From 09de0c1ed33331b206ff816549b33ac3a3c85cdc Mon Sep 17 00:00:00 2001 From: Danial Mirza Date: Fri, 1 Nov 2024 13:32:28 -0400 Subject: [PATCH] update vision ft example (#1544) --- ..._GPT4o_for_Visual_Question_Answering.ipynb | 1233 +++++++++++++++++ registry.yaml | 10 + 2 files changed, 1243 insertions(+) create mode 100644 examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering.ipynb diff --git a/examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering.ipynb b/examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering.ipynb new file mode 100644 index 0000000..8050aaa --- /dev/null +++ b/examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering.ipynb @@ -0,0 +1,1233 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Vision Fine-tuning on GPT-4o for Visual Question Answering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're excited to announce the launch of [Vision Fine-Tuning on GPT-4o](https://openai.com/index/introducing-vision-to-the-fine-tuning-api/), a cutting-edge multimodal fine-tuning capability that empowers developers to fine-tune GPT-4o using both **images** and **text**. With this new feature, you can customize models to have stronger image understanding capabilities, unlocking possibilities across various industries and applications.\n", + "\n", + "From **advanced visual search** to **improved object detection** for autonomous vehicles or smart cities, vision fine-tuning enables you to craft solutions tailored to your specific needs. By combining text and image inputs, this product is uniquely positioned for tasks like **visual question answering**, where detailed, context-aware answers are derived from analyzing images. In general, this seems to be most effective when the model is presented with questions and images that resemble the training data as we are able to teach the model how to search and identify relevant parts of the image to answer the question correctly. Similarly to fine-tuning on text inputs, vision fine-tuning is not as useful for teaching the model new information.\n", + "\n", + "In this guide, we’ll walk you through the steps to fine-tune GPT-4o with multimodal inputs. Specifically, we’ll demonstrate how to train a model for answering questions related to **images of books**, but the potential applications span countless domains—from **web design** and **education** to **healthcare** and **research**.\n", + "\n", + "Whether you're looking to build smarter defect detection models for manufacturing, enhance complex document processing and diagram understanding, or develop applications with better visual comprehension for a variety of other use cases, this guide will show you just how fast and easy it is to get started.\n", + "\n", + "For more information, check out the full [Documentation](https://platform.openai.com/docs/guides/fine-tuning/vision)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from openai import OpenAI, ChatCompletion\n", + "import json\n", + "import os\n", + "\n", + "client = OpenAI()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will work with a dataset of question-answer pairs on images of books from the [OCR-VQA dataset](https://ocr-vqa.github.io/), accessible through HuggingFace. This dataset contains 207,572 images of books with associated question-answer pairs inquiring about title, author, edition, year and genre of the book. In total, the dataset contains ~1M QA pairs. For the purposes of this guide, we will only use a small subset of the dataset to train, validate and test our model.\n", + "\n", + "We believe that this dataset will be well suited for fine-tuning on multimodal inputs as it requires the model to not only accurately identify relevant bounding boxes to extract key information, but also reason about the content of the image to answer the question correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "# load dataset\n", + "ds = load_dataset(\"howard-hou/OCR-VQA\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll begin by sampling 150 training examples, 50 validation examples and 100 test examples. We will also explode the `questions` and `answers` columns to create a single QA pair for each row. Additionally, since our images are stored as byte strings, we'll convert them to images for processing." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from io import BytesIO\n", + "from PIL import Image\n", + "\n", + "# sample 150 training examples, 50 validation examples and 100 test examples\n", + "ds_train = ds['train'].shuffle(seed=42).select(range(150))\n", + "ds_val = ds['validation'].shuffle(seed=42).select(range(50))\n", + "ds_test = ds['test'].shuffle(seed=42).select(range(100))\n", + "\n", + "# convert to pandas dataframe\n", + "ds_train = ds_train.to_pandas()\n", + "ds_val = ds_val.to_pandas()\n", + "ds_test = ds_test.to_pandas()\n", + "\n", + "# convert byte strings to images\n", + "ds_train['image'] = ds_train['image'].apply(lambda x: Image.open(BytesIO(x['bytes'])))\n", + "ds_val['image'] = ds_val['image'].apply(lambda x: Image.open(BytesIO(x['bytes'])))\n", + "ds_test['image'] = ds_test['image'].apply(lambda x: Image.open(BytesIO(x['bytes'])))\n", + "\n", + "# explode the 'questions' and 'answers' columns\n", + "ds_train = ds_train.explode(['questions', 'answers'])\n", + "ds_val = ds_val.explode(['questions', 'answers'])\n", + "ds_test = ds_test.explode(['questions', 'answers'])\n", + "\n", + "# rename columns\n", + "ds_train = ds_train.rename(columns={'questions': 'question', 'answers': 'answer'})\n", + "ds_val = ds_val.rename(columns={'questions': 'question', 'answers': 'answer'})\n", + "ds_test = ds_test.rename(columns={'questions': 'question', 'answers': 'answer'})\n", + "\n", + "# create unique ids for each example\n", + "ds_train = ds_train.reset_index(drop=True)\n", + "ds_val = ds_val.reset_index(drop=True)\n", + "ds_test = ds_test.reset_index(drop=True)\n", + "\n", + "# select columns\n", + "ds_train = ds_train[['question', 'answer', 'image']]\n", + "ds_val = ds_val[['question', 'answer', 'image']]\n", + "ds_test = ds_test[['question', 'answer', 'image']]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's inspect a random sample from the training set.\n", + "\n", + "In this example, the question prompts the model to determine the title of the book. In this case, the answer is quite ambiguous as there is the main title \"Patty's Patterns - Advanced Series Vol. 1 & 2\" as well as the subtitle \"100 Full-Page Patterns Value Bundle\" which are found in different parts of the image. Also, the name of the author here is not an individual, but a group called \"Penny Farthing Graphics\" which could be mistaken as part of the title.\n", + "\n", + "This type of task is typical in visual question answering, where the model must interpret complex images and provide accurate, context-specific responses. By training on these kinds of questions, we can enhance the model's ability to perform detailed image analysis across a variety of domains." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "QUESTION: What is the title of this book?\n" + ] + }, + { + "data": { + "image/jpeg": 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m73uaqq46WINY0iPwjqatD5a+HdSbyJYZjmK0kYkjI7QsSSyDAZlUE4OK818Ra3qujaNL4GdtlnBO0zOWAkaFiHjjZRwp5yVzxkDjBz2M3iuPSvD+u+F/E10+omC3P9n3hUo14MlVxyTkODhsnIUnkDJ800WNNU8Qo+svLcRzOXup2J5dskFm9275r3MgwNSpF/Xlzeyej7voYYmtGnFzRp2HhFoES71pzCn7meGEMNtxCSA6hgRhwCDjPIz6V0svgPSLfXLu3KSeRNCskA8xsxEEhgDnnBIIzmttTb2WnjTNVhNzpagCOYqW8sDorY5Hsw7elWLu7t73W7V7W4jljht5DI8b7l+YqACQTz8pNfTKVRzvOXyPi62ZV6svculrqtvL5nncnhO1h0ZLh5ZfPgnKXGH4YBypx6YGOfrVX/hHbaLUrm3laSSIorRZfBAPB6dSCK6qd/O0JtoIa/nZkHUhWfd/6CKoXZDasNuD5cOGPoWbOPyH616WHTla7Z20sXWd033OOl06O1VHGQbeRklK8Flz1P4GiSeQxkwhY4wQQQCGIyDkHt04rUZkdbmQ8xu7H2I6Z/Ssm10y7aH95KFU4IVwScDnGOwrys1yadWpCeHjq1qenCakrzeqNCeR5/OmuJZZplUOskjlmX6EnIweavm7u4L+Cys7U3c93gRRp139Onoevb1rAvTLbF5hfKZAu3YseQR6Hk/rXY/DPxZpPhuW+fU4fMu3Rfs9y5HypghkBOSBnBwoJPfpXy+Py3E4ahKpKHNboaU8PCq1zu6PR9C+GukaOY9Z12X7bqESB3acgQwkcnauAOOeST64BriPiFrUWu+IofsbFHthhZlGHRQDhfcsSWKnnaqjgkgSeI/HWo+JGaCz3Q26ndkAgAA4JUEZZvRmAx2UHBrlbkRafaiONPNeXiIAEmck9Djktk5B/wD1V8/gKGIhV+sV5e/0S2R01cYqbVPD6MmhvH1SKVIJzbajsaEyxEhZlwCVB78EEjqK7LT9UvPEq3FlpznSobSFYpBJCsjFmyMAZ4UAHB756DFP1/QI/C/wZtra8jB1R7hJFk6vHO7BmII5JCgg884rhIb46vby2gujb35TY5jcqlwv90gfyPr6V9nlmaQx8JPZp2v/ADWPpKOIVdKM/wCJb7/+CWLie80S3t57S/Md3pty1ubmBhgqeCfTGNuQa3tM+MniG2gC3dvY6gAAd+DE54JOSOD0PQCsC9FrNFrMVlYSWUIjSQQyHIDjk4HIwSB+XbpXf6p8N/D/AIr0hNW8J3EFpdSIGxE2YXJHIZRnYeSOAMdxXl8RRwinTlioaS0u1tbu9znzCFVuM4aO35aHM+IPiprmvafLp8FrDpsTjE0kUpZ2U8FQSBtB7kDOD9a5/wAG6TFrHjfRdNdAYBMZZAQMFUBYg+hOACOhyDTbrwv4m0m68q+0C9KAkGSGIyoRgDIZQR15x15r034TeCbnSfO1/VYmjvLhPLt4XXDRx5GSw7McAYPIA5614uIq4bAYOUqNkraW6tnkU41alVe06HGfGG0sYPGzGwizPLAr3qKRguSQpx/eIAz65B6k1zcul+JvD+m3Xm6fq1laT7VnYxEKwPABOO+cYzznFe6S+C9KtPFuoeM9auRJ5eJIlfiOAKgG4+pGMjsOwJwa5nUL+bx7qMNzfI9h4atXEtvDdYha9YYIfMqiNkIyApYdetehkmfV506eHpRU4RWsn0fZG1WhF3c+p5nLp/ip5bCwm0/WxKATaQtbyKx2jJKjAJwMc9hUa+HvEt1eXtsulaxLcoV+1xCFywJGV3jryORntXvvw7s4jZX/AI2u4gizI6WUaqFEVqhPIUEgM5XJwSMAYOK5bwbo17qeg3fim7027m1DVWluGmljs3iwSdu0vmQADAwRjjgdK+rWbSWihH7jn9hFbHjssGqpAWaC/WKzJJ3wsUjKkrzxgYKsOTwQR2Ne2/Di0g8M6Tav/aesaddaqqSyPqVkPsMkpGdoOAQcHAJZd2BjPArovhbYQXfw/ktmAIuLaOJzgdHhV2GB/tSyH6k1k/DK18V6n4dvdL1fUtPltNOuG042d3Y+ewCAcMdyjGCMZByO+MVy4rGSxG6sXGCjsV/ENxZw6prl9eeci2cVvprJ4ck2vcSTMxIZSMEgEAKc4yecnixHd3i6i11qWoy3NnIi6cbuaIW0ulRyYZ1miABVpAFAkBwDg8AcrrWk3vh651aHTpdPtbkw2upWr21mIYozDLsk3JkgjbIMnIzk9Ota+teHn0s3msrdfb/F18oiiiiTbFcqFC+U0JYgx4GWYnIySCOBXGWb3jDS9Cu/DC2WoWAuIwAtlb24xL5m3CiLHIIHccAAk8A153aaj46+Gnhq3TxJPp+r292fIjhuLljLbEgklm2ndGoBLdcAcHFPka80maWKJn0qa3iiOp6ek5nezs2cGSW0cElVOCGXGQACMYXPRXHhePU9Ku49DtfsBv7Z7ZdU1aeS4mMbjkRo7FlDDuSp6HaeKAOa8KSadZaldp4baDxR4waEyS3W7ZZ2gwq4VjycgKCRlmxyRzjU8I6bexx2vjIWl1eeI989lrlsZB5jDfnCBmCqVKxkAEAqT1OKd4WsJ9F0XTdais1k1DRRLo+rRW65aaGNsBlAGWZcIw7lSR1IFdUdP0jWribV9I8S3FqLtV89tPuItkpUYDEMrYYDAJGDgAHpQByg0/T9W+Kmj7fCB0Z7GObUp7maOJGn/hXPlsQcOwYknOR2yc81oPiT4eSPc6vrltLqutX15Pdm3FrJOIU3Hb8pGwERqCTyQO+BWtDLDoUfxIv4fEE+tS29jDDHc3Eodo2ZZMJuGBwxHQAA8YyDWRY6wllbJEmh3Gz7N9lDW89vNKqGOGLIRJC2QiSnABOXFAHZRfFXwe0SNFpOpGMqCu3TOMdsUVTm+NenWk8ltDouo+VExjT/AEaRflBwOMcfSigD5ZooooAKKKKACiiigAooooAKKKKAPWPgrpA1eTXYxoVhqsiRRbReTGIR539CFY845AxnAr1jStZ1D4e+GtP0XxZpb3Ubv9ltJ9NHnpIWJ2xMrYIIHA4IIA5zmvKfgnZ2Nzc622pW+pS2kaRGQ2c7osfL/NIEYMwGOMA45OB1r2SNInN94Zu9VEq4iv8AQ7q7lDs2PmGHP+s2OoJOSdrDJINAHL61cw3Wh6nrGkfC/RpI7N5FuGv0hEqtGSHzEoJOOv3gSORnIrO0/QPFOgvHFF/ZlzH4aifUXs0Vo8NcBy0KsSQdqgkEjqQOe3QN4l07VNX1iWyt7qeHVNGWGdbC2knUXI8xWVygIDBSoB7gdcAU7UPEtpLrd58tzp8l5ohs3F/A9v8AaLlmCxBQ4G4rukyR2YdccTOCnFwlqmC3ug8OeOfC/ju28uaOKK6jAY213t3L/tIx6gHuMEdwKNe8CQXBOo6ZPPDeKCVnhf8AfrkEErJkF+CflcnIJAK5zXX6z4X8MHw1HbappNtPY2EASINH8ygDGEIwQx4Awckkd68qh+H76D4ej0a18T6gnijUmVRYW1yPLhRiNxkUAnaqZJbIyQAOvPgRySpQre0wtVxXZ6o19rdWkjz7xZ4on1sw2sghEVmzb2t8hJ5ASqsoIBCiMKqgjI5zyTWj4Xxp2n+dIwubO7UNKAmTE3fI5yPXvx0rm7S0k07WZkmMUn2Gd4JUjyQCpK7h6jIJFdLbkwsbixZGjk5aPPysfUeh/A57iv0/C0YvDxjDZfi+54OYTck4dzpbWSREH9m6lFLCBxDL84UezAhgPbtWddajqF5LdWEcdvZBQFnni+cuSMgLkDBAOSTkjIxVE3MMmWm0pjIevEbA/iSP5VS0hZbp1t44EgsLq7OSWwxQcsBg4xhTznnPFTUgqbTki+HstoV8TzYiPNFf1q1+pLNdXFjLbMLkX4jTyY4FUBwO5GOpwMHIHHf1qytqX2RythP58xzJJ8px64AJPA6CuvuNLGsWckui6DaSxoWSK5MgiLMDjKbQCQCCMkgE+3NZ9xY2raHcvZf8JDeXtvmOcpHhYHBAOcAAkdcAsfU45rD62qbdvkfT4jKsvc3Pla7JHLvZ3Is7GcxeVZzvIsYfh5PLwGIH90MQMnqQR2NS28AvQzyNttlz0ON5HXJ7AdPetzXrSXU5oXkhW1ieyjOlKjZEUCjAQnpuOQWxkEt7Cs7TY474RQBdtvbqPOQ/3hwEP0IJPrx616GFxcp0/f6/1Y+fzPAywtONVfC/6+8ehkeArYWXmJ0DHCIfpnqPpXJy29zpV4ElRRPD8wUEEMp6j6EZFeiwwi7ga6uZjBYKPlCnaXH94nqAewGM9aydc8Prf2qS6Lo8vmq24zMdgdcHoGOWJ4xx2rOtUjO8Z/C9DycLi4xnyS0v1/4Ja0XwZ4q16wt7+wSyjtLgCSKeS4BK4yOQMnOBgjHUetej+Efhpa6BcR3+o3TahfxktHldsUJPUovPPv8AkBXhmneKdb0KFrWz1e7tLXcXEcbYCvn5uCDgnB/GrF14u1nU4Wju/EF9NCSQyGYqCAccgYzwQcH0Nfmea5VjlVlQVRRh5LVo+gpuhTtJR1O2+LviWLVNZttEtJBJDYkyTsvIMpGAvB52gnOOQT7VY+E+h6V4g8Pa7aahAk6tdIck/MhCfKysMEEZOCPfNeZxRxhfLXaH5+jEdQe+c8juAa9j+CGnzReH9S1KVWVby5Cx7upVBjPvySM+oNcONgsDl/LSduW2vncvD1HUr8x58730Phea+cpfWmHiaRCBJCclVEg9Dwcj1rW0a/gOoWV14avEs9W8qGOSJwVSYrG7OHTjcDhRuAJBwQaq+IbI2Wma1pZYl9PvWCh7djiIuHXa6gAAhskNkemOKW9sY9RtkngcxXOzMUyEhsEdCRzyDg/WvvMPR/tHCpS95cq36n0tGjVxsHrflWx7P4U8VWninTBcQqYbpABcWr/eiJGR9VI5B6Ee+a36+fbC6bUWl1GO5k03V7WF1aS2Yp5ePLSNWzwykhiQfWu40j4g3OkubPxUkjxcGLUYYDypOFMsa5KEnpxz6Ag1+aZzwnXouVXDaw7dUee4uO6LnxRBvLDS9Hm1CLTLC/u9t1fTqTFGqqWCtyB8xAxkgcckUzUrW2svCdzF4f1NXVrdkiksY5oYCxUgYfzxGeT0G4+gPSt6W78MeONJn04X1nfW8y4aOOUb1PYgdQQeQcVxb6N4q8GyF9smuaVEDtnilYXkaYACFjkrGAOREATjqOldnD2MpUKP1WsuSSfXS9zkrQbfMtj0Xwj9k1v4W6faWTFI200WbBhho3CeWwYdiGzmuC8BXekTeF4tOl0jw3p95ZFrW4F23n3DSLwzGIKGOTk/e68DtSeGPELaVd3GteHVfUdGun36hp0KHzYmGQZkQZEfC8q7ln68HFVfFWs6VYeJYtf8IeII5l1ho/7S06zLM8gwQJMJhgQDkrlST35NfWpN7GCTbsjqfhFN9ltbzR5GObZ2iXchjLmORlJ2tyuI2gOD2YVS8J6j4oHxB8aWljptpLYnUi8l7dTFBEQoAAABL/KFOOMdyMiubjm1qDWZNY0aJdKhcCSebUUWNdy8b1jDMRlSwbexyCCTkAjHtPFkNhfarc6lf+INU0S7kH2uayaOJZrnADAgAMIyoUDDDgEcg1bozSu0aOjNK7O41q9m8RaiYo5rm7TUtKvtNhuRZGC1eRlVlETEktkxsMkkEAEep1fCd5Y3HhhL/SNOl0HSI08p7qSNWu7hlO0qo+bPzcZbJJ4AHBqXxHFI+nQWM0V0dVMXm6VpeljK2jJgJI7cAhWKgliFwSACeTzMM91F4wsr2Qytd2c0zXGhrNttrufaVaa0ZsKzBiSUJBDZxz1zMjT8QWh0TVdL1ex8G3qsbxE1DU7q4SSQwP8AI4YCRmYENnnAGBwOKd4a8Opd6fPHeaM+uyWNxPYQnUbtTFCkcrKojBBKnaFycZ4GDjFZkkl54v0i0Nxqmo6XrsmrqLnRppmUPCZwQPLbBIWPB3KADtOfUa2haE97a3msHT9QMOoX11MzaZqbRPPGZn274yVUjAGCrZII+lAEulR618PG1vVL3RbRPD0zi4Ntpc5le0KqFZyHC7gQAWI5BGcEZxFLqWm+Kr2T+yvh3aX12bZbpZdWjht/MjckKwBDMwJB6gdskZFadvPp2h31oYb2f/hG9REtrcwX0jOlrOANqkyEsm4blKscZxjGecy71ew0jVtB05Xe4vdEvWt3+xxtPP8AZDC2zKoCSvMYYHuuQOhoA4yw8N+I9RhlitrXRtFPiWYxvp0cDBIFtGLFyQ2ASwCkAHIbPHNdzovxV0fVtZufC3iewh03UYZTbukrCW2lYHBAYgYzzgEYxjkmoLrX7W2k0fzLfUbGWDW5rj7ZeWcsEUdvI8jOGdlAAZX2gZ64JxjNdVoGjaPqvhmaW4srW6ttWuJb6QSIGWQSOWUnPcLtHtigCf8AsTwr/wA87JfZbjAH0AbiivNbrwr8PjeTmLwdqU0ZkbbJB5nluM8FcHG09vaigD5vooooAKKKKACiiigAooooAKKKKAPUfg74tTwhPq1/O8DWhEC3EBJEzLlvmjHQ7c5II5B4Ixz3/iOLUb2zlmi8PXGqR7xeWugIu6Gz3DIknI5Z25YQqcDJJBzk+ffCDwu/i2XWNO+WK3ZYftFyJWWSOPLZVFHBLAYJbIAB4JIFei69da1Yw3NtZ6+2mTK4tItZ2AWt+VXASViCIplA27x8pIIyCMAA2dO8O3N1rVhNe+I7y90HULJ47a3t2NmsE4wdoWIqBhRJgHkFSDkjNVbHw5rEOjaDY2ery3dxdTzx6jHqzG7gkiQsGJRjwQwVRtIzu5Jq1Y+KE0/XbGy1LRNQ0rRtNtHnS6uEMq3FwQASHTcGJDSHJJLFieuM1tL8V319pPh++0fSZpLmGe4fUDeK1tDDE5YsTKw28sUYbcn5TxQBWlml0+8vYGXVNLtNEmjkkuI5Te6ar7Qy5R8SADcCVU4U4PGAavaBqEOmWB0vU7qz09pInvrrWYZRjU4yxJaJ+uSCN2TlQQFHIIz5TJqd1qO671DU7HW5lR9P0+3MNiX2gY+1SgZDBRkx4yeAMkCrui6RbavpEWq6vptnd2CQyWT6RBFuGkqGIYIvJZ+PmOA3AK8DBAOZ8f6FA/h+DxJpXhePRbO2QPJNJLHG1zExAUeWucvkggsQcZByTgecwyQP++trjZu5JRsA/UHv9RXpXxD1nyPDkHhq28RafrtpdIqhDbq0tvEmCrmRWwW4AGVBOSexrzOKC1iHkwwCQqOQqbj+J7fnX1eRur7J89uTpf8AQ83GcnMrbkpa8vCIIbm4nDMFcQQ7yqnqTtBI4z6V1WmxaZJcY1K2kdo2EVhprQlWnkIA3bWAyBnAzwMEmuYhudQ0oiax/tGyXeGl+yzGLco652MD0zz14rrLG4tZ7wf2tqd2bguJ9O1G4uml8pwB8vzkgZxkZHIyM5Aqcwdbmdvh+Z7eTp/V5eyt59zptC0zUofJ0bW7mDTdP07TVubiSzmIdhkrhmIG0DaxO3nIABxnN6HzNF0G/l8OtcX2n2KsZdOv4WhnjBXduRmUFgQScMCTzg5GKoaPq17csmr+IrGC60zVNMW3nFlE8jKMlgWQAnBDMDtzggdByNCJ5tX0LUIvDL3aW2oRkSavqTs8jKFKhY1OGIABGWxgkkZNeFLn5kaz5+bU4670a/S28O2lzepJarGUgu4kCsjmMEIRyGU44PBOMHnk4DaNPDNfQyO0d75u9tjMI5VOCMjPQ4IJHI6ZrobvXLqS28O3E+nmCwKF4YEcM8riPCkgcBRnjnPIJxjFc/Jrsk097PMha98zy9igskSjpkgcAEnJ784FetheW6vt/wAA9OH1Z0n9Y+Hz9PzN7T5hfWsGp3UHyltlpZJ8x3gkZI4BbIOPQD8a3J7WdbfztV1NNPiP/LOEgMM9i5ByfoPpmsTS4Gsra30u5m2yxsXtLxBjcxJJBBzg89CcEGtq4u2ltxFrGl/aVjwRNbfMcjuFyGU8fwmlPmsz8gxVvbfu/hv+HS19P1PKPEtjZaVrYj0qa6KlRMj3EZVg4OcjIGRwDnHr1r1O18XfDrxVpttHr1nawXciASmW2KAP0bbIo4Gc85HFeY+KtRttY1tJNOm1C5RFEUa3crSNvJxhQxJAPAwT1zXp9p8O/BHhrTLabxLfK9wiBpVnutqbyMsFUYJGT05zXzHE7oqhR9q5e01tyb2PuMDzezXp1Ne2+Efgiby7qK2lnhb5lxdsUYeoIOSPxruLGwtdMsYrKygWG2hXakacBR/nmuNsvih4CtkjsrTUo7eFPlRUtZEQfT5QAPyrtbS7t761iurSZJoJVDJIhyrA9CDX5bmLx+ixHNy305rnqU+T7Nrnk/isJJ8QtZhjmaJZLKBZh5xiDMQw4OCuduMBhz2I5rl9LjnsWn0y5ikie3bdGJCCWjJO05HB+o4rp/FTxxfELWWWNpI/sUDTgxySqGAbrGuA3ygfeIA7ZzXL6XNPqDzancyGQy/u4cxhNsSkhQFBIHXpk/U1+zcKOX1ahy9tfSx7WTc31iPL539CyYLqC8a7067+yzOgSQ+WHV8HIJB7571Xh/4SC1kWSK/tpWQMQZoySXbrIfVgOATkAcAYqw89xLdm1sLN7y4VN7ojBQg9yePw6mqsNxrl2UW30dUaQPsEswGWU4KEcYbvg4OBnoK+gxKwXM+bfyuenjFl3tHz/F1tcj+02UlxFBr+n29rgny7sZwWwdzu6kOzE8gZA5yegrq9H1/xTa2UsNnfp/YyoDHqOsxkSoMclRkFl6EFgOuOQK5SSxiLRXHiS9tXtmyYrVXZELYOVJGGVwfUYyCM5NdNpWmeJNTsJXtdLa/0FlHl2+ruI53HXCE5yBxgtj2Pevlsxjl1/wDabct9L2vc+cq+y53y7dO5is+jfag8sWp61NqVwP3zj7PaTTZxnAAU9D2PQ9a1rc69/aFzpNna6Zom2HzYfLi83zeccEYHBwDkZGRxzWUw0cXcFquqXuiz6dcKwsL8FoY3B3AE5x3OPmzg8YrYjPiOPUJtWEFhq6tD5duLWfyxGCckjdkHOBnkngV30VT5VybeQoKPT8Ci1hcSRWWs6jeXN6bGUrqVncFSiEZBZVAAwMhhkHIwc1u3Wn2VlqUZSCI6fqp8i5hCjYX2ko4A4BIBU465B6isMapIYrTRbuG5tbrVJS1/czx+WvIyyKxODkAICDwMd637y6trrVbe1jkiWz0w/abpwwCxkKQiE9AeSxHYKPUV0RULM3goWZb0CbXrDxR/Ydr5l0+oW6xQapPLlrK0iJ3IFx8zAvwScklc5Arf1mzsdUMyrpsEmjeG4ZoFSb5vtFyYwAoBB4XPLE5LHjoSee0CTW7/AMUjXrBDHBpsAZdPnjKy3ttKfmkQ9ASU+UHqVGcZro9Wmt9MN1HHf239j+IopruCaUhPs9wsasG3EgFWwG5wQR6Hjya/L7R8mx4+I5PaPk2KfiDQtd0eLTdE8O+INT3T28sk8VxKsgaOJV3KjspdGYuoBBwM9OlZPhjVl8PeGrLyNcuLKdIFeG21Vme0vYiMqY3IJjYggEKflYEFSAK1PEPiy+1NdM17wzpOpXHlW00U0zWjrHEkqqS43AM5RkU4UEEZway/C9kNd8N2K2dlqepXBhVLc3Uk1rZWMQGFJwV81sAMSoYkk4IABGJiWr/WX8Y2f9p6fYX8Wn3sLQS2FuALjU3XG5cnIjjQ5Uy8E5IGOM1NJ0nVbnQLG8S7l0K1sb1V1XQbSI2whg34JLjDt8mGLEkEZIwRVu80q78F28el2mqXssVtC9wdUhiEs2nM5BcyoQRJC7AttOWGCecAivYa3qdjodjaXtpdat/al4P7U1+1YTwSW+/oCmSo2fKVIUKCxAyckA07zw5eaSfFNxpWtaql6ksH9nQzXjyxKXCgKVckMGcsvzAkAcYIzUOoabfaXqcGiLZ3y3OoW8kk1x4bm8pABgMz28hKLknhlIJOcYPFPu/F6a0/im30WzvrzUBNbjTttrJskaMKQdxAVVWQMSWIyOmeKZqmtX95rMer/wBoT2VzYW7x3VloMBvmKnDFZZWURKVIyAQSMnBOaAK8fxEjto1gj+IGgRRxgIscukSq6AcAMA+AR3FFVh8NLG4An/4V1FP5nz+bLrx3vnnc204yepxx6UUAfNlFFFABRRV+00jUr+6jtbTT7q4nkTzEiihZmZf7wAGSPfpQBQoqSSN4pGjkUpIpKsrDBBHUEdjXaaVfadpXh9NcOmNPexJ9jtFlsVNskxJJleQ5ErBQSFI4J9AMgHD0Vqa9Kk+ryupXcVUSlYBCDJtAfCAAL82eMD1wM4GXQAUUUUAes/BO/sLO41tNT1W50+1ljiV2hTAkwW+VpApKZGQMEE5ODkV7Ci2M63viG60kNYwrFZaHaXUOzJPy5WNh8u92CgkA7QDwOvkPwP1Y6S+vSrrOkaa7xQhf7T4SUgvgA71x7nB6jj19U07StY+JXh7TtX8QakdLiSYXVnBpfy4ZCdsrM+7PqAAAAQTk9AConh6DRNV1S0sNSv7SDTNFW5mFjcskf2gmQsWTJVSQoIUAADt0p2qeHozq91Fc3d9qxttGN8yX07Spb3CsDGQvCjftkyCDwvGATmprenalYaHq2k6V488POb95Xum1HZHOS/3vnViMkcD5BgYAxgYy7HxN4n1uYvbWOn2//CSwNp32v7SZUEluHDzKoUcFSQBkjIBzjOYnNQi5yeiBdkeq6xrPh5vDKXGo6pbWtjdwh4ZnlVCeAysnPLDgjGSCBXkcPxFsdV0Qa/p+hXq+K7BleeW0tm8q5RSA/mMv8LLk4YEqQMZwCei8OfDrw74OsvO1KaK9mC7DPfBQiAnOEViQuT7k+9Ta942sNKg+wadDIZ2U+XFBD+9YAEny4sZPAJ3EBQMnLYwfCjncq1X2eFpOSvvsjX2VleTPA7S5bUdauGeOOBr6d53aM4ABJbavp1IFdLAm5ja2EaYj4djwiH0OOSf8k1leKvC91oTwzyrGkN2znZCxdbeQHcEDEncCjIwbPIOexrU8LY1HTxAoNta2qgTuGw0jHrz2Hc/UAV+n4WtGNCMobP8AB9jwcxg4pz7E72TpnzdUVCecbFGPzOf1qjpRmgdHaSC6062uyDlckIeGwACCMMTj2rqba3jdf+JbpMbx9ppQEB9xn5mqjdabqWny3V95cFzDIN00Ft8piIAGQGOGyByODxwDzU1ZxqOzNOHsxo0cTyYiXKn/AFqjUn1KfQNPli0rV9OWA5eC3nTcy552oVYcZJIGDjOOnShLqVtDoFz/AGfqGv2V3NmWaLy+JmJy2AFKoTyAVx1Gc81iT2015PbrDaf2ezoZY7k7dxX0AU4OcjOTwO1QPLqotHYX0hmiOJY/LTPvg49Olc/1NVG7fI+nxGZ5fGbhzX7WNjXb2XS5YIriWO4jt7ONdMMK4FxEwyJMdiQoDAcAqfUZzdNdLIJIz74LpQZXPTef4j6A5wfTis+S8uGtNPt3k861t2fypH+/GsmCVyOCu4ZA7En14lt51swYZlzbMTg9QmexHofXtXoYXCThT9/dHz+Z46WKpxpL4V/X3nSxzpbW7Wl9EZbL+CQAsUHYMBzgdiOgx0rP1zxDJptpGuk615ru23ynCyFVx13EZGPf1qFftMUW6yvAIwMqsgDrj2PUD8TiuRnuLnVb7zH8sXEpCrtGFUDv9AMkk1nVpKLvPRLV+h4+FwcJzc5apf1sWLHQNW1pZLi00u+u7dWK+ZDCzAv3OQOSM5+tTXfhzUNNjMl9ot/bxZ5klt3wBnPJwQOB69zXXaD468S+HdNg0+ytdOms7cBI42iZHckk5JDfeP3iT616V4T+I+n+JZhY3Nu+n6i2QsMhDJKR1CNxuI9MA+ma/Nc2zbGOrKt7NSguz1S9D6GnGjUtGM9TwCJ4yvmrsZuSD2BPUn+Q7nHFeyfBHUpp/DuoaZMzMLG4zGWGCFcE4x25BOPeua+LHheHRdag1eyiWK1vyVlRAFVJgM5HYbhknAzkHHWtH4R6rpeh+HtfvdQuI7eNLlN5fIJ+TgAHJJJzgDk15+PlHHZfz01fms13vcvDU3TrOJz/AIj1E3Wn65qv75W1G+ZULXGxWiVvLXaoOXwo5zxz36UXt9DpVkqIpeUJtiiUZJwOuB2AGSfaqDC+m8Kz2SwpZWpDu5I3TT4YsoYn7oHHA9PQ1q6XYQW2p2UOiWx1LW3iilbzHLCPdE6sWboqglSQeTkAdq+8wtb+z8Mr+6uVb9D6WhWqYKD0s5LcjsYf7K860kjkvNYvImJhtd0jSn93JEQByq/eBPHQ12mk+AtQ12RrvxJJNZW2R5dlDKBLIAcq00i9SOgxggdTnOes8JeE7bwtp4Qubm/lUC4u3GWfHAUdwoAAA9q6OvzbOeLa1VypYXSPfr8jz3KUt2cx/YfhPwbp82pnT7S1igUu9w673z7M2WJJ4AzyTXIyeJ/E3jJ5I9OzomlYO5whe8K4BDFB8yIwP31VsdeQDWv8UittYaTqV5YDUtKtbzN5ZNJ5ayBlKqxboArEcHgk88U3VrzTpPC80mgRMHt7dnggtr1btI2CkjaF3mPGOqFT711ZBgqeJorFV3zyb6u9jkqzafKjL8O+H31G4uvD3hqVrfTkYf2pqhcSlycnywQTHK5BBDlVKjGRniq3ijRNHi8QW+geDdJmtpdOMf8AaepWskq+UuCRGxQMCzAZLMpA798el+D0tNC+GNje2+6ZfsH2+aRj808jJ5jsx5JJJPXJHA7Vw3w+022Ggf2q95oWpahqLNdXMyXr2tyjNyULrkkgk8YUA/nX1qbjsYJtO6ZjbtXutUk0bTxFr8AVVmju/KB3MSQiyRkqx2hmO5VIC84JAOHaeGoLy71TT7ptb0TQreQG7RbIzLFc45DsucIF2nJJBBB4HNen/CWA3sd9rUoZmuXeVGdy7AvI2QWPU+XHAM+g96peFLfxZ/wn/jSaxmsX03+0dktpelgXYqCCrKDt+UqMkEEY44rR1ptWZpKvNqzNfxHPNbWcWozT3FprUUBi0++05Q8N/uIKRFSCPmYKdpxjJKsRnHKrHLd+MLDS7gf8TO6mmJvmgMtjp020u0NsrHa0uQdzHODnAHQX9Xt7jw9foUGowWuk6Ze6lb2U88ctvG6Kqx+WQNxCmQ4DdBjAFa3hSxht/CUdpY6ofEejSZl82IqLq2kY79w55IYlsHDKf73QZGRzxu73wppVo2pwarqniFdYVbjUZIpTFFCJtuQ5G1VaPACg4y/NbGheIxp1lcaNJrH2ZNPvLmFhZ6fJcSwxrM4UuwDJGMYA3KeADTfEV7c61eaVpWmeMlnSW9QX+nywRx3AhU73LgAMgAXkFRnIGecE8J+I1htLuWTXLPSTeXM9+seqWLRq8ckrFWRzIocbdvTpn0waANKCDSvEV7a2tokl34dtPMutQu7kERXk+AF3MceZj5mPBUYUdgBmT6LZ3Wp6HqNk02mza5ftIRp8rQSG0WFihIUgEEqjMSCQXxnpVmxu9e+IT61o8+qabHocJED3+kZZroMoYorMWC4BAbqRnAPemyaHN4TuzJp3jjTlu0tFtIY9cVGMcSkkKrKyEDnkkEnAznAwARXujtctpRn1bVr/AO0a1LZmxnumMU0CPIrFlXGQqoWyTgkAHOcV1vhq70vTPCz27zW1raaXNLZyszqix+W5UFicAErtPPXNeSWXijxJpeZDFpWsTeG5mkuL62vsxTreMQFwF+8HIOcjAUjHOa7DRPhHaQa1deI/Fd7HqN5PO109uq7LWNySckE/NjsTj3B60AZ1x4j8Hw3Msdv8Q7u3gRyscMEgMcag8Kp/ugcD2oruN/gEcY8Of98w/wCFFAHxZRRRQAV6dbWXie58NLDZaNDavqMcIubp9SjjnuYFUBEVXYFFIAJwCCQOwArzGu1uI/BdzFZS6hq+ufbTaRCfybGNl3BQAAWkBOAAM4wcZ70Ac/4iilt/EeowXERjlhneJ0Mm8qVOD838XTr361vWFzpes+HI9O1PxG2nLbvGUhmt5JY1VRJkxhAfmYyAkEDkHn0xL28gtNW1ZdOZbmzuC8MU1zGGcxlgQwzkq2AORyMkd63j4m1XQvD+iRQ2uhGKW2Z1Z7CCeVgJXGZCyEg8YAz0AJ5NAGH4nu7K912WTTo5Us1iiih84Yd1SNV3sBxltu4/X8axa1df1O51nV3v7uCKGeWKIFIowi4ESqCFAAAIAOAMc1lUAFFFFAHffD7Xb3RNL10WOmRXU2oeRYJO7D/R3l3gMBg5zz3AyBnOa+hrKwttfuxoMUm/w7oSR2s0Sn5bqdVH7tsdUQbSR3Y4PAIPzz8N9Ha/Op372ltqNpYiIzabcOyC5MhZF2kHAkBJ25GMsenWvT7O2uV+H+oXHwu1Ddp9wxkutNnXddWpYAN5TZzuKgYBzkDKknGQDs7nVfCxsJ4rbwfdahYoWiBs9IEkUm0kHacAEZBGRxx1rhIbK31D4Y+GprJW05LXxBLEUmhWQ2yyzSoFdWBGRvjByP1wa7nR9UTxFbSQ+GNbOmeH9HhS3+0xwoTJIFBIPmqQqou3JwCSTzgc8ksIv7Hx/wCGE16LW55YF1WCeHbkyY5UhPlDAxx9P7wOASaTtZ3AZpfhd9XurcXmuyIspWJhY20Vs8bPEWGXQbsb0kUgEAkD1wdCY+H/AAV4ai1COOKwF5DuV8NIyahBklSxyxDEMpycfKR/Eax9E8L+CPENpE2k6/qFnJMI5ZLO31AqwdcsDtcFiQxJB7Ekg11+kfD7w/o8q3Ainu50kaVZL2dpSrnksAflDEjJOM5714FfPcNhk48j5u1jVUpS1vocHN4WXX/D+ueJ9fim0y3e3J0y0kcs1qASykgAE5YlVBGQpwMAjHnGhTx2GuJHqzS29sHK3cLhgN65wHUc8NxgivZ9e1aLxZqi2dv5cmg6dIJrqaRtsVzKCQqbgQRGCCDIMgMVzwK8u8TaVqms6fN42eHOn3Fw0BygEnloQiSyYJBYkYYjAyM969zh/HVIxf118qqPRPp2OfFUY1IOK7HfRCPULL+0L6drTScZRdxRpl9WI6Keyjk5qW4sLXTtYtorS3SOK4t5FkRAADtK4J9ThiMnmvPLPxct7DHY63HuRTDBBIq4jhjBG+QjJJcgADjABJGOh6abx1otxr13MbkiG3gEcTbD+9JJLEDH+6BnHSvpEp89prXufF1suxFKfu3a10W3l8yG4j8nQ+CWk0+ZkVu5VX2kfTbVC6UJq2VwPMhyw9SpwD+Rx+FUpPFdlNoywMJBPcTl5wEOEUuWJz34xgc1U/4SC0m1O5nlZo4lRUiLIckdT24yT/KvTw8uW19jupYWsruS7kZRES5iPEaMwHsOv6Csq11a48n97BvAwAxO3OeMng5+tPm1CK4jVQ3/AB8yMzgHlVzjnHTIFMmgkWFljxJGSAMHLAZA4Hc88c15ma5zOjUhDDy1S1PThTUVaa1Yl2ZZS0EViySMu4MkmFx3J6Cuu+Gvg+w8VPf/AG24SOeFFENu4bJUg7nwGUkZAGQcdc9RXPyfuxcRyBo5doQI4Ksc9sHnk4FaK298dTt9Q066NnPZf6t1H8XcfTHBzn0xXy2PzPFYmhKm58rfX0NKeIhTaVRWR0XiHwXqfhuQywB54GJA+beeeSUbAJYgfdYZ4GC2DjmrhkvbeOa2m8qSHDwMr7fJII+ZiOQeOO/45r2HQ/iFofiJU0bVkFpqEqBJLe5UCOViMEI2SOT0BwfTmuA+IGhx+HfEEf2YM32o5EanLvkHDAE8sCCpY9ipOSSa+fwGIrzq+wrxtPo1s0dFbBqTVXD6m34k1yLxX8GLbUbl1XUI7iNQgwGknVtrBR1yVJbGOAfavPobIaPA95Nbme8x5nlKCyQDszY789TViK2fSbd5jC1xfbWmW3jy6QAgBmxzjgDLdyAK7LT9NvPDYurqKF9YhvIFeRlZFKsucgAnlSDwBkjGMHNfY5ZlUcBCSerbbt/Lc+lo4ZUUpz/iW+7/AIJyF6Y4I9ZaDUv7QjESIJgMLubqF5wQCR09a9C1Dx34Y8DaONL8M21veX6oFKwY2bgOWkkH3jwTgEk46ivOrqKfVrOCGy01jNqV0ZhaW0ecIOSABjjABJ4HfitnSvhF4nu4Q8yWemqVAKzSFnPBGcKCO56nPFeZxF9VnKnHEzsoq9r2vfv1OfMalT3YR1dvz1Me78aeLdWuvMuNeuYIySTHaMYVAwDgbceuOc9K9L+FPjm51xJtE1aRpL+2TfFK5y00WcfN6sMjJ6kHJ6EniPEHwy8Q+HrCTUN9tfW6czC3DbkXqWKkcqO+OcD0yax/BWqR6T470bUHcLA0phkJIAAkUrknuckE9hgV4mJw2FxuEkqKTVtLd0eTTnVp1V7Tqe1TeLNF1XxJqXgvV4PLkbESCTlJ1ZQ2Aezc8D2yDniuXuIL7wJfQ6VqdzcX/h66YRWU88jSLbngCFkaVIgOchmyMDp2HK/GGez/AOE5zp8wF0tugu3Uf6t1OVwezYxn0AHeucvPEnijxJpk6XWpX97ZQ7WmCxgIuDlScAY5Gcn0rvyPIsTCnTxFFqNOS1Un17o2q1oq6ke8fDm/gn0jUPBNxcxyvaI4t5IpEfzLWQnBBQlcqTtIBOPl9a5PwbqlzY+Gbjw9dvG1/pbS2stvPaWwAIJxtZ5ULDBByR379/Np/Evi5buw1GfVdWE6oy2kzkk4YAEKccggDjvwahTxZ4ittQvb5dWvY7ucqLqXaAzEDC7vl4wBivrFlNR7Sj95z+1i9j6A+Fd3b2ngBrg4CQQRSuB6LAisf++o5B9QayfhXqXiaHRNQ1TVdAxBqd2+oC+kukjLhgAMqxyFAHB4GD07nwltV1oWrwJfaiILzKsglaNJcszEEDAIJdjjpyfWvavAN9a+K9Es7eWLXtYk01I45rG7aJbOOVRgEkAFwCCQDuI44JANcuJwcsP8TuXGalsX/EWq3mt3WrTWdhFdXEdrb6bb28dysiztPLvkBYcAbIxyegyTWnq2vw3rXWm2VidO8Z2ihoY4wGEalQwZ5AAphwcMCe3Aztrn9fsLSz1LWNPuVl0s3aW2qRnQIXnaFoiysSVUbSQAQxAGSTzg5sJBcT6jNbX+n3VrZhEvbqK9ZZbnV4FwrNJIhIAjBU+UODwDwcVyFFOZ59VvLu7VV1K8uIYotX1GxhMYNkHAeO0XlpOp3OM8AAc4A6S48SWtnpFzLpUtpr0OmW5uBp96hguYo1HLKWX5gAMcqDx94nArovFt3o1h4b+33uoxackC7rO7jIDI+35fLH8WRxtwQRkEEV5rbx+NPir4dtpdS0vT9JjtSZFu5xIjXYIIZAoIKxsOGOSCMYBxwAbfhe8u9R0LTtKtp1tdU18y6xqMkBw1vbu3G30ZgURSeQAT1FdC7+FtCuZdLsvDcl1JAitOLLTxNsLDI3tjJYgZ5JJBBPUVwvg+206bULy78Grb6B4rSAx3GlXRMttOpCsGTkMBypyCCM4K886/hDV72a2t/C8V5Ja+KJpZrzXJ5IQZIMNgkBgVJbMargEBRn0oAybe303WrX4lWGmaHcaOZbKCVLW4txCzOqyEOEHQFlHTvz1JqhBpVxqNlHI2toYBbi6Ih063SQxhIZTtbaSG2SPggA5Suo+1LpHxT0g3Xi2LWDqEM2myW7rEskJHzgkRgDBZSvIBBOOecct4c8J+CZZLjStQ1y70jX7K6uLN2jvzEZkDFVI3grzGQMLjjtQB0c3wclmnklg8VayIXYtHm9dvlJyOe/HeitOH4R6bFBHHF4q8VCNVCoE1FQAAOMfJ0ooA+SKKKKACu+vrjwnqNnpM2ox+I5ZxZxW3mQLEsbMigFEBHO3IHHXIJ5OTwNd6PsniPTNKi1DxTDYx2TY+zTRSmSBCkYAiVVKsSUJGCCSQTyTQByGrtZPrF62m7/sRmf7PvADeXk7cgcZxjNdimv69o3gfTZILrR0hRvLW2NlFNPhy7CSQuhxnaQBnJABxjBPO6og1/wAT6rcaRasls0stwqOVUpHnOWJIA6jI7E4FdB4MtZta026hk8N2uo2to0ckt3cXxtETAYIruSFI+YgAYOO+AMAFeK2/4SLx6X8RsEi+y/apvsiCMzRRWwdfLUAAF1RSAAMZ6DGKqalJ4a1yPGi6VPo98n3LeS7NxHcD0DMAVf0HIPQYOAaniHUtX/4S2e8vEWx1GF1Cx2/yrAFUBFTBPyhQoBBORg5Oc1izTNPM8rhAzHJ2IEGfYAAD8BQBveLbC0sX0drW2W3+1aZDcSopYjzGLBiNxJGcdOgrnKvajqt7qrW73s5meCFYEZsZCAkgE98ZPJ5qjQB6B8M7LVNUbVrDTlkCSRI11NGQzQxAsrOseQXfDEAA8ZJwTtr0oR6rbeOdI03wukNvfLZtMs7MWhmtVX5YywJ8yMnAUnDRnI5ABrz34UeIdJ0OfVxezTW2ozRxvpssELSN5y7sJhQSQ24Ag8EZGQcV7h4B8N3GhW+q+L/EkUNtq2oq080MYIjtIgS5QDJwScseTzjuDkA5y5t114T6noZmsxFfRnxT4cTDM5RhvYADJJAOduA4HqCDYtvFWj33j3RtW8PaCyaY9w+l3OrpEI45S6grGVwDw+zBYD0Gc1ytndalCvhvUdJVIPEWs3zyR3EoKho5XaRlkKgrLHgjIJDoe3Ix0/8AZa6/rUVpbXUvhrW476C/1HQ5SPIu2jcEyxEDnO3ORkEgZAIzQBzDItpLPpuqeFvtuj6Tf/Ynv4vneKJLky5KgbgBHIw4PPH0qxZ6L4c1LSIri2uZ7iJYY2ljW+lZS4tfNdCNxAJMcmR2yRxgAdNPrWoeHfHHi1xpT3ulfbLVpTbEGaN5YkUEIfvAkAYHIP14juPA2geKI113w3qc2mvcZYyWePLZirKS0fADYZgehGTnkmvExGYrC1msVC0Okt18zWMLq8XqUfHPkeD9Gm0qzPlyMhSxSAqH2yAqrKp4ZTjy5F5zhHxkk1P4sCeGPhXpmjSphZRBaXAU4JXG6UA4PLBWA92rX0vwPcLqOl3mv6sNWbSYfJsAbcJs5HzMckswAABPTGeTzXWz2tvcSQyTwRyNC2+MuoOxsEZGehweteFmme0KlekqXvRi7/PoaQpNJ3PFrf4TWL6Nc61qGoXWiWpLTRW0u2QwQ/whyQCWxzgdMgcmuRsPCkN5oMupC+uy007QafEkULGc5KoGHm71JIOflIAGckV311ez/EXXrgF3/wCEbsJ/LiiRBIl068EyBZFcdQVwDx+Od7Q4hq/j6RpWkeDQ7VREkjSELNLnLDzFDfcXuWA3cGvp6WbZjhMJPFYio72ul27IxdOEpcqR5v4k+Htv4am0W0n1qSa9v5AJYwkaLGgHzsGZgOCQBkjPND/D2C68RR6Rp2suzGJZHkkSKQAs4UKPLkIztEjEEg4X3zXpH2e+vvjHfTQw6q8dlpsUIexeJAu8lsMZCAQcHGOcj2pCktn8UgtxBcxGS3tpgbm4WViAZ4s5BIA3SqMDv9a2o5zmCy327qtycbidOHPax5LqfhiTwp4lutKudQjMnlo8cixbRIpB5wScEHIIB7U7TISde00TQxFftSbpFYYb5h268+9egfFzSbT+2tB1V9plkL2rRt/EANyt6cEkHPXI9K4u9ENrbGdFjjljIdGwMhlIIx+VeTRx0sXThWnq5LX12PIx8lSxCjbc2popLnSluL+5XV2nu2iFnDGDPbctyjA7srtyQRggenJzpYH0uAZBmtS22K4UcMSejg8o2Tgg45zXZ6BJDb6tq9vZ6K0l0lyXa6wqph1VsFyd3GTwAcDHrVHVNOZHubzS459V1S4kBuoo8fZiowGQg/LwBgcls4J7iuVV1z+zlt0+f5GVSlGqrM499OtbuZ5ZrSW9uB8g2AhEI/hzxjBPX+VVEXV1uNTWJ47u8s41kmuJJGkk8sj7q7s5CjIPPQDjNa808OmFLVJZVs5UEtrEineQxOUJHdWyOvTvS6LcwWPiqwlFmbYXIa1mJK7W3DK5POTuUDJHevbyyq4YhJ7dDoymvUo4rkb0ZvR6NaeHL6DWrV5ZrWaLybxnPmEq2Csn0yADjjByBxWTruoadDpL6fpviC9LviO3tY1G0AnG3dtB2gHHLZxxzXVRQalogaCzthfWIJMMfmhJYQf4BuGGUdskEDjnANYPi3VNRa0tFudENtbfao90800blTngAKSRn19MjvX107JH2UrLcwotc1Dw94gWfSUt3ltrUQAzqWCbuSQARzhPyNacfj7xwrhzqVrJ8yL5bWqgEkZIJHOBnrmpPDngaXxadS1Fbkxot00ACXGzO1F5I8ts8E9+/Snan4C8Q6OzTRETwoWYbxvHIxncoBzjuVAHrXxGa4nB1sbOM+Vy80eFmc8VLETnS+E6vw18UoL6/Gla/BHZ3LuY4rhM+RMfTnlTyBySPcV5z8T/AAunhnXpGgjK6dfAy26qMbH/AIkHoASGHsQO1Z15H5q/ZLmBo5QnyBsNkE8spGQ2TgAgnuaqa/4o1TW7WwtNRnWWPTIzCjKSfNbPLEnqcALn2z3NXk2T82NUqGkH8S6W7o5KeIdWHLUXvId4a02LV9ajGsfaJLOZ2Wa5JPzSsCRub1zjqa9U0jSriw1OS1mtYzatZLC06AAT7DhSwxw21sHscA1zPh+A6Ho4gvitzYXI81pVQnYWA4Yc5U8YI6e1dLZ3d7bRYs54L+0/gEkmHX2DjII9M19vVgpSTWy29D5PM8VVqzlCD0/D/gMyZ41Tw/Ehw72F0sacd0l2D9OKp6jaxPq06FRi5tgJBjqQSAT+Bx+FTX17b2bafp11dwJPLM1xcZkAAIy+DkjqxGM9cVRnv43NzqYIdGAigA53AEgY9csSfpXTh4p6MVKNWK5tbNu336GHKudAAIG6EAD2KtjI+uP1rZ8LeJItCuLjTNQ1M6doepsGu5o4mZwVHKqV5XeMKTgkAcYPNY92PKsoLPOXkOW+mct+vFUZ4kvJRET8keS+PUgjHt3z35Fd+IwixOHdPrpb1PZw1RwlzN6XZ9AaYnhiz8Wu2jrp9lpkujCS+KFY4pQ7/uiRwMgLLk+hAPasGz+I3hu1k8P63darBPBBYzabOqnMsUmUIfy/vFWEWM44yuepx49HZQSMY7ezifb95mUYX2JIOT7c1S1TSpbVkncQLGf3e2JcckE88c9K8SeRyg0uY9GGMhKXL1Pdfhr4DvYHTxD4inmNvHvfStNuJd6WUTHIYgkgNtwAB0HXnpo+MPHP2y4i8PaDHHd3N78ijzAomB45J4WM92PLDhQc7h554Z1jX/EvhpLSTVYRBYkQOHDSSsFAKAISI8socKWVjuQAkEg119td+FvB1jK1/dxSxzqiai8kheW7jclobpedxPOCF6c4+4M+JUpypzcJdDqTTV0c5rdjdaFoNlHEskusXOoBUuwCjxXzkZJwSY2GcFRmORACMEDPT6hFqWr6lLpcl7Fo3xBs7Vha30GBHqVuc+oOASOQeVIyMjNP8JaDeeK/GS+L9W+fTdPXydJd4jHJeKC22eRT1IDHBwM8HAxzz3jPUIbnU/GOuMhkSwkitLSZAQ0U0S53JKuTGweRuGAVhkZyKgZLrWu6TB4dtdJ0jwvcXXiPRIYrq6YQhJbF42UszMQGck5JK5BByTg5qbxU10/jK7TTvDya7Y6nawaw0O5SwXyngYqjD5iQyHjkYHHPFi6F7fxWtv4muj4e8R3doLe1161A8i+jZQTHJkAK3scEEEqQDg3r6LV/CXiDwalnZwaje2+hTWl1GJdnmxxCInYxHXIyM4B5HGaAOfstQ+EkVhbx32n3kN2sSrPF9nufkkAG5eOODkUV3MXxT0mWJJH8PeIQzKGI/s0tgn3B5+tFAHyDRRRQBJGVSRWZBIAclTnDD04IP5Gu81jW/DY1SAXvg9jP9ltOf7UkC7fIj28BScbdvfPrzXBxyPDIkkblZEIZWU4II5BBr1eDWfFNzbRTXXj7SLGYW8TtBLGWkjQqoUsViYZIKk8kjPOOcAHIXFxDL4v1uHxCH0sXsjpObaLzBbt5iuPlyCy5XB5zznkjBn8S6/ps+maf4X8Pyyw6Jav5ktxcKVa6nPDSuq5IAHAHJAqtJ4Z1HW/EmtR2t/b3bW0ztNeXlwsHmAvjeTIQckke/NWJPhrr0KRSST6PGkqlo2bVbcBwDjIy/IyCM+ooAw9akhutVSKxle6iSGGCOTyyGkKoq8A89QQB6YrXsvC99rF3Fop0ubTtRtIGeeW5BiRV3M26XcBsADABu+AMHORmXVpN4U8UCGV4p5rKVHJglBViAGwGUkZGcZGcH6V1KeINI13SP+EagvJtBtCRKbq+ma5+0MDkJKyqCqDJKgAgHJIycgA5nxNYafpeoxafY3a3jW8AW5uEQokku5iSmeSoUqA2BnBOOawq6HxY+lre2dppV59ujtLRIJbwRlBPIGYkgHnaAVQE84X0xXPUAeufAzxB4e8O6nq1zrUscUzJEtqxgaR8ksGC7QSCcqPfivRvE/i258cWg0PQ9N1CLT7jD3d3cQmMzQDkpEuQ7bgDkgA7Q2ASMV5H8KtO8SXtxrMnhy2tbiRLYRyxzy+W3zElGUkYyrqrc45UcjNeiXHijXtLunF54P1i0u0lmkiEMBnjUkmSPDrwQJGlUgcbJSM8YoANQultfGPg0QmEh9TjIQEhnVlKBgVxHKoBI34DjADDJNdB44s9Pvfidp8Gq28MsMmksIC05glEgmBzC+QBIMjAJGQTz2NfQtNufGPxB0vxK+jano+n2UDTzWt6pRJLtgV3RrnngglsDJUZGSa3viWjWUmga7G4T7LfC1nJgMwME42MCg5YbgnA59OaAOD8N2njSZvEGtaPdLqkUWqRwy2WogJdTrbEFRvHyhsYBz1xnOeCaJqjWNuuu+H7VxHBHBZ6xpJjKPLdBGaZ1BOAyhQTwAwDZ5HPR/DS9t9G8Z+IvC3mW+J5BqNqILeSBAGAEiBH5UjCnGT354wGfEzw1JpOov4x0qJNskTWuqoVJCxuNhuAB1ZVJB4PB9jWNehTrwdOorpjTad0dFonivRvEGVsL1GuFGXtpAUmj9cocEY9cY9607yA3VjcW6ymJpYmjEi9VJBGR7jOa828S3XhbUtTu7v+37CO/VmvIL6zmANqmFVXdhzI2FAEI65OeuRpWmt+LbKNlk/sjWY7eLzbmRZxbvADyiyNym9hztB4PfBBPxWN4Xq05qphHe2tmdMayatIxfDeu/2FIfCF/oel6ffWqBVlS1EjXihTmbc5VFBAyWZjySMcYq3p+oJ4X8Vf2lPNH/ZGt7YXmAVUSZQQjAhVDKQcFlUKDjk8k3rvUfDHjSSLRPE+kzafqLRiSGC+Ty5MHODG4PfB4yCcdDiuX1q4HhLWl0q41ZNb0u7xHNbzEyXMIGMCTYQ0iAA4Qso55Hc+7Tr/AF+m8FiYOM2te3qmQoNO8NToPHcUmieKNL8UIjSWEgFlfr9qkgjAJOx5GjySoLHOQRwBjmqGvTWltqeka9aJp9vaMTC0sFv5CNuZGjcs5DSKJI0BYLgKScmsq18V39np91o9tp5vtHdNsB1hgrRqcgqVUksnTAJBA4JOBXMxXq6fo8mmS6xizIbzILOMLvUk5V3A3sMHADHGMCvUyzKsVDDvDV1ZLRPujqWArTam9PXQ6/4s6lo+swaAp1BI5UnczQBv3kYIwQwGdpDLt575xnBrmks45rWTyJBaWmwr5zR5eTIOcbuo/X0qFtCnlsrKf7OLLQ7h1Ms0LAyqCco7ZzgZwepIHWnTrf6HPNaarK1xe7sWcjD5ZUOACo6Ag5yOo+hzXPXyiWCoxhSd4x/U8bOsHUTVSm7pGzY6hHAY9Q1LULmDSzEEvrdFPNwqqq7sDcFZQD6HGD1xXVTXsU2kxXFzf3lhbXURkghs4wWjhAGGJCswIBBJBAGQO2Tyeg+Hb7UU1XUYrpRHBCTcGaINHcELkRlQQMADJI5GR1zSaTdT2Wp2ups91bpFaqgt7stLbrE4yAsqg7Pujhhnj0xXkVKEKt5Resd/0Xr5nJCnN01O2hX0pdJM2lo/2/UNLSW7RZIFIkaP5GDEDBIDE5wMnqB2rqjYfDq4njW507UVhLAJd3P2lIgx6HcxG3njJAGe9ZOiXnhk6+VvZ2W2hglkEtoJREsksu4kSIBhVC4ycA8jsQOw1bRdmkSXFrdvqOkzQsJQSrskZGN6soG4AZyDk45BOMHSXsJVFCrOdOT2a2+Z6+BoU3DnktelzifFkHiPQ9an0+LWLg2BRZbQlUDuhJDKZMZyCCM9eQT1rBj0izv72O8Yy3TPbuyC6kMnzDA53E+p46cV0Wr+IG1jQLS1mitlfT3Ea6s022CVQuDtBG5icAkDIBHU9KxbaKK1tLCWPV7l4pCYbeWPS3KOx4KjkFjkYHGTg+9dHt8W6ajVk7rTvf7vvPKzGeIlUahPQoQNqOjHTpvDV29jM4PnYY7HK4++vIOOeor0C2+MI0+2hXX9HuFk+689oysjH1AJBGRzgn1rj0sbqFQYyL2GHcjvAp8xGLZJeMjcp/DjHasu+vYJJAu3zPKcARkYJc59eQAM9e9c1bDUsVJKrDmt16/eYUMbiKMuTdG/8SPFHhjWdOt30R2N/M/mSDyWTygOrnOMMenGQQeegI4bR7SbzIr6KKOa2hYgxnkuMYJAx2zkZ70uqwPdguURrn5dqg/cUEnBJxnOf84q3ptw1vF5kEY8tj+8gBAKsOCV7fh34r7fhajSp0Xh6d79b6u3+RvXrucOZaNnV2FxJbwhrBkntGyRC7bSh9FPOOe2OPaq+q3EIsZZ/wCypIbnGElhkVdrHgElSCQCR2NZYurJpC6XLW0p5b5ihP1HQ/rU0MD+ILxNMg1GWRW+adlKkIgIz0A5JwBz3z2r6KvSjGOp5mEwU6mIjyrrre5oWtiVP2OytBdTBN8zOwGc92Y5ySe1VE0eK3vd2sXFzpKs5MMESq0ROMZDfMNxHbA9smtbSoZIL2a00m2kitb68W3W/LAjakeW25yWPyyc4xk9T0rT1OCx0m9S50m7um1KGQQSTT77iCJSQW81iCEAGCQCD07ZrzK2JbStpY/RcVOlVpqMoq0ehzqeHdPvtViitNXvYywMt1NdQFVit0GXfJUDgYAA6lh61hzQCPUpbSC0ms1nlaWKOUkssJ5ViSTk4wD759MV6PrWiCLUdLvNV18Xun6lm1lQKsUTfK0i4KnO0sq8EnJAyTgCuY1yWP8AsqUeaJNU0aVosvw00Qbb175BUn0I96eFxc1U5pPTseLicDCrCUqas1sU4IXkJtbMLHHGMPIRkKeuAO55yc9M89cVFd6NbXsbqq3l7MgIDI2FRvr90fTmrumeXeImnwtIpRQ92zKUZQfXPdjnnsK3rVZruPZYbLWyQbVn2g7sddgPb3PXtmvRrVlNWPi61aphqmujX9a+vY820mRNL1uGPWUu4rPzFS/hglMblAwYMCOSVIDD1xwe9e06O3wY0SeO5t54Jp0UeXJdpPMVHUbQwIHfBAry/wAYabplupvLe+mub1nAmB+cFcYJyAAuMDj9K2vAOqeK9aRNE0R9MM2nwrIn212DyRrKroAQCDsbcBntIRyOnj5lTVWKrx3W/wDmfQ4PEKtTUkepax8UIryyktvCdjeX15NiOG7ktzFbRMQuGZ3xnAdWwAeCCcDmuD8S2P8AYvw4vLKRwsqRtHK9wGV2lLbmBZTlWYkt5blkbIKsRWje6l4m0LdHqvgm9MaoqxiyIuYmCyOAhK8qDBI0eTz8qnHHBfHU/iVZWOkDRNcsZku1hutRnhMUc9ipJzITjL8KQuDhskEAmvFOw6X4gPHceCvCct5BavaPf2jXCXZIiCmNhhyASBkgE4OM9DXLR6NrVz8QBY+FNTe2j07S3mt4NUYXESCY7CsUikkxkBSCScEYx1FeoePNLN94B1O2tQEmt4BcW2P4ZIiHTGfdQPxrzjSdctdN+Inh/wAQma3Fv4gsxaT+XYyWxDsVZHbcSrEthdy4GDnGOaANeHxl8R7CCOz/AOFdGX7Ooi8xLsbW2jGRx0OKK9ZooA+AKKKKANvw/YaTdzyza3fTWenQAF2t4/MlkY52ooJAycE5JwAD3IrV1q4ttIv9N1HQr6LUbAxNHEbu0QOMcNFOuMMQGGCc8EYIwMUPDur6RpVveDVdEj1cStGYoZJ5IlQgNlsoQSeQMH1NbJ8Y+EzEsR+HtlsUlgv9pXHBIAJ6+w/KgDkr+/udQv7m8uWHnXDlpAqhQSTnAAwAM9B0GBW7Y2+j6tp9vcaze6jYLbAW/nRWfnxSYJIUHcNrYJ4OQcZyM4rE1m8g1DW768toBBBPcPJFCDny1JJC/gDiumsprXXfDEOmXniS306O2ljKW91HLsQASbmTYrBixcE5wSQR0ANAGF4ju7O91uaTT7aW3tUWOGJJyPMIjRU3P23HbkgcZJrHrqL7XrGz8ZrqWlW8V1Z26xRKt7CrC4CRrGzMpBALEFvUE561PN4+vvOfydO0Pytx2btHtw2M8ZwmM0AcmyMgUspAYZUkYyMkZ/MGmVra5rUuuXEFxNBbwyRwiIi2hWJDhmOQqgAfe7DtnvWTQB33wu8Q6poWr3y6df8A2TzrYsw+wtdeay/dTavIzuPIIx3r6M8BeIvFGvW0jeINA+wIqgx3IBi80kDI8piWXGTyTg44rwb4O+MD4Sn1p49Iu9SlngVgtvhQixh2Ysx6DHPQk4Nek3vjT4k3MksltpukabHG0w+zy7p5D5aqTyCAcs6oMAHccY4NAHstcD8WmP8AwilnDgeVPqtpHMWiMgC+YDkqOWGQBgcnOO9M8OeL9eh8T2fhrxdDpsOo3tmbq3Nk7HBBOUcEn5sAkEEjg074qzJJoul6Rv2SajqcEYIR3KqjeYzAKQxxtA4IPPBHWgDlfEqNp2oaJq0clxa63aX0UWn6e94XW7jlYLIBExLxArxgnAx06V7M6LIjI6hlYEFSMgg+1eQeDdLTX/iZd6kUU2Ogr5UIaw+yu1zIPmLBsu2AOrHOWBHqek+JPjCbQrOPSdKd/wC2L5Sd8a7jaQA4ecj0A6e+T2NAG5ceBPCV5kz+GtJYnqRaID+YANcV4v8AAPgjw/YHVob/AP4Ri7gbzYJ4JjgyAcfuiSGPsoB96xvEHh5YXvpLnxr4iSC2jeOU3N6VaJlIHnKAQJE5AKDDDIxkkA5ukaJp15drf6J4R1rVZnAeO8unKpDMuRujkm4kjbkkMpI44ycAAw77Xdb1rR7PSfEVoNLs5VSVUNsyNfMACJWlfJYk8nBHXms5BHZyzQPIuj2ixkx3CQGUyNkYBx7En14r1+38DazrVosXjC8tLLQ0Cu2kWDnyyy85Mjcxr/soQB2I78DrOm6RN4nh0rwFd3V1Hkm6BzcW8KDA+Q8u55GQu7+lehh8TThDlas+56uExlOnT9m1Z90ZMelafe+B7XVbuZ3vy5MYaUkTsJCAm3P8QAHHIzk9DXXf2fZ6dr9sljawxfbYnS6to1AXy1BIcgcDBIX33e1cmvhu91K4ult9ETVI7NgJLvSySob+6AQrFhxkAEjvVS2vpFmuXsNbmhu7hPLk+1jdJwSMAtyCDkcZx9RXZDlnrFp/mdkFGprCSf5/id3okERs9S0vHmadBM0ERY5yhALJk9QpJXPoMdjWXfww6h8NfOvXIeC1MkM5JDBlBCMD1BIAz65PrWWmvmy0ix8PzRfYkceTPfNIPLCYJZgezMM+mCapapqx8TWjLYKU0mxdVittvM4XHLD0A6D1GTzgDnxmJp0aTU99jkxuKp4em1UWvY9D1O0trDwz4b0aylZdJvGInkRsGfMZYKzDqHOSfXGOhxV6zdrfwj4muISY50EwUAcpthG3H4YPtmuN0HxHpq+GL3w/fqbi1kjb7CioXYSdQgAGQQSGB4xzyABS6Zr+rGZ9KW6iiubuJRNawRrcTFlXazMxwiZAGQc9B9K+C9nL6pKi9Gp81+6PPp46i6FxtrY2unahImn3Rtnit4DbRxwtOJZJAwaLy15bcEVuMEHJzgnNcy6nba4+i3sb2NtewG4fTQjRLM4zt5P3Q+DkDGdvOean8M21/omvGLTdRjizaN5c1zbCSePDhZADu2hhhFyQRgAAYBzp3kNjd293aRy/brm4O+6vJpC5jI6OzA8MMfKoxjHGACR9JTwbxlJzUvdextGE8VSbg7RKel2+njxLaXFtqi6jeSxmGawaIBYIzjdtXH7sLgDDZJ5HU1vQ2dta2sNrBGotrTUwLcDoMnLAf7pZx+HtXJyWmpadaLrsiXcUM4FvbaihRbqWNhkF4zwwIGQeGwASB1qtbauYdM0y0ttftzFZzm4EsllKZJDliQw5BHzHPI7civma+HlN+7O9tPz7fceDVi6cuWT2Oo1LSrH+ydf1QQhNQEkrGdCVeLaAFIIIOAoDY7596wLyCzvri0tRdDV7A2/mTak5VXtW9Q4Ax67Tk+uapSalFcXNzNEj3kl23nPJdApAegBWEHnAGMsc8Vl6rJqF2Ehmud8akNFAiiOMgdV2gYyOxOa0oUpQdpS1/LQ53Whzct9Tn7iRkfYjnyS7BZ+hkAPBweRkc4NQW808N/thVpQ65bJzuxxnPb0z0q3eSLbRO/lcKwDwtxgnoR14qWxgZPljhBuJcM6jhUHYH0A/POa+xyChOpiFUi+VLc15koNtaEkk1yEytpuPceYK6DSY7CHTdRlXxGLeWaNUnA0i4kMIwcAOpwM5PJ9fYVmGxdRvnvQg9EUAD8W6/pVnR76z0nUW+03kc9jeKIJwzL8v91jjGRyQfQHPavrMzUqkFaT09DXK6kFXSva/U7nQLOHxFqT2GuRfZorAKbGwTdAJV2kGXGQxwDjAOBkggk1qaZqN7oXgDQ005FtYJbCSeS4+yPclp+CIyqnILEsdxyOMdTXIyzvLLa2mqQR3el6ZfBTdM/zrG0eFLDuAXU7geQuSOM10N1qV94R02K20fV4l05pgEtRCJp41Y/MIST82CSQpBwM84AFfM1aUn1Po61GbVy7rOjabF4q0dVto42urWaa503IMUbEKDIE6Aksyk45yT1BrlLnSNL+w61ZkqtnaXLPBMTny2KqxA7nDHGO/TqK07+Xw9qGs6RaaXqkzalLI8l3dGQi5aPy3BVmYZUkgfLgYHIA4Ncpq1okPhu8nglka2lu2/s62UnBy/DEk5bIUsM8YOeSeNsMna1rm2HkqcW5bLcSaJbiXS3m3RyTzJFIqsV3q3LKcdRxnHtjvXX2trBqGqS2t6dsUAXyLQcLIuB8x/vc8be2OhrkLSBLieK51JormC4j2IQpVYGODkZOeTjk8gjtk1vtLHDGlvq6vJHGcw3ig5HpuK8qw6Z716VaLk72sfD8TYynjcVz0NFa3n6o09Rv761NxbLbR6fZJGdlz9la5D9f4Uxt/HIryTwxq13o3iXT7zT7420q3HlC4EO/92x2sTH34JOOvHrXb+JfEd5pmmr/ZfiBJnkYII2CSOAQcndjPHv69ay/hvqEXhvW/+EgvNC1XUAsTx2P2SDcpfKq7EkjGN6rkA8v61zS/dUpzk9Hov68gySjKFLVfn+p6/wCDPGHjDVdZ+yXejfbdLDsv9qC2eywoOA2yQnfkc4XBHevTa8gv/H3j7UXkTR/D1jpqrgL9ulMskjGYwhQFIAO4N1yMKTnFS2fjrxdocNjqHi2PRzo95qAso57YukpU7gJgpJBQ4z2ODnFeAe+eieJpZIfCmsSwrukSymZV9SEOK8gTSdPufhfZw6jczadpo0+OZb6O4a2R5VUMo8hyQ5LDO5QMkZHUV6v4z1OPR/Bes38nIitJNo/vMRhR+JIH4143B4fXVr/wt4NDCRDbpc6i0mmtGTBGFOFlkJYgsNuVwvPGBxQB6RoXiPxVdeHtNuJvDxllltYneTzgu4lAScds+lFduIlUABVAHAGKKAPgOiiigArqbHwh5sVzeXupRWmm2sMbzXZiZh5kigrEi4BdsHJxwACc4wTnaZrMWm6feWzaVZXUlw0ZE1xHuaNVJLKvpuyASMEY4IqzqkXiC10+yjubi6k06JUntgsxkih3qGGMEhWwehwaAMi9tvsd5LAJFcIcB1BAYdiAcEZHODg10UGnt4nsNPji1fSrT7DCYDBeTiDYNzMXBIw4JYkkEkHIxgAnmZ7ia6uJLieV5ZZGLPJIxZmJ7knqa7nTobuDQNOk07wBFqscsRaW7uLS4lZ3DMDgowAUDbjA9eSc0Acz4mNj/b0y6fcC5hRI0a4VdomkVFDuAecMwY5PJznvWNXX32rDSfFt1c3nheyika0ijGm3UbGOBjEmGCk7h0zgnIDYJzW3od1f67aS3kXhrwZaWUTeWbq+hWCMvjO0Fn5ODkgZxkZxkUAcHqNtZ232X7HffaxJbpJL+5aPypDndHz97GB8w4OfaqNdF4vnuJNWhhu9Ns7CWC3Efl2QAhcbmYOhBIIIYHIJBOTXO0AerfBbw3e6/d619g1cadJFFErE2yzCRS5baQSOCUAODyCRXo1+fE3gy/tZ9ajsL+xlmEcd7bqwCTszMrSxnJOXdm4IBYJkrgGvLvhFba0LjWtV0nXV0tNOt1kmR4/MW5J3bEKZGQSCMjkEjHJr3rR9Ys/ib4J1HTb61e0vTG1rf2UykPbykcHB5AzyD7eooA4PVkmk8f8AhHUIvMkuX1VY5bgRKwYEEOGmHDMFBASPKKARknmuu8U/arv4oaVBE06rZ6VPcq0brGql3VGLSMDtUKMZUFueO5Hnh1a5t7Xwj4iuYXupdPv1guhGm+UsA0ZRSxGWJBIijGAMFiSQa6bWBpt54jsNU8fzfZ7m8EdrY+HrWQs2xnBBnII3fMQSDhRgDk8UAZXgjx1b+HbTXtL06xn1nV59ZkMK2rtJDIZMBC079iVIyeTjPvUcdvqUuofaLmNdT8Za9bx3FuibkjhtJonR42OSFSPg56k7cZJJrdKayfHHivSvC2k26kXOnt9qlIS2txFGrBSo5YnphR06kV0WmQ+F/hdp27WNagGoXCgz3Vy2ZZgBgKqjJEajgKBgfWgBdN+HHn6lHq3irU5NZvBKJ1tvLEdpHLtVdyx85ICgZJ564zXb3NxDZ2k1zMwjggjMjseiqBkn8hWB4e8eeHPFFybfSb8yzBPMVJInjLqDgsu4DcAeCRnHeuhnhjubeSCVQ8cilHU9CCMEflQB4bqSa/8AEW8bUtatb608LEbrGy2uqXEZBxK7xhmVsYYblK4OMgZJntNPXXtSt/COjOkFmqi41CeBIwI4hgLgKDH5rEACSMqcA5UEVB4futR8Fa/deDdTvL0hX/4ljtcTsJ4TnaI440PQDkbgAcgYArp/A0xsviP4hsrpm+0ajbQXcZk4ciPdGwILs3GVIBIOCeAKAG+ObiXSbbSPAPhK2kgmv1JlFmVEkNsp+dlLEDc3PJOTg85Oa5XWdGi0zTdO8JRRzmO4LBxNbsrLEoyxVZA6b2JCho2UBmBwMmt7VZLWT40alBqFho90f7OtzbPqV0YGjALZER2tkkkk4AIx1601LcXfxWs4DHiC3toRGovmulRmlaUkFumRbgYGMDn0ovYE7HJ/EPwNY+FNO0VI9RlhN9cFLi2fEkK4G/KFuQASF5JyCM985RlhtcreyJC4TMdzHHtDjuGA4yPTuK7z46LZXk/hqylfdcefLL5XYxhRkk9uQuPXn0rze8hEGnTJA7xqU27VOQc8Ywc9c+1eZmE+epGMmePmtR1KsITbZp6fZreXaWEd9HY6vqVuZmuNmSiYG2NeRhmHJOc445rp7bT7WwitHF5YaVqtnbG0mkkRSHXCneASuckAgnPUgjORRplldveauf7OtLvTmnVEUvtmBRFU7cjGBjjkEEE5p+q61JaaXutPs+rxs4gaxuiBOrE42ng5x3BGcAkk9a8KtWnOooQ/Tr/WzKjokkcxpOnvd6hp1rFe2N8zT3gF5dqTGwJRi+3PJznAyAT3xzXet4Q1e5hNjPq1iNPcFZJLa2KSshGCoBYquRkbsnjsDzXmV3Zr5sFjayQqljGUAZMpJKzbpApPYHA9eKt+HvDQ1/xlo2n3dpCkDytNOI3JJjRSSGwMYJ2jr3r041MVL91Rq8qe6sdmFzJwfsVqXvHXi2xutaGmWcitZ6bGsUO1vlkkOAcHoQoCjP8AvVm2DxWr28HmJLJBau+FIPTbjH+e1e53Wqw3LT6Povh5NUhtf3MxYpFaxsP+WeSDkgYyFUgZwcHivNPGPhErqWjsvgq20kXWo29tLf6dqBIjWRwpGwKoGQSM44/GuiGWRhTUIsyxeC+sTcm7HBX2oRaVBpryZdZQ5IQDPzYbgfU4rpIvhz408TWdpPbWUGnQOwlV7yYrIBjAJQAkZBzg810fhd/B3gzxBrtrfWUH26z1F1tZbm5g3RQFVKBfNkDDAJ5A5z1Nbl78VnvpGtvDmmz3s21ipgTz2wo+bJBEYxkch2xkZXnFdVLCQhZy1YUcBTg+aSuzy/4h/DW78K6Xaajdawl/M3yzwxQGPylB4cYJyoJAJOOWHriuW0W/MK/Y44mmu5nyjE8Nx1J68AGvWLvTtQuXk1jxRcQS28SRXSW4cyxsrcxyyvgBlyGjKhQqB9wAySfHtQhNhfi406O7t7OYtPp8k67XaIkjqCQcEFSQecZ6Gvp8nqxSdJaPp/ka16EXDl6HYW9pEkpjWFr68ABdsAhc9snhf50avp95Jo9wzWFuiqhZsyjcFHJxheTgGr+g3K6lp8UGkZSJFHn3MiE4cgZAz95ieT2H5VeGn2VxI0SWt1q0iHDF3zGp9CSQn6H6V31K942Pl/bujXTkvhf9eSMvSpF0h5JLK1FxaXSLuj347HBGTggg4I+lQ2d/aDWPPinsvDlzCChTaC0qnpnICEZHUZPHUVPLpUIawnsbi5tbK5dlkgUqRG2CQBkHbyCCAcemKoNpSRxzxKN9/bybvNflpM8rk9wVOMdOKh041rKJ9xX4nwdSnH2UHzdV0/q5q3mpaVdanEdZ1qwure6jNpK9tEFeAMQUmBDHlHAJ6fKW6g4rn9Rv7w6rEt8sUUWns1r5MRyikfKXB9CRx2Cke5MF3HGPKvYVCpLhZQOMg9CfcE4P1qmix2spiJAjmJKg9jjBH0Ixj8a6sNlyhUdRvT+rHmYjMZV4uMVZPobsUyWSvDOhezfJBAJ2HuCBztJyciriarLZ2rPb3dtdQIpIWWTDKMdNw6/iM+9YUFxNaAIF86AdBkBlHoCeCPrg1l61dWd2Y40t3ScNuZmTb8uDwfWuivStp1PIhhVUnaS0Fgim8U+JYYU+z2k1/KEBYhUiXHLEkjoASfU+9e+6V8N7620+3g03xmDbW6xpEI7CJlGxzIMnJySx3H1IGegryHwpZaRHoc95rWnTul04EdzLbFrdFUjapcAhd0hBYnGFjIGd1d9ZfDuzLWdz4S1S80y7uFT7LPBKdht0OHuJVz8xkJwq5AwV4GGx8vmWIVSp7OHwx/q59BSgoxSRo6t4d8X+GNOa8M9rrthaopn8qNre78pVKkryykhWlPY7nJ5IArC8e3ieIvBF1fnEqrbLLAIIN4gjJBUKDgQocDLNiR8YCqoxXe+CfHEuparc+G9ZYNqMG5rW8ERjj1GBSV81AeM5HOCQeo4zjzvxHpL2Wl+MPDMAQw2EjXNrEwyI4ZFEmQDhEAJcbySxwFUDFeaaHdeOpLqbwT4YsT55kvr6yjmEKqzEgb8AMQpJZB1OPWuWXxTYeEfi3qd3qbzX1zJpQjWO0la8lVg5ZkY4AU4XJVQFAx7k3NU1UeKPDOm6l4iuJPDvha0McsTFit3fSqhA2AcqvJxjJI54HIkvTaWOteDpPCHh4ypd6Vdy29sm2MgSLFh5WJOMA8kkkk45JoAuf8J58SJv3tr8PQbd/miL3i7ip6Z98UVrWmm/E+Gzgi/tXw7HsjVdn2eVtuBjGe/1ooA+QaKKKACu7i1LQ/CcttJp+n3V9rUltC7teyFIIWeNWwEUgyA5B+YhcY4I5rircObmIRx+Y+8bU27txzwMd8+lemapqGsrqUEcvgDS7iT7NajzRps7nBhTAxuAyBxjGARjnGaAOKv763uor9NS04QauJR5b28QhVSGO9ZIwABx0wAcjnPboNIjDaRYw3fji905xA8kNlbW8kgSPczHJVlGT8xxzwQM9hzfilb5fFWqrqZjN8LmTzzGAF3ZOcAdB6DtWvpcb6sdIsNHbUotStlMqFYjKnmFslgFBZBgLnAYEgnAyaAItW0mfXPGTWWn3s2oyy2yPHcXQEbTBLcNnknAIXAyc4xk9TT4NLs7m1g0zXtUk0G6stwCXdrIyOrHduwoJDZJByMEBeeKhvNN8Sar40udPnc3euMsiuIZFkLhYiSqlMg/IMADp0wOldLZaFfeHrVYvGPiCy0+yAyNKuFF7Nj/AGYgSIyfXKkUAcX4gm01rq3tNKmmuLOzgEKXEybGmO5nZtuTtGWIA64AzyTWNW34nuNAudYaTw5ZXNpYbQAlw4Zi3OT7A8cZP17ViUAejfCjXE0HUb67ltVlgAi3TSOSlq24hZnjAJcLuI46EjkEgj0Yao3g34iprMdldSWmoxPaXSR5knuLnl0LDOPMJ42qMICASM4HnXwumhkTWtKj02XUNTvViFpbxqMMBvEm9yQFQqxBJ7NkAkCvRkvLH4c6L9qnl/4SDxPZwLbBoUL22mqeMEgYTJ5YnDMc5wCSACaFYPCWq2k+pGGTxbrV4x03TncvDpQnkJZsDvljkjBOMDABNKnhjxFpvirTtH1NbPVIdT1kahJrLDE7CJVfy9hzsAKY4JGDgYzWtZ6RpuhTa3p/jDVEuf7WZL2HV5gI2YgAbVbkK0bAMoB4DDA4NZem6laxax4t8V2Ou3+sWWj6WYLWW7feFnfLMqcAYysYyBzuIyQBQBBYeJPFb6trcehWEMMGq6wzJq1wwcLGZFtlKR5BJBUnk4wDxxVK08M2GBqN/cHUtWu0SU3N2Q7GSS0I2DPAUPKpAxxgDsK29K8HeG9M0qxs/FXjEvIlvGGsTepaxqQzPgqpDMQzsck8nn0A6i1+Hfw81Sy/0HSNNuIcbRNby7z6ZDhic8dc5oA4n4ixG3+x61oMotT4bjAs8OFjEanDluCW3MBGq8btrnkV0vj7V9TufA+geJNBu5bN3uLeYupG0JKpUBweCu51BB+vUVhXej3fhbVY/Dl4895pt6rLpl0cGXcVKmI5BBlC4VXbAVAxABBrO8O6le654Sj+GzxmKWFpEvr4qTFHYK25ZEZhht2QqnHQZ44NAGzqvifwj4y8Lz6V4uvbPT9as5GiZoX8wxTL0khK5JU8HH1B5FcNp3i1INOtb+S/tU1vRZ5DbuI2jW7iHBVY44AoV1wAWbIIycV0dt4t1FNU/s7wjpenaR4etJbaFrme0JneGRtguQCQGTIHJyeQSeTjq7OHx6/ijWNJn8S2f+j20dxZO2nJiZXLD5gCCNpXBwT1B9qAOP8AGfjHRL7xDoPifQtTj+1Mgs76CWHDLC3zAkvGwG1sgkAnkY4pll4r0rTfH0OpLefaYJYot32eEuVdWZSCEgj/AIJpCMAklcZ5ArorfXPiEfDemaxJdaIFuL0Wt1G1k2bbMxi3cOA2G6jjGfqa0bO58e6xr+uaHPqul6XJYRxNDcWlgXMwkDbWxI5AxtIIwec46ZIB5V8R9ZtPEfxCe+Zry1htrWKO3WeFoXcHc24BgCASTjgZrK06Z7nWdOidJhA91GNzydRuHVffFavirw1rNvodj4nudRurq+e5lsdXadw4ikSRlTbxwpAwOwyPWuPe9aCVZoS7SwyKyyE5CsCDnH4elcGIpuUzzcTTbq3ep6HpepmG2ebRL2aTVDeO97b3RYQRKS24kcAAYBBBySPwrPur1p7q5ubAKbif/XX5G15uACIx/AuBjPUjB69cG/1nUdRhmW4uQsSMJBBEgSNz1+YDk5IPUmpG1WDS/wB1cblVhujCrn/gI989O3Neb7Bp80VeTOWtKdrU1dmpZs6WjW9sIJYEJPkSqd68n5c5x1J5P51N4e1S/wDDseteJ9O0jUZ4Z7b7FZ3mzfFatkGRm5JAUgYOMEgjNdP4b+DV5rN5b6v4muPsts8asNPtyRIynnbI3GDzggZPuMVa1eaPw740iXwTHBZLtIurfkWk0aZDPIACF+cpGH4wQ+SQK9TDYbkfPLc7MJhHTlzy3Z2n2+2sbPRvC3hW9tzdXEQcXC4l8qBRl5mH8RYkAE9WbJzg1yPjnUHsooLmb4i2N3/Zd9DczaXshjeYxyKxX5Du3AgnBGMjnGMhstswi1bxR8P9PisvEK27WuqaPKmHiYkEuiggbgRkEZVhyBng7Meq6Xr3h5/D3hLR7LXYILECdrmURpGzZCqxKkmQkMx6EEZJBNdp6BgeOJoNP8bakf7In1KPUrSG9U21t5pVfKkgkY4BIABiOfUCm2nxN0e3aBbWx1CS5huorpLaCzbgSRhbiIDA5Ulm7AkjHTNWYbnXLXRvBGqQxwRa1ZTvoN5Hd7iuWwoDFTnkxoQRkZYHkV0Fv8UZbaBG1vwvqVuzRrJ5lkVuUwVZuSCGGAjkgjjafSgDDTwrr3jrUhHeR3GjeFLaWUQxuGiurqGQqxiZc/LHkcAgEDAAOARk/FS1h8QTtpekxwwW/hu2IVwgCySlQxt1bPykRKWA5yRjgiuq1b4l3WpxNp/hrSNTivHIR7u9tTGlspyNwUnLvwdq8ZPHJ4PFX+iw3E+g+DIZy76pdLJeyeZl54h+9eQnGJEYgurZBUllPGQLp1JU5Kcd0J2ejOM8E6lNNdR6K1xHBYTs0zy/dfAXJUHOBnH1HNenaVqSz3yWVpZiLT1tzLDLkDzBu25VewJDYJ64yOOa4H4teD7Pw14rlTS4yLG4hF08MaHFplivOBgKSDj8R0ArntO8Z6tYW13Gl3HK88McCyyfeiRAQoXBAAAJ7deepNe/rXtVpNa7+R8/mOUqu3KO/wDWp6BcEPoTzJz5l8ZYeOeZsjH+e9Vr5lXWs5x/owLnsAGOCf1rh5fFuoy2VhbLLbxrZlWBUHLlRwWyf0GKqz6/qFxJdO95GGuVCthQNqjIwPQc12UadSNtF96MKWV1VfmfV/obMuDoDHGAw3KPq2RUmn6CPE2qw6dJenT7YkCW9MBkSJnyqK2CACxyASQPxrnH1e5a2ghzE0UJUkKcFguMA8kdq9n+FdhMnh++t5ZdC1ePV0W4m01LsefEhXGCMEZAI4JGD3BrPMsa6VB0ktXY9XDYVxlzPuXo/hN4dtvEsWjXDXjQ3Gl+ZHLHdMGSWNgsjkZIAYSIRkYypGK5SL4PpqUWkWcGr3J1O+t5Lx55lBiithgKNnUsxZe4A+bjgA9ZeZ0XW9ZubC/fRYYrCG2upNdeS5xJIz+WEcSEqAOSQSORkcGr1tq9/L4hsRLbW1jMbE2GjPaSmeC6DlS8okAAAjWMEIcE89civm/rFX+ZnoqMVsiTwFrXhrxjpr6JLp8FnrOlx/Z5o4TtbamELxyDBKkgAjPGQDkEE5eseGtT8EXzz6XHNdaPdlVu7KEiMXKgkhAQP3TkseFwsmSBtJAPS6z4A0LSdGjv9JmXQb/S43kj1OJVLn5Tu83IPmA9SDk56elc7ovxE1Z/DHleP/Dl/BHdqIre5gtji5ZuFXaOUcnkHgE8jGKxKMbxPqn2ldH8aadEslzYvHdGVNy7bVSQ0KKcBYxllLMAXbIUY4GlrVxpl3dXXjzxLHNZ6CUiSy0uQ4fUnjLGOSRD2+c4U9gCeBgwaXo9roQkv/FaPLcTzG+tfDVpGJpHk2jMkiKDk5BYKflQseSTWjot3JqWo6X8QdeuIptJv4J7P7K0eYtNy+FJJznOwqzEDlgOnQAzvEth4tS0l8YtHp2rxX+miCKykGE04SsuAgORIeQCeCT2xgVY1S/1fw143tLXQNLXUZNL0OHSw8swjiikbMm5h1I2Qg4GPc9MvFtoeqeOvDeiaB4jvr6ytJWu7iwE/mW0McYzGBwAcOVABJwPTAymnabp+uS6n4j1vxY2mWOo6jLJBbxXEduzpGGgUmQ/MQUDcAgYJPOaANm00b4lX9nBeHxzY2/2iNZfJGmIRHuGduScnGcUVCPDXwlwP+JnYn3OtP8A/HKKAPlOiiigBRkHI616Rb3W62jXUPiFrsV0lrHLJDbQySJGhVcKD5i7iFIyQMcHk4yeS8PR6Kk017ryXU9nBtVbe2cI8rtnALH7qgBiSATwAOuRpa1qdpZ3Wl6v4WvL+3gRXSKC5cNJaMDllDDhkIcEcdyCPUAjfwzDf+INZgh1a1srazkLCXVHZGdC+B90Nk8gn610t5p2rweHrHST468O2mmtASkUNzKhnQs3zPiLLDOQA3GBwK85vri6ur+ee9kkkupHLTPIcszE8k/jW3pt34du4YE16DVDNAojR7B0Pmrk4UhhwRnGQTxjjPJAK9/HN4U8RzR6dqSSywKUW6tWOCHjw20kA9GIzx6isVmLsWYkseSSetbXiyZp/EEpbTjpyCKFYrViS0cQiUJuJAJYqFJOByTwKw6ACiiigDrvCVle3Wl6pdWelvdpYyW9xdTRSbHhgHmCRRghiGBwcdhz2r6P8JaPpXha7v8AwusSnSdWJvNP8z5llVkAkhJPUqACAeSrd8E14t8HFDJ4gQ3us2++CNNml2n2gyg7wVYGNwOOhOOp5r1rw5rOjz+CdP0LxqjaTd2iBUTU2Ns7CM4SSOQkfMFAyVOQc9uoBvw6Z4x0jThp2lXWj3ENuPLtZL5Zd/lj7quVPJAwMjrjOM15/dXIi+GMTeI5reObXPERXU3UYTbHKQ4GO22AAdeo7mr+u+IvDdno9xc2/iTxlfWSgq0mnuzxr2x5zKB14zuJrmtGlvdLs/D9hq/h/VP7M0N59SuZElDSJbzeZ5TuEYMGBLEjrgE4xkUAO8N2Pw5mnit5f7OPmSRzzi44KrHG0jBS2Ccu4UgdQhHOK27vwXa6fplte+H5G0TV4LeTVLm4twSf3mfKtiucEFjtAwRhDxzz19ppXgzx5o/n6ZdG6txwT5zS7CR0aOXcAfqufQ1zep+Cda8JQvc+H71BaJIs5gkBa3LKPlMisSyAHBDKxUEAlQASABLnxJD4r8D6/pHikw6d4h0JBPMY2DKJFGUkTB5ycKVB6tjuKpjTbmbTrPQNQvo9P8ReNGaa9kRBm2tUjJWFVyMDACgZ7t1xVfQtO0nxT4jj1W6smtLLRFafU3uScrIrErCx4DEENM5wQSwHTFa2jJb+INVudW8W2EcUOu+XJod+HG63jUExxhx/qpOQ45+YkjkjFAGpPolrqmmWnhTxLMbHWbaHybHUYDsFzGABlCeCSAN0Z5BGRxgjprskfEXShFgk6Zdeee+3zIdufx3Y/GqN7ba0tgdP1rRbfxLYZGJoikcxx0LROQu4f3lYZPIArEs7yNrjUdP8I6Nqa601sIp31eaVUsUO4pzIWJyWJCpkHGSQAKAJ3Im+HeomMBxqeryi0H97zLwhGH/oX05royAPiSDHnP8AZB87HT/XDy8/+RMfjXJfbLrwj/YJ8VWMNloek2whhubac3CG42hFZxtVl+UOBwRljk9DUVv4/wBJtNMu9aXUbKXXdYdEt4DMNlsnIiWRuiqoJZjnqzAZ4oA37LTbPWvD/i22uwDY3moXK5PYKqoWH0dGIPqM188aD4F8XeIdPE+n6LJNaNjZPvSNHHGcFiM9xkZGcjtXuialYvDo3gfRLgahHchl1DUIm3IEALy/MDgyOSQQDld+eMjPQ3N9d6hevoXhxo7WG0xHd34jDLbnAIijQ8NJggnPCgjIJOKmUU9yZQUtz571rwL4u0DTbnVNR0qGCwiixJvvIs+gwAxJPoOTXZ+BvDGl6V4olk8VypealZQQXNhHDG0kbo6kmQKASxBGMkAAjOOQR1+v6X4Ato5rTWhPrWohTujaSS7uiSOyqTsJ7YCge1chPZ23iXwFperX2mztfeGlNnqdhMhWZrXGNxBwdwXEgJGMhhzUxpQjqiI0YRd0tTo/Enj+51oz6X4diLhQouJQ+FhViF3TOpPloM5IB3kA52AEmg2kWfg3Sb+91OaZ9RgQG/kUJ5hTJVJbcEAGMAhTEflxkEZA3ZemeKmtV2eH/COo3ihh5C21iyW1zG5KyoSRgIwVZVODgsR0BB6PSfBOueKn06bxtBbQ6bpsryWOnBvMmKkjas0gOGCgAYH3sc++hqc7p2hXPhn4c2HjCOX7F4muLtJImdtkckczqqwygnAjCANjPy44IxWndXGqyWGuap4Lt103xBIFXXNIkQNKjDP76EdC2CSDghgQcbhg7PxB1Bb/AMTaJoEDgLZyC/uiGVQhAKwplwU3MSSFYgELjIyDXP8AhDwm+t614m1CK/uNO1mxvUis7iJHQQqEB2GJyf3ZyMocgY4OMUAZrG0ufB3j7T9N1241eK1Nvq0d3KSJkkJ3OpbA+YCIcgDGccEGtjUfBXiLw1pzX/hu8l17TnhJ+w3T5nVTFKq+W44YDz2O3AJwME1Xbxbp1x4K1y71vRE0jU9X0+eAX8SH7PfsodRhhwrZzgNgnPBPFWNFvNV8NW2m3Phu8l1nSntrbzdIaXzWRmikd/KcnKkCPIQ5BzjjigCLw7458O6lrZ0++uWs2uhJbulyDC6OEhdWJP3SJBKAc5zgirXw30++8R+MrnxXeLssLDzbaxCtmOWVjiWWPjhGwTjJGXODwa9AtI/DHjbTLfVVsrDUreVfleaBHZT3U5GQQeorctraG0t47e3iSGGNQqRxqFVQOgAHQUAeaXHgPVde+IWs6jq0/wBn0CZ4laBZDuvUjRSqMBwqBtxPc5I6c1geMbyy8d6gNC8PWNt/ZFnMp1HVI7Y7WKkHyo5EjfbxyWIxwB069d8V73VH0S08O6JG8mpa3MYAqSCMiFVLSkMeBwAOexPWubvvD8ng/wALOsUFrHcw2rCDzfI89iFOAGjEbMc98kk+tNNrYC34H0HT9b1a68SXdraNpen77WyDW8IWRhgSzEpGgZRjapI4wx4zXMeFZE1b+2PFJNmUv5GaKzjXBiRMqqlRbyAHA6g8555zXqHhmwR/hHYWenMshm0kBHX+KR48k89yxJOe5Ncr8PZkvfh/p9pp1z4ju1ih8qWOOOKBI3/iUOwXIBOOGJ/XD5pdwK/gTwTo3iHwPdy32m2k97dQKBM0SlleRPODAgAg5mAyMcKB2qLwHfXvi/wtBa3fhExtpTC2j1G0uFt5VkQAHbkBlbkZwSDkg+lb3weuGj0OfTZQRNa7UkBOcMjPCQCODgQqePUetZ3g/wAXW+n+OfGGhR6XfzzPqjTxR2cO5BlQrEkkBSSuSSQDnr2pNt7gUdX0+bTX1wXelXGoP51hqP2XULlZWulDtEwLDgAEqcEYHGRjitTVdFvNLsda8QajFFbaTKY5G0JGAKMqqBJFIpwsxbptGCcAkE5DPF2qSajrF9bJ9lhvW0C+hjgiu1lnjkUxyAOqjCn5TgAnJB6Y51tD1C31O1trmz1Z/Ed1bIGk1G6IitLZtuS2FUKWwTwAWA6soOSgMNvEOpSxWkjStqVoZobe2lv7fyWsZ3YAPeRcFiARtYYUkDgEhqt+J9Jutd0u9g+1z+Kb+OGRLWGCFILW3lIK+YzZwZFycDcSD0AJyM3XrjQ7LxTa3d94jk1a41xl0u8toowLUwPkDGAQpViCCWJ5PqSNDQLzUbvS0h1PXr+1GnM9mkOjadIWLxOyFpD5bhiQoO0ADk5BPQAzfAOnWfhpdI8VvHLCL2BtM1gzOzGC5V8eY5YkqCyFTzgFlPAzXfHRta0rUL5/D82ni0vJPPa3vEfEUpGGKlezYBI7HJ71yfhfWYNIude07xFDqMOjXlyZoL/WrQQpOzqBIr/KFUEgkAgZBPfin3Oo+E7RJIdG8Ta/cLCuTaaNPJeBB2AO1wo9BuA44oAijvde07U/G2teJGsDe6VpSR2hsAQgRhI/8XOSyrnPpxx14DSrTwDAFVpbB7s2phDzg7DK0UMYYlht++8rZB4Cg0+3n1C+03Xk0fSdc1Cw8TzQw2s19cRmeR4CWlzyDtKhhnoCAM54r1HRLvwR4xjn0+ymuUvIwVnsp7iWOaMjggozYbB4PBGetAGWZPhBYH7G9rozNb/uiTEjklePvd+nWitNvhB4dZydicnP/Hlbf/GqKAPkSiiigDoPDviX/hHorvGlabfvOUwuoWwnRAu7JAJ4JyBn0zWyfiXOUCHwp4SKg5A/shMAnqcZ74H5CuGooA6rTJdA8Q+Mb288S3Umm2V08k+21j43s2QmcHavJGcHGOneuw1DU5PDXhtb/wAKaBY6ePtLRHUIXS/lEYVSGMpBCEkngBTx04ryWrdlqN7ptwLixupraYcCSGQqcemQaAJta1G/1bVHvtSkeS7ljjLySHLOAigMT7gA/jWdV/V9UudZ1F767KtO6RoxVQM7EVAcDjJCgn3qhQAUUUUAevfAp7n+0dWgttfg0hplhUNJCsjStlsKu4gdzkYJORivYNQsrrxLZXml6tb2t1q+g3UF7A8aYS5x86naSdu4B0IyRnJB7DyD4GarY6bLryatbM+m3KQQzzsoMUJJcL5mTwpJxuwQDjOM5rrvE2v21n5WrPqVzZadAxjsb+ByL7UIh1jUE4aIEnErg8YIBJ3EA6W7utNgk8Q6bFeRQ6Vq2jnUIY52Cr5svmKxTOODhSV5wTnjJqW81K1l1fW5LC4huo7/AMPma4EThvKlTKxgkHGXEjADrlOOprD0jwzqaeJV046PomnWtxZNc21+0Z1CWVwV+VpJccjfkgAAjOD3FWPS9RbTdCvNR0OxvbzU7hoWGjxCyubdl3MGDhgrgeWSQwAyAOaAOng+E+iaLpUT6VqF/ol9BbbZ7+zuCplwOTIGyCMjOMDHYiuXsb/4p6V4HbVL3UrG5kumSG0tbu3zcMZGCqQRgbjnIDZAGc4wRWxL4lv7q6Ogvq1leywSobrTtQiNleSL1Ee7mJy3GcABhkcA5q14fkbVrgXFzDPL4tid2uEvyxh0oEkDamQuNvCleWGSWAyQAclot5aX0em6B/Y91ZeB47l7bULi4dVkuLwD7s+05RS5AwcAnAyAAK9Aj0J/CVpLpsOnyav4WkB/0IgTS2mTkhVbmSMnnHLA9AQeOSsW8OeFdYkm069vNW0TVmaPWmuIGkgVmOBOZNgj2liVIBIIYEcCu9i0TWNKQDw/q8UtlgeXZ6iplVB2CSqQwGOgbdjjGBQBjw3PhSEMtn4l1XTlAwLZriVdnsI5VJUewGPaue0fxY6jVdK0iXUb7UNQ1kWi6zLEuxAVUBicAFljViFC4BXJAB56bxJrvjLTNMGLTQrea5mjtYJFvJZG8yRgoIUxqDjJY5PQHrWbd2VkyxafZ6o+m6L4cczXeppIBJPctGwKhjkbiJCWbkkuAMHOACn4os9DsZjPb6n4i1PU9LJZYTFJqEKykDIcOpRTgjOGUgHIxT9W8aazp/hNdTuNb8Ho8kP7q1iR5fNc8YBMgwM9eDjByTip/B2san/Y2leHNK02ex1AWYvL281WBtuWJ3OFDBnZpN3UjgEnPSm2sVj4V0bVJNdsLHVbS5eR73WbOISmSQndiePkrg4xtJUccL1oAyNEt75TcXvhxnW80i032VpPFsXUopcmW4PfMkigqRjAjUEYYY6nSmE+h6L4f0K9cG7tRe3+oJxIsbHLMD2kkcsATyAGPUCsGwufEtre+C7Q6ZHa6tBp8kXmTPuhu4hEjGPcMFXyqkgg4IJG4ZqDw9a+IoNO8ValoR+ySS6jIJ9MnhDXNugAY+WwbG4bnKqQVOQRjJyAdpZmRjLpHhC1tbGytZDHPqEkZZTID8yxqCDIwOdzMcA8HccgcbrWp6f4M8XRa4fE1zrdxORZ6vZ7EkEcGD85EagR7DzhskgkZ6mt61vbDW9DVraeTTfBFjFh7hi0T32OCAeGEecgnhnYkDjOdWyutVn04QeGvD1rpthtxFJqIMQI7EQIC2P94qfagDixrPiPwZqE+gaPPpEmjCA6jpkt6sjFrYsC6KysARGCWAwSVAxngVdk8QePL9mgk1PQ9NXzPLMtpbPK64l8pj87YABaI5x92QHsRXP3Hhue80HUvBGp3Vlqer6TGdV0xrZiA0RYh4CAQVHUYz0dcHgY1NO+H3gTxNpENx4Y1i50uW5gyYbW/MhTcFLK8bEnOQARkcqPSgCfRPDjwWt9fLNc3mqSgy3zS7XmmU5BwCNrbSrRmJgVOzI2kgmT4KhLiXxXqNuIRY3F+kcJgDiNiqfMVD/MAdwOD06dAKktfhl4tttVjvV+IEivHJJIGTT1DEybd4ILEEEqDggjOTjJJrvPDPh608LaFBpVmXdIyzPLJjdK7HLM2B1JP5YHagDw3VIryy+F+uaUiExadqLQXkcUyuNgnBDvG4yoIIIaM89x1Ndx4i+HQ0uFfEfgFVsdTtozItomTBcDy2Xhc4DhWOCOCTz1zWd4i0iK98d+KtOWxtbmC7sre4laSxNwYJSrJuGxg6khFOVDc4JA79L8JtfbVfCjaXczrLfaNIbGVgCN6LxG4DAEAqMcgHKnigDitBvLuOJvEHgqRbOCS3d5NLu4/wB1ceQsSElVOUkZ2YBgedoJHWu+8N/EG01O7fS9YjTStXRigiklDRXGDgtDJwHAPBA5B4rH1rwZf6P4m1HWPD2i22p2urRAXtjJc/ZysqsGDq3TDEZYd8H1rm76DxVd+V/wkXw7fUoo42LRwzxMpYH93GgBJjiUAZAyWIGSQMEA9M8a+EbfxloTWMkz211G3m2t1GSGhkAwCMdiDgjuD6gGvJYtQ1Hwo40rxbpv2MNmMX6RF7WdQAFBK4aV2OciRwo7jtTvDslvNfppegalr+havHlpLGXI8xyCxCwSEokS/wB5jkgYwSQa6mw8c6vem88NahoMHia/jQLJPpbK1m+RysrPgIw7gA57AdKAK/hTxFH4Lka0upA/ha5uD9kvlYOlnKxOYmZVCbSQSNhKqSQTjBrN8R6XN4E8TT3kdrFdeG9buVYGVi8VpO2cgxmRI8MTkMxwB1zjBpWnh618PXF1aap4r0/SheSFE0fTIxe3EStkGKNmDMoJPIC8k9as6dB4eW7n0/QvAmsa9qGlwqx/tyXywinO3akpOM4OAEGcUAR+HtWvNM8UzyeFtPk1u3ncyGOzK+XEzbQ6NIEWJQwVWG0tgoRyGJp1r4oudA8W+INN1DWPD+iXOpOt7POgedrcldpiBGFMihQ3PGXOAc4rWk1bxTqlv4fu7ye003wrfSCC8j0wNHNak5VY5HIyo8zCEqFIORxnNWZ/h1oWmXM3hiaCRtD1x2mtXZt0lpeKvO1zkncgJGSeUIOc0ASavb2mleHLPUbFbPSLfSz9osZL1QLi/kCnKkcECQEg5BYls4GBnIsdSfT57O0QR2ngiGaSbUbcwMLiylIMghnAyRHvIwwGCMAkjrorrGo6d41Fjr1st74iFstrowihPkzKSS9wzHIQ5C7gOQEOM5FTazpNiutwtZXN1FeaRbT3GrapaAJIzMgZUZsEMSfm2EEAYGACAQCpq/iHXfF3hK21PRm06fR7vVEtmhkt28+JBcBEYEtjJwpIKggNkHjNT6Je6hANUuLbX/7Os7rVLx83umtJFGPOcbo5QVUE4OQ5Iznj1papb614PjsnXTNIvtQuQ05ktYXgaIxoDJM8Cny5CoIwRgk4wM1o+Bta1mw8JWxxa6xa2i7LqC1Qx3UBySWCkkSgg7gRtJBBAOcUAbhF3cTjw1r93b6tpus2kpt7uOIRucAFlYDKnKtuVhjocjoayrP7P4cuPDbo8dlPFN/YupZwkMiRxO0bNngMSFZTnOJCDnPGX4k1TSryylOi3luuiKBdTai5YR6dO2CpgKkMZWDHMQIAJGcZIONZw3WsQ6Tr9jplrqGn6jeLbTatrLG6uYxv8sOYeI4wSMADOMjPWgDrYr6xju9Aksbu2mvIdbu7dLeJ1YvDNLIWIAPRV2tnphT607T/AIa6DrBvtclS5g1S81Ca5hv7Wcxywr5hCFCCRggZ5Bzu59sTUtH1O0XxFdXljoeowaRLH9nhTTxBPKpVW+WWMgowDAAgHJHarZ13UPDFrb6PHqa6PLNAVs7LXot6xYAGI7mMkFVyOHBI4zxxQBVu9Nura9ngPxevYvLkZPLlZC6YOMMeOR34FFbtvPPHbRRjwFDchUAE/wDaFvJ5mB97cSC2euSATmigD5GooooAKKKKACiiigAooooAKKKKAO8+HXhy48ULqumWSStdyrEit5QaGNSWDPIT0wORjknGBwSPYdR13V9Ma+t7DRbC+1yK2jivNLuMltiLgSW+P9ZERyUGCCTkZJrjP2fW1A3mvwae9rFJJFDummYkxjL8qgHzHnuQAcZzXo9xpWka1c6i1zczvouhMJPOt3xLc3ZUmRzKvzFlBVQFIwTjoAAAT+HNe0O81bR/Dun6ilzJpdvJfXMoUqqEgp5YBxtA80naPuhADUFt4w0u0tPC2pI73S3tzdxrDbKZZFDszbgigk4IVTgcBjWXcaJqn26az1H+ydRe20lb6V9XsBLKuS4MayoVJAC4JYknPTngbRr/AEm4ms0fTtLgbS21HboVmLeR1jZfMjkkYs4yGABUg5z0xQAmtTXFxJ4l07UV0/S7DW5FaN7h/M1A4RQQII9xfG3KgkFQefQJaaSniOysLm4udU07Rm09tPsL8XBFxPk4zcsOikj5VPGSQSCQD6C3hbSoNK26PaW1pOpE8FyiAt5g5DM3Vs8gknJBPPNefR+MfDtr4fGuaZq8YspZguo+HriVZAQ7ASiJD8wILFgB8rDPAzwAWfGEOt3nhKz8E3WkrDc34jtLW5s7wGNjHgszoQGEYVSTwcHA64J3NI8G6X4K0FIr/wAVajHEFAaSW/8AIiUjqEUEBR7Ak+9cH4M1C9s/FV5YNHqTeKr2V4rGPVd7Jp1j98Nkk7sgD5QeSoBI5NeiyWeieHL2ET29xrniGddyF1E1y4BwWG7CxICfVVHuaAOC8QW/hW1khvbTxZZ6sZLqJZYtZKX+yNmClo9xB+XIJAJyAatr4Kli8USRiTTrbU9MlS/sLa3s1tba/iAGS20k7gcgnJ2nBwQRXdXbeJNTs5babwvpDWsyFHhudSO4qRgghYSBwexP1rz/AO1394mk6R4j0q8EWka0LNdUtbg7QjI0ahpV2sGUyRgnADY55yKANuHXIPEurPNqFtfaHZ65oa29vczsqqW3OxCuCRkrJkA4JAPFAP2LRtf0zRxZa3qd9DsMOmWwhtIFEWwNISzKGIySMknCjAAyNCWPVPCfhmTSJ/Dh8QaVZqRBIJYyTCOVWSNhkso4yoOQAcA8VzlrrGoeHvAl1P4f17wzd2QDzJaqzZs0ZslVIIMgUE8Mqn06AUAaX/CX6PfN4RSOaSPS7UfaX1K4Qou6OAgRKSAWY7juxkcY5JwL2j+K7BZdf8VXYe1WWePTrewdds0rxjK5U8mRzLgDsoXPfGXpV/Y2tpBfa3ara2Hg6xW38uXa5kuWVQHj9QVVdpwMmQ9CDU2geGLG21WVvEVnbXMviiJrkTuoJhnOWaBW6gBSpUg5JRjngUASaNZXt1oNpolxBHZ+I9HuWvxYTsDBcBndhgjIKfPgMASjAEjgA6Gv6hpHiPT4rDXL7UPDV2jgmKeTykkb+6WP7uVc9gTn2qZkjuprfRdeuZbPWrRidO1RSFa4XGAyMRgsRgPGQQSCcEYqv4m17xP4dsra1u9H0zXvt0ws4THIYi7tnG+JgwIwCSQ2BzwBQBx2j2Fjovj+/wDEcWoaRPpOg6Y7yzaVarAryPkCJtpIZsAng9SowM0/SPhBf6zo+nzardafpoYvclbSwH2lfNJbaZSRgrnAwDjHesvxBa6dpGnr4EstWstPNoo1DUrjcqrPeOR5UQVjgoGKk5BAVRnkGh7QeJy9nqHxButUMjtGIorpYkIMqorGNTggKszkc4G3pnkA9s0iXTbCzg0uHVxdyQqE3T3QlmbHdiTkmtivENO+Hfg4v/aVxpbNbrI7QWkbtvuJJADHEBnJ2IFJ5ADMxJAU10/wb1y41Dw1e6VeyJJc6RdtbbkmEo8vqo3A/NjlQfRRQBlaxHBB8UvEDTsyiSytJhNEY45ISA64E7kCMHGTjJPGOlaXw4muPEWv614skhW3t3VNNt0jm81ZxETmUvgbySQA2OgI561xHiPWGltPGfiK1ls5Li4u/sEA+wG4kjjTEQJkJ2xBiWPIJJIxnivWY5dI+HHgGAXMgistOtlUnoZGxzgd2ZiePU0AWvEHjHQPC7wprGoC3kmVmjQRvIzBep2qCcD16VzEvxo8LbW+y2+r3hERmAgsH+aMHlxux8oPftXJ6Xql9az6z4v8QXn9naheW0gWGYKDaLE0TpFHuyG3xyA4xyc8ZzWjo8OueKLtZvCyW+kaXbyNt1KSFikjFssbaI4IjccsrErnBHIyQDM8SjUfifqFrDe6RJoWmWo89bgoJL2SNkOGVRjMXI3BSxBxn2uWPii2Gh3fhaaGZ9KgiCHXPC0LiKMHn51UHY2OWALDk5xXTx/DDS/siHxBrGpanb25aYQzTiC2iJySVjQAKACeM4AJHSsSb4jW8ccehfD3RElQFoobt0ENpuH3hHkjzW5ztBBPUZoAyNN8MXM0fh9vDF34e13TtJvFujNAoiv5AGLFWYkjPODkrkgZArfk8YS6f41u9a8ReH9V0eztdPMFu/2czfaSXDMGaPcoxgbQTgZYkjOK4uDw60+rzWMcSaz4qvnM9xdHdEtqMkbyyFJIcDGY2U5OMGuh1nUNY8Caho+haV4tvtZ1a52+dZ38AuFCY+Z8riQDgkKCxIBxnuAWoPEWn6t4cOl2d1azat4vuJJEso3DizjdQGZwOhVFLEcEuTjuR2WtKtxrfhzQ4GaSaCYX0zsctHFEpUMT6szKvuN3oa8/vvEOjy6rHF4i8PC21WMeYuq6AWM0BJChmUqsq5LAAMGByRg81z8Nxrl7f6xoHh3xRpmoXGq7J31m6vDFO0Jyq24XGVcEMSAOA3QZFAHfah4sW6+Idrf6fZC90bR0ls9S1CJlIt3lKHOM5KrsG4gEAE88Grd7FPpz6/o0kRc6q82o2E6EFZmVVZoWHUEFeOxXuMYqG5MeiRy6po9tZ2Eun6d5OoaPe/IJII8spVlyCRlwGwwO7BwQRXNXE9xquq6TpMjTrchJZNI0ZbgwTJAynm6mUkqu0EBFG4jAJJyaAOj8Y+NNI0jW9E1NLq3ume1uYFt45VL7pRG0ZIz8qkpgscAZGa5G00yz1Lw/pT3V1brcR2q29haafbJLe3aqMB2Y52xkjIBAAUjJBPF/SNSi0TwrZza0un6c95qX2A6FbWsYjZBL5Um7ILsQAzbt2OAOc89F4IYaPoS2Gm6HZ288NzPZtcTSxwLOYpWUHKhndsAZyBk5570Ac/p8GpeCdO0rSb+304anEZprBZW22t00hDPFuxiOVSdqsRgg4B5OJtN8TWY01tM1VYtO8Q+Ib0W0mlRxGMWgLbC+DwWKjcH/AIiVA4HG/q2m/wBr6xD4euL1Z7q/hkl1WaJFLQ264CxIGBEaszcE5Y7Sc55GHdaLOr6dBp1xBc6Te6i1pZ2+swC9iijWNiXVmIkGWjYKA2MYORkUAbOv+I9OtovFtybiJpNNntC0IYbnZCkgXHcsSVx7Yqvq/iG5PiHTvENpZWttp62jxPPr0wtANxBBiUguDxg/KARgZ7jKTQ7vTbiyvLe38P2EdxqrafJcafpgF0DvaMOjSM4HzKcjGQMnnFdl4a8NaRNo7S3lol7qEnmW95d3Q82WV1Zo3+ZskAkHAGBg8CgDxaT4eeHJpXlbxHr252LHyNDuPLyf7vH3fT2or2Nm8X2jG2t7vSpIYT5cbzufMZRwC3P3sdfeigD42ooooAKKKKACiiigAooooAKKKKAPWvgnp9pevrxu/Dp1oRRwsqRmMOnL52lmXqOCAecc16jHPq3jrw7p3/CI6XBomj2t3HMF1BRGJzE+RGscYICblGSSCSMAcE14l8PNW0jSNN1+XUkvpLmWKOKxjtZZo1ec79oYxkD0I3HscAmvoixgvILfT/BukXLWw060i/tG+QAvHkcImQRvYgsSQcDnBJFAHOaz/wAJjpejeIZJ/Cz6lq2qI8P2yyuhJHHEchEVCA4ChicAckkkjPFEeO4dT1PVWsNF1S6vNY09LKyhe2MflzoJPNhLNhQFLBiQT3z0FdRqlh4PgS4tr/xle29yoIlZvEEiSIR1yu/APttx7VxFjDPZeA/BuoaBc/aZYNburaGd5AhlWWSZAxLKwBbC9VPJFAHReGPh14jvtEhtfHPiC7mtoYhFBp1nP5aquMfvJEALnAwASQB3NWdQ0P4d+CdKvIINI01pTEVm88mQopGMyOxJUHsAck8KCayPtfjjxIYbZbmGzinHyPJdGQs5iEqjEccQOVJHzEjKnIPGWw+FdOtLCLUtSmbVX+xjUbX7UqrCAozPCIRiNSVOQcZBJOflzQBD4WbUNb0ldHmnmsvEFhFJceH9RnUCVoQxQxyA55BADIckAqTkjNa/gXUp7vT/ALJpMzXfiScB9c1G9jJFpICR5ZHG4gghUBAABJxnB5iyj1rxJcah4p0W/I0vQiZ9OmljKfbpFXDhiwBAMYEbHIDEA9c1vLcT3+vaXqHhzUEsNB8XgvfSIo82KeOMllRuiswUqTyQVJHOMgHQXZ0a2u3trzXfEGs6ig/ewWM8pZSfVLfaqdeN2OO5rM064stEh1nTvEGiX+m+GdSkM0M1+5lwxVRIsjKzlCSNyliDknkEAVvWc0l1EdK8GpbWWmWzGObUvL3rvH3liXIEjZ6uSQDn7xyA5LSez8R/2Hf6nd6nZ6tp0xb7UVJVo2VWwFUABllHAHVaAOM1uC11i9sbfwnqF74hvYh5r2WoXEktk8OMbnLEAkHAGCxJPII5Fy6v7+fw1ba1P4R8LyWRIjnclma0GdrF0MQICEEMASRg9hmtWymksPC+h6w5DXeizHS7w/8APSISiCTJ+qrIP93Hetu2gjs/GmpaaUDWWqWn2xoiMqJAfLl49GBjJ9wT3NAHEafoM2jeNPDlrr8MV7bSQy21tdw7jA+FLQq4JPzopkVSc5DjByDXTWtpbW1uPBeub/JBzpN2WKmSNTlFV/4ZY8AepABGeQJtHs/tng290WWQiXS55bWCYnLIYm3Qvn1C+WfwrldG+K/h3xZ4da28RaXcs0flpcmG1knhMjEBdrKCVYscAHBB4BPWgDqtTh8RW2mS2t/pFj4qsQOhZYp2A/vRsDGzD1BXJ6AVwmgyWNpbXnxDh0q5tww+w6Hpc07yM0zHYWAJO0s3y4HACse9Q+K/HEWjabdWWgeMdZt9QWPMWnajpjvIQeyyPGGHGcFienWp49PvvFkum2fhrUk/s/w5p8Ua3FuyMZLqSMhmAYYJC9yVILEgg5wAVtB8XWvh3Tpotc8E3l5Mkpmv9SRYp/NlklZN4zjgupCgHoARkcnvNMHw++IVnItvYabdtGMSwSWwjnh7YIwGXnjI4yODXHQa3qHhi9MPiG0jAiYNb3CRGOKKU4jjedMnbHGoAUqWXrzuwRP4l0ho7Wz1rweVjvrGVmtrmKEPNrE7HMgJBG6PAYljkHnGAASAJdaI3hDxX/Ygkkm0nUIWbTlnaSXYQwMkBAy7jJDBQVDAncSBWn8KVWLUvGdzLOci+RHeTy12qsfGRGSigZIwDwBg8g0zxTrNr4m+G+ieLogkS2t5DNOJCxWIMxhmRtuGKjecgYyB6GuXXTtPsLTUvE2u3d5beGb+WN7fSSFik1ORVwpKKBtjPZPQAscDkAzkS61z4UXFpp1hc3S2QmuZdRunMVtBskaTEKgfvXIHUg43YJxwNbUIJHudK1nx1qkmt7khaDS7eABD58E2FWMfefeqgNx1BOB01tQ1jxAmgePbHWrewsrSy0mNbS0siGSESrIApOAd3AB4AHGBggnR+z+EvhjYW+ta5dy6hrotljjkmfzJ2AXG2JOiKORkYAHU0AX/AAn4FkleDXvF8UN5rXlIsUDKGitVVQowOQZCACW554GAOfQgMAY6CvJLPxp8QfEmtRWem6Rpuj20kjKXuy08karHHJuIUgciWPAA6tg4wTXS/D7x0ni2C8s7oRJq2nyGO4WPOyVQSBLHn+E4P0P1FAGX8YbpE0rSdP1G9uLHQb+7MOoXNshaQAKWRAMHhiDngnA6HkHLvNPs/Dfhe4m0gSXFpFbF2RtMliEoVc5kAjMTEgDlkU/7Q611+tt4e8dwax4PlvAL6DAki6SxNgMsig9QCQcj6HGa80tjdaPet4V8WxWy3HllbW8ZEK3cfQt5s5c7ySAVVM57UAei+AdMh0LwWmrXcgkvL+Eahe3AJbOV3BQSSdqrhQM4wOOtcX4P0i+v7e/8S65ZaklxrcouObSG8gMX/LL5ADIMKccY4x7Y6bwHcvqngG68M3J2ahp0T6e6vkZjKkROMgEqVxg4GcHiuM8B6tbW/gSO3nkSC6tUkili/wCEiljkVlJGTCThCSOg4/PFAHQfC+zTVNU1DWph5mZWkhcqwBXe8UYAYkqFWNiATx5p9BjK0O5+2+NPG2l33hF9T0qTUGMl1bxKWhbaFxg4JJChvlyQSTg5zXT/AAj8m28DJMXGxY4WZieg8iNm/Jmb9axvhV4207Uz4gvLiO6S7vtTe52x2kjxiPaqoAyqRkBeckEnmgBmqXs4udOstUubPU7HR7e41NjLbvFdlYFHliVXA25ZlyQAGK5wBkVf0bR7iwsobnxNb2Nvrhma6ttfgIaNnck7JGwCBg7MH5SuADnFQ+N9asLyXWbhhcm1ttIWxlBt3R2a6mUYUMASdsZPHXIxWo13pXh/wu994XmneK0Ii/sm5klPnEgEQhJcujkEFQAM5GQQaAKHja1v5G0+JfCWiyHU7+GKTUoZhI8ZLA7sGNWzgE5BIABzUXhfT9L1f+2r5vDdjrDXOo3LGQ3EUjqhlbaNrn5MgZGCM5zWPdBLLXX1BLd9Iv7tY7eGwEplh0kynY11KFOyNyGwqjGSST1OOmv9I0Tw5pUdxrOkzW9nY24iOo6NNJGJIx0EqxsH5PPO4AkksMmgCGz1Ca7i1nwt4P8AC8uh6hGoW6u7sIiQl14bchYyMRyPzJGMFbXTfF2hT2E+oaJb61DpVklvYxabdiMI4BVpCkgGWK4GQTgZwOTUfhC/az8LRzaLA39o+JL6aeyS6dpDDADtDyEksVWNVOM8lgARnNbl9pWhae6Jr/i6/F267t0+sNa7uxKxoyqBnPQe2TQBwdp48W0n0qXXNF1S1utKvLqe/tRaOwiFwXMUoOMdW2jJGS5IzWp4f8JeN9Y1XVJ9V1O60Pw/fXj3Q06CRftDBjkKXUExgjGQDknPAzmshNM0ubSfiXHoWsTasqW1rOtxJOZ3DRh5Aoc53AFeD+HOKvt4i8b31sFSSGKNkDPM18CoTERLYjgVgAsyNwwOM88UAdI3wV8HSOzvBfszHJJvpSSf++qK5K48FfEkXMoivrOSMOdrlfvDPB+Yk8+5J9SaKAPnCiiigAooooAKKKKACiiigAooooA7bwDbPci+a6a9h0W3eG4vbqyjV5Ld13+U5DZO0EknAJ47V7Gmqa78PfCGqaja7PEkGpTm6h1uJxhWcKoaZecBcDGCRxg7a8l+HGqXlpHq1jp+Dc3vkhMghUZWba8jHhUVmDEk8kKOQTj0jTdQk8EavaaFpFnc6jHOjLfaTtBMiKmWk2liUlAGCoysgIKnngA7y3t1tkTw9oulaZqktpAsl9c30u1WkkyeSEYs7Hcx4AAI9QK497TUrbwf410KawttOvdMnTWbGG0bzI0jyJAYyQMjdE/GBySMDpSoknh3TVl8H3Ef/CI69fR/abpWYTaYWKo4znKjAABPKHr2NXom8J6L8V7W007VZ7q61WGWwv7ee4e4UDaHUmRyecjbtyfvdB3AIrHwN4yW0hvND8XWNxBcGC6ikurQh0C7mAG0kYIkYEY5GBxWnbfCu91S2t7bxfr7anZwXMl0llbwCFA7kkgtksVyzYAxgHHTivPdEuNL0S9h0WDWNT0TVILoWEkkLssMrrclSxDAox8tlJJGBt9+eqN/42ksIpD40PlPDHM7Jp0IYI0KysQwGMgb8HHYGgDX8c39rcfZvAejr5NuFV9RFog/cW6/N5SjGC7AE7OpVW4Oa43RpLPxJHrXh64tRa+Htbu3j068jwYItQjALGPoVRyNyg4zyo6mr+t2kfgXw3qSNJ/pK7hJPMWJuZSCylmHzB25eOTOVYMhOAM6etWVvoHwG0rSriPbc3Qt1DlCWinkbzHk45yo3tkc8UAYV54lkvbaDwFcK+hPaG100WkUmHu5WYKX3ADEIUFvViwz6H0qLWdNvfiHen7Zb+XoWnbJmLjEbTOGbJzgYWFc+ma4/SV8R+K9MN5eeHvDWv2MTeTb6hfxtBNdxrwJQCrYBOeeMnkDFczba9oEthf6hF4B8JrFau6MJNQUNOV7xqY8uCeBgcnigDqx4o0W4+HwVdTtln1/VpBHG0i5jWS6JJYZyAqcknvj1FdHoviPTNc+I2uS299A8Ok2MVuJFkBUlmZ5GBzggbUBPQEGuS1bUm8P2mly6j8OvDFudQlSKGEygupIySwEJICjqRnBI61Wv9Ut73UItIk+G/h++lljDlLW5EbKpdYwdxiXGS4AwexPGKANqTxBHc+H2t7C8SG68X6vPFZzZyUgBEbSgeuxAR7utM1Mw+G/BGoeH7rQTbXQKQwX9laGO2lYMohneQAhCp2ltxyCpxkEVz3h7wkZPF+q28XhOJ10uKEW+n6tqrH7Lv3MxiKqwIY4IJIwc9ycdJrGoWt9Dpuhq+t2xvtQgtr3Tb2JpERA4LAyupyCFwNrkEHgdcAGx4mbbF4i1u2aL7Zpui+VDdoo3IzBpH2nnnAjI+vvXLa5pWseHfFthcfDrSd0dnYK2qQqwWO4XJKKwJG6UgMcjLcg9+aUlrbnT/7U1PSLbw3YS6lts7m2mP2aUhzs+02wIUqSoywwehwBzXofhvX7O0sbiDWZ47TWELXF4sjYEpJ4kiP8cZAULjJAAB5FAD7TUvDfxE0hRb3UbzCPLRBlFxbMRghlOcdwQQVIyCCDXnF1HL4I1eXSr+5C6VNE0MbySMEt+CwRWJ3LDIoY7F+YtGUBwATpeI/DOmah4ul8Xan4hXwzcxQqLaC3dFuWAyRJIpySzZxsAJIABOcgc7L4pntr6bVvFejalrHiDTfL8q3a3WG0tEfmKWTaThjuz8wO0kgYIzQBd07S7Twz4am1jxTcXEXhlLprrTtFmRUkuZWxgugAAXILLH0UHJJxWxJGi6lq1z8UbS2Nve2qNYmKN5orWIZ3xqyrlHBKksMZ4IOBgX5/D+oR+Nre88Y3dtq2m6taPYIgg2RWUrYbYoJPDBSA+QSQAcZFT+Kr3xb4Z8FzwRXmjTNHGLaGa4Z/PnBO1cJjaZCMdyCQTjtQBxht9Ft/h2yw3k9rpvijX1AuruUCVbVGyWLNz0jJGcn5gDknmvodjAxa+0TRNb1y8uIcvqE0ROWaGdWHmSFR1ljBAyDsJ5IGd3X7yHwp4j8O6aNEn1e30HSQqxQhSBPKQqs27pxExzgkE54rTT4oeKVCyv4AlNqfI5i1BXYedjYANoyxyMjjGRnA5oAz28Q3+iXrDVtB1LSYbzNtb3UhQqpcqGXcrEK7CONVJIAPJOBzWtXTRfit4d1RGjS31FZLGV0kJDgriIImMLCrKqqxwWIY4Iwa7zTvFfh/xm9/4b1C2MF+qmO70q/ChyMZJGCQwwc5U8cHjivL/FGkvpFnqnh+/vp4/wCzNmoaZesSWaMELHn5gZJAwWEDgKCWxkigDW8aaJe/8LVc6BBLNrd1bRXlrLHcCNbN0Ox3lGDlGVVXBHJyBVjxjb6/4k0+C01jWPBWlz20qzRTLeyCaGRTnKsSu3PQ8H8wCK5ivpHh8KTaqtp4v8TQteapelNzQRhSUgUAjHAIwDwAx5yKd4L8GaLa6tqvhPUtGVJrrTY2uFlQsA6MUaSGQjJVtysMHKkEEDGAAO02z1+DX4tZ03xH4JNwIWgm2Xc8guFOCA5Z2JKkZBBBGT1BxU1h4U8Twa3quo2eqeGg2oyCWWCG9ukjV8AMwVHHJIyScnPTA4qWbwrolx4B8N3V1plo0+jX0FrOREuZAk4gkVvUE5Yg5yR71ZvPhn4auPE2u6UmkWsaalpyTwOsYBtpVZlJT+6MlDgYBIOcg4oA5qVb/wAH6BfaXP420G3FxC0MdnZQPcznJkIVQWyCTJtyQTgDnIydvwlrLap4R0iwGv6VYWMdvGslvpG83zEKAU28lDnqQCTzgjOasR6HpUXww8P6zBp1tDf6N9muDIkYVt8bqswJAyc4fPqcGpJLKbwX4t1PT9Ft7e2PiWQXMF/KVCWzKD5wIJ+bAO9VAwSxBwATQBl3sF3YXusx6bqDWl0J7LUbOXxFOQGjXerAliWKggnBwwyOhwabaz22reJrvUrS4il1GUwxaprNsmxbOJhtUWysSxyQAZTkAEkcDA6P/hGbObxubW+uZdb+1aQ5Mt8Eke0AcBShCgKH3N0Gcx9cClhvrK81bw/fapJCtrd6NcWksrMBG82Yy8TN0GAkmBns2OlAGzfeFbDS9Jmk0i1tYZEibzkuPmivFxllnJyWzyd5yQSTyCQfKhrFl4u0lh8P/DepWVw8bQ6rHbxxrAYGBDLgsFaTnKkAHg54yK1vBtj4l+Icax+Jrl5fCdm7LAAGifUsEhGcggsoHJPAJx15I63xJ4h0fwXo39kaRAsDkeWkVlFyhI+6qgfNKR0HJyQWwOSAcp4Tt59IlPibw/e3PijTbaxOnjT5dsV5ZKpBCBcAEjaARgE8EZra8N6s2o6Pbas2n2l/4h8RTSOtvM2EghjJUKzbSVVAADwSWY8ZJrh/tFz4Qso/EUcht9cu3EscMJDJMjYVICN2JYgoVQy/NGwORhga6RzMuo6h4y8J6VHD4ot4Gt9Y0O5zuJOG3rjGTlQQRw4z0OcgGnZWV9pfxIWHUdE0vT7DXbGS0Y6fKXSaVAXG4FVw2zeBxyO5xgcz4Y8GeKNR0Xz9P8TW3nW5n0y4sru1yibF8gjKnJ+RVIyOoGc1J4gufDWlaJ4d8T3fiG6uPEMPk3saG5klEpLL5q+WSRGpBYcbcYxzgiqnixtE8P8Aj/VJLqa9sXvZIb+G/sWkQrG8UituZcjb5qoeQckn3oA7SHRfivDBHF/wk+htsULue1YscDqTjrRUWnW91d6XaXP/AAtG6XzoUk2kWuRlQccpmigD5OooooAKKKKACiiigAooooAKKKKAPWfgncx2b+Irn7VAk0Nqkn2W4kCpdRDcZEIPHTGDjgkZyCQfUfhrplzq2r3Xje/SaKzMJtdHhuQPMitdxbLEcn0BJJwDyQQa8w+BnhbQfEOr6nc65bJOtgkTwpK+I8sWyWHRvujg8deK9Z8deN7CTS5fDnh69hutSvR5DtasrraRHh3ZshQQDgAkckdKAOG0vX59IUa/ZwSXy+IdSlS40pSJIrpZHYqAP+WUoUjgghlI5JzjTs9Fc2Nk/gaG2vdCOtwX08EqkXmmSBlEiAEg4wuCDyBkcg5qK5hg0/xR4IQWzmG11FbVJNgZU+UhULMRJGQSD5bggdVbAweg8VWBX4rxT6fe3ek3TaQZnu7aISIzCUKDOn8SAEgntkEkAUARx6t4b07xZ4w8PeJbVRp1zqEU6zXMBa33SwoSrNghGyCQSRnnBBFVtR+GOu6Szy+DdUgudNlJcWF+5O0GF4gEkGSVCyHAOAMDk4rCs/E2pS3fiTUtY0CPVNIl1G1jvL7TiJoF+zkbmCHJZSADnoASCa2vCfihPB9jZMLp73wlPbW8ksxYM2mTygkpxyUyMkYyu4djQBdj8G+I/GOt6Nc+MLKKxttNgVblYboSDUZFYFdyjgKCCSDk5JA65HeeIvCmleKo7ODVommt7WcTrDuwrsFKgN3IwTxkZ75HFa1tcwXltHcW00c0MgDJJGwZWHqCOtM1D7SdNuhZbRd+U3k7+m/B259s4oA8f8YeL38R6rc+C9Burex0q0xBfzpcRwySAAgxQh2VSBgA8+3A6z+G9Fi1DxbY6HFG0ej6LAt9LbB3EbzMx8oFPNkTghnypAJHNZ/gHUdA0zQpdMRdaj1SF8agl7qH2aNbk8MAA2Tkg4CqzEYyCa2vBt7FoXxH1Ozu4VtxrscUlsWY5DxqQUIY7xuBLAsFJIbAxigCp4oeDWPjSlpeS2Qg0zTFaGO6Ep/eO2SyBCDuxgcnoOncO0YRp8WVD3H2hmityr+VPGAu25BAErMSN2w5BxnGACDV7xbeX3hX4m2OoQzzQadr0C2czw2qzP56E+WBnpkMAOucHjjjL126ksfHmk6g0l68rp5Eou5oGlYpIkyKI4jhd2x1GQCSwGPUA0PG3hhpvi34a1Qahe2Vtfo9lM9lMYnLorOilhzhhkH/AHe2cjoPGGirp/hmfU477UZJNMaO9RJbpnVjE4cggnnIBHPrWJ8ULfUtX1PwRL4evo4J5b5jBc8Mq5j3BsHqNqtx3zjvUuuWWpa3aaj4ftrqXXbxbVlu7me7+yW8DspCqEiUhm/i2tkAYyeRQBHo1p4Zste1mx1G+uL26tdTlni0yRnmSISDcrJAM54kIzg8knisrxJCPDVmb7X54I9HmulNjpCD/S9PGR+8gkBO1lxuKAFQMgHHW5oeralNr6z2klhZWOvWq3l1qUoBlilhRYpYVJO0sCuRnIGWOCBiumtfMe0lvPD0emQi4ysGo6g7ySXWB989Cyk5I+bBAyBgigDD8PMunW99BNLpaXFqfttxr12A73MUpLJMq4HUfLksACuACOKztOi8OD4jDyNcl1ZfElrNaahBdkB9yqGQ7dqlVKqygYAHGPbL0uCO11Hw0mu3WiSXlrLe6d9quEEsAZHSSMryuCNxUAkYyRjNdnrWlT+JtStLGTx1YQXtlOt1Fa2MCLIJFGVLBpGYgAnjgEdaALX2uLS9LbQPF9nNd2UYCQ3v2V54riMEFDJtBKSDAzkAEjIPOBxPiZvh4NY8OLpViRqbaxaBJY4JY1VfNUtuLAKQQCMcnPPY10V94w8a2txq9iNG00vpIhknvN0jCWKQn95HCACcAMSN3G0gEnFZmt6D4h13WLUar4qa/wBEaxk1SGLTYVgLyQlCoU/MSCZFIOSeO3BoApalH4l1nxj4ouNFtrZ4BeC1Mk0MjEGOAx4BXA4MrkckgkHHrYg8VappOoRJrXh6WURXMl45sNxaRhH5aAQyBX2qvGQGGVBz1qjFdeKvAtn4fu9Cnk8RHxFm6u7OZAzNMUVmeN1AIBBxzkDbnnPHaL8SPBupWwsfEDpp123yz2GqQFTG2ehJG0+oIPTB4oA4vW7bS/FGmR6hpd9G2qWYadL23fy2F4+JJGckbgkSKOCM8qo5ABt2Op/8Jld2Os+KdOFlB4RgabUXlQZkusAhRjsAocr1DFQRxyeMtGt7LUtL1HwncRXWpahMsVqiS+Z5oDBiJCDmSEbQ24klSoGSpwI9bXT7Gx03wnLbXl/4et7kv4k1OENtadsth2Xn/WFXb+6Aoz2oA0vDdquqy3a+MdM/s7VtWu/t2mXquu/btUIkcgztkQAfKcZBJwRmuvj1jVtHkWLXNJnuwgKpqWnw+aJFyOWjXLoTxkAMCRkEdBRMMmmaQNI1rTpNe0AKBb3kEXnyLGOVEkY+YkDGHQHOASAeTjarq1hommTXWjeLNWtJEQmCyvoGlSRv4YwJlDgk4AwwxmgC7HBN/Yeg6VcRNDc61q76hLC4IaOMStdEEHkEYRSD3OK14L2AazrniW4cLp1hb/Y43z97yyzSsPX5iFHuh9qx7zRdFsDY3Xiy/wBSvvEMsbMGtJbjcmcbxFHAcogJAzjnjJJNc7oZ1PWb5tE8P3NlN4d0y6Ey2+rStFcyMMthlClmjEh3DcASQMkjigDp5rWa3+H2kaHOpS/1aeNGixyu+QzTAj0VPMyfb3pnxM8MP46l0vw7A8cflym7uZyCTDHtZFwARksScDPOw54BqnqF54n0vxJDql5baLql1cYsdPgt7yRBblgWdtpQ5yFyzZBCrgD1Z4W114vDt9fWl9Hquvaxqr28LONo3AYQlQcrEsamQAfwnrk5IBJd+FdE8OslvceKfEst/PbJbrDbXG64liTO0BY03BRuPJwBnk1xdr4Fsx4utdDn0PWbLQL+2njtzqNyHX7XsLLIFUkKwUNgHk+nFepQmDw1KdP0+3k1jxFdr5tzIzBWftvlfGI0HIVQDwMKDg1zXjmDXWtrW+1bxboujyWMovLWyiiLCWRM4BZmDNwSPlUdTwaAMm08ceKptFTSYtNaG4sAbS6lQLDHG0W1XYyFmYgAhiFQHGSDgEjQ0Tw3BYXUWpa1eH+0WY2y3X3F0y5DEhUBJBSQEfMxJfIyT5mBl65cf29aaZ4w0LUI9NsNZlit9UeSLzRazqSquVyBzkxMTwVYZBzxtWvwiS6Zf+El8V32q26xRxNbRYt45VTOwSYJLEZ4JIPA5oAoadZ3fjv4hIGt/J0PQ7rzrwIVaG4v1LANEeSFIKswzjI5GTk1/FmptF468Ra/Z3T29zoVvDDFJEwYNhS7rJFwXjYyKpYElSAcYyR3Oq+K/C/gXRBYWD2rTxJstNKsiGlkc8KoVckZJGSR3J5NeU65aTp8NdUa7Vrq9ula/uSgWQCRmBL7WJwoBCiWJiMABlyKANmG2i8QS6nq3h3S7Oz8TXunmLU9B1JcLMrgMJYzwDkgHPQ55weTpPfp4X13whda7aXJFx4cayvkWEy+UYzExZ1AJIBLAkA4znpmrvj+2tr3w/4NuUS5ine+to4rqy4uIkaNiQh65OBxznHQ1zl7rfiZfHVrBDBD4obS9NuGZ7QCGYpKAoEsZ4EgKjKgAkHoDxQB0n9j/Bq6/wBIx4c/e/P/AMfKp15+7uGPpgYornrfx98K7e2igvfDbC6jQJMH0pd28DDZ985ooA+cqKKKACiiigAooooAKKKKACiiigDsPAkVuZ9Qnu9AudXhihDDybcyrDIDuQsADwxUKQeCGb0r1bTPGXhDTmZ7CS1gjhedBA8YiMiLlwjKQMho3aPJ4LRp14rifgx4ru/DWraqlppkd959sJpA90sGxIySSCwwT83QkdOte1+G9c8G/EmWZ5PDttJfwKGlF5ZRzbQQMYlAKngjjIPXjg0Acpa22m+Lfip4em0G7gvtHsYft7yKCZbcjISFjnoGwVVhlQWA4wK6v4h2y2fiLwxrnkl8XD6fIVuDAVEq5RhIOmGTvwd2Dwa7qy06x0yHybCzt7WLrsgiWNfyAArjPiywbwla23l73utUtIk5UYPmhs5b5RwpHPHPPFAGf8L3fTdW8U+Hb2OeO6W9N/GtyyM8kUoA3EplTyvJHHI4HSsDxhocngXWZLrT43Ph3V2dWtYkUJb30kbRI5JGQhDk4BwCOnSm3UaeH9fsPGGlC0W0tLhbPUY47TypZUlIUZaMmKUqcHKAYIwfSvYNV0yz1rTLjTr+BZ7W4QpJG3Qj+hzggjkEA0AeV3fh668N67c33hZzpFx9odobBiTBeIqqCDFkCOMEMxlOMFuO1dDafE1olt11zw3qtk00bSLLbR/aomRSAZBt+cLyDkqOCPWobv4NaHdedt1fxBD50QgcrqBbdGDkIdwOVB5weKz9U8AeKNGiub7RPFM2oBgrXNpquB9oRAcIZVwVXBPyjAOTk8mgCxqPh7wv8Rro694W8Q/YddjQob2wbEmCMYkjOG6HGeDjjJAArktYt7/SJbbw/wCL7Rbe2klDWWqWEgSIyjGCNzKI5eCxkkLHqAMGnaD/AMXIeC78N266VqVoI1utYeYI8OBzFDBGcFOSAWwCOuSMi8w0qfxNfabaaZP4q8RafC009z4huPKhjwQMRxsNuMkEYUDHO7vQA9PFMHivw9d+F9esLvxDPGFMN9ocRlVyD8rbyFVZFOMkHac8cEiq+neCPE83hFNN12Wx0ixi5N1dXJLpg5V1iiZY1cAA7mZjkE5Oauy6j4w1n4cweKLbXbfS7SMs1zY2NoqbYUcqwjkYsSwCkjAAJwB2zoXPw/0aHxLDFq73mtWOtxNBE2oTtLNbTKrPlCcEBlDZOMgqB0OAAcVrenabeS6XpOkm+1e8vbkLb6/qhkWyjcNvKQhQFO8gqcDHOAT26zw7qjnw7q8evaVbaWukTiJtB05PLFw7BdrMc/MrscAZ2nBzu4x0mgaVHeeFdQ8F3xLtpJFmlwvUqFDwyDHR1BXI6grnoRWTrdi2veAbPxeghi8RaTbG4WdlBSRos71YdGUlWI9CQQRzkAydf8PX0w1ODUdNtbqzvF+2XmkWUgH2FFUKs0crBR5pIb5cAMAfQkwaJcT6vqul286WOveHLXTjarZxxCO6jDBdsksEhyThAMrkYJI64rq3ttRg0nTNGa9V9V8RFzqGolASqiIswjXgDAwqg8AZJBOcpqGj6O+heIr+fTLa5OmRNBZmZAXiWCEYAb7y/MWOQQSCD3oA5/w/4ct7HWrDQLeeG1S3try7NrdxrKdtxMoijdCcn93GSQDkEDnnnptQ0K0h0p9Iv9I0u0ilBFlqFjF5SQzn7hIxmJtxBBDEE8EgkA8Na+EoPDet3KzWNrrUMenwXN19teNZWjkLAssrEAOroxBJGVbGcqKt2Pi6XWJrzw1p93O2iy2kk8l1ePHPc20KDEsShWbeSCoVm5GT97AwAdAvjCNdT0//AELUJfEdvF9k1LTobctJtK5D7uEKhxkMSBhm6E4rN0PVtU0Wx0W2m0/SluNNtXtmjk1qFHk3bedoBCklASMnHQE1DB4YvY9RtLfWLeCHwnqCmGK0srxzM0z42yTsMNMxGckFgMg4wCa0LbwTotpommaPPZw3A0TW1TfJGpMqyHcobjn5ZY8g8Er0xigCXwjfRadd6VZa5C+mXFvbz+T9pCiGSSaXeVhkBKsFUAAAgkHpwQLvxAsdC8Qm0tNbdRpunTJd3UiglmLZWOFdoJJcnJA5wBx8wIy7nQDpeg+KtWsbx0tRcTGDSpYkks/k+UqY2GFDSBiSCoAIPQVlPph0zxDoukSwQaTrtyw1C1TTXkl0+WZAQVlhPKccblOMYOeAKAMnxXpB0G3vPE3huxuvDxlWKwtbK3gVJ7uPJMz7CC0ZxjBwCApJ6iuv8Fy2fhHw9G8Mk9/4T1Em5hvXXfJaMww6TgD7uQfmxgHIbAwaXRfFU2q6jBbafHE3ia481bue9jcwWyxNhkiIIDruIACtz1Y5GKzB4xT4b+NrjRdZm+122pD7dIbS1MSWsrkgiNCxLK2ASASc5OCSQADtLHSLi0tlm8Ja3bf2c/zR2twn2i3UHtGysGUe2SB2ArG8aN4rudKi0RH0j7bqcixQpEshYbSGaTJOAqgZJIPYdSKTWLzwBBbNqlzp15AZRuaW0sLq3ds85Yoq8+7GuasNRFnf6xrnhvRkPnWa21hcXGrW7mPqWd98xIJYrwT0QZxk4ANTTddl0bxNrVpNqTa14nnuINOtVkh2KoEYkJOwBUjBeQ4zkhDyTyJPGGj3sN/p2p+IZ7HxAscght9KhtzavJI5xlDuYuQM5ViFxkkjrUXhvy/FrwWnhueTS7bQ7nzri+kWOee5unjZWOQWQ5DsSxLEkgAAAGorMaUfDGnX2qaJDr2t6jZS6nPdXWyN1jXBO2Qj5SoZQFXGAM8daAK194S1CTxXY266Npfhqz1S1lslm0/a8yEq0jKcKo3FY8ZGQAWAJJyL813aaPZ2HjiWyjW70bfperwWyjGFPlh1HYghSD/ccjOAKvXWiS3niLw7Y3GrX0+jTRS3lkWkZbqCZUAAMgIYqFkJGcnIwSQcVlP4IkvtM8T2n21m1i2vWee4JCJfxMqSqkwAx04zgFSCRwSCAdHp1zNBpFuunXFtceI9fY3M1wjCVIl/ifIOCkakIozgkAdyantjaaVqM1l4f0w6vrKkC9vp5QoVsdJZsE5wc7FBwCOAMVj6lZrFFpPiTwsYtIPiFre1vCsA3bJTlZFHAEik4zyCGJIOAa17S2muLifw7oE7aXpmmMIru6UBrmaVlDELuBxkMCZCCSScdM0Ac5qGn3nh3Ur+bxVc6W2geJpVtbq0tgUFtKy7VkUt97OAGbAOcNjiuCbQrLRdRl0C48FXer3+nBRcS2cJYSxCUNHIQDn54zIrccFVI5zjuvEEfg2S+vdH/wCEfm1zVoIz5smoXgiYZHUSzuGI90BA7YrJ0zxJqKeGvC3jCCBJ9Tt520G9iecKt0rNhCZMEHDBTkZGS3bNAGXp3iDwbpJa3WEaXJCiFhLa+RNgOYi2SMlinlTADPzRnuTVjxhqWl+NtItNM0C8spdWvdQFpdWSgkLJkhrmLByFIUksMhlbnkV2Wn+OvDni7XB4e1/w9H/aAdkSOWOO9jypw3zKDt57kAe9d5p+g6PpBJ03SbGzJGCba3SMn/vkCgDmfiLoyv8ADe4it4jK+lrFdQpzz5JDEceqhh+NcxoN42n/ABW0/UZo7hLDX9L8i2lmuVnUyqQ4VXzuI25xuAJJ/AekeJpltvCurzuu5Y7KZioGcgIa8Vt/DZ1HwZpSaY1pBrtjZxXsU0tsrOhRQ/yzwnCg4wBKCTz6ZoA97MEJOTEmT/siiuR0v4i6Vf6RZXksNwkk8CSsqwsQpZQSAcc9aKAPjOiiigAooooAKKKKACiiigAooooA9M+EV34LsbnVrnxglo6qkf2VbiAy85YthQDk4A6ivWT8XbCyg8nw94O1KWwiLruWJbaMbU8xsLg4AXnkDqPUV5F8J9I1PU7vV30zSrXUWjt1ikjmuBEVWQkNtJUjJQOvJGN2ecYr1Ea41lrMdr4l0S702WcyOFlCtHcMzmR41lB2YOIlJYgBIiDjOKAO28LePLfxJdx2U+lajpV7LbC6ihvYwvmx5wWQg84OMggHBBxiqfxVHn+GLKzjiEt1dapax2ykLy4cN0YgH5VbqQPXiuJ1vULkfFTwhrEky/ap7oWwj83GYXBU+XHjPljJzI2CxOQAAK6bxzKl98QdA0uWO1litrWa88q5RpQ8jERriJQTIwG4gZAxkkjFAGPp9pc+Lvilb211LLNY6Aq3NwftoljeZv8AVJsRVjUrgkgAnjBJ795448XQ+EdFWfCS391ILeyt2bAklY4GfRRnJPpx1IrgPAPibRPCGk+KLvXLuG2vDrEoa3EKxzOAqhVWJScd8AEgEnJ71j6jqFzrV3ceK9dimiM8ItdK0kKsrtb3ET+VIig5MhkUEkcgAjoBgA6TVNV+JSSSrFq/h+NUUxiS2t2aMXAIHkyFyTGTn5WIwTgHBIzzGsXKapMLXxT49bUY2xJHp9tGYllKnEkE0cIMiMDwCQRweCRg9fD4Q8R+LL+a714R6HpV05aawtZy1zMjKoMUsi4XYSC2ACRuIyMk13+laDo+gWwh0vTbWzjUYPlRhSR6k9T9TQB5TZeH9W8VGwXStEm8PQWiIItauofs94ijrGqIw8wEYG5gARnIJ5J4uvjpuq2Hh/xVZ6P4va5YRwFALa+jB7t/CATwCGXJ7Uus+PtY8aa3c6N4RvvsGl25Mc2owKstxKcHmOMsGKZGCVBbuPSs210v+z7+DS9AjSbxBqJZZLuaXziicGWSRiMsgIGY5UDZIAJxQBJdp4Ukt7fw+viLXvCn2N1nj0zVI8wKQ25SSQQy7gSMuQccV03leOYr678Q2x0XxM5tTDp5tZ/IS3ycsQpyGLELn58/KBkCofETad8MPC8GkaWtvNretSiEXF6oKs3AaWQYICKCMKBgZHGM1xi6MfDWj2aWNyI9ZuXaNL+yuCFyAWkkZ4nKuiAFtrRqcADJxQB2WkeLYdN8O2nh23eb/hNdSYi4huIWR47h+ZJXyACqjJGCQQoA46Q67fQ6h4Zfw/ozsfCultFZaxqEUgDuvyhljJBBIBDOx7EgZJNZnivSPFN7Z6Fp2riw1C71G58uzvXY2l3a4BdkZkBHzRgg4GATyDgE9L4Y0i30SK+0jS9MXTLiMCW90a7uTPBdRsNvmxyEEjIBUnGMjDKOCACxfWC6X4ffSr/UpEuNHga80jVHcK5VVIAYnglc7WBGGVlPUnGbH4on1HS70rpsSWOs26NJdalc/Y4RK0QjdV3Au44XBVSCScGsM2b30Zk0HTFuJZw95omiXUqmG1RQqNdMCSDub7iglecjqSNzR/DM8ep6V4xtLS61TWDYvb3sF3chTFdfKCct/qwCJFIUEAEYGOSAYWh6Xr8/ihYdV0+z1+dLKKeGZbxRaSgEpHIQVyQgB4Ck7nY91x1upeFxbn+0rm8L+LJ8Lp72SeWsG0HCIhJBi+Y7y2cg9jtA5fS7rXbfUNLa/tr4au9xqdrJbaa0ZJ3SRyhdzZVUBY/NkEDHOcg9e0erG1urLT/C13aandxGF9RvL1ZkiUgjd5m5nbGchQByB0HNAHB2U8Gj+J7jW7RrC01KFZLQPcNI2kSStgloJgMRNkYKHjkgEda37S/8WxaFosT6E99dx6kbzULuK8txDKpZjlW35/iXAIAAUDpiuhkvdGsbo6XLNGuneGLOJp1Cg5lkUpGAoByQoJAwcmRccisbRvB+g6fp+gQ61oFgs8elXF1fGW3VmYr5QJfg5b5ye+DnFAFNLm+1i717TDfvew6lKZH0nRzHI0EbKEIluW+SMNtJIGTnOCeaY+kajpK/ZUsV0uyt2hS9jtJJLu9lsmyCVnPzBQwIKoMgAkEZGdPRNVsPB1j4UfVJYtPtb+2uIVMrALGhcSwKzdBtQlck9ScmqXi7x9c6hcaXceBtK1DVry2ujuuYrRzbSwkEPHvwAwJCnI4BUHPHIBevb7wxpnhu2ku7ppvDdpJssdQsZHWayfBHlMYyGHBwD1IOGGcEt8NDTrBV8W61Je3M96TFolvdAz3S2+SVCgAsWbJYnkhSATgE1zfibTdZ13VEHiC0Xwtp+t+VHsWSO6SS8jJMXnEAbQwOOM52gEitzw9r8PhufUbjxrcrJ4s877PDbRJlnhOPKW2QdVY5yRjnhiCOADs/7R8V3vz2eh2VlCen9o3Z80/VI1YD/vo1yFz4cLeJ7yLxRpOkxWviGFbdLqzJcx3Kg7TllBVmU8dQTGO553L3zDbx3fi/XZNNWc/utMsrgxbfRS6fvJWx12kDPQdzy3i+y8NT+FLsWNv4oLjayzSDUPLiwwJcmU7QFAJyemM0AOl1B28R6VYO91o+twX40281KygAguUELMnLKYySTH8rAkFiBkc1f1C2uvCvh200fWNCsNb0u2nWK11G8lVY4VY4VplKkrtztLKCCMdMmrH9mR+HbrU7DxHdX2raPq6RSpeGBmZJUAUhjEMhiqxkOACSp5yATjwwTeI9Xjm0L+0fEWl2rtHc22vXDRW4kGCCoI3Fh0w8bDnOQRQBsanp+tW3ijQdQg1i3udbvGltSHjP2aC3MbSEpGDuOGjX5icscAkDAHK6tLrWn+A/Esz3q/2dHqcvnXbYM+qN5ixlABgIuQVJBzhQAAASZ9X8NXlj4jtdTsdH/wCEamkhe1tbmK7WWKO4bJRdnRUf5lPAGWU4BrS0e/i1C58LqdMltvC9rI1oguT88l9sIzICOVDeYuT1kOfTIBevob7xPq+mwX8Vz4fsVjE+itE8bk3CjIMgUlRhclUBIILc5AA0I1bU9VSO6um0PxdbpsMkQBivIwT8wVuJY+SccMhJGR1LorW2toD4P1eWSCENv0e9DbSVB3IqseBLGcADuoBwfmAfqk1zDpzW3ivw6dYtYBuF7ZQrICAPvGIkMjY67dw9COgAMvxvqev6bpMUGqeFtH8QC6mW0gZHIHmPwMxOpwCfRj05I61y0jeENJ1PSfB+uX1kmk+HbY3F5HKcrc3knYLyWCgscc43AHpU+lSw2trdeNrQalcR3MotPDGm39w8hErjaXAYnAJ3Y5yFB55GH2XgbxVBawJd+HNGvZ0PmSzz3+XmkaUSSsSYjguVjX/ZVSATkkAF5fih4U0OKWHwj4YuryNV8x2sbMQRkbgmSSAT8xA6Hk4rZ0P4pxajdxW+p+HtU0pZbw2K3MwVoPPBI2FwRzkEcAjPGa5a71HWvD0ccviLQLjT4w0Ykv4StzbowZm8xguSAZZDIQR/Ao5qn8SrpL/wQGtn+zWOnsrWYacKPMU8MzAEySnJwi5C5JYg8AA9g8U3MFp4T1i4uSPIjspS+e42Hj8eleK3Fnq934f8K+CRLNDPqMUaypFeKht4lUNIzRRgEgrnl2OTngGu6+IeptcfDzSoZTCjaxcWkMvnPsQK2JHBIBwCFIPB69DWDolzpuh/Fm8vtWkstMtotFBtd1qLOMqZBuKhjuY/KRkgE88AYyAevWtlb2VnBaW8KJBBGscagfdVRgD8hRXm0vx38JJK6pFqcqqxAkS1O1h6jPODRQB8o0UUUAFTpbTyQS3CRO0MRUSSBcqm7OMntnBxmoK7/wCHV7qkNlq9loFrY3OsXRhCR3nlkGJd5bYshClgxQ4OTjJA4JABxVlp97qU/wBnsLO4upsFvLgiMjYHU4AJxUMkbxSNHIjI6khlYYIPuK9rGny6RpHiH7DqOlW3irUoLa1Nlp0yo6zly0iRgcAmMLkKcbiQMHAPn/jvT7jTbjSYdTfOtGxDagGfe6uXfZvOT83l7Ac88DPWgDkKKKKACiiigD1L4L6v4i0fUtVn0XSYdRtFgWS/WSURsqLuI2sTjcctgYOcdute76i2l/E34evcaVPuMiedaSY+e3uE5XI7MGwCO4JxkEGvBvg5qdla6hqVhqEkn2e+WJBbRof9LkBbbCX6KDkk5wCAQTjIPofh3WbLwV8R7qOe7ig0fWbaS6kYBY7eOdMkmIcEx7QVDEAMRxnGAAZJ12EXXgnW7/bbW73wmuWchQGKsrOd2ZHw2QZGYDIwoxzXVXL33izxLH4js9Rfw9oLWo09L+ZVSe8DSbh5Ib7gJAAYjJycD0xdL0q2jsTqOuQsNDuNSeXRdG2BJtRkeQmIygnJADAKpwAOTx1q3a6hrXi/S7LxVpN/F4ibWonsdjN9jt7SPa7BDna5IDAnBOcZIxggDks/D3hbxzr9lYaPNqerwXdhJZRJG08/Cq8r7znbnJySQCSPTjvvBvgy5sZLbXPEjRTazDarbW0a4MdjAowEU92IJ3N3yQOOvKaf8QLTSfFPitLLS7jUtavtXEEUcKbU2oqQxl5TwqlgQOvJ7ZrH1LSda8XI2oeKtSkkjeMTw6dayFIEja1eRVxgEuHMYySe/JyMAHu9ve2t3uFtcwzbDhvLcNt+uDxSX9ml/p1zZyFhHcRNExXqAwIOPzrxDxDYr8NdU0vUvCsCrDpcQt9RjiRme+HDSFgARhFwxY8KXUe1d9458YX+g6Bo2t6LHFdwXV1GrxyA/vY5EYqAQCQxO0Dg8kDBzQBxfg7XNSj0+98I61a2Es2isIDFJpykCMcK7M0qKcjkHAJHJPOa2vhy1u/j7xM7vDLcNbWpt2jlEgWEbgyqRJIAAwGQGIBI6DAD9a0HTfiRpEPinQsWHiSyysbzxrujlXrDMjAjIJxkjIyCOODzGmeIr0zWnjFbe7+16Y0llq1oxkkIhB/efMzBcrgOFRQM8GgDodTeG9+NN01zcazBcabp0Qs30+285VDli5cbH6kgDjnB54FU7tP7X+Kmn20knnfZ7ZD5kmnm3lYvMGKvkDcPLhcZAA5I9aXx1t07x5ofiuyV7uy1S3FgzW91NEQ5JaNswgs2QSAMEcduobZXZ0/4nQ3F+XiimhhaNriWZj8rSREBpQCcG4UkDgDJ9aANT4raI3iDXfBdgl9LaGTUJB5kD4lXCbiy+hG3r2JFWPHPhueLwXqE15cW+r/Y7OUxS6hABcJlSMiSPAPY4K8kDJrJ1ODWfFvxgS/8N6vYxWnh+2WOSaT98heXcWUKpGSVAB5GMDnNXdc8SnWPseiDVba6ivr+G1kaHS5kilUOC6pMXKHhSDgnvj1ABVtLDTI/GWrNcWerWs+leRaWGr2scjRRRLAmYyFBXGSxIYEENyRgVr+I9RstK0p9R163vVtblVB1TQ7qRVkJACsyqwIJGAD8w6DPQVnaZ4m1bQ7J9ZeS31az1TWZY49Pskzcx7mYKAxYBiNoypAwCeeKpiyvm1HUZ7GC6vZRcid7KOQyafpcmQSQuR58ynLFF4DE9CASAU9D0W+0ttNt7ax1C+XSLWSe98m7MVxHNcyB9ox/rHWNRlcgHI5zgVqaxfalfavounaBrPiewe9uhG7XtptRogpZypkQMGAHGeDn8asaZbPo+mPNcaXqV9IJJJ/+Eg0iSNnuFZiwd13BmIBAKFWAxwCMCuZf4gpp/jjVNQ1XW7XU4tNsAmjJDHt+0POFbJxxuwAGPAAPAGSKAOu1fwv8P9Ou/Iu4bltWfbM0ltJcTXjMDkSN5ZLZyMgkYyOOlcV4hmbTfFOl3OleIvEMmo30i6bHba3YSGIxSsAw3MqAgcHAySQMnvXp0WfCGk2sEcI1HX9UnG8F9huZyMu7NglUVQexAAAAyRnn/G1/4rW20ye+0HTotLs9Strq7uIbwzPEiSqSwBRSPcjJwTx3oAwNE8N6NrE8ll43177avh67ewsrSV1gjMahcOwByxIwOWPAxzXcar8QPDXhiySCFo44okCxJgQxBRwNuRlgMY+RWI9K4Hxlb6TpfjrXf7Q1Cewa9ENxCY76SANvhkjJwrAHEkcZJIPBOeOKv6RL4G0KaK/jfTUK3KM8s8okle2uYxgszEk7XIBJPAU56kkApa3cap45uCNVtZ7TSY2G4Ohjkk4DhIlJ3JuUMQ7DLlQoCgkVNFq0mu+Gk1rT3s9V8U+DZGAkPzreQFSN/BydyHd1yGUjgmor2bU/Gt7J4Z8MRiWztw1jfaw+Hga3VlaEowPzSpyBjvk9DkXNbmsPAPifRYvD9qZF0bTz/bARhuazZwMsOrOGJk46c54NAHR6Nf2tvHbT2Pl6/wCKdRgWeaVZAFiUjPzPz5UQJwFAJOOATk1Jr32hrc2nijxhbafHdIytY6bCqvIpGCoL73YYyCVUZ9BXP6LM/hLxBc+EvDmnoJdcnbULLUiAYUtmUEk85YoQwC9DlSTyc9d4duPD1l4lutE07zLvVhC097fuN7OwZVKtIerZYfKOFAxgcCgDLs7/AMQaO2haVpt7p+p6VqKMthf3YkEiqqb1WTbw5Kg4Pyk7TkA8nEfwsL2K58X6kslxrlpdOmqW1szQIYkyCsYQhiQhWRSSS3AJwQBsIvk+BoLpQMWHiBmsyD/yz+3NGAPYozAexFdHAot/iDexpjy73TkmlXtvRym4j1KsB7hB6UAcrP4Q8P23iCynuLdtR0XWVEcL3NxJKbafaWQqzMSA4yAc5DAYPPGponh8WcWr+EdZuHvLe5le8s5mOJGjYgtyP40k+YsMcup6k4pg4+Dccysf9DYS2zdwIrjMWPwVQPaq3xbGp3tx4e07w2wHiH7RJcW5EwjZY1jIY5JAwSV4OQcEEEZoA27i9/s6zbSvGdqLywPypqZg8yGVR0MygHy3HGTjaTyCOg5PXbDStT1LR9H8K+M7zZqFx5dzY22pmeMWwVmcjJJj4XAAIBzjFbFo3j5NUTR7nxBpwuzpy3UUkum5Er7isiZVwPlJTkDkODiuIgg8af2zaeIxpmjSa7qNnc29tbWIMT7B1uWY5U4O0DJBIYDqeAC1rmoWHijxE8Om6udPg0NvsmmQ2U4jkBXAldQD1YlIUGDncTggGr1tF8RNCMcum+Il1oeZHbC0v1DfaZhkzbH4KomCNxPJU+wN/wANeFPCHi3wZbRaY3761jEM8dyvmPFKB8wYMdyHdk5jZcnkGs/7XrPgLUEs9SmZtNEIgivWUySadDuyzIAv71T8oLEblIXcCANwB3vh7xNYeMbTUtIvrZIdQtS1tqOnO4fbnIOCOGU84P4HBryHXra50zwd4l0OTe8mkubOOZyAzQEB4lLNkn5CMRxgZ2lmPPOnrt3Z+G/E2ieLdMU20McsdrcRoF2tauSWaZycvM24PtBJAAJwea09St4PEXi/XtXs7+Oz8MC3jg1TVcANKU3B4oH9GBVWYD+HAJzQBa1TVZfHFlo1n4WeJItKmhvLnWLhR9mtmRD8q5++w3ZIHAwMkdsXxFo3hfSPFGg6jq9zJrov7G6ke5uQbg3blUEYRFBAHzHaFGBnPbNVPFV8U0wx6n4a1C28EjTz/ZNrYkgPKSoR5yp+UkHIDE9eQTmta51+w8FeL/D1rqcNxcy6X4aSCK3tYTLI1w7KCFxwDtiPUjg0AdBYeLtfh062iHw81RgkSKCXiUnAHYnI+lFEPjH4gXkEd1aeBIzbTKJIi+oIGKEZXIx1wRRQB8k0UUUAFdZ4VtfDiaVqureIIbq9+yNEkNjbyiIyby2WZsEhRtA4HVh61yqgswXueOa9K8G6f410O91/TNEGlpcwiJbt7iaBlTJJXaXJU55B6446GgDE1fU/C154bnXRPDdzpt0tzCTcSXRuBtw/yglRtycH3x7VyLuzsWdizE5JJyTXqXjZ/GbeCZv+Ejn0qS0+2weWLMwFt+2Tk+V2xnr7Y715VQAUUUUAFFFFAHoHw9h0qXRPEg1S5EYKW6RxA5ebczAhF/icHDLgZyoGQCc+pR20Wm6LZ+I/iLsjeMLJa6QcGS6uAMCSQcZbsqYCxg4PevGPBuonRJ31W2upLW/inhiSZYFlWOJ94kYhgQCPlwcg549cfQHh/wAF2d7L4i0LxY/9rarJIJY9QnAMrWzKBG0ZOdm1gwIXABA6gjIBFZ+Gr/xVreqXHiUrZ+J7CaK50oRyl4bSHhoyqggOCysrEjJI4xxU/wDamtx+NdXvNeudOmsvDWmNdItkrKolkU/e3EkMEVxjPRwe9Xrx7g6bBDr3gzUtV1CxQwi6tDFtmA43A+YGAYAEqRwTjBxmuOsLK3l+GF6LOybS28Ua4tqICxLQJ5gRlJbnhY5CQR3I6UAanhbwZ4wutC08z31hpEZjSYyJEbm4ZzK05Y5wqksw4O7G0VtN8KnFvGkfi7W1liVRGx8koCqqqkrsGQAqjGeQOTya5Sx8Kanrk0Av/F+us08sQQxXZjEUexpX+UAAfIYgDjgt6YFW44vFvhbSbHUNG1671UyrPfS2Oqyh0WyUFlO4jcHwVHXBJ6AA0AVriG8UX/hbxKEF8bdmE6s6xXkWSfMyOQob5mQEs8jAcKAKNP13+3vhVp3g+ONn8SecNOSBso9s0DAiZu6hFCk9yePWt3U9UtPiF4I/t7S1NlrmhSfahDcKBJbyKNzIdwxhlBIJGMgHgg457S7S7TSrnWdDsIrTxB4pza6RFIQrQWqKS0zt3dgCxY5JJXrnFAFK4Pg+z199Ig0G/wDFGpNdRWt7qdxeukLXT8AMQSASQR04wQM4q9baFYXWsarph+EunrcabGkkijVTmVXztMfyANnaepGCMHB4rXtfCaap4a8rQooNF8SWsEVvqNlKgCSvGQys23kncNyyjOQSDnkDt7gi3+I2nMqndeaZOsuP+mUkRUn6eY4/GgDymBfDcuk6ZqH/AAqiUWmoXAt7dheKR5hYqAQWBXJBAJAHHXpV6103RdQ1PU7DSfhZAdR0tVM8Wo3SooZhlQANwbOCR0BHcZrppQq/DrVwoISLWJ/sm0dxekoF9t/Ax2roEiW3+JUrR4H23Sg0wA5JilwpP1ErD8B6UAeN6Xb3h1jSdUvvCOj3Nt4iY2cdnDutUspIWdSrEAg5AJIIOdpGOOe68X6omn2UNo2r2X9o2FzbSWmk2NuFXeHXbGzHOMg4B+QAHJGK000b+2dB8T2UMhhki1eaexnQcwzqFcMPpIWz6gkd64PTfiHoep/D/U9DWzuLbUZwiTG6VpnmeZgJJyVHJUEsOhwBgAACgDTuNHvNHh1zUdO0Sy0MWzm51IpdNLezQMC7+VIRtjyNwAGclSAQQDXc6HqWk+GrOTQ7q7t7RLGLzoWmcJ5ls2Sr89SDlWPJJGT94Vw/ijx7oUt9qVjt1R9LvNJ+zT6klm5WKRS2wkEAkYc5PQnA9SIotFb4seJrW78TWP8AZkGhW8YuLRmAkuHkG8En+GMgAgdeSOOoAJrdfGHibxhLqPg2W50Hwzdxq0txdxLtnck5lihYEgsCMHABxknJxVTUrOx8F6nF4cNq3iPQ7uRmuLG4iVrmOUqZJHiZQCxChWI4I3KAcnjs/FHxAsdHtIrPR2We5lIht/IQOGboFiUcO2cDP3V7nOFPIWWkXNvDPr+vXMKXLAwOszkJYPu3FJWxkM52sJlIw23AKkAgEl1cHwjO3j3Tru98S6GdOa3tFeUyNYsWUgEk52EjBJG4Ywc9a0fE+ieG/wDhHGsdWn1jUtWuLM3LvYvcTs5P/LTYpKKpY8AgDAwOlc94al1nRfCM/jsubm11K9YXWkSIpW5hZxEjIFUAyk5JOAHB5A4q7bX1r8P9H1TxV4UszqujaqsewM5B0+RcjZIDyIxu+qkYPBBoAH1K2v7LwR4mvtG/teO9tJNJvbbykkZpRgrhXIGQ0cnBIPPHJwdHS2+DdzMkwsNIsrhlDiHUYDAQDyCFkAUg9QRkelZOotrlp4T8Wxai9kuraTqFvrltJZDMIV2BBVTzj5JCQTkknJOcmtrcOu+GLInxXpsGo6SItseoWUW5UxDcKokQ8qS0qDPQBRzmgD0DV/iF4a0LTkttFnsb+9f5LWxsZFK7j0LFeI0HUscACvNdVtNWFpbWBumOveIdREVzcBWHlsRiRV5+QohaMqcqyFWXvjp/DUGh6nqMyaX9hjkuUcRSQou3zUSKVRgcEETSBh3AYGs7wXG3in4qrqkMBWy0CF4i7qd4lYFBC56OYwXAYclQuSTQBYudFm08/wDCCnV5LLUrdHuPDOqGTa7RkENAxHPGSPcYIGVAo8PQeOvC1tqBm8IG4vEsorTTja3MRiXbuLs5LBiWZgxOCSRjjrUOv6Bqnjr4la1YGxQ6XGILV7+eHP2aNQJG8hif9YzOwPBwMEntVXxto+k6dfW3hXwc+qJrUhV5zbX9w62kIIyzIGOSQegBOMnHTIBsqPGTeGvDOkWvg+5H2GW3m1B7i8gRZjGQxC4Yk7nG7JAwQODni1cj4g3Goa9fReGraG4vLRLSyZtRQ/Z1G4ljgfMSXzxgcAHOM1j+GPCyeIfFFyovNXj0nTAYrlhqlz+/uDg7PmCsuwZJx3IGetZWh2Eus3mr6pCmtf8ACP8Amstg7Xl3IGSPKswKSZOSCcEcdAaANzVtX1nQ/CPh7QdT8MX9tYWr2qanex7Z4lhiZSxXyyWwSozkDAyOc0uhzt4ul1vXNd0bUbW2vZY20jUo4C7QQx5MbKACynJLE42ncQTiqfhHwxqGveDtQ1BfEOuwTzWxMUKXrPGGdWkVSr7srseIdc8HnnNL4F1vQ9R0Gxbw/qWsafrVlAkc8UqXN3AxAA2suSu04ONpUgYxjGKANhNT1hPFd1PaTjxVNDpW2F7QRw/ZGdzwVL4YsUXOCCAmMc1Np2u2x1+FtOtbmNtH0r7A9veRmKSWaRoxCgU84yjZYDHI5IBxzmvPHew+I28Q3srWxu9Omu5LK1kt2S3yygKr5YgMOTyTk45wK1JorlbibUxFcx6FphVtM1q4VmurT5Bv3K4DyQEkg55AJI4AIALFr8N9Q8FSDxB4ZvmutX2u2o294+2G9ByzAYHyENyuc+565fo3xJ8LfEHQbiG8nh0m+gQyFLt1xGQPvoxwGHYjjIJBGDzLqPi+W+t4NO1W3HlXaqsA06bemrszBQkUnARecsCQ2CewJOZ48sJG8MquqWWl6Ja6cjXFhZ2bieeadVOxQvlgBASCwAOe5ABJAMfw3or6noVzLqcw0rwbCzFLiZtkksDYYwR5AKx7iwL/AHnAUDgca1vbweNdT0myv9PFr4EubaVNJtopDH50yEANIFIxlRIyrk9MnnIqLwjaz61rOha34p1Bda0/WLBltYrmFRDbXQI3RiMfLkqHAOMnaR1xnqE0keH7S58PyeG7zVtDE32iwFt5ZMAJLGM7nUja2SpGeGxnigDK1az8S22o+GPB82padc6XcTBZNkbLcvBAA/zkkjBCqpIAySB3qrZ6b4k8U+Ite1XTPsFtZS6kYo7y53SsVhjeABIwAMBmdslhzjg4OYvD8lhbeLfFHiJPDk+iNoulhTHdsS8jNufeeSAMRgDBOc9fTA0zwzqv9lQ28/ibWUt47V5BaW1x5QMzRxNtBUZOZpwOck4PXrQB6TD4O8UwQRxJ49uAqKFAGmxYAAx60Vkx/DPSookjuPG/iHz0AWTGrFRuHXjtz2ooA+UKKKKACur8L6VotxpOqalr+o3sFhayQx/ZbFVaWd33bfvEKAArdfXt3yvDuq2+i61DfXOn22oQxq+ba5jDxuShAyD1wSD+HFdva+L9Rl0O/v4fBnhRLWJ4Y5Y/7LI8zfuKkDOCAUJ5PGRigDE1d/Bg8MXKeHjrf2xrmEuNQ2bTGBJnHlnGckdfw71xtd/q+tXuueBL9/8AhGdH022gvbffNYWhgZmKygA9Qw69wQSOueOAoAvaTHay6xYx3zbbR7iNZzu24QsN3PbjPNekxeEdK0eyVtX8OyXus6tc+Xp+kW16xa3gA5mLKTnJ6ZyMc9M4820dbNtbsBqBxZG4jFwfSPcN36Zrr9F8LajqfxKjs4tAuYLKS8IlgCvshtyxB+cnoFyQc8kDFAHG6lbJZand2scgkSGZ41cdGCsQD+OM1Uq1qEEVrqV1bwy+dFFM6JIP41DEA/iOaq0AerfB5LqbT/FVtHJpCWk9rHFc/wBpSNGu07wCGGcEZJ5HpgjFezeE5bfxx4M0bU7TUfI1nT4/IN3DhijrhXVlP3kbAOD1BBGCAR5B8DYLme71oWvh+w1aQJCc3sqxpFy/QlWOT7DtyRxXqGvaVZeIdMe+hsLjRdU0e6i/tKC1fy3e3GGkTdGRvUxkspHORgAHIoA1dc1rX9DtHF94o8K2bY+WW4geNj9EMpyfpn6V5bp+saT4g0bwp4f1vVolI1K8uLyeRGSN9xkMcgLADaxcgHjk4I7V2dx4O0KC08U+HxpdjdI1g2pWd2Yw00YfftRpDljtZcqc5I4PTJ1dXtLKO/162uLaKS1v/Doktl2grHHAGDIB2AMqMCO5PpQBUl+EOnpb+dol79nkZSBLC7QllYYIDRnaARjkq30rJvm8VeGfMi1S2fV7OURIwKqszxRZYRRsuEkBOcghXILYU9tHwx4C8XeCdFg/sDXobmV4d1xp2pxsYFkxn92VIKnPHoepqha/FPW9Q8F3uqa74Ikk05QUFzBMBGzbtoO1juADY+Zc4PTkUAZVlo0firxgrabqs72+rwmbWnjOEmtlbksCMqXcFFAwRGhJySa3NNWLxx4zu9Rtru60u70lFXw+Nm2KWAAq0oQgCRGPBwRhdoyDisbw9daW7p4K0fWI5NS13fNq2tW6YUkqWMUJwAWwcZ6AEnGTgdbbabDoGn2XhzxPcMkFlhdI12LMRQAYCMw4jcDA5O1xgcnIoAuapfWM8kMviOG+8P6tbgrHqVsGMWOp2ygFSh67ZAPcZ5qv/bVtp08mqpryeJdUksmg0+30+3DuQDuYkRkjBbywWIUAKO5ro4rTxRFHiDW9NvYSMo9zZNvI92SQKfqFH0rjdK16y0K98QTzLpR8WXmoLZQ2VsdvmEBFj4xuC5YszH1PoBQBPod/ayt4Z8N3sd1YrZwC8uPt8DQfabhMEKu4ANhy0hwTyo9617fWIYItT8Z3SsYLhUtdNgA+eaNSdm0dS0sjEgem0nvjD8daVqtxp8J8Tazo93pUDefcWChrJpsdAH3OxwegABJwM1Tu7PRLfQE1Vvhxq0VtawiVZZr1YZo8DoP33mDHQcAnsKAOk+0NoWh6d4aedT4i1t5GcJyY2kLPNKf9lctjPUgD1xejNj4Strfw14Y0tJ77ywywK21UXp5s8mCQCQeSCWIIAODjz7R5ptCvptejhOrJokbx6s/nma4EsigkIWJJSEBU5PIaQjJBrtNPlvNJ0izitRDdeJteY3Msr8omQCzsQf8AVxqVVQDz8oGMk0AR+J/Cd9rfh27j8R+MJLO3dCZUtI44LdR1wxbLMOOcsAfQVweoxprvhrw54vtdQ8iHyF0rVrhIkk8sqSscpDqQAJCCSADtYYIrurzSdITUPskunS+K9eUB5TeMHSHPILbv3cQPZVXJAyAetYstnc+Ftcl/t6y0ez8L+IitlNZWUh2wzFSFcgqoO4AqSoA4UkcZoAwbLWPB3hya+lvb8z3DYgvTcXHnXTRs20FWznfFIpGFABXawBIBrRjg1X4p/Zoktb3TtLG6HUNTYeUNTgVgUCoQDk4JyRhckAnOKj0e70vwVd3Hh688Dpf6jpbKy39raW4aaBmAilJYglskKSMgEEkjmulk+JWqXP7rSvBt95u4Rg31xFAgYsyAHBYn5lK8Dg4BxkZAI/iF9nhTw14YsoljhWdbqSNN6rFbwLwWZAWRdxQbh0xk8A1zPhe18R6hrvinV9HnhlEVysE2mXIj8q/TZlizR5USEEYcDBzyBkgW7Ky1O6urvxVrM0Et3OFVmiLRpZRgBo1Vx88alWB8wEgFmDKQCRpfCGSWXV/GZYzeX/aCYM0caP5mwhgRH8pIwORwevegDF0pPB114Q8aXnh6WWzub/S5Bc6Vct80DIkg+UE5K5OOCQMYGOg19O8Yav4ZsrC28VLFqOhz2lv/AMTKOPDxiVGIEseSGUBGBYY4wSDk1wes+SPhS9tfW6vd2d7JZpcTQtHLA3nkOI5VBDAgnKMVPJPIxXX6z4c1z4ewxaxp95ceINBtEDXFheENLGqwvGpVscxgSElcDA9eSADpZfhP4NvbuLVdOgmsJiRLFcaZdNGOR95cEqMj0GMV1ehaFp/h3TE07ToTHCpLMzMWeRyclmY8sx7k15X4b1+78HWhGkI+veFVhMn7mUGW2KJG0zR5wGjDSAbQcg5A6EV6b4f8UaX4mtpJdPlfzIW2T28yGOaBv7roeR/I9iaAMT4n+Krjwr4UL6eT/ad7KLW0wpYqzAkvtAJOACeAeccV5/Y6KfC/hi7vJ7O5mnETXN3M1vLKJ5ApJdhcWo29MnDj8a9G8e+H9U1WwtdS8PXAg17S3aWzLgFX3LtdCGBHI6E9CByASa87svFw8SxyaVrk9ymoorJc2V2wRhtA3sylVhjQEnBYSMR0GaAO78Laa2kfCGGOBf8ASpdNe6cqMFpZELk8e7YH0Fcb4K0vSrf4fadHNP4UWWe3LnzUNxIxYE8jcCWAOCAOCCB0rofh/r/9kPH4N1eQxyxjOlTS7gLq37KCwBLLyoyBuABAxXMWss/wz8S33hyaae10y+nNxpc0EUKK4bO5HlcEjacAAAseMDkAgHVfB26EnhT7Of8AWRJFuGMEbV8nBB5BzAeKqeAtb8P+HtU8XaI2oWdjFb6w8kMM8qxthlUEDOMgMCBjJAwPSsPQ/Edt4C8TXUeqvJFZ3kjTRu8chaRXbJADKrMUkYnhRkSsRnbgyeGJUi8beI72z8IXk+u30ovLJr0RxeVbt8oclm3KC4YnaCcYHXigDY8Z6lHcX9/cWkM5H/CP3UiNNA6LLJDJFIm3cBuxknI4weDW3dNeapYW1r4lWBzcoJG0bTgZGm74dyQNgOM8KucAsQcHAv8AS0txDrED3uuXeniQ6tdPcN5EsTKVnjjQnbkDkKgAGwAnPBoeFp4dDudO8I6VbuYNZaSZPEAlBE9uAzBVPUOFATBI2nLAc0AWNR0S707Vlju00zSNE1aWO3s7K2k/e2l4ATFcKAAobcAGCnGCMk4JOn4Z1HWr3T0vNO0iw0+RVKXl5qk5ke6lRirqrKxYKGVuWPGQAuOawfGE2h3NnZ6hZ+FZLnOppb2erwzA+XIk2zLHJbBZSACCDxyCRWhoMby3OtpbeFtI1lW1a72SGeJZIj5zAiUMCQueQVycHp6gE3gyXTNcTxF4Rvrmxea3vmuohp05KxhyJAYnwDlJC3IHBwOnFdDeSeJNCtS1z4l0H7Oq8XGo2zRNjH8RWQKT7gD6VmXWm22uwzeHNT0K30PVngeawurNgygjALRyKqsGUlcqQCQe4rI0XwzpNhe6BdXdhDqL6qn9nXseoD7RLFcxpIzurPnglGVgMDAUjpggHG6j4yEtl45ttS12zvptRW0gtrmxhfyWUMRIikgjKoSeSc8nJrt7X4U+GdQsReaRfW0pYAx3MOSAQQQQ0bq2QQD97irOn6XZ6DH4ba3tUjsLXWby0MIHR5ZJI43wepAAX1Ac+lZmgfDrU7PWNY8R+HNc/s2abUZxBZGPdavGshXbIoIPJBwR0GMUAVp/g3fzXEkr6leyO7lmc6vINxJ6/wCoP8z9TRW/Lf8AxcSV1TSfC0iqxAfzJhuHrjdRQB8lUUUUAFeleE/FPi1fBerW+l69eI2nCA21pFbmVihZgwVv4QMgkEHI6YANea13HgJPEC22qz6Rr1toVmFiW7vbicxAEklFBAJycNwB0zmgCv4h1rxtrujGXX7rUJbC3lUAXCGNfMYNjAwATgN9Oa4+vUPGK6vF4QvYdY8cW+tvHfwoLaKWSbYwWTJJZRtPpjIOD6CvL6ANa00/Tp9Phml1VYbp7xYGtjCSViIBMu7IBAORt4PvXo0L6VBANCPxgvP7PVvJaCOynCFc42hskBce+Me1efaTodxPqlh9usb5dPlmj82VIGP7okZYHHoSRXfPqkNj4gNpY/CyzaGC58uIyRTySMFbAJYnBJxnOMfhQB5hfxRwahcwwndFHKyoc5yATjnvxVWrmpEnVbwshRjM5KnqDuPBxxVOgD2L4Bppdxq2rWl5dtbXsyRfY5I5DG4YbyQp6E4wdpyCAcggGvRNV8QX+m65e3Njc2kdzbbbfV7u6A/s91UEoxIIYTDcQY1yT0PGDXhvgqPUrnTdYtdNm/fTG3SO2jlVJppdzGMx5jY5UgsSCuADk46et65P4Sh8OLJrui6heaRbRrDDBbMymxuMfvFmUOCspbJ3tkEEYPUsAN0yGTUNfOkpb+Ib3UItNOGuJzpUMluD8qRhQXZctgbycZOSOaihuY0sbHVp/wC2NCGqqLO3e9mGoQuqsWETggSxhip+6QCAc9BXoumvIt54btLqdH1eOCWWb5wzrbFcbWIA3HcYgSAASpIAAIqpa32m2kXhq5u54I7Jrq7W2eQgKshMnlnJ4HyBx9SKAG3fi6/utJEUumyfZ5mEc+p6VMt3AkX8bDb+8BIyBlOCQSeOaenXY1/StL1GSZH00y7tK0KxjC7/ACmwhlYknCkBjwqqQM5IGcK5jttQ1zxbd6Bp96+tTPGbLWrZjHaKAiD/AFmQvykNuzu3c9egl09NVv8AVJX8M6jYQ6pNpzpe3/2Yi31C4U4zAMgEqc7nBIOQSpxwAP0zw5J4a1M6F4jvLWXT9dvHubGS1JjewvcltsZPIBH3WGOVwQN2D2v2jxFpsTW2paYuu2n3ftFmUWVl6fvInIUn1Ksc8/KOlec+J4tEj8BPp2m6U2n+KLhIYl+1adILiebcpZUlIAZyedwY9M+47vwvp3jy30eOPWda01rjYAoktGmdf951dAx+g/E9aAMLVp/C2m2M9zF4Bv8AzSQiRGw8iNpGYKoJyF5YgZAJ54BqCHwwdGudM0jw/a2D6zbT/wBqardTRnyYyUkVQQpB4LEogIwFySM5K+KtP8XRSWcmu3g1CxN/EIBpky2fkylsRs4eNyRuIH38AkccZrJ/s7xA2o6lDGt//Z9vexya5YzXaT3N3GUGGXbGrFSoAwHIIUgAkEEA1dAvfDmpaLaNPp9n4o8S6pGbq7ijWKSRCQCQxc4iRchQCRggAAnNQ2mgaZeaXNpXjCXVbC8kLyW1ncXjC3gUEsqwOGKvtAHUlgM4AGK09O1LS9X8R6hH4NvtPSdtARbMxoAsTeZJ1XGRjKEgjuDg1Dq22x8E+ILfxPDdtBdptsrC7uhdXLyiMZKEEnG4AgD7uCSADgAGboHiKzsn8MSWWnTvczaV9m1TT44dknlqgdZhG2N4BMmCM5DkDJ4qPw1rl7o1lresWdidU0uGY6fpt6JcLa26ncokUgMI1MnzMMnC4IAANdTcXtjPrXgq1huIJdYRPPeSIj5bbySJCfRWJXA9RkdDVjw9e6ZJf+KdUtJIV8OFwJJMgxPOqt57r/skFAT0JUkZzkgDbKSVC2geG7qOS5VjLqusMoYJI3LEdmlPUKeFGM8AA5t3YeDr9JoV0q/8W3igxvL81wQ3cCViI4znspGOw7VR8PxR3XwxsrbTLeU2VteSf2ppsIMdy0Bdj5ZHB3bShI4LLkA8jPVXc93rHhyD/hA9U06COMhWQRgbVH8A4IiYdMMhx6DFAHmeo2+uar4QuDfwXemeJfD8LJKCVeS60yTIbnkOQoPP95M8E8bcngPxf/ZwvdB8X2+qR3EZmiF/b7Sd7JIGDqTzujUjIwCSe5qtpNpq1l8X5LjVEvobWDR5ZL+W/vluUkgJIAUqqhRuBOCASATgd8jwvoXjqXR9Pl8NLqunQu0kkU15qSG38kkmLEJDHAUjIwM9sZzQBv6fD8ULXV/MXwxpgt3eVZo5L1TG8TtuVeCSArGQg4PDkY4Fdr8PfCC+C/Cyac3lNdSSvPctFnYXY9FyAcAAAZ9M966DTI7+LTYE1OeGe9VQJpIIzGjN3IBJI/P8B0F2gDxLxNpsbeI/GHhstelNTiivYIre8jjDSMMFdkhCtl48kj5sevGPQPh9rj+IvBtub6Mrf2ubK/iccrKnytke4wfxrktUd4vij4jjkuoITPY2gjW5ZXjeP5wV8oozSfMCcKVPXkZqz8NVtJfFviO40G1az0VVigmj8hoVe8XJdljY5QAEAg+ooAyrrRv+EB1vULWRdTbw3ewN9geytTP9iZpFeWIgA4DFRgkEYwDnBrm7jxP4TvNYgvpb3UPD16EeSS4gjdJ1UNnbI2D5ssh55BVRkDJ5r6FlnigXdNKka+rMB/OsW+8V+FbeN1vdd0gKB8yy3UZ/DBNAHn2k+KvFQtEn03xBpOvwjDTR3MYRoQQSkZli4eUjA2qpOc9sE2dY8Q+CvEtl5PjHSp9I1qJVaOCWFhdAn7rQOoywz0A7jkDFcrrs2k634ptbj4XxLZaokjRz6qsflWZyjDy/mUrvOeCAM9ieCOj0i70vS47rTYymieO5Yxm518+e8zHjKy5AZTjA24A/unBFAGFpfhrxRqOm3tnPpjSaZ5xltNQ1yb7HKuMkSyLGDK8gJ4LMAABwO1zUbjSZrSC28W/EOXWG0/bMINGthuVk6O0iBmBHc5XqenNUNV0bVo77w5L4n0nW767gv0k1HUDMbmxaLeckRLkKMY42rjByDXV+Htd8Ht8RNUn0GextLOPTgNRbaIUlcPlSoOPujcGOMHco7UAZmnapZ2Oo6NbeF/CX9lrrZIt9d1RRKSNhYEYYsWIHAZhknp1qlf8Awwm1DU9T03WdVkn8QXebzS9YYsolRQA8DqDhQCQcDs2RwCK6OWS0b4fXSQxsINQvifDkA4kVmYGJl7gCTdIOOEODxxXTeId51Dwrb7lfUft4kDIMfIsT+a3spBxjPVl60Acm2s6frNxNY67ay6dZ6VBHbtoizCM3N0x4VUUgyR4Chc/KdxJHBxBrnhyS1vLaySSyaTUkuL7U9NuV32kYVc7lVcNGQSF3qQSeSCcitXX9Q8OJ8ZfDq3SRtqEVtNEZiMiORyvlKx6BiBJjv8w9RSzmKAeNLW7ULqszPPl1O6eyCqAEPdQNy4HRiSRzkgHN211b+CdBsba+0rWrGxkuUuLbTmlgngaYEOqrMSGRQwDHfgDB5651PCuo6X/wiemt4l0A20chd/7SbbJGsruxfdIp3RHeWHIAB4zmt/xjdabaa5pN1qxT+zjYX0ZLn5WZljIUepKhwAOTzXC6ZFq8fhRbvS5302zS2VNTuLu6jW2uJQoWTbG8MmWyNpYABmB4PJoA6fxPPdabfWVlY3E93q0CPc6XMhWRo4yArJcliB5RBH7wkEhepYAty11qj6lqenyXUuo38moagptm0ZTa2CXAXyyRcMDIxwCCVwDg8Hmk8PW9raeEEsNc0rUZNqtcanp4Ym6ngOPs7ggqZIkUbSq4wRyBgit/w1caY/gAtpaPa6Ve3qx6Da3MgaWOcScMDkkKJAW2kkgKxJIOAAY86vZXOo3V3p2vafHoF0JpLuLVPtsayOufNEUoG8bXySACAcjB6dPofiq/sdBFvaWUfiAW8OYptOmVZWOMgywSEMjE8nbuznIHar+uzWsdv4plm2fZIpbRrwN0KgIZM/WPA/CsbxLc+H9X8f6RK9jPriw2sio2lgyG3kLKQZGQjAI6ZIwQTjkEAEBl8FynzNQ1TWPtr/NcYguY/wB4fvfLt+XnPHaiuWez+LCOyReLtOgjU4WKe/jMiDsrHB+YdDyee5ooA8BooooAv6Np39r65Yab5gi+2XEcAkIztLsFzjIzjOa6XSNKhuptb0iw1nTpbMQGdnvopog3lBiZECAkMoLYB6hiMHoMPwtcw2fi7Rbq5kEcEN/BJK56KqupJ/AA12tp4g+GOn3NxLbaP4kJnhlgbdPFja6lWIHY4J70Ac3qvjCW+8NpoS2GnRKHja4vIY2Et0Y1KozEnsCewJJJ7muWrudavvBEnhK6g8O2epW1611C7G/lRyyBZAdu3pgkZ47iuGoA7HTfG/jG4fTNF0vW7y2VRHZ28MMxRck4XPPXkDPsK7a/1HV9PvDp1n8Wbm816J9hs2jlELyg48sSklSSeBkAE8EivJNNnitdUtJ5vMMUUyO/lttbaGBOCOh44PavRl1TwNceK1u7HwhrV3LLe74M3oVJXL5X5dvAJxxnjOKAPNLqWSe7mmmGJJHZnAGMEnJ4+tQV3+t6z4PurLXltNDv7DWLnndc3AlUP5ysyhQo2nAPPYAjvXAUAe0fs/Tm21DXXg01728MMSw7FAC5LZ3OfuKeM9SccAkYr0e/0G71HxLem2voLS+t4kk1bUnQvFKrKSsHkEhWQKASWOQCMHJJHlHwVkt4JdcuLjXb/RwkcR861UMpBL53hkZcDsSAR2PWvT7zVbKTRF0/wXa3viFLm9jk1a7iJkaSIEGQNI2AzsqhQoOADjAGKAMSw02Sw1eW6g0O+stRk0wXE9xoV8iAW7EhcxXAxGTsJCqSRjjFFvpL2EVvpqaLcbLC2N/ayeIrtblYoiwEjxwxfIzLuBwzAjIx1q/rHjawsbLxLq2tLe6fqlzayWFhYzWckbCIbthLEbWLMxYkHAGAOhJuan4s8OyX+uX0+q2zwjQUi01UlBMiy7t6qAclyyxgjGQAOnNAHQ6l4IjlsVubq5vNZu7dxN5F3LmCbHVBCMRgEEgccHBJODnKt2h0jw3YsJ7a88IPMv2W8iJiuNODsQjE8ghWYKTwQDyCAap+FdQ+JHi7QLdJEg8NwxwhDezW5lnuWAwGWNiAgwMknqSMe1dPhDofh7w7dW+q6/rF3HPnbDHOY42lPIKxA4ZsgH5iRxk4AJABT8M+L7jW9WfVtYv7bVZrO6kstC0+02q9zJ0M7DJ2/L/EcAAsQBxnubzT3FkdQ8Y+IHghOAbW0uGtrdCeiblIkkJ6cnB7KOlcF4cn/tiN/E+mWNo3jDQ0aO4jjXbFqdvgqHTA6kKQGAIyuOQRjo9B1V72DTvEOqQrqniLUofN0/TrYjZZQnvknCdRukPJPygHABAIr/SvBl/ZSQW+geICsi4S5t7O7G09QwzjcQQCMgjIFZSeK49c1LRoo9S/snxXDdnTL3zbc5uIyjlSYmILKzBDjgqWIyM5Pe3M3iO3tzc6lrmhaVDjkGBpAn/bR5FB+u0fSuW8OnSfEJ17w9JrEV5em8GoWmoxRBdzEIQ8Y6ExuuDgkEYyeTQAiXGh6L4bsdK8Wm60/UdHXyIr20imQuoG1XjljByGXGVJ65BHAqCwvr3wfoF5qWp+F9TvZ23JFqc0qy3E0RbCCXcxaLgjgfLxyFJwLXjbUPEekQWkOpapbXtjckw3NnpNkFvJUKnJVZGcMOu7AGBzn0y5D4VTw9a28Wm+M4dMv1WCKNLiTypd4woy0hQZzgcgEmgBfD2j6BqNnotnGBDYaLp7T6zKhMJadlCGOUj5jgLIWUngBQeDil8PeGf7Zn1Gz1C4u9Oh1Fv7X0i0gYJDGmQFJjAG5l2xkqSVww4Byar2FlPqniK18N+IftenrqUDtf2TbVN7JEoEcu5SR8ycuAT80QB4PPY2FtPqumx6Pc3ZtvE2hMPKuSuS4wVWXGRujkXhhnruHBANAD7dbjVrl9S05obHxRYgW+oWr58q4A5AcDnaR8yOASASOeRWT4l1nw3Zxi68U+HdR0rUJP3aXNqh3yPjhUnhOST2DEe4FXNZ1K0RUufE+m6jo1/bAqNV09WkjA74kQE7O5WRQPY9a56x1RtVvrzxLqXiGXVvCGggTWzyWqRGe6AIHAUb9uQAcAFmGBkE0AYWo6dqei+FZvD8LzXviDVozqOrvd3BaSO0U4SBn5JZiQgx1LNjggjYutd+JF/avaW76PoqKWhjW1QzOCsqQqoJJABZmAIHHlt6Vm6LqPgDV5bi/wDFmtTw+Ib6fzLqLz7m2WDa3yRHG0fu8AZPQgnPSu3Hwz8PXcCXehaxqtpuAMc9pqLTI2NxBw5YHBZiMdCSRg80Actp3h3xLJfy3rfEPWItPt/MnnuJHDL5WcRYU5UFgrOeCApXjJrtvhj4pufFPhZnv939o2c721yXj8tmI5ViuBjKkceoNcdbrq+i63P4Z1ac3DW6i7s7iGIL9oj4Achv3atGAF3MdqAKQpJBGh8IICmq+MZUMQie+jCiKd5lJ2ElvMcBmJ3Ak4AJzjjFAGH4o1aSC98aeJxDdeTGyaXazR3/ANmBaNSGA2kOxEjE4GAQOp5r0nwdo1v4M8C2tvdShDDCbm9nkbrIw3SMxPocjJ7AV4hfPZy/DWaFTHPrTyy3klvaw+fKoWZmd5pGB8lcKThcZwCSckV1WtX+u+NobaPxK0XhvwrLGsk0aXAMsqtDJIjs+MBcx424BzwQTjABHHfQ/EDU9Q8R6rp9ve6LFbSDSba4kYIqxyIszuB0YhgQcHAxwcVUg0TQ49aGl+HfCUWt3dqHhuYZ41RYEZvmjnlI2lxjKSKSxAwQep6Hw94Uu/HESarrK3OmaLLCoj02F/Ka5/dojvIQAQjCNcKCMgAnHf0vSNF03QLFbHSrKK0tlyQkS4BPck9Sfc5NAHAWngjxdexRx3ep6ZpEKIYmFlAZ5pYsELHKzgI+0HAJUkEZ7nNTUZ/h34I0+50ZrRNd1SRcTwuouJ5MDAMkhGI1HHUgAcgV0PxJ1jVLWysNE0a5hsb/AFmVrdL64k2JAoXcxzgneRwuOc9OQK5K18If8ILpLXctoS8CNPLewyiRgduWIlVVlUHGSrJIvrxk0AYWmS694cjlcajqOl3d3NjT9DtHF0Iwc4XypsblOAfMjYgZOcY46DxFqF/pdlpw8f8Ahrw1q8l+6wxrbOEuA5ABADgg4zgsGUDI5GRnovhxoAjsP+Er1ONVv9QTzIVZQq2lseVUAAKpYfM2AASegxXE6DJH448Rah4pvrmwaSSXyNMt575reWCFSRlVKsj7gckEEE5zjPABeS08LJremXVhqWo+HNbsAwsdP17zPs4BUqVUOcbSDj5G4yDg8VVvPFvi/TtX1gXWhzXni9oxHZJZwmW2tbM8mVSMltzA5zySoBxjFXfBfh+y8U+INSur+1hltIZGiFu0UYURo5VRhQF+aSORmKgZ8tOxIOb4ePhS08beJ9GOp3uh38F35en3C3kiRpGoACYJ2EBtxCsCCGwKAOph06x0Cyub63hvNZ8PajbZ1KVX8yZLhCS07KxBBIJyF5UoMAYrO1LxBe6pJpdzBcKsFmbiCHUBbmeTUwybT5EAwWIUAsxwgIJGQM1a1i8uLme0sdU06502e4jlk1a9tpF+zXlrCmZNuCSdwKgEgMoYjPYweGrA6tqdr4mlt72w8T2W/wAvSrkeXG1oQVWOJSAMBCMMDw33sA0AZ+kWel+IPDNlquo2uoSaZ9sjs7a71LVJDdRuXESyRxqBHGVYjpzwSenO14JtNBt9FsPtf9oatqdg0luIHElwtvJG7ISqAbIySMhjg4I5rO8dwaAG0+3e38QJfXOpJJYwGN0t453l3MSCAhySxPJPJxgVf0QRXD6vLPruvWkX9qXZtksreRYUHnNlt4jKuSQeCSB0x1oAv+J7C+1q+01JIhb6zdGT+zv3xX+zkUAvKzIcvIcqNoO3kDJGSeX1DQHi12zluNNTUr2PUDBZ6jpUosriSdU8x3MTZiYjawZiRkqQQa6GPXtFtBf3Og6ld+KPFbQNFErDe6dwpVVVYowwBJwMkAEk4qhZ6/HYSaKniay1HRbLRoFuC1xaOwubwq6u3mIGXADMcZBYvk4xggGc+mSfa5/P0vXbpNavhbXLavqcS2rTqSFDrACxGV2gAAEgDOMV2Gk+EhrfhuMalqNzEJYmj+xaaxtba2YZUqqIQW2kEZYkEjoOlc7pvjPQNYh8Ozy6pbxac2rXlzOs0qo0coeSSEPk5C4JIPQkKM9qZ4Y8T+Mte1LVtP8ADlhDHo76hNLb61dxtsSJnLEKvHmEkkjBwM84oA1PsWk2n+jXHwyt7iaL93JNDZQbJGHBZc84J5GaKmk8C+OZJXf/AIWNONzE4XTowB9BuooA+SaKKKACt3RtFs9U0u+mm1a1sLmGSJIFuSQku4SbhkA4I2jk4AzyRkVhV2vgWW3S31GIeFP+Ej1Jmie1gMTssQAfczbeSvKgqSAePSgDnNS0PUdJCPd2xWGT/VTowkik/wB2RSVb8DWbXp2ua58RLOwF7q+nDT9HhlWFdOlsVitW3BjtERGGGFPJyRwQQea4TWrvTr/UPtGm6b/Z8TIu+ASmRQ/8RUnkKT0BJI9aAItHkWLWrCRrU3apcRsbfGfNAYHbjvnp+NeuaZ8QdSbxNeWmseNZdNtrW8Wdf9FdVeIZ3weXtBVgNoAIwCGwTwT5BptwLTU7W5LSIIZkk3RnDDBByDzg8cGvQtS1n4cXniOXUrvSPEUi3NwZjM1xGFmBY5faVzgkHIB45AxjAAPPL+RJtRupY5JZY3lZlkl++wJOC3uep96q1a1B4ZNSungx5LTMU2rgbdxxgduKq0AXLPU7/Ti/2G+ubXzBh/IlZNwHTOCM9TU1pr2sWEAgtNVvreHJIjhuHRQT1OAcVm0UAak3iPXLiJop9a1GSNuGR7p2B+oJ5qjb3M1pOk9vNJDKhyskbFWU+xHIqGigDX/4SrxF/wBB/VP/AAMk/wAahn13V7o5uNVvpvlKZkuHb5T1HJ6HuKzqKAL9vrGp2ro9tqV3CyJ5SmOdl2pnO0EHgZ5x0zSWuralYyPJZ6hdW7ycO0MzIW5zyQRnqevrVGigC7catqV7Mk11qF1PLHwjyzMzL9CTkU641nVLzyhdaleTiI5j82dm2e4yePwqhRQBet9W1G1uGubfULqKdl2tLHMysR6Eg5xTzrmrG0FodUvfswxiE3DbODn7ucdazqKANOTX9Zmmiml1e/kmgyYpHuXLR5GCVOcjI44pT4h1trhbhtY1AzquxZDcuWCnqAc5A4HFZdFAGufFPiEgg67qZB4IN5If61WXVdRSzFmt/dC1Vg4gEzbAc5BC5xnPPTrVGigC1Jf3k4kE13PIJBh98hO75i3OTz8xJ57nNS2etarp0RisdTvLWMnJSCdkGfoCKoUUAaFzrWq3kscl1qd5O8f3GknZivIPBJ45A6egqSLxBrUDyyQ6vfxvK26VkuXBc9Mkg8nHGTWXRQBcg1O/tYp4be9uYop8iZI5WVZB/tAHB6nr606fVtRuYVhuNQupYlCgJJMzKAuduAT2ycemTjrVGigDXHinxCOBr2qY/wCvyT/Gj/hKvEX/AEH9U/8AAyT/ABrIooA0LzWdU1KJYr7Ury6jVtwWednAOMZAJODz+tVGnmZQrSuVAwAWOMVFRQBqjxJroh8ga1qIh27PLF0+0LjGMZxjHGKZ/burm0W0Oq332dV2iH7Q+wAdABnGKzaKANG11vVrGLy7PVL22TGNsNw6DGSegI7k/mabHrOpwyTyR6leI9wczss7Ay/7xB569/WqFFAF1dTv0Qot9cqhVk2iZsbWxuGM9DgZHfFWH8R65I0bPrOoM0Ryha6clD0yDng4JHHrWVRQBqy+I9bn2CbWdQk2MHXddOdrDoRk8Edj2pYvEmuQRiODWtRiTkhUunUZJyTgH1JP1JrJooA0Ytd1eC4luYtVvo55v9bIlw4aTHTcQcn8amPinxCcg67qhHfN5J/jWRRQBJJI8sheR2Z2OSzHJJ9zWmvijxAqhRrupgDgAXcgA/WsiigDX/4SrxF/0H9U/wDAyT/GisiigAooooAK7/wG883hrxFYweI7TQvMNvI1xPM0bPgsNoKgkjnJA7gcEE44Cus8KweHItJ1XVdes7jUXtGiSGxhn8kMHLAuzAEgDAHA6sPUUAaXiHS7i08JXEsnja11tBdwg21vO8oUlZMOd4BGMEDHXJz2rga7LWNV8N6h4YuY9G8KS6XOtzCz3BvGuRt2yDaSyjaScHjrg+lcbQB1eqaxcQeDNB0mARraywSzzERrudzPIMFsZ4CLwD3960/D/wARdS0TS9I0jStNsLmSGRiTdWqys0jSZAU5yBjb0wck+xrL8Oa9osVj/ZPifSpb7Tt5khltpNk9sxADFSeGU4GVPGRkc5z0SeKfAPhhGu/Cui6ldawBi3utVdSls399VU4LDtkccHPagDhvEUEVr4m1W3gx5MV5MkeOm0OQP0xWZT3dpHZ3Ys7HJJ5JJ60ygAooooAKKKKACiiigAooooAKKKcqlmAUEk9AOc0ASzW81uVE8MkTMoZQ6lcg9xnt706Syu4baO5ltZo4JeEleMhW+hIwfwrtNbs47v4oaVo2oMRBB9gsJmc4yFjjVySfU7ufemapcyf8I94g/tIGKe/1mLyYnGNgiEu8gdgodF44wQO1AHHfYbv7F9s+yz/Zd23zvLOzPpu6Z6022tbi9uEtrWCSeeQ4SOJCzMfQAcmvRNc2aZe+OI3XZZJBFptlGeFbEsZj2jv8kbNkeuT155nwBafavHejbkZo4blbiQAZ+WP5z+imgDKvNG1TT4fNvdNvLWPf5e+eBkG7BO3JA5wDx14qkiNI4VRlmIAHvXW6ZHpuv+JdK09Yr9lvb8vfRyTAlgT1BCjkAuTxnk8js+48JhZfDVt9mkiuNYuW3AkkIhlEap7MNrE9xkA4IxQBgv4e1SO9vbR7XE9lGZrhN6/u0AyTnODwR0JNZqozsFVSzE4AAznNdvNLcXM3jvWPIlDSHyNpQgxiWcNyO2EjYc+tcudNjkeyhtL2G6numCmKKOQNGxwADuUAnJxwT0oAS80TVdOiMl7pl7bRggFprdkAJGRyQOo5FZ1eh+L9ZtrXXPFOl2a3E097NHYnzUCJFHC6hdoySxPlqASFwCeOeKniHwomheHtQaSxfz7bU1s1uWLBmCq29iucBS2zbkZxnk9aAOHors/EGj6RpNr/AGfFGj6jJNELecTk74WjVi7KCQuWI2jAIBOQSAaqeMNJstCuhpdvaTrPa3EsUl3IGUThdoGFbjghjkADDAc4yQDDs9Mv9R802Njc3XlLuk8iJn2D1OBwPc1Fc2txZztBdQSQTL96ORCrD6g8iuo8Jwxx+G/FN/O00cItYrTzIow5UySqTgEgfdjYHkcGpnj/AOEsee+gtJZLXR7G3toomb5pcEIrSMPuj7zHnAAAyOtAHFgZOB1owcfSvR7bSIdI8f6/Bb6T5g0uxlZIP3hAfasYcZO7BZ8gknAII6A1ixKsHw3u7pdNjY3uo+WZhvxEsaAgA5x1lGM5zjnOBgA5Giuu8QaHaaLFbaetlcS3rPCzXQLKrhowxRcjB5YYIHAGSTnAvTaT4em+I8Hh+0s5BYQX7RXM5uGJkjUjd2OAoVjkckdxxgA4Oiuv1HRdNWDw5JbWd4kF8ZZJZFBd5IllK5VegYBWOBnAIyT1OV4msYdO1p7WBbcQrHG0b27syyKyhlf5iSCQQSDjByMCgDFooooAKKKKACiiigAooooAKKKKACiiigAr0P4Yz+IYf7TGjavYaTbStBFcXV6AcMWbYiKQdzHDcY6A8iuAiieWVI41Lu5CqqjJJPAAr1HTvBms+D7aa71Sz03VbeAx3d1pkGoAXVuY8kSYUHG0MwIwwwTkcAgAxtb8U+KvEvgy7Gq3kd3Z21/EsgEKxtE5WTaQVABDYcHPQqPWuDrs/EXjOwv9DOi6Foa6XZTXP2q5eSczSzyYIGWwMKMnAA/rnjKACiiigAooooAKKKKACiiigAooooAKKKKACpIpZIZBJE7I6nIZTgg/Wo6KAJp7me5k8yeaSV+m92LH8zS3F1cXcvm3U8s8mMbpHLHA7ZPaoKKAJ5ru4uVjWeeWVY12oHcsFHoM9BRBdXFqxa2nlhYjBMblSR+FQUUATR3E8dwLiOaRJgdwkViGB9cjnNOF3ch4XFxKGh/1RDnKc5+U9uTnjvWjP4a1G3vtPs3EBm1BFktgsykOrHCnOcDJyBms25gNrcSQO0btG20mNw6kj0IJBH0oATz5vLkjEr7JCGddxwxGcEjv1NJDNLBKJIZXjkHRkYgj6EVcfSpI9Bi1czQmGS4a2WME79yqGJIxjGCO+eRWdQBLNNLPKZJpXkkPVnYkn8TVuDUpPt8M2oGW+gWdZpoJZWxNyMgnOckDGetZ9aWmaLd6tHdyW3khLSISzGSVUCJuC7uSMjLKPxFAGr4i8TjWLCOzzPOkUvmQzXYUywpjAiUqPue3TgYA5zgXN3cXkvm3VxLPJgLvlcscDoMnPHtU2paZdaRetZ3sYjmCq2FYMGVgGVgQcEEEEEHvVGgCwl5dR27W6XMywN96MOQp9cjoabHPLFHIscrosg2yKrEBgDnBA68gVDWl/ZEg0KPVzPAIXuTbCPJ3hgu4k8YwAR0OeRxQBW+2XPmTSG4lMkwKytvOZAeSGOckE4qMzy/Zxb+Y5hDF/L3fKGwATj14A/Cres6XLo2qS2E0sUkkQQs0RJX5lDY5AORnB47Vn0AWZb26mMTS3UzmFQsReQnywOgXngDtio1mkSQyrI4kIILBjk5BB59wTmoqKALCXlzFJC6XEyvAMRMrkFBkn5Tnjkk8etRySPLI0kjs7sSWZjkk+5qOigAooooAKKKKACiiigAooooAKKKKACiiigDZ8JAHxloYJ2j+0LfJPb94tdtY6R4J03WRqK/E5JbhWYsX0W4YOWBDZIPOQTnnnNeY5xSUAdxrfhvwrp3hO5vNG8Trrd2LqFCBZyW5hQrIScMfmyQo9se9cPS54pKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAO11G/tY/CPh/UI7iNtSSzmsBGGy0YEzneR2+SQAe5yOlXbHSLCx8PTXHm6ZI0miNc4maJ5GmeQJgbslCikEAYJIOM9B57RQB2SW0Uml+DtPaW28q5uZJpg0gwrPKsZ3jOQAsannAwfrVjxLpumJbwPC1hAb3WbjzRAyH7NCCoRRgn5QCxJHHQdRXC0UAdX42hgsdS/sy2s7SCC1ll8p4njd5Iyw2l2TORgDG4k8k5wQBa8LxPB4R167W1t7mS4NvbJBNJjzE3l3OAwbAMaAkEDkVxVFAHok/9m6j451SaJ7S5hTSmKrcSoYo5RAFCRsSFKxscKeeE4zwajSz0bTvGmlRSrY3OnW2lpcTEFWEzCFpWyM43FiVAbnhcjoK8/ooA70abpmpaz4TspI7GCKazMk4hkVS5LyOqO2chiuxcsc859KNYtvtOneEdLkuNPUTzyvL9lMarGzyiMhtuM4EY+Y5yDwSBmuCooA9EgjttR8R+KdYkitLq6t7oS29tJNF5bI0pLHLEqwVQFPXhsjoCOCuH825lkCom5y22MYUZPQD09PaoKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKK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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ANSWER: Patty's Patterns - Advanced Series Vol. 1 & 2: 100 Full-Page Patterns Value Bundle\n" + ] + } + ], + "source": [ + "from IPython.display import display\n", + "\n", + "# display a random training example\n", + "print('QUESTION:', ds_train.iloc[198]['question'])\n", + "display(ds_train.iloc[198]['image'])\n", + "print('ANSWER:', ds_train.iloc[198]['answer'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure successful fine-tuning of our model, it’s crucial to properly structure the training data. Correctly formatting the data helps avoid validation errors during training and ensures the model can effectively learn from both text and image inputs. The good news is, this process is quite straightforward.\n", + "\n", + "Each example in the training dataset should be a conversation in the same format as the **Chat Completions API**. Specifically, this means structuring the data as a series of **messages**, where each message includes a `role` (such as \"user\" or \"assistant\") and the `content` of the message.\n", + "\n", + "Since we are working with both text and images for vision fine-tuning, we’ll construct these messages to include both content types. For each training sample, the question about the image is presented as a user message, and the corresponding answer is provided as an assistant message.\n", + "\n", + "Images can be included in one of two ways:\n", + "* As **HTTP URLs**, referencing the location of the image.\n", + "* As **data URLs** containing the image encoded in **base64**.\n", + "\n", + "Here’s an example of how the message format should look:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "{\n", + " \"messages\": \n", + " [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"Use the image to answer the question.\"\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"What is the title of this book?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": \"data:image/jpeg;base64,\"}}\n", + " ]\n", + " }\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by defining the **system instructions** for our model. These instructions provide the model with important context, guiding how it should behave when processing the training data. Clear and concise system instructions are particularly useful to make sure the model reasons well on both text and images." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "SYSTEM_PROMPT = \"\"\"\n", + "Generate an answer to the question based on the image of the book provided.\n", + "Questions will include both open-ended questions and binary \"yes/no\" questions.\n", + "The questions will inquire about the title, author, edition, year and genre of the book in the image.\n", + "\n", + "You will read the question and examine the corresponding image to provide an accurate answer.\n", + "\n", + "# Steps\n", + "\n", + "1. **Read the Question:** Carefully analyze the question to understand what information is being asked.\n", + "2. **Examine the Image:**\n", + " - **Identify Relevant Bounding Boxes (if applicable):** For questions requiring specific details like the title or author, focus on the relevant areas or bounding boxes within the image to extract the necessary text. There may be multiple relevant bounding boxes in the image, so be sure to consider all relevant areas.\n", + " - **Analyze the Whole Image:** For questions that need general reasoning (e.g., \"Is this book related to Children's Books?\"), consider the entire image, including title, graphics, colors, and overall design elements.\n", + "3. **Formulate a Reasoned Answer:**\n", + " - For binary questions (yes/no), use evidence from the image to support your answer.\n", + " - For open-ended questions, provide the exact text from the image or a concise phrase that best describes the requested information.\n", + "\n", + "# Output Format\n", + "\n", + "- Provide your answer in a concise and clear manner. Always return the final conclusion only, no additional text or reasoning.\n", + "- If the question is binary, answer with \"Yes\" or \"No.\"\n", + "- For open-ended questions requesting specific details (e.g., title, author), return the exact text from the image.\n", + "- For questions about general attributes like \"genre,\" return a single word or phrase that best describes it.\n", + "\n", + "# Notes\n", + "\n", + "- Always prioritize accuracy and clarity in your responses.\n", + "- If multiple authors are listed, return the first author listed.\n", + "- If the information is not present in the image, try to reason about the question using the information you can gather from the image e.g. if the author is not listed, use the title and genre to find the author.\n", + "- Ensure reasoning steps logically lead to the conclusions before stating your final answer.\n", + "\n", + "# Examples\n", + "You will be provided with examples of questions and corresponding images of book covers, along with the reasoning and conclusion for each example. Use these examples to guide your reasoning process.\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To ensure our images are properly formatted for vision fine-tuning, they must be in **base64 format** and either **RGB or RGBA**. This ensures the model can accurately process the images during training. Below is a function that handles the encoding of images, while also converting them to the correct format if necessary.\n", + "\n", + "This function allows us to control the quality of the image encoding, which can be useful if we want to reduce the size of the file. 100 is the highest quality, and 1 is the lowest. The maximum file size for a fine-tuning job is 1GB, but we are unlikely to see improvements with a very large amount of training data. Nevertheless, we can use the `quality` parameter to reduce the size of the file if needed to accomodate file size limits." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import base64\n", + "\n", + "def encode_image(image, quality=100):\n", + " if image.mode != 'RGB':\n", + " image = image.convert('RGB') # Convert to RGB\n", + " buffered = BytesIO()\n", + " image.save(buffered, format=\"JPEG\", quality=quality) \n", + " return base64.b64encode(buffered.getvalue()).decode(\"utf-8\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will also include **Few-Shot examples** from the training set as user and assistant messages to help guide the model's reasoning process.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "FEW_SHOT_EXAMPLES = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 1:**\\n\\n**Question:** Who wrote this book?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train.iloc[286]['image'], quality=50)}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Reasoning:** The cover clearly displays two authors' names, 'Evelyn M. Thomson' and 'Orlen N. Johnson,' at the bottom of the cover, with Evelyn M. Thomson listed first. Typically, the first-listed author is considered the primary author or main contributor.\\n\\n**Conclusion:** Evelyn Thomson\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 2:**\\n\\n**Question:** What is the title of this book?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train.iloc[22]['image'], quality=50)}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** The cover prominently displays the title across the top and center of the image. The full title reads, 'Computer Systems: An Integrated Approach to Architecture and Operating Systems,' with each component of the title clearly separated and formatted to stand out.\\n\\n**Conclusion:** Computer Systems: An Integrated Approach to Architecture and Operating Systems\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 3:**\\n\\n**Question:** Is this book related to Children's Books?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train.iloc[492]['image'], quality=50)}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** The cover illustration features a whimsical mermaid holding a red shoe, with gentle, child-friendly artwork that suggests it is targeted toward a young audience. Additionally, the style and imagery are typical of children's literature.\\n\\n**Conclusion:** Yes\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 4:**\\n\\n**Question:** Is this book related to History?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train.iloc[68]['image'], quality=50)}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** The title 'Oliver Wendell Holmes, Jr.: Civil War Soldier, Supreme Court Justice' clearly indicates that this book focuses on the life of Oliver Wendell Holmes, Jr., providing a biographical account rather than a general historical analysis. Although it references historical elements (Civil War, Supreme Court), the primary focus is on the individual rather than historical events as a whole.\\n\\n**Conclusion:** No\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 5:**\\n\\n**Question:** What is the genre of this book?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train.iloc[42]['image'], quality=50)}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** The cover prominently features an image of a train station and the title 'Railway Depots, Stations & Terminals,' which directly suggests a focus on railway infrastructure. This points to the book being related to topics within Engineering & Transportation.\\n\\n**Conclusion:** Engineering & Transportation\"}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Example 6:**\\n\\n**Question:** What type of book is this?\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(ds_train.iloc[334]['image'], quality=50)}\"}}\n", + " ]\n", + " },\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": \"**Answer:**\\n\\n**Reasoning:** The title 'Principles and Practice of Modern Chromatographic Methods' suggests a focus on chromatography, a scientific technique used in chemistry and biology. This aligns with the academic and technical nature typical of books in the 'Science & Math' category.\\n\\n**Conclusion:** Science & Math\"}\n", + " ]\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our system instructions, few-shot examples, and the image encoding function in place, the next step is to iterate through the training set and construct the messages required for fine-tuning. As a reminder, each training example must be formatted as a conversation and must include both the image (in base64 format) and the corresponding question and answer.\n", + "\n", + "To fine-tune GPT-4o, we recommend providing at least **10 examples**, but you’ll typically see noticeable improvements with **50 to 100** training examples. In this case, we'll go all-in and fine-tune the model using our larger training sample of **150 images, and 721 QA pairs**." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "721it [00:01, 518.61it/s]\n" + ] + } + ], + "source": [ + "from tqdm import tqdm\n", + "\n", + "# constructing the training set\n", + "json_data = []\n", + "\n", + "for idx, example in tqdm(ds_train.iterrows()):\n", + " system_message = {\n", + " \"role\": \"system\",\n", + " \"content\": [{\"type\": \"text\", \"text\": SYSTEM_PROMPT}]\n", + " }\n", + " \n", + " user_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"Question [{idx}]: {example['question']}\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(example['image'], quality=50)}\"}}\n", + " ]\n", + " }\n", + " \n", + " assistant_message = {\n", + " \"role\": \"assistant\",\n", + " \"content\": [{\"type\": \"text\", \"text\": example[\"answer\"]}]\n", + " }\n", + "\n", + " all_messages = [system_message] + FEW_SHOT_EXAMPLES + [user_message, assistant_message]\n", + " \n", + " json_data.append({\"messages\": all_messages})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We save our final training set in a `.jsonl` file where each line in the file represents a single example in the training dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [], + "source": [ + "# save the JSON data to a file\n", + "with open(\"ocr-vqa-train.jsonl\", \"w\") as f:\n", + " for message in json_data:\n", + " json.dump(message, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Just like the training set, we need to structure our validation and test sets in the same message format. However, for the test set, there's a key difference: since the test set is used for evaluation, we do not include the assistant's message (i.e., the answer). This ensures the model generates its own answers, which we can later compare to the ground truth for performance evaluation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "239it [00:00, 474.76it/s]\n" + ] + } + ], + "source": [ + "# constructing the validation set\n", + "json_data = []\n", + "\n", + "for idx, example in tqdm(ds_val.iterrows()):\n", + " system_message = {\n", + " \"role\": \"system\",\n", + " \"content\": [{\"type\": \"text\", \"text\": SYSTEM_PROMPT}]\n", + " }\n", + " \n", + " user_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"Question [{idx}]: {example['question']}\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(example['image'], quality=50)}\"}}\n", + " ]\n", + " }\n", + "\n", + " assistant_message = {\n", + " \"role\": \"assistant\",\n", + " \"content\": [{\"type\": \"text\", \"text\": example[\"answer\"]}]\n", + " }\n", + "\n", + " all_messages = [system_message] + FEW_SHOT_EXAMPLES + [user_message, assistant_message]\n", + " \n", + " json_data.append({\"messages\": all_messages})\n", + "\n", + "# save the JSON data to a file\n", + "with open(\"ocr-vqa-validation.jsonl\", \"w\") as f:\n", + " for message in json_data:\n", + " json.dump(message, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "485it [00:00, 490.79it/s]\n" + ] + } + ], + "source": [ + "# constructing the test set\n", + "json_data = []\n", + "\n", + "for idx, example in tqdm(ds_test.iterrows()):\n", + " system_message = {\n", + " \"role\": \"system\",\n", + " \"content\": [{\"type\": \"text\", \"text\": SYSTEM_PROMPT}]\n", + " }\n", + " \n", + " user_message = {\n", + " \"role\": \"user\",\n", + " \"content\": [\n", + " {\"type\": \"text\", \"text\": f\"Question [{idx}]: {example['question']}\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": f\"data:image/jpeg;base64,{encode_image(example['image'], quality=50)}\"}}\n", + " ]\n", + " }\n", + "\n", + " all_messages = [system_message] + FEW_SHOT_EXAMPLES + [user_message]\n", + " \n", + " json_data.append({\"messages\": all_messages})\n", + "\n", + "# save the JSON data to a file\n", + "with open(\"ocr-vqa-test.jsonl\", \"w\") as f:\n", + " for message in json_data:\n", + " json.dump(message, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Fine-tuning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have prepared our training and validation datasets in the right format, we can upload them using the [Files API](https://platform.openai.com/docs/api-reference/files/create) for fine-tuning." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# upload training file\n", + "train_file = client.files.create(\n", + " file=open(\"ocr-vqa-train.jsonl\", \"rb\"),\n", + " purpose=\"fine-tune\"\n", + ")\n", + "\n", + "# upload validation file\n", + "val_file = client.files.create(\n", + " file=open(\"ocr-vqa-validation.jsonl\", \"rb\"),\n", + " purpose=\"fine-tune\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the files are uploaded, we're ready to proceed to the next step: starting the fine-tuning job.\n", + "\n", + "To create a fine-tuning job, we use the fine-tuning API. This may take some time to complete, but you can track the progress of the fine-tuning job in the [Platform UI](https://platform.openai.com/finetune/)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FineTuningJob(id='ftjob-I1GKWTvusx0900L4ggohrGCP', created_at=1730479789, error=Error(code=None, message=None, param=None), fine_tuned_model=None, finished_at=None, hyperparameters=Hyperparameters(n_epochs='auto', batch_size='auto', learning_rate_multiplier='auto'), model='gpt-4o-2024-08-06', object='fine_tuning.job', organization_id='org-l89177bnhkme4a44292n5r3j', result_files=[], seed=662273734, status='validating_files', trained_tokens=None, training_file='file-UzGnMr4kYPgcFeuq121UBifQ', validation_file='file-LoWiW0fCIa3eirRZExRU3pKB', estimated_finish=None, integrations=[], user_provided_suffix=None, method=None)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create fine tuning job\n", + "file_train = train_file.id\n", + "file_val = val_file.id\n", + "\n", + "client.fine_tuning.jobs.create(\n", + " training_file=file_train,\n", + " # note: validation file is optional\n", + " validation_file=file_val,\n", + " model=\"gpt-4o-2024-08-06\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the fine-tuning job is complete, it’s time to evaluate the performance of our model by running inference on the test set. This step involves using the fine-tuned model to generate responses to the questions in the test set and comparing its predictions to the ground truth answers for evaluation. We will also run inference on the test set using the non-fine-tuned GPT-4o model for comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 485/485 [02:03<00:00, 3.93it/s]\n", + "100%|██████████| 485/485 [01:35<00:00, 5.09it/s]\n" + ] + } + ], + "source": [ + "from concurrent.futures import ThreadPoolExecutor, as_completed\n", + "import re\n", + "\n", + "# load the test data from JSONL file\n", + "test_data = []\n", + "with open(\"ocr-vqa-test.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " test_data.append(json.loads(line))\n", + "\n", + "def process_example(example, model):\n", + " response = client.chat.completions.create(\n", + " model=model,\n", + " messages=example[\"messages\"],\n", + " store=True,\n", + " metadata={'dataset': 'ocr-vqa-test'}\n", + " )\n", + " predicted_answer = response.choices[0].message.content.strip()\n", + " \n", + " # regex to get the question ID\n", + " match = re.search(r'\\[(\\d+)\\]', example[\"messages\"][-1][\"content\"][0][\"text\"])\n", + " if match:\n", + " example_id = int(match.group(1))\n", + " else:\n", + " example_id = -1\n", + " \n", + " actual_answer = ds_test.iloc[example_id]['answer']\n", + "\n", + " return {\n", + " \"example_id\": example_id,\n", + " \"predicted_answer\": predicted_answer,\n", + " \"actual_answer\": actual_answer\n", + " }\n", + "\n", + "# run the prompts through the finetuned model and store the results\n", + "model = \"ft:gpt-4o-2024-08-06:openai::AOY1M8VG\"\n", + "results = []\n", + "with ThreadPoolExecutor() as executor:\n", + " futures = {executor.submit(process_example, example, model): example for example in test_data}\n", + " for future in tqdm(as_completed(futures), total=len(futures)):\n", + " results.append(future.result())\n", + "\n", + "# save the results to a file\n", + "with open(\"ocr-vqa-ft-results.jsonl\", \"w\") as f:\n", + " for result in results:\n", + " json.dump(result, f)\n", + " f.write(\"\\n\")\n", + "\n", + "# run the prompts through the non-fine-tuned model and store the results\n", + "model = \"gpt-4o\"\n", + "results = []\n", + "with ThreadPoolExecutor() as executor:\n", + " futures = {executor.submit(process_example, example, model): example for example in test_data}\n", + " for future in tqdm(as_completed(futures), total=len(futures)):\n", + " results.append(future.result())\n", + "\n", + "# save the results to a file\n", + "with open(\"ocr-vqa-4o-results.jsonl\", \"w\") as f:\n", + " for result in results:\n", + " json.dump(result, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we’ve run inference using our fine-tuned model, let’s inspect a few specific examples to understand how well the model performed compared to the actual answers." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Q: What is the title of this book?\n", + "{\"example_id\": 6, \"predicted_answer\": \"A Wrinkle in Time\", \"actual_answer\": \"A Wrinkle in Time (Time Quintet)\"}\n", + "# Q: Who wrote this book?\n", + "{\"example_id\": 10, \"predicted_answer\": \"DK Travel\", \"actual_answer\": \"DK Publishing\"}\n", + "# Q: What is the title of this book?\n", + "{\"example_id\": 11, \"predicted_answer\": \"DK Eyewitness Travel Guide: Peru\", \"actual_answer\": \"DK Eyewitness Travel Guide: Peru\"}\n", + "# Q: What type of book is this?\n", + "{\"example_id\": 12, \"predicted_answer\": \"Travel\", \"actual_answer\": \"Travel\"}\n", + "# Q: Who wrote this book?\n", + "{\"example_id\": 437, \"predicted_answer\": \"Cookshack, Inc.\", \"actual_answer\": \"Cookshack\"}\n", + "# Q: What type of book is this?\n", + "{\"example_id\": 482, \"predicted_answer\": \"Christian Books & Bibles\", \"actual_answer\": \"Religion & Spirituality\"}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the fine-tuned model does a great job at answering the questions, with many responses being exactly correct. \n", + "\n", + "However, there are also cases where the model’s **predicted answers** are close to the **ground truth**, while not matching exactly, particularly in open-ended questions where phrasing or details may differ. To assess the quality of these predictions, we will use GPT-4o to evaluate the similarity between the predicted responses and the ground truth labels from the dataset.\n", + "\n", + "In order to evaluate our model responses, we will use GPT-4o to determine the similarity between the ground truth and our predicted responses. We will rank our predicted answers based on the following criteria:\n", + "* **Very Similar**: The predicted answer exactly matches the ground truth and there is no important information omitted, although there may be some minor ommissions or discrepancies in punctuation.\n", + "\n", + "* **Mostly Similar**: The predicted answer closely aligns with the ground truth, perhaps with some missing words or phrases.\n", + "\n", + "* **Somewhat Similar**: Although the predicted answer has noticeable differences to the ground truth, the core content is accurate and semantically similar, perhaps with some missing information.\n", + "\n", + "* **Incorrect**: The predicted answer is completely incorrect, irrelevant, or contains critical errors or omissions from the ground truth.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 485/485 [00:18<00:00, 25.58it/s]\n", + "100%|██████████| 485/485 [00:17<00:00, 27.09it/s]\n" + ] + } + ], + "source": [ + "from pydantic import BaseModel, Field\n", + "\n", + "# define output schema\n", + "class Result(BaseModel):\n", + " example_id: int = Field(description=\"The unique ID of the question\")\n", + " rating: str = Field(description=\"The assigned similarity rating. One of [Very Similar | Mostly Similar | Somewhat Similar | Incorrect]\")\n", + " type: str = Field(description=\"The type of question. Open if the question is binary yes/no, otherwise Closed. One of [Open | Closed]\")\n", + "\n", + "EVAL_PROMPT = \"\"\"\n", + "Evaluate the closeness between the predicted answer and the ground truth for each provided result.\n", + "Rank the predicted answer based on the following criteria:\n", + "\n", + "1. **Very Similar**: The predicted answer exactly matches the ground truth and there is no important information omitted, although there may be some minor ommissions or discrepancies in punctuation.\n", + "2. **Mostly Similar**: The predicted answer closely aligns with the ground truth, perhaps with some missing words or phrases.\n", + "3. **Somewhat Similar**: Although the predicted answer has noticeable differences to the ground truth, the core content is accurate and semantically similar, perhaps with some missing information.\n", + "4. **Incorrect**: The predicted answer is completely incorrect, irrelevant, or contains critical errors or omissions from the ground truth.\n", + "\n", + "Ensure to consider both open-ended and yes/no questions.\n", + "\n", + "# Steps\n", + "1. **Analyze the Answers**: Read the predicted answer, and ground truth carefully.\n", + "2. **Evaluate Similarity**:\n", + " - Check if the predicted answer contains the same core information and correctness as the ground truth.\n", + " - Determine if there are any important omissions or errors.\n", + "3. **Assign a Rating**: Based on your evaluation, assign the appropriate rating: Very Similar, Mostly Similar, Somewhat Similar, or Incorrect.\n", + "\n", + "# Output Format\n", + "```json\n", + "[\n", + " {\n", + " \"example_id\": [example_id],\n", + " \"rating\": \"[Very Similar | Mostly Similar | Somewhat Similar | Incorrect]\",\n", + " \"type\": \"[Open | Closed]\n", + " }\n", + "]\n", + "```\n", + "\n", + "# Examples\n", + "\n", + "**Input:**\n", + "```json\n", + "{\"example_id\": 6, \"predicted_answer\": \"A Wrinkle in Time\", \"actual_answer\": \"A Wrinkle in Time (Time Quintet)\"}\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"A Wrinkle in Time\" is a very close match to the ground truth \"A Wrinkle in Time (Time Quintet)\" with a missing tagline or subtitle.\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 6, \"rating\": \"Mostly Similar\", \"type\": \"Open\" }\n", + "```\n", + "\n", + "**Input:**\n", + "```json\n", + "{\"example_id\": 437, \"predicted_answer\": \"Cookshack, Inc.\", \"actual_answer\": \"Cookshack\"}\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"Cookshack, Inc.\" is exactly the same as the ground truth \"Cookshack\", with only a difference in punctuation.\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 437, \"rating\": \"Very Similar\", \"type\": \"Open\" }\n", + "```\n", + "\n", + "**Input:**\n", + "```json\n", + "{\"example_id\": 482, \"predicted_answer\": \"Christian Books & Bibles\", \"actual_answer\": \"Religion & Spirituality\"}\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"Christian Books & Bibles\" is semantically similar to the ground truth \"Religion & Spirituality\", however there is a key difference in the predicted answer.\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 482, \"rating\": \"Somewhat Similar\", \"type\": \"Open\" }\n", + "```\n", + "\n", + "**Input:**\n", + "```json\n", + "{ \"example_id\": 417, \"predicted_answer\": \"yes\", \"actual_answer\": \"no\" }\n", + "```\n", + "**Reasoning:**\n", + "The predicted answer \"yes\" is completely incorrect compared to the actual answer \"no.\"\n", + "**Output:**\n", + "```json\n", + "{ \"example_id\": 417, \"rating\": \"Incorrect\", \"type\": \"Closed\" }\n", + "```\n", + "\"\"\"\n", + "\n", + "def process_result(result):\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": EVAL_PROMPT\n", + " },\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": str(result)\n", + " }\n", + " ]\n", + "\n", + " response = client.beta.chat.completions.parse(\n", + " model='gpt-4o',\n", + " messages=messages,\n", + " temperature=0,\n", + " response_format=Result\n", + " )\n", + "\n", + " return json.loads(response.choices[0].message.content)\n", + "\n", + "# fine-tuned model results with scores\n", + "results = []\n", + "with open(\"ocr-vqa-ft-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results.append(json.loads(line))\n", + "\n", + "results_w_scores = []\n", + "with ThreadPoolExecutor() as executor:\n", + " futures = {executor.submit(process_result, result): result for result in results}\n", + " for future in tqdm(as_completed(futures), total=len(futures)):\n", + " results_w_scores.append(future.result())\n", + "\n", + "# Save the results to a file\n", + "with open(\"ocr-vqa-ft-similarity.jsonl\", \"w\") as f:\n", + " for score in results_w_scores:\n", + " json.dump(score, f)\n", + " f.write(\"\\n\")\n", + "\n", + "# non-fine-tuned model results with scores\n", + "results = []\n", + "with open(\"ocr-vqa-4o-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results.append(json.loads(line))\n", + "\n", + "results_w_scores_4o = []\n", + "with ThreadPoolExecutor() as executor:\n", + " futures = {executor.submit(process_result, result): result for result in results}\n", + " for future in tqdm(as_completed(futures), total=len(futures)):\n", + " results_w_scores_4o.append(future.result())\n", + "\n", + "# Save the results to a file\n", + "with open(\"ocr-vqa-4o-similarity.jsonl\", \"w\") as f:\n", + " for score in results_w_scores_4o:\n", + " json.dump(score, f)\n", + " f.write(\"\\n\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To fully understand the impact of fine-tuning, we also evaluated the same set of test questions using the **non-fine-tuned GPT-4o** model.\n", + "\n", + "Let's start by comparing the performance of the fine-tuned model vs the non-fine-tuned model for **Closed** form (Yes/No) questions.\n", + "\n", + "Note that with the fine-tuned model, we can check for exact matches between the predicted and actual answers because the model has learned to produce consistent answers that follow the response format specified in the system prompt. However, for the non-fine-tuned model, we need to account for variations in phrasing and wording in the predicted answers. Below is an example of a non-fine-tuned model output. As we can see, the final answer is correct but the response format is inconsistent and outputs reasoning in the response." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# example of non-fine-tuned model output\n", + "{\"example_id\": 14, \"predicted_answer\": \"**Answer:**\\n\\nNo. \\n\\n**Reasoning:** The cover shows \\\"Eyewitness Travel\\\" and \\\"Peru,\\\" indicating it is a travel guide focused on the country, rather than a pharmaceutical book.\", \"actual_answer\": \"No\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine-tuned model accuracy: 90.53%\n", + "Non-fine-tuned model accuracy: 87.89%\n" + ] + } + ], + "source": [ + "# read in results\n", + "results_ft = []\n", + "with open(\"ocr-vqa-ft-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results_ft.append(json.loads(line))\n", + "\n", + "results_4o = []\n", + "with open(\"ocr-vqa-4o-results.jsonl\", \"r\") as f:\n", + " for line in f:\n", + " results_4o.append(json.loads(line))\n", + "\n", + "# filter results for yes/no questions\n", + "results_ft_closed = [result for result in results_ft if result['actual_answer'] in ['Yes', 'No']]\n", + "results_4o_closed = [result for result in results_4o if result['actual_answer'] in ['Yes', 'No']]\n", + "\n", + "# check for correct predictions\n", + "correct_ft_closed = [result for result in results_ft_closed if result['predicted_answer'] == result['actual_answer']]\n", + "correct_4o_closed = [\n", + " result for result in results_4o_closed \n", + " if result['predicted_answer'].lower() == result['actual_answer'].lower() \n", + " or result['actual_answer'].lower() in result['predicted_answer'].lower()\n", + "]\n", + "print(f\"Fine-tuned model accuracy: {round(100*len(correct_ft_closed) / len(results_ft_closed), 2)}%\")\n", + "print(f\"Non-fine-tuned model accuracy: {round(100*len(correct_4o_closed) / len(results_4o_closed), 2)}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a generous allowance for variations in phrasing and wording for the non-fine-tuned model including ignoring case and allowing for partial matches, the fine-tuned model still outperforms the non-fine-tuned model by a margin of **2.64%** on this set of questions.\n", + "\n", + "Now, let's compare the performance of the fine-tuned model vs the non-fine-tuned model over all the open-ended questions. First, we'll check for exact matches between the predicted and actual answers, again allowing for general variations in phrasing and wording for the non-fine-tuned model, but maintaining a strict standard for the fine-tuned model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fine-tuned model accuracy: 64.07%\n", + "Non-fine-tuned model accuracy: 46.1%\n" + ] + } + ], + "source": [ + "# filter results for open-ended questions\n", + "results_ft_open = [result for result in results_ft if result['actual_answer'] not in ['Yes', 'No']]\n", + "results_4o_open = [result for result in results_4o if result['actual_answer'] not in ['Yes', 'No']]\n", + "\n", + "# check for correct predictions\n", + "correct_ft_open = [result for result in results_ft_open if result['predicted_answer'] == result['actual_answer']]\n", + "correct_4o_open = [\n", + " result for result in results_4o_open \n", + " if result['predicted_answer'].lower() == result['actual_answer'].lower() \n", + " or result['actual_answer'].lower() in result['predicted_answer'].lower()\n", + "]\n", + "print(f\"Fine-tuned model accuracy: {round(100*len(correct_ft_open) / len(results_ft_open), 2)}%\")\n", + "print(f\"Non-fine-tuned model accuracy: {round(100*len(correct_4o_open) / len(results_4o_open), 2)}%\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The improvement in accuracy here is much more pronounced, with the fine-tuned model outperforming the non-fine-tuned model by a substantial margin of **17.97%**, even with very generous allowances for variations in phrasing and wording for the non-fine-tuned model!\n", + "\n", + "If we were to afford the same leniency to the fine-tuned model, we would see an additional 4.1% increase in accuracy, bringing the total margin of improvement to **22.07%**.\n", + "\n", + "To dig a little deeper, we can also look at the accuracy by question type." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# seperate by question type\n", + "def get_question_type(question):\n", + " if question in [\"What is the title of this book?\"]:\n", + " return \"Title\"\n", + " elif question in [\"What is the genre of this book?\", \"What type of book is this?\"]:\n", + " return \"Genre\"\n", + " elif question in [\"Who wrote this book?\", \"Who is the author of this book?\"]:\n", + " return \"Author\"\n", + " else:\n", + " return \"Other\"\n", + "\n", + "# get index numbers for each question type\n", + "question_type_indexes = {\n", + " \"Title\": [],\n", + " \"Genre\": [],\n", + " \"Author\": [],\n", + " \"Other\": []\n", + "}\n", + "\n", + "for idx, row in ds_test.iterrows():\n", + " question_type = get_question_type(row['question'])\n", + " question_type_indexes[question_type].append(idx)\n", + "\n", + "# plot accuracy by question type]\n", + "accuracy_by_type_ft = {}\n", + "accuracy_by_type_4o = {}\n", + "\n", + "for question_type, indexes in question_type_indexes.items():\n", + " correct_predictions_ft = [\n", + " result for result in results_ft if result['example_id'] in indexes and (\n", + " result['predicted_answer'].lower() == result['actual_answer'].lower() or\n", + " result['actual_answer'].lower() in result['predicted_answer'].lower()\n", + " )\n", + " ]\n", + " correct_predictions_4o = [\n", + " result for result in results_4o if result['example_id'] in indexes and (\n", + " result['predicted_answer'].lower() == result['actual_answer'].lower() or\n", + " result['actual_answer'].lower() in result['predicted_answer'].lower()\n", + " )\n", + " ]\n", + " accuracy_ft = len(correct_predictions_ft) / len(indexes) if indexes else 0\n", + " accuracy_4o = len(correct_predictions_4o) / len(indexes) if indexes else 0\n", + " accuracy_by_type_ft[question_type] = accuracy_ft * 100 \n", + " accuracy_by_type_4o[question_type] = accuracy_4o * 100\n", + "\n", + "# prepare data for plotting\n", + "question_types = list(accuracy_by_type_ft.keys())\n", + "accuracies_ft = list(accuracy_by_type_ft.values())\n", + "accuracies_4o = list(accuracy_by_type_4o.values())\n", + "\n", + "# plot grouped bar chart\n", + "bar_width = 0.35\n", + "index = range(len(question_types))\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "bar1 = plt.bar(index, accuracies_ft, bar_width, label='Fine-tuned GPT-4o', color='skyblue')\n", + "bar2 = plt.bar([i + bar_width for i in index], accuracies_4o, bar_width, label='Non-fine-tuned GPT-4o', color='lightcoral')\n", + "\n", + "plt.xlabel('Question Type')\n", + "plt.ylabel('Accuracy (%)')\n", + "plt.title('Accuracy by Question Type')\n", + "plt.ylim(0, 100)\n", + "plt.xticks([i + bar_width / 2 for i in index], question_types, rotation=45)\n", + "plt.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It appears that the largest performance gains for the fine-tuned model are for questions in the **Genre** category e.g. \"What type of book is this?\" or \"What is the genre of this book?\". This might be indicative of the benefits of fine-tuning in general in that we teach the model to classify genres based on the categories present in the training data. However, it also highlights the model's strong visual undserstanding capabilties, since we are able to identify the genre based on the visual content of the book cover alone.\n", + "\n", + "Additionally, we see significant lift in the **Title** category, which suggests that fine-tuning has boosted the model's OCR capbilities and its ability to understand the layout and structure of the book cover to extract the relevant information.\n", + "\n", + "Finally, let's compare the distribution of similarity ratings between the fine-tuned model and the non-fine-tuned model to allow for variations in phrasing and wording." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from collections import Counter\n", + "\n", + "# extract ratings\n", + "ratings_ft = [result['rating'] for result in results_w_scores if result['type'] == 'Open']\n", + "ratings_4o = [result['rating'] for result in results_w_scores_4o if result['type'] == 'Open']\n", + "\n", + "# count occurrences of each rating\n", + "rating_counts_ft = Counter(ratings_ft)\n", + "rating_counts_4o = Counter(ratings_4o)\n", + "\n", + "# define the order of ratings\n", + "rating_order = [\"Very Similar\", \"Mostly Similar\", \"Somewhat Similar\", \"Incorrect\"]\n", + "\n", + "# create bar chart\n", + "bar_width = 0.35\n", + "index = range(len(rating_order))\n", + "\n", + "fig, ax = plt.subplots()\n", + "bar1 = ax.bar(index, [rating_counts_ft.get(rating, 0) for rating in rating_order], bar_width, label='FT GPT-4o')\n", + "bar2 = ax.bar([i + bar_width for i in index], [rating_counts_4o.get(rating, 0) for rating in rating_order], bar_width, label='GPT-4o')\n", + "\n", + "ax.set_xlabel('Ratings')\n", + "ax.set_ylabel('Count')\n", + "ax.set_title('Ratings Distribution')\n", + "ax.set_xticks([i + bar_width / 2 for i in index])\n", + "ax.set_xticklabels(rating_order)\n", + "ax.legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results provide a clear picture of the benefits gained through fine-tuning, without any other modifications.\n", + "Comparing the distribution of ratings between the **fine-tuned GPT-4o** model and **GPT-4o without fine-tuning**, we see that the fine-tuned model gets many more responses exactly correct, with a comparable amount of incorrect responses.\n", + "### Key Takeaways" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* **Improved Precision**: Fine-tuning helped the model produce more precise answers that matched the ground truth, especially in highly domain-specific tasks like OCR on book covers.\n", + "* **Better Generalization**: While the non-fine-tuned GPT-4o was able to get at least somewhat to the ground truth for many questions, it was less consistent. The fine-tuned model exhibited better generalization across a variety of test questions, thanks to the exposure to multimodal data during training." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While the results from vision fine-tuning are promising, there are still opportunities for improvement. Much like fine-tuning on text, the effectiveness of vision fine-tuning depends heavily on the **quality, diversity, and representativeness** of the training data. In particular, models benefit from focusing on cases where errors occur most frequently, allowing for targeted improvements.\n", + "\n", + "Upon reviewing the incorrect results, many of the \"Incorrect\" responses from the fine-tuned model are in fact due to inconsistencies in the labels from the dataset. For example, some ground truth answers provide only the first and last name of the author, whereas the image actually shows the middle initial as well. Similarly, some ground truth labels for the title include subheadings and taglines, whereas others do not. \n", + "\n", + "Another common theme was miscategorization of genres. Although the model was almost always able to produce a semantically similar genre to the ground truth, the answer sometimes deviated. This is likely due to the lack of presence of these genres in the training data. Providing the model with more diverse training examples to cover these genres, or clearer instructions for dealing with edge cases can help to guide the model’s understanding.\n", + "\n", + "### Next Steps:\n", + "* **Expand the Training Dataset**: Adding more varied examples that cover the model’s weaker areas, such as identifying genres, could significantly enhance performance.\n", + "\n", + "* **Expert-Informed Prompts**: Incorporating domain-specific instructions into the training prompts may further refine the model’s ability to accurately interpret and respond in complex cases.\n", + "\n", + "Although there is still some progress to be made on this particular task, the initial results are highly encouraging. With minimal setup and effort, we’ve already observed a substantial uplift in overall accuracy with vision fine-tuning, indicating that this approach holds great potential. Vision fine-tuning opens up possibilities for improvement across a wide range of visual question answering tasks, as well as other tasks that rely on strong visual understanding." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "openai", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/registry.yaml b/registry.yaml index d2502f2..229c09d 100644 --- a/registry.yaml +++ b/registry.yaml @@ -1676,6 +1676,16 @@ - evals - completions +- title: Vision Fine-tuning on GPT-4o for Visual Question Answering + path: examples/multimodal/Vision_Fine_tuning_on_GPT4o_for_Visual_Question_Answering.ipynb + date: 2024-11-01 + authors: + - danial-openai + tags: + - vision + - fine-tuning + - completions + - title: Steering Text-to-Speech for more dynamic audio generation path: examples/voice_solutions/steering_tts.ipynb date: 2024-11-01