBH+seC8/uP0Qx70fhX5/Q/tzfEaNwzW+iSgfwtaOAfriT+taln+3t4zj2/atC0Sbk58pZY8jt1dsfr+FZPI8atop/P/MuPEGBlu2vkfdoOaSvjXTP+CgVyhA1DwXHJwMvb6gVwcf3TGe/vx711mlft7+ELjjUNB1mzb1iEUyj6ncp/IGuaeU42G9N/LX8jrhnOBqbVF87o4z9vfwR9n1Xw94shT5biNtOuGA4DLl4/xIMn4IOvb6J/Z38ar49+D/hzUjJvuo7cWlz82T5sXyEn/ewG+jCvFfjl8ePhn8ZPhPrGkWutNBq8ard2UV3aSoTKhyAG27QWXco+b+Ksj9grx3HDJ4g8JXEwRpGTULRWONxxslAz9Izj616NWhUqZavaRalTfVPZ/wBfgeVRxFKnmjdKacai6d1/X4n2XRSZzS18wfXhXzR+3t/ySbRP+w3F/wCk89fS9fNH7e3/ACSbRP8AsNxf+k89ejl3+90vVHlZp/uVX0Ox/ZCb/jHrwqPQ3f8A6VzV7ICK/JrSfiP4t0HT4rDTPE+s6dYw58u2tNQmijTJ3HCqwAySScDnOepq5/wt/wAef9Dr4i/8Gs//AMXX0WIyGvVrTqRkrNt9e/ofMYbiOjRowpODvFJH6tZFGRX5S/8AC3/Hf/Q6+Iv/AAaz/wDxdH/C3/Hf/Q6+Iv8Awaz/APxdc/8Aq7iP51+P+R0/6z0P+fbP1Zz718F/GL4r638I/wBq3xPrGkS7lJtEurN2/d3Mf2WHKsOxHJDdQfUEg+Nf8Lf8d/8AQ6+Iv/BrP/8AF1zuravf69qEt/qd7calfTY8y5u5WlkfACjLMSTgAAZPAAHQV6WAyWWHqSddqUWrdfI8nMc9jiqcVRi4yTufdQ+J+i/Fn4t/A7WtGn3KX1lZ7dj+8tpPsQyjjsffoRgjINfSQxivyc+HHju9+GnjXSvEmnxRzXVjIzCKUHa6sjIy+2VdgD2Jz2r9M/hd8TNI+LHhG217R3byZD5c0Egw8EoALRt7jIPHBBB7187meWywMuaOsHsz6jKc1jmEOWWk1v8A5nY0UUV4h9CFFFFABRRRQAUUUUAFFFFABWfr9lJqWh6jaRY824t5Ik3HAyykDPtk1oUUAfGvwU+CPxs+Hfw41v4d39p4UPhfUrHUVa5juJWvBPNbukYDZCBd+zOVJAz7V0n7O/gn47/CDw9o3g+90rwdceFNNhvGWSO4nN68jiaaJS+dgBmdVJ2cIT1IzX1NRQB8S+FP+CfcuueCNevPHPiDUR461aa5uZBpt8PsTSMS0ZlBjJc7yWJyPvYGCM1bn/ZX8eeMvBHwY8OeLYdLu7bwjfXEGqAXrN52ns0WwIdg+YIrIBxgIpB54+z6KAPj7T/2cvidpfwC+IXwlkudN1TR5plbwzfy3RV1iFykjRyrtO0FVLgAnDFhkggjV+M37IU/xM+APgvTIFtrb4heFtEtLGG4WTEdxshVJbdnwMqSGKkjAPoGavqyigD4r1b9m/4raXdfBrXfDNroMmt+DdEWzuLfVblvJ8/L5HyYLLh+oYcj8+j+Nnwf+MXxi8I/D69vLTwvB4y0DVpb+eC2nlSyKgqYcbizH7g3DI68EdvrGigdz5u+EnwF8c3nxe/4Wp8V9Z06+8R2ls1npWm6QD9nso2DKTkgHOHkAAz98ksTgDr/ANq/4V638Zfg1qPhnw/9m/tOe5t5U+1ymNMJIGOSAecD0r2KigR5H8c/hlrPxC/Z41bwXpP2f+2bmytYI/PlKxb45ImbLYPGEODj0rg/CmgftA2Xwt1jwlqVh4Qthb+HhpWi3OnXE6ziYBIVeR2YqNsW98hRllXgAkV9MUUAfDVz/wAE5Yf+FLpbQa1fnx8IEuPs7Xo/swXOQWAXy8/cJUPnOcHpxXqth8HfHmrfGf4OeONe+wmTw3ok9jrTpdF3kuGgmj8xBtG4MzqxzjGSOcc/SFFAHy18TfhL8UvBf7QOp/FL4W2mj69Jrenx2OoaZq0vl7CqxqGB3KCMQxnIYHIYYIINckP2PvGQ/Zn8U+G5rnTrnx34m1mLV7xvOKwIAyny9+3kj5yTjGWIBIAJ+0qKBny/8CP2f/GvwC+KU7aRJZXXw91uygfUrJrgiWzvljwzxKVAZd4YcEZRxkEoM5P7Mvwv+OnwRtNL8K3Nh4Qk8IG/a5vrkzzSXqo+N/lkMq9hjKnvnNfW1FAHxP8AGX4D/H34v3r+DdZv9A1Dwh/bL6jD4iKrHPDCwwsXl5LAKCQFUEk8FyMGvs7TrFNM0+2s4mZoreJYkL4yQowM4A5wKtUUCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAPBP27P+TVfG/wD24/8Apdb0fsJ/8mq+CP8At+/9Lrij9uz/AJNV8b/9uP8A6XW9H7Cf/Jqvgj/t+/8AS64oA97ooooA+MfGH/BQHUYte8QnwX8O7rxR4W8PyiO+1szSKmNxXedsbCNWKttLEkgZIGCK7jW/2ybbUPB3w/u/BPhqXxF4m8aSyQWWjTXKwi3aJgs3mvgjAY4B4BGWJABpv7WHgzxN4a+Dms2nwq8P6NpWm3dtct4iWytoreWS0EfzbAAAxK+YDwTjgcmvNP2efgt4f+On7OXgO88P63qXhLxV4S1G62axbqsjx3LyLJICpIDIVMRAyMDg5y2Qo9Y8Jftd2114B+Imq+LdBbwx4h8DSeRqOlG5EySStuWFY5ABkyOhUcEdDkg5r2T4beIdV8W+AdA1rWtLXRdV1CzjuZ7BZC4hLDIGSAc4IJBGVJIJOMn4M8R+F/CPw3+Kl3ofijxNd6/oGkX0XiLxrrV1FltU1AhzZWKRqWBx5kjFSTkPLkgIMfZnwh/aH8G/G2bUrXw7d3Meo6fhrnT9Qt2guEQ9HCnqpPGQTg4yBkZBM9OooooEfmZ8bP8AlJFpv/Yy+H//AEXZ1+mdfmZ8bP8AlJFpv/Yy+H//AEXZ1+mdAHjX7Iv/ACQPQ/8Ar+1X/wBOVzXsY618V/A79rjwn8MPhvaeGdT0zWrm9sb7UPMktIomjO++nkGC0qk4DgHIHIPXrXfD9vbwJ/0BfEWf+veD/wCPV3QwOKnFSjTbT8jzZ5lhIScJVEmj6XoxXzR/w3t4D/6AviL/AMB7f/49R/w3t4D/AOgL4i/8B4P/AI9Vf2fi/wDn0/uI/tXBf8/UfS+KMV80f8N7eA/+gL4i/wDAeD/49R/w3t4D/wCgL4i/78Qf/HqP7Pxf/Pp/cH9q4L/n6j5u/a4/5OG8WeubT/0khrpv2Gf+S0z/APYJn/8AQ4q81+OPjyx+J3xS1vxNpsNxb2V95Plx3aqsi7II4zkKxHVSRyeCOh6elfsM/wDJaZv+wTP/AOhxV93XjKGVcslZqK0+R+eYecamb88XdOf6n6B0UUV+aH6wFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfAnxZ/5Pgtf+w3o//oFtXaf8FCOvgH6X/wD7b1xfxZ/5Pgtf+w3o/wD6BbV2n/BQjr4B+l//AO29faU/96wn+D9Gfn8/91xv+P8A9uR7B+15/wAm7+KP+3T/ANK4a439g3/kkeuf9huX/wBEQV2X7Xn/ACbv4o/7dP8A0rhrjf2Df+SR65/2G5f/AERBXkQ/5Fkv8f6HtVP+RtH/AAfqePfsHf8AJYtT/wCwJN/6Pgpf28f+SxaX/wBgSH/0fPSfsHf8li1P/sCTf+j4KX9vH/ksWl/9gSH/ANHz17X/ADOPl/7aeCv+RN/29+p9Jftef8m7+KP+3T/0rhrj/wBgn/kk+tf9hyX/ANEQV2H7Xn/Ju/ij/t0/9K4a4/8AYJ/5JPrX/Ycl/wDREFeJH/kVy/x/ofQS/wCRvD/B+p438Wf+T37X/sOaP/6BbV9i/Gv/AJI544/7Al7/AOiHr46+LP8Aye/a/wDYc0f/ANAtq+xfjX/yRzxx/wBgS9/9EPWmP/5hv8MTLL9sX/ikfN//AAT1+/49+lh/7cV9imvjr/gnr9/x79LD/wBuK+xT0rDOP9+qfL8kduSf7hT+f5sazBQSeg9a+FP2kP2rdS8UardeHfB19Jp+hQMYpr+2crLeMOu1xysfpggt1zg4r6U/ah8XTeDPgn4hurVzHeXMa2UTA4IMjBWIPqELkY7gV+aNerkWAp1uavUV7OyXS54vEOY1KCWGpOzerYrOXYsxJJOSSec+ppK3PBPgnV/iH4ktdC0O1N1f3BJUEhVRQOWZjwFAGST7AZJAP0ZP+wJ4hTSfNi8T6dJqO0n7M0DrHn0EmScdedv4en1tfHYbCSUKsrPsfGYfL8Vi4udGDa7nyvRWp4o8Man4M1+80XWLV7LUrR9k0LYOOAQRgnIIIIIJBBBBway67oyjNKUXdPVM4JRlCTjJWa0aCiiiqICiiigAooooAKKK2vBng3VvH/iO00PRbU3V/dNhVzhVA5LMegUDJJPYdzgVE5RhFyk7JatvsXCEpyUYq7Zi06OR4ZEkjdkdCGVlOCpHIIPYj1r6mb9gLxANLMq+KdPOo7c/ZjbuIt3HHmZzjrzs7dPT5t8V+FdU8EeILzRdatWs9RtH2SxNg9eQQRwVIIII4wQRXFh8dhsW3ClK77f8Od+IwGKwaU60Wl0Z9L/sz/tWalaazZeFvGd619YXDCK01S4bM0Lk/KsjHl1J/iPIJGSR0+2Qc4OeK/Hmv1D/AGffFk/jb4OeFtXu5DLcyWxilkYkl3jdoixJ6klCT718fnmBhQar0lZPRo+24ezCpiFLD1Xdx1T8j0Wvmj9vb/kk2if9huL/ANJ56+l6+aP29v8Akk2if9huL/0nnrxcu/3ul6o97NP9yq+h88fDv9kzxh8TfB1h4l0q+0aGwvfM8uO7nlWQbJGjOQsTAcqT16EdO3Sf8MH/ABB/6Cfh3/wKn/8AjNfSv7IP/JvXhX63f/pXNXsnevVxOcYynXnCMtE2lp5ni4TIsHVw9OpJO7Se/kfA3/DB3xB/6Cfh3/wKn/8AjNH/AAwd8Qf+gn4d/wDAqf8A+M199UVzf23jf5l9yOv/AFfwP8r+8+BT+wf8QR/zE/Dv/gVP/wDGa8U+IngTUPhn4x1Dw1qstvNf2Pl+ZJaMzRnfGsgwWVSeGGcgd+3X9Y6+MPHnwD1T40ftR+KyfMsvDtq9p9s1DHX/AESE+VHnguR9QoOTnIB9XLs4qzqSeKl7qTfz0PHzPJKVKnFYWL5pNL8GeBfB34aP8TPiB4e0S5kmsdO1OeaM3ix5DeVEZZFUngttAHfG9SQQQD+mvhXwtpngnQbPRtGs0stPtUCRxRj8yT3J6knkk5NePeJfC2meC/jL8CdG0ezjstOtf7ZSKGMdP9DBJJ6kk5JJySSSeTXvB4rw8wzCpjp3ekVsj6HLMshl9PvN7sQkL3o3r618ef8ABQj73gL6X/8A7bU0f8E+GKg/8J6Of+oP/wDb6qngqPsIVq1Xl5r2Vm9iamYYj6xOhQo8/Ja75kt1fqj7F3r/AHqN6/3q+Ox/wT2f/ofR/wCCf/7fS/8ADvZ/+h9H/gn/APt9H1XBf9BH/krD63j/APoF/wDJ0fYe9f71G9f71fHf/DvZv+h9H/gn/wDt9H/DvZv+h9H/AIJ//t9H1XBf9BH/AJKw+t4//oF/8nR9ib1/vUb1/vV8d/8ADvZv+h9H/gn/APt9H/Dvdv8AofR/4J//ALfR9VwX/QR/5Kw+uY//AKBf/J0fYm9f71G9f71fHY/4J7Mf+Z9H/gn/APt9H/DvZv8AofR/4J//ALfR9VwX/QR/5Kw+uY//AKBf/J0fYm9f71G9f71fHf8Aw72b/ofR/wCCf/7fR/w72b/ofR/4J/8A7fR9WwX/AEEf+SsPreP/AOgX/wAnR9h+YvXcKA6n+IV8ef8ADvZ/+h9Gf+wP/wDb6xfGv7DZ8IeDtc10+NPtf9mWU959n/srZ5nloW27vOOM4xnBxnoaawuCbssR/wCSsmWNx8Y8zw3/AJMj7fyCM9qQsODkV8bfs6f8mifFH/uJf+kMdeL+APhrYeLY7h57+CzePorqOfzrSllinOpFztyO22/4kyzWXLTcad3NX32/A/TEyLn7woLj1FfmpF8NbWPXPsDXNq65wH2gg16bB+zNbS2wlF7YsGGf9Wv+Fa1Mqp07Xq/h/wAEdPMq1S9qX4/8A+3t49RQWX1Ffmv8R/hj/wAIewMaW068jKIB/KsvQfhXrPiOzFxZadHKhHVVFaxyaDip+209P+CYSzirGbpqjd+v/AP08Lj1FJvX1GK/KPxZ4X1Hwjdi31CwSBzjG5RzWr/wrxpfCB1nzUUgcR7R7d61/sKKSl7Xfy/4Jgs9qOUo+x1Xn/wD9SN6nuKN6eor8hbS3mvZhFFAkjk4CgV2Ol/C3xBePG8ulPHbORmUR5AHqaqeQRp/FWX3f8EzpcQTrfBQf3/8A/UkOvqKXep6MK/Mf4j/AAfHgfSLW9TU4LsT4zGgG5TjPNeZhCxwAT7AUU8gjVjzQraen/BFW4hnQnyTo6/4v+AfsGHHqKNw/vCvx+NrMBnymx9DVzSPD+oa/M8dhbPcugLMEHIA6mtHw7ZX9t+H/BMlxLKT5VQd/X/gH66gj60m5emRmvhP9he3e2+LOqpJHslXSJwcjkfvoOK4/TfAdh8TP2q9b8NanNcW9je65qnmS2jKsi7PPkGCysOSoB4PU9+nlvK1GtUpOppCPNe2/wArnp/2xJ0adRU9Zu1r/wDAP0cMik4yM0GRf7wr4N+NH7NXhr4c+Ovhzounahqtxa+JNRNpdvdSxs8aCSBMxlYwAcStyQeQPx9dH7BXgQ/8xrxF/wCBEH/xmuaeGw8Ixm6r95fy+dv5jpp43FVJygqKvH+95X7H0t5iH+IUbx/eGK+SPiF+xb4L8JeA/EWt2mra9Ldadp1xdxJNPAUZ442dQwEIJGRzgj2xXxjXdg8qp45SdKq9O8f+CcGNzqrgJKNaite0v+AfsJvX+8KUuoHUV+PVehfCT4ead49/tX+0J7qH7J5Pl/ZmVc7t+c5U/wB0YxjqetdtTh9Uoucqunp/wTkocRyxE1ThR1f97/gH6hB165FHmKRncK/Jz4geHbbwp4uv9KtXlkt7fy9rTkFvmjVjkgAdSegHGKydJ0i/17UIrDTLK41K+lz5dtaRNLI+AWOFUEnABJwOACegprh5OHP7bTfb/gky4lcZ+zdHXbf/AIB+vO9cDnil3L6ivh74EfsjWPjzwje6h4yj8ReHdTivXgitii22+IRxsH2yxEn5mcZBwceoNfLdceHyeniZzhSrfD5afLU6sRnlXDQhOpR+Lb3tfnofsLvXpkUm8Y4Ir8e61/CXhHVvHfiC00LQrT7dqt1u8mDzEj3bUZ2+ZiFGFUnkjpxya7ZcPKCcpVrJbu234nFHidzkoqg235/8A/W3cD3FOBOBX5feDfCWreBfj14U0PXbX7Dqtrrmn+dB5iSbd0sbr8ykqcqwPBPXtgiv1AHT+lfP4/BrBTioz5lJXufR5dj3j4ybhyuLtZj6KKK809gKKKKACiiigDwT9uz/AJNV8b/9uP8A6XW9H7Cf/Jqvgj/t+/8AS64o/bs/5NV8b/8Abj/6XW9H7Cf/ACar4I/7fv8A0uuKAPe6p6rptvrOmXdhdRiW1uoXgljyRuRgVYZHqCauUUAfAifsrfHr4U2nifwZ8Ptd0678E6+7B5p5USVY2Gwlgyko5TCsY85ABGCAB9KfAX4K3/7PPwRuPD+mS2+t+JSJ9QcsTFbz3rRgKgJ5CfJGm44OATgZwPZ6KAPjbVf2Q/FWqfAjT8XNpJ8Un18eK9Sa+cNDdXJ3ZgZgCCFVgB1XdvAIDkjvPgZ8JvHqfGzxV8VPiDBpWj6pqmnR6VBpOkyGVQimImRmOef3KgcknLZwAM/RtFA7hRRRQI/Mz42f8pItN/7GXw//AOi7Ov0zr8zPjZ/yki03/sZfD/8A6Ls6/TOgD8dW/wCPvUP+v65/9HvTq++/2Xvhf4P8R/BXSdR1XwroupX819qfmXV3YRSyPjULlRlmXJwABz2A9K9WHwT+H3/Qj+Hf/BVB/wDEV9hQz+NGlGm6bdklv2+R8LiOHKlevOqqiXM29j8rqK/VH/hSfw+/6Efw7/4KoP8A4il/4Un8Pv8AoR/Dv/gqg/8AiK6f9Y4f8+/x/wCAc3+q9T/n4vuPytor9Uv+FJ/D7/oR/Dv/AIKoP/iKP+FJ/D7/AKEfw7/4KoP/AIij/WOH/Pv8f+AH+q9T/n4vuPytr6F/Ya/5LTN/2CZ//Q4q4z9qDRdP8O/HTxNp+lWNvpthD9m8u2tIljjTNtExwqgAckk8dST1Ndn+wz/yWmf/ALBM/wD6HFXr4yr7fLpVUrXje3qeLgaToZnGk3flla5+gdFFFfl5+uhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHwJ8Wf+T4LX/sN6P8A+gW1dp/wUI6+Afpf/wDtvXF/Frj9t+1/7Dej/wDoFtXZ/wDBQZgx8BYOeL//ANt6+1p/71hP8H6M/P6j/wBmxv8Aj/8Abkew/tef8m7+KP8At0/9K4a439g3/kkeuf8AYbl/9EQV2X7Xn/Ju/ij/ALdP/SuGuN/YN/5JHrn/AGG5f/REFePD/kWS/wAf6HtT/wCRtH/B+p49+wd/yWLU/wDsCTf+j4KX9vH/AJLFpf8A2BIf/R89J+wd/wAli1P/ALAk3/o+Cl/bx/5LFpf/AGBIf/R89e1/zOPl/wC2ngr/AJE3/b36n0l+15/ybv4o/wC3T/0rhrj/ANgn/kk+tf8AYcl/9EQV2H7Xn/Ju/ij/ALdP/SuGuP8A2Cf+ST61/wBhyX/0RBXiR/5Fcv8AH+h9BL/kbw/wfqeN/Fn/AJPftf8AsOaP/wCgW1fYvxr/AOSOeOP+wJe/+iHr46+LP/J79r/2HNH/APQLavsX41/8kc8cf9gS9/8ARD1pj/8AmG/wxMsv2xf+KR83/wDBPX7/AI9+lh/7cV9inpXx1/wT1+/49+lh/wC3FfYp6VhnH+/VPl+SO3JP9wp/P82eA/tu/wDJEZv+v+3/AJmvz2r9Cf23f+SIzf8AX/b/AMzX57V9Xw9/usv8X6I+N4l/3yP+FfqfUP7BF3YRePfEUMxUajLYJ9nLdSgf94B/5DP4V9y4r8iNA8Qaj4W1i11XSbySx1G1cPDPEeVP8iCMgggggkEEEivcZv23/iPJpX2RRo8UxTb9tSzbzgf72C5TP/AMe1ceaZRXxWI9tSs07fI7MozrD4TDexrJpr8Sf9ua7sLn4xW0dqytdQ6XEl3t/hcu7KD77GU/QrXzxVrU9Uu9a1C5v7+5ku7y4cySzysWd2Pck1Vr6bB0Pq1CFJu9up8njMQsViJ1krJhRRRXacQUUUUAFFFFABX0z+wZdWUXxJ1uGZkW8m039xu6kCRS4U/kSPbPavmar+ga/qPhbWLXVdJvJbDULVw8NxCcMp6fQgjIIOQQSCCCRXDjsO8VQlRTs31/E9DA4lYTEwrNXSZ+u9fAv7dN1Yz/ABdsY7Vo2uodLjS62dQ292UN77SD64I9qpP+298SH0n7IP7HWfZt+3Czbzs/3sFymf8AgGPavC9X1e91/U7nUdSuZb2+uZDJNPMxZnbvnP8A+oDAHFfN5VlNfDV/a1el7a7/APAPqM4znD4vD+xoq7f4FSv0k/ZA/wCTePCv1u//AErmr826/ST9kD/k3jwr9bv/ANK5q6OIv92j/i/RnNwx/vcv8P6o9kr5o/b2/wCSTaJ/2G4v/Seevpevmj9vb/kk2if9huL/ANJ56+Py7/e6Xqj7fNP9yq+h2f7IH/JvPhX63f8A6VzV7JXjf7IH/JvPhX63f/pXNXslZYz/AHmr/if5m2A/3Sl/hX5BRRRXGd4UwKqliAATyTT6KAPHfif/AMnC/BT/AH9a/wDSIV7FXjvxP/5OF+Cn+/rX/pEK9ioA+Of+ChP3/AX/AG//APttX2HH9wV8ef8ABQn7/gL/ALf/AP22r7Dj+4K9jFf7lh/+3vzPCwn/ACMMV/25+Q+iikJwK8c908t+NPxrt/hbaw2lrbx6hr93G8sUEsmyG3iXhridv4Yx045Y/KOeR41Y+KfjX4/ijvdMk1uawmO9WsrGz0yArngxPcFpGQg5BIBIOccc09JtovjX+0PPJcqt1pD3sl1Ij8o1jZYhgAyOUkuS7Mp4Pl8g44+u5Z4bSFnkkSGJcZZ2wq17UnDBRjFQUptXd1tfpY8KCnjZym5uME7K35nyzb/Dj4yX00Lzz+Ibc4IdZfGMQXgnGRHa9xjpn3NOh+EPxhged21XUpA4JCx+MJgwPbG6AqPpgCvo648deHLTf5+v6ZFs+9vu412/Xniq0fxO8HyOETxVorOeAq6hEST/AN9Vj9cn/wA+4/cbfU6S3qO/qv8AI+dR4K+N2lRj7K3iSZgCd3/CT2dxznptlgUH8W/wplr8bfid8Pdas7bxLb3Wp+e20WGradDaS3HcrbXEBMUkmCMIwBPQZJr6es/FWi6g+221exuXyBtiuEY89OAe9Y/xP8EW3xE8D6toU6o0lxC32eVusMwBMcgPUFWwc/WrjjIyajVpRs+y1Ilg5QjzUaruu70NLwb4u07x14ZsNd0mbz7C9j8xCwwynoVYdmBBBHYg1t4Ir5n/AGRvFrvf6/oVyGikvI49Zjhbqs3+ovRjqv7+POPRwe/P0x0xXFiaPsKsqfY78LW9vSjU6i1xXxs/5I744/7Al7/6Jeu1PWuK+Nn/ACR3xx/2BL3/ANEvWNP416mlf+FL0Z80fs6/8mi/FH/uJf8ApDHXz54O0O51fXfIjeaNGkwTGSOp9q+g/wBnX/k0X4o/9xL/ANIY6+evCXjW58NalJNBB5x3ZHHvX3eDUnWxPJ3PgKzgqeGdR6WPp7Qv2VodU06O9/tSWOUrnkHI/WvK/iHo3iDwRqf2O01SedRwBuPP610Phj48eL9c221jB2wAOwqLxD4e8Yazei/u9Pc45OBnis6ftqdR+3kmuiPUqKjVpfuE0+55e1xrep3cUGoGV0ZgMvmvpf4ZeKdK+GugRx3xR965w2Bjv3ryhZnudTtrS4tPKmUgcjnNX/iP4U1G/t4Fgjk2bR90VpiOSvy05aIzw6lQUqkfeZn/AB41i3+JOrRSaZGrAEfcGe2K7rwH8D7zxT8O5LW5lNsMEj5fTB5rjfhv8P8AU472NpIXYZHVT619l+FdHa28OfZ9mxiuMdO1cGMxX1aEaVJ7M7MJhfrE5Vqq1fQ/O7TdMT4bfEf7Nfx/a4IZ8MQOwPXFfW5/aA8G2Hh5Io4I428sDYygf0rw/wCLXgO/8PfEk3t7ATZzTEiQjgimeLPBcHiHTUXT1RpcAgLjJrurRpYxU51Gziw/tcH7SFNLyRwnxIv/APhO/Ekr6WG8qRshEOVHbgV1/wAL/gPrDTx3l3YB7fOSJEzxj3rmPCdrcfD7X4zqtsyRA43MMDr719gaD8YPCo8O5F5CjbcbdwBHAqsXXqUIKnQV0Rg8NSr1HWru0jz7WPh9oN1HHYm2t7aZhtZggBBPFIfgLH8L9DvPEWk3gknaIgo6DaQRz3ryv4g/GSBPFv2i0k3xJITw3BGal8Z/tTXGt+Fv7It4tildrNu56YrCOHxjUeVuz3OieKwUXJysmtjW/YsnkuPjXrkkgAdtJnJx0z58FeIfFGwudR+MXjO2tLeW6uH1u+2RQoXdsTyE8AEnABP0Br2b9hmUz/FrVZD1bSJj/wCRoK8t8V+Jf+EQ+P8A4p1f7N9r+z63qH7nzNm7dJKvXB6bs9O2K6qF45lVsrvlX6Hh4lxngKTk7JyZxM+nax4Wu7W6mtb7SLlX8y3meN4HDoQdyMQOVODkHIODxxW3/wALf8d/9Dr4i/8ABrP/APF1s+JvFtx8afEPhvR7Wxj026luhZxPLcGRC8zoi7iEGACASeeCcDjB9IH7B/xBP/MT8O/+BU//AMZrtrYjC00vraSl6X0POpYbEznL6k5SjffY878R3fxgstEvJNem8bwaOyGO5fUXvFtyjEIVcuQuGLBcHg5xznFekeDf2afDfiL9nG4+IFxqGqx6vHp19diCKSMQb4WlCAqYy2CI1z83c8jjHs/7UOo+MJvgX4lj1XQNFsrD/RvMntNZluJF/wBJixiNrVAcnA+8MAk84weU+HHxB8L6f+xze6Hc+ItKg1ptI1SIafJeRrcF3e4KKIy27LBlwO+R614DxdWeEjUpRUHz293qrdT31gqNLFSp1pOS5G/e6O58YV6P4H8Tar8Job+a/wDDV48N60aCS4DW6hlDkAEoQSQSccdD17ec16F8Q/i3/wAJ7osGn/2T9g8q4WfzftPmZwrLjGwf3s5z26c19fWi6iUOW6e58nhpxpOVVS5ZLYzviJba7rGoN4rv/Duo6PpmpeV5E9xBIIZP3Q27ZSqq2QpYY7ZxkDNN+EnxA/4VZ8QtJ8Uf2f8A2n9g83/RfO8rfvieP721sY356HpjjOR9L+O9Mb4zfs6/D7wt4Lu9P1/xJp8FjcXemW+o24ngjS0aN2ZWcY2u6KR1BavlDxb4S1bwL4gu9C1y0+w6ra7fOg8xJNu5FdfmUlTlWB4J69sV52BxEMbRlRqJJ6q3l+Z3Y2hUwdaOIpttOz5ul9/Q9J/aD/aC/wCF7nQT/YH9h/2X9o/5fPtHm+Z5f/TNcY8v3zntjmX4Ffs66/8AFeCHxDYz6UNJsdSWC5gv3ffJs8t2AUIwIKvjBIz0IA5OD4R/Z2+IXjvw9a67oXh432lXW7yrj7Zbx7trsjfK0gYYZSOgzjjOQapeBPjj43+GekzaX4a1v+zbGac3MkX2WCXdIVVS2XRiOFXgHt0yTlyp2ofV8vkk4vXrbf16hGo3iFicxi2mtNLX/I6r4y/8In/wi9r/AGF/Y32z7Yu7+zvJ8zZskznbzjOM54zj2rtfGvwZ1D9kaTQviBpfiC38QX0d6bOO1u9OaKMeZbzAsdsxJ4BwBjkj0xXzNXu3x5/ahPxt8JWWh/8ACNDRfs16l55/2/z92I5E27fLXGd/XJ6Y75EV6GJTp0qbvBtqW22nz77GtPFYaaq1qi5Zq3Jvv/Xc5nQ/HeofEz9orwz4l1WK3hv77XNO8xLRWWMbHijGAWYjhRnk9+lfpsoyozX5TfCD/krXgn/sN2X/AKPSv1ZBr5nPoRp1qcIqyUbL7z6jhucqlKpOb1ctfMdRRRXzB9iFFFFABRRRQB4J+3Z/yar43/7cf/S63o/YT/5NV8Ef9v3/AKXXFH7dn/Jqvjf/ALcf/S63o/YT/wCTVfBH/b9/6XXFAHvdFFFABXxd+0R/wUAuPhj8Q73wr4U0Cz1R9Ll8m+vtQkfa0gALRxopBG05BYk5OQBgZP2jXwf+0h+wH4l8cfE3VPFPgvUdNe01mc3NzZ6jK8TwTNzIykIwZCct2ILYAIGaAPpv9nP472H7Qfw8TxFa2Z028hna0vrFpN/lTKFb5WwMqVZSCQOpHOM16rXjn7LfwDH7PXw4bQp72PUdVvLlr2+uYVIj8wqqhEyASqhBgkAkknAzgex0AFFFFAH5mfGz/lJFpv8A2Mvh/wD9F2dfpnX5mfGz/lJFpv8A2Mvh/wD9F2dfpnQB43+yJ/yQPQ/+v7Vf/Tlc17JXjf7In/JA9D/6/tV/9OVzXslABRRRQAUUUUAfmt+1x/ycL4s+tr/6SQ1037DX/JaZv+wTP/6HFXM/tcf8nC+LPra/+kkNdN+w1/yWmb/sEz/+hxV+k1P+RQv8C/I/KqX/ACOX/jf5n6B0UUV+bH6qFFFFABRRRQA3NBrx34h/tBWPwv8AixpHh3XI0h0TU7JZBqHINvKZGUF/9g4Az/CeTkHj16GVLiJJY3WSNxuV1OQwPce1aTpThGMpLR7HPTr06spQi9Y7k1FFFZnQFFFFABRRRQAUUUUAFFFFAHyn+0d+3bpvwT8aSeE9I8P/APCS6raqrX0sl15ENuzKGWMEIxdsEE9AMgZJyB6Z+zf+0do37RnhW61Gws5dJ1PT5Fiv9NlkEphLAlGVwBuVtrYJAOVYEDHPyT+15+xz481v4sat4t8I6W3iLS9ZdZ5IoJUWa1lKhWUqxBZSRkMucA4IGAT7p+w3+zdrvwM8O65qnilUttd1swr/AGfHIJPssUW/G5lJUsxckgEgBV5ySAAfUVFFFABRRRQAUUUUAFFFFABRRRQAUUUUAfJ3x/8A2RPEPxI+Id94o0LWtOH9o+X51rfiSLydkUca7WUPv3bSTwuOB82cj5u+MPwI8QfBM6SNeutOuTqXm+T9glkfb5ezdu3IuM+YMYz3/H9QOCM181/tj/CLxb8U28I/8IvpP9qfYPtn2j/SYodm/wAnb/rGXOdjdM9PcV9NluZ1lVhRqySgrrW2mnc+RzTKKPsp16MW5tp6X1110Ov/AGuuP2d/FPp/on/pXDXGfsG/8kk1zHX+25f/AERBXj/xc/4aFHw81T/hOQf+EW/dfa/+Qf8A89U2f6r5/v7On48Zr2H9g3j4Sa5/2G5f/REFOtQ9hlzjzqXv7p36E0sR9ZzSMuRxtC3vK3U8e/YO/wCSxan/ANgSb/0fBS/t4/8AJYtL/wCwJD/6PnpP2Dv+Sxan/wBgSb/0fBS/t4/8li0v/sCQ/wDo+evQ/wCZx8v/AG08v/mTf9vfqfSX7Xn/ACbv4o/7dP8A0rhrj/2Cf+ST61/2HJf/AERBXYftef8AJu/ij/t0/wDSuGuP/YJ/5JPrX/Ycl/8AREFeJH/kVy/x/ofQS/5G8P8AB+p438Wf+T37X/sOaP8A+gW1fYvxr/5I544/7Al7/wCiHr46+LP/ACe/a/8AYc0f/wBAtq+xfjX/AMkc8cf9gS9/9EPWmP8A+Yb/AAxMsv2xf+KR83/8E9fv+PfpYf8AtxX2KelfHX/BPX7/AI9+lh/7cV9inpWGcf79U+X5I7ck/wBwp/P82eIftg+H9S8R/Ba9i0yzlvpoLmG4kigXc+xW+YgDk4znjoAT2r8/P+ES1z/oDah/4Cyf4V+uGPzr5++Mv7Wdr8IvHE3h2Xw1LqjRQxzfaFuxEDuGcYKHp9a7spx9elF4ejT5nvvY8/Ocuw9aSxNepyJaHwh/wiWuf9AXUP8AwFk/wo/4RLXP+gNqH/gLJ/hX1t/w8Dsf+hKm/wDBgv8A8bo/4eB2P/QlTf8AgwX/AON19F9czH/oH/8AJkfM/Ucs/wCgn8D5J/4RLXP+gLqH/gLJ/hR/wiWuf9AbUP8AwFk/wr9I/gT8aoPjf4f1DVIdIfSVs7r7MY5JxKWO0NnIUY+9jFem4FeZVz+rRn7OpSSa31/4B6tLhyhWgqlOro+tj8j/APhEtc/6A2of+Asn+FH/AAiWuf8AQG1D/wABZP8ACv1x2j+6KNo/uis/9Y5/8+19/wDwDT/Van/z9f3H5Hf8Ilrn/QG1D/wFk/wo/wCES1z/AKA2of8AgLJ/hX647R/do2j+6KP9Y5/8+19//AD/AFWp/wDP1/cfkd/wiWuf9AbUP/AWT/Cj/hEtc/6A2of+Asn+FfrjtH92jaP7oo/1jn/z7X3/APAH/qvT/wCfr+4/I7/hEtc/6Auof+Asn+FH/CJa5/0BdQ/8BZP8K/XDaM9K8r+PPxzt/gdpel3k2jvq4v5nhCRziLZtXOclTnritKWfVq01Tp0k2/Myq8OUKEHUqVbJbux+cf8AwiWuf9AbUP8AwFk/wo/4RLXP+gNqH/gLJ/hX1t/w8Dsf+hKm/wDBgv8A8bo/4eB2P/QlTf8AgwX/AON16f1zMf8AoH/FHlfUcs/6CfwPkn/hEtc/6A2of+Asn+Ffo3+y5omoeHvgV4YsdStJbK7RJ3aCdCrqHuJXXIPIyrA8+tc98DP2orX41eL7rQofD0uktDZPeGaS6EoIV4124CD/AJ6Zznt05r3fFfNZtj61dLD1ocrTvvfofUZNl1DDt4mhU5k9B1fNH7e3/JJtE/7DcX/pPPX0vXzR+3t/ySbRP+w3F/6Tz15mXf73S9Uermn+5VfQ7P8AZA/5N58K/W7/APSuavZK8b/ZA/5N58K/W7/9K5q9krLGf7zV/wAT/M2wH+6Uv8K/IKKKK4zvCiiigDx34n/8nC/BT/f1r/0iFexV478T/wDk4X4Kf7+tf+kQr2KgD45/4KE/f8Bf9v8A/wC21fYcf3BXx5/wUJ+/4C/7f/8A22r7Dj+4K9jFf7lh/wDt78zwsJ/yMMV/25+QvY9q8h8X+MfE/jbxpf8AgnwNcQ6WNPjU6x4hni837KXGUihTIDSlckknCj3r11jkEd68j/Z6+aH4hzH5pX8Y6kGbHJ2sqj8gAPwrzqTUU52u1selXvJxpp2T3MrRv2T/AA5pEkLf2/4hjKWcVm8VnfG2jkROTkIA3zOWcgseWOMVuQ/swfDVCHm8Ni/mHWS+vJ52b1J3yEe/FeJrF4K1vwd4u8R+OPEEtn8R7K7vFDPqLxXNhIjt5EdtEGGVwExhSCSfTj6a+GV7qupfDzw1da4rJq8+nwSXQddreYUBbI7HPUdjXbXnXprm9o97Pp93dHDhoUKj5VTXfv8Af2Zl23wF+HVp9zwVobcY/e2Mcn/oQNWP+FKfD7/oR/Dv/gqg/wDiK7X8KK891Zy1cmekqFJbRX3HnN5+zt8Nr0Yk8GaUuTn91B5f/oOOPasuT9mHwLAM6XbaloMw6S6ZqtzER9BvKjqe3c16yRivE/2hpoJdb8CaVr1/Npnge/vJ01a4jlaFHdYwbeKSRSCqM27PIHyjkYyOmjVrVJKPO18znrUqFODl7NMpr+zXceDpodY8E+KbyDX7aa5uEm1tFuluTME8yOVgFbaxQHPJBZjyTXf/AAq+Ikvj3SryLUbE6R4j0mf7HqunsciKXAIZD3RgcqfTPJxk+efB8aPoXxl1zQfA18174KTSY7i5hhuGubW0vvMwqRuSQC0eSVBPT2wOg8Gny/2j/iEqfKsmmaa7gcbmAkAJHc44z6VtVcqikpu9kmnbXdaP7zno8tNxdNWTdmt1tuvuPXT1rivjZ/yR3xx/2BL3/wBEvXantXFfGz/kjvjj/sCXv/ol64Kfxr1PQr/wpejPmj9nQ/8AGInxR/7iX/pDHXlHwM8JW+v6jcG6iDooz8w+ter/ALOvP7IvxR/7iX/pDHVb9n+xsorcyO6oSoB568V9hCq6csS13PjadJVVhr7KJk6LreleDPFVzG8QjVThcCuws9U8WeMriSTQboNbAkeXIP8A6xry/wCO2nwWOvPcW7g5YH5TXt/7KOsW93YmL/lrnnP0rTEJRoLEJXZ1YeblXeHbsjzea11TTPGtt/bsAjfePmUYBOa+l9I07RdV02BnCMdvcVhfH7wpFqOmC7RljnjywJ4/Kud+DMEl/AsUk5YqcYyfevMq1PrNFVU7WPTpQdGq6b1TO91LVNC8Ix7iiqQOMCuVvP2ldM0yXZsYoO6gVofEjwRHebWlYhBjn2rE0X4K6Jr1iQWRycj3rCnHDuClVdzabrqXLTsjnPir460X4qeE5G0+VReRAsI24bOO35V5J4IgudQtJEhvWt9RgBIRzgHB96k+Mnw0uPhXr1tc2DloJGJKgnHBH+NULZ319YbuzUwXoA3ADGa+ho04Ror2bvFng1ak5Vn7RWkv6uP1ptU8V3Y0+/j/AHynG7HWtC3+CN7o0Au76ZvsWMkBj0r0fwF4em1RYTdw7blOd5HXmvUviTpDS+AJwg5VMYx34rkqY1wkqcO50wwUZp1J3vY+Ob/4YDxZrP2XQh5hzjLE9az/ABv+z/4i8EWH2u8jHleqk16X8FvEq+HPFNyZYzJskzgdeCa6f4+/G+z8W+Hp9M06zneZOJGKjCgA5r0VicTCtGnBXieVPB4WpRlVnpI5X9hNNvxT1P8A7BE3/o6CvFPi/wD8lZ8bf9hu9/8AR717b+wo274oaoe/9kzf+joK8S+L/wDyVnxt/wBhu9/9HvTw+uaVv8K/Q8zE2WV0f8TPvX4m/DzwP4L8MWet2vh3QtElstY0qU6jFZQwGBP7Qt97eYFG0bSwJyBgnPGa5D9pf9oqHw94I0+4+H3jPSpdXfUEjmFjNb3biAxSFso24AbgnzY9BnnB+U/F/wC0V8QvHnh660LXfEJvtKutvnW/2K3j3bXV1+ZIw3DKD1GcYOQSKi+Emj+FdW/tX/hJpLVPL8n7P9puzBnO/fjDLnoueuOOmefPpZLKilVxLvZ7LW607npVM5VeTo4RcvMrXelnr2PZPF1x8QvHf7Ld14013x99u0u62edoX9j28e7beiJf364YYZVfhe2DxXyzXtHwP8OfD3WPi54gsfGE1hH4YhguTZtdag1vEXE6LHtkDqW+QtjLHIycHqOS+OOl+GdF+KWt2fg57eTw5F5P2VrS5NxGcwRl8SFmLfOWzyccjtx7GCnClWnhkv72ySS0003fmeFjozrUoYlvb3WuZt31116Fzwj+zr8QvHfh611zQvD327Srrd5Nx9st49+12RvlaQNwykdBnGRkEVwer6VdaDq17pl/F9nvbKd7aeLcG2SI21lyCQcEEZBIOOCRX1Z+yz47+Kt14a0LR/D3h3Sr/wAFWGo/ZLy/lYLPEjyiWYjM6klVmJGEI6DBIOfSfEnw3/Z4vfEWqXGs3mhJrEt1LJerL4hkjcTlyZAyCcbTuzlcDB4wK4ZZvUw+InTrxTir25dXv11PQhk9PE4eFWhLlb35tF8tD56/Yv8AFGj+E/ipqN3reqWWkWb6PLEs99OsKFzNCQoZiATgE49q6f4i/DWy+J37Suo6rqd8lv4Cvkjlk120voFRUSxXDB2JGN6AZwRj868Y+OGl+GdG+KWt2fg97eTw3F5P2VrS5NxGcwRl8SFmLfOWzyccjtx7V/wrT4lf8KQ/tjzfC/8Awj3/AAj/ANp277j7V9m+z7um3b5mzt93dSxChTqLGRnyurGyvuttfUrCtzpSwVSHMqTvps/L0PWvO8S+BPh94c0T4FxaV480Wze7jvby8vYZjE5dZUXcksS5PmvwATjbnGcn4Ar1f4TftJ+J/g54eutG0Sz0q4tbi6a7dr6GR33lETgrIvGEB7nJPPavKK7stwlXCyqKot9n1lvq9Tz8zxlLFRpum9t49FtsfSP7SvjL4ReIvBOn2/w/t9Ki1dNQSSZrHSGtH8gRyAguYlBG4p8ueuDjjI8K0LwL4k8UWjXOjeHtV1a1RzG01jZSzIrgAlSyqcEAg4Jzgg9xWHX2n4SPi/8AZA+H0VlqWh6T4lTWNbEcDWOrSROJZYgFUq9vjH7k87h94cd6zqt5VSjRoe9KT0Tf/DeRdKP9q1pVa65Ypa8q+4+WfhB/yVnwT/2G7L/0elfqwDxmvy78BeGNT8J/GbwNaarbfZbh9XsZVTzEfKm4UA5UkdQRjOeK/UQcjivn+IJKVeDXb9T6fhqLhRqRffqflp/wUH8XeIdS+P8AqehX93cjQtNgtjptozEQ7XgR3kC5wWMhdS2M4QDOFAH0P/wTV8X+IfEHw88T6dqtzc3mk6XeQpp8twWYRl0YyRIxP3VwjbexkJ719F/Ej4HeBfi69rJ4u8N2msz2oKwzuWjlVck7d6FWK5JO0nGSTit/wf4M0P4f6Bb6L4c0u20fS4PuW1qm1c92J6sx7sSSepJr5Y+yPy7/AG7/ABh4h1v9oTxBpWq3NwumaWYYtPsmYiKOMxI29VzgliSS3U8DoAB9V/8ABOLxh4j8TfCbWbXWZ7i907TNQFvp1zcksQpjDPEGPVVJUgdt+OmAPdviR8B/APxcubW58W+GLTWLq2XZFcsXilCZztLxlWKgkkKSQCSQMmum8KeEtG8D6FbaNoGm22kaXbDEVraxhUGepOOpJ5JOSTkkk0AflN+2p4w8SeIf2g/FNjrlxcJbaXc/ZtPsnYiKGAKCjIvQFwQ5PUluuAAPsz/gnf4v8R+K/gleJrtxcXttp2pPZ6fdXLFmMIjjbywTyVQsQCegO0cKAPY/iN8Afh78Wb+3v/Ffhe01a9gTy0uizxS7P7peNlLAc4BJxk4xk56/w34a0rwfolro+iafb6XpdouyC1tYwiIO+AO5OSSeSSSck5oA8Z/bs/5NV8b/APbj/wCl1vR+wn/yar4I/wC37/0uuKP27P8Ak1Xxv/24/wDpdb0fsJ/8mq+CP+37/wBLrigD3uiiigAr4g/aJ/4KBav8OPiTqXhbwdoemXsekzG3u73VfMfzJh99ERHTaFOVySckHgcZ+36+H/2if+CfmsfEb4kan4p8Ha3pdnHq0xubuy1ZpU8uY8uyOiPuDHLYIGCSAcYwAfQv7NHx5tv2hfhyniBbJdM1G2nayv7JZN6pKqq25ScHYwYEZGRyMnGT63Xkf7M/wFtv2efhyugLejUtRurhry/vFj2K8zKq7UBydihQBk88nA3ED1ygAooooA/Mz42f8pItN/7GXw//AOi7Ov0zr8zPjZ/yki03/sZfD/8A6Ls6/TOgDxv9kT/kgeh/9f2q/wDpyua9krxv9kT/AJIHof8A1/ar/wCnK5r2SgAooooAKKKKAPzW/a4/5OG8WfW0/wDSSGpf2VPiDoPw1+J0ur+I77+ztPbT5YBN5Mkvzs8ZAwisegPOMcdai/a4/wCThvFv1tP/AEkhryCv1ShRjiMvhSls4rb0Px3E15YbMalaKTcZP8z9JP8Ahr34Tf8AQ1H/AMF13/8AGqP+GvfhN/0NR/8ABdd//Gq/NuivM/1dw380vw/yPW/1nxf8kfx/zP0k/wCGvfhN/wBDUf8AwXXf/wAao/4a9+E3/Q1H/wAF13/8ar826KP9XcN/NL8P8g/1nxf8kfx/zP0k/wCGvvhN/wBDUf8AwXXf/wAapP8Ahr74Tf8AQ1H/AMF13/8AGq/Nyij/AFdw380vw/yD/WfF/wAkfx/zPdP2uPiX4c+J/jfSdQ8Naj/aNpBYCCRzBLFtfzGOMOqk8EHIGOa6T9l/9qB/BEtt4U8V3TSeH3IS0vpDk2RPRWPUxk/98/Tp8z0V6jy2hLDLCy1itm90zx45nXji/rUdJPdLZn7AwzJPEksbrJG43K6nIYHoR7VL1rwr9kTw/wCM9A+GqJ4pmK2UhV9Lsp1P2i3h5yGOeAeCqYyo74IA92/CvzGvSVGrKmpXS6n63hqrr0Y1JRs30FooorA6QooooAKKKKACiiigAor8p/24fiz4p8QfHjX9Cn1G8stH0WRLaz0+KVkjA8tWMpUEAsxYnJ5wVHQV9F/8E4Pip4m8beG/FPh/Xb251Wz0RrZ7K7unLyRrL5gMJckkqPLBUEnAJHTAAB9m0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAePftZ2lxe/s/wDiuO3gkuJFS3kKRIWIRLmJ3YgAnCqrMT0ABJ4Br4v+E/7SXif4OeHrrRtEs9KuLW4umu3e+hkd97IicFZFGMIDjk8nntX6X4yADzTHRdrYUZr2MLjqdGi6FWnzpu+9tdjwcbl1TEV1iaVXkaVtrnwV+wecfGLU/wDsCTf+j4KP28P+SxaZ/wBgSH/0fPXnvwS+Ktx8A/Heoanc6FJqF19ll06WxlmNq8T+YjNuyjYKmMgqRkZOSMcw/Hj4wH42+MLXXjpP9jeRZJZ+R9p8/dtkkfdu2r134xg9OtfVrDVZZj9ZS9y2912PjPrVKOWfVW/f5trPufbf7XnH7O3ij/t0/wDSuGuP/YJ/5JPrX/Ycl/8AREFdh+12Mfs7eKP+3T/0rhrj/wBgn/kk+tf9hyX/ANEQV8xH/kVy/wAf6H1kv+RvD/B+p438Wf8Ak9+1/wCw5o//AKBbV9i/Gv8A5I544/7Al7/6Ievjr4s/8nv2v/Yc0f8A9Atq+xfjX/yRzxx/2BL3/wBEPWmP/wCYb/DEzy/bF/4pHzf/AME9fv8Aj36WH/txX2Ka+Ov+Cev3/Hv0sP8A24r7FNYZx/v1T5fkjtyT/cKfz/Ngetfnf+2n/wAl0vv+vK3/APQK/RA1+e37bVhc2/xsmuZIJEt57KAxSlSFfAIOD3weD6V05C0sXr2f6HJxEm8Fp3R4DRRRX6PzLuflvLLsfcv7An/JPfEf/YU/9opX1FmvmX9g7Tbq0+Gms3E0EkUN1qZaB3XAkURoCV9RkEZHGQR1Br6a6V+VZo1LGVGu5+xZSmsDST7C0UUV5Z64UUUUAFFFFADe1fKH/BQD/kVvCX/X7N/6LFfV/cV8r/t9WVxP4O8M3EcMkkEN7IJZFXKplOMnt0PWvWyppY2m33/Q8fN03gaqXY+IaKKK/U+Zdz8e5Zdj6P8A2Dv+Sxap/wBgSb/0fBX3wa+Df2DLK4f4q6xeLBIbSPR5InnCHYHaaEqpPTJCsR7A+lfeXUcV+Z5008bK3l+R+rcPprAxT7sWvmj9vb/kk2if9huL/wBJ56+l6+aP29v+STaJ/wBhuL/0nnrjy7/e6XqjuzT/AHKr6HZ/sgf8m8+Ffrd/+lc1eyV43+yB/wAm8+Ffrd/+lc1eyVljP95q/wCJ/mbYD/dKX+FfkFFFFcZ3hRRRQB478T/+Thfgp/v61/6RCvYq8d+J/wDycL8FP9/Wv/SIV7FQB8c/8FCfv+Av+3//ANtq+w4/uCvjz/goT9/wF/2//wDttX2HH9wV7GK/3LD/APb35nhYT/kYYr/tz8gZcivnbwJ8RI/hPqXjbS9b8PeInnuvEt9qEMllpcs8UkMrgxsHAwcgdv519FV4R8RvBui+Ov2i/DWma9p0WpWP/CP3M3kz/d3iZADwe2T+dceG5JScal7Wb0301O7F88Up0vivbXzEv/i38PtW1mLV734fa3d6rFjZfT+F2edMdMOV3DHbBrc/4aU0LP8AyLvi3/wRTf4VX8Xfs6/Dex8KaxdW3hKwinhs5pI5AG+VghII59a6P9n8D/hSngvP/QLg/wDQRXRP6s6fPBSdnbXT/M56f1lVOSbirq+n9Ixf+GltC/6F3xb/AOCOb/Cj/hpbQv8AoXfFv/gim/wr1vYPSjYPSuTmo/yP7/8AgHbyV/5193/BPI/+GlNB/wChd8W/+CKb/Cqmq/H7wprtjLY6j4S8TX9nKMSW9z4elkjcZ4BVgQema9mwOwrxH40eFdK8ZfGf4WaVrVjFqGnTQ6u0lvLnaxWKFh0I6EA1vR9hOdnFpavfsr9jmr+3pwupJ7Lbu7dxdD+OPg3wvZ/ZNG8FeIdKtCxb7PZeG5Ik3HqdqqBn39qd8I9Tm8YfGHxt4og0rU9P0m4sbG1ik1O0a3Z3QPuAVuuMj8+3Gd8/s1fDID/kT9Px64f/AOKpv7NMSwfBPw2iKFVRcKFHYC4lArScqCpTlRvd2Wvbf9CKcK7qxjVastdPu/U9QPWuK+Nn/JHfHH/YEvf/AES9dqetcV8bP+SO+OP+wJe/+iXrz6fxr1O+v/Cl6M+af2dP+TRPij/3Ev8A0hjryP4e+J10kujzFARjrivXP2dP+TRPij/3Ev8A0hjr5k1aKWHUTFASCGIIU19zgqaqV8RF9z4KvVdGhh5pX907jVZD4s8R/Z/N8xGPBzX0H8CPCM3g/X4UJOyTkD6ivnr4eeD9WfVbe6MMhTIJYjjrX0Lr/wARLTwDr2lvduEURgtjtTxt5L2FLXQ7sBZfv6qs/M9Q/aIkEHgkzZK4zkj0xXC/s9S2jWyyx3BdiQTk9OaZ41/aB8H+NNB/sszi4mmG1VCngkfSuc8HxP4dtx9jYpGeRivHp0ZxwzpTVm2evKrCddTpu6R9P6vp1vrNkY2cHI4IPSvB/EUet+C9XMdgxeNskAnrVDWPitqWgWu/zXcDsM1xf/C6JPEF8PtZMe3gFutThsJVgm7XRdfFU3ZXszL+J/jbWPFUa2d9ZkzQk7WAzmqHw98VWlhNHBqlsYJFOA+Md666Hxlop1WKa5aNgSM5Ga9OvfBvhfxj4eN3Z28azFchkUA5r0pVo0oKnKNk+p58aUqs3UjK7R1/gvWtCuNMSeFkZgvbqK3db1LTdY8PXSLIu0qQRXyjod9e+EPFT2JldbfOACTjFbXxB+JS6LYSJaXBQsMlVPevOlgXKouVt31PQjjIxpvnVrFLXPg8LiS41PT7l4juJJU4FeR+OtL1XwLBJILkTR3eUfeOa99+AF9feP8ASr+3eTKHoxPQnNePftMw3mga1/Y13GP3ZDpIOhBFe3hak/rHsJvVHhY6EPqrrwVrnTfsHtn4m6rnvpU//o6CuBk/4RP/AIab8Qf8Jx/yK39uan9sx5v96by/9V8/39n3fx4zXd/sHf8AJT9U/wCwTP8A+joK8V+L3/JWfGv/AGG73/0e9XThz5lXhe147rdbbHkVZ+zy2hO17SejOq+Keh+BvE/xR0LRvhYyJpmoJb2e+ZrkILqSZ0yxmBcAAx5IGMZxk0fFn9m3xP8ABvw/baxrd5pVxa3F0toiWM0jvvZHfkNGvGEI4yckcd65X4Qf8la8E/8AYbsv/R6V9h/t6f8AJJtE/wCw3F/6InpV8RWweKo4WE24u176t3fcVDDUsZha+LnG0lslolofB9fRnw38H/DDxX8Db/bompax8Rreyut39n2moz+XcM0v2bd5QMQyFTGeDg571xP7NuveB/Dvju7ufH8NnNorafJHGt9Ym7QTmSIqQgRsHaH+bAx0zzXVaj+0VD8Lfil4wvfhlZaNL4c1f7H5ay2MkUa+VAAdkYMZXLvJnI5IyOvPTjp168nQoxaas072T20v8+/Q5sDGhQgq9aSaldNbtedjm/hj+0R4v+Bekah4c03TdOAa9ea4j1O2l86ObakbKQHXGPLwQRkHOT2EXgL4R+KP2k/EXirWNMuNKs7tboXl2l1JJEm+4eR8IFV+AUbgngY5NXPB3jnwlqf9t6n4ug08axqWqT3rB7Bpgok2thTtYhQxfjJI75zk85oPxh8Q/DTxD4jm8EaqulWWpXTMRHaROrxqzmLCyIdoAc4AA64I4GH7CXvyoQUartq9r6X7lOpBKmsRNzpdluuxhfEPwJqHwz8Y6h4a1WW3mv7Hy/MktGZozvjWQYLKpPDDOQO/avXvgd8dNb1jxFongrxh4jt4vh9LZT6ddW10kFvELcWsiIhmCq68hRneCT3JPPivi3xbq3jrxBd67rt39u1S62+dP5aR7tqKi/KoCjCqBwB0966X/hTWt/8ACMf299psPsn2P7bs8x9+zZvxjZjOOMZxnv3rqrUYVaCp4m3Na17bPyOPD1KkMRKphU+VO9u67M93+Nf7MGn+INN8Oaj8GtDt9S0+b7T9tubTVlljfBRY8NNMQcESg7D1Bz2r5Mr9Iv2Qx/xjx4VPbN3/AOlc1fOH7S/jP4Q+I/BWn2/w/ttKh1dNRSSZrHSGtH8gRyAjeYlBG4p8ueuDjjI8TL8fXhWeElFzSdubW61er3PdzPLqEqMcZCSg5K/L0ei2ML4D/svH42+Er3XP+El/sYW189n5H2Dz92I433bvMXH38Yweme+Bx3if9oLx342TTYtf1ldUt9PvY7+CCS0hjUSpnaSY1ViMMwIzjn6Y9s/ZL+Ofgj4Y/DzVNL8S6z/Z19PqklzHH9lnl3RmKJQ2URgOUYYJ7e4r5Or0cNGpXxFX6zG6i/dutt72dvQ8vEypYfC0XhpWlJe9Z+lro9L8J+M73xz8bfAl9fw28Useq2EAW3VlUqLkMCQSTnLHv6cV+nYGK/Kb4Qf8lZ8E/wDYbsv/AEelfqwOFxXznEEVGvBRXT9T6rhucp0qkpO7v+g+ikFLXyx9iFFFFABRRRQB4J+3Z/yar43/AO3H/wBLrej9hP8A5NV8Ef8Ab9/6XXFH7dn/ACar43/7cf8A0ut6P2E/+TVfBH/b9/6XXFAHvdFFFABXwh+0j+334k8DfEvU/CvgrTdOW20ac211e6lE0rzzLneqqHUKgOV7klcggHFfd9fFv7RH/BP65+JnxCvvFXhPX7PTH1WXz76x1GN9iSEDdJG6gk7jklSBgk4ODgAHuH7Lnx8X9oX4a/27NZR6dq1pctZX1rCxMYkCqwdMkkKyuCASSCGGTjJ9iryr9nP4EWH7Pnw8Tw7a3h1K8mna7vr5o9nmzMFX5VycKFVQASehPGcV6rQAUUUUAfmZ8bP+UkWm/wDYy+H/AP0XZ1+mdfmZ8bP+UkWm/wDYy+H/AP0XZ1+mdAHjf7In/JA9D/6/tV/9OVzXsleN/sif8kD0P/r+1X/05XNeyUAFFFFABRRRQB+a37XBz+0L4s+tp/6SQ1J+yp8P9A+JPxOm0jxJY/2hp66fLOsPnSRYdXjAOY2U9CeM456VH+1x/wAnDeLOMc2v/pJDXTfsNf8AJaZv+wTP/wChxV+lynKGVKUXZqC29D8njCNTOHGaunN6fM+nh+yD8Jv+hVP/AIMbv/47S/8ADIPwm/6FU/8Agxu//jteyUV8B9cxP/P2X3s/SvqGE/59R+5Hjf8AwyD8Jv8AoVT/AODG7/8AjtH/AAyD8Jv+hVP/AIMbv/47XslFH1zE/wDP2X3sPqGE/wCfUfuR43/wyD8Jv+hVP/gxu/8A47R/wyD8Jf8AoVT/AODG7/8AjteyUUfXMT/z9l97D6hhP+fUfuR+df7XHwz8OfC/xvpGn+GtO/s20nsBPIhnll3P5jLnLsxHAAwDivU/2Wv2WvI+yeMvGVp+9GJdP0qZfuekso9ehVT06nnAHvfiP4LaN4w+Jul+L9ZAvjplosFrYun7sS72bzW/vEbhgdARnk4x6JjsO1epVzao8LHDxbv1b/I8WjktOOLliZpW+yunqKAAMDpS0UV8+fUBRRRQAUUUUAFFFFABRRRQB4t8aP2Sfh98dNYi1jXrS8stYRVje/0ucRSTIvRXDKytjpnG7GBnAAHafCn4ReGPgx4XTQfCuniys95llkdt808h4LyOeWOAAOwAAAA4rzf45/tmeBvgR4jXw/qMOoazrYRZZ7XTY0P2ZWAK+YzsoBIIIAycEE4yM958GPjd4X+O/hY654ZuJTHFJ5VzaXSCO4tnxkK6gkcg5BBIPY5BAAPQaKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDldZ+F3g7xDqU2oap4V0XUr6bHmXN3p8Usj4AUZZlycAAc9gK+HP2z/DGkeE/inptpoulWekWj6PFK0FjAkKFzNMCxVQATgAZ9vpX6FiuF+IHwU8FfFG9tbzxNoUeo3dtGYo51mlhfYTnaWjZSwByQDnBZsY3HPp5fi1ha6qVLtK+3oeLmWA+t0HCkkpdzwr9or9o34e+PPg1r+g6Hr/27Vbr7P5UH2K4j3bbiJ2+Zowowqk9eeg5NbP7BP/JJ9a/7Dcv/AKIgrI/aN/Zy+HvgP4OeINd0Lw/9h1W1+z+TP9tuJNu64iRvlaQqcqxHI46jkCuY/ZI+Ongj4YfDzU9L8Ta1/Zt7Nqj3McX2WeXdGYolDZjRgOUYYJzx9K9edOnVy9rBxk1z9Vrt5HiRqVaOaReNlFe5pbRb+ZzXxax/w2/a+n9uaP8A+gW1fYnxqx/wpzxx/wBgS9/9EPXxF4s8X6T46/a80rXdCuvtulXWt6V5M/lvHv2i3RvlcAj5lPUD15BBr7d+NZz8HPG//YEvf/RD1z5jFweHjLRqMTpy2SnHFOLunJ6nzh/wT1+/49+lh/7cV9inpXx1/wAE9fv+PfpYf+3FfYp6VzZx/v1T5fkj0Mk/3Cn8/wA2LSFQeozS0V4x7g3YvoKNi+gp1FO4rIQADoMUtFFIYUUUUAFFFFABRRRQAUhUHqM0tFADdi+go2L6CnUU7ishAoXoMUtFFIYV80ft7f8AJJtE/wCw3F/6Tz19L180ft7f8km0T/sNxf8ApPPXo5d/vdL1R5Waf7lV9Ds/2QP+TefCv1u//SuavZK8b/ZA/wCTefCv1u//AErmr2SssZ/vNX/E/wAzbAf7pS/wr8gooorjO8KKKKAPHfif/wAnC/BT/f1r/wBIhXsVeO/E/wD5OF+Cn+/rX/pEK9ioA+OP+ChH3/AfPa//APbevsJHUKORivH/ANoX9no/Hc6Af7e/sT+yvtH/AC5/aPN83y/+mi4x5fvnPbHPjn/Dvd/+h8H/AIJ//t9e8nha+FpU6lXlcb9G92fNtYvDYytVpUeeM7dUtkfYm9T3FcF47+FkfjDxJpuv2ev6l4e1mxgktVutPMZ3xOQSrK6sOoBBGPfPGPnn/h3u2P8AkfRn/sD/AP2+j/h3u3fx6B/3B/8A7fUQoYSEuZYj/wAlZc8Rjqi5ZYX/AMnR7be/BvXtTsrizufif4jkt542ikTyrQblYYIyIgRweoxXf+E/Dll4N8NaZoVgzmz0+3S2iMrbnKqMAsfU45wAPYV8p/8ADvZv+h9H/gn/APt9H/DvZv8AofRj/sD/AP2+qnSwlRWeJ/8AJGiYVsbB3WF1/wAaPsTev96jev8Aer48/wCHez/9D6P/AAT/AP2+j/h3s/8A0Po/8E//ANvrL6tgv+gj/wAlZt9bx/8A0C/+To+wty+orh/iJ8MoPHuo6FqUWt3+gavozTG1vdOZNwWVQsikOpBBCjt2r50/4d7tg/8AFej/AME//wBvo/4d7vj/AJH0f+Cf/wC31UKGDpvmjidf8DM54jHVFyywun+NHuUnwl8RtGVb4p+JOeCBFaD8j5XFdj4C8IWfw+8I6b4fsppp7axQostwwaRyWLMzEADJLE8DvXy5/wAO9m/6H0f+Cf8A+30f8O9m/wCh9H/gn/8At9aSp4ScbPE/+SMmFbGwlzLC6/40fYe8f3hXF/Gt1b4O+OACCf7Evf8A0Q9fOH/Dvd/+h9H/AIJ//t9H/Dvd/wDofR/4J/8A7fWccPgYyT+sf+SsqpicfODj9W3/ALyIf2dP+TRfil/3Ev8A0hjrxL4a6roB8aNL4gIW2MgIJAIHPOa+0fh1+zt/wgPwi8UeB/8AhIPt39t/af8ATvsXl+T50Cxf6vzDuxt3feGc44615G3/AAT5Ytn/AIT0ZJ/6A3/2+vVw2Nwsald1JtKT0aTPJrYHGqnQVOF3Faq6/wAzq/FnxZ+H/hvwe39jy2k13txGseMg4614F458FeJPFnhBvGF3Mv2JQCFLc4J4/nXqZ/4J9SEY/wCE/wC+f+QP/wDb615P2LNck0o6Y3xMlaxIwYP7JO0j6efW1HFYLDtOnVu76tp/5F1qGOxMXGrRsraWa/zPkHwZd29l4mspLt8W6yguxPYHmvpDxb8a/Dlho0VvpAWWUJgvkdcVqD/gns3fx6P/AAT/AP2+nj/gn2w/5nxT9dHP/wAkV2Vsfl1eSlKo9PJ/5Hn4TB5nhabhCktet1/meYaN8YrCd3bUYVlXBwrHisbZ/wALB19zpyrZWwzkg4GOte1j/gn8w6eOU/8ABMf/AJIq/p37Dd/pSkWvxAWPPXGjn/5IqPr+AhrTnr6P/I3WDzGdlWp6eq/zPm3VrRdI1+C2N55gSQBmLccEZr62+FvxE8KaR4ct7e5vYt4XB3MK5K4/YLuLqYyS+PEdycknRz/8kU9P2ELqMfL4+Vfpo5/+SK58Ri8DiYKMqlreT/yOjC0MdhpylGjdPzX+ZY8d2+keMNY8/RLmEMCCWUj0rwX4yaLc6HKhkufM3DPB9zX0DYfsWavpp/0f4iBP+4Of/kio9X/Yj1HW8fbPiAsuBgZ0c/8AyRU4fG4ShJfvbpeT/wAjTE4bFYim7UrSfmv8zwD4K/HS5+FkkyCPzYpCMjdjpXpXxG+IXw8+Kmmy6jqdw8Gr+QdqrggMBwK6A/8ABPwt/wAzzH/4Jz/8kUw/8E+GP/M9qPpo5/8Aj9dE8Zls6vtlNqXo/wDI4aeGzWnS9jKmnH1X+Zy37CgUfFLV9pyn9lT7T/22grjdN1Twzo37Vet3vjCO3k8OR65qn2pbu2NxGc+eEzGFYt85XHBxwe1fUvwG/Zgf4I+KbrV/+ElGspPaPa+R/Z/kFdzxtu3ea2f9XjGO/XiuK8YfsLnxV4t1rWx41+yjUr6e88j+yt/l+ZIz7d3nDON2M4GcZwK41jcLPGVZTn7ko2vZ36eRrLL8ZHCUYwgnKMrtXX+Z5R+0V4++Hs2seDdT+Fa6fZXmmTTXM8thpRtdsitC0DMHjUPgq5A5A7jB57DwD4O8cftd/D2a48SfEDyLKw1Qxx2Z0aBsyLCpEm+MxnpMw2nI4z9ND/h3u+OfHoz/ANgf/wC31t6F+xn4n8MWj22jfF3VdItnfzGhsbSWFGcgAsVS5AJwAM+gHpVVMTgoUIww9Rc0XpKUbu2r3sZ08Hj515TxFL3JLWKlZX+8+Uvi38P/APhVnxC1bwv9v/tT7B5X+leT5W/fEkn3dzYxvx1PTPGePY/B3jL4Q2f7OM+larb6U/j1tOvo0kk0hpLjz2aUwETiIjIBjw27jA5GOO61b9g7Ude1CW/1P4lXGpXsuPMubvTGlkfACjLNcEnAAA9gBVT/AId7P/0Po/8ABP8A/b66quPwWJowhWrO6s20nul6HHSy3H4etOdGirSukm07L7zkP2afGfwh8O+CdQt/iBbaVLq76i8kLX2kNduIDHGFG8RMANwf5c9cnvk8f4Z+CV98efHHje48EXOlWekWWovJAt15kCGCaSUxBEWMlQFT7pC4BAx1x6+P+Ce7d/Ho/wDBP/8Ab61tB/Ym8QeFPPGh/FfUdH+0bfO+wWUkHmbc7d2y5GcbjjPTJ9axljMJTlOth6zU5d07LX0N44DG1I06WIoLkj2au/xPkv4h+BNQ+GfjHUPDWqy281/Y+X5klozNGd8ayDBZVJ4YZyB3/GX/AIWb4m/sX+yP7S/4l/2f7L5PkRf6vbt25224cZznvnPNfUmrfsH6jr2oTX+p/Eq41K+lxvubvTGlkfACjLNcEnAAA9gBVP8A4d7vj/kfB/4J/wD7fXpQzbAuEVVndpK+j3+4895NmEJydGDim/5lt94z9ljR/iF40+GbLoXxG/4RjStMvpbOGwOiW931CzM29iG5aY8HOOxx084+PP7Lp+CPhSy1s+JRrQub5LPyPsHkbcxyPu3eY2cbMYwOuc8YPs2hfsZeJvDFo1to/wAXdU0i2d/NaGxtJYULkAFiq3IBOABn0A9KXXv2M/E/ie0S11n4varq9qj+asN9aSzIrgEBgrXJAOCRn0J9a8aGOhTxLqQrJQbu0o6tevL+p7E8vrVMKqVSg3NLR82ifpzHxFX0n+zT4M+EHiLwRqFz8QLnSodYTUXjhW+1ZrRzAI4yDsEqgjcX+bHXIzxgdd/w73b/AKH0f+Cf/wC30H/gns3/AEPo/wDBP/8Ab69PF5lg8TT5IVnB33SZ5mDynHYar7SdBSXZtf5nBeNNB8EeHf2lPh9beAJrKbRmu9OkkaxvTdoJzdkMC5dsHaE+XP4c1+gQPFfKXg/9hY+FPFui63/wm32r+zb6C88j+ydnmeXIr7d3nHGduM4OM9DX1av518pmNanVcFTm52Vm3fv5n1+VUK1FVJVafJzO6Stb8B1FFFeQe+FFFFABRRRQB4J+3Z/yar43/wC3H/0ut6P2E/8Ak1XwR/2/f+l1xR+3Z/yar43/AO3H/wBLrej9hP8A5NV8Ef8Ab9/6XXFAHvdFFV7uSSK1lkhi8+ZUJjiLbd7AcDJ6ZOBntQBHc6rZ2dxDbz3cEE8xAjiklVXck4AAJyeeOKmubqK0geaeVIIkGWkkYKqj1JPSvzp1T9kTxV4g8JfE74ifGXU7rSvEltavfafIl5BNFI6IzFX2lsLkJGqKVxkYBwBXLaX4u8ReOvgz8Lrzx8mra58MtF8Q3VlrBtWld5IVW3aEzFTkqvmSopyCArAEErQM/T6C6hubdZ4Zo5YWG5ZI3DKR6gjgj3ot7iK7ginhkSaCRQ6SxsGV1IyCCOCCDkH3r84PAfi7VtD8KeO/BngK01Sz0j4h66mmeDLe+3jyLYmQXtwoYkhFjaFS2cgnJJKNj9A/h74I0/4beB9E8MaUm2x0u1S2jJGC5A+Zz/tMxLH3Y0COjooooA/Mz42f8pItN/7GXw//AOi7Ov0zr8zPjZ/yki03/sZfD/8A6Ls6/TOgDxv9kT/kgeh/9f2q/wDpyua9krxv9kT/AJIHof8A1/ar/wCnK5r2SgAooooAKKKKAPzW/a4/5OF8WfW1/wDSSGum/Ya/5LTN/wBgmf8A9Dirmf2uP+ThfFn1tf8A0khrpv2Gv+S0zf8AYJn/APQ4q/San/IoX+BfkflVL/kcv/G/zP0Dooor82P1UKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD8vP21f2f/G9j8bdd8R2Whajrmia3Klxb3lhbvP5bFFVonCAlCCpAB4K4wSQQPoP/AIJ5/BPxR8M/DniXXfEtncaOdcNulrptypSUJF5hMkiHlcmTCg4OASRgivsCigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDm/H3gjTPiP4R1Lw5rCytYXyBHML7XQhgysp6ZVlVgCCDjBBBIPhf/AAwT4F/6DXiL/wACIP8A4zX0v2pOn0rppYqtQXLSm0vI4q2Cw+JalVgmz84dT8B2Hwy/ar0Xwzps1xcWVjrml+XLdsrSNv8AJkOSqqOrHHA4A79fuf41MD8HfG3zA/8AEkvfx/cPXhv7Qv7I+tfEvx/N4n8OapZRPfogvLfU3dBG6IqKYyiNkFVGQ2CCOpDYXxzxP+xh448J+G9U1u81HQntNOtZbyVIbmYuyIpZgoMIGcA4BIHTmvoqjw2P9lOpWtJJJ3V9T5SnHFZd7enToXjJtpp2sj0j/gnr9/x79LD/ANuK+xT0r46/4J6/f8e/Sw/9uK+xT0rzc4/36p8vyR7eSf7hT+f5sWiiivGPdCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK+aP29v+STaJ/wBhuL/0nnr6Xr5o/b2/5JNon/Ybi/8ASeevRy7/AHul6o8rNP8Acqvodn+yB/ybz4V+t3/6VzV7JXjf7IH/ACbz4V+t3/6VzV7JWWM/3mr/AIn+ZtgP90pf4V+QUUUVxneFFFFAHjvxP/5OF+Cn+/rX/pEK9irxP9o25k8G3/gH4ii3eex8Lau39p7AWMNjcwvBNLgckIWjbA9M9BXsdhe2+p2UF3azR3NrcRrLFNEwZJEYAqwI4IIIIIoAs0UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABSYpaKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigDwT9uz/k1Xxv/ANuP/pdb1x/7GXxf8B+Fv2bPB+l6z428O6RqcH2zzrK/1WCCaPdeTsu5HcMMqQRkcgg9CK9s+O3ws/4XX8Ktc8Gf2n/Y39p+R/p32fz/ACvLnjl+5uXOfLx94YznnGD8kf8ADrL/AKqd/wCUD/7poA+1fCvj/wAL+OvtP/CN+JNI8Q/Zdv2j+yr+K58ndnbv8tjtztbGeu046GuhrwP9lj9ln/hmj/hJ/wDip/8AhI/7a+y/8w/7L5Pk+b/01fdu872xt75492nhjureSGVQ8UilGU9CDwR+tAH55/tj/tKad8U/Gtv8MdM8QxaL4KtbsDWtc2STJPIhyQFiVmdEI4ABDPg5AUNX11+zlr3w5uPhPaW3w31EXnhXRHeye5lhlgPnKqyyM/mohLHzA5YDb83GMYEn/DLPwj/6J7oP/gIK6jQfhZ4S8L+FtR8N6P4fstM0LUfM+12FtHsjm8xBG5YDuVABPoKAPhX4n/ErxTr2o6h8adLuxoun6nq6eD/DWrzxArp+ngyGe7AIOGkZSA3JAMoGCFI9p/Zt8feJbT4++MfhvfeOf+FkaBY6XHqdtrb7GeOUmENFvUkEHzTwWONnGCWFfQLfC3wlJ4Dh8FyaBZzeF4YVt00yWPfEqDkdcncDzuzuzznPNM+H3wo8IfCq0ubbwn4fs9DiuWDzm2Ul5SM43OSWIGTgE4GTgDJoHc6+uB/4X/8AC/8A6KR4R/8AB7a//HK76vgn/h1l/wBVO/8AKB/900CPMvid4g0vxT/wUM0fVNG1Kz1fTJ/Eug+Te2E6zwybVtFba6kqcMCDgnBBHUGv1Dr4n+H3/BNz/hBfHnhvxJ/wsT7b/Y+p22o/Zf7E8vzvKlWTZu+0HbnbjODjOcHpX1b8TPiJpXwr8Eap4l1mYR2tnESkWcPcSn7kSDBJZmwAMd8ngGgDhv2RP+SB6H/1/ar/AOnK5r2SvM/2b/CGo+Bvgn4W0rWIzDqvkSXd3CwwY5p5nuHQjsVaUg+4r0ygAooooAKKKKAPzW/a4/5OF8WfW1/9JIa6b9hr/ktM3/YJn/8AQ4q5n9rj/k4XxZ9bX/0khrpv2Gv+S0zf9gmf/wBDir9Jqf8AIoX+BfkflVL/AJHL/wAb/M/QOiiivzY/VQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAaRk1znxD0C48WeAvEeiWjxx3eo6bcWkLzEhFeSNlUsQCQMkZwD9DXSUmMihPld0TKKnFxfU+CPh98Iv2hvhY1+fC2knSzf7PtH+k6fNv2btv+sdsY3t0x157Y7Ej9rIHJz/5Sa+xsGjvXtzzWVSTlOlBt9Wv+CeBTyaFKKhCtNJdFL/gHxz/xll7/APlJo/4yy9//ACk19j5ozUf2l/04h/4D/wAE0/sr/qIqf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OP+Msvf8A8pNH/GWXv/5Sa+x80Zo/tL/pxD/wH/gh/ZX/AE/qf+Bf8A+OM/tZe/8A5SaM/tZe/wD5Sa+x80maP7S/6cQ/8B/4If2V/wBP6n/gX/APjj/jLLrz/wCUmub8efDj9o/4n6VDpniXTjqdlDOLiOLz9Oi2yBWUNmNlJ4ZuOnPTOMfdeCRRVRzSUGpRowTX93/gkTyeNROM602n05v+AeZ/s4eEdX8CfBvQND121+w6pafaPOg8xJNu64ldfmQlTlWB4PevTOwpB0pcivJqTdWpKpLdu/3ntUaSo04047JWHUUUVmbBRRRQBWvLSDULSe0uoI7m2nRopYJlDpIjAhlYEYIIJ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to ignore all the warning \n","\n","import warnings\n","warnings.filterwarnings('ignore')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BInvs2EbbvCb"},"source":["# Step 1: Import the Data"]},{"cell_type":"code","metadata":{"id":"Xi7QTysuVZCU","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5f8b5109-0f49-4d6a-9055-37503fb0651b","executionInfo":{"status":"ok","timestamp":1671466527489,"user_tz":-330,"elapsed":58133,"user":{"displayName":"Himanshu Gupta","userId":"10164527740482211978"}}},"source":["# the data set is present in the drive in the zip format. \n","# we will be using linix cammand to import our data set \n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","metadata":{"id":"1SNSKY-yWTt6","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1faa2c60-f2cc-4c5e-ddc6-9a3c672acb2b","executionInfo":{"status":"ok","timestamp":1671466527491,"user_tz":-330,"elapsed":35,"user":{"displayName":"Himanshu Gupta","userId":"10164527740482211978"}}},"source":["# In the next step we will unzip our data set using the linix command i.e \"!unzip\"\n","\n","!unzip /content/drive/MyDrive/kagglecatsanddogs_5340.zip"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["unzip:  cannot find or open /content/drive/MyDrive/kagglecatsanddogs_5340.zip, /content/drive/MyDrive/kagglecatsanddogs_5340.zip.zip or /content/drive/MyDrive/kagglecatsanddogs_5340.zip.ZIP.\n"]}]},{"cell_type":"code","metadata":{"id":"6u8dhEtSYIGd"},"source":["# imports \n","\n","import os\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns \n","from skimage.io import imread, imshow\n","import shutil\n","\n","import keras\n","from keras.preprocessing.image import ImageDataGenerator\n","from keras.models import Sequential\n","from keras.layers import Input, Conv2D, Flatten, Dense, Dropout, MaxPool2D\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3l4s9Ob6b2G2"},"source":["#Step 2: Data Visualization & Preprocessing"]},{"cell_type":"code","metadata":{"id":"riIn9KBsWToq","colab":{"base_uri":"https://localhost:8080/","height":235},"outputId":"828dedc1-24eb-4034-f62d-cf3c21c1140d","executionInfo":{"status":"error","timestamp":1671466531211,"user_tz":-330,"elapsed":158,"user":{"displayName":"Himanshu Gupta","userId":"10164527740482211978"}}},"source":["# now we will visualise and see how many images are there in our data set \n","# this is a important step for data pre preocessing as it is essential to ensure our data is balanced \n","\n","TRAIN_DIR = \"/content/drive/MyDrive/PetImages\" \n","\n","dict = {'cat':0 , 'dog': 0}\n","\n","for dir in os.listdir(TRAIN_DIR):\n","  path = os.path.join(TRAIN_DIR, dir)\n","  dict[dir] = len(os.listdir(path))\n","  print(len(os.listdir(path)))"],"execution_count":null,"outputs":[{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-5-648cd9027388>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'cat'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m'dog'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdir\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTRAIN_DIR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m   \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTRAIN_DIR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m   \u001b[0mdict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdir\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/PetImages'"]}]},{"cell_type":"code","source":[],"metadata":{"id":"-dU2MZ6_NJ1H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"KC22OWzA44jV"},"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z12SPepSWTmt"},"source":["# Now lets visualise our classes \n","\n","sns.set_style('darkgrid')\n","\n","x = ['cat', 'dog']\n","y = [dict['cat'] , dict['dog']]\n","\n","sns.barplot(x,y)\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"4KazUqvRWTim"},"source":["# lets creates a function that can show the images \n","def desplay_images(img_dir):\n","  \"\"\"\n","  * Input  : list of image directories\n","  * Output : prints out the images in the list \n","  \"\"\"\n","  i = 1\n","  plt.figure(figsize= (10,10))\n","  for img in img_dir:\n","    plt.subplot(1,3,i)\n","    plt.axis(False)\n","    imshow(img)\n","    i += 1\n","  plt.show()\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"f2CL2SxOWTd5"},"source":["# this for loop will call the function display images. \n","# we will be plotting three images from each classes\n","\n","for dir in os.listdir(TRAIN_DIR):\n","  ipath = os.path.join(TRAIN_DIR, dir)\n","  count = 0\n","  img_dirs = []\n","  for i , img in enumerate( os.listdir(ipath) ):\n","    img_path = os.path.join(ipath, img)\n","    img_dirs.append(img_path)\n","    if i == 2:\n","      break\n","  desplay_images(img_dir = img_dirs)\n","  "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Pj2b7PNh_Onz"},"source":["## Split the images between train test and validation"]},{"cell_type":"markdown","metadata":{"id":"DS5ay0IRlWXN"},"source":["# we have created the folder called data_dir  \n","\n","\n","*   Taining data will have 70 % of data\n","*   Validation and Test will have 15% of data\n","\n"]},{"cell_type":"code","metadata":{"id":"1ATSfogBpELE"},"source":["TRAIN_DIR = \"/content/drive/MyDrive/PetImages\"\n","\n","dirs = ['train', 'val' , 'test']\n","\n","ROOT_DIR = \"./data_dir\"\n","\n","if not os.path.exists(ROOT_DIR):\n","  for d in dirs:\n","    os.makedirs( os.path.join(ROOT_DIR, d) )\n","\n","for dir in os.listdir(TRAIN_DIR):\n","  de = os.path.join(ROOT_DIR,\"train\",dir)\n","  os.mkdir(de)\n","  for img in np.random.choice( os.listdir(os.path.join(TRAIN_DIR, dir)),  int(len(os.listdir(os.path.join(TRAIN_DIR, dir)))*0.7) , replace= False ) :\n","    org = os.path.join(TRAIN_DIR, dir,img)\n","    dest = os.path.join(de,img)\n","    shutil.copyfile(org, dest )\n","    os.remove(org)\n","    \n","for dir in os.listdir(TRAIN_DIR):\n","  de = os.path.join(ROOT_DIR,\"val\",dir)\n","  os.mkdir(de)\n","  for img in np.random.choice( os.listdir(os.path.join(TRAIN_DIR, dir)),  int(len(os.listdir(os.path.join(TRAIN_DIR, dir)))*0.6) , replace= False ) :\n","    org = os.path.join(TRAIN_DIR, dir,img)\n","    dest = dest = os.path.join(de,img)\n","\n","    shutil.copyfile(org, dest )\n","    os.remove(org)\n","\n","\n","for dir in os.listdir(TRAIN_DIR):\n","  de = os.path.join(ROOT_DIR,\"test\",dir)\n","  os.mkdir(de)\n","  for img in np.random.choice( os.listdir(os.path.join(TRAIN_DIR, dir)),  int(len(os.listdir(os.path.join(TRAIN_DIR, dir)))), replace= False ) :\n","    org = os.path.join(TRAIN_DIR, dir,img)\n","    dest = dest = os.path.join(de,img)\n","\n","    shutil.copyfile(org, dest )\n","    os.remove(org)\n","    \n","\n","  \n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ke9wYvfkKUkx"},"source":["## Lets Now try to visualize how our data is distributed !"]},{"cell_type":"code","metadata":{"id":"RpIBVA5Blxtm"},"source":["ROOT_DIR = \"/content/data_dir\"\n","\n","x = []\n","y = []\n","for folder in os.listdir(ROOT_DIR):\n","  dict = {'cat':0 , 'dog': 0}\n","\n","  class_dir = os.path.join(ROOT_DIR, folder)\n","  \n","  for cls in os.listdir(class_dir):\n","    images = os.path.join(class_dir,cls)\n","    dict[cls] = len(os.listdir(images) )\n","\n","\n","    y.append(dict[cls])\n","    x.append(f\"{folder}_{cls}\")\n","  \n","  \n","  \n","\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-8uJfP_ilxku"},"source":["plt.figure(figsize=(10,5))\n","sns.set_style('darkgrid')\n","a = sns.barplot(x,y).set_title(\"Data Distribution\")\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"kNUnQelQzAxl"},"source":["# Using ImageDataGenerator function from keras to load and preprocess images "]},{"cell_type":"code","metadata":{"id":"2jQb4BSIWTav"},"source":["# we will now use keras module to pre-process our images \n","\n","traindatagen = ImageDataGenerator(\n","                                  rescale= 1./255,\n","                                  rotation_range= 0.2,\n","                                  zoom_range= 0.2,\n","                                  horizontal_flip= True,\n","                                  )\n","\n","train_data = traindatagen.flow_from_directory(directory= \"/content/data_dir/train\", class_mode='binary',\n","                                         target_size=(224,224) ) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"U3ltTSdxWTXq"},"source":["# what number representss the class \n","train_data.class_indices"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KLPwFVHV_KFL"},"source":["# reversing our dictionary for future use \n","\n","CLASS_IDX ={ j: i for i,j in train_data.class_indices.items() }\n","CLASS_IDX"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ZWZfDYyozVzs"},"source":["valdatagen = ImageDataGenerator( rescale= 1./255 )\n","\n","val_data = valdatagen.flow_from_directory(directory= \"/content/data_dir/val\", class_mode='binary',\n","                                         target_size=(224,224) ) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"NUW6uQiWzVra"},"source":["testdatagen = ImageDataGenerator( rescale= 1./255 )\n","\n","test_data = testdatagen.flow_from_directory(directory= \"/content/data_dir/test\", class_mode='binary',\n","                                         target_size=(224,224) ) "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BpEMm5qBk11v"},"source":["# Step 3: Build Model"]},{"cell_type":"code","metadata":{"id":"ihV5cOtslPON"},"source":["model = Sequential() \n","model.add(Conv2D(filters=16, kernel_size= (3,3), activation= 'relu', input_shape=(224,224,3)) )\n","\n","model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu' ))\n","model.add(MaxPool2D(pool_size=(2,2)))\n","\n","\n","model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu' ))\n","model.add(MaxPool2D(pool_size=(2,2)))\n","\n","model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu' ))\n","model.add(MaxPool2D(pool_size=(2,2)))\n","\n","model.add(Dropout(rate=0.25))\n","\n","model.add(Flatten())\n","model.add(Dense(units=64, activation='relu'))\n","model.add(Dropout(rate=0.25))\n","model.add(Dense(units=1, activation='sigmoid'))\n","\n","model.compile(loss= keras.losses.binary_crossentropy, \n","              optimizer='adam', \n","              metrics=['accuracy'] )\n"," \n","model.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"UIKS_4TQBak5"},"source":["model.compile(loss= keras.losses.binary_crossentropy, optimizer='adam', metrics=['accuracy'] )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"dZ0X4ihOWTR8"},"source":["\n","## having early stopping and model check point \n","\n","from keras.callbacks import ModelCheckpoint, EarlyStopping\n","\n","# early stopping\n","es = EarlyStopping(monitor='val_accuracy', min_delta= 0.01 , patience= 5, verbose= 1, mode='auto')\n","\n","# model check point\n","mc = ModelCheckpoint(filepath=\"best_model.h5\", monitor= 'val_accuracy', verbose= 1, save_best_only= True, mode = 'auto')\n","\n","# puting call back in a list \n","call_back = [es, mc]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"BodMzRuzWTIs"},"source":["hist = model.fit_generator(generator=train_data , \n","                           validation_data= val_data, \n","                           validation_steps= 16,\n","                           steps_per_epoch= 32 , \n","                           epochs= 30, \n","                           verbose= 1, \n","                           callbacks = call_back )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7M1DBAu7lJru"},"source":["\n","# Loading the best fit model \n","from keras.models import load_model\n","\n","model = load_model(\"/content/best_model.h5\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"J6owJqufoevU"},"source":["# Step 4: Model Validation "]},{"cell_type":"code","metadata":{"id":"q-0Yn3FcngbH"},"source":["h =  hist.history\n","h.keys()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8tQfYaYRnhOm"},"source":["plt.plot(h['accuracy'])\n","plt.plot(h['val_accuracy'], c=\"yellow\")\n","plt.title(\"val_accuracy vs accuracy\")\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QtoB4cb-oWLl"},"source":["plt.plot(h['loss'], c = 'red')\n","plt.plot(h['val_loss'] ,c=\"green\")\n","plt.title(\"val_loss vs training_loss\")\n","plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"GiDCCZVim4Tt"},"source":["# checking out the accuracy of our model \n","\n","acc = model.evaluate_generator(generator= test_data )[1] \n","\n","print(f\"The accuracy of your model is = {acc*100} %\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rVOGVC-QDcf-"},"source":[],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"dkxgpJixArND"},"source":["## Lets test our model by passing a unique value. \n","## Every time we run the below block of code a unique image is fetch and the model gives a predictions "]},{"cell_type":"code","metadata":{"id":"XO80HQ-deaAP"},"source":["TEST_DIR = \"/content/Cat Dog Data Set/test\"\n","img = np.random.choice(os.listdir(TEST_DIR), 1 )[0]\n","image = img_to_array(load_img( os.path.join( TEST_DIR, img), target_size=(224,224) ))/255\n","\n","plt.imshow(image)\n","plt.title(\"Input Image\")\n","plt.axis(False)\n","plt.show()\n","\n","print(\"########################################################################################\")\n","\n","print( f\"{image.shape} is the dimention of image before expansion \")\n","#expanding the dimension so that it can be fit inside the model\n","image = np.expand_dims( image, axis= 0) \n","print()\n","print( f\"{image.shape} is the dimention of image after expansion \")\n","\n","print(\"########################################################################################\")\n","\n","# Now lets see what our model predicts for our image \n","\n","pred = model.predict_classes(image)\n","print( f\" The model predicts the image is a {CLASS_IDX[pred[0][0] ]} \")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3Nrt-uy4-xAS"},"source":[],"execution_count":null,"outputs":[]}]}