diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..df47150e31964bfe2ddc63ca6ad03cca46a4f878 --- /dev/null +++ b/.gitignore @@ -0,0 +1,163 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +u2net_cloth_segm.pth +u2net_segm.pth diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..132dde8a73bbc6c512b5b64ca21c9b79ea9cbe6d --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,20 @@ +## How to contribute to tryondiffusion + +### 1. Open an issue +We recommend opening an issue (if one doesn't already exist) and discussing your intended changes before making any changes. +We'll be able to provide you feedback and confirm the planned modifications this way. + +### 2. Make changes in the code +Start with forking the repository, set up the environment, install the dependencies, and make changes in the code appropriately. + +### 3. Create pull request +Create a pull request to the main branch from your fork's branch. + +### 4. Merging pull request +Once the pull request is created, we will review the code changes and merge the pull request as soon as possible. + + +### Writing documentation + +If you are interested in writing the documentation, you can add it to README.md and create a pull request. +For now, we are maintaining our documentation in a single file and we will add more files as it grows. diff --git a/README.md b/README.md index c11d57bb2edcfd266a1e6945cbb47dd14f0ddc66..de1154c14e2b6dd05b2bc0a73af6371b154c7428 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,38 @@ ---- -title: Tryondiffusion -emoji: 📈 -colorFrom: green -colorTo: purple -sdk: gradio -sdk_version: 5.7.1 -app_file: app.py -pinned: false -license: mit -short_description: 'TryOnDiffusion: A Tale of Two UNets Implementation' ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# Try On Diffusion: A Tale of Two UNets Implementation +### [Paper Link](https://arxiv.org/abs/2306.08276) + +### [Click here](https://discord.gg/T5mPpZHxkY) to join our discord channel + +## Roadmap + +1. ~~Prepare initial implementation~~ +1. Test initial implementation with small dataset (VITON-HD) +1. Gather sufficient data and compute resources +1. Prepare and train final implementation +1. Publicly release parameters + +## How to contribute to tryondiffusion + +### 1. Open an issue +We recommend opening an issue (if one doesn't already exist) and discussing your intended changes before making any changes. +We'll be able to provide you feedback and confirm the planned modifications this way. + +### 2. Make changes in the code +Start with forking the repository, set up the environment, install the dependencies, and make changes in the code appropriately. + +### 3. Create pull request +Create a pull request to the main branch from your fork's branch. + +### 4. Merging pull request +Once the pull request is created, we will review the code changes and merge the pull request as soon as possible. + + +### Writing documentation + +If you are interested in writing the documentation, you can add it to README.md and create a pull request. +For now, we are maintaining our documentation in a single file and we will add more files as it grows. + + +## License + +All material is made available under [Creative Commons BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). You can **use** the material for **non-commercial purposes**, as long as you give appropriate credit by **citing our original [github repo](https://github.com/kailashahirwar/tryondiffusion)** and **indicate any changes** that you've made to the code. \ No newline at end of file diff --git a/caption_images.py b/caption_images.py new file mode 100644 index 0000000000000000000000000000000000000000..4171b37322b78b34f76e2add502fead0437fe593 --- /dev/null +++ b/caption_images.py @@ -0,0 +1,52 @@ +import glob +import json +import os + +from PIL import Image + +from tryon.preprocessing.captioning import caption_image, create_llava_next_pipeline + +INPUT_IMAGES_DIR = os.path.join("fashion_datatset", "*") +OUTPUT_CAPTIONS_DIR = "fashion_datatset_captions" + +os.makedirs(OUTPUT_CAPTIONS_DIR, exist_ok=True) + + +def change_extension(filename, new_extension): + base_name, _ = os.path.splitext(filename) + return f"{base_name}.{new_extension}" + + +if __name__ == '__main__': + model, processor = create_llava_next_pipeline() + + images_path = sorted(glob.glob(INPUT_IMAGES_DIR)) + + for index, image_path in enumerate(images_path): + print(f"index: {index}, total images: {len(images_path)}, {image_path}") + image = Image.open(image_path) + + prompt = """ + You're a fashion expert. The list of clothing properties includes [color, pattern, style, fit, type, hemline, + material, sleeve-length, fabric-elasticity, neckline, waistline]. Please provide the following information in + JSON format for the outfit shown in the image. Question: What are the color, pattern, style, fit, type, + hemline, material, sleeve length, fabric elasticity, neckline, and waistline of the outfit in the image? + Answer: + """ + + json_file_path = os.path.join(OUTPUT_CAPTIONS_DIR, + change_extension(os.path.basename(image_path), "json")) + caption_file_path = os.path.join(OUTPUT_CAPTIONS_DIR, + change_extension(os.path.basename(image_path), "txt")) + + if os.path.exists(caption_file_path) and os.path.exists(json_file_path): + print(f"caption already exists for {image_path}") + continue + + json_data, generated_caption = caption_image(image, prompt, model, processor, json_only=False) + + with open(json_file_path, "w") as f: + json.dump(json_data, f) + + with open(caption_file_path, "w") as f: + f.write(generated_caption) diff --git a/demo/__init__.py b/demo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c91c6528320f8cdec58e34587c368976823d678f --- /dev/null +++ b/demo/__init__.py @@ -0,0 +1,3 @@ +from .extract_garment import demo as extract_garment_demo +from .model_swap import demo as model_swap_demo +from .outfit_generator import demo as outfit_generator_demo diff --git a/demo/extract_garment/README.md b/demo/extract_garment/README.md new file mode 100644 index 0000000000000000000000000000000000000000..83571019b26ea855e82addd59c4455d67ae2cc2a --- /dev/null +++ b/demo/extract_garment/README.md @@ -0,0 +1,14 @@ +--- +title: Extract Garment AI +emoji: 📊 +colorFrom: indigo +colorTo: indigo +sdk: gradio +sdk_version: 4.44.1 +app_file: app.py +pinned: false +license: mit +short_description: Gradio Demo of Extract Garment AI by TryOn Labs. +--- + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/demo/extract_garment/__init__.py b/demo/extract_garment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..39e479c43f2a8ee8e560e96a9e27e53e86410567 --- /dev/null +++ b/demo/extract_garment/__init__.py @@ -0,0 +1 @@ +from .app import demo \ No newline at end of file diff --git a/demo/extract_garment/app.py b/demo/extract_garment/app.py new file mode 100644 index 0000000000000000000000000000000000000000..4d36059c48be2d9ae8f9a471b68c65accb3e309d --- /dev/null +++ b/demo/extract_garment/app.py @@ -0,0 +1,76 @@ +import glob +import os +from PIL import Image + +import gradio as gr +from tryon import preprocessing + + +def extract_garment(input_img, cls): + print(input_img, type(input_img), cls) + + input_dir = "input_image" + output_dir = "output_image" + + os.makedirs(input_dir, exist_ok=True) + os.makedirs(output_dir, exist_ok=True) + + for f in glob.glob(input_dir + "/*.*"): + os.remove(f) + + for f in glob.glob(output_dir + "/*.*"): + os.remove(f) + + for f in glob.glob("cloth-mask/*.*"): + os.remove(f) + + input_img.save(os.path.join(input_dir, "img.jpg")) + + preprocessing.extract_garment(inputs_dir=input_dir, outputs_dir=output_dir, cls=cls) + + return Image.open(glob.glob(output_dir + "/*.*")[0]) + + +css = """ +#col-container { + margin: 0 auto; + max-width: 720px; +} +""" + +with gr.Blocks(css=css) as demo: + with gr.Column(elem_id="col-container"): + gr.Markdown(f""" + # Clothes Extraction using U2Net + Pull out clothes like tops, bottoms, and dresses from a photo. This implementation is based on the [U2Net](https://github.com/xuebinqin/U-2-Net) model. + """) + + with gr.Row(): + with gr.Column(): + input_image = gr.Image(label="Input Image", type='pil', height="400px", show_label=True) + dropdown = gr.Dropdown(["upper", "lower", "dress"], value="upper", label="Extract garment", + info="Select the garment type you wish to extract!") + + output_image = gr.Image(label="Extracted garment", type='pil', height="400px", show_label=True, + show_download_button=True) + + with gr.Row(): + submit_button = gr.Button("Submit", variant='primary', scale=1) + reset_button = gr.ClearButton(value="Reset", scale=1) + + gr.on( + triggers=[submit_button.click], + fn=extract_garment, + inputs=[input_image, dropdown], + outputs=[output_image] + ) + + reset_button.click( + fn=lambda: (None, "upper", None), + inputs=[], + outputs=[input_image, dropdown, output_image], + concurrency_limit=1, + ) + +if __name__ == '__main__': + demo.launch() diff --git a/demo/extract_garment/requirements.txt b/demo/extract_garment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c886cb4081318a5d7b0b1ad720d1067844b8cf6 --- /dev/null +++ b/demo/extract_garment/requirements.txt @@ -0,0 +1,3 @@ +gradio==4.44.1 +pillow +tryondiffusion \ No newline at end of file diff --git a/demo/model_swap/.gitignore b/demo/model_swap/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..9afe987686573afdb32d7c479a6e145834634774 --- /dev/null +++ b/demo/model_swap/.gitignore @@ -0,0 +1 @@ +.token \ No newline at end of file diff --git a/demo/model_swap/README.md b/demo/model_swap/README.md new file mode 100644 index 0000000000000000000000000000000000000000..138650d0b432df7ba12f3413eea86f5b16f13a05 --- /dev/null +++ b/demo/model_swap/README.md @@ -0,0 +1,14 @@ +--- +title: Model Swap AI +emoji: 📊 +colorFrom: indigo +colorTo: indigo +sdk: gradio +sdk_version: 4.44.1 +app_file: app.py +pinned: false +license: mit +short_description: Gradio Demo of Model Swap AI by TryOn Labs. +--- + +Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/demo/model_swap/__init__.py b/demo/model_swap/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..741b1d6dfc75bd30a50191f4a13450f64fc9c47b --- /dev/null +++ b/demo/model_swap/__init__.py @@ -0,0 +1 @@ +from .app import demo diff --git a/demo/model_swap/app.py b/demo/model_swap/app.py new file mode 100644 index 0000000000000000000000000000000000000000..2013922c285324545f7013112409521e4d4fdb95 --- /dev/null +++ b/demo/model_swap/app.py @@ -0,0 +1,321 @@ +import os.path + +import gradio as gr +import json +import requests +import time +from gradio_modal import Modal +from io import BytesIO + +TRYON_SERVER_HOST = "https://prod.server.tryonlabs.ai" +TRYON_SERVER_PORT = "80" +if TRYON_SERVER_PORT == "80": + TRYON_SERVER_URL = f"{TRYON_SERVER_HOST}" +else: + TRYON_SERVER_URL = f"{TRYON_SERVER_HOST}:{TRYON_SERVER_PORT}" + +TRYON_SERVER_API_URL = f"{TRYON_SERVER_URL}/api/v1/" + + +def start_model_swap(input_image, prompt, cls, seed, guidance_scale, num_results, strength, inference_steps): + # make a request to TryOn Server + # 1. create an experiment image + print("inputs:", input_image, prompt, cls, seed, guidance_scale, num_results, strength, inference_steps) + + if input_image is None: + raise gr.Error("Select an image!") + + if prompt is None or prompt == "": + raise gr.Error("Enter a prompt!") + + token = load_token() + if token is None or token == "": + raise gr.Error("You need to login first!") + else: + login(token) + + byte_io = BytesIO() + input_image.save(byte_io, 'png') + byte_io.seek(0) + + r = requests.post(f"{TRYON_SERVER_API_URL}experiment_image/", + files={"image": ( + 'ei_image.png', + byte_io, + 'image/png' + )}, + data={ + "type": "model", + "preprocess": "false"}, + headers={ + "Authorization": f"Bearer {token}" + }) + # print(r.json()) + if r.status_code == 200 or r.status_code == 201: + print("Experiment image created successfully", r.json()) + res = r.json() + # 2 create an experiment + r2 = requests.post(f"{TRYON_SERVER_API_URL}experiment/", + data={ + "model_id": res['id'], + "action": "model_swap", + "params": json.dumps({"prompt": prompt, + "guidance_scale": guidance_scale, + "strength": strength, + "num_inference_steps": inference_steps, + "seed": seed, + "garment_class": f"{cls} garment", + "negative_prompt": "(hands:1.15), disfigured, ugly, bad, immature" + ", cartoon, anime, 3d, painting, b&w, (ugly)," + " (pixelated), watermark, glossy, smooth, " + "earrings, necklace", + "num_results": num_results}) + }, + headers={ + "Authorization": f"Bearer {token}" + }) + if r2.status_code == 200 or r2.status_code == 201: + # 3. keep checking the status of the experiment + res2 = r2.json() + print("Experiment created successfully", res2) + time.sleep(10) + + experiment = res2['experiment'] + status = fetch_experiment_status(experiment_id=experiment['id'], token=token) + status_status = status['status'] + while status_status == "running": + time.sleep(10) + status = fetch_experiment_status(experiment_id=experiment['id'], token=token) + status_status = status['status'] + print(f"Current status: {status_status}") + + if status['status'] == "success": + print("Experiment successful") + print(f"Results:{status['result_images']}") + return status['result_images'] + elif status['status'] == "failed": + print("Experiment failed") + raise gr.Error("Experiment failed") + else: + print(f"Error: {r2.text}") + raise gr.Error(f"Failure: {r2.text}") + else: + print(f"Error: {r.text}") + raise gr.Error(f"Failure: {r.text}") + + +def fetch_experiment_status(experiment_id, token): + print(f"experiment id:{experiment_id}") + + r3 = requests.get(f"{TRYON_SERVER_API_URL}experiment/{experiment_id}/", + headers={ + "Authorization": f"Bearer {token}" + }) + if r3.status_code == 200: + res = r3.json() + if res['status'] == "running": + return {"status": "running"} + elif res['status'] == "success": + experiment = r3.json()['experiment'] + result_images = [f"{TRYON_SERVER_URL}/{experiment['result']['image_url']}"] + if len(experiment['results']) > 0: + for result in experiment['results']: + result_images.append(f"{TRYON_SERVER_URL}/{result['image_url']}") + return {"status": "success", "result_images": result_images} + elif res['status'] == "failed": + return {"status": "failed"} + else: + print(f"Error: {r3.text}") + return {"status": "failed"} + + +def get_user_credits(token): + if token == "": + return None + + r = requests.get(f"{TRYON_SERVER_API_URL}user/get/", headers={ + "Authorization": f"Bearer {token}" + }) + if r.status_code == 200: + res = r.json() + return res['credits'] + else: + print(f"Error: {r.text}") + return None + + +def load_token(): + if os.path.exists(".token"): + with open(".token", "r") as f: + return json.load(f)['token'] + else: + return None + + +def save_token(access_token): + if access_token != "": + with open(".token", "w") as f: + json.dump({"token": access_token}, f) + else: + raise gr.Error("No token provided!") + + +def is_logged_in(): + loaded_token = load_token() + if loaded_token is None or loaded_token == "": + return False + else: + return True + + +def login(token): + print("logging in...") + # validate token + r = requests.post(f"{TRYON_SERVER_URL}/api/token/verify/", data={"token": token}) + if r.status_code == 200: + save_token(token) + return True + else: + raise gr.Error("Login failed") + + +def logout(): + print("logged out") + with open(".token", "w") as f: + json.dump({"token": ""}, f) + return [False, ""] + + +css = """ +#col-container { + margin: 0 auto; + max-width: 1024px; +} +#credits-col-container{ + display:flex; + justify-content: right; + align-items: center; + font-size: 24px; + margin-right: 1rem; +} +#login-modal{ + max-width: 728px; + margin: 0 auto; + margin-top: 1rem; + margin-bottom: 1rem; +} +#login-logout-btn{ + display:inline; + max-width: 124px; +} +""" + +with gr.Blocks(css=css, theme=gr.themes.Default()) as demo: + print("is logged in:", is_logged_in()) + logged_in = gr.State(is_logged_in()) + if os.path.exists(".token"): + with open(".token", "r") as f: + user_token = gr.State(json.load(f)["token"]) + else: + user_token = gr.State("") + + with Modal(visible=False) as modal: + @gr.render(inputs=user_token) + def rerender1(user_token1): + with gr.Column(elem_id="login-modal"): + access_token = gr.Textbox( + label="Token", + lines=1, + value=user_token1, + type="password", + placeholder="Enter your access token here!", + info="Visit https://playground.tryonlabs.ai to retrieve your access token." + ) + + login_submit_btn = gr.Button("Login", scale=1, variant='primary') + login_submit_btn.click( + fn=lambda access_token: (login(access_token), Modal(visible=False), access_token), + inputs=[access_token], outputs=[logged_in, modal, user_token], + concurrency_limit=1) + + with gr.Row(elem_id="col-container"): + with gr.Column(): + gr.Markdown(f""" + # Model Swap AI + ## by TryOn Labs (https://www.tryonlabs.ai) + Swap a human model with a artificial model generated by Artificial Model while keeping the garment intact. + """) + + + @gr.render(inputs=logged_in) + def rerender(is_logged_in): + with gr.Column(): + if not is_logged_in: + with gr.Row(elem_id="credits-col-container"): + login_btn = gr.Button(value="Login", variant='primary', elem_id="login-logout-btn", size="sm") + login_btn.click(lambda: Modal(visible=True), None, modal) + else: + user_credits = get_user_credits(load_token()) + print("user_credits", user_credits) + gr.HTML(f"""

Your Credits: + {user_credits if user_credits is not None else "0"}

+

Visit + TryOn AI Playground to acquire more credits

""") + with gr.Row(elem_id="credits-col-container"): + logout_btn = gr.Button(value="Logout", scale=1, variant='primary', size="sm", + elem_id="login-logout-btn") + logout_btn.click(fn=logout, inputs=None, outputs=[logged_in, user_token], concurrency_limit=1) + + with gr.Column(elem_id="col-container"): + with gr.Row(): + with gr.Column(): + input_image = gr.Image(label="Original image", type='pil', height="400px", show_label=True) + prompt = gr.Textbox( + label="Prompt", + lines=3, + placeholder="Enter your prompt here!", + ) + dropdown = gr.Dropdown(["upper", "lower", "dress"], value="upper", label="Retain garment", + info="Select the garment type you want to retain in the generated image!") + + gallery = gr.Gallery( + label="Generated images", show_label=True, elem_id="gallery" + , columns=[3], rows=[1], object_fit="contain", height="auto") + + # output_image = gr.Image(label="Swapped model", type='pil', height="400px", show_label=True, + # show_download_button=True) + + with gr.Accordion("Advanced Settings", open=False): + with gr.Row(): + seed = gr.Number(label="Seed", value=-1, interactive=True, minimum=-1) + guidance_scale = gr.Number(label="Guidance Scale", value=7.5, interactive=True, minimum=0.0, + maximum=10.0, + step=0.1) + num_results = gr.Number(label="Number of results", value=2, minimum=1, maximum=5) + + with gr.Row(): + strength = gr.Slider(0.00, 1.00, value=0.99, label="Strength", + info="Choose between 0.00 and 1.00", step=0.01, interactive=True) + inference_steps = gr.Number(label="Inference Steps", value=20, interactive=True, minimum=1, step=1) + + with gr.Row(): + submit_button = gr.Button("Submit", variant='primary', scale=1) + reset_button = gr.ClearButton(value="Reset", scale=1) + + gr.on( + triggers=[submit_button.click], + fn=start_model_swap, + inputs=[input_image, prompt, dropdown, seed, guidance_scale, num_results, strength, inference_steps], + outputs=[gallery] + ) + + reset_button.click( + fn=lambda: (None, None, "upper", None, -1, 7.5, 2, 0.99, 20), + inputs=[], + outputs=[input_image, prompt, dropdown, gallery, seed, guidance_scale, + num_results, strength, inference_steps], + concurrency_limit=1, + ) + +if __name__ == '__main__': + demo.launch() diff --git a/demo/model_swap/requirements.txt b/demo/model_swap/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..33cf3e69d1fa3a563ef78571378a0c13b0482181 --- /dev/null +++ b/demo/model_swap/requirements.txt @@ -0,0 +1,2 @@ +gradio==4.44.1 +gradio_modal==0.0.3 \ No newline at end of file diff --git a/demo/outfit_generator/README.md b/demo/outfit_generator/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7d9df6269ffb03b634480183b6e854f14570c4b6 --- /dev/null +++ b/demo/outfit_generator/README.md @@ -0,0 +1,86 @@ +# FLUX.1-dev LoRA Outfit Generator Gradio Demo +## by TryOn Labs (https://www.tryonlabs.ai) +Generate an outfit by describing the color, pattern, fit, style, material, type, etc. + +## Model description + +FLUX.1-dev LoRA Outfit Generator can create an outfit by detailing the color, pattern, fit, style, material, and type. + +## Inference + +``` +import random + +from diffusers import FluxPipeline +import torch + +seed=42 +prompt = "denim dark blue 5-pocket ankle-length jeans in washed stretch denim slightly looser fit with a wide waist panel for best fit over the tummy and tapered legs with raw-edge frayed hems" +PRE_TRAINED_MODEL = "black-forest-labs/FLUX.1-dev" +FINE_TUNED_MODEL = "tryonlabs/FLUX.1-dev-LoRA-Outfit-Generator" + +# Load Flux +pipe = FluxPipeline.from_pretrained(PRE_TRAINED_MODEL, torch_dtype=torch.float16).to("cuda") + +# Load fine-tuned model +pipe.load_lora_weights(FINE_TUNED_MODEL, adapter_name="default", weight_name="outfit-generator.safetensors") + +seed = random.randint(0, MAX_SEED) + +generator = torch.Generator().manual_seed(seed) + +image = pipe(prompt, height=1024, width=1024, num_images_per_prompt=1, generator=generator, +guidance_scale=4.5, num_inference_steps=40).images[0] + +image.save("gen_image.jpg") +``` + +## Dataset used + +H&M Fashion Captions Dataset - 20.5k samples +https://huggingface.co/datasets/tomytjandra/h-and-m-fashion-caption + +## Repository used + +AI Toolkit by Ostris +https://github.com/ostris/ai-toolkit + +## Download model + +Weights for this model are available in Safetensors format. + +[Download](https://huggingface.co/tryonlabs/FLUX.1-dev-LoRA-Outfit-Generator/tree/main) them in the Files & versions tab. + +## Install dependencies + +``` +git clone https://github.com/tryonlabs/FLUX.1-dev-LoRA-Outfit-Generator.git +cd FLUX.1-dev-LoRA-Outfit-Generator +conda create -n demo python=3.12 +pip install -r requirements.txt +conda install pytorch pytorch-cuda=12.4 -c pytorch -c nvidia +``` + +## Run demo + +``` +gradio app.py +``` + +## Generated images + +![alt](images/sample1.jpeg "sample1") +#### A dress with Color: Black, Department: Dresses, Detail: High Low,Fabric-Elasticity: No Sretch, Fit: Fitted, Hemline: Slit, Material: Gabardine, Neckline: Collared, Pattern: Solid, Sleeve-Length: Sleeveless, Style: Casual, Type: Tunic, Waistline: Regular +*** +![alt](images/sample2.jpeg "sample2") +#### A dress with Color: Red, Department: Dresses, Detail: Belted, Fabric-Elasticity: High Stretch, Fit: Fitted, Hemline: Flared, Material: Gabardine, Neckline: Off The Shoulder, Pattern: Floral, Sleeve-Length: Sleeveless, Style: Elegant, Type: Fit and Flare, Waistline: High +*** +![alt](images/sample3.jpeg "sample3") +#### A dress with Color: Multicolored, Department: Dresses, Detail: Split, Fabric-Elasticity: High Stretch, Fit: Fitted, Hemline: Slit, Material: Gabardine, Neckline: V Neck, Pattern: Leopard, Sleeve-Length: Sleeveless, Style: Casual, Type: T Shirt, Waistline: Regular +*** +![alt](images/sample4.jpeg "sample4") +#### A dress with Color: Brown, Department: Dresses, Detail: Zipper, Fabric-Elasticity: No Sretch, Fit: Fitted, Hemline: Asymmetrical, Material: Satin, Neckline: Spaghetti Straps, Pattern: Floral, Sleeve-Length: Sleeveless, Style: Boho, Type: Cami Top, Waistline: High +*** + +## License +MIT [License](LICENSE) diff --git a/demo/outfit_generator/__init__.py b/demo/outfit_generator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..741b1d6dfc75bd30a50191f4a13450f64fc9c47b --- /dev/null +++ b/demo/outfit_generator/__init__.py @@ -0,0 +1 @@ +from .app import demo diff --git a/demo/outfit_generator/app.py b/demo/outfit_generator/app.py new file mode 100644 index 0000000000000000000000000000000000000000..37b6a1589d56eb7f4f82fbd47147dd1d83f51aa7 --- /dev/null +++ b/demo/outfit_generator/app.py @@ -0,0 +1,164 @@ +import json +import os.path +import random +import time + +import gradio as gr +import numpy as np +import spaces +import torch +from diffusers import FluxPipeline + +device = "cuda" if torch.cuda.is_available() else "cpu" +PRE_TRAINED_MODEL = "black-forest-labs/FLUX.1-dev" +FINE_TUNED_MODEL = "tryonlabs/FLUX.1-dev-LoRA-Outfit-Generator" +RESULTS_DIR = "~/results" +os.makedirs(RESULTS_DIR, exist_ok=True) + +if torch.cuda.is_available(): + torch_dtype = torch.bfloat16 +else: + torch_dtype = torch.float32 + +# Load Flux +pipe = FluxPipeline.from_pretrained(PRE_TRAINED_MODEL, torch_dtype=torch.float16).to("cuda") + +# Load your fine-tuned model +pipe.load_lora_weights(FINE_TUNED_MODEL, adapter_name="default", weight_name="outfit-generator.safetensors") + +MAX_SEED = np.iinfo(np.int32).max +MAX_IMAGE_SIZE = 1024 + + +@spaces.GPU(duration=65) +def infer( + prompt, + seed=42, + randomize_seed=False, + width=1024, + height=1024, + guidance_scale=4.5, + num_inference_steps=40, + progress=gr.Progress(track_tqdm=True), +): + if randomize_seed: + seed = random.randint(0, MAX_SEED) + + generator = torch.Generator().manual_seed(seed) + + image = pipe(prompt, height=width, width=height, num_images_per_prompt=1, generator=generator, + guidance_scale=guidance_scale, + num_inference_steps=num_inference_steps).images[0] + + try: + # save image + current_time = int(time.time() * 1000) + image.save(os.path.join(RESULTS_DIR, f"gen_img_{current_time}.png")) + with open(os.path.join(RESULTS_DIR, f"gen_img_{current_time}.json"), "w") as f: + json.dump({"prompt": prompt, "height": height, "width": width, "guidance_scale": guidance_scale, + "num_inference_steps": num_inference_steps, "seed": seed}, f) + except Exception as e: + print(str(e)) + + return image, seed + + +examples = [ + "stripe red striped jersey top in a soft cotton and modal blend with short sleeves a chest pocket and rounded hem", + "A dress with Color: Orange, Department: Dresses, Detail: Split Thigh, Fabric-Elasticity: No Sretch, Fit: Fitted, Hemline: Slit, Material: Gabardine, Neckline: Gathered, Pattern: Tropical, Sleeve-Length: Sleeveless, Style: Boho, Type: A Line Skirt, Waistline: High", + "treatment dark pink knee-length skirt in crocodile-patterned imitation leather high waist with belt loops and press-studs a zip fly diagonal side pockets and a slit at the front the polyester content of the skirt is partly recycled", + "A dress with Color: Maroon, Department: Dresses, Detail: Ruched Bust, Fabric-Elasticity: Slight Stretch, Fit: Fitted, Hemline: Slit, Material: Gabardine, Neckline: Spaghetti Straps, Pattern: Floral, Sleeve-Length: Sleeveless, Style: Boho, Type: Cami Top, Waistline: Regular", + "denim dark blue 5-pocket ankle-length jeans in washed stretch denim slightly looser fit with a wide waist panel for best fit over the tummy and tapered legs with raw-edge frayed hems" +] + +css = """ +#col-container { + margin: 0 auto; + max-width: 768px; +} +""" + +with gr.Blocks(css=css) as demo: + with gr.Column(elem_id="col-container"): + gr.Markdown(f""" + # FLUX.1-dev LoRA Outfit Generator + ## by TryOn Labs (https://www.tryonlabs.ai) + Generate an outfit by describing the color, pattern, fit, style, material, type, etc. + """) + with gr.Row(): + prompt = gr.Text( + label="Prompt", + show_label=False, + max_lines=1, + placeholder="Enter your prompt", + container=False, + ) + + run_button = gr.Button("Run", scale=0, variant="primary") + + result = gr.Image(label="Result", show_label=False) + + with gr.Accordion("Advanced Settings", open=False): + seed = gr.Slider( + label="Seed", + minimum=0, + maximum=MAX_SEED, + step=1, + value=0, + ) + + randomize_seed = gr.Checkbox(label="Randomize seed", value=True) + + with gr.Row(): + width = gr.Slider( + label="Width", + minimum=512, + maximum=MAX_IMAGE_SIZE, + step=32, + value=1024, + ) + + height = gr.Slider( + label="Height", + minimum=512, + maximum=MAX_IMAGE_SIZE, + step=32, + value=1024, + ) + + with gr.Row(): + guidance_scale = gr.Slider( + label="Guidance scale", + minimum=0.0, + maximum=7.5, + step=0.1, + value=4.5, + ) + + num_inference_steps = gr.Slider( + label="Number of inference steps", + minimum=1, + maximum=50, + step=1, + value=40, + ) + + gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True, + cache_mode="lazy") + gr.on( + triggers=[run_button.click, prompt.submit], + fn=infer, + inputs=[ + prompt, + seed, + randomize_seed, + width, + height, + guidance_scale, + num_inference_steps, + ], + outputs=[result, seed], + ) + +if __name__ == "__main__": + demo.launch(share=True) diff --git a/demo/outfit_generator/images/sample1.jpeg b/demo/outfit_generator/images/sample1.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..0cbfe0c5fce6444c668f60829327d0a0609a1c6c Binary files /dev/null and b/demo/outfit_generator/images/sample1.jpeg differ diff --git a/demo/outfit_generator/images/sample2.jpeg b/demo/outfit_generator/images/sample2.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..cf21363c01f6e46494ba96b8afe6f0fcf763e55a Binary files /dev/null and b/demo/outfit_generator/images/sample2.jpeg differ diff --git a/demo/outfit_generator/images/sample3.jpeg b/demo/outfit_generator/images/sample3.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..fc4ba801932242c2263375020c86a2a052fa6266 Binary files /dev/null and b/demo/outfit_generator/images/sample3.jpeg differ diff --git a/demo/outfit_generator/images/sample4.jpeg b/demo/outfit_generator/images/sample4.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..0ce577bdc552ba428937babaccc8044dbbff6bb8 Binary files /dev/null and b/demo/outfit_generator/images/sample4.jpeg differ diff --git a/demo/outfit_generator/requirements.txt b/demo/outfit_generator/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..eefc24b928b6cd97f1e242bcf125c1c2de32917e --- /dev/null +++ b/demo/outfit_generator/requirements.txt @@ -0,0 +1,10 @@ +spaces +gradio +diffusers +torch +numpy +transformers +accelerate +protobuf +sentencepiece +peft==0.13.2 \ No newline at end of file diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..774c5fe4eecbf484779f4c29d283f3230579ec43 --- /dev/null +++ b/environment.yml @@ -0,0 +1,179 @@ +name: tryondiffusion +channels: + - defaults +dependencies: + - blas=1.0=mkl + - bottleneck=1.3.5=py310h4e76f89_0 + - bzip2=1.0.8=h1de35cc_0 + - ca-certificates=2023.08.22=hecd8cb5_0 + - cffi=1.15.1=py310h6c40b1e_3 + - gmp=6.2.1=he9d5cce_3 + - gmpy2=2.1.2=py310hd5de756_0 + - intel-openmp=2023.1.0=ha357a0b_43547 + - jinja2=3.1.2=py310hecd8cb5_0 + - libcxx=14.0.6=h9765a3e_0 + - libffi=3.4.4=hecd8cb5_0 + - libprotobuf=3.20.3=hfff2838_0 + - libuv=1.44.2=h6c40b1e_0 + - mkl=2023.1.0=h8e150cf_43559 + - mkl-service=2.4.0=py310h6c40b1e_1 + - mkl_fft=1.3.8=py310h6c40b1e_0 + - mkl_random=1.2.4=py310ha357a0b_0 + - mpc=1.1.0=h6ef4df4_1 + - mpfr=4.0.2=h9066e36_1 + - mpmath=1.3.0=py310hecd8cb5_0 + - ncurses=6.4=hcec6c5f_0 + - networkx=3.1=py310hecd8cb5_0 + - ninja=1.10.2=hecd8cb5_5 + - ninja-base=1.10.2=haf03e11_5 + - numexpr=2.8.7=py310h827a554_0 + - openssl=3.0.11=hca72f7f_2 + - pandas=2.1.1=py310h3ea8b11_0 + - pip=23.2.1=py310hecd8cb5_0 + - pycparser=2.21=pyhd3eb1b0_0 + - python=3.10.13=h5ee71fb_0 + - python-dateutil=2.8.2=pyhd3eb1b0_0 + - python-tzdata=2023.3=pyhd3eb1b0_0 + - pytz=2023.3.post1=py310hecd8cb5_0 + - readline=8.2=hca72f7f_0 + - six=1.16.0=pyhd3eb1b0_1 + - sqlite=3.41.2=h6c40b1e_0 + - tbb=2021.8.0=ha357a0b_0 + - tk=8.6.12=h5d9f67b_0 + - tzdata=2023c=h04d1e81_0 + - wheel=0.38.4=py310hecd8cb5_0 + - xz=5.4.2=h6c40b1e_0 + - zlib=1.2.13=h4dc903c_0 + - pip: + - absl-py==2.0.0 + - aiofiles==23.2.1 + - annotated-types==0.6.0 + - anyio==4.3.0 + - appnope==0.1.3 + - asttokens==2.4.0 + - astunparse==1.6.3 + - backcall==0.2.0 + - cachetools==5.3.1 + - carvekit==4.1.1 + - certifi==2023.7.22 + - charset-normalizer==3.2.0 + - click==8.1.7 + - comm==0.1.4 + - contourpy==1.1.1 + - cycler==0.11.0 + - debugpy==1.8.0 + - decorator==5.1.1 + - diffusers==0.29.2 + - einops==0.7.0 + - exceptiongroup==1.1.3 + - executing==1.2.0 + - fastapi==0.108.0 + - ffmpy==0.3.3 + - filelock==3.12.4 + - flatbuffers==23.5.26 + - fonttools==4.42.1 + - fsspec==2024.3.1 + - gast==0.5.4 + - google-auth==2.23.3 + - google-auth-oauthlib==1.0.0 + - google-pasta==0.2.0 + - gradio==4.39.0 + - gradio-client==1.1.1 + - gradio-modal==0.0.3 + - grpcio==1.59.0 + - h11==0.14.0 + - h5py==3.10.0 + - httpcore==1.0.5 + - httpx==0.27.0 + - huggingface-hub==0.23.4 + - idna==3.4 + - imageio==2.34.0 + - importlib-metadata==8.0.0 + - importlib-resources==6.4.0 + - ipykernel==6.25.2 + - ipython==8.15.0 + - jedi==0.19.0 + - jupyter-client==8.3.1 + - jupyter-core==5.3.1 + - keras==2.14.0 + - kiwisolver==1.4.5 + - lazy-loader==0.3 + - libclang==16.0.6 + - loguru==0.7.2 + - markdown==3.5 + - markdown-it-py==3.0.0 + - markupsafe==2.1.3 + - matplotlib==3.8.0 + - matplotlib-inline==0.1.6 + - mdurl==0.1.2 + - ml-dtypes==0.2.0 + - nest-asyncio==1.5.8 + - numpy==1.26.4 + - oauthlib==3.2.2 + - opencv-python==4.8.1.78 + - opt-einsum==3.3.0 + - orjson==3.10.6 + - packaging==23.1 + - parso==0.8.3 + - pexpect==4.8.0 + - pickleshare==0.7.5 + - pillow==10.1.0 + - platformdirs==3.10.0 + - prompt-toolkit==3.0.39 + - protobuf==4.24.4 + - psutil==5.9.5 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - pyasn1==0.5.0 + - pyasn1-modules==0.3.0 + - pydantic==2.5.3 + - pydantic-core==2.14.6 + - pydub==0.25.1 + - pygments==2.16.1 + - pyparsing==3.1.1 + - python-dotenv==1.0.1 + - python-multipart==0.0.9 + - pyyaml==6.0.1 + - pyzmq==25.1.1 + - regex==2024.5.15 + - requests==2.31.0 + - requests-oauthlib==1.3.1 + - rich==13.7.1 + - rsa==4.9 + - ruff==0.5.5 + - safetensors==0.4.3 + - scikit-image==0.22.0 + - scipy==1.11.4 + - semantic-version==2.10.0 + - setuptools==69.0.3 + - shellingham==1.5.4 + - sniffio==1.3.1 + - stack-data==0.6.2 + - starlette==0.32.0.post1 + - sympy==1.12 + - tensorboard==2.14.1 + - tensorboard-data-server==0.7.1 + - tensorflow==2.14.0 + - tensorflow-estimator==2.14.0 + - tensorflow-io-gcs-filesystem==0.34.0 + - termcolor==2.3.0 + - tifffile==2024.2.12 + - tokenizers==0.19.1 + - tomlkit==0.12.0 + - torch==2.1.2 + - torchvision==0.16.2 + - tornado==6.3.3 + - tqdm==4.66.1 + - traitlets==5.10.0 + - transformers==4.42.4 + - typer==0.12.3 + - typing==3.7.4.3 + - typing-extensions==4.8.0 + - urllib3==2.0.5 + - uvicorn==0.25.0 + - wcwidth==0.2.6 + - websockets==11.0.3 + - werkzeug==3.0.0 + - wrapt==1.14.1 + - zipp==3.19.2 +prefix: /Users/apple/miniconda3/envs/tryondiffusion diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..5ae83408c0b40a3045f4d928105fb4ff74c19f00 --- /dev/null +++ b/main.py @@ -0,0 +1,44 @@ +from dotenv import load_dotenv + +load_dotenv() + +import time +import os +import argparse + +from tryon.preprocessing import segment_human, segment_garment, extract_garment + +if __name__ == '__main__': + argp = argparse.ArgumentParser(description="Tryon preprocessing") + argp.add_argument('-d', + '--dataset', + type=str, default="data", help='Path of the dataset dir') + argp.add_argument('-a', + '--action', + type=str, default="", help='Ex. segment_garment, extract_garment, segment_human') + argp.add_argument('-c', + '--cls', + type=str, default="upper", help='Ex. upper, lower, all') + args = argp.parse_args() + + if args.action == "segment_garment": + # 1. segment garment + print('Start time:', int(time.time())) + segment_garment(inputs_dir=os.path.join(args.dataset, "original_cloth"), + outputs_dir=os.path.join(args.dataset, "garment_segmented"), cls=args.cls) + print("End time:", int(time.time())) + + elif args.action == "extract_garment": + # 2. extract garment + print('Start time:', int(time.time())) + extract_garment(inputs_dir=os.path.join(args.dataset, "original_cloth"), + outputs_dir=os.path.join(args.dataset, "cloth"), cls=args.cls, resize_to_width=400) + print("End time:", int(time.time())) + + elif args.action == "segment_human": + # 2. segment human + print('Start time:', int(time.time())) + image_path = os.path.join(args.dataset, "original_human", "model.jpg") + output_dir = os.path.join(args.dataset, "human_segmented") + segment_human(image_path=image_path, output_dir=output_dir) + print("End time:", int(time.time())) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c7efd1917bc6da8b9fcef41a99c11cc4d87b13b0 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,15 @@ +torch +numpy +opencv-python +pillow +matplotlib +tqdm +torchvision +einops +python-dotenv +scikit-image +diffusers +transformers +gradio==4.44.1 +gradio_modal==0.0.3 +python-dotenv diff --git a/run_demo.py b/run_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..27aac1441f9dea536e84dd1e26a5510227018b31 --- /dev/null +++ b/run_demo.py @@ -0,0 +1,18 @@ +import argparse + +if __name__ == '__main__': + argp = argparse.ArgumentParser(description="Gradio demo") + argp.add_argument('-n', + '--name', + type=str, default="data", help='Name of the gradio demo to launch') + args = argp.parse_args() + + if args.name == "extract_garment": + from demo import extract_garment_demo as demo + demo.launch() + elif args.name == "model_swap": + from demo import model_swap_demo as demo + demo.launch() + elif args.name == "outfit_generator": + from demo import outfit_generator_demo as demo + demo.launch() diff --git a/run_ootd.py b/run_ootd.py new file mode 100644 index 0000000000000000000000000000000000000000..8fed0be62864a4351dfa240079520fc3ac82c233 --- /dev/null +++ b/run_ootd.py @@ -0,0 +1,37 @@ +import argparse +import subprocess +import os +import pathlib + +parser = argparse.ArgumentParser(description='run ootd') +parser.add_argument('--gpu_id', '-g', type=int, default=0, required=False) +parser.add_argument('--model_path', type=str, default="", required=True) +parser.add_argument('--cloth_path', type=str, default="", required=True) +parser.add_argument('--output_path', type=str, default="", required=True) +parser.add_argument('--model_type', type=str, default="hd", required=False) +parser.add_argument('--category', '-c', type=int, default=0, required=False) +parser.add_argument('--scale', type=float, default=2.0, required=False) +parser.add_argument('--step', type=int, default=20, required=False) +parser.add_argument('--sample', type=int, default=4, required=False) +parser.add_argument('--seed', type=int, default=-1, required=False) +args = parser.parse_args() + +print(args) + +if __name__ == '__main__': + ootdiffusion_dir = "/home/ubuntu/ootdiffusion" + + command = (f"{os.path.join(str(pathlib.Path.home()), 'miniconda3/envs/ootdiffusion/bin/python')} " + f"run.py --model_path {args.model_path} --cloth_path {args.cloth_path} " + f"--output_path {args.output_path} --model_type {args.model_type} --category {args.category} " + f"--image_scale {args.scale} --gpu_id {args.gpu_id} --n_samples {args.sample} --seed {args.seed} " + f"--n_steps {args.step}") + + print("command:", command, command.split(" ")) + + p = subprocess.Popen(command.split(" "), stdout=subprocess.PIPE, stderr=subprocess.PIPE, + cwd=ootdiffusion_dir) + out, err = p.communicate() + print(out, err) + + diff --git a/scripts/install_conda.sh b/scripts/install_conda.sh new file mode 100644 index 0000000000000000000000000000000000000000..cf3cfef7ab5c3056141f51e276ec46ecf080b343 --- /dev/null +++ b/scripts/install_conda.sh @@ -0,0 +1,10 @@ +# Setup Ubuntu +sudo apt update --yes +sudo apt upgrade --yes + +# Get Miniconda and make it the main Python interpreter +wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh +bash ~/miniconda.sh -b -p ~/miniconda +rm ~/miniconda.sh + +export PATH=~/miniconda/bin:$PATH \ No newline at end of file diff --git a/scripts/install_sam2.sh b/scripts/install_sam2.sh new file mode 100644 index 0000000000000000000000000000000000000000..540ab170b6c2c99746015b57bb0590122eb88646 --- /dev/null +++ b/scripts/install_sam2.sh @@ -0,0 +1,11 @@ +ENV_NAME="sam2" +sudo apt-get -y update && sudo apt-get install -y --no-install-recommends ffmpeg libavutil-dev libavcodec-dev libavformat-dev libswscale-dev pkg-config build-essential libffi-dev +git clone https://github.com/facebookresearch/sam2.git ~/$ENV_NAME +conda create -n $ENV_NAME python=3.10 +conda install -y -n $ENV_NAME pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia +~/miniconda3/envs/$ENV_NAME/bin/pip install -y -e ~/$ENV_NAME +sh ~/$ENV_NAME/checkpoints/download_ckpts.sh +mv sam2.1_hiera_base_plus.pt ~/$ENV_NAME/checkpoints/ +mv sam2.1_hiera_large.pt ~/$ENV_NAME/checkpoints/ +mv sam2.1_hiera_small.pt ~/$ENV_NAME/checkpoints/ +mv sam2.1_hiera_tiny.pt ~/$ENV_NAME/checkpoints/ diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..7f9d52bcf84de36b34fe662a50100aee7794f00a --- /dev/null +++ b/setup.py @@ -0,0 +1,31 @@ +from pathlib import Path + +from setuptools import setup, find_packages + +this_directory = Path(__file__).parent +long_description = (this_directory / "README.md").read_text() + +setup( + name="tryondiffusion", + version="0.1.0", + license='Creative Commons BY-NC 4.0', + packages=find_packages(), + long_description=long_description, + long_description_content_type='text/markdown', + url='https://github.com/kailashahirwar/tryondiffusion', + keywords='Unofficial implementation of TryOnDiffusion: A Tale Of Two UNets', + install_requires=[ + "torch", + "numpy", + "opencv-python", + "pillow", + "matplotlib", + "tqdm", + "torchvision", + "einops", + "scipy", + "scikit-image", + "gradio==4.44.1", + "gradio_modal==0.0.3" + ] +) diff --git a/tryon/README.md b/tryon/README.md new file mode 100644 index 0000000000000000000000000000000000000000..fbd459cd6b38a4d26b80f611d4f572619cedb2a1 --- /dev/null +++ b/tryon/README.md @@ -0,0 +1,34 @@ +# Try-On Preprocessing + +Before you start, make a .env file in your project's main folder. Put these environment variables inside it. +``` +U2NET_CLOTH_SEG_CHECKPOINT_PATH=cloth_segm.pth +``` + +#### Remember to load environment variables before you start running scripts. + +``` +from dotenv import load_dotenv + +load_dotenv() +``` + +### segment garment + +``` +from tryon.preprocessing import segment_garment + +segment_garment(inputs_dir=, + outputs_dir=, cls=) +``` + +possible values for cls: lower, upper, all + +### extract garment + +``` +from tryon.preprocessing import extract_garment + +extract_garment(inputs_dir=, + outputs_dir=, cls=) +``` \ No newline at end of file diff --git a/tryon/__init__.py b/tryon/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tryon/models/__init__.py b/tryon/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tryon/models/ootdiffusion/setup.sh b/tryon/models/ootdiffusion/setup.sh new file mode 100644 index 0000000000000000000000000000000000000000..8530fabbefcad553a34542dfc6cb8fc65d9c4ff9 --- /dev/null +++ b/tryon/models/ootdiffusion/setup.sh @@ -0,0 +1,30 @@ +ENV_NAME="ootdiffusion" +PROJECT_DIR="/home/ubuntu/ootdiffusion" + +if [ ! -d ~/miniconda3/envs/$ENV_NAME ]; then + echo "creating conda environment" + conda create -y -n $ENV_NAME python==3.10 +fi + +# clone repository +if [ ! -d $PROJECT_DIR ]; then + echo "cloning OOTDiffusion repository" + git clone https://github.com/tryonlabs/OOTDiffusion.git $PROJECT_DIR +fi + +~/miniconda3/envs/$ENV_NAME/bin/pip install -r $PROJECT_DIR/requirements.txt + +if [ ! -d $PROJECT_DIR/checkpoints/ootd ]; then + echo "downloading checkpoints" + + # download checkpoints + git clone https://huggingface.co/levihsu/OOTDiffusion ~/ootd-checkpoints + git clone https://huggingface.co/openai/clip-vit-large-patch14 ~/clip-vit-large-patch14 + + mv ~/ootd-checkpoints/checkpoints/* $PROJECT_DIR/checkpoints/ + rm -rf ~/ootd-checkpoints + + mv ~/clip-vit-large-patch14 $PROJECT_DIR/checkpoints/ + rm -rf ~/clip-vit-large-patch14 + +fi \ No newline at end of file diff --git a/tryon/preprocessing/__init__.py b/tryon/preprocessing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e863f7dc3eef9bd9385768ed40c3d7dc5a221ada --- /dev/null +++ b/tryon/preprocessing/__init__.py @@ -0,0 +1,3 @@ +from .preprocess_garment import segment_garment, extract_garment +from .utils import convert_to_jpg +from .preprocess_human import segment_human diff --git a/tryon/preprocessing/captioning/__init__.py b/tryon/preprocessing/captioning/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b42bc4e30b278e0ca8237e7ec90a7bf7eb46f9ba --- /dev/null +++ b/tryon/preprocessing/captioning/__init__.py @@ -0,0 +1,2 @@ +from .generate_caption import (caption_image, create_llava_next_pipeline, + create_phi35mini_pipeline, convert_outfit_json_to_caption) \ No newline at end of file diff --git a/tryon/preprocessing/captioning/generate_caption.py b/tryon/preprocessing/captioning/generate_caption.py new file mode 100644 index 0000000000000000000000000000000000000000..8582ae58e7c8366a81dd9f26531dbf5b8c87246f --- /dev/null +++ b/tryon/preprocessing/captioning/generate_caption.py @@ -0,0 +1,108 @@ +import json + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline +from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration + + +def caption_image(image, question, model=None, processor=None, json_only=False): + """ + Extract outfit details using an image-to-text model + :param image: input image + :param question: question + :param model: model pipeline + :param processor: processor + :param json_only: True or False - if json only + :return: json data + """ + if model is None and processor is None: + model, processor = create_llava_next_pipeline() + + conversation = [ + { + "role": "user", + "content": [ + {"type": "image"}, + {"type": "text", "text": question}, + ], + }, + ] + + prompt = processor.apply_chat_template(conversation, add_generation_prompt=True) + inputs = processor(image, prompt, return_tensors="pt").to("cuda:0") + + output = model.generate(**inputs, max_new_tokens=300) + output = processor.decode(output[0], skip_special_tokens=True).split("[/INST]")[-1] + json_data = json.loads(output.replace("```json", "").replace("```", "").strip()) + + if not json_only: + generated_caption = convert_outfit_json_to_caption(json_data) + else: + generated_caption = None + + return json_data, generated_caption + + +def create_phi35mini_pipeline(): + """ + Create Phi-3.5-mini-instruct pipeline + :return: model pipeline + """ + torch.random.manual_seed(0) + + model = AutoModelForCausalLM.from_pretrained( + "microsoft/Phi-3.5-mini-instruct", + device_map="cuda", + torch_dtype="auto", + trust_remote_code=True, + attn_implementation="flash_attention_2" + ) + tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct") + + pipe = pipeline( + "text-generation", + model=model, + tokenizer=tokenizer, + ) + return pipe + + +def create_llava_next_pipeline(): + """ + Create LlaVA-NeXT pipeline + :return: model pipeline + """ + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf") + model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", + torch_dtype=torch.float16, + low_cpu_mem_usage=True) + model.to(device) + + return model, processor + + +def convert_outfit_json_to_caption(json_data, pipe=None): + """ + Convert JSON data of an outfit into a natural language caption + :param json_data: json data + :param pipe: model pipeline + :return: generated caption + """ + if pipe is None: + pipe = create_phi35mini_pipeline() + + generation_args = { + "max_new_tokens": 300, + "return_full_text": False, + "temperature": 0.0, + "do_sample": False, + } + + messages = [{"role": "user", + "content": f'Convert the {json.dumps(json_data)} JSON data into a natural ' + f'language paragraph beginning with "An outfit with"'}] + + output = pipe(messages, **generation_args)[0]['generated_text'].strip() + print(f"Output: {output}") + return output diff --git a/tryon/preprocessing/extract_garment_new.py b/tryon/preprocessing/extract_garment_new.py new file mode 100644 index 0000000000000000000000000000000000000000..60ec4dc3aac748a743455317f6648a0abfb2aef3 --- /dev/null +++ b/tryon/preprocessing/extract_garment_new.py @@ -0,0 +1,91 @@ +import os + +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +from torchvision import transforms + +from .u2net import load_cloth_segm_model +from .utils import NormalizeImage, naive_cutout, resize_by_bigger_index, image_resize + + +def extract_garment(image, cls="all", resize_to_width=None, net=None, device=None): + """ + extracts garments from the given image + :param image: input image + :param cls: garment classes to extract + :param resize_to_width: if required + :return: extracted garments + """ + + if net is None: + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + net = load_cloth_segm_model(device, os.environ.get("U2NET_CLOTH_SEGM_CHECKPOINT_PATH"), in_ch=3, out_ch=4) + + transform_fn = transforms.Compose( + [transforms.ToTensor(), + NormalizeImage(0.5, 0.5)] + ) + + img_size = image.size + img = image.resize((768, 768), Image.BICUBIC) + image_tensor = transform_fn(img) + image_tensor = torch.unsqueeze(image_tensor, 0) + + with torch.no_grad(): + output_tensor = net(image_tensor.to(device)) + output_tensor = F.log_softmax(output_tensor[0], dim=1) + output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1] + output_tensor = torch.squeeze(output_tensor, dim=0) + output_arr = output_tensor.cpu().numpy() + + classes = {1: "upper", 2: "lower", 3: "dress"} + + if cls == "all": + classes_to_save = [] + + # Check which classes are present in the image + for cls in range(1, 4): # Exclude background class (0) + if np.any(output_arr == cls): + classes_to_save.append(cls) + elif cls == "upper": + classes_to_save = [1] + elif cls == "lower": + classes_to_save = [2] + elif cls == "dress": + classes_to_save = [3] + else: + raise ValueError(f"Unknown cls: {cls}") + + garments = dict() + + for cls1 in classes_to_save: + alpha_mask = (output_arr == cls1).astype(np.uint8) * 255 + alpha_mask = alpha_mask[0] # Selecting the first channel to make it 2D + alpha_mask_img = Image.fromarray(alpha_mask, mode='L') + alpha_mask_img = alpha_mask_img.resize(img_size, Image.BICUBIC) + + cutout = np.array(naive_cutout(image, alpha_mask_img)) + cutout = resize_by_bigger_index(cutout) + + canvas = np.ones((1024, 768, 3), np.uint8) * 255 + y1, y2 = (canvas.shape[0] - cutout.shape[0]) // 2, (canvas.shape[0] + cutout.shape[0]) // 2 + x1, x2 = (canvas.shape[1] - cutout.shape[1]) // 2, (canvas.shape[1] + cutout.shape[1]) // 2 + + alpha_s = cutout[:, :, 3] / 255.0 + alpha_l = 1.0 - alpha_s + + for c in range(0, 3): + canvas[y1:y2, x1:x2, c] = (alpha_s * cutout[:, :, c] + + alpha_l * canvas[y1:y2, x1:x2, c]) + + # resize image before saving + if resize_to_width: + canvas = image_resize(canvas, width=resize_to_width) + + canvas = Image.fromarray(canvas) + + garments[classes[cls1]] = canvas + + return garments diff --git a/tryon/preprocessing/preprocess_garment.py b/tryon/preprocessing/preprocess_garment.py new file mode 100644 index 0000000000000000000000000000000000000000..7b6a5aa5f63c6d228dbd10a5dfbe11e9287c980b --- /dev/null +++ b/tryon/preprocessing/preprocess_garment.py @@ -0,0 +1,107 @@ +import glob +import os +from pathlib import Path + +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +from torchvision import transforms +from tqdm import tqdm + +from .u2net import load_cloth_segm_model +from .utils import NormalizeImage, naive_cutout, resize_by_bigger_index, image_resize + + +def segment_garment(inputs_dir, outputs_dir, cls="all"): + os.makedirs(outputs_dir, exist_ok=True) + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + transform_fn = transforms.Compose( + [transforms.ToTensor(), + NormalizeImage(0.5, 0.5)] + ) + + net = load_cloth_segm_model(device, os.environ.get("U2NET_CLOTH_SEGM_CHECKPOINT_PATH"), in_ch=3, out_ch=4) + + images_list = sorted(os.listdir(inputs_dir)) + pbar = tqdm(total=len(images_list)) + + for image_name in images_list: + img = Image.open(os.path.join(inputs_dir, image_name)).convert('RGB') + img_size = img.size + img = img.resize((768, 768), Image.BICUBIC) + image_tensor = transform_fn(img) + image_tensor = torch.unsqueeze(image_tensor, 0) + + with torch.no_grad(): + output_tensor = net(image_tensor.to(device)) + output_tensor = F.log_softmax(output_tensor[0], dim=1) + output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1] + output_tensor = torch.squeeze(output_tensor, dim=0) + output_arr = output_tensor.cpu().numpy() + + if cls == "all": + classes_to_save = [] + + # Check which classes are present in the image + for cls in range(1, 4): # Exclude background class (0) + if np.any(output_arr == cls): + classes_to_save.append(cls) + elif cls == "upper": + classes_to_save = [1] + elif cls == "lower": + classes_to_save = [2] + elif cls == "dress": + classes_to_save = [3] + else: + raise ValueError(f"Unknown cls: {cls}") + + for cls1 in classes_to_save: + alpha_mask = (output_arr == cls1).astype(np.uint8) * 255 + alpha_mask = alpha_mask[0] # Selecting the first channel to make it 2D + alpha_mask_img = Image.fromarray(alpha_mask, mode='L') + alpha_mask_img = alpha_mask_img.resize(img_size, Image.BICUBIC) + alpha_mask_img.save(os.path.join(outputs_dir, f'{image_name.split(".")[0]}_{cls1}.jpg')) + + pbar.update(1) + + pbar.close() + + +def extract_garment(inputs_dir, outputs_dir, cls="all", resize_to_width=None): + os.makedirs(outputs_dir, exist_ok=True) + cloth_mask_dir = os.path.join(Path(outputs_dir).parent.absolute(), "cloth-mask") + os.makedirs(cloth_mask_dir, exist_ok=True) + + segment_garment(inputs_dir, os.path.join(Path(outputs_dir).parent.absolute(), "cloth-mask"), cls=cls) + + images_path = sorted(glob.glob(os.path.join(inputs_dir, "*"))) + masks_path = sorted(glob.glob(os.path.join(cloth_mask_dir, "*"))) + img = Image.open(images_path[0]) + + for mask_path in masks_path: + mask = Image.open(mask_path) + + cutout = np.array(naive_cutout(img, mask)) + cutout = resize_by_bigger_index(cutout) + + canvas = np.ones((1024, 768, 3), np.uint8) * 255 + y1, y2 = (canvas.shape[0] - cutout.shape[0]) // 2, (canvas.shape[0] + cutout.shape[0]) // 2 + x1, x2 = (canvas.shape[1] - cutout.shape[1]) // 2, (canvas.shape[1] + cutout.shape[1]) // 2 + + alpha_s = cutout[:, :, 3] / 255.0 + alpha_l = 1.0 - alpha_s + + for c in range(0, 3): + canvas[y1:y2, x1:x2, c] = (alpha_s * cutout[:, :, c] + + alpha_l * canvas[y1:y2, x1:x2, c]) + + # resize image before saving + if resize_to_width: + canvas = image_resize(canvas, width=resize_to_width) + + canvas = Image.fromarray(canvas) + + canvas.save(os.path.join(outputs_dir, f"{os.path.basename(mask_path).split('.')[0]}.jpg"), format='JPEG') diff --git a/tryon/preprocessing/preprocess_human.py b/tryon/preprocessing/preprocess_human.py new file mode 100644 index 0000000000000000000000000000000000000000..82b2596c0b35ae83625d768b1d659dcc17f60bdf --- /dev/null +++ b/tryon/preprocessing/preprocess_human.py @@ -0,0 +1,86 @@ +import os + +import cv2 +import numpy as np +import torch +from PIL import Image +from skimage import io +from torch.autograd import Variable +from torch.utils.data import DataLoader +from torchvision import transforms + +from .u2net import RescaleT, ToTensorLab, SalObjDataset, normPRED, load_human_segm_model + + +def pred_to_image(predictions, image_path): + im = Image.fromarray(predictions.squeeze().cpu().data.numpy() * 255).convert('RGB') + image = io.imread(image_path) + imo = im.resize((image.shape[1], image.shape[0]), resample=Image.BILINEAR) + return imo + + +def segment_human(image_path, output_dir): + """ + Segment human using U-2-Net + :param image_path: image path + :param output_dir: output directory + """ + model_name = "u2net" + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + images = [image_path] + + # 1. dataloader + test_salobj_dataset = SalObjDataset(img_name_list=images, + lbl_name_list=[], + transform=transforms.Compose([RescaleT(320), + ToTensorLab(flag=0)]) + ) + test_salobj_dataloader = DataLoader(test_salobj_dataset, + batch_size=1, + shuffle=False, + num_workers=1) + + net = load_human_segm_model(device, model_name) + + # 2. inference + for i_test, data_test in enumerate(test_salobj_dataloader): + print("inferencing:", images[i_test].split(os.sep)[-1]) + + inputs_test = data_test['image'] + inputs_test = inputs_test.type(torch.FloatTensor) + + if torch.cuda.is_available(): + inputs_test = Variable(inputs_test.cuda()) + else: + inputs_test = Variable(inputs_test) + + d1, d2, d3, d4, d5, d6, d7 = net(inputs_test) + + # normalization + pred = d1[:, 0, :, :] + pred = normPRED(pred) + + mask = pred_to_image(pred, images[i_test]) + mask_cv2 = cv2.cvtColor(np.array(mask), cv2.COLOR_RGB2BGR) + + subimage = cv2.subtract(mask_cv2, cv2.imread(images[i_test])) + original = Image.open(images[i_test]) + subimage = Image.fromarray(cv2.cvtColor(subimage, cv2.COLOR_BGR2RGB)) + + subimage = subimage.convert("RGBA") + original = original.convert("RGBA") + + subdata = subimage.getdata() + ogdata = original.getdata() + + newdata = [] + for i in range(subdata.size[0] * subdata.size[1]): + if subdata[i][0] == 0 and subdata[i][1] == 0 and subdata[i][2] == 0: + newdata.append((231, 231, 231, 231)) + else: + newdata.append(ogdata[i]) + subimage.putdata(newdata) + + subimage.save(os.path.join(output_dir, f"{images[i_test].split(os.sep)[-1].split('.')[0]}.png")) + + del d1, d2, d3, d4, d5, d6, d7 diff --git a/tryon/preprocessing/sam2/__init__.py b/tryon/preprocessing/sam2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7a8cf59d8cb50d032ac9bd394579f57ab3ee544e --- /dev/null +++ b/tryon/preprocessing/sam2/__init__.py @@ -0,0 +1,23 @@ +import os +from PIL import Image +from pathlib import Path +import numpy as np +import torch +from sam2.build_sam import build_sam2 +from sam2.sam2_image_predictor import SAM2ImagePredictor + +SAM2_DIR = os.path.join(str(Path.home()), 'sam2') + +checkpoint = os.path.join(SAM2_DIR, "checkpoints/sam2.1_hiera_large.pt") +model_cfg = "configs/sam2.1/sam2.1_hiera_l.yaml" +predictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint)) + +with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): + predictor.set_image(Image.open("img000.webp")) + input_point = np.array([[500, 375]]) + input_label = np.array([1]) + masks, _, _ = predictor.predict( + point_coords=input_point, + point_labels=input_label, + multimask_output=True) + print(masks) diff --git a/tryon/preprocessing/u2net/__init__.py b/tryon/preprocessing/u2net/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c006cc9c16ec275db1b98080821a255466dc6c9d --- /dev/null +++ b/tryon/preprocessing/u2net/__init__.py @@ -0,0 +1,3 @@ +from .load_u2net import load_cloth_segm_model, load_human_segm_model +from .data_loader import SalObjDataset, RescaleT, ToTensorLab, ToTensor +from .utils import normPRED diff --git a/tryon/preprocessing/u2net/data_loader.py b/tryon/preprocessing/u2net/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..08a4959954016a4ca138e3750eb18f6633ed6e78 --- /dev/null +++ b/tryon/preprocessing/u2net/data_loader.py @@ -0,0 +1,277 @@ +from __future__ import print_function, division + +import random + +import numpy as np +import torch +from skimage import io, transform, color +from torch.utils.data import Dataset + + +class RescaleT(object): + + def __init__(self, output_size): + assert isinstance(output_size, (int, tuple)) + self.output_size = output_size + + def __call__(self, sample): + imidx, image, label = sample['imidx'], sample['image'], sample['label'] + + h, w = image.shape[:2] + + if isinstance(self.output_size, int): + if h > w: + new_h, new_w = self.output_size * h / w, self.output_size + else: + new_h, new_w = self.output_size, self.output_size * w / h + else: + new_h, new_w = self.output_size + + new_h, new_w = int(new_h), int(new_w) + + # #resize the image to new_h x new_w and convert image from range [0,255] to [0,1] + # img = transform.resize(image,(new_h,new_w),mode='constant') + # lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True) + + img = transform.resize(image, (self.output_size, self.output_size), mode='constant') + lbl = transform.resize(label, (self.output_size, self.output_size), mode='constant', order=0, + preserve_range=True) + + return {'imidx': imidx, 'image': img, 'label': lbl} + + +class Rescale(object): + + def __init__(self, output_size): + assert isinstance(output_size, (int, tuple)) + self.output_size = output_size + + def __call__(self, sample): + imidx, image, label = sample['imidx'], sample['image'], sample['label'] + + if random.random() >= 0.5: + image = image[::-1] + label = label[::-1] + + h, w = image.shape[:2] + + if isinstance(self.output_size, int): + if h > w: + new_h, new_w = self.output_size * h / w, self.output_size + else: + new_h, new_w = self.output_size, self.output_size * w / h + else: + new_h, new_w = self.output_size + + new_h, new_w = int(new_h), int(new_w) + + # #resize the image to new_h x new_w and convert image from range [0,255] to [0,1] + img = transform.resize(image, (new_h, new_w), mode='constant') + lbl = transform.resize(label, (new_h, new_w), mode='constant', order=0, preserve_range=True) + + return {'imidx': imidx, 'image': img, 'label': lbl} + + +class RandomCrop(object): + + def __init__(self, output_size): + assert isinstance(output_size, (int, tuple)) + if isinstance(output_size, int): + self.output_size = (output_size, output_size) + else: + assert len(output_size) == 2 + self.output_size = output_size + + def __call__(self, sample): + imidx, image, label = sample['imidx'], sample['image'], sample['label'] + + if random.random() >= 0.5: + image = image[::-1] + label = label[::-1] + + h, w = image.shape[:2] + new_h, new_w = self.output_size + + top = np.random.randint(0, h - new_h) + left = np.random.randint(0, w - new_w) + + image = image[top: top + new_h, left: left + new_w] + label = label[top: top + new_h, left: left + new_w] + + return {'imidx': imidx, 'image': image, 'label': label} + + +class ToTensor(object): + """Convert ndarrays in sample to Tensors.""" + + def __call__(self, sample): + + imidx, image, label = sample['imidx'], sample['image'], sample['label'] + + tmpImg = np.zeros((image.shape[0], image.shape[1], 3)) + tmpLbl = np.zeros(label.shape) + + image = image / np.max(image) + if (np.max(label) < 1e-6): + label = label + else: + label = label / np.max(label) + + if image.shape[2] == 1: + tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 0.229 + tmpImg[:, :, 1] = (image[:, :, 0] - 0.485) / 0.229 + tmpImg[:, :, 2] = (image[:, :, 0] - 0.485) / 0.229 + else: + tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 0.229 + tmpImg[:, :, 1] = (image[:, :, 1] - 0.456) / 0.224 + tmpImg[:, :, 2] = (image[:, :, 2] - 0.406) / 0.225 + + tmpLbl[:, :, 0] = label[:, :, 0] + + tmpImg = tmpImg.transpose((2, 0, 1)) + tmpLbl = label.transpose((2, 0, 1)) + + return {'imidx': torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)} + + +class ToTensorLab(object): + """Convert ndarrays in sample to Tensors.""" + + def __init__(self, flag=0): + self.flag = flag + + def __call__(self, sample): + + imidx, image, label = sample['imidx'], sample['image'], sample['label'] + + tmpLbl = np.zeros(label.shape) + + if (np.max(label) < 1e-6): + label = label + else: + label = label / np.max(label) + + # change the color space + if self.flag == 2: # with rgb and Lab colors + tmpImg = np.zeros((image.shape[0], image.shape[1], 6)) + tmpImgt = np.zeros((image.shape[0], image.shape[1], 3)) + if image.shape[2] == 1: + tmpImgt[:, :, 0] = image[:, :, 0] + tmpImgt[:, :, 1] = image[:, :, 0] + tmpImgt[:, :, 2] = image[:, :, 0] + else: + tmpImgt = image + tmpImgtl = color.rgb2lab(tmpImgt) + + # nomalize image to range [0,1] + tmpImg[:, :, 0] = (tmpImgt[:, :, 0] - np.min(tmpImgt[:, :, 0])) / ( + np.max(tmpImgt[:, :, 0]) - np.min(tmpImgt[:, :, 0])) + tmpImg[:, :, 1] = (tmpImgt[:, :, 1] - np.min(tmpImgt[:, :, 1])) / ( + np.max(tmpImgt[:, :, 1]) - np.min(tmpImgt[:, :, 1])) + tmpImg[:, :, 2] = (tmpImgt[:, :, 2] - np.min(tmpImgt[:, :, 2])) / ( + np.max(tmpImgt[:, :, 2]) - np.min(tmpImgt[:, :, 2])) + tmpImg[:, :, 3] = (tmpImgtl[:, :, 0] - np.min(tmpImgtl[:, :, 0])) / ( + np.max(tmpImgtl[:, :, 0]) - np.min(tmpImgtl[:, :, 0])) + tmpImg[:, :, 4] = (tmpImgtl[:, :, 1] - np.min(tmpImgtl[:, :, 1])) / ( + np.max(tmpImgtl[:, :, 1]) - np.min(tmpImgtl[:, :, 1])) + tmpImg[:, :, 5] = (tmpImgtl[:, :, 2] - np.min(tmpImgtl[:, :, 2])) / ( + np.max(tmpImgtl[:, :, 2]) - np.min(tmpImgtl[:, :, 2])) + + # tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg)) + + tmpImg[:, :, 0] = (tmpImg[:, :, 0] - np.mean(tmpImg[:, :, 0])) / np.std(tmpImg[:, :, 0]) + tmpImg[:, :, 1] = (tmpImg[:, :, 1] - np.mean(tmpImg[:, :, 1])) / np.std(tmpImg[:, :, 1]) + tmpImg[:, :, 2] = (tmpImg[:, :, 2] - np.mean(tmpImg[:, :, 2])) / np.std(tmpImg[:, :, 2]) + tmpImg[:, :, 3] = (tmpImg[:, :, 3] - np.mean(tmpImg[:, :, 3])) / np.std(tmpImg[:, :, 3]) + tmpImg[:, :, 4] = (tmpImg[:, :, 4] - np.mean(tmpImg[:, :, 4])) / np.std(tmpImg[:, :, 4]) + tmpImg[:, :, 5] = (tmpImg[:, :, 5] - np.mean(tmpImg[:, :, 5])) / np.std(tmpImg[:, :, 5]) + + elif self.flag == 1: # with Lab color + tmpImg = np.zeros((image.shape[0], image.shape[1], 3)) + + if image.shape[2] == 1: + tmpImg[:, :, 0] = image[:, :, 0] + tmpImg[:, :, 1] = image[:, :, 0] + tmpImg[:, :, 2] = image[:, :, 0] + else: + tmpImg = image + + tmpImg = color.rgb2lab(tmpImg) + + # tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg)) + + tmpImg[:, :, 0] = (tmpImg[:, :, 0] - np.min(tmpImg[:, :, 0])) / ( + np.max(tmpImg[:, :, 0]) - np.min(tmpImg[:, :, 0])) + tmpImg[:, :, 1] = (tmpImg[:, :, 1] - np.min(tmpImg[:, :, 1])) / ( + np.max(tmpImg[:, :, 1]) - np.min(tmpImg[:, :, 1])) + tmpImg[:, :, 2] = (tmpImg[:, :, 2] - np.min(tmpImg[:, :, 2])) / ( + np.max(tmpImg[:, :, 2]) - np.min(tmpImg[:, :, 2])) + + tmpImg[:, :, 0] = (tmpImg[:, :, 0] - np.mean(tmpImg[:, :, 0])) / np.std(tmpImg[:, :, 0]) + tmpImg[:, :, 1] = (tmpImg[:, :, 1] - np.mean(tmpImg[:, :, 1])) / np.std(tmpImg[:, :, 1]) + tmpImg[:, :, 2] = (tmpImg[:, :, 2] - np.mean(tmpImg[:, :, 2])) / np.std(tmpImg[:, :, 2]) + + else: # with rgb color + tmpImg = np.zeros((image.shape[0], image.shape[1], 3)) + image = image / np.max(image) + if image.shape[2] == 1: + tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 0.229 + tmpImg[:, :, 1] = (image[:, :, 0] - 0.485) / 0.229 + tmpImg[:, :, 2] = (image[:, :, 0] - 0.485) / 0.229 + else: + tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 0.229 + tmpImg[:, :, 1] = (image[:, :, 1] - 0.456) / 0.224 + tmpImg[:, :, 2] = (image[:, :, 2] - 0.406) / 0.225 + + tmpLbl[:, :, 0] = label[:, :, 0] + + tmpImg = tmpImg.transpose((2, 0, 1)) + tmpLbl = label.transpose((2, 0, 1)) + + return {'imidx': torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)} + + +class SalObjDataset(Dataset): + def __init__(self, img_name_list, lbl_name_list, transform=None): + # self.root_dir = root_dir + # self.image_name_list = glob.glob(image_dir+'*.png') + # self.label_name_list = glob.glob(label_dir+'*.png') + self.image_name_list = img_name_list + self.label_name_list = lbl_name_list + self.transform = transform + + def __len__(self): + return len(self.image_name_list) + + def __getitem__(self, idx): + + # image = Image.open(self.image_name_list[idx])#io.imread(self.image_name_list[idx]) + # label = Image.open(self.label_name_list[idx])#io.imread(self.label_name_list[idx]) + + image = io.imread(self.image_name_list[idx]) + imname = self.image_name_list[idx] + imidx = np.array([idx]) + + if (0 == len(self.label_name_list)): + label_3 = np.zeros(image.shape) + else: + label_3 = io.imread(self.label_name_list[idx]) + + label = np.zeros(label_3.shape[0:2]) + if (3 == len(label_3.shape)): + label = label_3[:, :, 0] + elif (2 == len(label_3.shape)): + label = label_3 + + if (3 == len(image.shape) and 2 == len(label.shape)): + label = label[:, :, np.newaxis] + elif (2 == len(image.shape) and 2 == len(label.shape)): + image = image[:, :, np.newaxis] + label = label[:, :, np.newaxis] + + sample = {'imidx': imidx, 'image': image, 'label': label} + + if self.transform: + sample = self.transform(sample) + + return sample diff --git a/tryon/preprocessing/u2net/load_u2net.py b/tryon/preprocessing/u2net/load_u2net.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa0320d136336b17d132ac86950ae58b705ac7c --- /dev/null +++ b/tryon/preprocessing/u2net/load_u2net.py @@ -0,0 +1,47 @@ +import os +from collections import OrderedDict + +import torch + +from tryon.preprocessing.u2net import u2net_cloth_segm, u2net_human_segm + + +def load_cloth_segm_model(device, checkpoint_path, in_ch=3, out_ch=1): + if not os.path.exists(checkpoint_path): + print("Invalid path") + return + + model = u2net_cloth_segm.U2NET(in_ch=in_ch, out_ch=out_ch) + + model_state_dict = torch.load(checkpoint_path, map_location=device) + new_state_dict = OrderedDict() + for k, v in model_state_dict.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + + model.load_state_dict(new_state_dict) + model = model.to(device=device) + + print("Checkpoints loaded from path: {}".format(checkpoint_path)) + + return model + + +def load_human_segm_model(device, model_name): + if model_name == 'u2net': + print("loading U2NET(173.6 MB)...") + net = u2net_human_segm.U2NET(3, 1) + elif model_name == 'u2netp': + print("loading U2NEP(4.7 MB)...") + net = u2net_human_segm.U2NETP(3, 1) + else: + net = None + + if torch.cuda.is_available(): + net.load_state_dict(torch.load(os.environ.get("U2NET_SEGM_CHECKPOINT_PATH"))) + net.cuda() + else: + net.load_state_dict(torch.load(os.environ.get("U2NET_SEGM_CHECKPOINT_PATH"), map_location=device)) + net.eval() + + return net diff --git a/tryon/preprocessing/u2net/u2net_cloth_segm.py b/tryon/preprocessing/u2net/u2net_cloth_segm.py new file mode 100644 index 0000000000000000000000000000000000000000..fe7a8901a73c087d77f507b599dd6f3c54ece3a5 --- /dev/null +++ b/tryon/preprocessing/u2net/u2net_cloth_segm.py @@ -0,0 +1,550 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class REBNCONV(nn.Module): + def __init__(self, in_ch=3, out_ch=3, dirate=1): + super(REBNCONV, self).__init__() + + self.conv_s1 = nn.Conv2d( + in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate + ) + self.bn_s1 = nn.BatchNorm2d(out_ch) + self.relu_s1 = nn.ReLU(inplace=True) + + def forward(self, x): + hx = x + xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) + + return xout + + +## upsample tensor 'src' to have the same spatial size with tensor 'tar' +def _upsample_like(src, tar): + src = F.upsample(src, size=tar.shape[2:], mode="bilinear") + + return src + + +### RSU-7 ### +class RSU7(nn.Module): # UNet07DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU7, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + hx = self.pool5(hx5) + + hx6 = self.rebnconv6(hx) + + hx7 = self.rebnconv7(hx6) + + hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) + hx6dup = _upsample_like(hx6d, hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5, hx6, hx7 + del hx6d, hx5d, hx3d, hx2d + del hx2dup, hx3dup, hx4dup, hx5dup, hx6dup + """ + + return hx1d + hxin + + +### RSU-6 ### +class RSU6(nn.Module): # UNet06DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU6, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + + hx6 = self.rebnconv6(hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5, hx6 + del hx5d, hx4d, hx3d, hx2d + del hx2dup, hx3dup, hx4dup, hx5dup + """ + + return hx1d + hxin + + +### RSU-5 ### +class RSU5(nn.Module): # UNet05DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU5, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + + hx5 = self.rebnconv5(hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5 + del hx4d, hx3d, hx2d + del hx2dup, hx3dup, hx4dup + """ + + return hx1d + hxin + + +### RSU-4 ### +class RSU4(nn.Module): # UNet04DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4 + del hx3d, hx2d + del hx2dup, hx3dup + """ + + return hx1d + hxin + + +### RSU-4F ### +class RSU4F(nn.Module): # UNet04FRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4F, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx2 = self.rebnconv2(hx1) + hx3 = self.rebnconv3(hx2) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) + hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4 + del hx3d, hx2d + """ + + return hx1d + hxin + + +##### U^2-Net #### +class U2NET(nn.Module): + def __init__(self, in_ch=3, out_ch=1): + super(U2NET, self).__init__() + + self.stage1 = RSU7(in_ch, 32, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 32, 128) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(128, 64, 256) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(256, 128, 512) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(512, 256, 512) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(512, 256, 512) + + # decoder + self.stage5d = RSU4F(1024, 256, 512) + self.stage4d = RSU4(1024, 128, 256) + self.stage3d = RSU5(512, 64, 128) + self.stage2d = RSU6(256, 32, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) + + self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1) + + def forward(self, x): + hx = x + + # stage 1 + hx1 = self.stage1(hx) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # -------------------- decoder -------------------- + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, d1) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, d1) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, d1) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, d1) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, d1) + + d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5, hx6 + del hx5d, hx4d, hx3d, hx2d, hx1d + del hx6up, hx5dup, hx4dup, hx3dup, hx2dup + """ + + return d0, d1, d2, d3, d4, d5, d6 + + +### U^2-Net small ### +class U2NETP(nn.Module): + def __init__(self, in_ch=3, out_ch=1): + super(U2NETP, self).__init__() + + self.stage1 = RSU7(in_ch, 16, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 16, 64) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(64, 16, 64) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(64, 16, 64) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(64, 16, 64) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(64, 16, 64) + + # decoder + self.stage5d = RSU4F(128, 16, 64) + self.stage4d = RSU4(128, 16, 64) + self.stage3d = RSU5(128, 16, 64) + self.stage2d = RSU6(128, 16, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(64, out_ch, 3, padding=1) + + self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1) + + def forward(self, x): + hx = x + + # stage 1 + hx1 = self.stage1(hx) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # decoder + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, d1) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, d1) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, d1) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, d1) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, d1) + + d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) + + return d0, d1, d2, d3, d4, d5, d6 diff --git a/tryon/preprocessing/u2net/u2net_human_segm.py b/tryon/preprocessing/u2net/u2net_human_segm.py new file mode 100644 index 0000000000000000000000000000000000000000..e35efcbb90bb1a6ecfbee91974d217c5d397792e --- /dev/null +++ b/tryon/preprocessing/u2net/u2net_human_segm.py @@ -0,0 +1,520 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class REBNCONV(nn.Module): + def __init__(self, in_ch=3, out_ch=3, dirate=1): + super(REBNCONV, self).__init__() + + self.conv_s1 = nn.Conv2d(in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate) + self.bn_s1 = nn.BatchNorm2d(out_ch) + self.relu_s1 = nn.ReLU(inplace=True) + + def forward(self, x): + hx = x + xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) + + return xout + + +## upsample tensor 'src' to have the same spatial size with tensor 'tar' +def _upsample_like(src, tar): + src = F.upsample(src, size=tar.shape[2:], mode='bilinear') + + return src + + +### RSU-7 ### +class RSU7(nn.Module): # UNet07DRES(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU7, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + hx = self.pool5(hx5) + + hx6 = self.rebnconv6(hx) + + hx7 = self.rebnconv7(hx6) + + hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) + hx6dup = _upsample_like(hx6d, hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-6 ### +class RSU6(nn.Module): # UNet06DRES(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU6, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + + hx6 = self.rebnconv6(hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-5 ### +class RSU5(nn.Module): # UNet05DRES(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU5, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + + hx5 = self.rebnconv5(hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-4 ### +class RSU4(nn.Module): # UNet04DRES(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + return hx1d + hxin + + +### RSU-4F ### +class RSU4F(nn.Module): # UNet04FRES(nn.Module): + + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4F, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx2 = self.rebnconv2(hx1) + hx3 = self.rebnconv3(hx2) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) + hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) + + return hx1d + hxin + + +##### U^2-Net #### +class U2NET(nn.Module): + + def __init__(self, in_ch=3, out_ch=1): + super(U2NET, self).__init__() + + self.stage1 = RSU7(in_ch, 32, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 32, 128) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(128, 64, 256) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(256, 128, 512) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(512, 256, 512) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(512, 256, 512) + + # decoder + self.stage5d = RSU4F(1024, 256, 512) + self.stage4d = RSU4(1024, 128, 256) + self.stage3d = RSU5(512, 64, 128) + self.stage2d = RSU6(256, 32, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) + + self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1) + + def forward(self, x): + hx = x + + # stage 1 + hx1 = self.stage1(hx) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # -------------------- decoder -------------------- + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, d1) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, d1) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, d1) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, d1) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, d1) + + d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) + + return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6) + + +### U^2-Net small ### +class U2NETP(nn.Module): + + def __init__(self, in_ch=3, out_ch=1): + super(U2NETP, self).__init__() + + self.stage1 = RSU7(in_ch, 16, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 16, 64) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(64, 16, 64) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(64, 16, 64) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(64, 16, 64) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(64, 16, 64) + + # decoder + self.stage5d = RSU4F(128, 16, 64) + self.stage4d = RSU4(128, 16, 64) + self.stage3d = RSU5(128, 16, 64) + self.stage2d = RSU6(128, 16, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(64, out_ch, 3, padding=1) + + self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1) + + def forward(self, x): + hx = x + + # stage 1 + hx1 = self.stage1(hx) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # decoder + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, d1) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, d1) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, d1) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, d1) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, d1) + + d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) + + return F.sigmoid(d0), F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6) diff --git a/tryon/preprocessing/u2net/utils.py b/tryon/preprocessing/u2net/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0b8e6eab37bdbfac5d8477d523b254acfd1f587c --- /dev/null +++ b/tryon/preprocessing/u2net/utils.py @@ -0,0 +1,10 @@ +import torch + + +def normPRED(d): + ma = torch.max(d) + mi = torch.min(d) + + dn = (d - mi) / (ma - mi) + + return dn diff --git a/tryon/preprocessing/utils.py b/tryon/preprocessing/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1770316011d9d7115a54045753f21e1d8050034e --- /dev/null +++ b/tryon/preprocessing/utils.py @@ -0,0 +1,91 @@ +import os +from pathlib import Path + +import cv2 +from PIL import Image +from torchvision import transforms + + +class NormalizeImage(object): + """Normalize given tensor into given mean and standard dev + + Args: + mean (float): Desired mean to substract from tensors + std (float): Desired std to divide from tensors + """ + + def __init__(self, mean, std): + assert isinstance(mean, (float)) + if isinstance(mean, float): + self.mean = mean + + if isinstance(std, float): + self.std = std + + self.normalize_1 = transforms.Normalize(self.mean, self.std) + self.normalize_3 = transforms.Normalize([self.mean] * 3, [self.std] * 3) + self.normalize_18 = transforms.Normalize([self.mean] * 18, [self.std] * 18) + + def __call__(self, image_tensor): + if image_tensor.shape[0] == 1: + return self.normalize_1(image_tensor) + + elif image_tensor.shape[0] == 3: + return self.normalize_3(image_tensor) + + elif image_tensor.shape[0] == 18: + return self.normalize_18(image_tensor) + + else: + assert "Please set proper channels! Normalization implemented only for 1, 3 and 18" + + +def naive_cutout(img, mask): + empty = Image.new("RGBA", (img.size), 0) + cutout = Image.composite(img, empty, mask.resize(img.size, Image.LANCZOS)) + return cutout + + +def resize_by_bigger_index(crop): + # function resizes and keeps the aspect ratio same + crop_shape = crop.shape # hxwxc + if crop_shape[0] / crop_shape[1] <= 1.33: + resized_crop = image_resize(crop, width=768) + else: + resized_crop = image_resize(crop, height=1024) + return resized_crop + + +def image_resize(image, width=None, height=None): + dim = None + (h, w) = image.shape[:2] + + if width is None and height is None: + return image + + if width is None: + r = height / float(h) + dim = (int(w * r), height) + + else: + r = width / float(w) + dim = (width, int(h * r)) + + resized = cv2.resize(image, dim) + + return resized + + +def convert_to_jpg(image_path, output_dir, size=None): + """ + Convert image to jpg format + :param image_path: image path + :param output_dir: output directory + :param size: desired size of the image (w, h) + """ + img = cv2.imread(image_path) + if size is not None: + img = image_resize(img, width=size[0], height=size[1]) + + filename = Path(image_path).name + cv2.imwrite(os.path.join(output_dir, filename.split(".")[0] + ".jpg"), img) diff --git a/tryondiffusion/__init__.py b/tryondiffusion/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tryondiffusion/diffusion.py b/tryondiffusion/diffusion.py new file mode 100644 index 0000000000000000000000000000000000000000..a12c7d1ffb8fff253ec85f90fbd25dd7810a3c4b --- /dev/null +++ b/tryondiffusion/diffusion.py @@ -0,0 +1,275 @@ +import copy +import logging +import os + +import torch +from torch.utils.data import DataLoader +from torch import optim +import torch.nn as nn +from torch.nn import functional as F +import cv2 +import numpy as np + +from .network import UNet64, UNet128 +from .utils import mk_folders, GaussianSmoothing, UNetDataset +from .ema import EMA + + +def smoothen_image(img, sigma): + # As suggested in: + # https://jmlr.csail.mit.edu/papers/volume23/21-0635/21-0635.pdf Section 4.4 + + smoothing2d = GaussianSmoothing(channels=3, + kernel_size=3, + sigma=sigma, + conv_dim=2) + + img = F.pad(img, (1, 1, 1, 1), mode='reflect') + img = smoothing2d(img) + + return img + + +def schedule_lr(total_steps, start_lr=0.0, stop_lr=0.0001, pct_increasing_lr=0.02): + n = total_steps * pct_increasing_lr + n = round(n) + lambdas = list(np.linspace(start_lr, stop_lr, n)) + constant_lr_list = [stop_lr] * (total_steps - n) + lambdas.extend(constant_lr_list) + return lambdas + + +class Diffusion: + + def __init__(self, + device, + pose_embed_dim, + time_steps=256, + beta_start=1e-4, + beta_end=0.02, + unet_dim=64, + noise_input_channel=3, + beta_ema=0.995): + self.time_steps = time_steps + self.beta_start = beta_start + self.beta_end = beta_end + + self.beta = self.linear_beta_scheduler().to(device) + self.alpha = 1 - self.beta + self.alpha_cumprod = torch.cumprod(self.alpha, dim=0) + + self.noise_input_channel = noise_input_channel + self.unet_dim = unet_dim + if unet_dim == 128: + self.net = UNet128(pose_embed_dim, device, time_steps).to(device) + elif unet_dim == 64: + self.net = UNet64(pose_embed_dim, device, time_steps).to(device) + + self.ema_net = copy.deepcopy(self.net).eval().requires_grad_(False) + self.beta_ema = beta_ema + + self.device = device + + def linear_beta_scheduler(self): + return torch.linspace(self.beta_start, self.beta_end, self.time_steps) + + def sample_time_steps(self, batch_size): + return torch.randint(low=1, high=self.time_steps, size=(batch_size,)) + + def add_noise_to_img(self, img, t): + sqrt_alpha_timestep = torch.sqrt(self.alpha_cumprod[t])[:, None, None, None] + sqrt_one_minus_alpha_timestep = torch.sqrt(1 - self.alpha_cumprod[t])[:, None, None, None] + epsilon = torch.randn_like(img) + return (sqrt_alpha_timestep * epsilon) + (sqrt_one_minus_alpha_timestep * epsilon), epsilon + + @torch.inference_mode() + def sample(self, use_ema, conditional_inputs): + model = self.ema_net if use_ema else self.net + ic, jp, jg, ia = conditional_inputs + ic = ic.to(self.device) + jp = jp.to(self.device) + jg = jg.to(self.device) + ia = ia.to(self.device) + batch_size = len(ic) + logging.info(f"Running inference for {batch_size} images") + + model.eval() + with torch.inference_mode(): + + # noise augmentation during testing as suggested in paper + sigma = float(torch.FloatTensor(1).uniform_(0.4, 0.6)) + ia = smoothen_image(ia, sigma) + ic = smoothen_image(ic, sigma) + + inp_network_noise = torch.randn(batch_size, self.noise_input_channel, self.unet_dim, self.unet_dim).to(self.device) + + # paper says to add noise augmentation to input noise during inference + inp_network_noise = smoothen_image(inp_network_noise, sigma) + + # concatenating noise with rgb agnostic image across channels + # corrupt -> concatenate -> predict + x = torch.cat((inp_network_noise, ia), dim=1) + + for i in reversed(range(1, self.time_steps)): + t = (torch.ones(batch_size) * i).long().to(self.device) + predicted_noise = model(x, ic, jp, jg, t, sigma) + # ToDo: Add Classifier-Free Guidance with guidance weight 2 + alpha = self.alpha[t][:, None, None, None] + alpha_cumprod = self.alpha_cumprod[t][:, None, None, None] + beta = self.beta[t][:, None, None, None] + if i > 1: + noise = torch.randn_like(inp_network_noise) + else: + noise = torch.zeros_like(inp_network_noise) + + inp_network_noise = 1 / torch.sqrt(alpha) * (inp_network_noise - ((1 - alpha) / (torch.sqrt(1 - alpha_cumprod))) * predicted_noise) + torch.sqrt(beta) * noise + inp_network_noise = (inp_network_noise.clamp(-1, 1) + 1) / 2 + inp_network_noise = (inp_network_noise * 255).type(torch.uint8) + return inp_network_noise + + def prepare(self, args): + mk_folders(args.run_name) + train_dataset = UNetDataset(ip_dir=args.train_ip_folder, + jp_dir=args.train_jp_folder, + jg_dir=args.train_jg_folder, + ia_dir=args.train_ia_folder, + ic_dir=args.train_ic_folder, + unet_size=self.unet_dim) + + validation_dataset = UNetDataset(ip_dir=args.validation_ip_folder, + jp_dir=args.validation_jp_folder, + jg_dir=args.validation_jg_folder, + ia_dir=args.validation_ia_folder, + ic_dir=args.validation_ic_folder, + unet_size=self.unet_dim) + + self.train_dataloader = DataLoader(train_dataset, args.batch_size_train, shuffle=True) + # give args.batch_size_validation 1 while training + self.val_dataloader = DataLoader(validation_dataset, args.batch_size_validation, shuffle=True) + + self.optimizer = optim.AdamW(self.net.parameters(), lr=args.lr, eps=1e-4) + self.scheduler = schedule_lr(total_steps=args.total_steps, start_lr=args.start_lr, + stop_lr=args.stop_lr, pct_increasing_lr=args.pct_increasing_lr) + self.mse = nn.MSELoss() + self.ema = EMA(self.beta_ema) + self.scaler = torch.cuda.amp.GradScaler() + + def train_step(self, loss, running_step): + self.optimizer.zero_grad() + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + self.scaler.update() + self.ema.step_ema(self.ema_net, self.net) + for g in self.optimizer.param_groups: + g['lr'] = self.scheduler[running_step] + + def single_epoch(self, train=True): + avg_loss = 0. + if train: + self.net.train() + else: + self.net.eval() + + for ip, jp, jg, ia, ic in self.train_dataloader: + + # noise augmentation + sigma = float(torch.FloatTensor(1).uniform_(0.4, 0.6)) + ia = smoothen_image(ia, sigma) + ic = smoothen_image(ic, sigma) + + with torch.autocast(self.device) and (torch.inference_mode() if not train else torch.enable_grad()): + ip = ip.to(self.device) + jp = jp.to(self.device) + jg = jg.to(self.device) + ia = ia.to(self.device) + ic = ic.to(self.device) + t = self.sample_time_steps(ip.shape[0]).to(self.device) + + # corrupt -> concatenate -> predict + zt, noise_epsilon = self.add_noise_to_img(ip, t) + + zt = torch.cat((zt, ia), dim=1) + + # ToDO: Make conditional inputs null, at 10% of the training time, + # ToDo: for classifier-free guidance(GitHub Issue #21), with guidance weight 2. + + predicted_noise = self.net(zt, ic, jp, jg, t, sigma) + loss = self.mse(noise_epsilon, predicted_noise) + avg_loss += loss + + if train: + self.train_step(loss, self.running_train_steps) + # ToDo: Add logs to tensorboard as well + logging.info( + f"train_mse_loss: {loss.item():2.3f}, learning_rate: {self.scheduler[self.running_train_steps]}") + self.running_train_steps += 1 + + return avg_loss.mean().item() + + def logging_images(self, epoch, run_name): + + for idx, (ip, jp, jg, ia, ic) in enumerate(self.val_dataloader): + # sampled image + sampled_image = self.sample(use_ema=False, conditional_inputs=(ic, jp, jg, ia)) + sampled_image = sampled_image[0].permute(1, 2, 0).squeeze().cpu().numpy() + + # ema sampled image + ema_sampled_image = self.sample(use_ema=True, conditional_inputs=(ic, jp, jg, ia)) + ema_sampled_image = ema_sampled_image[0].permute(1, 2, 0).squeeze().cpu().numpy() + + # base images + ip_np = ip[0].permute(1, 2, 0).squeeze().cpu().numpy() + ic_np = ic[0].permute(1, 2, 0).squeeze().cpu().numpy() + ia_np = ia[0].permute(1, 2, 0).squeeze().cpu().numpy() + + # make to folders + os.makedirs(os.path.join("results", run_name, "images", f"{idx}_E{epoch}"), exist_ok=True) + + # define folder paths + images_folder = os.path.join("results", run_name, "images", f"{idx}_E{epoch}") + + # save base images + cv2.imwrite(os.path.join(images_folder, "ground_truth.png"), ip_np) + cv2.imwrite(os.path.join(images_folder, "segmented_garment.png"), ic_np) + cv2.imwrite(os.path.join(images_folder, "cloth_agnostic_rgb.png"), ia_np) + + # save sampled image + cv2.imwrite(os.path.join(images_folder, "sampled_image.png"), sampled_image) + + # save ema sampled image + cv2.imwrite(os.path.join(images_folder, "ema_sampled_image.png"), ema_sampled_image) + + def save_models(self, run_name, epoch=-1): + + torch.save(self.net.state_dict(), os.path.join("models", run_name, f"ckpt_{epoch}.pt")) + torch.save(self.ema_net.state_dict(), os.path.join("models", run_name, f"ema_ckpt_{epoch}.pt")) + torch.save(self.optimizer.state_dict(), os.path.join("models", run_name, f"optim_{epoch}.pt")) + + def fit(self, args): + + logging.info(f"Starting training") + + data_len = len(self.train_dataloader) + + epochs = round((args.total_steps * args.batch_size_train) / data_len) + + if epochs < 0: + epochs = 1 + + self.running_train_steps = 0 + + for epoch in range(epochs): + logging.info(f"Starting Epoch: {epoch + 1}") + _ = self.single_epoch(train=True) + + if (epoch + 1) % args.calculate_loss_frequency == 0: + avg_loss = self.single_epoch(train=False) + logging.info(f"Average Loss: {avg_loss}") + + if (epoch + 1) % args.image_logging_frequency == 0: + self.logging_images(epoch, args.run_name) + + if (epoch + 1) % args.model_saving_frequency == 0: + self.save_models(args.run_name, epoch) + + logging.info(f"Training Done Successfully! Yayyy! Now let's hope for good results") diff --git a/tryondiffusion/ema.py b/tryondiffusion/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..661c2790d0de7ac9f98fbf5a5838a3af976a9570 --- /dev/null +++ b/tryondiffusion/ema.py @@ -0,0 +1,26 @@ +class EMA: + + def __init__(self, beta): + self.beta = beta + self.step = 0 + + def update_model_average(self, ma_model, current_model): + for current_params, ma_params in zip(current_model.parameters(), ma_model.parameters()): + old_weight, up_weight = ma_params.data, current_params.data + ma_params.data = self.update_average(old_weight, up_weight) + + def update_average(self, old, new): + if old is None: + return new + return old * self.beta + (1 - self.beta) * new + + def step_ema(self, ema_model, model, step_start_ema=2000): + if self.step < step_start_ema: + self.reset_parameters(ema_model, model) + self.step += 1 + return + self.update_model_average(ema_model, model) + self.step += 1 + + def reset_parameters(self, ema_model, model): + ema_model.load_state_dict(model.state_dict()) diff --git a/tryondiffusion/network.py b/tryondiffusion/network.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b43a0a8c8405689258981eca60799a0df31dae --- /dev/null +++ b/tryondiffusion/network.py @@ -0,0 +1,865 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import einsum +from einops import rearrange, repeat + +import math + +from .utils import GaussianSmoothing + + +class DownSample(nn.Module): + + def __init__(self, dim, dim_out, t_emb_dim): + super().__init__() + self.conv = nn.Conv2d(dim * 4, dim_out, (1, 1)) + + # positional embedding + self.emb_layer = nn.Sequential( + nn.SiLU(), + nn.Linear( + t_emb_dim, + dim_out + ), + ) + + def forward(self, x, t): + # slicing image into four pieces across h and w and appending pieces to channels + # new_no_channel = c * 4, conserving the features instead of pooling. + x = rearrange(x, "b c (h p1) (w p2) -> b (c p1 p2) h w", p1=2, p2=2) + x = self.conv(x) + # calculate positional embedding from positional encoding t + emb = self.emb_layer(t)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1]) + return x + emb + + +class UpSample(nn.Module): + + def __init__(self, dim, dim_out, t_emb_dim): + super().__init__() + self.up = nn.Upsample(scale_factor=2, mode="nearest") + self.conv = nn.Conv2d(dim, dim_out, (3, 3), padding=1) + + # positional embedding + self.emb_layer = nn.Sequential( + nn.SiLU(), + nn.Linear( + t_emb_dim, + dim_out + ), + ) + + def forward(self, x, t): + x = self.up(x) + x = self.conv(x) + # calculate positional embedding from positional encoding t + emb = self.emb_layer(t)[:, :, None, None].repeat(1, 1, x.shape[-2], x.shape[-1]) + return x + emb + + +class SinusoidalPosEmbed(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + device = t.device + half_dim = self.dim // 2 + pos_embed = math.log(10000) / (half_dim - 1) + pos_embed = torch.exp(torch.arange(half_dim, device=device) * -pos_embed) + pos_embed = t[:, None] * pos_embed[None, :] + pos_embed = torch.cat((pos_embed.sin(), pos_embed.cos()), dim=-1) + return pos_embed + + +class AttentionPool1d(nn.Module): + + def __init__(self, pose_embeb_dim, device, num_heads=1): + """ + Clip inspired 1D attention pooling, reduce garment and person pose along keypoints + :param pose_embeb_dim: pose embedding dimensions + :param num_heads: number of attention heads + """ + super().__init__() + self.positional_embedding = nn.Parameter(torch.randn(3, pose_embeb_dim) / pose_embeb_dim ** 0.5) + self.k_proj = nn.Linear(pose_embeb_dim, pose_embeb_dim) + self.q_proj = nn.Linear(pose_embeb_dim, pose_embeb_dim) + self.v_proj = nn.Linear(pose_embeb_dim, pose_embeb_dim) + self.c_proj = nn.Linear(pose_embeb_dim, pose_embeb_dim) + self.num_heads = num_heads + + self.pos_encoding = SinusoidalPosEmbed(pose_embeb_dim) + + self.device = device + + def forward(self, x, time_step, noise_level=0.3): + # if x in format NCP + # N - Batch Dimension, P - Pose Dimension, C - No. of pose(person and garment) + x = x.permute(1, 0, 2) + x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (C+1)NP + x = x + self.positional_embedding[:, None, :].to(x.dtype) # (C+1)NP + batch_size = x.shape[1] + x, _ = F.multi_head_attention_forward( + query=x[:1], key=x, value=x, + embed_dim_to_check=x.shape[-1], + num_heads=self.num_heads, + q_proj_weight=self.q_proj.weight, + k_proj_weight=self.k_proj.weight, + v_proj_weight=self.v_proj.weight, + in_proj_weight=None, + in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), + bias_k=None, + bias_v=None, + add_zero_attn=False, + dropout_p=0, + out_proj_weight=self.c_proj.weight, + out_proj_bias=self.c_proj.bias, + use_separate_proj_weight=True, + training=self.training, + need_weights=False + ) + # x = x.squeeze(0) + x = x.permute(1, 0, 2) + + smoothing = GaussianSmoothing(channels=1, + kernel_size=3, + sigma=noise_level, + conv_dim=1).to(self.device) + + x_noisy = smoothing(F.pad(x, (1, 1), mode='reflect')) + + x = x + x_noisy + + x += self.pos_encoding(time_step)[:, None, :] + return x.squeeze(1) + + +class FiLM(nn.Module): + + def __init__(self, clip_dim, channels): + super().__init__() + self.channels = channels + + self.fc = nn.Linear(clip_dim, 2 * channels) + self.activation = nn.ReLU(True) + + def forward(self, clip_pooled_embed, img_embed): + clip_pooled_embed = self.fc(clip_pooled_embed) + clip_pooled_embed = self.activation(clip_pooled_embed) + gamma = clip_pooled_embed[:, 0:self.channels] + beta = clip_pooled_embed[:, self.channels:self.channels + 1] + film_features = torch.add(torch.mul(img_embed, gamma[:, :, None, None]), beta[:, :, None, None]) + return film_features + + +def l2norm(t): + return F.normalize(t, dim=-1) + + +class LayerNorm(nn.Module): + def __init__(self, feats, dim=-1): + super().__init__() + self.dim = dim + + self.g = nn.Parameter(torch.ones(feats, *((1,) * (-dim - 1)))) + + def forward(self, x): + dtype, dim = x.dtype, self.dim + + eps = 1e-5 if x.dtype == torch.float32 else 1e-3 + var = torch.var(x, dim=dim, unbiased=False, keepdim=True) + mean = torch.mean(x, dim=dim, keepdim=True) + return (x - mean) * (var + eps).rsqrt().type(dtype) * self.g.type(dtype) + + +class SelfAttention(nn.Module): + def __init__( + self, + dim, + dim_head=1, + heads=4, + pose_dim=None, + scale=1): + """ + Intialize: att = SelfAttention(1024, 1, pose_dim=16) + Execute: att(torch.randn(4, 256, 1024), pose_embed=torch.randn(4, 2, 16)).size() + :param dim: + :param dim_head: + :param heads: + :param pose_dim: + :param scale: + """ + super().__init__() + self.scale = scale + + self.heads = heads + inner_dim = dim_head * heads + + self.norm = LayerNorm(dim) + + # self.null_kv = nn.Parameter(torch.randn(2, dim_head)) + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, dim_head * 2, bias=False) + + self.q_scale = nn.Parameter(torch.ones(dim_head)) + self.k_scale = nn.Parameter(torch.ones(dim_head)) + + self.to_context = nn.Sequential(nn.LayerNorm(pose_dim), nn.Linear(pose_dim, dim_head * 2)) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim, bias=False), + LayerNorm(dim) + ) + + def forward(self, x, pose_embed=None): + b, n, device = *x.shape[:2], x.device + x = self.norm(x) + q, k, v = (self.to_q(x), *self.to_kv(x).chunk(2, dim=-1)) + + q = rearrange(q, 'b n (h d) -> b h n d', h=self.heads) + + # add null key / value for classifier free guidance in prior net + # nk, nv = map(lambda t: repeat(t, 'd -> b 1 d', b=b), self.null_kv.unbind(dim=-2)) + # k = torch.cat((nk, k), dim=-2) + # v = torch.cat((nv, v), dim=-2) + + # add pose conditioning + ck, cv = self.to_context(pose_embed).chunk(2, dim=-1) + k = torch.cat((ck, k), dim=-2) + v = torch.cat((cv, v), dim=-2) + + # qk rmsnorm + + q, k = map(l2norm, (q, k)) + q = q * self.q_scale + k = k * self.k_scale + + # calculate query / key similarities + + sim = einsum('b h i d, b j d -> b h i j', q, k) * self.scale + + # attention + + attn = sim.softmax(dim=-1, dtype=torch.float32) + attn = attn.to(sim.dtype) + + # aggregate values + + out = einsum('b h i j, b j d -> b h i d', attn, v) + + out = rearrange(out, 'b h n d -> b n (h d)') + return self.to_out(out) + + +class CrossAttention(nn.Module): + def __init__( + self, + zt_dim, + ic_dim, + dim_head=1, + heads=4, + scale=1 + ): + + super().__init__() + + try: + assert zt_dim == ic_dim + except AssertionError: + print( + f"Expecting same dimension for zt and ic, but received {zt_dim} and {ic_dim}, indicating mismatch in " + f"parallel blocks of both UNets") + + self.scale = scale + + self.heads = heads + inner_dim = dim_head * heads + + self.norm_zt = LayerNorm(zt_dim) + self.norm_ic = LayerNorm(ic_dim) + + # self.null_kv = nn.Parameter(torch.randn(2, dim_head)) + self.to_q = nn.Linear(zt_dim, inner_dim, bias=False) + self.to_kv = nn.Linear(ic_dim, dim_head * 2, bias=False) + + self.q_scale = nn.Parameter(torch.ones(dim_head)) + self.k_scale = nn.Parameter(torch.ones(dim_head)) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, zt_dim, bias=False), + LayerNorm(zt_dim) + ) + + def forward(self, zt, ic): + # b and n will be same for both zt and ic + b, n, device = *zt.shape[:2], zt.device + + ic = self.norm_ic(ic) + zt = self.norm_zt(zt) + + q, k, v = (self.to_q(zt), *self.to_kv(ic).chunk(2, dim=-1)) + + q = rearrange(q, 'b n (h d) -> b h n d', h=self.heads) + + # add null key / value for classifier free guidance in prior net + # nk, nv = map(lambda t: repeat(t, 'd -> b 1 d', b=b), self.null_kv.unbind(dim=-2)) + # k = torch.cat((nk, k), dim=-2) + # v = torch.cat((nv, v), dim=-2) + + # qk rmsnorm + + q, k = map(l2norm, (q, k)) + q = q * self.q_scale + k = k * self.k_scale + + # calculate query / key similarities + + sim = einsum('b h i d, b j d -> b h i j', q, k) * self.scale + + # attention + + attn = sim.softmax(dim=-1, dtype=torch.float32) + attn = attn.to(sim.dtype) + + # aggregate values + + out = einsum('b h i j, b j d -> b h i d', attn, v) + + out = rearrange(out, 'b h n d -> b n (h d)') + return self.to_out(out) + + +class ResBlockNoAttention(nn.Module): + + def __init__(self, block_channel, clip_dim, input_channel=None): + super().__init__() + + self.film = FiLM(clip_dim, block_channel) + + if input_channel is None: + input_channel = block_channel + + self.gn1 = nn.GroupNorm(min(32, int(abs(input_channel / 4))), int(input_channel)) + self.swish1 = nn.SiLU(True) + self.conv1 = nn.Conv2d(input_channel, block_channel, (3, 3), padding=1) + + self.gn2 = nn.GroupNorm(min(32, int(abs(block_channel / 4))), int(block_channel)) + self.swish2 = nn.SiLU(True) + self.conv2 = nn.Conv2d(block_channel, block_channel, (3, 3), padding=1) + + self.conv_residual = nn.Conv2d(input_channel, block_channel, (3, 3), padding=1) + + def forward(self, x, clip_embeddings, unet_residual=None): + + x = self.film(clip_embeddings, x) + + if unet_residual is not None: + x = torch.concat((x, unet_residual), dim=1) + + h = self.gn1(x) + h = self.swish1(h) + h = self.conv1(h) + h = self.gn2(h) + h = self.swish2(h) + h = self.conv2(h) + + h += self.conv_residual(x) + + return h + + +class ResBlockAttention(nn.Module): + + def __init__(self, block_channel, clip_dim, hw_dim, input_channel=None): + super().__init__() + + self.block_channel = block_channel + self.hw_dim = hw_dim + + self.film = FiLM(clip_dim, block_channel) + + if input_channel is None: + input_channel = block_channel + + self.gn1 = nn.GroupNorm(min(32, int(abs(input_channel / 4))), int(input_channel)) + self.swish1 = nn.SiLU(True) + self.conv1 = nn.Conv2d(input_channel, block_channel, (3, 3), padding=1) + self.gn2 = nn.GroupNorm(min(32, int(abs(block_channel / 4))), int(block_channel)) + self.swish2 = nn.SiLU(True) + self.conv2 = nn.Conv2d(block_channel, block_channel, (3, 3), padding=1) + + self.conv_residual = nn.Conv2d(input_channel, block_channel, (3, 3), padding=1) + + att_dim = (hw_dim // 2) ** 2 + # pose vector length will be same as clip pooled length + self.self_attention = SelfAttention(dim=att_dim, pose_dim=clip_dim) + + self.cross_attention = CrossAttention(zt_dim=att_dim, ic_dim=att_dim) + + def forward(self, x, clip_embeddings, pose_embed, garment_embed, unet_residual=None): + x = self.film(clip_embeddings, x) + + if unet_residual is not None: + x = torch.concat((x, unet_residual), dim=1) + + h = self.gn1(x) + h = self.swish1(h) + h = self.conv1(h) + h = self.gn2(h) + h = self.swish2(h) + h = self.conv2(h) + + h += self.conv_residual(x) + + # self attention with pose embed concat with key value + h = rearrange(h, "b c (h p1) (w p2) -> b (c p1 p2) (h w)", p1=2, p2=2) + h = self.self_attention(h, pose_embed) + + # cross attention with query from zt and key value from ic + garment_embed = rearrange(garment_embed, "b c (h p1) (w p2) -> b (c p1 p2) (h w)", p1=2, p2=2) + h = self.cross_attention(h, garment_embed) + + h = rearrange(h, "b (c p1 p2) (h w) -> b c (h p1) (w p2)", + c=self.block_channel, p1=2, p2=2, h=self.hw_dim // 2, w=self.hw_dim // 2) + + return h + + +class UNetBlockNoAttention(nn.Module): + + def __init__(self, block_channel, clip_dim, res_blocks_number, input_channel=None): + super().__init__() + self.blocks = nn.ModuleList([]) + for idx, block in enumerate(range(res_blocks_number)): + if idx != 0: + input_channel = None + self.blocks.append(ResBlockNoAttention(block_channel, clip_dim, input_channel=input_channel)) + + def forward(self, x, clip_embeddings, unet_residual=None): + for idx, block in enumerate(self.blocks): + if idx != 0: + unet_residual=None + x = block(x, clip_embeddings, unet_residual) + return x + + +class UNetBlockAttention(nn.Module): + + def __init__(self, + block_channel, + clip_dim, + res_blocks_number, + hw_dim, + input_channel=None): + super().__init__() + self.blocks = nn.ModuleList([]) + for idx, block in enumerate(range(res_blocks_number)): + if idx != 0: + input_channel = None + self.blocks.append(ResBlockAttention(block_channel, + clip_dim, + hw_dim, + input_channel=input_channel)) + + def forward(self, x, clip_embeddings, pose_embed, garment_embed, unet_residual=None): + for idx, block in enumerate(self.blocks): + if idx != 0: + unet_residual = None + x = block(x, clip_embeddings, pose_embed, garment_embed, unet_residual) + return x + + +class UNet128(nn.Module): + + def __init__(self, pose_embed_len_dim, device, time_dim=256): + super().__init__() + + self.pos_encod_layer = SinusoidalPosEmbed(time_dim) + + # process clip embeddings + self.attn_pool_layer = AttentionPool1d(pose_embed_len_dim, device) + + # =========================person UNet================================ + # initial image embedding size 128x128 and 6 channels(concatenate + # rgb agnostic image and noise) + + # 3x3 conv layer person + self.init_conv_person = nn.Conv2d(6, 128, (3, 3), padding=1) + + # 128 unit encoder person + self.block1_person = UNetBlockNoAttention(block_channel=128, + clip_dim=pose_embed_len_dim, + res_blocks_number=3) + self.downsample1_person = DownSample(dim=128, dim_out=256, t_emb_dim=time_dim) + + # 64 unit encoder person + self.block2_person = UNetBlockNoAttention(block_channel=256, + clip_dim=pose_embed_len_dim, + res_blocks_number=4) + self.downsample2_person = DownSample(dim=256, dim_out=512, t_emb_dim=time_dim) + + # 32 unit encoder person + self.block3_person = UNetBlockAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6, + hw_dim=32) + self.downsample3_person = DownSample(dim=512, dim_out=1024, t_emb_dim=time_dim) + + # 16 unit encoder person + self.block4_person = UNetBlockAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7, + hw_dim=16) + + # 16 unit decoder person + self.block5_person = UNetBlockAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7, + hw_dim=16, + input_channel=1024*2) + self.upsample1_person = UpSample(dim=1024, dim_out=512, t_emb_dim=time_dim) + + # 32 unit decoder person + self.block6_person = UNetBlockAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6, + hw_dim=32, + input_channel=512*2) + self.upsample2_person = UpSample(dim=512, dim_out=256, t_emb_dim=time_dim) + + # 64 unit decoder person + self.block7_person = UNetBlockNoAttention(block_channel=256, + clip_dim=pose_embed_len_dim, + res_blocks_number=4, + input_channel=256*2) + self.upsample3_person = UpSample(dim=256, dim_out=128, t_emb_dim=time_dim) + + # 128 unit decoder person + self.block8_person = UNetBlockNoAttention(block_channel=128, + clip_dim=pose_embed_len_dim, + res_blocks_number=3, + input_channel=128*2) + + self.final_conv_person = nn.Conv2d(128, 3, (3, 3), padding=1) + # ==========================person UNet ends========================== + + # ==========================garment UNet============================== + # initial image embedding size 128x128 and 3 channels + self.init_conv_garment = nn.Conv2d(3, 128, (3, 3), padding=1) + + # 128 unit encoder garment + self.block1_garment = UNetBlockNoAttention(block_channel=128, + clip_dim=pose_embed_len_dim, + res_blocks_number=3) + self.downsample1_garment = DownSample(dim=128, dim_out=256, t_emb_dim=time_dim) + + # 64 unit encoder garment + self.block2_garment = UNetBlockNoAttention(block_channel=256, + clip_dim=pose_embed_len_dim, + res_blocks_number=4) + self.downsample2_garment = DownSample(dim=256, dim_out=512, t_emb_dim=time_dim) + + # 32 unit encoder garment + self.block3_garment = UNetBlockNoAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6) + self.downsample3_garment = DownSample(dim=512, dim_out=1024, t_emb_dim=time_dim) + + # 16 unit encoder garment + self.block4_garment = UNetBlockNoAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7) + + # 16 unit decoder garment + self.block5_garment = UNetBlockNoAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7, + input_channel=1024*2) + self.upsample1_garment = UpSample(dim=1024, dim_out=512, t_emb_dim=time_dim) + + # 32 unit decoder garment + self.block6_garment = UNetBlockNoAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6, + input_channel=512*2) + # ==========================garment UNet ends============================== + + def forward(self, zt, ic, person_pose_embedding, garment_pose_embedding, time_step, noise_level): + """ + + :param zt: rgb_agnostic and noisy ground truth concatenated across channels + :param ic: segmented garment + :param person_pose_embedding: [b, 1, vector_length] + :param garment_pose_embedding: [b, 1, vector_length] + :param time_step: + :param noise_level: sigma sampled from uniform distribution + :return: + """ + + pos_encoding = self.pos_encod_layer(time_step) + + # concat pose embeddings + pose_embeddings = torch.concat((person_pose_embedding[:, None, :], garment_pose_embedding[:, None, :]), dim=1) + + # get clip embeddings + clip_embedding = self.attn_pool_layer(pose_embeddings, time_step, noise_level) + + # passing through initial conv layer + ic = self.init_conv_garment(ic) + zt = self.init_conv_person(zt) + + # entering UNet + # 128 - garment + ic = self.block1_garment(ic, clip_embedding) + + # 128 - person + zt_128_1 = self.block1_person(zt, clip_embedding) + + # 64 - garment + ic = self.downsample1_garment(ic, pos_encoding) + ic = self.block2_garment(ic, clip_embedding) + + # 64 - person + zt = self.downsample1_person(zt_128_1, pos_encoding) + zt_64_1 = self.block2_person(zt, clip_embedding) + + # 32 - garment + ic = self.downsample2_garment(ic, pos_encoding) + ic_32_1 = self.block3_garment(ic, clip_embedding) + + # 32 - person + zt = self.downsample2_person(zt_64_1, pos_encoding) + zt_32_1 = self.block3_person(zt, clip_embedding, pose_embeddings, ic_32_1) + # print(f"zt 32 encoder {zt_32_1.size()}") + + # 16 - garment + ic = self.downsample3_garment(ic_32_1, pos_encoding) + ic_16_1 = self.block4_garment(ic, clip_embedding) + + # 16 - person + zt = self.downsample3_person(zt_32_1, pos_encoding) + zt_16_1 = self.block4_person(zt, clip_embedding, pose_embeddings, ic_16_1) + + # 16 - garment + ic_16_2 = self.block5_garment(ic_16_1, clip_embedding, ic_16_1) + # print(f"ic 16 decoder {ic_16_2.size()}") + + # 16 - person + zt_16_2 = self.block5_person(zt_16_1, clip_embedding, pose_embeddings, ic_16_2, zt_16_1) + # print(f"zt 16 decoder {zt_16_2.size()}") + + # 32 - garment + ic = self.upsample1_garment(ic_16_2, pos_encoding) + ic_32_2 = self.block6_garment(ic, clip_embedding, ic_32_1) + # print(f"ic 32 decoder {ic_32_2.size()}") + + # 32 - person + zt = self.upsample1_person(zt_16_2, pos_encoding) + zt_32_2 = self.block6_person(zt, clip_embedding, pose_embeddings, ic_32_2, zt_32_1) + # print(f"zt 32 decoder {zt_32_2.size()}") + + # 64 - person + zt = self.upsample2_person(zt_32_2, pos_encoding) + zt_64_2 = self.block7_person(zt, clip_embedding, zt_64_1) + + # 128 - person + zt = self.upsample3_person(zt_64_2, pos_encoding) + zt_128_2 = self.block8_person(zt, clip_embedding, zt_128_1) + + # final conv layer - person + zt = self.final_conv_person(zt_128_2) + + return zt + + +class UNet64(nn.Module): + + def __init__(self, pose_embed_len_dim, device, time_dim=256): + super().__init__() + + self.pos_encod_layer = SinusoidalPosEmbed(time_dim) + + # process clip embeddings + self.attn_pool_layer = AttentionPool1d(pose_embed_len_dim, device) + + # =========================person UNet================================ + # initial image embedding size 128x128 and 6 channels(concatenate + # rgb agnostic image and noise) + + # 3x3 conv layer person + self.init_conv_person = nn.Conv2d(6, 256, (3, 3), padding=1) + + # 64 unit encoder person + self.block2_person = UNetBlockNoAttention(block_channel=256, + clip_dim=pose_embed_len_dim, + res_blocks_number=4) + self.downsample2_person = DownSample(dim=256, dim_out=512, t_emb_dim=time_dim) + + # 32 unit encoder person + self.block3_person = UNetBlockAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6, + hw_dim=32) + self.downsample3_person = DownSample(dim=512, dim_out=1024, t_emb_dim=time_dim) + + # 16 unit encoder person + self.block4_person = UNetBlockAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7, + hw_dim=16) + + # 16 unit decoder person + self.block5_person = UNetBlockAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7, + hw_dim=16, + input_channel=1024*2) + self.upsample1_person = UpSample(dim=1024, dim_out=512, t_emb_dim=time_dim) + + # 32 unit decoder person + self.block6_person = UNetBlockAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6, + hw_dim=32, + input_channel=512*2) + self.upsample2_person = UpSample(dim=512, dim_out=256, t_emb_dim=time_dim) + + # 64 unit decoder person + self.block7_person = UNetBlockNoAttention(block_channel=256, + clip_dim=pose_embed_len_dim, + res_blocks_number=4, + input_channel=256*2) + + self.final_conv_person = nn.Conv2d(256, 3, (3, 3), padding=1) + # ==========================person UNet ends========================== + + # ==========================garment UNet============================== + # initial image embedding size 128x128 and 3 channels + self.init_conv_garment = nn.Conv2d(3, 256, (3, 3), padding=1) + + # 64 unit encoder garment + self.block2_garment = UNetBlockNoAttention(block_channel=256, + clip_dim=pose_embed_len_dim, + res_blocks_number=4) + self.downsample2_garment = DownSample(dim=256, dim_out=512, t_emb_dim=time_dim) + + # 32 unit encoder garment + self.block3_garment = UNetBlockNoAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6) + self.downsample3_garment = DownSample(dim=512, dim_out=1024, t_emb_dim=time_dim) + + # 16 unit encoder garment + self.block4_garment = UNetBlockNoAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7) + + # 16 unit decoder garment + self.block5_garment = UNetBlockNoAttention(block_channel=1024, + clip_dim=pose_embed_len_dim, + res_blocks_number=7, + input_channel=1024*2) + self.upsample1_garment = UpSample(dim=1024, dim_out=512, t_emb_dim=time_dim) + + # 32 unit decoder garment + self.block6_garment = UNetBlockNoAttention(block_channel=512, + clip_dim=pose_embed_len_dim, + res_blocks_number=6, + input_channel=512*2) + # ==========================garment UNet ends============================== + + def forward(self, zt, ic, person_pose_embedding, garment_pose_embedding, time_step, noise_level): + """ + + :param zt: + :param ic: + :param person_pose_embedding: [b, 1, vector_length] + :param garment_pose_embedding: [b, 1, vector_length] + :param time_step: + :return: + """ + + pos_encoding = self.pos_encod_layer(time_step) + + # concat pose embeddings + pose_embeddings = torch.concat((person_pose_embedding[:, None, :], garment_pose_embedding[:, None, :]), dim=1) + + # get clip embeddings + clip_embedding = self.attn_pool_layer(pose_embeddings, time_step, noise_level) + + # passing through initial conv layer + ic = self.init_conv_garment(ic) + zt = self.init_conv_person(zt) + + # 64 - garment + # ic = self.downsample1_garment(ic, pos_encoding) + ic = self.block2_garment(ic, clip_embedding) + + # 64 - person + # zt = self.downsample1_person(zt, pos_encoding) + zt_64_1 = self.block2_person(zt, clip_embedding) + + # 32 - garment + ic = self.downsample2_garment(ic, pos_encoding) + ic_32_1 = self.block3_garment(ic, clip_embedding) + + # 32 - person + zt = self.downsample2_person(zt_64_1, pos_encoding) + zt_32_1 = self.block3_person(zt, clip_embedding, pose_embeddings, ic_32_1) + + # 16 - garment + ic = self.downsample3_garment(ic_32_1, pos_encoding) + ic_16_1 = self.block4_garment(ic, clip_embedding) + + # 16 - person + zt = self.downsample3_person(zt_32_1, pos_encoding) + zt_16_1 = self.block4_person(zt, clip_embedding, pose_embeddings, ic_16_1) + + # 16 - garment + ic_16_2 = self.block5_garment(ic_16_1, clip_embedding, ic_16_1) + + # 16 - person + zt_16_2 = self.block5_person(zt_16_1, clip_embedding, pose_embeddings, ic_16_2, zt_16_1) + + # 32 - garment + ic = self.upsample1_garment(ic_16_2, pos_encoding) + ic_32_2 = self.block6_garment(ic, clip_embedding, ic_32_1) + # print(f"ic 32 decoder {ic_32_2.size()}") + + # 32 - person + zt = self.upsample1_person(zt_16_2, pos_encoding) + zt_32_2 = self.block6_person(zt, clip_embedding, pose_embeddings, ic_32_2, zt_32_1) + # print(f"zt 32 decoder {zt_32_2.size()}") + + # 64 - person + zt = self.upsample2_person(zt_32_2, pos_encoding) + zt_64_2 = self.block7_person(zt, clip_embedding, zt_64_1) + # print(f"zt 64 decoder {zt_64_2.size()}") + + # final conv person + zt = self.final_conv_person(zt_64_2) + + return zt + + +if __name__ == "__main__": + + time_step = torch.randint(low=1, high=1000, size=(4,)) + + # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + device = torch.device("cpu") + net = UNet64(8, device).to(device) + out = net(torch.randn(4, 6, 64, 64).to(device), + torch.randn(4, 3, 64, 64).to(device), + torch.randn(4, 8).to(device), + torch.randn(4, 8).to(device), + time_step.to(device), + 0.3) + + print(out.size()) + + # att = AttentionPool1d(16) + # time_step = torch.randint(0, 1000, (4,)).long() + # out = att(torch.randn(4, 2, 16), time_step, None) + # print(out.size()) diff --git a/tryondiffusion/pre_processing/README.md b/tryondiffusion/pre_processing/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8c762276bc1ec21036bac64d9744b94c7110e56a --- /dev/null +++ b/tryondiffusion/pre_processing/README.md @@ -0,0 +1,24 @@ +# Using Different Pre-Processing methods + +## Pose Estimation +Human Pose estimation is required by tryon diffusion to get jg(garment pose keypoints) and jp(human pose keypoints). +We have chosen to use a [Pytorch Implementation](https://github.com/Hzzone/pytorch-openpose) of [OpenPose](https://github.com/CMU-Perceptual-Computing-Lab/openpose) to get pose keypoints. + +* Pose Estimation code can be found inside `openpose_pytorch`. +* Download [model weights](https://drive.google.com/drive/folders/1JsvI4M4ZTg98fmnCZLFM-3TeovnCRElG?usp=sharing) as `openpose_pytorch/body_pose_model.pth`. +* Give image path to variable `test_image` in `openpose_pytorch/body_pose.py`. +* Run `openpose_pytorch/body_pose.py`. + + +## U2Net Human Parsing + + +## U2Net Cloth Segmentation +Run the following command to segment clothes using the U2Net model +``` +python pre_processing/segment_cloth_u2net.py --inputs_dir inputs --outputs_dir outputs --checkpoint_path pre_processing/u2net_cloth_seg/checkpoints/cloth_segm.pth +``` +Note: Download the checkpoint file by following the instructions given in the repository https://github.com/wildoctopus/huggingface-cloth-segmentation + + +## Segformer Human Parsing diff --git a/tryondiffusion/pre_processing/__init__.py b/tryondiffusion/pre_processing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tryondiffusion/pre_processing/garment_pose_embedding/network.py b/tryondiffusion/pre_processing/garment_pose_embedding/network.py new file mode 100644 index 0000000000000000000000000000000000000000..b143d4002b02200aebcabdbab5451823368b3052 --- /dev/null +++ b/tryondiffusion/pre_processing/garment_pose_embedding/network.py @@ -0,0 +1,29 @@ +import torch.nn as nn + + +class AutoEncoder(nn.Module): + + def __init__(self, inp): + super().__init__() + self.encoder = nn.Sequential( + nn.Linear(inp, 16), + nn.ReLU(True), + nn.Linear(16, 8), + nn.ReLU(True) + ) + self.decoder = nn.Sequential( + nn.Linear(8, 16), + nn.ReLU(True), + nn.Linear(16, inp), + nn.Sigmoid() + ) + + def forward(self, x): + embeddings = self.encoder(x) + prediction = self.decoder(embeddings) + return prediction, embeddings + + +if __name__=="__main__": + net = AutoEncoder(20) + print(net) diff --git a/tryondiffusion/pre_processing/garment_pose_embedding/train.py b/tryondiffusion/pre_processing/garment_pose_embedding/train.py new file mode 100644 index 0000000000000000000000000000000000000000..6c24e83660ae9d759da3ada9eb5c449f753992e7 --- /dev/null +++ b/tryondiffusion/pre_processing/garment_pose_embedding/train.py @@ -0,0 +1,89 @@ +from network import AutoEncoder +from utils.dataloader import KeypointDataset + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +import numpy as np +import os +from torch.utils.tensorboard import SummaryWriter + + +def train(train_dir, + test_dir, + num_epochs=100, + model_save_path="./models"): + + writer = SummaryWriter() + + if not os.path.exists(model_save_path): + os.mkdir(model_save_path) + + train_data = KeypointDataset(train_dir) + train_dataloader = DataLoader(train_data, batch_size=16, shuffle=True) + + test_data = KeypointDataset(test_dir) + test_dataloader = DataLoader(test_data, batch_size=4) + + model = AutoEncoder(20) + criterion = nn.MSELoss() + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, + weight_decay=1e-5) + + best_loss = np.inf + + for epoch in range(num_epochs): + train_running_loss = 0 + num_batches = 0 + for keypoints, _ in train_dataloader: + + model.train() + prediction, _ = model(keypoints) + loss = criterion(prediction, keypoints) + writer.add_scalar("Running_Loss/Train", loss, epoch + 1) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_running_loss += float(loss) + num_batches += 1 + + if not num_batches % 125: + print(f"Running Loss; Iteration: {num_batches}, Train Loss: {(train_running_loss / num_batches):.4f}") + + print(f"Epoch: {epoch + 1}/{num_epochs}, Train Loss: {(train_running_loss / num_batches):.4f}") + writer.add_scalar("Epoch_Loss/Train", train_running_loss / num_batches, epoch + 1) + + # validation + test_running_loss = 0 + num_test_batches = 0 + + for test_keypoints in test_dataloader: + model.eval() + test_predictions, _ = model(test_keypoints) + test_loss = criterion(test_predictions, test_keypoints) + writer.add_scalar("Running_Loss/Validation", test_loss, epoch + 1) + + test_running_loss += float(test_loss) + num_test_batches += 1 + + print(f"Epoch: {epoch + 1}/{num_epochs}, Test Loss: {(test_running_loss / num_test_batches):.4f}") + writer.add_scalar("Epoch_Loss/Validation", test_running_loss / num_test_batches, epoch + 1) + + if test_running_loss / num_test_batches < best_loss: + print(f"Best Model Till Now: {epoch + 1}") + + best_loss = test_running_loss / num_test_batches + torch.save(model.state_dict(), os.path.join(model_save_path, f"best_model.pth")) + + # torch.save(model.state_dict(), os.path.join(model_save_path, f"{epoch + 1}.pth")) + + writer.flush() + writer.close() + + +if __name__ == "__main__": + train(train_dir="./data/train", + test_dir="./data/test", + num_epochs=200) diff --git a/tryondiffusion/pre_processing/garment_pose_embedding/utils/dataloader.py b/tryondiffusion/pre_processing/garment_pose_embedding/utils/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3be820698feb3ce5119873e7c1b581bb4bbe3c --- /dev/null +++ b/tryondiffusion/pre_processing/garment_pose_embedding/utils/dataloader.py @@ -0,0 +1,62 @@ +import os +import json + +import torch +from torch.utils.data import DataLoader, Dataset + + +def normalize(val, lower, upper): + return (val - lower) / (upper - lower) + + +def normalize_lst(lst): + """ + Normalizing Keypoints between 0 to 1, Image size is 1024x786. + Will be adding padding to both sides of images of 119 pixels to make image dims 1024x1024, + to keep them in sync with paper, so networks can input 128x128 and output 1024x1024. + Therefore Normalizing 'y' with lower bound 0 and upper bound 1024. + Adding 119 to 'x' and normalizing it with lowe bound 0 and upper bound 1024. + + :param lst: list of coordinates; sample: [x1, y1, x2, y2...] (total keypoint are + 25, so length list is 50) + :return: normalized list for x and y coordinate. + """ + # lst = [normalize(lst[i], 0, 1024) if i % 2 else normalize(lst[i]+119, 0, 1024) for i in range(len(lst))] + normalized_list = list() + for i in range(len(lst)): + if i % 2: # y + val = normalize(lst[i], 0, 1024) + else: # x + # condition so that non-existent keypoint remain zero, if x and y both are 0 keypoint non-existent + if lst[i] == 0 and lst[i + 1] == 0: + val = lst[i] + else: + val = normalize(lst[i] + 119, 0, 1024) + normalized_list.append(val) + return normalized_list + + +class KeypointDataset(Dataset): + + def __init__(self, json_dir): + self.json_lst = os.listdir(json_dir) + self.json_paths = [os.path.join(json_dir, i) for i in self.json_lst] + + def __len__(self): + return len(self.json_paths) + + def __getitem__(self, item): + json_path = self.json_paths[item] + with open(json_path, "r") as f: + lst = json.load(f) + lst = normalize_lst(lst) + return torch.tensor(lst), json_path + + +if __name__ == "__main__": + json_di = "../data/test" + test = KeypointDataset(json_di) + dataloader = DataLoader(test, batch_size=4) + for i, jsn in dataloader: + print(i) + break diff --git a/tryondiffusion/pre_processing/generate_cloth_agnostic_rgb.py b/tryondiffusion/pre_processing/generate_cloth_agnostic_rgb.py new file mode 100644 index 0000000000000000000000000000000000000000..f8352bb044353cab57130870b00faef54d3a8775 --- /dev/null +++ b/tryondiffusion/pre_processing/generate_cloth_agnostic_rgb.py @@ -0,0 +1,33 @@ +import numpy as np + + +def generate_rgb_agnostic(img, img_agnostic_parse_map): + sum_img_agnostic_parse_map = np.sum(img_agnostic_parse_map, axis=2) + sum_img_agnostic_parse_map[sum_img_agnostic_parse_map!=0] = 1 + img_rgb_agnostic = (sum_img_agnostic_parse_map.reshape(*sum_img_agnostic_parse_map.shape, 1)*img).astype(np.uint8) + return img_rgb_agnostic + + +if __name__ == "__main__": + import os + import sys + from tqdm import tqdm + + parentdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + sys.path.insert(0, parentdir) + + from utils import read_img, write_img + + imgs_folder = sorted(os.listdir("../../../zalando-hd-resized/train/image/")) + imgs_agnostic_parse_map_folder = sorted(os.listdir("../../../zalando-hd-resized/train/image-parse-agnostic-v3.2/")) + + for img_name, img_agnostic_parse_map in tqdm(zip(imgs_folder, imgs_agnostic_parse_map_folder)): + img = read_img(os.path.join("../../../zalando-hd-resized/train/image/", img_name)) + img_agnostic_parse_map = read_img(os.path.join("../../../zalando-hd-resized/train/image-parse-agnostic-v3.2/", + img_agnostic_parse_map)) + + rgb_agnostic = generate_rgb_agnostic(img, img_agnostic_parse_map) + + write_img(rgb_agnostic, "../data/train/ia", img_name) + + diff --git a/tryondiffusion/pre_processing/generate_segmented_garment.py b/tryondiffusion/pre_processing/generate_segmented_garment.py new file mode 100644 index 0000000000000000000000000000000000000000..8795791b72a45656a271831a2db6e21d3ddd5ec9 --- /dev/null +++ b/tryondiffusion/pre_processing/generate_segmented_garment.py @@ -0,0 +1,30 @@ +import numpy as np + + +def get_upper_garment(img, img_parse_map): + sum_img_parse_map = np.sum(img_parse_map, axis=2) + sum_img_parse_map[sum_img_parse_map!=339] = 0 + sum_img_parse_map[sum_img_parse_map==339] = 1 + upper_garment_segment = (sum_img_parse_map.reshape(*sum_img_parse_map.shape,1)*img).astype(np.uint8) + return upper_garment_segment + + +if __name__ == "__main__": + import os + import sys + from tqdm import tqdm + parentdir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + sys.path.insert(0, parentdir) + + from utils import read_img, write_img + + imgs_folder = sorted(os.listdir("../../../zalando-hd-resized/train/image/")) + img_parse_map = sorted(os.listdir("../../../zalando-hd-resized/train/image-parse-agnostic-v3.2/")) + + for img_name, img_parse_map in tqdm(zip(imgs_folder, img_parse_map)): + img = read_img(os.path.join("../../../zalando-hd-resized/train/image/", img_name)) + img_parse_map = read_img(os.path.join("../../../zalando-hd-resized/train/image-parse-v3/", img_parse_map)) + + segmented_garment = get_upper_garment(img, img_parse_map) + + write_img(segmented_garment, "../data/train/ic", img_name) diff --git a/tryondiffusion/pre_processing/human_pose_embedding/network.py b/tryondiffusion/pre_processing/human_pose_embedding/network.py new file mode 100644 index 0000000000000000000000000000000000000000..115cdacf7b9584c8dceb70833635bcce41d7ff68 --- /dev/null +++ b/tryondiffusion/pre_processing/human_pose_embedding/network.py @@ -0,0 +1,33 @@ +import torch.nn as nn + + +class AutoEncoder(nn.Module): + + def __init__(self, inp): + super().__init__() + self.encoder = nn.Sequential( + nn.Linear(inp, 32), + nn.ReLU(True), + nn.Linear(32, 16), + nn.ReLU(True), + nn.Linear(16, 8), + nn.ReLU(True) + ) + self.decoder = nn.Sequential( + nn.Linear(8, 16), + nn.ReLU(True), + nn.Linear(16, 32), + nn.ReLU(True), + nn.Linear(32, inp), + nn.Sigmoid() + ) + + def forward(self, x): + embeddings = self.encoder(x) + prediction = self.decoder(embeddings) + return prediction, embeddings + + +if __name__=="__main__": + net = AutoEncoder(50) + print(net) diff --git a/tryondiffusion/pre_processing/human_pose_embedding/train.py b/tryondiffusion/pre_processing/human_pose_embedding/train.py new file mode 100644 index 0000000000000000000000000000000000000000..55dd558dd7d860eb924e264830a5de78a3c41897 --- /dev/null +++ b/tryondiffusion/pre_processing/human_pose_embedding/train.py @@ -0,0 +1,91 @@ +from network import AutoEncoder +from utils.dataloader import KeypointDataset + +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +import numpy as np +import os +from torch.utils.tensorboard import SummaryWriter + + +def train(train_dir, + test_dir, + num_epochs=100, + model_save_path="./models"): + + writer = SummaryWriter("") + + if not os.path.exists(model_save_path): + os.mkdir(model_save_path) + + train_data = KeypointDataset(train_dir) + train_dataloader = DataLoader(train_data, batch_size=16, shuffle=True) + + test_data = KeypointDataset(test_dir) + test_dataloader = DataLoader(test_data, batch_size=4) + + model = AutoEncoder(50) + criterion = nn.MSELoss() + optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, + weight_decay=1e-5) + + best_loss = np.inf + + for epoch in range(num_epochs): + train_running_loss = 0 + num_batches = 0 + for keypoints, _ in train_dataloader: + + model.train() + prediction, _ = model(keypoints) + loss = criterion(prediction, keypoints) + writer.add_scalar("Running_Loss/Train", loss, epoch+1) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_running_loss += float(loss) + num_batches += 1 + + if not num_batches % 125: + print(f"Running Loss; Iteration: {num_batches}, Train Loss: {(train_running_loss / num_batches):.4f}") + + print(f"Epoch: {epoch + 1}/{num_epochs}, Train Loss: {(train_running_loss / num_batches):.4f}") + writer.add_scalar("Epoch_Loss/Train", train_running_loss/num_batches, epoch + 1) + + # validation + test_running_loss = 0 + num_test_batches = 0 + + for test_keypoints in test_dataloader: + model.eval() + test_predictions, _ = model(test_keypoints) + test_loss = criterion(test_predictions, test_keypoints) + writer.add_scalar("Running_Loss/Validation", test_loss, epoch + 1) + + test_running_loss += float(test_loss) + num_test_batches += 1 + + print(f"Epoch: {epoch+1}/{num_epochs}, Test Loss: {(test_running_loss/num_test_batches):.4f}") + writer.add_scalar("Epoch_Loss/Validation", test_running_loss / num_test_batches, epoch + 1) + + if test_running_loss/num_test_batches < best_loss: + print(f"Best Model Till Now: {epoch+1}") + + best_loss = test_running_loss/num_test_batches + torch.save(model.state_dict(), os.path.join(model_save_path, f"best_model.pth")) + + torch.save(model.state_dict(), os.path.join(model_save_path, f"{epoch + 1}.pth")) + + writer.flush() + writer.close() + + +if __name__ == "__main__": + + train(train_dir="./data/train", + test_dir="./data/test", + num_epochs=300, + model_save_path="model_exp2_300epoch") diff --git a/tryondiffusion/pre_processing/human_pose_embedding/utils/dataloader.py b/tryondiffusion/pre_processing/human_pose_embedding/utils/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3be820698feb3ce5119873e7c1b581bb4bbe3c --- /dev/null +++ b/tryondiffusion/pre_processing/human_pose_embedding/utils/dataloader.py @@ -0,0 +1,62 @@ +import os +import json + +import torch +from torch.utils.data import DataLoader, Dataset + + +def normalize(val, lower, upper): + return (val - lower) / (upper - lower) + + +def normalize_lst(lst): + """ + Normalizing Keypoints between 0 to 1, Image size is 1024x786. + Will be adding padding to both sides of images of 119 pixels to make image dims 1024x1024, + to keep them in sync with paper, so networks can input 128x128 and output 1024x1024. + Therefore Normalizing 'y' with lower bound 0 and upper bound 1024. + Adding 119 to 'x' and normalizing it with lowe bound 0 and upper bound 1024. + + :param lst: list of coordinates; sample: [x1, y1, x2, y2...] (total keypoint are + 25, so length list is 50) + :return: normalized list for x and y coordinate. + """ + # lst = [normalize(lst[i], 0, 1024) if i % 2 else normalize(lst[i]+119, 0, 1024) for i in range(len(lst))] + normalized_list = list() + for i in range(len(lst)): + if i % 2: # y + val = normalize(lst[i], 0, 1024) + else: # x + # condition so that non-existent keypoint remain zero, if x and y both are 0 keypoint non-existent + if lst[i] == 0 and lst[i + 1] == 0: + val = lst[i] + else: + val = normalize(lst[i] + 119, 0, 1024) + normalized_list.append(val) + return normalized_list + + +class KeypointDataset(Dataset): + + def __init__(self, json_dir): + self.json_lst = os.listdir(json_dir) + self.json_paths = [os.path.join(json_dir, i) for i in self.json_lst] + + def __len__(self): + return len(self.json_paths) + + def __getitem__(self, item): + json_path = self.json_paths[item] + with open(json_path, "r") as f: + lst = json.load(f) + lst = normalize_lst(lst) + return torch.tensor(lst), json_path + + +if __name__ == "__main__": + json_di = "../data/test" + test = KeypointDataset(json_di) + dataloader = DataLoader(test, batch_size=4) + for i, jsn in dataloader: + print(i) + break diff --git a/tryondiffusion/pre_processing/notebooks/agnostic_rgb_parsing_map.ipynb b/tryondiffusion/pre_processing/notebooks/agnostic_rgb_parsing_map.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..87d56694b36f648bca12d3291fd271e36ba87947 --- /dev/null +++ b/tryondiffusion/pre_processing/notebooks/agnostic_rgb_parsing_map.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "d362cf6f-ef4a-44b5-9578-c36927269587", + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from PIL import Image\n", + "import numpy as np\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4fa37a5d-253c-472e-bfc2-b2278833240e", + "metadata": {}, + "outputs": [], + "source": [ + "image_name = \"00048_00\"\n", + "\n", + "img = cv2.imread(f\"./zalando-hd-resized/train/image/{image_name}.jpg\")[:,:,::-1]\n", + "img_agnostic_parse_map = cv2.imread(f\"./zalando-hd-resized/train/image-parse-agnostic-v3.2/{image_name}.png\")[:,:,::-1]\n", + "img_parse_map = cv2.imread(f\"./zalando-hd-resized/train/image-parse-v3/{image_name}.png\")[:,:,::-1]\n", + "img_pose = cv2.imread(f\"./zalando-hd-resized/train/openpose_img/{image_name}_rendered.png\")[:,:,::-1]\n", + "with open(f\"./zalando-hd-resized/train/openpose_json/{image_name}_keypoints.json\", \"r\") as f:\n", + " img_pose_json = json.load(f)" + ] + }, + { + "cell_type": "markdown", + "id": "316ed49a-e947-40c8-bb34-e2f72bd4fe9d", + "metadata": {}, + "source": [ + "## Generating RGB agnostic Image Ia" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "97d70126-7633-4a35-872a-7ae757506138", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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gHlUiCbxWAnqbx1/5oHP4jQoavTLx+oXv+qt4QIdw56Duc/mnjw0o7L4VHnW86QIKJwaADhBWMBmVoFuwvPDdM6sHbJzZjG/rROGXdbdU/e08/Nqdwu53YzflMM2D79KTDkflJEz/3am7oFC0VLzIAkGQSsdyDKZTJDom2Dk5HGQSMfZ2ffP1l3e++uLOt9+Q1bDf7ZEdutuWhUOOnjR4IMN2qHp9Z6feVIKcYgmMU6tjy2EblhLqgAF2djbZCQaqKFbKCRsCpgEh4flSOp/hkDSJoAS0PaE8dONZgAwMPChMhygkTFQ0pjNkXRb6gfkWWQubAAvDMcRNZwQmaKeFT1ANS8ukZPshsxF1rC18QsTv4vvibRHoMJyRyQpq4M5To+ERNnkNmmzpIjRbzi8Kw5mSWVg7hrL19XXmWl19sbi4tLi0NLQt+ohm8cq1Unm651gwia2LtaSIYiLchwRGzA4dnxTow1qghgAJGxIrhCf14w6b4MQ705/CxjK4t3/aEcddGI5KJIEjSUAv0kmU49EJj6K+3zscvuVDvCXcfqRFnBgAOtKsuzv7Yna3n0bLWc51GvxfKJoTb97uy2Nwe/gHdEBP/woFHYLKMfh5B4cgLpcYYMXMOYlMKjkk/V99J5OIl4r5leXlv/nrv/j1L3/xYuV5bbuKEqY/27swu5hPBwcWuXnAIkpXiO1oemYGHV9vNRUxk88L5dSbG1vbRNAg3lRWCZbR7ih+XFo8OwwhbgJxKKA+qRRxP4AbdD/gYHp6mk/uKm3OaAuVzprASEMjzNAfOqAi6lQgbvhG+MnvQhOY4pBCfdjVZZHRQWcHTGAP2vmkvwMgQIYhMBmfnD1oUmcgDPBJ0BD9240mkeCgNzxk3AVyQQCLF0AQU5Fmwb7GyHSKxVIYwhWFO1wyOxiRqeGQYrOP4FEAlWCbIuwzLpD1QoNuRSWSwBlKYOKVm7jczcghO9CNt9qH+5DdA3e3eP9g4O7ZaTkyANpvmj2pH6bxxAkeZtKozwlKwJ9g8ByDygFTBC/lYTrvpvPaUQF9Kq/tvJv+O9iClMKy4pKC3iVwRTqe3MfYadqNuakSW7rvfPvlX/6X/88//P3fkdiQUdg8kBjoBG1NCh+Ch1H5ttcqQbJCLCXl8jSoiGMvAEPXr19vtNrVWp0UO6h8zDOgH4wojGXfOzMCScyQI+8SHcAKEDdAoEM48oWCW3owETE1JiI6U8AZXqDgLfDvvLVbLcKPtWUtI/8UjYAMRxvUVXDaDZRykMJYgyD6tQWCAGiwHAE4Gl2dQQbOgyv6ENxNB1qIXOoP6zZI5h+G0OicA4JIEr2xutppal3YxtbXNwkb+uSHP+CELzx0IMK+bQpDwlBDREzNXP5TTwXfX6FUhivucgmkoQOX1udVIxA93NI53sZvDdFHJIEzlYC+TFb8pQ0uafOWA7gJdw767zkq3Dgxajd9Oh/Q58gAaPcER2pxVsILONLwE+kMD+fLwIms4oIQOfj1ei2Tr30Wu+lHz263VE9EJv4sAoFziToH3ABw8BRlU/FCKjfstH79m1/9L//+33379Zeke9aecMMBzVabUBqifggWAjUJB1j4NKySFIdCEBABwlSAFGtra6AfUIKgTC7rLiTS7eC3ogUzCKPAKxABagAdwAeofCrsMJ+bI2woBdZxWwtjgTJc0of+4AOnJs7HCYQgEhdz+q1jUQF6oM5YCdMgBS48hihNooEk9nkhCi+0qJsSQuquauyrT6bYLwYb7UY3nUlOlyvZXB5xOWOwBA8MZwq2xz+6/2B7Y3t6dp5JSSd9/foN4BgT4jjDKgRLjpnoD3E+4ZnhIt5uc8vpWA7FkW9uxIZt18cEBFkGUmybnlf1ycDg1svWqBZJ4M0kcMj3im5HncdfVwbuN9ZvTbzVXO7Z/trZjwyAwhPvyaKzwsR+N9w/4CZo3JNC0O30KgEDwRTnxUnAwFtTCV6AYEWBbAOx0xLUd/cPBlIJunljQCrcZ3c9oH/I/rspXJaWk1ogcvbCwqmgkimGKjqo6jxhuenExovnP/1vf/lf/7//5e43X3MaKBHKHGrRYQ9VV+d/FctTw2Sq0dGxpgAO0eFYeA5Hxc7RJ6thA50Nt48fPybHD9q9MrOIOYTCRAxH2ZuviQPXZQ1CoTsCANOABgBGEMTtlcvl6YyVhDZ+XamDIejpxOlMgRQWI1YhOEVaIaUHkk+NyGtwFQgJUuxSH6Effqm0qy0GroGIefwEtmBfspW1xbfKK1yaFmTDes3Kk0QIhC8pMza+ME5XJTMiu+TNMddttUFQJK7WJrUUaqCzPaiywe1LSSX+T//oT65cvwZqBLYpQ0C+wFEesA3zNotgGULgk0cA89C0dhr0dChc8glzagr5wGj0V4LPoBI0WucT+4DsMWg5V+GBx6MTpnBm9YD5S8TzKQnHRXGCcoAg1AIJT7Dtd70xPGl4VLidnhOXEwT98sgAKKCyJ6M+JZ9+N8x0MPBiVgKez5G9PUU6wc9hHurEkFO9DJ44swSPOyzM8KK8frwlhOkcZkVH7X8YmmfQ5xzZ5rlQYIBPVio00FVm56lidtBpP7r36Jf/+Hd/+b/9l/t3vsH4geoF0wBO2uSA7g9KlWmGoq1dRE6ETwBH2g6poD8EMfPs1OuocwqXuL48ZAfFz75xsuYAFyDCZaOhPV8gG1xd0KEzBbhDcSYh2OSQVTsBHmoMoT9jfSL6ABG4TFkeHecKSOHdIEsLZPmErB3ozjjtsaLQzljjVx5AXHw0Yluh0Q7NEObwFugQQQ1ZCvClMRhgowIvQorEP/0OYUVKY82uLsJ7oEAg9fbmxs9/9o/YhH70459cu3kzmytAhOHQhg9sTMP4gLhoGphCfGrzvCxJgDlIBatjiDg3DAS/dDCWfKFaGmV0MV5pcHlSFaYIT/pasmGWwp2PSic89rzq+63lTfjZT5j7zbVf/zfh4ahj9+PtqHS8/8HUuDuxZG/Z3Q61iZ676XuHYwKg3YyGmaBOYUq+rj7x7k/vsLv9pFr2o7+nXJh0v/4nxc9h6MCbs8HnxeRzTyk52xO39mx0IQQ991vjblkFQ3bfuuwtZ7w02R/MlYP3CtH57DTqWbCtSc6UUWO71cink3NThVZ9+6vf/fbvfvo3v/rHn2+sr02VprCmEO+M1WSnjtKPpbOcXMGXXe4wfErkZiYMGBygfD/9/ub2pmGXDhEtHOPFAagoclAD+RBBP5h+uCwUShy5jj3ELDecyy5TDbAG9oAEfoQqEcH8nhBRTB84BwlRoYPACrvAbJOXM0+jNqLFdTw7IdxAE0iNMEqnzq4r6GiUIR462wYvvns4o5I582op189ghOcYSBdmBAnh4aJwjUstwWnz3V5ywO40wsMVK5TI5ppNMkcrWxLZIzPZXD+BK1BkcWe1Grj4GjgBEdeXX/yOBNXXrlwtz5WQIbIkMBzvFdmJWDZjYY2nxGY6zHDikyAlvJG2/x5xIU/YcBAJyFNME/iP9EQgNG0Ie/nr75zzefjvGsMPWcI0xyKatAnRx29B84D6IWd8u7sFgjrkMk/psR5y9nPpNiGi4A2cEEXQPsHkRDujjgmAJuiGLyFKCbecTT2YdGKRZzP7icxyYZeASAPeTmSlTgSap0T5BJk8VVKnIdXXMuwYiKlJ8Cybg6EfGqU9UfKDLrmbOdcU/025UkzG+r/4h7/9q7/4iztff1Pb2SH4t0O6PmBIn0TPoBPpZFxCOGSk9aXkZEcBrKDpW50uJp9GvWVHtQumgL5Q1cT+oLYpWDUcmgAyADT1RgMgwV14owU6YCa8bFhBZmdngUQgHkw+FBDAeDrXuDKT0J/i/82liuEayEKKzsAIRoFb5E1j7TAPXKIfPA/Yu9ZTEDQBSHHLEiQEE293mvCjuBr1UjecakyED4xPFpLP5aAJXe7CMweEVbE5pZTFB26ZiDrzYqlCZMyPhDV8UGfH/IvnTx/cv1+ZnkZWBIfrccTxGLL6tFJGS47aUAaM094zQw+QosK8CA3GIMWlC0o9k+rnYqESLs5/uOXE6/tNwbqCufarBx2iSiSBE5FA+E0LE5x4S7k8YQAUnhjqlHBLmJWTrYdnCdd3z3Lw3d39z6blYnIVrN3ZO4DJiVsTlwGdcIU+Z/Z6hOd9x+uBzFHppFEW+uGPKSndSgw5oHPYbeHzqhRzw173b//+7//zf/p/3/3uTp9DRQEZsTgBx2hfvDQOAtDHngIHvxjtbKYiyIbsz1ADr6hwgFa7TRuYQKE2RAKNYoqly9n6xKg652n02hwrgTp3SETENPqeKRjFcWBQwIjCROAhRz+4z5iCscrEHNexGxRa7CdHNiioqyk2TCXiaZ34Hi9YwDX0e8OeGUxYvGJ3fJcWhh7yNvY7faM4ICUjQJAMQXABJ3iiADnAHMKRiICGMWTGifGsiklgSXQAhgNSXbOnH58c2RVjUFAiH46djyXyee1fI1t0k8CfTvce4eG5wtT09Iff/ySbZt9cC9tNMs2WNxl/YFfPhgKjfaxrsqYjLpbnFZbD2mmhMDvtsGeLv6Av+AG/Cf5TcEH5jth6iySw+007MQBk30CJar8Xfb/2Nxfv6VF+c96ORCF4PC7Mc1zX4acOegaVIy056Mzw4BUKGqPKiUsg9JiI3VVUMnoXySslIIhomEjFhvV2i+BhbCY//8e//1/+X//Tvbt3CGDBh0TEjVAQ6X3isVw2Q6JlTjF1gwd0sOigm7FnELWDbgbBgFfwatEoo4viZzAqSVXzo4Padk5AEmAkLEq05LIFYmhyhQIDaQT6UJCAWYs6zAb6YToKRHw67FUMxFrCWqjQGcwg/5eK3F7ZYgnEQzsogSaWS65n6ilcbJZ3J0XIDcPxeCWASolUVsAO2xUDIIUrzNEH1hX+Y44PloEFRjxoh1wPY4/vRIOfZrzZx6nXBOEojSG32PBFT+YlYNwcYYzQenEgIiK4ogPJsW/cvo1pDQ8icc98DXTerJnThH/Ycc+pqyAr2ymG0GCeOjS5y6UXBEIjBLWQsdElqNB+ZuV4k06M0vsYlUgCpyAB3rTw27UvAJp4IwNOwoODxqCy36j92oOBb1KBpVOl/ya8HX4sS3DZHizhwxM8ds8jCXNPbn0tR6JzbG4v+MCLJoRRAJC5VEAL0pm4WDBtGBgiOXG33wX99NuNr7/95j/++//nt998Vchm0dZbuKLUN46pA12O9gUi0E69kMsTzcPJ6AQyT03N4E7a3t4CAHF+u6n5ODoe5EPQDiHDQAGyNmPRsX3rAzrzCkGNT+EVHSWh4QAFdDotaHQOk3ftTh/55ggtNnRish1gpoF/PhVZZBvJIALBQV/hTilMQxzhhQVHfi55uoAOACEGsQKOPSUZTyGfbbfzsAR9PjHgJOJKVEjhDBB0sSMrXYMRcYiZiwpvGYTgp9uJ5XPFSqmcy2SJ1AGlCVPCIemL7HAMugEBiXFGSqyIs1QRJCytr7745c9/Bht/EvsXV68TEJ2Hfh/voh3aalAOOWsWszEJO1JnIB1YO6RocVFQod3FqMe0/3+I+t0L/snqJOyoRBI4lgQmfnUn3qXgLu37AqCjzhsQDQ/0xj1vhbu9Yf206b8he68d7vzv95BeO/zEOzgnR5VqmP9w/fDsMePxBh5+irPpeVTRnQ1XwSyBnLXlyL1fpi8BFqCEWK+DC+zbL7/4X//zf2K7O2YSLBjYfoR1QBWAlVQyk88RCIQ6T2d1AIUbbEA207Oz3K1tbxH6w9ldwCXukhkIiwgACNCBUQS1DcjACgLEgaUeodPxOO0Qp4UUQYAUKKPXGeVbwFDt1BnIJaHZXI7fE6oqshRls9DBwgNEoMJwGOazyU41wx8MhxnrKPsNtyhwwnAu6QMDXNLIcFpoF8FBshdnI/rI7qLTUO0ttW5CbHDC8nGVsUUfCbAuLhmIh85ngXMog8UgKFRn3DKKgmvt+dPl3/3mNzBGHw7KSLBjH/uNjtwQ/oEOo7zOjAwXS7ZA2jEy8UkJP1mv0y1oPOMK6zrH2c94sacxHQLcj2wk2P0kc0D7wfI8MQC0mwOe1hl8GS77O7Gbf5fbbnmeZcturg4z+1E5D/oH0wUth5nuAvYJFnIBeRuzBHoA+BD9gtUnOcobjNLSpTIYthr1B99++Rf/23/+4le/0E6tRLq6vdNs6QAK/FJ8o4s6ySFTrzVBKuVcTu3NJsBifvEKupkMh0pp2GI/vGVVZieXwxcDH2CnelPoR2rbYq6RGHUzFJGbR4V26FNARXxq75jtq4I4CCCAJtSFK8x0xC3P5syk2i5lBbL8lSMMoJLBOoRfjEAh+fsAJfDgkIJLa1ccD6WYKyIo2BBYMesLxhra9VvGH7OyODShgUVgSYIqnxiTwGdzs9MQB0iZRcZDc4gzJ3BqfDIrdijLIu2PY5hMbG6uf/vttzNzs6y3ODVN2iJinvQiETFN3LT5J/kpdc5ZFLjHvyaSIQs2AfIJV05TY63dL8/g0/k5xkTI8IxZPQaTF2GIP9PdnATtLsndHd7BlkAmrH1CLMGtoP0UARDTB/O9+WM4QVJvzsxpUzjBxR5MKngPjv2woO9EfKJgujDl3eKaGBXuEFDwxoPphAeebH2CjZMlfmbUwquYkKQ9AjGidlLdEL2bjDe7re2N1X/425/+8mc/67aa6XyBjDVK12eHdEn75rIYMXBvEZyCwYPhHHueTWdmZmbAQJvbW6urq0AH19ME47ilJ18s5HLk+uvvsCXMNrGjrRXpYjAB+AK0oB24A74BsaDjiQeCCI1gEUKJ4Ray1HERMamvyw06NGIxIuaY/kTScAtq9IGUqCkFtAw8DG/WG9z16bzR6QT9YQCTGLPTR8YhM6M48gD9cBdsA76BuKMQBgKLMmklaQSo0YgdCCy5tVnd2NhgdRi1mAgy9CRTNZMrpEc7wgSqRCeXxcG3uvLszjffLiwtLiZSpUqZwHBDM5wF2wVcMZSekpjZevw5cgkFXwUSYCHU/Rafvi5GnWoJzxKuH3JS5/aQnY9BP6B8pImCUedVOd5Kw6Mu13pPVs4uh7A0oL/fN4L2IwOgCdITlye7GKd2BlOcBtt70jzVtRyJ+El9SZzO7ql3t4QFcvjZD6YTpvkm9bOZ5U04POpYVoSQKb40r0ME64CAgp1ahQZFqds+7i7mH7Zrra88/4ef/s3f/+3fkJGGoBY2nJsfiY4WvGyeI2wNmEfYm0XewtUX62RzLpZL/CO/85MnTxR5A4ZJc057lgO/GFjGqoGNJQakaIFUwAcobw/9cU74BFXQh8IoeINPdkX5ErCGyHdk51SI/5gindnVJSKEYw/a0BZ7faVLZpkAHW4xlgqfEMeEQzwSfbjUMDtRi1HkkmZG9TdXHXeotzjYomegKS2PGNgCmsxIoeIcCoWZ546QI6xAUCSwmpTUxG0T1LwwN8uorW2y+XDGGEkie9lMuk7CJEw6DjcTbObCCDfsMqomUxNGMRyE84sLhXyFvNDyLQLucordhhSDqCAWluLMOCd8sihuwRV1VuoV6hQWS/E6fajzeaGKs7cnS7Dt7ReQ7T0ZvlCNBwj2QvF5gswEL4xXgkumQBqU3XPRh3JkALSbUNQSSSCSwEWWgH/VnUPUPJeOBtCp2seF9sUeQaK+6tYvfvZ3P/3rv2KrNnl8iO6lGzo1Jh2t4GFOLW20mmyWwlmD3sfmASl+XNi6BVZYX1+nG0l+uIvlptlpMxfhz1CA0PbOhhgAAxhG8YEBJ2AX9lLJAGPABTrU6Q4DFLqh3fmEIBYaCKLZ6SN6KkJDSdP1dKbdiSt9TzxGgDYhPNSJQGJ3OsOdGpiqWa8zECI+KcAIgKGYaXWKETHdabVbnRanwPI/ABYh1kiMIfTHOIQTsRfvcRoGa6eRgu+PhbAPrpjPMRewJsOmOooO7hCaoXRl/VGBCAV8A9yBZ9yI9CGmanZhPpGJ12odfHbWBQkriRFjuaQC/w507O7LX3baKTTyaVONPmgJX0b1SAKRBJCAf33eCABFX60jvUkXSlzOjP9WXijGjiTSqPN+Egi0YFDxnlyiqgEKPHQUIzYI0E9y2Gs2al/+5pe/+uXPNjdWi4CGTou0h/hisA+hm7V1yoxG/JUuJ6RX6QAVFs2+r2KhrPNNt7YgzpYxFDYnx6OqiWlJZdLsamq0FfKsIye6UvyCK2YHwhpFKlZQBU4054pR6mk4BoYhSH8m5S63KAAUeY8sjTUeM+XbMURC6BLNFC0zrvDkNIYWwoxARGAXdnLF08Q8gW3AL2xiAwNCbQSg8JHBFTOBHEjHjKOrx2ntbRIfciOVy0Kp1UBQbHzXBnUKa2AKJmHDWCLGPn8OHEsOep1Wo1bMZ+cXZhHa8xcrMt5wvNiArM2KqmZOx3ls+xdsGiY53J5FcgsY9MUXXwCAbly/9d5HH8Fms1kHSmbZWWYyMTno51psG/LTQg3u+CeXLi6vUKfiJegwbrhYfwO2T4ktlh9QDoslaIwql10CR33Dg1fi+AAoIHHZZXd6/IefysUU1wRXYYZPTywR5TOWgP/o+7NGC7v9gHO+yGQDkskmY51G6+Hdb//x7/52ZXkZk4YcTpzJkC8QBLxT2+LM9wLbvshdM+gT98MecHxPABhWgZ3mypUr6HKif5iFQB/Ubs1MGhx5QUbAJvmd600sK8CmTpPEyjjdtAkLLQ4/qsQt3Y7FQUNB2MLUFU4x7sKqX4Js3LRDUkCUPu12VEaa09J3OBBDEIIkRoZ7iLIBnshbpIO8ugnFxzATyYIIdZIZCztLVrvchV/kjdKk1OnFLKwFDISpJZ/mXAwBL+bFx5Wr6IxVRCB7DkfG4wJLElaNVYjj7ksgFeFCQyf1ndrs/NzSwhxLIECKXWGMTOfygnFxnXLPXPleD49hE8HIKGZ2nQGHezS/+erL//bX/xVqH37yCekGMMJBHQ5ZHZxQtM6Xm+D0Hrl8VHu17i0X+ZN17cnefu17dj5qY1hcpzrRURmL+p+LBI4JgMKv0bnwfcEnDeQTVC44wwF7+zEc/VgEIroUFZ5X8Ci94p8YPdDo6GacX5hEMNi0atUHd7/52d/+zZ2vfs9+KzQ0HdCy2E1qtSqZD0nrLMMI+7czOZR+pykvDsYS9j9NV6Y5Xp0D3vH+YC4ipLde49iHTjbLBq4cFfPv9IkKajbasp7gRcqYAYm6HTrGvjDMKr0Be+ZH9gywBfTBE5ZsSPHRXUU/d/FigQBS6Rhnhcqw0u2CpzotHQWvA1cJACJBI1iB+BxjD5wBzRTpmAE68Rj/yPFIiqMcUwJshkrtCEEExUBsYgAUTn4t58saLgw18hUCifDMwQYnkKXIjIhRqa9sirFBD/CWzRYK2Vy5XGILGDzQjVWzbADRwsw0FqFqrUFC7VgP/6EOtQe9YKAie8B0ogLYw42IvQsO8dLhratvb/38Z/+AS7EyM33l2lXyMeJMhBkvYlRH08tu5S8h7cEX0/vs+XIGffa8e16NAfNUwjzs1x7ucyL1YKIToRYRuYASmHi1whz6l+LIAOgAimHq72z98srnYM65ezF/Rt/ZN+0YC+cJUsATmEdwrhTIOtNr37/37U//21/97lc/xyaUJ/I2nez0MtXadq0uoMMJFtrM1VZYLluY0MeocIGSZocdT/Pz84T+sOMJlcz556h+3hMKKId6vdmkJ0BMtpOu/ESygpjyVkVb1hXRAoLBNgN6YCD98fFwl0Ldd4+zUm5ZpsME+7zkVcJI1WwrnJnN5znZVHBP4VCT2aen6Byo4TxiQkADfi9tBlPgEPl8ur3WkL3+ZDFsKfMyLIF0FPWTy+KyI0lhVqeKxrSvHucdsUYp7FrZrGJ0sAYBC8UwKAeexIyySINgEsoGiXsLGARK2djahHkigWi0x8SeOTnrYI+s18kufjXJAUsPnONxo7PO/+KEV86OXX3x//vrv4LU/+n/8n+eW1jEmUiUM50p/sQRBXLjkopLxj/1aEOoN1z3gdHnbgkgQwS1uz1quXQS8K/DUdk+MgA66gRv0v94S3qTGd/BsYcXcvRjcRlfD/9992eH4uToTj4xRmQzZDFuP7537+d//3e/+82vdqpbM1PTmETI00PR5q9YvFiuoLdbrQbqFlXNFnAqDAfcsPkLAIRAnj59ChYQBKGAq/J5M5AoqpcoaubFXYT5BvTjEMdlKH7wUGFq4jBz80Yp5RCGHSBFStTATEI5RsHdRliAABBMDVqCCMFAcAuywRbVaTdxTOEUI44a5ATU4FM8Qy2GcafLzjFtRkfTmbkJVMQp7dJ9qWyCg7iUJiiOzaaYzViOJM4OSwFIEgoeIjm2go4Ai9mkNq/BWJuIohwb5jWF0FA8zknvnRxOMxIZlucW5jlEFtPO2vp6qZBXiBA71LLsthsiQAxUml1WqgGAkgIMIysk1iimwT0HX8+WH//1f/3L6enKv/43/322WJbJaKykNSP7x6wgBNZgy/AGfdKBdiq0u6gv6edl5/98xR5IjzfhfDm5sLP7F0ee74vJ4oVl7GKK6xhcvZUSPuqiLvsPhOJ2UXj2JfZPLmlEDn4Lre/txM4Su4J9AuiQxonU7Wysrnz9+998/cUXtc1N9m+jOxutDiHDWB3ALgzD4eVUMEhw1AWKvFgurNU2gQ/TM1PFUml1fW2rus3YBnkF2/25uTksNcOh0kYDX/jtJfCZT3bFu2KGNyrofnAMnNAHpAIIAAn4zxCXeJfYfu+6XHYTKwwRWgK6yebi7qqM1HxswPg0+XQsKZD2cFmQk16DIUeQDvvawNWNZ4aZYQ4wRru51sQQhAm+ZgoYyGRTpEBME7bE/n4DT5oxj3sKR57AnFxR6UQvkSZ/IuiIVM+QQkY6LpVz0IiRbmYLuQwAC1lNTZXpD/AikJxltvqtfCrTSyr7gOQDdDJPGXmJtAQvsRgB0QT8sHYCmB7ev/uf/uN/mJ2d/aM//TOO+WAUogOH8RAVzmRRQYyjkeIVb2QKb+SSEvTxxovzCWMHsBew7d1Ole1grtfOAsOv7XPROhx+dYfn/DLKYc/V8SOwrwUovMjTEOKeDNF4lnPtx8Mh28+L1aPOe9T++y0//Ers1+d824+x0mMMOd81hmcX0DElR0QNbhuF1NoHm5wAO9owha+E4xzY7SUkguuEnDFxFCkRwdX11Tu/+/Wd33/x4uljIAAhO/VGqzskF2Kv1sLB1S0WSsQrkxnHnEGk7+uXy1PsCsf+wu8G3i98M8urz3OlAjYjhzIYX5iR9wRTB6wgWyjrtHkMMXaJBsEH5H1wYMkoJEygjNSYRWSbomiDWAo/FkiCXyj2orMlTWaN/oA8iYzAw4abjFnYBMa5XUqcTCYfuNTuLUASPi91BzpluDPo59IpzmzHjSUvFDvOzBjFrNDPWTQ0pDDAENCNcMg6xHkfLkpFOBO7zTHysUErqe1XnGI2GJRI5thReh6MVWAlQTTONoUTzhTDhYgJxpI55xdnZ2pb291mIxXLYt2BJ87xwoyEM0+JBwgMj8faPfxsMtvAA3vWBMXymX5iQMLIp48f//Vf/eXM3PwPPv9RH44UmYRjDkCL5BCR8BA2J3ahYU9iINYyMW4BTP5W80mBMNK4aOWQXB2y29ms7sIK82yWH8yCHKhfqEcT8HakCkvYFwA5IV8q9aBypAmizm+TBKLv/wV8mv7F5HP0o2Q5bODTWtCHwA/bNq0GVPCA2OdiNt3Y3nj66OHdb76689WX/JJhe2g02frNsFS9WavVm0T8sL8d0wOJADHnsGt8qjSFoq1Wa4CE0nQF8ECkC2AFIwlTQz2dSNMTYwl7vKWGBbUM9yh4RwYMCu386FBQy4yx2BYyG0r9c4tCHzfAAFegxl501DwGEHQ+H5h/MLEIdCiemIPb03iU+uyxJzS4P2BHOnQwJXErl89UiqWijCrJfC5bLpSyIBTghQbqsFMKRiNabNrRGVsAES7BVfBAhRBkeCByB6dcpVTgbjZfxOsG1OCkMxIdIS/nvNHplsolDD/kAFKkE2wO+0RbT5fy7XohPqhzEkZPQUOKBdKMGKrAXCYQIBA5h2iDlASViBM5hGmqVmv89le/+sEPP7t+8+bM7AIeSVCsFQK0eWiSiF/zKeFyaURUt0I7fcZX0d9IAicmgdErd2L0zo3QvgAoWGH0FTq3h3PxJo5ehgv3TLAboPkwWTjckH3A/uOMWF1zhbmqBEBgANLp70TMNOqrz5/f/e7br7/+mvw9M9MVKBD3w1btdqevaN90mo1IoB9ML7RzFwxDY71eBRCUS3mif+j24sULFC8xMITaEM4CeCHwBfDUAYvIrgMe0UBwAxTAMYzFUEQdUmauAhhpsxWuMJkuhpisdCk0NYw1awqgTiTZuqVNX9yK60SMRD7Nh1BOCsgjX10HFMDW/XyZ89gL0xUwSAnmS+UCAIg0OkT24N4iOgjQoIWYcUgbuGDDQoX4oQN88FhlNONi6EdhiGH8dPK7MRG5s5Pa5R5PpbkgmkfBQNq9hb0mSyxQo9kiN3Sa6Qgtz4GyUjuNBj44wrArpSLOtMFOndDxRJwdcHaAGegrqTgne3T4HGUG6iF9JICLToY6rHgDPGi//e1vb7z3/mefF4qVKR4xqMyMfKMIaLEtlrU0ZIW3k4IMKb6oC/e6RgxFErhIEtgXAAVM6vvF1zT6j4lAIlElksDFkIBCYPRf/orykTtsFO1DHTcOph80IgpX5gJ0sHTkgA1IsWqt+vzJ4y+/+P2LlRViljl7fLtaM9U+JJSHbzrJfjCToP5BP3yyZzuXKlAHDdDOWQ18PnrypFqvKS0QwbwZhdR02z3yRCtRjjm8mNzCe3RAFWNR9i4z9LQ2gClUSD8s7CgnLhu3mtgzqEQ7E9EZ/w5ASmAFUKK968q7A3TSKWKD7qCLpo9NF/Kz0xxENnVlcWlmdmphdm4KHITdx6OhcS3FdJ6oeEIoGJu0g4rRyhYNyBDOgIoVRAToMUawmggLZQd5xptbiRSOffEdSyAR2Bt2xTAmJJCW4r6HcR3ukUxgGGN2IFovk24mEuyMj1WKPA8NqTdj7Cmz5Ea46dqJeDfFDrPcVFnpFbdIJFCrwxqwDLcgdirqHLv24P5dQtSvXLkGqkO0MMy83DKcyM+yoppGgrX8lrplYqSRJfit6DOSQCSBPSWwLwDy71Xw7Qoqe1KJGiMJRBI4FwkAfaSjTdV5FBC4Ar0nhEE7ylE7lUii0+OrTtxIY2dr49nT33/xWwxA/U6XA7/IyIcFhLw91Z0aSAUvkfQ3B3CCoaSPhTzQu2T0IWyFveLADfa34/8CZKBrMeOADAi9IQ4GDwzBRLZdizbBC3SwEivrcHg8U3JdUQFVENFj1gpZV4Te8PyY+OjPrNySoQQDklaA44idYWClHnAPOIJZJZfLlwpFOFm6snD9ytXZ2enpqakC6RqJ6hF+0GZ5K9ovZuBPyAEAhBwEEWI57jpQ0BIMMdBOoBRwkCHAJh8Pf9rCjxNQgc9IUzvG6IA3CkzDcvBVEc9DHiJORssV8thvoIafLlEuxnrAzWG+odAjQrOJXSKZJC3aGJcidAifYSaZzWGzaxNxlS9MlRU8LnglysQK5fCNba1vfPX7L3/8459cv3596FYo1gGnerTwCZYEEmmZ1PgH29QpcGsM+1X0GUkgksAeEngNAApGhL9XQWNUiSQQSeDCSEB+HC+GgaStKQaDOCYdRDIctNvbL1Z+88tffPHrX7HRC9sFGpeSKxaww5DmmDhakALIRq3s2bYN8zoQg+3qWDgGMcw//Hv4zRMmUspE3DqWKxnkRDQOjjDmTKHv+eSgsa72TwFoAB9EvAiXmLnCtjTZGaegHxAHZhiGUUOtm1mIXelYZgAuiUGPeB2CkwmRBgCRame6VLyytHDz2vWFhTmyELFPaqpSyedz8MMUCr/B0pPA7CS/FRJgOYP4aGMXsA6nk/UUpKNCH37ZJCYYZiS5FpVIWn2ALLoLExhv7IQNKg6LwD0MZ0sdPTEbqbPBPWxYhOHgSbMs210ccLFhibXxj7AjQqR3qnUERdFeO02VIiYaAMjOeLbZx+MlpsCKtr297QfXA6+ISX/86MGXX/zu5s2bC1euY6BCTJKtsQSHcAIbwSqoBHVfCOxFJZLAyUrAX7mTpXku1PYFQOfCTTTpkSTALx39g5+/8Fj/7fMO4fa3rB7+HvqS37IFvnY5/t/7AjvjriMLAJec84BmRFMSGgz66TRrWxtPHj743a9+ub62OlUqAzg4pRwTC64zjBBoU9CMrBrZrOtpFC36lcbWQNl6wDeV6an1ze2NrW12jNMCTELsTW2KIhmgII7SPROMMiA2yNL4EChjx4e5o2n8QmIGMScTth9tRIVLhpB0xw/t0omk2I4ULmM5GzGU4FcC+lxdWrx18+r1q1cwh7D3HnsP2t85hDIASLwKTBHCLD8Rji+uAX92+hhxx8k4yZ4NnLHjiiFgOgoVhMY/BupoL3MeYQmCCyWPZmO8eZdY7EjGkGDr+yhCSLmz2R3GJekikQAE2w1t48dtV8TDSPyQ5TzkgNRiLru2utpt1ThojVhxsBOZpNnKNiC6KZlGiIRcwRKeNZ4ZG+k7dUBqCQvUb379S5b8z6dmOCNeTk0EZrgTGTPj+Mm7Tc1XM2qzlQX3o8qbSoDn7iQiwbooLrscXn55Jl6N4ElPtB/18qToHHXek+q/H//n/uADBoKKLznMMPWJuyclloPpnM28vtKzmevg9Z7XXWCE9Pa4ADywEKC4iaXFrIHaVvxKrC/nUaLP+Qzrqyt3vv79yrOnnIEliMOhFuCGbAao1O7qCItMjn1TBdQqN9GsmIKAQBgbaAGpcI9syXfuf4WbJpfM0cjMqGMq6GTgCA4k3D1w1WjVsZswC40AIDoAM+gJWOEuhhgRNCCC1QT6WFvg1ew+cuCRuYf3Npdn03smn01du7p0A9Bz7cp7t28R5VMhrrhYhBp9rGi5xB3Tgt0GFCNABZLDFCWzisMCfeLKI1iJeSkYnuCEE+fl9gIt+X9LOPTRdnJoCAnpyA/9oUXAirVw7dYX1sIdhMxiOl3W22NPHEJTyDaYz3I9x9J9nHFgSuTpvDKY7fgIeafWWNsgjKrWy/WyuYIehCFH4pPYsIaZjYRA27UdxXfn8y+ePfvy97/7/g8/Xbp6HfpQA3JBUKKzfe9iclxop+rL9EldVuP70d8TkIBeoZMu/uBOmurp0juqHC7aGicB0FHXc7rSPVvqR1r7kTofvI7jkQpGHfxKBd0O5uFk7x7M0pvPNbGoics3p3/pKCAB1L6CiYecCBFLptHscVQyJ0WgwbVjqddcffH0u2++enD3HudngY44sxNFz+kPnNuFCQf1jasLxCNV3ZfjCa1PwAphxyj1ze06nzdu3Hi8/IxgIGweir5Fz8f7yhBoUAYUBWRoNGo8fSo59qzjEgKlYHnJKBgINYy25qBVvR4oewXz6gPFj/uMsB2sVEQVGXgjcXO8UswT0vzxR+999OH7169dnamUgT6aWtvLgHrKrEN3Vsvz4tLWbg44QSFBANCBvxt8WptADOjA0Rh1PWjuCDRypQJbugAJabcXVBUzBNJgvUk2fFE1exIdZFXCitQjL6KAiEf/SCS47IgKB2Gx3l6X6ZTcWuHeQmkz08qRuL21s7q+zlo4KazJw2EuTprvD9lCD9whOTVQ8cqVpWKjTE8eRzmf21xb++r3X2D3ypemmBpQBU/kjcadlsvaDrUYkdGgORWm4/O0v4Y2VfRxYhLgqb31j8zfzLDIznfJkwAozFlUv8gSCL4t4VcqXL/IzL8Jb+/CGveTDz8W4eWjzNHIYAnsQOhp/i8NbiecS+ETZkNMLVkEB73a1ta3X/7+17/45fOny9hz2NqNe4dwYY4vJZYF0KPdXhyDxXFUeGE62voO4vFPNnijj6dmZjjIdG1jHbA0NZXH2GMbqmSBoKeNjRPxQ4ZnAp1RzzTCraEHGSQooISX/MvxJh7ZF07ETJoN4vArml3S8FTK5aWF+Q/ev/nezRvv3b6xMD87VSoKyoFdIIGtyOGKebiEM8i7Y1iHSQ2CjC06LMCgAHCAYaAirEu0MMQ4ggoePmEFRGhdkZn9BfRYRTvZNKWWyQfAix1d9KeJ+1TYn0aFn1GxZcVucciaXIrk0s5mud/DHIfxDKcgIBFbV222AZSZnd2u7tTXt2u4FLd2alilsJ9VpisER8s0tLOjrEJzqR1OJ+m01p4/45iwdrNRJB2lUgrIpOQ8BPN6xT+5RQm4Ct+K6pEEIgm4BF4BQHxhIrlccAmEn1G4PvFreMFXEbF3bAlMPmicPpZIEByDckedo94hju0BLEAWw2G/w4mna5x68dWX9+7e6TQw/8QxwxSKJXxcHGG6wwHu7RbQgVHddouBxP1iCsL2k2GL03C4s7MDoCHcmHNPt7drnDFKrDQBNwAa8EAuo/M+iQVud0FRXQ7Z0KYtonikfPk9EWTgdwU0gEWKnoAPmVwotn8p1mV7Gt6fBMeUsvOJmOZrS1du3rj6vQ/fv33r2sL8XDGnXWkYVGQlEWMcVopNRTSNjiZnwQAVSIJ+AEOahUU6QBG2wTiGBQfIhdtLViluOW9kZXYoNeo8/gHEegOikWWIAgDi5DE+8NRpyXK90cwnRcuQyIX2uEvBgccEYBTqSBWYGe8Rn0Qvoa1Wsw6TmHmuXs1NI9KNrX7scbNZ39zuthqkE8ogO9xbHMRa36lDgZ1urGtnJ9ZqNh89fPB0+XF5ajqTKyILnQTr6xyzbasQKrKKePcCb+Nq9DeSQCSBlxJ4BQAFzXx/ou9MII0LVeG5TPy6OXt7Nl4ozk+Kmf0kcFL0LyYd/0ru+ZTBFihp2FYfXDm2AMwpYAqABwFA5HZefvhg+eGjTrNFUA5JeUAEpD3Ee9TqtOutJiNAPISyUBAvShuLjjLvJDkJlTOt+uXKDNab+w8fABjY7E14CnEsPiPqmVEccMF7ScRQLsNPijaTgzj8N4RPr7hrCewCggBMwC6Bz9iF8N+khrFKpXh1ce77H3/v4w/ev3GTsJ/FfCGjrVjau0VvrCNJwrPZPEVhpZSgAicE3dAilgBKFoVjXUh9ZGzIMmZIZezrsiv1F1fWFZ5lIMKZNjbnOEGaOR5DGEoh1lDTdjrBKaxu5ne0boJKLJNnYTAMXEWSagbITwjTAn92t1gok2IAkWIjy+Q4lgOZJUi6XZqq3H/w6PGzlc3NzTw73a5en5uZZUfYztZ2eXoqXioO6w0e4sN79997/3ulqdl6q+0P2gGfFuLWLfbHj/EQjVGJJHCRJeC/DOfI4UsAFP7anDtb5yiRiz918HTCj+zisx1xeFISCD93Bf7ov+8dFsjaoYKlQweOtrvNOnu/8J6QT6+6uYV3iXvEnACDGMDOr1qzhc4G7pCoGTqm3qFFop0ckSuobVQ1eZ9JeEje5+2der6o7U7kChLqMq+Wuc+IaI6xFQrkJD8VF1hPSFbjSAK65qcjhzNFHidQDXOBKIhiIZkh2Zvz+c9/8P2PPlC4z5X52VKpIH7kRbKUzZyR7jHCEAT8CIgIA4FKVLM6cA9+hH3wO2kTGQBwZKrRrCNMoMrIGGIVxkBZuEcMjUGkbD+GLvzT7uIBgz7zccNsSLIM6QJoJvkbzBLgcUMUwVMKhZaUwIX5nCLJOyQ+RD4YijLASgpQkA38S1cWC+VShXM0ymXsW8Cg+nZ1LZ4i3pkzMXB1AaawjWGD2tmu3r939/OfrJVnZoFZzGpT69MXGFS0TCvwHJVIApEE9pQAX5MRAPLvz56dosYLK4Hg9+7CchgxdlIS4Fk7Kf+quoYLEzccoChjwIVpWaJ/mgP+X6uBfh7fe4CHiuOwqvUGOjlbKBLP0lDkT0dOGm1TsuTLaSUBQsWb5hZMkQEjm2e61dVV8wsRDzwsForECRGH4llwlLXP7B7wo1M/5RYSEDHnkdiGCE4yAw8y+bDzCnMFG9I5oHRhdvYHn3x48+q173/yIQdZzEyV2C5Oqmb8YkIkiqvW1io+tXOt11WktcJ24EiARU42+8T2oooZiHRrBGHMSAOOUXekYhhHXxvt3rKxajEYo0/rJYpGVZeiDlmoeW9rGZFhjZqPP/LzUVi4WtSHeOTUAK6VSgl3IQLAoyhSnPzFyWK4wGix1JE6s7ZclDlNUiKAK5l8/OTp2voqQG9+cXFmerrV5kgyIp77BFg/efjo3nd3F67eKFSmyW4pMKbpYELh3jy2cSZrzUWxu16NPiMJXDgJ8H7ylTkXtvyrMbIbnwsH0aSRBF4rgd3fkHf5N93X7jLxHw60nxoVraKUemyvsi1LUoeyNXQ7G+ur33z19bMny8CRVhOd2yftIUYUcvdg4EFp44iRMQPl3e+yIQsrEViC3DOgJe3mTsXIT8P553VONVeSmxjZCHlqICcAEEiHnWOu+AFGUvYxzDpCBmy/Fxog0CclN5CCcOCVAyU4YiIZx8E2Nzt769q1jz68/cPvf3Rt8Uq5mCPKmMM6hFnYgJbgRCz9MgpBWHA3cTYgBB0cP9rrJJ61TR1oQFpBUUcMQiFAByZmRn+7DCDAiCTlQuMTECXmaAHf6O+oQG1sRmO0/zRDHHhi8UBO0e09PqOIWCy6oI9sXBoGcgOt8Ccj/omgllHKYBy3ETsPCasbsU3NVoslAkCnyxUhnpnZ5jV21QFDc91+vFGr4VLkTFYFePV1Smu9vnPnzp0ffv4HpelZorUcMNmMgn4SuMctiYexTIwlZ9yXP1pE9OfiScDfH/h6d54USz7Hxb50gb3hyxA8uTekEw0/vAQCmZ/vO3R4ho/XM7y6YMnHI3VxRh17IS4NfjK8yNtkgbi6JOUP2g51TOIfNgo1G7XtjRfPnyw/flhv1KaKFc6bAtJk8wUOJwe+oN7R08Aa8tgQosxYVC/p/fSHCGjp4B4hzfiSNjc22DqW4FhRO76K3V7NJtu14qS0QaS4d6BD1DTJosEDXMKLO6i0DR4gpiMgBM0IViKmeaZcnp+e+t6HH/3osx+S2JDTVTlSlJAZfGQG55Q2moBlkANkCQkGH2jjGsCGiCZcfvxgmrkFPrV2EMjI/DNS+VhLFF1ktMSJwA7I0MCOoxbDVWNMA1VIsEr9SfTdMiQpKqhceIZmhzWiCUFnUjeo0Y04IiKfbdXGD31ssiHHmbHJTvkNFYDNdvkeaYGUahIrGatLx3OQ5kFY9kjsYZkriwtc7hD+PIi9YMfd+jrrm5qdwRbXxA7U6WUL5aePHi4/eXTt1i3Mb9jDdOoHy0Hw4k2fzq1djgRC3Ut4IeO26O9FlEDwEGHOXrmLyOQxeNL30b5N4bG7W8J396yflExODADtyeXFbDyGuA+/kBMhDpEjPeAjdT78Wo7d86j8BxOdiPQCaqdX2Y/P/R7Efv334tDtFmF7Q7gut9S42CEMkAYAYDzBLDHQBiesOFhM0Lq1zfVnj+8/enCnur2Bz6XeaKF/y5Uy/h/AEQe3g2YwqoB+dM5Xpyd4k85CHKMFe8QKxeKz1RecjY563qjudAbDSiaTzmbaTXYyNbJsoSeOF5OH7CdmjMH5pnQ2KHeUMmCCRgMeOiiC6oADKRhwZXFucXbmex+8//57t64szc+U89hWSI6ITcilRFYcNl1h6Emwf0pAiAw6AzIYKuaYcBh+P93QI1MHyAftP8IlmoQ9X/JKCRkBBlgLBBiAAQbcMhKQqKiHDGY0grEYSCcDLX0FPGsgHZhHKRpN5I60FCZtOMM6gDa4588QzIjXTliEggDoIE+dzF6kQVLSQskJiEdmIIgzF14y5xgUY/kMk0U/hGSB3EDrW9s9sg9k0tv1BqHshAdxkgZbxpB5Yth5dO/O9z75uDJ/RQmclL5bjkEBQ1JgA/1Mx2hFUXkrJMCj1Oszei0v92M9qddyNx0X0eEfuPd/FwHQ4WV0Sj2DF3o/+kd9lvvROZf23a/mm7DxWlm9CfG3YCyvih/IpdidITl1KO1Yp9XYqe5sbjxbfvjw7nfdFgeTZ9CPZBxGnorHtXO+WD66EwoUNCvohkvGkx2RnUdUstlcrlJZfvaMzWK5HLvfe8lBirEMxIPDmZygH6OgDeoQURZACyv0WBQlzhn2gRfK75fOcFppqZC9ff3ajWuLH77//uLCHFodww8uMmAO8GQEJpQQGsSiaBjdMZOG88mnQ4zgHRuPmXySdBAIo5AZ0qCAUNbof1yDgZQ+SYdlcCW8JBMN3VmUaAIpzbATI3u0UhhK/YjkGGzRYo0MGdG3uUJQVem4+XUFSGXwRIIpIYG4sA1BDKiqhJWJAaeHkKwakxdGMv7Gs3lALGfSPl9de7a6xpMixpw0kpU4SYNmMbth3Ktukw/o2dqL1fL0nIYo8ocTwRQnbiyJES/cG1ejv2+DBKIHeuJPMQJAJyxS/w3a700N2oOK/Wrp53V3CfrsvvVWtuy53onf9Ldy4UdalL8wgVgQGsXz0HB8aafdJM520GnheNnZ3nr84OHq8xU6s+eIMGJtqE7GQDZoVkZhI8HNo1GdTiGbo4U7mDLcN8QQdnsxdmNjC5sCHjHS1XTQ3WQY4oDSFHpXFhChCP5vdhJgxNhnJMiAvQcPm6GfFHG+bO+6SnrDWzeXMAItzOH2koK2PVZQYHY+vCI6VhwAuXysA4BlBDL80m+NhzuR0adMP3KaGU2jLIijYYJp4a8cSxa2sZ5MbGvxUYzWgEDaAcChxbphdxtBNPGgDf4BYVGAXSwykBDAskJVHjrqcldhKsNgpkPvkZs50WLFXJ6jXskI9OTZ8/ryM/bhccZYs9GYn5tZWFh4vrLaxENWr29tbRH6xLmz4DUeAGOhSBnBvpE8fc7o89JLgFco9Gpd+uVcnAVEAOhUnsXul1W/o/YT5ZXwrMd+ufV7Z2TD1M69fjyuLuBCzl2S+zEQvF0uNJSr9ghxuCb7ghR+oot+r7O5sf7g3n0Cn0l42Go1MtmCNsf3BYbAQOb+km+KWTD/UGghjoU927TU6nVipMtTU+tbNQ4nxybDKFQ+ihZTE/NSF4pgLs0pFxifNFKcmgKZSRqUSZbzhWI+c41zTG9cuXX1+vVrS8UCzeAnpcaxDWQyw1ixilxSKrS4BKA8IYrglldsrH040An19g6OQoJmC5VRkLZCc8aDAUA+kQ+hs0xDugAeCeRxd8SQEYJDM+twd8yevFwjItx1gQQ0jRaGH049kyWKszyIiCKomwkAgTwjckwT6APNqXLp4w8+ePr0KTFbOysvWq00cAcL3NLS0tr6JuFQvXZnc32Nu7OLSxyFAc7kuWCfY1IZz8Zyc2bE9rglkEBUuXQS2PMh+tt16dZycRiOANAZPYvgZ2jild3ztXae9rwVHr5nhzNaz4HTBExeWA4PZP98bwZWhoANaV8kiVS9+A2zW6gZ5Qf4wbkiAwc+rAaHkVdXnzx8eP/u5to6OhuVSYRKJp9mGzkApdUlzFn7jJQ7D8qJVC5PAhoV7uqRCSgxVaxYLj9d2yRGlwPVCY4u5NJK9kO4CfYGrBjGE7jAYQEjUcDgFjIWgqWwLrEPKpdOTJcKC/PT7924+t6t61cWFjnUAp4tOoZDswhYgsoIV1BhPGR9geLAoIUq4+LSCSMRjQ+KOdLoT4O5sPjLFUBHWXPYQA/04B5kLeSHqXRUvDrjL1MiRM1ksyq+Sg4y+ayoClXyR5iJyHGbbjxLwK+7tyQWbkGUnnRkRQrOsUu1GE2fUC7EPv8HBindgHpiq+t0cMOVCvlPP/kE6Lm5VaUPD3Z9fXNqaqZUqrS2ttbW1h4/fHj1xu2lK9daw66SDCgKzKKBDDhCh8J0UXm7JcB7FT3oN3nEu39t34RaNPYgCez5puqHaq+fquAXc4Lifu0T3S7C5SVi9SKI67U8+KvibwuyDYpcUUQMW9FupG6z12k8e7r86O5dHS2eZid1hkyGOohB2EV6kTyH2GmoELvDJ0NpwbRDYXc7Xqssuft0vnpKrhZ2tGsPtg7gpAnrEcoW4ILOFRKyAnKCMaBPPsvOJiJxmTdZKeRmisXF2an3ri3dukb082w+kyT1dLej7NLq5XjLjCvB6rwC1aAlqFPxVduc+gg6By1U6DNxGcgK+EEBjyiBstXl71NcjooTDI8FSgW3vN0v+TSar/x+BrMIxlmhhTrghgoNjBLYMacV40egRwiL3W2SBi3EpStmaNi7sjT3/U8+JlQcPERhKxm4BwBKnWPClh8/ebb8lAB2ngWfzMIUTtCnjj7fBQnwLr0Lyzy9Nb7yBT69ad41ysFP4UTlLZbD7pWe+JeTKd5iAR68NKlNK97NRUEDgIa6vE4cXIF4cFIR4Nxpb62tsosaAET+GMBNLkduZQxFHYJqGcUedwALu9ZJnAfo4XY6k+1y5kSPIxq4LHAORnlqZnN7a3V9jZ9YsJNOKQcwacM8x7ArjzP2I9Quah2WUMBgIzgRsmH7dzKez2Zw5QB6ri3NXV2cX5qbLmSShDwTQqThQgKAEGXQsbDjkfsM7Q57EAx/hpfsdU2NqcV48Baz3wQhOHCiiGaIGILRawNvbjei4gWeKdRHY9kpZhvIRFBhOfptdDasApHRryWA0/8hekw9hC37P41TUbMvg5VoMYJaI350ZUXzmtwUkW0T9YAytvOfsCIQVDabvn796qff/4T9eNWtbY4bwSBEtBZGM7iqVrfW115srL1A1HJoetqkMZJztn0Kn27Pz9d22D2KIccYtZtO1BJJ4CJI4MgusMvy9u/HZ/CL9obS34/+fmT37A8ze7bvR8TbGXJSqzh4osPfPcYqDkncFxvQ98oJLj+gfEh+TnDqQ87o3ZjXCwyHeQYx8N/90tYYZEjP3KpVt9aeLz968vABUANsg+uqN2y3Y9pNTZiIII7ZilClbr9hOIVZ2AwPtdn5OawQnEU+v7jw5M7dTruHZ41Typl0SPRJT6YdrBRUGEUjQ9D7kIS9Phz0OuCeWc5zz+crhSz5fpbm55fm50iryFnoZB8Cb4HWfGof5cthOEYQ2NDJp1a0XqvQwft4+8Sn31VYza4iAkYiPFxzCOEI/QjBsS5vMUTidHzm0ad8e+ril04qIBj+L3DvQEfuUrj0Fuqa0R182rrfw6WlW7TzP3oShU26JSQg8MRedqX3YecdR4J8/PHH9x89/tWvVjODFFyAX5EeB4F1223wEImCFq9cEyoimZOdW6JFGas+qa1s8iN8K1yf7Lf/tY/y1e3fK7pzKhIIHhnyD+pvMtPb9ByPKpAjA6A3EfQpjT3Smo/UeT+Gj0Fkz5f1GHScpWMP3G9FJ95+UhxOfDknLo/K9ptz9eYUDuYZ+gYppBfp6eul7hVHKrT7XXqiyImAlsWBLv1ebXNzZfnJ73/z6+WnjzEMAF3wi4B7yPucKWbRlBxfQQpmEhCTBwhqKE4+FDuLCYct2ZlcJs12sGSxWO52+oTZolWzeTL4SL9ieQC+4DJSxIlYG5AMiH3iOq60S+SKzEEcX5XPZkv5Alu9uDEzXQH9MAfoCusUYUacmwH/zJVCyVvMjwiZL4zhrAu7jrxj2r8lgGDY4aXMvKereZcPwykmKukDbgV3ubRoHMh4aJFmMHsMArNGMyTREkyAEUhxOmaQwvKFrUcMYDsCffA/zFvSOnsrHrrCHpmIAmrGgKxB1oKESHVNFcBq6IeqsiUl+7bxjs5xS0NNam0eWTqWLKZS7c7w+9/76OnjRw+fLJPFIJfJskttc2ej3W4RYgUMYpcYz7HVbrBqFw6fJgNdajILkYa28RB9XHoJ2Nt+kqvwt/QkKZ4HreO94S+/+efB86Wc83iCvpRLjZg+cwkc6deNzl60owrrS6NO6uetTbI/r3BwJrcS2omeUtTOcMgnvp5CoUBFmQ9tJ7yAiPIuJ9yig5+LFeMZm5mdrTUbuMBQ/bzwKFEyzfAXOjjO2EYvG4U5vNzwgK5lixg6llCicrlYJG1fKr00v3BlaZGwFY41bdZrmC4KhRzTQQsgRcWZ90+Nt+KV/QTPpJRgYNCNlqBOhT7+ad314Zdu/vFFKbx5fBsJjKsTfz3ieYQegns+18SkEzM6CvFPBnJXqMvcXqPpjGVuIRCArBulrCJAFCf5YyJRLpfIGck/4rC2tzZI9k2jUmu32ohwp7a9ublBBWjrE/HpbCBGZ49Pn532qEQSiCQQlkAEgMLSiOqRBN5IAhOaxi8nGo89wQQ1V2/WiMEEQ0CzXt2sbW02qtsdMgR3cFGliS9B2aN2Mb2wFwyIgf/Jw4CI+6FRqjceB8TQH0XJv1ZLGfnASaSb2drcARgwkWtT2ilu52AVGDHYKu/oinZUL+gHG0UJ+EPgdTI+VSnl0ql2s16v1bAUYb0AA0ENnOPMQ4RRjPVIING0Eq5Q37MEPcXTuFgkUkpBzQovwmgzgjVQoIujH6/7vD5OpqtX8Y/ZhOT34p/WO8I/NINWZOqCvtMZUdCVypiRl3+DibyDSHh6almBCH0mk/OAo+PZcSZHWCIlWIY9LgXCZEu7ItHn5mY+++yHV69e2dzc2dzcBD9CnaSImKqAQS+ePS8WFOCFbNmvxy0kwyct4xmjv5EEIgnsLYEIAO0tlwNa+X054G506x2XwEvVN7Y6HFUgToFR4TfN6wFxp+lbilCxHEyhPdycWlrdfvpkeU0nt6MfkwTVcuI7x2SgVolwBseAVygYYIrFIujH6bjKZAi3Ov0eh2A0O+3tKjYkO9TTdKqsM3KpGATgwFTQz6CvMCIsDWQnjA9JeFgpFaYq5VI2W0EnZ3VKaqtB9sQGMdRTU1NM51qZ6WycTBRegsVOLNDZm/hkiLcEnb0C/16C9mAgLcEQrYAV2WfQIagEPWkZczf6G74VUPPG3bfCY192Hpum/K7DSupBB6EfooEwCeGQ1K47xWtl0qmbN2989vkPWcTKyjMCreZnZzghlU1h7bZOZWPVYFlgj4sXZpwf/4S+V3yW6DOSQCSBQAIRAApEEVUiCVwECfCV1LcypBdlhgAzyBhhZXwXzKN/no+wxeleW+vrKyuP7t5nBxinmWI+INamo4HK/gxZtsRj7IGyQx8aUZnUXdHySU9p3Exue3tnZWUFOwfRz3ZEBHdgid3aymKMRlWmRU7x5HpsHSGpTyEn6JPPpbAA8Q+zVL/dSqUTU+UKxNndDRFUNVP4OoSD7BisMBSAPYrmCwnBL8MtUuoYY3AVjWWiuxh+xvwEQyYq3j8YFczlg/1yNx36QwfgZM9CHNIHYBke5RPZvckPjeUBYO4ym5zfNriGSUmeuNGMSos0+peMY0RDeHGCqqanpz/77LNbt66SA5rArEqlgkXt8cMHGNfgiBbgLE/TISasirsx5vO5JoQQXUYSiCSABCIAFL0GkQQukAQCxew8uTIDH4RZDFSad+YTWFPd2nhw/+797+6w+x2gA4zRPnElHB4q25+lewZ5oCP9E5oUR0JeRx8DUwgDApRg/sHbYvEkSnsYwCbIsuW70+sTVd21lEJQoGBSKhRzpO/DDkSqROKkMUd02014IywasjqhzAJy+WQ693lBObwW4QMr9Anaqe9ZvOdE/4lR3N1vLHNxKxg+0S1MPFwPuvlEfPpd5zzgn8agHgzxnrrUNjeMZhpLN/W0n2EuYUj/VBCFXF3cJUaLkKpbt26+994tbuzsbNM4NVVmFxgMgHs2Nja8J4LlSVGnQh+jE31EEogksK8Eoi/JvqIJ39Bv07iE6+O26G8kgZORQFiFB2+aEIMVWgLVSwNKbqSDCYttd54vLz968BCfiEws7C2KDRtt0j73pG0BN2z1YtOYFacMagGaQBwlyieNdAMAoUTZX+3+L9opzOIMUOcufjKgC7afFKHMokF0c4Fci8QVYc7AZYNph2BpzFPYgdhXxhAGuOGHcF1CeEFH/UHXQQDzemEKKj5RsMxgam/3DkE3b9SnxeWMRsHZeC89d4LOYVLhupnQBLlYXUCQUUiDTwqN3PB73uKNQWefhUaGBKOCnqOKTqoX6pGNSq4u+0fFiwCPj0WEhPkQZK5/PCMni43no48+stisHficn53FCASsxEaEWQjxMhhKXqGDXzpjoylO7Q8cnhrtEWGX4WnP8s7SH72ip/8cL5qEX9kGfwbv8UVb/2H4cbHwyU9kuH6YsW9Nn/3ejbAieWsWe44LCb9jLnPgB/oM3BI8Apc57yNKk5sk92nXG71OiwTBeEPQfPQ0ZJMcttpuSODUzFSco776nIbBXQjyCU0HJayXW0Al7UAfDHBvPXv2DAMEOAWNTZJECpMyVlCGKeWMi5F5kR1eqGF2xmv3fIZdSzLvEMNCwsNYOoFuBmIB0xhFZAvTQYSJMtww6MbsLmraKeA0ZwzeQGvBU+AWLcFluL5nY9BBNF81hIiUjaHif70zlzRodpPeK2S1Yx2x6PhYgqVZoYkUUAilURgTVh2GcN/ojP4GRIKKURBsNQMXm+DsaA2GEq1tBejDoah2NCpPV+kA+pblmRr48sc//slf//Tvv/n2HikM5hYWs80uVjpOiSfTE1OALPOFAu8ClFgIn4haLJ9aCRMP109twhGW1aOKyulI4DSe40V+Xq8AIBg9jfWf1JN6Q958+AEPI9whmCvcf8/GidUFfSbaL9fl4Vdx+J6nIYGDZw8/u9OY/TRo8p/+sK3gEtQiCtCKlinVnCSqRg1segeymPaV4WPAxvTOyrPn+ETQeVJ72j+U7LChqzMgotk0IrE/iv6hTnQturvT604Vi9lcjlSHRNQqYIg0NfJNxTmKvMGBqSSMTgup4BjrALM6pDEUBfaKMTsJfdjoRQROguTFaf4X5zx54q45UIMRnNaZSpekrzhFtYPzazA/vwj9dltxKpzMxVogDh1UNQzzqXYrGK5oS/YEmCDljZpWFhPU+Uij02KYBOggw0t/IBOIAKGkp0JnisEUZfaxFuEMa0SFjn7rnLKNsFHAHbuwXM7qQxEw8lTOxgAb4bSFCyMTXS0/ELNBRzYi2Z5kUWI+tcRGEER1403GH7hSfwCO2BC+5YJIdQbHeb4gTDgQQlXfFAd9KU4rkUwRSH7z5s279x69ePEiX67UOPKtWqUbN6lUKtPY2RgJEgJZYtWzdejD1jiSW9AYVSIJnI0E9MLru3ARy+j35SKydh487fmcaNyz/TwYPPk5eTu9nDzp86bIus6bhSPPv+ebxkIcCnDX4QIqGdLAojTKtNclT97vfvPblafPaHSjDtYckA390a4MAb2wvYsEP2wvkv/FnGJoTqg4+MDGI3uMAQ60ab3VhBSuJOYR8un0O70BXiv0M5xkiGMGYgEA2MEdH4KFpitT5EREXyvG2dIqYqVIZTMgOc5wwHHDEmhhrFt9cJwxLwUOueVYx58XNAOpcddL0EKfUTev2SOmDxS8PehJhXa/DCoTjU7cP7kFhYmW4FaIjv1mhlxsfss/HWmFWwIKVMLtqkPE6PhS1DDqo4frA+VplD2MhJapcrn86aefEgHNQfHgGzpgAfIh+D0D5hnrOI+KF395xlfR3xOQgIv9BAhFJM5VApcGAPH1PgNB7TkLjXu2nwE/pz1FeF22ytHHac97XvQv0c+WnoROuNBfxAWSwGzjAELqEBxjfitW1GrWl5cf37v/Hfui6ZzPFUEhwB22qacI8ZFBhV4qAA6F4JidwA0w5ARqtTD/jDITMhADDEG1eLqAJTII2RDMReyox7yAvgbBMNYhC3VihihcQhkQBdOumwE9cAt9v4uGpkILbMA5TjEq+tRx6FqXCI7xh2Egeo7giBlMsJlo25VAk60HIh4lw1grhF7rH2xLZOPiaA8R2r/Rb4hkYWxwzT9QpYE9bDdQHSEP72OfsCFy3MLcI950/Jc+1dtgU/BpDZMfI1JQNuIvbxsd8KJDRmfZSfl01JGw11k1Yv/8888xAjWbHAm/hTz9aSJkHhlC4MkiRqaTp9L8XzRChLEvJ41qR5FA8CCOMijqe2kkcGkA0GlLNPyiez38uXt27u5Zdve8sC3wf2F5OynG3o41Bm8a+gwNR0EvUkcZE/rD5ve1lRceCeu6FvWHaqSe5Wx2038YEbgUdFCErVxLjktAPyhL2pkC/Z/JZUEqq6urjKKPD+Gud8BJ5dBHNHtd8Ae5fgA6uXyGczzx1MAV6KfRlKWnXClid6InoIoZqXg8LxWKAyDn1lv4hAcwkJdwY9BCxYfwOerPTObVGg2zP36LqqHH0UuONUvDQ6+8E/GJAk7C9Lm1uz08SlOEyp6XNHoJOjoFGo13feiJ2kPxxtEA+8Moa1RwEEBtbm7u6tUlVk8ubw/MQpKInecIHSjzyZAwk7svA06iyoQEJiR/gOjoOTE2uryMEogA0ORT8zc7/DnZw675idmz7Nn5YjbC/8VkLOLq1Z9XvqTSkYiF9IOEyLrVBBSjboNhbWebg8EJuwGLAFDsxNMeVgGPAqGPTCe2e4snTh8UJ5ci2I/hGAP3YC5qdQWY2MalDfDbWxD21xuvmUEHNOsI/dAOZSgwF6QIgib6B5TDNiUAmfZ/CXhlAWToZgjCCXNxlxaWgLb2TwdVdKYCMHMzFbfCa6eORvfky9hyWIjDBT4pdHYmNd6sNAnt+NfRsETUCOtZ6I8ceVBl/FhphewhID8EoBtmX/JvhHSbtXHnZa5IzaUIZQvD2uu7o/vjokkFuTBHvbRIOcMi7v/GFj6bC6b0T/yEIJ06h0o+k33v9u2pUrG+s0OGSYSNoxPZImSzwOnJMhwh+wvj1j6X+Zi16O++EtAjHpc9O4UexagadPNxwWVUCUsAYYUvL049AkCveRa81q/pcWlvX9iX8mQl6j9U0AwqJ0v/bKiNf5bHb6OwBjvNu5wPtf5i9dnyU88Kg8IDdsASDjAgAZeoQ3QhjjAijVGEoJbguWM2oANghYIGRUCcHYZpgVce3IEHjRLIjVGQAq5gXeIoUQxI2WymmCOKWsd7sccea5BY6vfzREMPB+0mKRCZWR4ZrEpwRd0fAf2pcOmKmQ6CQAECAtaYL8r59Lt8enFp+61A8k42uBVcQkek3K6Eh80SJwpD7SqjIdqlLjhE8S57ThGeKOhAhVHhy6Al3O511uI9fVG+/JEExrd8uHNCnZ6wlM4kb9++PTMzw7PjSSFVZMsGMW4BgLybE/eJJqbzSaPPE5SASxiCQeUEiUekTlsCIwdz+Dtz2lOeL/3dvwgHvLiI5YC757uQN5z9bV3XfmK5POuVeYKoFwomDPtiSq1iLKCOEtcNDAXYZfB/tTvbGxvPnj6pVbeII6EDL2wylSaOh7GYZ1i1Lxy0gUmEWwwU6GELVyKJIYf2bFYRPJweBiap1zgAdTuZzhDxY/o4psOpkmyDsm1OoJ9ejw1fmBwq5Uoum6JC6kN2kUGBM+HBP2wkA9YAp6anyuAqNo6lc1kODOskO6jtURpprBxsMUvpXDCMFUzEiuATyMYivc4lNVYtQ45g2Kg9psOyQCeyrgioybAjj56MKbYxS8OFcTAXqdADCuqgIyY846AhIaYy+jazujEQygiOdmLExYDtJwOdGUt8QFmcU3Qhg41NYRdqtYfjbjbbEmaMw40inTWb2HEDkg3hg/Em5xFNkbN1+X1amRzmzdplDA+HiHFqulJtNnl8XkrlMs9aljYTpsNWxkAKOk7f6+Np35K/waIQ1CGXFAyZ6H8YCvuN9XYo7NdhYq7o8uJIYGQB4smd8cPzGQ//ebIiC8/rlA/4AoQ7v7Z+snxeEGqs+rw42U/g58XPec0LwkAUKDPOLe322pyvThVAQvKf58+WMbeU8gXu4v+iW7Va433GOMOWd9QhheEUGkEwZuOR0QX1iZqkBXMCUAaXzdrGOpHU9OeQC4cX/uT926HPPtu+EpVSOU+2aSKMslmigdrNBqc2YGLqqAJcwgzVwS/msb1YJvC7QZ8K2Mhn5BMGECYV582whJ62AA6FG2MVzrya2ord0wdX3u6ffgnnTtZRA9Cop+hx5duh0EdADlhJ4LXQjVmedN9BCwjFg86DSey1t41a6qMxNpA5xsWY0oez5JWgriFW9uwQTBMe5WODUQiHu1bApD2Q0OLi/IcffsiWOp4a7XRg1Wvr61tbG8iZOmNNePpth03vYxTeko+w3HxJ3rLf8oL+Lts9u4X77Fffc2DQuHtUcCuqBBLYLSVvCTqcceWVPEBnPPdZToeUg+n4gaAebtl96X18CPWJzgGpS1F5c+bD0jjVJR+e1cP3PFWGT5w4WpmloYfRYWg30AnhMdRRbMwF6OEulhWgRp+9P2ura6srddL/EIOsI0/jmXQWUGGbtrqpXJ5kOwxER8pU0O4wkEeJFwxSOzs7wBEcWDKnxOMyF6WS1ZUV4oHYek0HMs8QOQ2gECaBmSEHivU4dZVsNK5li7k8EGdnu86uM/qjjzn7HXtLs9GoVErifDDgBHigTzYjIxOFbpCSKUZeNnnBWBEt2H64RYEH76agbZLomBWjr4gjCQE+3UNle8O5kIJ3CjDLQFbHJ+mPoMuJZsAh4mFYDpE7ZrxhAs1vSY/kaMOoorRD/N829YsBS8YoW5RNhuUIWegXwLgzLJQyqUhEdPE7bitSF+8s1oQ/+MNYGev8Nwei5IlU0dN0CMcfdTfbEuLBE+gtDFERgVGLwKU9/fn5WbbE4/xqNrD7tHmOPBQ+sQnSgeJDXGgmInH71pdg4awUCXDpSw4EeMYSCPNzxlNfwOmCx7Enb7vv+lPbs/MJNr4rAMhFFr2RJ/jqRKROQwL+Q8CXH73FJ1DD4QhamxZUN74dzDiyYvTBNDprnXY0bL3RQM9jlpH5pc0udgWFoE4drNBHOMP8U7ANZRARp46DezjvlHO9UPjCVSTtabexGqSSCYX7skfdYEq33UNvc65FKZsHnJEyqFwssf1re3MDPQ+K6jRbmKOKuSJwJJkiSXQqB+rJ5rBJEC0E5IJ///YxNRG6aGvYgB8YgEM0ldgzQMMa4QQ056YpuhHXTH8RkatLQU4sn4FS+SqyglDY+S1wFYsVcjnAF2uRT0sB0f3RWLCcEiCRgpnz0dhWBagzp5hSIwkgSTK9l4qTS02uIvxn+pQ+WIFwulnzGJ1A0K/Dv+O7f23UbdzT++/5STf1HBfoUJAS0IisBQC7GzdukAD6+eomXTJKypThtNR0VpKkhbH0Ry58UpfQQtTGVN/Ov75k1kYlWGG4HjRGlUgCSODdAkDHe+TR9+d4cotGHUMC/guOxvKCGpPxwgKAaCFuGNwDBAAIKBqZJD6NGn+YiIE6AD6ZxmKULxVT6SzJBlst3aKgBYEQIh6P5QoCMeADRlAUuWM5Y7Abrayt9vqgATxdHPwOnhBZU6WxtG0sAkmATghDmZ6ewvdFIO50sQhj8qbFBVlID51NJUvFIjYJrBTskKfAFoCGucjoJz7dlmMqCjagCboJGoFiEJQa16Ebwm3aIQ4bFt8N6OFQDRhuduoofi4hKHcgiR+7XaBYr4vFK6V9UClhOJjHTMXhG+YOG+iMLYNNBhKU0llzGcwhJbMqpjclKAndIneQP/hnbJhRX/x9Bk+5AQHJd2TvscG0iZDG6ixafeoimEgtVhS3pJuwCfoSBhM3LwvU9OhN/iIGhgTkpbOJhYUFANDqRhUhUxC+27TEtvGD0BARn4z1T275pG/957uz0rf+UZ7BAt8tAOS/DkcSa/R1OpK4os4nJQF/8dBeDlNADVBGn0nbSZcNtjfXSf385PEjDi4VmmGrPCfAc8ZFvwf0ITCWzh0LT4YU6jCVyUKNkcCRVkP+E8KfgSyoWLxU5Bfe2Ca73hbOErQpCl4LGUqD4gQi0plNXsydzaWny5VysQCh7VpN4CaVwBKFbia3j5RuVvabUkkACBORGDUdLEAA6EClm5J29UydqWmHDRIby/2VSgHwOHQMNxncOuoSCxiWmEMgQWCOa44lk13K0+eQ35rlZTPaidZug5ziSWKVoADgA86x4V+ABRRi6+0AhpgLOwmz4NUSY2PoAHjhkqLV68MZ1pVQDh+2ENXVEaPVqECBMr7a4++Iprk4/ba3QFADfTK7QbsVtY+Lnhorl1Fu2CuVdPQsLVjXhPnAnRbwjt2LgT5EUgqtaw+GoqZIAu+8BN4JAMQvwmEe9O5u/IIcZmDUJ5LASUlAgTymdF2NSRFiNkH52TtMBDThz6CQnVrr6ZPHd7/9Znl5uVGr09n7oJHlMxOkUDAsthUUJFin02njGAIQwCeIpNFgA7yCgQAi7NCaLhHPk689f9pqYxZSByACh5lykwGoXbQpU6BlMerg1aIDSX0IKioV8rIf4XDDXMR0bDNLJKenptinVCoUc0oMPbL3wD+6mxPg5Y0CnsjHp+gf4pNhiQrnZ0CJ4+u5BTxBlZPUB1jQaePn0y76FHacVAq0Z+tzLw9hSTrbFcgFyilPc7hZsVgu4d5q1jEsZfLFQjoF0FEoFfHPuXTOUzhDp9vpygtmRXYWijJRY2gSZyR5FodAExauU7q4pJM+aKdGRx/jgMnqIyea1UcfPDsBKtmPtBcNw9F4oPDTOBLIOsuWpJ8bTeG2J1Xkm4NbHgSJp7UKM8hhYOOx8jj45C7tyI+HQjwQtHwK3geZ5cYwKMxVVI8kEEnAJfBOAKBjP+zxr9WxCUQDIwkcWQKoQHOivDIQJae4YDsGFVW3Xd3cXF/b2dkGD9HOi4rFQyqw30NrktCZOuBGOtU8XNBCFwYU6Q9UAMpgtIEyypKysbGJrseio2ATXC1mXEFzg2nQ9pwJr6ifjOKdISQ0A30sK0oKTbBRv5TPMSlkcdBg/oEgs+jTdDBccSk8YSocroAQagD3GIfdlmw61EFILL9YKMIYu9vY7rS9wd587E0NTigDeOHqgmkGJ8h5KH9Uwk4WE1wgUfJHn3yMtQo0M1WZBgyBEqZnZzLJNNCq3W+xM45bOtUVxnvEPosfCVzIR+IhvXVYSrQH/NPOAwAoARH5dPgiOmq3wVqUA6MRDb+0xVo7p4twHNu4y37/eRUmYpSRqyE3PxkDaCiHpjgH7hmKJawqpbNKsPPZPjv6I3lnAgoOg0Y8RX8iCUQSGEsgAkBjSYx/xcK/Pi/vRbVIAmcuAbMJyH5AwfHB/1D8JPDptJokP9zZrmIyIRKIgquLDd7EwTTJu5PBKqBwH050d5MDG+exzaARFXBjkc4MwWrCglCNwCBKo9UEZAgPgFfsqArSRDOtOZI4Dx5LkvZzoV+BFFhFKGhZTA6ZRKzLRvdM2mwSaWWpsR1JwlV+grplsgEKMZb/MymggTKCCvBtx7WCCrK2PQ2Is80Wte1tDBiddpcpWpx91Wqx4wlTUEvwp08rn9i05ArCItVL1OtNlrBTrWp3G56wXBZjFRWigxfYOD6/kGSHmlBLLJcvYgXT11zxO8AXqrLHKKwKq1RMQTPyc3FDSM9/D4A2gjg2RriFVqg5OtHjcSwjk5HdGQ9jiCizs0y2H1qVoBqUZG43EWSgzWK3vK9NAhWnCWFEPerpxG0uopp4msgcAERPHgqH39KNindmlBMJSNEelTOWAML3GUdvyBlPH033OgmcOgAK3oDXcXIq9ydm18/N+I2cmM9fUO6G+4TrE/2jy0gCh5HAwe/bfhTQijIweO4Z6WJikLPNRg1UkU9nQQXbW+vVnY1qdatFAkPFhwzwQ+G3QR06sgEf8PaCOMRAIkEjLiFOx0DHozjdc0QHAA8IBHixvbm1sbmpPeEdTE0eQ5xqE0IkRJQQ6Elyj31e+alKifCjneo2wc6ABqih3gkSwqG0OD83NzNbzOdhmIEgHqagTgUe3CYh6GPACxMV87L53kUE0NnYXH/+/DnnepKpiFEsX4jELBvwXyyVZ+bm4azZbtWq9Uarru35Kew3RPSkr16fgT7pcO7fv0+WRtpAS5m8PEQw/9FHH/3g+z8sVco41GLTg1yxQKSR+GB1rAJjiRmiWCFikzg5axbgqc3q/CAgJK3COHJANNJq1iZzmG7Zbwtr8eXQElRe1o2Q95i4O5rAKAa3XHpc4lnk0QHQhGUzcn4Rp4VEMbYViuUdYt3Z+tfrOkIF+jAQ0bF26pH5x4R6zh/BMz1BPvz1OEGCF5DUUeV2VJmcOgA6A5nuKaMJQfil99yzf9DolWA4l0H9DNbyJlMES3gTImcz9hKx+oYCCV6eYMlBBcrBXZ8F3IM6R80l5WrBakBgM7o2zc4s3EPxGAch9EmqU12pE/887LaHg26bM7zYFdVro/XMwgFJhf5Y7InO5AJkkAwQ/NFotomqSaazXfQj+59ADiAajgJLpsFVm5ubwA78OWhiNl1BpN1pZlG8acwh/GcDp662Z5YWQBSN6nZte5shWFSgRxAPXUBHxXRqtlSolAtszhdgsgAjtC8FZYzvC6WMLUSoyXxerpvZAAbEYTvZnTt3gD5+yHk+y5lX2pPf6fSAOLlCEUNOqVjG1YVRB7tMfQcApP9RbzfbpADo1esCefn83NLS89UX1InfwZiEMAGF33zZJmv27PRMpTJ19dq1xcUr+WIZ3NNXVDGmrH6/hfiUOggfH9w2W00ekwAcj4Gdd2zmMs+YoUGemlbEHxmvtC43BcmsxKQMpLBEIU/DNaw3eIt4SDgOhVAoWIJ4vGQvUAeDY2ZCAoaqMMZ+fJCPpw2iDVhpyIqdYwN28xXKJWBcvljscS5Yq4OxyQaJAVAcSxu9V/v/h5/3927Bp08eXEaViykBe81evloXk8ljc8XqjjF296iDX+a3AQAdQ0yHHPJ2v2GHFELU7WQlMFZtewNrvwtoEArRWaW4TNC+Umwo3SIhOJx+ur2lDfCKh6ljy0GB2sFfgCf7yTAVjFuK/hYto3AQoBSQBShAIaoGgEIFUwrIaBabzdR0+/ET3EroX3xl9EQzEw0D6ipmc8VScUCg9Nw8BgYw085OFZhCYBDbyxV12++R55nLYj47VSmmFeqrg8cACTKl+N6lsTmE1cEVheXoVixWa7Zw5z19+kQnW3W7U9PT8Gapg7LE6RD4XJmemZ6ZxWYDvAN0ALnAUHRmOHSASEC3lWfPX7x4sb61TWMOVDA1LdzCXjadltYhcpzU2A/vP3iefY6zb/X5yvTc7Nz8YmmqMjVTwSxEXDfZf1hLv1kfpjqEFGUBdwltvJd1KTFs90ZhNPAPpuBh6HdWwtY2Mv+V4Fbw4+sPkU9QDP/3dl3YQK9QHxfoSSC7SzAEqMxdQVEvvBBYhbKkO1AMUIegbTub1tCmbdx7da6Asd1TTLT4WiYao8tIAm+rBCIA9LY+2WhdF0UCu9XPLhX4klU6o+ekXgm1HWk78gSi4fq4s/i3vr668vw59gzFyWxs2k51oQpl7zGTEnX+0x8QQzEjCnu1ChzghYLHgsSWdZQ4R1hgTel02QLWRbXyr1qvMdz1n+UIlJonTBosAnVCiBbm50lsWN+pgp8IV84UMoQic+ZqPiM/F2gI+wp2F2bntItgyaOVGNwRkzYFepoKvOGlqlUbGGxerG8QOTQzMzczP4fdJU+0dQ4cU2F2GbYEy5glzpH18VQW40YsLiQH3GFqoMz83OKV9XWoVbe2IVspT7fajXqtCYJsEYc0TNaatbX1LYbk843aTiN2/wEzVISspsmqfPXq1YXZGe6CMREB4FNyIEhaXjKsWzpiXjvruCtmRmiFh0JTsFJfWvgS3mSBMTuQyQESjEHcDJTQzT7kCRZHAct0lzCZwoAifeDAaDJAk8uWpyejR4xVDKHRDmTkOWIqyxeyDIGC0+GTgtjDXHlj+NP700K3oB7uENUjCbytEninAdDuL/zBvxRv60sQretUJYBS8fdq4n0LK5vgllxXpgWVVQfnB2pS+6gEKbJpvEe9lWfL9767A2bYWt+AbUAA+74wVDCHNJil9nPowyimoNAHfclNTDhoSuo08onKx47Qj8WfP1/Br9VDn8Oqkj9j8Iix6bxSKfc5eazffe/99+emZzA7YXNiILG3TM0sxFoDQzibrFAgNeI0wSncTRCga+jE2YZ7OjM7n96CSqYwvNFokngaqw+h04Ae8FOhUGLvOrgHTU84kxHSaRSGA5JlEgsls1iAtM/LLCIQZC6Gz84vcCRZrbqDKQhoiA2HxbZbXYU3xarJRhtR4iRqNttbiS0tP9YX+pmZXn1WfHL3TqlcXJydn19aWrh6Qx4qZMksyhkd67ZbxBjZjjPZtmQasrUANHA0CeMQRjVeIBUK9Pnw+u7P0XCG6B4fkz0ZHvQxUkiAvy+7gcSaluEJLxhi4Bawb7o0pSeu0z9kBKI4kYDUbk6ilkgC77gE3lEAFPwoBJXwe6Afm/HPzZ4dwp2jeiSBQ0og/FL5exW0BBXprYT+q70H8DBlhj7jP+OTRKuQ+G/Q3VxdvXvnm5Xlx7Wd7Ww62+gII+EqInDE/FdKdcxwWGIKKgwfG2a0DwtNCblcVh6lZqfFIaXlSmWzWsUdxitPZwrAi3Aa0gpiZyK4FgPLRx980O92tnZqbMACDBCaI/wkNx1bz3qZdIzYZ2CLJuop6jZcHABpXeAGZdUZtG2/Ej40Ziekl3yM2Ja4BXtyP+VzMEqIL5CFbfH41JIZdqdpOawtlib8ZZgtcOJHH0MQE2HwwhNF9HW3W5iZneKAsmfPnj1frmH5qUyxSayCMQkjDp4ywB/hzUCfRpNEkPWdWnVrfW1hZrqQz2K12pybBzlBtjQ9k+IkV/a1labIbkS6I56OsI5ML0QxAUQEXbg0i4wePqxZH/HIw+Imz04XHj2NAcl+T+xacNDjprm0ulGzDtbNG6FJT+1M4y/tZmyiTWmNdK5bpzM7Oz89PSsuDIwqnsi28iMTWhgBfTeSed3mmvwYPRdrPqBbMGyiz3gJwf3RSv16ovPLTlEtksDFkMA7B4B2f2MvxoOIuHhrJXD4V04WhpEYpMOkwDjqAU2OWYWPXqffqm9urG1vbZLJBsMQ5o1OL5bIZDFXyGdDhEwGo0wCb5crNj5BFRT0JZYS0AlDsN9gHQG4QB9wkM8XMrkCphGCgmnBOMKerUIuCzOYdggYvnXjGhHQHPvVaMhNhn7ViqRtyb/HuaoDDoaX+cfMQhhtuMu83o06xRUhn7SDMKhwl2AjPguAk7jm5dxWNmx5Ch/AHBubYBjtziYw/jEnq+DIDSKLiOtRvd/FtCVQQgokciDBTDKWjiUXryxi2qmUiqCZfK7AFjR2zC/Mzdx78GBrY4NcyeQRwD7FP07tIMRcgdLdDrvbyAIweDQkYujmrVuVmVmkzwoxRynDtRhOgTxgcxhPG9BgUUKZQbGF6mMEj0aIR/dZL4WK3R2NUN0avQOS8crubmb+GaMigbBYAw9fs/X+zffZ4Y/Yn62sIlUcYdCkglQDUlQOLmGWDu7pd8P9w/VgLI2+WFr27BD0jCqRBM5dAm85AOKreIwvYTAkqJz7c4oYuKQSCF6hQCuwEBq5DLcEq7P+UpZSY2AZU2Zox2QMq0xqbW17ZfkJx0AAW1Cf29WNOH4ii4/pNpugCzbBQ4Ht7iAeHEnaMJ3JmLOpMTczI13cH7DBihZip8kKyOGpJNDZ2Nrk9AkAEO1MnSLRD3mfk/GZ8tT7N2+BGJ4+IUS65VzLTUZNp0vgmJKzDOvLVLkIXiHoKMdpqWaToA9wwR00vjquFQWjLf2ZYPksUAkXUzovjJuKZSGhdDoF8oAZVgT/3MfyBP6jndRALIJjzzh3XgT7wDhAiQAZBi2MUsTB8O+j3EcKMNqudokQjsevLc2w9+vJ48d3793bwhTUbhc4qLWYB0eyZy2RzBZK0+QGuPPtPbAF8AEbGKJHiDMLi0waI0Zah5Up/EZLHznCtCxchrY6hy9UhVTsydpazQzkNiFbst22AXz4SWQIxRp0PxhudTXDDK3ys8WT4ELyGoD5WBq35uYWMHCRY4DcAeBYMKgShY+L0dQHDUgguNxd4a5Pd3C38MCDe/rdME2vhylE9RORQPAgIgkfT55HBkCBxI8332UZ5cvkU7/yB/58nPGK9mMm+gKc8YOYmO7g58LTmXhA3p9GKl7GBFHq6EYpVNo1jFcQ002nRco/tqAvP35Uq26j1tgJxU3sKEIe2lot0wpDgE60g35Q2ERBC0BYEkIqHgDEJ8S5W6u34zs7JNRbXV1lOzqJZNCTxAVhTOLf3Mzc7evXOM5ie3OzTnZB3FI5Jd8TN9iiAGEZnaQBl9eWrqCJaQd/gIlI7ENnClPIdgJXSUWlcAl7fCqmxwp1XdJBKR61aV/nRSQxY/TczAM66nZaNII9GNHuaPMXEmG/GJv106APjEOkAuIAkNgQW8iw1ydVIrYxTEeIhV3iePEYCGgpl/Iffvje1FTlu+++Y8t9s9Eol4qFcrktf5vmtXyNKfaIFfMc4YqTEAGmyuVmOlHqxcmPiLUJqswlBIi1TQkOWY5+HGhxEDN6hgoxsmLtjpB0DfpkKQqEFtTRpzrwf4vTgs9XyTgN6+NVC8GmM9kg2cD22Y9/BH5d364C0cCgmP3MhPcSzbhsGeov0ojcq3/0wlgHbw56evurfY92FabgdTg/Gomo96ElEJb2oQeNOr7Lz+XIAOiowj2p/jykYzzj8BDq4cvdjPl7MNFn4nL3qMO3OKnw27a7JUztSFMfqXN4llOtX0yu3nzJ+60r/HAnZtlziDcyiuJ1B9tcStsmFL+MXsQOkM6mST/4xe9+tbn+gkTH2ADQ+mlifVNZUuJgLEHfkxWYSXFyoflAQ2hEDDPgHnU2rxPdqGBYMbIyrgBTaOm0m3iU0IQ50hlq09Pg6sL8++/dAmusrqyQPJBuOtrLsgptba4zC+o7nRiymf7KwpVypSQM0Osnc3aoO9RNFdMNTliXLUdmE69zSYU+6gAhwxAEFHFSGGFMcfIUgYKwlfQ7vlkewIOVBCqc0RojgaGAAuAk1dbxYoNEmv1xJAdStmWbTq4pxQURyJNMtGLYy+iIrJJENCGQm9evtgkAqtYVYg3ASmBQaZU5NaNcARrGBy2wHcfCkkAJ9prlInyCkBIZICbi6+O6I+ZKTiaIAoAs9Y5gjKdDHJlzBrgsuWvQaIRlATIAM1J3gwwRBTDSuVWGbytIAPRqOBavF64+UfACD97OJVHxPNC5xYVr12+QuWB9fRPgSAeoAbHUYQxoXOy0eIX215ZgiPecuHzt8Nd2eC3BPbnds/G1c0UdDimBt1u8rG5POfireGkA0H7L2K99Ys2H7MYolwsVhgT1CWrHuAwYOFmyx+DkDIYgt2C9ZzDdxZ/iAGkE75hXPLLEXxJUGkXhO1Kl/UcPH9z/7rsOjp40YbBYfrALDZKZdLLDdqoG9g9gChEtRLGgDtnsxVjoUFx3ot3R0wAHu9vFVFMuF8mvvLK6SvgLCjxHuFEiVsxmFxdmbly/ms9kajs7UIZKvqiwIXa/uwUI0JLL4oRKAJjY/VXE55RNcxwYc7kRgicCLe1XR6ebmcTRj9pVRk/M6jLv8H+8aayRW6CfhG36EgrQ0WYyGyjdIC8VHcgwBOqBTDqjCKl0BjShW8MYeIbIbp0MIdcci+4T4pMo5as7xGz3gFQAPvxN8+x4/+TjzfUtckjSBzch5IqlAggSgXRwJHZ7jZ0aK4U9XEuwly+VlerRtsfZm60ZbFkyXwUFPgPbjxYlyKN0SCrkRdR2LSLHMYAZ5oGCLEcq3GcuIV2r+CezUaFYu+yAzIhFDZ536s3bt28vLS21Om1yMlWmp3j09IQUfahAzcmOG8cS5/rQJaBw6BGv7whjB3fac9I9Gw+mE92NJHAYCVwyAHSW3wSf67Xf2MNI+V3r40KLRBc89wNEwS0zaUgL8sqhOAEoMgCoyB6AuwdUUdvefPzw7qDfJfqHbDfsAgKOYCpB5dHH0IY6Y/Xp9AZuEoAaRgTSCENflh5mSurkL/4Chsipg2GjlEq0yECIjSeO+ScNlLl6ZfHalUU8PetrL/AfYTgBZwmVxJL1Oid0bUONbvQHXhRLeZwvpE8GJCmSx9xexgz+LwCSXFEqlv0ZfliWqXtX1XZLChGQM/L+KPGNFH2cLIsYhHCFMQiXFkuBZ3akc0QFCG6geKVuq9dP5fJJzj7jdAjCutmcBquk/QEYAeUYGRsSQA1IAi2CIGRs6vewW6U4sb5Y4nhUTCkgPOQG+mFdio/OsZctB3rpdAg0rjdqNQ5BA+7kY/jaMklwHqLgMQloqMKTotjDcleYnF9aGDvUtCiKfwq1udWHfjxZxYADj0j2CCEEpf1edJY1iLskFwA8YbHjPDXFHdGX90SQiTllS7t24zpQdmNljenApvCvOV8JMRoxdew/RvDYo0cDWc5uOrTYMt+U+AHj95z3gP7RrXdTApcGAPkLPfFdOsy36E2+bBPTvckrEnwhD8Pzm0x0LmNPUFDnwv/ZTDrxKgZC80rwhsAMLWpE55FnmS3i/dbjBw9IgEi+vu31F6CQXj/OKfCYQHRMaIvd7AW0OLocRdgBzpDz0DQ6FahBmUs6oFDz2QKXaGMsHNylhYzSeG9AM9Pl0tL8wvzCLPmGOBgMuJPNcJBWLpaJcegC6h64AMbIpJVFethrs/d+ujI1PVUhayEanBPXKZg5AHAU9B7qGFQ0Ut6m3ideftboLfo0nMA1aAC3ErmtUfc4wjj9Cq8XYIjOoJ+hxXeDkdJ5jnJPKvMQuIagIA6vSKULOpG+B0wgZRH9sVjFUjH22AszYdchr3a6S3esV710P5dPd/K5VjGHEOhMGUyVQT0wTx3QB45EeggqSSogDkTLDcEcxCILA8nQAseATsLGBeJk8aGNYah8+MUJJ0SC506gT3CGP2bq4g9JrvFw9QB7Zt+iI6M0L9MR3sTakzEgIJ0oDrBggMD1QaLHBj8OBJmbXSDj0ebm9tzCPMY/CVzIyf+JGlNrdudhLGdv3O8zGOUDfax39lu0hPvsRyfcHiYStB+VSDDw8JU95z388KjnOyKBSwOAeB58bXitgzfbL/d7TkE3OlA//FcuPHA/4lF7WAJvgcR2L+HwL0wgij2H7PeWemefNzw7dRSe/iOfl9ZgSqVY2qlv3f3uu7XV1TzHMjRbZMpJkIS5pzQ4oB96UtDicILOJoRGfitFhIwLejudxZuEBu3GLf9hPE7KQdLkcLpmdWsjnYzhyWK7O4CGlp3NGqYmnFxwgtmlWCqxO51d5dVqlYmEfthrPYwVc9mlhbmpEoefZvE74WCSIYMgnmR65Pyyrx6cMIr1sGSQDXWideCMCp8jTS30IwsJpiBtjmJnOwdx6LAzWUEYxL4t2U/Y5WTAAjtJvN4kBVJ2p8lJraWp6TRDW22kQFgUvOVJYgjo4QxRBRBx1FcHLEWGRUxJrVgDU1Imm4518BKyZYzoHi0T8xUps8lzTQF2cCyY/F8poSh8gIYtDPOYKSdFkA/2OYKdR7ADxjH5AP+0EhyWBkVsyTi8WJstmQoYhhUxHbIlrZGHYeHKJGZrp15rNZSMgIAkNsEhHkYBsBglhErAEtYl9qa1uxzfwTmvG1tVpE3aa8QuO5E694kFGj91/dXYw5XdPWnxZxQQ2N0nuHXIilOYIHvIsRPdDk/kzdmemDq6fGskcJkAkAvdv5bHeKcP/4UJni5DjjFRMPwdqbiIjiHeCyKfPTn3xiM9/T3fljDx3dSClqAbLagxNC3/Te//6Y8Gb9QUsUvM8IP7d9mxRVButd5s9zGuyAHDhi8QDwNzubwPQZdToMmntyBqOkAT1YvSxXaSTWULufzm9hZ2oMX52Zs3bxZyQAGwAXkKtctaZglCdodyq6EKQT9kRXQK4LNKLjs3N3f96jX2f8ld1QWHZYV1cO2ghLGUOL4RyDGsA/gRJpNOla3ENn9xC3DkQtA9i3TG6MXEDOq2mFfiQLVvrK9Xq1tAEwAfm55Q/EkdUjadr7DPfZjfqBLEjKmGIndXit1bxYqSPBaBGEyBx0kAsU/KomSctIcp20muc76USps5AC/YjQSMMplymcPkk8JzqSTJAmCVklXANExLJrLwKLO24rJZgphUBUnrEseWPhUmDufgHRnetMtNub37HODqhQtOKyOEi844NLe2qi/W1zizDB/l/NwCPCiHY6WSLygtJAzAJ27M9jC2vVNlCI8jmSuRvJpbsjJhG5KYVbjLp7FEdYSBuPRbflc3Xi3hIdzx/gGRV/tGV5EE3hIJXBoAFHx7+U761zJoOeBRTPSZ+JJPDAw6H9xtYtQhL3cT390CqdOY+pAcvkm3YC1vQuS8xvI67cf/xOOYuDwkw3sS98aJqbnkv/hRZtxFsaFCM4UC3hwyEN68eX1rdfnu102UaJt8hp12olBB10sNm/kH2w+2GfQiihZIRCPUIMIlnxRMK7ZlCZPGgFMeYB7Y1GrUp8olUsiUCrlWowk8Yh8YnVv1BueOgR44LF7eKEAJ8UP5IsdNwE4un5ufn79x/ToJoDn3AjjgogBvsS8fCMAlRLCE2Ir6HiNji+LDdngZXjDIoGAdz4zMKFoGnS44jDWSoQf0wBHxmL6ePHnSaWshc4tLZRxd+SITFUtTJPCJd3rZTOGTj37A3Ua9vrm2Vq9V7397r9/uVCqlucUZDswCq5HaEVFwrCmCATNwSEaKJeH6o3DgeyqDrw38BBLKl/JYf2BER56xTy6t1EoU2NPzMiYJioopvNmuJVhBELnEiJWWrwtDkIxk8pLJptXHbgd6w4fIOfZ1Mi8heYAmgVxkN+r06s0mAIigbE5uRYY8R9ZCoueb791curIIFMsVsrOgopkZgqMY22z2St1uaSYvrKY3QKzxf8lff/cozj83gsruTsEtLTMEoYL2YDgddg8/TIuTChPfb5T3DCaaGOJ39xsbbg8oeOME2XDPd7keyHNCXG+9TF4BQIEULsiyw/x4nR8ZeAseEo3Uw9324/y1fZwUw8M9g4n2I3u89mCuieE+9X53Jzqf8WVYLGc89bGnC/N81EcZPIVw5bWcBJ3pGZ5998CgZ/gF9rrf4lUn+Q8+mDWyH66vPV9+/HR5Gb2JfWN7cwcdnkmmwErpdAaHCuiHhM4MByVgcUAvUocCGpGpUed4qUzdDvCVNNuNfILNTbFWo0vUCbuiADEdLCsgLvaiY8yhdDsZ8A9599J5cALmBx0TwT4y9p3FE7Pl8q1rV5fm5wimIf4GBQwD9IE4XaX0DXsBFuTrQTEDeiywxqTC+qzYWaNYZRQeoy+19LhWDUADFGAU2dgk3HtlZaXWaJLNZ37xKmkJM1i54omVzeqj5XuJlBI5si58Vd/ce8yJYHOzs0Rn3771AUd5dRqNYiGzvb6x/PAxkOIamRCvLBRKRbAF04OKFAKlHfdmLCFDTxaXlCJpsGHxqYNIyGkke5YQIbkloYxJTSASnIonTr88itl2gtC0Q8FEjSdHZiNwI6YZCHWbbRay9uI5KKe6zSGtpHEG/9DcIku1YCIHr/J/HXcfI3Fjr92vNaorq5vPVteyuTQgbGZm+sPvffSHf/zHhYryQ3Jy7fdmZzB6gTV5H5CZ0DITSpAjoBa8ci7s4NIrMDnRsvuSgUE3re7VH97w5e6xQQsUfCwt4UpQ957BRMHAiQ5B+1Ere9Lxxt2TOvHwkP36HJWNy9I/vPb9eH6bZPIKAGJhh1n/fnI58fYJflzuE9I/EsMTY8MMB7eCSvjukeqHYWmiz8GXR5r9zTtPMPPmBM+LAo8yWItXDv9wwz0DIiwkXN9zXa/tsHtUmE98TpgAusMu4STsOlpdX3v08E6nuk2sMgdXERCCyhzgt+E2ESgofwWdAHS0CYizPwEPJAdi3RY+QlwOzUTloJIJrcXRA7zAoINuJ4sgDi9Udp/cPew3QqMDVsAEzbbCU4AUDBwMk+QS5Pyp1ZUXzVqDdIjYTK7Nzc2ST7CQZft7SkG/ZHaWH0eKHJyDo8jMP8ACWkBgKHUBo5Sy/rBS2uGWOj4kXFnYXYA9GFR6LXLwDAiC4cBV9mpxFLtlSo4XKzOp4nQ/lmr2el8+fs7qt5uteqv3+NkLdoPjMEor3nq4sPB0em72wcPlxfkF8jdWcpkffHAL8iRxBJPNlqYfPXry7bffvv/+7e998hGoB1tLPpNNpDOIUHvtU8JqGNRgDCMQ7iQWBZcY4gBAsO2cSyYy9GDfUkAzjWAE8Avni1kdYRj8UdB2j236mOvqW1WOLtlcW3/+9NmLtVU27OGzw41V13Y9BKbDz3rkPELWuMSKKYCXopDaCgtqvNjgiNhyMc+E/e/uVxutf/LHf8qer0S10R8OSlOlVDbD2baIFwyEjOU61ApGb6lYMuCiplcL7dwN2rxbuMVvBcOdVHAZDAzaDxgbdKaymwKNu8d6Y9DZO/jlnp3DU+xZP9LY8HR7UnvHGxHm8Z7ChZKbvxKvAKALxd8EMxMSh3tfwES36DKSwKWQQPhHNniTqRBpgmsEfUwH/rMe+wouqVq7ubK8zOmd/Be/VBxWi0Qa6KCBQzuVPZ0BQxBIi9EC9wmqFD2NHCCC5QXMgZq3YF7UI2pPoT6toU4TIwgGtIHux67AJ6YJFCr+IPrnC6ZfOz1CsDH/4LlhcCVfnJ2ZWpqfnmMjeT7HNjHgjttIBH5sauZVmI9pO1l+tPVJxZdJhTbnjWhn40XowS1BHNNF7DEs2fZ99nVl49Wd6urm843VB8vPNxsNEhBtN1rrO+25xaul6YVGs/bk6ZP5hbn/6//wP1y5eu3/9n//H796+PjBizVOGPvHX/2qlE788R/86F//y38hnJcrrL149vjhQ2xdtz+4nc2kQYKsGk5kR4HNlPAKWRC1wT/FSSBK/UwRerNCHbjHKriilUt7iHwAJWT2wpLFJzFMFB0q1m+Da5DkztY2CLJeq7GpLp7N7qxvAN1weHGMG2eb9hOAV0KcM1jasAeR5RmTGrvxeaAcCUv+yRQZl1JpXIHb1Vo8lZu7eu3WrVsLS0swJnepDGxAMYUnGfhxrvTpAvdPrcQY9kux/GYlTCFcD6jubgxzEnR7bWU3HYbQeDxq4ekCCrunCG6F+0f1t08ClwYAIXpeU39TeTv9BeXTW96+B3OOK0KkLt5z5OHdnFqSl9EBGEB4T0KZaBo1zunc6PWX5f9qKF2yBdygsPGhkOEG3xMPK40VYTjAb8VYub1Q0uaKQvuSNRGygBughmJEABry35CXD5Qy5ER1Il+4S0GZCppg3JBRg21iReiTZ494I+5CaqZSWVrk3K+pSrHAaaPEExOdyyfYxv4prkjGnDHW4VJYwQAQTFq7YBAFlnRLqXmSPdunxdeb7mytj2U59wwDWG/QxEPUe/r8+dd37m8R99Lv37j9/tStG988Wu5v1krExExPPXnyqPr0cX1j7f/xP/+7m7c/XPreR71Cubq1+YMf/GDQ3NlZWf7m8dPCr3+zNDMzWyktLV1ZXYlvrK2zzLmlxRjb/TECSVAglg6hQSwSLAHCxDgV8OkVj8vhhqASB3IlcB6yFhZAJiBkb9jHfqN4QJADcgGFqLVIO91q8lx5VKz1xcZGrVYlFjuXq3QQPy5G5JzNYb1LpjOAIoKIOPOESeUe63XAnZudVqVZxK1JPqNvvrlzqx/783/7B4vz89UWWA3kFaABaoJBXnZ/g2inkc9gaV7f3ZMW+nh/v8tl0G2/9qDDSVXCE03QDPMzcesYlydL7RgMREPOSwKXCQAFMuJ9jV7ZQBpR5e2QAD/3FJITS6mxDXwY2yR+5dHjXDxWJXhkaxuAglKSciVLjeJM+imObLf957RgpCF0h/NN0ejgFeAFqrqHNQHDAhAEQ4+NxT6BXmaTFn4oAAuwhmyAUIYONMkBSEwKzhnCqFH5BOtCU9NBMBabqZS16T2dmqqU5G4hyw5p/NKjcxjoJnuEtkdRLE2gG0vG1qAJAKSn5tiIQ+w5ax0gBav8IMmpk8DotFVV3kXWRUcFMufytz/+OLmw9HCrXkkVSrMLi1ev7JCk59HDrc2dn/32y3Yi+9//2z/sJvOtfuwn/+Jf/tPPf7Dy4M5f/Id//x//y18Uksk/+vzTP/j0B6C67mZze3OrAMqolFi1uNDJISBCq3AcR1B8Jeh+RIgLz4xp+KxwnIGS5Fok/gljD+5IucK8mC/MIpm0nY+wa+WxTJany7iucG3h4itWymxQAxJhBcLfmeEA23yOqCGmZWe7bfJSxmdwIXYmshqBcfU0Uyk2heE+g0irqSRMhIvzjHqki+SBgmaBLMg/hFQgSKOvxiu+3omWcB86hC+958QnHZxO0DM8yjuHJ5oY/oaXwaRvSOcNhwdsnN5K35DDaPhhJHBpAFDwngVv3mGWF/WJJBBIIHiFgpaLVuHdxmBDUIpidjpdjj5tN+qNeo0UiETncJdPwSDLaogHBGWF9YZdQuQ+ZIcRZh+CgrG00IfdTdh7wCgAJo1FS2PZQN2TCzAZxwIBwsLdw64uDZQPRidCEENNbAsKm8NAQR4ULoBT+Memp8qVQj6TiufS8UqhwOZyppEBR7BFZiuECR1cQFgZZBxB9epDn9yCGcjKq+cqWfvcyfbHXYwgcCMXEFucCDsiegb4li1UssV2kiiffGlhgXMv8ulCYbpY5rz7m1dvVr+7n05nZxevFKdmyG64+vzZ++9/+Kf//M+AUpvV5vyVa7MLS7nS1AeffDb4t625mcpv/+4fvr13v5BKf3jzGhvkcXSR4pk1srUclthlBcDBpcgbApgQjDMwBISEW2GfhIcDGV6T/wtDC5/YgBjubjHDGcKQREULVDGQ6GekwcGqgzKniCh6ulQuLC4ussePOTALEcw1xIU4TJD1pzI9x5Na39xaW19vdLq1JkfAwkgqns0QwM62NIjyIIqVaU40W3m6wsokveEQ9xgyM6GO5mV2LyNR2wVLo4zvvPwb7kPrxOXLfqFamA79w5ehXntU6XkY+nuMHDcdabrxoNHfw/M5MTB8uZv/3S3h/icyaZhgVD9ZCVwaAOSv/sFv28mKJqL2lknAX6ELtajdKkFoo98hhV91Y6Ndr02XSg+ePmUnFIoZ5QqUgX/0Md4RnQJmwTS0YMiRkwX0Y1viRZagWs72wlGjyOImJh92jbFJm6Mt2MmU4qiHNsEw7EVqQxkspZzDeMo4EjWZ4DQIlHG1ugN9CqHRZAyan60Qi1LKZWenpwqZZLlA/A8UdTqDIAFQCIcWuZpdFcMB6tbqZviR/4uirlbgkwLQIGgpIX9fDt+b7EBAhrZimwi95hMcgqdP54EN4lOFQpm9ZuXyfK2ZT6WmCqVWs5PK5/7ln/932KtmpmYxpOAhYsvY53/0R9///vc5VH59bWvh6rV//W/+zbX5xd/89KdPnz69uTD3/s3rK+sr9a0alpeicvtwcLzZfrRtTgUGxappa+NBHNt6aGbvvPa0CblRJ1gbJulkK6WbABzOLwAOeIqta9jzCDhnbfpACOl2i9xKbHFrA3xyBY5vS7ONq1SeYhcfTxDH4vXrV9mbtrGzvbq+yQMiJoxAb46/BelubGzhEdtudPkkwgjcxsODDSFdA1uITLzuX3xRfO7f5VB3nA5d9yS1Z+Oh6O7VCdn6dC5kv9yr42vajj0woOsMBJdR5S2QwCQACt7d83rYPq+zMcFDcBlU3oIHcAGXcKnF68zv+f6ck6ilUFGp+83urPKJzgSEoEIVbhIbdlqNIad1dto725tk68EmNNLO3DMFhtrDZpJOZAExRNT6wkFCmBUotrUpC/6hGyON/gBtT/Avm7P73Q4HXdGZPfPoXYwzGiOVT/iRtmiRawYUkC8WwU+cyc5ZobhhSLs4Vcwtzc2UOfwiww54xf5CCpCE6kWpWgDN5EphjA7QHE8hJR28Y26qYs8VcUlsJ9PmKlxN6RRb9wmjAQ6yf6pYV1aeSi5fyRAtk/3sg/cAB882V9Nsc8ealZsuVGbq7d63975+eO/BRx999K/+/H+Xy6X7rdbi0uz28ybADjyUaDVX7n6HTQvUN1eZ3m7UyKfUbRJsxPZ2vGdkT9TCQZ/OasCwIBE4g38mdmEHrQioJ6GN0Y/g2ghWIGzApDbRE1wEUrUM3WRLwiJkWa3BoVkQJ+LkoXLm/CDdSdUVTp5IFDKZSq6Ei212burK4gJb3oCe+M7Ao0RAF4urD5efDetkTVRYemV2ga30+DaRO0ypmLfRq3zCkrgKwR0ug7tvUjkSncN3fi23Ex3sjT3aiiYoHCCEsNwO6HaYW8fg8zBkz7FP8ExPUErnspxJAHSqTARSe+0se/Y8fCP0L/uDea2I3rDDnsJ8a+S23+poD14M7xNcvqE8jzfc+YEHCuiHAhZB8XdanPwA+Gndu/Pd44cPOJQT+m6cwL5AHBBKTQn24toAr6AeBioJnlwMhIxgD8BMJKsA7hgCXHoEi2QwGBUKOeJEsCvg9iLPMgPRrGaYwFkG3CIPMsdmZdgTD/IoT01hYuI4eEKD81MlzDTlUqGMPwYTkcCXbXpCq6N12UWOQ0xQgs8RvkGdMzX8cFefVoKav2lABR3hrq3zKcAUvibtwAJODdPdejNTKC5lMm0SFMUTNU7hQEKtRiWdKBWLf/qTz3/62y8fP36Yr82yEDIG0QGD0Ae3b/4f/w//5ubVJcw/uIbirW65WGi1W0CTTz/9QQoM9PB+c6f6+WefEoZTb7dwt5F7B9ZYEMzgLCTEB1ji7OmhSMrcITkP57pTEQrSqrQ0+QDpiQ+MGxQcY4SY0x9QyJ46yy1gFiDgEA+DM826CF9QVY+FICo7JSOflkiJJYIJHTnPAbDa055cmJnB9cUovJMgyEKR4y8KjW6/FUutrK2CgWYXlxAub4VJfIB/DsrGiD6owxvFG71iDa98hHu+cmP/CycY0KdjuL77cn9Kozvh4U58gkjQeDCpMJ2g5+4FBt24FXSbqAS3gs7eIWgP95/oE7711tcPs/Y9hXZBJHOmAOgN14wcDyNun+XwPY/N1RlMcWzewgMDPhGgyzBoCXfz+gG3dnc+95ZjcDsxZOLyLFfkz0KqFy9Vq0XELNYXYApqkvO5+u3W1trq3TvfbG9udAFEFv0De8IeSTZsK2EgJykAYtCRRM+QVSYx6ChJDEeg43bxUGq2OPX7QCLI6myHIVE+HHTBYVg6YQo1zfIzBD5zTBjBtN1+NoO3BstFjD1HJJbGzIC3Js/g2LCYzSzMTOMCww7E5nw8L9h74BzEBlcCZ0m50QRixqqXmmMgbVUi5sgKd33hooDyZiOV4SQ8eny9BYGAUCCQdDJbKgzb7Rs3b3Jyx4sVcOGQyUkpmBj0by1M5//ZH/3qu3svarVBfYcdVvOl4u1Pf/iTzz+/eeNKhsicQVtU03FMOxi42ALHsRMLS/PpxGBnff3uvXsc/cExoj1QA6aYeIrsSMQS4d4ClmBWk00L/x5GGnNwsUBnG85ZqTadj9do6MNMX0Ad2/8FJqQTubq1o57H1O3Vdxo8LAp5uuXRo9hDyZPjp1wmC2WW3Isyp8nONIx3iD3iKacFhJuZTBajFFkaiSWaT813+7HmILHT6sAIu/PgBx8aljzeHOONxyrxBq+x1/kMHkpwyyv7tU90m7g83qgJIvtd7kncG1kIZb+BfmtiePgyXN+PiLfTM0ztgEnpH+65J9nDz7vn8Kjx9CRwmQDQ6Unh3aEcfRUvyLP2ByHcYMrVgk74kC8DMLS5vjZoN188XyGfj4Xx0BFrBboVkw9RPBlsP6TRQ5OCPRiCGsZwYEsjMXGKGGeUMS0AKvYqYTsBTpBSmC1fKEtZJwg0ls8JN47nkhm2O0onjb7nPsqXOhXMKrmsEvEVstmZ6QoJoIn7wWwkuwNphNi4ZOdmQIfZgAQk5DMeXn4IA7FUgwv6DOkVawYCMacABLiL+4zXEPanF/KclQVASw8HlflZpABuw/VU39nq1XbKw9j1xcXSTz7fYrP4MAE6KJen2CQ1P11Mk0e5AUjoYVZh9xrQEihZ3V5nhbfff++P/slPnty9+1d/8ZeNdvsnU5WZhXkijgGRQI4OEBRB9smG7azZJ+atUciPW3nM3COjj/IxiW2dMfLS3gzE5Bn1CdjhpItWGwykeB1AZ1uBO4A8Ehcgak75KhTygNraToMg6CLHsOXYwJfDUgQ1ciPqHFo2yWeznKIBFAMboWWBsFOzM6XyejzX43ha7FP22ogHeHUbopi1GCZagscQrgeNl7RiKx1hoDdcV5jUbmk48Qnos+eQN2Rj99RvWcueQrsga7xMACh6z4730vAddtFFAjyeAE9ilCODcayGqUx/HGg6vF4gH3dlkOxna2Mdk0+ntkP255npKVQ+wIWffIWTEESSlgUCvxK0tGMJywGJiwnUGfTIE4PZhkAfDnhQ/IkhJGwqCm9RZI+MRoZ+SH2omOphnJ6YW9hIDQbA/IFOlWpBdbbbrZ3tKjgnk4oRgpvLpNgCxqYprC+2wV27yVzxOmgbASB3w0EBt5cpKb17Jr5AkbBeGkxzj3ED9iE7n8tRBUYhivLZsJIM7p9CmSidXHpna5OMOrlUIpct4DKqLj+t93sEPs9UprJYo9q1wVpjbWNFMyZIe5jBsNNpNgaNWrtWIxuPoppnp/Ol4uzVq5lS6e9/9jNQz7/67/737KZniz/b+UkWSXgQUxGjo41fCM6ABbz6CyCe4J7nJVsRD4CoIN2BW1uO4n54RFCpVetkF+AS4MNyienJJTNTM7Mzc/Ns1CNdkww8ZrnhbDBOuiW+J69ElX3O/AIGYWOTdc78ZqQJSuWTqnPqK0ylUpyAMV2c4qhUwrzsHA0EDFPkIsILJo5MfibD/Y0lvqJL+qnXY7w06qxi4vJU1+UznuoUEfEzk8ClAUDRa/cm74T/QAQynLh8E8rR2ONJgEeA1qTwUPg9R4OBIdhgBPLY3NxEoVebTZQxuq3FkV1SdTFsNnK4kBtGUbnoPtSATjalODUsBPhuIKa9WUrEx+71YSaZIXQHJU7cDyYHWYDQ0tyVuQVyTA11JUpmrACWnZ9qJ242lfQHFDIcAn7YOAYsyxEknWTPETBIoc32RmECERCAB4riWwiGCRUtwiCR7gK6THuJZ4ulwdUkAVCwvrA0Dma3JISKBaItRZxNmgBlLEBDvHiF5qDeyabhIgWEYyc8ICnWbnbboIgGu6XYDqaz4ykdnSfa67ZxoiHi2dlZtozXGo2vvvsOXHLl9u2Fp893SHLd7k5jLgPhxGPsRW93W0oCoAPlOR9kjGxMxdoSMMPg6JLBiNhvBbbbioQ5FPkjfElBwMi53+7j8IIRzmEDrHCyO95JdnsBaHjwlna7C/rJ54uLCzHOl8U+tEPWA3b5TQ1JSEAazHxSwie/gWxCuSIssdMP8eWLpdkrS2yOb+LB1H4+7cFjXpgBHrlUJc5xgQcxH2oZ37nEfyeWM3F5yIW5ZOhMZYJCcBn0OSRNBh51yCEpR91OQwIXDgAFb95prDaiGUngjCXwUhExsTTRuFDHhoJiMrCCVwoEhE8D35NgUK1D4HO319lYX+fscJDN1vYOSg5PDbYcfFfYflB+8sLwg+snOYClhoCBljm8Ujhf0PTpbI4oE5AB6hVMQ1ZiQlAEfizwmAgdBflCgpMf8jnOfoc5wA3b5qmYlULxt/lsmtTPaHsmUJwuW6UUfsSWe8Uso/hBWwqaQY+YdWe8wtFfFAI1VwysFmynrW70ZLAwlNAS61APUZQ5xaAV0EeFSJhYnPClUoV9YdODRnUHO1Cr28bVUy6xdTwJNGQQ+8fK5RJHmEGNwj4yQ3I5XHqwDQBhPznprB0ofDo//+GnnwHAaG9ppQTQCEgyhGgcooUYbtYd4IQgqviXSUoATms0DITcacHKxl3GEniFVYzN7wBNnJidepOHNT8/z4kieqaZLO3NapWzu5auXuVnl61c6Vwxlcl3ms1ciSRHZGYsbhP8tbpGJNT0dIXIJ2BrIpODZlIuMZ44zzzJia/8c5mKT2lcWDD+hCHFLYzx6cU6vLwcN0d/95YA4grf8MuwPMN3d9cP33P32Kjl7CVw4QDQ2YsgmjGSwNlLwNUSnyg2AnJk0ZFuVaYfIM4K25q2qwSRoLBpl91GZpp+IqnTTVXtEgWCrsZkoj1HGB/AKyhMedLknMJUJFdSBaVaLGJFUCQK0dK2Xwy4QZCteXbihALRlYM/GU5/CKK8KVSUfoYTG7KZSpkA6AxZbdJ5Dk5Noo/ZtyRugQVgD5AMXh6CXwAJtgQABwWR6o8VIQVda+eYVQ1SmKYRArOuBENTDG/oLq2SCDXgED4pWFR4TAwLSoEoJgJsGk1uCxYIwsUIAAKsyYkG+iFBkXCAEBuGE/oQKdRNIiKQZA6RgvZKixkWCCbZ3t7EZIMQe502aExiJEc29h8DPVCWUL1gtIIHgcYk+9uZRotRTLSwLMzTizTNGPAAr+VC6fZtThzLN9odch7WtqoEXtWazXS+xFH2WKowGOWJymo1CHKfKZeX5mbhBwq47dqd5tYGPs3OzMIiKSlphOfBUC4wslgClTK5gvBvRg8csoSG8bwQkwvMPxklgZpBgk8uo7KfBMLyCaTnnf2Wf07c2o+adwvT3K9n1H7uEjgfAMQrEr0fZ/zsJwQ+cXnGzLzL0wW/j14B1qDeXPGjqoE6PBo8QWxFBwlpN9ZmdqdWo4PQgGk1xd/YNwizBP3R4nhkUJ8ZiwFClaO+e60hp53nszm0o05gaNZHQ9CFWBLwMCUIZ1YbhoocWfjIJ0PkCTvmySTE3i4d0aCNY+ShIQYIjKXT5xWVgq5l+1YKK5ICiGTCUkwSQ7QEMyB5ghyRBjGAeoAldDCXHxUoyOAjNoAu3IAXw23U7IbDDXoq6+BLLU5wMkT6qVw20UmCYAqVcp995WwLt8SJ4CT2Q2GGEn1inNoyzMCSkigOdfY7qIbda8T3YE6KgedicQAU13jW6ts13IUJy5EtIAgVIBcYTPwr1gpuWbwQnUTPyRfgToAQ+I8FC/uIW5YzGGBmwzw0OzvHYWTcvH/v4ePlp+ub20Af+mzu1Eg0yYmnJEUslmRzw7rG2fWzMzPrHJj6/BnbvuZnZ5KJmc3N9RppuFPJAjYg8l8zBP7jCex3nUG7YI5PTkhVoihsgQOMWAhRbIwApb8n4tjfGniJyqQE/Dfw8CJ6bX86QM27TU72bl+/VnTnJR79Z9+ZzX34V+3MWHprJtrvOe75hdyv81sjjYuzENQ7WtP5Cd5/r4Q/wTo8FEw1AIR6dQfzAHU2rLc4Q7y6A77Qf+DLNaQN0mycRxMyXKHLVtD0tFoVha9oFAwDHF4hvw+5h828BMzStiXDLaj+xIAMOIAMssxksBI57mFHFOYKlClBP8TjYuIgCBpjElCjyB4lK/CAmcVBD44fkkvTLJcc9pu0NC7FuaKFQp1PmFLdUAJWK7jFn4O21k4yeZmsg3SHig+BHhVnXuhKqC1F4DEnmMZTYKUYGR6LyQwbrDg2VMNY3IBt4bJ+MQo4IvMRnAIHO90UqQCAPtrenuFELUAgu8MUFA6YIMaaM2W1pc5AkwEbsaGjJl4uR+sC6IB9/B8icJwhaKjirM7NzJOM8eHy05//8hfPVtdXN7d2ag3mw7KTLRT++b/8Vx//8NO/+7u/+/nPfobTkLRL39x9kE/Gy/nc/HTln/3hH964dW358aOdeoMAoO2N7UJ5lpUSUp3Kl2FJKaaJ/GHPWrfLYjHg6c0xt5e/DLAhUVgxpiJ9rKd6QEFU3PVPJHZAT7/lPffrtt/dMOXD9NmP/qVu32/hb7iosGyPSuqELUC7V3gAc7s7H5X7E+kfsLEfq0GHE5nuzYkciZ8jdX5z3k6JwuVdBdpX1gH7XcU8wTuGulXKOhlgsB4o+186ngZ/yK2lJD1k5+uk47FWs7H6/OnG2gsMPAqC7vbZnl3UqZmdRruBZ6cfH9Q5MYr//sfkwwHquHAwjaTyOEzqjW12bHF2KRPrGHdo9kEGmRbqfkDPPvvFsO6o1u/IwkGaHOUW6it6d0DIM9u1MTh0i/lSPpMgWzHmIIKfhSpwCaWIlVbiGRlHADCAEe3HFioiIIijvTAuKWJGZ84b9GHNMucod5GcduQNsoTXzMr/2KaPYYfni1VJYgEhYVLSmRiSEwXrEFoJgMRhpNwCtZkxBvkpfhoMx3GsmIV48WyekQIz7xSD5COjJLMGv5ia/JBMD3GChjHxKDlhj4lBdCSZxpWFnY1J8epBTROLqohgTVK7AbI+IdYwjqUIC1O/i1XJzE0YnnpF0hXG0z//xW9+9tvfteOx2jC13OGsr8rCwmL7+fq9p88+i6dufv7jRrb4cKvx3Z1vh/XWAJNQo9apbv/40+///s6933759VSxODM9B2prNnYIjcbzmM1PIfNkqdhv6awSHhMoULmjBZt5du78UniW8ahvoXi3pbAodbJr3YjKXhJ4Q/n4cH9D9iI/ehLebb/OwWOiwwGk9qT/7jS69E5qvScMgHazFT3I3TKJWt4dCfBTZmpU8SvoT74OUq+mpvhEg6Jc0Wd8q1UnhAVfzDDGpqaVlWe16pZprmG7pT1HnD+BucLS3yWARM0Gh6bGsAEwljBeqAFQwFCgKMBHaaaCIUcOLUuXBw5DiQN/2AqWYe94HoNOHivRiAfZkzj+okF0DcCIoF1MR6R+5uB3TMSgHEAGmACUgjYVesDZpdAbLCHKiMN0hHMr7ogFip0RCuFSF1YE9SyYhis9fW3gl4uKuGEPfXaZOAXRkndNfX04dymAJI5f174no8yEuquoaRx5ljrHeyNzyMulRl4fpG1YS+hGch+RJFSKvEodjonFygWcY7kWmk2WIxugKTD1gJKMYbfuUBcfOMFYsh0foiVgxxKYGrCFvVFrPl1+/uTZs2u3bs+/9/63y8+Xm92b73/0wQcf/uJv//Hxb7/8H/+nf/cPX3372Y/+8L1P/2Cr1dt+8eL6R++/d3XhH//6r//257++e//xTz7/bOnqtRxHx29v7DQ7pUaj2O2mMsBf9o5h2eunS9mN7a2lbJHch0KTVli3FglrLl5vHX/qblROXwJ6GfWK7Vu468/i4G77jo9unLQETh0A7cdw9AbsJ5mo/e2TgP8yGkKQltLLb58CPbaJnStUGVHPZN+hReqWY7rQdx0dTUV8D1u4CCGmhVtYkMAuwCY7wt1Cg9kRrsNJE4Qq4/2iP/ubmJQdZEQ0QwHyQCVGMSPKHpRj8+sICFroyQZ5tm0zbSFXypI+kTgXc7cJBtiOMD69JwyAN5xtBlLRWuTh4soKNhqMQ9IEQioUtdonnSnQobjfCFLckZUH8EdNkrEtVyNVbqAQIINM4l3uyqKmWCOjCRSjCFgSsEN8MoDMhouMWNC6Zbnx+bmgYoAG9AJetBVhS0MELBagJ2K2zY0Eh0J4/J9nBtZhlGb0/9t8XBsPWK8UmZThQJIh8UYNzHXDwfsffe/q+x/2svkXrU5ldi5bLGaLpamlxXS5dP/R03pneP32x59+/sHclRtPnjxNFIp/+M//xczMzP/6H/7n+3e+zTy4V5opN7tKbpSrlBlIjm9OXosn009WXtT6sQV8Ycm0XoNQgXOX8EHqN9Q/qp6SBOxNF229S3uVoD2o7O61+1ZAdnfnd6rlxOVwbgDonXps0WLfWQnwW+ZfWj6Dn8SgLqAzHJo7Sdt/KPRHxbM5iI1Csamp1ZUWqIWwFTk+el3wCvuY6pbQh46CBeZOAk8wClUNakLvg36AOMAp3GcYJ3AR4akhXpb+hUIR+IP9pW/BzgZFBMJajYYCZVIZjyVC/8uTJQ8XFdlIZA2RUQWgwqEb/NXszoPcVFh41KBDz+kSftzSzEANM7zAHwgGtEEh+Jv+2UyWT4bgCFM/jDfgH4SGZMwjJnmA+LRAsUAnQA2NGuL/wW1by0CLGs1IyU+UvLtrIYZ7f79haIHYo3hCO/9l6JEYDYGZlUq9FHEkCEWCQRnnYF+tMEfSAWk3owE6wpNGILnwljHJo8rmZubmSPlDEHulPIXdji14RFbNX71GAFC1Wrt24+YHH/+gxha/WLIyt3Djo4+Lc4uLN+t/8i//VavT/e1XXz9//vzzH3z0ox9+H9jzYmPrGo8sl9+u17/66qtrH37M1vqdTlu8WBFXI3U7eiLeEn1eBAnobbTCszoMP3qtDtfzMNSiPq+VwKkDoOiJvvYZRB3eYgnws6dfPvsZHP0WmrpC69Ps/x0PAMJThakGrIPzhl9ADuFCHwNG6MY3CMMPiAHjBo4eYl2x/XQ6xIKQkFDggVOiCENRXMgwls0kcgT0EtIbj0ON2wwUplHiPs5YkPHH52WHkWgmhuCeppBSg8OvdNQpP8HsAsOeRBQQs1sWaVHA62MFnplOWQwJ7zYPjCligI0KddgIKi9/zYVqrOimumE54VqbrXCr4URieSoCHT5RYpgaH0shnMY9RKCdZ+73gYLbbCRGcj9q4KhIhPxffwAvNKJVmNPsTGYkIjYag9EYSSmsyM4llUCQiBV/bprEC6s3DmGdBrrApAhDFt6E2vrsTOfodkAjsVmtJ8sx8jonM+VCZa26zd74mbmln/zTPy7mSWlImFDu4aNH61tbizdufPSDT5O5YrY8tXD95o/+yT9pthtP7n2XzadmF2Zn2dufzRUrsxih7j54+OvffnHz409J6rj55DGvBAzwPhh34hgxGPQbMzziO/pzDhLgXX3trDwyutmDm+zrtyZbo+tTkMCpA6A9H/ApLCQiGUngIkrA33//QfRfRdUFMsyuYD+C8I0+AwOhSklYQ549zoFn73qnUacxm0pzGGa3xzkJuW5/QDpjAoBEVjovDniisDdIATlEucTIW5jh2C8UPDQFHJIpjBigFuZlHxQVfnfxvGWIqGHru/a9s+GsRX+2zXOSBrYhFDo5hpgX0MFeJKAQUMMtMAZNZGkQzrESCJ0rQR9DHXRgZm/hU4FBWFSwAyUVOCMerNCHOp0hokhkO4UL4cjQYsLybq5MRJA8QCyN+1YY5d1srpcqRyFKVujlHfikDrAa9QeQCtGpxfiVcHwInxqlT4bAHbHmYs+ZBGKwCu2gU9gT3cwaZFxAgT64Iqdms+AxHlm93ihmMp988H7y4aOtFy8w75WnZog+Z9q1F6uPHj3Ca/lnf/ZnpAvi3DfOAuOksI8//nhna7269mJrq7ryYuP9W7evXr9VnpmrdXq//s3vHj586BJDnuDdvJI/KRcQvDM1DFCCVUSVs5fAUeWvd3KvclQ6e9F4O9tcMvvJ7RhrPnUAdAyeoiGRBN4mCfClRVOyopEbBeOJVKZFEwuLYGixyB6FzAxIDAPyoQBKQCaMAsdwsjg4BkVsth9FBUnlc26oLBZSfqRJxKFDMhjogocICm7U2OPN7jCF+HD6JnVGcZcaif7ImIOC73S5gWONw+SHBMLQCi3IAqDIfIh1KK3gYtGEH8JQoMBcqFp+gPjDp1egy2VSuXLsBnXZV+jIsangCLPBGARRB7qwmQwTEql2IEjgDOPR4njJBDwkKmZklNbIP0J7JD0oAa6oSYo+uzVKANz2ECIIUuSP8jAg3ZOOQVgCjGYCwrtHd9m+8BmaBlIPRpnnzarCQFS0SBZhAEiX1mpt+lAuIHHJSfLEZQv8QVN762z+Ujq/kMm1h4m52dmrC/PfPXz0ZPV5be3Fzma1N0zUGvVSIfun//Sf/fEf/ijL88fGl4zfurbUWM+QHKhSLoN/11c31ta2SpXZmVprdXv74aPHHCkGisKwh4uzoS38ZGfSdjyekd4dLVkPwtbCGqNyFhLQq2pvy1lMFs1hEkDmE5I49iM4OwC0m+mJNUSXYQnsJ65jP+kw8QtYv8jrDfN2VPn7WAdAprBd26KwOKpc5zfRASgBEsACAkwBFzVqVXbCyz0lVT/k7Ew0JLvLAUQE/wj8xMnv1zUwADtS6+hqpiBNDviJ/D08XyCLlDSwSaedyuTCOJxORFHnMhh3lMqZ6QiaBlKgQSkADswx/J7jUUOpphKYW4BAIAjihfocyAWTWr4wktCAdC3/hznTu8IrCqNBH9gmdOtBowYRUh1jxzgBM+zLEr5Ru2sOk4BTE6KgwKg8O4yCJ3nBZErSDfS8zFqqWtFrzBjhKEOE9DLedD0uPpQrRvinISI5H4WTLAidduumMVT0R0VIQpc2ubXYHjfWZ6vmA3TJjGRKgnSTHfXkNyIbtaMv4NRgkE8l8HlNlwpLczOrmze/e/T44fIaLslbH773/ge3sfcUMskhz6DbKqRipDR4sPxo0Kp//P6HzUadLfD/+Ivf5AgmWlj45v4DdvvNzc19/fXXP/6jP5manu50NlgCgBgOOT9V8vSnY4zaciaVhC8h+jwNCfjbdQBl7xB6uw7ou8cthvvY8ETHprbHBJe8KZDPUddxZAAUfgBHneyC9N9vCfu1nzbbR5r3SJ1Pm/MToX/wig6+eyIMHEBk9+xBS/Dr4y3BzxOX1IPLQJ0yixS0Al/kHrIYX5IOK0YHhEOqHpxQ9Z1aZtBlDzznPMjwEo9bYFCfFDX4R0gFBAUoEwKEF0y6FqyBCoYoJ4Ni1+GkDKVDTnIUA0E9DFcotMUagx6AKx0OBx0K1sAMSAI26IM6L3Eygx2GRXivGg1UMBb/l3CY9p1rjz07pJh9rP0FftjFDofEa2MxIjUffXSuFvvLZZRRwmgm0u4obaSCKGiB7dxJREBPmXdQ2wZvADm0gHjAPqyGECNFtyjkKUUGHkEKBWTDnPIQ0hOeWCZ4C/SGJc3MO7YryiCUHqjgIRjErD1CMDaXbogh4xOAh+gALeKN20hTQwR4RtgMOUjEEEN2+Cyp68KKRjGjEskylvwDopDRVnxakHaMo8EGnRiR56RWig0r2Wx5af7K3PStK+uEPwNZFgkQ2lkHQ3Fk/JD80c3ms3t3V588rOSyV+dmEQZP+c79ey3QazL1+MlTgFBrMMAx2mrWZxcWCPOCA2KzmBebkFmexBhz8+kLDT73bAzuRpU3kcBuaR9AjQdxwF2/tR/B3WMP81iDUfuRfS0/b3eHIwOgt1sc0eoiCbyhBA7+ofHfI/qgv1HDzIXCatUbKDY0OW6mJPuk+j2U3M72FqHJDlO08cj2IzEE6INOJKyHry5E2PoOVMLBhRstmYth/gETeDx1Np9DNdZaHM4gjSh7T6+b5TRys/2gyGFGapv8OmlARgb3ilBLMs6hWihWnGKGgfAf4UcSShghOzQ/oUMWgAwF4BSfEMcaAUxhk5oLkP5CSGZI4ZPB2lfOdikK8GQMJAyDQUCcGCKyvsA1QTH+gTCIteYmqYHMJBOzPD02HAiG+HQL2GHcOVlngE8QDABBgVGvbhxjOsNPWLMgYFObIY06S0OAxjxLVDGEZLDG+Jbr8WUZbb1XqkugKMY4BuvuACgqyIScSCykPfpKYMhZ7demS1tVHlBt+0m1+ljGNg5aQ9QcIVZdfRbvNuZniPlZYMnJdO7aBx80e53VtQ0eic7BsMNcebiIH+hj0FCmLNURBbFJsgO9UiTsqLy9EuCNPfgRH3z37RXMYVd2RgCI53RYjqJ+kQQupwT2e8ntN0howztwqRY5rUyxW4I7aVrTZCQq5twDHfXJDnYlKtR/5dOfoBoBHwwWZumAlECBwQeG+iV4hQIWIToEEdIIKIFOMpFFp9NMf2wPsmZAmdmFgWKoXw78on9bG8E4ckpRP8TnYP5Bp7MTjUAaMBkdxLYVq+iXF9sG3WCAiVDMAKBKRZzp285tOyNC2MeKL587YAg60MZADC/eV0uweCPFQmNTMdQDTJGaF1xJs3QYEQgCewmWgMkwGcEsjeAO0I430gwxFfWV+cqDpcyIZaYh2uhsyEbPBepCKjbeIootLtuNQ3xaPBPihJiWJoyBwMei8BAk+TGVrNqAGP1ApkocgC1MHj8i2HsdTGwYsnKxzHQ63humupxQ29ghAgtwA+LsNJq55HCmUmKPGLHohcpMrlheSKcb7daDJ8ts2SuWShyNy9N8/vTZlavXCaYmaTcA1/nn0yxZL18zcTv+1UXUQZ32qLxNEtjzyfLED1gjQw7ucMDYC3vreIs6dQC05+O5sEKMGIskcEgJTHzfDnjPuWWuGBH2bvz6UKyu8xMoHLrerHEgeSuGcaBWQ7vLROFZiQ0r6HCvLopUBTq6q6GkoEmR4gfLUSGn483BDuhIAAQuEqbA/6XZSe7XFUgiVTEDZUNSuiB0uLICArCKxTz5o7H7oLeFtWwPFz2pMBfUZB+xCo1mTBllUKSRWYJu2h5vLUE7k+K0Ahs4YJCFZKIw3oYwi1ilICQQkgYJJWos6MbSQGNEEhoTftLve/g3ng4uUkMcQoeiRB+b0OvqAz82HSARvjUZ0UlknsZhZQhCcE/2LuEengvx6axQLBmJUcUoi5oeIiLSEoO5gFx045KkAwPMNKk4aQOGhGS1BZsSvWaez1xqkMoVUi9DmjCz5XNXeHa1ehODXGW6DAjiyDSSQT3f3J5hI3w8QSqgRqP29OmTDz/+pDw9zfPHksjDhU/Wy5NhUopxqudi7I0a/Vb0GUkACfBivJVy8Dc/vLTXrvTEANDuucN8BPWAoUP2DwZGlUgCF00CwTtMJXixJ5hEn9ISdLBupiHR4UCHXp+jK0A2ABfhIBxJHP60vTnodbGsaJs2OhjnSipNijzuGv7xXIJmFpLrC8TST2TTBOIUzZADNabD0tNoNZkaFxUcoMPdPgQF5qUDpgyYhiIcsgGe09GBBnjY2IoOjMEFpg1k8Ddgj5hMMhoSfGL1IVbZdq1DjQKKKqTTMi9xlAeAwHrKOGOFpRqoMsQA6BIOAXjojzbw002BPvK1wSrwAbzFOIZg8hK20JYxugldwYNWwMnusDviX5I0GtAXrKOnMJKhEC4pACnNYsgEwsHPP40Oo3SbBWJLghbAgsAp/2cnk0BRyQ6ty4iMyMOsgqJwdGksxdgGQNFVcIo2PJQUEE0qiacqruAoKHf5x2SETsVieZhh2YSlg8lwPSYJUc/pWSY5Lb5QfPBwmdk5rXZ9c2NqutJskQKKDJeNQr/MuoC5hEm5+Q0UCxtwrwdpixZrEoJfWlv0EUngXZKAfSOCr/seK4++G3sIJWqKJHCwBPheBR3C9aAxXAk6mEJCnWnsCMrQ1NN2JIUHd3tpbb2KkwWRcBzgDjelPg1MMGr0PQag6D/3AQVGxwAElxRQCJ0UoQMkUoSvBYWYi4q7ICEhLNtF5ZdMxxQAJoBWsVgggJo63VCr7BPzfMfOc8BDsC6IU+cuxevQpJsUv7XYHbe/aHWUERaxG07nZTyNYTI14m8C82B3gQ7oh/zVyAEYRp3PcRE1xxwW9ULdCwwoiMkkBjEafSI+QWyjFu24xwgDTBKMCx6Er9cvmQ4sCDjz5TCQGUfDx4CCFo/vZnUihoQxoZEMEcwBXdahSCn8Yik5ucCnhWKukE+RYYn8SmA54BLwKM02twwGHIw9JIbG/8UhJoVyiUdJ4Vmsb22ur29gTJqamrp+/TpxXOsbq9q7ZyiO1dONqeENVvUojU/EEPAcrlCPyrssgfEXZfS9mLh8ByVzYhagQ8oOiftX9JD9o26RBC6FBPZ7scNvuzuADAwINDAERYWaZIHeDS1Zj5EVr05aaKwItAtVaK8VKhVsIR3sOEPTGQVUtUJIsNrw3/0DoRmnLA1OYTJmNXMPdTYWcZQGXhOMNC7VXrvV6DbZd5abJQkihhMUse2HUkQylhdmE5N0ZkYKldGKsHaMAl6kepkKUKZP2ymmJIMCP241sV9b7CVmMnEKgVgsgAd7D6YX3/UPdMNgQy+Znzi5nTgfp8/EMKCNdPgHxZJ8cfAHo7Qwh+7yyXBZfKDAIuFY4EB/9ctjCSRlINERqZpQni4hLe32oiqvYkcWmi52Giqylsmapd1gRlG01NULAueeIIeSANFBAoQJOGG12v3G/n9CuGSTARilMM6kSZ+IjayjsHFMeRxKBn3inbutVjwb59mwFhAoNqNsnsBnodntnert92+T+6dar3Koxp07d/5kY21+YQlLE7YfxQzZKblwzFiENPG8wk/N67aI6OPdlcDu14AWXul3UCL6aTvjgqwpZzxpNF0kgROUQPjHIlw/eAp/81GQKKrgP9yJciUNjKnwAbl22A+PjwM6KFzUr3LLWHgK+hJwI1ghza14XlpcDfu3CbVnCXuU/gfLAeM4OZwCsBBmirNRSHHQUIYB2jkJHZsTkcucU0VsCRVtuZIuBwV0pNpVMDCM/F8j5kPfXJ/dlyzkYXjOG4NbXoElrYhfWAtefokgzFYkY0/w4wvIY40cJ6EYZJhXRRYV2rHHDBWRA+5xalAFAvFPI+yfYJPZX7RME5QicrSZX1AMkTIRg6l7h3EFmsAWiYzMSEim129Rt3+yPFF8IRpuakIMG5KFqjMDHuUfjdCkMKNWbZd6NOBXRV7rM8lhs4Ui/8AuMuPBXiadLWAi4iDaIoeQAGGgoh3uEgZ75HVCHAAom8qSVnFna/vZ8lOeF0Ct3yUwmgiwNFMgLbpZpPxLbuHNixj2RzBuif6+xRLgTfDVUQnqtBz7NXA6/vk2ye1QFqCwBC/R4i8g2xeQpXN5oBdZDsfgjZ8VKTkryBMKlNElhh7pM9OWGE3QpWQCppiLiv9qJ+QZLMNg9v50O631tecAEEJdUIGCAOhQJavBeCE4gy6HHEoO+qrGhpwggY2G+GX+0YEM0twCAHWao8NBGZfP5+RWwkwyjGWSGSKJScKI3SGVJHFioVatr69tg5DyZEhMpIk8whmEDQVMgNkERY6+xhDBuhxGuEdO4c6y6MiChauGu6CTPsrcLS72UwuUAjkgCC2XkBqQ21hKcpcJjsCUlDxLQUIYXpiCtepSBp4EATOK4NZZYQpSgvMxBS7IpwRt/sGltuL7m0zCIK/AkdmAyNhMoVXtDJfxaBDPJlNwzKEWAA18RzwCgYluMz4k1ooUyx08cWIOzCLHIk4mZhDYQm5igisxH+uwLV9WL3arQZYHJcipJ4540iygn+h3NBDjECzwzIicQq5KnkhjmtAgnjIWqXQWkkojlIplBFp1g8edxCFG8Nbqyipzkgjxg9vv37/36P7du59//uO5pYX1zU3uchpcOpXSWxRLGoLDtiXYKgMY8oFLmEFKPBQTEp+SiEnM8dxYYtHf85QAD+WA6YM3PNzHhwRPM3wr3F9vq5VwY7gz9QNu7e7p8060X8zLg9d1KAC0p3wv5mojriIJ7CeB/b4J4S/zfn32oxm0Sxm+WpwUP2n65bFQW6vI+cSvkSlXfaKn2L7VadbY9N6uV7c21puthrOEj0TmGzm/lMUHSqg0fYwDq1GzgBgFnujnS1FEgiNm7aCCHpWLBqChnz/BAOZSxC4KOhYv5LKVUhngwIlj1Wq1kOdkqtlMNk1XlCe4iv5CWECnlCG2BGdl6OdCylxTaLVaka0FyhY/rF9wWPGiHla45C9z+6pdJn6LgX5XhIweOIFpJSZsGtrZBM7BA8V2+MBx50NH8qAdClqa9AcfYpoeRpaJNTVtI0Qlxxd3hLv6YEPZuhAItp8O6EexyYAYbD+KOFf+Rq3LSDuIEbeK/YYT7cBHuuaIg8DIMsRcAjS9AYfOMpNYY3LZgTyNI9HbygOpFh1dItpJS0FJyBDNRlzrAvZxi/GEYxGbxcPELsgesatL1wh1f8zhYhtrlUqpUirgvyT2vFCqdDtp1sRh9Biu6MwzcisjC2E4l2OZ8NeFo0pULo4EeP4wY6/xJFN+a7J1fH3w3XGv6O8eEjgUANpjXNQUSeCySUBayn5inPHg5ybc+OZrmqDGpX7R9Ms2Uv9oNnAA1gfpJItFyWQz1c0GVhmsQRQ0lqsrhqgyTHAaBo1cihrK0xwrovhqQc8xGeoQ1Ud/zQJswNShqWVFghrqH3WPUQUAdPv6lfb8TL9bf/p0bWtro1BQ9DP2J3XDPoEzSb42UWHeoEj/q4yUKGRHBQsP/4RCRsXbPeAGTEXrqEVGGjNLGD7RMzHTxOiuERAhf1iyUcjyY/PpgjtIAmojlkAhMu3wqUG6azxIVCZ8N4FgrwKtGEmFLMlaQ/y4ttEpV6EMbG18guAhYSE5wczoJcBDyJQVeABUKMpZUpQ1SiyY081XBkgCGZqVTwhMx7saLIMUBJzbAAqLf/nmhByRMMeiCPUJ9rCpXp1ZCN48Arsa9Vpsfu7G9Wsbm5u9Xu3K0kK1tvP1l78nLPqDjz+5/+ARMUTpfIFAaQLASMlNTnEsi2ZYYn4VlxWfJlpZg1SX2KJy4STg7+gh2fL35ODOh+lzMIW3+O6hANCRHslbLKxoaZddAlIqrlZNZZ34cgLiAeXwr48jCTjQP8JfUZYdGVcIZWVfTz6pmFbXuyhSWOUWqQjxgvEf9AAazAOQpZ0CKVwxPouZE2S2QW8zBBBjwT9CPBT6QNODo3GNsXpsEoT/lvLp929d1xGn3Wa/3dze4fyNWoY8hmwvM21tpiamkOlIyMHsM9yCN5UxAOIOxTmxCcWb+6q8kU9nwy/pDE/eU3XjEM3sLXzCoDWaggbqYathBDKjkyWStunM5ENN6xnLYVSR01F0DO8gKoEtUTQTHZfgH5IAgTgMAxEGhe2HDEy9TpvY82CvGZRZJRLDjiKLmK5Zs1l9BLdkvWFFWoEkJR58iBilAHFl3QHimgj1zE0A9DevHxcIU6Pskzpy06UgkTxoIBiaCADa3t5sNheXlpZwNX733XecUMI++GfLy8uPH924cQMzXr3ZpFu+UFL2bvacEQFmIWK8TjDiS/D3SmKwAqteiT4vuwR2P8rgKbM0v8snjbt7Xva1vzn/BwGgPeW1Z+Ob8xFRiCRw2hI40qt74r8X7iGCh3GRqkPnoWUb9R2UIBqaY1AV6ayQW1kFKNgE4IS8MQiHnoyVZrUi55h8JtKjAiRjPUqjaz56QQHVLXUIBflcsCf1U6RvTsSK2fTVuZn5uZl+q95tbD94/EQHrA56slugoWXfEEVCi3S6GCfDCwoQD5MiboW5UPl8uH73FcGhMya2mW3kk9JTpR0erJsZQqyJbrRTVU8MKRAE59Cm4G//sR4DPpmg6KboGn7SbYiYA3eItrVYjdvCEEZ2dHKGhZK7xESWm5h8hmz1wvkFT0CJVp2TRwBAYFDizLUrTLHVCBaZkoyJ1RPLI7bhU6wS5CSQqEgg5oKgnIxah7yM/BFaQ+ZEehlCtVWrERnorhcTzniZkgCzcQkFn0nONVsIAVhz0zNgoEePHixdvfLBh+/Va9W739UJaH/+ZPmbL7785Ec/6tbqYj5WB/uyJuKHsAbFO5z8pjPjJDPFeEukQr/jQlNULr4Egpfk8KzqOVuxF340jsbw5eGpHaPnWc51DPbCQ/YFQIEQg967W4JbUSWSwMWXwH7f//1e7IN/enzUnjRfIYiaUVyudDDtwS20FHXgDpCCW4CLxtb2+vo6CgxJ0uIFw48igWystC8/bWbIoA/D5Z9BsxFCO8YVUOMWZH0iG0k4r7Q6StYaaaN/ggPEc8nYVDbz/tXF3vc/xtT09MULnCcYpuhMEX25ZMQ26hnKDoCEohQJpCnRp9LZbjEytn2g2s2eEaxlRDAkBFp8Xb4Wp8Zn0CjEgPYGYNCRedRDEcfCH7Z8REBF9p3RD74I6trkJ/IKeAIJyeQifCJow6PQaWs0kgKg3aiTBED/2h3gh1I/CiIIODIb67KlyXXI4pECmAcvH9vS3O1mPNt0IwgkdigIRydrCEcyFnBDZLmxIBhnj0o4VtyKYfGsp2lDxbrXaKFMT09/9NFHy8+fPX+x0uq0iYP+8MMP79+/3221yXnwdPlxaaoyu3QNyNlrkwE8BshlFNwChmCD1wmCWBDBr6JsRZNG5aJKYPfT8RYe3VFZ3j3EW3a37570qHMF/febwjuc4ETBjMeuwIz/R8yhKFwo1g/FcdQpksBYAge8vQfcGo+e/Bv8ggQV7zFBiku0M7dc2wF6KFzSzkCUE6YaKnb4aHJnZxt7BJeMcRiEskRJ8h/xeEMwCXBpBEdh1HKTuKo3COJTY6uRQgaZSJeTiUaKnNmtJ4Em0Of0BM7/SuYBQUTL1qrAoGvzszcW52c5iArdbwkAbcu2hbQYIoE4BVLwoIR/GEUUJgRPOqyUf7KICJfISsM/cTbmzcd6C5KAXlD3S1qgTHFXl99l1JiC0vMAJTyMSe1mSuGuEjfLVIVs9A/QRQgzcE8+QYZg6dHQPoLmnxAH4LLb1zntRgRHGGeu4fjj4DWif2TuSmkzHWTBXvwTulOUlnjmHx4wBy+wihyEB7GGsQWdpD2y4Ojp2i0qmI70sMStwA2PQ8vypWupLknICPmMFJtqVveB/skmr6nZuanpWf5hz3n0eJlsih9/+BEHt9V3au16/dGDe9tbG71Wg61owDMhZisM531LZzNECBFcpFcopEC5K3EFaFrcReX8JcBzOW0m/B3bc5Y3n/0A4sz45vT3ZPt4jc7MEQDQRVvA8ZYdjXo3JRB8M6mEy0lJA5oBKf9q8ekFJe0VtKbcXmZgAOLw3+WcVQpG4r/O0Z3o42wuwxeSW5hwSHpDI0NMcVskiX5Agt9H06ycOj7a1qTQEsXzjmEHA8ez2zBuDwbMSAducWh8qVhMDgeN6lZ7p5bo9UtsCiN6GksGAAL1bLociOHrclJ8cglXzpiWY0V4a1yg79WAEyrhElrCy2bZsazAJH/903s6aKAu+WLHMp1N3bqTjVEwCHSCQQe5UbQbfyBPloYojZAZftD+7KiXJ1BuvjgYqAsqwumnY2dbLZ6CSdtsS9QDBqjz9OQUtFhppoUJ7G34AzGxkLgym895kI3LhLX7qhAOLEIH/vVPmEyPTARNRHTwEohrdG1eKu8m4yFHyFM4x62Q/94n3yf+ul5r/uCzT2n77rtv116s4gt7fP9edWMdkAcKZAsYBWDNs9bsZg0Cq1GnOAMiaMVZjT7fEQkEL2ew3t0twa1DVg6mMH7RLu77JnvpIZd6vt0uIJ/7scTTDmQV7uNvQbgl6HZZKheT+TBXYeEfUqo+PEyEgcFlmCCN4UunH/SkQvEOKJtRTyrSl0Qf06BQGPTcMIWbaUguO7Qmn7lEkpgUdDMemc3NTeAP9gX+M972m+uoSwZq/1FQUHSKMAF/8Ka9VKtQ0xQWyEKF7uhpU3xiBr3IWQ0CMGSJSSbBQEod1Gr0MDsBevoD8ukJqsmmAlHZT1LgLnl9hK2coLPAwv0dZy20YF3g0xGAu9tkHWGZCsDRtm/1R5WPrV8MogXGRM0wAWmOoAJOQD6Mlb1E1h3WB5AB9GAqkeVM+EaxUIa41GdkwcBGA9YQ3ADZwD8ADsLgj14fiQB0cOWR50ctbAiHWxxGRP00mrXqTqvRKBd09ASuNphkFmxAWLTUFx4ERjvY3ORAUt7oXiqbk/EnGefMLiqaE7aNNxl0mF4rQ1yKeRKcHLKLLCPrlhbCH7Fq75dDO0EiCg8F5GMOK8RgdOg27LPDfXHpyrO1DXbz3Xrv/Q1OxlhZwSx07cb11ZUXd+58Mzs/N5/N1LbWp2YqHAeWzmVIpWmAEOvXIJ62ULNhjwAw42H00+SCh9uoXCgJ2HM5FY4CyrxsTOCXXvfPoPGo00+QCl9OkGKigI2JW2d/6ZzsGwN09gxFM0YSuIAS4HsS/smAw0N+h32g4wAUEr866EWGo5YEbuJJEAnnt3dbw0Grhs7td7rrq2vbm1t0+/+z959vkmXHmSfoWqvQESlLoFACKGigyW422QOQQy6f3lE9Ync+7V+2z36ap3dnOD0k0Q0qAARBAARQKK0zs1JnRoZ2rd3395q53/SKyEhVKSIz76msG+eee4QdO9fdXjezYwc0waA44I7lmCuHHu/Nt7YL/gCRUEQoXA572iW4QQn0bkPI44f6iGmpLRCqpg6Z8FaxbcBj6VKpxJZp4ksPo729egOEg3Mv0KNnQIKN96il0skMX1ky71iS3DfNEJ17bxAJGMHLxUts47ygT1AhyKimg6YJHWLjJAnJKHFLW2/udWmiWYEatKdKNahqPQFrxEx3xkFFozmC2QSbcMER2gCEUBuNiK+fWQzJShkDtuk0mmhN2AKG2WsyaJw4hIKGXcWhBNtYdEfwjc4lw1DWROETS8CxQZqTUUGWw5Scy2VkU2ISMMVn5HMR8SyzoRkHO84BCgXdgiSLG+QK5irikfaa0YYWrC4mxUGr3UXVxJgQ8NpXv3Yu+fG5M58OxpHvfe97169ff/vtN3+w/CcYM/e2NwFGhcpCxlyhYSXT4eXhZFumY4wRw2aT6Jms5GxxmH9qOeBvINMLMg9kqrO9zebpnLc9KNGbf5QShIUA6CgtSEjLkefAHT/D/oEPrvrZbfKdD5sC0UjYOUwRNCkWcjG2JLENO8pBXVJJoBCihkxf5p8nXQfqIjxXkGBy51VC3COnqQJq4bmBE92KNmCAyVa+4RC9yL2etpUJNWiDt3kuY7shhAwHjGv/N04wnXY0BQBLJlIZoFG/2bRBgGgQovB9dEvnjAIAEl2ALekrTEuB4Bca01Y0dqw5lAAxyU9ZkY75R309QoGjmauVeKBW9GtfiFx5RLKhVKg8ktmqodzBi0n+KsxNGhq0MkI72HuUTItkRjChoMkXLKhElRhJx4gAOImYDcRAxUN7/tG+22g092r0B7ZgUiRCFxoSGaTGKUMg0jmhMqNyuyVLpeBmok+EZk1Naij+6QAKUWuJTkSeJfJAJc+bIxH4UkZJKjA12AKTWRIa6/0w0Ell+gMyORy0tiMO/2r3uql0tlgs15utlbVjxEygrN1sZ4rF33v+uQuXLn766acohFgs1jSVymQLZWJaa0oo5wxzqyt4CGOd4TMD2CjhJeTAfXLAXulbt+Uz4Q88c5uat27/kEudnhAAPWQ2h90/4RwIPrdkgo/0fcyJtggkKWpMfwMsMQnFb31k67DdbNZrNaAQIAPRDdrAJIYnB1ILCd3s9CXJDIiIDOEIYR3MXbKXpCTsRCd4iCFmvndMN4GyYOJIhO8z26rxucZB5MKVyxvXY4MWw9bSqSzUsGE6mcog3sFQaEDoEx/q2VnTObdIbojUdMziwxCgAWcIdhbhPaTtNN2SUXTi/ejpTE0KueNKMSPrCc+5Y0jsTPhcK+qgVeKB+CGDkvMVjnlnoBvBKymEsHfpjBFBDfebhvPCMeNhr9tpgys6WdiBKzO+zHJktqUQfEtSSzogO48dmJKIJvCXpuN2t0VUSnRPVAYQghqFKqGMtvK71je+02+0c6dbIBi3rD55ksiepmk1mfkE+lhOA51MwcEU54Ml9+LNVr00rOzt1ipzC8VS5dip0wCjrd3db37rW//63/7BL37xC8CVrQZIk/PkS4yFxgtGMgkmhB+9SLE0HVE3RowXh9eQAw+SA7Ov2YPs9wH1xZvvPYUA6AFxNOzm6eJA8AnZNy0vv5uP96QOIk1y3MU5gglxiUAntqECLiOLSdVqdWvjBg5AJPoHjmAHGbQHeLKaq08M0wYnptIReh/5vXBEuul7BE+IOGyWKZx/uVOfbnMxKUvnFAodSfFATKAYZ74jazlDathr9loNzsGwoEGgqEwuX+4i2zmRKhLFMkZTjr7wWdAJib4AE6b8iBKUDxo0KzOK6TEqG+ltbiaGFJlTTMADqFVF1SaprgS1JXrSdAwfqFwj01YXlCr2hG1ZBOHRqVuyIZE0e22zk4XMgJeacwt/EflcBXwAM8JKVMcNiSEiw36n1WzVqnhA48mciMQyyRT2QnyGNftoLMtJpakM43Y7/TpOQu0mqrYc4BHTGr4+aKPYjtXtjdI9aNGmOrRdGkUUwXWNaieKAcEwaYktAZiDZE3S9qkRAkpkQa8MljYbGGwLa6+JdRYpZHNoobrtDvveSevrN+bn55OZ3NKxkxcvfHZje/u117928fIl3gGAbL3RSmeKS0sr6VQGbRlhgIjfJDAk/Y9YKBKNt1y54Z9RbaXh5VniQPAyMGl9zu4x0WS2h3tsfYSqhwDoCC1GSMrTxwEEmvsIu/Ax2S9ZZGoJud2gxWlUa6hk5OfBKeT4RFcqoI9uv4/IRuahYGGrUVfuvcIDtEXc0y1JghMvYzQRUl5oBAdAAg12hgWFJMYiUUhFouCAhTLZ1Jdfe4XIMRcuXMDtepuTMDYb2XwDFQ71OQvLkVMylgTDUELP/pXHlXFd3eLDcUtGdZD2FkOIOkHiEYlb74QreS/kaoWTW+WtmtfxapN+eOCVMW3JkiSxbZYkIS7zEpI9UQNJCwbEERYUDAKvgAvjKKZEpPeGKVBQs1aD9Wm2q8ORAeeVojtJpsBD+DYnEuy0On/+wqefnF2/fp0I0dlkcmm+tLKyRMqVinFWtNfFeTySGCoQwDgpJtniaOu8piUPdzytYA6D+tUocowHhaLN50hGSCgWQQPHzAArhldYWFViJujioBN3MWxqN27cqMwv8o8l+vTsmUvr6998/auvfeV1fKMJZYS30LUrV+cXl4+fOMU7M4hBDt3gQybbYZhCDvDK8R7Ch+Ddu2+e3FMPPuh9j/XAGwZ8CAHQA+Atq3tPb8MDGDLs4iFzIFjQgx/d4NFdkaDf3hLK+tYxVYB9//gXkHx69Lu+2yUQIluq+a3vyAPfVaojOMngx5zMyvsYUxa2Fh89kKkItoAeMiQG4krP3kNUuhwceqJJGW0Q9CgtRnNz5W9861vLKwu//vWv/+nnv+hs7XZjkU6zMx5EMul4VwdT4WEtJ2tRba83BKh3aXzM4mPlToaLfGxGPDGfICLS6N/nhK5cW6ZQSRnTcliHkMojZiZkZzoQbqTXAcOgRJF1adKTKGFvlu40z4kbEToXh0f8oQNT/FBTiXLGIVSAsZ/nEIUZjH3ivW6biNjU5wTZwaidzEQSebbBJdly9/Gnn7z7zvvvv//hhXMX2To2X8pkM4mPBr35ufLp0ydf+eprWKAItYy9UkgFChMyWWogQVIOQmXR+tjhMknUZNIaGfgUFIE+sI7NF1pErWFaTR5C9cgUhRBubwyQCoMeG7gMLo8G2Uym1mx2+z1CAc0vLCUymd16axxPlRcW6IzGoK5qvXX+3Bk6XFk9lshmIAnojJpNR7daoqYxUGAxTM8gB/jMPahZ2yfsrjpj0LuvfFc9fuFKTlIIgO6Tkb6cwbpy+wBfrPukKWz2EDgQLLGv7x1Xed/nXLdWJGHseEL6C6lq0BWwxRplhI6mMiTEniLUD2iD2PsFniAWUF+SUBIVkcaWa8X2lTTjEHjb6mQQQQBHRjH1zlDUNzZISwTiAUohcRmOBAZi6zuqpnw2zZ77dPpYKpPe2t1p99ndncRqpGPQI+w8Gwi1RRSDMWsb6elQQtqgFRlPDEWfcs1hABPyYBN/BJeMUcyUzwWkTnCbEWZE2ufF6uiWzfN6hKh3Hlk9+nTcoiGmBjIquOD2ttIETb/Q9cgeBhm64anZoeQwjoFL/tps72IL3oCoSN1uqy22JccofhQTKBL59NOLf/8PP/3www/h1vLy0uL8wnypCAxpcPz61saZc58RlnBp7RiksY+eb09hmGF8yPEgBMgmZk800udBt4tBETjFahpnDG2ggTPPHEiCwgBcGgZiDoAdFao+Z6AKDgprtmp1vQ693sbG+unnXugDFrXJb4RVlNPB1q9c4d0gIFGhVC5ksmCjzqUrGxsb6WyelV1OH+McE1rbLj25T9HQWBteQg48AA74mxx0FHwSKfFPYvDoaGYg+FAANDuZR0n94xr39nM8SBUl+75ngx4OVvZHh5UHDR995giSdDdMuA3Zt3nkn0nJHksMdJef0tv0eZBarxz0bJId1xMZlTiiCSXMQA69JOQxyKeLRQbrRq3d2NjY4ih4qtEnoo/IfehtEskUzrqJwdCEaarT6WHRwHOX5tSkc6DBaAjKGRDjh/3PJj6RwTrXCpMOEhRM40QCnrhFFGdSOt7r44/eu3b1YrVeI8xwvdYhqjAif6Sd4FheMLthfMEUhgEpSQAZwI/2aQ/ZFaVvDEYBMYB1mIY2ggMyzC2aW3ZTiSZ0IdrURQ7QgdxXkZNBKzLwxxPWPDJeAoKgJq5GmphYRCMp0LBvKWsN0eEAMsAJ9IwKhwY0h0x61D/ZoXgqPRCeNtYBs031AHdxnUtKb7jFsFc8mS7sbO/dGG+vrSzjcE4lHtT3qs3t3cVS5dvf+Obi8lKJvXI4IafSHBzWZd8VqVXPFIqcQwuyAXmAdGC7ogpkc5DRG/VYLPzSU/lsuTRHMRThSqWlhhSptxhf5GsXnSXK0JlRaMXMRdMVqyhTWCCFxIQqHLHZz4/hLZdNSWfVkxJubWn5/MeftKr1fDbHtr56va7ewJyxGJiJKEFw6MSpk+ICzkoaGXexFI84MlUD2mZDAexpUsUwhRy4Fw7wzvAieQvP+Ft0l+9S0PZexnyQdfcDIAi6S9IfJBVhXyEH7poD9/1+0nD2I3rXA36hik6tR8oBgNCXtlaZvgSXDujBGoNoRLbiCk2JSUh+pgtAIAYRoghQBCGP+NHPiV2SpgZBKET+g1oUOcZKbHMSeXZ0mcBFPWFJqMAABAqGYZ8dZ61BPz/uJzvN2nPHjp868fz773/605//y+ZuFR8Yjk+QpxCWJn2v6TApj2TjXLBCz+oq6CMX64lPEqNAvwa7mSai3htSfLPxNOd1J/hmio0MM00qB51pRKiadgIO45Y5eTnZWQ0HaA/+JJJpx05gFqGRDiggmc4Xlo8dr+/ubW7vMEHciok1kIknQBKvvvrqy69+Fe9l/Hz6wx5UwUsgB2wHOgCk2C2fyWTqzQbIFfSTStNfor63u1PbQzmXAjAVivSDii6gyqchSi2xhOzZErX2gNkZOBF4E+tZSgNLvAugXbR1GLY6nHSRSl5fv3bi9AtAYTk29aKcA7+yuMR2NqIy0kMql2/2+9l8jh54VUBOm5ubhVIxkysxFp3DN+Fj456u0wxPSbRyPnveKA0vIQfuwIEv+Lbwyn3BHu5A350e7wdA/vG4U6vweciBJ5UDs2/4bP4BzudzH2kz6zCQaV9Ma+hnNliQGBQyCDcMHNhihISi0WKxiMhtdboIXdQAnkA/ZGI5xTakH6ohtklIZ79lRCswc5N9p9DEfHUkwfGmpQJJyoFoot8dMO5iZY6tZstzhedfeGV5bm5zY/1f3ngH1QD+tmnMOXJJYcuZ5HTAGcYlSZhOfYqRmdwp1M80YTrzalyDhrMZOrzlI2vlaEmkurfTtCZ9Av1chzLwI7mAQjJqmdhW/8ZaxxQACxgjjhtVEOnUUZ2OKAbCLK6sdhr1i4Pe+u5OazhYiowqsXh5LluolOPJDECHUDpZU5QQK4nu8ZRO4YNj0JLokTZghJ1Z29Uq26+29qq98XBuaXnleIYQ2/lMVpTbooiBUlMBQLC2mWZL4ZEsGcalbzAWXHZFEPXZJCaK+R8VVyzGuNTmPdnZ2Tn9wktUAIT1FKkymS3keZvARrVGozg3R3BLCCWcFDPG0NlotkHVsUQGc5gUS6jr7J3RUDfhjjAliRKuU4YbeeEl5MBtOeCvzW2r3Pkhr9wD6efOI92qxgQA7SMi/Bjcildh2ePnwBd8Mw9rflj5fUz44IfZS3wIgylSrgjK2AYktDrdATvesUDFC7ncfGUOs9Ror8nQCD/cfwAFZGgIpqETftxT4MoPSUnDGQAcnYGFCgLrjMl9FAA6CMMSHr/cciomfVKf4ff28KZtlvIcoVCtb29W8tmvv/blSxcvNJqtTJp9UNICgSCw7iCEHUjQkOGCEbmVtERKI9lxhLHEvGYr8Dwo94yXUO3gIysUedTkGiRvyJUKJqtFfzCKCq2+Z8gb+hEuJDEDjGVgPsiEY5ghwT8KFB1L5Cul5778Zc4KvXj+s3a7vQlDBsN6d5Av9LK5QhwzIXGyM/J0Ru9DkEjCVLIEjEI3MBJ4gQfRjWvXdQ5XrZYrlk48dxpN0vz8HBBWdQCdgkCTmYo8kS2XIKEdI1uUm2c4NDNfVdY2e1CdrIrwlj6u31jnBPhaTcoqMBB2rnhqALziNQByOT2YUKv1ZqfdS2bS8/OLvVz7xo1NOqZhq9WKxveWV1eoKT/owQDWKVSSjcXVCWE0iCFN7v0mvIYcOIQDh70nwYt0SLtbFx9sdVj/t27/BUr1k8uHD4gIMl+g27BpyIEHz4Gj/2Ye/NyiBbB/PEHWsDfIXHxMgBGhBdcS7CkINkwoBIJ2uY6MBOt4V+SZtWR5NIokppA4zuQljE1mqVuTshSSgemS/ATwmSaELuUYi7wAW04sldzZq37y8dmtrb1Wo33j6npzt7pQLJ9cXi5hwMnn0glcgcyHxgCWRKZ/S3BrlDCWJx4pmQswV4sI6EVCZp4mbaffM06GiJ6CAM9rRCvx9lTz8plqxkl4iDeS/ROMMEQidIjjEs42BASADvBbgvPqJ0xw/quy0SBcgstyNku4geMvvvSVb3/npa+8Pr+6iqalP4o2UcSh+MqkoumkwvgQNhIFDIESUzgJscMLE6LOI2FqrFGjXmft5ubmXnrppeeff35pfgEIi2JmMheDMdTEpYt/0KadYoIawrSCtc4Lv2LF8rDWto7MGi8u4AtaHzpcW1uDLSicans7eEj12h1/Q8C39VaTgzlk+WILHvZKvMxScsO6evUqAIhb8BlEOhsZ3bnq7HU6RZOhW66zFIX5kAP3xIEH+P48wK5uP4WbX5S3rxc+DTnweDmw7yPBbSAa74mwg/3cU/P7rixqDYRAADoTfoUj0uRV0h/Uq3vNWh09BHogfuK7bMPFGclpc5RYojLlCDzEoSlxZNKQSMa92ixB3A6FrohaDP5RMBvaIuG8rV8RxJyMkU0ndqvNa5ev1OvNXndcneujz2j3exykUMjl8TJmWEQpbUkGGya/kaBBxEO+fJPxKlbyQr9yGyyKqlkiQyEVuFPjablX9nKv6SX76nAb1NEjQUiRh2j3+k6iYwvVhEbbNcZTHtECvmFSor5aoymDa7gnq4sBvsrE9Vk+1scvqsV5F2wIy+YiBAcioBE4CSTESSTDJAAC5QqsBmSwm1yaOyNgYXFxeWGxANfKFRyfwbI4gzNZrRFM1E4umxnIzPko3AMBN5M9nlyw8NlcGBXwlkCHxMoWCoVqtc683K0HKlePZxKpVLPRInj1/MJCo15l8xd+Taj7jP06P7VYKF+5/FGtUQcDXb56g1eLY8xi7AU0h60JFpwZG87M3GmZpgTPFof5kAOHcmDfK3RovSP24BYAiFf/CZ3MEeNtSM7D5cDtv6b3vcP+hb6v8IHTF/Q/Kz+QXghxSpCvVNBWaMGSCGFn+DXPzi/2FxHuhd09CFYwB9iFWlSnGXkwhlQftjuMp2QQq3Kb1bGm1q26NmyFAEQPMv38mpCW4KcTGoINkKrpbArFRrs/ur47rLUubdf6iHB8R/qDNsI1mc5wDGoEJxf2lXEWx6CfQq8DbgIVaS81mEPnaQXTZBZQAp3av2X7vBjIuep1bO4Srtx68qfBdUo5DycIiZlQSJ/2CJjIoPR6E8nhyquuNLBi23jn2jgnm5dpVUAushLqIfUALgqEqMMxKFGiCcEBcRVGIwfYo2Epm831BPo4Fs02UolmBsWfhjhBKJUAp7jyEF0QcAOGgbe5RBp0glZGpOsgtTS4I8lGPBFnybbCMZz44w5MAl9QaCYwkSF1IAlyqSbqRLRsi8wWRRNrUZmfv3ptgziHVKNThYTudQBD2CkpweQ2HPV5hTgsTB1BYjSRK+Qq3QF2sUuXLv3+7/9+LidsDWZi9WQ468nVjHWRbVAtrN3nL86iz5eFdyEHbnKAV2j2JeH25rMnKqdPETPZN58nagohsU8/B+71A3aw/sGSh8q14APlHy4f3VVA+uKwCDqK0dtH99DodtuIJdQMSGLk66ivQDzS9yQzqDn4dFJOBVfqOBKiEzJUliRDVNv3D4XIbK4kmqNGMFQg7Qs2I76y0IxgrYIYoaFkZLcT2Tt3PXvlBsqLWLSP+8rKymJs2LM914QmGkRHOhGd+sIW0+84OqdQ45qGg5F5FDx1eryVl/s1KCHjPZAJ8rSiml/Vu9VhLpR4ZQdAqmP/rMqkHwohT8eW2tSsub6dPYEr6QTFDQwB4KgmR1kIJTIFdk/BxgHb3JLsqeePdvyPU2nNGu0LPsO0lSKOWMzmiYxXFRgCx6A6aplmk1M5QJysQrEwVyiVQBQ6dYvzQ3DGUoxpUahkLKJPZoF6TZ0D3nwOhkB8ypRBEm7SVINYjj7d3tk7duJ5trifv3AOyrNpeUPjvIVtrVSptNstwCH0gFxL5Tnwm7CacQxdFK5ImM94wVBQVeuNArjZjiSDHDhGP351AGQ0TkumhIV/Qw7cDQcmL9LdVD16dSYaIP8ABOQFH5Kg5AFmnkR+3ZIhT+JEHuA6PvquYHjwogaZR0/G3YwYkIfcAqn4LfQjkDiPAllECcew1+tVZBUS7kp0hKcuco/OwTqIQCrTFhBDazsWQ1FheKrePKLPWEeR+wnstEUlgBoJIctPe0XSi0SxdjEU7szdiOLmAQ8IAIMfLGgAfUkX0Y3/dX/U6o6a49bx5UpleVH+NYOeTlAYxYklFMvlnOfQIyhgVJGBDK4ol9zEQx1KPM+4futXysnsa8vcSTwiSSWiDrUNCgUMeW8IXrFygQavTzl5F9h0SVteB3Uut28BIAEygv056hBQwtuXmJG6h+1c6ZCrtD5qBqXMh26AO2hcsA4BNocEK7SHtI0CdDRxAg1EZIHC+wpNDw2IWlBtNnc2N4geVC4Xs7kMblWcWBFPEQgReoCGGggVGpUhmJG5FfSBYggDiDBNeWWZmzTPhI1UF2UVw+HG1O72tndqjRarFJtbWFhcWN6r7vCe8LYQkVG+Vg1hYiA0SiC8fGgL4rmxsQGzMGhiicvkcsQA39raWlpZ29nbRUs0n8nAB71UUwBkI2tcL1EuTCEH7o4DD/ud8f79FQ3G4jbI7yMzeJn3ld/+dgKAbl8pfBpy4IngwGGfjUdPfPDp5WPpQp0MQl0CVY66EfxY2V+E/oFDNgnognZHwsm8m7GGoahJmCkFKc2pTxL8JqXI0DMJMSak4J7RWblFI/mR5v4toDukKhENse8gTklRzrhIcjgDjiPtTgulBRFsOOwznk4cX1v69je++t2vvzrutd5983fdlo7/xJmXEQw3TMxMn+OhIIuSxrXkYEUlouJm8qfBPbee93LdTmp8rhV1gpqeD+qj5IInPogsR9bOcAymI5kWmbhAhnFAebMs+c557xOEg8c0lig5BgFmmIsQo6l8+HYFvBhNbi9jXsI9HCSimD/SDLFDHqyTymVL83NEpMQDGqsiMSgZFFQLYRAlzZJmOuGST9knMsF9upH6KEgsEUed0IXOPkmkgUEcSNJDBZVKsfhzC/OYsUA82XyetwHDHOAqkWZtY6h/6IQFhU62rrU7TaolFSozij8ZuOfUcyCiLLiZ8hQgWOEZLQVjh5mQA88qB0IA9Kyu/NMyb77LXbD59UhNC5KQiUgpBy5IW9QEFKLUQA512g0gETvgEVStRpNyZBjT0S97QzxCFToWvsXhCsRuRmsg6QuCQteBDzIiU2YdBSq0PjHn9HmAHgi/Fpmnpgn0g+WLMIqEhsbQhnDOZFKlQhadwcuvvvwnf/zv/qs//P2lSu7Cpx8x2Nl04uMPP+n0OOpcZ6f7ENADYVw13HjiXg1x0xH0d8L5fRk4AFyBGoS9m8yoYIJfWhCmYinox+5UqHbTZOohSvRPgZOFf+hP+9TomsrQZsBHzNb7oIiQYjIUE5dI3LHn9KO2lmgEfqIVdPOUBLc5tEPMlYaMJrTnD/oyeWKhAeLKWSQgFLaLzS8uLFTmMJZZqziwAquXwvvIF0qsUAfSR/kEga3QPMmj7nGcBqnus0Vt1hVi0R+xOjhbc/RtF/1RNN5sd1imYrmS3d1lLXQkKtGMRqNafS/WjJWLpY0b18vlOfauEfsQCnmF2ECI6ZSaYCNOuoVyOtjb2+t02RIfQ4ckJaISk4UHpqYSA8IUcuCZ44AUvs/cpMMJPy0ckAQxYeKvcfAye/lRmKWTJNwgBCNlDGoVJBkWFQoRTqNuzDcqI6X4pd4h8AwbrZFO4A+TmRKMbO7CgZmN0GwMkvib6IHIIKspp4JBEzyVJe65ZbgUbr4YhjgpA2HPkeWSz7TAtSiNEaaUQ1amf/AH3/t//T//w7DbvHzhLGdqvP7aV7aubyKDicTY6yv2NN16oiUSE8aS8UReg03TLLe9zEvIOz2e8aXxPOXcep6rJy/kyi1XT573tl5CHr4YmJKFSVBGmEOJCtDM1fNBfWshXgg/qpZ1QNZ6vzmYNfS2PDFIY0zWArIkMp+lc1kcgHS2l+mLVGjU+pWuBap0I5KcALuKcxQ58DJzHM9Vgf9RDwqeJZI94jwPR+lMLjWOVJutuYUl4kEvLS1z8gawBqsnp77H0/EbmxsY6tg5qF3uIwWqBlVjCSXDC4ZjO7c4DPG+0YoMhabS03AibZauGRLDbMiBZ4cD/qPk2ZlvONOnigP7pM7nhc1jmOk+AriV7kEmLGlQIAiCnWakEYkSRDW/1HW0gp1wCeZwnATi4RHCjIYgFQpVbgdrBROzPhHKCddw0CSLALRo0QJAbPyxTeBWDWEP+NFPf9x8KoXsiZWF+vb1K2feHzV3alvXtq5dRRvQbvU/PXN+e7fWxfGI7VNISccFOKxYJCFooE9IhTbr9uaMNDEDAUapqgV0kocerjTxVv7I2RKUUIHkj2jhJqTgqba9QZLDhWnfQX1aWWvAzAGIJtLlW2wAiBXRXOAYfQjM2HZ180fv4onOooASHENQB2ZiXEIBZD5GtstMiiLFBcJhHWKYF8kVe6KBnrXUjOFKONFDOQUEKUDNY89EjMEvlXuCFqxa2no3irS6XY6LI7JzOltEp8W5bhz/TpACaNN+rkSCK5gGddXWxgarSghMGbaGIz9JA3zd6nYIEl2pVFgLKnOlPvyZLhzZyXI4t8NryIEnnQOzXwV3P5fQBHb3vAprHkUO7HvvESdHikpEDvS47HHayCPDEFfc8vO9bUHq+BGPhQSHVqSpww7ErOxbpslAgDlW8KkhTOkD+e0wgKf0CXzxDHUQjeZyS2Q8CXNu0fQi9YecwzqSX3AuHc9l8skXThQzsWuXz0oXlEl+duHSP/3sV+9/dBbQVa4onKBTTuckz0MeeSgP5uUkWZVJnSDPo2DK3oPfelfecLb5tOHNflxng67CmzMoc4E/1BBbNW8l78Q75+qvgJXTQhWpaqWTmpAFZ0AqICqeAVOBJqrkfAb2qYIQnnV/cwivA9oQh1HGDeGrOV7zAJQlvKjXT2thRjfGoxOnxzIakUdKMy8qZj1QKfOCCODOXr2RzBWz+XKmM0zlc61Oey6f7/aHu9U9zvZaWlyBNs5OmZ+vnP10HaxGsB+FAcfumWQbmo7pYNbAX/yTAHO8Y3gvYflCs8gEKQHSUYdZTSgTNWEKOXDUOeCvK1f7aO+n9v5e5hAA7edjeP9EcGD2MxB8MKB8tvyxTCT4fJJRMnMVgseIUXAaTGHAiC6nfdkh50hTLF/Ip/hQO5VwvuGHPo9Q93D1IIfWTSDL5XrDPLmnHIcVnFPoU9467JyKcMiGHFPAWOwWA/2grGGbkvx58PUdDRHbuVSiW613qjsvvXhqdWXud2/+OpnOfvTJ+bfe+fjDDy+wk6yQyRF4D4kulKaoOOKqD8eIJPQ/wnCW9jF8OtPJQvCU5DXpx596oXfLIwq9ZHp1sGIMcwg1xbTqinDJegKKmyTvHGZ4P4Izbt2hlWbgPi7qwg1SWhFNbUAEHSblg9KWHV5oehTVOqqpRcY9DkNl5zznx/KQfXb0KxbyBCvWENOi+0BBymRAATWmYtTSM95EUjcJxBkIUy09c4INBlHOP02ItRvJBQmXc8591w58tH3s5CqWyyAYTJ/EHALTuMtzZa6ESYvdYaiF8PJp1ht2WmrW5o6hDi9sbHVx3ivadnsEAZL1TpoqUyuydAE3bHDuwhRy4IhygE/ow6MsBEAPj7dhz4+IA5KL09/Tnnmon5l7m5X2O8vthFYIIAM95Pkh3u132zJKDdgQhpszkfkUFLHZIZQMslzOeVTGJILooi23yFRkmOXl+OOIhHLMS9RBXyElgnZBKzaMbCJyJdK+8EwyFRn3kdP8wyu422lHMymEKAcs7Ozs/Z9//de1RqfeHu5U2UwUx9qCZgUI1mv3IukUtPt8JchBGfZdRB5kxj90RORJXsfpDG6hyvOq8Xltisi25OVBc+9Bzk+yWoEyrKFJaar7U6+shuLsmF3laFC4G6M7m35XklFLoznoHFbQRHZAMI2UNWPtOBcqkNmRHAAtAZ603V6cS4IrMvuwqE2ikxiRCggLKY4wEuuieM0R6ZBEpdPGEMpbxB1UeqKQW5u7yDAE5LPTnch3J2T28nO+6RDHL2hK5nIxXMRGI8JP+wYuXoyVtdXqzi7b2neqe6VKudnm5Rnh6AMwOnfu3KnTz2fyhQHzYvtgn1iaCr4AE9AsAq14bfp9vUgOgHxoXimRFKaQA08aB/SZshR83oOSe53KQwdAAYn3Stmjr/8EkTrLnIBsfwmC26BO8HIcfBTUeQSZ2dEDkm4z7mx9rzbbat9kD1a+Tc8P+xG0QY+TxJVbgA5CCHjBD3KcSqSqAcHgbdJrNqu71y9fQI6mc3mOeNIuLQWDlvkJD1rUEbQma963JlwpxucEWwaKItw/0NHwbRDl6IbYsC9rl3WOA1CiN+wIO2ErG0udkIyiCEjliuliKc/RVy+//HI2nTr/1pvsH6q3Whu7ERxdIhyhlYoBwXpDdhUlOH8VWkA6PheBp243ncooaI6i5qBRGqMGcW2MIAUCHLmKGmPKYoQs01dii5NZ6lwNI0Tn/4By9oAW2uGmW2LhxFDdWB88UzO7QYdB+UCBdoAydCk9DKBFmhi+EieHtJM3uAn2EAjATRl02Ce645DpCwCBNRUFWd3GsQf2x8NOF9+iAQqfXo/KHIOaKeYzuUwqV+inUL9IM8cKMIW4bSkbDXpj0RqDIUAazcSgHmukPBoiTZglRb1EmfRGwj4AIgEg6rL7XsonvRdY6IAsI6iI9sas3bDJ4SixaD6Xwwk9ncsBZbOpVGN3gEanvLBYb3dwi55fXkFleOPGDa7H146dOfdZs1UvLcwxMPoehmYEbJrgQtASoAezF28fbyBkAY5lhhPxtlxT9DZdsfBvyIGHwgH/Drn7ru1Dxzt6E6aT98Kgk9nb2ZpUONh8tnLQw0MHQMFIYSbkwN1wYN97fDdNjk6dgx9RZCQiEXnDI/tRLvuRQRwEU6zVqG1vrTdqVfJIJqK/sAmoxhFddvA7rZgarfyjyy2IR11xvqm+FqTO4chMrCGZRIHzvxDu/Z58PnKFAhqgUbMBUGE4SuJpOe3iFfvd73zjhRdfJLhNrV7/+T/9U787+KN/+0fNbu/dDz/55Oy5XreTiI85Vqoz5CTOTjaRY3QQlUlQThAV1EGdhRMw0yFJmTHlvhEJqWh9JFtF34Gr1/XyaTt9Vc3WdM8e7a6SuUsIAzWJVwAGgX4EO4AO1p4cBApn8L9AhjaQj0ao00QAz6AQQJaiU6rouJE+yhBMhqASNlU1FVsHbAQO0jyER9g6l073CEGYTacLGQ4ZRbPCKEBBao67xNeWmQo3Zi594hoIb7lebPKdq3H5X4hJ+iDL2pe4OQmx6vh9oYqDHlAUGAS9XR8XnijQM8UoLfAZPjryzdYrAWrBpIkeiH1hrODG9s7c4hK6N/ph3bPJMg5ARE3crVYXV/vojXrtNqfY85KApEk2uvNLqAy2OGf8ylMKvU54DTlw9DnAe/tg39gQAB39RX+GKAy+l4M5P/A3Puj5IWWCKfBBVZKGQHugEDUcVUCe399I5nQqiSmqXqt2200sLYTZo04un48mtJmZIHiQN9sVeWTeUJYy9QbuoW8J7NGYLT9zo5zCEA+iqIWAOxzLQGWAC8NTmZq0AsogF3GHXVtdjacz58999qt//Hm5mD/9x9///h//22+++uW/+Ycfv/HOR2yAx4KDX1LfNn7RkL+0JWkwyUt6u0kbdFKoZMKeSjRB4NvlFldnu56qZxm3vKb3Q37SmdebuepRNNLXyWgArGn0I4upLYwE8LEk4xxqK4NExBW0jVGKnQhmHBGAoN1BAwSO7HWEfUBICtWjIU0hFI1mCMWEcbLTbe+O0b0U5sq5YgkeCgO2gYUDjtzgaDGhrQQTBjZx7ikWMdANuhS5+eh/SxAywYdiGuWCQThfK89yDHrMJ8oRpVjwOOkW1Ebi1RhHUP9QgUFx8Wl3FbcJOxfUApqcOVxZjsXFxb2tbc9cvX59ebW6sLIKZmKLPq+T16QHtEQUAmcxFVJspIWXkANHmgO8vYfRx0fjsEcHy+nH6x/WYQiADjItLHlsHAje14CCw17coMJRzoh45B5yEoFnYAUTvGEsWgABAABJREFUlWkshCFw6SD+IUGARkTcMZuXqthxEI5aHFl4IYCGTzIJEMOUHY7wSCDCBaoBFD0VGhhbzOd4n6jEHGzO4WIoh4aDWrPxxu9+s7u3+d3v/d7q4sLrX3kZlU9k1N1ev7S2UPkP//6PEb3vfXz2xl4NedzpEYpPR2w6AVyZDonhfVDy8nGxGz2QUgRkMPnGEVmWnGw9P5D8kRdbDxpLNjDpd1SfCrrq7+RbD/WPtZK6R03kogMRQ9mkJtwgsrNvkxP0ox/MTZ3+QPuk2GHV6Y3Zaw4SNVQHMDK7mpRGbGWXiquvuAOgRYoIsNNttvbSu3HOEAFz9WDnEA+hRCYZzSelpzG4JvqBPobclHdEKDcmWGWz9rnrKqcvbFE4ItlMEyifUCaxRpyL0gEIpfO+0CxisZjHxefK1auFfA6dX62KmjBx/NgJzq0H2rKTC8cgXMQox20LZQ/+0QUMrBYvChaRoRq738nQP+faOm3hNeTAE82B4Ktg3yz42O4rOezWe/D6IQA6jEth+ePhgL+Xh73lj4emux4Vsvd9DhXWV4VR1CoIUfJYrohPOBogkrpsZr588RJb4eeWS7FEEqFI1BYTjfJxnqoSNLwXgm8QZubvjARVZ0ArwzyYw9A36CxVSlAlsLGdyIr1Vpu2nAnV78sVGi3C7u72Jx+1cZiZK5VXV5ZbzWo2HW812Ul0I5Mtfu/br33p5S//3c9+de78Z/1RHgWHwMJALtu4BGnjk29omkFFuCMh/pk1w5PcH0g56kCe+cXom8kQDBnRbN9U8CpI3tZvrSnV1CdX3ZJ3VIEF0ELvKNZPX/7jsAUABKwQO3HWkQlLp5CS4ng/wRtUPPjC4NPNtrtOj+5kPlQLgCheN+jE6MIKBv0oW+PNtoZaCOshWqTRkFXq98A/JEUQ4Lw2MYFjaqX0EdKS7ogZaf6GBTFEqrLhswkAckdnFcl7CmqAJgKmcuFi71ei0eleuHg5W5lbLc1Xm20807O5wvzSEtNHeZPLF9BaJXEQ6/XZ2b61swX8ZRIEdz5x4sRnne61K9fnFxbAUvW9aqZQiKfSMlLG4+1WFw0QdlUzomquFHKdMnaCKUVtmEIOPFEcmL7Dd0U0lQ/W82+YEAAd5ExY8vg5cE/v9+Mnd0rB7CctyPNJQ6witNht5PPCG7pe30Vg86v9xo3NPKhFAfeS8unpsfVaTj9ek7aeuKWcPAIMSwmi1vMCVaYQ4kwx4A/CXtJ+MMiwtT2TwfDBDmpVQCjHOQWswz54Hn/w7nt02G33d3a2N3Y2/uxP/3i+WHznnfeyxeWVEy+efu7kxcuXqMc+LPCGMlP7l7aVW1Rr6V0Q4EyMK98vctQx/ySDLE6zwM7nE+WzBZNqQjgq91u/Aj/U7TR5XldGE7IicuEAjY7ctAmd3R90AI4YkGR/M0clnIUs1h+npAJRQC2c7QrpoBZCXPe6wB8adTFMwnbFDWBwmwvQRIBGW9xjqUyWM7/i2Rj7xjRNKKQaeAvTYp8zMQhaOE4kdQAqTGfggNyAWp7cnIXhOZbROYZaEII4kQ1j56Ur5z89d/b5l15eOaHTTlrNXjyR5kh5NFa4xqPdyaczxIi6fvVao9XEHAangTI4BkE8iBlv6LVjx3gx2u0OeqlsQQd+QQ9zVLxE1D/drtg7BUCUQ9XnaJvyOfwbcuAocIC3FzLsLf3c90BQ6Jl9pHr9g4WzJd5zUBICoIAVYeYIcWDfa3qEKLt3UpgLuITf7ATskT9yJMohmuv1Rqve6HW7uGvgV9vp9HK5AhYNUJHJJw1DQxeWxg20HDJ82C94+RIhjGVQQzRHFAOIY8YoQ7ADDrilE2oStpCs60So2UaZERk16q3YEqHzxriF3Kh2ru5eLy199G++9410YX5nrzFKbp04fmpx8UxtZ6O/vBjJuHaELumJXeHorlI44KDbAdSxMZ8/fO+QpDCZukRLG8KNqLyJeIL85Klx0gsD6KBZy2xIc1rzDei+zlbVGALMMp5IhDNHVGitvb0eh4P2RqloXPu8mDGGwWhkwL51eGNb5PH1BlmixqEJeAKTIJoVdEboYqiDToZF4GqqmQG+PckRR00kMhmOoMinCjkOaYskDVfFpaXTQRX1dhILVyYRxR8ImnTMCPRJuaL1kAe0Cpz+CZCTz7qYqQfEQQCY4gEdidzY3jnz2XleByARgLVWbWQyOHHpIAtYziJW63WOI8tnskRB7Fy8kLfQiMVCDuKuXV1vNFr0xJzZu4Y7t/fPlYTlLpVJk2HuDKnF4FW8icmEjMMUcuDIcoDXFdr8GhCp75qZn0ZB+cGas48Oy4cA6DDOhOWPgQP73vXHQMEXHvLgh5ON1XJ01XleKaQvgh3Z1usrQAuZubmFVr2Ggw7ird5sg1zowRQ9QhiecOOghHLEId8J/CYyQSa7DOKNcq8GXKJPfFm4ovNQJD02NJnwk7bCrC17e7sZ1API81Ssks3stXtXNnf+5h/f2NjY+ObXvnL8+KkvvfKV+iD6xhtv7q5foWsMYAyXkBUMZIa5SMMh8m1sxyIUSNqzdpAX8I9bT04k+eCRZ4KnQbn349egMKhMhv6l/xENas0VJ6rtzZ32TjU2iGTTmUIas12sx+zHHCY6auPGZGqzLkesDQfyj2H/WjyZKhZSpRLBf+iEPe2cJDoG27HZrdfqtQba6tXvgoGSjXa22a4Myhz8DtcBdIqDPY5QX8qkWDLFoeugOTP70RUsgHgjjL+zoE6/ZaGf/51soCQBK+Pp9E6tfvb8hfXNLYIQkHAIu379+nPPfYm9XeCbvWadiN+ocHD3QYl3/Pjxs2fPbmxtgpix36EK2rqxwSM295XLZaphqeMKf5gLE0f9wwtGxnRVRgCLINJE5D4Oh7chB54FDgQvPxnm+8AA0GP8RD3GoR/IG/Og6D+sn8PKHwjxBzu5m+EOq3NY+cFRjmwJU5CEMRkjQBBLoHbhFzz2FM6wJGxPJq1wiO6wQhwd6qAUqJTnT5x67t3330NFJDEvX2klpmlSn/7MumOBECnnnnISYfMk8KRO0CHxCDygDzFqcC/Bayc7yvZkaVM/nZ4QAKdMgQGoQbw/EFk6m++Nd9BNfHZ5/fd/7/f++I//+MbG1o2dzVdfeuH8px841GAUmtMNCRMS6ixNzxIVIAZakbvUUZlAhSCaJwq556mqWaLcC0EOUp2YLU/bwsEiU+WEumAgEBWaGeOnT5mmlAmD+HBgmXSm2+q+98774+5wrlDOJjK0I3JzDZ/iwbAB+EtGTz/33MoKgXNSONasb28SRGd17Ti+Mh7/MJ3WKVrDfmdrc3N3c73IDjLiK+EBvlev3thq1+roUVjMFKesEa0biiNp5tcnWHOkTVSgZGKUjOMNDVV48wjiCCeBUHDFZl1lN5MdUbNQFfQvnHAyADuxYOyz29yp1pqdfHkOgIZCiKZgVzCrIOxoxAYuAjKBY1B0wUMYiSHs6vWrPMXyVV4uLS0tXbp4EWcvdoSxtw2nn3S+4NxmsVhu2csImmDvjq0DQQC0kVBrarRBra+ILwpXXzjqkL+/dFjbYKD76zZsFXLgLjkw+6Yd9jZ6+QMDQHdJWVjtqecAL99h79xTP/eDE9RHUbuBCKOnX99UgDmobChHFBHKhaf89D9+6vTC0iKPkG1ckU8GPqhlOABf3ul58vy0pxNVMMVPUsGa5Tk0QJKjKJKbkdQeUhqheorG0iniG0YaHHPaBxgkIpxxwXHwaQRlttHosQ2+XClwFCvjEv+GAH2ffvJBtrTwlde+/MtflHZ2duYKx8EvEqLmOsxA0lTQJ4fMEwRHE5LFChoco7gLjr8AFOqpiVKunlGDw5PX4ept6XdfK8hAVwIc7GL1i0czheLSiRPP7zU+fuvdM2c/Q/MDDthttKqdXqqYz5Zwh0l0xtHy6tpXX3ntyvr1nU739Orq3OlTBALA65x9XxpoPMrEIr1MNlEu84XIvqlhu9ve3skvbOxcvXajWiPKTjGfZ1tWsVLWMVocj2q4AQtmfKAzQ4ATM6YlHMV9b9z+efIKAE8JWQnA7Y8i589f2tzdqywu0uHVa+sgIIApbsvFUonlILqBcJSlSJqNbIKyzz//PP7p0MwS7NWqPCTPWvO2EP4ngX+T4RtWpF6vg5x45G8Ujk56JYyxxEsUZRZTapKfvJZWPl2y/dSH9yEHnhAO3PwCOYTg4FslBECHcCgsDjnwIDjgghyJiIsM/fHBQwZhw0HTgzmsWasDKZBe+HxwrgLHkSOlNKwpXab19VvcJJxOdwdn2HOMILJAaYM2Tik9ST6i5tGYEVEIIfaSOg1eshm4QytGRD+EPgPDULvXXs5W2NedK5Ua/YX1jV4pm3zzd79ZKWVPnVh55evf+uVv38QyVMrN0Q/SluZYwhgCmnE8gsgMsaddNuNCQ2JUub+M2PsNkV6gcntCCXX9Nrh6Na/ghQ6nZJhSjGcDT9av15R+SN7cbH6LwwJ8lWPZTK5Unp9bWF078eKXXvn4/Y8vX7jEka71yCi7NPfy61959Suv59LZn/zkJ7994810oXgJL+Ktze+kc8W5RR1gAVEp4RlUJsSBZgMeMXTQpoA6Bs1mtFzJLK/EiqUrH31U393t7u1yVjwTL5RLyVyKrXrdmMIswZ+McB5wVFMNZmf8ABRNWMGteTbFscSl0ln8q8+cOfN//tUPF1bWvvPdf9W4trW9V8VOFyPAE0jUEo15H7DZ2VHzcRzYWWXeE2ANYKgAchkSKBwuxFBX4c90bGWtOx7Ky8tialOCVxloig5p6CecBOSRCZYpWIjZp2E+5MAR5IA+R/eS7vhuhwDoXtgZ1r07DvCa3vHNu7uenrxa/hH93PSxME23hws9gIRGXfyWsYNp75KpfMAW1WoVWYWIBW3geUNi8v4TH2BBok86n2QEBSaRXSgBEMBytmpJm0F0HqL4dDq48NIbPWPrwRRCPgYCSqZazU6NM8c6nWQimk5EVueKjepWZMhe7xhqA8QzUEk7qsZRNB/zpSKEsnsaqhiGnukQJxj6FHHIZ9AD48al5iKBXaae0AIvVmblUH9bq0pQmSFoYJ7EMqVhpXfw5M0ZTjamSBzhTmgf/YsninPzuXQxVyidOH36+Zc3Nrd3G532Cy99+dvf/s6J4ycLufzFa5exXlXyRXBABpLHOPikiawMKuEsDbAATkCJTDaXIxRACbjAIWjRbC5S7BSGo+weGqUmajXOoIhub+EAni4Vhpi+0LhEs+mBvIyhkYnr0NPpLH06wRX0prlgm4wn0fhtV2v//Itfv/HmO//+v32+UK5s7+7tbO/iAY1miM3ttMoW8mz540SSNKHBtblPqBfwRwDM+aXFK1cuS4eXze1sbYPYWJBetw9q0uCGNXl5YBSEoevrNhpipnCpkhioCIy8TI6BtEbccQ2WgHyYQg48cRyYfY09f9gUglc9BECHsSgsvwcO6Ot0RtrdQ8unsSqsCD6K+OJIviBlzH2YP2zNQj/T7/G7vU1wGtBDjkB2zdauDBQK4SPsQzMuOIzI2QVAIExAt6AQ7xzXG8QbHjJeTt8KdoMItlWgIahFgtOsYyAKnEXwganWG9TLpXNAmWq1VsnlgTNLhfxeLnv+fLO91un2Iu9/eObC+m4yk1+YX7ty6UKz2S6wpT7FnqcJ/MLjyBBaD1kKkZhp2BrFQCSfNRugPONrO/vIqdU8kdOOjjQDTY2SoAdKeOpKIKErrz+tIN2HGwSHOCvjRRUnxFE/MS4XisO5fiVXePH4AOMd6rDNM2fHrfbiXGl16fXl1RX8gT797PzVTz9OR8bzK2u5yjxeUDgzD7r9dqOGrbBYLCSzaSCqjgTDOoa+Z21tvtmAsbUrl6qbW+2t7fawX+rOZdDY5YeRlE5eg0KhHyFPmxI4h+k4ntBfccMABlZLAvQkcMj+9Ru/++nPf86ht3j/ELkZ/zDWiKVPG6Rj0UGrRGHkyl539gyCkimkQqwbwT/6woXzcIAEYKUcChgR2Ap0S6V1sCtU8RSrK1fy+BLRFcmJMbWUqBKddlgZ5WEKOfB0cMBf7LucSwiA7pJRYbU7cGDfazcrzO7Q8il6zKxJTMi5Mb01RQMHJvgjlDhEpWlzfmWdHGIIpEIg32QukUzFCU4j24xpV6RvMdhEb0gvmiPPrHPvWM4f3Fod4SFVwCMEV9qhJB+JCshm5CJDxKIJjhLH1pNGvYHOo89BpsNOq1tMlZfm5na3t9h/tLm9U15Y3tqrptJ9lEZs/ELEFjkSi5AzdpQ9ARVxnGEsbD467XOEmkEqqwQwxRQ1TpnjMOeDU8hDHpGfTZCx75YST5SjnxHuCzQTUzuasAkKKOiBEOBif1DFxJNIo5ipRCONvWq/u1fMpomd0+q0Ni6c70dGhUJuvVWHvZlOO1rd3Trz6aBWL8zNx7O5RC7T6nWv37hWqBRxBKrML6MuGXW6aEw4DBVIM7d2nGNr0/HYmZ3dxt4eYGIOXMpUeoMohjMhDZKdUGZKFRpOlkcwSNvO/T+bCt4+6bNnPvvpz/75s0vXjp083enhOp0sVeZBPiAW5gWrSRi2mFk6y2GoKTbwUY7li+M70FgxfZYGRRF6QtAPC8T2fyAqwBrawKPgJNR7LPriQoWVwh6KGskWR1EARKu0USyXDnQz052TdrsrNfc9Pria+yo8rttZUo8skY+LOU/6uL64t1zW2XW/4zSDyiEAuiOvwgp34EDwMpGZfTXJB4/u0MVT9Hh21gAS3F5hAsIf3gg16KhzDgMfctxUs1FjR5XkpSEVfGztGANMSUjMOMocYRoEI2fCm0qAWxm51NVNfxqEmX7+88w0M+iLQAfUxyjCRiE8nYE+7VpD24iSaejRSVYK7Ec7tRj0260G52ZEsf7gmLKwtvbn/81/j17kg/c/OvvJebAOriT008/nOJOcidDGhajkKGgsxpXt8ey11s4nKiAnucQVstH2PZngdAJdBrPUaHekJTLdQyBXvSFPVTIFkS52GVS90QJHIrk8oXHhYFMFv5FeKilC6gTnSQ5T8/lKDtyS7tWa0f4w0YoQsocBi/Fxp1Ftd1qccPpcMY9D1GjjenV3e4BBKpseJ+Od+u6osR1p1btz27lCmUNQCcazV63Wa81soXDsuRcut9vdeKLaaGUT9Vx6b9gZJHMZDqCNVPrEioYu4jC5FRDame8kAaZgGqZDrRiHwEVRwn3w4cec384DvJ3ZmFYolgnvBI4hKiYuPoAYrHvMlCnzPmiR7PRWAFBLx74KGqME2rxxvZgvsLvtww8/HPU7VAYq4UCNtY699GiDwEzcQgb1ueKEZmzT+6MV4h3iNeQFc4wJdbZw9nf/hcr7i6yyujpiaR+pwe0RJPWIce5JIidY1sOIpsJdrngIgA7jYVgecuCeORB86mY/oqheEDn65+BAG9YjHLHQqNaQeQh2fFlT2Uy5PMcmJsU2dOhjWEcdqqk0QPyyN20IAMhFmD7kehSN6QAtQyfIXgszlMRjV66y8VIincLruVpvFhUphtPBBCZMVSF3kEKpAAhCfKdTiXylfPq5FxZWVlKVxRFnohf+S3swwgmG3ftIf+JKJ1EZDNEnDfDfxqA2jClM37AfGyf7eJu4MxNTswEEVtjs5ByETk+MvI+nLkIpJeNXwQer5JV1h0oGTQrgkXL+Ep0a8IQDsBnJiHWIKxAUMgVcknq9bq5MsMBMew/VSCzRVqhJ2J2NJ8vlRRyCZV6MRAiaPO70QJfdWmRuYe5EttDutvbOXbgxPLewuLqwtNbudv/pV78hSuFrX3+dc9OubWxwTjtoqlqrpzhsYzwspoAUsn/ROQiWsIsspAhkxQwDkWcKgDdVAgnDvEj07fc+OnfxMhvgiQGtXfzsvLP9gKBMIvpwzhcHWaABErsM6/CGgFWJ8Q3wpDJMJVQ0C9clxPdIW8bQhbWwpZorvTyROE2l2aQanQBSeWcARrw8lNDK7Y78oZqRKjAtDhupfvVb8p5ml+zmitgzbvdVnjZ6zH+PLGGPmS/h8FMOBO/tPQOg2c/DtLej+DeYoRN3lMl2Up3CgOyjTHCw3geJPFiyb0bcHqwTdPgEZWZn4XPU7+lpeB6e2i3AhT3byAkEjSLpyVEE/Uh/sLu9iY6GMD47u9XV507PLa5sb1XZq44wR26hInERxZVYOOgwfDhuEWMILOpQgqDFo5VtQt1+hO0/GbY9SzuE7mMIFMAHBFVPsTSf3NzF67myuMSxUm3tlNZRqWOC0cSS5WJ22G0iU4+vLKy+8OXy4vzZi1eeS+ZHyVSj2x/FI4QSbnbatVYbZ1tiJKNPYmikbyvZyEay7EFLptL4FI96XfRbSFmhAMETaLRA0XLRiaNJYrcSc4dwTQRohPbB3gRugDKSzDTSFi87U8NgEnotc4CW8oQdb5TR+xBUxdEWRNYWpiAQtdAiBODaDBDB9SWdQSMU7Y26kTyYLRPrjjhqjUjQzBbNVjyl08TwiEniMDPoJ8CJ3W772hZGpaVcppyJbzdrvc31rVptY3vv8ocfvPXB+3/3n/+Kua+WK88tr3IyfKdRa6G+G/eyuHJpJdHkRfNxwg1kQbbaAycwpzdCLwMILBHvtPHWSiazmQ/e//Cf33ir1uxm8qXWjW1Owz139tNXX/rSyWNrpBYgqNdnDzxKGw5xy+DOk0j32lX2gcE0VpOZE8OJmIeb6+sdTnnrj3tt4gSx2kOWCQw9atZ5PTBJwtFcWofAGw3yhlYQA/kq+VsjxCPdoiIX3fw8ammmSOiWn0TN6vPJm3y+7NZ3s23J72u47/bWXXy+dLZDnsz2sO/RvtvPdxPePZ0cmH0fghl6Ie+DJ8rvGQAFfYWZkAMPkAO3fF8fYP8PvCs+Qt7nbIZZ+ESQSY5gDLvIEKKPnO24ppyf9WykYjMVUo0A0LlCsViuINIJ/itZBYiJSWi5wYtR9NvdEnl6RrCSQbHk5V6NHe4IPHl/DLG0EKIvIeOZEQkkkl4mNkZMIpCBDKl4msA2eNdygFixXO4Pe+WllRdeeZlYwxvVNigHG0qdMMRALG28H6NDqreamHwiHJuV0PkbkrvxxCAlh+jRALlMSOXYOE6QPQldEWvuLwGL3NaieIEmZZ0wnkq1gyePKXi4pVzzt4zfem8ymVlhbITPL5vtQVIyf1GHPOMpb8NS3/yqQGFp0CFxB5Pgq/SQzeQ6t2Ikjyv509iZa/KV6baxM+KVNWwQP6lX4mD2wZD9XeWl5YU/+LevPv/82x98dP7SRViwe2NzASWMnSnb7vcWcAxPRMBPnGyCqzGQLhFNIoc5mYzE6W7MgwwPgICodbZ2qu++99Hlq9cW145TJLDIEvf6LCLECKywZskk++SJZ5hLEr1J3jwQT81UJoNTD7Qmx6PFfHlhYZG1FxJtdzB4La2sEgyaynQF5tMrACRKERVBbQW7DZfDK5LznyuFJC98BFcf0QdyGr7goN6JT+GBdPgF6QmbH2UO8J7c8g0MAdBRXrWnkLbgqyrIPFmTnP0UzVLuHzCeInLII9tI3JKohp5Ckt2gDGoKhBYYCMME8CGXK6F+QFwRE4hyKnsTmnufEzFm5TzyridDcwtUYgO8STLEHaKPH/oYPRRumD1d0nbEc4hqNAAcas7BoOx3H6A4AEhl8QgasOM7W0Csl5ZWl+aX8tnCyfw8uKhdr/ZadQJVMxBkIKGJ2VhiD3kKpY9ELyUQluinsMyh4pG9Rv+hUuB/OSlDKsBLsMSlrNmvwCjeoU9TvJLtS3V81n4N6qDkEa6hgrUzwMMMpACirca0tvCKvyoA5YFEOOcMXUciLmAEXkuyuwpMyDQEdIApuAxnOTsiq3POOo16u9VqVvc4VSOHlSkeb42agzaBCPsLiczSS1/9xouvNNut3e2dbpMAi3s7N27UW1WOsAW0rYxPLJ86rj1yaQUFQAeG3dLXHWBnTJCfDZu8QJD/8tvf/Pa3vyWOULlYAl+CwwArQmMc61UsYM27evVquTK/dlzHmvI+SFdoiR5Qa6V7HJ+iVwugI68spe727tb6+vpzL7xIhGgCBfFqUcEdgISojPMUwiWWbNKdvZCUcBvwmcwjSIzoJAVjOQ3B7W0yNDys8mHlt+ktfPQMcsDfveBt8UwIgJ7BN+HxTzl4Cx8/KV+Agn2zmP2Onv2weT4owWoCmAAAoUPZ2dkrlOfnFxfASNXaHlIKiYjEQrhJmMlYpASNPKIH8uhLXDLKooS2Ay2CuXRQX3oIkoxQSfZaE0UGq1AqHsml4ghgGa4GUgwAxNAmsL+9l0vXm61UIp7J5bc2d46/EOV81sL8YqTbuXzxXLddX1rIMTTqKKQPfXOIOlI4GU1x9CfHS0EoMMj8S6RWSkbwCUKTA/wRQnLmHGSRKDTBDH6i86AChUF+35p4fQrV0qdtsMmrqQgAJPBDMs2ZatI1qAjLG9vUZHAjeKIOvOh0UGXxrzS/UInPp9jdls+Djgo4zeRKbJHrNxoF3IOxEHEkBU5XUeIB5RZLxdNLy+i6qttbl8qFXVyjey1ACsGhiYvIyaXpbIYdcayab92HJ/TAdFKpZLc/IOT0pQuXfvLTn21sbX//27+PJW5vZ5c6WNZKcxVWBAS8urp6+eoVkOjJkyd5BwB5EEAEIwIUuZHLO8yjw9Mud7zBRtjCtre3sYGRwXu6fv0G7wArwhU8RCeoAL0VTch4gl2H8Xla5SH+/SJD35LyL9LhQ5xn2PUR4AAvzEEq9hWGAOggi8KSkAOHcmD28zOb9wb+69/L+WpG8JAHDUwlECJZsXO6OOzYDnN+ynOK09LSCgWErmHHDzVpSCP+4d4jM48l8A8SceI3Qw3/HR+LUmbeIRK3Xsi+HjxOEKsDAEu3xxlYedxiOEGCyMKdFtXTuWy/MdytNyrFHIfDd7s7+UIvkcnXG82zZz7pjT899fxz77715ub1jRLxoeMoaXTcGAkTTLqHx1FUKiXiTeNjZBJX+9owfwnRsLOIbdf46NiWqMlBE5K41Ocqz1vQiV0Rzvy7KZm9UJOQ2CZZdpKnuZfgfYwJTwCHLsVdFGtuEgQtukpIAwCHrA9tvxLTqJkYE8EZ8NduAPMa1VZ7a3cHnx6AAl7kmAJjmWS/GxkXi1iXZC+LKTAPLkK4CoFBWBdcjAoL5S+VX4viVZxO9SNsBEvl54qJbIo5gzxAfsA/A6UEtFT4bDloJdP4XbHt69KVq+k89sYy/tTgMEIyskZuuoKx2Tz7wbSr69SpU1AOt5mWTzmW1HEWzBRmJ5NFHgF78XEmthPqHzIYLqlJHVypeKMglfDYIFRMavCQQzC4OiZzntAVJVz99km5QvOTQmpI52PkwD292CEAeowr9ewO7V/BT+78D37GKPFC5BPz4svaE3kv9wxogkOsMFLIgNHt4rjKEZvEgEY+IS89UV+3ptfQ/iLbwkN9EiILWBX0zSP2NJmoi2KbIiHWcG4FVyQRwN0O/zKF7Hy5RLPiwlJkI3bp6iaOf5wbhXAnEF88EeeQsDFCvr33wccffeWb38wWK59+9NHVS5eII13KoxkhBCI7sXVeByIWNRM4bGAnaAHlIAnYAwBC6LIPTCwwfQwToQT4Bi+AOFyhmUdokwK2eIkkvSU9neEV+SD5o4MNqcAjyqGBDDkGkqKMZCgLiakBFVGIM9wBTtF0LC1z1ZAgPmzfHxCKG+rARzFYgtsOq5EqYUCkTG7d0QgxmHEdItwjwALPqzgn2Y5x/Yll8drKpPEBInrTAKsie8uwwdk0ffGZPvvj5W6VTl6/cPnsufOc28opZp999tnO3h7QB2UPSTvVTayjEWQiBqT62BEBQ9AEdqaQykxI+NJ0Zh3ChxPgIJ8Hcl3SMmWPHTtGCTY+r4A2iCZgLFkrnTPGErpyjjljuRWfnpDkxB8klikc9uhg5bDk6ebA3b/PwTsTAqCn+5U4crMLvrD2vazBG3nkKD5A0D7Kg+dMwWdBBVKQp4LJGn7Tqy4gAnHVAgONxng9t1rtjY0NDrlcW1u5eOHs9Wtd1UeZoKMMJp7UaCmAGvrpj0rBTk6nzhQ6GAiQmgPVyMTwBP5QZbOPESEvly7PLSyc/vLLJzZ3tn7040ajlU9lGIJdXpl4WgF12GedT6YL6BLWOB7rzTffuHaVwxayOOUCXhgd3QpymkkJAmFJssPQBXHiwkAOgIS/0A4xVU7RwE/IzC5yCoqgTKFEzAHU0YokRkzgDgDIb10YM8gEMqrBzI/+4DZobnO8yepph8JbTB4TGE5AqNIQ+4zcN+en6ABqkhn2h6HRYRe8+YkPB71mtzPgbFRQJJECwI0tbIZtcGEEECOFyyDF08S4Exn08CVPJyNpAB/qPG1PEwAFJ+J1LY8mDJJygYIYzTMWw9j5/ocffXL2DCof7IPnzp1rtuVmjuYPZQ/wRcAJ/qcyi0sZdE9EOCyW51h9s95xZIfwpYAn5lHZIsUlmpPWN9eJXfnCCy9gSsOoyiNiQTP03OICnPHRufrCeYmzKOCk90bhEU+zrwGkOtkB8WT2VTji0wnJexgcCN6HO3bO2xJUDgHQHdkVVngUHPA38on7IgsIJuMJSUOCZcFnjDxyyAvJk+G3O0+RVZxzgCdsNp9fWVna2xpjcEHO8Yh9S0F92gpZTEGDRpFlTbGeqQMI0XkYQBO2lSXbQAzsIPZI/SD/gRDsXFo7tvKtb33r+Wb7nffR73xCG6w36pWt8GCqVOIP/90fvfzaq7Vm841f/gqNFMgMhCZbE94zOEVj3FHQap3licmtFx3QVGIZmw+i2vRAIKFEL4H5DRGNm4oRrE1kUgkBFQjtaBu8xBpjCFem4lfPkHemMS9KPE8hiVtPmtRU5UM10r5q0DNp4nJRrsnyP2dXFmeLECUIaNPttbNy/0knopoE+At3H/bKJwfJHsd+jaOoT9i1r01kRGEGimDYIs4hu+1RAnESfDYzTsdwhWKgARo39DRyfGKCGCSFPOQV7qOPYxsb6x999NGVK9eWltfY5NVsdXAAGvWHBDN86ZWXcR7C/uU+YYBgPIFYa14JlEMo8tj5FYsMOdvCp0nP0I5LENgOfdzubotRXn/9dSAOMYQ6vcH27g4NaS7lYop9ZOIboIyGE/bNcJW2JMqdXUfzGpDnGQi+JZ1HfyK3JDssfCwc0Hs/fZHuDICCV/Cx0Hrfgx5Bsu+GpLupc988eeANg9foMLL3lXO7r+SBk/QIOjxsCi78IMBmKQcg8vIL0U5y9h8N2dGsc0R7PUr41Y4I7A9wwekuLy+DHs6f+4wDMZB/EqjmPyTZjDSNJTgnvN1ieztYRJohOTjbsakINwpGbA6SYwxhguXqgYkkFs2ikRj2Iq1ui7M11lYWFpbms8Xs4slTf/T9P7p06UKjtlteWcgkhoUMOCP13AvPfeW1VzDr/P1PfnLi1HPzi6s/+fHPKngAZbM6rQwIgwnGdFccmMU0cW7BaQY6AXKiMB7tdQlOSPDERBzzmDAPsABykK8Ea0YEI2UBGzCEfWiy7dGnkJOxiDzJ+eYlxkKyky3cPKMLLvRiTjaqD/oDn8AQaXuUdF4EsAyqGAbyIAGnGdHquAocSQTDWLbPznVcrhqdOlviMQHiTEQwQwdSwxRoAQiqCATaxi8a/ExaZtNHv5UlXmQmlo4PcHaKsPsffALkA3ai+qEFq51wL236w/eZaIq/e/vtz85dAJfADvi5vb3L2uH9jDMQWhxIppww0NgsFdYglcQqCsG8D5jMqDAYyvjYbjcBNJjJKmxei8elJSoWwakLy0v0QwadYpezTcZRHIN4wahDfbRK9KOX0ACQ88GZRZ5xxeEvkOjK+wn6OFgSPDos401u+VQv0FRQ7cvwiHTLVgcLZ2sG/RysFpY80RyYXeXZidxyxYPKdwZAs32F+ZAD98SBfS8ft8Gbd0/9PBGVmZrPd3aaDmIwcqDTkGTiyAQ71Ysf/QgmJCACDJl99crl7a0NfD5cJmHroqHbVuiWCtw668igYGC7Nr4+JOrrasIA8Z7JplutLiKToy06jXG70602mojV1bXj7GYCR33rO9/MZBO//Ie/27x6sZAeH18q4odLgMQ3fv0vqydO/7t/9/1r61t/+zf/VzqRzqRz7CmS5EbMYeUxRQIXheHhPyS87T6DNhJkIGtBS2ASHIQIOoQmhCkL/YFd2DaGJFMLBK+8ggRnDAntk8EU+jRZcfKmPLqpFvLJ+suAjCejyuI6nJNqDZZQ6JGOeQSjuGpXPM+0p1/uQLgw+y62Vqc5IJ7yoM8xWli4cIUexvvZbAHARkuDDXKrpgd5FwN9MvFRJhnJcNKavLAJ702nqIeIZQQaoxrgjAtBFhWIkkM2Yonz58+++877u7u7oCMSyEt/NPsIIQ94B7705ZfohAoAGhaCCZLBeR0MlkE3F1U/VGAV0EXRO3CJ+EwgnkqpuLW1RX00crVGHRUXfkgALLyzqY8bEPxxBzJeGPgzy2eINDKO0OUIknSEuBOS8tA4EAKgh8basOMZDvAV7N9xZCh+ir/vfILB1G0nDturJYRkT0klUJUg+ZB//GpnXzqsYKMSOoBev8UPcgt5iPbC9lSPhJYQXSAnpBp5FB70w4kMkmr8sEeU02uffdz0jjGMxIldyMJuMZ4nwjKhYbar1d1aM5HJFucXrty4Mbc89z9+/X968XjlV3//o2I8SsS/bn3v2uUrF27s/OEP/vTE6S//9B/+fx++9yF+tcIB2gyFZYdwPJA2EaI+Qa6QhNiHElRQaD8ggg1IQIP0mJNDpXXA9AR0MlYIGZjUBcYJBAm2ACKEbVQ8+z4IXFDGc23lMnQzfWc0KEXWE1fzhrJ6CHjFU5SoR/DDQ7iCDw270DiuLDKEnxxcyp4sKY7QZWlmWfQ5KQBFpIu+Z9xTeJ1RN4bGjGNQ41GsfjKQpWLY9NgVj/0riSUwFc2kh9J89SEwCZoyh3RcjXC91pw0ABPWfjEAFfvs3nzr7U8++YRDSeKJNENjvRLTzEyGoQrEQ0jGjc1t8ArBELLZHI8oJ5Q33GMY5sibA65mLyDHjwCD0f1sbm4yLn5jn3766etf/wagB4cl7Hr5XBn/bpaDbnkPaG5arYlnNB3CPVLwZs7yPCgMMyEHnjIO8J7Pvvb7ZhcCoH0MCW8fPAf8/QveQn8jn9bvX6ZJmp0doohN78gwMoS44ylijK3YSCk0Pboiurs4nSD3cTWJSSJbEgCxhPSiQ8oSKTxvZUfDFyeV1FkHRDAWGELiswFKB6PKINQ1Mw9oqdvGj2Twydnzf//jfyytHjvx0kvtVnPQ6+BytLq8VF2/3OwMen31vLywvLm5/dMf//TC+UsL8/M5TpLqdGRgEhzRbAzCaGrAOE1mlCAeM35BIIuBbFl6TuhFzzApFQmrcACWQkSjhsHEA5HohOT1a99KTFyOw5ZUdYpsKPC8hpvmg2pkRIfY5UacqSnH2oB+YJF6MrWHDs/ALIcHUgyFGk8UXcn/ZVKFbDEyBpsSIKnT7rfZGQb6AnyA3jjCAwCCpYvOUWbRH+MBs7R5DJADiFMhXuqmiMJWBdqIE3KRElkqU+ye//STM+998FGj1cYUBXZl1VjlRqNWLJewc9GEaD2sIGohIAukQb4fX8oWsGQ6i/YOUMfiEshAZ3/IChnjBAyAFIDp3LkzvBqvvPIKc+XAWsCYTsOw81ABVSwo/GEVyGghmNLnk7P382XPxN3se/VMTDic5Oe/QPbxIwRA+xgS3oYceDAc4KsWIWQyntjDSZxEBpyWYCCAQmQY8KGhzTtEJ2YzOcoFdA7a5iNbknlJ4/6DAxBqHmwzyGzKYxyNgEDm6FEUFOk0GQQkck4b0ocRhO4YUZ9M9Ig8Y0ojdEXZQnkQif/lD3+0vlv7H/4f//PLX301W8jRmgB9aI46LbDSKFOcY4f3e+9+RIgazuXAHtSqVhlCHjJIeo6sN3xjEzLFj4LUCJrYBL0EWizRq3yk8WcRepN+hsp+BhVwTyd58U+Yj1Jk/jAibQfHdDm7/BosAP27xKLEdD/ohCZJNSNxNE+gRJECCFMYIp4DASnjeRS3ZT+XXtmh3HYwhOH3rP11KEgIg01dcTidymX67T7wRs1QLaFaA9ngOK1A0nYiGSuYgFp5+zAGlaCHCYFq0XFhykT7oybsiR+zlLkb16+98fY7xDZELQd1gD8cqzmRjTq4fEXjKUyfbAQDuNRqdZQ6KH5wgtYjc+SC+SAnfJjgIexBDxeNtqCZ14YTwi5fvgyG/s53voO9bGNrE+fuE8dP4XIEcppfmEe/COv5xxI5vBaDp4n+p9nwb8iBZ5cD/g0WAqBn9w14ZDPnO3dWkjHuU/8t7BNEWDJX5os84zc8wozf6EAWSpBV9VoVHIC4Ii4zIVtQheDxagd4y8eZOqAf+qGOcw9hRg884haRxhWvWEQjaAaZ3B2Omt0udQBGyFqqoSZCWQEomquURzc2/+qHf/vOBx/+d//Df/tnf/KDnWub7V40WVjYql7d3GklOtH+KFGVW5LcdRI6PSuBIoFbzkgXIShCQEJmBWNoX1DKXReDYGY4kepKLm7YvgQgMRcgogFBKudw4k48sYfJOUidkGAPT2Vhk07F9srZEwot6VVRNY3EZZIoEbqSOolHaquMNZyyy+xjkVE6nhpqozoQBO0YiiuwGdoRFEPjns5C5eSuQVSoMhPLGDgDpqJLQzFlY2DBFACFPEaDVMEw2+flTJD5UfVBRzg4gcbwHkJlRFilC5euvP/hB3vVeqlUId4B4BVPdhgztzRPIEp8oF/40pdQ/DTYE99uE9AZmyNREHk9WE8gFD0IxCjwUIZd+sxdgRNa7aWFBTaCnTlzhtV59SuvYUjFYSjDmfCpZKUyz6vQaQsz8cawKOKy0Sm4aTycsO/AH1hN2e3rHGj04AseGRnM1Md68HMIe3wSOOCrzzUEQE/Ccj35NAbfrUHmyZ/TLWbgs9s3R8Qev85NIMlYw293Eo2pxg96MohAwu41mjUd8W2JR3JhMZRDc6wkyDCkoyl7TEKjeBhiIlE3pvFAS8Hx72AI8IZUL0hcuQBHkjvbtWymuLS4Vmt1P/7w8v976//z25//C6doEiMxl8kSEwg5mRixs3rQHw8XlxbwkMHAhQM23w4AKfYSaQxFph4DaCAA3xnunU49MojGcMADtqSBNBAuDA6AQKoCOlLsf5L1BkxBS85kxysFI5jPDyua4R4MZPgnC4EIMpLomSmThwmADiX2VSHO0bqoBlRMHKZMkQS72H2mWjTgL4iF0cCUnNGFPggnJtcMoTLDhwfTFRQAJ1G6QbnCCUidw4Um2CilcKM3huc6wOkZ3DMhQkMo3rVRKTzE5Ngsr2nEAUPSWCXjl69ef+PNt65cva798CJU++oJ5YOFC/JYzdPz86dPn+aV4E3Ac7wCHnrhBRRCwJd8kW3vOG91gC+ohYjjrJ372FCHQ253trfffettNFUvv/YylMMu9hKCeWkC8BXCYotZueDe1rwGVGDFuBozxVnINybbRA7kvcLk2SP/Mzv6QVIfOTnhgE8tB3i7+BRwDQHQU7vGR2Fi/p4FlPiXWnD7VGaCj5Zmh7Q2rIAQ4ne8CyR+tRO1pTK/iKRGbqE8GfUHWJsQYLlCvtGtoUOgJniC/5FbOOkgzxBsACCkIKFm6A3dEo84PwGXFPZ0U98TIo4K5NkUhZsJ9jD8UQBG2FNAGNkcZ3YlLly83qzXuu323NwchjSezs/nC/kcehJkagqZK9OXDlHPlsrCMKbXYVc51GIaEq6QRkUYQEDDKIEYcAdEUgefaTLMXlVUiX1hSWCW8IvZzsQSnIv9O0jwje8iS7Y9SgMaZyiiOVf6ETaZ6jAoAazYZrSRdBzmiUyGqoZSxFUq8z+EEppn4r0d13cdFjF59IwSeCbBpVhavsYK7Tgedfo9JgvAooaR4FAqgAuCDlDuMMtpM6UV9DG0xmPXlW7icQI0/+53v2U7XqVSNp7I5AdVIBh0To1qDeh50s6vuHbtGpWXV3UCBu7MzfYePUMYwYF0GMeYg+KlzGN/XToZX6hU3nrjdz/7x5+89tVXX3jhS1evXj956jnUP+De5VUFg0YPlM9mZAIzEAzBZFhTrmLjlIH+ivL0MaZ99AS03ZKk2cq3rHAfhUeBCfdBdtjkC3Jg35t2KADaV+8Ljho2f+I4MPsC8AXk9M8W3n5GXjNo6JX33d5rn7cf8bE/ZcrCKy77EYiWEJkACiZOqL1UUiaba9evIJOKS/Mcm8XmL+QWP/c5T0oWq4FsJfRgehAT+eY6TSe2oxvFBDoY9jlLPYIeAgMM3tDyB0qmtF1rNMIolpLzSgyZiSBkuEanXcQ+hQ2OSDbjaHs4KnIAQzIdaTZHSfY0YagSCEDosvUdDEMcPryVMpkc5CdRdaD7GEcwxDTrDex0infMzyZiQ4MGLAmN2N5+JD9QzCR9Du9uAFNvqLiOOE1HxlmmQDxHIRf0KUMOm0ihwMFjGE2QlEUK5oi4Bn1YBiChvfMaywAHGiSZyugK0xfKHoxyeqTwR7QgfjLaHDn84hFONzKGocWCLPZTyWJlCEtc5YgyQSrtWzNHbBCNQvHEkvCUKI5iPrvAWCV7mTjJw0YRkJXOSEY9qaP0GOUPk2H0EYGlZd+kEDCWSmev3Vj/1S9+zunx6M5AsFDuMZEKhSJjpqKxfCa7trLCkp0/f/6tt3+Hm9E3vv3N+aWFeqtRKIrtpnjLJOIpCwKEe7S8xBbKpdre1j/8/Y9wFHvpxS9dOn8hUyieOH366o2NpeVlMJyO+LAAQpxzAitQEZr3kb1Ftlg89Q+grZtPcfLUpquLVwhub5+hn30VvCQov01vQR3P3KYmQwSV9+X3jc7tLfuZbT7b5LDy2Tq37DCoMNvD7WsGTcLM4+XAvmU6FAA9XirD0UMOPFkc4HPl34ZcyZOAAkxBIhBk0O1Tjqjd2toAlywszCHnrl+/gU9KuVIGu3ASKtgFJQT4xrxX1QN1kOQK5GdJfSLWwFhYXKxzPdd4GkV6mn60TeA7zqgypQ3ASBu6DR/ocNRsrofdbBRjOzXHXuHpw9Fisg9x6udw2CF6ELvMdJYnGME8ckwfg8oHjRJDQDzoR0nqDkQf4IDRGRtQYaoeiDS0IiBirtlQRw5xrifoeQAZIglTGIoZFE/AGUbidjIdqmk2JlYhQ4NQMlW6aFCDRIEFTqMJf8nJCIYETWhFkm5KMIUORJ7gkHRNqsYkuBqoFyRj+5zqo9+SfU90WVAhdqg5HzB/wQJ1pyfgEdvkr5Nq5QWFPU3eXe1uh7UjlOWHH36ITw/+PR0CORPPMJGmEuo92MKYuVzh5LE1INbFi+fPnr9Eq29+49snTpzgEbNjyWABZkeWlxlBj8JWd9osAARcu3K5kMtUjq3iNF0qL3ztG9+o1Zv5Qgm8h88Q8b5RLwHmoNB4Je5NZknu7hINb1nxnvqxudy6n1t2/gALD6P/AQ4RdvU0cUCf8DCFHHiMHLin79bHSOfdDH1Q8Mi1RXoEKYdQ9jBZdvqk4olLFy7W61VsInhD0zN2sY5Aknx3EGNcXaIHg9JQP+oN91BIxvMoFbwOshMRPmk+HMh7F79fBWuWKojK5hUsEIZKhw1eDE1D6nvPWNnIuPwIrmQQ7fSM9M3YaavcMgppNmOeLtovZolN9VKDBb0xNLeYcvAD9imA1NBtUMYtjwB0wnSAC5+gNogJ0JAMpDBBCAFsCe1pCNtT5oNxZSASFjEZzoBXOn1MCVdiEAeOyWAgKaGUpxpP4BmqHPZtURO3a3zPGVwAiJnK0SoNBlXSqAYxucI3SsCA6NpI9MKyMihqH9Ccs5EDTi9eufzTn/707NmzIJ58sYD1jfiEPNURIVA3GhOMulTMs2H93Jkz4KTXv/r1b3/7O4QBYtN7uwNAFdBkOOJYdjstsGZkOGjUqpjMwJHvv//+2tra8tIqE1k7cTyTz0HAzYjSE3PrBPRMeCVOhOlhcYCXyrsOMg9rpLDfh8OBEAA9HL6Gvd4LB56Orw9m4RNBgHlCCCE1EVdcKeG4LqTnyuJiu9N85923kIv4vUo3gCw1mw4CH2WFy13vAS4G3RogkPYCGUkdT1QAN5D4JFPSw1giUGGWIKvB0PTgBCCAh/0eOIz6OttzKO8ihD8iF0ylsQSxsBmRFWrTNiQdFCYoADgggQpcQgcT5BF5rrTxKz3QPzQwLiUkMkyWpN36HP8B/sHQBx5iCxMxjSi05KNTmR4majRuTMb4lSGCUazfibD3p5Dh03R6qAC+YRIoueSmQ8ZsbJjNhIH0SDRrAEvec9CDJgiItOQ1NbaxCfIwOcFqqKaE+WvTViK+U917970Prly9husVtkqwL6PABzCZcVUASx7akREnm3As/Mb19blyJVcsAH3gAUPzjDqQYys46nfbO9tbxWyGuM/vvfMOkQ8brQ6byJ574Usnn3uu1eyUihUq04orxDjxNpuJYBZ5jhrJ3W/yIe639VPebh+3n/LZPnXTC01gT92SPiETCsTPE0LvHcicnQ55SU+XrzovAjE5lGohMm7U9/jd3+J4ikg0z3boYhGNAlIRF+l+GyOYVESIZjsXSpiJUenGpPBEpNGtzESy+mgURJ/+mdIFqxL6HjuWHEOOtibRm4QiVpVkkjhDXZxwUbhY/Bs8V3iKXgGn50IuC6wROOr108kYyh4sXHQuxCMrFf44mg4lQBoxApOQMIS0TxRaEXRSB5whHxniLXNeWTTeUw9QMuLAUbRTaopDdBwv7TERseXSFCFEspyenVKZx/AVMnCiKZJoIuUOQ8vmJnMbhYxtOh099jrGFnEKYpxIOV07jDJSaSePpjEaL9ZD3dqEqI+BS1hNqIvk5jxxXsYzerQ/cNy4adymF1rDWJgGNZ1uBx0YKqR33nnvd797k93uqUwW9NPqEMgHb6osTGBdGIDjRVgF2Hz50gX21RWKeeZFQEuO/YLmfLHIS4LTNpHBa3ucR5vd3dlis97aC6euXr74u9/+BvMZI5564UUONmEKHC+PEojTVbNpAkGh2yISoxJIyPGW2P04krgYppADTwIHQgD0JKzS00sj35UTIWfagid3okwEqcNcSORJzIW8SyPEqLQBKIF6XaTazs5WLpdF/YNoXJhfSC4td7vteqtZ5VAnk83URP1CYmcWnQAIyNMhAzAKyIZuTcEghvEENRKikVAzemr7peX5Ygk57YWqGR1nssTFUSsnEsGJbKYCt+g12G2Eg3KyoLM4UP+AhJDWPIJOyKArGoK2NKb1zyR5CqUiUqBCiRKuPqjglz2lgpyUIrK4sTNbV1RjFhpIWXkjC8kJFQndYVfSTjHQh3aXCe7I10h0GiyS445hII0lFCRDF0BGMA26J2ZBOOTgjJMs1Dcjcko9YYH0zmnKEwDHiPSgCsoZdzQLOptMSaMYDPVBBTvNEQoUJMOdIh9mL16+9N77HxRKlRdfyp498xlRpdFsgZEwNZJRDyCwaAwYjEmr3mydeuGlL736OrvfWTt0ehzvhgsRCRQMzxmCmNGtRvX5kye7jdaP/vN/ATevrR3/8suvHj9xir17nKKSy+bRprHuUMWi4XbuL8lkIoxoiaEp8fz9XW/Tg+b1+fQFx/p8Z+FdyIGHy4EQAD1c/oa935IDB783qSbxc+D79JbNj3IhUyAFYgDIgmEGyUQ0l1G3jTjkaXVvD1FHVJhqvVaeq7DheWdnr86JXXHcSLA39RSAGAxk/QBokKBy/pGJR4USv4IJQgPy7BnFJUTBGYhpXFIQSVyxz4xkE0GRkE1z5jt7zqRm0qEQRBDqteWTYjYjuvLkS0BeQwgBSMkBZkEhkU6lTbdEGThpAnEEwsbaci+kYGmyQ8r3nVshAIsmDOSd4zCM1ci6F4BiRoRdZHcaaA58xNnuHLBh6Ij6SQoE2RylCO5IKQXQAbzg9kyHIDkRJIBHGjAs4EfWQYNc8rDWFABWyujYChAqsYyIBDQi5hEMZap0R09sTwM+CWsCQNWnQSjZtwT4pKESx6cgyQEWeiQdQxIZ54ulZqP9j//0i3PnL/7Jn/4ZZFRrLeyM9MRy0w6MCYIETeLLzKkojUG1NCfsi0PP4sICoYBwLMrmi9guoxgkzXrIuK1mY75STsWi//LWm5cvXoR6gh8eO3lCdEfj5fkiR6YOutrMz9JkUmmawB/8qUiagnP5i0Efzf/w5EPse37Lwn11ntDb2anZ6/CEziMk+yYH9MUUppADIQe+IAf8C5GvSAlpkz10SIYf6CRJJksS6Gx+soO9tN99MCCQHRAFU1Qixfb0vJNBbyRt8jaRzBVPGa5B/2RI0ihYeBv8cJGC4CQ1sGpcDTYJLzA63RLXB9QFMYhMCUkLF8TOdogFPXg1q8kmJylCMH4J35gWhFYMx9PZZCSokA7JMwSdkHwKlJOHBpJQzzSBlbj1JBTC1n+nRr7Ttmce+xlIhUfQDkiCXZqE63CU8USjSbm24WvXPa5FFPo41CEjqgwDQQxeyNAZT6Q42oJDu2wikC2dlk8kuHoJoEfN9VQ5nyP9o5NjXYRQE0muUIfv8qdnz//uzbevXl+vN1pod1jQQr4IDbQCz4B1MHOCQ3kRavU9dDyUZ1KJ5cV5bKBsiOM1oA6rI9iEHisWwfUnm06ePnnq7KeffPTBh2wTQz/07e98b3lljW1l+BvhNkRiLhyygYs0YwFo6UdMm74nEH+vSY1vlQ7248zx8imjxK7Hm6DkYHq8JIWjH2UOPDEaIF7ro8zHp482vgYPTioovONyeE2uXpNr0Dbo1gvv2FVQ/8hmmJqLXubiKZjsRCSP2WGOh/Ko2uDAiSaCisO9yeTzBUlWsx6BgbB90BDPYLQalONEK/hABMJkgojGgIZURke+UxNY4j/6kaYITjjjgIM6ABsapdlwDsziYC6Ohu92cAyiWrKXtNPQo9kUO50S/VELSrJpKQ/UBO2QgQEKEcxoLMjQCVcNnUI7pdPHqEwJghY3ahm1LFFNBJNYaFO4KA+xlBIh0AIkQiHV1AP/0wNbvtmyPVSYPlqgDuIpIXoASNjDpMSSJknu4cQpRJ1DtCNowC9Zc+yrOr5SvKTWGeexAkcmkEsb1IFlppaiMiz17w4eiyqAGjjU9VhgUc4bYUSmSfgj+ZJrgvRuuAilmlAoebyyhXsS8Uw8BdSkf+aB7zLBeK6t3/g//uIv33v/Msao/+1/+4/5XEGnk7LhK5ViSx+jo/qRA1hk1Gy1WLVyqVzd3SEEYnV3d26xmSKwT5qQ3I1SrITGqNtubtxYr5QKSwvzFz47e/HiRfaUgXb++//wP33tm99qApTTKZgCD3C1xoDJorB2RrMWAfAIltLtjB6I27tMdHGXNWer+dvu19nyg73dTZ3ZHm6ZP9hJUO02j4I6TtXd1KTJwSl4P/vKD+ttX7WAhsPqBxXCzKPhwBMDgB4NO8JRHg0H+Pzz1fA0fQv4N93B7zvJe6T8QCIKMICjCiYPhDy7eNq9bqvbUfxlO+mCmlTATSWWkNREMWLuMEh5qVboeV/n1Hfp7mx0ltIB1VRumiHyqFJc5eIVACFStZjKgafUR5Z7D04h2irKJQcVlZC/kzpUQw9EzSmaMXxh0EHVAsE5zfvEuUNb4kPQlkSeQq5ii1RWkjHynUbxhNaHvWnwARCEXmfIgIRDxhwlZMYZHSo2+KR+DFpLP0VVkamH/IHdsQiexCJVM5PzMiMQzsi3bEm95LRRYVbaO1XqRlYvqJMeDDciaaFspx000xDn7gQHaAwIatDNFyvVZvfv/v6nn549x3GoJA78isTa1ETfhmWTwJAwAJhLw0a7AdbkzHYmxRKcP3f2p//44z8vlueW15IE/tFxGS2gzM7WRjGbniuX2B934/r6r3/9m+s3bvzZn//77//Xf9rinC8iWHLaCYTRr71RWm85TkGmKclmpySKjnSC9iNN310T99RM5K5n/JRUDAHQU7KQR3MawfcCX9D7KNxXEtTcV+1JvPWpcWVS7rTLLLh1uVutVpFzhP/hl/rJE6dXV1evXb4MDJJAI0iyaSwAGwhzmpAmHJAbiqQFnVDIlaTKkv9sejclECd+InfVi+xHQhVmvvGaQAThBCQ80hudynDy2ReRJoeohsIG4e2JztnCxbEYRoXmguT2iZAXVDNKyANmbtLJvWSzwSOdx8mONiOEeYA9pN3B1Uc2QZ8FXuHMBuziZ46ijwEBcn7YAHflZIrZ6WR2DvTgqHeUNszVutZ4IBugic0HgCIu4Tg+tkCLiowogkcK6qwTPAzVTMxw6GYUD8gqqJUlKjtJPDPKYBYPQF9SNZnJC6ULbNVysHwEbcImRQCh3/7uzR//9B83tpqZjOARTlyo1mAODMCeBTczmRSt8O/BZJbK5KAY4JvJ5tv99icfffz6Ny4urq6xUw5N0sbGxvnPPivksyeOP0fww4vnzr711lto+P7kT/9vf/hH/xUxD1OMwdFgGApxdhIeFGLWqgGJpu+JmG95Mj61I3UNyDtSVIXEPLMcCAHQM7v0R2viR/lb+46cCoRNkPHpcAWBSC5yOmmP4y2FGyjL54u5fBGnEPZzccwldg3OxECsYiiRdQnBi58KotsgDo4t1u1Ea0JvJHpW51PgQh757XSSJzN5ahW8hELJy75kvyf6USEYBdh0E0+oKxAKZjoeuj0F2nws014IUlAu+CVEAhmT0ckLdFkKhmAUCtwgw3Ai3nZUeTWBOGE+zsUwq19saIYt7hOUREfaJG8xiVQd4xt7vZmebsAxdOZdCVwNtOcLxKG52/9UMaUP1MJ4quhAUz+fDDqUjEbO/7IbuqQmII07Kk4TbSmRuQ06RUM0Um8248lsrlB4890P/vKvf3h9cydXkMExMY4QwDCNbxB+3RbmMZtTTEXUfLic4+EO8uSWE+BH0cT8/Py1jc1//qefvv71r3EWWI044O1mLps+dfw4rl3tZv3alStvvvHG6omTf/j9H8wvr+zs7a2Wyuw4Az6yHCBVOAnBvDCAKnHJXY6MfoinRNQevQRVR5a2o8etkKKHy4EQAD1c/oa9PyMcmBU2JleFQvRdz+lUhjPQXuDhsXWDIzI7K2urq8fWbty4sXnu/LFjxwz68PM+JSyCfQQnGrMTgS5AC3gJg0cQZ3JrmXjk0DlVpGlg6zMIycfiKngBzgE+GQiQXDRZ6MLS4cu0AHFOVfqZICeOnAILgCnwkKFnrFM4xNBEo1hidO+B3igMBqUf70QTpxTMYBAFBlBAZZUp0QFIaMzRGxw6BoRiJIVWBG+wHQtVi+laiMwshZBaCYfEURqBLARecG3RoNaTtoFp5xdpTExnlDR2jinaJtRRamsMkAoHKIAKihIgAv3NwAKTxAIOWin+qjNP0AnDMbtJdUTfqJjMHYqnvX6PK8RzFunf/O3fv/fBmVQOzU4eQ6b2ott8oYIp5LM5MBWWsnQ2Q7QnfMGJwZSJJ/Zq9Vq9Nb+4dOLksWtXL3927uzq8WMb61eJlf3ic8+hAdpYv/bRB+//8z/9HCZ/+7v/KpMv7DUbx06d7gM45fMkfyy8ryCZcUz7Jj8nxQnQ/CZTOMp/YO9RJi+k7dnhQAiAnp21PtIzfbK+E01Afk7UOP0OAGA0mdkSPJGRWMls8dq1dbxfT6yt4gxEHSSc11xeWIwSCqheJ/QPzYVjJEqR3RiFhHsQcFRG9lNOBTLc2hCU3NxqjukFmMFT74R+qMOVRCHwBetXrCflAYlHyGz6IS8yjGzPQwK90wl1wG3mSd2lJj1QAThEOcnp0dY2S/LQsYmDHfTIhrCJSNHj9Z0w70HoioPn5Xcs3Q0VsIYh4qkz4LR2cSCi8UbsidfOdzbJY/qhFSNTx1nBFb2NmamEekwzZBCIGjhS07vUcNrF9jmMY80xsWniop8OaIUyhRYkgTiVintqSLLdWgO246WzWaLy/Ohv/v5Xv/oVURsz2SyRoKmCOzV0wVH6gUiaaKOWrZSOgJXmL8+hb9W9Wr5UhOI/+P1//cEnH//FX/zv6LVe+8rXeplhNpcmItT169f/4e/+7sP33vtf/tf/9bv/6nu4xKeTaVQ/hNAWHfba4P7swJX+nWKI5dE0H/4NORBy4M4cCAHQnXkU1rhvDkjq2JcyV/LeT5C5726PQsNgarPEeGEwZR4hSOXhYviDW3Z+tZo4h7TxcSZo3ihSAzJw61vDkNUABQEdkIpsYTItCZuIe4IvLu0YhbyfLUXPJEo8UYEM9SkERiEbJ8ACVYYtgTo3FCVEYgYrg1vqBGsNSiBv63iFrrC5JNPZdHZIgGPZmIjYR8hCk8E8ZccRNQFAJGbH/8o4dhBKE8GoKHhE/0GfEAJ1GL7oHlwgCc5uLyATuEc9UYLGRiGrUX+NRknDQGTkFWRO0ooV5LOwQVUPfsjzSfd6zXwTu9RBJOhBtzSFNkJb9iraUD6cAR05GTnUEcFWBzqtlj3qcp5Zh1CT7FSLnfn07G9+8xvOWCsuFiGcc7twl4IkwCIZRaSMjDhmBHdpgvdgpGLuhOJu9/pbu3vASAoBu00i/czPv/3uz/7mv/zwlVdePba6BmDa2tj8+MOPGP0HP/jBd77zHeY0jEfXllcbFiUcpQ8j0DNmMrAg/cI1n7cRzHxvRhzQ3MMUciDkwOEcsC+Iwx+HT0IOPCQO8H1tX9kPqfvH3C1i0ykA+xjw0GEUfNjy+SynV3b6A6L6lkqV+l6V4IQEpeGUeHQDKBIACphXEJCEhaEH+qEEMCQkYYlC/oJjkPEcf8BT+qcQNKOtUoZvKEXi0paaIANVhh5AhyXvhxLqeDUUIWAvdSv9kWmM8LS1wIO0gB6ShjDcQG+CXzPJB9VyTpMP5Hd489CzgR5RZQRrUp7RyWWGuqCHZFBK0E1YUEeGcV5Yf9TXgaLs5u932rhS8Y/N/fyDL+4lxSa7EZZC7dYno6TT5m0J0BvxT5YsUyzZILo4AU4D1HplewozJ9+Kss0xax6PoqbDY0ABwY2NLdR4RCsolZLs0We7FgEYCduTThHOOYlbt+jo92M4CmWzHKAKmVqs/ujihcuMWCjkqMDxt++8806pWPyDf/1vLn924be/+lU6Huu3O2c++eTMmTPf/tZ3Uf+sHj8NCq7MLXBoPLRp4aLSKgFx4TnJ6Nd6QTPcdrYfzeth5B1WfjRncRhV/qofvB5WPyw/Ihw4chqgp+PzcERW94GTca+r43LFvxecGJMxAgf+iMy99vnAJ3UfHQbEO/1MwUu4ukz1Et2yM0tx9yyaTa+zu73JcaBzhfkbG9vpZCqXzSLSMuk4nh/NGnuhI0SFaTQbHLSZ8GguIBOd8sQ27kg6KasK51UgkC2qXsr0RrK2mKpIqKjT5gjxaCqveNPQhtWGQy3SaeALXjt0qQOqkJUWbocz4ZOECyKEoAZIRDirNTLsZVNZdDZIbo+Og5qhP+hCFRl2JJVKJdQP+Gib2/Q4ipzvddF5ECMIoU+AIMYFrsnqxJRt3zjwRrGiY3ELXCgTVRzzllyIgG4S6YhvpgVGQaALVpmrk7x9nasgmgGHWEjpxAGq3En/k2QTFrgKRRADCQ/qLCypRMgSzgiFDzhOjyZAFDiEQ5XBK4OLk7cOakmgKF8+6qNNgS3gLSmcQJVqKADGFvVuj8NrV/EJOvPpL65cu44bV7pa36ntaX/aKJZPp8tEAOp2wWCwEw4Abuiw2WzDTLyELl++TOzoUrEgT57ouF6vbW2kzn386crK2mKh/Iu/+8nvf/07LO0vf/ZzjrkoLy+PU/kux8IXKpqFDqwVnXAKByAmyxiwRZvmZBu1wD+or2Ssm7zOPjWf13284Q+8yWGUHFYeEHDHCkFNMsx69jbIB50EFYJM8CiofB+Ze+3ER+e7wl7R6Zrdx8Bhky/GgSMHgL7YdMLWR5oD9/o1caQnM0Ocz4sr32gkMnzB6TsOJ1/sIyCGIYaceLNW3d7cogKGj4X5JXx7wBOYgvZ2tqhDf0ABdAPIfZQWUmO4NWeqTPKe+cZksxgAAl0KsaRBODgTE2lPupCJa46+T4mPJyBiGhK6EuAwHQ/SXUcuWIJOqiIxkPSAB3xooRv8AbagPoU09LYMSolZ5dSzU0uhsALaKckdoQ3qMCIZT5BBuQbhj/7Hamff9cYoehYLjVVgGeVN20InJDa+O2l0qF1OgmnmHS59WGzcTwxsLz1aFiqDMdmUztkgnCnBjOW9BGBUZGm54DCwJznzWBJCsRQ8pVjcEMpKoGJhacBSvTZ6F+lXOp0eW9i7g/Gb7364euzY0tqJ/vAt0CfT4RxZWsXymnuPzfE0loYmmsvl4RJbxYhbSPCevVoVLQ6KPfbAw7HVTAYmX7t8Be/o08dPnFhduXLl2o/++q/RHTUbje//6Z8tLq10R8N0NFYo5NnC1h9oC6EnJ/XWct6n93RdmTUcflBzois6fFC9fcF+HuzUviAxz2bzEAA9m+sezvqBccC/Url6Ql6ScQQAKOCWrzluUQNcv1K7dPkibiLHjx/P5zJ72zvYvxC8DTYFNevElonaNiIq0wP4gKtROYEU3GL34QEboemTuMWUIHdBJAIlvaawiBQvspGRRztDJ2S4Im6hhHLP0JCETkT13WomyxqCQfveKWRc0zQMwC1eh4b0Q8plCyhiQGiUU5kSulIlsxyBLUSC/IERXCKfcbnqlq68SC0mqjKdKo8OxiqgrNKJFkRmxsFprNPN2IaFoofurQVACniDWYqNb8PY2M7+jEmZxPFeI/bDoSGD2xAST8pznNjZAjpSRIk8mxSNbUu8FCWUiDINLXpIdtcfRzBqKVQQs4B7jXYrnkri2/zuh2//p7/6zyDOP//zP//6N75N2J6d65vlSgUGFPMF+IBVi078+HewKwvNbKAOSIR9Ez70hEjH5dIcaqxOp4F+qVavbm9vvfj8C3u723/xf/zH5WMn/+y/+b+/+OKLyWwe/yEOiock3gV607oaMwmdYC5NRuzMxVnrc5gpDrP7OcAa8Sp66XTR99cJ758RDoQA6BlZ6Mc/zaf4u4apkRBuJBhtsl7fsC6T0Nd4EKBabQ8vWiIfctAEdTY21+v1qsEEYSZkLYoMb2vnY/E17XoR7f8i0aH3T576SNxgUblF34CgzWTS3oNfvSbYRRltqtdAACNMKmTYhi67mHaTWc9yrBUeAjZQJ4e3bRJllE2E/WJ2UDlDFAsCSaAvhqA+eboij5AGl6ixku1X971gPLZEKQCIpPDK08Qs6ITEXCiDQjRbFGrzv3UO2oMkUBKoEubyhPhIsuLJnYix8MEh4GEiEu/H+slBvOdnfvmBWbYCfS8xqkwPhec19OHMDeLhwFjogTU80fazMXZAo0YWOpAN9EDAOJ78xa9+95/++ke/e/czoF+j85cnjq8RwAkFHponzq+FKjzZYQVcgip0bODdbC63V2+A2aq1BgiXWeDVtby8Sp/V3W1UR8eOH69X99783W+Pra28/vpXL125vLy8+OqrrwJaQX+smmNQTd/0WCLYEvQHS+95Hs0WBk+fjgxTC+b+QGb0YHu7D5Ie+Izug4awCRwIAVD4Gjx0Dsx+3Tytn3zm6IkJurjymUofgRKC87aiI9DD4uJipVLpdtqVQlEHQQyH2Fk4FwzTEPaUfr/rGEH+KpJ5E1hAhywS/ZNB2QNmERaahjNGoNIz+MkrcOSXAIoFVNRuKMEnQE5yGFEwQyGMvsLnILNdV6TOUSoYuiJLHa9GTQCJyNJpVplsJk8EP4w8gA9hAm0bFybB/ERoQjpB1wEd5o+EuJLEEidsr7668q1K5okMIpEAl38NPiuCYtonp+1LyhN8yKrDAlBOP47x0E1ycqYSToEqmtKCqdGN6CdsNO470T40KH40+Zg5CWEXY5Lqe+IjPPEZgrcCRzLISaEE9KFjMRQ8BCtAonE7RG2QZ7FqvV/88l/+v3/5w0/OXibkULGQu3Dl6qVLV5fn8suLi7TDaNhstxIJuTyztwszGH7u+VwRdyvm0Ol2KQRLQRqGLSaztbUFh9EPAbzw/QIM/eoXP0MtNFcuQ8Qbb/z2D7//X/MUCMSqZnLShPnqMHMyQaKTIB9mnnQO+GruW+InfVJHn/4QAB39NXqCKeRT/Sx8pJmjJ+brv9dZs0kRhqf4OJfLtht13GAp52c91SqVMkFfYA9qIQ7CBMHwSFJcSgftxkZiCx6YzPMvRyALz1wckucR4IQIe2wNl8eufIOEfbx/ABB1+j1V84bqBFUCAtuOdgdsUOI769kzxcmr3CJpqU8FtA+0UrAZ24ROn0h3huApyRrKIgaZkK3gQ2bsw0WZPCWaAuY5FU9ccAB4lE9mZD1YNe32IuMU0s7qq4TmFJKY25gz11MghzEnsGPaUieK14PaClQlcnRsGrOTyUuQCD56ICFC5yRSSdypgUTUk5rH4ClrRFuAHAyhGMijKzCJsYzedk+ey+l84er69j/87Jc//Lsff3Z5E4DGKIDA5YUFYlb22cXeaHKkCbofyMa/B/UejkHFcgmM22xxSLyskNu7O0T6xv18bn4e8LqztUP9Sj6PPxSzO766DFba3d5KJzMnTp6q1ltbmzdwWuf8eDyW2HePLZHFgGAoZ/W5QqrmeCBpfk8XJDpspgemfucC58wD7PDOQ96pRrBYR4qqO1H9tD0PAdDTtqJHbT4Hv7IPlhw1mu+JHv/+8itT88Stl3BFqgJHOALs008/3bixefzYibm5OeRrNp1CjdJutbRnW9t8ZI9q9jqMTic0RECSDzCNQNJogDi0PoEOdq7W1MxEExeTPMUcJsVPLNbDecbENs4nIAfIQIjSIdCEPDjBmgBO1BZbU8psVIhzRDWqE6pgS8Ljx1tJ+WTHgVHBWUSGR3go055dV3HwD+YtEIjhEGlZSHJIZoO+DE+TSbGbSQkC2eRmwAhTlCCdyumbaqAbrrTXljJxog9y5HhUiFdvgEqglelsaGJAkU1mAlmjMceRKnwk/uG0Hg241b4wVTOIIJUVifPj2flmBj5QlMgcE1YAOKZYR6lUGn+si1fX/9MP/+Zvf/LP69scXgFalHt4sZiFmnqru1QqlStl12PRH9Cn0WqW5ypzlYVmg/jPHbAsXkEccYFDUi5XYMHxBNqr7qLqYenbnVY6GcMYWi6Wmk1iAjWrO7vVZmvl+Kmdna3j5XIml2lKOYhuKZFOC32KSIFL+RWRebqTVn+6ZGR87j5lf3Qf06eT+257H8PdZZPZqd1lk7Dag+JACIAeFCfDfm7HAZc9t6vxJD9jdiRmwHeZf50Ft4hU7ECgDU66QIZRjqqAeDH8uMfegQcLx17SECEnH9mhzoH3fpBzQAJ6M/mXRm0gyKLd7AqTSDUSsICvdH2rAw2Q4KbPYRRqug8Nolr1Da9QXy4vFpFZw7FljB3Uct1B6o+AA+mk9l5RzXqiLyEGxDa9CVFFow6AVMGSrxgdgsXIMwpjoUTReDYi4lqDwgBugSRmTaM31FXQTh/Yu+gLfGR7rZixlBw4+OgpuhxNTTKfJvTPQJCNToVRpOuKJ2ENzkTaEk6dvk3ETg8RGkPTgzOz/J1MDYXiyNCDOgQKAiC0hd5MYFAv1Zhcv8EY8FA+Q6nsm++8/x//4q/efv/DJm5FqQT87LM7nQ3txKhstZiLMCLzHYgqpgk8hSpiPUOnfKFSyd2tLZx/QJspvIFyOcAQqVTMMyqrz6FhtNpY3ywX8syh3q4STKDe7nzw4XuvfPMba6dPF/I54Ge90aJDdEuCrTYvsXQmMaF9JTMPn+ysz4urr92TPZk7Uf8Ur+Odpv44nx8KgIIP1bPw8j3OFXiixg7eCqi+mxcjqE/lu6n/BDHDp8MEJbPNMkWJy8LZWSuqzRgtTvzK5maz0+ZYqG5fu9A5CGxvb4+aLs6RnWhgCLIXH/ficofR5iUEPxWojPzjKhhhA6H44B9ykZJkIomQRweTnJwEKrggSRlT7D5cSeiZthQOeh1kNk0gCfcj6uCHyy12GnQiTAHmcwsIoC5NsML4oLTCnUWYI6kYxAQiRG9BBsUGG/FpACDxJvgD+1iQTTl2NPqkZ26FdcxslUhzWoQ4RsJ2BfZhIqAkJuw7x6TlABtSYtvK6BDwotrAPe2B1+wcBpGB4WId6ismpXg8qKMUIYf9b7CdLugVjyGKoId+wQ6cmgr4oiEl0vdE4Tc1OIk2BSpKZRLb1cZf/+gnP/zbH392daPDyesp3Kc4vmyAv04pnwOMsN19ZW1laWmBvXvQ46odMsdPnmCee9UadjHWmrBJPGIgwBCEwzHQD/ESgbwyVJonF/5ARHp87rkX0AB1+0PiI2zsVc+fPffcSy+l8oRkKpbKhXZLh5BgJ0UPRIJd8ETcM0jHLfNiLuYnLnAEDTwikREvpyWef1KuPoVbUsujW5bfTeG+tgGL9rXdVy14elj9oMJdZg724yUPe9y7JO/ZqXYoAHp2WBDONOTAvXLAv6f4zkIOeVtKgsLZbzcEE8IPF2VkNtKLve6gGfQE5SInJBA1pjMcFoAplFTrtaAHwA4qE24l10z5wZVuQUXIQjAK0pRbniL/oCGbZMeWNCkkFBUgFbaU0xzpS0N3MOIReQo9Q8dAM1QeKHosDA/xBYFTGtQreGVuyTAQyfNe7iU8sMqixBuSoQKFZCDVC4VLXBjL4yfGge7AFWIjqVpEDseD2CDaN6FuU1BEIhflo+jQznbw5jTG9kPepT6dMwRzJKVSeA0TwoiYQLBhctgZNXsWQ5ma0CXabEc8XGBRhpjl5PUD0IrKdbo/qHW6Vz/86DfvvP+rN96/dKPB4SCxdBLLXG/QzRfSxVx6QCjqTntlYWFpga3shC0cgw4xbjJB3Nu5wmqUPSwxyIbhDGBpEaHEEGScOJbYv0A/+KJzDkghn3/5pS+fPn36zLnPPvz447l8AXD585//7Bv/6rtffuXlTr/baHWJdURzQBXTpX8m61f4TGJqnqGO8lPQ44V+DerMFob5kAMhB0IAFL4D98OBZ/YrdSJmTAyTJ8E+JBwMcZ5QgiBEMFNOXtLYNklRB++QdCpbKfM7f77TqlMBaZfJprotuSqjMEANgqkFG5F8VkyIU4fe/Ec/PVDsyg/6NCQUGxF3GFhgUMMlIUGMtasdx2CBg6TQz0jSF9MSJjU6McKkBGFQx0/gEEMRuohmB15AN1MyURJXaB81JCkcscCVJY4y5bH2VmGyMgWMamqXux6bXYkmgjaGgWSXwuIkmZ3UPq7xgFaccQoeGsY5LxZfKB0Frx7FWMElR5jGZtckCbWplSVmZ3RgnuswX5AfSUoudGNY9aKxrEJUCyOCeEQ8ftOoh4xo86AG/cRxwWLrVrPeWt/Ze+ODj98/e3FjD1WbwkrTciAGo4Viq/24020VMunVxTkWgqFhMNogMuyHZynxATKiRjjxeNRsP+XDxsUyiE0Pn6dhOpMiWgHQM5/J4trNggJn0QChFGS//Ve++c23Pvjg17/8FS5EX37tq8trJ3d2djFp5nM5c1u/CXeYks/d+6dD5jtljBESXkIOhBy4LQdCAHRb9oQPD3DApNeB0memwKcvoW+JebsQCkQRxc4Mz8gFxixQ9WYDqYz789LSEuqKTruK8iBRzCH/8I1lHzU+thxN0e61vE/6BViZbkbuLxQi2tHZ4AxkMl4+KJhdkILEVAT/kGETt5NBn0AoqkED9Qu5HMiGytyCEpwwk5dShABAEJuZFCGLsdZQTbvlIZWaLmK5engiJ4xbEp2QHBWR0SNF8DGwYM0p4alX8IbWgpqcDUYBG7YATGit5AgFtaIcpRDKEhy3R0J/IBBrMrEDMhqN8PjBdcq2c2lQKvigwnm2FoyI1w32RLQszBoWGnnaX4ZrNoOAgkBXcK7HKR69Ebxqsumr06lW65eub16+ul5vjbDdwUqdvwWc0745/JVR3gyxIK4uzWXSSbgq/gxHABfQD4gWxOPmsK3tbQrd+GVe3fKQAuIwNKe6QxMUDAcCT9gUh73uhUvncYGqVEpra2ubO9uQfOLEibfffrtFsIRO/49+sISCEJ9qjYieKiM8x8o6qlOfU4dom+bnFELicphCDoQcOJwDIQA6nDfhk5ADh3AAYePyBqlqMlWSmLqe90bkVQlHl6h2A21ubiLDQAitbg9r1872NqglWyhgUmk0WkR8IdYOXtDIY5I2k+vsJ0S9guzRStF/OB0MIWx+zaAfdETUBHdQTk0SWEB+zQZcUP5AACNSx7aCS2MBOYhggx+4+MipFq2NEMM0oYpBRyKydTKXGZXw8xX8AjNAixyNeMYE3S7D7DVZU0Ug5s2nWeBJG6ymAEiYiLxzwxyW4ZOV2ZkVmqlMRFaFMM+EKcTqJT0YJcxI1jDUGuIsI4NdGCYCiYyqbknmKgSMI4k7UDtq2HjyAZJGSfvLzCSH67Ktl/ow/yAiIgFNhmNFEDCMOEok8+NIm5PvCTXNVrAuCJM9+Dq0LRfpt4qlPGdT0AfnsuHqzDlfoBM3fmEIy+Sy9MZaw1j2+EEOGQjXEER4YmUyAplspYO9DNfttlUeG7fajVdOnRpExru//s2l8xdycwsry8unT5y6fvXaL//5F9/57vfymTSuV8KYbGrDaUmuYHAYbk0wKHMUhywFGb8NryEHQg4cxoEQAB3GmbD8Fhw4+N2KaDlYeIuWT0sRk2XKJCZEnuQZZBIZv3o5dfTUgAMohEe5QhGLCb7P7eV5IAcKg0Ztt9/tbOkc+MHcwnyk2VYTmZsQ62AYyXxkG3JULrqm8HDgQgXZi4ZDeZNwWFVPPryZlNQDamvqIhAPagnagpaQwSTKueXKKGRQAiHFqU1gwkm3GMaERuhGoMELqU9iFJogtn12eNtAvz/iSiHjmsZFiBAA5NX8kT+lQxCMl1gW1gCZGF/QbUyYwwlL+4h3AvtABtoXEpWpM4FZU+WT9S/1jy2FuqAa9fGdQkc06E9OAiFyI3OhGuRxpCtcIjER9C/gOZRR6VSGHe/grGqN0D3YmmB8Cld0MSIR0252QknnWUeFKiDCoU66ANaMx7XqDhB1aXWNntHhGT3R7e1teA7DWXGwUTKdIQoiLkHim0VaYo2AUEwtn0s1FQc8gvWMNdnc3UimovPzFYBUqlh+/rkXXnrp5VQu/+FHn7yfyX7ppZfQHXIAiV4ke68YlBFhC1fjj16V2eUI8mEm5EDIgcM4EAKgwzgTlt+BA8EXLhn7Lr5D/afjsc/a5xvMmkIEElJ2XzlTphwphcAlD55AScIe+EK+FM3FkZQb6+tz5RIiOZvN87Q/qNMJNekQ2U5rhDc4RgLbtj4h2BniZh1MNGzFoqbZtuiBvJu6EPo9Dmvg6K5cDhoCCxEVRO3EdCIAARCCAKS1SLWJBCiHEtceQRJd6RbtBRgDnyCjxxELkxT8cVwV08nkEEmBbE5CLoqs6GQztD3lQodwbIAMFzmwiGuScNLs2cI2Bg6ReU7UIuNR+ZAsVDRKEFoI5DCEhYXGnMVDNrmpRDvNaInSTa5XVAWL8Hr6EiSxjFmgSOhBg4VHFLvpMJYBABXkutNhOBQt9ANOkxd5PJIBlqWwqaF5aZUKeby4Mulsp63APWhjKvPldDoFZGm2m4YyO41GHbKYA0hFbsvjEXu+II+nsINx4TMYCGbhMM1EcrkM7tXzCxVsXLgirawsbe7U0dux82t7c+c7v/dytze8fOUKjWA44bjpodtTLChx0BI0e0YgUZ9E1iFMIQdCDtwVB25+kO6qelgp5MCzzQFEOAKS5GxwiU4e+TSV7npEnkQ1sEWn1UAE4mKCeqBUrFTmygsLC82arBjFQvnkyVO1vXq13qjVGkAixCShi8EXdghFDB2LYRSNSMZxDBmpMUzW0bPEKvFpzOMH9EMdUAtXzop3oQuKoIngl+EYgAVbsLFmyUE7HuWoslw6gyORyW2dhEXn6p8ZRT1AEc7QUgpJa8SWrYmNTH1SmRK6JeO3YBCsfpSIYv4yScuJHRRyAXvJ/0gV6JJBcffWE4GCeEJCHP1IErXLcKSNYUJQuEUL1Ui9I5AkZyP6Fu7TJFybFUupnSWCIJk50Aa3KIioemTjUjhpCJbtj5ZpMNBQEQV8SaED5Q+dQxgVcHvG8Zk9avANMjBKri4szXP0aX/QrNWxc1XKBSI+w2fyzlhcf+BDJpnBqgcPwEAsB2yRvYyITeMklizfZpfVUbh1tHfZTKbf7VVK5XS3c2Nze75S2t7d6zTqscgQrdK5s59987vfZm8e2rv19Y3ltdV8oQSn6ZnJMF3nMxOwDAsxeS25DVPIgZADd+TAoQDIvr3u2DyscA8c8O+pgw2OIKsPkkTJ9Hv2Dl+yB9s+EVM+SORhJS4w/WkwWRf/CDzgCFIQEcWVfK/PRnfyggEIS5QQL3zpebQTO9Wa3IBRQgwG/ODnEaesc4tIRvoilBU/CBHX7/I3n80i27lxkxB7iDDHcNZ7OknkHQR0MotSIRoFQ2FwQZmDIGxjFRN6iBmgoDX+xrLqKABjBPyUFvQYDXNoRLSFa8QOMU7V6PT7aELw6k1lsqAByKYHZO1w2GUuDoDcOQkdSzorzAEK0buBgYdI0lP1g70qIAq2tfHQXhg58lCgOMvMAl9mJmhQCOOS1DXcwVJsciTAHk7Hsb5jSlQtaGO4V5RGgSEakAj0bNEU6UV3DnpAnqagojOgC/2jwuJ/EB4k4X0Dzc5hpoOpEa1PEtAgpZL65H/6BwURzRoEgx2P4zd6nQ5Q6eTa2srcHI5UHcX+6eXyWVx/6OTaNU60beD9A2lAzgRMTCRAQulcFhAJcGFlcYiGLmIIwSE8rxLJWKfZIOIAMZx6zTZavo31G4vLK3OlOSoTJXpzt7p948rJU6fRyIGucI5Gl9fGXfr8pZOnTxExwZZAsZoEF3GaMkdvTTBh55MYaOb1m3D+Vu8xlW/z9FYtns6ye2XCvda3D4J+Du1r6OX7Cu/I4oP90ORgJ7es5p37uLepcEcanrIKhwKgp2yeR3k6T9br6NT6B+koc/Vx0QZnEKWMzhfTLJcwRYFsEIrgFeRiv4cBZWH3xvU333x788YNRJeBJ+lxkIJRNBbxBCET6UF4CNNMR7vDaE7ek9BFPIFwZSBtlLfz5KlMNTQPTgAaFu3yMsMW+ALdDz2wiYnOIRMjEdvYBaAYdTTstIBtCpAIMVwBPuZrrIk4eUzM6aFzKpD3xK1nMFYJ3SCYx4ouKL2EMApWM7RVvqNNpjHu4trLBcbQjnnT5qguCdwiO5y0MMAyVFYaEY2YFDe2PV7RdwSDMHvJg1te4TRDIyTPKe/CxjKwJq9toB4U4H/DDrdUIpJk95wmKOd0YwutDKDBQv3DHSodS0WBRfRsLAWocHLtfLnCZrpuuwW4QV2FM1BSxq96rdFkOUSjnW9KnslxvgXgyQuZL1vfgUU6tQ2dD7q3ZJwjZXnKKhBhqIcvNBa4Yjufz6IaI/zh3t6oVdsjkiKe1L1eF+5L35eAATH0QPOLc2Agi66ZxBQo06B5QDMuQBGOi42GJo0dt77cscKtm4Wl98WBB8XtW/bDiwRR/sjzd6Txlv3csdVTWSEEQEdiWQ++uEftHXUKuR41wo7E+hkRMMelu3MJRjlSAfrUa1WMX3hyAH2wc1VKRWQwQpRH16+1ACXxZKrV7NAcYIE8Q9tBWzymkc4IWvIU0gSVBhIO4UdNbkmMzFMiyZDxOnwXgg7QtlDiq+aYRjokg1MMR3MQD6MBdUA/lFAZBEYF+aiY/cspcVWWEBIJGUvCacYGZYLqZ5rAKgw4UdXIymUGKZEgQxU1tTPLojvSXBYv+pPZyfCi2lpVgvMA4UzFg4HN+47EsYWh/vEKUnJAOwek4moDHELmoySDM2hA6IQYP+ofvx7NLRHB2ofOZRRPZZLy5TGIBjFUoJ/UGFZE2HvH3Pv9HmxGQ4UKj5mCZDpdbSHDy6dSVJgf6hPLAHiaxKMnm213+zc2N0GyppnSLCh1HoJKffaYF7F50RCuZtjmpxPWYvgsATCjI+0Fg1Qa8krACsxhRMnklYD/wCxCP/OIF0ZxjRIpIB6PUAihL6QCIBZKeB1YEN4IasI/5gVTDn5CYaMXciXPoGF6NBw4yHAvCZYDMoIVCZbpIG0H+9lXJ+jEyw/rKujHCZgdfV+Hz8htCICO6EL7Cx28pkeByuAz5pkjSOFj5xLSiCVD4PkXjbZlY6mKjHc2t0ESiFaYxlPoRHpxpRphgQA6nBmOOaZvSg7EGB4n1KcmohTzCjKPjmhLogmRhBmIEIhUIDAOAhXpDUbhEeV4nVANDQ7nVCAXKeGRiUY5Dpuk1HcuBjBQFyT5UyqQ5ym3JOekCLbeeEoS7DKIJjoMS3FLhlYkTEYyP9mkKGfWqCEYj65kJkMpJBQ0sctACeW0BabRBSjA+2EInWmKKxDaqjib0VXOrnfq0MDyUWJGo+AaK5a0kApaFZiKCUsu0FRkJOMS80bBJeUJSXo0adUwyfFUFSwmJDYkGAuIcQ9oqBJW4w8gQfBKx6XJQSqf456AhI1GLZXNZPPZzqBXr9ZqOP3Ac1tWbF2EcWIUWgNQ3IMbTsJDgRjzNOchw7V7I3bVs/8MzQ+0YsS0g01YbhYxnsum8bbebYKUOU6jWRov2GuQ7HeFgNH9QDwdwkytvkYTjLQk5Z/uLcFATdP47FdfU/KUez68PgIOzDL/lkvgK3LHRfF+gjX1rrzVvsLbT2ofPUG3t2/1tD4NAdDTurLhvB41B/zLSFhAdhCJGTKIQPQK/HDvtDj/u80P92wmj9BEhkEfMAg9UDvRurZ+3StjZEG/gP0J8RYB/bA5CyxjR16YNJWQc6RCoDz0DZViAVUIFRC9XBmUhmSQwXiNyE0Y4GJqBq7UkZ5GuhPUMQYKDB55W5rg4gLhiGEjXxUYiw49uZHJvzF55MlH9Pk6Bxz6yGVHLjSCI1bu8QuR0BLS+iPoAyYyRIJmZmI3hOCk8EeUINFQL2Z6HRQdJKAPk7Tz5bXbCncd0zwBvoRZeDgx22mfmjRAoD9hpz6xdprJcaJYKTGudaTKnD6r/3qEPCQCkCCgOIl3EvGisScNhuAXDiXFQxyEhEqGqaG1o8+t3Z29vSrmRexpcteKxXvsvO91UdKAKFF5cfoJowDO+Ec5mFUO2CA0IB173+ztII+KCG8npsBR8LwJ85U5tFF00ugRp7EToGRYzZvQ7siyCUNYDoEqw7UAZRbTd5mRn7CXejZNXyy7Cy9HiwMsDYmXxMkifzf07avmPewrpJ+DJXfT+TNYJwRAR3TRj/gbHHxuyRxxUh/NAsMEWCFxbcmxCIVIK4w8SDVEVHBclMLhGbBAiTOXLwOMEF2CSgSxcV8ZszWhB0LUEYoGQSirB8EQEzoHlFuuJPq3IdC+TL5GGZwSKuMywsTpjVtqIs5l2Uok0XdAJQoQAQRkPVBJgnQCnpgCstYGwnFHoMr1LjSnN24hmwx9cqUyyct9vtxCCkN6HQ4BpaYK0YdM3xNKPFFH+Mx6AzHwFxUTlZWTMZED2/FyVtRmmKAmwARLgnugKZ+xb5WXMxD0UMEsfzoXlllh80qM1AmgiK1raKd0xCsoBHbRLzEFaKCw07EYmrZUpg0GYs7pVJqdWcBEKMELp1DAeSvaabDDK5JKZ6EPhRz4g8YwRKetkojqxBAGPSnUUw6LtQQzGQneC4hiycOMGdU58CwKmEZno+o8OJ33TsNSIbe9OcKZPJ2MK0bTQAudSsvalS8VcbOmZyhkaiz4hI/SOGpfmAHIm0sDe0ni53SxPOPl4fUocIDVcTJYmiB/r4Q9kGX9IgTcK8FHqn4IgI7EcjyQl/hIzOSZJ2JW5GCByaYze7vbu7u7CO25uQV8O5BzSD4EE54fCDb2bVEIRAAMdFHAmCsPcgzpiXimAgYa+iRgjGEmHSqO0CbRD2IbfuPCgqsQNcnzIuFfQk0MKkIBhLKZmtsc1ggASYMSz8jzB/OQnKCpTzVQygQKmJMQnjWAEExsFBIgEIKoRhO2wVOZRD+Ot6gAMZRwK9Rgiaf85ZE8gMibMQvyKFGhPeVWFjIaAknYjBZ8DNBQqRnQRFM1zyeQjex34I+ESQ1wjToZKfSRYAyYzq4+BG7TkAQvhADBCokIYChOIETBESXUU6A6eoAYJgVz4IOPD/tihIhW3KZ4uVDkNDEBGrbj4Q5tO9LZKMdyEUmIfkBp/g8i2enHoKAZFhS/dLqlE14A9tnBbQEvvM7pnFIDYPlioZjL1mrjrk6vH9b3qhXUTblcut4s5LLFXJ6Kcg9Ks0y9jNlDobnTs8BCSbazKYoAHQLbyKMdsyncBKY0h0Im5fPSsMZ2MmF67BzQi23Y1CnxfLhAj3hdQgD0iBl+i+FmPwa3eBwWPQkcCL65WE1PyB6EK7STAfHgS+vghjxijIToQnNx5epVRCMusp228Ae6B2SZVAWIUqlCFI7ZO+RXfpfzofCBxZlFBi9tKXK1iCCAxKFCFKJJATzgV0QrZDBXtpGj7EEDJHpUdYAbL+H4OP8LyxNl2nKvfVUWUiiXpRp5RqBtMiUxj3yGEpL6DyLlGFlMkL/Qw1Pl3aRllSclk2oT9yO708WfBnXU1gCTOKYtYhIPIAYq8JdCEn9xnfERQGs8i8SlIsL9edohtDuOsisdoPaJ0yqWBEGm4vKZgrniFdhL+iooN5xksETUTGbKcHAvl9c5a616jcGkdUJzo6UTbIPR4DuK4RW3XLFvsqagJdQ5adQ4lI60fFgUKdfspKCi/gDISTH9EyyRhv16DZ/nq9cuz1U4Vmyu2WhHE/2FxXm645SMTKnIpABV+UISyFtrVBkiPTdP95DCNZ3xs95YqIntFToDpmlOtkazhZSE6ehwYPr2Tii640oFa+oNgrUOZuQVgtswcxgHjhwAml25g+t62DSeiPInaDqzq7CPt/s+e/70CZravunc663P1K+wwrlBJ5QgpbjlClJB4LkkplyHG6RSuK/iRILcI48nCK7NICGkL7/4CayHJENqAi9ANXYaqMLoAYBIPKJboAjiDTWPBLaHFEpKrLbqDexrdEUhTRD4mFUwnNAEEYsAhjYRFovlMllwzqDf5VQxBkWUIkfRNJCogOikN4Q83dOWMDYIZkZMKMBQvMtubdKgmx6AoiiQPshZx7hQByagE4wzbMpWbzr1HIOe8tSlpnOGW5XYFCihf67Wg/LSkQjrWIkhO+EudEGmoAJIguWc1WplSEWOzzN6DryOplRJI8V0pEhCK0YUJAqkCwKzYMzjQDQdoiZcQijIgTmtW4dAvW6jjvsUZqVStoD+BgMlrIAMGNUmvBKe0ew+N6iBm7mWbMRudoCIVEEcakt9RqQJyWMMYG+s1/Zge3FlqdWsw0C4rtNW9c4kwEC4c7Va+InV0BTiYQ1/ut0GftMoiOKFCrdCvdkMK8CaUp9qej0I8416Ks1WeZXDWEq0ZBbFABYxUYYQHyx5JigkQxInp/iS29m8PQ8vD4UD+/jsi3X7kYKlpNq+5gcbUuFu+pxteMc+ZyvfR/5e6bmPIe6jyZEDQPcxh7BJyIFHz4F9n2e/9e8driQnCZCBfEJcI5nIUEhNNm1xRUAJsthXFaKZk6rYtkUFapq6wYLdoCMyr2ekmjcPevaGbvBSb2YUQwpSjiDkKj0BkRKpQXjiBMY44SG2jLGFOo3jD0Anoj1H+OGig3BKsnmgFGdjCWNNKJE+RhAKMeySFaeZydwMWvmIToBoMN8j9Cs+U4EAVDDmoO01gysVyDuS8LEMzMAfoSJaUQgZQQUK2SIvxOPEQRXwDjUVV7Ok0Zs6RKkTIYa1YJcpaqSTYhcYjTh7A8IwmxEiEdSIcxaGMoxj/WhXPJ9Op6cjweS1QxKkGMhdnbmTp9xjTGumjBOL9IecXCaCmSPVVME0f6wxGTWn52khCBitExgYbtMhVVhTQKAYK3Wg7Jio7tod9oDpzLhWo7k4t4LLD5ATiEPnzALwitmUboE+VNOUFRGKJDxHkEYxyqbjfHI2UkjmYArq8Gg2f7BmWPLIOHCvC3HY4j4ygp/cgUIA9OSuXUj5Y+OAf+Mc/N6hxAs9IxFox6ASRYYjMNkohJjK5HJ7Ox2kFz/lEWYoIDrNFl95SC92XstpGjMTugtJ4QFKBZqAFJDW6B4MTGC3miADxCUwgbYMZ9Jf2g2ELiVAjnFP+7l4xOkYaHwIQdztEFpmkEpkOBadaqgP0EshWbGtqVWMLdllbukPPYnrSvDtxfRDt1JWWMJ3mD6FFywxlmrahClUyEHAk8BABG8hI0akIeBV0Q5JlbePqBZMQsPFUypbB7rE3KjlaAg4B54wUyBIismosil1iJQkPAdpAKAZ+AICpAZ11ArGkJGhTMiHCzo6MYwheIwiCCWQ/MfpYognOKF6wIWsGf+n5aCcAMewCrCOVcAXvT8cgAQhknkRrZGrelHMQ0hw7CGcYVMT06jAO0BtJo4TEusFtqSrdod9YUTfHnYHWXN1p8tJTHCcfgRuUgl83VH8wHLOOUH55t1CDEopB0PsGqv1dH4ZCFP0jCcn2lJCEg1OyfTKrScGm2Zv/vX6N+/D3OPgAKvgq+PLEaxUuDoPaTVCAPSQGBt2+zRzIPhi8knOfj35I7+iyQG4IANBBYguZBtneiPCeUoeQwanU80VSxvXr/FTnpPDwTmgHAQke5bwfUYs59I5oR/b9ozaADMKie3ZjEihBCTy3WAB5WSogzRPIGuJtmdmOByrOfYLoYmXDxGH0ZiwGUyBhsajVBJ3bELXSHCyU4nm6Dzo2TukNRlEKXMEaXDlERYfjl6gnCwlQQU9MRWIP3KYgpVn0hzk4c3VhRp6W65+6zyhrZrbjjZmZ4+mBhpz8+GZ2luiZ69P6B+UVCQIoB8ywVBCbsJBkQQ0C+XEBupPvlVxmAQnKSAmt2lWmDvaL51QoX6inC4Cb3kkkoBpI0XfYRx2zTmy08EZhDnIyC8HtmPA6kei5CEDg+FkkqbkwxuItaATMJD7QNMtKigWYAFEh4dzr8f7gPVwa2cbSAaWkWUrnYYJ1ITOjjYC9krlIlcWnRKa06GvF0yhpi0UA4kDzk9j835W+3S4BswPqoWZo8ABli8gYzYfFIaZB8iBIweAZpfcP6U+29nyBzj/sKuQA1+QA/5mct0nWighSUjaz3GOGcfTtVgptzGxDLvNdntjYwNBhZzD+0c2EXZfm2MH8sw8bfGS1ukJNEfsqXOLbmx9QjLoRJufcWBmdxJyFbmLFOQB9UEbToyL9vm5SiGb057qXoft3mg28C9Bb+HSmu9bKYpGOiLUS4A8dMIQNoMJHqJn+bsYwkCtQmVRgkWHmuZ3wi2Dynon2IHGg9pxFEhqyCMpkthWJggz+WdE8pTkAAuZLJ3OBOWYEkkb1k2ZoXIADF0rdpDaKCsFEFe5OE2THumZRlU9A0C6kTM0BBoQkvuzfGVwfMJlGb7RmhmJidEYUZjYM4YxkAnAedBVPKPA3DjdEFlRG+MM+6hvb6VpYgrTfjlmCmySWYsTZ7FqxRW2IA7iTHBOxYBN9cQDUj/ReKfbQm/G0IAYbFhsqmeIRr21m9oj0mJCOwXl1wUwKrDKxiuoY0TCItCk3ZfTmNqSDKfylCyaIDIketZkNfObbyZ5SrzQM+E15MCzzIEjB4Ce5cUI5/5kcSAQJ5At0WryBiGElOIRVwQ6cgglEE43CGIMYWiAqIYuAahBWKBxr8UR8RhWsjl+7pu1BWWMmU4AGaAfekYG0yfS1JmDQEV9MB2aQRTxBjxDt1QDo6CfkGsIm6NRpbBjC1maRgfEUyLsZYscOKUHw2RSAY6BQb2OohAxASQuaAzZiZSkK8iwPEPJPIf81mDypxYQooRyB0BukoMA3IMURdDnLvwTtRM05NRDTUM0ksd0wtWZFlx9dsGVCiT69IyqwcFp4laUTAW83/pDK1TWumJc8cpuuZjpS9oo7ZinBzrV+piLD1eMWMlohF3rzJBNc9iWULYxV+7pB4bAROe/wKGwp1kYpy4+fsuS0xUMdAgCPUaS/L1gr/x++l2wLFi3N+4TA4HOqQ8xOPFwhYe8MG7sg+cspRnCUhhGyXtNcA+u1gCgTLnIoO46ZvSjr/LJileU7EtOyb7C8DbkwDPLgRAAPbNLH078/jlwULogWpB5gVQOukaGUc5PdyQSSAWQgf6AInoAD61zEvjGBnnKmy3OaZdQRX7Kq1XAJo3UxfiCcghZTVfeG/XBHxyFgQQ3JxbpmRCEAltoHnQS2BivZ0DKsC9bDDYitD9I10qpIL8WZPkQhU8CTRAexGzaBmPhj4R+gp7pKpEW8PJkKhnpe5ggT5ng5Kqzt+Rg44/Ik0A/VEBUm8uN1Te7GJ1KWQMKQ6Nj//ACUhJQYH+VKWi4mNQGTk0Sz20wr0n/lDOAbg0bTet5s4m89z9UkOmLPvGX0j8ViyCu1swqqNDXQshjCCTpo7/JsBFPPslRfJ24nYBLeGqxGfFTl1JL8MtQLxMAj450sqlWn2NJbGs6C6RbY5pTTtwgdudxqC3rinMQPAeU7tWqKIdw8SESEJvooUo6p1inWFkgD8Sh7aQ5up92m/eEbn25ufKI3nAyorI4zOwsdhR1nACbq/HHc+E15EDIgRkOhABohhlhNuTAvXBAQtREjjfi1kv86o/43Y804ie7izEgiiSfzivtsc2n2arjw4PfSbMrnCQAExmgePHK6CGoSXOi9LE5iKeACxQGFm4mMRj3XM7RIZgJmxaxBtlzRnRphKIqI+rZBi6PaUS/thoBHhDikt6csaoDvzCuCVQxCt7Q2XxOhI1HaIGoxDZ2yKAfIA7JzE/aEaYkU5SOHY3jX2yV6QRi0DbRhGdcSSAaoQCDLEyMzmk1eWSmGUpcbJMh8VRX6Z5UjTydk6EHSuAPV9mgvNoM52lGIUmD2lM1mSApdUhX6hCMShXThlGB4qHcpEGbIA9MXWBYjqElCnNiwEkcAEnb2yWEYUuD3xSzpVDqn2gCRQ6hmvFWdjrpkK5QsxkuAc0o6pI6F6YUbfTDy9DtoPzLt/uYsAjOFMUwKh0Pu+45iwOHIcBxvZUZ6dzTdEpRLuGAEwDe5ag4dEj0D22sJkvD0pPxalxZYYYgMTQNnQBoCDLUCdPTxwHesYOTYt0PFoYl+zhwpAHQLdd13wSeoNvD3sgjO82AsFnKg8JZzs9WmC2fzd+y4WyFJy6/b9aIHGQSoguRxiPkVqPR1g965FZSAGgiyeyE1PlyZT2fQTFTyOWbteoQL5K+rC2SleYOAspoN1v0ydYkoiO6+QNzlga1gMLyaGFHt0U0BmDhV9IdRlo9ubNkFfE4hWcsEprDpoAAbGlKRfFFASMMsP/ki8VMVv7OEo1IWaRoMk1UY/ANx20gYmWxkgQVLcCLbl8u1WiGsNYRZJmaiGqZ29hNZooVP/XCVtDUNg44ppiGptqzJW3PJDELEgSgiKKI/ORKQ1E0STfLb9rjBImoQdvga990HxPLo7WcVBGHzFmKTXViKkocphTj9K6YDpQf4+MMWMzmstGdTZagB3/YFWYKHUEuG4E6hKYcAnrwagLGsUx44dAP3ME3nWBNME2O5AlpaGALASaT2QzKG/RIbPUDAhL1IBrNElqpKz/pOHERhLWiEXYF4oktTIkX85htf/18LBNL0O240+EVwuKGqoi5gNJkTOXkDKhKxHUyhrRpgtGKX8lCQaGmxtymQYAgBpzEK0fiKcy3CmK1Z7TINkfjWHh5Mjhw86W/E723rGkfnTu1vLvn3n/QYZAJxg1KvL+gfF/3+6rte/qwb480AHrYkw/7DznwwDngn2c+7fwQR/BgE+FXO4UILQSS/zqX7ImOwSSUdJsNjzKIuwlyDrk6sJM4EWZAEIVtHkT6VgN4RH21RdlDzyO2Ro+6CEDbAYRIRVICg5CW9EQt9A8oLdjXxMZpto+h/6BDbDqE+GPW/n1EWzJQmMnnQFGIRm7BE6iKSJDNLRnMZzfDJZsPEEFn/BFdCQdZQ0rAOFxJzlj1IsEsHxcK6Tl45BX8Vo9sLOeeP+LqT4NCv/UO/alBH/k8W+ugc+9P8McJQtTLcclJk+JH8I4d7nSSSqbboybYCEeqXAbw0sHmCDxRTEmgAvEWURMZUOuxFkwPzZgwCzhCqIIVwaapI03pnoaMKUWTEgob2Os6MCoKtZjDll4KU5hxOIdaxTC4SdelhSSctDZ/MS0KiGAtt2xeDLpCEQS4YY1YcDrHwVsVCI5g58TRj2qaUYw8CUogmw7Jk/GryJriHgpJAW8DnoeZkAN3wwF/wW5T84l4u0IAdJsVDB/t5wDv9P6i8N444J92rggYFzaOeHB2lpQZDQled/XqVfx+5ufn93a2NjY3EE5UZvv0RLuD6oWzP9kUTYw+yS0JRdpSR7f89Lcd7+TZqcSVCEMAIIQ0gzIiwpcyyTOdDaUKONtgoIEApKbIU8C9GPIcVCTXFgS10JViM1IHuetkc4vtiE4BAQS+oaHEtP7Ju5mnVEMScyUJKVnp5Ja8I5EpT1QAHVK90Hji+TMrdNnTZXUZVcoJxzKTEsr8feOBwIy570yOT7X3cPIYLgAIBG/sn2CImkrfBAiQngP4xpU8dDJpaYAIoGi6K0qAI/TEnJIpmRflUzPAVZlzVA32aQoWIABkCkPAK0wH1GcBG3118FeHP3RIlbgsbpqJ/RFCYn8d/aMT0oFiGDGNvTCc0zFAxO3mmO15PhUqD/DJ6oPNunHz0sL6KZrpd2r54pYXA30Vq8CMacH6ijlSFPFPjKIOih+njQo2u/CrHsaE6UFywF8279Ff4AfZ+yPpK/xUPBI2P5mDzL7fT+YMHjXVfAvANK4kABCABDnUkXMzp33pNFNEFwlZeOPGjajJMPIAIKphqhoTvceiCQONEJrUBKMgxujN2pn1R/1LyFHOU6Qw0g6VRL83xJUEdRFIxkSjtC25bK7A0QpgC8ILGVYCIqFJYDirIxiB+MRsRyfkuZIYjqvEvWkXKGd0xQ6SlBVG4SkZkuMT6vOIq1KAWqYlgiEgQMOF3rk1nXCJvLcNrmQ8WWfKkqE5Ga/sJf5UuhjDN9w6WyAWIlDU8BdUSGvBRNWiULhOJjzpgEQAha6TY6McDEGFwzSJjA2WweYEZzluRNgtAQrUV6UCIUKOGSkJlkhMSIyNbOwapllots8n+xHFDWK2XNwNCB4mtfs9VWvWiARdyhaJSQSKAoOCMe0l4Yg3VDzE/hGOoQ5avU6nrcWV/mmIfigdTXKDHxgV0u4HbY7wUAn9jEU1B6Xigt3CahL1HRZTzi1XltLrUI2nYQo58PA44G8a/ZN5eKN8kZ5DAPRFuBe2DTlwCw7waSchYLiiOpGIjUZQ/Jw4cYLoz9wihPgZjxsschH5xG/0NDuwUJAkY+1WG2komBF8ZbDDa6JOGGMLQbgiwSgBIuHCwvCId6wwnLzAiBKWdrA5ugXEdA7TVpZg0wruZzAIbyQOQmUzGsepikKKqY8HNB3SFR63gjqSlGg4IHsSXNgIjil4DijCnXAESIAeExgRUOsZJLcSV2Xoxu54Nk2SvqhRBNIE70iqPmWaP5U+x8q5JW9XuSZRDAQTtPHHnqHQukL628gJ6mOl0tgTaia4BBglmgwNkAFa4YbV7yjmMnWBEYnBKDHAOYmIj5AuQsFfBEpC14YvF4sVG5lXDaGTQGZDBR3gnLVcHufmnIUIEuYgOdMYggwJCIe1jFvQCm5FrGImGWPvGJPoDDosOSRBDL3ha8UovCds3iMOEE14T1h5mjMneladuBaa+lhWzXsMrEMHIGzKTGXInenqaE4TOmSClHAbppADj4wD08/oIxvw3gYKAdC98euZrX3E3+MjtS5IKYQQHEPe4DqLHiglwCEnDwrRvnCIAQqhdm0Hsq2apLU2r+O+gwg1pxOXVZJm6grBbL7J8vu5mQAG2tPEoaO4BnESQkpRfJCL1GAsAAu3okEuLFixEPbDdI7nqkYF61nhEJGv1GF0GnIl2bAyGJF8PFWAWvCPpvU5OcojT9Qko/pTxGM1J/4o/ujmFW9nw4jedtJwquYJboPRyQD1oNzri1YbZ6a5qEKxYk+EFQScUPvYLnFmRAdgOJAMkXbAkD56Ihkn5nOngU4shiEwGWcL1QCuoRPCCOb78Hr9DsdjoBpCiUfEAnlD43zc7WBRrHNwGGzFthXNA4PQ6PjGLucQ02ehYW8XlAJyMuWfsw9mQ7mrnUQ22/8UwEAKJFZEUyBUo04QUdKsxiAY6fZYOLSGkOeLS29MjZeKFgJnppyjhH64MgQl1IQwL6QaJQHTqKaxwhRygDdu+iVAxvlxH6+Ht72Pho9lBUIA9FjYftQHfbJe4iPFTVjnQAepA2FIHa5gDjao7+7uHTt2nB/9+JkAIxBmLsO40gR0pI3ZZkJBRJHQ0UzEOJVjcexjfK1QzlXCbIRFBldnbSBiHAU6tsQjztPAQ1eSV0ee4ssiQYvhBhikY61wYDG5SD8ITmgTzSAFFB5TqckjucqY6wxan4nsNOOXCOOrcqpBoaaP6xl/cyixDIjEQIljQftWFUOYjQEE+kW0U3XyT1YquhMl3inoRZ1PMRUzxDNK1FvvjAiJM83VSKBHo6NeAmHpCAzBxIkvNL0JGogGWxdsgbQZ9br0Bys4JIQ9dy02uI85dYRjXTWSMEe/PdCJIrKTcZwFPFTUyui408PihV/QEMedXrcNY3WGFz7mmM9YSh5weHsun8pwjHybHWTYy9pNHWTBatK3LGEgG1ZIQQLQFA6AMKwazLH3QcsNSdS3lYdSpgb5EwTDLEiQJy4SqtFAjyY+RbE059ViCloyy1CHp858r8Y1TCEH4IB9pCbXgCEU8hYFt/syPPJWQfm+26D8yGZCAHRklyYk7AnjAB9+BIx/ZfhXA3lF3uH0qEhEhz2ZBojf4kgj5BYO0flMHtGFoEPr4wjGxBjtJMX9qye4TiUcX0n0pKeIM76BJKZ1/rnErltfQD/I1nwW7YOOU+BLDNkM5JJwFXiRUJf1zcQnAEgSF62IJZ5y64PKgGdIRX0YTJlkpl+XAW1keKTb6XeilUymQMdqPp0RpV4y27nnvY53S97xE38m46oTZSmRXw9jmTifPFWBBLxXcMchrma3s2ZSlvBcqBQHG1YBdRmws16Xb430K5ilQI242kRlY6InykErfEvSM+EEQJCgNxhOeAHiGIqr8Wi91Y6AhzotPKhRy2SJ95NMKLz2oEdk6URJrAYfMRq9EdkQt51BdJCKoPazTV6AM9lDiVQpdMqSQaQ0Se1epNnUSmMslPZNSa9QLMGSoQGCS9jCmCwZfzcYiHfDa3LlEVzlEYnRbUU0o9nygO1BqzATcuBuODDzIZ1U98/d3bQ9OnVCAHR01uIIUXLw5T5YMkvuYa/+YeWzbZ+4PJOCGwgVrsgPFyHcUk7CDMGMKJzc9ruIK566KYQmbK0CbCwtLdV2x81mE8nET3zbwB7vtjo6E0qCUc1pxdYfMtQZ9vC4VRw8Okf2o0TgBHfwjxAHJ1olYrKc0dCCviBj85n0fKUEBhr3uyliAEUVWw/5zR54dYhCSBqmKIWpbMZMSzi0SGDaPx1FTm8IVKCS5D1O1kS4EaVJwYQpmvGMryD1yXiJy2uhP9f08MyeAj/ELyqZbKb+RMdDQztDgwlTZt0bZPGcD2DAhSzEqA8by4ekKv80hPQfwCKHCzY46wJOEV6kZ8EfqjEK0APPqCEBCGEaYZ/tii9UqzvirDSdicrU7fQOME1yrsyZFTrHKxWPJeQz3u40t1u1xWKBGAPo4TY3t/v9Xg5uY1yTubO0O6qymnP5fHQ8dOyL+gcHZ4iBhwpr2euWx5x0i+ZGruytZoezN8qlHO+PgjGm09W9vUEi66CZzoHLO9t7qJo4sxUGsjS8FVSmJvP0alyZFv1TTsKUxrxI1GTiPOVKHubDAcrJ+JXyMD0LHGDF72mavB6H1bdP1y0eehN/tW7x+EDR3dQMyL4NPQc6vquCEADdFZvCSiEHAg4En3A+lqTgM0nG8xRS2W/d5TkVSyAF2fHOtVIssTeaOm3cnW0LD4AE8YQrK7dIKf/OwciCHDdAMBmCDrl1YWaerwO0EegD2DVPkBlGlIEGzIGuIhYtFQhqgz/zGCckRHkOxYYBF0ATPSDyvR+OloASRKYUUVMMxygUCvpAFo5E2lel5BwgQ1sJT5s7VCFr+TakkBLq+NUrB028nMc8pTLJq/lkgyZB5mA/zlvQj9NBTb6b6SWoqRIX6mZAU1cqQZMzwT3ekKEJYiSf5klPIknbsqQVS+g8tI40OJjpcDGncqmQx3sKp3KOV0ukFBEpMuyhTqnkczitz82VgaHlbPraOnENBpViFkd0jrzgkHn2swNL5fPcHbRa7Tg6G6yQGMUKBQ4A63ZaKHZYCRYdWxlICFbj3MOOe53FYbEuOcK2wAY9gdwx0AditEHfPH6Ut8nywkASefjPIy+HJ5R48jzXwxLVxKswhRx4cBzgpbr7zu7+9aPmPfV8RxpCAHRHFoUVQg58jgP+CeSjGHwaXYRwRQIFVf0W4xT+qdlUZnOTg7kQh3L9QQpyABdHYSDwQA/eleQg3iR9bBxADPnp4LArgGI2DXyjVYjgZc97LIXI4gQHahIzBt8g+sQ/hXYKDxOPFfKZUjGb4QTWKF4+cbQF6J+ozbFVCP90hpiHOlJKPj4JlEMAIgWqRgg66CE+NbMAJVBH8hWvF+2QkoyEWgibxTo09MTcBYxMmnplRTJmI7rtn3e2AJEMssAlelNlMIEe6fTPyTfmpK03MEFO5elTZbzCvuu0gprN9kAeHDT9MkYfhb5MCIy1olB2Q660sQ1TcKmYj3a6g3qqSbRJgiRBLUgSxg/6OpRU55Qk02y64xC1bD6DiznxttPZzMrcHLqhi5euEf5ycX6BkkI6xTG3uEXPlUsYs1rtRrPRaVSrbAbET4jt88OuEAz8hEECoHHZSXuDzmjcxmbqbwX0UIidi5qMruA+2jWG3pFiQRwacsXsZcyXopEbTdnkBHmqkVdtS5738mlZ+DfkwAPjwF2+WsE7eZf1Hxh9BzoKAdABloQFIQfuggOzn2HypFnxwy198PFGRGk/lrlrYDrBKsEvfmri5AG2wKCi3eAR/JTxq9W5GezNIqwwSALnDbQ53i01aQU6oRgdEY2waqHL4YCMDh67/UghKZvIoNfFIzdZSJfyOVQ+CXbKRziCgWMwFOSGrUqQkeKkBotIBBajCX6/wAGBLdyGTBXEWJAHRKAJkAutBvYh5kIJ9JDhqYgxFY7KpjP1+SK5IZ50s3wKWagQJCgRBOF/hx9oYD4vrf02qE+GEu9zttDzQWUqBHl/RInQlqudMDYx3MRxyGxp6lYrRQVohiG5TCyf6yeTNTll2XEZOfaGoacBsbbbmCE53iyTIGK0nIWyOYINsrWdoD6j5fnK3vbeTq2eTcaPrS6X81k4zuoXwaEEhern2ze2YiOiWRI9qJNOxNOVUjaVlK8PgQ/h/lh77NH7ANFYAmyjwCZIYjoCQ7Z9DHMlp8pBr5ezEA5YWQ5njlbZbKasC0+9kKkxQWeL550zs1evOVsS5kMOPDwO+Nt4f/3zDn+R5vsGDQHQPoaEtyEH7pYDfA79ozj7mSSP+PFy4QOzX+zu7iLGKpUKT7vaJ91BNLIZHqcTSpB/2L3YT2Sb2dFJADikF+ERia5oy62kmpGGCERtQJNWp6ct83GZbzCgdNtI1kgxjy9KIiOHWmRqMl/IogsC5EjXYKdesPEL3RLwg1aIfKeWq6w19k86Ho5pSGoWLmsRyVQgVCMJUS0q5I2rKECUO21GmuARdbyQDIVscpLZyjapeQmFxh/534gmqmmXljrjzhChd6ZnlhMTVI8a/sfKyaurSd1JRQoChc/sE8+rNZykY0EEcw1CCcSY0w1uWMEAj7YXbBKzIJ9BnZYHPqIEApSANmLJNJYpWF3IZZl9C2sWHjyJxHylvLG1tX7l8tLC3EIx31tZpIkAWDw6X8hzoFuavV+jUXN3l+XL5tLgVaFSTRyLGUGhElkwjhYuBjBC5QPzSc58ZyyT5db8fshIUceLxMrSw2CkM1DF2SmvZqdP4W1ueeStZuuE+ZADR5AD+97kL0hhCIC+IAPD5s8cB0yGuqCRUJm9Je+y38vJI4wRXdV6DaiB8wd5YEejLhfjUqlEBeQZ0s5O7/K4O+InhYIGYAHDUkAIRy3CRWhrMIiAofCmRvKxeRvFUDLV7bZwXJ4rZOYqyOsMbrxEisllk5xygQmL4VyaptPIbI4ylezEFIdTi0s+gu/hvw15DO3AiLmQB3FQn0Q1ZD+tsJfRFSVUYP6Ue150T0Gb5/2Wq4YIij4va51jXL2amszU1K0ecVGx/gCWpvnPVzz0TvyCAF2UaA792knn2MyQpTrG7YnZppPFaBxFXauzB92CmmNOIU0BbvC/2djYQjfD0bKCKdERyAelHogzl85Fk3B+Eaj64ZlPzp/95PXXX3/phdPNvRpHoNRGzUgydnJlCa901rpWayhOwXjcabfEankxg25tn6CZLzm+hMngrs5TeE4PHINKXmAIs1yv5zq8wH7qq8NiMTp0kXG0RCc+X5v3BEEyTW6dWbP5Q9kXPgg5cNcc4I2ibvCCHdYuqOD1D6v2CMpDAPQImBwO8RRywD/DwQc+uPUSJJCXILfY+0M+Xyz4b3p+viPVkFKIMSQWNbl2uuy70oFQQAs0QNaWuwkYEMLgPHWzYaFJof6AH/yAIaL8mEGE3oBM+UJiDqfcAtvrOUFqCPBhCCIBYV+iS8IkAsIYXd2O5XdCmgxh290hlY5ZKoEhzoPouWEFwCHZCUkOgGZ1OUhxI3ECBKmG6YwrhRPSZ1beinWPyojuIN/mqUvwKKhOCeV+a8Yyue5QyDlcasYM5MxjaTKU+pE6aNJo0pPV9RYquakcmsRLlF4NYMHcdai68tgI82y52q3WqA9bWTV8bMrFIvlqta5DM9hpFcc3qMCJXXCMvXWcYpEhFHSuhL9UrVG9cvHS8yePP3fslcVCfmcnndiK1tutVK5wMrPKJrA2x8Ebn/GI52B5joZv4g8/iOzubQOJBjXtbC/Pz7NYEAYNsJ2550pF0cn82Ek/3bmGUxerlE4JMOFvJXultpVJUei8UVVLQQkZT1Qgw5Xn07Lwb8iBB8ABf7VmO9r3jh2sMFv5UeZDAPQouR2O9bRxwD/JLnKYG7ckbj2RR6YCgJCvIA+2bpHB9bhHVJhkAvUM+6wpoQ7oBwyEsYSN3CakKVNr55fiwKCbkW0Msw0mswHVUOmwIx1jCjKYPdXZ+Hi5UkbJkE3hpBJN4lqbiHEIvHqgvQVjhAbsLEAlugb9MDQZzF74HCHd6Q2/I5LLXUpoigKIuUAehSTqq4lZxDzPNZCffM3RrX/ZBV95qoCWaArmgqeS0pAGUjSOUY1bv9oTe2qymSbWCkw5qWC3k1pBEx8oaOsZ1TSAZb1jbRIwZUZmkZPlSysk3QrMFMrkv0w2j67OTl+XxouVAWdwzkV5mGdvna0su7oaKNo4dhbXdTBHSrENIwSMXl1a+vrXXm/Ua+fPn3/x9Km1lVWMZUCZ7d0ddoctLC3OzS2AflhZHKXpvN3p1NudRruzvVfb2tra3tzcq+5wJD3aQRy50DOVi6WuDJZ4w/MySH3oqwYUZL5yHRuMYhmtEVPQvKZ8vplxWGic3Mccv70l325ZMywMOfAwOPAY38AjB4Bmv9pmeR18nmcLn478MzjlJ33hfMn8c+sClRkJO0ydTxFFoAQAB3YKROAgEW8TA7jd7A2642geJMQjVD8Ai24PjYD8c0boTiKcx4nnMXFrtH8awwgyj8DJdAbK4aAEVAB9ixucwh8WYwdx8CLEJ04hh+fzmWPz5flcssRhqKMe528Wc3IwoiPsXL4XzM5YoG4nlU7m8hLAEM8zkR0ZsykfLMaBVpqR7Swjwx54MBAZSML5VjO0AIBcTTGjP4YxgDF6iB6C8yPk22zKIVU3fMN2MGlwTO8C9KHQAjgCFgdoLnAvcg9rC3RswbAFWBzuAFKE94QCCJdkMl54BWQmPRBDyVmH5CN6Xm0topLTZ51RGc9mdSozIoNa8sNFQEL0JhREnKNkvFDIwZYkbj4jcGa022rGyqVyqYCCDS5tbdXZ2bYajxIYCR2RoUyIGzBAKpl+9eUXW+3ae+9+8Mabb/3Bv/43bPtaXl5GFddo1tq17XI+s7a6AgYFZrKkpeL8Anqb0bjebPVfOF2t1a5fv6HoUMDi4aBTq+IwtHj8ZCaTa3Q4jgNv6Egmhcksge6I7XUZHIl6vWa7Q/9s94+Cd9k1KJcg+TmxivCEjBaBMEvSfwlSK+gUVzygYIZ88CeJl1Yllmjo+enD8O+zyAHeBZ+2vw+eDwrvniM0me1hX8PbPNpXk1sfffb9vGXzoPAwar38yAGggxMOS0IOHDUOBB8qMsFHEXka0BkIfoQcXjjVTlvSBQ0LB5di9pKsjfbatgPIzGHE8tE+9yTKFSAEH3LEj37W0zlAiqbcIyb5L5ZKG4yI6ej3PsdFIYAjlWJlpZybL+dK2USaAxkSSQ6mQtuDwINCdAaEDmbLF+S58oYSWX0syCFWNzAXiEA0e4AfyUhT/gBaLDklNKc3T5SQCebreaeWq4MbCp05QcNJxpp5Exno4NtMV0GToPMgAzNElnVLNXji10m3dGJim1s9/FxeRdBMAmpaU2AVOIoQlD1GJ0e30oER9ygaQf8CEkJzQ30QBh2y325hYfH42mq72yWe0161urm5+eJzpzETsqDCHmNslONOt1mZn//m115vNtoXL17+4MOPv/mtr6MrKpULnWZpY3uzVt0ZjnrlcgV8xeqAinPZgsYqF3EEI3xlpVzGIQwMxHtR27qeSGdLlfJCKc++sc3dPQ6KL5TmiPDEZISABwLZEEAC3Mmji5PuR0NtF+T/KGdosK+QVTeWQCV/5Uc2uTLlSZkV+UWF0+XwjLPLnwb5oM5M0zD71HLgqC33g6InBEBP7SsbTuxhc8A/hFw9w3CIB/JITfLIdbw3uPIzHXmWGGrLlQRqv8s+oxibguQKIhADIkJ+4XuD53N/rD3Pksdy/xBe4Sndsk8aeILfMqoD7vvE/xmwvT5CqJ9iNr22NLdQyhUL6Sxb1yPDbIbAPzkoAQPg0IOMRNy6jAdC4ftMEbfCNviz2JlXEMadF8o/2H9pCWwo8UgVkNhmhNIEpcKYaG1E6PSXmU7BUlcCF97WH0nFAqySWgEwp36VI88eNgN0XuIM1IOb4EqKC9NeqOhmw6kotqeIdetaLbUKJAONynvJpPQmKhJ1EInTE0hI8ZBMhaeozKPR/AIwZn64tcPGulaLCITYDUFC3XKluLK6hC6nenGPMD+njh8rlEs4CeGazFpjCeMKPMrl8t/8+uvV3d2Ll87TVfG1VzGe4UmdzmUvX72yfm292WydOHECnkCDcGg61R+M2t0GU11aWmC9avWmwkQNWlvr54ejTm1zrbK4CLjhZDBtr2/WgD+sYrvVJqqTOokleSF4l3hL2E4osD1MYFBF78a7yPKJL7wPukM3pjsSDHX++K1fxTs3Gk7XaPYpY83ehvmQA4+dA7yTvLG3JOM2r6u/5CEAuiXfwsKQA7fjgH94Dtag3IUNwAW5hiBSsELJV+3NoT5XSUqTIvjASmaZdYtHEsZ8jNnIzOETnD8F0MHoNZSCwbQWUTZ0YeNAV8S5qnSC4E/GIkuI5PnyAq4i0v1wmhRnUQFvTNnD6Kk0O+2xj9A/tEEP5IHDSKITAIPWR7vZASIyvTlqYVS+OBB0qmNfLNyRpwfvhKv8ZcxF18okyEnknSd+61dKyPC/P4IGWWi8cHrllrZBfd36wN5m5upDTPu/Kb+Dob2r6e2EHuT/7Bekz8XMW8BMU5LFCdbDJNkdpjXKplLLC4tbmzsQBa9BqiBIAGsmlwWg4AC0tb29s7Ozvrn1cvnFTA4yMrilw0FGb7WasHd1dflrX/vqu+++/9FHH7Gp/rXXXsM1naFWVlawirKy8AFfazhPgh7Ztdgrloxn8fxJpObisWIp1+p2m+1+betabXs9VyixmoXKQio2zhTKcVyKxokcIyp+gpBuhBdpGE0Tn9pWChOqkLgwpwIuUchATFHjyRFKV3nRT9dF5Z9PUx7eZDLPb1P/863Du5ADj44DvJbB6+qj3s2LSpMQAD26RQpHepo4gHDhM+YfM658lkhM0MUPqMUfueABL+h4UttIlSoVZdca9NfXr1X3aghXtCbIXmQL5TiXoDpBWtE/kljoB+CkIow1ICo8dRDEPeQ5u5GK2dSJlYW1xUopE0/j8hzTyZ2EW8RBB+wE7skBf/J5hDeAic4hAC8iQkHj3gJhAB5gD7ocyh3/4P+iNYIexW+GIES2L5qUBlQjye9aTyZykRLEop7yhx5nBa3XsZ5clzTpy7AOeWeRF6oHY6BjgkmhPFQmFNgIQV1lvLlossQtaXqntaAJ4xpdgD+VqAYRqBnKQIBci7A7YljkqzCVRo8DO0ho5AA6qc9SbPPqjWPVvfrq8pKh2D4A5fjxY7vVvbffev/K5WunT59Gn0ewZoL40BugkHCGJMZ57bVXYPFbb731mzd+C8u+9a1vlIoVlDQnjp9CaYSNbG5hCczEEqNAYpkVsCCZZIc9RLNKpXRhIR7r9ofNdqvX7bc63Wq1WqtuN6s7pfmlbLGczheWltfyufxwFMMwx3GswOUh0DmVNJ0aL45MkTCVmXOBJNMqTtku3vpDcUqMmSanf3oX/g05cOQ4wOvKWzpL1sGS2aeH5UMAdBhnwvKQA3fgwOwnkI8fiQb61W2fTDKCGBbMlytAhKeNWvX4yhIbpte3Ns+dO4cfyf+fvf9+smzJ7nuxcueUr2rvu6+fwWAAYjAYAIMZkCD5nmiCodAvUuhPVLwIKRQS9fQeRcKDcDMYwhAAxwAYf+fe277sqVNGn+/67r0qax/Tp1x3dffJ2zdr5crlcu2snavSba5y1t/oGp/Z+6NxCHadjN892ONYmAbolla+2PCxv7/N5A+3RR9MzE9NXLm8evfqpRsri1cXmc+Z5JK9Fv8YOxU8IeNAARAfQM3FL6YdGOmnubFP586g0xROTP/E/IwHSw2Tqo1LcRCiSEfiIi6IOSSqYFSMFI2FGLL0FEVgaMgrfFGbZCWLkYmxLCQIE7wVHAIPNaWsArAQaw90tStIcZBadjjGYyNIwjkWvwg12XBFYMjKEd+yQAih583rN+7fvftP//gDVsG4lTmiVJ0I40Opq1dWPvzww5/+5OEPfvjD+z/60Zd/5UvIZpNQe5rnwHXS2sjFLT6rK5e/+MUv8mT//M///I/+5I8R+7WvfVWfwpidZflsbYMPYzxbvXypxU75nQ7n4Tl6xtYsTpxxbxOmsj18fpapqJnFRU65T7Fxfruz82xt4/GTZz95+oQl0Jm5xXfef//2nXsrq1dZ52TBa5PrhfY6PFQeKGtqyBCIYp4Yz6QOg/CMXcHUkcPLo55RbYkBlozymRY+H4NjD7x8D9Ahe5WeoIuOA6BeN44xYw+8wAP5m5YDgxnAK3KJFQdyQg1GIIYuxRRTU6x9cGcMR7+Yp2EI/PEPf8L5HcY2/m7na5iEE4QjjNNTe5Nc8qPohymVVpsFNAVAE1whvcX6GX/LL7YmmPS5f/v6vWuXlmd2F6e5CYbtuxwh05ElRlxkEW8R/WCA3hMMXRG16CMbM3N85AKrnPwWwbYW8zSBVLAT0ZE2+GhHduwoibqYk6KIREgjvikDlBgj3XaEA8S5L+mBgdmnEK/ZF1jBUB+mRaADJEwEKEGPwdRXlFWtRMYuFs3eMJGDr+Dh+FbgRWRdQX6YyQJ2IBEKKBoIMpZ/qgNhky323zAd192dbs1NdsHq7kduLiBSvHPnzs8+/nRjbYPple2d3SUCCu1h32E6jpWsL/7iLxCLfOd7/3D3/r1bt24hHM9zv1NrqoXn6QYETty29IVf+CJcf/Gtv/zTP/8m9x9+7Vd/lbN4CoPm5x4/e9qem11aWSEkipN6nOHShBBN51g9c3/0HK2GagPWLrt/iGjZR7SysEhw84yNSJub3/8ff/PD7/6PxZXVW7fvctZs5fLV/YMdgmWczdk6ol121usx6IZHAG2L0n/h4ZgQ1JnDQ08FFO6qelGjiqJcWSQ8X5TG4NgDr94D7qIj9sxxAPTqH9jYgtfLAzkGNH7HwIMpAyAwRrIp5NHjz7Y3Nq/cvsEXu/hbfu0pR56fM7axmZW9G0QaGl75S12zHgx4DFbcOMiX3PnLntUx3dPDV8gZwBdmJq5dXnpw5/ptNv7MTa2y6WeGo9FsSNZJdaZ1cCazPgyxrblZIijbgFUQzDLbwEWITAdgJ0t4GhujnjNhfCCdERLdii20IQmtEZUpsxBsEE3EQbKUwa9n/KPWxOaifRQtGQxFSxBvJNNThAw9RpKDIWXRmCxSlYyaxiCFliQwoKbIkchRAGBLXCV1WgFU0/amdnCElv/YAxQzY08ff/bo8bNrN25zI+L68/Wtrc2Hjx/duHmFRa7N7a32/uTK6uIXvvAFFq6+8Y0/+8u/+e//4jIBzWJnc4sT5tiGt3niSObBEgx9/ud+npDlr//6r7/xzW/tdna+/vXfuH79+v6TyXZn6/n6emt+jnkgWLjamzUwbOazY8xL8RAJxygSsGh+kBuJtjmPv84T5Krv69cusaz56cNHRGafPfz0b376/eXllbv3Htx950O2fc20F2jOTHuOFnEonoVJnEDkqidY+ZgIST5kd709g3wsx2Yne6nM65rDZ5S1ZsziGBh74HXxwDgAel2e1EW0k3fiRTTr/G1ywxnONYTEOE3EAEyiihwTqHUeBBrArl27xoDKosbGs50nTx5xAosRlLUVXZHDzcvkkO7PEO7o73KGZBZlOC2/yzHs/TnOkE0xMTBx48rye3fvXL+ysjo/u9CavMyXvlqadkJpe17bT9iEQvTDQe4dTQEosW2Iv/25rJjZDsQwBGoTrjYYsWNEJGTMiOj7X2E8w2QMuDRBh9GQ6XNhUMrASNo9K8aIb2qHUxsrSvIJVVVtfLWepoWrdgOpyIPEahsAxJFQp3DEUQ+YIHAHkz8rFv1U2FLxqEKU2myO8RQiAZud6AGpIU0EaOfJUMWsW0zZ0G62ihNr8CnZLq5jhkdRIDuStzeYcLl79+5nnz3c3djnYmjcuLQ829nYZnc7m3iWFq985Su/ytLVt771zf/+t3//67/+6+z/6bCWtbnV3eEzt9yuxEyVWsftQf/sS19eXF4lBvqbv/vbzc7mb/7mbz549x0e+sef6NYf7l6am114vr7Zbs8R9RKOMjVICMWUIPdSRwQ3tba2TuQ5zw1OM6393Q5H3PkWx81rVxBydXXp8dMnT58+/+kP/uHRz346x4zQles3H7yzuHyZB89KGe7iciAmunhmm9vb2MkmMLyGj1mqUxfhgoZIPHqcQwpHwVclMEDGG3BFkGmpNzwsRpJhk5Wwa8f52+mBsj+c2APDhVDr/lb2xuG6dBXscIpx7dgDYw+UHsjfLn53PE6AIQQxjZGMJvmryDpUl6Pv8YEFTn5xpwvhzSYnoTkFFtEPIxyfFPdf4NorzVjDkXhtZOZQPGHKDks3izOTV5bnryxcuXv96o3LK3xyfGWhtbrMKpc2DDH8oxE+hjZmAhYWl9kjQpH4BnswgJzVFsY9oh9srr5XESGalqYYwOodS5JjUzTkIZH5KEUM4Cm5jcASwvjJLIJGefkhxIQZMVhCbA9AEpUVbyDL4EYiU7LlQ0OyhEZVWYTG9OQ2T6YfTdBzNFx70EVLQ3TAjVkaCkSccgYhHiuDbe5O0qE7NkR3tra0c3xGB+gePJj/5JNPP3v86NETQqD11cvc/jzP19DYm7z9+PGduw/++W/91vrm5t///bevXbvxc5/7iFmZhYUlHhnLnRx6n5lpcYhse3eP7+B++StfQeB3v/23P/jRDw/+6x/9Wnfn7v37hKqfPXpIQ1DNXJHcva/giesW9/d2FjgmRhX9gOL+PsuUJMJXMLonk/ibQHZ3YnFh9srld9Y3t9aerz9++OTH//Dtn/30x9wrffnm3cWVywdTswsrqxzBD5YZraLq+nGuMVCv4DRZxN2VJ5GZ/qPWCQxA5ul2gHB7eDZ6mnlNnPRGmiurjBznb5sH3AGyC73y5o9ngF75Ixgb8Pp5IN/m/D4bJjfQ+4oPjA5nQfDw00/fv3NTwMOH7IHl73IGY0Zi5iEgY5AhWGA8YYjb7m7vTewvzLKQNcPnu/jK6Z2rV+9cvcznpRZb03OtqUtL84vzc92dre0DfT2KMQ0hfOuU6Icpjb2dXTD8oa/Fn/gCqxZltMLG5hkd1dLoX0cqDlCkX5HRQXsqlsO0PqKzbMwx+LpkEURcAqMDDo2XVbv1ECGgBNKJzUQsqsWEC7EHvARWal0VColDSWKdYIeEyET7VESuyR7N3xQ0vscvYjLqUFfxsiMI23zlX3XaSTWyjqRpIsWkioGMwLYIhGJ5iBYyN8aUWetgr7U/0aHN3N5EUMuXK9778IN/+MH3P/3s4ydr6++15haWZ7Y2t5mMgfGzxw+vXr36L//1v/pf/m//y5/82Z8x03Pt2hWCkqldfVWN831aXmNqcHKKY2I3b9/+0pd/+fKV5e9853/85Cc/+b0/+INf/dVfZZsRc2Z8ZP7K5RvzEwfra5vsVua5MftDBIQQBTuAEf3Iq3hCS1n73IZA5IpXFcsQQPNJslbr6hW6yTw3gD/Z2PrBP/7Dd//h+7fuv3/jzoP5pWUuLdjtdCbaM6urq61ZfZOVJ8M6W3hFcQzrbERCfhZyWDwXKSwfkJ6Ik+J7r6bxo6avHhP7tCo52u81TmMPND3gF0UT+yrK4wDoVXh9rPON8IDGgDoxVAAykJAAPADwZz14MAQfzIMwK/D06WOKz588/fTTn7HTluiHwzradewvTkxNcuaZv/IZU/ibnxsKV5Zmr6wsL8xM37qyeufK1evLS1x1ODc1uTjb5lNRzAZ0pE8TMKxtcQkiW2v5BhjDJrEPiVUtLFFi5kBi9cEHBRQxZwPaO5N5GkQ5FBGGKD4SBoZZBy2UiUPhS56i8vIVbfQzlGyWn2KkhJ2S8Rr6CGRgrOObAKrB0jRlTm0lMbD2YWpJSnwMnDKNL4ulHNirJLpqLBaB1tA03O9PsGNd4SBxBB6cmu5yLIyrBde7a3wSg7u2O7vdew/uffj5jx4/e/KDH//k/fffv3/vzv7eU6bv2GOzubXeWm8TA/3bf/tv/+N//I/f+MY3/s2/+TfMrMyyJX1ne3NrCy08Bbaxs2W59bjNGui7777LhUCwfPvb3/7TP/1TNhK9++77O6ycbWwur65MLvKZFC1nslFagRiXhesTuRzu01we8208lEnNFqlfhRv0yAiguTaaFS5iL5Y57z54Z2F9c3d6/tMnG8+fry1f6kABC+tr7B/CPDaMHbTbWufkIcZzRBqm2rH2mPwTSACRRRIcE4SURFZ5VHXqhtHtRaPYtUoRwgoGX+PGP8ceOBcPZEcdXfo4ABrdV2PKsQcqD3hU0BgQCSyjBSAAI4GJKGqginGX6IQbD9m/wQH4jfX1H3z/Hx8/fMRgNjHd1thETMIWZc2RaF6GyxHZscq5r0vzU3curdy7dfPy0vyVxYWl+bmVdovQZ57xkKBjQh9V1cl2JgGYNODINItc3AU902a0AQMRlmh/R5x7ZwzSwB8RidaAWGbTFxMUe2Eyx44gpl0krQsRyDCyTsdXOAiMGLwUOR0OYyKr51eAaX56gIbr+D7BkHiCjKya4QoVdlCw1yBO03yGijF6ym+Hw6iGW1MiG3XEjTYAJJZSSzQjgmidzq/VwgkOw6vB7uAsqvV0JFMriGJFHL7ACeyQ4bMhO0zNKfRjy/PKpUtf+OLPf/rws+9973s//OFP7959Z3nlEkHnzt4OoQQXW/JhjM/93Oe+9uRrf/QHf/Tf/uqvvva1r+kj7TvbcQd3iz5A5MJT2Fhb1+dp59tLK8sEPayU/d3f/Pe//m9/vf5s/fa9uytLq2w5otFz822+86UZmgie6FVEanzcFvciKqIjuhgh9Qw3INLbMJxtOxGt0iD2NE8/7zzb3p1854PPf7S4+nyju76983d//10iqp//hS8yR8WRfqajOFfIh3E5ZYYL8Jlc5zwAe1te8b/6WYuuIlMYTMgjxkgRAEdUJKRWR50I5fNJVajxj7EHztoD9NjjdjPTjwOgs34UY3lvmQf8i+S4B1jzN5EYrvxrCZJxi1GH1Yfu9euffvzT73znO2tra/gJLv5811JRDNhEMywAdXcP5qYnrl5qv3v35rt3bt24eokvaM7z9XhWaaan5jgPzfTO9CR0bMvl6JACIIId5m3afAKDTbJtbGBYi5gEkLsPY0lLg5e22GIVem0quUKh0I7pwVJFcsDMXrAK5qYpPsM6mOP5wmgufaSDhbUQrhoqqjFS0z8IcWRiV6iIgJCRZJYj3jrAOhQhbP9UG0Kt3n2WnzITkC4dkOP/anuvxRW7hfQhWJbDuNq6dcBe5D3O6HGka2tjg4YTiLCr+dYdHXr/px98/++//Z0P3v+Qr1hwoH2qOx0TN2vLS6vb25tf+tKXPvvks7/4i29xnc8vfekXV69c5sJorNIj1ldsmaTRTYaXpld5XN5evTS/8Bd/8RfsjCa0IsbkQS0tL3Mj0UTM8eA9opZJfZBVxkYDfSRet0QhU88ljs1j5GZnm4fVnp3nc7vcE7W4evXyjTurV27dmpr5+GcPH//d/3jydP2zzx6hl7Cs3Z5lPxA9jcehEDWeF9LsUpXrJ1i63kjnSZkEmlCLx0puoLK53lPUV2ayj4G3ygPZQ86p1XS2EVWMA6BzegRjsW+yB/K3K3/TwJAYUUgADFHkDHUkDVexRZpLXEg//ME/fv+f/oFPTDEss8bEoM82YRatiC+IiJgOYqMtp7zeu3fno7u3rl9a0acPmDlgcofLflj0igGfrat8Payzx1affQKj+YWlucUFLW6wrWN2jqGUm2MYEdkUHRZFTKaPseo/Rs5q+QTrNdRVix1Bqe9jcAwt/tDXkXqEYL+GX4Y3BTv6sx8uLZiRYq4IkxTBOQaK2SCdyiKuinEUKniDWpns90ipSR//3cZ8CrACsaoK+gjLJLROnNsGLKMYiwVZAiqGueZjWol5EmCCBe0u0lxRNSZnZAY5hvGk2CC9NLO6MbFOuLLD7mUmSPa4ZHl7YXX5/Q8//NxHn//Lv/jLv/zrv7ty5dr83CJzbc/W2O3c3elub21Ns8fr1776a0+ePfmjP/qjxaX5L37xC5z4i++66wQWy1pEwEwGEfjiUq5j4jzX4u259le/yrrUt771rT/4/d/d2lz/8Oc+z6dPiU64rFtLllzsPNOiyQqB8Ag7llm94sHzdHfYy0ywvMNCKoCugdrbe85Bsv329TvvvvvR51eu3ursTnZ2D1auXf/KV69scJp/fe3b3/7uletX7j24T3t3OBt4wAft9RE0OVB7tSKQDc9DAFI+rKNSweFq8urp0IPrhF/1aOPmBOPMXgmpydwB6tL451vqgcPeRZfpF203us0oboKlFGX4hXLGAdAovh3TjD1QecC/uo3fKxfJSSbQX+cRPRBP8Mc6Q97c3Oz2M+7f6/INqc8+fcQxIoYfVi64B0gs+qI4w9jEg7uX379394N3HnDU6/ICO0GmmAEg9NGcz6S+tMBMDwL5lCqbZFkdISyam+eszyUG4J3uHjNBDOQI1IUvmhWScF4yzCrom6ctndUXhv/r9w4AXCSQCHQdwRBcTP6gT3MDEdXl/g8HeTQVPhpIQmjMAGhKAQz/adWs9oZ9lxp7O1NvFZ5MskZtOrkkgIYED7VEaVRRJI/1HZmoVgQDBFheEYTlMeKzUMihOa4D2MWlXGjYmm2tbW9xvfL07uT2xvrVy5e/+hu//r3v/AMbdz744INf/KVf2Ow85xbD/f313e5OZ0cXGN68eZ3NQP/pf/vff+d3foeNPh988B5KwXPGfGKqO7Gtre76ciqxC5/+asscPrjx1a9+lakgbov+wz/8Q67H/NXf+OrV69cIUnWXISFnV9OEUKpdnLhjPqnLTVIdxBKiRVP4QNwsHYnIlDt9Fi9fu/3eh6vXbrMdjAs4D/Y2mRdcvbx46/aNTz7++Mc/+SEBExdQsQAWjziE166WB/FhJAArJSc1ikYmHgAmFXUdQeFnkJIm+1Ubwi3KmHH+tnnAfSBbPagzNMiS/qyA1PvaBED+Bevb/mxM39peZCnquLy90s4PU9pZarloNpd2Nmwrqy5yE0rbXgAfMFVDikAnBn/iGIYmtZTGu45DU0EGksFLYUrsHI5rdg4++eSz9S12eExzmIewQzuBJibmpycuX1r66J37n3/3zjt3b6+uLCG0zTe+oeACw5j78XYiYinmEnaYTFAkM7m0uHL16nU2lCCkRbSkfT1aydJ6Vr3lBTKPdjEUaUwyBvNIHFNi3NJ1goxPfBOC4IUD0hEUsKhGs9jFy4wPm1pEopBHjWRdLcIgPKHtPTRS24s0/nFxEIEIjSbsUFAVIYh8gy4YlQzH/AEjOgZjcdgSBBiBoRVxhGuhUVqkzQs31SwOYp20jAh3jMI4O2zTSpJ0aXoK7TwoPjShRsTSnLbUhO1YzefAiB60p0a1+HG2O8UM2rzu9ZnaYQqHGbrue+88+Be/9Zv/j//7//OP/uRPb929d+nqlT19S3Fqff8phnEwa3Nj7b13H/zWv/znbIj+4z/+U65iunbjuh4WNxxysCu+4sYUFutlXO60PbHGWtR0m4sLpj/3hc/xhY2f/ujHH//4R3/xx/tf/xf/nG+a8hV67pWea825LdoVRF86OABQuEM0pFXPFldo0kgu+wE3PTV7790v3Lr3EYTr29w/rg7EbiEmZnjMV69fac3OEIE9eviEdTrW17QnuvZ8PB3cQFPYtK2LNwXhDiWgqEC/sFVyhYj0SKsJpJj8k51gdc6OPKbc4Iy+UIkjMrSUWoh6kORUe7zUHfBtSI4KU4/z19wD8VjVhnzux2pQspdciQRoiM1i0pjRRfLXJgAqGzyGxx54tR7w71WVe4Ul/kyP32tt5mD8YIjiF4xPiTPM8I2EA67RW5jn7+8f/OjHfLiSbdF86ZtjOzNTE6tLcw/u3Pz8Rx9+/v37N1aX5lt64zPSM0ACEFAAsIcFdcRASFYoEd9VZUvH6soVdnVQxZivQEJBFhMO2vus2xR1AQ7vmojUYhzDKhJ22oEM/r4UiEFPIQtjjgIXTZnE+hR3AM3EqMhwZTxosQaZ5LL0Ef/Dw90yMoQkIMZMqzHSGhs5VWBojvEAZkmyrDKmpK+rxBu2Sw7JNCABcCOe05NQSqkCkgwYHsKjiOII7XC45lTkT8VmGobZwryyevlXfuXLnz18/Md/+s0/+fM//xe/9a9WlldZeeLbJdubOo23vrkx/ewx57z+3b/7d7/zO7/ze7/3B//6X/9rFsJgR4hXxLrb2uHOt1CZB2rNEa/OoJqghI9pfPT+B3//t3/3j//4j7/3O//ll37ly8uXL3U2Nrb31okjCWigJPQht2HETdrtzormVIvJoi0+nNJauHXvvdvvfHAw3d7ubLNgxsYgeo62Syts2qa9Widta1UUjUwjzS8srFy+QhGC6BXhMWJB+hgOafiLZjScVhNAGc70Mw86E9N52VxVdzyLlVejr1qpeaUunp5EhNhaoATV8g8lj6GxB9ID7mBZHAKY0h0MMorjAGiIu8ZVYw80PcBMQ6JyqGb7TpyJ4TdLu210lTNxg07JMOTyt25Xwyu7Lvb2+QDqD3/8I4YxxhvWMviExTsP7nzxow8+ev+d2yx6ce0PkykRPGlDD0EMn4Xiq1RcC8NaSCTmIohfmBtgEYS/4xnSWB7jVxmZvvWHfS5EA4wx8Q0NDT76tVf0ovCEiCaCM8UCEf1AWIUtwmjpSiMflJq6kihGR2IklnGm/cWymhwSJfEUiZkGRAtfJ9fjIMTWW4WquvBPNeAZpgKghE0qTTEuar5N8YTQigw0EeJwS5hkVMFJ0w8xO8EYr7mqKrlS+4F4XDH9BMaLN7SGaTbC1gPdTKgWdra7+60JLq5kUocbnz99+JhvvHNXz7/8rX/BPAquYZePrrXcO+DrYJdXJz/3uc/x2ZNv/Nmff+OP//TrX//6yuoKx+m5UBL7FohWZ9vbWtva5eg7MQ2PhieCupWF+S/+/M8vLy39t7/61tra7//a177KnZbrClyYuGJmjY7Ag566dGmRVhMGMWU1Ozt3MD23O9niJqCrt+4/+OiLc4tL2zpHr1UnbI9oI3ahxcl5mqaAaGUFjRiM0dwASRPoPHaImlu70bDzw9qCwEj73Hk+driqZxFBscLQus624eEAZCqUJAjotnoI8aAtfJyPPXAmHsjuhzTD7mbjAOhM3DsW8jZ6wG9tAh3mQQgS/Cr3+11vcwcc/LHLRAz1E9Odrc2nT5/udLYfPX64391hn88XPvrw5z58/717fNdrcQYBfIqc71co8tGkDoOBBuP4vBeA5YNHL9EPgxuHlbQ5iJ0+/NdmZy13ITKLcVCFPh5yql098CnJGiUNMwztmsLRzuL4fiYqhFKilgQR0lDN9hhp1w3E2Kcz2dRaILGeQ6vgU2b8IVSPfA0CJBgjTTUs9qQLAGlHESpBj1VmMq9E1AmC5Do0RpKrURnCJLC0MscpOJDYgvk2ngRhConJNsIJlseYqvkP//7f/8f/9X/94//6R5dWl37zN76yz6mu2Vk8s761TSjEZ97Zyv6Lv/iLB91ddjd/65vf/OVf+RVO5/ElL/YYsZtLLt+bIwDiGiG246AaXkIQahcXlq9cvQrv3/7t3/7ef/nt3/j6b965d5997hiiSwsnJoiTIOZEPM0HObuwzMLk+vbE0tXbN+5/MLd8ebOjrc2YzaVTXCxk4ZbPE6TbgGEGhukoaIiimApCM3D4Ux4sPQZxI0HQwJSe5MlkbeIlVFNs+o2gFjPQC46iExiSJUOl/ng0paij6HFp7IGTe8CdahwAndyDY8630AOeASLooe3KPXHP25zRVd+XjF2lTEowiDJw663P4oIWrdiI/NOPf8wRMAKda6sL7z34uX/2i1/8uQ8/WJpjSOTWH74BtjfRZqCYJeiAmYWImNZhiUzjFnrYQosCxgw2k3Dr3eLyyuLyEiOrPmU1pQ+J84+NP8QFMd2iqQ2lGGT4iQRsBlnhCSy81FU8RQ9IyBEpkY6mgSQNNqoYJiWBjzVwrlsDVY2MUC+qNNihy5qj/dUaHLUkIi1qtTNIYQ1KIJcn+RkYg7UQbSdR9CPqAASLJDFRYl6J3cGYyjRIDM8ehCVFC4jSEUxwYbPROnqn03euUgvllwj4RBtzY1yrTQzESSvau8UHSrGRk+ac5Lt9++a//Xf/h//3/+v/84e//7tXVxa++IXP77UOtqe7+npp7NHZ2+lO7R38wi/8wsaztb/6q7+abbW5A3qu1d4igOSUegQBPCdcOj87xyahtfWN5eVV3bu4/RjtfDGDGOhb3/yLb/35N9jgde/d956vbRAC6fsYRKzMOOkDp1NzC0t86mtnj9um29z4vHT93qb6I08ttjLNTDtaYgsS7dd2Lj6Iqz6iCznZOb/CBz20mUwhEfFHOpkmpLfli6PJ/jeuIqO/ZqrB9DlOVyWnFgW5WrqY+qI7KeyLhAF0cryDeX68FmljoD8UmLrGwNgDp/bAOAA6tQvHAt5WDzCGRlyh89Vse+H1rk00GmP1MQcwjLHMnfDZqAXmD/YOvv+97zx79OkvMudz/z67Za9cXokFrw43ODO9w+IZf7lzeCs2jmprcDXARJxBzGFlHipYQeBWocWlJW1YVVwS1+0wOMY8AaMFtzjLlFgLi0ElRn7CjQg4wGhEieiInAFS/zS2VkkDj+a1aKHON+nOvRhEJyc5G85I3WU49yXTEs6/sDXogarkkUycAxJ0jkAATCKrjqZamAioteVJD0DU4vFbfEflQI+4JI76ShEs1qVcdEoQREsUGjHtwkPBC6wZgSdoILFni2kVHH7v1o3/43/4t//7//b//e3//J94iO9+9MHKkvb6IJbdQmxRfrjV4Xu13BzN3T8//P4PuAP61v3bRFJra88Y8QkBCRsIUIiNuBb82d4E61BcD/3Z06d0Hi414BMZy3ML3/jWX/zpf/3j/2l15eate1yOON3dZZsRlLtx19QaB/S5yHB29f77H65cvcnJsu72jsJbHvH0FMujBBk0KmaM9Lg1/RNXM9ilxLM0jf3ersKxGEaV/UBbSHZdiTG+N+9lTC6q/NRwjmVaEUUt5MUZSQhiUYxJqQ40FEnWknJ6lY4xYw+c0gPjAOiUDhyzv10e8NyP54E0cmpbCQEQgQKTLxpBq5HVh8UYb7i7hVFmv7u18XRmcvfrv/6ld+/cvn75EvcZsjdou7PF3YazrWlCppkpfatL+6MdlzDex5KTVrUYDRSLMMsxzecRWA65zOTP8qrOqGvIrrbdQBWDjWZY4gw747fGMFI8JI2CpEDoI++Kj7SA5c3LcEfIJV7Nm3C6KKpFHiffmINgTGI+C0qtxzEjFfcLaQYAGpCKNrBBO3NlCRMr1RQL6rXbBtUx3xOhjHCaOYvwK6wyxhaSy+YqXBEIhsQqHQoiXsPp0bogglyGRkudix/hIV/8JMusRYEIjmrIR3gQHckgYJ6CTVbsWdb5cyaCWpoGIxh6/937/+5//p9+5//3n37/d3+bCPi999+fa81oCmZvbuP52tbm1o+ePeXi7y984fN8IuMb3/izL25+8cYtPgMndUyusWd8YX6JkOj5xPOFJUKcO7cfPFi+cRO3s+i2vr31/kefY0PP7/3R7/3ub//2v/8P/6flS5d5ACx3rm9u6z4D1ua21g9arH3dv/fuh5sHLda+uhz2wkUTE3zNjfugEKOYIxoZ8yt6jI6HhCfpWqh4lHFpJ4bZIeTpjQSO+KWn0EuGEFHVMqvHISdrNoiwfQ9b+KZZcWMWMRAc8RukubRKQo+uMWLsgdN7wL1rHACd3pNjCW+dB3i1V6Nl7OtlxYRhRitKcXULlzTz3ucyQg5itbh3sLvz6Sc/XXv86ZXluQdf+qWZyS5jzm5ng1Puy/NtYgXN7nAIKw74UOTvYxwKklxRRQzM/L0OwFwHAwYTB5zB5gi17iMm7orgg1wrOUxa6MNWnhaKJbqehwMlKdAKPgRpmI+opV7MChy1hFbsQ0KaRHNKXvNCnJJnuY5VnrCcHBsthFZ4WQ35WhQMy2tdGogrp4VutytAj9GHgy5IapPRRTU/ZrDQZ66KjSmPSNCXyVy9uYnxWiUkfoDkJxFcCNcajU7F8V32iHBRwKH/nTif//TJo9iVtf/400/ef++dzm987T//5//8+7/zu1C+8/577OmZ3OPepvYeFzV1dp6tPUfslWtXWQjjgX5l7le5RZr5M83NzOyxcNnZ2uarFHzj7e47786vXJpZZo1zf35tY+P5s6dr6++8++5Xd7/627/7+7/92//5//x/+b9u8W3aaT52MsUq2gYTQguX5levPvjg8xOtOcJsvt6B8XSjVszjocLecM8hqtBMYRwepLH0IohJwAlgqgMjGMGTh2+UNYqJfyFgIbBDaXXExLjCeumoaKToySqI2WsFzMSQWeACScriCzWOCcYeeKEH6FGm0QnJF1KXBMlZIl8v+DVtwgU0OzvPcW1LxrLnHFdIyXtOcGmnzWOap57V0DDMFSzkVLEPhRkbRhAGGf6wbk3yR/7+FPfQPX7+6JOPP/npT7obz9vs1uCPf233OWhxwbN+9fjHXICu4dnVplZWyrQngyqpY8MykHYec2MQw+7U1sYzkJevXOOyvt0OXzzVPS5QaiJH4UUFMKgIIlAAICTxHEko8ypdtstkjIEaHSnE1UHWjlVM4DDyeAjSkhFyLZm5IN2wqDuOifmYGCLUE2+EOKFStZr1iRaogDuYdYivuDuoAIci68rH5zHSSHEdDsGyQq6OttjnfnOBpSpmgwTZIZLAxhOeFtXoU1zID3kVMfrfzIpp1DAQiuJ4DgQZ+Auz+bQoa0aaKZGRPAKWq0Axtbe1sb67t/Pk0ePdrS0WuTjn9Ye//we/919+57d+67cuX71ysLNL2MgKFE+KsZyLD+YXF8D/7Gc/+6d/+ic+L9/m8oJLS6z1zLXnn089n5mbv3brNvc1c20hD5NuNH95dn9qemPv6Wa3+94HH/2zR0/+5r//3d///d9//ou/8Hxze7o9v7u1s7YzcfnG9Xc++sLM/LI+6MUCpbrO3vzsPM6gvTGzggmEpKqlybQCPAnYi1DgexwuankvvA0g5mA3cNzccuCyKIr82tDbLIcHokkoLlDAw7v7m93tuXn2lBNtzuM6GkG3hAXnp0kp8LiWjOlfLw/4QWe3sfEunqAhSCt5szu9sTNA2drxL8wJusuYBQ9kFwLOXiSkYgL+01yL8byguZaFV/jBbpcVEs6yM2CuPXn87OmjR5/8dHPj2V6nw9/l83ypQssOkyxypYdZ0tFaDuGQ/o89rNU4HZcXg4lRipwDO6jjU5pEP4xbrVntIUU1SI/6iBYQIRGijK+q4o/7skWFARoiIauig2gsvBAz5xOTNpRITEHpVBo5VdXalUJALW6BqqdlUjBCNQlkA+CQDDCgCxK0lSVJjiYbCUdikEPCJlclXQypwonY0UpIwE6QioPq5FDRQUBs73HrxMj8jUMlvk3rpTtuNsS3BEBws2WaqyiZl2BIprG4ubO9u762Rh9gTezu3btf+cpXvvnnf/4n//W//tqv/dqlK1e67FKenlpZXeWynWnOvfMV9312/6x95zvfu333nZ/7+S9g2PXrl7gNvPvs+dzScnthYaLFVnY+Rs/M3w7h5cLKCruQdjiiNTH1K7/yKz/9+JM//uM/fueDj/h+xuPNTndi5srtBzfvvz81u8gHdYkU2LNE61iDCx9UXZd2kdRfI4CTj+JBG09ujHMjE5O1an6j/ydRD5BcPTXVbxDSksaw5Sc9u4IgIO5hOQx/u3uDISXNGHh7PNB47hQbHWZEV/Ryufu9sQFQ+sUtb/gxa8fA2AODPDCoz8RvjiIG/mYl0tGwTKyyu81uHuZ19ne2158/e/7k0ZPPPl179tnMQXeBzyksasIHQu4pJATiBhf/aYtq7VKJP8qzoyJUeP2uxx/KMYHDH/Qc/uJ6mqWlFfQQdswQZ3lUII9tyDJEGAIMXhMM/wwkHvSRLZmRqvBDrVCwFAETP4gtiE4kSpqVCPBIseUnvtKqXdIQ0Q5iiGphhdmuMuaAPAREMAdzpRRhoboKdjyLg5CwSlUZpZhMtlVJCE9a8JOJKh2FQ26EQUgPyZrSgEPS4upnx0iVEGwAj9EMq1IFE3oV3RDoxKyXZeGw2PqszeM8AXaRsz2ly/QbtCKO0IGnxicoaPvi0gry+Zo6h94P1g9u3rz5hS98gUkajr1/9WtfI0jd5SHO8NhXLl29xjfgbt+7T1j7jT/75nf/4Xtf/IVfZBGTuY+NrfX1ra0rfA7j1m1ueWIuRN9a06YuruuZby12+ZTY1O7B1UtXf+GXvvRffuf3//bb3/vil77C105bS6sPPvxCa2Fll74UO8SYriIo5jyh7pCKJxKekfc07QM27gEyMqvKIrCL9ps4IxlZl6o4hqK83YegQiZ9A0hplZZ40PDwj0aDJOzD6ROt+OqLPsNC0KkL09M2BDJJlnLid6WhZFwce2AkD9Df3vwAaCRPjInGHujngfK163pjqvevxkY2+nBRz/7iXIsvU3bWnz59+Mnjz362+fwJ56cJfWY5f8zkz+QMg2X10ud1r5EXPsUijn4EVH/cKE6CkqQxOxYLgGOSqcUBabYIMUxGxKLNQyaAl4QQDSQxOIk94qHAS5rtT8BF0cS/kteiwGj2IAyDjJYqPOJ7WQonutgmUVrkkl4ScQRQ8gqjsViRIkBlXpghAUWCpl6RApSETJIaSRVDk+VBArkiuSrBLidXQR+1zObQJIIR/unG7NBM2MGKFwGQPnnBV0a0+4oDX+xz0oSE5vtoZewmJoDdY4JGqzQo2tjabM3NarFpcvLB++/B+KMf/ehv/uZvvvqbX7t85YqCRPa6T/M5kYPl1Utf+epvPHrynJswf/rTn375V7/y2aOHXDBE1LN69dr8CqEMhuKqA2JcWTKxzw2Fs4tLnMF7ur7x0ed//n/844//5M+/efPdz1+5cWf5+p3l1WtPNrcJgrGQC4SIEPg/lrh0BJGEeWoFkXlspRcqngXGGC5zExsjB/ajKemBD1mOyjvEB4NFlchSuPHkIBOPG4E5e8fiGPNAPA2KpYQSblg1Lr7xHnBvOX0z3d/GAdDpPTmW8MZ6IF/K2UItT/mW5/0DNvpoHYqdNnzwYm2N0OfRpx9vrT0l9Jmb5lQyn/SemmCrzi6j6B4fZWKRRe92/tb3xbtaEFKIwPs/coYBhmf9SmpEjqkORjHiE4ZY1gQWCbJm5yt6Ru8wjjETjAboCqhKGtoRX51MK8coxyIRFsQYyQvF7xS0o1QrQTFSUgCgSuGCFGiLijCaBNI916rSpJCSROAYTSMxgVHt8LAo/b0Ol+eTWDFTfEZ2JNFuUsRv2C0LohjeAUvMFVwM59SIsz5NFkVtD/ekUGgikrA0ERICxA1KkEgIDSBKiA9OwSKhRD2sdjHNgJMJFigSUohS00lMAu3QNuIdENBwHox/iKSqtTC5NDvbnl/gUkHufeaew19aucR61re/+53Fv/37X/v6b8wvzO9wBVRLtwdxQdGNe3e//q9+62effvLXf/vff+nLv4xKdkOzN2jx0oo+5s4MFlM4OILHfcC10RNTyF5aZg8R011T7cX3PvrCf/r9P/zZo2ef++WvtxZXN3f41Bf7zRRccif0/JzOTykUi/k/HpKjTGZ+qobXjwkyYwBwUwmDeWHyc0ku6I2pGOPhlEKO1EZFL8b04BGrTUsTCkbZB82VkRHfU9KkXZLho9KAUt0YfuM9cLaPfhwAvfEdZtzAs/QAv34MJYyFnO1i2GJ/Zmdjs7P+5OMf/2B7/dkux9pnpubnmPQhONDVyezskHpGZt2ywxYNxS1ICJuAGT8Yh6txQ2ODBy6ISEFkkEUTDsjwRzGDAX8Zg+QPZQZmDxvkTo45gGO8U1wCvaUbCSNVXjCyBkZ62SRtIoQs1MboGGsNihT0FziJeRD9MS77K+uEhVQ7m0DqmmmtVgipmMnNDJlWEX/Ku5YcXIxtbmhoDLHiD8vluEjBLeLYilTR286w3GCVm5g8NqyLWJoiaW6FFA3WRi0k+sMREQFRogyGZ8IdABRQjpOZhwDQ90Biuy6SpvZacwt8xkvhVXt+bmFxkQZzq8EcX7GdbX33n75/+ebNL//arzFPxHkmAkgkbe8e3Hnwzpd++Zf/5i//isUyQh/cuHrlMjvZta7HVZYyWBuY2UPNCpBC5empvThg+GRtY2750sLS5bXt3fbqlU5HC130B64Fkm1EPVMzzD9iGO5nCkhxXfiWJtBcwgh00QQ1PTBqTo2RQyIqMgFwAlHTJztCEB6tMFXf6cPSFyWD65QwvyhEcdhJJKqpLfUvHmBNFz8DcxR1pH5cGHtgJA+MA6CR3DQmejs9kC963s5+QTOYMVqx+0fLJZ3O0+dPnz38uLv5vLu13p6eWJznmxREAQyLGolYL2KLxiQf9OZ1Dv/+PgfK5cl9To8JwbjDuASJ6iNcABWUvPR1BJ3EmMIYzHQAoy9DGyZxMw3RDysdbPqQEP7F8EyVxj6hNLuhFLBGEEY7BBM8xZYRghMEOOIxoXO1l/8jaTymqM3Nnqbi725M1fDI2Ikoia9oD8e9ilejrSaCwprKlqhSSIQO8Vq+FUdeE0tqwq6RH5SYmIn1qXpRK4TIEreFUADRIOW4+N54hGAxoRYSbRC+pzr8oSAV2LmQuEYuYI6HiSKuaNI3RvA/IzFPgskYTifRfMTyFIia2Jh1oDNxbQ3U0zN3Ll9Z4sKe1p9++zvfvXn3/ocffUSIvLm1NbuwKMkH+3wm7B+/+11OxX/9n//m1evX+BwpdxnwdTfmkpiIkms1CaTPsjIFErZMbWxucdB96fLVSzdvP9/knkCm2dR+7k3GTax/YY5OCioM0kF3GmPvkcsv8bzUpDoBZzLOlElfE+onlOSuKvEJmyCLACUmJu+0PFompEHjHHwJmJ3QGSQN5Ing6/B3JQC8IvJxGnvgdB5wL31bAqD8HTud08bcb5cH9LKNFzSAhkleysQ2vL7399jcw2U+HPV68vCT1kF3ZZ6rkXWiV8ENYxTJb3gGTsU2nGJXOcIOzo0T3CAnDhzBEmMG0hl6NR2jr53qr3bQKOUXtTWDNl1XSCXFajyIUcAwSOipU6Tl0CQsZ5hELNJI0ChV45NA1zA4Ae/qfL6qVUGKQUYtrZvP7A+VYCyK4EtCVWaWgkWqaK3CD4w/nAQyCVTiRjWfRBBpxQo+UzREpVohoDwZyfiYDImBkxBTm38hlakaThlotRyH+xQgTChSFBNJzuSH/teD04c88DB8chQzeXqsqUgPTp6ErauLjlqzfOaLDctKhD/EHXw7lGBUe4Nw2sTuAnuA9nVGjB1e7SWinMnV69e++pv//A//8A+/+c1vsgz67gcf8uy4g5lHx6aiy5cvP3jw4J/+6ft8XOPd+3e5jYc7u7mTWYN9d4+LDtkKjb3swO5wJyZH/3Ynt/ZnLi9fvn773p0ffra2vrWzzWfk57hosdvZZfWNZS+t3WmRlG+m8rzkXOzHeLUi+k860QC5mjNaQsgQwnCL6iuBo0mFGLFmScBIK6s7gbuzavT3QP2YbJLZh9g2rhp74IUeeBMCoOG/orggf1Xyl62vX0o5ydKX8pUgT/ZrP7zJZ9WQ0nWWeVwHnoAepdZb8vZibI/HA2ATZMPNK0HxoqfoRDGGxqioYYSwoYS/uPm7VJtBlgh6ZvkK1+ri4nx7gouddapA9ykz3GuPDj8JCLRyEuMNshwVUUUloYOmbhjHrZohEmiPXdUHDJlwdLp67/P5KUU/YbEMiICJ0Y51Ai2dMNIx6RRjtsQxfRDLByLX0K4GC8YQZPFN05j4QblMImCITUIMm5KspHkRKPUv/mzXN7aihmkvCeNyo1iAY5zVUSwRIpomaJDCAEVMISS0quEoBQ0WCnJajky1P0AVI2KLoTqkSZLCQHkRDg5r7XMcTjYFcXwHVEGWokUZwD4YS5NUYYhqZFx4IxASG5JQpggTO5kfkngqFAmpluk0zeJE8Eo8wXyMvlXVngUfXmbuiSiFWT0iUdok6+dmdV3NwUR3Z3diYWGRQJVgiAsUr9+9/aWv/ApfsWCm58qVK5evXd1mfQpFLGmx7LW8zKfcPv70k1/66q8/fPqMdVG2XhMY8hhRwdFvnioXEBE0dzoHndbilWv37919sLC0fPX6rfUf/nhtbWMxlrT0fdb4LhhBHX7FOHomVtEi9wfDFO12chIPWm0OJMVwWZVHveCsTcBVFE3vYoV0xxkwK+O5n4YcGHtFVdZqDovHJNlMqbECBuDfD3UzGywcKYjcO40Y52+BB9yXevuhMb09bbhL3oQAaHgLS4+U8HCuC1vb++Avpqkn646jt8WjZi/9oEeMPTapZEliACdqIUO4k1mAASBg8OCVTJFBCwSllZXVjcf83d+hit0YjJQOAbQewVgTw7PHmhBbXXJosXqhZ7DiIYGRW6Okhii0YAzqGIZRBMZRC4CiDQmFbIpPVtlm8kBqtJDx8S0wY8jRaKUa9aGAXcYpnBIN/1cBkJqPaAICyTnYpVJjmO7nQ4MmTlQh3TNMyIhYoiSlNqMCjEmrANxE/YhQBl6zQxmAKJi7kQtDhcpVqCRQkZkIYlYjEFKuOFL0zOQccomuEiFrDkuCiaVoof4LgwgW7BPMwNc2mx9s35F3WM/UeT0UESHpukc+RqE4Er1o3NfkHH2CkIgrfBSlsS42MckH2W/evvPPfvlLn3z8k3/6x+/NzbOdeXFzq0Nz2C7PRYgrKyuscG1sbGkSCkZsIILZ7eokfHefc2iTM3N8+HR7f//q7bu37ty/dOnq9sbGTIst8Fq75HA+c0s8F8Iv/EVLZDkeKpxgB9AcJ7U82msgc/otlNAkpgSMr32ZTi1JmnBDVPI26epyqgBIS4zE6/odiQ3vUQtB9UW8mnv88y31gHuIG+8+VmJKeHgPfJMDoNILb2k3edXN7u2aZ2WRH26+MVNso7uXfSBZTJzFNNKAogfiBw7UxMAAGYBhGNltqskD7X7eY6fq4uql+eXlrecdvkSgjc9MRzAzw3Q9X2VScBFLPyHASpGTgAzQioxCGGCYtQeEG321lsGsgK694XOVfPkLOdQjjqTZkYhGYGD04gyaOGNgAMKwgy7FaqxKs0OpFDHahg3a0qugJ9TKgPg73n96027M1IqYzttrYJXV2tWkYdINwBBxqX0RAFXsGozRjdhQTUEJGlwqShqmMU1SInKLahFUZNITcY3iAgUpaol+Bp4qRXdk8kfwiE/ykK/KgHELIlUjKVVSLf8regyDJvQRBur0mQ/tnZJ5DoD0ZFl2bM1MsSrIVmgcHwIV5hHkyCHhPU4s8R9pn71ZSJLTFE3JxD0uLPjwww/pEg8fPvzxj3/83ocfEB9zNebm9sbKpeW79+51d/aeP1nnEBnTPYpxJ7QTiAk21iIJq7Y7e5vdySvX7l6+fW9heUXn02LGjZwlObZdE2NhMAGQLuxGMQWs095o4d3mIUDllPBqwtCHD4VI3qztCzTIGsXhchrq9HCOWu4i7rdqCOqn3NeWMfIt9UB2G/feqtsUnXmIX97kAGhIs8dVL9MD+VYtlWavNbLsuyXZIBh2WBpCIDbeQOamLM0AziJvVVLygqeoUVDf89IOU5KL4BnKKWqQY0vHPmfd5+eXrjx/9phPfvH1KMZS/pSnWjMH2qbKXFE1GCOttDa1g0ztSDYZjACMwSzCSJ0SIUn8CFNTmlbHGP/qP/2BndDu5CKcDHaSohhHAElxRxUVRMzkgUYLKlokgkjjo9ezWLxjjBeHEkLIJT8CoEpcVGnKhvYHAblIYj3DsEIQIQ+DnyiKTIlgQ4FLVRukliANKiKL/5Cg6Td5WHCdQr+QlW3Cax+V946IGemQx4BK9Gd/IocUzpAAkanVCjenWnocYoBCcRLSJhSgKAjki2i6iVs8TIx55kpSiBtpg6aIWLy8cesOEvgg/Gc/+2Rp5VKn08Gi1cuXrm92Pn34fHNtc+XS5X1imV2dlick5RnvTu9u70xsdidml67cevAh9wzhz253d7u78+Tp0+s3bqxevcLhdoU+YTkxrGyWW9RgEgUZFUnmBZyA8dCY0sUSmbAZKSYAbMYGsiQw+4h5MtrmFF7Jp5qknqcUv31qrIvjfOyBhgfoLIl5YT8xwZscANHC0iPpmjFwETxwJk+n7OV+1uQG3EYIMoGhR1AMoMohroOHiAdi9GOIY7TTcKZxUDEQA17IYTRgn2rspJnWnTaMWouXr8w9v7zXecpGFf4Yn9BnD7pMFWi8jAG16W1mJbBBA1YVOUDAyx0tDKgaXTWaIZl5Ad3uK3bGbFmi4IDhUEMSbeFmGo5LSwwTEDQnYpWYu1K4FjMuYblU0cbKLRqeNXRSFINkKcZCsoZSycGWGE2hQA+uYHpJQRE18odYZIBqiUNknswhl+HhqKgDo0kcGok0t1cyBDvEUUACAQgwsPBsQk5MZ+gbG6R6Bkc0/B+xh1WLNL7OITKxV6GqqEKg8dLItBCY8Bk6ZJPotWMd4rBGzqY50UjFO6bBLRHMRcNiHAZD3AQT6hSbKoxC3DT7lXCMpCEekB5CULO/z6zPdY5uPX38k5/8pP340fXr1xavXuMjqczi7HU7G+vPZ6fe4T6frd3OdHu2G7HdNh867R4ssOn57kdzC6udg21Fqa3Ws3VWzDa/9CtfnpiZ2l7bRBNBOt/P4FnrQShkjzk2NztyPaGjyZWysnoQ+CGcEBjTFgL6gNCU2EaRqhKDorJIrVWXEnphWJwkq1ZHhBlw9LcKPmJJr5wx5q31wCjdzM55kwOgt/bxvy4N730/jm45vCTT51vSRVe5toSj1n/TC0x24JQAAJ6cuR8DpowQQVwkapVabXZtMEe0fOnajZ3Oj7+/ud3Zmm5PaEJml0Et1lQ47RXzH3CBJhlQiMFYXugFzkQV4gmGsAFkzCioEvbYrVsZQKhBgMBNfWGNshgFGRCVgKv5icCHcmlnQIHS0vgRAYtCFw0sEZEo+NEJ8BjIfdsiUUgdUyDH7M5DILRhW5itWM3hw9HWybzKCKbWRF+EPlQR7lSPhiqM8aM1RzXQ6TsRjv8Om4ABtgH5PKPKqno4d2VQBEimOMfyFAEhX4tfyCMa0uJgxBCx+EcwJAqteGo6kI+7EwyxIolX9e1XBVA6yieHkkmzNoeF5xWwEszwXXY6A/QHe93PPvtsbXPj0qXVSytLOJMACDFrT59ub20QaXP4C5d39iZZ+dqbaC1eunT91oOVy1e399gGP70/0e1sbv7gRz9cXFlmWW17c4vpn9bcPLc04noWx1Cu5vGMwrJoj5rpFDZWLgS2S8nF0i/BBRoCA4Z7CbM2gSE0WZXCE2MAvE1KaQL4vzampDeyxIzhsQfwgDtt9u2yDw/yzzgAGuSZMf5leKDRRxvF4RbkezDfkrBrBGIcq8ILBRAMT7ztG8TC+t2qIU8JDIwkJgAYoGODjZAKR/SHvyYYol4LW55t0C4hhtTZxUvXbj55/LO1RxvT+93ZKU4t6RQ2VwVNz3C7nQIdUvBqVgOBwNhppVFZZSBtOUfMGGs9uDLHwDinNRa1RJQ69qQfkWFetg65MYOE3eKIf2hhbPfMTTRajZXASGwjUhSkqQ9Jk3YJVEnRBmWK0il3IQY25AVCY75hxIX/ajLsgV5yNYjKZslW4oAbGsJgm6+2YTRV/ND9SQFZu8DwlSTUfovvserQmWyUXlIENQFZXIDKkImFamg0uGqI1sXCQs3aSFU8bx47S1DyqeyBWQtfzOkx0SfPH/CwRQwuLkVUsMUXULRbSPI1dwUP9JrvwocEVpxo5yl2d7bx4MrlS/vd3c21jWf0iemZlWU+F7b4/Pln6xtPli5fY/ppY+dgZ39mf4o7FelN7Pu5tNnVwTHNU022f/qI72f89P33P7x06Qrn59mgxvWa6CMS4gHZu3gBgF5K26hSGyMZIO9N1Ifx0YCaBTK1J59oyAEDvkRaWmIS6NWSGAtJOVlMAoSghtwp8QCK/aueSP9xD6x+s0qyMfzWesDdqexU9KIh3nDtmxwADW//ENeMq14XD+Qjpt/HYB1/glOI97XWImJpg+YkZbNp9bSBJZgSYmIRxhIAiwIgUQsSRax/qEiUo10b+zPtudt33t16/nh7k9Fub2aCm10m+fiXdoLE4G8hCglidLEuC7RGT/Y4ekNFTP/EJSgaibAhzkblwEZkQoCmNmpLjOKpaK8GrqrhWF41GYxE1EnrQU6xKAYdZXFFqKGAw5uyGWUmNfDzYQj7wTlibHAlL1xU65K7KuFuZrQdjK3ScZ6I1TzMBaWGbyeRBb1iFR3qlvaqRRp8q4QZtkRcEWCVjzhdgSWQhWQcEbFj2lb7Ax9CpvsGwidimZ5m/VIH0aLncAAerTZvfnHBd+1ARrI12omkXoCiQLEqpo1f+oSqDs/v7fC8Oe2FyOvXrxMZsSH6s88eXufzqHNz87Ozj58+0ac2uEOou7/BfM4Bs4nXV6/cXFy5zJEzpn10FO1ggn3TbKPmuXCJ4uamFr/m5+cxBjN8VzJOlE30OazVk68evZC1qbX/9FOeq5NhU5bE4MuiCRq8JijJaqn9f0JZqu5LxFMDb3WWTOuSssHeMDLJxsDYA+482WF6+54Jjh0ApcSGiy2ugXy1xeOaegGbcCYOPK4fzkTpiEJKnyecBicGgMSrkATAW9KAtXjiBAyMhAqarYkpGmohzrwwqQ4CVCe0NGruJ4bEYCEGgRe8RnU0Mr7EYB9/809x3EZbdLjgji+Go3Fmhj/bb95555N/5GDzFkenOOPDAKLxyRtNZFk18MrEelgN2cqojSke/ZGrK6QVAKATlYRQajJlZjpkYcyFaL2jnjjRZpAIFyCjNkZ06rU9RbMBsT/XVbIBCifNcGh8hZS9LI57mNLwlBWO4HgarZMdLeYsOPdf8WmpKFiQSVKcEYGL65GvtkTiVJJiGSmJWRZsloWq1eOTLs3koJvAK6zRY0WtxIb3CBpCvsSxUsQzqGcBZDvC+GGIwE6tCww0QHiNRsSDZa1KSmWuXB1NAYpwloqg0YOGi85TFXFKnJjjufNBUwyLC3sOdnZ05SCP3roQqgkoNjrF+hfGtPhciW4+4s7Hma3NDTY+t2a5J5p99J3V1VUWsNafPZ+bbbdmOTu2v42DJyefc7vh3jShz9Ub95ZXLm0TNO1sz8+3uQ2Ra6L/4bvfe/jpoy//0pdXFpdYR1ta5FAYR+b14Qv1SVoRUb7DBPZzVd6PBh/CcqEejVLALsYzkVtoSOKBReK85goUOPVVkljCpYdcUbRYsfdLJbE0HtpyqA4+GRTyQ1XFBAb3Jsa/r3SlfnrGuLfOA+4wjWaDdB870tMKomMHQAXvGBx74Cw9QB/t7cRgGFpIACS/AbMICyM0cYYx1Ho+AICUxsGYcACN4pFKE/sXRiqLFzEyKcmaGJiDTRgiDf7Mv3Hz7vbTJ1tPP52bndrb3fHNNAQniBJDtC6k6YUegtUc61YEo2hBX3c3/dREd19jqxvCXiO9+vk6J7MoTgzy8GoGoh5FDOgodAxaabmioIoGFnFLO1TpoVjsEle0jSGUGI3NJXwglMHf0lwLn4KJiOrIo/FqC7WYSp4tssagD4Iw4DBooyhDmBHyRIwkUAz92g+OasVeMWNEETloQ5BuzAldtF48bo/kuCRdNYhAXn/yfBBWGdLAmIiKpMYORIu7TpCZEgTPBX7NsUQiNEQOYZCluTU6ABgS5RaukiaWi33Qs222Ae1vwrO7zdwPa2HbLGPtHyxfunow1Xr4bLN7MLNy5eb1u/fn51aIftDA7um9/Z3dnc7HP/6YgOnd+w/ee+89OWRqamtrq16sq4yp7dXPI02N5tAE6KIpygxXnEfbCxIJrjIggcGS+LI2hZSA6RNDseRNOMU26JPAElybNK7NYmoZA2MPHNcD7kWjBkDZL8ed77iOvrD0PMp8rBfEyEbvchEjScAMJ9jpIgBFogtOCLNzhqKuYNH4GDs86vd4tKsa1wLukzX8gPwkAmbky1EZSkISLGBAgSZqTUwANLG0eu3q7fuf7XYn9jvtmXYs43CyRwMwlpKbpRSOhGiFamNiwiGRojo1NvbkOvTDjF1WViKcquUoFICTXNySJTFkVlGPzSxvCcEpLwix3sTUMvcTM0Q1Ay0C4wAqRECDXgRLrqj4/Kc+V694RNuGo0qjfYguhk+4oXCu5yHFIaGSE0HJodqKmABP//BYtEgameNgi7An9CJYZLZKbQjp2hciSsmPSR9dSxR2hsZKVxDHCXagIBU3SVwRtCEDWPNrrJbRQhGFxYF3ySyc8gPwpyckgXm2WfHKFTpqhvd3rY9aHhb5zNwsO7q63FiIaw8m+Y4G+6K3drrb3YmNzt5PHz5fmly4dvfda3cfTHOvNF+P5ww98d9+d31t7eHDTx8/enzrxu0H776DqA4fSJ+ZRSyRlLSHkwXU/+g92XzwqgqaRFIsYdeSOyVLjaiIwZdcZRG8tZilJEshAKbpW1uymwWMkaYnNyCkTj3yC1FhLNm1pboxPPbAiB4YNQAaUdyYbOyBY3mAl5dfdoO4INBQXb+4gfV2jPUR/UXN39FBwDidVRaVkiEeJLyBLymBkQABAJIdAwnWUhj2UKkdIOgFoJalj87u3qXrtzubW88e/pSvlHIZIjMEjIjURlgSA3U9GKSRZUwj+YpW1ChSMf6ys0QDKLJsEjkyI5cDSdAz/SCuSEYaBkkxYQAwTobJLcE7gSjSRu65ET5mXCqycAVVSuFwfQRe0kIgRGFV6kqgYq/18hOMa2lq1qZZNBaklOiKYzmcBH2sbVFTJdkWzaBMLYkfdWX1UzKUqhANSGSREnCxb4548ApGw3iz2BiqmHpk7QsLObEHUg+ao2oc0mYNdG+HZ0b0qD5wcNDh2p7JSXY4EytBxIkyvuve2ZuZW7p6/c67K9duzXCqi4BJlz8RLXWeP3vy+PHj58/Xbt68fevWLeaNnm+sE/2wsQw5aEwfyiT+r1O2AkQJuyGJSQB8SdkoJlkDKFkaVY3iiJQNstKMrHITKNZ9/IiqrD2CHRfGHhjqgXEANNQ948rz90D5suvVRq2HQ6o8FnrWh4mfCCwUHjHGUOUXJfs2UkiFKUaCrCqBeKVWYYTxDZMgQAtVaNzlHhj9nQ28x/CLakZz4rDZ6RaHc5bnV1dv3tva6XS3nvGibrFZiG+HKUgSu5MDF4+XCLT9+frWFlrmMaphuxrDYOH+PUUjbguhDhZE2AEKsbpxSAOh4gEFaZGQzE94VeP/KcZAroCAyRLo+c9cGlHh5aZjVpr4/he8lZzacJg0zE/zXVdq2REjmRFYVKGYCME1vBcKFSOC1wTJYcrW8YwVYaAZlRgBMatJ+sdtzGEka3RMBaFPsz5qaLQwohm8JZFYLpziFSegsFDYGuXa1Au6oK/GVdW6CWaU2UeHXORxHB4jSVX8Wi+J6vi75rEm+JYpU4B8L46nz7YhNsvPtDj8Psuu505nZ7O7t7k7yRLYgw8/f/vdD7c56b6nS4N2tjuPHj3d2dre2t7AF/fu3VtZuTQ9M7u2scWlUEQ/Wi+MJ8UjUKPUOCXZjKPCM8IfTY0mZHOSN8ktimICwNBXKo76IbmGAw1RJk6ZFE3QUJFG9gqP33H6bcybHjWpFNvLOMaMPdDwwDgAajhkXHz1Hsg3pl+CFI1hvPGsDzlWgiT+iErNf4DRqKDdINUIZ5qy2Ldt+dJMSgOWaSHkFDGAqqylCD5igukdPkU5M8u3u+eWL69eu/Hop5vsYNVeaUUoinKsOmRW1loUAyQJvBPbPwi0kBp6FehEcosIfBjmFRyAdIASq0BsALZ45cYDWKm1yOaYy4EwzD50UbAklUII5jBAsiUpLroBDJcS/MT8l0gR7eiq5pM9YQMIMUQyTBUaSQA4MauiCYfEqpWeKlGqwYJGwdARPDQYgkwCj5CMmEoFdNbVUGSkc9jLYsKJB5DEWiatgIa2O+Ym+mE/slJ3l9hUD1LyoGIHOxOEQriMHC6O5oJEaLcJiqdmb9y+f+vB+2x8ZhuQPk+/v//02bOt9Q3OebElnSmf+cXF1aVV9kPzIVWCIXYUsTo4wyH6uJe8sjAehY3k4aT9CZTGl7DZyZ2gT8CwJYCEy7nx6YokMODctdCXyL5w0hhoFFNaaTPIJGuYUVb1VTdGjj3Q1wPjAKivW94KZL5NXm1rRzRDo0ykGIA0tsQoHJMcMUvA61GTHUffvaO8keGBjNwp37nIz/csVXhJVeRADDyMbdp0w6YNGcK9vyx3MAk01W4t6dOVT3fXdve21yPY4S9zJecxmAJajHS6JkIE9j3HnIrGWVKMaQzt2rGsAd1m6KI+CpoowqAILGJnDzQ2uCKLqCthz5ToYH6EVcLzfyQYPVRLi6IUbXsKadgcPuWLHpoQ0ihfM/k7Z1ioEAS9QNZuM4CxDRhRJG2A0QyFEhLip+hRh4SInnCIZONMcpoITQ2jXP+Eoc2os/vsk/BGtdEJDYLCXSEOb0ta5FaaOXhgcjU5sCGWjToq8b/O0IlCaLpWkMhmmkNvrNmZg4SYSzH5YFeXKFM2TzD5F6Eqx+r06dI91GAmC7Zb3f3O3hRxD9dncmxwepa1rU19UH5v9+nTx3CtrCxxBRRzRQRQ61ytOTlDlfZExwdQibdoDNcLySx1WhulVvC/CtG94me0AqJIFV38UJMjUcJackqZB8mRDAFZhjKL5sqqQQD0SZlASdwXmVqwLoglJEWJRX8MeAtcKWwMjz1wDA+MA6BjOGtMeuYe6Pvua2hhsCF51OENSOTjv7+TzC/xLAJYbPEOLSubcC8Z7KgjR7JrSzvBgCdiYFjQ0K73OyWCBm1NpdRqzzPCrR/sbzJD0N1q/I4FDSwMPPB6wNIIIzzjkd/yGpVICgV0Uw1JO5ZdrgY9LQbJGxqSM0kcAYLHQo9qIRmDQRIeaBy2zfpoFVt3lTNYs8BEjEJzFI9oBawaF/VxVlPGHnNZSRUzGbQTD1V2hsMru2SPrU+Aok0yiSwxTRwBy1oD1JIIq2Qt/ynywhwl/IWnq/Ew2mxFoo9kQFyhMYoVbAy5VTuHQICegmzmf8BKVk0JPhP0MiNmIkECo1nTkPtcY3jA3h0udJ7Veiyfs93FDFy9y/SOoiWmAmf0ldODvfbS0pXL15dWL3EubOfps4P43i0TP0t8QHVxsV0fu9vparsPTSYeIoQiJGIGCMkgNRXkb6TIZCU1xFDk2UYAJ9AASQI9sPNENoCSxrzSUghJepCDRJVVyT6IGIG98k1sfI8E7NHvUQrsZU8jx8DYA6UH3GcaL+eS4BDO7nWI6oEG9bxReHuEnS+ir6mD7IR4UNX5Wnn+0vv6AbXn3d6UjwG2AQypKOrvaIYZ4gf9Ca1jL5T0mmvP8A0BnT12kciD0TFttjTniQQAAy9A3+SqJCiLKQqAhF5k8Q9ZkDGuEUsgmCiEXbDCMARyn93MxMLKVf7cf7a2MTXZiU9qapuLRkuM0VwCH1Ll5hvmJVj80lYGDjYjIFZCuGFIIQ8NhNhDPwAzASx9eZSzhZqZgFNEkhoRjiDGZf6PQRfCwxRVzP5grkH8CbSvL5jFohszGw6ePB+DtREFqaWixHhyJqhonsxCl86l808bomiO7FX4EEZhl0wTzCSZ4oqIZjyVFS4QKpB4QI5Vin3EYT9OEK/kK/4JLfKfYsZwPixMMKG3mvsJXWgXedzfg9GiQZe6lijNSFGqQgg5WixTKGRHdOhaWm1iaDTtJHsUhioECxkIVlC5q3k7Fq2mWlwbjTq1C4uZs5E9LGpOtzgbxke+Nvc6LJFeuX5nYWFhs8NBsG3uS5xmX9T+5LWrl5nmQS+XO2tBjZ1WBEx0llgei4Yw1Ie7ydWiqjkyO5L7Rl3ST9rlBJyAmI8makGIoUi9GCoRkiQNAoplbZIlUNYabkgoCZIrgEOlxlsX9Dg6hFTGWwJ5Q/JRaePS2+WB4Z1hpADIHevNdtvb0MYL8gT9hmo4nPGJt7xf9FSx2KXAh/UDYiD9qa2/qqkFphY8MD3bnds5VY1itrfU1aBxVUqg2CBIIQasHTjjMEtQMMPg6ZGGE+dTU+35ldVrt9Z+trk/1eGzmQc62rxDKyDUp7w1YDBY8wr34S2CHyQpKKGKBCVaDJMzCCjWUNhRJUd+FGwAgEMbiiS23ApTJIq6eXDPJ/khceA4wywQR9giPiDmqFZ2qEZTcMfwYwiVGoLlebbBIJAErMAAvNQKSrwB2RVGiiYWbo4QxEAOxrV+xCLIfeC1QHM5lyIlmCp1xjfymuwQLYYYqskTa1jWI5YIx8JpUL21CnwdeEmlnBAJmAejbkqEHjgCWT1ObsckEIaLXk0k02pvdXY3OjsTrQWiH1ZIO132QW/OaKVrFhaveSHG3V5irUJ7qw/XHBOfAFbI6MobgrHCRWEjmYbcvrVkS0g4WuNHYHBYbi4oDKAEgLwhxJgkbkjsxTcwyT4UcGMr1TbJcpILpOGGAePi2AN4YKQAaOypsQfO1gONV1JZBGa9i23OjAfAcTCId5pGaf2IgSF+Vn+dNww7HEGLF5+5oEx2cyW+FGJjyqrSPPCMarrWR3+PE4/EhhoFDow9BDMa/rr7B0xSsYu1de1Wd+vJ7uYTphUwV5uL9/aZ62EvKzKYJtCOHMUHWntSMKRpoSruSQvRSMIGA8JH7BLRiGRSxZqV8B41ZUg5YBsNlcYJCYnJKhU1fUXURCMkTFufQTDkRphCm6CvBnvNFamkPd3xLABV1tQRYmUekyG0XaXC8yrUCRLNP4lEsxckRJktAImTrJih4jlKZKSwQvGFS+CDW2oxn8YbIyFR4aLZa2KxVlz1D6pIIVNV4TMFoXU9GHxblfRoJELdMCK64ICCoFcIJCBABMwSQcjtz6xqaS1xYuLp07UnG50VfeD93dbCCrEjV0NPrcwwncasz3QrzjBOTPPVES4Qhzdi37q99nN0flTKhmiIcxsX7ZChNt5F5VFd/1QhuI/4oZQT5KNmvYwoSvnq3qE/MYPkWk5ppCnBpwQwFp5krnXuqpRvgVkESFElcgyPPYAHxgHQuBs0PdB4oTSrz6fMwMJL0ykiH729QbIQYIX57gNJ0mgZKd93BkxmOKtMSd7ApMwkGBGAEVNtCbBmMRiWIjJgQIw9snwMfLo1v3Dtxv2HH+9vrT2dm55sz7b5thbkEeRMsTNW1/oxTrBuImYNnjloYWomW06xbsLhGCb2engwPZ5JpJqD4IgMGE88KMEMpXx4qE1Dl2anYgxTRSS4zQuHRVkFOSKEIZqpKVWsx+AEqDRFVB7JJCRSAiKu2wJgfOQVpQgox9Bo4qCXFCP5kUJA8oxkYiTw0Bgmp0gyUCKBpeConESavmaUO9nrw74kzVDGTnUCIi3JTk5zrH1mdpFrnR8+eb7WObh848799z536fK1HZYKo/OggoNd5DwIYn1OypNctHbymC+s7KFoMzIH6E1qVZ3ghMDSDACTernOBJOS0T+6wCHECMxawz25WkcTS3XJUiLH8NgDfT0wDoD6umWMfAUeiCFAcwO81BgVnHuWApiUQU+OZYGUqQI0mwJ05OWbBKoaIb2YXutWChQYtJAXdmr7MxhmTBix+PueYVcTA7pUeHp+9fpih/2suyx6zFLZnuhuc2Pirj4vFdMl0qgYJQISPqmhXToRXij3zhtIIiIJ+00tTIztB9V+l/AA0zPyWxUayiQZBaGmp2I9J3wEb6VUHhORclkR0w1ioBi3XmvCpnKrDJUWGhVxUaCPBkDUhTDxUw2sPMz2rFKACIxZrhC2P1XcDBSTLJqLMm+lSzuljSH3ST9MldkeaOWRWo8xQS2ew95gYsj4FwFiTL5RH0mkAJKCb9T1oNNUnwTXKeDDeA56RUBT0/QE5jt0caT4wHJ3EXu8mBScer611Zls33nv/v13Pze7uNylrToPr89Z0AnYNqS7f3ezW0UAAQAASURBVJj32dkJ/8OvxyeXC4q5Jj2rKtkQCrVF+okJLgII9g/D0ibiWkD9gIPBSPOeYZ76U+9ZCcdghDfyEF73urPSNJbz1nhgHAC9NY/6IjXULzIs4nVmu5j+JwBi7oe9NSTj42XnUbF6j5eNSJoS+UI4Nb6QMglKFg9UVPlF7wAIAvZviF6HdMBpgopBsTOx3261Vq/eYm3n0cc/WN9+Pqt6VtEUkTg0kCsikPLxJga/mE5CZDVcCaoDC8OhtBq6wdhOV5G7VnNp2pTEiMgWW5FolomQKr7sURkvU1RHMYUIiKKrnGuZp3hYQSKWSjdQ1KotPaNUVB6qcDFzs1S6IuoFJjm+LMlqI0Nvodj0rq1pKj6KVcOK51X5vRYtmqAygDQAcuoNlwBVMatUBxZRh8MJ74hWCIhoZ6zITW3tTHCrz2Rr7t2P3rtx9wFLYpusjPLc+UAY3wGbnODAF9FPBP3KuODHDbFdyCQAkvgIB8sqEzgvLQejIv9HKsmAUwJAo+psi7X+yodWDTK1HMsAiEveFGKxcVNVEUCX1WN47IERPDAOgEZw0pjkTD3gNxp5+Sqst38yQOtT29QyWjB4UzS9TYDFXEYa1uxG8YaFkmKSGUj2bEqJb7AnTQlUurwZhvd5jDUMh1pK0l/smjDwnhHPIcS00CR/9M+z7+PyjZ3O1vNPu53drTa7XmemOMnMqERYgiCNdvr2OHNG2gSkgAhxbILxUKWi5lwwxmfi+WH94GV5aA9TRapAR/QzOAVhjMdlKzRPJmYhWYNjlD2YjFtqmMvgKw0e+Kmsh0nGdFLlK05CSaN2HUm4zaCgzS8yD0g/9DPYBDPbI2sbZphSdNr1RNuDzw3h/FcYSI5U/qW00CLJYgwVqddI6YuUtTYLnDfxwBk+19MLpAMarw2Kk3pcL0gJMZqSxBDm9MIL+pIFZYdqHG+jYdwTQC/lsJukaGpwgm+8P93otBYu3bz74NrtuwfTfCZFcumnHF/f6nbbLe1/3treZp8QTYiYP069hasV/ODb8Eb4NTSHHZmhSDB5/S+UBzKfVzDbRSVjiSnhpDkWgA9QbTmV+hoD0njnlc3Hkh5teREjz0utj/54TOlj8rfbA+MA6O1+/hej9by8iB7IefMzljAAMAEQbzSNX/kONQCGqjRcSA0u1V+KyZWAKZN3ULGUmSoaXKnUBFjN3+6KXiJZgvNkZNjc3OnOzcxx/IcTbRtPPunu77D5mbmYqWnuTmRKQMOt9pAwmGqYlBLYSUgtrTKSHALwWVXgg1kC6sRIWg2UlcfKCQzkQwc7olIsGJjIQ0M1Cldk/KhTRH6H5plYyPpB1ITVT8t3wZY7Nz5rAQ4f7dEHjbWqDVOL5y+RRlaa+v0oNboeTCYE2AAwCVisaYC1GCnt4gawMZRjHw9hZtzTEFcjcbvPk7Wt1vyV2+98dP3WXQ7A73R3OUWv66L5JMb+/lx7noin0+kQ9FsLfV7S6yQdkaIDOAJTGWOMJ8fVkFcPKazMKgBVxY+E/axdrGpFcTYJgem6lJiuS8yJAcsv2VMjUWLpFtP0GlPyjuGxB9ID5x4A9fZO66YHuxNnMX9hwJfItPVVAX2bkEY2rHozfvcarSifVNneQfiSpgEzvvmdxUufKIcpCAC2RGxvbjEqtKbbbPrhHhdGmO5el+MxDXYXY2DQWKVDN/WgoJJS1XmAylb4IZqmpgzyOiuRyZhATcXParaA4IGYjfNqCmUYEXX8eXJ3r6uhkQCOvTdhD1f3YgfTNwuLK9du3+dzTs8ffdzmjJhuz9NheG3mjlNdHAqbnNEFwplgjbFM4y5TRBijRZRIwFWSviJR0rEutlcjVtMekFkgRACyMxIwP4VU2MljkTInWRxckhEATaQ5kJnAjIzB9QSJxmPxkGohgJNs8pUVStYuZAgxIbCKmFTde4R1CgXK2kM4PIWowARnbU9oUEaVBMa8WiDTYAxkrq5aWgXGNpwDA+E2LYMRDwCBjWJo0SpibANXsxSXw4hwpmwQ7kU6JoTYEiY8G7Cmp54/29rc2V+5evP2g89fvnZzd2Jqe3tnpj3DLdG7ezuz7TY9HFW6A4h7DiemWQWTav0BkDc7hOHyGDZU0Q8Ebp3rKMqx9cMta6P5tbcrSSqKJZLlJFyT9P9prqyDy+zWiENcFW4RCAFVLgInAUiS2c0yeg5jX2JL8zRZg6ahqFHbV9oY+XZ64NwDoCFuLftlA3YPLpFD5LySqots25k4hAbme2RQYwfhhxiATLjKVyQjwfPnz7e3t1kUaLfnICAhId+eLoKxOhdFRKrDnbREuCK5mMgEkiQxKSGrALK2L5Khk4a4OVA6CRMHxFzEaoxkD/TmxP7Cwur1Ow9YJNl4+nh7t9si+NOAyhCrT35C6G0fjkVgD/1HmtNrBoOMkbafHD5yBg3ltatxJgKVx8od+BQViiBOhIAKqWAqWGppxkNQP0ERp7SsFTaSLAjRWWVAcx4QEHAQfxWPzGJhgcypElQZoOgk4huhLTmBLJYsCdsDplFeW3WICU+jFAwJS9DkooXoyUbSRE7NTjuIhLjsh/jx2fPtre7Bpeu3b9x7b37x8hYnwYjlW9yaDcked0FhA0KAyZHsx+1m2jzgNNh4itZlfMJpjPEmTqt6hTQkJ8ELgV6ZsJTSXMQeAIhtWK/YBksvQV9MttfCTYMo47O2Fn60E9cSXWvimrKuq3+WtSm2rhz/fGM9cF4BUPaz0TsTlCaGFyAlvLG+v/ANO4+n4EfM654ETPRD6MNyAF/Mnplp89BJ1gsBHqKYfjJcYmJ7SYwQNZXZk2UQcERIEBlj80AMkpP4oGfyBO1658PCoI7NjI6Mb9QCk7MRGQQX/jIW8qGEheWrN+5OfHIwufns0eTB7kxrhogEfkiZ/yJW0uXXMSEUJoSA2jzbVlpIteKdeowkXIGXsV2YGKwxDvmaxKiTdv6IQDQkSYhpBgBjnGeRQVoao5GucrvMdRi7oCjMqOTWstLmFEiNYXIS02akRCYgbI2vhNRzbwhAlWupci2MAEYCZ1Hu0AycE0Q4W98QITeZKGP2y8G0FiWphSWqxWYJLHPtdTUTRxBD6CI2Zn84+Ac81enur22yz6d97dbta3ceLCxf2uV0ngw9YO6Hz2EwldmanoOJa54zAKomnng+WgLTk+RfNkeO4XdEobESeCc1vkhUycZou6yNFByHGTiTZX5YNxhCZm+lRVXqotpExmOZ/oVJYUhlvDEpra/krO0LDGFBUV2rkDXj44acysIGti6WtYXAunr88w31wHkFQKdxl3+7TiNhzHtWHjiPZ4FMDSMsctXRD6EDh5N47/jVw+AUmOqTnLQFfNkik5WYhOtXYYVoFI1tSANZYrLJvVpC2uGokFzgNaxm0BMLOjQwJWskm5jgLuCD9vT80pUbdw8+pfj0IV+QWpxtsX+WDbRsCdIxosKYlA/SDSltqyjrtz/ETia2PTVOQ6siIQb2OvSBzJSWTL0xZS6mIlFlTNKYl2IC1msCJFosOck0SQkgbHz3A3q7C8CUJVkpn1oLL/MGsSOEmktKgPUjngvEGEmyBJDQNwiyCmKHkpgnWA3Vvh/1T92FzSF+bvieeMo+r/0Z1r1u3n+vNbfIJmhug+IKaHr47m6HdS4tdXELZsz9pK6w6NA2K23k0lcnqgw2aCiWolzspRmC70v8QiTGhPdECFCbeehM8CBfKOc0BNZ7Gglj3rfZAxcxAHqbn8cb3/Z8UTJTEsODdo+y+8fjkYclv1h1ZgZ3aNw+TOUL9xB7FCrfuaZPjCVX8utxseRO+clS1gYcMyKCoNXYSVklMl3+o4EcPA1htAOmwNIe22BJjPYd7a/ZX1q9Ts3HB5PrTz6b3p2Ybbc08wNBTAZIWk+yPVIZKespARtJxKWg60DrLMa41lf0abIFwuDE5KoqDEc4QUHKFFDxEzIJT705yavZjyjbKufJnkVNXhAUhq5EmiwZcxYkdRqAIGlSctqcmAagHiO+6rNr0ONny4FSNZGAmZlTrlYoxFFzmWyJ2TjhQ44uOazHb7arCebDXyQfBNM30WY2t/n82+RUe/nWjTvXb9+fbM+z8sVjYJKIeU32/bT5KEmrjXe57ye6t1bCLFZEGIBvMURTF5VFlVKirZirophJJGFSWF4xyPjaz7QKWBV1crGBpBKZvciaqf9P6OFyndnTMFfRQBFQOGpDf3EjYC0WwgTSgIqbqTpZdLT3jiB5TDL2AB64iAHQWf3yjB/wmXjAb7ozEYUQXpH88Qygjc/b2wj3ZA+nvogYSFTRAUgMDxowjrzMR7IC3iF0foFmoxggk9iM5KZJfAKlZNOASW1gCCM0NRAyGQqwP1Y3NAyTICWe2NmfmN6bnF+6eued6U+mpxUD7U222BO9I2/ovFEky7WW0gApTJW1ryAAaS1snTZsLiRUIVrNZZnOLYq8RJoxkRaeeYoFMJcxDVGJ7PswLLxUYfrMkWyBYAxnMWkSSGnoyoaU9CAbiVoTgK9hkeBDidU4XiVqK2QI5/lOzmhLOOE74exm94DPm167ee/StRsHM/OshRHOKEaME4LcAjU312YaqLOjOxDpztELpJC+UUkOPeikWAUXdSugsRHUJmB/UpSddZLEqjMIFaW6rqd4WHE6qGFDFmVMpNOJr7iRZMkGyiIUqfRMdI2FvG0euIgB0Nv2DC5+e/Mtw9vn9NYijTe7vnq9p6UERpe4B8VfgmQ00jkgawGotlscfd2nPZAZdp6Mg4xMRigNexwCTl4Aw0mMtMRoEgDzYg4AtGtMKTbt4mEUxWo2+aqZuhxmd48G0lKmBNSOyaltvhk/fbC4dPnmHeYcDjprjxgu+VXkc/Ash+k8UXgAsYeSAyMVtXNsFQRGGk+Rv4g1bVO3KNe8YgaIv5SrGZ1kt6+Qal0u1kg9iMT7qfgeoJrg8NBaktVVlaXMA4VVRit3RVUdDwKCtP+QLqAjxIFpKDI9ZEoVweHz4hGAg8VA0pjLOS3CKLmfx+OPk+ijsJUQ+qeCnsn6xmfKU1NM57BvrbM/vXz13uXb91cvX2N+b2d3j2ueEcR8H7LY0j87M818EQfe6QX08pirqMxXWMW5NlknVWFjFdOktSYlN5A20wpj1BwsQ0JMu5gATAhVXibjS8xx4YYZJTvulZXxHMNDykwAEsBVJcvoMJZbiFnckBJT4kcXO6Yce2AcAI37QB8P5Mvl9C/NhnTHAUQ//AHNK5KidREiQGmYnPcpN/nF7YANAUfepFD65Wg7DTcZBpdNbyGmspzk6FeMcaU5uOSIqykuuGhaLZwAgDPXnJZnmzObQna7xEOMshPTW132RF+5eW//J9/f6aw/nZqdIWyK4bDU78Gskp9GYjMKFI3VTnOV2kL4E6NRmmEaqkRTBDplw81e5qZ3HGCYPJKGutozh4ELyJJ9EGzGmr2aaoG4LztkhS6JNFmD2DQQ+7H4p3n9OCAAgF3PRWLUBP2UQE06BgBOu7GANcOjXck8jqqWW3xoNO5jBzQ0nb0DPu2+vHTp+t13Fy9dY3f0VqfLN06Jfnd3dujMkLLvhw+gbHd3pqdbxAJUcQGmQuRoRdFDNO3kpUbZUbexFzbGrYMli2qJ7K2u/HbJtUmTQDa8JDg9jHud0jBkWpcfxOlVIMFtT8mWCTIiyDPRcJZC0tqG0HN6BA0t4+IoHjiXAGjQgy8Nyt+NEjkKY0l/MeG+/XtQ0yAeVPUKWzfIpN6mDaJM491AXosATPkwALGJIuZCeO8T4zju0WgxPz8fw4y+EBkDH2NPFUaktBLoNSYxVprEiW9Ym/gEYEkaA2WVauOveF1JU6XqDBUjqugVXsSYyuffudK6Nb3X6errBxAz1cXnUVvsdpIfOnt7rf3JzvTk/MrV2+9OfvKTH62vPZqdOJid7BIuMWPAXcEwaDpJtwoxYST5XMfDsBI2sogCpGts4pwQZgpPtUZp/VTOP1VEYuYpzKsGflFEopKfCA9ApDUMRlar1qRVjkWhKNSIISQYkBMcZwjIP9y5l6j6uoWMChqJpRFx46VZyC3KvUV0tQqqMllRUMqvpCCkg+GtqqjaKFDF5YO6rUeycBlzbOpVMV+hLT4RqWIqzNQq7GEg3zmIbfj4dmJfC4j6ggXfaSH44Zbq6Y3O/vb+9OUbN2/ce7+9uLodMU2r3Y7pvv0Wz515TfTqPKA6eWjBjeo7hClx1ZEfJBOJCmIJXbA7WiT7gWSQmxCtcFtopyorErUowCrTTFu4wgTGNmgOVQRlyd6AoTRvg4UiiaoEYAQmx28G1D/CNpOlZNeCbOCToD+gb8OQollxOM9ySuLBAqseUhCH5+vWFfhDsFf+Yd3IECaNTDsmfGUeOJcAqOxAg/oBNI2qshM3ql6Ze85OcemTs5N6oSX5EftRGiYm2N7SFXC8K/2WpNYE5GDYFcSwwUciIQDD3+2+dI529jowCPRaTOAE7khe5JOyiDGWBqYUC01ZTDjpTUBOE1ibILDjmxi7unWPwZSlsRmCJw217Jiemd7mNuCp/bnFSzfuTT/8WYuz8d3Njf3tTeYSZlq6Mc/yEQ49PtB4ikGYhKXsReEioXqKwpRRJS5YgjCGzFpOCkyzAcRSj14YCY0xrsrGG2kJmYMsZSYjSKdKSKgoa+WN2kgogcsEBuKkL6sSjtogqgdjqiwpBWJ8Q7SqFBoRdih+TBVEHPwHmoeFZ6fj4ypsgkYmHbK7y17mNtOVB9Ptrc7+zsTk1Vv3uOxnem6BbUA81Hjacp1Cn+g5cB2aEc1X+Im340pptyIJXCRPTAKuquws3OiWU+uqKk9BAVRcR5EjlgbxJh6gYaQlu+MBl7UlcYkf0ZghZLZHz3OAPb28NoDcvL0Eg/C9lC/EnG1jX6huTHBcD5xLADSKEX07WV/kKNLGNK/KA0PeI67imXq088P11h/gfFHaco0608QKhEfMEs3EVFD1hoLYoshJ0JNbmqtO2XaLSskGaqti0IqBZ0QtaRJyuNKnxT3Bmi+ixCwCX+jSdUHQ7DMhssvgqZruxP7S8ir4x9NTz/a2qViYX5pirqjLPAS6tYgWn1mVMTGiCxH/YmYjpNs8tKimTkds9hn76cNxSzaFPyGX5IBLALh8dmUVxOYC6USxRAI7ZSgGAAZiyzQXSADwtcmHQkxsISZu5GYBadWSU4WDVfAKLwFiFKpASJTxLOKMITNGinzsVkyLKSAM0LIXX/hingtTCcGZuuOqw/X1rY3O+sz8Mh83vXLrXmtunoUwNSrsRxQwCV5m+JjZsmFolGFWJIspKYFxLpOKlFUlAOxkfokJQWUOAUWLNVzxnO5HyrSuvsJKpXjAlCCHsPSVc2JkGjlUQtXTkua8LUy3nLeibNEYOK4HXlkAdFxDx/QX1gP5e54vPkzNdx+1JIqMJd76Q22MFNWLMtsVW6FnHCGBZBRps6xQbTeuBgwTp3CKwJZPnqJKgkQOAZI3gRz4U5SBLCalxZZF0xjT2dVWJy5U9ijHxIA+f8C00ORUfAhK7dra6XIafG5+8dqde9wGtPb0oRZcOCzGpIHahC0HTAgJiuHT7WQEPNyBXA+oYUw1oGIGKQ0DaBTBOMHlsAwAGvK6pvIpRQ/tAKGiyrJorjIvyVKgkZBlSrHGJFdKBkiYWsiSBqCsKmorD2hRLKaazGJeKwqpkFVLJCoyraZmIhRHA8bkmvofx91bz9c723ucdueTbvev3r7PlmYu+5luzTKlw74unhAOJMGm6KdHqQwoPAeZTRI6YPJMWZW1lc1RUXoAForOTVzWlnLOD0Y7wtVH6xDQJp2fxpR8AkUnYEl1Y+DN88A4AHrznukra5FfhVbvl7IxDndY+eLoO8NDFPkqkyZCImnVAABiIh4HSdydQ6xApOTA4VybVJqdivoiqS0HbIpJlgANsZAKo03J4mJXCG3n02AH8f1UJNHGuCpGwQ2jaafbWZxfuHrnAWfiH378Ew6QLc/OTvE5LUZipiIIiLTzhsUZcq0ParzWTJD8xiyEhiEPRTHSCqkpDM0TVfbY2mrO6BBPLcQOFJK4QoYUYD+dbJoFDsphghLtEhsmHUqLKmqdjLecpEykMRJRC6GqZjXaYYS8YSFVro0+WnMMRgeNqkFMPL4wTKuIeErzN1qSVI5wFry0hVlO5VGxIYhZyb3Jrc3Os83dlas3bj94f/najS6XH0LMFB1ff9vZsdiQrDiA1LCnLspIt6VsUdWS+ocsKZJ5yTNRCQw5uQkTKPjOHWw0gVajUl0ukmsBS7Kzsuk4YmP676wUH1NO2glwTNYx+UvywDgAekmOfgvV5O+//zImACKBzMTLkeSXg8dLxh62AREDLS4u+i9pjyuw8IaFuK8bqQ05/d+2g7hSFOwJJ2CBFF/IniwAKSq5uOlYQ6L3POlTnyLn2wjEf7EveoqbguBjtGUE4SD17NzC5Rt38Ir2A3V3uGaYr2XgQKaEGLAVVDi+0WSQlfCKr+xv2EzR/hIg0MGSYOwk5ZgtVO1b8FiYGAAwmbvKNGpJUPYWTV/iEwZwSnYDFpUwNH0JGpKziIclwc9LPcWJ75s6XAhnVeOxqjh1ZQrP9sgkYpqIybmSQHvPdFvVTHd3YotbfPanV6/dvnb77vzq1e29ST08bWafIH4lRiJq53wfBhCyk9tyyatdimGBFMYpm5nFyv5gsYSkoeiElFpU1dPAJ9lLA7C5ocutoDuR0qQEGsTnVLS6XtvOSd1Y7BvgAXrLOAB6A57jRWkC76DyBVQWGRsYLYhp2jOzDOd+UULg5AaAd9K8iO5NrqQlMKSdphmFsiEElsRgfFkEXzbHZB6okgWgwWIbzEgzDcAFwDBJhAfA7ATj8u7OLufEoIkLIVmEauGgzsb26tLi9Vv3n8201h9+2ulucCSsxbQN21BkKVGQkk+WRewDohqQSkvAqiLGKudeTgtujMHsKnIKN4OpFoOCTxrqyEAxV0quBQrjcTAw0maaECu9GQqExqguXG0kZDEDoxktSwafumRE6DBGs1wyCzsr1TETJkmSEzkH7MRSL5sGo50jVhvM4qoeRwRMdlHkfLieebYpNp8Tf1PPlN3O/sH6VmdqZnFh9crtB+8uXLq2P9PWl92mZghHWflqtdqsUtJpEY1Mkq1R3q+xNtW5rSE3IJa67aUHgBvFktLwy8xtj22ufBpNIKMns0+chmeLEjgPC7EE+bVzDpWeh66xzDfPA+6cr0cA5L7+5j2DN6ZF9WvocPTKd5OrGFRItBc8yQ2nKt6Z+rsbpPcAkTNRBDGA8eY6J1950LKRpYpSNbYlQWl8SW8jk4wicBSVo8UhAaeEqGCiwBEeQ/XU9AxTCBQjBOHc9fSz9c7qwuzK1ZvTE9PPnzzc3ngO+yyu01dSGaz1lXI8qE83aIf1YQp1dRFDI9Xl6tHk8ESlqxBOAjYm8yQwGUXLb+BTvgEeaCnNLORwUWUtwKRa7GGfSVHQEyubC8oUIvboQUkpTIhyCAaoojGwobeekkkWCLBQZJzN1xqrWXCo1sPYwqPJucnJzv7kNmuws0ur1+7cvPvO/NLqjnY2s0o7x+F2LvhBOKGPAyY6LPJpoI2xVRAAOAcfRsmKgA8zoWoyA2YJtNgzUTCypE8MwKGOEnsOMCalLrfERuKBsuocNPcRGRrlqNIkin1IXy7qotnzclv/emjT36P0FXeXfGAXzfYLa9iZOGpQ6/xQzkTFyxdi4xmw+cOaFZ9uh89g8733w4BbfU7LQBoUSbw6HRMwqBD9eBAFj3Oc/G6thq5gcS25ddW4w5+JR0LCh9WjQWZ0jhwPzBSdUkaqSGtdZfw00y0xgbPPqhYTCOw1iYtn+NgUR6tbTAxNTe/o22hdvglP/mx9e3l+bvnanan23NrjT7e3nne5Cmhib3qKMZslGgUHgJrGiDWeygzdX6xhwKN+/FRkA2VYG4NEmAFSZDEm41ISVhkJZVlLi4PXgYL2uBAliMa8scAEvbnIoUe2nroSVBggyRTgBSBZr3PaYcoSL4OCOKqqjGDRkBlDkkQhHTx6gXXLThxwr4VjB6uICrZQpMu5lfhS2AS3+szOtjiMx+e6Ik6Rfjok8c8Uk0D7E8+3uhPthRt33rlx5/7swtL6VneGT7ZNTtE5aRPfuOAxYiTb2lDq25JCS+U9m2ojyzwMOMyyClSy9wVMidhk6QWG1CLfJllRL+8LMZaQuekp4gTL9G8oMMmWUGUAzAvlDydAziAhwuvXAhVH4nir7hU7CN9LeVaYl6/xrCx/g+X4oVQD0qC+dfr2H1fyuK+c3ucXQQLPXa+/GO00sHe5SFcvQb8l8ymDTDyhDyyeAWJViD3CboiJ8z3rYjJCYwmNVqcK4/vSUJXSEgaA2PhSSEow4Jc7BKZxbl0JByUvZYZITljrOr5c6+G2PCICPquJZwiqcAvNJ1FssfH2YG9ta2d+tjW/ckUbf55M8cmw1tS+TmPrWuFp7azW3l2tINmeyKtBDjmY5xa51o0iSqhXh2Sp7czQJynBg6QY6XBQCQ4FLGIOvRAYRp1ZjDcycwvKogFrdxURWoPGVkFpMsmP2R2cCJJa8BVv/LBRIJ2gbwikWKFUsTfLHZVdncOD3sQcqtP1TJOcQzx4vrU7vbB8993PXb51hwB1a29ienYO9ri2kQN83CWgFUIFW5UD3CblSM88AemU4VrCMwEwqoU6Ctj+vngYjD9BnmIBMCBVjy6qL0u2hSeSDwWZiR9d/ukp3bSUc7JmJvvpgb4ea4i1o0z5SpzWsOdtK2oGiDaP8qjeNteM23saD2ixJqYKGF34o5lxHWm8IntlugeS+wXKEXHo+cN6eXkZDOwM5/RPooSkMcsgUb34IRiL6vsCAlkqyiJIkllss+G+WhgpFQXGkBAAraDA+K3fOxrGD74DS7Q4rRWVad2VyATF3sQue2y7B1Oz0+2l1ct8K3Vmim3R69tre9MzczMtJDKnxJcVGLN346PlHhtxF2LTHsVH6JcSQSRhIoagCTWOeoWrlqC5G93tB492CseoLYKIexju46FSqw+VM+MSktEXlyZbryQfSboRJ1PQHHYDCSExu4MfkigASwNUlBHhC7Dnezyz6HCPgAI8cpTqM26AZmFShyboJJ1ocM+uJMRpLYrsu0JLl/3lcvv0zt5BZ5dbCSbmlq/eeveDKzfv7kzN8El3hLAFi2VL2GfYjeULuXU35WFDkN9IsiEMcx4WkdmjolXDI6Yhh5i8/AXJKlOK4YySJY8ubBA9NpMc0Mly6KI5IC18EOPoqktKpNWCS3QFF7V+KH7i8mof6guDKs2LJjR+dy6MoW+oIYdLEm9oA8fNemUe4D3Ia5E8Jznyt718RYIkGQMAC3M/GxsbbIVmcwxxD6NWkFTDA5QmPlbDkAD9IEbjTVOKLTHQZBGYBCW2lYMWGGhKMinVmBev44nYaRsKWO+DkXkf5GgIicGZnANF5Iy4jNkcyWblZbY9tbjENYkSu/F0Yo8vi+9NtHEslxRrwwryxW4zBBWjRNMSyFh1jIBHhgWlc0mPp5DFMFPNMVASB6/RVR611UM05ZHqcIsxUXv4lkf6IX2oshkQg69goKJRrgoU7q/6BkVgeFxrmB4jRrdAEZ1MiEU6tUtrWERdzMJpA3qLkG5rd2dje2/5io67r964szvJVd17fNOkNdPiOmgUwKLVSlbKdnWVN/EQgqwxHSUdZaMCDvtth+sPczOSJ5B1DeSh15LiVQDyfCSUJ2DjS3PAUFtizg+2LrvrpSk9v+aMJb80D4wDoJfm6rdUEREMARBjBdMbuMAvyjpnhDt8RUJjvC/IIQbiMDwYv9EASBp06kkOio2XXS/mxE5HMtJK9ixaS6q2PY4/kt7E5Fr1qkZggiB2KpjkgOGXCoSQPE+jSZVwT8jnqHzljb39KUbl1vQcHw2bm1tYY1v0+nPmkeZmpvXdqlhSqdUhDC5m2jTHgdp6zD+cqMBaE2OHASyHLZTKOODMQXptirmqxEcgYecQWxxK9moQW5OsVK3Wdm0lKToyFh7O9Rw+flQHpfhr5xtO3jA4FDho1AdGJUB2MiGm+cWQB7ux6iHRIpys2SMIsUTdBhKtSu5zoJ1vrrU7O93NbQLL6Uu3795954OF5UuUiC5nZ+eYnaMHcy0Tx/BYguTJEXdqPzR3AdFGd8J4lGpqpJAvSwAO81gtq07x2SfwBn20S6jqn2vLqoAvWubWYVX4X4uSthAgPfDSbA6lL03bWNEb4oFxAPSGPMhsRr6VwPhNlC+mpHk5AAMSwy1THYwV2OCVLADw5KSGGVgOkhxK5n42NzfZCbSwsICQbJSB4BZlQ8LZFpHfa6QxthN10JAMWDvFGIlVgqyGjzQ2mGy8VLD6ErwKTWis16KYZ+CzGcQ3Snt7HDdi88/CKl+kaj2bmOisPz/o7s7xvYZ6FQaZEJLbNq1hHfWPlSo2igSZLXTRtWZxDkHQVAKBjTe9edEQNFWeVQ0gGU3cqHVRNHWXSDKQwg94EFETBHoQ4W1LgN/WggVNlZ9UsfwkXzHNxi1Nk63tzi67zmdm567dvHfrvc/NtBdZE9OaHyE7ASzdd3e33daFBeAoUeYoPdVYjsfJZWSRZFONMRB55XkIw8DDzJgSXwi7uKAaGY9GnolfaoqY69wAjTyPBvj5Ok/51lUijUmCMTD2QMMD4wCo4ZDXu5ivHjejUXyZbePVQ2Ks8PSP349gDDQssZ3UGg8XO4EoEgPxiXiQroIsaRoSzqTYK7/hQLSnqYMsgYDBFXtoKXDdXgZLBoccJmsxYTdjrehjZoLNP97I0p6eYbKhw1YgqqZbzE4cdA/m2jOLl6+xI+jZw4+31p5tdbbnZhnGJQW1CCVhIv6CDVgVSvHRsTichS+NEmU0Rzy1zQBuFwBkwKSANdDr2LiwkQWe+SH0+AJBYZUqsQJjs5EAUvCmfOPIka/KQ1OFcZHcgGmcJ6N42UOFUZyoQ4I+1Y4LJUoTO7VY+zzk4CDNl0GLQ7X+dTDBpp+t7Y1Od7K9sHTz9v2b9949mFta2+62pvi4O8uvBzudbYfjsCBEFx0ihSaylMgdiMRAmoRShBUqZJ2BLApVNdOOq5qcvxG97TI9Ekh9a0Pky8vSmanStrlILW0hUSzxSfwyAYypnVb185epfazrtfPAOAB67R7ZsQ2u3wjHZjwNA0phLwMgikb2Fcubi8RrlPEGLrZaMAKxG5qkbRcRTCQ7oxBkfeWcEtnXV6nXwrEztZQwSFNiHvgyp1gKoegW6QMXjI6MyDF+cJxacYwEmZ1Jn2kFRFAp1pjqdLWLqDW/eOn6He4PWnv8sNPdbOMLDtYrxcCPJp0JrzTaQnID+BY6xS2aI6ryuD8wBFRDteadRFZzAaf9ABZFblgG18nIunTkp7mOoGqxKZxawyUm1VG75yU/b3bGEi2z0XbHNYrSaCBrgAQ4OtUlZ2Kdo8H4LlgwEsl12To+Mb3dndjam15YufLgvQ9Wrl7rUOTw3cIyT2F9a4trBuh7XGFA0lG9+JKJCp7tYJGSaEjyK1eXlg+yn+k+WkGLymSMWYwHc9EShmEhuftGmpcNAQMBCSCRLibxaYAQdeRXr69w23kaRWPet8cDumuO1tKTDFzYll98C8/cdX1/vdEy5EkNqTpz8yxQgyj9R8OAEmMDZsty+hWj08HuTnebmYBWuzXJEeOYBsBIJ8g1hDE5EMM/SDGy3yR2/LITiBkg0pUrV1hHMx4CppRMAMb0AI2EqBLTKJZVvXBDZhZTCBgSRWOcN+SYBiS1GjBjyFRTo4Ei1o4fHCCQpEEFGqIWoXVoqUsQNLE/zZfIg4jRFzT31ux0mRiaac9fXppemJyZ33z0M7avTHcnWrqPmOCGo/IEAQz5sgt1jNHMdlDgSSFE1wh5gkrTJwdsAYbocCFNpsDspxn7fnRpjrzJI/ZTltxMMQUET/X4IePjEwzy5dxPLDwRkmBLxVfPBlG0NOcKZUKQ3Bu+whKqvDfKKojVRIUrMDOiGXoccjger5Zgo1yqStqrGZoDrcBGG9RVCYKmJjg8xxU+kxvbEzsH05dv3rr94IPZpZVN+GemZ6e5h0nXUCn0iX7I3BJftJUQ9gq19BcjtXocSmEeYVb9WGVBwORYTgoyNdMgNgSsXmHABOQVRTTcVeQprZcgGUcBUvgoxMNpGlZBbGttavKWlifyeMCRXmT/VA5BTqpz07JIVVh4PFUXgbpswkWw522woZoBch+6CA0ed4KL8BRObEP5+IAZJ4yJnMFAQyAwb/9U4b4X7yyPcaqBBgwjEKEPk0AAjDokquAtZKaYlwHYqr6abBJVvc3B7GSk1slCgJPRgM+FcXCMr6Zq6OdemlYcAGPM1ZISW1IYtnWBNFfTLKxen5uZef7oZ9uba1DPTHGUif26XYT6qLb3qSBZrnOUojAmwtPQpzgjwokqFFPgEXND4X9g+zyB0ngEHD6waE/WAgSieo5mN7mqmnyV05LFgKTV/pSuSDRD0U6sGoogoh/Rx4QWdbQ9GquAT2lyjxPssGrjDrF1a5a1su3uwUZnrzW7wh3PV+/cn5lf4otsXMlN67mu000mQkE8MNNIAPRA2G2DpMaDc9E5GONN5iJVRlI0JuMeF6ntTUOqTNwgyCLqEu4VezJMQ6D8UScEUkuLSA3hbngDeSZFNFq4AZvXUGcDrc4EZ6L6vIWUpmYbUdpo3Xmb8VbJr2aA3qo2jxt7hh6IEbOPPH6ZiX4IX6rhRL/c/K+Xl8B4daoccMkPJln4KxwJbIVeWloy3m9bv4Ut5EzeDgh/oZy03NZSTEy2woBzaktKF6miCUnfaIIkxobxakRhnmOqCkE4gURME1/RUCCz09Uq4dzsPBcjcjZp4vFnnc213e4uUx0tdvZyQMwzTDHpolkgfUpVD0DbezXxpukStIdVtJ1ZlcPxG6yKOWETZPGgZLkT/CKp22ik2kvQVjzTgFWJDNVGKgmQ0ChappH6JkUYpifuKR7PJ6kdlTB+IILmMiejCJGAjhgHWPNhzGdxii56nbzCFc+Tm50dLvtpL65evXH3+p13ZuYXt7VqpsR0pbX7GQG7SBX2uNeBKQ3OrmgaSakTZFWb1Yj4F3JK9pr28GejVkKOajwk7YEavD31Z4MofYJE/0qCdDobHUOl2CeQJFDDwpg1jv0B6k+m1zFlQzC+bObr2JaLbPN4D9BFfjqvk228/kpz+aVlbCB8Ac/QTVEX/MXBmZKshKGEzMmwAyAuBPJ5eIiRmX+LU0ziUs4QGPrSThfJ+7KUlCYoMQnbBghSTgJGQknKkZK4ENg0DnSS3pQUSaqKARRiWNgTHaOyPAmZJ8YmuKV4ivPx16Zn5/lixvrTh92OpoIUYik4OfxjXatX/BcOxlL+UY15zg0gGaCRjER1UFYBEDTGlywJQ5lwSkNCesBKe2nAuyq5EoCYRC0fPxMgwwWpVVrYUrwnf5G4ogf3qlqD3+7erpbQtAg4s707+WydW51biytX7773/vKlG1OtuQ67gZhOm5okWvdz8QZ8PIwSPYXodeRSEnOQADZGSupkODEyN/xpgNyiLMdVNWv1sy+yQXMRithZNvOVm5SeLx1Yw3269Cs3eGzAxfHAOAC6OM/idbUk34Y0wHC8fRgsGEQUALEdVYeAFADVfxNHW+uX1JGGgySJKxLDBpt+WAVjTzQIhJIMkB/hHK3Q4GoUXyjDhrmZmSMEOyka0yuzJIDGTTAZuQFUA/B5+JDCThw100lO4B4aNVeTQnIkH5Nnbmz3oDM5MdeamZ1fXr3Rbs/OPn/0ydb6493t7uw0W11Y12L8JkjgCBmzIwQJeJWlHPbOqKGyVkC1fsFgjm5pNCKWzCBBu80QjwIOjfpgoljxBnyY2Q8um9IYNQ2JddigKqav1JqIXUK0Zn1yiGVKp6rCZNa94OWnBJOxIToovZ9KdyxJCjgt03BLjxbLiKHZMcWS4dbO/sbO7u7k7Oq127fvv7e4cvlgemabPUF4c3rGF/twvA6x1i5D6zhVNqOxjnuAeYIyoiY2bDL4hQ+n6UEFjZoUsIUYdm7eC5vb4F7z3Fi1KtrVS/ASMJXDh2oaYt4o7ENljytfew+MA6DX/hFeqAaUr0sGCaYubB4wQ6veOB43BxvttxKvLQPM9yCESSCtANWLRwAegUwzWNjZ16RhpWjMKC0B9mvXxIax2SxyRcRAzksaMOX8FvTUkuD1vJElgNG4DlLHnCa39XHyCRbDlq7cmJube/Zw9vnjz7a6W+2ZyVgfi+9Eau9wfEaeAEL/KVoom9ALowVkGBtRUT2WYyR4/R/mxc8q8AWm1ozG9+ZRW6kWHGItE2JhQrSFAB4+aCQrxJFILXEFscIhBVS6PUCKudQZElwDOCnUxFRr92ByY2tnszs5v3T11s27HKCbW1rZZm/0zh6n3adm9PFdpMX3Tad4NkSWVoo8d2AJLtrlJ1gZEN6QNbU3HPrA4mQ8MICFGHYuttcq0QQnrKYJ4enqab6qdmAGJqX22kBjlL+mrs4WjYHz88A4ADo/3761kvkbfIoZDE10xB/KfgHxJzNvytjQC0IJBxlIGEy+Xg0TEDDYM0RxKAwYShMYOHMXI9zGDJIMQVYlZWkztbatrDXsnFZYS4pKduGrk02KluQgrhyO8YWRxvQpfJrzYbp9WYe/2OOz1d3nbujZlSvXWrOtubm1J492dzY5vsTpOw5yM6wjBl4oFXQAqChvV3t94nh2FdcQNyjJLs0G4XLNpkAeVsWXsKgMGk2EQBWwMtx36KDASq+YrVZP0MRujnNjqlyNEr2qAPkRSbVF1CYCykGAl7g5m+NdovGhMT4loqNeEwQ622wL352aXVq9fufB9TvvTs7Mre/sMtXGBUpTLX3RHSb6GDuEosNqgZKui8OlMJLhLAI4qVJGKdUNrGICisL0pL7IHqoLhOg12D7BRLUwEnB44hWYjf5D1Uwz6/dXBtoUahu2GdNAvgK7xyovhgf6B0BHetXFMHRsxQX3gPsMuUc6z1jwJgqMXkOMIqQs9m2O31zmMgH0RAzMAPF3uTdnMD4hHFEQ5Juur7TzQJYa1dQ6lXhwVBljIGGKJAgcBoF3lXPwtI6mBVoZxLqSOP7UpsohoKcoWLlhfYiN0VyzrRNMu/vrXGm8O7kwu3j51oP23NKTR5+sP33MQuQcG2emWOXpKsghPsB1GiYY+Sv7UcQuGO1rLlzKfAsRRswWVXEA1HI7UUikkhi4bzKlc7S4A0AJ7NxtBIamkhCDF3hSpdgwNLXBoo8D75DozD8LWWoN57yInuS0rr4aP7mzP7Gz39qbmlm4fOnmnQeXrt6eas13WDmbmJxttWkgASLB02y7jUDPA/FhWmA/AmkJq/A8QFhxxEUQJA0EvUlNiARAreE3Js/2upnZ2GzgS2g1KpxQag/jcgB1VCXNtgagrIQTOTowiP3Ne7Kj++R1p+wfAA160q+2tX2tOu/Oh9LzVnFcrx7LDy/BfvyDFpKWJiLRIl5AjCg5Zgea3Sd+K6nF0JcjQhQ1QiQADJdzQh9/GxWALUE5PlE7PIVVw0mqWnSVqsEaUzL3YqiFyymtzSKA5dSv4+Yr2PiKvyIWDSGOGUM+2QQY5HM2HoWeiYl4hZtrdNvS/i4R4Ux7jssCdtc77BY6aC9duTm32GrPPfnsk63dbaY7+HI8TWTWY2+/y5PSItr+JM7kofC1T2bs0n4DsfhFhHX4RHQHj07X6OgoRjrnESMBWHcvlk9UVisRwYKPpKIbWwLCFnhFWDWmDhlQ4ThQA1qI0t/6ICHEQAAFLuwmmpjmKNzETIuJsS2+Xjo53ZlsX7117+atuwtLlw4OWsyT6Qtf83M7hD7c+8MD4LuyTFhGa/FGq61XIkY6NxDmVFnaT8MSX7WvbiQ0gMkLLGlkgg65kn04YPYGjYWnor40DZZjFS15uFhqnZAc3aCpgdom6izKpWMNl4oMg48qPUcwAYf7z8KAsYwz8UA+qTORdjIh/QOgk8l6JVzu2a9E9YVSetH8kDNA9lIZAWCqrSXP3wEDECdg2JSwIxDYEVWy9z6Ckr0hrSS2hBIzhLgkQ755S0XAZdH0OSRQBYsJSrgh1gYQp0CcCSEs8iQva1YJg4WFj8MzXGtslTi+BTGtMCdWrGZnW1du3p+dW1h/8tn600f7uwcLjO6cFodmf5+D9FoC4l4cpoY6O0wi4WSSZMakCrDmiiLIwB7w5GGPQjHmoowht0kuJgyQMFUwkpfJBKZxe6m1ogSymIxgzAhGO5d1ozOLevvskSdGJAza42Olu5Ndbkvab0225+69/9HSyrX23Hxnn3k1ffuUcIwG4mV2/xA+IlCMwkwSXkNj7eBJ1ptwAmkPQDYkgax160xTRT6VyCQ5FZDy01QwJXxi6W5Lye62qwNEsOiuAoGL1KYxJdfosNlLOSO6qmxyqqulJeIMgFM28AwsGIs4aw+89gHQWTtkLO94HoiFCFiqzc5AGol1nY2SipEMkDdS35d1vmjybchwyyhFooqpCwAzJjtik6uhom8R4pK3pEk5qb1BmQTmsqhEmrjMIUtRkJEaRQhAViNK7AGyZN32x7hSDMOxwuOWEv5oQZEIyMuKYKVIm4CnFCqyfLS/35qZW7pyq92ew2vsClrf3phvT3LWibsBeTqKreIabhAySWZGeMH2Ikkj+uFeaYVEEpd5AMZUdobq4GbPkCiZiYmGKlOpYDHeinIQreXb8OpRiqZewpCc+Ic7dvd3WfaTajofsR6um5jR4XeU09K9ifW1ne7k1OVrN/m21/zla0wI7ep8m+TBhlv40Cw7y/S5kV0tkRDzCE/79d9hS222FIX9BrBEFJGidbK2TGVVCR+yGXu6HI0Y3pDRi0kCV8GVmOHAIEqHO/Dy7Pz4kGzhwwUOqS11lfAQlpGr1HvVUcZp7IF+HhgHQP28MsadyAPly4t3Iu9KiwGmym9JXprAjZTawCcMQBEhRsLICoUnHkqaE8OI7fviNrK0pKQs8ahuEDcElsSuIgdJSkYX3YoGnmEZMlOWedVkPMzeHYZ/hlbiDuIZGORdYiC5bqe7w0IaV0XPr16dW1hcWFx99PDjzvqTbof1sgM+naG9RkQNfB2icLseUGxzjisTqweHNJSSkxQ11YmiwGhOVJJFdUFjWlXEgG2W3uYYE8IO+wDIxKccAF0SQMeoNoxrLooFsO7B1M727nbc83Pjxp3rtx7oAxcsGrLoJYeolewfCznaTMWd2UTSFFk9pNWIJCiEqKERghJj+0NI5RAwmYwvc6qyCFyKSvwFBNJUDC6bgKlgcJdT2RxTJmPfRjVE9aU5c2Rp5JkLHwt8rT0wDoBe68d3UYw/+tZT3KMBKmKX+pWnP8VqWGaXsMqRel9VYHjVIsqTQCFVf7X3ZUfGkKpKR/EjhfTVWxBW1iYZjIYTKImBUzJwciVNYgCgzGRGisQP5HAqjyRk8Rc/sO+Mroj0IwIg8SnBNMVBp4PpLpHRzu5se2Hl+t3ZxaXHn/x4Y+3p5sZma/9gYVZfaDvY40NXukK6GtK0OzoiV903xGm+mJWp7bQtaRIfMGPOxCZWenW8DGNM4rar4J1AARB2VTKjGKYSwrGtORpeI3nKMT2TsqjwxYZyhWojzEMf27c58j71jJ3yE9NLl69fu3nv0tVbrbnFre4uUz3TrRbdkegZC/lqbNyXSDH8NakTeW4vQCP6ARM6q8xedW6Um1zmJb1hhCRLQ2Av8SkxqahUXSotjRmuy1wlb9JnewdpScoGUJpnuK/8Bte4OPbA+XlgHACdn2/fLsnxRtOA4Zcsgwp/T/tdyRAA3vALnZLvRL8izYU0B0DI9F/tWdsr0Ab04odgkJZ6X0hWqh6kaxRpKDJZCnFjU34CUAJDRiq5KPLtBk3ZEA1E0mQ/WLYIx8rQNEe7DyY67Pjd3m23Z9oLl9kUxNGwx48+215/vq19QAez01y+qK3UwanQgvkkZpIIbXhsjkGstFKhY/FV3JZrC1TZSAx1bdqJWGBy0wSs5hjozWEPCaoxGUXgzOkDBHVgWKljmorwbWtnb4uD7QuL167d4bTX3NIlZoO4JZJLgDRl6K44pe+jSWCY43go5YdG29nHMMis3fSyrO7PYMrkKnLTZ/HMgUHyMaasShiAqlOa4UjRgbLbaPnO7YdTqjgT9oYTzkTmWMgb6YGBAVD+5jSaffrfoobAcfHN8wCdh5217LCYmdH1zQw5eiVptkC7Wmjv8F4Eu+hzlK0dBFKLFHwvNHbg1ujq56Ae2yBrFG2JdTWqymIKN/1wrlGkIcEyM09MqgCgseQkjIncC4LiJSrAQ7hWEYsWeBTFhDSClmDRXmj2xUxPt2fBcyRvd2Jyrr24dGWmNb+49vghn87Y62wyFRKf2YjPZnnSLm7tRiEDHgaETKvQzh7tlQkFmARgWPhI4GpTbUzlRZPVLDRHsy2ijBU1IAh0rWMlBHWCoENyxaVgTBf2EAET0lC5QzC8x1H2yd3J9uzy0q37761cvTMzu0AsxCZtFHS6OxO7sLHsxak3XMm+aW14Zs1LMvV92Mqf1HleCoVUqTaiLsPkcmikxFSPprYQNhlcBxkJJP3ZAshvWIj8xJSWpN6+yKx9IUBnsHxUu+3D1fUKLA1I3l6yU2LYfxaKyNTBxulieuC8f0FGbPXAAGhE/jHZ2AN9PaDXT7yA9D4KiqpIx6+H8wCr8SaFmCyLSQyG1y4BEEMgSMhclZQAfZElQQNOCQk0CFy02DRsCDFVDRuMSd4EehW5yvTUphwwJNP7ZxYZfBVHFC96wyBj1Cemwfca47lOUiL3d9c6u3PtucVLCwsLS4sLy88e/6yz9nxzZ2uWI2HQEURJh5aFYsyXWqvD+ZoVYtlJEnXkXOtfcWuOCWxzTPQEUTBGDGPbKzk5jtZYL5nRBmzP1ahqT73iu2izquJ8Fh0ggro2LetssednorWwsrxy5faD9+dXruxNtnd0OSL7fhS6kWhLu8U1P6jVUS9+kIjOtVOqSPZb6JI/XSzqwxlUHz4IwSG/jpiCOhmTshRytjAqUp2BLKII+PQ2pASkkXAdksOv1Yo2SDcqKYe3MelLshF5S5ZR4FosNleOAtPXgFGkjWnOyQOv9qFUY4nb5h4z7iKNJ93XIfVvV4P2bN47TaGnKx/L/uOqiqUTMeEQ/kQkZ9mFv9H5Kx1walIRNm9Mxp7W7DTfbODzDKGCF6jeoU7Z8QBIGEwCoBaA+R4SEsiRy/VCnHkOkuYr3iy11P4/k6avhAYPNA1MsoMHHk4AjQmgNHHmZk/hNibpS3zCAeAT/fMf4WAYkxw77LKVR87XaXaMZrASkYIFGbm3qx0wxDcz7Vm+DrrR3Z+ZnF28emt2cXH9yaON54+2N55Pc7pqYm+GpSJpiGXLCfZHxwxQHJJHLNfo6EaeaSIhdg3rCkIHAWqm2MHDTCilhysR8VlSaomeZCiIMBdbgHVdsyYLFZeoP8Q+JEioAYkAMJqkUXsU1+zyQTQmb6Z0xw9ft9ibYE3v2rXb91ev3JyaW9jmBBwGoFlDtfY0xzl/TraT6Jz4CrxgkckYOUdaqiRkhEXaXASsL6iFr2sC/ZQbIwFD4+abQE06UUIOIvuygqe2b5WRZa0NKzFDGKnqK9zsWeUGRjej5kh7Va5tS2C4xlPWppYwT8LSbVl1VIWed2AUugV4+No5SnleJUwN1cMe4nDddRNElc9lOItrYbT2UYhfMk3ZqJesulRX3QRvVNoEUP5ilwymaTyGC+vl0vKzhdNXZyv2pUkbZL8fZT7lk9kDu/9YBCDxBkCsU69A8CCdJwBXUoYM/SaHKIkhDGKkh6AkS3rjU2CJz6pkTKBB1lvsKxDkcAkll4lLjLUgIZGmsZ3kuBEMyZSGnYMxnl9V2+AiMMmw//Q1RhKIIuIYlFaxWCci1plbWbk2u7hyae3pw7WnhEFP2TTU5q4cke1xYJ7o1ePGbtyUiC5JPtBaJM84qnhZxNOZirP0VNfWErJgAMTaT4S15Pr0KEgGoX2dVVOcppEp3jZqBUVWqaBjni/O6WvFEwkKqHXMfYp7nNnStLs/1WK389Xbl6/fbi+uUuKf5ogUkSnso3tghm59rK+PitBHquSFwQkbeivdosN2RcdzEYXQK0SLVNJUqPqHJfcl6Is0X197apH6OYS3JOsL9xWOwMRbeFkEYyQCS3xf+SMiUw70Fl5hql7/AjEmhhHAeQ+D+lhtdn+hKQTKSnuPlBMgaqUnYD1kaQhpFA/paijtfyFlzfH2/qyWwNx17IayK/Q6xrXp4mQZ+7rXV68jpnyyw3vCoNbB5VEnRdE33D3IQRruy15WpXazZBEa72CNUVNiSpkJmz619ErOqlMCSE6lw0UNIksJSWCAHIGG0/4smstOAC69kTSlPUIe7LMThuBD38vS9dETxBetli6MXm3Nt+aW1p8t7Ww973a3ma7hLLz252hGRYmnigrCCscoYKhQOBPRDwaQrJf4A3KNOUQuWsNyzMGi3B7zMwhQfeSiU1jET4yCW9M8TGHBopupY1PQLnNXnOSaaU3sHXCqixWug+nZxUuXrl+/t3z5+lRrbp9ZxqlW7IqWWDQSAinkVoClCNJJKg5HQZeO5NJ+NNGiRBguMaan9Q2aLCbQKzmrBgGDWEoD4G0UzQWyl70vcpB28A0JKTk1gknkEDnHrbLM0blK+sFtjKhdM4sDU7YLisFyBrJfqIrX3f6X6cwqAOp9/GXHahiUxEmTmAbluPhWeYBuQKJXOABivKSYHqh6S4HJqpIskQCwIIQBDAIAxAKQmA/wTiDgBr25yBtVJdmZw0N0UTX6r4mJLQ2uXrFGpkAaYp/gnLJRMPbyCrW/N+UrK7UZfYo7oYkx8OnUZLs1N7M0N7+wfHlr/cnms4edjWe7u9vd3b1Wi0sRtQ7GwTCHEzwVxRdENnGoiiBJ8QwH0RRTKbBSgKVFMm0wDquinjrZrWUlIitySmhXCWH8jD3JVCn8mZrihBpPWQe9WnPcVri2uU1ENNWen1tYuXzj9pVrN1vtBa433OcKRK5+JlyK6AW1SiGQbqOgSvacKslvkSwF0BIBwGC5gYYOtWhoGkTQwJfypbqfOuN7tTWI+9rZywXGlM4R4mQ8SOOTkdoGJqteAoBqDOhVZJP61fTSjjFvrweqAKjXAX17VS/ZGHMsD/R69RW+O45l+RBiv4P0HmRWoJoh0K4O8BqM4n0KrHGDF9KRvadNqRLSL6YxUkLqUccDv1c6LAXekh3YqrM2lVlIFk8AHEvCKMTQpMENoGGea0EmYF5ykokBqM0iSNWR+MlOHa01sWZEkW9iKEjgWbGEpOuR52YW23w8Y3Fz7ena88edrfXNnc7M1F57ZnqOORiOVO3q/hzE67lqriVeICFYC1laferyOY4jrg+bsAz9tspzM7bfGN28qE9VaFtR7B7Tfd8EvxzyWtva3eh0OP7FTuelK9eWr92cW1rlqFd3n+BPq2nERQjRjp/oDWoW5VgPoxMC2SfOXSSXMwYn15oE2EXIDWRxsICBNQrL6tSwrUYfPllj0toEkhIAYyyHnFRaSLGkbMBJ3MCXRQuX3KO/TSXNq4ObrSuNbNa9Oitfvmb74eXrfR01DgyAhjSm179gTvNSGKLrTarq9Rutu7CuazzQQXb24sEQnZQvepqZ72VqSflYgXsVubaBp2gMOXEVKsgZ8kLeocCUbMC6zJhVaUADnwTnClhp2pC60hiArO0Fkj6BBk3KgSBFASjFNuMICogYtEOHqOVgSvEKBeKJXX0fdHp2fnV5doG9NVuba3xEbHtrfW1rqzPVnW/xCY1YGUOE1r4kJDb0yBZrYAkrJoGEScOyFkxYcdjA2KKtA14suiGNJC4CKX1dtbW2vbm2tdOaX7px+961m3fb88tTi4udnf3tbYU7fBaFOSq8RbREkfjLStlGhBwOgmEB/9swGVT/usmGwamsLeFBHFWjjrbXusxiPzgvMb3wIHXmpTZ1mTfpDWTRteQN+sQD9BL31lqvc9f2cvViSjkvAcYALGwYWbxjXoIJr1JF2fBXacfrqfvYARDuLnt8Fgc9hpK4dNFx6Uve1xT2L2qv8enD3qpXhSmfWj6pEe2ErBEAldLKFvUKTF0mS48Zn3IAPFIaX8rplYCoBhKMRZWMpWFnDqflKbkXk1UAg8xzQxrNoUhKgVkkQAROaQAsUOlj6CyDKTagjjCCb7trNgd/MlvH6lZXN+zwvbD59vICJ8yXli/xEbGnjz5lNuigOzE7MzU7MzetT7/zT7IjWNEn4xVeITuk2QaHpSyzgaeaqQ/+UcVhL+IcnQDTtA+msA5H9KON1VihVbSDqa2tvR0+3zqzdO3u5dVrN5YvX5tpL2Iut/5Mzsyyb4hD7RDyMVeayRF3csnWHuruAbuoVdA/bQey0gh6ggy8TaOmSolB+9DoKOhLb1eISmDKAZ2wgTKvtNYPOoumyUeZeAPUypP2e1iZsIEGfRaTKzGjAw3Jtg2kQszzTvX+s0JP88FRlU4w2VGD1Sui31Gpfn5+adBTOz+NSD7a2HNV9WYKP3YA1HjMjeKb6aS3vlX8mh3rQecraXQuKBu/zGASydvWRR6FjWkEQOAh6H1Qx7W8V0JfzMnEZnP6ymwgs73G2zmWMEiO8baNnJQyaxbmbZgr0bwZ0QaDGOGH9yszfxIxkDZFU7W+3SVMmeeD8nNTV2/MLa9e2uC6oLWnu53Nrd0Os0Zz7TbMBFSx45gJJMWkCi+0+SdCj+rtTHwjKzAgjbFhOk+vT1vwk4fLZuIp+PlwBx8w6ypw4bKilZWrN5cvXeOK54Ppdnc/PvKKHM0VIWyqxZafuPAQmL3SiOXr8GqFdo4pCVPNDsiI0oY05mSAmwDvoYtDkPHkBlJ4Fg30WgImkQkkey8ATSlzFJZeISfAWC+qrf2l6R1kqs1o1PZFNmjOtvjK/XC2zXl7pB0JgPr2Gz/avlV93XRc+lIIWt6AnjS6r7LtvSzH9cO5ui6N6bWTJjgWYbzJ5hjDwgQYj0MwkkoaqlIssAkMuIpakvHkII13ziC+vb1NjkySyaBHNXnQVlnyutyoLSlHgU/D3rBkiLq+WozMKktze400JskoJp6YBnUsL/FhLIIHzdt4fmiPLUFsm+Eb61yxw3oUMyjiYiMQj667twsxy1FTfFZ0lh3SVzsba+trzzobz9d2Oi2CJZ2T17OLk19VgyiyIoWQ3Z0OkQrnzgi3mPwRKh6otulwlI9FLgzS2tck9UwodRX6zKC7PTd/5eqNhdUr88sre5N8zowZn2m+4a6QS9HSZFv3l2mdbELbhhCimAgtQkodnwbTcTBSqBKxjbOLKkPjRwOTlA0a403cS2N8b14KAYbRvL05Zps4q5BGqh5TFU0ekWfKI6gg64tvkKUuVPRWpQ1UmQCMkb3EYBpVR2Rq4g8phy+HvhKENGVZ/SIuFFl1akzAYmrDjrwQSg2GU05v1SiYWssotGdJ02jsWYp+a2RVAZBdWXam7BOllxPZ65+sGpE+JWTvSQlZ9TYDpRtfvh+Oq718iAzJHm7TbGqdwCTgWooApTpjEplF05eUYFCUI4dZGgTmKoVAQNE5tb0Bk1l6GUshJc0g2PR97YFleG2aVwrviyyb73b1ZUkz8BiwttgwKOmTX/oKGAXiCB7Dvg54UUGsEmIoHbQUo3Csi80001xmOctnNHZ3rm6ur3U217a3Nyc4kTc5weYgPnrCpiydBSMoqWbspvkABb4mOGm1dOUY2rnlh/NnqJAT+JjX/sT2fndHcz6TM/NLK0uXllevzC0tsxFpihs1p4h+iJ2Ia5mrkuXI0zctIvqhKNmKgPih77qTAmmwKh4WTgEh1tx28qGroy+9UDD0JMjyebnoPCX3Aqm3VAFXL96YvgJLXsiSxiwlY6OWIv4lIcFcJs6iJZdcrkoyE5xV7obbkmxFQ3hf1Ynsy9WLBJMsDfm9xZLSoozpFdvLexpMKb/UWNpzGvlvA68CoIa/jvXsz9BHr0rvGTbhrRXFX9v+sx4PMMb5jQnMM3VKzzAAgKFIryurGkXTu2ea3iwpx/RcCOTz8CV90pRAKcpw1jaKxqeRWexLlkLSyMSU9CUMAcQNTKPYS2CxkPUallWWXBaBU7IBlr70dGKmhOeggIdtN9oPzVbo6rlM6CpCSFjhmmAqRUfgdQCMgImZHT6z1Zqd25+eW57dWmNCaG93e3+ns7fb6eianz0uUdSXtmQlx7Ng1Gkz4pa4f5GAa4YvozCcdru7PLsd9M7MTrSXFlYW5xdWWPSaXVjk3PvETItAakfXNyswQjszTERq3AekxTZs0z/GZgVUxG6EXMLWaZD36vpT/fQjqJzJ4whjkCi8BcfD9WOqEZSUKCZg2ASNHFEkI3lYZmzQUDQ+KU3QlzhpAEqCEp/yjeT31IBzc5EnSykneQcCw+eBeud+BgqqWj24vq5xKE+31aPxU1IYVzanJu3/s28Ds/n9eWr5roU4hZRwyVu6tMQfF05FMJ6VzOPa8DrSH1kCKxtQOjTxfZGu7VvVF2n67IjZM4xJXWPgNfJAPmgeImOSn6mRZU6LktKty6efeICyJ7jYi/Rf0gyis7Oz1JIQmIFXw3Up0ICJE9lrVbInDYC5smpEAC4LcZ5CUvKIckyW0pKrIadRTLIEFKfGazooyTTQsmenNcUpdyKJKDJHEwELVXw8g6mfYGHeRsEGsZMWs1qz863WwtLKQXdnZ3tja/1Zt7NFTLK9s8N+HkQSsqCLVTJkEgoRdyGk253oSgRhV3uKJ8eC3MJye2EJObNzS1PTbWaGINTCW6xmElbrvwjFMJzZJF1AhBqW1OqkbdQ9SX5WU06bMD4f2RBZ9lsSlCzALiZgMiOTJQFEkbInA2dVCcQ+riO2+TcCsYNYSvYSNj15r0mlKNufGBNn0UADWWp5CXBpDJZQdP4SVL9QhT3TIEuDG/hx8aV5YGAA9NIsGCt6MzwQrxs1xQGQG9X31x6k8fn77z83G0hLSBoXyU3m1z21XJcHRtfGRALuZUles5cEvUp7MWY3V29tL6ZUBxx2VcMYMPSlAQ3isyqW1lqm9bKSRZErEBligUCS+KmvpmIYdVpdUiSktav4ZhYbdoiDaEbI0exRHNzaJQzSHYntmbn27Ozicrezvdfd2dp8vtvt8L02+gHPaIqlK91oyKctCG44sD7Bt+BmWsQ+81w3NDO3MNmen5pts66FAUz5yDLNERJ5yS5N7cSOIj5qAcDaWn1avgp2NcWkCAnaw7/v3d6zylEbXlEU2EjhOuESEGwitaHCH6kNvEkG5dbYqFXzwxIDPD7EZvKvA0VzmTKLDVGjFK3FedJbbKkiq4YgGzQvuWgnhOXeh9QnXH7JJg1x42ke2UtuxZuhTsMGLSl6iWAXz7WFqaIEyp5xrtrHws/cAzw73r7kGlljmM8nm7rAuAqMaxvFpDRg+kSWRd748JIzuvLXMDQUk9JwqkhpDbJSYF/ekhG4ITBZeoGGMWWxQWyZDeQQ+gbl8GKvHE8t6AMUPKl4TPF9C3YN+wPsmrYhpFBcETGIT5vnbEpY62UpTQghg//5/G1rtt2emd/f220tLu91O50tbnLuMF/Et2sPpnanZyemp9uEPXzFdmqmTQA0OdPSJ1VnWmzx2ePTpxJFbCMPq/doCzOB0NTMTAtMjOpEYNjOVUXsISJV4YVOrcejr4f+4f54cS2iIap0vpi8D0XFGwYZJrdY2f0iQyFwNGPRyQhA8uOjynF/yoclE7UNLVQlYx+LX4RCWkPgizheTT1G4iJ029oo9rcEsovQogtiRn8fvR1YzQA1usL4qbwdj/5cWknnYQaIt62DEuvI95GL0cEq7a4CQ7nRD8EYb1JqXUwkGCZ+NL0QKfGV6KPsvdLAwNerFHzqasAUnVJXSVlXHvmZlEewRcEEacYL6QvW/qBFDZITV+TgmurvYMiIKrIVDL6EFozREhL/+14f6Cs72dbjBxHzLkzUEP9wAL3L50n5kjpbqrkscXZhfmF/jueyu6epIK6dbrVYNyMR0ujbF9PK+cAFEY8OcGn2KZyORimlNyAdBv2PXtZUFUhpr48CH1RCDQeTUfCDFFZsKoRT4jgYikRzkiRRYVEyG9NAZu1wIHkhS1gNHZCIV7Im6QFIVJFTy+KvaejDAPzSOQDCyUgGJg+OIyO9lVpCqkjA+DTM7K4tkUlfAklQImsYCw9bVCN7f/pZDaK0u/x8g1c7h3jig+gPXd2r6dVi8FXD1bZnqA9frclvrPZqCSwfCQ/m5TyG7AGpESDhN9bfb2jDyv6TARBIUtnixPCgwfs1nTSjP32zI4EAiJGAIskjAUgXS71gUosBMCVBAy7pS8rEl0h4U3gDX1Y1VDSKvYwNAhdfSIYlDZpGMcWmzWAcCAalggkdTEdOREI+D0ZV1IrSnvNIDBkTSOFxviwGFwRM7EwS6sxwKgzSrW3NVXAYjJF7cnqXz8hrXY1veLEGx4rbHvIZrBXwRFcJgTEFwnfLuNNwh33SCFX8hRZMtRluRfXEYQcvaZqOclXmJX0iS8B6EwN9A5NVJwBSe7buBELMa4fDbpku4gG8RAIZj0Z/fvBLARlcxhiGvm+70sKGYcaTJ9CXvcF1uuLAUOZYYrHTNg/hOnFbRhHeVy8mlUpLuC/9GPlyPKBfFfXx+sXhB0MxgbTjbJ9ZSmsAtiSRqf1YQNp/LK4x8Yk9wJ16HsI0Ju3tMxWgjbBsZ+XcT/PP2eO95ugJPE2SbXPR3YPcr3WmGVhbQbVHBShNls0xvYsWBcbIlGyuZEk8ZCUMAUVj6qoqJggJRz51ZjLLtLqU3xeAfhSy5K0NSIRs65VgjPM0ieJhcGowcj1BtVFOlNNj4BSxp4RAW1s1h6TPqYoqqgVMT/M9r/04Z0+wFHum2Smkc2H69qrW0jQwB4t6QmyolkjrAcDCvVjnYiyHUsJSN7U6mAZVTN2FKWQYp3bpuiDVhbUxP1B3G5AjJrWzTvZViXEN+L5ItS0sUV5PLpqyFNXLa65araJM4CSzOksA7z5PDhk5iVkffEWgSaKYeEsAAwCBAUu2RmSSTAZQKgWJHNcmo2kg600pp7cKkytk72kvz98MnsUppMm8MvHKwUklJmG3LotnCAzxgLU0/JBFO/kMLRki6oVGDuF926qqGaC+zW74MZ9lX+JByIaQJDN+UE89lq4UlcINpOqX2fkaNrzxxfIJ4nC/NGk1eFfFo6zcYAy5y+UDSmRfj5nRVSkEdpIZeb8PkgBNX5nmzdpSRUlvgiQutdS8h/JxQEmAHDMaSLENmuH4BnGtVExp1RAJyZ6WJHFfQNFGSLYiHigSnEwP3ABKOdTW46UWawh66Aqc7ZKPFKNUvNCA0EfjOd1eJ3ceuEiGpd3NJDdQa6+Z5ATDww1L+jMBUJrqSu2JNJBFaAyntWAMg+9FurZkt5Yyp5aEJ2EnJ/Fb4EQkRFTEyiNIakGmLlhSSAkn0gCeJzWQJygOURHSZEzZ/BOogAUt2cAEQvJhVUp+kUlJ2AdIRX3qAlUKL+FB9OeBL/Ue9cbhm+o89PbKzCdbmtRL9goxAwOgNL00DuTZtmSIQBuQ6vra09c2U76QvuQdw2flAV6a5YPz4wOTz7GhaBC+JEuafKaJQZ23AfHG53WfXKZMevAl3CCjiEAITAPsYoOM2hIPbIJ6/FXJYrLKgMWaeBAmeA9HJhOXeTIClAJLGHoXTWOWUkhJXMJJkyypxWTkYEgMqCYGTi4AkxkpuqhNFQAVHNNIrmeTtSW41rn7j+Fe+SWmhKFHJnkKLGuHw+YdTlPWJn2qc2OhqWLHkrqAk9G4ZE+goK1AV5VNa/CGJ6uMR+PfBRaF+XXAk54QggU5fnDkaW0C1JIs2bKAYSevRMcP0zhPXnM5p6ovvqTppUzVvWTHwqA6bbMljdzSRrRwiOqTSWhwnVWrh9jZ29500Qu5Tk/Q0OVncXqxZy5hYAB05prGAt94D9DLPYDR+53cZOBse11zSFDWJtmIABr5S5cXPS99B0D5ZjGQxYbAEo8BvcQlAbyNojHJSNGtKMn6tisJstaYUlRpapL1taGkLGHLTF1lVcKl5ESWQF8CZJKyCqAsRmUVKpWiwNMxIGYAzjFY+3rAxzc6zEgOV+apxUgXXdsQbjOMTIKSvaQ/FtxXCCoSX6qr4Dr4K61CqYvJ2Ki1Va7tIzOqE59NMAYuEr8I4HFvhkGeDaIKJJQmdl6qMwweypScgJFijpT4EwAIMFcCPOwTyBnEgqmW3Lchg7jG+PPwwOEjPg/pZyfzlQVAZ95HU2D+Gpydl8aShnmAvu5RjSHNAVC8i/n73sscPJAqDZNyzLp801m1/+RFr/FlXgo2vsSUcHJhrmEAE1BMZLIYmcUhgKVBUAocQj+ILOUM4R2lyvJTyygsVj2oyQ1RkDkhmSoSj8YAOUj6SSaKEDdsMJmRriUvkQ363tpeTIMli4Moh6trsKcQAFclJovJMghI3pIlkQlQa9saKsyFq/mlIPFXATEQSGC8bS5gk/W2LmUOIhiOd+2gvNd4UXpX0Eh7gAYJPsSXKkpYeg4j9WZPO+QfQ2ftgd4+dtYaTivvlQVApzV8zH/BPEBf50967zbgJUvRqddMv5uodRXFhHuJR8QgBNXozRdfAqmlr6gkS6CkB5m2lTDILJogyczuomnIU3XCDfrEQ0lVWUzeQUBD1CCyUrJZehmH66U2bTMlRRKSXZUqKDLiGunO4EdjpHNqSaW1Llpg4oNKZA18EhiftQApJ5ENRck7OoCElNbL5VprwVbbkJaY3ux95SRl0vRVYaS1pMxkqbSHnXjbG4D4pfDfBtBD4DDITwSM9WZumaa0NAs3JumpSnyyDAIsx7ymMW/iA3m8Dj9IV+ItnNyb4ku9STMGzs8D7lHI7/esz0/tSSS/sgAofXQSq4fyHP3VGko6rjwjDzDo8Fc9nueFy0s237BHn0U1J2SdVLkPZD7EFsvp/XUCb3VoRHXMPFViGh3MvENU9K3qy2UkGs1S0pSwa22581Rhsr5IaMD3ykneIUDJNUhISdMritqGVUlTMkJDMjG5ExgR1/GQi1SBpU/wDwwPi1RRRkuZrAgm6YU41SWNaxPfF5CWIpYq5fSl70U2tJuglAmmUWwIca3ba5i8tCSLCSAhCQASLiVblCkbMPSJSRYLMd6zblQxFWQC8I1kfKnaBH0FgqS2lyWJE4CslJl4gJSQyF5MVp0ASGlhQNNFWWvJg4w8gd4xS+nSdGzD4RfNS68sAOr1izHpOHsKZAMzigdPxjWK5DHNEA94eCNv0Phx8Bz9KA0kDDFw36ffkOOiick9lKZwa8+4xGL7SuhF9nYYm2chtq3kojYJTFPWGs4WJXtqSUwv1wkwvYrSgNLIXslmTGNMPIjF+CRGGjDIUghFnkJDgh9TPiwYTUBOshAjLQqkhZNnMjKLIwIWOCLxcLLStgZlqUVwbX/ZumRJOdlMA41+mzIHNTzx6XAwRsJrdoqI9V8FXBVhZFRWU0GmTy6MTBpg8GUVirIVBlzbQA4qWvKA2sPAdwDBGaAx4FgGn4HKwSKwZKhDBnO+VjUXv42vLABqPMf01Mm6aaNnUyRZJgC6stjQ+8Ji2mM50Kepg3hTr1leSD9IznHxfRWl2Q1paWQDf9wiclDBy9HvWaZhstUAhlMmxLyRG3jTNCiTxQC1ZesooshVVo32TqdDzg5QV6GowQJ9iSnhUl2vJSWmAacQ8IYTk+aVwhM2GVykRA4ChtAMqUJawxgwvfS9GJthfEpIoDTSSPJD4n5+MBk0JnOeyLTT+FJ7qasBm93INKBBQzHJSuG9ZINqwSMhhZSMfZGlRhMkGY+ZyROK0oXYlEW5dlqAVeb6ij6JayDFlpETvwvZ7S0Fcv/GQc8vCMiMY/hNMU2pApgEF1UA2GwaJvCym4IRAf9HW6A2MQgnSxD7CH27Zjqzn1ZqG14o9FUZiWF9nTOi2S9s1yskeL2acOwAaFDz+j7O0R8D7IMkg3+h8F7exJidnPRCe0pFDXqKZe0LRZlAWusXyogsZ0tm7Wcrc5C0aGsfJ6ffANKeRJbSSmRSmsBViSxf9BBQSyL0gQAATFK6NoUk3mTGN/K+EkA6WQJwyWVkCqeqL1myNNgH4XvJejHJWwJJNtyMkgU4uYDLtjTIsljSJ7IXgCylJUtfZC/vKTGpDgMS7pWZ5pVVJb0JSrKytuRK2ARJdqS7BFEpLeOS5DJQxjdg0gzXUjQmlfYCKce/MtBbJoADJrNARlHi6vckGFKvwBIj4toq45O9JOuFkTwiZS/vcTHW1duWXsxxJZ+G3s1/yTbYFacx+w3jPXYAdH7td1egW5ASRt3JnpklNKxFcmJeSJCUCZTsiRwCHJd+iKgLXKUlD/xKYzP+KH1ruMQMaktJiTQX+/rQtc79Nidn+gcDeIkDUAWMBBKwaazXmIYNIBsYiokEcBoggaUBsaOIv7ehLEWVjCW+F24wQjAKJuU0iCmGPUeENGiS9wRAKaqEU1QaYMwgmqQ/JeDGIqSvIgvvdUivUmgswcRlDrGLDa5SYwlDRm8gbyAb7CrWcuufYnEyO7ABEUcyBjBZjM+igWTkl4Jt0SyEOcrhNwLAKpybntyALS9lgk+lxld54P1iNe+R2p5CCkmAG8LFeEZnwRoK0WLLpWNQExo8Z11s6KWYGko4kecKHLp9BDUv37wRjDpLkgsUAGWzGk+o0XuS7DRAQ8VwUacx4I3vQHad3zJ+t6YzQZKyeCwAxtJ1llNiUhpV6KXoAIgzLwAU/dQGsST7EMBKnVugiUuZAZNVMVayAAhb4IFLXSYoMcCpq4F3cXhtL4vprfe4vL3SBmHOT/IgjcfCY17D8w323trElEBDDkUSopLGRedDVJQEDThFGSA3gDQoG0G8VZQSjEmWLEJDgt33A/lQGBh+axwG8ftCEfpQqMxFMAmkWGMSby0j5ifjGlH4EDLrdRPK1g1hOauq9FsCZyV5LOdMPHARA6BsGB33xP2mwegiOTJJqWJEoCENriFCeqt6MSPqfV3IaCAucgDkxtZN1smvGu7TmrLKT6cP0WAU7HCRIOFtTjFnoVx0LXmpqFdeb60xZjd9wgAceQMZQBVp1TS7wUgH41/VcIvqq8ISzNtLYPwotSVlAx4utkGcxeQqLaQWfIlJsmSsPnYRW1waxNDA24flkPm8oNJmwzajxDd091al5QbK3LxJ0BCVRWT2pQFJlyInQVyqNkxuIGlMmUWrgKaU4KIZ+XUg0CE56DEZMLWZStUgqU2xBo6VW0U1r9P7FbBCFrqK0rmDqKtsO3dVYwUX3QMXOgCy886ws1rUmfwCnImQi947jmMfDnEApGG/CDFLeBR5yCnJhrBTZWLnFEkEQCRv8LQckEkAhmIpvy8GeshojltU0lMVqfq4kgsQO0HJsJElAPMagLivukRCZhpzjZ43FI3OOJyyYY+1GJkah0voraWBJ+btlfZCzCB/Jj6BFFWaV8IQuFjmyYUcizrSfevqZDFNjdZPqsxpGmOSoJIpikqwydzNQCZXsjQkUAxuURIAsRDGqXhjzG7Y7NBUxEX00/gt6Kux1H7R4LKBF822vvb4KfStGiPP1gMXPQA68S9bMtL7cZmLZ9uxkJxazvapvI7S8AaRB+9Kvm9p+3FO7R/+lGyGHaZp+LBRLP1AVW/R9AwG6KUWgJc7k/wAFP3irm04Mv2QSMhKycAumte5MVFDpjaSSkakeUAih4JiJN+HVN2ADL2TCerSkZ9UHSn3KyC5H1q4IVXJMpymYYBNbSBHUhQfPQ1GTjy9uFFp3jkBtLq3FehqIEuydFQCti2LDd6U5tb6IVVwaE/GbGMpIX3UIGsUkzfVlRjgkt6wtTgnAIKGXxCzpwEJlNKSnVpSSk44gZJrCPwC+jO9Fbq29shzBznCb9iQFhy7KjTms30xe232keeI317MOaY4kQcuYgD0gt+T0dppIe46zrNvjSZgTHU8D+BkwgK7Gs5jeRuupC9hI1NmGgQeXc49q28aY7wNiFjE9mRQYqss03kKBLCEbAJFY5yDd3jHJyaBScbDiHySzbBAhJNAEiZhqeFSVwlTWxYHwagrKRO2GVlMdjBpYYlMuC+QXKU6I60iCfqyX2Rkr+W9/sF+yEgAjVqQxjTwg5qcZMmYlJafRYCSBsa+BIk0kPINZG3iE4N8kCQ6JEgmRwEogqcbOyQyjS1JGMApuI9YZXZqAUpFNUf1k6qkbFSVxRHJSpY3Hi59Yg+P4skzd0tpxpkLvwgCL2IAhF96H7Y7wbFcZiEpCsDwcFGmQZHJhhMPtydFAZxGznAtp6lNC1PIce20BN3nCxTN3GPZnz/7PZbE9dDx5k0NfYCGUssxknd0WQscevQ2p4rc4soieBKUhCyuhQVCMMzb8MGyyQnthLAoflBrsooxzgJbAngjyZFG6u53gc2CBJKX22p67PF0F2KptOCqP2c5NTYwWazY4oeRjaqUYEqKwwkGkZWKDDfkNJANvb3sR+d7mn+59hXeR0gPCsYXqw6uXldYWF/2kjhtMzKLQ9h7zBTCz5wuIrjuAQbI6SfmAiYZlsZghOcQGZV021KO6ftiUlTWIpaURVS728/Ozm5vb9OZ0VX32wm6MWfEIDaXLv7hdyT6v3/L+NINRf4vZaoNdStsQJmbUhif8NLHcpodVZW6Y0jNPGXCkkONloVeeeDwPQB6iMGnNKAve6+6ppHB1iAraUq4r4rzQ75C1efXqFLymQVA5+2pQfIb/Ya2NSgbxV760h3AJQFwg71B/KYW3erSFYNaahroeZOSk8AYOYgF/AsJoHmhqFIIxFbnv2j9cocAwK94CHiPV6/vA4U1ZldIVPMCCL8vUQBOIEkKfWJxbW9CwwaiUFQmMEZSWyU2QQ9uKDQ2mBwYFVk0hhxkSVYSDIFPwDJE2mmqbEmjaccVmM1J4LgShtBbZkoG6OvzQU1Ixl4VdIZEJllf4Ul2toBtLjViRibMo8rJSLQDgKHbA9h+E9iw7K/UVpgaOJblyEwJwMGbso8laRixLbd8twvqAIZxvZy6bH6vutohvTWvBpOuezXqz1/rmQVA529qfw30mL796cQ9qa+0/rrfRGzpt0G+7dtuiHl1ZhVuDE8yw3I4Eri2rkra/kCwqyoB25bFZCtt5g9ZlsD88SMCFKp2d3d4m5OA4SVnGQuALaEuIscS4g9UvZIpEvE4B8iksWGm2knKZlLke0EB4baTuAiZlc3SBlxFNsZXVUWjDumpO5oaLEcrD93SwJ9tMW1ArL10XPmnl3BcjUPobQwNKa0aBFvOkFYPkTPEBqpKjQ3KhkwXSxvMmxLKqpRccpnA/d+6gOm6ju/zdxYAPF3d9JYPsizCbslpc5qRmFEAyxyF8qxobOfL13ss+9O8hpOPJeS4xFY65DkOqeqrK1vRW4uoIbW99C8B89oHQPjo/LrLRXtaL6FDnEwFjrKv6OKkFGI4cFVYYDIIQCbcoHdtgybFNriyaALe7LzEvc3TKvwS9/vdNGbBTN744K0dpOd+AEgIAU8tonx9HJLb7fbM7AyhD3EPRQ8k5JZggzNPZAlkK0rkCeBBcgbhh6so3VJSNqRRxDMlwXHh40poGHBcdYPoS7ElPIh+FPwgOYPwQ2TayfXciHxeCimLvY8DypLAWpLMVdDkrwk9nJR9GAISRZKrKJba02wjXWulWZVK+zI2yM622LCkMvJsdYylDfVA4xGY9uX3hKE2Hla+CQHQYWvOFNKbYMAv/+h68sGfXtToSl8yZfWWqd2VTe5rxqDaEt+AXUR8CgQT2poYv7V5uTMDFGGQ9jSQeJWTg4GRFDgiMNDViD4VN1l73waSqYAYgDxEacao1WrPzc1OtRT6kCzEZpCjGgwAlM5djH0Upj3fPNs1upoGi4sYj4RG1egyX0iJZKt4ISUE6clRiM+JpnRLqnihf/paPqTtgwRaTumxIULssdJIGM2buTp9rHBlj/Vvh5F0eNiBSSnNcOztq5DGpCILdxE4a0s4iV8OUNqfhr0c1SfWgs32ZDrwxKLGjCN6YBwAVY7KzudfWnInV5+gR56AZcRndgHJ8BWvUXLblgDF9EMCfWnMYhrgBAwnb1kF0kVyA5bMm50AhZkb3ubAJE8IQWN7UtqhebEryOyOfsjhIsHFfA+zPmwdJWf/tPWaWIrrVhuTMkstWXUCoFfOyTAjqu4VPiLj6GSjqLBXR6EcXe9pKI9riekbXL1dBZMaNL1GwtVLU4pq1A6np5bfCH5B6NUZ5VsCRTo8sItQkkp7wPN74VrwWdvAgE9MyT6Gz8MDdnU+i/NQcVyZmHSh7Bli/5sWANnvJ/D+cEZqj/srfQIbep9Tr5DjmtEr8zww2OmEcM5YsbjUqwUC1fb8ZdlLCcbESZ80yZ5+SEoAkMbzfudNzTxOMjLHQ4QWArHhUL4M4rUOc/2uD0ZlDAYMCYwTDn0A0h6II7JSMGQkGMOpsQSGVJVkhlNmWdVXQl9kyXXmMBrTUWcuvBR4fk078yb0NbUvsmyg4VHIemnAlE+hhE1c9kYT05/psaYkzxjIndydGbw7PL8+wE6wW0IabEyjLfwaQJ80WQsS+iy+NMDGvzR1r0rRK/HtcRt7kY187QMg/4Kdya+Zf2fyaSXA8y7h4z7+k9FjzMkYXxUXb9jT2JweTqDREOOtImkGafT7nWNbfu9Dz5pWEgMYBg9QSkZpVFaboJFD3ENiSHBVHpuHq2QEDlENq1U0mStSXR+6oISgwdKXcghNqW4Q72nwbulpJLwSXrulzNOM07TIAlPUyYAhBjTkN4qD1DV6keUbmVUOeqjyPBCiqOL3BQxVwKSG/F5MSWAtJcYwXFT14s8VY1NL1YPMO1czjiU83VuaPYqEC9u0l//cR3FXSfPaB0B2sfPs9GULe+HsZ71VyLGorGoUEz9KH4V3kC5XDRFivb3sQ1jStvMA+rYljOSsd/XHZdrWMNv42CTDXpxq94yNTBYX3eq0n1rexaXqJADgxQ2ldRmfL27+ro1JIP0hq63Ne57iqV7EFkhVvu7NDsYJvKsQwrIXYlEEjcOgkthVabyr0v4oSqTZM0+CBmDKBvKFXL30Z4jJFpW2gSyLZ6guRfV1Gsi0JykNZFXDsIapfckaLA3JLqbeYxH3FdUQSLGUeagozg9msRRV0pf4BgxZspddGiRFaunb/GFAyGOBbHejygkMNAik6NqyqxvjWghSL7+rCRs4iAt+TN9TWdOe6R3QFmqzS9tqZfJ2X3wSvCrANqN9RPOSLB/HCSxPISfgHc5yfpKH6z1W7WsfALm1p+kBpb8az6xRLClHgQdZVeKBB2kpyUZRdx40aZ6NKU3CbKbMUcp7ze/TJM4WGSA3MMRC86YEUw7nCqnKbJWLnrAhAPLbnLf23k6X3G9zcpOlfIrASLCQEnb0Qw47ZM79R7GFmLfMYc9iXAZpPYc5WkxQAsmVdFmbmEEA0tLyXjmDuEbHl8JLLrfCmEE0Jf1x4UHyj6url94Y56WW4RaWcszlvMRbwnCZw2vTBsiaoUTWDQV622VJKQ8CfgvozIgBSff2r0YyuqpUkm1MIWXtKHDN2JxSGoV3TDPIA/lcBhFcNPwFNPgNCYDKJ80vG46uf+XKmpPAZ/7MLLDMbZYNPnN1J2nzUR5M6mtb4CtSYE+kQ1knZnr67ARK2ZAlbMCKUl1JAAy+QV8r0p+zVJme3CtWnU5nelrv952JHZAAWAil5Zu3ITDNcFtgycUv6H1dLUablzzZDRufyGxIYlK+6bNFFA0bn2QAJaaUU+JL2DS9mJL3WHBpZC9jmp1kJc3pzegrNpWiq4RL1ecHu1GDmjYIjz1DqhrWHlHR7PUN2hcXcRECCWsASPwKWL4DenKS8PWVoRRTKMr5d9jRB7cCCWUDE57U9dEkNYP5XIE9X4avmhiEo384zirQK5lDE5SjkA2VcVEqR2/1ySxuOCqf43Gl9TI2JB9X4DnRH/b1c1LwSsT2en9EM3hIjTQi4xAyBLrWkpPS+EQmAIGrkvIiA7ja3rbNhp03GpLIsjm9yIYEEzcckrp4WUNPAuPghhwkF/YQvoAkp2ikiUvtwGbPHBaiHxIY6EklvfWaq8RDXBaBkzLxYBKZgGuHFF3VIEiZQ4ATsAyR1tvAkrivruEsJXtfuC+7keQGYEygr5AhyJDRfGovpE91x21yMlrvcbUPMWx4FXY6lXrB8BtBAoDdVe722eezdrj8stbSLLDEj+Ez9ICfZinw/PoSkktFbyT8Bs4Anew55bvgZOyDuPKlYIJGMbnAZ28bRJPELxlIw/rqleUx3qfZAAmXLCBTVAJJ0IvJqr7STO9c+iLx1gYDyLktbi/c2NhgbohgaHNzM6UVgIMnWUUCzxigYSEGBs/9GM/cz+QU//V5HaDLNBZbFoEDWY0xvQTGZF7KSeTJAKsujTmZHLiGWFU3sI/sIVx9qPuheoU3ZLrY20bwvciGhl7hDYKy2NCbVccSklwGBslskJ2yaAvtkL56TdCYHHXQz+/CaRqIOrex/utvlKZoHnf4tPEoUt42mlM+JrurV8jL6aKv/GEd+QP3lVvzCg3geZ/fI3f36u1ktNdKS9Ul/AodMqLqbBpxg1nAkGjFCxvSS5AYJAwxIMmgAXYRFt7a5uKdPjc3B8xmIO8KcliTxCncGOdIgB5K2OFiJLDkkiYZAdJIA5knPokTY4GJB0jhRrpYEvSFEdg3NYihaWDOsJimZhMSOEMtFoVkAOcN4b1t7EtWcr2QoCQeET6WTD+7UvKx2EvGBlxKRmaKdf8vfWUYAvdzCEieAQLI2pSQgDWWoho2JMELafoyjpFDPMBTaDyIIcQjVvU+pjNXMaIlL59sHACdvc97+1OvDtO4n/X2tl5Mr4QLhaE5apE+A69DXtg/ShPEcjQlpmQPYUcymJKyVAcSiby8QRIAcciFgIaT7LOz88ziaCKnStX+JJfSBG+dRkZO/8BCm0hJU7KUsM1wXtKb0ZjElwBw35QaU3KJSVhNLtyYcAJJeU5AX+NBnom6hhwa1cCgpRczXDVCSMNpjlv7QhtSqYFeA3oxx7XB9FiSxhjOolVkDkCgYxpgT/k4ADLNyQy4sFxvUqMaT/aUPs8ecko5ryP7OAA6l6c26JetF/+adz6vIh2OdjTHf1D2/oqWLe31Q2IS6PtgLMTCTQDst7archmL6IfFL6SRgE2WOQDJEkyjv3/rL2B4+sdi3RxTlnCyl3aWyBLuSwNBmUxjDHBWlbyDYJowqOpV4bH/TFSnQ1Jaeqas8kNMmkHACRz1QpbRW/pCUYPMHo4vHeIuWnrGvCWGfm532R7y7PlmN97tMuVwA15CrY15CYouuAo/jsyxtnyyF9z4C2jeeA/QyR+KXxMNfnfH8q1hsgZxvlxK9vKXnFcSL6Oy9lXBDcsbZmhnzH51rQhVENvytD8bRVXCpsziC4mTEkZgEtIs0LAFCsVKXNiwtbV1/fp1zoI9f/782rVr8/OL6+vrHAsLCVoamyZwmubr7dP6DtgkmC5IBOB2EqtgWIU0B0OWT57J2rOIGcBGGvZn4A1XmNr45DJLFiFDQhYBGuxl1SDYElJjw85BXMfCD5HZsP9YYhvEDVHZIpPZBiOBeWQNdoqmSfqSIIU3xFLMqqQ3xpQmSJq+epPRQEMFyF5MRRk/EA7B6OehzOs8DaOYZktY9CtyEjbbAGDI6OH8mnBtBH8xgKfbmxeYPg2NiblMS78UVjO4CXV9/bNohiPi6ixYXc/PvpHyod6gtKkF04tBzDbXAZ/8i7nc3gNoL5Zy4SlO4JneNlWPOCoQ6GQkOQlML9cbgOnz1ngDWvUKm+CuUxrgDmQMcFnVgKt+Fz+oGtTnelU05LzM4hCbB9k/inkvdFTDq7zT/Vrn9U0cgwpHMAsLC8Bsgp6fnwfAJBih4aWfEvzGp5Z90/AyXcQealMiE7wT9L2NPVYbkVALa/48lpwmc5TdnMxLmtMLL6X1wg239BKcDJNtMTBECAR92wg+uUo4kX2B4ZR9FTXkpASAhJOmF5NVxwVC/GEb+7L76VDVAODld8TN4deBPp+rYPxGmNi18FpRFktFpmxgXGxUudhAlowJQwPsPJGnAQpHHf5Gn0bgOfFi5zlJHl2sbUhLRnleowu/aJTjGaCX9ESyP42ubzjLiG+H4UJGN2ZEylSHecBZ7GV3rQmGk/XygrF8A+QR/0z5De4gxgHQ0tLS2toaAdDy8vLi4uLG8zVTqvZAn0qdnJ7a39slJuIvYBJ4RgLGA4IhYOQinPHAkZB9bmvTAAhIjaKRZT6kjZANry3l9MLJmwA0NjWJG8WSMmnOBBguuWHGEI2W06AH2cCkhFJvX14oe/ElV4pKyhLTgPsylsgSLnl78cbQrgRK+oR7GbNqCGB3pWSKTrDQvYn16dskAP5C6K6tQUni96iUCSaLsAMnxsXEUHRV4qlKZAoZDpyGd7hkbCuFDyd+VbXp22MZcLbtajzEIcJPZu2xmnauxBc6HD7Xll9w4a9Xxyp/Q16+5Wh0WMMzBcAYR0IAbAMiBgLgSPzq6qprFdnES9xmUyRm2t7eppYZIwYDIiEIqGVsMA25k7sNsAGTlcXEJzDIIYlPIFkMDMJnLQSmSYAqwyWmFDsIX9IMgpMXoKQxPmvLqgbcYGzUlsXS24lv+NnFUmba0JfSckr6lPzCqgblyejhcrtK83pb0VfXyZClQ1IpvZoOHwF/1/ZAxqEBfln4FeB3BwIAO8oSDKeE0xhjgYMknFJFKfYMRZVi3x54+JN6M/wwngF6M57jq2yF36G8NPsa4Vdn36rRkajolZMYA35rk1P0Sxz5MBIAEf0wD8QkEPEN2x2oJejR3A9iQzLjAYyMASQAikwCAfAZMVHW430vUDYhjCwRL4ZTIKTAJISYDXgQf1lVwqZPCZY5SMhx8akIoFRxXDlnS29L0jYLT/MSAA9sskQ2uAYZ1iAr2anK4iD2Qfi0ZxDBGeLdhMzVsaOT84swNztLrEORDs9vh/8YKFVjJwkM3REJ+pXp6ZkQpPCStwEf/tHQqCiKvcKLyjH4Cjww5IlQ5b7xCsw6I5XjGaAzcuRFFfOSO+ig35azMqNXPphMvL5RFK93ZeB5xbOkxZsd/JMnT1gF8wQPVbz3eWiQOVoCT/QDGX8ZWyBwpny8VBlOIKuGAL3EDUwWAZxKaZhRFk8D9wo/mbT0zBna1teS2h993FLSU10WgQcZVlKWcIPdxV4CMIkcpKKvqF7kKdl7BfbF2FpyT+qQ82tCjna6OrG++z8E/KaQ+xeHHIJMSAYmtzTDqc7ILA4BSoFDyMZVr4UHGt3gtbC5YeQ4AGo45MIV/cpomJXvkVfeBfu+EG0tVa5N409praWlzNIzwOB5rfvNjkbX8h4HJu4huGESCBr+0iXx6ue9TxECYAIhLj6kaC6APQRw+/PRTdDZkBMDSD4xb1/G0gl9CRKJ6tSeQNZeTKDXTrUhUsNg/GBMAn3JQJosgQZXQ2xZLAWW7MaXmJLrlcPpkLSEAIhw338tsNeNBI0jHn4pIKNIAsgmu+gqy0mMi6PkZiE3MArLmGbsgXP1wDgAOlf3nplwvzLi1dF8fRg5RJN5hxCcpiq1G8i8V6bNcN5beywM72VCk3w7w2tY4U8dsqDI0Q850Q9/3QLwroeSIgB/+wKbzOymZ2AAAI8oYNOYwEYC91o7Srv6MvaK6otBfqpIIClLTGlqEpwhYF3WclxdpZ2jmHQseoiH0PeaCgYbnI9ijGka9I3i6HJS9SgSRqHpq9oOCcdUGWR0bP4GcN8m+o8/ADQVym9E/lKgsUwWntLKYqnXBIMwCKTKYkuac4Kt7pyEv6li3zanjQOgi9uT/cZK+3pfLlmVQKP7uthAJvHpgTTJQCoykPZTTAxKkyvxgywxF7VDKF3lnHe6oxb+kGWvD+908H6z+xTYp48etuZmCYAc4viNn/bAS4IFOSARQnKRHAx5w35bnhIGNaQkQ4jlpCgXE5lCejGuAt+bkgtj+tpT4vsSpIReAHUgLcG5Mb2UwzE2uy8NVXa+FZEPIc7apMkWGUNuOY2iybK2V5erwDu5sSWcihJZkdYGW6Pb0pCWlAlAUKpIfAMYhabBQtGWlHgw9Hwnjn0RBvGXAJ3ck0DUYja/FKjDffoXboQegAlRpkUt1jJdS26gVDQELumTseHVIezHqnInqNpCcw6qFp2TumPZdjGJ3zbPjAOgi9kPj2dVvkcabPTmQalBefoiigYJsQ3U+t2X1iZ+EOOx8BbrHMl+y/NyN8CbnZ1A5GyIJuftz4vetWkYgE2CJVUbQ561hk2QbUn6IQDEDfosJpBijWngqTWmtGGIxuNW9YrNhqdqDCD1Uo6iC65BjGWVVfz/2fvTX8uSLMsPe/Pgs3vMkZHzUFVZWUOzQBIU0ZTIpiiIlPRB+iZIgD7yHxMhoDUCAtWgiBYFopuNLrK71V1ZWZWZURmRMQ8+uz9/89PPbN273n5m55x77vQmvxYe9rZtW3vtbXbs2LVrZ7h9CMFEw8IEnkJTFGMwkqPG4MKFwnMuWOGrkQdkQYVGPPY1JwG/DlgLGnKeD2Ctz71xquW8IHFSUNRZQ44cQyqaSRUApQjrkAF31M626nz6drYxXyxb49FpVBJnPRguNvgJvK+1tW0CrrmajNvX47ZrXP65Nrab3KGqjSoq72g1gIjvdjFurZjrXDz2SwxK6KVEQFPAVGu9amNOFeYCWACAhgldczd6pnLt5aiKBRC3AZG2Nza5K4hXRefN/7TWwZAkF/qau7IEP7tBTNYA0h2jgkEOMjqVlXKTWJl4c7KmQFJZVBXFNoBDKvBtxTaeNjx6uVBjlZtEVcbUJEaqqsC7FiFWUSTVbCM1sjIVeDSx2IcBfJv3Nr1pC1+194KBopIYojl6085QgNZeWOjwBYCvAVvb219+9dXTZ8+2b9zg538B8FAAB95+0cgwRuuYY6hC2lCCAKKzdyOtiTwFQ120VV1ljXswModmGXhNhNhMmtSni9pa7qMjgKhqQjzWyjbOS6tf7ABd2kMzRmDF6LelB6gA5BIMmJOAXzlCIOEle/akNNrtyDgNiAIyKx4S87tcs16RMwT0uvuHTSA2/7kPVJtAIE3iyNBISS4q5QLUGvTGRxLrrYyCTKKmTQap1Abo0NeGaBrx1ktwEXCUbUs/WO4jRDyE4lRuF1ndkIm/qJBVweBIrLfGQqwSp6gM6BDi0Y9yNIktNbOdWpBJBBdVkXMaGVqFGoPhCwDfBNgHevToEd8WhNH3B84giko9/QLuibwQmJt/Id6vn9Pr0Z+LBdB1GJlxAr2o9jiG4bR5um6I06hPG02Xde74VeVio1BgKJI0d4Pn6pcugMkpMzsLINY9bAIx9etOIJSOXC74pMj/0mwOW+HXmsqqAZmiaWKolYWXGRbbfHXoVeW8DdnYP4q8zQS9UtFADpmOGrVFFUWPmUKmOORrsKp5Ck2jL2NibXGsjekpiEq5momhimpabGBPznFhsTnYstzhRODWH74JvP322+R8K0BPeOTEA14mFuQxhhoBlgUbmcNTYDiErLkKZWNRvhqrFsqZ94AGQMxn7uICCRcvQrzAzp/QtaYnzyAqwmWNeIvihM76mcmXTpJoUcSQi2fmVsBxOoty5GmUCzBF+MmZxNns0RdZcuZ6Teua9LkQ9uThI6Z7vfXk6OhQtdGFmYftipWnAcvdmbphITGUM/ywrsffgtnxRNNGZQSMlNsYpC9iaGMrSCawKhjkCJ42j+hV22goK6rMYJg1ZnaVNTaPmgiLcsT0lwsGR4VQVPXn7IlkqOOFE4HzYjj+j+7cuUORN6HrBIEKjFIRj4vUIiu3a9dasxCudw8wAGjglT7uV2YHSH09v/E0b/7ZRl5HGzXIsThb1zVboy8pR54bKdDwUWc5q08/AotiEYOtcEdilud//qFPCyCe7x3eCs0Ur4dfWACxCaR1ktgwtBcJIee20CMejEk/NJ9TEYCKRRWE1kc8sFgcKZsnIhuVESC5J6w2RDOZ7UgrAB0Y9WHPLjLYQt3q6Ct77tpjEEAmPWNo7LpCKapGwo6qgmTKYvROAzlHPP4p8isxbAKhEYyzBoEkodE1tdJLSOicCjCgxuGuTi7BQ85CP32R0KYnWTA09sCV7tsrswBq7HqUOuvqvA1/LfUdQ9A9U/TVvPvBITHTxRiiX2OicqRcTJ0ikQvZAvCmDjO4kgDc9MNEz28e8ZA80z2y9JFTmpiLFg2C9HYkIeaCWeOiBVdZsHcLdZU1Uwox/kaqOoZG2GTKSO5I3DPWmBx8RzLMDNZ0CBB21DZWRRMF2ZY3mqNUhI5T5m3gOelxyrkAOet+Rj7Xv9S3fCtA6ecigUkzMgy1wm1BwES5bYui9dMIfTjVtMJLPI5F1TUrzrWl7tu5ejmfI3JlFkDz7us2fk62xnQ+h2cCL0QbrdyuQs9UWGiwMjgydOiHVWmNkWyXj/mnbXYxoIy9Z1opYxG5jseAxsBEEl24UdxMwI3QfNDlLaAjLoNRBRs5ayA2/wny2bNnMACjdoVfR10+Xl7hPSHHRDx4HOa0i9hMWj6hJrXy9ONTAShsANzD4IBTc9gwSg+RDdZMyTLbKo9Iy6Ki2IFMLCGGKJvHggmlcdGCkRaockIZZWMmFsQWac2PgN7NSY0MzWz0aIAFkQhsZW3bWNWorG2lcdgUJSN0Myg2g2Voc9VSJMHTSBUxQvbPI6di4GsApwOLIc4IeNIzAbwd/fCY/IStzuOlzY0tiulpSG7OyWO7DkAaEUISizqc+ipiQEfA4JPJjFLhMTZfHhTqjLxdIhqNnClbN9K8AFBUausIatuqar0OVlte46fRnJmypyGat213/07vfVz+cfHTR9iHoR5n0iha51DpPEGwUoI10R1VsRjkwTNWQTMQ6/PQHmtwrekAtwdzhoaZN8bAXK8i326p4hsw8z77QCjVcAQSFBS1WrLsojTOEZxsbg0CVFGvyOVOMGuilcJQHvXI/ZUCF/iiWJB3FPsbguwDFkzImCuGPgwg1Xs2EadbEWutHEuIR6rNsL+XtkYp7FhrvxL6uyiCjIaRE5iZdTqgYT2Ekm1RzgsS8bgq4gsXHUW76MAsqmbeA3T79JxxNE7PBsNYhBo5td82fY3sr7liN0HP5Oh29M64/OPia9djjYza3Jq2SMTf5sVWFoy0BhdW2p0E6y1EgJTwIIitERZNotwBFqfAMU5rsGWVo2+3ALSCScqTJWZ4BC2AeBXQ1ua6YzNVWi3ll0GjgYecVOxHZE1aMMGmZFlhKKcqFm1lIda2yQZbMBKNZQmFx6K2P6YbiZfatX0phgiQjF5Vygt8oXRtoxDJAWBbaBqtULbB2vQyGRmbzA0r4rE+RiUlhgiSVVtHUpBHkgnkgl8jnJw1ENuiDx8+fPDgAfGwDELpkM4O5IFbhy1B+YA/HxEDJohzYdK/B3xM+3S4wea3lQVX9RH6WBlTe2900RPWaNtfeWV2gPo3aYEsesAjz/qoYZz1HGqNyMI2YmJV7TEiCSyCFWetcfyNQoHHo5IckTOhk7O48czOky/cCZGWO3myRhCJ8sYwqIq1jZFIKe8miVZUtRm2VUXzNlv76gbMqjY2cCRnBKuNbS0dSSVAYR77BzkWI2GbPmIkF8jEGFKNL6zAGlOEar0EaiMgGhZIit21Ed+NpFYAXEtgv4efi3nx4sWvf/1rZD8foFqHmsxC06LHKyE7+NjnVyLyjiDdqA6Mq2rwNF2B7bjm4+Id+TyExQJoHr16uTjrEW+NhTyMWz+V+7dHhOQSMIzD3UoLABK035TaAaurYotUqwUQMle+yNdWVg/3D3ZeveIZseWlVe7vYf+fqd8hyeo0/hNgrZf8BCaX4B4rNIrKsYncuQTZylB5gQcQkfYlfaySYdRE8Exku5AQOaPGMAOKjrJ+YsE9NjFDNCTgWOwvj2zXSIB8uccsSK9mdsfT0wUkLPpJauz6+govx/ryyy/ZBGIBJKXdqRj9WmNBtRSlUR5NLol8aQOboH/6H+vi6Lg4gdMpTfr3P8giTem6ML9il8CK6BfFPj3AAGqEtZ054NuqzAOghslKVUZasN62YmgMz2CbdwiAXWtZgsgl58VNahorIWTt/LMY0rNgRRjmETPFGG1RjBhHYkFgFy3IY8xdhaAAqI0AyYJFORo26huV0WpWcuGore0zcYcv9dJM2GqSnvxFk2ueQtON764tqGKxf1dEpC9y4Xdv7/Ddd9+9f/8+bwqFmfNC3wfkRVZt4VFbVFEkRV8x2oU82x5QV5P3pO2P7Ek4c1hbhGrprNwtdoBm1ZMT8lzIBBHH1rgBgJeJclGRKxW9kLGDzFUgJYvB+igYE5WNsknsSzDpFVWqOjziBiAtfZaOT3g1kABcAuNCGHs/WPFhYL+qNVViCMusGEm3vrG2VtYaXDgYu2uEuVZCB6ajqiCZebFuy/QuiubMw8VIzpGA6ZvZh6HoipEmxhM/w55NIDQsd27e3PrBD36AhjOCHGWfBrbB7GVkPDMH8JXo9FtRE3ufdjXZXS5d7OHr0SL1b0dbYpOnPxiLBdD0fXjZGeKIkWxNLUzQmDhYkWMRNrtQVayNcodfwyzUYHtxFZqo1D4/U7wEchJgJnrdCYTMjE9uQ5mr6Nz8URAyhmfbCJMsKuQCY71gkU1g44Vsyw0Wvi6Kf055EZW9OBhrphQKQrrLqYMZjGoL8w6TkVXy2wazRwGKYpuV9B1BxqoodxPWtZwFmOtcYMuHTaB79+5x/YvzglCp0kkhw1kFX4dxDhraQjoHRwsXI3tg4gMxsWFbSItLYG09c076seaUOiZNUrU+aqKLQo5FmTSOMJQ10i6oZZYEQErTJS/TyckAM2fyNAcVAIoFuLEYwyhku0CgSrWiVZE8z+n8PTo82j/hh8JODnnrD2WefGFniLSymsLAilZQIfMYW5QdIUrpoYqfFhSlj7mtVBtzVdmqQLbVQo6JwQgqOqRYFWsFKGoNqPVtmsK7aa23RgyF3sXIj7KwirWWbWuwBOujx6LKJmabk1C0pYit0akwbUhH3gZo5ESJISZKxjBi+VagO360DOKt0F999RUA/VIYSjDZ9swrvswgZucIuEi1/U7qhJxbIg4Fo0kn5Tk0KdUhc3N+HsQeDHI26Pnz8DxfH+fWkEu3A0TLG9N8+/vi2NXYcf1jJRMLHQzdGAWgfCRJpIrnXk1iwgLW4SJWyZHdRRLD7MIaCdLbVkoY0DDXk+sZeMnM79wGxCYQABJFAAjmjDzITkK6iIBJzBsZrIwubKhakRhZ1EZ9d1WBLMC1lwJQm3drUveFfjMYR04oBSOXUhqDJZjHmAIQiwZHZZs8FriNxHqx9QnSJlHAMBYLGXKlqEcTi2PJje5QsvrRMoghzesQ+XF4nofnDRH4KkyKor1LX9fWGptMI9TdMg3bdbKdZniM1Q/n5misqCK45yC5dDtAl79nYy/PSp5gpsCkf1/15O8Jw6+RCDF1dAgwasm7wy4AsjKtaqUUTw2QF/RKsuXh9+EjL4MvsmoFX3P5AGDeTz8Of3LIZwD6lA8vh2XzMzFDK86hh1Sc4oNJZMPvzYPS6Z/u7jrFDaVGvGMeotLfRmUEtMmFoT0iFFVmQC+YchWtNKynUHupNR1UHXE2WilmqqIXK6NJo7IRIKpIKBgMBUlRjGw95cJLTQiAMa/9S34tmNOBH8tjK0inA7W1SYdrgXWSFLZj8XS4oGrgZXgy1mAAseFRrsHXRlO0en7tmuxQjnUUahd9zKOV5A6rS7cDRMSNaX4HcsE8WQ9oVOlgmQGl9Na0CRi6yiYSYm6MBYOtKQQDLABgv0ceyXV/A9M9qx++7H7nO9/hzW/xWTDmfXFGBmmK9ja2ooinjaoRNo2yiC1SqUrRxpgjpr8cGaLcn2EsZDwKyC7iegLvE5jYYwy7VrYxGymBXEJksxyr2ggNHleoyXGRogn7QJwXfCVgT1TrIVxQSy5kt8fMNPh6UCNn3pzahTXRl+JXVdQbfM2Ea9BGmnA+rbh0O0BxsF6zcXltmuNjhKCdEg1WikkzXNgUIziNaNeFraBsMrQZ9pGVsoqGggAYYs/8Rcs/rmOlS1lZphrzk5XB10EWOjsvX1LJKoeV0IMHb37/+9+/cWOLGX9vf09c0V2ORGRd3xYybOCiMk/6qIwRU6UIrYxIydZYMNiCqyxQJXJjEGJt1I+U+xhGd8JbY3NrimCkF4w8wkbGdg6AxnhQul2KoS1ym1voE3MBrt31IRmJcZ+D1D6QrhEXTaO2fwBt/TAymPMEFN17nq4XvubXA8Uo7T7KXXP6/EJcMM+qB+pJalbM3TyMKg8sYohhxKpuEmpNImTkkaYAdBDKb8RLIy8wswnE/K77Pfl2i8xEzx0PJFZC7AaJXBjbylx5owsDZK7cYRQtsj6CG2UMZascjIVGfKNyApNGnloJs1JdFTVDVFrgIseqKKtbDOjoJWOi+TxkYihS7QVAoaw1NUC0hT4WG0mkbKyKtj1ld6NWPFghcIJo+4fLwcgkYHh0bqtGL92xdds2Ei6Uix6Ydw8sFkDz7uH58ndPOvPwXXikWGjs1HrPfdYkm5wMNkYaF4fA8pPGAPCRyngEUVlDkVmeW541y6NnMUQuJbVaG4kQpQwjiaqsl0AuTJ03Vkkp29oktquu7aNpY+5j24FpbEsHvqhqa9fMo8VRm68ipLGKjZwoY/ySGzuqVtYax9PoS7UdVT0B9mJBYVN0SNvb2zwUyVcC3CkZHAXjo60AsSqaLORFD/Tpgbbxw2jsY94fs1gA9e+ra4VsG2EdjcREVso1FqMm2kqPxkPWGgsRH5FRL3OTIFiOMGRoxazcmqxOmdY3PN5CFSRM8XzHRc9ah6Q9IYrUUkwGw5WNZQuKoS0SGLpTZI7ISGhfbeBo2Cbbtg0wll4hObCxbBvBHVSxKxpto9LNrK3Q1MpoO65sXzIsioVyAtdtJqkZs579iVbfAWgFu6GSOSk2NzfJ2RZVc/CrZs4jALk4h/xKB38O/XOBLhpPovOJZ7EAOp9+nr2XKc/ncc3bZkDWC8dLbJKnffLVpeW15bWVFW4sG4yrk/TrWYMNkpWlYwBtY70tHuHJSaxLmJe1OqFDpSSXTA6J1y5JL5vBaoYVD7c2s85ZY2YnMd1j8vzZk1c7L5j79/f2jo+WVpYHd8VhCpRm8Og8v4qUfhjp5IgmYOIk13Cmpg8TyoGexp8MdgikHOiHSP3tqDLQhhKIheTayGOlaW3rKgvCuNgo2JEJBSuKjbZRaR4rFVitN7+RFtrA+UAlVBGVijKXO1O1CY0uCmWkjU7t3QA7NQOCU2MMyST/4wCXx7jR4KzSHlHj6GxlQ4mTAyDPwR8d8losXuGVfiiGi8I8Gbm3xwKIMyC9KIgehpmTq6D16SZqeVfu+KOyIYKZquQrUqq30ZOQyant0zOR5MrJdQNzBwym4o7mqLtq8w6TjqqePDWs1nR4maZqsQCapvcu0lZn8rlFwIgsBmV1RqXlRXdUvH6wIHH8kU2YmBtmczuKGpQu2qQQ0rImJxZAfOvlLYhff/01AoZM9OTIBY9icw4hsmij0o5ca42jcpUFqlxrfIcQPZpE+ILHtTaxxvjCpPYrk8KwgKUGjPqsLXi6CcUfMYV5EQC1AhR6B2ahANTFmqdoWiyKtltTuKj5C0BRTA0LqajtLmIXAUWxqLITTg5OAXJWQmDQI6s2tlTmhaYoRheiKjTnViSw7tjOLZKZONLhGItqrB6AfyzyNrB5LLQhC/24+DbzkTyX7imwoiWLYkcPjDy62J7baY+jtpNmEGfn11EwiWG4gnHYEvq0lMYm82F/IcR4eOdzevnz8MSWADk/EPbRh3/HjUH8DOSd27d5Fmx3b/fmzZvpBogMciTi51t58nL2qyQa0tBz6vNUXE6vFMKD9ShVRS5lLZjHVdEc2QxCilN6IyUUDAW+ABfFIgxRFYSFiQIrlBRlBaE4ya1xrawEqBlibUcMZi4wFBuZYxh2EW2jXETlKgsFgKLjqav6aGLMhReKsbabTcgR+OGAFFX+grDCSaGi3MGAHg1CHQ96Ky3IPJo4jBpjcH/BbLVJR1UNfh006vCR3TISMEFfRU7CiMUONsHiOKkNY62ojLHQ4QLz09m5A7eoWvRAWw+EYTpq+wfocfrgZ9iR2gilL4YvxahRMWoKNlUZJoHtevZ4JCNYJpjf/va3v/rVrx49fMjShxsgtLGvvGCui/JV69s0anvqgtAJImmj6o+002hipQR5Se5DAAWmqMrYEYcMhsKqUYOyTwBFPHVx4pBEVYcqPbEp1R6tkW0bg2FtAvxtVbWeNUftaCyGmjMSigqNHMUi3wFQopEy8kSN2CJnRM5JzkE1dOM5hzGn1vWkjUehp8llgHGMJj5MjU2egC2HkCa0xQLoMgyJaxDDYCDlieko3RdUp5OuwRYHsUZ5pkpzXCxGpWTVRm+FXkX4EfJNDgMsez/ck6TCy2fPP/vss4cPH6rM2qiIx14s1B6jplH2WRfJ25DWG2yBKlEpV9F4CYqT3AJ6yQUy4mOVyDPBgMS1bTxtehtKiA0pquqiWxqrzCChERPxjbJJGmtnouzZIR2+pmToY24MJxv/6BbWPSTOAnJiEwC9kVZ2RL6ougw9EA+Z42lUuna2gn1ZmC0/bNMwd30mzTzQBeH598A0g6NntBN8/HREpSoDLMRgGpUC2BzBKe7lSAlYYVNF4q3/h3v7L168YMbn3iCug2nqNydWyCDJJbvKgpmlEVLKaILfaGIwQmNPFrbCRKs2WYZyV+QqthnKhXJFRV6E0WY7Uu9IopcO2YRFPI1FK201jRCbHOWxOCc2lBfMJ2boY0iP4YiBnf2kDA33AJGQ0VNEACMhIXJyJwwVCbNIl60HdHyJKh41K88z2imdEj/RdpDEBvZv12IB1L+vripSQ+dcomcaZfunOTF2NXwVj8ZrkdtSSBWz3SAzQEKjecEvpKd4BMnS81MYUDPdo3z+/Dnb/sj8FhJKkQMbCOl5mcE64KxfLqsNrqyJM+YKxppoaCUC7mKxkG1V6GVoW8Ps1FVCFuaGxdpo0qEvqC6qWERbhxHbWNR2VBkJRgkNgvV1MVa1yY62oGrDn4M+j7uz7WIkrixzIZgguQ/aMRNMDBs5FmNtoZ9HK2JUjfznEEOj34WyTw/0OTocYhJIJdFK6RzlsP7MGB4ZA1bCzOwmaDMWvom10HQXx+Vpw7d5GTeeNp6Fvq0Huo8ItRwC5TBYYP2hQ8PWS+MxkjLaKgAYSJYt1Eq5y/CBCX+04tHLgdj4Sfc9pAfC0qClVu4kJDmcb8IIlp0OOLN8JpO5VRSRZV6RlCuhwjYaike0UY6a6Et6e1SxyGNtwelixBTmdRGrsfDzYKg5z1kzk06IMUfCabo3HlPxoCFxMvIdAI9cBctbQXxPHpxiCgNMo982PVYdVbFpC7m7B+h2erIbcw1qPbpie63saGDjMGtUimRmC6COmK50Vdto63MwLkPDzyfOwgudNtwKSluM1JJQkRd9Yk1C5NQIoMZ6ydnF6USgopWioigrb/kwoZMoKqd2dZmfRE1Pu3Dli2UQT8Xff+MeRd0BiuCV2dHJ8QoPjeWE3mnoZeDL4QFw2MKQKzDZZqZTq2gogDUFD7VDpwPBADNbI4HcMRgTvbDuNKf05NaYxFVtAiZ2DcayqWToeNp4rDeDNRIKQhfb8IV5Y3FkVEXrGklqpUNSkC7WSGlGwkYywCMS5RQbTVAyvHW1V4JgyOyMYsvBHNrmAqNocHcf+vSPM8wP0oBQ/G2+VKtc4EZkhHXLKb4wM0Rw43iOgCsqu5MVf0cPqGcK/Dm3emLvblc3Q6yVrMFMM2NVPUisuTKXwGhPbNLEB/Ky8UzckJ6Gs2pvi7vT8eMh1YLsUmPrZJw0KsZDrxZFjU0QXCtBGucI0JKTCqSugvGCRASSMDE3YWFbY0BGpWW7tqYQCkMXLRR4Fw1AsKxaafhIs15hxByka21l8rGExjZCjl6p4C/8juWrBuOiVvbREIZSH7Aw3b5gM1WUUY50FDvKJG1CQd4Ga9O7FRZAInMKkOdhoy5dGS59EpPBFqSkqJRAw1RrhjXT/oW5jULdMrKr28wvud4Nt3A5A3Z4FhRnUayDLwAUC023yciTwoCGib6mjhpbRmWHPC6+g4qqWbFdHp6Rx7W7Q3rWzqS9raEOH+9Kg5TviMP5MTnlf/IwTaUi9xjkp0sUPCYSVBVb5CqUdW1hBcBJeBUtI5gwIgUgpMOj9OMYbAUBM1ICeARytcXMCTdI6LgTaLDRlZAhAXHJVVGpGNC4Fo2LEoQHIMGEQqoYMSBJEWZkJERpmM3NRpXdGWYewWIewREfMYUcXRRV/Yv2298kIm2umF00pmhLDTCyUajNa02j4em44TA1ItIjl6c1xcu4xo1THohtbWOdO4E4HaxBWOap4WFAp8yDxe3pKIphcj3bxdMorcrC6cu7cNEGEjKPZ7lWXnQjKPS1Ej1K5qNMcx2y0/5vaU1jJ7Rgx1C3dXsbRVucOkx1LWFHZZRrF5FEhv1bLfzYC6A6iIXmNekBja1iRGoY0QPWW0BJLcn9k+TT+TCpc/2ZES+lTSQUnK4VuUiUq0p65FhVKKmFlnUPKx4tyPjWy60PfOtl3o8eIyfhw0Ot2MxpjDS1uYKJevHYMFYVSiGNlwthZOWqqESm1lXRSjDl0gtWY+qoom0hR3AHFVWkGhw1yGAK/nkUo1P4Y7ExAEXu+IWJVgqy1sTg26wi5qJkNY0zgqQ4lY+Mx00Ww0j8WADIHca4/OBJY7m76uD5tXeGzB4wsbcblRFQyOPiozltWSyAYocs5Al7II5C3rjMKmfw81mZT+cML5JlvYFscJQBWi+ZojVCischqhhzqijWySZ8V/bXZcJZWl1JP4O0lhZALHq2bt4gDa4W6RVwRJWNyfm5L+KBXF6K2IbKFIDcGaAqKcmj3koJ2HbUghF5xBBtLFqOYVhpd6o1BgGMcmO6BdvW5DK0vpHZtdFLEYAwdhSR5yAXfh0w+qJq4mDgMe3EJPMwJLC8/lnhdjlGs4qM3BhwIWtvxc2hlsAoWphHnOafB/mCs+4BHU3pfaxr2BXSnN7DcYWCXoR6VXqgPkmixrLOq3h2qYFRYxlByZ1A0XKjIEABs3fN9RiyFaSn4tG4VoS2tWBHaAZrpuECxSYCO0dQAoCgXILZZi7QEKdIHv0CiFV9ZJtbaLQSc8Efi8ix2M3W6GImSvwWaSSt8CNhPQGzZevpFBh+C7A0nALo+VZQAyK+qKXoJHKKUYi208sin55nwfDa9sBiAXTBh/5qncPxs8odlz/CBpmVCNZLqaIBFIWRxhOli316psBQZLr1P/tC4Ec4+Gc83hM4fwVXYKx+WMfo4S/N/lgBMIne9gOTHnDL1mUmE2vNEHnalHY0lqC1mjqzzdDeu2EKzOCCTb0UcwHaOIUUpuaMtWBqklpTxDPbYhFPT/KZB1l3VM9IJoN1tNqnA8zskrJvSupo77wj78/fHzlZpy2srlMPLBZA1+loXkBbOuZER6N5lrz4tK5tjZRgBgvds1tNaMNGQWyyYsbnETCehJdrqlQrQxWtUZGqqIkupBfMGDkqlNFqpKzYBIuyDU0uX+jlvYjBeATxGB+rOuRuqzY2h2HmNqQB8xbcEATGp1PUOwbHr1rrJxZMODHDnAy1ADo82tdv+uKFUGl1f3dqmk0s9GdoQ86Qqs3FVdcvuqjnEVwsgEZ01LxH0rz5RzRv0mrCVuQSlHvK040pntwpknClXD6pNQCN2NCAifoiQLsr9BQTHXN0eo3tsq5gScOPwPNT8PzsF7m8IPKPvR2KTPTYcu8ziTdB8x6g4yNuDVpngyg9DMZdclwLOD5GlsBPvK9wg8TJEv/EP/CSA7LG4aGxHIVBJEOrWGU5YqQ0P4I6yhpb1cg6Bmng5/NeXtAgKBVUNWEjICrFT65EFYKPLF6kQUAvw4Hv4UesW6dawWQoW+l75iavBUcYqRyVlPZrc4OlcXECoQigcB0J6bXGBQhD2f8ivlt2o2qPeRXIqD/c3d1ZW1s5OTrk/T/gI7I05/nH8AOAjd1Sxy9CB5/OyR4JKxmOxDpII6XpaW6rCxdyixuytsDm1EAd1saD2xhJf2Sj+TkoFwugEZ08p5FkrxrULl5ywb3hsP2pRuSaXCQYQFFW0lhv8DRNFolzCdGRXNcuQKaU73SWyEoI4enTp/ohMAxzfbo5Wk2gpXww1FRRg0ksFrLbjl6BOTxXSXCtAQWVit21jSZ2bQaRdEfeRjWNvs0jeldJVi5fqjJg4gDMKSo6ISZop3cxcWwXaOhWM9QZ+fwUhrpC3UUXUVQ+kyDtrieb8RZ6Gl5p2LgdPi5+Vp2D38J1rZmVr4Kn8FvUdhTTL951VC+qzqcHpj8K5zYjVKGiSEOIAGIMKCkqpzaDNHs2f7QYWXR4oXdR5AbHYvIVkjDeUXANeqyUeAAeAJtA33777ZMnT9599129CA4wHwN8GFBL0gJIDBjauwWqGvUAqJLHAmy9BNVC0qgvGCgCa/NoR4LFYpscYwDTyNyorAkLKjdH5uQGNBJaKaSKVtpWfim6KkbSqIyAWhZVdIdGygKMEo1yhJ6+esIKX/MrEo+boOZQJOmKMEJ07aKE/m1pRDYqo7tGeTKrRqoroXSf94x2XHxP2mlg444W+zqHtoz4RutQFoJ74ByOin1dfoHeUIdoYnIxKqlSrXI1KusGawiB2xorzoK5ADcyyEV0JCtT4Z6JnmUNyx3SzZs3d3Z2vvrqKxY9LIZY8ThUBHiiF8tms8axWRMxUrrKISFYacFUFooqoqJKeRSMHynU/SMTc0aGRmUEWC5oXSR+JSMnEIowiiKEdjcBeW1S8xcuGgGRR/GMhEWT85eHRyb9xTtnAXfFISjs/sH3QfbBzKQHcHRuvmYScCTRgYiabnlcfDfbWLVtrtv03eRYTWbYTVvXLhZAdZ+UmngkolziXstynF/SmM0p9sTI2QeLiO+QI1JykVOUJpJIqThZ60hIeZ7ZtakjJQsghK+/+urZ06eshzDkM0DmEEqjYszlC40FyVHjKgl1nsOp1Q2aNqSagIGFBuOhyhizSXBxCDzzt7v2DHRYwKRI7roh5HTZZ00U3JnRUMqCORYjQ4eMCbXKBYtymyYSymnUFPJIQIHvKDLC+p4qHSztVQo1Bsx3AIqyoM8tt3N01cw7/i7fi7pFDzT1wOJFiE29Mko3/VwwysOVqY9z4uBjaRg7RSVuOR7qTv9SFafa04pOSVbkoJwjFHJdlAarwil61kDa7GE3aGtri/ugv/jiizv3brMJxD0QVLEMkqFCw4SU5NwEyVk31A9jE9557CsrewpQT2MevUQeBY9GArBYa6tGpWv7CGJIHTRcKdpqXPKawVT9BccTTWBuDAZl7bQRGdmuokyjWPBwRtBeco18ZNLI9mZA3xVaH8Kr2IGLmK9WD7ymO0D5jO6V6XACveTH9aIilF9Njp4iEZRip6GhqLwx2kZlZOiWozkyKT32ld5km5I05MjLzO38DPzwq61uf05bQUfHt2+kTaDPP//86eNnm+tbrIHQC8lKSCSRzbQWitpoFassI0iWUOQKOCqlsbsoCGZNtIqyGAxGiJqIRKbtan6hH7doF2KbhnMa2zrsGA+yUg1DM6zswjQaXiGlDpNytjyJXJugtHmaVvjoR5JGZQQs5GvQA5ONnMmsJuiu124BxFk31ok3FniCA3ClTeIwbZSjkpZSjJooT9APNpfgoqjigSuqKCopJFY5FFkGMddvbm5S5D5o7oZWrXIALqYBdHZBLI1zkALE3EqEQpamyGUrZE8eYEIWVG1FgbPReIZthB16eYmA2KXoVYyA/rIa0h8PsjGejhiKKooxjeX6UoHVCncI3cKSnYQ+fSVo6qgZxj/BgZuh9wXVnHqAwTMn5pnTXpMFUOzxKLu/dKY1VhnTLWjG7HPGCskkYpM+VoX3cUMdF1+4m6xIu/Cr1iGQmDQ1b0KI3p1gfpQk1UpQVZQNtiDyIncttlSpiKCiwpCJyMklmAcMr/w5OjzRl12uf+3t7d25c4fixx9//OLFi9XlFf6BB2lzOVIxKqMMxgDLMlTRSmBRH62kl3fJEexam7td1jQKwNCLVrKKzm3lWmss1HG6CsG1FqCSHP2anyqGSmQoZJAkYNKraAxFy4XgAAp9LLaZF16iiWklxKpaFk//vGaYh0adqfjdA4xzEt8EOBza/kRGA8yYtmBEGGGWJciX8jaSOekdiYKck5d507oVhSPro4BM4thJcF7YTlmEFoa2Xr2QYz1Wi678Aqjuemnqrq81Y/WUwDrefQwLZOFdQXbwCFBYdeNHcnaYz6TKTXbY0lgvL41Kqtr0fWKLbS/c2Sn6OtmQmYJ1D4l5X3cCcSGMWvR8GJBkG4NxM1FKjjlKTAq8io16bFVrwUVrCsOCTTDlila5YM7NVpg3ktuqUWgzqfWF00a2kcqadqSJACMNOwAdVT29zwpGJEoTE0Zz5DYerXjYE+V0AONjZ6Gwtb6NsNAX5kXtougeKDqq7mdprO8Q4KTWALuYUigIFbCVjt/ClO5mbn5hN0G7j3o2qRuvWuX0dQTHri9gqkIpfVskkUGYqOm2FV4YrCLYyja/BiNEj2149DbpwKiqJ+FInuhRMt/lIffsmr6CLOUvIuFW6NPqoQNpaj31hVK90eB3uIDAJL33mVt90uYNEr2HkHf18ytz0y3NpJMV/vHllu+4/C48W0H8jn0qryzzk6iseB4/fnyy9OO1dd4RBOSIDYqDw8ON1dPvDDgkjCISDFOT3X75yocGJeCiSpqoB6A0NB3jbwym2ywicWcwsqqi0rUWVHu6eTWsMNEAEA7KEJL+2oXc9fEl8xh2JGyTu5mxUgBt5taP5BGyCI9iT8OBuf0NBfWn+rno2yFk7L8OCYHkYwGRZYYx3wRYAKFhmAOzG8vUWKY2yxl+9pxFP4g/kJhtGiGFdt1T6r2mZrrzVVvDBGi0HbfPavKCAUChcbGjyphxhZpz4maezubjBlHgiaAIgijrQAurGRYdgIQiGBxJU+vHjSE2qg9bH8y4MUyPJ6qZBFaQZNbTyxkqjuz82KVF0xqrCqfmRwBvE8nKC1oVI0CrFnKqmPd5JP7ly5fplzHCk8BmjmxqY1suZB1wwRCLlhvdqTZGbny3MNJEQTpUC9203bV22gYToK1WeiJx6kaOW4t3TJQXtlEZ5QJWFCOSmIvaS1WMoRKYioo5fQ3Ie586HRQ2GjCCjWyIeGI+0mQsAMwiH8vqioLb+jz2QJTdzEala/sI+YC3Lm5gwMX0XvpEIszIePpTCTnHHSD1y7x7R4OjGCIUa78FZtyeMh7mml9KMBaMv94CXUGijcrVWGT6gTQ8PwY9ViOn7Bw5iiR68kufPIMYBtVg0z6QI6RMYvOHX4knsfGTfkZsbXX15IRboXkAnsQm0Pb29uraKr8SD1v6Qnx05ltvamNOUCWS4diwa/RWCqliIaNEI7CqYm4TC7G2UTahahVeRApgjeO3pk3o/lRvrT17ShqmG+UKX/2bWRhOXFTvJ7+Zou6uiZlHGrorRiK7AYp8XLZiGMiFlFwC0wLIY6Pulp5Hqg0WCSeLv7tPrk0tHVUcqdh116aZ59+QOS6AzqcxOrWKwYHreny0nYQTxGl3FiCJsjkbla61VR1txFwtWW1h6kSQrPgHxfbN0gmaqe6NXjgO8KAvkpXyQq3dRXM9C8ZCR1M/P4/Ky3C3b9wi3z885ELY2nLDKRPZkCGUdzmVI3txFXopZeJ4olBXSdOmj7bIwMgVjwOwvgDPuxgDsEyECrLwbqWRBWAmRbzAH33pY3gacgUs5ml45m3bGGHujcGwiQsgwKS4ITRueJjP9VCOG8+Vw8eBeuWCv7QBN8zm3bEyjhsBPu0ba89BmU7QpnMMJd6VxzCssRBrR8p9rAoMRU8BUW4Mb2QAMwEUYcyEExKaqRRlaZpHz4wcp+YMqPg7EFEqqSbKaJjTSVznUi1BAiBn9uceCH4YlQthN+/cXlnbYGfIl8PAgNepABiZKnjQmUEYm4ifXABqM34wMgVWLTlFwWRlGUEa5zKUXlausmBya2YruNOhHfmD3g51tjFMzNbYOdMHaQYJI8MzHmTsz/m++3k40vAe+wGZxPgnFVUjG1IAOE+gauyERmVh3lacMqo22kuuP/9Wtx27i+qotngm7pmxF0DdLe8fBy3pppqgFu+FVdFfhdOiWNg2FicwgafoFpEor2OOfsF0AyL4MshEqzRoXZ7+tBQZaIYvFwHmgCduJoYkeGCTIM5BEGf/CCad5cKKRQnbPKx+2AQi7bPxs092gFLrFTtKjvMQ5g9VucjBkv+ufGDW8sHg2nrlBCm1CsB5l6ezdWaWGoaz9WVpJKA0mLSstmB9bh67Ix0ZRgSoV6MGcne19K7V4evwLiRXjjsw86jCL+MNZgSCVNJZgJKiF+tuy1hhwCBD8cvRWAyzAfPcw9LgEYjZEC5YrngPzHgBRG94iJ9Pz7SdkPr8cO3EUZmBc7hni2qkNRZ6Usm7Y+iwamxgozKSmFnCuOFBJRd8liJwMw0M7JQknvzxyn7AynAnhlo+c5lfsdJ8GiMZS5bT5CUnCymGNI/zN/2f/uSUhbw6GSpdRT0y8ZCOjnnM61A7/3CmD4CTk7QA2nuVngtbSi07PjnERLcN4QTrpMzbP/x0GPM8RfjI07NoTSm6zrAcbkZm2xRzxFAs9K61eS3Ys2xlgszzcVQN4h/2j8FRECehRGWjXO/6yGkEFzHHKmTXFvrzKQ5amp1x2BobLIy7UYLDHgxEh3u2005rWV4Y0y5EUN2T2OE3xiymPsy1T3gY+dGLyMlJnAJ2B6w2j5pMcqqIEUKlCislZCeDtowVv3lO/XVKZ/FqCCEN9n07TS9XpXrybHPmGyG+fPjaPPXBtNk26tsaODKSRrYO5Ygx3WHZWEXcDj3KjeAZKu3UnLPtqZrfjqYU+vRSH+81ZizmPuDulrrDLdR4BVkAKBaa2rDQNPIIIzYALErUKOWRQRjnjVVywQcAez+simqSaIUsQOFUJEYKY6WKsnVusDXC17mQ7joLNoxUBbiumq3GwVgwf61x1VgCPLOiin7bOLO306VRPBzRfCayfDmHs9H7rHzBo6GoXEWWOziV66JWStUiO/XRGGxBPeliT6H21dNQsCnNx/I1D/A5xz/yGM08nkbCRqW6d2SEbUchveahrQ59dFkgVWWlBbMVAOtnK9R+xe/I2wA9w5jSfGIvjl8M/cOI3Y5VwVPH05/ZtpFT5tJEvcEAlJhmC0BRtIkErACQq4hMQramwBsmd8qtlGBbUaHMrMmLqqR3zjNhWgDxecDiJoEzkfZ2/P1RtpkthacNoeGXXnlOelkjkOR6UJcb5dqhk1QJkmJbXpCYzYIcuYigvR9pBrShWq5PP+cJINSOKxYNwXzAHw7rSM5IUoNdW7fUVbVVoQFp824rw2CQ3IinqlEf/UYq9G34Nn2kmkaGX5EUMWsBJO/COGCHZGGaACa2lXdH1c0TQo2ju9voUtfSouKQdYcbOyr0RpdRNGnD9aRqM+/QdzP3ia2D3FUjdoDsxoIsHZwFMy6Ei+qB4hjNL4xGR4wED4ZGwKziKbzgKya8UJSvHNFp5gDMgEbVEa+pn70fqpCpItfdoAWeol2Loc4jQLKsjHS01ghgvQUbFppYFAkapcjZR8aqD2x6TDwE07MVDPGYFlVtRZmMjGokwN1uodHjufVzo/daqXjUCYrcLbWAVZRrkkIzFriwXRT79ECfHq4xtabNV39kG8Pl1w+eVaapReoO3SewhW78onbcHpi4YzV/yR3HdFy/0+PbItcA68PfFrb0kT/KYuY2HG/MSCMreXfOmobkogTw1rAAUk+iYfXDm4HAF+5YHPFPyqJKts5hNkBKaaSMVY1IlG2pYBCMmNvwUc9ih8uEUSMZlf/VtT01jsEChm5pT5JxYfiK7sY1H4mfVfxzDXJkK2pA7rYzXSeNkHUt/eAkjIqRGU0sNsp9MI2GC+U0PcABncb8Ym3rkTZNPKNvgi7GKEV1X9RHeZpoFraxBxp7tXvs+uhEnnOTY8ATR9LYwHrIqVHoo9PYUvMAKDBUOWEiGQyCi6LSZS8WQNwEvba3xw3RYISMnJYRLMtWyyyUMhQ/uTQWZBXJFYzCMEzFxioT2uSSCGqagymK1k8pFH0yJdtI8zZ36H0ou0naGLqt5lGrkVPEUxQ7GiVzAksYHn6oPlytMTK2olEZAQt5mh5IB6X64iHlNLTXxrb1Ehh9VHSTNbX+2nTH1W1IcVDm3ZAOd57v6hioUq2FGmONkBQtIEe/1otNufAsN8I+EKUhK697Tm98PsMjE7MNGPLTMeJkEaM1UMQMGdMmEFfLXCRCEqsfCfnRMLalUjIGQRrnRVUsRjm2MeqRG2MrMC5qg8fFhdCzB9T/RVdbKX1R25P5YmHF4KQJjSNTQRZglAIXtY290UF7sT1wjb3H43UVB+dcD82Im6Dn6ntBPkEPxNEczaX3pKMi+VgjHnAbf/TVKEfDbqexNsqNtBFQuFDrIqBgoIrE8gW9bkyODCipbSNBz1UwXSESCWsgNBDJyr4oWo78YibPgiHp00ImCI7B1ZHNykIwRlQuFrC6aNd1lTT9qdoYZqs/53hG9k/P1k0QNiYaDz1dzAmmGBQMslI6T4YJjVyjQHBx3Hgwn9h2XF+vM34mnQyJDvf168nFAuiaHFMN0DgrjRz6NhmJrPsong/IutwjAXAizKsCBJLAFiJboRRSObBYBFkYDhxFLSZ5TxPn+f1DTLKpurBlSUTA0lvGV36vT7rl2X7hQZa5FkC8FZHLYTwYL4zWVYltuAySXuS5dcl73gdKkWTydHcRMCMtAHBPIlsvPDlKyao1wAJ6VRkmK+sl1Png7cPDg1UDJtNE740MjrwR6VbUtsabocaMq5mAalwT8I68CM9UbYACP31R3Su/yKTMiSIlnn/UaJQjalFallBr0BfIxFW1uo1ZtIt8Vj0Qj0VxaPq74PD1B88VORyi0zpxi1ovgcmD/Vno1k8b18J+uh7wYbLQwSdMREa5w5CqAunxJKuiaCpbISi5qlEYogZLH2iVDKZo2YKtEKxEiGDxaK0j2bmQLiKggQpBt0J77payYI7eC1k8ylWFrGTkUJH+FhhXobfcR2jj6WM7GUadhq2FyXj6WJ1/6zqiGtnePscOEqcOX/Ookl+YidOrfGT0/d3V4LrVERPl/l4WyD49EPsWORbHNe+Dnzdm3Pjb4vGAHPsmaDOawppLJZxDeB0Ho/beAZ5tvxWuKc7J9dBR+jDW7IhGypS3TJiNwTQqe3YLtnIqfKTinTeNrzxmt4N/IAWWkAspaCkHa6O8Z4OSRQ8afhl+79UrfTAAO21sfhyMD4261cJ4zZSt9DL+9C4hR242vBR6im0Jc0VrQUjTWkAvWfiakLcPJ8CwUQK0gam13waqsx+WIGHuoCoYOpgL5DyK7rGOgDuqCKm7VjHbi5tQa1w1cyFGGOXCEb8AQ20C5MNHhEWQqWqYMpBidaDDodeyPVphTTH+9ll8C/aQu/VvEU8r7jWuKHp7gp4Yi+EcjkhjPBP4xQSqETtAE/TX62PS2OkoG/UX2C1t8dQjqdaMDLsgT40/uz/RyGklgmTlhe1I79l6wCBwH41pBbYhiw9SwYAm3fqTZ2qugm1tbUkGhkBtbvEgY6EjSZzksVZKa1RrZSxKKQ14F6NgvYRIK5gB0apDVos6AHVVbVJrsGpUFmxglKIeTSzWsgHjNramihrTRuUMZbW0zqMLHdCoOWdZCyCcEknPDukJm74hF9450zdhwXDhPcAoWiyApjoK9OBU9udl3Bhnraw1IwMspjzP6Y2GkV9Iw2KVlVEoHMWqKBewWJRHa7QPJFtXIaRFULgNiNWPFkAsbgBzAxBFoiXZygGssIdynIaEAM4FUNFgikYWSumtrIuxagKZZcWIlcUEpOduokOpHOfqzymj0DGdkqTDvJs/1ka5g3DcKmj7mADTgC/GcJttG22tbzwL2mgX+kUPzLUHRl8Cm6v7a0A+k2n3HPqhjpO5qVYSST1nTRzeDKl6xtDYIjWKYJRqKqxSCj/8ABIYOhZD/Cw8Re4IRcMmEMW9vT1xKgcmvAgSU0rpYfi0wR/6WVUDcOa3nE2yWTaPevFHW2kKE4ryFW2N6RZk2I0559ruVqh2+rDFo749hwbakYVzcDqxC4JkDLMpitB9OCZ2sTBc9MAF9sBiB+icOv8SzndtIbXp23oKfGFSFNsMoz5Or8hKAiBHZJscTcC4GM2lrHPwdcyGaUNIAGQ2gYQHYCtkBYaGfSD+IXQmXg40eD+QYUUY6K2RrKJl1yIoxaqh7kr+dX92RC9MH2QbiW0ttCGt74+0SR+BA+fUBz8xBi99bLX9Qw6eJve0amN2pyEoGUnRcn9hMqv+/Avk1eqBicfDYgfoah3oWUY78aApgkg8w00Lc2rGrOfNAgCVNZEWZf+Z115MheCkpZAx8uhaX95iZaMdIFcloQoPnTaEICTlPZ40gyPH+KNMFVYJnTHyaICUFLO3AY9klNTKVrlhNr9YwcHPNgy1ug9nY7e49zoYahemilZ9qCK+v1y4m5+j/iFFpBZAOr5FqBHWU47jRC2Nmp4kC9jl74Hph8o5t/HKL4Cuyok07wmuGHlFsXFUNXbdNHHmbY+BK+6wOV46Wc2bITjKtINX74Cg6KRiVCI7NtYlFGOiioS5lAhp7TJMyMzdq9ziwoIk/2ObBSQwLVYEsPdjXhm9crK6xgKFR70G7wdK5CxZ0k9lHW5sruHsMF//Sszr6+nO55z8nHDC53SypCe8Ej1Nh2WF5wwgOkqvT1xaHbxhSOAipwXWKNTEMmR2VRQwIRBrDEZvWbWF5tRTWGOZx0JhZb0FvIBxcXrBbBKKVhT8YEhg6AQEarvx0bwRiVI8Y1FF2qsoq9WpJ1fTeacezScQvUqHcBE3ndnp0cnQP7Glsokay7Gf274ojPXkl5kl1K4VZAzVMQAuzBfF17wHTmfP17wj5t18zr25nn4itwsLk7VrSvMOp0xGjSmaRO/ILmIITOZRqG1toip7lFVURlvVml9FrXVsAjM7QCTVSk+eojw7vSraqJfclpuqTRBhUVvzC4DeyNgiK6MQwVGPHPmLqnkUYxstj4x/5pHY9cyZI2FsV5QjZlx5+shzJNy/droWJIZu2u7acZswFr523TaY2/RjuVuAYw/UXWoNgmWZ1JpINaVc+OrPdmE7QBNH3L9tlxA5k1bX57xaKnLyNkCfDrF5FGw4DTO2beaKHC8SgNm7XUtoY5AeKyW/AQgrsyEUSbUo6TLJyQsy//RFOD8TBiDpMxV7M6x+2AfaOziQhlt5EFhvCKacMNAl5rAQQcQccNi2GSxTjJQ5GCWZW2lhWJ96rMOWKpLBEwjmL3gcWKGfwEVhEgkLWU4jvgDEKmQFXyi7i5GwG6naGNK4tmKIVlHu470RE0lieI1gK5MVqx46LY/P/YP0Guh0D39OCZaFSG7bDsEBNBo2KjvYRlbZnQVMcuCn3wdGkiwAI3vA3WuhMKmPrDTg66rCdmSxzelIwxqw2AGq++Sya2Z4+Buban4Ey0IWxcI8wweZ8YVJxFgGPNZZIbBNakHe0TtpI4eifBVCgTcGGIYCC0PMaLiPh08H5FglK8Ni61C6WAgFXsW2HNu6SoS1XprCxN7b8IW+aGBRW5AXtbMqtsUQ9VGW3/OJbVZtvCQ8dBo9SdI4KaLq6NKOqo7DMdKqCKC7aDYL3fhF7YX3wLhHCvy4Jt1tXCyAuvvnMtYyPc07rCnHWUuEDLY03lRLHpNahMZCUeuq2HZhCg1F6U9WlvkXMYPCmT/8lDv/UsJsYKnikAdCdQhqnX7pnqF0u098yp2tnbS7c5q4uyi9FOgo/csLF5HE/BQcpAgO6hRDLNZyDWijwrYG14QdminNO5jrKreirro8mjR+LlnqeYwKGCOb39GjMW7PQDM8KdzKwhB9rTE41eZ/UTPSpAAvile3Bxgb3cPjQpp2YZfALqS118Mpw+h8Zts4Xkd6NFjImMdub9ODcRWC5cK28OJa4VWMtknm/2EyYKg4/Rup0A6Kw3rXIrD3M9gWIpqWRckpw9AyIYe0yAaIIRaRDVCtOISpNUMPzX9NVVTbY6FvK0a/woi5jb+NZyy9yWvv48Y/lt/+4EsSRv+Ax0VqpxOr4eBNBG61D1B/WplMYNjfBeHVA6a/+QI5QQ9oSHR0+0jABE6nN1ksgKbvwwtgiNOHxpynpDlFYy/FEGdaTMEMvVZhDHZ9hvUZPJyeCnDiCbNslG0+UpCVqSgeESH7QPpVsOHcjZ5FTL7JAZHEj13wBVXPcA3m99RSrnctr4HEL0USD8Swo8MlMO4E4imww5Pj9LTXMGzdD8RGFxq2g/Q1GqslokiY09//SsrEiR7y9GppNQ2YZTRiRhP1Ugpf5wJbb7BoVRwEZtAUgvmn4Ogyjb1h3Lyd2tFrImjM0KsIJLdaRa34m7ZvEhBMz8NhpFy4aHfTCOI0g9qiosIrAEYuhAl6wF1aHPqiaObGzpeSvM3K5hZANlIZMK6wWACN22OXBV+Mg6J4blFq+Bbe+wzoNgx6pcYmUIW+cNeIjErjZW5+CS7mFcvATkgKxpgQNt0GlNY/6dMihQTMXigi62Mk1VUp1eb6oXA6BaCxa2GwFrnzii8pZBhzGTaCJ1A6KtnGOCdgW5icQw9owPR0lI9vHnt54FLMmjyS81il6AHZk/M8YSPDGwk4z2ivhy+NkMa2NA6VRmWj+bkp01fbRbpOPdAxKGfSzMivOYWcFMk7BrqQxifLPLFKgES2MY/MkgFLEMxFm4vNufSGSa9FDDKCkVFgm4etI728B0dUyR1smLADRJGkG6JRAiAnoZRgWZqEzslIAdDpQsOgevjHJIZZAOLayCaAQhUNslNsqc37CDAUMGlqfQGbeVFtmTltT8LhkTn929Pw/GEKUX6jjIZijCcWGSGD4jHv/1k6PjziXVgowYft0jQYikNvF5EtepGJDp/zAjBlsQjJRcXWEdiUfl9nc3eyO8EjwRoLOu6xKPkCD81iAeTDcU2EeQ8m8WuUR19Rrs8Kd26EWVnghWlEFiaFIbU6x2p9rHItLiRbg6AkR8NSw8c/6x59MIgZKlJtBYMAqhImY5szwZwbj0YGsarQuCoKYpBG+KiJyAnkGVJN4P38TXQ07bcoWn8ZhBgbclGMEboqHk3JVCEo2YQisnIrTWLNhQtFhBcez7UMIHYyciz2aa+HjWyd97GdCWbsS2DjtnAmUS5IxuqBPsfII68nszk1JxZWZwbuCe8+TgtruRjXUcGsYk2CR0Wi3DAhYy0aEr/oyLzNnUBOdoQGOee6Fyc9zGVlXuXQHCb9ZJxejZK/EKfbgNINQYPbhuRRd//INt1VNFxgDX0lZnTmD3qU5beR2IqKKpk2KoecXX/HMlRzIp00kCgG8hoT8TOU5bSPOzACz9B7pOoTQ8RLjiFNxlBztmkae6BwagyBHfMeoFydBujwmCZ9fvcVw5ORqyqUBc8Q36CnSt8MsI3NTz6yl0Y21S7yS9sD9aGcYagir8fYDF2IauwF0MwjWBBeoR4oBn1RVEPSHDq8YiLBRQCpbpiEhwRF7AQVyTtOA9U6L6jMED0mDBUtKfhKCBGy+iFRdJAIOS15B4ii8bKqNdKTUzX8OGheNGTAAF7QniVJJQGiiTHnIKhb5KijyfOIpKe7tg6cJqR5cE4TT7ftyGjbelLDm6WPEl5EpcGmXJo67w6pqG0LoIBNXFSoE5svDBt7YOa9OnKgNoYxvXKxAJq+D18LBgZo26BHr9TeEawh0jJCAATLGFqmNtckmJSxqianFvNC36YULL0ZCIfJZ0MaUqUqMSOwAEKfGsjjWqkJ+b3Px8trqxvcCLS0dDCMYQAbkmCDJt1HgUaEjnVQPBv88fFh9pWeFANwyhMkR2VdiqsCD0MyavDNu0DKB1EVepm59pRlIc2uBxr7fHb0YzB5tKTxnX8QGA23uDEauc3/6GjwFisYEzIPtjSSGR/5FRODUd08YAc7Rn2imcl4o1fVHDeqj+sF5gJ74GJPhHLX/QI7YuH6PHtgfhMEA7p7TNu1kMrb2t5NVVuZ3IIwBY+candHcmMOgK/C9gKnYAjpEtjaGsWCn2/M1JJshVAUVZVRp1kbLPLYsFb21DgSCxgqiJEMhsk2Moy0XQDUAx5ml6RDioOo8cwYzmvxNDA0nok2Ij3sUUb9JWnUIoxFD/TsgcUO0JmOqk9mn+pncMPCuPih3aX4S/Ddresfpfuhg3CG7hoDs2sFo6KcIseU7gfim2y4H4haZnzApLwhkuDIKEmS9d13oF9aWmcFtJpeIQ2ejwn+Zusz657GOFFmL/C1fGsOZkKikF+boC80wagUTSJBtiUoR2X+xtqoNKeUHYbRaiFf5h7gIObjmMYwx9cXvyRrtGZAQyNaB1XAtmECZAYiEeJIRBZmwLuguI49sNgBGhxVTpWxzpZx8ddm8BS91NgPBaZoe9scWsC6iwWJiwiNCTb04ozhGayqoqjVTx0JDKoCrx6InNFXbWtNYVIUgUlDrmRDNQSlNBYai7aKgkwKWgAFVTS5VLJ64FKFdEWDKcYSRWm4IBsHQx4q6fSJYybKan40GatDJjZs9KImNFYtlDPpgWvTw4sdoJmMhytPogHdZxoqhr6K5LJNwlJ+u/Exa2uWCYNrRnTQKiuQ9v2RgnZkh+LOHiMYJVQk16rIeoVvtJLP1LIXlLaDUhIPm0EIMseKlH/k65g7h9DDi4b9Iu6NYAdISIFlrjyHcfo0GUVS2kpKiV7giRuR8Qda7STp04V7jBQ8yMEbopNtvhUJlfgtVE6pGWAcSfI0VPLeatLwtdgZEjYCc2VWEuLgb/mHYFCZUNUoC01p9tqX1T/qvZGdUXRmT6uRtA2AdFdcuvEHj5wfvP0HzPr6+qud3f393dXV9eyacydteZ4cH62sCh/OMszzzUAM3xQ2pfy1+swolOPhIGwIo328NYJR4quxW1CSVJviGZ7L4mmzavOy0Df2AD3cqL9yyjxUr1zU5xhwPpvGONjj4s+xKQ2uPI77h22TBrqsYoohpVVCToY1GhYYg/sI2DbCoqO6XfJI7gjFIz2EMjdMtdbLo2kNa4zESpO38YiwMTdJITiwyKngjSyK1k8pRNdTUk1mPqd2TRZMH6txe0wjQcxzbazIFV4MktufdfdPjIR4WFr0aW9/THTa3ypHMpiWFWHspYk5xwpgAb4GPZDeZnupmnHZ4lHnjBvVuPj6EJzbOVyHOq5rGDwpJtv8+mSEJOc0cDEsShkBbr6UNrR+GgG2FGFI2k1hEwbheJXvALz0dhCqwLhDINddPu4iBJZ1YnNIIBPP8N7n4CchC3AsWkawRwSK0qTAznaanQqmWsAFrNbYUHs/xtfIRo3N2wQTtgFG6tsYiGekbX9A4WVKcthGMhQei2IdeSSMco2ch0a7pDAfHBywBtIOUH7ZVcPDid3hUTuysbEJYutpYtcWoJJHGEwVa6OvhXx1e8DHdyZNWFwCm0k3XisSzSNFk7qnEmqdWDggeyJD9rOwVhbkKnbXNppYKe/mSU5z0tlCXifWOEBYu8jWDBaojQsgGExrAUyUXQSM7KSikAN8BkgW1rXB0aAbrRnYZl5zSrCvbsFgc4KH1noVyaMmctb6WhPxl0RuDBJl7NKeoUaqKPc074ZNFlI3Z0dtfeg5IxiQegBerQOjRE1NFXsAmOK3oWuTvjJ2bVXTV5Fohye7bWplrRG4TW+qhXAJe4CjVkRVj4EC0FZcXAJr65kL1mvGqfPzCWuC8WSTImYH7FFrpKssGGPNuIIYyJ3yhO5SEho5zyCGGC+AZCWMzN1MimqRNI3kjUp7rGvrLlIANbJNUzAUxbo5bTyFvg6j1hQml7lYdMtlDnXesdEVHEp3iHY0dXBRkrzHqUjQIFyGo1/HoNgae6yuqs0bDRfKy9wDEx/ExQLoMh/Wi4yNmSKmGAp6FzXypIn4iDEYoVHfqIxWbXIc95YROhJUrjUtGumlEQBZiycrjYkBR1nIOgdjmHwZE6ukrJHSFIZmKASbWygAddHMFmrMRWkaQ2pUdkTYhm/TT0DVYXLJq9wJCIyZnA/OU2k0kIbDKd3aP7809NLXg4KPeSNDo9I+ZO7iQnh9emBxCeyKHevuM7luzGzPbbwXhI7HgmKIUxKawqqO05r+SDtSVM5NZcHBILCmQZ/AWeB7rTUoSbJK74zmcZZ8Kzfffk2l1+KyXIKKZL2EQlkDDJMb3AlTCwXzWEWz1VZuYBEb8RQabNEI30FYu5i5pg5s5i76EJ5bGO72PlFNiSkaVRxojj+PDKLMibF/Bo7yTLlpCE0ZXpu5u4gYhCkiQemqDpK2qoX+NemBxQLoNTnQvZrpaaUXOoAa5xpNSeQSgGdYuWgINGOIkRMZZmkaI4HXGAQSKxtugrYMgJWQYGBzHINaMNzfTA4zSUJjG1Q1JDn1iBVK2YLJ5CmTpi4aL4CKgkXZhgiDqAJ5rI22RQA1zK2oq+aniVHNz0sjs1yrqxsBr4kyDq0o03wVC+X8ugVHfcaDB2qBt35+ES6Yr00PLC6BXZtD2dwQZgdSc12lbUN6PrIQTVGyeiAhkLRrkn8XffB6myGYqtVh7VAX1gEyp0IkIAWWvshtbz0a4m80ETgioxfLESYe7gGCE0PlCGCQUfEv7QZl0th8kWAupIrOsZBewUiW0hh7Ea2ckEsoTMRDVaEXWzZKGUV7VJU05NTq2BVgVQmskCTPI1eE82MeyT/vBvZvmo6C8CPD7k87EkkP6NVWEhjAvA1of38fQ84CRoiGNEVFBYwkWgsO2ELhV8i2WsCJdEhb2NZFgQu8yDtcmAdMH5jxC+H69cBiAXT9junsW1RPE7Wm8Kq5KSrbTIr5SyZt4EhoOYKjDEDkCsaOYtFKsakKWTyxFo2S1kbIXuXwCeFgxCAkMoKr2mS7k1WEyTZqxF8EZhf9BfkC74CjbfTYRx8xl0qOHXWpAmsLJgYc5Tb8TPQ63B4JFBnSJAWgoVIMiViMsuJpizwi2zBTtki0BXlRjC5iSFG/kDt6oKM/O6wuYdXcL4EthteFH/U5DdbhkY0f8HwjPLuk5hfRuZtG70T00/CjegRmYhZ/z+CHwQwWLniwoQQRkjsNPvmHb/GxCW9iztspg3oHiwt5gUFbJgjUHhwdbWxsSBa5TSjayso2weQRgFLMUtaYAhBtG2Xhz1i1fOGufTUSXqzSnaNo24IxrA1wqfQaNucWkrvOvcTwXl1bWVtfPT7Kp8JJejs58WTk4NwUGI0FAGZQ8BQLjRvVplcwbbU2RxiJhMQYCdE8yt21EbmQ3QPFMbrwPvThdoR9hLkvgPoEscDMtQcYmsVgnbm7OPrxJXdZSXa6QmrzK3xjrc07MDI00jyYoHRuBgSSwpJMjhU7Omllk3+JAg1FUq5KtUpC6oqAviLzxhQuFggpd8IIX0c1ZEozuJDC1LKRCG08WInHbIBjAJEkVkV8gbkSxaKNRfFKNKEjyHGb0zY8OlwUVfJIDpXW9wzv46NDw6RnV8gjx4IxhQDAmtiiKBsQhW7mSBut2mTw45q0US30bT2gY3qx/exx1T+Ms9/X2xq30F/xHkgTwNl0sQ1ipHqwKpKiSLAxwqI2VlkWhtyCq6IggHLp22SvaWyekVzt4qxhYbS6traxvr7JMmZ//3CZ3/PK/9jx8j9+JN69bpI2QUjVFs1vM0EvZH983fw28tgtEdPfV7RayHPtAQ7WlPw+rAheAIlTVbwPOr8S+tRPo9MhmL+nCRsKp5azk+ZEO7sAF0yXugcWC6BLfXiuUHDFTBQ/aDURqi1tH6tFSz23Rp4oN+I7AI3e7YVayVEz3P45s8igLfp4kMnW1tba2tre3h43kEoTGeS0Z47hxLaFi+JYFLVFMR4dVXVHMqsgizDGKl6GGMYK+DKD687UCGdgq0rDaaxB1djeyFCPukaTaZTR3TQ8C9v+PVCPpf62F4Kc2SWwxWi7kOM3sdPu4zX2ONa2x9loEsnUX0wjZZyOCz3NccyxaVKSC+BiEvh/mFjrMOmTA+OWJX0AUCTZUfq5MP7ln3FHrxXPjRs3bt26xcMye4cHS6srG6vpx/VIENvQsryhR4hfh623CUImSQoE8mQyFJK2KRV+BSmUbSS1Xj+PtjL4afomfwvd1e8BDQ9GF03RGFCbGOG+CVpVwoxscYadjv/CSu5MUhSt7xbgjKF2gxe1r2EP9B8hp/P7a9hNfZrMmTZW6sP5WmEYi0q02kKfHqjBOhCyLWopmtNVVkqQPtZGjeS05Mn3/ZDzAUBuWgsg0bMAIrEDdPv27c3NTX48kvCoEkyyOG04ruD2WoChkGtOAAUsYlwblQt50QOxBxgkDF0tgBA0ZgB4eCOjtF5FMxgmgVxJAFnZ1oLNEcDH4pRy9G6qRqVrF8Jr0gMN8/tr0vKimZyHjamALYpFDyynVyavnCwdLS3z6+rc9MLuSXqjYO5M5s70jyLTDffDHC0ds2igSiSag7TgMG3PiQmYTcwWNQWgLubAkzcCUAzSmIQPADZ88isS0yuO2AvJ2z/IxJ/yk+XjvYPdja3NO/fushJKCyIeIEs/L5+XeuTmCkJymYPPf1ljdezCyldmy2hniY++XRr8kmugzzX5A0xeEnCYkM8oqSosc1EHiFbyzwdLPM5lZ7bGopTdeeSPyEEM1VEWps0qMkwoLx8zkgf/MgW+5uhuwiibzBR2U02HTkcZAIeSs3iZU3nl5OBonyKXwDgv8unMMEvDnVvf3BX1oT/VgMxnvcA2iWGcgqM2j9WzioZSI2HEQa5kZVFED8lIHpsvhKvVA/2PbMfke7WafFmiVddzvl2WgM4rjmLM8QXSnSDBxSIiDNuqhGysLdwVRQxrDUqo5K7grMEAGjEoWeeQszxikmfjZ3V1PV0F293jcbDCb03bR6NWK8/4wY5O1Gc5r16aBpoity83pNBQdJXd2baoAlBrRGi9+atQWxW2LRBt+gJ2DsXLE8k8GkvrdNSKY0cxfwFIPs9imgZcjgwT9ZUFB1xrXFUIs+ptPEZmFXNDonohv+49MPYCqBhY59Z/bX7bTpg2/PkEPFfvbU2eVdM6+N2uAmM9MagKDal7HZgBg/m0ICzaYk7pKWIbMbXGSAQ7sgBeiXAj1UCZW6FtIfEIY3MV2RM6ODxcXV7b2ti8efPmzs5O2gPCNttE2qQc9sVAn4sFoXwVuexkZZKESV/eC+xpsfDuohjs99Sgkho8ZsyZGLLG5OaoMa5qFBrxjcpG81kquZutKRFM3cwm4Kx17OuQWqKKzqaJUONBHa4dXHJe7iDO3PS0cUtKw2640Ine2+RscrqEEmwshjbmPnrFjzulPiYLzGvVA2MvgF6r3rmcjeVk1lQ1p/Aa+VFGdxGDrARAgVE0uKiK+j6tiJg2GU68UBv92hGCqyxImQiH6xJpZIWe1U/8Bmw9AlU44hMCADIa1j3+tKAYlyXJRU4WVCTPMQ92qupaw6IQG4hJZkj80vchsUmknbncx0ufaGce2LiEMUjJ6upxec4HT4ThzJvQpxdAfgos0w7OaB3ZeHyj3NNl7NWeJgvYogfm0QPNX3fm4WlOnJx+TnNycQlpafJco3KXWpjSnaa8OPFFWeRoamWbXyGNL4pYuapgqPVolGSFLBME97MEVQlsDQIXv1gD8ckR8RwhHySZyDzGk1+skr/iR22nHKmaCO1zwFJjOunPVGLrFp2peD0K03TdBfRQ05OYI8NQG2PuBRCL+xVebpWGQDmooJWysWqk027AFev27sYsai93D1z5BVDs3nSmDlPUX0t52NDB30vVRqYwJaIaimf+jhUtljWeZqNUbsFF4WXo3DwIhaxiVMLgnR7zS8ALSLZ/yO2RJ+F1/UuuixyYUqQqMI1Fh+Ra80SNZKqsLISaB0CjMho2EjYqo9VCvro9wMFlAeSBwSC3RsedXEJsY9RIjpqIPGf5koRxzq1euOvfA9dqAdS/2dcPyanemM6zpQogevRMilJyzCNyehnvNUnhjmKRFJiUNjfGmkguGQyCkAjaARI+gs1gARNkTjyde2aTnqrKPD2JhjLqVVQuk7rWHscSIo8Ma81YhI1gN7ax9tIq59EVvRo70RNevZjzePPhSA3kac78tQVzXQXWyr5+E3TkV8/E/kGOxQg+T1kx0EC38Ty9L3xd8h5YLIAu+QG6pOH1nE3iDGjZtha6G2nDGmYGBMs1DE0NkMZWFmSe9v/zC4EKvYqEpBUPnxAkvQSow28kQY5FxWZbNdZNtiBAUbRVoxDBUTa4UUltm96GI4WigSPxC8BF9YCPdSFwBNFwChCYq7qDNMxCG34koM2wv34xAvv31euMXCyArvnRZ66Zx3QjTs2S9CBF7ZxLo9rCr6akYmKiqFQfBrsAUNcWmlNfegcJWzP8XJf+ZQfCt/liESMvyvPiJ/0MavEBgBcSenJMciltO7EYYg1kML+nyguByHmxUIwTQ5IjQYCIM5B3r6wu6Q0sp5el5AhOm8gQfeSs5TZArU+htLAl74Q33LezbW5Bar4SBNI4SPSZdUSQddiFxoSFXsWitig2msxE2d+R+sFOMRyklROOdDrYfttQt2CK+QiKimglcBaTsitejL7Erf2MbQYhSo3VGEXHsS6aH61qOYIJowZETQRHOWKQiyqKSgVMxZFOG60WymvQA4sF0DU4iJeuCZpQ+k8rzE1qgwWKMo+aWbUT5iKJGWXhInqPcsTz2aDdoAioqQpmM3QgIVRqtEVJbVFlTaSVHDXRyiZRaXLXSiCXYLA0VrZ5MV6C8YW+KI6ECTASVtCeQzH2Q5TPwfVYLui6xt5DSdiKXDK0CTpM8kLJ7gR2sQYUVY3FSNgIiF6i3AjuqRzptCfPAnblejL9dNHisLkH5tobHadr7bcD7Gj7C/BHwqLYnyciFbNoY/zRkfC1xjyOxBhpXAQZHblohihkq7Q/gSCrVJv2hMrlFAAl7WAULqjSdk6yHnYdyugLWZqTwyP+8R5siuz6WF+AXWzg0X5AeNeL4iEngc85twHhMb23gmIjm8Cqipgo21A8gyYM26jasp3RZijXrRjWpL/dtUIqWsVW49tithcDLLiqFmr+GtNTk6jyu3kGfsNRqxlm6Lcmn6Fm0JbhzWYwK3LtjC6fHK+k947nc6oYKtVJ4ajgnKz5Iw0HkdhTJQyaE/SOJJw6oXohtvdA3Znt2KtXc2HvAbps3XoO8eDC56FHyjn4la/CUSzWUTm8NiG2BZkkpKjIa6GNynqRyBClBQHswvgoAO4GCGzOU1+ohrauRYgXxax3JNHk6Dg9AI+GxAWrGBUySnzJnYoGqIoitcjWF4Jqa0ytKQzFTC7yAk9xAMiui1pT1YFJY84OpKvaBMVAbZv3NsPLoB/2TPOBc9MuQ6gdMbTFqSOis0AY8iQoz4zDHhh9+DAyGNMod8TWUWWGFNLZVGvO1qdSEU8NWGjUA30680r31YUtgK50r00c/OU88SaLSucGX4fpjYF89lPck9Rk3SVO2RZU3QEDFiAyFDGAESzqrUHQzTfc2mSAa6WBnPuG+HcwuGdiAASGdOqaHYI0R6dfzzCVBStTwNYGIemHzbFa5La1fkJBLirjmfFXzFMqFJi6xVRt0aoDDesvnBLm/Z42Q+7m0eGoAHl9OYsP+1NmRdK553QKnlqiXSyAOBFaGtjsQODT3ptotQFJZGj21KQdK1RcjIVvcrjQXe0eeO0WQBc+4nsGAGyyKeD8x6NbRMBKRQwopZHgIspajpqCx8WIQVYAFgwbKSSTEIMZEEgy94FwM60nemTph7VnWpqQ+Quq2DLrACAS5UPb/JEZ8K51MNEqymIYCYsmlrutYBbALqRR0SQXIhCYo2oLoLt13bVtnFGvADp6Y2SEke1iZUJVtAoDmf5J14KP0teAWBUBE8Qs5gkMpzehRcO7vKcnWzBc+R64tgug+nS9csfqAqeJcftqZG8z75Cgjbm9SKlay66VYL19WYMgZRZO7awXM3mA5YXGKTZJJtT2T6zE0LURiZ759JQ22LClk5ZXog1tD5BTMTEsp98Ry37oKH6IPiU5Nb+UMY9VklUbo42YdLtR+i37yHEqo46Gqoi0yDXg1P7cpYmDGWHYuevT1srYzxEzwleEXpAch4MPsQSCZxQjk2J0FFNVGA9RjsjLLKsJlznCRWxz7YHFU2Bz7d7LSz7DSdkzI5w1ba1xpxRVyTgnAySgi5oB6KzSgAJsvakEEImVjTAjqa1lm2gBJMJIa3LbGmPbRkH9WVOhVxVWFsxQaBI0JJmgaLQ1iUJ1sadQh9rT0LCx/KoVtkUYy1yG7pvII6qx2AR2PpZt4foyFIu+pTlqUaFXMeZ18DJUl9a1c9LY6Zz4Xzfaa9+fV34HqDgzz22Atvmd7Yhp89LWzP7ex2WOHm2bhLwQkab2bo0A5NJYMG2BlB6l9UZGgTf9DItQDj7dJZhBQqpMd3Ce3tODPrGvrfJLXkvc6rC8dnx8yAsO0eUdIGiSnHgzM3gLsgWG7f7B7slSeldKMkzdsXyUF2wp4//8tXmZHZ0kDFZyST3kRJsqsMtIvYQ3BZvToCoXEyYKx7w7SLdgJQZS5BRYesnKB3s/2fsZjIOTzTAfRJyLwucwh9X5r/xGtjPVnQVsMVTeCTxTGVtKRVEcK5LCdlhM7U5v6xmVhvh0BC13GKWRoINIzuuixkpzu/vnhOcXif9Yb/PiiCRhdXVt6ehkfWX5JU+BpQfBKFGkW1LQNJYx7/ApFt0uTaEE30dj2ikF+YrHpU2e0tF1NY/ddS3beDqCr2XzJmsUR33iAz+N7WTRzsRqgvYyudTzi4MpCAUWHoyFAq/eK2yN6SnU5DbsqBIGgDDKUdbBKEgBWACReA9QG956xzBSEL9hRTGGVMdmqyj0hEXmaC55ZEP6e4nkI2kjGBn8SBP1WJEXPPMoxtgkR408Kqp5eJ8tp+N0b/fpTzAjwzCzkX2sDF4Iix6YYQ/MZgfoGozgugm1pn+/j2XrKaY/fyNySh7F3IfEmCTkGU+2bjX6VFUllGBUVQNiAIZVHA0K+6VOLgSKcjRr09tK2zkUxWz+GDMPyFDNC6BZAAkQawt3sWgv4m+zgpMUa2tNTTuNRu7IIYl+zakPt8YqYWTbZm6eWujgFLgAqGh3NeHEmrOORn+c147OMlT1w7uL5hF85WxCRYzN74OAy3oEy40+qG2aAE6xGdAwRRgxEiCke9vxoLFstoXw+vTAWEd/BgugsfxNfBjOx8vE4U1jSNN8Gk/MMz2DXE8QDCZKMXitIYiqMbA2PQxQkTdaRX7JAtf62lwauBGwIpcQbaV0bhdoIszmxMreD8X4IRGRiSr93MXp5yiKohjxyKpVnCrKJDHlBMCy8QhRKWTPPBLKe+GxJ0+ERc6or+WRYXcAOqpqR2jGxTeSTKlUD09J0mFetHECdzp24iH32BYVOSmOaorEAzLph6NUEbqKYpKrWuOFFI9sx80VgKwcjAMo+F0c18sCf2l7YLJjurgEdvEH1LPAxYcyZgTdY87tsiD6WERWsmcXLbiqQeAJlfyolSLpjgdzOAsSmTB782iUqpBzMZUaCSGxOz1SG2+GML99FYKLRjYKdm2hhnVUCSxfbR5tbkFWRbHw211bgM+tSBuVGj12VDXir42Sho9sS+qc4Z1JLOmFt2Ha6QyJo6+EDiHUDMRGZQ3ro5meKjWtRw/0CWaBuZY9MO0CaPoxei27daxGXbY+7IiHqrrWyjjXWKmuyBNRmosjpuglY7phhVUswoDfyBNr22SFpFyYKNdW1Kp1rH5IEYwci7IFXJDUmAJAsRFTU9WaSNVdG5FRbnQdAecm94+Elio1xkZVo36hjD1QdKA6TYt7ZIY6YCl9XFQUiZSuiswXIsfYLiSAhdPL3wNTXQKbxwibB+flPwy02hOHesDFnsG73zC0jO24PHIXGboDAKlUwAbK4fdPigZEuQivKNpkAqHohzaGwmMukqWUTNLTMfRhmvdJ6TEvfuE9V+XFT/pIqLd/BrbZJVb57yRZ9jjswXaCkTABumHdtYVzwGhiMwvAJSyO1cBLGP85hEQXKXFkvbL3JbB0LTftkp5+ZwasqOY3EnAxAbkDa+u0CTjbqBb6y9ADI494W5Cno7kN0aaf2GUb4Wz1hNeYZutlhmyOVpwuFsJIj+BHYiYDKJIO2zit1GGgYVYViakEUx6ZRVXrIybK0XXU95QbzRu9WykBQ6WejrphJodTSHuhKNkYBMs1rass1BhzqkpIu27ER2U3c0ReBrl/u+Ya7SUJw230QURQUpXiJPfNfNRSpRwhNiTKZr5YoYj2YoNZeL+0PTD5AujSNqk7sHiSdyMvZ+0Fxs80p5kuxoCsZY2qlGvSVAdKg2wlGimxbetkuXDeBjNVDZBtrbcm2gJWeBKMkYASAbxqDZayAKso8ujC+kZ8Yy2OSFTB02hlvZGCyUp5oSmQqjUPRVlFjRlq2+hiQEW0Z/9JfyXyutUzD7vusZm7GIuQYe8jLkEayccnh7wMaXV1+eBgT7R0UWwCRRIzgGsLwcEAs2whUlkZhUarCKhlcRaGVqIvqmqGhebK9cDEx3TsBRAjSSn2EZpYXMiXpwfqgzXb2OLIk6xc6wnk7kQw0wyeonU9qRQSrhGcq1scrYp1bhdC0syIcW1URk4DpIywPrLMTVKYtOkLWF2sDWtNbdWtmYABkzp1e5ltLd5nS3h12XQgFL8GuTon6qmNPRarCn2BdLdwFlielTAPzlnFtuC5bD0w1T1AbswEYy6eIeY5NwHvMeaLDWaCVtfxx+bUhCMb2G1eE0a8yKMGPEUnFUUSlYapavI8fSnlo+t0Mh3ZXvsiHoWhfSzJUiZM/e7dfD+QWyEBd+n2oPx5kFznb8kDTGI58zlxSt5UVUeOhlRbnXkJdHYBBiSsBsuwNs+eB1lbba0/7d9oP5TrWjFI372yADmkOROV9G4OdXVUhWFbsaZqQ85Vr7bU7T3jNI664XuDzgB6F0Y4OjsyIytvqebF6UtL6QPCCyDOEeL3ol/HIuaRIcp9MBG/kBc9cD49MJsF0DnEOvJMHjeGmROOG8CU+CL+ohjJ4+dH1EcZ8z6waCIZQyWKCJAUPEmZ34UjPXMoE6jkBM2ppkVDDbaNVW3KNpM2vXhy7YASWanNRQzJyNzwU4tcPLMeAImSZI/SULSALEDSnJKdSuhT1dklzml1iwQeq5bKM2rBIri/7RmiYSFSDXXlX32mShvxUVaTbTluD9gQYRrbyDOlTOuKRk1JOKV5iucshU5SHQUvgIhZCSyCLOKROssxGNsRUFhRjLU9aQsvdVGc9gUATSzWJgvN69kDfRdAi9FzRcdHMb+0taInrNGczzD0PRkYSErCd1ulWpZPib3R86kS5HBCzvAw3+WqYno/Y5gLupKVXoLCu9/4d4pokWhFBpeRJX1YcPDbW2ojam4BzyaqB6bXaA9y+VkdNlVWtfMhG58cdeWppi28U0Q/ST+HFX89vnBbFM3aFr8B3YLN1RCBka3vNu+unRVPt5e+tXG/p69NL1zsupEGgAd49pxSLw8OLEq9CmhQm4kkz+pYQNlGhaO2qrpFIGOQNWChWfRA0QNn7mAo6lxcjCp3xUJQD9RDgtmHRK0FIa2sq9DUPNFKcs88OuowEUwAvJOkQUCJrNTBEG1l1QGGzbWWk9ecVGV9jUSjeIzBzrBCqKv6aAqSmRQd7Ug2IdVGg3PfDDIrEfrTRquFPHEPaPx4Nyj2vw5PcVAEkJWdFkXpG5U2iY6snFKYB+eUIS3ML7wH+u4AXXigiwAm6wFNNPM4+QfMw0WMXUggl+CwVczq0zWBa6cX0ktKINaO0TCqCWiLsNsYUvPPrkXc1XGFYjYEbZUVhLKKSpBmsHkEYFLoT13nqgJQFCNVT7neB+o2nN5jI3/qmeE6tRHQXzkrnv4ezyDZ9Tk7eM7UzqKgBk7ApE4edHU+U4eXwNgfHbwHaCR5AngQ5yCSJnwZiIHFwdwmR3wfeRD/WWhWnlUtSq93D/TaAXq9u+g6tJ7Zp0hFq6gtNP2LkVlWWZN+JCv9TsXpp3krZZz1WkGdFUX8RdGmctThrqPKJBZyM1O/dVtRK4AEySKJcaoWvZURaac2NKwQXDSyw7YNU5jMr6hoi5jbGl7A5hfVvJkvYUNOfJlzeEnOA1KHg5gddpRjXxkQlY1yfYjtDnyUG83blJG2TW6zXehfwx4YsQPUf0Bf9b47n5bGc3KaHoNnhgHXUTWSAyNRRUJQ/INdjZMVJtCV9APpSS9g3sjgK+OI+2lgK7rC5Ek/WD8N3BVIF08DG967IA0A8Svm5Ip/6de+EiFKJULleXZ+2DQTEjNPu6z4/lww6PkejMD9QUmfiwfZ2I6ybcrSfkl6sRBuUpFcjQBJSppMGHMr02M2GZS5U5wkZAmJLqTEkL5XZze520Nlsyg21Vk2uakMSLd3dSbF1gnpqpTHwm+XQa7r7xTm1EvDPhez3YnHGPsdaLQUSG8Dn+XphhderpNC8prDjmthuBypaxo1DhUBgNreiBwqB01jeDOwwadxfswXYwiWD/MjC2tra7kWC6oIO53TqXo48NyNaJBJQDmlyOQFo6TJpNJ05wOGIX832LWyUjHKaFzMAg1I8SxSzx6g03yse5pcFVjrDhBtJp1/M+S3zs8/ksvscbaHpj+bkMXJQFGJHhuKmvjSJ4d/ZLStPzPmTGX/eM6YtRTET25aBMkoSbKTBjkqVWwhHqijoZGmXVpJ/ONymgdB/OQSYlWH3A1WbcQUmqJoR6ftGqpqzbBmjL8xkjHMekBr5jrgGlNreri6FJAYeZTbgtPIdJ/o9n8XZUVRq/+jo/SUgGktSEnRGjNYKAIw0oTZepAV4MmK0cWAIa9lJ2N7na3aDuI16JMRO0DXoIWXqgmck3EwFcXzDFWzg4NpmCzORgPSJlFmjcwXR1JFxdp6sLx21VnKMUp1RxUax9b91Q5YQg7WPA0BEGodbdTkC3ujNrUycf7RsMHqR1FFHvtO8QxT8p0XZCglqMaY4WptaND7rxk6LGqnxBCVMaQOnvOpcosae6w7BpsYFpsZlcj0uX256vILPWMWjFyC2+VjjcBeJgugpaUjTnPByA3IXZTGecEQNYO90LPnXUFi1zKEPwJMHv1Gk4U8jx7QUZgH8yXhXPPAmlNAbfxt+jmF0Uh7ITEUToui4+w+z2XVjTFVh9DovaZFQ2L6gwohEmpOhEdJVcjZYpBF/EhZtjUMfa2UZrIqBQcDAtwisbLNl/TACtcKO1MN2l6EaxMM7QtZ360RatfSREM5lWZA0rSaUzDKZVLnrq35xWwTIWsYgEalDc9HcLQS2kKyHsEyJrIiVATrzyfyeXhxc2ryzqr0NaYwET53Cxe3uZCdLvmCIVdSdyG73yRTjJpT2uEp4VpVuYh5XmkFi+qsB0O1TU6h7ZLAwTAtrUhjkbTTX/OasXqJXr1a3dF6CexqNaN/tHnkD7L+VgvkyB7gPGHyIj/7Hp20D5SV+Y0+w0e0OtgAU9vnROqDEZWQYi7IrUQ/iBOpPY3ERMJ2msEMLrYil5WVNYkbbqEDU1RFk1pGE5XRtlHfqIxWV1dOTUtvxBnseVzdhnRHTjMZaeReAPmYSlCuU7umMriugrZQpn2gcpV1ZilTmxQMExd9Ns3PxcSxXSfDK9e9r90C6KqMNmaWtsnFegvn0Ch81YMbJQnvml8s1EhHGKuibEAhiL9Qupj9N3/nKKqirygX/FSpttDbYxSEUS6rWNsoR1jylBOfLvqAoYSVlIV5m7KA9S/WDaw1ZnNLjbFgzEK45D2g0RWD9Hiz0hgdX4oIJManMdLEomWbowEW9fZljAXDZFUb1siIieaNss173XXeSLFQXtMemNk9QGONyHPozMsWz2RNHtkKAD69J3NRW8lppLUXlDEkZF0CE0m+g9I/Ls3zUsNJM238UML69AM+8g/NBwC5sCOEGlyHjSaZnM66aSUhW1OJ5wxouKUfCUd65Husvt5mjzSZ53pOCbj7hyho7qkqS/I+iGH48RCLdKaKwC2IRE2IlACMGRlwEUlRFHmhLIrDAE4HwJROG/kLpRtY6NuKffB9GtvGf431jd1Cf0Y9spL7gaIwfXpeVvkRTPbW0taa+QtzVYFHsK8JhOgiUOlchXkq8gniWZhcth4I0/ZlC20RT+8eKKaP3nZdwDBfpGmoKNqyqLI+CtE26qeX25itV8909I+RBAMsIqmKtXW0AkSMGcSjvACIJzqSRmy1l6gRVeElAtrk2h3IGFibYaG3SS0UyJkU7WVWbCKM+UyYrxAJbVci5kKgKKWb45EWwZYHRE1rlJrKnFGIAUT9zOW2eBrPi5l7v8aE6tir28CZ7QBd3S64WpFrwMXz1pNIR0MivgNWVNmX5kGKCHInQsm+ewDzDGDnY7iwZu8Ho+Hej/hFK7B4kEUlgIpUudb6QogA04LJpmkDRkrlAhuGIFlgchVlbrkQ6IM84S+vrq6tLB95Ux2YGOSliNNFsRlJURo5dR4F29ZKbNPbhrJrwdq8R4xlgSlGFx1yJLdsocPQVR3g/mGYLQpqlJsWqyRPzN8RM8zUirmNP5obXIc3vUaO2sKAX1WGUdSlV1XZ0AEjaH93bS29ykt4w9oCtjkA+IUnR69UGKKURsi2YptVoVcRkoINvZjZs44mgkXNQh6rB3y8xrK6PODhB9XcImKENaa5Obz+xBcy5jiI9Kzy2MXSEJKjkuBiBEturCqUFJVq80ZNY2BRyVSu2dzmY/HbSgLMIpdADhtVygtwn2JhWBTNYKeFpq1ofZsgwrbametrd20tlevu2hhef2S06pBHEgIg1S0qOCMgygVs+iLk4/L3x6uxCrLbKtZaxjw20PqonKEs/ujUsoUZultQXd0eWOwAXbFjN9kJzIwQDYtidxcYbMF4NFbCLxfk6ZbJ5HDw/Fe6P4Yr/+mVgGjTmjtBz86J5kQwZ1R2yOCL2qiJcgFTsQC0BQZMVQakF/musg10Ornr1b4GiD8Vw/uaT5VnoymsqCw0KjoMWRdFlFETZVVF2qJWhLPKe5IDm8aju0g8Lk7DmWy5R4WUQ2trSIw8ysnwbGpkyCanI+esxQWUOppAFWe0h5+Dc7ssUOVDIEIXC3P92nzaTh2eVqa1YFtrxhXMoGBkjnKoV/+f2RMa18UCX/QAfRuPaez5AjnbovyOyzn2Aujc2lO05KL8FmFc3eI0HdhhSxUprXhySlPLMKFAjD1WFNuqOmDRRDLea2VdJc4ITnEPbSWDESwSUoXSyFiFPjV8dVW1CTZsMjIJcJEXPKq1CxWjC8uRR8oa7DDsF0HkEmSoHHMHE2XV2iqaRH7DEGxehxTNo0mtt0Ykjs36Qih8FcUCXBcb+YOyHFShqiYbrZnSfKSDgr8oYh77x7UIUV97MZJxTtIeKkqlbtuazRrMJfM4AiQUa6paY/O2KlEZVgiyKo9rAVoUZ9ED8QANun14xGdB38oR/QrkkdZmM/dLYG2OF/pr0AMMr+OTQ74uM/LqwSeN9fVYdJW6gmKRkj7fRTRG3tKtMFOjvAXSqq4jB4qS5IAbjaNTy20xFPqiWPPjXWHUVYVGyELpYmpGmJ7qopESCgDKaF6ARxZrtpEm5wngKOhAWJD3FPbxsu8AO8+Qal9F/xdFB1wIKqp1jSYGUKsFUAeMKtUqx7buMZQZdTpu/WYgW8npxLmaU5uf6vmFMr2j/ex3s9pkoZlhD5z2/wxJe1CN9LtYAPXoxesImWzGiVZRVg8x2pQm7rDIiRyLY3ESRoEXVSNhcjNcAchQxZrEzUTI9xSdeUueSQrXFNUtylW00kUEpTa/qpWXOh9aD/4WwRRFQLWmjQFkBLsVBb6jWDBEZM3WAbZhdxcZNqWg2GKE9uuqKV3Mw9xBdpDHRglWdzsYHXdyFkAqCia9De2oQy9DIycW+rRuYvKF4evWA62XwOJQft06ZdHejh7QwHDucaKJqZiedPcPC4CasEACMNUZcHX3zJnacy8QJJHH5qe3/pyk9RB62ilAR1xuuARyN7yuqnnAKMUqMaCPSstFSDXYAAvYRjbLFkzeU5jAcAKTnsF0wXT3zxChvhqWZv+3aOP83MUjO24zZEuoCEowDMWGa1hUqV0ItS9VueFJyOd48Xau2nAajaOdhmRhe/16oHUBNG5TG8f6uCQL/Hn2gOepnk4LvOaUaBunNuRYjLBajszIAGQrZK2pGawxlRhk61oL1kdHhWyMrSRITy4BKxILIGrRIEe8MLQHpaqilfEDWLYUzDwIgiGQxJOlJLd9bNhLpmzI7DqRVGE3GAzjp0phyEUjcjKlaCezna2VOsQN7Nk/Y8WQG3tmqGCO0k7HYpsOnJcprPzS3+Y7ggmMRLXCGxmkAbJSeCgpKpdGtTVGtePmIh/XaoF/nXtg7j+GOm7n+swZ13CBn3cPML8cHx+mzXA8neSf+Bq+zlWuz0xkVCVteoMIeiUOLubSpMrhjE+tixJUlWTVpHdJN6R6tIiKvKgqigYopKP0JBeb/KcuBjs6mSfP26pL9Px/dHQEFEw2X8tF2pVYTymyhAJLLVMAJ/uME0zFaFJojEfI5mmfiSUiObtraPKHViQYyNQjyaPkApQYMid6CeQFJvEPo421HbRmiHhkespUxkQh4tHLRQRE2bUxwghokwsvBSzTJh2wxMz9IoOU+typm8SwZmHImQkDaTN6PK3CJvIU/PDAiYIq1ZqRYpZBpsaSKEt5snSUivmBTQTOeF0CSwN+JTGjZMxLacKRgsgHfvP7qzR887vCBsO1mB9GckYAUcWiZNxJP2iaMDSDbyzV2VqbLzTXuwdmtgN0vbtp0Tr1wGDyygWmFaW6c4AxkZEivoahgaFRr6qR5m22I/XRb1srTFKEEfGqElvOyx0gkQggWSYFJ0Uwyu0XIcKiHDGWay+uQlBtJIn4iJyJrOZEqug66iXXwdQMtVU3Z43v1mS2wYDMcrki7Da/8Fr3Yf9uGSAHF/5YprLAb90EooG4kBcZInf4clUtiKQwN6zQT9OxclQz4Kt16qnRC8017QF/xbmm7Vs0q7MHitmhKHaapspGvGexRnOZkEswRsVC6dpxhYKHYh1VgQGghK+iKl8XOP1U0NUuYFrk8WWYfwXGAcNpuVGw04H7s38qE16tOyAkyBhnlCurEYqRQWJf8yf37evX6LInLJpI7m+YQsmpJpHG/dwGGAvWTXKxtd1Hs7kfhvtSjjyS0K95f3SwSWNM1rtUCmKwOwsDHBvE2iMu7aYtx8gjl6NtA0TwQp6mB/KJmDKTRNnK8xG6D/fYO0CzasmseGbViZctnu7DNqtWw9O/4YQUhnQKAVsSeiU0CKliWKVaachdK01Ra1gtgCyUUNXKAuOiwTIpDF0cblklX0Wo1gAWHsBwATSIDU2RbBUFZJJJJKDBlhzOVD3sQMkqZrlcycmKKgsycRHBsngKjfB1TmAyrAXxtJnEpsHgYo3vo5F5H6QwE7iL/UPHw+Ow+/u9QGTPaIueVJFcSdf7GINc8tImEPp8FYwBmbZylVCqf9Re2VIVm18UjWzUjzSMgJ4yjhRnje+oqsELzbg9UHR7URyXbVZ4D7w6nrEXQLOK6aJ46i64qEiuot/8e+8Enu5EYSOExKtQWAj4nc/UUaUkubGZAKTncFjuwNck3cfRtSa3xgKclhGGq58zrgyQlmJq8snJ6iq/dZ8mWax0MwT6M5bDAhiQJCksDOsHn7XWS8AKgGRyFW2iqsIk1o6UFVUjLLo2wHgLrioEmVtZFK2flSB+d8UEtNPYJnfV3skEMUxvUgySojiS3904+Kms00al5bhqvTQv2FRrhqI2FoWRhghJTY+HRot5ybiOwczLzYL30vfAa7cAuvRHZBAg52eaIM49tU1kjfEY7NWDY5aGohLtQFBuYfrGiUphmK0oou+jsXkhuI2RRK1jGYSSBZBWP8ik+DkRw5Ms8kZY4VdFrKKhixagAhkxdlEQ1hgDRELejemoNZWFbjbDolDwK6oIqOXCxIAJvNv2egj0TEcHqqqp97jGelTr0UipXF0ECQmN8sZ+o8r6aGulqQqeNs42fUFYF8Uf9bUm1i7k16EH5r4AiifA69Ch16ONI48aAJI++JlH2BnK/06XOFl5mqlbVEZGOLeOIk58KY9OrckC+zfM44p/dHjEz0IHQ20FxeagjEV7lF65AQhojLEeTayybECjF9ea0OSxqlFpkzZBDJGnDTmZvmaWxtFORruwUg/QmW09ib6uAs/ZrdNUtWhIGTvIpunbAacf8myaEIRx/HaHnkhcnExIbZnMcmHV3gMaIe31F1xTh3fpHoO/4B66TO5ncp7PqkEEo5QJB1/70Ijf85EFqiyDQVaSLKu55o7NQuGOeNqqCmRRtJXWQMOWlWzoZWjBRWtMpSqK3kBylQUwyNiSO8nQ5maOemT0qsKwqOpTlK2RFEfyFCa27SnYhYWehlPC7C4LU5Kdk7ljrv3pMAFwVQGuBxIAkvAIPtBWUlVbmb+nYFrweFMxuujmAd8fbKrYHCsXwuvcA4unwC710fdEUwjnH7Q/7HHtYJCZUzytSFZuWFuoEWyTCEYZi4VMDFGjkKQpDIuikbEVGGo3S82MzJIzOD3qFdkAk9bW0jYqgggRwAimnNqsGDQHmPmlt0nGp3tOARgmTGTIsFOAkBFgcyHtToKRdlEI0UpyAXAxIgsv4xbNKcOiaLY2vQESpg+sp6PC74UUHaoFh0E/qCtchWAZmGqXebtPelXTYGRmSFphDE8KBtsRm53gVSVkos4naWGoYkTakQKLAGkilQyldy4TF+XXxVowoaocZ9Tb0Ui2mn+huYo9UIwimjD3S2BXsZsWMY/VA55TJBR5GxVjUfOOhTZko162riqK1kehHv2xVmF3Y8ADKDAsX/hs0EeFAKKFEGRjYAWDw8j4VMIKWgmqFRs1UlIkqco5mg5mVRVW2WPJY0KENkJhRppHqpFyty+ZF/GP5BwX0CeGcTkvLb5/Y4VMA6zpzZsaeD40pkXQMKYHopKiwY2d01gLQ6O+kSF6NMDmCGJzVMYshNeqBxYLoCt5uIu5oCjOo0maKZQv837n9OtXB0O/g49q3wnE/KJ0NpJ0n43eDX1W31wakmvq7PqQLuwVZKOyrkITlLoTqHmepUXmHLYv/c17QOm9sszPuoFUtYAjzLaFAEbIsznfttPX8VybvpoPOdM+k2lNNaxt/lwJDbRFEmyFLAyaKMtXgmaAayVI35ZH22jehh9L3yeAIeHpUVOXDvWDv6nh+dV/kVO9USAvf7FPE8qmsfGT3/ucW6cz9LShacGThoZO8DTwuPGPckzR6alllmJVMhle6qKSo5Jq05+B0fBvwTFeESeJPK9vCss2ZW7gYOurMFkUu3tAvQ3GB9qabsMLqW2MbWYLoEb2GbZz3vwzDPV8qIoOKYqOwUPTmukFTSV4JC0vr1F0qsmpqpU9NfB3I0cCMG/EoLReQluc6AHUtWiU4rNg3gfCL7XRu4pqDoS67GWYa9ViMQusjwg0jtNgK101NBl4N8D6/kLdamuiOytr5qIqWtXgQuM2Fvo+xclsFV4f/kuIKYIvig64Ud+mjN0oDGOQEU6iWCTv9OBLhuTCoEFIueNoEsDYYyEDd5VMRVjorbQQ/UjpqhxerF/IY/SAu1E2RXEMovOCtkU4swXQeTVk4WfePcC3QFL6zteRGE+aB4d3vHStcphrcjozi6ERfy10+B1ZpYFeDPeiaBL0scqRJIB+EGDw3bdsnfe6aD43BjH7k7I5OzTJmH9iBjksnjYfJDtHwzASeb3Sy2wJoirHmW0H8UhJbjD4GpBYsl5CnWNi/qK2IC9qVeyDaTTsr3QD2+LsT3X9kEWfFEW3t00PgJ1LMneyTSTk2jSuGORsdOofAz/t6Az/oUyD3v8YUSx4vOUzGLCJT14afRGh9R3RxvCiSdQXsthirkh6einYFsVr0wOLBdC1OZTNDek5QTQbBy08TH/kJOYwchZAvAWOCUtzlvJgkcRGZYe+MB+rmALLyUKbeQQgq9gWaiOJTOgQ1SKwD5Q+HvLrEFWb++l0pWJ+C43MUtYYOFEqdRjWVTKs9VGjgAsNvvprzFBYRYZpZPOPJJlTACP9XldA7Pnct3mBf3Zs0PYI69kVjUdKPI1VbbSY1Hg0RUhFsY1toX+temCxAHqtDvdsGstUkrZ/0hNRg89ICansxOvz8+V9K4SlWAdhJcJY81QBLoo4QuMkvxSjXrKq5B2A44n7QMJkfHo6RjwgtfphAUSSHqX0gL1IsgsEalUscvT2HjDpfiCeyDE4VFl3RjDJGW1VcBNUA62S9BU8daYANFNCjenWYNUNWNSefw94gydeoypGQi6mo6+xQR5TR8w9jzhsIumJrz2aQVVFUbRRmdtS0yw0vXog997gkPUyuKygxQLosh6Z2cXFaT/xtBKjKAa9ZhOUMfF5H4sjP+8ARxfIaOI8VdR2FPtYCeMcIcuna4sOflUpvKFhilYaGq7E4gCk9BIKTqoKTYTVtUONrBpsCzZik8nQ8DQYkK61Ve6BhLGmjzAuvg/nAjPzHuAw6fgWzNbH42hlBFs5RJ6OE5idhrWnpjIE4CoLp6CpJfGTj2SS9wKZgxxpugBc2x5YLICu7aGdtGG+N8UE+kTnszPdKJA/QwfPIuXZhwkuzXF8/A8mON0XkOdJNE7Z1pxJoMq5KorpKSnTrQW9ktiSRZgNUVKURoJkMWaTwXolvwkIRWoItTzpJnzGnAbA+obfATs6Olhbo70bYNBo6ZOMc5JS9wYx/9sXlcgKQMjk6OzKQ3rXGs9eUkRKNpVcUCwMY1EYE8YqZFNZEJ4qG0qWF8GoMsCC8RZS1XDjrQNm/EyF0R+NU7kLO3OX4XfBfFzUqKIoZTwE+Wiyxcip3XDiCM/wzoM8f1ikX3FfTv84pqvp6Ist86hEnn4pD9ucpfNLtTEYnWXiZ2gM8Vil4yW8aus88siwxtQaWyWT/NIjPdBWIxeakT3gzhyJvOSA+tPukge8CO/CeoBBr3FPzoRIzlTiRFiWERSl8EUpziYaAAEAAElEQVTEri30ExTlsTbErxO1bR7ByPb0KsBZrtoQjZltjpHWQF4JWagZYjzUFsn+IzkY6zuENlitl1NT2ZeFusoaC5GkdmEYQk0baxfyOfdA4+EolEVxGGH5eQFMaQho+AsArXJXN1oVGIPnJHQP2jk5XdBeqh4oB/SlCm4RzKx6oJhZimJ/L3r6CTwMrIGioWcTCeQxgaTo3IKUkWcCeSwSgYlfCXcIeX+F78Ep5eKZ+dpIbPXNVRjhUUrPPUBeBlkJRrLAzlFatiCkc+kbkXWVYB1ge+kW1NjYwCjbNjqKsgNDWeupbWQz7UhhSvOR/GkXx/9Goy8doq3bHag6kFxIBJJro1DrwwHl3G94GH5oQtVgS2moScSWEWKKTi0DsDxDQa12QyzM0MWC6gr1wNiXwOY0Lt1l8+a3o9dNKDq2KLo32mYE6ZmUEJiZtPrRasAmCJIlqCQ586c51466BSMnmwYxVwPJJdidi7lmkKnWVhSpcAy2lSAbK4EJSU6HUEvVUJe7a6iRSa46XQuaTSQwSBCJvXQLMol5xHe0JcJq2W2RUAP6a8aNocOjo6q9u+vqqtdKQz/EDnTnS+lid5/EzswyWbn9CRXJPNEk6g2w0oJMKBYCJsbI3OQIxpt5XAES8YxruMBfpx5Y7ABdp6M5g7YUk05g5KO9XL7kT2s2Tk4/sxOeBVJaI5WJ6caqKFspIU1LAVnUdhcJ3vFHkqiHQUXnjZziiSQyPDo62t/fPzw8NKcxCFq+WGMSa6Iv1UZNtwxJI4+sVFUA7MJCosifH7qIab1IimJ3PK7FqtEQR8ZIoMy/RnCBnHkxx3iazZz/shG687t72z0yMv40bgYjJw3+kXi+JeV/A6AdWVCFisi1ULiIhkRCrfIC1quoTb5e0AXoOvfA2DtA17kzFm3LPcBEw99icvEFL2r12cmHvT/vARep7kvR1vpCU8HKD9ECT1EmleEAiF5VEhpzBS8qAC6KQkUZ7ubED6CyEhLSGHWIHobPytMPCRgEMzgWrRTMtAiGFQzo0RhpWC10YFxVk9c8UePAomFUitkabCMyUs1QPgcXM4z2nKnisYiu2/QRYzmPOCwGM4D1COr8yIYmFsEURVtFnkJuPKbwNOoL2z7FzFPG2cdwgbkePbBYAF2P4zj7Vmi2yhOEtgnTh7GUwwmIjZ/BXS/AMjKFMRDSPlAqJdUgDbYbjRzqp/0LoQKriQo9RSeBVYSB1GgOgCpyln0sevb29lgCbW5uekWoWmwRWAOhFxV8WJlTSrFJKY1litIot6EBUVNjInNEzlYuvKiBRTDCFMjZhlF4hLzWFB4BKNpC/9oW3RsW2rpCAJ8fFJVqPHqU5DocKhrmYp+DhZVgPi3TERxymWqoGO+vzBNhCph5ifzMHY3j0S3QV7YHFgugK3vozjdwPtpxqL2fOPswiRQJWKGhODLYyBnBepti1PSUIWzjjAwFpi5KQxO0AOLiF8uguPqBzVZuuDUIUkanyOoTciHJSTY3p2BUyUSCqWKtlCYxUhibIBgTlZZV62ItmLmuskYkbVR1SDacq3BRfufaqJHktLo4ZJP1g0hkWxASg491UeXvAx1xdsRDVePcIXd22kHeWAVrEWcjbKG89j3w2i2Ausd9y+l2+glXDIiLwhdhzK9YdJcmDj6peQJMrzzkt9DzM+BrWiERCRhSW0iC1bRt+Ph22jZMwaZiobSt9QianVnQcN0KgKoIHoHcqxxiBkMuvIpsBW1v30QWWLlIsM369CCMk/rEuXxRi8ZKFVVlvYp2ZEILMncRweSSBUAZkfYSMREQCdGDJxkQZSHRIABQiuYzlxu941cBKBLJ5C46DCFdTHFftUQT3K4ox3aomYZRJWTUIKuYqo5Pj694UOosMBVgnxeuEkMiyiNEYBgomkeCAMgmNFiAthyY2WyOIGVPEpNjldLwnUOI7TOWjRbCNeyB124BNMExTGfKOGne+HFimTGWpinBy6TDp7IEuYnTELJSUYW5NDFvVEZAHxl34rGgUJVLKZlf8RASWiZxKaMLgWmdpnhyVkisgWRFjkxSramwImmlgnJYO5jrxY8ejHJrEFC6GGulpFZsKhZ4K7sFu+gQ2hgISVWOrSChKI2RBZUN0Ue5gE1fjAFEOTKX+tblejS6XHJsQpQVpTQckbqqTzOw0tEUWDK5BJQAGJAMSjTIJNNalpCNTvvXRWojm2ULIizYTt2EU8auo60NXdshZMPI3YFdVF2rHjjvBZDHZRysl6dH42lJVEWxjrMAFMXzx9ceZ6ihdSQmPnIOH5/05DHhi6I8SlDtIIbqITL0zJw2GcDm84eYs7vUhCKhz9M4GzanSxMwWspoGcRdz1qCrK+s7i8tHx0eHR8cxkUJrcAkU+WX5CKl38vmlbOns/+gNveSWi0T9E5R456JSiOnF6B1GPbVk9a24G2L0CfU1CP5wCfbnv6GsI6PqRjSED6fv/Htz/Px0J+1o8PrqloTHfk4WukupYrEuUAVSk0CCKR8D40eEQVSHh9UZkNQ0TmCU6ZK5Kf4PD5DOdWAR0M+kCM+W6oqi4PsDOcZzOB8rwHRfCFf4x5YO7djXzgqijPv4on5C8OiWMdZAIriueHr0752PRONGog7pWIZhAv05MAQzvTGdGudM1SaB6u5Tw0EWYCtV2DKDRu0ZBi2bS1oJUSReR8wRe4E8s1AMpcLU8mWvLiHCQBI5RKEjCSmkhDxyGZ2bbdQeJS5OSmSjLHczVnU2kqCW2d9gVeRWgSDGzGTKefHPFk887BSGxuZiyoXLdgqdX6xvsh1bQdFeniUxCOZUyNPBemAOtlRLeiiM0iTWIBQMnUCiLAmadTY3LWYoxRVXWvYQriWPdB9xM97B+hadvHr1Ci+AvLvSFNSY05vaK457ZYTJiCUSVFWnYK6pO5BHC2P067LYFbXByzXu/CavrOiJ4YV7nVIADay+JdDSps9CAMv/E5Q0gI4Xka9rOtfB8NrZYOFHW8DIm1sbHgql7n7JAWSWjuY4rOjJBc9oKKVFkCS3HDpKVoQoMgL86JWRZFEZtMCkL6NR/q61lap61LnnUaeFWWrpZx57kbBHBs1laPLtN8zVUPOGtcHUfXoYzdKeXp8hyMw69NsQJXSUJMOPiQk2Ra59K4NtmmQUDzFI6btVHj0TzXplMrzSZpVlLAqbU9ZFtKiB1p74JwWQGeGdWswi4rJe0BTwOT2/Sx1HDV5KccOQUkcyI1K6fv56YuKrfYYk1DkFJ1gH8is5M66yg1JNwYhgHFbkG2Vv+kuswN0cHDAnUBgtH4yUyZJymSVmAYdYoAE6QsvUgogp9Eq1ka9kLE2yhEpfVGroiOx9wImfSNDdBHlgjNWLeSL7YHuQ8NR1qAqgkRpPYLPlAJGUTB/tSjGEidObQLGMAQusYPJulO9rAwziaOypkMYC9zBs6i66j1wTgugq95Ni/g9ZUhom5Wst5C7brDMqKet6TuWeEybYitWNF7rUDdMaTdo+OURIU+zeb7mhp085Soq02qVw50/0rDlg5Avgg1eiStiaEnYUptS/is/qZSTBRWVY+4lFADFlgNrtTKPwC5G2rFkubNTCyapNa4qhJ7I6kAVNBdavKa7PvQpR4fcA4aiNO7uomg9JsGKLVX2RtNoX1le4xTSv7Q7nBJPD+Bi8J2BcuRM/oYxRE6Rn8nzGZQJT7MEmG7oyHuOgq0kItZSTKEv3gZ02tXXQNKx7mjIYgHU0TlXrIqDreljTnHnKSNxI/CBja+5upu+FTngQaawrRkU04JnuI1+1h9LGd/4zAJIspqcn/zPv4Z2dJjfn5aupDHji1zMieyEu6BhT1MqVbJFjp0mE2miPpkHK6pAShnzRmUEWFYALhZCd63BdlfHo+ATIC/1hOxJa/6FMNceqI9acpdWAGXKh4/9y7R7GevEQK0S54g5k2aJxyQHvyhsPZhUMxz2MiQHQNJ3CQR5ybogh5WOSAQ7eyacTnrmiTEjn7FtOo8K/KL4+vTA3BdADEoN9zgKX5/+vXIt1cEibAk6fFFJ1XC7gqkTMa2E1EwEyeCHmFQjpXL01KJULsOeucMo8Geo0q0+mTxpB1OlAORDgfVKktMMvpRu8REhaCbx9AVzhefBVnjMnbVOMjlZ2tzYPjneXV4+oK3s/ayvb3IDEJfAVpaO+ccQ56H4RJjuduIjhaVP6orj4W9iwx87ARkYSXr1lTT6UBF4ENUQJryUysUTNcjRtqhy0a6lwURWyqlFMKb2ax4LMqE4GAq5IpEOx4aRUwoeaiN5xnAdFwHj7v1E25ExnRfAx84O6Q2GFkUEaklcYsp/kioN3JzQpD2ddAPc8soqg5/dkbwpspxeA7G+vq7zFyqKq2sbx9Sv5tONwX6SCCGDOZ1JiScdLg2B5OskvUhCCR7VKqQhMg6f/B0jN4BarIzXIBsWPSLO2Ga7MhuaTDL5lFyL8lXoAY2cjkjnvgBi4Ha4X1Rdth7wHGEhRogyfVofp/UNyV/g0DuhL0yG65DB5BtrkTEk7zNOhJR5ja81kZZaASRIFlVjTiuY5UnU3rp168mTJ19//fX9+2+sra2vrnLWJJrjowNeh7i2tnK8lC6KZXDqHz4CsEq9wKtShg2kpPjdChcRSBBKUA7eSbUuIrTFDzLCOuQOZEdVB+F5Vqmvuj32wXQzXN3a3kewbbSkNVBexLgP2OxZ43wnMebZu6F7GfCMdg1F8iww3gcaGLLmNE9fEPLHgXJRO1SUSnbJNTTXGoyGbyRRH2XbRqENIP3iAyr21esmn/msmrjxDNxu25GAbvNFbc8eKPq5KPYkibDIYJlZz4lJxAlD5CKPbFGOVlEfZTwqRaVk6eUODcUCY0NVOc96JuI0F9tctikkvs9mNr7gkphrb924yU7PP/pH/+gf/sN/yDLoxu2bB8eDn4LfO0g/C+9vuofpW3H6jpuuhuW7C9gdKFwUQSaPORX6uqj4rcfIMsKA5ayyAMRiH7lwIS99DI2pGVw1UijaW+Dzp3N5xAuMit08jSZXXdnY7Wxtcs/O2abRgXTPoBvT9szgbpiESj2cTpG8KEfOMC4EkzgdUkXa9Ux3zyCQO+UaVvPp+wAJjATBFJsjzOC0kIowKU1YmKCP5paN7ylMbNiTfwG7Ej0w7Q6QhjVNtTCnZs+bf05hnz9t0VFFsY6n50TAJAXV2bkuHXS+EYpBdUZIOZLc+DqwnlW4iK4Lq6IoLyijvghShIN2nSzdvn37L//yL/+L/+K/+PDDD3/84x///Oc/f/Hi2dLRCr+HyicBEzf/r2GzunKyz56QXhCQtvdJLBPpNzctOi2UgK2pBQwNkCBNT0Lb1szWRBdWWjADgmTyRu826Sl0kKhK7hrZumPGRAwjYY3kl1apRnWH13h0Uj8MzdSrpsrFM+uYzDBED3uS8cx5waJfhjEXdKhJc4U08JCQcz6QzQuMM8iAVJ3BMldRmiFDMqXWSjsyZy2MBa7NF5pL1QONR9yHeNxQZ7MDNK7X/nhaq9TfZIHs3wMd44ZuF8/wCJz+7eC3VSOza2sG8EpFVW1SawoTisKQO7Uq2aphfs6XrfLftMPOPy5vnRwfcpvEyurSv/7X//r3v/892z//7J/9s88//3x7e5ubIdbWV1gAkVgIQE7w3E/K/g//8J+mcf7Ps3kdnvDJZJiIU2JUDiv14eHSeEIjYaSIAIcRAY3ySGSkbWSYXpn6uB/LyGhPadi1G+vfqeUlkQa9ctr/bPykG3TyAEvvuMo7PVKm25b1DFTqyZWTdN025WxiDn8dXTyYY8o/rendnwja4NEXJFb7+Z++LA2GNP0CCQlbCWgsozFAssljh6oKjWrJSVZGZC2DtGEUauRCc0V7QId4guAv9QJo4lZN0BGvmwlzx8jpo7H/ZVXYJq6cPK+19SecMQGTYRue2raqWi9m6ZEjQMXo2jKwxhhoC9s/Dx8+/Kf/9J8+f/4c2D/+x/8YGT2rIr4Kw7Czs4N+jSeB8+fI8eER34/FJo+Su3MHAAy5TqKSXmFLNm00kTJqZihH7zOkXVDNqQfiyKldNNUONixz1eApsAjTCa5FD3oSMrs4R4dpLycuhnDHUBReA1t45SANSCdU0z3RdcDSwNBW5TPCZ0EttNku9K9hD0y7ANLIfg077qo3uWMSoWmutaD2ajbRbKVcmqiXDB5hZC/1wZiEYIp4pIm5wOkWHP4NE0pE5tyhYvBXGmq168Njv1SIgcC43eHmze1f/epXf/VXf0Vjb9zgVuhnv/nNb9gKopZ9oI219f3dvf29wRUBMBAe7vPO6PwpcnLEvpLYnKu9bjWCkgEXIhDDWH7VS+rERsNxCRtJUPbhIfTxom9zdhX0Hp9twXYA6s7MRzB1Xl0l/jR+wxmBknU/4/zg6JB/FMWgpY/XQFnJ+bea/uUkDTmnBklFC/I18CiDnEc9JrEouVBiVGBqTQGg2AdTWy00F9IDxRGfPoZpF0DTR7BguKge0DTUmBch1cNOswZ5TLaKeADonUtoRFrZKEROAxS8ixLakNQKH/PSNq2B0jKIxETPAy/cAMT1L55751cvtra2nj59/tFHH73a4eEvHopZPzw83t3dBcyPpPLJwH2j7AAxs6tb+LQo+EcWZdiW27wA1HprZi7QWDiVR2HmjhaE59kDHNSzv9ubz9nhtTONN+LhCXfktOWT7/dHw0jwKZPlQVYEj5bzolbaBFrOOA9sIWVFLkdSgil4DC70RoqhMHethcJ8Ubz2PTDtTdDXrIPieRKb1naGtOGj7Tzktnjm4QtO3HkOQiYxVSmpVn5VRe4wJFvj7pImctrEQrQCSbIGDMWUZ1dJZr+HYtYDS+gMEFJy1iUSUvICQCTp8hWtWd7be5XedLK6xJ3N/NnaWP/qq6/+yT/5J7zyZ21t48WLnT/8wz+8c/vur371tyeHJ++//256ZCw9C58mdpZDK8v5CTKuA/B0THolXHoREL407yMoDAsphrA0dMCD8FR9Ni9sXVnrO0hsZcGu0SDL1oKUAlOlWtvGYpQNmEaIMTTyKPKRfkcCGskvoZKG0GTSGC3Kbyo6OtpnbGPIaCSxfN99lTVLx6zYWcvTWLh5kpF3WCV+dkU5O9Ie5tHxUXrWgdvd2PXkOwBIVvl8H4Bnf/8wfxNYReYUwoSUzqU87HO0qRdRpj9hdAlDLkFVhmUs59bAiqK/S4zR8OA3WtmLBBfldJH36YF8ZE+PTh+TmWBm7veS7gAxKM9/XI7rcVz8TEbA+ZPEZjL+lBwGRcsILhYwY8RGLkF6FZUbWQhmRh9tzWC8ayNhlEEqPGZ8/jEFixOlYGJgiv/4448/+eQTBHaA+Bj43nd/+Obb7zx7+vKzL7785ptHbPWw/fPtt9/ybRiG7c1NPmP4nGDBRBFaPhUi4dBp+gsgJnlEI3ys6imLvCe4EeYYVFsH2WjVqCyoGjGzUk4T56xiuDw86g33iQWWKYxGkkJlBcNjjHqZJxgGMMOVa1jra5sUvdqIx1FU8KxvpKceMWHlpH2gTJtXP8OLyACUcKchLSqUOIKfXAmNADm6QSYNtiIBaTYRqhVT5nDKxZQ8C/Or2wN9d4A0fN1ODR2Khd6AiYWZE04cSTQkKjcZfVGMyPORiwCK4gxjgLmRLc5HAIqiTBqjUjcqbyNv9GilrLT3YyXCQJ+PFLKK0quoPE3WfAoMf7mCp2H4lsuzMITEI188xJKFJd76xhWvTz/99JtvvlleXWPOPjw+2dy6sbl162jp6fLKxuHx0tPnL1+9fL66vs5Lobe2kmO5YO+HR2mIiP9hI0lQMORKrhoqEhgG11ofBbFFzVhydNFoKP4OL0TYpzbC1KgOq8ZIxlJqRdk8WM+esOcQzFiRTwDu7Mk88oYdocYmFzyieHiY1hyMMR7+Oub/tOgBQL62sr5/sEMVZwfLGq7t5rNCE91gDIOkNn0ZYO/n5S4L/dWVA1b8g0V+vusOK14gne/+SV8qwOed2eSfmPPiZ7CUURMAQEtKiJwIRkLGn8nQy6rjni+ohImcIlzkI3vAvVcj1Z+D/h9OtjVsMk23XzuFvOOwiqSg6sAr1NELoEaKRuVkjb8oq9gEujgWG0MqAEWx0WSuynMLQI6U01FKahoygvIoIDs81wqA3hoJRkYrZKcIsLJREJJcQoHBHRMuafC1NV8NQEkCT54+D9K74NInAjM7Wzt//dd/fZi/HO/svLp75/53vvu9g6Olx89ern/x9a07d9975+2XL1/+7uOP796/f+fevXW+HOerDHw24IJvyivrJ3uHu3iEvAgmFhVtjVFgETmNXPOLTW3vydwIdpxqiDEqmlnFtjAM6y+MRdUYjHyNxdM/vHkgi1bULiJAsnOaSaLIgGQdw4ONm1zSWtt8vvNyeYNrTOvc3pPHanq8kRM4g5MHBDk6OE6/hkEanEEsfvKWEqdOol4+fSUYAEw4kdK6SAsfCsPrDYqBXEnIVJ+TfCU+rZqG3tGDT+qQGjWhfiH26gG6UTgLjWbdtY0m3UoTWqjxHVUFWMj+eMxHL4AKH1e92Ng7jcqr3tLp42/sljQxnU04kiJ6xBYlmkSSTy6KInRVxEdZMGtUFBtK8xjQJDDX4lX/iIEliO5Hzm9qHnzvTJO8gxkKScM8zL0OX3755e8++n26ILCy8vjxk+9//8dvvPXu7sHJ7uHJVw+fnHz4IRP8d957l7emfPn1V3fv33v77bc3tjb5Ery7zzIp3T+BHz4JSMTsyV0NUaPqyKl1qmun0QwbOA1Hqy0xt9bNp6LRo4IYzOW9/fpYNHL2ppk70HGO60mHPrcuXbpiWHIa8Y+XWt26eW9tc+PRo8f3799jTwg116fAH6dTJvlJN7rlhxkx18qJBRBX0ICl5U/m4pxEYITrxBKeIubkGJKQoY0pkWc9uc4OgdEPqvKBxISi8gFPXlRl1IBB8iJf9MC4PTBiAaRhNy7pAn/+PcCR0uwwV9e4KFKbO8FibYywGFcUmfsEiFWFDCeEURn5CxlYWxIysQ1/bAjvzOD5xYVM69zgucbtEewA8QmBsL5xg192f+8733njzbcfPX+5srrx1cOvD44PXrx48XLnJ9/7zvsb66t8n+Ynw2CGFqojLphtpCfC+JzgIyfGRlRqCLkEx6OilSCj4fRyN6H9zsTRDNmmj6cngw9NT/xlgxXHtzgEap0WGbqj+Zf/7a82N2/+u//ev8cN0U9Xnr1x//7u3s7GWv5hu8O0dmGo0sYzPMvLLIBQMshjYplDUS54UgxbEjcM8UiBwfktoadDGlqSkOTICTpMiSrfiF3rfQmsqBqaLv4ueqBvD4xYAPWlWeBegx5guilSR6NBUqtcn+OSZaKJ0uaxSlYAbI6gonJbtQjpeyffYF2LFUmzM99I878UGEowultnbWWFn/ViOcREzJdfpniE3/72t/wAKsjDQ3b+N3/wgx9u3tjee/zi3ptvPX3x9NX+weHjp//63/ySZ8e+/93v3H/jPvcWYYVz7ozmBYncE801scP0TXrgK7kbzvIWFCdFJUWF0oIA88vxOz/ygnmGvhr7R/xqz+knbRFEZ3EwKs6xTzrDmaqS3gi9lHqFIkOUK7R7e/t37tx5+fLVZ59+/fa7760sHW1uMeq5m22Nk8CtB89ZwroGqqTMpwx3T7OyYXFPgo2cOq2K+F0cNCv5V1NX8nUxvgt4naQ77TLVaQY8U6eWJmfDJKWrzgj52hhAlNFkaLr4O3YPXFRPXpRfd9BrtwC68B53189cYC6gdTOn7SDs6U6wRnBUIqsJRUPGneOEJ1dS/FoAWSMBJbMoslwzAyseHnp/+eL57373MbcL8ejvo6c7d+7e/e4Pf/Lsxd6Tpy+5SfS73/vJyxdPdneef/P46bN/+a8fPn7Et13uCnpw7y4TOm8PYt9o4ILrCvmNKXryhWBwQdJ1MYSO7nUVMNhcnLnQM4yZ+50VoY4gbBamZJ4Vz5Rh9DBPqxLBPEIcvDUAGJZaiKBkdX5weHhnaemNN9765PPH//X/55/86Aff+873f7C7pwtb+YotezMMVbgxSFeKKRylNwUtp/uH2NnhV4Dz7+ClC2HsJ6V1zMrgNYka6nyRYG10dDz45VQFw/cLDTYwCKTEPjwLsqLrjDCyaHKPjmqAKCTlDdUL1WvQA60LIA/NiceHBuvE5nPq/MsWz5yaOT9aTVL63kZnFlOS/CZ9eiDEj4CkT32qyNX/jVaOWbUgSeyoyBL5dFcHaH7Fjp4FM21mSPcu5Pk6+XIyuQWqkqOj4611HumCbpXnV3YPdvPOzdo3X337t3/1N9Qy17969fIPf/H33v/gh199+/zZS+b04zu3b+4dv9g7WT1Z23r2auevf/O745XVJ8+e//iHP7h36/bdu3f3eTsiOAV0lBzRITSAb8W0R1+ak3J44UCfB4SUXm2Uv+BSlYqk3A6HPaUgTsgTcc7HImwzOY0207XBBn3ew2V/ZN2QPkvF2A+N4YwVQCNDizJ+wI+OVOgBLl8mTu0djAtEzo/BaSFMbhfdz9WrdLN/+s2WtEmTKlmXb2xusz25tr55cLL0+08+4x3mb777w08++/KLx/sPnx6c3Dq+f2dz/2A33bl8crh0vLqehuLaQXLJ6XF8xJrn6Eg7QHt7z1eoX11hrX/jxg32iVZXNxjGYPlBGP0YPMHkeNClFVhaGOXfkEnxDe6ITsELozGThn9eeyUbbkTKDxCgMQ9y6pN8hifMFCnSTkFzHUzdvdehMeO0oXUBNH2PTM8wTkMW2PPoAU0+zhGUCt+1ksGAsm1IWC+Y2RKPC0zCw7kSXayxecAOZlVrwDihRFYVm/l7hwdbPPfLBay1jaP9Peb3p0+f/uVf/g+/+93v0hbRCrv7mz//4z/d3L799Pm3h0dLewdHu988ernzcnNjnXuDdg9fvHy1+9nnX33yyWe/+uu//ff/3X/nxz/6Eauf/cOD7fVtdn+4JJCbPrgMx6cX3h1/auNgSh+EpMAcoZswE0G+ss/YtdNyzyrayKNeUj5tfE327gpXRu+qdZWFiLGyDWzATIT8FSB/+rOa5gxIpMNw0qgaOGGdwTKFKrZqtD2TNWl3h70fft13ZXXt0894xdXH/+rf/Hpj+72DpY1Hz179t//df/8nf/Cd/9G//Yu9/VecaSxQcMEfrn+lK7lp5ZNuZeMOOX4GmBXP48dP8Y2SNRDNhzzXpgUN97+RKzKqSIlNl7qGjx1QqyrVWsZdsuX/9Md/05U76Zyb35qFsOiBcXugdQEkIg2ycUkX+IvqAU0r9l4UrZ9M8CQlAZKoMSfKxmFTBxNhli2YEIFpVXNhTV7jsyZ/TgwnUFMJHHO+qw6m5hXu9Tnc2trgmtevf/3rf/kv/wduADo6Pnn85NGNm3d/9OM/2Ns/2ts/uHH7BpcQvvzimxcvnj24d+fG9sbdu28vnRx89c3jm1ubf/fRJ7ww+s+/efwXf+/P1ra2D9LeD+ue9OMYfELwBjkEyenrbfoMSO+U4/OCSIk49WeOle/TQzGVkRWzG9JfiDz9rRqRooqEUW40qZW51WrlmcqigYLFXOg2c9X2j6cR2ah0lEWE1o8pDD7Ue1vlDZ60qSnDwTqAYro15/SbAAMmvcSBocQrrhjSPOy1v7tPi9ZY3LOfQ+JaFXfnHxz/+re//ud/+S+XTlafvzz5/KNfbW3f/uGPvsvLrtgT+rf/4k+PsWC353ifzUpu6eEdV+zdpEGbV0UIfB+4efMmASTOw8P1tQ284DFr0iIJK/UkY15VEtKP0qdVTUJqQUMtyV2BXuu4wcnuiixEpGuSxWknWL0QFj3QqwfWNIB6YccBzYl2nBBeU2zR80XRndI4m7i2Q0gz1jAJRqnAo8Gv9YqhUWlD1bqIgJUMk3zWBXqRC5BwOclKJChMglzwqxYAX2HZ4Tk6OVxfX+WlPg94EGZ3lx9A/Zu/+Ru2cFa5WHB48ud/+he37z748HefvHy1c2frHltETOjs67AVtLGxdPfuPX4GY3tje29358Wzx68+/erVzl9+8/Dbn/74Jz/+4fffeAD+hJsk+OSgGfmTKf1iPPdRhGgHHZg+FfK3Z7VODTmHXP0zlqMY4bjm6vyCofAea6ny8ZKsWvKoLxhmXixC6s8fg5yAhGVOYGA9lJYXLKtzV7DgSIMnadIrrJb0Qy75bpz1/FgjxkuvGNO7u0fLKy9e7v3uk8/++T//V7zL6g/+4M92Tm785V99uLz6iItX/86//eef/+6XX3/7+L137r988ZJLwgdHx6sbfEDsM3TxscYCi8GZl0GcAoTEMCYfLn0Gv41KIDz6hTI9AlZt2xAnCT157sDBSZrbkuWsTlJqmDCpdQIUQkaljDCGhNaNIYTuHcNqAb0GPTBiB2iyFl6G8XQZYpis9y6JlTow5gTGLJMT291ndqRR1mGPewggsQmC5Pz9N81wdmFBHo10AEPTNC0quSqaUMUEy74MwsHR/tr6Ns//MjV//unnv/zlLx8+/pZVzrPnLw+Oln/8kz/aP1j+/IuvN27c5MYebvbc2Nza4F6JvYPl5YOtGzwAvLS2eefweGX7Fl/ADx8+ffH83/zyo99/9re/ee8nP/j+T3/yo7feeuvwcJ+bMAie10rrgwG/dFtuTuo9anKOSvO/o6YPADT0sBFTCkWXTsCW2zJehKn/W1LPeDoYWogb1BNE3sDSrpoySF4nlTY/U5SnPiix0EnMaWWSEmsUrZPSichNP7yIiiXLKoP0aH93j6pX+0uff/n5r379d5988dXh0ep3f/AHd9547/bu0o9+9geMxoODV1s3tpdWN/5v//f/5//4f/L3f/yj7yflq5e8NYuvB6zb+T0wDgrr+DUW7rzhYWMDQLqnjWXX0RGrq9UDTp50YS5t9vAk2fBcJkgl8NwEhD4JabsoRciopjYebqqzPp8X6c7rQQMFK8BCTpzDNrHtwvB69MBcFkDXo2uudyt08nt+cWPjpCDZGk9eBjcK4EEqbwS0Ke0IgAKLGhGKWQypdvipgJyKw6SiNS4ipI+KYdL6g3s7od3b22Vjn8A/+eTjDz/8zbNnz/gGzJt+3v3Ojz747g/3D/mdyJMVfjaSe4b208+/44qLCnzAvNrZP9pcu33jxvaN1Rs3bq4uH+++ev7s2ZNPv/iap8N++9sPHz99zhWxBw8erG+y2OKpMH5VjNmf5Q7R55jzLJ9j454itMNWhebMYwGkTsZJ7NWhz/KvwWXFmGWaWVCpM6200EFck/SxaiNUAKqdhqeNfwJ9DAnzuPoRG29XYPHAojmvJNhQSTtDS9ynfHi0sp6W2AcHrIIO+Hmv5zu7T5+9+Pyrr//ql3/z248+2bxx5yc//fnNO288fPJi72iJ61m3bm2tLG1/9NFHjMX/8v/1X/HzL/+7//3/9qc//O7+/i5PcbHXmQbpEXfI8W6INU5sziB+D4M3he68BHDI6oeQWBKxVFrPD8DTh0o6TMh5zUOWYkeZbycaXOSlFqXwqVrF4SkghpgnzOwSzLMjWzBdvR648gugxQieZtD17z2m2vxPM1XKo99YZI+cGVP16Hu6aISJNlYhq8j1pOEkeRqIajNg4NcaBCZuktAZAwHfRHGyvLm+sbPzcuv2xqudV3/32w+//vIrYGnCXln5/vd++MaDtx6/PHjjjTf2jg65THZwmH5G+/79+7duMZPzSpW0r7N2b3tt5YRdIRSbN+5tpAdh+G2x5c+/evhP//l//+2jR3/8x3/80x//+O7tm8urxMArotNOksKjk5IvLjCwNuNWi/x5kOMcRBvkMxtvWT+DTL3Rh0hHBCQmlm2IZiRVo5UZ+gt2VBP2J6mRc6KtHXVoHEPGnDnR0KTbnUns+KQfsaOspwsZGPREuqVhj1eVcwGLV5bv7XB7/qefffXbDz/6+uGTR49erG8/uHPvzY3te3tHK4+ePeX+nl/+8t+89cbd//R//h8/f/Zkc+X4Jz/5yfrG9t/+zW9u39q+fTNd58IFZw1/YGd3J51Bx8f8KPzN7Rs8Epn2flhy5Se8+G4AJqd0gRg45xDrp6Th9qT0osTUoMSYTq4kU6PcgtYjcpcQORXFofr0r8xPywtp0QP9euDKL4D6NXOBmrAHNFuR56v2zDODNJIOnDAIJrFSmpEkBkT8QC4/F4xNgj1aGxlQKhJy5m59lKclyNLS48ePP/zwQzZ+FPXK8trNO3fXNrd2H768eefmyu7+o0fp0tjt27d57zOGT548efTtQzSYc0Pz3vODnVc797g5+uY9fjB16eTo/lubr/Z3/+Y3Hz99sfvVNw+/98H777z11hv37/KTkku8I45nlY/T2+SIJH1KpDRY9FB28BML3SSxT7qREwdgQ/ijO+kblcCkbwyp0cReZigojBkSQtXYosJF7CXJWKVFz3A4nCpZwKfbg/jHE+955XC8undwcnS4tLu/+/LlztePHn/8+88/+vTzZ8/3b9954/aDW8c80rh16/BkbW/3gBXUgwf3X7x8cvfW6n/8H/79rz77+J//03/yC5bpP/3ps2ePf/vh3/3ij36ytb3JJd3V49X0pSY/2p6/LyxzDxA7QGn5f3TCLdZ8H3DrCO80wuFB166rMLQmn2tnxrlMUs4WKB2lS2Opw3Ix/R22v+ivfr1aGQ0U0Ob1WFv9Qn/Ne2BmC6A8krR8T8NWA/qad941bZ4OH0fQBxENbSWPSSsGadwTFNOG+dmEEqr8jTBN1rFSLnJtcpH2dZyYBWFjns7eHYymyDTxn02mslqGLgLQl9CI5IeO+Hd8cLTOXZurq199+SWXAPhey1fY/UMuESyvb2zx8PDu/qu9o2MWN/h99XL3xtZNHhfjJW9379zidzB4FQoPD99Y2361v/dq7+DW8dKt2/dX17b2d1/xg2C8Y3f/1c4nX3zzyadfvPvO2z/76Q9/8MF33n/3nTfu31vH+Gh1d+/VenrQ5oDv0IfHfM9eOzxK715hXUWoRSvcnD5CYQ4VPeD+jMzqE3HqyDbyF4QFJpJ0V8m1IwGMbPMYWB+eAjOTYowhxjYuebTt7j0xy68OEzJnRf5ZLrYL0wnCioMqBsbqWnrWnYupiNxWRsUur9882lteWnu5s/vJ51/85rd/9/vPv3x1cPz85f6de2+dbLDu4cLrMrfz7/OA2MEBo/qdd+7zQy4fvEu++f4bPzvZfcmz8fhjs/OLL7nl/9UmP/CbFlk8ArbCk/DcMpf3g9K7EHmRNMOeV4YyaCkSG4GpgYQ0XNMTczpPucqMwH1Lah0FbSfnryoYpR3Q1FjGAHVNCQxqAHWlnNb6bo0IuzGvT23q+WpGPYfmX4jT2K6ZLYBiS6IcnS3kK9cDHMo46VBkmmPTRCcMOWlko/qMB2H0EGwkNHvhiGKkHZjzJ5/G9bksPcwZcprBw3zNNgybOgf7R7//+NOHDx/y/Fd6lRv/Do63tm/w6kOm+J0Xzw8O9jbT017PsOddQSyAtrbSL2rfvXuHTaNXr17xCD3Izc3t46XVzY0br3b2VjbW+cjZ3Xj55MWrl0+5XejrZ89f/PWvfvO999/58z/7kw/ef+/e3Zu3b905Su/SXeIpZX5EjCD5ksy90gj+IMRj0WS1pegWlB1JJAI0GkoZYR3gno4aYW4OggASateN5rVSHTXQ9xiTNUOhiZE4yALTp9hoG8kjiQ/K6RqUi6Lp6tbKwSGLm3SbvKpYjqBlY+bV3n6WV3jZ4cHhMXefffn1w7/+1d/+5ne/39k/XF2/ubx+c+PmvZX17eMDlvUrXHkl7e0949Lto0ePvvP+ez/47psrh0vba0v/zl/8ydH+zpeff/bjn/2YRQ8/b/fgzm1CIrE84Wzgd104/xmoaNLroPn9+HxhWW1Uo9LaLD//le04SU+/8HCoPZ6HrR4sa+oOwTx+GwKfNIt0vXqAY6rBc1HNmtkCyA242PY4jIUwcQ/UR1Bz2WjCtE3fOEnlC/7t9vAfhYnSQCIZfDwGVuuAJUBOkp0TxrAmESAz8yJY1mSKkrs4+TzgFk8281+92vn624d8oiyvrnN/w87u0U9+/qfvfud7+XHidBPDnVu3T475ndRDHodhohcb5JzFd+7c4gMDKhZAfDI8eZwWSavrW7z4bXefV6uwZ3TvxXM2j/im/vTo8Juvv/7mxc7+H/z0hz/76Y8+ePcdPtf4kOG+iXQFYCX9UMbRQXquGEJC9ac7MrSpGTmpFcNS898cXui+s6iaodactTgtFczdxVOzoRQbMtQN/nZU1UgHXARQICcozpxQMbh1RB5lajnQaHSEqU2f+GnbRre78WLm9MRihqUhzSDkHucdhmt6688hjx5+8eU3v//4i7/+9YfcqLZ16y77NyerG3cfvLm2eYPdGxbaXLdaXV9haAFmiLFA39xc/8Of/ejG5tLzJ8/Wljf/4u/96f/5//r/ONh9xTB++vT58XtLJwxKIuCV6flsyzGlOAGQeKsQ8WiUpo0fNqbW1sEAGDTlJN0hNGx4Aq6kbc3haZ13eVNDOxc33bUiH7o484UtVi1k90Do/8HMYI0xcxJw1P9ozimGSDv2AmjePTVv/tj4hdzWAzoKzhmyTF1p9soJKwnkbQxR3wbzsbZgpDWRxzK1II1BsCy9NM5tKEHglC+f8AHARg4fBmzbfPH5N5/xkHB6kdz63sHST/7gF//hP/hPH7zxFls7LEp4UdDeLhcdltj85yEvdoy21/lVSK5EHHAtgI8Wtn9YRaWnb/bZ0OEnkNhbWufNKc+e7/Dm3K1tbiu9fwvM4e5nn/5+d+/50m8+fP7yBa9b/Bl3R//weze43yI/dUyf5tVVuuGUDzauCuh26aIVsQeKqqIIrTu2qJqyWDB3F6f01W2uBio/XSHmgUpU3bYdtYVt/24cRNKj5+VCXRf5Od3SwoM7w5bTVcvldFtxehCd9QZ7PhDv7OyxSH/+cufZsxdPnj39+puHX337cI/fZ9m6tb26cuPW3dv332YJzs0+6VnFvZ0bN5EPDl/tb2xsQc46/ssvv7y5vf2LX/yCJxlXl453dp68/c47773z5tHh/v07t7lGxteDzbVtFmaExy+B8R2ANQ4iegY8o/rZ0xda6bDrkx4C44pZejoyvR+IXteEwZ3TKf6cpCcfTiaDmUSHINlkWLH34wNEpZE6qNagT+ajUoEpiqOsr3x90d6iOE3zIhUHJRYL2o6qAnkOxbEXQOcQ08LFxfaABqhzTTHDCStNQNLEqadnwNnkdAqj6JOhYHMRgGSBXbSh41EMQ8M0FYKJMAGsSUJ6eRzXEli3LN+4dYfPj2++fbR7cPhiZ+8nP/n5f/Af/Se3777FY8LcXcFb3+iBw8M9PpLeeOMevwDP42A3b22zAOKTAJmrCdwLwc2hfAa82t1f32QfKD0YTBUXy3gSPr0+cW1rffvm1spNHifjB8FO1lc++pTPoK+++PqbR08ef/D++++/+/b62ma6tYPXM6YfVkofNuyN8ThZambaG0qNIHL1hlrUJ5fJSOSw9wYuRuJnApBTHRfLjmSki55NG8kzW4CaU3NyAGlaipm6NAK5LDRApetMVGVtqjxKFTxIyCNW/GEpzJsadvd5LeES6x5ewfDFV1/zhBcLoEcPH+9x1xjvaVhde+u9D956+4Mvvvp259UBj6qvrm8/f/yUy2es8o+P1w52T3gdOa8739ze+vbbR88fP3zvD7//7rsPjo93lo/3b2ytsVP6nffeffz8xd37d56+ePrq1d7tGzd5lOvoiB/3PeJn3lkK5RBPWO6THn77eGk5vd+cYU8iYK6s6YhwRx1YzRuMZJo2mEPSKE67pzkxsPmXh3VY9QAeAgazjYszEXR02o7RTFy8PiR1N9aaS9sbiwXQpT00FxlYxwj23ATGclusQ8DpdNaITLAMafM75Dm1libi0SgkKV2FEGUoXMSEz4abK5usVPiW/OTp0939va+++fbWvfv/s//sf7V96w0WKD+69YB3JD59/g1v+GfXhy+3MDD1f/PNNyyfuPXn5s0bzOz56eATnism7bza40vw+nq6THD71l1e2fJ852UCLK08fPycz4KNG7fu3b29sbb86ccfvtzb49coWSR99/33/vjnf/C9Dz7g5mhWS6mpeQ+JaLlSR6gkBe/4EyYnVQ1Lvf5CgpXyNgPRTkDeRjgnfdkh4eOzrJpTBENau1OnuTisH6x+VKQ2A9JhFV67KchKucgreJZ3d1+9fLX3/PlL3r3JkPvsiy/5Mfavvn74xZdfr23c4DdWtu/cv8VtPrfv3Lh3f5eNTR5cvLXFqDs8eL67z+Xa5RubGzyKvrK2oqGVNmmWj1mmv3i58/TJ3oP3tp+95GHGde5ye+fdt7749uvbJzdYhTMs33pwnzug2cPhdh/CZNRoocKahuGtpY84wafRn/Y++fmwtc10LTi9kWjYmtNJQO1GT8NzD5x2y2AicH9lgJBBl60yr5XicbFR6INpNFwor2sPLBZA1/XIzrBdTGHpG1uacPjqxk/5pPnn7Mdn/kI7gUsoNStJcLGgEibmbQB9hCha4Y1UVSTB+daNzfT7jLy9kA+ZtBb5fP/45D/7j/+nv/iTP/83f/3h4dHyw0dPgT19/nJ7m0d/0zMx3z76Bk72d/hA4fvu8clL3qB45+49Hhzjc+XJU+4Feo6vjY30S0w8LMaPwy+vrfPxwAt1l5bTUoZfZXq1f8w1hLfeff/kcJ87T588456jD7/66qsffP+7f/6nf/LBBx9wxW2d20l5+Vy6FMKX7HxZYfiB4abBRjAUJbixEwjTM0zgVCa4ji2y3J9QPRDz/rb9keJvw8ewoxzxA30ac6jTYoK2k+K4ZW1BHSuJvb30tnFuSuNtCzu7LM2/5hnDF893P/38c+7N4Ufdb9556/bdNza2trn1bJ/fdl/b3DlY4YLu05c7jDcub61uLG+cLPGSTvi5pLW9fYNlOLSEwbDc31r/+uE3v/ybv/3+27/Y2N462j9i6PLWcq5tpcu+S0sU2Wpa4y2Ly8fsQ3EtLMXM+xa5hrW8xN4n3xxY/RA/p086F/KNP6x70PC4GhapbYNXiKVdTJJ7BoFEtfRpfzPJZy5jnQUMgZk24oCd1vWWcDeZYW8Plw44jybPg/PcOm6xADq3rr56jjSyyUmKXrKKbXNTdzvrGWdAHj7IhYlOzWmnCE6adikCkzJP06dfKyNSVGjgzzs3+XvtMr/xvs8q5ONPPv2jP/rj/+gf/Cc7uyfPdl5xB8+jJ0+WT4529nbxku7pWeK3UF/wEXXrFhs/6b4HFj3cjcodQY+ePrmxuXXzzm1uCXq5+4qPB2AHfNXe5aUsL4Hm52PSK3SxYEHD+3kf3H9wY2P1xfOn3OHB+6O/fvz8+c6v+XH5d9/76P133/vhD3/A763mtuQrD6kZ6RuygqchNCH9OhOJpqe3OqbG6dcRku7sp4I+Ivxpo36YVS53Dmwa2iLsnlQEAFJ5T5PZwmLYyAytqGn0JQAxA2a9Qk5iGcDjhyxWuKj6+AljYZ9x8ujJMxbNn33+Oc9wcdH1kHX1xvatew9W1zZZmBwsrRztcQv/4dGrw627Kzu7rx4+fsyjWm+98QabNC+fvyDfef7iaP9g6356fw8jltU8rybnqtq/+Pxjfvjlf/kP/pRVEYN5+SA948muD5tAG6sbXMxl2B+sHqcrVekXLnjuPn3X4cYkdnZYJ8HMSohoaQuJtrAeohWMfHLtACGgzymP1Syxrcnf2C1C6Jn/qC9kYOnS9fjHmvBMlUhyMQtWL4TXrgcWC6DX7pCP1WAmCPCaLNLsLHk4c6k2EaZP4JzynJamQq4YDWH5i+Owfjj1DMrDP8X0NFTzl5ohefr2OQgmz1yDqrxBhZxu1cy3FJAnE+ZY/wPPVK7bF4Dl24qZ69nHucESgr/8Bvbf/d1H21s3/4O//w/efuv9X334yd379z754tdM5ht8zmxyaw6PqB/w4p4HD26trvDmt5WXO6+4k5RHxnb3D7nLh+eR+VnV9Q0ucN25ceduenUKES2dvNrd4cdWj/YPb/JT8je2+TTgh7j32PhJvbTGA2ibW3e4ysYvD+zvsdB6+quPPv7dZ5+99cYnH378+z/82U/4YabbWKWrbOkxZO4JSdfEjtNVBm4PSq9JSp9HS3zj5jDQ6nyU+Fnw9AHDDbSpX2h2QgFIv2eJOisG1zIGVqm/ziwgMOBfvvuIuhEp9/wZ8xEGw2oNEpkPdaP/Gi/zmCfj1ObBjbeRy8MoKPN4PS2nQ5KHaxrqHtSp/iT9GFY+BWBPiSKJ7ky1dHpWJhkp9WV6yyV4kn5rBQwLX34olKPCOoJVSLpnmIPIXV48VpUeKD/a3Xn1+MmT5zv8lsrLb799+Oz5q4dPXn779Cl7jXdY7tx8wI9bHK9vb22ml3Mubd54wSNgx8tbN9jH5H2bj7gBjTf8vHzy7M72TZYmvKKBHF8veYXDDj/sdby9kW7bJ4x79+9ygfXWvZsfvPfu5598/tnnT96694AXR3PNin3Km7e2Dj/Zu3vvPhdzj1eWD1eOef8Pl8FY1y0xkpcO+bFVhtONrc2t7Q12idIvxvO7wZu8I2KLt/2Q8LvBL3LQNzzSmE5MuoUzn7FPx6bhm/eT1FmpJ5OUbrVOu0D85Q/loT7VDWRCaF/9+PgC5mgks5DEMFQnpxGTS+kQDhwFw4V4jXtgsQC6xgd39k1jdug/QYw1m0ArPHl33EIWMBclkKc1Qv74l4YitFk/+NlqZKj4HOIqFZP3zqtXT58/++lP//A73/nu82c72zduvfH2yf0vvnn48BFfnbnI9eY7b/GxxQKIrf693T2mdv5l1iO+AbN44R5mpn4+D5jx+YbMommf52j4/Fle4rOIKxpcJsAdX435qs3HHh/R3z56zL2ubPNwd9HyCu9cxO32zsun3GL9249+/3vucf36Wwh/8fOf37tzK92icXzAZyWfmjQnfXCy66NPktQwFjipjYMPCz5BuLA3KLGSQaJj04ZRghSJj/rTmX8g5U8lTJrwhXl7UZ3cXj9hjcaAjfFimSpk5VbOQkif3AwZLY9OlrmNne6mr9JSiaFGUu9Txrt2PVCmwzRYjC1z1Yr3LrCIWedQb6XLUnv7bBGmi5ssNXjs/OGTxx99/Mm3j54ytHhZ897h8dOX+/ycxZ2799c2b65tbbNeZluINw+yDEnuV9do+cb2Nle7CIPrVlpXsfy4eesWtznzkCMyA5aRyQKFZdlq+iH5FCkRAn7v3Xd//cvPP/y7Tz/493+x8+IlD8kzUHkgESswNOTFq527t7dYoqVW4HB1la8BMFDFLwIzetOA50VZebFFLSl2PkiWgKl3SHmRmI9PlsJRw5cSKMxjPqyZ5d8Uy9lUa87WL0rXswcWC6DreVxn0ipNCsxHSuYsiug1YUWA5HpawdawfkKJj5zs/eTimekMDdNuzskGKb2LlruXhimHnKZaPiCOuSCVZ+333/tgbf02CxNepHv7wVv37t374z/+o0+4RfmzL/ht1Jt3bq1trH7+2Tc45QOMr6p8ADG5c22CDzkeUn7+4ilPyPOwMR8eTONsDuGN+3hYHvGpwB1EL/kw2dm5eeMGRe6d4J5RPrG4Xsb2wdomL54+4J4JXiHN1Y11bk59dfD42dNf/u2HT57wartv/+xP/vi9d97ZTg/tH/KaoHQI+BrOpZPDdFMR7tJiKG8P0av5oyZ9fKXXXJPYJeD6Xdo6SquuVE+mRREFPsXTZhkNohv5+E6fbclI+ekDO4mpTrQRpcB1bZveSJm7OK6AuVwgWDZJrXHVUDgzclDmXZ+0pilT6h36mZamFQ4dyxBLS8psoI/29LqCYVewy8LYSDYgtHVK37NcZe1LWEu82JDbzjj4+zxpxTuXP/nss9///vd7B0fPnvMjFnt3H7y1efPWycHhGzfX3ubBrY0tthu5mIU9CxRe/cwzVuzWcBsZq+ojLrnmdzF89fUXG+vb0KO8dfs2zyryaqmVkzXuCrp99w5R7OcLsje3trm+xq9ZrK6v3bp9ly3OL7/6ltawrto/2OMGnrv3HjCGGdWcFs8eP7l7O70O6Ph4P735kL1Fwmfj6yTdAwQmXxHe4JeAWdbzG6mszukE9If5xE17RblPsMr9dTpXDAZYHmbqbSAjB4yQ3Xly1Z7q2uw3BdZudCVr3CL3qjUzbM88OGcYXjfV6AXQDJs3Q6ruVi1qZ9sDPn+gRY5FOYoa5I4D7aoCJn3MiyY0Vslv5LRVVqZInKiSLIGchQiLA5ZLXH66dfuNP/17/9a/+Bd/xTPGGzfWeTyd7Zh3336blQq/kvHlN1998dXnuNvf5QbSpTu37925s80mPxP9sxcv+LGLW3duspph4cUDYlubNyB/+fIVtXjEixJKbpt48803QfIRhXz73n2eT37OXR7Pd/j8eLn/ap8H4Lk0sn7j7n0eid/i2sWHH7MP9M3vPvr9X/zZn/LGoDfu31nlV+XzjwuwXMm/ncryBW5WL+kDlzYPvnZnXVrr5AVP+tDCQMhcNczYD8Aov1l4KS2tskwlYl4/DXEdf2lmPBaSIx5ALNaAWItc4IvaWCyQjkQY1Y50Fwlb5EHHpXbkdU7qWPood1NaPKaKvOWWr9Hkt1qmSz7SEwD1LHpWT1b5+YqDvfT84De8dOHRk6fPnvEQ+7cPH37z8BE3lfH41r1bb9+8fXttdWN7ef3Zy5fs4XCtdpVrWbz48Pj41p27vH0cwnRxjVUYSxDeKZXeTXj8lMXKvVWutD7lpeM7O9zmzC/D8zMtDC1qWTkdLPHjFWsrN7gYd8h+EDkXf++9+c5XD5/9q3/zMc8gHu+d7B0cbG+vMzjZkeKS1pOnjz44eZ+V28HSyTq7Ryzj0sI73bQErZY7jHnwuumNscgrE7kHmqtYqdvpghyqejUfnSSm1eEwuUrCBDlMOtAT2GKCefoicI1S0RtFcbYNnbLzZxvMuGwjFkAz7LgZUo3byAV+hj3g45hnjTPEaKRMeed8cgrzoiR8EaTWXuRAxWE++Chi4kIj5Zk4ciG54P9MaxgfWmzbCMwUnD4MuHDBr7hzE8PKyoM33ty+fZcfjHz24tWnn35y686d9987uP/Gmxs/+8n6jc0PP/wN9zLzdjkew3n89NnN23d4Uw/MXIPSDRBcJuNnNLjc8Nab6QoCu0HwY0IMfCpwRYMinxN8FKXvx3nnZn1r8za3rS7zBqH0wDDvoaYKHszX+YEN3h+9ffvVzrOXz5/89//qrz799Iuf/uSH/9af/+LnP/vJ7VussY6P9g541JgFz9FJ+jFM1j/pcyVP6HhJn9Mch7QJkZY4+V+6UTopUiHtTRT9lj7UU2X6hKf3kjxc2RTIukhXqLdjVdvRsb42ieYjZZunTk5LkHmk1CNKg0VPvjEqXf/K3Z6WPVzzTGu2FEC+EAZimdc0swKiHzmmHBdeXsiamGulbMOwF/jtt9+y0fjZp1+wpnnzrXd4WPCd9+/u8tjhyjo7jC92eYfzEkf5ZO/4m8dPGRK37j6A5PHTp6zI+SFednfYlTw+3GfxwWBhq4YFxbvvvs1P8N66fWNti5+TO2IU7acgllhQc5H3yVdP2AFkVfTx73/HULx1M98htHS0efPmJ59/81/+V/94Zfnv//yPfnzClbBXu9wizaNnbBqxDNp7tb99+9bRyi6to42EgUCjoCUw1j1cXGOJzxXZtKLigld6eTR7T+lcY9dRXefch0waH7V8pmotYuwYQv8BUCCL4hguF9BhDxTHdKju9dcDoBd61qARC6BZu1vwXZkeYFySGNlKRdwoC81MiqKVXxEiR+ZcHHzUqYrcqc0EAFWCIbA44GIFMzUfG2niPthbX0tfal/s8NTLwf6rl99+8+j5s6dUffnFZ0zxb7z5DhcLuGL12WefHe6dsIh5DnQnvfANzL27D7jDmfXNyeYJrzEceuHz6JBPINyxBuLd0DjlmhqfFi+ep5ci0lIuf7Bg4ocnt2/dTB8qR8dbN25xNxEPKe8d7PGczibvaeFB5s3tdMXk6OSTL77+/Sef8HOVn/7Fn/3hz3723e+8x+9vsGfAhs1q+iROr+rN3vPnE8cu3eqRjiCNz+1P2znAkAeHj8/zVAkeGLduZDlBMyAJYxxlcQ6YE0cKJv89k0XAmYoxC4U7FeWRfFZemoPKGz3ppmCNJTo1L3/SbTU5YcXhpvdZBnFMOKLsxzx+9vzxk2ePHj7lXQj5jp+nL17uHiyvbN24c+uNt7a3bjFGuA76aveACn6E9/7t2/vstWxsHS0/5953Vk4MNu4SY1Bx3LhwxiYiL7BinY0X1kME8/bbbz99tsOFVlZFvJGKFRK39jOA2XHk8ujG+ubyenpRIYnNG16HyLblV9885IfheWnnb3/30f/x//R/+c//8//Dd7/zDtfa0u+hfv4pCywWOtyRfe82t6CtHp7k63qMVYZRWmBt4Z3BzOCmyAYTPZ++VKzme9T4Xd+zswS1OaVTL52DwzVrASuKzYdgOq1dQ0NIuTgd48J60h6Ix2JSjsntFgugyfvu9bFkjCoVTR6qmz4m06cDn595L6EwC0Vmn1AaiNBajyxt1qSP6ogfwNLDKXkmo6zEPn368qmbU5MKK+ZcC3xSMaGT0mWBdGXqFq/T/ebrh0+e7Tx7kT45NlaX337zzYP9w08++YSfMH33/Xe4/MRPBHz77bNbPOR1K32osIJhQbOxdYOHwvjU2eXXs7fSxS/u7eDaFx8zfAKmvRzezrLDPdavtAOEUxq1ort/Vlf5uq0W3bp58zsffO85dxLlxDd4frMgXUrg2tXqxhtvv88uwMNvvvzo919g8uHvPvnTP/75z/7gJ2/evb+xubq1yQotL6LSJT3m9HSZgsUNX8LpiPSoEu+yy6sZ9oXyxg49nJ7koVfwjibVpn5PvU1Gbe55JNWf6XYFrNwHSEX1cAQgozQsyqoS2IDCtrEocDSJfqNe5oXTkjMvaAbKvLuDLJJkmLtA7c+DOrUnAfgvrYDpMbotLX34YGfUpV+0TQ95pUtOLKa5v4e7evjBk28ePuZlm7zMcH1jk/vdl9c333z3jXc21nkScPPGNi82fLG7Bw/3NR8cL/OmZgDPnnNta4lHrThqaXG+v8/imy3LZ4cvjo8OwbBDyBLnYC/dKI1XdneW1ld3nr7YP+TCV1qO3Llzb3Nt88X+C9738+ab6c6ebx9+/cZb79y+w43TW3uHBy92XnIV7IP3Pzje3/1n/91/84Mf/Df/m//1/+LuzRurS4esnB7nNztww9A7b73Bg+0sh7gUR3/k3qbFyzdvbrOW4mfo2fs5WT7IZ1XqHAAZgzhIKktNrtM6UQxPcHAuRuWQoPWvwQiF01abXJGDaR3Y3baXvHbcrrjA5ox1yGYe58UsgK7Q4Zl5j19RQs8yjr/WuEpCAlTTiw99HPcoWZsU5iYRMrvLH8/DT1MzUEVSkTyntNwxoVTWUBxOtqxF0k4+n0z/8l/+/759/ISbWx8//fbx46fvPLid7mzYXvnd7z/++tFj7nF+680HP/7xj+/cfcJHy/b2wd/93RMmfT6TeHKdNQ0fLffvvcHXctY6PHrMWodPI/Q7L/d5jF7fubmmwCMz7AMRGJ8umJDYBGKLCIH1C1tSb779Fg+Ogefzhps/0t7S3sEal8o217jwwZYQH6kHR3u/+u1H/NzBr//u4z/66c/u37v1/ttvvvGAzzTWQevsaqWrevQS3/q5VSg9hJ2f/9fBSF0YUrpWQ0fljmURlGu5kqY1UOql/FkWPqSCbe5EynQ+ee7VQW6l0AJIFrLQuGosQR4Lk5kwF5wtRd3ikyrzI1JpFc5KgK1BHgl88eIl17m4k+zR0/S76+z98MDX3Qf3t+/eY5HEth5pa/umxh4Xj9i42Ts45FoqG3S8RoFbnFmF8/bn/d0dWsQwu3//PgOEIcdqiRXvyVp6mJGzhh+jAMAeIYfscGl5++bNPfZsnj7jV7+Sj/WNQzY1d9Nv97LNwynAEorH1QmVt0qvsKezvHrv/ps8Y//Od767fev+P/7//tM/+qM/+Pf/vT/npy+AMXb4bsDtSq/29/jpu/ylYnCeJp9Lx7oNaD8v+ngbUT4iaRXI0p/EWUCQHhgYZDmNFmJWAkYxa/T3XPMUTx7z5+p14ezS9MDFLIB0SlyaThgdyGULmLljdNATIWDWTglNdqs1VYkvugbg+St6Qw8smw+my9Pa8A3b/NTKROSFntrh3bgDtlNwZsvfSNGl2vyuav6m1Q8NiW1Bzl/a03Ur9uzxxZfmLT5NVpd/99Fn/9V//f++/eb3Hjx487e/+5RVBNs86yvrO3usZ3a++vZx+ihZ+wNefsgrg/gQ+uyzL7hR9PPPH++85CcyVgDfuX2Xe5Y3tzfv3tncP+BuiRV+fJsYXu2wWtpmM4BPkXTNYnOTmz/oNFYzLHogJwY+N3gJ9YvPP+ctQe8fHt6+eZN12fHJIZs1L5/z3pcN7iF6ubd/cnhw89a923fuHR7sPXn06MtHj1/tffTlFw/5vv7Bu2//9Cc/+OGPvn/33r2VdT7U0j2qdCrPqnEHN+9uSc8f5chpNf2Q9y3SgkiHia7jsyr1Ffev5h+fYnil/kw3SNEICmG8BVF9njo2U2VoynQcXawFDEdiaitp5FTmktGrITYpyIsisEiS2pyHK0pu56Er+GEtNDz0xAPe6d1L6V0+gxcVJsxpe6lZYUHMwELLbgr3NLPWecIl0pe7rGC4ZYeNGy4VLXHjOiNmm73DOxyatfxLbwfpt9z3WNywVuWx+Bt3NriQyrujNpdXeSEmb9c8OTrgzUBcjeL60osXz7jCpdvnGTwcQgbV4yePH3/7kNvqeY0CO07cu3bzDg9ksZ35hAX0Gw8ecLCfPPr21YsXLLtZrrO3yONgfOZzpzN3VHPJ9cbNO48epp+iY33005/9/MmjL3/929/9yS/+8N7N9XsP7n/2+Vecegx4Fu53brzJ9Vb2vSiyaKHhdOP2jS3W2QeH+9xkRM+kV/7knUT1Zx6ECUkVGmpSf6V1Pr+pOhgkqtIRkVUC5gOqgaYimphshdIyQsTUckamYFQFM8Fo9qjBV1rjNl62VugQx6hqTaydt3xOC6BLezz69O+VDr5PAyMmNjZOPZYtYKWxGzWRqlGO/AJI00YyxJ9ObTVyiEl8yOmDfJiQlZiLmez4pXbu0+Dq0hobJXme5TvuxubWZ59/+e03T3aPb7z73Ttvv/kAnh9+8D3wbP6nCxn7u4+fPOTHv3Z3ed/KzXv37sDJgubrr79Nmyv5UfzHjx/zkXL/jXtpa2c9fbKwBuJjgyUOHxiEw3dl5n3umSASVkJo2BDC0f03Hty9fx9HgNM66eWz9Y27PFbG7wqwjmEVtZeeGNvZ3z16xqfp/tZbD97g2Zxb/EDT2haXXx6/2Pn8i2/+9je/+83vPv7Jj3/wve+//+ab91k8sdLi8hzLpVs3bvN4MqshNgLYUqIq3SmUn4pHyW1BNJOuIEhy3tLHWocn7QkMPR9V+hkyih3JRwSBdnUgqRLASNmaobZtI0QfrUwLQ9TXRbmgbQhYyVBKDHm8nFy1/vxGYFWk48gbDKjlrhfWj/y3+2qfLR82b548e/75Z19/85gflHv28PEjFgJ3797fvvXg5j36dO2Q/ZK0r7b6bGc37ZqsrrHkgXA3GfNKzKXb6dUJJ1zI5B4tVj4coS12b1go8YpMbnPeWme0sJNEGLwriOHHgoYXNDBmgDx+vsOd+4wuFh8HL9LP9PK6xLXVdIcyP6BKxTtvv8FuDJdCb925zW904ZcnzBjWjx4/Y1uI4fX0eP/OzRvvfed7b7/zJm8e+uTTL+///AcsvG7evM0ajkHLMGbBxPUvrgLnzkmnGN2CU0JCZuimhwEOec8R51h6uJLvD/RkWus45c0WgseQUZJ50sFCINchcA5nusjYlAxGANYEadYZP5ZVM9dCO1EP1D1fayYintCoawF0sZFN2KDXw4xD41mAFhfFafrAB12TBTnJhMgJMPyKZv2sBHuvhDPT3CAMvKZLZ1QNIkTOxcGnbJpC0w4GH/RpnmWORmDy5ZlivnfzNZpv3Tzk/s23T45O1j743g8//P3nh0vrN2/defPNt/htdpY4XMxgfuf2GnZfeB0iS5y1tT0eK+YqxE9//KMb2ze/+OKr589epW/TxzxKBiItbnjtIY/f7K6k97xQs/tij/tCs7stwuMb8+07NwGzFmF7gJD4RLxx+1a6tLG2tr/3ku/69+49WF7eIAA+P3jDHLeUsM/AI9I80LOM4vZN3tnCu6BZmfFV+nDv4NE3X/O4/BfffP32r7e/887dtZV0A8d3v//Dww9++Gzz2a0791lI8UGTjh6LHi5M8NNOvPH3iDcgHa0v5WfZ0oKAdREbY4OhlT/g9FT8oPM1EtgkScLgdhjCHyTV6lNtqCv/puafHU5CFPrSrCrXeGsif2V3qgCfC8AHgwcBJZ/f6JHp+bzRQyFtErCg4foUH968uTDl/PwJY+jg6OXLPR5ff/T46UNWPryicHPrZHlt/2j1Nntxb/I0+x0WSowQBg23BvPDt6yvbty8ne4VY2eOnuR+4SW2kPb46VHWrCzQX73idrE9tuv4na1j9v7STetHG8hH+HrJdVWCJACeKeOyF+9TZAHNvhGEB/yuL/dRH/JG8NUbt+7uvnjCqoUf3L11e4vVEhFwoY1rbw+5r+fZ8xs3b+68fMlYZTXPaoXfLb179zYvd9h9+fTZ4y95PO2nP/7gzo1Nnm3/+NNPHrxx7/mLF9yFfff2zWNu26bD0hvJucGMdXa6mTpt/eSoOLlIyPQaCA62FzdJOexqZEq0IvW+ppTU52eOjvTOXYfG8kJY9MCUPZAW71NSXFHz2HCfhFeoLTF+wi6Kbsg08wW2TiLEC5pCjkpVGeMwJhaGVIOvemqmG4uQk1c8p4N5WHX6lwmXaZkJmjWN4uFOjNW19X/xr//q13/7u5//8Z988e2Tjz787Z/9W3/x/rvvgmH9wTIFPM/53rqdbtlhuZVeS/jl13xW8dbEP/r5H3L14TNe0/PVt/9/9v6DWY40O9MEQ2utI65W0Dq1qsrSVSySRRbJ7pmenu6ZWZveXdv9A2O2P6Fnt613zMZsetZmOWvLJbupmiyyipWVWgOZ0PLiahFaax37HHcgEHnvBRKZCWQis+CVdeHh8vPPP/fz+jnveU+1DuVZI1Et0bWjRkaLeBY/ATeCCfoD5pkRATqLiKxgaFmFWSWoUW00mMdhgxEhzQdboDbv9rWrinNknzkIiPBxz156EuftDqPVhgyd0dk1NwVooSbUrufa1aTHYSkYDKViHmwXm5wNDvSFKgRto9Nu8xIBoQ9wSol55/udD3Q9UpCc61ZXYpbVL3JJI7tlomiPusHIzJ2RoLZWXTWc33NGvSJONNx4OK/O7LnX6Kphnwxn1EMNt1Fn+LtjAzYbbsOq0UtTT8oSNiBhnLvAEhXJiTkn3GMw4IYDElFwFG8PRB9iTIQySWiH41UsQzoW3DEwEORyWtFg9pGOzggCaOJPgtfcMZg4iJFoK/4RgAi1U/AbKuAdIErJiB4hM0JXjDeFXN/1+VwksDdbknlOw4DeIFoQF5AFhxDsGsjVtBAshfIhipsNSqvwj5asdfLkjS47ow4WUYnxLnFb8JTJ1Kw1S4X86voGGA7vTrlcbTXqDAmP2x3w2d0el7ZvurSxQc2WbKFMNqLNZHd5CZwB06UZXPjAITeO88qTpAwYBi1RNBbSderEcjpKqP8i1giIlM6kG/nL08cSdXizGZMsVIaWMn/n4WUhRxOUvSvIzhJlpzsDUt14uFCdefz3Ue6B4U38yht5Lw/QA2nco3Op6uXs2Z49Fz6Qy//aHWRHVyivqZ2fXGwzfHmp86NLPuslc6jh7syou6sLRw6FJeadKC1hG6bhBvJDPqXFbMms8p8sVD5Jef/yBuY/3uO8uAkDYdwJJYBOlNe0GWTw9ntnzp298n/5vx575smn/uIv/2prbZkCSW2zk/rskFW7jZrb5QgHQ7zr+TTf2twiGAFjg9gFnAxED6PRIN/BaxtxHD75fBb0g4ki+QpjhtEyG6gaprU55XSNRg2vD0YWI0fwgkuwKCCJEt9AElJ+IDz73G6j3VJBjRExX5udbQiuAZ6Ih3DtTThF9Tr2TAyJFMHQUmujQhSGr/9a2W7oOr1Wo0GQFq4DkvnrbayvuVghLU0zPT2JCe4Xa3ia7BYHe5O4DMtEGKxd2KxYauFoYJXpS+l5spoMQDdlVrFVNEZ+3J7o4d1Lbq/89H/ZXTa6dU/vvf0dS8kObLrjvMMl6jHVhg3/Dg9964y3fnPMW8NJcRPKMbDUKOXIOOlSoUJipixkpk1kp6et1lrpbJb7jp8F1ebNrTjOQZ2JIiotSQj3+GCq4x4ih8vp8oKeqWSKVLLZaiNdyyqYW8thua3wvQBMNqtQxNoCnLQ4ioA4lADDjcT9RUgTLxM0HQYqkUypp0s+l9vNsGE0Myo4SBksk89Do+ZghRK+oZrZbCVW1azVNVZTU9OxEPckfVA0piy0JJ0tQExqtcWDxeABLeGVREnaabUFfR6HA44S2Wn2ar3eZ0R0dZVqw+Ww+P1eEuxzuYI34AcG9QYe+kjiW0of0iq0n2120Z7m+UKvGtAD/KLAHgOJTVgu8g8KBuKWyaiUm87sJyblYDvvKVtwq+Xv7XMN59Uln+8vB6EN6t/Pd4THe32TeuChA6BvUmd9Ha9Ffdrvs+XK6+nWtqPzLBo9zgN5E402Sd5Ho7/vMj/62lKbt7uRLFEXqjPDvxxS/QxVD8JyiSZoNe+fPrO6tuX0+j/86OOTx47++Iffu3Tp0ttvvTExOa/Vm8TqtOoOu8VH8UiNnryeYqGSyxVtTrtenwDi4AGyOZxRc5gi8BxnbXUD5AKTmARjMQUmtBL1cHfgSBMCG/QFhDHRBjANhkHoHdQcuK0gR3CqgC5wq6Vu5rDCNcGyiMViF+wfV8Eu/JVoXHdQqoKUKAIuMRFwUt+ss0+N21yWSqUongeDGeb01mai3U8SvqFiZqPe3tqKYybHx6KIXHvdTthEUuG7T02DjoAmwTsgR1xMcsc5rBIQ4+Rih0b/8nPHxPbqBjuWf/Gfo+cF4XJAzjV6WPXU6sLhqmFjWMI0/KnuLnBPca7wl4ujw5XleIaUe6SAFZZA3CGpC14zRpzC7Bub22Ty0bPFSq1FNTAqoiCPbNIPDCYFCziFaAN2wexDp4LoIypNlIUzgJ5J7gOiMygYNsASloszT/GxsVBvMuJSwn8DKc3pMOKAYQMAKXw1tmEX2s8w4N7BzWazVCpFpj28+w6FuTpdtu+0+2aTxe/z8DABbtkSfj/oBDLQVipx/eYKOWVOl9tudQABum3kOgMuuz0a9A0GrXq1CVVIo20C2etVLsyAnlA04ne4HIR+YR35gyFORy/hyZGGKV1Hk5gYkAxPBhHoh8HMX6XBuBihG/c5rNq3bMkMf5V+Vi7uNriRfr77xKbqXmxyt/m77/14zeMeuFcPPDAApD4V9zrV43WPdg/c7Q7y9hm+gNR59e+XfzV8T/IOxGBx6t2tVdkSt9eyDSZNggzqRJsBQHz08lJWnfN2pw2s88tf/ZPT7VvYd+Tdd9+tV8o//MF3nQ7La6+/vba2ZrQ4gyJ/onc6xRODlA+1CODA8kK2GM0GrQG3S6vd0LcwcCav19VsBfkox86p3h0iGiZTBaopERM+2Ql7wT1CeJp4E02COYGd4EJolWreQEjME2LD0GIvKfuFDwDzwXI2azcb7Eiiu8Nhx3BKoKTThPRBhjP1MDiq1WQNRUJOT7Te6WapJtVoBkMem8lLdKtaruBFWLWuEX25cPkKjN1oODQ3N4Ok9P75ObfL7rBZ+FQXIECPSTI1/0jX0UIUhve80bcIqnIr7kzsQkfd+f1Z5th3dPPdxxkefDjD9swzMaNuv2NeWah6K5TcH36LA0D2Ur0RgAp1F66UGf6Sww0AZQZVp1y+iKcE/xyOH5woRpOtQm5XtQEv2eEOeANj0FyIfzltYAA78ISDMlooTwpmhW8DmOAn91uOPOgKHOrrwbfcO61Vj7+Q+0iH8Rd6DdABeA1OdbldFnw8DQFRnF0Cpzj/el2Epgi8amEHdYSlxH2BCiaoGmKPDqK0HRYacCfo9zYbVe6jKCaazYS+wGqFQp1yd4Vyo1zrelwtwqBOm5maKswA7re3kuVKvt+Pma1UXXUQS0OME84QY8+g13i9nmS6BJgD1jP4hwBI4f3QEMmr5/oJ1oFv6DcZ70KOppdRptICgJRelz9szURvMKnPqbpk9K+y6tbdHC5n4XD34cLdM/ezjXr80b+7j/N4ycPrAe6R2vkP7xSf6cgPDAB9prN+hRt/VTfgbnf9fh7aL9hdnPpznGXHXsMj7J7ZseUXbO3dducst6dbm/Bzx8ayRDFvYnFum3B1L75fsUZEM9QXMu4fPrV//Zs34oncgWOnCBMcPXr83XdeQ3flxeef+53f+Z1X33h/M7FVKmbn5+cIdYGZ4KfyrmevDl/I2DJFIQcDgMoKeemkWkWCvmIsJJasLl/S2IFsNl01GKanp7GIeGgwIcjk4hUoFHIWk6QNa1ttCEEUrCQMBS8FnGS2CqOCkt0wW/kkx3UEBVupN6knXoaNwW6yioKZWBoMMCawXqVgVA1Wx+HDR9ng5sZqpYIJH0y4ItHJqVIxV8+wQTOdKXX6g2S2SHo22drxZGplZWVudnpiPMaf6ckJSEJ8vxPeINNHaQNWD2gl1khuuviH1IGE40xQkSxU78CI/069KbLqk5O6fLhMNYDyVz2CsmJ0X+Y5DWvlr+AETskvfCgCvYZHk80UhKPIMnMUVt82n2ooDwqOjBNpuZDAb6+lo2RrxWGD2RbZSdQDe91WnZzxqggP1uur6+tQcwiDUqA0Xy77AkGHy+c22MGKNqeLQFWr23Vg5qG7CH9rQHRMlf+WNuLks1g4shovQgxahh98mnqrXKozEAAucnbVMQkJqMW4InXLBLO+2RBFQVrHeKAlTgsJfZCkwc1aSPcMQorDzy04a9U6TDB8VIV8yel0g3cgGHEW9IWIfOXyZS6RUQF9udXqotQAXQl1IpCxZyzq9zsddhNCPxQoIzCXSuepSxexoLXpQFiIynSEamUk9PqhoH99M40uAE5Hqa4qd1Z0AZRelcuUxlnsnXZNro50xQ5l54UZzSomtuSv0tXsITd7dCE/hxNr1S2HS5jZsXB0A3Wev3LcvfYdPY46z2Yy4pRmDJfIK+Px9NvaA791AGh09D+Mm/5Zj69uP/pgP/BW3c/B1W3UxvCXabgX8zQJ08GbjP9U4zNcO5y5j2bztt/bnSCnu/1KvdtxlBOxO42R5vESljexSvwRYo00UmwkvAd4GxhxyeWWeAQZKqLmQoZwp8MXrRynP3Danb959a2LVxYnpvf3NWZIG5FIFJxx6eJZnVb/7IvPfee7L/36N79ZXVpNOs0+P3lhVEXVk+OcLZYanTZYhM9c8ZdAJ1Zqs1c6OT7cZycnpsYnqOWElSIX+sKFCzVsCS1Btw4qLBWy9WaoEg6Xhy9kFOqQVRm0uzazpdlq5UQzpmm2uwBbmHscWo1209wmV6iDEpCFqhdaCsu3C4jMVOqYXkis8Hi47CpiQb3O2MTC3Pw83JSOdlBT+Csdg7lnsrf1DXdsmhwhjdFcyeepNgUHhcR7kouWViirsW23mCcnoqdOHj906GAo4LHbzIS9jLoBwkeYPI14vFDu04K74CHR4Ux4vJQepcOx07SVO6sMEtxsMkAgXcn4wUsh/U+hLG6uAlNgL7G9LFQADXAGDrByx2W1YiiVe6ocToaE3D3BNrhWaAY7wkgGD6kmFQUmDgMS5Vys5S+OFnE5sDupUBodYRgcM8KVEo+WZLSRS8UuuDaob8UuSOOwX62Ja4QuHeD5yGVLSFMCJjhoKl+F+QvX3OTy+q0uXHoKPCUr0Ax9HKVDfDjAIwJYHA3zT3fJqMARY7cx+IAvTMALNqAZrELaieQ/ImcEMAl8ErviEmAIlat1oppWuwtfDiV08SvSGx2EoZVeJNOeQqNuhxN9TgjY1UHD4ws57A5yGgE3dD8gGAhnNTlh2mfzZbLIODQg22HjfmmpgOGwWS3Q8QeDOpdg1MaCuH4sdsSJuKskN/baiWLDUe4yxiEzMXSL6fjq8tqzp/bxuADoe2QzNus9K6TsipUxILeV6JzYDr4rkAuVHgBkNeB6i/gCcTDZQLxrElKl29VJ0KZQiPY2Ogrm45DCu+cfdRqdZwm3VZYIjpW3APP8ZV7dTF2i7rjnX3V75fDKgFE1gFTu4J47PF74Te+BvcfiN/2qH1/fp/TA7TfFnTeRuoP6ilFfNywZznzK4e5jtfoiu8eGygby2lJmZEN1ZvhXVig2crgKkyO2VoIbQtIUpgIOHI0GgZbzF678+X/8K4vD5/FH8uWa3mgiuefl737/2LEjH5358J23333p2996/tmnoQm3GrUrV67NzDRNOjPiy9BeFxYWqACAHeZFj+vFbrUR+iA1jJz2yckpt9ePSXN63JBfMfeba+vJdAYF54mpacihYiZ75H+RfqZr4eCxU9WSok4SFjHq9D0TjgSKbFt5wxOXMuu1nW6DsBmQCLNESpFGS1WNbnFQx5JwadVaFXRXLmHqrLGxCEjE7fUs7N/nL1I9Suvy+Uu1Jhk9RChm5mcJkWwlM1Ceke0rV/FStUAKFo2mXKkmM9lEJr+VSB7YPz89NeYnMmIzE9CAbqLYMLqPTGy2FXSCoRIzr3hWugMqdeApIFLGSmmS4AvgkmJkMPHq57V4cCT4KJNyEHGRyP+QqlaRk9xMFspaplu7S6wHFMR2WlWZEFcLwJafIC/h16rVpgbEnmQvekluNVeug5pMapVRwzVLO2ktDCeRUaZgCfXWG00RyxGb3e5UatVMrlhpNLXUcdPrCzkBEF63ye11ByJsKToI4F2nw9UdQEEn6NjGXwjCYBUHdFjMLOFQjC4h+kgWvQgi0B56SfQQiX4qopdgF0kp04pEOCMEgEuqu7QbTKDXF4tS5RSRHsYsg0GgnqS562q1CvgG/ETaF5uFw1HS2HO5vMFixWtIbjr4ixmciJDVwGSpTD6bz9FmWGcmwyCZjDM+SYN32FxjkWC3hY4CSYU6yO86jQRhaSHBVCQZ44n03GTUbgbQ6dfXVyvJteeeOrQwPxaNRtkMv5HOawMs6cwWOpk2K7dSAC43i4bhMBN9BSXzi59M6k1kW2VLuUyWMPFTmW7dZVnxyYm1bPbJZY9/fUN6gJv7SF3JZwNAj4fm8OY9wBv5CD/tGBbxYCstlPnh5d9eOFxwPzN3dh/detiTw5nh2h3jbXQD7IcSChEHA8uZeBdjTfkAZ3d+8n/5qcAf7DLv/VZTModhTpSqzX/49WuleufU0fkylk+rhbVDPjBf3uNT08CFV37zy3/8x388fvz4y9/97tbW1s2bq7CFyAqOjI2HY0AcDxUv0GvBNlssLl7/mUx+aXUdDBRP533egN1lB46Ew+GZmSmqSLbPXyDDCyiF2I/OYsYRhZk0Gqx2n4OmUpGAmEuj2cLB4LRScpUmE1Jrg5CQVRSZHk0X3WDMLTEaLYn2VgtmhgaLd6FNzGUAzWQsGvN7vMgC43zYvzAPNRpRamiwSzdvLt+8MTU15Th4kAr25CgJ4bqvK+OXIhuNCFrXDpYAJGzEk9STurmyOj8zuTA7OzkecTvoCbMdqV+mgcZisgKDcPmgFUhPk+1EZ6pxJEwbnc5WMlYU68UGYE9Zj4SSai/FCyO3Rvl6VyRiQE6QRYxwZriTAp5wF6hDRMCRxAFxcNG6PqJFsoBbKoBGlmlBD1pwoRB3gbjkTJlxVPTaktpGcpukmpPaBkARjEQDUFRu1Mv0GwXbqo0OTrRSucaBGSSEJrO5IuBSZAXQIBhorZRTdziN6C/hDbGLah+ICqcXh+QIAOViuSS3CZBqp4YX8IHgFGiYTC+ozZKyzoRTBN+J00mtUFShJAdQT50M1uL6g8nuEHFwLpm8Qg7V69LAFtcCSQ36PMdkX47G5VCEotaqWbpW0A095HZ79Boj9U77SGO2W6VKjZuEHg89ncqncQgl4ymwndflnoTe7LN12w2Lse91mb0+9H70zfrAarak0olcLt1u+8mQ73S0iAlJTTGw66Ab8HiWr5cpg1o1akrlKk48tNFh9eAJgwAE1OFEjF5utNwvwXg6rhSsL34+rZZ5phH0Q9/LxCU/UtMj2KQH2D/f7Kt7gB3Foe4XAA37dDjzYNvxwI/2dWknF05TMRwPvAe+4AHv0SQazKQe/x6b7dmA4Y57rmXhjg12/FT3UhcOVzGjzmPS+GyW+YGOgATuA3lNCzbqQmbBuPISN1scly9dO33u0szCAZvLu53dttrsLMa2bm5v9ftRXzDwwkvffvvNN3/xj7/61re+tX//QbPZ/v57H+YLOeIdlDe12eFK2CTeMhhIqfaBtt3XocZbrnXqK5suVxHaBGiFD3cMDCSM40cOb8a3MZyoKfJND2+6XqnzsY8nIJvNEUtCIo9kecyxSbJ+qKDZJnm+Wi6CgcBEHAqiBZEUbCWmHCkaOgHjiC9K2yaQRil7rVfEqcnsaYZCQQRdoLLayby32vOZ5OTU2OzsDGGdD0+fpYJHU7Jz0D6U5BwgVD1XhgHt83lcHnd70NtM5EGH/D+ZydCPBl3f63SEwkFUAEgPl8RpoAChJMIYKNsAWgS38EcGA/ZPGjYMk0q3y3qBLFI0VF41Al3E8cOokf5jieIyES+C3Dl+KwOA7cXTJAeU0BgwC9jFMuwtrFqCZrDR8eyxnBCXifpnZgMOB8JQrFvbXNve3vb7glabUxqqjIdKOZvP54CkYKJzVxZzhSpeHQg9XE2lbWhrIesggA2yo1tRBjdxeSgTcLGwksFGoOpKnWIkgjNoFR4aZsCXYBRqndKVpH5j+GVzJVRHm2kq8xTwkkvpqZcJy6eLZEGpXBZo1qeie4Ohqh7TZbPjTuv3OkQkCSJxSRyEEaYUY6lyKwncoWDY7nAoRL1JSasi+8SxAXdsDwwCY8OYDga9AZ+LYKXFbAh4PTZbwGoygmJpV6tFNJY7YAIhra6ulcuVuYU5yPZ4M93wtXUam4kaYd10Mk72+7FDBxjquNtAhFCgoJwRvyUPwONykzXGk0Xz1AnJHxnoeCgRslY6gUtmldxBOGS3p9ub39XxM9zg/mc4tjJI7n+Px1s+7oFP9MD9AqBP7PTb9EN9mB/2Fd//Wb7kB/5upxs2+G4bfNYeUw84POzu3ZVVO2EiC2W5+Al44fLa5Y2ogZSD80dYz1LLSZgfcJ/lhYyGCpigVH3j3Q8HZmd0cpbUGJPNBqwBgpQqFVwO8GMy2XwwHPnBj35ievPN8+cv4neZnp4FCa0sLW1sbYvqbzEHrKH8VzAYBIbAHbHYXZ5ABCnEWhmlH6R0EO8pUUa+mMvabVa0ememJsvVKnQhnAcVakWRVi1GsQedGZuBmksfcjOxFXwahIU0favFIiU0iDhobJJ6g98GYpERZm5Loiqo5WHMBn2bxZBLZx1W5KxRiq6DqPBJEBkiIma3mbKZOJUVvv/d70BO+qu//sXFixf1RmI64ELQiw0I1ekR06kaBwat2WF2+ol74XcATsHxWU3kyOkv53N+lw3N633zs9h7Uqz5i1H3+r2C28R0i/dIRwwKWAMkkRAj3GNAkEgV0fMs4d5AplaAzi3jJ3QcZtmIgxjxJwlCumNRQQ9UqjcqzjAh/1DblViWgoQg3wjCBSyhWAjXxASkwD/RRnyyWo7Ht7xeNy6QK5euErGymux4ywIBDyAyE99YXV1tN2YC4XG9zlSpte0mr8EWQIZH77A6TG6hmVssIAUApWArhpFW3+lydIg4t/yIjChuFsE8sw1/kwkSDReleiJBdeBaxiETLh/5R+G0kQFGDxAewt+Dp4hauY0ueXttXaNO4jueL6eiBsRBrE4XEJr77PG6uXx8OTUkebha7QBwI0fTGwCv5WxBozVCYycTHgTs8ThtFmMA0BPwRUNunhoOi8+q3eqbDLpYNIyWD9HJdqtRrhA6q4ijioxFGEAmMxmL/mIgYAjQ7LFwiAq82n77wtnLPEA/+tEPo+FgBsZYc5yOl8xEIRvpWyg8yDMmjxz3nR2ZxQ0H+qFnyMOXCWUjKHly0wTbKQ/yrbCX4umTBZ/7pbF7Rxqwe6Fy0s/whyMomO0z7PLIbio355GfRm/ZV9vgxwBo52AZvR+j92nndl/R77s98MNmP6g2Kyf69ItUz3uPk6qrhs3b84iy9j58YGw2nEaPw0L1JwaDeAl+EuWNKwEwZZW4ajBqePqRxnnnrbc/OHPh5DPPzh84vLq+hZYuBY8QoDOJd0OPWJzFKkwd3um/97Pfv7m4fPXKlbXVt5566qmXX3754oVLN5Zuri4vbayt+oOB2fl9gWBES7q8w+1x+5KpHFhA+CaUNdAM+M4msFEplzCgHM3jcihGHE8JD50OjwJeFocdDToHKWMWzA6f9tQW4NvfaLRbIT07wSiI01H6m8ANDi1sDpI/9CcohGsrIf/SwsXSHouOuZ0O3DkBNF00eBF6Rr2dfKa11SUylbCCF69cP3fuHIQel8erqdSAFJgr8BbiftBMlP/MrW6fVGmyd1x+dKSNhXyu3tG2NYZCvbu6lSxUanRhJBQIhQIBv18UICn76nb7oDqZqbgq0SyMCEc2GQElIl6khLoURwGmUFCd3BMALOCUSQCdaDmKBWWSRWIexb8CLxizKSEVhbLOSglrEh8CL2kHRsVpTfFQyoYmUskshJdcgaLryfj21vb6s888MTczCZGlmE6u5UtWm62zMOVy2/CEtWul+NoK/jPQQqneb/XJeXO0+52BoQM/hsQqam/RnG7TQBtg+XA9qkEHuwBOyd6DuIXnhrVwfUB3EINpNewfwBBDRxhFnT6Z6jCeWc4SHDwSKMNh0hKVcC4AOM7GHNykt1gtNnrDoDEQAMU9JoiqbxEUrNwYqEYkonMEp9MK4Kw3u0UUoiqiZQiBGkRCTC8S9lMHNzYWDAY8CGqaLT5I4yAyetCkN9MGFjZqFQQUJKTaaRM2Neit+AyjsVBwy4ciFPeIkYn7C19jOl66cO4j6ODPPP0E+P70hx9NxAJazUmkFoBKnBGw7nIoqYvirRMIK9cGr1vRpjKCz3oN7jvXyKm51cpfHkyhyQ8neo+Jn+pfdfno/HDLhzTDuWi0+nd4dpY8pNM9Puwj3gP3C4BGR8wjfklfpHk7noQdP7/IkR/gvmqrRt8a924na4cbj87v2ST1UDsOONx9z13uvXDHofbcWEbXnitkofoRKR+UoxOHHU5iYJWJDXjt8m2KXcMCqWa4xw90dAYGr9N/ZXH1zXdOO9zemfkDVA9AwxdMgCWG0gPogAPBhzcT39MkCBdLlamZWRKMX3/1tVdfffXksRMHDx6MTYyfv3iBBPJEIgFWIQVYb+bjXpof9AdgCkMPknIEBik9hkklnsIBcUGROsTGMHW8voDL40+kMmvrGziEiG1hg6CdULYJ22cCvFgdgDiEfSF54G8CEFFYA48I/6OjuEC/10cF1cS29uZ1ql7YcS/BcsW8YXwwnOApemB7a6NZq85OTXPMD94/nc0XImOz2Nl+vwaMoLuxlLivcDjRX4A23Brtvgago5UUHgeiv2anG7cEUonafqeIdA37FEs5znLtOhCFGNNYNDw9NREJ4CSCJUz/EYwy0xUgF4APmwNnBPMAb/AVKPp4cgelJqhigURrETyEJTVqoWgxYRoBH8wqigDcF3Cr4EKJXeqblMnC01NrAIjAFclE+syZc/CXKbqZSiRTic1epz4ZCx+an/TazYOGpmVs63uDXGo7td016Xt+p63Zbm6vLE0dcDrN5kK2RhodETQJyZGyjufEAEARUUly0ZFKwuckES1ikxL8A6CY4bBXJepE3NPGXRDE0+2yEIa7xWilVf0ewUodAIVbr65VA2GCeaijIrjKiMgSKBmmEkcA1Na6RNZa4j4pI9Y94LzNckVOaDQLiRnnpFkifNoWGWoMWPjrPTxggaDL4xwLQ9hxOyaijDpdr9MyweHRUaO3xu5EUytoFjXZqUZ4zu/lNlLOhSgVQ4j8Pq3TbvW5XRGqpHq92Vw6sbke317n/M8/9+Thw4f/4i/+4pe/euVf/Mkfwu5uNInyoTolBV6COg8NkMijAsS5e6oTCNQH8kM8mouS+6h4iZgf/mQJl69GJJX18kdZIn/UQCcLhqse6gxn5CY+1FN8VQf/pl7Xw+vP+wVAD68FX/DIv7W3/G4XznLlnbJHv6q73GODPfa5y6K7nWK4+e6zqGf/4q+54VWMnmu4kIRw3rN8hPIdTLFthTfSw8brTfZ0vvbKWx9sp/MnnnxqcnJybW1d9AYddkpGEP0BOuBWEekbLcGXAeoofG+XS7XZuZkf/OhH//lv/vY3r7+G8vLzzz//4x//+MyZM9evX89l0tg5dBQxOT2NkQIaVmtpdXW5Umk4UXHWwXi2YgOwggatFa8IdlG+zyFiW61ISKPXEo8n2QB4xCZUSNVrqThmwZRub263uznsK5/SZruZyzFazNBduDQOUq5g9bF5UF40AUiqfj/YCiCBpWQt5oSAztbWBv3jD4ZT6ezVGzf0RquUosKKm020mc24g7hrutVWu1q31xu2RovvfhwbzVa3pKubDGZvIMzxHcTURCmmAaEEQ1spF8vpZIU6DEUUsfPpdJqUMY/T7rZDpEUTmbKubpcP3WwP1BAVunIQcKBkH2EdFRso/h0xemgIQHthuEoEk17ieuWSlbgSwER8IWgsKYUgRI6ZuuSlEioApIUDJrlrZ89dpBTV9OQMEoV9jQGtnZW1rc3tFFHOojCZJLPd3IQi3Q25bLVGjWMyDMyI1uQKm1vb5bbOBZRwuUqiG1myGEm1s2LI4a4AaYFvFAOlVTDHIfmoWd/EKJWWigICsAbgAlRhpHE5XAVrWULvAhjE0wNjWhH1xsnHT1x0xIWIsXIjAFScArDNjtB1uB2gZIHsXIYJpUPCSFpS7iFzgeWJmTkoWBHwccsYmWBBdLwnx2NW6M3tBlAbNw84ljgX6A1J6ArXnq1tbG8TekPGcBx/Txj0JnVX6pVGpVpAABynET4bgBH7bW6sLN68GvB7jh89cfjwQQq7vvKb127eWEJeKJtKE0WFY+QilmancjBhO5qp41bRciblPUDIFLlp8ZzxGKpPIvPcarVn1C3Vvywd/bnn/P1sM7ojZ7z/XdhSbeHwCDt+Dpc/nvnSeuAz3cEH26qvDQB6PEzv58bf7UWwo/f4yTTcWH0pqAs5CzPDcw03G26srhouH265Y2bH9qwdnmXHqlunu+ebURwJ8oGI4aFtEtLC3gAdmGG6/S9zWFRMO24DgRWsEqPS6hodrmK199b7H1+6thIen56dX+Aj3aQXRwVJQDAqqHmVy2TJ/o5Bhhige8KXvQZuDk2FtON0eX739362uLh4+fLlbP7vf/jDHz799FMuh+PShQtbm5vpRBwfBtLASMv53Y7+xNgWFSWJQ4FHKA4FHUOx67CSYcpg54rlDAlZ8H7MdvvYWJRTQN9pN6tOqwdghM+BCqbC16jWMXWiE9PrgFTIVcZjoe1rYW9Q7ylXLva7zVDAHw56IDiTpkRIjkNhaJmIWWQymYmJKaiwH509l86V9CYrGdwmm2R2gXK4HYALOFLoBRMVolXQXMnNBgPhKkC5mnwsq92JfwhXBYxgs9ljtHsiGGF/PRyZSCa2KsUCyW7bqWSlUtJAscJ3gkVvNS1O98zs/P79+6F7E15h4rACNK1WOoQqscodE4k/Dk3HkP1ES5DhofFYXBAV94K9SN7GD1QuFFkO/Um5IpSWUBxuc7/oSZT9gsEw9p7AULFSpsfS6RQFKKbmD2wn0uvrmxSH8AUi1PqMRMI2r23t5tVBp3X0+MG+2aFz9CqdQTVXbBmstXYfUjo+FIqo+QitWe2MGXHnKP4Y2oZXhliktqMlEQ++sw12ldR3AyYxCPVU1yKrr9aq452BDYPfhV1APLQa9AM2VNpPjnqzVC4yhvGPmRV5HhjriD8xkI3UZYN+jcew28OlCL+4WEYTquTz+WanJ6HdJOOJcDQyHgnZTDoOjl8tErCHfDZGONT7RlX0mUh7g6HMsKcMbzZTWltPxRMpuGL49iD143YyUo9MI8isWa/hIJqIxTS9eCmXjafi585+RCX4H/zgB5DEU+kk/f/0U8+VSw2GEG5O5D3Hx8fxXfGwgYG4fcAzbiX3jvGJ/5DMQn5yixlUAhCZuEyBu7e8QfQAq4ZP652XC0v3mjiAusteK/dY9pk2luYp0/BA7M6C4c+v9czX9Fo+0x18sDfoawOAHuxl73m0b8xjsOfV7Vh4n2NOfaKkZ+Ql9mAmjnafZ999PvUeKX+FOMIMf1UABOZgwjgR2EIIRup88vEOJRrSr0Z/6frNV9541+0LLBw46HQ6MU7Efyq4dJgxGSEsY5J5S+OY4XOehHDYt0SmDHoDaeq4/2G6PP/8ixhgKmb8v//0T7/33e+geWhDY05H2tFWs1kr9/vry739Bw8tzE4HvG4IzFgF2g81GNUZiMuxGPq4Zsit168vUmMBZvTswjxkGrKGISLXqxXiVoAITg21x0o8Tg8vWpxG2DzJ94KyQ2WvAcELXC1Ed7SVWiPox3/hKpeLGM4I7p/eANDGNphPj8e3/8ChdCZPESg4IbCeYbQgG6M6ojBXAs56Xf7C6pVYW6EASPH5Bnzld2D00KmcEKcCPgdND74MXhZns4smkdMTgjhVLuW7nZrb5wVtlgv5KjXSSviEcg5PKxiNZQt5HCJmc8Pt7VkBWE1CZxU0eLjvJDTR1ZhP8UM1W+K6aLepbMWFwzLBacEdhWBEPJHkf8ArTCNcK8l0uVRsBoxwsP1dvZ373sZ1ZHKVs42VjZvgqK7GWqh2HbVOta2rdQ1NjbVvsjtDk77xqbGJcY9Zk0xl+53WwGAvVFtW9J8CoXyVDPOuyQY5CpeMxmonmQrKubhzJF1LUrSEtUNTCfrgqiLZ20lcCltvhpYuQS7+IigAWsIvwkXh62N3JnVYwnpG+lD1A3Ec9VFibRN2szJkAYVoc+PlY5jqDRakJsuFisAvpXZvpVpsNf1gMatJB3QxDCwhr1NGlGTnaQo5SodlK6USd5AxwHgj6sitajW7SAGtb2wh7cMNQPKgRXVWuGQwqbUUCOuB7uVy+oPtzY319fVCtRSOhl544aX5A/s/+OAMY+mF5579/o9/srK6uby8urm5HfB78ZKCMpGzOnxkvwaSG1w1heREY7hSJoY6sJt4GMtvLVTAj7qWJXtOrB1dzs8dr4UdP0c3fjz/uAceSA/cFQCpo3M4BHcM1gdy7scHeeA9MPoS2XEH73Gu0b3UzT6xRHKsJJoxHAw7DsXyu60iWWS4sdIefn7irTdc+6kz7K4cYeig4hULALq1n7qWvxTb5qT4f1jNSiwLNZiSmeKZcxcgkB45Pu3z+/EuYNobtXq1WoZGTKlujBleBwITQqPpdjO5PKENl8fDNz1RF9ScSaxyOrzz8/uAC9dvXH3llVdInDl6cN8Lzz89l5xcXtlYWttIbq95vC4AzfRkDNsGuRUWaldIJn0yqDHt7XI9m5NADsbVjYOK4ga9FrwclH20fYSXOZu+1m6QyeVyevAP5EtFPDMERPhkBzxp0Nxrdpp1CjUMAEzQYSzRIGCOzieJneulmgPmEzwBZTUQDPsD4VfffC+eLnj9IRL1kchmRzbmL8hKOhMHGbwUXGHwWmDykquFfaSYa7cN+DMKAkOdSAkDddqZdIGzAwT9Aa/VbLC5PDZLUCHYipsB7ZzolBZXBF6EsfFJzgK+wSzyXy5bRo3o+uIS/hIUtymexbmAQTBsCuCknhQ+o8NZLgXQiECJII21ZyB/ztbq1XsGhwEfkltX19haBkdNZyNpm/T1WqmsgXbks3VKkLoHPp8hnq2W6/1Ko+d0+WMDJH/aXn8wOjZhtNpNFm1scqZeBJbB7y539XB4KSxKTrcPiozGoLOb9dTHwkvFeAJ0crO4R2AXZWCLWQcwMkKw9PQbv1VgKr4pg7ktLj7+L64gB8KIFhSJJIVeMIEoRpKbb8AlInCHRLAauV2ilyMY1GJvtvvFUrVQrEJzogdwupBSF/L7Aj632QJJqh2A5ROwszHDWMY6IpNksVWbNxGeWt8k0IvyuMUhLk+CVKSoMcjlzsopTHaXgwijENRoixm5INyRBahPW91eOpk6/eEHtH7h4IGXv/89s9X67genC4UKbqdEOufyBU4++eTH775x+doi2YuQe6wuH+QunD1cF3iPcSJdAeRRPkC4UibOS0fwxDOjTnTUrfQ+WXrnVcB1sAF9Kwl2SmuVfpbrG87Ijy9lUhrwpZzp4Z/km3QtD7+35Ax3BUDq6b95HfrNu6IdA+XzXeDoXsN5XkbqNDwFP4fzzOxYO7pqdH54wNGFO+bZZnjwHfNsufsILFEWfqI9WHSYDWQ3yee4To+TgE9fnC7vf/DRyvLa0aOHx8djQnc1UVOih7HBZe+yO/lOx2wTtkAOjsBKJp0sEfZyutvtTpdy7FRUoEiTwUTeFiwTCUZMxC6fP/vxh+8V0tsH9+8/evQYu3sD/gtXrqWT23gjYGOgPRj0ejBmPeCFTuvSmaCwFkuFbKGIOWLCNcSzh0mqd8ULgnicnfKnkIhhV2j1FFfCXkKjabhdgoFwXSjeCCxPu95r93EWNHAFUdIdS40HizJVXDIUFsgi0GSIJYWCse1E5sq1Rcy/2xfJF3BRyYc7BQsgsCJHg3wwmUOQj7iLMMG5fAJtmHDk75CE4ezYWpLJKS2F2UaIB78UrjWZOji39HCDcMHgSCA+5vYFQwEv8TGsoMfpqZSrqZRET0QQ20O2UfKjj6/otBQAsdUb2o2NrMPl1iNfrBkUi0Uu3GzhpO1ytWPrml2Sa2Wz4qkbm4GYNSAiRsyPaJfDbXDW2r0+pULIhmtjihuDKkhNb3OGxqB7a4za4NhMcnsplcmJFoDijMGHBHkFWIkzjjubjm+j14SLqNiqFIo1i9tGZdNWHm0but5WpQRXhyISlFERgjOjETTGFTH8OAJeH/AQN1duR1uYWEwk2VEIotej2JZoc3Nb2QZkwC78RYoAICWwzynQh84XIIXolB50YuYucFiQOr6xdCoN6QfIRZk3MgJhnsfGwja7CSlrUJDXxd2w0iTgD8nw5Vp7cXn5yvWVRDoN+T0Y7uPjQfYRRx0XyphBALESa6PuiGeIBENAaCaZNmrBzqXVRUlgJEuOO+cLeI8eO/H088+TMPDO6dPgxYWFfbEYBWHKRCRPPfFUMZvcTqeuL5EFmP3d+X2R2EQL4QSLZEvi27r9VEq4EN6S4CFFL0C5THHNspnyhLLhfcGa4eM/nJE9PzlxwNG1zA9P8ckN7+sX+36R3e/rHF/uRl+wQx5qY4ddPXoHhwsf6qnvcfBPAUD32PPLWfW5O+hz7/jlXNejeRY6Te035T0jOcmjk9rm4ZJPuYRPfvCNbswRdvy8fVLx5wzn1c3Un8Plwx1ZzsQXpvLCVf+KgQc/9LCtFJHAtBvITrFf+fjS62++Cx14YWbK57RKmdJ2m2ANNRD8wRARFjJcEB7ExpB57rDZC7k8YQ6oqXy4p9JpLF8wEoUhizGDFe12O8FP3/3udyMB75n33v7gw/dg/Jx84sljRw7FxicuXbtWKJavX72USmw99cSTsViE8BMhDjwsTaw3n8GgG72BmpOci2QaOBSYUAwHxq9Ra2rsOtLM+eaGdwzJyQW5WOuoWU2SAYTxETw26LYaBKdQYhyLBkEt8WSCXCXuF/4JkuHpgUa9LmbZZid+ESfuM4CngvaPHsvKSfA/IHqEE0KD+0l8aAgB4x2ww/hBWYdfeH3IxqcsPW3A8eDx+AmCGSTEgUxjH/8TEkSci/ZUaw28JiaDBtVrX8gXDAdq5cq5KzcS8QxeptOnT1dK9aNHjgMJCvk6Jcig3LAzxchqXTLuIR438X6hptOnulXT0NM74R0Z9VaISnqnt0siOtlPFF5AKAj1ZCKSJJtRKqveVbLBG4TzsjUqWLTM+Ecg6hq1IV0kmV5udpvmnp7YJnKVwqxuV4nTmSQ9iv0qXl+kWqxtJPItjbVeBv1USZWq+50Es1wuBBC0vWbDaQF+Ut4EJyB1TkWIiAkEw9hDmYlDDnokyuEF6jf6TVGiBBIqtS8U6EP/am0OO5sxgX5EvwllIIN0voH0dLMRpIL/Cawp97wCnGqRQ0eRDZfDGIt4gSDRqKAfHgVSCU2Sw4geEUwpsu5hHpnSqeLaWiKTK7e6+nbP0OgM8C8SvoOFTPTMbjUGQ94OZTYY0gBiQFipsl0rVTLabGqrmCuC8KLBAB8DYHTQ5NpG9r2PPqZYBmlnyCKk83k8ggTXYoHAsVNPnfngzXqfDmutbSSee95cb3R6DvEUoq/FkKOFPKQ8hvLhcVsAWnkqIUiRXEe6n8qDl6d2+NQzo8zfdt4OH+nPNaMc6nPt+Q3dSe0QuSmP6vRIte2RBkCPVE89qsPpU9pFH977HTHcYM/eZt+77b7n8uHRRpvFlsOD77nX6MZ7zqu773kQFoo1kgmlYHkX44fn5YtJw+mwuhl/4+0PyFt+4flnyYiB6qEbdLa3s9RCh9yKxK3DLTwY7Ivk9KBlguuo3w9FwpFIhHc6X7osGRubwP7BbwW78OEO7MAuzs/P14pZSLywgD/66Mx0oXjyqad9gZcWl5bWVjfIH/vwg/dABmNTMY/XT/aVdWCDcGu21khBwn3C8W0WE9/IoA2qgIF32kq1Cy4f4gnpxwQ1aBh4jcwkrgVlILquYW8add1cqkZxS3L1IQhhm+Hu0EhcHVCSYDVBf4VaFPCHVjcvD7RgHX2pQskwrQWQYES018WrEWJLr1rFw0V6HB4igAjeHcAQfoR2p4l7gGbA0kWKRqe3iO9h0CUVyIdCkMNJBhjxKvFWUS213XY5rJcvXdH0Dxw6sK/f07/z3t8k4tnp6elEtoomoT88Te9Z3cGNrWSbChLQtK3OgcaA5DLyS1weWApLSkkHgBfYsIdUY3ewlSlQNLbtdeGgajU6YvlRsxQg0C4XoStVCsgg1wkSAtRkMhso5GXoFmHdkFKuX1iYI72OZS6nE2yILg8VXnHwUaYtOjmZb2zVm6lqT0sOuqjZoCstAR2zg1qkmg56P4gbgfzweCnsHzHh4BsmIBEdqA5gcW+g9CO8H8qZyfAGFkNdwgPEHWEzRhEEZUajSSt5YZLspjNa0RrotajDhjNR0HpvwKCym41j4UkGFT3PGZlxCfqhVbi7rLougch6mzrrIMZWp9XV5nI48/BtSqUQDgxy4idSRnarHpBEM8DN1Uqn6XEiFU1t2HIhpR90unWarnv61MmD+w94fB7UzBkS758+98vfvEPB29/7oz/GG7edSuu1PXQjo+FQs92JTkzYr4ccncHzk/vW1lZfe/2tn/7oWxE3vHk6SUaITERnlQnHHdeOFildMZzUVfxVlwx/3m2GvlW7V/2752YcSgVee679fAuV08mFKAf/fMd4tPbiir4x1/JQe/aRBkAP9cof6sF3D757PNIPtSX3c3Bau6N5w5+sYtrzICwfbrbnBixkA3X34ZZ3O9qeR2AvviRHd1HnR5eoO7JErMJgAFLBM0GBa3cwnK20X33z7Xgy//RTT8xMTAz6YmDAImwD5Rbrl8pkJdDjtKN2AsLAHwJxh+95pOewYoj0EF2qlepoQ7MNX9NkAxEV4SBYJOILJ06cJGrm9bm348k33nnn+tLyM8+9dOrUqRPHji8v3rx44fzqys1KNR+dmPT4YxqDZKqTNUMJLyrCo6Mr2j86TalYwBVjd2L4HDiZILdifUWbhk3lvw7Op55VKm4i8UKVDCwcfaLYWamqAVvZ7oJzDQOF+mKGbJmK73kwB1YZdUfxNBms5bpSpVwJ5fj8QVKopNK43gDswymFd4cPdiw6aAPpPcFkSALDnoUAXqvlC4v4xrhM8QwheWczwyrny79WbZS7sLvNzbrl8vlzbpcznSjcuLmysppa30qV6oNKWxOamndFJzR2tzUYDVps3BfY03C8JaRoslBSjZP6XJIwZYMXJaXNQDAdoBC0lVIZxw5eDSBpQ9fvgI2o4YXsYalYJypZKJeAEeTqsy/55Hqzrt4ZwG4BO1Lli4hkFSCYKQQ6ABo9aE+vE8RJYI6zgBtIic+Wi3qLk/vLfTfrDVSicFisZL85nDiA8P+hpAAZmUlYL1w4gA9la9wqdA+xQJawHNgEf0jQmVZIVOyIxA8OHtAKBHZQqQj36BAuIsVLJHNwLMK3ph/wRcHlwe9oNulDAfLUQjQDeAfWpGZqpZRD7giaFuwwbiA3R4vkpWTjQdTSAkAi4aDGaN1OZaQb3W7J3bMyKgSz0RAsOalepQJezrQpGgZNdZrNqVjk8MEDczP78D5evHTpwzPrkK/fP3NRb3KG/KGzF65FIgEbSf0GXSKVZrQT/3MYHUeefOaNN95A6XPh4OFstri5kZiNepW6I9DsJDGCp040v4ljmskHoP6MsOfpHKXrboEknsrhxKrPPXEQeR086OlhHPNBt/EzH4+++sz7PMwdHrX2DK/1MQAadsWDmXkE7/QXfHHsuKLhz7u9OHacTozDyNOo7jW6RO330b1G51k7sgumCPMrf3nDshkHVv4qc8oHnDgERCM4gR7c2Us3edc/cerkyRNHlq+fNxoIEFgXDhyxGbWVQq5aaxYQneu03R4nxglfBEUriUqQOi4sY0X/DQY0JwKXgDBwL+EbIC2aBCCUc7GsZDAfPnYiGPQfOTmwe33vvPfBb1799dLS0nPPPPPUk08szM+dP382nty+cf263rhhtrnNdg8ZZQgT+30kXCPnx0e4fEFzMcq7HUayQcp2tEmtFvcPIadatSJ6gloSu6gBnquVq4ARNsGbRe2veDzuC4RxomB6YRoh5wixiY2hBFHZA8K1TmJMjhbieYgPSe5Sj8rkEJBxFdhdTvqW8lKYXNFrGUiaN3xtRHwkw6nTxsmEYcMGUwWqUymrNwKdJICKKPi0Gwj5mYzarRx61laEf/7pldfOnrtSKDcor7meyJDjPe0N5uuNHOE7eCJGS0fbpKyo1B7p9VxuL3EirD4lVxkgFJeAHFMqV27evEm5UrvHJWWkcE8ZTG3IPlCXOp1MNqckt/cAZGTCk73fs5Cq1iFVX9vSN3rcsSr1TyleC7BgAr2pzhg6BGABSoDhy33kJ4UkNAMT8U70hPQ60tBa3GiToeeQIlzE2+gnDRmEtToXIUnm+EuoIcr1ghQRAaw3ajhvoJ/TEjbj3in6hERdyZIDQGqFLgaXvK+BY4QMAoEwfnIoDu50weg2i/q4nat0IzSAC426XZp+06TvW1x2+FvrqxupdA7PHBix1u4geilySUAoXJQmI+U+YKBbneC9MtHLkM/r83gbZbBduVDIFYrNYqkeT2SA8oMOfqbWkaOHIgF3IOAK+vxrG+tLN5d/9etXSCF88eXvUeeuUOt4Q7GrS4vERA/tn2ZL0v9rlarbagVlzi/spxv/4s/+f997+aXpgwvw50CoorF1y1kispxgPsGCKARwn8C2eGHlKSXu28dpyTyT+pCq8+rPewMZtuc2Dbd/4DMcnFM88MM+PuDXrge+MgD0TR1/6nO7++rUJQ/1qb7b4PvUk35yA77baCwvIFJX+JRjJW8LvvV4H936i9VmYp0sUVTglO3lnaIsl/3xYdAeZtRWDVexkSzf1VZYwSyVkwjikVRkdVdRopHtlRWyktcsnhoCR1JfE58FX9JwRyFsQAzmW58Xsdft3kgmLpy/8sGZs2aj5qXnT9rMmjNrlyCT2snQ3lrMrq/F3Bbb1OSrr799/dKFSCwKnuE8sdg4X9s4U7BVXB3lvVvdFo4Z2EK9knz9Q2Bxw0i1GZudfhlXQyUf9HmbmhLFI3/4k987evzk66+/+fHpM3+2fPNbL34bV9D3vveDTGr79EdnL99YLlQ23f6guGTs9lDI7fE64UgIbRTvgtXCX7jYkDm4eiNJ6/gV4E/TWbiIKiXwCO4Fqo+xtlIqYp8RYiYRvVKuTS/43B4fRcWoOQ5QYyLviYy1y8tXK422zmAjVGSzOTC9VPoiSIH7h2QdmEx85QNlAArIz1TR50E1sNNBGolrJz2NgBrKeNqQtlqqIZUEWQXogHXDHBKwg2kF/ivU8sKm1fRefvkl3BJ/9w/vXL624gpOQfGVEJ5GQ60P/GnwlTGTUnijgyIz/Cd0mKm0bjY47UZkBvTQqInTifofbcJ5UzSQsyTBJpaLzKDOlCvA6y5zafQDJTnh4YB+AGlSWJ1j9xAtZFiIRgD/IZBIcQzEb4JlGELNYrFkMflV4jnQATpTrG8ZD0crba2ZUu8UG7HqSf7q9AkmaqiH0W3XaoUmEaZWZ1Cv4W+jtJcLDW5GJOE4xMMhBkE/F8Fo9AIYKzQdR47RCkCmWEm/Q3k1yNRQZAzkatFzeLOQz8H1BQGo3aqCFvwuJ7cP6R1yUABVVjME8zoOPjxe4NBWq50r1jZThWDI3OgXfL2mw0pGHEnmVIDt4DJCy8nQ7rdsvQChrmq9msvkjP3E9lqTJLccJPsKMTSfxxrwOYOhUHQ8AvEfzL22svLKm2/m0nl0mch4f/ut99OJ1H/1X//rj86dr7TrTxyaq9Qq+n7TbHTPTo0ztImGchMbleKJIwevHzmYz8Sff/KA34VQgBFpdcRCee74TiAJjk7AZYhSA8E/nQZnm45gJflorKV3LHCgkITi8b4NaEBL8voYPtDyUMukvivkPSBvAHkb3HnolQ1kG+V9pP5SD8FWn3Vix1vvJclvFX8nR5A3lpxP3jafnGTt7Ul8gY+nz9EDd/r8c+z8MHf5ygDQw7yor/jYt56ur7gVd05Pe9SXxZ1FnzanbM+TL4wHZf7Tdti1fvdJdy/ZtdOdBepJR3uSeYkIicsH5rOEbJSPUeX7W3RTYNEQAOnzCU8iVTqdvXDhElXBf+cnv7swP33x3Gl8JBMTEwXcCLkSme4nj546/sTzVN5+5/0PCqiv1IBQMgUCITz8yNLwkuS7n3qbqOwJHdZgpSIktr/bc9paUu0SXxEMkEYaEFCGTdvV6IKRsZ///OeUy7hy8dISWWdbW8ePHtm/sO/b3/7O+PT8hx99fGN5BZ0VSqhqxkI4bLCY6O3hpSApHcI1dCKAguAMoIDDjuGlzid1uCFnYOdUvjZWB/lpG2EMpPZ63WBQxOggvggjx2xOx+PsOz07U200icrhNQBg0Ga6Fa9Sz4yyEe4WgTiYWj7d8fT4/W4wZTqTk2954AmhNZ2GYA7f+qLFDAdIp/F4XOKi0FJz3k2aEgQiiOGVEuzdNo6NJ08ex6fx3rsfVMoNm9NfbbbtTj8M5n6pRNAGTxL3DW+Eltb0+pVqLa9NkuSPeYUIY3dYXTYabtborYQn6WagA34PqzSas3eBZcVKZXVlE64MsR6v20cwj7uvtXfg12BwAUS0GTjMHSGoSb55rV6h8OfMzEzGZaPAJ7CpUrMhNAiGoB9g0lC8HK9JM416Mldqog0wbWw2kI7Uz0KeEJAHCIS2zC3AwJOuz+AZdOoSfKQ6PEleNgQFyA2UowHI6Cswa7lYkEKzA12TWB2X0euRjyYJ5DYHuVp+vwdJRv2gDScpHArIJTOWca116aGOHgloqpCJkNCAuusIDEFdryI6YBdpBvA+ETGJrUlZUyhrJMQPDJqux2kq5pIrNy7l0+5CLuGGOmQyzEyP7dt3IBwZo/IdpVcARDdu3NzY3sjlMtzKF7/14hTymBI61C9eWy7ms4cOzv/TK7+enJl58qWnt7ZXCLnil0L/CglKOpZQHXS3hblpi1Fz6tghUskolKLDA6T4LAVxEsoFPUiapDiBAEbynOK01AvKoT/x/ckCgTSCbGSt/GTmDiTiJ5O6gTo/+nN0+eg8xxn9Odzx8czjHrj/HvgaACD1gbnbWFfX3v8Ff4Vb3u0SvoQm7e6lB9EYvofkpaZO6vtI/Xt72c5/WSsvv09Oey375Ba3f/GRpsa92IVXK8ae72+hkipHlbiFVgvtIpXNvvf+mbOXrpx86tmnn3t+GyWccuPU099B8OXy1Q0MGGIlS0uLgXDs93/6Q0o//vXf/f25i5eLhSzIAIPkdnmxpsxTI4JoUafZMVuN5JcjJMiJqk0pF8/XPzYM1NHskmvfh2STzpdIkvc6HUdPnFzYd+As0aArVyAGvfv+B08//fT+gwf4HJ+4cOH64o1KPp1NUo3AhN8IiCmSfkJKltoC1DRXqopp+JhGdxczKj4PvBYI3VBgm7CZkoUk1THrdegw6Lhw1dwDCC5UfSJ5BxvkcDi3Ehn4LlwpzcQ4N1qoSJfRHIKyQas5EalAZHhbzG6uFGyBKXU7/MQs2IUl0Ilg3nBABonHTe56lTR4jDHKQK2aKBE1qvyp432h+tdYZGx9hcjViscd7um7xRYJZxaKdFqtDmI9RLn4ws6mU5CzNOSQaweEeMr5TL1RTmdThJAILcGm0hqsXcm07wM6p8ejJJeRXFUqwI2uGs1SxMNmp0y6D5NcA1f1OladXVx2Gh3sZY4hCjWduk7TqtptlUKyVi0atcgzJtE5JMNfAjMDPVvgREnnMmYzQUbiRAXArEPn05u1Jp1JZ7Piy2yQndYFzDgJL/ITWi8OyFaz0tC3kRwCi9AhsI8pzYqnh9uNXiYIVVdDS1NPYRDI5jSq22jRtySXoYoTDrhDkQjKSQEfojyEO4ED8lEhEbF2m2MBTNvNFv4+XF2MLxjfuJYcdmq8A8Tqxkgg7I86bdCom4Iyuh2UwbvtXqlYpDx7uZwul5OoFw3c+ljMd/Lk8aDfB1nK6XQ1mv1SuXnt6tLH5y/wpBw6dmj/voP+gHtiLMph0PP++Z/8/N/9j//u3//7/8f/8H/7H55+6mSukMX3Nzc1wbCHeQaaKVZrDH66DTo40cN+s2jS9j12a6dd5TEAjMnniA7+DwmXDEDBIkyMRkYyg1X9yaBVJxUAqQtvP813/ZfNdq+7vXD3+2P3tp+y5HaT9t6MtbfPtfcGj5d+k3rgawCAvl7dzfNztwY/Uo/WfTbmM70Lhsdkr7t3wye6527dNTwUWzNPn8pfee3KJB+WitYcf5lYooSuJCKRzCSXV9bK8IXb3eeffc5qsr51+mM/FBX/+NLyos7sInhid0IgTf+n//jnzzz7/Mvf/d5/+6/+xaGPz95cXltaW1/fWPJAKvX4XQ5JBapW6oAq7C72PpfP4oTQ620EY2xEL6g/0GhL0XKrsdJoUbGdlvD2d3ad8tF88MDk7Mz1K1dfe+2Nv/rrvz167PALL7zw7DNPzc5MIpADU+fG1csen4+MZ6w7EtEgD86CNawUCnw0Q8rGxyMsE5uTcIzBLdU04SynMykiEygGkYtOiMEtRaYkew0bj16dxWbz+wPwgy9cvEK1DY9fEtlEA0coGl4KYCiQsUtaGYhB29Ph4KJf262KxWRAOBFQxQaEpzDJ5BThZ8AQKuetd7DcXS5WY7Na6i0RkMxnczSYcB59EvIHjh0+ghMtXdrAscTtgZbk9bi8HqfXbU8lt/OdGgEcSoaN+SIFNwrU2kqjkS/nWnVzRW+gyJfJ6mh0+kg2g1VSqaTbNUfhjiwSitV6MBzBABPuBCfhnhp43XQvd5ysdhi3iEfjEUKqu0JgUICHCdghBTSIz7JWolEEpjTQmChzywz+Foqt4RbDbQYOAhdWG5W220ZNWQp+2W12o1NH8S2cQIwxipGKSjO4Ax0DnRGASJYceJegIVlrlTIYuYObhwYAkdmexsAutxiMPuDkzDiBOap5wdZCaVOv7ZoMKENaK9WOlDOjQnuXNHiY5V2aKlDaIqEigHXA7a63B3izGMOCDoEzNrPW6arXyvGtDTxwNCmTSfNcEJabnRyHgjY1PUFNlfFYFNSFs+fchfNbm6liubG9nXO5/aeeenr/voWp6bFqtZRKpvE70lTiYj/7+c/+7b/9t+uryz/48Q/OfPQB1CuAUaNetThsBDs5PsKMfbjW5ibjBN5XPp1yTQTBT0AxQo6SPidAReres0heFHC1+x1dF2YWfUZnSXicWW4W2zGNvkxG59W1w7/K4yxRKnWb4e7KEWQrdfnuI+xeMjzmcGb0aMOFj2d+a3tAPJaP+MXfz7B+2JfwoHrp/o/zJVw1jeEsO07EslG/Dh27Y4P77OrRve521cPl6gy7qDP8Hc7zbgX2KG9RWcn/1YkXq7x5lUlBQR3scTyVfOvtdzfjCdT8Thw7efzIUSquUy7gyMHD1xbXz5694vU5u5rW8aeeJZf7zdfefOed99Kp1MK+fftmZ/iAXt/avnJjcXlpLZ1JdFpUdELWmYql5Iih4dOn6GW5WGrDFFakXVhEgEDiJhrkd/voBtEMIlk9TZXACWQaQhBHjh9DIvna5SvFQu4f//EfZ6enyRA7/N2XAUDvv//+9na8UiqjquLz+Mcot2SAE2zyutySE47B0bbMFiEsk49Gn2NSKAIKMII8q/St1u3xgJDwBgFBELUm+QvYQfyrUmlvbMTzuVKzoyPWNDYxCaGVECEWCccDFhSOMGcRLtCgT3I4jGSibK16CYcH7gTifS0NOkN24j1sj8tBynIRTIJzAx/HoINpDH4ifEb+eL/L3wH2FfCUurmJ/8xq90zPz/upXzHo2o1akvc7TqN2IhALe/fPzUDXBZEgwri4sv7RxYuLq9ukxZN6hKcHGcZ8Pu10OShnXqjkiLsxCmwuNxQbi51SriZJ3bah3NgmTZzoFA4IPGTUDqOMSavaBiHKyJVCFvpkOo909OzMgcH6Osxxtx+FRqtmYIME7XTmkAq0OWE1mzRIGEl4UAI2uBIh+LjtIDpzQ9szGghBKnn+HejX5IkLJwm6Fre40UK1qJ3JVVPJQp1kQmqFIUZg0gBE0KWkOorLZqVIu8/rZHwSDIICVaWyWyUHXwp5Ahjn6HszckAI1Pki4U1igAhIur2MGc6C4pKzRCo73GsptQWzu1nON1u1XqextrrYqJWkfIpepDshe7kdSBjg9OFe9j7++ONMJqUnf63XnZ6e1OjMkUhJbyCu17148SLDw+2xA/hq1G6T4dQ/cer4P//nfwz0+fbLzx/cP3f+4kUt8B1J8W5HJ8IBlkFNpI8YJTRQJyJSUq9N+uvWOwEynlCe4cVzCoA4E6vkLoDR8LLCCxL3EPXrhrvcen5hv3GblKPdsT6caHh8dX50iRxCaYy6zXBLdTl/dy8ZrvrUGU70qds83uAb2QOPPUCfflu/ksdj98P/6Q39wlt88kqJcI0SAD/96MN3kDT+dnRs9JhsMPy59wzcR3kZ3dnsHmfFGvEexxCyB/RhqhBgCykiSsCDF/1PfvLT9ZXlK1eved1eojxnz56n7oHT7aEats6MJozO4QuTzIWxvLn0K4PV/K1vf/vwiWMHDhzY2Nh6+90Prl29mS9kfd5QWARO9NlkGg0/GCDo10HA4UIU/4cLrgPBFBi5ZiMUVwtlLqAX+8ntdtgkydlpoew8LGWs/rWrl899/DE5Si+98BzVJQ8fPDQ5PrW6LuXLs+kMDY6NjRGVwgBiGYAypEVJSM9gxBSTVl0qlLPpJCEwhIVAMzCQlORn8AqgRN9o9TY21vi2P3DwSAn0h6wvBJpsgbQliqTSGC1qNE2pvY4WNf2FR8xgMcL0CQWo3G7POaQ+F9U2OCtgSKnRYQC6wbjFSDd0emrOE6qh8mgNpk+9ChDAY4UZY2GjWXXZ3MnUdr/XDgTdJndw377Jap4AUxfpAKRqDs5F9v+ESOMRSDj99sBqlZyzVKZ75sKJv/q7X529tKYhl4zsKYOpWixBwYEjC1vFarFDnbETzak1ekKSalDpw9Gz9SmL1hVdbPE/6PQAEFwS5LoBjMRrAX1eb8zlCyBIzDYLQHKSziY8XIg+DlLPtAMjSoOWmxvgOByHOF2AuaVKHWiD5ycSIhVMdK4haXU7DagspK0J0xq+i0Yy3qkmmi/Wc9kSxcXAMSLz7XXAvwmH/BPjsQCuJLNhQKCoVgKYtrp9XGXJdKpUzJLxTv0Ss9lJN5JgBVsIdMKRUR/gQhAhp0fhF0P8MprI7a8XsuWG2Vwv5WhWtZwdHwuhoWgLeJ584pgfNr3dQXYaYgTLy8tb2wR2hYDj8QVAwAYI5mguaHToX26sxy9cugoHC0SCP8/ldtAPMOR4yqDe/97Pfv/f/9//xz/90//tv/83/wdUNGG74/WE+G+yWBkYFgt3XEhmoDIClKUaxdwoISw9z388esAhHj/QGMiQaKwKgG49sPgtaQH8KYhat1jPPDQCjgCc93ioaZiy2d6b3GPV3jt8cunwnTO6ePfCe7dhdN/H81/3HvjaAKDdw/RL63qeuq/k7F/yc6he4+2T3npJcemgEfnvvidFRU8wjPq2Gu29PbtR3UA+HHdNw+05vXjSFQ+Q4vQB+Ej5bNzsvNlZwns2kUgtLa/mC6Wt+Pbs7OxTT5569623C5kknIZz6XS5mJ+emTXaLJNzM5V65+OPzzt9oZdeeOH8x6fXP4wbmu13330f7jNhm1Ao8i/+2R+vb2y++/6Z9fUtUr1sDpfQWvFvNNpwhGsU1Ww2oKQ43B5sA16Zjk63tRXHhGAMYMi0mnXKOQVDfrotQChISiI0NYeOkArEcTbiyWs3lhw2K3JBcHX5NL8MTFu8mc5kIKzAJSWwEoqM+wMOSbmR49czqXQqsb2+tkzCO5I81VqZ4+AcIpUdmEW30bRmq+71+AE+N24uIyGDOkzPQJUJC8fHqnk8bhpGSpSk83c1RH70Na3X7SjmC3y745jBhkkgg452i+sCcNnt1NHuExK1VsdJSfviCPBv8FdQVh0JIqCF10eIxo6PhChJNOr264waqxO1wpDfZkV/KJsMh33f+dYz87MBsFcPlncXEQFLt64Jeq0//u7xiVjw//sXf//Xf/eqwxPWk+PtcBbLdacDl41X2zXWS81qSSRzxP9E3r7FjAoRE31CrIj7DjUIlhN/pQdaNZG0hsqjG8RTiVQuAUasNyruHtVtMdjivJERRLZ/owq+DIeCqUINhja57aL3iMONgTvoUNnCGvLC5QXqEWcFxHC2ShXQU4b+RPX1NIVec3ju6tVyDb9LKBbBfzg9M45+IBwp+gbGOf8xTvCZlaotypwVSnVEmmDMFMsdp71Nj1kUqUPGL+xmKMnoLzC+DHhvBoZ8uZiOJ5ObW+sbCSJ6szPj0OWtNtPkVGxqgkIuWkjK4DCCdOtXFqkIBujHbXP8+LGJiTHoULDYSsWGwSCRQYrZHTw8ozUOFm+soPWglNOFatbvknqn028nUgf3z1OR97VXfvO7v/u7YxPTxfKVXLFARRgeNjQCRBe0loatjkQEegGVZpfLCXoIm0oIkkl5M0gaPBMPIBgId5AsV/wxgn74PlDQj2yxc5IHGgA6XKy+LnDHsb/8/y7Trc3wBikbKAeRd8dw5i77qV9We69U27z3usdLv9E98MgBoE8dx9/o2/GoXNzwLUODRufv0b4dm6nvlB0LR3dn1fC9I/O3Xmijm8g82zCJo0fCYPLdOZzUVeR8qT6AazduUvxhazueyWT/9b/8b7H6RIiOHz9+4dIVCll7vIF0Ku4Phfct7L92/eqFS9f+6I/+mLJeiX/6p/ZA/+wzz0N2vXFtqVjMI/HcqNamZub+i3/284tXrlIWO18s8fWMkg4cHSkRKoUpRCYYr4nTI4xp0qeqCmHZ5Xfj3yBqEe9ksW14Gqi7hM2mJBnKK95gKOgP4b+5eeNGOhH/m7/9zwcO7j927Nh3vv89Koheu3atWKbYRgVshzXB9GKnKRaG2M/y0mImsZXJpudmJ+kIzu50eUA/WE0LFrffw/NEHfvjx56o1lqLS8tYwWBkAnq2fJdL+QUT/Uh2OAgPjT96FfyEnkuDXLBeB3uMr8VhF7cWsAajSw/XalToLJLxjweKbkSkmPAK14ubAS9RrZSHbhIIeOAwOV329NYG19jXmyfGpmATc4E2naFZzkUDnm8/98T++QDBu0alANUXy9lAbZCEMP7q9IcWYv/VH/3utStX3/ngQmhsTm/2aDstAyQblAoLBTK8QC0oB9LDyCBVjFJHgswmJghSxVIFR0U4GqPxrYYRnoxe2zZq2sntFSKMqO7gqBOnBLXKFJFi+gqvGGCOwJSX0q3RcKufqVDIVKkIC7YyKGlWiINTuIJYI5Egv5tYFnrK9UwmtxVPpYA+pNa3wA8mv89JvhXcsnAwEAoHBF8ShwNxdanbQfFastlbxVINto/EM6VYF7qXiB/53DikuGdQmbvQtqqgB4vRDp8/vpWA2oWvbntj89zHZ9HyDPiFheb3efYfmJuejOJWJOaYTGyf+fgjsgI97kCr3QuFgtHxMe4CnpYqbKYq59elsxmwaV/LKVrhWHR6ejwQDOICTCWzUOMJVhJxg4OEmkEqlT518kmCs6+98c5//2/+JTqTeCgZISaLplauI7uAGgIXgloCFCjRv+KRo8WEDIHKAoAEhsjjSX6+MjFulYAXnUwWGIFFmbiJFMWQvxL1Up96/jK782HfY9HtTW6vuoWM5Nm/vYrZ22tvL3r87+MeuL8eeOQA0OOhPHrjRh7zW4sfbP/seXwW8k7jXcZbjLPyQ04KRFFeWDsbICtlutPs27PDhcxwkDsbKMcc/cm8uvGtcylnUndhOe9QdWOWMAnplc/2rrBSIKywASQMlItv3LgB0Ekm00dPHI+Nj12/cWN6fg795quLN/ky9vndW/EEpbSuX7kIS/rgvgNPnjq5tLJyefEmmerki928drV47iLFCuwm2wdvv7t4/eaLL387GgoCLBDEu3TlOiXWaQbnN6BLjEAfZKJ20wY0gB+dy+FnwllgIbhmNaOqly9k8nlqqtbgvsARRqzZYrNSlZ0Ea6o0WOyumX0ebBW5atcWl0Fphw8fJvqGiCIltK7duE6eVbPTjkTHMIFJamultkFGRFuIphF1QnIFCggBKzYDkGBHk8mkx+2LjY2f+fgSBcapNQGFttZogR4CIT8GDzjldruWluDedir1MslhiDqCDDBLRIvYlO3BCthmloAz8KVAv2EDST4DGHWhu1QVqybsYi4aZSBMHtERcAa6i7bNRI8QE3n2Tii0RiqDF5OVl7714sF9kW5DA7Pb56dkufBdoJ/ksnlABmekrOuhA2N/8vOfIFyJmrPVYSZkVi8lankqunsISOHC6gwEAElaPjlUnX6pWQHvQk4HjVlsRJSkx8k566Lf5HB2Kxaf25eJb2xup+hSnEMAHCmgZTAxSLhS+jOTTTncaOjgO6I8CGXtveyPB6hWKmp7Gnjn9VrLqEFNyuuwu6v1NhxtYj90ArQkQV4uJ0MCISXCSbCjYKYDKzPZOJ1BcTcY0OSQM9zRP+Q4iACRVFgTBfKuG7qW1wuXnMARHDEBBoCsFkCxxAHK+SKlUUJBLzcIAZ3J8dDhQyfJGSOENzMzAavs6pWllaXrEs3UaYOhGFFU+GlKVr6Z+ClMqUYVwrYAKwqScEPNNgOwmJK3kQg6QI58rmx32aftzvX1dXoEpI33jtJvcKdi0YnfvPrGD3/0OxDhUU8QDXCdeSsTD1LG12QulwqWFowpE0dGy0n0nymmC7bGTTggrEb9jRajBdzDbWLwMLToK55TLhneHH8lvonKJPlvymPO4wwtiEliaLLhXeELq3e/JdSF7LVj1f38VM+l7js8wo4Dqtvcz9Ee5W3Unn10Wjjs7UenSWpLHjoAemSv/P7vxDfgEu7/YndsyYOkTjuW38/Pz/Eq4Vw7epufyqR8aEq1L5l4h+KMAaJVKnxDkyRuvnFzKZEUSwRYOnbsxNLqyuuvv/7kE8/yrcxX+IEjRz0+d3Qsls0X337rtWqluW//QUJK5y+cJ4nq6WefweBfunwNtu2Pf/JjCCP599/B78IRiCiBKp55/gUIN5vbCYDOxsZGl6rk8jlrxgjyqU2MBmNsQz+6p4HsCq2Vr229gUrjiOY1HbaGXl/SmayhSIwUoJtLK7gu2J4jYHuPnHxqY33ll7/6zZVr11/+1ktHjx7lA7vZbhBQu3Dh7MrKciAQRMqGepkUN8CM+XwesSgScSCZv49xxTUV397M54r+QCRDlaxqLRyJ2mudRLqAHwjaNH4U3B5AQMKCIJt4YgtTDV/KG3Bjx2h8pdQlcYwkKTw9fPIDK0EVRExgx8CMGXTRcBZlZeSLpMNRG64WaQNIAktI6I0bMTMz9e77H5ZqNW84Wktn4M/CFEFrJxoJglrqrQYHtJvtogRNHKrbA5DBjCHoSX5aNhGfm4z8y3/+Bx0NPB4DxTmuXr5y6dIlMxwSksRhAJXI27LhlBBnjtFY77bKmSrjBBdWs2YCfuKI83hdMHZKhQJpX3iwsLPMszXBGngtpUKRMJ94nbrUS6eie7teqxBII5kMr4/ZpAOF4DRCg2B2ikqkMa8LpWaCTha6nRAhbQ6Fg3P7Fmgwzjkh2EhdWLl6MNXW9tali1dQpaZ7gcuEfXA1EU4kPkubXUZ9T6fFUQQ0HouGYfngY4PnA6IDTaABRDs3N7ap4GY1msrFGmG0Q4f3nTpxxAJZyeYhzS2VyXz4wZliPg8ARI1pbuHg+ESM8U/YkVItfU3b6dZ3NKJDSHi3ImXCRLCAsmng1GIBJxTpdD1QW5mqr41OKBhjJBdyGUZguayLhMLktT39zHP/8//8H/7sz//iX/+rfxEOR6TICfoBGmO5QUVac6naNJrrJq8TzFut143mEMKWsKMMVhP9g1OPm0v6pSKYBCQSfw8QjEkywejg2982wzcG9w4EL59Uu/DNcBtmeOpH9+Wnur26fLjl8CDDmeGq3TMPapvdR37Uluzopa+8efT8o9YktU8eOgD6yrv+cQPupwcYner0KRuLHwivN54hhvQttVZ23LHXjhfN7g1Gt1fZP6NPiLr97vaoS3j7M7E9JA/2xQDzmX758uXN+HapXD5+4slgKPLOO+8srW9li29KNrjbN3/ENj49A1ghDIQ/AZ++rtd89Ve/yJfL07HIzNjEyvLyxlb8xLFjU/P7Ll660DdZJqLRrfgmnrDlpZukGgFNIuHQ5HgEHu+V6yUUd/hcxhtULhUJwdAGssfNFCWl1EWzA1DADCONWK63zcUa3+R6kz1otMDbwPbPzc1RivXchUvYyIWF+YnZA1Q2WFm8/h/+w/+LTDQKF7z88svgkbNnz25vQ/xZxVZB48Vwuh1S9QJMgyNBwlLkKgP3+lpoy5wxHIpQgIESWV5fwCRVrXTkypMtjzsqnUyAA5595ukjhw7NL0zhv4HAzBupViqTr4MbiZ4kFYvIXSqTx58EDCKqQicDdEiXag9EZJLKDNCMiF4JVQWpaoOBC8eaQh6qVMrx+Fap3p/dt58IFZ/25Up5POSfGrN32hSiImBnY4wACBrY40aHsqG9XplOQzWHJpr7ncNz07lau9LsIxt4cCHqsrRT2xsNSp/V6wapFKGT6hMASwqY4X8SBRpDvUd5rA4Vs7aQQC5IeKjbqqAExPDEenLJHodjMhrdTqRx/IC7gD5qm/HbsPFYLIRfMV2sl9BwgjKUy1Gazet1RUJBt90E3mQJFW0RQyY5nPQxGaVUzWDCkdUk5VDmIddncqVEpthp912ustPhNbkgZWk7/SYuEzLHtAZLVxLFxXhDGa8Wq+16BVnpTqG/lYjjpCMgBYdsLBZZmJkmqQ9H4QQySC4XDOtzFy6srW+Ri85Nn5+d3b9/AfoOwAtQU6PWGDHIJlwfInw2PJTNZokhR5QPfQaGBTeO7EJY0jDAKuV2ZIx0PHsul4SLJsO1h8+vRtodIVezXn/kyDF8kL/8h1/9zo++T4n402cu2PT2rsa8uJ6cnZ/rm+yJVN7jsIIfOS5lWviLBgROWPyvClCR5xKQzSAUdK6EV7nHLGQtFz6cuDHK/K2nX53n7/BtoOwi29DbsnzkvTLcTNYqO9zZbRfGGh7w8czjHrh3DzwGQHv0D8/h8HlTn8k9NvqKFg3fEQ/p/F/O9cpb7JOwSf3JX2WSi7s9d+ctyBIxQMpfZnjbom+MTSA9hfBWLl88cOjwH/zRz/HPG622sfGpZLbU05tLjc5r77yHgT9+9CC008NHj06NT16/ce2dt+KoEZf6uaXr1y9dvsJH//6DB/liXlpbc3i8c/v2F7Hi47F9Cwvnzp19/bVXiX688NKLzzz9JERpPDTADwwP/oXY+LTb5eDtD7iByEpjNjc2MUwo+BETIdJBfG6gSQIytP0BppS8+nqbSIHF6bWjmwxpibTuQ8dOrN24/pvfvHbh0sUf/OAHzz777NzMLOGw9997B7oMxh0yEKBH3Bt6AxAHe4OnCo/OQCN4CxUYNKzXN5PIDMKmbfckoRo+sJMwjd2OiwjHFfTU0v7ZqekYtGz8TPB4ieMQCANsiWnr9PNwpPOFcq3eaLQqlQa3CGOJQCMgoE7ml7Yn2jmdhiSCK/eOtaRkE1pbvXEdPWUcUyyG/J3LpuGhR3wODFi5CMaCN402Hkxt0XWEnUvYDQY3AIjDknmOF+Ha0sWPL1/3RyfwHBw/uPDf/Bc/Pf3mr9duXoefTaY3Dczl29BvJOfaaKi3OzqjrWUzV1qaOosQLAIQg056OpwcRH8QLSrlSyhlW8m8Ejsp8VwpEdchTIl2Epn29UhsXGeIUlFrO5WFYO12U4YLeo6hBcHc6IA/ja5Sp98R1rndggIlhyXEw+0kNEkIkNwxbjGIk6w4XH4deOXVNp1HMqDXYaLPGZ1QlLgNOBGNui4gtV+vEr7MZREZp+CbhnodJqsJAs+B/QuxSHhucpqE9kI+fe3ajVQqUYWxXq7B6AoEQ0cOH5uaGGP84O+0gN7Qi+r00xn4SCJWRHyPhgGyjcYBreJu4vkDV4H5uDvFQg0IpdFlZmZEVgooT2+AcUEpwOhiuQz5i4Sv73znO6TK//rXv/k3/8f/M6Vb01n0LPVLK9smp89ocW1vbO+fneRDAs8NN9Fmc6GwxJ2iVzkah9LpRSKSDmFgwNuiUeoIYeHtSWFKq986D+LNxWHVU3yOg6n73vl7+xDDA7Lq9rKvx7+0fLTNwwt52K3fcd57nO5La9I92rDnqq8MAKk3bHe/7F6yZ7s/deHdjjM6UHYfZLjXcGb3No/CErV5XMuntvPe17vXtUiKx2d6A+w4xY6fe51Clo02/m5XwXJJQMZ4QrpU3qqyhDR3CTFItQEwEACIv7yLmf7wD//wyLGj731w+tQTTy+trDX7S1hNAAc+j8WVDQpF1quV733n21ShJB/+yaefgXNTrTQoZn79xk1U+HAMLK8sQnUYC4yV6w2Hx7/vwJED++ZxBly/eo26m5fOXSBk5PF4j/3wB+T2XL2+mM2kklur0egYXONms4pxhHYMdUrgjp7io8gJ9Ygn9DI5eEqRYICrZpXX7UQemiSy5aXVtbW17a0tRHhPPvWcLxj88MMP/vc//bMrV669+Pxz6MocPXwI2wnFBxcIwal0Lgt0wODxyQ8YQigPnhHLg4EIMAsCB3xt6jFQ5L1JEIMWmF34D2KxWI36ltUqgR6Ko0MLgUpCrjskEqfdgSlnX3rJhYnz+ShjDuc3lUkTFwNTer0+u90musVtTH+zUsrXC2nkkZnof7w7RPsy6TROoViYfHtXs6utNXvNFnny1FtAwYlyWhS1QFbYSrUpnF7cJvxAhOEQ9UHRGNRhs7vR5oHC5SmUosHA6o26ZdJvbcaD2uTBmWlIL6So6+ZD6VwFw2+yOBKZQk9r65mca+n6Zr5pdDo0FjsDAfeGzkCtDzMV04v5YjaRqRRLlENx+fxEP5tk7LeaADXO1c7j3ck36x3qS0SDHrc4qJjQ9+s1qkWnRQMXCd1k7iO2HJoZAw4SEoOWtHYEmsn/ItUcP43RYHE6PDYb4aG2HTKR1w9gwi1iRn9bZyEKRV0OsEilkIlvbhvECaVNJuMkbY1PTM0vzABAIVATTOy0uivQ0S5eTqeSVruFQOe+fQdD0QiJ5Jkchdiam/ENgfudpr1vd2jc3Fm3LxLPLBKo1RisqCDiOcM5hIIjN4qMLTtaALl8rSHzhma3kK9YzBlCqFRXJbbm6fdwYYLUkWxgVIDwJqenTpw48ct//Kc//pP/En2hV996z+YLlyoNcgUOLMxTULfR6kfR1NZos8XKRCTAI4hMUgeZAMWAAMcZ2ILORyapOieY+NYHjNLD8ocnn61u/+RtQ8PRyGapAjsU/476DlH/qoeUHWU35deu9566dnR7da/Rv6y9dZDbS3cvYc29D3J710fl3+EVDWd2XMLo8gfY6OFhhzMP8OBf5qG+MgD0ZV7k6Lm4YXsO8a/jjbyfNt/tekf75Kua3/MFNNoYdQPlg1JeXsxgdMXuKpPU4dZRJr2MpSHz5Wd/+AcT01PXFm+MTUwQJqBewfjYZKFSISblNrr5QL946UOqYczM8UK3IRdksNpPPPF0MV/COxIKBXC0vPvOGygFU1aCvCq+lZ0OVyQydv4iLOrl6NgYROVqo3rj6hXSupBj5vP6iROHENEh9YycKb3GTBGqRrOLxwAdOQIUZE9RfaxKjagC3gqKQthx3tBwSoiRrz0zPYXQMhdYrhQvnb+wurltszpc3nBsbHodSeqV9Vw6hfvG53V53S5IKhgqONcouPh8dIOIQIIkIKXgjaBnPF4/VepxKsBDYstciXoFyOkY8OXgCqJOKl//POrweDwul/B2qP5Kwg5ixLUybUDeEAcKwZ9gwIdti8bCyVQQPSRSrwA6FO3C9IIGKAEGXKkXCLqVMcdQg0FUnBRkQaK1P9K3+/KaQgspZ9KfqGOBwA1XTeIXWULZTJXYGU3l9tH//UGbOvP6GpnhRT1UW0M/7HMMOpVKsryd7Vx7Y8XYLp44NBtwOajIDmBq4NRo9Cgljxpht6fNV4pGpykYHutaOjlI1lWUk3uc0Qz1Fopvt0N1M1QxrTbS0bT5ouA53C1WkxFXFxgFunKnjbgS3pIB/HSz0yqja9CF1mMDu2CKpRY9MANQKz1PVztsEsWj+ZSMBcKaqE/i87m9fr3ZCV/95uIKwaap8bDV4tfg6kFogSwwpjKDs5NJJ5Pb8VhkjOgnHrhQMBwIxTw++M7t7fjmytISuVe9JsNbMzMzu//gfvhqkhGvkVJlkItXV1fJ5wdB8hNIRAoiUtpIH3l8fojk5PCD/PCx4eFCFMDqQI+qq3C2CNEOKLtGPgMBUBUxi58vVwxHgsQuAU8tBYuzNhKOPfXM06c//OjV37zxJ//lH7z1vnFp6SbknvWVdaAtJK2eaEE6CqXclasXDi9MHV3Yj0ePR5VUL5w+6jNL8xiWcotvf6twr/lJxFLZQDIqoErLHyY4XrexkbJW/sjzfjvuxfzt2Z2ghB1l7a7pbst3bfgpC6QZu9r2Kfs8SqsfVD88Stf0sNrywADQnp1+j2F0j1UP61pvH1c99egj9BU25najHuK/XN3oxd7tTGzGpLyF5OXCLvez192ONlyuHHP46xMz6qo9N2ChGuui7TAfyILnZYrvR+IYkvSFz0K0aJqK+UcL7tChQ3zCIqXTwEc/0MEp9nm9s7P7sA/YQhJUyE7X6k1oCV68tmxzeyk8iQFYmJ8dn5+rNGpur6vfaVJntIpmS8G0Wiya7a79zyxAq/7w9Bk0dp544snDh/YRGUHUZ31j9cbVS8uLBpxG47EJogNovaRTGVRkGrVMOpGDyQtJGx9DlUpYlBSgkjmUkS6xsDYf9xgnohWYCgi8AY9remKc0g0YtUqrS0RMozMdOXwi4HFSQaJRr6xk0yraQ5ZQ6mZQoV5qmUHKwbaBLQY4kHA/4BbKQfYtSTUM+0BTqMGNodJCGwuMGYP1AliKhoh9uaPwXinLheANeVVSVVOYNWTkUAW912+TR03OttmKk8KhmYjiWarU6ngL4BNjZ8lFH3QgMosvQhRc+nCNK/lMaXJsWq/94Ob11WSelC8zCWLWQcNrvfLT778YC1jTxJiovVUsO91eaEC0GVREmAYvHqqGpLYTOrSb+6ZmJkixDL2mXkx7vPb9C8fsTu8Hl1e2sqVqW1eqUgbLYrCiyFc1Wj1dEpEgvQz0tZ6+KhUpEEEkeUn4ORbKlYe8tWJ2M5c+NXUqEEVhCFWcnkM0u/uFUgmIQ+Y/FGirxYgXhPtOGMhgMsIhosaoi5x5Cm4grUkvacWrBLJB028Q9FPBHjTgcToo9oFGJV0KKKTo+sLsGBKOpGIBjCD01EjoKhDpolBXGR2lYBBFQcv09PSxo0dJ9KOgK/HM1fXExYuoMySRXQAHklZ2aOEQPjOQFviM3D3CX4VSEaUnnDShgIc+BLvgcNJSX7bZE2Z7r8VejCSeBroUBxhwLmALotdUKmUQOgAwMcYossKDI4+NEN5rCG3iESR/kM4H98COjoQCDFHIQ+OTk/sOHPjLv/qbF1/+zpHDh67d/MX0/D4yFq9evua0GGbGo8Va79qFa9urF8z6wYHZebiAFK/lzJydiYPQGN4YEgBjEvAiT7C6SnmX3IEsLFReLkpcbOSVIBvf2WpkhTKrHETmmNm57nP9Vtt2j105kXJl99jkkVil9ueX35Sv6rwP8EofGACiTTu641OH1wO8jM9xqEe8eZ/jij73LnTFsDeUZ35vwPQ53jvDww7bpi6526GkHTLJWFLeovJD3qeYHHSMxfYS/EIZWNgk5KRQ2gFaDxbwhRe/he1GFA5BHQpmhQJI4s5TGGtzKw6FBZ8BScgI5/QHB8fGYlSPuHrtcrHauIR0UDzVaTYP7Z+16nXt6XHsFnJ/ZFEX6vVBr/nmq6fJJJqdnY6NRRCbBjLsm9/vcbvbnc6lq9fOfHh62be0b54SF9Mx/7zHbiaP+/qNJcQZ9TaKrldS8XX8OuKKMZhqjcbmdpwQFeEnoidOZxc+CnGoSfSDYzFCYZyUOpf+YGw8HND3m7l0YoCenVKWNZPPTdrssegYpViBDhSqxM8EqRfzifMAYEF8BCS0ubbuxBUEIQVriUQQZBHoUAYpXD8YdKIRP24f6qsT8wIb9ToDanVB3ObWYLSqTbSLYZ4Iq0rflNgitdBpp71GUpdZ/CgUHN/eQtoGfMNNEGttJGmrTUxw39TU/n0Htt8+s762ObD6IRiRSPTaW+/+9AfPh7/7FF4AbqDQdCtbFPbCAFP4QoMfhzqyoCDtAA/T2tL1mZhvPOjvtxo6n4vsp4Ep+Kv3Lp29umFwBjVGew1kSHFSrZVisSathSR5kFkjUzJaHHoTLGqpR4E0gNuqt5o1zWr5o9PvFcplJzQlrxf6DliEiBtJe/iaAFz45xhLFqvTYkGzB+fGwEISOxJDJqdJPv4hsgyQT5QyYAgA1JuYduQtuYk2wBruO7OVcCCxP1xNUsYLO95tEHJbvtFNba00aySggZOAUmaP33fs2GGS1HxuFwoLZOBDtdmGIZXIc4uoeQq5fmoy5oCpZbGBSLhT2lYffUYZ2W3klQVe4GtkPk/ttFxObyA/zElfKI9CT3AbMeH+gFIhEJsqK2tefxg0TISRjQHceP7saBPY5AhMQCKUDkgFAGOhXQS5HgzHzeYa8ScdOfHEX//lX/3Zn/35f/2v/9WxQwdN5M1brImt7dWVtf1zs7lE6oMzZ4NOjccbzObz4Gn1GwXvIY8rTzRXzV+tiD0p+AZhJpCKKv6jQBb5KREuungPBKPuxV8GpGy5a5JVEkCTO7TnBrv22HuBegr17+gWymH3PvXoZl+L+S/YRV+La3wgjXyQAOhzN2j3WPzch3q84xfsARzY3A7qYCqxeFEz410z8rpRJHnkRcZ59nhJ3ePso3dZfQHKq06OIsdR34mgneHbj6WKL10UigUCSctYQup1rwNPpyUeIOAPLh9qrfPyxaNDcngiHkdvLjoRczmtRG/wW6DeAk/VZNREI1I8KxgJE94CfMTGuol46uLG4vzcdKHa0LttY9Mz8wsLlUKe+ptrqxtn3n0dbmkkNua2G7qkGpGIVWkgvRwNjpNWTsqSBi6u1UTA4+JHHxp1RD5qKBP/6KUnKLC5lcwkMnmqBzSqPfJ0MMQEovASdAZgFwdSPeZWrwEXWigyxFJc0bonm00vbyeNOlMTAmqn5bLat8s5qajkcBgbaP/ocaK0en1hLGdzWqOZsAURN6I4DtcAw4lU9NbmumYrjpw02ocUdCd93e1xECGDhiKSzkhIN1vEJww4Syh3RYI+HouOuGDq4Bjqh9f7lOoEXFIwHr6z00EtiA7ivgGPm9Kv9DD+It2g1WuVRfQPx9SgTxjLF/Ai1Bz0E4XzNPJtnYlaCh0oKbntrV+99ua3XniK9LF6hoCgu4Uad7lUatQomGUkBb3XxztBhTMq1+dSSXhUkmJfpam2eKa+/NH5q2spT2T/5KGTVKOAy4I6ZZ1w5mAgQn02O4QmG0EiixUPjcVpOTy1b2pyLOh29lqNdGJzeXmlXGpaLNS1sK+tbXEfhW0DccfuIJDHQMJNE4jYEHQSnUeJ0/Xw/JAY3yWhq983kKdNjhlqBd020SLIxS63W4KGeJk6sL3JOif9sKcn46rXzMbj22tLmWTc77LYfJZGuU1XHDp0QJELwoPjBDKur2/C66LD/X4vGgZzc7MTE5NAENxI1UYpS+U2PcjYYRyYAKk8BWAXeOiNBulmArlAWQCXJgXkdT2cLNTJ7VB6vkqPNHl2gGIWi/jV2IHe8AX8arwMCR+oSwge0Ya+VeFt8UjUyjCb2s0u2W3cUd2gwy6wraPjU/sOHR6fPvfeB+//7A9+TjWxN989TV/hlCoWqzeu30xvrbbrjUPPPe1yiaSWF1+omdQ2xWcDcRoNJPrXaGJgyOdLT4NbDqgoT67CBFLfIaxR+EIsUzAObxrRO1DfJVCMbk+7PEG3X0F34JH6DlFeU/IaGZ3YePRtM1ylLr99qDuL1Y2V5czeQVfKconcPeLTriu6BePU6314jd993od3rod05EcCAD2ka3t82PvpAfXhv58t2WY44mVGxS+79hx9g+xauXPByNlvvbPUJcPlvECF+dOVUgaqwIjqBMLK8rqHEkH9B77UbywtQ1KAhZlNJYk4QMqhdaTpYk08Xqe2VPG47VAtqX6V3N5iOTpysGeK1nIsNk4piwuXLs1Nj/Op7bE7sOwTk9NQbDbXt7z+AESOq5fPo5ZjobIX0SSzL5nOnrlwAW2+Y6eemoiGuo3KB2+/jraKw0oRqKZ54PRQS8BrGAtOLK2nVrdKOBdwXuEmIJ+b2IXCDCGfp0ZEqt1p2J1I71LZ1Kl6JiZnxvG76BpdczCYLaSxxnxn9/q8hXW5TB5KDqlfJNabzFZRWsxl4R2HwzEcNhZFaaZQAl4g+2ds18m6LlNIHP8NmTqKYqFVZPSY12pMkHQVpjaRRLKl+1pDAfdPqwuZBo8OYjj4omD76qwQmUUPM5vNYartdmc4FDUBnvSaLBlPyAU5nF6P59x776JQHI2El7aumY24EgKxoNNuGlCA9vS5K8+cOKjV5ymjRYZ5s9fBbJO5hB8LQ+P3skR3/sr7Z6+s+tyFbDpXRx1nYCp3dNlq3zd78tSzL6PTjfgyxWapMWqHrgQ6Q/aGyqIoz5ilBjuaRtC2Tp14gtKhN65f0/baAE+E+rLAqUod9ABRitpsgAAiqQwijoEXqg2hyGxHE5JUdDL2rLCQcFWZwHmi7Cf11YQj1WMXh8thVWql4TwBgFGVAxjOVeBhgiBFdIw4aauBTrWG4NXczNQzTz1BYVROxxiGvvPBB+swpSBvsTEhsJNPPiE+sHKDljMS4M7DI4bmDEmHe8Q2lWyWAQB9vdeFR1UkzIS7kEx+4DYULuQ0cZoVKPBbqtBVqhAimkS5fN7rdPiCgfh2mpAlMUcAKy4xzkJ1Mbx3gk7gZuu1OMOQRmTAbGxtdfC3DTz0CaOhXqtBgT9x4vj5sx+d/vijyalpAmf1WokHh8y4S+c/mgwHfuf3f2//wtji0lXq0kHbd9mcaBlRLIUHk2Ei7H/wjKYnRB75lJHvFrCmPMuCcfi2ETABOBqtgLHzjbDr9/Cds2vNA1hA28St9Hj6be2BxwDom3/n93yDyIP/BSZ2Vz03u4/xqadjg+G5ZX5XS1gyOvEahVMgAEj8/kx8Hjcxz5yaFbx/45tJHP7kMBEdw0LjN8IzhC1Bj5etke5H0R/ABtGXKAHhDBO6h3wW220LczOtjgbztLp8iWKWW8lUPl+0Gg3kPs8fODo+vUB4AYKRvqAHapQGZZfb02hWSBnDE4O4IsXfry1vFFLJekvj9oW9dq2mU67m1hXarHFh4fCB6Zl/ev9yvUeRKzteBFNzUK22S7k0nheyh/S6cTRjaGcmnSMjjGDKRCz03NPHipnk8pUMV0mCDyUEytUScAe1ZQyYFt+Hx5OrVLB5CAFSngLGK64gny8wP78wOTmD9jSmUb7pyYDKFouFAeld9BrojRQdi1HncrpMVB3XoteH+RuYCaYQ62n3UBykVS0S+7MZXa8NGzrk8wiJxGjqDHT1dJ4bJWUvyTqnQKvOrKGERB9lal8tV6ZsVTaRNbtDFD7zjoem5/dPjUU2fM4rH73/H//6n8h+mpsKep1831PDtWrRWLmbLdHr8+jt4fOnL/7lLz68udrgImp1B9LPEJC8Dm/E5pzbfxjn2emzl0jNJ6xHHQnSxd0AyX7PqG2PUd5Bp4EvDBByuN3Ej5YWF69evgRxym014ZnZim9RhxRWLplf5JoxVJBQMjckYZs+cQzIY0ddiegP1XO1gYAPoIdzok6yWK1B5rtei3YiVWkdpNoRFUUJgDAeldrKdGouj1MNh1yxRI0UQ2xi/OXvfYfuw+/iD4YpDEY3nTt7/tLlC0Af4q2Qt+bmyPlyK5RwRjFOlyyl60CfFPACdlPOdTsOdUzLLUYcCGIULYT11ejQaXrClGAHNgb2AcsgwrUJ69YqJpvD4xZhTC4tlysT93QO+pzC0IGaI08YR2N08R3AJwGMLe44cJCF3Gp2YSGcKb3R6vbZAVK5TBKO0cnjh8vl4quvv/bHf/LPpmcnqvVKrdwgj29+PPj9l1587pmTS2s30C63GXUNeFcGQ68N5mEsiGOYJ4tJOO/cYHk45SGWGWUafS2MLr+9fu9/R/fae4u9lt7t+MPl6szw517H+CYs+8Zf4Be/SQ8YAH2OHv8cu3zxy/6tOgI9/JneI59p4909qZ7uHrdVVin+b3VfTje6MfOjk7oNS3irqgAI+MPE25wJ/wcbsArrArOEiuJOp4O1bM9fTDv2CXljfsIbhaBK0IA0YAOqPToL3AhEeSjTZDFpW80Dseh4oVjt2HtauxNcYhoYoBNpBj1/OISvSD7ik7kLFy+tx7dwDu07eIhK8mtLFOlaQvmOWk3YeLO2kdy6ZjNIIclUMkOy1dxC7NQJ49JGkvgXtIye09ILeDc3CZmkS1SddNgDQR8uFkgqTUJW9cbczKTV1I9XKdkOyKlKUQKzvT8o+ynQGo5xvQSk2J6sKOmNTiuR3K6T5dZsX7t6/ZVXXgEiUP8BnjU+IImtgLbabfLjyoU8FcWLJA25Ld2WMeAnXzuAb6MBotKg3quHS14oVrYLScUr4xVohFMCeWsKocv3cT8cDRHgI0UcBWdKqsJCb2IbCyVh1Jgt3IVOP+W0WSenJiKzc5HxMbxWJqvLYPP9+s0zDpv9X/7JT+1jbnLmqTAFGd0Ih8fnGeisl5e3/+qX79zYKB164juo+1gjiBfZYuOTLi/OCL/JZn/11ddvrq053T6y9oi6EVSCeGQ1GuFuUzAVKOxzeYiCUbn2xvVlijyADyDPUEPLYXcVU0k6Kob+stGIrwU6TjgcBPkQvYJL3irW4EbR84gBAmfxmQGIYUrBDm71Bg4DwU0HHgvEBrkv4GYQNmOJuCR13EhZx4MYG5uIxMJmqx0VQUAMnrNkMgUT68rFy8wXCnl675lnnlGgD+QzSV0sI8BDjVVlIjGNG+REl8jlwcHpcQcJZDFgGw0quzUMJnIGTYSLoGW5Oz2jkMdx/0HkEX4x7hziZcT1ECZAdhlSM0wvhiiFL6anZyV5PpOiDYxDTkXZOm5SnYBru02TmMdFRJ/gT+p0PMhgavs9hLJdDjuHlgz8A/vPnD33i1/+w49//OOF6ZihWxkP+F946smp8dCNq5dPnz+Lu4dKeagQNVtegs70GCgNWCZhViM+OaFOKW4eSYQXl5Ay8QyqM/zd8w2z50L22nP58FD3P6M2YHjA0fbsOMg9Vu3Y8pH9ObzMR7aFj0jDPjMAelCD40Ed5xHpx0e8GXfrbfXlMlzLz9H5u7161G1UDMM8044t1SX8vVu3YCCHu6ib7fjLT97dspAtlcPwU/w/AnskC4xPW1LWQyFbPJGWD1yTFgNN3AGCQrlS8fmDvOWrjTpvecwML31UfdmRTBkObDebU4kElgcx3GarFAn75+d+xDf+lStX+Nxf2dgC06D7nC0VpJI77iCrIzZubzU1FAoj14xgUzaZunz6Q78/2K/UUU2empppD1qJVNJs9k/NjEWCwVw6m07lz51f3Ci18E+MjUVwmxC3QCCnG4K3qod2CshgCZ40jNmg3UfIz0/yV3y93SxTJX5jK4XmCtdK7AO7hQOJ2mJGs5NMJSvqMzLZQDJkQ9EzuHzIWq9cXaQUPFiQaWFh7sUXn8cgvf3225RZhRfidVluXLpAFvnk5Phg/0HiLCJKqLgYsK9un9ecStebbX8wwOnAT5DAB1CtKgXcDVQJo7U59JW311lSha7b7OAFwdalcvnljU2cDPkSkgINXTpdrLYoMRXf2moOjK7w5K/eOkMxs9/7/gtH9k/ZzF6GBBGqlUT80s21X/zqzWyx+8KPfsZgoFqsmTqhUHw9XqSxa70BSk6ZStUdDEoAqN8lbQ2dwIDP6vIH9UQFB8ACPeEnAmowyLl8EC/1RKPBMX2/bdaY+o3e5tLmWDhWLtRSyZzHB5fJV9Y1FOavgf7X6CB3E2uijKoBqMltRr+gz0HsVICw0XUMG64aIQOBv+k0fCB4M3QOd2duduH4yVM+v5/y6Rtbm+cvXgB+ra2sMSpJSJxfmIffA4jD9QjCYBt8l3hfhMPGoNYOoD/7gj7x64gzs0XtNlxcCBnq0WZw+A0mtD078Husdiex1814goENLxsBRcYwbQ76/IAzBnwDKUa0mW1SFZXwLvwtRhTeI87F14Jk21HKtImCYgWda5IAWAi5Gpgod6GEpHQNQAkpHoEitKlIE2N7HpnDx46uLi0jxu2zG4NH94XcTk2rfPqdG9xrnc0xO7ff7fGmciTP+6E98V3Ca4MjS49ZJCjJPH/h8H3yGReqH5O68NZfNLIJIt5mEw63V7e8x1+2VENX97/L8Gjqq2b4835mRnf5HGe8n1M88G1o89elqQ/82u//gJ8ZAN3/oR9v+Yj3wD0ej92rhq8A5bmSK7vHA7Z79/vpCvUU/GXiBTqc1CW8VVXog81g4k1NRIAAUCKRCIRCTSTyTCa0VfgI5f3OBpyRXQT9WGx8u7MQOUEaZmob8G1IGKKQk5KWTlkL7iO1OxwMwXiVMulkbG1tQ6AeGw8Hg5Bn21ub8ZvXl6wu7/MvPoOczNbq6sUz54iCuKwmm8kALChWirlCBs5vZO4o1Fibz5ZfLXx49rJ7bProwalQxAfHFntZKFQgjCbTeeGKKkpxAsug3lCKC13gSKhdE5YScobwtwl+8I2PGYvGYlKwE78V/g+Hfcbr8Xn8wAKQ39ETkxhaXFNgGS/lOYTe0b967QbaRRtb2wf3H8CesUEUBKbXnz7d2N7eSMdTjUqd8plMHq+P6Bsgs9IQOjYuBOAOGsF2p4Nup+D9oD6guxBtpuuwlzgJkPOBLUMvc4PS2RzWEwsdCAWtTtPGpSvxbBmRP969UIsisajVMrG9cfPCjXXECKZiAZ+DbHRzkdzySiNZrCxvZPVm39JGKhoO29wBqw3kaWy0esnVjUq1Ds0X1hZEXbjJyPBA50IOaXV9DSKwDw0AixXnQi6XXlvdQrCRJCQ0kQj/mbxu7iZlUJFeSqcziBfDENpOxAEagB5gp58qoKT5O12QgHCR1Jtd6Ct0Gt4gsrrwqSB8newlOD5VTdkJFwv+DIZKwO9nhECgIUoH6wVdzQ8++ujqtWuFYp7xxgBjqJAPuH//fsg3ABvYNrU6sFvcafCYGZ8MV24TkoUef+CgwXK1f3V9c8ModeLGcrlapQa4gUZvZ99SVUEnJiNUf4A+ISrgl741EITR7FR0FZjb8lz0+njFuEFEtrhM3EUgQruUcZfbx9DiFnM3AUBsA0ChnfK8+EMgKlrFBuBvNoaMn0gnwNZoOhP3PHX8BDra1y9dmBsLLkxF2xXquTTBSygpzXF10VAhk4RIxrUIE1n4PfLAwmXjFMzgE+LRk+YNP4HwN8qmtyb1/TB8SzADnhn+vL3Vg/z3Tks+7ahs+VBb8mnnf5Dr7/+qH+RZv1bHegyAvla364E2Vn3URx+S0fkdp7rHS4FVO9byUz34joPwU111+5Pv1no2Vl+V/DOKe9R5Xqa8TpW/8ke+gpUgF+af0IbywqX4FBI4LovFCqujg8per4v7B9uBL4RYkRgPncbndWMGsGoUayd1F9NIIcyIG90cXSKRxB2C/4Bw2tT4BMc8d/EcEbNgP4DK23o8/dY7p+OJzPTM5MFaM+ZwDnTm2ORUwOVC8GU7uZnOrnojoRe/9UIgGmsMLNSj+E9/8QsKWfhD0Sf3+QYmW7VF/ag2LCD0EI1WBywazCqxAtqPwVVY0uTDxyh0ta0QZtP5BrI3lNziYgh/EXAR+o7fg8gyoanxUBDDtnj9ejKxOTExjSwAaUTVerPaHljsHlSG99s9165efe/9c5cvSf3wSDRksJoP7T/wwosvItUC0Rd/xuLiYjYtRTFdDfxiDSIaQBYzNUCNhOQqWCuAl9lo66NZ2CfyxkKIOIY2aePUmpIU8QEOA/K5Y2Ph7/3g+5j5RDZ/7PCB8PicyxuCToTAM74K9GxM9oNe51MUPf3wwsVmGW+cBc4NTCbqlbgDs8lUMXHpJrTlg4cWqA0GkVawSLWJeDF8LBjCWE6H2dJDTrpdR8mbuGGz0S1rWpquIUOlUJkqhD7xj+Anwr9VqJShlUPP6ugN2XLVYNFFY4FsIQXXh4OLJhAIl2plVFDXgoi6ye2EzeFqNdpZLLm2j6ATYlFsuX/fQiwWQiU5EELh2sFgA7pRRS6TKSyevXL9+lI8kaLOWijsO3nq+NGjh7kjhXyWzTweW6tdobuQBGK4AoyAPvDu1ZBspVpuJdJ2WwOQEgyHitUytTIUdGKm9gXHr9areLacHqfF7kil04xeorr0P1gDDW+XyQpwEXlrlwt/JJAR3xidL8R3q71fFcVtIGwu18LBUy4ZiM2BvWgbzwJ6VEIY6mk4HdfCRdGwYrlQrZWJ59LUUDBIeKtf6wSglE3PfPTuW0XDIHbqqFbTJWBHNTgL0kxeL6lebMyjK0/rbSYg6IazMKnPLH/VZ5m/6rT7VTCyBF+YCo8AH0KU/tSJY+54h3zqLo83eNwDu3vgaw+A5El4PH3eHrhH7+3ANMMz3G35jg3usZlgoBFmwO33o2AmJo6jzqjvUObxOvDCZVKX81edx0KXyhksmclqxaqF3W6ICCVqWyiftrzreR1zNE6HNVLbwxcqBgnvgBwBJZoWMnc5Mcnk7LIDlSZx6ji9SEtjsaKxcXLgOQLWlHLuZJ4nM/m//+WvUC/kx5F9+08ePVot5958q1SNF2JRP4SeQDBSrnVfeev0r974gIa5x2ezlao1Ho+EPSa9RpT0yGq2mDFLgC+hoAAiapUWpSEMmqMHZllN9ICEJ/KryQZH51en0UeCoUq5vJZLEyHCzJcaxTGNZPGkkmmRnm7hyipReV5rNGm0xlqzAyvE5vSOz8yRNw6ewYdxbXH10vWb8Jr9Xs9kLPqt55974YUXnnjiiWs3rq6trVBeg1oc2FfoLNpayRxGsFpn09j7ovfTb5RqRHAITKFmjDtNsnoUo460IklYCP/hXnr2qZchnXx84fLhY+7J2cPUS6X+FI23Ol2Umy0Ui4SNNAaLL1vRRbX1Cua52Nc7AXOkDuH/0EGzaTWzhbzf7zPZnJ2muCvq5Uq3jl9Pgy8KkZs86VqNmkOSyp1oQmdTGY/La4QiZXHYHC3kDbmt4Aa8D2AC1IvoSAO6A5Uiba6WyxCYEEdgPOAORBQQjwiBLUjQNBXJQjYpZs21asmBTiBeDL3O5/Oi4r3/4D5whs5IElz28tUbpHQlE2kq1ELFiUYmDh09Qq641w3zaYzRxd2ESU0DKCKmDlH0d4ABAGsmqaLV7hVLVYQHyhXKw8I+NkLsYjAQAsN9mC9RfIX+7hNJDIXQUhI06nLhWNLCQIKRRecgcS363joDIx/sgigRY7hQLnJwJtpAP+MvQvSQx4FtAE/K4K+DgZR+Q0Khy/1iZDLOeRboq2pNT4dYHW6eGnl2mh2L2SWseb3RR6mW5ZXLV8dPnjoGsZ30QyrlcWusVrPTQcq9xMs0GinMwhPLI42DkkkAkPIUM6KEr6RMw5eDOsOyHUuUn7cWyqO65/q99nm87HEPfJEe+NoDoC9y8Y/35V2jvqF2v6fUl5S8jEam2wsFVdz+9htZraAN9bd65E+sU35whB3HZDFLlC/Gna89NuYdylsbWoF858rbXagnvIWRAcQGYLpW1lZLjeb8gcPwWBFChE1MmAFpQeSDoK9iYYl2YR4wlrjo85Wsxe3GcvClzmE5GtkxBp2WyMKgIUoqmKsr125Q4pQ8cfKgpG16I24kyMUItySTiVR688aVy9RHN8jxKE8wmF3Yd/DoAUTjUvFtksDf++jiL3/5alujP/HsC063i2ZIvK6NR0fbaVJQk/aQue0gNYlv97TknWU3V5dQVQmHAlhxuCDeQLjaRmMQBvZgEjM8ty+d2eLSKdzl9joxjBSD1/e0COcEfEEoM9upJIYLHR4KQujI2DFa2gPy6LXFRktvc4TGIyabq1yroIkMTWR5czvxN3+zb2EOWtLkeGxq6iXo2NeuXVlcXBJ3FNIA3Tb+gUbagYgSMpP1Tr9GbjrKNHRWp6MXBw0eFJHVoz4GyXbUW2We+Jqk3NttZKE3qaVeLllNlmImT937dDFvhkVltXmDoXQ8Qb0Jq9OBdcSHRBzLZjXa7fqerk+trhJ4EJSD1wW1ZgNpWorF70u9egEu5GrDV6cFtKPfTaYTdKPD5rS7rOUaLacOPJugrlSq6wEddbNFlyvmgCzIRkG66tIpRBD15nq1TtI75GjgAqXiqP1OI1AW13Ub4UDg5IlDIqjo8QGuqo3G6Y8vr65vojaJ04WgGHdtfuHA+NQ4VdMDXp+RMmot3DZVEBV8HCAFWosWqwPvC2OVCV1uGWlEFnsDXEItwKlGh5AVujvmklDTiMU10mliXuBK3FcUOBF/z4BswbIwaug3hw3yOfnwLLeKC6qBHxFYhvoUTwFnpLUV7m6lEg6Lm8rh6PFcCBPIwvh04w+F+y9oyWZjGNNzDGmOzxKACqAHEWiEwnkAWcKTCYCD8865kn0NYcR0tvrW++cm5/cdPT5f6zZXtreNGgMy5VwaYkudI3MgMG49OpQEwbRGniCAMYoHwCEUguTdojz0yuti5E2ivAGUTDElMV7dZvTv7lfE6Frm2WD4EbVj1Z4/ZfvbjVE3UJew8DMdZ8+Df+ULuYhP7bGH0civ6rwP8FoeA6AH2JmPDyXvVvVRlBfL7Wm4kAXq2tFtWMt0WyNEnZfjyKekAouwr/LiHmB7u7z0mWeizBO78C2Ld8cs0jIENSor6xuEjVAd5ERgI3LF2R6WJe9rUpCwAUAcYgQYHo7PG58ZEVZUJvATOc/QSDHMRDcCYT+JY8hP6wZo/urnpycwSJubvrVV43QsWixWiDr9+V/+J/Rw/8U/+719c9Nwcs9euPrXf/V3//DKm4V6Z37/IVKZxsajAb8bEWApmNRugW8GHYx632Y189lMqALBmE67bjYZyRWKxaJwVnASbG2n9SaKampsVsOhOYoQhHqdil4/FRyPtWBdDAY4iorU99BoxicnKA6ayVUwqDioyF0joQlFbKudkumECaVawUDHYkpy6O3u0FgspNfMGslTs5qK1Xr67HlAw1gsfOjgwdmZGVSwsYg4SxD/HTTLMMYpKao125DDobAGf3AM9FrNfq+h15Aaj5kDkjbW11dnxiP0KvrC1E2LTC6gIICF3txKbqdyRKXMJvjjZKHloRJxd3QGqlLYoPhgkkgCr5cLVjOJYYZcqQgy4I5EwxGjU5fZTvR6Ldg5vX5Lr7GEo2GN0bSxudapt7weNzctUyrDV+L9RVa/y2ap9duEL9vwtir5fs2g1Lpolmt53CdhRUGAMZPLFpxuGLs4UXTcUxxw9CSdT9DlyKEDscDzC/PUTLcwci5duXpjaRVOcCqdhwBFqInyuocOHohGgyAVss+QiK6UC5BdSMgCUYCl1VHEwekKiMWDAUo8JvC04hYh7kb1EoSU7NSpbffrFFkDCXFuOoGGMTB8sLu5ZwhvOhyFYg4wCrojIsnaYDAMjEmm0/gpKeOLFgDdy9G2N7dwCuLaYWA3CoyIPHgIEhvtBwCxAcuZYSCwPJPJAk04Js8CDxR+IBASjiAeBpAsyewAWQJt6C2iEo4ggIte9gUHBvv6dnY7VTyJny1TadUbRos9jhMsudHrSIBPnqB2k68ImdcCv0XIkVODmMUPROSOYTqcAC3i27mvINdwpz1neAoeh8D27JnHCz9TDzwGQJ+pu75pG8t75C4TbzEmVqp/d2/FvnuuGl0+PP5w5m4HZAN1UmDPLQLBcJ7YFozLWz/hHUglRdK+arymDx0+gqIy9qeNinCd737IK04axsZUMhdSc7dFCAP7hJmh1BKhEuwMX8xYDpitVHBCWlfqdGp15TrM4z7UY08gROSFigfsgO8DyR0L8jVGg9s0OLYwQzGNRrt76dKV1aUrKPIQOUNP+fy5yx9+fOlvf/Fr5IFxF0XGJ4iPhEM+4jcevsyt2AV0iBSybRU+LlaJj3Mqdkl9da/PeeDAPs4FLKgiRNPuOe2kjGVddh2p2as3LoFLgugMBkIoMsOGbtWry0s3i9mMy+OMp3Prm8lmV2M2GMSFoOT3x8ZjJotpezve1QwAOhR4J4aXokJYJ9Hv4//QHdq/8NSzL/Vb9bMffXjmowuVUhFHwvTUxML+Q5Caq/tzSM1srq3i9oC2i5oA1jqTy2aLdZ0JfaJmT49FbnbbxKkKRNBwcCDoXIfKjbOi0SzDrOloiP1RtoLAnBUeT23QbNVt1KFwIKKsR7AAJIXzY2J6rJqzXb58ud2u0SEBd9Dn8ThNNrR4TPiALHoK0eNvIloJvQt2l66BOmCv1qHSRrmWzYE/2pUCw8ao1UF/EQXlWslrMQFOLFZL0GGsZRuVUnZmapya9xSdzeTSfb1WCMLUoC2Xl2/egK/94nMvdPpyX1rNwYcfnEVZgCDU2YuXys3mcy+89NzLL1FATTIBTSJlAFcKyg1jmB6jIziUljw6PdhMD9LmEiT/aaDDPUm9CBjZhIy02hapZoxf1sL9MqNa3deUilUwNNAcQE58qqsbsAti3yAmBoPFqtekpE6aw2ZVBAbBFNxYI8gGYhEBT0YvsgrwtDgmY94IWV6RVcRjJMO71QUh0S0sVOG+ODgHA8BQrpCHKw0w4gGRLwSQTt/gsDs1JpGNIKrFjWbY84B5An5jPIWuUWJ78/yVmwuHDvjcFuq4ZpKZ9WQcnQgCf0gkoEfKoeVxo4IKjC0lEqecmm8YcbrQhuFfZm69bxhQ9weD9nzD7H4X/ZYv+ap66as67wO83Y8B0APszK/9odT30+5hPVzCzOj88ILZkeXD3W+/54br78yoq3bALmVf8foMJ96bTJgNPrd5VxII41MYEMNC/sKcYIZ8lmAAFoX92s0VXDwwV4g1WG2UnwwKJ9RkYks8QPCd+fJuyWe/JD7x4cu7nu9gOUGf3B8ql5epLUU+tKbVLVWEOg3IAB7JXggulvJOfBRmfWIrFd9cn5oc91A8vFL1eqw//MHLaNxFxsZz+crb73/8d7/4lc3rf+b5510+5Kl905MTfp/L50JItwJpGFvD2x8StN8TRAeZhpULUFW0a8uaINu7Xck0ufkpak4ROItEwr1uLeYj96m7unkd2m84Fu7ixHJZvHb7Nhn+5SLlnLwBf6tnqgLHEKUTqKqnMCqkIq59bGyMGldSQr3XI12aS8eDpTGYcVVsJJMUcWfzZ0+dePb5l2LRiZuL1y9eQuIoTfyOtLjpscjxwwefeCY8V8pDM1pcupnJFTCllDAdQOq1+NOpdq1O8pq436iGCd28Qb6600XYbn19q6uzkV+PYHEXWRutNpfJIqJjN1uCtLbaJBMcOUHuAgyVZr1K5jty0kAiMfLU3zAZ66VcPpvWdGoOKyW2CoysdqOdXIf9YjJ365X0Zo0qrLq+ZdCsUJisUwmKplEfucjYxNiBZ0598P7b4PbjR45ybxFAim9tI43DPYXG64mMwUCnI9CkxgWyDkVMq13b3Li2uJHPFTqVKmR5om8TM9P7Dh2FTE1SIWRkHCfFUj7brNngQRv1uHzQKKQ3mqRONVsGSqJh+KXQfRWoSGgPZAtah86MVLTb4YZCxC3g7kM4a5PZrrdwm9AuArGXS3X0CwhyAdlr9TIeI4ZoMr4JQEElvE5sjfQuZRjTzzhvGJCAGDAQNem9btSeLILswRIwuPN5sBeBp14bfSkHJDOeDq6aQwF0qpWOP+DlX/hGPBT0GOflXLCjKbLHDKXpCCNSUoMsORyhBJEJYZnt1kA4kohvX7p2/eCluZ/99Lsui+nm9ZscYW5h/+xcjIuFgmagahqUOeSVlI8lLnb4tCsYSJ5rlqh/ZYZgH4ljnwit39pj+G5Rfw9/7p65tcOD+Ed5+dx5IQ3P9SCO/Q05Bvfum9otDx0ADcf9FxwLD+o4X7AZ37zd9+zY3cOdJcpCeVMoM3d6Qn08hsdRf95Z/cm50X3Zkml0vfpTWXzL4QPm4ZUKUuFtLh+p+Gj48va4efUjqeMkGAL9OTZG5tdmfJsw1sLCAsaVulosx7KwF1/nHBDJHzxH6jerxF/4KB9oKZ/OqmwmD9uCNHXmc7lMrUZODTrRFLagHKrEv7B7RUpBxhPYTUoqkDiD5EutVjp16omnnnymUGz8u//n/3T+wo3nXn55cnaasA5qcrCP3Q5Km2vz6TRtgFchPN9sFsMSi8QIamC9+BbHbQJnZGZ2kk/2paXlTDY/NjbR1+mIsLVaIjOY3LrRqKT0FAir5IjdEbdLrK1vr6/GULyenHJ6fSsXV6o1UtuoE44505C5RrY/5pl+oCwDtorcH8J/lXptMADV+R2uEBYXsWmMtNUCwquXak1wxIETzzg8XgwtmU03tgtXV18hB4xOCAaientIW+074dEGxtGG6dZylASxGNs9lCZhaEnRjEbYF2rGitkcSCFd6w4mZ+fotZgvSB0ucpUspmgHmWmU/OodiLkoB1sMZsBUKZOj043anh8xAqMGMeKNmxkpTWrSaVtVj9mcyCSQXpyemNLXuS+140eOLC0vghVeeuHZcOTYL3/5D0S9fvqDZ4uF6n/+21/0+k0qioxNjMcTG5RpM+icyCbl8qWteArwNzU5h/sMZg9a1ulsAYA8O7/P7vJdfpsSn2fxr0R93v2HDsIem5yZLjfr569eJnGP3DENHhoKi0D91tL9uNl6aEDS14hQY/bxRWqk2K0ZMha5fciN48Bj0BKVFDgCEbhHpTZEyQd4f6jORgG17kBkyhUfpNXt8UPMAkCAaSBXgbo7HYAyCYlWQZatdiqfZVRTsJXLR69IwfRtA13IROdT4J5QpHaAbiQAlU8Eyomo3k2zyYq/B8sFlOcJYpB3uqIkubaGBydBur4c0Izf0AB4qrYaxP5wieLFwZvKQwrDPRIJVeerqeRWtZChzNzm6lq1mKOjDh46Mj4z0RughdkEV+NAJQLGLlwjDw59QvyLeZ5s/s9PJtaqf4U5qEyjT/2e82y1Y7myRPnQUlbc2uAT748dezyAn3IZn5x2L/nk+m/Ur+HFDme+UZdH6uI37HoeX86D6oHdLyCOvOfCz3RGjqA+S/z95PSJ5Yp3Rv7w7gbK8BcUBCNHOD0kchuNJA2BWrACrMX8NAABBsNUJMZXMv4GQAAwiElgEBRcGCr5vPAV+n2+oSHGogcNVQgQUMoXMCFk/lDfkpAF8oUQL4LegN1CYtZAPvH1A0R62B3PCnEizri2kaJdoYDv5LFjnOh/+V//P6gY/3f/p3+zsP8ApgvLMZCwTUeKd9brlTLVPU2UUsAaEXah8e2OBsoO3F3MPOUYDh8+GI0EQUisImBnsfuwH/VGAfgyP+7PbbUccxMgp48/eDU4NvPsc9+2WRw4eVx21+TUbKZUu3D5CmJ4pExje7g9ZA/RRUgVk9J/6sknSVrmpF6PI5svYEQJmZGTjb9B0xH+LL4xLC5okjS39XhKkynyxT+x7yhUGgqql3Op60s3X3/3Iu4ynAoejwveErVnjVqjx4MQn5aKp1hZicp0unhFCEDStyhJwpiBVwsjGu8CAbVGpYZNdVocdIjDZs5kynBmbBY73XPjwkd+lwMeMWJ9Trs57HHA9GXGjRvKbKH3AnZTd2bC5w2sbmx6PREyzIMBdzcB37kPFkbPB19FPpsB7oxPTVL/66OLF7nXtVb35locIFWqdgJhm9PhRicRN1lTY7S6fP2yjiieWSusGnuzj9ig1x9sdwYwzCHBKOjBHpseb/XbFKAL+AhgQq4KQHTu9tqAafqNAisa4jukjgNxcPe0ekj0wJ+hsBdMZ5qNzeRGQJOCvOxwORlRZNKBhi12vcFsIP7JjWYsMVpk4NFFjQaHZXzyRDBYeUQY7ow0vGUmKXRqJSGRvs0XS3anC0oWmfro/ZCoPz8/T2iSYBkRMu4+R2DMM1quX7/O8MYVCtJi4gnlueh2zZVqCUmFtZUVaN00gFvm9+Cl9KYzKeA1HjkeIrPF3B3gDNW4nJaxWDAa9V7cWqKObTFfAJbNzM1HolF0R7O5Qg2XnYhHGAQzKU8sZ2EcEpKWRxsfoDiSOLmMbVw//B0CIGXJEAzdARnq8ru9Uu699m57fdblnIXmf9a9Hm//deyBxwDo63jXHlib7+c5v59t7r9Bu4/GEiaFKqDO3vL9KG9U3vZiYsAx2AMBQf0+FgPtl4sXLz7z7PMkr8NQ4bOVCbPrInlHpxOfv6L+jO2XPBetvH+ZMA/wSODhQp5gA7fkRevbrR7caL5VKWfNJ77DaiLHCuIOxFwybvrdtt1tz2bjmBmUdC0Od7lcTSeyiOv+zu/+hOSo//V/+9834smf/uyPnn3x20SE2J4CUclkXM7FF3enl8tRRdVGXK1QJq1K8FyplownsxBcKSDfqpUmJ8MYLYR5MGlWq8fns2MWyhXhwsQinl4l3Rw0XfaQO+DapHb9mdctNi/p2CanPzJ3pFhqoVwnNFinC4IRrCLMD4EM4kErS8swrG0WE6U83c6Z8VYUYISzAsHDYjGv6zRJqNJpyXm2RCM+tkyRMF6h3lUukalSVQqrH/SETj03gQ/k7NmPVm4upqifgKSNHnHJnFXqd9ZsXa01QOpWrdbu1CE09TuwemfHI5litRDP6CwVoorghlqpAgE8V9uE0EP+NSxrsFR66wY39eD8jMemo+yHdzIEoEStm/qbM6EwGCK+uom9p1KVLxCWyJ3W2NGZiy3NzKEnLK5Iz+COZ5oafcBmNa1uFODWmJw+SrnVcyUGSx4NoUrDiwSUBnGdEr677cQWFx7WmqMzGsAiBT67tcLG5rbT0641+zqTbXJibDYakUKrbShaVb3ODfIBdzvs1JLVt4jQUThdlCs1JCIS44Jqw1CEc01lT0VZpwbJi5vOABVE1BEdThxIBO96Og0IjGhRDZjX7fmCgrVxWHJwvDcQbrgFoORKsQxlnuK46BjgZ3ISdjVaKFECICOTDh8T7hn0G7mJpGgRw4KaQ4LdxOQ4Qa4GnhhlhFOejA0Y8wxvngiUQtldDfjiziFcq0oUUjaO0mk8U1xCpV4FCdkajnqzVS4KxOEXxLduvewym2yxQGYqtnJFn8smeQRmnn4afEtuXSlb0uq66WIynYn5J2ME9RTQRo0OC0iOwcGTzFC/9TzfYgLB22O686qgyeoPpe3DX4KKvoQ0+E805U6jHs/t0QP01Z3bs8f6r/GixwDoa3zzHlLT1ffRXQ7+6Rkc93hU7rx0br/7hmdh1ejEq1n9yYxCAMKGKunPvBoHmuW1VVK0wTqFQlGPuI7UdxRZZFJaxBzLRDUiLUp6vJexT9A4kHeRxHiFVAH0AULBJ8JdQXwBLgTShaIp19NYzSYCZzh+WnoDVoTtMahwgfmKnZqKoBYEqMqk0tBXXU7fBx989Mbrb//09//k4IHDlJtge66d4qkkAQUCoVK5hnFyeijqLiIv8pE8oPpmnbgKAYx0u0Nh9mY5Hwg4BSgpHG18DVQUR9iaQMTFj87k1xfnJ4IUJWUXZ7NFdCyTzm/HE/0OytHGdCK5mYHYm6MiOxpEfOjXsln8DVC78QgQBPzwwzPYv6mpyVA4wGGhReGqKZebRrjSIJh6VYggZKl5PdR2qPf0W6kCpceggaez+bXCFoGr2cmxp584YRSfSofMIqI/DrO2ox9kE5v9ZrWj1cewbdpBFR9Uq0W5djT9BrVcKVWgEiv5aFwM5JgiVevzBXCZzzmZS28D0bzu8WI2S8IZyga1ehcqOqlxdLIafOGupTI5KTXVHRg1oBNrsVLpoNDXG2wlstXmIJuvZvMlr9NVKNVL5XSt1eY/o92GdI7FjhwBBC5PfH0dU42EZKNVID2KgKOpJwhYnHAGPUJBgWBobv8hEEZP14IPBDhYOHSQOGNmCwpS0hP02KA8N7sIIusAffj04D9p+3q6QKdBqgAGuKYpUSvpS52ecq34h/Dr4CojJFUr5BkMQAHKs9NBACaGXK9bkKT0VodYVS6bwGdJqxBMcjvtkIhoFQPc4XSbeyJSBbfGgktvIK4mj8dHEE2rrVPnhVUWqzUSDuPogq+NGjVDjoXUfCfIyqPCAMANA54WRSuP0Ld5GEBUDI94YgtUxE8Kw3PJVitsJC3x3HQ2xaOTy2TIT0SmCGkq7gjsbK6rqalD5B+LhK/fuEFW2LGTTzq8tvWr15PZTGwskM6QC1nuTMQk7wvNAvqGiRx4Il/UsGESKSCmIQHoFveZzXnw+eZQENCQEM3iO5Bo+GZ46DOiZK3ylmjJ4+m3qwceGABi0P929dw34mpvvYlG7p24QkTDQ6bh+0jm+QDG7S+EAzZQ/rv1strZEWzMIt6E6sxwtfpTDrXLw6ys4u2jOsk5y63/iHlhYzgernRsFT4VaAe87HHknDj1BGQOTDh5RrYm/OAOqeOUoKrXSK3q2dD008BdLWOQiDp0ex1ECDE2RMdg23Ac9AclXmO1YXVooVGvyefKFO6mrHq31YEWjU8BoTfKYOIW4qMeEwKjFw8K2AhmzNTsDNIq5z6+2mlqbUb7+vIaNl9rMlAsqUSqsEnTL1VrlXqhUmdHWEwmq4NfFKAg7Uvbp1Yr+KyTiG8FPQ5IJxSAIMEYgV3MFblABm1vfjy6dH7wyq/e2Jqd+YM/+IOTzxzO57B3GxaDcTIWK9Za9Z5Jb+4XswmtXjxX+XJJzIzeANOGrifXyOXzw3fd2EqT14N9HB8Lga4GvXbE52hXfVw8hHFyvBFlRv+QombRUh31I/KtnC6HpougnxQP387krtxYpMb71MzC0tISJUh9flcDxKjTZDNJaDTExAwib1Th5mD+iRqury5Dqw353OxI1Y5nn302ru1d3t4IefFnuRtj46lMtt3RT0zvB/TgKsPlgvMnk0oQsnE5A+CG8nY5Hs/gzwN/9nu17VzZbjaRyJZI5ZBO1Jg2ES7yW40vPPuk0aI140MhbtjVoAWpt1gjkTGQVrq5DXsGpjQS2tvJHn0RMtnxRwCjEGyE3wOH2jYzGY5GKV9CotqkxuD22utwlLo1BINa7arFqHHZTLlMIqsZhCPjZrszW47TNpcLeOkmwEnFeEJ/0NIAliS64+HDa4UYM05Is9Vpa5MLKBx8PDc8DMBoRrjDaZeiY31NKinUHHLFqE7PMcEN+GCQ6gYyeiTGBvdswMGTqSzKQQxXytLpDUaGOsVWSZ1L53LME95C8huYC6QD03AcYBawJuiHOW8zW020hyR/8g1xbXK/4JjzeQAmA54gjARLGkeRZG0ZDDwIBi/gETY6slUM/w4BO8YtnwR6k2N8ahoVxNXN5KXry2vbGXd0/NrqRqZQD40v6PSeBN3Z7dpAP0gt6XV8P5DrV+jBpe7YHMJ2FlVNICItUCap/gLg4AFXWdPCGiclDHoe74vhq4Klt14F6iI6RD3CrffPrTeP+s/IbncOsHNOecN8YqHyFmIJa3ishxCNJZx89DNvFBWNLv/E0b7ZP4aG4Jt3mXcFQLtHzDfv4h9f0e4eYKwPp9G18p5Q3kqjC+82zxHYeM+1rNpzOQuVM9wSfVY+HOUPLz5iM1S0BhXxecpPhI957x86dAStYYILCOtApNCQgNTvI0iHFefDk49mPosJVwnLh/c77+WBplgkXENBKN78kFJJK5NkeDjVhA9IQ8aEYAwwIRrDAA8EPo3NrThBjG7HjTAfvgoypwg2ETtAP4ZySyTypNI5XDucJZVId7Udjz1AOM7icA36xm3099pSVwvJvqmZaafDtbVNge4aKnHgGywAWsdb2xth3xFoOuTvb23FcVQ5iOhBAKpWW/UihnZyZvbMuSvridKPf/Kj77z4zMKhEzQP3Z252JQzMPb6+5e24luoJZtckGHFG4GHA99PGT3fdovDYjuh3axvxLkZPq8rRAUoGxEyC0rWfO5LoERqY1EIwok1Je2IeNlAgxPFhp0ukWhtBY3pV9fWcaphrZE1Au7A/zWZbaHYBL6EfCFDlJBCGtRInZua4IxPPvlks9wCgIUoCdLpAolggmujEWJbGH6T1TI2YdkW5QABAABJREFUMXn0iWea7QHhmWwuTeGSTDaduLYKxKy2jebKANOOESU01KgNGk3K1qI96PA5pDRpayPusrq1Vsf8/L5OMSUK2rhTkJ8xGMnBtjhIV3cjQoiTBQ8Nl8G9IBRFeXfRgO5oqCpK0lrAYQMukPNVDQaWbtykxmkkOgHy0uu0pPBbTNTzCoaDHmAEbq2tdV0hm4OEg/YA3iAqVfAXfWZqgWXyVQAKnjv6zetyQ0cDo7c7HTLe6Haf148HhKHOoAJHMmjF72LBP2WvUSmW0JTJQikRRiZYSor4NhsAOApf0NV0LG4zHgcqexDSYsSSMA8cxEkIHtIZGlluXiYzNT4+MzvFiOUs3He2B/Pxlz4BeHEQoD/3hhEL1gH3E+1lFWhpcnISHAQ8olV4iRCJADAxj0uJFAELeQP4BqvFXgk1IA37kmN/5NhxtKmuX1tcXt84/tRTcKPWNtNHKm1vIIq+Fm20hRzNaoPUTChf3CnV5SNxMaquKmBCfRuIDJDCI6TNTPLE31FTZR0lWlksGylrZeOHP8mpRl5VwJ27vqAefmMen+HL7oG7AqAvuyGPz/do9wDvCXlV3J5G528vu/Xvnqt24CEF68hrR50Zrh3iHuALE69yqUaJS6cHdwfdvQZQhXc3FE7+0hwETlx2B+nWmqrQDjDqCrEU9T/RNaH8tXiATJgHLcAIi8h/fOPyXU44DDcSVgeTg5QtAoPYBkw+QRwCAThTONTq6jplSjtTY1TxhLCxuZGAe51Pb5P+PX+oSsYTn+kElW4sr6SK+Xq7vv/wEZfHj50sVqjS3YZjjGel2qi2CSsMKnyC+wMBYj7tOpECqLs5gFQ0FkYBmegP3iYyz6hpRcspX0UdiPDY+M/+aHZs5sIbb73/P/0vf/r3v/ynF548GQo4ua4np45aPZHN9LvFettqd2FywBb6LqUshQoCC4WUcbJ1QGkQn+lkXF9r65t2G0xloio61IrpN4wi116v1OlnvC+YugP7uvlCeX2bKqWIEeFT6+E2I0+aeAo+DAAHrO1mreK2IBUjpRrAjRyE2p8EDUGcQqOZ2Tc1e3BjM1FrDoJzC9j7c+fOURnTPTa+trb20ZWrNo/vw2uLfn/M7nReunQxn8u47Tb4tYAnKsDjfnO6fXgCaBL3lw9zDDnYldImG9tJQmITEV9HayQT0Kg1LC2usr4LyhnU9TZnbAIaUQQWSzwexxfCtafzeVfbXEY7p1bzmZ2lQpEMLNSFtHiuNDqbyUaOu66rATdVWgNwsHDF8Ee0e5k05d/zxJtAD8Ae8DTUZpLs6jVASMPtqtk7KENRfBcx8TqjjmHmNrgZq3Q1LQd9UlOCGZAQKJmSrqA6VnFrdLoM4AaQgYeyiaOpWmM5gIcQojioKo1eJ8fdRLecw5I15vOHNtZXqdZiobKa04GXBhDDLnQsWe4E3UIBP/NgLMHugSB74e0jtkWFEzANAJQRzkKSv+hJ4A7z9AZIWUY71WIlY4CG4MrqMl8qVfFBIj5EGLVSLuI9AppbLDYw04EDB1ZX1q9evfrT7u/DvL5wbWVzc2P85EKtXEwk07Gwh3QwXG4MOy4Tvh7MPBhTNFWdOK86jcILtlTfKMLflu65s/3t/e78y+7qBszI5g964uB3+WR70Gd6fLxHqQceA6BH6W58FW259Wb65NO/+2W0Y8no+2jPVcMNuCZ5Z+01jZ56OK/OgIQwIUzMsAQ4Ip/LHTw3vXQ6ScluPZ59hJ7RVuHzX0ftAyo41KBisHFkLMaOGAAsEDYM20DeO3ZFqleZpf4UITT2JTWeIlF8p/LhruvD35SCAEIObfY4F64H5fxgCQPuGfYmmECB1YCbbCEDXA0cUSgMAgTeP3N6YmpSksK1huMnns4k4a9QqV5KbRB+onwCLiIgFo2xmAy5DAXH7P12vZDPU5bryMEDWK9CpYQLgUqTWhLGpL5HvViu4Nxye4L7Dh8zOAJkkFEu491zlwrpOABoNVU7+fSL4cm5J3R2sBbeFOwxsAAHPYEMm9OGk4tUL4ylcJ6sOBu6m/G422Wbn5vGKUKRTvqEnsEigmCoUcXGPj/OETtEb8SItxOXzVhhqr532+RlddoS3wEMUQcVbgh+IOi7YDWKVVFTC2RD2SxcHafPfIzlzhZqN1c2b9zcIJMIdtHNmzfpbbSG8eBxF7qaTafbU2klnn9u7tTTz148fw4vmS9kER29ATba6vd7cWbRsFaH4qxWynKVq825yRin05UbsGcozEYw0qjrnjzxBPynRLYAy92g1UOyAS4BYa0Op3CVtYNMant2Ybqn7XJPx+bChw4diqdThO8mJsYBfNyLubk5iqUgvJQtpXPlmt3joIYYBVdbRgHH4y6XOEWyBRhdJLul05vpdB72DAhZq2uyxOsVHjR3mawrKnbRpWAI/jJ4QCc4F9kdfxLjUIYdwEjQeWcwENeLzYK+gL8rpefFV8cYAGSwMZpJOCZB7fQVbh4ytoS5TGohpelRSjSYSONiLVXGSoUcICYaDoFyyHGDQR8IhTkRtDbuLAu5s5yIPmX40QPszonc7mi9DlWMButZ6PJ4kLyitYAw+sTjMdN4UKDLLVKiPLIMIQ4pzsjJiYMHD3JRtCocCfo9nlq1TMIbmWnxpPnksYOMFh49OaPRwPeDQvuRR1k9zuhf9YXA0WXtXrhHNt7rhTE82ui7ZM+Foxuo8xyTLZlXW8KM+lOdGVbDGK5Vl4/+ZMnj6ZvXA18ZABqOv29en36Nrmj0LozOcwk7Hn7WsmR0G3V+x2bqtaur1F3u2Ru8km696thYcf/w761JxUDYBt7CSh5YBxDDOxY3+6FDBymydGNxCcoFDoM2InNEQ7qGWr3BW7xGzo7B6PX7yhBx6nWiSoAeCp7DcQaF1JqNNPIzJqNApmbDYpVyoUgJo3LHQbAT/mCQKpGJVDIcjmY0WQyApoPNo1V93vKNZhtXBz4iRH+JekDbabYaa6urLr8/nSU/ptiotVtQVjWGvq5HqrDH66LmAYYB9xPsCoyiDfnmjRUyrZ587iS1FzBU7FhvdOZ9Y9S1oNBZqUB10mQg0NdbXJSdnJifCbZiR08dq1fLlVz+4vmLZ68s1/rO2cOnfEGd34A6tC6TLqAjDXginMElcNXixUL3iGuHnKE1kteeTOWCIf/89CTBIEwXHYVjAKuMfw2z12p36C3CPZFQYN/sLB2HSFK5LMl3bEPeuFHS4pqafodqX1aUheUTn9KtvXMffUxFMzjg0Wjso48v9LWWnsbY0Zj8wXA7X+lrTRCqsKAEdQx46voaq8MzObHg8YWKK2UEDRxOK1BX6oSYrfCdDRYbCgL9QREOFpUVnBZLKZf2wvKx2dbTV2ACwXIuEcYslWYnotCdNdkCLURsIJFOIMCdTm4R98Ht1eq2epoeCjeoMjtcbpheLivEZw9J9iQqIRpAvYrVrTWu0O7yg0J0RnGYQVEvFKuGQXttbcPjdnLXRM4ACUQjegrI/zTxnxFUsztNpLABCuGLwHbjEQBP4Cejo+hV2avfB4WoWBa8Am7D7+JyeUg1J7NdC89L18Fn5vEsEFdlhHMWHGzshQNGDmbUR6OCZoD1iEWZzMaSUvIFQSUGMKpOdJh3coqRU4fy73KBkzgddxy8i6Qkdz42PhGKRGGwaTQ1sClHruWLFG+hHi4ojbR81SkF7nFJ/EtPSgF6UT4PYz8IHY2Sd8RMoQGh5wBeBlxyCiJub73z3o3FxZe/+70jh/fnMimwuNPtzRfRu+5YdCgCEIw2MwIRVWdgAA7lK0PJwVTfJ3QUk/rA8zipGEhgyW2PDmuHLwRl/tbLY3R+x/vkHqt2bDn68/Y75g4MYu3o2dlgdPvH89/UHvjKANA3tUO/RtelPuTqu2DY7D2ffF4Nytthb1Q03Hf3zJ5H23MztRmjf3mHYtswJ1gI3pZYF1TpfuenP/njP/nDxcXFGzdXcAWhOXfk2Mk04n3rcSwdWrqII7MXNQGAO7z6PT5JzGF3qi/B8IVSwYcvDGSnFYVfPdxLaEN8JuPMQOEXM0/WNhljICZ4qZAqCCQBgAgFUDMqGHClshlqbQJrIIrgOGnDL4EVjP5dv5fPpPRJKqYniWmggILAoJFDWiwoLpp1UF9FwxqAhcIfM4VcHsHEb73wgt/nubF4jUuz2DxERmiqWU+FVK4sY7U6Gy18SEApeKR4X3Q2SC4wcYKxza2UyerGktlcTp2e7J866dYcFoME+HO53DgAsIWYUoILOBWgGzucNq6aQBhMKZfDRl4aJpmwF6BhazNOn9ABZHxnclIkfGoimkxn8SWY9boKUUAsKLQaUXXW4luiFgQuEbvLTZyvCzWnVsP2zzcXQuHoxFQlV0Qcz94a6A8cPGYwGTL53PLiEqQSrDX63UaLaAiRIgf7uNVoT0+BtMqZZApzAxXG5e44XEJewU1VpgQr8cJBPRyLLq+s4OGw2z10YLtfBinTsecunOcnyj4Q5SswzAsiVekNeJutaiadIMbHbWXkVGtVZKwBRGu1amxq7PDctMFGuavawCtl3lfX1gIhcGmPih8UPKVcCQgVuWqq6lI3zR/wlWtVarTTmZxxfDyG7iGdIJ4e4cfr/BMxVK0ZtJh1DD8Dm8aDe/jLPQXBwP/lvgCVBOUIr7zl87hFcYeMts01s9UKxwy3CoOce8FfOEnZTI6MeroIpW+BOwgrVEo4BvHf3FhaAqZHgiFoWOKus5E/X6ZnKPgC8ILZxqHATHQLt0zqAfd6RH7xAOFXA+zShlQyyaUhkkkjQb2MAWNXKpFFowHGAPeRy4QKRvuJf3E5JoOOYxLs43uDwUf+AUKUL37rpYW5aYKjpVIx4ncgNLC6Gj92YFo7IE6NY1DKrBK0llAsT4Iy9Hmibj/au5/+ey1RXzv32uL2utEtOdftxXf+ZQO1DSzasYG66s6mt+d2bHZ78eN/vzk98AAA0ONR8s0ZDrevRH2bjL5Tbq8RDKROLGFmuPx+ZoZDRXZT/M4swbPCfzt2V5aLT0jSWCCTlivY8mgkApN1afEGkiRmEm5cLiIsociYFkVEqk4aNBgJLAqkD+IaHJCXvyRGo/uHSTEZnUY+xEndgTBhgpmhfKmjY9yzGRBIhHPaNZqMVADdSsQhXMTGxyBVGHWmHFEnIeiYsBMAi5ZV0AwHx1qg9gv1wReM8DFLPKKQzFy6eN7r8lqsbiomtNDFsZqshLUQJaZUKTJ3Wmi0ZbxEFMY6Oje5f34afwASvWQMkZEEAkP7pVbKUSbK7XJNjE0SFUvkCjg8sCt0CHnpxJWglUxPT1FLgixsSC0AI3w8XuJb6ME0GnkYsxUKiRsQjUEjgPKZNBVMA3JCT7iQp57l0vz8DOES1IBSArOsbEBX8GHONfq9blgcsHNx84xFgnptH+vLBh63m6AYQAr4Rm12gkIeL763fpIq95EI+IZ9oaU32trW1ZV0trKVyFMVFK1knBuASMRt0CtCpQ/zCO6Beo3mULNWB5bJRP3zboe7TAob7BngArUz4CBjsEhDIt3KbuiT3ZTMVag0Am+l19K1W2VqfVAUqwxWQvOn1STyAghw2M2ry4tYeogs3HhuPWE46o0YLdZ6jepvVH5HAXxAQJNdkGsEMJE3vp1PQ2YPUGzN6zJ0qm1znygTfShRU1jrREj1/Ug0GIyE9Rq9YtdFx4cRIevBiBUJvHIuXDfU/6K3GSfME8hj2NF7+FcUUR4zHjA8PXiIyI0CWKSzWcYwPCtgBxQxCrQQSoOhRU9x7WgC1esMW6qM1XnSuFPgTjAuSIV+IxjHuXlAAP8ciuPgmiK2BeOb5Zsb26hSAar8wQCuUAXAUclDUgqYqCXGGdkMZyEt53nBs0W3c3CWM0F/zmUzfA/QNsYtM/9/9v6zSbIszRP7Ijxchqvw0CJ1Zomuru7qmR65M1hgsYCRsAVJg9HIFzTQSBhf0IzGF+QnwefgG5jBAJhhsVA7uzvTM9Pd07JkVurM0BEe4doj3IO/595Mr6gUpbpqukTeivK8fu+5Rzzn+Hn+95Gs70mA5H35xS9+wQVhfW2pebB1NgjA123t3b5374evX7OOqOkIaPUq/f1a3o9PEsyRnCearyf7hlXnd41E6aHAkztPLp37N91tfCYyI5+Jfi0sgj6q4Vzx55zGs8/DRoomt57zyMtL324K/K5+fS9aT99uqn07RveijSO9/uynUSdbWLxduZsW+GRSfOrymFSS1PexjSxtK/lkKCrA8dDb+L/7d3/9X/6X/+Uvf/lLb7RvvfWWTdyObH93EvKVfB4I8OKbuM+EHUw2X2TUsie0y/YO02m9xXC9XmMn3pK9AbM/PfAuLHRgq80Eli0OXYBYPnQE+VJ+YXlezaH/mhoJo4LBqJ9oJNQufV5UHaZChWJlkVvUpSvTxdzm3Q+3Nu8OB61+91ioYk5YiS6A7OdEc/qpk62jQ9Glf/D913vt1l/9639969ad0ASVa1yuOMZvPXrEZ4pnEz5k1LZ5giaITbcxGDEVMSQ2PcfHRxiNNFMaGlFadI49BQZdWF/NzfDZGgvFjDdzNxPSBZeCCTy+tLRMhfeLX/z68ADG4i9dISVCioTnZdj6eGXnJE03GFYYZyeYMed0nBcLlEwUj9Q6QCM8AURlCgTnw1CRaG1tY33tAqsnZuWylZAPfXj7zq/ffoe2sYUMPbGqZynFLl++SpiBDsG5m829nV0njfl53t31+QbvKMo7UjQ83iJzqy1yQLe9cfHim2++CY5AmboKAtJqXbx0ma8Z7IjlG4JHqH7EjAaCfQ1S98S/Yfc9a3gLc/Uf/fBNVrlH+7viR8/XaoZBCwnwLS8tVEtF1jhLCwsUT5StMIpGwQ5zbVmivHqwTn+Viig8hYPDfUvGqK0fS8IUI4IyiOPcRXed6IbrUBh0oh7lHaRNMr4dHRyx8AI74D2iSjGuICdrw4ygKrWXTGFMc+7cvQXcqESdvroF05h99RMmWYRoAmyp38L2VQf02dg16hFXYC+yT1dcNztpL65duwYsIp1A1fG20O3CaqjNDomkB5E1Smtp4NYbgqOtBbC+vnr16mUuaBLxFgvZ5UWBDVZAKPXsHzQ9xdKrmJ+Kdcu0R4BIu0UaGSixzX/ql57+3icX9X+ytzy7paSFJ9fTp85/Tm596smzT31Cu59a28sC33QKfAkSoG86Cb6z/ffLf3bsNojz1321/9ocQ/SfHL76d1LG+bOVTK48dTf9+tSOo4Vk99PI4+NJ5a4H9PBiGjaV01PUA++9947gb6xzXCQD+Of//J//y3/1P7/79m/EzKtUFl2UDVTrhPa2Zq+fGD+/d/XY+tnhCvHnLgMR6g8ZLY0F86DkwSicY06cm8gS7t96xHBGJN/XbrwmItzeo+ODg73j9lC4l3IufySd+Jn40bMJ55tpEEvMMnddPhm0Hnz4XrfT7LT3q7VGGAMNOYMPuDcpPD45Y82Unc4cNffX5qtvvPHq0f5e5KwoV+aXVniiTY2HXMIloMB7HkOWE6HtTr1VR/y8/oi10+nMoFwqQWS5mVOBpMl+SrASw1txpnsdJsvADEER7NXKNGVwWFle0knYK+XHwgFzOH//g/d+9Zt3f/CDH8zP1Tjqk5nRnrAUh7dUBRIKZYwMghXVa+Xr16+i6u7mI5ySeSwWTqIFgiT5aGlhKryJKKa4LN2+fefRwy3AaDo3avVPc5UzCsoMq+FOFzOklKQTLNdrM4ViYCvRdkIS1MqWpFhnnnVy+fJlRj/AhykzF/goUNJtHTXK87Q2OLrFAf0BcPRKrXbvb3/2C1iHe5TQTELvcLv7za/fHnR6Fy5cLhVKv/31rzhV7WyKJ5R98/Ub/N52t4QDGKzP13JJ/ZdWVqY7nV/d/TvK0DqXMHkthv2jpmRt1HDlniAFDx5AG2DKTvMYuOl3B1vb24d5UYcWAIPhkElyFnggUKxRZTEFi7S7M4LmWOS6CvLONRaMIlDjyWwSeqrLu34hSbhmNebyBT7msAULN9jYwl1dE6uJ7glYYeUTv02ogijLOhcMCyIn9iFegi3S31eR9zvAyixuhk/fIbgvVGYzsBEDowYQw0Q9/a1ZFeYMJDKVovswsUdhIBLOc8Luh9mVrkqEp7wF5UcnQOisbLQkXaesvsd+MhbW69979d/+5G/v3P7wT3/8o7XVxaGgCEcHgmftbN3ifLf8xis0pVaOQw9TOqQ/ea1MTp78umMQ6cV0OM6T4+lNycW0QJSPF/ZJgY+uf1Tg3NY0uXj+5NnW7T8W5Pnr58u/PEcBxDk/C98mmvxOAOjlovlGLwVr+pNn8KlFnxZOP8/fOn/+FEHO35q0lV6cfJ2cpL80zAO3fgyFUl+w0zOyn4iOeDbNaHR1ZY1lz29/+87r3/v+H/34T9rdwf/vv/qv2r3RX/57/6H9mr0x/20+Qyrh7s6nZoxBhJfODIZMWqF1PMa7shPXZwuzXr4xcvYNDKW9tQNPrj98sCkY8Un/pF7K0U1wOQY6Rl2+5mFZQkrRCdzQK1SwuiFA4OCw023uHEABm+qe5ijFENeb+smgn1tdx37ivN8S7PCPfvwW5cLdu3cvXLwqmAqrYcIPoGXz4YN+55h3kkCLtblGs90F+3SG4AhBdJuRcFmurfJsFpQysOnp6mx57yDySxBv0InpJpQAuk3x2KrM7m0diEPN+z9XDK0KzmqM7LGpncKOm3BMKOPTEVZJ/EBexFAX1NB5bPu4dVCdnl9eWSJvYAxMYgJImaO5Wu3VV68vLjUe3JGqIic/A/KkIjoUnV9s8Gob56fWF5YxYwom6CGTnV5bXzPR7EXAu/bRgMBKX7FzmkQMjbdeAlKnSSm4kQsgg2WCsEuLK3AY3q8/GDwjqFFmmsps527/tNcfnTW5zoG5HLUgsMzUDEujV29c67Y7wl5ryINLmerKIkFe9fbb/3A6bE1fu9jdfYikTSbdR9vjbrPX3Ok3W8XMab2UZZIl6euh+Ifb9JDHUAhq99o7g9N9UQX0B4AIIoxF6OGnJmloTI2+oZj1ps8JZqqbKcY3IJTrZC3snSFad6Ei2d/YRLOnJh2UU4PuVy74k9yQAI8wUpQmgMNNC3ZtZfWoFYIZ6BlcQisT4ZNxz9ajh48ePmSmBp3rJFCoIY06tOLQT1cQHLghBwKz3n77HUBCV9TGAoxsRmRnoAfCTjsPbBkakdJ8PdzfOl0U6IvJFFg3Sbrn8+KlS0SMH7wL5bcW5+v7SQoznnqkUYSslMjCINaEWxCXy494aEVVkcUhgY0fr/5MjnSj8FXrT20aT31VJr2Sjuj8p+uETU+V/4Svk6rSMmlngr9/BKo+4emXt76FFPiCAOiplfQtJMx3fkjPbkzppNuR7bApeZ4tk15X0q2n7vqaXj+3eOLK+UcmNbuuIXu3Pd05zIFJM8OVW+DatSti/D94uMkAiAZKrOFbd+/9t//9v3pw764wPKRFME8zMA22MaZ+YOmgHiYRQM/iypIK8T+Njs8iCpDrlAvLjYYAMQILH7e73qeLsxUBgfAburYbVy/MzTew/6Pu0IPlXGFpofHgwb3cbM1XIXxqcytMTUCo02Fnno6p2+7t7z8Yjaq1eSYv3bOTQn7m4d0+IRa7nGG39db3XnntlWs//elP/+3f/GR57UplblWGdP0hwgkOJpFkJicMY39/D4TCkIhPWIJQbMzRf0WgoIwQgLyRvWpfuLDBFhbXNw7iE5wGg+SljJPhuMabDaVejrM1RAEaum68XL24edMJ9vt81xpc+tfW+aiHdok1t5IbFwKuvX/zJmuPJBrNnPAyMVOkaMNIN0b0on6gB2MWqfBiuXLp8hXaRt1nkLPTbJ9lZ7//1o82Lq6LIzkrxvGwB+EBB/RKphLWJP4xNIjwbDoH2DRkVz3Lyss2Yl4lAk4x1GRh/p2ZqZZnly+tae7dD+8xSZ4uTFOCUcwt37hMMkSCtbq20uyeUORI4PAf/Ad/2WkdsSHn7E3o0Wd0c3LKiqd7vJ89640Hxzd/87ePbnG1k/UzozNm8MEHg9uPmpni3Fw5d/XiOuv10aB3uLtjuBi34QOI7d2DQvWMnMci5FkmZjf7dAqs5eUlA6QFM31s9NEfek5NgkJo1O/D62IokDLSXpWKs5AN3RaPLVgqtVZmilRi6lUWjHuBbssfX3U1QIfiQumkrhqm1QWUmD4PLjQCpFIghil3ekyF7TY91GGz6VcGl9PpstqxAAyQRb94DdAJCRNt6H4zoi6JLbR/5w5hzcrqQmZKhpbIxrqxsQYHR8jRbGju1KPK0qCE+BKcCZFI2PnDH/7wf/xX/5JAdGN1AfRJLMMCqrLe1hcRlvzumLxBxnqSQJ8wA0Kf85++PnvQ9gaWeYJnlJ/sGFHYVhF+jcmOkb65JcgprTatTfn05Lmf50ueLxDXP+m582W/o+efTNhvNFE+NwB60TL6RlPha9t51P7qFt/5qTx/rsUXNXr+1qTMs508X+w8bV0/39Cz5/H2es5igDGlHbzTiWD/1A2VelVabBkAWDbYi3kY/cMvf/7WH/zhf/6f/18Om8e/fucD3r+kKRQItuyQW2RFyKtUyhGhJFjOyUn6Dm1ztzUTMTCcoMxiA8y9C1sKi9Ow0jgVE6hcqTbFzjtuf/jh7UWmRLmZ2frC6sr6waNH3v7/eHW1PQhMEPmS+n3NEQwxyslGFL/xzc67gE5nagyfFc9O+63cuDDr7f9ob+vSxvKP3/rBSW9w68M709ni3fub+Vl6kSVQoNuSgzNz45XXVtbWMS1GKNJ28r8HWjgNsekNCcdhKzPVmp9riHbD/afd+2BpaR5zffX66MGjHQPkgg/BkB+cCR0p9+f6KtZIEAJ1YL1e4qX9IpnwLGf4oya5yxSnMFCGVRCnp3wuQuQdNY/nFhqXL1+5def29s4mMQzhFnteyJLTnEdwYmICtAIImMoQRRBGeRCU7FBX5mezM6M7H95av3yR4EL5xKxowAnsZJjZazapq0DoWZomqCG8yiLZLSTB/47sisrS7EBjHO8X5yr5GQbyZ0LzJQEJOwv1eebbrcP9qTHRS7tUqbOYKfWGDx49pL0Kn7KdbSZR0OHJKcHDKTz63ju/Xa2XLmysrs1f6xzvw5OVYgYIE0Jz7cLGWaZ0NlPojRIb737ggEqFRTl4FyY+jXmLpfrBrbdPt/eXVzbgA8MUO3F5WQrbWQtAtxHHwjZ2qxTU9okg5DSJKhMU46ve8RQISlsJ4kkYrzzTmfUL68CNKD5Uk4RPzJklAklxoeCfcvQ25o16rOZisQMh6ZuvoWxaXgZWtIVulh/zZxhdc9HhRkMZQc+1GIIclkDjKYvfI5cvXYWbf/3rX8NGb/7gB9PTBxSrOqKHgkgVGuzL6TRnyTtdYfsDPxkLGSFA5jrxIQHS97/3xr/5q//V+vn+669IrTE6yR8dHhK17u4dHLbaDek+ZGRJtNoy1Gp68hs35MlW4Dz5+vjlZ3Jd4Y8KTa5+npNJcy966KkCSTdAr48dT5X52L2XX75dFAgvgK90RF91/V9p5z+18mdHl/yiPvW5z1Qgrdznl1jnUw2rPD0m18+35VZ6Pb3oMz1glEl5ZVxMP110Prn11Ela26TOeET1iRNHUi2pTOjivVjThoRCRMWsXuLALzP+sZvbu+3F9x89ZBesMcKPv/u7n/wn/+m/+P/8f//f//V/8y9/8rc/PRN6sFoHa8juWWhgpbj1nXt3efKCKZgFvuW9PDb3/onM7dRJr7766m9+8Q9e07ETgoRisZLPtYCnxtxMIVvqHe91Z05YprBrEf6OJxH2YGNPR80Hmx2GVFaXL1+YLstu0C7MNubmV5sP7/X3d2YWGmeiyhyPjk+nCVfEcfnB6392/cqFn/zbv9raOcgWylyLpPzUSWzj4f0H+sAdna0MaYpQN6yJiaOGp9N7u4csjvCwQoG8Z09AvMX5RjYPf+A+h+RE4CDYx7ZD37AxIgpx6mA/mgiZs4goDI1t0/x8pkRnND1l1AQPnK3o565fvSKSED+pze0dEV8YuEBLTJHplQSBpFbD9Zkor1+80BSeT/AbDkrDIRS1uRkpt1rtVmN+gaXt/fvvrq+u1RdXf/bL33aP2+8234FQjQjKCeMhk7Wz63Hu6WciKYIOFDdnYTNrtlEAkxbV0nCI/jBaBub0lVNMc0Ynu839h9KD7R+98uYP55aX9ra3TovZ8KY7OBBxGMPGuCUU47rFfoYZLztl4WeoxbTe3H+k5n/6z/7p1t137t15l6PX9RuvxkLNsm4uHuztFWbrp1Olw7bJpehjccUci2gry1gKtsuvihgpWenMUbtH6Ahe8PbXQ5pEiBbIgGYIe4TJef31N27fvk03an0+fLgpmqX1BgmBIzJvoCGkMu4BzSeZbixmwXIgp/mleTomRl3oIMkpzzjIlWSFhVAYZGWzG+urEmeEAdb0Gc+v5DcVfvUsvdQcJJqaAuuZZFH0sYtGbTZD2tIN3b5370FjYYkODrL44MNbsVpmIuIRuvmkfZM5dqYS7wNhji2XKlWdiE3ZbJ8SLQJLlvwiIX5LC84iu7PS1Pzee+/9xZ//iYFPzZ6JysRS6vjw4UHz6MLSUlAvN8NaDq6FrtJNwI/dkZ5/wmfym4oUIsqc/5x8TU9UFCWfqUgT6VPP3Hkahz3bmeRKsh/Fwy/cxJ6t+Rt95Vk6fKOH8wU6/7klQF+gjW/rI89dPS6+6Ef4zaJDOrr0M0b07H7zzHg+YeBP0eqprx502H/PH5MyuIUNXcKB4emQkIYLMf2UqII26+MOKc3+//A//PdvvvnD/+z/8C/e+P4P/uv/9l++9/6Htum5yKswx4gI79FT9dvG5b0S1J/cAp8IN5ypqYOmaHItHtFqo2fwDi/ELudchkT4TaRSPTuRX1IQ5HubO96er1/agNFUJcUmsYf6SazwETqivd3j3f32/MKG9FDvTp3t3327s7t5JtfSyhpXajFz/+Q/+md/+oc/+PU//Ozm+x/AKisbS2sXrpA/sZd4+PCBpr2y06YNRJ877UVQ43L9mIs3qx3mRP0TuQhIoqrVykwu0xv2kEK20dNhhOdBH2PB+QAdeisIYHVFDGIpQg/p+ggPsCWyiuPjpuYIJwiIBM6BAtHBsVpfbWaZmwQQYdVLGIZ1oRKRAwzBW0729XK5LvShTo5nIjXszNQJ851HW5vAFmAkyddcde5/+B//1/aDB+xOJCbf3BEWMhzsCMVcIbWiuKRXARFMqEMmVVxf3jI4Q6Jd1kICTGuRCiZMmfhVFbIA7sJcaW19nURnKreJMednePhXCieLKwuvsGv58N7DtSudi+trACtxm2mXYwR4JnuoVyuiGELMf/DjP/6zf/IXf33SHvQ7q+urjdXLi8tLBIOQzbvv/Hpzc7d51D7unqxaXYI9DgSxTHQ62+I7Pty4eIFCB3Zmzn06ppBiUD9drYimWAwzl8cOWeHD7wpZlP5Dhw5Qw0hNKJSD/qAS1GJQlUodZhL4UQeADHEZqOpATTAdnrAIYSzqSAiDNOmVV15llUWtRgPlWXZFaqYBRD3lVS5IlZQdMA08dpTUAHGoxxTHQKazpIMPHm2CLIZsQt16660/eLi5qW8mZTyUL++YBz7gS1q2s7tl/YjP6FMTEAtRjj/OZyZic2uPhZbw5TDTrTt3e3y+rLDpMdN2WmbY67B5RGBJ12mxsQCC8BLdUrwsPd48whL68eHipx6KKpN+pnV8/MqnVvCcAmqwsTypkzVXoqH7rgCejxFkssF+7Op37MtLAPTFJzz9LT37/Pkf2LN3P9eVL7Gqz9WuwpM94vyDaX/SX05a4Pzn+ZLnz9Py6Wd6/VnSpVce70d2OzKcxBSa9iQV/2B+cnfawfWM+65td3W+sXvAkKW7tR0JIr7/xun1a9f+H//F/+3f/e1Pf/mLX+0fkMrvZ8J4lEjjJF98HKllZ3uXTkL9amB+yixZRiq94mI2dzrnVbfXxjkytFFHrU61NCufV7FwVquWpvNl2gccnU6qtrD8wx+vRWe63cSiZcD7WHSXzIyM37MjCUpxpoWGoD5yXmziRSejP/4nf/6f/e//E0kwfvHTv5fVUtDm8XRuPJOJlGSyT+3vl/Ne+Qv9ntxS3OyHnZb0YRI2cLsSf0fG1rH8mrQ6fdhvSEyVB1N0OyiWmDMfHDQFGS6VSzgQauBYKMbEG/clcAnGnOS9x/DoSjBITnD3Tu8AcOBCBMUpVyX3oJeh8SEnw1A9QhkkqC/WKHBRFtySrn1leeokAsb0O03DZ7uTslXSqZv7h0QgncFoaf1SrlgXgYfUQfIQlMlmD8uV0qroMUviZR9kclmh+kbTI0F8OtLFRt6rKtGIdvkriSbAthcy4GNUmsmUK7PsgjPmb2aKqdOsvFsZqdGbUsRj/8BBgwBqXs61Ib0VQuPlsoiwKqezCwkEJNfp/sOv3jlsD1euvH7h6vVaY7UzPjts9nfee//d3759sL1LWyisFCVhYD5GTbDeXBVOOzrYZ4I9P8/mfm4q25/KFFjSoCplFrkU2rM6MrcCY5Kl8R1HWHTQLfShhuTwDwrs7oVxDGJK5oWhk/eIHUAGmaiHINfBiIhvWjLR8Ngy30RH6llaXN3e3r31wZ2l1SV0JhIDsMx1CPN4oAGCEUTRLB+uQlhra8INUEtBLUUNyeclmGRywK9LwjwOJLubNln88jisQ07kPeWIN9nf3NuTlIPhEX0W7AWlUZgGoE8FNtMzKAw/hV3dvQfCC7FwhofefjciKWwsL/qRsq+3TuBmASeZhCfGZn6yGTAy4jHET/ncy5PfeQJr0n0g7iUy4MlXwheXPmba/BxZT1r85efvRIF0y/2dqvjmP/wSAH0z5vD3u1htSRFwLzlSek3OnUyufAFS2uoSxVdshA41xN4pWWhiBJ1goMiLSUohBRc5yNnUDCEENRlMg115GXWX6/veaO9//J/+1Suvfu8Hb/34j3/0/T/60Q/eee+Dv/n7n91897d0TDPZwrIIyBfnsP/bt8IZ2JsxrsDcZXjivTySVGAe6sSxHLVSjfqmfXRMv5Ktzp7MTLMGwlAvrm8cHW6LwJhwlLF86wwfODALeMtulBgpc9iy6YuCs7i0mhm3uPqM+61B5/i1H/zg//R//E83lhsSR6AYU+NyrSFZASNnKq3bt24OOu0rFzaEFJKlNeIb8sNiB/1oixl19KdWwdGxLtkpGMYKbYcXwmpMRkTL69dPMBudZ7TUG4RLOfTI30oIHPq+0XAYrLiQQyaMkyDGjIlphNTMPHzSkqyvj0mPmHqgrUhITDpY1BojBqk5LBlMMgXDIf/quYXa2qBzdJzUZppOej2OYCQW//DTn25cWJO+4t7WbnacHUnRUYgwgHKPCCGD6UIM2UJe/ldxq9GWRiVslRLzLMGhQavMfmTolHqCyx7k2j/uz1ZLj+4/mDntiYdwe3Ovsby+snrBuKC6D2/dYT2zfOHSlWtXWIizRtfD8eni5UsXrly5YlUACmyKd4e9//nf/M1/89/9d7ya5CwTr0k0JZpCKqnTfqff2v/jP/jRq69fF7DR4mPAJbtt51iExQ71kG6wDp5bnF9fWa5WR72TKTGO6GHNQq1eroUIpze/uECfaEWJe2wtS/FmRMSJohsw/rGs4AkzTtyCFASHg50edGLdEnQxawNunIPPvQ7ySrVxxqfv2rVCY2GV/lY8qsGIQrAm4ainllaWmQ4JwG2ufSUEcgiDJDw203ik05AVfv/+w/WNDagl9Fot7lpzrKhC+VuS6ay/tbVjEimzQExCLBEcHCokRLTMTPqgxz7digGjZW6hts2LEyGHRj6bO9zfh6J+/OMf3733oYBAy406LTOEJIwT3ExDRiamNiXPzvykIvj15EiSvaNBAoiSXzpCPdk84rrl4defnn3yZ/LgFxTaeDatPDlJd6/zCEsHdOPl8V2hwAsB0GShfFco8bUcZ/oD1bWvejrUP2nrPCUe7xAf36jOF0jPFXtRDQpM7p4bRex0Y55YidmPCIT4PiYXyaKUT+LDYvxQEP9zonTvoXg8vxhSicx0oVFfsLPvbu8TtMwWG9gqs4PNrQc//cm/vXfzg/n5hR//0Z/9B3/+4z//0x+/f+vuX//dT997nznvh4nYf0W1VBXCuIl6Ms1FK5M9jEjCw7magLw8hMGA1sbcxtJZPX92ure7Td8iWCBbWqkCyGhooOzpywsrHKyGnWMeRkYOPOEbcEttvrS6tCjOsSjIuc7COLM1NdO/tLHy//p//t9ff/Xaz372M4Y+hWLttH18YePSxoVLXM33tx+0j3ZZu/RPR1lJv2UbH3SxXik0QsFRr7Hn4RTmC9LoqNYDbwWxROQpTfMZKzDUOMMmsVGWUwL8UqLhJagqvwRCYUX4NBdr0qR8yJmyDK5BhEB8J3Jd9YW03j8Q46ZIijDbbAaHrpQ4ZtP7yBPPqOqoMxDrbnNna0lg5cVKvdTIjGSMug9sQaxwFrbHje76jRv8v+7vNsPJq1RBltMTViMNCVkZlDD8kVO+Vi6zN2Y4RTrFWWh6apAvF6Y4u42FFSbTmg5JBo1PlotZYUrwpOFgfXVeOlijayQYZXjS521HD3hv+8GFqxuztfztd2/duyuC9+7Z61PLi0s6f+XqhRCxjC5vb28ILnCw0xxP125utm9uv8PWd3FppTZbIbm4cvnGpavXq3O1u/dujtsjpuUof0jvKKBNNlR7dx48vFGqRlylw+7JGQlfkWkUkBDegoE5CKsyMAK5oJjiQI8Rk3i1j9spkvfJlllJ4zIRrKAho4AJMYkjWb3Iyeg36RUl/sjnIzCV3+GdhzuLA4iMNyJjnZzoDVMzxYPj9snZtOkTH0FzbI5NuuUf8QzlbNP6rLWZEQ8A8OIkD8SorVgoAWooYf2wE6Lbs37YV5nrbk9K11ytUknEZJm5eoTihGX7+S77I/JFPYSKZKZbmF+kp60LvjAlT2qlXLpeKVXe/e17b37vNZY+pJuibnKqH3QHVibkRFiVmeIDEOJFg023iPRTwKFMzpJJt4vzdxIXL798q9Zuk8KbBJnYIia70ESUFDtJct3J5O7HqvvEL/F4HJP6zpd+CoQ99fV8ya/veTrACXEmHZ3Q6gkFJne+oycvBEDfUXp8V4ed/jCeO/rzt5yf/6p8+hv7jD8nz6Yl08/062QTiqpj7wsMlBZg4xl2uDZSgYNbx9O5PDmBnKDYqjI4IkGF4MLsc3ET4XyKWckk8g/u3aMpAC/e+vGf/Okf/vCtH7555/7W2+98AAZt7uzY2dutwzt9LMoL8KmgxqCAt2GhjPnC8Mqxby8sNrBw2Ev+I10O88/ElggzJrSATdg+MKPpdo5GPVKoAhwAAHG/qpYLe/s7pDIqwdUkV7/x2vX/4v/6f+aC9Ktf/Yrr+737W7WFCzMCO0e2quk+W+WD/TCtlQpqpnACE7JfGY2lykKBBndrRzjkH2KaXtwJEoL57R3gMXgYU1l+Wzgo/IFXkUOoCg0HB/zeWYPE3o0RCkII9MCRpAI7+3s0LB4Ft6T4cJ0KySOUKShDRXJw1MS0wETPYuea6w9OxRHSOkKxItraGr9+7ZK8qryoDjJ7og8RX6ysLJEh/eLX7wwEE5jJaIJRtq4nRktVDJhNzPwCzVKVWRIAgU/LwCZVq74xxiE8M7/99oB9NNJhagQksQYigAwrk/BIIgMzapFpxAuQqEFfLl659NYf/Qg3ZeQuNVd1tW7dGAu8gTxhaM0PLn+JS125MifWNEGiVBuSY9AZ8d8etltzpfyqwIkzrImzAlabxEZ1sTG/CE0OesfSdVkSCD1Xq/7il++IAbV68TpS0+tF3IHps8VchNWRypUYpTloto+bBCSkJ1ubO0zOU8My5XmrkQOR1jBIWl1ZRl5fZ0j/cgVkEZCQAROT5JjrdiekdHOiAfGMO4V/+ckxQFaU4lW8ckKahfkG2MTBMFkJexSvqM1zK50y10EQNfkJeIqqU1BH4k0m6xWmXdmcnoseWanWSE792hTePzw0GdReAJPhNI8O5DbxQ4KDgSdV+TnyY5MVYzoz9tWIpIi5efMW1AW2NpuHMzOLlnrzcPuo1VpuLCig0XrMH7Mu/8RbjgNYTyIZWllxmKn4ufv/3PHU13N3nnMadX788ecUet4lT3n28R0bzuNTv5fYWF4e3x0KvARAX3yuP/oJfbyOL/ab/Hgdv59vn9rzdMjnt4/zj3z2/Yi2IPac9N0u2Ykmzzrxghx/eP7ZdH8wHpxMZws5tr2ztVq2cNbePQ4E0icw8GorWdX+2ulKLlMYD2cuXny1Mlc6+s2vGNbQj4AEhX/4GeZ34/XX//k//bN/8b/5jx5shbsulCMNFuHSsbg1h4e7+zuMRY7aR4fe489ORbzF+L0lgwLEFdg5VZOME2QDXJzhDOJ9bIApyGw5N9sXMnkoUm73SLSdEzFsmASNIYvO0Y1Ll2785Z+8foPhy9Jv35a89fZh7/R4OCqcjq5cWM4aYKeze++hvAuXr15ZuXCRVsJOjEcUiqXaWcA73kP4U6WOdzcEu8Ns8Gk0YW1aqYwqU/y0ecVPX7p8kcUJlEY/MjyNVAycs+iU8L9ExDPCXMuzFRUSQjAKwWKHvdPIqrFQjOGAnOFPFFtBOpuwDoQkzDRE5aIyOZxzim/UtNh2yqh5dGL4gasIfzBOjarcsd89qtSXpKQ47vYEsGRYE3VGKJoerRyeqjagJy8dWLtLlUksQd1CetE63tEcHzEohzwvzEAKuZ5HZs5MpQhAKSwj/qG9EumH+u8P/vCtjfWLdx9uq5PUhyELoxxOZhE8MsL89NijCNtYun41spVlxUfo10VIqtUBkXC+p6CMBKUnVEcA1sHhgMM5oykdoKM87WdhkOPmAYNlEZyxxrv37w2n8/NLa1SE5C5Wl/Eau9ax+aXlZQF9IpBjr7e8sghzwECcrBg8lXuRbIQDvMADd+6cEUCuLAv+dArIGrI+6DcBp9a379+HYyr1GvxHAXl4eEBlKQozd7+gm+jYJ6Od7X1QFdLt5yMXHFRqkZgvEjjIMlB4vTE/szh3PMfpfXtnN5ZQoi+T1XZJ0KSVFQ6SwJNxFatl8xsipcOD9z744NqVS9SLKj9uNpm7UX5RGgKtsBR0OMoHLDbdog21THe7ubN7MDwZi/bZG57O1hrNg91HW3saAOLNKYyBOMkR0UfTw/JJLyW45QkE0YlPPCabQ4qVnn1M5Z9YwUc3lVSb75OTj+59685eRJaUAunnt27Qn29ALwHQ56PXpPQnrB63XrTyJo9/9pNPqOrLbUiXkgo/6pqmn9t62u6k9cnJR08+c6YexZ65/PjC5BZmMDkwPKqQ0PZMZ6V72jrcKVVmvVFOZWYWlmmycnfufshA1ab86NGDg4Pjk8H06sqF7riXKVSO2nut411ZqIKZjce//vUvV9cvsBBiZCPz0/V/8meULyLfUordf3hP4kvSo8x4uL213Wo2GT7szozZlGTZcY6HVfHxLm/s7GZvf3iT181xu4VtQ1EsJJgqU420jve9PR8cd9heZGdyN65dXV+eW35940dvfo/L0eaDD//qr/5qe+/w4dZ+luFxdVHuyos0AaPBz//ub/jzM3M+YguyfyBjBvZ5/8FDHYaZIBWGzEbnpdzBhCexa84HA5NNIjGdCcOOfE7+8GCBqe1tktkA50spixljdSQfCwuLyVSG1S3UgutrRdQX9WRmIlAkariS8Okxvn50fMoMHFAAUzwI3LVhwm63MlukPuQ2T8pi1OQ47pLOHAd6PCTHmh3lKo2V6ZnC6RTQc3rSHYEdZck7i9KJ13We6xLNCMkH1ZvoPpi90emtPvgECAK0HfcihDRtVL8jriMwMp7qhlGO2HrDoZhM3L46/blLl69yOH/wcFtw8ELxuForLa/ME9EZEbpxKQcjKNqq5dLUKXFUD4kuyhRWroSNVKtp7IppUSRpkAuiEs64VJHUZJ2Eh72LeH6iWt65dXvjwuW11eUP721JX9obxcD5qEPBO70OQykm25RHsVwT5yy+WlCIOTo+PGT/BE8Qa8GjjcacRSUfO/iYWysgJnmJXk0nWdkBIJcRUCVugTKwYLhoFcJsq3Msk6u4iPn5+UWJgb0asNyyQlZX18k+RWdg7RSrRZjE5ZWQoYkcRZR4FgopxfyGIBLWyijsK4DClhqEmuJwd3KyOLcgvZ3oSkzLzxpncqfJ5nq4v8tVTJdEs1QJhC0ilIBVhsn7zysEGPpwc2dufv50ND04OZtbnDtotnb3m+JAhVEdiM2rzk8lOZK1ly7Jj4l8zl9/fPvL+Ee1ky0lre/ZK5N2lHyq8OTW1+1EP78sin1ThvxVT8GXBoC+HQT9skbxu9djoX/CWp/UPzn5hMIvWkPpI5MaXlQsvR69eXJMrnzyI8+9q45nW0wrtrc+dYtIxCsvNi+aL6GO4ITdnkjJ9GK5Sm2+Wj/cOxSEd7y8uLi792hqxFimNZAUfma2f5brjzPV+YXpqfHe9o5MC/dv3tx98CjipmRzG5evXbh4cW5x8c3vX/+TP3j1n//lH2oFr/rg/Q+ZHlNVEPlcv7T8wXvvYSWtw937g1aY64YzDYjiHTb3cHP37XdvRn6ubJbfVXb6dLFeuLDyhnSS83OVi+tycNc//OC9e+//7NHDO7yQGguXsnmZ2QuL61KGdduD7v47v6T/Wl1f7gwYKRcWlzfILfBOwhrgAHRreKkvluSqAEcCB/IqzmSc41jYto5RNGgO/6Zh2d3f85a/tLjs0y2RiIhk+kLVQEiFEukLtyO4B5tnjiN3B5ihNkIQZQqlID/RP79rht7UFsaJToViIUBDomYbDEeRArbIJCf87TWdOaNRqdYq1cP9g+WlRaKC995+pzc8yk6H4TNxhWf1Ad+NbKTSyi40CH6AISEMqJDIRZjfkm3olfIMr+SvIPqjlURoadB5n83QTyY2T/Vq7dHeLrPx1157bW1p4eHdWypcvbA6v7h857605xJHsOtiqyRq82Bz65DrFytqIaRJWcQ4yOeoDhd0hgiq020Rs6GnryxahAICyKjgYBpwUFQjqXDn5yPqI4+8laVVqiXe8Gtr65cvX/yH336w12rTbElJ1SWRGnSXF+dNEI5vcnb3Dwa9R1FPVdDLAcUQGEoNR95DVUlaKYInIyeHu2QqBg4R+mSirj/EKoBgrRZG/Q6YQ0gAhKqd1sjqXn39DWCd0urOvQfiHIl8SEH2cDOaE5KAyMsC4Q+vHmIkAjwkFVlS1FATsX94RL17/formzvbswyzgsLTi4vz8scJvzlXq4xO2Vk3gC2w17TAfLVqeWllPbRphTxfyFq5xumdulkMpJ3d7Zs3b/7lX/6lOOy/+s3b/+J/958e9064/u03u42l9f/l3/y1xZDmXQGOWfPpUnKICh2BoeknESz9vZ8X2iQ/fC9IErq88DXJoByefWqXcCW9df4zvfhUSQUmVyYn55/62p5Pejs5+WJdTR9/LsW+WIXf9Ke+NAD0TSeE/v+Oa+vLpYDOPHeZvqiTLyr/ol6p/Kmq0q+JiuM5G0pKn0mX0pPJ1xe18qLrkwcnfXAlHQLGjB376oQDdrjDT7G0YWHDuLLSalMvnVFFCRbnTV2Bh4/uxBv12kqn1Xvng98UagvHrfbWzu61K5f/+M//4t6d2+IOtzttNqpMGhioHu7s4c33797hvPP6927M5rPkHdJvLTPvffUabRTjUDiDtECuLpIAlbNE4ZoEGDFCEf85E6aoZa7p33v9Aidt2rdUq3L//l186+GDuz/9yW8kyn7E3mdrqzgrAmOdmkAu9GJ1/sLF9bOT7s33fgO18KuDCQSYnsrN9vpc28Yi//DDIizRQ0zdgVtoF7cW6wW/BBS8py8sLqFP2JHMzCgJzngEL1QYG/Op8wqT/aChc+xZ+XiPT7JEpRc92D5pY7oYp5JmykiVEX1nyHF9RmHJPoGSPoba7tBYtQ2ZlQz1k+jMS/NV/MzsmDeCIsZSCt++d3c4VaotDNc2LrNPl4HBAJnj6J6ZAt34lOmPkuQcwAfBm1GgKgtpYwF49E0XxCDU3tTpAAcFB5uHYOcMIyD8EaBptY/gBlqzw8NjSHTv8IiOryoMUU1eMLGiCGXCd71cnjeP/Ji0SD1EakX4oQ8c5fR8rl6dOh0KL0SrKYMFXEJIIzgT56Y5fl/zc8QqYjiBvNSkYASZ3I1Xru/9w9uwy/zCYqJ0CsN84XIITkhTuOIfHx0ZpoFYWqtrG8gCc6C8GTk4OIQ/DQ0GIoRzoPbS0qIuiR9o1J4yBQhlLnQSbPIVLrEegG4e/uvrF8hdzDVXsstXLuoebSCpj2JApeWkBibJVKXCGXBsN2pU0gptsnhRUNS7Nz/k3XblyrW0NSjWnCIRlzrTLfGIn1ir1SW5YSIvjhRVIZNnIJonGrd5QktK4enR6Ws3LkuASk3m1YKumqm+ObIwhG/c3m/+5Kc/v3rxAljJm8yfaFqGT+YXll0vkAGnP/zUZM1wnjoQcHIlPT9/ZXLri528qEtfrLZvylPfzVE/d3aeA4C+s9Txu/rajl3HPvVn/1nKTBaBwukxufLJJwpP6JOeTPozOfnkGtKnvH9jx0+95rlF0ZU2gRmkHUvf1E9HzE7OBl0O2JQ4peYB7cCQlkMcHUbEK6uXEluKXbFPWIGIDn14sH331u1TeRk5jnFJKjdqC2v90c7aheUbV6/+9O//vjdqFk/Ef9vEkadpG3oMgfq3PvyA07Y37++9+QMhBzEhrtSG024e0EosLs/v7p5cWL0q02eTLXYmWy7O3ZNKCWcv5W/fvDcStedgi6INs4rA0Fub/WPB9dp7uweleuN7l14rlOfaLYahx9t3e9nMSBRkChp8pbY4vn75SrxAH8k+MS94TeYshx/DMZABeQFYg7HhYyhE04Qx41j0Ly6CEbROqbGw4C+4TC8QRtwS8gePZ6mCExsFUrhIOISwhFsyy1b4JvFbjujELJdC0+hBTWDMlCksZrje55IUGeFyRskRaefznOJbTdKL/tJ8fWqqihkrz1rW35l4TWejCJMtIeZs4fLlDSodGj/CJ1xWnEPpIyqMhgrFfYFx9vb0QuQhJtLYYgyQLVGx1BocE5mQHAExEfuo21lpzF1YXbl5+5ag2L0MtLS30zk+HfbNjiiXO/vH0odJSKUGlGflLSZSSLci03y/CKZaKIIrBr5g33rGgth4Kb+Md762dHaCs54JS7jfHC3OVS9dvMKoZe+gzT1QSdrSQXc8PDmTPu3O7Xs/+PHKG9975ebdh1sHx2qFSPKBKqf2Dw6AHg1A7Ay4SWKYasExSM05PLztCcPERTxqE8IxvtYXAM3faCTaeHjPoaHpJgUMuVry/jGBs9YeB/gHD7c6XUrMBWbmoIYA3F4JVtZXNO83aby0j7RjlXKdKKnb21IJ1RPI0mpGaCIozTpcWSnos0V17/4DD1pXpiNZWQWJ7vfHktQu7GwfMjyCBUVikt+2fyQmAOXhcHt7008DOj87Pbm8sbKysHj/7v2dzbt5KXVHdIuDre29Bvt2sanmll5/863ZmYAsZsSR2LXF1pEOzUnSa2Kej2CNwsjlw8nHL8eFTz2SZz+l1IvKRG+SZj/l+a/HbaPQ4a9HX74lvXgaAL1ooXxLhvuNGsZkuaeTMvn6pQzi2YlOf1rP/YElTX+sWcWeWzIt9KldTZ893wfn6Ve34pUxOWy+vKnY4vjhLzSWsLresDkSo7latFPjlKDA69//MRf3X/zipyLjcfDhPNyYE7y4LZ8lHQxOr1pu3tdv8J1fktBAyBRWINCSaDxhLqy2Uvmdd29KyKnzB//2b8AFx+bDh8Qbezu7guBxWA8T7NlZrG4wFmXnbIUlrKyT7bCB3draI+TxOs5/u15b8GxvcDYcM++tyTElherOwdGff++Hj+4/vPXeO71WszIrIo6ESVmCnHJtkWIsO8OTa4bKKucRca77YqvEezPeFvIbcWUSGIT5yWngXOQ9PJLQRT2UPRgnOIWJEuq4TpIBTyS8J2oAmJh4kwRgbK6H4KdNHjbEXHkUgXqPtra1Bcspo7wDmgEemF7jzax/RPDB34UpUKe28tnIhquS4+ahKM6eQig1k5HM5Mtbh11CrxnEHQ2XFurH7Vyve0xNI/cGfZkOi0NDpsP5rpAPOZ8OE3gwntExCjJiHl5vlCWqyBGPlQoc0yDOYjmvKIHHzv07zLMvXrlMvLS3f0RphiYiMvc67X4lL6Yfl/VZnnGcByM1aZhGhQFvN4kDXo788/zvgG34ClQknRLTutVpU1zyhoPSHj06ODrcby0uRlaQxLcO4HjnvXcbKysXLl+RxqQ3uA3UmZRiXib2EStgNIGWeCZaTeozJ/zSeb2nLL9SjqS5WwMZ3LeUB2J8NSlozpc+ZqFWCxjaH+YXIn8q+kPl4ioBrIlPWUHAJxZaIM6FjTXkQm2Ps8iJlcZJTFqMiN3oqKifmi+NroTUILI8bvX6zL37jwi3lldW/RBu376rw6RTpULFyu/3uo/620RF7LEg1i4lYGswms4CbRb7YK4+W8w9uHdf3CMBo/mf3bi8Ph727995T8ikmbMSGyKkoOzTjasXVhkAsaLOTfuNjsedbpgiBb4IqJgeeuVILqann/LpqadKpFfSGpx/alUvKvBZnn2q6Zdfv30UeBoAfftG+I0e0VO/3qe+PjW085uFkue/PlXS1/SuXXhS0rmLPm3QTtJDSQVs5WkNk4vpdV/T6099Pvd62nm3kpN47Tpf7HHrAqSc6xJmgCXoD/CBdWGNGNp0QS4q0hMRaKZpjgbbRxsXXm11Th/eu727F17E9BHrq6s0JW//4uckCuE0PpO5sLosrB/OgW3wicaHACgHkxf133m4fzLis1Pmzo2bMaTY2dleXVpeW9vQBfwHL5GxgWtYNbQSw277Lp+jW7dueavOZUvj3ilfrZW1yqPNPSKTxvzFa9/7IUHFwfBXw6Mmo9EHd25Lxj6bn8lVqgQze9y4GgsXb7xen1tmvi3uXgFomJkWvfp0MAQUsBOwYFH0PdYq3RY/LX3zubUVZh8UOvAWQKP/tB6sVwBBjlgMTfRze3uL0grjJ02AYKAW+iTUI9XwrBPjVblRMKBWQD1RLPx/JRUvIzi9FX3Hzu4uK2AqmHx+xAqkfbwthNBMppjNjJXJnAX9IEKaLXZCWvE87UzruF2Za+Rz0zQ7/M9LxcyyWNqr6LqgXfqrm++9zyOfcEhPCEtgXDoaJkieHQ5Oc3XyGnm4BoQ3IlxbDJKOdfsn5Xrd6iTakWt2aW0Ffrp7777EIzPZWRhODhN5Q4k0hMsGEWZzPM/pymLURmp0ogvGwHPZWr3KVMhUCgGV5QRezE1FB6ZgkTnJMWpz7Mvu3PqQPRZZoJjN5dp8sVJj7fvu2+9IrvKDN25s7ewcHgusPNJhUij+5MIagtEQ4QCcjqyugTutbak/FhaXoU+TIpg0qMCYTdI0dKDA0iu/UeRVoF5rdLNdnZ+TCMVo+8N7t+8Q+QBVuk2MR5fEgBpcK0BY4mofCBrQgXQ1ZPjc6el22fZUxBOYLQtVMB2G0Izcq1ZyxHIuiu50zL4Nqmt3Q2FqusX25IEnSo/E8PW5xf3mpuFELotigb2dh4SAtsxGw9atD99/681X33zrB4ZfOOtte7U43ps66e1sto4Odmv1BYNVv6ywjJ0ePtq5sNJAB55g6W/c2shkwtAs3WrST7ecQEeup0eyH9hPYuN6cij1+HAlPUseenwxnv+iR9STHF+0gpfPfRso8DEAZD18G8b0cgwfxxafhR6TqT+/0Tz7YFpsUubZAp96ZdLQpKQrNmt1pifO01dnJ5iEV+KpbMGrMBOgncMme0+v6c2IdzxipHncPpQEoFRtXL32Ri7LdKYrdCGL3f39B4DOSPJxVi/9LlZ6/+6HB/vNsQ19mmmHlAul0uwsIxdoCTtfXFknXSjOZt784Y9breO//+kv2Ldcf+2NamX25z//+ebOIaNpfOvg/ffbbcFPRm98/4av05kHbTbMJ0Oc7C9uvMI09b1bjyLNZmtQri+yzp5rPCRKEbfmr//qXy8vLUFLu71DViPVxfW55dV8SQy6rPSrvJ2XCitczOh9+sOO+klTEMSJ/ldyFSwEq4PDQAR0E8UFX3QFJMLpSZ4gGwKDiI2YeKQriX1CNuRJ6kkf9+lZ2CgICzkNBi2+1l7y5xdQXlhplbi1srLGOkeGNVKJhYV5ZAzklyRqcFcN6bxEytZEaESEIFVFopTMe6p51P3RH82Le0QRRXGD9WYzlXq1BLu0TyJBh77ZZsBBM4tN4+9aF5xRbb3+MW0ccxg4IlMCPCoriwtGRy83XRjOzctQNm1cr7z2aqk6d9omJzsFhE0EavDDMnaiCGhsivgII89SJgpM4KFpoEExA9T/U/ECziI+TZFhjPCSQ/lIJGSdOeXKvrLKGqz7m98+eHiPxzij+5Dj1RdkugB2r924fu3V1/7ore//m7/5+XHneLYqPKAXg4yoBFAguClPmbGIi8kLrN3uUQJSwqb0h+qZqKMJSRdVJnFLucZyOWyxzaBFiM5OTCNzcshSV/VNAaPQebUjO+EN42UQDWaiRDPupSUPFokGS6VAXfCK+Q3Ix4YsE3ncbt+7R6X76uuvwaM0j1evX2dd5NkwvcoOSDetLDBU4jlWVmSQbOT9MnqdI/4EhezZce946qTzF3/8ox+++dobr984G7ab+1s7m3dmc9Ov37h8d/Nwe/PhWkk7taOWpCcRkXprZ79eKdLoRTACQQ/jl20lP37hQZ34nqCZ9NOMpMdTX89f9NSTUp/0rxqeLfnci59Uy8t73yUKPAZAz66b7xIRHo/1JRGeO+nI4njyAvbcIr/TxXizU793wYR/p3zaG6Td3xuwBFbyEM02sKgMxsCHhTQomy+Iikucw3iEgaeoJBQE2UJ1vlwvV+dYsXCjGksilp2pFAo1kXWyud37d7x8lwjn+52th/cJLgRNFAHFdu8FWspJkQBzBALYSfPhXjMSZmVLVcF/j7ujznD69cUNVx7sRg5y+oLLN96ABLb2W+pkNyqxqNd9KTL++ic/6wYgEKzxYCY/c3C4u1AtL5Tn94nWhlNb7aOD9qAsA/j6JdzhoN0dMK/JMYoaYTm9oxy/6Hi17/RwNZxeGBfv94xnQBnUwBRFk8EIQ91jX5+edp0mCBMlU4Hq2MkgDm6kT6bM0JCWSAIMcoVQpw+reccPKyust5XnYY31FgpaSdGME3c1oXK0BaTrtTleQoO+aHhe1lmki3PTOT3pc/HB4PFi8WdgDplktcvGZWpmvzpbMqLZWQqdrpZoMMXRlv6Cdof+i9ktP3W4gWEzZhvYRVSns2mqIvyz0+tg3yIhkVNRX0ItDsY0hXIFQQb9NomFVUG1IoiirB2MdPB9WsUQWo7PSO/ABWNXmLgqEr6KZsP6O1tk66OkeIkkc6IgMhorGAM4x/psembb9E/PAKmXr1y5d//++zchhzv1uQZqiFpZrS+cDNu3P7x16cLFH77+6oP7Wz/71but8QF4lECr+HEMAeMMFWExHA0l7M1yFdxG2uhJTnTmrujJEBU7ITbQhVyGiZv+BBYkkDsdHR2F1ZfgkJRiZj8UcLOzSjLnMgRJz1TCAZ6gBdIVdZBRE5BnTsyyn4y1IUCDXCm6Qlw3Kpa1SAEo4V27tUvOpBgKUaX5EQHK9IDGH6KgSnk6nxH+ihud2D97e1v8wlrHh9wu65VCfTZ/9eqVP/nDt2rl/OaDu+/+9hdz1fzFjdWLqxuLSxePu2f/6t/9rVUqlsFZxtI0q9mbH95WRm9PevRjoQJL1hsEkxp6P4YyOuNn/9TGERddfvzPRzeTwvH16QeSIpO7vk3OtZvcjA8Xz3+dXP/GnUxG943r+de2w7FZfPbOKfxtnYPPRYfPTrF/zJLnZ+f8+XP7cL7Ac8c+mejJyVP1uO547rNKPnsrveIzqefxg76mh2XoxBZnN8eAfSbaBMYEU1NY2tQMI4l4Fe6PhL3BG2L3n87QPrARkTVTgsYF+a6bR4T5GKoUEDu7m7liuT8Ybm1uLsw14o0/n/d+z6DC6+jh/jafnI4U7VsPX3nltbNhd9RvZ/k0STs6OmFOdMqPmxHDeEyBhQlZ+F64IbThiPXMGmtft7zgpumcvP0LTDw/X7t0YeXdd99rtzp3br7LzVued4xnb3dXzvhqYw66WbqwPL+yMZrmDFXkos9YlbDHcLl3GTJWwTbZSzkuRS7lTZ28BALwWo+joJuL2hWe2AmeRxnGgzvkBGGIE4fCyKiMB3FK5+p0KMCy2EXKI2yPCQgNDvGPOhI2jPwREdvhWfIeoo1yqQQ26RWyTyV5psRH9vVgb5pzuKf0B/biHefcQbrj2atXr9aqFTZJLJDpaICP5lFLBgyEElKIOMdTOqlOGimAk7RJiMJWs4vCOiO7lpSyBG+NamFq2Nt8+AgB82XpQU6bB/tkaeUCqmchpN5+q907CYP0cqkuoN90mDRDhCo2aq56EQVADE0hnjlJydd2JpwxfNVVAdEPL0LFDMfkkh7xdCLCEGUHqS9fviyezd7O1oMH9xeXl/nGLSwtH+yc6Pzd2x+++cMfSTbHmObW/W3ojhxLhGomUqIA8SnTehxFeUtmptuSwQkutatXRqE3Qcx8kaBF9lQ6XLjcRKBGSnbzaN51UnkzhfhGAPHT3LkFfwOj5h0GMhb9H4+OrQSN1utzXhYMJ5ufUUC1KGlGifnMtdXOcV0eMctgd3tTc34+nOBkuZfmgrpTvIBd6YRN0hGB6ubUoMw1cm11/vVXrly5INrAUnN38+dvv7+3tfno4e25N19/5dorx0c9LyFvXtv47//1T1qHhzPluROx2jvdUq64ef/e++9Lrzsk/QK8oGcTpm/m3ad/dcP55DCW9PzxxpB8cTF9ZFLsRScvKub6pObnPps++Mllnvvgl3jxsw/zS2z0ZVXnKfAxFdj5G0+dTxbK5OSpAt+Ur9/0/n8ynV80uuded3GyTTy3gLuT40XtKuCWz7S288XSi+evTM4VdqQPTppwxUbvUJ9beCrGMBrLUjRPon7cZF4zxWn76OBIrDjv+hG8p9um3KAX63SPJAn3X4+3ECDBKabWuHH9VfV0hlMuMGde35jrnc20+v35uaq37+zUoM20eX7+3rs/EyKoIujh0dZv/v6vYJF6Hgjo7t77IMxThvyxT472trpH+1y9uq3to/IYK+RLtbl5x1u3UM5nUye/+Pu/+fCd2cLM7PULK/lSdjZ/dvv9d3V7Z7fZHxpO5MUezWTlKSMm2do5OJMUaSZTynOugoVmxVkR66giIVmkOj/AuuA/ShDSKeoDWgyOyrjd6ekISxsXpnBT3AWPJJIh6yK5MdIQdUzjOhFcGBndxbzZQrETUhURi6c6w467zHZxwbRMoLAMrVNo2VjFzpaLDJ+5VWPG6tQ6tCetacKks+phLb6/26dPGYfu7wwD5s6tNoX39w7HU97+82Ei3WlXpUYLX2sRn0PLqT9yoCoJ6xpdkZYkWTbqbE/3uxRmpj6TlXlqsVbcfXBrpV6Bkzh4JSYp0p/L+5arlliWgI+yplUe7d435CtXrp+c9s9Gp4V8lnl1MrqSz2kJtgxDus6TIXkH1VJooLJHJ32iCVIxeDjCdovYN5PP5WeZA5/uH7Yuri/KrHJxb+/+g0fbWwG/asLqSG1V4vE0YA4sLejG2oW33nh1R/o0MXIWl8lvyGaAGzY3DICax/uwCsMn0AriImQS+oj8iZ8c6Y6UW4g5GG4OOyeQ2Wife3wWTZZW1sQTYmDU6ckde8EC3t3cDtgqzQrdkvCP+eLSQlg9awjdZD9hIsV8O6MF7nvDvoVRqSyQs8Ei0XDEXAqNmM6wH5Jtz8Lz4NFhc9BtsZgiVGJzNnVWL2Snj3a33/3lz6qzs1cvXXzzjVd++P3XN1YXWGKd9No33/ntzvbm3vYWUdsr199YXln/xa9+e/vm/f7gbO3Ka+99cLOwsLJcWeB+Ly1YfblBcri1vQ0SZqfCkF9vLZJ0on1NzQvjtx+Rp+Jn7oivyadij79aGKSCSiV7SxRKjscPJBf96uN4wft7WmdSIj7SDky+/t5PJt2bnPzeu/Td7MBnBUDfTep8g0bth5RuH8/2+UW/sU/dFBRwPFvhU1fSMpPPFzWXPpXeteM9OXm8gWHG0RhthJgwdv0kESOW72WfffEpDtRn8Xy6WPG+Gsa/Ho/X3Ck5uKfZJh8f73HHZkzK/AK3s+mz5x2Ll4gnLF+QSnNh9eLi2hr71oW5BdF1t/b2yoXsKQn9VEbcFOGHl5dX1cmIBNchlWk0Tg/39wQ+ufHKVbKEne2H1Ez5mSkpNKGMv/vJ31C6iUbH9uTS5XXqFFFRzECpOCtjO65z/fo1ns/vvPOh2D9/8k/+EpD6zbvvnZxNMbNlSEMIwfSC9qY/m8cIoaMwOD0dizwno6T87sH5wiw5hDfYBs2Fc6ABP8YvMRVCgsQgJ0AP9gkAkRxQQmkaWUAKzM+DEIBbjMdZfnhEtSqkSCFKQSKGO4FyRCVgCTwMwESLIQUZAYy25JxgQYSRIle1GM50bE1UzoWuc1xqi2OUTJYoOo5255gXlSZMn5Jgk/5rXXM67KHdvX0yJ82x4yWWM2XQIYmFgtCYjgFnVoDMGJFCq1aWv8PjLJlPp6bzlRwpoMAz+VzGrOF8tKIYPASDPF3yv7k67RxBjpY8pCoVxrhEzGTnQ93FeSoTNtTgWiE3y/yZbz9QhsCwFLSRk2+3NIxUWSzIRuR2RX7dR0f7d25P3XjtexASMHfSP370aGvQ/+s//8t/7wdvvHLYav+bv/sHSSRoyFIpjnmxyEWzNHYITc06aeL8hhKaQDsH0oigxtx8Q/My45L4BFpqBFI0j3fu3IEdU0EUchHPiM5gOA6qNJUYl1GbHOZNCIu8HtThHIFMvij6gajflpZJh5lcNyOa84iOeaRarsjdQuR20usc7Dw6kfDsYEcus4VK6ft/+afXL1/+/uuvFLLkozt339sm50Nzq4IQdGVt/a0ffq95tH/cGcqLQnn74YcP/te//fWd7cMf/ukSmZxRWjzsrK0gAR3mioUHd/bSPiQYKCSUFqQ+T/aBADlJujoLKb34ok/PxtbwacVe9Phzr6vT8dxbLy9+dyjw+QCQFfPlrsLvDqG/6pF+wry49Rl/6kqeP77qPj/uFUc0gY6JehIZuT3dTm3TtOnzh8mxXchnePRANgPinX4/W2TYHK+SitnipYInBTnhdx7yI87VRXaXtCu12TK+xjH+4uXLWcYiIeGYyhcqf/AnfyE9+Faz01hf0USTeAF8GQ8WKgvMY/cOm/W54NAhXupECGYRi6XBwpbKc1VmNPn+YHqc7RwPyC/wLIlCa/WGiMw/+clPjvjxZKYe7naG2cOVK5nC3FJnfPfsNFOeW8LaO6P3mIVyJjsZ7JFV4MJETTt7m+QBlGtCP1JVdDuDXquNW4M1VH6IQAKE52F4dCKm4+ysj2iu47gQWPwe5RM15OqUT9xFbm72trrtflBSsqisJOMEKSE8wMzoB9UP3BASQQNheCXpVqHQWGAFtU/sgdudjbkXsWvGtwWtoZTJSCOunyyUwIt2p3DUFIxgSF2obxYMQ5pkxsLQZH5xiY0O3RPZWYpTjS7tfwCg4UDWeB0jZwqfJinKWTKfnkJvQMPsfAP0oZoxLFErs+M8b3XxAjrjUe/Y3Lcq5fzyYs0jHkSdCFpTmz05Barm8zNFc9RYXKBUerT1yNpxW29zJyPaH2OkwDo7ORGJSIoJCiuj065eRrToHuVgu9NMZGMC951NLTTqlGTD4T0GLvX5xYuXry5capyd9h/d/oA05Vf/8PO/+Gf/7I9+9INmu/vX//CbThdAosubTeEIzIEmBISBBRPoA3YYLXdCoZzv3r8PE8KpdlIkNUHWauIxlyGjWr9wESmIi9R24eJlEwrYmHpD4ziGjFzZ0M0kWLrKuM/QCrUTlBPBh5wH9OwPgrbFGRG2zaCO8SOj/mNttX+wTXlm4oeD/vLiwqUL6+A4o/U3Xn2le3zIVO3mr3/793/7E6jU9JmFH7z1o3bnpNns906ys7XlYuFUBO1297Q7HO8fHDMA16AonbAkSuqoZbuyIiVJ/e6tTZ1EBHROf+npJxikP5O9JdZPIqFJT9LzyV0n6VPpSVomZD+fCJkmj5yvx7PPvX6+zD/yuf5MRv2P3PTL5lIKfD4A9JJq33oKPPcHmfxQXzj08z/jdIs5f2XyWHrL16SJp1VmpAkhOEiC8vlHYbmm7eaZYti12EnxWic2Za/0xBuu4ARe67NTspeWGRwoTA6PB3vP9jjLicgoeXoqSM/lq2WoSFzmq3P1TG52ujBbX16fW1nn+EPa5JGp/PtrV64T/+we9+ZWLjOe7VOm5MeX1jcOxMA9Oak3Fqay+Yc7d5ca87KCeuNlgTSdz7UHh53heLpUG+eq93ebby5fLS+sFmrls0JFiqlcubZ28VKhXBYEkUc01RJmtjBXZwYDM0jpKo1DlSWMoL0zU6LX4JB4A8UXKgExzjmiY3LkWK7AfAaODlgIjkhyQMagbylxMH4v3CDOVGSCD5co48oJ7psPnKQY0nmK0ERCrlPRceAampSZXKhRMjJbdTwl7o6Xcid25tnyLAJO9cmAegejkaA4eCrurA8q5+8T5hziF59JmRaBAXc2N0GouYUVuhBpw7RFn4bx656srjQvui2VGDbJXY79rQlSwEUMn/CAxTo7Gp75tXIxM+yOs5mluWXorFSvv39nSyTlSimzhrHONwg+IBs443R8kpMIQpwnQXdAQb5s+wcspnsdISFPND3qTOenQZ3I8wW9AUeGEAEwpVgBbYcj0hJ4wWBjVeoADRFh23SmXK1rwoIUu/L+3bski4ACBN1YWoJj3nv/pgxq/+Sf/rP/+N//CxLKn/7i1wRogmEXJEXJIbuImhGjGUAhwjk43EM0iIcsZnF5xWKxDs2mWb589arh6zfRmlk2s4ohl0ViLCYUdNDvrjCgZDX9DuCmjMVAQBqVWOSJZ0Bk4U0OT3nE7CCRXwcK8OBjgd6UMWZ82jneXVleWKwUGADJ0MsOWmAhJj4ADbx454Pf/v3f/cTF3e0t2VI3Ll18+513d486qxevH/TG/9P//Ffrl1/73uuvHDUfvH3z/fvb+zWar9P8HW8SbfroEHehGOndaYeQsiDHm4Wn2xHr0pFoqoLM5wBNIhuLS+mGkIzgUz4M8Lkb1PnHlPH1s5Q8/9TL8+8mBb61ACj9GXw3J9WoP3WbUMa+dJ5KHoldg07oqder2HM+8YXrGSo/XT7e6cPI2WfmDMv0X2RRiKZsVfHJW8jmfRYZtxJjFLHxvLwSDM3ksWTcosBeopQt8m4mYCDV4OrFhYobEZlQwsAy3vmHJy3MmzS+eXAMc8i+JKEjsVARJ2YRXY9E6Pc2H1IjXL56TYLJw+PW4sIyT+dLV15tC6lzlt248trwbKa+uJ4pVGuj0fd++EMJLvCn9WtXWFeUdo+vvvF9TjiHR829wwNGvrXVy5iWd+Pi/HItVzoaDJYlgb9xAwedye8w7FjdWBd7ptVpVUs0SWWmsuVQSoQNdXZQHAzHLHrDZzuXZfcMwYjWw1QFL5lr1BlJM6CGVOhroBlSL0ITflv0TVRvYcLCzz/YCBOotsi+HhHkZndn7+i4SbqQgI/Qg6RzzVYX3JilRjKZAMf0DI0JXskZiGwGYkA61sSCCzj3il6YLfM3gzNcBNewVSIxVeFtw1yWJ1gvzIm8/OfCXinyiM30OtyUDlc21qUQtwZgHYK0UuLCZOB8vYghhMiTA567u7umVfc7hAmCB4TheefgUe/C91+rVOsfPPqw3z+mDCuUq8VyS64LejAeXbQrstIftFuMoIiPZsOGt8RgHX2qFUnODYT06lQSCR0+rQUWTCUQvMmdGJn4QhzESLSIXmTfAOSUnJ8j4BlDLVvCQFfK8MHwdFDfo8Q5pdzcfHhPagguVfXFNWlOj37zq1/+8tfkOn/yZ3/+v/33/2xlvvaTv/vpwdH+cNB72BlAUpSlRG4oL1EalRrUctBsj6co7wRnyvDq48Nl4iAqoNaaFODbGoZEYU0dhhvkeiHaYTZ2ctadznJGPGm3mxJQoD+0MVPLHB8eW9JATpcJ2vi03ToyZflCTjjs0xMxtI/Ti/SsBr2xsjgzdTJXL796/dIbr16fr1TJtyjv2Aod3t/jyiiFxb1HmyRP5KHD6eLS+uXGxuXu+w/lGXnY6s+tX+ycnLzz/ru1emln+97hce/qG29VqvOj3K3CO7f63ZMh/bQMI2REiwUR299/+0OJ6uUsM3AruTEn5HQgcj30wycHTZSCk13FcpycJxLJBMHEHvJk14nzc3taahvkynMPhe0l6SPnC7g4+Xr+PBB87E4R4sFqTD7T8+T05ce3mgLfeAD0saX8rZ6qzzW4524Bn1zD+UfsVekOEhcn+9AnP/+F7qrfAYh5+2ZAES+LYJkgyHgq/y7eX9hqnxkrnU68yhPva8cjPHzxUOdAA9MLyYYw1Ax8NT3FhkRUG2YQNcZDSZyT8VGTRoYVhTi5xrVWaywsNu50+5zYKYskcfDarZ5cEtv3vZu3GKsyuN24sML+6NK11wYfvNcbnuWL5cW1S8Xa4kC8XSoA/vlTubWLF725E4pwx19krMRUZH6eUci9h5tyiq1Konn1CjclVjUry4t6qz+proTshBmL135SFNQ2XNzXuMoRXqeY8uzl5RUnxFISR9iiMXs16CrfIoVxF0CKabOL9IMpiNG6x1ko060QEpTKVQIDAE6Z9aXlUXm2H9BBoglGNdkwTwZo6nXSFxXCEArrA7MenFgxtCLDQXmFUR2UdJ0gKAs1RBIGUYvEIgqjH3zOKMAITuD881YWFhjzPNzcMpv6qTA0cMIWWG61CMQ8FlNRP/nuUXMe7B9BqLybMMZBp905Oly4sKxFupvTdidTrEgSoZPZYTewC7ukGcRL0JXAmBzH67XKfNk8Wy8BsyCMep3qMNg5h/MkJRaVo9Tw0IBcssYBcMjRiteXI4R3pL+dlr+DdiyfPdpu80JcnJ8TvdFiAxBbrQ+3H97ZFltzbUOkQVZcN155/f333v75z39hdv7oz//0n/zRmyvz1b/5u1/88u33Nx9snc0Ir7AAuilcL9VbjyKPhNBNfAMhTuZi+UaD9ZcJCvHj9ExEdzTw3pCUyqiRC1BAMWpFdLfoDXmUDS9Iw3GHDk6AALCbQBFNAVrAGEkhHwK/Y1opfmKHzX6hQB94YW358sWNjfWVuuwt9dnW4UGrScy5z8ft4YN7bKFAMbhL1MKNi1e8NXxwMwJMzs5trE8VuoOp3ojb47QcM4sL9fu33y8XpxqN2hs/fGu2vrq316rtHpdr9Qd3720/2h6xtB+est2vlcrv/ubnR7t73BTWV+PtJdlBUmDhWxwWjAEmp65/hEuSK/Hx5O7T55MCL09eUuBLocDnA0Dn1+WX0vxnr8Rv5rMX/kco+aL+/B5J9KJRv6irk/JpgUnPnaTHpICvk/Pf8SRtK1w84kUtKkuvJMgnJFKuOwdHsA0IyBvsVFeCRu+xwoqI/oMdh4wHn6M9UBKHE9cGJgAsUm6Hs6rE4xgbAxQqJrXJaOVxBqoXL27wjxE+EYtaXV8TUgcjJ6OXLtsjb7/9Nhyg5Lg9Psgehp6FyxmTijCzKIFQOkxDkfRnxEhaSBusCHqwactzSZYT0ej6Q4mf5MjkN047c3TEGDaUMhiYB/FUTWB1FBlpJ+EYnXdRt/FFqTuBiL39g6J4yWESPKCSwxOYA1ElKECtQGckqyenLV1LeigW0WFQRphELkVLS8YIbVD/6V7aOojgPV2uKoIyAgjaGZxJBwhXPJLQNt6CzQFyIS9OLJpfj6U3rzMKuGSmEAra0/voCUxjRsbhJ688iRSxHIxChhc8uB2aMiyeGzyerM6IXGDm/EPHJKYReYXo3t0gl+BEelsphpZT9vXMqL++vo5ygYABnggFVCuMCYMqFGcyVJ2MwsCIxIXuiUhHbarSd80xCSI2Yh6Er1szOR5zEZ2ZKOqU65ShMqwGjsnVoChIlwmL2+AFkZiktqtrxFQd2SBYVGVm2CDLk1ohlnxw613+Z9XZy2JXC9Jz9dorN2++/9Nf/JKQ5odv/eDy+vLav/iP//gPfvTuzdvv3by7eyQVXPOwuSefaKhKLWmCmOkBqWRWopZaBfIAOrmyM/eHY/wQAN+A0bLZyUo7E1nNuq22ucuOZjbvP0JJC5klHFIY+Gi+HibFlHdTYj9uem8gCwLy6sXF6xurubDKl6Atu76yyGlOanclu+390+bR5q07v3371zNhPea3NeZZdu36q2f7R7/57Qer1+qEaXcf/rJ5JHnwaac9DAnT9PFJ6+jwsLUwO7OwcuHC6sKMlGONxpY0tNs7UsosLa/+9t33LPL55TW56wmxhq0eE/V/+NWvrly8tLa+EjgvMX8m/DU0v/j077kbSmwFEZbpsbwnJMVf8aGtaPT3dKQj/T01/rLZoMDnA0BfNc2sxZdr4ssl8mf5eU92gQnxPfXUg5NbX6B7aVXnKvxIwnz+lnNyGvWnPj58f8EXnk1ekEGchDXH/ogF5qbCGMWLMgAEo5Cve9tPeHCY+tIC6C3hCG4tWRNvIwwSL7l3754WiDFklJLhFJphuSIJNt9pyMmD9nHgaWNjDaBRP+7OI51RsFYIFZrNA+ogAnPSptAUAFVYyHGB1AQUSGoeDYh9sNJMxP8Fd9SDy0NOOC6pDA6HgSWuWnN6CzcIJwOCeFx59YQoghnK0VEaL0f3lFGPn4UyRAi6hNMT8+DcCuuYuyjmKQNMOyziMJ8vSjrNwWroA984URJywt1t+QJMCpPsuovASrnKf5tWigAlctGDYiimTiIiIEdXNcHQnANXVQKyE6hDxq0AXkadth4zJNPqYTNbKNMA8cBWM4kKBRDVZnjMnYa9lgMBDV2sGggqDFso+NgkJxRwLsyP6Rh0m05oqCq1OfINSsOF+Y3pfqvXOjSJGTq1eiRLtxYIpwI/Ue1F1ERBf2ZMjK9O2KAYC2c7swBYoZse8leShLXDSb8TqSRccWJFRZRICDKfCznLkOBp0JkaS3BRbSxeyWePD3ZbR8fbD+82ajV9ZzAtW1ZjaZWuSYZ24XF4wF298kptfvHf++Mf/9mP/7jZ7Xxw+96dBw/5yUcgBaIRVlER4rJztL89t2Cu5kgLQS+0YA9uCkRn0JlqhdFzlkW8kIYBjlvH0mKMeoNsKc/qOFfMw5mHB/vSUxAdVrjo1xu9ap5O7eKF9XnJWJaWpOvKzRCEnkrelhmf/vY3v7gtXVinIx9qvTEPBFpNtdnSyuo6ULi5vSsoeamx8ev3Hgjh7bcmRHZV1CXtDgevXr8y7B1LYFedldIkf/XaFRJNgYyYHH1w81a7M7y4cWntwsX3P7zFnb66si6XWj5ztvdgF0QSDeioRTEWgRJMbhxEVAkGMnGTwxpLEFH6FvR72Px1b9KZr+5EK8lIv7oWXtb8BSnwQgD0j7MyJr2eNDc5mdz6ep58/Rf056LkZDhOPJgeKO9r8kr2+J0svibHZ5kUlUyKpec+J4ngk9e8x/osxXAom6QC9A64J6ELSw56Li+5pC8KYGDYnSgnqnBOjAG10B7htZmZAs7KWFcNxbL4KBh29NJFvFyKSlCA6QlPJqEDUxSiToxTtW6qkGrs7GG4lLPz0ESRAXCljIU/fBSB40AD9XkHp5JIwvIWNUSYgZHLxEl+hFNyzsfjIYbFBTaqguGAEWroVSs1OKZU6nKoidEnAzFMPA9eARGKc6x1AqJhkxBYqxNpEHjF4+KoY+MkJMChwaUwqRkJikPGFPH9MHI9INRRW/SceW8aa6fbIdTB+1HJ2AMRJjnDYSCwCZPzNfzrBtzIiZCq0ZPRGYNdZkOwF76owzBlC8A6Iw4Z5osnFDdgAfipofHZiWjO2kJeX/mdkc7RfMETFy5cyFeYZhfouhSATkQx1NVbt++yNxccDwX0Np1KGIjDF0WRWJdHRy0z1WsFmPO4/tIEkgAh7Hy2OJrKCXOcH/XDaDoAGSBXNC6yk7nasvxkKMXdT6THSHuS2kRnWSaF3RJgJOGEal1HcFco1OCurABG+jk8FScIrbLy2Z9NdVodOJWWtZAXBrI0UyiZfaKVk0GfqxvY9ODeLUpOzvpms1ipjk56uw9uCzH16OHue2/fImy8eIGp8Bv52dKf//D1P//DNw9bx7sHbINO5eS6f/9Bt5fdb3b2d7fEI2d5DOIwqZ5fWqQM6wqRJJ1tTTSBBmTJatvaOB22j5pHlp3ptN5mq7OLjfmzjYV6tULnVSuXX7lxTSApQjKhHR/cv3/vw1+1yyVrhJWYmhu1OgTvV+UXkclV8sVFxlbFykxtfnX54g3Qbaf5cOeoJ9hjvjwr8rU07zwep6ngxr2dh7dLs7nlhTn7wcVQ437PhB70T7rDTiZXHkF1MKDlU4vAB4j2ygznzdE+G6yzqTZXR6GxGHslTnAo71mHExNh+flMD1eSb64/dTERzCTXkjIf3X3y6Bf+N6k5WQ/Pq+Kjd7Pn3f3c19Ihe2xy8rmrePnAV0mBFwKgr7LRl3V/HSlgY5rsTX6u6THp6Pm7Lj71VeHJs+kjrjh5/JlcSs/Tu+48LjYVYQ8dNslUP6IYRpVE7x3I1hTGpFAKtmzTnSGUDxMQrDfQj2wJlUrz8DiTnZILcyBwCSEHvpHPe8PG6hyuQDy+AjFsYnDouNIb1Kp1zP7unXsSQPKKknybQAjPSxjGSDEKEO/rwAaGJGe5Z31lBkLuwjyGgoYOCFzRc5zYoRgGoLckLvHu3m7H+/RZyFE0hKWpPIFNgd6UBzt8pXYxXgdqBErjpx0BfsIxLRFpPIZrCRsO4ZZHEklN2NVi28biwagnk9FzFx3pkLUYsp9KzRX1IxS6qZ+JsMKaMByTRrKlh9ExIXPk5Up0c64oqDnDD8QzolYjThu3JTmfPgsTlcRoXXmzMOyH6a46NYHOpTr51DwpT6u7z4OJOTCsANRyNVebRygvIbwaxVK1liCPGc7kATGTOM5iEDcqhZP+kYZU3qcDlS9CrMvksFRExaTGMzpBhmOCRqeaFRBcz1unkRWELQoRRYCkYqhETYSxpD5uC0CSVBSsnceh/TFNCuiDp1zRbV/ATUBEqO3QEM3wv6udnRTyMfBp5lNwzO0790h63vjBHywtLVOmnc4vCawjCQVPt/GoTVDGElwKXrk6vveDN69cuzrFrHy58eZrl02Q4ADt3vjomGuW5C323jEDL6TTh7P1FUvCYJER1Ke9XFqcF+b7aP/A0NdWVsxmsZRfnG+QexLLMaICEKeHxxbd/UcP5huN/Z2d93/zq0GnNd+og9Q7eweXL1995dXXD5jEH3X8bhY2rgieffvRwea7N3ujWFpTBGJyA8O+mcwROdnUeHmxIQnbbLUscCf4Mr+wYOCUr+Nsbv/o2PQhezE33VhYag22AWJKQ/K4MC6XtrjTlqX14urCEev4qZkANdANadS5FyGz/GQqnZ4/nmwIyU6iTDz78nhJga+YAl8vAPRy3X/F0/109U/tTent2PKS4+nS6Y727NXkymefu2g0eTU8XxMO5Dqu42DyjBNg5LlZzIpwYpZmY5tHSvMYn8YJ8Cr5sBQgdNFTHM4ntqpCvNz7KMOLauCPAB/QAwZDwjS1vpHPFSiSlFEe0xWO7+LlK1iRAzvEVlOo4atzAo7gOskhk8PJyZGMmKxj9XB5NRhSwg9CZwT9uOgpOEwAvJ3t7eOjI50kVhEhhnEr/IbfKIPbYbda0QGf41FicJMNbqRXmF/EBtQ9Vk0DiouOsQNSkY5DBish/cqsfU9xaG1BG2rA4EEKnLt5dISHyH2G/ysAuHiWpYkCiKmkT7KKhEqRpAkoUZW2kN0n69sQquXyQc8hO++RYEVTZ7MCTSumOMDHmKbfbWOWnPVwR1jBiJRXWOW7O1t37ty+cC0HF1JiErB1JLYYNMvdnmn1x1x6lghuZgZYQTTU8/hBs8m5DFnEL2KGLDyxdOc89qbCwHrY6Q0Llf5UMVvOSk9SEpnvtFhitD6djexXMJrOIwtZjvG60jrMAh/Z0TTxz6xkGzOG39RDPa/Psk2vGTBDoSKbp4G06wNSH0vFiT8p2RjiQDDCEhIWHh6FqVAms1QrV5fWrxxuPzJWVmQHB5uPNrfJz268+oZwz0tLa/XqnCU3HPHq6k/lMtt7O+Q3zkWOpjvb2dunhL3+2muc8CDvudnya9+7iuQmV1KvYvGHcI/HQUwLJgGoQ8sGDWchkUhwC5hmB/3u5ubD9vFee3y8t7/F3mtlaUEN+7sHFIvb27urS/KYLa0tLQ9qdb6Hc42lmbv3xUBvnZTu7+w0W72rV6+PC5Xthzs3790B42ATetJ8ZmrzzntT/YPT7kExN8pOjdbWVsaZPCselIEga7M1v4vxTPE3733YOxm++eabtXoWjPM/iSiTZ7S1FDm73bl1sz6/HAuy27cm/GxJNMMsPVlgijngZrI6iCj94cc+8PEjriQ7Q1L44/e+vG8q/1jTYfn2dE++vNaiJs19pSP6cnv7nart6wWAvlOk/zoP1s81/cUmP93n9DTdQdIyk9vP/s4VSEtOypw/cSu5G696WKBPvBZrof2i5gFu0ixCJBCYE4OOufng4rgmPqE80JC+xA87XSw/iadTYHdgz+XlrAwejzt6WyWiIDCCGJpHpBItfEW7nsUDwAsQAVbwVAosqHLSczzVW7vCrpAP4VIKzNUaGK2IvcGuEiCFAThUiCFh3tH6zIx29VNvZZ1yyxWjS8CHDE2hk/KIIRBPgQKa0BlfyWMEfUlRl14BIkYxq23+a80D/Vd5O3JenqhKJQ5X1OxEi040qjnP6jyEgYf56sSBtj7T8s61qyEPpt1DTFccKW2NBf5zk+oP7GFQzCIVlQAgUSJXFhvjvOwij6GV0anQEDzLeqk6tyCTOyEEvePR8eHRcUemz9lSuXPWNmr5xjUawQu4vueD01NvCalkDEmfswLXFHO8jRqn7IP5d7c6B9sHRre2sbzXk/eWna24iAClQFCR1Iw5DY2P9GUJRdliUwfmSBDBLL1CKFOMeoaZUKkoIKCoQiW2NhKn9Hooph7ERzThBpije4qIhRhGeWY7YQSenZlb2sDK3ZIx3rSahHd+++tLl65c3LggcHNpttY/Hd29e/d06sSM1+cXIHmSns7bH3BGE1lxc2u7VKmGf/z0GSkOoBwDaR9LnWbW3v7tb8XRAS/0HdBBTwJRrl5N8a+PWmZNJx88vG/VrW5oPQynHB7c3do1KZ5ilgTK88/a2WvKEiKyQ6nS2X7wKPdwh1G2gOZar85Ve+yo0Epatd2txXpV2MNb777T2d8hl/OTAU7o9XZb/aPeYDwY77d7P3xzKVdih54TyAm5hBIgOeuBi7KgTE+ZOL9S5AXIth49XNu4KNyEZS17rKPda4nsgGIIn5j2ReiLVJSn56bDsnVy7ggZ8Lmvj0/NwrMXv/AVtSVNf+EKXj74raLA1wsAfblr/R9hol70W/pGDOR859PztNvOJ18nA0mvIGl6xWd6MiHy5OvkxC1PxdcnO1hSCacYHIFAwLePXrxs8Q6aC6gFp2nb35k2hKlsWBPTn6gHJ8AbfMWBCFCKRb5LI1Y4Xt/dSjGBrRY/kNdaSe+xDJBHJwN+LGG+TMI0OqnPSdwe6irN4cdO1OZWikJ8Td77Qyvhuu3eOysmjVMyoHFxaXFBW4+2HkJfQtdE/J5+YpucLWD/AJCSrEYYWWNRaRM48d7BHhbrRItsco1Fgu9gtPhcTf7OvJf5hO1xyO+xGCJZOC4U9qcOlNSQGHq8dkTylQgEFnORXCaUDv1gMPqcVq4DCQoJnoQaKjRKxiw82p2DcUYkrrNeLeHQiWUGgQtZmT73x6fEEUFh8yVNLNxx3JLlG2MUekafWfmEJdBgACUYBTmORjlbZafCDh31NCrT2XSpguNRQFWqOSawKmw2j/BLoNZkS4iOhtAMF3EO/PPLC6xg9ppHkFuhyOmsPcPVrlaRqMEyoO06Gc+0mSQL2HTc6jaqXOv5yfOAA0WQN+Irh/tTvjDMGxqBYEzoKeAbkjyDcsKAGqtWRgH41ZrUJcWciJ/Jslujs6WKsdBGzRZjglDVCmONDFu0jrvts77okfP1amN5nRqR4xUhGzqP+BI+eihp7tXrryysrMrCe3LvISVttlo3QYXpmX6vK39ovVYTV/roSJrRrQUm5JnxNkf/7Rhss9V0SnLTb8t0tyMZXa1cgbn1XMfQ0xy1OgOAxjAfbG7NFCvTORcLe+12ZXH2ypUrtYUwou+02Wu1sxbHwtLgsAVYeot4/85t9cgTLINpv9+7+c5Z5kS+lNaV9fmVWmFpsXbSOVjhkF+bZeVULi1CTvlyRUjP3tGgWJ/Pl3MHe/tmZ6FaJ1y6cemiOarm8zSIIF3YjZ16FSEl9V5R294W2OlkaaFuNmgAp4dhe9dsN4NKyc/cEkV4w0HndJ07OQ9sUMyRSGLS0/i0S3y0hzzGTI/3H9dfdGjlRbeevR6tPC7+max/0sp9evB8benXpLbP0fr5Gl6e/14o8PUCQL8XErxsFAUmv2cnk3PXkx/8c37S6UaQljxf/vx5WiYl78fOKXhsbZNa4SGHXEIJFsG08KqwUI58CGF6EqqWpIfemTFgWycu5brDua1feVwNR3TicRcxjLgLQk1Pgy+p+ISvdZjNzjd44KS8ECfWKPselTiPbiRheW3fRAuq8mxggiSMm68QhvK+Eh3Z3KlLitXASXrF+kdJTEs9dElQlIuQgfIYKjiiP2n9Kvcs5qSk6wqMRgFQ1EMkQDukFWPx6ZF0ROoxKI+7rhWojh2S/qjBLReVVBU2r67EYzoC0BkjAEd6lFaImJ5VRlvpWDzuQY+nJNVP5Eprc6I5CJTAJlurMrmFzBRwkTM7chXpSxJSYGSqVafxalFGEdbZ4CmRm74Rk+koh/UEc5z5RCLW1g7yBA/qA0t2jtk6CVYUZqbLa4tZsQ1bhxAJo6S5uUqpKUnD8aNs5sLyvB7UaqyH5iMaJn8nlSdZycSwJrZpN0+gbcCFIkZ/1K/P+gmY+ppOBIcmkEnvrcw4S+bdqJGa2QvlHecy7n4qYR8tXZkRe5boUIjqytw8iomVPJMtVGeJ3MaHB7uJm5toTiIFhHV5NxdRK+usdvSnO5B7bO0i/6mNBw/viREwW8qJ3IhQMPVsba7amN/lLt88smJlPesQ+U1NHbRa6+urcwsLYTY06r5CS5srbB7SdeY3Lr1y996D5vGdRzudV7+/UJtblbH14ebu1vbOUbtXnaPgmwKUDfmATdDm1tLyQrk4s7G8RuB11NwXnDAWLY0wD8RO+9rrr0Khu3tN8RnWLs/5wZAATe8ecNL7/vffaB036wW5SWa9Irz2yg0TZFUkGflCQsblTlKTqkCiqytc8wgJBdhs1KuHe9uVbKBMHpEe0ZN03aebSfoTsN7SbeGzfGr0sxT7XGV0Iy0vMOPnevBl4W8ZBV4CoC8+oZNf0VNVfBW/2Kea+Cq+6vak55OTtKFgey/ehlI6pAVeRJNJh72OKwP+PNmCkjsR0IeJRZz7tw8KiA8TpjC8n4Jbc5PBO1lOsGcl+LGrKonlpyCgkW3gbYfNfd7b9lY10CVhyWAGUxfAROHj4yaDGEjIS+q4VhM6WRdIcGAtu3GjUfciiy3pVsrLcd+0fp+GhqWpxImeUEYk9jdhzRN9S9zk7KRUNtIgKKmtvYNdMesYc4RpDvfmBMEoPMoqzpFeTi/ZxAIHYBIQHViA93W6YgnO0TWkbJsViLB50Ebr+AiOMS6cWDfwHqnTyLe0hU/j3JoIribtA/omQg7Qje1RJts1Xp5gKsRzlJmrhenS1taOl3gx/dSgV8jF5EM9COuT5pFTGy2HMoVhiegLHCSIAuxkVDCBpE+shPRcl7hywRCe2tpkqrQ3u7BOscXUmPuYPwIAY1Qs+QxJFWkWJzv4TOV4KFqpKeIVh5044VDoLgvFPDHdEQ58dLS47oGSlA6ZqRqzpFGpeCaM49Q49JICKCd5xDzFu1s9JkuLhUiNkuFOb7yVSjlyoM5EorQxwxyRCCCVGVbGHXcZSoE77KaZKNHbkPnIAtfu9mqZXJ7uZzajQ8lCag96fNPOKMjY3DSPBF4+KnI+FPB5MOwe727d/aDbPwXBWKVt37tXLFcI6qq1em1+SVioKTCPT2BtniinWp41SbsMavKzi8vrGzfeeOedd+5s/4bdfW3l4mJj8cMPP5huDZcvv3rt2pXbt28PZx7NNhbg0NNpwpVhvb7Cmb3X+fl7v725urT+4z/4UXmWDrd7dMCi/w6jqNu/fUccJhOa7R1dXKquLDf6QxqxQrvTo4gUlWCKbVi1Kux3Nlcal+daU7nN457oT9evXY34kAV+jvwNh6uN0lxxXMyVxSTd2n7kFq+53f39JNn8WMaMKclPU7s3S3Np8b13P/jg3ffEPT/c3elkGACJ+DA9TIRwik1+7c4dSGqVPnv4fX0kLn729rn3tOfdfHxNHZPmni3m1kd3Wf88BkKfSfwzqS26+bzjo5o/fvdF5T9e6uW33wMFvhYA6EXr5vdAj8/c5Cf02a1v9Ip/tvOTK07S8/TzPLVSgjx7/XyZOI9YIJiUz49tOlgv7uhxJ94beQ5HwLxzexm2jWOlr/XOhX1zsMvhIm4bY4sg0gv+R2Nlh1UJhsdzHKNlTSKPASuWuflyfmXFjodDKID3q9CbPQyBPWPzHmDNgLOqlghHba4TnEBdbmFRun+wvwdbpHILQ8b4l1ZX9Bx4cpdcR+u+QgwMijUNFGjLxXRo+uyrA09KLyafIRqxaE5OyRvinIYw0FJiFq0PyRMjvXW4nr5YKxlyi5MwXUrJDn2lrbiiY3pOSBIkTyyF9Qr1iHugMZUoAFql5fVB5GuDMjTNyc6hn6Q4EXQnl00xCr0Tl3uysAAp08Ve60jN6tQZT6XzUsV442hI/X7nzr3NrR2ObfrcSXrIOAb6Ub+A0do6aB6bYHlFFAi7ZtKFbq+Uy8A9neOTRpUKLzzsTDSZzVy1dP3qtdzU8JSwqTQr3Rl6ke8Np0YUjurUDcuHXi1kKx327LEA1KljDmXM8oAhU4KwjdfhblrGRTWwvUZnq4i/f6fXr1AvnuDgHZgABSv0b3kKMklqT9i4iLWQKxL4FXe3Ig1E3ioqQRuL8oPu7+5h/wc7mzubDw6OD8CDrGjLtKiRFIIN9Nz27k6rPypUFmTJzZfnZuuLudn6mIH43Mri2sbNew9GM4WTqWzvdOp0Oid8kmRg/YPWgwcPlhdWRbE82t1uH+5b2Hy+GlZuYeZMBMWps/2d3VGvz2tMyjBCpgtrq7liIfLPz+QWl1ZW1ot7Mru3u99/43tXrl5994P3d4QGYmBUnJopMNyhuyucjvr5qdLlC2v9fnv6tCeqVSZXaPXa9x/dp0UVvwo0ZLpv0i0RQREgUTo18ZpEnvrgw9sffPABHI/A21tbzYNDxSyzlNQonB6WosN58vl8GPGk7Av/NXEvvJfcUCBt5aliTzX6pMzHNqKnHnn59dtNga8FAPr6kPjJT+J37dFnr+dTf8y/a1c+//PnO6976TtZnDzZd9KT85+f2ojC56tVPr0Sn8lbGDEMnIDnMyvBsfCzYincpKUz8KBidtKIdAc4TUEY0SDuyCCjjT+EjUuuXq/YcJmwhKkK+1sGQJJBct8KZ3VpkiCmGVyQQShA40Wc/Q3WzikMF+x1O+LLiY+sBzID+RNehQeXN/zKbOnhwwNNlCVPICM5OV1dXhESEAcCoYAMubG0253py2WuG4xnbO8bGxs6q8+QELZqCJ5VzEC0S0yjQhwidZMR1YYIJWUYUWYKWwtbIlESi5zKiiXPIhNPrL6MCcMw7yVEIQ9TmEjDJ/6twsgMn5gu6Zsr3rP1IQIN82xaWCBwInTRGUIswYgJq7g0MwQiuJKwsxThnbNsiUMmJIlsz+jKiyvLJ30pVPlaj6q1Ms0l4QHzXm7TZyehYPKI2WJuzoyGbjGbfQC5kjRAkA6RZriZl1aWA+7s7169fFE+CYoiCGo8VRJ8SZ91VbVo2xl3TqlBp2R/y5ay+b3DvePDg4x86pnRyuri+sry6vLCnQ/eyRcycqkNMrODs0xYSXc7C42GwJMCJVTY/ZrR5uHpmPnXWN5PPTw8bgZhTZwgUt2e8NNB9oTa1hjSKRMSITOXC+GTZKjdfo80KTom+I8SU6FqpImNJUQ5VCzWGoviC5ijuUalM850pnfPKvO5Srm3fyjU9Ovfu85Bvbl3F1JF+XHu7PZvunLBnvRPxFvsdga8GQ92Ivup9BXjk7NGbf7a5esWRi7SbrS3Nnfv33m4NL9sqlmUy9gFcUjyuvvo7mxmfOfdn7m9XD+bXZkv5/of/vbvmBAtCBg+s5o9HdOEMdihOHvn9p033njj2o3rcMn+YfMsX3vrj/94d/9QCjNxfsTR3FhlDN1hE7eyWF+Yrwd9Rl05LdCZPZelNeiPmKcfhfHbYOfgcH55pQ1mWffeGVoioQ/p+wq81dpdcbDK9Tlx1Y87betcYKfm1qbhsE6CDdMfr8/kR+xy7AMOv98nJ3Huevykk4tpmfiSXEsLTwrE9d/heFLP4+3oSR8+qjFt7qPvn+1s0skv9vhna+RlqS+fAr9/APT1WTG/l55o9Mlv8suf3U+t8fyQn+3G5MrkRIXnz32d1PDU9fNNu5UWI1Sw9VE68PdKq3I9PZTBa1KU4BMrqkYIm5y8jvESORUAQplgG8lBehHigULJieCELHBcVjDlapoIZUfice0RGitmtPqKq1HM7J/ue1XF1NVJHAIrkGEQ+QhaqEXnPnUGC9e3AASYYOKh5nF1JtgiqyEX02c9ItIvMERyotq9vR0t0qo517ra0kqIl5TBX/VZza7ThznRcyVVCCN1zx4HNtQoVu1w1zlW6pN4Q+4q7ZJOUZwR01Qq4XGmfiUjxWliZGpQiKNYwrnHwgzq+UKZP1lDVD0v7orpidBKKX7yqYC3dX0wIh0hWyKHY2yUKRdIQVhosXSSrsO4pHEQVme/F0NQXvc0py2j09D8aHyw33QqL1uqjzNYXty8rqBP0McouKCTiLTYPrMcijwkYY2uS+rJimGswPQUfNYf5CmnKAAvXH1lbXU5unDSVQ1zbLpL8hiBeWgCQZOZcUQ/oi+UHMPj+DSbXCcoTLDnRMAec41orihpElNg6ly7VqcyTozIbDCcAsoh6EqJSRJjshDFWQI4eqUSDVPPqSGOfLayAHQXV1aW2cocm9qOSJjC8MwgTZcJcrsTATcZNZ+EXTbNGbuYNm942GBqfPfWrePDQx1rihiez3/4/vsocLi3wwZ86+Hd9lHFmjw42Gse7FWKhddu3BCXaHdvU1Ck61cvobzOIxuwMoYbM9Nsky2Fyzdeye/undy/n601Vi5fz5QbN2/fbg9P9o/aU2FqFcub0RsPfBK1yKqbmSqB+eXC3v7OTIDz07aSU2MhuPzahKY6EvZaAINswXksZhJWODviW/rxVUQYIk4V0snsto5kWhtYinoVkDexjUNQa4OsELT1j0URn48vxo30CLp/iljn6Z3nyaNfo3+fHdfXqHMvu/I8Cvz+AdDzevX7uWYr/72s4OTH/2m//t8HSSYdm5zoxeTcia+Ilnbtqa/P9jchb1x+XEPkP31sgegKLojNpJ82d1eSjEUBDhzsNrBFogsMjPoLL1GP6/iWwgIE4yIgBK6Gs6oH+8zmZvF1chOfuEu9XlOnzRpOEBoOc1KYBkaAPmJ8rFGddu3woO70vFXTIjEDUhWG4RbmrS0sh90OixoVAhA2bXGQI4n7QNTBx0zRK7L+Hh+3ZVlXBvvXQ53GX/FOHCjHvz+xCqIrms6GsxK2oAxuF1CgkKebYTjsD57ph9hI1tTg02ISMTg1UiNi4ptgoNzwJAAiJytN6KcOo4zCuuErEhkUAw4E5tRDaCFiMsGHAEiYGU4fJSWRyFFOhV4MDNLDDIyEUSXmREk9gTt3d3cUBjj4rxHd8EcPlGpowYPDWkgnjZcNiROHccUEMakZt4BOyIn0JUINJZbX+mb4AJCp2D9oC1jUaCwMeu1ZMb1nxhyklufLi9kwe9Lncglsm2IQTrYHqlUkwT0bUzwJtpQuA88QimkOEQyEcgzLRnm0qowiOpRDV0uFMBrjphQTndDKRT3hVmhoTjQXC4/czKJOUAXZHBmO4chHG2lfpwlFBJCcmmsskBmpXyhLdmojTZIGNpbvPdyebg8urK7lZzJQV77QrCexBnb2dyhhx3J/9Y9orOrFSLXWO9ppHzzSN+f5s9H+o1sUkG4t3rgISXSaXSLE1cV5Wq6kVyGj4u4F+EJ1N2/fAj7+8MbrYnKaHUia5yNb6cVLl6cEH28s8qDcPe5LJTu/sm4uHu1sMqlios2AfP9gCzoX81Bop0GrWQjL/ZLkq5JgMPxi4YWS1TqpXHamcNYbTi8uFcaUcaJBjfwAYzFLXkt6CECjJ1wl5tba6sbB7p4Zubi2DAMd7J7Ks0rYiKRqcxiCw2DTrzElzzvcfd7lj659aoG0aNriR489OUv74FvSm8dd8jXtYFJKJxMJ85NHXv77LabASwD0LZ7cL2FoyX6RvLo92bxUmm4i53ei8+ef2KpoeMFZpyMaWmoJ9BgDpZzbszJTChuIrTB2todiS1k5wG39ow5rVjsqd3I7vusEPzJSQRWcjBxOOBXlCsMIXNPvJ/KGAo4+HPQxPCIie7EHcWIncj8tzDW4ZxPzgC+L8wv2cVbDYuXhhSlmgnImwAgOAIlE4JmuSW0xq7eq0hz/cOdK4gSuEJz45J8MNuE6OIHCwIEKQQp0wzKVDCRB2UcUcRZuSuxqDdyI9A3/ZjarHj0n5FAzFKP/SfmTcjkkJeno3NIE0AiQHXYPybRAOpVoRUoIHQbm1ONBo0je+zedoBJBjgrxfiXdNbv6rFeIrHucgwAjPdelQX9QqwAW7KMD3LTaR7l+Jp/JqkdKz87xnjKTsdOy7R129e1Ukek0HEAo5nJnRjDDER5CYz9Uq9MYhs8aARYnqcHgEMQRaIcQRbqL8mz+mD4uEWiJmoMca6uLORKKknjNZEyHwC4501jCt3yRvEICeT7ezJZYbhkLqRIsIQEIpyzOYiYaDDK0uUa4+pss02F0wDYCkxjFGGGAVhu5LDY0t8RAZOpPxi7WoQLW9hCXz2QJq/qnEYhIFAcqtlw2MqYRTgKjjza32H0DvEsbF4jR2sNxbbbAbb5UETVqlu5MSg4rTYDosRS2xbD00nmzUKkvaVQ/N9Y3/DKY0RjFyuqSJLasx8zRlavXG4uLwp3fe3B/Olekdrp85RrdFmWkCufXLwlWDa9H/ws6MyaF2tmP3HaVap2Vjx+RgZsaQp3jo8NGpGKtxW8qm5ENxZKeq5UJgGB9YTepYiFyVl+G3O0N+cDLUotcwL0ZGUl+IVqB3MOjADeDoTeHHpBsAbh7+fLlD979LavtC6tLayure9uP0uWkS2io9Yh9AVnEx2c90l3FJ3J91mfOlXvug2lVX6zCc3W/PP2WUOD3BoCeuzp/v0T9bv4qjBocMR3xGbbJ8eda+hoU1+Nt2PcoM5mg8+eTi04m152ke5+TdPdKySvxZUCBMVcYDY1sjP6c43a2e4dive4wMyOGXyV5L8/kkzRhTDvtobREZPOh3xBnN/FzDiPoaOusL/hdCBhKVERY4JRvHJpyhWqpIvyIW7Hd0+DMzCwtLnZ6HeIYrI6kSPCV6TMR8Cp7W5uJYuFEZBW8jRMWboqje41lL238gEJiaRvSIOY4estLuB52P4kVcJgozYA7XtA9jpOBaJFGavqM2QQAgZdIWHZyOijMik50+vChJJoNfEI0ZF2VqxLsi5DEZ2MO2N7m5+brTI9xK/oI5hrGy7wDkelkmLPAR6yCACp8CFcmJUMKEXmKs8XjTjNXiFgsMp6xkUKr/oABh7wHEe5Z+s20eyZI6ojoJ4Al3F9OtrNp2eUl4ZSly0EnqUzkWB+NGPCK3AfKqPa42cNx2TIf7GyFOUgiIbNCwnXuLHt1aZkVEVTqupzzYKs5TfGop4AzaiQAAuMcRmjvM67afN9KBXFwpov5mfL6evtoX0J4ed0Bo1IuR/CDaHsH+4INzmZn+faNqQoHJyHsOYug3oaAOCZFBgYolVUSeKGfs8zAs1miNXcTvDiKwH2z9QAihgVtA7Lm2EQncql0iVozksCLpnM2TT4X6IQ7IfoTe8h6kctGGrLmAcf1E3NKjgVGo3+LBXd+LMmobrD4ub29yzJnfXWth3yjmeWNq9lihZmbtU+HND7L7O5FvJ9CfbkzOqC5Ky9u1Orl9jizv7+bkbp3cbEwt2LtFeqN1YtXljfGxbk6xeJytXrh8tXZWv3C5WuV+nyuVG2JtnRgdY1ncuGvLome9wA/VJ1tNqF/jvGRp8LwL21cgjWtNMP0JtEmrum0aUUlcwPCIkdsp1dfCDs20KotknUIiEiDzkQ35EAn0Ca0PBiGYM9cG6YfcrwMoPpYso7F1aXFD99/z2/H74vca4cXQwL0Ned4sg3EFuFrumn4h7QtrAAfby0CXz/BOq7EUx8VPr/PfN7zSYu6Hr150oFJPcmOl3biSQcm916efHsp8HsDQN9ekn4jR5buCJN9Ifa4J8ez43FncnFy/uxJWibq/Kh87Gh2oDQ0SLLpPK4JD1PS4d9gX2ASY4fYLadpngjb3XI+YgbSawthZx/HttneymYKTIARUk/DBxAAjmVfTqoKPmfrd9d+jSNqxV7vhE7FCe7INYneK0QgCZPAkyjFiBOi4fB+x+26RA50JirxSSoASKkcT2WKnD8TGzDi6CisCfUbD0kD3izFmDIQGzbmH3XirACQZzFO+j+HfqoWctNP/aclIfSBG1QIo+gnAQbvIWVk/PYJYeiqp3RetfQvKY/RrrtIixPzoofAPMh4OdqIpO6B5zhnKYZuypCjqDxgE0STaMr0SlXEA67Tu6VI1FgAJoF/kNqDbukntYcAPNEH9J+FC0PNgSa6pHyCM0LWEh0bT8GdvPIgDEAhYNbolKlKocjpixAivORki0eNUq6wu73Jz4qDv4GwaeodtAEX+jV+T4x+dJW/G4cjArSpSARXAHx4R0FIVZK8Ph/vI2Ak8QfUnSkDOCsK7FfKZ4oI5dCWkZrfKCkzWmLUpS0DUZtbHmPiK/YCcy7D9xVc4KiPI8eDTNAYDIXnGZYc2VcsGbAMHFWDwSKyyATmPVavTFrTuQM6r509iUsXZN6g5RyzrR+TwKCSEDlnbJg6Jwet3ihTnF9bKi+s8C27cG2qWJvTw2Zv1Gis1LOZYa972Am4WZlbmMmXvaTsH7UkmdcT6cp6h0eIrLbjNpwdgIONM+h8eLjvS1BvNDJHKjES/YTUY2YlWjn0bgCeVvzMSGKJi+AesaWMWvAsPxtvPIhjNtVAdHQ2rlr8tGCQd1nO+liiVnCsmfE007o2uei161fu3/rg/p27K3/wI0Kpu3dCLKpXxutQ0uHESptcTE6egKHHe0N8nRSIJ7+MQ4XandT01NfJ9Zcn3zUK/N4A0Pnl+HUg+u+xP1/6r/1z0TMGnmxJTtKeTPoz+eokCXaT/Huu9rRAemHy+GekpGJpyag0ttL4tP/iain78TVe65llsv1J4BE+FEglgMYwkmDnskxSKIxAB5w736hj0nZtNhUsNhS2kVIPMWQg1j8TBnE6G7gqElCc2OWPj9qsiRIpgrfhlpqFb+Z3g1kyJ2ZFpBs+OUxpnWQIefAS57kZSqghdqJRShlZMHFQcE14Z5UIfeIl3oC8W9Ohtc9C3TM8YkU7llsChqD700/iAZ5ihoJx6pLaEuVLyN4QQSvK4NYK+HTlrFxJ6eyc8Q7ZEtEFLRwbjXo11HbG66l4MAkdjHqtVhdSJPRi4xJkGZ+12i0VQiqLZ4vD7OCAw1TiQeYpc6EGjNZYSkxdEkd6pABlDFOQYd1QiU9YpFigk5qNfGP5fOT9TA5DAPLYN+lhOhEDycBE086GwkXlHk+jOoUyMoxyeuxoUYzfXGk+7LjlNJUKjThAu4EnSPX6nNhOKDrr/OCh0cKsCuUVy5ZAijP6qrNSRLw0WD0xXxYMRRvWzAbJdQoXn3qnQmNEQGVAlkA8wb/jKXedpLcAWJAYTaQyVVJVlke4/SfJKKIkfCbk5Yk4nIy6IkS4pUFtRD4kwGA25p+I5ZSKEd5CQ/ZGfKdEMxCfWs+JT1rdiD80PoOoBsx0UKBHXHkyOmweCT89Hk3nCSyP+0yjRCVUG0EOCc3gRHrdMPYS0KnZjKSkGxsX2SRx1PJr8VYgZrdRMJMzIhbK6oeKuOUHHc6mW6IAeXPgTX8gDlYxwqMDRjKRLS0BOmSEnvG4ASJ7aTayvNGdJVMWhnEwr74hksqFWiD4QaL0bs9vL8KLd0WJ5Hf2zi9/KR/IH771w0g0hrbW3JMjlSj7htQpwdP1/NV9pm19dfW/rPlbQIHfGwD6FtDuyxqCH+o/zo4w6fAntPi79OT8jnO+nnPXcZqP3sPotHxzNy0Qr++J+Y4t3fZq98fP4nU6OWzZ9n1c1uarmO3Vs8QDpSSKiW0dGsCtcWi3FAgT4+lpCikM2xu/x91SLXZOogPZYPR2b1mZXEx96dc21hM2GfFyGL5iYFr2rAoBHUwUV9OrlDUKYEM2oEvzCw1aAI+kJdOxKIPZaEt5leizAtKZqUQ33KXOc0sTWI4Xcf3H83A4OhZ9gBZ03q3gLonQyLnpUwx8cRFjDi6OShmBInlShZ1sepAfaI44AKd2ovM66RG9DZiV+j0lAbK1rh53NaFyfYtiGB2F5GN9H9VhqJZcR0Yn+tYZnWZnZkkyth6cMb6aZakEmrBBSWrI5cugIPkS4VePIi+bKSYpTg02NIMVFjxHulEolQ8OWweHTf1Up8er5TVYcnd/BxfnDi+1hmzsR81DHXYUS6s88IvHrTwB21GbHxP4Wa7W4GAoBBRU5+HxkW6Dm3s7ESanXJUpBewIDq31lIyoIRaRwhyykAsNjRrZdR6FneQLZU8VobYkXrZnASDVekT5dJgANKLh451WO5LXnp6Qr5h9WNN1VFQbWiELsaJFGhOaaItcl8SDYfVsqUbo1+0M5UAl2TJVcq8GJEqshmsNK8rPJBPdms6Npghr8lMZ6i2Z0XqSgoTMZm7euqLyA+VRjMcAmSD5YqbdF1kJoO0dH9fnGwxvvAOYNnkoEkJF6G3D1CU9BJ05DfBdyxWKtIFaYwEmBqSxsGRPyBJmc2R1CWoEDXkRQGNDP84zPmXCAgTgi0Qc4/HixY21a9eu/s3D+/q5sbYqETBKpmsmhfUImICfxzIhzbky+YLIrjgmJ+nXL/dT5Uk3opVzIqHHjaS3vtwWX9b2daZAeHA89/jSO/1UK+pPr3zpDaUVPtXcp379irrxGav91O5NCnzGCj+hmKrcTT+fKnZ+60lbTAtMrn/Cs09V9Wz9k0pS96Lz5ZMdOdReNm4shwYmX5rFV+2O+uq9W+jkyCbOOtXbOMWE7wxUxVGZIdEJWMCSyNuwspJvto9a2JWNXtSWkP2csREhppjyJi3yjc8k1ssAQnBR5nmH1FTYhjddDNvG6FN2pm77+GTQS4yVIoawE39OePd4eaas4+6OUTa8fFMSiRrEpavb8bmyvESbYzz7e7v4KyYBZ2BXaKItzsz+nGBILqZcE3tOOXTKd103qLS8gWDqyrvo3CfSYbSqRTf+3s7VnxQm2yjSOgmCTPBhSGQ/+5yskpABWFHyLP8jRGT/GycYcHoeRr4DPuc0a5EzS7eRV+yfVHWFXWCiZkYrmlOPI+Wj7MOcKN/tdcgbcErdSzuvRSKAHNskKry6RFEMmTtKWgkeV4wfmb4ptr9/SA7UaXe3t3YJ4Uyfwin7VJ5AwkW90gHhJVEaJAIv8Gn98ThSKBwQLTRuMniEAhH6REyPIJ0KQR+NIpSanaR0Vrm7af/V4Lqqou0kUyxqu+iu6+mngahK5WqL/qO1AlDBySmJEmsbLv40ihDjwd4eeZTyaXP6qUXnaU88TkkUWrlchoE263UlIUlBC8UhIvHSEWIoEQfZiW/Bc72+qYJQzNnc/CLZDxOi7Z09OfLYylXrcz4pLqkI66oSKSBbHHE5MzKOhIzYGNgh02ikURNhpaEVyRPiU6p632AdRqpISaoPGvFuQA8oqoHFgNSGafgIbrgWCWEqKqmWkz9VcmhTiwUx1k3r9VdfAe5u3ryJYsmiBasCVznU4FDGgRqu+AzIQ0KVHFHoCfpJr0w+01tfymfadFqV+r+UOl9W8s2lwO9NAnR+IX5zyfeP3/PYNV78u51Q9UVlJgWe7Xm63Zy//lThp5p+UROTGhR4qobzt56cPxaS2ygdIQQKw95huVa3hwZwGVMvRLg/e64t01NCAyfWxiFZgZWax4fyNogO7HWcj7ft2ubOXpWQwJtxghjC3EfNeAw8RNijEmDLzs5AlJxGC2Ggk6RH0GecQkmbNXbFfcYQUr2YdBm4NssKLMTF4aDL1VthXxl3yCwA0ETNCb+0+6sk+GtWutY5cQtT/q3O3EwJ18SEFMD5nMsgsbO3B5ld2LjkQcMPvd+Y5U3iBZYYGNGwaNRT6IDxK8YTzkg16ut8bYH0y4jUiSyplAjH0hyqTmZKeXICV1x3MToTkoyAUypPT4K3JSITA4lnk/lREp7gKITFB4YoSDPSJX1CXk8pplHNGSnBDOOnSjW0LezWp8L/KeI6KsD8mfhHHxyn2HNIp6QWFzjxaHdze1EehuyMp1SY1okyOlmRRD2bh5AOdx72W23416RMF8U0WqACY8i7trSsb3i5p1DAPOphqVKKIJDWRyKJ0QfjUqFOohkyrq+tGJRRp0hI91JqKG84QAyqmkzkdcUtlWvCV9MNJaC/AQG0YdGvkgBUEXoqOQ+QxHOKdmw4nGJHMx2mPHpyAq/77QbNgctqBZKghYTTZbZVv2XNH67X0c+QcfI6p8YD40XGVhJaYqfMrpvVFo0VxKI/ZqG3uytkZUn+tZnAHPMLS1AgO7nkRyI1bBLoKAGp6tRD/UQlPUQ0U+woV6tcEcCu1MIpHWY6avNLGOkTTfxkXCzT0IXHYsjtwKNiKYdQYPjh/jYAK4UZPfKtWzf/9I9/tLKyxHzODAJzPg3Q4cSBSg6X4vMJCHFZWdfSkuc/PXL+66eeq/ZTyzxb4Is99Ww9L698gyjwlQOgdLl/gyjy9e9qsms8Z0f41B/wUwWe+vrswCf7TrT4xE/jfLFJgfMXz58/O/uxM0WE3ZATpIcrKQiyF9thY+uX5rMk2dC0GMNDQpPx2XAkDLSziAeIh3nQFgzTENUEMApHbr5NRTkTcDKMWyJGyi/qpHxIYtCKiiqclXAg6aWE8RVuh/NOuVQQaZeJBc6hddVjMylTx6VwC5AJt+akQ7xDKZIgsFG1UsYkajQpxRJPY1EBKS44tEkdiqkAOqfDpMIRbjGrB7qHU/ojPcI2pnjAUTCxIp2e4qqOGSsQPDvsfEpYpHHokjLss5GCDiV6komeGEIqe9BbVsvpwL2K49BzdQnOIgIPBgk5ATpkCnNzAbB6nXZARvKEsGk1mSE1MUBN0ItRMjo8mNQm4doZtOJPSnTDZA7lwdwgJBkAkNoU05nH7/KJpgzQErDaof6VxSXQZDDc1oTBagVtI5LB+JTwZq5WhyE0q2QmUbKokI93t8Ufu4JuVI3AqEf0Cmdleq42GV91D5wlgdAZC6D6REmnV6pSibacS8yqew4jMhFUS8BTBDqaCXuytPNuwQ0hSCqUOomduGdd1FVr6Thx6JuaCogJPyYBFgJDUCQeHwtLeOhZchSNuu8PuVj2+KpXbHGYrLNLC5lIqWwFBoGTA3FYHUEzMtYVApSejBQ8HVIXQiqie1qfUr7pRq1aIY8h9KIdRQSTqwILwOwjiCfFcNJuTC5I2u0S4aABlS74KF9t6+hYtQm0YA3WA4fd0oruoW0a1NswUVVtqm0sLAa07fYlxjDtflPobO78dubnF/UN0Vx3hDiWM7sQEgR7p2ehGRSeNAI8ojlL8ECTEPCNGze2H9xDk4sXL24+um/FIoIZ8ZmenDt/DFMsQiXCF2zqI7Aes/iPeOj8P2JrL5v6ulAg9tCXx7eAAk/9gJ/6+tQA3T1f4Pz5UyU/9Wu6rz37+akPegm2b0axRMigBmbCggBxoiHhZxPrjTbcryNLFFECjpuJu4lKwpt9vNxzUJqenqtWwhS6PwhJTKJYwUGhFW/YNn37tb0Y+4GQnCufwJrg/Wqgm8EJnGOWWAssQtijEhchEizTdZ84hMqdeJY8qn10rBuewvZYAinP2NYVHAXnUC16qs1nvV6lEYt2z0IrhIsYrltYrbsqp1IgUwnAxGe7KIuqTA5NEiklDUVJV9KaNeIEg8GYYYgQoiSGqPqACxJ3uaJLjwHHzIzCaU/0Wc89rhta1w3FPOszOp6AIQNXhvRFVektjyuPeynvM7UrShvVK1d0UEnF0poVUAmiBfGnp/f3dgha4FzFBB0Wdo+WRIUQAKEXYgLBRgFgRd8iOUbBg4FgEuWUvoXdeQK20vnSIvKaUBc9ooDOyBJiahQg/dIBLEyBlPgqRApdRUmFjYtrt5IOrYC56bkOGIXDVw/SmeqeZ51rRaNGpEso6VC55txFRmNx3VwkyziEOgqbC9AQHdQmGZZP3D/tD0sdRRSrVuqMuKNAs6mGRq0WPpFTo431VYUP9nZJKecX6pYN+ZHwn6yxPctizSK3tvpyh7Q7ltOw34PAFxpzKCyIA9ADqjrpHB/KYMEsG+DiT1gpF/g5xkFE6S9x7DIKRLC0TIGeGAs0jUogjgKGadQOQ0bVlOB+bQrQ0oEJRoRiUSUN40nyuK+QZiG7sNi4fuOqkmBZIn+MCUUuh4uO9HxyMf365X7q3pdb4cvavsUU+MolQF817V4u95TCNpfzpJjsNe6evz4pnF639Tk5Xzgt8NxPxdKS6edzy0wuPreMi+c74zw239h+IzCLGC2kMsER44U7XpPtsF7iISFuJumDzBrsyLiaux5kfUGYr1F+YeVcxS3v2eRAvX4H2xBvph1IZSrOI2Vm0XbvddmeTCCRMqTSbAkbEJKH4kaFuIIKxUVk0sH3SXSXYAlF+qYIcugWmILDFXN5jjHhoeY1e3iKEcp47nWZD5TMlzJUialjsB7RTx7f2En2JEIUkvoYXQxtOsMXvdM65pyGtRt76rXODgN7A/xwI3oVTEUSCc0J8Ycz4b54Tzpk3fagc62QbuDxkCPGFmQBX0iaRuGQ7650ZkhEA5VlcpIJZzFD0I0gV+I7ZnRqTh9UP0ZuFsIUJgI8RpRkA8QOFQvzpSQKpcKe9ZTCrqgKZw4EkA+zG2jDlegqYFdiRByaJpBIAYcCUmw62J008kIQnWbLNbxcUqrb9z48Oj4UA3C+FA7YzcODpdXVtCo1aEhYy9CnMXg/bpM9kKoJM60XqG0ZwVVIMV+fqzVqHief008H4qT91ygdTTo1ZpbILJ6BBVFmJgdw1+sgRXZr65HJspacG74CELRZAH9d0RJhD50ZQZ2TWAloQrIZ4hamSwyDhtCdDjdWVy05BU74irdD9WbKCG9Iceib0sMiYiod661a3t8TH2ib6ZXFSbsUatA8caAMsAEjZBjzLiDRqUhQpDTM1JjUZ6blHtmZLeUbc1XiQ+8UsAiEDW1WquadXDPiJJECqkQU75joyFLHhc3S6NDQMYmjzPLrkErWEIRD1GdpvKAfay9bZO0Ulvjiosd0i0TAMBxUzdIstyKw4SjEh9NTidV/eZYE6PqNGyIizlW+VymXrAGtO8yCIcQvPfldO4eGEDYFRa4/QUfufLRlJQU+tmnE7U88PKK25xZxK+3Gs2Vc8ciLHnxubS8vfgso8I0HQN+COfgqhpBsKI/ft579tad30197fL5gv0gLfBXdO1enrSp2xghidxZCIO/PiQwoSRTKcGcmBxzYNOlhsBgli0VyHT4s8Qci4I4YScpWbd+2ZnuuT1t2PgLTLbHlxMK46hx2Oh5X0ls73yZl2tRVYtokEg6VEPyozb6Pf6fs1iec4RFaD7IBu6crqZhBW0w3tMKmGVfDMzzrJIEjiazo7KxcjiSdYg9F5MecEMwRQjrRjBAO1ZRXWDeYkRICmCbu0Em74WZPbqMheENDJsJ149Jc+o6OCfnqFo8mFSJASDVE5w34GDInIyVJcpIeatC0TywwGkpSzKokKKb1iHkTGMJ1WEdbCrurEp30VfDogAtkKjyAEiBivMoEn05ERB5HwM2d/anbt0uVef1kFK1CvWBxW6tXnRDFaQiwM1j8PgZSoSIsQgYZ6j9qr/EQ9ARWYAp2Taf9Wuv4EFcCWDWqCcCiMw61WrZQzLSw7S4jnPSW2MT0MQGqioUklvEMLzSP4OvybqGzsRusERmjT+f6j8JOUFJXlUmqCp0aTp9SzF23HOmJWwnRYtROEIeyFeozdpW4axQqR5xuK7CvAqARGUzanOtwlJlm5owU0Lby5GGGTDVGvCeGOPBHfFJJJE9I5y4MhmTeBDJnM34kmVAFn8I67HskWRNyenFuQyvAiT7npgPLEpmKLTUl1erZCBaHytkLWX4y/OpJ8gsKlKwVF/XKWFCVCBBSB7mQyKFvZGxGYUQooJMxzOlYdZYSqRkBIE8Cvvw6ydxH8KF6pXxhdfn69es/+Xf/C2EqKnlcrzzvM1mKXkcCZ6hQtZNj8jU9iUc+84vZpJKXJy8p8Hkp8JUDoHQpf95uvSz/CRSYbBafUMatZ3eZtHw6I8/Oy1NXnvr6VFuTPkxOPrn8+ceVTECXz+DTNnd7YXIx7A9w9pTFpmzJBhquPeluGEHbIoU5/hoikvCg5jV2yqBGN1g8+MRQmSgAIoMey81gAAQGRCdYjx080amNi0OZl1jiRLw4Eh2bPvSAH6iN97V2kz4wmg7lAXhBxuBNFwA6PTuFh/AMsp94903yV6iBcoEMABexxQdfj3yQhCJgEMFVGHZ6x9c6c9dapUzqA4GFTxmhFC9xwegMX2qHfH7/8IBFEUdt0hc9JyWS9YrlBSdqujYcmqE3psJ8JSWYrnLpJwhxYHtsRzySxnTBzJCO2U02ea3XhPp9GiZmlMrA0AeLDfsYlivJEQAlYVQxHeNpoicQJ+X9Rodh07yI2nwyW4Bg0pIKGK+aSTUePHp40BmublxVD2UN7uh5XvhCFYgmTHqgk4nYLBiiMvrP9MUCIKOaq1V3dh+5fv361dWl5V67RRpYnZWoIazLUYOn3kEudyTpmECOBXZBVW6BYYUcocQzx602zRNt6crKGlNij7CO5y5ePJUNo2tFWRKWB6buPJnrMNxGExcdOgM8gQLRYSinH2JI3XOdT5lhODdet9KnAA4TqiogwRUGMdaxagc8rhh5Zafn5iIqVRBRJ/U+EXvApYRBCdTP5oqBwOA2QhmO5fA8Sc9co9FN1JEe8VCsgXBylKhLsrGZE9HHhwMqPaBP+HKjE23bVC4uNKB2iVhJsUYz4k4HvJDQAmTSIkSYzHvIb6yUWJCZjKWEXDKe6ANoJbahzqcQp1IOrWKv3RGqigCI472uWHUoECMaWeFhD+TB0PiFCVOGMo4ESAp5NvbktVevXv3rf/M/gXHVcoEZk85o0aduPPvp4uRI7/rqJD1PP9Mrk2Jf4MQQPvWpz1LmUyt5WeCbRYGvFgBZUpMV/M2iy9e5t5+dqi/6Sb+ohvPlP+PEnX/kk4n23JLpRZ+agz9ssgQmhCWCjNjzqQm6XW/SIf9QBnOSnV3QGzbRsnnlirnQgTGFtvs+zmrJlSoCD+oJmHIooG4iMmHs3O+HR5V2mscEQrQ/gvHgCBHg2L5/4cIFLETjUI7d3+uvHV+LcIyT40PpzQ9TNn/YDGBBVad+rehSWlIHVKs5nxpyS+XqwUG5mhvd2BvzsC+gX61e0W0jwjITL3TWtcfclfnywFLQHpamWjAOx1CV4fgKImk3MBC12lkoYjTkQTgiZU6kVgCG5vBteM4J+JXo+yJ0kGdVEkTmf5WwdoPVQ2PUZ/2kp3PdV4BP4WDts1X6DuYpmopX/wQBqDatyvBdxCYHc1UnyKgeo3ZO8uTEoTaCIfmn2EIBfKAS610ZLqaGkSgDxUhsdJ4BtVhKWtE9iPDylUushA4e3gXOIDB3T9m2kEidyERL/zTF4GmRbXVVKjfT/xhxykBPp8PoW7sK7x9I/DkwZan4J/XnN0xjdBfWUYwpjB46DNZFPXfXYQEYLLLrj3NqoHRCXUzLKK+Yi0AYqjaPDhJF4RQLLmMql6vprBmaVganLMlApWkgSZztTncAR6C8camf2MazsCs4bmaV96y7yKtLQhqC9uRHZpZe9JQMaOpU+G/2YqQvhlOdZe3ehUqFBjAQeNcEiV0OFNbm5nSS9FRvtZYYOMN5wgJ1WZ23OhFTwLNeCdCBqx1tKYzocYfWdQaIhOR0Sc2GqVcoYH2Kzmix0JpWRxlBonQD5ZBL1loqYcjb8jMRIek8CxoGTcOqHnKLKfZFnZ98KJYW0PTk/JMf+R3vauh3rOHl499ECnxVAOj8ejp//qk0+lyFP7W2b2uBz0sl5Sf7yOTkWeI8deupr8qfryf9er6SZ8ufv/vkPC1lu0nEP6koI+le7K7i7oV2JkL8wUDyPSkt7s90IVRLJDKlcpJfM18cRgxodi6hFRuMUpwR8goG1LZvVWFF3lbFCdRnezEvGdkJWPPABHxe+r2ukC0YWGt0sr27xWNlaWWRqKA46531VEg5b/693pDJj5i2rBywUgxJnWwjeJVh9qr1cuuDZfbZIFQqWpf3Kulw6ESCkyTqAxkJnDLXwQX1uiQh1zSgwGvao9QxEWtONowjCQcYBs0WiXzEEAoL4oyxMI8Oq6NwGUt0HOijWrALEwaAmPUwFkpMpwZnsseGOu+QMsUwk/6esFfxh60GG4pIQqFKSyLmhJEv5z7sHqvG/FKihcQrkttH5GWjE+oXA9V/V9gpM0NBBNPkWROqMyCC4NX4owCD0s4vrKzMFIouegSbVFhJDnqyMQz6PdGStEJhx5SLdCFDBJadAXcWG/OkeLJGcPAPu5a5CI1zKsxAJyILLIfX1dHR9pb+A1NEgmmkn97hoR4amsdw8ZYgRsPh5vZuvRLqRQdOTGWDzSumXSWdMEfRJRfVlg7BRYVdpMoJDHEa5DKbyQSG8YphEmEoYHIVJsAjJpKuzij4IrrocfDOrLPGt4rjfCZHmIZQ2iMtUy2fMvMqRSv/QmuJhTKy6A5ExFaGHEVMbY8LJoTybqGPdpNQC2F9pVp0AzXCn3Fo2RMLkv3MH+fCAMuM67OeeIKajMeh3jhHkshUL6U7hCsF28kJaMi/TLqzsvgC+UI3zPypbkMjiYxmX8pblCFvs7pG+0FD0MfYNQ0/mVlRSFXlivQhp5G0l0lQnr0Ry+tWR8TqNstuS+VX925VSmFx/+SHn4p/AmQ7UuJPbn3CicKTu+drm1z87Cfnq/rsT70s+S2mwKeD8d9x8C/X3O9IwC/l8XTj8Dk5JtVOrjhxcTJf6ddJsRedKD95ZHLyosKT6+crTyqIV3x36Z5sx2HjGjwkkZ8n2OgxnkhMVZTHCRhs0lmxE6Y+gI3SA5hwF8dSHj+AhPASnM+mbMMld3HLdWzALRwd409L6o+vLqYFnCufikaUV8ynmlMm6hF7v0p0OK2N9EgH8Btf3XVdAeVxFJVidcEICa2G4ViOxMhMs6fR0O8N+vhfqZDjABbWMJG+I40oGBqeSPSRWNuoTZ3piJw4DA3D1rf0liva1aLrmlO5WyL0YVB65aKh6b/reoilOYzRKBzuIng6XidupYWVV6ca9vb2dQNPZnTNuGd3l5NXdFINapv0xEWdgTJ1xi0HuUWK8EhR1KktF3WSQbEwxHCPvpFzqEr90Zzplsch3LZ5rxddBzlQR3UaIgXylRBN/ezcmSTDPaADbYt+JtMXHNrdcrkCzpJGuA4cGFTaN7fU4yl00Blf00O3PegzAERi6YWGTtSJGjrmekpnVTlxxYFiPl2BNY0L/iMI8ZRGHabSRbNgIFaUFQ2Vsweq16qczc/GQ5ZpjHhOhj3CHJCFIqks6CWPRYISwRutOs7no1gBKX1MIHIhjg6Am0tLC5rWQ4eGFJNnLdFnVQvlSqsjUVjLXAJuO7v7Dx5uAoh+Xz5huvnGYtKlUNeiQ7pgTDSy+ESrdFo15IeJAiHgSX5Q6fAVAxONPSaURVfSOmoYu0N/vDa45dwtGi31pETx6dyRkij9nFx56vr5Ml/u+fmGzp9/ua28rO3rT4GvSgKUjtza8lP5+lPhu9DDyUR86g8+LfBssefO5qRaNDx/PiGpi0mEj8cXfEmPKIzXiZ+T7ImJm0i8amvF1mzP7UY2gPA3dkg+7ToLIeYPTDL84Su2e5WM+mGuay8WiFfJU6KTqSkvoMCBjdg5sb43VuwhGqJWAID6A/s+EwfBTiJRw2zR6zcxiZ56z9YQlQQpS6Y7xcBTD8PZZ8zeswyRBGMQVyYbWeLDRqJQsO/rAJ6hSxc3LuiDV2cds/XzCvbC61ldxGUwDyxDMT0hVeL+zcJH9nViHlwNg5wJjUfkww6zJ+YtZzkEwUjwHhHvVBgxV0LuFH3SRHCvBDJilPqQFg6T6oTxjNlqJDTxoEEFGZOASU7gtHJ5Ud26OujHkBHQIS42DYxXeWwPgEB+mhr8F1PUV4a3esLgCZW7J2H0rQ8ABCKoGQUoTSb80jBpM/XQLKCnYIC0WHpOrEWTxY5khpWu2RE/ZjQ+FI7ylIFwiBloCeHWmSE/cI7fJ6la5+Bg7xDM2d2V32p1bbmVpKQtFiTDKPfETmY3PYKTSvki+RZlX6j2dIyhCxgRcqDTU0pMPcTI0TmZ4pg4vTUi3Ugp5rorpsksB9kpWZ0nJtjo55ZDYTIYB6q6qxUXU9omEzTDzJgfoiJu1RL/QfgCZLY+8/wZu7JGEA1ym5JMtX/ab48IjWbOcmfSyfG4KlDXqpyVvsWpad1Ip0+XNJ1iHVcYDeWmM+3DA332lQGW2Y+xWApmczzm42WJzi0s0X/NZHuMrPOz3Yb4ojGkmVpDytVDPTc1YKIHNWT6+AGoTUM+Acd0vKiSoi4TPYoXj4i6FNLNREdMT+pnctRuCk6knnQRrq2t+RUjeOq4oML0MPQ065+VExtAcIfHPMLaiC9fActQp9bTz+eePO7cy3++exT4agHQd4+eX98RP/v7/4S+Kpwen1DGLWWSLSxKTU4++ZH0rsJxhEMImU2IGYIpjM74MYVTlRf07MwpK9qzKRwLV8YqY9NPeLwtFJwgRahV58LSdgbbDHWP5ESUSoq6UuHWyza5QIQwKJUrp+MWVYIGsQRv/7gIGOXKWJZsohouM92IsMxKAwfGTZW012PheIC+OVePHqZ9wE1dVAzHda5OPEUZV45abZoMVxLWUpDd3Us9xx0F1CkJlOv4R/AJKRqogSCR6RmCEfCkmrpEqbZAohCebwxd0UFtYIpwwCrRjls4K1K4LlykpCDKqke+Up1sd1pzudB/sd9NG1LedZ/K6CShmNo8i4CELzGDU3KWDkL7FnAqXuuN1Dmep4++BuZL5ARENHnwpFI8O2EkGxEXVasnPvF1gMmcsOnGHMPydYrAow8teTZSfySpNEEuN6FR1Xqwl2RaSNU9nW6fSEOwY977aAVn0YjJ255YsRBUlB92e1u7O/MLc2JEHR40BaUBQEGzo9Yxgiimn3Sk/M/TtAyNxnzKXIPyiWW6LiZECPKm84VDp1MMDHH0cxGy1B9p1ARTNgSfYB99nPGE9C5Z84jmxBCcoJJHgEZPWa4J2gs4iIbwpFQgBkICJLilVCER9slSE0YBtspnKC8JhOg6OU8RWSIIlaS5o0h1ZEHMQVgLWYd+Gg7AAwA2P4OTwDogjoaMXXxvn0qSnDaPWqY7nirxqoPVPBSZhqEV/YcFPUhvGVg/iWugBkRIbdE0ZLJ8JY5ilq5OJNWTwLttMSNbHndF/+MnGT8Ho4hGlUFSwA+0ah81j6tHoZKs1fZ2t4Gn6PpjCBKioPRcu8nhl/37f0P+eJeedO3lv98BCrwEQN+BSf74EPGD9AeffH5MB+pWWtaJQwFHekL4EZeCN0WZ9Pqk8KQFd5OHIrrKRxcTGXj6oKu2TqiHjXNSI1ChD+QZUyxzWKwI2sMI2pZqn/UIj2dqouZRWOb6iqmz4rQLy6qddG6mUptjzex6yrnVdjY9gw2cJKImDAmPOBX5JBtWHURODFyonOzpQADJkdRFs9UKlhLChqLwQjJ9hp0sNkMgoRO4QqNep+QCNHiMk0BR72BsBqoP2AlFg5ddOQrkEOXgI/AKMMG1xy1cuds76/H/SpQIOtzpNKuzEUgmZXKymdXrZedMbZDLe39eqm3qAlIH8IJ5bLaIAbM1ZgfMuYnshBAHZ0oABDsb0KdIh8IjWj+pTvBCwY3n6usn+HLwOcGmczyuMEXM9QysHJ4Af7ycGL02GgswUY1EJznarJinx+yOET+CE+azjxVwUyP8GwKklWJCDrpw6SpVI9mWEVVmqxjs3s6OhOP5YgW/3drZOzw8ioUzRaSVo8QBDUnaYB9CO3i03eHMPiLHO2od4aUKCQH06NEjqPO428fS3UKLcJ3r97Y2904yFYi0sbB2lvng0c5248FdVjyN8DxigtI7ONg3g5VaxWyGcGc0lD+LsMxFPZDNjWTKyiHqyOdDFyY8Nx4vGiF6CsKs8+Q4LorHw2Pd4qN9I7/rkRiOTuvcCUMqSfQ4zYiHgMqKcsV6gN3llhtmxmSKIZsjkCsWEue2AMoABDLGGuv3mH7TeXHSMxeM2t1CE+uDDDKEMKMzckjrXMxqD5Bn+QWMBqfMt61z0ASGUT/5nlmiQz3Y2+fYBQVCFgpEht1BQDHujIRyO9sH4A5dbqhHZytsgNg1Odg/06X5q3Btz8/IZwflWM+Ils3k/Byqcw2d96efEdTcGjVHRmUq/N4FKBXRIbz6peMLezL9VmVIRv3eInNIlZUUNCNiN4lcba4qGEFiqC1wQ1iXmwL9929sC4KgTod32BP04zQSI1N0pnIg+0vc/WgLmewln36CFOcLTb6q0HlUm2xf58u8PP/OUuAlAPrOTv1zBp5sEI+v2ynSzWJyMnkgve5rejLZVlxJtxvXP7YJTZ5MCoSRZtzO2G/BoJT7Jvsp3+mTbEjUQ/XDZMd+BdnEeQQRLrjuhOgixTp2f5wVQyIl8nYrkCCWgzOoypNAFkyUL9hqqZum+BrBDVoOqHQyhnJUqCp2QexRCknOL/XQArB30H9RTBQAwrRu8/asAC0i4XIJcpfmRT1kTg5fqVcU8bYtgb1eOVz3YGJVEloGA8Z0PeLQSXelUYiINXJVAj3ZsNHBTYW5wwIwP00r46lU7+Ic5358JdBj6LO06xKJkmfBU2CIZgx2wJOwZxfFJEQIvMezyiOLQwd0z13tKhav/Kkci3W2RzLRitZRE1Ti0uwJwgNP4eVnOayQu3svyxi9yHk7tDOhKDkZqUqeNZybW367RyHIyW40W4iEU8AWW+/TUVkl5HjgRXjuJayI375yVkK3P9g7kBFDNOq9i+vL+klilnT5bHN7+/bmPkB9eWPt0sUrghqaFKovRuWAZpA/k8xpmDedWQ9QR7UaRtC6hPUGHpyu0+wgl2rBRIsnXT/Ions+45FaeA6ao8XFBRRQGJWYA4snTp2KGuxi+LJZIWYTYm+3cdOwYg5/8PGJ4Asgo/pV4hO1EdM5Cy+QIrSZCbIMWRepSiWM9B0WG1EKASFQDgB5KkAjayRLJFkkBogyCXoI4yRCMlPj1UI3LHqWQZYC8efc/IJInlNHLd1md0OpC1SxdDYQv44UfKASaMtBz5V+t3PcRJwkhy6DOUKsU86AHZVTZsWvIxc211AR8ZQ6QXp0m6tXwxOvb1WEgZfCiGwZGmYCKqZ49oX3ZqIdzgjdoF5uazHR8Wv3k1dVevgK7XszSU4ef7g1+fq42EcXJnc+/SStR7vPLXr+upLnvz63/MuL324KfLUA6Pya/nbT8Ws7uvQXnk5E+oN3Jb2Y9tn5s69ak/KTGZycTEZ6/sqkwvMXJyXPnyiQbKp2xOCmk/J2fywB3khsjkuR+ons3mGfzs1w1NUEIY5Psn2MyhbsoHxSyaR1m3Jc7GMMzHdywYNnK/lS1jsxxqyeeq2WCmbIUdzCpUL7w243sWwQqWZ2qoJt6EwIdUINFBooAonCqeg7UXlwqcQrOGGl40I+kieE59SQxICWIQJJ45/KOPBjZiSAmJ74GsOZnj7iKtMfYr0RlOXkjKXGzGxemGGBVPKl0HBxLdausXBzw0s1qife1FWQMleVuGggSAND4tlYEfsYd3VbDwEyKAELxzUV5tCuBuRNJ0Lltbk68KhXemYS9C1tMQVAvnqQZsddRAvTnJnYKNQ51yATqZgHZsFq1gf91K5HiBy6J1MRS4ciMjJlinvEtom1ykk2wC4gGgNRrNdne6T1CLRI1tCSCqx5dHzUHh/DiEOiAUOYLYch7aOdg/tbe3vHvaPj1tJ89cLFdQH3NLq8HAlQx1MzB0cPNe2KVtOeIEW2avJzyOhw0ScjYmUsuQgWRS4VJi8BVlIXd/IY7vdGR58HAhDIeZy5zBDWIsyoliMktIg3sn+ccMFjvnNGPGaM4kQRn5AwgTXEOQndekCPx+EeIhYDMWSTpfLoAKyXzVsHAjn3pFU3o5GeQsqXM0BHhUhqfRFHEq0IB8qTzLwIpQNicrxKYhiFxlZYJUsLfvJjoS5mZ6UheWEXFhdpqbQubRk6UKEaIFNxQkTksmJBEbAGroDpGTr55bglyy/sbul6SbBmVMWqjW2Wn5t5BXYg0aBV+Mb3Wq3jaj0kf9a5izo84JCJmvFUT/hqrYBqU6dJQAR1JUhdeaODeJT3lMMVx+RrenHy6VZSKD4mZSYnk2Jf7CStPD6TvqTVPm7xi9X48qlvLAW+HABk9XxZq/MbS8mvdcfTCfpdfuRPTfFnme7n7iwupgAoThzJBmdPt1H2hyd8gaguMC2pHeyqGiVXwDnIG5hqhqVOPicYIE2K/ddbsk9l7LzJTksoInlTDfOW5cjjpEGgkkDLKuwcP74SDJh+bdQTdgdvxpy8g3vEiU/Mw05NeJAyrRQGkVMlsoHFwDqtYwxV5TgTq2IMw0U9ZFgTNSdhhyhdFMByXD88PlQeg1Szws51GzRTUuVserxwC43D5bvb7i3nFi0jI+r3hqEiTNiGhjAVZjZO9CpokgiE0pLeotVp+NpyooC7Wk/H4gpOmRohuW4UWkcNb+eGmRLNiSviHeqSyj2YjkLoRbnKlSHIIY1Lp0slRD5ydI56pwYIDLmiaTWoJyQCmOsMQU8IQnQ/vUsHZLBqoGJhv4UOyrNEwjeJQAaDLlTBaoX2JxHpBM4bnQTng+SOjpr5iHoYR71aah7u0ENpWs6Q43YHrlWDVsANXVWtznS6rdlSJURTVJBt0ZbD6tzQtBtdzZegJQISVxzkN6QRibkLRBp2VMlATqK5eh2uTdGtiNumDjHBwRDAzJYQmVwnsf6SJ0OohpgvffC4VnxN50KXiOU8GNM0CkMiDuq0b6VyYGuVhIRImOz+kLKJFknKLvfJByM4ouxm5VnKShZgaX+UV4sH05UPA2ka6LUIjw6PCR5nyWBEfBiHbZYOWJZ6xXArnaBkaBE8UXldBVKBe61DW/oM6/JX8Juk+szna2r2i1OJsWgUkSkreV9ajdZX2nnznY5abQaofmZA6Vaow2pwXYG0jOtqS++mF336qv70Ypy8QHIzKZOW/F0+VZW2m37+LlW9fPabToHfFQBN1tDk5ItR5Hd8/Is1+l146rm/9vRi8vmYBs4dE4J8rulIH5w84qvz87WpNr3oxC2HC9FWcm4PTd4w5QdgO3wmYjD+zrLSfurwIC7rvRXbw/o9yS4Vk8EJsHOsyJbKgsb2jU+HToGLe6nC19dL7Wxljg1EuccHJ79+4RKLaeyNNRB9SqfXrhNvJFkL7NQyNxB0xIt7JtyI2p0uFqs842Ov1JlcBqeKESXO51iL7R+c0T0sBAtkoOONGYiY4nnFbrSfUhIoyfIJhycwXiRgthvJPiJsT7itMeKlcSAZkZHqQKLx3QMmL9xnWHF3mQdL9BGB7EbGqGNiHPrAPYJlJkwCP4rzkxELIY1GDvmTCO3DewhbN3yMCvuJ3g572Jw7GFu3Oza0s2Hm8LDpCj56KmtFt4vZU3BAJ6YlJewZF+w4xqVkmnSG3gsBocNa8NOI4xd4ItEk+vTUYJSpNhYzuZP9vUPzJRoym+WFBfm+4KvHb/+bm5vV2pwOO4TvbjaPo64CAVWEzEF8ramWaby2gDH90fT1G9feeP01CSa2NgPiaJolysHBfaMzTL0gokJS52YTuOG4J5k7lQ7nMgZCSBdMPhymQAzhwkXhjkDPjJdjhEidzcn/rg96EBSL5FZTrLBVYrwuggk0qeBCxDngky9Acz6xnWI9U67ppDTs6MmZ0OpNlj9cGLbqqspwFWeFNq18Mq0WPxNkme8GIQOD2s1jKMkCWMvJla3XS/rTPmoPmoP5hQVRC3f39q0HtnFgB2pEn8PbvCJ+tdU1OiFnIqbKHh+19KyWnSGO6mswE4bPvPHHp7M6xhpJLgzvE0ZRTKIEicPt9YAVuQ7PViqIA64YnxUSY4mUY4IYhQiKaxkxoN6aKq8rOuwwM0jqdxOEjRgWkWPEkMOREXQrUjqH7tUR62gUhRVwJCcf7TbpxfR6oK1kI4rPpEj6dVLmU08mrTy3pNqeKvDU1+c+9fLit5UCvysA+t3p8nL9/e40/MI1PN5ckn3JeXpMavtcU+PZ9MH0qU9+1t20QHrik8DD5q4SUv2wDkikHbZOdboL2ZCtS9eIVY8KYckBxLgbMIL0IklfgJdjNmQnhEZAgwfdVScbDrsvBk8ZQYzEaIFswC0tyoWkAEUHptLc31eDrR8jT6tVAw0IfqAhIEzlbqnE1g8o6INbwX7whyQDRjCb5HDLv8o7GId6RAfcVa2ueopPOTkEwYzWMcJEFxG2z83jdq0quRiYxCIo5DrGrqrJg2EunsifXAw8lETHCeolmjJEC3CQSHeihkTYY0Q67Cva6tjpTChlcNN0rj2rmE8D9KmYuw5jN1jdpo/yOH2Q8oL1CCxE49OYm9U/JjLaxVDcCggjXE82f+nalcNWd293111XTJp+qo0cBa2wWzShfdM+OURKKNY8pXxme7uF4xqKQYmIXcmfdVoRnnGIw85Wb9y4tr6xKjr00sKip1gum0owUrsoiaQjFtAJQVDAXPtEQFbU3JSQnCkPHKMDxogYHrIGQqaYJGzXf1IQjJzDE+zoK0oqTO0FTjHcAZgSu6Bp4plYFRHzOpaEemR91y66qRm/h1GyjOATO3rCEXN4MuozAYp/R8BqsuxCNxpCSrhA7guGRYZMOxfUjhBE5IMKR8JaAlGm3FrRDfWTA7mOqmREQJ63ALdItdSPvGms8Jlx4J4kcnQAaL0Vd0BvoTbLOxkauB7yIbeY9JATIZEKDTBdb2GulIQINxfIaHQ0X8R6fCqhUz2LxB2pdDagHkO4sBtLaJKovQLFCtY+CQIUwl3dhpQU1qg+pJ+TE18d6d30PP10RZnJp4vpI+fLPHX+bCWTAs+99dyLk0dennzrKfDlAKB0jX4xYqVL/Is9+/Kpz0KBCYXT7eOp33zMXWwsH72QTYq55fgsTTxVJp46V+HH7uI1H68yaSTsWLuDIatPuc/tlqwy7cL2zbz3zkh9Ko4O2U34lSSb6VkhnNwbwgvbo3FEF+2zsXHj/2xcSnO+PtrastH3BuFNwzYXD2DFTLZC/VAqD4hi8ny3qnXO1QGJzqYOjo75oBFCMNmlW1EnZkk8g03mCW8yWWBJcykHdWKMWjE6wMOerxs6kG73aKgATgC64V5OHHi/T/2lXUmhz2gYtsyhtsrmavU5zLrdg44SRsKdPuL+TTPARSLcKxBGwoDRyhXNGRf4lzBKWS/4Likcmh2ynPgYUpSEnZMKqUICeTDADe93ZiiPUz1gb6zOkz4EoqJOVEwNHsHFNdodhO4GyhiPSvt7u65HXgvCpwScecSzHtEl53zxJRUpNlt3G3f3ZTYPu2DKkoAd6KAercOmHrc+RIjsdA/r1GhVNkosdfgyMRMJLyQs3LOBA6bOoJmNq69cu3YNYWnEoBYyOdN61OkaCNqyIDY63VW5KdMZFDYRnc5Bq3Uk0NBSdg6EOWCIfUq+UlG35Kg6az3Iy6Y/YBpmb4nRh0lykmJlHSYaEWgA0dR5SqoR8RfghoCbCX1ylUggH+GPQQ3Dp30iwLBklKDY0nm2aJY0yErrGsG9ZzLM/PWO95nrctfKyEV2aWnBSbKyI+ZcA85grMwbP1wLiXbk1qhWGmbZyjTkmCDCqLC5IeWa9Xsi1EFbcbWFSjfvMCdZjqTwMXGMq/0oWMdniGSKRD5ScxiRnhq/yERqg/C8CaAhS6OYmnF4HmTzBDZSsiRGdX58uhT+CiEBgoydxBC5gFm9iem3NxPvBXocBfqxZpDImkxXSLrALAOH6+ln0PDju8Tj224kx2SncD0Fc+mDTz31pPhn+jeaSA6l/fuZnnlZ6NtLgS8HAH176fMtGdn5LcP5ZAt4dnjnS56/+6Lr58t85nN76eN9MN2DfA+fdJiEXWvi4x0beqKTSgvg4sF7EpNeuzP0wBgIM7At+uoRnM8rvsLi02KK4STsXTl5kRW/pN5gDhKskUDCuzI+p6s4CvzEKlUNdHArKyubj7bxlfW1C3dPbqdv2CQsieiChZAdP9g56w3lQy/GOPTJvmyXp88ha9HtkFHF++4ZUIVh63batDJGoQ/4h5I6gKS4hXqkE9B5V3x10ehYfjjxICJgzK5ANjghPyTXdVzhYGMRECjgncMVjeqbp5wjjpJOfCrmup4E+MhE8CFXtKUzUf4xX48K3dJ/B+jjcPdEtgdKxiTIniGAgEn9mgqE6vCIVgzKU8XanCueQx/DhyS06C66MXDRqKeYQ5FTzZUqlC33HzwipBufFhv84BtleiuFS9QzMbKQHKDY3MKyHOkMklrN5tGhPxIXkRHUxEv9TObO5gksJGtYWIPpr25YS4aGSvwJw6+JDXLkNgmD4ngsyeABxeqh8Dbu6jPRIcNhd4lwiMtIp1yExlTYfxyL+TEZEQFcjet9SIKZ8JTG2BoTekyL3pjL9MhVTvrFfIQFVNKSgKeF/6MEg4Z4kVtIzNnVb9UZAmcuw7RoYTukSqdA/dCDruqeT1OjPFGgYo8ePZSOhemSEVlIKK+JmCz6LKG6a6XTEcXiAakVKANKKmn98C8kVD2h1UrEkFaFx6FnVCE0FSgU2chbjM4z+pfAnRAVAWo64LWhPwiRnszy+mapJXQItSYsi25uIRaKUUcKT+S6Q+cNxyfK+3RFVT6Vj9vn0E96Jb3r85OPSeFPLvbJd1XyyQVe3v0uUOAlAPouzPJHY7TppL98+xFO5jXOvXQnmuwIvk52Kxcd6VNpsY/qet5ZWnJyJ31EDZMrzuJiXNJ6vCZqy15Mb0DggT2wjPFC7P3WPst8weENlezdxm1n91wiBDphxmsLtstDBrPZkGrgEADH6CwDTGDdCmDDcIqvspF75dUU/QJrkJ3DHZXbsnf29mld6FDwpHqt0e0E51iYHzHcOT7axR6w1YgzNJLpoqUM4pH/c84KMVIkUg3PLBwqrc3Lt0OHcf0EJURzM1n5v/vC+7oITol2I9aLZJXFwqw86fRxmBOTIhRBB+4+vT47nmxHVjIHsVPQarqKqbRbSqo2piPJ7UVEoQ/YsdYBO4cCynvOJy+6lLZ4p5pRIkEGdFihoEEKZXBNlQilk1brwUTCgUeic4FftCscyKOGRMyDYjhcALIkvcZjQJPELEBkKjfEdNI8bqUzTlbEa4wIiMqrKnu54NfJdEA7pDV8wUxlEvW6s7jUUDN5AVftxnydgQ5AKUWGHoIyAhBAMwtVxkgh+dBz9QcU2N6HNqxWSk91GaCJnqvVWQ0zrubxhz6mHURACq5wcBypiabl5RJ2BhGq9Tl10jRR7Vlf4uLMNRaO2i2gIaUG8Z9Ri++EREjH3R2ddWBqOkdgtLvX1BNTILsoA+D6qdTrOcXIckaz09VcvtsWovrUMqZzlFIMajfPSZyekggPoL7KkVFbAUeKRcNkvhY6UGbhrLth2UyIEsmOjJdFmtWu32ZEiVBLJfll2RPpBijcbkNZgNHIQAd9RmP5+Xm/AIKulqvR64A4UmmIjR6ujmZEBywoZBHdQCXc68DgqKobWT50zPKWzOPoeA+cNXwvDR6ko0ymJjwNycCULFut2ezhwd7ZuMqzzKJKtLtxK52ydB0G6T4uetFoetH1Fx2fWuBFD768/pICn0yBlwDok+nzbbtru0mHNDn5ckf4VLXP3d1cdGjXvuZFFB9yinthdRGuUEAUli5sImgLTgbYoR1cQEIcjgYBt7CT4hPkMDiZelSBU9iOUy5C10Cpg09gGBG2R4CWZIR7O7t8vz0OJZD2gyO2fpzSSzMej0fyN1LQrg1FQWHEA97IazU5JlmihFmDtqAn3VAJ2KAeHXOR9gMOcT25FSeK6YyECQrobdpJnwboUMCzQgEHZ+p2jEXncRfXMbxE+xG4ENtQwAWFtag/0AlxlCsLjYhtp0LnwIq+DYcRSUgxj6jHORdn5TWXtp4KY4wXUVWIK6khYX5BQJVHy4mMROedI5xqEUqAgGDAicW3Qalch/u9AYsOBTSvsBq0os/OUe/+o22femIW1IkCniK2IS9J+8ZeG4+H9mYyHLx79erCwnxNW4sLdUBtvl4JmHUa/VfD7sHRnYd7y4vzP/7BmzqAVpozBCblOuAQsscw4d10pOZdDzWkMO+ngQDMnbbB6k+AXzkiWmIlDwFQ+MO5ko36HIvdXofyMSy3jEIMZpiCqEd542oszAN2pDUqIcthbxQGRpGEBAKQKrbAh504UHgD/omJV1pO1MCZTgBWa5vsS3IM3Wsdt6jYUmobnSuaM0aV623QOQ1WJHZ2knvOdBupnwZZjtXudWV7e0udUqzUa9y4wq7L0ChLmQfpG9xGOEjUQtOIMprwqUBaM9KhqyEncQaMLLRaMHpW3hdBCAWZDtnYCeWZ6x41C9avJrgdqIdKznm6olSlG9aPHnqQ8b4qkFdbyZDjlUDrPn1N50X3PO6IE/EOk0OLrvh0uDD5dDG+Pr4TtybFkrJf8CNp8/EG+AWrePnYt4sCXw4A+h1X5+Pl/u2i7NdtNJ+8icR2kxznp9J5erjzyY9/wmAnNT8pY1d7srE5S5pVZnjCG0kA4llCEbutwrZOGyhmhoOm265OuI5VKJBurE5w/eP+MckBDkEjkGmHsmOqmBMDRbXEGAp45z3Y2/OVQIZFEWmEc25E0A/xgY1W9m2RaKhe5ucXKEdWVlaFV3z48BFeDxjg3JIEYEUaZZOB8TT3m1pkXg2HJLu8z1BXYST85GE4gCNhLrniOPRNuo3B6xfOAXsFZxrRO4iLx2Us2KrySI2deHXWN1V5JDhfwiMDUZVEbQ6PpOBbgkTjCOMpoCIJORiO5TqATWA3bXRMZC2oq7Bu8z/CAiOggGQcxRg7erqF9WoFNUrFEpEDYqZsOOWXvjpS3pyr1gW1QQoUYACrzqnIyRCg0CO6Z0QuqtYQRCTyVNp/xEFwA6mVs9OR0nQ64hCHBxaX8rb0Hcy92I0QS7Bumm8AQMQ/EUdJgf3DQ0RTVfdEjthwgltbWSHMMHTNMdvhy81sa3f3wBXIKVo/OnCRjIPBjxDQMnG4aLEACTGY6PsJWQwDZKEUCrnQMWmalMUfCx6rCvImBzptj8gjc5HG5IwjIWkL9y2LgdRyWk/1D1IZTwEJLOAFUvYwCQiBDPkXKoXJlNAJSWgis48uRTCBXXaiqEVe7aI/qy5Tn86RjhkCSyRYnD9hAnfYRLHjDgABlzidm5tPNUlhrjR7przuWeGGADYy/EJ89Wg9BXxWlOG7AqmYJuf6bJl4WSC4ZPrHrU8Q0HZf3COLazaAo6GyYhKbYTQlFJNPqVF1QIUmUVfTH6O5C5CdpGThaOhif9DTNzpHWj0zZdQhvk2cFj1lvCrxqUs+01E7DzCUXEk/3ZocrqQ2QJNbceXJNjUp9gVOVJIe5/ahL1DNy0f+/+z9B3dkR5YgaDpcwLXDoUNRZWZnVavq3t7ZObPizNmfvnLOmTrVO93T1ZUlkkySEQwF6VrBgf2uGeB0IoLMJBlBMhh4DD7Ys2fi2jVzu/ddZb8GDLwZBujHYMJa/DHV7+p+NwbW0SudN5Gcme4yvmZHVk3lkh5f+3ZV7IclcpvB+oQdZVy6c0fVyhQlEf342sBFSWTLvgxwuyripEe7bYguNorkGfZ0O7vqythM2Xv4IueO7SNbeQR+Mh61W1uKkeTTfyCfvnmJfB4/Dg9qieOTU28Vdufxkj9tvSJICHYqeWAlmYH3wTQIQENQokeWE6Li8qPOsCEzmAOmJHksugYO4EP9kTxukATf5VpQMvEroaLCDMUAackWC2l1Jdz1663hK6lZj/LdQZULoEZMhb1l5oxRBHbGlfZV9NaIXAl1V7Q/2gS/vpjNytdUHpQWlAchaZkWvJLj0kvuvcpvPDFwWsjmJmyK8CWF5iZmQV0WPe65TZqUi/O+Yi4kbz4PFR6pXWAJ77C4YP+jLtbTDOJKHf6lCQyBKEgMT0QSCEtjmiaStlkMIVic8/FyGnqrOFn2ckks5+yRjCKSJKzei6NTNs8KI/+CKWVUeDReYwxKHII0tinW+Qbux4JJRkKgWcIGlPIuZKvNmgeVxiCJzRjcXtLP6gjABFokj8EvXhVNEzmUNiFNO4hyap+PerUUUJD04HNCT5cxaUawhi6AYSNEd1TIRHira+AZPsh1ZPVi85SHPdI9xZTROIAtLkh2EVyqG2q7ycgxXBz4zVTmnmdh/dYUjluxPH2QbAFYP1YyeMCQu2s2Wjn2D3UzGzh6NeyZ3x3dbqsdVvwEhMEqzsEZy96pe/B/dBy/FLMAYGPXIHSxaaezk84sl9HwoiO8VcAFVL0bWr7SCL5xy/mqu7xIf24ScpLOLufnagqo8o0m/rKH9Vq5L/V+SEN/WXd3pd4VDHxvBmh9Jf2YQb6pdn4MDD++7qujWP26fnzjb7WFVyHX3S3gPa5ycmL1+GrhvxDa3C+2wI4uvbrszhzCW8mB3P5u37f5U5SQXoROoRK+6PZlwhVSI7stcoVOZEKCJundo/19pxtGD7x3aVz4HXUjnF3n5clxs1GjQGOPoqnQDXB+Hw18CgtPMxj2SUGwM7PoZUlpQv6g+nY660rAXR/B2vdhTcbhHCt7OvqGPjgLrO4Qb4qwjVKz03YcAU0EYoOuAMaZFsgMip6R5oPbly+AURGtSaCsaAmYacmQG/lBn9K5GTmN6siBGeV1ipil0yo1PjQCHAqd4N7eDgC0houMCEAgD1vqKotyn/Gq40AgWfWEFqfGRmAhTWmT3gdWgYFSxqByMN9kKax9YMusc1HfxOvE24CKJ10Y9lKuBTMtMyBJ0gtkmPPPJVf5Oadvw5zOt+qGL5bOZMR4iKVOGRN0dnbCm21zc5+wJzjaqyUNFNsejWey7RAFLbsMCv0kiwAAjtaaMRCIFZ6Rk1iju81p3QAxSQxeKLAU6LY7ZBqQW6u2tMZQ3qjpK1FhyBQRfLHos5jH8Qi+Q5gnxCX1DVcvw0OuAwnADF4WWxMRoQIGJ3ck/BssORGmBDx53cpx/pXIQo495TJmMA6moECCXv3CszswVNGyS85O0nXmpRuLRDd4XexOhI+aEUlyizPjI5ZtrnoYxVsaQiMqSZbGljkQWYoD4SxhUh8LJqJJspcXlbp0fYKKTiER5GafaMonRhJNAUBIpE2MK/gLDpPHu7AQn81Hw57wiWDRkJVvIzAO574RO2G/DMfkOqyXbM50GzUpHXYQaxk4waek6EGRFi+7VD5zJC+5XZJfgj8gTxIv95jXxOvIXKUDmFc2n1VOLu/xB1+5/TxlWsuPubX0+IMbvqv4zmPgezNA7/yI39wA1n9Ib67VN9nSqxC+mpP7W21Gq+5f3Xdyzrfl55a9lYh7aigXXu80p93XE8hPJskot3rUF1HbZpqMNtxtl6gC3Q5awoZXswhAFEm0Xy35Nlz5mZYLVOLzmgAA2cOR2Kxzy77gJZj7eGu/9gm/09366tnTC1bSpdAXILg+O4krfPQzwsV+iWejI0J99kBgmM25l/MSqiwKYeWg30YjBP5UWoppXCbplBzGy9g12hWR59An1M6l06BVyTBIa6irKugibHirFviNgiExYDKQiJkyqkARg1hvE1W+VjRIyzFSBTpbLb3LCWJfCpQi1fIzb4GehuzBSQtJFKSviEicohy5Z0w68irD4BGooIJVFZnqGB3IQy5SvmrWjSjYBZl6Vz3Gm4RzGAS19GugINGITpVE9fCgLGv1BL1xhESlOvzyC3NomKgkPuzl85d4kXaTOU6HMA1CBCPQ8r39Q0e1R++YqqRrYxa9tbsv0h7NKYxhLvMJnaynM4ahNJbNBiYmEG7N4Mpi+tjcjKeIunwyEYxaYGxxGYdGbNb6/XNGPLCdZ8FxpFgNhcFgFuDBetCsIWsTQw5tJGc5R8su6bwMVhMNky4NwlIs0RRTygRR7RFhysTx6BGu6JWcsIGfs1xBYinyqNOayeUdqGWMj6aI0+IeYRixa7Eyc7hI65yJvVeETKbA7GPpPboAAzZDJmvTV+98oFkNYtr8yjB0lxsRZ5wjgWWPRVOdzMcYR4uQsMbRwklCCWwrxFsY0KwCwNaO0fHnF2iR+LFSD1N65RVTRkfuyntcXVaNzJyvQL6u377CCclXQAvuOX1d4Qf9WW8hQ/WmWv5B4NxV+ikwsD7pt/q7Y4BuIeR7PObfz60K34br/DO7VfitPn4bJOudvgpVrrXK97hK54rrzXq1eiuxqnur1uqVFtAqb13SuW4mFQRA3jFltaXarLEjCtgiBTz0MYpYC1ITx0pRzVyGNIihhuouu7DMTMI1Oxz1x5MIQ8IRJiwoJmw9uNeEHahmdVtvhbFwe9Y8pVwZjw/v3z842HOMQFhRXFyx97x/eF+TKNDOTvfg4N6zZ8vBsxcgk0kOMRZoMXndk3JkuuLbGHBIMk2TIC7UGSxKWJY60YlageVH6FZ8m9fDTBu0VEjGRecAeOTUWz/CTB01aNTSTMFBiydArtBdwKuoF1Q3DTpitAAD4VEFo6CwNJUcxRxsOLQ+j1cZV+JDUGt2zex3vQ9tnY4wBOxIUFyPAVKpRByifa+kOSttLEtEShp3dob2ARMmxcI1LYuNahH20nDCm0waGDv3Pqg0uvXjHk8nVE8j/hiOBEkOOYGWw3IlXeZmPAjr2uPj09JOW0joJ0++2tnbIWrQIwYIYFtbvYtC6d79g/v3D8XaPp/GiRCN4YgtS2Cm1Zye9cAgEwOEpWD2rUcXtJAqkSkymKKxEsHAYF2BQ0NKakfMrGVhmKPphEM6/ZVpOj8/wTwJdkBqppgp42mYcQIDWiaVtNJxPwQzMImBgHNMiTGy6yEOsVQNGW8Q9DowHsbS4vlgq89PTvvFjQ8+fIiTYEM2n0VZzRmLtaq4BvEMzLX41UMCzSBRpiO9MGfax9NwavNKGgCwqCOXfIvTklfdK5K2GH5EbQiEsxIzIgEBjo5fcE/78MMPeXgZSLNUJhsDnJ+AoQkVMWTar8/5jOBVC7CKTzW5MAx12s+ggjafd4YtZj5GaKkXUiFKTFpm3K1iqrtUdE+zHb/6lOdGJnmt4VVy9XaVzjmv3vOm8Wr+D8vRXWDn7vr1YuAvWTB3DNCvd/7/3MhiCyAItwfd7AR/dg+61aQVpkquldO3Crz6qPD6usQN+DpFoblt2669She7gbCzwUGQ+EjYSYP8JOmCHXUaB4b6tE8flxGMjjmqsP0Rmb+06aAJ1QsvXxwPBwg6K5EYo33Xh7V8iXD7mlTY6hZY9Vxd7O3uDvp9pLHbDsMUGz3y4HM8FGTFILSOknAclROmkARf3uQ+GkEam6xlUbjl0qe8kaJSGBrEBgVCk9AMMDudgEQJVVIMU+IzfVqeYJI8ykS9FFMXsTdM39+BzPDiEWOvjCCBCgXCW2RiHNQjudkjdRJaYP2iNcU0pZh8Ca8wT+561Li+MCgKawrW5Bug7hZc2pN9kiohCElGzcprRIP53HuP2BTG21vtDiA3R5Xe2SnBzEa7xuWJlIHIROFE5i6NbvvgwfFZP5mck5NhHYJ7YNwzc8j9nItexCIi4QMMEY87OD/99NNa+XekM+fnZ4eHB/cODhwizuyc2oW3EITjO7XMTUocyF7vzGHmDz74GNtaKDPgdb5Jj++/piDHpX134kKXkMUkgKyWh71B0OHiBqe2GnGHk0ip3xIyzRpezYhQfZfAQqQ+6VWsxkBLskjLSJOPLSlQwya5VwjUzD9W+GIDPxeT1QyLNOo/TYGWxCpLH72CVbbI4LXATDp2wXIiQwoz9ghZGcIe4q2Y63REho4UIx2z0oBhSWjZalQsFv/lBY46T5yWsXHKCJq4WaqcOQk1JJWx4Jk244v8BHxCMNbBtT189CG2sCzEw6Zw6oIYhdm1lnm2H73AxNfp9GBAF747LGC8DZSm30IZk6VHSMBxb3c7jOHkWEkakW8KIM5PDXIsOZxQWPndbBE+ZaQDs9TCKc5QfmU4yrvnK6fdXdkI+ubNm/kbzSaQApibaz19k3f3913FwPeazTsG6Cea5m+bFT/InwiCm25ehSQ2m7hoMYIGSN2U/frvdWb+agp7lrgUdn+1wfTy+rb+Vjp7wPpOthEq4U5PxPccFQjPJjBcXrUTDWMUg6XhV+XfBgFCaBzKM4H5FiGuIK0ZDkfoGS0EUYvziOz5tmAuSRQYOkI8GLT6YmeSgkLMSmHCjKYw5Tk+PsK3NGq8csToHfNb6TQbw955mzdUbZN5KWrE4sEHbtQrBTuC8GiQfIjWYTTu29+RNGggzXGON7jrzZYoLUx0cVedbrOQThRXxreysNO67o16J0cnvJ10Tdcg/rK3iBwlDgMbp50jM/fuPwieIo7BDMci4RBFDUbLx9Nxt7qVzXJDizOZ+rr2/Q8kViDNWjNTLPhEsdBLXAW9iQaRQLiSg54hkDFfzF5ifiFf4JlNJiWQRnzjLaQxMUEvi7X47tda5gmmEeMQTjCgYddC9hGqwWLotk6BO51pMQ55uLrENZYHPYidcfri9bVZdE7ZDHwYr0UPDIL0UDgywXHGlInCnwEVq8iIiiSKPVO303A4BOld5arUPz8vbxQO93dZCyH2QjE9efL47OgYv57lW3hmSwJI3PKxVzRDBDJhV2Re8F7ppKrpfFK8YtYsyhSFIA+0mPRkoxxCRaefYWDY+BosDgybJUHGQwRHpmJaw3KbXUxIIMuUZJBlFEg+cm+YNIimQORlgZIcKV8z04R8Tr1Y4JvDNMdArF/8zXgU3PPDB/fSR4ccZj1s9kOsaJltsHMqFavs28B8gTF14gSepczSypTFUC7I+QbgMZUc6bEVhFVRYrMuxPnI4e2ddnokKXQs/EBFscuprioT7S/FWCcGJZWLMOv8KBtNq1r4KYOFBPLVWANOja2KrG0ENYfee0WqlbDht3Y5mYu1GKvISsYTGpRlCPNWhkfR0imICeG8vSQXE/oxsBZOguRwmQkGvKHI0GbkR8JHjuzYTOKtCbvZezBK8tI/L19zqZ4rvuZdan/VlwJKpu6i7CqxqvhqzurVXeIdwsAPm8c7BugdmuKfCNS/fCUp+R3b0C1w8za0ajweb0r4XCR8z7ENFUAfIlhuAdUJTYRN1SkDNmr7L99mkh47OAaA2aYyaIa4dpgdu7y92+cuj2VfwNpGxVFKNNI3ru54rZODYAFQbfTeq06njZUhLugwCUoGDWopj+OxgeoOCUO3nISFjKTv9fJ8NOWJY/dXUZt6BDAq6BqNh/JxHoQ3wNaUgwhQMCU1pVkls2BG43p3Ed7Ix7WoboAoCrUOVmE0yWbXYVGkTQNBoqS1o5FACNRUxUJajKdDZBsd1QIBgNaMzqWWZg1c785SUFgtBhz4HnwVgMCZouqEgkObGgRVZhrirIZ0dLzq8sHJ8hljEfxgigWgNSdVpPwQHTE2UTfC3/UHjhWXQ2bT2ygw2Rk7aG005jjuGE6HgKDlYKMhJHlzYCcU6Rq2jbq5uzXonwXfcLmABjZThoAGkkwQBQHj5MWTf/nnfyYH+u1Hv2kxfNYwycdGpVFvj4ZH1D/gATY9IFAwBFyyB6OhOQoGGhfIPGuJRRsL5KMu9pttD9TxoyJ4Awl0QW8wiFfhpS+mokCcdFwQALcKgME9TwFUgRxTEix9CPC6yYZMwSndlqVNe2UurD49B+OV4iqlXvAJYbhsOmAewLRIVwnhymDutcDADdi8F7muS4dcp3JBM0XSFNyb8MxkaGx6fAzMMX4Y6AiFNSnNKQEByYTJTTEnaejF62A2imVH05omFTFoFVOQ7NwZurFodhWuFszV2Wkps1mhsIuftkagSLO+BaxEKDK5FMqiAhmLtzScjH+wOAJaXVb9cJczQTyTblQ7lmVqBoqCywGMR6D42eZlrIz8YH6Sw1faEqKkrt2/4/ruAt5qX/V8v9XO6m3Ov/V4q/Dd468bA9/KAL126fy6cfF9R/crQNF37yMQYoyuW8VuPb6KNwViq7vZhm4ViPy1LNuiXdW+j174BnQ8o702NslkiUJNBID8lSoRn6fkQlxX2NNeXW51iCUExQnjCe24o9nIPCIj3ajSSVUns3G45yQfIi5hKMSyH97OvubVyWPBRjEzjiMiQ+nQRkjpnIyeuMi5YajyYjhzXlKtEUY8yAAqYpdn4cIwVSXuOcQYzvREy4PAzMN0NAVyDMtZ55+rxfbFGM97PWXQG6NSEs3LlwLYtpKD2zGCcU5WWNWIugsJIMRwGLvqHoNOpwA/0ohxyF3C995X+rUjlcb17i2EGKxHFTPYmQlDpZjPosp6yc3KV9gjux14k+muBZfevQVbhtaCwATNJ9PmZkiDjCWmb0a4whg82E0QQQiWVI+mKfdoKPgD9jhCPYM/5mg4fHT/fqPpKXLUqm21Hjx4IDwQrSWnvOm8T5yGXyxWGwqfn6HCA9Im1J0mDk901MPCjYjL9nbDbQtzgEObzLMxr5PLwE7egBCSk1TmpCpJ4pgIcLj9uzBXzUaVR1r/NAQt1FfQJRIPSV+pugk0yi5LiHzneOGE3WChYINujba1YiQpuGJqNRCVabkEhMMtMR2DbowIvGk24YHKkktXQAa98GOJxwRtxuR6R+iXWPdFLez8y6GwY39zGaZGYoIzGjo9Phv1sK01MYqErcJ6bdYiHgQWBF9B5wTD7Jg0Rr5IQrS3F4Er4da8gNxC9ujQXy1Y7eBk+k121Unnq+BvCH+ci5YwUxgNR0BTi8bTQPxkeU3yQ2PiBFosqWK4bIZjhoABsv5yRZ6JLp0aYL6UN2R3U4C5lHAlpudrXkdH6VJM2bd46WW9dY9vu8f17u7Sbw8DaSq/9+r5Vgbo7QH662jZb/iNDCS3c+tn+UZa/sGNvBFgciPrizKP9BbePLJKCX+VOKqTwUF8VrrLjw/3sBsIMk/BkElpUJ3lFb2FAhq3s0eEOBFLsqAiUVz5tmCvhP3DUSCi9vpytXzWO48v/uQ2hW8YJdMZ1Df6SoIWXejdI2AkPCqPEfF9qoXYzefORW+j7lx5fLsqS1bBN4tVJ26AjUuSWsXXLU6KxqpWT07yVEvJYNkdBQJeJooZZvAgDF4BWloBPTLxxk94IqswlkBj4tWA5NE9coDNaTk5EElqzaPqRoTCaVA7SsonD8rMk8IMwlEhwivtKCNDOzgMOEfAAKAKfhFIGgFDghb/FCa3Lv1jEEdXC2eHB2uUrGWVdFaXuiGBaES8GSYp7pVEa/RyEWEMa5jFIWmQKxyyCIYGezu7SCk7YWdyDavJdDf9rAwEkoP5bLQBdXJyxpTHqSQffPCRZmEPWOZXyxzFKuUBgROOW9Cnxaif8QlvpC5GgV1uhBee01hHjjWBVqbYhsACjAjp7OyyFu5ZSUq3nAvJuFkm/FhiG7VjXJApPgIsZR4CYK5QfSYAZGJ3YRJLB9tKaUtFOa5Ab3JjNCmQjBnCGDli1RKAyVg/9DzMlnmS1Tb5J/oRKKliDEEUpckY04wTOj9jC08K2MJZExnu7R0ALKYVzx2xHUNbJBrTbMofEJ8fsR/Bl0CI4NdA0ntCF5CK/cEZ/vliOW22mJvvcmGn3AxmOTkKAEzS6DgBqGtVG1fIhfiCnZ971enuuEOCkuAU5MGywQWxKGIgL1+/eWXKV9Jay+Vxijfcjxw9xBpW2N1lCGqZfP/SY9y/Y4dVNBcDxndc0UhaUasyq8dIJIeM1au7xDuNgdXMfq9RvKcM0A9D1vfC7PcqnOFZ/7n+JT/v79XF9yqs9wzAKnGren57K3P9MRcwLolvYnt9WyMAt10KwB9XpSksWxgCUCHZSbNUHEnwaqMavsfasZmyOLFv2tDt2MihfTXv1FmyEsRjozDs9RFvX65Iu49rJtKMjhAkIgMNJGbloj8YiYMnE1vjPLDMLiB456cUMVWQ6IWoI+WXdra7iBz5zFa34+PZF/lsMmRAsrfdvWi3lKFm+uKLPzlRst3dUpF0YskjJ9mXOw9Lp3b4vf1d1Oj46ASjIIdrN+YPA8fmhIwk6DDL2c1KUhhcVZyx4FjvpIYIgpkPPUhO0bQzWZ6ExkCL4eOg1OW5BW+BgUSijMVA8H8yoE4X2B/FIBRWdRc4r0TQYfDjGAKZl5fkK84KTy2TTwgJGNoW1TlhaZldh2YXTvIgcbhIMh4uSyQERRbcp9OLQmen1GfPdX5m+hqbYeSUITQd5mU2j1kwKczGt53sKqhgtWIiAKN3GEabedAFZU00O5itOK2VSKSytVVbHIyPj48Vay9EWxbG6IL7PO5uN9zmilSiOEBcgrqUcQavaxTWYCUMTRe4F8wwc+rDgz1IwiDBGMMXb+WnQDvlTjpHgtlOTThNqLi8gvCEN+vomjvPEAq8U6yThIVfoRbgQTEiMABY2Fg3saQtUyYy1lGjHgG+se4wY/VitrjZi5iD6wY+IOP4LBZFYfKc1jxpE4essHsTZGi4WQn2QlNs3cx+qdwn+esNRgxuiEQJuHBC6eDXkrhEYYaTZs1kmUSzLLyonxpOZn93h+G/6S6Xuq12vWASGf4vL9hpgSGhKKYVGOk3mI74dUifUNfCpjNrt8hS2AVL0pF5yoNIzAW/CBhIKyS4dgj3ewW8kSqp5VKc/2ohB99jtcjUiyvKiX5powhfDI/exCUn7xQK5Ef3nI7XqUBOfPc9tZ9b+rpgbvumh6/z71LvCgbe1Ny9ngF6U63D5hts6k3NzS8QpDy0dcCk13/tb2rst9pZ70V3r17r5TN4yqxnfnda4fVBKZwe046WayrA/pQFh/MKrnfD5C6SpCY2UFyHPdcOnpuS4xgCdEimsMKIDWNPggsEmxjAHS1HNX0I590clbTJ2przXizTI0WDzrM0SEyUOouMOMopXH70wqHYDo5uudBpObZ1vWnEB70dXw66YmeP7gTkrVT1qDphhnyUnopJ+WZTb/b9cOPSqUY8qiuWINUP5gmT5NtaLZRG4ygW1YR2MEbuGBcAYBEAo25UTGIJhbVmOB6hJQOWcQkerzKuJNAwF2Yrw4+eSOhFv8ojotI+651/CXUAcGkckBRgged07DyghxE+u7BX2VGLtI1buBFtFpbzCbFQqB31q27qMaQyiumUXKFewUjNULsAEm92SfTSJIczNMjXSDj+FS6pYEwBnRHIHbSKPWqw/SLREdKg32eEUx5PmU4T6Uz63PX626xzSA3nCG0wNAxiKMvErQEkfWi9HOyIgRg7gZxglZZEq9OE5/P+0GG39DwOAgOAxUgMc1VA3UMmxz3OKICRg2VrAeZNhHG4B3NGY1XdxC/OsSRJzKMLdFt3EjCW+fPcu7vqRgQtmg3umVV+ipqT0BVcO16eyZqT6nFhBDVgCH496Xn1LhI5pmUwS8dThFmSqcAXxiQGm5KiTClM9sMHDkhWLG7SrPmB6s60WI3SfmzdLQep9kklYRvjRZxqqrHb4XtvrW6AJJafS0J97ZsdCaMw6mgyH/Eb3yr4uphx6z+Pbic5bFoGqhPmhWArLSRwwol2NBIr/4rRFVSEHVVuUMJlCIB0T8mvORVl1gq8Jp3f/iX33FTq4uvi8ZhavZX/dYm71C8VA29wyl7DAL3B1n+ZCPR7eCfGCMjVLvCTYXK9R+n1x1sw5Fc/AEgVkYok+8myilA5aQfp9sXICgitzH35mo+tM81X3pptqbQaCqMQHNbpKOxik97INyz1RRD7q8tOp1U/2MW72KoZwzJOtlOr4sI5YWwQA7SBSuX89Jjr797+DuEB2iIGtI9vPIdOAyaKsDgkksTCERpDoPhSH/YHYcnRbjEdJR5wxqotHjVFMxhuSIB2PKaXKWNuBmMezktWJEE1HWXa7yHAunYEhChEIlA3I7quQ8rjNHjf6HQk3rKnQbN9Tic1S3k4jCCK6axMEWkYBbPfYMrqIztcxBEpcKI+MAMGI9UCkJAf+dCIi0r8Uii2FHDybCK9QfCUAa2EHDycNEqsDMqNXdAgMNAIb1n9QLjWYEbEl1DNXMzOZkOTwvPLBUvCFs8uiKNKe6xyQoaHa4wfmleJ1cAJlEIWMomwNHzl1EXTiVsYsqDNwwGuqDUaO9Cqz48OI4ZRk55dbhyNZn/4wx8O22R64lhW7oVB95XoQWe9sV8IkABg1LhgjQuHTeaAPOMe8ugM0xC4koEAlYYQPFzCFURS41X4HBndYhpKTwZYdJfNVgd4lhC+jcUySYyliLtqlIPDuyqYnUuPXLeYMw0GZGyxSkECRcE6FErCQJp6Mh7lSTxw64RsXP6C71QYFrl20V6F9XHRTBDOhWOg4y1oax3Nlk5zA6plSem3iTW2bntDozMKI8Izwi1vdv16ZYU4AEwhKGCCpnPMEL4HPKzKGKObnf4iImPpDkBeRdDIRQQfwgERvPHPhwxoNDTIwZpqDfB+LF0OXlXKu4F+yeOYXUMU5VcOGmnNQ/tx/8RCCm+4+HX69YSfAXmOBiGAzCn0fYn1Ab/qWpaQo7Bsf2/S18Xiz1u4UnfX7UrD3lvo5K7Jt4iB9Rn88d28hgH68Y3etfBOY+C1m8L6ZvHaAreGnMu7u9ZfreraZ1eX72Dh+e2JCBXVBosRm+lsMuPFgzZpQUgVJE1dW3CYoCInl9PSZXxf9pKJD5pqP1UGTdKIlpHwbJ+Rm8Ub2Xa9R+m1jSMhfogOkytZ3sTt+/ZrtFBTSGx/NGwWWrHp+5rOxhk88AUdjrDRQqRwuQpTa4WjgOO00iHwiIH2dQRsmjhuO2xZFPDWMR394QDwUT3sRYJcYANCkyLgEOpnnIyF01mwuQWNgCcR+F10bn4VPI1MZUgdNKsvAIDcPeh3cvPJr4CUGCnf9GGW1N4KoYvqGiT4yVygXgAsJ+eHhiMIdjAuuXFUEOSDUXj+KwzcsAVp1LoYsSq1FE+l68i/XvGua24XO91tUXycvVArb5gXtUb9SWerTW4C2hhwiE/idFhRtflPUQ/iubzCvQAYkHnIIFGdqGc4mZi+3fqmaH1kDPQkhAw0LxI0j2fHp2SIYDb8csS2CR4O/L3hQEAdjWATYTFPQRpNKFiB4VWYai2DgYhTQRbBKOOA5MNGLq9Z1kJYCLypOiqGgXIyXskYCx4x6dfU8lam1gxZvrnQi7cWm0zKO6h2kJm32MQJDdZ0Km283mJBmKx1E2cAeLUMudVq7+0ezGdPQ8DjPNwki9KRBpPnGqPyCnaK5FS9tDIFSdzg+UfQAhmWmMmMIRVCwcfA2QCtCqPuNDpeWTKAsLqAjaHpdJq4OievlTev3Q+3Ol2rShiGdrfDu973QKu9JSiodgzwMg6D023ItzRqvIY+7A2tSkNQhjCoknzM/IoBCfL1yxgV07UE2/H4pSc2KD3KzrxRfhuPrtWr/PgX3vWSO/oLy98Ve38w8MYYoHdohb1DoL7thfjqhhJbxRrLokC+7G7rwMjMj6vE+lvpW/mrZknCE/nJ3152183pPJQUiKLT2XnoqiudYcAZkFvYNFEj+Q4NIIC3seJdUvk4qAFR+vDDR8ojD/gYKg/kRO92ZHQCjRFLxiMtj49vRAXJkVDYuQE0Mhsb9zXSaLaLpVMuSqxNeBZlTsLx9Oxwkm2C0V9v+oLyRBzFEW+j4GMwQ6iaO9dmQOKV4GV7exeQYkMnyQ1LaEd7TugdkLpSt0MixW1KZrNSZ0Q8W8w4QtXqOwtHq9adlB5yF4PNLJ2xQDzRCL4NVCIRaVMmtsN8MHMBQ2IpwhrDMJURxiaO77gq5rCKSkKOYhgvoMKbsYNEO0alWTnBfCTrEwbY0tFROjhTSbRaMRhTTPvGRWm4pCeq7HlryBpHYjFuOhpNlxoU0WdDLL7F1OnvykO4OVOSBMi4QOIyEZg1/u/0TcEVFcMwmeFtrAQnMEwixjQM4OgM9v6uI93aD+7v7+90Xz5/SuFV7wgatHc+mAp8eX7eoxEzHA5TGNrcS9h1WRJx+qxjTsIKhpgnKQyhB49T4DQmsVHlgIZyx4Q7PYIJl+CCokqdpUiVIA/cYnqsA/5WxdC9Ov3dIyZIHR5P1UYdrohzEHhCTCiyCiwGPFISbMiMiNuaun//PiBZ7Rg+XMmMhReawuC5sTju0ph0whhO7wLpOMNOD6Fzms6LTWetl1lUwViWF5JGiSd1enbM8Il4JR1s59ug4jh2rI/WxoP+zu52u9FkU0QshAEyHKeINWpb3g56Q7MTh97HV0eVkfhZrw8TIMYAca8XAgmHI+fZ8yMWQFzL/HQBb7XGUo+ImvHDZMZmXGzazaWmDEEvegSVSEJaNhF+KhHHMiIAkLhpMjYT+S4Yc/fohwOLfqwRwzoxjpnXvNlpbm8pqqxf142sSt/wT+tlVulV4ZxY5d8l3jcM3GaA7hbE+7MC0tbzmuHKX89df5Ref6qp9gAAAQAASURBVFwv9hemYwtOl5WWhOUh4SCqYRwqOzLd0JOkeUF6EwFaFi4TkZ6E9YngtkenJ2w+sVKhkGjwwwrTZq8wQKgpYpNz8ECZqLg/ePDIXfsyNY562amRWBu0HOX5YA9bA1xLrqJhGhA8mcIaj31csN0wFSq8fHmEA8FdkQ0gAPghzfqMViy0IiHbD/hVwXXpyIXgeeuVksArLZOLlpaDHMj2cQygYBd8ticIg5RKaF8CqLkFJBOoGpSPlHgFRUahzQwMsiowoUdaGEyDwi4HwgJGXVoMFWFpp5GIbsJPRoL+IRD2XNqQqVPQimQoSA9OkRoIncNuAlf8m1Bk1ULp4wK2Ng1QXRYtuyEJCCmI+H4pJh7badS9wuZYg8DA/Wj/YG/PQPBVGBpHjhggyQHllFcxQKsgmc5s7d3ffvgIo7zXEnt6ACpDNpCrcp3xr3PHGfRAlNCPzGNQ8lY7jE4uihccx1BckaOBJ34BkNjrKoncqxKaL4I0hPwiDJ5CduRM+CUfOmNpm00CKgDgb4Rx1CM1qEKmNR0BJ65glfQroShiDWjKpZi7AUJg/pkYHWCgxaKyGBiYg1mziqmLrZQ2fK16dEGCrs97p2rBAwYyv3UahhjTqc2IzFhvbJKcEeSINoQ/DmWoVVe4LF4JQxhW51kmtBQUfT6HKzkkjnmy9AU8sxMJh3EEA03cJXpWSMvot2L9N7gEOuD1YrM6wsFaoaHLS4ekijFRnMZCdR0dnUBCiJ9ImwixLq/wdKy+uNfFMiI2KweLDAlipGOADFC/etG1gbvnK+NKGlpy2j0nborE39Xb9cxV+tXyq1erhBak832VeZd4nzEQHpKvLp3XZv460PQOrf5X5+WtToHuvq3HnP9tb1+FSsnYaV65IB9ht/MlFiuKYHfsj3Zkr1CmTEKiWPqQjx35omBP9cFIGmRLlc/qWfteIdguX+FkABphH2vvZsAxGvTts75TEVoEm2MV24sI45ycU3BUPkCROmwHzQh36852lx4BnYsP581KY7OJUKFDHtEPjXDyZSGjx61uu9psx/fumE6kTsqCz3AXuoYWD1T88x2lnTd9pkr8rgXY1YitPw/NHSVIizCshl3SCD8wtU8awcjD5zLyQKumIwPi7obAkFrgI4wL4QE31Rea6S1CDTkyWxHUJ+RnZGCqM3mR1jV6nMgNy+6ggAiVwkGxnAqeZD/ahBnpRJMYAjPgcGBFC4sgE4Sq8CcTnhH+F05pSLMTJ2CkI8plhoNRnJ0eZt0eMaaoMs4u5ACqJC0hMk2DlAYe2iUJzRqa4NpYt93dfewaNyUjQqE0pQB8Yi43mzXCnjiJ9vzYiERyarZbxWr9pE8vNhFnGN3VC/LfaMb6UUZFnESrRv4nPt9Ys2JYkzbJhkg4iRCC6dgsQiM1yFRETbwKXrdW2JiHvIc4JTmTR0CdZHNjekKMlDSSwWwU+NX3deStvvRpsrCOEJvnKw0E324gwQ3rLoAssn+/tsqXE+rXZHGssNhQFrMExktJ3I9AkXg4Z+RZin4gyhPhbHXaQgYwRSf7AaYpqzf2te/SeGhSGcmRRRlYEkZqajQYWloYKBNq6IYAxoCqiFsPrkjkJUsCuoEeU0nqykyIyLNa5nBJGiTuKNYxmt8oBktrDTDqIUoMq+tNQiLQYjAdBiJOKQcvPzrBosS/dgYKANTQo9FBVOgYQ1TmyoEVQH09ZYYAG66ciCFdX5ED4ddP3/InV9Rufr9KrIrnnPX89fSq2F3ivcJAfNNYB3n15JHfLYtfyAq4NS9vHKo00Xlzia3nu9tfbUyR+DN70XVL37aQtBCS7twjly4Hgc1ouEIPEdYqSfyjCcQArULYbLVCKqPTaVf3AbygvCCAYbtpe8W4SMd2T/sQ4XwiEPSjR2QGIV3Il50XLUYGMCKK5Zbj4zXMdyJEoe9sTACSLK1NyqNgKVLEQlUwPMNkaloZlGo7Xds9mqSwPV2tkEY46rzZRo0QKvdgU6jh4nxvzEcYLOOijEg+GKAFEyaR04DRCGh15JWBBLeU4j6DwaVlo0hNFTAEOmV2rFPIyTh88eKFKuBx6Z1OgjxGsSzD0Kk2XSBRLGma+h7165XyLv1mYHJCszLhAWC0EQaVZ1R11FRMRM7deseeaSFjADx5ytg+J1ofshaMLRhYSbMDwYRpXJuANCjjJSzCdjDs0YjC3iKTOdwSjoujPB0lj7H5WX++Ub334GG3umleNF5t1h1dMnlxBicshqehtZs6RIOY6iLocsT9YzGjweEwDHH0ZbDkMtJWAkbw7PxEESwG4hvLJty/T4lp8Kb0PJpttwU7KItaqREAKwOl7k581dT2dldTbKFUNJY0s1EmY1JFl1rRbDIO8wp6PSpJK+dSMhnxBAJdXgHbItFdTPlm9KUjDDHtJ3binNavUem0Y1UP6CCHk26ZEU9Ya0VHEQXTp0RwEmmk48wUNmv1UEuVfQAwdQpnezNIhye4Nl+8EkavspkPRhV4wvEmfnos4AnIAs4k9zJMS11HqkcLmK60huENqHkglKfSGKAYfoTYxN1SYV1fxFRSxqTrxP1AeMj2QK5WIj7X1EeOS9f5ks7bjGLr+Tfvb/9Nrb0+89Xqry18u/Ld83uAgdsqsF/NkH8dS/zVUbz6Y/6RU6ZB3IhG9OX6tvbXX31bmQzJd7+NPm76QpLtlbZ+9C8DkPdKpjRiG9qLEQZ7vP1XvmI4HjuyD0rlIxqdczrTIeTMe/Z3du3O2SC0XmAJWqAd4GGENmJuHOOpIgmTXZi1Z2zlpevjrpykVFpuOI6AhgIJ/eKLL3rnp/qivmJyxO8GG2REwQ0IDp38vXWkX1/GEr770VTUERlDadEwJB9huMdPCqjLCwH7MEPg1ykzj410lIEGlcHl0MEo5lFThqMM3+7MhMELqwmZbHfYofRvTH8Y8+gOBsAggbRQ1WEvyDb47sAYxJEPXJTKYQIsxFClynUHE6MLBB4DAWbFAADmQKxeg2ljToRlDJIcaA+aF/IYxjT1yzBzoaSkDSqXzwb9c1xqcRNjF3RX4zEdyf4X15UNugmBvEnzHPMrBKI2Q66QZDOYMKsA/ICHvZ2tHR//eh9DXjrxirhi0B9BJkf3r47Oe/M4vrRbr56dHG8UF82tdqW55Qh3bZIA6T1pu8LWRF9JtldaFMiqYsiNkukju0C6Q36TVxMJHyJMFhGaxwrYZrpLLDj5ouHj4bhpBQuocRyUWdCUtPbdqQVjuq8K1JdKYgFZK1snxCdqKa8f0xplxFzgMV4N9h2TBQC8K8bGxFlf1Ha6YP8PexBuvORDWJZswMTxS37oBFv1Yd/xHcHocQyMqayE4NBbc4QBi9P04gpoSbwACYHM2mQ5cgMTREoq/CNRjKPtzZeD5xxgJ7A5IxtckUsoAT5romILVyWNl4JLXZgdzZLzkPRY2ASc5s5YDFnXuN7gDpOAETurd9wPvRwtXRJ9uQXGDE07SkpY45AZO07YMdkJgi2GLplpE0p/rZIbTijeJVbpJiMKvPbKJddfyfGY7xK5zdXjesm79LuCgTc+fdcM0Hq7ecG9Kxh53+A0U6vd4ceM/aaRtPXccCQad/2wZm8a/Atqp00vlwsKShGT7A+YNnu0UV4tBCBMzl+21yScIFFXjCKDO6191r7pu99HKutbnmI2epty2l6vkJas5kCZ0CSbe3z1JgKjKyVzYeSfby4xvX1YoEJliIhs6xrBCUECMFyOlNfgvXv3ECfKCG+fP3+OQfFKWrOJEqTDMZJtDQ4gqHLy2xLNWe/cgVFEXdv89aIWwo9OIAkwFkeeJpmQtE9tre0eHMJMbqRSvhYq6E55UEEC+OnD5KhiFAoDj8WOV2qhT4huCKWSokqtIJKX6ejWrFhM0Z+Rc8WUD/wvo1+MAPA0oiJkatxb7AIjbCZAbK9JLNSSQ04AG6VOo3hFYDDnl628ushzrqhlhNbFJBk8kGCMGDNCBvBMZkH+tc/ARX41rMtD80KTcnR69oc//HcAOPniajRkNMIEeOO4B2PPn7/47OxYfIOPPr6fJXMwUByBn/Fva39/jx89R+vLDQcycDQrOMy0eLlBODgYEZVxMg9WEir0K72zvQdZzIPIUFIs78ll65JO01sXnBgsMIzXKDJiUWGJPEDo0ppxKUnnpFmPQf5TYG7tK3B0dKawFtRy3pbEOC0MYRpgz2PAn5Rr9HEurUGLID3QqDowNjd3YfVYeMnpZHd3J7nuj4lRGDnpjvxSyX4fQzWm7eU4aJ7pfRlqc6Z3SEa73SIAc8otN3d6YcNpVcq4Rr+X+XRhPRE6VS4DMK3BsOludbbgFi8IHjBon/IxF1BGQiMuoOKhrTEJCx7jqDDFXHB/iWtM6+oiKT0jBx7MuxWnkdSAv1/zJTltvJG7dsnJTmGrvFwm8r+5R60ec2K9qZzjvqr7avVV+3eJnwsDqxn86QG4ZoBudby+hm69unv82TGw+j3/SEjSXpA+jFL4eY+u17b5bfkKZ2BuFYgFHY3FVrV6FZnpO+za2TXFO0QeUA5bre3VXmnrDLlIItUohB3WrhktoJA22rTFekRWmZHYhSMmczUkMbgHTW1tdYgelEND7LmJhIc5SJRMlsjyw1aUvMT/BRSOKq3LQ0bUFkKj2MaHA0Sel5ReopF6KEroU0rd2L59XqPZVG7Sp+fn7kRS/pGEZADYCydqxKQ7fIMRBmTpYhrAUw/4uhfzZjSZ+loeDDUVh0Aao6jAkMBtGx62ExKgIiNEPpEPQmgUmUNShlYQUcn8iv63SESSHMgrLBFjF1IQAgL0lVOZsC+bJdGDghPiq+YMc0MXEBsjBUjYg1x9eQta3SWWJcgwoqjTVitMko0aeskAoFvXTrbnd790AKdQxaXgLVxUmUrCeRp1iDQQPYARfhkCRyVduLTsVSaKhB/Qpe7ZWY8L0hePH3/66Z/AQP3TgprlxWg8BSGwiPHQdgtDdV1IyDchGiSuCPrtPLJqSYxDfLKhyayVQ/AWVjqF4EJevDhSCzzOKafJIgQyJ7M5/20+22FBjwHSuAZjTc4YipWVwRDINHDIV5cIreoM91JYvVDuCGtEtGEC9WjUDMKMSwtYHI1gfCHDI7YGEohh/FxacXoGU6rJBTbkwnkj1VIzwGNGFgZP5aJzfOFHLfpc+DeE2WLa2t4RLRJHgA2i6cN8YG5ASCOlgB+ZY2PIdUZjsqgOBvwk1HPCZDsZrMQWDQuoU5OhIzCTKfp48NVxRcNprBEIKoRVmnW3bhUzEC17pxOCHT8HFj/SvWT55NFbXwJAhXxazhhgGP8E9kCugGm1AahioVo1ysdcbSz8LzZmGM+ln7aSwerE8e8hCl1l5oT7TeL2h5nZzG/X77kFOa99m0vmMt9RYL3Bu/TbxsDPOxHfygD9vGC9baTftQ8DaYoTj5K4FXufax0z62tAerU9vdYG6LVbj8xVI9f7zqVIztc7V5L+ZAbIthjnMZHih6x+PmMRhBgEkKFJiSsD547yIQziLfd94F5wTokIcEwr5sNLogwh+KqXVa7sLJcXE7RtGUdmxsHhCx/NxE1BAtOlkWalog2AIWzuaJiPdkJ6AgC7NniQAfRgWQvJAYkF4wn8ATvlXBhUQbSSzkhh1TFVNnpAIpzyE3mI4IToJf8yzaIx6I2KaGoMxDd7nEcZzB+5BSWdPxpvNTtOVlWSSkIjmYrAZCJO8yC5yRrauMhg4BiFwze4NOtVEoOFKzu0pfFG61qj66HdUgwCNCsyi7e5DFg0roqB+2JnmKx8yFri9IYQGIhPzdhIeUyeczG5mY9mgdXpRqhCeKfRczEZNjReZGgyhhKHBy26K4r3mHyItK+KzEajqW926RfLggMmjo5PP/vsszjq/OJqNBgfH50VmflsxkGhThUDJ/jpag4O9oiVaFswW7BnpLRqGiQ0UdOx760SY9sixaWJviyJsEBnJC4lW+kIBUSggx8VAinbBrHKnUwHp6cnYOt0tqgL60WCwIBQLRfIgZp/FN4K0hQzcrl8+OABLuTZM5F82HhtsGWGzEBxUoEBxoxYSNeHhyx5mQX7mC+W91CkcbNpCBrHwMSMOBBtMhQ2R441pjFhfxTD3QZjES7pLOE2R8NJsrGCw153e9eipPTD42AeHBbL/ezeveD4mSC/GJ+0O82L8cX2tnChTSw5/kjXsWBI1nBnDLlwaYUin/8PH30kQrklSktsjE5Ve3jvgZWAteGm79cBwqvytYaRh5zfn7Uh0zCBqrVqffPyosI+zKQnN87AB5ZHuIcUujOtcGo1wy/yVvOZE0hQ0T0jxH2VyJm3JEC5QN5PVrtKzlxVfG2Z3JpXr9ZaVb9LvJ8YeD0DdLdQfvWrwaZgll0S+bIZ2cfttt74l77J7E3+BSPibcaJKnYSVdzk5LoSKT8Xef09qihmxwsz5wj24UMQAbPhujZKrVLRZ2IBcTo/O0EbUNMQPRQLwh0HoEAtxubO9gGJImDHBzBriE9z39HN1mWxNJoK+oKKOiZBVKHQUp2cHhc2irv7B7p2oFIOhUKNpAVcgjvSoi9hTpA6BJX+i+yfnoLhDhsZ/AQaYONvNJvOu2RfvNfew33o1ElWCCc7IWS712ckjVZtaQE3IIZLZpXoBZgiISS4gWQIDF3XijB1kTRDIuTgj8/bRvRhAOPnyIeQamCL0UKSZiyl+UI0oWbJGenczOOEeUdJojRaxj0EZpiz1hrjqbPUoy40ovQG4lWUWYYzlyq+wKEaKki2CDxkEkednpwAW+giVVQMFzeMA23a1pav9PHCGZuXPJIYCZGIwJKwx/Vme84dmv5qgzxjqjWYdC9VGpcl3l/FZjs++h38iRsYDje3cI20YEsnRuGgSucMmoaTeq09mV5O+A5NwxMeqjGv2L+yuJFOT8U+VUllCnUKzysRnLmviyMUB5XgRYPVWAod1BIlCt/W3e4gtjy0z/sDNvImVOQfzJaFZl7M2uBshGVxviqEOGLCHH3++WdYOiG5sa3C7tCHkh3iC/DP8zLpSwjDCBz981vAi/MDtBRjoVqUhGbc/cRsTpMIe9gB5fuDgRPJ6NSIfAAbDlMR1WFDPExGNSAxdu2rBfMw6Uc2HgylxSfAHAvGcO/hg42ySQnZG28+0hVN+RmQTLFZxnZYM6IEOTW1W2+SdRL6UKBZoyx+Gp32VfBDGOLAsiV6fHQqgVcRCSnY6rDH90MT++dCPEZxH5589SXgRZ4+OTs33a1O14/OOme7Ppkt6Nrq6WsE2Go76xdUrJS0eXURfmKiHDBwmwnGVAz5H4mpTk1H2kDgSDTGahs26DKreKPJImIohfGexWCPgYcAKu0hsT7TphN/UqZ8v3F3l2mEvpxO9+svqLWcSEYL6cJ65XS0lS7ZuYB7pMMVFSBRPmreXe8rBl7PAH0HNu5WzHcg5x16tT6P0vkx7RX2orwLxVMekber9GqMr+asXt1K5Majj7hsOsiNjSdk5vF1my60iuCBvEPoG1TDFeYmV2FaIaoIWoJqXIXGilxkurEIik6yzkYD5QwRvM/S4Thptco+fJn+Pn3xkm8u/xhVsDUIDCpbDFtpp2+Hz5SWiYZYtyDeW51t+yJiQAZj4wVRpm3GiIHgGmRLDaYtpBcXm4UIp8vsBseAlPKi98nOckKV4GDSpQW0CpDYAmoLXsOodzesfenOQj8SOpcUIw4BFr1ZRxgXVX3HS5MYITZ4IG7yvv3ng36xtCU/UeRQFLZKEZyGDZNG0J5A60boXzJR8ZWvkjSwjVqIYdoKqAZtaMSIrLAPG+ISVbK7EGiLSWsjkeRYoWsEjPKo0IMH9wQawE8g7VCtAAotch+PNazeditMUlA44BmXmH04CifCirbICx1sKCLmhmMQtQsCCvHsYbBZeAae5XgycrvAVXA/IWOAB2MHiR75KFEQhuhtd7NTKUxGPdxvs7vb3LvEpAX3wICXr/uoxDOLyfnZaXhmkXgQaEEOZ0PCMuCh2Uk52DKoYAJI+8ZhR+wy9eMUoRE74hXCT6yiGAxoSkkSEHgQnSAMgkLaN3OOPYGNMsFcJsdyfOm5FTAabXW7gA92iugyqVZhOHRfflMqJHNgbUroK7dvJZipDKRODTy/hWfrFlTtZgv2gJorWiGMxZ0Y7EgKXQLJYbGawtBb6ta5lqMnTAYTKmfhlX0GnBuCQ9/aoglMxy+TJM9aFt2HyI1RV7LfH1hvPBGVEdPh+PTEt4H4QwFDgYa377fhI4gUimn4whnEIl2VK0aa7NtKnC8hAKunI0NwAbirrW7Xr8ooAtcs1EWg9N2TxmIBC+5gUJr67kv5+HT4c1cUS1duc/3+2qqr8q99e5f5PmDgezNA7wNS3qsx2gVsG3Zel+Qa8xP7bXx9pcurvKFEImRCPp9egydlUnuveXUrK7m747SYx6J6S3qEIH6V0tbWtgCyXHhIPnzJI/ncS2g0CAa4qqBq4ifSq9CR6SvUIGXHiUfoudiVl4VReYQnEIYXqc6Dig23uHHaO5fvo13UEweROkmerRG6VN1sGg9NE/lEuCkRpvCbqoX+i+moKxiCFMAtODZHJqWIyUxhgF6pBScksnMK2RJnquM2ElSYknBcl0aTUEQuNr6G0WCEiogCimKwyaoUWjK10JTySDWwpRGwxNwEDMo4Swlz4RWii8/zoT1IFJoghxSHd7pXaKER4X0wTs32FsoKJwQJWtCg7nA8yJXPY4eikYfRnMikh0O5ldEyrkUjaJfygYBknAukeaiDWHMDcINR1HTiBKxe0M/BoFHZQPMQQo2MWLYsNxeFkN2EEGp51eT13RLduDo4743nF52tbe0jfMcnpxFeptliloucc3CyBkyf2DckK5jH+dWcCThZi4Huf7D/4YcfVi9nzx4zEYtoApo+4xJ21O/u7mrNI9DFJh7VmLEHAmM9zIM7lHb31iwIko1RzhEj9/d2MpulbkZ1HNt+PjGDGLXgOQSBDEVqeOyn9IQgJBoJeUz4PFkYmsLh7afTZPHHTI71k7jk5SjZDCWxKR1ZxBOfOmzMcXJ44tDrxUGkNFsxJTirFCk7WJYkiBLrE84p2GDbvBgLr/WYCxZkESJBG6Wzs3NGRSI6hi15Cp5ukRgpDj6FFC3AuQYxvpb185fHeE+QZ5ZL46yzuHfRg1nUk+HErwxWLRC8pBXreFrgGbWcsLlOXA5JFPbMGkwLMlSruBey1uC2F5dgHob8OLzv9AL5qjtl7+Gj+9ahfJfh2Gsg3H6DOXNILv0fIPPbXMBdRSVXmR4jfc3b5Lerl9Hm6uG65HVHUWH1dvVqVfgu8f5g4NXZXy2MOwbo3VsGq8l7U6BbH7ZXG5OtLfbE+F69Vnitd5GX0Wt7v5Xp8Wa/um5A3XzRWaz2Mu8Ie5AT/ZL0oy4sFdAVm5w0GhGWvChEpTq5CO9i8nVHJPkqVRHAvvgpr5AEGij7tRZwKF7hPPgJs87RCKJsO6ZWs6vv7e34QkW0ZGZ5gG2aDxFoz0777uEvHf5KYahLaOHjdXt7Zzx+ophmRbmFFq88ohYaCeOjWo2BrPxmvQ2Bs00GH2UCIYIfdhisOjQqR5xEAKNkqqMcrsyfSairjH59wUsrDBtYCr202p08KWhSAmwBbD2q64scSMaOzjn+CtVRkg5OUxRPuVmYpLZQDOYhwd1bjEAiYDHFHOa9DWjISVDp5Nomn600aF1JbnQVYfeSbEkXcgi0iCAURubwmHrRoBZAGPqZixiLnBCZ0VkGfa+RRGzWWlHy8op+jZzPAB9fPD64fx/3w/RExB1TDzzzpy6bZu7uVcdD+TMPo12zuTEbnjqrpBDH0QMHAKRH+t3epaorcm569vQ5idH2VtegCCMgECRwBXhol6aWcsFzdBRKlYheqDsKs3i+uiLMOz3vG2Y0HiIoWI1gQmrR+BqRdpQ0Ry740bIh+9lAGmmHHuWbZc0yH6Yx7GxveySski8qjwJkmVfJaNoCA5GOvNd+cDYpKqZmwR/rv1xi+XM+7OO2cCDqahZUwVI3Gxg1mkFLFEgu8+t+eP+eTrWgZIZQppk2ZJwROHCNOsKL6wtmcOMiUcAh9R88jKzkShk3T/FMUYcLw27iRTFAWWcEhlDppd9JzHB8MsT6CXXXggIxdFuA98KOAjkffPABRZvgSkR78m0q1F+XV34L1xtOIDZZAuXF6W7fyHOR72q5Ih8MN5cnb10y8v26TJSLV7lglEiXzFVOTtxk5Ke7+68ZA6vZf+0g7xig16LlF525/iP/NkC/e9ZfW8u2ZXtN4VFsG7ExfaMR37BxuX9D9qPYrdZyxVvVV483pUMrYAe3X5Hk+/S3FdpJfU86EEpEf0oMggosmd2PDD/oisg304nPdHs0wOz3tlH7+E53S0g6Xi6trY6P4/5ofPr0VGu13zI+KEVQk4L4dVMCJ4KAk4uz8czhmtP9e/e3trefPXvGGMIRBRMWLYkRQZDIRRwU1RuOdHT/4LDRbPns1SC6BEIxGIlPJss5dDk7Csp8dRMDbU75Wm3Y67XgpG5nss5xb1cFNqQ+jsM3rRa+Qi6aO9gQMDnsSOIQsU2o1T7ClvJ9l8cVZC0xWybF24xkke2AwIyZ1sOHNS9ldMur0k4QWlK0wGFi15A3FSGKiEgZwyeFCsVNIZjLJAcK21XleQohTOMUx4UECSUlT0HEyBnSFfSe2RMtDOclV2bONFLmpZXcxwAQVJmt7OaCzq+11SLTYGMbxjfDESpKzcRA+mJJcRIBaTAufKzjVCwVLkJiYhnQ6cQYU3SbiC1D1VhmDj8/ZGZejWM6yuzUiQbHIyB7xIo9fX5KEoaiL4KUX/DmM1KMxVYKYwhsmSxaUFhLBW6tALWUZ809Tg590WO6vMTPmQYto/EGDnXuJEY8tozMmV2oqBNYzUX4XvHpqzc/+vg3RCa0ThaS4bMyctH4hMK2xBKnjEHBmjx78TQ0d42wi9/QanATG4RMmEO1dGTK8IoYO9Nk1iCZH5kFZi2BLoQlSz+WC6bmjNcoraCX9TyYvVVYI9IUc2zOwp4sRSfHVlt5w8EQAN4ui0sxrgTp+d3vfnNZugzn+bSY+XbBJ7zpReRuaVN3sYzYPxXiIaKcYjj36Qp6DR/rQOaUvpjoPZXdXFbYuY/6A5poysxxDA7oZLqVigAN1HI2kvxDjrEkdkcCbt0VlOOStsDinvIj8U0mZf1xteXkigrfKi8/l79VYP0x17q7/4wYWJ/TtwTGn+3iL2WA/mxDb2kA39HsLxCk74D2zb768WPXQmrEnhDO2Bk8OZncStih8n6x2jVWiVXhWzm3xuht6uJWdmxtaID2hd2XtkFL2y5t5dISji9FEqjA7LyYI7stzQiPG6IghBa35HvV1klxha6Ez1OrHWeDi7UjmEqzZXPXYOYDtO/zFxkjfpAPFG16JV9HPK34iFEosOTIY2GkgsIR6Sv58uXxVitOO++dCyLMMZgCosH4WMVsfKMRMPMuVsWVxynHxs7PCf7EVsSE6C4TM7oCJI18CHeiEeks9UFapOWgQ77dNeU7PiEgOBhUM8OMKBqFkrCUeilwhoZNBFimIaub25GmvvCoupwEXRidTIVTmmVdGKIaFMjoAKJlZTQuJ7FN1wbdmFFcsZPUHeqkPBTBJCBxCbRg4v+odVngWS1Qza47ddu9R/cxGUxPAkuMzZdc7ucnpz3CMPBcEs1VNnd291udlwQ2JDL1SgTU5ptN2QNa5jf0XoIMNkqFna0tPBv/LizS0FFfnM4uLmAGQnA5GocWAMQAJcJqu8PXXVhIFuidzi5LM5gBBoQbmsZDEJJsvwwkoUheIrrZPf5iwUyMXg6icEh6wTFr37qi25OAgMBnzexGFEqoi5Gmy0CsTIPzlFu2wvPps6ooaewuI+20O/lsClDhZTRrFCaUeYy61jb0AkBhAyTSU8yknJ/1Faulo+seP3k6OZ48fPiBuo7jMDRdwxEJkxMowga8Tia4HI6GjeYBCU05xemp1tpijmbuSheEf5V0NhnxkkwwGx/5IlaeUKYVZtyYvBD8eEXRDFGgAgOenLwnSSrjh2Bh+8aADW0CWGCA0OKlqEjbW23rn/cc2FQXfEGZgAfc+rjZXhSGorj7P6U9miBXyri+2aRyKuVfF5DOLNSqZFRL1yonJ667SC3n9K0Cd48/AQbeNuZ/QPt/EQP0A9p929j8BYL0tof8xttPOLzeSmxziIIu7Ib2qbyJyHStvL28lX8LDI28mnmrjAK52K3CHmMvnpNhRIg8JMfHN1GFXW1jSQYV8KANaDDTS2ISjk5MdMiBAIm64DPsvCQNHHxYBNnfOc7bW7ud+NS2315ezPmv2HNJ4O3CxycvSUG2u7ssJ3wlc5YGGHuHMLvWeHJm5uMtcM/VGbdiOo5wB3NY2PxIZOoQAjFNEioYGYhD21kT18NWl7VHUKCLuc99Jx0huqQCRDFkBuDHdwkGpCO1DNUdlRr2z0k9EDz0Jjg5V3KAws8ZFMrr+AuEjcIMWUIiVed772BOYIcbXTqsStdEAmGIaoT+R0fnFzy4NsNwKhnSLoIm6UVkIAhhqeFi4oTdgW34RtIGvTMBB/TFrsv3PLcoLI5rXt3c3RYFp3F+fuZs+fHlFPFmfKIa3yZcqQZn04nGycPSHBlHSBDxH7XO1j1Hz3KmW+J0S/RwX371FCSqTF4cibbHaGZ7Z7c/GB7cu09RuFnlhhYSOGWIIMlAzLz4imVGWhvFP33xZHNS6A8npaWgydNm7RBraGK0RsPC4ASvgDHCgeEnjCihMxhrJFMZ5BbMAJMDWiPN3B7GQmZGBWkKYZVpgGctBF3H5VzG6pLGqDJ/xnOaC41sXm4Kr2Q6fCIwXcL2MmXSkdURPM5wwk/KLFseZpViDnJifgvCToZOjUe5RZ5GGo8KYqsx+/ohWYFL3gBaJkjEODOQstxIlUCugHzmyprypUCTa70B2ED421vtWDNufE+ffbm3eyBwqJ8D63DHTpDTSGj9t598BA2M94FJFapZok3fEvieZNo8IZnD4yVPvdr5yanIAvr1AzIpODQ8mkWWfiXEw2EkztjIVECjZUnmkwQ9JodeNSx7KL9cIAzkh8tbbCx+OCbaW7Mbq5paMnYeeL0W+Pgr/+YpEOfx1n31uF4sZ97KkZmvW23eZN/9/ekw8G1T86Yg+F7tK5zX1Z9ngNbbXVV7U0D/yHZ+afD8yOH8lNXz9OfJlc6PAIjtLF02pnxhgNbxvCr5vaBVK/dFxiSB4XDTV2KAONLEpzCSTLZPKjQbEzNEQCC+w/ZZYv/CaIiLsWcS2NhJNcaSVJtzRHg4am9tS2OMyPOpxXa2t4QuJB7KcniR5Vz8vl2np8+Goz7BD7Kkx4BKPOjpBB2ya5+cnPNwko+eZdkMQhUk0EkAFXYtl1xcLpuX7QthGEOtADZ35YOBSNu07d+I5LgMMPciR9eQyY5IvkzFmp12OpM8TElkQiZijDLpWkllaJp8E2tWdWWQpjDxwU+4kkOyMvKVRwiR6qyZ0pQYwl4ZslfGJa2RQLkrwi4zygkYgixl8VsygNWJHGAANY9LGZQXfWVIVN4IAo/U0oXFIynaZMzOBrU2hcq7EFQHJygGwOg3qCa75vD/IXZSYDTuU/kwfx0wBrq6zDIMFsbG5S2jFtjmMq2F2XS44ZzUYZ+ajFa0d3T09MXxbrveadfBTVljIPAu6OQFx3eSlXQ6WyiGkiEZAHh3iaVkgGxxYNXQdIE31bgETBgm/LCJCS6H9jKhWvngMYpFIEE7eYY0TEJFnBIWDmXBeOXj59TILZtN48VGqEIb65H8BIOuLzjRAnmVWtVS5fTseDIaE2IBAEhq6VH7epcwg9CSQQVeeitwQCPpj0IyxDW/Vr8idooVGSxRiToKe6wksNPJszHpkykG+9QCK1wuxL00g5zyXeARdrHdarJgt7CoUvH6Mq0Oi5/Vmn8Wspkd9sLeCBqjEUsCi1II2zhRnzB2aQnFxwmYQw6Lc4w/jpPTSUSBYuekWfovUQZk4XwIf3BIFgIJrLGbAr6YsR7TZSfIrclZJTQunXNWidWjHJcyqxyJ9cdXC9wqnwvc3d9zDPx5BigvrIym1Wr72bH22uX+s0P1CwRgffpWk2inyJtFlvrITxtRLAZ7U77bcMNzNwIda+Oa2EvkRlJ1ZCOazznr9yifxdqv3MPAooRqsoRc+gpERIeTMCK2m9vBGZvwtb5cTASpY0sjRJuOkJaIA3dlA146+AjPQhqhLulCLTFSvf4ZsuFD3/5LPMFglShI+Fq6G+/956iv3Z0dZHLU74HN0NrNtmb1TiqD6ZET4voISiQWIirqyKQZax+ykMBUolWsKyCHhAOdVsYJWTzRDARt8Ih6aTkn7P7oU693blwSTvBG5JCqZovPTtDFJLKiCwrVDa4j3ZE5btvXCi8MDOYDN4V2ohKapdxwRBRDYR0RU2nEE/rKxZ2cgOs52BhIxegWTjoLAiltUhB4rA7MpIkNDWBManL1NzKPSBPsZZbo+OilvojLIBMqQioX58w3Z5MFg6ftna7y4XlU6pkQeMDjoMPg5AKNT4CNzk7z/v378MmCm10R/3mcgzY5c2E9t3f2ENYXR0cfffTRow8fobJsrVpbXXyDdKiYLjFL065z3bN1bXKLI5oAIWSi5e12sCbDOWOjFht0FsoWlAFi3ZlAkQMh27CN1yHEC8OdZEkT+jfhhpI1NF2q1nREgKQ8xgtvjKhDqfkglfEPsYcB+FRyPsLWnJks8gzdQxrEAsaxb1lExHAdS4W84zEUSKibnZyeQSnRoFbPewMrNhZgow14MXu4hx8fH0MgfAosoEHMTcNpFUSJMRcgDfkcAyaKLfGwihSYYZG9dFBdiDB3tmNFkc1cLsaDyRXVao3I0+JhddQxNoxPxKlKIQ8EHyCYhF7HdFVqEXZTbB6rhcXSQhCsCsuf6Un/3HB9JPhqMEC/RPeHDx9epNAAFL+UW3ASgsjNEKfhlkE+EiIrcZPx29/g9j/zi7NdJAb0yoHE+Mjk+k60FF87fowG5zsGtjMa3UFincJA3lmcJZ+S8ZTz4tXNheeRfy0suimQcq53IQU9rl+6cMnJr3J6vcBd+v3EQF4Jf54Bej+x86sZ9as/+PWc9bQ9Pe8zOeFV7HQ3Dj7rCMlbyWqvWSVWZdRd6exzF+sdXe9VqXTuRQskHLYoohaR0hAe+QiwzTEovQ/fCIoY8RDRIR5PLt+UbH+0IR28VLJKOD0+oeJBZiaE/MyQC/FBHLqbTQYP2wo7shK1U4UiALWozkq1riDAjS+/eEKwYXdGgXSkpLHrPbQDVS7cg6DuobSqj4th9Eo+BDMUaoicAgqrorxM8hgtSIQ2J23HaJUCIEkUlhVy+Lo740k+MwyQG6zyCKe61DEeg7dIY1cSWlKDAU+25SVdCGNw7lpxVuWY6AgrBdQMhrcuHv1oXgJDdlikGlp8aicZAyGUxoCkLyI5xTyCFulSSyCADBWs49XUnY4dS97Rnbmj+FNJNBrNkq84IMG4cIpaQP5995NwcNpLBJvGksQAU9tWfsiSBqOY4izG8SMbBW062TxoJJeryQidZk9bhiCeQmEVg+NVKnRYWzvt+3tdSjHI363ddzzZsfZGobsMgRCD32YwhWowasEKYBMpj6jwdGG6rQFrOcmoaqcnR6CFfGW8BbYCMJaG7wCQwHyeAuWNhUURHJkRc+2CjUBvyPNCAgFjGveIGTp5eSRx714oc6GUd9uzly8wAWQrBEBekSFp0xC0o6J+g4XlRWWdhPgkVgLA1NVaQmlon7ibe4X/jf+8w8eAAfufvAj8big9jbRwuWPewCnszuHhvvJeCTyI69goXLSadX70WNUXL7/aFvUqQkJH8AjqVD8+jZpYvVvM2HTzPp3MGu2WdUGY6u7nlRImJfg/ANBVw1NME2O9UGTHivLKt4pdg9c9FSrM+E0w346tJE4gvhb56N2lO5cq7h6zb0VOexk4ueFacmF3+eket1zgum4qLL3+uCojkV+5ZyTnHPe76z3HwLvKAOWF/p5P3o8f/q1NxM5un7JH5F2JDsn+mR/XS+o3P2YAzMX6Y87MLbwKYUiM0iYWBezNF5QUYXKUuw4Kw3CnUiFFPz9HpeYoHJnAVTmcm9CcRrOTPocnPfHW0nbmazzOcUqxYRBvlATJIcxH3sjy43Au9rgF3kPZWohPCn8oJ1jx8fLFLyiiCMgb9w73fYv2z5+0DvepGNjAIvzEMCBBVqlAoAW7M7oKO1aDYt3C6QZVNXZRAZExzSLwSHVwA6MBdgg3QPSPO9GG72ZEIrAREXTD152DmK9tuEU20DzkEIMDrXmD11ogIdSRMpB4QqD4aCYYIZcyWDa2EKJWhNkVGSjckJeYRPgLk2rKKAfCL5aiASOlQFIGAyR3OQsnbVIS8GjQPcw60ikQma4DGH4m48DSeDS89+BQPsDBgyrV6jWuZ0+ffIl2yoSYia9+5PwqZEv1VhtsQCQOYuIjYjHxjFOulhd98irjY4d+RghXEnCvydYZ/8F7nN8+eQxyXisXh6NxGIxU6iMRop25IdQ31rgY0pf7u1tPH3/+bNDHBR886ggj9PjJC6yViQGJCItJica8fUl4qGW0XNga6wSiWEpZFWYWiyB+MeBJ+wilHMZFXoK7FXWSRE7oHeIjQwjftOWct5flZMYPDw9hmGILl6Q1kZ9MEF4NAqPrenDJJhEG4BPCPfJJNCMwYxkId2RheJvvMIxpt/io4zA4lofySvbPzoV21E5aRWXOg3SOou/ghZRj6Y8lUtK6xcS2W2FhNrOMLuexxi3jKiMhBtdkdji/tiM+Quu1UeifnSr0waMPR+OhJTecTvqFDfjRLzabFM259FaXpQiGMbHfokEMRIBqAq0UYOP7I54Bb3dsU1LYYazYa8eqUyUc5caiaTtVzDyGiZhT+bY7B4d7oC1dCtwVl/aJHhn9aCQ9RmsScGiRwydNu/vN5adwze3knPy7UGX9SnWjnTwRq1eazWl1V5m5zPp99eou8d5i4F1lgN7bCXsjA8+7QNodrreP+D4rxyZjn8qXMi5bie1Mvn0w70QZAK/W9pbbQCmpwO3ctWff9Uij1mMD5UJiN032zqgRWTqzTVunq15rEHW8OHoJPAW0yXLZx6+91BaXv+A9IFQudEUZdJrpiLMv8iaIW1IL5egte/QdCAMCo5g7toDtp1HY4DWJKTg8PKCA0yBpiirKM3fWNRrm0QkeXOIpdHicBd8QyosgdVrDUugODDIRSHorCXD5MEZStSatBU05A8vYdK1Nhhz6AkmuG6QgsZLe0jVIa1n7iiFXEJUEJaGuku8tf59zzMT2lrc6BVIGBlpolOitlAzkJPZFLwi53qdXBVEateBSACugjASSLIFPUh4TZQYlNHX5dLmzs6cYtRDfNzxEnGtQDp0R0YkWOWfpmvUVyVx/cLQsVAciTbKsCiGHUxtENAzzILgiA4IVerJGtQlFlHpa2+1uN+rMu6vkCfz0sMRkVArf224S5sXZcMW6LLKxrU7zyVUYdyPM2FtMsUE55wETtliMnEUFWgRbyZ3OFgkfl2y9WBWqsBKHMZ5oF4MLVjjBElWDEuMg3eFfj6JXswEXMUFxq0uOKcNUkZ0on7FkIJBsLhITGZ5l7KPNMt84UisTbWYsbZDAnvUDM/lXQ8o1L0SgSxWBLd+sKU+jqhen2YMh2YaZR/or4skKlLqnxiOmkfJ4O3qrJCcS3SDERZWw5KHaDalhEuXFrzKJ+kKstZc85rjogxn3Y7CapPgjmvJtYwaNFF9o9g0WMB7FV7TusHSkOqx+VLGC/BC1hukRYlvCVZyRP4WalXBoYnEMIzI4VZohxEGyFySsTS3HwraYcWFh5cYOO3wU8ubglQSswj+YE5YkY/9JOZGRvrNu5EMpP0rcXAYsme+rxPqjFjymdqJOfpTjiue76yfEwC8W53+eAfrFgv4TTt+vpKu8BawPZn1TsCWtz7WdKD+62yKVlONaa+RaxSNnvU1pVfIeE4m17UZJvaR+7EwRD8YhkILXeUBykBCCH9+v0jZWcVM4pXO4UstGnDZecpyxbTgOwUxWxjZM1OiajIkjXIuwzidnp+T1tndSG3sxmMGvV8UQbAYVnVYjSPvFnFsZSq53VhoGj2Ao5lgDhhdqIaUgUZdHGEJFTtDsNJlaK4NBQfmWV/Q8wQllXYAgN8LoGX6wOyk2td0eu4Z1SQF9kIj5aBwHtvvEdwJ8lo4QkICWg70RadnwA5+0BmQqYYUSzkEOhg1ORX58Qxfl0UOxkskGsHoEg1ooq2Ez3UBTETlD0Nfe7n5/0JODG3DH8xm1WEfGxbEsThBjJIRqzcPhCGGGTxOOl1MXPHQ3SAeXIbohSFOg3Q3KrfqgP2iKa0N3Ut58+PARac74yDHkTvqKA1pZrMAEXR39B5cly0DLzHAZ8dDRMSTSNw8yDAEhluAF7gyCmK9gVmKFhLtfhANvbO84Nsrsi5VIvYJX0rsZOTthttLQpmFqXIFPPvnk7ORUg+CHDaIy7CdU41pEZgp+ZhSsGAkOCzPkGcNq0UH78srRVw43m5oFHFfCDERW2E5rXAtkRYNeb9jfuOhuiYwMLfwKI8JTs+kcOiot6jgqNiIdwTZFIeL8xeeuhTtMNJh0DpLn07C89uPgKjYQBbEYB56QUflHzNO0Mivh3hVyKU6HAjaVNl+cH2MZQRLHwlZKjXoXcqazvIwj2qRfEO6Cz1fMWhHrw3lQmjvkRhYH6sIJGFZ+LGM8xNXG/t6BH53yomGp7hXs0YBaz1DRam9Rs4pXKRxAIDbO4wpWGOekCqGgzMCtoqDahFUfA8FssRvCA/lN6cgFacAGoZ/KZoQ4YpEWLmaqYqTinrmb610lfXdds0HXrxRwwbR7Kv+N3Sa3APKcSGXjtsrPOQpIuOfLW4n86u7+a8LAD57Wb2WAfnCLPw1aby30n6bTd7qXWzvFzfzGDnKLf/HKFfn5il0l+bKmvSO20ZvNS/6rOFE3Z64Sr5ZB+21uOrGp2r2JcHSCFNAaaJMgnXGJQP/eInrIhh3WrmrXRbeQGZQe9ZKJ3GYK561t2p5LAQRClJ50we6pBbUynGrJV1EVCSwFBRzWyDbtFVKB3pKy+HBXXkfaUTe28nKcA0oIIkePgrM0Wh0kDTX1mGhBiBPAQN4AWlX0i3CRy1xt0mSNgkI4gMJ5CNPgRVBk+hit6VetIvPRWZAWAGeCZ4y5a4MK1dsgpEealRlNofApOjOo0HvwB0/gfIkXISqTKU0+hLYBQ3mPRuTRvdyKUMhsh8k2cHjSWgiCLgZP8lALsurEUxIaJucODBd/knSkuWX48jWYcQU/k/4ZkNFOY49gjzBwcdXvH+8/+CBETWdn/PgdRR6Izdqi4oZJiWjLLJRVMZ5ajZzmiy/60+G5M8T4v01D5WVtFBnLEymE4mWzNpwsP/3002Zx4aiI7t6eoN4OviX320D0HT5aJ8YTWXEaUqJ2509OlXfNpuaxl5z8Q4MTwQXCvQ4HTH5lIBBrXByfpDOiHj9+bEK3tnfMIEMieCBKgTFTDD8kodYX3kx57RiUxQq5gnvTIxIcYuDC8Gpjw7oiJlQGFAqHRX2EDAhuLuzc8LOiVxN97e7qDpuu99w+JdfmzuZ01jk5FklhAFO7O12NcBXvdpytFv7pL58/49nPmYsabL5kqiy+pr6gM/1KdZ+Up1RdOtWLUXglAR4DMa1YZ2WY8qSfJ+7cugrWR6gh0OJ7AKOKtyZadXMkHyqwvy7dQJ3GsS7ygac1OcqmUaSFVAjeTr7qYgCURVQMMybhvTAxcaqXSxdA0q87+OUkI8BvyGwCxLSZ5ESq942bRlZXbtOjwrlQfpXrrtq5VT7nrzLvEj8eAxml69Px49u81cIbnLVvZYBudfnLfITlN4iLX+YY3xRUtxB183j7E0p3efuIbSldqKd/vlRVscfd2lxy+Vxy/Z5/ADe9fP3GNyUxUH6LXrJTQXD5VNkPyc6pxuali8KUA5odMdxMppPBVreDvNCM2L7RH/TDERX2YpFb2N8ghAxruAGhl2BEVHjeYIPkIGZ5h409PVvbRCCciOlc83Uewymw5xD2cKezE+5UoxHRi93chaKn3T8sQ8n3d3a7YcviuIkU+w6tNSTdNZfhkoYtCI/v8BuPI+ITBQmrCZ/G4MwwBN/VqHO5R1ecuUT0gkgoKR+xx4clXDlPKlkUpcMo5KA0Og27ivlyzCRrQ5BGR8STkC2qKRqyoaG4GZ+oEZjROfo1NE/7+tJIW7CjVgf2NI6llANL2Roj0JwmWkfyWbVuddvmBVVmmNRokYSFxlFTGh+OYyxaIO0gV3CAwvDsxWwQoh2cXzLBYRgUXJ34BVrTOJSyTtGCWRPfGd529vZE99EazAADt/H82fGQf/h46GhWpktejaaj0sG+SWRrdeYcrOny+OWLxsb8k48esUAqMamestppTKf9pmACjH43nLAxwcHs7+4BrI/3mox2Ch14EGYcuyhIowGCwayBShewCnXkEhKwSgtqyAl/YSs94n4/XzjkFj6desaMB1uJWGNPFWNmruXZ5QxHIY45JpLNFb0tXoHrvnDMDICwQqfn5wSHBri3s++OK9IjRg0jAxhNpTg94inXKI+c6OVUVyEeOuG1xR19xG2cIMdx6qClQSMMoWSEVdIsLPlFxI7w5VBpdZqGZFxEWFCKSzMKj3g78DLTJ3IhQuwNB+ypuBIeH5/GjEeodwx3GLf5keHK1HKsCktnDDpjaxAqxuEf5Jhe/B9mL/SGSTkLh2bZ14rfmQIFhtmEd+l7Q9dyLD+rFK5IqK7C7iq0w1hWt7yv5IVnObl8C6mFdZEIRujms8pRhMH/hFLRTSouGLi+Rzxtz9fbVHp5d/uZMZBn53qC3g4sqy7eSPPfygDlblZr8Y10dtfIz44B05qvdUjM8q2Jtve5lFE4l1w92ubSnhXZq7fSuYXUUhC/62q58s098uONltllhgUu2knw46PcZuaNw1C1L/4KGlHYYFg5xFUolvbN8AZHeFBsOZqMUzYr4X1jn43v1HREvHyFgaEjUgq7OargbfrgDOWUC0VBC2V65Kf9xeePeW6XqhEdZ8pqJZ3NhDBowaZPih/u6qIST14IYsKEArFH2DTb3upqDY3HA+WBZ2YIbQASIKlaQswQgw4dH4GKZvU+vZyeHh0DVV3kFgMEpciGCEQyJVyAQV0cSqCKpAJ4kkB+sqXVhaM3FZYfwymGmTP2QqYek/O/HoPRBKc76xbYUFLFwWBo7C5D8xisie4uwnJFSQa/oWmj/kjRJh0MS3OH/pGjRe8OP0imS04nPdhu//HvsUBhREy9Fk05zGHJeolMZYxLMsn0ShebAXyzifdxEmp10B8SyZDinB8fQZNV8PLFV+fHL8uXTtS6ataqYvo1ap2QAGEqI5zgBuaDSXRpPmi0yX6IT3aLbHRFABqMKMXwhTgSmIdMhB0vAhJxAQyHCXyeCONylAraC0U4IkwzxsVaMkyvqERBGK5nGALrEbeB4CZyboF5Bf+MbzCvZpVbFpsl9u8qmn1ee7hD6xj/J0EpadghaKkylbJg8ROj5eKFIbDKQuAxAGkphrRPc2aE5hH+s/+5hUNp63dl0jViqeOlcG4seLimk/3oWANyapskpiRQAvYMQvsUwSFDVwtdVppHawNPozxW6bQX0h06SqeGWB/YSueKMOoy/DCo2tzc2uoenRxbCUYn5kBAJZQoPZpDPzYE6dzEXOHJYM+AyUnTKhoI74ynwtbMrgh6SBHDaZSYx9LCFBqFAQZzX8EnJVzw8Yzvmvh56hpi8+WHH7+Q2ENiz/E2XzI9ZgYoDs5JH2Du+YpnbhCpShS72Y5kSmshJ9Kb68dbmflVrujV6vEu8SMxAPPvCj6/lQF6VwbwI6fqfai+2gjW5xSpsAXjOuKH7yMx7UewofDqUj5TiKh4sz94azOVn19lBK5a9naF0pyZchLHE194dBZR3Xd3+PAWxHZzPlRhI5RXfFgKTCjBRY6CC7nacBRAG9Vh30pPhSKxB6IO8ArkKCMOjfx/NimMhwOt+dTkEFTaCDNn27cP8eFm4+jo2BZMbNCMIzBLL14eNZpBscqFjZNnz+3Uhwf3HCjx4uTF9u42m10uSAwk0JvBsI9j6J8znd6zrXO5FnAX83FyfqZh9Cw+p1kRpS9bPAeBT6ftoK6K4DfomTGqBc5GqzUdD+EKuWKHAxtgxvOhNGcnR3HAmUB8pAOlCoUR6xDjwoIQX8EeRyF/aBCYZWhhb7+bSSa6620QOWrBQuGB8pyPRsPdnW02NMQSLXxEUvNhMw2EJAUhRHeDD2PhwdT86urg3uH56RkuARFCg7EdQ3Gui0UhcwhXyAeIGFi1Cp3oQHd2J8mM+DL0dw1mMc7pCpHAeMKUanF8dNpubVPumAJzeqHaEgtVYuHMAnd5OW21unoOGtzu9p89Z2ZzuMdfrHJ89AyHN5tgiSqz0azViANTUcxmOGAvTs/P6u2D4mad/W6zWj548GD/3r3mzu5oeXUs3PY4HZbO2fsqmAkqP1DxBjScvcODGQmKIaWjRTBnFgD5xwWzoI2I/sdCK1gT/nG4nw4OI2IlOwKdndBo0KMvs5xMt1fIKPGFOcLyBrsjplGVrJAhkbaphxrMtOHEqwjOlOzN+f3jXSqzJblRt4MxsOZDWwTr2CPMtB8I7NDzAlvXZI0WjK6tDSReU2YnbJDZT9UiLJa5Zq6sAYpL9s54DWxom5bRzynFqVIAqEDqG5LYPIUiHtHaNrm4VcGp+ZeBlhpLR6zXyEyxy6Q+2nnae+bUjFY3zvRg6KQpl1406HdEDIZdfnT/fj3ACCaF3AmySW0tTl8CcejLfCJ40yVOPbTPoQhWlyiOuRJzOoyL5TAJvz/axgijIOS46TZNCqePrGDTXd4YP9497unRopcO//j0qMAqEflRLxgmVzx+81o1IuHyMteNk+h0lVguc5MrrZr9Zht3T98bAzAJ2+8QPr+VAfreQ7+r8O5gwAK1CdiX7Qw2LJuRvVhCjkGslu83EjcMUM5Mu8ratvK6Rb8qkxGj4ipHR4ilC8HDDbD0AEC+sDE2eCCpxSKYXzDrVwYTqvgQV96HvreUJ/Z6BMl3fJaq83LRxTX3E7oerkFxJjaRjv1aQoOYBrsvhkBdJZEEOVpgpWunVgbZiG9f4iVMUrl878F9nA0TVkY5FCoo04vjE7XAA10vXh4rjJQaCNbHvg9ItfjYo7jIgy7iEM9qmaJtJVgiY1DY5ovyh5IihEyC47U0CBhNCZEnVjU4DdOIyBnAAyMYLsoMxsO4GDnARtBhy1g86l1xLUxY4E7bAUYZZ1AE6jyIP5lZwKZrZZieaF/baRUURU7qX0RYRVMAw8lhiHtUHK9hsNgcTbHUhhNYu+iPjBkZVDJPme9wwZSarTYXd+ZMm2UymAtnospUhjN1q91kLn52etyoNdqN+tOXR0+fTcpXi/7gnAc16Q6oSBd4VGPEmEPTeGJxxEsSWRKPCKvQItTwZaE8Wlye946//PKJsbATNi51ISoPTVqP6FsIWi4LWB98krp4KejFBfKbU5hYq04HSn0Zs715dhIxCdlDOcxUdwi8zwOYsmA1a0kImaO6BIwRh7g8SmuKiTgYdEFyNBz3JJw+29naJhPyXkk+dMEEhfSCm149RER+ccLvhHlZqI28shKM0SSaH9OacjAfBJ+hKwyajWqnMJsmJX8AMCnTlEs5QDItl5+GO9nZDp8yEikrU/glekTBAsS8fv78+dZ29ze/+Q0UgUEU0OKk5BQ2+NNOtV6Pc4iDKSk3koEd7ocyFFTsnIQAiCPQLnhQctQMtNBjwxJr7Xm4AbKk9uzbxkpbULjuhBIwDIwcscpIWiN6hAEdEaoZl+G7YgCJuTFkQ6Mcx8sonB6v77mATNeq8HrmKl9iVXG9gPQqP1q5u+4wcIOBOwboBhO/3r+39oI80Ez6fJbmrVyZvBevCq8S1+U933yH5Zy8p3wr2r5ZWLFcftWIHV+P9uLQMYX5RK1AqMJQwYnl6dlmGrAVSj5ha9WIR8LJS3VERVMum75NWb50knAU8DE0EqkA4sT6WPQRR25X7eMseHRnc0dRfLJjFw72D6WfPHuOFag1W4xInIIqjp/qJGO1dFwrJddXXz2jBnF+J2cfQH7w6COE5IyUpb3FUjYMX2YR9Ri/xfEnjvguISqjJpPVJAfSKfbCGfKYIZcRkXCgAWRLvnaxF75iaTG8MgQCDAn8ByWLNGESLgpRZ1HRH4UpLrpJLmW8uBTsSIsrGWvYLEhQvRqHeWkBJMvBAIbRYCNld5R0bU1sGYSn8x9i3hPrCXtxnqX4diNRdzIYoa8ILpDfsrjJ6qJLpqiaPNdQbPa/na32x5/87uT54yd/eiwkz/bOARVXGNw4y0tpVUvV0WRREgPQJLJZ3nCiallYZdioFJcvn33F2CWUoPMIPlmvYIeCyl7Op8RXTF5IJ6lsnDqxUQ5dVbfVJkPrnY8L7T4hIV7NuIgWCDmMWhhA0p+vvvqKOgb+BY7ifgf/UIGvsmaCwb26wgvClT71yLzGLGcuUAGrKDGFjdI02GioU9fJFSaFRU6rFTbmLqIdSjLCMO1kxouAIvEToaVlyiMfJBzERKHWDiTLUdGUuYNN/1bK4jIkSFicvHQ1BXFkn2zANyZjVSwna1W8H78bnIGfqS4FVCRm05EClmuMolJ1SruvD2opnn34wMOD+yZOICtDvn//oQFijMxmLPiDA4Icddk76xGnYfUCUvuiWuOciR4Vxk/7XcwW+MgNznqFjU1WZU6vx63oepFcNR1vqoyPBRLH2SRNI3bJwGhDwxKOG3+EghTTUux2HRgmZsormdIATveU+nqjuJbHrPaKYIXSlYB8zYeZl9FWunL6dc1+zTbl1u7u3wsDUPra8nlSXvvqHcq8Y4Deocl6k6BavjYpDJCNTLu2RZt1XuvSduq8s+QuIx3bSFxy1n8S62mvcoFc67WFkQEdhbsXHUByv1JYpk5zy7pGSDaKvkjxABWbpnzMh10V/dYdMokOZXJyfhpGpig0coVWhVAkCYEykUCZ7N1x1KXP8eRYFZtyufzAUZ0pGp4+c0Wt4Yp46mhZI2iAkmxuTk/DyesS2aJwoX4rb86G51owAsyQ8xwQEi7Qqqioit6Dfiwiwq82gRS2JRiXKybPhAtzBS7mEZeI45u6IDcLSAtq6DjwIDwsiON8jLD7YfeJy5EOc5ZktT278RfD5CR6GsTepQy5EWKPAdIgGIxdX6F3SRe8GUjIReIo75CleVRAgsdd4DzJxtAlFee++ONgr5Bs0WLQZSg2WQquHY75TK0ikMyi+eH9fTwKzkF3WRtC6WSycILlUstagEDR/kzQZDwivbkSA2g65Zd//97Bs2dfzRZBmAf986047qG0uHQS6mJ3hypHsAAaN2eq7mztHWrR4Rg7O9uFxeVgQmM4oSWCugx8ClkU5uRwRR2pLwCQNkCvQXnE22GFcI2GgDVBhuHKuLSjQGC7WIBbzAdOw6t85fZhToFVLmiVzNjLXIV2jo5PTBpgLM6d9o5epNXyi5GGGauFLkxfgfkSxWHI+fD2aSnGGgCke5K+hHRHyfgYiEFgLNjTiBsVelZLhR8Z9W+8DQ4t5D8GzkaHFX/6gTR29naxTJlpxu4oaR69UlfYdGy0jjA9ADM0AKv++RdfDQZn4iDAsB51pApd38UlyVmYo5Uvi+PFIKpQlLNKDp3l7GK2JGgkE41zZzBBixmVLjkWFshWAqXKEBziSw0BrxlrFcgJZl0bu9Xn2RCM1z1mLe0eq7RH6M2Pq4RHl8f8dnWXcOW3ab2HemyVk8unIne374eB70DdalLWW/yO8uvFfjnpOwbolzMXPzUkNrtswmI/ctn63fN+lNfxrSWeH/Mus4J1fcXnTWf1Kidkrj4hUlqNuHQXu+QU8dBnXPnUUgojX8je+va2l4ltZ+MmJNEaDxoMDZdghZEWuzkxD2lRBj68onZ32UngYxT21hZr4643WhgUXFajwciEfiF8ekkL0CGfvLubNYEWdeFjehaUd8aEQuMAsP0StKAHWCDhYWI4xZLINIf3H+ASfGO3O1vd7R3aFL0YEZLj7tOc9AelhF5wGgLCwHVNfSSCGRCZB4ol/DKCijINen2UQMzH+VTjgjsHO0K1x/WdJAJtV0yz1wxKOsQeIMqg8eQLCDmsskxBE1A+FIuehcpDeUC6IAckhozQGBeaV2/UoFGnzKGAhxvDvUFjqIaS1IQr03n/rObkzWpNhB6KyAkv/iBxc5G4YVxgIZQaXYc3hEbg6MHZce/0KNl3LZk4k0mh5fVahZ0ultGqEvEJPseNFh6uVG/Sj7UbzeOXT+ejyWazvkG3spgc7m2ZqX/+7PPB6KIRYQhqVGYkeM9fDI5PXopYOaWROb6s1BhUd1v1llgA/f6V2D8kMnuUpBsbz796ipPPYjCxdtB/UwMD9/YPBtdOUvynFp8//xORkRVC9gW9XNEcb2E1ot84RNjGrWgNPRc6SQH8k7sCqkgIAk4ZhD/hpiRYc6EQilTMgYmwbMiHFKP9kQm9kO9K0zHanAkyOTd3pkNh/DcsmSMtZwbXCjdeJlDyhYcgSCEVBUP2GCAxwkWJ3I1NPknnsZBp4e8vr2Z+JrR2FGx6JOYhqzPXs8lcAQb+ppizGGZ4wo/SiSXdrfzzgRmDDcd4tl/0YotQCpOHgkeViOewCXKnYURkdlKbajnkgjTODKOsH2mthRY2nXAsLUeeoWnNEAQGIGi7qpSCpXJpLoWczhWDqaL+TpfWrHYVXamRr7mf/Lh+X5VZZRpFzoz6qQXApDavW1MyvyJpU9i1qnuX+DYM/NKw9DbguWOAvm32fw35eS9YXzc57Z43ADt12qyRZhYm12ZA8TZdqvsLEbmdjJH19Ks4WvVoA8rVUwuS1zsRCiFl08PjoDj+AcDWyOzXnaAeA0Q+ZHdVxdcqymEvtpmq4mxLtMQjpgTroKLQKbZaZFt3LkwLAu9OZBIbrhZzSJ50VJNG+IPb9Dc3g/z7AtZCtSxiWw89UFI7yhB8KLO/f6g6PgkMDlEQsUXX8jEZ6Cs4nz59epGIEDhDR5C8z7SDJxAUJsuESErkBwvFP6o0ZQiq37RXX3/46jQTIVR5fBkwjHk1pQAqmkUEjQ4N62yFhQdUIDQG7kKoNJvKRD5aEo5sxY0siEo+22Ef44K3sLDGyqUzMdhe4R6A4RVMQlSG0Pc6bLCtiglKk6XNqDUIbKPq5FI9x1AUi7v7Dx88uCfK4HnvzALhxuVsiN1uvdusHJ/0ne7A6xmzhVfZLJCoHU+GPQ0Khoc+Mkn54+ef3//g4+2dHZK8Ya/XqJbKDglfzB7d29873P2Hf/ynyWzpvLGD+w9gkUU2Y9d2rfH85IiCrrt/SDIJclMDA8RmYSUzDcWiTAsA5g3HlJkg82vI8CZfmr8Y+GPG46CMFilEMBPp0k6SFkWsPwhMCylUqxpx1zgcqqVTCTgxHNEm4VBhBje6ODk9Mgsa17sFr4VcRV2TReThSwMaBVGEWuVJgFQHS54IwOMQcBK6UF2ztFRecTl0+Njp9DhYz4gLGiIi/NLJyZGSU8GORAlKTmrYd0EzwaBHZfBv1sC8vsh++MEROvH05ETLygvBoLqSDhtW5eD+ATaXxCYUdelAVrz1eBoyQsOBWnjAwWIoe2fnTnINnCzCfNsrQjtl/PIYcXs0BGDDVT4Tgxc8rGkHxpRn3EUmtemksYqu9E8/GCrCjC7rVLHMp2gzX5HznZfuVAd2Lukux6NM9SS+s/b7+PIN4mSF9jeI6nXwzOb649uYrdcwQG+7y7cxjLs2X8XA+t6xPqfSLnuG7d62a7eyQ9k1IpTNzWfTet3cspycqcwqnRO5wRUA+TGf+CW9yr9uJxmE2qY8MhlhOkruYHPEgaUjPsMdxvaKFitgq9UFGyD2lagLvQ8/amIj37IH+/ds9KjyxWRZLyF7oTPCQNjQMU82ZocWkbsjW2ig4+KFFUYy0UvwM55GxjRo1LVyyDns4U5IQDMYxmJ6MBN29sePn4XQiDEHr5lmjRURguqwKo4/mBLinwCAo3I+8fv8zMc9YxHbOr7n+CRspXF23NAEKNIXbiZoAGUN8VUyMmWa7Afe6W4lkJjaUDSAeSP8p0NbFeehs3pGHWkxoAKfgGQy29WFfuWjqQcH93SHLcPWgL9cCX4IqfNKd4myEhkEmwVaOXDSG5zDbbPVpCfjFF0phY2RMkyXfOirKCr0ZD5zPsjW1q55YU8O5mrJcVod8ZOQMzgBHO2HI81KxWWrVm5VNy5GR4vxSb3UWExOnOjaqjbLy379alyuXZ33hqXaFgdr/K456nbalxfTl189LyzG5mArjmtDvquPv3w6ml5Vt/bm4wWgt7ot/vSkHWIpQoDTyVhH08QAiXiMIBCKgpbW2NXiS5zPFWEF8DpMcMwvIxijxr3zl4ITcyeswuhi8ODhvYP799FyKI6BlDkrBTEODiLNi7RfhLrWnkmEassYhk0T5KjiJ8N+ymGgGAW9KCwTl2DNOMCOQASqsSByIFa/Zlkjmbsyj9L4lZjQUCxjLR09UclO6azcyiVm+w6MDbVafzjYhoVmZ3A5wIGaO9EhIBAQRmSViDJAdWe6K9U6FS2o8OKgDfshreP7sWtOOGk0ICS/MorZtGepaF9T4XJ4GTpWx+FNiJquCgPmZufnnL8M9qJ9rTHE/sdZ8enH6zeC6cRcsG9nMuSQDzNCfOsXqBc8zscfPiINFQM9ll/CJGxxWIcoFzMmIGGaQOgCT06441hyCAawuaxSl0QukNMp7zrfn1QrHnMi1YuOVlW8ypmrMlE6lb/pIWe8F/cVut7UaN9sg7dau/X4pmBeb+cbDNBP0N9633fpnwwD6zMrHY9X4RJlR3XZi22FbCttWPYjG3QGTLG8d7hnMhAVby6ZucBNRuxBLo9xX/tyW2VKqIXAhMictWy6tGzX8w+FSOd8BVnjG8V7iA1Q7MKLS2QOnCQWaA+wg/NI2xrqEht6Ot4L8EHPksNw2EYk7QPlBZqtHyyUtxlUMGAFEgW6Oj09/tOfPvNBryPgIYe2ZH2RJmEptFYodDXl8vmueghakmILeQsWJ0kU6EF8/gIMXQGk9kHolb2XECqp5MBbHA3H883QRsUncrK2IeEBCPhRDgDohdLHHUEyI9ErAcZiyYBJ103dpUBBeCA2r0RQRnR4bfQTsh9lNOV0KK1BJviBIc1xKdCIGxAxeeqkCEdmRqxql0RVhMJk38pWGuVleiWcTF4DKDpTJa7jtCpGpwUIr5Qd9H2IVWI3YwmIgMwF+ujlV6PB6Xaj2Oy0XpwOx8OXxQvmMstOVUQ+xs6zk0F/zvi7VP3w4f2DbuvLx58dPfmyeLnYPTzAr2LEP/30s+2Dh3uHjx6/OOvu7B4c7DEMIgwa9gbPXz6bXW7s7u1pajl1OnpQVsxbPs+cX3qGFsJHk5D3UNnkZQCNESqw1ZIJAxKGDC2whN00dkNQkvJKAb575HZWTsak1qwQ+fpyWTAZvdZSrNj0i0jMzYYZx6xqtloNNogKSBkCCC0oCdvJskt4gGBZtE9BqmUckopaNnZ8NiisaxUlKDGZbntLzSr+A7bDxwkOyisNJpDCb8DPlCmRTHwItppkyFxrljchyRdTffxZdJdCOVsJhoA7VMYXhbQhsPOigNYm8Y9m/VhwkGBwyTReZbzm337p+F3RtKnYYicIGPzKBc2yKkQwchmaIBIQsrvV/f3vf//JJx+NehF4WiMw4K6W3UaDknISi5ZNvLMeLcoEVv2fGJP1u1d/9tKmK9WOm+5yjoo5c/X4Z5v61RfIiPrVD/MvGWB8YUNHLrqe/ksq/5RlVkD+lJ2+u32Zygx82qpiC/CY5ze2K19daduy9dmLbRbu9jp3e5+3SuZrtWvIzzkrnKx+Rav81RzJCZvSdMnUW+5dR2hPtOkAo+WFD2ESIJwByntVrrEP0DUqhYTgLhSwpXNm7k16zUZHJdURMHs6wkYoIhFUKkxhQgOigHzLWSaRgswQLKBb4utG5QKdi0eEoTIOvl8IFUITdrtiyBC6dDptH77o3YcPH8SZHCLszYKJgRZ9cDdzQjomhy0Mqx0SCHSFON/NiOh3fOlSbOAG+j332s72djAf0xlep40JSwwHFomMh51RNJgCSXPe8bkq4Eu1Hp/aiK27K7r1tZ28gQL2OB8tYsPoixavVmvyZQebUX/17JkCdAxIEjtozRJoQaNGKEhQR/fTxSnvNtKIo+OXk94YbpEiBfBPKIUqVsYsDFFmZo2URS126KRdTNUb1UbA4Yg0p3E9OxfAcGdrG1EzERuXTjPd3ds/ROe+/Pzzewc7Dw932ey2a6X5VatSbdw73DYdo9nUupp8fjw6HT16uHtvu7MYnSyHJzv14l///l8/uHf/v/yX//LpZ/9Sq3cvC9XZlCjv8rfCLD486J8eXZaWm9XiixfPS83Ob+79Ta25dXLW13UcB5GMg3vn52I/brRDHsUOmubl+OgFqRiED857xghyZkjtVvNeuDhFcISIsXkZIbYNP1npBufq0fH1pgaoUI1ZsbqIjlB0sh8LD2cDLeZqNBpDuEciPc0zFsc2sY/CWNTrO6zCM65Y8OgbGBaAX5hfxGwW56u0tzoRvzG5xPvcIDphJn50dKJfnRJtUldZS/UaXWKws/z241cptqH2riISJv8tRmQYE+ZG5lG/e3sH7qenZ34Hh/u72L/nz18aqS8H/DoAjAL/p3dDtmxCVeWE4B6FJkYBaxLeiFoQQdsIt0U73N31UzJqp5JZBg5nlQ9F4BEGnNhQde2nhXMdBSCATNptE0pzZ7EVw5eP0X4Y5EV/N1Rmlcj7xuoxF3M3Rl3Ho/+/eSmsVs6Tzo+rdnKOt3IADEI5Eh4lNJsS1wyZ9Ot6+GZ/v7qnjIo09l/W2EzQzwJQSEpXHa+nV5l3iXcRA6Yyr3Jbyfq0ysyXWbdhIdJGp4BdEttgs3Dl8opJ5HTcb5aJ/Nci5OuS6TXH55xj30FRXCBxqZ42ypDo3ADg0/Ba5JCL2cFwJUoiLQ4lAFvuNBFsW1ucT0kXpQUNSttlV33Z9QzBPdeyZUvI4ZeNPOBGchcJKrf4sneIpt1/Z4dDk9aC03p59ILUJI4uQinrIQaYhryB4J/kYOY4TpCzINYsyoG0ZO2VtK92r+SDDczKQwamgeUxOsrEV3dPnnyJY6BuiZKVcoRpqdMubWG/UFQbP2GRV6iyOQzXYWO5DGMRmgZ6MsQsxAzEZJubpA4etRZ9TfEZgdU8QARJgRhhqIFirhMk2MqQn8EobYujDGgVE+mNfiDq8oojVZ1eCQ+gCmEBW+1ofDyBZLGMd7rbNBgBieBD1eJkJDRy4eXR2W8/fMAahl1H7/xYWLxSlam18MG9R48eloaFwfnpZuHig/t7hcv5eHAGi//qNx8Pz894Ap2FdlLcaMviih6MZTEB1NV8Shw5FQdctCR6nXJpRxjp7u5OcDYnTnUQJ4lIj8IEl5LCifPwCqY2q5zMJsxAhbVhLmDSvGTpDv9wFkLSJsuQSYugS/m8HgwZnhMOM8kMY7WYfczObEa8p33IN1Nwogt1EVZSE4EHlHFpIfPi+Y7qggpKs8mLCcX90Fsx/ncSrY4sM70rzO88Yj0nAacI4NpnGucf9k6zJktPGFzw4GksuSR+i5NGXBhoQAo5HTwb34EaTiVoPw84awPMyuP+9aIvLRijsSsvG5fvVAzoHfV5ws8FvVTGklAAK0IEiJ3qxAmwzfFV2MuLDaVr+TYMNksOgteswpgtIxXj4Le//S3MkCtetpvY/eEy9LwZvZBA9ppYLhuBDQoksZkA1R1ULgPPlxxXfiUhM2VEziq9SqwKeOvKj0ahX4/uSmo8v8rt3N3vMAADX6vA7hbHr3JB5D1ifXLl5Mu+YOfyKu/ajH+RTPujtzYL2PDK5TFhJu65YkbUejq/yvm5Vt76b6oEAb7OT9tTaMiKTuwiZRHVRWwT2yIRlAC8wfdgiJBY23Hpgkswsc21d5Kt2QaZTRBs9ID3neqLOcEXdMuWnbbjsGB1kTMRaUxGfaSdbMV42eggySBBNogLWJH6okVgNOsRjUUwfPrbsUVVJikx+vCdQSB9C19dxuFS7W2gkwMhBpyGQUwyRBfgBKdgxcSYadYHy5GA0vCqIzmO5EQw9AJC4+QzQ6LDf2yfHet0EmKbxM04+owKS0gbDJAetQ8VYNYCeQwOCfqDbpBFiVUDR0YncDYeLmxZUK6I3AjPZlBHuiPRwyKWN6cBw2DIfw28SJRH0gXFMg4BCT8EP6IH0XVogT2N5vWIC4qDq8T8JbSgrKltLCbj45dHGBeiDJP1bDA57Y0/uXf4b/79//Df//P/4+jk6Vnv9KQ3Ovhg58VXz8dnx0+fPXOU22I6dN5DwlbhxVdf4CHu3XvAMem//rf/PRmRXNU7O4RhglOjudPxycsnn54fdk9OTp+9PO1NZg6m/+T3v8N/1MrBKVqY4GWTa7xIM+sT4h8SQejCn8EDZUugul4rJw9/E9/r93GOgjt7S99IqsGKy/qB5CzqsOCh2vpnKxbLrEhT2WBWEwe6me4Na1UYQOxMUGgKJjopS0FaA+YdAuENYxTsSCm8xkCocauYZNE09PridE7IOBsVi4pR2mCxOLd0NeLgVQoxo8MAqf7i2UvOa/gz4iVQpd9jVTHVfSeYLM1Gfjk4MPrKly/HDK3D8ilJ/rCqId+qMGtzhEXoUv2OjBrbhYfD0Wrq8N4+OSjIcWu0q05/2eh0jUJQQ3XJbMjSlIVDZ4y0MFVheMY3MxhKPxhcGsFtcMmUeklRmMCs4Efh4dGjR1lipFNMs3NFQCvCpFgHqsNwJRgwO0lsLJgx97xvpF0nfog5x91gV1vKqpicVf56Gfm3ykQ3cWpeyLugcb2i9N11hwEYuGaAVkvqDim/MgysZnaVuDVAG7ctUqbd3WZhk0p77jW7kzcRbyNO283lcbXXrJrNOat8X165WGot9iwduZAZOREOMAXPZVDsSEr5uW06Ijuq870d8o366D2r6tTK7A7aGZzNVQQsQeNRBaIIX8pyXJrXlFq4Jo/BBCTRiDv/dt+7TmawZRsvSiNTa5vJ6ReVwg0gLXZw/MSDe/fIZqT1q6QNXXcKbHW6J2cDnESjxs98ioD5OGaCikWhDDLejD3mTRLoDbMZ2zqJhY99+jLoPe+dKoYIAUabGBof6MYAWgmQ462C5UiWTMpnllRTxiLTo5LgVH2z04YcrYHT3WXssBHf30njBnLtydeRV3Dlq51yTN3kp88wligopGua9VYCYCjx06fsdlG+2qg/4Go+3QgZEpEOzSCJQGljyTXMkZwoVLXeFp25P5ryaxf88OisN5lfCNQznF9uVJrdveJ4Opo9f/HRow+eHY/+5dMv/v2//w9/fe+vj09Pv3z85PmLk/myKGqgwyk2623BnZ++eOLQ3U6rjgw3I5L0/mfPTi9LVfxazWkaTibpOYakR990fHRu6JgY4yLbMFKcq8GSCxosWWbkJzNw55yYRAhxxyTJhB9rKbMFVJ8Qa/guSBYrW6Bx3I9HJjWLxZE2FYYZPA2mRL5JkemCMZflqk1lVGc8Dv+lYkTZ6XbjvHfASHuLI7GgHEmLMev3Bxhr85gvAAAGhDk4ukdhG8w13iX/HnES1oYcchad6NTodve23b2i3ByNzGx1sxXHtvsdKyOUg7vlndch5PBTs0qjl7QkAqSq8JxXVnuntUNZXK91qX2NVAGMz97O7mVBOKWI3GipQ6GBGE6bwXYswgR/MdY5HLjP00m9Vi55pxZw55y9mFR55ReHhcKr4aMs+fgqiWBU8aFlXUGCe067k9d4vHWl/NWX2NdVct31wrkdOaB19+jKeJZYL/l+pl/F2PuJh9Wov6ECW+XeJX4FGFjtLPn3v3rMQ1vfIOTYaokfbKC2CRuWHLuYdL57lM5Xrp7vt35OCqze5pQcLch013hqP2QPYZKTREJMPMWW9T1t3xcLJ3enWJwEnrgxZgTUMSxuw850mz/4lk4dkorSSNheJXgP407UlbPV3TG0QX9gs8bE8JwvtYPjQSFQ9zw08PhORWD4T4XUIZ14gIgS1SBaCIwCSOP5KY1DhaOLigLFLBdFJ0UR9jhEaafbYaVtP7+YTbdazRb9Ard+8fcYzhY3tgS+c+BX2LdyUwM4Mc2i0RHRh+3zJZqQEUUXIwFyJdX1YQ1m4h2Ep9luuDurC/IQdESIYzTqy5CbI5ABXlYcO7pwclQYZCA2Fd5bQkeGbs4YDRbNg9KcBppR6GXTeaDpKPhClQkUG9uXCmgWfgAGYSrCIQmRfDGT9g/v906OJ6NBOkWj7ICtZrfaaW+NgvMciQNJ7vT3+wfD02OngghkUK7UT16eFxtb5doWsJdDEgIBj8pG+uDBb3f3P2SuK0Sxk6eeH5+MLi639+/N+ufzwhRr1Wk78+u8Ud/8+OMP/+XTPwl6vb33cPtk2Hee1GTM4ntvCxuwfVVsPXn6NGRfs8WJI1QddUKA4cDzUhh60zNiXBh8k4/h9mDPcDKxb7fa/fMZtKDKlorpxiJU9vZwO+Q6zfZWo3VFQWZg4ZBWqoVyxi+gVMRkwEagHLNTKYrilFAdFutatpjNXmZWUPiD/ZY5tSahN63VSPNkw3Ni7l8c46iCs0HM+QNavRqx1MlDlXcIHR6L9TcFnRlZzIUijLNsgepnZKRmKv2grn9TxugbwalbB3sb5IjiHOUfmkZQ/+N+X9fS1zyTI4fD/79DlnNy9BKDsr3V0TXZ5N7eAxIZojB8T+hHlwtl4M16sGb8BKQFI8BiOapPKExH9Bq1ZgETOD/HSPFcw+2LDx7cD7RYZuRPYi1xsDdMVzA8vgyEECpqMMuD81eSOQK4HQYyLqFds9dX2jqk4/XatcqRCNDWrvVXORuompVWMpWP7FzrlYZzjbv7e4SBr1Vgv/pBr34qqx/Jr37IeYCvjldOZhfSHu8pLvhBPu1rsQ8lOY29w7arZLTzjX3mejeRnd+qnvvKd49ohwZzm7kXrzSbGgt7IKYuhEq6qBQiYiFDECqYq0tyjqJwaQoDxk4qgD4WwQYqbTsGnldEL5OxD/S4Es0LGw75+t3qdiUyUR847SiZdATrU6sGKUr2DWQAyqCFyAMCg/bQjQBVMV1oyraO6iicDxOQ0hEY0CCHrjpEKTmijdEJlkA6dZAECrG90+33BvyinZF5jwSgu3s4PURThQimAMy8xWy+1AvSqH0w5OEEXU/yqm63w6KXcQvAkFuqL2UiQPEoxm4ukCfsDw8gY9EyIsqQHMh5OFgBhNMr+FFd45QyJDo4Bwgh/3BqqQCAChuaYSqja5AoRhuiUw7kWtAReGCGToztDW0L12hyF85hRCcMntRlD5VtivOCefjo/sb0k8/++N/hFmvDIPdw/8AacChrt111Zsje4f3js/H/+3/9z/cePPw3B/fNSxnftnE5mI2YhI/Hg//0H/8dY5I//OEPpXKV7Gj78OHj58ft9l6xOt2sjZiY4A9y5EZDQHuNgl4PVk27NWCRkIUg8JhaswMJkEwxxOEcokyx6TMoaQwtnMTCq8TBW8YrU2uGDy04VsU8KqMAJJzjpgcDjyEySbGzvYUBhZW0VLRMQueVNuHZTJkmjE5iyvlXNnDDUAoGbIq2Y0Y6MeMJ+FirWBlCOZIdDeOSXdabBumL2p3wWcP/ucNtjKhcIgmyzEyXMvrSb17n4NSs9i0JOaYVHowL31avmFtrNQyJ2LZlrs7YMT3OaBNPC9jklP6piL3ksEdhZZlAAt4letmIKEQszmBHj36egUNKrVoIIP06IYBtli8BI51uzJ1Zl23MVeep51vAnCWDnFiuJJHu4E/32GGkA/M3+4ZH73KBVcn1hPTqMSquXR6vW0stGBFoc+ZaqV9J8tbYb41qhcDvLnar1lt6/CXAcGto7wsD9AtE/a2ZeEuP3zZwm4JXadO+lkVL+7XYKUDiVaaC0nl7RYdlqiUn/6hS9di/XNI5ke+x+7AI+HovCz4pb5qEGfI1LuoyFkR58WaRE0YNNmjCFSUFrLPdi3ti72ZE4GN/FCqLuQDCyuedl+KjMGHh49TucHKWb/fXBSthxEP71GG+ot2JALjzgFB3ukaulFHSOEgp4pQq0e04fCWtmTJ0PD7x9WLrp3BAGNT1KIEfw0ag3fpCWlhHGCdpiEEpMOid8/xNEp9C+JpV4oP7YG/v+dFzrwiBQHWJoSM7GkTEGso2oizjQo3gzzlW6DE/spyDLioJTtFrOOygJZm4YkQQR/8zZQESIxX2QD7P0Sf+U0gU8gMb4iSJHxMWLbPFDjol9l2tvrMzVQ1WVXfdv38v0XjYJmwIWq47+RJaQMl40/kir7c7U6fJViuNYh3V1z62iTXTYj5h6rS3s/1ydP7lF59dzsa//eiRw9v/2x8+ozMUXO/Z2ZOPPvkkAL48+i//7Z9O+tNmd5/qyhFjGxfTy+l5/6j/8cP7u3s7//CHf2w1yJwO/vGPn//Tly9++1f/pr6715wtS8fnF4N5t7nV7tQpFB1XjpkO462QVPDuW7IBYkZmWl0Aw8zi2beaoeQ6PT+xPuieMA3kdi6zTEQHjQarvjKZP7DyTY1R37tXEHwBZ03qQgJJwmdaEfjSMs75cllpyDKxolVK+mPhk+5MLU5SIFNIylIqamAyn2NxxObhCG4lU43hxggXYZ4Ayarws6E5FYZQDlMz2OaqljihkJP4keH5rQsrzYzEr2A21wJo/+Ef/oHFEXYTYLEUk33x6ckJwLpbO1aj3w520EIFoaUu3xqnv8VjufCvsOSU2tCyOq0t1FMRl8EhG+KBb7W2lDk9PcG6bbXZKsVWYG3wuZNPeuNu3ZIZctUP0Ja4cfZ2WLYIPs4RDrTWJDhFM02H3cwjcEPEgi7Xqx3H3udfCrz5xZsF7bvS55K2Y+yha7/ZT1YFUpnEHqVXika1dL2aXq91U+rrLSttY4pE12vNrAq+Y4nXDnZ9DH+2wHrht5r+5UCyPsz3hQHKYzYHfjD5vo6F9zCdl2PmS+z+CQPXzE3eU2ydNtkV3rAJr0VdLvOabSgxQNpP2I6tU1PS6I6N1EZpJ/UVi9j7UiT+QaG5ngstmxkOqgd8B+sZdBhhIKFBpHwZJ84mymhZvr3V0Uma9YglkAB16jT8k0ns8S2is4RUqVYjF0GMMD1RbFmIcCykLFcMRHx+1228QxKiMYuQ8vmTpwRfO3t7cZx4shFhr81UA0FFBT/66BOheOEH9XDXmtGBzSNWJsyc0se9RBCbrTZn/Hv3DzA3hFwYCARVefQso90jgJVkwIvXg3T0Qw6xDIqNOgp+43TNsJx2aJizT5NOB9pjyLM5DQN7qrPTU8gV785bF9yOmxFXWkd4NRqowFVMIX42voajbjIBVliaaEEm+HGpgJcTo0IAUXQHklxFdEQ2LI4D5wGUTqYKiyjFcCN7+zv/7W//v//1f/v//c3v4+jNZ8+Pi5cbv/n4E6qbk5OzL7+s0uUxFRpNWHbPEM1Ou3n08vm4f/bR/QNGXAjt/p7Ef/m7//y//U//1//7v/2b//T3f/zT6WD0J1MQ4akAvgkO8QBxeLy7PeJh5nPWPDRTIWvhgGQ5KYzwHx0fYy/QdYysCSFr22wGu6OAuzLepvPLIpIQLOEhMHNWpjQMyLRCcC0seh48eGDK1PLWLGNGvTV9OAYnQsCekkHpkwldAHlNzmOdQ1euC/k49eoGwr+kDCptb4c4ajQgDoErPoYYHXPU651j3QhJZKoObxgFF20kIWnMSzpXTu8KANiB8y5jjFgA1SpJj8LJaI2ZTTUfrkKDzYBdFb8RkScVJkLD+rD0VyACJZWKonxqHHc5rQg3RW537Ox67RsO1h8k0qPRUIAov1M6R8b7wnX7mRi7b5WrcqF3dspLL0yc02IzHSCBGywS06HRgPdAuFYEGCEi3bJk7AR+g7rPO4w7VkRfySqIbVEJI5kyvyF0zujNdxhzqZIvmes50jdzEe/zWwiU0OwN0/X1q9zIO33PY3ynh/BzAf++MED5V7H+s/m5MP4T92s38LNPG0TIThIGwuEl7xE5B2GwQeQry4FS+WBTJHzJpSaCLuac9d8bAxRXnGV9c0V025Bkf33lDc6zjshFSEvGE+efh+bF53Nnl/9RbJrFmZB4ofRByHmXc9DlA8yCpFGpDoZjEpp0RFiIskkRWHbYcymJVExQXe10W7QVy0vHW6LfviWLaC1zCoYOSGOJZ1OxJDAPcTjiTbZhV2f4RD1BgMIUSaOwUq44FwmvhYbVlCE4Z4Ik7grnZYacuAfGzHGU0mzKxYVPUZyNvQzllMK2fkGfbfd0e9rCANn3edLXmgxFw8HHK5/jSKnyhonZIgsh0BBozixRRkSowsQbKcw9p86jjJfTfLm9f+Dkc+O21S8EwmZsFAeshwRCaCKPGMPpaASLHMXY4ri04MtekLqIxpIkZLil00GP9uTw8MArEQuVyVIQfBuCKo3M1ZoNIgNM1ctnz1WFgQCg0zYAcLLqsEIy+cdKnJ+ejvq90sbVYNirN/767Gzy2Z+eXZbMXYEZkMS/fPnyww8fiTp9PjyrFS2Si1q50G3VHh7+2w8fPfp//X/+l8+fHo+vqhel5pOnzx6/OPr9v/33x+PpV09f/PGPf/zd734H4ThIbFNenL0zh53VHZWF4nJnM62G5rjWdqNOeYN9jBlvNYQ5xy3NpwsGUoI6uihMOWdhEfCgw8qEBTFHQ/IVSkNxBGnMNGVVEBhCXafVKNDlsB6DzJBwiP4nYgD+r4DzGI76z87D9sjwt3eFpgz1EIbACqa7vJhdlDfKnWaHDZnpCIVjknU4Xk0khWaZsOfSjAc3sVjQVZGrBQ/nRAi2wQ51n4Vtsukyb+7GbXLZ3JgsrM6ec2Tv3+OKaOKKBfY6C8qs8XDy4PABTsuvxc9Yv9XE3eDsIY1A0cXESgLrRl3XaNb8ylyM1coh4rpQ0U+pvLfj5BU4VN6mgSPHWkEvS3lrwA4RIYuWl9X6VrF8efT0xW53m50eUZP14FdsffohOURuOh7YEmhIOQwSfWGdNGKMzKIePby/v+/wsogy6seS5tQOs9qObFMR2yA05mGBlfaRWMuvufL25ceS33l0rT9Ky/FWwiUBjFTqumTKvs7IBV7TzTuS9a7D/zOi+X1hgH5GFP/sXaef+u2vorw7JNbGdnTthBW0Qvybm20lJ/A2uQUDsYm450d3WxgaLCe3ll+550um8i5bk5LYFK0HlzBzdjSjAZwDtQvn7Qrjn/iYDiuFuk9SHSyvBJtBf69K9XDPoQJgm6MKCh0FQhHCfDiEQCyFkpA/Topw2CQA6dRIawhp0HXkM7rDIlxxpdlxjoWdXRnEj1nJ85cvY8tltTqcCAYNQoTw4f1DMn5dn56c8WRRFwL0OJmHWYbqDDJQICAY4HhEBxJWSiDBzwHPGLWPILkrX5mHySckZLAVy0hQQOFEA1DZeMtNTJhmmeCHL1wg33T98sR3nutGIby0BuAbDJGu4MYSepF/1AvkKHAghzyNXblDCabB3IBWdxn5YAiSFo75185lFxUsYHBvisFS9FUMHWithfuLUzgiYGM43FewtKEuKTcUE6KJIZRi0ho0InIajROM/fM//RMixsaZsqnWaP3+r/7q+Jggh+HwlNG2uHo46ocPDkaT8dHJOcOqf/jDH3cOH5Qbk1pru1TrPzs6+d1G8eOPP8bvwg98ktDgBf7q97+D8GdPX8DMeLIYjibkKzCmawM3C+hriNGSK7tRhL1OHEJypDrO1KVYHql2rAqcEBsn6LLeDEQVoo5op98n3CApVEX5qM7iJkkrAy01vmgm4jqwEF5CSJ74SVyeAkBJM47lNU0Kgx+HdHCw7xVvcBrdjGQMCa7ccz7ARHnTpCSYg5+jaqSMazWxDtKaev7yBUyC0A+kVY9A57Sr0hJ+OypGj1dX9GtkPM4QwzNbjX6T9ZAxhd3byfkZZuBwb5+xv+Hj9rDhInludWIsYjAOBxN6ZD9Oh4GQubaEqJ6En78QlL1z1mdOsKfhE5696ufTH38Fe+zbYA9bz2IJOwVXsKDxxbhnvJPhAANDIxbuY9ZvORTTrOZ90WCE9vZ3Qag7Q8hTZpguY4S3nM73vLfIX13mSDrfc2KVVmWVzuVzm6ti1y2n3Wu9ZM5fz8nV7+7vAwbuGKBf+Syv/7B9qxmtH3zad77+eMr7jkyF89tVMYk14U7gyk6XS7rH3hF50ab7el+5qfTy67cyfVqLxG9D1I5aQcPwIHy/q3VUoVyIrRyFY6ppO29vxdbvmFaE2ScyQ0sf2/ZAH9DinTDORQXZIujZEKgVRHb2MUqpxBQVbcMGffHFY9+T6IR25DhDiSe8/FCHVWdXxarO7d/YArHe1A0+LH34np+fIkVBZYU0botkeCEiHVpFJUED4JVim/WGM8a1TGZETkYXgEAiADIQS7i0xQMMA5iNiw0K0ii/0GAAoJEahASU3D2LKMotrnA4kjFpD7CDsi6vnEAJUQLGgEdmEsNssA3COTmJ3c6PR2GJEgMvh4sTznE06NP1GJQRUbsYOye7MKqttFGz4+MTRMsQYJvI5+j0xIwATDGcqio6QpLNCKKEG2pwaoPxcUS9g3DnpycL9RmPKuozYioMquMy7jlDrfB3vf5Re7p7eK9LJjDszR9/8ScRDv/df/ifqk3nqE8++M3H/OE/e/zZyfGnMye7t7rOU2A8vLwq9gcjUgnH0D56RPUYMjNWPljdTMghU4IEK0IoFDd4VKH9SKmFQVCEU/OPsRQ5xIPDe5tbZfgJsUqyVjYcmFTdACXoBFnKxMQldzBijxg77AhfmaSe5jfQmMyZg2lIx4CYSgXMrIOuNALteBpQiWDEDN3UK2/WQOeQUSb+cjoOLMNfji7xfC7pEBVWSWGau91dLRNhjkd9MQXazfqFQ0iC1b5klYx1MNHhChZdnxPLgJBwy5CtTKuFvleDVpS0wTsOj9LKAK0l8qjMW2CWjRFbRtAlgIA2M7vc3OsUin0xgIgpxbEcjJ3cfoEtcybHxdKPj6wn3PdEup5NR3zy2Y8Nyf8uyKgKozhmeBb84kLQB8FC6cEYS1XPcflXcVTwk6++/Jt/99cEYwM/MzNbKW9vh2kbdR6AP//888Gwb9Xt7O5BlVNYYtNIWlcIT9B+/YnlMV/GmBMKR/l05YR7frvKz29v3b11Ba5it7luYVXm1ZzVq7vEt2Hg25C2mqxvq/iLyn8fGaB3a4Z+5HKxTPPOktuRNnyXLzM5mQuRUEzaZRPPu6cy8uN+45iqjCu3k6vE4/XWFC2sX7l6biG2nfR9pnGesIgQjoMbFs9bop12+vKjSrtywngh/JNt2UhVtRoH1KfHYJXQHtocdxfyQ5zENUk+lUGjVmVpiwNAF33w6wWhQTDQDiTKodkuFJUx7Pb2FupFpG+kYENjEFQCBAlldIegonPaz+xLfJQLgdJsaTNlXqFqgc8kZVEF5xE2KskjJr68ExohEJlkFmrHPe+fecTxGIjL2LWpYvAxKXAiqhAisjgqIfyKofysd45s7Ha7mB5lli0akzkMAsDoZGI+sqgAbEYa6gQUmKDJYCaBExIucNKpiQ8A2tAakiCha5cLujOMEEiQUjDo3YrQZqLdYSUDvTgzfQE1Gr/2xzZv+Oc4PdQJtmGHfHXBspi7kMIwCaOf/ulPMdgNDOXs/PjlE/zIckhbtfHgoLt774OPPvznL796fnz2//xf/nb34FF778Hssipg0PHZYGvbQRftar3R3XHaFzFDnNuAwaw7ptbxXqMRkRtOETBQV7AQlImYBYFGmNG78XfbIURRLDCWNCwaifWcJFUh9Unxn2DCwNWiOrQSlIEfS16+abJmYJjoSJnMLWlNRxpUUg4Y9AUJSupLvhxlwCChEa256+tiTidJCBcaJcUUxibKZ9/D/NlxE052M5dqYdIM5OyEnRYD7YoZISaBWyI3cDJA7nS3HW+iLkhgW4NqUWi6e5SPnwKwKSMYM2SZZh8YIcVJl7dffPEFGFp19tTCFPnBlwZ9jpTzsHGbzHuzkXAA7a2IpAA8kAeDUhA9S9ArB55sb211j49PyeQoSzFtJ0fHeCddUO2VCxErioDHI/WWjszX/s423/jFctzotFXvNBvNhkP9WE0Nnc3iI0QAUtA6CRhWCYIgUHVX+gl8/ckkP1/yvV2lb7K/8XdVQO6qqZzO5VLj+fPvWh32jfp3D38xBtZRfavSOuZXr76j/KrMz5J4Xxig9Ql47Qz9LNj/CTpNUp/YO4JXSayMPTq26TiPOZRf8cJXdvpn/0klr2/AS6kA096Rto/rdH6lBUQxsl4nBPJWdff0pZ27Dd+RqV0unQRJxWDrt+OzT7Vlb9Y3S0mJQGBg00S/T8/PfFNrBl+GAtvBd5ttvmAYnXQ2tSjMU2a5RCZgm11EvKB82KddlWyfC/e9B/e/evKM9QbqiJwgE/t7O8yD+BMR4JMA4J/IMHysfvX0OWdgYPgCZqYaLNF8/vzlERSJ9YeOQA5zaTomAgMCfeRnm9+vszMvLo5Oz4wUjYSH7b0wtWaNu1hGDtak76N6EszNg4cfaIdkiyoBefAd3N2uilqMrtAGmCmUldrLq5P5yVkvXK+RJeognjjcnrBDOiWIMRBOc8hg4D1RBQoFeMYCDvs9HXH4ErHwxfNn2KJGs7Wzd4Cb4A4GV9QojJAMM3yjkicU+t1s8qQLsu0yKZpSAJwGxeeIiXql1A4HPceu18rCFVg41E0MeK8uMGfBO7EToqxBeg/3duuI3t6jo5Pjw73tvd2t//Vv/+7l2eOXg/LZaDm4KBXGy0fd/YtK6/nJoOTkV/GkC8WTXl+oZZQe18h6BKUnrSNFWFyGgciXX36Jg+H1Ld1Ew1O0gn/1r/4VOg117thcNNUZnGiqQbmUNBZDo82BtLTGQvkHYXgXa5HkTBpnkLjVhlfGqxYJEIFZ8ArJu9B8qa5ZVbTmrikVo4Vk3UKPG7UwnSmQtPJWIG1mpRkec6ybgxFfhCzEpRcs2f7enqPs4ceJ9PuHBw/vP5AGT3Wz7agTXyUff/QBTIKhUnc2b7HNKqtcBh6tmV54exHFaM0APeqFdbx+2TiRR1qchr8kDBsO93YPLufxw6G0MkFxPDu1r0NSy2Wz726hWqUCQRiUIeB1InM658xIomnUtgcOjKNBfDz4rbGpmo5Hlmr0HkK3SyGFRPTipogfanI7mFQ/++Lzf/rnf67923+Dl6fIBhi85Xu102IDRFJldLrWI77czObIn1BqLmRCr8vac0lEsZt0enN9y/nrOdKrKqv0qoDyrpudLHrRvPIpc1XqLvF+YeCtM0B5Rf6MSH0tAK/NfFNAfvcvar1rJT1+d/nvC9V6a7caz93JtNf41EfabWSZB3L3NuXHklg1kqDNm8X1ZnQLnlXJXGvVhYq2Mxu0y1aeH22vGC8bInL72WefPXnyZOvgQ1zC4Cy8wUNL04goNRiU5Tw5njicKJ2dbse33WdqB1RlLhcj7AINFfK3mPXVvbZeCktbXsRB0iSMCH9jKxe8BxhMFhASHI3WABN0rt2gsKByypsv5Qi2A+UQsllH2CDteKU8oYmTKZMAack7DCULEkUutbykjLOh685GL4Em4cB0HedL4N2cnzoSNVFeSKcyWrIJCOqY+9VX0C0zsojRMehBk1CdQM5gaKc2BKTChs1VpxcCAPhB5yIUZEwcttJh3E6AopLgYrNBVdEnnCLPIawZMB7uFZ10T5VnIgwcBcI3qAvtOgI2biyTdkAaAlS7b2/HefJydE2+RN7jkE7WwBgyPAoNDwd16IJ5LGCj1fnkd789PQ6zm1q9+eBh48kXn/7xsy96w5CTPOgcTp9PStXu4YNPGu3uH//0ZVj+MktxFC5pwHLpbBBgQKm5M0dZSRrrFG/HjoSSq7LZrGySgUAO+JlIm1kwg9AiIWIguYFeaJeALkpGozPefBmFyYJbl2k9P4tGoFemTl1Gl6ascXh4KIGBUEDL0poNMJLy16NmdWqWAex1f9jj+I4f2N3Zh+HZPFilna0uEyZrGTNEQqMj8jnQYn3Qe01ZD3ScdF7SvMQBCgYCEtACXstwol+g+gKQjmIp4rnerRkNgkEBj8SfRqeAWdNF+l1UY3S1Zl6WyuO+lOyfMVs/9XVB3Jon2iMTbyo5SHv8+LF+jVTknhORFTWVTONfvnj24vlTtcgO+UnioixqoRjiS2DYp8KLuASY8nSevCPe/vZv/45Q51/95re+J1hK4Zv9gOiJfQ/RdxuRAeKqAW8IwL5g25WOE7GSPa62lPUEtOS5W7+vCsjMBVY5HlfpXOW6QJz/eq1ry03mkrlwLrPexV36B2BgHfm/cJS+dQboB6DvjVT5ZeL9FlS3Hn/8wG/95lePOspp+wuugLAh77AStl3bqwJe5csGIeG+Ak9daTkSrgxnTuQyKfvr/FVFVVKtUAHY71wVMWvSdmcTtAXLV5ezt13X/q6w/XCG6No4G43yJtvV0PLk3omP7Jt2dgB4S43hMxR7YVdFIL31QVlKm7H9/d7hA04qSILdXwsoGZp97/4D+x+VFUi8Yv5yuLmfuBwB6MJZl74MVLpTQMKnM0KITnODrzeaDtf68ovHB/cOEDRlaCRoKzAHXKa58KiyuVFiyIIYoM30Czg8ohcjIchxfMTpWc+o8Qflynw5ElCHrof7VXhrmwI4hnYkAaJ3d/e2tsizloXpTGhH/A0bElILH93GGZbObIaqzshqUoVwYGatITqzukxLScMWl0GbhaURdAnviA+CivGExcm0sldEO1liKRxuQJUqnyQSMoyawaJt4Sc1ig90xcwO5kNgZCeRQ66YOuyhm5sUlJtnvSeffvrpfUGEy5vNVvvs5ROu2a1Gd1GqHPeH/Y3T3/7urz75fRNNPR4+3t37qLPzYasze3n2ubMoCo630BSZWbPhzAy8KZtfAh5EHlsHYVaS2bEY8Fg65fDFGZyyxowYMvTi3oh82K0HzkvlznZLAk8Z+lDBmQg3l4t+78xKJ6YyrXKGg7DP9ZYcgzENhk8XJlEOTZs2PbpbhLGYk4W16TAvaQFHqGXrBG8ELRzBLMLlYlaIcDeid7KkmXMXlGPdCBhkzRgdgyyGxi4ALIvzwE63g2uuxlH24uJgLsNzcETCN+ddH4vfWjG0k5cnShIGnk/OC5uhXMPKWNKkOMoHeGGMH3bi3MXMkVHoRQuO6MInWh8MolhlcaxTvt304wpeH0PTc8BcfIQUT3s9v52BGI/DHrHLYOMc+PnnHzsDbozXIYUyD8m0Jt1Pj450YXkxoIYWLXPaZM3H73KT8lrMKtbSiScjC/zf/+vfA/h/+E//sb3VAZ7yREpMtazDEvP6SqVVbmmB/MxY/JwVoC5MksUQAmWcr++Hudh6Tk7L1/4q/WoBOasCq2Lav5X52op3mT8YA+8Ken+1DNAPnrkfX9Hcf9vP9bWNf9/yr23kOzK1n7vIm8UqjTNIzFA4rdiMvF0VSDWCHkez2IokSskF3Fd95fL5MTcbxdN+5FWubvP1/WfHQVNjy+MtjCPZrH7yyUe/+c3Hn311Is+BXKwcbGUkOyFsuPKxeEFvZEve3d1XBekCpF1e+3RAidKT1S82NoXTrfmgQ7l9T+qa75h+g5XBDYRVK2/kEAVhunItbzVol/dF67gpgCGBp6fnSqJ/eWdURYMIsRy1HNju+1i4GgKbYBmqLDqDYkGEgsogReiWHL1kvkEt7YuqJyF6zNbWNoQQ72gfdQQAUBFUfJJHmaDCAqJbSAKeKGPP8FFl0+RR44gIsCndYAlW0VeNHz1/4SCJyzh+yge2fyGuQL9gG7vgnHD2R/zD+r2+aWxs7wbVTGdjaTa3rDwYoF0X8IaNILGAavQSnDV6rqsNHvtOvUCCUczuduP+B/exRJ999jme0kFpRnd28twZ5+B8eT7YbGwffPAxs5/p4PTiy2csQ87OB6fnjnYv7e4f8MVrJbuQYe801gxPPsZbhfApC1TP57AKh8+ev8D9wN/pQkQfqiDhjy+whWx3AAbPQM2YJMqLwSdzKFNpFMEQb2ywZTFfLNVzs4apDFRLYMvwrBoxa9aGwsoAwHgBEGs4kVXYhha1sq4WzvUI5kXyFa90oBZP42pPhhNckZXOVseR8SmiT6h+AKkREgdQ4X2BpZy+tCkfJKbAjy8vgMQlBCtwWbTGgsP2YaJHXP2sHitHRWMPtNBEJWt9q5FNtJ8pjLkbiF9q/EYCkQROfWmSJ92BJA9NO5g5KldhHjTOSIrjPFVdBkmxUTFCL8aM0AXjgKkd6w3cMzmQERkILGmWoJPLAsd/Jvk+S4RyNDUuXyI6/6d/+Wfr/z/+H/7GpwnWykDUimNhBAnwWaNQiuMVTKPfAkY99h+tRoteGUgaS+S4Yp2k1+uZ+ZW7zO8o4FUumRPGpbzLuNxXFVfFVs3eJb4DAxl131HgXXl1xwC9lZn6Xj8ni+mNAPEd7YDHhRHxk/fLt904DcoWbGNSKxNCu5S0ty57kPI5IZ3B8zbvTamx2FYkbD8r4D0q41qVl4jNL/lDoccede1716e507iYcTx+SSG1qFeavt1tjgqIZMsdt1QJMQyn3Exy1LLjMydw3MFX469YZrY3Ipw/2QdvbWZEaAMaYPsGMwAYVRgdDsNWTsDAY+jw8J69G/mnsjk/7yupvOFIkDpQl3iLAj56xMpI1Lhzx6sTwHgrtC4ZGeKXx05yYMt26RHbCAbdqYgFQgkRVP0aqbdUZqQ1SLgPX8AbTjtOYqJiCF0eK3C+WuISvTw6po4zBYhHUBd8jJOtkgQi6MmcIIq+gzIt3JsRS+3s7HYhCruYv8UVGvbOzUeYBYWR0IhMyPc3GJhAj4esSc4RQZY+F9MIns1QWlMJyCAALkgzUp0ao1fUSdq/qsdZDURUoeMrlYeDMZbFBBIKwCGHO75QHNuJrw7vP3zy+LPjk7NhfXN84RDy2uyy+PL8bD7oYQHnLLPPJuXP/1goV+/f22fIJIDAzsFOMEWnZ8I4xTIrYEFmCGVIm5Ihs65xJNRqA/ZVhRJlDxRhMBulmiqwCJM8utlcUyI5lgPw+we75GEUTLU2HWssAwRbiATiFm5WsDRIh4ZaFRiavBLIfr56HNbBWjNkryz2PFkQwjS7U8Bzx7JneySkIS5HMQbXab6CxbRohZi8KIelDmOos5Pjne5uaSvsnSnyHFlP8mQRKs+nP80gHsvMhJ+d2MqudgtXdzWajsjh/BZx8A58dfgaBqJUiSV9dnZk/evL5Fqtkyn5ULdcCSkgxvTsrOd0Nv2ID4nHI9LhscWc5pyMJwl+8vwGkv3CKOrC9s8gLmfYOLZKpWosBqrNdF565qv8NDSla+D5AUIOuZLfLACgF7qI6zTbPz2xigle59Rh+JhkCe5bxHhF5vQj5e+mM/FNiw3RLoLWuNsQIDl351EXhJRe5W3DoAK44OZlfmM/kS8zF5B2SadiaSPKWTf5629z+qbw9e6kX18LXuUWUr1373YzqLcI+TuNnz+LlzsG6M+i6J0v4EfiMozVZmEPsqXag2yU9qCcn4utl8xpFVeJVSNyVq2t0uuYyltYrrhqP29/enchJ7v7O4QNPEd8HMeGGCGMwxio2MH/VFB9sUfQRYonO75tl5zGpReN22S1tjG73iJX+6lXCtjTET/9Gqa6NjvyACTEYaoelWEKiopv7WyTslCmPHr0IXKrjLoq4pDIEZjg6EU+6vLBBx/s7e5TaaHoKJNi8qFPAYC51ELwmCK5IxgAYF2BFZIQnlGO8tSAdCjSJBNaAJ5MvBffH2mNsHwRxE4m/GSkZaYETmrtJlLEAsNI8TckZBRbsEFN16nX43O8ioYTGjgcIzhCYQOoK7QpJiK/dUofU81amACJNkIs4I3uVlNQykRZ9UWWo7DhZAQGPjdCVDY6Hm1vhfFHT3jiOOzhYjovPXn2HOcHDxggX+tUUee//W3wJ40WaZCASX/87PN//fsq9zNENxyY6pXOVr27d6/abPQH59NlBL9hmqs03NJOwXa9vqNTfAnMWA/kHEZNnnH/wQNnRogGSLkTaq/7BzBmIiCWXxaOU62MLmMBPLRDoEx4tvxQbpemvIIdKyGWQXL68yGgookzFuWBxAQHwoOxTk5nDjLDBITXOulXOrxX3Twjfjoxp4x+ZwSa+nKSRtVvDDxn5yfKgAE87nrUtbS+DI0luXiDEVoiMBeriIBHd5hsplPmtNHsHDPgPz3DcA2GI4ZIXOg5sGPD4jj1ag2Popam1IlBXRbwc1gWna4Ga+ErXE0SUMVACF1GEVE3k0cbhEC134JJVCD/hC2w7d04aA+DZl4arfB3szbkP4rzzmbBWHMxjPDrYfQmzKPjUKyIIWP4y7njdLGj7IfCwDzJLHHbDPNrm34owfG4dAQZuFNgSIdpl0xgJXbHoL7tyj8Hb1eJ15b0No8lJ9bLyIEuDHHOzO3k+3qxu/R7hYE7BuhXPt15I7ApGGdOu9uO7XpBGUL04KMwWJxcJqPDTiFfSdfKDf4WpnL52IBf4ZCi1trlMXbqdNmmpX3EFyv19MEdoiYyb9AhRSEAIKW4vJqKna+DUqh+UGWfxccvj9AS54LNpxPnT9ma8xeqTEKgpt0e4Xclxq6bXLi581B4EBqhPmI0c1hHpDFIm/Ua9yQmKxxkCDCceEq2pDVtUnVhCQlbWHXw0cUn2aNRDm9RVlwC9/yHDx+IIoPfMBAkBC3h92u4qCO+RfsIMcmNLR6tIg/SezqGlePYTBcRvye5T2vW0Az6q+fPkBxp84EsKYMoQBEmAGFDpjP9NjjSI3SfGgX5CKHQgi90UHS+Yw5IZRtEo2ByIQ6XSL+w4fBVRQsLoa41xfgInujCqEXQX3THKU9IOroEAKhWJnuBYf5Yjzx99qR/3gu6uhGeUE5BZeE0mjjpkz3SBYC72yyVRF1cEozt7O9xMdvrdE4HM3EVQ5k06R2dHk0X01a33tlp1ZqV+4/unfbO//4f/5HSxOi2IhjMhqjBDlilk7Lk0FqN4y10h72L1ZIsSGQG6mazzz/7095OlwBKTqbcFUR0g9Jnyu7bWsoBijB+fI8IKurGu7w4OnqhQePsn59uChhVLNQ7LcbqulBMBiftnZ2HcBVKsXBOYksfCzJGys/L+QxJWhC8XJ3YJC6FBQPyoyJFcboDtFPjVssdbQ57EW6R7zcIocG0Rjts7RInRHvI9ou/W1hczRacBNVlDrMpetOwR3DSbHWsIxI1rA/+B7jmlw14GMgT4RRFHgoVJ2Wl5bd/eE+PYhwIPhkq2tDR1g0JQkD44OEjv630s5jrEg4tWugFkgIBWMRxCG2dMvTGVmxrqyti9ma9aSU0BAESVuDySssSwCSgJTbzQ1AyZkfWpSjbJqGyDLPo5IUmFkO5RN5pQg2Mss1PGfNMghxCZLxfiv4MJ6KQhrrLkWZ2jGs5UOxU+fLGtUrfZF/zQFHlFSFQzlyVlJCT23GXzmsmp1fVV4/rFe/S7wMG7higX/8s59+/H7/LaD3aGe3L9q+kFIv3mQ3KBWyRiknLz9iJEjcsVG4h5+f06u2qcE7k1qQltJkvhX30+WC0Fek0gZA8spL+JcxXk1UExswRWMiAoyUzJMQzdnCKM7Vs3OiQzbaWTmUyFryUvdymHBtysl0Nunwjw8e7JHOiXbYRpAisahFsR6MrwB4F/6AFDaL65DfIibeQk7d4hJN9thY+/9MXPpeRkIw6AwGSkioqkzdWNEkZwGgZ2OxKMCTch1VJ7NFSO9NRfMF7izNDAjKuEjWdojEYIPDrPRGkUEuRCmjcYx7y2dk5taIePXqrsI394N4hnZ1oATrCb2kT26Qj2CB/oGSMRJFkZZNHGPUKYERzmRdD/bSYXeCcYBUM4AG8wsCTJtoR7HgUSDnTY5FMKdykw8Bkf3cv5GTFym536+npi2Aci38DOSyCQ1nEzcdcYLMoTZoNTEyv3++NxmFOVi49f/oVo3K6qat6KIGgnYub9gk4IFCP+A9U2XQj0MJOtulogikskR4IvQ0chB8NRsJVH7OCT/6GZkQOXKHW2vQojaExLuUzUwVjmFTlTZNR4QnBHF5PSflrXOx7IKfd6mDFaJu2trtiDAqmrKIygdhL7EsdLOIjBv9QCodzvRgvs2UwK5Avkc23N4qg1WxIRkLgWu6zABNuitRHPCWs7WJJuoNb9Hb44sj8cXOrb9a7uzs4VD7q+JkwimHWg59cBo9ugvQIUToFkoqxSIIdvwiGphy6vK0tsk7StUDUdhdXhLEOCZ+SJlcta0+9xySaRFzCW5c2F1dzCsdxuOxfOH4OkpZX5/OLwkKs88XVeX/QWFzx8HdyDKd5bDDrLfrzMJ2OY2ksMJtGfMzgF4mXcT8AkC/8OBQl3mPFiKTPrWB8Ap+quMdDPv7iZs/J+V7lKxdeT8tZ1b0pFe3kNJx5e6tAVFlTq60Km69VetXUO5HIA3x7oL7t9t8e5H9hy3cM0F+IqHe4WF7E6R6k0SVtU7Yt5t0zPsnSTipfAsGwI+SS+Z53h9zOKkdmLi/hWpXPxdzztpLf5gIyw/WZUGWjiAAkqhz2Luq6gyeff96sx8mdjJ19ATvRCE9gf/fVzskE/Q5XmCUnlwmzIbTKLq8jMGMS+K1oRGtGhwUhYEEw7M2k/75yfXOPJqei2AxeniC3TJBxFRgGBB0LpKnTs3MOTQBBYLrbHf0qBnJdINQffvgh7QAf8/FogItC6vTZ6511WlvEJIrxkC/dDzbCuDJuEYE4g/wivJQ1dXp0jOLhcuzOnMCpxpwphpoixgDWnS4EuUba0W/nVuKWnKfU3NtbXrLzLSrWOz17fia0TNg2LZhU899h3Rxnr1b4V2Mv0OxBd+j4VSI00xjSJpoJ/l1V7vp84k5BslhOcUpIeSDNQWalCjIvjXsAA6IFPFwgkFjV8OAmMKQ7wxkgaeaB3xDWAafgYHvFSsLATOmhaN74wbEiL2yzkTn4jcMuyEp+/2/+PcmEQ+nFvKPZm49HjvLa39qmytnZ3lcdwHSRwutZimADBusWM2jigloThzRqjnfYrNbBw7iYNEG8GcFpzLcAmKDF8uAJVIGTfoR3mpijVjsc/rVPwoHjbhTJFqsKxDq8SqE4rQoe6jQiIE88BMGPMYZB7nxxJKbRKNiaxunxJ5/8pt3gTy7AdwF3aVnPkheeoDiGHAfHlQkgYWmZ13Be9oJnWlfs9Bm2EwYG3RU+ZwFX9GL4y8HGcIRtsAAKXPCs6OWy2e7QIZ6JlDwaVltxuArGNAi535bvhTlutYDNwBLhPJzUu+WkUn5c/f6DB/fgUO8KO5kFQAItgpYi0ajjuLxQBO9EU0kEkti1abPh51/mvShU0mAwoV6mUmRMF6zMeOKX5NCV07MBJAmA8OTZU78gimXeaVBxfHpGFuvQMQyO8mVqTmaFAm876A9fF+K9logAeY2BxKV3eA78U8Mll8Ocv7p7FW9v2Br5oM1vc+K6wDflzavq2peOFtI9z8IqRwty3B2ht0rnZldlJO6u9w0DdwzQr3zG845gkBKrH7wEq0Xk395t48AI5c1R/mofWRXOLXjMLawavIW4Vflb+blrzeYNCCCsHwgc5svgUZKtS3BjzGX4P0+HIbZp1JrpM9LB43FUkwuovmVt8Yh0nPmwuYk5cNepIXiljE9wTAbFFrKXSZGtX2u2XRwJJ6f0PRoGvMQVyjtywWc0ZgwLQtWFfSF7wPqcnZ2y1MHrcJZRQPsyNfLhRx9QClQug8VBV3AD6K63ulPYK9wD4iQfiyNTdUxShLepR6RgkMAAuq6uWuy7YVLXimlQQiaRUoZcjrcuVdT1NnMn0Jg8sZNqhrgi2R4ZnUt1mgVw8kODCoRfRzEpnIoS7dEU89uF42AZtF5egY0Cgi4MCcfACcGkrpGCHPXSmkmBZOBKgwGlcwMStH/44cfqYv7CMqawpFo5ONzT3d7uLvbh6dMn48vasrh572DXWfGPH39BEPav//rfMYX/8vPP8K//l//x/4TIzubhYP/kSRggg40G5lyYgE4rYxse9LW8cK54sOmiH3jknu9AVubGclymG9qBp2vDAS04yeRgD/AyXXLcjQhutSxxdhpVTJMZabZCS2XIMKwFaeUhTe+729iypnZiQSbNESCDjUpHrBu1hUz5CvOyVIRzIKkbc1cOsx70lt3V5XzjvDfwuwqWTkDCsNvZcPYpiYcohJNxsJtYsBSepigQNtkMODVCsqL3AAnjkn4nDnrVhe6sN95aGjrY2yNDhQeNY5pxIKAld1QRVhWzbGKYycjJqLWms+HAwWovo3yBfY940Pz3hUofCrXpRz7sDTFVwaZvlJ0NI/6CpmiK0yoq4trD9w3v5fh6v51qTcjr8eDcVIlLRVm6eVGbFceBp2QvBZO+bIzW4gn7oThbTkvBhcjMF5znS2YoZtOVC+S0Yq9mvvpKjlr5nruAIo+5FzkSjOdyAa9y4dx4Tnv1s1/fDck6Kn4YqG+7/R8G1c9V644B+rkw/5P2m382eTvIn1XSuAqbgi0VCyTh8ttQUo77au8AaE5LrH5+Gfp4vN6aXjMcDcrVZu4xp9kMiUKtC5QD4ZFph/R57NFmbd/P2yXJeoIoWrCD1sth0UyfZC/mCoWYUQ7ZzRH9tMGGHXe+NM0DHj0QLxghUZ7nkI9UYXLqi6YeE6FSd3lyduorH/FAJ1hgIAxe2bU9YmWunscnI/4Q3wgSvYSSAy3ZqNHXkDb5GhbMBnUBoV7QLfCjMViKi3JYj7pgGIrEe9YvzkOOAlpDkIaTOIFcd0Gw6aF8QHPqCZ1OHT/H2Jln8pIGolAgcEGNUBmjRqfJgQw894u4lRshxqBjIW9IVJz5MJ3bJKJQ0xIGl8sk2pGW0ZTZ8IHOvGU5L4gZLWB0mC13t6HOcfcmhW8VIMFsIgLVrjipwFmhW8YLeHqbS6dgJZdsj8oMRv1JL8RFg/Pewc62tQP4LZFp2sl+tt87fvHcGKthq1R7sLfrBPOtvYPxfHneG8+WF48++ICMAUJevnwO1LA4C7MQUoxYh2JzG6zEYj4GPPMv3JDRId5mQ4zHsMG5XJ4cH41HQlNGdACXdiBcp2aN16N/56cnBF6kG4Qf3kZ+MYyIqxfhi66i+yxsjfEVI0zS9u5+ICtYHOexM3OKyEMq7myHDEwpmiLSH78MB5dCF7rdaLcYWIXB0Aa/sxqbGIotvyctk7tap0SexJZJU2RswZ763bEqBozqEI5BPO/3DXY3+JsJNZM4lrCgHYKZmAvxHpvNbidC/hBTASl8J5lglxzHgbWa/8tnn1q9oiuxVfd4ccWNoLJMx/cy5DIiw/FzcNAGnow5l17id6G8YN7Uz2SKQm05NSxMsxOzvlnuMDVL3Dks4R4WV84qvtreC373/OSUjLO5v/v0cWhfibaY219txudBYrnom5YMhsxIbBRpG8Gji1/lwqfIBI985QQ/zcopS1FOZEaVSOe6q3tO5GLuq0RAdyPmSdnBiQZWU4Nq6cslara7R5c3+W3q8Bdx+7Pw/NkC3z2MP1v9zxb47vbfubd3DNA7N2XfG2Br2q89V5Ne/exj05/xKGYRGSYgaU+IYhL2iCiX0qnK9efaa/teNb56KyfvR6tXuU3ciX0y+KmrjcFwenTSE67NmdRsOhd0LwxxqEYQ+3FwQkwoosF0SJZMlWjEQGs7ZnHrQxdVcWE70ELCGJcRUV4omSEBuQ0QndM7MQF1kroyNeKy10eb6VQs+UHhFhHAUJQ/zarK/iUZew7t1/Z0jYi2zOnXpo1tUoZ8yF2+YhKYAOIZMgntoL4IFuCVhEyUxhC0oxdNuUDonoHhguxLP6hCzFQ4YysPNnQyb9ahlWC5s8kKNUh1msGoLj0cT3bZqTSdXCFiUIVJk893+iYjihgr01nv/FRf1BbmmsKFQ7NEox7sY6IOYRHvs1wZOlFtWg1hp7JYgFwmYk+xRcyQuUNV8AdEDJQ7FEChu5nNeMY9d/zT7u5vfvMbB1V0L4oPHh6UKk2qsTh19PRku9168ezxxx//Zmtn69mLl3/4wx82KjWxiPViIoKN4Vve3eGEJRF6mUTMvOV0bSVgN2FPlwf7e95y9BbIJ6yLqhHpANKgyxRkgM2CGXE3TO1wjDeVxkrkUw5JSmiB3fG46posV2aVzJpXEH3eH+JFcBJa1mw3WVubOBeCaaLji6EULmC6oN8kO9SUki5yTGniTN7pPOAVwxLFLyrxlHm1QLimNE7DRU+EizRSQyCfgw3LwN145Zh9CxJg+sFSkNthffBO2hkt4i0zalx+KOCwsBGMajybP7Xw2GkZ+7Nnz5W3BowR5xuoTgEpctBO1Y03TMCFTEgnzuqxXNzc3iJPiniP3sIeSDQIZkgDycLxu4zHCsHEs6ln/30piGWnjXu6JHcbX1gueCnVoSJ3kaJ3+sbif5V2oXTeTvB2cQUMYHN3eQa2u7QRueX0+j3lx9t8KZ8LS9zkfeOVt15l+KWZXWX0umOB5GjcpY70L+QCDJDeHjBvu/23B/kbb/mOAXrjKP3ZGvyO33B+ZT/KBACIcuwLNlaZsZ/fMEAe/PaEbbWLxcaQtobVDybn+ET3bn3DuNW1R+3kLmKHSqfQK5/bEwioVsOsXM0XBU6/9qbZQkiVOgH6slJkMsM8N85G3yzban10khCwO4n9Nx1ILiquQDi2ZvFDhIFhiOAkgqtlfW9nhx0Dm57dnW3bNaKhZ+QEwbjaCMsGer8HDx599dVXWrY/2wELe7sYCHCyNG4VmxVfsMWNUHOEv5hwvdWT+YJFhgJE/WT8xmw4SA7tl69+ZWDSSIPolgpb2w7ZvhL0UVgiiCXrZ4Hq05y2izWG7mi7SLO0xoQIscEt8WlTUk5synHqxcC216zRPTmf43x/71DzIiATfulXq8xW2Kiqq3yzXWtVypDJTVtwvdFsXqrWHWTmG5vFjH+wuCgteLShgrgitsyYmP5ogKRdjEXtK7cgnyiD4KrEYarFu2k6GhpVq4HdDCKs4rPHjxFmEhcEj3c2lIZwxpHarLjmFziKEJCV62e9yeePv6LCObj/4Wz5lAMUjLNNwRN89mlxMBocbBye9k554D07Pv/vf/gnmrMPPvyE7AXBj/VXCasjKwFsjJHcTQKsYokgGWwwYzE43A3Cz/rnOAbHbbJDAhU88x8Sp3s6CzkN/qPVbM8rjo8tseLBVWuMLx7xHVRsdbpagA3LqeQIuUJREB1aUSsTcrRDFdioN4Hk4HpLR1OEpAAzZP8MGZuhPAWZRhYXJ2HTVixb4cycpVn5MPpyvjqAGQtjfcUFoLoKE6D067OE+LAnLocURNjPsOVvbTEAjyBVRDwGrh3ma9gjRudxtgaHL8E/2eUIK+Vc997AvICWFhKihqNxvzc2R9iUjQId8cWzF195pQyxDLaPE5zFD41AYLeFW8Wd0CuGiVFN/IUIqdDtxDkk2ocHAkXgAUM1XWuWIo6VoKBIvDKhlCPadDxTzMo5OXqmmAWsoh6Zo0f5qRDnFNo1p/H6R7zE3EwxCHfBf2De/hJSn2uuJXYGjFgI3cj1rvmh6w0osQLeq6ijfJfIV2wvaWOB3pxY3dcSIYezZnxjTabiG/lFi+wVv1+NaFkjuX1V1q5oMLHEa3lvOQmMDNJb6udtt/+WwH57zd4xQG8Pt7+4lv20Mkx+Bi5pv3x7XKkU6g8blrt89ywjzluMYjaXnL5uITWTW/A2Z7qvctYz09t4dX3F97d4P1y5Z/gTn5cctYGhd0IKwoi0+cZ5VsGVxb4UlANVQ/VjD03xWtA54gcbayZ49m72H0iakjyxVbCto0/k+pplKOpz3O6MjirgUlJ5iVB+XYZUQFojQGUexHkePHkHd0di5QNAGWyTL3IQOohey2yGkG3KH9SCWgmokOnukcoJbB55TuV8j4aCE0I29K5lvQSVUj95yVOx6QLl971OtoTCOQJC79Bv4w6ymlixcLohfJiElgdCFDg+OyVAAr/uXPRorG+DJwt7HWDWnNyAcjY7HahmpaFfLvLBXmxEj5UKXYwZKyB3xC+0LOZDC06wREdpEq8uxXoOjx6mPFozOnVNxGQ+JQmo16r5OC+nt3Lq3tk9/OyLJ5PxaO/eveb2Dg3Rxx9/POidsrZhOz2eFw7uPegNJk84gs1CH3R6fAylGswIMcskPhCuF+khyLGw6ZJjdAQR7jhL4zXFccQHp6QBro6yrg5Idd1lajPKJ9Ea/hBWdvf2kGcj9Qrq8KMy9WvGPRLbxD1JQzXilS4UtjzIlgyc8hFynRXKEw1jhJcyfZBsgi4i2gBBExMcHc2xL07w2GrFW7An57lwm/dTwTqKqWgsxqW7UHpubDDWx1SdNliUhzaNSAzzjTc1QEEKCICUNBb4t4owXOBRSwuaIIobDcXqNOQA2Gx22+ENZ+CMcAhpfCVYvZauScRvEKNScjFW00W9USM/tQ2YgkBv8qrjWmAKLFtH81FgxRidvRq/0Jh30HVaHeIeOMfoUPhmhHsFV8CwtreaLXbwpHfhFuf84BR6RzGLzZVRmvAmGVtQ/MHEfFObroAfctzTpboy7vnKaXcN5gKp7UjLdCmWEykncAU5BoHVJrbKtRTIdXPJu/t7iIE7Buh9mfRv7gjXPI2N1c4lAGHeOOzy9uuMkZzjfqvit+Erl3/1bar+NQMk6K0u0CWshm260+4SXWws5kQF08msXgsLYsoiO+liEMHreKDYghldMrhBZryzkQWhSvY3i+lFqcOooCbhQKzuTsiTED/Ds+Xh41AdyjJS/r3tHZCcnByxZbH9YTKYZJIkOXSJzQrgEcK0G8eXolo+EFF8aT1qU3k9I0GaRSpevjzm3NTe6iJovtSRJZ/gMa5FBBew2Qb8fLCnTpY4wqOQ5yDnzvEmocG+MCnVZlBKlIwIoTAVFoBoIQhYOvcblgi6lEFmcDzAo30T9tqpVmBTjBjm7EQ0ojPBihD1tKHTowVzAGkUiSHf4/a86aCCEs4JL8jAxxmlvrjBb9LxTxAFWkTEY2IJLgQIcAY88ZNOcYc8BcHAIU6nMKAX2IgTygohLxG1srFDxFXrnwb7hdazqMVrjrhWR6ygcr83JInDd/KiHw3H/MLOBpP/8f/8yf/8P//f/vbv/jNeAhtHIsIEW3XmPlcshtmIpS9yA4FPhvHQiBszO+IeJbYmBGbQArAw+o6jNIJzffLkS/yo5acMaNUCrcnCr8Gewu5QgunJS1QtCSVlFjcE9BPXO8Qky42wGlGRnM8lDQZDgLTcQtackuHJwYiQMPXP+9txxGl943KAs9wgRmH6M48eBdWGydl4WqyLFeQslKUAla4SORqiXCnjbfzAHLc+K8+D+1kWOJ8psMWuudpwcinzfKAG2uNammuUXacAM2ciDoDZW/nBUF5YWmyUCby6/A0NkORSoFFj9Ja1mIetrgBIU4uWCMfQrFtI4F5nJbAYg+Z2o23lQx4EYkcV4K8wno1Lo1Ji92lhr9h7aTOkWfMJQa5zgRmKDfkGzibssv+P/+Hf/eu/+n13i/AsxglCl0GBOV8AMyN5IuRI6CszI9I5R+Kal7nJua58wwkFHtOlrr/5vl59rfx1195ClMIkQO7RRbormRNy7q73CgN3DNB7Md1re8H1bz5vSX72diIXsiHHJmLrtyGiuarkMna62OzW9gso+7b9YpWv+muL6aLIMGJROD0hO1m0t2pCBaM/ojzb6HPvIdtYLlAJrAOypB3sUSbtNn2fszgnrceWnUyn0VtlpPEWJPwIBSNNxIltg/K+iRl5oGG+TbXDWllJ3kmq6JGjmfIaNFiXt2CIr9v0RWs4CIDtEWmhqkLz1FJewh0VBIMtNVEpVg1h94oZcuajljlbyVFe4zzfkDpWzEaEtsEBSgyrTGH1ZbzK81d3FzNIp8bobiICsErZI3wCTBXAy9eCAhz7tS8zU2siCq+UbDZ3aExIJUwC1geXVt5EOKnnQqCgiurwg7k0TK/B6S2yqil4ZkctcABsTJOHDxUkwglyrmzWSVJoBADgEX5aRZCQ9FCMUvpEXAHSr/4Atrl7U3JhB4k3dDdbFo7OIoLAX/3VX3l04AlOjCICzFrQNRGFuuysXXkgBg42af3qDWyOWId8kXcURMK9UgYbCwNKGg78a0ph8Megwgc8BIfgl++tlWDKACBfdQXkeKuFXAbkRiffHUh4BTla1q8GtYwhMJVyaAZbjTYRIpaGhRcRVIxehAOn0ToCpSCq8iF5w/HxKTMXPJw7jk0OD3YsAZkNEQsJEe7k6GgQBZI9UyyGhUNhvdx8cfQSGHJIMMCsU+LCjC6MgfztnS2RJL33CpfMggcXjSXCi4CKCTK0MMZi9QUJdQtsMW1stX00YML4zP//2fvzZ12T5D7sO/t537Ofc5e+t9fpmR4MtgFAgCYtIkIiZVKSRXkJ2ZYj9KPDjvAPDv9bVjhEBx22GKQcdohhUgHIIgGRIEAMMGtPL3c5+7778818z9unF8za3Rjce6pvP6eeqqysrKx6K/PJyqrCJs78srQOc1AfmxANrpa08MCg4MuMBuyCygnRfm5Wknw8WGSkZrFNmSzc9cu89Dd+56//j//mX//622/Sh6S44oViY8nM1OKLolY7RwYbfYE/aumAgDqM6SNNRY1RaspihC1CQ4ooeLusdK8F8tGjC8rqJNUJlv2lqytOiFWqMKXs7ddO/Et5ouQLrfeLxv+FEv9FIL9TgL4Irv6i4/Rr59LQc4HpgGm9JtYsiHg1zfF+EB9PCh3x7JnCl5/JjsDrn9MYrJstUWhgKYmUkJboFX4S6fg45n0yxg2aFCDGiZXVNfP15FXP48fxuOD2O030uiQhfhLK8jZVSkAbiiPiyIb4OuQgIK9aQaRJzn1jJJ91qdIVfIXTYBDiEGFen0cXZ2urObHXziOXrbrn4fjogIMRUm1+gdz9TZ7LS6+U7sIAsEJEQbWzl3QbaFSBHpJACU/ieeP+a87UYbnxj5vB/pGjcezQyqIbz9GmH7AIsxL5RJ6ZjwldKQSwNuZOsrNTyyWrK+sM9YS9rPmrfL5zE/UPap4UhLeqySrY6nPW3V4nJC4vDSYlxAh0DlmuZqD4OLWIzYaLDOwzblqdmaRyUYGoDZQkBMCvIn1BPrmF9uw4RwZbGLT8RCjSXTSB2EMzLyUMcciQ5oczThrk4nt6YimF7I//UN1ghTxof/ju+9t7nHKPHt1b4xKERZpJU3EVBuOfSuHLNrS5uGMbG9Ye6Z8St58dlCqQ5qCT+YMDCVsRQ48a0SbwVTZKsUhQXKCXdJYIbDQDHJBuHOFVv/Ke7rj2MmhpBWBUiYDUIkSyM41qWV7y6pdBAcW6zuXRErLLXYbjMgqXl66xxV2oKqUYbe8FLeVgZXVJLRH5/NTihuOEJP7RSxR0Pyw6EFejIrVd2mcpLgaYn+XK0j0EnBiQtoxdnjAcLfPVWlnzY7Av/YO9D/QZ/lCjHfDgaOdXHjy0h3Br+zk9TNfw4GfUhGp/94jaPXFh20E8+pHn1+ITwe4/qsgyPWmwwAeKtgTYgrT9jhtrq8YCjqIWbdQguxMc0j09N29P3/aWMxq5H80x78GGIY7eZgW8Mr6tVe7uvPH6o//F//x/9rv/zv9o6tq57U+2c8j4NKd69RoSgoFEn1EWZ8Q089NBrsSG97z92ukKjhOliAsNLzJG2IljVCJVMBNCx8dViIzBbieatD7+evf2AnLgc1OAxqPwS2bSX1a9X3Izf87q/MJvM8qrOcbDZE0UmTdM8WQA2akigsT3bIFkApJye9LxmuKl1uRZ4UeQB2a8PwNYwJkKDg43d7IFbOrgeMAtoo66oXo57Daoc7yIXTbx4aB3mHZNW+j0BSyYedGD8sKc2+MFWhFprSU0M94N1SizYcDMtjCw/fBFJRTJHkXB+8N8wZhvu5k4odK1QI4VPJRZB0yCeELmsQQwA+Ra7LqJyXd/URK5CODRo4eaRup7Ar48iUqERpKSFCDy2ZlW15a5a/iYFkjfaJllbPPUEK8ICGU8QpYXcORyaQm2+iKPqrG2tqoVrtfklxG9MB7KMUg8efKBJ9sPYOxqeuD3Cqe2OPgRJDEfBcixibtbGMI1RHUMZvBYP9EEVSs+XQro86hHK9BmMemab0xsA6LOjhS3BaxfMRefLX9oJBWW4QOG7iCuIFc7zpvccWiTa6WG89OMcL/+W39N7g8/fIJ+/rc8aey957GMt+72QiGa2dLs9hbpHoRQExDcfU1j4Z4kS6KAdOnahXiBUQ2RchEJg4D/VA+YdaiAIRJ1k3S9CUzQUqVwAKqOpFStMza8IjBIBGOlKHjr2lRlDTw6lGtH4YQHS3n/qMiNpTyp5ybcdHF8fuDqiwvnJXFjlru+vmbsPX9uKdZKVQaq1sFz/36US5iNh+rovaXVFbsdgeU+XctOcTe/cCYk+xxF16FPb77xmrq+/91va7uFXf/cU5H7ZflmlWM+Ow07Feud3fJU1YwrLl+1DXDz2fPtXIwal3YNaTZS7ViwaDyoEkrTo6bTcbPs6Bfkm8ENHxknxcbtZ89pybr9aH8b8ffX195883UONo5KcAfJwlx+QVzEenUrd3iUIqQuZIyfIh1UIYS5N+H2KxjJ/VS8s8YA48gYplO0QmSMtSuCxj9ZXjuv0d5U+yX9/UTVH1H5OdX/ReP/nMj8S0bzuSlAf8ntuKv+x3Fg/GsHmN+GK7YrmE2ceSZqRjAXm+UzPdVdBBKV8r/pKruHbyagimYy+tE/WrkN4NkyDGbel+5eIPA+eP/D6+uBSdFNQ9lx5vvWvDbJlHHpY1ecJKBG2BJvgvYxatr91re+RcwQolIEsz8aojrUcc9B03NrNtrEs8EC15IvdNUMeahEvL33wx+4TMPJJnQJZ9Xy47Hfber6anUpG39c54g3A/Yv3LF7+YoVxOcyq9Dl8WGOfXOWMdpsB+NLxByFBjywYx1DrFYMOebmOJbL6+3L+Zl46arR+SiuxuDuQZWZXcBkVx8coTzXmOds6+yyEbAai5g6mBOYrzjALizF0sPhwkIISY8b/FK5eEuElYcqfZEiYk2LSy0fDnugscgpyfa3MRY4p5gH+MlMXHwYghDlpBe0kdGnw3zB2xBtt5ivfUQKaod5MJnNzyg8nc+JjmxaFnQmJ3KhrHQCsJmvXmznYOPrP6cauJl1wEp39eTps1cfPsTqDkaZdinIhkVFqGWjCyYlgbkLtVhEG+BMRHGjISEKZ6JSnV+iTa5OKSbHu5wFgu2JwoQoAwNPWrORhR616BFFKH9S1OsZnW9m5uErrxDe4pbc/NNS9kLDCGZxVTtRwAhXHw2mTh5QbZRgoxDao5OcXMDjPoNyCvO5G0dlEcA8ebaHR/izuraqRmuMnGe4+NsHyAh0HpeiyyV73csuZSmKU9QVO6djkVyLytA6mD/Z50Rz6SJba6Z+g46BZmgU0TrcfvrBE8d/X83O8O1Ji06O3B5/NePsyi13kQqG0IEmXK8gjuXGWdaUfb2j7DR3pOu4muHP3PTcVo5IjIYK0uY/C2U5aYKZp1Z7D48O7t+Pn5w2SjEm0eAXZZU2K511g6/iI04yu07YX3+Uc5+ttVFLT452uXptPju4t+zwJT1Fxb6+NDx1RdbWqRzZb19MxA2/O4Mk6RXEqSUZlTfmH5HWhkQExDSkJ7B+3obv3M4Sh3kcHxdpmHqlA6l6hAf+cdaXExnTNo58vvWO0Y4jny/+FwbbnQL0wnTlT92Qmljy6EmKMDChC/kSvY66QMaYSQGMf0XjSCd6Fbld8W1gc1ZPWx0xJdGmmNyfbj794z/5U/uANh6+Dd6sSqsgCLn9mhJNl5PXUbxIHTT46Ddl7+5s0wwk8k1Bkv1EDOss9fc2NhxOaL6WRcQqIphez47OfUa7JwqwemNQcV11BQCsNjBraQ4myXpEjDHEMyRgM2FzH5mIR46IZ9NPOhIkKCMbrDpptZUyHha8dw+OfDRHsCpuy6964CfA8IeQoNjQrIgEMzqH0qmL3FHQ1guU0NXsr0FCMdLNmlnBIQtc44nCi8tsS9YJyI1zBvtWbvnmrJzFNZqObTjDxQEhRyfI5qJhDl8R+CNjKSagE0IoCGzNtF5BifQpT1EgQPEWBmWBaamubyZsb25K0QRuPQ624yMjXQrIq+sAwwbeCYQW5xjGnNGnlbURyRf/NDV6aW1A0ckJgU4HnuE4dYqe6fklalfUjtpfzTZC1xTHDSYZaLQAkzF89jJbk1QkFz/Tpw5lPjmnAKFEFsplqYjeo5uU0kwkyUKkBkqkw0GIjfBoS9ZMK6gOi+CUrmDjR5W+55NUC6ZZI+OyA1JxvSmQ/UrjXtkFLavFg1hurkmdnnZE8smJ0xzmrs4nDo6czROVWhWoRZUxAwMOqE7qIvMb9f1qEoVOLKAA0XJw0uGKPL7tulKKH5VESqd94bTF3e0teoTTGi0a+lk6+fHdH3z3lYePFhaHDoF0yQlqNcE4QaTfAuZQNnCyGWg3Fpz5aU/kTpUoX32G1slxjRr2pyPpdGkqshYbMFaQNcGaF0WIYzv4Wo6Me1yGuEOiT9xUk1Bg075q/vRP//Qrb7yqGcxAlHfNp1DF6nnFi6g+cmoyQadfBHp0mS4YhZHCMzLzJBFEwXiqQsJteHFB7QU1ejQq6Q3ckcYU6FvVFc4MaRi61Jf/RA8yvrh6v2j8XxzlXxrmOwXoS2P1X1jRlzBMVSE0BR2x7agj9QscKQHmONO0GYFcEak5wswz+krr4v2LBROzzcd/vV7HtQDu4lKCopAoNTeIzwcvEMZ2jh0zvCjWVk2Hx6cutXQsGyHoJna2Fo4sNslnrzg/DFYic+rC4hJfBkZ78WfPn3POME2jWRFPFXlttSNLEkvLVgUILWKYtLY+YqZ549VXzcZMLKSOa8VIto37D5hWTOW+9mkhGj557HSi+GeQFphAPBBgpmxk0yfe5vfK+8cpvU4EPjh3NcOQ7WcwslUggwDjwartTg1WxB0O1hBizigzA2fVyUXOo2Ep4LkH98gn+o/zYCw/nlJwLuMeEc2G10huDWF+yj5o24vYxXJEy+xMHTTMteXg8GBf1W4ypySBI5XoczhGr8ruex49pUCQeZqjCepSw7zNYMtXBDwBZwZ2i2pvZNNeJGkvDCL2B9IkqATcrWwUj3VkYspmLjvPqQUKNsOjkcTFZNJ6ll1v+E+OfvDBEyL2zTdejXfQzNTZics7P4QzWouDvLeeHfL/HSwNFhbdR2/n0XvvvY/zDsGkXtBEi5JZPr3qwg1ZutiuNBYUTbCjHm/1tV7uYaYUUqkXICPF6QgnuS5Xn7KZPN98hnKqBmNPj0+sgFAf6V8YtFfwqr9YRKhBOhtCVdOD2fZopaeMNGWhcfC08aAihis5zdidzR1ouF7FC2dmznECLJdOkJpaifGDQ7SuU8rz+vLUXRDuFLnOOVhn2+db1JTnW5vPnz7zGbC86F7SKbsPPbeeb7GplPf0/J7N66cn7i2jZn34wfv6iCLF3mMDXdyMqHEnhwwyeOI8SXzGpXwPzGVRVQPdi3LhiCP67iBbHa1LuqPDXR7cffwDoxXWfC1K4g+XeQNj7+DIXSl4YiMhvda9YFjkmja/T786KtfhzqGDihi0fLAYlDqaEvYHf/Avfvs3f+0rb75amoUTIGMgcqh7kTHpZw6hkF4zUov//SoF5/O4CQgxE3nDQKEj8IgD635MqZsQymuKawSSRTwlinRWJ4LqrMr8YlWQJsDzM8MNGZ+Z+XMldpO/OPw/F3G/SIXvFKBfpN744mnJJNGKUMX8QmpCyRTTlZsKzXRZC6rjUvqHZGLt3Nu/KPHbrwBuUDfsJ5+lWmWvloP7nL7ve5dSw2rCejHvwBAy9crJwufO4BNnoIFNhGwQIaRbVrET+OojzESkbO1sm7h99FNNSCxVkkzgTZTkH08Zr5pDKoOnBomQDRJFNHlnc3Nj4z5IEleuoEU4IAUSIiRCtDyXFSF1ANC4YCApW9lSr3kfnUMmkNpt5JAVZCAJzoODPU4bNBv6DdnPAEZIQztL4cyxPfG6hda2JMzzCa4uyC0rRCbNgneUHQlNkYkVwV4l2inCCBs6YQxCMdtYcJgg8tdn1hDpTg/tlY5GRcThVK+IunCY9sOpdlBH5hDtmOAWT+ufmgYGbbiqoNb1kECJoL3SyX6YKSIgNZzRi3+JNc3p6xzapFaWOdvB8NYpxNqlm6gjSwuzpzkXMJYDmI8ODiijdsg/fvUN3tNV0aR1yT//8z8HoxSeww9SR6iyTTtSVh064LCl0tzxFicldt/BLIL/uEd/Mr7QIBHN4WftaoSc8MQLDAQJAxgRYPBorBox0I1YfhLa7xUkIXx2faYuqhBSWWiksUQZn9QRi6WUNIxFqv6NhsRPPTfM2tye7f1edb1KD/ayg90qqgVK/PRUL4cnB2LhPPY653rmcsbSHcKQqghW62JWHbQhoCkXByBLe1FydLAnzmzjdtxzfTEz8847XwWDwwYAzIANFQcZWH2yUsmJ25jUkOYADCE7a7Wx3sV4dhq1j7bOhObLRV/KstkwvkEHRyC5YzPmWWOlR9kxdn0+eU5nr8vsdjef/fEf//F3vvOdt958hS3v6syPnet3exMaHTHrQi6kdTnycKyIiH6kANVLskCKI1IHdUR8VPxGVWpg6ULHu5TnOKXTP/38sQCfLnKX8oJx4E4BesE69CdtTiaMCpmMnK12E5Q33Vih9zBLNoxE+SbuAs7cJdJxTzCfrrUTu7hncNKrCJQcsZPzD1k3THGSzNCTJvlJ8+zSxekFv5wZ3gnln0FFMtETD/29TtKYu0s9uuQygiQGHjgVoQnZX7Myk8UX8EqpxVe6vTa8hsiX1157FSS0yICNbFDcpQfa2PBEhXjLDNYRkABAwsNeJW72l2uJhJWA4CcatQrvIJRrv4xSa9nvnS1dEre2stF95cE9rg+cdOJLyvTEGHKwpwnkouNn7O2CnzTBW2te4B0V43ta7e4ssjIiXRoKNVxwiZdlrRwnwyvCVh0VM3KcHqONgzdjjI99kjXmrutDdh39on9oLyeWLM6Chy/qoUN6fJtfXs8sODBojpy2X04HrGNpXdYB2PZ1uwE9J8+z+Rk9aMYiFioReBxbQBfk60E2348DzLrDjQhdxwP5StcQ3MaQ5G644TTeylQZPahFr77y6Oz0h9nYNTjZcZjQwvCVx49W1laIWPA0WlUQwKurVMCcB9hqEBqoU1aCohLVAorugE17ZY2Hlk6kxmm4PW5yl1dWPY0KIZsba0nXIMRV/5gD6SPcjk6suzHXOUDSbVdCDo/WO2U8oOzwYi4bpHu+LJDFVOFCOzehOkOAaxelmZZMkT+2Dy5eLiQ/vtOm7KmbvFzVdhw7seVtaJHRaT0X3LBpZo671C328aNn69mWkbA3n1VI556DB4CNHMY1UxFtRM/GxvrM3P1uNQUuRNbqniGnFjDGVYb3ik1/6TgMZ5dz4rN1M4q1kXM1MVy/twazHKyzw/Fg/2hnb9vPfWllja48HC5onzMtXRbG/MPVTO00XMzQvze/9Ggk0g0nA8yodtghYyTt0zEHSXfwEW0rhy+gS4ji0uerinhnBYVB5Aahl0wjAb2ZT3pa6VcsEqmOG5mRGs/H4Kugpo3RjlHdpNxCHso+qvwFi40b/oK163Nvzp0C9Lmz9KdGePPj/KkL/gwFqq7MAoLZRDCP9FTSTwCRXrVbVa646cxEaTIFMK4RmACJZyeK346kgkrpiCdU5IcIbEQ7ScMZ1WZc06TJzYwoSxUqBVBqCTNAPtZN65Fky8skn4leIMwAt2AGrF7SQllBhFZkcidOyDPFRWCI7C+FxmduS0Hy47XXXktjC5WntpSHR9QRQUXsRlxMyF14YICKbFY7GaMiEk6KOISkoizEkEmQgGkkLmOXTmi6gtuecbVAC14A1joB5JqACdxdwMOA5TxtLYA1EpQTGXoDKsBErRSNEgegCV4hFGFJIGi00bmBkGMyfiLGk1EDtcxOCFiMBrgsUX5VzWNpGSpFoEWk1gkgLRtBjiRIBCyyd+zoZKRlBvmJnVzLwbmYu2ZxAGPRGRHuCljK17kFoA2QuYW+JBwYdUmhAfAx5wKly7ARMeC1ohslbiAk/SouKdQaY1aEuy4FCJ0gvXavwUB1UNArzAgGoAk0KoYoSFTNyuLZjDJC4GfRAakU5IobyqxiUXHqV1D6pXEZ7d8apKtSc4zhzCTbz/X09RnjXIYed3nnal8fRzEqdbb82a3fIdsP5mBvm/KEEkhye1oOHjQUc6aRYNVsd2cz28m5l9Ecs5l/Afd4+Vg81BFLDF+rG2gW9zRm2J0URLZXPWIMOAHS0GKjQrbXo+NDVdnDhS6YQEJO49Uv4fPyEhjMkYNRmGBlysBgCHvyxDIc3/h43LOi+kHwJFteWuHE5TeKYwp2peLWFs/t0bTCFSSnNpTKwsMYEc8v6EXhWsZtKzphaStAIkLf+t7xMLFOHkqkgsbKMqHkWdMIVB3pJ6hxRBzYuGBnIbWRj7MaIIkqu5mabpdtgM/3eZvIzxfzHbbPhQN3CtDnwsafC4kfySd+pT8Xuh9ZWF1++/m/jsHtacKnvNearSapPMxB5idh+jJbkxoy7yajmtayYeMmFMKR6nOTNvob+KqIoBIpBB5T1IB4NJzYdMUzwr3cpIvtMtnPbK8KEabSzMsuVSzX5pamLZilmPQj4RyCd34Rl9WLy5nB9Prqmo0t5jazMJgYsCZyR2ZLdJgdzEMArFK6nOvv1usS6mwt+eDeOyAYrOzwLNY+phF7mC2jEOeqhgE9ci1MEPNefeaydqhFLrWMS8RI4s4N3PEOv+9mRw+uX1pNYwwgjGfOr67n7IqvWXtY27xJ1fKicsYKY1XsAbQ1VPEmhi13o05OrS5meQIjfGfbOkdDcs/48tr6RJ0LMOdkYVrFjONxdN4kcQtJryKhSrtymKRLsuZZX2ICsZ9LL7B68M6xW80FljniZXbGccCu7Xz63GrgBqecbA1zwA1v7WJidwEmayxU/LEi4co/TB+x3pEmGDU/O0lTZPCgMNv9ZERh1NoDG7nvLS/GlZ4SgAbif3FpFbwhokP365L2ldVli0G0TNIdh1uL0nBSFhgecoLaWF+njG49z0mS7DiFKp7RwHSuDqI9REpfXRi6bjtwkuPk4qRutfMJT2RBBUYREbRlGevMRWk2GMZgwEWdqSmssUxF1uoCHmA12qmk6K/EcyNWdpa5ziiOhndYys/dOMRGm8vcuCZFRbyV9Yshem2RyuITZSQ/A98S2dVIAUKDkZMDfqbiQOOUBLQdHB67/AsraGyoMiQcAbSyuq4obmi7oKw4hQPA9cnZzt7O/ZMzA5ZJ0iKY/YybW8+dzQAbp+zw2Xk/jHnZh5/FPm3x4+MD7SOEAZMTvqYZMEYCtPNObdjdTyOnZ6yWap71PA3xa9Kn9HLLtvba08/1Pmw+AjTSbKFhitPd9CDbnn/zc9S/GIoEn05qKUXFbzRXCSqL/s7FyNqP5Q08sARs7GmlwbrjRunJG82Z48hH2Kp4Q948bypqVemj6SrTnEo94bkpd/f3JeLA56MAfQmj50uo4i+x239E6z79w/6Z6VSLUNpIhKY5yLwMvynVlC3iaRoAM5qqamWsXkfAioERxjRAIt4wt0mNwLjRfqSPg9lPbYSWFHOxg57Z6G19d4oJci5n7SABElNQbaBxTZJ7zvfM++QisUd4EPPIXl3OeSSmRbVAhaTrs2yiJpvZdSyHtSogCzAKO2JRpou0SQC84g7t8Qq5J2BoxdfWV03lkKBEKRICtXyBV5ejoAhewZvvwbRwsll4ZXbFvh2rX/lSz+qMO5VmjmPwyi6w6alZ+8VIbfOtb2vi2bYasvn5sy01qo7XKrRIgh9j07Ty+ZAb31zXn1m2qa92pQhde6ysKknETyFkVS6OaTLLB5tKcS/Wgt2d/RZaWkGJsRzD34Z6cXUR0xGVRxHcDja3J5Q9SSIrGjwogZ8OtDZctVHPMgc9SRudDmDJR5EDV3xcOE8vS4r37lkOW362FbdZcl9d1EA6nNOCcHZpZZU2ycd2Zzf7tgzHJhsPaTbdTVonoBY2xSWCAavv9A7avEKOY3pHiiciDRLn7mA7nksEoGDzU1lxiRAC03E8nxotsB7PXglmVjKmjBhGIuBTKcsKzkuhx0i5Oo2kVCPwWH5scKIFxlc8O+ep3NIRFpyWGS0Z5dYFy2KMqkxguWsVG6GlAQNbXd/w6leF86iyb29yhjribGsadry/qcvAaEtGvrZU2Ul+Ut1fXv1aLAE/fu3VDz98ylXaIEo78pP04BLHpYyuOTQMFPGdQJ/xD7dxAz+Z39wHfLCzTVV1wZlzIPND2/NbWJLLashpzVSBb3pEKYPWQq4vIFj43eMDpfHqLOPQ5rXHTsS6fx/ZqttYZ1N0rHzMkyGlZom0u5QdRHbo9HqmR+TW8yP4EVw1quPhXjcvUPlpCyKd3lkqFdG5lTiCQavXBu5nIcijS41ff+bIGO3nhfBno2TMkC7+C0LVz9aWL7TU56AA/eX29BfKnV8E5ONf7OdFTP8YMtPUBGFqs+IBufnOrGHirg7NhCKYMjqSxPpOEhEiXbiuBEvn5yn9o5d6laK6Risrh+rXioyZ1IRuSgXAL8bJKAsTAxM0pwrH/DnVxuKMGogEKsbh/gGfGx/oPsqz6WvSSUJnR9Px1uQrhGxJ5mNiD8Ico8zfYWFopkak4N4xR/7H2YIomJgkm4k0skrD0YYAeDjh/uAHPzD1w4BO5MXl01WS0zYr5Xpwn8X7BzlrMZen2s1+emZHjArJNjoCzwqb8o9zSuHQV/vW5pPN7Z0cVwj+jGHG0Uozl4cMPNySDlmPXNbOFMXMcz0xg66nz56jx260yemzS7uDstMt+844Ydi848icnb19n6kkoBa0usB65Izf8HxygssQJoB3YaoG8s119DQwLR0uOAEo9WIkJYwcpR44FVKkjp+0dFiybX6ggFNiNB+DXXZxenaOObY0qaK6edRxGTyTEVSEMZyaQzGyZ01Ag6Ux7kdvvPZYLnpoWmdn781OX732+MHpycH8Yg7iUzUu2We0s3vkJji75HA1KaWOP332hNIGG9GuCToCN6LZsGbY/1X+wngFj3S0CRIBy0WJNTNkKU4GIwBJQsZVrd1onVdl0c/oIqJ4KK+tc33IoRph47NFGyXpUWXznaHlLvUrikPJU8Ud1Q3V5Nn1xFzMjap26KGUxeWVIsoZzGpBYgYnnjP8GCqaqTsc9wBydo7z0+Lq+j1xenvcrBaXN+ZXeOrQk5xglWMzp9jrZuiWfLLcvoow13dpqU6nGqN8wz2yl1dMO7uMeMxHbnqPX/r08oLjrWlLC9NTMURhsAOGDAN7xDRQl3KvZs1ypJDTQWd2dp4/1+dXfOLxSkFX2n/7u99/8803saj6K18dVldx6kYJO7k4naEI2Wx4TTO8uHyNT/urjx89uO/3AtgvlWsWxlLbIcHpjByLhPkJpuNww6OnD1l6MwB/cdARVepjMOMiPRj62Yn9VHEXbMTi49zx+JF1O/4Xk/AT5XyOqH6i+n4k0Jg/Yygpv1AUjgn7y4r8vArQHTe/oJ7D2PHwvR3/aasbdxBsQs0INRkRofVjIGZqbhkpQHa/ZLqKeT+hCmXSEempRGLT0LmAb6eMsySC9xS63qoo4paeYVetmZGPC5m0PLXcXggl866pHrMX2fSkoJmU1gSneZmcYF9XXesunmDM5lK0QxXw25cLWL0kjSwpkUlWDWZndja3uAJZV7KiccQTtXaHESFcIjDFqhaECIiIKt1IliUXqFTdEtrua4kq5ROtOJODV81BvIJK+eql0yCA/Wl4ffXwwWpkm7s+aHRzs3ubMcDIzXarS0cBLU9MLDNvQLBxL5vhZTEO4T1NgCMqYNaXCWsdU7MElZNgQHaLACOAqGMeYDSyooFXiktnayKiWIacxktlQ20ILN8UNVJZvFKkNIqiZosyAL2kIWqUdXbqmrMIOQYJBAgwlw1pEX7cCMJaEgIALbYDZjBy+KK25PLX588oQLx33LqQNTuXZNWJhSTR7s7um2+/4xyj3T1Xppchx76ksmqoCGayVnXNWCnoERT31DR1NZ/Tds5Jdf6h4uIAlFXEAECkrvfUa0KPPcDayLMZvIZgI8mNGyI9fmCgnlPgBJDsfIaNFuk+GCgz+nDyPEYd4pLRZ9oKGA+taMCTw0H6qCuyqMS2pBewRF0upUWDSNOPaQ4O4n9F95/edsbm7NoGu8sKynd399xmMr9gFS+GpovrKTv+/V7t6mPUYzRVh3bRcTAKQr8qTab0wZ/l09MzC2e8vLHI0UqjH26Za+nrWufsqMkJN6Xo+RxWxBWtx1v9rKx8LmKF9sbYubru48Erqqad6Bzd1PaxmYnZK8/rBTej4lMOGfK7s8bns0R9f/Nv/s2/93f+9rmlMbf2Ogl9duLASqQfuCIJaht9K4WfNZZwvtNMFcnOHHMDWi8NWcXzAwcxQnQDXX+T2KFfQQoGQEf0ptq9goFAYoPdPe84gAM/uwJU4+mOh18UB27/UPuXrKafgeeNpwv6DGvlpacDE5CPQlkmO5+wZgrv2dCcSCDN5uOqCzhfzB+lxA7uPSly4TGR+b9hqFWmmlaBwLEWTfgenOQacvTs2XMmjezt4aZzfkKMDOrqafKCR6+LUK8nZ+an7XiKCPSdy+zhG3dlNQf1qkh1PtodHiN3q9x0pJvTc9n2KSvIDpFA0rj3woSOHrurckDzsXPbzgZLywixCEEgEXWr62tuG/DxbO8uD5gHD+9Lz0an4TxvEk7DdDWv5MFXv/pVVZMeWRW5mtjZ3xmfXMye5N5HXqhStD9ol+PLwkjAORQ3EAMtsdmnvCDPETJEDo2EQPKPrGWGOtjdyzkrA7aTaF1KcW7Zcfwx54uyiOgj3+7UKa/iueGUvcqX9OnJ9t4uybeyvh6rUpxUyGC3Ol3sH0ZXIFYwbW7eN7e1oQkXwW7XtQZugwox5JmTCGKs4jJsvYkL7VRt45cT+8Hy6krhYwG52N1nJMueIsDPnuSYANpPBOHV5Zqb4ZnKjo5XN9YZD548caMG95bhytryysa61aEMs6OTe2sPd3dyZOXyyuTm8y3aAhlsQH7ve9+TqGq6hwa6cA3/LURiXa6NPzliYmJBkKUt+OOpYPiQ8eDYpAsjI5x0HOLMFO8nbCNhjeiF4YCzd5sy4WdUZAaycKkn2V1gYBhiO6EcL64ss7iA4eBsh5z1Krzxs5tzqyhez8esiB59p2rdxO9K/w4X1ziPSU/i1SWDJZtc3W1HDGfFLfpK7WNn46Gg0Zihuv/K/OHeIdJ5VVPy5xdYdyztXe0fHjy8/4D+yBs5WwWzB94a38TlIY/j7N43bIwNg5BbPWuZfg/HppypaBNkPhU0B0vooCxWpUdezaKXjrZg57y9bvo6V6kwSvmJ63EKrqtWqftqNMBMB0aYe7ycNE0ppOho+fTAUqPbv6J+KTtxNfBbi89dHTzh8PR7Dza+9vWv2Jp4fLRtIdD3iCzcM34030DQTWaKzCo1b1g41VNmi6R7tIkos0nelM08UqEAGuwm6eZvylcoVOO4pMSrwhQUMW2Yi6QZadrYmpZSjXyMp7G9GM8XslGfe9f87ArQ507KHcIfwYGfZzR32Tz9nymjpoRSp+o11Y51IOs+ye5QoE1VJ5iORaR4juNjgJsaMq00ZppK55p3Au9L+iy5FC9mdrOzU/ed4SfFMoVXkysvYIUZD8YF5QpOjiEoIs7LrYcoUoUipI4IEQheFUJN+nFZEAdm/l2vjWBeQXqFgfxTRFxxKossgkoEH1RROkG2FJnrAUNF2fJKWLKRqIj7i1rAy408cInEdB21V64n/Gf4mRDY5IcvZOtETDOELkhSWaUWIEidluK+5+FhOzk5PVm65HgRhw/SKMpfCKab2jiWpSgYio2pHdniWpFltpLiSnG9dXsC+ml+/ETWNxYpJgSzQ2KkaFp3iuZQAQ8jljh51IE3DmQ6Y4MZdIs0DaSbO7VOX5G4R043qgOauy7EnOam0gnaDxsKG4crymmKLB8Yzi6yvqb04u7ekWtGuiHOB5p8GotBjjayDFp78mmZuCpoi5ogVLVGEfBgWCPwW2Nb6BoQcvGQtzXWAShhH3OIKoq3WUeCRy1YJ9DstFFE54JXkVys61aI45WqW3fRZHF2FE/WD4ngV9ZW0XCU69ZtCLeqNMxGcl1r0TBXhWY7HjFKRaCPIgMKZjnEsOoxf50eH2ZQ5vDKoboXllaI4DB5Zpqh7ZxiVhfVoY3KZTXq9DqWMEqDlhoo8xTK/R2+Xa7tSHMcA5HjoNje8iPSECmaJkv/6gU6zdMnT/hg2dwOA7d3AH5SzRb8xCtbxixTKuLn6PW0LsTVmwbkwtIy9yyJ+lqrPQ1guq8jK3WNBUH12iWna6ftWDjN14V0jT07PqK/Pn/+9M3XHy8v3Lcs5hhPY08diExNHwv5jnK8dbci8bxF9UqkXj/xULrr6vSOj1PGkdulbipMmng/9extmLv4HQd+FgXoMwfcHSt/YTkwnj50XPVdJgfUVjxWcjOUibhTzMVye87yHDdqnNLYClMmq9swyjVO6eO4lEiUWNTjhAPe0oNlju3DeJl4FUZ4cqcAfwW7kGwmcgLvLBIjbqIz5Cw+tVm4O3eBF8+M2s7jKd4CjD2JPJs7z+beJs9Hb64NmJnn7uDpVV0EDPWCEDLXezq/n7GBlFVH6z3kPZ7AaWYXECngj7KyInTnZxl7ejlJOoId9lswDguYXltdZlIi5xggOI8Qfb6bfffa3ZNzaOamHVNModEcV9AztDgpkQFGJ/C+4YTBF5aeR0uwqKRp1lvST1f2u2W3EouD3LnZucmFAUosaDA5YOa99dW92en9ffdz72Zre4It8VHguKGUND/WcMsfR2hbYhCYWkSDnUHH2eOmB9wQDljN+HBqDeOYk1bcsQ0TBZ2dxBxFKBLKqEI8iQWevRBbVCbLFifdF1epvT3WPdupELawsmqP0/r6vcWFufd+8C7lZvVI9020/mcpBQfUgnuxeNk6fnZOlqMBTr0pAj9WA+MVphamIOl8tkjxkFH2S2ASFYFQd2Az0uDknaWLrT8+uHe/xk+Gn9HBeMO8o5fDWoPzwnnUGTackSk6Ch5dx0fY2Y5RQgqMUsI2SrlxUlBWjyk1cxOcmSGMMw6NaDGrlqqDR59xO2NfxCJrjXyk1ocDQzktMgAG/P0zaKtcDnvUzdAgZsmp3m4Hq8tHpyeuNp89AWnXPQ4APuSFFi8mnsWX7IDslMYAnktRtaArEY9LUAlSlAIAXtV4Lgt7byALJpbazAbKphPLoR4xYUJ2daW7KbVA5MIjA05BYYNWBDBD6bvf/8EP3313dXlhODu5spR7Xll79Mu0K8/qZ66KrqitL6wwUm7rOxAm5eO6TiV8UvtpyFBf/G+0TXMw3CzfiwGQ2wAdkVIwBVcNT4FPVTqGuYu82Bz4qRWg2wNozJoeZ+PXu8jnyIH+9f6cCPsXfoPK38w+NY9lPUU8E1Z9U3b/iptQepIKdL32E7DIJ3r8ptRojhtTa/Y0aZo6zdStALmYnPbgM/TDrQNVyGWkgJAtxitIqJRijSeKLBdwMYWN3V6iAMbTjE/OmccJBnFNgIaXplbQdUzlDn+zAgAVsSrRVC4AU6plKoFqs5j7yI7qSCGaDbQAUAKy8cMjBRJFPMk25gEApHhDkmQIBsaSoSKf0VQ1iS3dQ+rF5frKelaMTnJwEYIxkvvtg/sbxAI5R0BYkcFpt53yNaKX+HR2QItrKZFqpxh9yFoZ/YmShIu1YT42FZDl0btgCUzbrVOwuRCOu5ZF+KZOx4vcfV88ezTN4lozDdnh+fkFhxKWg5Nc/c1u5BCBVcchth7pdWWYLWyKgLFEpTqtxhMVYYU4gecsaTC6lX7i7g8qhcAhicIqrl+0bikbute5UiuoFwAouH7PhibnYsceQ/ALbZ3CN2Ymr7pbU+ViKTzcsxRMG+3FKmuc16YHAEhZGgW/grMXtAX7qCgkmdkcIM54I3A9VqSR60cpGqKIg5glYjWcAjwa21nSIZe4tBCbU48fzlitaSkLiYJBW+vF6KfwQS5Qesr3PBozDx2qRbA5QfqUWRFTeYElGKtK6Qt4LEUpKALSAKOdizsdQEPUJRFD2jqFXV7FlXULsLGH7LW1FYkgcQx+ZSVSq8SpxbLQScunymQHX2+wrGtQOVNrEZVR1+AYGiDBAYkQqIvGSTFVF+e5DDy/Ne1wF961UWfFyvlIUUPBa9GxY9Ad7MXYmft5M9g6qF2Av8P4lZKk7E3yJ/822VK77BjSa2c1nk88WwG6wRXkDdARTVC2jeG3s8bIbwr+1f77gjXni+uMn1oB+kxSjLDPTL9L/Dk58HkxFh4zUX9w+YTzaj6VwujQWZ4CauPwydKQ3UU8LkJ+p2fWqBC4myBXWoDqU+5mKguem3TftaQX2Z9Tamx9slxgMiUYwBAw/IyoD+Zrfgmwkp2ebqk2vYKHxFzMT9nEbQWfIAGXHVIE6mR8jc25ZnwlzWuEsdPdfOP7Rs9mJiRpDFUgF43FF4GkYewRcQQO5JvbsQOx0ZPlrAuMGUC4K8Op6Rbr+J4QFf0ZrZat7W20meipI3QRyhliAPNM1RDqCJs+zxj/7HWh1jmDCIdZX0gUn/HqYsDJhikHQ8cJg1S7JCW0lL8wSFdKUMA0mbGHCQGSEEQYRb5ODt2zMJUrJ6y76EzShd/r0NVRk9P7FKXYpeLiyxTEm0ID4MIT5Fh1Y/KhJWC4/WwcgaXztOW6ysJmMzz9TLv6KjEkHR4dT8U2ER0i6eeTFucIuah3fEwoGTMclZz9SAFl1YtaKZPFCCsUol3wcA+XJq8O3RH7bJZaofbXX79HNdG6sHdqwuF+vK+2djbjt10mJdVFytZBheo1CsndjZiOco0J718jAR48B/Ns87lXR+8YrYR7uikHSM7Mz8a4MpgfWLqh2xmQdvDpfad2O8RIcbk2uMWPe45/cUxW9ntjPp7o6BpdcaO2eWnRyZBWsJwCdTWxurB0SOi7XWRw5R7aZqbhSNGkbjByUINj61tw/DH/nzkKDmy0Qr8NfvU8qYwQu/PivuVM8Frs4S81V3qXnWvUFEZBg9ywRIwFodpxFscddWkdbvi54io97OhoH8zZyYIxoDstw6mKGu2JJ1zZjDd4xH1pGHXbbhTb212ZyIk+7IWefilQCcFcftOnE9TugYvre7iqg50yqrMLT5wXkNXNaLmBOTvHExZCrFOjVw7Yr7/6+BvvfF11qDXQ4DeqeUzjau1QzEormnXrOIzmB1PFR5NMzyQQjwJgsS7SEaU6sV89oR0n3mSlVKfXPDRCK0U6O2U0z5sV9iLjI5wA7sJLxYHPRwH6HFk2+mF8jhhfelQ9g2QCquPI2ivCa6a/uqlHnBjzNPVb2ghk3jJhYZ6neHNxnNIRiTdZmWX03Se6D5gQXCUFyezrScfuOIM/pnuTpbNsBLUDU3tS6lBj5/SensU1hyfnfatEteuH6uCjHwyJ6Lu94YkWGDy7rEb5Mm8FS0XVxjRERC3ReMpaYI4mbyC/Opm4f/8+m01/XkuHjRQhQgCormlTynQPCeni2XVpAszgpRQNJ77JxckJwKSp7d3EJmBg+bD2aaxFpRJx4sYwLeZMAY9aqE/Yb0d/t0vKvfsb8HhthKogUSaPyOyca+zrnA9JqbVyqFEXHJooQQrqFpXOLVADnZ+YAwPBQ0UO6di8Vs9KirysfTp8O5zLTLCpwnE+qGXPQLZXcWTDoHj4c5Wd57HnlWyWjotMKdBqsK1g29u7rz1wquEST3fGjONzIpw5Jx5U1v+AMawQW7bcqYJyLAWrPZk9sD1WhzLLwcxtBQ3qNQCaEhoUyHiY1zgBwzYmjjA76oxokPhz7aDC68lFt565bsyRgyXpEQA/XuGnQJAP66BkZQEINCpb2qL0nKX7ysMpOhN6oMVAvEWMhksU8SlBA1Mdw40UxEBCO9cBmAZejfCMev9mAxrIdFgt3hl4SGoYEcuPbgJBj7gaQ+SRNTUDw3poLD3QSkQAbqgXZouYxiCfflVjiR8hBkrH25Pjs29961sOXlLKbwFaFLJ9Uk3QZmxDSDdSlx88K5e4ggLa9Ihc3M5y7VQGjyCdHchZiCcOm57g3nbaq6tff/utX/nlX+a7fX3pIyT38Cpb4zAraxAWqugZIgIyxkGieD/HibcjnwkgcRwAh7CPP1ELQNoYVdfrCViWp1C5H8GMgb+EyE3tn11VET/+jPxR/Gk8DQ/Xj0b72ZW9xKm/cArQS9wXn2z6eEx/MuNnfYfQ79/E5NlBvBO9iuR1OhPESEDeqigWFCGbNBLA5O0m0nHzWCV8NKH0r5GoMGs71+TomofH3g9/+CEHDkVUx87AWYVIICoAk1ridhZFWNYCvwiBZMaXy6Un3+ZzU/P2s1xNLi4scyYVFLGaE8r5WNvdfcayMk9/ainCpmT5LWKDnkELcZeFids3IIXQ/6w+JZlUpDlQmbLTwvKKQKSC3VgCibqAUQ1vhmVrUXtuYtfsyyuXT7FBcOQhYAgbpUgkKzfEpyu3YlOays+tBbkDEFUmSxWRtblHswRM6YXaix4VqU4rwEhRl0jXDg/C5ApMRFOENSTWSliPCMfLaIQkov7QogjNeQpEFn1oJIgd2OEznwNmoJ24LAV0FcqJKIX2VZWziGtoYSiVLiYi13moS+7FeQxvsXnU5nkwXhGTyMU5xxw3vasQZuGaeST7mPafPEkPtjP1xMwS7u3tnTMPIAnOMYfFaRcpWfvY0QNA7Z6qU4WFThYcwhukxUE2jWeb27x/EQ9JNPyZOQYxJgvazMbGPZqZe1ZUd350YomNckAzsO0bwzDfmGYOgtDds+xndKbUNUxbmGrgXFhcihtaXH4g5gMd1ZM9yc/IMQS5DoKgtZ44SCv0D29o8l3vl+9aRqbu9sQEPz4K2NlpdCmvqFVWbsiubXfGubJ+BDSZSs8Z4vPza8BqOF3myhGGw0t70+JVJpeiFUskc5bjoBSbnnDulM8Dg0QzNTDtjSsSBShKnnSsdbZWa5ziyIafmQ2H4YGZySeWw4sLi48shlEd8/HgKMgzR3HPO4GdydL2xuvo1mLYZ62Svqsh8BsJqLFcqVF41ccAde94YgUwTO5cEYn9FBmHT6d01ifSoRqHBvAKXydKyYR1Eyorg6RDoYrO6rXLfpnPH1vpJwC8fqLtTe0YbBz5MlvxAtR1pwD9QndiD/qfZHB/5i+kC/YzqLKwYOqRkJ+TZ0/NJrvmQhKvapK6mTikVxG6RSKmWM8OprCexTx7ojGTdF1dCnLTnbhpUZGz4cXJ2fkHP3jXnnOOCLm0oLQxJ6khgPgGY5HFrN30tMxzL6o52hQsgDdBoxHOCJ5qglzzuCJy6Qq8EEzuZv+e1iSCzDd9OfSAJDu1GwbBK/mnFPeIFjOkrA9rVaOZ1DGbqyXNKE8RGgyfGKoGGYZOxwKjyiuqFFcXhNLlwikFbQqKq53hB062EwhliasL5jElINUrSATTfAAPgNaiotYAFJQljmbVsTBhulIAYGgDP/drSq43BHQpDSnVZ8KHPv5AopkupIqF5uRYrjs0WcJQTmTC7Bv/3XffRUlzFf0lqmLYwLLqhTjzytUCbXXsQDHwzLnAdCO3b55fnVMwi13Wy+biCzU3+93vfO8HP/jeysbrPKPPzo/tnWZS6d5BkuZoLMLU62Rhr80frZCuT6UgT7sQAF4/NJO1HWFpUV2R1uzCw82d7RxeWPyEASvAiwjgXUOh/xXB5GBgflGTi+WXFmcXs4Co47SRWqCgVSCl4DQqpDMx4tLBYX4VBoADDnSK3KZB2a2nz31qyGpDS9PgVS1lNYnVhzEG/t7spogWwez0yC4lBZFaWURZcqWZxIJFvweAA3KRlFE6ZFqci3HmKrvx4UEJJvDpxjR4UvbiAs3SFfTrw0NxiZCLKwW+EQKGFqvL58c+ykPaMpzB49AfnnY54GeSeYnqY4ybEpYXaICuB86Cl3VOKbiKaUo5P1JcXUIzsCPNkE7x7NBUjSG93uSMJpbOkiirc28jvwHOzCa9QPJT6nQ1VkoenSJLfAxwU/zL+/t51f554fnyWv4LU9OdAvQL0xWfRcj4t/pZmR9L+xE/40ZSP5LM4KY8Jb1mjqv9Uj01mDKIgWx2qc/fnlk8BfAKCtEyPhVgaVIAiMgXqeoYSEbeFV57xj/6t3/6R3/0R1ZbNh6+BdgMapZUxKckAwV5ANKHMQwSg6RMJqqQRXKYpokNclpBuZQC4hwecfM2iUgesO74B+Zsh/dPOUas3z8+OSSieh632iW+EyM/NWLRMgT3BfN7B/XKRS25SCR4VSkCWkj46LfKwy5CzFMFNFftRAPm2X4EA8JoA+QjeGsFnJ7IMOQ5S1rtDE8aguHnM3Eh8uUNuSxi0gEEtnXlik2iui7LdGjL4gKPqKF9XXS2WJCmyx37aBsSRgnYMKoQko0nrBgqQgE/FZcxsASwQ6jLIhFByDPX9jRnFIVvozW7eHBbB3QdWvpu5E8zx3NqZW7+0SPHEOcGC1vHgJFnHE1g26PGnp9zNqLWaH73IIQaAgwfukd00/uHmxj46muve77++v2T44M//7Nvc7adHd67nOSqFQ8bI9Chidr+/Nnm9T376p1JTbGMZmmgKtgjAf97hxemqUWQ7hQoGDjx4IBXusHGxhoBj1S2OqrS4c6+LCYQTrvqdfKSBiNYK3jqsPHsxz38+v79B8AUxGIDjgd9bIOGcQ1mrLcGRfVHktFVfND7uRE2GofOylANHziYUM6R5Lk4HDjRh+LQa454zpLExpJjdWamnx08s8RriGoOyo03TBPxU/PTcR2qWtBf2+yjlUk3GuGQKMt9aogRlDJ6N+6tGTPABC7g99bW9w4PKFIGVV3iwceMGdUx5S5AzaoZ7kHlGW0puw3maFWumMVvvUaxo5OdZj/dwDcH967L0xPjh2aDhpMyPmmMHxRLEWunm+IXl4ZUIMPbIMJbHzJagSeoUp1E80D+lP24UjI/yBVEOp78m1cwEqvIR4kFmFK305N9MzvdpAcRMoT+1GlsncIHqMGq0pF6dLuuwveFP5qkG4J/ouqK4M+G/KnwfDaKlzX1TgF68Xu+f2zaSYqYpvMhVy7DXmuSyfxtMiUDPM2GZliyQci8XtqMZ88RUAmd2JHb7OufqIlmnGgqBOxVIoSEPFXDZ7CJyQdrXImvL/koEAM+N81bdjCp3Fd3SM3Sj2MMB6QODJDDQGYAkGuu97WtOdJVIU7+NYyGaEXbFUg1RQBocmTh6ipKIGzBI9Kf7ABICwBEAsgIs1vmemhhA0xQqVft2g7SkgqS5EokNhAGQFmQ0iUyY+BoWnd5yd4AgA6BNroCMJUK1bSscOmEXFGZValz92EQnWpBv+KqUETxyKHatZf7LHf2lFZei7QR2Y56JITEaWZWAquDPGJwIoyRpLGoAqMOFipF5MKPDClqoRaQ9/vbB1RXZi6Q7judvXRA5WjZVASXujuU7aNlYNbRkFPljK6DfVa6yYf37n/3O+9NX05uPntuieRwgxoXhxirJpvPnr7+la/E9epg9+oyi3qYiUhPyLuDJGIpCtFGnST4sUIWIsGoq9S7Psc5hhb0CwhTtVKlGC0AFvcUZCGegGd5g1Ac2TodQxQEoF+0HRkglVKX5R6JXtNZdfWsIgoCRhWAMLOWqxCjg+zYawIAPHr0aPLJk/aUaobTonLcX7o4vkSeB+5OWVygq9FjNBDlFAvAiiMJN7TChgDpSZzwDRCNXBUZtG7gqjOvtV3VCAutwqT1wHzhiOIYHjZkc1KlcnFeWTiVEsLP3HU625eQiChF6wNsZdeJA8+efJhPAlcmxzn77MSh23z4YiCytSG/TavAGCLiqOwO4lrB8GS2aL1HijDKbmWnXoF1lojchumUz3wWWuSPQvdvQ94UDyXiBZme7dyO0AgxXyhgj1K7PrOmu8SXgAN3CtBfpU7uX/VPSHH/4G8/zUkm1lr0j/AzZ/nIM4GaBM0IIqZCiydq8SqR9qKuTBIVxBUR/URip0hUV1cnLqirZkD5LT55JE5985vf/Lt/d3v/ZOL9zYPp+Uy7PZOp0ZTNu+IKae65ImZc9TXIuoZjA6FiquBXYJczJQnxFgPIJ4cHqnF1dR0en7y+8jVRK8zd4sBIEU9IuMUMfVjPRL2DwLe+w2lIDpKX3YiaAhKdjFBcWWBQIyT2NFkekUgAkJKklI9g7bElivUlMmd+QKSYY3GVLtTTvRoVN++iig2FW7ndNznjmc2klo34kqh63kEpdqkx20xSeCau3IEVz5OcC8m6wJ8ZB46iEWIRZpLQXJsGtCvXuqPBGgS5wzEcWlYTa3mahvK4AXFrdRDPLEcovcxtyhX3E/yrYuK5okdOWhjS2FwtEmeNuGlTHmgXUYRVU0NCK2J/c+kDGqdjqMjWntlZKgRTmvYeHJ0Tscxpho9OxGSS9fmHT/iOHB+d0i2o0BTc9957V1lXTjmbm0WECZKqoz/lIlhBIrmLGzPiUeBGmvqFdSlxZNi4R0zrI31/hPmsVdOzLrKSS0uQbvQanJgjxatRYVmIIUfVjC7IpvQgyat+BQNSdemRKVqmo3Gy4+/wKLY9q4lnjuXkGTQR7RBYkGQnFgsGn6GcRSSFS3t+PLMzUTcxHBQR7uYyx4hz2Jq8Xl9djlLOz73u04ifFzVlenLgTpXJa4d3W9KanBisLC/SKowWTLBb30/FwMMZwFpEX8ZYPw35dmPZDJgPmFxSFjscAF8UOOC1tgEOzo/jlsQiM9y4xw5ElUG/0XtxsYYP9B8HVOrZ+tLJgMRp/4ZOs37VCU3ZoKCBlBYFNWpn63nzTRWlKObDAxB3O9RpsLI2MlJ+/NL985GiupoEMksk4v+b0HNJzyEYCEAYR8av4xQRQWlZSt2gyV8p2NVFPDvFE7y3cXqXAtkA0hslrBnOt5bsx0XuIi8VB+4UoBequ2+mgI81SqKQpHqaWGsWyGclN1LTq2nCHNGJcgF3XImeZQAIEhuPSCEbTUzindKRUV31wS1uOuZFY0KXSy8gMn73d3/3g+cH/9U/+acs867BqNuynIwfL04zFc0AMFnF/tCktkKDEmsj5u5CWKsOZfhRBTAih5AQeAHRUczdSrXkMKdLnznNF7P1gmCuDcBKmc3pNEgiIQRzPcgI/rqAE05FSMqa/c8UdJdkq3Q+2ZtISBAkrggmqFopibQ48MwwLsxWHM6p3OqRdY0xbSD5c9AkmrG0NfIeATDZx85nHMdsJr+cj6tN2uIwvXLXyDd9zs7lPHV1XiuZiEeqhoT4dFQkd52Ac1qO2pg/yXhFGeRg7lBnyhBiKHAgIceHzWdb2CJIURdFTKKscn2hKMRiYSEJWiGETZxirDZ6xRNtIQUVl4Jjmtn9wmWdL7rc2ek5zAEwd3jq/Dz2DDDkGl6pRZHuNTjVLhgG6ZE5HjMxWpDHCMYcurm72Ci7toypQiJSoIJZKRyFB5h0DLHBzdpLURsJqhatY7iQhVGeavcr4PjmNQyhrdYPwatKAatdLeGq+7+KgMYmSyT0TMQ62EwzOosncZRWBCWOCNAK8WharcRMUmTzyQGmR7IISAMmDC9jTyipO+wUxF7WCv9YnYztUlnOKPekPxheNgatiEGluB8MAAg9FVyksdbvAnnsTNK1BaP8CkQwREWqBtPV0SrFATcTJCqirtEP8CKGJWT6Z1hmyRmWmHmy6srbiyLo0Eb8UzwjJ1VkxujQtTffxDty+9mJY4DxqxQY+ilR8NosUlG/ShEBUyGfbV2ktBwUjoJRV2ABBiB0Kc8vOXTVX3Kld9V9mgN3CtCnefJXO8VPyywwboN4/9g8M1fdTHw1sUaimObG8A0cyArkMLcG0yJsphC5gqktiRX3BVVeKF5GX2PKddUqM0mZiKWY6OvrkPVh0qkq/CXfeOMN4urJ1uE0qT4/5xD87D3hix1qoRrJIeJEWSKS2JabMzxAnrlPe8ivoCnnhMlEhCotUp2K1IvaPpVESgme+IqiBAycCoIxs1eio33ofJpAlPL+iSVgcXEJgLi2WJkyzZa8yeqD4p6IIXhUSuposLNelCJaVOdUHt/mikvhMQrSyStuJVDKAhPZQ1atr2fBSFwVRI6CUInYQcbeEZbnRu64qkjnFqQJ7u2CZefYlZnXc4Oh8xsx0y1n3E346LQMsy3HFiziCrhcrj+MHVGhnPY7HePK0nLLZuYeopFCmWONWJWmHFLMr4QlbX1dA6kXTBfV1SPNUhcQ5LyFpudyTyqqWvyVUaG5FLuFhmrXyYFDGy8sNjHAWCdBjoGAv2fHZ8QkEx25KJFUd9LS1ewVtc+6mIsgWHrwxFlNKGHyka4WLccxW+ud8WhdicJO+9SVmokkEcToVrwKJ8s/vWwS6Rq9oGNIYd77bh6Fp72LwLOeKFID7DSaU+T2pObnpCiSvOyj2Sk3Nc0iEi8g3VK7nGp4LKpUgL90P6MnupRhwu/r7CRdH7MT69owuotdabVN3enbQxvlDvZ31UgbdGK2hSQ0ZLQ4VbLOgFDWUAnlc/Nyg5ZLuB37w5i4vBozmma46mV8tr3Lfnltj4/U3IVjNnOmk2rOT50FLt2Y1HBDDDzMNEudDgMALcJqMDi5t7cDMa5y+taVfh1g0GmVk7mVnfBqavb4yj41drUJV6dN1oq6hTLqrCpgTl/EfBwFSMddTnGQYqGsLyfZUVACVdHRfNLxfioIBJKK3M5JXLowTg33a9GtUzordRcSiaEgIUXkamOIGt2KOjII3cAX4N3jpeTAj1eAxsOuxtbnxqQx2s8N4wuHCIua580r8Z+QaWMwkQ5j3tRvvmePfCeZE0wN3oGN+7ezu0gqHReuSCBvNsOPc7r4J+qCmRA1OUIS6ZhJaGImm76yUcVKCw9LAqZzrU9ZzzFlmoJJFvDM9OZlzpu+/k3EEVq1WQ0axVlBVEeDiWI0OUF0mfHM6V5RBV6u4g3MZtCCpOqNe5DJXdvJHjIF5tBWftaKNAyqyABZMNs5pQh4rh5FrXWx7BtCZLNOEdU1EnFUETY228vltUxViuitfezA0CZXWQhFpCBYrki+32uTl5QWKzCA9CqdfmGNhm1m+jLbdshId2qSdtGxzo5nBimt6pZb1oogDJdn51zeIAT/NJOPBse1mx3o5PQQZtxTuxapq1kRaDXiv+1Oqyv2AMG5ufUMmBxxAaRgcQd56kWP4jqbFnqwc0Fq/vqvvqPhz55v8RKxAIfVuaPLdW8zs44HYHMhwiGUQvtBRlv+4McTLdI6mpZTjClSsvQF4a8iubogrjyzs/yCQBLnzBtQsQPNTMWnx1ELEBo2ILVan+osBREIzACTqBeUFUd56cHXQ7ezFrxcTcMQ28XTqOnYSvO3glzIlYVKAsxl9IoOao0NmCx1IUPcVnxxC3bFUbpANto7kdDtKGyDQsg7zCZEeMAHuIalSLZWlZInyyIduwsy1IhaMOKqgBn9bYHDB4lqp/vobMwXhVOjYFOXUoLEjiuOCQBgwGG1GHMgc1xV3TKmItX52sFA3XrkN8BoZ6nL0p1DyS2+lvebIy3X11fj310fPNBQZGSpRVvj9sR/vwK+jSPNRimCuPR+3vp2S5b0/LmJ3C41Tqz8T8I0Qu2VMS51U3tw3sSTOwYYo/rcI+OGfAl1fe7Ev8AIf4wCdLu3bsdfYI78QjXtJ+d5/+BvE3+7rHh+8CNFJ1M5SLNDw5gExwXFTJmmBymdGw0oB5+YvDO11eQxAs+vOnilBeHYDtTZmW7LHmMtq/GZfUmpq5mBTeHyTLYgrXCgh9DxtEqjdvWSizkFJUpGXCyc3FvxKBykgrI+fRU3m0cMnJxQnhgnYJBlfwrZoZQ4uUsqqMWKCfXFnK4UpYddQbpDYkz9UtSorCmbBUK6uoSWgp2l0p7QvSIQGaouv5BJ3+NkpPSzgygBhMq5drJ9nJOjruKyQhcxqUU5kvj6ij5BhcRU95hycYU2dyfEwyKmGi1gaGEOU4RxDEILTzZw2aejFSqFhDMPrcV9pPP2wwPgrsJCQbyyG10cEU/DGJ7ISF5GZbHL+drcn+KAPL+45NsctaWRXjvgGGFaRysSI+O8+qKnb6oa92ac6lIrpLiEIZyh+YKjpNmVJiy4bT6rOUwUdqKBkeuGtd/85q+8+uqjvf1c86qBVKKZqXXNJKe1HH0L84vMD640N+CUoqSSxMQ2dZPExeZ8sJfmh+NLpQRLJ9E0rY1bsCmYrqI9XE8wgAnpiJOom07NoWzxAEJwrcuQx/RIhrooPdQeMNbNtMuLMUyzxGc1wqB1GRX8smo91KsjD6N5xnYm+fL48EDcEiYXfhduEPdahFE4yR6Jdv8Ojw/OLGuWRmCflA6iEw9yFlXUI7qs5qkXGmWxung49IoeTxYpzZIYNaJ+FOwjbHx+FlVVlCrnWiCYEQiBi+Us3/dPZADWAp+2nBweuA/P+DD4Adszj41pXQd7Ec4z/GqRcdYv3S/UTwCAthztRxPlHqguYyP9Eu/5mI58oYjQhB48uOfwRvA3P5xwsOgtHSiNTD9iTk0UWT371AfUaKoBNg4NrCIpnl47dIpEr58A7lfpDd8wt+MK9WAeF5eraePXxvBFPMdkfxHI73D+zBz4MQrQz4z3ruAvCAf6B98/v54yeibylDgOY2rNvOI9iXQEpAjIMUynj19ljSejcUSimbPxg4wYCJKJ6aXh/tH55uazHab1y7nLAf8Dh6xkI5XjDc3RMPBNMZk6AWh6gqVkOtd61mklcolPUy1pAQwIbcMpLaR/1J2siuUEtqP9uKcwEqyuDik9Jm4ytc0MxAkXXa488EgnPslgyK2qEL0Sm06Ug4QcQH8cR8TmA73PhcumJE1zM5SKLHHNERw+lDVhZtYqHVVBcZwEdngUUxPp4km7Iuq6jRyLLVDMTMc4oy0aJUsR90lol900zXa5Elu4dtyriR8836n9yX1+uAQToYh4glMoGcwwRjo6XCDV7R866WfBAhw16vJ8BzY1olBzPLGo10EU2Z7cL1bTBmJoYcAgHcBoLEtYYT7e39klH52JRwupPX30npj08Gpne8sKHU1id2+fZvXKK4+/853vkZV8L7CU/alkjcP6bIE/Xlpe1VzIwVMX0eBGca12RIK4jWO4p5RWG38DBqo6GNrqDdsMImVhAqpEQNaNsxml+pHAZThsxpLTOKb7FOmGgCfIxdWFOZ56B8w4d8xwjBY3bq1nsVpBqGBea3hgowgN3M4+zDcSVKEirADjqS/AKOUCea+y+CSdXWd51Cv2ditwFVqUqAvxmgAAMwFI8QnBbQwSRPZoRHAdpM5DfM6m9AZWChOQYQQCQwCEvKIgYTxT3dxwZPEK2TOT1CD+ae4qebD4ADwaBNeJWMA9PowblsSmmX80jeJo/wC3T9zmkjXHmNP4uWOZiqxLvvLKQ2RgkmdZhsK0/rxyvUbMQPQMBaW2ZvSR6vIxPUa+hggFiPbRr0AKVnjtdE+hwTrezwYQF8GxcaJ4B4hpr6mgqugIYKFTbmO7i78MHLhTgP5q9LKf6I8mtH/At3/JHR+lt/GmJpTY9CvSs7Dffr/CnyK2+5qqyvvEq9D1dmT8+tF80V6yH/+m61wzdX1fEfBmq5g3iAArMLsHx7ubz08PDyYHa/aZUU2mZp3wezHLB4E1wwm/y7VbxxQ2lb1L03YznfjejFMRAigu5l976UH67LRSoTXb21tWWLhNLDxcAEwlomGQQIp0KeLBFC/F5E6Zsabw7NlT0+K9Bw9ONjefPn++UDuiLWeAJ8zID4KWGGC6MPkyILFEHZyeOeqNakWToHoRbHZpMTwszc45LoW7MTHDExTHbPdyp4MNW+QN5YMLC7SDOcYP6wwWD6a2trbVRSgjSS0ocZA0p5OjgyMM4XOidqxFxuHhUSjxEV92JW0n5/AWVXBikn5kmKFdqVcbqSCHNuoc5uwW4pgpALBVoeFlfJAtQ2kRdYXuojh70mB2YNP8xIBNy66unG1DdMVx6eJidWUjhxXQNgwLRxIsLhPU9A5SXV2kUi7FvLre3d4h3a1mGkEy6InPd/bfe//JyuJSLHOMMxMxaD1+/bVvf+/bhP3U/CyRCG32d5GMV5eOLSJ6nVFEC9ne3LJssrayeniwv7y0wsrCSbm2GV1jslN/LHhpAqYVDfmCD5fqOMfdnZxFhBKWIDYdoyXrNXaN5fr3eU8j8zjciQKNG0wYbQLUF07rhgpPctCxcHatO/jxcN+RvjBYxfn93V1luUrhcxqiFwzMs5PllWV2FDSg7SLMu9Tw/n3pplg57dlj5KSu5RK3WArVbsDIdaa5I6b1OdWhnHYuDd2V5bWYEP0STzMGuru9+tawy8s+OAXZJL22/S89S6jXZnU3xqvaK7cpdPLx0mzc1iaDX8p7P3h3LR5XrFSXpza+ZW/igMPY/kHWeSGccmeJS9SWMz45+Z/xsZtl2hxcXS6dHmcv4fFhLHlH+5tWrl959JDDlq+kOgTxih7E38rwKbux2SM78TL93Dwzy+S4eYagnpXaQqQzEz6a5kpHMWsATzoco08wdBeoR+lU40LhQCBHuV4l9DPJUcNiIESSklVqhLwSb3De/X2ZOHCnAL3gvV2//6g4HcykwifiWCCls/o2gAb4CVkD+DZklzWTdmjkyCih6c7RCUtV9ktzAj1xm6TgGlGuQhw4r8q5p7xeWD3844TKz5fs5A0qLNkwXGsc5uulpUW2HMqQFLINjHqtoM1N53A5skpz5Jr3VQ1eikAnkEKigFczYUn+US8AEEtQ+c6WKws2ueLoB+AJoScpQtUAHJHJCcYCHB3tyqboAT9SPr5u+VDjm2++yVmVnH78+LWI3pKs0uGkvCmOGK+CiojSbhfMgOkTvlNFmgySRl0O1FNKSsrwXp6ccuqMVrjqsvicHvQqkD3dQFVwyqFM7e9lYYsfCPoRgEF0Smi8Ml6cLbaNhCo5s7W9K5H4RHxt8rHheZK/rjZzu37wgKmD9eXCub+oErs8syLCE+Xa4tHV/jUZy5zDr/3sYO+DD568/eZjrXDmcwJDAAEAAElEQVQqNGxcdC3UsTlt7364PreeE8cnHDewaFSglnpK69ALJDRuaMvm1nNPGgjKNdBRy8Qm3h7u7aMQ1pbx+K9Fns0Z60p0MLKySxksek2fGgnABPyBTS54dUEioriQHerV6Yo0c9Ajl2Kd3DLtQCVXZ0GlLA47iYBKBKdXhMGsrFenICASZKeIKyjuxEA4bQ9kEeKnpU9XllYUVDU+eAoA4DcGBHF0BmHWZw9QYuecV2AQxrvmKoaiJlipEEP7urnwTsMdbA2DcS5RcWNJW5jZ/AZ1IgcoV3Qgb3N7h9nR+mCfvAWnRN8ZjGn0s1x6Zj8/d/uzucvpE3rhOa/xi8u33/6K4LcMZwL1R2PKSIY+ulIu8C2tBoWtdER9qahpo5qb6aPp91pZ3mk5GNB/o31DQl+z+Fzp5ViU7Y6jglXnxx4Q4lIqrXEu73Y8FRXPx7kfK3z38tJw4McoQAbK3RD5RRgMeqF/tD8bMcqa/m5CJnrB3OpjDUJxWfXKpyAzmJTbz0DfhE6/efPFHzl6O/SAgXA8csxEpn6rAcCyd2k4fPvtty1Lff+9HekmU1Wb4c1RZmpx07RzaU3lM7NLRGZE7fS0GZyeEVNQLSuwVVgysmcFzmYOVAIHV5OuJRYf0AwZpC9SfZ2jh7/w7u622vkumN590SpIG1pdXn7jtcfEj+UG2pEhT+Mhkvf3rQVwFeKNG19RCxdEAgBxEY1CidqJycg5Rqc6L1EuMgg2MKpmP1hepqnkkxe1fGy6pRbo1Ih2EcUJqunFrDvge/l55ExefFBFC2mQOEPEQI4z6mr5TbWjEDUZFkDU65gaTeAFpfmOVNRum78seRBjkAztEi8nKoSdnmephcbAShLX2ShJ2VC9d5D97VS+rZ09Iub+1EYfkMiZ2urHslOX5mzvPuH+Q2PTnLlauzw6O3XbOU3z+mr5+d7+d7//7uuvPWQ5cxrN3u4R3eW73/2u5Rj81y6rgzMDJ08Sxpyb2cOsS65trK9TGTEQDHo0dizDsIhstkLkHngLauv37ulKDMEc/YADpfRSrbOHn1WGsqpP2L0AaEt3TVpYfk4S0/CS03QaZjjdreHS1a5DCdgO+K+sigAoC4CNh3lQfyGvtNh4hYNBv0oRbMPU8Ylbf3NxryqkQKVgq7y6w1CyXKu4huspChb8zGfGbVt9cgDRfhyx1c5d3GgEoNP1LSR+Dhle5/Yu0DeUZtiKQhaCp7Ks5rqx+TkfDrnVBGEUJlbK8GrGUeNnDKZ6cHtnk9YUfj6NHXR5zSnZUW2Z4kBSd+zz6sUib35gZ6dZhAKpopx2aInt8IiV6ze/+Ru/9M7XveJbyjlK25qXI8QTN9xyCJNEfAgh4rEf+SXWbBaFKERHQWo1RZly/MKL+ApReGqCyRRT9iJKkaLebp5RdpN3K8AEKVK1vYNXkU+A3SpxF315OfBjFKCXlzEvSsszw9Qc0zOUZ2k7niOVSEPH8YDW51oiFTpX9NORBoBIVs0v/n4U6XlfikjJnShAwK6uT2cHDrXzbZ0lKg6cA9+d2f4dRw1zaMQP54zTaDBSpCsOCeB9os+CAvfSEj+wkQdCieGamktOwEAKdL2I5CYijhKJbe9pM4+UbMZmi7i6IKdJC8CeBI8AEkBLa+loKH0FpnyLCyB9x8PJkRkBTCBEL/GgdgUBqNczBWo9ESQ8ENIbACguLqIuDdFYbeco4mYK5EGlXSpVBE4uSquOEaytbYqQiIpgCPwka1cXeTozAy1JCbPiyhJEam/pO14T1BA4Mx7KJDBGCGdbyJjWiph5/rzZYFYnJBHnMFuwQYYik0NykJvu9JIeuUyPaKBjkV3GpoGk9IcfPuWc9Pjx4x9+8H63BR5uQwtLdMeLudpalY6bmCCGCWMkdRPYo4pnUSw0HPFeRcBoOEuSdmUw7O+jBFVIxagieHaFqWx2mglNG6VQVmQ1JM5AiHLpmokkSIBB3hd2QqsubazBGb4hGJeak/DI8qpGoalSJHK1ffazmzJLWnGTqd7RCwC6OGxq75EpkYM9SuDRBL8LkFYqGx4n1QtYY0Hyf1NWRFCpsVGmt7PznVj+VCQdWqMIwgVuc5PXuztb3OFQi73gR13T9irHVtXZQtQjfcEIZJS+//771/s79x+8YuHXgiOfdTDNHPeyYBee497pYU5mQtj0RPQtWuY7X/vab/3Gbz56+ArVrcyjYUUoojLlB2diwRIrewZKvxa/wjFUj1LypwJW1ixT2pKyE64loex4Rr8vtSfvpe20xlPP1pxGOPIH6maLZ792Zf06Biyw8dtd5CXlwJ0C9FJ0vDmp5J1ngh9/fauZE3KK4E1KVCIzl7xOwRrxZpB5cBy/nd7xcVYw3kxJjcc0DZtaiD2zKh8gUseWH9/6ptFBCXizKrcFqKQo7vhgZa16uDWJRJErmDRlwSPOAdO13ID39w+IT7NxJkzzrM9H9oBYTbLvjM0AnrKFX5aUzeE9kBA8ZEM7PpOh8ACjmRByxKcVFRYcayyuTKC/lbkozp4kGUiSQHMcjjwsRYdM8mU97bhDG6xKlSF4VKEi1pTVVaKdtHAVV3bCoxwS63oq4n86PRNFhzWlNRJ2GDVrprogUZfGwqnGyOkc/zPPTwg/STVVEGyeVDBVKAJ56YqjnfmKoHNqJnqSuGoBsw61SqG9aEA5FmmCsnqHOpgLxeZmHzy8n1ZcsgcM5pivZqdyZKV1mdOcOHB2lRWTeA5dni2vL4ufHPGbrdXJicvnTy654Kyv3fvus+fPnm/++q//ErlO0NvWtorulZWtnWcrq4vOLqJYLaysOSMHSfBpJpLQubOz7dBktKuR6wyR7NYz8rY3t0d5YgXRO+urmra7ven1wb11cX5PdQJQrqp1ZRvLiFUteGAOn+sCEDgR7EmWp9/r5nYY5MLsX2w/Tugu9UjXR9mpVTmlBJzEdiY0RSDBUkxANlQ4ZiyEqxPRSGBWux70FNBADxOfL5srMY7njpuSv7m1pV5HPFP+vc658a1+c3GpoVHWVTD0Dlmlr2fBSwPX60eKGONhweE/OdkoIxAHDt2fenG5nxOhFgynnGidK9v8pi5Z2ZyUROViWK39f1LWGOeYc67vXTD87LmRPmOeXZbewy8oWzWRaqiwAk6wCgmX0csfPXj4H/9H/+E3f+1XdAX6VU0bppFlR2SsPmkD5mPRVd7GKstIO5EJADpzTSIKBghYVKdigJ6IRtWfZHIaZ8E2tpT7iwLgMfxnwsD8mel3iS8VBz5SgMYDYjxuxik/CUd+KuCfBOEdzCc48GM5rOMaxnPciY3kdllZHWSJtKTpuFcfbKPsmsJuFxyjFem4Zy+BjRHCI9HTPDnGbw6tePwwTb6bO8/+9Ft/7mQ7UoeoiEmnnU/pChPZNSPQytyxba6F2fxLXImYZFFLCImLeJI6ES+1q79kmPna8TAHtsQTGG0paUWBIEOGTVktrkjilriQtI6CbEUEpEaMlZ0A/n6V7lu5y0LrttH+mpfuQGVxCNEZyVGNgq1pDotKZfFqv5W61CuxVbHUVzVGftR5xIpjEVQIU5FclKCciBW0heomS6JXpZx8BCGA4gzv20hBcQqBWiYjkeOV0rV0Y9k/AMAjvQpewQAAnVZWoGU4gd9NVVQZ64MI48QThtM+oeMGYhNQGQNwNdaUnEgcr1tLjbRPSzDOgnn3+99lBPqNb/4qVVKW4pipFjTYSE2rpQG/8dZXJyd3/+23vsWq0VRB30Qio0jKIuPC0jJ68EScgtI8bOYYDF5R7lW8X+9trC8uxSyHXfNz3MVYqbgfMUsNQYJp856KaLFpV2nhCBdxYrW2UJjCuCJGEZQDQyECqGvGKJgmSS6Gey0ystJkoccThu4UDVFKXEj6MIu8rnKTDr/imNP8b7Dw036rMk3JYtuRjjC1+ElpEWchjWWzRJIW6QiM8oMDLFgmRcyR/et82FdXKTfcwYGpFBIN72GpXqh4Ar3+2ivr62vvf/BUjZacVpYWDMO9PUrn3AL1mVN8OTyl7Y6WcJalMwaO4iS/vLr8G7/6y19763WoHNxAkVZBjsqICtkh+h/THjdoSVqRMMrqFxNF0v0vWURPpA3J9JrcSh7FO8VTw8fxv0iNgaYLwzVCGbR34Y4DH+PASAG6PURuxz8Ge/fyi82B7rhPdV/54tS3lKwKowmn5yIpfal4NkyYYkv/+XRDWySPJqSelqq8SQtwo7pdyrQomKo6ADCtm8T39rbtdnYhvAl3sY6eIzCoO8qCVEQ6gXo9ncUv4pzEbMXCDC5icq/p3qEszAS5wZtXkPmdIcT3rv29pjuy31G4donXxzlnzawZ2ZAe8tLaK7uAyHhvkXBMRuc5Bia1M9acm9lX2v80lz1dz9vn0rTZcbJ7sG/Za81Cw/Q0+YoeBNvL4xwUKRxcCFHLChKfP38KIWrFhW4CmaQiX+cIlosWpQg/MoWfKnsDeqSAwRBlCRuWIBJLIvNAkPANd1np4QGScIf2ychT5r3oScnlV+7cY7vtJlK1WhgkiFkRRXTBxj2P/Oo5BmEg0w4yWDk8kceygv9Y4sYo91xKnLg4zybvuaEvdF5VbrBi9IjKgJexBeRYvOYeWwgaKIJWyQbT8xv3H27t7uzs7arv6PiAuwuO2W/N5HBwsjs/OyBotzefOfOAu5IeYSTjvQIrfQUf7GNvBqbpxyfWCNFCtvdylXr9MxKoaNEkTs+cX5yNY9aPDo/2ZqZb5GumSvW+hsDWSiqGe6WgSGGSwWfDzK5Ar0Ln0miVBTbSUUrjxH91GcPhoCMG6OU8t6ajXuBuRl02i2XxUSn2JxiKTzHOwax29MKpQSqtejVhenVl2TBORW6iqHMd3U+hjkZovLLSsXGyZVkpLSMfe57ujiJIN11aGFg+U4XcrDWVLzw+hS0gouOqKutK+owCBNIQRQOS1Is/CAYoy649rkD0VyOZLjubO/KcwRgXb8UV0fbFJTq3zY+He7vn737/22+9dt92wvWlgWEZnOBSEeXDz8K61VVtOR3xobIQSMtBqhFUw/LGjZCaFJ1lZPDJ+ILpM58pOsrNrsPPDPgsveoqhaqA6jWxzi0klXH3eFk5MFKAXtbmvzjt9pPO5PEXBLmCiQxMPwGaAT3NfeSodAGMiVik0XjtSGXmoUjHu+wYyRisI56mThUBMy02mGlfeSewmHM/+PA9WeZTEzqjuQ9jwCZijrkm7pUcK+LuKkfaMIHMmYJhk044kVtteon+VLc+yYWH5FPcDaAuqgQgtwkgReyCUQEATeOS4gteogCM6CKjaRgwkAqbHGxvVtmkIKmJb0hTrSP1NJ888YQQgIp887rZA23iqFNvxHDhFEeVGqVgAlZLQQYh1MyBCqSvd4lgAEsH0/Ibo+BUKhLM1qHaBVbOttmIJEsR7rx4AoApgZdPlrbOnEvEFzgn08CmD5VFsEoF7cL57lm58KRDwbifa/r6hD7J8efgkJ0NVQSaSkluqhLNb2Z2hk4TMuZcibrmLnedsru/A4m6KHCUUxetLS6sLQ6XX3vtje99e3f/6BB5CDivMwYfPX64vT198sSRwgfLSwN3bmw8fIUyunW9AwM7Ew5EcldQShejTo1IJc6QrV30PCcFSOxGYaAi2lVMiCEEqs7VcRrLpantJQDwVimYdQcmZ2tR8IcJzRksEqeEKdjFIUSGrXM4o7gs1akCQPVXbJYdRzXM0MKwu3ckV4C2lSoRyFvjjPZeK48QSu+OQFK3tBMhUTWSAVjqVaOfD5I4Nel3xBhX8HfTLEJRCfUM9VRo56eD46PVtWWjAv6mzfhM/97YTSGaenYF2MY9RNoPD7Ntl2x9PlSWVvJB0iF1TTgX6cJZjO7anb52EOnxv/r//d7w+vTrX//6w4f3Hzx85Geh+WqnWPio4cOM/nh3l8KhOR16kcvTq6yE7E+oNbCblPqb2cmsVmrKJ56t9thQQUVqsI+0nMI4eoyrQEnHlRAfw3S6rNuJ49y7yAvPgTsF6AXp4vFPfRzRMHHzYlLqF+5Hntmxru+Sax43u4m4WdtMavLq6UDEhK6UIC40j8aJjVmulPEM0jBeG6fXLqg+kZpGs7+anvHOO+/YOvtn3/+AXAbknBKzs29jci13a5fJHTxBQkOjQRE/BIOp/+g6yw3Eg1dFiM+SB+5sj42EdFEpAvjaAqPe+KjNUb/TszY5Me8zlxAGw8Gi9MODY8QoDlurBSJSCHuJUjBELdCOeYILpKAEuYS6FTFZP/jBDxaX8mVvUxVq2/HZd/y9jQ2SxrTtU53vy/5urpF39WywccU5rXNcnCVzGN1uoQ79Y49gxCJppGBsP5t12qWzcAZbOit2lPJ3ZhGyrqe93TvaSBOw0nh+HiuRA5acasNF1R67vd0DGhWPDU3LICi5ri1Oi8HqcNvdF84PzEk5+8QF53N7pWheO/s7FCCn57idLHuW3GN6tD9YsJd+1h74uYl55wBwvCJueUMT2HE8mZxkFfv+9/7YDvwHGxtO5zs/5kUepYGy+MMP3lUvnx4nYDv90omIlBfs4k5+moOIaDhMUEsaxdSBVCss8ezKQVBaN6Am7B2z8hg9Ya8LKGB7/uyZVUWbmOh0hOLx0YnG2mkIiVwho7lGLMZiJkoMGwcrFGOZSTIScBsST1nd+7T2mCvovuw9taqobagFXIPWmMled3XiKpYKsgwPuSfH2VYGv8s+kOGWMGjJeqy2YAkGPcAUlqIKccDuttfGIja/LxgMB5zwsxBPn7o4uLb3W/cD31zl7MRhiAGU5UzZpaV1zWeCocsYln7GF3Yvutw+7tKzYJpaxXf2XUjGaBq92dgzqBix/AQEylDO6inToE5gDNzf3jzYfXp+tLsymPraq6+sDqae/+C7B08/eOXRg9def3Pjwca9B/fj/cbNX9+YHKZzZDnjUE0n8YzmHaRNmqxp2lKaCFAk55Fpqu4rVVySqo3GZoWB7VURoaBDmOyAddnK+gimpqYUV1EVNDDKx6jgbh5jVDcJd39fLg7cKUAvcn+bDlqEl/6Rh6nHzNRtFpcChguHJ/ncMkCRnp7MDiKZYsbT0A23pIgqTnvqODBBoqeURt7gAauPOelsIj4Z/4O/+/ee/V//4dOtveX7D+Vaiui5nqwTyB5rSVOX7fXpI3g0Y9JOAAMgMLoWkCKNn5AbUy4ivYnnvsDdhHSBR6QZgpIoKKXhtddFp3uWuIwu1V/5RFcD+/QnFfY3dwgPZZWCUGhi0CCONkIFkhYwUCkrRe3wLNalWpAQMyCJwIZXF3hPwkMRyMkefdFN0ApVeEUPbJCrS1mR+NJEaOYgHPjxB7C6vIJRhPIHTEEw0AbzfnyD1CWdIgjS3h8kIV5ZpaSjAIzabR+/cmgkp4/LnFdpSzNHbGXRqAkkC/jT05j6Gr9lRzYox3JTXLVoZWVN1YNXH+LVzvb7z59+qIpXHqfH05Dsh47pyPmKmiNFOoYQfohBfCkEuaIBLeJSmDfoEOpCAxapYoSqVhIhpxdqC5MieJA8YOhhuAq+uddtl0URVykk2isR8o7A2VU3DyWymqgRJPzw4CH9FIyqpSjnCcyrIgakSGMQoYXI7YGhLDBqODAd2om6AJifHm73YEBbC/qGKZx061ieYNCuHiqUCZYbpdRolRaFxxPHyyvZniZFUFDVMCsFMx//otqFKDNUTCt1zm1gnTF24KQcg6HtNjCTMMOTK8t4t8NM6aUO55jM3a2jraezkydf+8prv/Mr33i0sjQ/leF9ur353snh5vsLT9ZW1+5trK3fW8gNIUuzw4Wsb8dFLAr6FWDuQEZfOQupy+wh+EuJkWwE0NIs5CWrgtxshk98lFjwsftka1nPZM2vLnDz7I5O8QqSxU0JlPii4Abu7u/LzYGRAtQD5SdhRQ+snwTyDuZL5sBndqLp1dTGUUOkQ5t1TJSmYxRKNJF4FcxVkEg3W41nkG5FzyNyBSm3I+MhMU4EAFtXJ7cjpLWp1oLRwtLKN77x9UePHn64mXOBXUZuHpuai3uEWRuwKdWTGEYd902lchRJ7lSPHCLhUMiFxSlBmoZU5/+CF5eeC7xLUJEBJFwQXk6srqxfnG9yDCK95KINZOb9vT2qF0kjzp5ERtqY4xxbqAh4rZCuUZ7obJmnLAkEEgZxxDDf7+/tIBBf9nMuTuQidQQVVoXoB7B1jTkkd2Iyq2I3W9xhBsF/BY9UxNPFcSyAaIyymo3dKV6hhVxEVSwU2gIz4vUYtvi+Pc9mcl2Nx7FI2ebjgG1oW/YrLt5cQjzONDdImP3dHa+ywAj4j371Mgd0q3WNPT5ucpi/GPIAtzTmMqzSWqeUhUdxfjwaiKw9C2q2yQ8oQPE0t7Cyvrr23vT7u7t75+9dLq8u3d/Y0MdEE0m8v79nJbJUHLqsGzgMg+hwrsFFLb5BPjeTW9nx2yB2ISfu2fUEs0MR9QuCKQeyM2YqkPo5+89Jm5hwdOgt55DnzIKwVI6eoqjqMsyKG1MpZM71VuPS0rLaVQohPlBQ2gqlFsQ0JFEMA54IRHsN1943HichfQESkXgI1cy8RcosnykrJUQ6UvwwzvWWwZgd7dKrWmKCpUqdnMTfHNsZl7S3eWtZkVsQbKwpkCurT/FKw6cOJ2l4uAGJ8ewgQwB+8HQMtiInSRpyetbOSjqTnWXuBTs7vl5f0czLnZ1TgxWSy6upw/24RkEFXqU2CM5dz00xyPnGuDybvHKa9rF9gLMXx28+Wvvm21956+H6mgtYbIGczL6EDBtrcFvPn+xtP//h9/kKLa6uU22XV+4x8i0uLDtg0cLfNM+g/Abcb5NbeKlDvYuitBvWISTUaYcy6lecuSaWvlEIx8P5qDVl+Um51o8agvbUkQC0agW+/tF+TBJyC0dDjb7WRi93f14+DtxZgF78Pu8fvBnKDGtSKMEdLxavGp/JsSzqzQjA5ugxU7psPxURgadnFjAiHRr+5i1zkDjgrlEVXmElHkjBg6PT4yN3GsSGkZUjm0Um8oHoY5Q46sUI/hUMD5QD8xhZklm7dCOooC0BeUz2W3KC0/crbLIgkQVSEa2QSDC89957risS5wvifjHp6m2RGYlyFRUEwZn0yy1jYS6o6ENvvfUWVK09oF+EW6k7uuXC7ylAxWMUHkYOlAiQNIXWGqAloQGrDipgAC5Po8QQk2iDAc0ingID1cJSjD3cbWXBIw4J2aZg22nUiCeIkQ4zK0+TrSLyDxg8UroTIdEuwESv4shYGETQStciGLCFEmDtQylUqQ43oGrKbVuDRy1yFffkgwxJlwVMSgFQRaesWD+aDB7Xwc7ORAneiRfTCUsDC4Td73VB2cTG+j1Ms6XffilLM8gj8fHHDm30aIK4J/xqZMV5eP8VNLT60tYdpiCtaAGnLTkBnCY6mdOVqDyIYXXQI/imLV4VR2for9GPG9KRt7y81I0NzaViAtE1JCW1CTxKBB0hSMcWZbEoDKw9dNJhVrY7CH5xhGOXAHkDgBG0SACAJ8FQehotaNxr2qhSWYC7liwZlSqADAURwFoSD3DGv+mk9OgFQ91EIWZqL+2nOBZDplC88pmT4YQq+OVyZAo3rDvGH9wwiv9Q6CuvHWrK9Pkkj24Xi0ye+/o4vTyhDl89fvXBb33jK7/0xqNFl2PMTsxPX3MZWhlaN5zJOqWjFq4uTlhwD3YOL85PHEn04VPdavAtuNZkbc2ZmfmBD+j8g3RSzEKxwMWUQ2OL2kMDNvIzOKWH+nokUnpnHIZiQcp/tJ3S96MM9X9BkcMT81+dGk07SjxaEqek7LqPr/UI87iKxn73fPk4cKcAvTh9Pv5Va5I50KtQC0GZKkx2QibHqD60jZhMAJh8KUCyxJsXIpkpSryMnx25zawRfKpIkCVF6FcYxM3LXvtphpNi6nu2tfOtb3333R983ykv80sREkNrIXWYDZlkxaEonfYBGtlzcc6L2bSMTldq+2JkN/DR7Hhh2o/rIFxH5YxAax+7+3to4FXDqA+bSsm/iPOTo53dLfN+n7QbEXvptLdDMmRl1SnAI/GfL/bBHAHi5oeV5dXnT5+bN329n09HJhEYadscWTJLXYCE8EcnghEmiKSKvmHqJAtzzsmtM/Ec+jcgJLLFaXrqzBVT2EIYZaXonLeFphGH8zyhhvE6gglaChLrl8/nise1FoyCIiWAs7OdIiIxtgTuLM421OTqQW1HD/7DDIyMRLwve9/eLjJbXnqYUm78zuF58+i8OD0j/cArRfjJwocW86Qp9jGfOBzPFjkICdy9nawn9gjR6oRa8iNwJlkolnKBidF3NOVc7VOLYjtb26SSfp+aWt4/Ht29ldOLz09n5iKMw6ubY/pUEYHL56hIwiVH4VCLab9cnoAxJNRuuWz9y/l7bE0Tjp/RpWdWWNRPfBoezu3TKLgAa5eBgWYdhIE0T3G/CC2tLovmDQYlGjVeLeVJdnwaJcn5ncXSnMro5gsYvMJQC4xEPttbAhWIh1gO4in7DUWKdu0XZgxbZPSs385UXPJjitA7s60RA8Jtr0iiOhhVlo2YpRzahH7BQIv/0NFoXZLiQqdVO+KpQbIOD470r/EQragsedfnV16jT9QPH1MoCmg+mz2J+/WMnXppbOw97CusMZcXq0tR+NiR/L6sD7p24+jiEPPXloZH7KH7J1enB/eXBr/6xptff+3hOjVm+nJpcc4mwcurY9oMDWPWmub01Nzk9HB23oY9t8Nc5VLBcwOdR9bR3rODrQWqD33I0liONnBEwWA4PZ9OMUfYD6jt/s9MUhboWG6EaEVTHJWKgaUV8f2pLFoN36eA57TELJ/RbTylGFQUn9Kp8qvhuMYAxrhkASz4KzRYo717vpwciAKUMXYXXlAOmOa0LNNo6Shedbep0NQ8TjFdSBeaB+Px0AC3GSPlE8HQ6RRg44IiJp1+9ewAgHQzLz/f3P3DP/zDb3/727PDdRvXiScy4PjgkEiem44rjEkcwZm/aB6zcbYAoCy9TXFfv750SXGJnuLtjkN7UCkw+3JdEqAUwurrNib6xsD2bgJWBQMPYAIAfAs/X/yREPW5D6GKlJIogn5gnhASFgqSPaGwjEyAOw4G5sYGBioBJCEKUgpPbESS3DQSRSAUT9trHQcYeBhQ1YklFyMDZUHuFYVQKY42gd5zenFq0aDxgwQGCTAwaoTHE8K19RXCFf1EDx5JBAAY95CxUn5OKCeIAG/cW4eQ+qjtUlTEtkSnUYoTxdU5DfPo/CSLa/Q+F61NDRa0i3cFhYlWsbzibjXXL2SXNUrgwSJ1Zpf+5TmLDulXvLJi5bjC3jcUcxH6wVOAtBQeFKJRF+Ti8dJokVo+K62vZL+YdHYoXX1+zXdYz0bxLRPSGSNCFMqznPUnETG4jb0wq53GpSxi1MFoBDMYuTGeVNApp+dZjcJPLe2gYOGPqQOLwDg8qOMoxygLU0AwSgCJJANA1SBhUNwJUg0viwMXbUwuyFcePkQJguUqC7ji1G56J7ZEqVVEtehXkdIw+xX32EhLS+vF6rC9Bl528tVo0SBVU+A8/SK0SFncgIrHuYhK0eAqEr/hg909N+ZadKZzKejMBEdKXJ0dLc1Nvf36K2892lhbpK9dzU1PzHBxdkvY5IxSfhm5fJbx5uJcf7AUZlPYTJYCEcnDmie7kX52cHhpSfN4/uRwh+dfrEEOvaT2OGhxJm71Flr9SNEpGM76I9zI/sbcH4ItUWDoMaUX5XOofpVSnP7gGR3IvBGlqb2GrqIhVwoNSRxDeru9psExKl49fvd4CTmQaf2nanaG4GeFnxbPZ+G4S/t8OHC7j8xJmTR8IZVhQAV6yixgVjLl9TQtpU++kT4u25Fxt3r9RFBcSqadCjD72w0QgQpAMPcMldyoDu4oEszF6Fly/7b7vOYXaAZMMuBPnUQdb+JymSxfSMX39nfM8iz8BG8K+4DMlJ3zUVRk3jehp0V1NoxTT+7fvyeRGJh3K2f8UvlzIDVaINcQ39qcEnZ34Yy+AgPzPDsRnKUNjDxm6AHKqk3VIsCIHxGSTCvID8C7z3LbNhiYJcLGsuTznEnE7M0PNaUGc3HIKOHn4lBypT53CaTy88gdYQwAvv6v3MvNCkOStexX0fb2JnFFBJSYj+jVTAB2k0nZ2d0N/VmHIqWiJuKB2f38wnHbE/yfEQbA8qGI7/1uArS+0yX5JwWhJCHuKDw/5XqLXG2mFK2FvCK91/gv7+zMD7OExPwzOeXAu5mFwwUHFuAMDBXiLsO5A1UidnrTA134LUv32XK3svyQtUZXpIuz4yjC++xk3jVhk9O5hpbuHTZOTDI4XdWlpMYD5Rh1inTzu7o0OUPaXr9D+5UMJ4Sp01HFkXgcVazfOUw5xiqWm5jK5BuD4vQGtegpuWhTsIZHrWfdjFgMV0XJ1xgeAChlbKCQeQK/eFj5oCgdMiqOYWWAQQUrQ1SNdrovm1M8nYFqqVyjRVxDLNey/hmrXkl+Mt5oV4UajSG1GDasOyDZjbCKmbOckFRhcRMwZRoH0gQamo3znMZcDAw/DawUVmdqGe8AZ9n2+M1NzafeteWVuKiXVdIAM5hhsAqb35E7T+dmnVNAIbNfz2eEnyeaDSQLbNczk0d723Sdx/fvvfbo3sri/GKWsJyEQOW5hA8PWPro1HM55Bq4IU2NtAPsem6WXzMiHd49Pc/qHHWFEoJALvB+9JfXOVjRbz7+8MowfTkATCvRpqfSTXrLOLCZ3u8rv7JY4ML2/KIz9eQk6Z6Iap4JkrgW6SQzU+ozXjwNSy3Cec+esvC/x+ftiPhdeKk4EBlwF15sDmSuv8xcbx7U0v7Zm0rEM3nc8nc2uWSaKBhgwpgzwG6nmFNA5oKNfGp99CE1LlJTz6i0RMBWGLxHzk1NPXhgD052v5MY5lJyW9WK+IS2DkNmxGBg03Xt1bK8QWCY5wN/s1TBWRUqAFLM14RNf9pqo5ZKZLRYqZU1MM5KkU40ZkpFzPkFq0CjWliMn29Owa2GSySPYQavYDNNvUqRKIgkd/NhahfSyfEHH3zA2iSdlFJ7iKlzBRGjCXQIicz8VLHphRSBhD2DdIAQMD6oRe0hxoIHf/DSVDgt2bsk3XIMMAfrIAkl4t1B4qhFKoSQRNbVShwAtUgn3vSMuD6jOFp2OHHJK6lgncIuIcsW5SoEQJCioCYrRQSqAqXQIhX9AuWJpmF/su3TXJtJWRK0hSj9tdgYRmWnmK3sc04KUFUMOQtLg8Od6/2D7ZOjIcOAYw4Wh0u0DkWsWlABQepo0JQmXiHRAk85zYSStdUNn+vIwEaCT4Siy5mWrkD2WkaJw9PVZRZwyDZNG51FZLXGhWXMUjEteJK1lAO8UKkIjnXnZvRG93Uz66n+okK58L5kK34yaVBsoq8DE3STJxowSiSmyApYR95jheZjC0VCrwmIRC3eIh4bgam0+RxiNPl6glrurEo9K13QCCOuyVMETr8FBMzMPKAM1LnisSEx8KSbatVMWfRQkliCmlTPHPBQHtbl7xvVh8RHD4Xer0CkexwqWWiuBkUdl04fjQ5kh9qFkw4O7fY/PTpgHZ6+Pn+wtvTWK/dWBzOLzDT2lV9eWNe1vEa5xxD0B6Hk7HK4pHt7lU4FMZpMPG6sn/HT11ulr1zm/lfqrv7Pd0nKGrkImrjKCRFnuf2LubKZnFWugIXnGkhJ8toh2zdKK/WK/hEA7ZgqXJ98o19EnbNlNmFbWl7fyK+i9SQ/iFtTHAx34WXjwJ0C9IL0uJ96/5j7Nz+OixC35haTjpncq0n15l9cWAoyM4tPr0wcN9qP2aSRNIPy2SShxOqoihuA0RcV6MjfBFHFx5GGl4BGIMurq+v31nwuO3Fvee0VRniqDq0C+pior3OXwqyZNreX+4g7U4zVwVqGf2ZV2DSFhcOn4uHBIRlsSly04ZaVZWp6Z29HW0k+4mFvJ3YmAoNwPZ8433j8KvHjOnFShC/zhx9+SDqYQMkqOko0DAoTYVzut4RiFBcE+YamrGTdkEFo1rm95ZSQi1QRQu1SrzUnCgl9RVV2LPmW929uuMA9eP/o2CEuJE2LK2xBpCd1h7lIhJTGMOtblCbNOz7Ldn29ZT63VET7If9IR8trgFPjMLuBBG3UOkyj8ZEJVtpIG62wwEaRynqSFcPhghYld252l7nFigU2c2rRyKjFWSIZLMbtulDNHPI7cWrLNW0D3wYWABWnOLt9nazHsdXlxeh8c5sO7yHUdY9VJO2JQhSXGmalqTP3fJ0fnCH46vzpk3dPFgePOY8MpuaPJl3RdW9tgxy0apdKeWBNHK5t3LMkdX5xMjk1Tw2gfRBfdEok6S/nBPFeCvd87VsgGXI3iQ2BD4zb6RfZpuYGO3t7zDuYbxuZdMo+jp1dOD96gjnJoHfWtqusKK5HB3v4hnuQ1yChgWXcGVLFM/ukcIun2vDE6T0X0VNdrcr5CDAdQinqhbhOD+t44WCo3NmZ86vLs5MLmxZ7LxpiVOIXZxTpUD8v8Oo1vLPaOzXNHR2ZhoMfhXEOmKWNeKcZ8Lzxz20repOFpIxQJ10pArBCnJIk7iwl1NpHjx4aYX4vbkCbiyP29u4W9VIr5hzCwFH9/Iwui0j0rK6uiTMPYScy0Pbehx8YcJakKMdzUzTRWHbYYeKodnZ8cbK78ejVxxvcly5mpzippUHMc37RVJtU6ndUv3fdQvOgkMbAWaYgv9dqI3NPDFN4TfGpX0CUTPeq6tb8CnyPGXWXDi7S+3q49SLxmMoEXcmQVz/+cF5IosvnS03SfK8YIt0Ugycqjb3Hilx+rjOnvr6mJpfW1qahzJ215374OpcSpmCIDAKrZYU2/tV06prBKiV5d+FF5MCdAvSi9WrPBeNW1VyRh/SeiMmtqDs+5PqLymyRyxMSpNZsHFkr3EbVWo6UzBq3QqffShhFx2U7ArMIMqB118Qbr79KWO5vcaE4mSxdh6VESRSaylhZRNzEafrnOWDGJE7ETfFtlrjgWOwr+say3cRDbgaEhHAHJnTcV2+0jTp8RZHY7GuyTkNIwbn5nfLF8QqGaFGKULf0g1HRFcqNRhahwkuJFARzVLrLV7/2NYQRSPA/fRqrDMxEi4hKc6zfCbl84LwWOFN17az2VERb4IcKfuqIuFZEuGWFa9GrGpt1cKpaKThVB8yrNmaJqryYu+MAwNZ6m7JKDWOMmVbAWoYUhMGACHtvMFTWRfSfBBUJWhEmnDrFhwghGNx44JL7iwHDS23OQpUjDHCgmlNu5ru5b9y4QTaKLMBNT11ypN3c/pDivb/9bH/n+d/4T/6j1x4/dIbeJv+eLF1k35A1Oo4qlAcLKks2SM/NuxWVvcTYcmgA6aaN3RZUbdxfT6uv0l6Kj7ag0yiyj0xkYMN7VIEj7OyGSJDOn51ZMNqhgVx8IwKl43fzEObJ6Vy6zldKdfQM+O2tN/Y0EJgnSE8sklWtzmdG9Vj3AEtGfjU4jyrslTrLc77Yi11qgw18q0FSKHDgbeCn21nK1E1pAkWq8FEolZWiFupCjeFYLmRKwXn0QKUUtLSQplOudH2UFAxiRzyLOu7Myxz2Uxvd6a/IU7W2IDUtclwhxctHxUypLzU/UHqO9uJChDkHuzuHu9vnh5tLU1bQBtz+HQZt6TTKjf8zP/j1l06TH1SmBe/5WMgzr76ookolGFH1w6tZJWZjr/kayicR4Cze5o+TLTE8zTdb+B9ExdNxUNJU0iuciiDWLxSwrC9jIhsSZYmvc1YhlYEChUicUcOkwxXgh5p6ZNRDEV0HZpB5SQjBd+Fl48CdAvSC9Hh+zJ8KPXfUPJAo24OZS6j0nh9aoclkYXJMetSej8IYZaf3q+yP0usDNik15UmHILOJeaeCFFIvzwpcdYaD+bfeeM2ukw+fbOeub3vgZzInZZbnWUkPyObWuPSYFT3oKZYzSBGGAXNr3CwveR3kwGBTefydJ65tAQt6M2t5mUiHxywPrbg1MjIm8jUHt+TLDxjpokZZ2NJrZLaWMdU0BlLE9J1LITKbEkw3bgQLOcLOJZFqA0xO9PZjtBDYFKY4XZarAXlDrC7MD1h6eHBgLxuDqlsCyUKSRNVRSjSTqpHDBkuiWmwCqQkotPxBxdJ8CCcmeBPHb2/Uuutr1iyvvEU8yU5ky9JST0HbI63hdipSnfAbB5LqPul6TO3okQIYfiR5Upeks1qwk0DY/hYoaWrBA8ZaNUKiCGDeQoxhNFDGtIPDQ/XayLa3ubmxsfKf/2f/G2fjffDDd22Lns5Ze+enx3vkFo45v5ghTxUOzeNX4yDiFkhWfwDoa5XgCesIrlAX5ueGzp5mElPakCAMiydOcrJm1LvfM7bRg9S0M4tcccFhA5BiMATXxfVgccBWhEiNwgbKhn9aRIXLWUzFEMzUcHEYxBWkecCg7eIiEGq+IFe8YPQCjccpRdx6qClUpdEGtyi2dUbi2mBt+f6DHVvA0jW2ktEkctYiZY5HuII2A6LKWJJFSVIXAvg6gTcQgWuZL4Tj4yi7IA2V3d1tSvba2or27uxu++GxYBkYFCAjoSxzRlrUaGSr4vnzTboU1x2vNkhKMYZZJikJjtKmLW9vPbePjvJ6dLBztPnk1Tfvry0YrBPzOa4rDjo4HOYy2uRPGB7lpTTF/FgyNBKkJNzA1F+PdHJdlDHKU1gJqOhRsRRVSGENZpmpCvxOMlzBMBlVno6CJ706zbsZV6IFXfE8Ahf3wQxuKLj8XJbNyG/eVR46NCOj65AdnWr8dpN69/el4cCdAvSCd3ULA41MpLQYU0C1OZ9ZEisrw6CnhVuTQ6DGs5h5KO8VPhPyJjNFGkBESHo/C0IKKzQ/5Qf31//s2+/6KCMm+A5QjCgOptFaMFo2DZq4iUbaxlFdEIZUMz5J04JExIQuEUCJgUO54FXiteRi6jNZqxGkSMdJLLLE57JATDvqhqggVrClEyE0zxID8Ai0HEshg9lc92iRgsmf8JDIuTdfyQcHFmuIKNvyYbaJBZ7eakt6iTqcN4tEdT145vO6zV4TujlIAoNdnuoSgRnOPTdTrqy494oAg1a6VhBUIsBENFbtEoFJIcYbBjBiuqWS1VJcT+9jAlMOh1PbqVUohWAHL8gKktrMD14KgvGBZFlcXtXwyIkaNmDU60nRkhI+lGqo0lByfr51dKh1dJQBLm7cf+XByjtfe/udr7z2w7/3PznY2/ng6c5gnpg655M0M3FJAqsIHn7TJCzPsN29tLdpwMAmXo3wWdmjsThFkFLl1D6diAkAFKfLakJTKBF50IpQWaLd0J5KlQSMS/CDNFRwwDgRFzRWE+SybnhVxGCwqR4NjQ0NpYLEGAkbSMVFGptXzddLagQpy6CXpWzYTkFbzAUsMHgV5/XlVUUAIJGuCBpm50Z7JyU2Z/Ss+MpKtsUpC8SruGCz+v37G3pJXL3oh013GG+rK2uxO15lK9nW9nMFaXEANEfDYdYK6o6OUC8r6yAqzRQ1On74xuHE9dHhvrMuL48O7XR/YMWY//f1pZW+chYKG1UUJaOmCK+dIlGj2gLUcVNOs71hxAVZZfdRvJWn4AGABn7syTXiqC6uh00VvnAsmWYXWBU2fSVKEaqJ5cpRQoD9ZhmFojMHV7JpPlUVzzcLuaHM55avqVDSmG5mqnq7e7yMHLhTgF7MXs8MczPdi/dP3tzUidXmCGN6UUGO0qXUh1zyG8MYT0eqYB6ZRGoaMUOZdG7Uo5G4LQir87CMS4wmuIZfXVn6yltv/Ms/+GPnk8RtoPQzEOSwiszOZqpD3q/M8os2puRIXGaUgJGXE2Q8mZJNvIB9pvMIIRi4M5u7qS3WRDTERE+utPBDhCyKi/OTjxw+lLNGzLbOylvgMwrJySnHCGGZRPQRbK4kxqT73iWubJMmPEy3FCCYiZOFRc68gTGT2kpDWMLv9ZX7D9S1dziyRsDgFSWKi5OIpFcnQiuuQ6y9SEc/9xU4hZBd2gklYzcXlTuf2k6fdW1RNYRWD4v50ZwYhxThMhKJ7tVGJ3duLC4qJSwt2uHlLJyRfFUjtw/KpgPz4Kk9zJE64miAn7xEXsCKziaVvweGoRBker1CvHBmZofzWVbjfUQE82uydqSgVTL0TFwcD6avXnt8b9Z39/nF3/53/9bK6tK/+MN//Sd/+q333v9Qh6ytLE4dsmrEIURFtb65rKFWh1CuUVDR9Cz92Q417c5c7jITms8RyK2604dHx2trDhxe1gUUB2ooDLpALxs/eFibBCdY+FaGK0YLOhGuFXhovNJitFeKitQujoHi3MW8aimDHLEZrlaPYxEk4FElEQzg5humSQeWDrYZvu6BB6DTmxJjo9qSRTQgT58+NWD0kV116GR2otvEbd7Zy3WTKwdwtUMLjJhXFn46p7IUD+Yid36A1PUKUbzcsHp07N6uGJC8Uq2U1cyzkyhMYLpbYYMBi+hGqHWw9+nZtN16Tp8IHxA3ef3uuz+YOL/UZU5TOjt259vOq6vDV1ZXhhYuowBlp17+ZeEqfmrIiLZSZprEipnewGT5qgCwBn6hX5Oan99YhUo5KdgZoEKaiSOYfRrVNOK357+qRTo7kf8YiwLi7rAq6WEc56AfIU/d4TMqEFDYSa96sw3bYQDE89sMDf42bUksv5/Kv3u8FBy4U4Be2G6uX/iodT0x+bjyU+95v375H03i5miTo2nxNjtuY7id/pPEu2zPLDUT3swyqfhqaTh456tfWV4ebu3uTq7OD5edDueOUK4J+WL2nerIfvM+kiwNDBdj2qHuNOWIhLbB4GKEMO/LEsB3RHszxd94PIAnb7SxpHVEQjMEWsKA46ePQwBI5DnB6CRXEcKD7FcFtIJVFCKE7SWK10QsMeQKVCiRgifIkCKiavBkATwkHNksAt7Xe/DUtz5URJFZWzNbXCmuLLcbVdPF+tWT0UCKiIKI8QSGRTCj2RMBEuGhDYpjI4RIUkq9mFBZUWsUhAElTbB24wBKwCgoV0FxKaHqOFXY9gbJfA4z+ujWs4aX0iRhgji0MHCOhmqBGnF2+PRg8xtff+fxK68c7G0RqX/n3/vdX/3VX/69//5f/NP/7z/7/d/7704uJtcfPD67mrH/f/3+AvXl4PLywSuP9QIuQSUgAzGH2e99OTs3X0pdlFFNk6s3cUA8Y/cybjpSkCEiSFe8UXlK0VgEd1kRrRvzU6IUlF/Ynl36kDjxSelRSnsbj+q0NGwZnUh5iiSVekqk9UECHrBjx8HQxnBPZ6EZAc03/Q6PdlnSVBeEnggQURD94gIwNPB/MsKlQ+IVhuaJ6ryKC8Wq6NkGc/OEuScj5ORciuVd2xVpRdqCPOmcoBVvGvpsAgS0DVAudXXy/NRWhLOjvcvj/ftvvrm+5LDCSc79VoXjv1U8VES96CydJ/FOaWL6KQW7xymAG8aT9jKGEZHlmeTLjKIGawC/zZxnyDOfvtJJ4GvDRsw5kmhgfuxlavI3KT2EXGHGDuQt/3IIkJ8y9lIzJVQVqjUwq3Zpd+Hl48BPrQDdHpovH7v+yrR43E1mzHG8qfc6nm5qqslP32TKs8b5KbeyRrNSp3xmy0fAPcN8HCLfax8P6u3gQ85k/fbbb7/5xhtPn/+JO5qcjoOSSFDXYLnw8pD76iQflLKlZ6OTz0V+RLYPZy0m2+Lz8UfUe6cO2K8beXB65jOacYb3kAP4CAYSBYXtbuzLmDFgc2tLhDgBD4OpfH5xfuLUzUrnT589kxXxxtuDh8X8vE0nDGJtNLI7qcU8ALwi8GBWRSPJrGrL1eKSKugQdm/xhnHnR8TV1ALI0nXyKU+q3sgPykqmY68tMsUhh5CgIvOUkiLerfDBDadd6ABsGmfNl0CjzQa37L2/ygk6ERw5NZmdC5Gkr7LIhp9gYzDgT+PAXVmQS3dvlEi3iErUwjU8sW0ufDhxrKDdPieHjpmOe3j2lJWvktw226BQlfZy8eThVHuwv8tjeuJ6i4gh4K1qfeXNtyzrXO6cUwb5qjy4t/b3/8O/+9WvvG7j0O//3n+v39mKnj9/pplrq8ubO7vPnj2ZG2h+/HMZR6gROgU9kWmTThuyb5y6ecTI5NoMaawG3GZtt3JhvAattGJa50UhjJBdWl7RNG5NYambthzaw+5BMz4WPTW0dAoRHsvYHPyXFFAqS1g3N59N+RXwpMdymF/b6bGUOYRbLVRyDUr/ZrMlLT7ChkdqL9VA01TkVSukaEZrHuKYyaUYl6qx6UTEwKZOeGwmPzg6GBwpF2UODLRqB4D/nqGhlr1ykjvzmKvusv52zK/fQMgvwi62icuF4YKNe3VX2D486mUbitwvUx/2GhtWHuHXksXB8Op8ev9g5/R438lUK8OZR/dWF+Y5hF2yxPoNsprRRZzJ4xfoN1tsiTZCQ40DT5yPO10+Ldbk43ZbST0ffOR+o3bEjIPm+JV3Isqki0MkIqtyw4ER7qqhrFD2eWVJK1kVUk+I8cP1/RGTkQkDE+oKjAkWZipQA3sqVM/Pfij52Rl3qS8QB35qBegFavuL1pTR9PHxZtU0kqRME2VDFum5ZZRYXhSmRa8mmo4E+LPCZ6Z/ZiJUaqmKUnVHPDPNIWN62lE3b775xr/6N3+W6TE7Puwcz2xHoqiZGCeah07Kn87ChGldWVmFIXOi2Z8YIEQ8hRYMhA1FjhYiHSTxIEuECBFv8wApIk72KIKwmctsGSMMnb4DUlx18NMSBCKfTGptSe32Es9c5lqJre2shakuXkFlZZmMb0YWRwCrSBx+TwQAEIdNkYKJuiaRmacVJniAAUAPGHF1KQIPViBJCiklF6RPeelpfs4JjDYjAIOTOxVgMBoCvrOU7eoUt0BAVfBXQZD2qTXxgCM4HXu4toYzam9SoZUIG1QgrUjJEoeBwAIJQGjliaFLc+wEW15ds61qZurCFWxfffstBwrTgWwRPzk+JIqIp2/++q/8n/9P/8f/y8Y/+G9//w8P93cIGg63D1555KrOg8N9DR8uxqqHhr295wjQBL68tqO7i17r1AgGJdLBIC+UkIeTU0srq24w15ZOB6ml4gKEmkAXkSJdERFqrngzEABUtBCvsnAYDHgBDHVO1TgJefcmHxVKlU6EnN1N8e4pvyfwslAIg0iPtzL55DoLuTBwFhenAImn1hp14EUMUTCYDPneTtazIIEfSd0c8CABCEgSpDsryuZ32FTHkV11UGkLgmk5+gseGFClIWjWh7KgAmPlNGdjFme8sU9tffh9SvWD9dUH6ys5v+fad4gao8FE/ajgLzxNasitbK/qFYB4xnBD/bmBlyuAVFAQkeMpseEVFutXf2QJDSMiIeUrvRM1x6sflkmlT/GQPjYvpQ6YOQkhNVf0Rv/TGJq3xlQ9I9o+e76ruu4eLzAH7hSgF6dza4IYNadnDS8iHc98UZmZAjKVZPISeh6RYko1e1bWR0hGscIjDrhTboFlHunXUQXA/CtB2zNm1ZtpSXX5MLSzfXKCx+svff1rD/67P/jg6e7GvYcmKoSZ40m1npMcq8IkNVhZts/FFE/q28fEKceZLr6+e5O8OX11ZUWWD0ECyjzOX4fzh5m9pQKcxBLZrGoT/WuPX5Uul8BAEOOQz16tNvtr17vvv2e9ADzDvXMXG9JxKGD8O3ZGW92NCoA0goSRwLd1roagfMxGpvKORglziHnZaSjYoiKYZZE9mu7edHqRuGYinjtOCKvTWexOsmAhHTa+TCVxs/moJE4URIWk+wQn4WC2DW5z65mP26C6iLeHW7d5/GARAGi1ThUguT7ojiXXganrgk+ppZaoAryPNAQw+UoHGusWGNKCFuWEcZpwecWQgLvg9SYuq06grmoadxRPKpKK6awrS4tnB7uXR7u/9mu//s4778CmFcUHiqCzeyxrzr7+2qP/w//+f/fw0T/5L/7B/4ONbeLShR5nG2vLJYvjAHR5vuAqNExQS277umJR23595fX5+XW5WNTp2fUcs0QtHl1NrC6toJlBiH0oLIrXsN6IniR9nV/50uL3v/u95j8MItK1Il1Ve9lsYq9ScTvjM76ytkq9o3Q6pomTFh9sPasUAvaPDh3JSBsWp2VhI+cz2DDWtZ8smSLddswRMWCQIaIq3I4lk942OWC5gSTdlGM/U8Q5BXzz7Wx69Y1XIXyulycmHj58uLKypu1dRUV4dzlH4ty3g5+MroEBHgCGaA88P41WETRWdUoBQ3D1+BFvpJVFh+Os+7FqNU8ixBxbgL4+Pdzfvjo7fnDv1VXHRE1NOC06W7BqWxxNMxpFzQZwNuVVrYvbw0Y2lk5ED7BKS4GsWAkFldSbACTo6gNMEWVKfeq0jhv/pS1lwSoYNYSiBMFN0QCjz/O67g1z/g9uA+5KTD5gUcvv24Mxyyxkb2CVCkjhDZK78FJx4E4BejG7u6eez2xbzwg3M0PeMi1UUCpzQU1tImMkHe8sueNIx4FlOhJudnyI9rTLGVFuhwZm3FAdC/XK8vAb3/j640f3f/j+U+fjsbXEbF2iwt8ZROWixyz2Z9mijsAheDKp9Tfr6Sl7A0pUba43pxMw4gS22Z8wImNa2BCiALx6mlgVkRtBXitTDnQWzNqMH9t7u8BIAllvvPoanKEkqlJtnBkOTrayJkKWtFvPVARodssTdSSsevf39pCHSBhkZfWnDCoSNV+ldugg8vHjxwBUJFd6S0dTtGa68BVAGl/7uYBJbJwUGpgRD7NEAQYnOqKHTgMSz4VmArQUGtyAfGV5ETbsyrLF5Gz8MkozowCN9JuylBDzGquKRh6zTTm7NAMlKgWbSj3RoC7jphvbqmRos0o3NXGyt704nP/lX3nn4b2Ny4vwJPt0ssfncmEwf7C3y/qwunzvf/2f/i9XVu/9w//nP3r/gyfH+9GD11aWP3z+3EHBblLd29u1QIaxxgxsqFUvdYikNk4560xkCyEP4RztJAt5mmm/mH1geCIFE9CpjdqO+W1HQaQmANBYkYbBNHHA1EftUmtzQ59qvhSlIMRS46QHWzdcXKIxVaMrZg/pitjrj2OG07gs/GCqCSqnk83v7+96XV7NyQs9EkQ6Dpj7iqdc5z6oRSs00PADACeCe+SIwCaFFoXI7e0to9FOtW4OtEjy7NGiLBgX4UmxRCbRQh76rXzt+hHGiOR00v2Ji0PrrIP5mbWlBV3gICW9kMMZy9yi3nTopwLypKUB9fT6iXC7BKjO/QQqiYUkXdlZnp3YxW/HowONKPE7osSMFKnRt1beC132i1GkWCennKshMjnB7/vGP+g2WXfxl4wDdwrQC97hPX2YNT4x0YybLcvNOiaIBhhDfuIVwO2pZ1wcWEN2CpgG690WNRlmvjQzBYyuQPbENfX87Hjia2+9YTXkT/7kO3u7m8OFZR9nV+fWLHKG7Iwvziv7XE65HkNiNhSIAXP9MLtgIvAEszw3EVJKxCtTPxGGANIunh8ul7AvJkeeTDqcJmKp7sQmMMk5rwrO7Ec8K0sMZIqvumBoASORaOEzoXVQlWCLLcppLUr5YnbMT6ksF9yPqC2yUBgjl+/42dx7xXaysrLkH1kIBxksC/KT8yPbcFbnV8FIUWNboURUarlMuipAOkaI4FxcWWSmgOTo3JrdskMOTeV0L03AGbShk9MulnBtRi1SNZO8hMSdEDYRPzt5Zo1keWUjbtI35/fIFdRFvqK8sSmuYCNBgNp9WTOtsc+xWqBWXc1nFgtgrFr8lDF/diauJytrK5eLA5eD/PLXv+osxq09F4ROnrhjwSFAKqOEZtEwO++sQf1n/6v/+LVXH/2jf/Jf/8G//jff/97+xsNHK0tLTgl0JcbZRe4xPTk+uLLY5TaoeQYPTi5RN51wSL3hDmPZi0QfTmdNxxig6OAefx7/Wmlwgg5q8Ue6FPx//c03KDfipCIjmHRnKmoUtGxFGq7J+t8uszCE+nE1keOl54enE+4s41BCG4AwGo9SGqTLSPxs0T/bTT/WquspMxT/tOuJ/cPgQSSDIEY16zBNcYnI5mZF7zB4sBSdEpm1JINB0tyA5W55a2uHtqxPjequgmGLPgJzt8sYYPrAAaMFe/1UlLeCZsBokacFtCJMzokqYPZzVDb2mNpEyXPe4QJuBGO6MubcnPZobene+rLLUDk+I7tIGmsYrQCNVJPiwEcqEUgplagR7ZSTCAoldm43MFpLzQzS4RKX7Y8mRLeJwm7q4P1s7PuneMY2nvdTBJ9SltFIP+F48S1b6EsbKkCwSum3FNRl4ZWKVHdDcjpR0l14+ThwpwC9XH3ec0d+/DfziHimgpupYpwu0mBjBmWaqDDOuo2tgU0pASttCVqzU89/5F7O9Zi4Jjd8hpGiJqWH91b/1t/4nX/9R3/yR3/8vb3dLaeY+S4OTh9oczPce83dZjQH65IWXbUpnhBqKUJaE9sREgRzrdpIt1IgBQwiqSNKERstC0UImEi+2pijogi/hUWWBZCmxXtr6wxCcKrOdjB1UZ6AKeJJbKhx5zTf4q65LnEbd5x799YbBlpI1GLeDsLyQ4oT5hUdKOt0wJzcoqC4KpDtAblS6IRfotyOkOWkHRhHBRLb+IBaVTcAGNgUhFwkUNPTHHilSNP2nujJPEGSFMVnsLIgLfyAPzyPeiSiXjAELf7gOWzVZNfNsm2UWKqDcwDD49k4QR4fx0vJSGnehryL07nZ6aVX7r26MXTU01lOluFtM3K6IklV5DxD5J1eHLtI1GlBf+N3fv3hw43hf/l//wf/8L/S6tfeeptfMLWQN89+vuqnZqfcYkHYz/mCzy2ibB5umKJHXk5xZbZxUL8szs3sHbij/tQw0yJkS8Mcuo54Onp5WZ8iXhd45bxFLQCZspzA6kAguYpoD/KUkotacQHbvXrqFDzEMUWkW28VkYV11HoYGlgiHmILDEphKabhz8ChxLXXXYdSYVQHIRj6hT4Fr7g98N2t1owg6aEiHRgk6kKh4l49jY1ul0SNlYISxdUIQHG1S19bW9VMgwUNRmP9RsIodOKGTkVM3MpyBtDlycHuxMXpowevLiKXSsfPPIqGC16yvz39XaOiI+J5vZ1SAEkUMTzz8TWaOgo2DylyhXGkX/vZYOIiWiSMUm4VTC60UYCCxY75VrDikj3WbHiG1Yo/dRAFJhc86S4uGlK75NtV38VfKg7cKUAvRXf7taedfu41E4mOUm61vjJrzapg88Ekkpmt/o9aEmWmM3tHfeORnakon20RHqNEpXzBJYXDAIGUbzUIzEi2fQ3MSxPnTjD7tV/+2l/75q/92Z9/7+zkeDKXPsZ8Ql6btRcGtVXYDG73U7ltwp+llsnr9jxFmDmd2IDXVG75pU4Q9vmeWY87TsmkyxS5WWYiYMzmVv/7y5gkWFhaIJt83/r6NxOyTPm3OBwwPB0fnTEE2ecz6y6hbJW/Hs6WoaUOUyGQ1Ei6kdmEq1c0WFeByjYcAob9SbrW7u3u+oonuhAGTJYIuUAsUYDEffgTUznXP8dcl08xlsaec+laSffJz83HYYi6gS2tHuFRCpam4qmlssAQFV7Fmx58E1cQB9iBqB9KAeZo3aIRPK4KSAWDJPAtb3z1J1KOwyqFKvpdyWBPrsYgfWhr9eJC7GEMNuLOBiLB1xem33iVnsH3eZ8DFS4x/yBPFXHGiBibDNenp7eef7C4tPL1r77xn/9v/9P/9vd/74//9Dtr6xuLy+sWtohq28qYOdzbRNBpFzvMDEMQm0W2RkfG40oZLbKjzqoZinkPOQBHpTqXnU9Ho5bC4EgdKoer6WFB7fSsXWssjWciToWemLIJ0pLTGT8q/FG1FRhdpsjs/JSdZ/yfqBr4IAu7sBfmRMpR2rqZ8WPxLePqIN5UyKECGRGKYCP6Iqxr7z2+CajqLKYjfODG4+IU/uuGKM8rWpFEpGKaC0dpXODVJoJ449wHQtTli1Mrlbkuvc5qgt+ClhRMVpBiRL/RHDibWr9BcYmaxnonUS22+PFtioKTzjHsjNhNd5q9cm9t9vpi+vpiZoqBrSw39UPWiXrQs/7GZFOvZd3r44Q+aeapeYNSMpo7UrwnEm2BxRyRnIpn1slUUh9RBWTGGP+rWUZ2jmZvHaowRMmKCajGlv3woaewNf4mNLVGBRpNU14UkyhX6BOA7nZ+NTdenudfqACNh87Lw4sXqaV+3p/uQSnj4IcPppvcEVmm9Wx1rTBO/DTYOAWgUgUexI3OLGye9Zr1CQeQZaN6JpZYRCI5ch8kLUOwIfnZzvHR3naOXp6bOp/IsX6+i9myiRlqU5YkaoVIFSoVoB0Ms3PHF7x53PRNDAglq0I5ycFBR0NaZns1y4uTXsqS+oRWlxIBnybPMowMrH8R/zQq2EhWMowIEZQlQhSx2gUeHkWkKwVYugDe5vP6lC9hVsQCAK/JRBdSlVJjWs2bm2HA+kualgWmIiDyTAQn6CsWAcFwbIVB8ZLHOQlGc6V7hQ08rUXiqKKa2bWRCqaUupDHv1ij6AFYh0hFCGxCAAxK9BMYT2zRTHiQpHZt1yjdBEb1CJYCA9rgFwdD6ZLCq0JFzdjj07M1q2C0k8tjG/WXFmk410cXdvNlXIFRUY2RLF8OWSmuL06Pj5YWZtxWvrv57Jd/6Y2//bu/+60/+w7HGaYQonx5YbjtPO7zU1obek4uWF/2+KuwBrFs2JLmutCFAWOV/WXZdk5D0BOQh+y6V06NGpVWlw+yxmKC5mimJTHdClLbIdd8jIJEIvW6WapUswgGnvjAuHiLMxJqNT6Ap2lKh0QwNnSNYaB2DvsNIB2ASlWt7/gG4bHqwEgXOsIBh38YejburaGBVg052ql1Ei1So0RQEEJZfkIQinciJFURPe50btJQHfiBGMTSfQAoJRgqfpj0wv5O0ToVSadfHGrSsQvKdiYunON4fnV28uDhysbKottQuaA7qTQHCcaNJr8dHQrtp4PxIIzTO55nkhNE5CJeoDd9BHkTSwZVJBpORSu94+Nnjze/KimmlRyEgKAgNjy7QBbRkpgpyPg1QJPb9iHFNcNvhcLnigyd0JBV8u7x0nHgL1SAXjpOvHANzoyT4EduAsh00E0cRwCMwo3zck8unQg4U0zNI+Ixfn88NB4OksEf3CL5us9npC9s0s6nK58b8+nFtY9yUie3f/vYPLVudWxjkjsu/vjPvv+nf/JHJ0f7C/NLpHKsPT694XSYzfSpJTPze93jHVFBWjl4hZoAF70mtDkS0J2XCxYl5vhzmNBtA2dsYA3xj7tDzfK8axSah6pbwPrEP2Z+GBHu1gkbfEgmwpjoenX5Vcs0RA7rS+6b57dxxNThiJfsxp88z2ITLc4KxNzE7LnvZitTtSfr8Phk+/km5xM4TbjSyVGKAgLI3bRKrKQykQPGnjXUUoCwhf82wjQw0lozT7Kpivh0EjJKdJ2iuK1qV8YCI/IBaIJjVghdkk9FmdlL77HbV4iyOZGVOAUjHixHMp4wNR1EV5h1bPDZyP8XMaiFEJ0tXFWqFgqrV6uG4pC3GiElvKSalkO6s5mJfGaV1uQkLuoKp8acPaP9MB/5dvdJD4OOh4Ef6vzMvEGhusurM4oZMxcAvlmnJxf/yd//n/4X/+X/zY0Zbo6YmuW6NEPF4fWj+WWKYlOxFe94dmb+dPZ4sDDUUOPDuKadRDGaZETByQx7R0ZRBe7dv398ypaTytilWMFw/eD4KMuxFPIMW9vypy44n01c2brnqisra3sHuWeDuipuHHZLGWNgYbjhTSQXQ1yXi1GGHwZKsUanIXMMS7U66Qdjx5ki6d86l4gRDZ8d3XPBplJLujRteHQHO5MbOFw9cbGZpTebHasro3pqjdGLvVAZszH0XEyurixvbm6x3CiOOXy0PP1ioE1vX0etl4VyhLViZ9MlDFqAHkPLgOFT41MBC8UxFgJsnMxhBQe49/jhQ/x3/5cKqREzcc6xhyDeSThcv7wMM9X1vCLJaz+TkXnHAI3FZTqTg1I1y3RWKVKi5hfPLJUHXn9krbZwRkeRWfnSRiqRxPpXtao4pQWqDw0xGEJZzDr+ZHLwS+emHyarPOaf+PbltYC68N3zZebAnQL04ve+2aCmmswIHfL7ryDLpNwTkMgo+9afhsyEUiE5rUrlo0uBfBqSIl0is11t7iVRovhkX8kxcU7nsTxRt5teSjXXzTsT+eLUCb73VwZfe+3he+8/OTzYXdjgzWq3rQkrugILAUltxjddmdAZYkzxpnB1qcfkTl0gs2X5+Df1m+vdw0khIDYQS+hSaIj8ONLWR79vX0VgODw4oi1pgG9iFIOcWppy+ZdSRBoArIA26tRwwXc5PKvTq5BIsdrELlU3DTAeBNgXuoJnHKWn2RvmTL/NNGjJ2XJUHe2spragE82mYF6vmlAHGzlvJe1qVAO+07UPCyXWLoKkDgjQavAnxznimYEEnuxiv879ZapDm+LMMRooEHI0tiaDHiYCmyfdstqIY7nFzLKMUrIUh5CpAA+1UdvVi5Mo0eBQVus1zAQw60eUiISkQbx59g+3SX01UoZWFge2Dh1txSChmcin3ECuXoS15S+mpBp4WeqI1IyPLSX4a2+//td+41f+P//09wYLS6sbDqKM47lsxgk6kyLDOQs3J6wHJ24TOzli5KAGDReXrbQxUMShqrre1rBDw+voSHdRYXd290LqzOzezi5lV7eR/Xbw028QhnLtolKjVrtsY9MKrWsOeOJbZ4nQ6Ut/jdJArab94Ix2YTJU4jDQKhTHFtyjPVzPzB6dHRmzsrB6pm4LAYA/atMc7VKEUiKF29OzZ88o9FD5XeojQt2JWcajFNxAiUELlX5X1mt3GYTI0Ez053zNk2wXKJNPtjcqCMzA0wILc2UEibocv+LZmZ3NrZBR9lHuP4hwULu7ih0JTc/1haOUtuBYHLvr15ekUj4QLyoRQJ872CmeQgPfwOZnG41GUR8J5ZpTvT/SYgqYmiI/tXRxaIUxHhg6SMkMQWWi2oymtyhShXZUCYWKqhNdN4pP1rjMV34j1k+95iygG7UMzsKSeu/CS8WBOwXoBe9uP2z6hKcPep/9ZINfe30z+ayf4Kngy9Xc3fNRCaqegMw/I31IjIZgVpJSsxKlh8Qy65hPLifdaJlVniwlmDp9SjL2ZPHk7CSr8jH/OKck340mUA4vtiMR29PXZ4P52cOL09fXFv6d3/yl999//w+/82R/d2t6YW1+mvw+Hs4zk1hfOOaiQYaZddW1vLJIhDDUm8rnstGXzwdTgYY4u99FBEFtF8za8srecY7AcSJzaUjxxiAP4qlD+yEXh4PFq0tHrfh0JOYtgLUYI1QU3zvccx7Pfh1GZ/IFvOSI3nLC2D3YnznLph7sIiZ7mWN7dwdb1lbXW1Gj8pDc9tSUxGeRmnNZN0kWmuc0itlmirZEQMLD85QjkR7xivme1j6MyPbISR/U+pSIhmPw9Nw81tpYBCYC9ZJPa5xtqWJS4mbiyP+ZSzSTeYS3XLoObKxLupGc47eTNaMTnteplIwECXm+v6cmKTHwGBE4p1I9iipqHUnJmCfFK1753EZGRNGpe7gcr0zATywuuw1+ikO5ZRkE1fDIIHGPGF22sFmv4c0TseVE3gCUhYBxkQw7Otx98631v/8f/Lv//J//892tp25/n55bVJH9gLPXZ5eHp9O1gZ+3jfGsacfnBhujxcxgfw9jT48PFoZLxDmeMSwZQmeXEztbz2OOPM8Wv/e+/wNb9GkDWIqtVBkDcjg3JPTYlcLAWCiuJuaysU7/dgdhsgjG6ipN1mMMaQuzy9MO4KZxErVxwIl612zEnNXl1ZPZXKau2fxRwFjQpMRS2cKKiSlXijrJW7+rhdEFE2psxBtMe3WEGgWaFna13xXk1FOqJxBcR6710vT4BUfmWVde+XeuqmsLc+xi07hBHQFmSENO9YdQ7+s+OgHtS9W7+3t+TrYa1O/UsTjUqYvDo31XgK2vDFYXF3ylROPzk+VURGUof7hU37eAqV5Aj99nnsaeiSInE+CjDhLkXzuiOla6hMCVvovQWO+SZAxoteSck5ApxxxVNxPnJ1/Anh0CnpQAC3Sbmo/MQlGbtEhIRhbx65uu9B6NMvWhpXBTkWj9AXMEE3oNBqXoeBmVNwHijIQET/G78MJy4E4BemG7thtm1jAtmPN74jBB+0lzNZFOAJMvUsTNjIIffs1QBZyZKMFU5cB5VnGTRj6HzRQMCnbunuZ73hZbZnWSknxmRXfgni8rpfgNmFbAO8SO+FTcVIMkk487loqMq/kpNEy/sr78N3/7m+9tHX7v6c7qcMm16qqYjKPPFfNSXA+mohkoYiqn1jDpkw2oFWcDIBuINyLcohUAn+bSwUNiajPpi8tFUrxh6jCYvgp0cXkJJCYoTlxBmHpLklELBHGHLUNIgVALLwkzbXSIWlCB3BSPkpXr7KmxA8vJeIwQ1tXUKNcTQDhYF4yXuhOaaU7iCobmmzkdDVJkMZjJFeSqujsRJYSZsuxDwI5PshONnF9aWLRg1I1Vl0qDM4sm8XRpiw5U1C8pqqDsTs+EJzCDx67GLJevDTwi+ACA/6y43OOJY2232Kd2qgh6cAZOWXpEx4MnXzXEK6tcCdFTiyYALDueHuzofbKl/u/WeLZ0iWhhJFHWtkAi6uhw73f/1l//2lff+B/+1bdn5jZmF6710exU6lJjrtw1Env1JBKLwsn+dnVynAVWvjfDYZxdGKVmlm1cmjq7Ot/f3VGDXj12vjQTndHpolxHBF1eHOssxzDn9HGe77kyYmZyjqz0ruHUExes47m2CwjohSTqHg6rxUARwUNNwAEB98QpHCyT4uGJamo32cIixZeN8GTWhnonZBefiX0dVE1LZ+Ge6lDrAhGK9dExO18OfLI6dXYa5senB8UVejA0W1StXnE+WOxBRrQzq0HBiXJ8E0EqGANRXIBZiuAmjnCXzuCcAj/nKzvvji5OT5YGy27W9S/6TAwwWJ1uwnM+fYJ4/pQuouoOSbelvueacVbG/yhkGEg3F8Uok99ah8JEHam5J6rRyKKcKqrIDWC0r07s58debjIaJjpnEahu6qGhEi0pe8XsP83Pk/kLJFc81SWL/sVidKfu3LDxr9zfT4yNpt/I+dENuVOAfjR/RrmfZu6P5exPhPeLB7J8n/ucKAMUnbjoZAIz286SVfHUyS3f5tCeYkwWcXwpNcjsQXRRQIwgC1gWoqxeHR0fRZtwMKEv7GMTq91RNh5HhaI8KU7wxEckUo2bIZNTpFRa6aMvM56vbCLgoxmQWPL69bff+p3f2D34/X+talNcdjtbk5iZYakx9c9MX3MsUZjksKhm9uzPVg1DvLkenVw5VKMhw6XFvcMDE5yFKpSqmZRSFp5oTqUYbW1FuhP/iqPcEzzk3JLIrdlBZIZEGsPiyjKrj+LAwMwvDI9OjohdIkRdUlSh1YoenfAwnVpe5kM9bScQ/HQ7ABCLACaNtI4Dq29ppz5SD4kxqkmYEjNPNDZgU/MhOJH45Yw2c4HUZWpRXeolWV30NHvqSKWxDEYwzUwzWec0jZoCOXm8HD0vpWBQl54gWDVICuNKUXi1PFwkjHWlccFLhiBmmdBKOMnBZgWErFMKNTORJ4IiA0siChVfYeXiwn6RE4ywyBnZsgwer7qVkMmfj0LUIF4lRVW+4B3p9NWvfvVv/M5f/4M//LM9nkBuIjOSkJCDauYtoqp0ZXkVEm3JoGPCrFU9FGq4g68tTNGHjU8WGgj5U1PgWYYM/KVyrHEzfYwllufOTl1bNtT11Hk7EKdzyTxzJcbGw8kxUaUlGDpRQ6qBOMDTSk21a4xybruVoXIUf6g6JpHBk3S3oY+27ufA4ERLQoY+0nF+LH5Ozvap/mVpw0DrjCujC0mmZhhvsCK9M223/5UdhrinyZRdzXQWkYbrU4hg0GSYDTaWQU5RDJlXE/tVi1GQ80J75MOgLGOnSA8VWfAIBqREvzYpsBnfvnGiLFycOLCJB7vBNZhl8wsqLG9twWhV1q9aYn7G0SoyEiSOn+OKkuJ/o65BSxqBVNY56RlB5bXjdRySezNIFERbPxtA7jh0SpAbRTFKKVaKdc0zEgMZHy+fZKne707zpGXMdBWSADeiu+dfZQ6MOvRTTZDeI+RTOaOEOwXoL+LMKP1n5uyPwfslZfvF9/xlHORz1UxgemKYYIfXNDfo+M6LEZmhW3DfQjZWmBdM5pFppFqceBh69g8uHI926pi1C7NdkJo6wWeSmpgxWUYYG2yw+VS31Suu0FFmypSdj0PfdpIurDWoP6TkYs4LSC7Xlxd+65e/vrm190ff/WB+doW7hFsLUzo+DfPWNJBGmk4Ps+9dFeZu0kguAW/uPkLbzWYcKlg0mQomOmIYQAOngeUYJNPUD49XM6xcryIOBCJHschCiRQFG4xckRv9I05Fc1QrupEvcpYvEeIv4ufymj7R8I1ZLRQpKejs4vgjIkDIFZcZBk5NAE//cDoMVxIn80qkTwATgUFjTdktRz1R6FWliJerSpAlo3OqTOqqW+shR790+G3hAjlStuJEHjNSyLiKwqR2Ke70ICa9qg4SBTUt/CpRCoksROK/dHHtQkwTFotfCVEagRXP2Qlb9znnxDsbcmymKFJiMOTTwqYAPHx+u1Q1po6/8+//e//4//XPfvD+3vWE12PrL2QUcUnIOQ2RKzG0MKGQaU0ztaLZro04NjVl+3ecY9BOMfGPgYcUtRSkXS7gmrfvfWryPK4wNN4L2gZ2o59qz3JHJvKzNnj1Mj2bCnp4mVVCtpTN6os0vIY0ALXIUbY5Zi0FAVlysSgbBynKWDZtRU/I1WnRT5C+trKEflEFw52yI2JO+JmFMiBRVaWE/6U8QauD8AF/BL0gYD5iXGeB2u5QhVTnQjZ9pE/9jpsPOEOLQqozkBTUjwoCYI+NLZBHlKW3HABx6o9FoiUO+tOT7GPzbmT1gaJz/HRrxQ9VHTQZG9CZSAVxLU3bMye0O45YvriSVZApW6qJBSzpDdb5mamSbXqpv1k3T8ABZZNzE1QheIMhu7mijqWlUqqePMQxTZAOfxGYK1J1VQpn2SsEpWGJ0MTKwhiUd+El4sCdAvSid3ZOchFyanC2RDC/M/KTmtensaCQb9dnPDIi6JwBYi2BAKj9UMcH+8cnR47n8cXkI9ms5DwXC1s+DXPqCYeeOqM5s0e0CH/yuSVkOqpppVScuAuZ65KRWSo3nhKbPrfzWssNZi3evK/eX/m1d9769vff3T3anVu5xwhiXka36W9xcclEtle7lOkHV7MX2mK5jneFrUymP5IgZPQVGcdRIxSkoFiQUlzFpnuhpTVIdzwRSAxCwCw9sa0Q8CQBExnpUnKlNldHFE1a/4oAi07Ah9dKDWUvrrWuOmj1AkK1ECfKIpIyxIeUHLIoJgUMsad4EZlP7aW6mMLqjNwljjPzA/KJKKLlmaBrth9Z6RTUcOcA+Xd6kuUYjE5zYpTQtJzlA63aye1TXtxWIh0NEGtTVDdSUL3g0YzIDiqpBtoRltvPSQm9S7WDRCcSIqQCSI1AKkhP3M7anFOkmSuYvsqgBS3FkajHuirrBBpbu69spZudOF3fyJE5qLU0SkEL+xLGkX6tpNHR2xkOVgG3nm/+zl/77d/+7d/63nv/bytuNB/6tyNvEHU9yVoz5TBleq2RO2C+4vtiAatoqx7WzIxPZMfw4r4z19jTbk4PyXEHHaPz9MwpCgt0F8p3dt8dHCI7jKILZifU1RwXrri4Zw2LrhAL5zFNRe+3KaRHcqxxatFAMPiws7dn1FE9jw+OsSvOKX5Pln8r4uHiWQyhh1CP5m1Yc9r59Oz1ZE7iubw+Xdad89mxdVUna2uFUReFqRzFVIRTGK5jINE7GuecKfFS0e2dPHEkeL5Gyg/aSK5jm3JsdAx7dco5K5EIJAiWnq7BmKNjvyI6K7ijwwOWMItEfMeGhjhT7tS13zgbXCwofl9FQ5S7G1Xjo15kZwVRHR1HJb8X4zR2HwakrGA2pN/7j1Iw4sMUQEQKY+SFOIP2M7OimNUM12UaWEeng/CvvKY0tiYomlBGYDQ5E9FHNYyrSqQUs4+l3L28wBy4U4B+9s71kxv/tn92LF9kSb9xk0p0m6x1nzvV3qG6rD4Xp4duGj04zh2iQEwWTpMjO/mUHrOmnJ8yt5is+EhE+HKrEGEWN2dzGjKdm+tMFPYV+zg0j1uKoBGZuOJRkWmPBBBU7TXzjPsJ84VmlZ2uEFsABYLHBP4Ru8zw9iovz8585ZWNr7326H/48/cuXTuajV0210SToELAQaSb2UkK877Z0BxHbokQzBKJ4SP3J1BfartNRF022sTJlywnnLSRMGiBrValYNODwOChW5AKFCPAUrACZlWTFkSRA1LEtQikFvGPcRWrKhSHXwS2njfBIM8youqkCz1IFCyy4/QjojlqFHHBgiLiaulEAK3owBk+1xcw6T69ECVGrkB8gtFdXYuCInI9w65yT6GVKSVxnKst4piigSKY1tQygzQ3UEvMww+P3NRkkWUy9ioM0X2Y44kzcmGQrwqJgSwDBgtOVwcMjCcCwKuovr1lppRHE+YVjJBRkcVKN5Ycv/bg1d/57d/4R//1fxOVnKmGWSLqHItFRh5s9FqBtG36DweHOQ8whznFm1s6YHEV4QY/YTyYmeedfRaFRAKrY1ZI3eqa4Tq8Wig3pJy9SStiCMnqpA2Mx9SkDHY/HyKTEqm7jBke3+vDeySo1Vj8UlEvUWEadTyjKGlxvkmK4ai9ungmBzgtGl1HsVxiNWzox1gAnLIQyfCjVHeBAaOXYJACWIvSKcMByiHXLgETdKVu5Z0GGwDNaZzaAoM4AOQx/Ljll4XP0IWtaZMFjJOPTXCGMY8l2iYnoIW5aX5M7G60tzhJ6ZVMdJlJ0n1Z1E73VV39N0+J1PZaggKVIAUBnoKXpFS6B3jGvHHh0q9KSaqZA6BmplQFEa8d7yLBWLlepd9+/QjnR0Pro4r0ZvyBOHxfnftlqi2mn8JV1d9pP83mF+qpe3ucfGarMt99ZsZflcS/XPp/8tp/RB98gayubyP+DSzcmaLivHNkCj7Y3dt88qFVLV+N5mXTPNsPQ4pvQYaNLITFRWbIkmGFx3RJ35mM8UNOPp4yYpJk0iDYYgEycxhmMtPMyCl74/NBJ7cbTgiYm8UJUEUFM2t8LxmBrHOd5nv0/vL8r37tzfeebT89iYcAGDLNvi7+KGigpWT72Mwkn4k2SJAESCIRBRFVmNJ57dAMoCUQTfWmTnGywYKT72kXbGqaGTD12l1Uc2uUmLLuLJYfMUrkkh8iKlKWigA/sKkzZw+uiOCj2hUnVGgAJvqjk2Oc5HBDV9FGuuNwPteaOt63fG0nrI/t7OZUYoInmk8FaDVzOFxCVYRQb0WOh1Y4s7A4ZN5qGUk3I64ghAF70caGxOQwHK5wvgZDhYIEsXa+wV7n01BQ0kYmrlpViaUOJCkOCWC1d+vAsBlIRAwRjkJdFfbeKDEKyrIkGh00O61i9MIiI0K74qbLdpit1FeO356ccnbwtPUjSibpHS2gZF4GzM1ko0ZIpAtQlfrclrxrQ/E3f+s3vvHO2093jknJ2FOMHFozV3pnBPhLtlG/s2/OWtuQM7hRrTmkMFmOS41ZKcMmjv1VBQ7EBkrjyt2iWVs0PMBQnk758UxHHUQJ/vrDFHRAHTh3XqIzwees+3Ka9pXgn9QQZU8awYrYi1NjcsWlodOTzF2MJZAgAAcgVHVsUNyXahN7ax4SqbCpq9REr2hmsAwznbwwmGcOVHV+d+X8DsyZ6DAgkAbImAiz9kpX0FiqGqGJ5goGpOBdulGRcw1PR7oRLy5VjNSpcp5zNICflR/X6dG0X//e7vbjx/dXV/hyhfxxHwW77opaUj/p0rR0IADJ/kkug1w8igQE6GH9lgJipcG03mSNViG0jXDmayqY+lWZrkLBDprZZHgFE7AKIqn9Vuj3MUDBU8Jq/BQYwmIwpgHT9jIYcsRRVx28Hw/5dPtowH487+7tL48D42Hwk5PwI4rcWYB+cjb+lYTMbByXAteFX21uPfnev/3jradP2bovjk+dovvw/oMNN0QRdxwweQywpwyz8EFITFn2ujiZymQbpcVHaHa/15SHEfUF5xvc7lJHBmdOorOoJXrOtVPT+nvRSyaewJfDhbnQpM7qAziTmCk7s2bUGpva15aGr7/i+sVFG+JZnBSkfxAWjcHETckQfKabPQlpSoD5XS7VgdiGjxeOIMLYQ368+uqrqIfE9ddr6+uARUCa8Xn5xN+5dkgBgJDwgLMr9VXd+FEuwoAEeJNfUjXH97RPZmU3NtZ71zqJQg71+EBMlMGSEOQTYkoeRHfpeEfEUaKg4KM0LCrLjXTtMku3koQ23dFCC4DcEvARsRFvyFpZ4fQd/+5SqqRDpS2eMFfnRXvrxFJZojyhQZZ0jLLKo70KSgTWNapFPNhmRoct0RigVbb82OMAVE2OQiYu6BqatFUTKjP2Qu6qE7mhwVIV4mGsoBVNpzdxQxTm5FOey0HqK2+8/pu/8Wv/7Pf/IB4e8TV2IHJgGFy0qbZF2cJEzeJBbWjlyHG00+EmFqYMJOJNr0HHE0k3hThnFw3DjWaF2nOIkvHOghi7YQ6/jp/18cVgvhR9QvrUYT+Hw1M5Q/jhcS4OTd8mqZ3NTZRjrxG8u7UNWzMQsbRhqg9SL6azj70bqBr8ZHlL7VeXxqFRCkRiuj/uybFsWUL2exKHdm/vABjCMVwpVQh4ZSS0ftDFi52j2y3SyvhHZ7zRxUHqMsOD9mt4xPxZfvQQivSotmrq6CwnWcFj+TT+eZcXy0vDGHvjEB3VpJtgLCSC29haY1steakeVLX5QZzGJ71HadYT3f1XvoZdlvqrrHhjEGmrkldBqcIwaayIdJDY6V47Pn4qIrGKjh5exmDaiKoxgEjF1WG6Muc4fjO/5uiJd+GvDgfGg+HzIvlOAfq8OPlj8HTP9Y/2x4B+rtl+7bSRnGc/PXG4s/ne9797eXK4sjBcXRkuLQwfrC2tL87bkmF7FR2GQcd5tq5ZYvJHhZUzZfMVZ9KI6dhaVpyetYKmE1Un2onvqEyE1CcT3FTco6lCaUMam2/gzGtg87FVn+GOoLcx7SJv0ZciyWEgzS4v7q8v0oH+7Q+ecsY0g5OpOdJmTuTkej/LRqZ4R5WEgNxyOkQR+c2SFK8Q0/alXS2j+TX2nvNzAknE8kkrRjJJMhO9aZp8Itsap21ZOxOjnTIgzZ7dUy3D7HdbfvR48v71083nEfO1KkECORJxeWHJbKoBjAO6mBLhXICWQMRNCzByiJQm53iOQAt5X9IkAn+JRgfMhEK51l5ERYSRpmI3XhDHqwmYIjqiNLFoftASZtLRA0bV0ErxpIi4SUKEFJQLIWD3qDsHGB+cfFMWBbYEMnXi+CjKn+CmUkWYPXRejo6azjIfRrXMJlmXeFCV0UJ1+Jc2cpNne8hhB1YDKaYHK6tvW7aaOM1oIIjs5MKMGiyposQqI47v6xpLRXYkYP5348f+wwePf/uvffP3/+W/nB8uHRyHORencfg1wnKyTqnX8XY/t/nubGm4yKLn+CFDQL+57p5pc2//eG19SV3DhWUD4fjyiJqBOULGwNxgYjHH5IhrrERkMwykH8+yhc3oNjDUa399bVSbZiJDq3OuqUEHO9tK6Qi3mEEOHu6w9PTEKqoBS2u1mYpVBM1OEMpWxwzxHMbYzuJKZVSUXqjRTj6EDbUGgxAzUn4OGaXYKEtELbJQpQu6fyEvJBPPnz+VPpzHJUP+nObkqdd0GRiEgRc0h2KqFgjRr+OUkgvYK02I7x+vouXhYH15SUmL3mBmKKE+D3J2V5viRkqGrDKujVQQY9sIlka70I8HB3rm9P7cffjV6DdO4dDdZRXSy2Fxsio3k4eEmxD4UmV0N22b1pWSOi4TSM0spWaJ0Sz7PbkJpWc7OT2eX7Hjar6sDDSDS121J9bKJBWZGeocU51uNSp7U30RNn65i/xCcUCf3h4qPz9tdwrQz8/DX1wMfvCsNLa62KFljmcZcGnEYDg75Nx4NcHV0SFxroAoF6Gc1sPwYMYwwnyy9lRjwGXypfDcBLleCGRWoZZgZjiZPRPdQEW7ie5l5a2+GmUzIAUy28Pqay6PTI55NXXFQDKxtDD/1usPl//Nnz8/PhrketToQGZ5X+dsLSZ0n9oQm9e4D3syohC9tqsrbAoEAAxOc73J3czu2xcASInUDhUx5+wd7EMi/cMPPwRWFwLk7EFCgqRXBXlAaWhBQiiS4W0XsQBjsUddqAXs0F5o54bxTRYBAz/4/v6m2wFrbBZKUFINmfUt3u0lhxCs7P5eVp2QAYmAWgDSRQBwf5YraE6nRxMtcTgzOWv9qxsrC9mKtGzzBO+o6hafnUsoSgcPrRTcQ20d3kNyjYQQDiiIcgQkzgfYilF5BSml3jCk+gue5oNEcYkLw/nj/W1ik0JJ+LIzUgIiuSLe0rSIshJFgBPS+YaSbKIrqpuTjfbYJC7Pv/5LX10YUGet6UR+u209a0px9w5nBiT03BzfFziiI1zNHXNqo57TFWbmV1bnnMaYW+St7TIS5WyHK968+poirhaUQJLlrdqjBD87CzROPGSIcbYDwyTm0Z411ghUFzOquLO+0a/Vwrl1sXSSIXF5eJlje7zFwTaqRflux7ST0IziPO+Mn05p8dxMoxQpq+Ow3fSeRuasiEhuYA5e7Lh69ZcnrJqgCqh0kEhrqFq3c7Wj1RK1iLHTUEe5V0/AhrRLNtTi1W8hken41TlCyfDUYkrZok2YS0zDdsDlHhjugVcImC0xUcd3oU1AhqDvbocoGvVDkKW6MK2O/9KQ/PCNT52V0unB+jt6ZGCMQylCI7SF7WO5BQa/v9Lh6dxQk5BBK6/50wBjmEBmJjMlyT/XU769uvi48hsCbyXcRV9oDtwpQF9e937qx/ZlVG3ucZiriZ7nn00fNr3z7GG7Z/QhxUyCmQim7TbPx2LcdqLdUEziwuxEYyLBd3fmBcIrH1ImsYixzGj+lSwkOqsleUaWEVrRRhSCKB9xlLCUyqJ7psJoZRyGSiw2T7LRfWbOpdQUs9dffYVd6vn+5sVZbiewXm8GN3er2lxmYjXRS3ENp+CkY0FZ8Uz0gyg9YIiWDjAoJW7OIxUsftmf7UmiI9A1F7A9evRIKZoKQXJyeORjlwYDieCTmpYyNR9RBEPSSxWAkwghPGC2HU5WB0XgIT4YJ3zuSlxxPrJjo4+zsIVCAJqM1QgWAYCMTkQDClk4pocLsZAJ5zZ+M25Nbu5sozNibIqlJ5KPl2qcVO2fP8j6HR9rZaEiSlUXrl7HAbab6XN8ZyfqI0gpvsfdNYFyAjWVHsafibbHn4kOx0igdZqGNumaU1uNIo+lUBU4xMCvOaqzsClCtTXAuJrorer4WIayTc8aRG7niP0gJN2EajvAGjDVQRkMPvYdVRjlm3g6e+uNx+try//qj7799i/9xsERVXrm5DQrUJQ3MMZLmh+7SIYT+x815+TM1Q3znitLy4OFlfAB+2YH0/McXmlY5UNEUdNmhyLmuAC5zgdf1ARBEzSfNWSw4KCgXEDb3OMizJiXam7kMdaFG+dnB7suHF0wnKhU2SRg+dLVcZzKDV2mhesF7IGEzxmlKYeJtwrrUyPmUBaUcEtzBP2ArHgic5CngpRynC7LNsr4oavUU6MMQkxAi95EhkHrXhX/vOqsrZ3o2UEyP0/nxnZjxs9nfkB7d+j1lSHN3qSivk0PKyx+OTnaPHF6fPhgfXF1ZclQ6+4a91qY2Z9ERTGi/ZNSCki+bDpzDI9+TuZoCDjO+zMOOV+shsk45VYEK/JWytQY2638EbsCJlvr4PYRhb/mNBT5F1u11wtzSrNoVLyalIKgYwVyNEO8rMqmUJXeruYu/gvJgdHw+Pxou1OAPj9e/khMfmc/Mv+LyjQj1HzjJEDnm5zwALQBwl1dNZJiBjB9W/Sim9Q/3jn0JaJiNp4/5tVyUzCPmEcBK1UFIwxE8vHuk5foimpTVpyYePKJ7POMOuWZj34fXikMhDtSnhXNDCaZvT+byJijrqyDTa6vLL7x+OEHW0dz9LSZodsviQFkqNHUn/mqbC2WbPg7kwpmeZJeboRtOaaAJ79JdxGzO3mKVE9iwO3oIsCIDd/3pmmQAqkPsqdLn8WwKU6Q2NEjFx5FvIJvAmgwaXKFtCwVXMEvwasiCAOMsDZckYK0H/Ip/KydVqqT2MQAFrwqTttIussyylOns/oJA1GPAPR4AkYVmhHjKbGKxyqDHPyBXLqgscxO7AEA7KKzRIVCMAqiR5YzZsgqGLS626VGkbicxxkrn9Sogkq6dQesUByFmg2sCaiGH1J8XH9lL7o2KFhDIwMjl6l8XA6C1wq0osqaBRivFhBtPLQW8+D+vbffevMf/+P/5vHr78xOL1KxHPlsqAiqo80f0HTOY/GytmbBbn6YJS2Ki917VvGGC0tcesOQQe5u42nGZ4gOY/ErqrZlu0h+17FeZOPf0gpirJhyw7FFnZc9/nQHaa9+lCs0EzwxylUtKCFt+Q8Z3Ex11AiHKQLDKNpSZP9VfNEoUojsHsReh4sbbMB60EIFoeooPGAQEK7WZgL4IUGVVnQcQPdC9XWcorqsgYF7utLZS4cOKK1jvrXKTyMdSl2Mwj2Dkr2dHbnw+CmBZ/Lxi7w4O8oJQH49l+f2ezroib00JyT5boqSGcdhZq1ogDSX8mvW+5og6DKhI0ksbcIAjD2tjFgSq2czvWQ2yPhNqNKjxxgbtXCsiTSMLOF23GtX55m8mxKd3hixpWEQ32CdjhK/2vxWM7fVMn8h6dy750vIgTsF6AXv9Hy5mnns2s2nez7QbfWilWT53F6WXBEVCWRSoUD4KCas+LSY8DLN1ORlZjEBtenGnN78MoNl0xeZRYLVNCgjFvACoN94pfdkAiWz83UPvAxMCsTIn5A5K99vPI+mLx14N59bNBbnp994dP9f/+l3KSuD+egoS4vDOqTt7GqevSo7jDjcmM3N44NhrgU135EQDrg/3D9cWVtV5PJZZkBymuzvhYD+oAdJIxm1yLHC/KA51R4fHtfW4mrx9dJgAUPeffddYAuPXoFBRC2EuvRtNxJYNGIt2D9wZZi4g/Vob0Sdr2A2G1TFb6P22sg92D9SyppTkrkfxd6TrVgkUAmGSAIEk2ESxb2LEE5K1Xztaip6XixbbD94Bhg7HTjE3BIhw26Xm7n057U1H5vOlCLkkC0LMH1Cwdb28JyPi14SAUCCqkUccofiWB/xTwTZK6tL8EjfZVhiq6jOamDxamJWFa0Lefrrf+5U0TEujhgbdDCVKfDXsXLJ1bRPhmjBnF3ysa4uPSjAwHHZ7aeP5l/5lW98bTg/9+73v7t6742VjQcsSQxOjtmkFlBiGbPogXBSimiWVgh1sSUwNGPGwDkFznugjLrj9uLS0UU8yaIh1amJ9CXmF65gzmxm9VscYE/sf34r+pqXDXXyPppqBVMK7ys/Fn0KYStG1Cnu/1aWQzrnuavrfVve+kTmqA60JfY8/kIZsa6tPTowIHNVbY7Cqh8MG4wY12z4schdKs0Br62G5oL4i6vdrS0D2PnmmKm42psG1Xb3GTt6Qq6uh0GnS8dwkKx6KLfv3/EVfnaoAgY/m5bhd3Y2QFpMeI5LOj91Hvbs5PXiYG6R9Y7diVoZw559DzrRr9dk4PosFc1BVT/uakgUWBRRbOpP6ROUi/5eyg+zjJ10RFVDFrMbDTk6VXqH1oUehSqeb6dPD5ToLDcDCLBot0IRTIjHEdpinI79x0STw45CVKYsMOKeHSlimbKyUqlffKyBSGKxpcHuni8VB+4UoBe9uykgUYHi5sw0H4kUbwZOqRFLtJIcyGFiI0iytzfznZ3nxKlZjMAfTx+fYJP0qDXQidCZgGZLxeh7rKYpJUZzIrUm33+FQlajqm+zkGA6zcdYGbNNVw4r4ge9NJh5trk5ObcQmZRrRHPkiZmdqCP/ogSUR4I5mlRQCk6UkH1FeDYoEQZSBGLAK/EmuFsq03CJTFnidCBlW+SIExswqA6qKBcz9r3nIjC1e2aDkxMgGYRK+SA2IZFlVQZORfgryXXmj3bBA3NkXlxkQj8CUMJkBlIihKoLB8qjKLM519ryvRVBG7UJjP8B02mUUku3lLB3JiTFi2VD+ulFLFu0qMamOapDnlcRmPENJeZ6PCx6cmwjCwFBqAqJ8CsOrNUv2CTmfBon45VxSL3kjLp0dFckwdmZAlSeVCHsYlYB3sy35nKdhuZKi5sP9aBBVf6MhBOAcI8VxzoOItVLKPFM+cYvff3extqTrednl3OTs3NDd3rUET6kJisdTipCS6b6qF3VPTY0VqCB0eOZ/uBRnfMKV/sYpBM9F/1ARY7iccri0fGpy2UZjVwFa3H45OJELlSCxupNHeGJpWrU+0ySSoWH1tuoRJfaHgd8Aye6o3MfL6IaCoprl15AHv0Yl8BL9wqniCcmAMNzuV6NQD+Q/X1e59EmD072ZGmaIQVecf2udWpXynDSCnsS2XwkGksohBBkcUDlVftZyIAcYzVEj4uoDmaU/P/Z+7MnyY8kT/CLw+P0uDMyEygUurpmuru2Z3vZIyvLP3nf94GyTysk52UoMisUiiyXK8LuYc90VaEKQB5xh3scHsHPV9XDM5BAoRJHViYSYUj8wtx+ZmpqavZTVVNTM9M7sQpfcjQbU/GdUs0UpWNZhswNLIwDaFz6B4LPzYfaX7BavOqQLqygSdEpSuWIIdnMAY+pTPLTioJbMkhLcYU8QarSDSzPGfCQqGhVRaYZJKZglYkqA07hMislkoJ38BvgXV2p3vqXtmtnihQOU9APf35mFHjvFKAeph9eL8za5ZObxV9r5uwDfi39B/60xAUCCxBunzm05RsH1Sw66XXeCT+4qXr5RNfi1TXBwXwTezjp5p1JKEHOzZSSE/41VYkUUqK4G0WqeY1KMrelF2lh4uFxgc+9QB3Rw/AyrDXtYVQgpKIOxSk6gBmf6GpXZMzTRzu/eLr/X1/8HqcyR3dcL4mbo2KoJpZ7nMJc22FAxua8suhvic7BMxg9SYO8uKOtNwBicwSDbC3MaDPySCQhiH8ixMqeM+LcB04DsFah6Px89nsDK95iiT+HPTKnt9cuSydjbD6HEjgtkJiUwN/df+QVOcavyFnDBKrmAkvM4PsaDZTlEjmtsDVkgg28xq3q1Q8JRFRlvljJ/e2MFtEPKILxaHZfFZvD9SRnATgouSb6LdgyBy/dDijII3G11B6unHBNUoKpFyF5dHKisUpJhEMIXzJVkT4XG8VymflCtgtlR3QcXyNTaYDuonCojTh6etsR9OyqPa2dkrJAwU0HzdqlBsPBT6/6bccyPCyi1aE43sLZDqnCdvTXf/1Xv/jFR398/k/2hR28eJmbRuKVFTdjil/OF3c09Fw8k85cc7sat60M75KXECPbNQ2o+OiwVjrWzx73CPMcc5U7v5wwvZaNVCHR5AXPmNDZueQX18tL1S+GD1eulcWt7d0Bvy4aoXtor6ziZtGRespH3bdlKSk2wbrcTRwW4DD3pRMv42BHYd/Z2/bLkFO1KowcT+oH45zbey1V6QilarXWV2AwO9jQ4UPMQnX+Z6nO6ayyCwIC7Q4ULP2nOxAcJXU9JUnoASznyEHROfQoxx/IgDKR/9y+qXGTG1qOdedR9kKc6minQ/jE8l0adWYXWSTN0UcoYapRHCC1zEL6N6bjqbpPCW7zbr7ofOHxvjIUXuWP/SWvZikdScrd2MggSZZp6HEiMSFztfDP8JgaY8aln770pE8HWIZh7FU1AZOuiuTp/GEKUy/paco9ZLqu15FrFB+eb4cC3TVvB3agfjv8904BenuEeE8gv9YffvYnOuuq2c8fBWGs3xcfL1G6zGVf003/4QLcWksWwpil+3Q7yLDMsxfFUI2rkCF1sVFwvuMgsKIMYSb5Pwce8hmiwlQriguxPcf/Z7qGFpaC50psxmfyFnWsao9ZPGIyoPDKmKIubzliYMrbw/XF8GmXjUcRQROs3K4fJn2ODmQJaE55wbJzlUR8WguCJYnrCGayipMyca1qc3cQaBXkBzhSZCCBAKEt2ShFLEkBgVoggzhakR9kKDjyg0PMUB9JNb4jcgIIgswCAeynUiJaQeJafCF0zi4ninAUJyg1tO5JDc0FMBkY5HfTQMQqZ6NaAhPJlrQyHrRmoL2Ia5YPPvTAV+/lbRxmaVe8R+xdBtxiXMDm9P+gJ7MAq6IceR27kWaSTqfnx2lRiVKVwkE5mZGXgGTyITvFpchGpvCmkZfDktqlQMCxO3BGPdkUp3OIAKVt9hZe5cRHR0PFtGB9Qv8S4X5kwPidfs/wmckYwp6ND0CreOsr60GZz/7A7q3r3a3tf/ff/O3/+//457WNVUcU2+y9PtxkyiFCjc5s+R5f8EGGKswB0Ea2MPQv9SJjhpHmYsAHOT1rGEhvnOUxvMV1jCdVICuY2/rOdj96QHZXKQ6XtDQ6Tbo1SLpSppaipHhrKdaaC5rzxkFSzUKPPrGa7lJ+dDf0Y943/H4aArohiUoVh/AMGoAaUqCuqfIaoSNUOL4ca+3yKEOadiGDxsLNMwhcXroLbH93t8ce4DWxyEJYm6yMKEV65DdZuLprmAYq3ho5Yjp7nXLnQhAHZORwwHh1B720uK1uWb7KtMikpzH3dsYTdG1W9qpT4abXa2UqEKKUVK+3dhL9JyVfD1mDT8kA6nd+VcJUgIWNVcjvIp1syZChUDqQuqrk1Apdbw0/mWb5UdLgK6OX+V1xsdQ5C9+I2uztQ+THp8Csa3580AXxz8J/UIDeEuXfFGx/5G+a+7vnw2FtA8kiSY7DWbxxmbopcGbOr6ZizQNwhsyOM/vK/CqKSSLOxqXj5KyfMKRcV5XT6RptbDcTwFJkaCTF6IizqUW6ke0hyBBUSpKSfAu8iUmjjNfit8xRxLzqLhzKxhl5MHj69PFw5Z8d0Tvcs4EF615hrVETJFTEGFAIuDrp3PSTtRvrtyZXvH5puDZkzfn46UdAU4M0nHgjciKkF7Pzi2K192iHSsZbxXogDJfXLLEsEZOKrA+jDGmjbKlQDG+fn9/e3GIBOjw64VEcqsYeRogu7T1+RA5ZUiDDuO5SJaGqIDVoxVWzC9mi5SkLCijEKYeKBBTqKQg31ekaW3GiOtadrFi5ZrqXfjg31CjLYsxVqKGl1JEsV1xm1QkP97RVyDNCLnfcrjFWtRID56WVWDLIvCg9tY1IXRRHWoMUwh7lHWcDc/RHKGcO5DjBEiWUS/oU9DQ86oLN0HXKMPkBB2qe4lQHhGUZsHO69DOWmTj8sqXcDSpCh3sW4Y2kETn5XwV3ej8kOUDzEr5ZuCJD9aMTtD3nFq73H2/+d//w3/xP/5f/eXlpwlbJif98bsHw2Bi6Qd1FWkBFnaqREMuHVvB9Qi5OzZ5qsTpmyyBo9Am0KssKz/qME/VbWYoN0jKNwwuzrSwUQElwRJRCOiFkTbcsrLtGrK5js/dQhuHmlhbFSXljwToa32rXy6sUQby1wxosvtnivjiHRBt7kKfeXS9cnJ3EDZ/GY70JpdZXbV5zlf0Z0iHv0cnh5XVWMIWToyx16ZSbpYnVN/B1riabBrBOdb9ThJ0FblnQiGAXjLNYBcPLPwO11jNzMKPxpglqYgjL+e2O/7kYYQVUZ5dgDLeWdoYrrsJgP+U8rt123rHQGg4mAwgKAnIhRdnUUB46GampjU7rhdYgac+OcAKsAPvI/onoFtBGSI9wmRD+tm438VlkTgSYFG/zLBA+h6QDXqvJlR69TB7pTDldE/zC0Ur9gULe2dkanKPB6019cVdhijQGMRphQPrDLC5aWsqX1hfYgsSH8LYp0N399mr5s/AfFKC3R/zvA3n6bX+fot9cxgcedkKcY1S+8sR5+dTafH3hYSthYNmOHCUGJwtnKXZQ2ka4QzGmriBDqmISZWxeJM+MXaRsbCLg0HOK3WBLxfiUZQCyLy0I1cJZtbdMQikT1oZh2RC2tTncyIFyYytfLdu6duybTyhjxnQ5aeJkv+UJe9BCye8L+bmhZIosP37N0NKSTGL0G8cG13IYnUAGSsPVJHcqmS57ttuHSXOIYQWgzEXya0xEWpoVEwjxKbN0OVloeikKQHh+/PHHqhO83drZpD04lM/hcpdzuZUsmkE5J5FbpSRFWDL5AO5VODWiLAV/MsOaCgzB9EpFYHrbQWLE2EW0E2WZf+SBpKefbTnw9HPDlRQrK53BZSAvXry0Z0rrCG6NLSBRpKBhNUzEEmFDgMzQLROsTfQyp2+X2UOG1hFbEKKDFCSFuFLClYPDDTP/yl+EyKQh5+YD1oPpzD6Cco4OolR61LweCwqFNQ2BjQ/BCwnWhn79618/3t89Hl2tWFlb37y8vh2fnSoFPUtQnNauL+azxVyMmloBGmjlvSe6ObdRLwMIVT/FBRm91QUko7jmS5RBi0T8hEMTvPuru0AiPQnxtUcf8dfZ4E8TxWW0OjovtOs2+BtHM2+wsKgOqoarhoHGlGJ515hr6mkvrLpekTRIf21sgAyUJ3x6tEvvfoeYUIP/Yri7CX/Fe8E0Eccprq/xjs+YXFhgc5IZNQwSg4EKJaU0v1w6m9bV0qrMjGPUP2elOv+wLEDhAEgSxSizHfrTFZMw7kEzojHUXKaYiYEUPSTO0enWWktVJrvjo2UkJL1aKmeFKD/d4/5oqTwgd0Cor6sdBSZa0TTT3Z8AFyq533ZKxwOZUmQsKui/CgqUFq4dLN9OME2THsI7pEB6552Gd6YAvfOWv1Oy/0UrD1+40y0w2ihENeyKJ7x6NKNhU+i3BIPJUjEvPIvWkjm3IJspX5hKmI//oh/lX8IUmpgVsJJHETAdil/JFlOPP11LPVNKntLBOG0MrJw82tl2gs5nL78oewPTQ67/vj49x7gd5IOzUVkgSBK1ACAwSjBc/uIXH9fBLcRHvLyJEEzfKxECI6eD1GnI3pIKnmbt5u5ffPmcYHv0bx4DKzM94Pj4hM7WC2cYpbm7nWgkilUPTzCFyJuz0/nzeUoMKag6idrCV9ohe8qCxv3Z7Fwi122yUDOJnMoTnw9NVgpKLeS0wq0e0gW4aayIPJ7wV2/rQyenwZwWRMuCLXKWrI2/C/iEqJ8NVhF1xZna7H96vGHqDQJF1Ba3fp5Q1U5OCHjSDhAVpW9pz6X80ZbQROugFE0nFp0oHHaNgQb/DIoSNhBjOcvGqJwhPr1qQOappKpRcv/RcOjSdAN1Vc6MMXl00N//5jeffvrp//HPv9OQpdX1AUdjzioOnLwdrG9ssq+oKPYOo9BgYGmhhF1cnN72VnPrRFfGCvVLM8F00QM1icMMHP309DkozDVKW9SeWhajUmhUnfIYg5AgXRvVlBouWVisuLng65KGoneQfbgwRBwKISBNfL2glEvKpFg5BbAbiKQAgkYr9aqVISnnxzl4SSnpAn1addB++mgPqnXcuQ2SuVJX8xV0HrrTNeU39gyzNHCQQWIVTb1MJgakXqmhBXmGHMNZQ7N4CnsLc5vrWQVzjcqYAqTA5Gp/d8cx38bQqlVEewtyXk4cvGVDH0qNSDpSG6oLtV2xqEsUi5y1Oj9Z8DNb+3i6yVL5k7c7tArVIyafDK0ZH+hXX9d+Ov1V8a74jnt4++pVJRbIJHYke+4gXlp3c6tpRdhKrVzfL96vHp5vgwLdHW8D8rfD/JZ6u+vfmQL07Xg/vP0RKZCe5l5aU+SIqfLOwbym/MsYCXuLh/KUaxBcJB8M8I4SRa+xiVkiGWgOiClJEUoRAiMhTLGYXDfEW5F+lWe4Uf2t12GpZW6BJNsLXHZ2t/f39xZ++wUxwPoxvjqVgQwuoRLpjvVH4BSjd3ohJ6fz03OijngggbwlKkS0kXxSqhEg15VqbcNbeeSEhwjRfnZGwaKC8L2IJBNI+UeP9ljFgEUiFqBKjAcGUFIUBA2ScCM4Iw9CtlCyK5pbrZNdSkGUAh8VwYdkIhQjy2vRREFvoUQ4TbWu8sAAWU6BRPJWkA0ECFBKhK5LOuEHOGgyd+2tM1Frjo6PJcKTsHS1lrrs0QYH8G6OUgCmjYM+yJEtyiXhZ/DsRqGeSmUDsykJRsSt6xeylz6VIgi7CbMNY0f+v8n9YtAzkqKgKHwXvvIjgwcWGULwz6WnpQaJQ8kZlX/zN3/zz//1c3XpDGYyKop1uvPxWU6a4QYOFik9uR2dnXLABiRwym1FeyEPARjSBtBKZ3lrtVLb3aMCHa/Ery6iKMipiNB95JlvpVaPFJRBf/kpXVAWzf20gAUmY0npTDk3Ul1UbYG+21ojb2KNYt9SxWBpmXIDmleAyIzIWgdVWQBEOmPyyZMnmq/vXh4d+ijcX+KtKlTapO6+65SulE6j0rX1dZYebVSLEVn9tQCT46MceglVKQcnJ5aGrWNZVLTNwD8+0Napnj55wn+LPmMVWK9xw5KfyaQWgTWxWYK/Mf9YE6P+eMJE/y65+IuKZDMFt/GQo5bEp1OoGHvRCZwMAZCLY4Qm4QTFKfIiLCGvvxpC3umrKQO5/z4Y1uemUxT37PyzUhLFPRPuKpMNqadpBf8+zIf4z4cCDwrQh97XWYhKyPpChWYxzQIwLzyBhQFHI9xzukn8g8IyBDbiMK67EAZSnGuaEO0ozIPaUbEoQCKdbZaz2U4tU4R/ZfLoT3lJToHFpM6mVMyrqhgs3GxurH78dH+4sXJVdy2RzSDvPXpMWuDdnmRSpNF1rluncpAB49GFJ/4uXWaSrM05PIhNqdVJongLMW33JLDlKZXi+smTp1YKbIeWh1mJlkDcET/HR8+tdKytDu37Uam3+K0736+OjuLIuuwajT2YqIhgzPHNrB+OljllhBqpVA5bjVzWDiVyy81cIQuGzaKzvFpVx2XbW8JJBvjnhw26izxOhjx9oCo7Da99dLSUpBRIPvnhT3DDitQM4W+uXegUn626qaobG2L3wmLtkhtdXJ2enxFzKwRZXL7acSqtA1C7Sd+z02P4sASAH/ScoHARTxQZBLP3uDdny948v5P4W6RFRoFza65PGZpYWqbTawMmVRhcGi5MTQf9o56FXjQkMtJAMJ6s1dZybWw71L7f/OY3/8t/+H+wzDh35/wi6iD3KXqSt7ydWO+AUT2VDihNZsdhq9MNw7X1rc2dZy++hAycPLtaJBVsvFIZOVhDZX1xKU7x9CrElA1wDR/W5nMFjQQ/UQPZ09haRTXqaT+Q8TNNrA3tIgjJ1FjO1DFEQVjDABfQljWIOqIWQ9qa1dn5+OY2B527mV4iUODIqZS6DNGLcTpI8KpxA8Q4EYeYIWTsWXithtdZCddZdFPQIKH503JVBPnLyfXp6Nx9YcpenJ8r7q4M7UgT5p1Ufu6u4I/3HxsTTGkOGLilRF7b059BEnXHEdUWxeLqXAucPtb6elESDlltcgeydXU5ZK/AxGNU+F7q35SN+I1UVDqDNQbGO+WmI/22u2n2bGj3n17JKSSPVfuMnYyx9obuV56KOGLVfMoXoETzvQYLbdTzTKqiDarf3T1bTS+t7y7p4e8HRAH9bpA8KEDvV5d2r/y4OGWSWmKoWQPg4bLhEBFUYQAVxPF9KwR5ecfTGxOcoiMyKic+LeJnaT93KVPdQp6poCiGrnjyVyl8PAy14gUqj4qEs8ITD8UzaQBP9ndY5L84O7fbx0IL0wcdhQ2D6Jcf3ycVuLiSE1cDVxumTQLObm5dtWVJyFuTbE0jY4jwxcvMgOWRQTZA6A7y2FF1djoabkUP8LMFDMmBU2fla2lCB3LKC/imvcBqBVoJXUVS7uaURJfipIKKpB8fnsATwsAqBRMReRqCDDKrCGRxT4gBG6FS4lwckjLw+yb5ptebx4G3d+xnlUdz/CTkLA4FpcGKKjREFZoshTiEDIZ/dHqimRSgvd1tAtJGKhXpHfldiBuEV3LmtVKw2tzcYnq7HJ8CRcmV3g1pVEu+pB+jOC0x23Bft0IUwlKMLIFosrggjzhpYxx+U4gkpfz0qEz+Gg9QYm9j6vq3f/3rcu+d7G1vXN+crs8Pszo10dFWiNioriyEab4qyGrjJzaMUsQlUptIaqBQAP4Ui9Zjgk9pySSgFCNB25FCBuYQcaTW6tyHxWZTi1/ajpJ+NgR4Nt28Fe9ulUFfyIC2ukwG6Z6W1aLinIcgMniLvLq1IUNPHkHxcgOLJUxBwdDd3MjwgBgI0mVWChkbZ1afNvxIN34Al9MT4pqsuHRwpNCKRJhzFAdqOFx78ewLVw7bIHhFvbm6ePLRE7MOHumrtgYMYmriv7y8uMw9mqYSVaG6UhOyJaBciX36VE0XaGRwV0WLmsu7yxc+oXvE6pz+7H/9yUfJzacKTj9FOp4ezJhpblD86at5INF8RzZlKzvYU9aUxDIizoB3Nj/vp1CusRc1ooZnD8lGoHM+PH90Cry35IXYW1SA3ttm/+gd/OMCvE+3+9/w96ulhYGyDZbuQT5l5lU+N7iVCZq3mIvEjuDyXW/mbhXP2wp4RkDVPwkYCSZYkDPt6zzxxzAlj9uk2Rm+mf8EMAM2NfIWCZDUVwyvuFmtHJmRx7SQxYedLTP49S/cjemI3rhyz8/UgsITu5uK+ZwMXUqGmfrieJFswN1aYpFG5FkLg6CXLSmsPvxmIgJJC25FPG2pBW4V5WlB7JFP4Mc5dDQiNs4cFD0eERvj2iDN08LGYxm0TnWkjtZaY1KRPIqADAE1ivjp4lUusQgcy8hZLECKMGCRdjIQMJSOxWFW7phZwCyxG6dsusdwPWKY9IK4iHs5tNmrEBbwcWwA1lu8ZWnQTB3QiCkiguYaKCfy0pNkllNZ6MHBK4LRS6gq6/ZuTyflSI+7TewHtp4duyoM8ppAigeNwQp3WeFiFAqrRSkuH3bQq3RhHgHZpXS9zo3WK4PM+QNK+vubQ/JkhGQUicskAlVk+dVff/p4f+/Z4ZmbVi/pxrZ0XXN04WtFRww9swpzRQ7G9YquzP1KQRhY3fJvOFwHEGSgUG29TkhSXGfpAtoDqugmcaV0IrXTK/vjpSAFIjiNWnk/lYqCpbWDJXYvK5EBXOd6e4ti1CYRcJCUQqDLms6sFLx3lLAsZQTK06MFPbulQaCmH4aV/lXKwFNc/OjwBKja+LgGH2bOpeVMVPQmIOqXDcJASaFX7SznRIO6Df3y6ChLn4wz9pTNj2P+pCOaRdAWjQHxi7Ojs4Mjt79aSvzk6RN+0HQdxxhEXc21bo5CZcbJGe5W8cI60DEfbtCGFbwQ5fT4JNfKLi85MmPiUhNdb0PdgvG2SDesjzxjQJFZ8BOV0tfV0ZCXIkmGzjl7yjMLXTwZ5ZoyD/apUGNKyfAb6GbwzOoSidWq9KriQXkTmGX+rGxTFapV8Ep5eLyiwGvEfPXiPYj9cNym8+Yfty0ZXl8dgj8u/J8PtB9OyVlHJFKOObM5E44pIGamURVwpfysIN7BL2U7w31o4v1z9pSn4zMgDWr2k1iS4meHlK8wAx4IXCXKs2NzbWW4tkRhIlLlYnVxARNR0TvbW8PA+7zCiQkDS07YfeRx/exJPGGgFaqTKC5/a1GK06sEkoBge/HihWmxRBnkL/l664YsUq2EYibuMkS0TCbRTiqoTnOoQYSroGrZBChJB1MgTBVREchwaAQ0U1khIm3soJfUKEKPabQhHOWpjg+W2Ss5ReQEXERQowzd2K5CvHHouiQC0l0AK0Cka5GDH1UNbYlap4hsoHkLuFJoIuItZDbqHG2U4e9MMAsyOEoAPiLwBKr7tNdYuWpDA8VB8LaJ33E/E7Jg8ZXQb2dPEUHthC7l5hcfPf3Vp7908ygDkktClbSn3Ub3ra0dbWmSQl4R+bVj5NTEgxej0Qn/aLbCaI13YhWq2qstmiyiN6WIezZJ5QRQ1U0QYEU0U0A9zyambOKypVNI/LISSUFAKaVUhYbAyqkiT2NGFV2XvqZey9l0UxBK4mlyWR8R2SAHR07pCkLDTxFV9BiQs8HKgxaywVCKPCJCd6iBJ0W6gipKWX9cjjF/u+moLedHjHK8ArPZ40e79AdGSBY0Z0whePSMcqOOS3O1xc+u1zdsR5tVOrWrSFvwlgCukBpjCQrD8WyNxFNc6O8fJgKAcJOoiCDiZ2qt0K9mz7vk/O1E+VWoaUIgl17VYP0UZtlEhBTM9kM0bQ1pqm3321bs7uIPf0OBpuH3oMX3LviGdaV3q3/fMP+fyvZWLEDG2Y+C3J9C+iH9O1EAZ5Efq3I/Jc6ty/EIDEAydw5eJN7qMiFTuHsKSifqykAoL4SOewreKugNedcpM6y64OznLJJs5piW/8sI1dmwPRnMObmwZA2MS+W8DcN2fS069m4wOFA7QazopT0p/Cx7x3KZ+skw6c3+OOAA2LKEAJBIbHgSJyLWzgD5wx/+II8z94hhBXFzpCBszM73H3+EmZJMy+7znLdzapE6Yspuu41s1AT7XBgDlq7jBSynRHPW/KvR7idkSDiv6BYiri9gUvKTECJz1S5nC1cCg0Thl5JjFe1OctL0aGwKT0jpI/nh1u4jGgICaODrQRc5QFgKOI6lcVaAs2z6ZyCfn5O7KCBn1iBvoxeCsD5M1cgYe0UhDFqINjcHK/GuRR4yb2PdzaMhKVLQjhhyXGSRAVPh6Ojg+uJaby07uyaNjW8Kp2Q16kQpqGviH0GTNZOMHAqSLm+x943aj3qV9ZS/8QSThLbha/Fm8Gj/8X/33/67/+t/+I/HhwcrGzt2mA1WszIV5455Z+/EloZiuokGtrG16QRCF5KdnB4BxaXJ+IzDUglXnlnPn/PRyZnOWo1iDn7UUXIK7Bsu1kABaAxpooPBy+dfhkpli4KhiCJ6GTRSHw4oL8L4IS6nt7PQS05+6g7ZlBVhhpNXXSAAaB0KGn56UqKkNViKnYLsGrTnFcdGray4wsUrI1lF6AQgCEZ1g1K14FignGHdilT5bzkrSJ4CHgOYcU6D9PPw4AUIRkshxpJ3sbowN1x3vcyVr07fM6RZFKPLhhpls1RvjvipuQQ0HEHJnudcpNzaOxpDmC4pa6xE6TqNm7IUBaPXVHslleU3WmZsutpwExteSFBjQEZkTuEKGURRohIkJBMdTaSyQD5B79YrT2CTp7b3e2NMqgACXgneoFoB9BHlehkIGN799uH5jRRAtCb+N779lkQFv+XtD3/1jYhB9bvW+1YUoO9Hsh9OlA8Vwvfo1/ukULxYTZgmUYB74ALh96Z3eEZx/LCSGlMeYRk9fP3IBxBggVBS5LXOlcHbTlQwkRJ2naLmFGbSqZAo2HQgfId5vYKZLPmQimpijXFlC8nCkvNw+WxKrSLO8VueH0Rl6Xn/2Un8ZmahhMGFRQhcm/Dwk5UCB+QBHdlQPsikJK2CZHr24rmCzYVnDFce+BM2SScCh9nEJABFqxDJNLcm9LKBTJ6xD9AwvFKEJlFCKzYA030/Hz15vL29S0K8eHHA5wcaCOgJCPggkEn2IokoAhoFiEWHewhMuiJKEpgyK0jqeHIEFiAguAtDTgEOaKJ1PRdWytvnL16CDAGGsWi+tQ+cgeTmdszVnY8IQS5VnnKVzXVR4CioOTQJ+eljg6W1pcVspIKYdEXE6aYEpBrL/zoIdBOskUGSk8nWUCdES9AKoSKE0DdLGmDlQcLKkHEiRcg6qZLXlinH//7f/5821pb/+Nm//s2/2wX5DNmdQFBWEzmbpJTO45sTQw8OXoFzdn4MsFOUKXCCguh8sciQFuLraOAltM4hTsmQTY0ICKYBgHRtquniaYxjzyuIKssxC0GUlSaPRGVBbpokf1nyZOhx0vDzrBUrSEoHx1jlyYSkEOtxYsjZrOaV4QfzojYf8/RwnSUUkxXgQOk4TaDC+hlTUB1sY4iATN2UHzKNm2vPHOew/2iP8WbsHtTRGS0nI8FqtcFHXbg2KrIjfnRyKo9tYtqlgSB7sgzlhPj62UQAGYZBqYJswaoO8jY5kEeKNjYzSaTmPmE/YRReRinJs1jQ7KecyVwhsehBUwYCQLLVy86j5o40Sg1EHG4NYVrFDJlOfXi+GQWatm+W9yeZ660oQD9JSnygSEeXMYvCD2rWGEYShaSYSnG3Yi7cIHP3T17VLGrKs4p/KYCvtQmgImFt4YMxf9xjc2FkAYzPCWVkNneWt1SbAsXwgl8rLStuOGNkpnHua+iM4WjY3MLEIbm8MelOVhoISJt/qGvJ47jbQXY4i5M36mo0NBDTJ37IEJFWfWQz8SXOKSXEAFmilJy4tp/MQp6XdkbPzx8fHAy3mE+yYEGCepJG29scgVlrLC31ShZZ40KMFdNObtG2d52fjpgZ6Bkm67W9mpaZNRq7wQ8Pj+lb52dnSEWaEqXUCARs8SwCGaYB6hEp1mjTILSIigZbaMNBOlkYG9WECWGFyzqs+GXDn5jWFmSTzf+Smhp2SN1Mam9O2cYQnA1Bv5w6GCYmhV5tiTLnFVJokepy7dPSQuwot7dbww2nIHIPJg5JVvgrmI6bW6RmGQ3qFXn0aJd7MdoKUQKXlpnWqANoSDdiVNNX6c3SaTKx/9riFxxmIYLuLigSPTp+X3PHR0f/8N/+/b/99a/+0//zf/90dD5cXzsfn0IAZSxkRfbz+V1Z3lnaPTh2SneOV/ZCu8jthewRi2UxppEVKvQiE4ju482ec26Wls7r2jL40zMyPGzSqmu27JDSd/t7OxkeVIjbOeodCrgqlcEDfbTWkQXr5XDDA80gkVMey7WuU9dFzlWCZHoEIQQ7AC+jiLA6aaVXQiyRNKSsIF3qSAOjyeVuMvYpTWvScYJX0KsXB88Nm43NLPxxuDGoBo/6LrMzKQsrYeYx25RSGJXoeo7H0jmv6NFY6yFTjkzTOzqcOMXDvTFRESU5U4Aov8F3sDxVLNSbz1rIwVGmS+450ZdpgmzoVkMxChkDsm8r7ld5G0VMYi64MUodQeSDr9mXp8GBSUSxysgNayhFKVE/5M+fu8BqKQWvSEKlY14yJDE5X20IgFwATHlbv68UupyC9SZ1JXxFHZe1YNebh8dPlgI1Hr4b9g8K0Hej17vK7fv8Hr0L2y6leLgPIOE10YDwNTunvJ2FzhxmFW4zZTdpb3MnciTTuzAOZYUpWyxulWz36hIPmyv1KDkLWjIUKGZsrKonaEnpuixvsVwQGLlcKQJ+PqdVz2PH17fZlETK2D/LhZk2AKZdSqwsWhThvbDg3BRzXSoFbk82S+fB01WzDWDc5Jm3VBCQqRSQwbtJPokipKY8GLqf7RXbmgHUyB7QyChFqAHcQiYDyygpBb4izaBhJWdrTtLlp/yBDyx5rEY5JSKLPBJF/FSLNnZ+xaFBT5LSOdUrUWZPVCfSuGMn28p6+qIUtdPzXOmqb7xFpVCsTDIx/NSF4dq7//ixKqSDb01FzpcvDqk7T/f3CUXSNzKMFaD2eUkBJL16OweyZi4ubu7t7iId11qIGULA8L5IvNYxoKQuxblru1skkpa2ZEN14GTgVifD9xtCjWsLETmrTnvlqKefkUnl1nP26eOn/8N//9//p//1f3v2xedb+58CyjcIbYfDDTvFYLi+ueHEG8SRqC1FroxS4t8iGdObPOPzmFKqYfkoxGVWBbRpyYqI8LPpHhQ3bDaH2V8GoGyK6EcR462qzqlCljhJcjgjhcRMNpxQtZlb5xBEUEuXQkm0VNHGxjqAMktvUoPZvSOHV7qbGxXtB0zpgOsvum8PGwBl0BertLUNy3RRy6IJWSp+eWTFlp7Xg1mTb6+y7qbfVaegHr+4vv3ss89Y6JjXEDoezc4dMGPwmY0vFrfWRFiNup8gJsin/7Qr+7f0iKPZ74wrWg835KVf0PC8j3s4vtDnKxdtqVFRUrwyCPyp/u1hX7+KzzRr8rs6Xe3d8EbjtWcytaIzZThJEKBK151llgJIEu/4kogfCse4GEYUftj5Z5FZ8YfIz4QCb0UBmg7HnwkJ3+9m5tuOfEno79xT3GxYN8Hds0OONbF7455RJ8ch1upVN7ELgmXOhe1iJFLYJFL8jvN0FUko5kLRIRVqC+2UEy3WRDJnB0UZy7VkbCZ0DsIymMZk5BVe5j6i7AjzvlGy3mR/mB1dLd7wUMy3pIUrEWpuam2gmqNqgoSgsiJAzDCoSCeN6BYarghrfhuQpEiXR361OAB3QoDWsh3/ZQVJjtgF1tbBjESp0/lUTciNxlEOlGJJIV5hpTm91KU61ySoi0Di5Ez7kcsreoNsvHeJK8ijkqqJKCsLhN884bHoCJk4dshAvIkAFd1gPspZ08ErQTrZ7yZUdggKFVsJwwYTC0eQK0cjCXVvFFSFltnOuOafsZkb5uNSPb/QJ+ggePRKzsJEFPRIymw2ovfUMYAqJcIcV+P4aaYU8jVaRXkTQw0ozYQnmho/JPtargmLCpRB1xttavjVdD+DrQcG/DuUXKwo6ZXmToN6dYo1MEONuvM//J///db/uPrl579f23ns7CHwu2my2fblklSVrq6t8xAzhqBjhGYRx/qUCyg4PFFGR+cWsA5rPcgYuLiMDqotFCNKpjMLVay7gdUjzIfiUAW2K6JVyODpLbwGK6ushTyidByKqShEyB61LFrRSJjQRtSCGlcsglYXpZey7qKMDCekgwaAigutyelrmaWrN93iEIfL3NvFuCi9FVzfE8pcz+eMg0Zyb/8JgEcLJ5KBhcvG2tC3czY67et4FaTbtUZFtUIUzmw0omu+y7UYBw5og8E+HUJ7DTZ3DIPprSUtW+9pMVFkypyjpSw61AnZFOQxxEQmY6s+3cUK+oxoKtF/8qW3mbY0khrVKKYAkM2VksHoiF5S/EqtFeSJ2lLMJPG70LiphS5j9NJpFI3f9V3OLs5051+wr1DdxMnwzkwtd/LfG3Z+RFG7q+bh74dOgbeiAH3oRHs37bv/bX8nDMKSyr0Gs2mu4lmMJwtVAftq5hQ+nrd3QQaKTjjMXZEZGikZFSaWnjY+d0oXnQFJ4h20aQZoZA5W3CoPvDMHqUVDy8GMsQ0p7kWL0iv+sOcuCs1VQ2dnJ3v7HxFCLgfFymWgQ3CciBfFaubrEcTz81QfwsArKaQLgH4SP3SazK2vsr6gbOtGslF3CAkp5ta7+4+wdQXbiBI7050nLNlAvNsGj/MyKfkJLDGgRrVIJGP8BEeKnzJI6YU2Yg8CApEgs5+yaQjcRqUMicivOhFtT111TRVQAlTVtUjBub2lt3nF9xY0EhQcUhzOhJ9WaA7BmXl4DumZ6griIBPfuhPNCHh50A3kRsZPCPupTYQJRceKl83m/ItgQpODcHeNvg787r4irIIqpcvRWDmQpBMGC8wPHV4fUlC5F9I2Q6GWLVoS1fyekHdEzTzBSlDqrL//zd/+1aef/Kf/1//nl1eXtWC4RLBxkuGFs7eSY3UQRDaaAw2EQQNKDtcUQZzVhawZ0T+ShkzLdNAtd7S5JkwnGjn6SEfIKZDi0NYYAKHWmILWQ6L7Wh7jRCmKe5SC8n83YBy9mAFwHTsfCKCNrVaW/xkMBUoGNBq43tRfAMqsCwDRlWyf4EBA2VpYs1yVqznkiYKyENMgWa8r9REblXSZLdgpqy2nZ1ljhTPg0vut6zNULXPAWjuOCfDMsYWOXbCDff46vnSQBF9Ek7vhMnOSp4QaBj69JfOWIoj0zuYv5Ok/ykpMP1bwNjj4P9pJRjIiepPi2R3Y+kz0IXGvp8XuSO1nJc94xpQVJHPB8YwrUYUAnw6vgPFKcvJVTilFg2Ttt9GTopRNq3jVliBVpV5V2wkPzw+cAj+mAtTj6T0h2LtFpr+394QUeE1UjOKV4jiagD73Q7hYWE04UliVDGRhswNWmRkLkyVspf4FiH9hwc3HMuMrNuRJFKmlWQ/xNquRpf2KXwAXg+KnNKFBzSYpRaDIBhNnqHV+G53IBvugMPT1rSHxQMjx+yAeyBcQCCDWDEJItq3tTW8VbAFDpAkQ8CqiyHXZtW4FN76kLcmkW8kjGyhgezt7Tgkk5zbXNv94+MfhyvDG+TsEyfXEHaLAEjC27ThshwJE/hGiLVcUIaJalwJWK9QlA0kGPlUjTWAVWF+nCcGQDmcDUBJN1nPi9CL1AihvFdcoACEpApQ4gog7xjfx6yxqaCBBCBQIsajNxWsDHEGNgiJq1/amhl5R1hXy6WU2sPmF4daWuHUkugryUHBP6lw+ypTknAe0Cud4VQNCplvRgiGtmZhkk9PdBLdKGz5E5GHFINNkgAaEmQV62PTgyMCqsaHImwT1xusm6sjN+enJ/v6Tf/zHf6QAHR69XNt6NHDiIumsmaVnULi0eri57Z8joS9GY0s5dHcaHscVBqRcfhmNLXv32IEMJxha+llcXWTMgxh90042VOXJBD3LRRJPz52afW4Qi1snHG4g2pnO0nd6nElScGuE4cdhH0DBq6wmnY5vlnPSAc+bheWVPhFZEUj2ffWq8FZmzUQfXblmQ9/Aqpzhcba5sW0PoSPO4bOy6BNYdVWFsr6YjJC5G+YlY0DtgMjz/OVLg2f/yeOLq7HyOVphji6YZV+KEUuSWsRjOhrXOdeTS7vsj09sxR8tTKLrW8cCTTYt7VGku0Kledfal6k4RxGxEPM90iHpQJkNPyq4pzEjRUMkeuHrFRek053yqgxIxgMSpfBd8MFLyfysSiR/rSP2b890TbGFuxLFYYqf9NtZekc6vyIiVVdUYYOehpOU2nAmpwzSO1tG6c/J6vMaPV8j4F/+57vF5zsrQIZRD6AZpd5tA2ZovFeRpknT6kdHDPA3h5ylfjPsmwjysKfoKMWDHEgYnuDHtEMD9o5zvQbfCxMt/+Mp6tYiPAWkblqgNKjSacCRDRtVnZDqa+YnszjZiBnFcIT3uf8yJva8MMsM71tiCMr2nzJKZYWI3D074UmDecddppWbiLE26pyNwORZ0XWR8r/89FNvbXc3SyYbMHEMukUF1YHt5JNPPuG1Co7MHHc4xVANypYfSQAOEVsOobdOxL0dZ2eWAA3QCCp3M1nXAPC3v/sdfYPQDJJ1cLC3AgHmLcFD4KmitQRyQqIf0htzskcG6EmhS2mF1nHseHnCRBQtx08RIiFaDmLO3fJz4nYNlBotq+kUbaQAqaIluvxq1ApVKAUT4ozg1Dky1LnPF/pNhoW5VepPPHbj8xSfD8iIe6WUV85lPDs9VS+0aUGPHz+CMAdbRe0CU1HXqKAASbVY/zo/eemERHn016UFwYyB6uoyR3Xmu6dBGPFHKs+co0sQTt+zb7kFhWcS4UlhVfHf/+ZvHIzp+qqtxccsTYaIXVDangtGaisW7Q4x5Sy17WZxZX7VsQpGWjm1OKyPKoBu+tH6nIOukYgVJNQony2U8rb6FNSclNhdgNpaBw7Vx0DNJ5CrZAewR4T2SNvejN0O8T2VEgwkFSGgznVcMiUbED9BVi88GyxoMhs27Hc5bbJOAKqBEeugV9yMOKm7XYze66tSqu4+i2VRWbWksvJHdjHI6fEZjcuCrL522hMI+QrGcVWWmYFK18Lh0c7uHz47sTiooE6fX8isxC10oMlmx4T0vo1EisaaAUnvcMOb2bGTUYDsIxu5Z84JBFokV2dmIBSyml5KkmcHb8FCO3CkaLYiIjI3yep9dGsZklhjRmIXT0qFlJoVTEqA5E8ldtzzfv4GIiV5Co6U9OKdsTn8J2Awt7ClQIPUFPAM5IcQeY1K77xJ7xyf76wAvYbxaz/fOUEfEPgaBcLUcrOPI2QwH+eA4d3F6diDowOZSltPweIXFqMB3bGt4keYRkKbdkRq7T9s1yQfi+IuwzYeSVRsCFjcKzDCDcNecduMkGIlzar9jMolMewwjKo2fcV/giSK1wiRhzPZkmv3UgxAVgRyzZNKV9eHI04N2T1OWq8yv1txIVpIhQikW84ux043idSpa1O5opDilB4O0YQBWbW5vaWNIADI3YVaM9xYO/ns+Iq1J+cdc09mazkbrCytDpfcj656Zx+7a4mMJ97oama0O4/2+ZeI26Zm4xjuT6rRG46ODwlUC2fuJCNyIAwxRpSWebK5xCmbkFdywi8i0bvIWMJ4fzfbrV34pTYyeBRX4xGE0fLUdVd1Zk+uLEFd+9Gyx2qAeAQVp5zbwQIqQQp8Av388MjR2eiztsbItBqsctjxZGt7+xe/+AVotAT4CBHHS4MJopFg5+e9aU7N0DUEvLUtG5Wuz8ZUT0tsW9sbE7vlUiXhdwMT5CWe4Ep4E8+WbBzVs7t26wpQRoN0osp0dsvpDBH3THlGwMxCxk508WkocZeHi9UZYxyL1F5hjof6zd/9+unjrT++PLh59MQi03Bje2GwSviqZc55S7dzzC0GOXLFSZxytLxoxc94OHj+AmHpFrSjucHc5enYJVxGXTbOXdEHIasQgc1PnGajRxaYtLYebdCDi1CtJsbIwRvG3gF7yqicYBo+x2euGT1yXmZpLaVDLA2cMaj53SS9Kc4njLaUT6k2qz95um8MvHj5zAjR0fGsYhKK3M9eP72Pqgat/Y7GO5XAhjt1MVnJ6c5T97FA0oDc291fXVneGmbB67xV59Xh5tYNvYTJUNNuz89YvFDP/XS6SREQcANoGNmYAQ3TuDZFcScqotFGrSpeX5bZRhN0zXTak++Vw3QmLOxlt5fsavA5OXL73LlhEG+nbPnK2Ji/BtcJP9dLNtXjAzHuLpaCQTsyBDQzPCW0oHDUF++H8W3JUjZTAZ1FidZwnZt6M5xCzuJPcmSENKBOiroU5HwlpVMnQ4xOHbCk7g6jfX5u4PCzmob5Zah3N0Ud0tLKZqE06IWb9VwxNf9UQ5Gu25gmvPbznbfqnePznRWg+yTrUXU/5S8ffx9w+Mu3+o1rzMcsM64Un+LwnpqZZcZTDkB30xxkDCVrD1EPyvodu/dUOIU7x51Z+czZsasoMtjElM00SkpNx3RxrDAS/0/D9DtMnpTyM68COBnCfUSxZHhk8QOPXolJhmC3O3l1PatFpAhzjQn06mqYoPxs+0I45lL2v5im98SXVod7yt8BcMXlLwtHtBkI0hPIlSdP9o+OTjg1E2mWEkgsBQGkZ4jIyaVUcTLJbqNPPnlEtHDtcEQ01AH3SoqyMnua8Tt3B27ye0sTKeFK3mRDEAzLyTt+PETg9lb2pmH1ajGDt4qx93j/4ssvqVQyC9CItrHAojN59uxZESvUAlM6+DQAcWALgfiOdJFORASQZctqUk7eW39h/1cUF2l0BW9DlqmDUZEXkRgO1Ez0Mh7ITAnIaZADms0GKprvp5uWBrSD8bmNYDHLQcYSG51jfRgzkp/S0wW5+ZuGnUEzDeVa36r2XdKrv7OMsLq6zLoe+WtToJONP/nl0198/Piffvu/Hx8fbW3v61qexVcrg+uzHGmzug7nNZYMI31zZ3vtes39Dy6r14s2gaPJ2kYOWKIOMvgAq7tPz06G65vE5vnlBfWF/gFRbtE0vG5CP2mHIWANBnBiybuYOr5oL5j2uUs06rQarajyRqwe0SoRibom5K7th+LI4imDUvIglJ8gGzBegQNkV+0tC9fW1iMUNm4d2ZzqJhPYbq4PAdQQOZXN+HEk42Bweu7+ll2nWf7Lv/yL4ukI13eUGxzcRFz4iibW7GQ+MaquR3NXbjOtzir354mL+BwgWUoS3ASd1R9+4kLMJDH7lftz7MrSpHhKzLdbSkvX3q/EBcX9n88eJzEqRCrup2BtPhYYOuRUdTbIKr3eNpxiFCnjZyUrNv07zaAKsZovdUrFk5myVSk9EdOk3PYqLRwNkOsYgeCmsd5NEZYyrbITfnrPJkLjfT/+02vJ28H4BylAbwelB6h/hgL5mN840FKKBeCWRD5pRzjFaTFcxmxPrMIMHnYm3k+coIxEYVhdqbyznCKdjGsL0yKV6FXzJZWLm8QrmN05xZ7oTzXfc2ZM+G6YjlKeJH0e3k9rxKMV6UrFBRwcIHxfIBIAJ7yly0bYiBADpIV0GgapIM7Jw1SedKHrdLobr4gflgv8GnCl4N8SK3PQ1I45x8eTaAH27PiUWGKAYTCQyCObM45rFrxShTwxDNQdW+bEsLLYCKaFj7Ojk8nVjRODepHLlPng6BCq6mXpUUQ2FaE3tWNhNXYseFKM6Cf9FnCZpZtjR3iXA28nwhw1GIUU0WqJ1ju0F0q0QMKVXtgA5dT9COKnIiissZ7WBjVH0zzRU1m90LqR0YH5SwTk0e4uySgdolKqILmu06JdwZObrbIcq+BAE+NkFkHYIwGUyKo08n7okTBLaYETot+FZDAkaryJa+BHH330t3/7t//3//i/WelZXlo7G13QohdXOPUu6TFdvOIQnLIc6Gh74a+OczrR2vrG5nYcjenyGs8SakiOeDXNzTMUIYgmqAUF2NPgz/+bpNS5nmWdWdMdmoNuQr4iPc70t7Z8enI+Os5VKoaP4giuOJxllsfTT0VQTC2eMgCF8oqonb3qyaN9Ves+YliR9MtKuvvFy1MqDoIz0tC5afZGWvURLSEbGBmH/Dw/O7VElpzzc7yt1bKxtvHFs+cy//pXf2XItR1xcpHlLI7iRq7vzgrt+PzQ0U+omq/A4eIx+ZW5rvQGaFSXRSUSqiPYy8qOU+ZbpMkn4op7Kqrl12wCayYTtdoydp2oUKaiYiagCQ0KuP62dW0gl4bRCpafGV2+vxqCNF8GJaXCHGpJEW2n48hvoYaKDD1OVDFVfYz2cpvzijkPPeGbbGE2OZYak4nCU7V4m92X6vhKiEGo2NJXUn9CPwr/9w7fGVZ3vTf9wN8hot9fAZo15h1i/zOsejZ03rTtmXDHbSC8qViJjhPA8Wxm9xqoep9HZsrFrpo/TNPL3Kx4Q+iyXomE15R3J158H2a/fS1FgQhR75qV3TijhAIUx4IyMRFFkStKhS2WHYgHE8jmzlkOqKUc3J94oXMQwNbDpLN3yA8TYkCpFlrEJ+8Zkl42KSSTImbue3u78pBAEqkph4dHRNRi3WzQEEgaguTlsxfKXpsVX/ZCAcftsEgVQQCGXIisf1HF0Blkdn6iRZHz3KC1KhtTgfw3F7fjiyyNwRP1sGOmerXrGnmcbKR1/I2krLuRsqSmp7e9OQgm3LTRxCqbWg5PjohSayvyWweUIluLc82MZCqwagHcz4PDw6plAwJkZWovFQoyLZuVVZNEvt5ItWllxSFMpFzIyNuYhQNVJmVV4SM8vUADhqpzIXzUuGiOGQmq8KRFZdbNCHRnRpQ4C3Lc13hm6R2BPMjgiFg01Ea99nd/93ebm8Oz4xPLo2vrWxDmqM9csbq+Sjc9H526rQRhs2dwML+6bGOX7flzOlH/u5VWL4PJ0qbta/bOZ7TEPKZGBFxdjwlHWxoBFEMu/aiU2hXpV6BJlx+o7Z3HlE5fktVVoZQhq5NpuzyCPIqLePaIyvCYm3PEotpptOINvzUY1QmuuYUJnL0SFM8yVt0flw7gplaHRWWsVjAKZO4BCc8eDEbjsZOx5+dlhhjk9bLxCb6lWkEDJdrqN7o8hQb4ICSIOQ67QbPAWUfTwqnqkeVy14/KBRFkcdqCfxlsPt7JbZYU7UzI95ojNKANWqws0VWZWqL9NDNRI73MoAHK/36C7IliFviCTO5FriX14lQa4u0UCz+iPKV1QiAUHBEhZStoAe0pP1NJMkNDxvZosuJnu79WxDSa9MLkrpl+PoQflwK6YAbwfnyW+E4iXxFUb47B+9OAN8f555kz/IZdmV9oaSc6rkOkzy0TDG6NN7yy9IYN4T+4RGlL4RoVlMJKyEy/kscErVgPLoaTYG0yULKa+2A9s1IiBUw1GJPJqvCKzbAN1X8lMsu9BEuiAJGq0rF1nBpkT+sKfAJymm+5fEJ7Y2toWo8Ly0BrESBmDUsES7OwJT1WE9J+Z5enAiPB3EUOZcb76RmyKUuTgTXth9hgOKFk4L+qo9nYO8UgRIop4qeD5uDGfcQJM/G2OT9HAb41JtZ2GdmwZllhVH482D5QrASxTt1m6zIjjtoZBKzlmYAjQYQHr6DhJrFEKMpZ2SZsPGSeDPBJq2MligBec3Xl0kArZK47VrM7WtneT6ctuhgmZJfVFYJNq5UqAerQwliALHwowl9bKSlbW/StRdIRNEJxezteRAaJnJEc5TM7mhvRNvRWrSVZf7QHiP/EgGsWw4HOdC5ljxA1srSdTU7TF4qTZHGpqOWGu+7OXP6roQ0/s7T7P8GZjb3OQMr+m3/zbx4/2v/Xz57Z8W+flH1qS8ur0DH0mDAuzq7GCwxyKwwAy2tp9e3lhFGEZkl1fnl8Mneda+1JPS4+GmhYWvDSuWwAyEXNiiCv0xNE9DUE0FDtNaJKipYlQ6Lxg7ZsMV7pKUNIwdEZNy/6UILKAelsBlXe1rVc7pm3T83ggQDI9b1o9+1meVwpoi8+fvqRDOmIpcHhyaE+VcvictQKQFLw/Dxj0v4AXl2O5N7fX9+Ig7+7zKiuho2NijDREeLOzWRDEgGThfLJ/u7kamSycnyZPpSuokYGzlqdK+ApsnYX+gcE4+R0HoQX+K5ZynIDu5zooLjzJ/Nx8fvhKc70Eg0tEWZZ3VIhmm66I5zmVVDLzPQSivlpUsDIFY00b5SNDpTlqdxgKtH/VWoKKgAZo3qAJS1Bhg4wl7u88KfVegsPjkqGoheoTQly90fh9CqP4pXy8PjRKBDKv5fhOytA721L3kvyvhukmguoW8QXj0cw3IclNacoJpI3ZY3G+3zw05wW8HGG8I9gPn3VcPCyTKeLAUU+0pCSTwioCv3Ts39Kz1tFaD7litTpOJt0bDboJHcKYJs1UasTEbMWlu1pfDndaq0u8okU4QTNvQYwTJ9CIPzhs8+93d7bxYvxccBIrhL/C9ZuyJLMcStQLOTh86HU7//wGd5KhIDDu0WcTw844ZilmpBbNBJlY8U5z02fynolg1pMjuFm/q0gKaiJcsqvvYgsDynFX2dje4f3dKbFtRNN/s3t2F3AUVAg1WJpqWMYwWcnUjbqGiF6kw3/8ohrRa6+qL05fqqiMuf8X+LHlm9z9J7i0/YU0VxPeeDTupQiWq0iNBxf5Mg7EkpdgjxaB1Q0AAtYpQR0Kf1vVxqLGU8pxaEN8vhkzA/dFjdX1moLgJzLvHLE4+Jw4/LsQOXdxowLXVpjQS1TjVjWClKMTgDvEl7/C7duC2gtoeH5V7/85V//6tPf/f4L7qvUPUtK12vMS+kXauKEYnpunGcvmJ7g80vdnqecM0UsLNgVpY1eMel8/vnntD0As362tEQrRMCjk+OmBkKJwE2iXkBJzVeFnCAYGwrSs8Vl81OApK7cyBUZVmbPPdWD7FoFeeOhxgmQOY8AnNZuwVfw8PBAHl0gPRbIeIbtyg++/G1rBEfO9bVgiyxqlJIRXsMbNImAOGFhw32wm0OKuAxwoIhvDLfg47Zd48TXqEVy0kTYPG8mcecCWYYYaXRK2UIkAgiCyDRk3EZ9CTMxbaqhbj3Rx2g64bAAz9zLu+CY9MyUrBZTgW8WjX59X0Yg4IqT5E8xjfsKMYDNjqJ5sR/brBHLTTiYKZagIuWwquRMtfkr9B9IijfO95AO7lpXmfPobNVSa+3XbQEqCH9yHKaOn1qoFr1jpN8HHN6EBH9SAXrfGvC+4fMmxJ3l+Rbkp9/kLOufi8xAKTiLd6EG1YnTV1E8sC3i0Gbmy+VoN27dCkcIs+j5XbkGSyGRwl/wm5hpirvcIZO3xW9UgaHcVV0sqSR6m5opBowKWDZ1JnlK6cHRcE4agw0YwbDYEDYagADkXwJpQZQyIonYfMNucrUwzzcZB5+s7DCqK+sNWeLQHxzcfq5m3BFpBB5oOf+XFI86YjsYIecnzj4teDFuEUhasNbQG0gCbiXOjiNI+HNsbNi7k2NsmBZs4mEX0XqvQGbYPzrMNmYpnEOv7L+6udraHNoRRlDxz4UV9xP1Emmqo6WBbJEGJV2MBe2XhFxmxVGzGIGcs8PXgWSFJB0iakTkDbvR87WNrFYMV4cwJyzVaPoL/ujgyDZ4KlmEJdHFGFBLOWQzeWz3mfRuryKEaAtaz1CmVhbEtR2Z4CAwEsCHRiVDcL64JBGINvF01vWVzVPxblpcsKfaweFUBWuKENG69XU3aFp5jDXCCdTzt9c8pmmRiM1CR7WFXtebZ/ocDt8gY7pq6H1j6LeqYJbyhL8ly3/7b3/9f/sP/3FjaXFjbdXql0W7k6ND5kBn51Bt7ETLMp7DcozDUul4aV2dsEZQK7dae9BPT58+tbqn0qvxiNZoyBtC+lFrjUS6BB9wNfqpf4H078Jep+sr1Kbq1SGNCxutgp+cqI6pZIm3Uamnrp5TnZ1diBD7Q7kEoapEcZVK1+PifLDozcaYwUanEaHgiBzNHcimvUqpMb2Ty8WOBzvzqjCiGBJ1tz6SzWiJira2yvjjY9ncBHKbR5khajSC5i3lzLMIsjI+P2HdArMDmtCFjUugtNdPT8oUpTG+ePSubNA0ItKL8iRbOMBUq0Agy1Q2rcFHuzqDjuu4YrHNVYi6I5pjNIoJiGdE5F0vGkb9SkJUPfREH1kkJP/roycjLKpRJk7NkUQTQAtypcAFQqEJmcZHC0uRK7RAgHRtYkjr7gVA6heaSJ826t779yt6h+37gtX7hs+30+VPKkDfXuzh7ZtQ4K0OhW8E3omeuEaeGJiP25eO3dd1E831sAPMQRyzE7QlBYsJeNVAZg3snymIM1Xo/BhX55HW/EPE7lWJWE+4ZCZsxcAgU1mr9uJ5gIa9xT/EUoRfthezR8tGfzJpBAEA7Ek57Bso8sAhuwwYG1vm8OtkuVccMrB1Ckj4eF2aYYkIGqUU2Scf875KSRHcXNPECTyJBBv59q//+q/bO640WOONoMaz0zjoZBPNxgbFiFcHO4Fa/KQYKZItxPQh5rQb/kN7tukD1buLmXzsJCd7FAFkd/fRxdXkj3/8445Vrrp6zJM+Rg4VJSJsiC7IQwnyYGuRRBKLMYBzEFWjQTkNQC1gUmuIBhAIYOtNWPlkdOE4O8681S3uR8uByGxgDTMUK9dvVUPbEgmdj+uLWmTzSnrXC6Xr8p0yBiAj8K1h9rLVHIMAxwjijgUl+fkGIe8oW+Wij+rC1QE/6HAS2iEji9q9Mv6681uCmc5Hxr5xAAGeWg1z2Hqqa2f38dPH+zSPRzvbKlAR65R1nzOK3eRme2ePfxKtxq0XijOuGUOb6xtEoib89re/NVr0kcmAgaunEJ8CpBYLOeBrlIr8FDdg0Eqf+kn50BFUItV5ZbQ0MiDIrxOpHU7GKRUqbvhIJH/07DpaCTFNKABRCsEdaYjyAvjGg+JNEl1sjKnCz241nEFQXIqIQehzorbWeqyEdRnopsYzVTi2u8UBFYrt5+RmsjHMQphaKOLUd1Uv7O0ZKkwrnNRXVxyrdXV2eihdHlWrV0tzLpQZBF3T77vPHfXyGfs9ZRDlUOzjzAK6yYj7WxYsHJa2lFzprJ75BAaK5XjM6YRHJNs/6VBRkZM5m82DntBMpGqK+3PIwhJslOdqHDwifKOok1i2KqRQxoA8s6dC4gK4nt54K8xy+rKg7WcQ8H+zu8D9SQYNaby7ge+8DTN83jkmb4jAgwL0hoR6W9l6xPhWf3gFQPnUsX6gwgKKO2QuFCl9gUmEHfj48yY5O09kldAL/9jNQhljin0AkEldFhCEeEng7ylcU71MDe+4T+wsuErWp9Qez0cArDrFmCS/Q0CKR1Ubw7PgZ4eHxQu104Q6ZZHR3N3vzuUz/SyNB7skAKC/upKdSkwnckoB8tmLl+pwDgqYXplScx0VJJKU1p4k8pyWmXOrWjTAK0H66flZDhWswLDhr53vhBZsPa+eP2/9iYTzijzDtnnwOLeZHsCFlr/Hci6EshEspVRK8LiaFXAyjAbz8uBQcu4ir4UMly24dYHgjHkpq2pxxFYKBGKsROzk+fOXGhUDz212Zo1pgdfW1yCybnuSlbvBEl0wUgoFlCWIyU4/z8YXJ6dH1g20UUu1FxrycBCx7V5OpAYQcM0plW6koBRPNEldddAijxKilGlhuLW9skQdzK408B3ka7av3zsoAm1k4acrxe6wXHY+4t7kFMfY2/RddB0ih99NdG8S5u4sBvJyyq7//GAPhGLumiNOSKpX6z755OOPnj6yi4/J5XZxhSrKasgIwcud1Wd35ykLUKBPro9enq+sr91uZH3QTn3ER/oiXXQduAFrmTLqwWnMJIZoZ1BRq5IoE3rGKJHPU6uRS0FjxjmPVGTKHzfrx4/2DDl6sKvBUJUGzye8Cas6VIJGf2WeSCQFMlrH+iBFFaoDXBXdfZ5wU5EuA4Hiu7u74wN1Cz1iy0bv4SVH41lZG/CDuzofm95s7Wzr14OjmCoVhwkVPkt1zvein/HQN3qPT0194AaISi/cFz9ZPzWCxo5hzFBUqaAjp/OhUiBkFqR3RCeaPvUwY4h9cXLCvGy4IhOOIcYwqi+ymmbSlW2n1Hcb69sVOu3yOaVPvahDOMvSAnjxgRo8BhD6GCzBJmMmGlRoEgwydypksl6f9Swei0DU0EqpnphRgfAim9LUG0OWHNEsw48qaxqgN3PBcrbtd/GMieKcMqfq1Pt+hdCjvotGa/YzyL4HYYbPe4DLG6HwoAC9EZl+Kple+zZwBd89lsRlwrpNdA6fdDIRKuGSwqxp/Qn1invDwSKSAVcjBDHuTAUrKF+KjbKdE8R+Eys3BoRBkkGRMQNqUhLDg3ImDIDJ4R+lRGrP+NgMyJn6sCMhFiY2llAQIr8rkBM3PFypYNnpHJUFnKgmZZ/A69VOhhFg+Hinm4IDYN6sLMikiHTAZXCRfEeIIlcZRMDsPALK7ha0Oionj+WV3dYY4ODmAdWBL/CjGCxHswFTFUgUjl8H/pKCv/3tv8LUWzKGukgtc/O2UiFj8V8YWmRwULU8GqIgOAStFolrCIRLmkZwSoE85TTV1cF3VZd9xtkeRRrI73hpieb/IJPTyuousk1/FOXibX18dKQumaUAO4OsaaqjvZHHiGAnFzyzEjQvJ5mQikiG+YUcMwMyZAQVEr4tZWVbWpw/PD2+uVrPPjHLM6vR7RZWVu/OVcnACDLVuY3SGz4VRBwoqdQz0uh28stPPt7f3/vy2fHjj/ZuFpbd0Yo4xg59kvFjdelUs6uPEC2HAhgDMIdtm8EoeaBurK9bt0QZid0LcpLZJROjTGsCJFEGAiBLN15VFII75nst51XqNUR7vP+ULVAEZNnAjFRus1ndMw8UvbPVC73sEG5A5JSHLUdfaB26SdHe1oRAhoOqpcigf5sC0qFkaIXCc9GDlVJWZhkANKbcYO/4JsGXAgIbJPMi05cDHPJpcAY6Of7s9//VuRAaYuwaY0YhXSoOzzYZZKS2F2A+djCDGAOpdVE9WLeI+FahpHazppVVF9AOECgLY6W0KeIrVzbn+tTHHiXjbjRGXykuEgqUauIZWoQjZX6Wrz8fi6Oo2+KUdxoi+FNjIDoJ2K3KgCN0BvykQCW/wnlW6IZ0Hi9UCFaqK4VPFhk6axWcfjjSG04DeU+es0ZN0XtP0PrJopEJ/U8W+Q8E8f4+f3hjdOXXQWFwl3ZvX2YpR4jiUbtM5902XRMqpXAQ0rbeBsLUWWeaf7qQgV+EZdwxMrym7NHJNJ1jhfuAZNaVnURyYirzSwAOTNBFs/CSwRb+olJzSLoCw3f5sMa52NQ0jgfFj9jtL7InKVNkTcC7TWmxWqyOACBmgmfNngkw59Kdnjqfj7Pq0MGN5vcypIo4IN/QiTLhZvDmL7tkqW2RzFCWsyqvVqLCskjLEnuAyJKN7YiTtIRNxQ6aCpQhC2SqXonlJfeuOwvm5PRlUlY4So+5cZQYC3pqVC8cSK8VQkJww1RxaTAl0mmYfDSKqICtBqqEfJViHVB2G7nl7PTFpSguMmhp0X9hbcUiYJZvUrtli+2dja0dCFsMAo2I6TUdJAWKiqNgUSOO1RbJ1OJn09ArglBwYKMq9AqYY54bk6tIkMn1zuaOviDCWbY2+KGvxk4AHwXpDK4LI634sdhpJWJ8AIKki3sbbqM3XvS3dmckKPO1QND9Ke4Dw2pshJOmqdTeHQNrd3d7d2/nn//zby2HbWwPL1fXbs7PWPDk1zouNDb03cwx1HEqH+Re0klW7hDBJj3dbYCtWMisrftailxIRH+qfomOqzopmgCgRukUS4elNmU8dzdRgLxFXsWFoDeYd6Q4/UATAZciAmVVZfff7TzXISrC9Rm9lte8KqJBiqCq74c6ZQ+/QYj87rJgvoGbXX38o7lpx/M5S3OLx4dHcJPH06Lp0nIW4wCxMxEyeiuaTCnNvjeXyLgTmHXQWD09taMwpyDavgfVT//qk5dffn5+duQ4Z4oMCF5BGxAUWKnzqIwioLxKFTX88sn7rnVZDxLHQd5k9+V44hqNK5aeUqUykpXSfAVlrYPERC2EGQ05XCooe13aT84At/czSVkCW7zNyT35cSeVkrNC6q2Jk2dP2hRKrijqwOeSHyFIVllqHD0n1rtaVQ8qvmlJpmMxDSVdA4NUapf9lRrkVRLfJ++fbhTE3qsww6p7HG6zlPcKz29B5p1ZgH5ylPoWIv6QV7Oh80OAvFZ2BtOCBB6GsYah53sO60D5sOzSM+TsjhDBuRqOuND6DeMJC06n9Fu8xqtmc9N0LKkkXF5ROmwOMbOcz1KIIA/XWiYjcy4Fw4K6LnYiP6yaBS2XgJWpnM0cA51j+XBCW7YiQzUww6RiXa/NJjSNtY2tHOhHOInbcE7Ihb9z9hzHj0fmKBN11bZEb0Gg3NCEJEqxLEXjcoX26dHpycoJIYH1ExJKkYlcell3LB8oognO8kFAJwkRVEQaIJa3XMFtKc39EiQHiw7Zql4ra4wBZNfOjtP3omQwPahaoxormJBeSmkLJJv4kcwVQDbdlmF+NWW7FG+RCMvFAbTl4sKiQ4lVnUv6IpIaL69fgm/1B0/nmQQyIE09JPJKQyDRtYBW8j4WJgEOaDLY3JRI5PFiRmZEX1+N4qUueVTkhjNP262dC4QaKgUHZKU4/MZ3ZDB3PT9wIJGLUWu/dA2nki50INhmNNwL09F2L+V+tIkzI5FX4miyPlx7uv/IFp7f//73nw4YP3a1mjEHcTiNIw6ibQypOHS+eZfAKYdqao/ZDNXqpB9ot8lN0xCKPUu/aLWf+kVd5SoUQ0opItEjkU4eo4JXDccjiovMEjkAQYxKgIz0MzSBSXeuuGzitGemnewuMNMoAwhiGa5eKUWVAcdPmPspsjLMUqbbaCGpiOJ+CkeTQ25J6jIylQVTZhh2D+qILYqRo5DWVlmhTDooaoOlXX1htB8dHWMF62srN9eXuzs7F2cnhsTVxanC3cYsJDHDUASiN9jxGfOkoHUIAjffuD9GUbvo5Xu5tllhzh934dJfYikqXiE/lKiD4T6K6/58x3QUs5AYcXrq1Wqx9VG21+gbylRwSjRKRbNRMAi9Ct7DE9sQosh4X+jlZxSh5IRnMCkuBxIE4J50yIeRRDdFKyZUQ0KQIcWrnsQDI4PtawO2Xjw8igJFsVe0eO3nqxfvfeydKUDvPWV+kggaiPl0K2RQVpS1O0fDYUfZAYQxhs/KMsvZ8fzM949ZhB0Jyeb/u8wy1K+8FW83gTb/dHpeYyz8Qq4zIwyfumZPcnLzoD0Z8ZTUH6XKthln58UHxQwQPyqfoDluOyBjVV0XXoVDMYbLZpaqHDaK8+L7+LZSS8MV5n1HvUq03EQqtNAiVOR0wzlYcI36Mh5BG0OXzlDknEKi3eIFyATbb/7u73+Xy02zh+3zL/4AQ7oOuSkzZKw5mItzEFXcpqfxKPP4w8PjrNNdRoYRkBAQIVTIP3XD0C4q9S5Fu+MTvbu8uHx4zNfnuWxwZjdSdTk1u+2S6zbJzRx1xi9KBvN04o3vSMwPL18EGZ5IpQLm/ipXbVzf8LmhN41GtZYxH/0PTE8oapoAc/ijCRqKwJUGQydA3NgPygIhfwShTUZ1W+dqDpdZY1vSg2Sp9tIP1W5rEe0JAsBy23AXluUXdCO6iUhWK6MFXi9eHKw4bcChAtXTNr7BIXjXYKJlmrBnYL1BUKiJ7wkNNJFCv3FE5EcfP9nb2/ndZ8+Xv/ji0/VN+7/4mxtzlFen8p0eHVtqdTUXDyp78ynPJv+nJ0eGHmlZki/LRhqi17RahO+UKkYXt3TH3f0clwBttNLFVFKvepzAhFWhBkwGElUjuNkCZ+9ZnUKOmOAzt3iLZijslTHgyKXkyZHJcVdSr519FqFivYshJxY1+YNJXaBhM2U330/ivPtRXSxeaxtuho+i5md0Ph10dAQlEKTA3A2yjhpqnQP0q8nYbSS+OT5o8oxG504Q+M//+XMXr0TZHSyc5j6sG75fsCqwlrPwCQtVznPKGlFUkNItfLP4B/VQRUZgnoJ+j50n6+zwj3pnRJSqp8vys/LnA3cVYUw8eEf2GkZJKU3Ls5gNrSRYmxa1VeYOPEqUBhNmloEktx4Nq6mCYRUa5qdHAUj2Ox2IIpWVvSqrlLyxFOFSFKC68ES79Yh6wZkqQaA9hJ8NBR4UoA+8q7EP/FEII8nGmXzo2IUQ9hWvoJmE8uIVNTo35lIFpw+vU7KfpRileBeqGVtYD7PO5dX80hyGiiPhNdG7wnswwGg/4T4Yk0n6cobfRcw6ExdZttnAW4qRMM05mWv7P2ZKObIycXp8bCL3+PFTeQgwKgUWRlBRoTRzZ3sLL7aBivghIRgq1CVuGm06TkZazaEqEDyyKUtm7D2KEwwrzuPHT6gmBJVE/hkR8Dnmx43cmTmKwzaJa9kuhE2TT3QX6kVhC/GsMcnvEGg1SlcEMSgqcIAqBGSQGKVtMoEJmWF1huwBGbaBf5mFLWRsCJ7KRolZjo4lQqBYAlt1JIzJd903TnY6R1ippeUcQihCKNLSNJnLiLdQ0kB10XXUG32o+g4yTQGt23/0SO3eykw5OrtkZ4qFiR4GIPsI8wqFJo2iXCyss6FADASOQ9yAVHd4wB32ZGV3lTpCPdabBBwj38KcdXZoT7WfjBBj5t5IU++3BLj1YICY5q+tr1oF05C5z547RGB+afWvfvVrbWSNQ1jyUx4WT09nFSEphHO449WE45eN6k7tMyZVh+xoFc34/PxRNNtYA7SXOiLR9WHKfvrpp8YP0iWld4qVM7UrZYuScd66WZ4gsnGlRnh2Zik6DsxWaySqUdU1irK8RWirLneq8DDbCJIC4htdEHMWhLFBiZTI31kiZAweqoM+hb+4gqqDMJiQQZxOtIAlnau+dNZMxHA7GMpsDbflefbl56w8By9zFtR8SJUzA3y/RiDjECSNKCvNvOBlBkcTzACA0tdTY0t1lRR//c8w2f0ST7S21/ar2bMLa1sWwRxnkRlQFI44y5fqo6I7FkKtKbtOOIpxEo1GZ9GUYkCeMqK8ilG59BzAy6IzxUfxpERXrr9BUuj4NOXOOGQGptUobDR6VcpVZ78zlU9/vfs/Qe8hvB0KvHUF6KHzvr3jZvTpj3aW2c9Z/A0jQH29FMZhusalIx4HxdRAw84y66yAzXWpFMdizd6KY1T2V4+77PkLJp41DXcvunbQUtn4wtoWGcB2c3tJ7TGbp/dkYhtVAtPiiNTY4n65hfvShTxh95NsMoIEDLHTcOjcMp1p99zCRXvDgE9Bkm4h6dnzl/i+JTCiaHEQLceZduAvLJ6SPVpmhYLwgBXXH0A+//wLNHB3qcQnH3/kIBbyT70k9yeffMIyZJsxo4sTg0gg6XQbFTlNWFvIIZeZagz/Hme4WIxYX9vYdTFC7lQykc9cHDFg4tIJ29CYDbJCl0stIly1aHfHTp1NE1dileiSgeD0Cg7kmRk2bWNtI9N6QCCpLm939nbT5Fd8HKQYRUCWTpx4XsaJ+dQ+uOBQx/BoLIRlgzl5LPLyxQvLRhbpEMPBAVKEUMahz8MhZKBht6CDqdlb1Ovueq+evTjo6qC6XHKHqmU9hemLqOZNzMzCvUZXaexWtmhnv1VNqd2Z0KLldQFzN2T8JX1mI/FecjVW7bqyUzNmGGAWF/i76Fmii1A/OY8Ku7u3zwxE00I0eaRT0jR/fJ4r1XiHg8NWpq0Wj7JWdePQowWDUfNntNI6PZIq5gdGkY6krxhvLsuFgN5BEMpBNkveZnHIT+Y/WHEfZ4OCD+MKCI8exSFafpn9hIynzHBgidBY3QFP7fP5WI1yNYqFKphAWB5lFYGGBSQnKeiR3z/7Uk6U9qSXeGtA+kJ39rYPjl5ePruEOcOf4yntiRcfOo4yuhqlLwcHQIO7txEyXM0hnGr38X7yyS/+eHP58sUz8NHRsQBqVyk/6EHsMH7euBP+fn9oqa7AKTIPqX6BT1SRrIjdHB47CnG8ubY+tzrlC6Ftbb+olaQaZnXxYFF4qkoFQuxyMtY0zGAA8m6ci8vQXJBduRWUUlR6OhXGlnDNIbo0pJnSU2Mm7j73+KG4vGkEyiqZLZp6117CAUaEMj33Q9hEwgWnWAWhv2wIiu9BaDSmHfA1fKS/J3h+DbXvlvDWFaDvhs7POPcPHE/hFfd0phk0HB/HxAGXawqFMSWYyYeNJOTGQZ7D5Xto4s6T5I5TZDoVmDVjBlAoBjXlcbjhrLu8CiNLbjdh3mDBWCrWT4RHCKotX3Wqw7fULz+UICaFwZ6NyFRUaZzOhjWMmswwGedwzBohnS60xFCf/T6BxAohTlpAANPnO+ppHgwgDUZZEh18+JYatOCo/rVBrDLq/fSvf+XZM/WPP/6FxSZGmt3dPaqDihT88sVz1iBALGJYUkCNk+sTbfHKcpefmKbq/uEf/sF9nJpp8UDBly8OwYFVAyFEEcFhfRpp1Whra5vKRRrJQFRRy4q0uaep8Lzd2dni17piF/rmRpy8s0eJ/0rMQsP1VVe+E2YlOJku1us4IhvlYhDikQM3Piuab5OcPDwz4OCYQE/AWy3QdlVDW7v0BLShpyC9R8T1oufnpzra2qBrQPlRwcfCzRdfPAPB1ros1KwyXZRbkp1fgZbbzoXAWY5/jC7d3n5kbFk5IqONHggQJHXzgKFC1+kQaRcl3CPD5ZuDPjI21O4pLlNSQKwrPCmKjx7tDredAjChiT6mkSzEm4dE014GRZnlDMLZR8UENXfqmCWKiFOGb2ONk0E66lo7Ozo4lNmw1Iqjg4P9p/uW1WwJ1F/yeMIBcBnYlqQYp1ZXKVASrxev6cqGmbfGBjzROQjXCQ7KGjaCr2At7YhrGoVJv6ObM4pAoKIxO3F5Ozw+VOnODjCBI7d66eJw81MVboDPgZO1uSyHcaNyEVUGYDqbUjqX+zZ3IKQQ0FAQoSsrYskOGZ3LcHx0AL1VV30JE7MO60rpNZU6jsLn3F0TsKWkJBJVY0GdcFDIX5n5ul2dnR8dn+9sbF4P0+1UPe41sqvXfCNznLt+Tl3VNVEy1NWTrepfrCFpWX+L8qKyqFdqTF1VXkmAio+FPv4vTBSiYCalQhiEHmK1BaLC7FVHAMRGaMnYiPcKVXMDD3ClWKfE30moJqZmkRD8HYX3BI2/QOsfFKC/AJG/TxWzD6A/0RmI1kF8qEI/OyVzlmgX9eUUj2BRwD3wrosRc3fsK1iz99QhYhyD7uK0n3mHvZQehGGpt6vO51fynuLiY5RY7CxMBb/Io0KYSe+nAM59pnUAP20GbzVtHSw4b9DlR+G5zM0y07jiwlJzvqtRrC94aMwY5aSC+/mNg3MoWVtdGa7QWhZ5DOHKhC72DY0AqUNZGCE0JezadZgUlLrM8vQsEkJOHJ88Iz+oEuA5TNHS1cr6mDQChAuzxQWLIjJrHX0oSwzhvbc5xEUzqDMX2RFNzSI0aHRIlLUzegA/72wii9Ulws0mMvtg6i50mqZbFRiIUACiWXtx7NDp6drqhiUh3uEuEghZyuZk2xrfIM2HLYlO1ZPZhF0T9UkcWLJKYaVjYHkCJlQ0glMnknYkMU8eRHaCUXBeGy4N4tnD2CMRwMtyjiaVvT06jjWLVYLT9+raOsqwFMDRqDBk4iIWF6mBY2aGqytEN62TPkC9Y2pAE5SnWVAaSBnuPpzBdaLBQH4Nlqxdkvc3Fmqy44n6ena+NVziKLQiz9yFnnIZSAabs+84i2TpIkP3TUKNlqkoQiUN0S7+RdpYpr6DtfnV4WTBEo9kVFmzHa88tdlT7NnXsSAgKa8Un0CUtoWV63H8lNsrZGN99fH+/h+++PzFl18YhobQ2UlWu0TW9ocMe8w4TFwr+/saqxMRY2t3R5zhJPOKC0ct005XXriA/erKWIqG5PynXknU03HwjxaFGvA3QvTC6k10dz1ocCKm86nhSUmMbfQmLlbOHDcq1tcGp8eHTGiMjqpurBRUNbtjKDNvNHIDW/PtOJnBPjFEVpw2ZiT7xFpzksd2OfmhQc3ViZfjU7f9QpKzP3vR5PJE92865NruM19fDZh8CLoKAZlkzY9ymEWNlagotzGA2uo5scOOBubevuWXh+efPTv41ad/rasRgV2Fjfcq9sJcR7pkhwPLUYHwrfk6WWjrxFT9AIC176hKmALd0k+cRkoYTT3wMWpJDDcVAhRYvC13jWVfp6LmW2Ejwdsgs72AvY+iW6dx5ltmFM0MzZAFPwvartCzBspdvQairyAWo2JKRnXSootRhjynI7AyvvWHz0MrU/+bfydvHanXK2gMX0/9af5+UIDe9377rqMtX059QhpWnxOGcoPd8I7FI0jmYjFxiPZPHhZs/quLtzerOYEDU8mmklml2FkTSEo76fgJjqA49qAKIcyqZvzmbFggRo8RUxA2BtuFDY9pbI0YnLN/SX6rQpm8EcKuOczGX4YEzpjlrwjfwh/8ArY4donpbdYFri/PT0akyc3Ozt5gOYIcKJzdVnNPzJ09Y3NrW8RsmbSAA73BDere8pLB3Xd2dwkBh9VY8OLnQQhhm3QYoOgl/D0oRposhWdLxFWtYkTulp+pVhOfh8eZ8aMDzmvuTlOCRhEqqzBkG/yfP3/mCYJs5JkIQwU1QkENkUKShYaOIY4fTzrCFQiAkwAwh5UNPKxBChLwNvkvXk1PzGMZop/IqYiDdwaLcJuQeaqWONnNYhmET89iMWJvkHh1nVrw/vQUtMmIluLxvlojKlhTHu3upJnXMQtJhl7D8dSzElHg6PSISIZ14bxk8xcpQzwRnNad5EmjbmI10dXxgE3RCJKuN5Duhakqfy/lflQRoUfC/XQSVO/YC6Znjk75b31EPaONzC3HhQVJPVE1ragLSh13JaXhoDzM+Up5q8PQ5+XBc9rQZCXuOKSoIYli1G1rhejmwEMqhc3/2g4m+igOmlf6CDTauiM6dRPN6fjsdHdrO/Ui6kUu/FLW2KMos2Tqd2su0LDC6LVWGJluX2mise6R0Fy2nNftdAZDUUMUJ4x9tEqBpvbPPvsMGsgrBamNIiT67A+/UyP6w8d4LtNb1pRl8DloIwSMSU1wcAPns6ODmxfPvzCtoPwavaU5xApLm5HH2KCGaCPInkKTzl/U9Q03a8EMDCFIpkYnEs3NPX9x5JK4vV3rzjSNjDSo4jFRV+ofHaihmcQMypaXjqnW3VXR1WUAC9NEdpxCBdvqlO5icSFxFj2YlqqOAoJRQ8fJhFCJjKCoT9NQClBSYJezBWJd7rdqLFs3A2ia+q5CcHun4esIfD3lnSL4I1f+oAD9yAR9H8AVx/BBkz0RdULzTR978whI9rCm08jR6+WmSOw694d7WE+Fzl/Mhb5iMpfAGJ5ZU7EYrAZo8GOLLrmOM5LKm3Zfp87rCUHN/G5CaJKobOzowcEsGSOm6nS9wGJqWBhQeK2S0gkbR+5rhSYRJBxaNacl0OrakJwgRXB5GobiW9s7LCUKMhJg6FJURAxQgIQnT596RTZI95ZgYE1QhZ8C/wkXJsgL+Ph6DAKdaWlh6fZyXvG+SjYwbye8MZ7s/+IPC3MuS6cHcMudn2xY2AKZtwnRr2xpVKcHl8ekFyWp1Ji4prYFyNoWfFhqysYz3cwMH9YHVQjQFuQRpyHpOhGmIHhaZHQtLVJr+GTZFRmaYwd2RKZJPhuVPHZ8+0e5Ut1HTz6uVp8ScAhFiiOyNlKy5CQ7LatJSQfdzrkoDZLygAYBCoGyqqZwTG6jCkR8CKT/IM62bARL6zlDmdXq9PggQIq2Q/JeR9YiBThg1vJCGvWGISXuBuqsCGSGHKFpIIOl46Pnm9v20y25O066EaLqLhIlzO6nEnhQQECDEGry+OPTYAIQ7NiSU6HyDCEV464lPxHK1MQ9Wd+xHiGOK1MODq7rfrQlxhVl9TVvN1oDfGrFx7LjmivlZbasRrdFOhFkjOHQIeaL84q4hA6ShrF+0UDKui9FHl3PAvd4KQ5h2uKnDKH2zdyjcrI2fg5evKT85Yv1DTANxjAZF5/1vXSZzL4L45myb6UM5ppmMB8fnVK2jG3NNH5uJxcnxwfj82MfBEyM1ewGj6N6DGyTGOroJZSW3EExI3tTdaYrwFBm1fE7o2v4pr98/uzw5NgplRJRIPlpFBkroqG/aY7xwHqUCpTxy8K7iVcpU/J036F9V+qnAVPxdCtlik2vI4EoXuMw1YXTBXllo3xVqdZ6QJ3EMBS/xvzLIMlTKT1YXW8mFrv1Q/h5UuBBAfqQ+x2/qG8+c7UYtPNvymiwgLAYGlDpHtIZ5OPQWMErIYws6kjClEwVD7QSGhLxzViow2RyxmvYDOu8tYDR+PggvsDm0zHuDxYnSxPKgaKKkEP4XsWzVRtesMl0jl1pYBOMuZ0VqGsHrCkbFyGAs5lrxc6u8Vw2nLPuwMG6R6UvETY4IEFC69I0cQsHngSADHIy/KhUKaoJLphlgqssqNEhCCFqAflhYt9mJJwx3sTZkxWvHbIENMBB4I5jYq2U/MASSD35JtKQhScsVQCplSWEiFVlo32VR9TR8WkbpaCUbLGE5dZ6GWACArCQt85FCsogERxNg48d5n6Kk/cW7zhFWYWw9gUryopa9rZ3HFkMB2uaSkFS/myZL7LIAFWNevHiuXTGA7rp6jKfoXOb3iI764wAWoWyMsgPkvYKJarjREJhg7Mgg+MElRI21oP2aOwGqrm11fVL/RYrC+Is60V9p7HRcb9XUFGXm0bSohzfTBsr3+EoNHTGiyvmtAz1NL+kr1KGkL6zPQ0yqBppx8e3Njohr4EjBVhvNZZktHKoNtYtW9rdB8dOc3J0PCmfdBQwqGm6h3UTCwSUEkoQZ2ccDQNZLs5jbtFH9tXTSygpaC5zTKcsDnWbmOOqQZPHiDqpY6zpOhAAAd20oUZU1sj4Y2nR4cFxlLOcFXFtxU1dgrrUrmfhL78KjZbsPfDBlHeXhoMQ4gR4vhfxaBrXuRDXB5sbXsuz24Fdwc3p2WEIqB0yMqKpYkrM6m5btng/l3Yy7RGsgmKSM798mnNzR2enB4fHY0dAGdWLS2rKxCoMwTJZDC3QhgFDE2RUAXmJqtEjXVd1dLQvGSqeF+KCDAIM+62U6KiVLjFrZF5XilfGWuZjtK/6lzJ3gQoFW9Yi7j+WdQ0Ky8CpDKi7PD/nvyHszyw8KEA/sQ4Pj8IZ7g1U8dc+31q6zgxMkBm/MUnCAS14G+Hxd6alsCFUKOYcZtdcyVNoKeJVZoKl7jDr5Kiesu5kRd/SePGMMhuJZYu7R3CxeoTp2k50fjo6/av1zY3wKpaeGwYGvDtalv9U3pV6CSub4iERjCuoGYfmnYChL0dVCF/GNDmaKiiuDOYuHRCLYhwVVofZ9H50fEKlaNmgydi9hSdhuLXpJ92CqLCLmjgf5DzlZduIZCaQ7KkmkGDOmQiv5TCkCPitnagOp1UvR4pHezuugmdwMhf/+KOPpEMyfPjmZmdzCyjH7754+YzZa0O+Yc53AYfOAeHWV+QnpskzkQiG2j9MfVCLKgSk9k/EWw4iRKNA1K3EIrVotcll9q50svYhD5+Wvl9sLuKP+0vOvdRMmMCJxuCOKtnIXWhslAsUcUdCOznRvNxFH5GpdUUDYwARrjg6KI4yiO2fFJBRXTqSy9+Yb21voCG0tcuAQOnFOZ5dvV0/44/sz5iIrOrh9t2eICig+L1nTmjMHrDBkhZZw3ryZFXEUokVPMPKG0OxaozQ1fDgNqCbZRghO2VgPL4+PnZf29jAMH5WV/isRHv2cXCD1kbuxjr52Zcvnn15ZNloPIqmS7A6OCenSd9S5Re4/+gUymhZHSKewZ+GwSIvY/v4JLZmw8yjIvMJRQqrbKGXAFujwhOpocpPThEaDx+jG6bGUvjAnHB1qdOrNZv61FYiYxv+NF0qru6offdU/4yZOJAZNqMxHVctcp6fj8CmANFOjTdtnDgmm7ObL8mHWd+RnKmLZhN7if6Ms41e7D4LSUtZEcFbzHSYcy4RU5VZV83pUHJqhfdpa66ziG9f2lKJKedlWWL6JtLUiOjFfGqkpbKUqETkqp9hFsgCuVTd8CEqJzrl1IxYtkEOd8tAyYwsg096Id/mH5gEgQpIhALJm5Q2dIWx/JzDjDhNhP5ZZP6QqfKgAH0Ivdv6UKtBhuzdZ15N86NdTzGfCjL4iwlesQRbNaA7OAeNO4ODW/CRTKCmIkd5vCnhzhOof/VX0V+IlPrpV+onXfxs5mJJiNEej7aidesYXuflZ85dOlUx0KoHfthlmc2DGLMG/gurJRYpR88QavHcxlLvTOtq9JPI50JhIaPVmgitmpdjbWSVDDgmY4+IVpDlmzvbEPv8iy8ceHiyva3U9sYmO9CzZ1/+4z/+Y6bFCwtPn+4T/0QZAUOm0iEUAQdMbwFBHxm0S6MIrV7mE5HuvGbQ1CizSiFDdFgQyWEjpajxLqJ+yQAf0g75wScF1SVFHolAKQuaOGXu9OwENLYOP+HjlcxK2S0WmIQcBecyFggZhDm3Y+aqhAuto9iJw1MAn+0HelrBEFBpubGcjWp9kbYXDaxtPyIaRUjL2Q2nyFL1dK1SzHFAkR3e0m1D1TXAa71sfLEdG1g2iHGPbbJ71tioB5n0vcK0cIESRxwynbA/OzvhA/z582ebm4+yI20hZLdwJ4+r0cUzlqvXNMSSS9SgcuFCFkguXcaw55J1dKuCUdpk4DqPnqPx2dbthgOidAL52hladxGXSJ21EJbdfHX+uLJUEN1n0D4/eGmQGIS6DDIhc6xKUyOf7isKXxohMijVGqd2QXnr8RO155rVWvmF88b6UHXMUehvozuVCRV1kyCPUoBooJy8vEQ0Kt9PqdSAS2mqN+nQxAi8PD9Wi3TDCUGzokrf7lDZQRZkkMaBWBCvZzSYBu6rHV9Pcn6DmwYpKEYjutbRDNJwFN80jgMKJO+KBOE2Bwa+H3kfyKnIq9Z+7qVLaUykySMOVPhMxWHupwV02YRgKAfI9bcTo/1U8NPEArcpjrdCpaSd04qU65yAzqLTlJ/NnxDt5xG0tMdzN/etK0D3K7tP4Z8Pxe+3+jvFZ13VtPpTlHwFEzeptYbYhAzo4lzZIGGSShiT4nQexpdMhG5xakYhnEdxFh3BWhVGwUVCiroE3BPTESkEWr9KbSlfH0zlAiLTwWQj2jMhc1JKJliuLvry8y+ICpuFuJtgxwvub6AEBSpeFuYHqMWFVm7uT8FAgwyHZzgQHpZ8dnb3IEmWmJxyTy0hfe7sQ7oLw7+1NjAJA9uZqBqm3Rg9/k5I0wIIKnd12SsuA82ABIKVmT2dQCL42PfaxhADfPzRE62nqajLE0w5Q6Ji0GQVISfuFSAt21QhHbZReuooPE80MIVfmltubUm9KEZMkr4kxfHxGc6raX4q2CpUHVfAXZ0lLGsc3jZjZ/LZ3dsG0w5/+52YC1z6hNaUMpQjUMmAk2My8spF7kDlKtDF+Ng22gHO8bxWPTRKT9Fp7LeXkkVACmf1h6m6kdKd6yhhO9t5B8OB8CV6LatBnrLGymH8iHsV8i7TIdYs62ims4YvJxduBFleZ1YJY9E0owc9YUqp+0bp0gNJ5teC9r72qgcbg9bqzt7+7p4BwGt47/yCwu7Kz8dPPzbkLOzAbXBrJdHpBvG8N8KHG1s6C31kEFAM8pPD0vmsyEyuz8YXgCMOSuaeNbe1n5z8djQyhIxP/uxK6SkFnzzaByfu80tnjkmoMxQYKxd8SMpGkS+FQ4MZdmhA3OyNDc49TErIxWPMMmvLGsQR9E4PDAutxi3fJn2tCahBx7a25f72g4MXepOlZ7i4TrfIMBiNDBAomS04d1sHGRBKZWtWdgm4XsNXyDN4Ym5jfILmoAcft30JtBU42PpHPc5x5BM++HECimpQ5PLUFriJSDCSe8cnIP1KPb5H2rbHsS14pdo6kIohiEphgIUUBpVpjx++c9BuaZmMRK1MZdkJTwEH69DRhqvKMZ2MgTCoMtUoPuVD6fmuHTixRqPW9PvUjDAUNkwW5lQtyKNAjZjEIaZC8IRwSFtC3C3DMy9zRblSojd/JcePEAqFHwHO2wDxhrg1zd8GAn9hmF9vr5RZ6966AvQXbu0HVt39zrvfbW/eTPxaoKLgcrXZfMrZijnQPqbL6tiGoaCK5iDe4il51jzNG3NEleb/e4E4nE6+KjHFufuUz43iWP/BixekI0Y+mKw0w7IjlsBI7cX78jecKuVVFp8jDDe6FJ/HWEqkY9AWGsgPzG0umlWUhsiw/I6YJABam0me27g/S6TcmIXLSa4QM6QRVYknEIXGSg30AI9yYL5cW/dpMyJm2xAhb8gYYFUmJ+UpalydU2d9zW1Q4v/0T/8kP6XH009FCCTbyymNqmZxAdyF3MQPUU0/kEdZeaSDnOuTyqcHhupiZfFKw+0hA1Dr5FldW/GKzAOB9CWh0Ul+GWi15xKty9Rd8WDqZQRHAUBUSupLUbbRo4H5qSw3LHk0lkxemJue0Zfi3Gnv/KXULgUcOAMlgqqAw1CKreZdBZ0mDRmPnS/Mt+nTTz5G3u0NRIgiCDH9wwwgc4TQa0MHWt8aEEfoLLM4UJVCG87i4rLmfx7Ng6JAKYGet5rJGzzaDNNG7VSCpH5sshdiOcJbX3iLPqUPxQik1X4qDojMUgwYMJU1VET0tXVFnavJqKF4mlY2s7iU6Lk+LshaVZ2pKI8FLXXJphYZFGEfAhl6YFKjGzGvdLLh6nNs+BCgRUsnsNULWyNzaSUqGvSk6ENbzADXIhVBWxUWRZ8/e+kt25UxI7N+d86Qc610kJMtDYAvTQAuUtbdbTIobt3TpjDBW4OLpuDQ5ornEUNOvtCwAlxCilL5MKnC3LpdIGIJafHGLoHj8/jX324Epe4pERMwIBXJIEhyejCVdXW1bd5qnUTBy+QKz/ECJpLycQldpPIEAoCx3kA3Npwps1J4ChZzWXRwfGk7MfyoMk3AemiF9HrLyHcwgxWFK+toP0aAwI8B5m3BeM/R+9Gb/afaK72H4oMC9KPT/G0B7A77FujJkMlMrDgYK16Fa1n1t/aULe54wCQrFFeTLBP4/vuJAYApntOZa+Ynzo2HJsM+4FXYz92pP5knYSfFr1r16RHmfXwhSyTgSvh7seMrU/Pf/OY3q+tDnNGCBUyImSw1TDleWpPq4j+BYbFyw2OeOLG55DhOoufZl1ubkyP+vZy6F6SgqjWH5rE23IC5DOKmkyKqa8hkgxkzlYgrknZEohT3JDnIG7oRmQdsn3xIWpB5ZAnNARBrFGHAte25Yco5WFx2LTyViFxxRrO3dBR3LDgo8MtnX97uZ5MXhwv+0vw5yFUeKEpBTxvV2NNxJLIAIwXFtEIAP9y8tn2Rr7JJ6VLcTHj8sCVQZ7oJXtFVFPdTj12W5ylS8PdVFwTSY9nexZSTNRFPPwFXka3dzqZmaeAdMolTMJVoAA0aG6nGXLa5scl+oAoFFYGDUjZX8/RwCJL4Wl1CTvuBAELZ80Y2yy/odE/toO6ABsLkyqXwiKYtgH0ltHj7StK9HxAWUnV7y5YDVoYWp5FJPIVd+elMI4NOD25u77x8ccyGp1tLUYZ1lOPQeZkrsYbrAp8DOT/H5R2JKt14i8+NuIoEpeqQyelxBq0GsWTY7GW/23hjxJbGt00+LlGchqChjRBwJo4I+08DaYLoBT0Ef0qADt3e3Do6OUYiWDVVdSAC6hbmH+uabBi+AxBsKjQGGhTkfT76jA69dBlV7OjklDWIM75Bvr21y/nJos7RyUvb2nS2lR0EtxPQAZpnn507fOis3LFt6psf5S4ReJ4X9zc2yWqjx+wIAg6lpgK7cgWVLIgbMeisj3QoZhGca/EL5j4PcKB3ej6mB2Vxen6RAvT85QvnQ8492m4DcHhJeiGPLgJ2ttvXkPe2Rilu5XzujCWWNJ8bsPhUf3eGAwT8p1KDwc+mCQ2JviKTdP+Dk1Jpz9SIbPjJD6YHBlLjKHqJnJ0j+qVF9pqEpZZAT9XiPzCA9HU4UlLDexC+Eb33AK+3hcK3tLe76a0rQF8fDW+rrT9vuP2NNbWLs0THwcnCarnUuJAUd66lGQf9xVLNLJx/4cV8fxTBgcRMgQkC6YZOcZB8t94yVocRVph+zCrzYdeuropO2Yc4icODGBc2nRU2dnZJYV4SLb0yl/QvUPyJYDThcz8B2Jgd3krccmc9H3MBMh3PKk/s7Y4HzHbdHKaihFKWcsS2tnZMeSEshZ3m8DjOLjQeQQ3EIeMQyB3Z3dsjSFzKZUpdk+ypZ4+CxEmm7OWFSqQRpbQlbjjoAwEiTQbrZaR5EwdPN6uWTb3yw4q/M9wtsXkFjrj0phipIwN8WhIgLCBS/Az9FxchIA5JXinq5RoEgrvSwC8vV8tP4+4OQASLF7Zny2/5z55otTTm8pO1xCe1LPmLzLL5CR81UoCI7azd5FCcEcTgr2oBkG41iSgA4qkJMphkK+7WNjBhKKWIn/MPjQwykUPM06dPf/+7MdULKBURw55g6hiU6Ak2zJWtQH9uYXSX8NW/gVCS6X6rjRzqRBy3ncpd5NU1B84gfrQPspSV1TU1Ns6l+6V1wbwOh9TjaAtgctbROIgms2ZqeHcW3UiPZORVdzRhAQHByDFsVKR26agKTsMkcZVCNmCNmyYjUJacDCrrX+jP6xxNZDCWAJfHWdvAguMnBQjBKSFaBHgryoAD6iffHoO82+i+U6Ep+eLwQHOA6k7RmzpCCpjK0ubFeT3DxPKsczRVBxpwhh2bn3rNRGoE8mnLtGGysuDONEgmlPkzn2gFMx++aflWY1vLrSI4C90lC++LS5OLS+3S2O64pLO11ERrupmQplIX8MgAnoEpkkWzDIkeFWVksrwWu2/QNHrkkTPxGg86IinFafpPNn21YlS+PsW5JKR8tmUYjsXKmlwAGXWwpSYmQxVM7nuR/vmhPqe0+1Cb993b9dYVoO+O0kOJb6BAf8Be3F9KyBz6tZBJnQ8/XMEKvCPpOQDlmDgKEDtEiRw8MUyhzMU4kfwEgESeteKXuWAh72ZsqWuAgIBrdCQfEmtQmFis4mahYS2lM1m/caMG/YNQ/93R7w+Ojp64mVIxThmRYBM3ImK7EkCO4xExefddThNtdVpguWLPmF5BALJ9tbnMu00Rqzc5nNhCwy0DzBmnVw05PjohQRivACHYgCQDSKycU3d25pAS4sHRt9L9JMzGJtF1K5OylCSrbC+evyBmnPZLXBEGRIAJOikiTdnoDU6OORsRJmoBAdHkJzixfnJLXAbqFwFIREXyWRe7k0OoRCwB0poK+C1xpZt5E0VI7kmzQWSmLK0wXwYw6uh1Fh3UoskisFKKDPNEw3RoOZ6vDrNEBXLUHSIm5M0MXoo4UMnMGyyGCdE5th9w2X7QATKAN90scaCJpB4e6MAEyFf+6oYCFLlS4vw8N3e44WGNqWtgUzog0pGJ9gAfWFlyM0Ai1oJIJuJfGb5B4U+GuxGRDKB5StG1YEVdKDuKSDyRj/5VqxEftsmjbUYJKXt1bXFHuXwC2YQY3bqJrzeZMfmP1GJOHHEY9gxKWvp6HQ6+zE5a0FBYRH7N0bMoGVvO9nZo7vhNBrALxCw3FljWWcz+bmWJ6jx9ZuQzVK05pyqHMtBlyXrfkH80bYoRvecyG/ounPztqCF3pcCctwwg/mgt3QXOCEsHGl1kfTPD/s6B7MWLl3oH/rIZJbKxz0HY4AxWpTd4FpVDQ6DMKWQARx1+XjpmNDqupdscXTEZ5nQcdMu3XQPvhg1FUfE71gOgPMnvwjUHVVjoXHbjjXMf6FTxJQc/9uZ0e4ZBhmkdJUXtSOuKY+Qd1SZ7t3KhBnzUaDTrU5mRMjwlJzlHk85+tDJUB5DidxpQxgabkgHGfi1weqJTld5jHMTrpwP8a+ioKzO2MhLrvpQMkvmIWKowtEI6qBXFfrRHqP0Q3gUFMn6+NXw4CtAbDrI/S5FvJdd7/RIFurtFMIqaPd2GvV5f4kMRHT7tiKC8ZXLGqhZjlUjA1DC9QdaRpmsxyey7JcDwqOIjUpqVBlAFyeVIqNopIHyGQHb9gW3Aj588+fKL57wNmqm5ixKXwomK64Q3ljijfkVu+R3mVsw6jNuGZPdwlX+P9OZZWPw4Kw85Zc7WNU4v7OikEVzC/irYfkIWElRkgIs/yQb3hGspmBg9SwghRHyS1ixhhIfamS5oOSQWbUldyhJ4CgJL55BS2cJhiUBOD1JkkDnEqjOvFW+lShEVOVgZ6WRTi3U89YIgyCyDxiKIRG+lyBl9IsteOR5GCmzlkd7ZdByYMkDAWwFk3q6hQxb1pss3eVHLMaCBQBpB0h4ijW3KEJzUFHqegMwaSM3SQIRSl9IR6sWp1S4o1U8XZtEovJqRBe5wkIXlzvIdq5LA5x0+gPBP8lZ+z/Icy0CKd+yrEG351a9vigGleL+ZRciupZVFChZ5rzXjq0n15peyaYvrS+VUMP6wEcNwiz4ksVunRbS97ghKMwroOKh5q9O9FUfnrrSbr7ggg1eg9VsR7bXGFCXgIlYxp6x70mnPDw+paaF07faS37JnA5Eij/43YruL9RTIbewJhcoXTYqTFXUKfdI3kis0rqPZGAyOGYI8Zz4IAAWHzcurR0/23cJxeHKo84DSod4ColIZjGrnOYBQi7yLm8MNBx5p+frq6unJyHUeskX6183z87cX5Q+U9a+77f0hYHspU1AEHapq8JcGy3V6e+wz0S9q5GuCuqBqP2KWVktRY+2EkiFrvGhQoET1iYrUfEOpZKjJG24ThbnsOrSuKDTFWhBCng5yg9HdBCnZVRRtLfxFcUpRAVQy6+fhXzKn2ipF2c3lP5WnGOYdtNKE1B28Av7PDNEpAg9/fsoU+EAUoFffw0+5M94Qd99lJkX5qF8FbAoRmg75qdu3XwAAvphJREFU0kkCEsgO1TqnRGbbQsIZyIMwnphtbAQjICxUFV8Op5C9IYCbCGWpZlGEgQ3npbJEbWG2ASEMBGebalbBpFGqV3PO3d/c2SUksGBiYOXcXVFr84vXbr4Ofyn4xGWmucV3sFQICBrijinMUjE7bCdLjiEe8x8iVC7qoqXGVjY6h+1NRGCUi3JWlcj7VwY1QoOkl8eiGMGwYmpvu1ZdQCHSIspbmQlCOeUnoqQ4RjeTeral09jzGYcwdJKSiKUAaf3FeG64vmcCeXiUM2MYdbL0dpg5N0ElBesXLIUQqw4UducSN5CGL4UNJm25GZHT6EBgoOe604fK3Tg133lwU0nF93Z2JVodUQp71zTaGTepxayzhWgqsasFNK/0FPce/wZL8T1yT/jakMN1duz3TBd21o6UkqKZItpOJAMlP/mRU5r4ypScgx7gMBwzouTSp9wg0Q25IvcWb518bBCgITuQRRX50fns+NT5TTCB9nSKr3DaOg2RcMRLZOC91Lu3s7/w6biISsU9yUT4oLlw9MWhWr3VQbXbad3Y9pb6M1dnMWeBlViUkovw4lPFrMgapu06nXIIoPwGjwggQOkwJFKXCEkuXhSOhzL6Zzno5mp8NTeIHsIgmWYqRRWgM4Ags/UVrlT0HpqIrVwkumFjgZl6S/dfoxmUnqpe50UhkVHn6SzD1rbZLV1bWiH7EAEXUJiasbAw7HGuLBslODtb22YabF2+SuZV2gnlhTpNQTRcubi5EGVruN0GMBoMvVvbAQQfkP5gC3hGAsagPyAsP9oj9NwVT2y6bOl/1r5KNUcEmQExmG3wXLJgpajNDRViacsRiakiXVYDiRqlSIZQDDP5xmVLiKLRYwQrCguqUqw4AHiNQRkmNW1IrmhDAJpDBXLWWGUL+4Czcetffoc/TbljqsgwyxQiNq1cgRHtzS91e5lBXOoRgIIcfYgaDtopD88PmwIfiAL0YXeS1vlW08aeGFVr+wudfuiVofOEo9TE3Jy0trnEDYUoxRd8/GEd83NkGl6DA5IL2CiOACRWLkVoOFNod0II++gpYFU+fYTXVJCrOJm/teGVZKBdyWW5xN3ifUUlGdQTsmqLsq+B8jOV1mRaHD4CPClDhLTVFqiTFqhARPM+ieRgra+jk6MBlYMz/IkN4o0dSFkyKayvTOtRPsr5Rs4IszpK0YYt8ol480oGMKWzE9BpAOmToFkTxAFR0NvOqSJNABxu4kSOV1ZkPGmXna5IFwRZWfhQB61AKegnslNlYNhgJQrprPLqUFZ+eRBBnB5mp5jMVq645MJ/e2vHT4JQHmJoVpzq2sApcwJoXskCiPxsFY0JKhGHEmUwBrRXQ1imVAcHVSvVb9OiWxBY3WKdgs7a+pAYY+mhtF1PmEwyVyZYIqbrcD/PFM9NBzUMqmcboJx3Qb/8yUk2BGSbFRGRIhDClmohrMs4XMEn2NLcWR3KsVypToSxtrgWAvYMLxKbsBAyIBtDKa0rUwFDrNH55qqtXjn/ScdxPpdBQBkdN4v3W/oKJ7AVDuV8ZeqmYRlg2MDZQWDSw4zOxDiE4Mi1ubFkvKlUHGIgd9P6HKaOcyOjvmgg/BsajR3kNKeOT9SbUbqsWm6tPn/2Rbpve0t18hj2gIirRe3dOsTR6XaB+cCMBgry9dIy1gHC0tzUmORVKqjBRtdw1DgSWb5K31Mg6Aag1wcFuJTKGxuhT7Y1EvkFtas/ygpNxU2lzmxAljtGEWg1yKPMCFW+WFuQp8NU+czvfPp6yquGmGoqFPhiHVMdK+lZ0a/gT2lIySUehemuRk2gpwXXOvch1X2TlqPYNyU3+Ifnh0aBWFM/gDZ9GK34sx3RzcxH3lmLQfS3n0TcLeZbU/hiW3drKJkaxvaeaZDX5t8YqA0fzYnCKe7GgAg5gTUwJ+AWsSZXJoKoWRsg3mJPdyHXXDAbmEZJ8xYE3JZTS3uWyJb9sacnm9tbXvdb3qr8Ih10HHwqyNaSACpwxRoD3ySVCZ0lf8HZejY7aV3O4GGRdwB/uKo0No8NHDZTOiUgr61qaeEhJWsl63SmHNQmnaIQ/N2McXxsb463zpKhDpVwyb0BxNKz518w2Ihk/n1zS1rcLN46ygUEAu/wxQsnq9gwA8h6aUuXNxd2gfkZcTKICIwd5frK2UPa8dHTXxCs0JPOtmTdDWT0bzrLasOdvVOIpn+svHU6GUxnsu14dX16xyd5LKysxnYFedAAqVLUsjVdm+OwLY7MMQfE0wUyoYa7bcss0XSGhkRx2Ip7BRrVypMQrSZkc76IQKtCdhqyZQsAlWpQMtAlFKflgq8jIKa9iMAvC9pACTIAoge7bI/PElyNy595NhoyiXg2NHGQPS3A6Ljr6/8f4PraUJUHncWtsTRxNBLCRjIMubTEFMS2aOX3KmdoKxhFeXWFY5NFUkJRKZCZT7ixcBIPhRjY1HX3gRge9seFqrZY1/whRGDPoMfEffmCFUcOEwpaCIUJSvIbXc7LZkG0SmVbJQToQ9aeFgdDxksQqFkwn+JcOpOCuq91Fx+vAcWOqxX0Cl+WbxzyvjGts840tsLLpDF3S2fSjxZjraDpGq2TQurHZYzqP79guKr7eY6L5Kse76Lri2PrapqjYODfzFsCY7nU04rADbljPXJiDpyE8qPK3/pgUFt3zI2nZmMQvEIfIR+sMxRrDHim7L1gYOidpBZcv1AeMxPCdcoUxBc6urMfNWEoT7JiCwpXqdakqGoBU9ZqPeZlV3eXK7V2YjSbfPsZD3mb0MMrkJLvXuh3lVCV3Xv1/kcL+fcfzT+D4V+mFQ8WoD/TDe/V6/uzk+YC/YRkf9LlLWiTBhNwrNDOJcxN7y5BotCEhdBdwl/M+fFYfEwevIwtyKk7MrXek4lSWFlCT6dAbxZjUAq4Fy4TRuM/ZmU3DkqkfoGEcZ6P7IZ9wffH8buLq4wUR2fnj7Mp5GqBs+d8ZD/blMzNdzyBMt+L1MSTbNIpJsUw5RUuv7S+Ftt3WN315dmFTRx4ImkVN44cYpLzXGXD14JS2WlEeFEQADQJipKfQjsSwZ/Elo0/CQ5OqpXzbrwxSB0TbpdHog/5QSwRDMjjeJ1Yhq6v7KNRPKXWo6CQY3CWQVzt9ACqiZRSCNzcxD/6inSTsr2cNSa2H4IBqo2tUpdnqVdO6GXFJfQRbtlmMGscWx5FGBvkbTVFihrtHoNtBOFVXKwSKVOLPOKKwIEEJeq0ovh+Olo6xaQlqxrTv3UWkQgJrQm6RnEhVFqMQimiFMFkjz3nG/C7UwhgCkJm+fMLbvyQiHQHhy/3draX16OjZBkl+3gik0AocZfq/LSdSI2GXz2/+SGb4F0XEekUlVLt1pbZAnkWR2/Q0qPj8/WN2/OTuKIjs8TYj9RTpi9dLI94Rj+z30LOyzbouhf05vnZmGpLHdERdjIx+03mohyjQ/esVisIBzeL1RmDC3okN74MciGaUl5pvo4GDdYSYaBI7yazHByMSGrHY/YWubqglyosP+VGThMG9Dd+4PbJJ5+gfPeIjy3Uu7TU6xaToStRmVaCRtWlvQDKjBTiqC2zuL6FvyZbIIOkE42yEDxcU0otqhO0ToagWoZAceoG9z2XaMSH3sa9yYC9zcdFpwY5NLWBIUUy9twWu7e7s72+/vLsSGcjbVyHbjJi8QA7FOx1gIwdV/FdN5ZVUAGtOgSJaEC6KqMk8zb9MwEqftPmJ3wAc17zwoDOzr4IB6CyfqoyTK6HxHQ5vsGDlF4ONP9nCEzNV2rGtLSAGdyHzwokQ2pO9hpmgXo/BP5PLoSi94KfafhPMLzWkLfXgtd187dX0wPkH4sC+sz3nW+9XSjyleNOEV3+ZJLrkj9fumSzqIsRaeYz8Mh+jYVsePHTd68I7mM7rO3Xclpjwn0it+ojAg27xHSc3dxnExuU4IdplNajUnDw05whVp8Z7kp087JwR/Szs8vr4d787i9eXC58aXtWDA+Qi/khg5tpvViYn/WJYsd0GprKlDtrBKljb24wd6vlSoQWlNSGs5nXNvv2xP0BYVwh1/FlIp9coqm4a9JOdpyV17RjTqzvnTrqhtZidzfFZSHrGrScuGxy+Lwag49fp3EL9hWvvTg8PLVQksN1Ls/GbreyCHJGjrID1RbpbFlXW3aelKcn8QNV816HF5kAAypCGCCgtmiyOC/dbFGJUSv8l6GITsQxmRY53LS3aDPeOa7IMAePEuO+KprkmfVL0oMuo1uoTy6xss0L+S4uxxYA3WOFsKQD3ZNPiVsUiFIikChFELhVH3HXGL18+VxZP13BwJHGymQt08RYRQXUOudEtngOzrEPEUMxRcQ3JT3sf5vvBvyKIEMnMjyMqJbuxoE8+oIuamTBqkZKGk7o9Mg3au45jHXatz2VFWAiU3VNMrsahXpqADC3qE4b3b/WSoDx0n3hXB+lSs/LNAD+TYpcDUszqRFOqIOpoNHujnTfEkuM4hn2C4u0B30tblRwlY+ALsOkzqYgEfEBZXZBMM8z4Ywgwx1HpXrZ6xiswCmHZTmdQmBpcXtjm9sdTBoywjpQ6ujwhKGjlpgs/S6xonG2k5JbgOfnuNBtbe8CxPX+5eHRKTj8a+R2Z62rvuzuXFh88tHH27t7zu75/Mtnz18e0MNgJUNuUHGWBGyuLjkEsYM+e/kidiqLwna7lZHGU5NF0yKGngVX+GUCoycXc416nTjumACO4ZQki3ouQYMsAuIb15d7a+sfU4GYn8yynM/u07F0dWmbWdQX5I2pzPdb/IFyM/H5l06sW6ObOJh1bpJlMrMbM66lxVAibIJylQCIk8N6DGT+phfoQI6YprvgAsVzssmQZgO0tpjR0cLsTbT7r67SgwnbmKpyOvrACvzgyrtlx2pEn0vRsjupFM3iAUU1j3Ye7aemfD8x+dg0qebk8drPWfr7H/mLYf5gAXr/B8OfxJBEIXXCM3y0UYJwPDM1iknEXlgEgYyxYSF4aqxANIhl8zj8mbJT70lCk9q7La/8WIHILJEWVbbiXOBuexj+lnpoUo3NbIDiIOH2dkmbwl3G30JmiwSH1qdWhrcbO8c3Cwc4NwfmSxPQqX4TIFMzQMMrrah0oxiqYwgPN6p3iYdblWpi2zC+RQzAk+KVmX1W9Iib1dZLbL+JkrSw0BN6M3szaR4e+CmbkHSYW+aQR4qMblvC81VEgyEJACTzMFMikQ6BiQtBNk4hlBJsPVdS2DgGlFcFJHNTwhKScoKsuIgU0k4cTGJJnMbQwq8LykNmSyxdKotubAkjasjBIRzskZZd21WhlJxAVXU6phb7dHF2gWX5CZDV1QkKpO3lb9w4aBQ0iCvGA/CpifRErl6a4xXg3nYDQYC/n2BCFW6C6vptvaKJDSVqi5/AqYp0P7o+Y0GpeuPcDbJxSLvNkCs/j6SY1EdZD31qrJI88SP5lgA3BQV5xGc5xTUNtg6Dlki5efx4Gz6HLw/IeON9bZUmmiuquojO1QRF0q7L9EsjaSAwbVGAEL+JY4TK7MILRABfF8gsg+LoD4KqVaQB1Ynr8kgBBLlonMCqVLCsJpvinvJQpuUCComUdU/F4lqOxEzOpXiAyaasWjp/ZZuuMcnjci34MORIB+fg4AgoYxtltF2jul5vYSi/fV5S6Ae6aaTBXOJq/D/+6OkXX3xueReqy/HJSQb6q4JWqlyQqotYduxYqAyahZmYC9Wk5fKWuoghxNITLdbnQJO5MeLX8IfYYyYEiUi+Lo3UuxSK6jQPfKeXukKudCusqSyGx7Rba2MFG3T2iJlyXV0GB1Sl+XFZo8SABrA/zYXQMQaeMAY1mR6ka8DyNqPfhM2fFMEhon1poxpplJQp5j/XlPSMTjsKV7BhGFhZygdniu9PTPuBeZEhTXgIb0iBBwXoDQn1vmTDkjJZ+WqIG2J4zjT4nrElcm9k0nZ9ZbJTdmLMLjbuYh755jEFUoqdmWMKboJXRtrVbg6Mu2UhJcjUCUMx8wQ9xe4FKdhkOAeo5pHY5PzAGcVfno4OxlfcdAebQ04lR3bcnjNYXNk05AYeHI2xRzF/AGuwEUh3DDFKWOlVXuHpg8VLh7b0FmhYUYCodUIY1lJUEOwM0z/J8csTRx16heUREn4SacSVJ3lv47pbLMRd0gqOdIyRWmAiyKGitRACiUnDYs9gxe0QQZKsYkCCCUuOvTVYM8EDPmhWuIJe+eIoLlFcCG7lYQ2aDNQv6GmsRD9hAlqJxhyi2GLP0yt1OeOvV0MUscYHgh1YCioljyBdu4gHHcXcdnrmhpCsNXQeEFBXnK4JOLcPFDYxl+JxvmALGyU1Sp4AYekIojhqVBszM25oLAfaZ7ezPFY8vCWQRBjaYF0ktSvKzvDodkwmCISYjFcg0aOMGqsZs9AiEJDvGnqEwKEL+tkjzmlMuSqrXG0i7Eejj37xMfewzY2oLwwfOiLUJcxoA4ZmxOhUForIo/k8eLpHUFWL0vTSSM5PT2kT2TlWzk9y6lNVi8ivLul++oK4+DCfZIEpx1bNMw1NRhnV8qOkinScnyIy13i4mFzT0R1ElDVfKZqGbjpdWxSRX9eIGN1QT0eXXzkgkFxZjfbJvDMeWedKAIGK75UAoKD7ys8pXtiAy8DSaRAC61QG/m3UTwqebIJP2/KkWcLZ5XhjaYG6p8Zb5/rcTJxnMHDQ0fxt7qfjz24mRRepRvmE06SbW1en4RTpHcoGu0v1TjccwtLzqp8x49GUTNHCrm4dixjjYNTgpo9cyCvV98UNHG47u49ysLW5lTK0qGpOZ+5npxj6VB8hA72gIUvjkJ94HmpbR/NRcnJyDvoKm2v0rRjLffx3XEjmKWsNTvXrPXukjfcZ/T30vLr36ycf/Ys150EB+imOleLlNkMVX60GtCZRipH5dizKmdPYfMKGvJI5WO2S9T4cKhIEr6GyYPh4GY6E3ZRSkqUlTCecSKjtEvSMsKriLD6//gLvD9DE5eCYAhjbz+jyv3z+7PBybt8tBNaLbA6nUDi7j37F/F4fqsxqtF5f3C2CBIOTAlT45Ry/ZvPs3OEwFwEBg4iDWKXDmugZEWXR+orJwhbfh7kgA+BEFAVIxKtOV9RGp1Y+pJAKnvxIZIi8mcSFFktk9Vlf2yCKKB8NswBiLmE9UhhpFJFIwqlFIlCkl5QoAWX4KWLGmgJyO4hooFJkpyBdkzzpHHIqLgJOw5SNrGp1x+qcdCmyeStCyImQZ+BApkVme3uQ3wv8teu+ek9VAwtJNagLhIyKgiBdEG9MtAI0FUlUUE4RKdLVNbQos7hoaS04xKc5kNGYUSWoX/NPOqMVFQ2tpmZoaav2KiveQSn2vD/BumX8hjDLDYLX939yaYam5vPx+vLlKeCITAVh7tCA7MFm1VBjmQGmlL+IAgfJRgw0YAOzpHM3v1NkQBZKAHo6yrPVl6a//F4hqaCIJ8pcMUnG5z3HVPbo8kpEXYCgEDxVDSyUZAPKs9FosJ6y6URF0Fx+mKgdcCl6vEsBQtGRmbakg3wSkAFTNj8BVJ3xcLEchak+lKkNkh1Rr8gZUPRjZ2mNnDOeZvrwUAt9D06Ov3x58PjjxxQc3ZlT360JUukuL1ZYSaIr0p/LAIPAvna9jCdYcSoPZdUBUhqfN6nLE8IiFKwk1U8pszhiClkevutcP+mbLrRzz4wRPuSDlSEcHdQr2ToUrPodlShr/zkCKNqMWV+CzPJ44hnTYrmY1Y4CyGRhsnukUWp8kr+gY3PFQ/MDXaapXfG7fjbC7xqLD63+BwXoJ9mjvszwrVeh9QpMIIYU7CAr2jYoc0S+tCuH2ddSO8YYMZSJm+0dLlKn9rhYwFQ9vpls89FywnEUyFnM4Sdhkcz8xWfDVIq5kMagqDK/mY1v4v9oed8s++B0/J8/e/5fPj+Y23tyvbhyZXGMGD53kcOl6R4FZ40mUKbyMK7oQGRDwDSPpsdlMhdXxcXhapbM4hEdiZqryeUXGLazJBcX4WghckK7hA2ZsSRSy03M+5ErXrVE0QRwPv/8Cz/JFctYGAoFSKJskJFIPHkLIGmEGiKnJ+fIQxoFxfmcOQSsVTLZZFbKFiqOrs57JJaQWU6rQgri2igJpie5pQrVicfGUycYgQZ5KV5Zm5NOipOdAocnrRCBAwhC/1SkhUFqKW9lMJUibh06EL+imu5TmyBJB/Gvz/5mRIqpvxQ4lpna4hR1s3HoCPFAdYiDCSlFrmSWrh1Oc86SqJVTBw3VDP5mbX3VtBlM44LWCHljzIgjX4C6Gt/YHUeKZBDeyTZNNuj8fPMAN80BRKSDn4IhZNM5xHKpWSwlx+TcL3/5CxdRqV06yqjF03jTTWx7enPCfnEXjFxGPXkkGILywC3brPihuwNuPR2nIspErq6rwx+89XnABvz6mLKZTr0+Mwt9vpTh+ibXaQtJ/O5474hsbK15lj911mp7XFUL6vMslUi3qqtHIK2Xgm7FVh7tgpu+hqS2I4J69bKb9RDcSJOHuKeiQzK9tLA0ZuO6mlBxdHsGZ6lZxnJaRzVfy+qq5U/VOZXKTR86Ctp+Hl9O5pf4y5//8ctnv9rdgg5YVBGayQorsvvkDYzbK984PH2FuR+sPnxfLb7Q61yQRHUsBNreNtoSRTrez2k/dnrMOkheGaI+OTksph5ziaOjE82GMDoYtUYWEsgdWlRZyARgiiagjy8FG0EJSlDMS/feolSsXLeMc9Bl3mWizF1A4ZWYWxgiS1u44hRa/3nPnk1VSM0i7xmCP1V0ZvruT7UBPzO8fbD1L1IqwZd7f5rSLEY6gcNL8fzk2Oo9/ovT4Ke4oWcYZjGecKv4Mybg9VyDsYSci7jM5k2A50ldwVBkKGmY2ZVQJfLwNc7kHKnLRfPLw6P/728/++J0fL24fOXcQ/JrYXBx7VTD0bUV+tLCGoGa+mFumYGCcx8yxDSLMyosUnWtNXhiuK1JwF9DPDsCB1NnpTBARgligD4BJimllCIkjQh2T2BQLMiSFjwmx+BTUKgUisjjCT2cV1AcM47TRk43zl3ryqIhaIB7ZdIvvZm+yCyA2YkyS1QKhgL43XbFu9LGSl3SZeiciovIIwIHrzQW5qSj4JX8pGCna0s3s+WiuEoFP1HIW+Kf3EKTREiNe77nqkC3Lu4pjg4w1642RUBPdRJFwOx2ydANETFTP6lbVCEjUQATbTuznpWiVL15NWykfO8AlHr1u+YgC0HOfZtG1CS1xgIy+hi+FLDGx6se9iKIrHj3xQw3bRf8lE0eXdZ50Fyn624BQbpUQ2iYTFDdd/SjpqS3yIVo3b8SBdBg5akKmKtLIuCtGSOdV1LUqyziKy6bKsQtBskmriKdqEavjD2mysbZE3BAvG2lofEHM+bM1WwTA1ypWreS8c5GQmXgK720usiItTD448sXL5xarsd9TSx7N/F/6rbYZgZaGu5f9CGHU+AwmWxhJaZSupvmHAXafKaYRvCviU03ObVWvVDVloQihWz3Azprr+ZryObOtuUq+VOkdZo7PgCmb6OVGJCs3gcvVeNtZlw1o6o6oidiFdpppd70DG4OksCRamCiQ4aN/F3L/WcqeKdhiv/dn3eKy4dc+dS6iM4fcis/yLZlavTq08UUcCh6j8/Z3gYzVwf0jM7PYipnLqpgRqhM/B17+suxURHMADuyKGaa36Krzn3B1PALhhnwhBYhmEXz8cArXhYuV8yam7M9r9dLawcXVyM3fM3bK0Siu9vQlpDLMXOUK7PnNnBJhnvIETeF1EwuFiATboKL0kHDwbfA1qKa7GldTZvDMMNM53I9agtaMkAKwwYZ4Ax+Gcyn2V+IZxlIDozVbHdtjYdKFCCZ6Qf4u3ZRKex7p/c0u/fKRir0cZs2hLQ6aM2bN5qeXvJksjRCrtg0A/La+nK8u3lp1LwzRrTWR7MDj24ZHwMmAR4b/DrBB1ZZ+IApTryRr+IIWCndrnjpWoyS323bwZYmWDy6lDbEm9vc3JYfQeBPYMjZd92jCgmQY4FK51DWIkesOr3nbjELKw4p1qhyjI82IA+3FXBcH64W6dRfqwVMbvD0kmO7JxuDWhAHoSJxbQezhHoZNc7PJ/uPqCNHhy8tktCxoQ8N26wcvQMTGodHd6KfbxigJGeIU7S69xNJo6OgEiOQPCHp+gYKHdeFJBtDzi4Uv7Vzzmg07mV+LbGgZOuYAUR2zueU59X56QHoQElHkx5X5LsNTC1MiVVjXEejgGEPhjza28YDjYSDFFQ1Lrw1eYCtT0wViuiveXmjZyxSGty0pRVGkiQmxxjx7D+sA34AN4oaPghGhZcSYUWBBYr56Th+1mu7u3t+Umj0O8hMknKigOIKGvYSjSrNV9Y+ROMc/oKb72TwtgKvPliY46zerKxOHC3kYzk9+sPLg4/3t63PXfAlRygmPbrP0rWZE8wzNrK4lrkKVEPM8vzJJMknUh+nipqHyOPTzbjur5XZtnozX3HpKDgQQBhQ1uF6ME9ujpyzdZgdAMxh2cqHAZgCBeuYorNcXoNhqkTV+I/2g0cxWeNXfBmtdhk1QS1fR3hUDHWuCvMhaiW/OlvAGK1jUpIhfKgY3BQwLF+FUrvuLEOvkt9yDFJvuYb3Efy7avV0CczAelcYvI+98RPAKdrADM3+oQfLmNL7L7INxw6v8BccpozG2JYvXqkIZ6woClOC6U54TDEv1obw9zhRh93LFh5yFyr7q0eGTe1dv7wYk4UL69srW9vXg9XL2/Fkfvl8fJ1tKDYMM8jP35ol202yaPP2Sg4l5FxJKIavqR5a7E+aJLVYkb/LAz4tWdFgyWGqxs9oFLFl1d6rtVWzwxg2wlLnI62hhe+PR1eOSyTVqBctRTzNJ73VHFySdCFa5EcNgoSQE7zieUDoEBhyeiUncrZcBAFAebxiNfMWm/7Vr34lD/3JVF694gFUs3ylYCUOflMOBKVoDxIBkSgOEzmjUtSChTxqsCKiLJmqLDVLTvCJN2Kvbh9b1i6lVNfAGWhAFuSMxC3qQRXCgDitR3VEVAGP/OjP3KuW0N4CJbOfUQpLGQJHSjU2LRLPeXhXXG22h8M1hHJqwJDGlGMZl+2BRky1gSDCuUinwPDyooaNEZS/GnWnrd/9Bf9bAjwV69A4d+YMWl7xl1OTnopg6PjBaGC5kC2qw2A5jVpweHh1JfzRp2kiv0TNz5mG9xLlV7BfyYkUiKBJWqRzk7+On5bB2+FWVmdCmlrx0XBgux+99cq3sP4ol/XOXWQYgAyH07pHZXOQBVZ51DhZCWJNLpgbVOKq1mowFcy4rbOq+61FYa8AV9Z6mVEBrBqh2iPEWxAMDBTr5hgttD150I0lUQZx2KYTXE2/tsbdP/eA2Wl/c/P5y5cnl79cHTgpw7fG8yc5ayqVVsR5ZiVeYuif9JqW0C5imDW8c7pYvhp5XLfLPCN0dUXnmNYEK2SlOtVEwWh02b0d6zUssSsUgLZWO2creqSZB6+g2HjCFYxdMAHJ/xJ9Yv4vE45W0SzRAQdhuwqXq2GmnJwqzf3OuMTqui8HWrltttbw5YRkcH0/AmzfD0R+Lli8EqI/lxZ/oO0MY8pUquxAFcEccZPMgmoPmLf1M4wj/EOw/lU3tDfnom/QSagYZs0YHDrJqQjeRwkK0yymI13EsxkHbkhE4rmCJJzUtNDFCXbRFosJn6VtcSLgp0IdAvPG9ip3vVccqCmcmpBRZthKwpAcwezyMFs2HORT2kPwLWTIBm3Bac13u70kCr6vFZoskC6UBvXIoAjhEfZdygrA8CQV/FTKk6GoFCniKtIF8yVagDJpPDs/kb/yxJvYvBdMVbscFBBCUSKssHyNzJLhUjZtqV1iB3DQTe02SmHWflJisGkwiVVBRH6y01PtgIMchGuLmxRFJAKigZARglstjUmXqI2p0ZIFi4ujBxxmUxENcIZ1Fg1dBkdml+8R0p047+c8a3kCZynrKsvO9lmMtgd5faJlqiBQTcYNFFU3BSgMOhpufgqNhtp/+ctfyqPLLIXk3LzbG8supGYhmyGkFYD7WULxTRmOUoo0GUX87JRZIl81tcugI0h2vSkDCapz5UGTxrAalTEsEbmkiyCIdG3sgaEfkb2JDAiAwPopp5ZKAUoExcR1n1INnOnFSAON7WEt+89pz+wh8jptcTGqgLMib+ddR8rzZm2Dp1D+Z3dhbJUCFDRKF/U3+LAJcr82EPTL1ta2L7VbpGo1qqdRRVUWTUOHjUengWMsaQJ1XAakgDmA0v1kxuLmwlMNBIk+OMYVr6BRDnx6Z55SowEvz86/PD5xGhITn3K+QX1qUIGghWWQzRBFBB818zA6hFDU68sLhq+drU3jIoOhRjL1yWDtuMRQzAb12vIOPYSi30RPqb4wDco5VLlPlyq5iYaMjjhR9BsFy59dGdVpu3oT7viYt7q1+wu07ho5O2ijtjhVMZOk9aHPFCNqUDLI3x91MHoPQmj7swzvquH5yB/CT5ACXxEkfhhAvmf8iayJ1OFTEs0nnoFe4WZ4KL7NFB2eWEGrvcIChPsU6ERKj5xKBWhpP+DLpmhnlk3ILjJbWrNpi+uomauqcuZYGFxNxOzGsQiEjVlZwH+bewXDON2yeZS52gZaNqqYqWrxLtcWcOPNrhBAsnF1Zcm76+t4qBB5VrTsSIeelsAQ6+8mYILQIMCC2GTCx3l/f98VWqojlKQ35uKytUqhLIGhIAjgKUiWyMaZmdJASkmHgyKQpx7RFsg8egzhJ1HKqZWqOnJQXBGQwZEZZLN5eYhkegOYCoLftTebVq/MhIZ0Ag4WjFHw5KsgG87uaQlMZtlgUv0W/RVkdYFJgHpCxpODiFdyakJIXepao+QtIGoBvPUA8dYrCDRotDbARUKcjSH9XpYSnrbrTuK+VVG6XjptY839GLpqzp5tUmUpmnTcYC+tGajX9jD7+GBrvYTwhRIlgKMpHH54ANWoBQedBW2yxgcxBDFqTo7iRLI0WLOQx44RI8dKdB19gSaa1ghACcJGCGizRKQGRE8JPLvlJ0uV9YlIP7ZPvHy5FPFVtK0CqRVX0DoNmF6lv8qJXhwcGSAJSOjJFFHnMnhK9Ly2Mlw6vTjbmYKUoR4kWiS/npJoXMkA4aa/n3CTolFjx5xGt0gbdbROVJFS3UB9o0jOY6qqsz30ds60IjYRHZNRR9Ggjtsmdu0a2/PR4dHo4uzqem1hGWbrnIXrW8+4qcBcUt81dx8rQ1kUS+1hOnPbm2tP9h7xtqYPyauPzLyobPBHCikiWZEybPAlaOEklyzQiaIwDM+d6HCSxWiqVb6IAhzVJwCmDEo2EBoZT2VhJJIcVvWy7u+36sxVnKWuvpAxIyQHvy9bA/MZ1EgsVHo0lF71ld936Q9/P3gKPChAP7EufiVGvrqU0Owg7OnGhqxwGft92GNYHq6c32om2szM2+K5+EK2ThSfasaEEIDgU55hNMVf8FlLbUaJFMErQc6wJDynXAI4eNjvZPp7fnnt3EEsOPNF0tpNDpObkTOm5xcpXzmC72LtZp2RAEueTsLCwUCdwmdQL+eSqEdZAjPr5JMBlKpVGtwiseIaSZBHZZobEAOUIhKlFRpM3yUcuK4ZrkQ/SRRiyUGAEcTMUxXAFAgMoBQkP5BFTi83Nze0THG8nVB3+QBFUqNU5LDmlj0EjZz0L2SQTtS1dyriAAImgNLVCziJJUV1kD8niUtSAigPDOXHnltSWj8B1soGiaosxVKfyAZPOb3SP3pEWQDRhBhGSVWQQyCULFykfJEqEQIWR2zZqn3U2qc4aBEDGRhOxc0sP8a94MYrKH4STFKaQPyrzqnEtCsRdOB0Cw1XP0Xdtd9nfg519B3NgMS3I4xxCAJ22FhelSdAdWH8LkipuzFrxH5fOdMDIINPzMCyqXvZkkeGmbVVuoS1ObLzfHx0ePjSUHz0+CkCBovyWYG8n56VkkHOQgNhCGa7m+bzwrlww/3ICxY+i1+MdhsbTBHhkPILiqs8/VKBz1lcrCrkTjpqjVO96zglRSw/KsIUpFPOzk7UBT15fEMGT4OiJm4YbHU5KyEuPwVIExUBQacbV0qhM/Ii4lVJfB3qlUT5x7c3RyfHWXhcXdl9tPfZZ599/uUXTulUkQzUD5mtbddgM/2wIn21s7XlM6N7rfJyox6NXSafmQEzsTtscmYpv6/luThC+f5sGUxASXo5rDnt0Cf476RvdTn6L5n8zM/tbW092dulLve9yxntQrgEvWSqA6E0yike+sWypAlpEvXRmHb+JzwfPXqsMUhglKI7CNAApHs+w6/0mv4JjC+EAmh/e0ZCsabuYq/StX5YtjZPmpvPgu3aOjIq65EXNTCT867gjBPm7dsPIUOFVwjcpbz9yt+vGmak+Auj9cEqQH+KoLOh9hqhv2v+14r/pX5OZ0L3q4sLjUnVnTm3X5mi5TD8jeFLC0/2eC/Hl7BEh0cOccF3FPEsBh4WYVLfbCvp5vQ18cL8IsTurax1HumhZJ21YXa/FBVo5eXxldP4CRICtVkJFoRR4sSWZZy0y1SA3YMvkI+3dqfmAjHOlkSmVZPwRyoP+SYWI/ggOJNhmcdS5MTj8TCwnUztwaE8JAmMMHrqxfn5Rp2uKxeNRB4ihMoCN8y7VJOU8hOrVS9BIsipOGVCHmZ4l2YrYgLqJ9sNkcb4T4BRuWL1sa5Rd6wmj6teSzUhFMEEGQ5yKtJKj+YoZaVGTvqQ9AjdEl1+NiXRn6+PXmsgtp1TZuSX08pN8mTRLacgSjCjVYsAN0UiTSqADA5Tk4hlHqVUjSyCnBoLuPwKKgDnFpC1ghEWPK26FDL5uy5AkKUKZpzIJiVNyF1PbFQROdrdXmDicC7UGELSxeXZNeXv3vKFBqHDTMwoIudd8hv9lV8B+FMtIUMyws1PhyIywDHG0f90U/ylVmI+tPAmWxEpfuVqRBPpBgas9KyyHbrVW2VfISRRCd10q1wyANLYKuunRJQRkbP7VFyGHlroI79ek0d+6dqmiDgcUMlPcWnyWKxUl9oh1hBkkI74NFEGrf7pVdeoddywPv30Ux7QanGgAz8wVkz54WCQK0t9gDwtSLzJCkgMUvo2VpGcDJkuSoeuXg1WODEtzK04yeKFa9UuGVpvj65GbggxR1pdHqxTUGpNEw6sahk2nnO31CnftTFoDfWRbVvso/Ym0OTKfmOFq0MjkHipHYiurG88Dn256eyKngVtW9sotZYKmdRCIu8MD0DiYN1aSzQWdDMc86yAqjILUAt2ZfNJC9VQpeTKh7MYDyEENOPBX/Iub9MjjWR64y8bVD2r8H58lvgQ+QtQ4INVgP4CtHufqghHMFEzRYtWcDtPXzDf2dzZdXyv+6UYg2XAOfzfkqlFAiaEx5HJlq+6OSXrwhPwKfxKpF9U8TxkSyI4dJniR7n5gBVowOPh5syJKXbJxj8Ah7XD1kL+mn98KmNvKN9rbJq94GbJBpCwNqAk4JrAZloYt8pM7OgdEb/F3fw1W3d9FfbtXq/sriqRXHjigQnkB8wIGIxOugAyOUcw4H3AeEUqeEtaiKuauDo8Pl2+vF4dZs1LOzjIxFdjfXVuHHcfDJIg0mi3aQ/XojY5i4UJysoaIQeC1SIVETMqksJihNsCqwqJ4hQdEQ1U1kYfmSPnKASljiilIVqniGwtBWvSH82JqxS0Xe4hpddcFAcZURTEyaW3QJBenuV6mJ6h3dEJ/AGEvCTm9/bW0vekSoQEH9UIsOyfoSGg5MItFxXn/lp6IyMt/CnobqzhRnxRobq+vgEgK0EOZFoyRnrFM/u8BEjCcWnpGDSYoHYNDeUSpHTEM2rU/RAnkFdv77/5ljiAcA5lHMgU20A8sZipkJHeqTUoTaBur27L5iqvmzWNlCVPWx0V1/v80NCcN748ukYrmAv1DONM6Z7RU/UdNMrKVR7xviyqgK68igt2j/9c8eF8mdKHwM+/Pr68LGTQkNGA8WEeHh6rRU49SEFpNZFrDqNJp2sRJNWrCgi2n5Cf2mUM6xCJcOajJoOBJ27xTXYdqpdpUVC1w2B8dekeFZ8asAZb68hOD8UELHHqX63IvoG1Ic/2a/fg0O0unQV6yWB8eu7yrdgJbYFwbdbB2enSeHHj6mbjZmM1a33okOlJzjri+pxztA35CxbIjx/vO+88y5Pz0bwZ2ERZblJZ9mH5knyP/qVj68MienzrwU4fOl59fM7CmjPTqZY+EKoP7TLq/1dZUFmODNrq0ICiiAOVD0rdIhSyvCseIhF8nMepSYNlB5fHJz8jziNoTQdfEoKX0CP0G+aZ/frHekLvxwL1AOeHUOCDVYDqY/gGyvTI+1Nvv17gu+b/OoS3kGKKXdMi33xM0VOxMmUUOfOZdfrChHFrZ4/f38XohNHdHvfYa2yKFoop+vqTYIZUbKg4Befb2OL9k8d/5ox4JZamFSlYQRwB6xMujj+xp/eKb6VdFiaE/jlaDF/2j7gFMCjm5FizSCw+0gsY1qPwSLwwXOsex8G/sKHwr3m2mPBcrkLztytLgwu7S+Qvlojv0ie4VTY03B/bpStEWpQ6QmMgD2SGLVHBJCAP9QXmJA2ZJ6cMLw+PO0MpFnGFsaJFw7ALK4KqAlVE5o8//lgzz04zuZctCsHdOT2gSfQT+xbvIAVuECC6gFGXk+jULk6SAS4/m42U7R23OqzLbKVpY5VPtNW6UPv6ItvB0FH+biDM1etV0T/70XSR5UegBJBTqlyz0dhb2aR7JtIdVuf0UPIgeZFFjEh6T5ijEmxVJLP2gka2Ek4uTwA2wB3cDeZtbg2TgRHt+PDEMtmQ50eNEOlyarinAE6fIZ74VD4Vonlk0EqXPEt6kwiYyqhIBMLVxtucWFjbqfZ5uKxu/vHzZ3Qg95UZXt0W5NXe7jJPoftLwxuNxjZN7sFZVjEmOBkE+WXT5NI5ljRGjwAY4GvZDiaPDG22Q8OG4604gGpHXn2NtoDIKT8E6FvqhXnrynJ6JXil308vy706oz0kotu1MxkFiP7NCFTKPYxyPhAcFJHHEG29TSkAg1t98ZYGIQyxE3edcrBjpbGCdn5qgWyRDpSjcWINcgSQ7319c2t7aWFzuGzdGsdwhKCdiAurfPLKuHg9x/YTs+XC/PgqOw23huuPd3eWaCvpUp8ujEMG/yMK5KV5Im3Uj2IdeSXd1KycsWiKCrm5xQ6M6tPMvVKk9ZjSC0OfO+XGK6Ep43kXiQWoQxR9rKiqRgQnSaB8IZdvC2jxVFAh6M1+3CX+uH+bAj8uzAdoP5wCH6wC9O2k+a7D8ev5+5P79lrezlu+Nfd2wFcd0WSy0zjiVkLx0QUH8Aw3N1yC+Pl/pQBd8s7FWeKgWkE2TcADmBkS7GbHxLm4EnXFo8PVc3qYw33Cpu+3pSFgZ5nW1ZK+Ce6V2GDJ/hnOsvxn5AE1/PfKChfuQqfKKR3EVxa/pjMtSExbE4/K6xsHdCjCA4jUx/pKKVlyl3zzaPjzCBbIZyYHR/5wL3AioO3ZSjmkBxrOB4QqSQMBOckSEqJamJN7yADSAhi0kkGEFCF+vKIhWf8SISTkoQCFhcfLBLx4k0dHGXF/XrORhHYlmwDDcN8SNp5W21hlSCD1iqjaliszchuY/SxxEEZLfNJWwFekBTPpJUAGBRrD2I2QpBQUkk8RECBMVIShV9NAYMkAUDbFWZZItUgZ5O5TDG5vYcifw0Wfca2YXzw9P0E0s2Ft6oKtWoEQaNmUFE9zjRXEaVPWgvwDZzn3gqWDNHC85nSlETFsEPH7gAbiO0IIVRlmPIXIwW8KXiFpDIz3QiVORdq95GkUob6eaAebLpA+vmBoud3a3jw5ixXk0aNdyCBsJHpCul62kP1OeeoukyJS27bidW58GnmIjBRyMhV1H6UxZadsKtn/77h0Nhg/7epSUESGrsIapL5z0/vFksWlZYcqOndI321ssQSNbc1DQPqDp9UbpVRHcbEABBkDBjR2OAdY63SlXG4qwubpXngtErRHGzUcbnHJySCap9E61IH2AwdACnic8PjDgaYKUD28Ojo6UD1rF7srLcUmLhvI5898NSgc45dlrRX7+Aa3qxubg81N15GeHBw6zN3C2PUAzlbx0NYJFxd0ZyNBXR/t7u0MN5n9WLYy/dJZMd2Y2pjjwGiqAOWzL8211A0vzL+iKI3OR9yfnVCFblBNezgAIU41rbldL4wDBFrWysF0QWAOgFblVNHvitJVrWb5XaxGpcjlkxTRqZIbpj9y1k/QOpLnN421zvVtzxmob8v0M373ftLnZ6oA/aTHYXjza6FYBVUEKygbfF7TXZZX1z/+q78+ePYlD0+sK1oJsVSOwGFSJbYtRuG8GB/jAh0oihRHFoIOx8+SSTGb4hQ9glOwf5LPDPa8APjFEsBkOSdS4Ow04ZJidofDZr5HLBORy0QyCLHtYE/+0HGcxMgwgVGmSYwZPITi71NCamL3/NDCPZM2jsvBIlvM6Gfzt24YK8VlcysbfRxzwmbDLMJeRbpEZk9yRQMcUlGtyJBVfmLW3rq8gtMPDYYmweFgc2vodDRxnF8tfGl3t7c5AVGAupmYJiarOL1Eil1uDPem9CQW+STR71qoo13FpTrNq0tPK3/OmbyG4a0ziiIXwYGMhiB3hOLGhsULpdLAbMIfKqs7BJV6qp0G1xF5pvK+fLf9VBD31y70F+e3RJ6QBgQLNZLDNW8dE3oqjuCQPHowgIjT8kApcfIVYgLigAlgQwNQvTEPZv1uPH9L1aMMRcu0AnJ0fGoXk6qRAqryeMHJghJG1pbkiZjTnCwV6TVycSpj7kZUBodKEmSbRdCh49/4zFt4RErFTGXA06HREwLWfP/wu88nl5Ph5vbFZQ4mkoFfuebQZyBJ/KvI0ZUEc3erxqql+1ccEKDlEZHolYinrtRStSCRetlgDCGvUFUK9UURAB02E/U0XmuZMxhjBpja5URJtYOJ4N5aiJNTJ+sIeAvSGxQ46dC6iUWnWIU00gBRl1etcEMpQNjkrmKF0oOQB0QeP2nbIGg7PUnVhpmVaKuX4kaxj4VGns3hE2azpbXVDQ7tToaYnB/73C2d52b47OHnKM/PfLC1PrSr4vTqMNe8OtfK7XzWAMsA5oRJJ7xbatvb2vQlOCsUDqYHOiXfNF3J3YAx+n7FvhKmQBMqZZTPkC/aCZaoYcslt8XMkZRUniof7pNfGq7JQjSmartmag7IlKFklgfUYi84C47kiCG6ej4EvalL4/Y+sL0/5Wu8gdDxfio7SwnCqeX++z8T7yJ/JtPP+PV7S5+pAvTe4vfejpk/RbHZV/SWMA/vTwhHuLPhlkYUh74sZ0jPl+uqcF6KJo77Tx798ldf/Mu/OIuZtwHOLCPeLifGEV5xkx0rrCROSIG89BxCU9ZiOgF+xOlDomyM5SZQmI15Hq6iVhwmW6Cz52VkrxcliyrFTr62nL1etCgHQdvF6tYiJ5qZQNqehJUTW/EB4mk5Z42h1AtbVWhd8WawO97E1LGJ8YxeG8ztrC2/PHVczQpDUh89onGqu7rOhQDF4okrGtgNC/zi1gacRuenFCdYpS2lDBEVB4cvCC2LNRQlx6WQCrRD6oiI5aUbq1oukry4eLK/v73pyvcTRxARPyATKhYJLixHtd8JVpv57e15/BxQA+OMcsNGcnx8aG2CbLB1V81Of0ZALcbGMWVWMVLq7PSEaWZ5uI7jMzhpF59rTkqAE5TwoUOY17548VJfQNuTuFUQ/Ag8FLqZX12OySqrnH4ZiCV9IU/5cUe6gyNJDQQ+HY1jEyLHViNCaD9yxgywtuqMResafnoSWkS4KjKiivUD3qTzihjRrk2rgUsLtilbMSHhJuN5niIWgWQgIw0rXlno4Cxsx21rvtMOjJzJrbtvuYyQfzAFXGuMMH+mAq0UbG8y8Lpqkfy+C6/9lCxbSyeaDyOI04lISju2iHUVXY9Hp5OroVMyFxfGV+7kshfaYo+VzShDsXNGKYdU4JCJACYFGX0MDb3wVIcOgl4oQFl3wcPKMoVGQ3URhQNMdkcAs7eRglU2xfKJcXJ2LuLlgn1xdtGudRaMzkbndAurjE3YeLetLPOe8ZONkKFodbiuOI0VYmfjEUPnzYJ94peDa8rIAASqBgyp5tBkkaOL6AihtBGDLhpzhs/C4qgOfTawnUZJ1wHHCHQEzxGT1eEhBejJo32j2rnslls1jZWPKn5ztTk6PuTsfGI6UTo9W+jKwur47GpleeNmeDs+OxjfjOYvbXs8H52dYkSjcY6VogHv7ZpGZJF3sLruuq1YH8NnEDWqkIGhmdDGlMJzag+ZMcMMls14VNKT0yxzL25YY7Oni1GqTomP1hwdOmrMdGTqO4DsLNRrbf4x8AyS9FH5Tcsc+xBozh27uebMNGL/1H2rcUM0yHGcjLCwUZMxkeAlpKaEjIK7UVc4V+q3P3pIfnuen/lbJH0/qTRVgN5b/H7m4+bNm4/F6EQhRfBv5vub29HkZnN1uPv045MXBxcnR24Fo44wXRf3z3UN0Wd4Ljh6+OrKXJWhnECwosIlGYfAWwjJQPZ/rRlVDV1L+YCqrgwV+NEtTpZRLgmbc64PZ4IELI94ZKK6tve2LBAByGBhYlxbxOJglGUbliR/SCSqwJytRn45iWRvY/0PR5k0Z+GGBrAY8ZfqasKH2YU7Mv5kmT/qAjM60QV16ovq1O7ZSskSQ/3ODjGmvRx6LAPZYjMaj/Z3dxEEVmT5k/3HrB2KkHzC2Vks/NxRzbxlKCUjbNQixu7ujrMT1SXOiuMVIH7Kxok6Eukqe4hgW5dQBo0gNjdHSQK5M0OYDKseo5VSYbFx/knxXe2yapciPxFOvLETyMOIJX4+inKW5ZTaWBTxU14OLGsoSZqCwBjRoOQpDB3JtEJV8ZMEVS/Im8OYIACUQbqgoFJUVRmsdIDnba0zpCA3G8JVraSFdFqIV1XuyvgaDKz0ZFDpJ8/oOyJRgmpEwf7OZQ3wPxuS/esqUZKm7UIcAxHmCMVgQVxGq+ASxDGGonYbByzN0cC2xKiRVPbUTUIAFXxx2wzF22Jh2OoXygH4jaQMRhT5ijLidCCO4fJouCqMZaRQi1dCRmN59ugU3bp6RZNO8GXKo9ckgkyHk8giCI7iKm8yepeudLxTrbLFHlM2NilNENkUXJwschIDBmLaCIKqc+1nCRsZ1CLnyekp1d8nKd3Yluin8fw5894Vgb9+cvjCkEMYhiL6rpFmoF4s3erpNifqYQdUO0P0cjwZu/XjKuoXClhwHF9cPtrefvpozxhiwKEfU7VUke813/50GGAByIjdxPwT7SNWXV7VDjqE4fj8TCtW19dSgFZavdJkz4/qIA3vlMCprzUpFK16SydSqRYgvSkHixutFM+5vLk2VZofOlp6GL3pzVSa+3XNKn2IfJAUeFgC+5G79U99PNMP9Ueu7RvA3UcAEzSB3H28f/z40ZfHByOXRtEhaAzRSfg7YAixMps+mWIS+6zEuFYgmLaVPBYlGBxrZ82AmnLnfBRlK3yICbqyFcSFaz4IZdmGFtiYHgREeFRLEacERA5dL+JRZAnQWQorB1sKUGRl1JmUDRdzU/3C/Mb6Kjlhu4qm5LoMqzzljrp4u8S2IV44ZB8W94lGhohit8Liz85HOzu2zFxj1rU0F6ciOoQngcEKAqXID14yo5EUa2Hm5TJDSzpBJXOEzc1cbn2/vGI6ckqQVyqSHREEebSLEcW8X2bFTZ01kxQB0dyaVUx+lKAJEHUivKpJwH7rFYYtneODS7lPidJFm7b2SEqiUbBGBRqCOBTglGLIlfU44lmAg90/KMDvxE9EKpUoWpSf0s9q1Q9xEEofWaGi2mbH0EW8lATZgoBT79bXlSXYiHP0kQ5/6X3CimaSi7ysiBdGJbBU7dhpHk83k6wKKQUxPYj4ysqg9pZNFUm3yiZVJCGDLkLx+4WMrjsnaDYPYg+q7smC1eHxEWS2nMs8v2j2z2qoCpnTj7VdK4LyztxTgzNrNEZCqSTGWv62kqSlgreaZsMZ4OjjtbGJVvxzBHqWuBpJbrUgqSdkAFlbYUgTjcUQ3W1OAI3Bxn5Hb1UNDRHTAqRWBTz9FPEKPrpXYUHXNJXYOY0GoKAkMUDmbnXuPP2bOlwXphqyIgEbHHJBR8AyDq2sPH78mPcS+NBoAtKy6yLUae9YO7OV4HJ8cXp8cr40f069m79kL+bUtb7q5rsNFs25mzNtol9gJvRlnUyh39r2QbGTZj6Sf8Um2jYZ04pQ9pXufOh1j0DS8jbqGVo2QWoRxNAk2lOYVJ5lPJyOGQW7LOSFBqKP2lWIYt21GKJsy9l2enN7EhP07d7W9srQXv5oYw/hnVCgO+6dVP3tlf7sFCAf43vbGd/eVW/yVtOaNWAuaSnJdHmNST968vToyy8uzo5jnDYXXKCUELuvGIJS1gfwNWwM83WMarhz6UOp17aRmtKJVhWRXapqlCglPAwnC84kJHDdrhUDRJje3Tw7piTIlJ2G8Kh/2Tpfq/Ux31xQfGheeKeNu4uuw2JIsCP5EjKWWHI95Wh8vZB1mUW3d1w71G6Z1y3BB0mXDsQAXkyf0kBHI6jG9J0K6KAawqDxaYQ1kDhUJFL/ImIeT44qMO/MupzeqxTIMgAo3SnAAo5MvLUUzBJPJrivzAPSCRuSTBWe4CMFYZMDaQYDWhEZSV6CJh3kKh75J7/MfqKbHrk8P2NMKD0wrs1ykg8g2CHsKXT+WYsUJAI1RGRyncuhwIG89TZlBTXCTf5I4vKcgAkgkFFK/ix0lgaDYCWNsuAoSJRNk0GjrGSH/02kcjYQDRbX3dR9fbG+NH96rCNzAhOquj6Kd60aM5qiAUeOTUNEo1+6SsJ05Ny9+85/tQt8eEBSdSIw10KrRYQzI8W61tbp5Oyd2gtteXqpUYPVh/KARNiG8gmwrX5PildpTi2aeCVurU1Eh6qO0UkKsIiTIVcavBTjRFmLWagKH690L0K09UWlUT6Mbb71sdZYAgodZIO54saPdPDjkZdBmBEIIKxU7WnARQOrK/AUhID26ilvjU/GFX7EDVCXOSCRUrq9t2t4dO+k2yrIDz7Iun59uLq5vTW+OLkaH3m5srQKcyd7Xg1X7FibzzJbrJgyZ2GrFMfQIQbbxbNRKPD40b5V0HrjMzSd6YEXqhq+NMoeAzq+LLy4htF9C67WSRqdcuCbrG8Mferp0Pr01NgheUphylJqhfysxKCkVlTKHpC8Dp+qgWdV3pi+9EFxMKcXP3nCAhT/bSVfjci7Oh7+/owp8LNTgD7UvsYOummYg7hQciub0tkfNvf3dz96+uy/OOaDVmHONHEuyLxFCVvFBCsXvH4wEdwqDizs0nGudNHlII9ABg23CTdjp8ncLItnkvCd3prF/ZINCe/GRoEklbAyZ5wRF1JAw5y4/qQ4xyP6j7V5QihH/+Ba2dJMA8rBJI6azTSc9Yd35gJBCy973gfry0z0pCjefTsaz1tYYMJZWl4fbjInWe/rJtPFZDg4Zvg/tcCH9UMShkQdgUECqYhZRYssgRFUWuYn2xITvp90AgJJTu6n4JTvjn3g0UJIASKKeCNfjw/jvsOMpr0SCS0C11ZhdHFHhy5wpApNznoKAVyXLo3sBiOtJNoWF1pq+82cRFFlhZQS5oLG+fMXwNraQ3r5Cb7WwVminCFmQvoOodxxTeprWnH3aCpy5vKTi6w5Ij4BzGuKaQQ0s+OkLMZGpVGenJXARygtUn/hk1eRRlVpV0c7LLUqJgqXlHvL6uRsJOsXivDCkQ0E1Esjyp4BjUDILP5PTb4p0t9HGQKQ1KRrGZKaQwwzYUIsN7TWHiiKwvX85cb2DtwKjdJ1pqczpMZuqbdAIYKAmPqLvicOcT+NXE9BfsBncWoHBygDDBAEg4kMQEEngHKgDiWgv4IFjmZMT4rLoosXHeNZasHm5tZoYI3akBvq4u79JqCKWOnyJejRgM1pRaqg6KlFZ3kqwq9o5YbzWbReKT4ktWTYuBcsQz1rc7qDYiGDFfFLZ5FWt9JeHGVkv/noasysakir9zh7CVOLkTBmOruKhsdczGhkzxuUriwuXo58oYLDEs8uJweHx/Ty3e1Ns6Ps65zPopvuYOqCdTq99FRqJHqW3uLj1yJjYjrbkdofIwSQMlOpjLqUFXoLGSCwQgEzKEE8fIrp2siKc1OGltbnXamq1HRXL1/T11aWdje3t5883djb17EUWo0NoR7CAwXuKDBVgHqQ3SV+yH/fVUu7Xl/1WyLuDL4vvGuRkkRnEF9dm1ruPX5y9MXnF0cHWSAqPoVxu8krHLvcBbI8X+h5YiXKLt3Gni9gMQFV8swkr/96dkqmVcXsME1cCy/D/pTACCNrU1dUpWZPnnQgNZJX3nJ4xNvMFOlJZvQyekkz6FkepSmeBTwZc1c8lEzx3JyVKSa4uCjk8XRWn9VhVrXCsotDqkX61vZOU7sm3Fm2IBdZPtzdhYk7DNdbFhocf3R2QvUhNYp9x9QkSGdnsh6kuIpkhrZEr4gZPwERh4zWAS6PnP2TYICveXl7assGclpdsgq5GrFM6IWamXslAzkgsyCDIiIgNwIaKCiSLimxTRba+CYbKQIx6whKKQKOMwfIEijJ7xAclBG0EY2lk0fySOlagIVFoNVON3FvVdp5Irxv8lPTIHk+4m48WateUBwm0Z8uuIoPeI7zvLHaU47YTCpeZzQqKJ9nIq/C9xFFgZDWZ4hCW1ukVIK7zVetVPLxgrBWj+vCq1xEWqeEJ7EWPYlNQUOUFZRtjMDRXtQGmYlRqbo2q0xB3YSy1igrpyddvYCsoKoU8HV6IFzFW4tSIU6RBzzboqKW5YsIMWPvHELeVMAvmdUo1Kv4rsFK4tV1FjclqoXqIy6PJ4T1uDwGoQiEVaEUlMRXNqe9JtFbdPBd66Cy4WUpVsGGqQodcHp2pHNrBC4Zf9QTNuNzhLKkNKmDA7I1QYezFvGWznAyz/E92rTGqvnxxtAHCoJGad2djSlfve++VJ+Qq1hF7DT9Hys04niBgsYtPINMNSpqT4WUamthjZluu0QhylQNPHX42XHPEJG5GKrU4tXV4d7+xqP91a0djnCM3Vmj5ItWBKwaHh4PFOCZWAPop0uJnxb+X8d2xn9/rC5ogCqquiKDnARtu+nK1s72/pNn52d2MWePKTcRO2MXTD0n9p7wMTFTJE2zkl9CqzlOQyMirFsV34n5oRIDP0YI+lMiMeRwmMV9Fpazln85iVNwucAGEfwO9xSoAHPuT+DkU3Z4XpOgEZJ+ykMdwmyj/vjNms3NdmmRK7TpcHQfqhqH3BW3QzPnjDYs7Zv6X/F/uLLdgxyCRksRHF95bQTLRm9VsAyAj9sG+bpcgqAS8FA57esmJ8QJG2shjDQvXxzC21yfV5B2KQWmFFNz2VptstdaFYqkhX1GcO2UlkFmCgH4MngqzvO0RR3idEVKdQYU71c0klyAVB48xgN5JkVZb1tH6brQUlmh21gCMXu95NRGx9qQn3KitvULddkMhftTThiKiAx5KJXSFYeMRUakU6kaYe0JMlAiMkS2ZW3UOuX0OERyRDbAXQF2nOMoxwuPdqSwQ4HmiBwbwvgksySCAwjKJDJVMwK1EvNIRbMffyJyB2T6OhCjHS4YkDmxe1Wrg7n2ahEE4FzGv0UrIDHrbJfz8u1U0ZGnqGQ4xVbRwKUo6Ke3Qeku3GU2wEM0eTRTRF1qVN54RAcFvUIHg00GmXU9+wpDK+IZPH5w0eJAbIWQRmjoAmLtFaoilFdFdDGwxieA3Ry++F5z46XS0XWCzN1irhoVlCgzJVulisvgJ2WeLz5MDJhuERzoIr4xGRi2bB5kWkK3Hvz8bxbmLtk7bYqMC6DxfHP73K3yf/WLaAyxrNBmncZ55Uv32VKA6JEn4yvXprIj+hB0tK42P2k6QNOilNVDFNIubRGyROVP/TKd8pUbE3KcumPu4nJtuF4b9WPOSS6l6oDEwK1snjogPVeqOUtQ8pQiTK8BnC07huOyEWVBnIPd1s7m3v7K5p6TNpi5IUPtR4EM6YfwY1CguuDHAPROYXyfSdg7Rfih8m+mAObw9RGZ9SxcDFPDlO332d3jDJlVL6st4YeRAQpiijivgCc2g5A4qwbDKv5TCy7NdVKVyRS35AgDU9RErh0TeOFfXBBLzARIb1jl0BwXEiLZtuFYTWbwRRqNFl2MP0Kqm/KpGz44LPDhsDlfugTVIGsWSsHQEytXljyQqGDHpYMLOPSIQ+lm51pHSmkmmdHN9KoPWZGT2V8281twBGKJXAFHIlnSM2xxKaoAVh4/1QsBOe9nkOgWJxBkhqRs8ovL5tUMoAg0WkrJBqyK5KGOCC3SgBX3liZE91JcCmxFZFD26OiESBKnYDViYKpLjRoLoOIyA4sCjbPicPYKWBDEFZeoCihJTPvpN3duUgoymwHSKRDYWF/TI37yCkdD1YGgCkshFvmA0ug4f5RqUj9BFV7Jnq6oU7/TU13KdoB5DqHOKHaN6IWePR1NtQptBxZBBBFv4Q8TSEqRX1zwqsebuETAwRSk9zhRSqKfBo9n55EZEKC8NZbcxiUzatBURLwCQR4FPQ1lxJQNDsioh4FyoIBe0wqJrVJ313eKV+pSXCIDpIj0xlNcROuMLrU3khLlV5cADYlwU7ZHgmwygwmxHksQkGHWxapaG+aWX6upWugz+OzZ8wOu/b5k+NuxxfV+fOZ+Ef7RORX64vrg7PyAw9PiknMac9RCGtoMBSIZ8EJi90JyhOD5uiGWHsjR50fiUPVKxLtXQ6TKVpHEvPTsnwV+Cl/by7UnLMO/zuPGE/veF1bXdfmVdbK4nU9pOAMYoA/h+1Lg6/37fSG943L5ch7CO6TA/Y/5h6ABjm97Ni5f/eTvY0pUV1LkaNfVtVrox4lwTU6rEbr2Mk8uL3BJ3DNGnZrMhbHEpdEMCmSMEfDoQC06wMdYBYwukzS11GE8NoZIBLxFRQHMNBf/dcBa9CTw5+dMKuPya0brZbheDDC4nCZ0wTQkl6guONKFLqBmPkPeNlvHvvE0KFCrbAOXh9xVlwzYurrIY8KGnCBdUBXfN0nGP6XYC+PoHXWSUubQQBHhChIA4i3DKBPekhNkiSJgNmJAmdhzNVUEKXp1zLILbFXkKeXJkyfAajJ8ImsH88+efzEaO1uFGcX60emLl89OTo+0bndv+9H+rgz6zT+GoTTrzs9DRGOrF+InoVHw4dmBslg9Zytv0Qp6pIinDMen50xZimiIOb1/ljZQ+Oz0vGTZdI+StzBEIu2qHowhqhWIxllit1eLmqpNKzN0rbYiaU2HAePo6JCeB5Rs7ASDeS5K5Y7NuqJ5pYd5ZlTfeYsnXgH8jijbEWjPEjvlG5/yC3JCEom0Bema2lDVqJ1He94iCLW+a5df/QQ5pY01y1uhU7xSi2xI4emnCKoK0vWyUFVM9/qhElo1qiIpW6ct6yDjx0mMrY3JQC1gIeuRczkeWXwBR36Qd3ZjCxSkUC6NGYmKaAUcdKtgxOYitpylFeMQHUsVEDSGfT62thmoz5490y8yG3uFGJNmmaZKRwdQ7UIjrIoeKkYLQqlawcLCwxQi61/WkfEHOuCLk5P/8vvPGKM4bFslt+fdt3bw8jkIl3PzB+OLlyejl4enal9d4+h2U0u4Maf5TqkjKkVhbWkNWLIuYkvyROswKTzgds6Qdd9cn5uoK2Nwqj2eqNRBnv5HazKiPPsfP6Sc9MP4QyPqxfLYpxmMFhmq+Ooz265ubNHncvxjGTFtXkReNLmD/fD3B1FA//6g8u9N4YcB8e674m0PpsB3s6CJmtWjgYM6asNozWLxHSwbX/j/t3enbZbdRp7Y777nWjsptaSenn7Gj/39v4Jf+YXtds/0dLdEcS0Wqyq3u6/+/YHMVIkSJWpIiluCxZO4OEAgsJyIQCAQQK0QSgQOmUCgaqdIxEWk0EgTVEJ9qoK6LMWUEmQmJaC/WHeMdyJYRUGCQyt4G1j5FI1CDJwLlQ8NLDobOUVCETHRoidXh0hO7oYrhGEgsvzaUaxDR2bCmHorO2HnCW1BCmpMCLDUhoNmyWn3wU/oSccwxJ8/f+6EOZjq1S1KyVbllcp1IKYtnt4CCI6yfsqpdonpsHcuzAJKfnnUoo2qri3CVSqDqXDwM9DCau5UL+DUToCAIoB4lt4LSoK3tdPUWxlVxRZLllPzQbb3gn2CKbNOqA2XX57yNh0lMzgASq+tkE1vGGtABAUh76kWcKTDDfKKKCvUsoAEDb4HMMXN2t0FOcW0Z5IVUigbZnXEIqQY+UpJKNKGGVF/1aTb+J+cgVf1O9m+MiqbVnjWCPxF5DaDxJ89ewbHWliX0ljUCayIkfLULfJrkQBnjZKioCbrn9pRekC8CsS6RfBWfvNHvEKo3QuCsvpKEa9UIZuydTSl+FmH3is56R9rEaMmm24HB7YiIHhV2yIuQoJXilBjdsEcTLKLOJgZiGI5ZOiBqh0CYdAEP6GqLLTNBI3yCpLi0iUKIqb9ZHJ0fHwqp22x0fCY3NDqDdvDsTuHf/fy5adv3kJoEYee6+li6tAC2xpm21ecKGwPPGGCY+khwJY44qlRhDYPldp6i/gi3H3sMPfLtKAqtGxaztjY5RCAnoGrEQIh+e+Yq581ZHxLACGQi6gKbCJxiJgN+LJHl5cIEPVPrj61A559NvSkzsBMmzo3Hp7fsAd+Mj35cArsG86Eb1r8/mv/poC+ojySQv9Dv0I6yekVjhCZhrQaLlxG+KwnO4x2qgGBMx7FDYx0AWKVkFFiVNp0P+lrRAbEyxNp3juMxeENw5/NDh2VWO5gDI8XZMTP0S2LPDgQayBFVrJ6LkINptJmhGTtSFEl1PPTBCkHYHDbYFkVCY6VIcIdttlZ+2kx0gpW/IgMVpMTuq7wHizHK+wk+G9y0Qcbp2tqlNmUFx9t9BbtZgKlRur9ydHYHlussPe58MuSGafBWlBbMh/dit6QXykRXA3Ft/JFS6lNrH0hXLQgGk1PEC0adnp6cpRO2EcnAQ5o+kpZFBooECp3hJrm6l6EHCl3DIa+SS2VJbMsxhodHBsN3XoGFx1OM5E+B42E6Ym3ZVluINpdB9Eqg4QP4c9bbl6gTfMGST2joKo9WX2rAmKQybq5GAN5BVsYyqCUIiBQ/HkSoG6mMweCpGtLL2qXtNpQ4oDg6CVub2iAvGSHasTkFIBK3QYJM7wNXr0rEt0lf72/5Wgzd51BzymwyvVhKGhg71E8E2pafzBim0Y4kx6DMzJ/MLnVbJHjyHVaXXtAzeLa6l/mTES9aA1tZOl/APWSnzLLKV4yE7jj0UBcpxFZpvMZ6VAfMrfJfnAxKROvl38BkitdNs3ZzZRMdnx2qhRozji2+XeIV+sYPpsVdeiBhbvWRUjS50V8sSsppbS1BZ861oDQFtVS+sQBLHuP5BsQvCq1+Epy8lAV3Jtn1Bpxhp5ZXZwomh+XNwSjnh0vEo+zmftu57OLq99+8tnzx6enhCLfEAmfjNtpvryafWr3S+d22nRU3HQN4mmJ/lJzbiUMXZ0xL6OfYae4qd8vo7p29FKIAC/dvA3ZWxtl6zbzWrt0mqlntlRxyogVUFkcRYEUGctnoid3EZiK+odxonqMJbBEtHjHGDJ8HzNg5wOcBI6YsPkPWJ2TiQivh/DQA7c98CAA/ZSngg8eq0E/6I9Z5jiZblXGhtFJHZQRCSFx2EVAVf0sq9DspCCmCEYJoRo6CG2KxIGKFF6C7SVe7FoJLhhDOOdwyAJDQOwINIVMdyoXQOIxIttwztawgUCIEd/QY7wk5FolNeQKBaQSwXRK3ykRVaso50/Wi8H4xFkR1tqpOqILJoueOmlmDrsAKutCvNlb1s0EivHkiG9HaOBJgju/bCLQfCjC8Yls2mSri+9Gz6PRGAHVduzBJgR+JpsngCLwrJjgYIWjNBQBTYZAKGepVBHEivW2iD6Ej9pBkEEXiatC3CuZARSXCD5+IIMgUV0S0zN3XnmGg2zHeFvLQoClk7elBF4QYx0SU2lR1FS6MrWX0RGXTb0VuIGWkvyYXHEe42fnaOKJ9aoXEMFPT5ird3ZzZWPCDgMIwJYOcciYHm7PEsjkgiplSnhSq0tQ6/cYl0SxAekiyoah3QUzA5O8+3X71yz8ctKXcrz7s6CnoyITu6oqJ+EPDU8yrhHRUo14/Ph8ODpmrkIDo6vT/MzJNAEoOyMF51RaoEUI1yfVSJYG5w8j5UxbUbQoqOvqeHlbO9ks9lZ1qtAzJgMlaxVQakX6qsrfdUypX0AYdfs3NChll7Z2OxxkyECU81ngU+oYDhtbENUAKd7CUE5xwCGgRokAyukblQiBMrJRqHALVbbM9jZ8fZLSbQwZdNPVptt8eg0BCiGYazwLN2cImhw00rR2Bk6Md4bHs4tX//nhxy8en/762Tmn0PPs8LZtQ39ycfWFC+XtaDVzmR0MgR0O4kPLIbBQCcQmBnjEyRjthWIwCKs+oInwkWB27ruwLTibXle9XW1gxiJzpnzgZWik3AeAjZe3ioCpeqV8KqY7YZtFPBmPUGvRY898NDn2CWWfrUALEHMshtkRgd6dTg/xn3kPPAhAtxMg38ifCz6hP5f8o0krH3+Elaytyi57Mbux2IqtMrKCfqGDbm109tVxMJoDZJvtSKHaKY3QeMpGEEJ5gZGi/bVfyitbCFn/YTRluRXHzyV/BBq6n9zYRKNUT3t7QbNDqdQjD3V47KAAUQfroFKXWvBu60GyEeqFMeTGKzWmUpjQYUWCKs4PSXNCjCRuzU6xHDzpk5efMVo9Oo7Nr5b1mAgNo4Cxp2CHDMPQijAnDlvsaJSzu9QYeAlirnXpjWJAWrkU5qGxleeZDA5m2VXD5yiQCE90RWrRJl0AF6ix7FAnAY/gUWxUiJ29kN8gjlWMY31SJpWnv/k/Zlm4BwFi7IO0fCfDQRjAoIK9AFjkFcjAJNsLkajcmjmbl1uuKHMqGtpYdBs6MXkA1wql9FiW3c0Gv9Ka4+J0b8EWF1GR4npAThGdk94u/EZBDBX21tUwIcGm+vhTofvhcynaLJ1WBC0jGJHUmSHai5h13YWMUv7L7z8nA93l+3p/NQong6drxwQdAluYC1B1dRoNHVF7fGhfZyeI3JMbpIjk9EDyNJcGOgIHrIT7lhb/nKZUZFByhX6jrvRWRSnVpQrjTGfXottkkJYrpSIAuWkLKEZRuhEXpgeyK5QxitPIW4c90V0UfSo4w+NTkwfavrDr2VRZ4o4qpnwvFyMhZclzJj5jfR3LOzMcZOucZUSMkXs3GscpReKBZIUsAyBwkAcc+iEXsLDoFzdAqmPjT93CWScZ54sFD4p9+h7AYkhfBKO56TQ/Xp6cMjBbQr/d//xq9h8ff3J0xMxnx1+Q7/7N5dXvX19OzdYOmW/HwUDs3uOTQkvzhZNJIOAPmJ6mhZ4gD2UhZOgj/UTMIUDPpzMuECX65mSNnCSXOqroUyZ/nTAq8M/KQkXR6EbaDixvkwHMRjrfKJv5lKTWc/R/dsKykkKIAjVlxcUe5B/98BDue+BBALrviu8nUr9MVOw7qh58ZKIswqzLGi7cQfiZdYQyoDX0MohtMwJEuz0lEFQyipxgAyFhhR4V3EKFUWeUDbYF4RA8zv3qKvm6HOiwKPSKFodmCblHYRMv+h6WsNVEt5SORCUgeNkBsvouDmRpipgOIGnZMsut42u7CfKrQgqBpVnOdYfc7RloUnuEf6O3JB8Iywkly9zrq5kFNOIf7nII27NIVB3eA0OcIyxqu7u+vqp+e1FkDffK09sKB+covHCl5SJ1gC4uLtWoK/CY1q5DAPJKTivrdF0xlBmNhuDLpvO4OlRQvJpfgIwMi1c5rPYwuRJu4AiqD+stuhY/w72cqC4bbbWfA6GIJmCGHa7Wxk5OP0ETMYZSqz1WBUvOkYf4olJlwfQTtprgiU3JVsGqGuZ6idoAQOoV3ES9tGyDVfYE9SRt0fFosOPVuhfGE1vdcihPvPSbGv6wyVX0eThVwTkL8OzLfJOgChgCx9aEXxdCmOlY5umtkIrlkxXlYd1i5kFY68L77mQdrwyfvvKsQYYIH3eo1cQ6ezVK8NY3InilD2tij78HHpZiO2cvNFtXILMKPzs5BVBOo1wkgxamrQ9NSzBlVkoPyCC/FHG4GQhPISO6WBogRbwtskSgQdhbEbVTZJ6cxE7feMGKoCUnaGY702oA1WIQvTGZ/axljTsklaUcUrbUvhqdntkhNiHIvVEHOw0/OTWxeqPj9fTNyzdXry5uzk9GnfGx3drPpl+8ni07g7GMnGnQ8ZA/cgg96LEFyywiqtTqxKGqlTo2s76KIEy1OtxMrK+vLlhoDfgky+zIZ1v7JPuQpc35GVUXySjBiINW1lOGMl0hUaft9Y2MTmCYnBZgg+FgNLbiKnukYNx+LKX2IoBLewgPPXDXAz84Aah+J3fo/f3+fl/11haq3Sf9nbS22D0gQ7FnabkW8IiOgv45bphRLMsv/7EV4A2mmDIgHOg1vhGW0PDvDxQExbknbVCFMEJvowphZQD88s2lM/BYiWzeesXvLGquaWXnq92cIdMIYk7qFn7UtE+Ps8Twh2aneHDO5ashZS5n3bLHXjMlcocq/trsbPbRJ3V74QEYjtW5/7BAgTDkqV4sx5Mcw1II8aQOoftxVjhodGIZaqWumTEmdWO2W9lZJXd7q8UMn4GVxSjM8R9tny9iS+S8FEYCuPU62xvsRKKmQSmyVLxdZ6dMqXRf9kFioyrgQHboqKZoS+hw/FQ7TsTcUx+GcEd9RQ2Q7oo6IDAjJFnGBtqd7CUugypyim6zs0lRNhLbWJxuYul5RDtUDrEbDgFkDQFNLdAW8dY2iV6kefAzXKfs68kgKEIAElGLumQQqUDK+858NjUlHKrxCsDCaNfc+E2ORjreOICgCGlZ09yrECQKnzYk2e/C/yrz++rZDawiahfxrHGRGr6U6CfELPtxSnEsnwRgGmomOYQPY8NELDAlTk6yi2fojaaOgSTIabLbrWzJFP1AtDZbo+IQIVm8zOcIGL6N22FSRW04ZGq76lM/0P/pmZqzdEt2A+/z0I4ZzdUuG6xkyCBJA5fAF2LOdsHcDIJeFWLAVy/cRJjU6w8tAtZEpTTznYpAGRwZTGCNtZnlS2Ftr4H2s2AiheAtA/kVJrX5mlyBAyh45Skz4BSNJiLDsd3KnfY5XkBmGjCfGh+z0XEp/dub+X988ukv90+0Yr7eX662rfGk2ek7hmXgIew0Fo+M6eFYklll7O1LeUfGZJGXKVA2hTUODpA3CqQk1j8kRQhMBtk4FrxN12R8yqZwmWxFpYVuFffxQPjtmFvuo1UJYZo8BK66NNr/mXEOe1L/WNiRmFRfdERRHiV7mZa1LtU9hC/1gP7/UsrP4ecPTgD6OXT6u20sn/3995/Itx4qPwp/azrhMXEYbLOfZhfFDlOjF+LDrLiEKq8gUkQNz3dwQ1zIJpktYSFoH67pe7Hp02uPG8PtfLO4ueSS0MXuqrPU5AaOMiE0vehvnLRltpArhBDcGb39krJ6eEIEGUBDXcX7Pu1C+D31NuetlnmMStn/WNjBJPWW2pNhE6lCCp24xXvocNkDovWBoe2J8VFOGlvXOpxTWRT8y5q4zSugMD6aFPmmq8ibN5f2TRSsrIi0Qf6ANgINrHQrZtC4a5PipLGjZBJDSdvZVsNRANR1KhJBiiEGx7rhaNcEAwrKGxkUChdkeY051UYBpSDeADgpB90O7d7vVQofmAyGtxtVMuh9KEHGE89TCuaqxhE98T+vJKqGeCdPbTusIOnuBXVJ5IySHbycERR2uf3DaMuAjRFDi7AVWyjBhQkS1du11ZOD+vgcV04SwrpYnWrCkmfopaGmkGuFr4bRUPTQu4Vv1x2HtPQrqWsmpgyeNYiDcB+/j9REYHUW6H7axrUxR7TQ4WqGlbJO4pspa1LtfG7SEBNNA5l1QgWqB5KTcWwkgyJBv1M7OOQ/fDQAd6yhdUofk9U/UvStETE6AiAZIOYvRcIwXiaDYSV74dWmqttDZSM6GFDTzMSw8UtdUWZC9nFdXg+CcSQMyamlEIpIF0fJ8XRlxs5uZvDlesmA0uyp3cAlZ6t9eRn3B4ZPpRK1EZIkThDq4MJXY6UbRBEyB4De2u8mnNUaISB+xdci87xh0Jgf3g4HR4fxarearpebDz57Od9sJ5Obzfbwhq10u2cI+KA4PRqdjEdWFGSLzFdbcPaTOxYaHRfZS/BRmu9GxHQvhvtE7Ya5Z3Pq5uqSC0QCLDxr7+kcWS2GTDM74jEkr+RH8dyf4VF6/M4QSqOQILOZ1Fo2x4qofYgGNP2TzTchc0fO2+ftnEriQ/hSD9x21pdSfwY/HwSg73mQ6/f53SGBoFgmCu6UsAq2PDo5O311/YZbQuvSQuIHlksukUQ7oLHaLFudExKISySHzSg/1qvNaBLyShmB7EYS4gIWqYuEFLrU6bUGu+aj0/HpqPvFzVW/h9nljA07DfwQl4rhK+1Kv8svys31rH/EeICmesIsegmPaLbDmNn92Otytxe6icmOu23Oc1rrq/WeL9dc78NoCE1jq4G8bRerbovzEmxjAxQeYOOAmYb9IqjiVZ4ovoafP3lMOPjs888JLpOTE4zKla2jMAmCxeHGHtVq82ww4FXFrUah3s09Sw50OQdJ0iFRW7lcCXG1QsXDIIAbkTmol/QPUUIt2K0atUKiVShKrqIBK+GWtfTyenpFSc+0CfCMNfvQDrGPO5+iIWuGpQEFTwLMsFwOhWHb/CKziuQt9mZvrthNl7EIGBFtpU4iM7iDDFYGdHI8hg8ubqFcy46KAa+djlSxXsb0Koqi3IOhPCZKFaFvtVQ1xYVTvCM6buOAT7vNv7bFdTw7j3pNS3+DisvQE1xd8LXDYKV/sbw2Dj28kbeFjeu3IzUaA/2mAhOGhFIkofBKAQvMH7LyH8SekvAnD+jBWbJIfUkq5gfbT1gVbmf3lo6LqNdhwEa0e3R28kmv8+r122b7gjpCS6EagcHUIJk0N7cugIoS0hCAU6UEpmk6hyQFOEGX3I65q5a6SA87uhXTHK6QpzO7vgQRLp28ah2OC+jBhNeZ0rTtwRVvi35n7xKO4WhoLREPDoct/5uEFew5nQ/RA8Swf/6+CWS6KnItoRMyNCi8bBKbyGgstvVWLPPYpB+Mr89vPz46gSSwvoTLm2tS2qNH59ribFeVdH1fVaEIWiQ5G2SmX+vAB1WVlng1VJ2ZStRwiExm8Fjl+VgN286tdr1h8/Ts5sptYcsPXr3tXU6PJyeRUjYr1tdnk8Fvnj9+YhvORXduCtOg1YL9NHc/LPX4PDUVLWE0nS92+9ks9Y1ZXNJzObZcv/r8c0qgx+en2pfrMqoZeEShjKtJZFEQnRGyQFkGitmjiuL0iyYtX3c7Ula22/SOVZDdt2ibclzOFyQDqZGIa40i1TcCC19e9mDrB/gnM+1nnmD064f2c+uHBwHopz/i+AQGaSmMjlgfjVmroFToRdbQUeeUE/FyRTmBB5NImA9IxhuqJCE9uYruB12+/VTCk7INoQe77Qa7kGdnp5/f5N4oBa0K0UbyREx/OD1j57ttrDsHBhS2ySZHJ+PJiUUu4QONxk9H42NO2JxfjVHOcuM4ODaAMyzWb6MAgiw6Zw3N8gNRA97Vj8tVCwsp9wDYt2ruuzYDXL7KLqmujEFOc8qmEvwLWZSGxreupjci+A2rXxnG4yNrdHmtjzk2TCGqnOLiSLfgW/iK/JjHjo1vhKGYE6moyBlR3siAKcLQ/tZ1MfUgg1hb115FXiBgCVwMvtnNOkEdS2oIqAXLi7hTrNHx0NrnCuJQIGtLaiknkrwSBxZKbiFQikmH3ubcCD+bznMjGDypgoCF53QavYWc4pXTG0QRLp+ybdFUL35NrF1bSZeTgtnTMay50TMTpBjHwIk+CxPSukZ2GDVFLZQHxEFw5E8vxVwo7iljBvJVXxWG9c0C8QRnhACWZj5Wds6tcdrlRYTyQyT7zYZE68JbtWlRxVkpeGqLt2aDfpBdBn8yOEWNBDQINWiGnDJ4G3GheEbQw+zVjR2wssmgB7zy07QxNJR0VGVEdlKzAfL2+ITgfSIDRaF+E4nVzvb2khNqP8I0pakq5DcPi+lVnPoYtWIOlxP+0LNjpSIdrCJsfjwc7yf7N2/eKK5dNHzqghIMwZFC+US9RCqSLgBYm1M6IYI4lVVsqYo1GKzcdmIvzLYepdeOdNSYdMg/K2JfDJmJbPRXOvTJ8fjX7z351bPH58c5CqD3KAZNDDJPthZJMBw0mAO+UmbPli+Z/DqxmDA399dXl7PrG3tYvAbYjtcumAiRg4xCITuy1r1vwmlmXZzVr/dOd/LdaqtVz2eLu4xS055ah3S0WC3tzbk/mBOg2y12tf7RRFRRWv0QHnrgvgceBKD7rvgpR1AWRN461pL+7NE5umM9he9GiRw1cnYQ8G/ECFcjdnA+iLogQlIExfVOjd+TlFgBFANnREp0Mhw8f/zog1fXF3QzY8rwHsq32btaqD1b7gbNjauGWOZM2UWcOUTSa/WdLbJQJF4MT07PaSCUoYmyKNw7UCwcGqsF34w59lJxuPW4j1WxHEJfiz1BoYL40HbYtRvSUwVUEXo8gOggEk3MYMBBc20RNgO2VjtQYkWL1q+a0YVUHkYiRMd5p7UIp5kiBvWPh3K6waPuPhDmQMYSsMDKz1x1wK1OOqc96OICpL1lTuCg1PiW6tRb+SXGQ6iI7VKfLQ4dQ7rd27KFxPSYMBRNAEiaBkPcVMQoCM32DruSX4skGiZxDQk/bjYJbdBbrOa17WldMQYSkRn79xYvlBlTlF/fqAWGNiUyjLfXldPTtfu9McipXxdT3NBfxJwoBhx0SHwOnZyMnDA3Olc3/CqNc/a5CBnaWBurRVLqU6TGVVrj3/AJrACaJ1DDUSxgRCCpsVQvUKrx2WLGckk/6MYiN0TZI6eCIhUIUURji+YhXwKwflbIchLv9LagXX56pS4/sWHewCWaCXYJMf1OKwKQgoB46m35S+enru2Gl6xgZQj8JLwS0gk6i8ZKpwkwVLUeBkQpAlCVUYCyJtCEuiNG2qDmjP41ytoIbXxWQYbZNB+HRAzFqYhev720iWbCK0grZ4ZzS9od5ziYoAlwMDNFDKLPwLFGBc151v6x5WIu3zgmj2znjZZb8A7NPkWLRqy3vD4/Pj7+p188+y+/fEH4GbUscgrO1ial67Sx6vWMTQY8e2E5fJD0Ioww4nn9+SsH4GnHDAp8vDV6vsTISOTRduRRK4J0JMErNtQZ6RyH8O0RduKEKmptZ8nUZafM/csUtBw2jo/P+sfHvD9l09x5NKL9ncAdofxB92NS3oU6yfVkTag/717+jP5+0wXZz6irfpxNNcUpgxGEzPXs1/OCc0aFnnsZIgNFDvBAnhD0MLmickCDfBLiMlhroUFSdED9YO4/Hj8VEWgPht3Oo2OnbFvt7WLADKjYH2zZLB7aLuaeL9ku9mw/MANa09zn1vfoF/rjyan7IJ48PTo7s6xmxGElpy7rbZSRazM8O+vgot9WL8NHFB+qhaTG6T5fgQQd1Bwj8ZSFGITfeCUPfi+C8dyzQD8Vlw5t/EsDaaqurqL+QYgxeHti8rjYAHUGDQKkDTlxKU+4KajhOgditVs81U5AqXxOhrC14n4XKP0m3VvQNApYiEFV1TAXQBYAARaTBbkClygPfgn5CKbF+gccP71Se2Uh4HsriAAOiLe0AjCXAr7mq1dm9d4Bj4AIoAxeKVIj3kJyMrFDGONiPxXxVqIxI01pDjxFbC9SLRiXmlKLKyKn+HcaVJFgUpeT/DC8w2rnYLmqTXRPXWGUjYK8FR/ZxDVH0Arx2nY/AfFTEKlxwKXLIKKg3iZJ61J5pNSZ4G2qK9D0s8RaRLqu8EpEyBDcpCBkJIq8iw/lkM1Zo1zT62eoRkETzJNahaefxCZoQN5P1RkgifBRnUQVVdy8Bc2UgM/jx4/r0APlp1LmhlciGkVel6hUJgaAe+rYboPiNrZP49H4xAaig/10uYPG/tF49M+/fO/Xz588PmL+U5WtmR50b8EoAn2wpZ/xebP3cZ4hb80d21jkwnZrfnXzmpeK3d4lYrDSOXpbkaCLzpRFF2VPzMv2dn4JQHb8kKrYqsdvh33KLA+yNrGvlpt1ttFiuqy1y+TK0s75fzNDBxvJIv3ccvjUIaTfHsL9DBSp4WfYJ7XhDwLQT3zoKzPQSMoW/1vPj46OTx89pW5BtlD4EAgUGWOgwuhmGY04eoZule0hhNJPofKMgLpbNySCglHc9No2o84nvfMxe9lVb7fsHbaEkTjtILu0O8vcobjfNNpkGQr66ke/yjGrDQfGa172s2DDcRBgR2jiptH6e8vPjY2EgmPINGQwBhzZ+WerVRKSgO6jpCY0zG0oVDzllAMLJHwg9DiQxbQUnJsGPtYPhfiSljB7bvT0ATGIYSnOaD19dXljVQr469evbWxZIMMEe0B07bSpCy+ESbglfcAg4o6K8Bjp8CzMhh4lTn3gVD82ET2GYwGrdvF0TvGupIg2KoUNZE/Oyr7VAhBf1EsiSqkOHBXqEv8MqJ24ytuwNKyxjpQ8OkQnKFILpqeKlKAflmVfRh4Nlyib2gR7c6MxWSHXaIADvcov6YDMBFVX5OMCu9FyAOqzl6+uOAQu1uuggQN/KhNxQbwGbRTufn07fytMT51m44jRB/TENQfmWq29kHdOrYiOA7V6VTtBJAYk+ZeAI8aBcBH7DGTGsghDmqMPga3Ip3+KcrQOup96w1VfGHwBGHGnRmqn6QE/Sz+QD6KF0vNWGr4Ek2rmXokikcOvThV7lKaP5YZ0kLVC0CYdKQIUNFTqKZRXrJIn2qUJ2itSm6xGKSQeGlZnuNSnVm9NIVXziGVfji5TfDSMgJtSjOzHE+Yx7kguHxOPR7FFtjetLcdEekuLxu602/zl6dF/ffHoN0/OTnptvqGPOu2T8ZiqKSZxxuLOwUHEDpI8sZ+tXlwmRXyUhxEPa+jXLz+/enMxtlU1yscIN1/AbV8RcOyTW5YJ1E07iydWQ3a+RBEfjYVRN+BdkcYokAQLvlN1zKnK9e+jR+fsxlESalZzjkVg1FE1RP3zIP3o74ia+fMQSg88CEA/8YlguYomolB4FSJFzGn3Bydn557kGoS+fg8ysOgMoURhC2UXCekoi/6QMOEufy2Sl/bno2gOd7GbfzruPz0a9Hez1cVn3dXNpLnpH9bdPWuSaA5uFnPkmY+1a9dKbze2Tqh5LNZYlVrUMo6Wp5DknPbCMO45AXJKIGKtSUQqVCw436JXD5JEfgvXxz+CfzGkgC+eQTKQgklgXQqH4ndzg4HMyK50EhJlu+oenT/RUSAABROmFZCqpBmEIFYUPznOXBzcVc6EgYGmYHqjaHSUAs3PGpSSU576UxxWlaUBq6AMhCc9LwMMvZIIeXEV1UbJU5UZIEOMrCMzUKQrmQXpcFYEcE+QCUZVVwSyZoJD8pNNXbG8KEE6yAoCBUhN9MtFp1pj/0uL1MVxMbCCvnN3FOA30/kFD5A7WsMBiZAoBmG6BKG2IrHvLEAAbB1FbaF1WqR2iZoFSXNSIsZNs5LGFrWHDILmCApqdQXip3Q4e1Z860/x0uI4kATNUwBKjwn6Sjb9Jk/tPXAkEmi89Up/giBSgYCvuBSjUKF55aci0JDZLAVcHgNUpwokQa5CbW2F/OAQbkoVEYVVCpqCnuAEs3KLqobDRAr4oAFSMVGRnHVWkJ9UAR9fdhXBUQd7wsQPpUAlC8Y3l1Pu29X4sHn/ZPTPLx7945OTo07TzhdjaTlodIi8EBOIMWqEFfEkBkB2okGOOWBMoClyfLyLG/6lX8t0epxDmipKwRKodWWITGM40KlISzx5r2yZEZ4ky6V1taXZHHPCgoUW6efQvlrtZqwa+yOX2u+dk4CSGfJH6p86uJ41UjD+WT4yQA/hnR7IZ/m9hIeR+Avdft85Pvu/kO3rvLLixGNpkBElwooDQ47GHD9+autpdfEabdm2sISQIQQRiUGFEVbENzT6bv1U+URNpyqAnrfWlwIDRRpv9rMOrp5Mui8eOdu+fv36w0bv0G12H3W3u0Fj0ALNIo2dpIVYi76aOSdaz+9+ucVnvzzQOmydn4rlpWu08ZVV7l6k4aDq8MkifOwwramruRLyqrhtPQ7ZKF8Qu9JRsdRF+mMhvduRGDibjbno0HLcbg77FftwbZcDOIJLHiKAaSbtlDj02Ew8Oj1bzW0k2QOKxYaFZqc/GDeP8Bgo0TlhIQ6wxflee8xMWF1gsqex2UVSjHPb+Tx2DeWIu750kox0Bb7FKOsFOq3lfDXrLFRtIa4bi60DZLOGxqugQZmEteINlT8RcbRd7fYHZKsMtdRwax89Goxl8MrAEc78r6Cn8ZFIH1aZroISMT97CEaNIbG32IqjzzAXHOdZkVAPO4qToUtzWZYUfZu3ChbuH0aLWQLFmgi2MeHgZqmIFPgdbg5OPe+uOVonlHH5OvP06+Yxn7FaE9B00EKeiHW+aQi3pb2TeoNEr0cI6AyP2t2BiVw4eroo6JFF+nhkrmkLbuXgdMGcMi8zf9+In+g64vLLw1YXcszipZce+AOqt5+JBOyWWFwcaCtSg3pVByW7poQ0XWKSU0oZPhUp620dZXmePn1s55H8I3E8OgIy05WIU6y+KkCJRlC6n5YNPkR4QhIQEThQMpFjDXppdaarEeDhHeaUq9fXUwbEajd2IDtV4COyiFE866KOCZ/TjiRGTTke9zbTi+Zm/mTQetw7+uX58ZPRwGHQTnM3tttMgYoG0P1EksxYpwp0JHvlNr1ZzBM1AI3dPwylUxVRq7I4OjniGIljAtJTbJ2UAiF9msOgBBl47bKTCrpAbUZL7UPKIcMus2kCrkZxTK3z9p3B6tB75VLVboeJ4a7dz5W8RfwBD1Bm2IFsfGiFfmasXzfVtv/Mn7Uf6hz70z753gSgn/mo/Nnm/+nwvDt4f7bIX03EL6yzwAk1ssSilW62xienE2eIptdIRMgT28ISUDG8XQrKE+FgH2ublEW/Ys+Y4JotlYrkGfU1M2f3A9jmP4z6nWcnk7NB8/rN9fbtp6wJHvebvZM+n2jbVo6LK5ZDWZusdF0n7gQ1f0EWinYTCnnC5LGbVvTgZY9fFdP50g/8OHUfgGTjEvELJw5jy8mjPllJyEWvZVHrr1dYAtwxDD/pdUSQ/surtw5MEVzwgM06mhuCIcZjCe6E8Iunz4hNCto5ojcJpmUzSyKJTGYduVjOXCsGgiZYmuslPafbABeHhopUT90CGb2HpcqsdV6BpzMxMOqZ2uH0QcSj9HDsSHRmY9+PsAWashQtcqpFitNycorDP/tmxRueGiXKKb8UVXhWfDTQKxwXGjAX1IIjClb/ftZ4eZNXulTThpjcZMIJt7I4hn5wDZvWxaNxubDM/QwwAZxfRnoC6RV5LCo8+O+1ws6+SbG80brsZrpIpNhiE5rtdR45H+VON/YoxQhMp2mU8dLzmql/4KyjxGkooA1IGcfbxDJwVXqILlB+KbXU/U99orSfQHkLmv7Em2vnGzVg9a08mWnFXEw2paAhs/FSUHoBEgnG2JknhhIECNMjeqsVBkLELJABEuIyg+xMu6/ExJYHKGC5Ic0EaOem91pWRUD5qZYBjW/cQaX2ijALQG8dRwcNYoJNci2yvzoa9yb91uxm3W/vnz89fzHpPRp0Rs1dm9/FbjGrTuPyufF8SAXoa/MD5tYMxBiSWUQO7UV0Iq3arNwQq+JBoJmbzuAgMwiZM8lBXgqVMSSRbK2qiiYxaiOhHCLzQZRKnDCzndZak35avWb3eLbpvFpc2Jl+3uofun3XA+YsPuiRwFJLAEen/PMKuu3n1eCvaO27/fBuvGY3PR62wL6i534qycY4mxSFW2S7ikUCD8X90fnTZ3bB4lYkR+KzMkbO0F+0GxnFKqQIofiFs4a2llDpvqdfitAQ0Hg7OxIy2mieTsbv8Qjk9vebN53Z5Ulz9WzYOm4sjzu743ajw1caWuhg12wa/2WbJVfP+A//MXGREy3HmnlFVCCFJaPjNssg6QkTRJZZElqvrkFR8lfuhY5HRCsBv4G2JaZsBBecZjmbV7IrDu0qmoAsSOHqxlt3PeRurzCn7CyI2JGwxUHNEwMYZ8KLiCM/+JgTtlS6Jw/HgULjC0Hnlw9fQL394/cluGsyQbGsceEpKKItek9Xw5DAJFHPn54eswURgZj+99Q6QdVyeioop7bjZH6ShLyFrQBafUqvibIZnVq2vpKu+eLgwMfb1WJJa1W6zQVZOU12PJ6M+kFAD+Ag8V5cOH3NA+1aVgYRrwCp00CKoFI/vfIUMj3u4jXlmz8pbyoQ8wiT0z2s1Smu1A49aOscY+RJdIO2VjukbwoxuMe6nU6XUycLtVFy6u3SV9pCsRBVAW0lr9by1HmuRk01LWvz/dSNqvOWqTDVC5gSa5+rUc/IEBzu0tUlv4qI2q9evYKnFNCkkJR0ZMUcGiqVLrDN95RSywLrpyoAV1eVsewcX1y8hQbgHCLIUL/fmiKnsoRgelARkIkcV1c3hGtqKDld7+Zk5WY5y5piF0dHbndnFrVZ3qxnV8Pm9hePjv75l8+fHo2OuPmOnO3Af56EF6MbxIhwUTsVx1FqjY6NftfBzXU8bxDStpRMG/Kalga9omOujfKkBEoHCsVZM1JFWRyjHlozQ+Vqd1PYmod7BVJu8f1N/Nk1evve0c1heNOZXLfHl/v+pRveDt2tS4LhZAwLdSriuL2yELdUEc3hH/idxLv08vKn9dANP60G/S+25r4fvjTW0uurBw3Q/2LP/liKZZhxO9Q2jImdIYkn128dnz8aHZ9M51MsusuKI1nCFCt5rWySiIKw3sYLEamThrI610v6QRuE4CXHjjdFy3KXXZ87Y1Y872WJupvJOji01diyAaZyi+5tixtDTH4XAowPWanFfb6lIHKco2nhYjmerXbaHYQrO3ghuZEtZEfwQ9z5viMo7LZH/MIRX3KwPAxPtuhsCBC5n6EcS6lrzbIgNnDECEIMNvXs2TMWEtRCgyFVDZOLq3u+Ve7U7JPGpFhng2ZfTKWXb7NxgJdER1L4kOr8dKJNNil6GRfGEqqsQ2aSQpCrrAs0QbsE6ZPjI0KPOPYgg7BaVo/M0UC46RRAienpctMTJYGckK9Vq7eB/+TQXLbzhJrT28qGKWrgCW0FpQjhUYU9pK5iq9TrTaRQo5S3dpHSBI2SWJ9R63SjtHDlN6bv8JBXOOXx5Ij8yB+geqWUYCShEOILSI2If7shkkOBCH7YcTHKMVNSXdkM1TQ/3ezrnpA+j3lFzvC2Bq2GsMZqHf/j3mYu3SlXJBoggNKcHG4KS5VBYs3mpyLq98qsoLGoLa0AQfYx1Iru6yXjKq5e2hr3pshjEMkfAFLPyFw1PRKNI7BySjQBfDs2jPQlozUpinjCkNcrb9VojvmpvUDxNCQOuMlf2wiUFKiCPC0Xv3hLVcQWyJwyMY4mY+K6EKGduY9tWvNpPac8bM6v3zsduAr+6WTQ3bQ4/DYhyD0qivKtoGE1xVYqzS+EAiUoPVPE/cjf5gCHQJyH0s61Vq7yyDSLQVU2pHztJahdqRQsp8DUEiqFJDAYzyCQ+VztxfaZ9JJD73bdNu3+ujl6S7a6QbyGN0ySdFfD7lg8H5K643iyaIDStiIHRTD/46DyP054+PUz6gHzrU6ABwHoJz7qkRcy2BzMN0gR2Wfx5Tfb4+NTvPfq88+QkVy1lZtSqXFi5WByoOyhnk6NFcsJfSQxJ2bvesvPpKBTIVicE7JzpmBCIZ08ssl/NN3R9eQAB8pK8Cn2RG7O3uHAhxVXudPFzfV4zJagz3SE2sIZlZBTclruiuZveNsejO0BTTk+acYdC/uWGFcWoqlqpNzy0RNNzrPTdOCFnGOZivhVrq8U3oAQ4xHILp7hp7La6KcVNksRzV8s+4VFRXqw7tdkfAVb1Vb3RlbGplRlWv3hmHdedNZWC/YZo1Q+c7FPQttiSb0PMnxy9+gw2xYK6kneYqiYyvne3CIOMtw8rcQLfBiy+yZsFftoPzjBg245AZSW4nmFKYqry2s7F1XDIY/mqEuT7fGApo0gV24KGVxTnFKktqgu4nWJflYqAOkV4gW4Q5iAbe00EEg1DvdUnqoJtpl0ItRIn+wx3LbFjMs9GLRFgAhmGUyau9j/QtITNM//haBghfCnZcurTJNYmxx4NIg7GZkFqJq1IloqG9GWvykzuqIhnRVJm2ebHMja4Kpmi3EzRgp6qwmedchEVK3VSSk7WXpSCn2nPR6JOrl+KVQc+VKK2KTrBG+pC2Glz8sEjNmNRCNldMwQFdUxAtDQwEcGQAhD5qGcdayl+FpprWBHAPKDKlEekgJkQLMrJAKU6a2N4+MjAJOBQFJUfeIgF6krxdMhbLx4iKCZopZZrVkb2WHNzEng1tK8XDb4iNrNHk/6v3n25L3TcXs9G3aaEY7K+Pps9KMmEmuyD1m7JUJYtH2wIvRoZoyFbKmhDczDzZnd1uXvdDoxwIo9EqmoHaqEdpAXixBJkxxlTRnXdH5UN6FUzuQ7YuqNb8llyG4BXO6HF5v2F4v1/LAZPeotXYocYZUSOfeQ+Toy4nfWPwatzHYgf7KhznDNM3lqI+9TfrJt/noN+wv94FU+1a8H5yHXj7UHygywhC9ST1Q2aANljV2wwfj0FGXJqXQUGVsuPjyQVFkQMhQTHRMXNF5JKSJhzhhAkYWK9OM+ZkRGNE7VSDosmf1btpbIZLu56+SAbJZjyDdq7S+aaFsKOR4eTSzuKN6t8PDWuA0JZQ6DicXJ4fDF24vpcoHbROVU9lO8Uhk06Ont5WFjjJYQ70GrjbWo0FF+Nklc1DBq1goMRm68ShyhJ5KhzlqEbQAFB41iFo3HYF30GZVveVZ26KlefVLTYR7cCrdT0E8ZKiP0Uzb8BuThfqg6rwKzeP1RSmZB1bbJ4CC/eEmLlCbUt9LhqYGKKwu4zCALmmmbQ05vZQa8Vu2VRPGNfYcSZJAZ/3MjOv4nM2gFBsuVWPBQjflJAJKNdkoVpZZoAwAQl0cf28HDvXC32uSyCOfIscO18Xo+xb/k0f/Uh0SAwqpZpmR0APH8jkKAZzpmQlId6iEVwdAhLReA1W7U/2nC9bWZRDlHAaaL9JhWKy6i4ZrvJyCKGCDBK/PLEzRBEYkiqTGfUIQkkZqul6Ss3PpStiDBUaknmNJFBGWTp/hxBkSKYZJBETiA4CeYAmS8lcdb8CsOsmkFT6Gmn8y0TVLIA0URuFBKNnBkV4sgxVMeoc4NGRQ0kXZuip3PjaDPxF3I3EixCZSoUp3RbR/4WHeDWmszby5vBq3V+2ePX7jcps/J8qHn86Yayn4Us/IIcxHiI2m0nJnXWl3jir3YJfl6Ccg7u25NpxZMD212oEEwG9EBcxmupCgrLeJN/GVzMB21jc8/oxrCQlFse5yMawTirqJLXEJB+Hem5Vs0ehfb1pvF4c2ySY9HL7SMZEQM1bgo7qKUruNIBrrb88pULcGr21gZ0Pv4jzfybovejf94W/R3w1x3PQhAf7fe/n4qIqrEzAdNzwY4klSWtohQtzc5PRtMjubLeZZdQtldh6Uoko2YjofhFgJ6Wqlz/RkCCJDlXcwVkz8eyazBXFXR5uEZzcKk0f6ya4WthKnETgXvPFgZlv2Fq5vL7mXfXRZ8kmCbnSzw6HXCQqLJbnany/Xnby+cBmsPBxaH4VXZewuCcKMOpzJ3LESciGUL7PzoBIW2bcTVsswCqcxuES+8GMCN27iodnp9ewqqwBWcQmEPpHdsQmGc4Uw4fSO6HHTbDfaIMn+1SukNYoRSqSrSIXOT3YjtTjl+hQPpEE+1yIPWF61O2y1jUEXxQdAXhBCow4rEZ7lMISQdh8VP03AdGOPN3OegRXKqve5bgRlmSa4q+3o4ojLHRdVEvEtDiqs9OGCT2EwtriIRyGhXlecAlHk2u7Hpd3p8ZueryGMqD9OVDbZh7WaJg31leQQCJ8LQAAFKEMbt+MUb9B45cYRPecF0Wrq2Y+GszSIs3PEYKN3HFf9WAhHXXNID4b86626zr9EKy4d/mG2RPpnr4qY6pON+ORfMtSN0ypCJUWa4J7QhKb2+klIj+krQLvAlingq61lfyUaP4glC2QCMLGIlQAA3Q4kauk7O9Kfrs6Ie5Ayixb7KaIBmEGXQY1x41xtnIxd1jwARfECQjN5uGe+LZgubNqBU19vHO6WydbKpomy80gZFZAdWCrB0oKacATd1l8vYzpOjGY3dXF/qEF4MASEGwap14G5nyV/QEOK7uSuMt83V41H7+XH/pNccmrad9na5iKvmDlVWJhu4Zkc+RHJQtxPn5/FNtS5mUukTHnxIJMz1ZDU9NESTM4e7fQITqak/GdF9WqhIlT9GePb+rMx4TyQQ0VXzS+TDsDAaDKPDoztmZn5ozXatt5sm9c/lrnkThfK+X47+ufOFmGWoKn0zg4lBRfoRC6Z3ZM/fSPk/mWC2/GTa8r005EEA+l66/e9aKRKPzZdFUfQ3FMuh1o0WvkABPi3bSuimXbKwlqKdRhmRzsZRVr2IF9qqBFJFAiG/oC6BKZBVcut4eDaLnzB2GmvEMv4/aOwtwbKCpqWpphLedntD7kMcorVwns6uJ6sTFihZ8eEfjokX9QbxDLafX1+9XcwsBGOwQJekxiz7iVmN1d6t6e51zbF5/gklYwNuA7U/hdoyibbMleKCMe2qfe0n4BqM1FYVC2kgbGC1YpSK11RWhFXgIpAHHSh8SDaH9oNe4UB+6hk/iUTiVDKFsrMtvQJBup+Yk3rrE4SQ+PKUKOgrKXoVnN5qRbywr4JN+KmNlsYKFh6GPUcrgGOBiXFWayFxiZXZ02A5kyVuvDTTqRsBnqoAny0IifAeH3B0hn/yw1MnZH1Nb5S5kXE3zlbdluMGjwgBklFg5Qp5YTGfEjzVAj1yDwFoNr9RsDYHh9E0b0GuHf7dPVVhLkBYP7AzIXAXBF2WkI0V7Wq5pYq/Ikqx4WTtSDWhyfQu0pjOEWpZPaNgHVCgpMOZdAG+/HWA5HSzrVe102oDM9vvmskEW7vzZRQ1ks6x1JABcJBNEqCMjgGl6VSqlpUo1DxwEK9jCohhUrCYxq98QLHEV103BeWXQQNF2ahJUdbTzJHS3rs0PvJ6xdaEN4jQ9jOCkYtZHV1czk3UfrfDRFxBqhUCTGe7cOpvMuwN27vhhA/o/qNJ5wkPSnt+COl3/aGLIf44OzG06gCQYY+2+Lp9/r7VsjnOzaEZtIkAyJMFrBZrQuJ0eq0i2sH0gCMCvCz6/H0gJCEfbukQUKDtB4JCqs6WN/j2K5lJVTdO2Rxz2rRxtTm8XTauD81Fqzdzb9l8dsKDvMHSnNwrvDN7yU9FR2w2RmN9F96N36X9CP/eT7wfIe4/RJQfBKAf4qh8FU5mP4r8VW//bHqKlBcRfG5XQ+FSlM+432gyrvtTshBrrNgq9Vwss3mEKiHNnkhYqFhhq/ik9SlZJJhgQmV7K1edcwVUDtFY/TlOa4WclWJZeUUDFG14VFA0T5aSwxDApMznU+d3kcV+cY0Y+Ywmv9GarTcffv7y9fSKQglHkQ4NJ2zxMpiGvZcD6vgN5TftvDV2+FkvjJy6otMb5i7MYhuBo0iUH58gAOFGt01r2OWhknHoKRsBWmq1nHZi+e2Du7rmUy5UWFgfodVWy95glxqtovCAWAVZXq/iJ4ZchvxvwpCwnFoXgG6MssyVE0tTRdTzZW/LK4l+k7/2rCIyIAQsHdYxLhrFskV1REjFkXYSnkP8SikCAvhFzivC3HJaJae8cgNSCbWWMpQ5zB/oRTIzKNiQFuEX+KjDPUymWO5kcJzmK3osfcV4HQRBW3LSvZw1041R/0R8jUTLeMmGSWzU18WhTrFqggOLlYgn3zhAGCaeX4JUUsyBW92M5kIvAo56i8sfvpikEBaX6zdY/qg9WnIzXiQzg6Pr7iHrN110L394BQglh56vcQgo6P/8K4GEb/7UODjyy6nVoHqaFIFWrsci6yf46opejUROANLnJGalqjhFgoSqIgDCRIASgKowapYngJiO8jPoVp3jk0WgSZPBjqsIR9lHfR+UIuCwPFu5a8/NXyUD7ZFLNghhdoRNGBDk8YwIZWYbOx28nY2b82F/92TYOO23R9Beceq+5sy9sTEjIiwOxyOypTu2HPQnPKVpfkbXlQES0tJso2fTy8XrgHNywT6MsbZZzbG7TxKpiX+sMnsgXDtU08TMPd9OYBnx3J3ja+y7WC8GQxzWIxMkKtLPYvdmtr2m9OGOvDnYtxZOi9FJHU+Gm+ussUJuAoHgE5SKwpt8lqliJOtMusU2GD+EH2UPfLsj+CAA/QgmwTcZ8lCVSmzuGhoS0cQsdtzW2gKjN0BGcU8n2pEO2nccfRF/d7n2wfxA2YuueuOOQQHxyjM0O6tqK2bwQ21qQA1dy86pGncgORgWPoCm4ykg2fABzIK2ObYe7WOeXINYIzJS4H6u5aZ1iNF+c9q2Xn306tXry6ve8RNLTmIRpoSX2EBrWklHFQGOday7I3L3cyxRioYGAcX1eEMRiDqMKCvHkkhfUnsSe4CGZbEi2IMrvDjNsaTGGLD5wmOy3YDub7ZT+APllHguRh3c2g/hZ2g36a0yM3mUCo9p5nyyGvGemmdXXT4WrYPMEMDbRCyI1Q6yvpdfX1uiM7GgbSpqG7skOZutq+UHXNCNGGTtZulASaTqUC9QaRQrrKIP8AoahcETSKJpMFIFseQs4lZ2f/QVcw14WjqTgHrdHq6Yjcvs3GlprIn1s1U4/gRJFlKYUkXj5uYqrHq742xSdZpStUE8dt+Himp9ynP/U4b7+F+IvFvkPlv4mynmfnB8MRJIBtpbyBBFoG3HpxY0lP0Rr445A++dSuUu2W7FREOsURL1Se1nxc0HBWWT7qdS3nrKrCJ5K/LiNU8R0iID6Sj5dTINh1e6TqIhgIx7J5RShUSW6uJyCkvSbzEaM4K1iDkpRXPob8ippeoI3BqVgbvzWkRCrigpyOWRSitA1WUucSnUn4BTZk00QNpys4pekzxrteDa93HX/TM37fmm15pNho0n45j78F7RYsNuA1pv7VetBpmboN/o9Fzu514uXyFqkS86yBjxsiyRGHHGIQWOLFrr+fXV61evry8uLVp8L7a6HNKcnEzI9GyUSdmx/jF8GcXsayM+Zl8JPlYLqWyFEYsYf3PD4Dt3NN6m4PW68cV883q+t43tkjJehdJ1MXtuoyVTK5Myv8pwZIJXpEoEafqD+qcO3/10qlPl/udD5AfeA9/6eD0IQD/wEf8yevmkvx7/qCWRoUSKMWAlA2hDEpAf154PxnTL3L+O3KO+t7TNdcv8EyJ0dA/YcqnrwGSUaWZ32Sba2PK31GUZAIQdKTQvtxXumq53R2jxlGYDL2yyg6HXIffklExZH6PCSHljO233u9cX7GzGp8dHZKCLL17GjoWnveHYNj9/9owr/v2jf//wk5fQUSo22pygOfbeGqKb6Blix0mIRR+nPNfT2XB8gosjpngMSmpnrTcYxeVxsWc57h/HOmm9H/fHaTiGNBrhE0AV/X0UForjItwmp3Pbbder4ivypFJmzU7rYGScFK1XofXuVx8M2A/5icfoTyzHk7xIsLpnpcGEy+Ss6WMiLFjpS9RElRAAlQUs16867s6QotFrWfM2OtnYWsbyBj9jozzqjzQtwlz8CRFNQAKgqMSKFRGlDhPUTs8+AoFpb22vjTgikXF6d7AoGJalMHHRPa/bzRT/iEhEq7CNIGgfiQzUdzlJCYFQjOJF4kUm1i20DvFJp8j11ez12zdkL9q31YYpquGhSNI+2SOY6AQDJyJANRPuLnzp513yX/n7DhydgIkTegk/vWx6FTkyyktWbv1o+G5mC/k59WbaNTk+MxG46zPgBkjnZAiaZtmEvGvEzdJYCxXJ2nDAPJJ91AdBuwjS2fTEoyNSsOjaRtbUipvlwgRL8X7Ephx55EByMXXWzHnAKpGsd+50y9Bi8+QpWHEfkLakk3LTuycHhqo3QCRss3tgg7Eby+j1fD3O5amHzz77DNJETzdc8Wak0fDJESgblr5TftXXNjd5CCz3l6132QvudK+MS8vsMr9W9Ci2oDTHbt3QSbjVdHBYDvc3w+bitHM4m/RObTf5knm98p0QPFjeEONcJWZFpD5j73+tMMnNQLJdzlyll8zrXGuTc56kveXlq9effvwhzDO7xiOnLKl+RpNhd9Bt9Ts2szN2ncwiUwVl0ubMmZxvtzxq8QNNx+u7sPZg622kdt3OrNl7Oz+8Whxer7rX3DmBAg+me5PRttcydmAxFXKokhCWL91RyjLlbLuXGWvClMrKFEv8HVn8r0y7h9c/sB741ofvQQD6gY3wX0PHDPhrWf78+3ekn6xWQ4Cb7cHwqNUbrKcztCIr5GpViuRRquNsjnOjxax3GXJiHZ1oAtrRROC00ceoqRXdQdxAY6GINf8+K+44+qPuaEVOwnOs9dDpuJextLOZQ7LyIztZIYN4J8OTT1wQ7TLn4ag5GL9erP7vf/vt//l//T8fv3rbHR3Z/0F764IbPkGbBEZHTlfEiMGplHKndFh4p88iBNPDSsIhVnOGB2en52ES9ydunHspGx/ECzQaHEyLAaWf1v1uFNUiHBRi2ohBkgIJTTaCZGDbqkY7XeqyUhdwHYiJKEWBJK4IiyKZSVQqkp8FsqegLofEgJU/3LG0BdYqtKy2y0flhv8SbiBgbHAO4G1lTMYx7IjslX03Z4xxiAhnxCbpk3Kt/fbqUnWMTItMEyMhKGEkMKw1yo/5KcoMHDPOwBFQstKPhsM+DAwztVwIxS6qiFDl7o5bzUfY25xUMTg6bcGfRbmWDifjyAplOAAUAuGPnyX523zE3iYqR8jfbiOWSjMvtEiztRTLZ+quj7l5jB6INNyIEEOeNkakUghVQ+M6VegRdAIVBiFRcZIxmPqtdiOdBeC1XYoL0gHXva6Uc+SJqQrDdjKSPHovO5ZlfxOEOkNqulLAEocEl+/mbdHNVoAQqGANRG88SSso9vj/i3TWdmGwYNAMl4KawIg+czKasKimDEdR3ZHvY4/P15Va5FxMb47Oz09GRzLwPmpb9zC7au1vRpStm6uzYfPJsDfpHUbOuZM94spCJ7mhgp1Ol1Diy17GKIfNGTGR2qW1s+ywcnKCrMpAEOKla7FczGdffPbpxRevqGXd9G4LkjcMciGFcn88ILZlL7vseueuLrd2EEMpcSP4ZOyyHtNN+lrPUo4yTnevRas/3TY/vVl8dLG42fd3vTFHimtrtLLHxx1jjrOWoUGq2NCVOZyVUihS0UmTlBLKM4dAsv7Jp1FDLXv36+Hvz7EHHgSgn+Oo1zZTCmCf48nx5aefzvsdXu2octANK9r+IBs6eAahBJkgs7S3OUWMxFAsZK2LlDdiu2Dphyu3Qz3d2UxoWN4QPprNabcn4tZTBznGzBlcokgPtFwcNgvKG1oj3pRpmKK5QdSWu8vffnQ839xsX/73337wL//++5vFujGctAcTZ91jXlE4utowg7L/ke0MiPkZP8yR3Qo7PMQ6GN1fbaCQRDtN19MYfr7/4plXbvDUCswAQD8Fy1bshENCC1mMRH6vQMjSGQfqZbNMd1WGJ8XKNgxphT+tLHDH4wmrDnnUVqg44Yxkhv6mfqjrQ6VkIG85kU96wFjpm1QBk7Gb5bHSToSPWEw4oJ7dw9iU1NZJ0eXwZKiElxOP6t5ZXpctsDqUOkYp+Mtpz08pbzU/S2EsscoltwnhGZiq6iGIieJt2C08k52oWpQ3sJIH25cIU0BwXMCVtbvnCQ0/BSKX2xNA0BwIMADxtmJ1H/ETDu/+rBn+pqfitSH3pTRZMByCCGSDQCOd/Pj8kcwXl29sw5w/IuTHDyThA8LeGpEYsZSgvbZRwt21t1gZB1Uye54RsjvtTBWQIeCT0E514dN6wHcAWrfsNalXHmXtZsJHpKAU7WCNyCDOZEePeSuzQfYKHHMDHEEiex37wjKTH2TTsaD5eCiNrDP8hBc4ksA5LKJlPD07bzXT7eBYA4CpyKPHZ2QnhyLH5GXy2XS1W1ztpm+2VxSuNuOa497h8bh7NGz13alFSUkgqy7dOWZvFfkcEiRNwiEx3C6h6UCpYr3CIpomzFaayWoXbLOy4fXy449vLt6q/cwJw7Mz2kFyp55hN911itNR9myRRSazmirH+c2qBrshw5CRde6S9VR8OMlsg2/ED9jNuv3xxez3r2evF7sWNdKQDqxnYDjbcFGvfiiDmJNoBhey9/1cpgrkb2UdP7MIK9NSl8oplMQafXj+aHrgWx+1BwHoRzP2FdHybd9+w38T6reLoVIG1UE+EHeXJj168uz3//PfrmbLyWgcU0cb/Z04tkeOCQqnR07TUI2H6CNemFx37Q7mrKUE+2KoTBbeLUvnJafElr9MaB3vuF4uf/vZKzeJP3324vnk3NGSHnU5PfyKS9k13kKaiguaxZZOmyPF5Zvp+vKDj95cfPj5F9P1fjg54SLI1YaWi8UCif4DR85KnaY7lI5Cg+QV7kL5lB0m8Oxv4FV2N4pTxsZyEVWQUBl8lXugTZLQOotUlBoPqoywsg1vQcDjFQnz6/eUIh7UDy8criwnaxG1Sw9KdybAwKLFpAod6K1ACoMAmBIhqBYR7MEheWyYiUQVyAoFR5wzsoWPRhMjADjM/QPZmYLS8XFsO6TfYysRHGjIo2ydHqm3uJ/BDOVXI5gueZdZ97HMcPoHVu1DfPThDVpay5b9K6BShLbPDhHtRiyUirsjQNRFUjw7OabIyPq+2JfcMxXdUjsKtC+Fr0r/Ura//DNA3pn7fuoHXQcx8bS6SB6aRrnllbFrvH2LYx+d7PW5FtFd6CiiJpwNCg2gzlTWT0EEKAVNKmB9MmD6oZl6QICeNsqZ3suGb4LpVNPrKwZvSilee6PG9TPgIMjsafMraET7GHWU3vazIiDlzfWbXObayQk+M0LZCNN0qfsDaZtkKqdXRlPtZshoHCEp0ylyQDx2LpZ2nkZEK3Wpdza9XlxfHhbX7cVVd3WVey0Oh0fj3nGvSfppuReWlqfcCwsZwIvYQ5ESUkD6JmzFD6bBbTUpc3x7uf8mskvz5vrqsw8/fvnR7+1FuwbHla4w5ysonn58LCjJZNju9YuXwoA1X8w3+4s0QNEzHbzKHTg2rkhT+ca7/UZntNh3r2aHz64XH76ZXSxszR61+oOlbUS3AUbc7FP+WBkxMEKdtFqSp3iGUhsSIhDnb1VBJ5YgQ408PB96QA88CEA/vmmQj7xwyq+DOlFDuKcKIugW6kCEICUwM3zy/vuPnr938fLj6XKFfDqZRNQpK96WlejieIE0y01eIWP0yCzdmHcUZXw8jTAcoDeIVQaPMSv3p+ZGIWLTW4xy//n1Yn3UbC9anfl2z4V+LpEeOEffcyWrW3/Y9FzLRUGCqA8688b2au6en4nryEkBjBuQfqdwsRABcYRGqrZjEYuk6E6qeoh+XTzr+jV7FFqZsXWrFd9y8YYo4+Xjs3OcgG3N+elZrlsvYoFVfw7XxMAozAMN9VQRTlm7Vy0kq1holg5XtV0SvbPerFVhAwivQL4puDDa7WE/nowBpN7HAJjM6BSJR0yaigtgDMnhHCmYNEtnHBQaXomo1BjhK0WkiN2G5uAHsuBhWBlZzNkixhs6pJbSnHJWyXZjPuHwkqoDK7qouMPmfNeGXdEfqDrw2WNh9jk/pcXZfDRSZDISFDmCsZM8nBmAbzT9I1DS5elvucHBzFSBu3uCnEZ1YgvCXMmcGg7Zq8TK5E8ZjM6siX86b+9fKfg1g6GIFJTezbQGU58YO7XvnLuO4iZ4mrQC+OQJdkImhtEsGoicLde98uuEKgDlwrZy2Cp5bsc64MHxpPDcLFa5i7zINGCWfsiczDxsN0z8dTvuDHSUV2DoHBGhNsoFWEHP5WWN/dX11F0T2a4tjhLkFIxLlVdgCwjMNUowFkCWyRCXDaN2z9lG4gpoc7tZU4Zc0cD5QlRsjKLHKhcbZyZvNkVl6APiZGG2nl22ljed9dXJqHHW30/aewfe+e0eZP7QzvBvace0zWBcF+4odqgOyYjFiSLpi22yCQK4fdoO/6iNw2q7/ujjDz78z/9488lL16E9Pz9//vTJ+eMzEzVGVfFSTeofk35yzDC6GBCzY5c+sfseNY+hIH4xINww4KLIseXL17NTmK8XW9teL29215TL3UHL5leTxx9WSXa9BwZGzxgoK6JsUOb4o58JvnpCkDEqPa8p5W8epd5Ebgcl0Yfws++BBwHoZzQFSD9oROyfo8Ohe26vD7vj8/N//G//7V8Xs5vVeoQ4MHzmsZ5TY2fBFhyMTdFffYRMIyviCTnvm0On0SNxGxOik+VijEu2G1sj7z153J0cLzgqZMjMTJLVabu72jXJQzIxeeQnjeyAIy0XvKyt6dib/X1/ND50Rs6ChVGR0nKypMdRG5yL+BX2ApNK3cgmYUU0RDG/DXOaxkglC3fiiPqOjqPgmc5nrFCtStFaLISEga8wGa7r1Ddv3hCAlNJMXnxQVWypcqDaZD8xm2wNFQUYiSDiQLnnS+0qwgVlAFl+nWZHDzR9VcUTcQt0LAocENJ1xY2Qgn5KlwEEWHk6TqU7yS4CULVqERWBplIR0KSoC2+z25icjM9xyhg/pfm6wvDKQ0kkvwAytGEIILHNuh2oChAy3lZk5KyVioAjgyIiAcErwK1YECMx1XmljeCIpCHORkcUyD4UWcokq0CUgqpwH6k/v/kzAAueIpqsdoHKhjitmTDkSEC61vHn0GxNmUVLO3/8RDaYy4MjaphIRVVDGI8IGg49BYWqXZDHW8ogEMTVKJvGJkNwSGZFVCf/fcfqComyBeid62cRgw4BQDx1bB0COYGSApQieLohk1JGIS0i7CriTFtcGORy+AGDaHnIyeR1pStk6eTRgZtKylQB0FsFpzdXs6uLzuztaH3x6NHopLujBOpzom2vi+hhiRDdTPm+OFx2ZiE9QFYmPkQUBp/wA06Oq++ZXW8+f/vFBx988B//47/fXL55fnzyq1//5vmj02p35VyWTW2qzRhc9fmlyNG59HeZSpkdcVth1UTWjCtDzqcsp2TZNs3G9s328PZ6+fHl8vPplt0Pi+tWe4AW0OXS83S4UnQejSSUHV4OMuxpRsxlYlcsrzLf/CxzQ7SGe9HnLuHh70MP3PXAD04A8pnd4fZ3/ZvP5scT9NLfivC9Eqi20kK5GiEyUnzxq1+tV4uLTz+bv3nFQWEPQeLTuSg/ZjdTp0yRYKQZPXW8ZUNtEPrfPtiOzyrZQXS4UBQU6aHVmPRsrBxRoyDHx6Pj37++ZJPSHYdaTecMJGIuQ6PBogGQXXPAgUlOyrBImUUp0u4OyWk0I6j/urMc8heEDJcQzcQd20P9MQXn+LElcKR7OkZlJU3oIQA56kTKKXZHueqB0TR3usiwf1i+bLVRas4+StHW1IpIEiLEmrqAJkVh97XD8R4Cy2R8bClM4HN+B/lm5GTLyKuryxuQFCTSYZSA4EbqIhVBQAY1yhA8kXJ+3soJGokVeR5N8Ao5z85PXJWmgSBQzzhSHV5eFGHr3ebI2prNMusiADHUIipRQqhIj+lzYLFDFVkVi8OcRxaBh5tOJ84bsUd8SIboJBLiKQAasfUgiUbDgb8YXN0bzUo4YhGJnAqyI2n7Zrflgi5aFkf8IKkW6iUQMaf7r1cRATQZRMD3rHGJNV5/etZsIn9bsLvhIjk2u2VvjlGK4iBrehUHNYeTBjt4zI3p/3Iem3F6O7JFbbJIkC+BxtH+r8Ew1QERYImLk661y8VvHUa5vWi5Yqvb625t+erwXkYHnNLAlo3NGgcEVuLgGKKKmDwye0qHwH6TG0uMGmwlespGxJndXJE//aS/1CCtiL4jp8ZW8Fcww8QHDm9dJQ6CukxLwM09qj/imI+ljr6Jwff34uqyNf3idHwYd1tH/ebIiqNsHWrgQAN9BBSEvknCgymzZ3dGOPHXBRo+75YdbKqT+WpDf/W7jz7+13/9l9//7rfWLr/5xYtf//pXz188NQ9JSaQfYgpHh+5vB4gMZUgcl9eKeCgkqWS7S7eqIpLj3orIDOLYot2ZrZsX692bze7Tm+Wb+W7lyOfAVTmjHHowMNuIfQ6aUh4r7yRaujdGWmRykyx3+eQf7B3ayEwz8czjzM+EW+qegXgIP6geyHT4/sJ3LgB9v837/jr2O69ZxyIof1M1dSxQcyHEiD+eOKbvjE/O/ul/+z9eDif/6WDw/BqFxkMC2r2lcYm4Qnr8ouCx8M+Nm2XlimLS4iD8CBhEcl1Qzwb+gFoAQbVEHb0YRTO03X9yNeXwx8Jw0OEilm8PVLbjgvEQylFzb9m+WVPlM2bBNlhY4rZR5mMetp/aoeOVMVcZIvp6xJjZ5HZr1wGDqsJZpJNiC6xFLqMkvhRJKxs5XsXEsqiyQKsbDZMiUnADZL8Amw9D2ucgMVZEJSPFdkNNxEJUQWYibFFNgeAMPLASsZz0ZrnV0k8BN/LUYzLocD8Vr8oD8AEE2dvrq2x2qEgzmSR5JY/8rg4oQ3Orh4B5LWvtC7GinshRI20pS/8IH3DGTcrblAIcBHk8Wat6sn0pPw90A9wV1MPd6pLoCYJWAKgiyj0pgMDHU+LMNQjlsJuGSCR7CWunwu1V8kC9Sd9u1+3cgXKvn1NlCRWU6B9eAVpklPI3wsp9RLYa/8vPZLsrVXPCXyL0WNoWS5UobKrw+ujx40s7uzs6qpGeZ18MZ8jID4xIjYMDExCk2t+xE5hGFrVNxVC8DrT8shlZEaVqHnE9o6zJWFHSb37Wp7eyQRJW3pobYMrvbT6pEoygDN7KQwBqlStRFTSUhG9VE4DgTySKLrOEim0FqLjqqnBmEGe7OEVkxUQEUcon65IXic3N8vHxo1HnkDuI40+B46h4N2izOzYJN1d2zWrflGkMO58v+WQdUabhJNr61eXl//jth//v//cvn33y0jz69S+f/eZXv37/xQuiSG/IZCr6WNcL2syyBWY/y/eosWkvopMDFuVsnTEnW2kAy0KOQBkTdQbbRv9yvf/0evlys327aa0YYvcn/GJw+aNpNlytx+ikLSt0kZt7lSHf2L91fYe1UlEywTbjaBoV0lhlnfrMzpj8tesenj+cHjBe3y8y37kA9P027+vXni/n65Hgrw/zh5azNhCZgpjTVQg2slDW943B5Pj9f/jN9etXH1++Qk/7iHbbzv2eUMJE1jKWygLVxt+RcJIQwSj03lZDC8PoUiYRUiKU8KdX1EWIHictv3p8iud3Pvzk1fWMpcWw07eHtNo2HYuy7MO92UMe+p2dlR3O45At77TUEtAqEphBQe+QSmRd7YJ4dsiK35Ghm1wZRXY4Ckq28CQ3RS/XPilNYFhhhYkcOnfDnIU5JmiVV4kg8eI6hDBEIgEfBJIKzoHr6B/MBjFXo3Q8xFM6GYicJxs0ZPBTHnBwRHkwJ69EABHkwWOoTOSpwHGywloiEikCwpNnT1VNQpNe+SVaL3J6fBwW3mweld0xWfujKLqyR3Azw0+onaiuaAjsdagrDo3abZc9QdLFGuUodhRyhNTSUma2uXe+9gCOmKErNk8Mpww0uyjWWbQL+o2Ay3xWD+g6GNKLgO9SdZBJGHiXxNp8tZfDQzaA9FMUbGq0oVh3KtUFyLf/TcV+39Vv/DDwEUPySAv1oUHcN3oMyymodJ0OPz07scO7d6CJ8dlgbFx0++vXr88enRsI7DejWXY/ffqQZb9flUHGWYtKX2UCGCn5VeFpy1W6HtC00Si8OTXEJSbXUcvTMwLtWNVyGkT9plLowUfv1TmmiN7zFEgf8vsJgqAgsIpMiqitFnkcU0hWahuaoWl6HljZPOHW3GzPnp4YTWUhpi6qmifjJ4c1XzgyZI6Rtuj+XHzqxEK/qyv6jh6wVwNEq5k+zzgiSn82F0sGfLF1IjF7QWLZ0Lw0mqvdZnG1+PTV5//+29/923/8+/R6/vho9PTs5J/e/8X7T54Abm7RN+og+iQSFRixl7NdVVwkIAUGiYxiSrH3oc/JJ72ax05dZ1A3t7o3q/areet3b3dXvCjFyoeQY1OTdV8oFfETRWAzbu82kmujeDzKCmjbGvZ86o4U+ARDwnOzXrR6xelV5vAfB6TvTif0xy8efv08e+BBAPrpj/u7VKCqf7QZwbUsyhEX/8dbf4s3t8fv/eLT//yfq9m81c6KFpG1ShMQmhShuWmHfKOz2cAnHsUKYWgXiQyEBbI2zU0XWQ+TcHg43tFpvH826bbe+/zy+tJGzPowXexnnPHn7k//8auWZV1xeG87Zc2piDOtfA4xNlBjFvQh02GliJp1X+EUhKW2WyvwFYoPy8TRYMLixVYdo0vkD8eiAUL6oOr6Ia0QsB8/I1UUvQsWhSdhP0Q6KcDiWCSAykoxM0VUqhMwJE9N0g/eIuuYiiLEHRF12SOL3FDcC0kUl1moXJCbGKVAUzWAmG4F6y3I4U/FpsQTemU7KwoDyChVXwEo0NVJFEC25K2vahUSwfRUDJLT2U1tI4CrXPcdOUyKVtA9AYs7w9dbpcCpAy0Ksn6QoQKU7q2CErWOhEHwnFaXUfRubl0Q1lnbC7Ip7hm3BSVJ2bz4tgOw2WfLkSgh+Osu/SCst5l5ItfXV6ZolIgyYv8uocqNXZrG8DlKEU3TRinKeuoKcMg2NjRpHAVNh7hs3hL4FdFeKbJ560NQUBD3fUiUrTY0aJQgRXFoGBGCWq1Cogx6WBYpvrmaAivFK0pSABRqdX7qZptRNWeV18cu7trlXCGM1CJARl1UXCb8I6fiu43NwsGD6CNd4ra9uby5ung6OPBXJMVyw6mvOPZpuQkOfDgu1N7sjVxJN104fCnbfsa9wfYw3+5eXV589sXrT19+9sUXnzMoOxn3/+HF818+fvze40enPrpiIacpTJoILT5o+GQS+D99yJSI7GNo9IZJHrsfbhMickrWXYfOzbrx4cXN201nNzjbNbo570AA0k8Ulq0O7xpwBpMsqS6fbU4YHKwUtHg3J6bPV1MSUJF6GQGqNCN4e76HuHM7NO9E6lg9PB964Ns7BVa/2Ice/aH1wJ30UxkVRcEtgniHWIgU2p2jQfb5uydPn548efLq5ibqIfSw2+cFDfXJomkfPXP+0qsw/yzLL2y/2Z33+qx2yDvygtbFInZusziQGJaYwNkYTe6eTPqXV9fX89XN0lmx5nzTWVI9OahFx5TzaNm3n26X2Cd9O5EsKqDcdVQ8fhRtTZhrrlsMeRTHXDQgZDEmpYvrmW2sMWU+vQ9yfnx2nrW1qxyJctvdfDNjKGt5qC0W65iQguJhIcXlj8Wog8idkzB7/AnLIXOoRdBkNBdJTV8VSaiyItlA8BZA9iUy2BdztF4EAhhSFSMaiziTtKwG0ysB4bdJ0Bu4NCm6BECo9ytHhI/tA31sMV/IeDRbWAiZxIKYuAosIW/eyJ1lwA7P+li2svCElb0oXitr6xR3gt2wqIHejJKrnAmzrI9EGMZRzoGDb1QNJq1KxVAzdbN2ZfnsX3y3uBltYxeJBxvKDqIO1y9aR4ZcrmY0E1hnxn+fu1RTsATw76Lf5t/QmTvA4kIZpaz7q6Ww1klUZWlyJBtitH9MrIZlu9Pdc5AXdHsVgiOQm2NFu2Ojh7RRbxcDWfGiZLL76wK72Fe5+qW5MwUj/Rj68Pd4rxlCwM/w5Hqmr4y13dqyOXm7caa6wTBmWJRneh40uWrvZGKU4CfpwGhqiAwqMtb8nct/enIum9tLptPssY7Ho+tZWL8Mgs8JMNupoxE1mF0icmEm0WG7vNhSJh0ec4705Fl7ceFTMJMObjXJOYTmwQ5Uji4OSUWfvf7s7ZsL6tn1bs9g72o6u5hP31xdTRdTk9xy6FGv95v3XvzXX/zi2enpMRMx1Tg1RhFrb5U/z3ImMW3KzbRZXun60rC4ytiQ+twch3xs6Dv7fGMvm/238/2n090Hl7634dmLx+NDa1RIEt0PGyuSnInmc0FqSDDGMi5QyxdCRZUTetuYyhkCHVCstyN3FtpW+pUuOUZGD+GhB/58D/zNGiAf6Z+H9JD6w+6BcI27oQtrCyOhTY7OhSaINOJoWH8yOX/+3tvPXrGhyLnUcnWo9SGaXlkC4qKgn2HMXMCi1CvH29HrynXiHd+xsHa779p4pguoEapIATHoTo767elieWPbvulWscZitaOG4AzRLgtjSfYys16OiXEU6Cj5YYOlYsRxGVSZROhaCX5WHkMEEbTJTwFiXgk4R81JMhDBRTzFZSYVieAuMjsCRqvBH4si+AcIqiD3yFybCU7gl3NG+I1Erd7sQ3AFa3EZpCtC5lA86+9ikQ1axJESWJj6i1EpjvOReFhV4X8Kqlc2iQxpIYl9Kmjxq0bw5SeGFWxzU4fM4FRkPIXKI9fO0ZXq5Lye3sCEFCWzBbHGCsVYKmeLFFf52dlJ7SulVFGlpfuU2jNe1Srun4rLAyzEMMKri9cssWHlUDcI3Vz6lm1Kcka65p2gCr/AeSftG0UDkN1aERHuJzTENF/36EyTQCtk0w9ECCe3xCFgsAw3IU+QImf6rR07G+oYiXwcwMzU9jSJ5a8zR2aRCsSTGGRvCMA6HMSmUipHBMwh6WrXJyLCYJhRxrHVJb/itnXEdWaFL57Eor8JDmVG+RzFAVGvDCKCaVYhqCgYula4NKEYtHWK8XX2QLluCsyG3WZOPqPns8CBg42pbaN9vdg4+dnuO1A/2EeqgBI5on292X6xXFxdz377u09ev70m10OUXnax5VZ749JRh7nsWR+Nxr9+8uiffvWL9x49Jv24BNhZgNgea1UUh0Xb6nw7wRGKNLwhB00aHBec+pgXs8zViPKt4erQuV423q42n16vL3fd9fC40e7D9ez4kQbSEfn8DZvMTkfkm+H9mVRHpiN/Nhz9oxyyuGCx57zneMn5ULG7Uq3+9Mhhsz/ofiQ/hIce+DM98DcLQH8GxkPSD7gHqgYoi6bQgzCw2wVRIdyxToztIA0QJUDc+56/eH/wwe+vLIRJEu4+GjhinUuUmDfyvhr74nCfmCIW4enQ6q24JysHcezTI1nhTSx6EEwCBRkJEVeJGJLdY4W7mIHm/MvpqEfoWi1dzxTD4vl2vW/3rlbr6+X6crH8ePMG4Y2MZqlatDWIeO57su+ApIaXRKxBTy2+cxQ2Ni2x3UEHR8UOAxldx3dRVCNYEf4zseEVl8obugtUkus2cXqhe6klzKAcGq8cVB7dAoyTvNT7frrJ0h4Q62MZXImVLbOBU/ebeIo7bMVFWNukeYwnoulftpuxjIE83ibi6ZYqaEf5VIh1ZW9w8BbHYnCkgwmNOplgxlKocNJ4uxG0Th5BKfmjfJreGLUqh0mBP5/RamG6TmPUPXcZ0yAOC3LUuIpc5e4ON5KU+029jQVRK9oLDewPhjZGzBQ9kytLduVyXNymF2ztNbx9e3nx9u315cWzJ0/Vq3v1T7t7GDAj6tkkyuSCMFCe4t9RqFUU4apM6SLCFmGyP59m/wti+gqGjRjOZoj9jBYQVsQ441H1N0AU2chPawHPinlFvv408KBpyGrB0CTbbYR63WP0Irl2suel9zI8dztZMkv01OfGmuxiROj2eBtipBYhoAg3gEC1zgoZiKemjSnkqCUE5PH0sUAJ8vcp7iGpr1RoKqqIlgeqMoBmqtKyAAV49oWur904DOHOcPL7V2+m11enw+6T0+3RhOavW5BscZJ0mYPy89cXNxc3s3Zn2BnG6Q6ITWfchn1izvxyxUfG85PT//L+i/cfu1tjMKJSoyrjuZAuLW4PnXwgC/qXjcNsKPpsN1El2u12WpKj87hQbffm28ZsvZ3vDxfLw+W6cbPvNobHR8dnlkCsfJzjB4LiC1VBmIhQpqG7YMlQzLUNNrui9KVLCdPs+EXsDyfzo4llWFFAyuINFbIhe9cPEEQewg+uB75rQvFXG/wgAP3VLvoRZwgFKTRJG8IHWE+UJXqeqIWnDZmoCMRyqyIJwxbYo/fe4wJxe+3cFodtAzaL8lnb0kbjBIhvuLgkNIkBsp0d+qN2v9Fn/cpLRywkipWyPbBVRB8qcsJW2AxfazGUtmblzH4YW5TBCqdeun2pcYpg9XqPd4PZ7vDaVoK7hS5e3/DL5jxZM4Yv2IYLFfPBuE8MiW1FJ4GCu81MIjknebKGjgrkZl4MSMfZbpAVBxJq/vrJaUKyFY/PlbvIxuygvsVLCBny22DSdRJrgAP9B8ZDs1K2fuJHxyv8T3GehMKxyppecekgqxcblp9KRlylintW1oVRKevOzipMeMVJsRora6xsUhXACn6SV0RIX4Brr2xgGhRbb56OakvUNAHAsksZXZSOlqKg/PX8Gq/fcCvXpoblK1sz1LiEVMorYjlXD6vmNihdXFx+9NFHvAIdTU7k1PlQ0i6bGq3jfu6EK7s/XtVO8/wOAkaHNZsEuiViVtmZjdaHeEm1khqJ5TFzjtKlPziODQqdZFHOeQp1OsFfGV2qr0x0Gi4ulIsVbXRd3taGkFtrt6jLFo42xpYoWsYMgekFeG2miJx6UrqRzdve7bVxZot0+AgqhYOcvCTIA6DMUmpxKaVRaU+dS36C760UQFjo1SBnLKOFaeaVETOfnZBk6GNWKCVRZ82mLod/7DaMT3+3+uD1xdlk6KCD2k2AVNqIWM/S2S7YtDVqnUyGR8ejo1zwwrVCm2n46oZJNbvoxydH//svf/EPj45P3YKSFQ+BhPH9LTIhMHYhkRLSdrHS4pnHlhcFEpllr5P6/ZPTo12r95+fvPr3j9/O9g7iH7XG5+77sxeWTUVmO1SwrvBDKFht8zcNYpnPXAwhNrYmyTY6L2cbQ9YicekQnzjZz3jpEO2iIYzok2eUcyWer7iEmnL36+Hvz74HvlIAql/dz6p/NDmfTwnvxn/4nfCXsfX13w8z8lS3KTK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", + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.fromarray(img_agnostic_parse_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "44225853-c88d-4c57-9f78-bfc3f12571d1", + "metadata": {}, + "outputs": [], + "source": [ + "def generate_rgb_agnostic(img, img_agnostic_parse_map):\n", + " sum_img_agnostic_parse_map = np.sum(img_agnostic_parse_map, axis = 2)\n", + " sum_img_agnostic_parse_map[sum_img_agnostic_parse_map!=0] = 1\n", + " img_rgb_agnostic = (sum_img_agnostic_parse_map.reshape(*sum_img_agnostic_parse_map.shape,1)*img).astype(np.uint8)\n", + " return img_rgb_agnostic" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6d17d5d7-32ee-4e60-b565-c24ee29de173", + "metadata": {}, + "outputs": [], + "source": [ + "rgb_agnostic = generate_rgb_agnostic(img, img_agnostic_parse_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.fromarray(rgb_agnostic)" + ] + }, + { + "cell_type": "markdown", + "id": "ea444fa5-b98f-4fea-8569-f7ca8fb35df2", + "metadata": {}, + "source": [ + "## Generating Garment Segment Ic" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c460aa8d-c11a-4dbc-af62-cbea76e7b21a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.fromarray(img_parse_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f2d18c6a-0866-461f-a0f7-114dd15ce91f", + "metadata": {}, + "outputs": [], + "source": [ + "uq, count = np.unique(img_parse_map.reshape(-1, img_parse_map.shape[2]), axis=0, return_counts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "dc53e5ce-f8e1-40ce-8dcd-8518c3f23e77", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 0, 0],\n", + " [ 0, 0, 254],\n", + " [ 0, 85, 85],\n", + " [ 0, 254, 254],\n", + " [ 51, 169, 220],\n", + " [ 85, 51, 0],\n", + " [254, 0, 0],\n", + " [254, 85, 0]], dtype=uint8)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uq" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7bc976ba-7034-4124-9775-72e18c9aa5ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 254, 170, 508, 440, 136, 254, 339], dtype=uint64)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sum(uq, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "18639dd4-151f-4b5c-b907-d09b19353d2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([437043, 20777, 119175, 18667, 10652, 19429, 19692, 140997])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "count" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ba24e642-9c67-41f4-8d25-81399d797c9e", + "metadata": {}, + "outputs": [], + "source": [ + "# color of top garment is [254, 85, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e97d8360-66ee-4265-aa30-10509c49cf54", + "metadata": {}, + "outputs": [], + "source": [ + "def get_upper_garment(img, img_parse_map):\n", + " sum_img_parse_map = np.sum(img_parse_map, axis = 2)\n", + " sum_img_parse_map[sum_img_parse_map!=339] = 0\n", + " sum_img_parse_map[sum_img_parse_map==339] = 1\n", + " upper_garment_segment = (sum_img_parse_map.reshape(*sum_img_parse_map.shape,1)*img).astype(np.uint8)\n", + " return upper_garment_segment" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2e5f250d-29ad-4321-b63f-1393f4190c25", + "metadata": {}, + "outputs": [], + "source": [ + "img_upper_garment = get_upper_garment(img, img_parse_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7442c47d-7089-4bef-9a29-57cd1b22adc7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.fromarray(img_upper_garment)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc67116f-a6d0-416f-bce5-134a4c81d5a1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tryondiffusion/pre_processing/notebooks/garment_pose_embeddings.ipynb b/tryondiffusion/pre_processing/notebooks/garment_pose_embeddings.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ae63bd7d00a7aa95c0aada88cf94e17bd8cade59 --- /dev/null +++ b/tryondiffusion/pre_processing/notebooks/garment_pose_embeddings.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "6f146f17-8fb7-4b14-9251-28685257c42a", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import cv2\n", + "from PIL import Image\n", + "from matplotlib import pyplot\n", + "import os\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "105730e3-4c44-4f87-8bc4-7d6d28f69c0e", + "metadata": {}, + "outputs": [], + "source": [ + "image_name = \"00048_00\"\n", + "\n", + "img_pose = cv2.imread(f\"../zalando-hd-resized/train/openpose_img/{image_name}_rendered.png\")[:,:,::-1]\n", + "img_parse_map = cv2.imread(f\"./zalando-hd-resized/train/image-parse-v3/{image_name}.png\")[:,:,::-1]\n", + "with open(f\"./zalando-hd-resized/train/openpose_json/{image_name}_keypoints.json\", \"r\") as f:\n", + " img_pose_json = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "05a6b7ee-8f4e-4c7c-9322-b15534703042", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.fromarray(img_pose)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0d4c80db-4504-4587-9d7a-cce2a4c4e078", + "metadata": {}, + "outputs": [], + "source": [ + "x_coordinates = list()\n", + "y_coordinates = list()\n", + "\n", + "for i in range(0, len(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"]), 3):\n", + " x_coordinates.append(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"][i])\n", + "\n", + "for i in range(1, len(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"]), 3):\n", + " y_coordinates.append(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "49eadc5f-58b7-4fbd-a4bb-3036bcac2227", + "metadata": {}, + "outputs": [], + "source": [ + "im = plt.imread(f\"./zalando-hd-resized/train/image/{image_name}.jpg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a3616db4-294e-42d4-8c02-2fd5576504df", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
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"execution_count": 11, + "id": "12dcdbec-d3fb-4744-b3cc-6b9b1dd40b84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[133.657,\n", + " 326.465,\n", + " 332.159,\n", + " 578.642,\n", + " 873.34,\n", + " 312.281,\n", + " 572.961,\n", + " 864.744,\n", + " 731.601,\n", + " 731.61,\n", + " 0,\n", + " 0,\n", + " 737.257,\n", + " 0,\n", + " 0,\n", + " 99.6869,\n", + " 111.044,\n", + " 128.095,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9f2e1768-c2e1-4519-a32c-04b1f8c4a000", + "metadata": {}, + "outputs": [], + "source": [ + "# garment ids\n", + "garment_coordinate_position = [1, 2, 3, 4, 5, 6, 7, 8, 9, 12]" + ] + }, + { + "cell_type": "markdown", + "id": "be7d5f83-4145-45d4-a320-bd64a4d7fb03", + "metadata": {}, + "source": [ + "Data Verification - using from data prepared for human pose embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e9aff5d8-3436-4bfa-b09d-9cdf6401c729", + "metadata": {}, + "outputs": [], + "source": [ + "# first run human_pose_embedding.ipynb to generate train_data and test_data text files\n", + "\n", + "with open(\"./data/train_data.txt\", \"r\") as f:\n", + " lst_train = f.read().split(\"\\n\")\n", + "with open(\"./data/test_data.txt\", \"r\") as f:\n", + " lst_test = f.read().split(\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "1dc11ceb-2392-4a39-b89c-d6d0b04dac61", + "metadata": {}, + "outputs": [], + "source": [ + "def load_json(json_file_path):\n", + " with open(json_file_path, \"r\") as f:\n", + " img_pose_json = json.load(f)\n", + " return img_pose_json" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7c289456-2bbc-40d2-80cd-0bafbb10ec49", + "metadata": {}, + "outputs": [], + "source": [ + "# using the 10 garment coordinates as it is as in human pose. \n", + "def create_data(lst_files, json_folder_path, garment_coordinate_position, save_path):\n", + " files = os.listdir(json_folder_path)\n", + " for file in lst_files:\n", + " file_name = f\"{file}_keypoints.json\"\n", + " file_path = os.path.join(json_folder_path, file_name)\n", + " if file_name in files:\n", + " dct = load_json(file_path)\n", + " lst = dct[\"people\"][0][\"pose_keypoints_2d\"]\n", + " del lst[2::3]\n", + " x_lst = [lst[i] for i in range(0, len(lst), 2)]\n", + " y_lst = [lst[i] for i in range(1, len(lst), 2)]\n", + " x_garment = [x_lst[i] for i in garment_coordinate_position]\n", + " y_garment = [y_lst[i] for i in garment_coordinate_position]\n", + " result = [None]*(len(x_garment)+len(y_garment))\n", + " result[::2] = x_garment\n", + " result[1::2] = y_garment\n", + " with open(os.path.join(save_path, file_name), \"w\") as f:\n", + " json.dump(result, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "759bc06c-7d4a-4035-aafc-c7d84e048168", + "metadata": {}, + "outputs": [], + "source": [ + "json_test_folder = \"./zalando-hd-resized/test/openpose_json/\"\n", + "json_train_folder = \"./zalando-hd-resized/train/openpose_json/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "61e83546-8ca6-4621-acff-ecbd46f98e81", + "metadata": {}, + "outputs": [], + "source": [ + "create_data(lst_train, json_train_folder, garment_coordinate_position, \"./data/train/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "82445c33-e856-495a-af95-77793d08430d", + "metadata": {}, + "outputs": [], + "source": [ + "create_data(lst_test, json_test_folder, garment_coordinate_position, \"./data/test/\")" + ] + }, + { + "cell_type": "markdown", + "id": "95c29776-058b-42fe-a6fd-7a0241661653", + "metadata": {}, + "source": [ + "### test prepared data" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "9e515e8b-a868-48f9-800c-9e86371992cd", + "metadata": {}, + "outputs": [], + "source": [ + "dct_created_data = load_json(\"./data/train/00048_00_keypoints.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ea89fb88-63cc-405d-ae04-9345a982eb57", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[388.751,\n", + " 326.465,\n", + " 252.647,\n", + " 332.159,\n", + " 230.015,\n", + " 578.642,\n", + " 187.548,\n", + " 873.34,\n", + " 530.341,\n", + " 312.281,\n", + " 558.722,\n", + " 572.961,\n", + " 564.416,\n", + " 864.744,\n", + " 385.852,\n", + " 731.601,\n", + " 298.015,\n", + " 731.61,\n", + " 476.585,\n", + " 737.257]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dct_created_data" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "f24ae230-970c-48e5-acf5-91499cafdbf6", + "metadata": {}, + "outputs": [], + "source": [ + "dct_original_data = load_json(\"./zalando-hd-resized/train/openpose_json/00048_00_keypoints.json\")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bd14c855-fa65-4e55-968c-cbaba0c1a1cd", + "metadata": {}, + "outputs": [], + "source": [ + "im = plt.imread(f\"./zalando-hd-resized/train/image/00048_00.jpg\")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "e09fc1ee-c6c9-4fe5-a4dc-4f9437db084f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_coordinates_new = dct_created_data[0::2]\n", + "y_coordinates_new = dct_created_data[1::2]\n", + "\n", + "implot = plt.imshow(im)\n", + "plt.scatter(x_coordinates_new, y_coordinates_new)\n", + "for x, y, id in zip(x_coordinates_new, y_coordinates_new, garment_coordinate_position):\n", + " plt.annotate(id, xy = (x, y))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56ecddbf-8266-4155-8614-471463777e4f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tryondiffusion/pre_processing/notebooks/human_pose_embeddings_preproc.ipynb b/tryondiffusion/pre_processing/notebooks/human_pose_embeddings_preproc.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8731e72b01c3e6b316b83590f066e5b3584e1f51 --- /dev/null +++ b/tryondiffusion/pre_processing/notebooks/human_pose_embeddings_preproc.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "22caf520-a089-4753-8b4e-1e0ca52af686", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import cv2\n", + "from PIL import Image\n", + "from matplotlib import pyplot\n", + "import os\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3388a8e1-ca40-4f9c-b316-001a0f739e81", + "metadata": {}, + "outputs": [], + "source": [ + "image_name = \"00048_00\"\n", + "\n", + "img_pose = cv2.imread(f\"./zalando-hd-resized/train/openpose_img/{image_name}_rendered.png\")[:,:,::-1]\n", + "with open(f\"./zalando-hd-resized/train/openpose_json/{image_name}_keypoints.json\", \"r\") as f:\n", + " img_pose_json = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "07e4331a-cbea-499a-b396-be7d2455728c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image.fromarray(img_pose)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e8d4b5f7-6455-4ed9-836d-02c2558592b0", + "metadata": {}, + "outputs": [], + "source": [ + "x_coordinates = []\n", + "y_coordinates = []\n", + "alpha = []\n", + "\n", + "for i in range(0, len(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"]), 3):\n", + " x_coordinates.append(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"][i])\n", + "\n", + "for i in range(1, len(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"]), 3):\n", + " y_coordinates.append(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"][i])\n", + "\n", + "for i in range(2, len(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"]), 3):\n", + " alpha.append(img_pose_json[\"people\"][0][\"pose_keypoints_2d\"][i])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "74399cc6-c142-4398-83e7-4a95e831e48c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[445.352,\n", + " 388.751,\n", + " 252.647,\n", + " 230.015,\n", + " 187.548,\n", + " 530.341,\n", + " 558.722,\n", + " 564.416,\n", + " 385.852,\n", + " 298.015,\n", + " 0,\n", + " 0,\n", + " 476.585,\n", + " 0,\n", + " 0,\n", + " 411.391,\n", + " 465.362,\n", + " 346.139,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6ebc6c34-446b-4f06-a212-a466cf92208c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[133.657,\n", + " 326.465,\n", + " 332.159,\n", + " 578.642,\n", + " 873.34,\n", + " 312.281,\n", + " 572.961,\n", + " 864.744,\n", + " 731.601,\n", + " 731.61,\n", + " 0,\n", + " 0,\n", + " 737.257,\n", + " 0,\n", + " 0,\n", + " 99.6869,\n", + " 111.044,\n", + " 128.095,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0,\n", + " 0]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3752b698-eda5-4b00-ae49-54adf8f7e09d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1024, 768, 3)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img_pose.shape" + ] + }, + { + "cell_type": "markdown", + "id": "3f29aaf7-893c-452f-b0a0-a86bc08d3dea", + "metadata": {}, + "source": [ + "### Data Verification" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a7debe4b-fa17-495f-8b3f-885b78b705c0", + "metadata": {}, + "outputs": [], + "source": [ + "folder_path = \"./zalando-hd-resized/test/openpose_json/\"\n", + "json_directory = os.listdir(folder_path)\n", + "files_path = [os.path.join(folder_path, i) for i in json_directory]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "12d9b478-bf3e-4140-8972-89e4963a02d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{True}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set([bool(i.split(\".\")[-1]==\"json\") for i in files_path])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8e93f0c0-51e2-4f7f-88c2-acd29b54ab77", + "metadata": {}, + "outputs": [], + "source": [ + "def load_json(json_file_path):\n", + " with open(json_file_path, \"r\") as f:\n", + " img_pose_json = json.load(f)\n", + " return img_pose_json" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "33a0f84a-a47e-408d-a886-008040e53a65", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 41%|███████████████▌ | 831/2032 [00:00<00:00, 4193.29it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./zalando-hd-resized/test/openpose_json/03032_00_keypoints.json\n", + "./zalando-hd-resized/test/openpose_json/01969_00_keypoints.json\n", + "./zalando-hd-resized/test/openpose_json/07072_00_keypoints.json\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████| 2032/2032 [00:00<00:00, 4413.11it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "./zalando-hd-resized/test/openpose_json/01248_00_keypoints.json\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# similarly created train data, write it into the function if you want!\n", + "# it removes the files with more than 1 pose, since all images in the dataset contains just one person.\n", + "\n", + "for file_path in tqdm(files_path):\n", + " dct = load_json(file_path)\n", + " assert \"people\" in dct\n", + " if len(dct[\"people\"])!=1:\n", + " print(file_path)\n", + " else:\n", + " with open(\"./data/test_data.txt\", \"a\") as f:\n", + " f.write(file_path.split(\"/\")[-1][:8]+\"\\n\")\n", + " assert \"pose_keypoints_2d\" in dct[\"people\"][0]\n", + " assert len(dct[\"people\"][0][\"pose_keypoints_2d\"])==75" + ] + }, + { + "cell_type": "markdown", + "id": "7ea4e38b-95b2-4789-a93e-fe4dcbc7619b", + "metadata": {}, + "source": [ + "### Final Data Prep" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "0bbfebe0-76d4-4312-9d3e-a150d11b40dd", + "metadata": {}, + "outputs": [], + "source": [ + "train_folder_path = \"./zalando-hd-resized/train/openpose_json/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "a899f599-6580-465a-8cca-2a9d1ddbe008", + "metadata": {}, + "outputs": [], + "source": [ + "test_folder_path = \"./zalando-hd-resized/test/openpose_json/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "112d5a80-8035-4fea-9e49-3046ac291da3", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"./data/train_data.txt\", \"r\") as f:\n", + " lst_train = f.read().split(\"\\n\")\n", + "with open(\"./data/test_data.txt\", \"r\") as f:\n", + " lst_test = f.read().split(\"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "1e80f3f8-291c-40f5-886f-5b683ddfb679", + "metadata": {}, + "outputs": [], + "source": [ + "def create_data(lst_files, folder_path, save_path):\n", + " files = os.listdir(folder_path)\n", + " for file in lst_files:\n", + " file_name = f\"{file}_keypoints.json\"\n", + " file_path = os.path.join(folder_path, file_name)\n", + " if file_name in files:\n", + " dct = load_json(file_path)\n", + " lst = dct[\"people\"][0][\"pose_keypoints_2d\"]\n", + " del lst[2::3]\n", + " with open(os.path.join(save_path, file_name), \"w\") as f:\n", + " json.dump(lst, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "92c12cbd-f766-4a2b-8d25-69b9095e9f75", + "metadata": {}, + "outputs": [], + "source": [ + "create_data(lst_test, test_folder_path, \"./data/test/\")" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "0f5ccb6f-eb72-484e-94f8-3b711fd28598", + "metadata": {}, + "outputs": [], + "source": [ + "create_data(lst_train, train_folder_path, \"./data/train/\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a485c6ec-8fe2-4cf7-a568-1985d84a17dc", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tryondiffusion/pre_processing/openpose_pytorch/__init__.py b/tryondiffusion/pre_processing/openpose_pytorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tryondiffusion/pre_processing/openpose_pytorch/body_pose.py b/tryondiffusion/pre_processing/openpose_pytorch/body_pose.py new file mode 100644 index 0000000000000000000000000000000000000000..d92b9cb35e89d91e1301bd0cb8caff4314b09ce1 --- /dev/null +++ b/tryondiffusion/pre_processing/openpose_pytorch/body_pose.py @@ -0,0 +1,233 @@ +import cv2 +import json +import math +import numpy as np +import torch +from scipy.ndimage.filters import gaussian_filter + +from .model import bodypose_model +from .utils import transfer, padRightDownCorner, save_25kp_json + + +class Body(object): + + def __init__(self, model_path): + self.model = bodypose_model() + if torch.cuda.is_available(): + self.model = self.model.cuda() + model_dict = transfer(self.model, torch.load(model_path)) + self.model.load_state_dict(model_dict) + self.model.eval() + + def __call__(self, oriImg): + # scale_search = [0.5, 1.0, 1.5, 2.0] + scale_search = [0.5] + boxsize = 368 + stride = 8 + padValue = 128 + thre1 = 0.1 + thre2 = 0.05 + multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] + heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19)) + paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) + + for m in range(len(multiplier)): + scale = multiplier[m] + imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) + imageToTest_padded, pad = padRightDownCorner(imageToTest, stride, padValue) + im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 + im = np.ascontiguousarray(im) + + data = torch.from_numpy(im).float() + if torch.cuda.is_available(): + data = data.cuda() + # data = data.permute([2, 0, 1]).unsqueeze(0).float() + with torch.no_grad(): + Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data) + Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy() + Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy() + + # extract outputs, resize, and remove padding + # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps + heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps + heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) + heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + + # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs + paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs + paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) + paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] + paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) + + heatmap_avg += heatmap_avg + heatmap / len(multiplier) + paf_avg += + paf / len(multiplier) + + all_peaks = [] + peak_counter = 0 + + for part in range(18): + map_ori = heatmap_avg[:, :, part] + one_heatmap = gaussian_filter(map_ori, sigma=3) + + map_left = np.zeros(one_heatmap.shape) + map_left[1:, :] = one_heatmap[:-1, :] + map_right = np.zeros(one_heatmap.shape) + map_right[:-1, :] = one_heatmap[1:, :] + map_up = np.zeros(one_heatmap.shape) + map_up[:, 1:] = one_heatmap[:, :-1] + map_down = np.zeros(one_heatmap.shape) + map_down[:, :-1] = one_heatmap[:, 1:] + + peaks_binary = np.logical_and.reduce( + (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, + one_heatmap > thre1)) + peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse + peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] + peak_id = range(peak_counter, peak_counter + len(peaks)) + peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] + + all_peaks.append(peaks_with_score_and_id) + peak_counter += len(peaks) + + # find connection in the specified sequence, center 29 is in the position 15 + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + # the middle joints heatmap correpondence + mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ + [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ + [55, 56], [37, 38], [45, 46]] + + connection_all = [] + special_k = [] + mid_num = 10 + + for k in range(len(mapIdx)): + score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]] + candA = all_peaks[limbSeq[k][0] - 1] + candB = all_peaks[limbSeq[k][1] - 1] + nA = len(candA) + nB = len(candB) + indexA, indexB = limbSeq[k] + if (nA != 0 and nB != 0): + connection_candidate = [] + for i in range(nA): + for j in range(nB): + vec = np.subtract(candB[j][:2], candA[i][:2]) + norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1]) + norm = max(0.001, norm) + vec = np.divide(vec, norm) + + startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ + np.linspace(candA[i][1], candB[j][1], num=mid_num))) + + vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ + for I in range(len(startend))]) + vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ + for I in range(len(startend))]) + + score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) + score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( + 0.5 * oriImg.shape[0] / norm - 1, 0) + criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts) + criterion2 = score_with_dist_prior > 0 + if criterion1 and criterion2: + connection_candidate.append( + [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) + + connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) + connection = np.zeros((0, 5)) + for c in range(len(connection_candidate)): + i, j, s = connection_candidate[c][0:3] + if (i not in connection[:, 3] and j not in connection[:, 4]): + connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) + if (len(connection) >= min(nA, nB)): + break + + connection_all.append(connection) + else: + special_k.append(k) + connection_all.append([]) + + # last number in each row is the total parts number of that person + # the second last number in each row is the score of the overall configuration + subset = -1 * np.ones((0, 20)) + candidate = np.array([item for sublist in all_peaks for item in sublist]) + + for k in range(len(mapIdx)): + if k not in special_k: + partAs = connection_all[k][:, 0] + partBs = connection_all[k][:, 1] + indexA, indexB = np.array(limbSeq[k]) - 1 + + for i in range(len(connection_all[k])): # = 1:size(temp,1) + found = 0 + subset_idx = [-1, -1] + for j in range(len(subset)): # 1:size(subset,1): + if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]: + subset_idx[found] = j + found += 1 + + if found == 1: + j = subset_idx[0] + if subset[j][indexB] != partBs[i]: + subset[j][indexB] = partBs[i] + subset[j][-1] += 1 + subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + elif found == 2: # if found 2 and disjoint, merge them + j1, j2 = subset_idx + membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] + if len(np.nonzero(membership == 2)[0]) == 0: # merge + subset[j1][:-2] += (subset[j2][:-2] + 1) + subset[j1][-2:] += subset[j2][-2:] + subset[j1][-2] += connection_all[k][i][2] + subset = np.delete(subset, j2, 0) + else: # as like found == 1 + subset[j1][indexB] = partBs[i] + subset[j1][-1] += 1 + subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] + + # if find no partA in the subset, create a new subset + elif not found and k < 17: + row = -1 * np.ones(20) + row[indexA] = partAs[i] + row[indexB] = partBs[i] + row[-1] = 2 + row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2] + subset = np.vstack([subset, row]) + # delete some rows of subset which has few parts occur + deleteIdx = [] + for i in range(len(subset)): + if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4: + deleteIdx.append(i) + subset = np.delete(subset, deleteIdx, axis=0) + + # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts + # candidate: x, y, score, id + return candidate, subset + + +if __name__ == "__main__": + body_estimation = Body('/Users/apple/Downloads/body_pose_model.pth') + + test_image = "/Users/apple/Downloads/zalando-hd-resized/test/image/00006_00.jpg" + save_path_image = "path to save the image" + + oriImg = cv2.imread(test_image) # B,G,R order + candidate, subset = body_estimation(oriImg) + # candidate: x, y, score, id + # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts + + save_jsn_path = "/Users/apple/Downloads/00006_00.json" + jsn = save_25kp_json(candidate, subset) + # plt.imshow(canvas[:, :, [2, 1, 0]]) + + # to save image(uncomment and provide path to save image) + # plt.savefig(save_path_image) + + # to show image + # plt.show() + + with open(save_jsn_path, "w") as f: + json.dump(jsn, f) diff --git a/tryondiffusion/pre_processing/openpose_pytorch/model.py b/tryondiffusion/pre_processing/openpose_pytorch/model.py new file mode 100644 index 0000000000000000000000000000000000000000..151aab30c21b39a4c03437e82ba3612f0c0a8a0c --- /dev/null +++ b/tryondiffusion/pre_processing/openpose_pytorch/model.py @@ -0,0 +1,220 @@ +from collections import OrderedDict + +import torch +import torch.nn as nn + + +def make_layers(block, no_relu_layers): + layers = [] + for layer_name, v in block.items(): + if 'pool' in layer_name: + layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1], + padding=v[2]) + layers.append((layer_name, layer)) + else: + conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1], + kernel_size=v[2], stride=v[3], + padding=v[4]) + layers.append((layer_name, conv2d)) + if layer_name not in no_relu_layers: + layers.append(('relu_'+layer_name, nn.ReLU(inplace=True))) + + return nn.Sequential(OrderedDict(layers)) + + +class bodypose_model(nn.Module): + def __init__(self): + super(bodypose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\ + 'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\ + 'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\ + 'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1'] + blocks = {} + block0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3_CPM', [512, 256, 3, 1, 1]), + ('conv4_4_CPM', [256, 128, 3, 1, 1]) + ]) + + + # Stage 1 + block1_1 = OrderedDict([ + ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L1', [512, 38, 1, 1, 0]) + ]) + + block1_2 = OrderedDict([ + ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]), + ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]), + ('conv5_5_CPM_L2', [512, 19, 1, 1, 0]) + ]) + blocks['block1_1'] = block1_1 + blocks['block1_2'] = block1_2 + + self.model0 = make_layers(block0, no_relu_layers) + + # Stages 2 - 6 + for i in range(2, 7): + blocks['block%d_1' % i] = OrderedDict([ + ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0]) + ]) + + blocks['block%d_2' % i] = OrderedDict([ + ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]), + ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_1 = blocks['block1_1'] + self.model2_1 = blocks['block2_1'] + self.model3_1 = blocks['block3_1'] + self.model4_1 = blocks['block4_1'] + self.model5_1 = blocks['block5_1'] + self.model6_1 = blocks['block6_1'] + + self.model1_2 = blocks['block1_2'] + self.model2_2 = blocks['block2_2'] + self.model3_2 = blocks['block3_2'] + self.model4_2 = blocks['block4_2'] + self.model5_2 = blocks['block5_2'] + self.model6_2 = blocks['block6_2'] + + def forward(self, x): + + out1 = self.model0(x) + + out1_1 = self.model1_1(out1) + out1_2 = self.model1_2(out1) + out2 = torch.cat([out1_1, out1_2, out1], 1) + + out2_1 = self.model2_1(out2) + out2_2 = self.model2_2(out2) + out3 = torch.cat([out2_1, out2_2, out1], 1) + + out3_1 = self.model3_1(out3) + out3_2 = self.model3_2(out3) + out4 = torch.cat([out3_1, out3_2, out1], 1) + + out4_1 = self.model4_1(out4) + out4_2 = self.model4_2(out4) + out5 = torch.cat([out4_1, out4_2, out1], 1) + + out5_1 = self.model5_1(out5) + out5_2 = self.model5_2(out5) + out6 = torch.cat([out5_1, out5_2, out1], 1) + + out6_1 = self.model6_1(out6) + out6_2 = self.model6_2(out6) + + return out6_1, out6_2 + + +class handpose_model(nn.Module): + def __init__(self): + super(handpose_model, self).__init__() + + # these layers have no relu layer + no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\ + 'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6'] + # stage 1 + block1_0 = OrderedDict([ + ('conv1_1', [3, 64, 3, 1, 1]), + ('conv1_2', [64, 64, 3, 1, 1]), + ('pool1_stage1', [2, 2, 0]), + ('conv2_1', [64, 128, 3, 1, 1]), + ('conv2_2', [128, 128, 3, 1, 1]), + ('pool2_stage1', [2, 2, 0]), + ('conv3_1', [128, 256, 3, 1, 1]), + ('conv3_2', [256, 256, 3, 1, 1]), + ('conv3_3', [256, 256, 3, 1, 1]), + ('conv3_4', [256, 256, 3, 1, 1]), + ('pool3_stage1', [2, 2, 0]), + ('conv4_1', [256, 512, 3, 1, 1]), + ('conv4_2', [512, 512, 3, 1, 1]), + ('conv4_3', [512, 512, 3, 1, 1]), + ('conv4_4', [512, 512, 3, 1, 1]), + ('conv5_1', [512, 512, 3, 1, 1]), + ('conv5_2', [512, 512, 3, 1, 1]), + ('conv5_3_CPM', [512, 128, 3, 1, 1]) + ]) + + block1_1 = OrderedDict([ + ('conv6_1_CPM', [128, 512, 1, 1, 0]), + ('conv6_2_CPM', [512, 22, 1, 1, 0]) + ]) + + blocks = {} + blocks['block1_0'] = block1_0 + blocks['block1_1'] = block1_1 + + # stage 2-6 + for i in range(2, 7): + blocks['block%d' % i] = OrderedDict([ + ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]), + ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]), + ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]), + ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0]) + ]) + + for k in blocks.keys(): + blocks[k] = make_layers(blocks[k], no_relu_layers) + + self.model1_0 = blocks['block1_0'] + self.model1_1 = blocks['block1_1'] + self.model2 = blocks['block2'] + self.model3 = blocks['block3'] + self.model4 = blocks['block4'] + self.model5 = blocks['block5'] + self.model6 = blocks['block6'] + + def forward(self, x): + out1_0 = self.model1_0(x) + out1_1 = self.model1_1(out1_0) + concat_stage2 = torch.cat([out1_1, out1_0], 1) + out_stage2 = self.model2(concat_stage2) + concat_stage3 = torch.cat([out_stage2, out1_0], 1) + out_stage3 = self.model3(concat_stage3) + concat_stage4 = torch.cat([out_stage3, out1_0], 1) + out_stage4 = self.model4(concat_stage4) + concat_stage5 = torch.cat([out_stage4, out1_0], 1) + out_stage5 = self.model5(concat_stage5) + concat_stage6 = torch.cat([out_stage5, out1_0], 1) + out_stage6 = self.model6(concat_stage6) + return out_stage6 + + diff --git a/tryondiffusion/pre_processing/openpose_pytorch/utils.py b/tryondiffusion/pre_processing/openpose_pytorch/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..35ab4743d3d7ca9b8050f9179b45bc00e66bf08c --- /dev/null +++ b/tryondiffusion/pre_processing/openpose_pytorch/utils.py @@ -0,0 +1,147 @@ +import math +import numpy as np +import cv2 + + +def padRightDownCorner(img, stride, padValue): + h = img.shape[0] + w = img.shape[1] + + pad = 4 * [None] + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + + img_padded = img + pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) + img_padded = np.concatenate((pad_up, img_padded), axis=0) + pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) + img_padded = np.concatenate((pad_left, img_padded), axis=1) + pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) + img_padded = np.concatenate((img_padded, pad_down), axis=0) + pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) + img_padded = np.concatenate((img_padded, pad_right), axis=1) + + return img_padded, pad + + +# transfer caffe model to pytorch which will match the layer name +def transfer(model, model_weights): + transfered_model_weights = {} + for weights_name in model.state_dict().keys(): + transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + return transfered_model_weights + + +def draw_bodypose(canvas, candidate, subset): + stickwidth = 4 + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], + [1, 16], [16, 18], [3, 17], [6, 18]] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] + for i in range(18): + index = int(subset[0][i]) + if index == -1: + continue + x, y = candidate[index][0:2] + cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) + + font = cv2.FONT_HERSHEY_SIMPLEX + bottomLeftCornerOfText = (int(x), int(y)) + fontScale = 0.8 + fontColor = (0, 0, 0) + thickness = 2 + lineType = 2 + + cv2.putText(canvas, str(i), + bottomLeftCornerOfText, + font, + fontScale, + fontColor, + thickness, + lineType) + for i in range(17): + for n in range(len(subset)): + index = subset[n][np.array(limbSeq[i]) - 1] + if -1 in index: + continue + cur_canvas = canvas.copy() + Y = candidate[index.astype(int), 0] + X = candidate[index.astype(int), 1] + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) + cv2.fillConvexPoly(cur_canvas, polygon, colors[i]) + canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0) + # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]]) + # plt.imshow(canvas[:, :, [2, 1, 0]]) + return canvas + + +def save_25kp_json(candidate, subset): + map25to18 = { + 0: 0, + 1: 1, + 2: 2, + 3: 3, + 4: 4, + 5: 5, + 6: 6, + 7: 7, + 8: "mean_8_11", + 9: 8, + 10: 9, + 11: 10, + 12: 11, + 13: 12, + 14: 13, + 15: 14, + 16: 15, + 17: 16, + 18: 17, + 19: None, + 20: None, + 21: None, + 22: None, + 23: None, + 24: None + } + result = dict() + result["people"] = list() + result["people"].append(dict()) + result["people"][0]["pose_keypoints_2d"] = list() + + kp17 = dict() + for i in range(18): + index = int(subset[0][i]) + if index == -1: + x, y = 0.0, 0.0 + else: + x, y = candidate[index][0:2] + kp17[i] = (x, y) + kp25 = dict() + for i in range(25): + keypoint_index_kp17 = map25to18[i] + if type(keypoint_index_kp17) is int: + keypoint_kp17 = kp17[keypoint_index_kp17] + kp25[i] = keypoint_kp17 + else: + xi_kp25, yi_kp25 = 0.0, 0.0 + if keypoint_index_kp17 == "mean_8_11": + xi_kp25 = (kp17[8][0] + kp17[11][0])/2 + yi_kp25 = (kp17[8][1] + kp17[11][1])/2 + elif keypoint_index_kp17 is None: + xi_kp25 = 0.0 + yi_kp25 = 0.0 + kp25[i] = (xi_kp25, yi_kp25) + result["people"][0]["pose_keypoints_2d"].append(kp25[i][0]) + result["people"][0]["pose_keypoints_2d"].append(kp25[i][1]) + result["people"][0]["pose_keypoints_2d"].append(0.9) + + return result diff --git a/tryondiffusion/pre_processing/save_pose_embeddings.py b/tryondiffusion/pre_processing/save_pose_embeddings.py new file mode 100644 index 0000000000000000000000000000000000000000..f8a1f68558cf61d449eeae1e99eabff418340f22 --- /dev/null +++ b/tryondiffusion/pre_processing/save_pose_embeddings.py @@ -0,0 +1,87 @@ +# This file saves pose embeddings. These pose embeddings will be used +# for training of the parallel U_Net later. + +import torch +from torch.utils.data import DataLoader + +import os + + +def load_model(network, inp_size, model_path): + """ + Network to load the embedding model. Removing decoder from forward pass. + :param network: Network defined in /pose_dir/network.py. + :param inp_size: input size for the network. + :param model_path: path of saved model for autoencoder. + :return: Weight loaded encoder network. + """ + class EmbeddingNetwork(network): + def forward(self, x): + embeddings = self.encoder(x) + return embeddings + + net = EmbeddingNetwork(inp_size) + net.load_state_dict(torch.load(model_path)) + return net + + +def save_embeddings(dataset_class, network, inp_size, model_path, dataset_dir, save_folder): + """ + Function to save embeddings generated by autoencoder at bottelneck of dimension 8. + :param dataset_class: DatasetLoader defined in /pose_dir/dataloader.py. + :param network: Network defined in /pose_dir/network.py. + :param inp_size: input size for the network. + :param model_path: path of saved model for autoencoder. + :param dataset_dir: directory of dataset created by using pose keypoints. + :param save_folder: path to save embeddings. + :return: None. + """ + data = dataset_class(dataset_dir) + dataloader = DataLoader(data, batch_size=1) + model = load_model(network, inp_size, model_path) + + # ToDo: make file_name extraction configurable + for keypoints, json_name in dataloader: + embeddings = model(keypoints) + file_name = json_name[0].split("/")[-1][:8] + ".pt" + torch.save(embeddings[0], os.path.join(save_folder, file_name)) + # print(embeddings, file_name) + # break + + +if __name__ == "__main__": + from human_pose_embedding.network import AutoEncoder as HumanAutoEncoder + from garment_pose_embedding.network import AutoEncoder as GarmentAutoEncoder + + from human_pose_embedding.utils.dataloader import KeypointDataset as HumanKeypointDataset + from garment_pose_embedding.utils.dataloader import KeypointDataset as GarmentKeypointDataset + + # define variables for the dataset to be processed(to get embeddings) and the network they are to be processed from. + + human_args = {"dataset_class": HumanKeypointDataset, + "network": HumanAutoEncoder, + "inp_size": 50, + "model_path": "./human_pose_embedding/model/best_model.pth"} + + garment_args = {"dataset_class": GarmentKeypointDataset, + "network": GarmentAutoEncoder, + "inp_size": 20, + "model_path": "./garment_pose_embedding/model/best_model.pth"} + + human_garment = "garment_args" + data_folder_name = "garment_pose_embedding" + train_test = "test" + + # path to keypoint dataset generated by notebooks "human_pose_embeddings_preproc.ipynb" and + # "garment_pose_embeddings_preproc.ipynb" + dataset_dir = f"./{data_folder_name}/data/{train_test}" + + # os.mkdir(f"./{data_folder_name}/embeddings") + os.mkdir(f"./{data_folder_name}/embeddings/{train_test}_embeddings") + save_folder = f"./{data_folder_name}/embeddings/{train_test}_embeddings" + + save_embeddings(**eval(human_garment), dataset_dir=dataset_dir, save_folder=save_folder) + + + + diff --git a/tryondiffusion/pre_processing/segment_cloth_u2net.py b/tryondiffusion/pre_processing/segment_cloth_u2net.py new file mode 100644 index 0000000000000000000000000000000000000000..7f1c811e00a996fae02b91faad49d15763fb4050 --- /dev/null +++ b/tryondiffusion/pre_processing/segment_cloth_u2net.py @@ -0,0 +1,33 @@ +import argparse +import os + +import torch + +from u2net_cloth_seg import segment + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='U Square Net based cloth segmentation') + parser.add_argument('--inputs_dir', type=str, + help='Input images directory path') + parser.add_argument('--outputs_dir', type=str, + help='Output images directory path') + parser.add_argument('--checkpoint_path', type=str, + help='Specify the U2Net checkpoint file path') + args = parser.parse_args() + + if args.inputs_dir is None or args.outputs_dir is None or args.checkpoint_path is None: + print("Missing parameters: inputs_dir, outputs_dir, or checkpoint_path") + exit() + + inputs_dir = args.inputs_dir + outputs_dir = args.outputs_dir + checkpoint_path = args.checkpoint_path + os.makedirs(outputs_dir, exist_ok=True) + + os.makedirs(os.path.join(outputs_dir, "alpha_masks"), exist_ok=True) + os.makedirs(os.path.join(outputs_dir, "final_seg"), exist_ok=True) + os.makedirs(os.path.join(outputs_dir, "original"), exist_ok=True) + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + segment(device, inputs_dir, outputs_dir, checkpoint_path) diff --git a/tryondiffusion/pre_processing/u2net_cloth_seg/__init__.py b/tryondiffusion/pre_processing/u2net_cloth_seg/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..80de3bd992f37b022e15bd231798782d8b6c6705 --- /dev/null +++ b/tryondiffusion/pre_processing/u2net_cloth_seg/__init__.py @@ -0,0 +1,67 @@ +import numpy as np +import os +import torch +import torch.nn.functional as F +from PIL import Image +from torchvision import transforms +from tqdm import tqdm + +from .utils import create_model, NormalizeImage, get_palette + + +def segment(device, inputs_dir, outputs_dir, checkpoint_path): + os.makedirs(os.path.join(outputs_dir, "cloth-mask"), exist_ok=True) + + transform_fn = transforms.Compose( + [transforms.ToTensor(), + NormalizeImage(0.5, 0.5)] + ) + + net = create_model(device, checkpoint_path) + + images_list = sorted(os.listdir(inputs_dir)) + pbar = tqdm(total=len(images_list)) + + for image_name in images_list: + img = Image.open(os.path.join(inputs_dir, image_name)).convert('RGB') + img_size = img.size + img = img.resize((768, 768), Image.BICUBIC) + image_tensor = transform_fn(img) + image_tensor = torch.unsqueeze(image_tensor, 0) + + with torch.no_grad(): + output_tensor = net(image_tensor.to(device)) + output_tensor = F.log_softmax(output_tensor[0], dim=1) + output_tensor = torch.max(output_tensor, dim=1, keepdim=True)[1] + output_tensor = torch.squeeze(output_tensor, dim=0) + output_arr = output_tensor.cpu().numpy() + + classes_to_save = [] + + # Check which classes are present in the image + for cls in range(1, 4): # Exclude background class (0) + if np.any(output_arr == cls): + classes_to_save.append(cls) + + # Save alpha masks + for cls in classes_to_save: + alpha_mask = (output_arr == cls).astype(np.uint8) * 255 + alpha_mask = alpha_mask[0] # Selecting the first channel to make it 2D + alpha_mask_img = Image.fromarray(alpha_mask, mode='L') + alpha_mask_img = alpha_mask_img.resize(img_size, Image.BICUBIC) + alpha_mask_img.save(os.path.join(outputs_dir, "cloth-mask", f'{image_name.split(".")[0]}.jpg')) + + # # Save final cloth segmentations + # palette = get_palette(4) + # cloth_seg = Image.fromarray(output_arr[0].astype(np.uint8), mode='P') + # cloth_seg.putpalette(palette) + # cloth_seg = cloth_seg.resize(img_size, Image.BICUBIC) + # cloth_seg.save(os.path.join(outputs_dir, "final_seg", f'{image_name.split(".")[0]}.png')) + + # # save the original image + # img = img.resize(img_size, Image.BICUBIC) + # img.save(os.path.join(outputs_dir, "original", f'{image_name.split(".")[0]}.png')) + + pbar.update(1) + + pbar.close() diff --git a/tryondiffusion/pre_processing/u2net_cloth_seg/unet.py b/tryondiffusion/pre_processing/u2net_cloth_seg/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..fe7a8901a73c087d77f507b599dd6f3c54ece3a5 --- /dev/null +++ b/tryondiffusion/pre_processing/u2net_cloth_seg/unet.py @@ -0,0 +1,550 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class REBNCONV(nn.Module): + def __init__(self, in_ch=3, out_ch=3, dirate=1): + super(REBNCONV, self).__init__() + + self.conv_s1 = nn.Conv2d( + in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate + ) + self.bn_s1 = nn.BatchNorm2d(out_ch) + self.relu_s1 = nn.ReLU(inplace=True) + + def forward(self, x): + hx = x + xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) + + return xout + + +## upsample tensor 'src' to have the same spatial size with tensor 'tar' +def _upsample_like(src, tar): + src = F.upsample(src, size=tar.shape[2:], mode="bilinear") + + return src + + +### RSU-7 ### +class RSU7(nn.Module): # UNet07DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU7, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + hx = self.pool5(hx5) + + hx6 = self.rebnconv6(hx) + + hx7 = self.rebnconv7(hx6) + + hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) + hx6dup = _upsample_like(hx6d, hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5, hx6, hx7 + del hx6d, hx5d, hx3d, hx2d + del hx2dup, hx3dup, hx4dup, hx5dup, hx6dup + """ + + return hx1d + hxin + + +### RSU-6 ### +class RSU6(nn.Module): # UNet06DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU6, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + hx = self.pool4(hx4) + + hx5 = self.rebnconv5(hx) + + hx6 = self.rebnconv6(hx5) + + hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5, hx6 + del hx5d, hx4d, hx3d, hx2d + del hx2dup, hx3dup, hx4dup, hx5dup + """ + + return hx1d + hxin + + +### RSU-5 ### +class RSU5(nn.Module): # UNet05DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU5, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + hx = self.pool3(hx3) + + hx4 = self.rebnconv4(hx) + + hx5 = self.rebnconv5(hx4) + + hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5 + del hx4d, hx3d, hx2d + del hx2dup, hx3dup, hx4dup + """ + + return hx1d + hxin + + +### RSU-4 ### +class RSU4(nn.Module): # UNet04DRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) + self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx = self.pool1(hx1) + + hx2 = self.rebnconv2(hx) + hx = self.pool2(hx2) + + hx3 = self.rebnconv3(hx) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4 + del hx3d, hx2d + del hx2dup, hx3dup + """ + + return hx1d + hxin + + +### RSU-4F ### +class RSU4F(nn.Module): # UNet04FRES(nn.Module): + def __init__(self, in_ch=3, mid_ch=12, out_ch=3): + super(RSU4F, self).__init__() + + self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) + + self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) + self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) + self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) + + self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) + + self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) + self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) + self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) + + def forward(self, x): + hx = x + + hxin = self.rebnconvin(hx) + + hx1 = self.rebnconv1(hxin) + hx2 = self.rebnconv2(hx1) + hx3 = self.rebnconv3(hx2) + + hx4 = self.rebnconv4(hx3) + + hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) + hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) + hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) + + """ + del hx1, hx2, hx3, hx4 + del hx3d, hx2d + """ + + return hx1d + hxin + + +##### U^2-Net #### +class U2NET(nn.Module): + def __init__(self, in_ch=3, out_ch=1): + super(U2NET, self).__init__() + + self.stage1 = RSU7(in_ch, 32, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 32, 128) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(128, 64, 256) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(256, 128, 512) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(512, 256, 512) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(512, 256, 512) + + # decoder + self.stage5d = RSU4F(1024, 256, 512) + self.stage4d = RSU4(1024, 128, 256) + self.stage3d = RSU5(512, 64, 128) + self.stage2d = RSU6(256, 32, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) + + self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1) + + def forward(self, x): + hx = x + + # stage 1 + hx1 = self.stage1(hx) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # -------------------- decoder -------------------- + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, d1) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, d1) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, d1) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, d1) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, d1) + + d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) + + """ + del hx1, hx2, hx3, hx4, hx5, hx6 + del hx5d, hx4d, hx3d, hx2d, hx1d + del hx6up, hx5dup, hx4dup, hx3dup, hx2dup + """ + + return d0, d1, d2, d3, d4, d5, d6 + + +### U^2-Net small ### +class U2NETP(nn.Module): + def __init__(self, in_ch=3, out_ch=1): + super(U2NETP, self).__init__() + + self.stage1 = RSU7(in_ch, 16, 64) + self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage2 = RSU6(64, 16, 64) + self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage3 = RSU5(64, 16, 64) + self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage4 = RSU4(64, 16, 64) + self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage5 = RSU4F(64, 16, 64) + self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) + + self.stage6 = RSU4F(64, 16, 64) + + # decoder + self.stage5d = RSU4F(128, 16, 64) + self.stage4d = RSU4(128, 16, 64) + self.stage3d = RSU5(128, 16, 64) + self.stage2d = RSU6(128, 16, 64) + self.stage1d = RSU7(128, 16, 64) + + self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side3 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side4 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side5 = nn.Conv2d(64, out_ch, 3, padding=1) + self.side6 = nn.Conv2d(64, out_ch, 3, padding=1) + + self.outconv = nn.Conv2d(6 * out_ch, out_ch, 1) + + def forward(self, x): + hx = x + + # stage 1 + hx1 = self.stage1(hx) + hx = self.pool12(hx1) + + # stage 2 + hx2 = self.stage2(hx) + hx = self.pool23(hx2) + + # stage 3 + hx3 = self.stage3(hx) + hx = self.pool34(hx3) + + # stage 4 + hx4 = self.stage4(hx) + hx = self.pool45(hx4) + + # stage 5 + hx5 = self.stage5(hx) + hx = self.pool56(hx5) + + # stage 6 + hx6 = self.stage6(hx) + hx6up = _upsample_like(hx6, hx5) + + # decoder + hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) + hx5dup = _upsample_like(hx5d, hx4) + + hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) + hx4dup = _upsample_like(hx4d, hx3) + + hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) + hx3dup = _upsample_like(hx3d, hx2) + + hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) + hx2dup = _upsample_like(hx2d, hx1) + + hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) + + # side output + d1 = self.side1(hx1d) + + d2 = self.side2(hx2d) + d2 = _upsample_like(d2, d1) + + d3 = self.side3(hx3d) + d3 = _upsample_like(d3, d1) + + d4 = self.side4(hx4d) + d4 = _upsample_like(d4, d1) + + d5 = self.side5(hx5d) + d5 = _upsample_like(d5, d1) + + d6 = self.side6(hx6) + d6 = _upsample_like(d6, d1) + + d0 = self.outconv(torch.cat((d1, d2, d3, d4, d5, d6), 1)) + + return d0, d1, d2, d3, d4, d5, d6 diff --git a/tryondiffusion/pre_processing/u2net_cloth_seg/utils.py b/tryondiffusion/pre_processing/u2net_cloth_seg/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0dfb3bf5d190467d78a82354c38e5e2a49d74dbc --- /dev/null +++ b/tryondiffusion/pre_processing/u2net_cloth_seg/utils.py @@ -0,0 +1,85 @@ +import os +from collections import OrderedDict + +import torch +from torchvision import transforms + +from .unet import U2NET + + +def create_model(device, checkpoint_path): + if not os.path.exists(checkpoint_path): + print("Invalid path") + return + + model = U2NET(in_ch=3, out_ch=4) + + model_state_dict = torch.load(checkpoint_path, map_location=torch.device("cpu")) + new_state_dict = OrderedDict() + for k, v in model_state_dict.items(): + name = k[7:] # remove `module.` + new_state_dict[name] = v + + model.load_state_dict(new_state_dict) + model = model.to(device=device) + print("Checkpoints loaded from path: {}".format(checkpoint_path)) + + return model + + +def get_palette(num_cls): + """Returns the color map for visualizing the segmentation mask. + Args: + num_cls: Number of classes + Returns: + The color map + """ + n = num_cls + palette = [0] * (n * 3) + for j in range(0, n): + lab = j + palette[j * 3 + 0] = 0 + palette[j * 3 + 1] = 0 + palette[j * 3 + 2] = 0 + i = 0 + while lab: + palette[j * 3 + 0] |= ((lab >> 0) & 1) << (7 - i) + palette[j * 3 + 1] |= ((lab >> 1) & 1) << (7 - i) + palette[j * 3 + 2] |= ((lab >> 2) & 1) << (7 - i) + i += 1 + lab >>= 3 + return palette + + +class NormalizeImage(object): + """Normalize given tensor into given mean and standard dev + + Args: + mean (float): Desired mean to substract from tensors + std (float): Desired std to divide from tensors + """ + + def __init__(self, mean, std): + assert isinstance(mean, (float)) + if isinstance(mean, float): + self.mean = mean + + if isinstance(std, float): + self.std = std + + self.normalize_1 = transforms.Normalize(self.mean, self.std) + self.normalize_3 = transforms.Normalize([self.mean] * 3, [self.std] * 3) + self.normalize_18 = transforms.Normalize([self.mean] * 18, [self.std] * 18) + + def __call__(self, image_tensor): + if image_tensor.shape[0] == 1: + return self.normalize_1(image_tensor) + + elif image_tensor.shape[0] == 3: + return self.normalize_3(image_tensor) + + elif image_tensor.shape[0] == 18: + return self.normalize_18(image_tensor) + + else: + assert "Please set proper channels! Normalization implemented only for 1, 3 and 18" diff --git a/tryondiffusion/trainer.py b/tryondiffusion/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..449881cdb5061e77320f3c7cbb2f62134a39dffc --- /dev/null +++ b/tryondiffusion/trainer.py @@ -0,0 +1,48 @@ +from .diffusion import Diffusion + + +class ArgParser: + + def __init__(self): + + self.run_name = "1" + + self.train_ip_folder = "data/test_flow/train/ip" + self.train_jp_folder = "data/test_flow/train/jp" + self.train_ia_folder = "data/test_flow/train/ia" + self.train_ic_folder = "data/test_flow/train/ic" + self.train_jg_folder = "data/test_flow/train/jg" + + self.validation_ip_folder = "data/test_flow/validation/ip" + self.validation_jp_folder = "data/test_flow/validation/jp" + self.validation_ia_folder = "data/test_flow/validation/ia" + self.validation_ic_folder = "data/test_flow/validation/ic" + self.validation_jg_folder = "data/test_flow/validation/jg" + + self.batch_size_train = 1 + self.batch_size_validation = 1 + + self.lr = 0.0 + + self.calculate_loss_frequency = 10 + self.image_logging_frequency = 10 + self.model_saving_frequency = 10 + + self.total_steps = 100000 + self.start_lr = 0.0 + self.stop_lr = 0.0001 + self.pct_increasing_lr = 0.02 + + +args = ArgParser() +diffusion = Diffusion(device="cuda", + pose_embed_dim=8, + time_steps=256, + beta_start=1e-4, + beta_end=0.02, + unet_dim=64, + noise_input_channel=3, + beta_ema=0.995) + +diffusion.prepare(args) +diffusion.fit(args) diff --git a/tryondiffusion/utils/__init__.py b/tryondiffusion/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a095608b2abf43d244e0978275740ec1c20dc67a --- /dev/null +++ b/tryondiffusion/utils/__init__.py @@ -0,0 +1,2 @@ +from .utils import mk_folders, GaussianSmoothing +from .dataloader_train import UNetDataset diff --git a/tryondiffusion/utils/dataloader_train.py b/tryondiffusion/utils/dataloader_train.py new file mode 100644 index 0000000000000000000000000000000000000000..78fcc84dd086140f946cf93b84e556ef92910254 --- /dev/null +++ b/tryondiffusion/utils/dataloader_train.py @@ -0,0 +1,92 @@ +import os + +import torch +from torch.utils.data import DataLoader, Dataset +from torch.nn import functional as F +from torchvision import transforms as T + +from .utils import load_pose_embed, read_img + + +class ToPaddedTensorImages: + + def __call__(self, image): + """Padding image so that aspect ratio is maintained. + And converting numpy arrays to tensors.""" + # cv2 image: H x W x C + # torch image: C X H X W + + img = image.transpose((2, 0, 1)) + img = torch.from_numpy(img.copy()).float() + + if img.shape[1] > img.shape[2]: + pad_size = (img.shape[1] - img.shape[2]) // 2 + padding = (pad_size, pad_size, 0, 0) + elif img.shape[2] > img.shape[1]: + pad_size = (img.shape[2] - img.shape[1]) // 2 + padding = (0, 0, pad_size, pad_size) + else: + padding = (0, 0, 0, 0) + + img = F.pad(img, padding, "constant", 0) + + return img + + +class ToTensorEmbed: + + def __call__(self, pose_embed): + + return torch.from_numpy(pose_embed) + + +class UNetDataset(Dataset): + """ This class is to be used while training, where all the conditional inputs and ground + truth is pre-saved and are pre-processed.""" + + def __init__(self, ip_dir, jp_dir, jg_dir, ia_dir, ic_dir, unet_size): + """ + Get all the inputs from ../data directory in the main project directory + :param ip_dir: Image of target person with source clothing on. Later + to be used to generate zt and to be used as ground truth for training. + :param jp_dir: person pose embeddings from ip + :param jg_dir: garment pose embeddings from 'ig', ig is the source garment image + :param ia_dir: clothing agnostic rgb from ip + :param ic_dir: segmented garment from ig + """ + self.ip_list = os.listdir(ip_dir) + self.ip_paths = [os.path.join(ip_dir, i) for i in self.ip_list] + + self.jp_list = os.listdir(jp_dir) + self.jp_paths = [os.path.join(jp_dir, i) for i in self.jp_list] + + self.jg_list = os.listdir(jg_dir) + self.jg_paths = [os.path.join(jg_dir, i) for i in self.jg_list] + + self.ia_list = os.listdir(ia_dir) + self.ia_paths = [os.path.join(ia_dir, i) for i in self.ia_list] + + self.ic_list = os.listdir(ic_dir) + self.ic_paths = [os.path.join(ic_dir, i) for i in self.ic_list] + + self.transforms_imgs = T.Compose([ + ToPaddedTensorImages(), + T.Resize(unet_size), + T.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + + def __len__(self): + return len(self.ip_list) + + def __getitem__(self, item): + ip = read_img(self.ip_paths[item]) + jp = load_pose_embed(self.jp_paths[item]) + jg = load_pose_embed(self.jg_paths[item]) + ia = read_img(self.ia_paths[item]) + ic = read_img(self.ic_paths[item]) + + ip = self.transforms_imgs(ip) + ia = self.transforms_imgs(ia) + ic = self.transforms_imgs(ic) + + return ip, jp, jg, ia, ic diff --git a/tryondiffusion/utils/utils.py b/tryondiffusion/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1342630100dc79f8b45c9e7f19ab3fb4b85f9419 --- /dev/null +++ b/tryondiffusion/utils/utils.py @@ -0,0 +1,119 @@ +import cv2 +import math +import numbers +import os + +from torch import nn +from torch.nn import functional as F + + +def load_pose_embed(file_path): + embed = torch.load(file_path) + return embed + + +def read_img(img_path): + img = cv2.imread(img_path) + return img + + +def write_img(img, folder_path, img_name): + path = os.path.join(folder_path, img_name) + cv2.imwrite(path, img) + + +class GaussianSmoothing(nn.Module): + """ + Source: https://discuss.pytorch.org/t/is-there-anyway-to-do-gaussian-filtering-for-an-image-2d-3d-in-pytorch/12351/10?u=tanay_agrawal + Apply gaussian smoothing on a + 1d, 2d or 3d tensor. Filtering is performed seperately for each channel + in the input using a depthwise convolution. + Arguments: + channels (int, sequence): Number of channels of the input tensors. Output will + have this number of channels as well. + kernel_size (int, sequence): Size of the gaussian kernel. + sigma (float, sequence): Standard deviation of the gaussian kernel. + dim (int, optional): The number of dimensions of the data. + Default value is 2 (spatial). + """ + + def __init__(self, channels, kernel_size, sigma, conv_dim): + super(GaussianSmoothing, self).__init__() + if isinstance(kernel_size, numbers.Number): + kernel_size = [kernel_size] * conv_dim + if isinstance(sigma, numbers.Number): + sigma = [sigma] * conv_dim + + # The gaussian kernel is the product of the + # gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid( + [ + torch.arange(size, dtype=torch.float32) + for size in kernel_size + ] + ) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \ + torch.exp(-((mgrid - mean) / std) ** 2 / 2) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) + + self.register_buffer('weight', kernel) + self.groups = channels + + if conv_dim == 1: + self.conv = F.conv1d + elif conv_dim == 2: + self.conv = F.conv2d + elif conv_dim == 3: + self.conv = F.conv3d + else: + raise RuntimeError( + 'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(conv_dim) + ) + + def forward(self, input): + """ + Apply gaussian filter to input. + Arguments: + input (torch.Tensor): Input to apply gaussian filter on. + Returns: + filtered (torch.Tensor): Filtered output. + """ + return self.conv(input, weight=self.weight, groups=self.groups) + + +def mk_folders(run_name): + os.makedirs("models", exist_ok=True) + os.makedirs("results", exist_ok=True) + os.makedirs(os.path.join("models", run_name), exist_ok=True) + os.makedirs(os.path.join("results", run_name), exist_ok=True) + os.makedirs(os.path.join("results", run_name, "images"), exist_ok=True) + + +if __name__ == "__main__": + import torch + import time + + torch.manual_seed(1) + smoothing = GaussianSmoothing(1, 3, 0.6, 2) + input = torch.rand(1, 1, 2, 2) + # print(input) + input_p = F.pad(input, (1, 1, 1, 1), mode='reflect') + since = time.time() + output = smoothing(input_p) + # print(output) + print(time.time() - since) + # smoothing = GaussianSmoothing(1, 3, (0.2, 0.6), 1) + # input = torch.arange(1, 4)[None, None, :].to(torch.float32) + # print(input) + # input = F.pad(input, (1, 1), mode='reflect') + # output = smoothing(input) + # print(output)