Repository: qubvel-org/segmentation_models.pytorch Branch: main Commit: 91c0895ef8d8 Files: 174 Total size: 6.3 MB Directory structure: gitextract_j1x_q930/ ├── .devcontainer/ │ └── devcontainer.json ├── .github/ │ ├── FUNDING.yml │ ├── codecov.yml │ ├── dependabot.yml │ └── workflows/ │ ├── pypi.yml │ ├── stale.yaml │ └── tests.yml ├── .gitignore ├── .readthedocs.yaml ├── HALLOFFAME.md ├── LICENSE ├── Makefile ├── README.md ├── docs/ │ ├── Makefile │ ├── conf.py │ ├── encoders.rst │ ├── encoders_dpt.rst │ ├── encoders_timm.rst │ ├── index.rst │ ├── insights.rst │ ├── install.rst │ ├── losses.rst │ ├── make.bat │ ├── metrics.rst │ ├── models.rst │ ├── quickstart.rst │ └── save_load.rst ├── examples/ │ ├── binary_segmentation_buildings.py │ ├── binary_segmentation_intro.ipynb │ ├── camvid_segmentation_multiclass.ipynb │ ├── cars segmentation (camvid).ipynb │ ├── convert_to_onnx.ipynb │ ├── dpt_inference_pretrained.ipynb │ ├── save_load_model_and_share_with_hf_hub.ipynb │ ├── segformer_inference_pretrained.ipynb │ └── upernet_inference_pretrained.ipynb ├── licenses/ │ ├── LICENSES.md │ ├── LICENSE_apache.md │ ├── LICENSE_apple.md │ └── LICENSE_nvidia.md ├── misc/ │ ├── generate_table.py │ ├── generate_table_timm.py │ └── generate_test_models.py ├── pyproject.toml ├── requirements/ │ ├── docs.txt │ ├── minimum.old │ ├── required.txt │ └── test.txt ├── scripts/ │ └── models-conversions/ │ ├── dpt-original-to-smp.py │ ├── segformer-original-decoder-to-smp.py │ └── upernet-hf-to-smp.py ├── segmentation_models_pytorch/ │ ├── __init__.py │ ├── __version__.py │ ├── base/ │ │ ├── __init__.py │ │ ├── heads.py │ │ ├── hub_mixin.py │ │ ├── initialization.py │ │ ├── model.py │ │ ├── modules.py │ │ └── utils.py │ ├── datasets/ │ │ ├── __init__.py │ │ └── oxford_pet.py │ ├── decoders/ │ │ ├── __init__.py │ │ ├── deeplabv3/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── dpt/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── fpn/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── linknet/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── manet/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── pan/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── pspnet/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── segformer/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── unet/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ ├── unetplusplus/ │ │ │ ├── __init__.py │ │ │ ├── decoder.py │ │ │ └── model.py │ │ └── upernet/ │ │ ├── __init__.py │ │ ├── decoder.py │ │ └── model.py │ ├── encoders/ │ │ ├── __init__.py │ │ ├── _base.py │ │ ├── _dpn.py │ │ ├── _efficientnet.py │ │ ├── _inceptionresnetv2.py │ │ ├── _inceptionv4.py │ │ ├── _legacy_pretrained_settings.py │ │ ├── _preprocessing.py │ │ ├── _senet.py │ │ ├── _utils.py │ │ ├── _xception.py │ │ ├── densenet.py │ │ ├── dpn.py │ │ ├── efficientnet.py │ │ ├── inceptionresnetv2.py │ │ ├── inceptionv4.py │ │ ├── mix_transformer.py │ │ ├── mobilenet.py │ │ ├── mobileone.py │ │ ├── resnet.py │ │ ├── senet.py │ │ ├── timm_efficientnet.py │ │ ├── timm_sknet.py │ │ ├── timm_universal.py │ │ ├── timm_vit.py │ │ ├── vgg.py │ │ └── xception.py │ ├── losses/ │ │ ├── __init__.py │ │ ├── _functional.py │ │ ├── constants.py │ │ ├── dice.py │ │ ├── focal.py │ │ ├── jaccard.py │ │ ├── lovasz.py │ │ ├── mcc.py │ │ ├── soft_bce.py │ │ ├── soft_ce.py │ │ └── tversky.py │ ├── metrics/ │ │ ├── __init__.py │ │ └── functional.py │ └── utils/ │ ├── __init__.py │ ├── base.py │ ├── functional.py │ ├── losses.py │ ├── meter.py │ ├── metrics.py │ └── train.py └── tests/ ├── __init__.py ├── base/ │ ├── test_freeze_encoder.py │ └── test_modules.py ├── conftest.py ├── encoders/ │ ├── __init__.py │ ├── base.py │ ├── test_batchnorm_deprecation.py │ ├── test_common.py │ ├── test_pretrainedmodels_encoders.py │ ├── test_smp_encoders.py │ ├── test_timm_ported_encoders.py │ ├── test_timm_universal.py │ ├── test_timm_vit_encoders.py │ └── test_torchvision_encoders.py ├── models/ │ ├── __init__.py │ ├── base.py │ ├── test_deeplab.py │ ├── test_dpt.py │ ├── test_fpn.py │ ├── test_linknet.py │ ├── test_manet.py │ ├── test_pan.py │ ├── test_psp.py │ ├── test_segformer.py │ ├── test_unet.py │ ├── test_unetplusplus.py │ └── test_upernet.py ├── test_base.py ├── test_losses.py ├── test_preprocessing.py └── utils.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .devcontainer/devcontainer.json ================================================ { "image": "mcr.microsoft.com/devcontainers/universal:2", "features": { "ghcr.io/devcontainers/features/python:1": { "verison": 3.6 } } } ================================================ FILE: .github/FUNDING.yml ================================================ # These are supported funding model platforms github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2] patreon: # Replace with a single Patreon username open_collective: # Replace with a single Open Collective username ko_fi: qubvel tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry liberapay: qubvel issuehunt: # Replace with a single IssueHunt username otechie: # Replace with a single Otechie username custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2'] ================================================ FILE: .github/codecov.yml ================================================ comment: false github_checks: annotations: false ================================================ FILE: .github/dependabot.yml ================================================ version: 2 updates: - package-ecosystem: "github-actions" directory: "/" schedule: interval: "daily" - package-ecosystem: "pip" directory: "/requirements" schedule: interval: "daily" groups: torch: patterns: - "torch" - "torchvision" ignore: - dependency-name: "setuptools" update-types: ["version-update:semver-patch"] ================================================ FILE: .github/workflows/pypi.yml ================================================ name: Upload Python Package on: release: types: [published] jobs: build: name: build runs-on: ubuntu-latest steps: - name: Clone repo uses: actions/checkout@v6 - name: Set up python uses: actions/setup-python@v6.2.0 with: python-version: '3.14' - name: Install pip dependencies run: pip install build - name: List pip dependencies run: pip list - name: Build project run: python3 -m build - name: Upload artifacts uses: actions/upload-artifact@v7.0.0 with: name: pypi-dist path: dist/ pypi: name: pypi needs: - build permissions: id-token: write runs-on: ubuntu-latest steps: - name: Download artifacts uses: actions/download-artifact@v8.0.1 with: name: pypi-dist path: dist/ - name: Publish to PyPI uses: pypa/gh-action-pypi-publish@v1.13.0 ================================================ FILE: .github/workflows/stale.yaml ================================================ name: 'Close stale issues and PRs' on: schedule: - cron: '30 1 * * *' jobs: stale: runs-on: ubuntu-latest steps: - uses: actions/stale@v10 with: stale-issue-message: 'This issue is stale because it has been open 60 days with no activity. Remove stale label or comment or this will be closed in 7 days.' stale-pr-message: 'This PR is stale because it has been open 60 days with no activity. Remove stale label or comment or this will be closed in 15 days.' close-issue-message: 'This issue was closed because it has been stalled for 7 days with no activity.' close-pr-message: 'This PR was closed because it has been stalled for 15 days with no activity.' days-before-issue-stale: 60 days-before-pr-stale: 60 days-before-issue-close: 7 days-before-pr-close: 15 operations-per-run: 100 ascending: true ================================================ FILE: .github/workflows/tests.yml ================================================ # This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions name: CI on: push: branches: [ main ] pull_request: branches: [ main ] jobs: style: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Set up Python uses: astral-sh/setup-uv@v7 with: python-version: "3.14" activate-environment: true - name: Install dependencies run: uv pip install -r requirements/test.txt # Update output format to enable automatic inline annotations. - name: Run Ruff Linter run: ruff check --output-format=github - name: Run Ruff Formatter run: ruff format --check test: strategy: matrix: os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.12", "3.13", "3.14"] runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v6 - name: Set up Python ${{ matrix.python-version }} uses: astral-sh/setup-uv@v7 with: python-version: ${{ matrix.python-version }} activate-environment: true - name: Install dependencies run: uv pip install -r requirements/required.txt -r requirements/test.txt - name: Show installed packages run: uv pip list - name: Test with PyTest run: uv run pytest -v -rsx -n 2 --cov=segmentation_models_pytorch --cov-report=xml --cov-config=pyproject.toml --non-marked-only - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v5 with: token: ${{ secrets.CODECOV_TOKEN }} slug: qubvel-org/segmentation_models.pytorch test_logits_match: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Set up Python uses: astral-sh/setup-uv@v7 with: python-version: "3.14" activate-environment: true - name: Install dependencies run: uv pip install -r requirements/required.txt -r requirements/test.txt - name: Show installed packages run: uv pip list - name: Test with PyTest run: RUN_SLOW=1 uv run pytest -v -rsx -n 2 -m "logits_match" test_torch_compile: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Set up Python uses: astral-sh/setup-uv@v7 with: python-version: "3.14" activate-environment: true - name: Install dependencies run: uv pip install -r requirements/required.txt -r requirements/test.txt - name: Show installed packages run: uv pip list - name: Test with PyTest run: uv run pytest -v -rsx -n 2 -m "compile" test_torch_export: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Set up Python uses: astral-sh/setup-uv@v7 with: python-version: "3.14" activate-environment: true - name: Install dependencies run: uv pip install -r requirements/required.txt -r requirements/test.txt - name: Show installed packages run: uv pip list - name: Test with PyTest run: uv run pytest -v -rsx -n 2 -m "torch_export" test_torch_script: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Set up Python uses: astral-sh/setup-uv@v7 with: python-version: "3.14" activate-environment: true - name: Install dependencies run: uv pip install -r requirements/required.txt -r requirements/test.txt - name: Show installed packages run: uv pip list - name: Test with PyTest run: uv run pytest -v -rsx -n 2 -m "torch_script" minimum: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Set up Python uses: astral-sh/setup-uv@v7 with: python-version: "3.10" activate-environment: true - name: Install dependencies run: uv pip install -r requirements/minimum.old -r requirements/test.txt - name: Show installed packages run: uv pip list - name: Test with pytest run: uv run pytest -v -rsx -n 2 --non-marked-only ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class .idea/ .venv* examples/images* examples/annotations* # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ 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/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ .vscode/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ # ruff .ruff_cache/ ================================================ FILE: .readthedocs.yaml ================================================ # Read the Docs configuration file for Sphinx projects # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the OS, Python version and other tools you might need build: os: ubuntu-22.04 tools: python: "3.14" # Build documentation in the "docs/" directory with Sphinx sphinx: configuration: docs/conf.py fail_on_warning: true # Optional but recommended, declare the Python requirements required # to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - requirements: requirements/docs.txt ================================================ FILE: HALLOFFAME.md ================================================ # Hall of Fame `Segmentation Models` package is widely used in the image segmentation competitions. Here you can find competitions, names of the winners and links to their solutions. Please, follow these rules, when adding a solution to the "Hall of Fame": 1. Solution should be high rated (e.g. for Kaggle gold or silver medal) 2. There should be a description of the solution (post at the forum / code / blog post / paper / pre-print) ## Kaggle ### [Severstal: Steel Defect Detection](https://www.kaggle.com/c/severstal-steel-defect-detection) - 1st place. [Wuxi Jiangsu](https://www.kaggle.com/rguo97), [Hongbo Zhu](https://www.kaggle.com/zhuhongbo), [Yizhuo Yu](https://www.kaggle.com/paffpaffyu) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114254#latest-675874)] - 5th place. [Guanshuo Xu](https://www.kaggle.com/wowfattie) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/117208#latest-675385)] - 9th place. [Jacek Poplawski](https://www.linkedin.com/in/jacekpoplawski/) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114297#latest-660842)] - 10th place. [Alexey Rozhkov](https://www.linkedin.com/in/alexisrozhkov) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114465#latest-659615)] - 12th place. [Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/), [Ilya Dobrynin](https://www.linkedin.com/in/ilya-dobrynin-79a89b106/), [Denis Kolpakov](https://www.linkedin.com/in/denis-kolpakov-ab3137197/) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114309#latest-661404)] - 31st place. [Insaf Ashrapov](https://www.linkedin.com/in/iashrapov/), [Igor Krashenyi](https://www.linkedin.com/in/igor-krashenyi-38b89b98), [Pavel Pleskov](https://www.linkedin.com/in/ppleskov), [Anton Zakharenkov](https://www.linkedin.com/in/anton-zakharenkov/), [Nikolai Popov](https://www.linkedin.com/in/nikolai-popov-b2157370/) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114383#latest-658438)] [[code](https://github.com/Diyago/Severstal-Steel-Defect-Detection)] - 55th place. [Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/114410#latest-672682)] [[code](https://github.com/khornlund/severstal-steel-defect-detection)] - Efficiency round 1st place. [Stefan Stefanov](https://www.linkedin.com/in/stefan-stefanov-63a77b1) [[description](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/117486#latest-674229)] ### [Understanding Clouds from Satellite Images](https://www.kaggle.com/c/understanding_cloud_organization) - 2nd place. [Andrey Kiryasov](https://www.kaggle.com/ekydna) [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118255#latest-678189)] - 4th place. [Ching-Loong Seow](https://www.linkedin.com/in/clseow/) [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118016#latest-677333)] - 34th place. [Karl Hornlund](https://www.linkedin.com/in/karl-hornlund/) [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118250#latest-678176)] [[code](https://github.com/khornlund/understanding-cloud-organization)] - 55th place. [Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/) [[description](https://www.kaggle.com/c/understanding_cloud_organization/discussion/118019#latest-678626)] ## Other platforms ### [MICCAI 2020 TN-SCUI challenge](https://tn-scui2020.grand-challenge.org/Home/) - 1st place. [Mingyu Wang](https://github.com/WAMAWAMA) [[description](https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st)] [[code](https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st)] ### [Open Cities AI Challenge: Segmenting Buildings for Disaster Resilience](https://www.drivendata.org/competitions/60/building-segmentation-disaster-resilience/) - 1st place. [Pavel Iakubovskii](https://www.linkedin.com/in/pavel-iakubovskii/). [[code and description](https://github.com/qubvel/open-cities-challenge)] ### [Machine Learning based feature extraction of Electrical Substations from Satellite Data ](https://competitions.codalab.org/competitions/32132#learn_the_details) - 3rd place. [Aarsh chaube](https://github.com/Aarsh2001) [[code](https://github.com/Aarsh2001/ML_Challenge_NRSC)] [[Pre-Print](https://github.com/Aarsh2001/ML_Challenge_NRSC/blob/main/3rd%20Rank%20Submission.pdf)] ### [NeurIPS2022 Cell Segmentation Challenge](https://neurips22-cellseg.grand-challenge.org/) - 1st place. [Gihun Lee](https://github.com/Lee-Gihun), [Sangmook Kim](https://github.com/ElvinKim), [Joonkee Kim](https://github.com/joonkeekim) - [[code](https://github.com/Lee-Gihun/MEDIAR)] [[Paper](https://arxiv.org/abs/2212.03465)] ### [ICIP2025 Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge](https://sites.google.com/view/cctgs-challenge/home) - 2nd place. [Ümit Mert Çağlar](linkedin.com/in/mertcaglar), [Alptekin Temizel](https://blog.metu.edu.tr/atemizel/) - [[code](https://github.com/caglarmert/ICIP2025)] [[paper](https://doi.org/10.1117/12.3096537)] [[challange results](https://arxiv.org/abs/2507.04681)] ================================================ FILE: LICENSE ================================================ The MIT License Copyright (c) 2019, Pavel Iakubovskii Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: Makefile ================================================ .PHONY: test # Declare the 'test' target as phony to avoid conflicts with files named 'test' # Variables to store the paths of the python, pip, pytest, and ruff executables PYTHON := $(shell which python) PIP := $(shell which pip) PYTEST := $(shell which pytest) RUFF := $(shell which ruff) # Target to create a Python virtual environment .venv: $(PYTHON) -m venv $(shell dirname $(PYTHON)) # Target to install development dependencies in the virtual environment install_dev: .venv $(PIP) install -e ".[test]" # Target to run tests with pytest, using 2 parallel processes and only non-marked tests test: .venv $(PYTEST) -v -rsx -n 2 tests/ --non-marked-only # Target to run all tests with pytest, including slow tests, using 2 parallel processes test_all: .venv RUN_SLOW=1 $(PYTEST) -v -rsx -n 2 tests/ # Target to generate a table by running a Python script table: $(PYTHON) misc/generate_table.py # Target to generate a table for timm by running a Python script table_timm: $(PYTHON) misc/generate_table_timm.py # Target to fix and format code using ruff fixup: $(RUFF) check --fix $(RUFF) format # Target to run code formatting and tests all: fixup test ================================================ FILE: README.md ================================================
![logo](https://i.ibb.co/dc1XdhT/Segmentation-Models-V2-Side-1-1.png) **Python library with Neural Networks for Image Semantic Segmentation based on [PyTorch](https://pytorch.org/).** [![GitHub Workflow Status (branch)](https://img.shields.io/github/actions/workflow/status/qubvel/segmentation_models.pytorch/tests.yml?branch=main&style=for-the-badge)](https://github.com/qubvel/segmentation_models.pytorch/actions/workflows/tests.yml) ![Codecov](https://img.shields.io/codecov/c/github/qubvel-org/segmentation_models.pytorch?style=for-the-badge) [![Read the Docs](https://img.shields.io/readthedocs/smp?style=for-the-badge&logo=readthedocs&logoColor=white)](https://smp.readthedocs.io/en/latest/)
[![PyPI](https://img.shields.io/pypi/v/segmentation-models-pytorch?color=red&style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/segmentation-models-pytorch/) [![PyTorch - Version](https://img.shields.io/badge/PYTORCH-1.9+-red?style=for-the-badge&logo=pytorch)](https://pepy.tech/project/segmentation-models-pytorch) [![Python - Version](https://img.shields.io/badge/PYTHON-3.9+-red?style=for-the-badge&logo=python&logoColor=white)](https://pepy.tech/project/segmentation-models-pytorch)
[![Generic badge](https://img.shields.io/badge/License-MIT-.svg?style=for-the-badge&color=blue)](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE) [![PyPI - Downloads](https://img.shields.io/pypi/dm/segmentation-models-pytorch?style=for-the-badge&color=blue)](https://pepy.tech/project/segmentation-models-pytorch)
The main features of the library are: - Super simple high-level API (just two lines to create a neural network) - 12 encoder-decoder model architectures (Unet, Unet++, Segformer, DPT, ...) - 800+ **pretrained** convolution- and transform-based encoders, including [timm](https://github.com/huggingface/pytorch-image-models) support - Popular metrics and losses for training routines (Dice, Jaccard, Tversky, ...) - ONNX export and torch script/trace/compile friendly ### 🤝 Sponsor: withoutBG Sponsored by withoutBG [withoutBG](https://github.com/withoutbg/withoutbg) is a high-quality background removal tool. They built their open-source image matting and refiner models using `smp.Unet` and are proudly sponsoring this project. ### [📚 Project Documentation 📚](http://smp.readthedocs.io/) Visit [Read The Docs Project Page](https://smp.readthedocs.io/) or read the following README to know more about Segmentation Models Pytorch (SMP for short) library ### 📋 Table of content 1. [Quick start](#start) 2. [Examples](#examples) 3. [Models and encoders](#models-and-encoders) 4. [Models API](#api) 1. [Input channels](#input-channels) 2. [Auxiliary classification output](#auxiliary-classification-output) 3. [Depth](#depth) 5. [Installation](#installation) 6. [Competitions won with the library](#competitions) 7. [Contributing](#contributing) 8. [Citing](#citing) 9. [License](#license) ## ⏳ Quick start #### 1. Create your first Segmentation model with SMP The segmentation model is just a PyTorch `torch.nn.Module`, which can be created as easy as: ```python import segmentation_models_pytorch as smp model = smp.Unet( encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7 encoder_weights="imagenet", # use `imagenet` pre-trained weights for encoder initialization in_channels=1, # model input channels (1 for gray-scale images, 3 for RGB, etc.) classes=3, # model output channels (number of classes in your dataset) ) ``` - see [table](#architectures) with available model architectures - see [table](#encoders) with available encoders and their corresponding weights #### 2. Configure data preprocessing All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give you better results (higher metric score and faster convergence). It is **not necessary** in case you train the whole model, not only the decoder. ```python from segmentation_models_pytorch.encoders import get_preprocessing_fn preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet') ``` Congratulations! You are done! Now you can train your model with your favorite framework! ## 💡 Examples | Name | Link | Colab | |-------------------------------------------|-----------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------| | **Train** pets binary segmentation on OxfordPets | [Notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb) | | **Train** cars binary segmentation on CamVid | [Notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/cars%20segmentation%20(camvid).ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/cars%20segmentation%20(camvid).ipynb) | | **Train** multiclass segmentation on CamVid | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/camvid_segmentation_multiclass.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel-org/segmentation_models.pytorch/blob/main/examples/camvid_segmentation_multiclass.ipynb) | | **Train** clothes binary segmentation by @ternaus | [Repo](https://github.com/ternaus/cloths_segmentation) | | | **Load and inference** pretrained Segformer | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/segformer_inference_pretrained.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/segformer_inference_pretrained.ipynb) | | **Load and inference** pretrained DPT | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/dpt_inference_pretrained.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/dpt_inference_pretrained.ipynb) | | **Load and inference** pretrained UPerNet | [Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/upernet_inference_pretrained.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/upernet_inference_pretrained.ipynb) | | **Save and load** models locally / to HuggingFace Hub |[Notebook](https://github.com/qubvel-org/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb) | **Export** trained model to ONNX | [Notebook](https://github.com/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb) | ## 📦 Models and encoders ### Architectures | Architecture | Paper | Documentation | Checkpoints | |--------------|-------|---------------|------------| | Unet | [paper](https://arxiv.org/abs/1505.04597) | [docs](https://smp.readthedocs.io/en/latest/models.html#unet) | | | Unet++ | [paper](https://arxiv.org/pdf/1807.10165.pdf) | [docs](https://smp.readthedocs.io/en/latest/models.html#unetplusplus) | | | MAnet | [paper](https://ieeexplore.ieee.org/abstract/document/9201310) | [docs](https://smp.readthedocs.io/en/latest/models.html#manet) | | | Linknet | [paper](https://arxiv.org/abs/1707.03718) | [docs](https://smp.readthedocs.io/en/latest/models.html#linknet) | | | FPN | [paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf) | [docs](https://smp.readthedocs.io/en/latest/models.html#fpn) | | | PSPNet | [paper](https://arxiv.org/abs/1612.01105) | [docs](https://smp.readthedocs.io/en/latest/models.html#pspnet) | | | PAN | [paper](https://arxiv.org/abs/1805.10180) | [docs](https://smp.readthedocs.io/en/latest/models.html#pan) | | | DeepLabV3 | [paper](https://arxiv.org/abs/1706.05587) | [docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3) | | | DeepLabV3+ | [paper](https://arxiv.org/abs/1802.02611) | [docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3plus) | | | UPerNet | [paper](https://arxiv.org/abs/1807.10221) | [docs](https://smp.readthedocs.io/en/latest/models.html#upernet) | [checkpoints](https://huggingface.co/collections/smp-hub/upernet-67fadcdbe08418c6ea94f768) | | Segformer | [paper](https://arxiv.org/abs/2105.15203) | [docs](https://smp.readthedocs.io/en/latest/models.html#segformer) | [checkpoints](https://huggingface.co/collections/smp-hub/segformer-6749eb4923dea2c355f29a1f) | | DPT | [paper](https://arxiv.org/abs/2103.13413) | [docs](https://smp.readthedocs.io/en/latest/models.html#dpt) | [checkpoints](https://huggingface.co/collections/smp-hub/dpt-67f30487327c0599a0c62d68) | ### Encoders The library provides a wide range of **pretrained** encoders (also known as backbones) for segmentation models. Instead of using features from the final layer of a classification model, we extract **intermediate features** and feed them into the decoder for segmentation tasks. All encoders come with **pretrained weights**, which help achieve **faster and more stable convergence** when training segmentation models. Given the extensive selection of supported encoders, you can choose the best one for your specific use case, for example: - **Lightweight encoders** for low-latency applications or real-time inference on edge devices (mobilenet/mobileone). - **High-capacity architectures** for complex tasks involving a large number of segmented classes, providing superior accuracy (convnext/swin/mit). By selecting the right encoder, you can balance **efficiency, performance, and model complexity** to suit your project needs. All encoders and corresponding pretrained weight are listed in the documentation: - [table](https://smp.readthedocs.io/en/latest/encoders.html) with natively ported encoders - [table](https://smp.readthedocs.io/en/latest/encoders_timm.html) with [timm](https://github.com/huggingface/pytorch-image-models) encoders supported ## 🔁 Models API ### Input channels The input channels parameter allows you to create a model that can process a tensor with an arbitrary number of channels. If you use pretrained weights from ImageNet, the weights of the first convolution will be reused: - For the 1-channel case, it would be a sum of the weights of the first convolution layer. - Otherwise, channels would be populated with weights like `new_weight[:, i] = pretrained_weight[:, i % 3]`, and then scaled with `new_weight * 3 / new_in_channels`. ```python model = smp.FPN('resnet34', in_channels=1) mask = model(torch.ones([1, 1, 64, 64])) ``` ### Auxiliary classification output All models support `aux_params` parameters, which is default set to `None`. If `aux_params = None` then classification auxiliary output is not created, else model produce not only `mask`, but also `label` output with shape `NC`. Classification head consists of GlobalPooling->Dropout(optional)->Linear->Activation(optional) layers, which can be configured by `aux_params` as follows: ```python aux_params=dict( pooling='avg', # one of 'avg', 'max' dropout=0.5, # dropout ratio, default is None activation='sigmoid', # activation function, default is None classes=4, # define number of output labels ) model = smp.Unet('resnet34', classes=4, aux_params=aux_params) mask, label = model(x) ``` ### Depth Depth parameter specify a number of downsampling operations in encoder, so you can make your model lighter if specify smaller `depth`. ```python model = smp.Unet('resnet34', encoder_depth=4) ``` ## 🛠 Installation PyPI version: ```bash $ pip install segmentation-models-pytorch ```` The latest version from GitHub: ```bash $ pip install git+https://github.com/qubvel/segmentation_models.pytorch ```` ## 🏆 Competitions won with the library `Segmentation Models` package is widely used in image segmentation competitions. [Here](https://github.com/qubvel/segmentation_models.pytorch/blob/main/HALLOFFAME.md) you can find competitions, names of the winners and links to their solutions. ## 🛠 Projects built with SMP - [withoutBG](https://github.com/withoutbg/withoutbg): An open-source background removal tool that uses `smp.Unet` for its image matting and refinement models. Check [withoutBG Focus on HuggingFace](https://huggingface.co/withoutbg/focus). ## 🤝 Contributing 1. Install SMP in dev mode ```bash make install_dev # Create .venv, install SMP in dev mode ``` 2. Run tests and code checks ```bash make test # Run tests suite with pytest make fixup # Ruff for formatting and lint checks ``` 3. Update a table (in case you added an encoder) ```bash make table # Generates a table with encoders and print to stdout ``` ## 📝 Citing ``` @misc{Iakubovskii:2019, Author = {Pavel Iakubovskii}, Title = {Segmentation Models Pytorch}, Year = {2019}, Publisher = {GitHub}, Journal = {GitHub repository}, Howpublished = {\url{https://github.com/qubvel/segmentation_models.pytorch}} } ``` ## 🛡️ License The project is primarily distributed under [MIT License](https://github.com/qubvel/segmentation_models.pytorch/blob/main/LICENSE), while some files are subject to other licenses. Please refer to [LICENSES](licenses/LICENSES.md) and license statements in each file for careful check, especially for commercial use. ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = . BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) import sys import datetime # import sphinx_rtd_theme sys.path.append("..") # -- Project information ----------------------------------------------------- project = "Segmentation Models" copyright = "{}, Pavel Iakubovskii".format(datetime.datetime.now().year) author = "Pavel Iakubovskii" def get_version(): sys.path.append("../segmentation_models_pytorch") from __version__ import __version__ as version sys.path.pop(-1) return version version = get_version() # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.coverage", "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.viewcode", "sphinx.ext.mathjax", ] # Warn about all references where the target cannot be found. nitpicky = True nitpick_ignore = [ # Undocumented classes ("py:class", "torch.FloatTensor"), ("py:class", "torch.LongTensor"), ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = "sphinx_rtd_theme" # html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # import karma_sphinx_theme # html_theme = "karma_sphinx_theme" html_theme = "sphinx_book_theme" # import catalyst_sphinx_theme # html_theme = "catalyst_sphinx_theme" # html_theme_path = [catalyst_sphinx_theme.get_html_theme_path()] html_logo = "logo.png" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". # html_static_path = ["_static"] # -- Extension configuration ------------------------------------------------- autodoc_inherit_docstrings = False napoleon_google_docstring = True napoleon_include_init_with_doc = True napoleon_numpy_docstring = False autodoc_mock_imports = [ "torch", "tqdm", "numpy", "timm", "cv2", "PIL", "torchvision", "segmentation_models_pytorch.encoders", "segmentation_models_pytorch.utils", # 'segmentation_models_pytorch.base', ] autoclass_content = "both" autodoc_typehints = "description" # sphinx.ext.intersphinx intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "torch": ("https://docs.pytorch.org/docs/stable/", None), } # --- Work around to make autoclass signatures not (*args, **kwargs) ---------- class FakeSignature: def __getattribute__(self, *args): raise ValueError def f(app, obj, bound_method): if "__new__" in obj.__name__: obj.__signature__ = FakeSignature() def setup(app): app.connect("autodoc-before-process-signature", f) # Custom configuration -------------------------------------------------------- autodoc_member_order = "bysource" ================================================ FILE: docs/encoders.rst ================================================ 🔍 Available Encoders ===================== **Segmentation Models PyTorch** provides support for a wide range of encoders. This flexibility allows you to use these encoders with any model in the library by specifying the encoder name in the ``encoder_name`` parameter during model initialization. Here’s a quick example of using a ResNet34 encoder with the ``Unet`` model: .. code-block:: python from segmentation_models_pytorch import Unet # Initialize Unet with ResNet34 encoder pre-trained on ImageNet model = Unet(encoder_name="resnet34", encoder_weights="imagenet") The following encoder families are supported by the library, enabling you to choose the one that best fits your use case: - **Mix Vision Transformer (mit)** - **MobileOne** - **MobileNet** - **EfficientNet** - **ResNet** - **ResNeXt** - **SENet** - **DPN** - **VGG** - **DenseNet** - **Xception** - **Inception** Choosing the Right Encoder -------------------------- 1. **Small Models for Edge Devices** Consider encoders like **MobileNet** or **MobileOne**, which have a smaller parameter count and are optimized for lightweight deployment. 2. **High Performance** If you require state-of-the-art accuracy **Mix Vision Transformer (mit)**, **EfficientNet** families offer excellent balance between performance and computational efficiency. For each encoder, the table below provides detailed information: 1. **Pretrained Weights** Specifies the available pretrained weights (e.g., ``imagenet``, ``imagenet21k``). 2. **Params, M**: The total number of parameters in the encoder, measured in millions. This metric helps you assess the model's size and computational requirements. 3. **Script**: Indicates whether the encoder can be scripted with ``torch.jit.script``. 4. **Compile**: Indicates whether the encoder is compatible with ``torch.compile(model, fullgraph=True, dynamic=True, backend="eager")``. You may still get some issues with another backends, such as ``inductor``, depending on the torch/cuda/... dependencies version, but most of the time it will work. 5. **Export**: Indicates whether the encoder can be exported using ``torch.export.export``, making it suitable for deployment in different environments (e.g., ONNX). ============================ ==================================== =========== ======== ========= ======== Encoder Pretrained weights Params, M Script Compile Export ============================ ==================================== =========== ======== ========= ======== resnet18 imagenet / ssl / swsl 11M ✅ ✅ ✅ resnet34 imagenet 21M ✅ ✅ ✅ resnet50 imagenet / ssl / swsl 23M ✅ ✅ ✅ resnet101 imagenet 42M ✅ ✅ ✅ resnet152 imagenet 58M ✅ ✅ ✅ resnext50_32x4d imagenet / ssl / swsl 22M ✅ ✅ ✅ resnext101_32x4d ssl / swsl 42M ✅ ✅ ✅ resnext101_32x8d imagenet / instagram / ssl / swsl 86M ✅ ✅ ✅ resnext101_32x16d instagram / ssl / swsl 191M ✅ ✅ ✅ resnext101_32x32d instagram 466M ✅ ✅ ✅ resnext101_32x48d instagram 826M ✅ ✅ ✅ dpn68 imagenet 11M ❌ ✅ ✅ dpn68b imagenet+5k 11M ❌ ✅ ✅ dpn92 imagenet+5k 34M ❌ ✅ ✅ dpn98 imagenet 58M ❌ ✅ ✅ dpn107 imagenet+5k 84M ❌ ✅ ✅ dpn131 imagenet 76M ❌ ✅ ✅ vgg11 imagenet 9M ✅ ✅ ✅ vgg11_bn imagenet 9M ✅ ✅ ✅ vgg13 imagenet 9M ✅ ✅ ✅ vgg13_bn imagenet 9M ✅ ✅ ✅ vgg16 imagenet 14M ✅ ✅ ✅ vgg16_bn imagenet 14M ✅ ✅ ✅ vgg19 imagenet 20M ✅ ✅ ✅ vgg19_bn imagenet 20M ✅ ✅ ✅ senet154 imagenet 113M ✅ ✅ ✅ se_resnet50 imagenet 26M ✅ ✅ ✅ se_resnet101 imagenet 47M ✅ ✅ ✅ se_resnet152 imagenet 64M ✅ ✅ ✅ se_resnext50_32x4d imagenet 25M ✅ ✅ ✅ se_resnext101_32x4d imagenet 46M ✅ ✅ ✅ densenet121 imagenet 6M ✅ ✅ ✅ densenet169 imagenet 12M ✅ ✅ ✅ densenet201 imagenet 18M ✅ ✅ ✅ densenet161 imagenet 26M ✅ ✅ ✅ inceptionresnetv2 imagenet / imagenet+background 54M ✅ ✅ ✅ inceptionv4 imagenet / imagenet+background 41M ✅ ✅ ✅ efficientnet-b0 imagenet / advprop 4M ✅ ✅ ✅ efficientnet-b1 imagenet / advprop 6M ✅ ✅ ✅ efficientnet-b2 imagenet / advprop 7M ✅ ✅ ✅ efficientnet-b3 imagenet / advprop 10M ✅ ✅ ✅ efficientnet-b4 imagenet / advprop 17M ✅ ✅ ✅ efficientnet-b5 imagenet / advprop 28M ✅ ✅ ✅ efficientnet-b6 imagenet / advprop 40M ✅ ✅ ✅ efficientnet-b7 imagenet / advprop 63M ✅ ✅ ✅ mobilenet_v2 imagenet 2M ✅ ✅ ✅ xception imagenet 20M ✅ ✅ ✅ timm-efficientnet-b0 imagenet / advprop / noisy-student 4M ✅ ✅ ✅ timm-efficientnet-b1 imagenet / advprop / noisy-student 6M ✅ ✅ ✅ timm-efficientnet-b2 imagenet / advprop / noisy-student 7M ✅ ✅ ✅ timm-efficientnet-b3 imagenet / advprop / noisy-student 10M ✅ ✅ ✅ timm-efficientnet-b4 imagenet / advprop / noisy-student 17M ✅ ✅ ✅ timm-efficientnet-b5 imagenet / advprop / noisy-student 28M ✅ ✅ ✅ timm-efficientnet-b6 imagenet / advprop / noisy-student 40M ✅ ✅ ✅ timm-efficientnet-b7 imagenet / advprop / noisy-student 63M ✅ ✅ ✅ timm-efficientnet-b8 imagenet / advprop 84M ✅ ✅ ✅ timm-efficientnet-l2 noisy-student / noisy-student-475 474M ✅ ✅ ✅ timm-tf_efficientnet_lite0 imagenet 3M ✅ ✅ ✅ timm-tf_efficientnet_lite1 imagenet 4M ✅ ✅ ✅ timm-tf_efficientnet_lite2 imagenet 4M ✅ ✅ ✅ timm-tf_efficientnet_lite3 imagenet 6M ✅ ✅ ✅ timm-tf_efficientnet_lite4 imagenet 11M ✅ ✅ ✅ timm-skresnet18 imagenet 11M ✅ ✅ ✅ timm-skresnet34 imagenet 21M ✅ ✅ ✅ timm-skresnext50_32x4d imagenet 23M ✅ ✅ ✅ mit_b0 imagenet 3M ✅ ✅ ✅ mit_b1 imagenet 13M ✅ ✅ ✅ mit_b2 imagenet 24M ✅ ✅ ✅ mit_b3 imagenet 44M ✅ ✅ ✅ mit_b4 imagenet 60M ✅ ✅ ✅ mit_b5 imagenet 81M ✅ ✅ ✅ mobileone_s0 imagenet 4M ✅ ✅ ✅ mobileone_s1 imagenet 3M ✅ ✅ ✅ mobileone_s2 imagenet 5M ✅ ✅ ✅ mobileone_s3 imagenet 8M ✅ ✅ ✅ mobileone_s4 imagenet 12M ✅ ✅ ✅ ============================ ==================================== =========== ======== ========= ======== ================================================ FILE: docs/encoders_dpt.rst ================================================ :orphan: .. _dpt-encoders: DPT Encoders ============ This is a list of Vision Transformer encoders that are compatible with the DPT architecture. While other Vision Transformer encoders from timm may also be compatible, the ones listed below are tested to work properly. .. list-table:: Encoder Name :widths: 100 :header-rows: 0 * - tu-fastvit_ma36.apple_dist_in1k * - tu-fastvit_ma36.apple_in1k * - tu-fastvit_mci0.apple_mclip * - tu-fastvit_mci1.apple_mclip * - tu-fastvit_mci2.apple_mclip * - tu-fastvit_s12.apple_dist_in1k * - tu-fastvit_s12.apple_in1k * - tu-fastvit_sa12.apple_dist_in1k * - tu-fastvit_sa12.apple_in1k * - tu-fastvit_sa24.apple_dist_in1k * - tu-fastvit_sa24.apple_in1k * - tu-fastvit_sa36.apple_dist_in1k * - tu-fastvit_sa36.apple_in1k * - tu-fastvit_t8.apple_dist_in1k * - tu-fastvit_t8.apple_in1k * - tu-fastvit_t12.apple_dist_in1k * - tu-fastvit_t12.apple_in1k * - tu-flexivit_base.300ep_in1k * - tu-flexivit_base.300ep_in21k * - tu-flexivit_base.600ep_in1k * - tu-flexivit_base.1000ep_in21k * - tu-flexivit_base.1200ep_in1k * - tu-flexivit_base.patch16_in21k * - tu-flexivit_base.patch30_in21k * - tu-flexivit_large.300ep_in1k * - tu-flexivit_large.600ep_in1k * - tu-flexivit_large.1200ep_in1k * - tu-flexivit_small.300ep_in1k * - tu-flexivit_small.600ep_in1k * - tu-flexivit_small.1200ep_in1k * - tu-maxvit_base_tf_224.in1k * - tu-maxvit_base_tf_224.in21k * - tu-maxvit_base_tf_384.in1k * - tu-maxvit_base_tf_384.in21k_ft_in1k * - tu-maxvit_base_tf_512.in1k * - tu-maxvit_base_tf_512.in21k_ft_in1k * - tu-maxvit_large_tf_224.in1k * - tu-maxvit_large_tf_224.in21k * - tu-maxvit_large_tf_384.in1k * - tu-maxvit_large_tf_384.in21k_ft_in1k * - tu-maxvit_large_tf_512.in1k * - tu-maxvit_large_tf_512.in21k_ft_in1k * - tu-maxvit_nano_rw_256.sw_in1k * - tu-maxvit_rmlp_base_rw_224.sw_in12k * - tu-maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k * - tu-maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k * - tu-maxvit_rmlp_nano_rw_256.sw_in1k * - tu-maxvit_rmlp_pico_rw_256.sw_in1k * - tu-maxvit_rmlp_small_rw_224.sw_in1k * - tu-maxvit_rmlp_tiny_rw_256.sw_in1k * - tu-maxvit_small_tf_224.in1k * - tu-maxvit_small_tf_384.in1k * - tu-maxvit_small_tf_512.in1k * - tu-maxvit_tiny_rw_224.sw_in1k * - tu-maxvit_tiny_tf_224.in1k * - tu-maxvit_tiny_tf_384.in1k * - tu-maxvit_tiny_tf_512.in1k * - tu-maxvit_xlarge_tf_224.in21k * - tu-maxvit_xlarge_tf_384.in21k_ft_in1k * - tu-maxvit_xlarge_tf_512.in21k_ft_in1k * - tu-maxxvit_rmlp_nano_rw_256.sw_in1k * - tu-maxxvit_rmlp_small_rw_256.sw_in1k * - tu-maxxvitv2_nano_rw_256.sw_in1k * - tu-maxxvitv2_rmlp_base_rw_224.sw_in12k * - tu-maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k * - tu-maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k * - tu-mobilevit_s.cvnets_in1k * - tu-mobilevit_xs.cvnets_in1k * - tu-mobilevit_xxs.cvnets_in1k * - tu-mobilevitv2_050.cvnets_in1k * - tu-mobilevitv2_075.cvnets_in1k * - tu-mobilevitv2_100.cvnets_in1k * - tu-mobilevitv2_125.cvnets_in1k * - tu-mobilevitv2_150.cvnets_in1k * - tu-mobilevitv2_150.cvnets_in22k_ft_in1k * - tu-mobilevitv2_150.cvnets_in22k_ft_in1k_384 * - tu-mobilevitv2_175.cvnets_in1k * - tu-mobilevitv2_175.cvnets_in22k_ft_in1k * - tu-mobilevitv2_175.cvnets_in22k_ft_in1k_384 * - tu-mobilevitv2_200.cvnets_in1k * - tu-mobilevitv2_200.cvnets_in22k_ft_in1k * - tu-mobilevitv2_200.cvnets_in22k_ft_in1k_384 * - tu-mvitv2_base.fb_in1k * - tu-mvitv2_base_cls.fb_inw21k * - tu-mvitv2_huge_cls.fb_inw21k * - tu-mvitv2_large.fb_in1k * - tu-mvitv2_large_cls.fb_inw21k * - tu-mvitv2_small.fb_in1k * - tu-mvitv2_tiny.fb_in1k * - tu-samvit_base_patch16.sa1b * - tu-samvit_huge_patch16.sa1b * - tu-samvit_large_patch16.sa1b * - tu-test_vit2.r160_in1k * - tu-test_vit3.r160_in1k * - tu-test_vit.r160_in1k * - tu-vit_base_mci_224.apple_mclip * - tu-vit_base_mci_224.apple_mclip_lt * - tu-vit_base_patch8_224.augreg2_in21k_ft_in1k * - tu-vit_base_patch8_224.augreg_in21k * - tu-vit_base_patch8_224.augreg_in21k_ft_in1k * - tu-vit_base_patch8_224.dino * - tu-vit_base_patch16_224.augreg2_in21k_ft_in1k * - tu-vit_base_patch16_224.augreg_in1k * - tu-vit_base_patch16_224.augreg_in21k * - tu-vit_base_patch16_224.augreg_in21k_ft_in1k * - tu-vit_base_patch16_224.dino * - tu-vit_base_patch16_224.mae * - tu-vit_base_patch16_224.orig_in21k * - tu-vit_base_patch16_224.orig_in21k_ft_in1k * - tu-vit_base_patch16_224.sam_in1k * - tu-vit_base_patch16_224_miil.in21k * - tu-vit_base_patch16_224_miil.in21k_ft_in1k * - tu-vit_base_patch16_384.augreg_in1k * - tu-vit_base_patch16_384.augreg_in21k_ft_in1k * - tu-vit_base_patch16_384.orig_in21k_ft_in1k * - tu-vit_base_patch16_clip_224.datacompxl * - tu-vit_base_patch16_clip_224.dfn2b * - tu-vit_base_patch16_clip_224.laion2b * - tu-vit_base_patch16_clip_224.laion2b_ft_in1k * - tu-vit_base_patch16_clip_224.laion2b_ft_in12k * - tu-vit_base_patch16_clip_224.laion2b_ft_in12k_in1k * - tu-vit_base_patch16_clip_224.laion400m_e32 * - tu-vit_base_patch16_clip_224.metaclip_2pt5b * - tu-vit_base_patch16_clip_224.metaclip_400m * - tu-vit_base_patch16_clip_224.openai * - tu-vit_base_patch16_clip_224.openai_ft_in1k * - tu-vit_base_patch16_clip_224.openai_ft_in12k * - tu-vit_base_patch16_clip_224.openai_ft_in12k_in1k * - tu-vit_base_patch16_clip_384.laion2b_ft_in1k * - tu-vit_base_patch16_clip_384.laion2b_ft_in12k_in1k * - tu-vit_base_patch16_clip_384.openai_ft_in1k * - tu-vit_base_patch16_clip_384.openai_ft_in12k_in1k * - tu-vit_base_patch16_clip_quickgelu_224.metaclip_2pt5b * - tu-vit_base_patch16_clip_quickgelu_224.metaclip_400m * - tu-vit_base_patch16_clip_quickgelu_224.openai * - tu-vit_base_patch16_plus_clip_240.laion400m_e32 * - tu-vit_base_patch16_rope_reg1_gap_256.sbb_in1k * - tu-vit_base_patch16_rpn_224.sw_in1k * - tu-vit_base_patch16_siglip_224.v2_webli * - tu-vit_base_patch16_siglip_224.webli * - tu-vit_base_patch16_siglip_256.v2_webli * - tu-vit_base_patch16_siglip_256.webli * - tu-vit_base_patch16_siglip_256.webli_i18n * - tu-vit_base_patch16_siglip_384.v2_webli * - tu-vit_base_patch16_siglip_384.webli * - tu-vit_base_patch16_siglip_512.v2_webli * - tu-vit_base_patch16_siglip_512.webli * - tu-vit_base_patch16_siglip_gap_224.v2_webli * - tu-vit_base_patch16_siglip_gap_224.webli * - tu-vit_base_patch16_siglip_gap_256.v2_webli * - tu-vit_base_patch16_siglip_gap_256.webli * - tu-vit_base_patch16_siglip_gap_256.webli_i18n * - tu-vit_base_patch16_siglip_gap_384.v2_webli * - tu-vit_base_patch16_siglip_gap_384.webli * - tu-vit_base_patch16_siglip_gap_512.v2_webli * - tu-vit_base_patch16_siglip_gap_512.webli * - tu-vit_base_patch32_224.augreg_in1k * - tu-vit_base_patch32_224.augreg_in21k * - tu-vit_base_patch32_224.augreg_in21k_ft_in1k * - tu-vit_base_patch32_224.orig_in21k * - tu-vit_base_patch32_224.sam_in1k * - tu-vit_base_patch32_384.augreg_in1k * - tu-vit_base_patch32_384.augreg_in21k_ft_in1k * - tu-vit_base_patch32_clip_224.datacompxl * - tu-vit_base_patch32_clip_224.laion2b * - tu-vit_base_patch32_clip_224.laion2b_ft_in1k * - tu-vit_base_patch32_clip_224.laion2b_ft_in12k_in1k * - tu-vit_base_patch32_clip_224.laion400m_e32 * - tu-vit_base_patch32_clip_224.metaclip_2pt5b * - tu-vit_base_patch32_clip_224.metaclip_400m * - tu-vit_base_patch32_clip_224.openai * - tu-vit_base_patch32_clip_224.openai_ft_in1k * - tu-vit_base_patch32_clip_256.datacompxl * - tu-vit_base_patch32_clip_384.laion2b_ft_in12k_in1k * - tu-vit_base_patch32_clip_384.openai_ft_in12k_in1k * - tu-vit_base_patch32_clip_448.laion2b_ft_in12k_in1k * - tu-vit_base_patch32_clip_quickgelu_224.laion400m_e32 * - tu-vit_base_patch32_clip_quickgelu_224.metaclip_2pt5b * - tu-vit_base_patch32_clip_quickgelu_224.metaclip_400m * - tu-vit_base_patch32_clip_quickgelu_224.openai * - tu-vit_base_patch32_siglip_256.v2_webli * - tu-vit_base_patch32_siglip_gap_256.v2_webli * - tu-vit_base_r50_s16_224.orig_in21k * - tu-vit_base_r50_s16_384.orig_in21k_ft_in1k * - tu-vit_betwixt_patch16_reg1_gap_256.sbb_in1k * - tu-vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k * - tu-vit_betwixt_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k * - tu-vit_betwixt_patch16_reg4_gap_256.sbb_in1k * - tu-vit_betwixt_patch16_reg4_gap_256.sbb_in12k * - tu-vit_betwixt_patch16_reg4_gap_256.sbb_in12k_ft_in1k * - tu-vit_betwixt_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k * - tu-vit_betwixt_patch16_rope_reg4_gap_256.sbb_in1k * - tu-vit_betwixt_patch32_clip_224.tinyclip_laion400m * - tu-vit_giant_patch16_gap_224.in22k_ijepa * - tu-vit_giantopt_patch16_siglip_256.v2_webli * - tu-vit_giantopt_patch16_siglip_384.v2_webli * - tu-vit_giantopt_patch16_siglip_gap_256.v2_webli * - tu-vit_giantopt_patch16_siglip_gap_384.v2_webli * - tu-vit_huge_patch16_gap_448.in1k_ijepa * - tu-vit_large_patch16_224.augreg_in21k * - tu-vit_large_patch16_224.augreg_in21k_ft_in1k * - tu-vit_large_patch16_224.mae * - tu-vit_large_patch16_224.orig_in21k * - tu-vit_large_patch16_384.augreg_in21k_ft_in1k * - tu-vit_large_patch16_siglip_256.v2_webli * - tu-vit_large_patch16_siglip_256.webli * - tu-vit_large_patch16_siglip_384.v2_webli * - tu-vit_large_patch16_siglip_384.webli * - tu-vit_large_patch16_siglip_512.v2_webli * - tu-vit_large_patch16_siglip_gap_256.v2_webli * - tu-vit_large_patch16_siglip_gap_256.webli * - tu-vit_large_patch16_siglip_gap_384.v2_webli * - tu-vit_large_patch16_siglip_gap_384.webli * - tu-vit_large_patch16_siglip_gap_512.v2_webli * - tu-vit_large_patch32_224.orig_in21k * - tu-vit_large_patch32_384.orig_in21k_ft_in1k * - tu-vit_large_r50_s32_224.augreg_in21k * - tu-vit_large_r50_s32_224.augreg_in21k_ft_in1k * - tu-vit_large_r50_s32_384.augreg_in21k_ft_in1k * - tu-vit_little_patch16_reg1_gap_256.sbb_in12k * - tu-vit_little_patch16_reg1_gap_256.sbb_in12k_ft_in1k * - tu-vit_little_patch16_reg4_gap_256.sbb_in1k * - tu-vit_medium_patch16_clip_224.tinyclip_yfcc15m * - tu-vit_medium_patch16_gap_240.sw_in12k * - tu-vit_medium_patch16_gap_256.sw_in12k_ft_in1k * - tu-vit_medium_patch16_gap_384.sw_in12k_ft_in1k * - tu-vit_medium_patch16_reg1_gap_256.sbb_in1k * - tu-vit_medium_patch16_reg4_gap_256.sbb_in1k * - tu-vit_medium_patch16_reg4_gap_256.sbb_in12k * - tu-vit_medium_patch16_reg4_gap_256.sbb_in12k_ft_in1k * - tu-vit_medium_patch16_rope_reg1_gap_256.sbb_in1k * - tu-vit_medium_patch32_clip_224.tinyclip_laion400m * - tu-vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k * - tu-vit_mediumd_patch16_reg4_gap_256.sbb2_e200_in12k_ft_in1k * - tu-vit_mediumd_patch16_reg4_gap_256.sbb_in12k * - tu-vit_mediumd_patch16_reg4_gap_256.sbb_in12k_ft_in1k * - tu-vit_mediumd_patch16_reg4_gap_384.sbb2_e200_in12k_ft_in1k * - tu-vit_mediumd_patch16_rope_reg1_gap_256.sbb_in1k * - tu-vit_pwee_patch16_reg1_gap_256.sbb_in1k * - tu-vit_relpos_base_patch16_224.sw_in1k * - tu-vit_relpos_base_patch16_clsgap_224.sw_in1k * - tu-vit_relpos_base_patch32_plus_rpn_256.sw_in1k * - tu-vit_relpos_medium_patch16_224.sw_in1k * - tu-vit_relpos_medium_patch16_cls_224.sw_in1k * - tu-vit_relpos_medium_patch16_rpn_224.sw_in1k * - tu-vit_relpos_small_patch16_224.sw_in1k * - tu-vit_small_patch8_224.dino * - tu-vit_small_patch16_224.augreg_in1k * - tu-vit_small_patch16_224.augreg_in21k * - tu-vit_small_patch16_224.augreg_in21k_ft_in1k * - tu-vit_small_patch16_224.dino * - tu-vit_small_patch16_384.augreg_in1k * - tu-vit_small_patch16_384.augreg_in21k_ft_in1k * - tu-vit_small_patch32_224.augreg_in21k * - tu-vit_small_patch32_224.augreg_in21k_ft_in1k * - tu-vit_small_patch32_384.augreg_in21k_ft_in1k * - tu-vit_small_r26_s32_224.augreg_in21k * - tu-vit_small_r26_s32_224.augreg_in21k_ft_in1k * - tu-vit_small_r26_s32_384.augreg_in21k_ft_in1k * - tu-vit_so150m2_patch16_reg1_gap_256.sbb_e200_in12k * - tu-vit_so150m2_patch16_reg1_gap_256.sbb_e200_in12k_ft_in1k * - tu-vit_so150m2_patch16_reg1_gap_384.sbb_e200_in12k_ft_in1k * - tu-vit_so150m2_patch16_reg1_gap_448.sbb_e200_in12k_ft_in1k * - tu-vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k * - tu-vit_so150m_patch16_reg4_gap_256.sbb_e250_in12k_ft_in1k * - tu-vit_so150m_patch16_reg4_gap_384.sbb_e250_in12k_ft_in1k * - tu-vit_so400m_patch16_siglip_256.v2_webli * - tu-vit_so400m_patch16_siglip_256.webli_i18n * - tu-vit_so400m_patch16_siglip_384.v2_webli * - tu-vit_so400m_patch16_siglip_512.v2_webli * - tu-vit_so400m_patch16_siglip_gap_256.v2_webli * - tu-vit_so400m_patch16_siglip_gap_256.webli_i18n * - tu-vit_so400m_patch16_siglip_gap_384.v2_webli * - tu-vit_so400m_patch16_siglip_gap_512.v2_webli * - tu-vit_srelpos_medium_patch16_224.sw_in1k * - tu-vit_srelpos_small_patch16_224.sw_in1k * - tu-vit_tiny_patch16_224.augreg_in21k * - tu-vit_tiny_patch16_224.augreg_in21k_ft_in1k * - tu-vit_tiny_patch16_384.augreg_in21k_ft_in1k * - tu-vit_tiny_r_s16_p8_224.augreg_in21k * - tu-vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k * - tu-vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k * - tu-vit_wee_patch16_reg1_gap_256.sbb_in1k * - tu-vit_xsmall_patch16_clip_224.tinyclip_yfcc15m * - tu-vitamin_base_224.datacomp1b_clip * - tu-vitamin_base_224.datacomp1b_clip_ltt * - tu-vitamin_large2_224.datacomp1b_clip * - tu-vitamin_large2_256.datacomp1b_clip * - tu-vitamin_large2_336.datacomp1b_clip * - tu-vitamin_large2_384.datacomp1b_clip * - tu-vitamin_large_224.datacomp1b_clip * - tu-vitamin_large_256.datacomp1b_clip * - tu-vitamin_large_336.datacomp1b_clip * - tu-vitamin_large_384.datacomp1b_clip * - tu-vitamin_small_224.datacomp1b_clip * - tu-vitamin_small_224.datacomp1b_clip_ltt * - tu-vitamin_xlarge_256.datacomp1b_clip * - tu-vitamin_xlarge_336.datacomp1b_clip * - tu-vitamin_xlarge_384.datacomp1b_clip * - tu-hiera_small_abswin_256.sbb2_e200_in12k * - tu-hiera_small_abswin_256.sbb2_e200_in12k_ft_in1k * - tu-hiera_small_abswin_256.sbb2_pd_e200_in12k * - tu-hiera_small_abswin_256.sbb2_pd_e200_in12k_ft_in1k * - tu-swin_base_patch4_window7_224.ms_in1k * - tu-swin_base_patch4_window7_224.ms_in22k * - tu-swin_base_patch4_window7_224.ms_in22k_ft_in1k * - tu-swin_base_patch4_window12_384.ms_in1k * - tu-swin_base_patch4_window12_384.ms_in22k * - tu-swin_base_patch4_window12_384.ms_in22k_ft_in1k * - tu-swin_large_patch4_window7_224.ms_in22k * - tu-swin_large_patch4_window7_224.ms_in22k_ft_in1k * - tu-swin_large_patch4_window12_384.ms_in22k * - tu-swin_large_patch4_window12_384.ms_in22k_ft_in1k * - tu-swin_s3_base_224.ms_in1k * - tu-swin_s3_small_224.ms_in1k * - tu-swin_s3_tiny_224.ms_in1k * - tu-swin_small_patch4_window7_224.ms_in1k * - tu-swin_small_patch4_window7_224.ms_in22k * - tu-swin_small_patch4_window7_224.ms_in22k_ft_in1k * - tu-swin_tiny_patch4_window7_224.ms_in1k * - tu-swin_tiny_patch4_window7_224.ms_in22k * - tu-swin_tiny_patch4_window7_224.ms_in22k_ft_in1k * - tu-swinv2_base_window8_256.ms_in1k * - tu-swinv2_base_window12_192.ms_in22k * - tu-swinv2_base_window12to16_192to256.ms_in22k_ft_in1k * - tu-swinv2_base_window12to24_192to384.ms_in22k_ft_in1k * - tu-swinv2_base_window16_256.ms_in1k * - tu-swinv2_cr_small_224.sw_in1k * - tu-swinv2_cr_small_ns_224.sw_in1k * - tu-swinv2_cr_tiny_ns_224.sw_in1k * - tu-swinv2_large_window12_192.ms_in22k * - tu-swinv2_large_window12to16_192to256.ms_in22k_ft_in1k * - tu-swinv2_large_window12to24_192to384.ms_in22k_ft_in1k * - tu-swinv2_small_window8_256.ms_in1k * - tu-swinv2_small_window16_256.ms_in1k * - tu-swinv2_tiny_window8_256.ms_in1k * - tu-swinv2_tiny_window16_256.ms_in1k * - tu-efficientformer_l1.snap_dist_in1k * - tu-efficientformer_l3.snap_dist_in1k * - tu-efficientformer_l7.snap_dist_in1k * - tu-beit_base_patch16_224.in22k_ft_in22k * - tu-beit_base_patch16_224.in22k_ft_in22k_in1k * - tu-beit_base_patch16_384.in22k_ft_in22k_in1k * - tu-beit_large_patch16_224.in22k_ft_in22k * - tu-beit_large_patch16_224.in22k_ft_in22k_in1k * - tu-beit_large_patch16_384.in22k_ft_in22k_in1k * - tu-beit_large_patch16_512.in22k_ft_in22k_in1k * - tu-beitv2_base_patch16_224.in1k_ft_in1k * - tu-beitv2_base_patch16_224.in1k_ft_in22k * - tu-beitv2_base_patch16_224.in1k_ft_in22k_in1k * - tu-beitv2_large_patch16_224.in1k_ft_in1k * - tu-beitv2_large_patch16_224.in1k_ft_in22k * - tu-beitv2_large_patch16_224.in1k_ft_in22k_in1k * - tu-cait_m36_384.fb_dist_in1k * - tu-cait_m48_448.fb_dist_in1k * - tu-cait_s24_224.fb_dist_in1k * - tu-cait_s24_384.fb_dist_in1k * - tu-cait_s36_384.fb_dist_in1k * - tu-cait_xs24_384.fb_dist_in1k * - tu-cait_xxs24_224.fb_dist_in1k * - tu-cait_xxs24_384.fb_dist_in1k * - tu-cait_xxs36_224.fb_dist_in1k * - tu-cait_xxs36_384.fb_dist_in1k * - tu-coatnet_0_rw_224.sw_in1k * - tu-coatnet_1_rw_224.sw_in1k * - tu-coatnet_2_rw_224.sw_in12k * - tu-coatnet_2_rw_224.sw_in12k_ft_in1k * - tu-coatnet_3_rw_224.sw_in12k * - tu-coatnet_bn_0_rw_224.sw_in1k * - tu-coatnet_nano_rw_224.sw_in1k * - tu-coatnet_rmlp_1_rw2_224.sw_in12k * - tu-coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k * - tu-coatnet_rmlp_1_rw_224.sw_in1k * - tu-coatnet_rmlp_2_rw_224.sw_in1k * - tu-coatnet_rmlp_2_rw_224.sw_in12k * - tu-coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k * - tu-coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k * - tu-coatnet_rmlp_nano_rw_224.sw_in1k * - tu-deit3_base_patch16_224.fb_in1k * - tu-deit3_base_patch16_224.fb_in22k_ft_in1k * - tu-deit3_base_patch16_384.fb_in1k * - tu-deit3_base_patch16_384.fb_in22k_ft_in1k * - tu-deit3_large_patch16_224.fb_in1k * - tu-deit3_large_patch16_224.fb_in22k_ft_in1k * - tu-deit3_large_patch16_384.fb_in1k * - tu-deit3_large_patch16_384.fb_in22k_ft_in1k * - tu-deit3_medium_patch16_224.fb_in1k * - tu-deit3_medium_patch16_224.fb_in22k_ft_in1k * - tu-deit3_small_patch16_224.fb_in1k * - tu-deit3_small_patch16_224.fb_in22k_ft_in1k * - tu-deit3_small_patch16_384.fb_in1k * - tu-deit3_small_patch16_384.fb_in22k_ft_in1k * - tu-deit_base_distilled_patch16_224.fb_in1k * - tu-deit_base_distilled_patch16_384.fb_in1k * - tu-deit_base_patch16_224.fb_in1k * - tu-deit_base_patch16_384.fb_in1k * - tu-deit_small_distilled_patch16_224.fb_in1k * - tu-deit_small_patch16_224.fb_in1k * - tu-deit_tiny_distilled_patch16_224.fb_in1k * - tu-deit_tiny_patch16_224.fb_in1k * - tu-regnety_160.deit_in1k * - tu-twins_pcpvt_base.in1k * - tu-twins_pcpvt_large.in1k * - tu-twins_pcpvt_small.in1k * - tu-twins_svt_base.in1k * - tu-twins_svt_large.in1k * - tu-twins_svt_small.in1k * - tu-xcit_large_24_p8_224.fb_dist_in1k * - tu-xcit_large_24_p8_224.fb_in1k * - tu-xcit_large_24_p8_384.fb_dist_in1k * - tu-xcit_large_24_p16_224.fb_dist_in1k * - tu-xcit_large_24_p16_224.fb_in1k * - tu-xcit_large_24_p16_384.fb_dist_in1k * - tu-xcit_medium_24_p8_224.fb_dist_in1k * - tu-xcit_medium_24_p8_224.fb_in1k * - tu-xcit_medium_24_p8_384.fb_dist_in1k * - tu-xcit_medium_24_p16_224.fb_dist_in1k * - tu-xcit_medium_24_p16_224.fb_in1k * - tu-xcit_medium_24_p16_384.fb_dist_in1k * - tu-xcit_nano_12_p8_224.fb_dist_in1k * - tu-xcit_nano_12_p8_224.fb_in1k * - tu-xcit_nano_12_p8_384.fb_dist_in1k * - tu-xcit_nano_12_p16_224.fb_dist_in1k * - tu-xcit_nano_12_p16_224.fb_in1k * - tu-xcit_nano_12_p16_384.fb_dist_in1k * - tu-xcit_small_12_p8_224.fb_dist_in1k * - tu-xcit_small_12_p8_224.fb_in1k * - tu-xcit_small_12_p8_384.fb_dist_in1k * - tu-xcit_small_12_p16_224.fb_dist_in1k * - tu-xcit_small_12_p16_224.fb_in1k * - tu-xcit_small_12_p16_384.fb_dist_in1k * - tu-xcit_small_24_p8_224.fb_dist_in1k * - tu-xcit_small_24_p8_224.fb_in1k * - tu-xcit_small_24_p8_384.fb_dist_in1k * - tu-xcit_small_24_p16_224.fb_dist_in1k * - tu-xcit_small_24_p16_224.fb_in1k * - tu-xcit_small_24_p16_384.fb_dist_in1k * - tu-xcit_tiny_12_p8_224.fb_dist_in1k * - tu-xcit_tiny_12_p8_224.fb_in1k * - tu-xcit_tiny_12_p8_384.fb_dist_in1k * - tu-xcit_tiny_12_p16_224.fb_dist_in1k * - tu-xcit_tiny_12_p16_224.fb_in1k * - tu-xcit_tiny_12_p16_384.fb_dist_in1k * - tu-xcit_tiny_24_p8_224.fb_dist_in1k * - tu-xcit_tiny_24_p8_224.fb_in1k * - tu-xcit_tiny_24_p8_384.fb_dist_in1k * - tu-xcit_tiny_24_p16_224.fb_dist_in1k * - tu-xcit_tiny_24_p16_224.fb_in1k * - tu-xcit_tiny_24_p16_384.fb_dist_in1k ================================================ FILE: docs/encoders_timm.rst ================================================ 🎯 Timm Encoders ================ Pytorch Image Models (a.k.a. timm) has a lot of pretrained models and interface which allows using these models as encoders in smp, however, not all models are supported - not all transformer models have ``features_only`` functionality implemented that is required for encoder - some models have inappropriate strides Below is a table of suitable encoders (for DeepLabV3, DeepLabV3+, and PAN dilation support is needed also) Total number of encoders: 812 (593+219) .. note:: To use following encoders you have to add prefix ``tu-``, e.g. ``tu-adv_inception_v3`` Traditional-Style ~~~~~~~~~~~~~~~~~ These models typically produce feature maps at the following downsampling scales relative to the input resolution: 1/2, 1/4, 1/8, 1/16, and 1/32 +----------------------------------+------------------+ | Encoder name | Support dilation | +==================================+==================+ | bat_resnext26ts | ✅ | +----------------------------------+------------------+ | botnet26t_256 | ✅ | +----------------------------------+------------------+ | botnet50ts_256 | ✅ | +----------------------------------+------------------+ | coatnet_0_224 | | +----------------------------------+------------------+ | coatnet_0_rw_224 | | +----------------------------------+------------------+ | coatnet_1_224 | | +----------------------------------+------------------+ | coatnet_1_rw_224 | | +----------------------------------+------------------+ | coatnet_2_224 | | +----------------------------------+------------------+ | coatnet_2_rw_224 | | +----------------------------------+------------------+ | coatnet_3_224 | | +----------------------------------+------------------+ | coatnet_3_rw_224 | | +----------------------------------+------------------+ | coatnet_4_224 | | +----------------------------------+------------------+ | coatnet_5_224 | | +----------------------------------+------------------+ | coatnet_bn_0_rw_224 | | +----------------------------------+------------------+ | coatnet_nano_cc_224 | | +----------------------------------+------------------+ | coatnet_nano_rw_224 | | +----------------------------------+------------------+ | coatnet_pico_rw_224 | | +----------------------------------+------------------+ | coatnet_rmlp_0_rw_224 | | +----------------------------------+------------------+ | coatnet_rmlp_1_rw2_224 | | +----------------------------------+------------------+ | coatnet_rmlp_1_rw_224 | | +----------------------------------+------------------+ | coatnet_rmlp_2_rw_224 | | +----------------------------------+------------------+ | coatnet_rmlp_2_rw_384 | | +----------------------------------+------------------+ | coatnet_rmlp_3_rw_224 | | +----------------------------------+------------------+ | coatnet_rmlp_nano_rw_224 | | +----------------------------------+------------------+ | coatnext_nano_rw_224 | | +----------------------------------+------------------+ | cs3darknet_focus_l | ✅ | +----------------------------------+------------------+ | cs3darknet_focus_m | ✅ | +----------------------------------+------------------+ | cs3darknet_focus_s | ✅ | +----------------------------------+------------------+ | cs3darknet_focus_x | ✅ | +----------------------------------+------------------+ | cs3darknet_l | ✅ | +----------------------------------+------------------+ | cs3darknet_m | ✅ | +----------------------------------+------------------+ | cs3darknet_s | ✅ | +----------------------------------+------------------+ | cs3darknet_x | ✅ | +----------------------------------+------------------+ | cs3edgenet_x | ✅ | +----------------------------------+------------------+ | cs3se_edgenet_x | ✅ | +----------------------------------+------------------+ | cs3sedarknet_l | ✅ | +----------------------------------+------------------+ | cs3sedarknet_x | ✅ | +----------------------------------+------------------+ | cs3sedarknet_xdw | ✅ | +----------------------------------+------------------+ | cspdarknet53 | ✅ | +----------------------------------+------------------+ | cspresnet50 | ✅ | +----------------------------------+------------------+ | cspresnet50d | ✅ | +----------------------------------+------------------+ | cspresnet50w | ✅ | +----------------------------------+------------------+ | cspresnext50 | ✅ | +----------------------------------+------------------+ | darknet17 | ✅ | +----------------------------------+------------------+ | darknet21 | ✅ | +----------------------------------+------------------+ | darknet53 | ✅ | +----------------------------------+------------------+ | darknetaa53 | ✅ | +----------------------------------+------------------+ | densenet121 | | +----------------------------------+------------------+ | densenet161 | | +----------------------------------+------------------+ | densenet169 | | +----------------------------------+------------------+ | densenet201 | | +----------------------------------+------------------+ | densenet264d | | +----------------------------------+------------------+ | densenetblur121d | | +----------------------------------+------------------+ | dla34 | | +----------------------------------+------------------+ | dla46_c | | +----------------------------------+------------------+ | dla46x_c | | +----------------------------------+------------------+ | dla60 | | +----------------------------------+------------------+ | dla60_res2net | | +----------------------------------+------------------+ | dla60_res2next | | +----------------------------------+------------------+ | dla60x | | +----------------------------------+------------------+ | dla60x_c | | +----------------------------------+------------------+ | dla102 | | +----------------------------------+------------------+ | dla102x | | +----------------------------------+------------------+ | dla102x2 | | +----------------------------------+------------------+ | dla169 | | +----------------------------------+------------------+ | dm_nfnet_f0 | ✅ | +----------------------------------+------------------+ | dm_nfnet_f1 | ✅ | +----------------------------------+------------------+ | dm_nfnet_f2 | ✅ | +----------------------------------+------------------+ | dm_nfnet_f3 | ✅ | +----------------------------------+------------------+ | dm_nfnet_f4 | ✅ | +----------------------------------+------------------+ | dm_nfnet_f5 | ✅ | +----------------------------------+------------------+ | dm_nfnet_f6 | ✅ | +----------------------------------+------------------+ | dpn48b | | +----------------------------------+------------------+ | dpn68 | | +----------------------------------+------------------+ | dpn68b | | +----------------------------------+------------------+ | dpn92 | | +----------------------------------+------------------+ | dpn98 | | +----------------------------------+------------------+ | dpn107 | | +----------------------------------+------------------+ | dpn131 | | +----------------------------------+------------------+ | eca_botnext26ts_256 | ✅ | +----------------------------------+------------------+ | eca_halonext26ts | ✅ | +----------------------------------+------------------+ | eca_nfnet_l0 | ✅ | +----------------------------------+------------------+ | eca_nfnet_l1 | ✅ | +----------------------------------+------------------+ | eca_nfnet_l2 | ✅ | +----------------------------------+------------------+ | eca_nfnet_l3 | ✅ | +----------------------------------+------------------+ | eca_resnet33ts | ✅ | +----------------------------------+------------------+ | eca_resnext26ts | ✅ | +----------------------------------+------------------+ | eca_vovnet39b | | +----------------------------------+------------------+ | ecaresnet26t | ✅ | +----------------------------------+------------------+ | ecaresnet50d | ✅ | +----------------------------------+------------------+ | ecaresnet50d_pruned | ✅ | +----------------------------------+------------------+ | ecaresnet50t | ✅ | +----------------------------------+------------------+ | ecaresnet101d | ✅ | +----------------------------------+------------------+ | ecaresnet101d_pruned | ✅ | +----------------------------------+------------------+ | ecaresnet200d | ✅ | +----------------------------------+------------------+ | ecaresnet269d | ✅ | +----------------------------------+------------------+ | ecaresnetlight | ✅ | +----------------------------------+------------------+ | ecaresnext26t_32x4d | ✅ | +----------------------------------+------------------+ | ecaresnext50t_32x4d | ✅ | +----------------------------------+------------------+ | efficientnet_b0 | ✅ | +----------------------------------+------------------+ | efficientnet_b0_g8_gn | ✅ | +----------------------------------+------------------+ | efficientnet_b0_g16_evos | ✅ | +----------------------------------+------------------+ | efficientnet_b0_gn | ✅ | +----------------------------------+------------------+ | efficientnet_b1 | ✅ | +----------------------------------+------------------+ | efficientnet_b1_pruned | ✅ | +----------------------------------+------------------+ | efficientnet_b2 | ✅ | +----------------------------------+------------------+ | efficientnet_b2_pruned | ✅ | +----------------------------------+------------------+ | efficientnet_b3 | ✅ | +----------------------------------+------------------+ | efficientnet_b3_g8_gn | ✅ | +----------------------------------+------------------+ | efficientnet_b3_gn | ✅ | +----------------------------------+------------------+ | efficientnet_b3_pruned | ✅ | +----------------------------------+------------------+ | efficientnet_b4 | ✅ | +----------------------------------+------------------+ | efficientnet_b5 | ✅ | +----------------------------------+------------------+ | efficientnet_b6 | ✅ | +----------------------------------+------------------+ | efficientnet_b7 | ✅ | +----------------------------------+------------------+ | efficientnet_b8 | ✅ | +----------------------------------+------------------+ | efficientnet_blur_b0 | ✅ | +----------------------------------+------------------+ | efficientnet_cc_b0_4e | ✅ | +----------------------------------+------------------+ | efficientnet_cc_b0_8e | ✅ | +----------------------------------+------------------+ | efficientnet_cc_b1_8e | ✅ | +----------------------------------+------------------+ | efficientnet_el | ✅ | +----------------------------------+------------------+ | efficientnet_el_pruned | ✅ | +----------------------------------+------------------+ | efficientnet_em | ✅ | +----------------------------------+------------------+ | efficientnet_es | ✅ | +----------------------------------+------------------+ | efficientnet_es_pruned | ✅ | +----------------------------------+------------------+ | efficientnet_l2 | ✅ | +----------------------------------+------------------+ | efficientnet_lite0 | ✅ | +----------------------------------+------------------+ | efficientnet_lite1 | ✅ | +----------------------------------+------------------+ | efficientnet_lite2 | ✅ | +----------------------------------+------------------+ | efficientnet_lite3 | ✅ | +----------------------------------+------------------+ | efficientnet_lite4 | ✅ | +----------------------------------+------------------+ | efficientnetv2_l | ✅ | +----------------------------------+------------------+ | efficientnetv2_m | ✅ | +----------------------------------+------------------+ | efficientnetv2_rw_m | ✅ | +----------------------------------+------------------+ | efficientnetv2_rw_s | ✅ | +----------------------------------+------------------+ | efficientnetv2_rw_t | ✅ | +----------------------------------+------------------+ | efficientnetv2_s | ✅ | +----------------------------------+------------------+ | efficientnetv2_xl | ✅ | +----------------------------------+------------------+ | ese_vovnet19b_dw | | +----------------------------------+------------------+ | ese_vovnet19b_slim | | +----------------------------------+------------------+ | ese_vovnet19b_slim_dw | | +----------------------------------+------------------+ | ese_vovnet39b | | +----------------------------------+------------------+ | ese_vovnet39b_evos | | +----------------------------------+------------------+ | ese_vovnet57b | | +----------------------------------+------------------+ | ese_vovnet99b | | +----------------------------------+------------------+ | fbnetc_100 | ✅ | +----------------------------------+------------------+ | fbnetv3_b | ✅ | +----------------------------------+------------------+ | fbnetv3_d | ✅ | +----------------------------------+------------------+ | fbnetv3_g | ✅ | +----------------------------------+------------------+ | gc_efficientnetv2_rw_t | ✅ | +----------------------------------+------------------+ | gcresnet33ts | ✅ | +----------------------------------+------------------+ | gcresnet50t | ✅ | +----------------------------------+------------------+ | gcresnext26ts | ✅ | +----------------------------------+------------------+ | gcresnext50ts | ✅ | +----------------------------------+------------------+ | gernet_l | ✅ | +----------------------------------+------------------+ | gernet_m | ✅ | +----------------------------------+------------------+ | gernet_s | ✅ | +----------------------------------+------------------+ | ghostnet_050 | | +----------------------------------+------------------+ | ghostnet_100 | | +----------------------------------+------------------+ | ghostnet_130 | | +----------------------------------+------------------+ | ghostnetv2_100 | | +----------------------------------+------------------+ | ghostnetv2_130 | | +----------------------------------+------------------+ | ghostnetv2_160 | | +----------------------------------+------------------+ | halo2botnet50ts_256 | ✅ | +----------------------------------+------------------+ | halonet26t | ✅ | +----------------------------------+------------------+ | halonet50ts | ✅ | +----------------------------------+------------------+ | halonet_h1 | ✅ | +----------------------------------+------------------+ | haloregnetz_b | ✅ | +----------------------------------+------------------+ | hardcorenas_a | ✅ | +----------------------------------+------------------+ | hardcorenas_b | ✅ | +----------------------------------+------------------+ | hardcorenas_c | ✅ | +----------------------------------+------------------+ | hardcorenas_d | ✅ | +----------------------------------+------------------+ | hardcorenas_e | ✅ | +----------------------------------+------------------+ | hardcorenas_f | ✅ | +----------------------------------+------------------+ | hrnet_w18 | | +----------------------------------+------------------+ | hrnet_w18_small | | +----------------------------------+------------------+ | hrnet_w18_small_v2 | | +----------------------------------+------------------+ | hrnet_w18_ssld | | +----------------------------------+------------------+ | hrnet_w30 | | +----------------------------------+------------------+ | hrnet_w32 | | +----------------------------------+------------------+ | hrnet_w40 | | +----------------------------------+------------------+ | hrnet_w44 | | +----------------------------------+------------------+ | hrnet_w48 | | +----------------------------------+------------------+ | hrnet_w48_ssld | | +----------------------------------+------------------+ | hrnet_w64 | | +----------------------------------+------------------+ | lambda_resnet26rpt_256 | ✅ | +----------------------------------+------------------+ | lambda_resnet26t | ✅ | +----------------------------------+------------------+ | lambda_resnet50ts | ✅ | +----------------------------------+------------------+ | lamhalobotnet50ts_256 | ✅ | +----------------------------------+------------------+ | lcnet_035 | ✅ | +----------------------------------+------------------+ | lcnet_050 | ✅ | +----------------------------------+------------------+ | lcnet_075 | ✅ | +----------------------------------+------------------+ | lcnet_100 | ✅ | +----------------------------------+------------------+ | lcnet_150 | ✅ | +----------------------------------+------------------+ | legacy_senet154 | | +----------------------------------+------------------+ | legacy_seresnet18 | | +----------------------------------+------------------+ | legacy_seresnet34 | | +----------------------------------+------------------+ | legacy_seresnet50 | | +----------------------------------+------------------+ | legacy_seresnet101 | | +----------------------------------+------------------+ | legacy_seresnet152 | | +----------------------------------+------------------+ | legacy_seresnext26_32x4d | | +----------------------------------+------------------+ | legacy_seresnext50_32x4d | | +----------------------------------+------------------+ | legacy_seresnext101_32x4d | | +----------------------------------+------------------+ | maxvit_base_tf_224 | | +----------------------------------+------------------+ | maxvit_base_tf_384 | | +----------------------------------+------------------+ | maxvit_base_tf_512 | | +----------------------------------+------------------+ | maxvit_large_tf_224 | | +----------------------------------+------------------+ | maxvit_large_tf_384 | | +----------------------------------+------------------+ | maxvit_large_tf_512 | | +----------------------------------+------------------+ | maxvit_nano_rw_256 | | +----------------------------------+------------------+ | maxvit_pico_rw_256 | | +----------------------------------+------------------+ | maxvit_rmlp_base_rw_224 | | +----------------------------------+------------------+ | maxvit_rmlp_base_rw_384 | | +----------------------------------+------------------+ | maxvit_rmlp_nano_rw_256 | | +----------------------------------+------------------+ | maxvit_rmlp_pico_rw_256 | | +----------------------------------+------------------+ | maxvit_rmlp_small_rw_224 | | +----------------------------------+------------------+ | maxvit_rmlp_small_rw_256 | | +----------------------------------+------------------+ | maxvit_rmlp_tiny_rw_256 | | +----------------------------------+------------------+ | maxvit_small_tf_224 | | +----------------------------------+------------------+ | maxvit_small_tf_384 | | +----------------------------------+------------------+ | maxvit_small_tf_512 | | +----------------------------------+------------------+ | maxvit_tiny_pm_256 | | +----------------------------------+------------------+ | maxvit_tiny_rw_224 | | +----------------------------------+------------------+ | maxvit_tiny_rw_256 | | +----------------------------------+------------------+ | maxvit_tiny_tf_224 | | +----------------------------------+------------------+ | maxvit_tiny_tf_384 | | +----------------------------------+------------------+ | maxvit_tiny_tf_512 | | +----------------------------------+------------------+ | maxvit_xlarge_tf_224 | | +----------------------------------+------------------+ | maxvit_xlarge_tf_384 | | +----------------------------------+------------------+ | maxvit_xlarge_tf_512 | | +----------------------------------+------------------+ | maxxvit_rmlp_nano_rw_256 | | +----------------------------------+------------------+ | maxxvit_rmlp_small_rw_256 | | +----------------------------------+------------------+ | maxxvit_rmlp_tiny_rw_256 | | +----------------------------------+------------------+ | maxxvitv2_nano_rw_256 | | +----------------------------------+------------------+ | maxxvitv2_rmlp_base_rw_224 | | +----------------------------------+------------------+ | maxxvitv2_rmlp_base_rw_384 | | +----------------------------------+------------------+ | maxxvitv2_rmlp_large_rw_224 | | +----------------------------------+------------------+ | mixnet_l | ✅ | +----------------------------------+------------------+ | mixnet_m | ✅ | +----------------------------------+------------------+ | mixnet_s | ✅ | +----------------------------------+------------------+ | mixnet_xl | ✅ | +----------------------------------+------------------+ | mixnet_xxl | ✅ | +----------------------------------+------------------+ | mnasnet_050 | ✅ | +----------------------------------+------------------+ | mnasnet_075 | ✅ | +----------------------------------+------------------+ | mnasnet_100 | ✅ | +----------------------------------+------------------+ | mnasnet_140 | ✅ | +----------------------------------+------------------+ | mnasnet_small | ✅ | +----------------------------------+------------------+ | mobilenet_edgetpu_100 | ✅ | +----------------------------------+------------------+ | mobilenet_edgetpu_v2_l | ✅ | +----------------------------------+------------------+ | mobilenet_edgetpu_v2_m | ✅ | +----------------------------------+------------------+ | mobilenet_edgetpu_v2_s | ✅ | +----------------------------------+------------------+ | mobilenet_edgetpu_v2_xs | ✅ | +----------------------------------+------------------+ | mobilenetv1_100 | ✅ | +----------------------------------+------------------+ | mobilenetv1_100h | ✅ | +----------------------------------+------------------+ | mobilenetv1_125 | ✅ | +----------------------------------+------------------+ | mobilenetv2_035 | ✅ | +----------------------------------+------------------+ | mobilenetv2_050 | ✅ | +----------------------------------+------------------+ | mobilenetv2_075 | ✅ | +----------------------------------+------------------+ | mobilenetv2_100 | ✅ | +----------------------------------+------------------+ | mobilenetv2_110d | ✅ | +----------------------------------+------------------+ | mobilenetv2_120d | ✅ | +----------------------------------+------------------+ | mobilenetv2_140 | ✅ | +----------------------------------+------------------+ | mobilenetv3_large_075 | ✅ | +----------------------------------+------------------+ | mobilenetv3_large_100 | ✅ | +----------------------------------+------------------+ | mobilenetv3_large_150d | ✅ | +----------------------------------+------------------+ | mobilenetv3_rw | ✅ | +----------------------------------+------------------+ | mobilenetv3_small_050 | ✅ | +----------------------------------+------------------+ | mobilenetv3_small_075 | ✅ | +----------------------------------+------------------+ | mobilenetv3_small_100 | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_aa_large | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_aa_medium | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_blur_medium | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_large | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_medium | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_small | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_small_035 | ✅ | +----------------------------------+------------------+ | mobilenetv4_conv_small_050 | ✅ | +----------------------------------+------------------+ | mobilenetv4_hybrid_large | ✅ | +----------------------------------+------------------+ | mobilenetv4_hybrid_large_075 | ✅ | +----------------------------------+------------------+ | mobilenetv4_hybrid_medium | ✅ | +----------------------------------+------------------+ | mobilenetv4_hybrid_medium_075 | ✅ | +----------------------------------+------------------+ | mobileone_s0 | ✅ | +----------------------------------+------------------+ | mobileone_s1 | ✅ | +----------------------------------+------------------+ | mobileone_s2 | ✅ | +----------------------------------+------------------+ | mobileone_s3 | ✅ | +----------------------------------+------------------+ | mobileone_s4 | ✅ | +----------------------------------+------------------+ | mobilevit_s | ✅ | +----------------------------------+------------------+ | mobilevit_xs | ✅ | +----------------------------------+------------------+ | mobilevit_xxs | ✅ | +----------------------------------+------------------+ | mobilevitv2_050 | ✅ | +----------------------------------+------------------+ | mobilevitv2_075 | ✅ | +----------------------------------+------------------+ | mobilevitv2_100 | ✅ | +----------------------------------+------------------+ | mobilevitv2_125 | ✅ | +----------------------------------+------------------+ | mobilevitv2_150 | ✅ | +----------------------------------+------------------+ | mobilevitv2_175 | ✅ | +----------------------------------+------------------+ | mobilevitv2_200 | ✅ | +----------------------------------+------------------+ | nf_ecaresnet26 | ✅ | +----------------------------------+------------------+ | nf_ecaresnet50 | ✅ | +----------------------------------+------------------+ | nf_ecaresnet101 | ✅ | +----------------------------------+------------------+ | nf_regnet_b0 | ✅ | +----------------------------------+------------------+ | nf_regnet_b1 | ✅ | +----------------------------------+------------------+ | nf_regnet_b2 | ✅ | +----------------------------------+------------------+ | nf_regnet_b3 | ✅ | +----------------------------------+------------------+ | nf_regnet_b4 | ✅ | +----------------------------------+------------------+ | nf_regnet_b5 | ✅ | +----------------------------------+------------------+ | nf_resnet26 | ✅ | +----------------------------------+------------------+ | nf_resnet50 | ✅ | +----------------------------------+------------------+ | nf_resnet101 | ✅ | +----------------------------------+------------------+ | nf_seresnet26 | ✅ | +----------------------------------+------------------+ | nf_seresnet50 | ✅ | +----------------------------------+------------------+ | nf_seresnet101 | ✅ | +----------------------------------+------------------+ | nfnet_f0 | ✅ | +----------------------------------+------------------+ | nfnet_f1 | ✅ | +----------------------------------+------------------+ | nfnet_f2 | ✅ | +----------------------------------+------------------+ | nfnet_f3 | ✅ | +----------------------------------+------------------+ | nfnet_f4 | ✅ | +----------------------------------+------------------+ | nfnet_f5 | ✅ | +----------------------------------+------------------+ | nfnet_f6 | ✅ | +----------------------------------+------------------+ | nfnet_f7 | ✅ | +----------------------------------+------------------+ | nfnet_l0 | ✅ | +----------------------------------+------------------+ | regnetv_040 | ✅ | +----------------------------------+------------------+ | regnetv_064 | ✅ | +----------------------------------+------------------+ | regnetx_002 | ✅ | +----------------------------------+------------------+ | regnetx_004 | ✅ | +----------------------------------+------------------+ | regnetx_004_tv | ✅ | +----------------------------------+------------------+ | regnetx_006 | ✅ | +----------------------------------+------------------+ | regnetx_008 | ✅ | +----------------------------------+------------------+ | regnetx_016 | ✅ | +----------------------------------+------------------+ | regnetx_032 | ✅ | +----------------------------------+------------------+ | regnetx_040 | ✅ | +----------------------------------+------------------+ | regnetx_064 | ✅ | +----------------------------------+------------------+ | regnetx_080 | ✅ | +----------------------------------+------------------+ | regnetx_120 | ✅ | +----------------------------------+------------------+ | regnetx_160 | ✅ | +----------------------------------+------------------+ | regnetx_320 | ✅ | +----------------------------------+------------------+ | regnety_002 | ✅ | +----------------------------------+------------------+ | regnety_004 | ✅ | +----------------------------------+------------------+ | regnety_006 | ✅ | +----------------------------------+------------------+ | regnety_008 | ✅ | +----------------------------------+------------------+ | regnety_008_tv | ✅ | +----------------------------------+------------------+ | regnety_016 | ✅ | +----------------------------------+------------------+ | regnety_032 | ✅ | +----------------------------------+------------------+ | regnety_040 | ✅ | +----------------------------------+------------------+ | regnety_040_sgn | ✅ | +----------------------------------+------------------+ | regnety_064 | ✅ | +----------------------------------+------------------+ | regnety_080 | ✅ | +----------------------------------+------------------+ | regnety_080_tv | ✅ | +----------------------------------+------------------+ | regnety_120 | ✅ | +----------------------------------+------------------+ | regnety_160 | ✅ | +----------------------------------+------------------+ | regnety_320 | ✅ | +----------------------------------+------------------+ | regnety_640 | ✅ | +----------------------------------+------------------+ | regnety_1280 | ✅ | +----------------------------------+------------------+ | regnety_2560 | ✅ | +----------------------------------+------------------+ | regnetz_005 | ✅ | +----------------------------------+------------------+ | regnetz_040 | ✅ | +----------------------------------+------------------+ | regnetz_040_h | ✅ | +----------------------------------+------------------+ | regnetz_b16 | ✅ | +----------------------------------+------------------+ | regnetz_b16_evos | ✅ | +----------------------------------+------------------+ | regnetz_c16 | ✅ | +----------------------------------+------------------+ | regnetz_c16_evos | ✅ | +----------------------------------+------------------+ | regnetz_d8 | ✅ | +----------------------------------+------------------+ | regnetz_d8_evos | ✅ | +----------------------------------+------------------+ | regnetz_d32 | ✅ | +----------------------------------+------------------+ | regnetz_e8 | ✅ | +----------------------------------+------------------+ | repghostnet_050 | | +----------------------------------+------------------+ | repghostnet_058 | | +----------------------------------+------------------+ | repghostnet_080 | | +----------------------------------+------------------+ | repghostnet_100 | | +----------------------------------+------------------+ | repghostnet_111 | | +----------------------------------+------------------+ | repghostnet_130 | | +----------------------------------+------------------+ | repghostnet_150 | | +----------------------------------+------------------+ | repghostnet_200 | | +----------------------------------+------------------+ | repvgg_a0 | ✅ | +----------------------------------+------------------+ | repvgg_a1 | ✅ | +----------------------------------+------------------+ | repvgg_a2 | ✅ | +----------------------------------+------------------+ | repvgg_b0 | ✅ | +----------------------------------+------------------+ | repvgg_b1 | ✅ | +----------------------------------+------------------+ | repvgg_b1g4 | ✅ | +----------------------------------+------------------+ | repvgg_b2 | ✅ | +----------------------------------+------------------+ | repvgg_b2g4 | ✅ | +----------------------------------+------------------+ | repvgg_b3 | ✅ | +----------------------------------+------------------+ | repvgg_b3g4 | ✅ | +----------------------------------+------------------+ | repvgg_d2se | ✅ | +----------------------------------+------------------+ | res2net50_14w_8s | ✅ | +----------------------------------+------------------+ | res2net50_26w_4s | ✅ | +----------------------------------+------------------+ | res2net50_26w_6s | ✅ | +----------------------------------+------------------+ | res2net50_26w_8s | ✅ | +----------------------------------+------------------+ | res2net50_48w_2s | ✅ | +----------------------------------+------------------+ | res2net50d | ✅ | +----------------------------------+------------------+ | res2net101_26w_4s | ✅ | +----------------------------------+------------------+ | res2net101d | ✅ | +----------------------------------+------------------+ | res2next50 | ✅ | +----------------------------------+------------------+ | resnest14d | ✅ | +----------------------------------+------------------+ | resnest26d | ✅ | +----------------------------------+------------------+ | resnest50d | ✅ | +----------------------------------+------------------+ | resnest50d_1s4x24d | ✅ | +----------------------------------+------------------+ | resnest50d_4s2x40d | ✅ | +----------------------------------+------------------+ | resnest101e | ✅ | +----------------------------------+------------------+ | resnest200e | ✅ | +----------------------------------+------------------+ | resnest269e | ✅ | +----------------------------------+------------------+ | resnet10t | ✅ | +----------------------------------+------------------+ | resnet14t | ✅ | +----------------------------------+------------------+ | resnet18 | ✅ | +----------------------------------+------------------+ | resnet18d | ✅ | +----------------------------------+------------------+ | resnet26 | ✅ | +----------------------------------+------------------+ | resnet26d | ✅ | +----------------------------------+------------------+ | resnet26t | ✅ | +----------------------------------+------------------+ | resnet32ts | ✅ | +----------------------------------+------------------+ | resnet33ts | ✅ | +----------------------------------+------------------+ | resnet34 | ✅ | +----------------------------------+------------------+ | resnet34d | ✅ | +----------------------------------+------------------+ | resnet50 | ✅ | +----------------------------------+------------------+ | resnet50_clip | ✅ | +----------------------------------+------------------+ | resnet50_clip_gap | ✅ | +----------------------------------+------------------+ | resnet50_gn | ✅ | +----------------------------------+------------------+ | resnet50_mlp | ✅ | +----------------------------------+------------------+ | resnet50c | ✅ | +----------------------------------+------------------+ | resnet50d | ✅ | +----------------------------------+------------------+ | resnet50s | ✅ | +----------------------------------+------------------+ | resnet50t | ✅ | +----------------------------------+------------------+ | resnet50x4_clip | ✅ | +----------------------------------+------------------+ | resnet50x4_clip_gap | ✅ | +----------------------------------+------------------+ | resnet50x16_clip | ✅ | +----------------------------------+------------------+ | resnet50x16_clip_gap | ✅ | +----------------------------------+------------------+ | resnet50x64_clip | ✅ | +----------------------------------+------------------+ | resnet50x64_clip_gap | ✅ | +----------------------------------+------------------+ | resnet51q | ✅ | +----------------------------------+------------------+ | resnet61q | ✅ | +----------------------------------+------------------+ | resnet101 | ✅ | +----------------------------------+------------------+ | resnet101_clip | ✅ | +----------------------------------+------------------+ | resnet101_clip_gap | ✅ | +----------------------------------+------------------+ | resnet101c | ✅ | +----------------------------------+------------------+ | resnet101d | ✅ | +----------------------------------+------------------+ | resnet101s | ✅ | +----------------------------------+------------------+ | resnet152 | ✅ | +----------------------------------+------------------+ | resnet152c | ✅ | +----------------------------------+------------------+ | resnet152d | ✅ | +----------------------------------+------------------+ | resnet152s | ✅ | +----------------------------------+------------------+ | resnet200 | ✅ | +----------------------------------+------------------+ | resnet200d | ✅ | +----------------------------------+------------------+ | resnetaa34d | ✅ | +----------------------------------+------------------+ | resnetaa50 | ✅ | +----------------------------------+------------------+ | resnetaa50d | ✅ | +----------------------------------+------------------+ | resnetaa101d | ✅ | +----------------------------------+------------------+ | resnetblur18 | ✅ | +----------------------------------+------------------+ | resnetblur50 | ✅ | +----------------------------------+------------------+ | resnetblur50d | ✅ | +----------------------------------+------------------+ | resnetblur101d | ✅ | +----------------------------------+------------------+ | resnetrs50 | ✅ | +----------------------------------+------------------+ | resnetrs101 | ✅ | +----------------------------------+------------------+ | resnetrs152 | ✅ | +----------------------------------+------------------+ | resnetrs200 | ✅ | +----------------------------------+------------------+ | resnetrs270 | ✅ | +----------------------------------+------------------+ | resnetrs350 | ✅ | +----------------------------------+------------------+ | resnetrs420 | ✅ | +----------------------------------+------------------+ | resnetv2_18 | ✅ | +----------------------------------+------------------+ | resnetv2_18d | ✅ | +----------------------------------+------------------+ | resnetv2_34 | ✅ | +----------------------------------+------------------+ | resnetv2_34d | ✅ | +----------------------------------+------------------+ | resnetv2_50 | ✅ | +----------------------------------+------------------+ | resnetv2_50d | ✅ | +----------------------------------+------------------+ | resnetv2_50d_evos | ✅ | +----------------------------------+------------------+ | resnetv2_50d_frn | ✅ | +----------------------------------+------------------+ | resnetv2_50d_gn | ✅ | +----------------------------------+------------------+ | resnetv2_50t | ✅ | +----------------------------------+------------------+ | resnetv2_50x1_bit | ✅ | +----------------------------------+------------------+ | resnetv2_50x3_bit | ✅ | +----------------------------------+------------------+ | resnetv2_101 | ✅ | +----------------------------------+------------------+ | resnetv2_101d | ✅ | +----------------------------------+------------------+ | resnetv2_101x1_bit | ✅ | +----------------------------------+------------------+ | resnetv2_101x3_bit | ✅ | +----------------------------------+------------------+ | resnetv2_152 | ✅ | +----------------------------------+------------------+ | resnetv2_152d | ✅ | +----------------------------------+------------------+ | resnetv2_152x2_bit | ✅ | +----------------------------------+------------------+ | resnetv2_152x4_bit | ✅ | +----------------------------------+------------------+ | resnext26ts | ✅ | +----------------------------------+------------------+ | resnext50_32x4d | ✅ | +----------------------------------+------------------+ | resnext50d_32x4d | ✅ | +----------------------------------+------------------+ | resnext101_32x4d | ✅ | +----------------------------------+------------------+ | resnext101_32x8d | ✅ | +----------------------------------+------------------+ | resnext101_32x16d | ✅ | +----------------------------------+------------------+ | resnext101_32x32d | ✅ | +----------------------------------+------------------+ | resnext101_64x4d | ✅ | +----------------------------------+------------------+ | rexnet_100 | ✅ | +----------------------------------+------------------+ | rexnet_130 | ✅ | +----------------------------------+------------------+ | rexnet_150 | ✅ | +----------------------------------+------------------+ | rexnet_200 | ✅ | +----------------------------------+------------------+ | rexnet_300 | ✅ | +----------------------------------+------------------+ | rexnetr_100 | ✅ | +----------------------------------+------------------+ | rexnetr_130 | ✅ | +----------------------------------+------------------+ | rexnetr_150 | ✅ | +----------------------------------+------------------+ | rexnetr_200 | ✅ | +----------------------------------+------------------+ | rexnetr_300 | ✅ | +----------------------------------+------------------+ | sebotnet33ts_256 | ✅ | +----------------------------------+------------------+ | sedarknet21 | ✅ | +----------------------------------+------------------+ | sehalonet33ts | ✅ | +----------------------------------+------------------+ | selecsls42 | | +----------------------------------+------------------+ | selecsls42b | | +----------------------------------+------------------+ | selecsls60 | | +----------------------------------+------------------+ | selecsls60b | | +----------------------------------+------------------+ | selecsls84 | | +----------------------------------+------------------+ | semnasnet_050 | ✅ | +----------------------------------+------------------+ | semnasnet_075 | ✅ | +----------------------------------+------------------+ | semnasnet_100 | ✅ | +----------------------------------+------------------+ | semnasnet_140 | ✅ | +----------------------------------+------------------+ | senet154 | ✅ | +----------------------------------+------------------+ | seresnet18 | ✅ | +----------------------------------+------------------+ | seresnet33ts | ✅ | +----------------------------------+------------------+ | seresnet34 | ✅ | +----------------------------------+------------------+ | seresnet50 | ✅ | +----------------------------------+------------------+ | seresnet50t | ✅ | +----------------------------------+------------------+ | seresnet101 | ✅ | +----------------------------------+------------------+ | seresnet152 | ✅ | +----------------------------------+------------------+ | seresnet152d | ✅ | +----------------------------------+------------------+ | seresnet200d | ✅ | +----------------------------------+------------------+ | seresnet269d | ✅ | +----------------------------------+------------------+ | seresnetaa50d | ✅ | +----------------------------------+------------------+ | seresnext26d_32x4d | ✅ | +----------------------------------+------------------+ | seresnext26t_32x4d | ✅ | +----------------------------------+------------------+ | seresnext26ts | ✅ | +----------------------------------+------------------+ | seresnext50_32x4d | ✅ | +----------------------------------+------------------+ | seresnext101_32x4d | ✅ | +----------------------------------+------------------+ | seresnext101_32x8d | ✅ | +----------------------------------+------------------+ | seresnext101_64x4d | ✅ | +----------------------------------+------------------+ | seresnext101d_32x8d | ✅ | +----------------------------------+------------------+ | seresnextaa101d_32x8d | ✅ | +----------------------------------+------------------+ | seresnextaa201d_32x8d | ✅ | +----------------------------------+------------------+ | skresnet18 | ✅ | +----------------------------------+------------------+ | skresnet34 | ✅ | +----------------------------------+------------------+ | skresnet50 | ✅ | +----------------------------------+------------------+ | skresnet50d | ✅ | +----------------------------------+------------------+ | skresnext50_32x4d | ✅ | +----------------------------------+------------------+ | spnasnet_100 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b0 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b1 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b2 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b3 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b4 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b5 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b6 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b7 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_b8 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_cc_b0_4e | ✅ | +----------------------------------+------------------+ | tf_efficientnet_cc_b0_8e | ✅ | +----------------------------------+------------------+ | tf_efficientnet_cc_b1_8e | ✅ | +----------------------------------+------------------+ | tf_efficientnet_el | ✅ | +----------------------------------+------------------+ | tf_efficientnet_em | ✅ | +----------------------------------+------------------+ | tf_efficientnet_es | ✅ | +----------------------------------+------------------+ | tf_efficientnet_l2 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_lite0 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_lite1 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_lite2 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_lite3 | ✅ | +----------------------------------+------------------+ | tf_efficientnet_lite4 | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_b0 | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_b1 | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_b2 | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_b3 | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_l | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_m | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_s | ✅ | +----------------------------------+------------------+ | tf_efficientnetv2_xl | ✅ | +----------------------------------+------------------+ | tf_mixnet_l | ✅ | +----------------------------------+------------------+ | tf_mixnet_m | ✅ | +----------------------------------+------------------+ | tf_mixnet_s | ✅ | +----------------------------------+------------------+ | tf_mobilenetv3_large_075 | ✅ | +----------------------------------+------------------+ | tf_mobilenetv3_large_100 | ✅ | +----------------------------------+------------------+ | tf_mobilenetv3_large_minimal_100 | ✅ | +----------------------------------+------------------+ | tf_mobilenetv3_small_075 | ✅ | +----------------------------------+------------------+ | tf_mobilenetv3_small_100 | ✅ | +----------------------------------+------------------+ | tf_mobilenetv3_small_minimal_100 | ✅ | +----------------------------------+------------------+ | tinynet_a | ✅ | +----------------------------------+------------------+ | tinynet_b | ✅ | +----------------------------------+------------------+ | tinynet_c | ✅ | +----------------------------------+------------------+ | tinynet_d | ✅ | +----------------------------------+------------------+ | tinynet_e | ✅ | +----------------------------------+------------------+ | vgg11 | | +----------------------------------+------------------+ | vgg11_bn | | +----------------------------------+------------------+ | vgg13 | | +----------------------------------+------------------+ | vgg13_bn | | +----------------------------------+------------------+ | vgg16 | | +----------------------------------+------------------+ | vgg16_bn | | +----------------------------------+------------------+ | vgg19 | | +----------------------------------+------------------+ | vgg19_bn | | +----------------------------------+------------------+ | vovnet39a | | +----------------------------------+------------------+ | vovnet57a | | +----------------------------------+------------------+ | wide_resnet50_2 | ✅ | +----------------------------------+------------------+ | wide_resnet101_2 | ✅ | +----------------------------------+------------------+ | xception41 | ✅ | +----------------------------------+------------------+ | xception41p | ✅ | +----------------------------------+------------------+ | xception65 | ✅ | +----------------------------------+------------------+ | xception65p | ✅ | +----------------------------------+------------------+ | xception71 | ✅ | +----------------------------------+------------------+ Transformer-Style ~~~~~~~~~~~~~~~~~ Transformer-style models (e.g., Swin Transformer, ConvNeXt) typically produce feature maps starting at a 1/4 scale, followed by 1/8, 1/16, and 1/32 scales +------------------------------------+------------------+ | Encoder name | Support dilation | +====================================+==================+ | caformer_b36 | | +------------------------------------+------------------+ | caformer_m36 | | +------------------------------------+------------------+ | caformer_s18 | | +------------------------------------+------------------+ | caformer_s36 | | +------------------------------------+------------------+ | convformer_b36 | | +------------------------------------+------------------+ | convformer_m36 | | +------------------------------------+------------------+ | convformer_s18 | | +------------------------------------+------------------+ | convformer_s36 | | +------------------------------------+------------------+ | convnext_atto | ✅ | +------------------------------------+------------------+ | convnext_atto_ols | ✅ | +------------------------------------+------------------+ | convnext_atto_rms | ✅ | +------------------------------------+------------------+ | convnext_base | ✅ | +------------------------------------+------------------+ | convnext_femto | ✅ | +------------------------------------+------------------+ | convnext_femto_ols | ✅ | +------------------------------------+------------------+ | convnext_large | ✅ | +------------------------------------+------------------+ | convnext_large_mlp | ✅ | +------------------------------------+------------------+ | convnext_nano | ✅ | +------------------------------------+------------------+ | convnext_nano_ols | ✅ | +------------------------------------+------------------+ | convnext_pico | ✅ | +------------------------------------+------------------+ | convnext_pico_ols | ✅ | +------------------------------------+------------------+ | convnext_small | ✅ | +------------------------------------+------------------+ | convnext_tiny | ✅ | +------------------------------------+------------------+ | convnext_tiny_hnf | ✅ | +------------------------------------+------------------+ | convnext_xlarge | ✅ | +------------------------------------+------------------+ | convnext_xxlarge | ✅ | +------------------------------------+------------------+ | convnext_zepto_rms | ✅ | +------------------------------------+------------------+ | convnext_zepto_rms_ols | ✅ | +------------------------------------+------------------+ | convnextv2_atto | ✅ | +------------------------------------+------------------+ | convnextv2_base | ✅ | +------------------------------------+------------------+ | convnextv2_femto | ✅ | +------------------------------------+------------------+ | convnextv2_huge | ✅ | +------------------------------------+------------------+ | convnextv2_large | ✅ | +------------------------------------+------------------+ | convnextv2_nano | ✅ | +------------------------------------+------------------+ | convnextv2_pico | ✅ | +------------------------------------+------------------+ | convnextv2_small | ✅ | +------------------------------------+------------------+ | convnextv2_tiny | ✅ | +------------------------------------+------------------+ | davit_base | | +------------------------------------+------------------+ | davit_base_fl | | +------------------------------------+------------------+ | davit_giant | | +------------------------------------+------------------+ | davit_huge | | +------------------------------------+------------------+ | davit_huge_fl | | +------------------------------------+------------------+ | davit_large | | +------------------------------------+------------------+ | davit_small | | +------------------------------------+------------------+ | davit_tiny | | +------------------------------------+------------------+ | edgenext_base | | +------------------------------------+------------------+ | edgenext_small | | +------------------------------------+------------------+ | edgenext_small_rw | | +------------------------------------+------------------+ | edgenext_x_small | | +------------------------------------+------------------+ | edgenext_xx_small | | +------------------------------------+------------------+ | efficientformer_l1 | | +------------------------------------+------------------+ | efficientformer_l3 | | +------------------------------------+------------------+ | efficientformer_l7 | | +------------------------------------+------------------+ | efficientformerv2_l | | +------------------------------------+------------------+ | efficientformerv2_s0 | | +------------------------------------+------------------+ | efficientformerv2_s1 | | +------------------------------------+------------------+ | efficientformerv2_s2 | | +------------------------------------+------------------+ | efficientvit_b0 | | +------------------------------------+------------------+ | efficientvit_b1 | | +------------------------------------+------------------+ | efficientvit_b2 | | +------------------------------------+------------------+ | efficientvit_b3 | | +------------------------------------+------------------+ | efficientvit_l1 | | +------------------------------------+------------------+ | efficientvit_l2 | | +------------------------------------+------------------+ | efficientvit_l3 | | +------------------------------------+------------------+ | fastvit_ma36 | | +------------------------------------+------------------+ | fastvit_mci0 | | +------------------------------------+------------------+ | fastvit_mci1 | | +------------------------------------+------------------+ | fastvit_mci2 | | +------------------------------------+------------------+ | fastvit_s12 | | +------------------------------------+------------------+ | fastvit_sa12 | | +------------------------------------+------------------+ | fastvit_sa24 | | +------------------------------------+------------------+ | fastvit_sa36 | | +------------------------------------+------------------+ | fastvit_t8 | | +------------------------------------+------------------+ | fastvit_t12 | | +------------------------------------+------------------+ | focalnet_base_lrf | | +------------------------------------+------------------+ | focalnet_base_srf | | +------------------------------------+------------------+ | focalnet_huge_fl3 | | +------------------------------------+------------------+ | focalnet_huge_fl4 | | +------------------------------------+------------------+ | focalnet_large_fl3 | | +------------------------------------+------------------+ | focalnet_large_fl4 | | +------------------------------------+------------------+ | focalnet_small_lrf | | +------------------------------------+------------------+ | focalnet_small_srf | | +------------------------------------+------------------+ | focalnet_tiny_lrf | | +------------------------------------+------------------+ | focalnet_tiny_srf | | +------------------------------------+------------------+ | focalnet_xlarge_fl3 | | +------------------------------------+------------------+ | focalnet_xlarge_fl4 | | +------------------------------------+------------------+ | hgnet_base | | +------------------------------------+------------------+ | hgnet_small | | +------------------------------------+------------------+ | hgnet_tiny | | +------------------------------------+------------------+ | hgnetv2_b0 | | +------------------------------------+------------------+ | hgnetv2_b1 | | +------------------------------------+------------------+ | hgnetv2_b2 | | +------------------------------------+------------------+ | hgnetv2_b3 | | +------------------------------------+------------------+ | hgnetv2_b4 | | +------------------------------------+------------------+ | hgnetv2_b5 | | +------------------------------------+------------------+ | hgnetv2_b6 | | +------------------------------------+------------------+ | hiera_base_224 | | +------------------------------------+------------------+ | hiera_base_abswin_256 | | +------------------------------------+------------------+ | hiera_base_plus_224 | | +------------------------------------+------------------+ | hiera_huge_224 | | +------------------------------------+------------------+ | hiera_large_224 | | +------------------------------------+------------------+ | hiera_small_224 | | +------------------------------------+------------------+ | hiera_small_abswin_256 | | +------------------------------------+------------------+ | hiera_tiny_224 | | +------------------------------------+------------------+ | hieradet_small | | +------------------------------------+------------------+ | inception_next_base | | +------------------------------------+------------------+ | inception_next_small | | +------------------------------------+------------------+ | inception_next_tiny | | +------------------------------------+------------------+ | mambaout_base | | +------------------------------------+------------------+ | mambaout_base_plus_rw | | +------------------------------------+------------------+ | mambaout_base_short_rw | | +------------------------------------+------------------+ | mambaout_base_tall_rw | | +------------------------------------+------------------+ | mambaout_base_wide_rw | | +------------------------------------+------------------+ | mambaout_femto | | +------------------------------------+------------------+ | mambaout_kobe | | +------------------------------------+------------------+ | mambaout_small | | +------------------------------------+------------------+ | mambaout_small_rw | | +------------------------------------+------------------+ | mambaout_tiny | | +------------------------------------+------------------+ | mvitv2_base | | +------------------------------------+------------------+ | mvitv2_base_cls | | +------------------------------------+------------------+ | mvitv2_huge_cls | | +------------------------------------+------------------+ | mvitv2_large | | +------------------------------------+------------------+ | mvitv2_large_cls | | +------------------------------------+------------------+ | mvitv2_small | | +------------------------------------+------------------+ | mvitv2_small_cls | | +------------------------------------+------------------+ | mvitv2_tiny | | +------------------------------------+------------------+ | nest_base | | +------------------------------------+------------------+ | nest_base_jx | | +------------------------------------+------------------+ | nest_small | | +------------------------------------+------------------+ | nest_small_jx | | +------------------------------------+------------------+ | nest_tiny | | +------------------------------------+------------------+ | nest_tiny_jx | | +------------------------------------+------------------+ | nextvit_base | | +------------------------------------+------------------+ | nextvit_large | | +------------------------------------+------------------+ | nextvit_small | | +------------------------------------+------------------+ | poolformer_m36 | | +------------------------------------+------------------+ | poolformer_m48 | | +------------------------------------+------------------+ | poolformer_s12 | | +------------------------------------+------------------+ | poolformer_s24 | | +------------------------------------+------------------+ | poolformer_s36 | | +------------------------------------+------------------+ | poolformerv2_m36 | | +------------------------------------+------------------+ | poolformerv2_m48 | | +------------------------------------+------------------+ | poolformerv2_s12 | | +------------------------------------+------------------+ | poolformerv2_s24 | | +------------------------------------+------------------+ | poolformerv2_s36 | | +------------------------------------+------------------+ | pvt_v2_b0 | | +------------------------------------+------------------+ | pvt_v2_b1 | | +------------------------------------+------------------+ | pvt_v2_b2 | | +------------------------------------+------------------+ | pvt_v2_b2_li | | +------------------------------------+------------------+ | pvt_v2_b3 | | +------------------------------------+------------------+ | pvt_v2_b4 | | +------------------------------------+------------------+ | pvt_v2_b5 | | +------------------------------------+------------------+ | rdnet_base | | +------------------------------------+------------------+ | rdnet_large | | +------------------------------------+------------------+ | rdnet_small | | +------------------------------------+------------------+ | rdnet_tiny | | +------------------------------------+------------------+ | repvit_m0_9 | | +------------------------------------+------------------+ | repvit_m1 | | +------------------------------------+------------------+ | repvit_m1_0 | | +------------------------------------+------------------+ | repvit_m1_1 | | +------------------------------------+------------------+ | repvit_m1_5 | | +------------------------------------+------------------+ | repvit_m2 | | +------------------------------------+------------------+ | repvit_m2_3 | | +------------------------------------+------------------+ | repvit_m3 | | +------------------------------------+------------------+ | sam2_hiera_base_plus | | +------------------------------------+------------------+ | sam2_hiera_large | | +------------------------------------+------------------+ | sam2_hiera_small | | +------------------------------------+------------------+ | sam2_hiera_tiny | | +------------------------------------+------------------+ | swin_base_patch4_window7_224 | | +------------------------------------+------------------+ | swin_base_patch4_window12_384 | | +------------------------------------+------------------+ | swin_large_patch4_window7_224 | | +------------------------------------+------------------+ | swin_large_patch4_window12_384 | | +------------------------------------+------------------+ | swin_s3_base_224 | | +------------------------------------+------------------+ | swin_s3_small_224 | | +------------------------------------+------------------+ | swin_s3_tiny_224 | | +------------------------------------+------------------+ | swin_small_patch4_window7_224 | | +------------------------------------+------------------+ | swin_tiny_patch4_window7_224 | | +------------------------------------+------------------+ | swinv2_base_window8_256 | | +------------------------------------+------------------+ | swinv2_base_window12_192 | | +------------------------------------+------------------+ | swinv2_base_window12to16_192to256 | | +------------------------------------+------------------+ | swinv2_base_window12to24_192to384 | | +------------------------------------+------------------+ | swinv2_base_window16_256 | | +------------------------------------+------------------+ | swinv2_cr_base_224 | | +------------------------------------+------------------+ | swinv2_cr_base_384 | | +------------------------------------+------------------+ | swinv2_cr_base_ns_224 | | +------------------------------------+------------------+ | swinv2_cr_giant_224 | | +------------------------------------+------------------+ | swinv2_cr_giant_384 | | +------------------------------------+------------------+ | swinv2_cr_huge_224 | | +------------------------------------+------------------+ | swinv2_cr_huge_384 | | +------------------------------------+------------------+ | swinv2_cr_large_224 | | +------------------------------------+------------------+ | swinv2_cr_large_384 | | +------------------------------------+------------------+ | swinv2_cr_small_224 | | +------------------------------------+------------------+ | swinv2_cr_small_384 | | +------------------------------------+------------------+ | swinv2_cr_small_ns_224 | | +------------------------------------+------------------+ | swinv2_cr_small_ns_256 | | +------------------------------------+------------------+ | swinv2_cr_tiny_224 | | +------------------------------------+------------------+ | swinv2_cr_tiny_384 | | +------------------------------------+------------------+ | swinv2_cr_tiny_ns_224 | | +------------------------------------+------------------+ | swinv2_large_window12_192 | | +------------------------------------+------------------+ | swinv2_large_window12to16_192to256 | | +------------------------------------+------------------+ | swinv2_large_window12to24_192to384 | | +------------------------------------+------------------+ | swinv2_small_window8_256 | | +------------------------------------+------------------+ | swinv2_small_window16_256 | | +------------------------------------+------------------+ | swinv2_tiny_window8_256 | | +------------------------------------+------------------+ | swinv2_tiny_window16_256 | | +------------------------------------+------------------+ | tiny_vit_5m_224 | | +------------------------------------+------------------+ | tiny_vit_11m_224 | | +------------------------------------+------------------+ | tiny_vit_21m_224 | | +------------------------------------+------------------+ | tiny_vit_21m_384 | | +------------------------------------+------------------+ | tiny_vit_21m_512 | | +------------------------------------+------------------+ | tresnet_l | | +------------------------------------+------------------+ | tresnet_m | | +------------------------------------+------------------+ | tresnet_v2_l | | +------------------------------------+------------------+ | tresnet_xl | | +------------------------------------+------------------+ | twins_pcpvt_base | | +------------------------------------+------------------+ | twins_pcpvt_large | | +------------------------------------+------------------+ | twins_pcpvt_small | | +------------------------------------+------------------+ | twins_svt_base | | +------------------------------------+------------------+ | twins_svt_large | | +------------------------------------+------------------+ | twins_svt_small | | +------------------------------------+------------------+ ================================================ FILE: docs/index.rst ================================================ .. Segmentation Models documentation master file, created by sphinx-quickstart on Fri Nov 27 00:00:20 2020. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Welcome to Segmentation Models's documentation! =============================================== .. toctree:: :maxdepth: 2 :caption: Contents: install quickstart models encoders encoders_timm losses metrics save_load insights Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` ================================================ FILE: docs/insights.rst ================================================ 💡 Insights =========== 1. Models architecture ~~~~~~~~~~~~~~~~~~~~~~ All segmentation models in SMP (this library short name) are made of: - encoder (feature extractor, a.k.a backbone) - decoder (features fusion block to create segmentation *mask*) - segmentation head (final head to reduce number of channels from decoder and upsample mask to preserve input-output spatial resolution identity) - classification head (optional head which build on top of deepest encoder features) 2. Creating your own encoder ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Encoder is a "classification model" which extract features from image and pass it to decoder. Each encoder should have following attributes and methods and be inherited from `segmentation_models_pytorch.encoders._base.EncoderMixin` .. code-block:: python class MyEncoder(torch.nn.Module, EncoderMixin): def __init__(self, **kwargs): super().__init__() # A number of channels for each encoder feature tensor, list of integers self._out_channels: List[int] = [3, 16, 64, 128, 256, 512] # A number of stages in decoder (in other words number of downsampling operations), integer # use in in forward pass to reduce number of returning features self._depth: int = 5 # Default number of input channels in first Conv2d layer for encoder (usually 3) self._in_channels: int = 3 # Define encoder modules below ... def forward(self, x: torch.Tensor) -> List[torch.Tensor]: """Produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ return [feat1, feat2, feat3, feat4, feat5, feat6] When you write your own Encoder class register its build parameters .. code-block:: python smp.encoders.encoders["my_awesome_encoder"] = { "encoder": MyEncoder, # encoder class here "pretrained_settings": { "imagenet": { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "url": "https://some-url.com/my-model-weights", "input_space": "RGB", "input_range": [0, 1], }, }, "params": { # init params for encoder if any }, }, Now you can use your encoder .. code-block:: python model = smp.Unet(encoder_name="my_awesome_encoder") For better understanding see more examples of encoder in smp.encoders module. .. note:: If it works fine, don`t forget to contribute your work and make a PR to SMP 😉 3. Aux classification output ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ All models support ``aux_params`` parameter, which is default set to ``None``. If ``aux_params = None`` than classification auxiliary output is not created, else model produce not only ``mask``, but also ``label`` output with shape ``(N, C)``. Classification head consist of following layers: 1. GlobalPooling 2. Dropout (optional) 3. Linear 4. Activation (optional) Example: .. code-block:: python aux_params=dict( pooling='avg', # one of 'avg', 'max' dropout=0.5, # dropout ratio, default is None activation='sigmoid', # activation function, default is None classes=4, # define number of output labels ) model = smp.Unet('resnet34', classes=4, aux_params=aux_params) mask, label = model(x) mask.shape, label.shape # (N, 4, H, W), (N, 4) 4. Freezing and unfreezing the encoder ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Sometimes you may want to freeze the encoder during training, e.g. when using pretrained backbones and only fine-tuning the decoder and segmentation head. All segmentation models in SMP provide two helper methods: .. code-block:: python model = smp.Unet("resnet34", classes=2) # Freeze encoder: stops gradient updates and freezes normalization layer stats model.freeze_encoder() # Unfreeze encoder: re-enables training for encoder parameters and normalization layers model.unfreeze_encoder() .. important:: - Freezing sets ``requires_grad = False`` for all encoder parameters. - Normalization layers that track running statistics (e.g., BatchNorm and InstanceNorm layers) are set to ``.eval()`` mode to prevent updates to ``running_mean`` and ``running_var``. - If you later call ``model.train()``, frozen encoders will remain frozen until you call ``unfreeze_encoder()``. ================================================ FILE: docs/install.rst ================================================ ⚙️ Installation =============== PyPI version: .. code-block:: bash $ pip install -U segmentation-models-pytorch Latest version from source: .. code-block:: bash $ pip install -U git+https://github.com/qubvel/segmentation_models.pytorch ================================================ FILE: docs/losses.rst ================================================ 📉 Losses ========= Collection of popular semantic segmentation losses. Adapted from an awesome repo with pytorch utils https://github.com/BloodAxe/pytorch-toolbelt Constants ~~~~~~~~~ .. automodule:: segmentation_models_pytorch.losses.constants :members: JaccardLoss ~~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.JaccardLoss DiceLoss ~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.DiceLoss TverskyLoss ~~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.TverskyLoss FocalLoss ~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.FocalLoss LovaszLoss ~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.LovaszLoss SoftBCEWithLogitsLoss ~~~~~~~~~~~~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.SoftBCEWithLogitsLoss SoftCrossEntropyLoss ~~~~~~~~~~~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.SoftCrossEntropyLoss MCCLoss ~~~~~~~~~~~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.losses.MCCLoss :members: forward ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: docs/metrics.rst ================================================ 📏 Metrics ========== Functional metrics ~~~~~~~~~~~~~~~~~~ .. currentmodule:: segmentation_models_pytorch.metrics.functional .. autosummary:: get_stats fbeta_score f1_score iou_score accuracy precision recall sensitivity specificity balanced_accuracy positive_predictive_value negative_predictive_value false_negative_rate false_positive_rate false_discovery_rate false_omission_rate positive_likelihood_ratio negative_likelihood_ratio .. automodule:: segmentation_models_pytorch.metrics.functional :members: ================================================ FILE: docs/models.rst ================================================ 🕸️ Segmentation Models ============================== .. contents:: :local: .. _unet: Unet ~~~~ .. autoclass:: segmentation_models_pytorch.Unet .. _unetplusplus: Unet++ ~~~~~~ .. autoclass:: segmentation_models_pytorch.UnetPlusPlus .. _fpn: FPN ~~~ .. autoclass:: segmentation_models_pytorch.FPN .. _pspnet: PSPNet ~~~~~~ .. autoclass:: segmentation_models_pytorch.PSPNet .. _deeplabv3: DeepLabV3 ~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.DeepLabV3 .. _deeplabv3plus: DeepLabV3+ ~~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.DeepLabV3Plus .. _linknet: Linknet ~~~~~~~ .. autoclass:: segmentation_models_pytorch.Linknet .. _manet: MAnet ~~~~~~ .. autoclass:: segmentation_models_pytorch.MAnet .. _pan: PAN ~~~ .. autoclass:: segmentation_models_pytorch.PAN .. _upernet: UPerNet ~~~~~~~ .. autoclass:: segmentation_models_pytorch.UPerNet .. _segformer: Segformer ~~~~~~~~~ .. autoclass:: segmentation_models_pytorch.Segformer .. _dpt: DPT ~~~ .. note:: See full list of DPT-compatible timm encoders in :ref:`dpt-encoders`. .. note:: For some encoders, the model requires ``dynamic_img_size=True`` to be passed in order to work with resolutions different from what the encoder was trained for. .. autoclass:: segmentation_models_pytorch.DPT ================================================ FILE: docs/quickstart.rst ================================================ 🚀 Quick Start ============== **1. Create segmentation model** Segmentation model is just a PyTorch nn.Module, which can be created as easy as: .. code-block:: python import segmentation_models_pytorch as smp model = smp.Unet( encoder_name="resnet34", # choose encoder, e.g. mobilenet_v2 or efficientnet-b7 encoder_weights="imagenet", # use `imagenet` pre-trained weights for encoder initialization in_channels=1, # model input channels (1 for gray-scale images, 3 for RGB, etc.) classes=3, # model output channels (number of classes in your dataset) ) - Check the page with available :doc:`model architectures `. - Check the table with :doc:`available ported encoders and its corresponding weights `. - `Pytorch Image Models (timm) `_ encoders are also supported, check it :doc:`here`. Alternatively, you can use `smp.create_model` function to create a model by name: .. code-block:: python model = smp.create_model( arch="fpn", # name of the architecture, e.g. 'Unet'/ 'FPN' / etc. Case INsensitive! encoder_name="mit_b0", encoder_weights="imagenet", in_channels=1, classes=3, ) **2. Configure data preprocessing** All encoders have pretrained weights. Preparing your data the same way as during weights pre-training may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and **not necessary** in case you train the whole model, not only decoder. .. code-block:: python from segmentation_models_pytorch.encoders import get_preprocessing_fn preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet') **3. Congratulations!** 🎉 You are done! Now you can train your model with your favorite framework, or as simple as: .. code-block:: python for images, gt_masks in dataloader: predicted_mask = model(images) loss = loss_fn(predicted_mask, gt_masks) loss.backward() optimizer.step() Check the following examples: .. |colab-badge| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb :alt: Open In Colab - Finetuning notebook on Oxford Pet dataset with `PyTorch Lightning `__ |colab-badge| - Finetuning script for cloth segmentation with `PyTorch Lightning `__ ================================================ FILE: docs/save_load.rst ================================================ 📂 Saving and Loading ===================== In this section, we will discuss how to save a trained model, push it to the Hugging Face Hub, and load it back for later use. Saving and Sharing a Model -------------------------- Once you have trained your model, you can save it using the `.save_pretrained` method. This method saves the model configuration and weights to a directory of your choice. And, optionally, you can push the model to the Hugging Face Hub by setting the `push_to_hub` parameter to `True`. For example: .. code:: python import segmentation_models_pytorch as smp model = smp.Unet('resnet34', encoder_weights='imagenet') # After training your model, save it to a directory model.save_pretrained('./my_model') # Or saved and pushed to the Hub simultaneously model.save_pretrained('username/my-model', push_to_hub=True) Loading Trained Model --------------------- Once your model is saved and pushed to the Hub, you can load it back using the `smp.from_pretrained` method. This method allows you to load the model weights and configuration from a directory or directly from the Hub. For example: .. code:: python import segmentation_models_pytorch as smp # Load the model from the local directory model = smp.from_pretrained('./my_model') # Alternatively, load the model directly from the Hugging Face Hub model = smp.from_pretrained('username/my-model') Loading pre-trained model with different number of classes for fine-tuning: .. code:: python import segmentation_models_pytorch as smp model = smp.from_pretrained('', classes=5, strict=False) Saving model Metrics and Dataset Name ------------------------------------- You can simply pass the `metrics` and `dataset` parameters to the `save_pretrained` method to save the model metrics and dataset name in Model Card along with the model configuration and weights. For example: .. code:: python import segmentation_models_pytorch as smp model = smp.Unet('resnet34', encoder_weights='imagenet') # After training your model, save it to a directory model.save_pretrained('./my_model', metrics={'accuracy': 0.95}, dataset='my_dataset') # Or saved and pushed to the Hub simultaneously model.save_pretrained('username/my-model', push_to_hub=True, metrics={'accuracy': 0.95}, dataset='my_dataset') Saving with preprocessing transform (Albumentations) ---------------------------------------------------- You can save the preprocessing transform along with the model and push it to the Hub. This can be useful when you want to share the model with the preprocessing transform that was used during training, to make sure that the inference pipeline is consistent with the training pipeline. .. code:: python import albumentations as A import segmentation_models_pytorch as smp # Define a preprocessing transform for image that would be used during inference preprocessing_transform = A.Compose([ A.Resize(256, 256), A.Normalize() ]) model = smp.Unet() directory_or_repo_on_the_hub = "qubvel-hf/unet-with-transform" # / # Save the model and transform (and pus ot hub, if needed) model.save_pretrained(directory_or_repo_on_the_hub, push_to_hub=True) preprocessing_transform.save_pretrained(directory_or_repo_on_the_hub, push_to_hub=True) # Loading transform and model restored_model = smp.from_pretrained(directory_or_repo_on_the_hub) restored_transform = A.Compose.from_pretrained(directory_or_repo_on_the_hub) print(restored_transform) Conclusion ---------- By following these steps, you can easily save, share, and load your models, facilitating collaboration and reproducibility in your projects. Don't forget to replace the placeholders with your actual model paths and names. |binary-segmentation-intro| |save-load-model-and-share-with-hf-hub| .. |binary-segmentation-intro| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb :alt: Open In Colab .. |save-load-model-and-share-with-hf-hub| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb :alt: Open In Colab ================================================ FILE: examples/binary_segmentation_buildings.py ================================================ """ This script demonstrates how to train a binary segmentation model using the CamVid dataset and segmentation_models_pytorch. The CamVid dataset is a collection of videos with pixel-level annotations for semantic segmentation. The dataset includes 367 training images, 101 validation images, and 233 test. Each training image has a corresponding mask that labels each pixel as belonging to these classes with the numerical labels as follows: - Sky: 0 - Building: 1 - Pole: 2 - Road: 3 - Pavement: 4 - Tree: 5 - SignSymbol: 6 - Fence: 7 - Car: 8 - Pedestrian: 9 - Bicyclist: 10 - Unlabelled: 11 In this script, we focus on binary segmentation, where the goal is to classify each pixel as whether belonging to a certain class (Foregorund) or not (Background). Class Labels: - 0: Background - 1: Foreground The script includes the following steps: 1. Set the device to GPU if available, otherwise use CPU. 2. Download the CamVid dataset if it is not already present. 3. Define hyperparameters for training. 4. Define a custom dataset class for loading and preprocessing the CamVid dataset. 5. Define a function to visualize images and masks. 6. Create datasets and dataloaders for training, validation, and testing. 7. Define a model class for the segmentation task. 8. Train the model using the training and validation datasets. 9. Evaluate the model using the test dataset and save the output masks and metrics. """ import logging import os import cv2 import matplotlib.pyplot as plt import numpy as np import torch from torch.optim import lr_scheduler from torch.utils.data import DataLoader from torch.utils.data import Dataset as BaseDataset from tqdm import tqdm import segmentation_models_pytorch as smp logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(message)s", datefmt="%d:%m:%Y %H:%M:%S", ) # ---------------------------- # Set the device to GPU if available # ---------------------------- device = "cuda" if torch.cuda.is_available() else "cpu" logging.info(f"Using device: {device}") if device == "cpu": os.system("export OMP_NUM_THREADS=64") torch.set_num_threads(os.cpu_count()) # ---------------------------- # Download the CamVid dataset, if needed # ---------------------------- # Change this to your desired directory main_dir = "./examples/binary_segmentation_data/" data_dir = os.path.join(main_dir, "dataset") if not os.path.exists(data_dir): logging.info("Loading data...") os.system(f"git clone https://github.com/alexgkendall/SegNet-Tutorial {data_dir}") logging.info("Done!") # Create a directory to store the output masks output_dir = os.path.join(main_dir, "output_images") os.makedirs(output_dir, exist_ok=True) # ---------------------------- # Define the hyperparameters # ---------------------------- epochs_max = 200 # Number of epochs to train the model adam_lr = 2e-4 # Learning rate for the Adam optimizer eta_min = 1e-5 # Minimum learning rate for the scheduler batch_size = 8 # Batch size for training input_image_reshape = (320, 320) # Desired shape for the input images and masks foreground_class = 1 # 1 for binary segmentation # ---------------------------- # Define a custom dataset class for the CamVid dataset # ---------------------------- class Dataset(BaseDataset): """ A custom dataset class for binary segmentation tasks. Parameters: ---------- - images_dir (str): Directory containing the input images. - masks_dir (str): Directory containing the corresponding masks. - input_image_reshape (tuple, optional): Desired shape for the input images and masks. Default is (320, 320). - foreground_class (int, optional): The class value in the mask to be considered as the foreground. Default is 1. - augmentation (callable, optional): A function/transform to apply to the images and masks for data augmentation. """ def __init__( self, images_dir, masks_dir, input_image_reshape=(320, 320), foreground_class=1, augmentation=None, ): self.ids = os.listdir(images_dir) self.images_filepaths = [ os.path.join(images_dir, image_id) for image_id in self.ids ] self.masks_filepaths = [ os.path.join(masks_dir, image_id) for image_id in self.ids ] self.input_image_reshape = input_image_reshape self.foreground_class = foreground_class self.augmentation = augmentation def __getitem__(self, i): """ Retrieves the image and corresponding mask at index `i`. Parameters: ---------- - i (int): Index of the image and mask to retrieve. Returns: - A tuple containing: - image (torch.Tensor): The preprocessed image tensor of shape (1, input_image_reshape) - e.g., (1, 320, 320) - normalized to [0, 1]. - mask_remap (torch.Tensor): The preprocessed mask tensor of shape input_image_reshape with values 0 or 1. """ # Read the image image = cv2.imread( self.images_filepaths[i], cv2.IMREAD_GRAYSCALE ) # Read image as grayscale image = np.expand_dims(image, axis=-1) # Add channel dimension # resize image to input_image_reshape image = cv2.resize(image, self.input_image_reshape) # Read the mask in grayscale mode mask = cv2.imread(self.masks_filepaths[i], 0) # Update the mask: Set foreground_class to 1 and the rest to 0 mask_remap = np.where(mask == self.foreground_class, 1, 0).astype(np.uint8) # resize mask to input_image_reshape mask_remap = cv2.resize(mask_remap, self.input_image_reshape) if self.augmentation: sample = self.augmentation(image=image, mask=mask_remap) image, mask_remap = sample["image"], sample["mask"] # Convert to PyTorch tensors # Add channel dimension if missing if image.ndim == 2: image = np.expand_dims(image, axis=-1) # HWC -> CHW and normalize to [0, 1] image = torch.tensor(image).float().permute(2, 0, 1) / 255.0 # Ensure mask is LongTensor mask_remap = torch.tensor(mask_remap).long() return image, mask_remap def __len__(self): return len(self.ids) # Define a class for the CamVid model class CamVidModel(torch.nn.Module): """ A PyTorch model for binary segmentation using the Segmentation Models PyTorch library. Parameters: ---------- - arch (str): The architecture name of the segmentation model (e.g., 'Unet', 'FPN'). - encoder_name (str): The name of the encoder to use (e.g., 'resnet34', 'vgg16'). - in_channels (int, optional): Number of input channels (e.g., 3 for RGB). - out_classes (int, optional): Number of output classes (e.g., 1 for binary) **kwargs: Additional keyword arguments to pass to the model creation function. """ def __init__(self, arch, encoder_name, in_channels=3, out_classes=1, **kwargs): super().__init__() self.mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device) self.std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device) self.model = smp.create_model( arch, encoder_name=encoder_name, in_channels=in_channels, classes=out_classes, **kwargs, ) def forward(self, image): # Normalize image image = (image - self.mean) / self.std mask = self.model(image) return mask def visualize(output_dir, image_filename, **images): """PLot images in one row.""" n = len(images) plt.figure(figsize=(16, 5)) for i, (name, image) in enumerate(images.items()): plt.subplot(1, n, i + 1) plt.xticks([]) plt.yticks([]) plt.title(" ".join(name.split("_")).title()) plt.imshow(image) plt.show() plt.savefig(os.path.join(output_dir, image_filename)) plt.close() # Use multiple CPUs in parallel def train_and_evaluate_one_epoch( model, train_dataloader, valid_dataloader, optimizer, scheduler, loss_fn, device ): # Set the model to training mode model.train() train_loss = 0 for batch in tqdm(train_dataloader, desc="Training"): images, masks = batch images, masks = images.to(device), masks.to(device) optimizer.zero_grad() outputs = model(images) loss = loss_fn(outputs, masks) loss.backward() optimizer.step() train_loss += loss.item() scheduler.step() avg_train_loss = train_loss / len(train_dataloader) # Set the model to evaluation mode model.eval() val_loss = 0 with torch.inference_mode(): for batch in tqdm(valid_dataloader, desc="Evaluating"): images, masks = batch images, masks = images.to(device), masks.to(device) outputs = model(images) loss = loss_fn(outputs, masks) val_loss += loss.item() avg_val_loss = val_loss / len(valid_dataloader) return avg_train_loss, avg_val_loss def train_model( model, train_dataloader, valid_dataloader, optimizer, scheduler, loss_fn, device, epochs, ): train_losses = [] val_losses = [] for epoch in range(epochs): avg_train_loss, avg_val_loss = train_and_evaluate_one_epoch( model, train_dataloader, valid_dataloader, optimizer, scheduler, loss_fn, device, ) train_losses.append(avg_train_loss) val_losses.append(avg_val_loss) logging.info( f"Epoch {epoch + 1}/{epochs}, Training Loss: {avg_train_loss:.2f}, Validation Loss: {avg_val_loss:.2f}" ) history = { "train_losses": train_losses, "val_losses": val_losses, } return history def test_model(model, output_dir, test_dataloader, loss_fn, device): # Set the model to evaluation mode model.eval() test_loss = 0 tp, fp, fn, tn = 0, 0, 0, 0 with torch.inference_mode(): for batch in tqdm(test_dataloader, desc="Evaluating"): images, masks = batch images, masks = images.to(device), masks.to(device) outputs = model(images) loss = loss_fn(outputs, masks) for i, output in enumerate(outputs): input = images[i].cpu().numpy().transpose(1, 2, 0) output = output.squeeze().cpu().numpy() visualize( output_dir, f"output_{i}.png", input_image=input, output_mask=output, binary_mask=output > 0.5, ) test_loss += loss.item() prob_mask = outputs.sigmoid().squeeze(1) pred_mask = (prob_mask > 0.5).long() batch_tp, batch_fp, batch_fn, batch_tn = smp.metrics.get_stats( pred_mask, masks, mode="binary" ) tp += batch_tp.sum().item() fp += batch_fp.sum().item() fn += batch_fn.sum().item() tn += batch_tn.sum().item() test_loss_mean = test_loss / len(test_dataloader) logging.info(f"Test Loss: {test_loss_mean:.2f}") iou_score = smp.metrics.iou_score( torch.tensor([tp]), torch.tensor([fp]), torch.tensor([fn]), torch.tensor([tn]), reduction="micro", ) return test_loss_mean, iou_score.item() # ---------------------------- # Define the data directories and create the datasets # ---------------------------- x_train_dir = os.path.join(data_dir, "CamVid", "train") y_train_dir = os.path.join(data_dir, "CamVid", "trainannot") x_val_dir = os.path.join(data_dir, "CamVid", "val") y_val_dir = os.path.join(data_dir, "CamVid", "valannot") x_test_dir = os.path.join(data_dir, "CamVid", "test") y_test_dir = os.path.join(data_dir, "CamVid", "testannot") train_dataset = Dataset( x_train_dir, y_train_dir, input_image_reshape=input_image_reshape, foreground_class=foreground_class, ) valid_dataset = Dataset( x_val_dir, y_val_dir, input_image_reshape=input_image_reshape, foreground_class=foreground_class, ) test_dataset = Dataset( x_test_dir, y_test_dir, input_image_reshape=input_image_reshape, foreground_class=foreground_class, ) image, mask = train_dataset[0] logging.info(f"Unique values in mask: {np.unique(mask)}") logging.info(f"Image shape: {image.shape}") logging.info(f"Mask shape: {mask.shape}") # ---------------------------- # Create the dataloaders using the datasets # ---------------------------- logging.info(f"Train size: {len(train_dataset)}") logging.info(f"Valid size: {len(valid_dataset)}") logging.info(f"Test size: {len(test_dataset)}") train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False) test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # ---------------------------- # Lets look at some samples # ---------------------------- # Visualize and save train sample sample = train_dataset[0] visualize( output_dir, "train_sample.png", train_image=sample[0].numpy().transpose(1, 2, 0), train_mask=sample[1].squeeze(), ) # Visualize and save validation sample sample = valid_dataset[0] visualize( output_dir, "validation_sample.png", validation_image=sample[0].numpy().transpose(1, 2, 0), validation_mask=sample[1].squeeze(), ) # Visualize and save test sample sample = test_dataset[0] visualize( output_dir, "test_sample.png", test_image=sample[0].numpy().transpose(1, 2, 0), test_mask=sample[1].squeeze(), ) # ---------------------------- # Create and train the model # ---------------------------- max_iter = epochs_max * len(train_dataloader) # Total number of iterations model = CamVidModel("Unet", "resnet34", in_channels=3, out_classes=1) # Training loop model = model.to(device) # Define the Adam optimizer optimizer = torch.optim.Adam(model.parameters(), lr=adam_lr) # Define the learning rate scheduler scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=max_iter, eta_min=eta_min) # Define the loss function loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True) # Train the model history = train_model( model, train_dataloader, valid_dataloader, optimizer, scheduler, loss_fn, device, epochs_max, ) # Visualize the training and validation losses plt.figure(figsize=(10, 5)) plt.plot(history["train_losses"], label="Train Loss") plt.plot(history["val_losses"], label="Validation Loss") plt.xlabel("Epochs") plt.ylabel("Loss") plt.title("Training and Validation Losses") plt.legend() plt.savefig(os.path.join(output_dir, "train_val_losses.png")) plt.close() # Evaluate the model test_loss = test_model(model, output_dir, test_dataloader, loss_fn, device) logging.info(f"Test Loss: {test_loss[0]}, IoU Score: {test_loss[1]}") logging.info(f"The output masks are saved in {output_dir}.") ================================================ FILE: examples/binary_segmentation_intro.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "id": "U3WUb8t2P2e5" }, "source": [ "\ud83c\udded \ud83c\uddea \ud83c\uddf1 \ud83c\uddf1 \ud83c\uddf4 \ud83d\udc4b\n", "\n", "This example shows how to use `segmentation-models-pytorch` for **binary** semantic segmentation. We will use the [The Oxford-IIIT Pet Dataset](https://www.robots.ox.ac.uk/~vgg/data/pets/) (this is an adopted example from Albumentations package [docs](https://albumentations.ai/docs/examples/pytorch_semantic_segmentation/), which is strongly recommended to read, especially if you never used this package for augmentations before). \n", "\n", "The task will be to classify each pixel of an input image either as pet \ud83d\udc36\ud83d\udc31 or as a background.\n", "\n", "\n", "What we are going to overview in this example: \n", "\n", " - \ud83d\udcdc `Datasets` and `DataLoaders` preparation (with predefined dataset class). \n", " - \ud83d\udce6 `LightningModule` preparation: defining training, validation and test routines. \n", " - \ud83d\udcc8 Writing `IoU` metric inside the `LightningModule` for measuring quality of segmentation. \n", " - \ud83d\udc36 Results visualization.\n", "\n", "\n", "> It is expected you are familiar with Python, PyTorch and have some experience with training neural networks before!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:37:36.751747Z", "iopub.status.busy": "2024-08-18T04:37:36.750812Z", "iopub.status.idle": "2024-08-18T04:38:26.758872Z", "shell.execute_reply": "2024-08-18T04:38:26.757586Z", "shell.execute_reply.started": "2024-08-18T04:37:36.751710Z" }, "id": "DYNdz8s56qOu", "outputId": "7f343747-532d-417c-fc72-fda5c713d4e3", "trusted": true }, "outputs": [], "source": [ "%%capture\n", "!pip install -U git+https://github.com/qubvel-org/segmentation_models.pytorch\n", "!pip install lightning albumentations" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:38:26.761388Z", "iopub.status.busy": "2024-08-18T04:38:26.761047Z", "iopub.status.idle": "2024-08-18T04:38:37.024102Z", "shell.execute_reply": "2024-08-18T04:38:37.023281Z", "shell.execute_reply.started": "2024-08-18T04:38:26.761357Z" }, "id": "iKiMzw2t6ika", "trusted": true }, "outputs": [], "source": [ "import os\n", "\n", "import torch\n", "import matplotlib.pyplot as plt\n", "import pytorch_lightning as pl\n", "from torch.optim import lr_scheduler\n", "import segmentation_models_pytorch as smp\n", "from torch.utils.data import DataLoader" ] }, { "cell_type": "markdown", "metadata": { "id": "H4RKHF535Twz" }, "source": [ "## Dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "lkghwALE5fIc" }, "source": [ "In this example we will use predefined `Dataset` class for simplicity. The dataset actually read pairs of images and masks from disk and return `sample` - dictionary with keys `image`, `mask` and others (not relevant for this example).\n", "\n", "\u26a0\ufe0f **Dataset preparation checklist** \u26a0\ufe0f\n", "\n", "In case you writing your own dataset, please, make sure that:\n", "\n", "1. **Images** \ud83d\uddbc \n", " \u2705 Images from dataset have **the same size**, required for packing images to a batch. \n", " \u2705 Images height and width are **divisible by 32**. This step is important for segmentation, because almost all models have skip-connections between encoder and decoder and all encoders have 5 downsampling stages (2 ^ 5 = 32). Very likely you will face with error when model will try to concatenate encoder and decoder features if height or width is not divisible by 32. \n", " \u2705 Images have **correct axes order**. PyTorch works with CHW order, we read images in HWC [height, width, channels], don`t forget to transpose image.\n", "2. **Masks** \ud83d\udd33 \n", " \u2705 Masks have **the same sizes** as images. \n", " \u2705 Masks have only `0` - background and `1` - target class values (for binary segmentation). \n", " \u2705 Even if mask don`t have channels, you need it. Convert each mask from **HW to 1HW** format for binary segmentation (expand the first dimension).\n", "\n", "Some of these checks are included in LightningModule below during the training.\n", "\n", "\u2757\ufe0f And the main rule: your train, validation and test sets are not intersects with each other!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:38:37.025511Z", "iopub.status.busy": "2024-08-18T04:38:37.025197Z", "iopub.status.idle": "2024-08-18T04:38:37.029876Z", "shell.execute_reply": "2024-08-18T04:38:37.028922Z", "shell.execute_reply.started": "2024-08-18T04:38:37.025486Z" }, "id": "NP_DttTvvyQN", "trusted": true }, "outputs": [], "source": [ "from segmentation_models_pytorch.datasets import SimpleOxfordPetDataset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:38:37.032330Z", "iopub.status.busy": "2024-08-18T04:38:37.032035Z", "iopub.status.idle": "2024-08-18T04:39:42.743994Z", "shell.execute_reply": "2024-08-18T04:39:42.743179Z", "shell.execute_reply.started": "2024-08-18T04:38:37.032282Z" }, "id": "OVHVkntIS6Cr", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "images.tar.gz: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 755M/755M [00:51<00:00, 15.5MB/s] \n", "annotations.tar.gz: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 18.3M/18.3M [00:05<00:00, 3.44MB/s] \n" ] } ], "source": [ "# download data\n", "root = \".\"\n", "SimpleOxfordPetDataset.download(root)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:39:42.745259Z", "iopub.status.busy": "2024-08-18T04:39:42.744995Z", "iopub.status.idle": "2024-08-18T04:39:42.761041Z", "shell.execute_reply": "2024-08-18T04:39:42.760018Z", "shell.execute_reply.started": "2024-08-18T04:39:42.745236Z" }, "id": "5Qyuw1YA5b7y", "outputId": "1d60699d-9dab-44d4-ba4c-fc0182b4a5d8", "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train size: 3312\n", "Valid size: 368\n", "Test size: 3669\n" ] } ], "source": [ "# init train, val, test sets\n", "train_dataset = SimpleOxfordPetDataset(root, \"train\")\n", "valid_dataset = SimpleOxfordPetDataset(root, \"valid\")\n", "test_dataset = SimpleOxfordPetDataset(root, \"test\")\n", "\n", "# It is a good practice to check datasets don`t intersects with each other\n", "assert set(test_dataset.filenames).isdisjoint(set(train_dataset.filenames))\n", "assert set(test_dataset.filenames).isdisjoint(set(valid_dataset.filenames))\n", "assert set(train_dataset.filenames).isdisjoint(set(valid_dataset.filenames))\n", "\n", "print(f\"Train size: {len(train_dataset)}\")\n", "print(f\"Valid size: {len(valid_dataset)}\")\n", "print(f\"Test size: {len(test_dataset)}\")\n", "\n", "n_cpu = os.cpu_count()\n", "train_dataloader = DataLoader(\n", " train_dataset, batch_size=64, shuffle=True, num_workers=n_cpu\n", ")\n", "valid_dataloader = DataLoader(\n", " valid_dataset, batch_size=64, shuffle=False, num_workers=n_cpu\n", ")\n", "test_dataloader = DataLoader(\n", " test_dataset, batch_size=64, shuffle=False, num_workers=n_cpu\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:39:42.762445Z", "iopub.status.busy": "2024-08-18T04:39:42.762171Z", "iopub.status.idle": "2024-08-18T04:39:44.501060Z", "shell.execute_reply": "2024-08-18T04:39:44.500156Z", "shell.execute_reply.started": "2024-08-18T04:39:42.762422Z" }, "id": "O4nq08ILaYhn", "outputId": "d8adb583-a5b1-4b7d-aab8-ea5e60381e14", "trusted": true }, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# lets look at some samples\n", "\n", "sample = train_dataset[0]\n", "plt.subplot(1, 2, 1)\n", "# for visualization we have to transpose back to HWC\n", "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n", "plt.subplot(1, 2, 2)\n", "# for visualization we have to remove 3rd dimension of mask\n", "plt.imshow(sample[\"mask\"].squeeze())\n", "plt.show()\n", "\n", "sample = valid_dataset[0]\n", "plt.subplot(1, 2, 1)\n", "# for visualization we have to transpose back to HWC\n", "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n", "plt.subplot(1, 2, 2)\n", "# for visualization we have to remove 3rd dimension of mask\n", "plt.imshow(sample[\"mask\"].squeeze())\n", "plt.show()\n", "\n", "sample = test_dataset[0]\n", "plt.subplot(1, 2, 1)\n", "# for visualization we have to transpose back to HWC\n", "plt.imshow(sample[\"image\"].transpose(1, 2, 0))\n", "plt.subplot(1, 2, 2)\n", "# for visualization we have to remove 3rd dimension of mask\n", "plt.imshow(sample[\"mask\"].squeeze())\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "jg4_bxKV5BaQ" }, "source": [ "## Model" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:39:44.502757Z", "iopub.status.busy": "2024-08-18T04:39:44.502418Z", "iopub.status.idle": "2024-08-18T04:39:44.507639Z", "shell.execute_reply": "2024-08-18T04:39:44.506577Z", "shell.execute_reply.started": "2024-08-18T04:39:44.502728Z" }, "trusted": true }, "outputs": [], "source": [ "# Some training hyperparameters\n", "EPOCHS = 10\n", "T_MAX = EPOCHS * len(train_dataloader)\n", "OUT_CLASSES = 1" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:39:44.509551Z", "iopub.status.busy": "2024-08-18T04:39:44.509240Z", "iopub.status.idle": "2024-08-18T04:39:44.532055Z", "shell.execute_reply": "2024-08-18T04:39:44.531224Z", "shell.execute_reply.started": "2024-08-18T04:39:44.509528Z" }, "id": "PeGCIYNlVx5y", "trusted": true }, "outputs": [], "source": [ "class PetModel(pl.LightningModule):\n", " def __init__(self, arch, encoder_name, in_channels, out_classes, **kwargs):\n", " super().__init__()\n", " self.model = smp.create_model(\n", " arch,\n", " encoder_name=encoder_name,\n", " in_channels=in_channels,\n", " classes=out_classes,\n", " **kwargs,\n", " )\n", " # preprocessing parameteres for image\n", " params = smp.encoders.get_preprocessing_params(encoder_name)\n", " self.register_buffer(\"std\", torch.tensor(params[\"std\"]).view(1, 3, 1, 1))\n", " self.register_buffer(\"mean\", torch.tensor(params[\"mean\"]).view(1, 3, 1, 1))\n", "\n", " # for image segmentation dice loss could be the best first choice\n", " self.loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True)\n", "\n", " # initialize step metics\n", " self.training_step_outputs = []\n", " self.validation_step_outputs = []\n", " self.test_step_outputs = []\n", "\n", " def forward(self, image):\n", " # normalize image here\n", " image = (image - self.mean) / self.std\n", " mask = self.model(image)\n", " return mask\n", "\n", " def shared_step(self, batch, stage):\n", " image = batch[\"image\"]\n", "\n", " # Shape of the image should be (batch_size, num_channels, height, width)\n", " # if you work with grayscale images, expand channels dim to have [batch_size, 1, height, width]\n", " assert image.ndim == 4\n", "\n", " # Check that image dimensions are divisible by 32,\n", " # encoder and decoder connected by `skip connections` and usually encoder have 5 stages of\n", " # downsampling by factor 2 (2 ^ 5 = 32); e.g. if we have image with shape 65x65 we will have\n", " # following shapes of features in encoder and decoder: 84, 42, 21, 10, 5 -> 5, 10, 20, 40, 80\n", " # and we will get an error trying to concat these features\n", " h, w = image.shape[2:]\n", " assert h % 32 == 0 and w % 32 == 0\n", "\n", " mask = batch[\"mask\"]\n", " assert mask.ndim == 4\n", "\n", " # Check that mask values in between 0 and 1, NOT 0 and 255 for binary segmentation\n", " assert mask.max() <= 1.0 and mask.min() >= 0\n", "\n", " logits_mask = self.forward(image)\n", "\n", " # Predicted mask contains logits, and loss_fn param `from_logits` is set to True\n", " loss = self.loss_fn(logits_mask, mask)\n", "\n", " # Lets compute metrics for some threshold\n", " # first convert mask values to probabilities, then\n", " # apply thresholding\n", " prob_mask = logits_mask.sigmoid()\n", " pred_mask = (prob_mask > 0.5).float()\n", "\n", " # We will compute IoU metric by two ways\n", " # 1. dataset-wise\n", " # 2. image-wise\n", " # but for now we just compute true positive, false positive, false negative and\n", " # true negative 'pixels' for each image and class\n", " # these values will be aggregated in the end of an epoch\n", " tp, fp, fn, tn = smp.metrics.get_stats(\n", " pred_mask.long(), mask.long(), mode=\"binary\"\n", " )\n", " return {\n", " \"loss\": loss,\n", " \"tp\": tp,\n", " \"fp\": fp,\n", " \"fn\": fn,\n", " \"tn\": tn,\n", " }\n", "\n", " def shared_epoch_end(self, outputs, stage):\n", " # aggregate step metics\n", " tp = torch.cat([x[\"tp\"] for x in outputs])\n", " fp = torch.cat([x[\"fp\"] for x in outputs])\n", " fn = torch.cat([x[\"fn\"] for x in outputs])\n", " tn = torch.cat([x[\"tn\"] for x in outputs])\n", "\n", " # per image IoU means that we first calculate IoU score for each image\n", " # and then compute mean over these scores\n", " per_image_iou = smp.metrics.iou_score(\n", " tp, fp, fn, tn, reduction=\"micro-imagewise\"\n", " )\n", "\n", " # dataset IoU means that we aggregate intersection and union over whole dataset\n", " # and then compute IoU score. The difference between dataset_iou and per_image_iou scores\n", " # in this particular case will not be much, however for dataset\n", " # with \"empty\" images (images without target class) a large gap could be observed.\n", " # Empty images influence a lot on per_image_iou and much less on dataset_iou.\n", " dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n", " metrics = {\n", " f\"{stage}_per_image_iou\": per_image_iou,\n", " f\"{stage}_dataset_iou\": dataset_iou,\n", " }\n", "\n", " self.log_dict(metrics, prog_bar=True)\n", "\n", " def training_step(self, batch, batch_idx):\n", " train_loss_info = self.shared_step(batch, \"train\")\n", " # append the metics of each step to the\n", " self.training_step_outputs.append(train_loss_info)\n", " return train_loss_info\n", "\n", " def on_train_epoch_end(self):\n", " self.shared_epoch_end(self.training_step_outputs, \"train\")\n", " # empty set output list\n", " self.training_step_outputs.clear()\n", " return\n", "\n", " def validation_step(self, batch, batch_idx):\n", " valid_loss_info = self.shared_step(batch, \"valid\")\n", " self.validation_step_outputs.append(valid_loss_info)\n", " return valid_loss_info\n", "\n", " def on_validation_epoch_end(self):\n", " self.shared_epoch_end(self.validation_step_outputs, \"valid\")\n", " self.validation_step_outputs.clear()\n", " return\n", "\n", " def test_step(self, batch, batch_idx):\n", " test_loss_info = self.shared_step(batch, \"test\")\n", " self.test_step_outputs.append(test_loss_info)\n", " return test_loss_info\n", "\n", " def on_test_epoch_end(self):\n", " self.shared_epoch_end(self.test_step_outputs, \"test\")\n", " # empty set output list\n", " self.test_step_outputs.clear()\n", " return\n", "\n", " def configure_optimizers(self):\n", " optimizer = torch.optim.Adam(self.parameters(), lr=2e-4)\n", " scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_MAX, eta_min=1e-5)\n", " return {\n", " \"optimizer\": optimizer,\n", " \"lr_scheduler\": {\n", " \"scheduler\": scheduler,\n", " \"interval\": \"step\",\n", " \"frequency\": 1,\n", " },\n", " }\n", " return" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:39:44.533601Z", "iopub.status.busy": "2024-08-18T04:39:44.533123Z", "iopub.status.idle": "2024-08-18T04:39:46.413802Z", "shell.execute_reply": "2024-08-18T04:39:46.413012Z", "shell.execute_reply.started": "2024-08-18T04:39:44.533575Z" }, "id": "8d_wsmYArTt6", "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth\n", "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 83.3M/83.3M [00:01<00:00, 74.5MB/s]\n" ] } ], "source": [ "model = PetModel(\"FPN\", \"resnet34\", in_channels=3, out_classes=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "v-YUI8oH-sfL" }, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-18T04:39:46.416557Z", "iopub.status.busy": "2024-08-18T04:39:46.416192Z", "iopub.status.idle": "2024-08-18T04:43:23.201628Z", "shell.execute_reply": "2024-08-18T04:43:23.200521Z", "shell.execute_reply.started": "2024-08-18T04:39:46.416531Z" }, "id": "WvKlqPH6sKtz", "outputId": "441f8a2e-6159-4e06-ddb5-c47df93d18c9", "trusted": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: | | 0/? [00:00 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", 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", 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", 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+/vGPz7iOPZBrwmMf+1hWrlzJddddt993dvq2Huj32hjDM5/5TL7+9a/PiBLv3r2bG264gac+9an09vbe77gKDj9FROM458orr+QDH/gA11xzDU9+8pO54447uP7662fcfYC8ld0nPvEJXvziF/PWt76VJUuWcP31108Z4nTvKmit+fznP89ll13GOeecwzXXXMOyZcvYvn07N954I729vXzjG9846u+zoGCucvrpp3PDDTfw4he/mFWrVk05g4sIGzdu5IYbbkBrvV+e9IF41rOexUUXXcQf//Efs2nTJh796EfzP//zP3z961/nbW9724z6iVe/+tX85V/+Ja9+9at53OMex09+8hPWrl37oN/Hueeey4tf/GI+9alPMT4+zpOf/GR+8IMf7Bf1PBinnnoq/f39fOYzn6Gnp4dqtcoTn/jEqVbcB+PVr341r3vd63je857HM57xDG677Ta++93vzojIdsdnjOGv/uqvGB8fJ4oiLr744oPmmRcUFByYCy64gNe+9rV8+MMf5tZbb+WZz3wmQRCwbt06vvrVr/LRj36U5z//+SxcuJB3vetdfPjDH+bKK6/k8ssv55ZbbuHb3/72ft/PfdFa8+lPf5pnPetZnHvuuVxzzTUsWbKE1atXc9ddd/Hd734XyG9uQt6E4tJLL8UYw4te9KIDbvODH/wg3/ve93jqU5/KG97wBqy1fPaznyWOYz7ykY8c3oNU8MCZpW5XBQfhQO1tq9XqfutdcMEFcs455+y3fMWKFXLFFVdM/d5ut+Wd73ynLFmyRMrlsjzlKU+Rm266SS644AK54IILZrz23nvvlSuuuELK5bIsXLhQ3vnOd8q///u/CyC//OUvZ6x7yy23yHOf+1yZP3++RFEkK1askKuvvlp+8IMfHIajUFBw/LF+/Xp5/etfL6eddpqUSiUpl8ty5plnyute9zq59dZbZ6x7sO+9iMjk5KS8/e1vl6VLl0oQBHL66afLX//1X89o0yiSt5y+9tprpa+vT3p6euTqq6+WPXv2HLS97eDg4IzXd69FGzdunFrWarXkLW95i8yfP1+q1ao861nPkq1btz6g9rYiIl//+tfl7LPPFmvtjFa3B7ueiYg45+SP/uiPZMGCBVKpVOTSSy+V9evX79feVkTkc5/7nJxyyilijJnRSnPf62KXA10HCwqOB/adSxyI+7rOiIj8/d//vZx33nlSLpelp6dHHvnIR8q73/1u2bFjx9Q6zjl5//vfPzXHuPDCC+XOO+/c7/u5b3vbLj/72c/kGc94hvT09Ei1WpVHPepR8vGPf3zq+SzL5M1vfrMsXLhQlFIzWt0e6Lpz8803y6WXXiq1Wk0qlYpcdNFF8otf/OIBHZuDjbHgoaFEiqqXgoNz3XXX8fa3v51t27bNaIdXUFBQUFBQUFBQcF8UQqNgilarNaPwq91u85jHPAbn3ENKuSgoKCgoKCgoKHj4UdRoFEzx3Oc+l5NOOolzzz2X8fFxvvzlL7N69eqDtrgrKCgoKCgoKCgoOBiF0CiY4tJLL+Xzn/88119/Pc45zj77bL7yla/wwhe+cLaHVlBQUFBQUFBQcIxRpE4VFBQUFBQUFBQUFBx2Ch+NgoKCgoKCgoKCgoLDTiE0CgoKCgoKCgoKCgoOO4XQKCgoKCgoKCgoKCg47DzgYnDn3GHbabcsZMa/ClBqv+e6jtT7byR/yYHQen/9JCIz3sO+pSlKqalH9zmt9cH3fxAOdf2Cw4uIBlTn3BAUHvCgPN1PXJRCUAigxaD8zPNFlOBV/nqUIMoDLt+eaLRo1CxrdGPMrO7/wfIM/YLZHkJBQUGH7/mvzvYQDpniGlJQMHd4INeQOdV16oAChMM3eZ8uIg72fMHxheQKFtAIAh2B0ZGSMLVkxovQXZGBR4nPX6vkoOK2oKCgoKCgoOCI0J2fHoP9m+aM0Ji627yPyOj+vK8IUEo9qElfV2xMFx3dSEb354Jjl/0+PdUVGxqP5FEzQFT+PyOgZfq6fkqKCN3zUBAldBVKcYYUFBQUFBQUHFaUQhkDxqCMQVkLxoDR+b9ekGYTiWPEuWNGdMwZoYF0p3b7LJ4mCqYLggOs+oCZvr19l09/vuBYZZpI7ciCXD5MExkcQDBIZ20l+fnR3ZZSILqzXXWgVxYUFBQUFBQUHDpKoaMIPX8ebsk8knkl4j5D3KfJKgofgAvBxDCwNqN29x787kF8qw3+8JU1HCnmjtBgZgRjerRhX1EwJQgOcQ/7RjEOFtHYT9QUHBsIaFznR41Mq9TIBUYnSaqrFyC/IzAtosHUj92EqpnnWpE8VVBQUFBQUPCQ0QZdraCWLmb80QvYc54iPG2C5f27WFkd46TyCPNsg0inlFTCuKvytZ2PZs2tS1n0vycw8KsduG07kCyb7Xdyn8wZobE3TWWf5Qep01CKBxXV2Fdg7PvcvusUHEsI4Kd+FqXxHcEhap/TJS+5YEpCqE79htqrQvJMKQWSR0PoFIjvV9NRUFBQUFBQUPAAUNaia1XcGSex7YIe3BMnuOKUX/Gs/ls43dYpKU2gNBqN6cxLNRqP54W9d3PHyl6+fuFj+eavHsvp/9iHvn0dvt2e5Xd1cI6q0DhYsXe369TBIg7T130gEYcDiYQDRUT23e7B1j8UDiZQiujIUUDRyVkU8kLv7s970+0UYFReAO6dRzyIyovCvXd45fOOThq8z5P5tMrFhnPC3oZmMtXdzHu/dwjF51xQUFBQUFCwL0qhKxXkzJPZ9Lt9nPbMe/mLpf/OY6M9zNMhkbIYVbuPDRgWmYALSylPPOGnvOSyX/LaZS9j3mceSemnd+MbjaP2Vg6Fox7ROOhEnM6UsDNRO1ANxfTXT193v20d5HUHWv5gohf3Na6CWUbJNLExHcGQf04+jVE+FxRKWboKRGuP9yniM0S654XGicZ7hTZmKtbhvcd7P3VeHailckFBwXHMPn8H1n3sCXzjWdc94Je/68yL8HF8zBR0FhQUPASUwvT3M3rZKvTL9/Cp0/+Zx0QNaiq6H3GxP0ZpaqrE4yPPfzzmc1z7jpfR0mdT/uEdczKyMWdSp5jWRWrfrlD50w/tTvF9RT+Ku9DHE1M5UTmddCdxHq0VWgliFEqD8gkoD6KRJMFLgnhHJikiCmUNOiihVYjRFiWC+LwDlTFmSmg8GL+VgoKCYw/T2wthAErzb7f8N4Ha62ej+S1GlR/wtv5rw8/5drOHz5z3ONzExJEYbkFBwWyjFKanh/TRp7L2GsNnn/YPPLXUoKJD4IFfLw6EUZqTbYUvrvoyL37TK7Gts7E/vX3O1WwcdaFx8IhFngO/b5epfV+7d1kxsSs4ODJdcAgEWqFchktaGO2AjDSJSRotJkdHGdqzk9HxYVrtCdpxC60NQVimXOlh6dIVnLB0BZXe+WBDMBalFMaYKcHhvS8aCBQUHKeYxYvwyxey5JOb+YeTftBZGj6kbQbKcFW1yXd+kLDh8Q99jAUFBXMLXakgZ5/Chuf08Ppnf5tr+u6hT5d5qNeO6RilOclW+OLZ/8TlL30LZ207Cbd+45yKlB5VoXEgkTH1s9qbDnV/aUhK5SsX9RAF94906jEc2qcoUuLxUSZ2bWPXlnvZuX0ro8ODtOMG2ghh2aA0eBEyJ4g37Fl/F0uXrmDFqWcy/+RVRANLAEjTtIhmFBQc59hlS1n9l4vZ8PQvHpHtn1HZxfonXYC66bYjsv2CgoKjjFKYRQsZvPxUlr3yXn608jMsMpVDingeCkZpzghKfPj8f+cvb3sxS/9xaE5FSY+g0DhAz9DukoOIiYNFMqY/r1R328Xk7uHJvufNzPPAdxZ1K3lUp17Du4TJ8RGGt21iZOsGRnZuYXJkN5I0CFVKJYSobLGhw/mE1DmUsWhTIk1GGdpUJ5scodGOWbjK0juwAN2JYGitixqdgoLjlJELTmLD0z9zxLb/toFN/MO7n8zS5xyxXRQcCKWg0whEaYV4Ie8OUlzLCx4C2mBOOYmNL1vCn7z4X3h+bRfRIdZgPBiM0lxZ3clfXTKC3LgU7pqcM+fyERIa0vkvR7G3UDaPRuRdevJjoHM/NPGAoKe8DtTeYzQlLu57knm4OVhHqoKjyV4Hi9xQz0/72FVetL03HIZSJl9PPOBQPiVpNti5aT1b1t7Njk3roN2gEhrKxmNKmtAGGAs20BgreDFkTuEyQZNSDgOyNKU1soONd8FE4jjz0Y+j2r8AEcE5D6iOW710GhvIfsWiBQUFxxb25JMIXrF7todRcDjRBl2KUD01VBjmrstaQ5wgjQbSjpEsO6aclwvmCNpgzjqN1W/o51OX/gNPLzcJVHDUdl9WIb9/6q/5+spLKK82c6ZW45CExr53bQ9aYI3glcOJRxGg995iRnWNlpEpoaE7phhKHHlbUgVoRKlpk8p84pj/K/mdCDE8UA71jnPXNPC+fDcOdXsHohAxB0dkrwt3fkZ4wHeEqgIMCoX3Ct85vE4UQWCQLManDdrjQ+y6dzUb7ryZ0d3b8UmLWqVMT1jF6ABjNGFJ56cTIDgsECiFdx6fepQHg0clMWl9F2Mbb6a+uJdqrYyoEl4FqM55rIV8jKortIvP93BjTz6Jxt/P7PJVflcJf/vqWRpRwfGI6e+j7/pJ/nvljUd8X3/5yP/gz157DQs+e9MR39fDlq778kA/yWlLGHpkmbSXjuuyYJuKym6hujujtKeN3TGMHx7BJ+kx4b5cMMsohVl1Cve8pZcvP+PTPCGSGc0ijgZGaS6p3c0XV/0uy39URiYnj+r+D8YRiGjk0zVR+aRQRHBe0KiOzUGGMh5jcv8CcYJkoLRHKZ+HL9FMRTqmJmoKVG68BqBEOjeMj3xU42AeHAVHA7Xfz2rK3jvvAKU7KkEEsjQjsJosrjO4eS3b1t7O8NZ1pPURegNHUAqwFrSKsSYgqpYIywFaK7x4XLeoGxAnEHmU8/gkIzAJsctIWyNM7N7CvCUrCHpOyE0BD9xRt9AZhxnd08OzvvVbXte/fcbyL/3rIr769MeRbd8xSyMrOK5Qikt+tpV3zLv3qOzuikqbt54uLDgqe3sYohSmrxe/cjk7f6eX+gUNfu+Mm1gSjlPRMSWd0vAR2+MBtrTmsWVygO13n8QJN51E321DyLad+GaziHAUHBilsMuXsfoP5vPFS/6e34nAHGWR0WWFFdqPbaLn9eOPX6EBIOA9phMNyDvyGJTyKO1AUpzLwCmUClHa4rM2RgvaRrmgEPBTBd8KrxRC57az8qiOP/O+87giV/54Y+/n6VGgzN5GAh5URxjozrlWCoX68Fa2rL+HzatvY2THRiJpUwmgWg6oRCFeIPOCDRQ2MtjAdDqe5eJCRDDaYG0eoVBecEYjCGk7AckY3rOHgeEhFvScsI++mKk4Cq1xGFGK9972I36ntP8F/JW9e3jsz/+DVO7bz+RPzrkwnzAUFNwH1665l6tr47M9jILDhLIBsmIpOy/sQ184wrvO+Annl9dT1R4NdK8orgapQFs0a05ZxL898XH8/K7TWfad+fT+cC1ubKwQGwUzUQq7dAn3vuokPnLll3lKKZ01kQFQUSEXnrqOrfNOhi1qTpyvR0ZoSB5x0Lpb3C0YLUBKEk/Qisdpt+p4B1FYIQqqhNZiowCwiPOIS1E6QCmLKEEJeHRHhOSTiYN9lPclNoqIxLGDmubsLQoEjVcaVCfOJR4tglECeJRPmBjezJo7f8nGtXeS1kcJVUI1UlQjTa0SUC6XEJWnWikboK1B2+55pdDkQkN0d+8KpRU4nVuKa0EjNBsTjI8OM7AsRgcBaDOtnKRTil6ca4eVP9vw2wOKjC6PCkv3u42vrfsx32328cnTzzicQys4TtDVKs/5zcZCZBxPKIWuVdnzuH7kglFeddpNPKW8gRMMBMqip9WQmmnX7BPtEE8+8b/ZtdTwj499Mt8/8XdY/u9bcDt3zZnc94LZRVmLPmUFa69dxHXP+wLPLDeOerrUvmgUT++/m88sOZvoNg0y+2l/R0Bo5Hn1iq4BXz4x08oRt+rs2bOF8fFdJEkT5wSNRRNSK1fp7e2nr38BpUoPWocgrpM6ZfIGEZJHNvbeNd7nXnJ3gneQjlYFxxLSsVaRToNajVN0Ilu5IFAojHIgGb7VJJ4YYsPtv2THvXfgmyP0lg0VGxFpTzkyRCWLDRTKWJyoPEKiAZOLCeU1WtvOudIp7BY6RegWJQFWHGSaWBxxu0mWxARBNT8/9zsvi3jG4aSkMh5q//FIBcw3dfSjzkSPN8g2bz08gys4Lqh+p8xr+or0u+MKpeGEBQw/JeUdp/+Ci6urWWqEig5miAzduVabTieqSAWUxTNfw/sX/4Lz37iWt696Cad9ZSHBbffixifmxN3igtlBl0q4x6xi7esM//G063hkGMxqJKOLUZpHRjsYXxmw2BhkDtQX3XeewYNGoZXOv8ReUN7jXUyzMUKzMUKaTgJtjEmANkkywZ7dW7l33Wo2rl/N+MgevE8Ah5IMLQ4tnTJxybd/sAncwaIZRUrVsUgeqQCH4Lul4FMPxINPcY1RRrasZe3NN7Fz/d2o1iR9oaWiFZGC0BiMMrk88Yo0E9LMk3mPR/KHBgKFDg2mZLHlAFMK0JFBRxYTWYJySLlSolSyWAP4FHEJWnVHKwf4w1Ocd3ONp5Q03/7OV+i7oU7zOU/EnnzSbA+pYBaIr9jrkmfOOJXmc57ImT1Fh6njDRVYmiv7ecqZ62eIDItBo6YeRukpkdGlu6ymS1xRGef7l/8tCz+0id1Xn41dvgxlj7rnccEcQEUR7QsfSfO9k/zooo91RMYRmk4/CBYaz+QpHhUevY5X98UR+ZYoRS4yhKk2oz6LiduTpEkd8S208VgToKzGG02mAxqTbXbv3Eq7WWfholH6BhZS7Z2HsSU0Qqf3z9Q+kHxyd6SiFQfa7n35fBQcCTpdxlBoZehO3LUIhoykPsbIlnVsXnMHOzetw8cTVEJNyVosgnJC5jxKQBuL0hpvDKIVxlowJo9odP9VoExeB+LzIiHE5x2kRDzaGXSSERhBfEaapZTwiPhpHi/dSMbhjWgUYvnwcsPKG+GTN7Lqpy/ntD+CbNOW2R5SwVFi19ufzDfe/hGu+Ni7AShfNMivH/PZWR5VwZFAGUPcbzipPEKPdvulSwEPaJIYKMNJtsxfLP8Gf/0HLX4y77Gc+O0e1LrNRd3XwwhlLfKYVUy8cZx/PeufWGYqc0pkAERK4xckqGBuCOFDGsUDnmB3buzmLUkV0Lnz7BJEUpRyGANhoNDK4o1Bq3wCn7QTJsYHGR7cRa3Sx/KVp3PC0pMISr2Aw5ggT6XxHtFHLjHlUMXEfa1fTBAfJNJpEasURgnKu05US1A+pT02xK5772HrPbcwsmsrKmtRNZoQhXXk96uUQpSQZgriPDaiLCijEKVQhtzd2xgwuSs41uSxlE4fda3zdZ1zIA7vUowRkrhFu9WgB0+eiKW6Ibfpb4IifWpus+b8f+KRf/cSTnzNQtzg4GwPp+AIs+mDT+K3r/xbarrGHe/41GwPZ4qLzr+DTU89F/2zW2d7KMcXxpBWFANBgwD2ExmHQqAMy22Z9yz+PstfMsrnTnkay7/9SHp+sg43MlqkUh3vaINeeRL3vLLEfz7ic5xky3NOZEDe+L+nrwVzJOJ2xFKn8updnedHaoVHcOLw4jpZ96CURmuNthYbWcKSpVQJqPWEhNYzNrKLdXfdwo5Na0naE+DbaEnRku3n4Nn1vSg4fugmTqFyy0ftEkzaxqQt4qEd7FxzG5vv/C2TQ9sp64yeSBNqsKIwaLQyHa+MkCAsY8My2uZdzkDjRXAiOBSiNBiLDkJ0GKHDELGWDIUYi4lKmCgiCAOi0CIuJW03iVsNxGXoTo2GmmEYM2uHruAQueOJN8CC/tkeRsERZNv/92Tq3zmFH7z8r6np+28ccLT53Ik/Z/iR5dkexnGHMpq0qphnGgSHIRshUIYlpsIb593KP1/yGea/bRODV63C9Pc/9MEWzF2Uwsyfx6YXnsCHL/oq54R21gu/D4ZGc0LPJCo4jlOnIC8BV1MzrWkW30qjjUEbgzEBRkcoZdBW0BqUcgQmJNKKahQyMdFi49q7iNttlq88nUpvP5gIo02eVLOPtijExnGE6tj0iUeLR+OQrE02OcqetXcxuHE1ychOQhcTWaaiD4HW2NBiggAbWpTV+Z2IQKMDkxd+K4UYTWrAoUg94DxKK6wotDEoq1AY0J3S89ATlhJ05mhOOrKkTbMxQZa0CW047XzP6ZYT7RfkKJiTXPTVm/nhExYXaRDHGY3nPZEL//QXfKjvOs6LQqA220MqOJp4QTtID2Dw+2DvRhul6VNlzoscf3/Kf/DHr3smt7tHMf+/7saNFR3LjkdMXy+7nncaz3reL7isuoNAzd2bAkYpesI2LRPN9lCAIxTR6LYF3evunefAmyCf/BkbYoIQE5TQNkKbEsrYvBA30CgtBKGiWrYsGKjisyb3rr2LTevuYnJ8GFQKqohoHP9oUAYk7z2Fi4mHtrPrrt+y4+6biQe3UXYtSqQol2C8oxRZonJAGAWEkSEIDTbQaCso41Hag85Ap2jtsIHBBCFog/Pg/N42usoGBKUyOgzxxoDNz98oCvICcPEkrRZpmkw7Fw/k3FdwLPCH8zbkNTsFxxXtAc0HF93RERkFDzcky4jGPLvTPtLOdbpb/P1QCZRhkanykaXf48V/9G02vekc7LKlnZtkBccLpreXPS84m4v+4Ff84YKf06fnrsiYixyx1CmlO94H0oluGEsQlgjDiCAIsUGEsRHWRmgb4FB4AyayRKWAUjkgDBVhIJywoJ/eWsCOrRvZcPcdjO7ahovbU+JCptkyC7J3mnfgLrgFc5i9H1l+ofaiUMrg223Gtm9m8+03s/7WX9PYvRXTniTwMdanGHFY00lJ1I5MYpKsTStu0GzXibMWiWuTZC3StEGWtXC+BUowQYgNSwRhCWNDRFuUCTBhRBCVCUoVbFhCmaBzrnmMBms1Ig7vHcXJdnzwj3d/B12tzvYwCh4iulrFnH0G/77tl/z8/R+b7eE8YG76k4+RXnLebA/juEKcozTq2BH30T5Cl+cBU+GN/Rv42h/8NSOfL8PjH4EKCmF7PGB6exl6zjk8/Q038f7Fv2CBOTb+PmTezJmaoUNKndo3YnDfBdMaUeBEYxC0sQRBQBCGOJdgjEVri1YBRgy222LUK1SgsMYhWYbxeWvTWi1CqYSRPVtI4iYrznDMX7ICbYPcMbxr6EaunkRkr5dHx128yJmfffYm0e37BdibZidTHZtAiUPSNhO7trLx5t+wZ91d+PFheiNFqAUtCgKDNxqvoRm3c6mpVMdgT6M0SKzQgcIGFmsVxmqsjlAuyztYGQudbWQA2hAEIVopjDF4k0GakGUZNs1AhDAICLTBqG7Refed5Y+9rZgLjhUWmSofvPNG/uziF5Bt3Dzbwyl4EJjTVvJPN365MyGYe7UY90WkAsQU14zDinME4ynbm/205cgV7gbKcEZQ5QeP/Gfe8+kn89u/OI/a9+7GT04esX0WHFl0Tw+jV5zN+W/5Fe9d+Gsqc7C260A4ETLR4P1sDwU4ghENPzWj1J2WtBrTERvamI4HQt52NLQhpahGaHrQporTIS00SWCQssFHHmxCqZwRmTaNkc1sWf1bBjevIW2OoX0770qk6fgi5CkwijxXzQJGZNamfEqpgz4KoBsJkK4rN3TOnwyVjDC29U423PJzdqy+nXRkiJIXbAo6VYiHOM2YjFuMtxqMN5q0Wglp5vGpIKlHUsE4sM5gvUZ7i3IWyRSSZkjaRrI2SjKMUmjFlFgxJu9RorzHANYa0izBJxmBaMpBiUAH4HNncU/nfBeNEoM6jH/YivPm6HBeFPLcb/0K9fhHzvZQCh4Er/v2d4+Zu44FRx7xQjDaYvPoAJNy5ItjKzrkTxb9mBPfuZbxy8/B9Pcd8X0WHH5UENJ66plUrt3Buxb+hIo+diJUHs+eRg1J09keCnAEi8Ghk/6i8miCIu8wpZTu+BL4vLWoUlhjsSbESYnMlEjSgDStI1qQDLTOvTjwnnJJUJIxObiV9ZMNThgbZukpp1MZWAA6QKm8NanueCGoTjSjmJvNDdS0n6aqGgTE+7yNrAjg8s8rbdLYs5nNd/0vW1ffhR+boOIhsBZxQuI8aeZoG0diBGcUkgmiFYLDKY9WYLTCRGHebMB1og8oXAaSxCAe4zOsAmMMJggQgTTJEKMx4smSmCxOCJzDdwSycp68I7vueLp0vcHVlA3UtCqio3mYCx4i1/bt4gt/3qD2u7M9koKCgoeEeNR4nfruZYy4GgSTR7wl6YAu85ETv8Hb3mTYbs5i4Jt34yYmjug+Cw4j2qDOPpWR19b5l9O+wiJTme0RHRIOYXi0xoJsz2wPBThiEY1phbFq779aW0QUaepwmaCUwZoAowMUFqUijClhbRmtI8AgGFAapTXaaExgCANDZBVJfZTtG1azZe1dNEYG0WRYPKpjniYiePKOQyhDkcYyF+hGL/IJuUfnNTw6F4ZaHFY5rI+JR/ewbe09bF23hub4GKrTIjkVhwSGTEM7y0gzj3gFXmG0QSmTT/yd5A8PWZqSxAlpmpJlDu9zOYAoRMA5T5ZmOOfztrjG5sszj/OC90KapqRZRpY50CbfhuRtmuk+7sO1vuCh8ZIvvxUnRy8U/JKT/pf61b9z1PZX8NAYvvZJrPvo73BOODf+uBbMEUSQVovydsuerAfPkb+GGKVZYip8fMXXGPiDLdQvOhNdOjbSbh72KIVdspg1r+rjn8/9AqcF0Zz0yrgvUvHIcISk2WwPBThiQoMpHwGlBFSeFmNMgFIWl0GWCohGd5aBResIa8pYW8boCK1CEANiEFEdwWGw1lAKNZVIkTRG2LjmTjauuZ325BhGeQIlKHE4L6DzPH1XTP7mAB4truMWnwsNpxRe50ISL2ifobI2raFtbL3rFrbecxfx2Bglo9FGkdERGUYTK0hRiGjwClKh2/O42yBAozB6WuNZnzt9K0ArjbVB3pjAhOStdAFlcu+NIMyfNyHWhGht8aJIUocX8F7Q1qKt7XQZKeqAjiQn/+lNZLijtr839m9lx4VzI8e14L4ZedWTePcf3cC9L/gMpwbHdvvagT/ejD1h8WwP47hCWm16Ngtr20twRym6bJRmia3xyVP+lclXTZA9/qyiQPwYwPT1svWFJ/Oh3/0XzgjCOeuVcV9MeqG8SyPZ8Sw0lKCUA1z+M6BEEdiIKKygVID3+QRR6wCjLVaHGFXC6gqBrhDYKkFYIwwr2KDUaYcb5sW8kSWKFJHJqEYQ0Gb7vWvYvPoO4vFhlKQYnafMAJ2aDSmSV+YYXik8uUaYkh4akokhdtz5W9bffBMT27YQZClGPEnaRgKN7SkzmbYZbTVoZRmpF/AaS4BBY40hMIbAWqy1WGOJwohSGGKNzWswnMdnDu+kI2JNHl0RDWi0thgTYG0uQrTptGW2Ia5Tj5EJEJZQNphKm9p3WtoN6BUcm3zx0s8z9vInzfYwCu6D+tW/w5+/5wtcXTs+/Av+47TvIbVjK1VjriNJQt+GFr8aPpn0KEZFAU6yFW549BcYeXeD7ClFN6o5i1KYgQF2vehsrnr5T7myuvOYFBkAW12Nvo0e3NG7MXdfHBGhoZDc50K5TkSjk7euQ6KwShTkYkMpi9WdyaDOzfu0DtE6IrAVwqBKEFTyCIeJUNaiA4sNDFHJUi0b+nsi5veWCUjYvWUDG1ffyfienUgaoyTPpxcp2lrPHaanTsFUgymXJ1JJ2mDPmjtZd+uvaOzZRpDFlCDvDmUgrIQkytNyKZkC0QatLKEJqUYVKqUK5XKZSqVKpVyhFEUENkDrPK0pd6SfVlQteaOCbrRMOqlcuQi2GBugjUV1mhmEUTnvdGYsThTKBnnK1PSObGrGPwWHkd+78pVHdX8Xlj3NRcUnOVdxFz6WT3/kOn63Es/2UArmMOIcwfYR1m9ezKQ/und+jNKcEZT4+qO/wBl/fTeTz34MulIIyTmFUtjFi9h67Vk86/U/4Z3zf0XtGOkwtS9OPDe3Tqa6rY0cv0Kj8yVWnQLuqXu8BoUhDCuUoipG23xVBUorjLFT9RrWRgRBmSisYmwJVIB0irw9gkPwkqGUQ3yCkpi+SgBpk+0b17B9wxrqw7uRtAU+Q5G7jtPpauS97wgQ2e/xgN7hAV53uAwD72vbx4MhYbfwW3Wn9F7QIijv8O0GwxvWs+aW3zCybQuBS6lYQ6g1obX01GqUKhVaSZtMC7ZUIirl3iyhCQltQDTVclahlUJrjVF5q1kleU1F3lAqf07r/F9rLTYI0cZ02iXn7uDGGpTRoHVuOGktNiyhTYC2AdaGiMDMv137mvcd+5/bXEFv3HbU9/mrt13HV7b+AvW4Rxz1fRccHHPOKr725U/xqPDYnBAUHEVEkLFxyhtCxn1wVGu9IBcby22Nv1v6U97xF//M1jefi1m4sLgDOhfQBnvScja8/lT+8NX/yh/Nv4WBY6z4ezoe4UdDqwiGG3PGR+OQhMYDb6vp8/a13QlWp9UnYghsiUqlB60D4iQmyxK6KVZ0fAgUOi/I1QHWRBgbonSe2pInZAlOhNSlpGkLcSlaMoyL8a0Jdm9Zz6bVdzC2Zwcqa6FJ830gKKWmREZXcHR/LzjCCHjx+O5EXwTlPIHPUGmT4W0bufM3N7FrywZKFkKtwAveecIgoFwuIyK02m2SNM2N8hQYk5/GaZLgM0eaZcRxTJIkeOdQShHYvLWytSaPbvg8dcp5j0dAgzWGMAoJggATWJQxHdPJKXVC6h0mCHMDyqhMuVzuPNX9KqnOaS+FxjhOqOiQAVPhO//1ZfS5Z8/2cArIRca3vvcvx+xdx4Kjj2+16dki7HGzV8MTqYArq8N86NVfYt27TsOuXFGIjdmkU/i99Xkn8vLn/IDn1rYdU21sD0QqjjV7FqHqrdkeyhSHHNF44L3894oMOr4CiEHrgFK5ShRFeJfhJUVpDzqv61Bqr0DxqLyGwwboIMIEAcpYRGm81ogxYCxKgZKMkoGqBdeYYNfGtWy6+1YGt6wja46hJJtqqJq32S38CI42guAElO04VroUKwkqbTKxexsbbr+Z3Vs34pOYrJ0g3pN6T+rBRiVAU59skLRjtCh0JzrispQ0a5MkMe24TdLORUaWpGRpSpZluE5bWsjvLhlj0CbvZIYC5z2iBBtYjMnbMntxeOfyf5XgxOMlrysRbdDWoHS3m1n3XNprPLjX37zgcCHO86GhVbO2///67/9HdnHh3DybuAsfy79+9x9nexgFxxrOUduRsCMbmNVhRCrg0so4H3/eF1j9lhOwJ59UiI1ZwvT3s+vKFTzieffw8v7fUlbHtsgASHG0h8tIksz2UKY4YoZ9dEz6pu7wCiAGhSWKKpRKJZx3xEmLzLVAYkRS6EQf8vvMgjIaY0N092EsGEumNM5YxFoyEZxLsThK2hGRoJM6w9vWc+9dNzO0eT1ZcwKZli5VCI2jjygN2uI9ZEmMkhTt2zSHd7Bt9W1sXnMnE4N7cmd3FHEmtJ3ggwBMQJxkpElKoCwlYwl17nGBOLzPcJKSZglZluKzDO9zceGdI0tSXJrlna1QuZs3IJ1kPKUEtOB9SuoT0iwmSdskaUzqEpzPcN6ReYfzudCwYYS2AWjdSevb93wqQhqHGz85yU9f9phZ23+gDJ//4keZeHHR9nY2qF/9O3z0S58sIhkFh4w4RzjYYH17cR7FnkUiFfD0cpMPX/7P3Pv7y4ouY7OAspbWE0/j3Gvu4M+Xf4PFpnzMtbE9EKl4wmED8dypWzsCR3VfkaFmLu8U2YZhhFJCq12n1Zoky5qIj/E+QSSj06cUlELp/A60sQEmCPNiXBvibIg3AV7n6VD4lABHT6jojRQmbTCyYyP33n0zOzetI+kceGPyTgJFutTRRiPa4ujYTuBojexiy923snn1bcRjQwTk7twOQyPxtDw4a5lotRkaHiVpJWgnBALGe7R3IBmeDE9XWHRS4bwgzuMyR5ZNi2w4h3jfLRjBGEVYCrCBJvNpLjKymDhpEydt0izFS4aIm0r90koThFFe0zEV0NhHaBSWGsclK4Maf/MXn2LotUU3qqPJyKuexJ9+6IucE5ZneygFxyIi6JFJ/nd4xVHx0rg/AmX4veoQL3rej9j5eysxA7MbaXm4YZYtYevvZ3xw6Xc4yZaP2Q5T+9IUIRpRc8ZDA46kj0Y3XaorNro3d5VCoYiiiCA0xHGdRnOCJG2SZS0yH+N8inRrPARQnY4/YUgYlYhKZcJKBVOuYqIKNixjbIBCMN4RKU8oCVUjBL7N0I6NrL/nTrZv20q9Xi8ExiyRN5dSoC3GGJLJMbatvYsNd97M6M7NmKxFpBVZ5ohTIVUWXamSacNEs0m7FUMm6MyjUofOXN6+zecpd/l1omOi183c69TiiHR+dg7nHb7TikxrhbYaGxi01SijQOXpWEkak2a58FUatFbQacmLNthuhK0IXDzseEpJ8xd/+AX2vOHJsz2Uhw3+OcNFd6mCh4Qfn2DdXcuY9HMjrSRSAW+b/1vOu+Z2Bp9zZiE2jhKmt5etzzuRTzzxBhaZynEjMgDGvCUalTnjoQFHUmjIzNu5e381gCEIypSiMi7NSJIGaZYLDe86KVSdmgqtwChFYAOCoEQYVYhKNcJKD2G1RlCpEFQqhKUSSmu8eJzLUCJEVlEONJaMoV1bufu23zC8cxs+bqLFo5WguylU0zO89tFGB+0ddJ9PPhzoOEfIzMLnbnXCvodGELx4tHhcu8HQts1sXbuGycFduMYkaatBlsS045i2cwTVCqVajVYc027HhEFAYC1GGwwKnfvGY7QisIbQGgJrCIK88NsYgzEa3REJWnXHIOT+j3k3qcw74jRGvMNq0AhIBj4/DzVgtcFai1FdUz+NthZl9NS5rYXclZ7px6HgeOV3KzF/9Y7PMfrKIrJxpNnxrifz6UdcP9vDKDjGkVaLBTdrNmfBbA9lij5d5iNLv8flb/0J2195FmbB/Nke0nGN7ulh5KqzufRlN/HU0vhxkS41nV1ZD9HE3PHQALCHsvK+kYCD1zh0U6Y6DWk7rskOMBiQCkZ5KmE/LTtK3JokDoaQKMZKFa1KU5lTkKHJOnefwzwf3oZomcS7BB8ZlIRok6eySJLh2ikuyfAihMpQUilJnDK28S5uG92D+Z0nc9KqszFBCa9M/uhoLt+5I6613vsWOuQT0HxSraam0tOOz5EMEM0iB44A5e9fi9DVq151joqAE0E6IsQanRdXpzGBEkwSs33tXaz535sYXL+OeGQY7VO8y5h0DcrzBqhEEdXeKi5tIe0mQTfU3WlLK0ahjcaaPK1OqY5hnpZ8udaYjsAwRhNEFoxBjIbAYsIAFQboUoQKw1ykuhTnUhKfkYnPpYy2iFFYW8YZS6YM2lpsqURt3gAmDMCozhHxKNkrtHwnfTBvcPBQj3fBXOSZlZRl7/tb3jD0Vkrf/PVsD+e4pXGi5wnR3JkcFhybiHMM3F3n+/VzeFS4es7cyR4wFd6z4FYe9fqt/EnPy1j5ydW4kdE50570eEFXKgxe/Qie8Lpb+MOFP6Omq7M9pMPO1nQ+Qd0jR9kv5r44MjNj6WxaFHnr2iyfdHafEoNSJcrlfsrlKjhH3JogSybBtTFkWKUItMJqRWAsgTUobREf4JzBie50AnIk3tF2jmbmiT15kbjJzdZsEBIaS0V5enVGMrqTtbf+mqHtm3BZmyxpI75TE2I0yhgwCt+NaCg1dcd6ymBuGt00HXWYPtPpXb0O9JhTdLu+dsVX50CJykWAsTY/lh3VGBgIXIvhreu5985b2bFhHY3hYVyckMUpRhmq1R7aSULPQB+iPK2JcSRuI1mCuBQRhxOHdNsFiCCZRxIPic9/dh7vMlyWCwfvM1AeHSpsJSKsldGVEhJYRFuwARgDWqFNHvkIdP6wKpePWmlCGxBGJYwNEG1QgQGr8oZqKpcW+I5fBzp3PldF76nDjb9jDU952+uIJZ3toQBwTlgm6Tk+bzIUHH2e9vrX4NZvnO1hHJ+IYHeP8Z9bHz1nrh9dIhVwVXWUT7zis2x885mYRYXPxuFEWUv2+FUsfflG3rP4+8zXx1+tlxPPtmQetpV1LCbmBkeu69S+E+/O90XoiHSlMWFEVKoionBeEG3ABDg0DkUmClEGZQwmCDqPEGVCtLZobUHnLUa1NiitEYE0TXEuw2cZeIdWEFpDtRwSWsXQru1svOcu6qPD5DfbPeARl3UGp6eygbrTRHWA6eL+MY2HId3g1T7pZgCIoLzkNRQIZAkjWzez9vbb2LR+DaPDQzRbTRKX4RQ4rXCimN8/n3JYpj5eJ27HCHTM90zHZC832AtsXuvRXaaVwqIJRGE67W9N57X5p2dyY8gwRNv83HFKIUqjlMmdwI3B2gCjbcdNPG99m7ksN3lSYIwlCPPzEaXyCIRMHY5px6b4I3FEEKH2r7/kcX/31tkeyRQ3/d/PEF/2+OIzPwLoahWpzp184yONac+dCcLxiEzUGbpnAWM+O+rGffdHoAxPKyV8+vc/w5r/cwp2+bLimnI40AazfCkbr4p46/LvsdBEx13KVJfhtIpK59Z5fcSOtNqv5Y7kk1A/MzKgtUbZEG1CwqhMGJXQ2uIFMpebr2U+n/zbICQql6hWq5QrVUrlMuVKhVK1QqlapVKtUi6X8glo5yQyKMpBSLUUUrKaSmQJLWzdtIENq++iMTmG1mA1iPjcBK5r0Db9sbfQoPNP5w2og8mQhwGdaI8o8J1H97PNa2VSlDisVniXMDE8xJpbb2HrmtXUh4bxaUzqPZlSiLV4ZYhKFRYvPIHmaIPWeCP3vOg0AwisJbQBQRDkqVKdQm/nHS7L8JnHiiLwilAUodJ5NKzr+G0U3iicVog10GmbKxi8yqMb0kl3EgHfOe/EQ5pmtOOEdpIiSlMqVwnDCC/gnM+Ly/elCHs/rPjRP3yO1lWPx58/e+13j0d2XvtoNl7++dkeRsFxgsQxfWsVa9K+WW9zeyACZXhKKeUfrvp71r7pROxJywux8VDQBrtkMduftZznXfxLHhfVidTxm4a5J+5BJ3OnPgOOaNep6ey9z53/X3W6AIEyFmNDMp/XOChlQRmUMqA0zgvOdcSGUtggpFSuUK5UiUoVwrCMDSKMsYjRuWNzGGGDAKM1BgiNphwGVEoBtVJANTT4pMm2e9exee1qmmMj4DKs2lvCrKSbAnOAiu+p7/zeVCF5GF4IpqeWTUUzOnlkSglKebQWFBn18RF2bNrArvXraezeTVqvk8UxThwqsHhjKddqLFt+Eo3JBsO7hgi8xojOIyOSfyYi3Za1GVmakqYpSZKSpRkuzfKWbmmKZBni8poJpRUmDNBhgFgL1qLDiKBSJiiVUDbM6zfQiCi8l47IUFiTRzmUzgWI8+BFYYMgb7MMeXE5MFNY+4er/HxY85NP/z1fvP7jTL6w8Nk4HNhlS5k4rz3bwyg4jpAkoXdTynfGHzXn0qe6dMXG+579r9z7ihOLyMaDpeP8veM5J3Pmi1bzxvk/o6ai2R7VEcMjDLZqqCSbUzc6j2x726kUqu4EXVAqn4wKoEyACSsoE5CkjjTN9k7yVKc1brfI12dkWYb3WZ4PrxSqc/fZo8gEnIdMBNEKbQxaacQLLklxaYLVnshCFCjKoaY9Ocr6u29j3V23Mjk8iPYZgQKLoMSj6U5wD5wkJZ19y7THw4dOAbTKp9SejshAQDwaT6BB42k3Jti5ZRMb77mb+uBuXH0S12ySxQmgEG0o1WrMW7gY0OzZOQipI8CAk7yoyQvSMd9zWYbLHC51ZGnHnM85xDt8kiJJgk9SfCfSICp3ke+KDAkshCEqjNBRCRNGWBt0UqVUvj8nKN+pv1F5QXkUBYRRhGhDknkyLyhtUUrv7bSlZh6hwkrjyLHolpi37XzcbA9jP5bbGn/74U+y8S+fRP0FT5zt4RzTNM5dxr3P+MJsD6PgOEKco7S9zrc3nc3IHEyf6hIow/Nru3jTi77ButefiH70Wajo+J0kH3aUwixayPbnnsxTXvFb/mL5f7HkODHlOxgez67xHlR7bgnoQz7iXWft6Z1xpi+bejAzfUo6rUXzfP58Uq60xQQllAk7kYvccE0QRBzgydPkPc7tdWuO4yatdot2kpI5wUtn0q8UXmtEKZQ1mMCijcZ5T5LGxO0GWdrG4gjI0K7NxOBO1txxCxvuuYPG2Ajapyjv0OLyVqXioZNOpdW090t3YqkQ6bwftf+xOOjxuY/HQ/lM7mv/+/78UJkSWkqD0p3PrXPMXJq3sY2bDO3Yxqa197Bj0waaIyMkjQZZ3O7UToANQxYuWoINQ3bs2EkSJ4TGopzriLzOuL3HOUeWZWRpQpomZElKFqcd5+/84dNs73mkFWI13hi8Njhr8NbijUGMRXcaBmiT12UoAckEn2VIluHSGMnSvM2y0dggr+3wSpMbdyiUNqjj+OI1V7E/+C1f/9VjZ3sYB+R3Soa1L/80O694+NQWFBQcE4igh8dI7+ll3RxNn+oSqYDX9G3iE1d/nt0fcAy/5LGYxYtAz41uWXMWpbAnLGbr75/GM665ifed8MPjypTvYLQlozlUQTXnVhT4Ac+ODjRBPdAE1vtOvnqeR7N3XQRRkncK2kdsBEEZY0wuLpTDGMFaRRAqjBVQGV5SvG+TZk1a7UniuIV4QekAG5QwUYSJInQUQhhAYFBhgIlCdGRQWuG9w7m8Gj8wUAk1ZSs0x4ZYc8fNrLn9ZsYHdyNZGyUZ+AylBK33igjVcYCe6gTVERvTfUP2msQdvkl9l4fSiWr6+oeynQOu2/UekbxY2vtOOzXvp6JBPk0Y27OLLevXsnPzRiZHhmjWJ6mPj3eiUx5jLfPmLaBULjM4OMTY6GguLrzvNIjNc+xEciHqsjyy5VKH70Q7vHMdx2+fRz3IPTKMtejATp0LKgogCBEb5p2mOulP3ehX/iGDT1OkYwbo0xTvUsQluCzBO4cyhqhcJYzKUyK32153fw7dTeOY6Dg2RzjzMxNcu+Wpsz2Mg/KZp/4Tw9cWPhsFD4xTvvo6yr+5d7aHcdzj6w361sJP6meSytzKZ9+XQBmeXo75n8d8gWv/8L9Y+7fLSC8+F9PfN/fTqZRCWTvjcaRFki6VUOeezT1/vII3X/s1/mTRL1hgqse9yABoekcwapH23BIah+SjcSh0p93T4h7dcvApXw1RGqVDgqiMMYosa5NlLQJr8uiB8vmEnwzxCZlzZJmQJvk9CGMClAbp3GF2SiFa4aTbTBfAowkxyuPaGVma4X2KVpbIWigFiKSMD+/m7lt+jRZh1SMeRXVgAaI1iEYZje9EXIwJ0J0vSp7S1Zlgqr0uGtPF19TxOEwXhEMRLt11u4XTkBffd0XQQ6WbvibiEBGszoWXchlKweTYKJvXrWHL+rVMDu2hPTmJb7dox23CShmXORbPW8C8gfkM7t7Dru078ElCX7UCgPd5Yhqy93qq6PiZsPd3pVTelcoYrFHYwBKUQmy5hKmUUZUKqhwhUYQPQ1QY5ilTUZRf+JRC+XRKWJDG4BxKDGiddzNLEpwosizD2JBSuYoJAjKlpyI7BlXkSR1l/O2r2fWik7juv0/mbQObZns4+/HMSsr8P/kobx9+E+WvFT4bBfdN/2qFGx6Z7WEc90g7pmdrwk/2nMab5/2aCuFsD+k+MUqzwFT5g76tXH7+x/mHRzyRf/7W0zjtyyP4tRuRdG44nec3IDUqsOhyCTXQT7a4j6zSKb5WEA41UVt24ScnEddtvnN49m3mz2P0macTvXIXXzn9Szw6hEgdf21sD8aY1wTjConnyPnQ4cgIje55M2PS5fMFU21uFag8daUUlbHGIFkLl9Rz5akCkAyRNiItvGvj0qSTlw/alDE2yGs5lO6k1gT53e3A4LwCLyhRiNcoAixlMmmRJQnep+DzOoCS9qTG45oTrLn9t/ikzapzH0dt4WLEO9I0zVNsrMU5x1RXIshdolFTaUCwv6iYzbvRXUHRFRfdlq2HZ9uAyn1OVCe1TLk8mpNmbfZs2czmtWsZ3LaV1vgoSatJ5lK8NbTTjPkLBli8YDHNiTq7t++kOVkntIY0beMkFxnW5tGoPGik0HkMCYfkAoCO0NAaaw3GGoJyQFApYaolTLXjmVEu4ToCI+jWZQQRyuZCyTvBZynSKST3SUyWKkxg8kINqxDRZM6BKhFG5TwqQu4U3okTdQ4MHWMVoVAeR55s0xZ2Jv2zPYyDcl4U8h8f/ztesvsNqJtum+3hFBQ87BHnCEdarNszj/EzhAXHyM1uozQn2RrvWXArz3nRzbzlsS8i/cfHMu/HW/DDI/g4PvJFwFN3/TQqzylGGYOKIlStih+o0Tqxh+FzApqPavH4lZtZFE2ilWDw3D62jO0/OotlP2oRrN2OHxtHkuTBj1spdBShVixn0wsW8ezn/Yw3zP8Fi83xnyq1L7tdjWhMIJ1bNRpHLKIBTM2z9kmgmnLczu00LEGpRKUS4dM24hIUKRrBuZg4niD1dZxr552FvEITUAoqhFEZpQ3gSQKL0pBpwStBtIBRqCxDAoNKDNrkefXWxrgsJctyEREqT09kaMWOsV3buafdIskyznjM4xk4YRlKCd47rA3w3oPynbqMjvNzN+umU0+gtZ4RRTjo4TnEL9ahrt9NuclTvtQDFhsPVBh1U8S0UjiXYTUYnXcMmxweYfO6dQzt2E5rfIzm6Ahpu5m3swUqYYmTVqwkSx0b1q6n1agTBobQaOKkDSojtLmAMU5PpdrlXa7ySbzvvC9tDBqF1QYThahSiC5H6EqELkeocoQqRUgYImEAYV4UrozG6G6XqQxJEySN0VmKzxJy5WRzMZIqUifELUdgq5gw6pwDdKRPFw2deJqSGdmDBUeQWx8DP7/X85TS3KyVWWCq/M+//yOXXfZi/G33zPZw7he7fNl9r+Ac2c5dR3YQ2tAeeHhNFAqOEuLR4w1kdy+DrszJ1h9TRcKRCjg3gm+ccwPffu9S/vS3v8e876xg/i/3INt3IXH80KIF3ciEniYqjEaVIlSphNQqSLWEq0UkvQHt+YbJEzXNFRmLTxrhkqW/5JKeuzgtmKBfW/S0LP10sWP9KYZ/uOppfPfn57Lkp0LvncOwexDfaHXG3clJOdj4O+PT1Qpq6WKGn7CQ+rMnuO7Rn+PJpUlquvbg3vcxjBPPhuQEwglBsrlVG3hEhcb+6VN78Z3/WWMwNiAIDK12QhJPEtkArwxx3KLVGid1DTLXxLkMo0OiqIbRCo1GKwPaECjB+RCPR/u8kByjIDV4o/Ai+DhP19LWAh7xDmUUoQlop0LSiumrBLi0zfq776SF4RGPDxmYNx8leR2CtQEiGifMMF4UEbIsQ2vydqjTJvmzRVdcOOewHXO7w7ZtuiZ6Oq+NcB5B4VxCfWSYe++5i833rmd8aJj2+ATtiQkSl9IWhbEBC5cvJypX2bhmHcN7hqmWQoLQYjU4OkLR5n4cCJjpCVPdybvkOVVKKay12CAgCANsOSSoROhSlNdlhBZvbe7+rXSeZieQOo8Xj89i0nYL127ikxh8ilWC0QqjBOUdWdombifEbUPUbwnDUv5+RXKhue/HvJ/ILiiAb37rei67+lWon98620PZD/2oM8l6S4jVfPeG++70tDGt85qXvolgtIW7a81hH4s85VzqJ5b45Uc+c9i3XVAAIM0WpUHNlmwe50UjHIuStk+XeUFtmIvP/yTfeMyp/MWvruCEby+kd+0kdtcwfnwCSZJ88g73PXEHlA3yesZaFeb14UshvmxxJUvSb2ku1DSWKuLlCb3zGyzr28NptRFOK+/h7NJ2VthR5huhpgIiZTHqwBP+c0PPR5b8iDc++0b+8+LH8KU7nkTPzxcxb3VMONJCNdqoVpzXGmSdtvXO5dGTMERFITK/n6HHz2P0mS3+z2P+k2fVNjCgSwSqdCQO9THB6tYSSmMur5edQxx2oZFPrvOfu94Ke90zptPtTqVQKshdml2KZB4fNtHG4rMWWdoiTlqkWQuRjMh6TBiCa5OmLazKPTSUURhrEbEgUe6pEQZIluJahlRAsgytPC4BsrxtrVG5D4IW6K1WaaeOejulVZ9gw+q7ETRnP+KRLFi8OK/ZMAFohc8gcx7nBBGVT4N9gjYq94Ez3YjCzHeuVPe7Lvsejb1ud+yNTu5/XZC9y7oqrvPv3m3n63kPrVaLLMsol8uEYTjj+W5fMKbtS+n7cTvfd+zi8veuFeJSBnfuZO2dt7H6tpsZ2raNyaEhssYEPklJvSPWhpOXLGfhCUvYvHUbW7dspaQ1AQbdMUq01uSNA7xDSd7EWOgEGOhmMqlcCJg8ZcoGBhsabCnERlGnCUCAhAE+sEigEasRrcA7xKW5iCHDJy1cq4lrtiFOIMlAstwt3Gu09/gUVCoosZiwhgkref2OdEXGPkXfhcIoOABGab78lU/y3Le/g+q//Wq2hwOAPvdshh/dx4v+8Lu8Y94DK0ReGdT43r9+iSvXXoa78PCMI73kPOrL8lz5n37oY8e1qdaB+MzYMnq2ze3C5OMGEaTZorJLuLWxgisrg8dsmo1RmkWmyst7t3PxRR/lc49+Mv+5/tFw1yn0rfP0bIkJRpqoiQbSbEKa5TdAfSfyXi6hemq4eTXaiyvUl1kmT4JsZZu+3iYDlUkWluucUhninMp2Tg32sNi0qGpFgCJQmkCZ/MYvpQcUGTJKU1Mlzgw8fzj/Dl55/m/4xeOX8bXBx3LP0GLGhudhRizhmCaYhHBSsC3BRYq4XxEPQLKyzZse+12e33s7S0yZQFWP9KGe08SS8avBk6nsaSNzrGXzAxYa3XSabiel6Sk505+HvGwCyVOYAKQziZTuxFt85+6BAq/RuowOe1FhBYmbpMk4oTaQpXiXEadCmmpCawmVYLMGoi1eabSvYHU5X9+rfCIo4JVBGQcmIMOAFrzOULFCtQSvwIhGUqHVivGpUK1VEZVQb6cEOOLRQbbedQslUgL9aPoXLcaYKr7j7wHgM0fczus9lM6wVkPH+E0psNagtZpyRJ+6Ge/zyXlXOIjkbXp193h2PURmpPo7lIIsS/N2qtPStbwIgbU470iSpNMFCiYn6zTrDWo9PfT19qI70RatdZ5Cpcj9SFTePUppvbfeZMa8ea+XyN734RGf4dOUdqPJ2OgQ6+6+mztu/i2D27bQHh+lOT6Z519mgteWRYuWsuLE0xgfHWPDhk2k7ZioUu44iStUBlZplLY4cXgUTnUcUwS0eLR4bCfNKrC5wAgijY0MJrJ5+lQUIVGICy3OGsToPMKFR/kMqxTaxai0hYqbSD1GtRJ8nOIyh1cZHkEZg86ATLBtg5YatrwYFfQBFpOf0FNiI9eK0yrX53DrxILZYZGp8tG//jgvfdRbmX+Hp/bV2RMc6jHnoP52lF+vuuHo7jeK2PTHM1sTX/Oc7/NH89d1fnt4iQyAv/nWszj1G7+c7WE8bJAkobYz41fDJ1NfcNOcLwi/PwJlWBnUeO+i3/KOBTdx7+NCbqyfzf/sPov1Q/1kQ/1EQwYdg0lAd9L4W4uEZHHGvBPGedIJt3NJ/12cHe5moc5FhEZjVCeDpJOncrBIxaFilMagWW4Dnlcd5feq/0O6wtEWR1OEMW8ZdFW2pvPZkQwwz9Y5MRzmBDPBYpOw0EREh2ksxzrjPmHrpgWcvXMH2Rwy64OHENE4mMiYel51izBU54a7mpZeklfPdr0HlI4ISn2osEZrYgwV1/M8e20JrCUIKzgMigRcgmtN4jLBi0FUijIZ1oQwdfe7Y+an87EZgcB6KHl8O8FbjRONeIuIwxkFVjA6oLe3SqYMI6OjiE9oj+xh0+o7KJUCtIHehUvQkUJ3JpmiFQ5oxwkmVPjO7XfxnR5bRjHVAkumTzvzSa/QrSTOM/1VpyOXF/KUpE6Uw3uHSIaQkcRx3nVLm473iEOAcqlMu91mdGyUNEnBQ6PeZHKyTl9fH955yuW8lbDWqpPipQnDANMRRB5Is05Xr06tidrnbr0S8OLwLsUlDRr1CXbv2MmWjRtZd/fdbNywjmRykrTVJGm00SJkiWfxCYt41NmPodVssmHNvYyPTlC2mnaSEBiFF03gFd7rqboXDHnNTX7SoQGDJxBDZDRBaNChwQSgAoVYA2GAKkVQipDA4nS3MxXgco8U6x06TXHNCWg1UW0HLZcLDTKIwBiLMRbtFFnL4RtgqlWi6kKUrSDSEWuSNzro1h4dqXBG0eL2+OG8KGT1qz/Nx0dX8BV+d9bExtB5vfxm1fUP+vWvWfZj3nH9Cw/5dcY61pz/6Qe934KCh4xzlHbWWbN9AbtOM8zXx1adxsGIVEBkAga05zHzVvPGgTtoimPMw6Ar0/ARTYlo+Nz87/RwFyeYmD5tqKgQjcLMQnSgKzoiFdCVDicBTlIo7cKzE6AjdixGHdvC8HCzJu2lsinAj47N9lD248jUaExP6eksOPAUqZuzoymHvTRsL+OJwmcxkdVUaiXKYYhEChMHuFadOG6TZSkSNEHC/M596rBBCRuW0crkk3271zgQY7CUMAJOW7IMVNvjE4U4Q7UaoNopcZoQlcrUqlXGxifIXJvMeybGRtmwZg2tOGHFGTEnLD8ZG+VfBWMVYa1EYAVlDdZqgiDI3aI7ruJ50XL+9RDvOyIrrxvZ24g3n7TmtR4p3gnO5RNYrQ1pmhDHTZKkmXtHeE8QBGRZRrPZxFpLWqsxPj7Orl27iOMYrTTtVkK73SLLUkqlMPcRIY+0RFFEGIYY04msKI33uUN7ZyW07qSFdcz4cqMJoR23aTYnaE2OMT42wpaNm1i/Zi2b7t3A0OAgKstI2zFpO0U5R0+tyulnnk1gQ+5YexsjQ8P5sfC5E3fmsryQ3CjIBK1BacF0hE4mgkUTmLz4WxnAKExoO1GMAAKNCxQuNASRRUchNjSIyWMN2nlU6jCZgGT4OCadrONbTVziidsZmRNMOSAqRZSqZcIgIG050iQmTTTRvArVWi9Km7z97lSErxuFOozfo4JD4v97x+v43ic/eUyl3Lx5YDOVD3yd/37zI2dl/xf0//Ahvf6qapOrLvriYRpNQcHRQ5zDDE8QbOlndbKYVcEQ5tA9jOcs+cQ9j3TUgEUGzgjASQzE09YLmMsRxK74OzYT244OTjzfn3wk/esdEsf3/4KjzJHtOvUAUWgCW6VaXkCzPJ94MiFJhUhAh4oosKhAE0tGnLTIMgNZhnJ1JMnwQUwQVlBVMFEJyDtDOfF4PGgBPCoDrxUYiwosJgS8z++YxylJmqLiFuVKlXnz5zEZt8gShwaGdu1mdHScuBmjneKEE0/G2BARhQlDbBDhRU1FAAQP4nGZ5Hfiu52oOgpMFDjJgNzEUKFRSpMkCfV6o5NqlW8vsJY0TRka2kO9Pk6plBc7GWNI05R6vU4Yhnk0Y3SUwcFBAMIgotlo0mw2iaKIyclJJicn8d4zMDCAMWZKrKRpmte4aAU+yztyxT53xO6kB3mXdVKoFPV6neGhXYyPDjExNsrO7dvYs2snY6NjpJnDeGjFGXGcElnLyaetYtHiZdz861+zY+u2TvpcfkyMNXtTwbopY6pTdNIxgFQieJ0X/murUYHOBUSgCKsRYSXChwGqEkE5RAKDdP5mWKXxTtDiIHaoJMWngm+2yOp1XNwiTTztTCAMKVcrlHvLlGolrLHErTqtdorSVXr7+qnWqmitO62OC+YK5a/9mvQT7pgSGgDX9u3i2r4j3MGpoKBgJiJIvU7fBvjJ+Jk8o3wjkTm2rh0PhuMhalMwk7rEfHfbWSy8c2ROzkvmhNAAhZKQWnkhfv6JjEpCnI6i4pgosASliCAM0c7j2jFxM8G1W6hWHW/buCBCSgnaCyZN8cbgtcKJJ8XhlUeJQ2dp7vdgNSYIkUDh04S0nYBS2MDQbrfwStPb18tC79i2YydJHIOkxCPjbMyEwGsqQcjA4sV4pcmSBNEGpQK8B+eyTi0LOOc6nac6xddKobXC+VwIKZ13VtJolDY06nV27x5EKU0QRhhtCMOQNE3YuXMHk5Pj1Go1jOmmTXmSJCEIAlqtFmNjY9TrdWq1GihoNBokSTI1MZ6YmEBEqNVqZFlGq9XKPwGl6OmtYdGkWUJ9fJw4jgmsQYmQJm2yNEErRRiVaNQn2bNzOyNDexgfG2V4cJD6xARpnOTtYoW8la0oTj5pJaeeeQ4bt25j/YZ7kXabKAzyOy5d80MRvOQ1KaLyNsh5ipZgusfPaJTVqFCjowAValRgsJWQqKeMDwy6vwbVKhJYMvFkqYd0Smui2im0ElySkU3WSeoTZGmbxENqAko9FaKeKuWeMkFkEQ/t1NHOhP6eHvoH5hNFpSNixljw0HnhU17Af9/0jdkeRkFBwTGAb7Xp29Dmx9tOZdfCG+k9TtKnCh5ebMtgdPU8Fm6/+8j7qDwI5obQEAUSYG0v1Z4TiNuTjE+2mWzVkdBQCcM8xadSw7c9rpkhjRa0m4jSEKSQgXOaLEwgCiEM8FbhyHKhQV4cbZQGbRHtcHhSlxFnCWknpSlNEurNJkFPD739fSxIM3Zt34lPEiRxDG3bRqgUlVLIqf4sehYugMCQZQaRAPGQJOlU1X+SpGQujxggkqdVAUma4pVgrEG8oI3BaMP4+AS7d+0AFKVSmVKpRBSVaDQajI2M0Gw3SZOEIAxpNBqEQUClWiVLUyaShInxcVyWUSmXES+0GnWMMVTKEeIyWo062miyNMZnKc24RbvdRmuN0YLR0G61aNXrxHGLtnQjM444bqMVVKoVGpN1xkdHGNqzm8E9uxkbGaPZbJKkKWnm8tQwNL3zF3DaWWcz3or5zS23E7cTQvTUe/BeyFKPVWA0OC3gNFoLuhv/UeTdpYzupE1pbGiJqhFRpYSyBqwmLEdIKUQig9aazOdO35Jl4MB4kFaCr7fxrZh4fJJWcwznU3wYYfrKlKpVStUKQSlC8DQbTRr1GOcN5Z55lHv60caSeZ9HY4w5bE7rBQ8dPzI220MoKCg4RpA0I9w2QnzPUm47exmnBHuOq/SpgocHv2ytZOFvwdUbsz2UA3JYhMZhmWSJBgkxtoegNACtEeJGHdVoElhLyYaEYYSr1HDllHhyEu/q4DKsNwQ6A9/Ki72DIHeELhmsEZzqaBkREI14wTlHkiakaUrqcuM+EUUYBHiE8bFRjHcMzBugMT7J2OAIoVHEzZg927chClLJWHn2KmrzBxAT0mzWUV7TnubOGScJSZKQJilZluVpTwriOAENQRjgfS5AtLZMToxTnxjHOSEutciqVdJSwtjoGC5LwXviOMY5R6NeR9dqRGFIkiTEzSZJHBOEIeVSmTRJpjpfIcLQ0CA7dmyjUq4w0N9HpVxGa02jXieJY9K4hdWdVsRKocXRaNRpNxtkaUa7nZvp1Go1RkdH2b1zB6MjwwwPDjExUSeJk6nOVUpZTCQsP+lkqr393HrLreweGqacpnn7Wi9440nxhCULkne90l6jnHQtMjq1D0x5piidGwfpwBKVywSlEFF5PUugNZn3eOcIlMpzOp3gEwdxhmQemjGu3iKtt4gnJsnaLcSCjsqUy2WqvVXCcu4Cn7TaTE7UaTZjvC6jyzVMqYyQO61P90kpohoFBQUFxxjikbEJ5t29hB88/WwuqWx7WKRPFRw/pOL48dgZDNw2gvNzL20KDoPQmN596sGjEKdQYtC2ii33o+u9hFGTpDVCk3HKyhJEinIQ4Ks1wmqNtD5BErfxaUyznuK1RUURplxCJSm6EmEjgw4MYhRZJ60pSxzKC2FUIhBLaEOSIKHVapM5wVpNJAHjE5OUgojTVp7MulbM5Ng43qdMTrRpbIyZjJvsHNnNwJJF9A4swOoaQacuwgY2bwSnFYODQxhjmJycpFat4fF45wlKIUEY0mq2KZfKOO+YGJ/EuwyfOVqNlCyJCYKQdjtGkUcb6LTDdc7l6UVpxsTYOLt37waE0087ncAYhkZH8C5Fq4BGfYI9u3dRn5wgCgO2bd3CyPAQJ520gjRps2vXTnZud/T1VBDxU1GEVrPJ5MQEPnMoLbg0JQote3btYHhokNGxMerNmNHxCeI4wWWOSqWG0pbli5dw8imnsXb9Bu66ZzUqzYhESLOMUOu8na+2OAeJT7Fe5TUvjtwXIzBopbFaTTmOG2sJwhATBLngMLn4EA9JK0OVHK7Vwktem6EzjzTbpPUWWSvBNWPSepOk0SRttdHWE1ZKBNUy5Z4qYbmMNRYyT1xvMDk8QRpDWCqjq33oSg8A1tqp879gDpGmvGzThXz55B/N9kgKCgrmOiJIq0Xf+iY/vvc0di/+PgNF+lTBMUTdx/x2x4ms2LFttodyUOZG6hTTDOpUgI36KFUXkKZ1gqxJUp9kpLmTvt6EcmUepSjAVMqoSikvZG6lJM2YzIGNUkqOPIThPJIYxBokyAuJvfOQegyKKIjIMo0jQxvVEUwehWC1IlSK4d17KJ2wlNNOPZXNmzazc+dO0szRrk+Q7tFMZDE9Q3tYsGgJvT0LcE5I4gRjDb29vcybNx+8Y2R0mMw5jIZms4kxhlJWJrEWpRXeGSYnJmk2mojPW9s658jSlNTEpFlG5jxpkk65fQdBMFV3UZ+cZHDPHnp6egiCgLGxMXbt3EUct+mp1nBpSqvZxKVZ7nGSOSbGxtmut1KtVGlM1vEuJY3z0JtIXgguImQdX440aVMpl5mcnGRsbDTfb6NNknqMjWhPNFkwfz5JkqGNZenyE5moT7J2/XomGnV6w5Asg0DlBfmiNU48WSYoLWgFXgsahWiP6I6PiA7Q2qA7YkMweK8Rr1CiMVgsBiuKtNnO/VpQKFH4OCWrN8karVxkNFukzTZZ0qnXiQxBuUS5p0apVsFYA86TNts0RyZI6wniQkq1eVTnL0IFEUVrqbmLb7cZufZE+MFsj6SgoOBYQLIMu20Ye+cKbn3sck4LdhXpUwXHDINecHf24etrZnsoB2WOCA0BI4jP8/ptqYdKbSFJawyfNkjjCZqtUVQ7g35PUOrBlkNsfw2rIVUtlFfo2IMH187QKs07N8Uq980IFJnKjfS0z1NxMjxREGCqFUbabbw4nM9I0wTxgnV5W9rB3bvp6enllDNORaxh844d+GaTyVaT5rAn8UKzkTAw0MQGIWEQkqQJrWYdazTlcsTgYDuvW3AprWadUqlMahRxW6hUqyTtNiNDQ2SZwxiL90Ka7i0qT9MMbS2g8J1Ur1JUQmvNxMQEY6OjtFot5s2bRxzHjI+PU69P4rIUl+Uio1lvkKUpLs1o+xZxHNNutejv76fZqDMxPkYQ5p2o8oOXu3TnvnOeJG7T39fH6OgoIyMjjI5N0k48rTjDYVh0wjKWLlnC7t27WbZ0GZVKmTvuuJPRsRFEHJl3pOKxaCye3FN8ry25EjBeoZVCOUAJTnsyPFrnD5M4UBlhGOadoxLB4PEOsixBktzx2zuPOEGStBPNaJO2YuJWiyzNMFoTlkKiaoWwp0rYUyXqCI2k0aQ9Osbk7hGyiQSJImr986nOW5ALpNn7ohQUFBQUHEbEOfz4BAvuyvj28CO5tLKlSJ8qOGbYmvWy8DaHzMFuU13miNCgk4ifN3pVJqJUGaBaXUhzchiXgSQZcTrGeOKoDmTokiWuRuTWchqtA2ik4BRKG8QJLk6RFAgMZBqMmTKFwXsyl4HOqFarLFq8iC1btqIyIYpCfCsm8B7JhNh5djZ24oxm8YoTGc1SWkPDJJkjyzwTY5MoMdTtBMYYqtUaSiviVoPhwV2ceOKJlKMgLxL3KS6NyYzG+4w4SbDGkDnHyPAQxlgUupMa5XHek8QpSZIQVaoEYYkkS0mSlGq5QpqkTIyPMzkxiThPpVSmFJVo6HpuCegccRzjvafVanZ8NxqAotVq4Zyj2WxQLpdpNJtI01MulwisRbybqpHIspTAGIyxjI2NMTY+wej4JEmm0TagXK3S29vDyNg4YVTipJNPZv3atWzZsjkv/DYG7xwOIfUO4yAwCmyI0aAlj2QYFBaF8aAcuExIvUcrwRiPzwQChc/ApYKPOy6IxkOStzL2SZu0HaOy3DfDxQlJq53XymQZSilsNSKqVin11bA9VVQpRFmLyzIao6M0dg/TGhwjaQt2/jzKPX2Ycg2v9dTpWjA3kS07WPWF17PmVYUhXEFBwf0ggrRjqvdO8MuNKxlcqor0qYJjhq3pfGr3TuLncBr3HBEaApLmhnHYPE/fVuipLSAzVZJUoVOHuIRmkuIUqHk9uFCjTBmlLEGQQZhC26G9Qjy4LENU7iat0FgPSgng8Jkny1KamSNut6n19DJ//nyGR4ZptlpYpVEKMpfg0hRRinu3bGbZypUsP2Ulo+2Yxug45aiEd57IRhilaDQa1Ccn6e/vJ4xCRkdGmDdvgGqlTBK3EZdP3pN2CwckSUat2kOr1aLRqGOMJW7HU2Z9IhDHMVnmKTtPqSJYawmDvGBZvCeJ89SmcrlMT08PJyxejELYvm0LaaLyjleQd2LKMur1OgCTk5Md345JVqxYQVQqMTKa92GuVioohDRN8S6l3W6zYN48mu2YoaERmq027SShnRmqQRllQwaHRknabR77mMcwOTHJ2rVr833EMaG1XXcRMgVZx4TPi0dEdcwNFZpOxCm3Akcpg/eKJMmQjtGh0oZyOReBdNKu8gJyT5rEJK1JkkZ+rHXmcWnupu7EI8ZgyxGlcplyb5Wgp4qqlpDAEGcJcavN+OAw8Z5hWqN14tRQWmAplWt4YxGlMVLIjLmMbzRYclMGr5rtkRQUFBwLiHOYwTHCe+ax5gmLOCUYLdKnCuY8Tjw7kgH06OSU9fNcZI4IDXA+RVuLwpI6CLSlXOkjq80j1hGNdoKWFCGj4YdxKsXM66UclrE1i40UlD1pPSFrpmRxRhY7cJ5I561TRQTv3VT9g8vySf/k+ATjYxMsWryI/v4+kiQm8ylGaUJjCMTRdhlehE3btrBo2YmcfMopqM3bIBParTZZ5gjCkB5j2LVrF1maUi6XMdaye9duFi5YQBwnqE7qU73RzH0jgDhuMzExwcjIKCJCq9VGRNA6yD03nEPrAGk2USagUqlQLpepVCpTXhreexYsWMDAwECnQDmfDCdJHg0plUr09PTgnKPVauG9nxIajUaDMAzp6emh0WjSbOaF5tZ0fEWyjHa7ycDAPMYmxhkZGyNNPSIaYwKCsEQcp+zevYczzziDxYsX8/Of/pjBPYM45zBaI+JQOi/2VlajjAGlSZ0jdgJK8ErjXV6nAbnwyJSgvcdiMN6idUAUhIQmxKJRmcN5T+YcWZqQxE3SVoO0Hec1Op26FicCgSYoRZR7qpT7apR6e6AaQhSgraXdipkYGqUxOkE20SCuN0lcgFGWIIgQr1HFH59jgtJ3b+Gcj7+Bu978qdkeSkHB/fL4m6/m9PfeMacnC8c14vH1BgNrHD8cP4sLSj/GalNENQrmPBtbC5A52ta2y9wQGgqUMflFVgSjFEoAMZRrA9hSH5kPCOIE41J0I8U4UKnG9kJUqaArAe0Q2oGlHTlcI8OYiKoIZeeRNMMZD87lBdFZhhaPCERKkfiM3du3MbBgPgvmDbA7GWSy0STzoLVCpR7jPY3xCXZlW1mx8lRWnngiu3cPkqUpzbiNncyN8krlCvV6nXqzTbVWZXh0jN6+foKozNDQMIlLqU828EBPTx9p6hgeHmF0bJw0TYnTDGMMYRChOoZ2USlApSl4h1ZQjkIq5VLu6K1AiSe0BsSze+cOxsbH8D73vojjEK01YRh1ohq52IjjhHa7TZalaK2JogicJ07ajDtHFEWkSQoImQMvimazzcjYBFmakWYZUblEKTTs3jVIX0+NR5xzNhvv3cC2rVtzR2+l0UZ3BJHkqU0iCAqvIUXQSqG1piWCd0KEymWSE3RoqVWq9PfV6K1ERIEmNIqs3SROAGvyIvLM4bIESRIkiSHLP+ckTcnE4wODsQFBtQQ9ZeitQF8Zqpa80Mfjx9u4PQ0YbeObad6CuFqm3FPBZx7jdN5+Fylyp+Y4kmUs//AvOGXJa7n3+Z+d7eEUFByUWFLGJirMa8ztycJxTSd9qmdjgx9uOYM3LfwRNS15i/SCgjlKhuOe0cX0tHbN9lDuk7khNAC6HYZEMGiUgDjBRBWqA4toDM0ja8WodkLoBedaSGbRThHaAFUKSMIQH0RkgeCtx5oEG2fYVpskjTtGeg6f5YUzSlRe9Kwh0irPzx8fpVKr0dvbQ5wK7YkGcZyigbTVIkDRHBllO5tYcuJJnLBoAWma4ESxe3CIerNFFEWUK7XclTvNmJis02i2CcKI0YkJ0jTNje3SjFK5RqPZZnyiTpLkIqPZamFtiPMKpTU2CAkAYw2lUkQYWKw1BB1hEQQWpfLISLNRp60142Njee0CinYnFcsYgwh5p6409/VIkmTKsK+/r59KpUy9PkEcx6SlNE+d8h5rLc45RkcnGJ+YxLk8ilCp9tCYGMOlMeecdQZp0mbN6nuI222M1mhAvGCMQolHyHJzQGsxoUWTd9hqpgneGJRWaAy9PTUG+vuo9vbk78+nJK0JWhN1yNpEBsqBoRwGuXs5+Wep0gxJUjLnSLLcK0UCk0fLSiGqUoJqhC8HuMhgrEIyR9pskg63cMMtkrGYZrNNy2csWDBA1NdD5gXVOS8LkXFsYObPQw0ksz2MgoL75O07zueUl9w628N42CPOYQfHSVYvZ/UjF7LcjBGoQmoUzF1ScWzftIBVyZbZHsp9MneEhkz9L/efUArRGi9CT/8A8fyFDI4MkUhef5HGDpoJRG1MuYE1ClXWRCpEhSEu89DOyNoZbZeQuIQ4y2sZlPcYNLoTSclcRuYyrDHUJyZptlrMW3AC8+cHtNopKYpWq40NAlzmsVYxNjpG6oUTV5zMokUnsHP3bqwNGBoaJooiarUaUalEu91mfHyC8YkJBgYGqDcaTExMoLUhSRKq1Rouy1vUNlttvIBzgjZCkqYYaylX8nqMcrlCb28v1Wp1hiN1uVwmDEOyLKPRuStWr9fJsizvIAVTYqKbZtV9iOR1GCMjI1QrVU44YRHNZpNWq0W1WiXpGA4ODAzgvbBp02YajSbOOaJSCQWMjAyzcOFCli1bxt133cWuXbugY2injYFO2ppVQjUsYVXeWcp5wWuV+5aEFfqrNRb099FXLhEFYf4+Jut5PUvSQtImIY6SBTQQKMgyMmsxxuQiIE7I2nGeSuUFpwQT5AZ/YSmiVClTKpWxUZiPL/VkzZjWWJvGcIvJiTrjE5M00gbhQJX5K5ZDtYyPLGiF756fR/XLUfBg2PHSM9nw9CJ1qmDuMuQafPv2R3AGv5ntoRSIRybqDKyG74+fw++UfkQktkifKpizNMXRu8Yifu4WgsMcExq5A7SAJp80asVEs0ktNFQG+snCgLYXIqVROsjdxFsJjE5i0wxVyTClGpXQ4LSQao9TKV7FOJXhyYuPFWBULmvEOaTjJt3qtLVNs5ShPUPUBhYyf+FCkt2D2CDDeIiswQCW3HBudGycJctPRBnDho2bUUqRZRmjo6OUy+Wp3xuNBosXL6bVajE6OorWeiq6kLkm9XqdNE0IwohqtYrSmjRNCYKQ/v5+giCgVCoRhvkEudVqkSRJbtqnFNVqFa117hAex0xOTpKmKUopgiDIIxRpiuu0QHPOdepAdO4O3mgyODjIvHn9eO9pNBp5Hcv/z96fB8mW5fd92Ods995cquqtvU4vsy8EsYVEiiRIAVDIJBQSaVK2Q6LDlExQf5i2HLJpegnJDoeDVli2I6SggmRIpGiJUthyyEQwFKZoBAWSIEEAwjYDQAAGmBk0BtPTPd39ltoy896z+o/fubfyve6e6ca8mXr1+n4nal51VVbmyZtZ753v+X2X6um4fv06AK+//jq73U6IT7egH3qMNbzwwgscHx/zyiuv0Pc9jbV7pXa5/inPt1t0WG1wWnO0XnPr6IAbh4cctB1WFXanZ3z19dfZnp9TYsJSMCQalXEOlDIoCiVKO3tOoFWSqYZPRJ+IKYkUz0rxoGtb2q6lbURGFgZphXclkzcD2+Mdm9Oe7bBjkwOhtTz34Zc5fP55zo1BLxckA1rPJOMqwH74JfwfOr3sZcyY8XXxk/1tPvHDM8l4LFDL+w5/c8ePffkT/Cs3/zFHTZpN4TMeW7yVNNd/I4gy5zHG40M0qBu4cvEfRSn6GDA2Yw8OaW/c5OxsQ8nQtkuZRoRAOdniBk8zFMxKozoNRUvpW6vIRQMiM8qpoHIhqppUVE/1U86UKOfVWiv63UBf7nF08yluP6MZXv8aSQW2PhJCIhuFc5bNdsudO3d46cMf5s69Y4ZhwBjDdrvl/v37U4HeOGkwxrDZbCilSBdEzvgg8bPL5ZLlak1RmmHw5FxYr9ccHByglKJtxWMxDAObzUamPqVMn4+36/ues7MzSim0bUuqvRvGmCqfKg98aK1RSnF+fs7JyQnr9br2cJxTSsF7T9M0bOo0BmCxWKC15uTklBdfeJEbN27w8z//89y/f18mKdZO95+jkBtr5fFB1vri88/z1I3rpH5gd3bK3Tff5Pz+fbIX34nJBVeQyUuOKJ1wyZBRJCuJU1kptI5oUwv9QiYFyAUwGm0tpmmxrsE6h9KGFDJD2BJjwPiM7iOb856Tsw2nfU9vC+unbvHMJz9JWiwZosEtlmQKWhXmIo3HH5vPPMWv/v6/etnLmDHjXXGee/7Nv/pneY6fvOylzKgoMeLeOMV/4Wk+95kX+Zh7RYJI5qnGjMcMqWS+EG6zfOVEwm4eYzxWRGOcasg0A7JWKGfZ+shBt+DmSx9mCIWw6WnbJcRA2JzQn53jTwLxtKc58JgDT7IOrMa0GrRDpYgKBWKilEQp1A4OaiO4RiuLHwZCTiTr6Ldbzvxr3Lh9mxdffpnf+sqr7OI5ymg25xuGeEqzWHLv+IyUCi+//DLee+7cuQPI6f3x8TGLxQLnHCcnJxwcHOC9r6f9ipwzfT/Qti2r9RpjGwbvSSnTdQtu3rjBer1GKUXnGqy1k7RpJBrn5+cYYyYfxW63Y7vdAhJpO04yVqsVzjmcc7RtO3k0lFJYawleJFRPPXVbejU2G0IQo/hqteLNN9+UFCljaJpmIlW3n7rNW2+9xVe+8hVKviAwQhBEomWNqdOZAz7+kZe5fesWKmXu3nmLN197jX5zRvYBXQpL51AacpbXqeQCMVN0Qdx5ElhcciKWJGV/uqB1oQQp69NGY53Ftg1N12GbFrQheCFdp/2W3XaHDYVOOXyKbHOgrBxPPfc8t19+EXd0nbubgcX127TdEqNNZTBcVNnPeCyx+oWv8OH/35/hlT/y1y57KTNmvCO2JfHc/3UmGY8TSi6osw0HX4afPPkYP7j8Iks7m8JnPH7IFP7eyafhzbuXvZRviMeGaIznBeMAqBSZaLhuwb1TT+Ma1ref5VaA3emGFks4O8VvzujPd6TNKTt1jF2ds7odUOsVetWhV45iFcVqTGMhZ7LW6FSqHyICioKmKIVxsoE+OTtnSyEby9YHbj/zHC++9DLpt1+lv3/CYrXCxML5dksums9//tf5OPChD30I7z3379+fphcpJZbLJXfv3uXw8BAAY6xMKIymlEzTNHRdR6ob2cViweHhIddv3GC5XGKMQVfp0W63m4iG3JdMKU5OTlBKsd1u8d5TSpmIxW63I8bIYrFgvV4Dcl/DMFSi4YgxcnJywvPPP0fbtkI+QpjW9tprr00ko+s6druep556iq7t+NVf/VWGYaCx7oJg1BdSKcVqvealDz3P7/7UJ1El8/prr/H6V7/KsD1HxYQpmcYoTG3ejkU6w3vvsSHjdMZpA8pA0ej6jokxk2IilwRKoWJBKWgWjsZYbNPhugXtYoEyjt1u4M7xMW/cvcvJ6SkL1/H07Vusrh1y/dYRR88/xcuf+STNwTW+eveEs/OB2x+6TqMbTISiiujuZjzWiK9/jaPPfQT+yGWvZMaMGVcGJVO2Ow6+kvjpr77Eb94+4rbZzKbwGY8dtsXzo1/6NB85/43LXso3xDdFNErdRD5qlFLEP6EU7WLJWT/QtppkDOvbz3J4A/zJOWfeM4RMvxtwIeP7ntPjDT4Urr/wAroT2RBaYZzDLArFWmLv8dteJhcpkTKkXBhqGlRS4JYrXCn4XDjf9fSvvsrTzz3H8y++QFRGWrGTZ7lcU9D0Q88XvvAFPvOZz/DMM89wdnZG3/fT1KFtW+7cucMLL7wwTTW6rqNtOxaLSEq1eA8hIU3b0nUdWmustXKdcxZPx94HiLQIIITAdrul7/tJijVOFlQtExzJxn7/xvjRtR0pyvSjbdvp5w8ODsg5s91up69ba0HBzVu32Gw3E7EaU6rGUZ41lps3bvCpT32Kj7z8Em+9/hqv/vaXuffmW6hSaK1G6QxZkQronIlaY5TGGo1pNEoFcg4krSjaULQmFk1JiZgKKcnrV7KkiTmnscqhnMV1LbZpKErjfeT45Iyvvf6WRAknyKvCTlue+9BzvPSJl7j27G30asnZLjIMhcauuba6SaNaSSlT6tKy7stjPh593PDsX/scH/6Of41X/vlZQjXj8cP/+A/9SeDLl72MGfsoheI9i69t8V885Kc+/nE+6X6OVqWZbMx4bJBK5k5K2M+tyT5c9nK+IX5HRGPcPH8roNSDEnhrGtbrIzb9OXqxRLsFTdugisWfn2HbjpLFU5E2G4akMN05q+0Od7Aie43uNF23QKtMIlBiomhFohAopFJIBZJW0HU0zuGsJeeECgl/vuHeyTHZWG7cfoannnmGIUEsG0LKKG3ZVbP1b/3Wb/H8889z7dq12ugtZGA0YeecpYH8rqRTNU0zfYAihkDTNiwWC9quwznxOqSUZN2VFHjvp+K9EALWWnLO0+Ri9GoA0iBeCccwDJycnGCtnbwZWst0IISAVrDZbFgsFtia5rRcLh9IuFqv18QYWa/WNM7x1ptvAdC2LX3eQZL3SNd1PPXUU3z6U5/i2tE1Pv/5z/PKF36D5ANGKzrrKIBSGmUU1ObwrKS7pCglpYYy3iJp8ECfi7R8V5IRo/RvUKCkiHYObR2mcShriCWRQ+B82/PmG/e4f+eM7S5ibEtaO8pixeLmdda3bqBby+n5Gec72G4868NbHKyuoYree9/PE42rgLzdYk4sr8ZznjaLeaMw47HAm2mDL4X06uuXvZQZ74ASI+b1e9z8pTV/81Pfze/5XV/ioDtlTTt7NWY8FhhK5EfOvovn/8EGcrrs5XxDPNa/NQrZJN+4cYvtLtKHRCiGYhboZoluWqzrMM5RCigMXdNCVpzcO2XYeCwNNjtUVnJaQSFTKFqBMWRjSEaj2ga7XmNWS7JzRK0p2mCahoOjIw6vXaP3nle+/GW2u51IhhYLci5stxuMlU3M8fEx9+/f5+bNm5N5W0gEUxrU7du3uXbtGgcHa7q2xTknPo7GoZSiaSV5arlc4pwjpYT3nu1uOyVNjY3eZ2dn0/2++eabnJycYIzh8PAQrTVnZ2fTdGUkKNvtlmEYpP3bWpqmmciIUoqTkxPatp3M46vVaiIv6/VaJF4pcf36dWJK3Ll7Z/J9AGilWCwWfPSjH+V7vud7sNbx8z//8/ziL/4Sm/MtpYDWhlyKSMW0RjuHMlbIX8ryAQSrSa0jtY7oDIOGbQwMKRFywaeCjxkfCyFBUdI6rhuDdhYMxJLpB8/941Pu3jlhczqQBwOpIeeWISt2IbIdes7OTzi5f8LJ3RPCkLl54xkW3QEYSxHV1owrhI/++Z/ih1/8Pv6Z//a/x1Ae/5OfGU8+/uSf+tf54Re/jxLmjpfHESUl8vEJ1379nN3nbvBf3v9e3kiZyOO/oZvxwcAbyfNXfu77sV987bKX8p7w2Gybarcc0zyjGsONsayXByhl8D4TA4SkiFkRUiEpUNaCtSjnME0LaMKQyR5McuhgKZ4prjXlQkaB0ShrUI2DtoG2oTQOr2EgkzUkCsoalgcHHBwd0bQNb7z5FiEmrl2/gXMN1jQoFEqJufvuXTHnHB0dTSbt0dew3W45ODjg+vXrrNcHWGen24wb+7btaCsBKUUIyna75fzsjM1mM0XVjlONvu/ZbDacn59zenrK6ekpMUq7+EhIQgjThGWUUZ2enk4xtzmLgRolHRw5S8Gfc46maaYejsViAYBzjsOjQ7abDdut+EXGaN1useCll17ik5/8JEM/8Nlf+AV+65VXat+3IsZECLFOeRRoTcqZmBOpyq6SgqQhWU1sDKmxRKvxCnY5M5RMLBCzImZpLUcpmq7BtgZlCkUlIolYMufbLcfHZ2w2AyVZdGkhNZRo8LvI5vic07v3OH7zLuf3jjm7f8qqW3Pr9nPopiMbjUfSxspsBL9yWPzhV/j+X/qXSI95DOCMJxv/3v2XscfDZS9jxtdDKZRhwHz1Drd/MfF3Pv8ZfrZ/kW0O898fMy4doSR+fPcRbv9YQz4+uezlvCc8Nmbwd4NWdVN7eI3QB1COlDREGEIg5EwxBowhlEIePFoF1octJSr8ecB1FqWlHyN4TwhePk+JojUoTSiFmDPZaGidmJhjwRlFTIWh71Ha0XVLzrc7jk9OOLp2g6eeeopXv/o6frdBKU0phb7vefPNN7l+/bq0g1c/xpjk5JxjuVzSNA1Gm8kjoZSiaRpc7Z/IKRGi5/z8nL7v6TdbbDV+hyCt3ftSqa7rMMZMEbTXrl0DmKYZo9zNOUff94QQWC6XxBjJOaGVIsYASKTtfhzumDC1WCw4OzuTBCvrODk9nb6/2+1wzvHyyy/zu77jOzg5OeHzv/Z53njzTXQlIhbAaFzR4ncoBe8D5IQmS6yt0WhjUNaQjCGrQslQoianJNKqXHClUEomZaAo6eZoNa4BdCHkSA7SGH5ytuHk7AzvM0Yt0LmFYEl9wZ/1bO+fs7njyCpw3mdUc5Nnn32R9bWbZG0JWtHnTKcLNlNJ04yrhMMf+hKf/s//VX7jD/2Ny17KjA8o/rN/94e4+dmfuuxlzPgGKCmRT045+Px91j93i//3i/8k3/vSV1jrOYFqxuXiPA/8pS9+P7d/4R4pXo0p/WNDNMoDnxUUta+gKLSy3Lx+m3t3j3HNAq0dCYgxkXNBW4duGrLakAGtLTFmzo7PAcu6LMEECglShlKTmpQhFEkuCkCxGpzIl0wBZQpGG2wWD0dMhTAE+sGz7QMxwfUbN7l+8zr37t/BVvnQdrvl7t27HBwccPv2bY6Pj7HWsl6v2W63EymwVW41GrtVlVkpDTGKqbofPKenpwzDgO97nJGXbCQa292OYZD27ps3bnDr1i1OTk85O9+gtEErTT94zjfyuEopjNZQCrkUStkChRgSpWRi8DRtO/k+xvWNMbht23J6espyuSTEwPn5GVordtsdxhief/Y5vuM7voPz83N+5Vd+hePjY5qmIaWEsxpdCq2zlUwVYkooMhowtTxQGUkASyASN6UkiljL+0EVzZAKOct7JJeCQZrklVZgClkh7eC+kIbA7nyH7wOqaKx2UCxgICnC1rM73bI56cBkMo7bTz/FMx96GdsuiEYRFGRjiBTsTDKuLD76p78IX7zsVcz4IOK/+4U/zK3Pnc01PFcB1RSu3rjD7V9c88uffJFffvZZXrJvzV6vGZeGVDK/Hlq2//gWvPZrEs96BfC+icaYMvXo06Y0kjWlgFxlVAqKAVpWq5sotcQoR4mJlCIqeZwp5MYSrMF1S5xegF4Sth7yFqM0qgSWS4M1hegTOSa0sbSLDqM1OWV8SCQFWEdGkVPBWEUpipwjbbfG5sIQzlHaMOx64vE9eu956eWXOTs74fj4mK4T43SMkfPzc1588UWcc1hrOTg44Pj4WArtnKvxsj3DsCOEWJOmWppGmrz7YWDXD9y/d4wPHklWVaA0MUZCJSJn5xuRg90/IRRF23Yo23L/bMuiXWDdis3uGO8HSsmoUsvztMYPoZboQSkiXcqpsN3s6LoOiiL4QN/39XYSH3twIKQpeGlTB/jQh57nO37XdzAMnl/79V9ns+sxriHFSFEa7Rp0TmAMSUHJGZUTRimUMSSK/N5kJUlhtS8jl72mb+ewTuN9oKSC0xZtHDoVklH0KmOdTGJSVtDD0HuGkwHrpS/FakBnsopknYlJ0wfNLixx7ZLm4Bov/+7fy/rWsyhn0UrhSsFo0GW2gl9l5H7gd/97f5Zf/jf+8mUvZcYHDL/6Ux/hwz83TzOuCkpK5PMN7Zfe5OZnX+DH/onP8P2L/5olzWUvbcYHFJHEf37/9/HUZwP57Oyyl/Oe8b6IxjuRi0eXPiX6/bcVoRWLwmCNY71aiL4/bUjek/stJQZKSeQCyjiMcqSYaYyjNQaVPGcnW87uD+iSsW1LaSzZFgoGsz5gcbCgxGouLkq8DCFidAOlMHgx7WljadsF3SJirANtODk54c6dt/jYxz7Gl7/8ZTm5d44QZKSVc57M1NZanJOeibFcDxWIKcikwej69AsxBc5OTznfbNlsN+x2O4wyNepWE2NitxsYQmQ3eLQxnO96dv4O6/UhxhrCENhuI23T4NoVg0/0/YBSGhsyxmiMBuckgarUgrwQIsPgJalLm6nwb7lcsNmcY4ymbR0nJwPGKHbR89RTt/n0pz+Naxy//uu/wa4fUMZI87rS2MahtCamQA5SsmeNprHS6F2K+GaUVhRtSUpRciF7j2us9JYAhoJCs77W0hZF2fX4sw1pN5BJeA1BG8igQ4YAwzaQNxmHo3GWYhRZFwqRqBQxGrZeEe0RbnmLg+de4ui5j6E6iS7WCtpSGL2AF36iby++FVHSHzjkxAs/8jr8G5e9kBkfJPzAr/wxPv4fvEa87IXMeO8ohRIi+d59bvzKTX7iKx/h7lOKmzrP6VMzLgUn2fNf/sp38qkv3pmSTK8CHhPp1LttoFSV+0gEqtYilylASpkQCzEXYhGpTMqZkCOLpuPw8Bpu2RFV4N7ZKbvtGa11tEqLl2GxInct2VpygUW3YOUaQlGcn2+43x9zenbKarGaUpZiyuRcZGKgA0prbt26Td/3OOf4xCc+wcnJCScnYtAJIUwdGlprUkporR/owQhVlmSNIyeJp/UhsBt6Tk5P2Gx2ks6UEkMYaFyD1qMc6pxh8CjAew/aYG3h7OwMZTQqK4KPaK24du0I17TstltCGCjGEGPBGlM3sOLToFzE547rTTnT1l6Pe/fucv369Slyt2kaFsvEhz/8Mjdu3ODzn/88p6cnNI0lBI+vhMsYKxKonOm6Fq00IQyEGDFOY5QixUgIiaB9jRjWGKNYth3Xrh3RNg0GRaM1frsl7QZCTAwhAiLJwmR8yATvwWdKyMQ+klG0XYdtWzywi0nkWNaAscSiMc0Stzrg+Zc/jOtWD5De/Xfo1RhWzpgx43HBa/eOePmVX7rsZcx4v8iJvOuxX3od9TMf4xe+4wU+4r5Wp+0zZnz7MJTAj5x9ghs/3lJee+PKyKbgsSEa7479icnoMShARk6+M4ZYy95iTtgSWS47rh2tiSWz22wJuy0lJmLRnN+9zzLD9cWarl0QtGEXRSbVNh1L13KwPOD6wXW251t22x1nZ2eykVdaTv+txTUNg/esVktSSnzta1/jwx/+MNeuXSOEMKU+KaUm6RQwEY4xocoHT9/3dK26KN7re07PTtlutvgQKaWgtUYrPSVnxSglfzIVUfJnAWcbBj+AUlg1Gscz9+/fZ32wwjlL3+/QqqCKeGFSMtJbkTOlJELIeB+mqQwonKueihhpmqamX/U0jeX27Re5cfMGb731JsfHxzjnOD8XEzzI2nPJhCgJV5vNlsY5FosWXaTbwyrNsmtZLY9Yr5Ysl0sWjWPRNZSS2W433HvrDr7vKSlhCuKV8IEcPaYUnNLEWEixR0XIMaMilASu7XDLJbprKT4S4havCspamsUSt+gwTYOxDTdu3EIb8wChEFFfnbrNuNJQuXA/bblulpe9lBkfAISSSHHW9V9Z5ES+d8yzP7nj//VDv4d/7qN/k9a4y17VjA8QUsl8MUT+nZ/+IT79E2+RdrvLXtL7wpUgGvsN5CPxyNqQtMNnxZAyEUhISlOKnuB7tv2O4/tvMYQtIWfO/I6oNQOaXVEchIQ7WNOnQtr1mM0O2yxo2hZnHYcHB3StpEX1fc/5ZsvZ+YZcYLlc0naLKfZ1JBtHR0esVqupTG+EMWaSVZ2dnU0+juAlptbZBm002lqS96Qk8bImZ7bb3VSwJ9dDuirGoj2ZtiRU4SLBSmtSTuSUUQp8GDg7zXSNY7VaEv0gbpicyTmilJGyxMLU0zEMQy3LK1hr8d5Psbvey/NeLpc8/fRTKAVvvvUGY2LVdrellIJzFqUkWSvFQGMt1mhKLRc8WC740PPPc3hwSOssRitp/A6Bk9Mz7r65pd9txdfSSEywsQ6VsySDGQPOkmJkFxOpeFTy6ASqKFTWaG1oXEOxjqA0uxzZ5kQ0CgM01tIuVyjrWK7XNO0Cqj9kIha1SfLqnCHMeDfE3/wt/sSf+Z/z9/8ff+2ylzLjA4Af/u0f4KP/w89e9jJmfBMoMdB84TV+7Rc+zFde1qz13BQ+49uH8zLwF9/8QZ79UUv58qtXapoBV4BojBgJhlIKYy1Nu8Qu1hTXErOWZu8MfRg4Ob2PVpqYI8H3aF1YLDq0Nai2w3QtPiZOTs84dC1usSCnTL8bKLtI0/TiwZDaarQ2In8yjrZbcL7dkXLGOQeo6l9YstsJITg6OpqK9kZ/wxhJOzZ4n52dVYIQH7jN2Nbdti0lQz7foLVs+FNOaKUxRhOTqqQjkyqhKaXUqFshIBmRN2mjqtxqh9WwWHRSUgik6OXaqiIekVSJSxQDuFIKbWQqMwyR9XpN27YoJaV7XbdgsVhwcnLCbrcj5chme06pPRxay1usZNBGy2tX432PDtYcrFcsmoYUItvths35GbvNRno8AFsyOUaUhlIMKSYiGVsL/xSgnUUbib4NqcBQMGiM1ig0SluyNuxSxueBU+/ZlQLGgmtQXUd3cEizXPHM8x/C1te17BGNUinGZfkzZjxaNCee/+jkGX746GuXvZQZTzDupy0/8Rsf4+P8wmUvZcY3g1LIxyc889Pwj/7Ix/nI4RdmojHj2wJJmrL83Z/+Tj79k68Sh6vXw/OeicajM32/P+xPMlQ9xXeuZXlwjfW12xzfeYNiHUlpYs6EvidsA8Enlqsl1lkwBtMuKdYw5IKzDYuuJRoxVTttaLWDnEmpUFKhKJkKhBCJsRe5k7a4puX6jU6kRTHV9u1zUkq0bctms+Ho6Ii2badujHE6MMbaAmy323qfIoka5VVKCYFYdB0K8WIsFgsp6Os9yl4UA4YgEbhUgiIJTQVnpKOilIyxmlLktsYoUooEP7BcLjBKEbwm54g2GmsMCZF1xShTDa01qDJ5SkYT++jZODq6RkqR4+P7tVhwQ4oBravMrcjkRmvD4cEBt27c4GC9Zr1aQcmcn51w984dtmfn5BQgZxQFrVSNsi1oFEppcirEEkErtJKSQUWhMUaM50qRooasAVOnNJqsLEMBPwzscmKTAl4j8q31iu7wGuubt7h++2luPf8hlGlqwPJDUil1tU4RZnwd/PQv8Zf+4h/nh//3f+WyVzLjCcaPbp/n4//KTDKeBGQfOPrlu/yHv/F9/JHv+TVaNU81ZnzrMZTIf3H/9/LsP4L05ltXbpoBj1Ez+NeD2pMJGWNomoZ2ucYtD9DtgmwcEU0oCp8Sm2Hg7vEx909P5YRbW7ANbrnGdkuKkijcplsKOQkRlKaxDa2TScZoOjdGT16LnBPeDwTvsdayWCymkryxdXt/Iy7N3qXG2A7T5+Okw3svZKEaxtumRaHkvpdL2radWsVHCRkw/bwU7eXah1GmCcckN0OhtUIiaaWfJOXI4Ht2VY7UtI7FcoGr/SFay/PNWXwg0tcROTs7myYvY5nf4eEB1mq22w1QyDnS9zuZPlAY/IBScHh4wPPPP8cnPvUpnv/Qh2iahjt37vDKb73CV179Cvfv32PX74R4GX0hE1OIZEnukJQKMWVSLqScKSjp3NCaCPicCVmRtCMqi8fIn8qwDYXj7Y7jbS/N4iiitbjVmsOnnub28x/ihY9+jOXBEWhbScYon5I/rt6v94yvh1uf2/InX/mBy17GjCcU57nn//Sf/suXvYwZjwo5wetvUv7hdX5s+wmGcjXK0mZcbbyRPD/yK9/NtZ/7GqUmoF41XBnpFOx1eGiNbTu69RGL9TVst6IYiURNWlPIlJQpO09fCl1uWDawODzAacOQJco2G0VRhqxMTV+SlnBq/OpovM65iF9CiUE45cz27AxjLV235OjoiFw9B23bTmlNXdfR9z3eizxpt9tNhGTcsCutiVHibV3jxOTcOEpBpFy1ddt7P/kvSimkSjJG7E+cSil7h+8XkyCl5PNc77Oxhq6ThvIQPEM/QKH+d5gmMjlnNpsNWsNisajXJGOtoe93eO9pmgatIeeEMY6cA7dv3eTmzdtcv36dpmkY+oHffvUrnJ2e0G+2lJInU7qpUi6RJknS2L4nIqvasFIK5EwBnNYohFyqKtXCNpRiiClLHUsuUBIpZs59oFeZYhq0MdjlktWNGzzz0ku88NGPcf2pp9GmFvnB6ABnn2JMX5px5aF+6hf5mZ/6p+DDf/+ylzLjCcRxjrzwF37yspcx4xEib3Y881Mb/uoPfh//7O/+DRZqjrqd8a1DKImf7F/i+o935NevVtLUPq4U0biAwrqG9eE1Dm/cYnl4xOZeR9KGgCKlglUKox1KWc6HzO5sy+GiZ3V4hMJwvtsy7HZo11Bcg2laXGOkWVp5YoqkkMkFnHM0TUNMhX4YKDXyNfQ9IITimWee4d69eyil2O12OOe4du3a1KHR9z2b6j0opUzSJ6311K+xWCxAaXrvpQwvRkL1SuScJb0pS8RuzmW6FnLwP0p9BA9uiMeJBiglbefOynPVWtG2DU3jMNrgB1/zmS9M+EqNvo3RJN7T95IsVcgsFgs2mw2lFFarFTnD008/w0c/+lGMsZwcn/Laa69x5623hOA4h6vkJkUvjeDGkFKkpIQ1Bl0nSNLfJ2WOpRb6lRgp1mKUIoPIq5TCGkuyipQjKUdyylNcbyywS4nktLS9tx2rgyOObt3mmRde5NZzz2HalpSlmO/i6hVAi8lEzW7wGTNmzPigosSA/eJr9P/g4/z4xz7C/2D9Kks1F/jN+NbgJPf85Vf+aZ76x3dIV9CbMeJqEg0FSlmabsXBtescXrvB6WrNuWtI2kJjyTjO+oAKieu3buJWh+xixhVQTYOKiVwGMhByIqQMKVOymK5jSlMcq5m6JsaJAlhj6GPi9PQU7z3r9Zpnn30W7z337t2bomHFMC5TBu/9nhxJPtquZbVasVwuaJqGXEDHQMqJtBdhq7VGG5EQee8nc/K48xU/hNCOcaIhG/Ms/RiA1lCKroV7DV3t9ygUnDMYvaJruxpLCynFaugeiwQ1p6ene8+rsFguWK9XuEakV9euHfH8889zcHDEa6+9zv17xzVxKxPrhOis9o4crlcs2yNyivi+J4RMKoWcxbsykoyxzyIDucj11ykTqjfFaYNRWh6jKBKahCaTxVAPhJLJaLRtsK7FNR2L9ZqnnnuOZ158keXBNTGHY0WyNREKVSmOfD6fXc2YMWPGBxTVFP7sT2749//p7+cHv+uv87yy81RjxiNHKIn/ZrjJ9v/7DIdf/tyVnWbAVSUaFcY1rA+vc/Opp9kef43+3j3CxuNLwHtIRaOx7JKiazqWh4fSOF0ypm1Ydi22cXSrJQXFeTVopxBwzuGcZRiGycitjUw2hhCrlCnRNG7yYoyTiVEWtdlsHkieGqcYo5QKwLmGw4NDnHPiuaixRs7Kxp06VShFJhEXXgzxXJSajDXebkQuIhnS7N22ntKnHMnF0S061us1xhpyluK8xjVTLG0pIs2KIaKNQqlC3wfatqXve/m+KpydnXJ+ds4zzzzDrVu3SCnz+uuvc3Z2hnVjytYpfd9PiVOr5ZJl10mPRr8TUlFERhVjBGOFNCkoWhrjVSmoXChkQp04FW1QtgGViSFSlCUpTVKajCYrxLeiNNoadNNgm4Zu0XF07TrPv/Qyt595Du0achbzd8nI9dRj4hQPkJ0958aMGTNmvCPu5Sv9z+uMd0GJAful1xn+8cf46U8+zx9b3ZkL/GY8cgwl8O986Yd49u++eeV6Mx7G1f2bsIgxfLU64OatpxlOn2dz5z7DuSf4M/wuoIzDdUuCcviiWDUtPkVySAQy1JNwdJ1YaEVJhZgS2tiaBHURHTv6Hay1aGNQOVdTeEfbih+j6zoWi44YgyQwpYyvZCV4Kefr+x6tFIuuY71acHCwRqGIMVJQIikai/lSBCCnTETkQCEmMUOXWmI4XZOLKYeqreqMt1FSFqi1bJJLypSccc6yWLTy2Flu75yj6xYopapXoxBjQimw1hBjZLPZSoJVEO/Icrni6aefJuXMV7/6VXKWGN1h8JwcnxJT4tatG2hVo4KVYrfbsttISpWSJyNkLEaZshhHHqVhMo6p8iVNjiIOS1kK/7KWvgzlHCUWSagiXRjltfhvnHV0iyU3bz3NRz7yMV740Iu0i4UQM6XrVEimPyNUvbRFMXlHZsyYMePr4X/9se8D4mUvY8ajRp1q3P6s52+89vv5wY/+f7DjXmLGjEeEr8TM6d9+ltWXf+FKTzPgfRCNhwvzLhV1CQqFazuW6xusDm/SHl7DrE/QQ0KlHbutp99mnj46pFms8CWBUeJ9SAmtC0Pfo42h61q6xjHkLB0Taky4aqd+i5RE2oNSOGtrClXPbqdxjcEYRSGgdKZpDDkrYkj4YSD0O/yupz/bsut3KAqrZcd6tWTROhpn0Eo29HEY6Lcb+u2GEiO6FHxKxJirpCtT6q63jJejiH+glCqTUrq+ZjUmVhUpBFTUfgmIPpAGj25bGm3BSMKT1tC2jsE3bLc7gg/TdMM1TSUgBWugax23b93g+vVrbLZb7t65S9suUGhOT88YBs9qdciRdahS2Gw2nFW5mUZhlEJTJK4WoUgomRxEClYDuk5sKKAM6ExRmYIhFTVNLLqmRSkLRBKFpCFl8XFkMtZYlHO0ixVP3XqKj7z0EZ669TROW3neY6Tu9DYbBVNlXNmccDtjxoxviH/rzd9NyfNfFk8qSoh0r5/zq19+lrdeVhzpwhx0O+NRIZXMj28/ztM/c072Vz/d7H0RjceCZFTUfTVaO5rugHZ1E7e6jlndQ2122JzpnCUFSBT64EkBXOMogLUWay0FiH1PUqBdAzljjcFZh9aGUlTtkBDfRkwJHwNFKZRWKC1SpBg9i2VLypLW1LSWkh2bs3NiGChRkrBiCOSYaFrHatFxeLBk0Tm0UoQYiD4Q+p7N6Rm77RZygVQIgyekjI95SmPKRRKwxLwN8o0MRaGVmL0LRXzMiOTHKOnt0ChyTAy7nl1NpdLOohuD0gXXGLTpyCnLR9YYYytpMTS2ZdEtuX7tOteOjhj6gc3ZlsZ1WNsw9B6rHb4kdpse709kMuIHMcArU1m6Aq1rLG8CLYWMIJMlZTSMxYNKYbQhFzH8C4GQSQ3GYrVGZfm5kBJJFYrVpAyJLIWN1kqQwPqQ2zdvs14dUJQRGlEm2ga8gzyqvMvXZ1xZXP9Vxd88P+RfXJ9e9lJmPCH4o1/4I8Q/4SHfu+ylzPhWoWT0vTPcb1/nN+MNPuLOYKYaMx4RIon/9Ld/L9d+6w1iTt/4Bx5zXE3plKicoChKUbhuwfroOsuDQ5rlkm69BmVYLDVGOdbrI7rVCq8iffB03UIIRylQxOeQUsa6gnV2kho9ECc7dl/EiA9e/ARGT70TY5LU+LNjhC21EdwHL5G0KaIVNFa8HYtuQde2bLa9NGsnKfPbbndEH0khEkM1kMdMiLnGv14Qv/HzfY9GKRk1/sW3F6BUChddGSmx2+5Q1ZOhggavMdZgrZuM6MPgq0FcEWPGGknU6hYtZ2dnwHiNcu3c2LA533J+vqHvh8mjopXC2jppqb4Sub4KjTwvXb8nL00mJZGIlZxRxkx9FkUrIUAly/QmZ2IpJO+JKZAoZFU9HijQhqI1GUVGSApGo7SpErRv+Jab8QTixl//Kf6P/8I/z7/4e/+fl72UGU8Idv/mM+i7n7vsZcz4VqIUyvk561fhs9uX+b7us7TKXfaqZjwhOMueOz/3NIfHn73spTwSXE2iQd1sUtAKtNEY53CdJAmlGIipMOy8TB2coelalDJs+y0xJRZ2ibVGok9jmuJbjTEYq1DasFdTIY9ZCilFUor4ECmKahp3teAuiYk6F/wwsN3upEdj8ATvp76Mtm1ZLpc0roFSGAbPZrNlt+vJCXbbrXg6QsB7aeSWDgkhPqrG3MYYJ4LxcI+GQE1pWdXRXDfwcvuUCz5ElBrq9ECB0VhncU42+DkXtFa1z8MzDF6ec2PRphC8I4QBa201uyd2u56zM5FN5TxG5Kqp/0MmE5ImNXby5Vwk5nY0YNfnkHOiVMKnlEI7h2katFLEEMnVz1GAVAqxZCkGR1cSmSlGo61GGU0k00fP2XbDZrclxYgyV/bXYMaMGY8RPvx3/gyf/sJvc/XPIGd8I5R+YPlm5pdOn2e4/vOXvZwZTxDeSJobv1qeCNkUXFGiMSYUgciXUs74nFDG4LoFtu3Rbovf9OzClqQNg0osD5Ysl2tANrbOtSgFu12P99KADWovzrZMk4pSSk1yghCjnKTXFKjdbodScHR0iNFS/FcK+ODrdGLLZruZGsWXyyUHB2uazhJToj89Z7PZ0vcDQ+8522zFND4M7HZ9bQ/PqOqfKDCtayztm8oMH/LSqNqqLVMHxP+QMylrtBX/wvh8lKmb8RQJIaKVfiDeN6UISLlezokQPG1jKkmL1ctiWXQdJRec82KG935K6RpTsMZpkkDt/T/T+sVh8iCJkuJEPRGXPDahI7HERZgn5ExOiZShKLldjJ7sDXa34/7ZCSdnZ4TgadruUb9FZ8yY8QHE7X/oSG+8ednLmPFtQEmZ7p7nleObbF4oJD2X9814NPhSuMnqdS9S+CcAV5JowHRALzKYXMgotG2xTYtyDW65ZCHuYox1DDFStjtuLle0bSuynJoapY1IhZSuJ/4otNJAemCTq5WaZFIjxjK+vu/rJl/Tti3WNiilCTFUw/iOFCLL5VJkSlqSlLwPbDdbtrteJgGnG7a7Hh8Tvk4QvB9IgLJumgKMRAO+vlFfFVX382NUqyIX6odsznMGnTUqKZGDKYXWtbtjL1I351yTpgaGQaEoNE6M9Frr6VpIwlasCV0S0ytSsoixZmonL2M/yCilGiNka/P3eL1HUuGcEz9GYUrlSnXSkRSQE+MkI5aMT5FUCVTOpVbvZQKZPgaG4ElPyC/yjBkzZsz49qGkhD0ZuHd/xbbM/owZjwapZH5p9yLu7rYeqF99XFmiobQYfEsWp7NxLc1iyWJ9QIiJIUaUtTjXALpKmzxnZ2csFguca6s8qXoplEIpafzOIQAKrQ1a573OC4fSmrZp2e627LzE0I4dGvfu3a9ThwvvBggRyPXUXzwP0kHRhIZcMtvtliFEdtuBzeacXR/EU1AlTjElYi7ooik1aWrs5fjGBn01/TlZncco3wI+JgoZq6WNm8hU0icfF9KykQuMMibXWJSCYRhknTEyDJ7tZscwyDRDazNdB610bTTPtVSwXHhcMuxPkDRgrMUYg7XyNh3/DFEM99oYnBLzuLV28tuUAiZpTH3W2kkJX1HQLDtc2wjhMVrMPlfEgPE4hTHMmDFjxgcaJaPOd5R7h9zNCzJptoPP+KaRKfzM/ZfRZzuelGPQ90w0HsdNjkwdMso0XLt1G5UiJUZSTPgQ8M1QuynAastCL1gul6A1p5sNKUWccyIFqg3a2sj2NOWEc2Zq9k41cSqlVO9DofpeyuWQTbAxjuP7J/S7gcViSU5CQOT0PYpJOUdyifiQOd9t8F4K/HyQkr8QhPTEkPA+VI+DEB6lROo0nuaPJ/37RvDRDwEIq1CglGFytRSIKZNLwE6eiEJMHqulZ0JM4Hl6XhL1K43iIUhzdymFnNJEMkYJ2XbbE3yQTosqvbrwkoAz4i8pOeMqkWiaBqUkvrZkIWT7udEjoSql0LYtxjlCShjrcFqjKilKKUpULQpTCjY3laRliipo52ialqZrWR8ecHDtiKZpv/Vv1BkzZsyY8cRBpYzKkIsoIGbM+GaRyXzpzk1e7u9e9lIeGd73RGM/2QgukYCMhgOQk3GlsabBNh3GtrTtggIMVSKjCjhtaFyDNhKnOnopQowTYWitpWlarDE1RlbIQimFNEqIKBhjp6+PxuwYxtQpg9aenMT83TQN1lliDIwb/hADwUe8D/SDr4RDvBFjupQPkZSqN0Ma92Bv0/31oSYzt0wiNJQs5mhkGbkU1CSpgpAzKiUaJ9G++5daKYXWCmN0nVJotNHVu5HqNGMgxiBToVzqREimI2OxoCLLfxsjhnCESBhdg6jGFY3ECWgbkWb5IF6PiTwaja7TF1WbCLW2WGPJFIZhIBXpz7BtI6lWRtMtFqwPDrCNm7oyZsyYMWPGjPeDYjTFFLR6Us6eZ1w2+hLZ3llSwtcueymPDFdWOgVMp96lQBw8m82OzfmOvveEmIkpTySiazrWqwMWi1UlA7YWKok0qetaGufq6fpFaVuu0qgxMSmEINMMRGIUYxTvQsx7y9qRUqRxjq5bslgsaBpHv9uQs7RpS+RtYvCRfucZvCeESIwZH8dyvmrgRmJYx1hbVXsvRgnTu1ycqc18LO8riDRMITXXSVzldU+vIVcyZZR0Tijqc5fHMVbjihAs66oxvRRJ1vJeNvaVGDXOyX2yt+b6sf85QMpJ6IWu64aaRqXk65XIhSgJDMpo2qahqeRBzOBUs7m8Ls6KZKvJSXo6tMKHgNWG5WLB4eEhi8VyknXNmDFjxowZ7wtKgQHzxIhcZlw2hpJxdy08IYlTcIWJhlZMKVBGWWLybGp/Q4qZFCVuNpdC07YcrA9YLdZY66QYr/oORo9A1y1o2xatlZisi8St5npiP3U/AM5ZjLOYJNKfvu8JOpGidEn0/YD3nq5taKp/oGmauqmVjXuMkVDlUSEMeC9rirFIGV0e/QowTkG04WI6sPfxrtON6cvVoLDnYVDT9Rs3/WXqr0gpo2J6YIIBoLWhacSgbaoEKqSIH4aaKjUmY+nqzxBCNJYjGmPI0b9tvaUWD5YinopRCqYqSRlTq5Q1dIsOYy1t2+LaRqRZOZNyQueMRq5P4xrW6zVKaXyOnG3OheBUc/ko3xrTrWbMmDFjxoz3BWcpTcapGkQyY8Y3iU0utMeKkp4cKd6VJBrjxlBTppK3nIrEtKaCRsuJfaxegK6l7Vq0MRIn2w8M3l8kGaGIKZH7Hmut9ERYU0/+y+QjyIghXArf5BRdNt2GlAoYqidDZFK7Xc+gZHPfNI2YyWvvh9aalEfCczGZkSnL+CzHgFclpXNKy2cP+TG+noxKSWPddOo/3ncp0qot3eGKokQKZYpMIcSjobBWSAUUchH/ipnauiPBy5RnjME1xqGmn99bxx4xmjCuu8bUovY2/cI66qciX2sWHd2iI5ciazWGohW6PjdrHSoUrNY0zrJerrCNow8BbQyLGOpr2xB9oN/tiCHS2NmnMWPGjBkz3geUJi8cdhXoZqIx4xGhLxrT84BP9arjShINAApoagu395ydnjH0Q914jxtbGHsxnHNopcVDEGtSlDG4Gm8LdTOeJQnJOjt1MYwn4Y1rMNbS+4FSDdlSqufxQ6AUPZmnJZ1Jvq/Ik8chJTE6a63qiXquJ/capcZcqAuyobV4PjAX3RGj9KjUj7fjgqCAtGpDoVwMZapPQyYmSheKUmg01li0qo+hqVMJRc418lddNJHnlGt3hqmeFTGAiy9E/pQSPyEi4+RkSrRiP6Y3M0aQlyLrGRvZtdFYPRJARxnJmhFvxpikpZUmxAGKPHutFU3jaBYt3aKl917IopXuj912i/cDzWL1yN+eM2bMmDHjyYXSirRsOFhvK9GYm8FnfHNIJTMUgw6FtzVGX2G8b6LxeKRPVXmNFl9Bv9txenbC2W6Djx60EAPnGpYKOteiMqQiTdJagbMWawympi6pnDHO0jiL1RqVCzFEUvVg+BDJRUlkbqNQKVFqP4TRGmsUg/f0fU9OaWq0TjESg5+M4N77muJkcE7hfSHqjE5cEI7pGmeUVhir0NaK5McnVE6oGg9rZERSuycKpaiJbBWV5fuM1KVgEBUVpYiDoih0LmgKxhaMKRgtpmmjtUjUcqKUjLUGrS0yzUgopbHWYK2YzY3W5AwlR3KRn5E4W1UnHlquLbVED6FEYzfGOMNBZWlCT5msMs44rNWg5RdxJI6Ns6ja3+GjGOp3/Y7GNdjUEHOk5MRy0XF4sGbb94RcWKyWHB5eo2mWKGWndK4H3mG/g/f5u/tl3h8ej9+xGTNmzJjxrjCGsLbcXm3oVEHPItwZjwB9sWh/2at4tHjPROMbSXQuAzknUgjshg1DHEg5EnIgRo+PktikUeSQ2J1vq3cgkZOYhskQvZS+aaVwyaFLIaeIBlL1ckQUg49kpeX7JTMMnuA9fvCkSl5iHgjDQAySYKWAHBP9difN21phtJATrTWNM8RGk5IihpG9jtMIZMKhClpTN/MQPaRKElKRiQNKX8xAlAY0JSMCL0OVOl30X9RBT/2LsaCKwiiN0wpnRRplK7EZrzPVbzG9D7SCksmjLKvG3e5H7wpkQpNzRmHQxsk0QjENbsbJ07jjvyggFGt4VmIAL0qRSpLXLQV0BGMNhYQqGWsV3bLDGAdGUXQhk9AG1usl3WoB2uLajsVyhTbjc5z/gZgxY8aMGe8dSilSpzlqd0jQfpl7NGZ809iUBju8l2TRq4MrKp1SkzRpu9tydn5GP/TS7K0Uu75n6HuC78k5YbXCGoPVF70OMY7N1XYyLOecp14IRaGkDFqjbUPKhd3Q0w8Dp2enteshk1IghZ6SM6fbsymlyqCrVCoTUiIGT4oJYxtKzgy9B1WwjaPJSvo1cjX/JKnu1gUw44l/jfC1GkoDJZBIlFwmmZh6YMNc6oRjTK6CUmSGMKZQMaZOaYUzFucsrrXVBC6xsaVIUaCYp6uXApkmGaUh5il5S2RQhVTElC1Ruhfm7lQjghsj04hSMiEIUaNe81EWpnT1r4P4b0KqvESaxrfbLduteGya1uGcpTENrtF1DYUQIoP2hBgoBdbrA1zbMQQJChh8eJuXZMaMGTNmzPiG0JpsoTNPTjrQjMtFpnCWF9he9oBPCq4o0WA6iU4pirl7GNjttgQ/1O/LxjunGiFbk6SUUoQQCCGgtZaNatNcTDvGU3+l0IBrWrTN9CFy5949zrcb+mHAaE3TOGIcOD2+x+b8HLSSbopC9SNI/4VE0SpSLmhlyKUQkzRkK62x1uGc9Fy4YjBRYm7zaDlXMrkoJU9JUONHrBvl/UjeC4wpU6PZfM8Ovu/30FpauCvpGtOi9m+rtZb1xNFTcZEWFSrR2J96yeb+wV8URWaoBnNrjPg9agdGGaVf1YBfciUbRVK4Ykz1OhisNbXxHZwzdG1H0ziK0mxPNww+QklQMjEGtNEsVwcs1mshUAnabsHBwSFN0zyS9+OMGTNmzPgAQSmyU7T6yUkHmnH5OE5LTF+gPDmHoFeWaOyX1skmvBBCoB/EqF1KIVUzdhh6YgikOsXYl/eM8bajaRkkIaptGxrrON9s6UNk0w8cn55zcHTIzVu3OTk55vjkmPPzU07u32O73dC0C7nvEMlZ4nFb51h0Hc45rG1x2pJzQikjxmWlMEZhnaoKHo3yGqUjKUchPyUTU5ned4UM1eeRKUJuqvJIVEf7hOLdr+Foyh7jZ611GMNENB4mKGMxoa+dHyllYkhT8/e+tGo/GeviNYMQxbcRQsRY8XUYpVHSRVjTsbKUMKKnVC+tNVqZ+no51usVTeNo24auk1hiKTpUxHRGvxvozYBWSpK9sng7usWSg2sLbtx+mmeffZZ20T2Kt+OMGTNmzPggQWuyVbQ6XvZKZjwhyGRe9Tewu7SXQHr1cSWJhkh0En4Y6PuBEMIk3Ykh4vtd9VfEujEOpBgmD8Ek5akTjJFk5JxrPG0ihsBpSty/f8rZdst2CDSLjk9+5jO4puHO3Tt87Y032Zyf1gZwzWazY+gHttstIQQa2wjJsIblYsnh4QFqYbHG0WiLtpqQIjnJybyUkSs55Q/QD1Lcl1Oq5EKatnNNkdJaoYuu8bDV0zFeI0Ypkn5bHO54DUeSJSTDYKyRro6pM6R2fqRECJ5h8PS7XW3nHk9xVE2ZUrU0L0/TDa0lyQrFNFnJUaYsPgSM0SJ5clZawuvPosTkbpCJh9yHpShLwZALDF4iazttQRm5bkazPjwkoaRXoyZSpSqJ8zGhjWF9cMDNWzfpFgtJKJtTCWfMmDFjxvuAUoqimVvBZzwy9CXyk3c/gjvuq9T9ycCVJBrA5KfYbM45OzubNvepnrqXKu8ZN9OKQqmn9Psn9WMZnKRBqUlKMyhFDIHjkxOOz845Pd+wPDjEGEcpIoPK9b7kpD6RsyKExG434IeBsgDrnKQhbQc2my3r9VpaqbsFSjtULhSVcc6IhEhpXMqYQVNyIqeIIlOKwVgHVeIFZYrRVUWhVJmmF2Mj+D7eqeBvnOjIJEJLt0au5msKqaZm9X3PdrulH4TUUQq6ThooEo87EpiUMqlkrLQLTm3mF3IoRQZSjJSQ0YNE0LaNm3pLtNbyWpHJIZGL9IjYgpQKyrPHuqb6PhBiYgw6J5q25fDwCKOl9CamJA3xKRHqdENrizaW2Qg+Y8aMGTN+JxCi8eScPM+4PKSSOcmJX/utZ/nMvTvE2Qx++bhIRJIT92FMe6qTjVRP4aUHQzb8ppq/U0oTsRi9CKPvQE7rY22kjuyGgZOzM842W7IybLZbXOPQpk4XCiht2PUbKIaYCzEXUpaoWdSFB2G76+l7z673XD86YnmwBCVxuVopcskYrXCuQSsIgyZFTTZOpi1NQ0hRujdqWJIutXKvAKpMPpSRbIzPa3yu4/cu+it4YMoRa3V4TgkfAn4Y2Gy39H0/dWEordHGUKoJXSsjiVgxEXMipgha4VS9jZbY3hIKRQtpyEXa2xXSUJ5zwVolBKUmf4VYMNqS6vW0KRNzxgdNLtC0LSlNsxtJwFJKXmtjScEzxC0oTUyFYfBsdz1dP+BDIMuTf2TvyXdLiXhUsbczZsyYMeMxgZaJhuHJOXmecbn4SlzSvdJSTs8ueymPFFeSaKhaGqe0nk7LQwjsdjt88FBP5il1otE00hmB+BrGlKSRUKh6Gi73fRGtmksh5kzfD+z6AdsMHJ+eslwuAF03sElSk5QiRdlkl1LQxuKaBmsduVbJpwwhRs7OzgghsPZr1gdrDg7XGKMIPqB0LbVTMhAwRmOVljZsa1BWJg/DYBi8h5RkI18UukDmwsiu9qJnH97sPrwpFk8LqCKG7BgSQz+w63v6fiClapI3ZorUVVBJRC3wKyJ9SilhbX1r1eQorRSiQKxFfEUSFqTrXD7PGUJJkCXxq+SCUQmto5jfrcVUM7wPQchKLix3PU3jMM7hmgUxFkoW3ay1Dq0NBeiHgfPNlm4tMqoyvd5vJwjvlxw8SVF0M2bMmDHj60ApsJa4UFx3W5r5MGnGN4lI4kv+WZZvFEo/XPZyHimuJNG4kLuI+3mU8ZQip9bDdlMlRwVbfQDOmLrZpZqZLyLpHpYViU9Ay06/KGLOhBA53+544403efa550T37z1KG5TOGGMZ/DB5QIw1uMZVeVE1bBclvQ85c77ZsPU9sWSW6xVd1zB6IlQpaF2wRpGMRmtp3E5TQZ70P4QYZZqhqnRKC9moT2q6Uu+0ad6fYoy+Cvl6qcb6KNOX7Y4Q4iSvUowfY/N6eeB+xuc/fm0kOaX6SHL9M0lJhkTYatGApSIEI5dYmR5ECkrVtK2JGELvA4P37AbParVitVyyWC5oY2E7DKiSaRuHNrZ6OjybzQ7tNhx6T4pZpiHlQnI244OHV/7t38cv/JP/LjCHAsyYMeO9Q3Udww34cPsWrdJzYd+MbwqpFF71N3HnBdKTlWR2JYnGxUYW6XaoZu6+79lszhk2O3KOOKspzuKMxnYdrqZLpZQe6M6IMU7JU+N/a21IGSEy9UR8u9vxlVdfZX14hHOuJkY5nDWceY/SoIxCW43VBqULhSR9GjmDzmilIWtSCPT9wPHxMeuDNctlS7toiN5TcsY5jXOGlDJGmbrJVyhbE5hMLw3j+qKsbzTEj1V8Y8Tuvgn8na7lNN3Rakrw8iEw+IHey4RImeq3qHG0Qjwkene8bjnnSiQKo0uksFfoVzIpi8RJpGVFuk2UIoFMMnKaJFraaHQxY30hOWu0KhQFw+Drc1XyHkiFEBOuC6Cga1q0NTIZqu8VgLZtWa/XdF1XDeyqzlRmfNCg2pZ4lFnrmWTMmDHjfUBpysGS4anEC+4ubq7qm/FNIpP5cn+D5jxLh9sThCtJNGCMWg34IA3dMcYpOUpp2XyOXo1SfQcPRuJqmqZ5IO52vF+oUqKYL2JbtSGFyOuvf4314SEvvvgC3WLJ+dkpqkiXhLUGpcRfoZQQDhQoAyqpah5Pddcr05jNtuf4+D43bhyybjuiku9NfRlayILRYhbHWmIco3lBG+nlUMSpnE8IgELVcj54+1Rjf4ozki9VTdc5Z3zwDN4/0Cui9EMfe7KjInf6NlJTSpkUrKM8KpcLUlRfjYkIyH0YkbNpgy4GSr3PalJnDMVCEUJku93hfcBtHa5t6NarOr0qGI2UNbYK2zhW6wMODo5ou07M4+8inZrx5OPNP/29/OYf/yuXvYwZM2ZcMShjiNeWuFs7buodRlkpsJ0x43eIUDJf2VzHbtIT1aEBV5hoxBjZ7fopbnWMfKVIolEMAbKBokkpT/GqFylL7+xTKAWZAmhDQTosCtItoZTi9OyUL//2b6OUYrfr2e52mFrUp5WiaRu01WJ0VopCniJUtdXkGqmLBqMcg/dstzv6oWfRWVISQ/vYfq6VwmiNdU6mKNbgfaBtG/reEUavgZZywExBKWkRV+gHNv37iVMPPucyEY1cIFVzfQhjP8Z4WvNgTO54DfdJy77Bfrz91MNRCrmoOv0oIzvZIxHCyoTu1O9pU5/LXrKWKtPrISRRJlIhRpZK46I0f2ulMI1FG1PXKqlY3gd2/Y4QAm3rHtVbcsYVgrl9m/vf9WSNp2fMmPHtgXKWcOi4fXSPpUpo5uLXGd8cAoVz39KGJ4tkwBUiGvuRrKUU6cgIntAP9JsN27NThu0Gv9sx1B4NrEPhxOSsDU3bQIGYEtpoUo6E6KukR4zF1N6HVIp4BVTBGFVlSIoYEvfv3CP5SOMMwWeUs2jj0AZc26CiphRfpUYWkLK9ggKthK0mMXCrYuh3ns3ZluWirRMYKWtJJJRVNfYWnDWYxmEUDL0YoxUilDJa5Fo5F5E3VR/FeM2+3nWdphb19oOP+BBr67jC1EQnVT0Wsu7xPnWVXIGx4NCkFFHayvOtvEZETtIOrnJGlYRSGqNAI+V8SishZYxES2EMMtXRQjpUnWqMnR0lj3cs1ynnQuwD27Ijdw22TnxSzsQU8cHjh4EUE/qBOOBvL2bz+OVi970v8cof/Q8vexkzZsy4glDW4g8Nz65OcbM14z0jlTxPft4FqRRC1rRPHs94/InGfoxtKQVjjHQ77HYM2x3DbsvufEN/fkbyPSoFVIqoIh0Q4rWQDX/bLck5kkpiWZbEHPExUKrUqCBpSgVFCDtiDmhbaDuDsxo/ZFQu+F3PeVG0bYuxhkDGOMvB0QptNP2uJ6aCcw1duyCnTIwbUgoUxPuRI+SoKNGwDQNnp1uuXT+QhuxSiCnKdMJZitLElGkVNEahi8EooGTJ8C7SMZGVJmtkfFKqQktdXMd3m2ZcfF2RYsGHSIhZ/AtKk1GQpQQQJc3dhYtWdUWhqCLTB6OnOFqZPIyRupI3bkjkEtFFCJzRMmrQ0+ucxPuiNdpolMloA9ZadCV8Y9+HyuP9qouJT9GUrCi1Sd0Yi3ONJIA1jcjRjDS2KyBHSe163Hx8cyTujBkzZjyGUAoax3CkeW5xMidOvUekKgeaycY7IwEp6wtD6ROEx55o7GOU+MQkreC73Zah74l+oJSM1ZpoFLpOJKhSG6UlHjbGhNZgncEl2ZhqLSfwMUZSGv0Z+691wVrLYtERYib3nhjBD56SC7ZxWGvQRRFjwZQxzUhmDRRdE44gFdmwpwA5SYFdzmMxXiKGSNNJfKyxBtCkJNMJ21i6tsVqRSoiC7JGzM4pF6mrL6McSbwQ73YN9+NuJ0+FUsSYGHxgGKT5W/weF9ej1L4LibrVaH3RSyLXS+OcnsiFcxZjbDWXe8IgLefUpKf9/f04WbmQWWXpxUAJYTEKY2TqUh4oKFR77eYWpxucbTDWTATHWEvbdTT19dZGggBM9YLM+GDBPv8cn/w//+JlL2PGjBlXFKptCQcSbTtvmb8x0kOeg5lsvB0ZiEnzJPY/PvZEY5T1jJvjyV+RMzGIATymSE6JkqWRerlaEGJCa4t1DcbqWgqXsVajkmyqQwiE4BmGAe89KRWMAYVEyupiAEmd6tqOmBRg2e4GISY505RCzhoV4I03PItFK8qolAhDYktPThk/SJyuKpoUEjlTjdoKrUTeMwwDxrYYa3FOYXTBDwnZwDvatkWR6XspG5TNtIYSL1KfqC3c9c36sDH7nf4cISWH4R0jah8u9xs/L0WidseN/mKxEJN1bWGPtVk89+KN2G9sHwnJO01bGAv49nwcMj1R5Mxkei/lwZhe01iMNpSS8T6x2WwwjUEZjXXN9PxApmNKv9Njz3iSURYtf/G5n73sZcyYMeMqQmmwhmzBqdnnNePRIWYtaTlPGB57ovFwUtT4USjEGIRoRPkYhoEUfT3tt7i2q4VtWiQ5VGOzgpQiqRqzx3hX2QNXIoNsZkOdNAA442gb8OHi50IM5KKBQglQUg1MzaWSAJle5BArsRCCRIGsCjknmtZijMH7hHWRlVtgjCWrTEoKhaVpGpqmIadKWKCmSqkHiMCI/Qbwh6/l/tf3iUROuYZhqTrpSRdG8T0ycPHzpU4W5HvjhMgYQwgB7z273Y7dbldfm4jTZiIN1to94/iDSVjiGeFi+jHJtMSEHmOafmb8nkQUS3kjSSRXY2fKKLsbCxu3my273Y6lXkh074wZM2bMmPFeoBTloUTb+ZR+xu8UqWRSQRQsT1i0LVwBogF78h6YolhTkkQopQrGalzj8Ebjh8gQI8Y4jLXgRLqTazpRSpaUMjEKWRg3qVqr6etQiCnQ+wHfB0JIxMB0+8ZJwkQIgZTFvC1xspqhryfmBZx1WG2rpwAKkraUQqzPJ6O1YbFY0DYdfT9QShLvhxHplbMOaxoWiw7rLKGkSQqWS67ao4ejay++tl+YN/73w5/LdEaSmnK5mFyM0bf797E/0Qghi/G7fm+ciAA1tSpMkcNAvc5mamg3xjxAiPZfZ4m+vTCZTNMs1ERySqHGAJuJgA5+oKSMbQ0L2+IaR9s0LFdLlssli8WC1WrNar3COTsVG86YMWPGjBnvCVpTKqfISAeCnrs03jNmQvZ2ZCrRqP1wTxIee6IxymvGzepYzuf9QAieXJIkLlmLdQZjTTUU59qaLV4AIRQWaw0h6mmDaq1MC4wZGIZEzoGcIESPj0GmHEUMOiXLHMFaV83jhewr4SmKmDLFMMmDVE5EMrpGxgL1fi5O19u2YblYorXhfHPOMPQsV0usbeSxnKNrF1WSpIn1pL880OZdN+h7yVzwILnY92SMX9sv2ktJiEaqpXsPY/JO7E1Oxocc79PXPpMYZQIkvpd08Tpqjamv5zjNeKepy0V0bk2rmiRzYk5XOde2dCEb42MqpShJIm9b1bBaLWmaBuvc5M9QSrFYdBwcHNC07WNnAh/xbnKu2VMyY8aMGZcHpRVllk7NeMRIKFI0kMNlL+WR47EnGg9vli9SjC66p3PJlJKwVtN1DVaYBU1j5dRaX5TWKSVFfYvFEu89fd9PJ+I5Z7yPxChlgH3w+JiYqi1qXKvWGqsshQZUIaUok4qYxShd94gpy2QjyxMACkbp6hewONfSLbrayTGglaVxRjbLMeNcS+Na2rbDNQ26yF9qMSVCjOQkE5JxKhOreaFUudG7lfQBk2xsJAUpy5Qo7W38x+u9TwZGOdXF63Fx//tkcH+Ksi9vctY9OLV4iGSMtxdvjBjejRFpmXNOpkOpTK9lKRdTrpwzJaqaNCaN7G3bYq2p/hxbr/WOzWbLen2Itu//FOqdNvuzz2PGjBkzPiCwhtwUWh1mM/h7gFH6bYbw94Ov97NPynSkL4YUNCrmJ65C+EoQjX2PBkhkqRS2iQTHGoNzFpVbrFFEa8mAbSzGaGllqLd1jcOhiTGw220ngiH3azCmiPzKB2LMhJCJcSz808RUQI8G8QVN0xBTIKWI94mcZNpQSkEjHRcjIdJK0Tgnj6MNStf1FUly6rqOxaLFmAalzLS5btsGaw3Jx4uErCp3yjmJcd0YTM6U9HaJ1MPYl0VN1zk/SDD2pUwPezv2Jylfb4M93sd+KpQx5h3X9yDJqGuspNIYMcM750gpE4h78qkLj4rIvLT0jVQviNYaax3ONazXaw4PD9FaXv+U0u+IaMy4wtCGL/6ZZy57FTNmzLiqUJriDNnNE433g98J2Rhv//DWW+9JEcbbPArC8Sjv6/0iFE3xBhVnj8alY/JqlCKRtkaK+HJu0CXiSyJrOXVXiERHGYcdTch70atN0+Dqxv/CfAylSNysH4vrYt28I2bjnBXWGpzTGG0xVpOywRlJShJDhkwXapcclIzRmq5rMcZQMsQkHRC6Sr/k5L7BaEdOcnI/nuYrJe3jKaZJmlWybLC1Mpg6MSgUcu2ZeNhTsY+H42TFa1Lb7/YmH+M1f9jXId9/ZzKzTwxHciFG7oupx9dLwJpISCn1dS4PkIn92+Sa8DVONMjyumtzMfmy1rJcLjg8POT27VusD66xXh9g7ZV7+8/4JqG7lt/4U3/lspcxY8aMqwqthGi0hZUeMJJTedmreuKQSp4IRiaTSsFM+xJd///thAPeP1H4ZqYtjwKZQl8symvZGD5huBI7rXHDOEpyUoporVksFpQUIEdUifQpkFMgRS1xt2RKLbVTem+TrGUq0rYt6/W6bmAV3meGIdaY1iWFgZQHOf2OmVziNNHIOQlRMAqlx4mJptEWa8wUwVtKqgRGonVb50BJt4bSCqVETiSeBYM10jIOGWMsXdfRda08bz9A9ZvYyXOgRyZzIWXa6734etcUHpQ7vS1gdm+6MEqo4OH0qrf/UjycBDVOonLOxId+oR+Wxu1PU0aykVLCe18fr5b1vcOkpZSCNZZCqSSsMPQDZ2enLJYLvPcYY1mtViwWXU0vmzFjxowZM94blFLEhSOvEge6n8pmZ3xjvFcCcDHJGEv+Chnp1tJoUOM+4sH7G4nHZROH3wn64lBejTr9JwqPPdF4uAdiNC1rrWi7hhxbUmiIwREHS3YORaH3gYIQghwDuk4xpHuiPLAZXiwWXLumiEF8FjK10DhXMCGhVSLtleCpGo8bYwJdy+S0lPN1TmGcmM5LNZBrRZ2AWJyVyNuUCoOXKUnbtsjvn0JrK/eFomlaurbFGItSBWvNRJAWXVdlWxmKIZLEoyFX6gE/xjtd01Ey9uBGnSmF6WGSMU199pKoShGC9k4Ybz8SlJyzTGLSQwb2h9Y1Sq0u4nOFUIz9F0pptBrXolEqP/DzzokPAyUej1xTtXa7LScnJ5yfn3P9+i1ACAt6NljPmDFjxoz3CGOIK4tbew70DvO4JopccYxHnyPJSNOx4AXhSKS9KQfsEw99xV6Xvji0V6iYRBHzBOGxJxoPQ6mL7oiUkkwuRoO2s6RkySlW46/BOAdTOZyucinR5Q9DXyckCa1E1tR1HbudJ8SBvu9rS3ZkbKA2KCJRYlZRIrNKiZw1ZFAlQ0k0TiRD2ojMarloaVsnaVEpo/Xo3HBCGGIi51hjWg2oQtO42vsgHpCpBdu5SfZlQ8TZVnwkQCmhvkfHX8qLiNjx+sFFmtfeld37mYvb7hOCfSIgk5AxHeqd5VP78qzxQ7/DbR/uz5imIVqLP6NOfKD+/u0t+8KbIfcbY8RosE6M4o1zXL92jes3buCcm5KxxmtZ1BijO2PGjBkzZnx9KGMIa83BesdKD2g0GvXEmJIfJ4wkI5QLopFrf1hC1BS5lva+nXh849fjncjIZb2OfW7QgXmicRnYl9SMp/DSpwA5ZjnRB4yzLJYLnFEMRmEGT8rSswBgtMIY8WyIFAlC8FOhm/fnaF1oWkPbGXpvcLHBRpkUjJ6HXORzVPUAIGWAJWW0Ah8CJRdScTjrcEVjtCJliLmQvHRLyM8L6ZHm8kgKkshtrME5w2K5pGlbQhiIsaC0wjqFNTIlcUazaKQxPIQkBvEUZT1kJJGrgDK14G/PCF6ypHHlWtKHkJtCeduv3j5R2CcdEm1btVpctJHDGOf7sIdDkZUUDRYZOqBr67fSlWCMfxqwVk8m8qZpKAVSypCp8qi3T2RiCaAKMSn6YWAInqIMi8WKxWKJNk5ew5IoJCpdevRv3Ieu3+N4XzNmzJgx433CWfxac2u5ZaXCTDC+Rcj1f/IvdSHUf/uy4oEDy1z//dY17jMXNZEO4KGJh2D01IxTk8dh+mHUk0cwRjz2ROOiP+EiEjXGSPCRfrfj7OyMFD1GSzN14yzWWSin7LY7KIXFYoFrLSl5tttzYhym3oXlckmMsUbd7mhag2sVxhWUVWinUFlR4qj7R8xgiPG7ZE0utm7Sg/xqKA1Kkyn4mACNMoFUzeEYN6VOWWcwRiRT1kE/bHG9pm0PaZsGay0h+Gpkh+VqSb/bkZNn2ToO2g6UYpMHgtVErYm5dohoIBWUymhVG9W1Bi3PSaksREkVigbpW3/QQL6fDvWwf0K+PvpnHkyjkujZMYKYi5+nkoRSZPpjTPXMqEoywBhwVtO1UrbnbIMzDUopApGYIymKzK2kXP/SUKRSfS9GYaz4QxbdCq0MISYWywNu3r7F6vAA21hSSVAUyrxz8tQsqZoxY8aMGROUAmvxh4rbi3OWOgLNZa/qiYRMispEGEYkOeGc/nsiC6VgUGTKRDpM3aeN9zehbuofJxP/DXNOuJYpyw4xFT85pvDHnmg8fIKbUsIHz3a7YXO+wQ8DxlQjtjNoXVBkBmfRKFKMhGGoG28jt2vkL4axQ2JMRxqjZFfLJT5kfOrxafQ+iBG8qEIKo9hG5gRjyzdFigCVNoCWYUHJGJ3JqcjVVrIGMX/b+rnBuoYYPbvdlmHosfY6xmhSijUedgkk/JAxRrNcdjhtSTETQ0aXgpoidamRvgqMFOWpcZ1qb+tf+0ZKGhvG372/bj/WFnhArvRwItQ4fdrHJI3S1eSd5S+DadLC2PKtaa2lbRratq1EQ+KMZaqUxXdTP4T4iMRKa41xGrMfpasUKI3RDl3/WyRt8jrMA4IZM2bMmPFeoZoGfwBPt6d0qjxWm9UnEQbFWGGXAEMlG3sYvz5aRseJR97b0YzkA4Bi3nHScVnTKY3imu5RR5687lDGUPJMNL5teCCBiFrOFsWXYaylaVqc07SNlnjZ6Ikpym5ayyQixEhRiqajtnG3k88gxkhTN7WjTMc5S+MczgWcNYRq7EZlVMrEyLRblzQrACU6TW3Q2sjEI0nEbTbjRlxO7vWYNOWaaT3WaYah4L2p8i5JRUox1dN5XTs6arqSa3C6IfrEJu3IdaowGbqnDXj9ZSo10akKhVRNr9K6vC2h6v14Lva/924/v08y1GhOUQWlinCdet2skQlPUz0o4rMRslYDqGSyY80kzaq+7/oeUTWVKk+v7TAM1ZchkrVSyiS9k3Ur5sHFjBkzHgXu/ODA7R99mvi1Ny57KTO+RSitIx4UnmrOcPVrs3zqWwOjZCIhZKOawx+6TQZSgQAYygO0z+wpKiSGuEIBxaAfo3/7D1TkxvUN/uiA1llK8Je9pEeGx55owDtvXMdo2sYZ0fObAknkSSVnjDEyuci1STsEgvcMw8BisXjAdOzqxrZpGoZhqBMEg7MGZzWN1VAkslYBzuZqbZCN7TjdUNlIEtI4zcgXG2CJnBVJkTYGaxs5rW+kjC+mQM6Zpmk4ODioa5RNs8FUqZd4RFIusjG3DqMTm82OGBMhpurJqDOCB4zcIi8aI10VBa2ZiMbDJXzvVPj3QMFfeVBiNf7su31tksAxxgGLoUtVsnCRynUxjbDGiB9Fj2tHygm1nlrRL9Yk6zX6ohAwpcQwDAx9jw8XROOB4sB38KTMmDFjxu8Ev/nP/nU+2f0pwp2X+fj/7GfGU6gZTxjK/I/GtxwaRaZKqJTIosbfp5FsjCRj3AXUIMkJ04QDxpNKNEI6xonG6M+4TLJolOaa1vzep7/MT7/8vTz1iyvY7Z6Yvz8ee6LxsF9Aa41rHItFRw6rWpoHEEm+ULKl7ToxGYu3muI9RUGMgc3mnLZtWC6XOGcJQUhF17Ws10tyTqQscikfs5iPS0arQIhSsFdyIdXpQcpjeeDFpnqcEChAK13/VpI0KWMsbbOg6zpsTabyPrDdnZNSnNZmrZWNdLlIVVK6UNBoa6WUDiPSqZRlIx2DGNWr07ogLGjfwM3YucF+zO3bidz+Nd//2sOTiwfu+6Gf3b8/+WD6sLrKzAClikjIjKZxlsaK18Y5i9EXfR8PmNL1w90eMMbhosT037YtTdOwWCw4WK9ZLBbT1y56On4n78oZVxV3/4vnL3sJM55w/Pof/BsAfN9H/wSvv3XEx/5Hn73kFc14lFApY3rFvbiiL2OZ3Jw69SgxtoiPnRm5KBwSgiOhmuVBsoGqr0GpMqr6D/voz9gjHGPB4pgWNj7eZWOpHT90/Rf5r77zu7n9s7dQp6eUYbjsZT0SXBmiMZb1jZMKa109Fdeoqr0rNXK1aRpKSPgaj9o0DUVBUaVG1vYsl0vatplSmkaJzii7ybmQkqqTiTyG2cpkwmpJkEqS7EQqZEaSMU4EpAlcaTPFp2qtpRtj0bFcLMlkvPdsNuecb85QSqJwH54q5JxxzhHiQKZgjCEliDGx2/UM3hOi9IukIoZr1IMt3uOmXr5VSQEPRsuOw5B3kz7tf/3rTT4ebhXf+w5SoKjrdEKjFZOEamw3b1pL2zY0jUNNcq2L+9PvMO8UK8bFmsaSv6Hv6YeBlDMxBJlueE/btjJleZxmpzO+5fjR7/xPgOVlL2PGBwA/8Z0/wkne8X/73O952/c++0PPE1//2iWsasY3jZwxPdzzK/py+RvUJxlCBMQUrtV4MDhtVKZJRqivgyROFRJCOqhSqgw0SmFQOKUf8Gc8DiQDwGL4jLvDJ77jVU4+/QLXXl2RvH8iphrvmWi804by24F3kujEGNluNwTvUdVArCiopiXnSNyNp/tCPErOxJLAKJrWkUsiZY91LcvVQlJeS8RYhTZgrKJtHMtFSwyRkiIyGhECoXCoECXrqCRKSSKPUraSHllvSiJ3cs6xXC45OFhXP0hD0zbEFDk7O+Ps7JzzzTnSBi4n9W3bAoV+iJOPRCmNcy3WDJz3W/ptT7/p2fWeXApaGyE/SouXo8qSSpWTlToBmFKftEbV2aKU5OXJYL1v9oYLorcfN7w/5diffDx8u1GiprV4MdQoVypV4mYd1mjaxmGMkm6UlFA4KT5EY7Ii+ESMsb6uWSJ8Van3beS1FjpISomzszP6YUBbw/WbN7h+4waD9/R9z2KxoGmaqV9lmhrtGdlHwgKjn0Vf2u/BjBkzrh6O9IK/8NQvv+3rP/OTv8D/4RN/4InSYX9gkAsqw5ANGUUqBaukjfpx2bQ+CRinGvK5kr6Mqe5LyMa+KTyhMMihr35IqjCG5Gj14DTjcXq9jNLcNpZ/6bmf5S/8Ey9w9GtPo843lCeAbLwvonGZ2D9BHxuirbX4MBC8p5SAzhE/eDabDf1my7AbiCFWMzgYJ+V33vf0fTNNSIw1U1dD13XTcw0+0jWW0DlidMTgiVHe6TFmiioYAzkrrNIUZcQjAZQsiU6LRcut69e5ceMaXduilKZtG5SG0Af6vp8+SkmsVss6dRlQCrwfRC7mHNrIRne769lstvjeE0IkxEBMhVykq2M0hqP2YmmpHgulq7KqTj5q4KzIpy5e431Z0uiDeCfS906v04iHb19KpmQhBsaMZXwWZwx2TN+yFlNTpIxRoAo5CcEIIU6RuVqPkw0hALYmSkkbfKnlhUIMY0oSCZzzJJ0afRopZeKUXiVrHdPIhMAICZFSxvyOPpQZM2bMeD/4Pa3jr3zx7/FnP/OHyZvNZS9nxnuF0pSDJbtnMp9avcGBThj12AtDrjTGqYar/RlZZfTUEQYytbiwiBs1SqSEoIxyKac0TpkHJFOPG1rl+P2LV/jef+oLfOkLn+CZe7dJb7515cnGlfoNGU/aU0qEIMbuwffk6EEFTEnS8l2N0tllkQkpTa6n3zlHYgzEGMg54pyU47WtI0aZiJQi7d0qKxpnaVtH1za0zrDZygRBlSIleVlhtQJjiVkTxshco2kax+HhAYfXDmjbBmvHaFUIfmCzOafv+7qJTXVzbQghsNv1OGfIWdKZQgy0tqX3nvPtlj54cs74GAhRnnesm+Esai4U5WJEWActuUqXUBry2H9RHkhxGo3b4/V+JynUw1Kq/dfonSD3K10ZTSOkrjFCLJy1WCOvg9GaUjLWaNSelEvWl+vEwdQEL41SUuCoja7+ExmcjoRGUskcxojJ3NbHVEpNBEqmORcTmLE9PKUkBK+SjZlgzJgx41Hhw27N/+Rzn+UvffwTl72UGe8RSivi9SX2uS0f677Gsp6Qw+MjwXmScOHVkH/bURKOs084NJlGSeTtSCygejGqXEqj6/ceL1/Gw9AonjaaP/3MT/Dn/jvPcHzvea79rCa/dZe86+GKRt5eKaIB+zIdhbEWVyyRRIzS8p2qzMhpTesahr62fydPHCvr92RB46n1/rQkhECKAVVajFZYo3HGYLTCNQ636VHA4AM+RGJKMs1IYGqMrLOG1WrJwcGKprGTJKtpJL1q2PX0/U4eKyWMsaxWC9brNdZa5NRe1820bOp9iAxeZFLOOXz0eD8SDUmjynv+5otIWBhTmUo1mCsDKe97UrL4Surj7U80xuv+9Tba72QGnyZGdXOvNSwaK+TOGJy1tI2jaRzOOazWkrSVJD84p4jW5kLOZCXeN0YhZjBKmuT5jalaKhf0WOBYpxcg/pzTszM2m82UPCaJW2kqcBwJSEqJvu+n+GGRsj3oe5kxY8aMbwY3zTnlD3w36h9/7rKXMuO9QGn62w0ffepVXrT36JR57CQ4TxreRjZACvdKdV8oLXuimmQJF4Zv+fnHn2CMMEqzpOG7mrv8Lz/zY/zbf/SHiIvnufG5JeYrb5DPN5QYrtx048oRDWAyTSeliCHS9ztSHCgxUEpC70WW5iKbxpQSiYTSihBEsjSebFtrKXXzvlgsyClByqhcKCmRkiUlR4wdQE2bSiLfUYo+SKyaUxpjHMYaGmdZLNv60dDVGFtrDX7w9MMOHwZylhP55WLBwcEBy+Vy2uTnLFIjtJCj6D0xCimxJrGNO4bBEytZGdOvshq1jGoiZaUuPOVUN8nSrL0fWTti34cwTjcejrbdxxgVu/+zxlwQhFEqZY1IwIxm6rEYX0+NECKjNc64iQymGOukJk3XPURPThdRt+Pjan1BbvYnFBJhLH6McVIxPreqxKKUQghh+vqIGGNNALt4/jPJmDFjxqPAH+g0f/I/+q/4j//cH6P92z972cuZ8Q2gnGXzlOH7rr3KbbPDqfax3rg+KZiucRn/HdY1gUpJOZ/av+0YW3vxulwFkjHCKcMN0/KDyy9y73v+If+B+YP4gxs89fMN5pWvkY9PrpyU6koRjXHjqrWm7weOj4/Zbs7wfosi4Yzo83LJ5Bjxex0KqII2Y4JVqmlPG1KKNE07afa7roNS0KgawVq1/7mInEpJgUwuWQrmrMf0A7FkXC3qc9ZhrciujFFyYt9YtBKD+DDs6IcdJRfpjnCWtmtYr1dVppPqpleM21YZauAVSol5uR8CZ2cb+n4gRonjTSkjAihNpky/gPulh6WMcbwSi1vKhWE7p9GvIWla8ljqoQnHhdldyYsipwijMTznB2xY+5MO6c0QQ5bRGlNfSz3qLCspsEaITUyR4APej4TMTPel1OjT0NN6dCVQ2lwoMEvJQmCsYblccHR4yGKxAMQwLteDaYIxTjHGpniRXT1IXGbMmDHjUeFfPXyTN/4v/4C/7X6Axd/6mctezoyvA2Utu6cVn1q8xoFWD2xmZ3zr8TDh0JgHG78rHvZgXAWCsQ+L4bax/ODq17jz6QN+hO8muzVPtc/TfNGS7x+T++HKSKneN9F4Jw3+t3rzte8HGE/IAVJMU+O21oWYMrlkjIYQAj56YpbkJ2U0yoKq/Q1aASWTU6Rki9Hiw0DtSaWMEVKRpUE65YwPkbZ1lNxhlGySrYFYJFINLVMLBVLMZxTOavFbpCz+kOoj6ToFRUrsVqsF7ULkOTmP1eOqerUNpRgpu8uG4BPbTc92O+BjImZIRZEREjRunlFK/OBFxrtFSfpUylnifmsql64lelpJRc7DXRVjT07JNX0KUFWTNRIHpRVjmNzDkwqhAmOvSMYaQ+NM3ciP5YhCuKwRiVWJhRxH/4hMdrRW9bVXRJWmmN6RcCglWRO5jIHCBaWFZPhhYLPZiLyuelnGtCoFIkXzHqVUJSAPJm6FEKb2+H3p1NeL9p0xY8aM94L/zc0v8Fe/75/ho3/rslcy4+uicfTPJF5u7rCssqkZ3348QBzK21UZb7vNFYNRmhbHS7bnjx79Asvf5fmby+/iqwfXuX3wIVa/scC8eadKqeJlL/cb4j0TjYfNvw8XuX07MBb2jZOH1fpgMhnnPFCyJ8VAClE26zrjGoPCksmE6CGBIpOtIWtFTpqcIil6YqhFb43DaGn3DiERUyGmgnUymUCVmusMSmWsEZJTlME20u9RMihj6NoGZw3WWHzyxCiawm6xAFc9EzmxXDhsU0/sa4qSc46UCkpZSjFSFDiA32XCUEhZkYuuWdJCcshZpgpFxopkTRnN36pQyFMbttHSawFFpgtKU7IWsqJKJWgy2Sg1yUkBar/gT8K1ak+HoujqE8l5SobSFHQBo2TWIo9tsVZImLUKY7VMnLTEBPooTd6ShqWn0LpaiCKGdmr5nxVSWECKFFOaTO/GarquoVu05JQ432zo+57VajX5R0aMcbcj0SilMAwDIQTGNvqu68QDVE3il53GNmPGjBkzvg1QCtW22Js7buodTtkrvZl9UvCkvgZOGQ50w8fdjtXhZ3n6Eyf8rcPv5jee/hDXn3uKG79+iPv8V0lv3X3sJxtXTjoFowHYgNLih2ga8SrU5CSR70hhnm1s3TxGcomkJIlSfT8QQsT7oaYMiZymlDHNSLNYrshZkZIYkIdhYLlc4r2vcbiO5bKTYrjBE1LB1dbpnAGtadoGVzX+3gdSyTRNS+tackjkGKFk2q6VPo+cadp2SqCS/gyDVobtZkvoPb4XE3gMiRhGE3ipPR+j4bueuuuCqnIpxSibEvJgTJ01lJrmVEsHsyokGT8ATN0aWiuscehaYlhKoahxmnAhrzJAqhG0qhbyifYrI0PN8b70RBJkLSItCyEwDCIJS7GQU5V8VZP3Psm11lazN5Opner1GM30xhiuXTvi9lO3uXHjBovFYkqSyiWQUp5M+ftRtuP1b9tWpFwx1tjhC1/PLKmaMWPGo8CP/vf/7/zL/+2f5/p//FOXvZQZ74acSdF849vNmPEIYDGsleNDNvAHl1/k2vNb/u7yd/GPbn6U7TMrntcfovn5nnR29lh7Nq4E0Xh4emK0IefMyekpu+0pWoMfevywo7EKVQolS0KRqpG3Siuapp02kfvJU1pHjo+POT09rcV6B3TtkpzkNovFAuccXddxenrK+fk52+0WkFPwxWKB94HeB0xtLM9ZWiuNEe9HTGL6dtaJJKnUdGdnUQoWXUfKsZqPDbZKitq2wxhHvxs4PT3l+PgeJycn7DabB03gyPMtuW7EyzjV0PW6qSl9Sp6zmqRDE5FAJgW6FKQQ7yHDttYiF9MyKWHsPFfjfcv9iqrKPJRAVeoGXU9pUG3bTh4IuJAoXWz60/S7s18SOEYAj+8N6VVRk7F+jOiV6VRTiYxltVrJa9t10+PG+hqPZGlMHRuLAcfv5ZwZhotOk4eTp94t7nfGjBkz3gs+6tb4QzW1Hs94zFDkEDOfO/oyk40Z33oYpXEYlsBzJtJ1X+HGrXM+sXqDH3/m43ypfYmP3Hse/YUvk3e7x/bvjStFNODiFLvtJAFq1w80VjbBjWsAKe1LKVJiqmRDpDnGXcTYTqV8IbDb7ei6jsViQYyRu3fvojhhvT6k62TScX4unRdKqSkZKoQwpRWVUliuD2pvxxgdizRVTyZ2Rd97tv2OEjML19I2Hdoo2rbBB9m6XxASXTfSmTt37/LG177GnTff5P79YwYvXo9c9rwcUyNmTZqSEQaZJOFuWlVNac2V1lKKl5VMOgwKVWRTrZFc6rFBfDRyl5xRytRUqfGhH/IpIGRwXH/e81eMRuvRfD8arcfN/RQvnOS1Gwv5Hn7dnHMT6RjldK0xUs7n/QXp0JoYA2dnp2y322laInG7IgnzXiKQR1IBTBOLYRiAC2/QPinabxOfScaMGTO+WXzuf/uX+YFf/zM0P/pzl72UGe+EGDFnhr5Y4PGWq8x4MqBRtMpKTK+KLNV9nrenfNfit/nPfuD38ct3PsPzfUB/+VXyMDyWZOOxJxrjSfW++VYbw8HBIddv3GS7Ocf7DYpETomSk1TVF4VSovvXNT2JMp54azFCK4VzslvOuXB+fi5JUtWkPPhBjNO5djK0LbkUUs5CTJZLAIKX8kBlLDlJ+pMaT+OVbHZDTIQY2G63nJ6eYo1h4caG6kqEsgYs1hm0uWjH7oeBzfk5m805m/NzttuNeBGq4bvs0Yv9ZCg1dmqMiVXTJKVOCOr/tJYphi6VOWiFynK9cqnmb62n+yslQ/V0KC2Oj9H8rveSwaZOivr62RoHvF8GOJK0fQP2lIJFqVnZQjDinulpnEiMRMM5h3UOHeMFQVKjXXwvWas+pvdevlN7OsY29n1SM8qotNZ0XUfXdRIQkPNETEbiMXdrzJgx41Hg7nc0PPcPWko95JjxGCFE2nuKt9Ihqdx7IFZ1xoxvBS6StmCpNE4nljpx29xj+cw/4H/3h2/w5unTPO0D5dXXKcFf7oLfAVfSRWOM4fDoiA996EPcfvpplLH03rPrBwbvpV/BWLS29XP5MKbBmgZnG6yVPxWGnArWOFbLNYvFCmcbSpFCvvPNlj4EbNuyWq9ZrVfiw6in5957MtB0HV3jpNFaihnQChprWXYdisL52Slvvfk1Tk7uU2oPx36ZnmyGFTEG7t27y3azoZRM3+8wRtG1LbZu5qs2jJxGgpUpSVrNS8lQxqbvaav9gPxIHvZBv8NIPsYJSSkS6RvriX9MScqJ6hrGJCmrNY2zdE3DarlkvV6z6Dq6tqVtGlrnaKzFNWaaZOhpWpMe6LUYP4AH/BIX10ckUTGKzOzB6N7CYrHk5s2b3Lp1S2RSC4m0PTo6YrlcYowh1ddtt9ux226JMU4BA8aY6Xvj18fpyThx8d7T9/1ENmZcHXz/z//wZS9hxoyvi1/6c38Z/cJzl72MGe+APAzc/NXI3zv5NEN5/NN+Zjw5MErjlKFVlqVqONINn3Ib/rWXf4L7399z+j3PoK8dSSjQY4b3PdEYN3XfztPbhx9LKfFbrA+OWB8c4JqWYbAYaykpk0qeNsgpxCqrkvjU8WQ8xlQTl6jafIl6bRo9afpd0+Eah1L6osHbOhbLBdvtjl3fs9vJRlWV6rOIiX4YiCnTVlO3AnJKhEFSsVw1sGutiSlCyqQS0RqMUaRcuHf/LuebLdY1kqClyl66k0TKFmSjnsnTybzKtQdjjLfV1a9gDKY2aJaaClWyREYpHvRA5CKxtOLfTsQop/rGKYyr10dplMpoI23p1hhpaq/dE1PhXtLkVI3ZVmPNaLa/eE3Hzfo+8bggGGoiSSMJebjXYryvXDszxnQouR24pqFp2knuNv6MtRYfohjr67RkNHyPREJM/+LDGT0l1lq6rntgyjZPM64GnvuTX4YvXvYqZsz4+vi1P3+LT/5Pv3Ilois/SCghsvrSKf/1b32S/9VTf5/Dkp/Y1KMZjyeM0hK4UxRHuuGfXvwmX/7O/4b/5N4f4qNvPo/97Ja83T5WEqr3RTT2y9dGfDtOdN9pE1eApm05vHaNGzdvYg344Zzgt+QYJslO76WuPSU36f9HTf646Rw3kmMZHjWm1SD+hKZtaBctXewkqWoY6JZLbOMo9wqnZ6eEwbM7P0drzeA9ISSGtmUYPK5xlJxYr1bcunmDlAtdK3Gr26GnlMTqYMVytcY5i4+JxaLj7t375AxHR9enEr3GGdqmQQWZMFgrm+lq0aAoZKqiNErbyZRt9AXhyTGijanFhEyFe2PvzVSqp7R4OVQlZKKbqh4M8ZzYxtB1QqhQSqJqzRg/WzBGUYqpRMSgSBNh2PddjFKlh70PSumpL2PE2NK9/zMg3Rvee3yddDzzzDNY11R500XR4DgpaZoGawybsKXv+8nwPQzDtKaxd2PfMD62yKeUiDHWZLCZaMyYMePR4JV/4a/yQ/+L3z8TjccNJaPeukf6tY/y2vcueP7xOzye8QGBURoKPG0a/vjRL/Dbv/cGP/PV7+SFrz2F+vJXHysJ1WPv0Xg3lFJo2pbnnnue9XLB2dk9hv6Mod+wOz+T2NpdT7/bEXykpEhOYdoYDoPE2o7+jxgjm82GGCOucbi2I5VCLJk2yc8Ya+mc4+DwEGM0u77n7p0j7ty9S7/ZsD0+QSmN63u22101Hnuss6zWa64dHaCA882GkhPe94QQaBrLctlxcLAW4qYCR4cHHB+f8cYbb7DbeYyxKKTL4cb1a/gQODvf0A8DKIngTUXata01IhszDqXEU1Ao5BjJSaYdI1mQgjxp1JYY2TL5LZyCgiMqMbhbrSdJVSm5OjxEpjXG6oacKDnWSN4x3hYgS7qtKm8zUe8TB2BvQw+UC0Kyf5vx85TSNOEoWf7bD8Nk8H/hhRe59dTTHB4eTY81GsGVUpgajTsaxUcSMt5WUsBE7jUMw+QNkfjdOMmx5ojbGTNmPErc/jHLG7/vslcx4wGUQjnfcP3XCz/fv8z3tL+JuZoK9BlPAKTYz/IxG/nXn/4x/q1/7oDXTl/m2b8TSV99/bE5qLiSRGPcBGqtWC7XaF1ARayGrnUs2v9/e/8dblmWnnWCv2W2Oeb68JHeVJZXqVQqZBAt1LSEoBmcZgbTWCGMgKdphqHttIHu4ekemmkaBiN6GjMY0WIQjRPCSAzIlRwlVC69z7DXHbfNcvPH2mvfc6OypExVVt2IjP3Wc/PeuOecfdbe+0TU967ve9+3wFmDtxZnLNbYaAVrmv7165kJafY+fQ+d4Hu5XCE6p6mkG5BSMplMeovUyXQKQtBOJuhz53DWcTybcXR0xGKxYlXVURwuBErFPI/5fIa1liIvKfKC0XjEeDJG62jbqrRCZxmT6Zj5fMErr7yEkhmmqnFVQzmKNq1ZlnN0fERrLEL4LkgvVvY+eLw1CJGK9M6qVQhE5/p0opcQnZtUOAnqi/NUqLXiP1MKLTX4gHUGi0PZgPcGJSW2c4qSSnai+nj8fjRLxtyTpLNYz9+Ak/Gtkw5HwLvTDk/rZGRddB5HvqJL1GgywTnH4eEh040NHnjoIS5cuIDWGVJGsialjAJ+qXrysFqtqKqqH5NLQvXNzU2yLGO5XDKfz9na2kIpxWQyYTKZ9J+jRDgGDBgw4IvFn37gn/Ab+LqzXsaAOxCahs0Xa77/1gf4DzafoRDZWS9pwH0MJSRjkfO+3PB/f+R7+S9+46/hNR7l8j/02Deu3xVhfvck0YCYTyGlxAfDcrnixo0bHN6+iXcN3rVYY3FtzGSwxhCc67yZTgrafldbxbyK6TTqC5TWBAE2hF4HkUau1sXAWZYxGo3Y3NyEyRhpHE1Tr7kVRcJgrUNJyWq14ujokMODfYy1TMZTtra24vNtJD55nqE8uHFgc2OD2fGCxSKKllfHc5rlimyx5KK8yMbmJgCHx8eorkNgrKdumhgO6EGIk4JeK4nOdK8zUDq6WpGywgW9wPtOJMtdrRTBeWzTYl2DDgElIXTZFknYnWUZI0ZknZhaSolUkqwoejF4Ihjp+HBCAq21UdAuIXkWpM5HvP8nwvLkbgXQtobN7YzJZEIIgeVyxcHBIRcvXWE8mTAeT/oOiPMe07QYY3uy4ZxjPp8jhCDLsr7TJaVkOp0ipWQ2mwGc6nDkeRrRGjBgwIAvHjtqzO94+mX+ygefvKvGIO53BOfQN4752ecf4OARx9bQ0BhwF6AQGU9l8N8//Hf5H377t/BJ+SEu/+0Kd3B45nqNe5JoCCEI0AubVSdEFjomaAuRYY2lamoWszmmNWgp0JKYNt11LdbFxClELs9zdEc28lGJzrNew5GKYediSrj3vn+dDBnWV+R5zng8JgBlOWJcVVR1Q9VEclLXNSEEtNIoKSEErDHUTc1oXFKWBTIENvIM6zzGeLa395jPF+y/cYPjg0OODo/6nfVyVKAXCoRAKkUIDcHb2NEJMf8CBFLFjkpZFhR5gdYKHzwhOLyPLlnOWlxro4WtEichgKkDpDwoFfUeWYZSHqkdRZF1zl4C09nGjsYl02nc8c/zPL5GSkTnAgYnnan1e7Getn3nXw3RjXCl8a1EoBLBkUp25CISg62tLUZlTG4/2N9nPJ4w3eTESjcEqqpiNpszm82Yz+dxJC/Pe0vjyWTSB//leR4dtUYjnHOnnKcGkjFgwIB3Gr9h45Cf+DHLp77qrFcyoEcIcDRj/OxlXv53NnlI20EQPuCuQCEynsgkf/zK9/E//M6an77xUTb+yafwy+WZruttE427y84zjk+Vo5LNzU2aekm9BOM9OtMUZY6zBWWZgfOYpoljQXiEPLFpDQRCcBjT4J1BNBKhFKpede5TGVmWx26D1mRZzmQy6QTPXUHsLDJTaBkoxyVSa/LWoPMc5BxjLVIIRmUJ29topSjzgqIoKDKNQuCdp20tSmuU0pRF1G3ovEUIsFUFzrJcLLAmdlckIga5dAnkLQ10gXs6U2RZHkeGlCTLNaOyIC9ypIhjXMa43ubWe0frmqgo96LLzgjgPd47rBBoIdBZRj4qAIVUjtG4QCmNtQaTxTbyeDxmNB5RliVaa05CBE/C7eJIVOgseU8+V2msDbosC++irkRGVUjwMd8jPSdZ3QovCUTiEUfcppTlqHexSt2pOLalWC6WOOMi8ZKKXMfOi3M2uootlpisjQ5TgTgWd3zMzvYOFy9dZDwa9V0Qa+1p++BhhGrAgAHvAK4WR3zmgx/Hf+pzZ72UAR1CVTN9LfCJ1eN8bfmZQacx4K5BJhQX1Yj/6PwP8mt+yxOMrz+J+qnP4ev6zNb0lonG3ZR+HCPpPAGHCKCzjO3tbeazQ+ZHh3jn0EoyHY/IRGC1XFC1Dc42eB9tbYMnjuakMR4hkcJ3WRI+htc5hwdcCxKJlxoZzZTIdI5ScSzKOANInBA0zrEyLc46goxrK8sSay3nz51ja2OT1WqFNaazkA1xBAiBN46gPMiA9TY6SOUZ0lmyImNjc8Ly6IiyLAhesFqs0DrviEGIFrhAJgRoicwVxShjNB5TFkXXSehE1fiopRAaDOAgK3OkljR1JDEieJQUMW88hI5SeYSErMjIixF5HlPNldaxK9IFFaaxJqQiXjQRXa6k+jxnKSl1N57W0jRtr42AgDEtxhq0VkzGU4SQtK0lBDC2QSnd6z4CIJRmVI4xrcO0lnPnNihGI5qmZTFfUhQTBIqiUDgX2L99G9sahBToFOjXtHgTM0naqsaZqPcpu67W4cEBbduwtbXN7u5utA12ntbFwD+dxREzIUQMHRwwYMCAXyD+8O4L/I0/8THO/aqzXsmAhNAapq+3/OCt9/Dt25+kUINOY8Ddg0woHtAj/syH/xa/7T/43Ty1ehzxmefOLAT0nhydOkHsRgghyYsR08km9cYSZ2pMW2G8j+ngXXK0JKZ4t8ZEskGIFq1aI1UcM5Ld7L8Qqkv1Vl0xqztdiAJkDP/TGcbWrKqWtl7hXU3bVBwdHLFYLtBSU+YFhGg5W+QaZwxKgNQago8ibMDarpMiZCQ3gLM2np8U6CzayBajgizPWC1rFsslRe66rkDoRqa63BApyMcl5WRMWY763I7opBTdlYQUqM7+VrpuRybPUUrjnO11LKnDkOc5ZVGS5zlZnqG0IMsleVGcOn4aJTpxilrPzjixSE6OX4nESqn65O043kTXRYoBgt47pKTrkMTXxPNV0ZZYgO5+TqNuh4eHTK1lY3OLLMspigLnYmfCGMdkPOWo3mf/9gHz+RylFUVedGGFUeCdKY2Wcc8qdGtr6oamqKk6v+qke4mfnWgnHBGS7daAuwChbXnsn/9OXvhl/+tZL2XAgLeMX//wz/D9v+rfofwHP37WSxlA1GkUNxZ89qXL3HhMsiOHPI0BdxcyofhY3vJf/dK/x393/Ot54q8/Ck+/cCZ6r3uWaMRcagXE8ZksK9nc2MYaQ7WcsXCeJjQgNCor0UHiPaAC3jiMd+AcLgiyANJp8lyTS43UOVmWo3SOFCqmi2uFyjKElLTG0R7NaI3leD5jNpvR1ivGRcycqKoly8UcEaDJcpSMQYFNU3F8eMh8MSfXGVplnU2vQHejOVVVoZxFKEXoCtZMaYL2FKOSza0tlquGxjhq02B8tKWVMnYbkJJiPCIvMvLRiKwo4rko2V+5PM9IhXrSX0Csh1VX7DvnPm8ESOsomk/C5xjYF3qd0XqYXu/e1Y0tJdJSlqNTYvyE5ESV53lPbLx3QPzuXBqVEl3WCRjjug6KQMqo2SG4XrCfksKn0ykAx7MZWVEyHo+p65rlsiJXiq2tLeq65vj4mNVsxbE/xjlHlmWn0sSjI9iJmF4I0b9PnkcSc6fOZC2+Y8BdgGAt7/1jh/DLznolAwa8dfxn557G/LeKH7RfT/F9P3HWyxkQPOLgmNHze3zy6x/gUX2NscjPelUDBpzCWOb86slLvPYrfpC/UX0Tj363xb/wypedbNyTRCPqgbsgtyBABJSSjMZTytUqCsBtwFpBQCNVgdQClQVypZG6wNhOQCwkRZEzGo+ZTqaMyhKVaaSQBGJuBMlCVcUuR17E9G6UZUNIytGE4FpKacFbxuWIQmdUqyVt2/Yhd97GzkdTrRBFgVeegCAvS8qyxCNiqnjw6KJA5zmZ1J3Nq8NpzXhjynZrqZqW+XxF3TZ4T7SOVYK8yBmPSsbjEpXnCNVZwJISrLsLiIjhfQECAuHoMjZSaJ7tHblSwra1prsDgSzTCBHwxiIEGJNTluWp8Lp18bwxpnvjk2OuE42U1F10rlQiTloRggMSIYqaDiFSZ8ZFdy0b8y5aY6LTV6fBkFKR5xlFUbCzs8t0uoFzjtu3b8eORmsJ3qHX3McWi8UpEpRIjzEGYy2raoULgXPnzlEURRf0GM0EUnL4emK4DwER3jx0csCAAQPeKv6r85/he5/8Ri5+31mvZAAh4BdLtp/z/KPbH+Zbxq8wZiAaA+4+bMqSb9/+SY5/9Yh/3H4tD3+Pw7/06pc1Y+OeJBoRnTAY2RXyAqULimLMaLyB94GyGAOxCHW2gS6VWsg4JpVGejIdi3nrY6BdcrMSSFAyipBTariQZHmB1JpNFS+f8x7hWmjntNUSJQR4T5Fn1HUFPo74BJcznpSE4MizHJkVCJmxsbnBdGODxhhsiFP9Skq0VAQkTutofysdusyZbEwZb25QG0dtlrTWoqRCZjnFqGSyvcl0PEJlJ+5OJynaSbzsCHiEiBIKIQLOGVrr8N51qekyjkllundZAijLstu9j3ka3ru+G5HsZouiWCMNMX1d9+NnJ3a1bdueiLk7spEsY6MFLn3hH0lE/H1ZlOgsj/cI0ed0GOMIIjqRjUYl1rpevL+xuYmQqj+HEGq8hbZpGI1GPPDAA1RVxfXr18my7MQGuLPoDSGwrFaEzukqjYml8wD63yfyIeXnu2cNGDBgwC8Ef+EP/Rn+80/9bvQP/NRZL+W+R6gbNl5c8uMvPcIbV9UwPjXgroQSkgtqzB8+968x36b4F+bjPPQ9FvvqG1+2jI17l2j0Iynd7rEXCDRZVjKdbpNlBQSPxNO0NdY0IGxfZHsfd6qtMSxXc+qmxdgWQexwZDqmaisFSujOnjWKm10IEOINlEojBQincHZJluW901GeZ0zGY4K11HWNVhIlJc3GBlJpdF6is5JyPALiqJRQCuf9SZpFCCgEeZYRQiRBxSgwmkwY1S2NsVgfkEpTjEqmmxud7W1OTPtOY0ge7wUhSLRWKGUxhq5YjuNnobO7dc52Gg0J6K674HHOEkLAmNhxELLTlnSFfRobWk/YljIKybNOFK911nc0IOot2ralqqp+ndbaTuchcS4W78YYrHXdJQkQBFkWx8LisUdMphvs7e2xe26v7zaU5Qid5wgBRVGgsxxrLaPRmLY1LOdzJPQk6qmnnuLy5cvcvHmzTwVPo1jWWpCiDwRcLBZRt1KWSClPntOdVxolizr403Rj6HCcLUxwZGKwJB5wb+FrSsXf/qv/M7/1l/023NPPnfVy7msE59DXDsmeeYjPfOwy78tuM/yLMuBuRCIb//H5f0n2mxz/rP1arvzNFe72/pfl/e9dogFRZwtEvQEIodA6R0iN9zEzQwhwTmCsI2AjYRACYyxtGx2Omqbpd9WNMRwfH+O9R0nNeDxhPBnH3XqdIZUmKwqwDiEsqhu5iUW5RCDJ8zir79wI76JAO18tyTLNqCxx1saRJaEJQiO1InhBlmcIp2jatsu2oLOejcLwlKytM911FUryvKapDUWeM51uMN2YkveheCCyrB/9SV0D4FRXIZGBEAKuuz7r2RZSSoqiAOjtYbMs6w2lUlr6xsYGWus+XyS9l7W2t7Ktqqo/ZsotSUnrp52oUuo3cXQspbgbS9saFssVZrXC+yVNk7I7JozHY7Z3dxiPx+ztneOhhx5ie28XayzHR0fovOjC9yYIBKZpuHn9OrPZDO894/GYixcv8vjjj3N4eMj169f7927ahsl0Gq17RyPquub27dsYY/pzSKNUiaQg4ljb+jgVDETjLOGee5Ff/lt+N//LX/7TPJpNz3o5Awa8LZxTE5DDvx9njuAJxzO2n/H8i6P3862Tf86WGJ31qgYMeFMksvF/Pf9D8NvhR9/4OBv/uMKvVl/y975niYZY+29EDHDLixHlKM7TxzGZONMfZABf42xM+F4ul33gWhL0phC4JOqVxBGrRETyPO2IO7L0HCnxUYDBclnR1hWmbTphtYyCchnHlXwISCVpm5q6bmlMg3F1pztQyG6Eq9/VxwESYwzVqqIxLZ5A09jOpjf0moQoRs4RQnauUgYpBVk3+qO17s4xCapFL2gWQjAej3HWYTvitapWWGP7wj/t3CfhuNaqs5Wl11aMRqP+fZIDU3KtSrv9Sum+Q7Guh0jjSUnnsE5+hEjvGYmfFJrzgV73EY+1QkjRdxeSUPvg8BChFQHBjVu3cc5HQX/bMp1usLmxgequxf7+PgcH0X3q3Llz7O3tcfnyZW7dutV3L7TW1HXNeDzm3LlzKKWw1rJYLGjbticl6XxH4zF5kZ8iGgPJOHvoH/gpftN/8kf4k//dn+Pry2HcYcC9haOv2GPjc8+deeLvfY0Q8FXN5vNLfuS1R7h9aUgJH3B3QwnJnhzxH5//If6Pv+ch7PX3oD7xmS+5OPyeJRpvhiBAKhXtVtsiJm1Yg3Eeaz3BW3w32lKWJUpFy1jn4ox909Rdp6OJs/c27qA779FZxmS6wWQypShHaK0IIWZyeNdpMITCB4mxoRs9AiXj5lNjHHVraJuWalWxWtW0LoranXNYF9A6uhyljosxLuZOdFarq9UKhCD4+K+ZkNFBKkzG5FnR2cs6rAGEx3VjTWVZRLIVkp0sSCkAiZQi2vd2rlSh0xuM6oKmORkFOhl9Svazvk8aL8vyVAcDTsTdyfI2EZr4uhPrW2ttLxav65rFYsF4PI6p3qNRT7qM6UTfPorDk6uV1rGTs7tzjryMzk8q04xGY7a3t9nc3CQrCwKCtm24dfuAqqqieLwxlEXBuIykcWdnhytXrvR6lFu3bjGdTrl06RLWWparZUeWVN/NOHfuXJ8U7r2naRqapukJV9R66BNDgYFw3DXY/Fs/xm/7qu/kud/0F856KQMGvC38yJ/6C3zrP/oG/Hx+1ku5rxGcQ79xgHnmIT73Fed4SC+HkcwBdzWUkOzIEf/Le/4G3/L7/iBPzh+HTz/7JRWH3+NEI3z+z5JY2XeJ30Iq8tGIPANvHW0TUyqCl1gTaJuGpm3wzmNNS9u0NE2DdxYBZEozLkvK0YTRuCTPNIrQOUiBcxapFDJAOZrGfI28xLQt1rXgHd5HslO1LU1rsC4QOkG60jkhBNrGoFTc0TfWQljFcSkt0EqR6YxRUeLweCso84JxOcbbAD4KjzOlyVKWA54YjRGw3fhWCMSUcx2DB533tK1DiLbrAsQ0wuADRVH040zeR1JRFAVaZ8TE7phB8nn5EZ1WYx0nwmjZ29SuF9ypSE/kIf08Ho/Z2NigKEqyLMeaaHMrhOy0EwHTGmbHc+r6FkIKxpMx5y6cZ2Njg7IsmW5ssLm9hXWeohghVdZ3KKyreOP12zR1RVmWnD9/nvPn42t3dnYQXTeraRrG4zHjyZi6aQhrI2FpFCydYxr/SlqV1hhUq8ig16UMyeEDBgz4YvHCf/JBHvnPf/Ssl3F/wzv84RG7n3qQ7//3PsQvLv/VMD414K6HEpJH9Ji/+XXfxf/59/5+nvqupxCfevZL1tm4d4mG8ERWEUgkQxCLuLzIKccjPJ6qqqjrmtCuwK5om6ofa/HOIkVAS4ELIDJNkWXIrQ0ypWJIm4iOU0EInPfU1TIKl0PsnhTliPFkwqgcMxlvUI4LitEEa1qMabAmhvgVzuFFIC/bGBrYtDHDI8u7MR8DQZDnBcoY2tEIQSr+Y2r1qCxpjKExBi0VZZ7T6hYjbGyh+4CWmiIvUBKQBYhYyMduTXSI8kH2nQ0fPM46pIwuXt6djCnFYDw6MXg4pfOIj6le26K17rtE6/qP1LEAThXkycVpvaORjpUK+NVyxe3bB0wn067DMSbPVSe8Nn1Oxrlz55FScTw7ZrlacnBwQNM0LJdLWmOwznHuwkWyrKAcT7hw4QIHB4fcvr3P60Lyxmuv9gL01WpFVVVIKdnZ2WF7e7vvYuR5DkJg10Tr8/kcY0yvzZBreoy6rntyEeCUC9eAuwMP/2PD/+WXfpT/8fJPn/VSBgx4W/jUb/+zvGfv9/Ge3zuE+J0lQtOw87NH/KPPfpDfde5f84FscJ8acPdDCclX5oG/+i3fxW/LfxdP/fn3ID75uS9JZ+PeJRoBYlgfnVQjpWMHvI+OT4ERNljqBowzYFoUkClFcA4XAmWesTEZU+Qx3doTsMbETkdV0zY1rbHdVyzyjTH9zvp4MoXgUVKxMd1B6gwhAqFL+A4CMsALUHmG8zYeu2lwLo7PBCDPu+6BC5016wglNd56jIndFa1UdF5ycawrOB/NXX3A2pZKRO2EVoqszGKyuSLmhJRlnx0CdF2Kk3A+KWNmSNs0fdG/7gKVdBSpkE5/TvavEDUTSWsBJ8LndcIRszFCF77HKX1GOpb3nrquqeuapjEsl0sOD48IAabTKTs7e1y4cIHNjU2uXbvB008/w2g0YmNzk0cfe5Sd3R2yLKOua1arFbPZMUezGSCxztO0LVmWcfnyZR564Cr16sNcu3aN69evc/v27f66zGYzDg4O2Nvbo6oqylHJ7t4eddPQGsPFixcBqKoKoHM0sxRdUno6F9u5dQ24+6B/4Kf4p6+8HwaiMeAeQyYUf/2b/wJ/jI+e9VLuawTnkK/eYOuH38s//Iqv4LHdn2EqyrNe1oABPy8yofjawvGXv/F/5Xe0v4v3/T8fxj37wjuu/bp3iQYpuA9SV0OIbkxHZt3j8X9KZuT5KO7umwZ8Q1YIptMpo1FBnsU06qqqWMzmzOcz6qqOu+vGHTBBvwAAV0VJREFU9u8WvO9IhUCrjLwomEzGbEwnTMbj2DEIIFWXgj0aI8QIY2rkUlNVEpyl1Dm6GOFMFKY765Ba4l2g9RbrAaFQmQQcOEBonDVYGzDWsliuWC4rmrrFh1jI26UBQteFmVKOC8oipoJ770EItIrXK2kEUnJ3IhC2KNayICTOR4E4CMoyjk7Fcad4XfI8j6Soc966k2gkQpO6SMZE4rBYLAkhMB6Pu0yL0HczUgbGxsYGe7vx/vgQmM8WLJYr9vf3uX7tGnlW8N73vo+Pf/zjvPDCi9y6eZMbN2+wd36P9zz1FI89+hhFWbJcrjg4PuJzn3uaa9euU5Qlk8mEne1dzp/bQyvFhQsXeOKJJ3j++ed5+eWXadu2J2HHx8dIKRmNRhwcHvLgQw9x6dIlZrMZRVFw9epV2rZlsVj0Y19aa0ajEbLToyRiBZzSrAw4ezz0h5f8V3/vA/w35z991ksZMOBt4WOFo/r+Rxl9y4tnvZT7F11434VPzPjLv+Rr+dVf90neO3Q1BtwjyITi60vDn/im7+GPv/Ibefi7DnH7B+/oe7xlopFmz9dx1oXSHauJvwtxrVG7AFpoynyEDRDIILNMpp6AxZmGqqk4ns9wpsW0sfAP3qO0iha0xmG9ixalUqDznOl0yng0YjQexyLSeUxbY4XHdR2JclRSjAqEVCgKspHHAMGZqL0QLSoIShReO7z1tMGhlERICFLEoD8haYOlslDXLdWqYb6oWSwrVqsVbRu1F6G7IKu6JYglrfNMTImxJWVZRMG3zJE6CsDj/QStw1rRG2A06ov+VBCvO3LleY6U8iRkr7v2UqpTjlGpi5EcvYqiZGMjo+26CXmeU1UVTVNT11Wn/4ii9LZtqKpVRxMleRYdr8pyzMXz52M2SQDnAsvFnLapefDqFR595BFmqwXz5Yo3rt1gsayYTqPeYnd7j4999GM8+9xz3LhxncVsTlvViI6kHR0dUZYlV65c4WMf+xhKKQ4ODjg4OOgF8bYjo9euXcN5z4ULF9Bas1qtkFKyubmJ60T76e+L1hrrXa/lmEwmGGN6cjXg7GFffJmf+IY9/slPF/zycXPWyxkw4C2jEBn/4oN/h49+72/hyq/9zFkv575FsAb1xi1GP/UEP/HRh3lUv8ZYDEnhA+4NZELxrZM3+Ju//AXmP/0YxQ8s3lG9xr3b0RBf6A8nOQxaZxR5QAqF15pQFJi2pqmXNE1D3Sxo2wqCQ8qA0poiuSPVDR6HLgtUOBlnGo/HfchcssgFGI0MwevOwUnRmobCFBSdbkHrnPFYYp3FWIuzLbJ04BymNdRNg6PFBQfCYq3DtA7rPNY6bGtZLqpYnNdNl3bucM4QvDgpWgPUocZbT12tWCxyxuMRo9E4kp+iABQEEa3Y1/zYoysVKHUi3l4fZ0qibSll747lu47Iul1uEniXZUme5ywWi87Klt5RajKZsFgsOrLUUtd1P6KV7l/wgbZtOT46RusMgaRpWqbTCaPRmK2tbS5evMTGdAM6O9v3PPkUuiw5OjpiPp/TNC0vv/wKGxsbPProo3zg/e/n8cce4/bt29y8eRNjDPP5nNUqdkqOjo64ceMG58+f5+LFi1y4cIG2beMI1nzOqlphDg6w3XmfP3+era2tPsAvZYqILj1cSNETr/XU8HVB/ICzh5vNaMPgFjPg3kMmFJuj+qyXcX8jBMJ8we5nDH//5lfw709eZMxANAbcO9iSI/7YQ/87v/5b/0Oeev4q7oVX3rHk8HuXaHwBJCFyKnYhzs47azFNE7+MIUB0UAqxWI+6igDdGNF4NKIcTaK1bZcTkexLZ7NZf9y6jiLr5WKJykZopZFKobRm4qbIGElNXhR9B8Rahw8OrMW0DfWqiuNSjcOHqAGpqop6FTM22tZQ10ts24LziOCRBEQI4DzenWRj+OAwviE4izGR2FjrMMZhjMWOXKcfiN0DwWmi4X3M7lgXLSfCkQrm1N3QSoEQuF5/EfpRLN/Z5Mbsi3G387/k8PCw74yMx+N+h7+ua6oqEqkUpGiNxduA8wHfmF47sVrVzGZLXn31dZ7+3DNsbW1z4eIlHnjwQV56+WUefeIJHnzwwW5Ea8HNmzd5/fXXuXXrFlevXqUoCiaTCQ8//DBHR0e9SDvd5/l8zrVr19jY2ODJJ5/kypUrMTNDa5x3NN169/f3qaqK3d1ddnd32djYYDab8fLLL5PnOTs7OzFLYzIGoGma/poM9rZ3H/7mza/hVz7yz4eRhwH3HC6O5zQPP4h9+dWzXsp9i9C2jF845N987hFee1CzI4fxqQH3Ft6XS779m36Qv/O5b+LS9xy/YyNU7zqikcZ8gL54zLIMnMMKEQXSUqFkhlRdGJzx3c58TOzOspwsL9B5TMO21vbC4qZp+tn9tEOtlEJJhRAeIRxKSrQSgKNuatpuDGkkxp07U44UMT9D6RrnJbKxCNkCUXAdE8wDUniU8GjhyaQgqFjgaynRSmKFxOOJzYn1RG3Rk662NV3hHwlMnhd9JkaW5V3XIo4pSQVKnRCNtPsO9ILtRCpCiHqQ5FCVfp/E5IkwpeMIEQmGMYbZbM7R0RFaZ33KeVmO2NkJGBPdnGbHx1RVi0JQr2owlrzTkAQPUmkcsKwrbu/fZr5cMZpOuLW/z87uLpubm+zs7PDoo4+yt7fHSy+9xAsvvNDnXiSNyMbGBtPplMPDQ5bLZR+AGELgtddeo6oq9vb2GI1HXLx4kaquWa5WLBaL/rosFoveIvfy5cscHBzwxhtvIKVka2e7TxNv25aNjQ02Njb6+zPg7sDh1x/wzf/81/Av3v/3z3opAwa8LfzdJ/4ZX/Pnvw3x17+G3R98CXvt+lkv6b5DcA72D9n+mQv8+Dc8ynuyl4bxqQH3FAqR8bt2fpp/8mvfT/PMo2T/+p0ZoXpXEY1EMNZn31ORK1Usqp2zWCdi+JszeOdj1obSXRgdZDpDSkXTpYZHlynXW7Mm7UFyGxqPx+R5hgSU1sjOFrZtapq6JStKXNdVGI/HjMYZuihRGSAzjBMUraM1joBIQhMUYEyDxGNyTVsLrAElREyzloo8031XQgoZuylSIaUCJWPYn/dd4njsjkhZk+erLoRvxHg8ita0WnZjU7ofnbrTjjXtxKcRKetczAPpSF0asUoaj7470dnHKhm7PVmW91qQ4+MZ3nsyrRmNx4zHYy5cuMj58xepq4bZbM7xbMZyEcesghD4bkROK02el+RliVSK+XzO4eyYGzdvMp1OmU6nXLhwgStXrvCRj3ykd5JaLBZcv36dl156idFoxIULF/qRrpQY33Tdr+Pj45gkvrHB5tYmeZ6T5Xnf3Tk4OKBt2z45/fz581y4cIHVasV8Pqeu656YAr1YPInmB9w90N/8Ok/9rd/K09/w1856KQMGvC382Ef+DnwEHvun3857/4jB3d4/6yXdXwiBsFiy9+ma73njq/gVTz5DIfTQ1RhwT2FPjvif3/Pd/Npv+4O8/8XL2Jde+aJdqN5VRGMdd7r6CCGRKkMpjZAyOjWZmOyd55qyLAjB01Q1ddN040am1x7EkZ2YsZAKZ+dcH2xXFBlagBeBum6p64YQJEUxIjhNtXI0jcE70LokzwVSZehMkxWeYmyx1qOEQiaJtfBUC4dt4qhUnmmCA9eaE7KhJCJ04YShI1WAkpIgJQgIIiA78hEdkEJMSg8WIRqU0lHMLTUxLfzEyjYRCKDvAqQuR+h0BolYrD8njTlB7Ag1TYM1FmNsJxzPKUcj8jwnzwqss1SriuNrNxBCsLW1yfbOLts72+ydu0BV1RwfHzOfz1kuVxwdHVNXdSR2nePVxsYGVVOzf3TIwcEBs9kMKSWvv/46169f54knnmA8jonhk8mELMvY3Nzk5s2bPP/882xvb3Pp0iV2dnYYj8e9biM5Zi2XC1bVKo7dda/d2NgghMBsNuu7N9Zadnd3yTvjAOtdL4oHaDsCO+AuhHc8/h0v8ej/4zt48Vf9pbNezYABbxsvfPP/m2+98htgIBpfdvjWULy8z9OfvMrTj2xxUUVL/QED7hUoIXlfLvkvfunf589+6tdx+W8e4ZcVwZpfMOF4y0Qj7WAnMXD6c3rsbkBaR/q+vlahFCqAUrqzaC2QEoRwSBFi96Kuu+C8k7GrJFROCdEpWyJ1M6bTaXRb0gprVqyqiuWywtpAXozw3rGYz/FBEIKgqVqkkOisIJcZQWqyYkxpHd46lJAIAVKCxOFtS1NrslxiWoVTDq1lzAkJoIzEStdpIzyCmNitpEBoTdBxNyXpLE6E8inJWyK7BPHoHiVPfa2PTq3/2XuPDwHZXeck5k6PpzyJ9cC+alVR17FTEDUaTX/Mzc1Ndnd32draYn9/n1u3bnNwcERRlmxt7XLlylUeffRR5vNlRwItBweHHB4cYIyNeRuriryIo1h1XffuWDdu3ODg4IDbt29z6dIl5vM5Ozs7PPTQQ5Rlyblz57DWcnBwwKuvvsr+/j4XL15kd3eXRx99lIODA46Pj6lWFa3t0r617kfqNjY2ePDBB3udya1bt1gul0wmk6j/yHSvQymKoh+Zaprm1HUeLG/vDrjZjMe/28GvOuuVDBjwC8P5v/gGt37ZBL9cnvVS7i8ET9g/ZO+TV/gH3/iVfPTCv2JHjc96VQMGvC0UIuPbNl7k+37jMxx+7gnKz76OOzgktO0viGy8rY7GvWLHKaXsBctpVz4EcCKgspxSOPJC4l2O9y2mrWhXhqY1KKn6lOfj42Nmx8f9GFDazZdSsru7y3Q6pSxLmqbh5q2b1KsZs9kcgWRzaxuC5+jokLZ1CKmRQmOtY7oxoTUNuhgTREz/LooSETxKShAOgsWbBjMeY22LD4G2mWNaS5ZpvC8QUqK0pBEtwQR8iCneUkl0phGZIqhoZxsJ1slX7FiIjmxETUKea7Js/fETYf16lyJ1MbxzmDXhdyJiicjM53OEEJRlDC+abkzRukUIwWKx6BPFl8slN27cYG9vj3PnzrG3t8fGxkYUwTeGF154kRdeeJEHH3yQJ554grIco3XGqBxz+dJlmqbl2rVr1E3DbDlHKslqtWI0GuG9J89z2rblxRdfZH8/7vI988wz3Lp1i/e85z0oFRPOr169yiOPPMIrr7zCK6+8wq1bt9ja2uLSpUtsbW1x48YN3Moju9Gn1LFZLpc459jb2+PBBx/k+PiYW7du0bYtW1tbTLONvoMS+lG2SM7uvCfrxO7NMBCRLw+yH3+a933Xd/LZ3/3nznopAwa8bfy1h/8Vf+oTj/H9H9w866XcXwgBX9XsfG7BP3r6g/zOvR9iKh2ZGPoaA+4tTEXBH3/w7/Mrf+sf5PI/foTtT2S46zcJxkLwb4twvOtGp5LzUSIZKffBtIYmtEhZ4DOBcwpjAtViQbWqmE6nnDt3Du88i87u1He6jHSstDOf5zmbm5uUZUlVVdy+fZvDg9toARLJeDxhPBrTGst8dkzTWkKQZFkOElbLObOjQ4LIybISKWOityxLBAGBBWdxpu1tUKXKMAZQCrlSCN0gG4nQCo9AKInKPIToppXnOTLXoCVSSJTW5Jkmywq0jjqJSDQkWivyvCDPFVrHP6eid51crudpALTGsFpGq+Bk51rXdcwR6fQKzrk+xG97axs76Vy3PFhrqKqGoigxxrC/f0jTGPb29jrdi2Q03mBrZ5dXXn6Fn/3UZ/js557hwQcf5P3v/wAPPPgQR8fHVFXF1zzyKIeHh/zUz/wUN27coGka5vM5o9GI4+NjjDFsbm7GDIyOKHzqU59iPp/z/ve/n9FoxDPPPINzjq/4iq/gwx/+MM8//zw3b96kbVsuXrzI448/zu2Dfa5dv47znsViEYXpIVBVVU8itre32draYjabsVytsEdH/WjZaDRiY2Oj/wylaxNzQsqevPxcn+8BX3r45ZLJa8O1HnDv4vfvPM3389VnvYz7DsEa9Bv7FD/zCD/ykcd5bPP5gWgMuOeghOSJrOB/+vq/xX+68WsJ4io7P+Si9su5aH7wFvGuIxpw2nmqJxtCIVAYE12Ygg846yjyEUWe09QVR8fH0VLV+X4Hf5wSv/3p36VRm8PDQ2azWR/+tr29TaYLDvYPmC+WGONASpz3hOBxpma+OMYLQetge+c8GxuT6ELlG6QQlMUkhvUFj1AKtMZLjXOSvChZ5SuyuqJa1cg6aiycjza30Y1JkeUZOtcgAUHXtcjIsrz7niGl6LQbsnOfikRj3bEqnXcSbietQRLH13XNfD7vheCr1QqlFHVd9+5OVVV13ZCox3jggYc4ONhnPp/3Y2hCxPeu64br12+wsbHB9s4OjalYLJecv3CBvb3zLJZLXn3tNV544SUeffQxvvrjH2dv7xwHBwdsbGzw637dt/HpT/8sr776Ki+//DLGGM6fP99b0QohervdEALXrl3j1q1bPProozz11FPcuHGD7//+72dnZ4cPf/jDPPbYYxweHvLcc8/xwgsv8J73PsUjjzzCSy+/zO3bt7HW9iTh+PiYo6Mjzp8/z7lz57hy5QrlaMTh4SFN25BlWT9qlz5DibyuJ6qnUao3+1wPGDBgwIC7GCHgj47Z+7Tlu1/7an7Fe59mJAar2wH3HjKh+KbRAX/iw9/LH1r8Bsr9y5Q/awmLJbTmLZONdyXReLOdeCklOs/wwSCsAiWQWuONpWlaVlVM2nbG4JzHGkNwts+OACiKok+wTvP4qfCWUlFmIySKum5YLSvausUDtutMKCWomwrZjc0qWbA52SITG1hrMG0LAnSuyRkxDh6pFUEpghBIociLPNrMrjRKa1SuKKyHIAg+Cq8D0aJXyIALDiGjk1QsiEdkWUaWaaSS0dK2G62S8oRgpOu2LvZOHQtjTMzV6ITyIYQo9u5E30k8vlwumU6nhBA4OjpCCs10ukWeO86du8B0GoXYzoXelSqEaMN7eHhEVTdsbm+TlyXXbtygaVqyLOfy1QfY3z/kc88+y6tvXOOJJ57k4YceRhctr732KltbW70G4+mnn+47HEqpfu1ZluHXMlJef/11FosFH/7wh7l69So3b97klVdeYTab8cADD/C1X/u1vPTyy7z44ovs7u3x6KOPcv78eW7dutWPgKVzNsZwcHDArVu3uHjpIptbWxRFQV3XHBwcMJlM2NraOqWXKcuyd/oaCMWAAQPeCciveB/+Zz571su47xCahsnzhzzzs5f45KMXuDg+RjEQjQH3Hqay5BvK2/yej/5r/vLLv4yHqitkr+0TDo/x1VsLCn3XEY20E7ze1Ui/V0qidNzVDxiCtzTNiqqKYm/vor1s3OHP4lhSt6PfZyt4z62bN6Or0XyGc548y5hsbpIrzWqxYlVXtKalaRuqqsJ6140ixXTo5VKz4wJaj1nMj5AydAV8SzkqGU/GFOUoEok6R+gMnecUeUFZxvC/cjViPKmp6hprHCAIPoranYsFNMJhvUWImPUwGpWMRhOybM0Ct3PTlVIihehscKOYO6aPW4yNIYJta1h2o1JSSopuLC3Lsnie3Q596nYkcrK1tcV0OuX2rQOWy4qtrZ2YKzKKuRSz2Yzj42MWi0UkBFoTbMzSuLm/z+7eOa5cucpqVXPz5g1eeuklvI95G1VV8cLzL3D92nWuXr3KlauX+q7MeDzm4x//ONeuXeOll17ijTfe6DssxhjKsuzXnLo0n/jEJ7h8+TKXL19mY2OD1WrFM888w+7uLnvnzrG7t8tiueyD/i5evEjTNEAkGcvlsiej8/mcqq7Z3dtlZ2eHLMswxvShhZPJhDzP+7Wmz+2b4c2smwd8aTG54fhrs3P81s3bZ72UAQPeNgqR8d/+vb/Kf/box896KfcdgnNw/TYXfmKPv/LVX8/XPPr3uKAGO/MB9yY2Zclv3vo3fO6bL/HD2Qe58FNX2Px0ibz+1v6/8W0RjTs7BXc71l2o0rJll+OAUTgHIUi0KtA6djKUFl0gnsAbS1VXfbq1FILFfM5iNqOtK4K14D1egCGwXDUsFkvquoribWtomjbqL5TEdzv+QghyXaBkgTGOW7fL6BglBFvbW+gsY7qxQZaXSJ0jdUFRNDS6oChyitGIpqlpm5amaWmbOArmg6dpWmxrkEohtcAHS/C+02hkZHnROU3Jfvc8+O7ihADe4rzFmoamOcnAMMbQNDVN58BlrEFJzWhUxLA/EYXomda0Xe5I8J6264JsbW1x/vwFDg+POTg8pOmC68bjMZtbW5FUac3+/j7Hs1kMIJQSbz2vv36N4+M5Dz/8CI8++hiz2YwbN25SVw1t0yCIpKaqap5/4VkuXbrIhQvnKcuSyWTC+fPnmUymXLp0iTfeeIODg4O+G5U6GqlzU1UVr7zyCoeHhzzwwAPs7e1hreX27dvMF3MudG5UpvudtZbxeHxKo3LcGQhsbMROVXreuXPn2JhOaU0kbFVVRS2LPRGXp65Gwp2duQFfPpT/4Mf5C5NvY/Vf/gN+7/brZ72cAQPeNi6qltu/+2s5910/etZLub8QAn4+Z/tn9vmZn3ycn76yy787Wg1ajQH3JJSQXFQj/uil7+f/88uP+VuXvhozPsfeT7y1179tovHWycbbdcd5Z4uoz59xj4V4CB7vBSFIAgohcrKss3ZVCtNWGNdgWo9wAS0V0+mETGfMZsesFgsknlGRk8tYXLZNS1Ut+x35ZPMqAuRaoLVEBo+pKox1CCk53t9nuWpQ+T6T6QZ5UaBV1E2U5Qidl0wmU4oiA1EixQpCiMLxPKe0Y1xrek2JtZFQOOdo6yYSKyWwzuBCTC8XRPF3b6kqk1Wtw1mHNS3ONBgTrX6buqY1LSF1ONoGERwSS7Atra/Bx3GmItfgHa13KEGfsaF0hm1aquWScrTBuQsXODw6ZrVaUjUNGxsbbG1tkZclU0BlGSrLODw6wnTzf87HnIpnn32WixcvcvHixS5pu+TatetY2wKB1tQIIXjuued46aWX2d3d4fLlK1y4EInGI488xs7OLoeHh1y7dp1r196gqiq899GiuBNlJ73JzZs3qaqKnZ0dzp8/TyCOgC2WSza3ttjd3WWxWNC2LXmec+7cOXZ3d9nf32e5XDKbzciyjMlkgmkNB7f3MU0M/suUZlVVtE0biYZSaKmYTKfkRX7Kohk4RT4G56kvHza++8f4a+FXsfHHvoffvDHkEgy4t/CAnvJd/+mf5ndM/xCX/9SPnPVy7isE5wivXefiJ/b4a7/o6/jYQ/+Qc2py1ssaMOAXhEwonsgKvmP3R7jw8Rl/mn+XfLH7ll77tkenEtFI8/s/z7Pf4lG/XDu1IaZFCIGUGqUy8qxEBmjbCmss1hpaU2PblpEu2d7cxnvPG6+/wfHRId5Htb0WAp1nSBGwbYO3Bq3Be4mUMUAuBLA27uzH0aMWYzoRddWgyorp9g5KQdtU5HmBFBLrPIHY9cjyAi01PiuQcowUDqRA5zmh8Dhr8dYRrMOHgAgh2s62Fhc8NrjeGSmuycckcSHizwFEELSNoV4tcW3daS1i56Jtmj6jw3uHIN5VLQVBKpyzQGA0GqOV7NKvT6yFE7ExrcX6BePpNtt7u7QujkbNlwtuH+xz/vx58qLAEbh89QrnL13k9u3b7N8+xLro2FRVVT8C9fjjj/PYY48xHo954YUXmC9mXacm6i8WiyOWyyXXr99gd3eX9773vezt7bG9vcP29g47O7tMJhNee+2VfnxKKXVK9O+95/bt231mSl7kVHUNQrDs7HPPnTvHhQsXepcr7z3nzp1je3ub+Xzeh/1ZYyjynNVqhTEmCtKVig5Y3XvNZjN88Gyp7d59KpH6ZIm77uI14MuDjb/9Y3z/H/oAv3njX531UgYMeNv4qiLnz/6BP8cf4DsHsvHlRAiEqmL7U4f86Gcf5+UrGTtyEIUPuHeRCcUDesSv2/gUt79yg//t2i9+S69712k0viBE3BVW0uNT4rXKCNLihEBpTVGU6AxkA20QMbHaWm7dvMWtmzfx3lEUOZnO8M70+RpCCrI8R+cKrU1XFIbOUUjEIjulY9uon1AZFFqjCJimQuoMvGe/amibmlFRUGQZRTFC63hsVFyndnHHXSQLWqmQuSCspXWnXAtNtLp1NuZdNE1Da2KWheueb51jtViwWMwJJnYHrLWY1tA2kSClAlcqiXceiONSdNoGaw15XjCdTGnqpk9Vj25WeRee5zk8OqToLF5nsxmL5ZKwWLBcrbh48SJXr17l1VdfJYTA1asPcOXyg7z44kvcvHmzz0Rp25bnn3+eg4MDnnrqKUajEZ/+9KdZrVbkue5Tt9P53rx5k9ls1gfwPfXUU1y+fJnJZMLOzjaf/exnmM1mlGXJcrns9CwjyrJkOp32GpS6qVlVFUjJeDzuSURyktre3kZ341+r1SoK5qVksVhQ1zUhhF4Xkka6km1yIh/rNsrJiCB1NdL5QLTJHfDlw63fc4V/+nczvnlsznopAwa8bfySEv7yH/yf+N2L/3AYo/oyIjgHN26z+ek9fuRrn+R92bOMRX7Wyxow4BeMTCguqoL/0/ZP8KMff/Qtveb+IRpdYRyL1ej047TGGoVUmlE5RiloGo9tDaNSkUvF8dExR8dHZHnOqCwI3iHw1KbpC3AtFWpUYp3tRMoG8NG9ysYuSbLIzTLNdLrBdGOTrBzRGIP1jtwHlrbCu0CmBAe3rrOYHaGznNFowmhUUowzilFBnud9Ia91RrAO2WkMTBtj4pXWyG6nXhAfa5rmRHDdBfIlArRYLFjOZ7i1It0YgzGRsDjnyLKcLIuhg9Z2Y00EAoH6eMF4HNBZTjEaUzUtdVXDqiHLIrEx1tE4j84Lzu3tcW7vPMdHM4yxGLNkuXwJ01qeePIJnnnmWZ5/7nkuXLjERz/6UW7cuMEzzzxD27asVqveRveZZ57h8ccf56u/+qv55Cc/ycHBIVpH+95kv5vOdTab8elPf5qXXnqJ9773vbznPe/hypXLXL16hZ/92WiJ27YtSimqqqKqqv7cleq0PQHm8znXr1+PFrzb2ywWi97B6tFHH+XJJ59kNpvxyiuv9ESkbduYq7FcopTqOxvnz5/vncuUiuJ8HwKj0agXiq+nsQ86jbOB/7ef48BNgcOzXsqAAb8gfFWRU+8NY5dfVoSAny/Y/UzL9938AN+28WnGciAaA+5tFCLjMe34LQ/82Ft6/n1ENE4Qx3oUSmaxq6Et4MBIpNCMxxOCc1SLJW3bkukM1e2mW+9omhrXOUlprQne452l7Wxs0yiOtQbnLCHQpT9njMcTNjc3KcqSxXJBNV+g8xxT1azqmrIcs5xJmnqFkAoCSJUhlODC5UtcvnqZ0WiEMYY8yxiVI1yApo42Y8ntKYXSee9ZLZfM5nOOuuC4VLBmWYazlqquWS4WrJYrTF0ROxoujomF0I99ZZntnZPatgUZuyLWeaRSLFdVp0mYkmUFq1XTjR1F7URAInXG4dEt6qrm6tWrXLhwkWvXruG9Z7lY8vzzL7C9vcMH3v8BfvInf5LPfOazvPzyK3zkIx/hG7/xG3n22Wc5ODjg6OioL9xnsxlPPPEEX/3VX81zz73AK6+80hOMjY2NfuTo+PiYPM85ODjgxo0bLBYLPvKRGM73xBNP8MM//MM8++yzLJfLnnDM53PqumaxWDDdmDLd2GB7a4utrS2apom2vV1Wi/eeg4MDXnzxRc6fP8+FCxdQSrFYLPpRqDyP9sS2c9UKIXThhDnLZfQ9FqnjpnXfnUqkVghBURRn8xfnPsdf+cDjPPLMJ/iachB0Drg38ck/8Gf4xue+k+n3fOKsl3LfIBjL6Pl9Pvv0A7zw8JhzakgKH3DvYyRyvm704lt67n1ENALgSXqQtFMslUJajRCKIi8pc03A0VRL5kfH+BDI8yy6OnkX3ZtCQaYzhIjHcsbStDXa6P64MR1bkeclwSddiETruEs9Oz7m4GCfqlp1xWcBApqlZTE7oChHlOUI62Ih77zHuBZjorWsMYbRaMRkMqEoChbzOVqpvgCeHc8ILlCtVhwfHzObzdjf30cIQTka4brsB+9975TUVBUihE5LYpFSkGUZbdv2O/Gj0ah/jXEOLyB2i0R0gJKa1niE0qgsRzSuS8UOKAXegxCS2WyBta+yt7dLlhXM53O2t3epqoof//Gf5CMf+Qjvec97USrj8PCQH/uxH+P8+fO8//3v54Mf/CA3b97k2Wef7UeSnn32Wd7znvfw0Y9+FOcc+/v7NE3D8fFxryVK4YvJbeuHf/iH+OxnP8NXfuVH+IZv+Aa++Zu/Ge89zz//POPxmCzLGI/H1HXN0dERh4eHtMagutGq3d1dfJcQnohNCuJ7/vnnee6557h06VKvJamqqu8ohRDY3NzsxeipS2WsoTFtTwTTPTJrTlUAu7tvTYQ14J1DsJb/+j2/iH/yyk+e9VIGDPgFIRMq2gMO+PIheNg/YvvfXuaHvv4pviL/2YFoDLjnoYTkinprn+P7iGh0iMKC/mclNbLIURqCVxhTsVhUVKsKKaPVbbR9DQgR8zTINc5G0XMIASskPrg+a6MnMVLhfcCakxl7Y2y3C39EtVp0Tk8F5W6O1hkHh0csVysmkw3MqOqF4R6YHe5jTENTNTSmZVSOyIucPC+4fesWeZ5z+fJlnLMc7B9gW4ttDXUVQ/YOjg6xxrC5sUlRFr0lrjGG1XxBtaoQItDUbTf+FUMKEdA2Fu9bnI0OVqZ1VG1DEJDlGW3b2QMrwcHBERsbm2S6oCjiObetAWFBGMaTaT/+tFqtkFJSlmXv0pTnOZ/+9Kd58skn2dvbYz6fY4zh9ddf57XXXmNnZ4ePfOQjfP3Xfz23bt3i1q1b1HXN888/z82bt/nwhz/MrVu3eOmll5jNZv1YkjEGpRShI1N5XrBcLvnhH/5hXn75Zb7xG7+Rb/qmb+LSpUt87nOf6zs30+mU6XSKj4yR5WoVMzKqqhe9l2XJeDwmhMD+/n6v75jP53zqU59id3eXPM97RzKtNcvlks3NTcqyJM/zXr+RCOFkMmF7e7t3nEpEJXU+BgwYMODt4vaHBJvft4Gfz896KfcHQsCvVux9puYfvP4hfuPWvxmSwge8K1CIt2ZKc18RjZTNEDwIPFJIUApk1o0cBbxbYY3HuYBSmtFoRFM30d3JR1F3cAZB6PMofAzk6EdoTtKdRex6BLkWgmf6pG1rLME5spEkkwJjGrw3EBxtu8KYGkQ8ZtNaVvNjmrqmWlUYa5krhZCSPM84ODxESMlyMSMvCqrlisV8STC+H7nBeepVhTO2L2BT0nciR6a1OBfwnm7sS5DnOVprVqua5bIiz/P+ijrvwXiEUMjeLhcO9g8ZjcbkecloFDBmEcXnAhaLxSntQex4nKSMQxw1u3XrFlevXu27NHH8LBboP/qjP8rOzg6XL1/m4sWLSCk756cFr732Gnt7e7z//e/n1VdfZX9/n7Zt+/dLhXscL4uC+tdee43v/d7v5UMf+hDve9/7+Kqv+iquXbvGzZs3WS6X/evK0YiiLAnQj5Wl78aYfqyprmvquu4JQ7K5LYqi18Ykp6k8z8k7RyofAkpHDcd8Pu87K6m7sr293Yf7DRgwYMDbxdPf/ud5Mv99PP5f/vRZL+W+QTCW4qXbvPSzV/ncEztcHtUMPY0B9zreamfuPiIaUbQsAKUkEMlFcArvZCfYtngvYlCeCASjMZ1zUFPVGBPwndVq2slO8/8B+iIzFc/JfSqEk1Z1sgf2zkdyIqIOo65jlyJ4j5QCY1qcd11XxFBVLd4HtC6pO+eoJGyXMmpH6qbB1DXj6QRnLG1t8DZ2XVJxLaXsx282Nzex1nJ0dNQRJIWUccTM+4AQGhBdmKBC66wTj8dujguBIGPORUwmF3T0CucDh0fH3fhPTlGUNG1LYx2poxTCSRc/rlF0lrjxPReLJfP5nNFoFEMO85yqijoQ5xwHBwcsFgsmkwnT6ZTRaMR0utkX55PJhAsXLnDz5k1eeumlvjOyngaexqqccxwdHfFv/+2/5datWzz44IM8/vjjPPLII1y7do3Dw0NW1Yr5Yo7vrmMIgel0Gl29nOtHnKLbVta7Sa1Wq1OkYjQa9d2vtm05Pj7Ge09ZlggpKYqCpml49dVXcc7xwAMP9K+rqqp3pRowYMCAXwie/S1/nkd3v+Osl3H/IHjCwRG7n77Kj3/L43xd+clhfGrAfYP7hGiEky8hkCIWs0pIgvRYAUKoLj1coZQGpzGBXjidikjnDJJA8GBMl49RNxjb4ryNhKFputfFYtq5gDW22/Xuci264hwBQio84JwnADrTGGvBg5TgvKVpK7yDTMWE7qqu+0wQnWXoPKNeVdi2pW0alJBkusA7v+aapHqSs1oukUKQFwVt5xC1sbFFUZSApG3jTj1B4lwsyLXSXTaIjbkaAjwizqAGepIhCAQhsM7TtDVKGcqiZGNzizBfdOTMA757URpn68iKiMTDOcPx8TFXrlxhPB737k5wQtiinsRwdHRECIE8L9jbO8fGxgbT6ZSrV6/yxBNPcP78eWazWe9adfv2bfb39zEmumzleY5SiqZpeP3116mqCiEEH/rQh3jiiSe4ffs2h4eHvHHtGrPFvNPbnGhyyrIkhEDTNP1jye0qCboTqZFSnhJ3LxYLZrNZH/gnpWQ0GrFYLHj99dfRWvdWuOtdnwFffgTn+PCf/E7+7R/5c2e9lAEDvii8+Cv/EvBHz3oZ9wdCILQt09daPjW/gtkZukkD7h/cJ0TjNEQnYEZKgotjUjGEMJGCGHhn2pa2NZguLyEVyM7bmJ7dxLEjZ+JYVd3W3Y656zUaIYA1jqqqu+Kys78VAiEVgmgJm+UFjbFIPKorUl2eozPV7ZIHZAgI5wmtw7cWH6JInDKA96gQwHlmh0dopdnY2I6J311oYKY1WipawFnHcrHsR3sW8wWz4xmTySaZjm5Wxtiuu2Bx3qOUREmNl4HgA0HEixlL/4CQkhAE3kPTtDgPQmmCEDTG4AKA7O+BEOnnFALZnU/3O4Rg0Tl/bW5ucnR01AfprXeT4KTDVNeRKBRFtAF+5ZVX2N7eZmNjg83NTXZ3d9nY2MA5R1VVrFYBa03fMbHWEkJgsVjwmc98hsViwYc+9CG2t7cpyoLRZMzrb7zBvOukJBF3SoRXSvUdjtlsRl3XjMfjfmQqdTzS+hNJqeua5TKmy083N8iyDO+jHubVV1+lrms2Nzdxzp1KCR/wZUYIPPi/vQx/5KwXMmDAgHsJwVryw5rnD89RP+jPejkDBnzZcH8Rja6JEEKIlrQerPOYNpID76KzlLMO520/h5/2j+OuvqK1LW3b9PoGZx3OGhpTdzvuJyNACXfuxEspUTqLKdtZEQt271EqoyxLfHD9iA6yIi8KSlEgrMQgKJTGhUDrW4QP4AJlUWCspVlVtEISgkQQA+CS2DjpFCCOC81nczY3N9FKceP6bWZ2xng8QesMuvwNEF2HpjsHRNRkiBB9vELs3qjQ5YaLaNFqmhYpA1pl2OAxpkZ03aT0RXeN43U5EepHR7Bo1btYLHpNScqTUEr1X6njFM+zoK5jLkgaTVosFr17UyIC3vsu2C/0x01heCk0b7FY8Pzzz9M0De9///sZjUaEEHjs8ccB+mC+mES+4PDwEK01Ozs7QMzb2N/fx3vfk5zt7e2uM+bY3d2lLMt+hG0+n3Pt+nXK4yOm0ynb29sdGYrhf1mWURQF0+n0S/CXY8BbhX39Db7hD/we/vWf/YtnvZQBAwbcIwg+oI5X7N/eZRXAhUEQPuD+wNsgGmtuTWHtj2u//uIgQHypRkIEBEH6X+hGolw3VuQD/TiKkAqpM3TIEd5gVYsQEqkkGk0IghAyjImFn5SSlhZnTVecB4SQXW6CwLtAyEQ/mhND/Ay+G2fSSndp13HkajKdUuQlLsTxJAQoqSmLEQVFp9sQ0XJXQMDjvUMLzagoMWaOklGRUi0XKBW1AgSPMSOcczhnybK8H+mpm4ZROWJjY5Pj2YL5fE5RFicdB6lA+D50UEgJIuoxrLPYTqMSZLzWobueWkeNh/ex66B0/LjFHXkR3Ti8w4c02gZCgkAipOgICCyXS3Z2dijLsi/m7wyvS10C56LGBWTfnUjEIqGuY3fJd3qYLNPdWiNShgXQu0A9++yzPPLIIzjvuHHzJg8//DAf/MAHeOWVV3jxxRfRSpFlGUeH0dlr0iWKb21u4qzFGsOtmzepViu2trbI8pz5bBbDFZXi3LlznNvbYzafc/3GDaqq4oEHHuDcuXPcvn2b1WpFVVWMRqMhR+OsEQLlzeasVzFgwIB7CcHD8YLixcu8/kumPKTdIAgfcF/gLRMNEUSsJFNRGLrxlvT9i0CUaAN8ccf5uSAQEBRSiDi9EzzOWhAeqQVSa2SekckAIiBlQAqoW4PMHGWmIThM2+CtifP5RYk1Bm89VmUQZFcEhq7QznrxtTUtQgaEDCgt0E71u/Or5Yq6rvE2IIKM19QJtNAgIJc5lAItM47qQ4w8cZIqJyWmNQQRaEwTLXm1pG0MPoCzBoGkDZ7lXJPnMZtBSHpdxXK5ioJuKVBZJAd120Yti4xdDdu5KgFkWdanZBMEwYVIYILtR56C98ius4NMHYxOQB5OxOAQovB+TawPvheKhxBYrVY0TcP29nafi2Gt7btGaQQpz/PumLI7RiAE17l5pc5F1uknTlyv6rruHcPKsuyPm2xnlVIcHx/z3HPP8fDDj7A53eS5p5/l9Vff4MqVy3zkwx/h+PiY69evU2ZlDEg8PKZtW6RS7O3uUa4Rtzxpaqoa2xqcMbi2ZXdnl+nGlK2tLV595RV+4hM/zuOPP87m5iZKSlaLJUVe4Iz9kv09GTBgwIABXwKEQJjPOf8zln9w9JV89MKPDoLwAfcF7pvRqRC3/7txotCJsWMxK6VAdn/fnfdYH7DWY4xHypzRWCGFJ3iDluC70SnfOReFAEppVHeQmAA+7q1YQwjoTCNkLKOzLO7ICxO1Em3b9laoAggudCF/kcQpFJkMKK3xIrZcgwSlFZnO4thV8AQ8UkXHIwI46zGtRWUSIcC0FXmekWcZtnNJEkLgQ4gBfTojy3LqpomjT9biWtddv06DAVjvo1WwVmSIqNfoMjkg0kUh17UYHWnghCClDkR83olN7GmIfixqtVqxsbHRkYnQHyNlZJzoPBKRCf31g+g0FsejaoyRvVVucp9KmoiqqiiKok/yBmiaBuccdV1jreOp976P3d09nn76aa698QYXL13i6pUrfOiDHyKEwOHhIa+//jrXrl1jsZhHt6u2Jc9ziqIg6wL/xmVMeG+bloP9A0xr2LGG7c1Nssce4/j4mBdeeIHt7W3OnTuHFFGHYieTd/hvx4C3Cz1v+H8dPcjv3371rJcyYMCAewTBWqbPHPH3PvcVfMfeD/H4kKcx4D7AfUM0EsLamE76c7QmXdMGyDj6JLKMIpMgPN61mMYhpERpFYtRIcAHmqbtRrFsP0ef5vlT1yKN4oQQGI1GOOsIgT5/wRjT51Ok16W1AP0ue1EUfVHdW7N6F8nJWpZHlmWY1uJdRZblKKW7HA+LVDFI0BiD85FAGGNRPjpY5d01Sc5JKfdDdJkh3nuENWiRx+vR6SRSGN26/uL0xefU4/Fe+DsIwp33Ko65zedzdnd3GY/HrFarngik67RuU5vu4/r3Ex3IyXulRO50fslyNiHpOBKRWS6X1HVLVdU8+eSTPPnkk7zwwgs89+yzvPH662xvb3Pp0iXOnz/P+973Ph5//HGWywWLxTw6Vr3xBjdu3GBzc5OrV6+yt7fHZDLpR9KqqmL52muMxmP2zp1jb2+vd9W6fv16/1mazWZ8+CNf8Qv7CzDgHYH/mc/yV//7f5/f/yf+/FkvZcCAAfcIgnNwc5/RT53jRz76KA9MX2Ms8p//hQMG3MO474gG0GsI1ovRJCz2XgGBTEnIFN61WNvgo2USUkb726LIUQhM2/ZkJYhY6Pfaje6xVCAmwiE6fUMSVwN9UZ+K4HUxeRI6G+v6fAhjDHVd91a664nTSimUVCiRYVuPzmJ6eNO0cZxHKnyIzlDWOqTWKKVpW4vOij7HIRGddOz1YD/nHM43SKliEN9aZ2Ed61qKAD1BWC/434xkpOckYXbSVWxubtI0TX//EhG6042pzyvx/tQ1TV/r4X3rXRK6NQoh+syKyWRCWZZorbH2JG/j8ccf5+GHH+bWrVtRyH3tGi+++GL/mosXL7K1tRl1KB0hMsYwn8+5ffs2Ukq2trb6kEdrLcfzGQeHhxzPZuzu7lIURa8vSeNidV1/8X8JBnzR2P3UjP/Ds7+cv//kPznrpQwYMOBeQAiExZJzP9vyN17/RXzTky9wVeihqzHgXY37h2jcIVqXUnQz81nvcBSTv9suCSIF7zlcJ4DO8wxJwLuWtq6om7ovRuNoFLRtS1VVaK1pmoaqqk5C/bpi11rbi4CVOinEE9EAekJire13tQMCuea0lELhUgGaimUpJVJItO6SypVG64zgBVVd0RrTdTU81npkcAQvQEicb/oCPAmmsyzrbWWBNaIRO0HrFGGdiLwZIrk63W1IP99JNu507ZrP50w7kXXKpUivS9c3vkf4vGOvk7Z0fsluNnU20jVP411aa8qyJMuyfrRtPJ6S5wXHx8c8/fTTPPzww+zs7PQdDyll70B1fHxMlun+g5d0H+k5RVHgnGM8Hkcik2W9ULzq7G7Xz++NN95ASsnVq1ffyid+wJcY4ac+zTP/v6+FJ896JQMGDLhX4FtD+cI+z3zyKp95eI+LowWKgWgMePfiviEaMTojFaapiJVIGQt+H9zJjvxacZcsUQXQmIa6rvq8g9VySdtEV6qyzGlNnOVPSdB1XZ/afU4752kcJ89zrDnRLKxb32ZZ1he9xhiqqkIqjetsWNPr0yhVIgLpe/ABgkDrPBIIG3fUszzH1A3eg1IZSqVQQY/SkazUdd0Hw6VrkLoF6/a48RqeHk1KP/fXfd3GFrosjTe5P1/AUCAV/Om6bm5uUhRF7yi1/rz17gXQj8CtB+cBp563PkZVlmXv6JTSw6WUlGW5do4n75XcoKbTaU9S8jxna2uLtm07MhQ1OevXMX1Za5nNZjRN0yd/Tzdj0KDtcjiapmFjI+ZqGGN4/vnnOTg44Fu+9Ze/pc/9gC8tHv/Lb/BLftGv5V996HvPeikDBgy4FxA87B+x98lL/H9/8cf46JV/ygWVnfWqBgz4kuG+IRqQgvrWAvs40QD09ra9UFl0BEMDlqZpmc/nzI+OWC5ntHVzQkJEtENtTdNrMZbLZZ/LkEiDlJL5fN7voFvjPy9vI9nCrluypsLWhViYGmOYTqfs7e31pAToXaFOksw9xnpA9sdVUqFlzODIshwpFa2xsTPRWT0l29tUpKeiu+9kJEF3Z+G7jvWuyud1KADZEZj1cbL16/5m5CH9nELxUodlvUt055hU+lonGr32pjuP1LERa+ecCMdoNOqv6dHREUCXZRGdrZJD1WKxYLlcopSiLEu2trb6hPHU1Wnb5pQ+JB0/yzKstT1xzfOcum3Y29vj3LlzeO85PDzkxo0bvYBcSsn+/v7b++AP+JLBvvgyb9z+yrNexoABA+4VhIBfrdh+dskPPvce3rjwA+xINzhQDXjX4r4iGpAsU090GqlQxXHS6UhFK+CDp1qtWMwPWcyPaeoa5xxZphE+EFzUXUgpyXTWF7ypyMzznPF43AuLm6bpiIGA5YmtakoeT2tad2YqyzKmTxvbO1Ql0XIacUq/X08wb2uLNdGtqixHUSNgfRS+C4FSEqk0gUQKBG7tvddHiNI5redREAJBfP4YVEIiAOmaAEh1ct3f7HVvNkIVr3ccl1oul91bn+5GfKEuSiIUqZuURN93XvfeBasbV0tdnXQOJyNslqKIHY6Ut5HI3jrBSC5a0WEshv9Za3sCmbQXd17buqq5vb+Pv32bzc1NyrJkuVxydHTEarXq9SID7h488ds/zW/4l9/Edz/6A2e9lAEDBtwDCMaibxyjn7/C5z52ifflN2FI1RjwLsV9RzTuxLqrE3QuVN7jfMBbR7WqWK2WtMYQa/HOPtYHnEguU2UkJTiUVrRt2xfp6za3aed7NBqxWlUAfeG5TjBScZyK39ThMO5k9z12LMxJt6PTaiTRdNO0eAsiaHQeKIoRQkShu3Me6x1KaqTS3fsohBQxZbxDKuJTJyAV5HCHe9Ta9VzfuV/vUnjv8SHg1rou69/vHLu6szOSiv00ZrQ+1rVONtJ6112+koYFTgTV6yQiPS+RztTNqKp4j8bjMdPplLojmanzYYzpyUNyG0sp44mY1HXMD0nOYsvlktlsxvHxMdPptB+L6olK22B9DECsqqq/7ql7s267O+DuQDAt1g8z1gMGDHiLCJ4wm7PxEvzY4nG+dfIGhRjGpwa8O3HfE42+SPWnR6cEUbjsg0eq2FXIFJhMYU3DYjaLNrf5CCWjG9FkMun1E2lnPLkXRWvUem1kZo7WmiLX/WhSCCcp1m3b9sVwHNnJUM3JznwIoXdfSmM7p87JeRyC4GWvF4np6woEUVsiHVp7YoMjjoG5zgkrrWfd3SkduycRUoKQPdF4M+eo0xebftJqnVh8IVvbda1Fen4anVoXgK93TO7M1EhIz73ThSqtJRGE5AyVZRllWZ4iFEIIsizvEuDptSPGmN6lqyiK/n7FTlMKUdT9NXTO0TRN/7rU9ZpOp2gXc1HGk8mpz1CWxWyWzc3NflRuwN2Dn37+YW4/suScGjJOBgwY8PMgBMJyxfR1w0/ceojj847pkKkx4F2KgWj0Y1InO+haa0Tw4DLKoiDLBMFbbKNoJNR4Mp2hcokIsfgdjcZYZ/qRKaUUVVXRNA2LxaInIGmMKoSYp6FVRtM0fQcjOUmlYjdZn6Yd77RrntYOsRBPIXNpnKfIC9rGY00aExNUVYPOMryLblMhpO+BEARKK4Q6ndGxbhmb1pEeD303Q5y+liGc6mT03QklEFL1Yvs3s7a9k2Ssd1TWA/bu1IGsj1ytj52tdzrWCVM6drpX60RmsVj0YvuyLFFKxcwR52LeiDK9CL+3K+6IX+pwTCYTptMpIXiMaXtB//p9TiQm6V6stWRFzngywXvPZDIhhBCT5a3l4ODgVN7KgLsHT/72n+J//+zjfPvW9bNeyoABA+4BBGspbla8fGObg6cyrg7N6gHvUtz3RGMdqaAlBCQBGSxSjLBOYk2FMyez/+WoxLWWuqnRSjMalRzNosNUKlxTIay1ZjQa9aLk2WyGFILRaITo9BFlWfYOVWnH21p7an2BcBLG19myWmsJPkSSsFbASqmQwtGKgO/0F8GD61K8o+uRWxNlBzwe4U+cr9L40boYfJ14+BA6vcfp3IpUwH+efmJt3CqRgPVxrP4834RspJ39FDr4Zk5dqcuR7sGpsMGOZKyTj3VdRjrf5Aq2XvxvbGwwmUw64fYKY8ypwMD1LklKHD95zPWfrWRnm7JPyrIkz/N+bdZaGtNiXbRUzrKMjY0NnLWsVivquma1WjEej7/IT/qALwX+5Hf/Or7tO/5HtuTorJcyYMCAuxzBB9R8hby1wXW3yQdYDSqNAe9K3GdEI9zxfS0JPCiclEip8TIgpAOpkFojvI0ZFlKR5TltqzFNy2K1IFM6FoPO0baWWGMLXKenSJapcTe8pVqtODw8YDrdQGcKbwNZpiiKPO582xYho2g9ahJqfPB4F/DOoVS05U3WuVLEsamiLNFaIRBdoSuQhcbbllVdI6WiLEf4AHUD2q/lXAgJIirkE/lY7xbYrouQ5Rm5jGGEviMR9g7dSCrQYW3kqdOdeO/RUiKEPFXg39nVSOQhPZ70DkmvkMjU+vPT9/WRrESOUoDiugUu8HnEoyea0K+3qupTOo/Y1cnZ3NxASsVsNmOxWADh87oa8Tg2ksIQzQWsMRjTQksMOhSwMZmysbnJeDTCeUfbWfe+9tprlEXBxsYGIgSauqZumiGw7y7FQ//Nj3D7dzq2humHAQMG/HwIHrGsKPYlL7XnMOWLg/PUgHcl7hKi0YW+dbXmnYLg9QJy7SVv+z1Ojn0yYnMypw/eB5x0WBxBKESWE4THWYkXEpFptCoIS0HrDEor8jzDOkPbWLwJgAQCSmhG5RgBeO9oTYtpa46ODrGmRSuQMiC1IASBkAEhA5LoBmWtpG0amsZinaVtDa2NGg1CLL7rqsF7gXOSPBsThOisdgXWOoQEnUmou+BBp9B5RhYUQWRQFlF/4GLwnnWe4DuXJecIAgQCHzwIMM4ilERLiSdgvcc7d6qzEFI3QwqCSGRD4kV02jDG9uNG60Tjzs7Guv4i6RvW8zzSyFEiEHd2NtbJw3oHZd2eN75HvO/eW0KA8XjC5uZWL9z23lLXLSAoirI7hsW5UZeZsUGWKeq6wVrThTQuWS4X5HlGluUgJaa1tE2LdSbqgQSYVUXrHK1xBKmQKgM8pmlQSmPqhsPb++xnGaPxiLZpaU1LtVy93Q//gC8Tvv07/yP+5V/6S2e9jAEDBtwDCHXD6Fbgc9VlzOZzZ72cAQO+JLhLiMYJ7hyb+bnw5hFvP+fRT17bZWmcjPqkwlejVRSBu2DxIe7IS6XxweJcG9O5tYIuddtZ248yKZWR57rb9XdY29K2UWwevCcEhxBxPMe0bReoF9ejteqLZOcsPji00sggAI9pa6QEJfOugFYQoG0sbWuBjCyXyETOJF23JKOuG4xpUVqilcAHGc8/SJAB7zuttji5DwKBkAIZJEHEx633ELoU7kQUfOg1Lmn0TKiuUwEEogYmdWbuFGPH+3HSlUiPrZOFdT3Kna8BTmk3ElIHYl0cfspdrCMa8XMQaFuD1m2XeyLROovk0zmapqVtDVLG/BVjok4jJYefO7eHUicdjrquqeqaqm7IyxKlNeMuN6Oua5omZq4YY5m7BdY5lqsVwTtMXTOdTinLkvFoRF3XHB0exfRyddf9lR2whuL7fhoXBlHngAEDfh6EQGgaJjccnz26xPz80A0d8O7EULVw2mJVKYXzCukV2K7glgKtM4xr+l31LMuQARQSY1OORcNkovuRHNcF4Z0UujG8T+voSFTVNWVxQnbWd92NaftAwDS/H4lAsrvNMK3HO4u1hrqu8N4hZIFSoh9Z0lqSFzlNG3fbVabRWd6NEYVO0JzhrCcoAeokGV1KiZACH0IkGiF0AnKLFBIpACmx4WRUqrugILtxqeA7MXq8jsGeZEbcmdj9ZuLw9d+nn9e7H3fa06ZuRdLJpOetB/+d7np4hDgJ9UsJ5HBCXpRS/fgUHXVKeonRaMRkMsE5x2g04sKFCzzwwAPM53Nef+N19vcPok1tZ1GcxP1pRCvd8+RCFbzHNvHYW1tbbG1t9W5mEJ2uUnr5gLsTi9CwJQadxoABA35uBGMpb9W8eGuXg8c1l4dNigHvQgxEg5Ngud65KJzkhsffS0TaGe80GFJqUIFgHda6PpVbykgcmrbBmKbPqEi762VRRtcnF0XNRppedLxu1ZoK4zRmpJRiPBoxGo9RssCaQKMdRjqEAGNNHMvBkWWKEDxKapAnXQZjDHXdUArRjwxFN6jYlVBKIqSK3Qfv+sdEZ0vrvO+6Eg5kQIqOKKwV8QBSiNjDWBOLSyljhySTn3eOb2Z1u07+kuYj/S4RlTtTxZMG406h+J3HTtczisVjN2s9V+NOW911oXkkMiePGWOYz+cnaemdyF8pxeXLl9nY2GT/8IhFZ2/ctu2prkxKjV8X3Gd5Tts0zGYzsixje3ubsix7C9230u0bcEbwjt/0sV/DP/rp7z/rlQwYMOAuR3AOdbDEXT/PG3aL92WDIHzAuw/3PdFIRd96zoL3Huc9ohOAe+8IITozhXCSIC6EOHFegt61qKpWLJYLvHeUZYnWUbeh+4RuSVVVCCFPBcAl+1alVC94Tu+jtWYymVKUYwiSJmvJ8qxb94m9qhAQQrTCzbQk+NOFadu2cVRLps6LQirVPS9qHpwQSCGQOj4m+8JbYF1X6DuHk13R3pGTNE4lgNATgY6oiW4MK1NrTlf+1L24s6Nx52hU+nMq+O8MEFx3ygJOHX89ayNd0zzPUUp3pMv3blLJKrht296x6uTYofs8nOhA1onPuujceY/Smt3dXZTWzGYzqqo65VKVUs9Tx0QAitDbGadRq7Ise2vk1N0YMGDAgAH3MJIg/LbkdbODHy0YEsIHvNtw3xONO0PcoCtQA507ksa5KJh2zq9ZtEaNhBAeJWPGg/eeuqmoqhXL5RIIWGtip6DrbJTlCBA0TYtzHudMP6+f53kvcs7z/NROvpQSJTRKKqyNBXaRZ0gRhd9N07Iuck96Ex88YS0t2/por5rlXaGtdXSd6ggUkRsQva0EKukjhEBxYukaOiG4lxLVESznXKfPUP11Wr/OwZ90QdI613UVdwr+14v2ddF36g7d2a1Y70rdmanxZqTGWovWWT8yleyE122CE9lIHQ8pU7r6CdGRUvZuU2mEKnUpAiCU/jyxe7LebZrm9H0WghB875TlnKOu6zXtzkn3bMDdiVDX/Lrn/j3+7hP/7KyXMmDAgLscwVpUAytf4EL4hYhPBwy4q3HXEI00qnTnuMvP9fx36m/kegGYxmo8HokiAN5FETPInlQ4IwkiWbXGcDdrLVW16jsU4Fmtll34W9G5Jwna1iKEIoRYNLZt2xe+iVSkkZo0OhQ1F5Kulo/EJo8C8qpqaNvQh9ppnXejQRqC7bQamrIUVE1N2xqUPhFCh856NQrYPUoKlBQIAhLwUhC6MSslJUpJQvDg4+6+CvpUQR+c67M1QCRPMQB8iJ2iN7vuSa9wZxDfnQLwdetb+LkNBO4cofp84iE68kCvxVitVqfITeqgxPc7EZQnspHWY61lPp+zWq1OgvwA6wPG2l6HkdaQ3u8UwRUCKU46OKm75ZxjsVj0KeUD7l64o2OWf/TD8HfPeiUDBgy462EtqoaVH4JYB7w7cZcQjVRwfuGE6P6ZfdEoEO8Az0hF43pytHMOLFjraFNxSOwIeJGBz2nrGiUlQmd47boMCkMSCwsRuk5D04mAC7wPSBm7IVmW98LqVKRWVdWLklMRelJ8K4TUOOc7UXmOEArnYmhgfM90HhKt8lMFrdIaKRXWO0y38940TXTX8gGCQAqJIKB1FMR77xEEFILW275wliLqMHr7W++RKnYZnPfxOsgT16lEOqSWdLrxz9MarBfsyco2FdSpg5E+D3dqO9YJy/q42fpx11PA122T15+TZRkATdP09219XCsK2CVSfj5JSM9LmR/Q6S+UwhiL7d4jvS4lhK+TqNQVkloh1joY6ZxSl2XQaAwYMGDAuwAhgHPoOjB3JR7/879mwIB7DHcJ0TjBmwmC4fPHarr+xzv+/qnAbZqWqq5oW4NAoFSGlOCEw7WyE4hrpPZ4nWOt64gBKAVSxvyMOPcfA/SapkEgkUKRZzkQdQHR5tRQ1zWj0eiUKHl9tCi6VTU4ZyjLEqUUTZOck+h1GiBgLBDkfXGrtcK7WExn1uJ8wHQp1IGAkrqz0g1IAVpCLPM7AibABRdzMkJAigBSIjjdVQA6u1vfEw0hU//pdBdifcwpkYH0eHJlSl2e9Od10vBmuJN0rBMYOOkUpGR0cH2HqF//mlYnyzKKoug7T97bfgwtuT/d+T6p6+CcQ0iJ63yDkxPWFxKzK6UIQuCshTWimToqIQSKomAymbzNT/WAAQMGDLgbEZxHtmDCoM0Y8O7EXUM01sdrvtxY3922XSaG8w4hJHleooRHSU+wAe9UJAp5iQstzrVRDN6RjCzT/aiLlHGW3hjLcrGkNS1axRA3KTV5XvZjU1FEXp1yOUojPidFbNRSOBc6ciEBj5SCvMg6kXnLcrEieIGYCrIsjlMFD1Y4suApAzRtizEOZxt8gCKXZOokR0NKEcPCu25KlmlIo12A1BqEIjgRMzjW9RJ3XN/+sa7r8nm/v2OHft3Wd10/s/7z5xPPz7+Xd45MpWOcpH/b3nVqveORjmGt7cXh0ZZYs1pF96g4ilb2CeDr3YlIAJtOoxH646fxqzQal8ao7hwTE6i+u9E0Tf/83d1dpIxGAgMGDBgw4F0A75E20Pi7phwbMOAdxV3yyf58Ue/PV1C+Y+8cTge5pWJWSU1ZagSOECzB1rTO42w3ry/iTrVds0cVBmSQ6Ewh25QPIVitVsyO5wghKYqCEALj8YS8UGid9QX1uk1qdKvSWGv7cajgQUqFVrrb5W5p2wYhAmWZU+SKEFYs5hVVVXdi7xIpY5aGlA4fVe5Re+LbPksiXmtQorNyDSA6NyYpJUKJU/2jeGaCIARerLlFpWspu8J+TdwWOhF1uu7r39c1C35tzGh9jGrdQvYLYf05b9YRu/P1Kbm8H1uSshflt23bdxOSM1TMwRA9CUhBjVLGe7sudO9OOpKXOz7XwKnxq153IgR5NyKVyE9ynqrrms3NTXZ3d9/ux3zAlxuf+Fk+9n/7ffzkH//zZ72SAQMG3M0IAWnB+qGjMeDdibuDaIT4n1PjN1/mOfRU2CUrWaFCnJf0sai0ProSWedoG4OzLW3Tgg/oTGM5yZ2Iu9ZJyKtjCrg1EGLxaYzr8ioyhOBUzkbaRU/i8KThSNa5KbAP4XDOxOMSx6qkyGhbT10ZnEt2twGlFUVRIoTqiIbEOk+W+WjP2xXLyV3KeR9zNAiojjAE5/DeQWftGlLBHlR0oOruV7TK9fF97ryXoRuruoNkvJlmYt1hav3+0BXjX4hs3NnBSF9KnbRr1kevYvfoxIUqYV2EncT9SRyexqzWyc+67iJ1xay18bpIF9297nhNIi+nxsgAJ06I1/q9r6qqNx0YcJcjBLJq0NIMGDDg54H3KBM7Gu4MJzsGDPhS4e4gGh1616I7CtAv8Ox37H3fbKdZhID1AS9Ev3svZNxhF1LStoa2bciURhLJSF4UBO86C1JL6QPWetrWMioFq1XV2dAKmrpG4MhyjZTxHeLMv0BKseZGBaPRKArJxUmehZBR3J1WFxXXoJQkyzQh2ChCdpYsz9BZQaY0Tmts72gk0UrirIjrtg6pA87Z6A6Vwv66sD6RRN2is8INJ7ki62QhCtg7QhLWtBoirfXNx6XSvV53YfIhEHxciwyyk9qfununPhNSSjrBSjxGd19tGlsSoksrj1a7Yq2oT+/tnENr3Tt/3ZmRIaXqiUHq0MDp4MB14hR86DtT6flSyn506lRgJPS6nUiyTkIIky4nJZcPGDBgwIB7G8F5soXj9dUWTRjE4APefbg7iIaI/xHQuyzdWYiujze90+NUaVxnfXyHtIPuXS8GVzrD2wwpMwLR3tYLAE8QAq1LAJrWI5VFa9CZI8sKQhCopo0z9wicknivscYhlaIoRjSNIQTZibrr7s8BpTK8A60loQuBC8EjUORZiRQWQZfuLQVFmUHwGBewbcB4g84bxiMR80C8RYqAkqCVwHVp5tYZhMowIeADZCq6Jrmu4Peh64ggEFICEtu6GGTICdFQ8iQZHH9iWxyL8xMicee42ptlXUCIjSAhQMk4gtQdr+M6KUGxe484tkXqFMQjdONg8XmJcERrX/rP3TrRWScBp/UdmjzP+sdS7kkiEGncq3fQAnzoUtXvCBh8M9cq0Z2OtQ4hfNfVgLY1LBbLtSyWAQMGDBjwboBeGG4spsx9YE96lJA//4sGDLhHcHcQjY5kJBnxOpG4cxTmS63ZOCl+HQSPD76rZmUcEwoxqE9neT9eFLxD6M4WV4ALEiE0UoPWhnIUC8M8z/FpFEpFYXdVN5RFwXS6QZ1FVyrnHNY4tIquVd4FbHBIeULAnI3ZHpkuUFJjbSp2A5kSuExifMA4j7cGIRY47wBP8A6tBHiBl3EtrhN+Gw9BSIKkI1Iy3hchCXSkIkQL3L47wWn9gRSyJ4zdE75gA+p0F+SkGA+JAaR/cKWM+RyAkF1noyMOvcJcRIKTAgcDcVotrI1gueBxLrpMZVohO4K2jnUBfrIbBrpQP0MInvF43IvBk4YikY5TuhMRSY2CfhQrjU/BSQdlndRITghZCK67eJa6boA5eT4kgw8YMGDAuwLBo1aGo+MJt9yIh3QYssEHvKtwlxCNL4y3Nkb1xeHUmEsqmOMj0AmiYwHpQaYRqmgH632cw48dAXfiLqQUGoHWGVq73jlITSbxvYSIIW51Q6YzsizrcxtSIZrOOY3RrI/qJFejlCB9In7uugsyjlL5EHUBq8qDCJRFhtYq7ugHcNahpUIogemOr7RGhpNdfNF1AGQ3KuV997ykR+m6BUlDsW4tlVZ0MuF0ItReJxXryd8n9zz+J72/cw4f4j/Cd1rDrou5e5Kx1jHouyqcWBhH21/Z2+/eGcCXOhtZlq1lWNCLwEejEaPRqM9e8d739yXdN6Xj+FN6j/XP2HoH50RMH8nR+ucxaTKS9mPAgAEDBryLYD1+kXHTbWDCEZkYqMaAdw/ueqKxjlMuQp0m4UuFNK0D4JzHddaucee5C5VTihBOZu+jNiPuagtOk6TkSqTHYwihL0hTsXky/y97IXL6c/q5aUxf0FZV1f+cdsqB3qmqH0/quwWxYHWZIpNxNx8fsMYRMoEU8Rw9p4v3k3OW/RrjmgVSdgTBB+iChoKPY2T9xVsr9gFEOK3DuHN06M4MkTT+dCcZXB9DuvN4b2YkcKe1LEQtRHCeIiv6x9a1Jnd+pWRw76NAPIXzpc5GTIDPqaqKpmlojUGFEIX/7mRE7E7x+2mELthRniJf6dxOEsoHDBgwYMA9D6VAgqwkt+wmTbhNEfQwPjXgXYN7gmisF2drv+WdYBqpaF3fIZdSIroCOlqyWoy1UZTcuQURCkQ3hmTFHW5DzoFfs8KVkjwvyLMcJQR1XXdjVqIfzfHe965FcDLrv+4+ZIzt7V7TCM66gF5rHXfRlYrjQCEV/ALwnYOSQSlBrmNXRqmo73A4lA94TkbU1sd61tcULV0dznmUjAntvbaGTtitVX+X1keqQid2OzVq1b32zqI6JYqvazruvF93FuN3HrvXjaxdt/Q65xzeOiQn5COd57oDVro3SXuRuk3GmP5r3SEqy7JI6tqWtmlAxmT2dMz0+uQmdUqbIkQka2vnnK57Ih8D0bg3sPMPP8NjX/N7eOHb/uJZL2XAgAF3I4RAdAm/qha81u6yDM8xZRifGvDuwT1BNBJ6spFm89+hY97pOtVrBoLrAvLibj8EMi1iWF3I8N5gWhlHlLynruvekjZ4jzNx5EUphexIke8eTyQhuR01TUNd130w3HpX4WS3X5Fl6pSlaiqEkytSOh4CpJIoBcZG9yfn0viNIJRFdJXSCinjddCZBiStNX1RD/E1icSkMS6X7G55ExvaEFD9P5Mn3QUfPMHxeYVyIkvrhEGIGBgIyfHq5Fqt27um190p2r6zq5EK9DQ2JWXUyITQdTbWnp/yMNJnInWeYhp4jnOW1WrVdzLaNor814MG03qdNZGgvkkH5s7P3cn1ODm3dKz1TtdoNIjB7wW42Yzy1lAuDBgw4AtASFCKoCWqErxS7XK8pdiVbhifGvCugQhvNmcyYMCAAQMGDBgwYMCAAV8EhhmMAQMGDBgwYMCAAQMGvOMYiMaAAQMGDBgwYMCAAQPecQxEY8CAAQMGDBgwYMCAAe84BqIxYMCAAQMGDBgwYMCAdxwD0RgwYMCAAQMGDBgwYMA7joFoDBgwYMCAAQMGDBgw4B3HQDQGDBgwYMCAAQMGDBjwjmMgGgMGDBgwYMCAAQMGDHjHMRCNAQMGDBgwYMCAAQMGvOP4/wMMRZGLEkohGQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "batch = next(iter(test_dataloader))\n", "with torch.inference_mode():\n", " model.eval()\n", " logits = model(batch[\"image\"])\n", "pr_masks = logits.sigmoid()\n", "for idx, (image, gt_mask, pr_mask) in enumerate(\n", " zip(batch[\"image\"], batch[\"mask\"], pr_masks)\n", "):\n", " if idx <= 4:\n", " plt.figure(figsize=(10, 5))\n", " plt.subplot(1, 3, 1)\n", " plt.imshow(image.numpy().transpose(1, 2, 0))\n", " plt.title(\"Image\")\n", " plt.axis(\"off\")\n", "\n", " plt.subplot(1, 3, 2)\n", " plt.imshow(gt_mask.numpy().squeeze())\n", " plt.title(\"Ground truth\")\n", " plt.axis(\"off\")\n", "\n", " plt.subplot(1, 3, 3)\n", " plt.imshow(pr_mask.numpy().squeeze())\n", " plt.title(\"Prediction\")\n", " plt.axis(\"off\")\n", " plt.show()\n", " else:\n", " break" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "Binary segmentation intro", "provenance": [] }, "kaggle": { "accelerator": "gpu", "dataSources": [], "dockerImageVersionId": 30746, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "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": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/camvid_segmentation_multiclass.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/camvid_segmentation_multiclass.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Install package" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2024-08-23T11:29:43.835761Z", "iopub.status.busy": "2024-08-23T11:29:43.835234Z", "iopub.status.idle": "2024-08-23T11:29:58.330049Z", "shell.execute_reply": "2024-08-23T11:29:58.328593Z", "shell.execute_reply.started": "2024-08-23T11:29:43.835708Z" }, "trusted": true }, "outputs": [], "source": [ "%%capture\n", "%pip install --upgrade segmentation-models-pytorch lightning albumentations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading data\n", "For this example we will use CamVid dataset. It is a set of:\n", " - train images + segmentation masks\n", " - validation images + segmentation masks\n", " - test images + segmentation masks\n", "\n", "All images have 320 pixels height and 480 pixels width. For more inforamtion about dataset visit http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:29:58.332851Z", "iopub.status.busy": "2024-08-23T11:29:58.332004Z", "iopub.status.idle": "2024-08-23T11:29:59.374217Z", "shell.execute_reply": "2024-08-23T11:29:59.372856Z", "shell.execute_reply.started": "2024-08-23T11:29:58.332781Z" }, "trusted": true }, "outputs": [], "source": [ "%%capture\n", "# Download Data\n", "!git clone https://github.com/alexgkendall/SegNet-Tutorial ./data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:29:59.376434Z", "iopub.status.busy": "2024-08-23T11:29:59.376015Z", "iopub.status.idle": "2024-08-23T11:30:06.026067Z", "shell.execute_reply": "2024-08-23T11:30:06.024970Z", "shell.execute_reply.started": "2024-08-23T11:29:59.376395Z" }, "trusted": true }, "outputs": [], "source": [ "import os\n", "import matplotlib.pyplot as plt\n", "from torch.utils.data import DataLoader\n", "from torch.utils.data import Dataset as BaseDataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.030406Z", "iopub.status.busy": "2024-08-23T11:30:06.029938Z", "iopub.status.idle": "2024-08-23T11:30:06.037431Z", "shell.execute_reply": "2024-08-23T11:30:06.036521Z", "shell.execute_reply.started": "2024-08-23T11:30:06.030368Z" }, "trusted": true }, "outputs": [], "source": [ "DATA_DIR = \"./data/CamVid/\"\n", "\n", "x_train_dir = os.path.join(DATA_DIR, \"train\")\n", "y_train_dir = os.path.join(DATA_DIR, \"trainannot\")\n", "\n", "x_valid_dir = os.path.join(DATA_DIR, \"val\")\n", "y_valid_dir = os.path.join(DATA_DIR, \"valannot\")\n", "\n", "x_test_dir = os.path.join(DATA_DIR, \"test\")\n", "y_test_dir = os.path.join(DATA_DIR, \"testannot\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataloader\n", "Writing helper class for data extraction, tranformation and preprocessing\n", "https://pytorch.org/docs/stable/data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.039244Z", "iopub.status.busy": "2024-08-23T11:30:06.038840Z", "iopub.status.idle": "2024-08-23T11:30:06.054834Z", "shell.execute_reply": "2024-08-23T11:30:06.053760Z", "shell.execute_reply.started": "2024-08-23T11:30:06.039198Z" }, "trusted": true }, "outputs": [], "source": [ "import os\n", "import cv2\n", "import numpy as np\n", "import albumentations as A\n", "\n", "\n", "class Dataset(BaseDataset):\n", " CLASSES = [\n", " \"sky\",\n", " \"building\",\n", " \"pole\",\n", " \"road\",\n", " \"pavement\",\n", " \"tree\",\n", " \"signsymbol\",\n", " \"fence\",\n", " \"car\",\n", " \"pedestrian\",\n", " \"bicyclist\",\n", " \"unlabelled\",\n", " ]\n", "\n", " def __init__(self, images_dir, masks_dir, classes=None, augmentation=None):\n", " self.ids = os.listdir(images_dir)\n", " self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]\n", " self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]\n", "\n", " # Always map background ('unlabelled') to 0\n", " self.background_class = self.CLASSES.index(\"unlabelled\")\n", "\n", " # If specific classes are provided, map them dynamically\n", " if classes:\n", " self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]\n", " else:\n", " self.class_values = list(range(len(self.CLASSES))) # Default to all classes\n", "\n", " # Create a remapping dictionary: class value in dataset -> new index (0, 1, 2, ...)\n", " # Background will always be 0, other classes will be remapped starting from 1.\n", " self.class_map = {self.background_class: 0}\n", " self.class_map.update(\n", " {\n", " v: i + 1\n", " for i, v in enumerate(self.class_values)\n", " if v != self.background_class\n", " }\n", " )\n", "\n", " self.augmentation = augmentation\n", "\n", " def __getitem__(self, i):\n", " # Read the image\n", " image = cv2.imread(self.images_fps[i])\n", " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert BGR to RGB\n", "\n", " # Read the mask in grayscale mode\n", " mask = cv2.imread(self.masks_fps[i], 0)\n", "\n", " # Create a blank mask to remap the class values\n", " mask_remap = np.zeros_like(mask)\n", "\n", " # Remap the mask according to the dynamically created class map\n", " for class_value, new_value in self.class_map.items():\n", " mask_remap[mask == class_value] = new_value\n", "\n", " if self.augmentation:\n", " sample = self.augmentation(image=image, mask=mask_remap)\n", " image, mask_remap = sample[\"image\"], sample[\"mask\"]\n", " image = image.transpose(2, 0, 1)\n", " return image, mask_remap\n", "\n", " def __len__(self):\n", " return len(self.ids)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.057523Z", "iopub.status.busy": "2024-08-23T11:30:06.056578Z", "iopub.status.idle": "2024-08-23T11:30:06.069829Z", "shell.execute_reply": "2024-08-23T11:30:06.068550Z", "shell.execute_reply.started": "2024-08-23T11:30:06.057475Z" }, "trusted": true }, "outputs": [], "source": [ "def visualize(**images):\n", " \"\"\"Plot images in one row.\"\"\"\n", " n = len(images)\n", " plt.figure(figsize=(16, 5))\n", " for i, (name, image) in enumerate(images.items()):\n", " plt.subplot(1, n, i + 1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.title(\" \".join(name.split(\"_\")).title())\n", "\n", " # If it's an image, plot it as RGB\n", " if name == \"image\":\n", " # Convert CHW to HWC for plotting\n", " image = image.transpose(1, 2, 0)\n", " plt.imshow(image)\n", " else:\n", " plt.imshow(image, cmap=\"tab20\")\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.071933Z", "iopub.status.busy": "2024-08-23T11:30:06.071406Z", "iopub.status.idle": "2024-08-23T11:30:06.480787Z", "shell.execute_reply": "2024-08-23T11:30:06.479553Z", "shell.execute_reply.started": "2024-08-23T11:30:06.071890Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mask shape: (360, 480)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset = Dataset(x_train_dir, y_train_dir)\n", "image, mask = dataset[0]\n", "print(f\"Mask shape: {mask.shape}\")\n", "visualize(image=image, mask=mask)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Augmentations\n", "Data augmentation is a powerful technique to increase the amount of your data and prevent model overfitting.\n", "If you not familiar with such trick read some of these articles:\n", "\n", " - [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf)\n", " - [Data Augmentation | How to use Deep Learning when you have Limited Data](https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced)\n", " - [Data Augmentation Experimentation](https://towardsdatascience.com/data-augmentation-experimentation-3e274504f04b)\n", "\n", "Since our dataset is very small we will apply a large number of different augmentations:\n", "\n", " - horizontal flip\n", " - affine transforms\n", " - perspective transforms\n", " - brightness/contrast/colors manipulations\n", " - image bluring and sharpening\n", " - gaussian noise\n", " - random crops\n", "\n", "All this transforms can be easily applied with [**Albumentations**](https://github.com/albu/albumentations/) - fast augmentation library. For detailed explanation of image transformations you can look at [**kaggle salt segmentation exmaple**](https://github.com/albu/albumentations/blob/master/notebooks/example_kaggle_salt.ipynb) provided by [**Albumentations**](https://github.com/albu/albumentations/) authors." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.482545Z", "iopub.status.busy": "2024-08-23T11:30:06.482154Z", "iopub.status.idle": "2024-08-23T11:30:06.495170Z", "shell.execute_reply": "2024-08-23T11:30:06.494101Z", "shell.execute_reply.started": "2024-08-23T11:30:06.482503Z" }, "trusted": true }, "outputs": [], "source": [ "# training set images augmentation\n", "def get_training_augmentation():\n", " train_transform = [\n", " A.HorizontalFlip(p=0.5),\n", " A.ShiftScaleRotate(\n", " scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=1, border_mode=0\n", " ),\n", " A.PadIfNeeded(min_height=320, min_width=320, always_apply=True),\n", " A.RandomCrop(height=320, width=320, always_apply=True),\n", " A.GaussNoise(p=0.2),\n", " A.Perspective(p=0.5),\n", " A.OneOf(\n", " [\n", " A.CLAHE(p=1),\n", " A.RandomBrightnessContrast(p=1),\n", " A.RandomGamma(p=1),\n", " ],\n", " p=0.9,\n", " ),\n", " A.OneOf(\n", " [\n", " A.Sharpen(p=1),\n", " A.Blur(blur_limit=3, p=1),\n", " A.MotionBlur(blur_limit=3, p=1),\n", " ],\n", " p=0.9,\n", " ),\n", " A.OneOf(\n", " [\n", " A.RandomBrightnessContrast(p=1),\n", " A.HueSaturationValue(p=1),\n", " ],\n", " p=0.9,\n", " ),\n", " ]\n", " return A.Compose(train_transform)\n", "\n", "\n", "def get_validation_augmentation():\n", " \"\"\"Add paddings to make image shape divisible by 32\"\"\"\n", " test_transform = [\n", " A.PadIfNeeded(384, 480),\n", " ]\n", " return A.Compose(test_transform)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.496921Z", "iopub.status.busy": "2024-08-23T11:30:06.496543Z", "iopub.status.idle": "2024-08-23T11:30:07.590478Z", "shell.execute_reply": "2024-08-23T11:30:07.589517Z", "shell.execute_reply.started": "2024-08-23T11:30:06.496879Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mask shape: (320, 320)\n", "[ 0 1 2 3 4 5 6 7 9 10]\n" ] }, { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize resulted augmented images and masks\n", "augmented_dataset = Dataset(\n", " x_train_dir,\n", " y_train_dir,\n", " augmentation=get_training_augmentation(),\n", ")\n", "\n", "# Visualizing the same image with different random transforms\n", "for i in range(3):\n", " image, mask = augmented_dataset[3]\n", " print(f\"Mask shape: {mask.shape}\")\n", " print(np.unique(mask))\n", " visualize(image=image, mask=mask)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.592216Z", "iopub.status.busy": "2024-08-23T11:30:07.591752Z", "iopub.status.idle": "2024-08-23T11:30:07.606959Z", "shell.execute_reply": "2024-08-23T11:30:07.606000Z", "shell.execute_reply.started": "2024-08-23T11:30:07.592181Z" }, "trusted": true }, "outputs": [], "source": [ "train_dataset = Dataset(\n", " x_train_dir,\n", " y_train_dir,\n", " augmentation=get_training_augmentation(),\n", ")\n", "\n", "valid_dataset = Dataset(\n", " x_valid_dir,\n", " y_valid_dir,\n", " augmentation=get_validation_augmentation(),\n", ")\n", "\n", "test_dataset = Dataset(\n", " x_test_dir,\n", " y_test_dir,\n", " augmentation=get_validation_augmentation(),\n", ")\n", "\n", "# Change to > 0 if not on Windows machine\n", "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=0)\n", "valid_loader = DataLoader(valid_dataset, batch_size=32, shuffle=False, num_workers=0)\n", "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create model and train" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.608742Z", "iopub.status.busy": "2024-08-23T11:30:07.608339Z", "iopub.status.idle": "2024-08-23T11:30:07.613493Z", "shell.execute_reply": "2024-08-23T11:30:07.612605Z", "shell.execute_reply.started": "2024-08-23T11:30:07.608680Z" }, "trusted": true }, "outputs": [], "source": [ "# Some training hyperparameters\n", "EPOCHS = 50\n", "T_MAX = EPOCHS * len(train_loader)\n", "# Always include the background as a class\n", "OUT_CLASSES = len(train_dataset.CLASSES)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.615277Z", "iopub.status.busy": "2024-08-23T11:30:07.614938Z", "iopub.status.idle": "2024-08-23T11:30:07.638455Z", "shell.execute_reply": "2024-08-23T11:30:07.637474Z", "shell.execute_reply.started": "2024-08-23T11:30:07.615245Z" }, "trusted": true }, "outputs": [], "source": [ "import pytorch_lightning as pl\n", "import segmentation_models_pytorch as smp\n", "import torch\n", "from torch.optim import lr_scheduler\n", "\n", "\n", "class CamVidModel(pl.LightningModule):\n", " def __init__(self, arch, encoder_name, in_channels, out_classes, **kwargs):\n", " super().__init__()\n", " self.model = smp.create_model(\n", " arch,\n", " encoder_name=encoder_name,\n", " in_channels=in_channels,\n", " classes=out_classes,\n", " **kwargs,\n", " )\n", "\n", " # Preprocessing parameters for image normalization\n", " params = smp.encoders.get_preprocessing_params(encoder_name)\n", " self.number_of_classes = out_classes\n", " self.register_buffer(\"std\", torch.tensor(params[\"std\"]).view(1, 3, 1, 1))\n", " self.register_buffer(\"mean\", torch.tensor(params[\"mean\"]).view(1, 3, 1, 1))\n", "\n", " # Loss function for multi-class segmentation\n", " self.loss_fn = smp.losses.DiceLoss(smp.losses.MULTICLASS_MODE, from_logits=True)\n", "\n", " # Step metrics tracking\n", " self.training_step_outputs = []\n", " self.validation_step_outputs = []\n", " self.test_step_outputs = []\n", "\n", " def forward(self, image):\n", " # Normalize image\n", " image = (image - self.mean) / self.std\n", " mask = self.model(image)\n", " return mask\n", "\n", " def shared_step(self, batch, stage):\n", " image, mask = batch\n", "\n", " # Ensure that image dimensions are correct\n", " assert image.ndim == 4 # [batch_size, channels, H, W]\n", "\n", " # Ensure the mask is a long (index) tensor\n", " mask = mask.long()\n", "\n", " # Mask shape\n", " assert mask.ndim == 3 # [batch_size, H, W]\n", "\n", " # Predict mask logits\n", " logits_mask = self.forward(image)\n", "\n", " assert (\n", " logits_mask.shape[1] == self.number_of_classes\n", " ) # [batch_size, number_of_classes, H, W]\n", "\n", " # Ensure the logits mask is contiguous\n", " logits_mask = logits_mask.contiguous()\n", "\n", " # Compute loss using multi-class Dice loss (pass original mask, not one-hot encoded)\n", " loss = self.loss_fn(logits_mask, mask)\n", "\n", " # Apply softmax to get probabilities for multi-class segmentation\n", " prob_mask = logits_mask.softmax(dim=1)\n", "\n", " # Convert probabilities to predicted class labels\n", " pred_mask = prob_mask.argmax(dim=1)\n", "\n", " # Compute true positives, false positives, false negatives, and true negatives\n", " tp, fp, fn, tn = smp.metrics.get_stats(\n", " pred_mask, mask, mode=\"multiclass\", num_classes=self.number_of_classes\n", " )\n", "\n", " return {\n", " \"loss\": loss,\n", " \"tp\": tp,\n", " \"fp\": fp,\n", " \"fn\": fn,\n", " \"tn\": tn,\n", " }\n", "\n", " def shared_epoch_end(self, outputs, stage):\n", " # Aggregate step metrics\n", " tp = torch.cat([x[\"tp\"] for x in outputs])\n", " fp = torch.cat([x[\"fp\"] for x in outputs])\n", " fn = torch.cat([x[\"fn\"] for x in outputs])\n", " tn = torch.cat([x[\"tn\"] for x in outputs])\n", "\n", " # Per-image IoU and dataset IoU calculations\n", " per_image_iou = smp.metrics.iou_score(\n", " tp, fp, fn, tn, reduction=\"micro-imagewise\"\n", " )\n", " dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n", "\n", " metrics = {\n", " f\"{stage}_per_image_iou\": per_image_iou,\n", " f\"{stage}_dataset_iou\": dataset_iou,\n", " }\n", "\n", " self.log_dict(metrics, prog_bar=True)\n", "\n", " def training_step(self, batch, batch_idx):\n", " train_loss_info = self.shared_step(batch, \"train\")\n", " self.training_step_outputs.append(train_loss_info)\n", " return train_loss_info\n", "\n", " def on_train_epoch_end(self):\n", " self.shared_epoch_end(self.training_step_outputs, \"train\")\n", " self.training_step_outputs.clear()\n", "\n", " def validation_step(self, batch, batch_idx):\n", " valid_loss_info = self.shared_step(batch, \"valid\")\n", " self.validation_step_outputs.append(valid_loss_info)\n", " return valid_loss_info\n", "\n", " def on_validation_epoch_end(self):\n", " self.shared_epoch_end(self.validation_step_outputs, \"valid\")\n", " self.validation_step_outputs.clear()\n", "\n", " def test_step(self, batch, batch_idx):\n", " test_loss_info = self.shared_step(batch, \"test\")\n", " self.test_step_outputs.append(test_loss_info)\n", " return test_loss_info\n", "\n", " def on_test_epoch_end(self):\n", " self.shared_epoch_end(self.test_step_outputs, \"test\")\n", " self.test_step_outputs.clear()\n", "\n", " def configure_optimizers(self):\n", " optimizer = torch.optim.Adam(self.parameters(), lr=2e-4)\n", " scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=1e-5)\n", " return {\n", " \"optimizer\": optimizer,\n", " \"lr_scheduler\": {\n", " \"scheduler\": scheduler,\n", " \"interval\": \"step\",\n", " \"frequency\": 1,\n", " },\n", " }" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.642407Z", "iopub.status.busy": "2024-08-23T11:30:07.642034Z", "iopub.status.idle": "2024-08-23T11:30:09.551807Z", "shell.execute_reply": "2024-08-23T11:30:09.550754Z", "shell.execute_reply.started": "2024-08-23T11:30:07.642359Z" }, "trusted": true }, "outputs": [], "source": [ "model = CamVidModel(\"FPN\", \"resnext50_32x4d\", in_channels=3, out_classes=OUT_CLASSES)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:09.553779Z", "iopub.status.busy": "2024-08-23T11:30:09.553376Z", "iopub.status.idle": "2024-08-23T11:34:35.862427Z", "shell.execute_reply": "2024-08-23T11:34:35.861229Z", "shell.execute_reply.started": "2024-08-23T11:30:09.553732Z" }, "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "GPU available: True (cuda), used: True\n", "TPU available: False, using: 0 TPU cores\n", "HPU available: False, using: 0 HPUs\n", "d:\\windows\\computer_vision\\image_seg\\venv\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\logger_connector\\logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `pytorch_lightning` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default\n", "You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n", "\n", " | Name | Type | Params | Mode \n", "---------------------------------------------\n", "0 | model | FPN | 25.6 M | train\n", "1 | loss_fn | DiceLoss | 0 | train\n", "---------------------------------------------\n", "25.6 M Trainable params\n", "0 Non-trainable params\n", "25.6 M Total params\n", "102.358 Total estimated model params size (MB)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "375eecbc1e1c4885aab6e8debc8a3f63", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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19t9du3Z9U9Wz3gd+7nOf+yfvAw/3e+vEE09kYWHhbr9vb7jhBkII65r1Gx4YaBnbhm8JMca7rGD98R//Mbfccsu6x57+9Kdzyy238D/+x/8YHltdXeW//bf/tu55j33sYzn33HP5zd/8zbu1ILa69YaGBw9e/epXs7CwwE//9E9zxx133OXn92b1/JnPfCY5Z972trete/y3f/u3EREuuugiwBbVTjjhBD7xiU+se95ll132LbwDQ9327/zO76x7/JJLLjms36+zI+9MUr8Zzj333Lu8j//6X//rXRTbb3X7DQ0NDy4897nPJefMb/zGb9zlZyml4TvkqU99KqPRiP/n//l/1n1PH8533vd8z/dwzjnncMkll9zlO2nttg73eyvGyA/+4A/y53/+5+tcPnfccQdXXnklT3ziE9myZcs33a+GYwtNsW34lvDsZz+b173udbzwhS/kCU94Atdddx3vete71q3QgdXDv+1tb+PHf/zH+YVf+AVOPfVU3vWudw1DtOvKWwiB3/u93+Oiiy7iEY94BC984Qs5/fTTueWWW/joRz/Kli1b+P/+v//vfn+fDQ0N9z++4zu+gyuvvJIf//Ef5/zzz+cnfuIneNSjHoWqctNNN3HllVcSQrhLDuzu8K//9b/m+7//+/lP/+k/8dWvfpVHPepR/OVf/iV//ud/zite8Yp1+dcXvehFvPGNb+RFL3oR3/u938snPvEJvvjFL37L7+PRj340P/7jP85ll13GgQMHeMITnsD//J//8y6OlXvCueeey9atW3n729/O5s2b2bhxI4973OOGsWv3hBe96EW85CUv4Ud+5Ed42tOexmc/+1k++MEPrnPT1P2LMfKmN72JAwcOMDc3x4UXXniPObqGhoYHJ57ylKfw4he/mDe84Q185jOf4Qd/8AcZjUZ86Utf4o//+I9561vfyo/+6I9y4okn8h//43/kDW94A89+9rN55jOfyac//Wn+4i/+4i7fP3dGCIHf/d3f5V//63/Nox/9aF74whdy6qmncsMNN/CP//iPfPCDHwRMCAEr5Xv6059OjJHnPe95d7vN17/+9XzoQx/iiU98Ii996Uvpuo7LL7+cyWTCm9/85m/vQWo4KtCIbcO3hNe85jUsLS1x5ZVX8p73vIfv+Z7v4f3vfz+/9Eu/tO55mzZt4iMf+Qg///M/z1vf+lY2bdrEC17wAp7whCfwIz/yIwPBBWs8vfbaa/mN3/gN3va2t7G4uMgpp5zC4x73OF784hff32+xoaHhCOLf/Jt/w3XXXcdb3vIW/vIv/5J3vOMdiAhnn302z3rWs3jJS17Cox71qG+6nRAC/+N//A9e+9rX8p73vId3vvOdPOQhD+G//Jf/wqte9ap1z33ta1/Lrl27+JM/+RP+6I/+iIsuuoi/+Iu/+GcRvXe84x2ceOKJvOtd7+K9730vF154Ie9///sPywI3Go34f//f/5df/uVf5iUveQkpJd75znd+U2L7sz/7s9x00038/u//PldffTVPetKT+NCHPsQP/MAPrHveKaecwtvf/nbe8IY38DM/8zPknPnoRz/aiG1DQ8Nd8Pa3v53HPvaxXH755bzmNa+h6zoe8pCH8JM/+ZP8q3/1r4bnvf71r2d+fp63v/3tfPSjH+Vxj3scf/mXf3lY87uf/vSn89GPfpRf//Vf5y1veQulFM4999x1kzR++Id/mJ//+Z/n3e9+N3/4h3+Iqt4jsX3EIx7BX/3VX/HLv/zLvOENb6CUwuMe9zj+8A//8F7PSW84NiDaEtENRwCXXHIJr3zlK/nGN76xrmK+oaGhoaGhoaGhoaHh3qIR24b7HCsrK+vKBlZXV3nMYx5DzvmfZfNraGhoaGhoaGhoaGiAZkVuuB/wwz/8w5x11lk8+tGP5sCBA/zhH/4hN9xwwz3Wxjc0NDQ0NDQ0NDQ0NNwbNGLbcJ/j6U9/Or/3e7/Hu971LnLOfNd3fRfvfve7+bEf+7EjvWsNDQ0NDQ0NDQ0NDQ8ANCtyQ0NDQ0NDQ0NDQ0NDwzGNNse2oaGhoaGhoaGhoaGh4ZhGI7YNDQ0NDQ0NDQ0NDQ0NxzQasW1oaGhoaGhoaGhoaGg4pnHY5VGPf/z3oZopWhiNOlLq0QIhBkIQSsnkBLELpJSIMVCyIiEgKCIRVJFQAEFEEAFqwlcip519DttPP5PrP3M93/md5/H5L17P17/wJUadsGHDBk4/8xG84FUvpw9juhjQksn+GkWVkjISAnXDORdKycTYoVooRQixQwWUQElKCIGCIEVBlNGoo4tKFyIqSk6JlKZ0sSOnTE6Jvu8RgVIy/XRKjAEQVJWcMzlnQhBQKCkTYwQBVIkx2DGT4MdAiCFCUGIXEImUUiilMJ4b08UOBYoqMURiN0LrYZNIUCGVnqIKqnYcSiFKpAAlKRIVYiRKRGLHaDxnr6NKVigF0AIlI/ZbdKIImY0b5pgfjxDNxCgEUVQLIUSKFlSx4wFkVVLfE0NgPDdmfjwGwY9LYWVlQojRrp9SECCGgBY7bvV49H1CggzHoe8TiB3fvp/asS52vtUOKyiknAgCGoScCkEEiQEtfs0FoetsOyEIIUT66RRCQFTIKZNyQcSu3eJHuhQo2d6/EiglU1CCBkrOSBBU7DJWVbrg60UCIQQQO/0xRr8u7LoLIVCKHUv7uyICMYr9sm9DEEIQu7YBLfahsevbzoFqQSmMuhFaFKX4/vpmJKAKJSeWlw5w1bv/lL17D9CrEFD27b4DKMzNbwIJTFcnpMmUU885m5POOIs+FW77+ldZ3LuX+fmNhFFExM6DFqVo8QMAiNp3hdi+Lh84yOrKItJPue2mLxzuV86xhQ//8pHeA8M1l3HVM9oIrYaGbxU7rj4PnvjSI/PiT33DkXnd+xpHwffjVZtecaR3oaHhAYX7/bvyML8fD5vYikDOimLECSBEqT8lhI4imZwysd6AqxLFbqpRxW6zBXViq1qQEBGEhU1b2H7qaUZUVQgxQGBG/qIgAXLOdONAQCBEI6z+WgQoCkGM4KkWECMMpRQEIxIqgpRMScW3oeSSiaNIyUrfKylku0EvmZQTfV9ACyUlckoDmVfUiBX2evaavj8KKhhBqgxD1ShLqKSj7mMmYgQ4xBkZqsfb3ldAjC8TQgQVe30gFwhixwdAg71m6IzMhxCIMVLUVxOKIloICFoyqJJzQsBIpaptrxT6lIgoIsHPgRNMP88itrhhl0Ohi4HxuCN2M0OAvQdj0TkXuq6jpGTESO0/cfKKCDnbNTZcNWs6zoIY/R64nwiai61niCAIMcjwMwnBFhuIqEKUQAwgIaCjEQW/5oJQJokoQtLhyiagFBEQRTByG3yhQUSRIPhKB6UYaRbx/Q52bKAgwc6tqBHeem3XcyMiRqBHtp8ChC7YRUSxBRKEfppQtWugFL/uCqgKSQshBlQF8OulKEgw0o+yYX4jJ51wEqo9d+xepoiwefvxrCwvUep1p3aco+9nDCNOOuUMosCBPXuY1w3E0ZhAoJSEBFssAkVUUQ12Uaod2xgCJQgNDQ0NRzOuesYX2bF4yZHejYaGhoaGbwGHTWxTSoApbFVdi3FETkakUCOBEoyElqJ0o0iQYKQlmAolrkAJrmYBiLBhw0ZG8/MwzQQBghoBFSH4Tb8giEQndU4c6w6KiY4i9ogUdYVYBrJZVBHNaIHUZyNNJTvZLUiK5BzRkp2UGNGyG/cAJVNyBjG1TFUp2d63vw0nEb57pSAysC/nYWLKWTE1MEQjvkqgKEQ1AhmckItxBUbjEVEEcYmyEnoK5GSEUYPaa8bZMQ8CQaKRsLqTmNpc34uUTHbCKcCIQC4ZotCngmZlFIwUJVH73WT7rpqJMTAajRiNOluAEGE0Gg0LHDFEVIWu61GElE1ZN7VyjRteZDhmtgTi14nMniOYOi4BRE2NDSKk4Xcq8Q/1sqBgJE38Oi0KXWeX/mg0ouRkpLIoXRcQ/2D0pTghdGVSiqmn+EKNiJFl8XMWAyJ5UGNV1dV5W8wZCGwxxXamwJbZ9kIgBkFViDEg0dRkRem6OFOTXakNIdiCk0LJBTS7YUEp2Yh9UcCPdc5QinDaGWeytHqI3Xt7Uj8ldoG58QZXxNW3ZS4GEVss6rrIiSefBsCB3XsYA+M456dOCKoUbKFD8GvXVmQGdb6hoaHhqMc1lx051bahoaGh4VvGYRPbXHqCdOSMqYoCqU92A1+U3CcoRsCKFqIEVNwj6kSv3sybbXKQME3n0UAMkRzNWjyKEdHihLKQtSBdh8RgNlYnaGuE0EH1Q0xNxYmRKlCgaBlISi6puiZNzYuBjJHYSlRVAkWzkYLott+UnTiaOlstwMCg3gW11ypaSYGrgaXYz5zUihSgqm5K6hN0HSFUBRNCMbpmdMsVZyCn3hS7rKRpj0SB7IqhQBGIru4qBVUjbuLHVTWTSzaLOE6ISiYABXt/IUZySqgIGWXECDsdamSwZFPHJTIf5+liIMY5s0KHYLZrCYgE5uaglHmywnTaOzGvxyGYHTgEP84ZCeokrqAEV3OhFLOVB2y7fd87kRVKkdkCgpPkiuCksV6Tqkp0Ahowwqkqg205iRKS2sIMQFRyVlNNc0HULfA5UNQUfFEg+jZ9RULEjoO4PT5KgFit5E7eQ1hjLxcjydTnd0iwhSVbMDBLfHUhgF2TKoqKUhA02bWnfTaF2S3yZnc2V8XpD30oi6vL3HzzXvrVKTIX0Ww29EEhF4WRXbtkCAoSI8efeApQOLBrL2He3BpCcAsytsIEfuz8OiYSZHY+GhoaGo5WNNW2oaGh4Z/G0fo9edjENoRoNmNXT3MpbotVcjKVSDojT8VtwaGqaG7JNXuluk3XVFRTsEy16mI0C6qqP9du7ovmgdCVUizXKGKkx8msKViVRJtKhJpaZ3ZeU2o1QO7t0ap8KpminVmTtQwZRhEha0Y8R5ix/dRSBr9mVWgFU8aqIm1PqYpvGfZPoh1yUythzabMLl3zicXsutVaTC9r1M1gxDkpuahbadX3oVB6GEUZ7M6FbOdAjVhrcsW3ZFM+MRtyzslIJkY6S4GsGLF18t6Noin25ukdsq8iQIimEMdIjJHYGYMztV1NJS2FubmREzVXX4WZ3VwEoXNyKJRoSmHsIn1v6vB0OkUIazKm6rnZSoDrAocRRhGIQQidq5d+rEOMw+91XYcqjEYdtnJT3AHsOrCCdtGIaT0LTqKldCBuqy8gazLUWtzC7Zb8EMNwHRvMmi6++OEvNSyIqJoFOcbOrcgQuw4pmaiBGGe57kKNCrg9PWW/tuw5Rt4hO+E+/xHfzVe/citf2LvbcubBz4eaOq3YMcQ/O1XJFQJbt55AP0ks7j/A/IYFYhj7woRdn/VzpEDw6AHSFNuGhoZjA1dtesVRedPW0NDQ0HDPOGxiW1wlNeFJ7KY+GFkSgW40sjIfz2vi+b+qaIYYCZ6RrapqdelmLzuKIaIhmZqrngE1+c0UQATNmSJluFlHw1BUFWLwbK0TbLd8lqT0KRMQs2LmbETbPJpGMkoaiKi9pzCQv6ABFSPvqkpWQaKpWqWYEq1D2hUPR7p9Vc1QW7IRqUq8bVvBc7JOyoGck+cjIZfs7wdSUUZe2FPcKm1E1xRaUVOcPeyIRrNea1a6UVed277/yQj1QD7EiZZZSVGzKYtaHtn4tBPrlMk50XV2PqUec6nW2koUR0jwrKyKEXBr7SKEiJAZjcZ0Ti6tcCwOZFNTIoZICIWUTP0bd2J2bHCbttvMff9FZNiOhGDXSrWjY9nT4KdoIL/MiqViCJTSUVTsfFfbtAQvSSoQAlGEkm1DoQOKGZRtpaLmZ12xjfgCgF0X1V5fbeXijgYQz6yvzd1aORUaLPfsCzmxC0jxzLVb3icpUcRKukQwu3IQima3Bgc0+GfXkrFIGLF9+8nMz3+V1dUDxFFHiOKLSoHYieW+ay4bs54X/3zMzW1CtiiHDh1gHojjMVCLtNxR4QS9ftYbGhoajhk0S3JDQ0PDMYV7odhKddwaCRLogilxMuRGy0zBw1QoBgJhpLZoIcbZy5plFysMcqVVgoAaQTbrsKlPfc5M+t4Jof2uVJutggVW1VVSE4VLsZv9nAoagt2Y52yETbA8rZqiG4NYeZWFMcmlWNYwgKh4gVaelV95i60OuWFZlxvVmdhHfcBim37Tv4a8r1WeSzHLJ9XWqQqaCcFIYcqm4qnLj+pJUgVQO35VeVUx9RyxEh8tvh+5kIuRPfdxu6I9U8xTStY+7dp5LgJYm7LWRKtYzjWlQor2XkKwq6JmicUbiCUUtGSiCOPxGEQGVT+qemuwEWUjp0JJdp4pwRdTAt1oBCJM+96txcFdAzOFUzGxNUotKgvWPuwkq5TMqAuVrvk+CqPxyJqRgwA9EqywSRTEG6UkeCmTrs1Q21EKft5sLSBQJBMiw2ekklbw0+ILLBKCtRi7so4r36h6+ZQtEo26Ed2oowuR1Js9WWWO2EdisGOSkinx1vYdjBj7NS1+wVqBlZWEjcZzpDxGSSh2vhXAVW7EPtvq6njxBmYksLBlKyXC0sGDzIsSujGqYjbkYAsnxQ9RmX0SGhoaGhoaGhoajmUchYt/99Ib6E3DDAJUfXhQEesPa1FPfW61/dpfZze4RgbCbENrs7NVZVzzO6airregDj8rtaEYf5w1z/FtaNVWzQ5dqeHsxXT4/WpXpj5H63Pr+5nt72yn1x+vu//XnV/rTr+ld3pwzfPqfqlbuuuz6vGy/9YcQ1m3Cf99mR2D2TuaHQld/9PhsWEjAxcGGOy/657Dmhf35+CW4+G/tc8cVPy7Ps9Z5LCfRnD9urnz85j9t24bMFieq12cNT+r+yByN7+75nd8S2u2OTwy7Df1FKxTKWXNnzLby+FYrH18zX7JGrXX1d0QhNjF4XdD9DxzCNZiHNbv/+C0qJvy/6lkFxhs0Gv3V9Yor+uu/bXvRQLdaOwLPWsztLrmHQHSSG1DQ0NDQ0NDQ8N9h3thRVazMXbBJ9TMCI/NBx2RtXcCst53WDzHGONoNq+UmZJbckHUW1P7fpZLDFXxtVxhFyJzozG5m0O12LYwlS+VROzsVtpETqNkZgu1sUBCVatkcFdG15GKZwBraY+V4dgREqkjWqCjI/vfg/i+VwXWX6+itvtWhHXEyTOXtfZ4DcFYS0YCgoYOAnRd9C2ZiljVTZH6ntQV11qc5QptmcUbg9t3Y4gDERSXDkVMaSMAWX0sjivbEuhi53NYZWYv9QWLrot0XVe1vuHP4Oc45YzWJulS6Pvec7jdzFbu0GK57eDtzsGt6PV6S73N7U05k6eJakXO2Zuewefg6nB9Dgsiw+JKGRYlYhAoSqHQT3uKmgU5J98vCSRX/ms7tjmgZ/lpO5Q2WzkEs5h7b7Vdj5RB5S2e4S6qSMa170LnNvLaxq1q9udSwqCCllSYlJ6JTm1RCGF1ZZVpyqxOp6bWVpdC9vfrKmsQIWGt0EHtOUECOSWmE2tGrtey4Aq+W8ntXARypao+gmi6ssTBfXvoujm6uXkqC69jsFTV7N86WxZoaGhoaGhoaGg4xnGUqbVwr6zI3lBbZuTILJTRWln7qT1PYk1qepYQtArDCl3shsZgitJFb331wiTLHYrbOIWsEMUVRLGsrg1M7YaRKiJmOdVSPKdr2yhhZhOOFjq1G3msNbhaeUOxRmaMu9v7QggSyYLlGYMR3Ri0RiLtDQXFmKMMSpswKwcScQIfrZKpIw5krxJaI7vR9iV0g5IWw+y5IVoGGXzkkhP1md3XDcOuWmu2GbYxzoh60UKIQqQji48qkkgAgiSyVIuqNfd2XefjgsQtsYFRF0lYK7AWAczCbQZWb5MuSs5iREnq+VF7TanZ4UKaJkLqARnU5+BScCl5sOkCqKjZnVNi4uVRpahde04CzSbuOe5SC8VAiprNPGXPEJvFvbgF2Mh2JdxGlFMupJRt4UK8pEuU6PtmdmPxcU/YKCCZlaoV8HZsW/BYq5JWOy+4HdnPkE9fskWVUmyhyKqXyKp00a6HnKyVGrfpr6z2pJyY9r6wpGYFrzlgtOaD7RrTYuVgQYXllYOsrB4iRBnGRFWlPiVrI6e2GYtZykHIpSelVQ7u2ctoPMdoPG9ZZLsq3XVQEJUhJ95obUNDQ0NDQ0NDw32Fe6HYZs8brhmbg92Ed52Qp1a+FOa8Cddv1xlUPaNxIdQ0as06mgomwYp/bATNrGzGnremiTZGiKY2hhIGAlnbmfHsbE46uDhVQGIg13E8IkYMooB2iOah8Ed8XimYSikaQIrlMxGSJrroilTJwIxcw8w5GgRyVVMFYuzW2XRN2S1DMRNUVVtmM1BFhibc0XjsCrZbgDN+PhTNvR0jt1ZL1w3vk6oMB5uHKgQkRgIZ0YhIB2RC7LCiK0y5Fiu76qS2/yqjriNE6KRDXRm1PLW3P3txUS5GfC3bGQZl1DLWGGEsdRbw7Dqwv9tCQcmZnI2sppSsGKwo02myucLeipzzemI7WIErmRMn/7mgRLwe2jPMleDW7YgRWxFSVvo+uXJfZu8v4oVYoKHOlLX5zaKZQnZCqXbdiaDFVPFS7uRl8Cyys0BKtuul88s9l0LXjSiq3kguhDBiMpn4/mRSKkynUzt+eRYTqG3P9f3Vf1tRmi0Uff3mr3DzTTcjBObmxmQt5Dz1xSCsslvzkCmG7NdmYWnpAIf272U8nmc0GhtprossqC1uqRF3s8mv8a43NDQ0HOXYcfV5R6Ua0fAt4JrL4BmvONJ70dDwgMLR+h152MQ2BmvWtdZUuwGPcWRlSSLIuKOf9qTcWwlOmRXVoGqKKnXuK4MdGWoWVMlajCi4zbhgqloXI5233FKsHEjU7pqndc6rl9XUm+cQrIl3CBWGOnYkEKISVFGCK8QRgtpIFax5WTE1uvjM1zDM5okDSUEgqYKYgZmgwxxUI9hDYtJu8l3VDcGahtVtwDbWRUwhjf5ePAtbSe6QjwyBotBJpIjQOS8qGGkvqJUVucodJBAlmpIuoCJosKbbUGbFSirR2LjYCKaSjdSFLtA52epG0bZbTJOXaGQ6xjrepdDn5NeLsTPBCHLf96ysTCgIOWcb/eMlRgNpZKbupVQGQplSQZL9vZ/2boHOpJyttVqCKap+LapUq7QMiqzZbhUreapN1l5eVgqlN+W1TwUBI9WlUI252ecWq6o5vd2qa2OoCkGDXc/FSF1x1b1a02uJluhM/RZlRrRdFddBcTWLtGomZwGxayv1iZQSJRdfHMhelCaIE+jomXUZ2fU+NJOHQJ+mjHPg1pu/xhevvw5EmV+YJ3ZASpTixWcEI+q9kotZ9VWskfvAgb3suv12um7M3HjexwvZKCmn6r6QZfbuokomD4p+Q0NDw9GMo/WGraGhoeGowVH6HXn4xDZ2gzpWVRvV7IU2lhUNsXP1yshX6TNdjNRxKbP8rWdr1WZ/BlVSPyFNp2azLM4hpY7CwW72tZBzIlbC4fnL4uRInCCKMRdU80BM0EKQaOFYhI5Izj6GSAu5ZGLs6GKA7KFUnylrbcAyEL+SbJ6qNSB7iZHvQ/a6pUpCa0N0Va2rgDlkcdXG9RTUxucI/rpKjMGm9xSl73u62Dm5ZLAQG2G3ZuIAhOIkuhK6ojaTVRmalM1uPEJGljAuqSBdJJBc4RablRoKXRcYx0hEGY2CN0djtuUQABthlFKhlKllZ10trjOPyZnptGcy7a3V2OfcyhpCV4/RjNzZAkFtrHb9H8XG15Q6foeqGs/UUOO9TqJkfbFVCDZqx+YoR3cJ6HCNpJQHu+9gynXbPGIqbC7qOWV/HV/oqDleU6rdWm1GYrv+NILkobl7ZqW3DHG1MpdqjVYI0dqsEaVPimDkthJgy5mbTV+Cnfc6fqpz6dfOd+drPEIJwle+/AX66Qp9P0G6SJHMNPd27aotvmgJw6KM+ufswP697LrtNsZxjm5uzi7okkCVxOyaRmcFbzVonnIjtg0NDccAjtIbtoaGhoajAUfz4t+9y9j6jWqM0fOORvDMSuqkN0Z/3LJ4JmwWs8Cils10E/OgUlE4dGA/B3btZNPxJ7nNF8iuDg4tyEbOcjbyXMePDOVJQ0mSOA+VYZathmik12aveGbWRvMUOkIxBVhipBuZSqwoOSf63lXMVMj09KWOYIkUKW75nc3AzU6MBbPAhs7/rjN7NK7IggWBZw211d5aLH/sanR2JVo9BwxOoLzICR/HFGI0e7erxiUpKfaIGhkUJ5te34VKQIMgqu7wdvVNhACMYmQ8GhFqllYUgtDNjzyPKeSUzDqcjICXGAmSmBtXG68rlmIqZIzRcqpDmRbDnyFYmVHXRXIpM9JbmS0MJLWq88UPirpiqNXiLsGuN1U7P+D5VyWpEjQwnfY2YkoDmsz+rMFs5D4h18+HHR/jbKbS5gzqxWcq9mfRmpu281mEYVTUYJfW2XsoRV3dFlP/qf9ZRlaS2/ZDVchluHaM/Lu93q+Lrqs7uTb7XQm6HciUJuzdu4+du/Yj0az7hw7sJ/U94/kNQ3FboRJsJZfM3t13sPeO2xiP5unGo0ENVjHeT11ccLdA9SGbU2PmpmhoaGhoaGhoaGj4duOwie3q6sTsim4PNlJqwleRmToTwsiLpmxeqaoVDNk9dUA1uVopTk6MRJXUc+tNX+UhC/PEKEz7BGrPE89J1iKh1GdkZLbVrOpqsJGZgPi4E7A7a8vHqpq91IOPViIUTcGtFuGiisRA6KxUKDhBFpTOKpHJOTAaB1KfKa5olZx8dqvlcochSqo269fVuTpTdX1zci1rEssul0LOlczrYCmuY3AURbMpsSGOELyIqNq+JSAUULdRFxjm2fo58a1gJMeOj1CsaEozwRXQgB+bOqpF7NgGbAEj5+yLC2439XnB2efsSpoSgmWLU85D47NS6JNdBxKcvLtiuVZdNQVzllMuampicdW5HuPKeYu6C0CCz3BVgpjiG0I0QpmNsJqQnUz1LYGi2e3vUDKmOGbrALZYanbaFsjFm5cx1VgKlGCqKoB4aZIZegc+bv1gru6GIER3OKhGxI+jXauz918ZfYyR4G3fdiqM2NZSbc2F2Pm5UvVzJr7IgbkrVMgFPv2p/80du3aRSyREZXHfXg7s2s3mLdsIxa5fX3PyXHRi/5697L3tdsbjebq5sbkXsNfVIddcvF3aEESZTCbklEh9JpZ7OV2soaGh4X7GjsVLjvQuNDQ0NBzdOErVWrgXxNbyjYkYO9IkDeNXUoKRRMup0pNTZuOmjawsL88suZWYquVYK6xF1RQhCYED+/dzx1e/Bhkb2yKuEhe7YS8lmzoogVSLm8DtowEtkDF7symWBWKgEHyUDaSi3uyqg414sEpTs5eFVApdCHQhkjuzC8cYGJVAn0AIlgUVocfbmKvFtDZJqaLBFNcQZE1R1ozEVevoQOpEhozk2jE4iquxONUphRIygWgUVOJAMLLaGCQlIyF7ljWgohTN9P2UEMZGyjC1LwZFc6HrTFsvqTAa2cJAKRDIaIiu5jEQzWnfU3K2huJcW4TFi6EKRXtrcfbGYnEC3E8TXdcR18w3nZU/BXLKPrKnHutqaV2rPtaDAXX8UwigyezK2Wcr2wJBoRApKaMCQYMr8gpi43vMKitDk2/JrujXDLMaacXH/ih1DnJB1dRhAONvOlOL/TMgYgsJqkrXdZ6fzYRQG7QhBPWyNHWSW4bFj5TzoHoG8X1UG9lT+9VyKd7GPLPri49ostw0LO6bIMxDnLKyuJ9De3ezccMmNm4+jkmagp/HkhIl9+zeeSt7btnF/GiBODc2FVpcptVZo3VdYLKTkEkrK2jOBAIbxhvXLeg0NDQ0NDQ0NDQ0fDtx2MS2n07MgmzNOTYGJtoInKJWUhSko5TC8tKKZw4hekYwYNlNs8uGWemUaY6IBOJ4gTtu2cl4bp6S0myEjcqQq+z7gnbWIIvU7G2wspsQyWqjZAK1tEdQJ44SFErxTGO1T1YbrM3btMIgU7xSr4y6QAxhIBBEgWxZV00ZDRntRr59ex82m3Y2O7WOAIoxksm4G9qPTyXXkIq/1xBs+M+QG/YdLWYVHlqjtZiVuI53kUhW0BDIISDF5seI2hJAHVkjAqKW0xx3EQmmLGbpiVJLjcw27C5rt9wGJ5di1uO+RxX6PlGKDtbhLvq4mmKLDlNX50O0EUQp+7ignMmumOdi5VNGfszSHUQYjSLFZ6kmMkKgC2LkXTCbMEZKxc97VW5tT2cLAYhQ1MhzPffWqq2uTto5Lmq54+JlUIDZ4gUbs+SLNKgtBihAcKXa26eH8y6uZDqBr4+nnCABpVBiHBY5rHjNSH0I1bJvCyvG623hJDuxBchYDMA+L8Fs1q7cllzJtY1GCjEwN7cBVFhd3M+B225hvtvA8SedSm8fdLc4FzKZlcVFJnv3Mje3kejN3KLqCxRWFCbWouXZdlP9p6vLRAmMxx2jbgOpn1JSf7hfNw0NDQ1HBtdcdlSrEQ0NDQ0N94zDJra51LweIEJKSic2SzbnPNgso0Y0Fc+BypCthVmsdDaix/OmEoDAthOOZ/euXSyvLDOZrniOFc9OGjlKfTalMgUr4lFTajVYWVMIRlrC0MjsIpKTjM7zg4IreV5qZTNOlVEOps7h1lCNRCeOObtFWTriOKAxWcuu36+LBCvsUbPsqhZK33s20t6sZGw+bW0NFu+zChDVyLmaPOtqbS3yWUOm/JitHR7TJ1NUi9bj7m3FNe/oWUsjj9mcpMEypF1Yk/sthRg6Qgx0XWQUrexJg+U3RyNTGpdXJtQYZS7VDm4WZrOFZ9uO2Pxje9BUWdudmU021+wpDGQtxDCUK6k3L4di5Fi1uNrqY3hUbSQQPqEGI7YxzHKoxWfZ2jzX2joNfW858Xp864zl4k3YWnOmufg59eOf/MDXkULekIzqLIsewvBaxe3LOWcv8fJrJheb3bvGhl1KsdZo/Djl4t4EX0SKcR1Zt0WSWe62qFpumDKMGNJakIUwGo1YXTnIvttuITBi28mnEkYd+AxfFbOIj0ZzrC5PmN+4wGhu3hwUNQfsDdGi9YNdVdzCdHWZrov2XAENBfHPRUNDQ0NDQ0NDQ8N9gcMmtrWsieCjbVxVq+NrxC3FqoHYBR/BkobcqpGLQqSqjUYmyposYRx3bDvxJHZ+7evsvO0WSghDCZRSKCWTiuckXd2ToTXZbqxDFC9vEkQiIXgRknjxkUakiz4eJZt6KMaGsqvIlYdmhVhARYd97ZwkRlFkFIlhjj6Y4itq5Ads3E0phTyaza/VArEzMhe6bigbClUZjZZNLc4dACR2rjAmt25Xkm+Wa8UINdkKjVCwsSquduPjb1xJzGuUzTqqxxKpdp4EJXQgEuliYH5uNIzOGY9HCEI2Pm82cCekdYyOKYcgQYidK/NdIOXaYOzvy4mcFROtydyqjVkKXj5WlUz7pVl5WP29SiShFpXNGpaL+nXpjcZosee4uljKrMBpKHRC0Ox/93mvqeZfixU8BcwuXLLPbHULudnDxXq9BpXdmrxDnM2TrUp8bdUueXZMalt0zv0weieIkJInoz2H7dzVy8+8LCwHsiYvTQv++bTPWnHlubYo79u5D3LH8aeeRjeet2tnzQIFAtKNmF/YROhGdoWoEpyR135jZWa3LiXRT1bpYqDvizV2B4ZFFWlW5IaGhoaGhoaGhvsI92LcT2R1edlykV1HKQLJSnJCjKbeuW81l0IX8WyllSGFMFNRxUt4DNVOaQrUaG6OuYVN7Ll9F938mBiMYHqLE1rMemykILpalmekT6u1GC9dCoN905px1W2y1jqbUrH5tSG4cmmZ3pqpzMUjg2Z2RlGMp2fL1UaBLtokIM/hVusndK72WQNwJfdmVxaq4FrLuDQUG5mkViQlcUbwSoyuPAenrOoKmLiK6yozimoa7Nu59EOW2UiiE6Jqaa52Zd8faxIujLpIDELnf0oQus6yyMmLpnKpGqDvkRcOmaW286Is224ncchS52L5zW40IuVM3/c2zqjriDEyit3QnCzBbdau8pdSyKWQUnH1V4eFgOIjm8AJNj4TVr3ESdVFaSWG4rNy8Wx0sWyuXS5OcnUYK6Ra25f9nGHW4xDt2tRs+xM9ew1Yxte3Tc1dF8vk1n1UAir278H9AAQVgrjSXfPb2LnLattljaIdQvAFmIJE8X03a3Ql/9VyX3Jiw4YFlsoU6Trb/2KZ9KBKnzOEwMZNGxiN5j3XPSPmVV022EnPJZNLIsZIP51QSrRWb4lQysyd0dDQ0NDQ0NDQ0HAf4PAVW2DaJxYXlzj++ONNQc1CAjonjEGEECKlCH3JdOPoVkojYbHz0iG/4Tai4KNxXF1ShDg3x1i3MFldZG5uzkayIORUsBZlKHh2kdqYm6zdttjeBlFCNHKXUqbPabCEihSKBswh62TP7cmajWzgNDZY7TMUa2K28TsQ/Ya+i7VR2LK4JthaW/MojkwJTskVZEh9stm5npksxVTEWAt+pOaJC7lPLnlBKniDcfDnGYnFCauoZVRNeSxorjlSASw7qqGgRYmYLTcEW0yw0Ux2LKIX/6BhIOuIK7MhEDFrrJVlTU2lzLOCI5FA5wsFRqDd2h0AV9qrjRVVUp/o+6llm8XmuWpOFGZKLr7IUHK2ll11Bb249dkpk62bWP6zWn8VX/iIwc6tE+FSld4hu1qbkquKbCpvJba2QIKRNP8dK6xyBb3M5utWJbiOyBLxRRbEiS4QLKfcdd2ahZjhNEBcO5m3zLK6flKKjxyiyPC5qS6K4Is8VXldOxoJhJXFFeJoBJ2QJfvx9DIq39MNCwt00TK1FLNo29geX1ApXvSlTuDVirtWV1YZBXNtSG0Kr7tSd6GhoaGhoeF+wlXP+OKR3oWGhob7CYdNbEu2vN7i0goh7Oe4rVuMDAZToNaO5Alu88RHnliXkhGK4MQr0yNOSlWE2EUblSNWpnTy2Q/hGzd/Be3TUGBktuNsN/gKpVimNrgKWG/xFWsLDgDBbspzspmixquyEzazHZsVF2+1FS9PEhTPuEpAg9VRRcGtqP6+Q2AcOycpM00qSqTromVabUAsMZi914itPXfqRLeLkdh19J717ac9SLXhCjEraPKSKqEQ6FNCRSgpe1NvJXgKxRQ8nBCJLwgQhC4Gougw/zZ24oRevLwIcknkAlMvDSvFmoODCKvT6VAWVUmNSLQCsS7QRYZiMMQWH7TkWY7WDi+575lOe1JOg01VS2HVz7mKkFKakf5cmE6T5bSrrVqcvNY5qaU4UbNiLWsHdsXSR/WoiluBjZzNFGEbL1RnJheZjV0KoXi0NFPECVstgqIguTBwX7dYmyXZe6yLmBUa1pRwYWrnkLstnkNXc0SoLaJkUVdvoai3nlXlFDv2ZbB6FzLi1nA/DhhpDUDJpqpKDLY4QeW/dfGAGdmuuV8n8mJzj7y12d5/LskItiirK0sE6ajtaOrnAXwkVQ0WNzQ0NDQ0NDQ0NHybcdjE1pRRs1keOHgIBbZtP27IKIbgI3OCMh6NSNnIsFkXw0AmbdxN9N/z8iTX3bJamZMUYW7DPJuPP5Fb9+019dLHpORic1ZLwebkIhQ1a3Py0p2goLnU5KirVbZdy4YayTGXrDXJDpRULJMqQYE4WFrF87w2J9foM66UWjZShrZnqbZsZqpxjJ3ZeaORqtFohEhgNJ2awtl1jMYd02nPNJUh12rEvUOz5zqp83Kh7621yv6tgzSmauqqanASYhlRU1WNzMco/v5NaY5dtAZdMhIjOU/JPfSq6MiUXvXsckppsBwHESNCXtzlqVCqap21GMFzZVdVyZg9u2aMc85EL5nqy6zIS4KT126EaqFP2ZRUZm2/g224Kvk+ZqiSqsEy6yN4tDghHH6mQG3pNsK5rqSr5mJruRSz163N3tRrt2ZNTcZdRyjxbC3R+Xey66qIH1spg+paRwMNny2BpDqUXJmtmsGeXKT4Z7Pmd/NQdOUvDQhZlZQLcdQNautg2q8zkKUq/CbVFz+2qPnyVUBzBlH66SoxBnLqWZ2s2OJMjAzNZPWzV2xEkmpN5jY0NDQ0NNwPuOYyeMYrjvReNDQ03E+4FxlbYX5+ntFojul0ysGDS4QobN92HEihqNmRtSip4EQJv6v20SkEimYXm3z2prqVOGUbijt2+24226UpaJlCIeVMSsoYTDXzEiR3y+IcihgC01wIavnO3GckxKGoSryddZg3i3q21PKLKIg3G5dcKLESNhlIcSV2Q8y1NueK1JCmEdwQ0aBEHxsEVn4Uu2hzcccb6IKpuF1nhFAinle1HGfXBXRUII5dRe1RipMhs3unonTBSEgpiY4OncV4zVYtNjomBuiCEbXRyPdj1NGnxHRq77/0mdwZiaeHopkuRrfa4gTK24ujnYU6fzVl9WtCKSVB0WFhoiq9WmblVoE4FIINDcm+wADCtE+2iJGLNx+rLZTUd6Z5KG4qa0Ys4X+3K69DNEOxWbHFLktXtXVGCmFoG84uq2opaIxkHy0kMORNTX22dHPw816LzazwzLYVpJYv+WtSi6xM2S2+H6hdK5W0+voINi921kItYdbCbKFve60Yo38OXaG2gPqsLKuAjOLsmvAYrwSxQjEvQtPipNvjAnUBwa75TD+ZGOGeTulTz7gbWenX8DmriwPmFtA0IfeTw/26aWhoaGhoaGhoaLhXOGxiO55fYG4+knJgz+49TPtVDh1cousCmzZt9CZhGRSvktWKY5wKROnAidBAeKUqoYE6tgQfzyLB7KGrq6uMO3F1r/iIF/Eyn2LzVZ3ABTVLbU9Bilq5kQRy8bIlz69KbUoOMhAgrZ5MNdtk0EDt7kGhC36zXoBYlT7LV9ZxRhXBbaW48sZAKmU25idg7dFByH0yxVIzSiFEa7RVrJCnT7V51rO/YqpgULFGXFcF7TVrntIVxGoBVSef2EJBzpXQBUSiWY9zT8nZna42Qoc4soUFz0LXRuIgNv7H1D6G95ezWXZTNptyP50SQiSl2ursOV7UW4wBn+MKDH9aE/SsQRjPj65VMtVt7WYXNjVdq7IYLK8MuKNgpuyuvX7CGqVT8Z8xWxCo431KUSe6OtjpZdh+JdOs2d/Z6+HFWdW6jFTLsfifxRucxZVdnBSqed9heFxdmA+yZmRV/Z3h+M3+m5U82WKRIIxitJI1rU3lCpKZzcv1361juVwdjm7tnqYpo1FgOlklpULXjd12PlscCNj8atXCytIBVg8cIPXTw/26aWhoaGhoaGhoaLhXOPw5tqlny7bjOGPbCZQ4x747vkFKE/btPUDOme3bt9molkoUZNYGXFWnGGz+po3XEZ8nawodEkyh8xIni7fqcENf3H5atJC86TaXMiiJKmZr1WoD9ZvyGAPFRxV1lUD5CBrLsFqWUcBmyYpQRInRGESMEIIPLVXLTkoWe38poxI8y2mKXSVkuVjDbFA18lQKWltvNaMlUEpCizDtJ6RstCn1mZwL00nPZHWFybSHEAkSEYHReG6w9TqzMZIcfaRqcWt3sebigM0lDVXW9uMtPrs2pd7IjNtUS3Z9XYWcMtkJa5SIei5XVZEYURViHJk9PETLs3phUEmFaZ/oU7YGZsJQwiRByDqzW1NkyGlLtsIpEUFzos/ZyZJQcrLGX7eki6v2lagbScfztLVkyVXGnNwVm0Aizr8H4lrlUC317zNiJqyx46q6W8CO5TCP19lwVWvxbcdKtiucVJv6Kb6AYWVceBY4ZCOQIQbP7Nq5tuZpH3FkE50GS7FqsVZkQLNv2/ehNkrnbOTU8uxQJHh22hY+bCEhg4qVs4mp4XhJV6HQr07pYmCyOrHtRVNqa7lVLdAyC3rP0sE9pMkqMc5R5F4kHxoaGhoaGhoaGo4q7Lj6PHjiS4/0btwjDp/Y5kTKiY3bN3HGOWczWTrE4sHdpJQ4eGCJzZuOYzzXuRXTyKgQh8bhnGbqUS1usn+A190w5CJdNfJ7dysaSsnLh1zJcyKEl/mAK7hYTnI2DqjUulwbzxLcNlyyd9x4nlbNdttVi7MHGUvJLs7KoPAlTUTPZZZSW5md9K5RHk2lNIKVS4GcKdnKr7qUiRMjJbOsrNCnREqJyTSxOp2Sk72PGEZDrayqkTyzm/pB1EIUgej2aCeDAkbgnQSr2Pbq/OFcymD9teysODGxplyyF1gFG3dkb6PQxc4WIUTQXE27NgJIczGSXHRQS6tDOwSfXYzY/oaq9AlBbL4woQzXQ05W4IXbYkMx5boqrdWNW8mbHUez0g6FZlWRr9eY1muxFp5lW5Rx77bWv1eVNfgcWLdfz7KpM1QleTafNgwKbXUT19c3zm1XuBbLzppCSj1jd1F+AzI0XrtEbp+xWvBUnQZ2MXrGt5ZfFUTiQKLt9WeEPAx5cT/HPokr+NxfsM9Mynadrq6u+Ln0cVS+aBK0HptC36+ytG8fk+WDbNm+DekW0OXVw/y2aWhoaGhoaGhoaLh3OPxW5CJeuCTMLcwj0YqJhOBzYK09OI4i08nUiGGwu+IQIiUUH8FSZ8uaFXdm07VtF1e4Uslku/X3Ep5C0ULfTxAf3YOuKQFSK/ApCW/xNaKpUsmCQlFGrnyWnJDaYqymmIkIgUJy1UpyoaQeIpToRCXaOCOJRkhKVkIwIjVZnRihWWOhnfY9QYSckucYjeiOsxKiyaarKxMngtCnnulkiqoMo26MnCeCCNPplC50Ps8123icYu8hdJ0rguJjhGaN1EMO2c+HMR8j+6tqirTZvE3ZE0z5UwENSkIRMcKZpr3ZTbuIBGE6mfioH/ExPNagnPpkJV6deOORFXohNmqokshS8szOir0vqfvjRM+UZyFEAQ3kHFG141PKQBvtf3VGpO1czCzDdn78sPpxqfN167xW8Z9bztWnLruSLwhZzJEeBiV2ZjfPA/m1jRTWKOjBW8D9J6Yqzz5jqjqUPtXFHSEMZHRGyC0D7keTWnYl3tZcFButRB4anqWWVMVAkEjQ4DlgQe3D6pljQSVTNJtyDq6IJ2IILK8sE4LQjaJlan2BwdZcAqqZNFlm/95dpOUVt1p3INGuz4aGhoaGhvsLR7Gy1NBwLOKqZ3yRHYuXHOnduEcc/p2m6JABVTErryCEaIQwl8x8nDerJ1X1yqQEXezoYrBZqBJJqafrOnJOnnM1llObe1VcHa2ZXZTxeGyzSDUznUyJXQf4fM1cVVVTGq29VY2YlmiKo4AUl5WCkRqbi2udzF0HEmwcTHZ7Zh1vkrUgauODsiak60CUTGGaMpLDkFesKhr4bNzU288yjMZ2HMwmquQ+Mel7lpcnbvuElJKNtFGbc1pKQrpInSUTQ0Q6oYQ6csiIjtmLZ+qdkSGbeYv4XOEQfAatqZt9n+n73oq+sLFBIjMVN4botlh7L6uTRFSh9JmUM3Hcmbrb94DZX0tWz3IGJqtTRDCC7gVKXTFCL6reIu1zjaPNhM1aSH0ergsh+0KEuhJbrb1GaGv+1tq9qmpaz0HN5s4eG8YnBZu/itT5u6aYK4pEY64ByMjMkuxW2+ivP8ypxQ61uCJqajg2J7m+fiXda/YHxPPhMzJdya5rta5Em9Q9+10ZFnNqURRqCw3mlrDMbqk6uoiXdeG2f9Py63U62P/LzG2gGLkuJaE5IRRWllcJIdB1HRqq6cIWT6piPF0+yOK+PeTVKSIjFrZsYzS3ZZgr3NDQ0NDQcL+htSI3NHxb8cCxIqc83Hibk9gysgwzQY0MKcpkMmVubo7YdeQ+M5n0jMedzTmVyKizjGrqe0IQRqM56k28UxFvL57NQxUxRQl19VMsO6petCOqSDGSm2MgRFP8SuoHq6fRhTioiwm1ma5BwImcumIYah5Yre3Y8rZio4ayqXfTPjFNabCp1tZYE4cLnSuanY/FQTtXkguTQz25KJN+ynTa+xxT2+ecTTss2bK0oYB0amRrFEglg3o2GUADmgu9Tk1Bo8ZpFaIRLS3WTF0U+j6hWuj73p7iyqzoTNUcaoOjHf886W3WbLDtTydTxq4sllyGsqySEikXpARScrt3zcKKUDLEzkYNhWyKYUr9YHMGdyeHQBSxOal5RmRTMrWzlISIj4wi+Gxhz6zqrKQrF3Wl1MhrcGVeFct4izjZs2s7pWSWaDHZNxQjupotK+0H3K9LL3hyXbc44a32cyN9a2bWDoS7XifiarEbyp1k66BT+4l0glw9vzXHjZ/j2gKtVaV3wmvZXFscKu4QyMX2WYLYQlE1LWCjqQBIBZmz+IGmBFqYTiZ0sRsWiRB8IcU+I1oSS/v2sbh/N6RMHC+w7eSzWNi0jcl0Ee2X72LfbrgPcBT/n01DQ0NDQ0NDw32Jwya24i2zQYKRA59HK54/DRKNSJbM/v2H2LZNiF0dmZO92Mezpk6igpfJ5FLoOs834jZNNXLjQ098nI4RmSA+xmQkzr+K5QSLNQlT3LLpTbaqhSh2Q5+lo/hsV9EAXUSjkPqe0Wg0m4UqPoPWc421IDkAOffkEpj22UbvaFVNk5cAiRPHRIyR3EVvm3UqpMWKmTKkkrxJOA2zXPOaQiPNVojldUSU5NbTstZO7ZbalNFoOecgpiaS1A62ir1vEdJ0iuKtx3jZko97sVKmbmjdrTOBLW9qBUJ4BlV8VFOInZV6FVNdtZjKW4lPDJE+J6Lna0mmdodg579PBeh9YQFX463ACBHCKKI+8ieI7aOptV6KFYWck6miQYhAHT0kXu5VRxIZn7TzGv31BwUUUJ/HW8dJVTJmKrstONR2YVSIEsglmeINg63eL3PLEYsVe9Vc+UCuBxJcR/vY1a8+zkcw5bh+BmZWah1m6A4Z2Epkax4cKzCjmEUfLEubXfn2IcZrMuR23Re3+KdJb3ntPlNSTxcjVMLuxFZ9QamkKQf27WH5wD4kFeY3buP4Mx7KppNOpV+dME2r/h1xuN82DQ0NDQ0NDQ0NDfcOh09s638ixGD5vJrZlNpuK2bvXF1dZe/exInddsbjOXBVLHZxRiJkloWEQk6BUcFH1PiNOrMxJuhAC4dZolKsdVhLoghIqbM8A4Va5FQgCxrE1MW+EDrL33a+DyUbie6nPdEJXdZMKNFymOo5zGItsaUoXRfp+2wFUQABUt+TxfZO0SG720cjIyFOjIwF0Jwp3hRc3E5asMUBlYED4WlOSrb30pfCyM9G1kIowVqmMeWyLjRYc7K9TsqZGDsy2e3CNtLHyrh85xWCmgKcS6ZzYizZTpIVPpXB0hpDVS8NwWuZ1ZXtnHVYmOg6U8mzn6fimVD8HAk+fgdf5AjBR/cUus6UfineDozlS4OTLBUjnYXgk3G8aCnEmdU91JR1Ta2KZ1gZyr6CjyKq17UAqWSk8jnfv8H8LQOj9HZjfy/BF0PWtCNX1M8KzMh0nY8sQza3+Huya5jix9oXO8zyW3Viex3NxVTlUj8nTmyTjzMq9pnK/vkYxc4WCbDZwKVkRAMp9+SUCVoV/0Kfe2LsrPULWzCoOjWq9KsrHNy7k8nKEqiwacvJbD/roWw8/ngII4TeM/VixWANDQ0NDQ0NDQ0N9wEOm9gGCeQ+YbNnrTAJmDXB+n2+NSFHlpZW0Dv2cOqpJw/KLIqPJFGzz/q2S5GBHIDdyFerp9fbELtAweeVltnsWqiZzmLERiGX5ATF1Kc6gsZGr2TQaFZespfsQCBTgmdYXaUNmt0i63f6qqRiedGYgqmp3ixrrcKZIMUUQUzpTOKEHtBJgRgGZbbuFxSfk1r8eBTwMi5yghhtlJCPh5GcsKKeqtAFP0dio3WclGot4OqNvYsEsuRBcbS3ZWOZRJ0oh86yxz7EV+mo7bojz1MXteIsdZt2znnWxlv3Sb14yQufui6yOjHSlD3LqyihC0SgG43svOfspVxWCha7aO9rfsRkYpndXNyaLrOxOyEaee5itPZpAA2Mum54jg5za/OQ6zY7t/rsYbw5uO67k+ZiKqpd38aI68KDUlwlFrejzxaAQi2LKkoXbMSOluF02XP8Os4Kne9TMJnWLNW2ykPB3eG+GINfl4N1Xv2T4pbwqupqqAsC1X1gam0RRbSQk3rDtlLE8vApr4Jmco+R2hgZ2prBxjOVzHT5EIt77iBNpozimE0nnMLxp53NaONmt0AXj3kLBAjxcL9tGhoaGhoaGhoaGu4dDpvYDrk/rUprmCm1OMEF5jYscMJJp3DH7beztDRh5x27OfW0k4etDAQjBG8vtnEutTzHZr9moqtYBiNTPoHHlGEtg+VYtSBFKVWV9XZflTLckHtXj93sZx9BlDMUs5P2pZAFhOSZwUzorO1ZndgUL8gBz2cSCGtyqVmL7VcIMxJRiWCo7bGZ7G21NeuL+tzbYCpn1oyUOgLGLKdVYQQh5WzkB9unEE0FjaPOioFcNczZ54lixV8BHeymQrAFCldL6594RjVVe7Nam7PmTHKLs2LsrWQdyG3wRl7FXtflRKQLqChZ8zBftjYc19xsiOYACCJI1w3kMkQZLLACpGTZYgnVYuwLCGqW75HPKc6edZYYjSQXPxfYNWftz3U+rV1hlhH3EU0UlicrCLAwvwlVZXW6wnjUIdLZAkodXMsaFdaV4WohHiwNMBQ0qf9eLcByfdn+7tdCUYjuQEAUDcFHVLm6XcAmOzt5LZBD8eIoHSz/w5xcJ7ZF1cm7mmVelZQz075nNJpjw8aNHFzZh9IhBLoow/vUYoVb6tfT9NBeDu3dTZlMieMFTjjlTLaceAbMzw1RAw3ikQU7PmU4GA0NDQ8k1DKRqza94kjvSkNDQ0PDfYSjvTgK7lXGtlpHIYQReA4zYLbQqj6ORyMe+vCHkMKIXd+4mUOHlok7d7N9+zZGnVss1cySNu6n5g7txjnGSpCMTOZiDa9mgS0UzeCWSqRmVtWba5US3MScgWhEUurYkmzbDJ5XVAp9URKe0QyVlojNuc02G7agpsaG6DN586z52BW06KN7SjElTKp6F+MwY7UUVzapiqr6v/1nVVHONuO0BGsPzjlD0IGIKdaSHFyZ01CzmoXRaIRIYFqyZTaLWVxLye44dgU8Mhx7QchuV1VvzbWGalMMtdh2SlFiNLKcSiKGzpqyi4/LcWUUqmXV3nfuizdGeylYCCCukro6XnL2bdiiRPHz2aee4NbZOnoHGIixqardYHu2jPAsywpCjELyHHf9Wc6+L2RbsChmzdZS2LVnJ7t37eLMs86gjOznh/YfoBsVjtu6nd0772DrCdsZj+bXLAoIGur5rMcvOBG342JqbiEQqVlr6rH3FaOhQVmVIkoUV5EVX3CpSq0d5+TKtrnIC5Apmvzz4Wq8n/XsKv7K6oQsq6bEijCam2fDwiYUWNiymaXFRXst6jxeahcW5J5D+3azdHA/mjNxfgvbTjuTzSeeiobOFyp8IaEYqdakZm8u663ZDfcBWgNow32MHVefxyPPOYvrbvoaVz3ji/bgE1/K9S97L1zxCnZ89s945PxlvO78Dx/R/bw77Lj6vCO9Cw0NDQ8yrCOD11w2+948RjB8bz7xpUc9qYV7mbEdMoPqY36orlMdyp1SKsxtmOeMs89k3+23Mc09Bw4ssmHDRkabaiuwIJKJXj5U6rxQzwN6MtaJkjevio0AmqbEWIvl/4oSkcFqGVzNsobYbCqZmtWSGBEtQ8MxYiSiNhBrKZCrIglaMirWAlzQgZzqMDsXs3RSbE6vtwUVLVaqhA9+KYUQq+JZfO5sQgqoZOIwV7eQyeuO6eDc9PKqKi92wfOlfoyCms1TsAbgEAIpBLccu7bn50jXEiipSuEsv6k5e0GR5W11TU7U7MOFjJodvLPcZr0+6nELoaPOtK2KbsoJghE6kVqF5TRYCyllm3XsqmJ2NiV9RqJZpRUj2DklGwfl+1awrDXq7cxVJdTaMlzVc5+bHAC8zCxae7MUL2SKgcX9i6wsrrC6ssKGuQUmfeLA3v0cf9JmbrvlFnbevpO5jfM+v7kbFNc4kFodiqBMvqzZ4HrMizdAy0w1rgsy1Xpc7LwUmGW817WP+/n3BZKUlUP7d3HdZ/+eyXTJS6HsfJraa/bsnIrNTM4BYmTzcVuZ37hg13C2a3Xc2fWsQ0GUna00XeHg3l1Mlw4BgY3bTmLTcSczf9xx1pYtttBTqNnvbGdHFCTeJZfd0NBw7OGR55zFdasv5fp3/w48wx+75X38EfDaLzyVr3zkbF72phfA7Ud0N+8ex8BNWUNDwwMH33nxBVx6wVU8ZukUAJ5w6lm89gtPXb8weJRiHaE9hnD4VmQvn5HguUCdzbqs1VKqSvSs43gu0o0CqTf7Z4xukxU8B2rtwlS1yy2+Km7X1OxK18zmCTYapku95xJndtDsSVnLADqBKGbvNLuzusJZSNkrqIrZPqWObvHMYg1QlqFcaVZcZUQFn7PrSnApSAwEQm0YsvdaZqU+No5oZp0Gm/lKsIGpgimGw2xUcQu0qCu93TD+SNWUujoWRymIjLzgp9YkGUmxzGxiWhtvZTYeptqgPT485DIrS60ETV3htlmtAIWsIJnZ2BkncPb7xa3StWHaxhhFk1eNvLqFW/w5IRTbXk4gkaLFFgmAqPb7daRN6m30kGZbvMj+vkzphVEwK68R2vpe7XUEe0+xM9uyBM9eM5t1e+aZZ7N1y4ns3buT8XiejZs3sbCwwMEDh9i7dx8LGzaQU2ZleYUudmxYWKhGff+smII9DAQasrE6KM/Usiin93YdzX63fujUo8u1BTnnbKVT7gTIXl62f+9u/vavPsGBvbspmiz7LdZcja69diMShTieZ37jAnMLG2dZ5VLt/jUobEtMIjBZXWHfnttIKyuMwojjTjiVLSedRsnRFqTUnBB27ZjpOKsO9uOhtbmhoeGYxuvO/zBcfR5wGq/9wlN53fkf5rqbvsalF1zFxR/4rSO9e/eIHYuXHOldaDgSOMZuyhseOPjOiy9g35v+lNUrX821V9pj1wKXP/89cOrHh+/Pownr1OVj9LNzLyQUz+uZlurjMV1Vq42pdZ6HWi5QPEc7yyAGv7mtaqkppsGJBwG3qTLcjBdr4vE5sNnahH1uasnZFSsjQblktwGXIadZLbBabMROKUqa9t4GW9YoUm5zVSOMwfdfXR2NEohiRUYxBC+4Cogrrk7t7T/PzlrxUKb0Gc0+Yzf1/h6S7UMu4HbS7PuUS6ZQsP8tVWAeyG9OZchc4k24iOVq+5SZ9D2oFQHVfYlBhuOu6lbXUpimnpQyqRRS6sklu/1b/ZgWslvCtRZSpUxWJXn2OOWy5vds33KyzGefrKDIiLzN6E05kXKy7fT2fkrO9CmRPOea+kTq+yEDmvqe6XRKn9LQ7lucyWqajXiqZUzqpMoWSjyzqmZmtny3jXwKAUZdrOsjiAS6uTHbT9rGOQ89l317DrJ35x4WFubYtXMP4/EcsRsxXZ2axd0t6IcOHXT5WYbXENa0LxMsK6tW2qXUTG4tEHNy6Z9Iuw4g+WfEiKF6gZTZl2tD9aED+/iHaz/Jgb37hnE9NvTICqWQgBKGRRwUItHyy3VBY/i82WfOVFeFklha3s+enV+nX1ph1G1k2ynncNxpZ9LNzxNDPa618Ms/b6kfSHh9j1Wpb2hoOLZx1TO+yA1XfAwwlRZgdd+M1D7ys89ix+Ilw4p/fc6RQiO1DQ0N9yd2LF7Cvjf9Kdde+eq7/OzaK1/NWz7yfi7f+p4j/t24FsdCfvZwcNh3msKsGKegfkMfBjtkcLWwFgSZkDsrmDILZWEymXLwwCFSqje9tfeYYdQKKgQNiHoBDpYzza64OZubERi32aaUfLRJQTUNJDpVAuw37yK1NKiyDiPOIUaki1Ya1XV0XUcXI6OuI0QjAtFn+Na8rVl/xYuMnES44mVimR2vaSkUsX0spZCylUip2rgVVf+7liHji5NEE9OM/oQYqEVefmRcRHYl2K28ViBk7zs5CaxqnLqd1WzZQknZiOU0kaZpsCqXom5/tsUCKmnxnLIWbxDO2S3CRtSzk5rUJ0rO5GIKYko9pWRy31NyoqSelKb23JQoKRuZTW6fzU6SczEyn+qxUnJOM1tu3Ve/KkoRV6dZ055tOeh6La0ur7Bn9y5y6vGC5WEBJsYAAeI4csYZZ3H713dx++23Mjc3x6gbG9Ge9kwnExRIfWK6OrEWZHydYRA93SmAJ4SlWpXtOi8126w+t1dlUM+p/8bU3NrOXAqoLyYcPLSfv/nra9i98/ZZ+zG22BRDIHTmDzaCb39KtdfXHC0Cxc5xSQn8ZyknDu3fz4E7dqLTnoVNWznpzIey5cSTkW5k1mZXzAsJ8HOUsn8mfSGq2OKM1irphoaGBwSq2vDJx1y77vFrr3w1l/7J04AjTyqP9Os3HHm0bHXD/Y3LH7b9bkntWlx75auPGnL7QCG1cC+IbZ+Sq0zBlVVB1BQoMLtsDG7v9OGfgUDNMtZ72r7P7N5zgEOHPAcIlinMOtiQFWwcCRGxDhxTDV3pFMwKrK7cmgeVYX4oihfwZLRka1f2n1vbj7GdECMhztTmEO2xGAOjbkQXI12IjLqR5wNtI2bHNhU3hEAMHRS7cY/DmCFQDcOcUUqZKa1VMdUCJZOmPcUJmrpiXHONs6imDsVKVNLmqmC1QKdkBDHnTCmZlExFpfhIF9Tn5RpxysXIKlqsjVrEyK8rs7NiLieNWt+Xn4tBBXf1PFvOus+JXDJ931NUBzU3FzUlNidKSuS+d6JvRDen3vav2CJGHSVUZ0kZsZu1N+P7EXzxpJp/xeX/LgS64Cp7tIUXwYjo8vISk5UJe/fsZmn5kL2HlG3ssZ+bgDAaR04/83T23L6XUlZZXl2kn64ynSbGozlWl5eZTqeUoiwuH2R5dWlwMphirF5e5nZ7wUcH2WuUUt0P6tltz8OWPGi5lFLN0t6qbAR38eB+/vaaT7Dr9tvtOhD7QHulm31+CL4Q4+o9Ojgn6qJULpmckynmkwlaEtqvcGjvThb37EGzsmXbyRx/xkOZ33Y82kXq7GGpCy2q5i/QTNZMVpuBLJrxFAHaBNuGhmMGO64+j52n/ME3JQXX3fQ1Pv0X6wO1j3/+m3n743+Byy/+FQD+6A2JHZ/9s/udZDZS29DQcDTjSJPbHVefZ9+TDxBSC/eC2BaF7KpRpKt9OK6KyWBxjTECShejW3Wt8Rg8yypWarR3736WFldMncMIqKZCEGuyzbl4aVOeqVfYXFsjf0IXA0GgE6HzXKU7LanhRFGIboGs6nEtqhIJxGCZ4BDNVhyryowpnLW1OMRoGUQnUEMG0f86crLbhbBmLIrN161qav3T1FlTIpOrWckbeWuW1zKXbjGuBNNV0kpAowQ6J+YpZ/o+sTpZpZ+usjqZmDJaiW61IMOsVdqJlRZFPbMLs/ZoBrKtQwlUnS+bU5pZwZlZlXPKbjfOpqZmI70p9ZbL9vdZ98m2UcuNnOw78Ve1nHP215xlf13htEM8pDgLRhxDULpobcjWhOyxaakqqP3+wf2H2LVrL4uLS0ynU1QLWU2xTn3PZLpC1sSGzfPMbdjIZGnC3l176MYjTjv9DJaXV1g8uMjePbsJUTi09xAH9+239+jHbHV1dVBHrT3ZSfra46pVie0By4ObSm9EUfFz5ce0lMLBQ/v527++ht233Y5gc6arawIsbm159mgWehgU4/pZyNnU/qzmalheXmYymZBWlzm0azerBw4R4ohNJ5zEtlPOYrxxI+qfi+qnDrYBL4WzkxMKiAWDh2s5mHmioaHhWMETX8qLP7jpm97wXPWML9L90j8M/37889/MEzb+d/7oDYlHfvZZcONZfOBR5/KWj7wfbjzrvt7rAY3UNjQ0HAnsuPo8++48TBxpcvtAw2ET21HXOakKXshDlUhBjchqJWVi5KuIP4+ZFXlubo65+Q2kBLv37GNldWVWWCQ20icI5EoGnWwFkxKJKB2FkcCogy6U4Xdm8FEnzHKWA+GVSmrdflzzwZjaahFfU3UlBrNxVquqz6cVZ0q1/Ejcmt1Fs36GGKwgi8ysGkhcWHYS4G3CGbMfJy9dqqHM4vNk1d+7FrMqD0qugIiTXc/L5pTo+2TEsk+DCmmWZHtOVVdrRje7qmskNA9qoT1vRqZySb5PZcjnKpBK3ZZniHNGs6K55niTzwF24uuv26fs2d1qMzfVsAyktl5bDPtjZza7smmjdIoti8wuaLtQbDxVtZ5XciU6WM7H8/NsPf4E+mlmeWmZ/Qf2szpdMdtzzhw6cJB9e/ezvLSMopxy+iloHjFZWWHz5o1s2DzPhvkNLMxvZGVxlc2bNzOZTtmwYd7aotUsvTkV+snUSrEodrwow3tKJVvb98oq1336Ovbs2zVYvksxwpm1HidzTRxcPMD/+uQ13HHrbUPmVoKXgVXbsT9eR2KBtWdTK8/cekxJlDRlsrKElkzsOqaTzLRPEDu2bN/Glm3boAuD0m+2YpBQnRNmjZZSvCXbPrvZVx3q508asW1oOGZw1aZXcP1v/w6XP2z7YT3/8c9/8+wf11zGw59365Alq3jk/GXfsi30le/9+8N+biO1DQ0N9wd2XH0er/3CU9f9d/nFv8InT3nKvdpOJbf313fXA1GprTjsVmTEx8yUBF20G+iaIxyKd2QodhpyhGs3IcLGzcdx9sJWvvrlG1mdHGLPrr2Mx2M2zI9tdqmrqaVP3mILuE1TxAheHa8iyECyQgyW/vUbb1HPWQZXqty+KtFV27U5xuC3/p6JFf8PjExIZyp0VQwFm7ebh/dvr+X1TFbSEyKlT3Wjlpt1K6/1N2PEwt4eJSVKEIrYvFy8fMpU2tlJKCI+I9ezpz5uyOzZSlYl5DJYkIGB+FZbsc1rxcYV6SB5+j7acQrMSIu/bUIwsjVTcD1biy14OAsd2pbVVVehGNFFQYNbiauibpngYifXSFioluNiuWe1MiOwZmM7gIXicrnUbKlvU0UpSaGLiM8rVi9Vmk6m9DmxsrpCjJG50QL79xzguBOOQ8vY1NNcWNi4iRgjKfdMV1fZctxmdo3nSdPMZDLl0IGDTPqpbbcklg7sp5+usnhQmPQTxuN5NmzYaMcNsfbizsZdWZuzL3746yHCQx72XWzbtpHUT/zz4mtHahbvooWV1WX+7ppruO1rX5t95vw6rh+3IMHj4/V607o6Qh0TZfEBa5ierk7tmu46cs50c2PG8/NoP2VleZm5jZsY4SOMtF4Nbg2vRW3VZ6zWRF4d9LXErS4qNTQ0HDt4+Nuew8M/uAluO+8eR1NUonr9u98BjzqXa698Nds+exofueBSYH1L8uVbrQ10x+IlXLXpFfdqX377Od97WM9rpLahoeF+wxNfyuVL/25dnnbxAx/nWj5+rzd17ZWv5gkXPgse9e3cwbvigZSnvTsctmIbAoPSB0AMnmU0027JduNrZU/iszLdQusWYBFhNB5xytlnMrcwzyh01vza1wIkG/1Tiah6sZS1Bft9OeoZPiVrQYP4zFLT5nLyNtmh2ddu7LMWUi0ZKoqqKZNVSTSVyUmc1hdzCpDKYGvGbcGaZ4SwFjKlUsgZz/wqqXeFUxQNRrKDtxcDJizjc2dTT8q9kXLseTaKtQzERb3IKadCzomUrQE552TKXymI89koYllWnamlZm/27GydmQqD/TevydWqFxUxFBYVb5JmKGqqTbq1DbmWTdXGaRC0yHDcctGBpIUo6+frFtAiPtOYYY5qbWcuuVCSv743c9eGbtwGq154VDJDw3RKiT5lUslMc2ZlMmF1ddXIdMqcfOpJjLoF9u08wGQ6IfWrpDQhiLJ3906mk2VyycRRZGHTAnPdBk7YfhL79xxg1+13sHvX7ezfv4/9B/cxPz/PuNvAeLTBlfc8kFZb0zAiW0vSammXeu5747aNjObGdj7Elm6qcq65sLp0kE998q+5/Ws3g2ZbHPFtiy9aDGZk9WxvEMQLzxC//rDLO02s9KoLkXEcEwnUD9pobkwYj8wG7pZ4MJdAKUpJtlihQPGLbu34Ji0ZPN8uxWctNy9yQ8Mxhas2vYJHzl/G9e+20T41d7sOT3wpl1/8K3zi6p8eHvrAo85d15Jccd2j3g+3/dJAancsXvJtJaKN1DY0NBzLuPTT59+n23+gk1q4N3Nsq5U1F3J2HcorZxVr4NUSGeRFGf5nHUrOiJOaIALRCqZKyUips15nREmLFdSErqOfGu2rapAQBoVIBMgz4hND8IxfpujMfuz1SIMSHLydluLk01gBzqw9j6mEWAt3ar+tWWW1uC1ZlZR6ooTB8mukL1luV6OHQ337agQjKNj814QUK6EasqSlgAQjFiFQysySXXJVhwtTZpbkGDq05IEQijcEF3UyUkujggwkxrK8wdXuAkSjjDkTcVXQ36+p52plTOqKnS8iKGZJjdEWJ3JOhC6QyyyDbWbzgma3yfqbCnXRATXCV1yRp5gCqzbntmZPg9jrZ1EbOVPE89lu81Z7T+ILMiJQ1BYR5ubmjNh2luM+8yFn0a9OueXrNzPNt7F562ZOOOEkNm/eAkE5sG8nJ554Imedcw533L6TOLYceMjC4sqEyTSxsrzKieecyni8kdT3NruZQOkzk9VV5uZHdu2qEsSug1RsVnPBztfub3yV7SdsYdSNkejEWBIIrC4v8b+uvZbbvv71gfxL9AWQ6j327ZaSkWik1g6pf9ZEbJawiGXLRyMkRrfYq2Wzlw4ioRvGXcUYqc3M4mp+XaxQD9pX1dY+B7hDoX5KfaHIr6OGhoZjC687/8PccAVct3gJrJ7Fiz8InLtecX3bx/+A6276Gp98/rX32AT6zM/eyA23/z5XPeMF6x6//GHbOen2u/2VdajK8D0qx43UNjQ0PICwY/ESuOaye/zOu1fbqhGQBziphXtlRbYG5FzVNS+xqZnGEKwUahizArPcX1VDnVzqkKn1JtcIiLK6vMiGcedqIvVO2sqdxCyWKgFC9DIerdRnUKe6LlAQrIPK7JdSb+j9oVCrodbkBUM2q3OuXmF/E5Wwidt8S8muSpsyGrBjUcfIECyDPJ1OWZ1MCAHCaAxiHdEEK+FysRul+CzfQBc7QucNvzGYfTXYe8w5IdJ5pNmVMLf/5pKMiChk7d0uarVVlcTPVGlT17xqyUa8aG2hFsvyRiPNxkfKsICRdTa6pi8Z0eBW4oKGaNsOkLUQqKOgXMUtioSIiim30VX2XIzqIp4pLgU0UpuXJdh5VfUsbahqvi84SPD2bSg5+Xb9vZUM3qJsjoO1BMsNsupOgw5OO/00dt6xm9tvuZ3x3IgNGzYxkg4BNm/dwupSPyjdm4/bwv59e9h+wjZuuP4OTj31VGIYEYMQ58ZGPgNs3LyRvbt2s3DK8eS63wK5FO7YeQvLK/vIKdNPe2792q1s3LKRTVs22vnzkUl9P+XA3v3su2M/Uoq3VFcyWwjSDS5f9ebsIFUflWHkUdVLBfGxVhFCRCisLB1k355dxBDYsHErIXaE0Ns1JspslJTlpyVU63r21y5YGB0/P358XbnP1IWKhoaGYw07rj6Pyy/+Fd52y2VcvvU9bHvZj8AVrwDg+pe9l+ued+s/+fuPf/6bee4zf4zX3enxqza9gic/4wJuuOIFAzEd1Nw734j5n43ANjQ0PBhw/cvey8Nf+cZ7/PmOq8/jkedYId91pz/7LvGOdX0GDwJCW3HYxDaGyGg8JkYh6awtWNUm0eZibag5V9tr1USrddXaZkQCXRCfHWpqYp8y4wwH9u2zEUAxOvVUb1R2xQkrHxrbX70sB0JtYw2mynUiaLCxJhQr1ImuSMZQLc+u+rqybHnSWdFQ0myjh5xEmIrsanLCGS2kZMQ354yKWYGrOplzQVNGQ0Sik3F/PSP+gVyykyshRFeCg6nOlt+01865EEJCxeaGdgKQCZjSWRcYSirQGXVXFTTGgdBkf38522vmNdlMXWNRVQoSZCgCsnE12RY3rJfaLbZ23BlUbPsdEbOnqiqBaCqiKrG4CdouCsueZstHm9qX6ubwZC5RTJ0WAZVMKRDxfK7/SbDZrqBEwpr8dEG95Itqby+FooHU96xOV+3hpCwuHaCf9mzcNM90chxf+twXOfH04znrrIfSdR0lmwtgbjRifn4DS4sr9P2UvXt2850P/y5OPvU0xiObbYwKRFPtQxeZ5mTMOlf7NHzj5q/wD3/zN0xWl+w9CKgG9t6+y2T8GCAVppMpKfdIEEZhzHi8gJRC368OjoIixd0LlThXe7I1ktecLmLzlgtrFoVy4uDBPSzt20We9sxt3uqLLpEYOpKXm9niSWfHMSil5s09c6te7jW8tsxGVUltdguN2DY0HGuoN0/XfXATL77tazzynB/jJRdcClf8Iz93yg6u5zSuesYX77EU6lUXPovLH/V+rrvia1x1Dy67HVefxycfY2rvq372ErjmsgfVjVhDQ8OxiUfe8j4u3/rvvq3brN+ln3zTtXwS2PHZP4Nzv7buNV9y7Vu5lKu4+HqLfHzy+f+OnQ/7A1785b2zDT1Iv0MPm9imnAhrRu9UYgs151eQYHNh1QlpDN4+HKPnDHUgcUaWcHtkNDIchH279zCan2fr5uOJ1WJMVf5qERPDiBSJ+AigNd00ntdd54au5U5uo81a5536r6hbbaWSMy/BKZUg2egiipErUft30mzqWE5ksXbkOsIlhsBkeYXYdYwkUkfWiLfY1tE9yZ+fyQQ6I4tu7w3U33GLtSt15iYWV+hsFE+s/87qDbZ+TNRtx166HCS4/bjanWsdl6utxdTfku0XQjCFt6rMta04ePO0KcWVkdpCgHVXKZ1mwGzctXBMdbZ/ub7P4RoyjbmOR6KILTh01cqtaAie3Q6UnK0XyTPc9bpAlSLRr52CqAyvRclMp1MWDy0iouzfu5/lpUNs2bKZhW0bOe7k7Zx08kn8w9//PaedfgYbN20k9UrOPerNyof27+fQgf2EGHjIwx5G6s2SvHXrcWh2a74qK8vLjOfH5FJ8rnDPrl07+dQnP8nqyopd91r3365JzWJ2f58LXJLSBSHOR7ZsP4FDhxZJaWqXtRN21BdytDZA2+erLloo+Pm3BQopUKarHNi/j8niQUgKPeRJj24os1yu5+eDBGLsyKkuxNw1MSsShsUlv0DqZUerRW5oOPaw4+rz4NQ3cvEH3s8nn/8UnnDuS7numsvglB188jHXcv0vnnZY23nyM97B5Vf/Ci++Yn0J1Y7FS+CVL4fbfmk2C/fGsx60N2QNDQ3HFl5y7Vu5dt/dxy++Fazu+y0u5Sr7u8c6rgX4yOw5F3M2q3cq5gPWk9oHMQ67PKrvE32a+DgR93Z6Njb7mBgvYzWVdMi04oTECAhS1hHPgBAijMYjtp94MqMNG1lZXmZ1cT/BW2frqBoV6EQJ6oqwuzE9mWkkOVKNxqZKeXmNIG6jNXUVGLKwdcyMuiILNmal5AxaZ7Nm0OyKrpFdfP4uaoQlpx4tidT3FM3ELrpaWOhTP4zMqTNkra230HWdFR9Nk9tPe/o+AYMb28ukZMgTS1WP1RuXcx0dYyTfbMdmG7VxPWV4DJzQOlEsPkrImpM9R1yqPXjtmJ5ihDtXMp38Z2UoeEpZSbnOxjWV2LLZlkEuJQ/zb1Ml5ar0pZCG8T9WkGXnto6ZsfmuPqrWbcS+j1S7sxeGYW1FRW10T07Z9ivN5suWYmrowYOLgBJlxNLiEpOVFUajyLYTt7N503F86fob6XPPwcW9LK8ukug5tHyQ22+7lX5SOOWUM+liZ0T54BL79x/i4OJBJqsriCh79+5hPO7IpXDgwH523vp1/uYTf8Xy8qLZvrHP0uBvkNohLBDGhDhHCHP2UQ2RSc5MUz9k2+s1q7Im0e7uBi0ZLYmSbZ6xejt1SZl+ssKB3buYHjzI3Hge6eYIYTxcT2HNjB5RU9dDCGsy5QDeMq5h+DxLiHSdz4juOiR2RG/5Lnr4yYeGhoYji9d+4am85fonDeN6rr3y1bzlv10HT3wpVz3ji2z7xR/hA486l4e/7TnrLHF3xvW//Ts895c7nvyMd9zlOZc/bLspEU986VA29ZaPvJ9H3vK++/bNNTQcA2hzTY9u7Fi8hMdcdMq3fbur+37rbsv3Gg4Ph01su9G8ZyCdbK0ZJVObj1EbAVPVuaJrxsyIjTzJbks2t2lwtdPI1dYTjueUh5zDeH6B22+5hUN791ikspKvkmxbIRCDcWtThoUYA7HOKQ2eMHWCN4ORvJQrGbfWXMvPFjJK0kxSn+Pq81cl6EDGYNYsi0Ly7Vkjc/ISrcxkeYUgIDHabFkfq6L42BSM9Jk6a6VZJGu/Tan3tty1o2rMbuxxZHLJZgOVmmcsZM3DMTe3cfEZtj5fFoxQ1zm11T7qjcf1OFMquYVhWMuafOXM6uuv4c2+2Uu/hqIoL6Ya5vYOVmD12axluD5q23ap5NZJe3a1tdgagy9mGBlUyrBYMZPr67b8+strCo4KTFZWOXhgP9PphCDCxvkF5sYb2Lp1G6UUNizMMeoie/btYbSxsHX7dg4dXGQ6nXLo4EEWDxxkz+072b5tOxu3bGL78duJozEbN29i+7bjEQIhdMQ4Ynlxmf179zEajVhZWWF5cZHr/uGzLB5aoqhlwVXFsq5SrdKmuG7aspVtJ5/G8aeeyfZTTieON5jrISdCgeDlY0PjuNqxSynT9z19b+e879dcS2pkX4vZ1EdzC0icQwioJsu6O0kFBndB0YIGWzAYsrO4gSIGiqy9HnTNebDFFMukl6FEqqGh4ejHdTd9jU1vegrz2/7Duscvf9h2XvuFp/KBR507/Pue8KqffSQPf96tXHfT17jhio/xuvM/PPxsx+IlpDc+FoBL/+Rp637vj96Qvl1vo6Hh21K+c3/jOy++gMu3vodbr/jHRnCPUjzylvfdY1nekcDhzhx/oOOwJZTRhg1WxoOR1BKCjdrJWHGRBESi5yfV85j1ZzPL76BNOWmyNichJ0UIHH/yySwdXGb3128mTXo2zM0xTYkQIjlDn5KpdwVrHI4+EqiUWTkVRqSiBP+7hWKNaGUC4k2wRhCR6CNjLMxZFcsQdMgMgs3qNL3WrZglk5OpVu4yRorQhY6iysrSkr1GTtbsm035Ho9Gw/MRiEHoxp0VMqUeSmFufsGOE96BpTpEWiXgo1bUXcuFGKPbta1JGYzMiTj5Uy+ZwhRriWZU1SqBSlVB8aIghgxlzRYrMuSOBdA6y9gLwaxfqFBCzXRamjN4hlk0kp30hlBbmBnG1JRSrbg+RiaYOm80VVyVrESu2p6D9xOpXZ/qlnfMQZCTFZwpvj+amPSrRInMzY0IsWN5eYUTTt7Orbd8g6/e9FWUxO237KSLcNyWbZZrniakCKNujt233s72E09mfuMcm7dtRqQb5gFHrOwq58wdd9zBmWeejlJYXjrE9Z/9DPv37rWG8fqetJCmyfOus7IsEWHa9+TSAyCxQzT7ebVMc/DMqkogiKn2IthcY/FrvKq/QAxqvWLYZzWiyGhM0imT1QmjKFACWRMiI/tc1Cx9qRefkWIwC36QYrOCg6DRPicp26JIJ0IQJYk5LKQ77HW0hoaGI4yrnvFFTrodbuUf1z2++Isf52LOHv6d3vhYeMwbuXzrU9bd5D3++W+Gax5/t6SiWpwv/NjP8cmL/pTVb6OVr6HhWMaOxUu4/mXvZd+b/nT4PF2+9T1su/gCHv68W49Jkv5AxUuufevdWoKPBK698tW86vZnwbnNjnzYd5qLK8qkdOw+sMS+g4t281qJnlr/bqiNtFoJ8F0zdcPcTbFSJxHL4XWxM9swMDc/x8bNWxnPLdB1nWcPAVH6vif1ZuvNOdm/U0/J9mfyfyefXZpLohRrls05oylTkll+c7XSZptnOzyWCyUnUymr0jRwc8/caqUmkNIECzDO2mPnxmNQJfVTJisrpOmU8bijpGT7C0i0w7946BCUQt/3rC6vINiYFQkykFcIpnY7cat23EoOLQ9ble28xiI8ayauinmuGVeAqrDZ6SAMI2JkmG9bZ9rmIWMtg+W5DKpy9uNi+eCq1qrWc2CWYzzLLFWp1eLHDs+b6nC+Ndte1vmoZnOfWajBG7EBzXmdQp9LpiS3UHvp2HTSs7S8TAiBbtSxuLjI3IZ5xuMxk9Ulzn/4w9iyaSsHdy/ykDPOZXVZ6acrhGgnfzw/5vTTzuLrN32dO+64hbPOOhuR6Ofd5iuHGIghcsdtt7GwsIHxhjliEK7/3Oe47bZbzOorYgs8vrhh9uwyNFtrycP1VFV18fIzy9L6IgRKF2fN1HXRac2HbcjK2nik2eOILTp1XTe0Tvepd2eADL9jn0/fvme21TO9fnHZUk+plvfk10Qm9atoyUTpCBqI+sAtj7r+Ze+FG8/i0j95Go+85X3NStnwgEH3S//wT/78wo/9HG/5yPvvolw8YeN/v8eb8Msv/hUu/fT5fOSCS222bUPDfYh7KjY7WrDj6vNsTvRn/4xPLv07PvCoc9d9nq698tXse9OfwqlvHOZJ77j6PL7z4gvY8dk/Y8dn/+yu86Ub7lPsuPq8+8SG3PDPx+ErtpuOQ0Q5uDwFpkgYM7ewkZWDq3ZjronUJ0aj2Y2+kd5qV7ab4pTTQJikYHld9Rt3jAghsO2E4zm4ssTyyiGgjt2RwSJMnZ1JmDUCl35QXKH2G+vQ1htCQEq1Q6p3UdXcpgx5zxAEmwZUhlZia/gVshQfpWJDVWMMLC1NLNcbI1EzuUwppWc8nmNlZZmlQwfYtm07aKFfnRCDGOHLvSmdipHO6ZRJymzctECfTakLIQ7qqWox62d2e+6Qm80UAqJWdpU1E0LnKisUAipGQEslJ7l4gdV6C6lxHjtyUlVogcTsGNXjZMdcvSHaj3NWX7Cw17bIsmebSx7ywARX9UtZkxXV4Vopmm0WMQzjhmr5lm3Xi6ukEMTztTESNDPkrkUHdTuXzGQ6JQRbKDl06BArK1P27d3Nzl07SdMJGxbmmZ/bwPc87v9gPDfPrbfvZN+uvXQbDnHSqadSUmZ+45jp6pR+OmFhYQP9ZEIYRVMxSyGOR0yXV8n9lOU0ZdPmBT5/3ee56YtfQH2+rMfDqfnl4Ae+mny1FHJK0FnLdojV9SCW/fZjH2oZVym+XWedbk9WP69aMkHF1X3s3AjE2BFjRIs1nqfVZVu8qKp4VfXd0j0sslRbs9icZK2fp5JdKcYyvSUznouQ8JFWD1wr8ubn/Fe+cRP8m63wb66/jPfc9CYe+cz3cd3pzz7Su3bUY8fV5zUV4ijEjqvPgye+lEvf+DSe+dl3DNbjO+PuHq9qLc94xd1u95OPuXYoXJl/438APv7t3PWGhmMGr/3CU7n19qu4/DGn8JYrXw3c/ULPtVe+mmuBy5//Hj75GH/wMfjvwONPeTPXPeoPOOn2F9zt7zd8e3H5xb/CIz97pPei4e5w2MR2sryXEDqKCiF2zI/nmNuwgZWDQh2fA2anLLmgxlrdXsr6giJqK7KsmedqVuCixQgJhURmZWWVANYo20M/nZD7qZMycMbnr1EGe29ZO0bGs5wxxkHVEzXyaiTBrJKl+n49W5hzNtKsSkrJ/46RRN9mEGEUOw7u208YCWyy5xYtLGwYIUFYWVriuM2b6fuenBKpnxC6wMryMqmfsLCwmZQLK8tLzG9Y4MChg8yNNzA3NzZy4Sp31oxK9AysuE3a1U5/HxSb4TqQD/WGZ8+5RlfnFCCbsm6Ke0DtIPpCwdqSJuNLNe9cz3UluFrULeZ1LBODfVkGW7SaRVedOEvdjzW2XPM+D2OE8ZZjSiFXMugXrQwLGK4aF9BQbPvqjdGiVQx2a3mgiyOm0yn9NLH9+O1Mpz0nn3Qqe/fsYn5+jvkNG9CY6fOELZs2sev2rzG/aY7du27jtDPPZNPGE4Y89+23fgNVZeuJJzE/v0BJBRkLB/bvY25+ROkTX7n+Bq77+/+FpkKUETj/rG9G1ezufpiGc1ZyIowSpWRijIQQfOxRnaFcS7lcwY6zRYA6S7qeL/yzVjRRG9cUgejKbYwsbNrCwVVvWi6KRBufFUUIyqDC1+vFp135deh2eLrhffXThGii5A5Vfw4PXGJ7Z/zYOb/I47ceD/z3I70rRz2uf/dp7KCR26MJr/3CUzn5MdfyhBs/ztsf/wucdvHHuOILT+VlT3kBi7/4zUnotVe+muve9CvAH/DiK15v44AWL4FrLuPyi3+FxV+cqVGtJKXhwYodi5dw8tb3cO2+V3PtlYf3O/eU6bz2ylfzeN7Mi/c/dV2WveG+wYs/uIm3fOToiVA8/vlv5vpf/BEe/rbnHOldOeI4bCvyrq/eyO6vf5W9t3ydg7t2IlqIXRyUpGrLFWtuMlW2So24TdEzgFUVrMQE/Ia8Fgz5aJZ+OiX3VnrU95nppGe6OrEynJyHVt6SEjkl1BtwSzLimaY9OWUjddnaja1YyUqVcrXD5llTsWgmlGJlTk4eKjHqp1NS6plOp0bStbCyusK074njEcuLS+zfu4fVlWWzS+fEZHXCdGWVPE1MpquM5kbkrPbeUkaL0E97VAtLq6uMRmPIUFL2916c9PvxLFbhk3K2uaiqXt5UrHCp1MKmWflScWu1/Tv7XNJM0TwrcfL5w3VbBZvja3ZiqN5lEbERO+AEqS4OONnSPBQ/VYWw5oBz8aZeXHUvMyt1fYEy/JJfF34FDc91u3Wq59PLrMqaa6z466O1/AqsMTsSopBLYjqdMB53bN++ndwnTjj+BDZsWKCfTgiiTPspDz33HA4dWGJ5aZE5XSAtwvzcHBIC09WePXfsYtftuyh9HrLjO++4gy/ccAO7d+7ic9ddx6f/4R/ISSmEmR3bx0ip76Op2dYEXXJGgMnyEsuHDqAluczqnyS1cjE7/GE2OqpuS2eLC+qzfkWMWGadLUCJF1fZhgWVDiSS+h7xUq7i5Waa0/qSr/paRe0aVt85hC6OGM/NE0Ik9dYQnkoyF8QD14l8t7j2ylc3S/Ia7Fi85K6PXX0eD3/ly7/57159XrPa3Y+47vRnWwvyR97Py57yAnZ89s/4ozckXnzF602NPQws/uLHjQTX0T3XXMb17z7tsIhxQ8MDHTuuPg9uPOvbWj507ZWv5iXXvvWot14f69hx9Xl88pSnHOnduAsaqTUcNrFNfSFNpjY/M2VsUIndGJd6c+yjYshljVVYZ6VOihGRkqljWoyI5YEciYrlAREjecagGI9GjMZmnexztrmi6tbmkkk5eRNscXW4jo0xsqvZ8puU9eNrtMzU1+LELg9jc9Tn25oivLK8xOrSEmm6auqwz4M9dOggWhKTlRUO7d/HwX17yX3P3r17WF1aohRYmayyunLI7MeaWV5eJqeejQsb6UaRpcWDaFEmqxNKyqYEj0fgCwBFs2d/vdFY79SerLWZ2C3CnnO2kUVO/rLnbrM1H+fh/fr793bjkgu5z+vadIXaDG37U4+ReNbSxuxUolqMiA6LA6ZgV421uNKb/DxZrtTO/RDD9SxuJVSDvV3XEH0TZAf1Euz3LFucSSn7fswyupPJKstLS5SUjbyT2bJ1M3HUcfDQQSbTVfbu3cdkZZGVtIiMAvPz8zz6+/4lJ516OpOVCTFGjtt2sjcbQ+wCOfWk3NMR2TS/wHGbj2Pf7n3kInTjDfY5kVkOdjTqGI/nGM/NE7sRo25EFyOjbkTsOpRAWp0gmlCdUrQnqTKdrJInK4QYKZ51LusWAeqMX5BgI3rELfzq18baxmr3RDuZDa6me666qrGu0g4N6H6cxbP1EAjqNn9VVJRu3NGNIkjwNuWagW54MGLH1edx+cO2s2PxEnae8gdDRownvpRLP30+jzznLHYsXnLPN2RPfClPfsY77t+dfjDjmsuGv64loo885yyecPu9J6Y7Fi+BU9/IRy649LCef7jPa2g41jB89536xmGU1rcTq/t+i+vffXjzpRsaHog4/MGSqgPBKK5WJi2kUhASS8vLrE5gPBqzfMIioy1bqPJQbcHtug6T/WaZTiONdnssofoz7fHg43tEAiGYx9QagiFrcSKkZCzbV52OpkR56ZA5jgdiVq2aqhAlWKYWvGgneLuy2I247Y2V/QSh76eeiS1sVGU0njOCkTIHDi2S+sTq8iLzCxs4kPbSl0yIkblNG+jTlLTSo1lZ2LzJFK1UWDy4n74kdu/cybgbY/N+o+UqQyUkRra12qsRSkqejzQFMOWa/Z1lh7VUq2/wjLJlNj06bK5rVRu3FMWLjIyUEkyx62JAxHKrORcvPbKRPMHbj2uWU+oxB4qfmyDZ9zFQinjZ03q1vvhcYu/6Hezfs+un1EvCc7Vi5Ksqya72R3HruRaC1nMbfP6qjTSiwMLCAocO7GfDhjHT6YTllUN8/eavc+jgATRnwmjEgf272bZ1CzAl5559h3YzN7+R6dIKo7kxJ59xBv10wtLB/ezbu5MDBw8iRVg6tMje3bu46etfYdqvDpZ2eyNmD6YUtIuEzt6nFgEvjFKCFT4RCKUwXVkxl4Ifi351lRgiadpDUDpxCzmKhmAzm6uXW2uu2fhr8OMUhKHYLYRoCwwUf8wT1q7OBwl2fopCBhsHZJ99CeIjtvCFLGvHTikxnUyYrq4yNzfyhQt90BHb/+MHL2L+2odzT7fob3/8LzwgM7iv/cJ6K1ydcfoybKzL6r7f4vrPnsZHLriUn7vxC1z4sZ/jwo/BWx51Ha/62ZfeZXs7rj6Pt1z/JF71ypdzw/33Nh7UuPziXwEntPPb/gOXP/0fSJ++lIs/8Fu8hffz+Oe/+fCVphvP4pOnPIVrP3LP+cE7w+zJj/zWdr6h4c544l2/V44Edlx9HpfeftWa0qH7pjht35v+9C7fww0NDxYcNrEVKaABNJF16llWIakiKXOoX3a1Z5nF5c9x/PHH06+szFQ2VxUliBFNH8FSSa2q2UolgAS7+Y7Rcq/RiQ2ePSx9j4TO98FmngYJ1GRozt6qi42AKT53NudMdEtkLoqUzsbfFCUGK4RSbDZtDEYzigpR7TB13Yh+ZYUD+/daxnChQIyknFheXESDsrS8xPzCPMtLy0ynE44/6QQSSsqJQ4uLdKOOUmyUT0kZRJmurDJdXkU2wIF9e9l43FayKiurS4zHc3SjMSmbipv7bCR72qPzY0AGwlCw45a1uLU7k0rPwYN7yKlHQ2A6mbC8tEzup2zetImTTj4DJbgN2yzXCQgS7feDkUQtuEJr5yS70ppzwX/d87aFQLC8dJ1ZXFU+qdZbRcVHK5ViuWEYlNVajiQEL0Zak9n1nDP1OUUpUp0DdeSQnUkRSCURCeS+kPopS4uLdOMR/TTx5etvpKiyYW4DWzYfh2bYsnkD+/Yvcsehni/d/kVbiNi0gTRVrvvU3xJjYm4hMr9xjuVDB/n8//7fTPsVG8eDKbfTPrFhYRMhjpmuroDiixCJ0ptSHGVEiCO/Yqud2nz6wQl613XklBBRfwxAUQmDggpKhyBFkShkspUV54xIVVTN+lw0eYbXcvGx5CHvDIJ0EfVxUzj5tYy1Gqcl2wKFk17xBaJMAil2nEsipICqzdCdm/Z4E5ufpwcPzv7Sv+Dsrff888dsfQ8PxAzuH70h8fDnrc/MXr71PTzhgx/nLZ6n/MCjzoV9v8X1v30jD3/lywfl4tI/eRqnXXz/7WtVjQdcc9ld/v1gzf6+6sJn2V/O/RBccRk85o285SP20L2xT9q5vfc38HW27YUf+zme+8vdA3IRqOHBg0pqV/f91mHnab9VXHvlq7n8+e/hulP+gLd93CIcjeQ+sLHtF38EmhUZuFfE1o2kxWe/ai0AwtWdWQZwaWmJpaUlBGsNjrHOLDVFr87frKG72g679rVKtToPhUUz9TfnYhnfYbyQN/z6tkpRui7Y/FpvFR7m23oGUdTG35QQBmUzOjUuWtDsxBqlSKITe7/J87z79+1hsyobNm5k48ZNHNqzl4UN8yzuCxw6dIjRaI6SepYPHiArzM/PAXDowAEWNm6kLzBZXGJubo7p1EYR9X1P302Znx8jMbB8aInlsMSmLVvIBfp+AiTG4znQQFq1kUa45brvp/TTnmnfm61alZwK+3buZPngHiaTZaYpoxnG3Rznn/+dlOOFIrXCidl5LYqqUJJC8BwlrGmlNrKTXc1TTHkMQYbscn2+/ybFfK1Dea+fUVP7BqMyg60YMXdAVFNoC9ChPovYfj8E/y2FXIzw2f4zzNzVYtvuRpHjth7HNCUe+rCHAUo3HpGniT17djGZrJAWIscdt4lt27dTSs8ojhmNRszNdWyYX+CWb9zMiScdz3R5kRs+9zn27trNeG5EN2cfpX5q9up+MjHSr0Ic1Hc7DjKUOblbwHzY1DbiOgbLu69swYHBo23EE50RTJQouFXd8sVRLXsrBPuMSkFcba0fVlWTkHNWQqz525rX9bMjtZE8+zzo0bBQgeIziH1OcVWO8ZFBXhAmPoBZwoOL2H4zXHvlq3nCzz6wVKkdV5/HpRdcxb7HnMKOxUu4atMrAHjC7R+/W9vdvjf9KZf+xe1U4rO677d47Rdu5rqbvsb17z6N5/5yx05XD9/ykfez6emLQxnRa7/wVGB2w1ZtzFc944t3b2m+G9Xmkef8/+z9eaxm5X3ni36e51nTO+2p5sKACQ6YdFcInZZOQ/sEjuWceIh0XZ2o00KyhSL1xQKUdmxdnJYs/2FFaoMEIZGxum6kCDkSiqM4+EgxJje5HOOTpu7p02kOqbgxGJswFOwa9vBOa3yG+8fzrLU3NuACqtgM+4e3a9fe77De9a711vo+3+kSOPlX3HZ9SBL9wBe739389Pp7EtS2Mskn/uCP+N5Dv81XH/krTlwg2eRrTbltEeTY0p0cOflbu+D2XTJHTr73EuOPXHYJNz3x1gWlHb//drgfbuJSrr3xTh677/vces2T8NLvvSc/13bnvTPnDGyd8yBTOBDWXwxv+ex8ZY1P092qx+mSb13rZw0MX6jr8XJhty3IxmG67NQWSBms8h5ekD7cSPhAG1AQ/H+2vahufYThEYzRXSenD0USoSLUX2xLB06GzlbfhYIzNshnI+9VdA4rvaTVOEdZV1TjHGM1VTVCyQgiwebmGkr5Tt4iz9F1RVVVNFXDoYsPYbQPsypmc0SSsra+RhJHFGVJEif0+j0m0ynR+mlMY8nSjDYlN4pSTr7wApvrp0nSHgsLiwwGI6IoIVIKFUUkUUaWDJHK97R6ia7g4kOX8P3//vfsWZQMFpboD0dk/R6D0dCTacYHPrnAbLf7SsrQj+tvBEicMKHSRyJb8BOqd4JGHKTvN/ZFQ1spyTL4sKVQGPAso4C2tskFgNvJ052XkhvjUDLI4HFIqfzxJPAqgsDUevxkkWFBwptPJUZ6sCWkII5jZtMZo73LlFVFUxeoOCHPZ0Qy4cXnVhksZDhh+V9+7aOceWGNx/77o8hMMVhcYE95gDOnX+S/Hv8eLz3/IsZYEmJkJP3SiBaU88qHZLlw3iiBEIpIRoD3LeNM2O8B9CkVwCBBfu/PBe+dtkFuTLsa4JOoReunBuH8OSiEIJL+XJQy8r3E1uFceCdcSKkWwkuTXRvYtXUeGmuJlM+q9p7oIOMO51jQrPvEawcSgbY24G2DtGFRiRaUg4yiFivvzrt5PnQL5R8HRuLGOzn6o78E4Ik/+CN4hVqYV2L+TpS3cOymGbMnHuGmB2H24Janc/aFRzh2xxf56iMe1B5b+gY3P/kRTjzzHHzoFn/BPLvHg9htPlGgA9nb58tX+p8dve+ngfB79uLvQ7dw1x9/279fX3iEzyz/IVzz5I5u0vH7b+fPH9fw1R3djN3ZnXfktJ+zdz0Mn//wV3Z4a3Zndy7snLvHtruwbgEDAXwEVkz4C2OlIpquXxXaxGQPeH0aa1N5KbO39LWsjkBbEzyB/mpYOtVmLfsQnBA4ZaxFOu8BFUHOCj+RDmtcdyHuQrIxBDktgUn0qNXLZK2Xr0oButEeOCiBUgJnPfukpERFiqqqmG5sEEuoywqhIpIsZbpZgdb0BiN6A8Wkrun1+gg7R1go84IsioPf01CXJbHIyJKEtNdHSkkxz1k/ben3F0jTFGctxWxOkhjqsiERPT5w2ZUs7lkOIK8FLnT+4xbgIIJPuRfzS//6WpwxvlbGebk1UmKN84Fewvt3nfP+WykFWnvG0RF6h2Xo9bXe3NpKS/3fvbfXWBPAjOjYVBEAMy2IctbvU9dytC9na70P2iCdXyjxYUUBWDsXPKs+lMy60Ie8jRkM2BiL8y04bViSE9677BwvvngShOP0yRdRqeTQ+y5heXk/zzxheP65Z/gX/9O/JBsOuOTnR/yP7/8Dz//4ef7ZL/4ii0sLPP0//oE1GpSMiOIUrRtkUxOrGJwjiqIAvr131dgGGflFGF3XXh7d1D6pOU4QCK8wEN7z2jLmtIqF7lwKr7MTGPv30gkR9rcIsnK/YCRle79tjxUY1uCM7vaNsT7cy4exad9Fu41Zd9ZtVQm5VuofFiWsa7UXnjHG+HRlrytHKn+eN2YX2b7r5+++xrU3Hvedi6F3MVt+AG4AzrHW5d7HruTDf3Drq/am6q/8Mrf9Hhx5/BM+BXT5D7kX+PBtt8J/jLYA7Ct0qL7avGdB7CvMA8PP8vHHb+DhG+6l3Ljb+wFXdxbYwm7i5+68s+dEeQt3XSBP7euZex+7ksNX7/RWvLPnyGWXcGynN2J3XnXOORW5ZV5be6Nn2ToCKQAgL7uUSm7JTH/i/kVR8IMfPElRFJ4dhM4vaY3xHbAYrLBYJbBt9FO4mG6vxz0YtZ3U1WifiOysvwhve1tbAN36R9v7bK9HcS+rMvEy1vY21mM7f8EvLMPRiP37DmNdRFM2WGNpyorZxiZFWQamtuxAfpTGpP0+a+vrGGPJyznTySblbI4zmrquA3CNAUFT12yeOcuw3+vCoLTx1Ty6Ljl4eC/9pQFWhFqebaypayWrtOBuq7PWCYfFdunFIDpwYp31Xs7wXllr0dr6Sp0g7XbCS8Bd937arUCmboHAvey9aTfFGOOZ9xBM5UL3qgksfltPZEIHb1cVxFb/cXf8BUm7NluJ1sZYCPsKhAdawqsJPJg2nZR3sjmm1iW61jz/T88zn86ZT3MWR0ssLS/yz37pl9i37xCXXHoJWZqRDRJ+/soPksUD0jTh+R8/g2lqlIzR2m9boxuaqqQu5tRlgVKt1NeCNdRVyWw+pijnOARZOiRN+2S9IXHsk5EFCiG9ZLmtxFIqAoJU3hmfJu6c95m3yd14dlZI5ZlewjJSWDAyba3QNsLXbVujan+qpAj+9rbCq71hh2ADm0+wEoSQLiGRUgSW33XvexxF9LMezhiqovR1Us6c68fNe2L+1f/6sXddHdADH32K6wZ/Srb8ue5n5cbdr6urtNy4+zVTccuNu9Ff+eWOhWgffzdJ9/zNVf/uRT783VsZ3nE9j31n9S2XIe/O7rzb5u1SD3PNxw7uVqe9yfnz/6TPa03T7pzfOXdga1sJZahssbq7YPYMoQyyWf+wQqiXPbwQ/vs0Tbnssp8jTXvev9qxjholJEqq4Nd0XmncsUqB/TJe8mitv2i3+CAjG1inrkpGe9bVd5s6mrbDVvsvrJcp+07bUF9j/JdPQNaBHXTe+1qV5LMZdVWwvGeBiy65GO0cVV7grCOSClP7vs+qqrz0VVisbXy4jpQIJakbQ5EXTCabNE1FPpuTxDHzPCeKU3pZH5xgMt5kPN5knucAzGZT6qrEauMZZe22jM2AEx5gisBqhhSlLaDjXEiR9qy3k34/mbCY4Ot76KTcHRAKbLwH/+H9aOtgwn5vQ5+cMR2w9qnFbU9weA+31cvQ3if013aPb7ceW3Qvo2VzdTgWfUhWl+Dsth7fOrvlC3XeO+xCLY01Bl03WAtKRuzbc4DeYMgv/LNfxDkP7HqjjCwdMJlsYjE01vC+yy5G146zZ89QNzrU5HgVAVZjjaEuC+azCXU1p64LinxMPp+Q5zOctaRRTBTHSCmx1tJYTVUXNE3ddQs76xcYjPYdyE1T0zQVTV1RljlFPqeYT6nyGVU+wxiNVAohvLLBCeEXAwhKBh9/7GuBrOkUFnZbFZN/PyVCSJJej7Q/Ik4GhJ2IlFuAtq2+alleJVqu3W1bsGgXNhxVU2NwpFnqWez3WHjUucyf/yfNE7d9iydu+9ZOb8p5maOzezhy8q+8l+tNzM8Cwq/2+9uu//Ruh+N5mAc++hQ/uO+73Pz0+k5vSjfHPrCy05uwO7vzhuaDN92w05vQzS4g2513+5y7FHnbOLsVgNOyPp4l8iBEqQgpQwerrbHWUtcVVRWR9foMF0aISNGadtvgIudcYIx88qpkS1KLC2DW6MAWB3aqBUudnzdcbFvbXnLjA3tEV3FijUWoUIETJK9t6JQJwNZYgzHGhxZZ3+tqmoZ5VTCfzQBodIMSAmsMSZxw6KKLmW1uUtcVbjYln8+ZzSZkaZ+FlT1UZQXaooiYFTm6MSTKv9Yk7dE0NdZBHMesrZ3xyc9SsrC0RJJk9AYLaAtGa1wUYbVDKolsQ4eCz7KlO0Wo7BGyDWNqA6II3bOeQWuamiRN/d2MT8RtWcc2FMgYSyQlW9im9cF6lrgNq5Jd2rUHNzbIg631QU843xoT6onxQmd/nzZIyR8HXjMdIpU6EC2t9MeM817UEFvlu3kDaG5TsNvttEFijhBkWUZVliAsja6ZzycU1YwojTBOkkQZZTlnY22DdLDoF2yUx3lPfv9/YIzCCYjiGBelNFXu2Wftw5WiSFFXNcZYVKww2rOaSdqnrmov88UE73K7f0AoBcYfj0QCbOt3FiDBzEuqqsRpD1CFFMSJQpAhhPJnkN3SSTjwHuiwoBGWD7bY2MDYSwlCWKLYp29HUYqIY0xdIZ1DCeGLrwIDLYTqKqj8OoeXWFvn65acgKIocPg6LBVF3lOtfXjV7mzNpT/8Rfjk/7v7+71/0XDrb/7NDm7Rm59jH1jhpj++dMckd0ce/wRHLvs6X96RZ393TRsi9eHvvros/K2ctxPI3p3dea3Zvrj2xJ8dZuOOb76tAOWvfPRP+MF9n97pzdid8zgP33AvV/HOvn44X3PujC2O7vI4JPEKIQMAITBmPp3XA8/A1iEx1jHPS86eXWc2m/tOVis7/2xLMGprMKbx3ZnB+9mZ/MRW6FMbumODFNc5H2ZlghzaOv9lDDgrwmMZCL5ch8CrYbdkx+ABu24MdV3jq4WMD5/CoaTCGkHT+PRi7yEU5Pmc/nCAFYKynNMbZFRNQz6bk08mYKBuakxTkGYR2XDEtPDMr5QCGSukirFao6uGwcKIvKqoiopYRphGU85zBN7fi/DscmNMSChu5cIBEAb5sWVLOm6DfNcGHaqg7ax16PB6PEuuO4m2MUHqbJzPjbIC4zy7ba313mPntjpz3VbHrT9MWlmyC+pgv4BhnGlTnjog3rHIrvVm225xYusA3LrP9j89U91Krd3WsWg8c9uxiW1ys3DEWcLC8jJVVVOXJf/wX/+e49/7HqdOPstkssnmxmmWVpZpGk2Rz7G6YXlpgfk49/U9wu87h0BFGSqOkVIQxwlKeel5NuyztLRElqR+fwsfDoWSIfVYhhPQh2wJERh4BCIkGiMhihVpkvrzxITaLKvDfgrLOw5/P4RnVnEdew/+HPUpy0FCDN6XKwAhUXFKkg5I+gukC0sk/ZE3bCsBKBwesArnwkpYkJYbg7Y6SJLBlJpyXhLHKdlgRJRlSBWBABnL1/Fp896c/8dS/I5npX7lo3+yo8//2HdWOXHRr++ytudhjt30RZ74gz9i445v7vSm7M7uvGPm9MGv8+g1x7uvtxuoBZ9Gv/sZ+cbn3/7HiGtvvHOnN+Nlc83HDr7rrE1vdF4XY9vKVOWWAhYIl9SBCdrKmHJboUZtyqvzoNFuByy0Fj7XsYAuyIm91DmwcgGcCSFCeqzsnkfIFhC4lz2mbM2KeIDT1tDIbUE6QjhU6Gn1ClkPyrXWNE3jGUgpccaRpQnIAfl8ymw8QUlFpRs218+yZ/8BxhsFuTX0sgxT1z7pNlJorVk/u8agPyDuDSiqHAwsLA7RjWZxcRGUoq5K0iQhTlJsU/s05DiiKUviOEYq/1rm4zFi0SLjFBpBmiWegbUhGhgQATKJACq7/dDW9gQhcN1UHlq5EDwkbJACe2l3FMftm+yrmgIzbEPKrodlwdvagk8luhAoF7pQpQNsSK5y/vbOesYwvB0+IVtsSZO7WprABNKucYTX5aQLjxkqpFr22PnjUYIHc2G7bHiAhdEIJwQHDx9mcXGR4aDHSy+c4r8/+t84cPgATVOT9Xs4JLrQ/PiHT3F67QzOOsrZFBmk2INRH6sN43FJIKORMmpPAJ+0HY5RJSVGSiCEaxkfHNXaWAkMtKFVKfjtDpi1O6bbkd2+CZ7c7lz04ViilfkLLz8Xbus8IJxP/ltJb7BANlogSXsIqdB1FUDx1oKD9783vsraOQi+ZpyjqUt0o4lkRNbrISOJlWIrqK2VL7/sFezOK82vfPRP3tFddFf9uxf53k2/zewLj+zYNjw6/xQnblph/+qObcK7Ym6+7/d54D5fnXTdv7+Fu/74xE5v0u7sztt62q7a4xtvLyD7inPoK3zpyY/s9tu+gTnxzHM8es1Ob8VPz4nyFuC5nd6MHZ9z5lAE0EbqusCI0bFA7UW39Mxp8LMSmFtaRtE6msognZc/elbRV9K04TcKgZARzmpkFxhkt4AzAUjL9mo+yC3D960f07N4bMk5w89lm6IcUmC3OkK9klM3te+3NYa6qDDa+CAl7VkyASwu7WFhaR9WKJyFycYGdZ771N0ooWo04/GYNE0ZjJaIZEy/PwwyzoamMWB8gmyUZOR1RV7maGvJiwIVRdR1TZL1iOKENMuo64K6KpjnOWVVk88LdNVgdNOxtg5HozXGea+jsyJIdiXOSZCqSy52zsusjW6IoohIKf8+OjDW+sAno30SbkjfEmwtNnhm3vkAJc8RdosFWz2oDpyXMbfSYu+/tFthUcEb61oZrWsXGFqRsQ+9cvi+Ytd+2TYozL7sOV0LmnGBYfZVRu2Dq1h1LLOQoGKFSiPiDObzGc8+/TRppoiiCF03rL60yjM/eoa60R74OZBKMVpYQqk4nAP+RIrjyEt0jU/2NsaELllwVqMc3aKB9WU+CClRKvYLNUKEyiV/XjkrvaqgXRxyLizotAs5FhHSzbqAKOc6D3RLArdeY+sswrZgP+glkpjRyh6ywQinlA+QIgREma33yhpHmRfe511blIgwtaaa51hrSfsZcb8H0gdeCSP8ed4+j5C7Htt3yRx96Aq+9ORHXtE39sBHn+Lm+35/x1azr/nYwR153nfjdEnRoUZpd3Znd157jlx2yTviM+j4/bdz18Pf5tjSNzg6u2enN2d3zsMcv/92Hj14/e5nNa9bHBguTANI7X4itsBtBzK28TPbmRpjXRfw1AUGhQt3cFSlZ398im7rFgxMqvB1LS3LpUK3rBSCSEqUlCFR1lfzyCDJbCk+Ebo724t9QajKMS3r6PtEtdYIB3VVQ5AqG2OwUmBC9+pg1KfX76GiBN0Yzp45BdbSz3pEMkYIRVUUWKtp6po4SciLCm0cutbEccR8NiNKYqbjTeqyJMv6KBXT1A1G1zjjZcJVU1PWPrwKY0jTjKqsKfPCh1U1DU3b7+scTV37FOfAfLbS4+1LEE3tQ4twgiiJiDxlitVbCcYqTrqQofbdFrSeWc8QWxc6il2QGeNl320wlbXex9umMds2UKoNj2rTp51B+BWPLem09RLdTpYsQsKxC5FTtj2OWoVAkGOH27fb5RDdMeNC96szhkbXOOE4+dxznD19mvdfdiVZukhTVqyvnWHj7FmeeOIJqrpBODwbqSQq9AY3jcbUDRLoD/pIqXxgWVAmlEWO1hV1XTCdbTKfTyjzOWVRoHUd9gU+/EnIwLaHPR2O45adlkFO3J1RnRd5qypJhP8TtHVBrQqC7r7eF+s7hRECGceoOPYBUoifSgwXAWhjLaaxWAfWaeb5DG0a4jQlznrIKA5V08537gbAb20rD+9e2u68w+fYTV/kRHkLV/3u7/yUnO3oQ1dw5LJLWP7Cb+zItj32nVWuG/zprh/zPM3Rx/+SJ277FseWvsHnP/yJHd2W3Qu2d9e8G9/Pzxz/w7ed7Pi15vj9t3PvX/wqX3ryIzu9Ke+oOXbTF3d6E151PGv73p7XVffTJaB652ZId/L8mAg/exlz2vog3XZw1IlJA9jcukhvaT/bVrh0lULhAjt8KaW2wKoU3kOolE8eFjLIlT3QVSJUlGx7sbL9gWirS9qOTkOsFJGKvf9Ua7SuMbrxALOqUVHMbDanKnN6WUZvMAAE4/VNdFVgmobBoIeUiqppGK+dwdqaKI4ZLIwwWpMmiQ/7kYKyLJiNJ5iqQgpHr58RRQprNGtnXqKpS4q8YDaeooTCaI1uKpw1lMWcqix8L7Dwr1BKSaMbatOgjaapm8B+tvvTB05FKqaqSs/a1rX3DDsPSo02qEihlOzqW4zxgV1+8WJ7MJdnI7sqpfC7dvGilaEao703u+08DaFh3gNrA1B2PhjLeQm7daC1xWrrA78C49mCaoFDBPBqt8nkO4ArWv92uE3bQOQcs8mEspgzm00xRnPpz/08+w4d4p9f8y8Yb8w4c+olTvzDPzCfzLx/WELTVB4gGoPWtU8Kt15mnKQp1lh009DkJdZosjQjiROkVPR6A6K0h4giVBwhUJjaBHCucc5XOvmVnnA+CLd9XaY7p9y2c8kFuW+o68UBxuHVFYEBbs/frftbtGu8LN8YhDNgzdZju5ASHhYQhFJEcUySpDR1Q61roiwl6fdRSRwS0bdXSwU+Wm5b8Go/L3bnHT9ffeTr3PvYlTx68HruXX2gY3CPPnQFD3z0KU6Ut7wtfJkfvOmG3VqLNzFfevIjPPEHfwTAdYM/7b7fqbnt+t2wm93ZnfM95cbdHFv6Rgdujz50xa7/9mfMzX89fFsuYBy//3bufezKnd6MHZ9zlyILsY1hAs9uBq9qC3PCRXnQwXaAY4vFpdOaSuGDbrZTORKBCh7FKFLEcbTFGCEwRvvwHqshyD090eirgDxA5WUgmOAzVEqFNFkCpg4hPkq28cvEUeIZZeGTgquqYDYdU1eFB5BNHSS8ks21TdbPnKUs5x4AaChnOXGSeAAsIOtlzOczdF1TVSXWavLZlCjyace9/oA4SUmyjLquOPnCc5w9fYrecIg2hoXlZVZWDrCwvMLC4iLpaIHGGGaTMdo0FGXBdDLFdQBH+Lok67ykuqlxdov9tm3vrPWsrpSKxtSe6Zag4ii8RQYhZQApBLYzSFydxFqHetmRsyUx7UKaTONDm7apkreD3bZnuAXInqwMTKGx+OxkizaGWjcYY0KqcAgoC+97u8jSMbYQemtb2bLrOn6tNThjKfKCuq48EK01ew4cIBv2mWyuo12NVDH/9ZG/Y/X55yjmmxSzCflsDE57AGn9Ak4kBcIZGl0xnUyoq9wHjSlFrCL/njuLUIo4y0iyHkmaEsWxZzrTNNT6VD4EKwBD39/rt5+Q+u27YreAYnfqiC3Btq/jaiX2Bqc1Rmsw/n33CeJhkUM4v/+cDZVYPm3cGC/dtkJ4CbiAtN/DWEeta+I4IUv6RFGMVJI28MpvmAgyaBs82e1BAV3G1e5c8LnQFyUnnvEenuP33065cTd3PfE/c9ODl8KhrwBw7NdmO/aP/jUfO8ij809x7AMrPHzDveiv/PKObMe7YU488xxXffWTXPXVT8LffW3He4J338vd2Z0LM8fvv53PHP9DPnjTDV3o1emDX99lcl9hjs7uedt0Er/S7IZIvY7wKNcm3ACwVa3TBv+wDbe2tTovv/+WVJLgB7TW+kqaNpUYUEoSSUlNYGJDjE0rb/7BEye4ZmWFbLDUXUC3rG/bpSu2b5CQwefYhvnILc+tkDhsSI0FnA/0aYxns5I0IZ9PAW889EDFkPVS9MKI6abGFN6vu7xnhXmRU1YlSEk67NOMxwgnGC4skKQpZ146hXSOZDBA1w0CRz/LSJKEtdWXSLMezhhmxQxtDEnaQztD2ss8+2hrysqzgRvjMVmSsLx3v/c01zWNc0RJQiQjmrpGRJ7BrOoCECglPPsZWFWjDcIJVJJgjENIn3LrfZaBhfcJTCipMNYhZUghtoBsmXoPbGwIkFIOkizB6BbYurA4QVdJE3Z453H29m0vX8X46iCUwllD3VQoKbG1xqYxQioi4f/E+eomERZPrG3ff3+w+QAr20m0J+MxWvtU67qqWNyzlyhOOfnss1R1yZWX/gI/ePwf2VjbRBsvx0YInPQLJ8YZojjqZNBV5Rc8tNbEsUKpKMhvLXHsPbiN1T6FGIJPNsjlwwJHU9fUukJG0qdpBfazrTMShM5ctl6XZ7Rttys773NQPXjZdXj97WmnQFiBFMqfz+ExWu+ycM6z6oHBNY0Ob7QgThOiyLPPOLZCpPwZBA4fqhUYZKwJnxmtEEPwCh8Lu3O+50O3wN997YI+xQMffYoP33TDT1XA3PXwt8keu5Lo9y7o07/mXLf6CHc9/G24H+ARsuXPvebt20WAzk/6M+b13v6dOkcfuoJjN32R/aueJT3K135mt/CFnnLjbo6cfJYTF/36jm7H7rwzp1WU7M4rT7lxt/9Mbxcl74djN36Dx+77/k/d9sPfvZV/+x8jTlz06zww/Oxbu6E7OG0FGm9jG/Xx+2/ntjs+De9hO87rFAd6utMhQEifTizV1s8A2EpD7uTI2FbE2l2AvyxJObCD2ng/phTC93i67UnHAodCV47ZeNZJnFvJpgtb0IJaKSUikp2O0wdJSaTyYALRlaEQfhOoMu2Z40ixtLRMf7hAXXswqeuKfDYhn4/Ruuok003TAI4oTTm7eppEQhQlWAdlVeCM94UWxZz+0iLpcEATPJ61qShmU6SDtJcRJQm6qUnTBK0byvmMpiwo53OK+RxrNEoqktb/ajWRBGM0Vntpa5ykANR1xdrZM2ysnaKYTyiLkrIsgofZw51IxZRFST6fcfb0KtPxBs60YV8+BVk53zErhQegUoiuWqf1PruAgX2KtaCsGg9Ot0mgcb5up/XQhqYen7JtTXeMeFa2wdQ1VVlSzOcU8xlVXTCbzaiqgrLIt0Kk2mNEbC2qtPJ360wAfp6VzLKUYX9AUzVIpdB1yXSySVU2LC0v8+xzz7K2dpZ8XmBMjZWgmwqlfBer0QZnHFVR0jQV2hqEVCRZRpL0wnFvg1RagNkWUBZApk9tBoJcOkoSQGJN610XICU2LO5IIVCRZ9V9Irh/Dm1MEEdsSa87D3k4F2V7Wga9st1mJmiVFVjvOXZWo+uCcjrGaQ0W0jijPxgSpxku8v52p8KChmUr+Kt9jSHQq2OcW09zqODanbdgPnThPTbfe+i3X/Hn5cbdO5aIfM3HDv7USnq5cfdrh6Mc+gqPXnOcF+/7fheItZ3xbqXMRx+6gqOP/2XHSr8d53xKCJ/4s8Pn5XF2Z3d25507rSrnJ78evPpybnrwUh6df+qnPjPfTnP0oSs4OruH0we/ztHH//JNb+uxm77IXQ9/+20pQ94+73V1yxtzvblWHulB7XaNYYdDt1yAtFU90ILY4K7cdp27PdjI+zoDcAp+TXBIHFf8wi+w9+BhEDL8XOCCXnZ7XZDfRLHF4rGNMQMfUmQtWAKbGBJqo8izY0qxMdmgrHImm+s0VcF8MmF8do31s2cp8oLecMRoYYnR4hLEMYPRAlo3bI7XwTkWl/eQJH2qomRzY5PBcBEZKWxj2LP3AEWeUxY5SZqR9PokcUJ/MKDXH2GcYz6fI6SkmBdIITHWEicx2hjiOPLPtbmONtqz1WEfyhBuJIQknxdMNibUZY2uKqaTTZq6IlIROItUHgxZ55hNZiwMF+kNhsRJ4r3MeObPM+Iggm+6w0sShAgBUSEQqpV/bxOpbi17tFr2AIRc8Nf6lmSLMQ11XVLXFc5amrKmmheU85zZdMp8vEmVzyiLGSYw67Ry426BZctR7dUAW3ICYy2T8Zgyn7K0uMDmqbMoIRmNFrAInvr+UzipsNqBktRFHkCxQTe6k95b3XRMp8QHcCFEF5glpCSKlK+LsrZVcre67lBRFdQGOFScEEcJAkmb/Nz2/BKYcH/7wI4H6bU1W57bLe9teMXt4lI4+8w2UNvdzlqsabCmIZ9NqYrC+9SlQqqIKE2RUYQLGn4nPOlu3HZwHABy6w+2NtRPuU6NsbXotDsXet6KFfSdTD5+tXnsO6uvGFr1WrKsRw9e3128AZ6xCOD16ENXdL28Ry67hHsfu5Ij2dfedqxP63F+4s8O88SfHX5TF25HZ/dw9PG/5OEb7uXm+35/6xdvwWJJtvy57qsNqrr2xju777Plz+2ytbvztp2js3veEYnI53OO33+7z1M49JW3Dbh9WWr/oa/w6PxTHHn8E9z18LfZuOObPHrN8TcEcL/05Ec48vjOBuid67zXjsOfnHOWIm/xsWCDKtk5QLaSR7aj2vDtFgDq/LfOUlWV78R0bRVNSGMNLGalK8+0WQ9ShLNd9UySJqAUUgraqlMPTQPr2ipebZAdSxm8l6rrdBUySDQt3mfbAjcJ1jgfEiRhujnGNhpdazbXNzDWQWN80FIUUfdKnHA0riEWGQjoj4ZMJmOauubw+99PrSvGZ0+jijkXX3Y5Za1RMkI7Q10YTFmi0gHLe/exublOoxsPQoyhPxgwXFpmsr6Oth7MVvkUAeSzMVGSMkgWyeczRosK3RhckPSaEAI1GI5AQpykzGdTmqaiiWIYDCjzOaOFEbXWSCW59AM/h1QqJD8LkA5rfO+v31USIbz0dCvUaLv8m8Do4oFQy84Lgoc0gN0uZMhX+UgkzhmckxjnqMoc8PJnEcK9dK2xzqGNxs1y4jSh16tIkwwhIi/dlaIDr/77ILe13k/a6IbxxiZrq6eZz8f0R31W9u9FIphONnnhhefIp3OSOKGJM3Rd4xxEWYwzDl3XtPp750CXJVo3xFGEsYaIiCiKaRqHkIq8arBGYzomNoReSYnRFiEtDknrlfULBlsqB+tab7sImgexBd2lRAbp9taigQ2vuX3/JFtLOiCc9NJtWuDrmdUqL7rO2SiK/D4zgW0N/nOsT1J2bXVS+2kgBGDC/1yQG/t91D27M1hb/8R9d2d3zu9c87GDPMy98BOS2WNL3+C62T0/BfiPzu7hUT7V/f2mBy8F6FjfYzd9kZs/tM73Dn6d2w5+mpufOcrhK99eoBaAD93CbR9Y4d/ydT5z/A+56tCTHH2F13suc+TkX3HTw5cC3+ZeHuDWVr75o0uAC9tj+5+v/Q8vA673Zc/y+cGfwuX++xMX/c0Fff7d2Z03M0dO/hU3Pfje63o+fv/tcOOdXPfSTm+JB58vrj7AsWsO8uDVt/Pgw98Gvt39vmVar73jm7D6yDnL07/05Ec4tvSNtz1Tuzt+Xkd41PY/XceWboe8oeXjVe7vf6F1zannn8PWdccwtSnG1jnqqkKHZNyWDPbNJPJlVSQve2y5BWg9M+a2ZM6EqpT2xp2EGWy7wcJHT1lHF05UzuZkaeaDgNKMXn9Ivz/AIVjet5eFlRV62RAlYwbDJUBgjUYKSRQlTKcTTp18njoEUGnTgJLMZ1OSNEHGEQvLK4w3xjS1B/pVXZHP59i6IlUKiaMs59RNzdrZMxT5DGt9h6xuavr9AUpFzKYzJuvroC35dM6ZUy8yHW9S5QXD/pBIxn5hQAqM1iRJQlXmGN0wm08Zr69jdUORz2mqEhtkqG2Qbcvw+d7ZAJyQSFqg2gZ/gc8T8vyrFARpbFtV08psPaAFOumsrzgyNEF+XFeVB7FB9CyUZ/rTNPVdvNpSzOdo03QS3GDmxBhDWRZsrJ9lc22NzbU1qqIkimKUUCAcvV6fjY0xDsssn/LjH/+QM6depK7mWNegbYNrNNIERjkERm0lNm9Lf8ahK02jNdrawHAKD+6lIkljjPOSZRmAZsfEdkfptv3kBDiJFMrv4U6JsO2g3xY0JYTsmoJapULXHLTtTvIn7x8WHpzw3nYZqSB1lluv1fqFgpaVdQF4e+lyK4O2GGG7xQrCu2aNpakqdFP7RZFX/mjYnXfYfOnJj/DoNcffMf/IH7//dh6df6pbof/gTTf41zD/1Cu+huUv/AZHZ/dw818PeeK2b6G/8svc/NfDtw0r8WqS49uu/zS3XvMkx35txhO3fet1b+vvfuu/ceKZ57j2xju59sY7fQjJZZdw9KErLnjS5scf/9FPsbHb/77L1O7O2312U7t3bo7O7uGDN93AsaVvUG7c/TP/bWo7X3/m4wb2950Iat/LAVLnHh7ltgXDhCtoKVto47wUuLtgb+/jb9vKg0Wo98lnc/ppiq0bzzxJAViMdTSNBzee1fMyUhcCgIQQYKFu5pjpjMFoGeh10mJfy6JwVoTuz9aP2JGHWwAY7/eUYZuFA+saynJOXuYIa30QEN6rquuSwcICew4dwrgaYSxRlJD0YvKiZLa5SZlPydKEXq+Hs5q1U6cZjoYkUYyzjo21NYSxaN2AMwwWhhSzTVQUUZUVS0t7mGxOKKspZVGyfvYsIpI0VYM1Bq0tg+ECSkhEnJLP52it6Q8GbJxZI4kSnIowtSbuKVSk6PV7OBx5MfOABQ+utTU0dcPADhkMRiRJhpIRddUgpffxbnWfemmxtTok3QagIxXOmQCO2l5aL+mmlZqH91EE+a2XK291DDvA1JpiNidSAqGiAGgdPevZbdM0IH0vrpdHO5IkJs/nDEYLOCuQEqwxqEhSFgV1VZIkCeONDZy1DBcW6PUHRJFkee8esiylaRrWzq7x46ef5OzqS9hGY4I31mqNdA4ZKzAOpxzGat8VjKVufKqyB3kWoXzHcjYa4bSlaSpQiigb4oxG4EPMZKQ8/y2CWkGARPn05JbhFH7/WGt8L6zz/CeIkL0lO+m2FQ4dDnDZAXwvy5ZhQagVKBtrCCld4Wc+wAoZdYFWWItuSoyut5/EQAC4UuBkkPkL20nA29s64dPLjTMI5xlgB9imwRh9rh8379n53kO/zVVvMvTh6ENXwIduuSCS5KOzezha3sKj0EmR3wn/4B+//3aWHz/Mly77CMfu+CZ//oXf4MGrX3m7H7z6cjbmn+L4w7fzcTyTePjKv+VLT37kAnOWrzwdQG2DwQ59hXsfu5Jbw/t871/8KgC3XvNkCPD6HN97CL4H7F899+f5g0/+Sx7gqc6TfOTkX3Fs6Rvcu7p6wYOjzsdxvzu785rzoVsuaIDUkcc/wXF2Jl/gvTxHZ/f4RcqrT2wFX53jPPFnh+Gjr3GDQ1/hpge/zXHe/v/GbZ/j99/Onz+u4as7vSU7M+cMbG0HagUiJKhiXaugZDsd03nv8NUnbaKrEIIoiqjrmiiKSZKYutZdyEzdNMjGJ9bGQWYsWj+m84yQVJb106dYffoH/NL/9K8QfR+800qSITC4zuKreQKo7rgyvHzaiiCT9JUmja6oihyjNf1eH9sYkn5GOhigZMTG6VOs7E3J+n3mc0ORl5R5TtrLmG6uY6qGSMSUecWeA4vEaUo58/2znuyy1EXB3v2HyLKMvCyIoohsMELrhuHiXrKsxzyfY2Y1UsB8OsWYBmOsZxqlwhlNbQ1CQt2USLHAtKkxxrH60gtESUqSpBTzmsHiAvNiDliUkjSN75vVVcN0NuXwRYcZLKxQliVRHJOmPbSt0Y3e8uu2SxnCbjF5iMDJBd9ykAu3AMwhfPhXSFQWbDH2oW2pA1wSR1nXFPMZUjriOPHHlvAS1kjF6EZjbB38qoI0yVBSUFuBQiDxTLIIUmSlIgajEQrFwsISL750ksFoiNGa4dICI7HExvpZdF1z+sVV1k6fwTlHnPSw+RzbendDqJlwoKTyoNx5AN1+4RwGg3OwuGdEnA1pak2cpkRpDDKiKWsvf1eiA/tC0smwhXAopF9osVsSZBEUBS54lwUChPTvRdgXXnsuEbLtvG0hayuZDpy3CJ22XTe06N5bIfw5bZuSfDKmyudkwyFSpf4cwnUdwP68avuN2wgxgnxZo433F8cqwVmDNrVfALB4EL07rzjPXbuH5S/8Bjd/5JPn7TEvxEXckZN/xYnLf51Twz3dz+77+LOdjHcn52cBbB94gr9I+YlE51d6rM9/+BNw1e8BnrXko0/xwE5UBAbwWv7x3Xz+w1/hiT/4I8qrL+fItZfw5eFnge/72qWH/c3Ljbv5+Ed/BMAP7nttFumVUp4fGH6W0we/zk1/fOlbckF37Y13ct3Tf3rBn2d3dmd3Ltz8TJB4AaYFtW9kjt9/O5//3d/hB6/y+y89+REOHPwG26XMu/POmNcRHrW9I9b7KqX/cZATg5ISGfpiXfjhwsIC+/fvZ9++vezbt5d+f0DTNDRNYGsFGGOxxiGcQ0mJEtIDImcRXfWJxDrDcz9+mvmGl91GKvKslHMhDbeVStJ5FQXB8+kMbXoy1t9eSIHB151IBL3egKXlPQz6Q/rDISqOUUKyuLzEgcMX01QNzlkilTAcLdLrD9hc36CcTVFKsLxniYsufT9CKdJ+j/0XX0x/sICxho21Mzj8hX4+m2KaBoljee8eqqok7SXMZhPP9OHIehlRnCCkr1lxxkBIC1ahRFYK6SXMVUVjNLox5NMpRZFTVRWRjLDGUMznlEUJIe24KgsSKbHG0FQ5CIOzxve/Wu9VldIFf6ffX862ULfN1cXvxyCV9bJyE963LWZfCoEM8vC26qclzoUD3Wjy2YR5PmMymzIebwbQ5iiLAtPU9Ps9BALdVOi6oSwLinlOHAV2V/hQK5/WDFkSI52jquYICfsOHKQ3GKIiRRd85Rwb6+u89OILJElKmg06AKmEJFKRl6Vb04WZiSAflsLXC4mQGt0fDEn7fVTkg72c8x7bptb+GFcKkF04NMEXKwUe7Fq/cGPxflpB67cVPoG50jQGeoMRSX9If2GJ3nCJNBt6sCraZOJWltAqh133mgCUCGDWiU6CDKDriunmGmdeeoH5eB2jvULAWEsTXo8NCoegSQ/vo98Ppqn8ceQccRyhlKAs5uTTTar5HKw/V3/CQbA7wPRb/08+dtUt3Lz5W74v9HzMBQz6+fKVf/tTTPCXr/xb7vv4sxfsOV/PnM9Aq7se/jZ3PfE/85njf8ixm7543h739c6xD6x0jOldD3+7S6T+zPE/5PTBr3PrNU/+1H0evPpyHrz68p8pRz5y2SU8es3xn0qOvvmvh+dn43dnd95O8xaEoL0XZydUOx1Te//tb/j5Hz14/aum5t92/affEWqkV5uNO76505uwY3PuHlugxSY21HzYgFBEm4ArgvwROlmk1toHRWE98MFhjKYsc7RuiGLVBf00tWdNpRQYpwPY8hfRNvSGrp1exTUlo9GAOIkCyAuMloC2Wqj1ZnaVQLjA+jpM6O70BUL+Yj+K4sAoCaI0RUS+I1UpRX84YnnfXsqyopzPaeqaKIlI+0OGoyUWFleIo5TxZMzamVNM1tfJx2N6/Yy03yNSCuEgjROmkynj8ZjpeBMlBUoptLGsr51mPp/SH/R9OJWKGIwWWNm7j8FoSNLroeIkeI0NdV1jrJc1Z72U4WiIilNknOGcQKqI+XyGsQ1lXlEWJZPJmCiN0OAZTGspysIvSEiBFD4hV8qQNN26OLu0oQCSnN1W/NQuJIjuNmBx0vjFhsDGt+9DKw8Hz/Dl8ynzySamqajLgqaqqJsSJyxVUbN29iwi8vJcozXFvKAsc8q6CsdSg25CMJHw3leppO/zjSLiOEY4x2yySVUV5PMZa2uneP6ZH/P0kz/AWEPWX8A5gdE1SkmEkn6hwPg6J200ZZFjtSFOMlQU+47XNolaKgYLS2jtaMoy+G8FcZxijQsSedF5YZ0VOAMYcMb6EK3gXW/rkKxrK7MkcdojHQzpLyyQZn2y4QLZcEjay1CB+XZiG6j1Yuctib21WOMBujYabRqs1pimoZhO2DyzSj7eQFmHtBJJ8AI7h2iru/C+6K2gMIPWpQ/6spDEMcpZ6nxGPl5HFzOwFhUlyCjx/mL5OtbR3iOz/v+6jf+sP8N/1p+BH13yjvXFfPnKv2V4x8/2LF3IuVApzTvd3/qT1Q0t6Cw37kZ/5Zd9b++rzJHLLnnNxz5x0a/z2HdWvSd3do8PX7nv+6/5mOd7jt9/O0/c9q237Pl25709bwef/LtxNu745lu6b5+47VtvGngev/92jn1g5Txt0e68XeYNagM9SLHOpwt1KbfhYryTUeLI85w8gFUpBYsLiwyHIw9sGw1RRBuxW1cVdbOBExJbV5hcdGE1dhvbZU1DL406qaZUHki7UOEjRCuLDQxhl3wVwmehA8KuCzgSGGODPxRSkVKVjiiLkXFEWRSMlpYo8zmRUowWFrBOYrUlUhLdNGT0iWVEFMeISHrgHMUIGVPXOdY5ZBKzsXGWVCnGRpMNfQBUpCKs8aFZRjccOnQYbT1Qa3RNNvCyUw+kHE1dk6UZptEUec7CYkraSzEG6nqOrh2i16eYFRT5lErXYDQqksxmY6yuGfSHxGkCCC+1jSOfghzRpfQ6532VrWwbtiXfCjr2ti2SaYGPCIZmF7y2gVhECIGSgqbRlGXBZDqhKAsarbG2wVlHcbZgtDjEWtBVDQLiKKKyntGvq4r+YIRzjrXTp7F7NAuLK13QlcWhlD+mmrqmnM1ZXz+NiiN0rZlPJrzw/HMIqej1l2gai20884uUna9UhNentV9kEQKy3oC6KhGxIkkyX+2DIp/MiLIYJRU4Q1nWDAdDRCthbv3oluDT9UsAThvAev+tjLwHVtItwtDJiCGkceFwfoHDm5qJiPwyjnWYVoJsHVY42iJbf9OgYJDSL1LhfECYiBDKksQRTvtqo/a9F874xGpAWLBWY7QGLFL4Wild11RFja7r0LUb+qKVQsoIIRROqt1U5FeYS3/4i933LwB//q13ri/m5vt+n0dv3LlQqce+s8qHv3vrz5QZv9659sY7ue6vH4GXLpw/79Xm6Owe7tp4ubN3O+j8WaD7pgcv5Wj5lzxw9b95xd8/MPwst17zl9y14X3FNz14gpK3Hsg/ePXlcNu3zp9qYXd2Z3fe8nmr5MinD36dA3d883V7al9p9Fd+maMH3/rP9t25cHPOFIrrYCKehbMmdJpuAVnnvAdQSBU8lII2mNUY0I3DGEuWJgwHffqDvk+wLQrqukFr7SWQde2Tf4sKT235cCCcZ56a+ZymqrE4f90eLvqFDPU+gsAOtcmvrgNYnYy2S4UNXbkBqAkhfEKslC0soyoLqjJHSBhPJ95LqDVZltLrZWSjEcPFRbJ+H+0sjalxzpDP5yRZStbvgRDMxpuMN89i6wpdNcRxzHA4Ist6LAyG9Pt9nLVEcYSKFFIpqromUpJer4dKU59cqyKkEJhGEycpprGYxjOXaaIwVYVtKqRwGNdQ1xXzySZa16ydWSVTkjROkFKg4og0S7A4qqYG1fouPQATQqBog7e2AJYPPvJVTVIKz/G1Plrho46kFERKEktJIhVxqGmSwaub53OauvLdr8YQqSikMjdsnl2nygvP7hcFTV1SNw1aez+x1Q2zyYRynlOWJXXtWV4pQQovgfbBRR6Q53nFZLzJZLLBmTOnqeqaJOvT1A1V4Rcr4riHE76zVeCQEqIkRiiFc44oTUj6KaiIJO1hrGW4tAJKYZ1B1zVaa+qqwjWafDajnM/RVek9uQ6ckjjp9601PjkY45BO+uAnth2veIVEC3Ct88ysc96Ha2jTmT0QNtZipOy6op30t2kVFe14r7Tn3IVSxGmCFVA2NWWRM8/n1EZjnQ6LVRana3RdoZsGGc4P21QU802KYoLWFSKWqDhDRT1UkiKiBBEFW4GkA9m78+rz8A337vQmvKH50pMfgUNf2XHp1lW/+zvn/TGvW32EI9nXuoTgt2qOzu7pgqHezDx68PrX3O5jvzYDdj4EbOOOb3Lk5F+97Gt3due8zwWSIy9/4Tfedt3eb+W0nbavJu09X3P0oSvQX/nl8/Z5dc3HDnbd5bvz7pjXrQ3cSjcWbF0utx5LtlJUuwjiEHvTsV/esyeVIk0TFhcW6fd6Xn5s/MW5kwKrBKZp6GUZMjB8Rhu0Npw+u8aZM2tMNzcCayjoWoBakA3h4t1BK6u1HvCoVrbp2vu6bktbvbXEEccxUkjKecF4c4PpdMrSwgKT8SblfEpRzGh0xaDfw0lHURZorZnN56y+tMq8yNlcX8cYjTWWqsyRwpLESbcPaq3966sblFLkeR6Shf0+Gw0GOOtIswwhJEoqpFDESUqv30cqHz5UzGdUxZzNzQ2KvCQvSqbzKXk+Axxp0mMwXKQpG5SMWVxeZriwiJKKpqoo8zlNWYK1QU7ug6OEcL6DFosUEoXqUqwloPzb7w8m6RcFlJT+tmGhwbP1W6yttb7LWOAZ2SSKGA1HnkkE36WrNXVVgFIeKDpHnMTESYqMJHESMxgMSXp9TGAZnfDJw5FSyMDyJknE0p4VDh4+zKg/wjaWzY1Ner0hTdVQlVPiWGBcE1KyRQfiTadA8OnFveGQprEMR4s4maCNT+EWSpAOMpKsj1K+UkjFEQiHrguEM2A1wmm/WGId1nipthQySKiD7F4IfOqxDMy382FWxsvnjbU4a7HB++usf+0CgVTKS/MlXWga4U8bvhdSdce+Eh7Ytt5bZyxN0xApFQC1wFiDbiqcNUSRREmoipzZxoaXkGvt93OaEWU9RBKj0hhUhFDSh2OFnurWY707rz4f/u6tO70Jr3uOPnQFNz146VsqX32lueZjBy9ILc1dD3+bmx68lM8c/8O31KP3wPCz/qLrfMxrbPf+1U8zvON6suXP+cCsHZrj99/OTQ9e+rKvR+ef4onbvrUrVd6dt/384L7v8th3XkcM+btwjt9/+wVfkDp20xfP3+ciW9U/FxqQ785bN6/LY4troWyQKSJ9Wm9rwnRtz+zLfZciAE+c7xhtwacUkiSJSZIEKSOsdZRliTYalMQKQdbr+ZAoKVFKYYz3l47nM/7pqaehaTqmq5MeE1gqGQCva6to7DY5pJcsC7FFJHlvaat5BqW8fLKuCmIVs7xvhSSJsQ7+6ekfsnHmJfL5lJde+CfOrp7ENr6mSCLYu2cPK3v3k/UHKKFI0pT5ZIouK4RwVLri7NlT1PkEaw2nVk9x9tQppmunSaKYNB3Q6w2Ik4y0P2AwWiBJUqwTJGlKEscgHWkvIUljtNFUZUkvTVhcXiZJMpxuiFVMkmYs79mDFIIP/PxVLC7vZT6bYJ0mz+fMZ1PKfA7WkEaRB0n4tGMpPDiRzoNYH+4liFRErCRR7MFUEkkiKUmUJJIhGCkwj9b6WhgP8D2zWedzL2E3noWs6xKlvLzXWIuMFMZqBoM+caRQAvpJD13XLIyGxHEKzrCyZ4koiUjSqGPvt2qeDAJ/vA0GPaIk5czpNYQQ6LpmPt6gmU/JJ2PqskBY61UIOA/KpELXFUb7zmVtNU1Tky0sIpBIZ5htrpFvnmW2vk4xHlPNp1TFLHzllEXBfD5D6wqjNUY3mKpGN83WfnGOxhoaXaPrhqauwBpfI2QM9SynmGxSTjZpZlOKyQazjdMU0w1/zFlDsKlvGeEdSNpqHwJgbv2xDlSbay1ARjgkptaoOEJG8VYvcDiecY5yNmU+3kBXOUI6oiQLCw0JUsZEMkbJKHxChMAwt7VNdleK/NZOqLe4kHP0oSveNqvdnRT5As2t1zzp63begjlfF1nDO67nxNXffs3qp6MPXcHNT693dUFvpzl+/+1dEBY/em2/8O7szk7Ph79763uatQUfaneh5nyzte28ks//Vz76J+f1Od7qeS8vspy7FLkLjfEo0Fof1uNaD19gTtuKEecCiBRbcNO19+tYXV8j1P3FgdYm1IPYrvcz7WVEkQdQnc9TQpkXYI2XKbfBVcG/KEJ4VcsUQgA84bm8hdQhhXsZ89yxzwKaumG6uYltGgb9PhJBWRS875KL6fVHvPjs87zwo6c5e/JFJqfX2Dx9lnI+xxmNdpa6qullGf3RiCTrMZ8XzDY2fcBQXTNdW+PUCy9QzXNMXeOsDSyx8KFTsSJKYozW6KbGaN2l0NZVhdGGMi+py9qjGme9HLmXom1DOc89WE1isixFSRguDBkujtBNQz6d4bRlPplQlwVZHBNLiQoASAYvrUQgpQLhvauRlF6OqjyYjSIfuBQFqbEQopMcSxFk3UG+WpUldV1RFnMPRJ1jOh5jjCZSEf0A2nv9Ps55P26UpAj845ZFjrGGuq5odM14c4O6KMAYrKkhVFEZYyjLkl5/gDOa8foaP3zyh5i6RilFkc+xusFqg2karHUUs5yymPtUYGtRkcLhfI+ucxTTGWmWboVAGUtdlNhaY6qaKp9TzmY0RU49n1PNplSzOTrPEUYjRPDVGot0Dme9d9cZ71u1ocZJhbRwBAgpMVVNOZtSTqfUeU6VFzR5gS5LbwnYVqUk2/Tm7bL79pgOCeNb4V/hv0h1rHsUe4+4oj2eGvLphNl4QlNWKKGI4h5xOkDFCXGU+PddKQSQCEWERDkQToRFLv/JoF4miN6dV5p3mhT5yGWX8OjB63eU6Wvnmo8dvCBS5J2YNszpzc6Rx8/hffnQLTw6/9TbDtT+5Nz18Ld5dP4p7v2LX92VKe/Om5oLteC36xH3n8NfevIjF+Sxj1x2yQUL83v4hnu74+LoQ1e8p1OF3+nzOqTI29lMryk1+MCmFrh63BvAYXCu+i4TL2UVgb2zba+s2PLBtkyq1hajLZGM/V2RJFkPIWPfn+l8BYoiIlKeyRNssUNKCG8TdW0lSXihQm6BWiE7ENwy0DJIQL2M2YEkeAwN/UEfKSWz6ZQkyzDOEMU+NEdXJeV8RlPr1hXpZbHWA5cszegPhyzt3UPaG5DnJcV0jqk12jrKokZYS12VWKtRShGlCaOlEVJF5LM5zjrqWnv2LHhgF5dXWN63nz37DzBYWGBp7wrDxUXKskIBvbRHnPlO28FoSFnmRHHCvJwTx4p+f0CkZAhZ8kD21OlVxpMNjAtMYvBRCxxKii3fcmDn2/RetQ24Sqm81znIYqWUqEihIkmkJFEUHkdIdFPTG/aIopi6abDWEGcpadYLtTHKS3ZxZMOBr94xlmI2pSjm5PMZTVUhgaaqwFic1hhj0I0mTRLiOKKuKp758bNURYkVjqZpGAz7RLFCRIo4TUljn3YcxQG4KtVJkX2tkWdAnbbUZY4NQFUq/1q8PN8nPduwACHxx6KXHtvuWPMpWt4fa4UJAVThPxeC15wIVVfBA269DNm23vDAhLb/dSrfkAAu2kWabeem7wHusKY/59rUcedwSiCV7LZZW4txGqzxHuQsQyW+hkpFikjFSOk9yU5uSwjb9pmBczijaYop8/Hpc/+4eY/O+ZRYvRVz4qJf57rBn/LEH/zRTm8KwAWRIrdz18PfhkNfeUska1f97u90VRZvdm7+6yEfvOmG17yY32l/7bnO8ftvp9y4+4KyQruzO290diXz/hw9UV4YtdCFPO9br+27RZL8TruWOJ9zzqnILevZsqwu/EyEuh3XJuW6jvzpLrzbO27JmB3G+iodgfeTbvl0HWVV4nAsLC5S15VPKxYyPIf362ndkKVxh1xdC7g9JsVCB2St8+xv24krArDwLKdndr3nMUinpcAJD+yk8smvVV3jmgaVZWxublLmBVVdUxUVKyvLDBeXscB4soGUgrqpPGttfbqtrhuiSFKXhqaqifs9kijDaM3a2hkfYmQqdFGS7Fmhyr1fN0tTBr2M/miBs2fPouuS8eYaS8t7mVc5/ZGXKefzMRdfdDkvnDyJdR74bq6f8fJtrSmKnH37DqAixfr6GvPZDCGgrmv27dvr5d3TKVIpsuGAct74lGYcaa9HmiZB6xoY+7AYYJzBmeCjtgYVqSA9d17oKvCyXSFwThOruAuAipRjOBoym88wdc1sNmXfsE8mMubTSUi3th5EWkNVzpGJQqmo86k6a6nq2vfh5jlJktLrxcSxP7SVVEw2p8xnpT9KrCVSirqqEVKS9no+xdp5oFk3NVKGcC2lmI3HPqnYCfqDRfLZnLKY0TSl9/OmGVVZYUzjj7sAFJ0DJVQ4VyzaWpQxOK194JMNIFaCo+1+9lJ9obyiQHT9wLZTQGwlfLvg1wVfOyS781K1Mnw8grWu7XIOagcLTvrbmvZcDsqLpmmQkV+MSNIkgN9w9iqJc6G2J/jALT74qq3QNdZ5FhkwuqGpcuoyRzclRjfn+nHznp3HvrPKdb+501tx7tNKXD/It3Z0O2BLivzgeU5F3j53Pfxt7sue5cRF/u9HTv4VX77yb8/rcxyd3cNdD58A3jyDevz+2zkOfP6rn3xNOfI7bcqNuzn2geu5+en1nd6U3XmDc+KiX9+5J79AXvmHb7iX8h2ySHQh59GD17P8B29NQvL5mvazMnvsc9z6m8B8p7dod97ovKFiybafVMifDDoVvm82dF+24172p+ikiaGTBwiSZOEvoK115HnBeHOT4XBEfzDoaoSM8WE5VVmRzwu0MR7MCn+RHay8dA8sRQcGOlwWpMau9Xg6i8Xg8KE81oZgJBkRJylK+joeqRTCWZSn6lhcWmY0GGGNY3FlmdHKMocvvpi9+w6QRCmxSlgYLmK1o8xLjDZEbf0LkKQJURITx7F/fAdOG3RV8/yz/4QzmjSJkVL4Wh4pyfOcSCkOHj6AlIoiz6mqAqehLgok0OulVFXBwuIiSkmmEy/13RhvcOb0aaaTCVZ7r+vy4gIyiojSlH7mg5im4wnj9XVm4zFN02ADCy3aAKj2fSSwtfi4I2vb3lfjv7RGh/dLa4txoHXj2dhIUZalD7cKoEvJiPlshpSO0WhEpBTG2K4TOYoi0qxHlvUCULb0+j2WlpeQkcJZi1SCOFZkWYJSkslkTFPX1HVFVZZkaYIEkiQhSXsoqXDaS26taboKKed8X3McJ14enCiGS4soFXVpzVL6RQOBQwkZaqOCxDdI3Ts5vvXPYZzDNgbXmLAwpDwLby3Gai+Prmv/Z6c6EFtnz7bzrWWJCUqBVn5PYJl9tW849gNDK6UD6eX3Ee0bKkJvre+dFrFEJv54l1GCUHGo5VKeoZYSJxVGtCFhyvuwCZ70umQ6XmOy/hL5ZJ2mLMCCkvEb+bh5T807Mjxqds+7RgJ8LnPTg5dy1x+f4NH5py7MxfnffY2PP/6j8/JQ1954p+8XfhVv8Jee/Mh5SV7eiZl94ZFdSfLuvG1mtx93a7qE5HfgPik37ubR+ad2ejN2503MuYdHCbpuUi/dld3FeyvvhfZivv0K9/W/oL0G9+mvdJJK6/AVLpYgi/WX40VRsLGxTr/fY3nvHmQch0ofRRJHDIZDkBJL6KJt01/d1tW/CC9S4i/6fctJ27UaLvgtOANNWXvPo9W+81MpqrpiPh2jdYOQkum8QCnFyp4lhJSoNMY4TZFPMLrxPaZK+fAra6lC1UscRSAFFmh0DcKxsDBi3/4DDBaXyEZD0iQlSmL/eqxgPpsxnU6pywqcJYoUVV7grOXM6VNIo0P4j8BJ2Nw4i24qyrqiN+ixtG8vjWmoyjm9LMNp14FqYxtmszF5McNaTZrFPoBJCabjTYpi7gGP8v2rSiqcE93zCRlkpyGoyfuZZXBZd6LvbceIPxgabZhPZ4yGA6q6oSwLpJBEaYqUks2z6zhrGIxGDEeLRErRWI2IFP3RIqapmUzGZFniQ6C0rwOazSZUdUmZz9FWY6xhNp1Q1RUqirBa0x+kqChCCEmvP0DFEVHkk6+99NYgIoWKE6I4Io59WJcFdKM5e+olmqogimKfNGx93ZRo90dQDGzNVpCTNgZTV9iqwlqD0Q1VWVDkM3RZYOsKW2kvZTbaLzxUGutMSKFuA7G29uX286w7N50IQuSwAuEswtquFsh2m+Rvr/BBYWGlB6e9okDrBh8OF9Kt8X20oq11ateNEAjrsKYin68xWV9lsn6Kaj7GGu1vI1up9m4q8rttvvTkR7jrj0+87f2ZF2oulPTwqq9+8rz4lo/ffzuzLzzCo9cc5/TBr//UheaJi379PS1Z253dOV9z7KYvXjD/5zt2ziMz/laC5OP33/6OsWfszk/P6wC23t/a3a1jebYukn96gh/w5Q/kf7OtFkiEi3acr0JplcIAs+mMU6dOE2cZew8cIE4z7191LnS9yq5ztfX3dl/Oggjdn8LXq6i2BgUvo3bOM5FSCqIo9mCpqTFGg/OhQy4EMDV1TaQUUZJSVQ2msSyt7GFp337qWlPlBadefIlqnmObBtM0bG6sA47R4iJp1vcdozb0w0YRvX4PpSIa0zCdTRgMR0gVk2YZvV6GdZamqamKOUkUk6U9srQHxiKdIEtTFpcWGQwHVHXJbDZmNBqSpRnz2ZSN9bMsr6wgpGQ4HLLQH5LPp0wmGywsLCCFwhnLfDqjmM0xjWZhtMhFl1zCcHERECRJ5PezXyEIibceuDq7Fb4lpURG3kcbKYVSKvhuZUicdsSJIk1jptMxQkqKeYGzBqWkB5lSYrUlTiL2HTgYALUjybxfVghBnGVsTCYMRwsMRyOKPKcsK2aTSdcji3MsjBbYu2evr3iKBCsrezHGS+DjOCKO4y5lGweLK/tZ2nMRSTokSfrESUqaDYiSFGsduvILDC4ENAkhSLMkeL2FZ/tbKbK1W0Fpzvm6nuBbhnbxRqKs9+1aY3A+0Sn40P3PrPH1WKrt/w17W2w7l7pzx7mOVfd9t+5l9Vxbp0ZnCvABaojuNhJBmmZkWa+rlhKi7Sj25xrG+fRoq6nzGeO1U2ycPslsYx1dlggnUCrxQLijmN2273fn3TBHH7qCY0vf2OnNeMvn2hvv5L6PP8uJq799wZjqe//iV3n04PXn7fFagPuT88Dws+/p9Mzd2Z3zNecU1LY7b3jOd83P7rx755w9tp4xasGjwliLwXi2Cg8QvQewvf3WRfV2cLsV4OQvoo21IT3XS5wlEhNY15b9m0/mrHKKiy65lPdFGc8/+08QfItCii5RVwafohTCB9pY08mdLRCFoCvZRc6Cinz3qVQe4DmnMEZSlyXj9U0v2Uxi6rpiuDgijmIvl1WCrN9jcc8K48mU8dmzNFVOlCaUFSRJTGMEURyTZZkH4QKyQR9hNCt79jCfTplPpzghqfMcU9XIRcnegwfoDQfUdc10suHB9DRmc2NMrUuGC0ukaUZ/NKCX9aibij379vEPzz3LoQOHsMZRlVNefPF5qtkMYR1RFIWEYg82lfSAJYpjdGNoQq+s1RoTGPXhaIDWwXusJBL/cxnYPSmFBzmBiXPW+s5SRAj+8oFHSIE1lnk+ZTaZIqVkOptRzOdUec7e/XuYlxVFXhAlcSfdHY9n5FVJU9cIBEVZUTUNla5YXlpBRIqyrkmSlOHCECEUcZwSS+VTf5XANYaNtTMMB32KeYmu6iAvjlCRIEp9CFo67LGy531UVQNYnGmoiwIlBUIkSOdTq43xEmEVSSIBTW2w2uCCisG2gLI91gUIvK+3v7BAUcxRQhFHidfxS+kfVxsvQ8fXKgklECqw3p4W9n5ca7exwm0Hs8MGj7MQXhbcre0AFovtWF3/voiwjdJZH8gmLEIEP7iuSBlsA8Yy+H4twhm0aWiKOeV8hm20B8chKVs4iZWh1sds7YPWH787rz0P33AvV/E3O70ZrzlHH7qCI5ddwoFrjr8nV7WP3387t93xafavfhpeugKu/jfn9wk+dAvlH9/N8fvf/ENde+OdLH/hN/i3/9H/U/9zVz715h/0bTKf//An+PxFz+30ZuzO7uzOq82PLoGrz9/DvRf/vdmd1z/nXvfT/hn8s9ZuSx3eDly3sTI+Rffl438dZMtedNtdQG+XcbaxU630cj6dsXryBfoLAw6///2otEeSZkipPJvVMkJb1loPWAOIU9u2a3uclW1lnYFFlVIQxzFF4SW//X6fNM0QCHRVYxrfM1rM56hIkZcF89kcISVVVWGaBiV84vNgYcSBw4eJ0oy1M2dJkoQ0yzDGMJtM0VXDwmiBpeUllvauAILZbEaSJMF7GVMUFUVRcObMaYzROAeSiCIv6ff6LKwsIYTk1KlVhgsLvP8Dl7Nn3z7qWlPMCtKsT1mWTDYnjDfXmedzsl6f5T17QQg21tZpqpI9e/ezuLRM1utTzgvWz5zmzKmXvJw0JPUqpYgjRRT5ih+lREg+9pJllO8ajlWEDAsGSkkP1JxAIomTBCElWb/HaGGB0eIijTah09UQKUlVFaRphnNQVxXOOmb53Ps/EVhtsEA5z3HGIJTyIVvDAeAoq4qiLJnN5qytnUUq/7zWGJI0Ie15ljxSMTJO6C/vZbB0gFprqqqgyAvy6Zx8NmU+G4PTOOkTwG1IXnbW0jS6O25cm13WSYXDUdZK8sPvlfLHvjU6MNptGJRASF+ZFMUpUkWoKAEZ+fPF/aT6Yasj+GUHtsN73K3DWu8/Ny4sNAQWuU1ptraVJ9uXnXtN03ipddc76zC6opxP2Dx7ms3Tq8w3N7C1r0FqO6PbM6wLDxMOg/9yAuwurv2Z805Zkf7M8T98211kXPOxg29ZRcPsC49w4NTahQmh+buv8fkPf4Js+XNv6mGy5c9x3eojPHzDvXz5yr9l+sRP9w1fqFqOt2LeLkncu/P6Z0eDo8KcT2nr6YNfP2+P9W6Zt9u/D++1Wf7Cb+z0JuzYvI7wqO0Jx+Gi10qglRuGCCFHkCyLLSZHSqLISz+zXha8mNJ7/ZzFBra3KydxngUWTiCcvy3OMB1Pef75Zxksjlg5fBHjeYMuSg+cCN2rEu8rFD5VFggeUL/dnnwMgUdt2A74UKpQOYOENElJs4wokhRVTlOW1EVBMZuRxQkqjjE45rMJztSoWNEfLFDMC9ZOnWK6vkE5y6mKnHI+B2uIkxilhA+8EoLhwiL5vCBWCcsr+0Apat0wmYwRDsqyZjRaIBuOyJKEQa9HL81IeglCCZYWl3EWNjc2GJ9ZY9gf0ZgGqSRaV1z0vksYLS3Ty/qkvQG6aqjmcxYWRqRJj/l8TlHkCOFoTI1KYkaLSx54z2ZMNseMN89SFTME3uupVKjtUT5QK45joigmUTFpHBOrCBVJ4kgRx3H4SkiSxB852lLM5yRJzOGLL+LA+w4zm+f00pQsy9jY2CSfzlg7uwbOkU9m9JKELEsZDvvMpxMQjtnmhDrPiVREliX0shSEoz/oMxoO6WUZWZKwsrwHrF/kSFIfKpX2e2htycuS/sISw4U9SGLP2OsGgaWpfW+wM5o4kn7hxDmvAsBL870cH4RQnWHbH/n+mPVsqU8ltlZT5fPO2w1sBZUpL6mXQiJkhA2PbdvFHhE87B1ADg9gfAeuwKF8dLEHpAFIeimx3Fa/FKGk9Cx0CH5r5fn+/I1ARqggCRfOYuqC+eZZNk+vMttYx9R112sstpVEt5YEJzsM38ZWhf8X/JQtYXdeNs/+/D9w4uq3v1f1gY8+RfR7f7/Tm/FT89h3Vi/4P+bX3ngn195459YPfnTJeQWHR2f3cO/qA9z72JXc+pt/44Of3uDces2THPu1Gf/52v/A0Yeu4A8++S9/6jZfvvJvufWaJ9/MJu/IXHvjnbudobvztplf+eif7AK5CzhHH7oC/ZVf3unNeMfMtTfeyfce+u2d3owdm3OWIru2ANMb+rwP0LYXrf5PKQVSxj4cJ9TnLC0tEcdxx/h5hg9/4W7bZFeB2w6cW+YrXB1HcYQ2/mfzyZQXnn2Giy/7eTY3J/z9//l/cvkHPsCeiw6H1FURPLUQCjmhu6j2kAN8CrMIXknd1DjnKIsChyOKI6JIYeKIpq6YbG5QhtCmKFJdPZHWFaaskUISJxlFXiGQGFMjHOSzOevTGTpIVZ3bxNJgGi87dc6SFzms+85QKb0Edz6bkQ3mTNfXOXT4EJU12KZic2NMU1dop0myBJUmzDbHnD11mssvv4IoTagbzwZbaxguLHpWeT7DWMf4bM3Syh7yuZfYRkqyfOggkYrYu28vjTUMBwsMR0OqsvT7SHh2r6lKhOwjVOQPAeEDpbTz262c6ACks7YLAGvfQ6sdURwTJwmyKsgnU5JIYbQhiWKcMcQhECzrZ6wc2Es5qzklT3oA6aCpG5qq4pL3v48s7iMjwWRzA2s1S6MhC0vLLC0tdUy9FILp5pTlxUU21teYzWas7N3DvKgYj8ck2QClInTj05BN0zAdb+Jsg3PGh0pJhTGmO/aF8Iyo1QYVR8goAmMxpsYbXFsZsgwhZRIsnm1uGpSM/OKJ8OnfTrQLMt7PKqUMvbWtN5awWuP3eatsEF3yGVhjQXjJt4MWsWKdRYb3UARlRGitpT3RnLUepArZhTw55zBNQ60dZZ6j6wrw73OkfKq3Ne5lSg3XhsOJ7pH9Z8LL5Me7lO1Pzv+22XTA4mPRf+bE0+eZyfjQVp/gAx99czLUo7N74EeXcCT7Giceeo67+J/Pwwaevyk37uaq3/0dHrxAQVbX3ngn1w3+lAeGn+Xz//4IR07+Fl+++m85MXuOB87Xk/zd14h+b8UHPs3vRH9lFfhpb+xrzec//Ame+IM/4onH/4gjB7/JZ47/Mvzmq8vbj2Rf4yYufZMb/tbOiau/zXVP7/RW7M47es6T2uLoQ1fw6B3HYRfYvmyuvfFOjmz+Fl/m/Fg1rvnYwfNiz3ivzHu5Cu3cPbZbWssu4MZZEy6mA0iUvpLFGNF1zyZJQpqmPo13WzKqMZqmMcRxvPXw7eVwCJSyIXhHG0MrxxQW8vGUF3/8Yw4cPsyZ0y/y6CPPc+n7L+MX/sW/IO0P6C6gnUV0hbY+PCdgMg+iHSgJVvo02ShW5HnhvZ9N42/rfDhSmmbk+dxLj6dTrNVIIahrTRzHJLrBWQ34ftfZbIoVguFgACrCOZhNxmAbrNVMx2OKusZoQ1VURLGinM+JIoUSkjOnV0mjhN6wTz2dEamI8eaYfq/nn99YXnjhOU699JJnJvevoJ339r508gX27tuLijIGwx5CCU69eJKFxQUOXHSQ1RdXKYo5l132c9S66VjIXpahm5okjZHKd5nGSUyaJH6hwLnAAvr8ISWUl0YLLzNWYec6BNK1XcABsBsvjwVLmiSowQCsY2E0Irk45vTpM1TlHKM1WZayZ88KeVzinD9GdFUxyHoMByOyJEUIx2A44MXnX8TZhjhRZElMEsch7VixfnYNqSBKPIBWSqGtYD4r6PUXibOMsshpKv/6rW7QVYl1NVgbKogUURTRUHaLOy3Lb63DOBOqqoIcV9huUaZNCkcED21dE6nIA9/AdvrgtCBPbgOnpAe9PktqS1TRLQK1HvQQ6GZd8I2LrXMx3MOHqjlwwi9E+d/7hRlwXWezkIHxRWCco9IGGSX0F5d9x7MTOGuo6wpblx78h+MGB1JF4bHDucYWg912Gm8B6t1p58PfvRV+85MAnOACyfPCBdzRh654U+D2geFnOf1rX+fY49/grifemxdx9/7FrxL93td5YPWznHjmazxwJee3H/ZDt/ArH/0THrz68jfMAB37tRn6sXt9Quv9t3PtjXdy7AMr3HzfK7//f/6fNA+eRx/chZ7Pf/gT8PSut3Z33vwcnd0Df/e1N73otzuvPCcu+vXzt+i3O+c8160+Aheuzv1tP+fO2HbiwvCd8w5VDxotzoExWxeurdfQmAbnYs8aCQ9dW49u02gvj1RtTJSXI9ttPbdOiM4LCIEBcjCfTFi1hgOHDzEdb/Dis8+ytnaaf/ZL13Dg4otxRD4/NoAwITyp5YQNIGSLP/L9tT6JVioPcOf5HIfDNBqlJA4N0hEhSaKIycacxjRe4qoU5WxGnKSQJsRqQGMM09kYFERpDFIwcEOqYoYOiwS9Xo/eYIhujK/qMQ1JFJEM+tT5HOsss9kUYzXjtTMsLS/S749YWBzx5BP/A900OGvZt3cfRVEwWlpiOp0iBOzdfwCtPTgbDoesGkN/ZcTZU6dYXFgg2bsPGUmyJCJWCoEjTiKMNj4YK0qI4pgoyGRVpHzti1JBigpKSIwlrBRYdCNC0nRgaJXEOJ8glEYRkVqgHvQo8xlCCGaznPWNTS46fJjJbOYZ7HmBMwYlBHHkn9fhGAyHCBEFRhJ6vYxempHP5zz/3LOkmeLob/0mWS8jiiLy+RxrDL00od/LSLOU2mg2Nsdki8solTDZWEcpyAZDpIrZPO39tLGSWOcw2mBDIJNQEts0XbqzxZLGCUZrz+jigayKIqSUXYCU/4XEOOvVCkL5dCglu7RhF478tkFJIPzrDv7VNrXYdzW3jynaFZpuvclZH+4WSnnQzhB1KeSdWrpjV0VgdX14FDilSAYjev0RcZbihCCKM+KQFm4aTT6dkE/WMXUFGM/Oh+AohEQohRP4ACvrsHhQ7OXau8D2J8en6r5FF+kfuuVNXch96cmPwJN4BvA9OC3QHOLlwRfiYviB4Wc5+u++xvdu+m1u/uvhG6pRuvmvh3DNk9z1sP/78ftvh/uBfx/Y+w/dAn/3NY5cdgkAn7nhXngH1ZQcyb524RaBdue9N+dR1bI7fq698U5OXP1tTq1+dqc35T03LVP+Xv6MfB2pyO1s8xL+RH2HDSyXH9f94dOSt1guIT1YNSZ4a4Mnt5U2+7v5/3zBydZTd8/lfEXNqZdWWdm3QlUU5JMZf/9f/guXfOByfv6f/3MGwwWfnBw8gS3r6BtPwusIzKO2xquWDdS196niQESO8fo6dZEjkNRVRV2WRMFLah3ks5w4jZERJFHKIBvglCLKEubTMQMVs+fgQTa0ZyWF9EAn7aW+wgZBrz+gKksPHIVA1zVZL2Jj7SxxmoJQDIcL5EVJalLiJOaSiy/mpRdfIun3KKuKpCo4vfoi77voIqRSCGOoqwrdaCSC6eYmg8GAQ+87jDGO8cYG/Syl1xsgIhXqdpQHMtYRJ7EHa0qEMCtFliYoIULIURTeb4mua0rh2VuExCUCJUBb2YGyNInYGE/Ys2eZLMsoS83TTz3F6upLFEVOUZZEUcRoYYFYRVRUGGdYXFxkMBwynxWkSczS4iLFLOfZ08/w4gvPU+clT/zjDxgtPcQNv/qr7FnZT5alDAYDelnC4uIiutFMxjPSxSWSXp98MiOOfI3RfGOdPJ/R1BXSGpSIvKJAa4QKdUXKg367LQyqO07acyKKiJM0LPJ4f661BoX0XuMo8l3AnXLAeVDovAlcCfwqjPOSbosD6zrp9/bjv/WrOlqLQGBLA6Pb+lwd/jG3wWzaZaTWs+sMxElG0hsg4wgpE1SSEicpsmWdcQjrQCpUlCKDpNriF0KGSwtEw2WEjBBSIawIdUc+YMtZxytkyb3n579Eln/9Vj9puJB7vRdxt13/aY48/gmOb9zO5z/8CY792uwVK2TerXPtjXfy2HdWufm+ozzw0U9fsOd54KNPsX8VHrgarj14PY99Z5Vbr3nyZ9b/tMD73seuDD95OSh+dP4puOZTHP/j28mWH+CmJzyYLXnngNps+XOcuOjtnRq+O+/AOU+qlt3x89h3Vrn1sd+/oJ+Tu/PTc+2Nd3Ld6iOcuPy9C2rhdQBb0f2/Z5EiQDpQQgT5ob/2tm6rr7K7+LYm+ATbItQQsSN98JS1QWosRAdjXUhqFR1X3H7X+m89wJhPZxhjGS0sUhWncKbhmSd+wNrp0/zSL/9Llg8e8sE1pgUC3npradXVAqUipLREsf++0jUre/ZSFCUbZ894yakxgPFS3SQm7WWoKMIGWWdZ5Mznc5yDYp6zsLzkk4OjiHw+Y6HMsTjyvELXFRI4+9IpVBwRxynZaIhUCXXTsHHmLLrR1DKm2JwQxxEXv/8yjAM3y1k7fYZenHDq9Gku/rn3+/Tk+ZzJ5obvWrWOuioxxvt1z546zWi4wP6DB0EpqqIkG/RJ05Ss32MwGnnmMABTGZKOkwDaVBR1nlnpIAkS5TiJfQcsEisVSRIjlQK2ErFj/DFijMUIwZ6VJeazGfl8xnQyCyyl4MzqaRqrkVIwCaFW4/EGRVFg6hIhLHWVo3VNVRT0+30e/S/H/e+tYTYr+K/f+7+46NL3cehXDmO1Biy9fo+qqlhfXydJEpSMmKxtYJqKJE0oiznVfIqucqQQJGmM0RZrwQmJNQ6jgw81eFIdXhJvpSBJetgAYI0xJFnfV0dVBVo3Puk49mC3qXSX/yTDIo6K8EDQyQ6IOuETjJHtT0THtLaO9tbD64Htlv9dOAetHHrbyev/6pDbfmw7C4HzSdFJhooT7/cNkun2nHfO4WRg6uMIEccIo3FOI6wAqYjTnldKCIvTBun8a7PSoLWh0s25fty8Z+Zf6x1C+2+Avd2/+mmuG9wDN94Jgz/l5qfh0RvvfFeHprTMw1cf+Tqff+xarvvNW3hgeGEvfI/O7uHIyb/iRHlLx9g+evDOV+2b/fB3bwUgu+FewHuNX2m2v0+vdpu3+7wTg6525x00u+zteZno9/6eB1Z3999OzK6i5XUxtsGTF7pQg4mw8/V5r2W4ZZtGLGUX4Np1YjqHUDIkE4tw2SwDa+s6X6wnr0SoCPHySsLzBOMeLdyuqhI7hsFoQDEZ44ylGE949H//3/m5D17Jlf/8n0OShecJuHrbw1jrfM6Uc1R1RX/QJ5KRr90RkiRJkc7hMNRlSRxH9EdDjAOlIvJ8jgPf2ao1RsDp1ZdwBhACU1c896MfeYBjrXdNOoeufQqzkhKrG3CGpirIxxOGy8tESUJSN9RVxXy2SV7W5LOCxaUh+SzHOstgOKI/HPJCkXPyR//E/oP7sc73lE42N3jx2ecZDUdc8QsfRDtHVdWkqZeW9vo9nLUkadS9w1EI+PJdqxJjLCqJfGUSvpLGOdCh6sZ7p33AFyJ4R8OChFQK6xxKSLSpmEynWGs59eJJyiJnNpsH5lzw/ssuZWNznSiKOHBgH5dddimjwYjv/+P3abTm+Wef44XnTtLr9di3fx9V2fiQrLZD1jqKouKHTzzFr9zwv6CUYu/ePTgs/eEQJwJYMxZrDUma0VQ5TTEBZ4h7PR+GpevOxxpFEU3TULcJyS4oEgQIpVg+tJ8yL7F146XEUlHOJ0RRhEPQH46oywoT7hdFEUpGncQYR2Bk/df2hRvZemnxzxX3Bn5fsa1btj23UOFP36UrWoBLCHgSPtV5uzSa4B13SKIoJesPESpGRnF3gvioqXaBiZAA7QOtpJIQEs/9uW0DwHcIYbEhUVrrKigzzLl/1LyH5t7HruTWy3fQL/g62dtjH1jh5qf/FIAnbvsW3PGpC7hxOz+PfWeVwwc+zZev/DQPXPmzb38+5t6/+FVu2jjBXdsY19daPHjw6mCm2ribz3/4E50E+d04uxdtu3PBZxt7Cz8b4D7xZ4fhmgu+VbuzO7tzjnPOdEGUZCS9PtlokXi4gMwGGKE8E+sksk2DDQE3LcPnnGdxwV+Ie2VjSNKNwsW4lP5i2Bqs1TjXpgiHgKrgf3XO4owJF83+9s4arDbUZcF8XpL1h2ijmc/G6KrgqX/8Pv/H3/wNa6svEkWeibQh6KdLXU4i4tTLgvu9lCRJiJKYOFYsLS8QSUEcR1RlSX/QpzaaPC9o6orp5gZ1UVAXBdJCU2tMYxDGs2lCQq/fxzSG+XiM03XXEWqMDom2kCUxaZYEKOHYs38PSjqqsgApOHPqDPPxmH6Wsn/fPpJeyoFDB0mzhH4vZT6d8IEP/jzpoM+pU6s89eQPKPOCfq/HaHHExngDKS1x4j2zcazIeulWWrVUXl6sFEpGXsosvdxYCZ8wLNueVaUQSoHy3kohBZGUJGlMlib0soQsTRkOhwyHA/qjIVl/QBzHnHrpJWazOesbE7R1VGVFv5dxyfsvptfLiJTANBWnXnoJ3dTEkWLvgT0sLS6TqJjF5RHLe1ZYO7uG1RZjfWKxwaGt4cnv/5Af/uAJhgs9lBQMBhmjhVHoPI7QdU0SxdRFTlXMkEAapWQqQQZvMM6FwCuN042vuOnk9B5+KhlRzQt62YD+yj5wEbaqMXVNU1c4a9C6xgkTUrctQnkPsgz6X5+GLED6Y8V2yd0tTxsWjyJFOlqgP1oi7S+SZH2SXo+0PyCK0+B331I3tMBZtN23bZIy+MRwXJAib7+9REjl5fjB5+46+tiBtWCNNwHLsIAhW6O6xdT+tZtqRjEdU8w2KfMZzhqkVERRShz3zs+n1rto3ha9tdtYip81N9/3+zww/CxP3PYtHrz68ldlEd8tU27c/ZZ2vb543/c7NvXaG+/0QUm7081t1+9KG3fnLZoP3dIpW17t8/HoQ1fw8A33vqtVK29k9Fd++bz2BD/xZ4fP22Ptzrt/zpmxXT50se8jTVOcc2jjpY+u8UxMF7TKNgmyc74vU3pA2QJeay0CSOKILTfgttRX/AU40EmTrbPeKxgARnsx7rYxU3VlcNrR6/WoygnWNjhjGJ9d47997//g5z54JZdddRU6hPE469DaeHbMGozW4CxxktCUNUbXaF2jdYNuKnCS2SSnaUqmzSaR9CylTBJ01aCtDjsAqrpCJTHOWiqtaZoagaDf73m5cpH7YCIjKPKcpmm8FzGAjcnmBtPJlIsvvZi9B/bzjye+TzHPkQjG401UHLGwsICxmjNnT7O4tMDKvr288MJJlBDsP3iQjfVN9uzby8FDB1lbO8vGmbMsLC+Dtei6Ic0y/34YSxJ7v6kHQj6JWElJmRf0+hmN1jTGBwVZHEma0E96ZP0UpRRCKJwUZEnq03edQyiFMT6Nt5nNqEOtktGGyy7/OVb27CGfzfnRD59i7exZitmcKFYUeU5RFGRJDyUkg0GftTNjqrJgMHgfeV7w/HMnybIeta5xPsEKKST5tOTb/9t3+PFTP6IuNSqJePH5UwhhkSoCBMVsjLSa0agXAJukrmuKvEI6R9pLkVIwLUsfmmS09xhLibMeyDtnwMLK/vfRNA5dGvLNU50E2xiDNoHxDPvDNgaVJmhjPUsbqSCEcF1Vkj/cxcvk/AKJEyFsSnrpNmIrQdyxlWjchkR18uH2gAxSZhsWVSQCTz5rIpH4ZGRPIeOlz/6BvE/WIpylLQsSomXutxazAKoiD+e8RMUJCOHPe/zi1+68jedcwW243RP4C40Pf/fWLcbwXTq3Xf9peBX8vp3tPh8Sxuj3/h6+AMM7rue6+67lgY8+xefhDYVIvdJce+OdLH/hN7qOwzcaULVTc/NfD+Hy926Nxe7s0LyKsuXIZZdw7JrdGpqfnGs+dpB7v/MA//nJj3DimefOi6x7d/Fgd851zhnYxkmEdZq68f69ptHouWY2XsM5g3C2u9htgacUPg1Vbr9IF6HepEtp9Y9nLbSCRydcB25b1rf1CHY0a+dW9KE0Qnq5ssGR9hLSpE8ap8zykqqsKZ3l+489xo+eeYZsuIgV20BFSHh2znFgzzL7Duwh7fUROPKpDxVaWFxAqgIZJUw3N2jqhtl8BoCKY5qqIY4kTUjJtdYihcQqizWWNE1RkUIb4wESIgBZ3/dba+t/rg3LS8skKqXXsyzt3ctgOCKKY7Ksh5CK2TxHNw3GGKbTGasnT7I0GPEPjz3GocOHWTl8yAMe03DwwF6SJGZpYcTy8jJrG+tsbm4QpRn5fMrC4jJNVTHo+zRhpGI+GSOFxAjBc88/x+LSElVVUVc1QipkpEiylNFgQJJGHDxwgN6wh4wjer0BdVVTG0NRVBij0doE/7FDKcXyygqDfp/NjQ3fIQsMBz0G/R7zPEcpxWw6xWYWZxxlXvLiiy+y/+ABjLUsLy3xgcsvZ/X5k+S5wjqBNoY0TVAq5pmnnuO5Hz6HQ2DwCyu94RK20lhb08sSetkCMvLy6vnMe3rBkvV6FEUeepj9kWadQxuLUqqVHICDwXCEUxE0DU1TkAy83xaHf++DdzzOkuDTtsimBusgakO1JAaLdaCEDcd/OMK7PmCHdMHb63wQk1D+fDKmAaVQzkv6w4nmg6ccHbAVUnqyVbTHncea8mVnlA+rckGWjxVeQRGUEc5osD6xWgJK+vMNIUjSBBEn+MIrgWql1tK/RmekTyTfnZfN8ftv57p/f2SnN8PPufY6fugWrjp0CQ8+/G027vjmu76/8cjjn+DUgZf/rAWx966Gi7eLfp17Vx8g+r2/5+an73lTFUAff/xHXPX0ER746FOcPvh1eHzYhYK8GRCaLX+O6wZ/Cl/9JFeFjsMn/uBP4F2+MLE7u3Ne5hX8t585/occ33h3f/69kWlB6LGlb8ChR/jgTTdw1b978Q0B3KMPXcG9Nzzwjkpu352dnXMGtqvP/LDz1KpIgQ0eUWNCN+3LE5JFV0fSegJDrUiQW1q35RXsQnFaS+BPeGC9NFRu68KlY6u8OtQinCLEwhIpBSIiSmJGcUK/Z8mLnN5ohXTpANlwEali7/EVIbwHh7Waiw7vJ5aas2fPkBcFZVmS9VJUJFnZu0JlQSQR7rTFNJqmKinLAuEERitUpEh7GU5btNGYWhNHMYPRkP7CkMnGmPlsjtbeWxtFijRNqeva1+lIyWhhxNmNdS46dAilJE8++STD4YCZkszXZyAFummYTCasbW4w29ykd1HM/n37sFqzfnYNay1XXXUV/UGPfq+HKXPiSLB3zx6kEFTG8uILJ2nqmv0H9tPr7ScJ8uEzWF5aPcWLJ0+yOR7TO3sWLFRlyWAwYLC44L25UYS2hjPrG+yLYlLnsInvgu0PBrimYVJUWOPQVc3GWuuhPcDevXvY2NigqkqSSLCyssyoP+DEie9ja0MSJzz33HMILL3M+1/7wz7OWcbjde9JFa6raUrSlCTtecBvfXqxDZ5fJ6FpCtIkpT8akqgYJ6GqGxpjOHP2DDjHYDDw4WIqJs8L7/WWorNzx3FCns/98S4gn0/h7ItMxxMUmmy4SJkXNFXp04SFrwVCQJwk2KryDPkW5gOEP3ad8eeScsiu1kh0LD7OS5XbRZOoVTmEBZ/uvAmBaAJAKn9uBCVEm5gskV7aH/6zRmN044O/nOt8y9YEyTQOq72iQEgfFNWmZHsfvfDnUBShbMsyex+u7/gFhNnu8t2dMB9//EfA2wTYnuMcOflX3PTwpcB7ZxX9Zczs7B7uXX0gSIbv5qYHL+UuTgAnuPbxO3niC7/B0a++vn7bow9dwbGbvsivfPRPuOqrn+zue/N9v8+xm77IzU//Kcd+bQXehH82+r2/h6ff+P3fDnPXw99m+GvXc/PTu6zt7uzAtP7b2T0APPqxT+2yta8xx++/nePAtXd8k0eB0we/zv7V12kn+NAtXDPfZcXPZR77zipcu9NbsfNzzsDWagMhPMiDUe9/FcGP145oyzKFII5jzwLiJY3WhrofIToGdktyuSVhbsOH2mti/3sv35QtmxTkm86J4PVzHdMlvHkR6xy9/gI61vQHimz5IGJ4EBcCjmQLvPEgyBlB1u+zd8+AtJfx7I+fYVpsYpPIg+m8ZDrLGW9s0pSVBxo2+H7dll+4qRsiIcnSFGsUVd1QliXaWZx1JHFMXZbhdTmapkYpSV1XGGt4/vnnGC0u4oAfPvUkWdojSlNMrYmSGKUi5hubnHzuOcbrm1z+8x/AKsF4MkE3mo2zZzl48BBxkpD2eggF/eGIl148RTbocfbsWRaWlhmOFomTlCxNSGPJaJgSx1DWJZONDeqipJekOONYXl7BOIcuCoa9HotLiywuLeGcI4kTpLHUeclmVVLkJQu6IU5ihr0epjGc1g39XsbS0jILCyO0bhgOezjb8P73X8pkOkVbwyWXXsr/+McTDBYW2BxPMNawemaVqir58Y/OIIRic31MUzVsbm6ijSFOErJ+H219n7LVHrQZZ1FS0B8m7Nu7QpZlJElKmVfMZzOkFGxsbJKlKVHkA5jyeY5zkjTtgdXout4moTfde2aNpZhs0lQV/YUljBTUZQFWY01NbVzIhnI+4ZiGJEl8pZNQCOd8n7GkC4WyJlT2SNOBaeHAaUMxzzH45GVhCdJin5zcHy0go3BehuPfhef1jKzocqS6b5wLC0Te32uMxunan+cB2FZ1jTE6gGNJHCUhEAyckDipQPi+XYzABz37aicngn3AGHSt0XVD3ZSv79PpXTzP/vw/8H//X1dx61c/udObsjs/Y47ffzsn7vgi+4Mc+dgHVl7xdteGhOjshnv5tyf/A1++8rOv+7l+cN93+cFP/Ozm+37/3Nn0V5nhHa8MBt+JjPuRxz/hexov2g2R2p2dnetWH4F3eTL8+Zh2/3x+9RM8cPUOb8y7eG695klu3f1cfB3A1lmEUF5iiAtJpz45NgkVLwLPkEkp/c+TBCGV99oFCafvuvUMrRCyA7rt/Z0Aa+wWgJXh0tz5i34PEgJrG67+RQC5FoNSKqQvC7S2TCY5WZaSJClKKqz0bJa1JgAKsM54VbK2PuiHAWm/x6WXX87C0hIvPPc8p15cxWjjO2GlxGpNU9fegwiAQ1uNbZyvQIoi+oM+yAwRVSghqYsqBAl55svhsM5gjPdkSikRUjKdjInimOl4TC/NmBcFfewEcwAA9+ZJREFUPSeIhKJ2NWWRU+uGZnOMQLB2Zo1GN74bN0tYXtnLxnjCCy88z/sueR9VUZAmCWfOnkVuKupaU505Q9brMxz2UUpimgpdJ8ymMzbW1kHAB37+Cl588UXqqiROY5ZHI2KpWFlZIk1jLrroEFIoGms9iLPeu6kjQ103FFWNCh7efq/HoNfDGh+idHZ1jbrOKYqK5194iTzPmU2nvrlGQj4rWF5aopiXPPvM80wnBfnGmIU9e5jNclaffR7TNETK1yVp472gzmmE9HnbSkUIKVjes8Ill17K5sYGo8UFjN5k7ewG1tYMByOcFDR1Q1VXaG1wwtHo2lcGGUOaxFRViQnBZiJ4UJM4RUSKqsopp3OwOqgQ2HKrOv/eIgS104goxjqDQBIL6fuTOrl+EARb1yWKt4s9ZT7H2iZIFbzG2DhDFMXAyEvbt/1/uw1BC7GN2fUsdxtOJQC/6yy2qqnrGl1X/rgWeBZXKiIRIVTUBVRJIdBdVrL3UQtjwIG2DVprmqrA1SVW+5C0l1UQvcvmG8/cwb/6Xz/G/+//8x3AM7EP33AvH/7urTx8w72UG3fz8cd/1Hkbv/rI1znxm7v/AL3T5uhDV3Dksks4cc2QI9mzfOb4H1Ju3N15Vz8OXPXvjnL4ytcnuWu7a9vnAB+YcuyhL3Lk8U/AHH7lo7/xhvzM2fLnuPnpV+5+vW71EY6/7kfc2Tl+/+0cu/EbHDm5C253Z2fnSPY1jnH9Tm/Gu3reC+n752OuvdGrha7aXSx/PXU/YUKgjJcgC9I0pdfr+QAhtoJkfKBrm+/q2rvSsrT+ItdukyLT0bP+9x4wtmFRMgTktL5Hh8BuA7i2RUTh/lJIHNL7UPM5vXSB2WSDYnOKcwKtG5yxAaB7Z2CSZDxv50TiYkYLC8RJTH9xgQPvu4iiyFl96SWctcSRoixymqoKWxK2OYT+SKmo64bZbEaUphR5Tpok7Nm7F2M0GxsbaGO6ChlrAljCgw7tLHVVYRrN2fV18rpi2B9QFjUO0HXF4vIys+mUg4cPse/wIZ55+mlsPuPA+y/lfT93GRtr61RVw9qZNZyzPHP2NGmaEacZUhqmsyl1VTNIYqTQRMqxtrbGSydP42REfzBAxAprHXmes7xnD71+j+XFRQ4e2k8cK6QSTCdTauuoa8/szSZzjNVEUYzWmiSJSJLU1zYZy3i8yUurLzGbzllbO8NF77uEtD9gczpjMp3RzzwbvLJ3LydfeAGrNXXdUBYVw8UFVvau0GjNZDbDWkd/0PNMuLEoJYmSlCrPEUqSZRl1U6G1h2C6bhhvbvDS6inqpqbXi+mNhozHU6pGU2vLyt79NNr4RYtGM15fwxjb+U/xlmwc0BhN4mJiFVER2FnnJffbj2d/IPs/pJRoo/3ix8skwjYIHbYt1oitB/DnUgit2ha4tl3WYK33abdpx4Go3WJmrcVsGdZ9snF4DF3V2K5bNyZqF5OUDwWToXu6pZJbr7z3IFvKIkfUha9GMk3H1mINPvQN3s0O2/s+/izD63Pui54NF9tHuIq/gd/8pP+TI/5ngTW7efeC/B01Rx7/BCeu/joc+gp//p/+iCP/8Wsv+/1j31nl1uAh+0nG9fVMK3O+9Zon2bjjemZfuJ3jPAJ4Od/PYlcfPXg98HIf7rtBgvyTswtud+ftMCcu+nXu/Yt3dzL8+Zprb7yTI5u/xZf5N6/rflf97u9w1y4jfk6zC2r9nDOwTeIk1PKIcAGtcM5SBB9qHMdkWUKS+C/PZLrAyLbX6HKrB1NuS0l11nv5vDITA13Kcss0+YvuUMPirPf0BempExKcDTUprgOJQkjiJMHgQcdk4wzjaQGhQ5fgB1RpgtWa/r4DWGt5+gdPIiSs7NnLcHGRpiqpqopIeVnxZDoPnZ0GJwXBcum5K2MxQC/LUFFEnMZUpWDfvr0sriyDcxRlSV03QXK9BYhaP7IUEbqu2JhNqcoaU1aMywbrHGnSwzaG9dOnWVpepjINP376hxht6PUXWDuzRjbo0Rv0uezSS1hZXuKFk6v8w//9/2WQZVz1i0eI04zBcEBVlJxdW+P02pn/P3t/HmZXdZ/5o5+11t5nqkEjQkwCbGysdhSgcX5BmMHBDha27xMRfE2uiBM6iSMa+SYBJ7Z5nvxyc9P+XYbExnEDDUm6m5/dENtPHOQntpEnGmwB7rYJJoqjYEMAiUGgoUqqqjPsvdda94/vWnufKk0lqYQG6puUVeecffZeeyr2u973+74QTI/mzT+B0886C6sVSaNBY2gIH5yCh+cMUWvU2DU2FhCTJssFxHTaHWyW450jLyzOTqCUYhyP9rBrbBfNgQHqzSbdXka91eS04TMpej1Gd4ySaM3Ia9tg/lxOOHEhvTxj19guCpvRaDTY1h1hrOjReyknz3LywmJqqchznbDdab2GVjU64+MCDL3HmJQsy/m3Z/6NsV1jNFsN8I7WQIPmwADjE20m2hNYbxkYnkPaGsTmOcPz5uGsIvOa7q6tfddU7B2nbGpNtMJoBV5jbQUc5col9IJHqbAqmdIS1FLggvtxaayGKg3VvBK5stYa6yRPOV470ejJITL92AYgDHqlhogSZB2AsA/o3CtPmiYoY0i8rvYxzjWVjbuEfuHonqxRJrg1A73OmNyLzqFrKSC5zsoFFtn3j+X4qxhD8tFLfwNm+/+Ou3r8/o/D/fBp4GM3/B5/dvavcuX4Z7knuBjPVD0w+Aes+eBn4dklXPjkcla/bwnXfmP6/cxTl9kXWwuw8fbPHbPmUY/f/3F435EexWy90WvNeU8f19nRM1VPPriFDYs3HVAe+JXr3sqdWx5g6mTdbM3WvmraGRwDQ8MMDs+l0RpEh9ifNE1LoNvtdtm5cxc7doywY8conU5XJJioPraJ8oF7kioxPOCXPavEqBLpmw0LESkoH5xhfR/7JA/3qgTDUS4aEERlcoNCFQ5fWIqiQHto1BtondJoDDI0NMTP/fzPMW/+Qp577jl+/MQTvLJZZK/KC+tHML0yJg1gxICXjNI0MWXubq/IGN2+g4FGg3qtxsi27YyMjGASQ5qmYtRjJZ83SrR9sNgpCkfhbOlg66ywkoVzFK5AKUXhC8ZGRxkaGGTe/PnYIB8dHxvD5jlbXtnCjh07eG3rq+wc2cXW13bws41P87Of/AtbNr9I3uuxePGJJKbGztFx3vTWt3HKmWcw0euR5wVzBodYctppDM+ZQ2tggDzLsdbR6+X08kK258HlMinhlRJ204OznonxCUZ27KDd7TJneA7z5s4lTRLyPCcvckySMDg8zPx58zhx0UKG5swhyzK0UcyZM8zChYvEbGrRiSg8J510MjhFzSRynIE8zzFG02q1MIlI3z0KrzW9Xkav26Pb7mILR7PVwjnIc0vuHBPtDu2JLt4rGo0BGvUWqASNIkkS0kaLk047g/rAMDFWJ1xtABhj6PV6jI6MhmtdJl+iMVrZMh7ZWqVLtUJRiFt0lPTHOKywoDCrgA7O4Dqsx/etrz8eq08Y0ddPG5UPPiLsSvavylbbAIAlt1jk27qMHnKICVrRbdMZ3c74jq0YREVhjBHZt1LYwmKLDOcstVqdeq2BMQla6/J6j33Ms3Vs14ZTPvCGzlf99ENf58pxcT5efe+n+Nhl72fN4itnJNICqh7eZWcuATigY92Yd+OMjGG2Zmu2Zmumavmq20g++cQB/418YMVPuezhNSxfddthGtnxU/M+cdWRHsJRU9MGtjpJUfUUXauhVIpJ6tTqDUySUqtJD6vWIl3tdruMju5k+/YdjIyMMD4+QZYVAiQDX2WtwwczqSIAYJEpy3vOywN7CVIRVtN5MaGKD+4EAOvLHlzCd8V5x3swGAGGjsiVgQcd3JnxYnhTOEW726PRqrHopBOYs3A+rVaTej1hYnyM9vgYtsiEHVYBWASW2SSJABetaQ4MsPDEExkcHsZZS7vdZtv2bfQ6HTHpceKoLPtbybTTNBFZaJHT6XSY2LGTXqdDnoccXeWwNifPCrJej13bR/G5ZaLdpttuMz6+k5Ftklc7NrKLIiswSSon2Xqybsbmf9vEti3baNVbeA8vv7KFPM858cTFdDpd0ApjNI1aii0yBoZazJk7F2MMab2OThMarRb1ZoO0ViOt12gNDjBn/jyGh4epNRokqThSN1sDDA8OMTAwgNKaHTtGGNu1k/nz57F48WJOOukkup0Oc+fOYWxiAucdmze/iLMOE/qNrXXs2L6dbmeCXbtG6WU9du7ahbMCqpXSpPUGKk1oNlsMDA5QS6WfGqBWa6ATQ2toiEa9iS1yxsfH6HYz0rRJrTnA3PmLaTTngqqJ2tiCJsXnFpv38FYmGEoZgfcktZR6vSk9xXku7KQKV3eUGQdwKPM24uLsvUN7hS1y8p70nyrv0V73Gak5ifUJZmMCXCNzKlLgcNWIzLewKAn4ke+H+8hokfB7J4qIKHeOjK7cHyFaK5qo4bE2I+9N0B0bYWzbFnZs2cyO1zYzsWML2dhOfJGXrK4WOjn07Xq8UhiTYpIGSVInMSmJSdHaoNSBdz7M1tFZyxp3vaEfNpa99DUgxH688skZA7UAi7b8Bg+c86v82dnfYWzjLTxwzq9OG9yuOe/pN9R5iUqJ2Zqt2To6qzHvRjac8/UDd0MO9a/3Psy8T1z1hvq7dqC1fNVtfOim2eerWNMGtiZJ0EkSzJwABa1WK7C2oLW4IKdpig4GUs45er2exNJs387o6CjtThvrispgx9mSTQKQ6B4nEuTQ1midr5hLJ4yY9Y4YWKLidwI5JTmxwoKlSWSV+1hgQk+uEnpXa433Co9jeHiYHdu3MbpzJxSWgUaDWr1Oo16XEfYbP9mCxGgajboACaOp1+vUG3UKW6CVAOZWq4VOE3JXMDg0gDZaMlHLnkxfGm7JJjzOWoo8p8hzvLXC7jonOaPBibnIcsbHxpjYuYuxnTsZHxllbGSEba+8hs1yJsYneOZnz/AvG/5ZvuvBO1m3xbFzbAyF5uf//b9n7omL2DGyg/HxMZIkYf4Ji5h34mLmnrCQBSefRGvuXOYsWsTwCYsYWLiA+pxhksEBklaTxkCLZqtFUq8xOG8OwwsW0BoelvcHWqT1lFqjTrPV5KRTT+a0M05j8UmLWDhvLosWLkArWDh/AcPDwyTGsH3bNhrNGgOtFt45XnxhE4W3jIzsDKZGqtwfkyQkSYI2hubQIK2hwXCdOPI8p9froLRieO4weZYxMd4Gr2g2WmiTMjA4TKPVotZooBIDCmppStFts3Pry+x47RWyXq8Egz5IfVsDA9gAPCuBbUXRxskWAcR9fa1xYSUANssyiryobjQlEy46TJpMvjd8nJapwDMBCEPZrx571n2Y6FHOl05W4Y4JMDi8dpa816E9NsrotlfYseUlRl99mV3bttDZtZ2iM06RZ0F+78pd1UFeHQelVdVjbpK0HE9hrZhy9as0ZuuYrg2nfOANO0P8vqeendTXOZOgdmrdvvIdpZnUdGrj7Z87oPV/6KbkmH5gLG45/0gPYbZma7b2UMtX3cbgrZeyZvGVBw1qY82C271XNC6c9RqoatrAlsg+aYUWy2JUAGPaJKRprWScjDEho1VAhzFGei+zjNHRnWzduo0dO4TJzQtbuiT3l8JXD/Y+PshriH2IfeyYDr9HytZaG5hfAQ5JklBLk+DCLKyvZJ2KdLdwFoeA4Fq9TrPWZOfOXXQmOiQmIdGGRqNR7k8SnJfF7MlSuIKklmASYVw7Ex12juyklqTMnb8ApRSLTz2FnvO8uOklsI6iyLFeQEaSJJNyenV0aw6vS8BrBdikISvXJAZrLZ3xcToTEzjnsNbRabd54bnnef7Zf+Ofnvgxr764JRhlFRTOkmUZO3ft4s1veQvD8+ezc2ycdpbRzXImJjroWkrX5RQaurlF1WoMn3ACptFE1xtgErxJUfUayUCLpDWIbrZozZ/P8KKFnHDqyZx8xhLOeMubOOOsN3PaGaez4ISFnH7mGTQaTenJrtfYObaT5tAg9YFBJrpdTlh8It1ej9bQMOPdHjoxjLfbjI2Ni6Q1SFudtwIqtSKtiyw+6/ZExu6c9IJ6aDZbDA7NoTkwyKuvbuWV116jsJ6k3iRttMisQ6Upzmgwwpj2ul127tzB9q2vgO+xYOEckqS/H1wAZJLUyDMb4nACSCyNnHyf71MJRTHGkJia5MCiy0mGIs/Jel28s4JEqYzWPNK3GhULYQBEdyjJpVUlcLaRkY2GakFCLdm50Z3cl73dCii6XTq7dtIZ3UExPo7PC1xeUK/XmbNgAWmjyeDgIAMDgyT1WjDP8jLJZRRoVRlRofBKhZzbBIcq2wvcLLA9ruqNOEO8fNVtLL1jJcte+hpXrnsrV45/9oCA58HUAyt+yrLGXftfEHjoXXceUPTIhlM+IJElh6FeD7n6eVcsLtnz2Zqt2To6avmq21g9erWoT2Zo4m8W3E6u5atuK0HtrGnU5Jr2k0le5Khcci51Aq3WIFoJiM3zvMrOJOTD6smYOUmSMmZEenJ7dLs9jDG0ApunAosVH7ilosNrH9WFF3MggleOFtmnU0HKXIJij0oUmS1o+gosRoYUxODGWkuj1cBr6NmCTg6LTlhE3smoNxvU6jV27BgRMAUhrkejAotaFJbEGPJMonySJGH+3DnUGw1INFtfea3MM21PTDAxNo4LLKxHYZKEZqPB2NgYIMdUa01aq5PnBR5xqi1y6cl01tLp5mgUaZIAniLPwvGQKKbtW7eyfevWkr2LIFkpDVrTGBhgotcht47NL7/M4JwhFp50IrrZYOuO7YwXPbbvGmXu0FwGhocZGGwJIAty2jSRPkznBMylSQqFxhgN1lNL6uJk7eoSydNs0O10yBUsOPUURnfsxNfrDA0NkWc5u8Yn2D4ySlpv0M0yus6SDrTQSYL1nsHhIebOny/9222RTOsknHdbkPe6jO/aST1J8cqB9mX0VHeix84du6iHWKDB4bmoNCW3XuKocORZj4mxXbiiR2oMjWaN4cEB6oMt0lqC7Yn7b8RmznuKwuJcIRLkvp7aPre0yhk53CtJrUGe2zARo+V4Oost4qSRFpZVyec+MsWEXuxJ/bUE5jfeCyKRj+WcLd2Vo9syKjD+1WDlnjWybYxG65RCdagPNGkODtLpZuRZjzRJ0SYRIGs01llsvN+0BufLe99rhTIptg906/7BzdYxXxtO+QCPrfrwGyrD8fH7P87jwHXz/pI7gTXPPs2yM5fwZ0d6YKG6I5+Z9DoCv33N5i9r3MU9q7404+dxWeMuruX0GV3n1Hr8/o/z0VtnDdtma7aOhoqgc/Xo1fzZ2d+Z8fX/670P8/K9P5nx9R4LFY9tORG55RGWNe5iwyyo3a2mzdh2RnfS3rmLXqdDmibMmTsHk5hShqy0QhuDDsyXVlMeYktnWFVKlZVSwrC5CjCWpE4p0536Y4ODsLBXWgdZpRFQlRhdOrf6wG5ZZwNrVK27Xx5qEsP42C667XFGt2/npRdfZssrr5CmCa2hAXZ12uwaG6MIY7VFgQumP0opiW6xlkazwZy5c2k2m1hnmRgfF3Cr4IWnf0Zn5y4SY5g7by7NVguFHIvoIt1oNlFaU6vVS7mt9z5IaruyLR2ln6qMc0mShEazWUlgo1w5ApjAMspYxcxn0ckn86a3vJX/453v5K0/t4zzfuH/4LQ3vZlFJ53EvAXzOfnkk1m8+GRarZYcS2dJlMAgjUfrKD0VQEZw4C17TBPJDPaJpjCQtpoUCobmzyd3DlJDY2iQ8XaHZ5/5N17d+ipvedtS6vU61lqyPKebZWThWC86cRGNZpMiK3CFOGBHZUCaigtvnvXIel3pgdZgsWRFRq/TRnkvjK9JqNfreA+1Wo086zG2YzvjI9tQtkurkTBv3hwajTq9rIctbHlco1FSdAoONkwife4DiuUkT1AbxN+jjNlojQ5j0VpjtMEYPXnSJbCiJUsb/jfeN9EcLa5X9V3X3jmK3OLCxIu1cgyxDldYfOHweQGFxRcFLs/xhcPiQGtMrYY2CRPjbUZ37KDIMorCkhcFRZAj+8gAo9Chcz42FHsE4Er0EKXRmzFvPIZvto7funv578ObNx1WCdiV697Knzz9ntIZeX/1scveP4nRePz+j3Pd43+5z+9sOOUDrB69+qCYkH2xsm90k7HZmq03Ui1fdRsXbnmECwe+cFhAbaw3oplUZGZXj14Nb95U/szKj/dc037SLFyBCdEeSilqjRp5UUONCyNTOFsC09j7J/1/INJMDVqkwtFJmbCMCz2lKIW3wWE3Grn66EWrynWDx/oKIGslrKdSWuSczqGMjEFkyRqtE5QW2aZHSW+kUmgvakoAVxS8tHkz46PbGB4e5E1vPpOR0VG2vbaN3tgELsTZKNE5o4B6kCgPDQ2x6JST2LVzF7t27WJifIJEKWppSi1JGds5Si2tMTQ0jE4Tat7TbXfBQ97roY2h3moyMT5Op9MJjLIwbdKH7HFKXLNc2C+PwhU9iV9KjPQieycy16AU1YH5M9qEYwXDc+aw9Od/jtrAAINzh3nrnCEajQZju3YxODiAxZGmKc2BAXCWtN4gTcI6lcKH3kkVepmVln7hIBTHKR+k61pYxETMu5qDAzTrDbZv206RZSil2Pz88zz9zxtpNhKKU0+h3myijObUU0/hh+t/KGxvWsOjGNm2A2sLlBcGG6fIuhlGJ3il6Uy0yU1SAsoiz3E2wzvLwOAQTilqTXFGtrmYNxU2x/iCRl2hkUmHXl7QarUYH9vJ6NatFHkuEzPEXGbAO2KklAr7GbFtzIONczux77XMcVUapeR69AEWGpPg43nVHh0chHXsk9UJSiVoJevxYftaKyyWwlmUTkIvuUP5arTeWlChPz6Aa7QWI7bSqAoxsMKjjcEbjcscRS8D59BKiwu2FRbYeFFH5L6a0LB4dJh00qFlQCuNVwijO0vYztYxWvGh7Z73jod3nmDDM/JQ8cDgHxyWbcaoi2s3fmb/C4faePvn4NZLJ73XHfkMd/7djQLEYY8PQ/LeF9hw66Use+r902JvB2+9lI0r9h0XdM97x+EwRqEsX3Ubqx+5ejYXerZm6whVBF0fet/VbHhlEw+cc/g8B0CyWh+bOKybOKLVD9o3nCMxR6sfuZoNd6xkw5Ea1DFW06dQvEd8YhTOWfIiJ0mSwMQY0Er66bwDGx7hA8CtiOHKVEc+VpN+rHNkWS/kd/ryGVxBCQp0NKsJa3a2QGtNUeQhYsSggzzTOY/D4r3FWSPGSyFaZ9KuWWH40lTkyEWWs2v7CC836hKv083ptNtVD6wxKK1pDQxw6pLTKHAMDg0xMGcYnRjmn7CAdqfDyy+8yPYd22mPt0mM4ZQlpzG6cxft8XGKXi7j92JypK1k4sbjY62Ms3+s1grzHE2kogy1yHNsr1e1X5by1eidG5ZVqsyNadRTWkMt5p+wkPb4OMPDc2i2GgJ+jSHLMl7e/CLPP/NTLrhgOfWFCyRap8+wyGOkvzOe65AnGw6qnJfcYZwH52g0G4ztGqPbbqOU4vkXXgDv2fbqa2zf8jL/8tQGdu4YZXjuAJ12mzzrlT3Tm597HhCX5BgHpYCiKGi326EP2TE8NIdtnV5gOgm9sx6LJ0kMSina4+N02uMYo5gzd5g8c2SFXEcDQy38eAeTyrXdbXekV7VkZEthMEpryY8NKl9tdOngHWZl5JxoFWT2URYc3Y+DEZkH6530ovsAboPOWCvdt2367h3ChI0plQPBC7yc/Ik3UJKmxDuxki1X96DcVwqMB00Zz+OyTsUgOy8y5rAfWmsclP96fOlEDdJzGyX1NvbC62kLRGbrGKkLtzzC40d6EIe53vfUsyxduhwuup7V94Ze14uul3/X38WV3DXjBlIR1E6VFh9sdUc+w7XfOJ3lq25j2UtX73Wmf/UzO2BAAG5xy/l73X7Mx924n+2ufmYHXPZ+Pv3Q4cmhnPeJq2aleLN1ROue946zfPFtb6iWjP568sEtnHzvw/wZHFBG7WxVtXzVbTz54BYue3iNTBCEv88XPiOfz07cHVhNG9jKQ6uwL1kvY8tLr5BoQ9bLRJZLNKPp64l1VT5rfICOQE2ciGPDoAalKIqi7Fnt/46sTpXGQbG/UGvN0FCLefOGcd7RbLZoNGo4l9PptEOGZsJ4pyjZzMjUxnV6xHW5ljTQSUKtkaAUFHnB6LYRGoMD4mw8OEg+MiLMMlCr1xgYGmRgeIikXiNt1GgMDbBr505MkpCPFZx40onMWbSAJx793xTdDq+89FLJ5qVJgrOaLBewWlhLt9erAAcKb6LTbcgNrRmUIcS37D5JUPZ2qj7nXAJj6Cxai5B4x44d/P3/+CJX/D+v5OQlp+KaDbSGRqMGStHt9ui0JzjppMVo5xhotar+yT5ZrPI2bDse08qYSAdWPNEmsLySdbpzdGfoi7WM7tiBtp4tz21idPu2cM41217dymuvbGHntm2Mbt0ucUdFISwwBuc0KkhsC1tQZBnNgRaD84ap1WqlUkArJWbAwRm7126TZV2ctYFZVhStOl5BkqQMDw9Sq9cx7RytFUPz52GSlKzbpVdER2CZfLCF5CZ7rTFpLQBHAXz1WkMmVooCV1gwCpOm6KQW8pQrMzRlKA2gNIpUp2EyRqG0SP0xCp1oyBXex2s3kb5ZL3FW2stYFHJtKO9xERwnYt4G1fUgcT1hUslQAnFXzo6koBPwBUonYZLEge+TWSsNyqDDmJ0SNt1ZiwsOzGiF16LQsKEPfLaOzlr20tf48s2VQ/d0DCkOV3/m0VKNeTey9NeqjNorL5Kc2dXP7ID1AnIPhyvysjOXHBBTG+uhd91Jdx/n4vH7P86Xnyrgjn2vZ/UzO7jnk0/QuGX3XNzkk0+w+plvT39Qb97Ex5h5cLt81W3yEDija52t2TqwuuORz3PP3Deu5H7NeU/zAG9//Tb47BJY/Ppt7nBWBLQbzvk6awZ2wAdnWdmZqGkDW2Fb+gxy8CUQja/7GVkVgGs/I9tv2hRfA5MAWqypoDZNU5IkKY2qBOgqFp4whwULB8Vd2SmgCBmzIvFccMJCBjIoOqMBa7tJ6/fWUeQ5zcE55IUjKQhZuJ7RHSOYXWO0BgdIk7SUilonUUPddofRHSM05w4zr1mnyC29Xg9b5MxbOJ/2RJvCO1rDg+zqdkujJWsdSTimcS99H1AtCelJLKEYRcmEgDC2seczfl/+DXmqCkQDSnkuBJR7lFW8vOlFvvxf/29eeWETF694N0WIa5pot+l2Mk5YeAI6NQwNvU3k3YH1jey58j6kJXkSrUUCG8+rE3DkrJWe4PC+0Zr58+aR5zmj27aTKs32ra/RnpjAFmKKpZRm07PP8+rLr5L3MlxeSKxSUivPXZS8F86W/dljY2PkNpfs4FIH70hMgrOIa3LhsUG6bUJmcJZlFN4yPDxMYQvyoiil8cp7jNKkSUK37xr13qMVDAwM4n0T7zWFzVDOoY2ujMl0jk9lckcrjWaySgEdJ2zkoEZgb5IkTMQEJlTpso9d+eq+mnqflOMj9D1Hv6g44eHBh/erCZ7Yyw4YQ61eC5mzuuwiDndh37UIPjK1qvoJA5q0uAoya+V9kHLP1tFa98z9Et84pwJFIxMfZsM5XxcQt5facMoHuOesr8P9r8cIX/+67OE1PHDvT8X5+KLruees+dzxyOdZ/dymwxrzc7B12cNrGLn1K/ucaBi59SvAF/a7rtXP7IAP7gHAPlP9+r11vwWfeGS3RURO17eNGQa373vq2UnMxmzN1mwd/1UpWY7tidTdAO0z+//ObE2/pg1s5RlZTKGMMSRJKsxVluFsZcykQh+fCssBk0BrfOjvl9jqPtMhUJMe1kEezJMkIU2TUg7snBPnYGNwzqO8qXp3A7jTWtEeG6HeaOEQttZacbGtxiOxJI3BOST1FjbvTgLatijYNbqzNGKKgNwVltGREdoTbeaesICBgRYGmNg5zgknLmThiYt4cfOLtAZazF0wn/Edozhnsc7jrCPPRIpcdSMrVNm72se09gFWo00wHzJYX+wmJ41gRkUprEL6a0M8kRx4cFZY8bHRMb751a/z/HPP8ouXXsSiU04BkzAxNkF7bCenv+kMUEaAtNco5/GGEnDH/mhbCIuulUhQVZAeJ0pTZDmvbd/OwhMWYrShZgwuKxjZth2D4onH/zdZr0dRWCIWd1lBkY0JNgrXSZ7neOWFXdRybGxuhUW2Tlj2bsa424kt8jIiJ1Uih81ziwvmSc45aklKXhSMje4kbaaMjGxneGiITruN9ZaB5iCvvfQy3W6XwrswbxOirpQSV+Y0JakNCitZOFzRE5CqFMp5cqWlxztMKDivyG0hMU/K4b0iUVp6yqPMF9BotIrmU6rs71VeELAKpkwEbtyVoFjWgxflsCj6Q7SPCwxqiLiK5lRhPgqURxmDD73qYlJVxWuJ67jGBPMquc6qCSviZRFuXY3GKoNS4drzXmKYZuuoralg6PH7P85yboOBfYOgPclNL7j8Cs4/+RIAnnj5e/zgWw/O/IBfh/rXex/mhrU/4sr3CKgtK0qRD7EiYC5r/V1w0fWcOPdLPM7R//C2+pkd3Dnvxt0ky/M+cRVMZfxnANy+76ln+d6632LpM8tmmY3Zmq2joB5bfCl/8vR7DqthVKyDVbK83jXV3Cr+t3XNtTfzaH0ljMGaD357FtAeppp+j62qOJuSdUJYHd8HdPC7S4hj7YmVDXwjpSkPlfy2//uTvxvApZOHdK0TbFEEsigyn+LWXOQZNs9kGWfxzmG9GD9prVHGYJIUZRIUWkyw8KXkWGkdZJyTx220wXtFlheMj4wx+tp2BhfMZXTHDpTLAY+2jkatQVqrUW80xJ03AH7pn7UCyhGHX6Wg18sCw6dCi2TFgIkpkGYS7g9sXASWfso4dTjGVc5on0TZOYrc88xPfsqcOXM4dclppK0Wp5x8Cnm3K7JSb0twJFLuqFr1eIcAneCCmxcFOlEyseDlHOZ5hvOWrNdjoj3By88/T6sxwJOP/4BEayZ27STv9gIQJcQ4OUrzMIJhVTARi+SjsxVbq1HCzDpH3u1h47lD4YxF6/Adb+UaCOvy3pNnGRjP8Nw5ZJ0enWQCU6uR9XK6E106vS7WVttGyT43WgPUhofRWgCySRR5F4kZQuG9xDQltZSiF5yqVXABD0Av3lHegzLV76XDcmB0nUfcnJsDoadVWFZh/BUYcQbXfVLi6N/lwoGUXFuPDudSxXmOcsLDS4+tUhCu7SRJQ8+6wXlN2qyRaI2u1Shs1Tcr6ndf3pcilUcGYDTKavCuYnVn65ipx+//OPOeenb/suQ3b+Lexgule695+/n87YIF8tlZV7Lm5Eu4896bDu9gX4da/cwOFp39HR6YofX1s75Xjn+We67942mbNx1sTfucTrOSTz4Bn5jmwm/exPtuf3a/rPLUasy7keSTT7D0mWUsnY32ma2jqDZ0r+exIz2II1iP3/9xNlx2/Yz9TdxXXff4X9Ll6Ae2F8YonqAoeXLejbz3D3/M3/oFwPdpf/pfufCDR3aMx3NNXxsYH8R9BLDy3KrV7qvoB6H9v1cxKH0S5PCZc3aP65kk0Q3fj8Alsr9ai7Qy9q+KtDKA2yDtVBqRuzpXgaJy3Q7nCnp5N4BE+Y42RuTHvgK28WE+SVOcVxSFo9Pu8OJzL7DpZ89iuxkvPb+Zjf/4T2x54UVe+OkzTOzYSb1WK1nfWq1WOjrH8QqTLIxgZGujTBQCs+ur/ZQc1D6kMOW4hsFKZ2ToR/XehR7PKgMV7ym6OT/953+hWa8z0GzgrPSziuQ5yJDDOp2XGCFnhWGN/+I9RoHNM3aOjIC1/NOTTzIxPsbJi08kTQyjO7bzyuYX2bblVTb88Eke+5/rmRibqPKNo0mTC3sc+oVFWlz1l4qsOiwbwL+1NkTZ2OB0rUXCiwLnxIXZi4uwMbrcJ2sL8ixndMcooztGePWllxndIU7YReGo1Zo0GgNoncoxCcZIab2BVglKG7QxpLU6mAST1tAmxXrQaYJJU0gN1AwqMRLtEynTsg+1vLX6+qQV/dM+Kk1oDA5SHxik3hqgPjBEvTVAUq+jS0dlj3ICWsvrJdxjJTAPxy9+Hk9tOYkAJSA1tTr11iA6TYUNDv3vtrDBSTm2GuiQcazQKrg5RxWGriTTSTob93Ms1tIbfm9ay2045QN7jWH427MWsObam2dyWIe9pu7LHY98vuyrPZS6ct1buXL8s/Kz7q3VB+vvOuygNpbIkWemVj+zg8a83Xtx91ZL71jJ6tGrp/2dj132ftZ88Nv7lMTP1mwdiVr20td4bPGlx63HwHTrT195dfLfssNQb7v2XZx3xdHdXHvB5VfwK//XZSx+63K2LvkCd/7dLwPCzr72kxUs3b6JJZsvFrZ2tg5bTRvYegxehR+vUV6Bq1jcCKI8fcCqTyq7N+Y2dPmJEU35NTfpe5V8uQKicf3WyoO28548L0T6qcF7WzKiwo4WIkV2k8Gys4722E5GXnuRzth2sqyLSQ3KSN6r1lX+LlQ5opQP7R7nC8Z2jbF9y1Y6nQ7WCtjd/to2xnfuQjknRjxomq0Wab1OlB4LExvYwzzf7ZhFEBo/L42j6FsuHHfrnPT/BnaudFh24lvrXIHHo00FqH1g+WwhMUGyLQGzkeH2RYF3BdbmAnS9/Ggv8S7KO17a9AI7Xn0VgxcpMMKkNmt1Xnt5C93xNpuffZ6tL73M9x96hJEdI0yMTVDkRVT5Tso+dqHH1AX8VYVDIXJa5/CFxVsrJlJ5Xl5bkRFVWpMkqcQF+dhL6qv1E4OktMQimxSV1MlyTzdzpAPD1AaGqA0OoUyCFwoVk6aYWg1lDEmtQb05AKm89hG0aoU2CSgtkwk6ZM8ifa4uzBM5PF4jbC5h0iH004rTOKAFTHutsSUr6yWuRyFxSyE2qGSB4xXiq/30PrDGSskERZxIkBkgyZ71Hm8dJigECuux3olU2vq+ezLibwVa47XcK9okJMYQDcU0CoMKecO1ff2JOS7qjkc+f6SHMOP16Ye+zmMTH+aes+az7KWvHfR6XnzLt7ng8itmcGSHt+Z94ipuWPuj8vWGUz5w0H21N6z90aR1lQD5ouvL9++59o9ftwfkI/0gvuGUD7Dmg9/m3ve9sFeAu3zVbZKD++ZNr/PoZmu2plcbujPTkvBGrdcWf563Xfuu8qcfHF85/tny/UU/+Tu+cc6bj/jfrX3V1Wd+gqve9E6+73+Nm09dxM2nLuI/XT2/BLcXDnyBjzz9EZ5503VHeKTHf02bQknTupjaFDneQVFYnCuwgWn1zu3OINIHfMu3p4JaEHdWXZopTZUfl6+jcU18X2tqtVrJfGV5Qb2WBvZSWCtrxWFWG1XFrvQxYc47tILe+C4SND5JqTWaNAfF8fi17ZJhW/SycizOOfIskygT57BeY4xGKYNz0VVU2MCxXTtp1lshU9XR7XRJTIEtipIxLY/VFAl3dazkSBVFPun49oPa+N5knXKffJzIBnuUd2hlAgOqMImmXquJi3BRkCRpXxawACgc2F7G6NgY27duZfvW7RitcVYmFzZv2kytnnLiiYuwhaPb7dKZ6PDUE08ytnMXvU6X1156hZ3bttPt9HC5TDx4J+uX/Y8S9L7jUILZarLEO9fHWIskupSm918f1lKEOChhLGNPaFxGYZIUbVJ0kmBqdUxaI0nqIsPVOoDqAq+jvFeJfL1Wx+saJqkFxt2GjGOPK2KfdzUx473kAKvQu1q2yPafKyVRQf371sfZTjo23lWTG2XPrGjOq3XE4+R8yeiXcv3ymIZ7wnlsUeB9l8ZAXVyaQ+4xKGxfi4Hum3Spzk2VZ9svIRdQb8DbydfvbB1T9fj9H4f74Z5VX+Kes75eAvgDMe/5vv81fvlnz/IDjs1+2+nWJAC7h89uCE5btw++I7wroPf5Mwf59OswvqOpNpzyAfjgt7nnrEu5ZMV/K9//3rrf4sJnvgB7j8idrWOoSifx46zufPLsY97IaCZqxYoVPPbqh/a73JXjn+XRF07l25u3AnD6Nb/LD/7hmvLzL933V7DuvwAwfMmX2fQP3wfgphdf44+Owv9uxIla8/bz+faCJUzVJN186iJuuhq2PlIH4CquYnV3EN58/N0LR1NN3xU5qVEbqNObsPgip8gcNrgiV66rAVg4hzcmPI/vubEuArLwLB4AJ0GCGZaJrsHel2xeBEBAYIhEoqowYDQFGuOzKTLmkKlpApMVpJfe2lLGaq2jyLrkvXF6WZd60kInBqM1SZJQq9dJtWFsbAyspdftCSsZewyNxtqMiluUB//2rgkm/HjVU1tA4RxJYrBWQHAJEryfDBioDKUiua6UEflrf09y+Ut1jMrzEIGU9+EYOLQqwvgSMGKyleqEzvgEpshoDQ2glCLVNZFtIyZaL296kX/5p43s2L6d7Vu3sWt0J0XhKgmqUTyVb6DIehB6WfM8RyuFLRztiTY2D2ZjUQ6LR8d9jAA/RhXrat+8KHf7+mMnZ8tKX7QrGfD4PWsteZ6XZmNKJSRpgtGGJEX6q5MElSTUmy2UScO1FyS1EuyKxOtkgEc5jyLFIw7QCnC+AFcQmXFU6M+2Fmcr0FedM4XyCk2V/YoOMuJwKyktb6i4z86KY3Hsw1VyBznvhK31sd8VuQ6VsLtOBoNzeeWGrICSuQ8TQdbR607QaA2AMajCo5BJEBeacgUQx35ZQdRaaYkbAlQi69XKoLQjrTfwRUGedVEx33q2jtmKAPdaJBN140evYukdK1n20tek/2kfD3k3vfgab33LP8Fzr+OAZ6qCqVN/XbnurVUM0Lq3ckY3ANaV7+Dle39C8sknuPnuf7fXVUbjqBu46PCN+xip1c/smGQ2NdtHe3zVWf92N4++cCNq4S9x4X7M6I6Vuues+Zx3xWIeP04d4Q+kFi58jMbaX+DVlQt2+yyysJ/eeDGLr/sUN1+yiNP3sp7Tr/nd8vdNVL9f98jamRzutOuCy6/AvP388vW5IwM88fL3MG8/n4ULH+P7/sr9ruPmUxdB3359d/PFvNvtfxJgtg6+pu+KHGS/0XU4gtgIPEs2DC9Nr+HDPTkc9zOxUWZbAmQqYNbPRsZoGwER0isZWbc8t2zZMkJrzjyKoqCehgd+5yiCY26SKmwhMkvnfCkp9s6R9cQoydYL2hNj5N0ertEILJwnLwpqWsBxrSZgr8gysiwXKajSko1q9OR9g7KvdmrfsVJ7jjjqd4uGftbWC0CqCUCyAahN7VkOK+ozoIqGS5GtFAmqNlr6P7URoOId9UaNtFVnfHSMJ/7X/6bVGmS83aHd6TG+cycTYxPsHBkn63aZ2LWL8V3jJKkumeJaq4nyYsiko8Q1lx5SXxT4Ii+ZwzJiBl+ex1ilKZgP56g6cALTwnXmqRx/SxVuJHYRKW3sZbZhEkPV6jSaTbJeRr1eJ0nroBOst6BEjkvIPzZKHLcTI1mwrkd5TpWzaJ3IZIUtyLMOWbeND8xtNH6Svm65b4xJJGYo7o7zOCWZurJtkSwLSywAWisBzzYvKJwViXKInOq/U+r1usihoWSEFTLxIq7Y0hudZRlpmpaO5ZOvu92vU/mpli0KmYxJ6yFHNxx3AeOiWpCsW2HGi6KgCMfez7oiH1f1+P0fh3PezDf+egPXcvp+TT3u81fxg28tfZ1Gd+j1oZsS2Ci/P7AiRP6s+AMA/uTp9/BvGy+mseUnAHx65GIefOoPOfP2L/NjnmP4Qx/h7Lvv5WZ2TXt7yxp3ce1eH/le3zoUhm35qttY+pHjA7zM1sxV98zPAJ/hB637OHVtzhVLrz/m45oOVhp7weVXHLNO8VPrgsuvEID327Djj3+bj31kGQ8M/gF/8vR7uO7xv+QH136TlZ0GN1+zaDdGc7p18SVf4McbX9//dlxw+RVc9aZ3cvOCReV7GxcAZwUw65eU79/04mvTXu/a1n3w1JOyvts/N2MmfrNV1bSBrSt6dMe7kimKcGTOuSC/VCU5K3g3sEKRWYIAQKoex1IKqaqcU6X2JF6OYLif/JW8z8iW4aHb6VGrZ4xPjDN/biuwhYW4udaM9HL6KJf2k8G0s6A0Ra+L7cg+je8cpcgzelmGdY6s0yHvdoFg+qQNShfB9MoFVi6p5NdTGNhJGaxhkqDfFCt+VlHXcd/jHguMs96V4A7oc9jdXcpMZDYRAFwUXnojESBuagqlQTvodDvs2LaVRaeexIubXuRbX/smtVoT5zVFYcl7PZTy1OstjE4w9RaNQU3WnZAWTaDbzki05NdqHQCZsxTWYfM89DvH/XflDgrD6rGUvLT0NSN918Lmilw5sX39tyX7KD9OiWmUVjGWKkVpg8Vi6nWMSjD1BkobksSjG3U8BpwX/yYv7tSECCijDb4oSGop9UYL2x4vGeFOexcWgyty8m5GkfVwWVsmThAJrrUFyvf1RNdToCnj1UJBK23KTl8PqCDLT3TfPQJ4Z+l1JsAVwbipPNOYWgrNukQREUy4qm5fuQ69D8C5mlySy62aIIrxPt5FqbIKxl42MN59vbVBou6FKBbA7TxR5hzdp+VvhBiMaT+bY/tGqKvP/ATfXrBk/wsexdWYdyNjGzvl6yvHP1sytleOf5annruKc1d/AfhIuczLwMushLa8fvrya3n+e9tKJvdoqo9d9n5g772rdzzyeVYfAOhYc97TfPqhGRjYbB33NdG+hqcvh4++6Tp4AzHzkf2baF/DiUsu49wzN3L2t+7lS8/deqSHdkglExYC9uZ/6q94Rn2R4U0X8sA71/D9C9/GRigBbT8AXH/aQ3zf/9q0trHptO9zweUPHPJkQDQEnM6ExFVveqewrfupJZsvFrB6APWDc84DYN2vXc3vfOfd5ftfeu7WMtLseJTuv151ADalHqMVzlKyRf0MoTy8VxJaefT1JbgDdgdyRBir+z6Lq9yDhLnvYT4+mDuXkKQa5zy5LciLojSI6mYZva6l0fAo4yTv0wl49ASzIqUCOLTYboE2Bq0U3XabXrcrYNJabDCQ8t5Tr9cYGq6jTALeYwtPt9Mjz30AZALiFXsAm/3HIh67ODFQ7mZ1JCOrGQFvjKnpd5SuwOHkYxfBbdyecx6TmCBxVWV/L+FYvLz5ZXSaUK/XqdWaNAfmodM6WZbT2bUTvKMxMCjAWEl+rdbN0slZA3mW0css2qRlH2lp1OUF8MSezOq8BnOjIG8tmfnA8Hl8GU9TgXUCkJt81LSWKKb4rjGGNE0BjVYpGINTSmTg3uPyDJOKrNjlBemAEWkyCoUjMYqaUixcuIC8VaOwRbl+rRxWg08UyidYX8OSU+S5ANrgRh1BualpkjQJ8yixTzXIjaNUWUmXqveCfSddI86FzN5SxB9YWRMmaPrypCNg9cIae1+xt6pcMeX9KMqC+BNmKmKwbnkNuZClO7kinI3qCx8Y9+ra352Vn63jry64/Aq6Z36Gd6ihPc7MX6O+wvPXmsOaa7vx9s+x/ACjZPZUdy//fdh4y27vS6at9JRNtPfNSjaeu5Ezumcc0jgOtKbrdrw/pmAqkxYzfH/uuy1O/9nP77b8V0dzLrjccf7Jl7Dtke3cc9183n/3Bbstt2m5SBU3nCNZtq/Xw9s9Z8l4jgeWcLaOvVpz7c2c0bDQAdRXYDOgLmP97zzEO7f9MfYnTwAccyzu1Wd+gm+fNrkR/vv+1+C0X2NT3yPexeqLbNt2IWub1Xvbtl3IkvbFXKO+Mi0AKQD6wFnbfsPCC7c8wp1Pnr1XE8PzT76ER+srOX3JGub76YHagQMEtf21YsUKXux7/U7+mJ3AK/eu49HTTuWf393mjkc+P/s36wBr+lJk53CekDMaJK3EWI+KmS0zR/vNfgIT2Q9wJxtEhT5L1eeGzGQgGB+fiWY3eArreG3rCCcsmgcKmgODtLM8gFeRZOY2w3Y6NBvJ7tLoAO5ccBJWslGC5Q6uCI693uOVgMLB4QbD85ukdY8jxxhDkiR4a9iyaYKJ8S62KDDByVbAnyZNU7q9Ht5XMuh+Frd/X9VuACAC18rAyJWfUIH0eKT6jm/Voyu9xvF3pUXObFRggRUUtqDTblNL6yT1Bipp4IJfkU4M2HA+lTDGSWKwaLTyGKNItMG7go5z9LpZydoLxhMQ7V0FcmJfbQnQwn4HT6uSlYy9uJG5L5naPqm2R2HiBIuzaCMS6yzrgZU+6Xq9gVcGbeRY570M8oKh4SGc94yPjnPySYsYGJpDYlLyLEcbjfJOzic+gEON9R7BmY7COWxhwXl6vR7t8XFeffllxneN0p4YwxVZOLWqNEjDe5JaHbTG2QJjhPqMkVWo6twqFYQNJftbfY6S+CJbFFi8SKhNYLoD8+pcUV0P3kOIiypdmkPurMz5SOasVkFCDuKIjMVZjVHgbOnJjTgfI328PqzL2nAvQaINdooKYbaOv1pz7c387VkLgCv3Kje7+dRF/C2wpP4ZLrj8xqP2Ie59Tz3Lfz7zLya998DgH4TffgTr7+LcX1zFo/V9r+f8ky/h9rcv2qeZVH9teG4Tnz5KpMjLXvoaG075AMte+hrXfuN0zl0tmuzumfD0mbsv/zagCzzKZ+ByOOvf4OnL797r+s/6Nxho3cejLzzBP7+7fdgB7m9uXMGLv/NtPpr8xhuKJZytI1txsu8MtXucJcBFmy+TX970TgCuW7qVoY3T+3txpOvqMz/Bt397/+5uEdTuqQZa97GWLmzfJHLlfQDcTad9n7GlfzXt43P1mZ/g6cuv5aen/VX53onqi/ynt87n5lOv3ON3xDHh+5NA+Z7qphdfY22zC4cAavdVK1asYN26dXAv/PAX7+ees9bNMrgHUAcAbIsQWdLHFHqJVjHG7JWVjbW7TLb/96pXsB8AT64p6wumOePjY8yfPyD9e0FKGfApzjtMmpLWG7giD2yUJT6Q413fw3xw1Q3AUwAtlE60Sh7am62UxoBHJaBIEVWyYtdoh067DR5arRp5UZCmTWF/u12SJEHnOQpFo14nLwpAZLtRNqoj2J0yCRDZaQGwBq8dnjy4AzOZgd3tGAdOPLJ1KhH5LRJFY0MmbZbldCfGyLpzoK4xtQZaG7rtDlmnTXd8FzYraI/upNasMzR/Pt47OhNtXJExOGeQpJ4QJx9KF904dqVQRiMU56TwHqrYnQCXfIBc5dyII05oxKzfOClQAXjQOiExdbzLCRpmiZ4xRgC+UnilSZXGKkOz1aChDYlJSJpNVK7Z+spWtr2yTdyeQ0yUODnJdIcOTKsLADHG8SQ6RScpXhtqtTonnX4GqTFs3fISm577GS7PwImDs8JIDI4W9tzRJzmPky8+9Br7SJy6Esh6XIl4FYhjdZajtJiSyfji1Icw3NFwLTLr/bnQZT4wSnp75cospcQYj8RnyTHViQ6MshZ5c1Qz4GMkr2wzMNs+SKGVnpUiH491weVX8LdnLeCmF1+b1sz7ptO+z457fne/yx2JWr7qNv71hgUwBYxGA5QbuB8uup5H9cr9ruvR+kquXLetdEGeWlV0kGzrjO793L/qzMMeadGYdyNL79h7juKyl77GRy/9De685XyuWlwPkuuZr4n2NXAmLHvqH+B1MBS6aPNlfOXfHoXTZx8Qj4Z6/90XwHu+caSHcdB1xyOf555VX9rn/do98zNco6afGX33pSu5DmYc3E7N5D7UvzHTAbUR/G1jz6B2aq1tdlmovrhPefLp1/wuS7evwP7kid0mRi+4/ArOP/kSbvlfX+H0a34X+Qv3/UnLSBTPtIazz7rPXwUcHlAba8WKFQD8mAl+c+MK7lk6C26nW9MGtpFxknIV2xP76NhdPtwPcCebIvX39sU3mMTS+SkzJlNwWynRHRpqUavJbnjnpC8yMIGR9VVJAnmOD86tYpakSkfjaJwTf58aM2QSg0kMWa/A+yBX1prIIioFWTenKByJVixcNIedu9po1UB5xJXXi0zZWYe1HkpA4AGHDjmgUyNr4jhKhrJfAh7PRjCKqgyiqsmF+Dq6AltXx6kaeU9RQ2MSj1YenxekOqHVaKDTlHanTWd0O1mnLcy1s2K0q4TprQ9kWGvJehl4cYnWQbZdRtzgQ9KMI7YPa6Ww1Y7Jeeq/bKTRFIUYKaHAWpG3OqdIUsBOZqcjaNdJHWMGKPI2qFxk5HmBtw6lDCaxeBS5zWk16jQbDdrjYxTtCYasSKRfeellYR3DRak0mESjEyUA0gpDKxMoMhGjE0NiDDqtybVhEhr1GvVanfrgMK3huezavjWw3YAWBrTXHUcnAYwruRZNmKOI51T2MQDcqTdLuDaiO7kPTsh11ZJJHuXxYb9sYcsbKE6UMOVaqnKPLYYkxDw5XADVsa9eIb3BLphZyX2mw/Upx8SGY+ii0kBVEvLZOjrrfU89yzfOOfB8legaOR1Q21+NeTfSHdm34dSB1jfOeTMc5EPb4K2X8uqWBXtmWKMj8neEvX1qXZ2LLzk0MLYnV+RXT1xw0OdhOiW5sHsHtRBkyN8c5Lsf+ghvOyyjmFzfevLLrD7za4dNbnfn3/0y7/3Db8Pmyzjx0h73JMdn9MyxVmNrfxfes/JID+Oga8MpH4BnH4FVt+0RKF595ifoLvmhyI4PoO6+dCX3XfIFGs/NnKrlwi2PcM97xwEobjl/P0vvv56+/Fqmgsb+ulh9kbXN6QHa/pqOPHnjgiXc9KYGa284n1PX5jx9+bXl5MHNpy7g9LMO76TpocqP91br1q0rwezU+vE8AbfLXls1438noyrneOrtnTaFopQBpfE6sKIRbE2SC7Mb6Oqvqe69FRCW9SXGhL7CvnVHkaoquSAiCNVaUa8nOF8Euap8x0Wpq4s9vhrnJV9UhJOyLjEzisCrz9jKIyDYe0BTqyUkDUhTTXOogTJaAEySBhMgSOsGhyKt1UgbnoGhmvQkO4sxhqKopCiNZhOTpiJRrtVQKkQK1eoh89OX4DwCVq0I7J6A8siSEY9QYG09hKxUORYR4CZJIq+1x2slztW+koI7r5iY6JAYQy1JyCfa9MbHcHkR2GKNU+C0xirIraXWbNJI69RrNWxRsHNkJ+1Or2L6vBxj28dK918LMbqnvL4ic+jDNWAMzYFBlEniR5i0JhMVyoBOQGt0kpLU6gFYdwIgFjY0qUsmrULTGR+nvWMHvfFdNFspYxM78VqujYnOOFnWwRaWQnksHq89ymh8YtD1BmmjQXOwRXOwRa3ZwKQJOk0g0VilKJx0BRfOMtHpMtHroFoN5p18Gkl9EOdNeY0533c/xF70KLMP/a5oBcGEC6cir4/0Cwe2FCjyDFvkuMLRHWvTHW9LxE6vS68zTq89RtYep+hMkHe7ZL0O1mbkRQ/J2423cWBxkdCqMgM3MsdRRu48TisKH85xcEB2GKxTFEpcqG2RlWuybjbH9nisCy6/goULHzvg7527eiNrFl9JY96NMzqexrwbd2Mn9lfLV93Gxz6yjJvv/nd7lQ2f8Z2LOOM7AkBvWPsjLj/v0OMaHljx0z6JM+W6r1j5FzN+XJavuo3BWy+FN+/dMCrWPWfN509feXVGt7+vuviSL/DgxrsO2/rPv7ZVSj4v2nwZ77/7Apa99LVqgfV38cDgH0z6Yf3hG89sSX1v3W8d6SEcci1r7P06efryayup8QHWNeornHhpb6+9oAdSU/8eXvbwmgP+G9lfV5/5CTadtndQC+xVejydGmjdx9pmlyWbL97rMjefuoiNC5bw7d9+M5tO+z43n7rogCdWD6ZuevG1wwJqgb2C2li3/K+vcN3jfzmj21z20td4cONdnLt6Iy/flXHOlovZ+NG1M7qNI1EHYB5VVYwPiVmqCmFmosvwVNawv6+0n9U1xlSgjODAGs1qYi5mlGjukcEMzG90ye1zGi4/C6251laSy6lssY9yZKptyerlvXo9wRlLbTBFGyuHzXswSN+pUzQaKcZ4jPGgLEmqGM862AJqtRp5XpDnOUopitADqpTCJAZrtWSRhkxZV4LqKuomZo+KI7PHOWGMfTh+MfM3HvPI9rkSuMqOKpvJejAUhcegqNXr+KTGC5tfIamn1Ootammd2olzhUlWjh2vvUKWS88pSpHnOWm9Vk44KG2w2gsQDsys8uKca60tQbbHh+tHAK/WhiQRsJbnEfjI2BOTUKvV6PV6OGfFgClNJQ+1CM7L3shxcA5nHTpN5fxaMZMSwzDpgbW2wDtFs9kiz3oYo8myHI8ny3poUpy3gc03GKMDW5ugjGynUUtRqcY6TZEVFIUYlqnAqNoiF2BYOMZHtjM+upU0rZPWEvLc4osoKZbzGJlVlET8gA+MJ+AK8l4PrAtO0xodtMdl/7KPsT++nBDptcexWQ+LBS2A2YcJHrzHWXCFSLNdYsO+hXtM2RBhJddckReQyM45G+8r6elVVIy7UpAkBpOm2HC/xzHHvw2+cgCbrWO84gPX6T/7eb59yYGzi/JgtJSTr307L98788ztgdSGc77Oqrtf/yzZveXYPrj2Dxla+Vd8aWT/6xi89VIAxj/xCABPPriF865YPGmZj132fhj4Ahc+M71xXbLiv/HyXdn0Fp6henFlyqlPLZkW8D7Uesd7h9jy0+s54zvRufui3aTnN3A/fEd+v33lO8pzNVuz1V+RtX18yvsHy9b210WbL2P9pQ8xtvkdBy1NHlv6Dr715JfZcO1A+d731v0WxS1bDmp9F1x+Be94057NAWMt3T4z9/BA6z6Wbt/ExqPIYX9ts3vEtr1ixQrOHRko/Q9moq57/C95+kPXlq8n2tfAvfex8dr/cEzHEE0b2OokraSNWkmvp1P4vAgSYHmAjcAyZqxGsGtLl2CITqsKLwAPeW20QSst4AIXEKcuzYOkv5ES9MnvAl6kedGig0zaWoe1Cm8tqnAUzgsIjYEyyle9nlqHB3SFSSpZM2iMAZOIlLPWSNEGsNKJqCygBLyZuqLRkixSj6c+YHAjGUVX+hGVSQQweEunPUFaS6k3asw98QS2vPQKyjpa8+bSzjJ8XxSOBKJWvZfKS5Yr0XCLYMgUWOxSrx0OtOBh6eeU71l87iFRoEPGrtIUDp577kW2vPoaCkW702XegnlirJXn+KLAUDF2eaeNT2tkzmESg1YJtVSRW0fhrJgreYezRZCzepzWYBKMTlEenGsL857W0MpQ2BANoxX1RoPBoUHyLMdZG9hMRdbplZx7ZOS9VxROQLH2SRmBpLzH2hy8onAFLvRX15oNrNektYReZ0ykz0aDEz7fKEhSyfnViSGp1cBoyahV0rdbbyYUNU1nvCgjerwPrLZzOF+QdSewXZGZR/bdeyuTDNrL/dQHShUepQyFzXFFRndiQqKovMeF3mK5IuQalSidiuGG6FZOkLsDVKy99X3Zxji8dcKiihwggGWFKgpI6rLeGEmtNYSMXusKlHJ4pXEYQgs6KjGhVzreZ3JtoYMh2z7UHLN15Ot7636Lxi3n7xdkxtiKhQsf4282f5dfVz+cdmxDf/0pN3AO8OdzOzz/wWV8+q83HOTIq7rs4TWMXDG9nrblq25jwzlf5+a7/90hb3cma+TWr7Bj2//kTSv/6/6Np4I/09D7Ps+13zidyx5ew9Lzfq980B689VK495Pw5umDsqGVfwVcezBDP+iaaF/DY4v/gQuZwV7b9XfBSbew8N2PwRTmTBjp6T0w37D2RyXQfb6xahbgzlAVt5wPH9y3LP5YqD05sb+4MmXlQbK1/XXR5svgUli7YgWP3v6p8v1+xnWqDDqaJm067fvMR1jG/y+Tj/OvzE3p/uFlNP7mF/YYN3T1mZ9g4aVzJ733aH0lPz3tr/YKavdlEnUodbSB2yNZt/yvr/DRa6dngLfxo2sZufUrPPngFtbs6T57dgnnX9tioj357Yn2NUzcBVy/9pgFt9PPsQ0P3hFUGiPSW69d2U832ZCmAljOVxJHeU/YI2EodWkqE9lAlKrycb0AgP4OQ4hRL2CtpdvtlTmbEchZ78myHNACNJwTAbIJ7LAtKjObyHJqTa1Wk9zaXg+lHLV6DZOAcSJJVmicE4kx1mOSyJaK/Nj2HHmRgXbMXzDESGEFZNgeiTKSweocab1GkeWM7RiVOBZrsVkhGEcrBloD1Oo1isLS63Yl9xYBKz7+X5Cv9tdUKXjsbbUuxMQEAyWCO7DDiityUVDkOTt3bKeW1uj1CvJOT3JLvcO5XOSzyKRFYXPpbU0TlJLjW6/VMbUaNi+wXrJ9USY4BjuU06HXMpgoBZOjoihwrgiu23KmtdG0Ox2KvCAxCfW0Fi4nYfbFekuur34G3jlXntdK5twn21aQ1hLanTbDcwaxMWc1MWgvGbhJotFGoQ2YRJGkBoUJEnmZ9MjzDK00zWadnsrICisTNTrk0DbrZO2EotuZdF9gLd56lPM4l2Mzh6rXSIzGK3EwJi+wvSz0x/Y5IQcAalX8vg1mY7vdHjK5ZKQ3vD87OU4Q6HCMnHfh2oCYKe2cRTsvWb61hrQHKIVJ5L5TxkjEkFN4ZSV/WGnQmnqjCTpB60SyisP5jFnFs3X01upndoQHzWW7SWQn1U/+jpWdBmy+jPW//hAXbb7soAw5Tr/md/mTx94D3MIZ37mI+1d9eNJD2vJVt/Hkg8Is9IPtxrwbWbP4ytJ86W3XvqvsSX3oXXfS3U+PbWPejdy9/Pd504kLDhjU/tFokzUf/DaPTZw5reXv3PIAQ8t/nw3PbWLZmZMfzpaxBF76Grev/INJAHbDOV+nuOV8/vxn02dpxjbewptueAdLxz/LA4O/ypvO+VUAbrj7R6xuTHs1ACy8dC5PH9hXjkytv4sHVvx0H+D/Ih68/Q/5pbf+7W6frFixgnvXbj/gTT6w4qeSaTxbh1y/Mjc90kOYkVp6x0ru/LuKAb36zE/wjs4B3nT7qZWdBmtv+GMevf1T5d/Fu5f/Phu61/NkX9vCb//KCcGdft9S4YWXzuVv/a/Bb8Ofv/ip3T6fv0dZ7+GTH++vLt6PqdTrURerL07bCOtw1s99t7VPA7xlL32NHy66n1/6L1dz86lzaf27uXx380doPHcj7zw9hAs9u4TFb13Ofe2r9rqe9GsfYeMH/vqYBLfTB7ZFjtKxQ1WhkIf/Is8EvAZmKTI2MZKmZHD6+lerqJag6A2utdb6YKY01VU59sNWTrjRAKe9a4I80Vjn0SQU1mNNNOApULoujJEVqa8xRiSSQdpbbiEA7263K2ApfGiMisQxKIv3CUp7HGJm5K3DyEc0B1Imih5FVwyUGs0WJyxu0mlbsl4X53fSawvrlXV6pGmNRBnmDAzS7XQY2boN8AwPDTE8Z7iUb3e7XUZGRiiyglq9TmE93onLc5Qdx8MVAYtMCFDKk6P1rseR1Dwm9ST1BKUTXGHptdvUEoMpHHneFYY6MdRrNcBQawyTJHVqtYacbySuZmiOxOo450M+qkPVpQ/YAi7rkfd6OJuhYuemBrQnSRLJeQ0TD6WkFkW70ymZ/1qa0GrWIDDxeV6UcuZJpmQauUZjL7ISgFyOLZpf5QVZp8NI3sPmViZZvMIbB96FSCBdyaWzDKUTiXFKNHKINYUrBLAlCooAMtEU1sp9YkRinngLaDya8bExbJHLcfLB69hbdD0aMnnyXhub5xXDqRTaxyzfEimWMuDYqdtfZf+uq1QUiiouKcqZdZSS9/fJh/tPMm9r4GWiyJTO2joA/cpkK/ZAe6VK9YBWCm+CSznirHy810cvPT7iRPbFFN73lup3AbUH39v0w0X30zjlflh/F/ec88f0GwhfOPAFLlwcetjOu4XHFl/K6tGr+bOz384Za++v5KMr/4Ln37MegGUv/f5eZVpRwvXA4NsZW3sLNxyEvE8A9rJpL//bv3ICb6u36S5az2frK/e4zI9v/2048z+Wr1d/c5CND6/hz1f+xR6X31vdsPZHsPIPqt9DndG9n+fXT49plIeiVQe03Zmqf1+zJVjdF3h8YDBOBOwuIZ5uLVz4GDddZ486tv6NVC+85Z+YAYPao6LuXv77fPnmgqV3rOTF7xyebazsNPgjZOJrzcAO1vABYBNr+uT7Pxj8FWDBPtdzyYr/xotfMxCA4kz0pi7ZfPFhi74BeGHTnXDakQW2R0PFGKD5f75nNnXZS1/jqueu4m8vXDCJWd902vfhNDDbN7Fu3TruvnS5nPd9SOUv2nwZ1/3aLv6U3ozvx+Gu6cf94NHaiERXxQfZfndWgiFRXyZrRK5AxZhVjFFk0ozR5VZgMnitSoUH8spgKPYA1kwdUHS7bRyOXq9A2gY1eZFjbIF1FucK6fcMgAlUmTGbpgnaGIqioN3t4hCHY6/ABiDhvcMVFpUIOIvRos57fO7xForcStascWS6Q9btsWtXRj1t4GxeSqm9daTNFGstxihaAy2y0RFAUW/Ucd7hnSdNU2r1OmmaYjNLYeX4G5PgArAShtNgtPSpxv5dOU7VOYjsZpoonC+w1qEs+Nxh84ykPki31yW3sq9Z1kUHB9ykUafT7bFzbByFk55graV/NE3xDrrdDnmvh3dW5OFao0wE3cJUe6UwiNO084jsXBph5doJ+b/90TDeQ571sC70dwfWuH+iIzooJ4kBRP6NqlQE0b1aK0VnYoKil1H0Qt+pUnhfkNbE9dj2MoHXRuN6jkxnmCQt2eDCSSZunBhRQK2W4gphxuuNOnm3hxKKE5NqtDY4EoqsJ5Jo51EBDdq8oFO0S3bVFjnWFkHOT7mP3vsQD0V1n7E7YVuy19aWsT/lcUDk0HluMSYcZ+VDX3FlShYl0mJkpUqlgHMWlMO4VCTSACFKiepbZd9wdQ9PNg87XuuORz7P6uM4TP35xip+8PZvQkdeH+pD0d+etYCz/+XDXHgRrL73U9xz6x+z7Kn3s3r0aj7Gsj4wtolV33kO2DMgjcZOzzfu4oyNu/fL3r7yHdwz98Oc8Z3niPE6h1JPPriF86+9T3qS9lGPBjD76F5ALUgG8H9+pHo4PeO5JXCAoHZ/NV2mcUP3er67j7Eeznq0vpKTv3gyN3R/VPYd377yHZOWEUA7/fO39I6V3Oev2i1y5aLNl/F/fmkHzO0c0BhvWPsjrnzPAX1ltvZQA637eOrd3+ed0+z7Ptprwykf4IF7/4Cl697Kiyt/pfz7ONP1vqeeZekze59Yu+CpJ9l42WRlyM2nLqJ9y/kkn3wCgKU3/B7/3f/KjI7rcBkqvV7rP5ZqxYoVfAkY+usfcfWZn5j02V+v/BU2Xrh32fbGBUs4/Zrf3WePdH+dfs3v8uj3HqiY3mOkDizux1WSRhQhw1JNkYSq0sSmX/II7NFESoE49iISSKU0WrsSuMaKkS55noe+XQBNVlh0LnEnE+PjOA89W4j81so6814Pow22yMV8R8kDfLPZotVsSV9vBOyuhysmcM5hNeBN2G+LtY6slzHQaAZJcTweHps7up2MvJeRqCZF5vAqJ6k3aDUSUpOUxyVKM5PEkBcFggEjC+0Y2b4DjydJU+r1moCtokBrYWfTNBUJrddYZ2OLpvRuKgErjUYDW9hwHEJfZ5hvyIsM6R+ug3JgLc1mk27WI6nV8IWl3mwyMDRMs9ni1Ve2MD66g+bgIAsWLCBNU7IiQ2nDrpGdTIx3cc6R9zqhHzZOQHhMzE81BpzDmBRfZqQKMDIm9GvHHGEVXbCrLFdno7uuJ89tCfyqCRMdTJBivFHYX61Dz7UcJ60UvW4vTNCIrFglYobmQ9STLRy1OsJUWi+TBVmcMNDYLMc2LKae0KjXMdpQoLCIrFcHc7AkTUEnWA+m1mCwNUR71xi9zgSenBIZW4nUcVb61W0Ar1Mndsr+9LJZvf/+nDwRVGbthl7bEhgHpYNcty6wwgG4OgVYkiJHBxbdhXtdKYP3lphU64OZl4BfcF6cu2MvcfzXunKp/f6NOR7quGBs198F7A4OQaS1/6lPZjfd7Np91aK3r4Pn5Xdh0ATAcsr6aY1pOnXiq9tF5nzmf+T2le9g0U/+jn9+tzQXrb73U5zR3XPW7NT6z2f+R940+A7WnPf38BT86SsfPuTYn0frK3lwbY0rZhjM9ldkc492E6Sd9/53nh//6iQH6sNR6097iO9+0sIBMra3r3wHjK/f/4Kztdd6Z28tz13/Ie64KTmuJgGvHP8sXHS9tGkcqXrzJrL0Pn5zozjsntGw3HzqIs79hY3885Eb1XFRL2y6k4HWkZciQ3BQXrGCqXBzon0NLNi3ZLy/Tl+yRtjwfdQ7i2mH5xw1Nf24Hy/sjfJQZGJsk3XaYg5ExYppLX2zJvSr1ut1arUaSZKU4FTIXQElESwYkxBjeKI0Msoh+8FwlYsrIDErCsbaHWxRMDY2TtHrkVtHN7NYBHR2JzrU0hpaJcKAeY93lonxMbZufZVXX32Z117bwquvvsLIyHZskeFd0cd2ObROSWs1vErodhR5L6EoUlxucE6TpjVazTqtgTp5IZm2WW7p5Tk6kXWU7BvS2+hxFL4gyzOKoqiyaAlyb+fJuxk2y9FIzqktXAlYdYztAfI8E2ffQJR3e70SIMW+Xq00SVpjzsJB0ibBjVhRFDlZJpJyk6Q0moMsOPE0GnMX0rOavCjKvmdrLb0sp8gdnYk2ea9H0etgsy4Qe3gVJk1Ik+AejSGpN1Ampd5okaQ10JHZM3hlBDAFE6TYE11m+gY0lyYJ2tRwCKDPbY7r+z9lNI5grhX6Qq2zfYyxx+EpbCFS5QCUizynKCc9AO/IsxxbFMHkq0mt2UKbFKU0eW7pdTNsz2IzR5F7fAGFtaEnOqMIINloTZo2UF7T6bRR2qCVXPM6MSitKOOm+vZZrjs/6ce5qrc6TpL0L1veqxHse+k3V776mRq15Z3cC95aXOiFtlmOt5Yiz+l1OnQ7YxRZh974GBMjI3RGR8jzDk450FYmp5SA6CLPycPxtNb13bfH3h/HN2xddL2Y5Oyhti7+3qTXMyFju2jzZZPjVw5pZdfvxvI931hFZ+j/MWl2+/yTL+HnvtvilXsnuOfaPz6gTZz46na++5a/4Ltv+YtDBrWxzrz9yzOynn3VDWt/JAD+KI6ymWhfw5+ur/Ovly7hXy9dwh+NNg9pfQ8M/sFe2Z5LVvy3Q1r3bB1YvbO3ln/98l/zfy9dd1xE/bze9XzXTKvfcfUzO2ik99NI72fLTx/nphdfm/T5Y4svPUwjPLx1sfriEd3+scAaD7Tu2+1876v217d804uvlfnHx1JNX4rso0xYpMOJUaSpZI92u5n03AWGzISoj0a9DkrYsqIoygdyFxgdFUBwXH8/A9cvNZ06jn4mOIJlQPJOEZbPWivxPyFCqN0ex1lx6iVk1pYP+VQ9tv0xRQphCp2T3zttT6/nsK5HWq+Tppp6LSVJFapmaA2mWOsZGxvFkaCsJh/L0Q4GmymNRp3xXJg67z1ZnuOcp1mvozwMDA0JEAsdvjpIi73z5YRBHFdhbUncFdbirBNJtweb58IcaoWzwaAhHHNXFIyNT5CmCYlO6HZ6OO+pJYYkSdFpinWaV19+CesyXFY5FefdjJ12BOcEVCdpwpz5c9k5ugubFxgVQGJg0JMkDUZRlXtvluUoXbHWBBbRhb5UAOvE3Vqk4ir0eGqU8+haIlm/SkNgflXcN2elZ1RV8mRnLbkTxjv28FZAS8k1Ha+pqD5wTnpctcIXYtikC48tCmye4fKCQkF3F4ypSjbtw36hNMakOGexWZd2u41RQWJtEvCOJMYm2bD9cN2Wx4V42iIzu28Qu8f7Ne7P7h+WcuNoLtb/PW/F5ArrRDJtpJFXaY1Oqh550ZLHPm4l5llK41R/VFfob1az1lHHVF10Pc+vX7VfJnMmGNsZrfV3cUN3MrP72HmPc43K4L1wwqbrOfHVL3HGsOW+Mz/Dr1+6hrO+2Zy2NOv596znT9f/kIsvmdlh/3jeBM839n+8Z6LO6IrD79QJAIDX3vt5zvq3wz6EfdbFl3yBR4M78m//ylpOnrfooJnbK9e9lR+s+OZu71+0+TL+z3edy2HTjM7WpHr3z/4Q3nw/dy//GjwiypbjQt3SV8te+hp3F9fBmw7P+rc9MsqpByqDf/Mm/ud//Jz8/m6ZTNAn7gLmzuTQjvs6WoyjDkfti7V9vmtoHIP36AEAWxWAKwy2UloDHpQjzzy9rGJkFFAUBXjP2NhYmWEJfSxteMg1SmG0xILEnFYVQEmMCep/iLd9ObX9LrgxH9b7vowpJfm2ERDkWVGyR7YPMCuk57NaMnQDhhd57kAlOOvpjY5RFJHxkv8xaUKj0WBgaJDhuV4YwMJgC4crumS9gjQ1tOqD1Os1er2aSEGVMKmSTaoEaCYmONdS9tGOj49T+CCTDn2kWml0AH0eKMqTRGWoFCTS3ucl6+2dkzieHZZ6vY6rwcTYOBpFnhXUW01qSYoD8l4X73KU9XilUF5TZBlZV45x2qgTYlFpDQ7SGRsHZcm7FuUdvvCoWg2ldWAkPV65wDInJNpgMwvO4pA+ZWPkWBTWBtdkue7kgPvQp5qjlAMnjHdAkgJiixyvTAlcfSG9nnlgT0ExMGcATYrNPbt2jeCKDK0U9UYLlxckWlNYMRvLez1ULxMpurXlfgABnHs8IpuOvaWS+ytma957Ep1ADWFu6zWUSTCpwWgY2bodVziMCjnQff2ocgkENhdCb3KY9OlXLkT2Vm7S8ntln3t5//bBytAvOxXUilJCo3QisvFUYWqtctInrlvu5SS0w2u89rjQl+2weG9weS4TXEhOblJvgK8zW8dQXXR9meW5tzqsoHb9XZVs9qLreZ49S0CjbBXYIzBc8vh2eO8QIGZV2x6R19eor8BmWHtOF56bnkPuPWfN52J99wHuyP5ron0Nj533ODz+3Iyve2+1J7D4/37uEVj9ug1hv/WPmTkkOfIZ3fu5dt06uHTlpPfv81fx7g9+iD//zoHJ22d7bA+ygsHRhlM+MHPqjKOsvnxzwc57zz/q5kqW3rGSZS99jY8e6YEcYm3bduH+fLEO77aP07po82Xcx56B7UFNphwFdQDmUVWJdNWitSPPIxC0KGXKJ+VJkT9TKy4DwbxGhcxTu9v3KulxZYIT/1V7e3CXN8Jm1JS3p45p98icaGDlnLDReW7DfgdXXyWGOsoriixjPMso8pzB4XkYk9JotJgYb5P34v5o6rWaRLno4LLsocgFdKatpOxdLvsoQ4mMG3SiKTo2SH19CUxKljn8WxTF5M+cw4ZD7lG4osDmBRJlq0tm2AYZbeqdyJ2zDOcLUpOG/l0fclN9YEId1kv/aaKTMrM29sTGnspGo067XYSJCg3GBPm0Rye1wOSKi7R3DiL7rmM2rMN5RWISTJJgnQDMeAFV8vTYO+rCBIoYVHl8cLb21Ot1lpx+Bu2JjF43x3nL+K4RbFGQZRlOOYaG55DUm5jEkNZqJMZIj+9UE6YopY6evx7QoLUhSeoiN9YajMjpB1oDzJ07l2YrYe7CYawteGTdd9nx6qvB2MyVpz26XFeyYYIDseoDoqpk4adWqX4IWdKTr3kf/z98V5W3SGnqlhi8lt53FY3IlCm/H/vfvXNlhI+XBmZqjQbOemxe4G2BVp60XiMJ6o3ZOoZqGn2th5WxnWYvaHREBmFN/uzs73DD2h9VjOTan5af33nvTay59mZCqPkB1/vvvoCnL595YHu01NjSdwAbj/Qwytpwztfh8UNzLj7p2gFp2+6rxnM3csbPzjik9c7WbL1etf60h/j6dZbVx4nZ1sHWUacQOg5qb+7Ix/I1N21gS3CE1YEV9E4eZEFiSLTWWO/FjCYySFA9eIf3CSzsJBMpecomPjQLg1tJlEtHW60pCmGx4nf3BJ4nyzP7XXF1CYqcc6Wns4qsnyrhQ7UOT+hpDRm5CqFbYy9wAHF5t8f2LbuYO7dBc3CYemsBr728CawlSTVKOYzWIrcNYMMWRRTD4hAJrjEaE6JmYp+itQ6DJjFpCfCTNIWQAVuOK/RtWuvwSs6L856k71iiVJUBHIya6q1BsizHugLthV0MBDpWFWhvcEqjtBgISWusZmBgIBwFkdb2shyUx8sBxRYF9brkz3oLA8NzqQ/NZ+eO19DakaQJynsMRuTHATCZWkP6Ya1FocErci9uwS6wr14L1y6gSyyqjQkMrxc1gfc+9LBGw27LK5texnlIaw0WnrCIufPns2t0lM7YGC7Pcc7SbA7hHdR0jXqjTqPZJG3WaDbqIncOoFGbyuQqzwvy3JLnBUUweSYAS6001ml27GrT7CRYZZg3f4hL330ZD33zm4zu3CGssAsMsPcoMSsur++ESskgExm+XM4phbLSyxwjeLzEyuIKWwLREgdHLKuCS3J534BTHu8LnEswIdwLlQrojWJxI6Zsck1F5lxk/kW3J87UmsDiBrm5c3u8V2frKK4pjO1N1/0L7y6GuXsfEQEzub2DqX5QG12Au5euIgLZ9z317G7fueCpJ4G9O0n21+uZ8fp8Y1WZ09rf8/zAip9y5bq3ljm+cPhMlo50vf/uC1i08q1ienWw9cz1/GZuOKMh18B1j6zlmu4vzNAIZ2u2pD50U8JfH8b1H++O+9Op+/xVbOL7XKy+yKk/+2X+61e3Tvr83F/YSPdMyTzfdNr0TZT2VTe9+BprD63V/5itY/Wamz6wjTjPx5diFqO8Cg/5pmQaI8AthZVTTG76o38qgGtKqXHsQe1fTv4FpXwAKpXJVFx/vwNz9X5cT2RwpTdTq2qHokVR3E3fxzr3rbBcPnJUFWEmD/BjO9sUWYZKmgwOL8CYlKIoSIyhKPJKktv/E9atEIZRlR22coxtUWBtgTI1ms0m4xMTFUin6jcWECfg05iUREmfc5F1y+MRzbi89ShjSJtNenmBNgatHLiCrN3BonDaCZBxCi8QUqS2SVJeCxNjY9giRNcoaclUWgyRQHJsx7sdlE7QOGyvg210SVMjslpjsElS9kEr50VerQQ4OedQzpcybIencOBQJLW6SKxVda1oA66IfbdyMuM14L0oDcZ2juKsx5iEbq3O4PAwi088iWL+Ql7evInC9miZeWRFTt7rorF4m9PtGCY0NBoNTJKiE029WSMJfeZpIpnHjUYNZyU2CKWxzpFlBXlR4L3B5zm9lzO2b93BiYsWMDR/Ibt27QTtUCaOV5djV0EWr4PcGhujeBTGi9Kh8F6QtBNXcqUIfeceFXJk+zQQpUTfe7nGyi7esB7vLC7PsRrpBRaUjgoTFm6K5L1kgPOc8W43nBddTi5EI7C99QPP1mwBh+x8vLdqbFoPb11evv6vX90KV88vX9/z3nGYBgl7+8p3cMu6/zLj/bWxvvXkl3m+cTtndO9naOknufPxB+Den/Dgw3/IY7c+Xi535VN/z2PnPc6VT/09AHc+eTbPn7eJjbd/jqXvWQnr7+JPli7hyzcXe9nS7rX0jpU8MPgHPLXuhzO9W4dUT19+Lb+w9l4+/dzFB7+SjRsYW/qVUo78yV+8inv74pVuX/mO/U4MRPb/eJ1AOBJ1rD407602PLeJjTf/B/gvf3ukh/K618KFj70uct2B1n0s3b6Jb957Lpc9/CEum7rAwwBCpvz4nN/l3NUbOX3Jmn2u84VNd/Lje5bu9v7Y0nfw678+zNrm8StDjjUdd+RjqQ4g7kdLj22U9yoN2oVYkxrepAJWAtPmEUllEYxxIq2oyvUFkOV9H4gNn09p/pMHfF0aEFU9hlOBr5oEgqU/s5Iu97O3/T2J0UG3fPwPgHPSg3j8KK6/770IxF1haY9bvMppj0+UcugkqZHnjryQ2J3oVqx1AB3WTsoQVWHbHocLMUM6sLbKBTASQLpSug8MO4h9nqZGkniKrEsZw4QmwiKQvtB6q4kyikazRt6Tc1c4h9dyzAwCKn3Ie1VG2FHvnDhQ5xn1Wg2jNV6ZEPWiQKciu1bCaucqp8i66M4ulElQLkwwaI31Iq9WWgloQmKfjNFg5Dj5wC4qXWCsI6mlsk0o3YyVA18LExxe/JFtkEf7iL68RSuLtTmdTkG3O0GvM0FrYJBmoylRSFpj0hpFr0On20b3qutqXEmUUlIzjGu5hmO2bu4KnJXtaGUEABuDdU4MrDwUOqWW1OiOF2zudDG6RqPWopdNgLJBDBAY9gg6fSWoV1pF42b53WuMl95ujwvKBuljR4nreP895r0HU7G/pWZdUfXlOoslQznQLkFpK1ywt/hwrpxzYnQW2WOlIE0kDzquJ2xXU7USzNYxVFOA5s13/zteafwQLq0Wuc9fxcD2+9i4YHqM5+GuP3n6Pdy+cjLte897x1lfPMRFm+Ux6Ld/5QT+0zSkyA+u/UM+dFPChlM+wBnfuYi3Xfsuhu/KDsu4QUyT+Nkt3PTef2H1N6+H8wI3fN7vceeDW8rlLjxPcnSf5OzyvTufPBvedScP/R2cd8WHuQcYuXX6235sAk6c2M6KFSuYaM+M0/NM1YsrUy74yRX84FsPHvQ6VqxYwd19vY9DSz9ZAv+R5V/h+TNlYuChd93JZQ+vKf8Fyf389O3/Rb73vheA4weMzdbM1tI7VrL+tOpvzRuhXrjvr/h//eJVbJs38bps74l72+W9ua9631PPwvU18v3w6CdT42R2V/Lw1LPkX4Rzb/9lfvw67dtMVGS138h1gMDWV2wm0UVYXH5chFbOYV3IsgwxM7ETMaxoNyYWJVEmsXcxPmBPlRpHTrVa3+Q+20nNqWU/YtyW9AiW/YGTenSDNrPEtX0AuG+5SWAYSrmu7wMGXmkxOSqKiE/oZT05Rs5ivcN5XzGNiGRXB0AQ3WrDwS331yPux/I6xMNYh3OWaGOkCDLtcFyslbgZYwxpWkdryVo1zpFog3JQS2vCxCmRtXrnSdOEZqNO3u3hgryYNEEryYl1rkB7B76gliiMtgIYvUd7hUcjRGsdYciFOXSZDcx0IsFOCpkICTE80swK3sdz5SYff+fQWoCkXHNyVgoXslYtcvwBE2XncflwbRZZHmS14RxqTV7k7Ni+DY+iVqthUumPzbodbN4Lx1ShteTdtjvjJIkp1QVRuu3CBIyw6Aa0jNOHaCFhssVxS3kwtRTwpMaQKxXuLTle8door2zvy+sMQo+v6OBRXqOMKzEqRkWlvEjDZaF4V5TrLB3J+zakQCK8vCNvFxS9nEZzAJXW5Crzcny10ZhU4rMSY/CpKydmojmb9DubwNraWcb2OKyjLQJhwykf4IH9LPOPmYHG/oHtQ++6k+sehzuB5xdfyUZOnpEx7qu6Sy5i9Td3N8lac97T+3zdX3c+WC1z55MCfs+7YvHMDfJ1ron2Nbzz5LX8gIMHtqeuzeG9ki96y//6Ctdt+Uu+cY5IFrn/4zwOcM6bYeQzfKP/X+AbD329XM89c7/EspeuZsNxxDTO1szVGd+5iPUMw3tndr2vPlJnw+lH1zX3wn1/xQlbLuHqh7/EP573Ib61bp3kqx5n9cTL3+Pb/2vrcbFvS7dv4tHbP8XyVbcB4Lf9T356yZVHeFQzX9OXItMPJglspDyIC9AwGKOxgM0I8sRKjtzP1E7NpC3ZI8TxV+m+zNc9gEoBnJTSyAq8RlA69b24DT95nXF9ERT2jTF+PplRrqCzB5Fhh21F0GG0odls0mm3yfIeRovJzuBAi7zIGZtol+uupNjyOkkF6FgrwFUMlCrwHFnnyBCLkZL0I2sd5bimZLN1kIgn9ZpIiLVGe09NGXwhJlHKeRwFOI9RCc4XeOdIgsxbm0TiW8I6jdISP+QtxkSAbiumORwf5524A4f3jNbkVhysG/U6Shlx+jVOJMSxB1t7rFOhhzTIcLWmsBad6JINjHJccSJO5LAYOeHW2pKtNEoLixqMnQo6gTH3GG1w1tKeGBcgjsbaAp3upJ4OghOTLOdzMAk6SHidtTibl9dKgQBGH1j0eDHowCgrVV3j3juKPA+gU+O8x2MDrBegGftR4/eqc49MHMR7S3TsYTkt/c/xeoznogTIlWJiUqurCtdweE8bg0pDdjTRXdqSFxMUzuFkhoWYpxsZ5SRJpE/cTu6lNdpgkgRjTGnANVvHT63sNGbOzGMGemzvOWs+i7ZIzEs0n1r9zA66+SpoWMmBXL+c9b/+/6HxN7/AiytTHv3EIzy/9FNlP2usfvC48faTGbn1Kwy0djHRvubQBnmYq3/c5e9b9g9ylzy+nacvv/ZwD++g6sfzJrjg8oq1Xb7qNi7c8si0vnvnk2fzwlv+CXgnIOzto7d/6oC235h3IwCrR2dB7UzUHY98/kgPYeYr/P3qv9Zmqn7wrQd550eWzeg6D6SWbJZWgO9/78OcsEV6Ma5++EvAlwC4cMsjfOt1Gkt35DOv05akfvzDpbB46/4XPMoqnrPGczeWfzefmHcjjXk38mSpAFrKuTzAC2fuLkN+YdOdXPf4X7Px4TV86KY9Q8W9/S2c6nz+5ZsLlt7we2y8/XN7Xdd0asM0g/kOYAu+Ahv48oEbJZmiuqJkiYDD94FXlCr7WieDOiX9eFoAnYvMVFzbVElyHxisKNaK4Ow3eZL1RAasYmpLZ2VK+EsgLSd9vieDqsmc0+Sx4sEry8T4WDCnkn2r1+s0GjV04cWEq0THHm0knzZN0zIGKUpvbVEIa4kP7KUtQWQEuZJva+j1pMe21mgAhsJ60nodrZrUG03pc1Uah8enIiMFj0GjVQ2MIstcAJLgrMKYFFOv08274CuZuAqacO+lnzr2GVu8sLqhpxIv8nW8w+UFDoUtcpGUGwcu+E+rio0HYVHlPPtK2qs9SZKgjaawBSawx965EGnr8ab/3IoZk0YmILyXOCCcML/gyG2BCnm4eOlJNVrTGd9FT43hnSGpKRr1YZKheXJ+vaO9cyfdXdvRXphYH6TCrrzUVcnIh8sPH49JQJE6SagPDKJQ2LxAYUUGHfpZ4xEpFbwqKU3OdJgIcXFCxnk0GqM8LoDcUsrvYy94EDZPumApJwh0yPWtNwZIao1wT+u+BT1170vgKrJkX/bQGmPQrsAj2czee5GGu0Kir5yct9k6hmoK0Lx95TsObw/mTPfYrr9LjJbGP8uLb/k2bL6MO//ul0k+OZ93F/IAevran2f5qtu488EtrHnq72HlrwLw2uLPwzcHy1UtveH3eGzmRnZEqh/kxopgF2DJ3PT1HtK0a6J9Dc2F/8DHLtNsvP1zfO/Wr/Pqib/B2659F0vvWMk9Z83njkc+X8qLY88wwJpX3so97/7jUo6+bt06Lg+MxXQrAto1szLkQ64Y+XM8ThA831jFo/z/ZnSd6097iO/eeumMZf66V4f3Gptz04uvldts/M0vBJAOP/7hX7PmvKc5+YufI4LZ/tp4++f45T96fQKF1lx7M8/d8KHXZVvHYg207uOX/+uzfHW0kmA35u3eQxzrxz9cym+fvJZH6ysnvX/2t+7lX8lL5dIe67yn2Xj753Z7e8MNvzfpvy0PvQseehLY17qmU785vcWmDWydc1T6UYI8s2KjQE0CoSC9sD7IHUVmXP0ugExTRbX4ktHqh5yx+mN+oqsyQXrbt1Qf2zq9/ZpCXgWX470sO4kxjmOcjGyd6zezkh7MuLxzVUxPZB6dE0BagR5xk7W2wHmHtaF/sijodDoVY+x9CVa1MaQBeCamBiictaA89WZT+mMDEIoMb5oYkbI6MVwCHyYnPLaw5EVBahRF4Sh8gU5FUlo4iy0K8BK70w/8PR6cRSPGUT7PSjk2gd1WGrQRpj8KkOMxl57RYEhmNFqXOBDtpR/ZeUAL2yt41geZsMaF8aSpuEc7b/FWAK7NCoqsW7KcPjCf8krG1hoYoDU0j87EBN32KFpBWhtG1Wpi/oRMOOjGAG5sNMykGJwChxc2GwHmSZqSZV3wck07ZwMwjv3dgDJhFTWJJFIGY2IPrS+peuc9DicGUuG7Pl4D4TaxhcXmWZD/JiRpUrpT+yB9Di/kGosGUgpMotFUcu1up4cwzn33VzDxisw8OjL6mkTJpIxTdWpNXTHOVONHKfQbBNgeNw9tU4Dma4s/z6//+u6uyIcawbD+tIc4y34ATuHQGNv1d3HHS0vY8Nynyjzboaffw5Xd63n1Bcf6Sx/iqyMPsfyp29h24i6+vXkdly+XJ7wo213D37Px9s9x5Q2/t9vqn3xwC+df2zqEAR591c/uPpoc3YqKJY9v57Hll8Ktl7LsKYDtjNz6FR6bgGVPwT1z31/2FceeYYDHznuc4pYt8Ic/BuCXTztBYoT2UXc88nnGNt4CSJzUhoHj4H6ercNfF13P6d/5eXjTzK1y27YLWT3+1Rlb37xPXMXSe/87544M7PbZ2VmNJx/cwjc5F8jhhwKI1uwFwPTXD771IO98+/kzNs7ZOrhat24dly//EPT5Muyv/jEzUJ/83gtv+afy/O+torfD1HroyWlv+rDUgUmRPSE+JeamggumRA4vslbnSJIUZwuM1qR9LK3zPgAuJgEi52Qd0gerw8P5ZGC7W7+tj4BaV7LIUqI8leVl0ueTsj3DQ3/sM1RKwBVTthel0v3y6erzfolzJSGND/5ai2GUdUGyGsbrnRNX2yDYzbOcmGcrywsLWvXcinGPNobEJPQ3R6b1EPMT9hnl0ATW1INVDqM8eCsQyYVeSA8YQzNNyXoF5BalpE83z12Zr+qduEd7J3nDKjDGEg8jAEmr6MArnylUYEvD8dFK+nDxOK1QLrL4LoxFgwerPQaHiwG8Crk2AvurxTa5lGJbjzgDT71e0XgdzptTZFmGRsy1fDTSCj3iMTbIFgVKJ4hLd4YyDSwJNuvhY5yO82KO5QkAT0CtgD6NqdVIWwPk4xqX98RMzQSJslM4b0kDcMyyDFAkugHKiLs4IZ4o7Ec1BdBXLowj3ksux6teYLcNpKlIwCVwtwK24VrW4X5QRqNTMeKSGwWsD7m9PkjVi5Cz66LLcdX/a60V9l7J5agRR2ZVrm9qH/xsHTM1hbHdk3nUTNRrP1nBWQMHaVi0/i7596LrWXbmEj566W9Ad5A/ejLkMzz+lzx2xWIU0Pib7SxfJX1S9idP8DvddxO9cR9bfCkgDwJL9wBqQR7u/nTdq4fNFflI1p1Pni2gvX2kR3J4qjvyGS7aLPLj03/286iFe6GsQt0z9/1suO7rrL73U8D08pRna7YANi1fAMycydwT97a54IMztjq+t+63KG5p88TxerO/wUtaLT5etk8cTA207uOJHx6718e0ga1WAqpi16yAFmFtMTpEiAQZrQ4BMYrSPAbvUNaHvlE1KZqnHzRWLO7k2lsGZv+ysee2T4UJOHwJACuGa581pQdXBwOmMkN0L2PpN52qfqBWq1XL9P0rfaUEt2dPu9MNTJ0qeyUbaR1dN4R2U7pZD7TC6FSAYN/5SMJmFJDqFK0jM6zRyob1Sv+kK8TsSZmUequJz3OKAKi9CtEsoWlWAbbISgauhK/xWMY+zcCuxkmPOLZycsH50Ncr7r1eC7h0XtyXnXMBPHusAu0UTimBoL4oj4sJ8uskSUKUjDhHE1j/yeckynBFZeCVOC+DTDp4H7urHRPjO+l1e9TrAwLknSNJU3S9get18bkDZzEmwWiDzXpifoXHKy391F4YbdvrUdgCZx1irSYGSt5pvHKAEaO0sL+lRD0IAeKprc7ulNIyYRInYPqvywgsdbi2ZP4nbMy6yjTKV+y8VlH+rUAbiQlSWoyovEabMFERt+d8OclVgd4Qz4SM34VJkGgC9kap44KtPcA6VNZ22hWA7Bnd+yWrlrvY+MWTeWjLL3PPFR9m3oqr4Ibf22Mf6ablFZhRC3+JTQvl9ycf3MKa857mwvMO//CP1lpz3tO8uPbnePryIz2SPddA6z6uWPoRrnvwLw/ZCOvFlSm8uv/llj31fu7ccj78XfVe8skn9rjsHY98/g153x9Mxf6740bZMqVWj17N+rf/hxlxRl5/2kPkD/81fHDltL9zz1nzWd3XRgFA45CHMlvHeQ207jvq/SOmW9MGtl6pwMjEPMoQT+MJBji6zKr0JXD1kx5qhbHTJQCEyX2vU6W+/azoVMa2b2QlUyqsr8g9q9ZeVYIs76v1VpE/lL2KEZjFtfWPMUkSiqKgKIoSgO8J5EaZdBKYt8SYkGNb4Ao7ad2RnfVFgVXBPTjou8s+yT4k7FE4K2DFpDqgn7BPJu6zLwGRAnCF7FsYj3WgQp+kSoxE3OSSlespyG0PW4hzsEmk50qhMGXPpwuRPGGcUDKH/RMCKkiuA4YsAZpPJK9YUaCMjF0TmGgvzL9BmE2cxyuPDftotEBEbIzFCedYaxJlBLdZByGOprCFeDlFhI6csygGiLJkAYdibGWtJUkUSns0Cbt2bEMlOymcDoy0F5m1zZCcVhDzMQc2RFGh8HkRZNlxmMEaSvKaKPKMiR07pEfXeYr2hIDopAXaCEOrKqa1VL6HU6xjE7mvrtnIkBbWUvepnLn+9oF4nVPdG+BQIbvW9t1r4ZcwKSWShipzOrQVeAHIWiuUFnMyFZhoF+/paLgVJjNm6xiqKVLk59+znu/qL3M3K1+X7bH+rtIACuCBwT/ghq58PrT0k1z51PU8dt7jcB4cDCaNBhr7chjeWx1PDwGxYj/d8V4T7Wto8g/TWnY3EP3U+/e43HWPn1/FM/XVPe8dB8TA7I1c95wludHRNOqjl/4GAMtmJwT2Wf/jf+ziT+9YOa1lI6At/u5spopDv/xwwfeC0mb1Nwd3+3xfNR0ZMsDHLns/3ZGBwx6Nc+e9N/G+w7qFN149+eAWfmVuWk5s2p88Aexbhnw01/SBLfH5Vno+C+tLuaRXiCNy4cFBYQuUCyCuD/jFh2JjTAkMSzA8SWa8O4CdCn6rUQlTW70dwRWT/q3gRfUT31YRjESGcQ/btnZyXMmexlsC4fJ3X+5rKfvtq7L31nqUgTSpBVnv7uBfjK0EoOk+aakn4oto7FUOKuSMWmE2A8jwEYMazeDgMFk3I896Eh9jLa4owDlMkkDI2w3i4Un76gJ6NiVNq4JhFuWkQdmH7Kv9VUIjI2BH1ozXWB1dlQPQBJyujplk0kovsAvr8SpKu8N5DN+t12syljy4bSvByUlSw9usmmwJTLNcV6EPFo93ViYk6g3a7TbWTqCVOH770NNbGkX1nWsB+r6cvNBqag84wfhJvueyHLSS3mCvAstZhFggJgFZqAzQZHO7T/hILnTIl0UAfyBQ5VgrUVPQB3ATXZOx+L67IvTHVq0CIV4qTFQ5DxID6idJ+3WQH0vsTzwkYZImjGe2jt064zsX8a+XrtprVM7BsLYXqy/y3XMsFz6zh+117+f2wXeUr69c91aubMA91/4xy5760iGZOUWW9mBqoHUf7tVhzn783qPWRfh4q4n2NVz7jaUw7+DXsf60h9i27UJWdr7CV7b9T9TCX5qx8Z13xWIeY3cmWXqBgS2P7NMR9Ms3F5KZPE3Xz2O1IqDtf70sgN1ZgLt7XX7eh4B9t2nsC9DGeuhdd7Lmm0+XEy3TremCWhADqW+c83XGlr7jsEbjiMR2/zm2s3Vg9aXnbuVcovP1sQtq4QCBrVKCEIrc4qzDFgkmTdCJwXrK/sAoWZR+v8my3ghmrbXhQbxCe/2S5KkM7j5GJVtU/SB2d4Z3qkxYl9uI+xZHHhnbyYC8fx3h3Qq4lfpRP5m1VIQMVWHljNYMDg2HxXRpLmV0gtIao30JuPrHDcKYGzdZlOoDmNKIK27w6JUeS3yQIhthpWMMi9coY2g0m3Q6PYpuhncWNCWjDIhzcOjPREVDr77j6MHjgqdQ36RAPOZxsqAEkAI6TZqQpDXyrCj7MqWf04TjZwLj7Ilu25EQjlMS3kHhXXlN6pCxHF23pW9VtmtMQuw7NfUaRScT4KYIIDBOrFQAtNvpYLQh01nFOAZTszgBoqgcn0UhEKJ6vEeV0mfZjkh15bgV8fgBhCgoF3JyrbUlQ+p9PIb9113fZ/hJ90WMedJK5Nm2sCHrtpok6bui+34P5k5UE0+RAVbh3Gpd5SWrqFQIzsfxXIlBVTiWuOBm7eOQd7uuZ+vYrF94bRUs2fOD1sFIkUtjlOBgTF/czu0r38GV459l2Utf47rH/5I7Q0LteU8dmhT1UEAtiDnHn550Io++5eg2W5qtqpavuo1Tf2Z59eXvcd+Zn+EH31rK8lUzB2z3V48tvhRuvZTr9mLoMnLrYvnsN1+3IR019dFLf4M7Hvn8cStNPpSa94mrYApju+ylr5UTBPsDtP1155Nnk7x3z1L6qRX/Pk4X1MZ631PP8g1gHRwXua8Alz28hqGVf8WLR3ogh7ka827k7G+lNJcv6IsDOvQ60GtpTx4X/Q7L06kDi/vxAlaKQoye0lqTenNY8ivzHKWV9Eh6j7dBjhoe7KveR0+SGEAMevodleNyEdROzZudymJOZmfVfp+bxUm46i+M46EPRGgIvaOTJdJxfNW6IiCqllWqYisF4IB1nl6WYwtLWqtRbzTFe0hX63feST9khSpLJlLpvkzSkq1VgQXuY+2oAFO1b5KTal04d0EGnqQp3YmOmFUpMQcCYaV1moJL+g6NryjG8rgHwIOZNAHQ/3kFvgAVz5sjLxy9rIirx1v6GEqPUi5g0iCzjkxs2P/wBj5KewnRM85Lb6hSpdFSdEwWMtKXfcoiRw4THMGsrJyjUIjzs7PlGGW/+ty9lQLvBFAHgy6lJcfZBzZVlAgyqaGVK2XX+Fz6UcNBsm5yRrIDOcelvF6FY+/RYTC2j1ntnwCKPccGiVyyYZ5Axl39O1UQ4eMmwv57X1lVaV9dT0oJA1zJoqsJDRmvIcrndWzwdVA2DKtZIHCs14ZTPrAHXurga861/6F8cLty3Vu58j3Xw7NLuOe941z5zU08tvjD3DP3w3DF9Ne58fbPwa2X7vb+oUiPY8UMR046+HXM1sHV8lW3HdID1z9mBrXwl2iO/RLLV83gwA6g9tUffKi9w8dyRXB7PNSGUz7Ao4/UZ8QZeeTWr8Czj0x677on/5IiRKYciKQY4JIV/w32Yo4X60BY2j3Vxy57P90lF/FjDq8k+fWsF97yTxiOPcfn+N+66QLDr47mB+SmPHU7e7puNj58YOva0zrWlNfs9PrWDwjYBiEsSZKQ1lKUMXR7vT5zXnmKjo6/Sokc0xgx+gF57tW6Yrj6gePU3NhS3sieGdj4/r5qMhDuk4jua317ANYlm9a33iRJKYpiEqidtE2tQWkK61HaBIAp368wcthnJRhASFJdMpRiduQCaepJamk5geC8R+nIdPvATopJkApMp/MqsI3yvneWXqdLYcVMSiuNToWB1dpQrzfLMfW6HbJupwRW8VjE/XN9oGcqYorAzUUmVImUWwfmzxjZj0RpsrwHODEnc/KvKqG8rCeCfOUN1lv53BGkscKq2iJOXMTrScYlDKqvsmR9uTfl2KutqSBt9/QvMpWRjtel1gaUoVZrUms1UEaTZzlFnoPWmCShyPMSTNu8Q9bpTEaX/Uy/c6CD4VkEnFAC5uqNihX2QTpskmTSdaxKFYFIkZXSJXtaAnRFMOfq378wUeN9KWWOkwPlBEdkrrWKgyvVDzLueB1KtFI1RTBbx3qtP+2hGTFGuenF1/j7ENfCeR+WNyeAxSLhfOwgnvMfCtED5015/1BZ2lgzsY7ZOrj69zULb2DwN1vHTp3+s0OP/Fnb7PLEl3bwJAfGVu2r9ub4HutQQS0IMHnoXWezdfH3Zpy1PXdkgAvOe/KAwdIbrZavuo0LQ/sDCDA8UNZzujUT18z+Kq5/6er/OK3lpw1sVQBMDk+ipR+wcF7MiYLk1iIRIUktQSda+ifLIFMmgdeYQ4tSONvH4PVtM2bd7smgaU8GVLuNeQ+S3v7v9y1Y9kzqKZ+XIG6K2y5K47xC6VRYOG9LZjV+P0lSjE4kd5UIBBXWVzmfCnBK4SXxpwShSoFRodfR+fLYKCPHs7BiYoUVQ6u0nqJ1Uu6fC2yrczaAEluBT6UxaSLAVqsgk61ATWTiVK6DVLYQ9i0eD60DSJp8HKPcGKSXVCldgkkfwCdW1mWMHJOiKLCuQDmN9h7ndbgqLaXK24uk2nsBpcJuB9TnNZGjFSY0nDdAsnPjeRTQq7URp16opNJ9cnatDd717S8qGKSJo3J5DTkn0m8t0m6rPJn1JMpjvcQgOedIQk+xDtJz61K8D/m2siY5joByHpvnOOvCeHSI4ZEe4RKshmsijIRKumyqAxaMrgK9HRj9MBmi+u4DN3kCR8ho3wf+K1AflwmHlrhQ7PfWffdR7An3BDf1iuadrWO4zvjORaxnGN47+f2DkSH/6JtjnHrmP4eonZmpqazX6+V4PNC676C/e7yZUB2u+vG8CZ68d/cIijcy0zlbR18te+lrPPA7J8AhTv4dDgOfvYGbg5Ue760ue3gNO/7oozOyrljr1q3Dnfch5h1mEHW8VP+53Hj757gMmdiYSYD7eoDaWPublOmvaQPb2AcIUZ5ayUO9lwfvQAdJxqp20vupJptElaUqiW0pD+43HwqgyQYDo6myZOdc6dI6FdTuj8XtX4+HSnqMRLeUPZAEwG7tpO/Fz1X4Xr+cun8ZHUBPJLu8d1gbJL8IQxvBZmmsBBgdWW0VuFuZSKiifSMzqXAOkrSOTgxprSbOzXmOi5pRGyKZwrpcGnJSfQBtLpy7/rFP6eQFhTIp2ugyakcZgwoAHCV5qEmS9oEm+R9vHRNju7BFXn6QmASNopdnUBQBTNmKzS68XD8hF9c5YaYreW48cdGoSQWQHsC8r8CeUjESKRx3rYTZlVMn2/MRSJZK2j75d5XKK0ZUqnS8dt6jnANvMYkc58jga61RXvrRfWgSdt5h86JUMyRJgrVWcoERRrrZbFL4CDqlF93FPlxVORP7CHQDC1tNTMT99tXoVQXiK2LdVQegr2QSopqgKatkiuV3FahZFS9uKlbZx+XKo8duUv7juY7nXrHn37OeZ8449Ic2gL9pfJcTnryE8w5AZjzdej0ArVr4Swy0zmeifQ32J0/wzuLgzNEeTW6ke+ZnZnh0B179+3M0lv3JE6w5b/djfOeD1e+zIHe2jodaf9pDfPNL53LZw2KSNNOAJNZMA9r++vEPl7J187oZYW3XrVvHJ3/xKhqbHmHjDIztQEst/CXWrfvyMd83HAEuHBhQ3Nf6jsaaNrA14ak/PigDJWuHEmmskTAWyv/0hIfg/tzMUm481Q05PMhP7WuV7VRmN6UZUXx/CqjdN9itpMD9y1jXx2JRPdBrrUnTtAQ0cczaGBoDLbLc4nrBfCmCpLCsNgZdq2G9wma2ksAqOSgOyVHVRgeaWCShJVgJYw0JqCgvESoY2T9jDDrR+BiLkzt6WRdLkOY6F+JyAC2gzek+V+TA4vlSFh7Ytz4mNo7DaE1jcE4w3AogShGMnYKRkPMYo+n1eqX0Fu/xRcgxBfAKbz2dXWPotI03BiXwOJgfCRNsbY4rrEiSw3Xn8Og48RE42hKPK6p9FREsfZAXvLDieAHJAhTj5EP/hIQKP7KeCNB8CW5lU6beQGtN0e1hncWR49q76HXGKIGwiyOMV1Ul35XXmlq9QS/rldcGhEgtY/AOtFGQiJzb+opV996KNN15nC9K52eJ2tHBsCziTZmE8gQjt3BeImhH6XKcct2L0kIRWPFJEmUmseiVfD4A3L7j3S+ZVr7cvdk6luqi6+E7U95bfxcv6IWgpgdsL1Zf5KLNl+3G6N704mv80cYfcd6qD83QYKVez0zax+//OOeuPvTHrB//cCnnv/3IxwdduOURfnDi63DgDrLM28/nznvbu8nBJ73esrtUPAKCWdA7W8dKnfqzXwa2svSG32Pj7Z9j4+2fK/sMDwXgTgWyh1PSO1OmS+vWreN3uu+msWn9EQNSF255hG8dkS0fvjpaQSnsDrrvfPJsHnpy+l7YB9Bj2/dMG2WlZc9dJUktmdm+B+L+3lnnXPngXcp8p0Tp7KlKaXCQJzNVTty3vknGUMTx9bG0u32vWqZ/XXF99XqdvCgowgJpkmCSlFQZDIpep8roVFp6kKXfURi8uD6tBQ0qY9BKgC0E+WY0kNKA8zHuFKM1HiQuKMhphRXzNJtNsszSbXdQroposa6AwmFMlaJaTg5MIT0hMOWBeRMQ5QJbWARQ58m67fJzYu/mJIkq5J0JlNJkeVZuYHe5uAAz6zwOCy5MUiQJtXqdVCeowoo8XVthbXXwfA5gSgUlAEFeq8PEhHUBdYf+WufDsVQQ44rk0hSX4iRJ0CapDM+Uk1Zk1w/kVMlgx3NsbYH3fdewF8l3/wXUP0ESryWRMwdG3Dt63Q4yV6FLzJcXBWhxXbbOhxxcHyYx4r2Uinl0nIBwEsODE1a6X2Eg7tsig1a+yqX1cewBhcZ7U/epFVSYxOojZcvLJyrBy/wiggKh/9abcktPR0lxPNRxw9ZOzZVFInjWf6uSIq8/7SFe2HQn17z4lT3KkV99pM59Z17FJr5fvvfCfX/FH238EctX3TZjQ309AW2sCy6/gm7gD37wrQd552V7zjjdX60572kee/VSBk68jx+3Jnjiq2cfkV7eO588m/PPqV6/s7eWf8wMj9//cd55wx8fceANB9fjXH5ny56NVGYB75GrO/pybFcfL383Z6C2PTK623v9/ZL9NR2geziZ2X3VqWf+M6z9ORZeOrd87857bwqRPdOrE7iEsYd/94gwtf31yV+86rgyxDpaaybUCQcAbCM1Vv1eSg7D064oLoVZNH3fnNqzWsaPhIfpfpZ2t/7XuMX4PYL5U1hvZGjjMpPBbWBpiREtlYvrnsZVPXcHzjkAs7RWC7EwsrsmrQEmfKbJez1iXEyaJLQGhzBpKu7H6EpurFUZieRjdike5QJ4CqwwgEk0RmmKPMM6h9Zp6NGUOB+DojvexQamu1//qUGY3ciUKzkKmkC1xn32BEfkKH0NLs1W4l1skQtr7D0EsOoDqO6HKPF8FnmIfPKuDzwZ2ZwWbtYY6f30zqGVZPzW601Mo06eZXQmJsIxkFgfry04h3NgtAkMthgbRfSqokw4gPDIOivvy31XPmQt55Qy5LSektYbOCcssbU5WisKG9JovQfiRE0Ac1oH+XEwDPPBMi2oGSJbiyLEDylhNEO/sY9qg7BcCfZRaO/w1oop1tS+VGtLxllpXTokiwpbzrUx1Ric95JJ7D3OFjgn+6O1KZGp0vGas31KiwrolkC0lHzLxVSyvSDnIZ7r/jFR8rdEB/E+l7nZOkbr+fesZ7G5unx96s9+ma/e+xDr/6+H+L7/tUnLXqy+yFe/9RDnrp68jt/pvptNM8TUzoTT8cGWSO3E9OpPLzvxkNZ14ZZHuPPBsznvisWsOe/JmRjeAde5v7CRiXYlid72yCgXnvnPXHjZ+3lxbc7Tlx+RYZUlPYeHnoW927WyB5YXZgHwbB2ZWn/aQ3x19Ny9fj4VnO6Nye2/zo8UOxe3O7a2eu99wLGWQ3s4DbFmus4dGaCx5chItg+1ZkpyP33zqFJj2AdugjQyGuzE59oqu9aKVDYCTjU5lzP23O2JSZ3Kvva7Gu9JajwVOMdyroKyewLM/d/vZ5YrJaUKpkwCRxrNJgNDw+SZRRmFc1ayPa2AIOddBWiE0grgQ95zzomTsfcob4kgomTBlEYbWS7Lu1hnSdIUAiA2GpRX2LyQmBs5oHjnsX0GTzFCp8ISAgI9cfIgAntXAm9RMAuA8lYYSOmRroyxolQa78oJDRWQYtxHnSQhA1UHqasHo0mMIdFGQDfxR8YaDcRqtUTY4uB27HFlpq5zTuJwdDi+QTTglTCLGi1sc1QTQDmREU99dLcu90OJ5FfpNFwvkZGefG1M6j2eNPnSPzlCn4qBcHDCGL0tJeBECbC35blQ2vRNrCCZsP3TB8HQSclJCm7Fvu86UxQE0Gs0RhvSVI6l+/+39/6xul1neeDzvmt/3znXNoH8KqGEkKgpwUxTYYFUfoTENVBsQKpRJCZKVYQynXGKI0QKQ4mmqlAVKQl0xkMUo3iYRlGmyTConThqpnFo6rFNSFopYGhoL+HHJLgEzI/EENv3nPN9e73v/PH+WGufc699rn3tc++56wnX9/z4vr3XXnt9l/2s53mft9aFZdw+h4paZ5dZ2/oHAPGgq7g2I79x/49+boAg8f59it3uRnDXwdXiRT7NNbaH8Wt/9EB+/ZY//NMjqu23/J1b8Fp6K972DJz7UiUdP1XYuZ8eoT1yvAuQrGcDT1Rj+6G/2OLrT2BMJ4nDa2vU8l56XC3/Tj6TCPJ4uDhkJAdfWtx03+2498Y7cc8995z395cT4b2cbcYXwqWsIz82sU31iILiRdiO1yRSe2yNh3+2xB9XepIBIUKhzpd4DPSkthFCI8KhJuVZ/GeNWIjM5yG20T5G/f+OpvkuCHETNbt+pNar9Muf+3xACXvn/hK7Z3ZBxKbO1uoEBqhVMJVWo0ogVFWYgKpO6LSRRTQeparYHGwAWPseKiusd3cAAAebrRNOwHrdso9Tk9z27CNCrsJ+au1/XNnUIDPwhF3NjQY7hIVETasVRMwQy04EQ/Fsd9vJDbMlJmfNpRFbMpk9U65D+g4ldP/cHjRqh9FSs0thS9qtTuRErJ5ZnHT5bgrB26U6wW3Etm1qNJW0V+zF6oEx2zGq252l3ZtUbru1G+p8rOvlxkyveOaLAPI1EDsB+Vr7vYVnhb3Zz573FqmK5td+QexjiOOI1Gyx1GqxJ2itrjj7+dTXoSvNrYc0wFStBa2IuQyI0Watra0emuPrdwRkMW9XC07Lw9oHb/4d4O5PPenrHr3+m8/b/udX9HX4OnwQb7z/buDv/Q+XZEy9Qvts2o6vBnzbw/fjDz/5N/CHt74f99xzD37js+/Aj7/sePbqX3ng7+On/tZrL8k43v4f/zW+49X/xyU51qXEhWp573zwFYPoXsV45ec/fNJDGHiWcNN9t583dOl/vvf/wT24vMjtlYZLVUcOXEwqMg6rNaG2Ap1Eg+QU/uNUzDxUqT/E4aTksBZHI5xSij2Ye32iHOznA7QeJhS92pXH68cWvzTdjZmS4GbPzRhXWqTFSYyRyGm9gzNf9uX4wp/9KWrdYt5O4GkN4slZkJHuea7gSayGViN1uI1HvAayqbRWbysiONhusSoFhdnaKZERxmlaY7utmL29krriTaGQKZpKi0buFknW3d/Wf9SnxIlbKW4bJrMJQwhUJrCfw+oqCenBhXEXI+ySfXszqAsWxhTjIiaot38i6wfkrWU0CWLf6zdcAmWasNrdRZlWONjfR91uvN8robid2QKqartOMiuzKeNWo6xVwCWWvKLOM3S7tXH6z6ZpsuO48rsQXzkSloNf9ou9kTeiloptddQ+J6WAJ1O0N+f2UX0DQ70tUawF3zbK8CZvZuRz3y3y+HwpEPWwzu2TZFcP1VJVsy9TtNryTSk/sm082NwUDYu0lwtoXfYshim0rX2Xf36KD0zaRgt5cBUp0mZ/2nESiu0rP/9hfHr/R57wNWfveCeuf90fHf+gN/8Y7rj1m5fn+cxP4ePf/kK86r/eZJa5d9+Lf/Cyf4x34/wtf/7DL38EXwYAePrE9qQV2qsBL37Zb+GhP3kNXvjwq3H7Rdir/84NP4jdh+6/JGP46a/6Suwf3I1f3bn1khzvmUYo7cO6fPoRJPZNr/kh3PbR6wAAb3zw5xavuem+24FLxHXvfPAVuN0DpAYuD5zvXnwvgH8LHIvcfuMj1+Lt//FfH3ndPfc8/QTpbIn0tI5ycrhUfXcvKjwq1KjlV0iC1FSuQw/8gJGYjoj2KmlvI949cwbEbOqiq0jbuVpv0UZZU+1tZKCpRq02MMhRl35Lpj4uLcvNRqtQzNsNtpsDmEgoqHMFc8FqdwciQJ3NJtrIIxbXNs8zaMNY71rqb86FKkBqdZZEKE4m61xxsL8PELBeryFVsa1WN1smhsyC7bwPVHGFzlVnV9iSwGqoz73YtyReQTIDoXZ2vB9wMsrMqYj39ywuBdCu5ImAsBbD032JIUF8yXOG0O5V3qcYEyGTjWPdTNMKZ67ZRVXF/uPnULdbxKaKVIJqRSkrlKkYAdTaLO7+HxHJ+mEuK7TUaYHRRl2q1p2anHPimy1GQnuNPX7Xqa9OVKNuOVoVmQLPtllQKmbaImzvxIydnV2sd3ZQlZ04G8G07rQaK7Wd1kk7kffaXejTXi/t88wUraMaKRffOGlz7mSWJt9QaddYfL3VqIkX8bUBUPQc3tochjLff8b6/552nBSpfdJ+sO94DT5xEcf9yj/5wpGf/fC//Vr8wdfcCdBN+PM//zZ86+tvxv/+4C8BuPXIa9/yh3+K//EiznchnGQd7dWIb3v4/iNq+O03fOaCZPNXHvj7+OmvujSkNvAvPvRn+KYfPvmk6IvBhWp3h6J7frzr/vddEYFRr/z8h/FLb5tx/Zt/NEns/Engzgu8/t4b78Sf/csHgCf553jgdOF7f/P38cjrfwa/esdP4tHrlxvCP/W3Xos73/sWfOvrfwa7D9+PFz78asifPCd//5JPfgHYxQVtzsCTE+ZIj37xFVpf2+PpqrfHr7FVau1d7CdGgGAWV3u4JlBaMK1NzeIYWBLAptAiyR8BmLdz/l5FvOcro3Br+5O9ZONr8nY43fcAMiCnqcWUJDscn6DOEq1mITayWzzd2EhQmdao8xaTAJUnWFDWDEVF65dqvW/L5HSEJGsUVa0+NFqyEMEUyM0WZTWBpxVUJHvFhsy73R6gznPWJRsRQ5K1vGaBhTSpX6OreezXb98KGJzJvwqr21UNs7bJfuT3GqnCHSLEQdDtG3u/GomLGmu7Dx2BRbsvLv8CqZzDw60UWgjTaoUzZ86AiLC3t4fN/j60VhTnm+rXJGmlrSAmlLIGUeuJy6tiCrla+raIq7HS2iKpzFajPFfUrc1tb+lWEAovQ0tyi8VV1T5FOW37qpZmHYFg8zZVbekdBwQoGJUI+9utOQi6TRz7r/ghvbNxKKhE6YZY9tVx5TdCqEKddRJMxF4Xq3mqsP5Lu7huDTFoYmulFPN26LNM1fruVqmY0dLA25pZrqHTimdTsT37prvxxhvvxCdO4MH5Gx+5Fv/iIw8DFzj1+/W1OPP6f4NPfuAnn/I5hkp7+eDXNwXXPvco2fwH+98J4Lcu6bluv+Ez+MO7/0YGVl17zfshL3gO8PClJdDPBs6n6A6ie3ngrpc/74K/e/XN78G9N96JN+K7ce+NwL0PHv+4L3z41fj419x73hKNgdOL5/7j1+J7AVx/OCH/oY/je3/z94HffC3Owuuh72u/fhTATz9BX9lPvOg1qP/5/wUA/Lv/+mf58yC7SWpf9lunRt0/XDtuFvDjfZ6OTWzZijHtGdiKNZO0Zd2euOYZSukh++Lhr4OopSVSFZvNphHn/j3+UMz+9ZGHZGrkI9UiuAaVL21KVneKTGaOr7MXLTOmaWU2aB/ndrs1iyqZ7VhqPWKxLIVTUU5igClJHxdGlYrtZmPzsJpA09SRjQg5MlWsJUjXnCez8JLzdK9LTYKiCAuziil53Km3WR8NVwZRm9VWjSAdDu1Cp95l+i2T/ZgJImQ1rkwZWEVMIAmS62MLXf+QpdbGrqBCWO/uYvfMLqDA/v4+iAjr9Q44Xrfg2UYSCTa3Vmds1uRI/FV4/ShpzkHk/BpBLgALeFJvlUM93/M17pnBIp0zwY7UiGG0BXJtlOx6w76tBFeGI5grCKi3daoCkHiCdBdYRa19UXz+ch0ccgPY17Y2o7WQEWJbh0qaGwj5+ewcD9GWaGE7V8006qjjztr6bo0okddYex3yIYH2aqmzfaZJbTyMxYPXs/mQ/K2v/xl88q6fxKde9ih+/VufjxtueRF+2R/4DodHPfQ1v4Kve+CDT+k8J9G+Z+CJEcnNwC8sfv6dN1xaUht48ct+Cx/6pTjXuRNLi75UOFyjez4l4tv+7jc8iyO6cvHKz394YQfucdf3PIZ33f8+vOk1PwTAlOEeob4CwPyvLqwG3XvjNz2tMf7J/Tv4+GsGub0a8VTI5RO957lov/tpt+n+3a9YAXdvAQDf/ddfiBfPp4fUng9n73gnrr/tHx7rtaTHfNp8zpc/v/ERJqx2drJOM3p52pEkA21CGQtSkIw3CBnQHqozvKaRD/tFPB+3usDexhwklogg2avUXhuq2KKOF42w2AQgLZVxbhUjqxG+AyJsD7Z4zvNfiFkVm8e+ZAphKW5zFWw2B5lyfM0112JarTNICbBWNXA1tM5WC8ps9aG8WoGVsD8fALOTaBjRJJBbUuE1ux70o20uYl5672ybIwuMyvpQt6VCGnFKt3TcQw513ecsFbwgUs3uzOxZwa6AJqmFzV1Q/nYIdeLmCrGTPY7iUBBWq3W7H07AZGu24VCdswY0N1IIFKSTCMqE1WoFq9udva0R5fH642u3sQEN23IL2aIIwEIbD/rvvVeuAEBsTtiEus3b3+91v021Ro6X4fW4hbPPc8xjJmpDUYhztybXbRf6tZg3V8Q5kqnVPwHxN4Jod58IDat8EFH7Y0FlHYlmXrzP1pJmWyxEqJvvvzAXiAr+4s/+GKcRd33uZ/PrvjfjpcSxLcfPMn75wV/Cu19z63lrbL/ugQ+CXvC3wV/5JZx9/ksAWBugP/3PF7ZVDZV24KrFMR/crjT0/z4Gnsq/kz2hvVQJqs8kvv4H/3v8PfrXF/2+T330UXzoL4y03HTfldUaZ2DgmcL1v308k/Wxm8KRq12Lh+Z86PW//XlevH5PnOykIprKUFhBBXWeUecZc53d5uzMCgzVFiS1GLS3Ism6WTsqLHOWoWp9SCPZt9kyg6D4NSHUOGptUkpBWa0wrXesX60TWxAwTRPmeYZUSZUaEGsX0yRJV3yRc8NktaDzvME8z1lTKmpEbPfMGYCt1la2tSNegqqWchuthcT7rvTXvqx7pfyfEdGaamhYVVWTd7lFNWyqTmKpS7IOlbezPWcSbyigaum5RsJDsTT1lGBiLynSFk5iZKzW6nXDNlZSBargYH8P+/t72BwcYLs9wHa7wTxvbK3U2VOSFVVnV7ErQJ5orD3ZJJTJgrjSdev3G66cxz2fpgnTNKFMK5TVGtN6B9N6BztnrsFqZxdlteN/1uBpDS72p5RdlJX9WZ+5pr1+WmNa7WA17WCadlDK2v6s1uBitvNSVlitdjBNa5T1GlQmFC6IVkjslvgQP8mDuTJZmkyBVRfaUy33r2ODQ9vS7NaWb+iEJTxeQfE54bTwh73a9z5Mme3cD+TugKhfDzs0dX1r1dfIwMXjlZ//sFmOP/lzlx2pBcKK+sToa4eeSMEYpHZgYOB8OPumu/Hp/R/B/PZvuiJILQC84pff+7TeP/4tHBi4eFxEeFRnj1TqlB1yi2THkVPxMqXGH4ERPTv7NNlGeJvCCOAICeVDNY59OE2quNpsl03pauqvKcvcSVuhliGPBTjBQ+PuSoqysp+IW4RbCxR/yKcgdT4+IrCTYguUsvZGZZqw3c7eUkacCKsRLCWz0RKaSkv+s9rUSCoMBmdIkl/cwt0L9eCg6PkaF+aKn12vq5NJeG3DwrhzqxUV1by7UdsaVta4aK2mTWbtZ8w3osWQ1wAj3uNEV009Ntt0qKHq9lft7oFfmacmK2ub+nhNbrjY11It9IunggLCXMVrwClfw7Bx9LZej6ay5F9ms+FW8bWvwezbtagHN1GQOw+MQqwHt0bbQltaeFW97tbvrd+rIK/ilv9Yj+opx7G5ASU/t8YntK0FbfexVVC3D5WFaEWfYGrp0t3GzPlAi6NYmySfjHZvO+U+jnS1pCI/Xdz18ufh1Te/J79/44134pF3vAg3nOCYnggPfevz8bn9iv/zmK//3H7Brx/62bAeDwwMnA+v/PyH8cZP/hxw43dfVJ3rM4kgnGH9vFAPzv/rs+8A8NandI6b7rt99KIdGHgKuIh2P5p/M4w0MZlUpFCQMqxNDOWDddhbBUiiq6ni9A/cC0q2ILH5NR8Wl01pNNUQZgHtHM/+VRKG9M525wFF8BWcELRfuxaatYa0s9M5OY3MEDfyzVxQdU77Mvk1R7pzKGjzPJtSpt5jtDC2+xYOlRZYJ2+mgnaEgNyOTEDVILrR0ia0bVqS1FCOo3ZXZjA45Tvxa2rT0s4ZQUEcv3Mbs6muhOqTLQwUsCnLCB3fba5O5pqirW5F9m8Ji/fYhHIKiY1wtt6wzASeivHcqpC5Js/u37M92ACVnNgWlGkFcuLWW9RjA4BAXSpyqLxRN2vrTSA5fkKzsacx19VK5ikVbau9LYgS5qx3hZ0n5GSnxBZq5X2KzUpcfOqMOLbtGgtoI79vqdinQhupyDmsPG/8Pkg4YG19bF6azdjm024GdVZzU2t900LzbCnYmmOiWd1jDgeO4nDNLAA88o4WPnElcL1f35QjNbYXwq/u3Iozm38zetIODAw8Ie56+fPwxn/1cyc9jAVuv+EzR2oZz97xTtyECxPcgYGBZw8X2e7HQJ0iBCQVag/NUbvo9Yv+Jn+35jushrT9riUZH06gpVQr+zEAy5Y/xqOXilyT8JqS2MbRXQuaqkyhxjrbCpJ1sL8HSM0k53xYD8LhpEVFsN1uwaVAmf3XFl5EVLBercBcoFpRa8X+9lyGVvXkza4p45aMSFQF2CKJFtZjQqYLHdHZnIyzk3+CZs4Q9XOE6FvaCDPgKm07kNdlGsnWakFICtuEWCRTo7022XGQup7cdmnXmfictbDRrsa+rW5XJ7XrLkHsWLEUsCkVT9nOEFKAOQnoco0gFXvmtlmjFsdtVmmOoLO2Ns2xEMotnJhq1hpHUnaopkq+YdF2TlLRzLLcCI6CpzartapimD1Y2cml2iZIBJ2FO4G5KcLMLZwNvn5yIVH7QRBfAtw2339munWUG1aeHB2Ketbad3OjuVxSKx6KbcPZN92dD0ER/vTIO77viiCxF4Ov/d2/iYdecOhnL7kdL//3R1vKDAwMDATuevnzML/96QU4XWqcj9T2iDYlg9wODJwcLqLdj+bfFBZOxH/MChlEsPrDfahLC/NiJK6Sqa0AgVzhBAWJbfm9CFLbpbFGmE43gLQ0L/rTJiGLR+teMVrW58Yo4zv23xMDqEYi9s99CUCk7boaV609z0TABqaBilQLQKIp7bVcClbrlRGkzYzN/sYUP6hbdWO4YU2ltPDa9XVzLYRsLhTXqv3GQNiiXYEMtstNzQvWEYdUVQi5xTtU0rB6q4201dTGuaz2FwIjjkSgmrOLbDWjSLLc5ESFes0lkyVa11pt3ThJD13UbrOkHVpgqdGsRtA1dh6o35wwq3W/7qCCmqSPU10Hoxun10zHCmc/nveiZXfpmoNacvwEQJpf21e8ZnyzzRuB8l639Scx9qpQnW3eTT8GwKgCqNa0/hPM8k3FgqpIff6zwLYn29Tul2+U2PQfasPUfzJS7Y8tpe6+xT2wXSQXmxWgRvo9d8vWHwCq6kr1UGwBI7X33ngnHnnRi3DDLSc9mmcW/+6/+2tHkpH/5b/8En76q86c0IgGBgYud7zy8x++7JTagYGBKwPHJ7ZBcpI4LtXTUDhTwVr6W7EoQAVaf1n0qiSlHTWfl7vzHoY/onfjOaJV+jH899SROf+ldkfqj2x1paZecfYHbSpclQqpRqyC9KeqHAROgWkqRjQJmOcZc60oRK01TTenabsO5bebZSVkKyDEfPX9d4FD96ddH/l7zbIcFaSaPVg5VcOuztjvR1icAW/nY7KiHb+aLVecZRcn8FSKW1Sl1YOGgteL8X794tnBzr2NUHn7GyIyEsvtfvQsLJ0BAIgKmDjbIuUC6P3O/t5sM6VqgU1ESdIJQZCjP6z/gYKKb+oooMpetuwEDkZAs75b7YXqVmcoPBAKi/tmrbMIRGY/VhCKAAqO7ktWb5xhTx4I1RFFszTb4mZfF9Sdpz/f4c/SkdZO1EoPFhtHHWzvwVcJL16aX7fWQPFnENtXfv7DeOOz3KLnpPG1v/s3gd1/f9LDGBgYuAKQNbWXIe588BVP2klzqLUDAyeLYxNb1dYuxay6rrQG4ROB1tlrFAWsTT1sIFCZXK3qSRx50nGerb2XLGCIegKHRmiSVHf22nhRe07vSC8t+I1BOuKDoFhBH9VqSSV62drvZK5eT2yEFVAQGb3ZbmbsnmGsdnZRa8V2nlOtXHGBiGIW75fK0cYISYbViZlpc+xkJSXJ4FiNMMUlyNKGbYFaoWp6TSQ1tTB07KjDRFiMJwZF6Jcy6lxB1d/sPLEXkI1PmcYpEDtfFbNfR31s3G9p99a4GbVaXI174FsdVuRr9lgQpN+cIAHYWxkFWTTp2NeLQMj6r6ayiC4GvFsrVcTqndkVUEIq3dK9NzZbqiqmTA1uGwRRY8wUfZSDPMPU4yCM2tYhAF+3iqyeVQVxsTZIHbHsPwEiALTZjFFNhe5D1RabEN371Xc7mo0/rg5dfbi2ns7q6rm2FloA0vZs/J/B5P+cTN4PGmL9k33irvZU5Fd+/sP4pbfNeOQdVw+pBUy1/Vr8tfz+u7/mhcB8ggMaGBi4LHH2TXfjjZ4zcLni3hvvxIVa8Fz/5h+9bAKuBgauVhy/3Y//Ny2U5A/5teLg4ADnzu3h4GDf+rui1b3GwzYzo3h7FXgNq7sZnSx2f3qVrFMQ849zvKMKUFiebXzUnbsfd9/GxJJvg7lo/r4fd/yJcwSprmJEXmGta8LOul6vsbOzC/IU5N3dXZw5cw12d3atLjV6tvp1h7UbIlCpHi6lbRzZu3R5N9CRDOD8iliohwTCVEojXdoUcbM9A72VV7VirtaGKU7XGWztdTHVTEky4y5JkNoYkrfYSctwEi37mailIMemxeIa8n45GaRurLEhgOVmBxGhgMDOj0nNlqvxP5PafZyCWmffoIC3fJqA0rWv8aJRcrVdSCHk38POUZhd/fXzEWdboVKK1463tRnXkfPr08OlYLWzxvrMDqadFVDavNlmDx9p90TnmQMF7L1MQGFQ6e7d4n/tZ3Ot2G63Zgtf1Md39y+XIC3OLd7CKd6reU/jHFcnXvn5D2cP2kfecfE9DU8LvoN+EYAruAMDAwM9fv8lGZ53ueP6N//osV73ra//GbxfX3tRx/7419ybPWwHBgYuHsdWbKuYImlkdgYODoCDAyd2TgbL5ASQFtbD+NqIH7sipkl8SMktqp2tEUCyLWBJeDryEujDiozIMsgThAkeXKMWeCTo0lxF2qN9JN4QeyowpdUUHIFOQBXrj0ooYCJr3+NjKMWI7MHmIOs5oR7QowQRlyqc3GV9pgQp9JpNMpIkXstLvU0ZQYybdZj8PE0O1HZfvGaXiIB568E/yOOxE8s4rIoi4pLJ5zvcvEHk1BXyUPcCDK8/lqglZVNlnbxnuBQIVaoRQW7W2fhd2s+1Ea+w2MYRWHJC8ppNOa1pc49roFxKndys7Vy+4KBKmGf1HreuQ0t123ebtCDVVY075j3w+6dQUBBaH8W8nVuter9+Y0jR/oiBWSJF3O59iaV0aJ4Q9wt2kd5YCdaGyw3FKfHn3o0r5pZkLbGJAm8BhRZMFmONDQ/7jPpcUzgDfE4kF3xuSOUaddX2asIrP/9hAMAbP/lzuOGWF+ETJzyek8aH/qd78by3vs7rbv8Tvn0+9r7qwMDAKcObXvNDwO99EYAHRf2rK8fCexxLMgB88gM/iZ9948VtZv7BQ3fipvvWT21gAwMDF1Njm8k4psTMM0opmErpUlHJEm6lkSR3k3ZprW7FdNumvY9b2x0lgARRG0nkfC3TXTWfj43HaCqyAKxOMtgaPGHXlTZiQEVQRaxmURvhCaIRD+FMlqCLDHKKFFpFWa0wFbPoWhIwoDNAkExMllqNTHoqsohA1IlLkAxnKHXBNKMHbqiL7XectmXyOtJOx1bAUqLzjtk4VFBrRz7CVuqbC5yEoxFJvy1xt5fKYtznaHUaNwLhVPYNA7LgKlYBlQJkWxgnY2T/iftgh2lW6n4sgN0nFUluKqLQqpkiHBsYbVAxDF+bTKCpoPoGQtRK21KUhSpviqOiTK72O8FNpb7W7nyefBwbOT5us5ATRGAthpyQltLuYfZwXtTB2qaCUu254WLzIE3L/pkgf1GmWBs1Tdtw32optxUkljblhgWij3DH/OMz1mYU4ZhHu5u5S2CvEl+j8M0gjRAyXDXo68Supnraw7j+Cw/hBS/4BH5FX4fnvfV/y5/vv+x/AX73J05wZAMDA5cLbvvodbgytNqBgYHLHRe9ZU5EmKYJ0zRhtVphKgWF2Wtkm4qXPEitfYx0D+/2B+hfSRpKn3bKD7JWMJTD5HGuChLMTpz9Vd1arFCz9PofEcG2VsydTbI7SZ6DiVCIwVzA0wQCsN0cYH9vz8hOmbCaVkbGXXUuXPIw81zx6KOPYrvdgrz9TRVP+3WqSq4CRy1n69XaKax+jRlE1M1d2J5t46BjcgvLbldv29tNmwiOxneCWbbNgpyTjjcvbM/9AXIenXACuR6kCup29ntKi/eE9Tbua79u7ISU1l/mAtcibXPAyfOilU0/OHf9CoCymrB7zbVYr3ewXq+bEqxx/zjvff6tgG6ttlhrm/MIncrNlFzL8A0NaVbcan+ktjWdmzn+tVZBrQLx18LfJ9sZqNVq16NPb/yJPsfdmo/1G+Q5Casr2eLjV9+gMfu12Ybzc6f9PPYT2uYqSHbU3LZ/Qvp1GA4A9Z7M0Wrq6lHoQqW9mkktAMifPAcv/t3vzu9f8l+/A9d/4SG84pffe3KDGhgYuGxw9k13X5GBS+ezI/etgG6/4TNP6bi7n/1HT3lMAwMDF9XHtiM1ZJZecOg1BEC6BN3uAbmrZzUbqabtMqQfSbXQFdS0h0Y7kWzRaoShIwlEAq2dCpi/aVbc4mStUhewk1cVqqeB2YKTQMBmu8X24AAqFWWa8tiiRkSCQTmVczs2g0pBWa0grvzBxxEKWBBv2yQgt7/O+TqF9V8NJXk5oY1gNkVvSRjy91JdKdckcqTG9jK19/xMt81Oinfcn6Cz+rbpo7g2tTVRUOzeOsO0Q/hsaaybpqyGug5qAWWmeivKtIJU28iQWiGzeJIwPLypG3f0olKAXBXe3+xDlaAyWysnW11AsWP0a8LUbkUVQZ03Zmvnbm6IoHPKzDn+VFZFoSQZqqXduNR3CiScBPBgNEXWCxMrGF2vZE9KjlppbTsQHVltmyNS65H10K/3RZq2xtV2vaKpNAYeGy5dWUDexVCL+2t0xOaM/xrJg68S3HTf7XjklqujnvbBjzyM22/4DD7+3+zhV/R1R37/Lz70Z7jmx58PADjz6L/Bt/zu/cDLnu1RDgwMXE541/3vw21f/f0nPYxLjgiWOnsf8ONv/lF8+5l93Lq3e6z3vl9fi2+ffwJnn8HxDQycdlwEsQV6K2IopOZc9AdrEbc3chIfIrT6WYVbRzvQoYf/lLKWZ2aK3qwMniicxmm1NOujppPS2p849XTFMXlY52W2x3XNoCkiQhXBvN2ibjdgIkxndjGVCefOnTMCSmjkmrqgqUIo0wTmglkqGFG3qEkGQ8UVqSilZPpumSa/xlB1Kccb6mh/rYArup0CeqT1j18jOxmnJTdJOyy1Nx2518G4NNOI6eg8xrHQ7MZWt8kgY9GZIA23vCpp3ncBvPbUNzMWt78nYPBa6RaOpIi1CDBZ2x6wb6B04xNX92OBaBBEgSdGN4u2ynIqCGq9jGEBURobMO6ft5628L66Tc2MoC2gbeQQtx6whQtICRP72g0hOjZ0fNzi7oTFmLrNjSPKO7BYF4v7TEFj/b35vt6e7Rsa2jaW+lZQy+MhN2tygyP+XYh/L3D14fo3/+iprql98CMPAzBV4ttusJ/9yf07wKuXr/vkB34SP37T9+E/fOEanH3+S57lUQ4MDFzOiPZnpxVn73gn7nndfwu85tZjvX73s/9oofoODAxcPC4iFdnqDImKKUhqbW6kVkBNoVmtd7BarS1Vtpg9WeFEQcySrG7t1GqkwkpIxSiA+g+k2SrNdhrJwWohr05LTFxiNPbsoVEUkp0RrBmKGkRHJP+kY9WVVjBhO8842DuHebvBtFrhzDXXYuIV5rDTakcgyNvnuPWzEJsaqIrirEuUkP1IC0NhNlDA5s/EOguL8upPP77NSdSP9v1I++TbIO+hnMWfUDxFCTbVBKE8YpdC7AMNlijUvo5jhkUX3o/XyagSMlzLeBA18uWbBgKrgZ5WK0yrFQpP2Q6oakVF9XHYfymV12p/FFAhbDdbzPMWojOqWjscW2fFNyWKq4JqbxOCxt9i69fIrYJVoayQDCZmaPULkk5e9HutAGYVVBXMavXZNiFt/lu3IjIFmRhgtppztp/ZfHX3pSpmseOKCGSuqNst6nZG3c6YN1vMm63Zk3svMtCt8/ZjcxqY40CD+HdrxTafvB+uqCvFvdrbPiMUv0+b8lG9VXzcsZ4pPn/TBJ4m8Gplf9Zr8GoCry5yH+0Kxml+OAmF9jhWu9t/+G0AgG985NpnelgDAwNXCN51//sAAL/0tiu379el/jfe1Nqrp1xnYOCZwkU9aYbgZ/Vz4jWo5AmrJnVJ9xBs5Cla8HQq0OHjdg/eYbVMXdfV4UUtqSvBACAugTEoabqcx/S4VDYlfxaKq4Kw3W6xPdiAuWC9WoOYsNluTFWVmgyiKcCWegsYsY/2KOxtYpQY0duWUFBnJ3DaWvmoKrjE/Li9uXP4EuVXR+ar/z7mqv8++Wmn9DZ+YsFaHGQw5j2V+KbOG3FlrJhRCcBcjfRQkGQ/IgEFFkykU7Ea6Kqo2jZAlMhbGwUxpxwzs93LJMqp1mqOPeZkQe67+yuiSTKNr+viPlOmNTe37WKuEGTW/sdkLaoKRS6xz+8hpTQcAzYGDzoTU2Et7EtTIQZCebavxTcOtN80cbT3htU5DA3aBtzdvwz78mM1RdbA3Xzntfafv1RmWz2t34Ij6APXJK6HqHMu+L8P1K/vqwP33ngnbjjpQVxi9Crt+fAffvkjeN6rf2Dxsz+//y/w4pcBv/ZHD+D6534TBM95xsc5MDBwZeBKae/zdPAP9r8T736S17xfX4vf/qVfwF+9QH/cgYGB4+PYxFYp6YLVkXJ7AEY1+25QrYVVlt3bCX/edlsyOsWQNNJ8o5+qEZ6izdacpIeC1JoaOxFBuQuzIbMpt2CfjiimdbTZWBtxMFK+e+aaDLkJe3W0wgGsHYqogJhBpJB560TGDZ5EiJlgGOEmgqmTnkJMTorY1T3SqEvljljkNDnr0hQScThhOm+SLsiGKd6NFhOT9afxY+XmQMjqQNaG2nu8H2yZME0rIzB1tvMSFiFftgGR02S2dCKglGbzZaBwcXLVricuhYiAEoMNuy3lhkIkE8d9TvszmjoJqCmmfr1B7NqcKioI4onKYjfHNOMquakS7xGymu6o5y1lQvFrynP6emXAa6O734mAS0lrfpUgipFG3Ai2tV0CEOuBumuneE1b/22jCfnZjHvuH8Q2r4fQbO2Mdpo2TxprR73VVC5E2FpXWq7VbkOmbiu0Kghz+xwFI79KcPsNn8EncDqCo0KhDcvxhfDjN30ffo9+cVFn+wd//T/hxTObEvGbDz7DIx0YGLgS8KbX/NAVm4R8+w2fuSi19sUv+y28X/85/h6dP3Ph7jP7+LX3/gJuGqR2YOCS4Ni+h+qKU5DReHAXJ3/oH4qbH9VUmuaVBWCP5OSqL1EX6ORkpRBhQqQd23urqltpW1eShWrpEppol3hMEQpkD/iZytrxMVV4CxjCemW9wzabDQ4ODnBwcIDtduutepCkODrOVpFMPA5lromQThycMEWNZ6tRdRtrKJHxJ8cVCnenasY1Ex0hCYva27gXbmlmainFxXurGq+kuBtplQW5qGg3BgJgO8/Y29/D/sEB5rki7clsNcNcOEO3wtIdNaVhF+bo6VoIvCqgqZii7T2Hm4uaEHbtMDNHGFTUfoaSGwo5XDEMshltbqzV0QzRCtGKuW5Ra8Uc9yys035scXIvcW5Gv9qhfrxavdUS25xVqdZCyjc4cj278lx9fNFqCPlxWBLJKha1BUYeG95DOeYn6nlzA6H7Obqfpdq9OM/y87mwH/v1qQoEAuuJqxBqNbdpg/dj9one1K1/JtvMEpkhUq01lcqRUKvTjNNiRQ5Se1z8wUPLR1V6wd++1EMaGBgYeNYR5RcX+2/72TveecGkYyO15wapHRi4hDh+H1tXB/v04l7dApDptC04irv3WwBRJtK6YomwGYd8BaBQC5iyAKJwcLqOuFCKGtSlqlAE7UHdfxf2aPSqr411miYQmRU5WqDkuDv1j8GoUlJBDTWQEMpXSMP2M1MA/eqjHRKR1UJiaa2lUC/zeH4NMDtsBP5Yiu5Ra+jh4Cig3R+ArEWRW5/n7Yze6p29fBE9VrtNA4KRz7iHThjhSnjw0biOUFSZGbXWtL6GVVsFEJ3d/t0zeaQF2nrAOhFTLGqbM43Zr3GRop1KdBD85bELF2RqMRREzcrMXVJwzk3MS4wr101sOoi1IqLJ2un4/WbvVVuYwUStvVRIrDbitBXHGRVk46BD9zI+D/HZInttqsbLpeD3k7r5OqraZs3twn6cZ23v8c2RTLD2vYaWAt3GqdHrC7FAm2IetvurBVd6eNST2Y4vhN+463o876329XfQL+KjH/nGJ1V6BwYGri68+ub34M7L3IYc//YFkT1731M/1vN+9l343B2vxkt327Pl+/W1Q6kdGHgGcGxiy43n9M+tqZQCnWKp8UAeknD/+G4HCDtlkNCezIUgKWL9NxtR9jrC7oE8zht/Rw/SGGCvXImI5wPF+ew9dZ6t/6hUP86SKLITFK0ChjphmzqV1uqICeTtdeKdMYYgJOr9a5v12mymLWHW3tbOTe5djhJP1Wb5PurtlPYem908z3Y75yuiLZONy+qAg3i0BOemJKpWQLqEXwAIJc9fREQgcQJMcV3ArHbeguLJysbgo6+xqezxj32wpBideiJwT1a9njVVxxiA+QnydbEuXdKM07ES2BLDfOzqqnF3HyPBGGQ9YdmCoMgV6Fg71RXeurG2UKEUVxEwxTGD5EvuIOQmRW5mSE5srG9gqcIjLM/dPehpYp81rsv/HCGUsb7t70ZwAwy4M92INg595nK/QELphW9yUBsUNVJ7WDEeuPzxVHsw/vhN34f93/sCfuO5j+Oj7/3Gp3ycgYGBgZPAYUJ7qbD70MfxuZe8Cr/x3Mfxq3e8Fd9500+MmtqBgWcAx1dsDz1UU6ewcqSzRp1tPNQSLNU43qRhkaTOthuk1V5SxXJ7w5qcHt1OgVood0BTwoJwdw/T/eN0cEn2ukIRwXbeAmkVPnLRdlgRzKpJ2tjts9nbtBtC9J/Nn2vUhvp0aTfm7t0tVsdVQVGUafIQHnvPPM/YznNe9+EQqZjEtF+7aryou6VGgvqIpmXacjfEYC39HMZXHdPNdjrduKJGudaa9dasnCokALPc+sZGrdXmXMjVVCOimRTt7xFPzU7+27sJnBya0k+Z2hzuASLGNBVMK1OwS5nynsa8KbnFPuqOFdY3VzUTgm39KKq4UithgQ7Cqnn9QGw2UCrQTU1vt66nq+dzJVB3O/rfHXrroXf0BBP+Oer3TsxSvFRUY614KFST9NvdD7m5U2z7kDgriW8138ChtTpw2eFibccXwu5DH8e3PAR8y1BqBwYGriA8FavxcXH2jnfi+jcD3/IQ8OW/+fs4+5uno1xlYOByw0X33yDA6ipBaATJIIfJb1oiJQkuEwfDAxAPxNkSNIlxlcPkVTslsJ1b4Q/QHanKGktXYDMlmQkTFUAF263VSlL/cJ52U86+uaotvRZEoMLwIOSF5TUVUITWiCRimvNCrVcpd2pWN2fGfCLgyq5hnrtaxrhSoqP2TgpCtBD3FmqbVKvzTOttVSeI2ohg7ifYzJFq2szbzW6KY3xtymtnFVeA1JKIVdWkwKJgbldtx+e2eZGbHgyg+o+7tRY3jMlrgUNRZ6CYYm41vRPKVKyumggAt80RbrZxUkC2FbMo5s0GWiukWopz9XRjqRV1ni0NWxU0FVz3ZV/mvYcnqAgONhtIDYIIJ4sE9l7FYcc1Bbd09mRNkhn3zALNuq2OToVu5LCzMl9IDI2NpEMbIIfp8uFEZK2mgIOqH5ugJFlGEMR64USAb88ooKSoub/DMZCFe2Lg8sFxw6EGBgYGTiueSVIbOC3ZCwMDlzOecmPJIFQZyISe3LlCw2FE7jKqFgJjEJpG6lLZ6l8ahBZL5bgPqQKWwVFhRVUxFXCaJgvDqdXCoKQuVLFQjwtbgm2tVkvK1BTF3g67vJhQ4OxnRIT12vr5gqw37rydmxKa1k/ybKBDx3ViKR5I1A0w1bd+7Pk+jdrR7CrTzaSpjexdgGPWekjfbog6VY8O/Z1jdPZCyxf09bmsDJRpeV/Q/Z4ZUqP2OK6RzfrOAKhY2xhmlFUxtR2WrqxEgJNHJra2ORpW7wigEmhVJ6cVMm9hIVDVa37FaqHJ78khxT1IZli6c3OFrfexANa/mKxXcRLabq0wM+bZN1LY+jtT8dpxEaBKtw65qc/nQawHoqX5+HybGU1UX6ru6G5ZfF7juKqaaz5fVxXEZuEHAJJmQc+6duamTi/GW32TYml3Hjh5DEI7MDBwteOZsh4PDAycDC7aipzqjqteZlVs2u2SeHXvB/IBNx6nk8dlpOuyLUgqoN1DevZP7XrRAqYuRhueDDgCQMXay1SpZst1JZLAKIUtqTbtxeRkMqtH004J6pRhJ+yiCg67KjiJJSuhbmer3fVriBZEJEDk/TIRSBTgZpkOlZsaK7EjpKoMINredESZvSWL6tKmmsTG1VB4M6JoTaSqoFbi2RFazd9DLak6fx5jJTZlWRd0tc2ZAuoSsIi6qmpkv0wTCMC0mkCFQFRMKefSyJ+HVoEJ4oSVPDEZotAqmLcC3ThJjcRiJ4oCAandc6nVCFyusab+t9Ao36IQdHMbc9PVSgMQMUJLBNswKBN03vjvnAg7+VURP51vrFS794UYEzFqCaVUIDqD1GtbfYOCfPPgsP04FdDOdrC06iN76QIKYXEF3d5fPbGcqRFtUphi2x1au7Qys4H7CiLKjC8SWSxXdGs567avEl77ptf8EO58+ytwwy0nPZIL41LZjgcGBgYuFte/+Udx74MnPYpnR6UdGBh4dnHxim2nEkY/WMviaQ/UvYW4f5YVT5ENSdCSlakxonxxY3FNk6XOEtsRt4UKRJloHHWToXr27WDiiKJwEmVEsdaa11Cy96grpxo9eZ38uFtzweicYFibGSOQ7EQux+qvD3IAGHmJdrvGHWUhqfW0sSefSfoRZEoWSp7VOLdJDIIT58/x5/DjnjVi32ynlO/t9HV7DxNKWn4PtW/qlG6AUHZ2sPL6VvJjKQAV8VpVccKqEK2uvCrmahsO0GptYwS+DnL7A1XVFNtQ8J3EkyvkhbhrcBWqo3bz63Olmip6zgEL1Ot9oUCtFVMoq648z9rU+NgwyGAoXx5RLxz9bplLkkswQ9Q3PryZMPsGkPKSFUramNvGS99XODYWQn1foHut+vVrrCnVtHjnLFHUG7cWXNk7d7Hh0k6Qn/6jYvGpx20fvQ6fuOXy7GE7VNqBgYGTxp0PvuKkhzBI7cDAKcXFEdsglmjPy1mXB+NjJma2djr+HULh6dVHEJqKo3HURtaW9aJOnHMoXpvInjqrgjqbAjW59bVWaQ/7XtxKHGoz54nF28mQMrgQVt7PNqyiii24NvLTq6LqxyYnB6YYm0pottllq+DMjgp1GUiy1PN6u+Rgu139I3VkottkyDpUJyaU805LIgxCFNIGz3VJsNnLveWMdqStRL41s6usk/WtZU71Pq6UoKmc2xoxNbn6BsG83WLebiGpYHqt81ybnTwatJqBHHCLbrJ/J+xNzfQ+waEod+nRSf6h7g7Ig8CylNUDq2xuom2siKYYSuyvF0CoQGS2c7kCX6ZiL1Qj5yBTPiXmyIfcSmRNtzfns5+RCEzFiTiS1JMzyWXgVHxW2tf9Bktv/c71oWR1wqItlRralFtfhxyp3XEfyD/PcUzpFOG2ZJ0At8TtdoP8d7g6cOeDl6daO1TagYGBqx3DejwwcLpxccS2FYkmmVnYOKmF/7RwmaW6Sj070Xg47+oFiRZqUrM0H1ZpTVUVOGFycnGk9pSw+NoSnAkqjTSpmnpJbpk92GzbdfaKlyttkYrb6i77cXU2YyJ/2O/IZ6deavOZLufofCobKImD8YWwdS9fAjJlO78Pwuovj9CvVFNXBHXbb5BUC18KazCBSnFbcFNkg/SLRL2q3Yc6z1BYDTPUgoji+kVcbaVu3tTXDSmKkNdsqgVKgWAtfJCkitBtpFAjUAQCOjvsoi1NP4V+b3qhP0zaCMWXKddL22wgV1zFlNhaARWQ92Eq02T3023EVt9LmYAMv/dZu8pOntU2X0ppgVYKeL1x2OqbtJ6p4rkJ0WhkKK3Rv7kdDbluJPX4fu1QEv78HIeVH/G5zQWFvp5cu3tzXifFBd0VpxOvvvk9eOQd33fSwziCQWoHBgYuB7z65vfg3hu/6UTOPVTagYHTj2MT20VYlNtOpVduQjFEexBuvUGdzIbKSo2ggPrHbyzUyZ4IWjpxI2mqijlqWMmDh7h/HwAl7x+KzqLbiDN5r9EIiUqCkL1z25jyuvJlIeVRXkPxCyEuWZMqen6laqHk5gN/zKF6nSoWcxrXkd2BuzH2mwlBUKNNUGtRRJhWOyBirFZTqtZKDLDNoahC5uokXixEK2pH3SoMV1qrWBufsHJbCrXbtDlU8CB0QYSrBS8FUWb7fXX1PTzZRi4tSAoc1m9PQmbKuUnSxbRorcNc0mabr6NQTpdBRn3icjh4k0Y7CZeUt50YO4kWqRBXtWmaUDfbtrGQarn4WbgjlJREW32u+8+Savv8GInul1yvnCLXXxti7Bj0GzCx1DTPQQRzWfhGR7ZRchKcGym9yB3rzn9vPz4/YaVuZ+C0k9rbPnodAODOG+/EDSc8lh4PfuRhAE+9L+3AwMClxV3f8xjedf/7zvu7T+MfPsujeXbw6pvfk1/fe+OdJzKGQWoHBq4OHD88CkjVCWSqzoLjIR64gyRgQdiaNdIedsPpCG0P9L1iZf1NnTSqggpDlYzcotpDN4fSZGR2Ik7rpYigSs1k5kCmDDvDtiTccFzauZh6EglX66izdWIRINU7QU05VAjpgij1M9nPWUxEknE0DmNfa0h7RkCYW3pw1rGaYgh2pZUJ7Op1n50b/YZtbsT00CoQ2WCuFq6l1cinkUTvw6pL8rLccFCIBLGSHDVqEMYIUmJEb1qoZl9be4UsyGmB246zj2zHqgQtbGthB2/OAb8xrv4uW9R0uVCd24Da5gfRcjPCNy6YY0PENjOsFrhCim8OMDCt1zZm4/ettZN6uJcoCLKYPyOmftxYmzx5711TTtnXuaU4t80gxNqwDySoswIH4Yy1Fp9fiKbNWNRrieMzxL4R0ZHZmHr1EKoIjErLcU+a47+5j7XcQDjN5PYTL3oNAFw2FuRRSzswcLI4e8c78cA9bwAA3Pbet+KDN/8OAOBt7/4UgLef4MiefZwUmQ0MUjswcPXg2MS2lJJqYK++UjzAAilgAp07sVOVvBLVX9eHTPWm0aaApnInEexkSly8J0gIVIFqgU3xMC+HHqjjLFmnSPHgr+CJ/SHc0nlNHV4SOUFLWubCTRlO1mDXmOPxqyVY4I46qYjjtTpiJ/tBgsiSdE3MtK+ZC4haPWvxvwFO9ZM7QhapxPM8g1zZVhHMtYL6MC2JFjeyIPZJmOJaCBD1vrk++32fVcT9j/TcnnjFNPapud19kYXl1UiVeHhWWtGlpU2rk6xU/bvjxfAj+TrSp5uw3TZjtBtqhj0trOJtjKpibYtyWkJhtXsUeiwV9gAr9TmJHs79lHq9MLW1wK5oZxAUU6rtJmOLdz5iVATZ7wm9fxYW7XaWKcpGaruNh3yddhs2BCs3d8W4o8j9RzUDp/qMs46Xa25WdYpuv1sz8IxhENqBgWcXPWG6/nV/lF//9nt/x0ks8DZ8ALj7U8/62K52jHragYGrD8e3Ivf1f93PRRTi3C4e0Hs1MmyqkaCcv+oe7DtmmP+18KcZW2/dQhBM05SkhBSWmhvqqts8xZ/4OZRlVnO4atRhxlXEGakRIgZUaxJgxHsQybP2dSOvjaSSNfrM4CUjqvakL1VMzdYCZQYXRvEQYUtfntIqHFZvLiXHyBl2pUCMr5plWma3ACsg8wyV6snQrpRKS+lVVVBpSmu7F0FQuhrQhUprcySQnIPodZoEy+cyxdXYzHCFT+ymgNSJqkmZaVOOjZHc2PD3J7FS21yIOVJXnG2CAK6aawMefGXvDmVd/Bo6pdbPQzjcFxipOKbmrW1bBACqCOZZMNGUKvTE1kpHxdv0qPiMekBT7Nv4PYwxMJcUXkUEBLN5B/Mm79EbDFmFAD82WfQ1qmw7iz21Gm8fr61Bauu2SdK5eaFixyZqpQQM3zDxz4242o5uM+AIfNPL5r2b69Mr2F4WGHW0AwPPHHpy9INvmfDpr/5+AMAtt/7z9qL97g2DyJ4YBqEdGLh6cfzwqFBm4uk02AihU5ca+sCZPsinP9ZhWJucCpXaUmDJWslQ2QFPBTKbBRRBrEITUunqbZv6pd6nttUwtofxIIuHha0F8e3tlzDSYgG9BETAkbRUWYK6HZgBkrQOm7pbwBO7Cjuh8JTKZJBlLsXVVCPFczVbsM6KWjeIYCCIK2cieU+SxvQNQ70ueZmg3JQ2U/mC7HpLF86b7be4I/YUaqe/rxfcnRAdtu6q+uaCIJOW7fDnZzoRkJQX1ZHsVAqDVFOQMaBSm4MIGGuHoSWhzXNJXkCERokGnaXWXioXieb81HnGNK1yE4C9hVHPGQEjjDEvzd7ejldrzfumChT/7BjJJVDpNgzQK+c2LklC200ixyYOFveeKdb7cu5D8Y1fKRQ6uzINZPsjjbZZ/rmIz2hc5+KexnH63Y+BS46h0g4MPDM4e8c7k8QuCOxZ4KVnT25cAxfGsB0PDFzduIhU5Hi0baQHsOfn9gMjA1mH29lSU5PVjiSFsqP2cF9rRRXJY5aVkb9CjCoK2UoSlkacLaFZMqAH1oIlbaY2NAKBJRRJe1QXb3PSxmTvRUdE2EktXKVitx8LEXiajMAyYZomlGnCxJMrg6EguhUVZlktzKhSsZ1nbFGhs9e2AqbSAebplGar9llNUpsKMzXSsgjjahIqekYRTuFsGqPR9zWUNfJcKspaTqZQ2xp5EYpWRu38dvxoX6SmKKsRWVIFE1s4ESNracMW3qulsZQojuNW9ixDddLOGSRm9018U0GVvGJXlpev3hO2O0cq7UHSnbXbIZNRpy3XWjRZyBKTqefdVQBlAk+ThY+pWq112NqZbf4jbCts4GybK+K7JszFkqNzmEZc+5ppgr1P1doniavBUUMNqPUEViPhEZBm69Hubd+vVkVBoXhDLRQsdjX8cxBG8tjPiszq+Pwu62nbZlG3jzFwiTEI7cDApcNd3/MYAOBd978P/+wVHwMAvPnWlwwSe4VgqLQDAwPARbb7iVrQQJLa4FN0SIUjpKLaVLfWWkW8dlakpurEbjUtqwIqxbJ4VNFonuZDd5DMNj5NIhvEWv1cC/Lkvk9TU72KMYKBvM2KeB2ncVICTQWFGYULuDCm1RpUJutfyq7ugUxpFjEb9WYDeN2vkVVTumatxnWWPt4WNmR+2WbHdWUs64ZdvpMggLrQOO0lfuxeKVRq89E2JjjtsvYav9PsLV+CmcSp/Xh5H1KBxIJYpyjopNDmiLOmVslJlo+nt7X2a8iuK0ir3R8jmf46ZlBsRMT5nJjaNWlughCRb5x4OnFcq5836mZjfLS4tlDdAQZDzO3tZJksLAyMaZow7x+4BVygFMnE4nZ+8uAuuFovOWiOzQt4grOvU1VpuVJEnkqORnTF70eXCq3uh7be0v4aVVSdW310fCakbVtJfkb7jQaFKf+trrrPsBKJDSc/f0rHy02wIdpeGoyk44GBp4cgPz/4lgmf/uxD+ODNrR4WeDvefHbYiK8kDJV2YGAgcBGpyB2pTRZlXwgo+8BSMhq0FweB8LeFOhshRnYYI7TTavKAKEBmcZtmO7G91FW6dmhYjW31+kgGs73a+qHCVNggNB1TI2LUanpfKRO4EMAF07Qy8lkYZVqBuCBkKFWxdjfbLbbbDUQVdZ5NafMaRKsDtnMptWsPG2eqWjk3TmyCzDirDtuo9T114kNBKrprUYWiKXYRuhXqeCh23NVKZ20lAGJuKlvSGuoUb3s9+VwGMQwSaS2YkCR1MTZmgItZyFNbjeVEqFqz5Uww4hx7EFpQS1FWRYlE7IXSawp02q0jSZu6VkgU5LFf2aFge+9Zppw3dVdBgQbbRfVxbWvFDlkVL3kglKxWOGDyPsnIY5vKGVsIxgrbLeTYa0EuCm02++ghLCoQCFgVUgkMNkJKbISUTOltyd9WWzvrbDXGIAufcgW8X5PSbr5vBC03ihQ15y+3NQ7f795OAMCs1XEtA5cCo4524GrF0yUu17/rVvvi4z+PW279gB8UeCkw6mGvMPT/Bp694504e9/JjWVgYODywkW1+wmyYTV+ocKaysOIB1p7mFW37TJZLx3x9jFVJEltKjtMrtQaGZtlNgJRQwHSJD72ME7NRklOIIlQiCHS1MpGXglTWbnyyqay+nVQ8Z6vxNn7lIk9IMf6lNZZIDKjzhW1blHrnKpnH8xUvLa3VdsurZhGaFqYVQ4xla/q9t/S9gZUk6BbAi83MqxABjA5SbV6Xu9z6411GQRST9R1ohEcMhKUTf0EWLgdi7ip3F6zudxiiMVhaqU4FQurbdq7ix2fg9QFiY+3h03X7bsUfWpd4QxbdFwPSadN94yJgjBTm5cccNBLo5qxhm3J+rry9eTT7oe0Prp2g+yvwmZFt9cJtExOihU0FWhh1DonWQ5pU9Tb/rjaHKptzqarsUE4YwNBYmx+BVbLascm9XvuaeV1nu0zEAquhz4l+ewO1FuHAyJBUBVh0VYQiJatl7JOl2JeQ+ltx1NtW1LdHRt4Chi244ErFUFIr3/zjx7r9Rfq8/poX+P6VPCx+OJVT+84AyeGYTceOA0Ipwhe9SM4+6a7T3o4pw4XZUVm793akzImMvUMpt2KdoQETtaA1mKmr8VTC0tK9SoVSfLz+U+7otKwlDKaYpvqJBOmVQGBwcXGayqstcrhMqULt4qgeG1u3czYygaqirnO0CquUrbgG2uB4g/vZOqvpIV2GUJFi8f4jsCm9GmvSrtxvFKbIma/jqAit/Cq5D0Iva0p2Hb9MddN/XPKEYKjTWiqaDlWbV9rx0YyOznU3V4hzVG4mqhAoTYmCnIc708FWLvNkXbcDBnze3207yl5PbQNstUYex1pVIISuaW8WdWDKIuTwgiKipZMAIGL27WphTwRguwBcHKX4VFRi2qL0Dh8aUoz+c/j2jJJOnaE/Dw2dvhmgp9XAZRWrx78UX03J8YOBWoVU+TFNo6aa0CSsDchtQtzUiB2K6KtUdtQCdtxnDzO75tXuXpaK63lqogNhvbZHTg+wm4MYBDagVOJ6PPa17QCV2ef14Enx7AbD1zpeOCeN+C2974Vj579gDlFPgbc8d4fwz/9zHct/g0EgB947H/FXS9/Hl5983uOfezzbQgeB1GOcRj/9DPfdcH3vOk1P3TssT3bID3KHs6LF7zwRYfeiXwwz28R5MV+NoslHGv1FjW6rI9ltp43SSTywZmcvNpDuFckopC3xAmLKTO4EMpUrGUKFyN3oYKaJJkKMQBALelVVCBS7SG/mpoc5A9kmiIQ2T4EtahlswRT8NRQa50o1aYQtzZATdW1nwcxRarWEe4Tdm8KxVfb8aqrucv57kipAlIa8aQQdoP8iZMMCjWt9fOlnskC0KyVdOWXCmrdIpJzsy1RHCcsu2HhFUlSQ97GqN/MaPZV/1EbViYMV1f8o345R5hWWP9PjBGMrGllAnHxjQ8n0f4GydPGPFidMZcIt7KbK2mvFZAKtLoZV00BV1ZMqzWu/bLngKbJ16wpzo9/6UvYP/cYihPV/j62r9nuM5FbptucBDknfx0bu0at8ceTsp2IZt/lWFq+MRMkNO5s9p9VAfzcIUNrd1+0t4v7IkrhO1dK+2rhPu73QSi157y2vb3HcRrxiQ/9l0tynGE1HriccKmJxL033omb7rsd17/uj/DBm38Hbx4W4AU+9/bvO+khPCO48433PuX3DkI7cLnhgXvecKzX3fbety6+f+n+By76XJ/bfb198aofwV0vf955j30S/5beces34wfu+boL/v6uH/4n+fWlIMDXv+tW4LvedqzXXjSxjQddUPeQGwqrfWdkVASbeevqkVNTZld4zf4b7UEk1Shk/82oBaXCAMw+XMpkahlz2l2NpBphkerBOB3hjITjqFMkVev/qqYk98nNPnyAgIICITeCKjcCFbZUbUQhqWY+3NvDvkgjthmM1L0mf04RzNOI5oLYMkFRm6Lp54/gokaui5GrxbW4YiyhYLYAoNxMOLICxG3IQW4JqnVxHT2xjb+F/OdiludmkbW6amYGqy5a6EjsQ7iKSLG5wMHMY12UxVqLIKOox1aJfCpjyRFMFfc++ilDYyMByf6DhEeAlngNOMIC7wOrvq7Y7wlPE3bPXOOqLMCoqNsZ83aGzFuISFvHvimQ98rn1cbuQVOuPoO8dRDc6VAFst36+93Gn+p/qMCNbEb4mKIFZ+W9ShF1qbNq9NzN+mM6RFJjQcXSalS3J7XtOuwchxOvN5u+0ePpwdMltoPQng70D+DHtd4+E+c+Hx645w247fe+eFHHfOnHhm332cQgtksMUjvwTCFr7i8WH//5p0RQr2Z87rs+nl9fjAod9yj+/9Bx/308NrF94Vd+1YIAZQ9LUFpgSQm1zqh1TjU0HI1lmjB5f1cqxcTUWt1yCaAwiAsmtvRhLmbvJWbMVUBiNlKzNDvx6FhZBOAQ3A7rJIGpGC91UjvPW4SWBXaCla1tKFgUQLQI1FFmUKhqjVMkjDM05VPhrYH8R6F0WqmxLOpII+Ao1EhJWbHXyZqaRx1ZIjJ1UQVAKV4OatcVpEq4q6WtsyUhdxcQavaRNNse0U4pPLHE3rbnaL0u9Wowm2VbRFGCsLk9N/umUl6Zp/g2u3UMiYpZhUUZUm0+KBR8f724QsgTgXkCEaMUBpMFklFh8MQAJigYNLGTVKuvLmQWdqsZZ9sL8LtK3lYoewc7Qa3zjHneYnuwDxIBqqDKDK0Vm3kD2W4tGduvVZrU3Cz2vmLMZq2Yt/Oh80TiseaGh2pLcA7yWIOo5uZOzfcEo42Nj/hc9h/+BQE+ZC7G4ne9C6FzCeSmgx4htIHtdnve417peKrEdiQcnx68cvfn8c9e8bHcOf/c7utx1w//E9z20esu2Tmir+r58OjZYd+90jGI7aijHXhqOGzF/fRXfz8+eN2PDVfIKcJx/308do1tIyDIB/z+QVZEPMV4RrYwgamIXBjTtEJhtnCcwlb3SpxBSrW4ijYbSRMI5rptLYFqqE9LpamRsRgltV6h1OlKasrXUqUKYqwLKieHnscJ1ne1tX8JCYvQ+2JD8Yt+s6GjxbnsfOz2XH+PthpcIjOGUhK7IA3kyjEFg4YInNQ59eL22iTC3bcMgERRNe5f3tWOSJ4PdgAR+L11Vk+EMk1pJw+xNMO3nNRwKakwmo27GLGVCpDXqzIhetBqaXNBzLbJwQVYFWu5VCbwtEIpa5Rp5X+sjrpMK3BZgUsB8QSm4uS+NHWWiyUnwzYrSrFrYRQwGOqbGnENKgJls8G3+yjpBhCtENli3h6gbvch2y3musXmYB/bzR7k4AC63aJuN5jr7Mo12RgByFyxPdhgu9mHeusrczksF2G2AYqV2hHKtjFh9zRrjRHKv7gUDmDRYgndCm2fjFBdz1cW27iqHvq+jWf5ej30GR0ARhjUqcNfewiPfmzZJual+x8A3g3ctvt64KsujnReqP40+qoODJxGDIV24DDOVzt6vprQw7X51nt6kNqrEcdPRW4O3LRtWq9OI7XzPKOFLdnDbCkFZbU2cgOFshFc4gIwIGqJw/MsZttVBbNCPNhnnmdL2iUGSTyXL0loryBlY10uiJ671UN1stdmqEnoLajIHrLcHTfqX+36KW2svf3SSFiQDT++kz8FgZyAgridoNsY6GuKbSDux3VCGam3orw4t4Xg9lZPyvtDbnU1whMWZB9iKW3M/SnRxhVtbdhDsUxxnV0lt2ObzXXGNE2Y1qtm+w2VMzkOW14yOzl3Uge1JGQuDJ6CtK5RViuUnTPY2dnBzs41WK13UMoElBXKagWiAuYJzJPf41Ai2Uks5XlT8Q1F3G8uq5FpCZU9FH3ipI4h5LPddsRGQcxVGLpVo1duBam1M1KxTZnN5hwe/+KfYe8vHkGdN5iqgkSgtWJ7sI+9/T1sNnuQeT/t+nbrI/wLeT3NJty+b+svxuIEN1dCszjH/ehrjJPg9hbjjjAfx8txuG44CDFxlBPYJhNzSdv41Y5hOz5dOHvHO3HLEyT2fvDm38HX//CNF7Qm9w/yES5yy61GikdP1YGrBYPUDgTi30EAuOXd34DDYXIvBUaLroEL4iLa/dgDMOeDr0LqjFobYQS8RnaaUCYLcwIoW/xM04TKFURAoYKpTEaQ6oG3OmWvKayeOAyQMeBOWWoKaI4tCWikzEZd6LKeNOppjzyvO+8Mwnf42Pk9RX0kUvHqDgGnkWBYJav9UbACwtoSj9ERy2CWwVC4kYz+WrP1Tqd+5VfkBFjhRC+s1u11TWAOshcX3ltIK1q9Jrw9kG9eBEHyexrHsA0Db/FDbb6ivpZ4wqrsgKcJ6/Uaq90zWO3Yn/XOLqbVGrRao5QVJt4BqEDJwpjUWzBZzTVc5bSeuIXZ7d3uJGfy0OKmVsfmBgGoCrPhOnEnZkwKzLCeuBqvV4V6knPMmXqv5bBvi8aGhMFqxQuA6vZfgGjCerVGee4KOhMOHv0Szu09ir0v/SU2e+egdYOqWygqILMfqCOUUf+c11Nbmx/0y8Bt6Kq53tNCH24Et9VfyB6MxSfiidXVtonj68prgZltg4LZLOqlFFDxQDfffLiAu/mqwiC1pw/Xv+vWrpXMUbz57k/hIwA+8aLX4MGPPIzpp34ta13vevnz8LbPdqT43cDbMOq3Bq4uDFJ79SFb3jhe+bKX4NNf/f3WZ/rd3zD+HRx4yriodj9mSVVUqU5oPI1VzVJKZISWSzFFTBRQU7AKEValYLWztnpV41+oUuERSNhuZ6iQ1xOiUzjdUtlbkJO0ROgPuy23t4t6+BPgSipCUkQfqhOElcnb0TTDZ6YVd8/zCB7fuEUQSaeEzvpSBe2+JiD7siIUQ6+VJZdQDwdN9bbh/ncRzhP9edLe3NU/uxzpYUlxvdR4hsLk3+hZ6vW/KmLhVGHHhdtZyeViMnJYpgm8WmNaTZjWa6zWO1jv7GJndxfTzi6mneswrXYwrc6AeGVjJwZPaygXMFmSdaHivYrZ10fMY5t3E1+tJjls31aXLLAM7ZqEVr3+Nji8zVMQYQEpQRjQSlBp9dSqBNEKVQq/9+Le2b32OY5QtFhzkKxvFf+MyJbxyJ8+hi/98ecgm8cw654HZUl8omxccELq9xW5xeCfoW6Txs4n7V7G6riQxNotpAuTWzuW1al3Cd7kpNX7I5dSUIo5LsD2PYjdzm410mB2J4Cp/vbPQL8Arz6MWtpTjI//PJ6sN+ott/5z4JOfBb4CwLu/AZnt+ASEeGDgtCMI7dn7TnokA88UfvAt0yJ7IHHWldf++7PA6DM98HRxETW2ilrVembKNr2rER5VpmL1k6VAoZDZagIZhFVhFCc+YIY1OgG288aCoES8ljb1qOxXmnbJTg2M8QAAh8JojlJ38jrBoEYW0mKMCEEydkowSyoXhkKSMIU/Oc7WJoIXxEAXv7PvpSPBQUQLXNnjsM/G/Klbuu0AZk+OdixOypJVdYpZT/Kj/vOImuskHo0IsqgFdRUGVbNNm+AX4UXebMhb0EyrFQSE3fWXY717Btd++XNxzXOei2naMWspeb20q3PMKxBPRm5EUcoKFL2ElSGztS1ardeAk6LId2ZmiMYmgmafYlsCbhk3rgmRasnaduEgECrYrOrBB7U1vFEVD7WyVlEzFFJto8YFT+txmzsWOfNtttVUd6lWw1rnOVO2a60gBrYHByhK2Gw3YACPffHP8YU//Czk4E8A3UBzzamTZOkIaZBrza/JN1vSZny4XjVU/CfxDS+DwbpNAV9vRIRpKpkgTURmC19ZTTRxAbS0gDBms/lXAIUxC0CwmnmwuSxQZ2j0/i3lCDk/7ejV2VFLe3oQ9a8AcNtHrxsJmQMDF4kREHX6ESnsj37sVaOcYuBZxbGJ7TxvQ4zKx30i8gTjCUpWUxeWVQAoERBVCNN6DZ7idIR5u8X2YIN5np9AzQl6RhaHs3h21ySOZvFVs4iqmEW1U16N2ARhdSWsd0ZSIxqW6uu2YoKpa9y/uB0nLMA4xCsWKbHdpSX/yL+1KYp+uW74RqfzLuzCh4kNuZJGAJQL+n6oHIFJWZsbyrETdj85wdii8oTVakJZ72C9u4OdM9fiuud8Bda712JaXwuadkBlBwqrF2ayhGtmtsRrwInRZJsVEEzT2uzEMIVTMLvSu/L+xO16Yn1J3Dn1rQZSEEoSPoVZo7PQVYwgirYNgFCg+80NEYViBhOhuptAfL7mauSuzhbuJNuKqGiVagnfda5GZOcZUivmjaUez/MG87zBwd4eSAR7jz0KguJg/zGgbrE99zCg53L8rY61kfjDKmrjq0ul/nxo9drINZzrwz4k2auXICCeMGUQ1wRMbEFbpdgaFOsBzMzphtAavYUZVaqr+b6t5D2bIpW8zlvzCpC9F6qo83zB8Z9GDMvxlYdFq5533epK7FH89nt/x4NKgON11RsYGAAGob0a8MA9b8BfefiH8LZ3f2r8+zhwIjg2sa3z7E/aptYweQhQQATitYnMLchHCBb6s1plv1qZZ+zv7VlKcU8Y/W8QeaIxgfOBv1mPzVZsx69AqmwsRkaCwJG2pOIgjSBuip4fOSy3cMIWAqk/k5v6pDE6tTJYJwwLxbZD1PyKuH0XTVWunuBjKnFz3DZyU7M2URWmbC4YMiBVXMl1cgIGx89dFI7k3VAuCQBW7Aq71UGudnawmnZA0xq8NjK72r0OpawxlR3wtDYlNqy9XEydh7XtidZIbAza+9BaL9hSKwpNfo3e4sdV/G3V3A3IzRJLvfLNkcNzWf046gnZYonKtmuB6gpv3FcmhUq3kQFgPphBDOzPG5vlOhuZ3m4A9dTnauFOdVOxf7AHgmK7t4d5uweZt9jbOweCYP/c4yhasd3sgUlQ6waqs9XY6myOABtVyL1JYqHdWj+8X5PXfKE1FUS2rYiwbTNZSna0y6KJMa1WWQc7TWs/kCeYMfunjgFtm1KqZPMwi1muSTyIrFkfeneF1JpjEyJQ9XsDMiXb18cxO4td8Rik9vLEXd/z2BP2cV0EQH0MuKAlboSWDAxcFIbl+PQjamYt7Gn8Gzlwcji+FdltmBYQY21U4jE12o0oBKWwkxN7z3pnhdVqSvVJRLHZ3wCzpCLHoVT2VmMnA3GSUEajkUkpbPZTlbQAOydGPvV3hbBRc9orYKkHO0FaPnh7y5V0hQaxDqKoSV5722j0q02FOOptyezPpBaEFEphXGa8fzk2NMU1E5kNq9WUGwDITQQCrYrXRDLWOzutBc60soThnbX/fMJ6fQ0Krz1FmlGKtc+xjQmzoAp5n1jNS8l7r8R5PzTIC8E2AersvwsrLVBrxGvBbNnO+Wuo73bxqKE6u2xJRGbl7jZBtAqEfb4B1M0WYKDWChVAZfbsJPGfKeaDGXV7AIhgc7CHOs/Y23sMWmds9/cxbw4w1w3qwQEgW8zzPogqtG68njx6IFst79bXbiXNuu4cYQukbmtQl86BvMVPoMZSrh93J4RVuLR612ma4MwVEZxmS8bXiFoLrEqAirsYagWrHWvrSjQxYa4KQKxc3F0F4R1Qsvch1/fSlh9rP1p4xfit5+6Fr/E0YZDayxe3vfetwzY8MHACGArt6UXU0D5696eWNbMDAyeE44dHBZkJQrawOy4JKQe5hPe3Fc0H/KjJ1WBJITsJzqt+tjRjf6l/f8E+m09wCdkWZ/HGpWSWPLax2PPMBbrqy/Pj8IP8+dUqs1Af96G/72NqJCoUX42T2OaBWuDTTGR1oLwBlwkyb1HqCjIfYFqtgFoxrXYs1IkLLBVZwFxQePIEZE6LaVqyXZlvhB6HZkN7vp+EuOdzC+Ie+w9R99mO0tWBJiW2r0IpLmx28VWBAChkNbA0rTHPFYUn6HZrtlqxOtJatyjrCdvtBrSaULcbrHfO4GB/D1IrtmUP0Bn7+4+DSTBv9wCZIfUAqtWN8eLtfQDzRGv7ozg8Ie2OXyTBO7zZkinkkTReJrteJpCI18d6mFMhcyj4JBN3RLT7GLSkY68977311N3I+D42jp7k2tr9Xm4AnWbccMuLcOdHRkjUZYlX/cgIaxoYGBh4Goi+sp/+6u/HB6/7MTx696dGDe3AZYXjt/vxh1mp3hfWrcjZaociAdVIFTnpkGo9bosn6TID6501VCrqVi3MSJG1eACMvpEpgxBTkgRNySQAEIVQU/TInuGtBlLVE3Y78q1dsnJvzUUr1QTiEdxfZ4MwEt1xF7vesJEmDW5q3AXmj6gRzxiz4qha3NdLKgCIGAHOERPmuRqBKRNiZ4DJ2+/4ULd1TsVPVXEOgKr1c2W2HYipTOAyWcDTehe7116LnTPPwbTawXp9BtP6DJgnkPeNZS4ok52Tic3m6j1km2prTJe1kRlmSzsWmY0reVsjUloQW1BLxY45Te93SLyqntJLaU3nidOKbIdS7KjdOzfgYn3GGjCJeDspqahVQVIxV6sb3W63mIiw2dugarWa7YMNtpt9QAWPP25kd+/Rx8AqONh7DExi/WhlC5UZUjfWuzfa+WhtAzuf/fgYFl2NdefkNnjihrZtXZC3RfJWS2BgmiYju8xYTSt3IHtbHmIosVvLLRhqKgQVD/nyzR3myazJ2sgxx/0jTqUWsLprlbltSOjSiTEwcFI4+6a78ZZ7nnfe373r/vfhn72isd4jCZ4DAwMDVzFCmbV8gbd7gvH4d3Lg8gPpMZ84n/f8F1iCrMBUK/IE3SBXXlcbCbuqanZJYkyFMK1WKKt1ykRaBZuDfczzbGRZo++sPzi76qdRt5leWIN665YShMrYkllcpVHV6qSOexsrA0GUGIRK8Af2CrLqUUvkDcmpD4/y2l71Fi1p0/RDLiaXluQ6+rsiiDOcREtTPs1G6jWQC+myKWI9USDifJ9GOnL8jjlfb+QWIBGr2WUCiQLV1Mcg3ChGdKbVCtPOLq79sudgZ/da7FzzHEy712LauRaC4hsFFh5FRB4eRd6/dvJ6Wg+PKlMSUNlazWVZr70tUfPpWr/VXrFVBCm3XOk236K1iYB+f2e/B2GFDbIctm9US9dm11xVxMgwBFFCGvcCIqgw+32tFaTAXCsmZswHGxARDvb3warYbPahUBzs76EocO5Lj4KJcO7xL0G3+/jSFz8LrX+Za7bdo1BINb9vNzxX0aHvz48L9V/u1wzHGidKG3NZTUCxDYtpFVZmq4u2GnCFfa4AqUi7PZdIRVZvF2VjLQrM8I0Y0QygquYLxxf/6KGjgz8F+MSH/svi+9He58rGXd/zGN51//vO+7tPf/X346UfGy0pBi49Pvf27zvpITwjOPv115/0EAaeIq5/16344HU/Njb7Bk4cx/338diK7TQVSLWHVqlWFWmOSAHrxlq/lCmJTtTTis6o0U+F2F7jau60tmAb2c7WSihqWoEFSQxSu1SA7IHZ+o4CYHJ3KqWqFKpn9jNV9QwocvIcLMNPmSTaT6/GY0kbqdSOiNqROkJx2NqJZa1hnCvaq8Q4GnVZEuE4pHbH6q3Y8Vur3RSAWm9TwDYP4GRXxAJ9WMhe5wo5EUGIgSpG9+oMEcHmYIvNY49j75FHUGeBFqBME3avew6uec5XYLXeRSlmV7ZNC6/jnXZRVmsQTxABallB2dv9oFiNJggrGMEOuzOTEajmdHUlH5QbJuh+5tpw67lbGCzV14STem3J2QDAExCbHwUEmawtDWuBstU9W5coS2Iu5oXGyjcGdp286bXXQFSxK9d5sJhis92CC2G7v4/nf/WLsdnso3DBY1/8Ag7mfcznFCT71kOXutZCaoza9lDcsdDV6trP+zrVo0T3fIpoO5bm2q9i911VMM8Kogra3+T5TNl19wPFZtRkqdbFeg6rEgo3e7iy/UFsSFRgtVp5MFzNz2Ep5cgYTzNuuOVFAIA7P2LfD4J7ZeG2j16H2/Aj5//l7wMfufsnAAD33ngnfvYr9p7FkQ0MDAw883jgnjdYLsHHXoWhzA5cSTi2YvtX/sqLnCgCVQWiNe2JEfLD0dN0KkBhD++BKagKrKYJ62t2jdDA1Z/NjO3mwPraajXlrEawFLKe70hZXy9w+euYmj3VTmmkIcbYamztQT8DsDwgh/1/EgPvFVd/a8xWT0hTZyNtim9osj726tbqsFHHmOMIPWkhKkvbdHeSJYlhF8vEiIUTMfWxAGHdbuqv+jGM7MT33l5Iq9tKu7Rm37yo2631jqWp1by6HX1ar1CmCewtZFbrFdY7u5h2djHtXIvVetdaBk1r0GS25jLtQIv3PiY2e6y4n9yts6nEK1nrGC8M1Ri/W2DD2m2pUU70fT7dFW2thdQ7+kbwEYxgFo57bjCbsh3PI8ds7tE2IBTo7kPc62o2XIW3I6qo8xZ//P/9Hv7yj/4A270vYVMfA4mA1FaIZr0ukoirW6jtqHFNstzYcDW7r/W+UJ/YXGWx4XFMgklMqXrH54qL1++6y6FMJT/3uVnD7HNkKdoa9fNK+MLnP3esc19pOKzYHsZQcE8x/vinnjSU6nO7r7caX2CoHwMXxFBsBy4HPHDPG/C2d3/DSQ9jYGCBS67YGowlFDb1TVAh4j0+3dIqqqBawW51BJsqV2uFANhsNlAuKIVR2MiGgrAVReEJZVIIwdQlaQpkU0VDOW3qbliGLYjWiWHaI+0YEgpu976gfqIE4lA+NY6+4M0KRNeeo4bRJMCSRN4cy41Ykp9X3d4aBxJFkswM7gmi5C9qnNeTmpPk9qO01krkr2OldhWdq1kAn1f/Yc5tEGz2sXASIqufnZMcxnX5hDlZF8ybikpbbPb38Lh+yV7HxcZJhDIV65G73kFZ7WC9s4vVegfTjn2/mnZBZQWUtdfvruz9PgdGnhjgCYXY1U9Tc3mhtitYjMxGma+RYaOR5P1uKW4qhSHaCP7EbEqklqxbjrutTrTV56FXzkkBLaYIQwjKCtnu4ZovY6xe8pXY7H8FDh59DHuPP4a62cNcN1BsQbK1O6j93YxFFhdQPUnaXmSEPTY/4Ep1tOvJ3Zccd67ZzkFwPoK7CIKT7jMTiHa0uXxszmKTwxwTnp5ebMMi6rCfLHDtNKNXcAe5PV2464f/CfDuJ37N2V/8q7j34e8GANx03414yz1vAADMb/8m3P6iH8AHb/4dAKO2d2Bg4OTwwD1vwG2/90Vv2TMwcGXi2IrtC7/yq1KBhNs/s3elWEAUvL5V3VrJXFBWKw8bMgVotVqjeIhNrTN0FrchW1CPhd/Yw/JcZ1fSCN5hpQ38PM/IQTGi5yooVDd4OrMAKp1SGwoWAPLAKSdBvcIV/WRTvfWApgg1CmtpEiQnGiDyACVvU+THCNtpU6E5iYqogqNW1v9jqmRNAt8mgHMeXIt0Eu1UK0hwXIePg0IhDvtrXluQIu9Zm/MjqPOMed766Y0wMjOm1ZT3N1Re7ggwlwlznRGzwGWFEhsdogADVMICa5sgvF5jtd7Bzs4udtY7WO3soKx3wes1SplQyhqlrE3dJVN5FUCh4op1bCc0MhU9d611EIUWar9nV5/B+V71DRcGbMMgZjjup98GI9cK1Rmksyuxgs3mAHvnHsXjX/xzbB57FDJvUWsFw1pVzdst9vf3cbB/DtuDc5A6O7H1jRVZeAGgHniVeyKu8C5fFUQ/3q/WX7p7XxOZtdscalp0rJLjoinI4Q9w4sxByBWR0szMePzxc8c+9pWEJ1NsD+PbHr7/GRrJwLONs3e8E7/93vsuSErvuPWb8QO/+X8f+1gR0jJI7tWHodgOnASiB+1ohzZwOeO4/z4em9gODAwMDAwMDAwMDAwMDFyO4JMewMDAwMDAwMDAwMDAwMDA08EgtgMDAwMDAwMDAwMDAwNXNAaxHRgYGBgYGBgYGBgYGLiiMYjtwMDAwMDAwMDAwMDAwBWNQWwHBgYGBgYGBgYGBgYGrmgMYjswMDAwMDAwMDAwMDBwRWMQ24GBgYGBgYGBgYGBgYErGoPYDgwMDAwMDAwMDAwMDFzRGMR2YGBgYGBgYGBgYGBg4IrG/w+qzyH+vD5xYgAAAABJRU5ErkJggg==", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "# Fetch a batch from the test loader\n", "images, masks = next(iter(test_loader))\n", "\n", "# Switch the model to evaluation mode\n", "with torch.inference_mode():\n", " model.eval()\n", " logits = model(images) # Get raw logits from the model\n", "\n", "# Apply softmax to get class probabilities\n", "# Shape: [batch_size, num_classes, H, W]\n", "\n", "pr_masks = logits.softmax(dim=1)\n", "# Convert class probabilities to predicted class labels\n", "pr_masks = pr_masks.argmax(dim=1) # Shape: [batch_size, H, W]\n", "\n", "# Visualize a few samples (image, ground truth mask, and predicted mask)\n", "for idx, (image, gt_mask, pr_mask) in enumerate(zip(images, masks, pr_masks)):\n", " if idx <= 4: # Visualize first 5 samples\n", " plt.figure(figsize=(12, 6))\n", "\n", " # Original Image\n", " plt.subplot(1, 3, 1)\n", " plt.imshow(\n", " image.cpu().numpy().transpose(1, 2, 0)\n", " ) # Convert CHW to HWC for plotting\n", " plt.title(\"Image\")\n", " plt.axis(\"off\")\n", "\n", " # Ground Truth Mask\n", " plt.subplot(1, 3, 2)\n", " plt.imshow(gt_mask.cpu().numpy(), cmap=\"tab20\") # Visualize ground truth mask\n", " plt.title(\"Ground truth\")\n", " plt.axis(\"off\")\n", "\n", " # Predicted Mask\n", " plt.subplot(1, 3, 3)\n", " plt.imshow(pr_mask.cpu().numpy(), cmap=\"tab20\") # Visualize predicted mask\n", " plt.title(\"Prediction\")\n", " plt.axis(\"off\")\n", "\n", " # Show the figure\n", " plt.show()\n", " else:\n", " break" ] } ], "metadata": { "kaggle": { "accelerator": "gpu", "dataSources": [], "dockerImageVersionId": 30762, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "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": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/cars segmentation (camvid).ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/cars%20segmentation%20%28camvid%29.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Install package" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2024-08-23T11:29:43.835761Z", "iopub.status.busy": "2024-08-23T11:29:43.835234Z", "iopub.status.idle": "2024-08-23T11:29:58.330049Z", "shell.execute_reply": "2024-08-23T11:29:58.328593Z", "shell.execute_reply.started": "2024-08-23T11:29:43.835708Z" }, "trusted": true }, "outputs": [], "source": [ "%%capture\n", "!pip install segmentation-models-pytorch lightning albumentations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading data\n", "For this example we will use CamVid dataset. It is a set of:\n", " - train images + segmentation masks\n", " - validation images + segmentation masks\n", " - test images + segmentation masks\n", "\n", "All images have 320 pixels height and 480 pixels width. For more inforamtion about dataset visit http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:29:58.332851Z", "iopub.status.busy": "2024-08-23T11:29:58.332004Z", "iopub.status.idle": "2024-08-23T11:29:59.374217Z", "shell.execute_reply": "2024-08-23T11:29:59.372856Z", "shell.execute_reply.started": "2024-08-23T11:29:58.332781Z" }, "trusted": true }, "outputs": [], "source": [ "%%capture\n", "# Download Data\n", "!git clone https://github.com/alexgkendall/SegNet-Tutorial ./data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:29:59.376434Z", "iopub.status.busy": "2024-08-23T11:29:59.376015Z", "iopub.status.idle": "2024-08-23T11:30:06.026067Z", "shell.execute_reply": "2024-08-23T11:30:06.024970Z", "shell.execute_reply.started": "2024-08-23T11:29:59.376395Z" }, "trusted": true }, "outputs": [], "source": [ "import os\n", "import cv2\n", "\n", "import torch\n", "import numpy as np\n", "import albumentations as A\n", "import matplotlib.pyplot as plt\n", "from torch.utils.data import DataLoader\n", "from torch.utils.data import Dataset as BaseDataset\n", "from torch.optim import lr_scheduler\n", "import segmentation_models_pytorch as smp\n", "import pytorch_lightning as pl" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.030406Z", "iopub.status.busy": "2024-08-23T11:30:06.029938Z", "iopub.status.idle": "2024-08-23T11:30:06.037431Z", "shell.execute_reply": "2024-08-23T11:30:06.036521Z", "shell.execute_reply.started": "2024-08-23T11:30:06.030368Z" }, "trusted": true }, "outputs": [], "source": [ "DATA_DIR = \"./data/CamVid/\"\n", "\n", "x_train_dir = os.path.join(DATA_DIR, \"train\")\n", "y_train_dir = os.path.join(DATA_DIR, \"trainannot\")\n", "\n", "x_valid_dir = os.path.join(DATA_DIR, \"val\")\n", "y_valid_dir = os.path.join(DATA_DIR, \"valannot\")\n", "\n", "x_test_dir = os.path.join(DATA_DIR, \"test\")\n", "y_test_dir = os.path.join(DATA_DIR, \"testannot\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Dataloader\n", "Writing helper class for data extraction, tranformation and preprocessing\n", "https://pytorch.org/docs/stable/data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.039244Z", "iopub.status.busy": "2024-08-23T11:30:06.038840Z", "iopub.status.idle": "2024-08-23T11:30:06.054834Z", "shell.execute_reply": "2024-08-23T11:30:06.053760Z", "shell.execute_reply.started": "2024-08-23T11:30:06.039198Z" }, "trusted": true }, "outputs": [], "source": [ "class Dataset(BaseDataset):\n", " \"\"\"CamVid Dataset. Read images, apply augmentation transformations.\n", "\n", " Args:\n", " images_dir (str): path to images folder\n", " masks_dir (str): path to segmentation masks folder\n", " class_values (list): values of classes to extract from segmentation mask\n", " augmentation (albumentations.Compose): data transfromation pipeline\n", " (e.g. flip, scale, etc.)\n", "\n", " \"\"\"\n", "\n", " CLASSES = [\n", " \"sky\",\n", " \"building\",\n", " \"pole\",\n", " \"road\",\n", " \"pavement\",\n", " \"tree\",\n", " \"signsymbol\",\n", " \"fence\",\n", " \"car\",\n", " \"pedestrian\",\n", " \"bicyclist\",\n", " \"unlabelled\",\n", " ]\n", "\n", " def __init__(\n", " self,\n", " images_dir,\n", " masks_dir,\n", " classes=None,\n", " augmentation=None,\n", " ):\n", " self.ids = os.listdir(images_dir)\n", " self.images_fps = [os.path.join(images_dir, image_id) for image_id in self.ids]\n", " self.masks_fps = [os.path.join(masks_dir, image_id) for image_id in self.ids]\n", "\n", " # convert str names to class values on masks\n", " self.class_values = [self.CLASSES.index(cls.lower()) for cls in classes]\n", "\n", " self.augmentation = augmentation\n", "\n", " def __getitem__(self, i):\n", " image = cv2.imread(self.images_fps[i])\n", " # BGR-->RGB\n", " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", " mask = cv2.imread(self.masks_fps[i], 0)\n", "\n", " # extract certain classes from mask (e.g. cars)\n", " masks = [(mask == v) for v in self.class_values]\n", " mask = np.stack(masks, axis=-1).astype(\"float\")\n", "\n", " # apply augmentations\n", " if self.augmentation:\n", " sample = self.augmentation(image=image, mask=mask)\n", " image, mask = sample[\"image\"], sample[\"mask\"]\n", "\n", " return image.transpose(2, 0, 1), mask.transpose(2, 0, 1)\n", "\n", " def __len__(self):\n", " return len(self.ids)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.057523Z", "iopub.status.busy": "2024-08-23T11:30:06.056578Z", "iopub.status.idle": "2024-08-23T11:30:06.069829Z", "shell.execute_reply": "2024-08-23T11:30:06.068550Z", "shell.execute_reply.started": "2024-08-23T11:30:06.057475Z" }, "trusted": true }, "outputs": [], "source": [ "# helper function for data visualization\n", "def visualize(**images):\n", " \"\"\"PLot images in one row.\"\"\"\n", " n = len(images)\n", " plt.figure(figsize=(16, 5))\n", " for i, (name, image) in enumerate(images.items()):\n", " plt.subplot(1, n, i + 1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.title(\" \".join(name.split(\"_\")).title())\n", " if name == \"image\":\n", " plt.imshow(image.transpose(1, 2, 0))\n", " else:\n", " plt.imshow(image)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.071933Z", "iopub.status.busy": "2024-08-23T11:30:06.071406Z", "iopub.status.idle": "2024-08-23T11:30:06.480787Z", "shell.execute_reply": "2024-08-23T11:30:06.479553Z", "shell.execute_reply.started": "2024-08-23T11:30:06.071890Z" }, "trusted": true }, "outputs": [ { "data": { "image/png": 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4gUjIJYR4Qd72trfxJ3/yJ3ziE58gDENuu+02/uIv/oIPfOADm31oQgghhBBCCCFuINKuKIQQQgghhBBCCCG2PL3ZByCEEEIIIYQQQgghxAslIZcQQgghhBBCCCGE2PI2NJPLGMP58+fp6elBKXW1j0kIIYQQ1wlrLUtLS2zbtg2t5b0xsXnk71EhhBDixrXRv0k3FHKdP3+enTt3XrGDE0IIIcTWcubMGXbs2LHZhyFuYPL3qBBCCCHW+5t0QyFXT09Pa2fFYvHKHJm4oQVByFNPHudLf/8t/t1/+Al5R1ZsOSb+WIuNP9o/tx2nm47zTZft2/ex1vG0b7faPtY61o2c174/s4H9XknJdW3k2aLzMireXsUfGnDi8xxW3pb2+0u3ndZ+GRV/rbtsQ5fTVMeHaTum9Y69/TIOELZdxsan6Y7LJcdl2i7TLtzA9SbKi4vcu3Nn628BITZL8hh8LT+Ei7fJRyOEEEKIaynA5yH+Yd2/STcUciUBRLFYlJBLXBG+H5BJp8llchSLRQm5xJaz0ZCr/fNugZVZ5+tuwVinkNVDk25BVLf9XE7I1RnKrbftRqz2TNAZEHW7bPK123bZ9mNoD7lCloOh5Gs6zk/u0+Q+TC6ffK469tup/fo6j719m877qNvtar/N7ftNjrGzYDs5TqfjvkhcSsjVOl+ep8UmSx6DLh6ukpBLCCGEuKHEfzSv9zfphkIuIa4GYwzIiybxItYZgnSGUO3BlOrYZrUqn9WuZ7Xtkq+vRsXVldhn5zPAat317bfRZTlo6rYfy8rQKtF+f9u2zzsrueDiYAsu/j6uFbi1n9ZeBdZZUbWR+7DztnW7Dclta38cJOHXtay2E0IIIYQQYjNJyCU2TRAYsiq72YchxGVRXBxYdOoMWjorfjoDnaT9rb3FrVsVV+fn3fbTzumyTWegs5b24Kw9SOncb+d+uoVrq1VfdWoPlNr31X57Tdu/q4VM7WFQN5qLg6puoWPn7XTbtktCsrUi+27VVBu12vF3a2tMLqvpHo51bttZbdZuvceFEEIIIYQQ1xsJucSmsNbSbDRx9XoxgRDXp8sJLNYKYja6z9Xmc621bXubXXLZ9gCjW9XYevvvdszdLrNWqLVahVm32VirhXxwcRiTBDyr3aZuIV+31sb2Y17tOFbb52rWCp66WS8oXO3xsNrxdgainSHievsQQgghhBDieiYhl9g0zWYTpWQ5evHi1W1OUqf1gq/Or7tV13S7fDfdKpXWCjAupWVyreu7VGuFY1er9fJydLYGXs5xbSTo6rZN+7/dtA/F38jpQgghhBBCvBhIyCU2h7X4vo/W8nJLvHhtNLzoHCa+XvDUbqNVYKvtd602us4qoI1cx1o2EgJ1Bjid4d1G7tPOAO9qTf7rbGm8XrTP6mrXPr9LCCGEEEKIFyMJucSmsECj5ku7orihdKu4Wm1VvEQysDzZNpkhpdrObz+tvVJntZCn23F0W21vtcuvdlq37TqtVqG22r4v53rWa4Hs3Ga9z9faH7yw0OhKBGQbmXPWOaMrudxqgdh6+xNCCCGEEOJ6JCGX2BTWQq0qIZd4cVtvWHi3GUnd5iJ1hhCdqwp2Dj1fbaZVe2iju1y+83rXGlbfzUbCps7zVxvKv5Hqsks5lrXOX+v71G3gfrfKu/UCvdVO2+h2a9lIhdtqz7SrVaO13yaptxVCCCGEEFuFhFxic1hLoxmgJeQSN7j1Br63Wy2IWK8SKeTyAqvOaqrrZRbW5VSQXel9XKn743JmcnXqtrLjpQaOa21zvXzfhRBCCCGEWI+8QSs2hQWaDR/Pk5BLiLUkbWbJv2qV07p9JDpPu5RZYde6VW2rtMZdT8e5XsB1PR2rEEIIIYQQV5OEXGJTWGupVKv0FDObfShCXPfaw6n1gi1F1Jqm438dLj/gSv69VoPbb8Qw5mrc5kupDhRCCCGEEOLFRNoVxeaw0GiGeHnJWcWNKwmk1mKJ2g3bWwY7V2FsHzTfOZgelp/obcc+DMuD7bvNiur86VwvkOkcwN5twH27bnPGus0QW+06NhredNvXagPvO79e6zZfifCo8/vf7XvReZn1jmu9WXDJ9ayl/TrkWVoIIYQQQmwVEnKJTWOMIVOQSi4h1qNZGU4lkhCpcwXGtVbL6zaUvpvOwKozDLsSc6/Wut5uYV776ZfqSlczbWQm10aCtG7ztDpDpUsNmTZyvUIIIYQQQrwYScglNoUFrDFoR2oEhFhPeytitxUTNSuDjW6B1OWs2NdZadUeyFytFrhug/O7rRx5PVjvfl2vkq3baVdjnpYEXEIIIYQQ4kYhIZfYJBaLxXFk8LwQa2kPlbqFPqbjvM7LbTQU6lZh1O5Sq7guV+eKfpcT0F0rl1rJtVrb5FrBVrfWxEu9P67ECo5CCCGEEEJsBRJyiU3TJMBJe5t9GEJsCe2tiO0cVlZ4JbO2Vgut2lsfk8+7BVrt+0wu21kxtpGw5VKDoG6hVrd9WKLb3u22bkTQ8fWl1pRuZJ7aRob2X2rMfzmBVbfjuJR9SL2tEEIIIYTYKiTkEpsmDAKp5BLiEnQLJjpnV7WHV4nOoe7JfpKAKGlFbJdUiHUOeu9WMZYI1zn+y6kqW29o/GrVbZ2X7xamrXdsL7SCrNvw/k4bCem6tXB27mMtyWOi87TO65BqLyGEEEIIsdVJyCU2hQWsNaRSqc0+FCG2vPYqr6SSq117wNHeDpiEW6sFY+0VXaZj+xdynC/ERtoYNzILa7NaIK/WzK3rtaVTCCGEEEKIa0lCLrFpLBbPk4egEC9EtzbG9sot2+W09vMcLg6xVludb7UVHDca1FypIKY9oNvIzKqNrIR4rax3X13OyojXy20TQgghhBBis0nCIDaHtQS+wfOkXVGIF2q1EKS9tbBzllZnZVZnhZTt2K5bmLRWxdhGdV7/eoFN+0qTmu7h23rHcbmhV+d+N9Lidznzsy5Vt9u7XsXYpbQrykwuIYQQQgixVUjIJTaNtQpXQi4hrrhLWcGv20yr1fa5XnCy3tyny7HWca0V7q113d3O30hg9UJCvLWO5VK36Ty/W/i2nku5XqkUE0IIIYQQW4WEXGJTuY48BIXYDJ3D3TtncrVfbrUV/S51vtRGVk283Na8jVaCXYorMS9rIwPvO78Xl7K/jV5GhsoLIYQQQogbgSQMYtNYa2R1RSGuA0mLX3tQlMy7am8F7Bxgf6mrK3a73s65XutVGF2rIeuy2qAQQgghhBBbj4RcYtPY0OJKyCXENdctvOkcVp+s0tg51+tSK7s6A6rOOV7t+28/DtpOv5RKp41eZr1Wy40MtL8S1rvPul1e2geFEEIIIYToTkIusTksmGaA53qbfSRCvOhtJJBqD06SIKo9+NFEKzG2X7ZzgHtnZL2R9sTOSq5ug/DbbWSWVGeQ1k370Ppu+95Iy99Gwqb15pRt5Huz3vmXOxh+o8PppaJNCCGEEEJsFRJyiU1hsfh+IO2KQmyCy6kE6mxp7La/zp9m0+XyG525dSkVSxsJfdYKky6nOqp9hcq1rmcjt/eFhkhXah+dK3AmNhIaCiGEEEIIcT2QkEtsDgu+30QpqREQYitI2vds20dnOLRaK2J7ldZ61VDrBU6rhUvdjrXbsXRepn32WLtuAd16LjXgup5JW6QQQgghhNiKJOQSm8ICvh9s9mEIIWKXOseqWwjSrYKqPdjqDKe6hV7tYdpGdavk2mjL35Vq9YPu88NWm7l1pUOk9fZ1qYGbBFxCCCGEEGIrkpBLbAplwam5OPIQFOKq28jspY203iWnrTV8PtFeIZV8dK7O2Nkit951J7o9a6w1/2sjc7w6Q6fVWjM7t1lPtzlfa60keTnWaiFNrmcj90G7zkBTCCGEEEKIrUASBrEpLJZGoxGlXUKITbdaFVa3z1fTHhS1twOudlmAkIsDmfWu61JnRF2NFRrXC5YSG51BthapwhJCCCGEEGJjJOQS115cvhEG0q4oxPVivVX+OquuVgteOmd0JRVR3QIzy8UrNnZ+3vn1WsHZalZrGbzapAJKCCGEEEKIa0tCLnHNWcAE0PT9zT4UIQQbC2M2uoJf+5D5JOAyXLztau2KnW2CawVeG3UlVh+8HFfjeqVKSwghhBBCiNVJyCWuOWuhGUDgG7S+3LHPQohraSNhTfILJQlikhUK15pv1Xm633F65yqHnbO9ulmvKi3Z71rHsZHh9etVlK01c+xSSLAlhBBCCCHExkjIJTaBJQxCrAWlpKFHiK1go9Vea52+kdCn2+qDlxpybeTYXsjtWe38zQqj1psFttrCAp2Xl2djIYQQQgix1UnIJa45ayEMg01rIRJCXBuXE/q0tzsqLl6RcSMzuVZrjVzLpQ6Nl+oqIYQQQgghrj8ScolNYAnCkLiUa7MPRghxlXRWZXULn7pt075t+2mJzkqu9VoPNxJIbaQ9sdO1WDpjtWOXZ04hhBBCCCEuJiGX2BQWi3FCPE9eqgnxYpP8VCdhVGe7Ynt4td5Kiu376bTafjZSldUZjHW7jvUquZw1zlvtGGzHx0ZWfuy2r877aSOrXXZeT+fXq+1DnqWFEEIIIcRWISGX2BRWAY7FS232kQghrhbV8W97+LPRoeyrhS/t27YPtt9oxdil6raP9cI3WG6vTG5HMh9rtVUnYeX9tdb1d15+ta9XC9PW+loIIYQQQoitSEIucc1ZwFqL4zirl0IIIba8taqZOgejJ9ZaibHb5Veb7dcZNHVbPbHdenO+1tp/5z4UK8O5zhbMzgq11UKttaqzVhvM37ntahVj3fZ5OecJIYQQQghxPZGQS2yajYZcxkQvHbW+nDXVhBDXm7UCl41UYSUVULbL1+sFXsnXl7PaYufl11pNMjmWboPyVwu9VMe23QKxZP8bkVxvt3ljElwJIYQQQogXIwm5xKbRzsZCqyAICMOQbDZ7lY9ICHG1rdae1zmrazXdQqFLrf7aSGXXRtsp23VWVyXH1n797dVetJ3WHop1C+zW+tx0uTwsv4dwqVVqQgghhBBCbFUScolNYSwYd2Mvvay1rWouIcT1a6NzntrDrM5WvfV+0i93UPulnA/RyonhOtdzOdVRncWrIRe3F3bOIesM3DrPW62qLAnZ1pr71f71at8v6SoXQgghhBBbhYRc4tqzFotFq41VcqXTadLp9FU+KCHEVpAMbH+hNrKP9nCnW2XXlTqO1QbQr0YRhXCW6Je4ZmWYZdrOC7vtQAghhBBCiBcpCbnEpjBhGM3kEkKI65DD6sPxE37H1xuZ/dWpc0j9RqrBLqeVUgghhBBCiBuBhFzimrOAsVYGyQshrlud7XurtSuuN+trPZ2BVbch9Z1hW/tlwrbTO4834OJZY0IIIYQQQryYScglrj1rCYOAdDa/2UcihLhOJAFNZ31ntxBoIzWg64VN64U/3bbvDJuuxHGYjmNxumzTHmqtNTur06WuGCmEEEIIIcRWJyGXuOYsyCB5IW5gl9KadzmX38jg+csZTn+podFGB9GvNzh/oy2P3YbQ245/O7cTQgghhBDixURCLnHtWbDG4rry8BNC3Ng6K8o6A7jONkS4OLBK9mG6nCeEEEIIIcSNRFIGsQksxhhZMVEIccNbrz3Tsv4KiUnIJQPphRBCCCHEjU5CLnHNWWsJ/YBsWh5+Qoi1dQ5T38hw927N0J0D4i/lelfb5nKGul9qa2J7yJWcp9pO00Ca5UHzActhl2G5uqvz2VZaFYUQQgghxIuRpAxiU5gwwHM3MrZZCPFitZGgpduKhutdZrXTLvW617PePjZSVbWR43CIwqok4Gt/5tQs/yJPri9kOejSbdsKIYQQQgjxYichl7jmrLUYE8pMLiHEJdtq7XhX4ngVywPqk+Cqs8INVoZfpu1fCbiEEEIIIcSNQlIGsSmMMTiOVHIJIcR6us3caj8taeNsZzsu02mrhYVCCCGEEEJshIRc4qqyNnoppZRqOw18P8TTlx5yWWsJghClQGuNUmrFvoUQL27dZnJtZH4WXba7Fq7Es5Nm9WNPgi/FygqvZCZX+xyvdt3mlq1GnmGFEEIIIcRWISGXuKq6h1yWej0gfRnhlLWWRt1HO5BOpyTgEuJFrluF0no/9UnYkwQ8yb9rBTsbCcC6XWatY+k2mL7b7VnvejpXV2wfLA/RsHkn/khue/tw+rDLsXQ7DqnuEkIIIYQQW52EXOKq0rrLyzxrCYPgMgMqhec5oJb3ba2VsEsIsSa7yuer2Uil00YGz7/Q4fRrhWDJv0Hb50l9bBJ0Jc/AnbfnUld5FEIIIYQQYiu4nBXQhXhBLOAH/mUFU0opUmkPpRTNpo+VV2JCiKvEcn1XOLW3JoYsr6rYHnC5rBxIL4QQQgghxIuZhFzimotWV7R4Ke9ytiYMDJOTU8zNzaMUUsUlhLgkl/OMsd5MrG4fned3u/xGqY5/ux2TYWXQpeOPtUKu6znEE0IIIYQQ4lJJu6LYFCYIUeril17dZnitPB+CMKBU6qVQyF3VYxRCXH82ElB1BkLdQpz12hHVBi6TBEpr7aP9Wa7bZTfyS7jbcSQzx7qFVIblgCs5Ro+Vw+iT+yaZ29U+tL7zeOXdMCGEEEIIsVVIyCWuPWsJQ9N9XtcGKKXo6ZGASwhxeboFQ92CovXmeG1knlZnQNUZJHUOlb8Sdakm/kiG0Ydt+9XxeUlI1h4ErlWtJoQQQgghxFYgIZe45ixgTIhzGSGXUuC68rAVQlxZ7asxbuTrje5zra/h4uH0l7qC42rX2x5aOays+tIsB3Cd5wkhhBBCCLGVSVogripjTGv1w/bVEP16SGgufkm11nwt3/cplyuk0xk8L4XnSRONEOLKuBqrDXYLsDqf4VZrRWy33uD4bs+aSdDlx9snX7dXccHFlWRCCCGEEEJsZRJyiatKKXVRcGWNpVKrgRNc0r4cx6VQKOC6slaYEOLydc7KgotDrKStb63LrKdbu2IyLys5jo1UaXVe76VUlXW2JSbXnVR0uW1frxbspTd4XUIIIYQQQmw2CbnEVdVsNpmamkJrzbZt24BoeHy9EeA3G+tub+JB9FpFL9XCMMTz5GErhNi4bgPVNzJPayNzu9bbx0b2u9rA97X2s971dh7bem8NdLtOu8p5QgghhBBCXK8kLRBXled5DA4OrqjmstbiNwOWFhe7buP7AUopZmdnSaVSFItFQmMw1rZWXxRCiOtdZ8VVtyDpcgbNX8528swphBBCCCFuBBJyiavGmKiWIJ1OXxRyNZoNfN/vul0ywyuXy+G4DvV6nUazSbGnB1equIQQ18CVCIU2M1iSiYVCCCGEEOJGJImBuGoqlSrWWjKZDKmU1zrdYmkGPrlCoet2juNgrSWdzqCUoun7ONpFKYWrZR6X2PosNp6JdDl1PMvhyeVt/eKTDFPv1Dn0vf1+aw+gkkAoZHlmVrd5Wd3mdq2ls21wtdUV1wrDFBffvm6rNq73WOjWWrlWO+Ja1yeEEEIIIcT1SkIucdUkQ+cvGjxvoRkEpL1M1+0cR+P7IUqB1gpHa5Tnxvu6FkcuxAtjreWxJ55ham6cTDZDT08PWily2RzFniI9PT1k0mkUas0AYa3ww9p4eLj8TABQrdV59vjzuNph7+7tZLMZTh5/nqNHj9EIffbs38udt97K4tIS89MzNFXI3t17cRyHyfFJ6o0a+b5eCoUCKce9KDjq1iLYGXJttHJrrRbGbsFa57E4HcezkYCvWzBm285ba1aYVIUJIYQQQoitQkIucdXkctlWyJXM0ko+bzabhE5q1W2TgEvrKNgyxhAEwbpD59uvR4jNYK0lDC1/+4X7OTdzgWw2S09Pnnq9Rtp1ufWWW3jpS+9meGgQv9YkCNpWGVWQSqXIZdKkUilc10VpBVaBigKtJNTqDCZu5Ee8BhYXq3z2r78E1vBTP/Ej7Nq1nRPHnuf+L/4T840qL3/tvRzcv5+nnjnKI19/iCVd5aMf+ZekU1k++7m/Y35+kpe+5hW88p4fINNTXPc6NzKYfq1h7mvpVoHWHmiFbbe72zbddAZVyeVtx8dGthVCCCGEEOJ6JSGXuGq0vvilkbWWIAgxxtDTk19ja4u1USAWhQYhQaCjF/1dXs3ZtqH0EnCJzdAeEIShQaPZOXSQeq1OZabMUrmB35ijlB9k/94K2XSO+flFmo368j4U5HIFCvkcac/FcT0crQnDKPh1HYXrRPGWNSFKqWjF0ZRHb7Hnmt/m64YFQocjjx9hdnaO/+XdP8iePTvpSWcZKQ5iqgs0mgF+EHD69Hm+9dVvcT6c5gMf+HEKOfji393P1PQZ3FKeu269g76e4nU1qH298Gy1AGutyySXk5BLCCGEEEK8mGy5kEuCjK0lWQwx+X41/YD5pTKOVgz2l7pcPtrA9wMcx0Epl1qtRrVaZ2RkaNXrWa708lZ9bMhjR1xtUfugIpVy+JmPfZiUzjI7O0/Tb9Lb20M+nyWV0bhuFFqxa2zl9hYCY1lYqDA3P0+ltkAQGALfYqxCa43nOZiwSXlxkcXFKrNzC9z5kn28/O47NuU2b6b2UKa3VGLb4HaaZYOjHJSCYq6H/nwvi0GTbC5HOpXitgP7eWbXTYSVLD2FEqMDvezfewspz2Pb4DZSblRh2j63a7XZVarj9M5ZX52ndV5+vdvVWdHVeZrpOG81qz3jde5TxSe29pdUDV5PiZ8QQgghhBBr2HIhlzGma4WQ2BoafpOzUxeoN5bIpy5++IWBwViLFw+qN9YwN7fI/Pwi27aNrL7fRpN6vU6xWGSth0eycqMQV1r73CSlFIMDUaVisa9/4/tQ4DmKwf4Cg/3dF2YIAsPcXJmnn67z7Qef5OmnnuX2O29+gUe/dRmiFs50CjzAGtNK1y0WpSyOMqADtFJkvRRBE9K5IRzXwVGgA5eg4qJChQWCMMT3m1hro+pRx40XC1AorVszsZJlMEKiaidN9+ApbL+8tQRYjLU4SqOUiraxJlqRVqnlDwvWGsIwAO3gKkBprDE0TYjrODhKY1VS9RrdF0rpFS2OFkiaYhXLlVnJMSbD9nV8mrHLp6GW2yOFEEIIIYS43m25kCsJuCSs2FwbrYrqPNvRmkwmgzEGL7VypURrLZVKndAEFAo5XM+lUq1hrKWnp/sL/kSz6UfDvbVa87GRHLcQW5XraoaGirzxjXdx10v28aUvPUa1Wtnsw9o0rcDGWAJbJ7QVbBzLGGvx/YAwMGjtYFE4jkMQBBhVIwwaaHpQKkDRpN6oEvpNTp07x5NHn6LWWGL/rj3sGNuB7zdIpdLkMqVWyGXipxmrNViLWi5dJYmYFBaMidpOAR+oN6os1qqM9vSjFVgUlUaF+coiTsolm83haRcMLJQXGB8/y0B/P14qTanQw/ziIuMT44yNjVHqKZHTGSr1Go2gSSaVIZfKgFJYpdBAaMF1ojBMawdlDQ5RZaBWUXBnbBS8aaXQ8fFHlV0WLaVcQgghhBBii9hyIZcxBmstWmsJuTZZGIZxS+H6gVIycL5aq/Hcc88BFsddGXIFvt+qWmj6TerNBpVKjbn5BWxogN2rHku1WsWN95fJZFqfd5IqQPFioh3IFQzNZmOzD2VTJM88xhjmFxexqozVZQxBVNFFgLU1DFUcJwQbUq8tUanO0vSqqLi+KaUDPB2ilc/jhx7lc5/9PN9++DvUggqZbIpcPkWtXqWQz9NXGsL3fVCKTG8OE1q27xhjaaHCwsICjqfJ5XKk02kcR1NeKnP+3AW27RgjlUoRBgGTE5OcPXeekZFhlHHJZXPU6jUmJibQWpPP5dGOg7WGaqXC1PgFBgYGcB2HQk+BWq3OxPgEQ8NDDA4OgAuVaplavU4qlaa3FK3gmc3lCPwQayz79+5jYKCfg/tu4fT5U2QzOW7efTOlYom+vj7K5SWCIKDYUySfz5FN53Csw2J9AduU37VCCCGEEGJr2HIhV/uAcbF5Go0GTz/9NC972ctWvUzniorWWjLpNLffcgtf/+K3ceOWxISXSpHHxdosrheFmNlMlpmpWWZmZlfsc1k08eb8+fP09d2G53mrDrxPjkWIFw0FWrc3pr34tZ4B2p4LvvPE9/neAw8yNz+O5wV8//jD1PQ8J54+zMlTz1BNhQTsY7Exy3PHnmJu4SzltEJjmZi8gA3q7Nu9jemJc3zxgb/h5PFTBLUQHeYIA6hUQsJQMTdXYeF8jXq9GVWGORbtaJ757iGMNbiOG1VHaU0Yhvh+iFYujuMwe6aCVpog8DGhIbQOp6bGSWkX3/fRKqoy09qyNF+J2hiNQSlFKTVMc95gXM3sQhWtHYqpIZoLigsLc6CiFktrLb4XUJ9c4FwwSxAEOK6DMZbvPfgwSiky2RzNZqPVitloNKLjtQHasWgneizlc3mUVlFw53gXfyOEEEIIIYS4Dm25kMtxulfoiGsrnU5zzz2rB1ywHCgZE02pCYKQ8lIVbR3qoU/Zr1+0zYXxcbLZLD09eYIgajnKZnP09i5PhQmCaFW5qJrPMje3wNjYGKlU94ALopDL9wPS6dRl3V4hrkcKhdZpQnPjhFzt5ueW+Nu/+ksefezbfOd7jxM4mtAN+K//138hbNZIOSky6SJeuofHjj3Hp/70z2ksVcin8wyU+vntj/9njDFk+nMMj27j+088wanD56lWDEFosBgc5YF1cHCjFQhDcJ00Vilca1CBwtVZlNatMF+HGg9IORZrQIcKKoqGDbFW4ykXT0GoM1gUmVTS4WjjcVwahY7GclnQNsRxFFpFz28Ki8YlGbyl0Tg6DvKtggAc6+A4KRztYDCkVDq603zIkENpjbKQTsXzy2yIsaY1VMyUFQpFnkzU78i5a/mtFUIIIYQQ4rJsuZArqQoSm021ZiO3q9ebpNMevh+1Abmu0wqeHMeS8jx838fRGmsuDqQWlxbQGorFPOm0x6HDRzlx4iTbt2/D2iisqtfrNJtN+vv7CENDX1+Jvr5opcbVKrV832dhYYHh4eEreB8IsbmUUjjOjdmCa4zlye8/R6VSo9RTIggMrpcmncmgcWlQw4bgBxms8tAB1Bbr5FIZdu+5mYMH96AIyGQylPL9fP/Jp3nyyaeo1pr4fpTraFdHVVeBwfM8sNFiGEnlXLNu0RqUo1DKYm20MEpIGLdLKlQyNUwpHB29SaPCKMzSOhryDjaqDLMhYRiCNSilAYUxFjArKrtcZ+UbCEZ3r3BWSkXtitbi4GBtEqYpjImeo5PWf6V0NOjeRMlZW7N5fAxCCCGEEEJc/7ZcyCWuD92ypCAImZqaYmxshHq9EVchpFpzu5TSKEdTqdWjKgJlqC02WKotMjQ8iFKK7du2k06nIR6K7DoOpWKJQr7A2bPnSGfSDA0OYG0OgPPnxxkaGiQMQ3K5bNfjgqgCMJvNXb07RIhNoFQUJPv2xln/rv1H3K/7OHgszJfxGyGNsAZNCHxLYFJkcwUymSxhEBDUG6jQwctmSafShGHIQH8Jz/P49re+w5NPH6ZWbdD0Q6xxUU4UMkXZ0XLQY2yIVjqq3GothEL8r41mFWqN40S/Xi0WFediWkchklKgHYVVFm0dgqAZvRmgVPRGDhfPNIQo1HIcTRhGYZq1NlpxGB0fw/I2Ngmm4n2GNmqJtCiUBaUsxoQYo+LnTY0xy8FZ9IZSFO5Ze2NWCgohhBBCiK1nS4Zc9XodpRSe50n74nVEa0V/fx9aazKZdFx5ZQiCIAqsXJdcNsvQ0BAAi4tV0oUUqVx/ax+lUoFyuUoQgNYZJqcmef7UKQYG+xkZGUFr1XrBNze/QG9vkUqlTF9f76oBF0QhVy6XvSK3s/VCdo2h+0JcGwpHu/jmxgm5EtYYjhw+hg4tQTPAb/qEoUEBjpMmn+3By+YJTUjTb1DIprl53wG2bR9maLiIsQ183+fIkSM88cQTLJZr+KFFEbX5Kc9B6WihE2zUughxO6EGlMVxo1otG7cVKhUviGItJrQorUBFQZc1hjBuZ1RWEQSGUBMFT51VWPGXWiuMIX7DINo2CEIUBsdxWoFUGBosy3MHlVIoZzkEw4IN4/NRoJYrw6JtiCvRoitvD8wajSZh4F/Nb6UQQgghhBBXzJYMucLQRO9eay0h13VFUa83qFSq9PaWCAKfIAhZXFxCa83o6DApz6GvJ0utWaWnlImWqm/7HmqtOX/hAr2lXlzXI+XlSXslGjVLuVzFmJCBgV6q1QbWQL6Y5+SJc/T19a0bOL3QQCp6wZi8GN2ccMtaix8GNIOA6ekppqen2TYyxuDAIKmUDIe+0SilcF0X3bjxVle0Fs4cr2DMOPWmTxhmCLXFUS5OqoDrZQgaTRwsu8a2cctNe9k2NoQ2ATZoYLTiiSeP8Pjj36fZCAjjqi2lNErHM6qMjbKeuJJJRetcEJgApRWBMbg6avOLMjAFWkWhkrVRxRQQojB2eWXgABMFSaHCah+UJTR+R9gVbaO0xoRxO2L8/BNdnSU00RXotkoyG2+DjsOzZF2C5KYQRs9eJj7ZRLu1GJSO5nBFBxdt7ziaEPk9K4QQQgghtoYtGXKl06n4j2/5w/t60mj6KO3gxy+4K9UaYRiSyWRwHIdGo0kul8HRUK9W8VJO16xooL+fTCbL0lKFMLRs2zbG8MgQzWZAX18BYywLCwsMDfZHFQt67cdBaKJqMoUilbq0+UXGLAeqzWZAKuXFQdflzUFaqwosDEOCIGhVVySzcnAcNIpTZ07zl5/5LHNzCxR6ihhjqVXr7N+1nR96x1vZuXPHZR2T2Mqitt4bcWKSUoqdu3ayMFcjMHOkvTxO3iVTymFChTEhxVyOHduG2LdnJ4ODJaxpYKwPNuTYs6d5+ulDLC1VMQbC0KKUxokTIGPAKhtVapmkfa917VE7oEqCsaTlbzngMtZGA+J1vJ1W0VB4FbUSJnVVNohbD8O4KgzA2qj6i2j1zNaqwjYZEh9VlCUFV9ZYtIoG01viy5u2O8smxxZVlykVTQprz9QsBmMtUZOmalWPGRPSmkYvhBBCCCHEdW5LhlyuuyUP+4aQSnkEgRe9iFIKN5Uil8tiTBTuQPTCyoQhmUw6bulZWWVVLBZRSnH27AQ9hQL9/X0orZmfX6RQyOClPIrFAo7rYo2lVCx0bVW08ZBoa+KhzsZQr0ctSj09hQ3dniAIaDQa5PN5klYlYwxhaHAcjyAINty2aG00LPtvPvcF9uzdh6s1N99ygGw2A0QhVxiG8QvYaH+toAvIpDIM9Q2RSxdp1i25Ygbd7zAy3Ecqld7Q7REvLtHwcr1JdYWbSynF3v2jPPnYc+zbexO57DHyvTkCHeB6mr6+IkNDfQwOFCmVMmgVEJqQMAw4d+4cTz59mNnZ+Sh4DgzacdGOFw1gV9HcKktbBRdJgZOK4ierWsGXNRZNNMNqeXEUGxVeWRtfJpmjtRwuaR2tk6iVjsJtpbDGrKjostg4n4o2TJ4P7IrAa+Vp7e8eJOe3nxeFcNGxGdO5SVslWrydMRJyCSGEEEKIrWFLpkUyB+n65LkujuOgdbTiWzqTwXE0nucxNz9PealMT08eAKWjmWqVpQrZQmZFUFQuLwGa6elZ+vtKDA71Mj09T6PRiC6HIpfPYULL+PgUqOUXhMZYmn4T13VxtIM1ltAYXMcBx8GY8JIeP60KirhiBqIwammxQibTd4krfUbtjocOHeXWW28jlfJa+wRa90Hy4lJr3Qq5rLUM9Pfztre+GWsslUqTVMpFa00un6K3t3QJxyFeTJS+8Z4Pk8KkVMZhamqKt7ztPfzVX32NdCpLT8FlsJRjeKiXXD6N41pMUCcgmmF18uRpjj13gpnZBYIgCqW066K1i3ac6HkjaQ20Sa9fRMf3tSGq1Ir7Gkm6ApOf3eQ5RiXzuJJAKml3ToIpCzr+n8WCib42tA1/NxajLn6e6WxtXHn/RMd9cfgVn2stNkzmciWJG8v9mEmFWnKS/M4VQgghhBBbxJYMucQmWGUUVXv7HVhcR2ONwoQmevFnDL7fxIQh2olePCpAY2k2/FZrTrtGo0Gt1sBvNnBchee5ZNIehUKWbDYaaI8F3/cplysMDPSuCK6sSSoe4qqrIAq5khUes9mNz65yXbfVipQEUkpBo1EHWBFSbUTgBwwNDHPzwf247soKMMdxVm3BVUqRyaTYtWvbJV2feHFTSuEkM6G2kBcy2S7Z1lpLuVJh177t7N63g8GBDH0jgxQHcvT2pMhkHYwJMKGPVZpmM2R6eo5Dh55lcno+vnaFVg6O46Liiqrlo7KszA9VK+vRcbVTNLG9/bjU8skqXnkRs3wxmwx+b599xfLsL0Bpjdu2WmJgwmiFRhVvpxWYOLiK9xO1Ni4P3rLxv0mVbBJyJaFZcpygUJqotdICxmKVWr4H4gDsxqwVFEIIIYQQW5GEXOIFm52ZZWh4CGsMQRhSq9VJpVLUa1VSqRTWOORyOXp7ewl8H7AQhmit6SnlW/tJqiAymSzVaoNisUChkCcMA5S2lEp5rLU0mz61Wp16vYHWMDDQR/JKUylFOp1qrRwWBFH1VLRKWfSC7VJmubUHT64bhXSe55LLZ1vnJ9VeG6kQ8xsBY0PDFwVcQlwu7dygM7kAqwNe/+ZXcurMUQ4c7KNvYAzl+Bh8rA2iIetaEQQhU9OzHDt+iun5Jep+CI7CcR20conCnihEMta2Zly1KqGIBrQns7AUiiTfNrRVF7cCpSgoSrIr3Wp/tCiWW5CNNfFpZjlbUzYpASM0JpoLpuJiKq1Igqm2tI8wOattyHxSYRZdZDnkWl48IwrSlNaopIcy/sfE+zCXVKkqhBBCCCHE5pOQS2zMKnmMtZZTp08xNDyE67ksLCwCUVtPo9kknU7juC6+H+A3m8nsY0IDI6PD8dDm6MWUjl/4Nf0ApTSZbAatNbVajUqlQiabJgwN8/OLHDlylP7+PpLl7tteY7YqKHw/avlxXU0QRJ+n06kXdjfEbYTt7YFBEMicOLEpWuHHFoq5XkgVV7KdtVFbcjYH5fI8Tz7xINt2jhBYaAZNQhMCuhVwTUxM88zhI0xPz2NMVOGpXRfHcbG2dScSmrBthcV49lZyvXp5zl8yIN7G15MsTpEkTLa9hVG13eb2GVdJIKZBKbvc7miTKq84FNMqbsmOVk6kVWkVJ1oqHjKvVGv/8aWjYC2ep5UEXcl8reSyrWrUeNZYUm2b9CrauCJXCCGEEEKIreDylojbBMuzkcT1xnEcgiAAaM3j8jwPawzGGnzfp9FooLUTDXBX4OpoML2OV/pqVTYYw/TUFJVKmdmZGWamZ1pzcxYWFgnDkGqtyhNPPoHnedxxx23xPmhtn7yI9H2fMAxoNgPm5ysEQXjZt9Fai+/7rcoIY03rMekHwYYfm3YLhRFia9ArVv27viU/m0EYXvYxG2OoVKocOXqYZ597nMe+9zCL8wukMh7pjEsq65HJpXDTLoExnDs/ztOHDjMxPk294VOp1UE7eJ6H4zo4WtNq0LPtHxasIQyjFU+TxTS01nEA1CqlaoXf7QFX+0wsEy91GHUErnwWUPFtSoIkFX9EXY3x8yOqtXBFGJp45UTVStEcpePL2BUfyeqwhuWvk+9Dp+R6k3sj+dxRW+bPBCGEEEIIIbZOJdfyO+Vg4oG5N+LA5etB9GLJtgKql7zkJa2VE6vVOkNDAziOQ6m3F6016XQaUJQrZTLZdFzWoMnncxftu9Fo0mg26Sn04HoVvJRHqVjA8xzCuRA/8EmnU7z0pXeze/dOgNZS99ZaarVoIHuj0eDkyTM0Gg1yuTy+H3DHHbdc9m32fZ8L4xPs2rmDIDCUy2Vc16FQKNBsNkmlUqiNtCxKWCuuKIW6xLlwV1KrfbhaRbmaYqEH3w8A25pn15q7pxS+HzAzO89iuczePbtIu+u3Drd+WuKfncWlJR7+3uN87q//nL6eIn09/bhellq9QTNo4KajAKvZbHD8xHEOPXOUhYUKYQhNPwTtoB1neXp9PNEqaVlU6Hj1wbC1siEkQVU8z8outxcqlayculwhpeLZXpbldkBr2obSJy2PjgMqJIx3reLnVItdHlKfJF5xq2R0bCZakdGG0XVZi41Xe9TEA8C0g8USxpVn2l1+bgpDExdqLS+osaIkljh4W570JYQQQgghxJawZd6ibR/wPT85T71Sbzt3+Z1rcXUlFU1TU1M06k0A5ucXOf38WarVOt948CHq9TrWWvK5HJMTU0xNTpPP5+nr66Pp+xij0Hi4XVYMq1Tr/OP9X+XZZ4/SWyqgTMDk+Dgpz2F0bIi5hTkOH3qGudlparVqa7sgCCiXy6TTUcBVrzdJpzMU8gX6+0rs3buTSxjFdRHfDzh/fhxjLMePn6VeD6hVo9s/MT5FvVbf0ONPHqLiSlIqquSKS3uuucAP+OqXv8mPv/+n+P/8u/8vtWaTbz/0bb729a8zuzBHEBrm55c4PzPNfK3MiSMn+c+/8F/50Ac/wtkL4xsPT2y0Sur5iQk+9/nP85W/+zS7tm1jYKAf64SESuO4KTxXQ2AoLy1y6NARDh8+xvTsEjUf6gFo1yOdSZFNe7jWgZDlYfCADQ2YEMea1mKDWiuUtljCKEwKDcpqbKjBKjQOnnaiCijloHQ04wtro1lXIRirCCxY7YBOVlw0EIbYUEGo439V62tMdJoNFMo4KOug8dDKQ+GCdXBVCse6OGhcFNFRaBROXNml4k5HizHEQVgUwEVxnsYGFusbMKCMxQYhNgjRxAsu2i30h4IQQgghhLjhbZlKrvYKmXq9jhevkNcKFqwhDH0cNy0DvS9T+3DitS8XrTo4OTXNjh3bsNaytLREJp3i9ttvhbhtRjua3t4iKEWjUccag+ulqNZ8FhcWaDQarX0m87gcx+F9P/Jenj1yBK0U23dsp1avs1ReYmh4iJ5cgdOnT3PPPfdErY+xWq3G1NQUO3bsIJ/PUalUKeRz9PQUyGTTlzRsvv2+SO4P13MZGh1Ba8X2naO4jtM65pHRURyZySU2iVKq1Q53za9ba0aHBxnqH8L6MDUzw6//6m8wV5/h137zvzIyMMr/+N0/5puPfZf3/eh7+Yl//i+4556Xc/zsWYrFUmtOeqf2uV1JtdgTTz7JX33mr6mVFxjuz6OUxfcbgIoqPcNoUYtms8Fj33+KU6fPUqnWQTkoBSnPxfPcaEB70vanl8uxkllUUaUUGAxo0K5eHthukudHBTZEmeho26vNWv8mPX/Kxi2lFmvDtnla0b9hXJnVuuWdvYxtVWJRpVU8xyu5gDXRcHvHieeALR9PsiCGtfFKkSpZfEMtd1uaaAsTD7+3jl3xu8Bai+nyhoQQQgghhBDXoy35Bq0JTauVI5rvEsQTmPXyiwFxSZIWxPUopfA8j2KxyEB/L99//EmCIKBQKKAdjTGW559/nnPnztNoNMlks2SzWUxoqNcbuK6L1hovleLUyecv2n8+n2dwcJDDh49w/vwFXM9Da0W9VsdvBlwYv8CnP/1pisUilUqltd3k5GSrZbJeb/LcsRNMTk0xMzvPmdPnudTc0w8CypUK1VoNgDAImZ+dB8CLV1RsNhoYA416A891VlQbCnGtKK027XlPKcjnC+CDa11Gh0a46+Y7uHn/QUbHxtixfTu37LmZPYO7yLtZ0p5LoZBF2ehnaiM/losLi3zmrz/Hf/34f+fCmdPks1nS6QypVBrHiVoiXddDa4d6vcF3vvNdnn/+AnPzNSpVnyAMcRyN6zpRRVMQBUk6nsXVHuprraPT43ZCrML4FhvEFVlx+18YTXqPqryIWxkVOCqpoaK172T/7f+GYRgNx7eg4ylYruOiVVKJFX20V1zpqASMZCHEOHMjBIwDvg0JbDR/y1iz4jGhomFgEBLfligIS7YPifM5C8oqtNWtMxVtgZgQQgghhBDXuS1VfrKwsEChUMAP/NYf8AsL81RrNcZGRwl8n3TG2+Sj3Lr0BmecKUVUEaGjwGtpcZEjh46y/8Bedu3eyenTp2k0GhhjOH78OK7rsm1sG57nYoyhVqtSq1UZHBlasd96rcLzp8+T8tLcdPPN1KpVTp44QaGnwNz8HIefPcLpM2d43/veR7FYZH5+noWFBfbs2cOOHTuw1uJ5XjQn7K47WFwsk0ql6O8rXXLI5boOxnqt4E87mp5iDxaYX6rhOpZ8Nhet4CivAMWmiUKQzcv2FY7jUq/7ZMIUjoKsm4PQJWVdPO2QVWkyZHBxCU2A36zi6ICF+RlGBkoX7bH9p+nc+CSf/szn+O53vsP2kRGGBvvJpFy09mk2GzSbTRzHIZNJ02jU+eY3H+Ls2bM0TA5jXZTWOI5urVDooEErwiBEu260sqsxrfFcYdTTF7cets+niqqbQmtwnOh3jLE2uu+hVQkWrbVI63QTv3mQrGJooyFdOI7H8rh7E71BYE1rXlerkiqer5XM+7NELYWhXV7NUakojzJJApbEY0moZtqqxNpq56JVGJPTiFZpZLkuzarlii4rxdFCCCGEEGKLuO5CrjAMKZfLlEoXv/hp1OvU601KvXly+QwA2WwOhabZ8Mlkc5ccZohl0QqIuhUUrSU0Bmvhlltv5vOf+QcGeodQWtFTKFDIF3EcTaPe4Py5C+TzOXpLfaAgl8/T35snm/XwstkV+0xnctSbTfK5AtXFJW6+5SYGRkfwTUiuVMQ/f5axgUFe9drXcejQM+zYsZN0OsXc3CzZbI50Oo3v+ywuLnL69GmGBocY3DZ8WfeFQpH2UkD0ujEMDEsL8wRBSL06j1ZQqxtOn36ebSOD9PdtfHVF68iDVFwZyUyueiXYnOsH0ilIZyy+rQGWwphLY2oJaw1GG5pOFZ8lQtMkNCF+UAcs5aXqmpVcDz30LT796c8wNz3F9qEBij158hkH19WE1ovCJsdBKc3pcxM8/PCjLC3VKNc8IMDRGldHdVXKKJRVGAVRuZLCWEsYhriuSxiGyyssJmGSSiqnbPx7JV7FMG4f1EphVRJG2dbc9uSZQKso8DJWYVvVUEmTocJxHKyJKsuSFWKNTVZqjI/GglI2iq2UgjBAKScK64iqrlzXJTAhWoHVthXa2XhWJfGqkE48tzJ5am+1O8bBmrEqCrNs1LwYfxH9fwNVvkIIIYQQQlwPrrv+Kq01uVy263kDgwOk0xkmJsYpl5cwxjA+Ps7hQ4eZn52XgOsFWlxcpFKpbGiAulaKZrMZV38p7n75nRhjaPo+oQlAWZbKS+w/sJ89e/egHYXjaHw/pFxpUIsDy+mZGcIwIAiiF+m1RoNKpUa5XMZxHPpLJawxHDp6mMcff5zy0hKLiws8/fRTHDlyiJmZGXK5HLOzMzSbDR566EHm5uYYGRmh1Fta0S50KZa3i1rBwjAglUrhaM2O7WMorZm4cJ677riFbdtGUMrd2FD5aEmzSz4eIValFH4z3JSrNibk3PnnMapKtTLN9498l7NzxzFOhWZYITR1SPnonEF5llCF6ExIqqCo1st0K0AzxvD1rz/I/fd/hZ5ClttuvZmR4UFyuTTWhvh+gyAIUEoRBCGnnj/DI488xuTkdLzqqwNOPFvKsVHwow1GGUIVYrWN3t5RBsdVKB0FSdHALdv2YbDKYAgJbUhoA6wyUQAUT2S3UTFVvJohKCeqFLMa0ArlONG/yQqJROFTEoZZFbWbaq3RjtOaHWiMwcShVNSS70fzvFR0rK6r4/lhIWHox3Vj0X4dR6E1bR/RbYyOR0P8YbUixGK1ij5aGV9Uned5Ho7jorUjcy6FEEIIIcSWcd1VcgG4roe1lvn5OXp7+1p/YGvtYMKQhfkFisUeMrkMc7OzVGs1BgYH6D7CWGxUqVTCddZ+QWOMITTJ8vPRQOahoSFK/TlCExIEAfl8nlKpSBiGVCoVpqdn8JtN+gf648o7KBVLHD16lH37dkYVH1ZBaNm7Zw9nT52m1FciX8hz/PnnOX7qeSYnp5ifm8PftoOlpUVe+tKXcuTIEWq1OrfddhtBEK34qLVmfPwCBw7chNaaarVKLpe76Ha02nDiyonVb7Ol2WxSrlTZtWsn5y9cIAgCMuk0ac8hm02htaK81CCb86I2R2Oi/WqFVrpjb1ZeMIorJ0o9orbZa6jRaHL8uef5xy//HQ9/4+tMz85QdQN+8Zf/Hf5cgB9a/vtv/zd6e3KcPzPO1Pw8Txz5DjV/isPfO4JxAurNahQYtb3XUqvV+dvPf5Znjx3DUSGplAbjE1qw1mCtiVsPo22e+P7TLC1VKJX6mJiYpVKN9um0BzMqeq7SWq2oZLImqlIyKoifz1QrqI4LmkAls7uiKqvk51mr5GsVtwlGVU/WGJTWaKWjdkBjoiqvtpbmKO9SWBtEx6U0yQrB0BawWwATH5NaMTPRmOR4o2M2YfT91zpqzUy2bw20bw/g46H4Oq7iUvHtjOu2LppRZq0l3JwMVQghhBBCiEt23VVytQcOhUKBiYlxqtUK5fIS1lp6igUGBgfI5PN4Xordu3dx55134mhNbWFpk49+a0ulUui4kmC1ai6lFI7WaO2QSkWzaXK5HEpH7Tezs3OUSkU8z6Ver3P06LNMTk6Sy+cpFosoYGJymlQqxde//tBFw5ifP3War371a9RrdVKpFKlUimKpyMjoCLt27Wbnzp309vZy/vx5XvGKVwCWp556koWFRebmZtm3bx+NRjSr59lnn+Xw4cMX3YbktiXVIKuJVkOLjq1er/Pss89iraVebxAaQ6lU5OzpcRp1n8mpSZrNJgDVep1yrUoQXPzK0IQGP9ic1jLx4pOEM75/bVOIhflFvvvgw+zdthMVGqKHtMfE+RmqtZBaJeDwk0d4+KHvcfy5M0zPlHnyscP8w99+mWdPnGRmfpbf/aPf5X//j/87py6cwVrLufHzfOITf87RI8/gaUshm8ZznDjHi0e5K412XBxHMz09zczMLBcuTFCt1qMFSeLZV1HlUlyRFY9Yt3G1k0nCsrgCS+vl3zlRY2H83Kejaq6kwstxFI6ncdxovpeKBl+15mipeEtrkutYXuewtdBi8gnL86+S2VpJayQs/x7UOhqY77pOHM4l59vW/ZIcv4pXXjRhGN8Xy9Vd0dwui02ys3hFzmgefVQ1ltyWpHos+bDWrvr7QAghhBBCiOvNdVnJldDaIZ1KxTO6erHW4roOp89NsVhu0KgfwXU8br/zLpTWuDJ0/rK1hz1JBUIQDyx2nOWVA1XbCyGlFGFgqVbKQPSuv+M4ePGKiIVCnqmpKZRS7N+/HwuUazWIqxzufcW9VCsVUimHk6fPcfP+A1QXFzlx/Bi33XIT8wsL+EFAUGti6gG7du5mcHiUVCrFxMQEd911NxMTUywtVdi1aw/z8/OMjY2RyxbIZLJRlUP8ojAIQ+q1Bl7KI+VFD/swNDgOzM7O0d/f23V1RKWiofOO1oyPTzA6OkqpVCSXzUazgE6dZWikn56eHI7jUK/XMWGI57qt1qPO/QW+f0W/d+JGFoXOQXjtQogwNFij6M2XWJofB+1QC0LCusUzeRw3hXItoR8SGIWJCodoLIU0lnwsCsfzOHPiNGdOPs+bX/0mmuUy//DFv6NWLpNyFdgArELrqMzI2mS9Qo2jPLTnMDUzQ7lcoVJp0AwmaDTrWBVXSgZg21cXjAMjiKpRo6/jdRDVykHsKkmF2udrxRVSxkQ/u9pxcJTGmoC2TsT4cxtVRam2NSlUVIkWBUnRSZakOi2uykoqZOPjtNa05mZFbY0azHJlF9joXSpjcZLbgEmituXbEc+jV3GIZrAoo1r3g7LxTLHkGG18dHa5vXKDa5IIIYQQQgix6a7rkEspRaGn2BqIvrS0SKHQQxCA66VIp4ooG7+L7ShcJ7XZh7xlJe/WJyFWEL/gMsZg4xdd9XqDZtOnUMi3qgiMMVQrldZ+yuUyxWKRXC6D4zgYE5LJZCkU8tGgZeWTzWWo1+vcfdedhGFIo9EABVMzsywuzDE2NkJfHDoFfhO/XqdeqUbXqRXHjz/H4OAgQRBQKpU4f36cUmmA4eFhKuUau3bvxHFcRkZGSGfSQFxlYS2TE5OMbRslDMNWNUc6nWq7D2jdtlaYB6TTHtVqBWMMxWIPlXKZTCbL0HB/NC8HEw1vDg2u4+C53qrNs41G4yp+J8WNRMXlQeE1DLmMsYS+JeWlOX72HDgOzTAkDEKMAYdo5T+Lg42ri7S1oBzAIQhCAhPguJpsNsPRZw5z/vnnGb9wjt6eHlAO1kZt0caEGGNwXY/+gT76+vpZXCxz5NhxLoxPYawi8A3NZo0giGdmRelRqwopup8UyQqIree5+Pa07rm4jc+2DaBXSrXa/5ItlFJoR0e3MWkhTNocdXS59jcNFK0nFYy1OI5eDtawKBu3VibVVNaJh8abKNzTiiTuWtFPSTQb0UZj7aPb2hbUJZVjrU308vqKWBvPIgNlkpUbbavKTMVhXHJ/yeqKQgghhBBiq9iUkKv1DnJH9ZCJ33k3YYiXSrXeffc8D9/3W+/EZ3NZenp6GB4sEQbRC6rkj3nfD+LWDvmr/IXQWkffj/YXa/GLnnq9geOoqL1Rq9b3jXjVsfn5OdLpYaampqnV6vT29uK6UftipVpFa6jVqmSzWebn50lnUgwNDHDm3CTjExP09vaidTSEudFoUqvVMCaa72VMyOzsDIODg9RqVUqlEqdOnSGV8rjllps5euQEjusShAG5fDSLa3x8Au04VCpVGvU61kZD9j3XJZPJUCgUWFhYIJ3JkPK8tturWveF1prp6SlyuSzGGM6ePcuuXbsolUr4gR9VuymF8ryo3qRVjbE8g8taCI0lNNd2fpJ48UoCmtBcu3bFJPip1Ro0/YBcLovruhhfEQQNoIHjuPHRrQyWrIUgCHEcTX9vP/v27qJWqTI3PUlfqQ9rQoyNqngtFqV1tGJroYDWDpNTE5x8/jSnz0yQzxW46ZW38bWvfpNGs4mvLdaGmLhdMFTh8uw9aIVAoLDGtgKqZG5VsrIhbYETSsdFXdGx2zgCs0QBU+t0a1u3t3UfxQGfbSVgcSCl4oAsDpN0ErglodqKgIyo8jXqjUTFM8W0E83xUq1carmSazmkikvRlMZaSApLo5lmxGEcUeC1XNAWh2K21Vup24IyIYQQQgghrnebFnJFwZRqfb0clFgazQaO62KMabV8ua5LsacIwNLSEpPjimIhQzabjVtBov0EgY/jaAm5XiDHceJqp+VXN+l0Gq2jsMj3G/T396M0hPFUYovFdV3CMCQMDb7vMzAwQCaTpVqtUqlVOX32HLlUnmazieM6lCt1pqen2bFrN2fOnGV2dpZSsYd8Po+1Ft/3V8zGymZzFItRdd/8/Dz1eh3Hcdi5czvpdIa+vl6mp6YYGOwHosqyWjzfq1qpsm3baDxIWrWGV18Yv0CtVmd4eJh0ymNxcQnPc8nlcq3HZhAEjI+P09PTw/z8AvMLC7gXLnBgfx7P8/Bct/WCNQlsk4qw5bZFSxD4ZDKZa/q9FC9u7YPHrwXH0WQyHqlUht7efhaqC0TzslysbeD7IUEQvZHhxj8XELU5Jj8XQ4MDHDxwgF07xnC0wjEBnuMQqiioCUI/vm2GRrNJsLDA1NQUzz9/mjC07N2zn2w2w/59N/Pk489gwpDQNDGhjzXg25AwDOJqrjjkMtGcKkvUGmhD22pRTH5/JCseRhVRrQFaUfcednneVhyQae3Ez0/x7Cqzcn5VMqMrCcJscl9Y05qRpY1NrmDFmwpJy6RSGqvjCrJ4r9Gs+uQILfEY+dYMsvZgXcUhl+sS3R4nuY44VIsrudBqOQykPahTmGsYogohhBBCCPFCbErI1a2Ky1rbCgOiVbAMjUaDXC63XAUQX35mZopmo8K2HcMUij04TlR1FFUYOV1nK4llna2JcPH3JDkt/qx1muNoUimPSqXM7Gy0yiXKEgQBjuOwtFRm//69NJtNxsZG0drh/PkLLC4s4qZcJsbH2bvrQPyiL6S/v5+nn3mC3Xv2Mjc7S6W8xG23HmT79jFmZmYo5HP4zQZnF+bJZDPkshmU0szMzFAqlaKQE9ixYwfnzl3AYmg0GwR+NCsnCeB6e3sp5HO4rotW0N/fR7PZJDSGZ48d58D+fWQyaUJjmV9cJJ/NksvlCMKQarVGuVylVqvjxKuNKeXw3HPHueXgwSiMC0KazSaZTKb1QlFrjaNXzuWq1xsUevJX5fsqbjxJS9q1DCGUiqobU5kMvb19TMyco1Kuot10HCAFoDSO46HdVByYB9jAJ+U69Pf1csvNN7Fz+xgm9PEcB8exNBs1/DCg3mxSbzQxxlCpVJidnaNSqeE4LgP9Axw4cBN79+zm2WeP8dyxo+zdtQsdakLtY2wTGxoCawhMPICdKOBKKohRKh7OHg1fbw1XJ6qyNEkqhl6u24qDdoieU2x8uSjkMnGQZVphmYmfY41V2DCq2grDaIC9MZYwCAhNdJ0mCMFGcwMhDtPi64sjOIwKwSYt5AG0wjTbei6Nri+Mv0dtc8ZsVLHVbMYVa3Ss5NiqYIsH4Le+z6q1QqyEXEIIIYQQYqvYlJCrcyC34zhRIBEEeKkU2Wy+9Ud4GIY4jtNaCU8B2jEMjQxS6u/FSbm4rkuz4bO0VGVwsK/rda6YzXKDi5aED1vfh877JHkxaOMXcVHouHz/ZbIZ+tUgR448xy0HcxR7C8xOTTM8NoqyDlo5zMxOMTg4SLVSY3Z6npSXojfVgzaWtOeSTqeYmJhkcKiXarVKo97gjltu4bvffIBsJo1SismJCer1GjOz05w9e5pbb7uNhYV5Tp8+QxiGzMzMcuDATfi+Ty6X46GHHsJ1PV7zmldRKBRIpVIEQcDc7Bx79+zGmBSPPf4kb7jvtfgm5PCzR9m/dx8j20YZHRkmRLG4WKa3b4Bs2sOYqP2o0QyZnJnD8bJoxyFfKHL2/AR7du3AGEuj6WOtYW5unmKxSDabwXVdPNeJ97EsCAIyGZkdJ64cZRXqGq/YWW82ef7MOUywwNlzz+PXfdKFFBZFYE38PFEgnSvRaNYJgwr5fI6x4UH27NzJwEAvEGBoUvcNftCkXveZX1igXKlgTBQIV6sNXDfF4MAoPYUCoTGcOH6CZw8dZmGhyr79+/nffuKneepbR5kL5gi8KjqwBMoQxvO8ksDIWNOaXWWMwdFeqyorGshu4hl8SbtxNLSqNZswbmG0xrSG2vtBFD61V3CFxkatj9oB6xAGCmMCQhsNhW82m4ShicOy6A0dY018etiqCAtNSBAE+L5PEDRRGAwhxgRR5VoYYIzFmqjVMqqgDVsrO5oVbYdgwwDTNqssuh8CrEraSmG5ci2qOrNhtDKlCSXkEkIIIYQQW8M1DbmSlsT2od4J7TjojtAlm82yuLhIPp9nenqaUqkEWLSjSaVS1Gs1TBhQLJVIpT2GhwbQju4aaCWVRp2n32iSCi43bgeNhjqvfBg0m008z4teMBmDUl5rRlcypyud9jh4cD8oy/j4OKm0x9DoCMYaXM9tzUnbuWs7fhDgaI3npchmc8zNLZJOZ3jkkcd4wxtfxd13381jjz2G31SMT4yzVC7TbDZ4/PuPk06nGRwcZGzbNvbs2cPM7Czf//73eeMb38j8/Dzf+973ePvb345Sim9+81v80A+9A8dxeP755ymWiuzatZPe3hLHnnuOvr4+/GYjWrExDPjs5z7HR/63D3Ngzx60dlhaXGRudp5isYcwiGa7pTwXx9UMDvazb/9elipVivk8WsPu3Tt5/tQpstkspVIJrRSTk5Ok02kGBvpx8jmCIETr5cd7Nptd8bgX4gVTtNr7rhUThpTLFTwnIAii54lyuRwNftIu6XQP2WwPzUYDv1llsDfPTft2smPbCK5WYOuEgaLeqFFrNJgYn6QZhjTqTQgtjnZwXY99u0cZHh6lVmtw4sQpqtU6YyPbeOUrX8Nr73s9t955K2iYPx3iLJ2jmSqjQp+mMZh4hmAYhK3gPqkIttbiqKjCLJkV1ro7k2Hzdvl3RWhCtNa4cXuiSTawdkUAFM+8j2ZJptI4ThZjnHiAfoAFfN+Pw6x4nqHxMcYQBmGriiu6TkOz0YgW32hUsTYkND5h6Ee5lYnaVIPQEIZRNW0QBvHA/mhwf2jCVptoUkEWvcnht75vxIPlg9AQ+GFciWaW264B329SKc9ei4eWEEIIIYQQL8g1DbkWFxdxXZdsNhuttNeaXaQuqu5KXpD09vZirWVgYIBatUpoDOlUGs/zKJeX8FIpSr39hKHpCLXCFQPoFxeX6OnpwfOu6wUlr7r2VsVk1crO+z6TyWCtJZPJ4PvBRdsropbSTMZjanKalJehVq0DsGfPbgBGRkZwXZcjR55l/MIFtm/fFl8vFEt58vkc589f4Gtf+xo/+ZP/K/PzZXYc3EO5XKZeq7GwsEAmkyEMQ6anpzlz5gyu43Lfffdxyy23cMcdd/DFL36RYrFIb2+JxcUl3vOed3PTTQfo6+tl+/YxDh85wvHjxxkbGyMIfNLpFG94w+uZn59jfHyCO269jTOnz5LLZMnl8hR7eugtlaIZXGFIrd6gXGmQy2awvSWq1RrVahNHu9z1kjtJeSm+8+TDvPUtb6TZbJIv5Mlkc+Ry0ay4ickpzp+/wK233kzK86jVGhhj8f2wdV/CjR26ihcqeuwE/sUhV1KxCcurMLYGnCdDx1ddA3T961VKk3Sxaa2jCi7tkMmWKBZ7qVaX0DQ5sHuU/Xt30lvKgQ0IbUC1WmNmdp7ZuXnqjSZz84to5ZLN5ti7azvFniIzs7PMzi0wOTmH52W55ZY7eeMb38Sdd93JwFBxxdHUa2W06+CmUmA0KvDxG3Wazeh+cbRun7CFsQbtxqs/KhWHOdF9EoQBoR8NcPc8j0wmQ9rJRuOukvvcJkPkQbVWRYxGzBsLSjukMmlcN4u1Xjw30kSD4a1tVYmGoUXbtjZFszyo3pqkOisgqC8S+E0gCrpMXDmmouUgozZRa6P2TBvNDQviNymCIJpNpmwUpIUqDuZMdHljDKENCcIwaqE0UZVZEEaVZACNRo3zF05c5mNFCCGEEEKIa+eaJj6peMXEpKolWT0RWLW6JQkAHMehWCoRhlH7RrVWY2h4gHwux9zcHBfOj3PzzQfQOlodLwyjSpxk3/39fRImsLyKpTGGiYkJSqUS6XQ6eqETmlYIWKvVSKfTpFJe63uTrDLYvoJYb1+JV77qXh575EkAQhMwOTnF+Ph5du7cQblcJZPJ0ttXYmlpianJKcZGd7CwsMBP/MT/yu/+3m+SyWR56UtfSiqVYmhoiJe9/Af4/vef4Nlnn2X37t0MDQ0xNjZG02/yyPce4ZlnnuHRRx8llUrx4z/+IXK5LPPzC+zbt5dPf/qveek9L+Hee1/B0tIiJgzJ57OcP3+WkZFBzp0/Sz6fZ3RkhEw6zf33/xNjoyP09fXiONGA5umpObyURy6fJZMG19XMzNbJZjMU8hmMCWg2Gxw/cYIg8CmXq/T199FoNMhm02itmJ+fJ5NO87J77m61e/p+SLlcjqoowpB6vUI+34O67KBBCABF01/ZTmatpdZs8O3vfhPfr9M30Ec+nyedTkeBbqFIxs2teOy1z+dr/7ftalqXj96Y8KhXmgRBSGgM2oXiwDDpVIGlhUX6erK87O47GRvuw9qAWr3KUmWRqdlZ/GZAo95kcbFK4IcM9A0zODiEsZbJ8VmOPHOCfL6HV77yVbzmNa/l1ttuYWh0EDfV/VeW1ZBJZal5TWwQ4oSGWmWRubk5wiBsvamSLHhiseDo1u8fE4Y4rovrOK1qqqYfRotZ6H7yORelHbTSWBuvmmhsXKVJNCOLeDaaUqDBAKEKsDqIngOiSwDRsHdrwU3pVrimVLRCa3KMJq4SazaaNJTFTfsYE6B0Eq4Rr2hr41UVl98cSp7jlYrCMqWiZkeMISqojlczNpYwniWWtGGuWAgm3me5XOafvvqVS39oCiGEEEIIcY1d23bFMCSdTlOr1eIV7qJVE13XbVUYhcYwPj6O4ziMjo4C0R/ZfrNJEIZks1kmJ+YY6R8lrTTNepWFco1KtYKxIeVyHVBk21ewS5aDFziOg+M41Ot1giCkUqnS19eH1joajBybn1tgcHAgmpe2YogxKLU8hDmVSjG2c4Rv/O43eMf/8mYGB/tYXHCpVoqMj08wNTWO6zgsLCyAsoyMDrC4tMTi0hIWw959Bzhz5gz79u3jyaeeoFptAg6nT59ldHQHr3zla8lk0szOLnLmzBluveUOjueexa/Ved2rXs3NNx3g2HPHKPX2kcl6vP8D/5yhwSHy+Tw7to9x+vQpHnn4OywsznHTTbtIp9OcPHma6akllpaWePe738GOHbtWtM/29ffw7HPHWVhaYs+uneRzOR59/Ps8+r2Hec+7fwhro/uxXC7zwz/8w/y//++f8+53/xBHjjzL3j174rbIEC8VvXhN9pvNpvE8l8XFOo52CAInWspNHpriMkXzwg1hXHGZPNbOnD/D5z7753zmU59mYn6eUCczDqOVbbO5DOlMmp5SD/lcD5lCmu1j29m+bTt7tu1lx84d7Ny5k3w+j5dKkXEzeHit63VTir5RhyPPzNOwCqOg0DNC2svQqM3zklv3cOvNe3F0wNTUORYrC1RrTSrlOr5vaDYNCwtLlEo95EpZrFWcPH4KB8Wdt93Dz37sXdxx9+30DZY4cegYjzz0TYZ2DPOq17+u6/2gXQ+twUVhtEdKQ0+2RG2pznxllnroox1QjgUVDW/XNoPWGmvDtkDPEIQBjqNxvTReNkXKddDajVdkNSg0ChftxM+XCohXO3QcHVVtGYvxfapLZaanJ6nWFwmCGo4DzSDA0R6htWilcZTCddxWQOW4Do6bIpPJkc/3kHI9tLZoDdY6KxYNsdaircUoG1+vAQuudklGbFmi6jSNixNXfIVE22miY01WVky0v+lkjEHLirBCCCGEEGKLuKYhV75QAGB8fJyBwUFcz1sOn+JWGmtCSqUinuvEA39DtHZIZzKkgaVyBaxHreozO7NAT2+B3bt3s3v3HpSCdNrB8zxmZ2fp7+9v7VuslE6nOXBgP37c5nTy5Em2bdtG8pCo1iqEpvei7SqVCnNzcwwNDTM+PsHOndtZWFjk1ltv4dixY9x0000cOvwMAwMDnDr1PIVCgXw+z8TEOOl0it17djM9tcjIyAjf+tYjvOMdb+fX/q//wu//3h8QhoYdO8Y4evQQr3/9a5mZmcXaAN+PRv24rmJi4jyLi4u8/vWv553vfg+LS4vs27ePP/yjP+LAgQO87nWv4+izR2k0GvT397K4tMSJkycB+Lu//xKTE5MUegocPvQsH/nIRxgaHsEYE90P8awyC+zds5tvffs7/OM/fZV/9p738NK77uKBBx4gm80xMz3Nk08/zUBvHydOnOBVr3oFjz76KNu2bcP3m9TqVfr7S6TT0YD5MA5wgyAa/HxhcgJrodZsUCSHpFzihXAcjZeKZhGePn+ar3z5Sxx64nvctGsbr7nnB3ji2RPMLpXRjtO22mfUOleerrCkylgsx585SbVWpV6vEYQ+tUYVa6NQrFDsob+vl8HBfnbu2sXYwC6OHzpJ0KwzPbvIwMh2lEpRzLjc/cp78Ztlzp87zsLCPG4qqoKypNCOora4iOum6OsbiH4OlkK2j+7hh3/oDbzxra9h2+4RHHd5hdxCvkB5cYmZQ9Orh1xtVabGWFxHkc9mqJTLHD18jMWlMtZGIV8YVyk5ThDNznI90pl0a3aeMSGe57H/5psoFEt4KRcI0cqJfpfYeCAWdvlHV0FbPyNocFyNqfgszMzQ8Mt4nsFJafLpNNpxKeR7qJSrKM3yrDAgCJv4QZPQBGhHke7txwZ2uerMrGzNB3BcJw614ple7fMutcLTzopnGZ2sWNzWvp5U+bafprUmCIKL5jYKIYQQQghxvbqmf7kmf5jv2LkTgEa9Hs0ccV28uJXRS6XxUmmstZw9e7Z12WTbZMbI4HA/+w8eQGnQHX+A+80m+Xz+ousVy5IXNcmLl7GxMZ49+ix33HEHSiv279/PwvwCjuOQSi2vBpjJpCmVStH3YHCAarVG4Fve+Kb7mJ2dBuDee+9lYnyCPXv28PDDj3Di+HFuvfVW3vimN9Js1rFmgTA0PPXU0/zkT32Abdu2MTE5wezsDEpZvva1r3Lffffhui6HDz8TtaNeuMDs7Byzs9M0a3XuvvtuyuUyTz31JLlcjkceeYQ3vOEN1Ot1jh49QrNRR2mH+fkF0uksAwODTE1NMToaVagUC73s33+AJ77/OOVyldm5OX7gB17ByMgw1sKx4ycoV6t4nse3vvUtDt58M2EYoh2H7z/5JJOTkxg/JJPJcu8rfoB/vP+f2LFjJzt37ojbcpfvYyee+6NdN2oDTaVoNAN6ewpoeWyKF0A7ikxGc+7cc9z/wP38+Z/9IcM9RV79ipfwxte/nr/8/32WE+4FwpwGJwpwTBiilIsxFieVwphoJT5jLfmeXmyPxQ+agCEMgyi80QrbDJk6O8f5UzM0m49iQ0inMvT2jRACe3YNsX/XANNTZ8hk0tTqDeYWqoRhSC6fo1gsUuzJEjQUiws1stki977y1bz73W/lppv2ki24rSCn/Tm7UW+AVfT0FLvfCYDjuoCP1nELvNVkchms9QhCh717b+Uld91NGNpoSLsfEAY+YbA8syoaBg+Tk5OcOnWKxUWF42XxvFS8iqFCa5UMOItmbxFX0ynd+jcKigyOdigv1Xnq8Wc5efooqBqW6E0FEype9/o38PTTh2j6NSBqfUQrlOtw2+13ctdL78FNpwitQTs6itXiIfE6maeWtJBqHbVMJoWhqq0RNeltNMsD7VvZnI5Du7aW9HZJ0KW1PE8JIYQQQoitYVPenk1ewKQzmdbsD2sMgYleVJWXlqIWRb/Rdvno3eV8PsvQcB+LC/Mszi/QO7hy1pYxhkq1iuu6pNPprtcfBEHXYfc3CmOWl6vP5XJA9OKmr7+Pufk5ent7WyuMtUtWZczn81SrVebmFsjnc9TqNXLZLGfPnuH222/D8zwOHznMtm3b+Of//H/hL/7iL5iYnKBUKtJoZvBScxSLRUZHR0in09xyyy14rsvZs2c5f/4MxVKRcmWBfC5PtVZmanqcmdlJXMdl+/ZRXnLbXRR6ipw+fZrjx4/z3PHj7N27l6NHj/Ld736X+fk5hoeHcJRmcHCQbDbHmTNnOXdunNtuu40DBw5SLi9x//1f4VWvfBV9/YPcetttDA0O4nlRS9ZNB/azb+9ezp07x9FnnyU0Ia7r0qzX+fznP8873/lOHnvke/zwe/8Zh48cYWR0hIGBfixw/sI4WiuGh4aiWUBEL0qVUmRzORzXJZ12pYBLvCDGWhpNn7n5WebmzvLwQw/wI29/O/vHdnFm4gSnnz+ONT5Z16MShgSEcVihCAM/Hn7uABZHKxwLnuvghyGpTC5amMLRONqJ25ajtrd6M0C5IbiKQqmEH2ryhSzZDCwuztFo1JmYmEArl7GxUXp6CszPL3JhfAplMty87yW89gOv5OWvvIuxbQN4KS9q86P7GxIzs3PU6j4je3asfl+EBp3Sy218ykN7KZx0lh179nLPy17GzQcP0mg0QKso2GqAVg4WgwktnheF+XNz83zta18nnU1hcVGOgwsom/y6tBg0SmlQfusUa4F4VUKlWmkTYeBgQ4eQaPi8MSHWOrhumnotoNEMsDZqNzUKlOMShhovlQdclEoTBXjL8yvbW8ijf6OwS7cWFGB5Bch4BplKuqOtxra1pmtHR8Pr4yqudsnvSXmjSAghhBBCbBXXPORqb7UI49WfjDFx24iL6zqkvBQWyOcLLC0u0lMstlYCU0pRXqwy2ZyjcrBB33DyznO0PPri3Dw98Yyp1f4wT14I3aiUUnheiqRAy5joBV6zGTIyUiQIoiHW+XyBpaUy9XqTXC6zomUlk8nQ36+pVqv09ZZoNpv09g5Rr/tMTMxQrQacOXOBUqmXVCrD3NwSJ06e4fDhwxQKBar1Od79nndQXiqjdQbXzfCd7zxKs2m4+yX3MDq8HT/wqVbOMH5hisA3DG0bYGpqlky+QK3ZpKdU4hX3vpIvffl+fvzHf5ybbrqJz3/+82TTWeZm5mg0Gq12SVfDzQf24jeqnD1zElAsLUYtTJ7rks/lsNYyPz9PNptlYWGBIAgZGh7G8TzOnz3L9PQ0vcUe3vWud1Es5Nm2bQTP8/jud7/NTTcdRCvFI488RjaT5aab9mGsYfzCJCeeP8XYyAgzs3OkUx6ptKLp+6RSbe26QlyCcrnMs8eO8MzTj+KgKOXTvPHVr+S2nYNU5+r84+EznD43xWR5jhp10BZHORijokBEg8JgwqjtTinAhcAajDXY0GJtNN8p6n6LmukUDq5yMXFgonHoyafJZh3mZ2Y5NTNNKpMBFL29BWanZnn+uVOMjO7g3e98L/e+8l5279pFvpAhk0nhuOu/0VCv+/QU+9i3f/+qlwlViDEBXsbDoLAEaMfBKgvKYXGpzBNPPUE6k6JSrWKtZqC3j9n5OfwwxMUhnUrjeJp8rsj2HdsJwia2aVEmnrOldRRiERdHYcBJYYwfXZc1YBVKRbMlNaAchdJA6BBaC9oAHqEJUK4iWvzSjTofrYn2qU1r5UYHhWuJAzK7YkXWZMC8UiqqCI3n/5m2yxhrcLSOZymquIpLobReDrTioflJNVq3NWCUujHfEBJCCCGEEFvPNQ+52gOuqakpUqkUfX19QDSPJAwNjuOwuLBAT08P0zMz9BSL0FqBSmONRjlONDQ4tJSrZcqVCqOjI/T09rYqtMIwjAcL2xXBVmdLxotVuVwmm82uqFhLgkXXdZcHF8cVW/39/VirmJiYZHCwH89zWVhYoFKpMjw8RKnUE1VCoAjDkPn5Bc6ePcfL7nkZSiuKxQLPPfccqXSa0bERmo16PM/FY2p6mu9+92F830cpTa1WxXUdHn7kUW6++SDf/vZ3GRvbhudaGo0mmUyG2fOzzEzPYI2Nb4Ni29g2ZufmOH7iBEopXvva15LJZNi1axfDw8PcfffdPPfcs9QbdXr7+hgcHKRQKFCv1zl+/Dg333wzx44dY8+efQR+wK5du6jX6/zTP/0TN910E319faRSqajVqFHB2Cg8feTRR6lWq9SbTV5610v44z/9U15y+22cOXuGRqPBnj27SaczaOVQKhXJZnME8epsoyMjNPyAgzft5/iJkywszJNyXW6MR6G4kirVCiefP8FT33+UU8eOkk+73PeGN/Lg/d9ioL8PGzZJuQ7Hj53gpffeg6NdrI3aFG3bqntJOOK6TqtNLxJ9EgRRZRFKR6FJaEE7WDQ4GmVDIEDbEGUCZiZn8WsVHG3Zvn0X4xemqC367Nm1m7e/5Q5e+gP3sO+m/fQUe0ilUlgbsrQ0z4kTx6hUK7zm1W9a9Y2Hu++9k9vvuZVCT77r+RA9pxtjqFYqBBYgxHE8TGhxtEOj3uDosSMMDg2w/8BNHDp0lOGBUUbHdkStxFENFNHIMk0uX8Bv1parmJRCoTAGHO1EKxTGyxs62omGtyelmZZooLzWrXZkm0yBR+M4LkE8G8yE8XltQaIJTRxUReGVVgrj6HiwvYOxNp7fFbWRKh1VYtExkD66xuXVMK21reJRG99nraCrFYyxYrGM5DTHkTBeCCGEEEJsDZsWcmmtcV0XJw6rIPrDOnlRkcvl0FpTqVSolMvkC4XWH96DQ0MM9vWSzeeSnaK1E5+vqFWrZOPtsZZmswlEL9wymcwNM0S32WySTqdXhFzJjJX2r6Oh6IaenjxKKR555Hvs37+XgwdvZnBwoFXJkbQ5ah0N98/n8/GQ/zkGB/vYs3c3f/EXn+Ytb34TA/2DTE5OMDe3QG9vLy+752Vs376dhcUFstkMvu9TKBQYH7fs3buHL33py0xNTbFn127CMODQoUPMzs6STqcZHh6mXC7T39/PyMgoCwsLHD58uNWiODo6itaafD4fB5sOPT09GGOo12pYYzh/4QL5XI5atdq6b2699VYmJycZHBzktttuW9ECVOwp0PR9Tp89S6PR4BU/8AN84xvf4Oizxzh4800YEw2Sf/zxx+np6cFaQxgaDhzYx+TkJMeOHWfHjm30FKJjOnTkCBfOnyObzeJ5XmvItRAbUW/UOfrsYb738Lcpz00zOtDHK+44SH+pxHBfL81GnUzKo9Go0FgKOHToMLe/9E4UDhiFNRbblqq2ByFaRy2LYWi7nK9Aaax2QbkExuIHPpiQTNrB0QrPdcikUtx1+62Y0PDWt76dxx5+koMHD3Ln3bexfecYvf29ZDJpGo0Gp04d59Chpzhx4jlKpWK0AMkaeko9694/juvQrDQxjmFyappMNoXjpAmCkHQ6y+DQMIuVJbKZLL29/WQyOVw3hZdOR4GPjQa0Kw3WKlzPJfCj1s6k6snajjdI4lUUVXySjkPE1psq0cSuqOTNLC+kGkblW0D0XNxoBFgToK3BKEO8jCIQhVEmXoURlew3Zlk50y++/vbvX1LtlYSAyXnt3+cVN+gitlVFLYQQQgghxFawKWlPMuQ3n8+vCJzaq63S6Wj4fCqVolavk8vnW+cNDpYoFnIoZWk06pgwIJNJ4ft1XDeN47qtFxtAtGJe22pRN4pcLrci4AqCoBX4JbO4ElorlpbKrWqt3t5etNak02kGB5N38x3y8fchGUi/a9dO5ueX6Cnm6emJWgPn5ufp6+uLWmMUjIyOsn27SzabY25+DkdrwjAkCJo8+uij3HHHHeRyOc6dO8fL77mT2dkZzp8/T71eJ5PJsGNHNMy9XC5TLBbJZHN8+9vf5t577+WLX/wihUKBcrnM0tISS0tL9PT0UK1WyOVypFMpFhcX6SkUKJVKzMzOouPrHxgY4MiRI+zZs4f9+/djjKFWq+H7Pk3fp+k38f1odtne3bspFAo0mk0mp6YYHBxkbGyUo0ePAjqa56MUKc/DdVNR6GVC5heXOHP2HL4fMDY6wtzsLL2lEqGxLCzMUyr2yMwbsSoLHDlyiO9851vMTJ0Hv8ZIX4n924fZMTqC57hYbbGhTzrl4oUap7eECQ1pNxVVH4U2rtZaXk0vCa2TClkgCnPin1lH62ggvbEox0FplyAMaTT9aN+eQyadIe15pNM5Up7L3v17CIKQ177+tdx62+0MDQ7SP9iLl/JAwfTUNM88/QzPHjtMpVqmVCxy8OAdHD585IrcUc1mE7fHpV6v47gOQWAxRuG6HgP9Q7ieGw3qT2fZtXM3XtojsAGO4xA0Q8JmEy/lRPeTDqM5W/GQ9uU5WB0zqzqOQcUt9a2B8CS/16LVH01ctWXiIfDGRJVb1oSAjb6XJmk5tK19RNe/3LKoWG5PtEkVF6sH59baKIRrq9DqbH1cLuparupK2hw7b7cQQgghhBDXq6sScllrCXwfx3W7hkphGBL4Pp7n0ajXadTrcUvixQYHB4F4vki8uh3K0AwalCuLzC80CUPDzj27iN51Vnie12q3SSrFkj/9Tdvy6C92mUxmxddKKeqNBrV6g0w2h4qH+YdhNFTd96Mhyj09vQwODkYv/sKQVDpF4AcEgSGbXd6ntZbBwQG0dvja177Bm970Wvbs2cGRI0+zbWyEVEoTBD6uC+m0i+fp1jGkUimazSaLi0ut+Wnz8/Pkcjnm5mYplUoMDg6yuLhIKpVibGyM5557Ds9zyWYzhKFPb2+RublZBgYGmJubpVxewnE0tZrfWjkyk83iuC6pVIogCBgYGIjCTsfhzJkznDt7lkwmw80330w+n8cC41OTTE5MEAYhhUKBvt4+wiAg5booLEcOH8FzHHqLJcpLS/T3D9BTyJPLZ6k3mvQP9OG60bDu0IRk0mmCZsC+vXt5aqlMyvWoVmtUKhV6CgVu0PUPxDqeP32Sx594jHPPHWN64gLbRwfZtWsP/T15BntyZF1NEPpYUjiOIpPWpHQGLz3ItqEBRgcHOX3uTLSiorFRexttwQiqbTXAaKlApVQU1qi4lU1riNvrms0m1gR4rkvKc/Bcl56eAiPDgywtzpHL5mgEVfoHexndNgKsrBSqlhtMnJ1j4vwiga3zspe9nL279/HUU4euzB1mo5V1C7k8rutgVYByQnzfp9aoohxFtVZjdm6OTDbF3MIsbsqlXC7Tk+9haXGeUqlIrVan0ahgVIjVFnQy2D2+mqTALQ6aMHZFRVUrhCLqaFRotHZwtAM42CAKlXQ0tSsK0ZTCYFHWooxFR/2EhBqsVtggadU30eys+LpM0uZoo+9vtyrd5HsQXaNdkVdZlkMztfwISfaw4jYJIYQQQgixFVy1Sq4oPAkIQ9Wa/5RIQgatNUE8I6ozeErmk/T09OA3G1hro3lOYcDi4hJ+LkUQ9kfzZUKLq11C69No1FFK4zpOq33D933CICCdySz/wX8DhFydoqouTaMZEBoLNiQMglabaC6XiwaxexkaDZ90OkW9XsfEM3uUdtBOFCJiLY1mk0w6zdBQP88ee46R0T5KpR4uXDjH/MIcgd8gCAMuXDhHOpVm774D0TD2iQnyhTy+79NT7CGby5HNZWn6TdKZNEtLS+zYuRPPjaoyFheXcF2PYrGIdhzK5SVCY3j44YcxJqS/v4+ZmWnOnDmN60VVfPV6nXK5zPDwMCMjIywuLlKr1cjlcqRSKc5fmGBpcZGFhQWKPT0opVhYWMBNeSitmZufZ35mlrGxMcZGRqhUKhR7CjhKMTszjec6nDp5gnwuy+jIEJ7nAhbXje7HWq3G9MwMjoZcNkMhn8fVDp6XJgyjgd6ZTHpFxeGN+Ji80bUtgBd/bVksL/Ls0cM89fTjPP3MExwYHOKum/exZ8c2cmkPjI+nFcoawtAHG+K4mlTKwUHjOopdYyPks9HqssZGw+VBtSq4lmdELVcHRfP54mojAKVRrkdoDM1GE2t8PE+TTrvkMmkG+nsZHRlg+7ZhTjxXwdMudRO9uRCEYVSh2HZbHeVSyPTTWxzj3NQxentLmDAgDEJeKGstrudS9X0y6TTGMWht0Y4h9H2q9TILi0uUy2XmFxcZHhlmfGKc7du2cerkKQ7s38f83DwpN8X8/Dz1eh0v5UUBk4pHXtFe1aSi9sHkK71cJWdsNNw9affUrQrlpMpLxTO7HJTWOKg45FIoq9BElXRKa4wCoxWOSmKoViwWDbmPbj3LR7JS+4D65DLJtK6oVCte3bhjm/bHhBBCCCGEEFvJVQm5lFJ4qRRB4BOGQdsg+ADHieZwJacVCgXi9cujocaxJOQCqNVq6EYT13MJg5CFxQUqFc2+vTsYGxshNND0mwRBg9BYXDdFOh29wDt//jw9PT1RSKEU2Wz2hmpZ7GStJQwsfjPAmCj8C4IAz/PI5TIEQYjneZTLNYrFPCThotIEzYBGvc7AYD/GhJw9f4E9O3fiug4v/4GXce7ceUZHR5mfX+Ts2XO4rovneUxPz6GA/oEhlipLnDpzilQ6hR8GLCwtgLIcvOUgXtqLKrx8nyAIOHPmDI4Tzf967rlj7Ni5k5mZmVaI9d2HHwZgx86dnD93jkcfe4xSHIRVq1UqlUrrcTAxMUEqFb2A3bt3H/V6nUI8w2toaIh6vc78/Dx9/f0MDgwwNDjExPkLHD16tLVKY7FYjILZIMB1XR55+Lu88S1vIQwN1WoVVW/S09NDynM5d36eU6fPkM+lsVaxe88uZmbmaDSaGAs9PTm8tIs10WpttFUYSth16cIwZGFpgQsXzrN/3wEy6cz6G11HrLU0Gg0mp8f5/hOP8uTjj+Aaw0v27uWVL70LGwZoE4AJcLUi5UVP3UlWpZSDwsNv+mh/kZe96uWUyxX8ZnubIh3hxXIVlzEGlMYPo1Y5pRTKdQiModGogwlJeQ7ZbIqeQp7BgQF2bt/O0GAv+VyaVCp6bk7mPjX8BlmdgbZV+aJqMUUqlcJ1XYrFYquy9oWKnrdShNWQs2fPMzDST6nkgdK4boqUl8aaMoV8kVqjhqNdHJ20FiuazQBjFEFg8JshcfcgsHx87S2Dy79DbGsVwyhAMqBsXCFl4pFmCmsNVsU/446OAiqtojArDrCSyrAkEGsXPSUkYaW56Lmi2zadTBxotfYZ/zcZYr8co7Vf73IQKoQQQgghxFZw1Sq5opW7PFzXIwwDrCVqB+ntJxpyHK18qHX0bn8QBgRBnUwmhzGGIAhIpVKEYUi9XmdpqcyevXtxXZcwDBk/f54dOwYZHOzDS2Vaw3xd1yObzbaOY3JyktHR0daLghv5j3UL+EFAtVqj3mhGL8JMyOzsPL29JSCal5bLZQCFMZZcLkcmk6HRDFianqNaKZPJZkinXKanZihkCwwM9PHKe3+As2dP8/jj3+fYsRMMD49y5x13MjE+SalYIpvNMT4xyUBfHydQONohn8uj0UxNTuM6LtlMlmajyfDwMFopzp49y759+9i+fTuPXrjAU08+ycDgIKExLC4usmP7dvKFAoV8np6eHiYmJqKwiehF2fbt2wE4fvw4juMwNDSE67rcfsft7Ni5i4X5eRbm56lWq8zPzdE/MIAfhpy9cI56vcbY2Bjz8/OcOnWK+fl5lpaWmBgfZ2ZmhtnZWebn59m/bx9PPPkUc3NzDI+MolR0HzebTTzPY3hwiKnpWVJemmePnaTZaLSqJ1zHwU2lUErF7bVKVjG7RNZa6o06E5MTPPLYw9z/wP186IP/Ky+/++VkMrn1d7DpohB5ZnaGs+dOc+jQ4zz6nW/w0ttv586bbsY1lpQ2rfAkDP3oOVNFYYfnOvg2rggzmmbTR9sl3vHD7+HrX3qQyclZAj9cEXBBUkG2/Fxo4rA1RGGUi1IONjTU69X/P3t/HmfZddj3gd9zzt3e/l7t1VXV+4Luxg4QILhTpLhIoiXZUsZjyY4ziWdsJXY+1thK7EzGnsROJpN8nDieseMkXjJjx7EtSqSohRJ3ECR2NBpAd6P3pbr25e3LXc4588e971U1CFmkSDCC9H4fdHfh1Vvucu599/zub0FgCDyPcjFHrVpmdmaKubk5yqUifuCANbieMyqlAIiTmMDz713TjEwTIs0H7PV6+GXvB7IVhUhbC8NBws0btyjXKijlIlBYnaCUy9TUDLlcjk6vi7WChYUloijm4MFDCKBUKuM6Pvl8kSRporW5h+DC2lGW1nD7Dc81xpqUsBoSTSPSKbU7IlOyS442e0p6CUlmp5cIa5FDhdWQJNtHRN5rHxz9NHp8vxpwvzp07zvPssef7wuwF/veZaQsFKMGzuH/jzHGGGOMMcYYY4wxxrsB72jw/PACWymHRqOeZi4VSoCl2ajj+T65IEBIRbfbob67w9FjJ9E6yXJk0ovs6ZlZ1tbXRqqw2bkpwn4PzwsIcgGen0MqhbWGOE5ot9sEQUCSJDz80EMgBOVyeXyhbi2TEzVKxSLdXp9+P8KRsLS0NHrKMFBeynRC2un2UFIQ5HIUinl2dne4cfMWD95/mqNHD3P16g20hZmpKtYKPvjBD/H88y9x8+YdpqamWV9fZ3Z2lpmZEv1ByMT0JP4bF9IJnoBSpchvf+l3KBaLSCW5dec2d5eXOXDgQKYuy3PkyBHCMOTChQs88MADvHHhDQRQm5igUChw6c1L9Ht9zp69n1u3b9Hv9cjncykZur5Op9Ph5MkTzM7OUqlUaDWbLBxYRACTk5PkcnmSJCEMIxKjuXL1Gjnf58zp0xhruXbtKrdu3WJ2dpb19XVyuRzr62vk/IDaxGSa5WUsge+z22jiex6u6xLFETdu3QQUSMEjDz/AtevX2NhaTUkHnQZND7f7vXbd0U8AxHFCHEdYC0kSj0ob/qiOaWMMcRLT7/e4fOUKX/zSF3n620/T6bT5mzeu8Tf+2l/jsUfeTz5X+N97Ud8WFtBJQqfT4e7KXZ5++mtcv/w6TzxyP//Wj30CX0gKCqyw6KifWhKFwFUSz3NxHImU4DsecWZ/dYMcBHlEFELUZmKiwtrKGp3eAJFPixH2CJA04DzNLN9vYXOQTo44Cgn7fRwFucCnWiozPzfN/Nw0lWqRfC4g8F0wCWEc4kiF1jEGPSKE3g5SpvZ1KST13TqlfP4HM4az9ZqbP8AnP/VplK9wHBdrwHV9pqamMFbQ6/WpVsoEuQBrJVEYMTExie86NOsNSsUiUimuXr1Go7mTBfbvBbELOfQuDlsVLcbozOZpszD4TB81CoY3o2ZEC1iTKtrSgPn0jcXQ1mjV6OaPFJK0GmCoghsyUMNQ+P3Nh6l9cb+Y66026P0kmBKpuox9BJjNmhuHarzhc/8o3xgaY4wxxhhjjDHGGOPdh3fUtze8ODbGUK3WWFhM1TNxFCOlQ7fbp9FoUd+t0+30qFQns+B4D9dNg8KbjQZCCFZX1mg2W2iTIKXFVR6eE+C4HjLLQxFIpBBsrK+zurLCSy++OFyQe5bnjyq0NiRxQqfdZfXuOsIalHKxFtY3tokTjVISx3e4fusmcRITJwmXrlxn+e4KkxMVjh09TKFYJEk06+tbzM7O0m63uHL9NluNbertLj/503+cXq+HNQm+B+GgSb/XJo5DmvUG/V4C1qXdafOjH/8Yv/b530LgUSoVaDabBEHAxuYG1lqazSZbW1tUKhVqtSqDcMCLLzzPYNDn2tXLrK+t8Ju/8essL99mZmYKKSy7u9vs7u7QajXwfZfjx49Sq1W5ePECvV6HN954jWeffZ67d1cpV2v0ByF3V9b4whd+nX63x8G5BdCai5cucOnSBaw1HD9+jJMnjnP9xjU2NtbSia3rU280+dKXv8ry3bvcvHWL3d1tNrc3qTe2iMIeeT/gYx/5IHPTNQp5BzBsbe2SJIabN1dpNJr0ej2SbDL91jFqbUpwLS/f5Wtff5ovffmr/LN/9s9ZXV29ZzL6RwXGaJIkptNpcfHiq/xv/+r/xz/6x/8Tr770GjPlAxw7cBIndPjLv/iX+erTXyQMB3/gjntjDOFgwM3bt/jVz/0y/+gf/B06G8s89eBpPvXhDzBTKmLiHvmiB46hnAuYLJco5HIU8sVUASkl1ib4vofyglRT5Cis8YkSiaXDg48e5eFTRyE0DPqDNP9waFfMav6GNkaBRDh5kC5JNCBqtcgpQaWQZ+nAHA/efx9nTx1nYWaaWrFI4EhsMiBJQiSSXC6HtZqr1y/R7jawJv4OossO86Vch3K5Rr5QBMUPZBwra3CUh1ECP+9iMGANSiuUUaAs3bjNyo07LF9+E+X2SeI2t27eBCwuhmuvvka/sUucdBFoMCa1+CmZBcUDGIxI31tYizYWKbNGRgvWpvHuwkokEikkwoLBgkiQ0iBVlpMVg8XHCjcL+Jcox0eIAKybqtOERaAxkLJUUgw5tgx7AfcYA9ogLGR5+QhrQRsw955btDVp0P1o3dI/b4c/qkT6GGOMMcYYY4wxxhjvTryjSi64N7/E8zxmZmZot1s4jqJUnsRRCmMMUkqU444uxJVSOI7C9ycAweLiAvX6Lrm8w9zcDOVihdn5GZQQ9HtdOu0uuUKJMBpw9NhRHMdlZnZmn01xeE/87fGH9UJ+/938NAtN0u8rIp3g+gHt7oDzb7zJ+toy999/locfPMPZ0yf5/Bd+kyiKmJ6sEYYRvu8hBOzs7nLulVc5tLjA7Mw0r71xiaWFOb7+9acpVgLe+8QMWltarTbnXn2V6ekqYThASZfFpcPILKdLKYc7y3f46Ec/SrFYxMtse44j6fUiVlZWmJubI4wGPP/CcwDs7u7S6/fw/QApFbOzcwRBwOzsHFIqPve5z9HpdOh0uuRyeWZmZpiamiKfL5AklqNHj2OMABQWQ6IN8SBmEPZxXMWJE8fI5QKKpQLnXr1Lq9VkdnaWwWAwypFLJ/OWs2fPsr66yle/8hUKhRyPPfYo29vbPP2tb7JTr3Ps+DEma5NsbW+xvbODtuAGPju720hhcV3JiZMH+fyvfR7f9zl+/DiLCwsjq+1wOAqR5va4jkc8EHR7CQtzRwn8H5AC5g849k/MjTFsbKyxtrnKzVu3ufD6Ba68eYVwEDJZq5D3A5SUVAp5vE2fv/7X/jr1X9rlT/6JP5up3uD3yi16p9clXYd1vvKVL/HNb36Dcs7nR556glPHj7GxcheJwJWKgp/HlYpAufi+j+NIlJNgzTDjSaeZTkohjIOyFqFjhB0AmmgQUZ6eYXFxAS+4yFa7hZt45HNB2jxIGj6faMA6KNdP7cyDPmE4IPAVMzMTHD16iNnZKQr5HK4jQBi01WAN2qTKWt/1cTzF0898k8u3bvDt577FBz/4gVT1tA8CgZMdR51Ol9XVVXI5l16v9/1vW7HvmJECx6ah7tJziGNDbCKUjamvrUDcRYVH6A5COp0mQhiarRbr66tMzlaZzR9AkRJEo0IUmZJXJv0RdHYDZ7huw7bFdEXTfU1KgDmug5QO2iqsUWgtSRss0/NdkjVcCpXmVpI1JSopsnrGvVKWe62HQ1ujTUPqzb7nkSnGhgs3fPAteKtqK326+I6f/yica8YYY4wxxhhjjDHG+MOBd5zkMsawvr7OwsJC1oSoKZbKDAZ9+v0+gR8QZBN7rTW9Xo9SqZRd6I9K2ymVSijlsLZ+h9dff4OwYyn6OSYqeS69eZGXXznPz/zJ/yOTk5PZeyUUCsXM1gE6Trhw4Q0OHjxEkMtlEwwns4b84Qyit9aSJHvB/5a0cj5ONDqJqVYKzE5XOX5kjih5krsrGyyvbrK0MAMW2p0epVKZOI6xQKIthUKe6elJLl56E9fPkSQJv/6bv82f/tM/x80713j1/Hme/dbzPPrYo9y5fY319XXmZmfp9/ssLy8TGUO32wUs5XKZRqPB7Owsa2trRFHMSy+9TLVSIYkTBv0B3U6XRqOBBQqFAqVSmaNHj3PlyhVarQ47O3V6vZBeb4OpqSmCIE+r1WV6epaTJ0+htebq1av0+30ee+wxvvWtZ/mZn/kZjhw9yjPPPMP09AwzMzNcvXoVRzlsbW3TaDQoFop0ux0ajQblcpmrV68Sx/HIIuj7Ps1mi52dHZaWDuJ5Pudfe42zZ86yuLQEQqCTBBNrkjimH4UcnpygWq1w4sSRbP9AtVLlPe95D4XC705aKUdx8NACBw8t/FDGzR8UDCfam1ub9Pt9Ll26SLfTxgrLt599juXbK7jKYXJykkLOQwqBEgopBAvzBwH4W//532J7Z5t//8/9h7hujj1y4Ie/Ho1Gg28+8zW+9fRXkTrmR9/3GMcOLaKiHiVPUjl+hH6rSeDnCPt9XKFw83kEBsd1EYJRPpNSCtdzSdkSiRRuGh4uLa4vMUqyvbpOd9AnVyqQ1xG9cEAjHOB7Ln6QQ0oHhEI4PsYqwn6HqN9nslbixMmjHD60SD7w8F0X5Yi0l8+mZ+WUYHPZ3a2zvXWb1dVNojDm4OIpzr36GocPH6ZwvIry975ipJS4joNyHOI4QmtNoZDHcb7/ryFjDFYysi1KlZ74hYXd3R1uXLtKKR+wvbmGh2FjdYumMXS6XdrtNstXr6AQ3Lpxg+p0lXajQb/XY0JURuSkHSrfSPP0tI5xhERkJJUxJt022ZeO1YYkjtA6QQiJsA7KyWOtIow66Y0dJbBGAS5GJygUSiqk2K8OS9sW78nIknutiZByYZJ95JTdx2kNQ+qlHN10+t1zu+5tVxxjjDHGGGOMMcYYY4x3G95xkktKycLCAkmS8I2vf5X3f+BDOK5LLpcnCHLsbG+xsbFGpVJFOQ43rl+j0+nyxJNPonVKEEgpmZ2b4+7yHQ4eOsG39Gtsb2/zta9+ldW1Q5y5/wwf+9jHqFYrDAaD1KbiOGTcDs3GbqpKmp7m7soyteoE0zMzP5DJ1R90JEmSToZINSyOUkxPTVCtVgkjjTYRL738Bh/+wKMcPTTPbrOHRfDIIw9x6/ZdioUc1WoFISS93gCt4dDBJa5ev82Z0yfJ5/MopXj66W9SquZ47LHHwUjuLt8kSTTFYoGJiQlmpudw/TzNTodarcatW7fY3NzEdV1OnjxJtVpldnaGVn0bbTRRHCGV5OSpkwwGA65evUq9Xuf8+Vc5c/oBlHLI5fLUagGnT09y8+ZNHnvsMZ555hmOHz/F6dNneOSRhxBCUiiUWV1dpdns8L73fZBXX32dVrvFY489ypUrV7DW0u/36fV69Po9Jicnqdaq9AdpQ2O9XufBBx/kmWeeAcBxHO7cuYPvuszOzvCpT/8Ya+urvOc9j2OBL3/1K1y+cpliPs/C3Dyf+tSPceTQIYw2dLsdoigEUocS+xQoY3wndhsNXnzxBaSUlEol7ty+yVe/+jRxnFAuFijkAzzHSe1aAjQWqwSusByYX8B1A/7JP/nnXL1+mf/bf/yfMzt1ALWv8e+HgTCK+MIXPs9z3/4GyiY89dhDHJqfxcFQK/roUDPoNjl4/4NcfukVarUaXi7AC3ziOCTI5bAYpGQUIJ/CYmVKsjheDgwIx2Nnc5drd27zzDdf4aVXrtO3ilKpiBe4dLtdur0B3V5MvlgiXyhgrKDVbeEYw9LCAc6cPsHkdIV8wSPvOEhrSWxK8kBKkrc7Ta5fv8Gtm7epVqf4+Mc+xfTMLP/L//dfYSPotgZo/RYbogDppEquKIooFAp4np+VLnx/UEoRD3VVGRkoJLhC0djY4fa1Wzz51BM8+NiTOMZiZMDRQ3PUKguUSgU2/TxLC0v0oh7dRp83L15G+oqFY/P3CKDSRkMww3XLyCxBSjIJQAmFsQZhLJVSiQceuJ8DiweJ4jaJCUkiRafXZGpqisWlBcIqGBlhRYJNNHGY4HsFkhhc30vD863OPm4v2ytJYnSikUqCdDJyKltOKcBkZNvwdcNR87uQV28luvaruP4oWqPHGGOMMcYYY4wxxnh34h1jeZIk4fLly5w5c2akmnr/Bz5Ip9NGZDXyQgimpmcoV6p0ux363S6Hlg5iLFht8FyPcBDS63aZnp1l0A+5vbxMvzvAw/KBD7yfh554mDixDMKUzHEdh+XlFQ4cmAcgjjXlci0N9xVwYGFxtIxvvYM9vHv9h0XZZbLQZSkFSoDWlihKJ8ueK3EdiRDw3vfcj7UCrcF3FBtrO9y8fpt2u0W/F6KUwfc9/MAnclya/ZjlW3f48AeeYuHALPMzMzQabS5ceJ1zz79GLh8wNzvD9WtX6PVidutt8oUyC7UqjlumtTBN/LLgyoXbNHaaHDl1mH/+z/8FVb9Io94gjhMOHjyE7xV47fxFBoMBjiOJo5jZ2VlyOR/XVVy/fpVTp06hdUyzWeell16g3W5SrVZZW1vm4MEDHDp0iHI5z6VLu5w9e4YPfvApXn/9dZ5//vnUKqs1/V6Pg0tLnDt3jqXFRa5du0YcRZRLZTrtLst37rK9tUO1UmN5eTm1FFowsaFUqvDsc8+jXAdjLZ1Wiwuvvs7s9DSPPf44UaKJowRrLKtr61ht2N7azvaQoN9uY/cpNP4o4G/87b/Nv/3zP8dzL36L+bk5CoUC3W6PUqGKMYZWvc6xY8dYWV3h8rWrHDp0iMD1+OxnP8uVy5cp5ItMTU7guw5KShACY4fqFZ2SPjg4HsxNTuCq07z87Tf4hb/4C/w//u//OadPnsFz91r9Ot02W7tbFHIFpqdmsXz/YYU2s5y9/NLz/Obnf5Xu7g4fePwhFmcmmZ2aJOr36PUGqGIeIwyFQpmksUsx7xL4Flc5uA7oWDAYhOhE47tuGjDvBvi5IG3pNAZrBxgbcfvOFleuvMpufYva1DzbfUEPgbYGJR08z0cbSxIb4lBjtUQbS6fbZqpU5olHTlOrVfB9hSXBdyyx6aMcSRzHNJttVlfWuXXrNt3ugJMnz/Dn/r2/yOnTp7l56xb/+l/9S7Y3Nnjvkx/kkYcfJwj8t2wUwAqsgThMkFLS6XXph9H3ubVTPiexaeZYSueb1FYoNDOzkxw+fJQ4SaAgcHMlrIJb124iPYftnbuUJopMHTuM4wd0ewPmDh1kEPfBgickRhiMTsPdpSQlsURadrJHPqarmBid2uOlZBBHNNotuoNdHEeTKwhEUTN5YArlK44eW8BTDkiBEAarDWESU52ZRrkgNSjUcOOl/2UfJEgbjIfjbTTu9gfOZ2Hy3/H4W17zbzr/jO2KY4wxxhhjjDHGGGO8m/COkVxKKe67777RxbHWGt/3iCOXr3z5i5w9+wAHDswjlYMQlkI+hyqVqO/WuXjpIo8+9hhCSXzfB2F55ptPU6vV2G3sEscRR+87xuyBWQCarRYXL1xlbnaaaslht17nwMI8vV6PnZ0dFhcXR3k8b71Wf+vFu9b6Dw3Jla7GXgW8EBZjEuLY4DgBYRhy5eoN1tfXue/M/biOJAg8bi3f4eiJIzzzzLeJkxiLZXNlEwNUSiVmZieo1Eq8fvEiDz1wP888920+/elPMxh0kVLy4kvPc+TIQZ566inOn3+VZ599llwu4MTx41QnarjFGuVKiT/zZ36OL3/lt4G04XH56k3uf+B+Nja2yOULLB08SKlcYXV1hcGgB0JSKpdY39jA9Tw63S75QoEjR4+ysroKwNT0NOvra7iuyxtvvMGtW7dot9uE4YAbN24QBAFTU1M88uijzM/Ps7O7y69+7nM8+MADeJ7HIAwJw5CbN67j+z7lcolCIcfu7i75fB6tE+bnZ9nYgF6rTRB4bG5vMzE9hee5XL9+jenpSY4dO0oSx+TzRfr9AZfevMLi4gLFQonl5RWMsUiZlgH8UTMFtZotBIK7y3fxXJdOu83y3RVqlUny+Ty9dptut0OpXOaRRx7h0sWL/Oav/waddptarUaxUEhz3URaNLFnrwJEmnc0bLlTnketNkGsNTu72/zlX/rL/NL/9Zf44Hs/SK8/4MWXX+JLX/kS129e4bHHHuIX/4O/SrFQ+a7XJUk0t2+t8KWvfo3Zw7OUCjlKfoCrBP/0f/kfKSD52Ec+RC2fo+i7CBOj0DiuwA8clCsJihN4XsDGxib5QhHXVViboLVGa42SCkeC4ykc10N4LsLzAYGODYNWA+m5nHvtNfr9BsZIXnn5NVqtDvefuZ8rV2/QDftYAUq55EsVdC5t9pMy4fFHznLfsSMI28H3wXEEjpMjSmLqjV0ajSZ3796l0ewyPXOAD3/0Uxw9epwkMZw7d57/7u/9fzh06BA/9VN/nE/86CeZmKrhOOo7zq3pzQ6F77torbl+/TpzB2YoFH4wDZhSZtlc+0kZaxBSorVht96g0+zS7a5w7NQx7i6vsXj4IFrDTrPB+uompUqFUrnK5NQU9cYOcRynVsSMtBSZJdBiMdaSmTgxCOxeJBcIgVQSbTTra2tcvnIBSx9tQ6zVOK7Po48/ybmXz6N1n0QnCCxSgJfP89hT72V6bgItnOz8sM8+OFJrDR8nI7/2FFgj++Q9ja2/u4LrrSqut3t8jDHGGGOMMcYYY4wx3g14R0guawxRFOEHweixYS5UsVThkUffQ6lUpNlsYi0EuVw6aQVy+TynTp7k1VdeZn1jg5/4iT+G63ocPHSIwA945jc+z+raGhW/xKA7wBhNqZjn9OlTaaiySpg+sIjruriuy4EDB0YX/vcs4/5QYfbyq1z33jvj8O4Npd9vXwmjhF6vjxBQLhUBCAKf0/ed4OTJoySJZW1tk6mJGo8/cj/KURgdc+nNNzl93ynCQY9vfftZ4iTmx3/8x3jk4Qf47S99mZ2dbd77nkeZqJXROuF973uK5bu36fX6SCn56Ec/yuRkjfPnX2V1bZXeoM/swRztdhvXc/n4j34clVM8++zzrK9vUC4HaJ0wPT3FxMQEB+YPUK1WuHDhdfL5HMVCkVa7Q6uZhsJ/7Ec+xvnzr6ZWtjt3iKMIIRQbGxsAlMtl2u023W6XarUKQL/f5+bNm3zh136Nz3zmM3iuizGGmZm0qKBer1MuV6hUykRRyMTEBFNT09y6dYs4jnBdDykVhw8f5vyr52l2OpywliNHj6K1YWN9Ax3FLC4d4pHHDmOMwHEVruMgpaLZbLOxsc309CStZjO1Ao18Ru/OsfbdotXqcPf2HV548Xnu3L5FzveQUnL37irRTMzS4hJTE5MEuQCk5Hd+87d4MWtJnZ2ZwfdSBZaUcjTZf6sa04wUKtnEXzqUS1USa1ldX+Vv/5f/T+5/8PPoxNBt9aiUKhxbOsz63bv87f/qP+WXfvFvMFGd/O5WyEASafrdmLivuXTrDTaWbxN3WvzIk08SEHNkbhJ0jCNAWIVS4Pl5iuUiwnXo9Qfs1Nt0Wm2Khw8ipMga+yxBEOB7DghwAg8EdDtdduqr7DTa1JsdYiOwjqLeaXP75k1Wbt/lwfsf5RMfuZ/dRpvV5TU6YT/NShcOQmhc1zI7O8XJE0eoVopIBrieh8WiTcztG7e4s3yXlZU15uYWOH3fQzz08ONUqhNcvHCRX/3sF+j3Qz784Q/xd//uf899p0+Sy+UIguBtz7eQBcI7GVEnXBr1LlubO7Tbrd//gHq7HZJ+GEIqNBbXd/H8gEa7QalYwfPyGG1o1LscOCixRhInmkK+SLPZxg8KDAYhidb71L52lLV1T1g7FpuxW4L0JsnwZoq1YLTGxAlojbU6zeCSkigMKRYKhIMBZC2Nlsz26FmkdBHKJ4kNwnUxSZhxt0NajRHhRpbHBel3b9aVmT4/U5cJAXq4zG9pG/7OUPu978f9DcljjDHGGGOMMcYYY4zxbsA7Q3IN/7wNUSSEYGZmBiklgR8QReFIyRLFMUGQBtEXiiU2NtbZ2FhDKof5uQNIpfiRj/wIv7L+eXBA+Q69Xo9BGFEoVNFakysUUJmFQwjxb1Rl7Z+IDS2V71ZC63fDcLLlOopiMT960GJJ4oQoivE8j8iETE9PoBzJ3dUtNjY2efTRx/jG008zNTXF0sFFjqwd5PrNm9y6fZMkSWg061y4+Drve+pJ4jhkbW0DsNx333288srLLC0t4nlpDpCUknw+j+d56URQwFe/8g2WDs4i84pCoZCFNkOpWOLGjZuUyzXe976n0Ebz4ovPkwtybO9s0253kUoxMTHB53/t8wwGA7TWOFlzoxSK9773vczOzrK1tUW/3x/9/sKFC3z84x/nwQcfJIoiXn/jDZaWlmh3OuzW6/T7fba3t3FdB63jLCQfisUiYHEcB60Tut0OgeOy09jlfR/4AH4ux8WLF+j1uvT6PfKFJc6cOZOF4QeApd3u0+sNKORL9Psh3W5KBA5b3IbWoj/M+NVf/QLdTocvfP7zNJo7rN5dRirFgflFZh+YZX5uHptE7NTrfOMb3+DGzZsUCgXy+TyFfB5hLAaDzVLm7D6iK3XDpce7xaK1xWiLVAohFHGU4DgenhNQ32lSKZWYrFbxHRdHSTynTL894O/+v/9r/vTP/VlqE7N4nkc+yKWKsbfbO9IS5BSzEyVyQiOjLg+fPMxUocCRQ4s0d7fwFBhj8V0Xx3FQnoNViu5gQHunjkSSxBo3l0sJLqXI5wtpzpiVKM9ijKXfH7Bbb7CxXafe6tKLE0JtSKzA8V1ePvcqpZzPxz/+KT7+yZ/g61/8KkKDjZM03FxKpBTMTNVYXJxlulaiGHgEgYNwJP1+jxs3brOz3aTd6nHk2HF+9mf+DDOzs7z55kW+9rWv0e1FLC4u8Sd+9k/w8MMPMTk5QalUxH+rNfFtoKREKUW30+fNS9coln0mJ6f52Z/52e97XBmjMTJTRZqU6ExkgtEJYAlyOWZzOXJBASklvbDLkcMn8Nw8AokJNNNTU+SjQUqMkraaDmGHrR12r70w/ddirMEYRllZQ0IsLWWUKQFl0kysNGMyfdwmGqN1aqvMVGKjz0OAkFhhSUz2HJt+FmRNjlmen80WaNSmOCLBuOffdD3uJbb2Pzb8+e3siWNF1xhjjDHGGGOMMcYY7xa8IyTXMHPrd/3QLPDdZuquQS9tmqrUJtMqdiFRwjJVqRBpzZuX3qSQL1Cr1ZibmWNrs87c5Cy+62JiTbfVo9MOOXRkCayl02lTLJZ+z2XcD2MMURji71MivFsv7JMkQUqFEBDHabuiUnJk5bLWksSaONGEYUw+H1DIB4RxTBzHTE9UKRVy+L7PhQtTnDv3BkmSsHBgnt2dHdburmEtHDl4hCtX3mR3e5sDCwdYPLTEN7/5TY4ePUIYhuzs7LC2tsrd5WVq1Srvefxx5g8scGd9B9dVzMxNkM+5LG+usbm5huNaqpUJzpw5y9e//nU67SZ3l++wtbXJ5MQkQgjWN9ZxXYd+v8/m5ia1ahWdJLRbLRzH4eDBgxirSDSsrKzTaDTwvBwHFg4yP79Io9nk5VfOAxETtTK3b99mc2OVwWBAqVRiY2ODfq9H11h63XQs9HpdjDH4vk+n0yGXC4jjkN1mkyDII4RkZmqacy+fQ0nFgw8+xOOPPc7ps2cRQhFFcUbyBcRxzNmz9zE/P4XrupSKZYRSf+gVXEPcuXUbJRx2t1okBiqlCg88cJbTp08xNTVJkvS5cvkG3372OZqNBoUgYKJSRkpQwpIFIqV6HTFU0qRGsiGxnh63JrWCWuh0BtQbbZqtPjmvwPz0LNVSCd93UEKiEEgDUrjkXcX1K9f5B3//72OwJBb8QgXhuHi+j+PKLM9OUMl7+MKyu7FJ1O5z9vR9PHD4ANO1Mp41BETIiQrbOx1mpyv4notUDpHR1Btt6o0uUsH0RIniZB5POXhKoIZKKGFACRr9AWtrG2ztNOj1Q6LE0o9i6s0W9d1G2pja1/zoJz7NmbNnmZmcZmJ6hu2dXZTjIYICqptQLZeZnZtgaqpEvuAglSGJB6yub7O722Bzq8Xk5BQ/8qOfolqp0e/1OX/+dXZ2vk6tWuWJJ97L8VP3MT8/y+TkBNVKJQ09/y4hhMCRgvm5Sf7c/+XnefDh+9A6ZG3rLqdOnfy+xpUSAqsNUgmsMGDTFkojJEI55As5vLyHMAqrQTh5ikeqJCLBdQtEpTy5XA7f5EAIkjgm0RGSYRNhSiwZYVLSygLCoKQa2QKFECl5NQzngr2gLqvQMlVxaWsRQqYklQEhzUifNVLfmrQZcmSPzAa8NXuZkdbwljGfPn+YwQXZ8TFiwvb2w9vvoP12SIsQNmv0/L52zRhjjDHGGGOMMcYYY/xQ8Y6RXPstEGEYYq0hCHJ0201y+QJhOKDb6eC6Dr1ul063S7k6Sa/XxlEuy8t3+J3f+C1+7t/+tzly5AhvXrzE6bNn8HMBQRDgui42DnFlwPTMNEaQZtkYg+M4GZET47hullGiMcakyhkhiaIoVZFl9pN2u83s7Ox3rMe7EVJKEm1wVKqcsDZVkgghMNaMsn5sNhEMoxjPc+n1eijlUC2XQKZdYSdOHueFF89x/cZNCjmHdquN4/oM+iGe59NqtfnCr/86f+wzn6E/GJAvFlnfWEc5ilarBdh0XxlDp90BC5VymUE4II4jIODK5ctsb29hbJrTE+RyKEcRJxEXL16g2WySJDGra2soKTl06CB37twhDMNRhlqv1+Po0aM4jsMgNNy+fYdSqUSpVEINBnQ6HYRU5HJ5VldX6LR3KBQK1OuNTK0l0FrTaDRQSiGkwvd9XNclikKKxSLbOztUaxUQqeWxWp3C932MNty5fQespVQsMTExycTkJIVigSRJ83v6gwHlchFjNaVKkVwuJdASrdMQ6XfpWPteEeSCkZLw6NGjPPDA/Rw/doRSucju7g7nXz3Pteu3aNTrVMplivkcrlIg7Ui9khJbZh+PkBq5jB26PsUoR6nXC9muN+n1Bviez8xUjalqlcB3MmIhs2NloehSSqRRhN0BrV6HlbUtZFBEegFCKTwlyAcO5WJArehSzSmq+QLHjx9kabpMIecTOAKFRBlNvlgi2mzj5PP0w5Beu0OkY8IwIuf7TE6VqRY8fDfNJrTZOUoby+5ui9WNTbZbXQZRTBjHNJptdutN2q0OWMXiwhIT0/O0XrrERz76MWqTk+jMshtHMZXaBBPTk5Qmy1QqRXJ5F0ioNxq0Oy363QFRZJifW+D9H/wU1lrWNze59OZV5ufmOHjoME88+SQLCwssLC0xOTWFwLK+vsbLLz+H5/l8+MM/8l3tewFIIbDGsLm1zle+ehepDLXad5+B9ru+97AJcJiNtb+FUgiEBOVIdGjY2NjEDVyU8Nmub3FwaZHtnW3K5TKFYh5HKaRKz39DomlkjR3mblkxanHMPmL0eVKkC2AzRdmQ/EpzutKyi2wUp4+T7KmuhBjStQhEur0AK1KCy2bjfL+Ca98/DMPpIVsesfe4TdPq02yxoWpUZAqwdHWGCY4wzCATqfXxD0dK5RhjjDHGGGOMMcYYfxTwjgXP74cxmm63i+/56DjEmhz9bodOu4XKLvqL+UKW5ZUgPCgWS+mddd+nMjFJPwzpdDr4gcf09CS5nA8CdBIhpEN1MlWBIeWoOU3I/cTBkHhLH5NSolSmKLOGXC6H47h/KMgGmSaaZz8LBoMEqQSuk+b8aJ0SfmEYIaUYkSye53Hx0lUef+QBjDHs1lsEQZ6dnW2SUp7ZqcOAREnFmTNn6PUHRGHMxsYW167fIE4si4uLXLnyJhMTE2xtbjIzM00+8Njc3KDdbuM4Do5KiakoCrl69Srdbpc4jlMirNsBazl8+DBhOODu3buEYYijHOIoYnZxEcdxSJKEfD5Po9Fgbm4Oz/M4fvw4165do1Kbplgss7i4yNTUFBsbG1y9epVWq8Xs7CwbG+t0Ol263R79fp9uF1zXJUkSjLEoJRBC4nkenudhraXVbrOzs0MQBFhr6ff7o88UQnDjxg2UUsRxanHs9Xq02+1RGHSr1QKr0zFq96QRrU4bY/YIG51NmpV4+1yjdztSshUeePAsDz54P/Pzc+gk4c1Ll7n05mVuXL+JklAtlykW87hDsoG9bCSTUVMji+IoJ0mMCN0oTuj1Q5rNDu1Ol1KxwMxkjYlqiZznIjCAyYhf9nKOrKRarlLMF/H9PHEk2K530VFIvpBnfmKCcsGnViowU8sxN1FgulahUshRyvlYYVDCIo3FURIcDysFVkn6UUy31ycIPGZqNfKBT7Ho4SiBicO0HRBLr9tlY6vO2vo2nU6PendAfzBgZ2eX3mBAEORZWDzCocMnOHr8FK4bcPHSChOTkwipiJII18uRzxfIF3LUJkr0ozbIAfV6nVa7RThISepabY7JiSksis31bYyFSq3G7OwcR44c5siRQ8zNzafkJLC5ucW5c+e4ceMaBw7MMDH5XWaXDSEEWiesrqzTGzSZX5ikVit//wNrFLrOiOAaKqMAtEmJ/TjWdDpdZkpTDLoh7WaTcHqaZqOJRFAo5MCmKiqZtRMO1YLp8g9XIyWfhiqukejX7i3QfpINMtIpI72G5JhUEqzEsJfLJ4TcW5dMmZUSvOnvE53gOu5ejt9btsM9CuSU/9176vCUkm2f4b/fYcW16XfHPTzZGGOMMcYYY4wxxhhjvAvwQyG5XNfDcSKE1XRaLYJcAdf1KJUrmCTBUZJ8oYgAgiCPFIZyucQnf/zH8b00mPrY8eNcuXIFhKFYyOO7Lv1+D+kKCn4eMOgkAWuRys0yttzRMqQWj7370anay2Q2tAK5XO4PTasigNpH8EmZWW2yCdBwPXWicQOfKIqQSuB5Lu1Oh/4gxPO9VA3mOFkOlcH1fKamphkMIjwv4Natuxw8eJgg53Pt2jWK1SmOHzvKxQuvMzs3y8rduygpyeVyaaZSoUAYhmzv1vE8D98PuH51NSW4rEUqSbFYZGpqisnJSa5dv06n0yVJYgqFAuVKmYnJCW7duoUQgiAI2Nrc5MCBA6PCAKUU5XKZanUizRqLIqIoAkjfo1ym1+sTBAGtVntEbCWJJo4TPM/LCE8vU1olGGu4ffs2nU6H6elJ6ru7WGBycpL5+XmWl5fp9/sYYygWi+RyOTqdDndu3ybI5SiWSmzvbHH7zi0GgwFxEo/2TaPZRGcqQ4Sg2+8jhKCUy2UE2Q9vzPwgMSSewigaZZIJBL4f8NBDD/HYYw+Sy3l0uy22tna4fu0mN28s0+2GHD44h+8qlBJImRmwrMBYDcKOAuWNSX9O1Yl7YfNhFNNq92m22sRxQqlQYH5mimqpgO+7aa6RMQhp9wiRLBdJmzTs3RiNqySzkxMYDUo6HFxaYGF2klJOUcn7zE4VmaoW8KREGosr0kywNBPf4Lhp8LnnuOgoxlOSWqVAIe9TDAJcKcEmxIlg0OtTb7fphCHNdo+tnSbb2w36/ZBGo43WBscLOHbyBIePHuPQoaPMLxzE93Psbu/gOn5GnqrUsqc19Xqddq9Fp9Og2akTJxFJrJHSpVCo4Tge1iiCXAnX9XDdgOpEjcfe8yhHDh8iyNSGQ2htaDba3Lxxh+2tXR595GEmahPf07iQQuC4Dp4XoG1EIV/6gYzxoYprn5ApI4rkyL5njMnOfymBrJOUbE/iGCUlSZKMVMhKysyKuT9oPhsvaWLWd2RZZT+kVsR7Fo59ZJO9hzSSQqY5ciNFmEBIkb2HYEjE2iEx/hYb/VCtdc/tnH3P2U+DDdsh9x639/BkQ2Iw+5jRa0bLP8YYY4wxxhhjjDHGGO8C/FBILsdxKJdKICyD/oBWq0mhWKJQKmONRicRSkmEMHiui8ASDSIWDx5iY+UuswuLSJXaxwZhyCCMaew0aDaqlCaqBIVCStQIi9UJCEnY65DLF1OrihAYrRFS3mOjHNr2fH8v18QYM2qCfDdDZVk56foMM37SuYrWhsFggOe7uK5iEIb0BwPCQcSxo4dZW9tg4cAs5WIBY3wee/QRrl29wurqGtVajc3NbXZ2dlhdvct73/skd1dW2NrcYnV9hw889V5KxSKTk1MYY9jZ2cGYBOU45PJ5lu/eZavZA2tpNOoox6HX7WETg+e4lEtl2p02M5l1VGYKs3w+TxAEKKlYXVnDcz2MNgRBwHYWLn/9+jVmZ2fp93sMBiFraysA9PsDjDEsLS3Q6XRptZrMz82wW2+QJOl7JElMFMUo5eA4Pp7vIZVCSoHrunQ6HbRO6HS6rOo1lHI4e//99Ho9VtfW0qBrrZmcmmZ2do4oirlx6yaO6zBZmyCMIu6u3KVaq5EkyWg/JdrS6w3IFxLiJGEwCAl8j3e7fkIbw/r6Bl/72teZnp7AGM3S4iIHD85z/MQhsAm7u7v0B31WV1bZ3W3he0Vs3qFQKAIxUqTktNWgs9bEtD0RsAJrBcZYtEnHdKINUZzQ6XZptrokcUKtXObA3AyVUh6JxRqNTeOQsCazlVk7mtRrY1JCWKbyl3zeZ2lhmmqpxNmTRynlXTxlKXiCSsEn7wlMolNrq+NmKjOD8hxc30cqmKoUcY2hEPgEOR8hNSQRSQKx1rR6fbZ262zsNqi3O2xvN2m1e0RxhE4SXBlw5PhRTt//EMfvO0tlYiLL17NEvT79fm+kbxOk1sudtS0uXLhMP+rg5CVePkccS+LYUqlUmJtfJJ/P0e/3KZZKnL3/LE888SRBPqDVanHz9nXm5w5QLldG50NrwHUCJiqzDHoRzWaHMAo5cuT4dzcoRKquVUohkLTbPfr9iG6n/32PNyvSUPbhNiAbO3JoVVQSx3FxAkGlWknLOFxJtVbBdRUTExVcz0cqiTE6vTEg2At6H9r6MmLICDNSQpGpDO9tKTRYm5JkFosRQ4+h3fseEkMFYUo+iaFYKwupH6nFrEFkSkWwyMx2zr6ssL3Sioy4Gn7W/m30tkzVkBweklnZMbHPfjmyOo4xxhhjjDHGGGOMMca7AD8Ukstay2AwIBcE1CanWN/YoNlsMj07RxwPaDd38TwH1w3odgccPHyEQrlCYjQ3bl6lNjuD63osLi2QGEuz1aG72+TBR++nXJ2g02ohsvZA6Sg67Qbtxi7VyRkKpWqa/WQNjvDuuVT3/WCkalIqzfHSSfKHguQawph04u86TkoYWEun02F9Y5ODBxex1lLI5wnDiE67z+HDi7zw4isp0RhrarUKx48cJhr02drapNNto03E7NwSfvAwvX6HYqFMuxVy5+oFtje3OHv6NFonVCoVms0GWscEgc9ufZdcLo/CIozmtdfOMzlZwWiLjTSB41IpV2g123Q7HTY3NgBBPpfHdT1OnjhJu92hkC+kLY2JZmZ6lmvXruB6Hru7dY4cOcLK2gbWQhzHI8WX73soJdjaWieX88gXCkgpsslsOtGUUuK6Lt3MalitVigUCsRRMmoCbbfa5IIcOtFMTc/w7W99m9t37lKrVfH8XBp2LgRCKlbX1nnz0gUOTE3ziU/9GKVKBT+XJxnEo30TBEVarS612gRhf0Ah8CkWC/+7jpkfBIzWrNxd4b/42/8ls7OTVMpFnnrqSR5++EG63Q7LyyupJdT3SBLwvRzBVI1Wqw1KpblE0kAW/K21xgrQJp3gWyuwVqY9i8YQxZreIKTd6dHpdZAYZqZqzM/OUM7lMiWMTckhhsKaLIFI7M9ASolVqSRaGRzXw/U9ZiaLLE2XqFZKWBJco8k5As9RCF+gZJrjlCQJAoVyFMrzcBzJzEQOYSXGaoTV6CimPxjQDxM6g5DN+jZrq7s0+n1a3R6NjS4vv/Iqx88c4YMf+CBnzz7GsVNnCPI5ICVhrNEgRGpzwyJsgjEgFcSJZnNrm2hgkMpDJwmlQo1K1UMbyOUKzM4v8d73vodjx44wNV1Lt4m13L1zlwuXLvDCi9/iM5/5Y5y+736UyqU71RqklfTbMZcu3KRYKXLk6IHvekwIIdNzkBBI6WB06qpuNrvf93gTrsQMzJ6NNcsbBIsj00ypQa8HRlIo5kCAoyxBsYKrBBPVIihFGPVxHIUQGkOCwUnJroy8FGZoiTTZJskIoKGiSghsZj0clSLYNFNraGuUQmBEuh0MKbElyfgyaxDGjhoijbVoY3Aso3VKs770XvZlRtLake02JaxGOV/pRhnlcN1rZ9xTfAk7zOIaOiNT1s0KwziUa4wxxhhjjDHGGGOMdwveMZJreCE9tIjUG7u4UzMk1hInCXdXV2h3OrRadVbu3sZzHaRUtNpd/uSBnydJErSxTM/N0djdYmJylmZjh3J1AiksR08e4cDhJWSWq2V0Qru9S6Uyxc72DqVCnvW1FY6VqvQ7bfLFUjrpGIXPf6dSRgiB5/vv1Cb53wVSShxHgYA4SUjiGG00URSyvb09yp3K5fIUy3kGgz73n72PS5ff5NXX3uC+++7j4NISR48dZXt3l1fPv87hI4e4fvMmZ8+e5Vc/92v8+3/hz/O//otfYXp6iueee4EDB2Z47fXXCIKAZjPNuzLGcPnNyxw9dgyrAoyxdLsdtA7Z3t4kDEOKgc8gGlCpVeh0O7TaLXL5AOUodna2cV2X+QNzuK7DwUNLRFHE+npKaBUKBYqlErv1BrXaBEmSsLa2RhAE5PN5dnZ2uH37Nq1WC9d12d7eHGVodTodCoUCExM1CoUCnW6PbrebLV+aJ6eUylorJdVqlZWVFV565RXurq6ijSHI5RECOv0esU4QUnLl6nV+8zd/hwdOneZHP/UTTE3NcOnyFTwriOMEA7R7XbwgwPMdCoXvzfr1Bxmu63Lffad4/PHHmZqq8tR7nyBJIq5euwEoypUazz//PEpJHn/8MX78xx9ma6vFb3/xdzAmRMlUnWWwJHo4sTckicnsnSktpY0limJ6vT6tTo9BP0Qph+nJEoeWFvAcB6HNSKkpbGbayuyJQ0+oUgpnaOPVCa4rMZlSzHEUQZBjYWGRMOpjjMVFoARINyVu4jjGEQ7apqourTU6jtIAeimwCKJBSNyPaHW6bO402Gm0afcHdLot4oFgfXuLwSDh1OH7efH58zz1/g/ymZ/5OXzlIoZ2RJEpgQCjTUqQSInFIYkTHNcjny9y5tH7OXToMKfuP8FLrz9Prxty/wP38aGPfogzD9xHLvDRxoxsvkN8+Ytf5cVzL5ErOCkhuO88OSSQlFIUCwXy+Tz+93i+FMNt7TooJSkU8oRh5/saa5DuRikFyZBYMhaT6FFLYWN3l2s3rtPrDpBS4Lgy3ZZC4ToOQlsineDmAu47eTJtPJRDm/sw1H7/55mUA1IydRoOA+KHyiyZKbWEHZHpRpAFzQ/J1ey9Mx3e/vD5VJG119oI9p7vrP2282GhiN2X9Xfvt1v2+qGU9y25XcNw+6Gqa58mDINJ7dxZxuMYY4wxxhhjjDHGGGP8Qcc7quQyWtPpdChXKiwuHkwDvBsNTp0+Q6lc4rlvf4tCocDJk2fZ3dlmZWU1DaI3AqkcXM9hcekw6yt3mJiaw/cDhIR6YxutD+AohZ/L4fpprlSv18IkKeEwNb9EudYliWPyxRIISRxFuK63F2L9bg08+i4wqpQXIgtTlwS+h3HTXV6tVqjXGxhjOHXqJNs7u6yvbyCl5PSpEzx4//3k8wWMsVy6eAmtLY50MRp6nR4///N/krsrqywtLvDSS6/Qauzysz/zJ3j55Vf41V/9PEePHebs2bM0Grs0mw2kFGxsblAoFinXZkbZNUEQUKlU6La6FPIVyqUSq6trXLxwkTAMOXPmDK7nUa/Xefnllzl8+DDVaon67g5nzpzh8uU3MRbCMCaONRcuXOLwkSN4Xlo+0O/3iaKIJEnY2tri+vXrrK+vpxPiJCaf5YUFQYDjKDrdbqqIESJrdUwoFAr0el201riui+/7WGs598orzM7OMTU5Sa1aJU5i1lZWGfT66Tp1egR+mSQWrK5s8v6PfoBWq8OFc6+zvVNnYrJKPpdjolbCdX4oosofKlzX5amnnqJczrO7u0s+HxCFCV//xtd46qkP8MlPfpJDBxcRUtJu9Wg26ziuYn1jC2MikAapXCDdV5JhwLcAIdFY4iRmMAjpdfvEYUIxn2d2ZpKpyRK+52ATjbE2zaliL6MpzToCkRUy5PM5SoViaj9dXSVJNG6+RKGWx1pDEhsQipdefIEjR5Y4NDeDJyyOI9OGT9ImU6kkrnLRsSZJEhJrEY5Lp9dlc6fO2tom9WaLSFsSI9htNrlz6w5hR/Po44/zwY98HGk8fuVzv0WxWEFJnyjs4UkHhrbBIWGXEUZKqlTVleU6xWGIyrts72zxZz/0p/nxf+vHKJer+H5AYhLCcMDGRp0wCjl8+Og9++zIoVOcf+NNiqVcZiu8F1IIHOUgVVpikcvlvvsBkSmTEAIpFcZa4ji5V1n0+4TWmjRqze7lVAmJkg7YmLAf0+uEtJo90JbExCQmRluDRIDWJDYluiZqM5Ry+dQCm8RYqyFTUQ3poP3thllq3D6iaI9MIrMuDjtPrBnaFU0WMJ8SXQp5T6j9sChFCEZE13A7KaVG4fV7NslMmzjMYtyfND+MC8t+Z8wokYshyZY9Y0TCpQq1jFcVYkR8jTHGGGOMMcYYY4wxxh90vLMzayHQZu8OcDjoc/jYcawxVCpVjh0/QaVSZWlpiTCKeCAMcV3J8899g1OnHxg1V+3UtxFSsXjoKEJAPpdHytS2E4UhOtGplceW2N7cZnZ+Pl05L8cb589TLBaYW1ggl70Osjvx1jL0Yfxha7LTWo8mQ4PBAN/zcB2FMQapFIuLi0xOTvLZz36Wxx97lDDWvPfwElEYYa3lpVfP0253mJycpFgukUSWY8dOcPjwAv/iX/5rLr15GaUU950+xfuefJKpmWleeeVVHn74ESYmazz33LM8//zzbGxscvjwQcrlEkkS4/sejuNgrGV5eZnJyft58skn+Grra0RRhOf7bG9tMghDCoUCfuBTLpdZXFxgMBhw7dpVmq0mg0HI5StXKJVK1OtNpqam8DyPQqGI67h0u6kFqlarYYxheXmZW7du0e/3s6B6xc2bN/E8j93dXQqFAvPzByhXKqytrXPr1k3CMByRWlIKqtUKGxsbrK+vY62l3WmjjeGRRx5mcnKKer2OMZZKtcLU1BTFco0jS6eo5AsI4fKVrz7D6sYK7XYbATiue499Du5VYLylnO1dBWMs9UaD5557jk9/+hPcubPM+voKx0+c4S/+xb/E6fvOsrO7hTExxmiEtLheal+8eXuTbr+FlQlCKECRJCGOdHBkWoKgTZJa9USqbsq5PrNT0xyYnWaqVibWCWiz136HyCyPNs1aQ+C6kiCX2lh7vRZrq8vU6y1cN8fs9DxuIY+xIJD4nk8rjPnVz32Bn/6pH+PY0gLoaK9EQILjOiOCwvU9hJQMwj7b27usbNbZabQZRIZBCDvbO6xvrOP5OT704U9y9oFHOfXgw1jg4kuv4wcFbGpiw8vey1qNEHLUjuo6LgyD0e2+Nj8SbGw4cmSJfrfL1cvrHFw6Qj5f4NKlC3zzW99gY2eTJ59873eQXHFkUSpIraD7QtyBkZVtOEjDMMxyqr5LDHOjbKqO62WKyULB/b1e+XtCyrSYQAqBNimhE2tDlCTcXV2lOFnmp//ET+IqDx0lxCZBJwrI1E/a0IsHxBgC1+O1c+fYre9SLM9ny85e/tbwfzF7XFa2HHv5bqldUWQB9lIJpJWZ8nh/22JGIO3LwBoSV1Kp9Ni4J0j+LWqrLHBfyD2STQoJSmRh9vtztfZyvqSSaU7lkI8btjlkSLPIhqUPIMd+xTHGGGOMMcYYY4wx3iV4x0iuocpgYmKvYv765Tep79Y5cuIkxWKRWmWCIPAJe336YUilWsULPA4dPIKrXISbWkkee+wDfO6XP8v0zALXrlzmzu1lFuYOECcaY5JU5SHzIATrG5vUpiYQScKF82+wu7nJ6U98DNf39yxKpEqCOI6Joj5SCIJcfrTc7yT2T1be+kn7p4tvtxS/13RS7HuOUnKUCVPM50cNXcamFhQlJK7n8OGPvo846mHjHs26oVabwFjLww/cT6w1N2/e4vnXXqWUL7C0MMXiwjynTx7j7/13f5df+qW/yt/8W/8F/9F/9B/x5JNPkMQJv/Zbv8nP/dyf4sbtm7x27hWSeECx4FMseuR8RdjuMjE5l9qtjGV1ZZn3vOdB1tfv8qlP/gS5nEcQBNRqNVzX5c7t24RhiBCCarVKpVJhc2uDQdjBCxRTc7O0en3K5TIb62toneD7Xqbs69FsNLLigYR8Lke306HX7eL7PuVSBWNIW+aQvPjSS8zPz+M4DkmSYIFCscjE5CRKSd73vvfxD/+Hf0AYhoSDAYcXZsHApYuvc/joMWq5GocOHGdlZ4fbr13G0TFeGNNJIi5cOoc7UeWhsye4GydcvrHOzOwUcXTP3PKecWIBbQXuqOns3zw+flB4O8Lt9wMlXQ4dPMI//Sf/jFwu4G/+zb+BQLO1uUHYbWCTCClTVZYQKWkdDnr4riLWeUINQkqwOiMEIdYGJV0wCmENEkMx5zA7M8H8/Cy+56a5WNaCsDhSjUggJVKbstWGRmeXWIdgLXEYY6zFcX0K5UmKpQpBqTIiwY3VDMIOxaLPpz75I5w5dYK876L7MWaYYi9S5WqiDY6fIzaWra06r12/RrcfYixsbu1y5cp14lBz7PBxPvWJn+Xh9z7F5NwcSa+XbjQdE/VCpmuTIDRgkcrLCCJFeu6ye8HlUuA6Lo5wRjlRKIGQllzRoTY9wW/809/hJ/+tGWozM3j+JJgpokGLanXyO/cZCqElSZx8x+/IQtyttCRGE0fx9zRIhmomtEDiYrRNM+/iwfc0rt4OKjEo4RDbMFUqWcAB4Sh2G3We+9a3WL17gyDnksQ6LZfw3NQ66TgpMSTA8zza7Q7nXz3HxNQ0J04sIYQcqbiG6i2NQUrA3mt9t1rvEVZSIVEo5aJQaK1AeWijiUON0h7aWhx8DDHpt4IksQ46y8dK31RmmWBgh+H0Ok1ji4wl6fZptJsszi0RDWJ6gxalCZ9iOU+vE6Mji/ItRg+9ijLNtNNDi2W6r6WVWZ5YtlvF8PPf/hw1xhhjjDHGGGOMMcYYfxDxQ/NIWWs5+/AjWGMZ9Ls4SnH0xHGkUty+eYOXXngOIQSPvedhJqamCQd9ZmeWQCiwhg988P0s377FqTOnOXXqFMViAasTpADX97IPMegkIQxDLpx/g5s3bvGn/uzPj9Rbb6WJHCfNhdFaMxgMcF0Hx/n+VQX/pm1w7do1bt2+ThT3EGJ/zom51x6S+lTuff2+IGMYTkTEaJKVhg+bLDfHyexLgiRKJ/muq7i9vMy16zc5c/oM4aDP8u2buCh+4S/9Ivl8uu6ddpsk0VQqZQ4uHWSiOsGLL77AN57+Jk+853EefPBB7iwvc+3adf7nf/Q/Z812GisUCwvzbG5tEEUh1WqVjfUezz77PIVCnunJCRYXDzC/uMDzL53jvvvO8MLzT/Pscy/QbDaZmZniG9/4OsA9Kqper0ccx0xNTY0ygJRymJyY4NbtFY4dPoI1CWAyW+JGSmKVi2kj52BAFFms1UxMVOn1emxtbXLy5Ek2Nzep10McR5LPBwwGPSqVCrmcTy43w/T0NN1ul+3tLb7xjZDBoEc+5zM9Pcns9Ay3l9dwXJ+Nu3dp+k3cXI3z586x8ebrnJRwDId6r8N1LSg88QTHlw7z/scf5/6Tx5BC3GsJs3vGoTDRDOKEcj7YU+j8ECRd+61jQ+XH7wdxHHH16hV6/R7/7X/3d2g160xPT3Jn+TaR1iTCpmSJ1lgLnudQKuUJcm6aizeIkTIdz8P2uJTwkRgjsVLguy4T5Txz01VqtTLKkWAThHLBpHauYf6ezSyLvV6Per1OYhOkI5CkIeiB51Mu1/CCHI7rp2UVw5Qlq7HkSOKQQj4PAqIoJE4ShFL4rksSx3huHqTh5u273FnboB/FRNLhxq3r3Lh+naUDB/nZP/GnePSpDzAxMzeys2mt6TSbTBSLgEUoged7aQHGPdv/bZiG7Pf7cwZTgkWOHi+VSlkuXpqnFfgBvp8jlxH7+yElo9bZjEe75+MFYnQ+jaL4exsfIiXfPc/BcRyEkBw6eJjnX/j2d/8evwuGuiObKfeMTkhMRK1S4fDBQ9TrO2xvNFJlcdbMGYV9tNZEUapeTeIEqVLbaRzFTE07WGOwen9iVfa3sdh923v4B5FaMoemWITCz9eYmD2EtSYl9KSg0+kiVIWZuaMIDcYkGJMQxzolKW0Rm3ioYX6WlQjSlkYpBLEx9LWmUp3g9CNHOX3qOArFv/pnv8a5ixdZPDrJgqu4cfMmQkuO33eMJAwzO2J6XMmRQkxmiq49q2e6pns3ZDRvQ3qOMcYYY4wxxhhjjDHGH0D8UIOArLXEgxYqI5KGPM7Bw4ep1qqsr68wGPS4efMqZ84+yN4sSzA1P8/dldv0ex2kULTbbRrNXYKcBOmQy6XWom6vTauxzZHDB3nq/U/9npMwISRKMbI1vZPodgd87nO/wZe+/Ft4foKxIUI49xALg8Egy2oRmRpOYkmXz5g0dNuavUmVFeA5bjrxFOld+GGroJTp7k2VL5ZiMU8+XyIKDdfevIlFUymXkNrwP/4P/z2/+Ff+Or4PnqtGn9+o14lizdLSQc6de5nf+q0v8lM/9ZP81b/yi/zX/83foVAo8OlPf4ovf/krfOG3v8jf+s/+Mz73+c8jpWRufo75uVmuXbvK8vIyg/6AaNAnFh4Cxen7ztJsbPPFL34RP/DpdltorQnDEGMM/X6ffD6PMYZOp8PKygpBEBAEOYyJ+eY3v8lEbYYn3/Nennv2W7RaTQrFIlEUkySaJElDwB3HwXU9tDYsLCzw5ptvAoJGo0mn0yWOE1zXY2ZmllarxebmFr1ul4mJGoPBgNu3bzPo97DG4Hke+Xye+u4u/V6fZmdAqZxH6Jh22KbgVXGt5ccefIA/f/o4U5cvcvvuXf7ZlTtcjRN24wS7tkm1lmc1HKSB1dm40yabrAsw++xnqaILnB+CbTHRmjBK0nBux8Fzfn9No0mScOfOMtbY1Oa5vkIU9Ue5UZ7nEkYK13WQCHZ3d7l94wp3bl1H2DTMPW2yU5gkBrl3LpDCkC8GzE3XmJ2sUsr7OEqkIfUGUn9VOplPLY2AsGzt7DIY9FPCSwikVSjl4HsBxWKJQqGUZlvdsyYWYzRxHCKFxXEVfi4gXypAoQCOjzUGHUdcv3WHOysroHJYleO5l17mwuXLPPLIg/zoJz7BJz7zf2BqZiHboanl0mJR0iGXH2ZbCeIkIU4S3CxXbg/DYKc9ac1wSHxHmYZJm2KFEFSqVVzPQ0DaAimdNJj9bayGUqb7e2i7vBcpASIzj5vOrHTfLZSSuJ6C0GSkZpmDBw/zD//hP/yu3+N3gzGGOInRwqBkNm6sYX5hhgOL8yjpjOzb1lqMTkme9DyRKZmkzL4HIAwjlAuOLzAmyZRcCms1Wu8p5nQ2nkekVrZ/NAYTR1Qna3zyM5/C9YI9q6IQ9Po9iuUSH/rRT+IJF21jsugveoM+VmmEY4gGvbSCkpSY0jrNmBNWEEUhP/vTn2ZusoYUgts37tLrdcn5PsIqlu+s0m52mZudy1pkUy2aNeYtWy/LSrN7Nsjs4b3vpre+ZIwxxhhjjDHGGGOMMf6A4h0nuay1DAYDOp02yaBNuTpFv92gVKnSqu9grSEI8qzcvcMLz32TU6ePEkURv/LLr1OpTnL69AMcPnoaASwdOsTm1hbWaqamJpmYmEQqh3ang7aws73J+toaH/roRxDIdMZgEpCZDQNnFNgrsgkzpESX5/n83obA7w+Neotex+K7U5SKCkuINmJkgQHwvCzjh6FCI1WbiH1Jx8MMGJP9n2NNRh6koc6OkiTaZGRYSihYHaeNdTHEsQUU1kIYGXzH4cqlG/z9v/f3+MW/+h/juh6en074NjdWmZqe5sEHzzI9NcE3nn6az/7Kr/ILv/Dn+ck/9sf40pe/zE/+5Gd43/vfx9rOBmHUp9mq8+r5c3zqRz/Jgw88QO4beRqNNuVSjlqtQj4fYIym0Whz+PARXnv9lbTl0XcZDFLrklKKKIo4ePAguVyOmzdvsri4iOu6PP3tbyF0QqVcwVGKL3/pS9R3ttE2ycghQalUwvd9jNG0271MfWLZ2EiJlSgKuXt3GYByuYTrKAqFPGBptZq4rkt9t8Ha2kaqepAOUZQgkEjp0G33uH79Bk9+8EfZ2Vxj89YVlF8isoZykOPYoVnmj0/B7i7z0uXJ25KdeoeyynFoYRqlYEoFVMplJJJEW5RklHzjuA6B66ANxDZ9fC/N552BtXD7zgovvXweKQVTU1M88fgjWSaUQGb8ysbGBkHgUymVcH6XwHxr03bEUqmI1gmvv36eqakKGHCFxFcuu70e7WYTJQVGG2rFAgdnZxlEdbabTYR00xwtazNLskRYmK4UOTA/w+RkCc8BYQw2s40p9sLZ91rqLNvb24Rhf0R0CKHwPJ9cUCAX5PFcH0iDjYzRGYk8tJ6lGVjDI0/mAkShwGC7zuaNu9xZW2Wz3aQXJ2w3mly7coucU2RyYopus82f+/P/IYW8oFQpYJIBZMtoTAJYjHCI4pgcjBLN91r17tmq2d82O58NCSn2qVUzyDTTSSDw3JQENzYlsayFwSC8l+TKTipKpdsx0UmqYnrLEqT5T6kVGgtra2vf9fiSUuK4CsdRaG2o1+vs7jbo979/u6KUe+d1Y9MthBRYTNbKGWFjm5FcOs3vyr4P0qx1SWINyPS9nCC1tRtjR+ua2r2zM7CQGdmVkoEm2yuZSTzdVkJgREJImygeIBIfxw3QxiIDh3bYQpsE7bjpDQqTWncdXxLqCKNjHFemuXUko6ZGV/joMMJXlkJO0W5v0mjscunCFcpFOHP2CPlqkSgJCXsmbdTV4UiRRkYia6ORQu4ptqzBZiHzdrge2RA0dsxyjTHGGGOMMcYYY4zx7sA7THKlF8+OklQrFXQxwPNz5AoFhJDUpgLQ6cTk5H0PsHDgIK12g2tX3uSRR97H17702wwaHV5/8XlO3PcAh48eo+Vsslu/SziokkRdBsJnc3WVZr3Og+95gs/89E8hpcRaePnZr3NgYZaNjV2s0UxNzlCpTVGo1BByGOorcVw3u2P9zkplNnebdOMBuAmRsYBLYpLMViXQ2iCFyiZVEjsK7s+UG2b/fXaLzTJTkowa0YlGAbHQmQ1zOHkRaCsJQ41SZmRllEiiMCEMYxxpMHFMY3cb5fm89MorGCs4fPgQ/ajP9VtvUimUePKJ93Dz5g0++8v/Gz/yIx/ns7/8ywS+y7/77/0Zjiws8sKzzyKSBGUNd27fQGDY2d0mXwhItObSlTs4uSk8RyDo4SiBEA5GC5bv3GRmZpYoDNnd3QaruX7tMomOEULS61ZptVpgIrTW5PM1mo0mSrmUa1WkhGq1ShQmlEplkiSh1WwSx1GWuwNxNKBQKJDL5TDGEIYhQKo0Gwzodru0Wi2CIEA5EpUIXDeg3x8QBD6NRp04SZCei+sUuHH5NaSvkIUCU9NzaC9PN+rDa+ew+jYyiFH0KaOR2rC6vYmNW/S6XfKFAjdWrjFIPkDeFsCKTF2SsNNo0en3OXRgDk9Jfn96qu8dRw4tsnhgLlPsSFz3LacIAUIqrtxc5oFTxyn+LiSX73s89MiDWAwbG+sk2uB5OSYmHeKwwY3L5zFJzMLCEvNLR/FzeS69fo4Lb1zi2JFT3Lh+F6NirNRoYrDgScGBmSkOTJco5gNcCZjsuB2SU0OVY3ag9AddOp0WcRxn5wWLlBLPz1MqVsjlcsistXA0kTdpXp60ksQmkESIxCKsBAwmSVi5tcKt67epd7q0u01u377DjRvLFEsTfPxjP86HPvpJ3nj1Ir/y67+Bl3PZ3l0lyE+kBJm0CJuS60LJVHWlh9bllGBQjh1Z+rAxILMUv4zsNgIUWARCqiwfarj1PRDQS3oYk+BYi7RpZpg2BqkcHM/Fqu8k9bWJ8RyXRIckOrknjElIieM6GG1YX18nMV3m5mvf9diSKlWBmRCKEyWeePI9+Mbhp//4T7G2fpfNjXWOHT9JIV/6TtLu94C1JrUqIhBWZ7ZWk6me7IicM8akN0AANSwhyRi+4bnVmtS6qByVno9NtnGFzT4je5U2SKnQOk4bE9lTPg1JVotgEGtAY00fMdhTfw0JWLLlwojUTmhTYlVJUI4ADMp6GBMCGpwEKzRKSn7t177A5tYqcUQ6Pq3E+pLQdLBWkw8coiQhtiJVNQJCGBKrAUli9FAgCahU4TciiEVG+O7fOmOMMcYYY4wxxhhjjPEHG+8IyaV1wu7ODkoKapPTKGkRyiMKuyQyxnUDjNbUtzfxvIBybQJXQWVyilyxmF7wC8lDDz/G4aNH6LabfPZf/q8cXFjgxIMPUN/aobm7w5U3z7G5vUmhMMWHP/oJkJqV5assLS7SaXfI53MUizXOvfIGB5eWqE5MkS/XUG5qA5JqL59EiHe+Pera9Vvs7jaQSmXZMOnE2mbqI2stsdap6kJrDGlTlxgqMDJFgdjXcmaxIwsY1mYTKJuGb2fWnETJVIVgNNYalBoSejbLKlL0owH1VosLF99ga7fOcy+9jOP6VF8t02w1KBV9Di4sUilPo5RDvV6n0WjwF/7Cn+dLX/ptlu9eYWe7jR/4LC0uonXC9vYm1WoZgeHE8aOUyxW+8pWvo40ln8/x03/8p7lz5w6//lu/RaIjXj1/nsmJGeI4wRhLsVTObJoJUsHK2h0C30cnCe1WlyTWPPzwI+zu1gmjiN3dnVRZZCUbmxvEcYQxBiUl2hj6/QFxEhNGMXEcEQQ+SSIIs2yeGzdalErFLJsrzenJ5XJYa3FdB9/3mZiYAFIyrdFq0ItaeIU80xOTFIsFBtahnyTc2tpheU1xqFombg9YCWMaNmTShPzrf/UrLN+6yn/z3/4d+t0erVabMIzoDSKq1SqT1RKT1TK5XI5Wt08x8HD8t1rXfvAQIlXQKfVvptRmJidYXtv8XXWP3W6Pl145x6989peZnKxx6vgR3vfE40SDHpdffY5T953m4LFTuLkCSkrW15Y5/9WXuPTmRbyCy+TEDAlps2KSJDiOIB+4LM3PMjtVJfAV+0nee1YgZY8QwGDQo9PpAALP80Y24JTkLON7OZSjRrYsY4bH4r6mQpEKQrXWKEcxGAwIW212Wz0uvXmNrUaLbq+J63l84tM/wYc/9dMUihVy+QJ+7hZGpzzRaJuOrIZipCgbZSENP5D0+XKo5HqLomtIsqR/GxITkwiN1hGQ4LlplpdQMUiN8lTaXulIfN9BuYIojGjWm7z1jZ1s/7f6g4zfujcTTJuEcrXAJz/5MT7ysaeYmqr+G8fKfrieh1WSG3dXePn5c/QHu6ydfwG7s8X/dPUiL27c4T/5T/5THnvoMTz5+xjvIiWyhDGpvRUBZhgWb0dlDkPlUqzjVCkrMqWvYKRiMqQ5hpJh1mH6EcMMx+HgN8P2YLt3Hh5aR+M4Jk4GREl6TidTgUkxbFTMLLgZ6WmtSMPqRUqqpQHzIlOnpQ2k1tj0u0NIPNdjZ7sB1kGpVPFrjEALgbEpGef7Pv0wRkgFZMuanvrvsUOn622yP8PxmbZCpsUNYyXXGGOMMcYYY4wxxhjvDrwjJJeUikq1tpdSojxA4Pt5ep0OquzRbtQxRqNcdzQpFULgBwFTM/N88dc/j01iLr3xGj/37/y7/Kn/0/+Z25cv8txzL7Cyss7hpSU21jbZ2FyjWAh58/zLvHHhFda2dnjiiScwieHChYv83M//OyjlUpqYwfMDhDXEUZ9wECKVIpfPk17QvxNb4l7s7tbp9QdImZJP1hiG/FSUREA6mR5l5oxusVuk/s7MsKEdcTjjElmQ9V6+WDq5iuO0ucsYM7JrpZ9lQAi01nT7IetbW+g4oV6vs7yyQr87YHpqimIxTyHnEMcxjUaTnZ1tgiDg137tc3hekY2NTfqDXXynSKvR5OEHH2J+to3nuoS9PnEUMTM1xdzcgVSdAvT7fV5//XXOnDlDIZ9ne6dDo9GkVp1gMOhRLBY4cuQo7U6Hra1NjEloNOqZwkOSJAmHDh2h1+uzsbFBoVCgUqmkNhydEn3D9Uy0RmbqhLAb4ToJiU5toeVyGWMs29tb+L6PlJIwjAjDAWQh20opcrkcURQhpaTX6/HUU0/x5qXLVMtFQiytZoOwO6AwcYBEeVzt9nnuyjJOwePu+gavxgn9wMW1hvZui5MnTrOyvsby7dv0e30CP8BzXXSSUG928V2HQj5AkFqU3nmd4XcPx1EjouCtsEC9UefLX/kyzcYun/nxT/DaK8/zvve9j6nJCWY/+Rk8R9FqNrj68ou89MorvHr+VdqdNvNzsxw+chI/cAGbKhMFTJQLHJidYrpaxs/sdEPiYnjsjkgqLEZrmq1GqkQChhN2ISCfL1DIl/D8HFKoTAG2N4EfEl0mU+xYNbQ/mrTZToDj52k0d7ly5Sal6Sl+7Kd+ltrUNKVCgemZaXSsR6qhfL6AMRaTmH3LkQaf37tP9wit4fE7Ot5TP93oHJUS4wYl0tw013MolvLkikU27t7kleefptfrE6jUolwo5NjZ3KDVqtMfpCH/xlh6vR7r67eYmzuMxZJEEUHBpdlusryxgk7ehtgQFi/wkErw+c//KsePH+Izn/mp72rcKKVQjkOn3ePWxdsErTU+MOdxKIl56ZkXaEddoijGWsutG1doNxssLB1hYmr6937zVEaVKlSt3bPm3fP7LJcqs2Hubd6U6NpPckqZbe/M+pjeCFFvIf5SZd9wPw3VZ0PySAiBIwVSDS2Smb3R7iPeACNTG7TWltjYLBcrfd6QePZ8LyXHhEiXIVuBKNZIqdJxRWq9FNkySgmO62WFAw5aDK3wQ+yNwP0k69DKP9weQvC2+W1jjDHGGGOMMcYYY4zxBxHvCMklRKqcgOzi2Vp63RaOFPTbaQZPt9OmUCwR5HLf8dp8Ic/Djz7Kl37z1+l3O0S9LotLBykXiuRqM5x/6TpbW3WmZspYqwh7IeVihfd95NOEUczk5CRROMAIh9LENMdOnqRaKXN3eRnH85BKkWhNvlDA81wc951XyfTDiK3tHbqdTmp7sVkGC+kEYjjRFkis2bO87GmuGE3itB2aR7L8FGtGIhaFxPfTYH+tE4ZhO1JkwdxJgus6uK7LYJAGR8dJQiFfoNPqsLK8Qi7IU86X6DV7TNUmmZqe5MD8JNPTU/R7qRpsMOgwNTXN5cs30tDnOKYUOMRhyGOPPkoSRdy6eZNNrVlbW2NrY4PFpZ00l8dJLU/f/OY3OXHiRPqYcsjnA6IotcdIJen2W6k6RWi6vQ7hIMRYCAcxhXyeUqnE9tYWhXyRfr9PEPhYBWCy3PHUBqqUm01cVaYKU8SDHlprfN+j1+thjKFcLtPrden1euTzBSqVKgDtdvuepr44jtnd3U1tP3HEZr3B0oE5Ds0tgV/hVu4G6xae222wtZ3QjmPulqp0rCTv+sxPL6Ckx8svvoISkkq5xPRkbTT+h+SaqwTGQq8fIqWkkPPf8XH63cLat6fcojhmq16n2Wrw4IP3c/ToYXIOHDx6gsB3efPNS7z26sss311FKI83Llzj0pU7uI6iXJ5GJxLPcbCJwfU8HE+wsDBLrVTAVSpTMWbHRSaxMUPuAEsURvQHfaLMojokIHzfx/cCglwe3/NRWTZVqlLJjjdjRwRI2p5nMVogjM2sxALlOEg3oFiewPPzFKs1HnjsCUwS0WnuYnVCHIcoP08UdSkUiggh0SbNceI7At3To3v0sGWUQ7ZHcu1/Lqm9MrNkR2HI2sod1u8u85Xf+BU69RWmqiUOHZ7n4FSJWrnIoaMH+da3v0271+bYibM4bpru1u90ePPN8xQKBYrFSbrtJl7O4cjJozzxkUeZn5+/R9WXEvOpzTKOEi6/eZW5uanvYcxYbGxI+gkyho8uLPHJhQEF2WFm4HHu4hrSCsLBgHMvvkyzXidfqHxXJJeSaiS5ssZgpaDd6TAIB1mBh5ORQXuElJUanQxz1+zeeLFpcYKUAuu6qTo0U8Xea2u/l/j5jpsQmTU2u02ByMj3vTN6etKuN9pYC4VCYZRxNyTY0rGxRzRZI4adCqlaa0h6DVO0jBytj7GZQsuC0eY7ylWEkPfYK8k+Z7h+KbGVqYzHyfNjjDHGGGOMMcYYY7xL8AMhuWymIEodGG8z+RUCx/VQEorVGq7nU65N4gcBSqWLEIYh/W6HXBDQ6bSwUcxjjz7Js898m9fOneO9H/4QlckS9xcfYrI2j40jaqUZThyrIKWDVHD0vvsI+31wLM3tbXo7Wwx2t6jki+TzRaqTNs2vyRoLPc9DKveHYlVsNjo0Gi36gz6+n87KDemEWmd3+K0dTirsaFKFsFhS1Yexw1DldJsOW7LScnmBkGnwMdaAVFirsp+HAcICazRxFGONIUnilGwDHC+PkrC6cpcf+eSnuHLtJq12Fyug025yJ+6xvb2LNSIjekLm5w/guopczqff69J1O2irKZdL5At5/FxAoVikXKmyublJs32BQdQnSUK0SWi0GvzLX/4XdDpNXEdRLBaRQhBHMa1Wi36/N8oq07FGa7LlTvDzebTWBEGA6xp6/R5xnCB0ms1jrEkn5FKMIs2iKM5sS2kYf7/fp9lskiQJSinCcIDWmlwux+TEBLXaBN1ej3a7nZKxWYkCwPVr1zh0eJG877PbDpmfPUhQ8OgnMdZqrFKEjqIpe0RxCIMcTuyg44jjZ45y6sz9FCoBxUKOwPfIBQGxTuj0erjKoeAFhHFCGEYkSUL+LWTwDxtv1XFYK77jwSiOubO8zPPPPUc06KMciR/45MtFXjr3PKsrtxj0FJ/73Of5sR//cU6cPMv6Zh/sndRKi0OxVGV6epJKMU+UaIRwKBXLOI4Bq9FWIDJSS2R2L2sNRmuiOKLT7aTERZLacx3HwQt8CsVSFjbv4WTKF7OPSBqGle+3KxprMcTYRKf81FCBSUqQ+0ERKxW5vE9zp0cSRlk4eWYZDmM8L1VlpuPOZJa1bPlHW9YybEYdKi610ZnK02CsQAiDyJLZep0W2+t32d1aZ3d7g36/RxT3mJ2ucmDaZ75cpeB79KIage/S63W5fXuZQ0cPMTc/x9bujYz8dbj4+kXefP0KH3z/R2i2ukzOLlIs5XnxhZc4eeogM9PTuG7WhpvlgQkrkNJFa0E+X/qeRpEfuMxMTFJxijw27TNf7GGdJifnBQvP93Gz81q/FbO702YwiL6rdzYCpBU4QqFlui9b7Tbdbhc3C96XMlVk9no9XNfFyLTJdX5ujrXVtX2lBAKdpI2KgR9QKBYI/GAfyZUSVErtvyGR3mQYUVhm+DyGjBViX87Y8FtyEA5YX9tBG8vSkkep5CKl2HsZe9+vIiNlhXTSjgRjR0SVNQ5aaazS6bJJhbICJSVKSEji0fLfS1zuU3FZRuuTRZClKmshcMQPtYh5jDHGGGOMMcYYY4wxft/4AV257pEzQwXCfuIoiroYm+C5BXzjIKQgCrsoR+AoMVJ7De+0S6lQUvLQo++hvtPi61/9OpXJEsdPHcT1p3A8hecVOHBgkVNnjyIcSZL00UmPdmuH3c4uV157g5ynkEqRzxdxPI+pmdnsyv2dJrXunf1bCxubO/R7fUCPcnmMtZih9Sp73VAtYYaKA5FOPHT2tjabeOyfnFhEaqUCtNUYbRBWpoQAEiXSHBchJMpJlUhRNMgUKynBI1C4rmRjfY3FhQXyhQJuLmBzZwsbD0iiCCEUruvieR7WWtbW1tnZ2UEphdaCTrdLkiScO/8q/TDEz+UwgOt7FEslBmEfQssgDLFCgpS8/sbrgMX3PQIvwHU9jDG0Wm2M1qOQeMfxCPxUiWaynJ1Ea9qdDq7r4nouYRingfI2JcMYNYWRTVxjtDHEUYixOrVpdruUSulEvdlsksvlsqZHHwtEUZTlgqV/BoNBGmjfauF5PsVSEUfuorVlfWsTnDw2jjgwW+Opxw5z4kSXzvYG9jd2uBJ3iMI+Bw7O8eDD9xPGA4Q16ESzsbHBIE5Qnku5UCDRBm0MSklc5eN7b3+o9geDVPXlON9zWPfvhdG4tJbEWMI4pOAHI4J1/yhPEs2dO3d45plv8Pr5V+h2WmxsbvDCiy+wsbHOV7/6VT7ykfdz7Mh7WF2vc+TYSU6cOEUh9wJSeCilKVUqLB06RBInHDq8yK1byyQmDdjGxtnYTxsDpUgVMlobwnDAYNDHWE0hl6NarRI4PoHns7tbZ3Nnh2bcwEqJOz2N66k9kmxIHO/PbMpIqZQuSFtKtU0VXzrRWKtxXAepHIxJsCZGJ9lxp+SoydMmGuXsCzffl/UlBAixR4QMQ8EtAp3o1GJsAWKE8Oi26mxvrrO7tUmntYtNQpSCog+lQhXXg/vOnMaXIdTrJEawcfsOfWeCO3dukugBcwemqdSKeJ7kwMIsSweXWL49YKI8gZKS+m6TqfmD+J7HM09/i0988oP7AvHThR7ez1DSRXyPfZ9SSXKFgFqlhBcbZGsTJkKEHOB6Mb5Oixcc6VAp1VjbWN8r3vg9PidKEjAGtMaqNMi/3x9khFZ6vkoJ7IBer4/ve2gMURSxtLBEr5uqOa2xI9WTlJJ8IcZxXAI/N7ohsGcltfvOL3vJVkNXYtp+kN5gSB8bGRFTUkwIOp0ug0GC1pooSrL3kvuI0PQVIlNhaaPTYgQhEFaQRokJEG6aDyayzK+hUCxT/ImEUUbjqGEY7lmnEQE72t1ydJyIH/C5ZYwxxhhjjDHGGGOMMd4pfF8kVxzHGZmQBtMmSUIcR/hBgE5MRkholpev4HqKxYVjxP0OyvFZX7nB1Mw8qjaRWkVcn2IpJSiqkxMUCgFuUOCR9zzE8y8+w+vnzzExmScoxvSjXdxcGelbrGPwfAfle9y+fYVbt27Tave4feUGP/Gjn6A0t8DG+m127m6kNpRoQJLoEVkj5NDGkt7dFqQkm1QOUoiRRW04MdA6zVZRysFx0rvuQ6uMGE4I9oJRsFnA8J07t0niPjlfIaXG6qypTYIVaR6LlAqjDVYOyULIOuuzuVKWyZK+6+jvVCGiSMm79H0QEqFAWIOQYJI0Z8ZxHKw1JHGyN2mFbBJoaLc7JFlQslKKZn0Xm/TTIHydBWJLSRiGaK0zUlISBAFgiOOQZ599luPHT5AkCe12h06njee7TEzW2N7dIYk1juPieT75fIFcLp+SJlaMctmElHS7XeIkwdUahMDzfRzXJZfP02g0cJSi3mhQKBTQWpMkMY5TwioJcdZYJ8gy0FKLUpwkJDpBKYHruoRhSBAEtNtttNbZ+yT0ez2MTifB6WS0g5KSdruNEILJiQmkE9DthTiOROsBjucglI+D5fjJRT72c6c48NAVWmuGervHnS9tYo0lVyhgkwE7O9vU6/V0ohsOUJ7L/OwcUkr6g5BcEOAGw3H59rh2/Qa5IODA/Bz5fP77OZzfFsYYtnd2+Oazz1Iu5/nYBz+aWr3E0HqVWrtu3bnD09/8Gi+88G163ZAwHLC1tcnVq9eZnJjhuede5a/8lf+YxQOn8P1cqjRRAq0THEdRLAWZAjDH7k6Xg4cOcv36DRITIkVKLFnUSJFobNpu5yjJZG0S10nD6HOBT6FQYGZ6msnJaZr1Fq++/AqvXXgDN5cjLlVwlYu0KYU1JIuHJOYQNlNeiUz51+n2AEsYRSn5JQVS2lQpOMq40/tILjEqfxiJW0cut/SYTonpe/OZyMiYaJASoKvLV+i3urR2tul1m/S7HTzXYWZ2jvkD89RKJVqDhN/4zW+k54AkxCZ9VG2er33jOUzuMitb22xub/HK+RcwCpQLwoHeoM/9Dz7EU+/9EDqO2dhokiSGYqGM7/p4rvcWe5sYnTOG58OhsvG7gZQS11U4Htiwz9ryXcxMBWEDmlt1Ol6AFQIpBbl8DkTWmMkwp+qt2E/JaKS0mOxfhaBUKqBUqlizWOIsU69YzKfEPBZXKQb9AYGflqForUlIspsPoxNvmsmGzc53YkT0GmGHjBXDuxBGjFiubBzt7fw9Kswghczs4y6pHnfYrHjvGlpD+j0g08ZTa4aGXfa2jbQIbRFWYIBIxPiOwpOSpDcYKXCHRNdoPGbfUyIL6h+uzvD7Z/+yjDHGGGOMMcYYY4wxxrsB3xvJ9ZYL3ps37lDf2USbHpAQRwmDwYBCocBgMGAQ9omjhCtX32BiYpIjR9bQSYJyXXa316msr1AoVbKsEImTNZ2VyiUq5QqeX8CIiPd96ANcvPA6Fy/d4OjJJfphB9/zuHP3Fkb0mJ2ZZRB2uHj1EptbLSbL0xw5dJRer08cDvj2M19ma3sDoTWDdpso7BMEHrl8EeV4KMfFdX1ERrII6eC6AUrJzMKS/pskCXESo7XGcV18P0AqiesMLSZiRPoIKTGZ1VAoxRuvv0qS9CgUPDBxmsNlLVakahSjUhVDmseTTraNsSQ6zsKSU/JNZCqRobXJZnlBQgqUUjhCphZQqdAms+xZA1JmE3OZ5aWlFJmx6euiKEJqQ+DA66+/Rr/fT5fBGpys6S0ZZcDYbMLqZtleae6NFALHcVlf3+TQocN0Op2UUNOpciyX98kFAcZqlJIUCnlWV2+nQytdUKy1FAoFJiYmqNfrNJtNOu02nu+PbDblcpmtrS3y+Ty1apV+pq4CQRSGaKOxGSEJYJxsIsew5U6Qz+yOQ+IyDEMKhcJIwTEIwzSHSamUGIR7njc7O8vUzAFuXb3I1ESNQtHH8R2ipICDZWquwPyJALzLuBPbLL4nj/87XZTyaff73L58FVsqpCqpOKZYKFAsFsnlAuI4xpiUwPm9dDJ37twlTmKKxcL3RXI1Wk16/T75XI5KuYzRhk6vy9raGs889yz/+J/+Y+47dpBSPsfp+86Sy7tp05wxrK2v8/Wvf51nvvl1BoMWgV8GK2k1m9x331ne++SH+a/+X/89+XyFXm9AEicUCkUmJmr4fhpiXioVyOd9+oMepUqRMAxptZsEOYGSJi2GEwIrUoLAcx1yQZ5iIc/s9DQH5ufxPY9+v0+312Nmfo7axAz3PzRFOQhYuXkTAh+MRptMMYMcWRSH2E94WaMzZY9gp9EkSTRxFKFJiY5UjCj32SbvZSiiKMRRqdpppOEZkUZ7z5NS4PnpMdntNLG6z9xEhbjb4sblbbprd1EYFpYOsXjqAUqVKspxETrGxgOsFphIY40Anap5RLHC8y+9yY27ayQeoBSXrl8GlePgodNcv3kLQ58P/we/gBAS6XhUyjUGvRCBJEn0PSUV+7cP2fFvshsb3wukkviBS2IEl7e6PHzVkvf6XLiyy1auDFIQJxH9fjdrPU32CP+3Rbone/02u7t1En+AdiygkBIKxfxoH1kTZGooM7Id6sSjvrudKSWdIeeDTjS9sA/CkiQRvX53RBLt/5MqZPfshSP+aGgPtxb2OK+UHMzUVNYa8vkAaOI4Cs9zs8bfPeVa2nYoMUIjhUR6cnRzYaS+EhZrI5QCo9PRVioVmauWsK2YwdouIjEIby9kfi+bbM8mv38fjxSHMCLUxhhjjDHGGGOMMcYY492A74nkeuvF7tNf/zYvvfgsvf461g6wKKxJJ0BJkowIGADP9/jmM99OyRSlCMMwu5OcEhNRFOG5LjlXMjVd4eSpo0xMTDM3fYTBwHDlynVu3LjJL/ylX2B6eoZep8vK3WV00iWOEhKj6XZCHn3kCYgTioUS7d0dOo1Nzp17g9XVFUgMKrFp4LBNQ4dNZgnzXA9HpjaROIowUqZh7Zkty3HdVOkhQGZqBplNbnv/f/b+M9i2ND/vw35vWGHHk8+5954bu/t2nJ7piQiDGQQKGAICAYJBgEjKEFwmTVqWTVsmS6WyZFGlKuoDXVTZLsuWTNHMIEhAIIEBMIOZITChB5O6e6a7b8ebw8lhxxXe5A/v2vveIalidfkDhlX7P3PrhtM7rbX22vt91vP8nul0Hp2SUsY2PykxzkZXlRQk2TJp1qOTRaHGB48PAYKcizGRLSSbaNOs3UrPocNCqigEQnObZgniAwEVnWQ+Cl5BCGBWdy8JuhG2RPQRREFKoVSKEJLhuMIRODo65h//8i/jk4ygNXmWodGYusaaer7ITRtOD82CKUYYoZfnnJ6ecO3a64zHY9IsoapKimLK0ckBIQgef/w5vLdMpmOGwwG5TkEltNs5SmWRrSWg3W5R1xWTyYS6rsjzFsbUFEWB1hqlFe1uB6ljvPXg4IiiLKmq4pHYrEA7jVKKLMtoZynGRM5VXUc4+enpKWma0mq1sMbEBbxzTOsYbwRYXV3l8OiIrBHbQogRy2lRkemMvd37DKYT1jaeIPgaNzrEnPZIly2EElxGqCDXGW+8/RrTD/4g5y6e533PPcfW1ga9/hJVVaGEoNPvzYWWWRvfv26891x9/DG+9OJXGU+mD3lAjzg1xOwP/3Pv6RA4ODzgq9/4Gvce3OfKpUu8/7nncdbxymvf5vd///d48cWv0u11uHH9Tf6r//o/58//+f8tlx57HOcMuztHfP7zv8sr3/o63lRkSRaPtwDddoeLFy5Q1Rbj4MzZs3grsT7gQmA4KqjqhjvmIsttebnP6aDipZdexlnD0vIKWgtCUIQwi085VlZWePLqVVp5ymQ0ZlpM2Dx7hvddeoz+6lrzmi14kMHTyTTTUJNogZIPX7sPD0sf5g2NMz6Xj78EMJmUHB2fkuU5aZpAiHyveCzMxA0BQczliaqq6PS6zNsgxUPxQ8gZ3Ntjquh8q3bvsHP3OpoJ/+5P/QhlOeH8hUtcfN9TJCIQrEUkCUIYsDbGlCXU4xFaJVFKCxKhFQhNN+3R7tQM7IhWux9dOyJH6zZL/RWWl1ZYX9tong88fvVxvv7Sq4DAN4D5R4+dmbst0DjYgnjPrXuicVZWIuG1WtJ+4y7b9YBrNnDY7iBEoJgWHB0fRicwkXEmGpfqDLRureXk+JCNzU1OT455693X2d09ZFAdY6UjfuxI1NxZ9/CcGZsTJQE3byxMkgRCQMu4X62xoCRJljMZjx8Rsh4KXFJKZCLn7i4p4oWER9+zYva/R29HvLAQ39/QasX24YCjLKbxGJkdM01EFyyqaew0xpBn2VxVixF3BwGchzNnzvDBp65ypdfD7494auUMn/vOS0x8QCUaHzwS0YDrmStzsyjq7NwphWrcdB7PI7HVxSxmMYtZzGIWs5jFLOZ7eP7/iisKmdJa2iDtZTgzxZrvXizaRrxx1kZ3ihU4r/BBIGUaodBpiuzGRUGiFMobMpnwxqv3kfI+Wr/Gysoa7VbGjes3+YMvf4XpeJ92q8WlyxfxwfPSy6/Q6XQAQTEac3Cyz+rKanwOwtPpdOn3V7AucnXCzKXROBKkVHOXkBDQFgL/yMLH+0CaJlG4axYAnXaXjfVNhIoQchHi69zaOsPh6Sk2hDlH5V+OQwUknvg8YjykabkKEmNqhJCNo0vyyPKjAQ0HpJIIncRIohCERlRMdHReGBMhw1oKQkgwxuJdeLhvCDHSFwK6YadJEUHXtnIMBkPaK0uoRKC0wFWRM5Olen7/UjJnc82EuLgtJWmqODo6IASPMc3iFE9d1ui0RRBQGcv+wRGJTPDe4rzl8Gif89uX8d4ynY4ByLIEYzRFUVBVBUtLS4zH8We378RmxzSNHK9WK41RuhC5XY9u+5mDLc9znMuoyimmrufHZ6/bxTtHWZaMRiPSNEPrh/vc1HVk+uQ5y8vLrK6uMji+z+rqMmc3zrJz9zrUDqU8Mg2c7Byz80aLC2cz6nHCzo0pg6TDGVeTCrjw9NPcvXuP2lRYa0lTTdps3xACxlhqU9Nutb4LFP3oVFXF1uoKpycnsQHSRYdcWRaMRgN8CMiZUDHn+zD/sxDQ6/f51V//J3z+dz9H8IHNzU1e/OIXSZKU3/u938daSyvJ8VUAcqrC89/+t3+TT37yk3zqj/wE33npG3zjxRe5cuE8Lzz5Yd68/i73dg+oSstqv8u5c2dBKIyNYlFlK2rnGEymjCZjSlNTVCV7e7vcu3eH973vGZTQJEmbJMtZXuqgEBgnY9uhtxAC927epZenfPLHPsHVZ99PmvXj+6QR2Ew9QaoarTqINEFoga1KlI5bwfnokJkJ9zNxK4raAdu85ywe8NjKc/v2LkEJlJb4suEUSdlEhQVSNIyqACAJzhESjfEOoSWicWI6a3HG4pxhMjzhwZ23uPXONaypyZXiuRde4Pz2BcRggGq3wVtINTLzoBrUk5BImWCmNeOD3eaVeGhpyNq4ckye5yyvrEHo8ezTz2B9oNNbxXlLS0u0b/iAjdWo0+8wODlGyl5sGvyXHD4xCg1JGiPexgaMeY8uHwESSWoloa25tyRwVlIcByQgRKDT7fIDn/wBPlR9hO1L2yghCCEyynwQTCdTjo+P+Mf/8P/Lf/S//yv81m/+Gj6zPPPC87z0yssIUyFFjM6b2s6dSdZarI0uSR8CLlhEc86SUjTn6OZcG0IEtjfHhpgpxo0rayZQoaLwpKRCNSLXLIQamg8UKRSiMWjNxLFZk6GUEi1ThBTcuu0aAU7ggwCpyPIWeatFpgXWGKbjKd5aEq2jg6tx5Art8D62hm61M/SNd3j9K19nq9Vj6xM/jNg/4db4BN8S6FSgvCBIQZJo0pnTTcTnrGR0r+o0jRdp4D3FUhezmMUsZjGLWcxiFrOYP8x5TyJXMZnQ63WZQeVtsFRljXU14AhBNBGwmaDSwG4RWBfwrv4uh80s/jX7ewUkQoCSIBWdfpeyqrh15x5Kpegs57Of/zwnp8d87KMfZWVjk729PQ6OTsg7Paqy5Nobb2CCJSD5nc98liTrg1LYAK7hTgkfr2Q/dCrE30MIOB/ZVUEKqsrEJsYQMNP4JX8WN5lOj9jd3Sfg0UqT6Ai5P7u9zd2dHRygGteAtXa+VJRNbHAGuZ4xtpyLXLMgwrw1Md4gxMhgI2DNoyYhRlmEUNRVOf+5965hUQUcEucd1tnI1PKzFkePDxE474Odg4VbaYaTitoYZtFLay11VSLxZFl0RCVp0+gVLAhQTRxQKY21Fp1oxuMxWitarS5SSSYTR1XX+BDjlEoplILt7W1OTg4ZDE4oyik7OzuUZTl3TM1cFkVR4JxjOp2idfZQSLW2gUwXJElGXdUopWKrXZiB6uP2rKqqge5XeO+ojaGua6bTKWVVRXeYUg3IPqXb6VLXNScnJ0B0giRJgjEGrTX37t3j9HjAcn+JJ55+juF4jKVDEMu8du1tPv9Ph/zRfJ29vS5f/I0bWN5HYT1PXH2W1fUV6mAIQaAT/V1uLWtjHKndbkfo+v/MjEYDOnkUmG/evM7W5jqbG+u88srX+Vt/6//N/vEpadYCU5PkKkbnRFzEpmnkLf2nf/W/JE8UVy5fpNddxhjLu+++w+HRMcbEKJp3HmNrUp3hnafb7XPjrev8X1/66zx39XF+8Rf/HNtnN9jdecDxaMi7N+8gREJZFhwf79PuncPYKvLMZIbSGiUlp4MBVRldeb1+l+3tbS5eusTbb+8wLgpUoknTvImsEY/dRjwvypLV9Q02zmyTpBnBW4SIwlVdOV7+2hc5f2mNc5fej0oVwnum4wkE0Yhb0Zn6qPjsZ3DvhrsnABtiSyNac+PWHdp53Yhb4pE48yPcLZibnwIO6R0yOJZ7S+ANk9NDdu7dYX/vDscHDxgdH3Hpwhle+MgLXLj8GMobqA3goJ2ADpB2wJnoxNKxKQ/noJ2jl1dYvfQYzn0+PqirIXiGp6dcf/c6I5mx8cRFltfO0kkTtrp92irl7LkLdJba8dwXpiA6BO/JsoxpYVldXY0R7Ec0LCklWickOkGIKA5Pp5P/2ePzXzfBe2pT4dyE7Q3Df/h/+hgrvT2+9P95lbe/tE89MaR5xtXnnsFbSwgGYyY463FWcHxwxO999ndxSYvP/96LXLzya3zr5df50R/9cW7fvkOeS8bjimlZNy7imXNLAfFXCFHcS1TaMBgb55UCoeaUNLSSKBkdTzMo/Uz0RhGFoEyjGmfW7BwxP54I+AD45rPFWQKB2lXxPN4IX96W88+BueNLKjq9Hp3lDkmWUUynnO4ecbCzw3KvT2HqWE4io2grQwUq5+zGJuwfo0c1l2xG3wiGL73COQS/8uq7uA6IxKMCGO9xziK8ic/Wh+b8EM/Rsol2A00BwGIWs5jFLGYxi1nMYhbzvT/vSeT6G//5f8Zf+a/+GmubZ5A6ZTQeMp2MkcIQhME7MXdEQFwfzZgt3vl5FMIYAzCvdg8hoLUmz/LIudIKGzxFZRmNpug0p64tHsXRyRHT6ZSvfeObvPXOuyz1l1haWubBzh67u7tkecbG+gamukciU956800OD44ZDycolcT4mmra1ZrFjQ+heX6PCEgNAFo+IqY8bNUi8rG0JtgYb0vSlDNnz/D6W2/y7o3rXLxwCZGkeA+hgdpHYUsigkD6gMdjQ3SU0TgAojLYbPAGTC0C2NqQ5TFaOGv863S6XL58hTfffqthX0VIuk6is8vUNgomIcRFi5xFWyRogXcOqRXToqCuDMI6UhGjeNPphFR4ptMpxWgC3qAUc6HNWkdZFs22kHHBpzLyVgsQmLomTdrR7UUgyzK8DxgTGVjGGIyxlKOaqiooy5LVzhJLS/15vGi2D4SIwPzpdNpEjkpCCOzt7TUuqOjg6nb6mLpxangzF8IgChhVVeG9j8D5ssQ0Alh8bjEGpFSMPrZbLfI88rGyLKPX6zGZTCjLEu897777LlJrPvqxj3H16lNMp1OCkEymAkPK/brFb35+nxc/9ybDYDFJj7LfQmhJMZmipeDqE1f42Mc+Sp7n1HXdCH+KJPk3vy1DCExHhyz3zvPJT3wfn/6t3+Hll17kJ3/yp+i1cx67fIbNrW2kyLHVBJU3bhZj5+Lfvfv3ODreozYlpjYcHx0zHA7Z39unrm1zrAvqqkbrBBUU07omSS0Ez2h4yvPvf4b+Us5r3/4Gg8GQNM/jdg2B/YMD7t69z+NPbpLnLaRUHA8GCCF45duv0Gl12Nndbdwsit3dXT7/hS+ws1sghCRJsvg2ELM+1ChNzdwlszbWYnSKqWr6axtYU/DmK6/xS3/uf8Fnv/QbHN6/zt6N60ghaGXZXEyggXzP3s9zDtcjbq4oUkSRWWrN7Tv3OHsmw/rmNgRUeATMLmmeX/y3qipIvCVLBO+8/h327uxwfHDE2toyFx47z0c+/Bxbaxvg4/2H06PmPABkKeQJaB3v2EkIUWgQaQrWQVFg6wknD+4ghcS5OjrLrGXlwmNcvnKFwzrw2JNPMh4cUB4cMBmPKLzAbJ1h/Uc+HrdpI1YH4nnYmAoxE+cfYYwJAUpHkX4ynXLv/n2m06f+tcfmw9s8ZJCF0NyJCOjc8NO/8AGe+uMJIbzFp1Yv8HtffgM/LvHO4JwiIDDGMRgOuXvnPq+98hZ/8JVv8fqb7/AzP/enyPINfuPTX6bbXuK3f/tfkKSWbrfFnZt7jKaaqhLzc2Vonpd4+MQaluTsuII8z5gVcMyOk5njN4TmtTcXeATRXZa1ZHSNhtDE2+PxMkOwEZpLKIImng5JmiBEmEcW/ZzrJR4ePxJsmFBZi3GWYlriJ1MSBOV4QtbtUrkmRCghEz3qIDm7dQ5tFe++dotsf0AZKuzKEhfe93305DXqxFKJIkbtXSARCVJrnLfzhs94IUQSfCxzCICp3ht7bTGLWcxiFrOYxSxmMYv5w5r3JHKdHhYomRLw2GBABayrCX6CDzXWPoz9PVzoxKvcVjp8A22f/Wz2gg8IrwABAABJREFU51ifXmOtoyijK0xKyDKNC1BMCuraIYQiCI31gklRI9SUorLs7B8CMT7HeEqWt3j/Cy9weDzg+77/Bzk4OOHkZExZVbHBUEmkgBDsXKCRDe+EENuxpBZN31WEqat5hLARC1xNkiV02x2EC1y9+gSHp8fcun0THxy7O/c5d/7Sw0Ves+CZcYBEs8CeLXRouD5SqodurgCzy/0ry2ukacJ0OsE4hxCS0WTCd77zKgIHQuKbbTBfuAeQSuGMw9bVQ5jwjFWVaJJE0woZQWhUgFaSkQqwTXwneE+aJmiZRRZZIwK0ckW71ZtziWI8NW7PwfCEsqrIW1kTzzJNnDAny9X8eXjvqaYThAiNa8xQVlXjykoeie5F8Wlzc5PptKCuo3trMpkgpaTVauGc4/T0lKIokFLPj8HZfp25r+YcJZi7tgDyPI9CVQhY5yirCmMsVVU1oP643YqiwHvPn/yTf5LTQcl0csRbb73F/QcPePzyRRKdYkPNQAic7jOkTxUEpTMoOyZQ8MSlx2h121gXqOvID/uH/+Dv0On2+MEf/Djb2xf+je/F4+NDVtfW0WmMT3pv2Nm5w9HhfTrbW1TFkOk4j+8jJRmejJlB1gWCsizJkx7X37nO/s4RB/tHHB4eN4UDM8E1CphZFsW+KhQoBcZWBGLTYG0NxycjpmVNkmYo9/AxtrfPs7a2xs2bNxAC/vbf/h/Ju2sxNprlbG1t0e324vvf1BhjYjGF0hhj5+UGSikQvmmti4KDdx4pFfsPbnPnxjUImk/97M+DCCgV6LU6bG1c4Nvffp2iNBAEmY7iQpChiS2HOYR8JnDNz0nePfJ+aSKwDpRKkTKhdlOcs6SNGzTG0BTIFJUpwLGxsca5jXVe+uLvIqTlox/7CBubZ0lbGqEs2BLKGrwG4xAyAR3dOYhG1DImnjikiFG5EOLPlATn0XnOypnt5ljXD12gxlOWBUfHE8K711FvvMmn1td4ut3h2zfv8creAeH7XmiUq3Tu2MrznBBKlFSNw+nRo06QJClZmrG+vs5//B//JX76j/3YIz+P+6auK8bjEUtLy7EEo/kZRPHZW0tIHVsfWAW+jOA6nUvn6VMThif8zu/+Ljv3Drn95g73do7RWYfHHrvCs888xi/9hU+Sd85wfLTPi3/wElUlefkbr/L8+6/w73zqI7z5xlsUhWNwavFh5tyS33Xui+fGgKf/Xe+pafHdDYSNJgVE/qI3PLyPxvHnhCY0rYyhuUF0aYm5oCcJc4eXlAJZPHoxaCaaEd1mIYqnyIBQNYESHxy9NCFP25y7fI52lvPyK69RG4ETAa8CGZYyBJ646sg3z3Hx6Rd44rlnWH3qCkYJXv3Ct5n+nc9xdDqi1EOE8Dgf2xgVTRuw8HNm3DwGKSKvbcbWXMxiFrOYxSxmMYtZzGK+1+c9iVxPPPMEvZUOWgusq/H1COfG1FUBeJIkxomk0kCg026RpAl5lrF/cIBI2ygtGY1G0cHlo9AliFfJ00SSaEGWtJAqLkaKuiJNVax/D45up81oNGIyLsiThFaa4EJ0CJlijMdx1zq+rr7O45cv8eorLzEcnZDnKf1+nzTNkDKKO7LhqcQRtNptQgjcuXMzOqiEpChjnNL52BKYpgkQMLZkY2uZ559/H3fv3mP3cI+vfe1rtDo5jz/xOG++cYPTwZROVyBkTaI7INO4sGjA7zFmIuNyQjaurbguwslAbSzSWJQSeGM4OTkl2IBUmmlVoLVCa4GxCikDnVbWuOQioD66mTxKSLSOrpiZcGNtjCtOJzGGGV11UNRDNre2GZUVwUpCHcjTjDRTgJ8776ytaSc5SinyPKUoSpxPsLam12sBUbgcjQZ475hMpgih6PX6OBwWhfMRDh4IGONIk/ZcaIjP33NycoK1ln5/CQBr6xhLC5ZUZyiV0Gn1qevoCEsawXK2SPXeEbwj0ZI8T2McazpBaYESCSEE6rpmdXWVoihQStHv9dnePk8IgQcPHsTHdY79/X2UUnzwgx/kJ3/qp/nrf+3/wub2WZaW13jzjbc5HQxZ39im1cpY31ih21rhYO8+w8EQqVM6IkHLLkGm7O/tsLa5xVs33uKJW4+ze3SC3T9Gt9tsH+6StTp0Wj3yLEEJ0GnK4fExL37zJaaTgi//7qf5b/7aX6Hd1WgdcFVFWyV0Esnh3g5vvXadztoVjB3y7NXLjCcFb7z1NkmiMbbGEUgk/Mo/+oeURc14WmF9mIOoBTHSuLq+zNHRUWx9E9FJpZWkrh3eaySQJJ4019S1x9kKiSZNEwKef/TLv4ZsLdHpd7n6wvvRIkfICHK/dW+Hw8ExJtQgW0ipcUFRmwlSRUC5VHHbG29iNEtIkAmWmtpO2Ty7zUp/mdODXUx9gkxbKJFyZm0DlfTYWu5xsrxGsA4SHYHkpWhcWo3AFR5xnzYiNrNFvQfvDN5bTMi4v7PLeDJEuQDBkKiAztpsnNliabnNO69+mde/+SL3b9/h6Wee4X/zF3+OtStXEM7BaAxhDFOiUBWhTo1rK4NiAjqFuoj/jgdrIROxNi9NG1eXBxfB784ahvv3CbXBloaJrZkcnrKyOaGsE8pK0Mn7/ODl9/GJ9YqLmwGdpJzcLemFhiFG2pwDA6lqUZYPkNo0TX+PqFwi4JyJDZi9jI9/4vvJsoe3hYC3Bd55EhWB68HVDE/3CSLQanXwDlCGVBqme/cAQ8Bjjh0FgXunU1rtLT700ef51E+fx9aB+w/2uH79Pl9/7R5/51e/zOHhPj/1Ex8hTyDLJatrHV5++Q2+8c1vYLGo0AISfJjpgpGrGPys5jBC3YPgIRexOR6ajwJ45BzyqPtrdlt8QEhJmDa9mTNBrNEiXRNH1wjU7LFoqiGa+/F40kQShET4KDb5xkGICwgzM/YqXFCcv3yBN6+/yfuffz+tfAlrHKXzCB8wArTM2Ny4wod/+sd59tnH57stlIZCBHzVp5SGUgmE8dEdOAP6qxDh8j7gPU3hRiBes/L4hci1mMUsZjGLWcxiFrOYf0vmPYlcf/CtP+C/+Cv/EbsH9zh74TL7B+O4+MJgjMHWNi4kmi/00/FphLwHkEqjtSYER1WXeBcXHrMr2lLGlkUlJcF5Ao4sy6idpdvr0Wq16LU7HJwcRChwwwup6gpjXFwIWIvDUUwrhoMRo9GEpaUlptOCsqiYTKaEAGmW0Gm3QQSKssRah7MBreOCTUqQKj6/WUzRNfySiamAuNi7desWVV2wurrGW++8yZNPX+Xdd99mMh2zsbnCweE9ZLJEnic4ZxiPC6oqinpZmtJu5Yjgmza0WRsXGOc4HQwJCGRwZLkmqSu6vS4f++j3kbXa/PPf/E1UIjmztcmDB/sYW9BqZ5hh2QhdDx0mQQamxRTnXeSt6Mh8mUVlQgjY2tPrtDAW7t6/S9rqkmRtyqqkLD2tVkKSxCjdrLEsTVMuX77I6ekpp6enSJXgnGNpaYkkydjb22M6bZoQm/Y3AGMc1lrK6RQ7naJUJDqXZQkhsqi8txRFOedk1XWFUp4kyXAuUNclOsmoqoqd3T3SNHK7ongnI1C5iTFKFUHKATg9PcU6R6ud4ZxDKcW5c+cA5u4xYwwHBwdIKecOjG63S7vdZjqdMp1O+R//1v/A9evXeezpJ/mhH/ohep02x8cHHB/HyN+HP/JhLm6v8d//D/8d47om8QHtLEJqxpMpr732KqRvc/f2XX7t136Nk8GYaVHz9jtvE0RAqhTvI+zaO4vHkbXarKxtkWcZ7XaX1dWzKKmpyzGK6NarTeCxxx7nB77/B3n79hF7u7tsfOxDtNM+3371TaZFQQgWIQUyzzg+mVDXZu76UyLyeNqtLr2VPvv7uxRlhVI6bi+pcC7gXIhQ7cYJGWHcjhAiqH3Wftnp90nbK2RZzu7ODsNBjIkeHh/R6nRjE6mQCAS1MYzG4xhN9vF+xEwMFgoXIvdPxZJFnLWMTk754hf+Be++/Rb/5Qf/a0KIRQbGlew+uMWFZz/I0UE5Z2w1Jhx8mLXJhX/FWToviPAeMzs/NTG1w8MTisLghlOm0zFZMeRrv//bfPsbX0SFwDNPP8XHf+RHWe530WkboWqYTGdGTlDqoWiSpJAm4CqoJ/F3PKQZ5CugNFFlcLPau8ji8p7gLbgoVPfOXWDiLGVt6C5tsLl9GZI11rbWeTCaxm02GiC7DmEFaT2h60HPfUpNsUGA2pQ4b/mrf/U/4SMf+QBZlsXHB7wL1FVNr5fz7HOPk2Xp3J0Ecf8TJFmaoAjgK5Cayipu3b7D4eER07Hl3psnpHqZV/7Z63zwh7cgP+Fbf3+AEWv4dsJL337Ar/zq19nbO2I4HjMtJ3hvUVojEHQ7PdK0w3B4QlpbNreWKAuHMZ0on6sUfBND5+GvIGZiZnzd0jH/u2jaEWdQettA+WfVBLNj1Lvm8y3E96bDg2tKCppjbH5c+dCIRgFHjMTPeJWRy+ixAQLxs8w094N4eEzSxOdLNKurS3SOe5w9d4Hrb9xhPD6kav6TxBssktPxAWVdIJzFjE8Z7T1gcHhCeecOp8U9Jn7KtCjRAZyP8XmFJCCbj4ww/9yAgPONOPdIBHUxi1nMYhazmMUsZjGL+V6e9yRyOaHYOZjgXc69W/tUVuCJ7BQpdYwAORe/D0vRQGxByYQgVSOS5CSpbtrq4pV1ISRSxi/+dRUzITNHT57nmLqmKg2mrGK8yHusMXO+UprmaJVB8JSmxFUeYxx379xjf3+fweAU7yPLRyBxvqY2JUrJeCUfSbfTw9pG8LGmYXclhCBi014IGBtr540pG/aK5ODoiHv37xGCoKwqlNaAo9NvYX0LIaJTyCCQOkOjccaikoRur0tdlpR1NW/5iu6jQK/XpzYO5wqSLLpiirrgiy9+iaoyJFpx8dJ5bt2+zcnpiE434eT0GB8cAcHgdEAIgtXVdYIUeAQhiMagEtBKk+cJUkiqssQ3fLI8z6nHRYRQpxm9Xj82EZqaui5J05RerzuP+qVpymg0otfrUZQ1dV0zHA6p6wjFDyGKWiGAVhohNEIovAelEixizt2qqgrnAsvLy5jaMW1g87PjQQgT41BVjNKYuqbVyul2OpycntLv9bDWIlVo4mNiHg+FCBM3rkY1Ap0zkcP0S7/0S/y9v//355D69bUNVlZWqOsarTWtVotiOmVlZQVjDEmScHx8zIc//GF+4Rf+LHfu3ufLX/4Kv/iL/wGHR0Nee/Ud3rh2jevveobTKTLRGBtwAdI844033+DP/4Vf5DNf+DyvfvtVWu0WFy8+wWhS8M6Nd0jylOfe9wG2z1/EOkdtTQRgC0jSnLKsuHLlcdK8w3jnLTaWOpzd2mR0etyIclGgSbVmqdOi10q4+uTT/E+//Vkm04okiYv28XjSiEgPBVYlFf1uj82tM+wcHDCeTJBS4JzHuoBI4gJ/phVonaCkmq3soyClJDJRTIqCnd1d9o+u473n3p17BJkhlMQ4R9qwr6RWDeQ80O/06Pe6hPC1WErRgMEhEHwUBoIzNIYzrA04J5FJC5W1MdUEW1v6/S737t7k3OWrDAeH1FWFV0lzJvPM6Eyz6NmjTq5HuXzx3dLwqXzgzNY2nU6PjfUN9A9/HINjqa35Y3/851hb34wNe3aKFAGmA0TWCEg6oTo9Ra+toVutKFZBvEhgPeQd6CwTbMLtd2/yzmtf4fd+/8ukeYvl1ZRWKyPLUlp5Tm9pid5Sn7zTYXltg05vjfNPPcn13X2udC/TStbjvuxoKlshkNw6HXCE4ZxTDPeGnPo+V1f7PPQpBUTjsr365BU+9MH3kzUMs8gv80glOHNug60zK02EreTkZJ/l5TW0buFtYDqdcu3aNV5//Rr37+1RlIG1tW0uXb7Aue3znDm7jHUP+P3f+SyfeXHIrT/9Mu3cc/C24kg9xvHJkM/8xjcYTRw6aZO1u2yun2F1dZXV1XXa7R7tPGdra5M07dNqTdEqx1mPFBleSKwH2XwuhRAlrtkBLma2K4AgESEgQtO6S/M55ENzDgkPxcnmWAnqYYzRh4CU0QXlfGQfCike/vfwUOQKHmc91kYmoZACpaCdpfEzSMooEIcwf74xORiPxVaqWFtf4ZK7wurGBu1Wh06nQxLAhYpESHyQ1KMBL/3Gr/Pu39qhvn4HUYzorq9h0zNodUiCox09dhgZCAKk9wgszik8Or4/QuPanR0dYcHkWsxiFrOYxSxmMYtZzL8d895ELikZlI5EJQjfMD2kQAQJ3uG9jbGQZoEoQ6w9t6ak0+9RlgYhYX19jYODA6yt5rBteCguKa2RSsdWvbqi1WpxND4h6S+RZAlpluGcn4ssxjYtUUCWZggdYzi9Xh/rarI0paorYqugi0VltW0cRAEpFDBFChGBvMHivaC0lmoG3dbxanvkd6WAoKxKTgdHCBlYWl7jwe4OebvFtCrR3pMkmqqOoooUCusF3isEgiSRZHmClILS1PNtHIhCVJZlVPUUlUU4f6/X5fjkmNPhiExnXDx/gaeffIaslfPSt15jaalHq63Y3buH0glLS32qylCWZXToJLpJSEUOWJakaCkbgSJgnWc6HpMoyeb6OsaDkoIsTZHCs7K8CcKzt7dH2TTiTSYT9vb2GAwGcbuohFarzXA4wDlw1lN7G4WivE2SZIiGQTNrRMQHQoiMMWNi26D3vnFjJfNGxNBkgaRUEBJ8cOQ6YWV1iR/7sR/lN37jN6MLrNOiqsu4X5vIUV3XFGWMZYYQkEoxHA5p5x2UUnz6059mt2l17Ha7dLodJpMJu7u7pGnKxYsXUTo2RpZlyfHxMWVZ8md//ud56aVv8GDnEKUkv/7rv4bSHabTKffu3aeqBpEB5wOhaar0NvDEE1dptdt86t/5cf67/9v/nW6ni1YJghrrAtKBUhk6aeEx5EkbpaPDzHqP1inttkIISWfjAgxGaCHQIjLkovgTY1jdPKfbztjcWAMsSZqipKWuS3wApeL+kEIgQqCTZyz3e5wOTjk6PmxiXBHAnWVtsjQlzzOkVOgkbdbgD90pUgqCdxhjyVo5yyvLHA9rBPH5VtZgnI1idbzRXLBaXl7mIx/5MMbn/Mo//udMpzVVZRiPp5gy3h80bY/GRCB987xnATCCAwFSaIx1QCDNcrROsUESXGxOlXIWY4tuRjuDzYvocHHBzaHzPua38M6S6RZKODbWl1jtfgiyHCEC0kzRoiZICcIhtIyW0CQFFYV5g4oXA5QGlYBUuLpmeDjhxje/zmgwZXf/FHSbbn+Db7+zz9FgiFR10xAYs8wzVpJUsXl0ZXWNzbPn+NLf/JvUvmB7a4V2mvPlr7xEoEcIcK0WpG/scv31IW9PCw4/dIXNK5e+6/xuTE2v3+ap5x4nb2W8+NXf4/n3vUCv130EwN60tyKZTmtee+1d6vJVDvcG3Hr3kO7WOR67ssqHP/pjfP8P9hkMhuzuHHDnzj5f/vIXGY5PabU0ZeIYiDb+cAtpFINaUqwYjEh59rmPEGSXrL1M0uqjswylYwRbC0WiQNDGWkma5kihm/eZxKNAxrbSGAQXyKDiMdw48nzjLg5CEBtq5VykjSUgspGBHGG2vZuCgSDAz27j43aQQiBVMncLesBFAh7I2JqLn4HloytRiED09EkSAV7IWFASIsQePGGGxpshACS0OjkqUSitSVSGkI0kFmKf7+B4xODuHmdHFVdMj9WlNfz6Kt/en/DMk23uTEYMCsNyf43dkwGOwObqEomoOR0VlJUhyxOyPKOcVkymBd1ul6IoOR28l28Li1nMYhazmMUsZjGLWcwfzrwnkavX78+5PME7vDEEZ2KURxCvQAuaOGG8a4km05o8zRhNx0zGUwQSW9nIu/GWYJqrxAK0FiQ6RqOSJKXT6zMajUmShOFkwmrej5E7qWJkJAiUVBjTgH2DIhGO4fCUPE+ZFlNOjgZEa4YgzdJYjS4kQqi4SHCOqi7QKkUKiQSquqKV5xhb4nxOsNFppFy8eu+xOOHwUmKtYVJMKKcF5XRKmmVUdcnlixcZnA6RTURtMp0iRUKiNVXtKUpFbRw6EVgTCFiMrYGE4+NDajMkbyXknT7GVXhhEMLR6qTINPDVb/w+x8cDsgxCqJEyj22DSUa716HVElSFpaxKnDEkiaDbbdHptkkSzWgUeWpZJ8UVFf3VJUxVYbzBOoGta8pqipIG68dxAShqqjqgkw5JmrGzs0NVVWitOT4+jbHTOrC0tAxh3EQQJUVRMZkUdPvdCDsOYIoaLSQOUCqKhtpajo/B2ti+6JyPDp6Z3ygI2u08usqyhMlkyFe+8qVYKmccUraa2GEU0mJDnMc3YluSpA27WzItJnR7PW7dusW0qFEqJcta9HodrHVMJr0Gvh2dPkVRIISgLkqkD3z2s59j7+iY7/vox3BVycAblrf6yE5ODVS1RUKMJIXohqpNwdLSWQQBJSWVqeirpegGoQJnwCisMdTe4VCI4FEhYVxPES0NJiCzDIRFhigIChnNk1KCCx4TAlZA0m7x9PPPo9KMTAumRDekENHFIolujeADnU6bzbU1dKK4dfc21hpi01qg3+vQ6vQ5Ohw0cPKmTS7EhXrj4UIJjfSQEAUvqZsm02Yb1qWJgpeMAoO3PibwApjgKZxnPKlQKsEbGByPMMMpaZLE6FwDXhdNXJkQUFKgpQIkIiiUhEQnzSszeFfHn0mJV4AI3yVgBRdgzucKWGfnsbU5IFzEVlStBFJ4VC5RSiGkIzgHMt6HEILGKhebEYNrdkyK7vQQssvopOJkf5+D3V0Ojgeo3hrTiWB7+2k++HSf3b1DDo9OGNUVJlhcJZtod4zQ+Vl0UYAQNYOxoL18loPDEYPTE3buHJMoxUlR0evmBFHi5JRy2zJSguJOvIhAYgE7/xhI85wPf/8L5K3oQL165XESpTk8OGIwHDAZFdy794A7dx7QanVZWu6T5gkbm1c4d2GFpbWbHO2OuHbtLv/i8y8xPC4YjMdMiwJTe0rjyDodzm1tUTpweYuxPsvR6SGlAik65HnG6vZZirqNlylOxf2KVwQLRhiC1HHfGIHwksrVCFICKgrmNO4rKRvT1ix2+BA+L4XA+viZENX/h3G86OCLwHVEmN9Szt4zQaIQRAlVMFNppdAxFulnTizRHHdRPPPORZFfhHhI6Pi+8UpF8SwE5Oz+pG4+Z6OjzAdBbRzWFFSTAcaU8/hufG4tpISdkyHDC2fINlcxhzdBtGlvnefGnd+nvey50u1w48EBFzcyTk4NstXh3JqimygOFUwqTbffoteRFFPL/R3Hua02p4cjbr6XLwuLWcxiFrOYxSxmMYtZzB/SvCeRSymJVFHUqa1tolEBKQIhxMWqILJN4pX1KDxpiAyZJv5Tl1VkYyXRpWSNbdhXELzEWj+PublQIaUmzx/ykFqtHIJoIh+aTCeEUGKb9kYh4+Ll4OiIEDxVHZ1S3vsY+UoSZov3JFFIJdGNKFeUBa08w3nDhQtPUBT1/LU478B5rDXYYAEfXVlpOncLOePpC820nFBXnuFggnOe8XRKkmVIrRDCI0VsP6uq6MzxwVEVJcaUtNtLMQYjwZgqMsyCxTeCxmA4ZDQZUlZTtMo4d+4MS0t97ty92cRdAmVVYKqAsbGFTimNTiRprkkygRCW2o4ZTycsLa2QtRLWVlaZjseUdU0wMR6X51mzyIrCSJqmDEcj7MBRlhV1HV1WQhQAVFWNNY7gA0mSYG0UqqqqxjpDp9uOCzcgTRWujmIUgTnbZzqdUDVNmFKqGOVxsyVq3CbWWsbjMVJGAU1KRbvViewe9fDY8z4KD1mWoVSEqadpSm0cpyfHTKcT9ob7pGmLJEmpa8NwOIxtkdawvr7O+fPnuHHjBsvLS+zvVwgBmxvr/Mwf/1lee+Ntrj7xOM5V3LxzmzTNUA3/KxDhT0LI2NDnPUIEtI7Q8SRJ6Ha7UYzzronixoVuTPt6vLEEb0jzDq4uQWl00Cz3+hGBLSQ+zNrZ4vaZteKVVUkwJUjJ0f4DxqMR02mNVo04FRRWxBjnUqfN1sYarUyzu78fWwgbcWd5qc+5c2e4dfc+SEFdG7IsxXk7byQN3uFdPBa883MuUVEWTCaTRgcIWOvwAayLwnYUv2i2t2U8mTIa6hjxsg7nA956ZCob/pNESE1szYvvES3j9oqesgpw6CRBikYowM15Q5GOJLAuwrVnx8js4PIzLlfj6pk3gYYAUjStiz5G3GzFTJMAH7PZQhCp+ToqjipjfHREMRmz+2CXyt7DBU2et0i6W6y3N1g9c44b169zd2ef42tvcevWHSaTCXm7xer6GsErrHXRyde4S+u6mv/dmkBdBcrKUU4dtQwILFXtadko1l25uszHf7zHU1dqlj59iz94ZZc7b7/Dc88+jaQVnUpCEILljTeu8eDefW7evEOiWyA13f4S2+fOs33+WXq9KN4fHw+4d/82J8ffpijGnA52MKOK4Ke0cJRThc+3aXfO0Vrvk7fb5N0OyAp1I2f17DnuvPMaw6rEZoqWF5gIcAShcbN4qhLz4ycIj3GBIGUExUtBMS7xsa4WGhEpNALo7KgIzRskNA7CGZstRmGjyEvjlgt+xu0Scybcw2O1KQchxg1dw+kSiAZcLxpBdHa+aoS04BuOV4znKxWFtujZIj7W7CJRPOAgSl4Q4ms+PToBDIOjQ4rpZP584gdnvOHxYML9UYF57DE21s8g6oqX9u+yUw44Guywvn4OgiVJAkp6sjzB2wpLhRaWXp6SyBpXOfJEk6eKTmZJl97TV4XFLGYxi1nMYhazmMUs5g9t3ltc0UZwdpJonNMopel2+yRJEllUQnB8cBQXvHiUlEzNiEQprDMzFDrWGFQDA3euEY9EdH046wkyguWDD3MYtNYRel5WJXVlCUGQk8VFZx1w1mJqg7WOwlqkktS6Im+3GrGkqXy3NgoqSiOExzZr0qDilfZAwDpDWU05Pj0CPGkanQNl7UE4lJLUlacoCrwLVFUEW1eVwRgPYkJdV9y8cRNTG5TWaC3RWiKkoNvrsbrSx5oaUxsEoYl1GpJUkiSBLJfkrT5JKnGWxuEVF07WWoQP1HWFSBUIz2Q6xhhLXRuUNti6RKk87osQkBLOnj3D8kqHvb37eCw6ESwtd3GuwhrLcHRKOSmYFCVStVFpbCp01lNVDhBY66CJFA4GoxgXkxJnHVJFUQtgOp02LqoZHys2iHkfCM4jETjj5nw1JSVZmmKtn7OxZBM7gjC/H9EoOUmSNO6IgBOBIONSsa7rJsYVwfPee6oqRlOzLCNN0waKnjI4jdHNEAJZlhFCfN4HBwcopSjLiul0ymAwwPvAhz70IT796U/jnKPd7lBUJVevXqWuKpaXl3i28yyjOopUSkgSoUm0aLhjvkku+XlE0JiK8XhCr9swu7zA+8guW+522Vjq4dqOtaUuB3sH5Ct9hrbCO8Hr3/427ud+ArKmKdA3C/UQhRpTV5iyoJwMKIsCrSS+rlA+sNRfQiRp5KNJgbOWS+e2eOaJS2yurzIaT3jptTd588YtWu0WL3zwBa5fv8F0GmH/zvk578w5S/COjY01nIPJu7vxveYd1kXh1tYGQSxvkDJGKr215FkaxWXvoyjgPVVRUZcQXBQTlpb7rHU6hIZtFHlBds7/AhpBKrbBCaHQaQYiRekEUBHcH2wUtpyJwqCncUeFh7HF5jgLjeg1A9D7uWAhHoqXsjlxzMQUIUG3CCLBVDW1sezcuolRLeqiYGllnby/Qb/VxziYTAt29w+5c+8e9juv8u6772Ktp6oqptMS7z29pnCDRkhx3uFdfH+Yxulo6hrnPXVtGuaTI0ovNO/TgJKai+fbXHrfEedfGHL/XsHK28sst5YZDAveeu0V3nztHnlfsbqyETlpss2Fi+9jMBhwejLm+PiYd9/epyxKxqMhk+Ex1XTEZDgk14KlvuTSuuTMlRbnNrbpdzX3bgy5O32CQXKFkMdmXZ1oqnIXoVICJafFGCs83oNxgRpBIDQXPEJ0HQaBCh5UdHQ6GyN6QYKzkmnRRPZEFMsDzfHwr2Glh0d+f5RHF2YiaWB+joldAw2AqwnEztpHYzvnv3x/cs6q++7HDIQQyz5Ec3FldjzFxxDzxwg8pKSJRrjyQRCQHB4c8cILV7l96w51WSJEjH+H4PHCg5d4K3nz1g6f0Zqjy+dpqcAXb7xBITytVGJtHV9YiDFfJWOMumw+J5QKCOuYVDVeKhwJeSJQ2b/2K8FiFrOYxSxmMYtZzGIW8z037+3ybPAE5/A0kHQkWZ6TZzn9lRU6/R5CaoypcdbEBZixBGNwrmmbmsVCGh6zdZaqEb0SmaBSNYe/O2eQiY7OCSlotVJE7Tk9HeJts+D1Fq10bGtsIkdSxVY851xzdZ45wDwKL7GuXYjY4FYbS13XEGTj8qkZj8c8ePAAgaY20wiGV76JhQmMsVSlQasEvKAs67ior+y8rv7g8AiJpNNt01/to5IEfBS7ZrwoIeICu64MPgQyKanrCYF4pf/97/8Qb7/1LgjP+Qvn2N8/5OaN22S5pjIlRVFydHREv9/HGkeatnj8sasc7B9xdHhCUcYmSykF4/EQ6wsODw+j4YHoKkiSlKqqKKclpqrxSJLUo5E4a7HGY11FaAQBJSNfaTwpohtPROh+u5NgjIlNeM32n/FvpJS08nwO+w8hYOoagn0Ilyfup1ljn1JyvhCdLTylko1jSTTstygMzfax8xZjHUXhUUo9bP/Dz5+TtZYsbzfip2z+m2QuqM5jibXl6OgQ56LTcHVlBSkldR3Fr5dfeYUf+uSP0e/3uf72NT74kY9wb/8ULWNL4cbyKle2zyF0ws0bNzkajogLW8lv/dY/49nnngMEVW2xLuCRIBRSCFppwtn1VfqdLlsrHb4zPuVoOEBOp4xGJacPbuIbNxQhijVCyAZgHbB1xXg0ZHhyxHB4yuNPPsdSp0W/l7O6tkZFIMnazT4IXH3iMk8/GUWuaVlzZ3ePjdGYp595mjyPTZk+NI1sAZwLpGlGr7fE1tYZUr3JaDjlxvW96L5r9pc1pkk4RxZar50hfODxy49x+cplrl+7xR3ncdaSJylnNjYYpgrvogMxzVLa3RZ1VTUdfyKWW8ydeg3Y23rKScn9W3coC8PjTzzLaDDAVCVKxH3scTjnEbKJwLow5zC55twRxTMJuLkL0DmH8I+qF43bKM0JTmCKgnJaUo0su/snpFlOu9OjshlZewWVLOFUxvHpEYOdOxwcHMx/7e7uMhiccnh0xNbW2bnr0jmH1jpGpBvx2BoTnT/e45yfRypDAOtqrK0bESO2t7ZaWePIc+jgURTI1oSkU5PgqU5rrt85Zf8kUJiEwcGE3f09JtMpwQeq2jM8OWQ6HmGqmmo6xBRD8iRwdqPN+qUuWZ1x6fJFltYSNlYdPWnIW6uotiItKkb7Xab0KZTEBIeyAuckxgT2jw4xIbprvXVY56M8J2LTp2hEYWstIkh0EJGHBQQZhe3xOFBVKVASROTQNQWGc7Fo/vFFE/Gc/yTMNcp/eYT47lvGX3J+m9n9xfihnB8rYRZZnd2yue8gwlxYm7mvGg0LGWT8LGuedJg9xzCLQ0o8cHR4zO69HQ5395DxBN7EjhVegHAC6TSHh1NeLN/hxJQ8/fRFbpZjzrQTlnWHUWmRUjdOztBwKsF5iWsixgFBWQvGDhwpUoMQ7l/dSItZzGIWs5jFLGYxi1nM9+C8J5ErxkXA1CXWelSzCBNSUVU1sixRWULWaWNNzfD0FKmj6BWcj81jTURjJmbQAJRdsNRWkKV5dC0ES3ABGRphKcySQFEUqmoD48jBabdaTXNfvJifZDnWGqqqpqpqkiSZM76Cm4Gk45d2FyIXTAhQUqNURlUbauMYDsf0e31OB8foVJJnSROdEyRJhlamcaVpggdnwzx6JwIEEYW0xDi8i6zpLE8wtuLw+CACs33kg0WOmcKYGusqts9fZH/vgPF4zGg8pNvrkKY53U4vQumrCq0yVC4YjyoSZZiMa5ZXO4QQ46LD8ZhiWsR4jBTcu3cfrQXG1ZFJY2Pj19bWWarK4YwBH1BJBPdLIaJjIkSIvJSKNE2oQjXff9Z5tAjUxtJqUEHO+wYWHzDGNCUBaQTPC4XHx/glHm/dPDI0izY+bLqLDjCpZkJWFOXsPN7YFBY0+9IYQ7udU5QFZVmitabdbpOmD2OTroGWWxfbNp0LUYCdganlQ/6Rc5ayDAwGQ6QUvPPOO6RpSlnVsS3NQ7fb4tzZc3zzm99A6YTVtRU6nZxOnrLV63F26wzd/jL9VpcvfvWr6ETynddf4/bNt/jjP/dzCME8ZjsTUYL3uLoCW7HUXmG5m/P4lQu8+elv4pWgOJ7QTdTMgxKFDueRBFQDqp5OJgyPj1hf6ZMqTZCCPNNsbGxy9cknMVJydDLi+o0bOG9pZZIzmyusLnW4cfuU8WjI6uoKZ8+d5cWvfpWiqpAqi1woQEnJ01efYPv8Bc6dP4dW4NljTjASElMZ0tyQJRnWOp587DGUaPGF3/4Cj128yNUnrvLl/tcIPmCNJc9yzp3ZRMsS76OAZ43DBxc5RlLG2KDzkeU9i0o6j6tqjnYP+exvfoGrV5/l/MVLfOW3f5ePfOgD3L91q3HRxKhi1LLiAn8m3MaI4iOiROPS8T4C5/EO7yzBJ5jKMZnUFJMppg5oqbAmwTqJsRrdajEpA6XIODo4YTAccnR0yO3bt3jw4D7j0QhrTRQYXGA0mlAWBUrKOQ/MWovWKeNxwdaZZaxxnBan1HVslS3LKjpJg0cnGo/Hmsifk0KR6Cy64xrX0O0HY2697Wl3Eg5uSI5Hhrffvc/pXkE1DYTacTI85uRowt7hIaejGl8NeeZ8i9xOOH92g41LS3SkZmWlxfalTXrtlPRkl+2rm1S2ZrJ/n7ooOTwes3Z+DVOMIlsuOGrpCcJGJpqLDszD4xOCkIQgCSEKzM4HUCIKo1Y0jZoBi8Ej8XiQITL2VEKnu8X+yREhTCHUDZj/IeSdR/8UZkJXiBnBuWYZZlasRz7rmtuK2buMKGA9eo/i4X3PHKcPH/qheiYa49/MKUh46NeaP05zw3koe5Y/boSsJI1MyGvX3kQQSLIEU8X3erQi2+gD8+CC4HRUc+94yGNJiktiu/FkXCJ1DxEitw8RkL5x/fnQSHmxBTaIeDEmuNh27OW/+n1gMYtZzGIWs5jFLGYxi/lenPckclXW0m4EA2sj+8hYg6xrjo+OqPd38UB/eTm6H5SkqqOggok16d57amvwBKy1yIaJZWtDUUTxQYj4SyexlRAR8N7ghaOspjhvH0Y+BAglEDoulIx1SOcRUqN0jDT5hm+ilI6Mn8YJ4rwDG1AqfqHXiWyYPYFWu005LWm3PcbWGGtRqh0zMiG6LVSSMJlOCM7HxYXxYMHjUFLihUcnCcY6BsMxLZuRr+WUdcVwMCBPUnqtDkJKVALLyz0Oj3YBWF5eYW/3kG998xskaUIIjpdfepnlpRWWl5Z55513abc79Pt9CAJbGLTIODoeMDx9iSRJGI1H1JUl0To2WAoIIUEQt0NVGkAyGhZMJ1WMU8YNhiYQvEEriVORfZYkkWc1i/VFB1jdCIoV08m0AcVHnlV0PdVN9BCMlQiTRAeEjOKMc54sS5t4WIyGOeviojF4tEoa3pJHKY0gtjBqJVA6xiQBnHfUdU27HXM1rVYLnWiUbtxaIR5v1kWWmJQGJXXk5CAbd4ybi2lxvSrRSsU4rlQYaxtHUMBLwdraGqfHBxhjSDtd3nrnXZZWV8gyzdpyj42lFSZFydQc8vTTz/D1b72E1oqvv/gS5zbXuL9zwGQ8Ic1aGFs1oosD75iMTrl78x06OvDYpTOcu3yBwck+ZzZX8W2JJo8Nb0Lj3ARrohAjiMKirQxnVpf58AvPsrq0ws133kRKzwvPP83FS5dZP7vNt199kxvX36EoRnhb4aqCwXHJg7t32d3ZIe2v853XrvHuzVtolcWolfdIIVhfWeUTP/BhzmxtkmQJKtWINMUFi/MWnMXVFWfOXqD/2HnefPMdPvDM0wTdxVWOVEu67RapTqNgFQLOO0xdUJcFUsZtMZ0UTDKFEJC1W7hgCC5gqijqgMc7g62nnB4e85Uvf5teb4PxaMxXf+ML/OX/41/k3q0beC+IjXUNA2wuAM2Eh5nWEaOW0fXn5w2jWarZ3Fzn/LlNlOwyGcN47Kk9tDstnJbY4NC9VY5HI27evMHB4R53bt/h+Diy3ybTCaPhiOWVFXq9XjxHikCikthEaw11bbDOYq2h21vB2sDSyiqpSpEypSoqvA9MJgVKThEKdJrE46CWCB+FeBEy6mqKTiP0/q0HBep3xtz7Vs2tNwMP6hx/OOJbL77J6XFBXTu8EmSyhUoFRvS5cmGZP/dzz5DsXmdt+wzrmwlhchcvA6GfMjw4QE3vI02LMAm8863bWJ2ydH6LDSERPvLknHNYEYVWZCABhFKU1hFkCkGgZXx9xniCkgQbYmGgj+ytIBzSx5igIrpAk6TFxYvP8+7tb4A/RjQMK9FcNKDhbkW1snFjiTBvIJ3HBEMTiabBWzETnRoxTDYOrpk4FRrBK8DM5dUUIzY/a+5BPPy7mBnA5nnGh/axIMKsz4V5PLIRuAKgE0mnl+JqT1nXiGCbsomAd7GxVza8MiccEokIkuAVAgUBahc4Op2weWYFGSwBh1KBRNhY1EDA1SGyD4UnTTy9HI5PK5zrMJP3FrOYxSxmMYtZzGIWs5jv9XlvTq45yFuhlYwOjLqOCwIhqYxtrhA3V7Gdi6Dp2iB95FkZazDWxkhGXSOURDVOHucCoa4bZpEjaE2W6chLQpGkiulhgfUOD1EcUtF15LxtnD3NAraBVPsGNOy8x86g0t43kTlPIIpcPsSI1Qzu3m91mIzG1LWh1WpzenpEWR43CyjZLIYFUijG0xhvM1V0LUkhQEXuV2R21UzKgp7pkCQZpq44PT2lleUIBEkSuSqr69ucnB5RVTVvv/Uuo9GQleVVtrfPMRgMeLCzy/LSCt1ul7qOoogxNZfOX6DVXWZtdZM/ePlr9Pstev0eLgSKhl+WNIs834gUEKNnWiuGwxHWWZKkhXMWIZrt6H0jfIm54wjibbIsxVpDkijKsqQsC2KEMG53pRSgCEQnXsBTVSXOxeih1illUSGDQ4qMyB1yke3VHA8zR8ujroxZBFJojZKg0xTr3Bwifv/+A1Qi6XQ6uMpiTIygWtu0TE4n1HXN5sYWAkVZ1o9ER+Pzj48ZuUA60bRasbXSmwhpl0py795dVlbXuX/vOkFmjKYR8r51/jzj8Yh2u8329jmkyrj21ltY68mzFiFIVpbX+RN/4t+ju9Tll8M/ZjQas2biY4Inz1Pwlgf3bvPEY5do97scnxwisXRySafdYzIaNXHbhk3WCL9xG3mWlpd437Pvp53LeLwdn7DUafHjn/oJbt2+S2+pR7ffpZW38DjSNGN3Z5fx6JTj4zHew+lgxDs3byOkRohkHnvtdDucPXeGxx670rw3Y79icBbfgOWDdSSJ5plnnubcxef45V/+x7TbLUhaBALtdosszwjMGgwjT204HOJdQrfdYXgiGQ+G7JVD2q0WW3mLREjyfgtBoNvrNiUSIbqsTGTynRwPCHWgpVPaeYdLZ89x5/YJjqbG0UcnGMwcXTFK/WibYpqm9HSb9bVV+ktLbGxscOXKJS5dOh8FcOcQrWWk9ewenrCz+4D9vV3eeuNNDg4PuXXrFhsbm7Q7LYQQtDtdrHUclEeURU275WMjrIuFDnmrzWAwxBhLURbUdUmSdoAYjW5lbVZXVmE5cram0wJjIntQJorD032cBe8F3nhEW0a3q9K44DmtUr784pRXmGCCJj/fpVNVlNOE/tI2sgWtzhmUA+NGVEZyZr1ie3sZZSUJh+gKJqMdhrWnk7dwtqIqxljjCbqPtSmFFzzz1HMk2Sgek7aOx4SQOO2RXiClor+yyeaZyyAGSGGRKoO0h6s0wiqEl81+8vEc4kV0aQULNuBcbB/NkoTUBOpQg6yQXhKEa94bDbctGn7j3xsGlpyB6+Pd4kKExwsenu/wYU7JmjG0GoV0fkaywn/XZ2SD7IqP9YjoFYJ/yI1r1KuHLjE/83A+avKaxxvjRRwi09D4Rv8KeBEQUiBwCCHxAgguxnpDwCGJXIBY0tLt9vBCEEQgzxTL/QxwZGlCW6fY2mJC/DxKNChlCc5gjaeY2n/T14PFLGYxi1nMYhazmMUs5nti3pPIJYXAGkeS5MSFZSMaOUuS5fTyXozPaEVRTHG1JcsyxkUJQtJpt8hp2FnEhYVzMS6hpcK6uBgK3hIClEVJXVdovUy7G7+Qp0l06iilaLVatNttsiwlEKhrg5Qqcrf8Q+dYlmUkTZOj1hojIjg9iEaEIS5EqrpGe0maJtH9JaA2FWkW45jGxMib0oosyxHo6OCyMd42Yzw513BOTIwWKZFAw3g6ORlQVxXTSYGpHVmS0u5kOFdyenrC6voKt2/dozw+oSgKsjRnMikaqHzNrdu35ttgOByilGQ6nvDh932Q8+fOk7z2EnVl2D53ntNhwXi0i7MefKA20ZmVpkkUoYKI4Hbr8fjYgiegri1S5iRJRlmW1KairqKDZLYd67qeRxHjv2uyNKOqq3h/LmCEbUS8BK01de2YTAu8D+R5i+m0IE+iq0IEMLaBHzf7xzf3PeMUWWsj86sRpRKpabc77OzvNy2LBd5bslZn3kYXBbMmetncp1KaXq+HQDEc7gBRYIm8LtNA6xOEiKyu5eUV6rpmf28fqRTT6RQQfPWrX+Pi+U10q09AgavRhydMpgVFWeG958KlMxwcH3DjxjuxJdMHer0+00nBnfsPsNYDEikkxtZ479jePsvjj11mb+ceZzZXefPVr9LpLpNqhQ8Ob0uKckKwFdB6uAAHIpjdzxfRtakbqLag3WrTW93gmdVNTo72+fZL32RwcsxoOuHu7Ts8sf1hPvCBj7N/OOL1m3vcOxqjVYJQil5viXJSk6SCp556knYrozJVFD6tAq0QMkWq2FwqpWJpaZknnrjKuYtXgMCZM1uMphalIyy/LMsmahqjWlqndHt9tErZ3t5mb+8enXabTDu0kATn2L6wzWNXrrC+sckTT15gqduJrZw+UFeGsqxJ05wzZ7bwITRur6b9cR5PnPGIHrYnSiVRIRYa9Ho9zp49y3NPP82TT15laXkZ6y3D4YggJffu73Dj5nVu37rJ3u4Op4NTqqogTWOjYzvLKccFakPFMgxnCT6KVUop6rqO54Ha4hs4v/OW2poGUA5VFSPBde2iS7YoY8LaxxivtZaiLKnqijTPsCZAUEiZYJ1B6TS6VoXAApUKDEXCgB5WwKqSPL7U5dLlC9R2k5IRpAmuqrFW4YLGminFyR6dakQ9sVSiy3AseXBccGErR+kWPghqJDvDCe1ehq01Ozdu8/hTayiV44OOkUMhwIEzAZl3+OBHfwQ+8Bx5NmU6OSHNlihtQqi7+NoRXA3eIfEoEdACUgFKWlLlaDPBlwM2lqZs6j20HmJ0ifcaIaNwGOUx5iJXCNG05X10aXoRCD7avQIehAIeRuoFfm6sCiIKRW4GAJzHGRsxKjT6KX5mDGvuNxAbJxqHKjOWV2whJvi5UUzISP2a5yBDwyr0vmmsdaRJjpQgRUpVlVHwalxpYhaPnGduHR6LIwpe/ZUlDgeW2mmSVLCx3uPBgxFadWm34OS0YDx19NKMfldTTWuMCVRBM5h8t5i3mMUsZjGLWcxiFrOYxXyvznsSuRKlqa1r+DYNRNd7yqJgPJngG4eXlHFBIJqYmtIK2cBzpVTgAs7buftGSYlUikzrKEzZuGB2qaOqS/b2djmrNimqCStLy+xnA4pJjakto9GE6aRAaYWzlhBU485SJFKSJAmtVv4IWNyTpinORVeZdXERqoVkeXmZspwyGo1ZXevT63Wp6pqqLiirirppGPR1TVUblNBMJwXWuMjISpLoIHKG8ThWvCslcK5G6rjgjfG6uIQytaU0jtVWD626vPb6NZ5//v3RTYXCOTg+HnD/wR5VGQWFJE3QWlGbyBrzHg5OBnzui7+PzjQqSzG14zvffoPaWIT3IOScP1XVdXy9OkFJHcVI59CJiLHBZvWWJJpAjCWGYJg1nsVWvdgCFwHdvmnKTHDekiQakigaAQgp8MFRli4u6n3CYDjEOdGY/aLbTwuFNQ6t1RwAPxPPlHr4b65pvAveM5mMaeVtsjTldDDAWEveyuateEpFR5n3jsoY6rpGqXjsHhwcUFeWLIsiYggzAY/GsOGpqprpNEbl2u12BGJLwWQy4dlnn+WjH/1hlpdyxoWldnDp/FnavT6f/vRn+cq1W7zxxlukuaLbzXjlre9Q1QXO1Swv91FasLa+gpK62TexGU8IcLamLMYk0rPUUfig2Nw8T2UdAY1popVxAlKA1jEKFgikqSbNFC5YnnricYKUEOBjH/0IBzt3+a1/9iugE9qZZqnX4+j4iKXlPtsXztLptglHQwiB46NDTAgkZGilWF5e4ty5Tf7IH/lR9u7f5nd++9NU1Sf46Pe9QNpZxvtY+KCUbtx8kvPb29GtFSy3b99iYhOMs+wfHLC+dTY6MpXCWsd4MmE4HFHVGctry/jgWdtYp5cJhqenOB/4gY//ECJ4rr1+DWtPeO7ppyOzyQeQirqqOTo6oZ33MAIKW2KIzYTBRc6fa7bTzKmXZznLK6tsX7jA5cuXuXDhPEmasrzcR0rJ/fv3uHbtGl//2te4efNdJqMReZ7S7XRJdIoWmiTvN1yvQFFUCCmpa4OpYknBcDihrqpGCE+oqjFVVaOVjm5YKZAqQQoZyxWkJE0zBqdDRqMSbxVpkpGoFCUV6AQloSpLRpMxrVaO94I0bVGNDUmS40Nk6iVpgs46jIwFN0FlCR3vsR7K4KlNjSVgqZHeYkLAo7ABVK+HPYgxNuEDqQzkrYQkCZSTklxJgoN7196mp1skQVINp7jxMtbkWJ/giK5f4SO776ioqHJNN+/wwrPP86v/9O9y7kIfIVvUxwYlAsGOyEzJUhbotgK9LLDUhtWu4Mxml9Wlijd7R/yl//UH2dr5NNfuKCapjpFnZswpIET+V4xCx5ZC27DcbHOOibwtohuwEats07CJeFhw4QKxSIXIkjTeEYLEAcGL+WdaCGIOmHcenBM4FzDJQy6gEJDlGcyEuFgP2fAZZYzRNrw4PIiqaQklkLfajEcDvAPhFUoEfBAIAjJI5Owz0BuC99RWcmP/BOWHnBZgXcq1d49wzjOeCtxewW6oGYxrpi5l5DyDiaOe1oxNi52B5HiUvucvF4tZzGIWs5jFLGYxi1nMH8a8J5Grdo40byEQczB0bQzVtMSY+AVcStBaI6VAa4WUiiACXnhqF51SdV0jAuStHADrHDb4eGXbWeq6opWnJIlCpT2sEwxOS6wz/OAPPM/p8ZhbN++jlKDdzgjB46xAqYwkSWMcMri50BJCjLglSUKaRQ5TURZ4H0WvopiSpinGtKjrmsPDI86fO8PqyjpvX3+Xg8NTBqdjVONSCR4MNVI6iPQqAopJWTGeFmjZCH0EDBYV4qLVmDo6iUT8edbOWOktIYJiOBwgVcKNW7c5PDjFe8F0WtBq5VF8EoraVEymk+hcCQIfSpTSpDrH2JpqUqGspdfrcX9nhzzLyfMWhIcgeCE01vi4D6jn0cDQtA/Kht5f1SVCpzjvSbRCCzl3RxljyfMWVWmwxjEaFTjnyLLoiMvznOAFWus59N9ai3dxoeiDYFrVWO/JU4VOEmozxZiSPO81DZW+cV/EGKGUMrYiphkhgNIJPjgOTo45OjoiSTJaeZsQPHXlESJG/4S0aK3RWpHn7bk45JzBB4eUAucMzkmyTKG0wnuLqaKIWteWV199nVae44xlbXUV4Tx/5t/7eQ4HQ3Z39/mZn/kZ7t/fpdXqcO/eA4rhkCJYvnPrBjd3byGU5nQ0Bq2wpuJDLzzPBz74fi5evMA/+Lt/h5PjE7SMLW0uwPHpEQcHbXqJoqMytq5cYThxyCDAAMEhkug6CaFGy8gEKo2lMi4KvKlmXI54+dXv8BNnL2Kd5etf/zLL3YQf+76PcuXxJ/nSH3yD3f0HLG/0Wdpao7+8xFJ/iVZngq0LJpMhQkiq0SltCT/zMz/NJ3/0kyil+BefeZ3L29tcufQ47c461gaCr5BaUFlHqj0nR0e88tp32Nx8DACDIGt1UJlkdX2dylZUdgIyIJQizRKyPGUwGnP97dcQpuDKpQs899zTnDt3njzJuXXvNsdlySRNyZbOorPoHnXW4Y2nnSZk6Sqq0+LHf/7PkLTX0TIn6CY75h0uODrdNo89doWnnnqGS5eusLm1SZZlCCkYDk75ylde5LVXXuLW7Rvs7e2Qpjmddh+coiod7VZK3urG45OmbME66qqmqip6vSX29/epXIFzkqKokKnkP/nLf5kPv/Bh/sXnvsTv/f6XkVpw/8HNeP504Lygmk6x3lEYT2k9Tz7zHALP/u4epamoSkdROhAJk0pjvaA2jvUzl1ABsvyU0ajAC4UJUNuK8WRAbTw+CJTxeK+oK0VtDLWbIkUCzmODgaAI2Oh0nRqEL/B4jNV0c7jYS0jSEaUrMcHS2ejy/Cde4O4XX8aFNs//0A8wvH+TSvYbJ1UBtJA+0HIGS8GonFLqMa7a4Oj+O7zviTW2lh2PbZ8n2UzpdRTLrcBaP6XV04h2gMQAU0JdMLp2l62kwKUPELxJ1lkmiBqNx6pYSCFDBL6Lhm+FJ3Lr5CwYHNlbs30okTRB+ChUMWtAbMKEISDQTWQfvNeY4OZusFj9MBPMfBPvhxBU4whTuKAa93P8rPTIeUNmELH0IgQLIjT3E+1nQUDwNYVTDM2Us8tttC/wriI4gUMRgoufzU5QG8tab0ROgQyQJhJCixXVlHu4GiVEjM3WUyrnQWtaGpwpOK0etose7e02Ee7FLGYxi1nMYhazmMUs5nt/3nNcUYSA0gql0ghTnhSRf6Vl8xU/LiqMqcmyLmVZNreOcaqiiPyqNEmhcc8kaUJt7VyASLMMVHSJGWvI8gwhBOPxgM9+5nOcnowiP8tH3lV8bpGpZa1trqLH55FlGUtLPabTafOlXWJd02zm/bwtzjnH3t4+1sYWxspWvHPtBtOyREsVGySNwTX3oYVGekcItrkP14CkY/Rq5lALITbEWR/bA/EBLyPQvCgq9vb3WVlZiW1hSYapLTEyExvsyrJqYk4apRKs8Zh6xq2SINV8u3nv8aXnpD7BW09dGdIkbcQcOf81az2ctQkC1NbR7SRIrSKXytjGPUXjgHrYWJamKWmac3hwzHRaxgKBxrnivWuYW3oOc58VFTjncUHgXXRazZ6P1pqqjhwkKeXcBTFzegkReWtZljVx0EBVlegkCo5pmkb+WYjMqLgAjY+bJClZOyfPM5x3VFVJkiRUVYF1jqIsGwdYi9CwenSSoaRDqhg/bbcyuu023lgmowk6ywlS0ep2eeXVz3F/dweQfPKTP0yrm3P58UuUpqQua3KdkWYZeSsna2dUVYklcP7SefrdHkrFeK1xdt68tr6+waXLV6hGp4xqw6/+jb/On/gzvzjfnpUzaK2jc6s8ReDQOkEr9ZAZFyBNUrbPboGQeOcxVcVP//zP84Vf+zXSpSXObJ3h4vY2WX+Zc2e36Xa7JNIj6jGpkvR6SyiZ8vjjF/mFP/2nuPrE44wnI66//TaXLpxn+/wmVTng3s1rfP0PXmTnwQ4qWwFi8QAOrlx5nCzrs7TUZzgc8+7d6+hU88u/8iu02h3eevMmIsDG2hoiwG//1m+ye3DI/sFtnnzqEp/6qR/DOsulK5f4b/7634A0pb2yRlFNOX9mnfMby/RXVrnwxBNcuHKFf//P/jzjWhPwfP8nPh7dOnisCdRO4L3E+oqnn36Bn/iJP8r2+QucHB9z7fXv8Nq113j11W/z+uuvs7y0TJrkDIdDxuOC8+c3CFIgE0nayjDeczocziO0VSNuTSYTptNi7iYUUkIQpGlOkiVUZckb117npZe/yYP9XdY3zzIaenwwhOBQMkHJDO8rJAohNN1O3rCktigmhnv3Drlzb5fTwZSqtjhnUWljXLImNtIKh3EW5WKEEjSe+LvzHms9vnnfV8YiAmgSHBqJRAVLQFBMC1bzhHRJkfTjRQDtLO2sRVjd5v7tI+7tt6Hucftgk0OZI7++z+3Xb7BzDBPVYqkt2Mgdvaxio6c4v9VheWuJjTPnufvuW3Q7BX/1P/0pVOsmkSFVg60xu/cZHxwwultgjMdagXU1xeAYX13hzuEJv/P/+qfcMZvcMQorJco7XGjikUEgGwErMrGisJQmEiUUrTR+BiFAIiLvL+Z8G1h8U0hAIITowBUEpuMpSaIIKiC8a8orBEJEB7GQEqljRFZ4h5RhHiH0gvl9KxXvW6oGTC8iL857B6FhdwlB8DFvKZBMrEQcG85vraKsQ4UAwSJF5LsFJBZHbS29NcmS9qy0FKvrfdJgESRNVBeEnMHvZ44xycPm0eYnjZusrA3X997Lt4XFLGYxi1nMYhazmMUs5g9n3pvIpROU1ggRFwS1sTgPSqpGBIlfiLWWOCfmglaMucHq6ipH+wfR1eM9po5iSt5qsdzrEwSMp1OkVFRlgVICLRRVhKjQarUZDE+pKou1szhb5EsFT3ST/EusnbIsUarhMXlPKB8KWyEEyrLCWovWNJBgKMua1669hXUWKRRpJ4oo0ovo/vIR+iuVbBj7cXGTpjORDWZsl5mwNuOXzWOeRPFrOp2ik5TV9WWqesLJ0QnWBqTQD6N03uOsmd9XliVIGdk+1lqETuLPGhFKhMhEssbihCPN0wgqbrZB8+JjZMdGkU5pKIuKtJfGyvokg9As/Jybv57QtIONx6N4AOl4CM3ihPAQGO99jDLO4obex22mtSZFNk4v3QhXbi5iheZ5Sakbgcw1Il8E4kupYytjnjIcTpoFWmSveW8fEfAU3gfG46JhX4VIp5GiWUTG551o3eyXGK0VBIKSZFkU3fIsZ319HYLg+PiYwXjM3/17f5+/8Bf/Eh94//vZPrfNyy+/wu/89qdZWlqi31/ix3/8hwlB8sUv/gG7+/sIIdjf32PrzDpPP/sEAsHO7gMGgxOSJD6mtQbnLEmWsLyyzGldMhpO+OhHPkZ7aXN+zM6OX7xD5B2YThByViX3cMbjMd/85h2ee+Z5pIiLfNHd5Dc/89t87Kd+DpmluOY4cEVJMZ0yOd7nrddfIwjodTv8wA/8IH/yZ/8Y7U7GZDTkwc59Ao5bt2/y6//TP+Enf/zjXH3mKUbDgjxrYxu+kRSCPG/zy7/8K4zGsSThM5/5XWTjtvvQBz/Epcceo5p8msPGKTItKo5PTth9cIcnL59la7XPp3/t7/Hv/uwv8J2XvoOx0F3tUnuBUBlat+gtr/LTP/ezTIcDynLK2bOrJNkKb994k2vXriGTguFggnEQhMIL0Epx794d/uEv/13u37/PzRu3qCrTCOWK48MB3fYSzlY4F7BWMhyO54UExhjG4zE+gDFmLqBrnQCQ5a0mguqIXX6KqqwIwfHPfv03KaZjjo7HPPH0h3nf8x9j/15BafYQokDJDIiCtvASU7omluw5ORmgdZs8b6GkJJMCpQImGKyvEB5UiIKOCI5EBBQOQbyPTrePlB0Qkry1hqkV1sQ2PaTFIPF4hHckAlppB73yOD0H9eiIm7fG3Nt11BaS1w45GRS89J09Nl/6MlmSk+k25x7L6fYUP/QnPkGnq2m34t/Tnoe2hwQgwsVcPWZyMKSrLOOd64Th19k/GLJ3d8jdd3d58oUPsrp9lby1iTGOl772Tb7wuS/TWW1z9SOXONAr/O3feJW33nmXKRITbHNaU7jgohPURfee9/F8cfnyeS6cP0s7y1m/8BStVguAuqzim6bh183Oc9H9F8+dLjiqScG3XvkC+/s70LT3SqXmpRUAQQq2zmzR67epiimmrAnOMx6PkDqeD2XzGD54dPPWTZQibdzPM3GtgeuRCAXe4lUOWZ/Xbz5A+QIZHGkj7DvvaHU6dHpttM6xMsd4Re0g6BRXpXN0n7MmCmguvjZjDBDbPn0jbEUHbYxezsLRi1nMYhazmMUsZjGLWcz3+rw3kQsxjwBa5zAuOoQSJWIkztnoArIR4p0kSSNyxdsNBgN0EheCzlkIUSSx1lBMpyRZhhAR7C0ITfOVAOcRiaTVajEcDRuhJgoxUuomBhjQOptfiQbmEbfZc6nrCPY2xlBWcVEzc9I4FxksUkZ+2LQocc7hXEGmU7xpmuPcjOfisCbeXkoVn4O3yGaRBDOTQHSVWRtjMgTm0Q+pFNI5BqMRp8NjpPLIAOXUoGSKkhKlGkHM1oRAZJt5h/eOPE8JPlBXFh88rVYLH6LYVBZVIzDpKLARmhhpjIsGYgtlWZaEEGjrnBACp6cDhIR2p49wDmujiyA0gpcQgl6vz9HRCVmWzZ1cQBRrGqFlJnDFfd24uxLdwJ0DzkWQftpvzd1kM0Esy6JzTwrVOMkiT0trHQH0rkYIqBr4/UzoNMZgnCFJJEoqNjfPkCQZR0fHVJVBKchbSWx9o2nctHXcDlWFD55OJ0fIQLvVQgrJaDzhdDgAKTg5PmVvd5erjz/Jf/Gf/Z+5/MTjbF84z9nNLbbPn+flV15meXmJg4MDbt+5yZUrV/jgh57k7/6dr9FuddhYX+bP/PyfxjnLUqfL6dEJWusYMSIKuUor7t29zZvXWmytrqG0ZPPcuRi1TdO50BmIfLh5g1vDGYrHfdxX1lkef/wJ6pCgk5zSGBABI0F1VvBKxnbTJGFSFNy+c59iOmLkAs9+4P388affz/bZM9y78w53d3fwZc1wMiZNc8bjEZe2z3D5wjmW+32OD/fjop3Ydui8JctzLly4QK+/yssvv8SP/MiPcDoq+YMvfJnvvPwSB4d7HB7vEoRlVEwYTksunrvIxdV11vuaPKl4sHOfJx97hj/xs/8BrbV1nlrtY+sKFwze1fhyQNpZIT93jqOdXQ53btFfMay0Es48/kHGk33Wljex5hXKUDIppgiheffdOzgfGWhLS+uY2lHXsZ2x3e5TljVVXeNcwHnBnbsPmE5HhGDROkUpTZKk5FlGr9ubtzIaY1EyCrLTyZTNM+eYjKakvdggOh7VTCYVWbZElq0zGicsLW2zpBKsO0GSsrK8RVWXdNo9TLHP8cmIaTFFqITBcMDe7h6JNJzbaiOcgCBxUhEc2MpTmwqZZOwdj6lKiwqWT33qR3CmAb+rNiZ02B8oAhWeGiU9WZDYYKGYIBEc7tf801/+HZbqewRjQKXkWcLGapez57s89/1X+FN/4ZP01/uQA7lASNeQ0B2ECUz3YTSm3D3l5OSE46Mhgwcn7N64ze17FVvP/CS1Tfm5P/1/wIwPEXVApC0uPvNBXvj3f4HHnv8AAOVgys29nMN//ho7ZcU51cMlEtJVTk3kvHmZEl20IXKz0LHFVzZsSJWikx69pQ3aeYruLoPSEAJZ1o38rllDafDQlJKIMItNS1RWYkSGIce6KkbjG75bsI2TVwq2W32WNzYZnpxi3ZjSTDk8NQRpEUojG+6XVBLVtCXmmaDVynE+UFs7d9qmaWwYxhq8k6RaMi0crrAYU+OsQQgNCi53z7G2eZVuHgtZaqWh1WFl+wrejiOj0MY2RmfjBZujwyPefvttjo4O5+dDQpgLZ95F199iFrOYxSxmMYtZzGIW82/DvCeRK9C03QmBNQbnFUImOBz4IopRxIiOlCB8wFrXcJpSnLERCkyYN6IZazDeUVmLKkuCEJTFFFcb2u323CUFoGRKCJEMHp1jBu9jpEwrjbVu7iLSWuOdoemOxxjDZDJiZXUlCiSNEOccGB+dZqZp2AvWUZvZ1/qUo6Nh5IXRMFKYNW5FXUGGgGuoLr5py3LeExo+UrxhaDhaD6+SQ8BVlmCiIBSkoqwNQigQksrY+XOfiYUeN98mQghccE3UqWFQESHppo6LpMlkgtKKVitv2ueieDePLvoo7Ckh0EmKrz3ve+Ip/uz/6pf41d/6DCevvYmrpxA8eatFkiSMRmOqKjYFGlsANDE5MRfDQghzlwREwbGlNSEopK154ekn6CWwv7eDc5Y0yRuhwDf7MzZcRqg9WFtirUGIgFY6LsBMiO4bZOTfBIFEg9cYD6ejAqlrpnVNcA6qKHgKGUHlzgWEZ+64SxKNkprpdMxoOI7bRUao/ng8jkeDThgOhpzZOsNoeMqLX/wiP/hDP8R3XrvG8uoGjz92hel4yu69e2QyLlYT3aabt7h0ZoX9/b35/l87uxZX0CK607x3OKvQXtNWgmAOuPn2lzjKJKuDXTodTeEDMaFrIigbhcPHZj4PEoWxAWTCxvISk9MBT146y8XL6/z9/95AgIPjEcE7nFIEGRk8zk0hz6I4kI1Y7bY4Ptnj+GiHyXTE3u4ud+/dYm1tjauPP80f/dEf5qln34ctDhkWU4KMoPLgPdKBVAIXDHduXWdlY8qkLvknv/pPSHWbo9MT9IMMn2WkWZul/iqJjmUVXsJgOiUVnjqU1GPL6vYF/p//j7/B577ydfqbS0wmJZ2W5tzaMiFYqmqEtiX9XsZP/Yf/S4RsReh3XfDyZz9HYRKEbuNLi9QJyAxhDBLZOOOiwCiVJJGSza0t6toiGseg1i62hnoZW0fTvBEnoSwtxkQmXZomOOdptTR5ntHvL3NudY3b5ZTl1SWU1AQLpvS0+6t88CNP89zzzzM4vUVZGM5uXqQYG9ZWz0Y3KvDyS69SDsZ0OxmH4yMyLVnfWEEEganKCJ0KHmkCPoCSgspYsiRDhBwVAl/5/ZeRn0wpipIH90YotUraWcUGgQwpfe/ppTWrXc/mapfVjmB7q8f69iYbm5dZX/4BOh2BagfoaETeQuqAYAz1mGrvHntfv07x4IBJknFu7RL/6B98mmE54bg4YTguGY8ltVG4IJjWEpckLJ05z8evZtRa0lp9nKWzz9Bq5/S6fZY3VklbnbloLnSCzNoYkbE7PKB2FhcUnSzDm0DtFSJNCVKgtQTn8T5WDwoZLxT4JCPvdWh3ctp5G91EpaPLUzT/DyADWijws1bO8PBnXjGZhBg7FLKJd8aI/ZmLG6RJQm0sm2fPsXZmnayV0+0X1FWNyDJCUHE/KcHuzj4hKEKwMfYeFBYZiyIat6lONe1eB6kEVSEQIkEg0DrDaku83pPEiGma0FnZpLWyRqI1Wd5Cp22kSkk6PQgZIQQypbHOxTZRrZg4iUh3MXJMJWeAe9c42GKjpHULmWsxi1nMYhazmMUsZjH/dsx7ErmEFKQ6wteTIPBFRZ61WFvt0msLRLB4H+h2uwxHp1y+dJkvfenFxv0lMXWNb+ISAoGSEp0mBCGaZsQYCZESdJ5gbIWUkqooCELQ7sSr08MwxjbxPakUwQWMqZEyAu+TJMEYg1Ki4YMZjDGNqBWFpyxJqIWMDq0mkuecI7gYe7PONa4MhbGG8EiMzjeNhUI9dC7N3GPQCGAN/yUC0Gfw+4eJspiwC3j/kJNVl5HrA5CmTZtgww4LoYmtJAlZliKkZFIWsRFQJdEZZUy87+kU1bQ5qkTRyvPv2g6z56q1nkc7Z+wsGSSddpvHHnsMYwxFURBMDT6gtMY5x2g0Is9jacDx8RFaa3q9HpNJOXfNWWvxIZBmGdZaqqoi1YrV/ioXt8/y8e/7PsrphJPDA8bjYfOaoig6d2MEMRcudcMK886Rdzo476mnZVykOocWgtWVVZTWSJlgnMeGKP54JGmWsNTrAYLBcEBdD6NQqCSicddVlaEoquiOcFG2VEqTZZFhNHMmjkcjXv7GN3nlzVf48Z/4KbRO+JFPfoK1tVVaecaFM1tMPvFx7t27yWc++xmyVNHrdtBasL62ymAwpKoK2lkLJeIi0hmDJC5+79+/y7fCCatL8E7iaGcOlS6hs3UsnhAcWiqkUGAswcVKuVQnaJ1SlYZiWvDsU1fZ3nwGqQV5q0tRFHFBLGW8TSPuOecIQqKVxJmKyWREpw17Dw4ZHp/Q73b48LPv431PnedHPvVzmKrmxd/7Mq1Oi4lL0bbEek9RxfZPZAzOWuu4eeMWg3GNUprKOpJModOcRCV08wyXFiTCR8dmAOehMBVHwynaTPn+7/t+gi3ZPr9FXYz47G9/mUQ7LpzZZDX/ABe3VhC6iwsBpVOEr5kOTkizJZI840M/+dP8xq//7zA6wyUtgtD4JqI7ex+EJuIXHYYPg1kzB+Z4MkEgkUI3DYlRHBZCNuUIsmHJJSSJQMoE56DV6hAkfOSjH+WH/8iPsbK0wuHeEb/7u1/k/8fefwdLlt33neDnuGvSPG/KV3e1B9ANS6BBwoMEnURSpChRUoRiNDHSyExMzGg0UsxsxMbGbuxqtZJ2pOHMylBDiZLoJZISKVIUQQIgIJiGa7R3VdXl67167+VLe90x+8e5mVUgKdNaaEjN5i+i0IV6+TJvupN5vvf7/XwPhlN0lpH3Onzwg09y69ar/OHv/y6Mzjg4OOalF19lZ3cH7xVpmjMaDbl984DZrEIKTTGdMR0PwZUEbwlW4NqIbllDVc4QIUELgZE5p3Z2OTo85EgOOb1p+KY3b3FmXbCaQn+ty/p9m2zspshOgqyGSOmR0iDqhnJ6yPHtI4YHRxzeGXBwZ8De0ZA7oxHXD0ZcuTXjr/3w/8xDH3wrQaSokFD8wpBrR5eQvYDqBVZCglQJdVOhxmPIUk7ed4EmX8GmPc6cOo8UnqapKGqLLsq2STUumPN1e3t3myqbsbK6Tt7pxlinlLHMw2QIaWJDoWwNZSGeVoGAMil53qXf6y/KUe6ecGg/l0Qsxmg9uIvSizknEQFN5ZAkOGwbA3f0+5ucOHWCuqopypJOt0OeZTSzFKccVlr6/S7OBfr9PnneYTScUkwtSAchfhaKluMoWtuvELJ1sAJZhnPxWJVSSKViU7D3JDpBaE2SZZg521BKfHtdSWJobFw3CAEtYtOkkBKdJChp2tsU+CjnRQKZiBFWf/fjbTnLWc5ylrOc5SxnOcv5PT1vSOQaj2PbmtEa52MEsdvVrK702dnpooSnnBU8/NCDvPzKS7z9XW/lC1/6EkVR0FQ1OoluKOfDgg2ijcYkSWyyqyqiS0uTGkNZlORZTp7DaDLh+PgY225ChRD44LFNA0EghFxAyZWKf09TE8H37UZGG01VVYt4iZIyEnOUimKOD3hr6XW7bf1WwLom8q3a+EgUpgJCtLEx60CBDODauErk+QpaAlcbL7x3Qx1asUssRLO54DQXvObiQ5KlAFRVHYU4LHa+8WmB4s75KAY5h1IRap8kJrpqWpbVfPMupVzc3twdZkwUr6bTKYlIKIoC3f5eFMAsVgSKIgK1q/lGP8QI5Fw463Q6HB8fk2UZvW4XqVQUoZoGZy1VKCnNlNlswuHhPmU5Q+v42EcuVdyT1nWFcx5j0pbJ1SClQGkZ46WubXm8p/Gr1+vx9ne8g4ODA+7sHxKQGG1YyzsIAYPDO0xnBd1uj/X1TfZuHdNUDmUS6iYC6pEJdVNSVA7XNIjgEaKmKEqyLKfbjZtqgucnf/In+BN/9r/g3LnzSKXYf/lF5Ehw6fVDdja3WVlZ4fbtm7z84rNolSOC5ZFHHsa3jh+BaOOxUNclihBbE71DeIt3Fa7xWG8JSmAp0akDH2O+dVmDA6E7WDvF1g3B+xayHfDOkeYJb373u5G9FUJoI08tXWd8eJPB0WFs+1Sxbi44j7cN/X7GO97+BMeDI86fv0B/ZZu81+VX//mPYZiysrVL42rKakoIjrosqaYzXNkgfQN1gzYZSgiUklRVg0BjncQC3kuc9WyurfC93/7tvPDM83zu858jTxL6nZz9yYSiOqKjAr/yq/8SnyRUM8nnPvspDsfHvOudj/CRb/0gjzz6OOtrG5iWgWXLgsHeMRdfeZU7V29w9aXn+ZYnvonV/gpb3R7V7Ztx7XGuhW+HlhXncM7GaOKifTMKrlIJdna20Srh2rUbDIdHKKkwJkEKGQWEVvzM85wkTen1em2st89Gv8P2yRPkaUZTxyKI++87j7p5h8sXX6e2jg980xM8/vg57rvvPkDS761w/33n+OrTz/CeJ99K01QMhmOyngEfyLOUo4M9rlx8BeEswsfnzVrbCkMp3numsylpYlhZ1Vw42ycTY4obBW8+VfPhJ3ucPAXV/g3Gh1dxe5pr+ynD/QOuXdvjcFSwd1xwPLWMK8esdlReY32CwKCyFWq9S606zLYqkt0n0J2dCDb3gkJu0tlNuP9NuzhKDg8GTKYllDNSKcEkJJ0ujQsgJL1eh+AafFMBatFUOB8fHEoLTp8/yZmVk5w/f4GPV79BtrISxRphkNKAMoggEYlso6aOCLP3CG0waUbazVFBtsy+uZAm7haWtHG9sIiei3nnYjzpEEK7rkdRTLSOQAjxfuDpZikrnQxbJDRFjZ95ksREJy8NWa7RSqJ1QuOq+HlkTBTeWnYhwSOUiBzDJLbJ+hZ0H8X3jKqqUFqgpEIYjdQ68iNbRp9oizykNAhRtU5rHePawS/uo2idyIi2VTJSw9rnQMIysLic5SxnOctZznKWs5z/ROaNObnEXfi4a+vFQwgIGTfr3lt8aJjNRkxnQ27dvkHTxHZFpRSNrRFKYVSCUaplI0UOylzsCSKgdLydJDUEPEqq6MRZW2V/f38hkBFC23xFy42yi3Y+IQRJkjCdTuMXdxFb/ay17Vnt2IhmkhSTpEwmk8hJsZbhcIizTbvhDYvNurXz5sDI4ZrD0OfsrzlA3TEHhFuEMG18zy+O665zIDBvOpyzzu5tQIQY/YyPXXysApFFVdsGJWXc1Dq5gGLneXShtbsvnGvi7fiwELacc2itv05Ya5p68Zxaa6nqCORvrMU1FoKPEReluHDhAb71ox/lR//+j0YmW8s7i0y0QF3XpGlKaMUoayMDyzpLULHt7tb+bayzOOJOMRBFwqIombd7WVsQW9H8IgoZgqAsS5qmQUr9dYy4G7ducufggGIyIcv6CC8RwmKMJrjAZHRMORvT7XbRWlEWM6Q2baOmijG0JMMHSWEnBG9RSmFMdC+WRYGzll4n5zu+6ztZWelRljP6/RUuX77IO97+TryzPPPMV+jkGWUxRcl4/MZI3vOe9/KJT3+O7Z1d5Px95BzCB3qdDo0v6OYZfRno5YZE12jhIGgIChkM3gmC9Xgb3VfIGOu0jVswdiAQnCV4T7ayHt2ELjZe0rqTsm6PlbU1AlEwlkiwHpwjSzTbJzbprHYxmWJto4/urJGmOTdv3eL4lSu8evki2kjOnd4hSRNWe10Mmixb5cz2CQbTSZQpQtyABydwHnAOJaNLZTIdsrO7QXjkQW5cu8ypc2d569vexq2XnqeoHCbXDI6P+drTX+Qtb343W1trDCZH3Hf+FFvra6TW8fynP81rL70cXYvOs9bysc6+5a08/sjDrMse5gvPIJVDKtmKGq2I3DokW01j4ZiMz8v85w1FAVpFp2Ov12NjY500idHdvJORpilZmrK2tsaJkyfJsigyndjd5YETp7gzOOTo9j513dDLV9jqr1KsWiY2ECrH4eEx09k+16+8yIc+8FH+1t/6Ef7iX/oLfPGLT1FVkOYa5yVG53TynJVeQj+X9E1AWItvPHt7V9g9sYOUgpdffpGtzS3KWQTUIwKrScWwPmLHznBXrvIv/tFFmr5lb1wymSi8E0wsWK/wpo/XfeqwDirHpx1Er4NTCU5IOmlKZ2WNsgkgM9T0GJmuAoqAhyCj2GQybBDYYPEiRA1FCpI0xUndRrIdHsX+wTGuLmmamrL2+ERGTiKLpRIE6FSTrySYJEEpgxQJUhhYiGIehAMfxRol5mKVRGmDSgw6ydCta0m29qy5oHavq6utXYwnRXx73SEgVIpzTWzbFB4hPFL3cCIl661G0LtQhCARQuODpKoco1FJkiRkWQJopEpiA2MbQUQoAmpxEkaI6CRNkjx+/sgapWXLKvRtIUeMdyspQUXR9W4BReuCi3YupIiFJO0ny12wvJSxlGHuLl60K4rF+yEKXctZznKWs5zlLGc5y1nO7/15Y0yuEFhZXY/uqaYBKmazguFoRGJqZGhIjUEQMEpzdHiI8xYhAkJJtFcoY1oYe4huiABVMaMso8gipUAhYyRL6RgPcRYtBdubG0ymI9RAtvFGmLNUQqAF8EpUK+AcHh1Ft08IZFmGUoqqqnDBLtr+bN1Q1wWidYNppQjeIRVI14pNwqOViqwt5wjtme82vRIvEwLB+8iGig9WPKb2duY183DvRuqu4DUXteLl/UI889YiApEfI+VCaBQiurtkCzr23rUClcNaj9Zt01fwNI37utsVMUc57+6irmsqaxFCs9JJ2dpYjb9nPY2L4ZXgA41zWOuYzabs7+9RFDPyPLoJnHNIER0yTdNQLYQoSZKmJElCcFGEmRU1L7z4Gjdu3qCuSoxRpKnBOklZ1q2zJjb0CSnodLpopRgMj3GuIgSFEJAkLFxqZVlw8bVXqKqKLMnpbnZwQjGZzvDT2B7mQ7zOqpyQpglVWWG0oCLy2MajCb4V6YAYfVSqdQlG8Y/gqeuSza1NsjRjOBwwHA3J04xulvPg/Q+SSMVsOiLRijRJsEXkPZ0+e5a8+zSzokBpAwLOnD5P+kCHlbUtOpMpp7e3kc2U4cE1rh7copMacKB1zSMrKdY1uOCpvItODAJCKLwWeB1A+ZbzFfBNFC1xYwg9It/fQ6hRRmE0ECz42Dha+wYvPCrRmDSFouba9RucPPMQg8N9nv7aS8xq6HT7nDqxzYULD2Fk0zLVJI2CNE1YWVthVFVtm2Z8nXvvwVm8cCBi+9/weMwXv/gllKuRsqaaHpAwJMsCvZNb5FpixZRvfvKdmJBzemub2WTCY488wusvP096puL07klWemv4skYWJfnmOsoJ1t78JnpSwK0RwnkcDm8dGIknivTzptF7o8bAQggLweIdTKcj+r11mtqzc2KVD3/k/XSzFTp5h90TWzjnOHPmDHmes765QVPXVGXFdDpltbdC3s156KEHcbUlNRmvv36V8fGIwfU9bpdTMlWidUAreOmlSwyGY5597kXq2jGbFTiZsbKyhnUWk3iyXNHvbLKzvoZRkslowu3JIccVSGFxKuFoUlNVJU888SaKosJ0e3SlYdNkzMaeL718m/FKn7qzTtbbBZky05pev8f65jZeCJz1BJUSVIxEeyKsfCYCIu3jco3QOcFa4hM9F0IEeWK4PZhxc79BGktT+whUd46Z9dgg2fIpdtqAl2yePMPqxjqrnR5GG1Y3V9jc2l48J0IIhIwRPaGi28kFj0k0Qiq8l9iyQIoG7+fCflzntFZ0Oh1WuytR5MpyjGvbaKOhkru2sXsdS3LRwjr3NCHiZ1gqOwgUQSgEgsIKbt05ZjyuqKuConqdazf2qMqKqqwppiWTaY1SlkkROB54mkoRhCSIDgFHUUmUkngfo4IIqIEwmCEENE2g2+uys7nD1UtX4mPgogMrCI9JVFwbZeTFSSHQSkSnn29agUvcvYcuOn2dC1gvcSQEGWIT61zQC+DxWBww/bd8O1jOcpaznOUsZznLWc5yfm/MGxK5rItxGG1M5D+JgHWW8XiKoiBRgX6vw2BwHIWHWYFonUI+BIyOLXg+hNjY5KCpLdbZNlohFq4KhKCpa+b+pTwxNFUV+TO46LKghdDLgNSKqo6n+4UQKK2pm4Y8z5nNZq0rqaFuGhpvka2zq3YN3sVz+SFEIDve07TOq3im27dV6m18bwEoiRXwsWXeta40j2zPnAfRtnQJQcutbwWvePZ9XiM/bxe8d9M9byR0LjrUopAXwLs2yiJiOxwteLq9jnvdD1EA8ljbLP7/3OnWNA5rxaJ90ofISIsbo/l1RXFOCIF1bhEFPTo64ktf+RJaR6h+3TRIIVHSx3gMczEwOqxE00QAfwDvPI1z7O8dsnf7DomJDoMsS5lMp8j2mAkBqTVax8ijFO3G1nmci660+eMlhMA2lqqMccpEZyAlSZKS2FYMVJr2WcYHjxCgjaTXzamKBqkNSZK28davd3REAS/eZmi5TaPRkIM7CVs7u6RpTlPVBB/I8pz19XWkuPs8CCGQWpFmKWtr61y+HJv96qYmzbucu/8BhuOCrCPZ3tygno25fXjE/liyrvtUk5JUBawwIH0UaRZvFBujSVLQBIf1DUrH19XhwRGhrqmGdxCqQqnYbikIEVrftgsKBCZJSPOczFkqZxkcDSgrG9tGTY6QMza3t3ngobfgm4LgPGfOn2N4cIvJZEwgliCMiykHx0c0wWEEi/c7Ir6/RFvO4HygqgNf+erXOLu7Ti83uGpMNRtwtH8dk2VMjaZxnsff9gQ3Xr6GJLZgrm/s0kt6ZCphe+cEpzo9wtExYTRGbK4RSotaW4WyBB8dWEG1AnKILpi5aDF3qtyNE8+dK/Hx9T5QVTUr/egC29ra4L3vfZJzZ+4jMYZer0NdVayurnE0GCDa9tLptOCZrz3Lh775vWysrbG1vRMfa6UZjUZ0E0WopoyKAf7sKibtkSSG27f3uHDhfo6Pj7n/wnkOD48wmabb69HJJE1dUZVjCtswnUyoyoLZdEbS7XP58g2aZkJVjkmTnO2dDT7w7d/BldevkvbWKCwc1ZYjK7juO9TskHROoXfPYkNC0wjk+gpqY5XgHKKsWkeSwIa4/teuAe8JZY1IFRKHs1FAWYj2EprGk/dWWd1Zp7eWkylNIgVCBILUJOkKJ3a3eenFy1x6/Tof+MD7SYzCyB6Xr1xnUlSLaPrifTR/LykVT55IQTSECSQKW80IfooNNSLEBt7o4BU4WSPCCkmiMEajg7snrhgbSe9GF++u7/P/CGj5VgFraxpXYEOJI0Tnmk6RMqOuCjp5Rq/XxXtPWdax1dd5mrKmkRKT5IQQHaJlWSzu3ZxHKKWOjqzWlOU9dxtohaHfW8VWHunBVRYpAxaLsBCsX5yA0UaTd3Kaqpkb4dqTNhIpQnSC+lhsYp3HB4kPUawU3I3UJ6lGaEc5uf07fi9YznKWs5zlLGc5y1nOcn4vzRsSuZrGcTwc0u90aJqGyPdpaJqKspA46anLksl4TG1rrA142woSwSOFaWHoGqViHKVuYkxRmShkeFoeCVEgcsG3DXuatZUVrt2Uiw2pb90YyghMolrxwtDJuzEylyT0uvHv1jmaNoIYPG2EyhECJImhLCMDS7Vg3uDjprjXy0H4tiWyZWq1DhXZsozmQPsYq4vxGMFdkWRu+ZJC4oK7xzVwV5SK7iy5aPqLLjCPWEQy5xGSQHBz7lIrpLSboEVTXHt9c6bW/M9ctItcsXlsK7RRyigYNtZRFlUr8gikUHgXYjshkWNjnWd/7w5SaaazAtu41iXUxHstBE3bcFm3jZVaa0wrNCmlIhi/aWJTmAqtG6tc/Ny3Il+MUkamV7w/4KzF2cgJk2r+fN0VCOvGcTyc0F3R6CRFSkWjYDyMtg1tBM5ZlIrimtYKaTRpmgBxw9tU4L1ro0Dx8e31epTTCVUx4ytf+TL+qw2PvOktfNu3fSfHx8fcvHWTs2fO0O+vEIJHaROdVjI6Qqq6ZjqbRkdhWdD46LoLQTCaTEBISusoKksTFEF1qKzCC0PSSaisb0UHh9YGKQK4eu4PxFqHsxbTxn0P7xxgZ1PK4ZC8l+JcgABJkmHrCoJACo0UitmspKgsYBgNS5579iXKqsYYgxefZTods7O7zclTJxkd3+Hi5UtIGTlCQmlCENSNpa4mHA6PkUlG0sKvrXcRgu0jYD66QwIeyXA44cGzJ7jv/CnwDZlOGB0OMV1Lf30djwEhWDtxivseuJ8xgf7qLj6pYFLQ+BCPMXici2w1fzxG1RW4Bro51jswGt86EqPzksX7Zf7nrsA1jy5GUVRrRbcT44kR5h3dcrWtKaoo0lpnuXTpEkmaMRlPqCrLzVt7HB0esrO5iVQSSaC2FVU5QYqGtY2cjumy0s/o9TKEkNzZv0FVzXj2mS9T1w2DwSCK/94xnYwpiyIKOM4xnUyYTSf0+316GycYHA+YzAYgat702Fk2djZ44OFHUdpgQqBwgT1rGYiUOl/Fm1W86WNNl6IR1MEysQ4xm5FnGSAQHpIWch68JBiJUYpur0d/c41uJ8OdSOlm5uvWu6pxPPTII+w+ukPeS3C1o5xMITi6eZeytNi6Bimwdc19u9uUw2OGB2Oee/p5SgZ86D1vg5Mn2ittUegCpIgcvyRJFoK0loIGYqMuLi6KoX3duUBZeorZGKMjz1AHQfDirotPxs+e+Xvp3hFCzG+e4D3OVjTNlDrM8C3zK+us0O+k1LOKXsdwYnsdaxuCqyHk9LIux4cjxkXB2XPn8c6z0u0wGU25s79PWc+QSrKy0gWii05IUFqSJBqlDWVpMUoig+fEzg65Uhzu77G9vc3g6IiqaTAB8B5HFFTzbi+2rQoNoWnXeVoeoFrwIYV00dXp6igAt59ZQipOnz1LknV46tOv/hu+GSxnOctZznKWs5zlLGc5v3fmDYlcznlcVSJD3LhLGQjUNE2gFIoGR12XCBE3hsPjyULsEVLigbq2JEIRubgCpRXe0gpXYREdsa6JP5cqxhadZ3tjEykkRhmCthGI3jQkSYJUkcElpcEYw2w6XbCygEUjYhBE55aPok+WGpIk4Xh4jPNR5BK0jKJgSbOEPE84OhrS1BZnPd4HlBQLQcp5HzfP8/vQ3gk/53DNN02iPSPfimDBz0Wm6OZSStHtdpnNZguhyzsXXVzt8SKiGyoClWkB+XcjV/fC66PwdnfjPhe15uJRdJTF+zB3DzR1g23i4yBFZI/VNrZOJsZgdIKScZPpvKMqKwTRFVfa2HYYvG+B8bG5EuJzHTlsUeTr93qx3VBEkW02m8X2PzRS3HW2zcHzc6Esxt9s27YWOVNzBto8Lto4x3hSIEyPNI3MqnmET0qF0Yq6qQiBRTTROUtZzvDBoxTILG6glZJRNCJE8a0tBPjCF77A+QfOcmI44vDwgBACVVkipaTb7WNtc5dZ196X8WjE/sEd1lc2AFBGoZVkPBpSzsaRS7bS5cTOJqF5AOXryP0aj3nr449ytH8dKXV7XFHYwUaRS0odAdhEdp5SmuDBW4furiO76zS1IzQlW1u7MbYUwHuBFIb9Owds7G/Q7a4wHpccD/e4dWuPsqpJn3qWoiz45ne/iVdeepb1jU0Qils3b1IVU2xds762gZKG16/fjuw2H9spoxvKxefUhyjIIlBCoJWitPG1duHCg3TzDILlyfd9mP7GJv2NDb725c/w+uu3KOsMqzTvevJdDCczLj73AueyLmvbO4h1j+jmIBVicwsqS1QIJGyt45Rs35Mtt+ke4fe3Clzzv89dPXGNSNjc3OD27UOGowmXXr9IXdUYnbK21idLM8qqYVYWpFmOC1Gg2Nnd5vreTU7ubvP665cYDQ6ZTMZcuXSZq9dvUCtF0utz9fXXIqfKWm7dusXZc2f5zGc+Q7/fJ01SRpMZk9GY4WDAaqfLmd0TbK2tYVQCDdy3scteWUMTyye8cJy//36KYkRZ1fT6q6i6QeiEoXUMrCOYHHSKSSLnK0sVvhvo93OyTHF6dwdb1FRlHU8ymBiB00KSJAlpnrG2tUqnayiOQEtPBLzHkTLgQs1odMCdwyk0glRn9Do5xWjE537zc+wdHnDy/Hnq6ZDV3JCLFfZfvsTRrdvYdIJr22Lnyxwi8hGFF8ymBVqbxXotkO06GxZ0rtC+72LM3FNVkeMnlUYHSVXZhWt4Ht0OIb4+7z0TEY19YhHGlKFtXgwOSYgu3xBQUlEWBbOpjnxI5VHS0+30uO/MeSabBfsHR5w8scv+3j7bZ09TFBX1aIirZmzvbHP67FnKouTw4JCmrpFati7igBQO7yuGgztsb66ws7HJ2lqH1bUNemnKweERWsR13YaAC2DSDCUNEhUZYiG2JeIDQQiQMor8eIKvCD5+fs/vt0ly7r9wmiTr8tSn/+3fD5aznOUsZznLWc5ylrOc3wvzxphcgNGGpmnodDqkRuO8xzUVs9q32GEXY4w2sqi0nleXi7j5ExJbNzjvSFMT26NC685yMUYUfBSwlFEIH5B4vPPs39mjrkvy3ETQvWtwPkGruNmRRqC1YTweLwSG8XgShYzAgl8Ui6s8tFHCKFRFyHwUkgIieLSJYPZer8t0WlAUTWRFyVgnH6Nz8ffmoHeYC3aOIOLtBREQUixELtnepvUuAo3n0bgQyNJ04X5aCF0+nm2fx6rmP4u/EhDhbq38PGZ37+bd+7tiVghgjMYYs+CFAS0bRuIav3ielJJIEW/fWYcx0WU33zAq1fJg2ttzNkaAQisE1b5puVoS7aP4V1U1dVOjdII2Bu8rdMvVahpLXdeYlqnmvadparz3GKMXz6lsY62L12VbXOBcbMDUczFLZeAl0/EU5yaAj/BzTIxgWcdoNKFpLFJLahcFNWMMaZJilCZJE9LEUBYzZrMJVV2RdXKG4yHf+Z1/hm/92Hfy7HPPMp1MOHnyJNoojocDxuNhFFiFwQeB9YLheMxkMkULQ5Z3aGyDllCVUwiWcjplZ+0BPvzBJ7l1/QzGz3j8LW/h5Wef5vu///v4uz/8/0YlsvVAeeq6wbu8ZYapVqhse9KEwQXFa5evIzuGXS2is8x63vPNH6TT3SQQXwOI2PDYVAW616eb5XiR4twht27utVHgmvT9Xb72zPN85CMf4dx9D/L8868QnMNWJRfuP8/WzikuXr3FtKwQtaPfXwM83V4X4QMreZe1/ipaCjppys7GBke+wSQpvfUtVlfXMErxx//UQ6SdLh7JtSs3+PTnnuXGzX3qJvDAg6fwznPt2g02zz2IrSzFcEzloJQZfjAh6IzJlX1G4yN2d7ZINlZxR0MiSNu3VKLfGTgf3ZjtW7J9n4q2zXTOZZvNSrI8p5N30ErjneP6tavkWUpTlwyPjxiNx1hX8crlOxzevsVXPv85bt64xrSYxgiy1qRrq6T9Hoc395lNp9RlCTLwlz7wl/jJn7jK29/6Vh57+AJf+dqL7foIW/01Hjt3P/efOIOral6qnuN0d4396R6EuD4XdUmv02U2G+K9I01zysJSBsWwCYydR2UZaSfj/JkTbJ89gZEZQgTyboJJFGdO7uIKy+HBMVVd4/B46xEuEOqGcVUzHI+prefWlVf4gfc/wlpuFuuY0ZKnPv855LplODrk0QsP8fu/8zt44i1vYTIYcekrX+XgxoRVYwgucOHBCwgpuPHMPqky7Tr5Wz6A2udEIInaucAkabsmRJenQBEX1ejLCjH5jGhjeFIqhBRt+UGMoPsQogB7L5otzElckUsVb3/OYozrnhQqFmcsfiwij7BpoiNZCIK3eFfjnY2fm90uR0dHFOMxG2urkaWlYlPu6d2TnNzdpSwrbG05ODggtDFR6R0ER1UW7E0m7G5uE7bX2Njd5OqVG3R8QM0/ggT4EFqxVaG0jidhWreaD6Fd1x1KGggiiqOtGzkSwQIBgZSQ5QmHg8N/r+8Iy1nOcpaznOUsZznLWc7v9ryxdkVpIMRN9pwHlcgkRh1cdPTIILC2iWKXjOwUpRRuzmUitvT54KmbtqlQCIQMsZIdT5ZrAoJOJ2M6nbVnsuHW4Q2UCnRyjbcNVRNanpDBaM10VqJNGhlKUmJMCjQt1ypgK4skOtBq76ib6L5wbWzRe48PFqkkCoEUkqIomU6nJEYtmqjSNEVrHdsC72lni+Dl1loFbYPXHCIcuUlK6Qj6tg6cj22T2izEqMHxIPK4WheJRBJcaF0GYcEzC3M3XbsdmbO25uyWOVdovkmf509iJDJu+Ewr4jVNQ1PXiCSJjBsdN36JliSq3fAEj/eWuo5RxizLkFJSVc1CdEvTLHJ0Qoi30zrH8A4rHc5EDowQMf6kUkNTFsgkRWm1iAPN46l1XdM0MfaolImiV93Eh1LGlsi5E8u34gREbo6UHi08wQXq6YyiGiGlxVpJUdSYJAqVTV1TVw25jDFA6S0agxYeISqUEC07xyGVJ+9FiP64mHBweIz3MJpO+epXv8qHPvQRkk7Ca5dew1vH6sYuW7v3ceX120wrz8HxiJs3blKtz4CAVgZvayQpUhrquiE1AS0KrJ2xtr7OztYmF2XNqdNnmBQFK0kGwiGExzY1RWEIOKQKiyY4oySJybm2f8yP/sOfoi5H/MD3/QAmSfEm4ff/0H+OSbt40hgnFJb1lT6raULX1ezkkpClXO8kMa6mc6alpWgCV69eZ7W3ymQyYDKxIBSXLt7kzuGYhx95EKcSjsclIcDp05BmGY8+/AhPff5LvPNtj7O7c4qnPvNZLtz/AO99z3tobM35c6fZOXcB7xzD4yGz0RHF9CJadZhUhhde+CpbWyv8wPd9Pz/zD/423/aBD3N65TQ6WeVmnVHfKDgejNnbO2Ja1wSjeOGpr/HFz3+c7/1D30r/xBriaACtOKJaoLhz87XhLisvCql+UTDhvacqGy5feZ2yGrOqNgnWMx4PmIwPCI0l1Jaj4ZDxeMz0aMT1O3scFzMunL+PX/y5X8KFWIzQyVNmsxkhBPJej3WTgs5wwRBcRj2bsXVihdX1dTwJD9/3MO9882O8/NyLDFrhwoiAHQ4Yoljf3Ob0ubN013JWyx5CSZTUJFaTKEOSJigt8C5w4/o+x9NAsn6arkxJSFjp5bzpvjOo1TUqK5gOjjkaHVFay+0bh7jKsbc/YFI5PB7vojvTBUAlmE6OTPqUxxoX7lGkXCA1XXKlyA34xHBiZ4Pz50+yvtHH1w2PvOVR3vvN72F8OONTn/gs3tZUwwFhEhANyMTwWxv95msgRE6VCw4v/V2nqpAgJNqoyGv0iuCjy4oAknkjrkYDwTdRxmqdsvOzFCHcDSzORaP2NELrCBYE0a65xMdcaYXUMZ7rfFznpIrXa63FugY7m5H31rh1e49u0qFqahIl6PR7bBKY3DniQEDS7yJ1PGEhRcCrQDTuqnjCI3iU1hRNSV02DPbuMGsaHCCFR8nWsQtIAc43bWTYwzwyrFRkPQaPkBHqH91wEikjszDQlprIECO/y1nOcpaznOUsZznLWc5/AvOGRK7tnZPs7d8m0ZqytuADxgiCs3hnMUq3sTsdQeUtl8m1kbUgBc38y3LL6vFN/JIdgbsCLRUIyLIofAhB66IAhEcbCSik1iQSTKKBgJCReVXMZgR8bFUE0iyJjXTOkecZQkqms8kCuD4HvMuWm2SdiywhIO92GI/HjEfTuDFSGq9Ba4lSgsbGVrooJLVn/lsul2gdUHMAPa0bJITY6OhshHobo0mMoSiLGD+L268YdZyDUX7LiHucX4h4tl0p0bq9HGtra0wmU2h5Qr51UUXul/j6+wyLNkfbqnV5ni9ENOfdgudlraUsS9I0pdfrUVXV4neFEKxvbDAYDKjrOt5O65CRUmKdo6wqsjRr40+GNE0pihgj9PdcNgKbYzvj/A+wcHcFYmRUyhi5i5HACPePdyHyZcpyjG08dVPgXUPwDtfUMXIaBMZovBOt+OXQGowxrYAWOWxVNWE4HC8eA+89iYlQ+xdfeoXj8ZDf+NQnOR4OCcEzGo341Y9/HFdXvPud7+S+8/dz+fVbC2B95KgJDg/vsLm5y2QyZm1zg6T0xFd2AGexdXu83lEUBUKKWPYA7UY9MBlOObh9m9XVHGx0zUkh8EGQ9Prct3meSTHDKLj4+hVIO1y9coXjUUWW5bx28TWqpsGGwHqnz6mVNXKtoKpQvR6PP/wAbmZBG57YeRTlBK4STKaWy69e5rWXX+XCgw/x2KOP4lzD8XhCUZZY55EyttA9+d4nefeTH+Rv/vAP88EPfYBep09V1nz7x76dJ598Fzdu3+Jrzz7NMy8+gzGKK5evkTYFD184y4OPvJMXnvkaP/AD38PBwQ0++NFvY3j7Eie2tvnEb/wGhyOF7qxTe4W1kum0JOvkbG5tMqPHQSF49sXL7J7aaB03AWej8zEKJqJ17Mi7Zso5PL1tCtVaEdLAeDSgKCdcvTLmn/3Cz9PpZri6ws9KMjSra2s456gmBfujIfn2Bh/5wIcZHI2xweGCJclyPJFHl2ZdEpMhQowpp0lCbQwbGxsURYGUgqqqYnFH4xCNwzrL4+9+Fz/wXd9BOZ5x6+iYdz7xXaz0c679/Z8CpagLS7fbw1pHluUoZUBB7TxeJgSZUJU1s2rKeDDiV0tLmaziSVAqQ4QGaRK6K5Ze1mXsEqbexWgbAS8sQQmUTpEqJageQVUg7kYVF9k/JM7HUhGPw7oGnEVUMzIlWVlfYTqYYW2DynJe/uTH2T8uKLAEGcWne9e8+LwIhJKLCOxcpAxtmYSQkn5/FZUleAxN2VBOp9iqjo5HpRcuvUB0fMUbCPdwFO9Gzhfttz60/K12TQ+BJDF0V9dQQqEyjTIGbZKF8GV0PEnhYjdJhPQXx5TDO+ycOMvo6AiNIM00Z888zM2rV9nor1JLqMsiljW0yUkf4udIZFZCb7VHt9el39dIpzi8cp1ZUSCFRAZQ7WNV1hV5J8UY4vqOaF3IbhFrBx85klIi/PwzJ55YmRd2rK2t/lu+GSxnOctZznKWs5zlLGc5v3fmDYlcf+a/+JP8T3/jf2Y0GkXHj4oxQyUkWpmFwLMQI+RdkLr3EFzcpAfiGeYwF2kELS9J4AWUdYWlJg0GlMQGS1PVEQhuLWXZUJZ1u2ERNHVDUzdkJmc4HpN3+3jnGY3HJEnC6uoqk8mYYlZgkoROpwOt6HBv42Anz7HWUpUlvTyj282pqpKm9lhhW6EjUNcVIURXlzEJVVUhpVqIeXFzJFFSYpIEG2I00Vrb8oD8gsElhWzForsti9FNwkLkugueX1jGFjMXq4xJEEJQ1/XiIlEMUS00Pywuf6/AFS8XI4YR2t5EJ1srTlZVFTdOxrRuLk9d14zHY4wx0dFmLZ1Oh9l0SlPXi2MVQiw4WS7E+I4QAqM1ntjq5b2nKArqllEWCwmiQ2seyYS7kbLotmvh+rSCatse2YJ7KIsZUkbxkCAIocLZiqhjRjErMss8tonR2iSJzjalJATZOsO+XnwTQjCZTimrip2dHV559TX+1Sd/jY//xm/w0LkLAFy/dQsPTGcFr712idrGw5q3S57Y3UUKwb/4F7/Id3zHd1G7KATjY2Ohdw7hA2rBh44xo0BA6QTbgvWVD/zyv/hFnK1475PvpirrGPFS4FyNEpKt7V3kYEieJly++gpHo5Jf/IWfpxE5vd4az3z5q6QmoHTkjuW9jI3VVZJeTkhyNnZOkUrN7qnTbOxs88/+yc/yg9//B5kMZzz99LPcd/95PvDhD7J7YpfLl17jk5/6DZ5++llonYvee5544gm2d7YYT8dkeULWySmrBu8t+wd7XL12lVdeucje3g1OntrlXe96B9/xoffT6eZYq3nyHY/zh/7oD/Gjf/uvIRS890PfjElTer1VZL6O6K0zszVlWeG0pZgNub03Zu/2awRXIWWKrRXBa0KIJROiFXwTr9uIVsB5h/eOYBuQEiGig9FjW57aMArowaG1oqpqdPDsdPt805veypvf9GaefeYZbl69xZ2DAQRBWdQ4J6LIIRVCJaAsQiu0TojOMh9B6JmiNIa1tbVF/O727dsE9xYUAq0MTfDQyRi4mttHt6mdoHINd44mTIsKG6Asa+4/e4G9vTucPLuJkhLfOkylSkhMB1ON8bMRRdlgNk9B2sPbhCY1SDKkTDAypxKGhiaGY11o48Dtq9J7ZPAQUYeEe9pEaSNxscUvOnWje7XC1TM63Yxv/sgHKEcTrr70OiZV2Kbi7R/7Lq5d/RTnHnmAQXMbqe8KZ60UuRC6pIi8vJg4V23DqAcvUMaASTEqR0pLUzfUZY0LUdMUSqHm19U6k8W9rrH5fZyvY0TwvFQy/pHxtp23FGWJFjqKSNa2McjWFRhUdFzaGQd3blIe3GE8PMLZwHSQ4UyKCILECA4O7yC14mhwTNbrkCUpozkbbM6SbHzUD6VkOBlR1gWra1tsndwhV5pr165H2a5de4vpjEuvvoYUAddYgvAxztlerw+RcUbrWo73SbT8QlBSoHS8z0a8oa8Ky1nOcpaznOUsZznLWc7v2ryhb66XX7/EH/qhH+SnfuafMp2OF+JF8DFYomQUD6q6boUT2v2uwgcbKSlCRDB1EJH5pCWi5UU11iJEwKQG5yxCJPjg8E1A6gjf1alGO+j2IpC+k2doDZPRBJlL+r0eXgqKsiTLE+qmpG4g72SYRDMcDnHOLZxckfcUWTJzgY40RWtJ3kkJh4EkyQlB4IKjaV0qRsWmNSA6EVp3wRzsIoRoY3YKZ+/ysySibduLLrCqdgt+VjyOOQB77p6IG6x7mxPntpN5JHF++RhFhOFwSBTxLEmSIIS8hzkU7jmDz93HwTlo2xi1NgtHFcQqeiXlAgAf+VkNaZoihKDX65GmKYeHgxihnAtyEFvZ5jHLVjCqqgon7cIBN/dPGGNwPlDXDdbahSB3LzxfCNG6oQJOgPTzts0okiqlMGkSuW5NidYGrQJWxpKDxOQxGinmMcv4mlRKt8+DgxD5VaBiw6UQSBl/niRZ+xhqJpMpP/L3fpSyrjkejvjRv/+/UeGYFCXSeV4avca0bEBIxpMRP/XTP0ExqcnSjNu3r/DOt78ToTTeE5sRffSWWB9oGo/zgcY5GltFiD8hXqYtOTAmpdfpYtIOtQ047xEixTnB8eEtnDOsrG1yfOsmg8GIbr7Cow+9iePSo5Th5PYJxsMDimLGeDbF54b+iQ2aPYsVmgceeYJHHn8HMu9BPaWYjMiMQRrDd3/3d/LAIw+yd/san/j4F/nXn/8Cr752EesFUum2hdNR1TVNU7fORk9RFgQVKOsK7yy+qdlcWcOXFYmH7bU+eccANRBIlKMYHZGnAULNzqlTKN3j+37wO/mRH/lZfv2XfpLRbIR1NU1dMZnNCAG0aFjfXGNrZ4Wqie9ZAnRX+oggF2KAayN4MYYGtmmAQOYsroqNjVIo6qoizzOGx8fkWYdOmuPKim6nz8kTJ8nTnLc89ji5yHj29Ys4H5DGIEMUYkLbVKd0bPHc2FxndXWVoixbM1Ebw2sdsE0TX5/HwyFlWTKejiMkPEnYPXUKJRWXL10lTzusrJ7AAo21fPO3fAvrvQ4vvPASJ89+SwT9R4sn3kb3ragrqsE+jQejoXAe72KsUSmNFBrnYnR47lR1vuVTzQWXAEiNVAlBmqh0tRME+NYk54Mn0Zp6esz+5efoVocI2Wfr1CNMJ2OcbzBZ2pZrePaPDvnBH/pero2us7G1dfc6aQXfFpAl2sdLShVPmhiFm/nW8SsJSuGVQmiJNMlibQv3uLOkFDTOttf/dUCuFv8l2pMSLNYviPdJG01ZFxTjY4xMyFWHPMvonj7F8eAQ1zQEFR+oqiq5U9zCTMasdUFgWO0pqiSjOJ5QTyeoXp8T5+9DCEFVV9S1xbvocvYLLlg87qZxZFmHc6dPMSkK9g72GF6/yXQyJYiT8fPBRZalUoBQBJmAb9rPlLuPKcS2yvk/RPcxdHtddk+eZHVji9dfv7lw0y5nOctZznKWs5zlLGc5v9fnDYlcv/nJf82f/a/+FN/zPd/Fr//Gpzg6PADX4EWsahdaISWs9VepqpLVtR43b16nqi3eg2nh88FbbAhYFwiYNlLn28bG6OgxqaaqSupZ3fKYBCsrfUJIMMZSzEoAlIln2JM8RxqNySQHdwYArYNrFuN5Ht7y2Fu4fPkyt/duIRLQUlKWZStepEgfN71aSlSSYkyKRaCDx/sGrQx5qgk4UGB9dAnIREYfQLsPMMaQdnK00FRVRXCgwtcLTfPNxHxjYUxCCLF9Mu5DJNb+dg7KIrbDPLYoiK1muhV7dAsPjhtA0G0zl1ic4Zetw6ppxUjvHMFbpElx0mPrhno6pazH1LIGW0XxZwFshsSYBQ+raDlciTFkWcZ0Ol241qB1YREjNCoEsixhVlu8t7FpL8T7JFSgqYu7ogN3nWpzcU2qyBMLvon8L9qbaVvFkAFfOpTUhK5HJQaddhCNR0qPUgLvLCDwTs8pPDRNw11zW0BpFV0wUrQQbIlUCpkqZrMZTW0ZDgaxHa1puOP2EMGxvr4GAWoXEEoihEcpSSIFx4MDkBnFoODND5xFETBaM5lMFrBq5zwmSciyFBk8Oxsb5FmH46M75FmKaiO/UsIf+IM/yM7J09za28eGT2CUIjMpo0lF0dR834ffzhPvfAcHNw/41V/4eT47usntO3tcePRRvBC48BjPf/VpkjKhmyW4esZsMGB6NCZ0NplWgn/0P/wlPvqRb2f7wkNs7z5At7cKAn7ml3+VT/4P/yNnzp3ibe98O9YJbEiRKmdtZd7EucbRoOTUaUs9GVFMK9LVLtSgBWytr3EFgRGWzb4gTwJdGfCuRuoUhKa3kkbn1toqz33tKxweHXHlyhWefM/7KZt9hsfXKMuCbj9jZb2DDA6tEro9Q6+f8v73vZu9WwOe+uJXAIckcvCCDzEK2LpQvfQ41yCkJASHDS2riVgWoXVKkmiGYoz3AhckVgi2L5zjW77/OzB4XCPZH48JqoNsBKEpqGSDUFH8djisczGqm+ckeQelUrK0oS4d4qZgduMI4xQIxQsvvAKzEWVVogVkAl798pf52ds3aYrYavuFf/krrK31GI1By0AIDVcvXuTg1h2cUDgJQntC3YBzaC1JkxXSdJvpbAoCjJFMVEIiA04qjE6YDUdQRhea91EUjCaqVpQO7X+FpW6KKKTNxwnAI32IrKcsZTitqQrHqTOnIFkFUbOxs41hDd9EV+srX3qKaxdf5eirHW4fXeNNp89wcmNj8Z4UIaBCgGCpXYoIEiEDgZKVbpfZsMaHGGWmClQyRiVRBpOkaGPQMsbOZbDtSYl4gmbuQxPtmhXayGKbNI+vlVZam68HQgioPVY28USHCFRNRdPUhODwwtOEEN1R3jI9HjG4OiR4z63sDt5KGivYOX2SndOnSIxkfXOTWzdvU1dRDPbeRtczArRAWMi0pJrOGA2HNI3HNwFDglYWITWhdacppVpXlkdIR3Aunqxp73M8OxUdu8F5rPU4Fy+f5h0eeOwx8v4KebaCa+BLn/y13/F7wXKWs5zlLGc5y1nOcpbze2nekMj1F/77/5q//Ff+Mv/Nn/8LXLuxzxef+jJFNaKTp2SphhBb/LI8x9qaTie7hwGlovBB3DD4ELlTTdM24mlFt9OJkbm6QKmEprEIRIQHK8EHP/hBfv6f/yL7+4dYG51PIQTyPCFJE9ZWVhmNRyRpBkBVWzY2N7l9+zaT2YSnn3majY0NNjc3mEwmUTyxlsODg7iJUfGMdozNKeo6RsCKosCYqIBoncSWOhnANjTWEnxYMMDyPMcYg8lSJpMZTduqdS//auFyijcWRZBEM5vNFjFGWoC9TtKFi2AucM1jgIRYA68EOGe56yKjvS1L0/iFUORDaFlgcsHR8tYu2CvBRzBx1u2QZCl5miJDoJnzbsTdGBreI1V8TrWKrh1jDH/+z/95fvzHf5zLly+3QP920xjCIs5Z1w2DwZDpdEaeGgjx3611OOejGNfGA6FlxcgYoQwIKhfFNaOjSOWDiHwlIeLrQqr4HCQGHwJFVVLbBi98hE37KCTdm/ycPy9CCJKkfVsIEeOm1uK8o2miq0UqRVlV0ZEn4nFfv34DIaHbzaNgise7QGM9tQVlNMJ6gnR4a2PDWlkiRGBWTCILiugQW9/cZjApaILDC7i1d8D+nT2QKY0FLxSCwM1bt3n5+Wf4/Je/wssvvcJbn3iUrRObXLl6gzuHNzk6vomzD1A2Q9714Q/x65/7TSbjAaPREf2VVaRwTKbHTIcjdndSBoMhl1+5xJ07A7719/0QvbV1/shf+otsnL2Po9v7TIPncDrFOs+5hx7jD5y8j/seuI+HH3mUgKQsK4SEg/190jRndX2DrN9lZWOdv/G//gjn77vAbDrjzNkTfPWrT3PixAYXLpzHu4KXXrhJ4zxHx3f47Gc+g9KBs2cf5urN2/zdv/O/srl7gh/7sZ+IZRau4fG3fhOyhYifPH2Cd7zzcTqdPr/5qadY6a/xwIVz3Lx9iaapqMoZ3nq8l0xnNTqJbj0h5mUNguDiMxCFDIERkhqHC5HfNpsVzKaBpnGRNQdIoxFGgRJ4D2VTs3XqJF5AjaVydhE9DiIsopHRwVkxm02pi9Y5lnRx1jFsnVtCCN75rnfyx37we/nJf/yPGdkbiKrixOnTPPGOtzMZjLBB8Ohb38LG1ho/9mP/lPzSZcajMTpJKGoLHpx1KA/v+ZZvYv/ggBu3LrPWX6f35rfy8rVDBBuYxpBLh0PFSLKIDauT6QzZuoC8UK1o3rpAQ9RI4iomvz5FPdfvdRSFEqWxZc3l51/mC/u30CHlzPs/xMb9b6KwBaWrsLbmkXd/Mw//2jV6p3Z599svsLu9+ds+g6I/SVKUs7btMIL4go3Rc2SMhro25iuEJE0NmezQ66ZoKaIr6h5n1Ndf/6KiY7GeRpE+hhpFe1+FnDtMbWSD2YbGNgwGA5qqhqAQwSCERggbWX9rq2yfP0un2+W1Vy7hhhWdJCM4ePnFl5m5pv3MhMq3J45UjHvWLUsQHyP9aTelt9anmMzoZApGmlEZm42FnLdKBoJ38TNGRjfaohulHWvtwhnLPQ9J3VRMphOckBwPpoyH09/2WC1nOctZznKW83thvulpx6//lW/h6PtmvPC+f/Dv/Xu//8M/iHvl4n+8A1vOcpbzuzZvSOT6Bz/6Y/y5P/vn+PGf+HG+7WPfyeDwkBdffCGC3psIU08STdPU+GAZDo9pqgoRILi4OZx/mQ7Bo3VsFhQyihPj8QSlIg/H2YAUCZLojPLB8ulPf5qqrKlrh3eBPDMoaSiKGijZ3NogzTLKwtE0NUUxRrZtiJEV5djfu7VgeMUv+K7NwsTWwk6e0+l06Kx2KeuSjY0NmqKhqcsW3q4wiUYZiagVVVlR2wotJGmaQHu8RVGghKS3usZ4MsY2NkZiWiA7gShQtYJMkqSEAN1ul8lk0jqZBP1en6KM7qbgA0qq+HiFudNAYExC0zTMmx7ncURjTIxLOosUAqU1WqkWORP5Xn4RYRRIocjyDi9ffIXPfPYzTMZjhAclJap1NKn2uuei1ondXUySkGcZnU6Pz372s1y8eHFxPBB5VEmS0O10OHHiFGdOn2VSNG2cUyMcRDdVfD4ELBhYd51vfhHhEj4w74vMsg6TcYHSui1CSFASGmdJ85y802cyLajKMkYncS2s/+71z48xBPd1TrmmrhfcNtGy2+q6jpvqLCNJU9LMkKQ9EmOQ0nFn/w6dbkank5MkCcE7hG9iM6kXSA+b66t88APvj4+nEAgEdWMJSiFlVA9CK1BIKSnqQFlBEBofBDZo6qbmNz/5m3TyhCzvs7a5zauvXeJLn/003X4fbVJeePkV3vv+9/PsU79JZ+Ms/dUVdnd2CI2lGE+xdUNV18zqCqUTpMnQ3R4nV3c5/+a3I5Vk9b4LvPjlL3PrcMiHPvYxHnnsIbq9Hu/7yEcIIdBYh9IK3ULERqMJ1zsJqysrnD57gkDclP+p/+wPRaHVwT/5Jz/NxUsvsbLSYXg8pigswRvKsuL1K3f4xKd/k/6K4i/+xf8Lt4+GfO3ll/kjf/SPc/9DT1DXNQcHt7BOMisbHJ5ON2dnd4t+fw2TKDqdnCzVHB+PmE6nPPvs04wmBbf29tlgg9RIECq2BAJ1XTOdTMnzjKooyLIkll2IGHFDKuomoJWMPKXJDGMUvU7G6OCQz/zar7Ge57z20mVee/UmblqS9/rYolxwCkUbl4Xo4uz3euyeOIWtGpIk4fDgmBACP/RDP0SSxHVEa02+shqjqEHSBOiur7O6s8PxeMpgPEPmOVl/BaEMiTLY2nL7zh3QGhEkWZqghGBy7Tbq6CYPaMfENzx35w5pb5vaSRCeIBzBS4KwCKIw7hrLbDpEJTmq00Ul3fiYiEB0j4KIQK7f8kkRRXHvQiuCR8dQiWYkU3oqYzoaovZu8oWvfYG8q3HNjF/4mz/M5a8VrJ/tc+2w4oGdLdZ7vXiVgbZkIjpUm1C1Yr9ACEWeJygZ3ZNSK5zSOKVb/hpkiSDvaBA+Nha2hRpzdU6KGAtc3APvf0tTrWgF9TmUPoprWscWSC01Smhc41uXW3TMRhegZm2zS3JyjdV+H2ct9asNQgXSNEf3OsimZi1dQeKp6yl4CyhCkPgQgfkueDyBRGt8gJtXr/Hi08+jmoBoarobm4iobgEC56IwF4TCi1gKE4W6KODFtU62xS/xcdJK4tvnU2tNnueMm4okTd/IV4XlLGc5y1nOcr7hI7OMn3rtE7/t33si5X/8q1/GCIW6twjn3zE/+Rv/ePH3D/6V/47dH/7sN+Q4l7Oc5fzuzxsSub723PPsnNwlN5pf+cWf5+atPbpdg7MNs2KC8GHBhMpzTa/bQSCwVRN5U0Lg/PzseEA4vwDKSyUJrbNCCEFZ1iRJhkTRNJ6zZ0/jfI0Ukl63H6NjIlCWFT440kzhQsPqah9bC0ajERcuPIQPlslkQpYlcTN5eEgxKwjek7Vf3IvpDAJordBaM5tOmVRTdKJJ0w4qSAiWpomOI+diFMWHeIa/aRqyThejY4QvTVPGsyndbp+sk1OUBb1+n9l0ynQ2i26BVjTSKjZIlmURhaBuB2ubRcPhtChj42Oaxmih94vYnmPOx4qw9zlEX0qJtTZG3qSkqsKCPwZ32xnvdVl5JVhJU5pZyYsvvsTwH/4YTmgkgso5BHLhUhuNRkgpeeKJJ/joRz/Kz/3cz9Hv91lZWePzn/88EIW+pmm+TjTa2d7lfe97P0EbXr9+i153pQXC+9ZJRWQZSbEQuu6F2M/dZ0UxRSvBfffdx/33XeDixWs0jWNWTCnKKSHEmJLWGqUVQcRIqzYK2cas0jSlLOu7HDbuRiNDiM+xbVsoVQvXdy4sHGl5npPkGWXRIKUhz7v0+xmz2TGj4TG9Xsb587uU1YzJ9Aa93NBUFcEHvvKFL/C9H/sQq6sZmISgNMYofG155avP89zOLhZBKEpOnjrL6bP3UTaOR9/8VuqmZjaZsXfrGg89/CYEjqKpuXT1Jk05ZjyaUJQWREK3u0nRKH7pFz9OKTTTWUUxqXnwgbOoJLbsCWkwWYeibBgXJVtnzrF7+kFUkiEQKA1n3/QmPv73/xF/6qMf5vbtW3z5K19lMp5ycHjE3v5t6rpkPJ5ydHDEZDhgMhoDoBJD7Szdfp+0FTmzvE+/t87aesaDD55j7/YhvU6HM2fOsn/7BknaJ0m3WFnNETJBpzlZZ4XGeYTUNNSMJxU+KLSJETQhW0eNjMBvFyykgvsffZjT991P9Zuf49u++zvJ1lZRiUZ4T2M9ZVlTVSWT0YTB0RFNXXH58kWCczS2IVjXNmREd1fwAm0SVlZXyLIMhGB9e4u3vO0JUqU4efYBTp69wvOXruBEoGhqpFSxCVDefW2FECjKkuPBgKayGKNxNr4Xz58/zyu3rwFw8eJFrr70AiKA0pqqbihqS9Lpkvb6TA+PqQiYTh/rPOfPnEcYx97+Pj4IPvmJT3Lz+ov8yf/yT5NpQccIdrTEDccUdyaokx2KcUWmd+ms7DBt4smH4GNcuJzNoI7NicLnQFv6EJWmNv4MgbvuVADnLcE3yCDQIkZ2rQwcece+i1yvK9eu8ujGSRSePFVk3S7f/sf+GOu7V7m6kVIzQihz98NnLsgEESOFcs7MYiEmBTxSajrdHqK3idc9fN1gxwcoF08ghAAugBItM/KeEa2AFe5ZI2HuqY330dlmIYwppcizDkIq8n6OUoosywjeoZQA4TFKgdIoA731HIdlODokSTwNlkYETKYRqeZwNKafxZguwt3lcYnQCq7xxMf21i7OBk6cOsX7P3oKJRQHl69xcDzEt7F4Mf89WufavBRFRKeymBe/4BcnVELwNLYB7ELgapqG9fU1iln97/9FYTnLWc5ylrOcb/Co3R3+6hf+Gasy/x1/3hHJG77Oe6/rl/77/xdH/53me//lf83Df/qp/+Dj/HfNf/vai/zwt3479//sHn9q+1O/42X+3sH7efldSxbmcpbz/8u8IZHr2z72YT77hc+hZXQMTCZT0izhxMkdpNzlzt4d6rqkrhuUEpSVJXhJlqesrvUZDSetUCRwQmCMRqoYaREqBoUSExep1CSsrW9Eh4mQrK2tcDzaj64RU+K9xfnoIshyw/buGp0sjfEcE6Hxx8dH9PIcnOcd73wHGxubvHbxIoPDQ5qqZDKZMJtNyTLDdDpBSsdkNkBKSaezSllXSGXiZkFKwLU8J4mWCuU9iVQkq2t0uz2sjULSbDYDHyiKil5vhTTrkKYZIBFSLZoUtVJkSYrWmpWVfnRFEDAmLrqz2RQhC6QCYzQrK2sYk2BtbD0sy4KqilGfYD1bW9ugYrSvrmtsE+NSUjmMSXDOU1VzR1qE1M+jkU3TYFY0ZRXjcDYoTpw4gVKG4bCLUrC9tUmWJLz26itoHdsLX3j+eeracudowGQ6o6xKTJbGY5i41skgcU2NTmB1vc9XX3gNKyV5t8v0uMD5CEIXgtYpFl9v3t3dOIcASuq4YfYNCRl/5Ht/gO/5w3+I/+df+9scjyeU1Zjj0YSbN69QTI4jRFtK8I5ECBItqJqAEW0hgGtaUdAjRCBNs7uA/wBGR0dP3Bja1tEWgfxNXROsw9qGopiRpgJr43VoJcHWjPdfpyhHBK9wrkKLButheDzkxJlzrK5vk6Y5/ayHtWO8hun+gMntY9Lt1RgTlZrd7W2881jnaFzASYkwCU5KJsMhDnBBU4eEMiR0VUJVNVy7doOqqOlubKFrwWh6wG/8688gs4yVTo+9W3vcurbH/tEhK3nK1s4W/Q3I8g4hWJqmxJgUkyQg4Od+/uepijGhcZgsxxjNmdOnoi/ER/cLLsYxo9gUwfkEKMqCw8mU29f3uHnjOYajAT//cxOKouDE7haPP/4oDz/yEE7SRmI1TW2xHhqhUMpw7dpVrJYMp9M5GhwRJDubp3jsobdTlA2DozEPP/wAf+I/+xN0u13yXo+TZ8/zzz/+Cex+QVXVFLMZVdm0DitAgkokOss5//BDlJOCwZ0jRscHrbDSIILHL4Dlsc3PO4tE0llZxdUl69ub7H3qq+zdvslmsoUXAiFbaLhUKB1/z1lPkvdZ2T3JZDIh73apiwopBanSjIqSyknO3n+BnQceYBZ+nUZK0k6HejLi2osvcHBrD8YFlz//JZrrN+hox/mzJ/jpn//nBCOYNVNev1Hz6usv8f4Pf4zHTm+zNzvi0uSAI5txZCeoySGlFaSdDvg+kOKsx2kHtkBUFiUNKstilDu49nHX0f0jVFxvZQP+XsUo8gQ9Dhs8hoAWkWsm8j6y0yPrJUihEV7R0AAJ/ROnMSfHpEGyCqRS3fP+D7jgcdKBaBAWYkMgBBKORhUqWwOZULocrInqlNM40cd6zbBI8RgELATrcPcGmAs9i39ikWts+WPxNe6sRxqB9wKlU5TJUNpgTEJ/rU9VFIuTDUiJF1CVNbevHOLtFFtV2NKiRELa1XT7fSaDY2gavJSI4No4aCAIT0DhrUchWd3Z4ps/9FGe/uyXsTMQ2jKzJSE1BCnbCKdg0QrZHrezPr6WEW2XSRS3lFIIqWKjaFvcgYSqqrh19TqdboeJPqYu7Rv5qrCc5SxnOctZzn/QvPq/vId0X3Hu/xpdVeFb3sZ//qP/jEy+xpuT31ng+kbMGd3jDCA6/+Gfd7f/m29m/I6S+39M8Cf/Pz/3O17m2/KC+tc+zkfyI3oy+x0v8/848Wl+6eWT3Lar/Mqb1/6Dj2c5y/n/53lDIpe1Fe9+9zsYjabcuHEbbQydTsbW5kZkVgXPeDTCWkuaGo6OhvFLfmPp5F08kvFwjHMOozR5liFV3AiYJCXPc1Z7PXzw2KoiTVOQgvF4gDieYn1FEA2IyPGyFrI0Z2WlTyfv0O32mYwLet2cPEs4OjxgOBxw33338dqrr5BkKQjJww89yM72FpcuXeKpp55iMhlTliVbW5vUTU1dV7hW1KjKEu+i8CGFQJro9pJKoLUizXJc244XAqR5h+A9sm5wjSPYQJblrKys0uv3OD4+ZjKdkqQJRis0tMwywerqCrOiYDgaoo1mfXODMPA0zTGdTjferpSkaRSmpBSkadqCgx1Sa2rbEGxDYx3O2bbZMEYLdWLwbbRUqFgbn+d5dHsVBUpJNjbXeOSRRxlOJpw8eZI0Ten3e21DXqCua3Z2duh2u2RZxquvvQZSxPYtH+j1Vuj0+vE+6AZjFFKAayyj4ZiXXnqRK1dvUXlBIDLC5tMaMyL9poXqR66XRyqJ87YVAgVaaaaTCaPxmMuvX8GkGQGLStIYkWvjjYGCuqpQbVOaFHIRVYyFAzHqubu7hbU2Nj8uWjclUolWvHSx4CAxNLbGeUtV1QQf6Pd7rWAYRUwILXg9MHEl3kfHoFYG56DT7XH19Uv0tlcwqeH06TNYbrFXTLhxdMjN4wGntlaprWU6nXJncMz2YBAh1q3bzYbA4dERRwf7VE6AytBpn8bFeFPdNDz37HPc2bvNd//+72M6LvnkZz/D7cNDNjfXcaXlgQv3873f871kvR4PPfQAp07tkndyhBDcunmdyXiI0glVbXnzmx/EW09Tp9imfc68x7Xte5FzpPAonEyRIsTGUQRGa7TRaJOgZEaWGTqdDpNJwa/92if4/Oc+x2uv3WRn9zSbW90YN/M+Rn6lipFWBNZaOv11er0OPljG02M+/JEP8JGPfJj19XVWV7b4c3/uv+Vd73mMs2fOxHiqEOzu7vLUF5+ibuJ7dGtzi6q0HB4cUZQztFEkqSExURhxjSVPc4IU8X1EjBtKLQhVfL8SQhQ8raWeFOTa8OJzz/Iz/+yfMmgmVHcajo6OmCPN50UTWms8kf+XdzoUZbmIVGslGQ+H5Gm+EMtnkzFGCrSMYPlLL75KdeUWsvZ0O12++OzLXN/a5JbyBJ0zODpEppreag8pBdUMvvD5z3HyOz6MNwlj65k5j/WW2WAfnXSgiW2gztZY69BKUtexbCLJOpg0RSjdikJzNqCM7tvgCeG3FGSIe7BPQlBXFVrE1s3KOe4UYx49eQZjJEmu6WQZhzduYic1uQ588C1nyRNY7f7WL7MCgSI4Ab4B4dEmrgez2iJ0h6ByCpvC1BOEJQSJ8BlSaEqfE0SGCAol74YT722dna8L7Q9YSF0h4EJASsXm9iYXHj1FUQ6RIqFuPF46tra2WVtZ5fatm20oULYttYHZpKKZjOkm0UXmnGd1Yx2TZmghSHygKxSmsYDHi/jK8eIuw1IoRdbrkpic+rhgPJwQxIzJbAxeEmxAeN8KdbFJ0voAIX7GRjeij244cfe+e+djlF1rGhudwrPxhBefe7aNMiq8/+38suUsZznLWc5yvlFz/Z++md934Xn+b2v/C2Of8a/+wOMAnEk+wQ/1B/+7HcdPvO9H+CN/+8+8ITfXa3/jSf7gBz/Pt/T+AQ+YQz79rgf/Lccs+Z7uDPidBS6Ansz4of6AJhxw66vvBODpd4iFk3w5y1nOv3vekMj1+uuvs3v6JHm3iw8wmRV47xmPR/R6OUlqUFpHYLHSaJNhUodzBcWsxHkbOVzGsLm1wfbWFv2VFYRSFGVFXZVY21DPKiazMUfHB6xs9jHGY31B3ZR0+4b+yiZNEyiLGoECPLPpDCUUZdngiVDy9Y1VRoMBDz10P8+88ByDwQEmTbh5W3JwuM94PKbT71JUBS54sk4XUWmSLG/jMQLrAk1oFmf+lYqMlSzPMInBek9VNThftGfdYWV1DZ1mBC9YWV9FSEliEoSIUH6pNZ1Oh7W1VYySTCcTBoMBlW3wIpB1O5w+dRrnLCbR0R2kE7yPjBspo9iytrZKnucMhmOcbShmM4L1gCdPM2pjcd4jnWF1ZYU8zymKgsl4gjaafr9PYhJ88Cgxb3gMrK+v03i7EG7yPKfTzXHO4puG/kqfjbU1RuMx2hiMDxR1xc76DlonoBRFWZIkTRSunCUEQdNYjo+PcS668Ob8MK1lawSJrJi5EBUI7eMOC1OFpG3gjPGhpmmioGUt2giUkpGvYz2j4QhtEkIbkY3XER0M0+kUrTVpmlLXNXVdL5o2Q4jsMylk+7sBk5j4/xeNfHrxfJgkinzWWqSKLZZKaYwJKAFSxS1vVVRcuXXApG74iZ/5Wf7E5gprKyt0eh3yPMWYhFJJCiVBa9IsI0kSxsWMqrGLOKdzHmsd48mU6awAndPrrwECHySzylLWjuODAV/78pc5ceIsg4M79DoZp8wOwTb0ux0efPRx0JpLVy7zqd+8znQ2ZTQaMp1MmIynzGYzrLc4F6Okqvb0pwWZd/gQ0CFEh1MIaAQNkgMhOM4iZ0/g8C13TihJahJMEu/T2toqu7un2Nk5yfn7LnDx4qtcv36brZ0TPPTII2gNLkh2T57haDpjPJ1QlTUdDGnWoapLdnY2efSRR3nnu95BWVpev3KTly9d5GPf/T5efPFFHnzoIW4fHHI0LSKYHMl4MubkY2+mKD2DwZSqGmFriy1rfJYhlGQ6K/AosjynmM3wMZCHCAHnHXVT432CMZq9G3t8+lc/wbnd03zyNz/HzRs3CDK+vl554ZXF2hlCpCFprfAhvkbmr3jhA6kx+Mbyr37lX3LybY8TGsf1K1f55K/+KsJJ8rSDVh2Oji12WNDNcrZWuhz6KR3V4cy5Lb76zIsEAjqR9Hs9godqNuOpz32Z86e32TsesXc8ZlRFAcvVQxLhcMUY5T1CqLklD9dEm7xQCoRseVgBIQNKzd9nEufja1GKRTVpdCG1jqjQVhQmJqHf69Hv9EiqmlXvUYM9OniMFRy8cpnptT3GyRr9d54nz1OUkvc8fvPYsiJ4Gfl2MjrJCCKKOVLFqJ9TEeAlfYsLk0hhaIIhoNrmwfhl8R4v1+Jkw/z2pKBV3kN0sLUnDJJMcfLUFsH3ECJFSIMNjnPnThKcZ7XfJTGaqi7bExMC23jqwnPh9P2sbvc5farEuA4qSZFKkUrFICjq2Yza1TTyLgQ/tCJr4xx5p4cAbNVQVlOGw1tk3ZRZUUPaiW4z4vtV3jWiRfak8Hf5aW28fT7e+zYKH3AhxuCtbWIk34v25MJylrOc5SxnOd/4ef2nn+AX3/W3eNh0AQM4Ppo//btyLE9mih/72N/l//7rv+/feBn5fUOu/oNznP8/W17602v87e/83/hYZx4vzHlzcuMbcixGKP7K7tMAfMevfzfi9w3ws9k35LqXs5z/o88bErmqynLnYEC3t0p/ZYXjoyHWOqaTIrpttCLvdmkceOtJ0i5CpuR5w+7GGt1+lzTJ8ATSLEUKsLZhVs4YDUdMx0PqMjpvQvslu84lpmeoi4aiLsnyjKaOLqVomnGU5QzvNZ1OTt3MolglPImSZJnh+s0rZLkhqwwIGBwfMZnEtqh+v0eWZezt7SGkpKprOp0udVNhrUXphCzLcW7aurhMTME4j265xVobuj1D6izj8QSEZHVtLQKrbcumSTTeeqxtwDmCtbimRgnD2sY6x6Mhx6PjGIVSkuF4GBsghUC1Lgpr7UKYUUrS63XpdDqURUPIMrQxNFVNU1d0el1KW8e4og/0ul3SNhppEoPRhizL8MEjhSTRkrW1dUajEdPpmH6vx3g8ZDQa3tOqGJvEEIK80+Hy66/H+9fG+pRSpGnKsAXnZ1lKCJ66im4say3WNjFGyF3mFswZOGHB2aH9uZQqMrYgOkRCaGHJsSEycshEyykLIGLjopIRnM0cAM1d14LA4YJdcMNig+YMa+86UjytS6ndrCulkUJQlEXcHLZ7Rdk6y0IQrVsn8m2kUggiQH6+Qdzf22d//5Citjz30qv8yi9/nE7apShmTGcTpFZceOwxLrzpTXSzlEQbtDG4QIy+KY00ElcUJEnG8WDIZDIlySXHR/E6VvsJja24fnOf6eiYf/oLv8i5+x7kifvPEqzl9MkT1FXJ2972Ll567ll+9md/hteuXGY8KyiqMsZfG4dzbRFAsO39kpxSCX908yzb4wlJbjBKIV108iituDqd8GsHB7ymHZWKYgIiMoGsCKgQEFIhpUZrxdraKu973/vpdLvkeYdr1/Y4eXrAxDlC5fjK0y+ATOmvrvPggw+xurbFy5dvYOuKo8GI9zz5JA898CBZp0NZTzgYHHLrzi0QnmeeeYayqvnKs8+xdfIMEsHqyhqDg0OOjwb01k/QX9tgNB5jqwJfW3ztSPIM1/jYoKgEQkqsj2yuuWbgnMNZSyMEh+Mpr9/cJ0tWuXTlFo0NoGOb4muvvv5162ds5AMlI7up0+lQlRX9bpdEaXp5h6qoaKoaAZzY3uHxJ55ga3fM0FfsH48pB5bhrSETGZtc/dYm+uRp0q5mUhRUtuahcw/ROIdrAkpn7O8PeOnVSxSTGaUXTIua4MEIB/WYcjwk9YEgAgKPAkQQMZ4pBUhFCALnHaoVoGUbJfTOYa1dwMtp32dRGL3L/CuKgtFoxGq3S2o9zfGAYlaS9jSFbVg/fZq1rR1uvfJaPJEg5dd1H0axxcd1o12D5sJMCFGgc0JAkEgnImBdBoIKIAKWObh+zlhruZCtdnMvg2tReEF0Uc2feClEFKxVoKimiFCTJRKtDVpKklSh0NjVHlVVUlUF1jcgoHEwnFnqkCDTFdZ2NwhFbF3FOlZWehTDCa4s8U1st0RrEDL+kZLgHFliMMbQ6ffo9jNCHtje3eb29VtMGkeQkW0Z16G4TEkRQ6bRoRYji3Ow/nxtio5Z4m2FGJufs8kC4a67bTnLWc5ylrOcb+C88vffyafe+zc5p3u/24eymA9k8IHHfunf+PO3/Ngf4+Pv+jv84F//4/zIQ3+Pj+bu33jZb9T8y0f/Bd+lP/Af/XaWs5z/o8wbErliVbygbGocgJSkWYJOU4KQBCRSKforKwQPSZpgG0uv0+HkzgYmUTS1ZTweUxZTrKuYjMccHx8zm07BR2FK4QgiYDopdVUihcUHT97t4INjNBpRVw6tk9iK5+MGyLsYk3TWUrXihdSS/YM7rK+tkaUJw+GQ2bSkKusICk5jTLLb7aG1pq5rkiShquq4iQoSdBRKXNsWJoTAq5atJSTKSDQCZRUzVVLVTYSnq8BkOl24i5RUOBtbKJtaMB3DVBJ5Xe1mah4FuzM5pGlqtNKx9UoqhJTkeUan240NZ1IwOD6mtjVIgUpMBPIbRafXRRSR+0W7oXE+wozzLIsbHAJSBPq9nLouAU9VFxweVtx3/wWaIGgOj8BFca6sKrRSKCmxjeNwcIwPgTTLqKqawXBAVddMptMYLZQxcyOljO2HUtL42OJoJHgVeTXIeJ1CSow2UWDwnuBC2/ilUEpEBjgxJiZFFCCc91jvsd4hlQClYnSzLTEgtE2MrcAAcdMmRIwR1XUTHVnt4y7a6yXEdkNxT6Nk49yCczbfHMZWyoAkRka9B6njdTfW4Ymg7iAEB8MjynJGCJKybPjsZ5/ibW9+HJ1kjMdjfICt3W02t7bwddG2QUqsl1y5vkdZW2azgnIyJk81L792mdOnTrKxuQ1iQt7potKMW3cOuH1wiKsbnn7+NW7eGfHeNz2Eayq08PS7OcFZ/slP/SS/9qlPM6xrhNCROaZjPFAqTaKja9GLgNSGLW04t3WGE+aYPJUkyoALeDzSaEKn4kTSZV87xkLgrYuikLdYZ3F1jSM+D5Wz7B8c8/TXnuftT7yd7a1tXr9ylavXb7J19iTD0ZSvvfQKdVXw5JPfxOOPv4UbNw/54tMXmRWOm3v7vPWJJzh1+hS1dRRNjBivra8wGAy4/Po1itLxqX/9ab7nD/4Ao8mEJM2wtmE4GpKubKG0JnhoqgbhLJaA1BHwbV3NzAVQKUHUi9eSIJBlKUprXJBUXjKyDceuZmQrLPGt7ENgNBqhE9VykiDYgLMOkxlsXTE6PmI6HeOqElFZpIDH3/VWRjo2fW5ubfDWd7yD8kvPIqoClXe5Vt3muD5GiICZKvbu3EYYWB8ZhsWY2tbs7Gxx+coVlEhJs4zJuKQsG7ROyNIMmGGbGiEcja/wzYzQxoOlkAgPqTZYU0cjk3fR0ehiRFBSY6TGSBG5WZoYB24nBLA+MtlkCNFR5i2zsmLWWKQXDOuKg+NbzPyAsppSKjj35kfIbx1S1w1aCZIkQy2uMzK5QtSxCD5G+HwION+2CAaFILYDzu+PCFE8J0R3k1AyFiO2ja7z/71X0BFflwZoxff2klJAU1fs3d7D+4LUZDGGqxV5N0ZeDw8PmU2nzGZT6qrCuyh2F5VlbzCCbhLjzBj8tCEUJZPJjMHoiHI6pfYOZxQuxBZQIWJQU3hI0wShIehAb2OLjlrBNQ1Zv0cxnUZ9qj1RcFdk9ECM2Ip2DUXQOnhla1bzd92FbVRSiBi5FCJEJ9hylrOc5SxnOd/AufjXn+SL3/rX2VK/dwSuf5957skfB3p85onfmbv1H2te+suP8ej/6SXc8fB/19tdznL+U5w3JHLd2duj16yhs5TZtKCoCrSGshI0ddwoRGeMwpgIv47KQkJVF5RVw/FgyOj4GB8sQniqqqKYTSEEVvo9MpOQpJrJdIrQisl0TAgSkyRIJRfxNEErNIgYAUq0xtuG1Y11Dg7GzGYVIQT6vR5CKMqipqks5bTEWx/FHyGYTqeUVRmbpZoaBJRV2bp6BFiHs3GTFwLYuqbb7ZIkCc7FFj/Zbq7wgTSZNxrWGA1aaeqmopjN6Pf6pGlGLSucd0wnExpncc6RJAl51iGEwNHRACkVWiV4AUqAkLJ1EUQumCcwnkwYj8YgIydM+NgEKLXCLeIocbMX2k2nb/ldsVVQxdjjap/rN4YcHBxQFiWbm6u85S2PcvX2AVdv3KQqCuqqZDKbQesCuHnrVnwehKCqapqmoWnqtp0rsocixN2hjaan+3Q6PYIyEGqMkjgl0YlBJSkieISSyBDB3tH1VX/dcSoVnQdSiLhB0wrnIzOJdlMbFQa/2LmKeeyI6OKQQkbXV8vKCW18EcWCySNF6/QI882twDtHYy2+Fc2i6NlgrYe6IFWS9W6fbm+F4/Exs8mMrgGpdHzutKa0NopxUuGsZ3//DtP7ZtR1HWNXQkQRZniEr0ustQwOB8wKx5Xre/ggGR4PUN7R6yRcufI6QWjWNndBaTrdVawPDEYFjQvYuK9lNJpikgTvGmw55b4LF3jqX3+aVy5dJu2vs5V3SZMcIxVSKYzRaBUdI9okBAkqSVjxnsskzFZ6KAnKS3AeL6Pj5yAEzOkTnNEwsx5fO1wI1K4m2IaqrvCB2FoJeNtgvaZxsLKyilSKOwd3ePzdb6dqLFfv3CBLFR/94PvYXF/ni19+maPDY1woWF1bo9Pt4ELAeocPFqUFZ8+e5Or1q1y/dRsXNDdv3iLrZkzLGWo4QCponI0ie122jsoGRUDp2N7pnEUbSTdf4bgeoFSJEJLgHQRPYgxSapyPMb5JMWXSTElWUtCC4KOQsHtqh+FwSN00cf1wHu8cxWzG6xdf4+j4EJUk+MrijyfYpubCYw/yxZdfBTyNq3Gu5saNaxxNpwRtODq+zdHkNpkxdCeSm9cu4XzBeG2VSTkl4JkVU+qqJNES0xYnZHlOEjydPCHPaiZlzaws0SY6Dr0QpCZG54zUmDzHKQ9GgxIoo9EqIcsTOt0eadpBqiy+HyaGe4xcrbAZRSlpbXQTGY3SCQhJ5RxjJziaHDIth4SmZv/OLaR0+EphnVu08N57pSGEyKoSUdiK7klwQBMEeIlEQmtAa1HyrRsrOrqEkvcA5eMl/ELs+u2si3gpgRChFS8dTVUzOh7ifIEQQ0CijWZrZ4MkSRiORsymRSu2R4Fea0+SppTWMp7NCNZhtMHPaphOGQ3HjI/HVGWNTAyYlOBVPPZWpPPWUtuGSTHCuhqnAnVw3LxyBVFWeCkQC0EvtCKXbCHzDfNl0rcil/BtjPYePhftz2LKVEYHLEuBaznLWc5ylvONny//4f+JVdn93T6M/2Tm0h/4O3z3X/0eWIpcy1nOv3PekMh169oVssEdTJ4ipCYEwagumBy7FjQdz3r79ix7khlkgMG+5qCTknfy1h3lsa6hrmqssyglMakhy1K8C3RXetTeYZ1HSYOQMWJ3dLxPp5vR73epCrvgZM2boWKEJZAkGU3jmExim2NRVEz9NLqwUGxsrEWW2GTMrJhFx45QNC3vKsZgQivSxKhGcIE87yxg5VJJJpNJ6zqCPM1J04xut0uaJkynE1KT0VGSyXSEEZJutxvv+7hhNp3QFFUUaIhnzfM8wuWb5g4PPHCeW7duUQdHkqVoKWmamqquyUKEfe/fuUOiTWR0pQllWSKkxAdPWZYRjO0srm5ItEYIibUW72IMTzVxd1MUFcPjIVImJEnC1tYWp06f4uK1W4zHI6aDY5q6pmrqRWxyLszNBaMo7NE2JaooRsnoQDDakCYJvW6PNE0Zj+rWDSXIsixuVl10mylz9yXpvcW2YPr5bSRJEl1WzpOmaYwcCUGSpAjRRHGvvezcyTD/+zyiU1UVSZIuBC7vAz6S05GS9vUbhVQpI6TZzyHUweOJIpsrC6yDynpknnH27DlOnzzNs88/x80blzFeolKJEAahFM6F2MgmRese85g0Pn+q5RtVRclrr76Cq0uEUkynM5QyrG/s0LhAojOka7j46osMBkOeeurLFFUFImVtbRedaMaTEm1S6rImiOiuRMYNc2Yk2ydO8dWv/UPOPfgoK5UjWVklyTpEXnVAqxg9lULgiRgf2baf7jcWvb1NYWsSFDpEiHrhLIPZDFuV5FVN4gLBEYXV4Ag+Nu1JBC54er0udV1TTaeU3tNNUk6cPMX+wT5NXaOkYDab8eH3fZjzp84ghOC5519EqvgcvO3xx9nd3sE5jwyghcAYyYkTm4xmY/YO9mhqGB2POB4cgQyUTYOQmsFggHWSYlbhXUO7dKC0ilyipsFIw+bmJpPjMdNWWJ+PtRbbsrWUEzRWE0LDzolNlJE0dYysPvG2J/jiF76IdQ7bNHOLF+PpmPFkwtZsytn77qMoCqiqBYB+NpshWgF+PDjkzp09jouSpNvFe4vWAqmi+KC0JM9ShIwNlAjJ1avXCCFQNxVSaVZX+/R7PcrJGAdknYRO0zCd1XjRQZoEozUraytkWjIdT6l9Q5L1QEiCzlAmIUtTOnlGkndQOsUFRVGVeMLivQZ3+VmEQPAelSiEFNSzAjeekAc4uF0yrj11yJAmI08T7ty+ybQApxJqD2kIX+fkCj62oEauXnSyxsVTAhrvY6xPCBBta++CDXYPQL79h+jauicUGVq3l2zbCec8sTjzWHJ8L0gBXkq8awBP08QopfeeNEkQIbZoWtNQNw0IQafbRUpJUzfUZQEIUi/R3uEbC9ZFUavV64UIrWgV30OEwOH+AXc2Ystw7WrG0zHBesrJDJFniDD3nvkY3VSCgMASULKtQWgFv/gqDa2zVdzjfoXFyZEwj50uwfPLWc5ylrOc5SxnOcv5T2PekMjVTQ2JhGArXGgQyiCkJARHwBGbzz2NsyAF/U6H/b19DBo70UwTjcoMSWaYtdB6rTVKKrx3HA8GkXcjaobDcdwkNDVCS7LatNE1UEJiq0BdWZAKj2BWWjw1B0fXWF/fYm1tlbIqqaqS6XRKJ8tx1kbEiRBxs9E0TCYTtNYkRrW8J0FiEpq6QAiBkpKmsbjGYUxsKxyPJyRpCgGqqmE2LZiqGTu7u3EDKgXGROeRyXNW1/q41tU2Go44GhzhmoakFX+qtklyzq7p9Xo8+eST/PIv/zLeRXeZm7c7yugqquuaYjolW13HqHjsrnUaKSHaZrgYjZyMJ2ilSIxp71N0sQUElbW88tprVEXB2loH5xw3btzkp3/qp2lQTKcThqNjvHWRDdWC2YUQ1HWz4IRlWQbtef/YiugxxpClKUmSoFW8TLzcKMZ4WuEgNjM6siyjn/eoqurrGF1Sxk2nlBJjDFJJVIhMrvmxxNhqjBKmWYZrKqBtwltMBMILIdDaUNd1e8x+EWUEuRBphYjw5iAiVyu+TqOIZq3DNi7ypVR01mE0QSo6nT69vI+tphS1wytJXTWRnRYE3rXwfBtfw01dk6Sa2XTGeDTipRdGKOl57E1v5sTZU6ys9Dl3/iyzokYiaIoJL734NbqdDsYYyrphe/ska+u7NNaSJD2SNGc6HiOEJMs62ADONuzubHHryuuIJGfrzA659eQrPYqmjpFJ60BJXIgx0LIuCb597IVmHBqUdByODzm3u8N6t4OUitlgyP6oYFKW+KKG4LE+ekBE696SMooDDZ50pQd1RVGXDKZjNtY3OHnyFBcvXeSZp7/KytoGG2srfMe3foSLly5y7uw5Lr72CnXdYFJHYjTOem7evE1VlphERdEnkZw4tcO5c2e4dmmPyWTGU1/8AmknwzYSoTXT6YTRZBLXKx9QSctsE566qRASRscDEArvfIxH1xWpSdr4cHRGEsBIR57EiHWiFM7WLcZKceLEiYXbkfadEUJAEtef1V6fs6dOs3/nAJX2OJhcYjKZAPF9dnj4/2XvT4NtS/OzPvD3TmutPZ75Dnlv3ntzrsqskkoqgaokgQBZtgzGBiNDBw3h7sbdDQTdjjAR7v6G+0P7g92BuxuHzSACAzbRuLGwEUJCYpCEipors6bMrMy8eefpjHtc0zv1h3ftfW5JhiYVMllI+x9xq/JM+6y917DP+6zn+T2nzCYTekWPOkLdWkJIDEAhBMGniLOUCqJka7yNFJrT01MGgz5GC3xwfPpTv4WDvcu8czRntrSUtcW2NT5aYggMtkbs7oy5dOUSRsDXjt6maiquXL1IdI6yhdK2nJZLnhx7gpBoU5DlQ4QULObLb/P6pEbXsMJCkfcKQutZTqaI4ZCtQZ9vvXeXuNWnjj36g232Dw7Yv/gMs+omh03An0148dI+W1qvHzPG2PmuHEWRY3SKDAskAg1Rp+bAbjvE6kV/6uO1W1PKxOZKOcW1wBXCyjm1EsH4FfD55BzWRuNtciam4yEymUxQSqf3C5dKCnwMBGJy/VpPs1wSfYUxkqI3QDtwLlC3TXK+iRSxjB0PLRCQMcWnjdHMZjPm0xlET15k9LYvcuXCJR7dvMV0sUQgV1oqKInUJrkIgwTpEav0ZoyESMLwR4+UqUwAu4p1rzhdHdtrA57fzGY2s5nN/DpO+298H4rPfdib8a/cnP7AM2wfnRCWyw97Uzazme/o+UAi1+7WGK0VUUHVOmwArSRaKhQepSI6UymCFz0ChwgeIfTacRS7j/v9gugjzntCcBAgWE8IntOTs8RlkToJU4Czkd29LXr9jIcPHlOVFiGyFLcgUGQZy2WZ/jDHMRr2sW4brSTi2COBPM/Z3d1jOp1zenoGQrK7u0tV1dR1jQ8BYzKsS8ByrTQXL17k7HTCdDantbYDUQdkJ5641ianjJbY4FhMlvR6Lcv5gsFghPGO+qRiOZ3T1HUXefP08pzxeEx/OODo6GjddDUajdA6o22T6NAfD/HW0tZ1WlxpzexsynKxpDA5tmnw3tE2Dc75TpARiBApy3LtkHI2MbWSc8mTZTm9XgFtEnOEkMxmc0IITCbHIKA33mI+nyURqmPfOJcWSskdFdZOJ2tbXNOsAfSQxCutzg8xKRM/je7nA0ksapoGJUgiWK9YOxmUVjRNanxL0TmNVJKiV6BcYoyFkHhtSaM6Z4CtQNhrxlZIvoXVAjcJZKFznMm1Qw04bx3r4pbxqfhPMjqkj6USZFIxHA2pnOO9+49YtpoLl67xkZdeQvgJ7z+4yTdvn+B9TNFF4dcweh8Ck8mc7b2SPFMsZi256XFwcEAILdP5jLv3b3P//h0m8xmPnxyTZ5qLezt83/d9AucsOzs7kGU43yNiEEqys3vA/t4Bp4eH6TjWhqZ1TCfHfP8P/TBf/tLrRJVTNoFl69juDQhKMpnNCT6gZXKPWOvSuRA6R010WAOnp2dUiyX5NcPu/jbz+ZxlOWO+mFNXgWCTyGWdT+yzGAlSoINEC4gKggCdZwQpqBZLFss5B1s7jAZ93n77bV597WP86O/4YQ5PT/iv/99/lv/o//ynqaoFy7rl09/93RRFzqPHR/w3/81f41vfeosf/l2/nWdfuI5TkU+8+gk+8Z9+km989S3+zs/9HF/91hf5xA/8Ft56+zahcWSuTaKtS2pAKkFYLe6TkBkIlHVJlhmuXHuWW+8skV0BRFmWiEGf8aDPpb1drl25zMHODg/uPEbFxGBbcfham9h0id2WxFijNSZPfKzUUOgheopej89/7vMsSbHVZblksVgyHG1xvKh4+OBxek1RSKHwIcVh+4MhvvVs7+6RZQYpBMvFHKlapITf9/t/H6eHE955+z5CDlgs5pxNJsnZqBQXn7nCxYv7PHN5l4d3H1NVNc46bly/Sr2Y8fBwStV6bISqK31AqRSNFhI6B1l30qRzMSaHUBRgihxk4Nm9A773tdeoHj/i5GTKb/3Upzl56w4xDLh45Vmq5RKQBNPD6ToB1Fk/7Fqw0lrxzJVrfO3rXyVID3iiS0w+JaDIJd6HxOZCICTdf9MJR0nETj6ulcCVrg9rTlcnbgeRAo1pRCdceehg7E+7nN599yZrf1jnVNXGUGQ5tqyQztEXOYXUZFoz7CfY/NJa6hDwQmJEYm8FHzuAvOxEqYD1kSJGyvmCtm0oywVCZAzyPjHLiLJea3pRJKEsdsJ9dBGyxBtblXcQA5EUr00w+tg5mJNzdXWdEiRX2WY2s5nNbGYzv17zj//KTwDFh70Z/8rNZ/8ff54fe/ePwhe//mFvymY28x09H0jkOpsco6RmMBiA1kSgsZYQLRpHMAqT9Rn0DUV/i7a1DPp92tqSFTlWuCQghIi1LVVVEX1IUb+sR2VLGtsivUYLhQ814OjnI4L3nB2dsVwImrrGOwBLf9Bn72CXXq/H6ekZzkUIcHx8gjEGYySXL19kPp9xcjylsYHZrKIqa3q9nOGwh0BRLiu89wQvuoWLJkZFCJLt3X18FBRFzmyxoGlbQtsSfVokbm8NcT4wXywTZNhFjNI0dclkcor3yaWU53nX3KXJsoKAYLEoyfMeWVagtWY+nzOfz/mlX/oFiiKjZwzTsuzYVJp+b5CiltMFrXU0rUWEiM4zAFxIvC1CxHuPCJ3DSEDwqeFwBW1u2uR2EkKgo0DrVB3fOIsxhqjarulrBTJeIa9i53hI0cQQAzKleRBC4EJqMIw+siyXVFVJkffoD4YdID5B2Z0XzJYVmZIEUkPaokntlKFjbGlt6A9yhuM+yOSMKLYK+tJgSdvvRMDGgEYQo8fjE5xagoupqUwA2hicDwnZ5S3OtiDUWtRKrXBJvOwXfYSU1G2N79hcAtkt/ATGdK93J2JoIwkSKh+49eABh3HG7rCias4QASSSQb9HXU4hJpaTloLpcsY//oVfwvqW525c44XnbqAzhXOaRVnx9tvvoDLD7vaI7fEgMdhi5PR0gnOWh48e09/a5eDZlwkaHj56yPLwPrlQZEp1PDCF1ZKRaLl44yXe+cl/TCl7zJsJ2XBAPu4hrCGbzphVJTbU2LZFRoNEgIi0IQIWYQUxCKLQvHPrHm/dvsWyqYlBEYPCe4fzqTigrpt0fASHUIqoNEEl2Pvhk0OMNrg20lo4Ppsx7g+5euVZvv7mm7z8/DV++Id+KypKXnvle/mrf/W/xdqG7Z19Pvldn+Di7j5ZXvBH/td/mDfe+DqPnjzinVvvszAl/ZsjXn72JV76+Av8xx//U/zcu5/nuSsv8lM//48ITYbF4r0lOAsxoEiicIgCVHL4SBT9nkALQzt3zP9mw/zREVJkSJmaXLd3R/THPYTRoAzHZ1M80IaWgl4SCjptQKmVXhFRmSTv51RNzd0H90FGogafSWzrmJ2eAZajs2M+/4XXaXRq2rx6eR8fPScnC+aLmsa3DHf6XLtxlXt3HpH3DR/72EvItuTevXvMmwUfee1lXnjxKtPjKURFCIK6qQihRSpDf3iJnd3rPHh4ytfeukXQCjU0+GnJu+/f5mwx52RWIlUBqgdCQdc4KW1Dng8QJsNk2fp9QgWf4O65Ihv0cDZAG9jd38f0+vz0L32Fdx6c8ZEy4KzjmeuXiNHy9huvUzV9RkA0hqdBX12oDqVyit4QrXocH83Y30/FAIg+QcBwEPmRT7/MzW/d4tFRy9IrbOfOklEgOxC9FKITsULnyoupOVIkZpeQSeBKFHqRdCyfxCEfLUqNu9ZZiEIDASk60UtEAhFFigg71+J8BBcwIRJdS+McZfkEhadqWuoQEXmeBMxOtBcuAIoA+CiIwjPOC3ZGI2bHxwitmD455lG5pHk8xWuJi6ET5iIiOKITeNkgkkqNDTaRtkLHKgsRKVIpiMwyjByivMW6FhElShlsXSOk+yB/KmxmM5vZzGY2888cffUK8MaHvRmb2cxmfgPPBxK5dJahOyi4jxFHap7LTEYhTVoYOMeiaanLktZ6jMoQBuazUxofKHoF2ihcSO2HIQqquqEsE7zck8DOLnhG4xHT6RmN9TRtzWjUY2d3wHA84OhwSlU5Fos5Zb2g30/QdiENTW1ZLJY4G5AKnG8pigIlDXVVQkyxN2stp6enNJ3DSQhBVaUYZb8/4uLFA+aLOc45trZGDIdDdGaomxrbOuqqQXZthUk8SqDtsqoZj8Ysl7O1oyiEsOZY9QcD8izDudQ0aa3l5ORkHccry5LJZEK/36euy+SkAmL0NE2NtZamadaCTC/LE+BdK4xKXCFCpGkqfGuhTTDnVTOk1vrbojvOexqb+FcxRjyR8XhMCGEN+l+zyUhwe7ooJ51TSiqF7ng4zjt87dcAeKM1bWPx3tPrF8kJgu8cZKJ7fMfcLtBGYpQG57BtvRaUjMmp25oQIMt62DK1YK44OcEDio6ztWoSEx1bauXkSjylLMuwPiKVARHxwbG/t4d3jrasMEryvd/zXegi5yuvv5Ga+UyGa1sypbDWQQhEIXCupSwXoATvvPUmIb6H9J79oeC5ZzRZzxNFDx9btDGk5bpDCoU0fSIG5yIxRBbzBYvZnEHRw4fAwyeHfOlLX8cUfb72+leTUw/Y3t5OjYXOYa1la/cCGo/SAR0sk5MTYrvkytUrLOZTnK2p5hNefuklDm/f4WxeUluNs5bdYQ/nHG3boLTk2WevcO3aMxwfn3Dz3dvIKAjR4Z1bt+aVTU3VOrYv7XHjo8/z/r17HD8+Q9YgGocWhhgDfrnEWosSEmygGKZSBi8itmlpywbvPN4HJtMJy90drl57lq9982vYpuWv//W/ykc/+ho//u/928xnU+4/POUrX3qHtna8+dab7OxscfnyJV568ffiY+Stu+/zEz/91/nlb3yOv/zTf4WPv/oamcz5vT/y73BZX2S81ePWtx5ipSd1KUaEUEmI8qkJECJaS4IPnC4DwUUujg/47k9+N5/7R79ItYTReMxoPOKZK1e5dHGfslzQOM+jx49TnFWdR+wEyamldQ6o1ARZ1mT9IXl/iDbperi1N8b2R5Rt4PhkhpI9rj//MnuXnuGd+/dZLGrmyymf+MR38eKL29y6/YCir3n5letIlXH3zkOG4yG///f/Pn75H/x9loslzOd88ns/wXAwwNmWGF133gnyrIc0hmevXubShX2mj5bYAMS0jc5Gbt65h0eg+0Ok6hGi6nSnSAyO4C3eWmSMKHHuaApAaFqUD4g2UJYlRudkwwFmPCIb9cmHPYrBILVySoEQmu/+1A/x1luHVEHSWr9u+4PziLkxBoAvf/lLACzmJUIYlFRILdjaNvyv/tDv5u//9/+Q19t7PJrXnDUNTkCUidmVmPYS1RVyaJHiuUGsIqWsr9vnscXYlRKkGxYhhuSszTOK3hDvA76tUrGHSDHYFeg9isQS88EjM00TKqLQ2BAotKKVUMXAd3/8NS7u7lPOZjw5OuR0Pqexfu2Ic85xfHKS3oO9ZzwcsTXso6XiYXyfw/kEYgC/et2SWzR4EEIB7lew05KrevX+8n3f9z185NWPUFcVb7/9Lcqyoa4ajo6OWC7mLOanH+TPhc1sZjOb2cxmftWIPOenv/DTH/Zm/Cs9vtDIDqWwmc1s5n9+PpDI5WNABInOEsjaiCSq5Aq08OkmvxIJMt0GbGvJBwWjUY/FfIaPkqauEbKg1+vhvWdZL3ErMUwoQuupXZtYMb2CwvRZzEtMpjnYu8i161uUVcNy2SLwIBL8uKoalDTkhcIoiNZTL+sUe9QSHTOESQsxqQSDwaBrvZIURcF8Puf+/fud8CNpGsutW7cZjcfs7u4CsFgs+f7v/xQhBt5//zZHxycs5nOsbTFK8fz16+zs7XP//kPOJjO2drZpmoa2bTF5xnA8WgtH7bLFtpbFfE75lFMruc9StLOua9KiO3GArHU0Tds9RoK/a6XQ2lBby3K5JBDPuSshRQy9a/G/ApxNjJ3oQton3XVyBUFftQw652iaBikVvV6fg4MDdvb2UcawWtQ6n5xdo8LQ6xVMFzNu3bqJtxaBwDmPd4mjI7qLspQS1ybnmPeOPDcE52lrC9Lzv/kjf5Tv/eT3cjab8KU3Xucbb72JqnKUUDTB4XXNeLyTtldqrLNoQQe9FwkO3cUSpRBEL9aLVCkEjXeYXoGMkapc8tz1a4S65eTJE/Z3d9nbGnA8n+GcRaJo6haiT1FLLWidA5FaEwGii1y5cJHx9j7aKNryjNKdEC34ALWz7O3tc/TkJMUsI/SKEVnR5/mXXmEyOeLSpQvsHVyi6A+YzebUtUebgr2dXS5duIh1gdPJBCFSe51WyT53+733OT45oWotx09OcHXLtauX+NjHX6JczAhNC7bmt//bf4B33/wGQebUTQMhkCmDtY7ZZMZ8MmU5nTCfnTGbL6iXLcE5Ah6d52iTYZuG6XxO8J5FueR7PvYxdi7u8Uu/+E85Ojoi2rQvt8cjZmdTbNsSZHLKlWWZnEAmMetihOgjwXlKZ5kvF1y5fIlnLlziW994i9F4wOc++wV+4flr3LhxhT/27/8J7r33iLe/9hZvfOMLzBbH9PoF12+8yG//HT/CjVdf4sVXXmb7mR3cQLBwnm+9+SWO2zN+9/f/bn70k7+VP/tPfoI6RMxA02IpvU28MCFQQqRomk8tilEbCmWoHy8Yjfr0Bj1mh2DbBKtXWpH3BhS9PlXVcHR6Rt7vo3TEOYuz6fheMfWyPGNvbw9xsM/152+wfXDA6emMLOtxcPkKcnef5/b2uPfgGO/BB8XpoqRuW2bLBTFGPvLRj7JcOj772S8RaXnn7a9y4eJllFY8eHCfUa6J3jHo5ZxOJlx75hmkgNY6tFEU/YLBYESW5SzrtC+fHB9S1ipdfzvGV0QwHGwRlaCNkkAyVkkBgZAEqOiTuhx8l/Lron4diL5X9MgLz3LZputIVeGDo7e7TXn3fSbLOUVe8PD2XWZHp/zMf/c3uPaR34HY3cfZ5DhdTbpJ4Ag+XT/POn5j9wuTQ5ZItB7nLLauENGiVXJYySBQEYxO7qrV34UrXlpqbF1FFc+jyQLW3K7V96ZjN7C7u8PZbM5kMsVojRaxi8uvrowChMSYDOEcpfM8fvSECzcuEaIgywuks2hRI3xgNBqztb+HCx6OZSKNaZ0EedK1rXWWo9MTdsdb2Lbl7s2bKKA5mUEmUTLFL4MPKUEaBSJKJEnMVamxAGcdzlnKsiLLM77ne76HrZ0hRWaYnJ5R1xbvEqvrytVLOHfA0eH7H+TPhc1sZjOb2cxmftWIFRJkM7/m+fm/+Vf47X/i/0Dvf/rCh70pm9nMd+x8IJHLeU+WZ4lB1bE6pJRoKYjesaiWOJ8cRT6mincQtG2L1JJCmsSBEQKBpCgylDKUZY2zHmUUuCTgKCWZTOdJMLCe1nmOT0959LmbaF0wmzYQM/JCA4q2sSid3FIE222HJgRSlK7jpDgXknskBJQSKJXiiXVdo5RKd+S9QylNlhn6/R51XaVGNee4efNdrHNMpnOUzrh69SqjQR/b1rz00ktIlfPue+9TVhUhNGtgulKKtm2ZzWaUZXJnOecQ8RyOHkJY/54QQgdqzxECmqamaVI8L88TTyt0Tqm2bVgsF0QgxNQ6GLvHkoCIHXeoE69WcUbXCWZ0DXmQFlIuptdxDcxe19GD6KD11jpCTKybDk2cYmwRvAuE1X/7Lh7ZOdmcc1hnk3sphuQgApqmazMU6RiITUszn1PO58zmM8qqpp/1UUikjLRW0NQ2CWjep8WclPiuITPP8nUMMbDaFp8KAaTEtZHoE4sn14bTx4dkArbyArdY8uZXX2fuGkRw9Hs9QFKWU2pbIaWmtjY1HYrEtEFIhoMhO1tbqMzwpFrw6Kik6GlCNia2MbnN1iB9z+7uNnt7O3z/pz/FG2+8wXDYZ+/iMwz7A1DHXImCF158kXfeeReT5SkC1St4+PBRYsM1SSgVSMbDHoWVPPfMxxn1trn/8C637tzm6qV9Pvryx3nl+g4vf9+n+Cf/2X/BZBZpbKQocoaDAY8nU85Oz1jOFtRlSYgWHzzRJ4ZajJ5iEBC6xQYPSGQQvP/OTf7sn/t/0UaPXUS0NSgB1rUsqiV1XeJbCFiEgmAdbYhIo+kPByipUlRQSFrnODw54eKFA155+SW+8MXP8gf/4B+iqWG8tcWXX/8cP/77/gD/2//9H+Cnf/LnODx8jFaK1pfcv3+Ln/rZv8ULR68yfu4iX3rrK1x+8QrVScneYB8dAn/3M3+T/8u/+X/le196idu3H/P+g3uc2AXWBFrhEmfNJdaS6q4VMQq0gJ1swKVnP8LlS5eYPTqEEKnKBbPFjKgUvSLn4aNDQoAi72EyWJYl3qdrTABcdAQCSkuEhKpeIuYZbXBIq5mWC+x8xr/+Qz+I+6m/mwouZlNu3k1A8dFom52dMdeuX+fe3SfETvS5d/cu5bLi6vMvUJZL7t+/i/ee0XBIkZ9x6eJFjNa4EJguSk4nM5A5W7v7zB48oLaR09mMWdknRHAC+kWOiB4RLNrkKJURyVLmUnXKj4ho3cPoHmWrkGsalEAh8ALK0AkpzjIucgwwe/CY0/fuUtSe3EW0jzx5+IhiOOK7f9tvY7LIcD50Ls/z954YYoqCr4U0QV3X5FmB8zWIMUJkDHt7GDUiNA2hWSahTAWCUngViSKglUjRQa2xrjm/xpHOz7AqvFi1LHYcwUAS/leusulsRls1xCAIQoJJzlGkxGTJXZZlBYPhiHZes7h3gmsrtFC4CCJAJjUDmZOjef7aDZ5/6UW+6TyP7t5DIai9x3cdkKsbIW0n8hVSc3ZyQmwssnWocZ/hYMDezg5PHj1m0Tg++tHX+Oab71DXDXkR1gD5V199lfF4iJSa3AywbaR1Fbdu32I2mSc+ZvREWsqmWb/3bGYzm9nMZjbzax11cMDf++rPf9ibsZnNbOY3wXwgkUsIhckSfyRGCCiiVlgZugWIAiLOBxACIzPqZUN/VHDh0iXmbYutG6rlkpyMTGmi75wDMtAGj8kNrW3xPtWyh7JJ7qoA9dwzrwM+LJNrK5P41uGDx/mI8xElI72swBiLwBOC7BxSGVWVRCdjDKID7FobqJcznHMYmWEKwXyx6EQiz2kXIxRKYPKMo5NDtNEgk0NqufS0TYUS8OjRI27dvcfZ6SFV3SCFpG0asizDtTbB7X1iZokIiq61q4MBx+ARRnaLbYjOr59fDDG1bAG+9bRVuxbH0vNJAHgRJb52XVRKpLhKOHdYiKccCat4IqQ7/DZGmmBREWQhsT4SpQIUMXQxJACZ2FQQkzPAJ8C4J6bIj0jtisElaLtzHrxHxYjwEeFSw1ywidvlk0+Etm0wJsdFz+PZCddsw7yxVC0oMyIoQ+javryAqDMEgr7yFIMi7c8o0EpR1dW6vXEFiG5te+7cQCDzjGDb5OAJDo1AEtASdJBUNiSGj1IsqpoQJFLmKXpkLXLQp2ciJpcsl466KVEqOb6sbVnW0PjIzn6G69xKlW2J1pEIVy3z6RlP7j9MLaKqT6+fMRolJ5f1gSAijQsEkQTJtqrW+8yFQNO2ZDKnnC7RmWL/4AJ7B88wtRXTs8ecTea8d/t9vuf57+L0eMHXnyyoXEZTLblwsEeeeaJbUpYzZssFbd1CiASbWuyiSDEunKWvFYoEHm9di9YF1aldA8FtaGlDJDjH4yeHlFWFiiLx9WxAC0WkxTpBMIqgFdXqGPCRs7MzTs+O+eRHX+Wrv1xxdutb/Jbv/ShndcPZ4wXvvv02wUX+7t/7H+j1ci5cPKBuHNPZnMcPTlnwVX7ktd+LyQsWJy3Hj45YVCUv7T3HD3/f72WhLW8/eJdxHPH8wXPokyc8nj+h9jVVs8BZS/QeIxU6BNROjm8Vl196kVdvvMrh2w8QMiB1YDTuE33k1nu3GI977O0esLW1w3R6yHPPvcgbX36DUc8kZ6sXGJHRLJfM53OapubR/fvs7u5w4cJljqcNX/v8L7OzNST8nt+NzgfEGBlvjbl4+TLzd9/He8f2/j6Pjk559+Y7VNUEhKCsA1tCExzU1vLsCy/xwAVO7t5DdbFCHyIigK1bGhuxuo/PtsFM0wntWoTqEbwFB7Q6lV+ESOYiw7wgBIGtW+q2omwX4Bv6Okf3hsyf3GV1VqU3ConwLaotiSaCcLTNlKO33yGTOXZ+wvX9feb3n4AQ+IEk6xd85Lu+m7//mfeg8gy0TuyoboL3eGdBOpyH4CIEQTSCqEB6ixERQUPEomNE0aBk7JoXE6wfEfBEooipXVWkiKoQAlvX3fUuubhcDMmNFyImM2il8d211HmPtal0ZDQYJp6ja2jbFH1va4UQ6YZEZhRGZ0il+eizLzHoSUJuODtZdDc0PMOiz3A4oCgyCpWhRU6Qiqg8yifGl5Zde6eUCKO58vxVXnztRWSM3P3mHR5Njrn23BV++w9+kq9+/it87a3bXLj8LE3ZUNoaUyi88yip+amf+inOzk6wbUOeF0hhyHqas9MpD+8cU5ctXlkcDcIHzjsrN7OZzWxmM5vZzGY2s5nv7PlAIldZVWgpyI1Bmh4hRJqmBRXREZTWrEretUqxt9F4yAuvvMDpZIpfTqnqEqkl1luqaUPbepwLuA7qa3KVYL4hOaAAhsMh5XxBWZZkRUFV1QghUdogpECJSC5lckdZiwW8T+6sfn+4ZkopqWlbSxVqpEpREK01qARlV0anJrRaEHyKHMWoyPKc4XBI0e+htOL07JTJZIFAk+kuDhc8jx49Zr5YgpQUeUFVprbHpmnWvC9r7doJsHIFiKcsC6s79sYYtNb4bjHlvV+LNTEmZ5RSqco+iIALSQwTQqBkgsGLDpgsdEeFh85hl3gxocM5EyG2DVFJRLffQnDE6Emr4NQIGJ1PsH1Eiq52PK3V9vsQyDINMdBWNbYukVKAlIgYUwti11gWOmZW8B5jkuMq0wbnPTE4bGvXPLDOx4B1FkEkBEfT1vztv/2TPHhwn0cP75L3hgn47DxVucDa9ttaHq21xBjXccaezrr/znFViQtQR4dJnaAQoC49rfXkmehcWAZtIC8ytnbGbO3sEO2CrFcwXz7k3oNblM0J27tj5uWMsprSl1vJudX6dK4ARa+gaVrqsqIpSmaz1HQ3OT3jnbZle7xF0yY4er2sAGjbVUw1OQDVU3bv3qAgL3LqumRydkLR7+NszeGTRzT9nBevX+GVT3w/92/dwnnS764r5rMlXj1JTDqX4sVt20LHCHPOpXyahqZq2N89oKoqQJAZQYi+O5ZF5xBJESvvLdV8jhYC7x02WGLwWE/iuEnJaNCn1+9Rt0kkKxcVk9MzHh+eUD6XeGKz0wmfuvQM73zmn+K94B/+o3/EX/9v/woHu1eQRhNiZGtri/5gQDy0LBcl777+TapmxmxestUbcdIeYqxBzTNsbpHoxGRbWJqzGbMHjzk+fcJiMUFIODjY4+RsQmga5FbBs89c59Joh299/Wsspqd413Djxsv8nn/rR7l69TrexeQmjYbZpORn//7f4Q/8gX+PL3/hdbQ26yivELAsa/Kiz/7+BcZbY4o8JyK5dGmLT336ezjYHdM0NRcu7JNlmrPJCW+9NcdZT3/QwznLyckRp6dHNG2dHFquxbomRSfznJOTVLgxGA7pzTNu3brFD1iLDwJtcvpFgfAKo0SK7hEJbY0MBucVWmqUiqkttpqxlfcovGXYH3Hh2W1uXHueG88esD3q0TQtk0VLtVwyLiRJIZN4HD4XLEpL7gzK9MljYGgyLu0dcLV+icezkru37rN74zrawr333iYLEuMdQSqMlt92XYxd60W6bkoQgYjH2cQJFFGkVk+RSi/AQUwlE0S6dtBzsSalpuN502vwxPXvSw7kGJIbWUmFELK77rp0TYuBnZ0tZsuapk3xViJIqREidq2/iTlpnaefZ8jW0R6esFvs0N/aYnBxiDCGs6MZ/fmSQdHDNW2Kea/KLoQElcR35126bodAnmVMJxMW04hrk/NSS4HwnjzAls6pJzO+/KUv49qWar5ka7iVnkOM/MiP/C7+4B/8cWTH+mrqhqqpOXx8zFe/8k2+8fU3sdEitUfJ5Pz9C3/pJz7Inwub2cxmNrOZzWxmM5vZzIcyH0jkMkajtCLLMmSeMxxs0VpLvZx2d4gFQUqkTNEwqVLb05OTYx4+6FwQCAaDPkoZZrMFQgryvEBZT2vTIltrzXK+WLcRzudztEiMpUExwLWpnW8ynSOVIC8yZOcuUsrgfaCpG7KsQEpFVTU4y1o0UkailSL4QNVUSRDqBBspJQRQWiCV4tq1a+zs7nH45IgYoW0c3kWkUEQfqcqa4FdizBzrPDrLUkOgtYjIOhbonUtthwJi8ASxikWtGDBiHRNcxRtXoOWVwAHnHBkh0kJsJYARQKoUjRRdS2QkIuLq9yTHHQAyRW9ihCiSAOg6h15cLwDFulFRRJILywe8tbiuWTLElVBHYg95n1ocrSM4j9CKGJMHLHSRIKkUonut036LCBHRQqIyRbBJEFvFNtO69BwEHYLA+8jrr79BUfRoqpqqaoGQGsO6JsnVgjZ43/F9UqvaSiByLkUuW+uxEZyN1NEhRUTpnKhzeplkMBwRoqZqa5bLit4gx9mGqj4i15L9i9ucTgWFGXLpmTFZH8rGorLURLlyfdA9DWkUofGMRgNeeOEaWa5hFoHERbLWsVgsmEwmPHzwkLIsESIJdCsXV4wJZO2cw2QCpcDahrpeoqRn2M/wjUX2Dc9c3GPvxgv83M//FZra0baOpqm5d+8RV1SGLjJi9NimIVgHIcVeYwzgu5jUUJEVOSpTSahdvX5dY6d1La5tsFWDbRrK6RnlcoFzluADoVugK5FaWRfzU3r9giAEvd4QicK2FeVywWw248LlK3zz9deZ/X//B249OWZa1nz1Gzd5/PAWf/KP/y7eu3mbyXRB23q8t+RSk+dbnN0+5N//o3+E47Lk4fFjTt55zE//pZ/kmy9/gT/y7/7veHTzHrdP36N1jtPZGc1sTu4EQvZxzlEeV0hnyMhRlWJ+f8obn/0S9XxOXS24+uwlXn75BS4/8wyXLu7TMzmDYc7krOaVl57nC1/Y4WB3G2kUUiucrdMxjqCqSrzPCSFQNy2j0YjxaMzuzpiPfexVnE2FE9qkco/xeMRHXnmO3/HDv4vBaMTu/h5CKnpZQWFyyqpie28ftKRpGnq9HoeHh3zX88/z5sNHGG1o25a2rbAhxcEzI3DRkZtIr6cY9BQv3bjExBZUTrNYTMizSCs8Igv83//0j/PytUupeVBKvLMcPT7m3p2HjMcZW3uaZgBf+fxnqaop/b5iVFzkd/7ob+N0PuPo9Jjbd29Tz2e89+AhRw8fcyo0pYvsjPdwURJiZHZ2yNHNe7TbLyOCRSr57XFFHwkuAAIfItYlwdha29UgRvCO4Fu8q4EWhEOQeIaiu/0CpFj1U7yv1YkZYgLMr24ihNCxrYgoJYny/DokhKBuG7yL+BjQSqJFTpQeqXX6WGlMnjEaj+jnI3Se2l2JYIxi0TjKuuX48SHHkyl3br7HeLbNfD4BEQgypi0OEHyk1+shhaRaLKlnC+7O30N4h/CR1gOF4fH9R7z+i5/h8L07qLZldnaMDw5vA08/43v37vETP/EXEyvTO+azOVeefZZM5bz/rdu8/94tThcTtPG88MIz2Lr9F/9DYTOb2cxmNrOZXzHqpef5j3/2f/ywN+M3zPwX/8//kj+59R+y/dc++2FvymY28x05H0jkSgKFJIrEZto/uEDd1Jx5i21CErFi5+bxmsFgxGQxw56c4LxnqzfCesd8XiI6iK5SCq0kIga8jbQdk2olcAwGA9q2peilReh0OqWsmiQiIfDWY72jV6S4WttaXFMDMBgMqaqapm4pCkVdd42ECrRIjh4XXAc/TzHCLMtAgNYKIRUPHz5kNp9zcHCRECNnk0lynJmM1tsUo/EpahhCSHfum5bWe8RTjX5SJmGNrv1wLUytXtvOZfT0xyuG1crB9bS4teJ8hZBcXJEk7tCZtpRR2NZ2Xq0kgIWQPpKrx5GJD5RijwmXnNoJk/uEGBNTKwa01GxvbfHi8y/w3Esvkg8GTOdzbt+5R1mWOJeeS9u2KQrYNfEFn9agqwWrkAKlJI0NyQ3WPddBr0e/32fRNLTerpsfn15wrkQrrXKUypHS8K//6L/B3bsPOZvNcY1FKfCkx6Y7XoWUKJ14Y0ZrpFLE1uFdcpbZEFk2LYVKsUxvHcFGPAKkws2mNI1HSUluDEYJch1p2yXRSR49ukPdLnAtHB63qKLlZLJgWTmG/T6y21+xUwulVkil8M4lzJFMvLQVN8w7m0QhzkH6qxhU2bkD27ZNDg7v8a5BqSFXr15lvDXm0oVdnn/uOscPH4Jdcv3aZaQIvPfu+ziXYqt5nlGWFQ8ePqY3SiUQg0GPUETwEdWBuAMRH5Pbbnp61MVPW5y1nJycUi0rnG1TpNZ5onPErvmxbeuu5VImt2CMND4dd9bWONsglcaWFi1NYs+VJeWiZG/7gLNFTXX3HsugyIY7LBtBWQUGgz7D0Qihc4xWlIs5Iji2dra58uIVjh/eReR9Xtq/zuLlJd/6/Bss7pzy+M2bNEdTHj44pvHJ/SKQXNi9xIWDfRb1EpMXZMbQM32stbzxxpe5dfN9JJbxsMenPvUp9va3eeftt7j97jsMC0N/WOC85tHDu4TgePOtr/Pss5fZP9jlh37bD9Dv9ymKHkXRI8/zLj6ds1wuOTw84oXnn+e1114luJr94Q6/+Iv/FOdaptMJR0dPuHPndoocmlRM4a3j4sULDAZDfuCHfpDWO/72T/4dfuzHfozf+snv5ez+fb7++S+gjeaVV17h/Zvv8vjJI5SCPJfMl3PqUmE07O4M+OEf/CSH84ivLJPFEc55bBtYzh7xzN6Q/Z1RJxAJHt054d0vfZn33nuPfFdDLpBB46wCrVFUaPcuLgSQMC4Er37kZWKUTB8+ZDab8vzHv4v+cIdoI+/evEk2GvDcRz/OnXff42h2zI6tkSLrrndJmgkhlVeEEIgu4L3txHWJQCGjREYIoYXgkZ3g3Wnv6bxbiWYhRe9dDB0TMaSvS4Xo6GK6cyp6Z5NJKwZiEN37BIlL6ALjrRFC5sToCa6lqWuyPKPo5WTakJucXq9HlJL+hQO0Dfhej1lwzJqS08mCebtk6SuenD1h7ueUyzk+NETXplbYoFInq03vI857liFQBpci4BGIiXkZgydUDcp6ciVoQ4sLqfFRa4H1sbth0vDgzftIEWlbx7vvvMfue7fYGg04fnTCg/uPOZ1N6Y0kz93YS6/rZjazmc1sZjO/xgl3H/Cf/Kk/xi/85b/0YW/Kb4j5ZJ7heh/2VmxmM9+584FELhnBtS25MkilMXmfpk08Ja0VeaEZDAb0egXlsmQ6nWJtDZVnZ3sbrQ3RNsS6Js/Nul1P64AuNIWShLpEaMhdQVtZ6roBL6hLR9EzjLe2aOwxVd2kYncBUsQEyg0enCN4QVHktG1Lr5ejuha64ENayMgERIeYomRBIpVIwo6HXtEjCoNzjl6vx3CwRa/ok+U5qwRfsEtsTLww5yytTaKEELIDoce1O0x1scu4ihuS2rzWd9a7xkHRfU/oYMeioy+vBJ9fGQ9ciT8pJNbFxwId48ivhR7RxROhWxB1opOUYr3NUkiUiPiQQPG56THsbXMaZwRbY/oZO7t7bO/uU7WBeTVjOp0Rfbd9yqfXOAiEkgijiK1MbW0ItJJkOsMIQytARI+WGhcDOjh+26d+mF6R89Z773P//gO0zlBKE4UiREmMaUErlUwL3eDIezmT5RyVFfR7kdOqxAlPFJIYPNIDUSf3mkiRsYhAIpF5kRxfwVOFSFlVmPEI68EGQDqEjFw+uMwnXv0Ey2XL5974PDI3EGHZVPiwRChBtVDUbYsIFXbaYmqoKvAhQelDTM2kSJBarhs858sls2mF1gN8sGQmI8sNOjMpAhhjEpTaNsVEpcAojQjl2rWCSBGqLNP0+zlKQlNVyCiIbc2zF8Z85OUXaMslR4dHtK7PfDml9omN5eclPFnFYDtHHiK16IWOvRcCzVIwl+l4q8sS17Ys5ovUNCiTS5KQFuKhs/+JmNw4SnTCgTaEDOg+JiqEA7wFFRK/ytU0bUkxuEJ/a5+yaRC5ptA96sUpVT1jZ3sb4e8w6Lhc3u4zm03JexnPXrrKO+++TQkslo7FsuRgd4vXnnuZk4cn2LZmUc07IVWglUYpx2CYoXPI8pyt4TahDZRWEIOjWtaIaMl1QGtB09RMpzUyCjKt0VoilWJZTiF47t19yPZ4zOnxMZcvXkRpiZJJ2NSKJOivnG9tzePHD/jsP/2nCBHY39nnZH7KpcsHbG2NGA7HKCVomgpbueT2dOlaI2VkMCzw1hJcw733bzHMBH00RTGkrOfcuHaVw8kMWzu0yslMTrA15aQiSEk2eJbRzghHhc803mccHx2xt7uHFBlKPU1jSqJ3a1tiTEJwDJqIhdgSRIbwniZUScxGohW88NKLBFXwRhO5dVxx+K17qOwRw6KHRqCs4u/+/D/hs1+7yUc/cYWwmGLGYyTD8/cerTGDHvmgj9ctSR4PiOAQogFjEVkfIQxCRAItHkeICqJHRoEIEYVK+yImh+gKNg8CpdN7gCAJsQqB6NoKEYLoI8gE1Q9CpThxbZGqA+KHQPCRtrVEoBGWWrW4ECiKSMgVdtzn1Ei2sozG1CxDYE5gjqAKoJsGZ2s8q+tYut6LrkV3mBXUixIXIyBBRaQSGCEJmcAHwAskqQFZa8OgXyDkKo4Zid6jpcHagHWWtrYsli1ZXpOZnNZHWh/xMeKcT69J2DC5NrOZzWxmM7/2UZcv8sKfeevD3ozfMPPRv/gnef5n7+E+7A3ZzGa+Q+cDiVxp4ZpYVdJ5vA/rxiehDUVPM94aMZ3NWNYltW0YjAYUeYHSCud8ir0oiQ8hiSECWtskAL1InB0pRbfAFjjnkRHqNjAaJQ7Yiku0WiyLCFZaIKCkQGWaKAJZkSMVZFIyHA2T+8U6rEswdCEkvV5G8IG2bvGkz+3u7CJNgXPJMdE0TRcZA2tb6qqiqSta1ybHirP42EUdRYIaiy4yB3SxvxULK8UARcepWlO7O6fPSgjzvns8pTrm0UqASPGYGOng7wka74LruDHp9yYhKCbX3ZrvklxgYsVzjucRlhiTy2rlrFJKY3SGEhoRBVIkgH+IMJnOcC7x2AQJ9O5CusxGRAJPy6SkiU4wEasVZQhIkZwRWZZhnSPYhmtXLlOVC7QUKJVaMf0qqpjUqbTgBEL0bG3vcOPGc7zxxpuEKNjbO+Da9Wvcf/KQ4+NDog/0iz5ZlvhXLrSp2U6IrjXME3x6zYRMkOnWtxS9DKkELliQATS0zlE1FcokXplSkiY2SSCV4GIqWxAxIL0EJ3Auds659NqunXaAdx4pFFmWY23o2jZbkIKqqgne0zaWtrFUVU3btsTu+IoxErzD2fQ5pQRlWXF8fJxOaK0oy5LpZEpdzrl44SqXr15CaUPPGA4nZ5SzM0rbYl1i14WweouU56+3SIJrt1OTyywmt2NwLgkrdY3wPh273bWA4FEr0UDJNa9LkthGa6eh1Gil0EKSK02RZ2RGMxj0UDiU8Fy4sMf7t2/hfMukcUxPn1A3c0ZFwb1bdzianHLh8gFaK7a3x1y5/jKHjw+ZT5b0Lx1w9/4dyuWSPDMQJe+9+z4nJ8fUTdXFah3eOar5hNnZcfIqCUGR9wjW0wbHYn6G1gopPG1jODo8plxWtK1N8WAp1oJx21r2L1yibh37B5c4Oj7Fx4h16Tw/j8gJhEyNsNZaptMJ9+/fJwbHk8dHjLZGfPSjH6HXLxiPRxyfHFNXVdcIqzrunqSsDOVyQSQJX8v5nKPDJzyzfQFrPb2ix+7eHifzqiuM0OT9IUWvx3w2JUhNfzCkaRuk9EityJSgLReInTE+1MRo8e0pQkWkzBBYlEyOV6MVXiQxCOFwootx6+6iHJPw3ev1mCwDx6c1p/OArWbITHD1kmGvP6BuIj/3S1/k6gsvc/WFl8mLEcEr3n3/IcvJCdE1TE8XfPNbt3jv1h364xRxBMHh48MURVQh3eQABBEfWkK0xGiSGwqPa1tOjs446Qu2jcG13TVrddMgnjMP0w2FzsnaSWreB6azJdYLYgPRC1ofiCK53GSMBCeILiAbixICJR21g/FIUYfU6LqoAq50lEFQIll4g1U98v6Y7aFhWrUQlngnccEQhUQqjdQ5RW8AyJTQZCW4CaTWCJXR+EDtHLZzFhNTE66P6diJOiKNochyrqpnkULgrOfCwUUOLuwiiHzza2/x4MEhWV5Q5ILHj45wjf1AfypsZjOb2cxmNvP0xH7BX772yx/2ZvyGmY//a9/i6LPPkd2592FvymY28x05H4zJlWVkmekg0ylep7VGG4OWOVkmE8S2aTDGcOHgAtZaiiJnuSwpF0u2d7fIMoMLpJZCIrZtsbYhRouzHq3l+g6yFCkC6EPAR8dkOqFp245dBUhBCB7nBEqCLjLapiYgGG8Nsa6hri1Chi6CAtGGtSNKiNBF4jzOebQWlGXJ9t6AT37ykzx+/Jjj4+MkJEjBYrGkLJe01uFCgnWvOForh9XTcHjRNSgCK5Xp/PtY8bW6KGIXg1mJYZHO3ZUetIPti46/FQmETuQT6+jj08ymGFYiVhJYBJ341MUS4+r/OxGMzin2tLixihkmBtSKA5W2LHHNJEqmfyKmhXwSNzr3Wvcs0msSUuxJpH8uRNo2kIfIg3v3iK7GucRki7F7HTpGzkoPjN3itij6XL/2PF98/StEoD/oc+36DZbWMptOaZ1Fa01uUrOhi5EQHVolHo406XkKJMsyMcGE8GzvDNA6Yz6fM1vOWdRL7jy+z2JeJgC0FCkyJgxKpzbMBLtOwoMxGiHPhcz1a9eJQFJIgo9EH+nlfZTUWJuOddt66rrpIlSsRd4Y0s8RIt45vHUpAqlUKm1wnrpuWS6TEKuUosgK9vZ26Q/69MZbuMqzt9NnWlZEelStoWxanEtwbu8TkDp0xx0AaoXgXsVG02OTK4I32MIkIZLk1kuHd9pWmao+ETJFmzu5E9UJntrodNwIgZaSTCmMVBgjicsFoVzy3OWLHN6/TT4aUDpFXRQgRlRlzZPHR7z57tuMH4zIC8MnPvFxnrtxna99/S0e3zviu25c5/L+BRbZFBZLFJqHD58k2H5XQhCsw7c1i8pTLmbp3Ouer4jQetc5HDOQoJViuawgSrzv9jFJdO3OYsbbuylKbDJsJ3JLKZKDVJ0fv0oZQkjNslrL1MBqE+x/e3s7AcwlZJlaR1QTgy8gOrGzrmvOzs5QKnGnnHPM53POYipwUEpx8+ZNjk7nWN8Qlac3KDi4cIEiy1k2Dfu7e8ltGz2ZzpChAlvSLqaEZkmMFe3yCOGmoPq4qkWSYtBCJvFSCoEPXdCvE6OTZTQJpU3dcnZWMp0scQ68SkcMUiGNpraB+48nPP/xbfrb2xjZw0lP1SyZLix2WTI7q3j4ZMY3vvk+F68M2dnrI6Xk9HSaOH3eIpWF0CbgfIwkDF+K0CsU9bLi9a98nTvvarZMkUpCWAnRTx333bUvhBR7TiJXinEvFhWzmSVKgQiBoCyB9F4iA0QfCN1NCYlACVC6JctqyrJCiAap4NFJSYiRsnS0NqeNkvH2PpcPhvh5RVsfMl84yijxIqJ1YkkiNEoZyrZNri0h0kspI8a2tEi2rz6DkZL3v3UT5SJN66mrlsPHp4TYRd21YlBkSCK9QpMdbJNpwdnplPlkhm2S07lpHXfuPoLVDZvNbGYzm9nMZjbzoc9///w/5JM3/gT7H/aGbGYz36HzwZxcpCYqrQ2wgpkn+G+QFqllEqCco+j1GAwGnJycpIVzjDjvaNpUsy5Veox1ZFBE6roletHF69LXVs1kLnikUrRVkxxKIYKMKaYXkkiV+F4Kk3Vig4jUddW5YSxKaaTQOJeYRgBCdMwVH9Zg7+VyyXBrhxgj9+/f4/j4mLppaJo2sWG8JyDWUa4QU2RwJTR57/HBI2PnjInnC+LVrEQwuXJPrQSrp2HLXcQw+OSIWDm6pEgOohWbq+gl+LrrWhhXQtfqQRJYn2TU6cSj1aJ5xftSUhJ9x+cK50Jd2oKVABK6VkTovFkoKfE+xS+D9+uF4ipqyQq0D52oBHTin9KGXm+IqJbcv32PYV8igNFojNa6a59cH3mI9ARAaJblkrv37+CjBQVVveTxk/vUzQxjIioqlAqAxZiIzDU6SyJMUUiKkcbaiGtATlJ8RxnB9s4Qo3OCt9S2IhCYLOcslksa15BrTdQKnSmkVElkjbGLNa3EuK4p1KQYbyR9XilDkffTtkVDZorkfAnJbBJFwPuQhCcfOt5QKkIQKj2+7MQ0SI5KYsCYgjxPjrW2bRFCcuHggKvPXuXS5UsIbZgeP0ILx1ZfYGRB3QbqIsOFgOuYdCH4tUNmdW4+dSAhV2UEqVwuCQAhrrdLyFTGmBb4dIv/JOSE2MViQ3p9VOfiXGHkNAIVAqptMEcnNMUjrlx+lq0844VXXqIk5/B4Ql2d8OY7N5kuK3yUeC/wHsqyZD6bEX3kS5/7Cl55wDCbnLE9HGEbx8nJGXVVY5u6E3P9WngNPgnNEUlI6VaC90gtcNYRZWI25VnG/pVkJrAAAQAASURBVP4edd1S1U3ia+kk9k8mU5TOMNp0CWSPVirFU1dAqO4YkVIhRI1Sc4RITa8xOIRQxJhEMCFBiLBugV0VNdCJkd57ZvM5RNja2gJgsViQuSQyOuf42huvE0wfZ5eE4Njf32J/a8itd29Stw0Gz6M776Vrts6YHD5GtHPqqaJenBJ9hRQzCI+JPgOnkcJBJ7ym01xBSJ9Lba3yXNSU0LQ1dV0SYgPYVGbgIt41CPK0H7zg9q17vPaJM0aDzmqaFWTDHULUsBC4mLGoLLsuIFTsnIySXpETJEgdUHiInti5EoXwCAKqY1rduzfnQXT0yJMLK66uT7GL2T4l7K+ejwis2hOt8yidEbrgMyI5OaUUiAAE0QHqIzImV1nEgWiSszN2EUjq5Gj2oILBESn6I0bjEcoYWhuoKk+FxAmB0gG9tJwyxwVoXaB1gogmrMpSqGkCjK9eRklJ+9Y7ZCHiXGRxtuDJo2OCEEQZGW/1+cRrr2C0YGdrlN77vKecLRLfjCSiW5eEcBE3ccXNbGYzm9nMr2309Wd564/vfNib8esyz/3Mf7D+8/hv/ch/xSfz7MPdoM1sZjP/s/OBRC5nLT5kaAHOWeazKYvFgvlihhIN1qYoou7EpmW5RMjEPRqOBljb0jQtVVVjTEjgb1LbHkIRadOCKUKWabwIOJ/uoAslkFqdA8w7DWUV+QsevEh17SbLyIxhMp0zmcxStAhBluX0egOs9Vib7lQLoZAiuXOESDD3y5cvUzWez33+czx+/ARnE2PF2sTxid0COK3X08IoEr8NJP9t+sAqH7hSseJTbq8uGhNDXP+M4DxCEyMEYhfxO3dqKSUp8pyD/QOy3DCbLzk8PFy7wHwI55yvuHocSAoWa5dZ6F6/1fevBItVkFJ0PLPVix6JRCEhis7NIYg2IgHn/VoeybOMWqnkchFpYe6cx/nUaAiCwWDEy6+8yv1330nRnDYgUGxtbaOUhk7QSfpASDHI7vGniymnZ4/Z2tlFCJjNp5xMHxNlQ95zmKHBqMTmynNJfzQg60uaZkmvkIx2M1obqeaRh/fTYllIjw+Oet5Q12X6eQF1XaaWQBdobEOuJNLEFJd14F3abtdaakHHlooYlZHnOXTupiLL2R5vo6NGCEWe5cmhaNMxriTEINaNhcH79f5ZOe5C8J1rJ0V2syxjOBiidY6USSxtradtWi5sD3jxpY/gKsebX/wy9XRGrMq0ZnceA2gJXkBUghC6+O8KFM/K3ZLEZC1SzDh2x2LoFr4r59+6GKHjJUXk+jwN3WkQ23TMS1LZgRISEyOZDfRtZC/ADR9xd26zf+Ey+7tb7F/Yx2cjhuNtCM9w68EjRru7fHR7m36/wIeWtgl85pc/j3OCN7/xLVpXsjXe5ezslB/81Kd5+OQRR0+OIQYyo/BeEKNERNU1eVq8T69nElQVGSkyXZZLIq4raEguwtZ6gvfoPGc4HKC15tGjR0CDlJosSyK76WDxIiTXpNYGk2UYY7rygV0ioSuSMN1rmEQvpSQxeozJkhjXgdbprjWra4FzjsuXLmKMQRtASYpRn6OjU45Opwy3DRCQ0bO7NaTICo4eP2K6mCJcw+Hd93HOkWuFbxtyBSo0uLqC4MkHGeQ9ghUY6aFzuEkhcaTY+MqJunKdrpBRQkrOTo6ZT2aIWKLigugkMUp8VeCHmugbpDc8uHWTh7fe49nrAgqTYq8ytQxaHEF7xjt9rly9jLMTQnBcvLhD6y0uBDya4UAhYhcflBGjLUWMeDxBKIIEEWUn5Ot0zZYp/udDYhau/HzIldAf1840lMchO3+q7K6JyW0siJBOna6pMX0uRocQqRBjfTKQmGXep0hhEBLrI4uqwnqX4uxCJ9ubELggWM4r3GKGJnasvyQPh6CSy9M7mgDOKJyWtD5iumv9cr7k3sMTWiJBwo3nLjIYDhhkgtGoT5YZMm2ITnBr5yFZniOlQnW5+PjUW9tmNrOZzWxmMx9kmucPeP/H/8KHvRm/LvPK//ENYsdG/fE//6fYvzpZf+3fevYb/JmDN/+lbMcfvvU7Gd/ZELk2s5l/1nzgdsUQI7WzuBh49OAOddPStksy7QFJZgyD7QFb4y2Oj48xRiMFDAd9FrMFzgdy08O6lrppUnxCZV0MLEcrjZSgMoW3ntncopUmywVVVdFaCzGmpsIO2M7KUdY6lrFCOYXVAWsdwSvapkFIRYwe284JnYNDSolUiS8z2tpiMpkw6A+48cJzfOUr3+T07Cw5a0JIkSCp1kKP78Q3iGvwu+8ECCV1cnb5FYcnLVBjF2FZRVmEkkS5ijR2UZcQOhEs3UkPnEPnVRcBQ4BRkq1Bn1dfeJ5ZOUcrzeHhYeciOmdrQSdasBIkVgvRtECT4pxFk3wsonOMpAVhlMl5ILtIohQd5J7UmthJMOn3IMAFjDKMhyPKxYLGuXVEy4VI6xMnR3QtlNdvPI8ta3qjHoVyLJsGKTNCEF1cVSGkwPkGEV1yPomAEB6pE4OKmJo2pQooE5A6YrRCBIVtBEIL8r5GGkeOZLyl2d7LMdmAxdTxzjfP0EIxHhnqak65bPBY8iItgutqybDXI/iMxWKOkBHpAiFKlNA0pUM4TfAOWwa0MkiZnFq56SVRMToykVGM97rygyS6hhiSK5DzmFz0SXRsbMNiucD7FE0KIVBWC+q6QkpJnueMRiPyLCfLckbjIa13XTpVUWjNs1efY/Zgyhd/8ucR0wWFbbAqEACvOqchaRG7Yrilj8PKltW58CIunguNPqaFfVr/JxFaCBLgW6YjaVVskEScruggSrxIzi3rHMIHhjZwsbRcKS0vFwXfdeOA6eSI5fSMt64+y2lZEZcBjydTGnSPF15+iUFvQAiB2XTCYjHlzp0jFouS8Xibg50r7O3tMx7ssly2PHz4iOl0woWL+xSFARTOpaIFa1sWiylluWBnZ0ye9cmyXmK1WXjnnXcSQ1BIJtMF7928i3O2iyomwd/altPTSRc9pXMZJYF/MBh04llkMBhw4cI+4/GA8XjA1taA2LG9Yue4DN4jhEoCqE9toHDu/gTQXRGrEBCzSJHbTlCUeCHYuXLAo+kxXhQY00NKTZ5J6qpBSc1zLz1Hb9Rnd2uMMYagPUYr6BUIHxAqY17naZ9JBUKvY4HBeiKJW+eFAx/REmwICKHT0dzFwL1z3H3/PU4nc6SrGBUhAc1DwNdLyqXE+5LMK2TjuPf2m2xnGWLUp9fPwDqEbyAuKIqa6zdGvPbaC3ztS19GAM9eu4hzLU2dAO/DgUrXKKlQMpDLiJAhNSXGBGa3IWA9iDjoHJTJ6+U7LWvNEOwU9TU6UXTfG7voqEwxZy1WhSCsb1xEumtnJ+jHGLsbF+nn5fr6HEB4BILFouIwzFhWVYqCShDeIaIkekHs+GCypxHWQ0yitAyRINLNB6nV2lVK7FiO0iefsAQRc6IE6yNnswmTtuTh/fS+o41iMa04nZ6xwscl5+B5W+1mNrOZzWxmMx9k9OVLPPitxYe9Gb9us/h9n2T4P36Z6Bwv//EvfNvXfuqP/TA3/4N/doDwr13/pV/1uT93dp0vzq4D8FvGd/g/7dz5F9qO+//ZS/R+9gv//79xM5v5TTofrF1Ra6y1iOioLMxL28WwAkanxXBjLUfHx9RNjZSKpqzZP9inrGq0MSyrOU1rU9OYySnLmqZcABGtJYNhfx1V8wGscwz6RYrhCYHSGhXCmvmU3BUrF0laOPuuFl3rFE1MAhcEH3HBE2IAIkoJtACEoij6SDmntZ4vfvFLnM2qtIjtFjYu+BSZlDIt8JOPi7XLqduGVYQwbVvb/S4gJhD3SqRaCVFJOCNFBcUqMpUcEHmvR1QK2zbni6bOyeED1E1LWde0bcNyuST60IHV0wpFyi5CKSC51lZOG9aM8UjsYoEKSPFJKRKzZSWUSZnceaLjhQkhSYVj3bZ2z1lrjQ8WLTVCihTb9AGhkjMlz7MUewkBGSNnp8fce/SQl5+7wZW9bYxqOb19j6os11FKRALrJz5O2vggusUXqosApW2yzmN9g1QebyIySqLXaCWS0BkcvSLFvoaDHlIr6qUl1znbWyNefe0KeaEpl2USpXJN21pOj+d4lzE9s7StTkKCDzgrUCoSrCIGhRYGrQxZVmBMTi/v0esNcJ0TSypDzxQopfHBY4zCmJwoJWXd4HyK0VoidbWkaSpctMgIVVV1bZqeLDMYk/4ppanrlp2dg04Ig9FwTG5yxuMdLl29wbu/+Fnyd+7TX5RoF3hEy2SkaXaGmCLvHCuAeEqYWh3WK1ELn/Z1F+eqW4/KBzhrKZREqSTASliLLcncExJfasUo04JcgPCSatFQnsy45jWfVj2+qxFsDwyD773OzknB5N4jLh7c4MgGpvNjhAwQ9TqqNx4PGQ6GKK3JiwHOOcSy4bXXXuPi5YsE79m/cMCjB484Ojql6PXo9wcoLTphKeJ9en5F3kMKwXAwZDgcUxSD7piCfv8B1kt6/SE+JDFCSYnSkrJsaBpHjIGd3QOkUCRRI2BdQ9tYYgydaBuYzxeJWackeZ7R1CW51hRFgZSSQX+Q9mnV8vjxEWVZI4x+6tqS3I1FUdDv9zuWoO1i3anp1RjNcDjm2rUbCKESeNw5WmtZLhdICVmWsbe3Q12X1HU6n6xSSCXQCKJPDrEYV+UP6dqVtiFFMGMIBJGEHoRIcHMZOpE1HUBKqiSJR8+gn84zLVPzXxRQVxW5FNx49mKC2duKu9/6Br1+Rr+XJ3HVRdrWMyoEcXdIvTzDh4DWiizLiNFTFAXWOSAJcFIk4VB1bl8RUyFCiB7hPQpBlBlaaHyMSNG5ZYmd0CXX18vkXl1FGQNRra74KSK/cs6m6GvnWOzeHVIcvYs0hxRhlaL7PgJKgIzpPamuKyauZFnWaxZYJCShH4nJFKN+L5VUiIjAr6/5WkiC8BglUPLcLZzeZ9Jz8tEihEYhaOqKzBic0xBTs6nWCm18uvaL0O3XdMPl6fe5zWxmM5vZzGb+Ref0d97gG//hf/Vhb8av2/yh/9vP8jM/dx0/m/2qr+395c/y5C//s3/2P//GCxjhv+1z/5//9MfY+u8+B8Df+MP/Js/8J3+D39V7zF+dvfrP3Q4z9//cr29mM7/Z54PFFZ0lBChMkbw7UuKdQytJUeTkWlLXNYjkqoJUeZ+bgsdPHtHYlmVZp4UfKabYtGlhr1SqZPc+CUmDQYF3SdU4O51S9DJGoyFGB5y1EFNkTyuF78SBFdtGKLkWvxITKS3YVpwoEVODX57nGK1xrefunfspAmYTX0VrTb/XI2saTk9OO9bVqtWwY0sJuthQ9wFdkReRLMvX8SbiuddJdo2QMSQoNx2s/emko9IKH+DGjRt4Ibl/7x5Gp3hb0zY0TYVA4Fzg7GxKr2+6uKUgLwrKzglAXAlYci1EpRhMx/V6iruVNiYQoyf5zM4nRamSQyIB6JPTJLlK4rc/DmlxtRLFoBPBlEJKTRQSoSUej1JwcvyQu3j+tU//QU6P76XY1+Njzs7OkrCjklAXfCB2QmraF2IdaVzFOOuyQWUu8aCCS3z7GPEI4tKhTAAfITge3J6wKJdE36OuA2HkGW1lKO0YjnuEQCciSQaDjOOjmsPHc0BhLTSNxbuI0TkCjZGa3nBArxhgTNYtkpPzTcnEWFo5cUKwxE4kdc6DUt3nAyKkeFAIgaLX6wRQR1HkhJAaKb1PcdvlssQ5z8X9iwTniEoyGAwYjUZc2NvllRdfBGmQ0yW/Z7CPdnN2nOYLp0+4c3bI3cmC/t4uvZ0+SguE8IQgON+VonMGghQRG8HIFEE9nc957YVXuXP7Dq6tGAkJOLxIfLbzRtAk7hhjukiux+QFy2nJ4miOnLdsDQZczcbsthW0Fis8Ppc8uX2T00bRXDhIrsmYFt4hpNKL+XzBYrHswOvpeN/a2WV3f58ood/Pee7GDZTO2Tu4TFNVtLZN4lPt1g6ddBwHfPSscnbWBqJ0eC958ZWXkSoyGvZBKoTqRJpIer3CudgdhOuuDRIlMwaDPjH6dK3JFGW5wLrA4eEJSXiOZFqhlMJZi8kMRd7DusC9uw9YLitcJ6BIKckyveaCXb16haIo0FoynS5WZx9VVSOlZHd3jxgFy2WFD4kplmUZWhtihF6vj1Kqc5mR3I8SMqlQWY/aLzoRx547RJ8Sf3wIRNnFxTvrTxBdrDKKNZR+NBwzGm1R9Hvp96suDhslX/3am+Ra8/GPfwSBx1uLRNPvFyA8KPBSoCLEcY/ceLyr1tvS7/fp9XKCj2RFQfBLfPA01pP1BmzvbqGyjBgirXW0bZtaLb1n2UaEMDStxdqV4zQVdoTuOpiuN+EcTK/S+5YQybm7imc6YTv2oejeG8X6uqelTCJnF+P0nrXwFIJP1yQCWoV0c8fH7n0wtYxGkdpuR4M+u3tj7t29gxSBXASCEuvf6QEtwrlYR3IVxxg6xpaH2KJQSK/ItEYoRexEtLTZKu0fERCyuz7F2F1rN7OZzWxmM5v5zT0/8+/+Fvz85q/pZ//Bx0a/6nNbfG793+O/8Tn+3Nkf4u/+mbd5+Kn5P/exNF/+NW3DZjbzm2U+0F+u1nkEHrTAB81wPKCsKspyRr2coEiL8CzL2N7ZpigK2rbl+OSE+XxB4yzzeYnWOU3TJmB9gMwYskxj24ZmGRDCYUTGdDLB+e4utPUs5nN8iNiORSQ7gct5n6J3SiFUinmEAG1bdwvgczZOjBFnU7RSStXFbs6FqnT3OwHo66bpHERxXcE+HA5xzlGXJcEnTo9z50KPlLpb/J6/bvGp/9Vad3yvNiUtSWD5GAV0scBIinzduXeX7a09Mp1z8eCA4aDPydkJx0c2RS4DuNYierpbiKTXY7VwXQlVK/D/+b+A9aGDjXfurwCpYTC5GHz3ebniiZHcZUKKBI7uQF+rRe8KZq+lwLu0P4wxON2u3Q5N09DaFpn3iCoty4ZFwYO7tzk9fALEDtofOheYB3n++M75bmEtyLKMIjcJ5NyJb9oYpPTIzk1C1zrpao91DchAXTrKDGaTGmPSMVYvLc04FRB4F7h08QoPHx5x+9Yd6mbJ/XuPWEwDIuzR7x0w6PcY9PvJzaM0UmqkUEhluqiiSgKXEiChtg7bOsZbW/Q7kWpRLjBSo7REak3dRGLX3CiFwCtNIOCDx8eAtZayrLpInOWZZ66wt3fA3u4+ezsjnA8Io5Ba4a3DZJKXPvISzeSUr/7sz3C1maHLCQMGKN8SGkdoWprJlCwDOcxAJUh+8GmBjBIJ2KWSWBgitK5JUHMCHoUXCiVkSr/GFHcMxMQ9Wh3/MRUb+ODRSqC0YDadIhqPlBn3mppfmN3nrfkCkwee+6tzmvKMzyzg8d4NTqdn6HwLJcEIQTSprTF2MT4l1bpxUEqZomkuMJlO+OrXv4aWffr9jPF4vD7+zwVY1RVOJJfc6jxNZ6aHqNM+Vp4QHMGLxE3rHqfz66TzNqboWdKXUzQT79fFDzFGBoNRJ4yHtXhbN0nkUEpj68h8OSME6A1H9AZDYhS0bYtzlhiTsNK2LU3TkGV6XQQipegaNs+dlUopQHLx4gXoonTpeaZYZLls07mqNUZrlFGoEEGVzGaz9c0ChEgtmVKtz30p0mudnKWJg7X2/AjRxeUkFw72KfpFEuJVorHlWlGX6fqP1GSFSQ5OKdFCYpuaGB02OFobaa3HOQcitemGzs07Gg3x3hF8xPpOpIzgYqA3HHBw8SImz7sIaFfeICRRCFrbEKNEScWyLDk5OVvvm3RtXO3HJE5bZ/EulUNEmcSoSDrugtfdzYrUeRsFqfWQiOxoiOnGg+zesyxaSFoLwUY0gUsH2wykZ/74EIJFq0CerVppLdVywqGbd++PoIzu9ovAOnAhogidGJyuTUolx52Sml4hUKTylYGWtOUClXZTV+Yi1g26kM4HKSVKKHZ2drh37+4H+XNhM5vZzGY2s5nfcPP3/vHf4ne/9jvxZ2f/izx+/jNf5OHP/C/y0JvZzG+q+WC3Z3WCTTeVR6qc3d1LyMWMpq7x1iZByDVk2mCi4vDBQ2rr1sysuo2MR/tIlSNkRaRFK8mgyFFEardkYidoIXFlQyY0g57BBU/jA9NFhZGRIs/JtMJ7h20aAIosQwhB07Zg02KidQAebXRixEBalHUL0Sw3KF1QuwWNbYkewKWInTZs7+6xWCzRKgcvEULhgoTYRfdS7R0ruHaKB0ak6BFdgVE5ukgL2LZtERiM7hOix9kSIRxKRmL0+Oi6djpN9ElowwmaskV4EDEQY02vp7jwzEWybECuNVI6nLAkV4iiNxoRo6cpG6pqmRwDgm4fxLWbaNXCmDwokRXeDKEIMoHDfdsSrUfrgsFwm6Lo49MrhIoygdFDBz0UEaGBIAguIHwSNpCGqAxepIWnFjLBjJ3HtRGpM4SCs7NjtkZ514TZArJbUKdttSFtp4wBFQJZPkDGLmokAyG2QMQ7QQweiUagiSF9HLwG4bFK4GqFDQ7vFwgvQXpOJ3N+9qdfZzjKuHDhjOViydnpGZnO2O6/wLPXt+jlA0zRJ0QIIa55Y0IIrLXJrWPM2pWF6AJH0aGloJdrtE6L0tFoQJ5lDLe3MMZQvb/AmIzd3R0AJpNpEobrhugteV4wGm0ll9aFC3zkIx9hNptxcnKClY7BoMf+/iWGwy18PePSSDDue959+3X+88/8Y16ZRcSipo9mFi0PZcu8bYmqJSwVI9VHm/NIKkqdi5mejkHnQEpMXiDqmn/y93+KXr/P5YMdolJ4PEiBAlDJtSJUioeORwNcWxNcS121nC5rGhExKrBwDe/FlqLwDICLh1OqEGn3RwxMpJCSwbAAJRN0O8YuSkfHj0tNgtFHpNAJmC0UUiawf2Z0cvC0XblCCF3zYxICQtCEsHrMyCraqwSE0CbIehdBzrIMqeU6FuzXQHgBKGLUawHNGJMilmvhjE4MW7HyOtYe5697CElM11pTVRXOubWTchWCE6sonpJdy17DztaA0da4u3EQKIqCPM87Ud9BCMTVOd+xspKA1UeJdC0tdA9HoGcMSueo2JBpBcEmwS4mtxwiJqdTTNdLgUxOWW0QXqyvh+k5Bi5c2GFra9zdDJBELQneAjUqFwwGGYMiY3fnCloVjHqGXlGkGwIxYq2lqqp1ZLduLLffP0Xnmo987BW0ENjWE4NndvaE3BhkiAyKgoO9XYp+j6ZJcWBITty0H3o4n1x2B2xz5fJBt49Won3XdNrdQCDG5AS0nhgiTdtiW7c+dlbnvXNu/c86myL03ettrUVLkCEQRSQzYPJI2ThGuWHHDLkv+yiZkeUZwdbIjgkZo0ALhdIFKl85adMxNexJWufo4RBtgw8OoSOZCkTpEVLQ15rgLUIECJFgPSrrXIQxuXhj9AnCj+je5zz9vmDU28QVN7OZzWxmM5vZzGY286/GfCCRS2uFEJq6rGldyWw2YVktqZsKGRzGpGbEsrI8eHJCjIIYFI1vQQqiS7D28faIYjBmOp2iheT6tau8+NwNquWcv/N3/idMZoC0oBRC4G2buDIxICKYvODK1av0+31OT085OjpKd/aVwpDcMKPhFlkvcWtuvv8+Vb1EiCzdfY+e8WiMNjlCGF5++aMs5yVt2+Kto6qWqDwjyoxlNcNkQ4ajHJUbfPDJxUUDhK4ZMqK1Tg6YIJDCIFFEEfExRdOETnE9T9eoqCVSGqSK+MpBlMioEUiuPncDUCipyYxECUlmwPmK6XTK0ckErXtcu3qVl154lrPTB1jrOq5YcoL1hhqTaepy0bksOoZWSGLfehW6Frq+fZI7wSOlotfTeB8oy5IdzlewWmuaxq4X7U+DsWO3MJQqCQQi5ZMSayamhWMUSTgZjka44BAqp7Utu7u7jEbD5BQhNR/KzmEmO+feKvpDIIkEAYL1yeGmkndCq+QtiSGJNN47pExtZzF2UHUPShgICm8V5SJyGOYM+mOuXXmG0XBIL+9htCb4VGaQF3na71IRYqRtW5bLJXmeJy7Ziq3luibJmNooZ7M5vSKj3x8wHPWpyiUnh0cANGWJ1y1TIsZk5FpR7G4DSVALIbC7u8vW1hZaa6bTKaenp0gp2draYqs/wFnH4eEhoZ4xUkO8bfjaZ36BNgR+aRDJM0NuwTtBK/s4ASoodKPIbAThOqAWxJBcc1neRS+lQKRUH/jAlQv7FPmSXq9gUBik6EQPIciUQimBtx7btBhjwIcELI+Rs9MprQ9UQrBUERcsUTq0ERRS8UhBay3CV7woPcLV1OWUKCVGKaxNrDc4j9IKsWqltOlas2JCKYXvosodmi6JW3pVXLHi+p2fATGmKLEniWGta9eOx2WzXH+fUmr9u5VKnKpzwQt8Yztx6lefWytx9OlzZvW11aw+L7ooYPoddL+vO686vp8S0J6cIbttau2SYQcel0KQZwVGpSZZAK002qRjuqlrBKSmyyJHAUpnqF7AGM0Kjp7O+84NGp5qho3nvL/QlWysaFbOe54cH3F0esxyWdO2nqZteXL4BJ1lPHnyhOPjY/78X/iLGKPJsgK848LBAaPRiMVigdaaLMuSQKRTNPXRo8fY0PCzP/P3yLVhe2ubg4N9bjx7JW2fSrHvrMgYjgb0+8X6Ovf0PrLWIZVOLtN2JTYGjFZobdLxEwJVWeK9pxcM3gfqpqHfM6g1PzKJW6vHT867VfOkPL+pEFO0cxW7tl2RyqJcMhoOaJYLFs0SlWsOhrvUTU306XxYN2qKdBMiRYxFJ8AJNLJr9aRzY0FyGWogcfNWhQi+cyHrKHA+tYiG6BJH0tskgolAlhn6ecHTQu1mNrOZzWxmM5vZzGY28508H0jkqut2LWSFkKJlK/C4Voo8yzCm4NLlZ/noqx9jNptz+9Z7LB7epW5KhkWf+XxB0R/TG4zZP7hIDJ7JbM4v/vJnePzw/nmcJPh19G69IAwrpolnuVyyXC6ZTCZUVbVebKYFWBc1IuB8S8QjhaYqG4wxbO/ucOXqVRCGk5M580VFaz111ZJpw2i0Q5RAlPR6fbJhRlEUhK5pkO0dFtMR07MTWtugBBiTYXSPGCTz+RKPo5dnzOfL5NTynuGwQMS0mByMxuQ9jTYK6wLL2ZzlrGT/4iWeufECd+7cYXI6wfsaESIXL+zQ76f4iTEZw/6QIktMMaLoXntLbVP8qKkbcmMoej2auj4Xn8SqPVGex5d8eq3W0Hwh1rFBKUUnECUhQEiBjOfiQFiBqWMSvbrcJ1rr9b8YWUcQnW3JlSaGgLcO1cWfnLNIBFpJlmXDYjEjRpfipiEmNxbJgZCiQGK9bc56go1ELwkuJodXgCg8UqalrYgaLSSZzhBCde1lJjnmhul7tNYpgiglbRs5aadMZguKLO1/LRVKacqmQSlF6BbKTdPgnKPsjkNjTDof8pyAYFnPutbM5PSbzxfUTUtdLWmto67rtNglMp/PKYqCoijIspwsS5FMay0vvvgis9mMz3zmM/T7fQaDAXmeky1yngBGFUhlMLHBPzOikoKvf/0byUmCohWSVgWiVAToBFrJvGkJZyWZ7sSWp+Knea9ACIHzKS7mg0D5jrkmNW1VcRZtEh4RSC3RUiBCwDqXnDsRbr53F6JHSkHVeDxZYqXFCNEhVHJWRSFohIBCEYTHuYpeUbC9PaaJkeg8/f5w7Zr8lQLrKrLoVnykGFEdPD+EQJc+I3T1zyGkeGZ6DNaxQu9IInXn/lHq250zK/EqxhSXBIFzScgQ3bG0bi1dAczXXKvzjyExzNZCVyc6E5JjS3Uw+HOBBFKs13W7qnNOce4CW8kRk+l8zVRblVEIVsw0idIa0x2vRMiVQRrJIM9xPvDw8W3q+tMgI6u6vdg1zHZ6Fz56VFcskPhUads7mxeZMezv7rK9vc3t2/f44puvczabY21NYy3zRUvTWJblDCHAufQaPXh0SFEUyWXk3Nqh5r3HmCzx6YqcyeSE0LYoKbsYs2ZU9CinJaO9Pbh5i9FoxKBXMN7ZTjvYe0SM2O71qrrz03vfXQsjxhiyzKzff56OeislKfIslZB41x0DIM25oKl1cX49pbtOOZuKUAQJ3t8JkkTIspzJ6THlfMp4b5vh3j5CK6qmxLskoPnuHFwsFuubCOvjsDsWL168SJ5l3Q2KJK4JJL3egMEww7mEFBiPh93NB4mQ6TwQkW97TCEkIfgUs9zMZjazmc1s5gPOo//oB3j9T/+XwOZ9ZDOb2cy/3PmA4PnU5CVjWuyPx9uExYy5UMgYKIxB9wZgCuY2EvMew+19eHgf5aCuG1SWI4XCtg7rfVokeIdtW/JeQWigaVsyeb4QfNr54EJkMlswmS2QUnYtc3lajAiB0oper5dEDCWI0TMcDrBZIATQJiN4uH/vCJRBSLNeZEYBZbOA0jGfL9i7eBFFxDYVk7MTZOfk0kqRqQJjBtRNS39QoFVGubQQNXnep26WIASjrTGz2SS5iWJAyRRFCkC5rBn0+wil6PVHgOH6yy/y6PgJjw8fUwSB0AKpZNe817kngLqumU+nCJ5Zg4FTZCaxtbTOaNqGoshQxhCaZs3ZWsWAEug8oLSG6Fk1p0U6uPRTC3Q6b0wMAec9qqvfS1ye5IpyziE8RJ/imdbaROnp3AZZZpDd9ssYUQiKLKeq5xAishPOptM5VVkhpcAYndrKOgEj+EiQnhAdQmRrUcG7gIwgVYYUGq1l53rpCgqMSRD4tbNGINAoaZA6dvyadGy2tsb51FjnQ4q4VnaJUboT8hJIfTAYpFY3a9eRqpXbpCgKsjxPbaCdANvv91Gqi9gpRZ4ZYkjHafAhga1Zwem7Ra11jMZbbG9v88wzz3D37l2GwyHj8ZjxeJzcIlJQGAMo2tZCtAihkLKg6BWMBgMGEZx3BKk7mH3o9gPELnoWfNcMSExipjR43wGpZY7SBiU1Rkh0DEglMbkmBE/rAiEKpAhIIkqkxs4QOkqT94ndJRzjIShj0DqDNZxfomREyYjB4W2LJxBnU4QZUVU1VipUFFS2XF8PVtD51fEsZTpXYudwwgdc9GsR6Vy0kmuBKDVKnjejrpxSQf5qV9XTohp0v0MkNl1R5OvvjTG16Umhn2qAXQlzfv37AIRM5/WqJGK1bSshQ4skFHZmyF/lmEzju/OY9d+SsnMQCSFRnY1NSpncXB1vL0SBdYlht6xLtNFMpjO2t3cgyxDSrE7/9XUgduK45KnG1tUNBgQhBiRd+YeEN99+i7qumU7mPDl8wqKsgEjVWqyNlGVJ09jU0InAufRYVT1/6vqzEhc1beuI0a2FVxmSS7HxjvkicuQmFDLjrHrIgyeHZEZT5Dlbu1vs7O6m9kHnaZxL17vOgag7Llk6TmwnDK72U+fA6l6H2H0teJuOn6euLb/SFStEEv1XNxfS9SCg4mo/CXKtyYzhyisvMxrvQtS0TYN1LbYD06+uLbPZjMVisRa9UhQzMJnO0jW2E6pCiFib9n+vN+DGc5ewNvEsnWu7Yy0VlqzdjEJ3FLHu5ofwtLZlOV+wmc1sZjOb2cwHGpFu7G9mM5vZzL/s+UAiV27yDpqe3DOPH9/H2ZbQNlgcVS0QDpbLBq0yrl+7RlnOadoaJ1oyvc2FS1cJwHDQp9frMxim2NbNd9+lrGp6maCX9wkClExg3aLok4sUsUjNamlhm4Dp6e66MRlSCZqmxsYkeIQQMSKB0KumwuiM4WALIUl8Fx/RUiIRKKPJdUFZOipbYzQ0zjEcbjE5OsT7hlzlCaC+qNCFYGs8xihD3SyYLQ6RIiP6EShJv29YLk8ZjbdQeUE0gRYY9nIgUlYlWikWrcO1FXmeU4wHnM1mTE6OMbKTG2Jq3JLd3X+jTHIjJfIRg156raKA4fYOZ2dThFAU/YxqEYnWo6QiaE2w59HC1YLLWruOMcr1Ii3StjXlcoZrq64NMkXzWEeRYL3qJi3ss5hYV1IrRKbxHZhbQhc3FExnC0zT4rxHZgLqAEia4HAkh0WeRSQtSiRnhhZpUSqAEB3Sa4IN0IdQWSQRozSKPtqkKJoUSVRYAbizLFvzm1LLnO7aE/W6CRDSz1hbMJnNmXcLyRAUSkm6QkTyoqAoenjb4LVCIRgNhxzsHzAcDih6vTXDp25aHj08wuYFr3705Y4XJjHaJMaYTA4hISVZntHr98nzfC2MFb2Cg4t7SKW4c/s221sDBh99ieFwSFEUDAZDHs8WtHWDby3GWPoqYzgcg8iQRjPKI1b3OkfdOZdotYAXAiSBhAQXa8HiV05M9kaUSa2CUkmiTyBuVg1uYSWTJTFHqYzgE5Mskvh5UoiO8xQZ9AeE4GnqMjVvKoHOFcKnGjofPZVtkNbRdpsUgiOG+G0xXCFWIlLXUuk9IUSyrmwilQGsnDVgnX3qZ89bIL3363hmoBNJZQLya6XTc167xr49Ztg0ScAYDAZrlpRrWoD18ZCE4yQ0KaUQUnSFDTqJnBGCDQjVtcBKhVg5JGNy2xA73h1PiW5PiUEr11bshLPkDkuunRgkdLHFFPkUayHYmCS4ySzHBfCuJQYLzkNMPxttBOsQwa6jz0F2IH95LoyHYJGACIFy2bBY1rggyfsDFo3r5FyPD0nI8T7Stq5r4kwssVVj7Kogw6+Ote6nZQydKB9wCHr94ZqJZ32SsaUAqSUog3WRqmq7dseVCzA+FWWNT8UKE39y9fqkmPvKpZpuVBhjyPMsOfnESghNAqlSGmM6gbsr1FjdYFhxu+q6xrYW51rOzk4RAt761ooVp9BKYzJDnudsbY8Zj0bEqBkUBtcoYq4QIkeIEULClauXeP7FqwxGQxobab1jZ28E0RCjx9qaGCN5VrC9vctiPv92N6FIseoQUy5ZpN4EziZlEtM3s5nNbGYzm/lNPr/nB/8d/GRTxLKZzXynzwcSuaTWlMtl+jEJZVWmKFQEIQ0uKITzIBqePL7PZHJMUy3Jiz47vV16/eQ8yft9lNaUZUlZLrG2WS8QvAtIEUBJrLNkWc4P/OCn+fl/8A8TMyuc19WvIoxSSba3x2wPxiyWM3I6t0qMySETI0YnFtJoOGK6nGJDik4FPFmWs7u7g3OevDA475BCsX9wgbOzCWVVkudpsdHaEte2NEKQ9XvoTFFOl0SROEBCQetrcq0IHsqyRqmM/mCQtjsKbNsQHOi8oNcfMm0ryqrkYDSiqStijOxs7zE9noB0a6dJinbRLdBS1CbLc7xPPqsUD0zsryzPqBbgvMcYtY79rFwmK8bQ2imxYuqEbvnpPasGuDVviA5e7T2x4wFF0uIuuBWTCRAds8f7LrqUxAdnXRJZ2gbnHcrkVMsFPiRA87Ku8N7TtA0xBIo8I8+y9HuSRS09/XV8ks7l5RgNBxij0FqitFzHbp52T0BqZtNd4+a5uNU1QxpNZhSD/ojtra0UPwuBvCgYDvpoYxgOhmnhabLEoepiaqFzlAVSlE9phZSKZVlxdjplWUqee+46rq4xSKJSWGcJPq7B6dDpBP8/9v401rY0P+/Dfu+whj2e6Z5zh5rn6u7qrh7IbrYoSiQdWRFhCxbyJUiEIEiiOHZiJI5gIEA+GPmiBIE/BImjOIDhT4kSJ7YVhZIoaKBES6G6yW6yxxq6pltVdzj33HvGPa3hHfLh/661962mJJambor7ady+dc/ZZ++113rX2uf/rGcIDm010+mUnZ0pJw8f8s4776C1ZjAYJHLOcHV1xXK5pPUOhaLMDNiccQ7j8ZAQPKuqJqBpmqongaw1dDfWVHfQkhVOqTVpqTdsSjIMy8/HVlSJnWKnIwwkiJ31vk2kB0rRBIc2QFQ4H4X4Avxi2a8zrRRt0KK88g7vIyEGxsDNW09wMato2gaTgsFDWgc+BIzW2Eyy47xzUoCQCBFRfNq0KescI60lb8m7dC0I63XlvSfLhUjP85zlapmy0aSYwgef8pe6HSnnpXctwS82crbS3y4Ry2qd26WjTiHn9AScthoThWyuqora1YR5IrhYk4+bCipkBffHyvvu/TyuOlsfTNnerjlVKTlW1iiUBqMsmV1Ru7o/7whBbH6AMjpZE2N/ne0t5aRmzkRAKa37GxJ5UXJwWDDdO3g8nL1tqeu6J7Patk3Xn87iSU/8dCq3DpvnubWW0WjEaDRC2UyuN65Nx4le+VVXK4gB72N//htj+3Xf7bOupTbPc0ajMYeH11IpQIZSkhnpfeD27Q+4uLjor6/QEeyqV8T2bbEbWNtf6W228tpufXyX8hwPH52QWYs1hkFRUlUVUSGZYFoT8VS1Q5uK3CiaRgjlIqkLJWLAoZVmOZ9Tr1bo9LOdstdYyPNG2lO9B5XWuwu/J+m9xRZbbLHFFn/Y4D++29/g32KLLX568emUXMWQEBUKTe08yhgyYxkNx0xGAwZlgUt5WW1Tk2dCZMWo0abg5OSE3d19SqWoqopqVXF+cUZdrbBabIauWtI0LToTu9l0Z48PP3qfpqnS0Ct39mMiaSCCMkBIQ2lIeTVC0kRkoA1Rs1xW3G+OqV2NsYZiWDAeD9nb2yfLcu7dO2axXJFnBeVwR/KXlgu0UZTlgOl0h7ppWalA62qWi6t+2DM6pyzHuFbUZCGUaK1YLlfs7o8ZDAaplSugYsBqyfvK8lwG5eBo6ga8KH2c97TOYYzHp5ZBkKFR1AC6H/RjUNRVS9WKuqRpaqQbXqyBXRiy0TrN4rEnFfqsnkQGrZVej7cxaq3JrE2qLIVRijaKjUbUSckpZdN/RY9WisbVdLFdRkXqymKNFmKrbfFthTGG2WxBpgLVSv5t7DprRrRBEvMTY0AFiF5ygrRSONdy7WCfyViaK1VPbGSSj2UtxooyIs9yBsOSssjFIpeUFZ1KJ8syyQJSSviJNCFrpVAdWdarQERZItZAJcNvIndEHaK4vLqiKIRUG49HUOT4Vqy6KssxMYVWe2lnnE4nZHnB7u4uewf7fPDB+3z00UcMk8LLWtuTAN0gbGOyJ7kgmT9a0dQVi7MHzCpPEy1V26CUFD/Qtj0BQlJdqRhlL3ekSRryN4f2ENfFBa5TGCblV0g/Q9ovndxPi56HGFM+Wsz6ljulm7UKS06jZNmTTD1rLbsHU8ZZgfeOotDYlKkmQ3v4MTszyV2no01FA+tg9LUNMCm0rOmJcpVILmlBTZlkoe0JmjLLEr8cyUxEXigpSbsyh7Q3XdOk80uDFiVYCGGtHmNNUHXk1uZ70Fr3Ci8hjORn4ifCv/uMrw0lIqRsPEj2v8cbHbsAve6fKtkWZVtCIr/EwmdNhupCzFCgDVGL0if20f5qbWXsSJzOEtkp+oDQPVobsizlgLE+hp2Caq3OjYn4UWk/B0LwGzlsazK2Q6fGWy6XhKROtcYwHo3Y29tjd28PrRSnp6fcv3eP84uLNdFkuv2WmifTOdm2juVyxXy+4PLy8jGS0RiNc575fI5zrSgl0/voVkOnBOsI9j7zKnaWYAWs7and8dokMWVNiOWjyOS6VeS52MCzrL+xYC089eQzPPnkMxx/dJ7aNeXGRVkWHB0dQECyLUNHSCdbsXepgbSRfC558XRzg/6c3WKLLbbYYos/zHj1m/CjX5rir65+0puyxRZb/GPwqUiu84urFDSuCFGjcrk7b3VG3XgWq1NCkOB4qw1VVdPUTWqbq/FeyJJqtWK5PKeqaparBa5tyK2QH8ZkNM0KnSkGwxLnat599w55LhXqdSODQkgKjhA8IXpaV+Ncy2BY4NrOWhHFClXXRAxN4wk+UA5L8jLHWoPzDYvlFTs7eygNl5eX5FnJ7mSfq/lcSBIGKKWp6wbnJKQ9xIALkseiDGiVkWUDnFuitEepQCQQo2N/f4diMGI+X9LUFa71RDxKS7NciB6jNa5paeqa0XjE5eUFOkMsRjGkoVQGsKZtUdHIUNK2SWThJY8p2cSapibESG5tUgqsG+C6QaxTOEmWTujtfCBByGKvMTjnE7EShNBTKpFaa4KEGBMpZ3vVgjWGsigw2pBZS5lIp2q1IARHUVpefP5F9vf2ODzYoTDwuc+NiVrz9FNPoLSmrkTlF0MgpOyl2P3be7yT2vvnn3+WF59/jqjEqqaQMgSbZY8N+TY1Piqd2hp1Ryro3k5kjJGMqmSF9WkQN9ZQ1zUoTbWqhFBTipgaH4H0POubPC54nn76Sfb2dlkuZmgiq1VFXTdgFJmxVFVFlmXceOIGbdPwo3feBgUvvvAixw8eMBqNeuLCe1nPXT5PURS9iscRqZtAPpxw46kXGO4ecf2ZF/ngzHN1csJqtUzlDREfRCkjVrbYEy+o2CsGtaYnw7SOYieE1Iyp+nD0jg+MMaJD12wYkxJHcuikiS9CCKJtSplZ3WurLjOKKCSL0hivaK8q9GjOYrVCmwzQiXgR0lOhUjB2ZJPI0SlTTKnu/bp0RaC3BXrn+/9WifBVyPXNGI0JqfRCK6TtVG+oSEkEWJdZphMJFohhTcAE9TiR85jyCdXvB9XZRPvcLN3vGx3X3+/eQxf0HqKo3UQ5JQSe0TqF3st2aqWJvWDzk4lR9OcUQaN0slNqSwgpqzCkfZ0OdujXTNqYbt8rBUoslZ1KTqf94LwXT3VHWydSpyN0NpWDxqQ8LzqlVUcEmp7g6pSonQKyI307u2rUiuA9rom0Tc1iseDByYlcp9oWozVPPnkLY+RzajabJSVWxHfq38w+Zi+cz2frvLUNEirG1K4bYipE2LSyqo33YCiKjDzPKQq5LuV5nqyjfm213VDYdtdS55oUQu/S9S+irWE4HKKsYTAsUAoGgwHj8Zgsy3sV3Jo8k/eemQxtLR4NydLtu/M6nZvyeCWZetsb1ltsscUWW3xKPPoff53/1n/v7/2kN+OfO/4PN7/Fr5hf+klvxhZbbPFPwKciuZZVjZRFKSKa3Dt0DFSrOVW1wPkaHSODwYBaG0bZlIBkYtVNxeHhEU3boLzm6vJS8ryCR8VA8I5oLFmZ0foqhVB7Io6qahgOx4TgIXoJCdcG5QJtGiqr1YrLi0uKoiCzuv/FPCioa4fUuIuayBpLYXOUVkQfqJY1w6KhyCzXDw84OLjGowfnzGeXGK0YjIaslktm5w/xVYUmgDLprrwMJD46sTL6CmMVNssI0aMVEAK74zG+bvANGB0lr0olC0uMFEVB2zQoo8lthjUWj8NYmxQ2IalBHG1To5TBuYwYfFJ0OVy9RFmbrFmRbFCQGUPbrDbsMEktku7Sm244J6Qg6g1VV0dYGrAGlHLEICogyQAKhOgJoUW5hui6sVyykF568QUO9vYZDYfkeYb3jgcPjnn7rTfRRjGelHzta1/k4GBfGvkI3PItASR3TPuUkdRitFhIRc0GuVVYFQlGMRoNiNEzX8xFMaRiT1QZYyjygjzPe5tejAFN19pp0/dsP2BK3pPY75xvWVUVdV2jjWE2m1HkOcvFEp+GRyEBm74BLiSSSGnJrVII8fXxnTt9nllIgf9S1rnPCy+8SN20fP+Nt/j+976H9557947ZP9jn1q0negJAa81wOOyJyqIoCKEFFHlmMCZnOt0hG4w4uVzyxod3eXB5ybJqadp1A2HX0hfTVKuM7reLmHK6oqLXyQR6AjT2yiJSTpRK/IXCE1O5XlJ9peeIGCFeUig8Wghc4XlElUeU9WYSiax9wMcFg/ESFyI2kWSiFExEHbKf1xZciEHUSGKblPcl51n4hOopEWzJLqZSDlGKT0ebLrg9kmc27X/WP6uEHO3IBA10QfooRds2kEhksULK+bepVFIIQxgRpU5nr90k07qweNX57kjHLJFiMRFdCtX/TXzcAqfTz24m6vXvpCMtQUhi1RHY6fWiT8yaJmIg5XsRWiIqfSZ0j5V2W1GECakVYhRrYNpv/dNuqEbzvOgJr031aLcmu3PLGJNad9eqvC5zrSNVpaDEiSUxiLW0aRviTIh47xxFUeCTskrIbSGf2lYUph3h1TVmCocnpQC9NTPt3454k6KLtTpPrOOivLWmy4STvTAoS7xzchMl2cnbpkmvJ2s6eCHXxDZpErEvasO1XT0nxIjVBp15Hty/g/I1F7OHHBztYhRo2v5mj28cHkcAbF6mz7GY7Lcx5T8atMlE2Ydbr9Mttthiiy22+H1i992G//KD1/kPD9/4SW/KFlts8YcQn4rkCqS72AAYiI6mXrJaLanrVWrwgqZWeKUY7UwZjkYsfKBQcO1gn4dn5xTlQCwhbZt+wc9SGHjGeGdEOci4tj/m9PyYpqkZDIc459EmpuEGIGKtwdh1C5kxYoOxRstwHUAZRYxdyLLYLuq6kqBlawlR2hNP/SN8CORZhtGR2fySZrlgOBoSvcf7ltVymZrzCtCi7PBOMp7quqaq5sQYKIocSNHwSjG/PMNqxdXVTBROVmNthg8tISqKQnKnqmrOcDQktA37O3vURQ0EDPIzIXjZb96hlJBLoW9ui9L0BShj0BEya4lRSJe1zUZjjCiaIOXyJK+YsRZtTFIzpT9J7TUaFEzHA9mGpOgK3qFwBN9ggic4B8qjrWIwyLh165AbR4cURQY6NUJWGXuHE4zVTMZDhhPFYBJpaml2K03Dcrni9GLB+eweTeNp20teeP4Wo+EOwUs2WFGWvWpCVGOa8/MzeR9GBkx5f4qiKIT8TKougLLIxZrWq7fEqtQTEEAgpvVSU9c1SinquibPctqmoXGy7zsFRmcjXLfprdVzAFVdJeWU7G9tDLs7O+TFgJOHZ7z9o3f44Q/f4PT0Ih2Se+T5QAi2RABorROxJQNwZxFUShoby1IG8bPzc+7du8/33niLxbLGt0n1k1QgqvMI9ie37wmTTjUUvOu3lWT/kq/7TsqUAuzXiKTnR/WqMVSWFEBrhYsQIUI66fQ8kv9k8BEhs7VCqUjbNgwGpQR8i+RE2v2C6i2EXSZajF0weejJCQCjFcauicKQyGGVVHxGJeat2x0hYIxmMCz74yvkikllDes2Pa3X2W8xilKvbcWyKM2pA4piyGAwwPQ5YJJPFwP4NqSGwSbZiyUwvbe76TXppNKxEOJblHWi3ErWxPS3Soels5xuqoM2GyK767kxhkAkGo1BLGw+pkwtkgpLaQLgfGLlQhSyMq2jjugk2T2V2XgPyqCUTv9Kqq9IUqFpUfL2+VL0524H7z1VOg9b5/rvdaTypsrNWsuoHKZzb+N9B1lLdV2nLDBp0e2OnUpkYp5LhmKnsGqapj/X1vtufdxlHYT+v+Vv+SP/vW7tlIxIUd451xK8o20VeSY2Xtc6mqZNeWWyRgeDAZkZJGItJBWY6j/zQhD7qFVwcv8+548e4lzk6Ma1pMxzaB3J84zGCyEWleapZ59hPNmR/dK0XF5ecHz/EWU5xGhLRJTBvVxziy222GKLLX6fsL/+bfjSH4Gv/qS35J8vnv+v/m1eWX33J70ZW2yxxT8Bn4rkAvBBQq6NFTtN09a0bZMGVIha41wArWmqmul0l7qqMc4SgqdtHaNxnlqxYt8e5ZwnoijKgt2dAdePdpkvLylNRogVlxdXGJORZTpZO+SX9tF4xHBYJsVDGvSUxrtI8BFj1tkpNpE+1aqSfBS9HhCWyyVVXWGN5fLyAh88ITgUkaqu8M7jQyQ30qoVUdStNGRtWk60Tu+naSnyjLZpqZYz7s6vcM6T5dKgl+e5qA6iwhpNUy3xvsbViiUwGu8wGg5EbYCQSfL8GptlkkelNU1di+pFm9RaF/ow/uBa+Z5SmExaHUlqgizlVOVZJsfAlgzHQ8qywGYWVGQ4KEXlZGQIbZzn/GpFaBxt4/ChpfErXLMiV4ZIg/NJxaMc89Uj7j64IgYnhFHwzOcLRru5KGCU550P3mZ6NqBtK/b3d7BF4OpqgavBO41roCwGXH/xFpPxPq5NSiytJT+mG9RjUoTYjDxPOVwbw6tKFjaV8n2c9zgv+zRW0Cnc+uBpMa715EZHFEjjG+RFTlTJApcG3zzPfzwnasOO1SlUmqZBawmW3z+4xt2793nv/dvcPz7BOUeeDyjLgv2DAyaTXbqmvu414BMWuCjWLryQdHVtePTwIbfff59qsSQ6ISZCsqV1tqpPklwQektaTEosa22y1EEMLVZbvPLpa5qQlD999pBKKjaAqGUfuwq0EFI6ajG6daRrVKKI6gkXn5o4I1pHtIpkBvb3djDWYpWlc4L1yUeRpD5TvZ0rKFG99JZaIMvEStu2LVopJhPJyquqiouLi56cjFFswQBlWbCzs0PbtpydnfXEqhCptlcRdU17ZVkmO6mEh1/NroSYzDLGw0R0JXIsOSUJAebzOednZ1R13a+T7o/Wps/96okuvVYUCem1VhE9puDaIEe7722GiK+zr7RcOzTpXEryLGWEMVNibQteyNx1/tqarIqqszWmJRUiWj9+PnR0XWfHJIiqtWmTCm1DbdYRscYYsjwjLwpc21LVFatVRVWt0jFer4bYv36RiPqUbxUhagnZ01os2E3b9pl8VbWiWi5F+VkUstZSnp/WmqYVK7nzkjVYliUmtV52JFeX/9c3lup1m6c0FgpJpY1FmwxjxXJdVWLlz7Kcxjmquqaq6p5Mdc7T1KtOW8egLCVj0BqMsant0aDQtI2jqWu0NpSlonUVxSATa7yT9WTzAm0zpjtTrl27BkDbtOS5paocZVnSlUF8suhgiy222GKLLf4w4+X/xbflpv4WW2zxU41P166oSU1LoKPZsJ9IaLX8ci815ARYLpYMRmMaF1BOcXp2Rogem1uy3NLUMs5Ya/DesaqWrFYlZV7QNhXPP/s8Xmk++ugOrm1wLiJSDrGGWKsZj0v2D/YgBi4vL9Lg74gxIwSxs1irqesWrUVhFaPbaPOKlEUBFlbLFV1lvU2kUNPUMsQoRWYzuhwe5zzVqpIGyCwT+4kPvYXItS15lotiR4O0+gHR4Zoaq0XNFUPANQ3eecq8QBFp64qzqmEwGmPzgkwHVHRim8pzRkNRJeR5znyxkHwnYDgYkNlCnjsXcsJYg1ZmbZnRoI1OuTA5w6FkuOxMR4wnIwajgiwzoDzDYY5Uz9cEFzl5cMb57CHRO6JbUZQGt7qgHCgGWcFwPCAqaEJLU7Vg5rigRWlhJcunapasFhVg0CqjaSInJ5LX1FSRYqCoKs/+3g0GkwmaDBUNMSrqqhFFCPSqKVmXQmYNBiXDckCRZ6LIsyY1yD1utgkx4IOjLIq1MgMSAbRWAPlkA1uTDSkzSMs2eFTfENfZBzfb2brmv+A9bJBd0+mU6XTK4eEhjx6d8YMfvsGjs3NClCy0QVmyv7/Ps88+w/WjQ7yvNux46/ye7nU6xE6t2DraqmaQFxRZRnQtuUnZVCqC7s6jDZjOurdW++hMMxwOenKCILYu552UGKh1aHpHngrJJUSFUaKgfHh6ymAwJKIwUSVuRCxyXfB596NagVWpZTKC04rRoECy0WJv7VJJgdgRO93/YlIaBhUSMbQmV8SS6tnJc67t73N0eEhmM46Pj6mqWhrr0vFW2uBdy3K54uWXX2E4HPDGG2+wWCx6BY1YoNuU4STrY2enYH9/PylTc87Ozrh//z6r5Yq2EXLNJkLMWlEWZnnOcFii9T7z+YL5fI6wTSSyTOMS2RpjspQlReAm+bBpo+vXelonm+vlsYD7jZ9XWuGDkPneR8m76r2llq5dc5MMjCEgnaKJwEr2QbE0B6IX4rGua6IWBVZnjY79mRlp6qZfe53Vt88hU6J2nU532NnZYRqnrCqxp8/nC9lm53rCuwuK/2TYPhv/1slKPJ1OGQ4Gch1RGh8Cq9WK1WrVtyt2ZGaIkSwTW2NHfLpko+wUit11orcsZhlKGfnMUp3qWCzSedtIxmJTUZYFg8EQO8sZjUOvCKxraUm0KpHwqUzFJXKvdZ5xNuLatWvEABfnF8xmM7yvWVU1zgcOD/ew1nJ+fo6O4FsHWvPOOz/i/vF9irxIpR5Cdq1VZB50p3xkiy222GKLLbbYYost/kDgU5FcSnfB0562add3epUQJ7obhtPgCjCbz5gt5hyM98hyg84D1gTywrBcwKpakuWGssxxrsE1DVUVGI2O+NKXvszf/Du/zt7elLpacXF+hTaRLDdi+csMVT3n3r0Z2sB0Z8xoOKKpIhdnElTtfcBYLcQNIdnS0qCV1E+dBWk4HOJDYLlaYRrXfz3GSGYz8kwGHuc9bdNACOTG0tYNrpEgcIVYIq02WK3QhQxUMZc7+ZJ3ZWVwbR0xNS1m5QCNWI9IZKFXQHBy115naA15BqPhSIZoa3ApX2YyGTMejSjzkkFZMhwN5H3nGbZXQuQYq5P7JCbCS1HkOTqbY61jOBYFQF5YjA0sFjPm8ytCC4NpxcFRYNoqjBpy84lrNOEAaw3jwZjpzgBlLVeLObOLGZmxZDYnyDTOcuH44EcnnOlLynxCZoaMxzuiaNMKo6EkI8vBhiGFmZDbvM8PU8ZgtJB2xsh+EuJOSc5anktTHBIY3xEcaYnSOcC0zvApk6sb2Dt0hFaSJSUlif0xQoAYcZE+vydGsTZ2zWwh2RZb72hb8I0QdIPBgCeekIytd997j299+3e4upqjjaEoS8qykDa43SlFmXN5dSEKKmuJQNO0PUmhlAzqRAnr7gbqPNPsTqfcOjri+t4OV2YBSn7OxyA2s08IMwJiRyPlOoWkLDHW4p2X3RFFPVlVHpvJ/ivLguATAdPlcWmSAsswmIyoqjk3bh4RQ8C4WpRiCH/S5cJppYTkihHtk7XMB5YhYpTm7bd+hMeisKhEQMoxloMavCcklU0nMBK7mBBBeZ6TJ1L3hedfYDAY8/HH9/joo4+YzWaPkUBioRTybrlacnp2yu7uC9y8eYs7d+70eVlCpoe0PhRtU3Pv3l201jjnmE6n3Lh+g8xaBmVBUZZcXV4yu7riwguR0LqWECNlWTKdTsmyjBAcw+GQ8XiMAhbLiqoSq15HdBLplYjSIql7DqcjYyWrz/eZZZtWuw7dujZaY7Qok3zw6ZjGXiVHJ9raJIKTvTRGsS3GdH7F/tgEjCFZAxupMEyWu02yrSs6MNYkG6Dq3093ylXViouLS7RWiSA+Yn9/nxBFGdlqlc7FpFy1WW+17f5+7BgrjfeBi4vLpLoSq+FkNKKpG5yXc9qHQFtVxBAoioKXXnqBqqpZrVZyo8R1DZyR5WIh1letJYsvqR2VXmdzif3bUa1WNG3bN6Y2jSeEJcZkDAZ5st4vubi4wLVt+ow1qCjNu6iI806KVAYDDg+PMNowny1ZzFc0bS0WdgKHhwdYm9O6iugDq6rGZGK3j0nF1rYtwUeuZnPqepUIa4PSolRct7FuscUWW2yxxRZbbLHFTzc+nZILgw4KFYW4MalqHqWISsKsJZdeArtxmkIbrPGs/CW6HjCZlGQqkBlFkUvuh7UeYx3WwqAo2dub8vD8Ed/67jeJqmJ3b4/ZPCMrppTFiKxQ7O2NKMqC5aLh9NElTVOztzdhNM5RccTF5XtEDD6Iymc8GrJcVpACv0XlI8qapm0JXoYrqzNMafuA/c4TFXyL1gbvxGozGY+ZjDdUEJuKin5/hRTArLsHpYEo2WeIYhGKgRAcWSYDvMkyjDYYaykyzWg4pByUlKXY2MRCmOyGeU6WKyY7Y3Z2p2mw8ThX07Q1ENHGE3CAByU2Q/BoI4oZYzU+LNAmUoxHOO8gZMxO50KGZQFrDU88O+b5lw9SO2Eky0yy1UiL2fRgzIPjc9pqRfCexbKhrq5o6sCwnHK4f5Ovf/kFynxIWU7IbCmKFcSC2LVMmqSCyQsJo+6n3TRFx7hWTXnvU65PshYGD0Gvm+GIKQtqbQGLMdIG35MB8vpy1Pp8no38ougd3kumFUR8apuMSkuuTohorWiatv85FcVqZ7RCJ6LxmWef5fnnX+Ctt97i1/7G3+DBgwc4LEVRkueS9ba/v8etmze4deMG4/GYq6srzs9XKC2tj2JnzPssIqst1pZ9zlFM9rHCKA4mGXt6wWjH4F0iPdLq9J3yJi1bhxBdkhukaVux966WFQYhfIIJDIYlPgoB3DRNIrSEB3EevAdr0360kTyH3HhCM5eWwESWqUQghBj71+2+h/Op5Q6CzoltQGPxQc5nkNbC2AfXK7S2dAnuMbXIhVbWeF3X7O5m3HzqCXamO7zzwV1+4+//FourK7FvGU1mTForQnIpI6RrXuScnH6DN99+l69/7efY2T3AexKxJCUSJhGtmbViReveCzCbzVIL3oij60e89tprjEYjjLEsFgvu3bvH3bt3WC6XYptbrcSOpjVt07C7u8vu7q7Y41Loetu2km83m1FVFYvFgthlcW0oubr1LV/zPQnV5c6tc8Q2SLAg1x9URlO3fXGG7OiMgBVlYkhh9FHhY0QTiL47lxNxrEgkoKIsLGhLiF1jp+oJRSK9Fdt7T/DxMXJq81z0IXJ+dsHZ6TkRGI9G3Lp1C4BHjx4xm81+7HrcIcSQ2liTqrF1ErgfISgJmU/GT7QytMH3+WPWWG7evM6f/tO/wu98+1vcv3/CoBzQ1Es5n7zvs8J6ayhrUlDFiGtqlo2EwKMks27eWUy7T424JtzEfp/KKwx9Hlpn8+zC8RfLmvfe+4CDg2tMplOObt7g6nImMQJJwebaGh8VxuYYKzc32tbRNJd9ppkxBrRkX6LX2WExhj7Tcosttthiiy3+MEN97iXi9956/I7hFlts8VOHT6fkSvYjrUk5JLEnbSJyo16UEx4MFIUlyxS3bh2xWCxYLa/ITI2KNc7XHF4fMxoNqKolZ+eXGJsxmgwYDAsWqysuLh4AnrOzE6rVirIcMB4XGAPBO+ZXjuAV49GYJYbzRzPu3pmjKAheWs6MzihzycHZ29snBkXTtjJgpz9FWfbvscvLUWo9IOZ50VfVy2BoelKl/xNjMsVEYkjEig8QZXBSRmO0hJxLfk/BcDCgyEWFNRoNmUxGlIOCwaAgyyUrqxxkDIYFWWYxWhGio2lrmqamaSqatiYfKoaDgqq+YLa4wthIUA1ZEWnahkDYaP6C3AAq4F2TrISGtm4wGJaNhDNvvvcsy/EusphfcXHpKcuSxWKJDy5ZZOZ8+MFdru3f4O6dE+pK8fnPvsbzzzzP3u4Bg3KMUQXRg4o6KbFsiubXqC4lWwWCFyVcCAHX1I9lXHWSrKCFVOo+X7rsnkhqO3NgtViMuuO2mVcEUHTOqER+ycC4toSplCHV/bt7bGc5DERicEDEuSZZetavJaqpSJbl3Lh5k5dffpmqavn1X/+7/L3f+PvJDmWxVjEalpRlye7uDkcHBwyKgsxannv2GbQ2fOOb3+Tk5IQsy/rw846g6ML0FV3+lyHTMJvkXNspkNy8ViymUUitjuQD1iRXgBBVUmoKCTEYjsmNWBRXq4oWj9WO6bjAGsNi6RmVBuehrlsyrVFZRoxeBnkn5EDwkYvzK2JIz995nxTrcPr0bwVYAgZNCBoKUFXFYlUTyFFGrNExhT8JmSE5TyGRVOIc62yMYs1dLpc8ePCAGKRddTSZ0LSO1XJB7TxO+Gi8Akuy2AZPVUsD3mp1zGj0Jrs7u1xeLZnP51gDWd4pdKQ4oygKvJfMtxgj1kjBwPllzen5Izl+WjPd2eHo6IijoyNeffUVQgh89NFHfPzxx5yengo5pnUisOLaqpiKBwaDAfv7+7z00ktcu3bI22//iG9/+1u9XbD7mV7VldavrDndlxCEpHQUsk9JVpTWaFMAWbqmKVSQNtjY2W+7fK8QgIA2j1shOxt7jDAaj8BkssbC+iZAd25unqdZbqWJMT3/J8mq7jWcE8tsVdd8+NFHjEYjbty8yc1btzg9PWU+n/WKsf4aAcS8KyOIxFAmIu5xAjzGQJ6L6tV7IZfH4xHPPvM0N67f4IUXn2d3d5+ryzmtcyyXS85OT5nN51RVhW/azS1On5MpKN5qMi12xc7q+0lbZXc96hSiWmvqtA5BYW1ODIE8L2kaKep4/8PbPDw748b1Gzz97LNYm3F2dsqDB8fMF3PyvMSkdkqtFN61tPUKH1L5gzFCVntPjK2UDSCNi8EL2bXFFltsscUWf9jxH/2V/4z/4Eu/gj8//0lvyhZbbPGPwaciuWTogO6X8RgjRVEQtaeuZOCPiXwwWlEUltVqzs7elNmsoamXtCW07Yq6FqvFeDzg5q2bXDu8wXy2YrVacnkhE2eRDzBAORhytPek2Cs8vf2uLAYMh2MG5ZAQ4OrqiqurC06uLnjnR+9jtaFuW6aTMUWWUVUVRFFcSJeeQGtNmdr3XCIRYtDEGHrbinwt9GqTGANGgbIa7yHLMmlszDJGoxGTyYTxaMhwOGBnZ4fJZMTO7oTpdMxgUEr7n1JorGQkqYjNNEG1FLkh4jg/P+Vsfsx8eUxsAzE6mrahaSq8b9Odfi8Doh/S+JqgazCGqFP9fG4o80zsOun4dQOfTkSl1oqsGLKYL3G1I7M5Vb3CmozFfEnwoiiYz+ZcnM8YlGNyW7Jc1iwXFZcXFbujZ3j9xZ/nv/Fz1ymKgSg8QhciboAkG0u2p7Zp0VosfaqzmNHZBOmbztbWq7XaKupI0wgRF0NSBAW/JqjiejDu7F2Sx7S2LKHXS7+zs22SA5CoE2v7tR9jl08jr9c62ffWRqqq6lvYrq6u2Nvb4ys/8xVefPllZrMFf+tv/21+8ze/wdnZBUU5ZDgYk+clxcAy3ZkyHo0YlCUxRup6xfHxPapqyYsvvsiXv/xlvve97/HBBx8kO1vos4I6sksrepIr+IblfEY7L5KdLVItV7I/0tDtO6VMOiRdfl33XrPckGcBO8xTU5wCNZGBOClVsvEo7VdDmfLPbKdAiRCUIviIUoblIuWpJYuhSvu9I186mZFS4GlFNaosVmnmizmtb0Ut5taEpdapCbDzXmpRSsordEHrAddKTtLdO/c5fXTOeDzmxs2bPP/CCzR1zemjU05OTri8uCIkS2dHCCukEa9d1fzgzbf52te+xs2nnub+/fssZlfYpBb1IeKjZ7GapzUkqkKtXa9Ucs6lHC24f/KQt370LkYrylxyvF555RX+zJ/5M1hrefvtt3nrrbc4Pj6mbVu5/iTCwznH5eUlFxcXfPjhh7zyyit89atf5emnn+TXfu3XePToUX+MuvOrW+dZlifLNo8fB9UpH6U5VelIZvN0gqRj5D3Bix2XnpxR/fuNUdM1V0JEGSUtliFKM6ZSNNH37Z4m2fiU1mRZ6LfTO9+3Bnb7rT9v0yLtcrJI5+t8NuONszOKPO9tvEWey+cT6ww/n2yt3S0Jo+WmRtCxt6531mOIDEopCpmMh2SZ5dGjY5aLBZrIYFBSAkWRMR4NKUohnapqxdXVjNlsxmq5XF+rEGWXMpq2lYB3lSyg3XkQUjNpnuUUeYFO+W8dUdZldJlEkE2n00TOtTRNy8cff8zx8QMmkynj8ZgnnnhCrinNiqqqZD+GSAyiCJwtZrR1d9MnUlcNu7sTfuZnXieEiHMt3jvatub+8SO22GKLLbbY4vcDZS3hU9eb/fTjz7/4C0S3Jbi22OKnHZ/q8lPXdRqq6TNTujyZLDM01SqFtmeMRmPqtiY4x/H9Y5SKlOWQGERdNR5MiVHz6OGC+bxhPB5z7eA64/EY8KyWC5aLFdPRlCdvPkleGBTJYhLoG7Hml3PauhF1mRLbX1tH8mxIjJrVfM7uZMLe3m4KCvbUPki+iVL9ACWDQNPf1c8wlEVBOZmk0OMJk8mYvb09xuMRZZEzHJSMRiNGoxHj8Vju/idLmVIKZSDP5e5962qCb3G+xvkG17bUzYpWVaxWKxpX8+jsBKUcq/qK1i3FVmjaPjOmD9jONDbr1GYykFb1lRCDpQyZOreivIghZfkIcZPnYgFcLpcUhQyx9crT+AofoK4dO7sjiiIjhMhsdsH9eycEF1BB4b1iOtxhMtxlb1zywjOvUBZ7KG8hiGXVOQmSjkrhUj5WFzGt5SvJGiVKm84iFKMhRJeaLaXFLWxKPxDiSbKCYk8+GiOWK6WEuvS+fbxtjrUiS8KbI9FL0+dmPlB33LqQ6c7m+Mk8rn5YDgGQoOu6rsmyjC9+8XU++9nPopTijTff5D/9T/9TfvjDN1hVDXleMp3ukOcDimLIzs4Oh0dThsOhWGWNoW0bVqsVy6bGu4YYPZ/57Bd48cUXuby8ZDab9WTaarXq36Pp1Bja4puKzM25MZWihaapZZhOdktAbJ3dvxQEp4ihU+AEKQpoG7SR0oRMQ4ia6COhs3UpcK3khWllicERfYPQ3WKdbBxYk5Pnmogmi2IPXBNVZr22lUKpiDXSEOpReAyzpsa1FVFL6LY2uTR4+oBnbTHt1oqEaIsFTCnJKgtJ1VRVDdXqlNOzU/I8Y3d3l8NrR3z+c69R1Q0PTk54+OgRLiQyR8k1rhgU1K7mnffe4fnnnycrc+YPVzw4mfVko7XSrleWpZB/MTIe76Og/5pNyjtIxyN4rPI0TcObb77Jd77zHW7evMnnPvc5vvrVr7JcLrm6uuKjjz7i9PRUGkU3Gj+993zve9/j9u3b/PIv/zJ/7s/9OX7jN36Db33rWyyXy77trzvWTdtCan7sVKtZVkhTn7Y43yaiUhNE9pcWSAC1VvOo3urbqRuFOJE1JsrRjhQ2xmCiwiiLsbFXuXXqLXmOuFY2kdSESmGsRSfiqSe60r7rlGp9q2669gKEtmG1WvZkaHeMlFLYzEq24sb5Tpeh59r++ZWCOnjatmExv+L87ISTk7vcuHHE9WvXaWOkGA24ujpnuVxxdn6K1prr16/z5JNP9O/H+8B8Puf4+Jjlcinn93LJ2dkpbdsyGAz6z47uZ1rX9u9XbJyPq9psR8alxkSNtB4P8gKtDc2q4my14vQkMBgOKMuc4WCMUkoaV02grlqCA20ybKbRKMaDEfu76bPSeRRynKt6ya//3f/fP/qXgy222GKLLbbYwLv/25/l3f/uX/xJb8Y/fyj9T37MFlts8RPHpyK5yiIjqIzoA0pbUY04yZwq7YB8mElLljHk+QBaUFmR7DwKa4sk1AlpwNJ0VfXLuadaHmNtzmiY89JLz7H7wi4P7j3k+PiE6c6IvNCUWYk2MqjkZYGPoljyIYBRDCd77DDExwylFKudHR6eHHN5ecnhtWvs7k64PtmjLEdorSiKjIODfQ4ODhiNB+zuTtnd2WU0HjAcDpnu7FBXqzRwhA0rm8cnu1oIjqpaUjdzat/ggwQtR+uZn10xn1+xqhbM5ue0bYVSAedbfGgYT/OegFIK8jxjMNHkPuK9wlpp/uqImC7PxlqLtRlN02ILnZQKkp8SlSK4QAwK51LWTRCrqQstmY24JnD26BTXKpaLhmefe5IP3vmQuvasFu+BAu8DeT6isCOeuPYkn3npM9y89RTeR5rGi90wQFgCOHwfyC1ZSClyTOwuiC2ma8SLKVsmKqkBIJKsT2Iz1Ub3ao3oJQjdRw8oykyJyivPRCWW1B0BqFcr6mS/EkIs9GSY1iYN2ELcxLR93T4yJuv3dReeHnwQzUeyEAFSTlCtWFzNKAc5r3zmM7zwwksUWckH77/PX/p//Od8/4c/5OGjUyF2soLBYIc8y8nznMlUguV3d6fkue0VN9YYbt54gieeeJLd3T2891xdXXH/+A6Xl5eMRwN2dyZobamqFTZlsrkIKiqCb2nbhqtZ4KqJnMwqjm7dwN97iDZGGvOih6gJUXK5VMrVi+l/sg5le5YNrJxLSjGNUfII50NvFQxAdMneikJHk6xra/XbznRK10pqtFoP6yqSGSH3jFFoI8fVomnqlsViycnljLoV5VrEQhTirms4JCmBuuD6GCMuRHxwhJCUQUklZoxBRy8kqgssmiWL+YI7dz6mKAqeeeZZXnrpBZ599lm++53vM1s2qNSkGVpQWB4cP+Ls4bmQcK4lxoDt1H7JNNmRbgDq4/X1c5Mw7WzLEjBueuWStZb56mPe/NEHrKplT9JKDlb3CslmqhSZMagYuX/ygHfefYdf+IVf4KUXX+brX/95bt++zcXFFVVVUVWLx2zI63NC0zYVWSotUComJRUUpRMrsYdYB6JRhGjwKFoCUQtprVKbpsf3hDbIunJtSIpHCWyMyRLbKWNFCehwnt7anWeqJ5k6lWdMxOU6u4tehdXZD/PMkGem39fdOuutiMme2TbdftBoY8iyDGszsizv7b9t26Ywdgn2F1tgZL5ouH37mGvX9jk42OVwf5fCaoZ7057g1NqQG5WC3SNKG8rdKTs7Q5RSDIby2TIYlDx69Ijz0xn37x/z4YcfcnV1ldaEWEW79s22XSva2nZth3ysTTMGYJkIfclRzLKMq8VcnlMbolaUZcnh0RF//KtfQyvF7du3OTl+wPn5KU3bYK1lWGSE4KVJVkG2+sf8YrDFFltsscUWn8AL/8E/5AvH/y7f+/P/ahFdf+PD3+JXPvdLW7viFlv8lONTkVyj8ZSiyFEhEqMiIPlUxmi0AaMLhsUIj3x/MBjhXLuRRyIvp1QXjJwG7ORS885T10sW8ysuLy8xWjMYjLhx4yYXszlqHsnMorfgaK1pvaOqKpZLyclZVhVNMGg0wQfyzPLMs8/y2muf4fUvvMa1g33G43EfFl2WJcPRCKFIPHlRUBY51apiuZxzcnqP5WpOXVeE0HI1u2K5mtP6FSFWKK0IwbGqFslG6JjNrlAahmNNWeYExLZnikA2AGtFueSDEFVa5ynnSgZRq3OMyajrkPZdt9+EqBLbXWQ+W0gYtfNJmVUQgk9Dv5ZiAAVlmdO2kegtWuW0dQMRdqdjbr//gL/+136Tn/3SF/HOYMi4cf0lXnjxRQ6vX6fIBliVk+kcFWF+GRJRlfW2tM3sm5CC9Dv1VBf61JFVkS4HqDeLAlJrr5XCBTYeF/AkNQPrFraYSLFyIMH1MVn1UAofZYBWSq2VYECTVHqdSquzoXb5N3meP6b2ioqUu5Uy1pyjaVrm8wVKKW7evMkf+6N/jCeffpL33nufv/yX/7+8//4HPDx5KGqZRG7ZLKPIc4bDIVmWce3aAYfXDjBGcXh4yP7+DoeHhxwcXAMUp6enPHz4kNu3P2A+n/eKGaV0T4rEGBkOBz0xgndoYznYv8b160fYLOfB3Y/46O3vcnh0g/vHZ5LvrxSGTAiqILbOThujohZCMtmWlDaPKUdChNimEG1rZL90dkM6fViX+SX2SZRCRXkuIvK3EhuYhFpHmjZQ16LAFIVcILSSd9Y4Tx0zqlqGeluUECWXKHHjGJMUYel/IQopqVxHeHlsUKhCCFOvdAr9dpgUjF6UA55+6mleeOEF2tbx1ltvM5tfYHJpyNOsbbNSGhGJpDbJEMQGzZrkAsmb6n6mVzMl8sJoQ0C2zXlPXXcEmOqtsnVd9eo0bQ3qMTVjWpsKtFIQIkbLOv1rf+1vUhZ/jzzPxSY9nbC7t8N4dKtv8hNLbC3Xy2SnM8aIkpWI0RalNUVRCjEdvFiAOwtnsja6tiXLDLo7+kn1pRTrhlIVJJeNQEjrrTvvOpVZCIE8kUtynqbPA+/X+3wDHUHXKXEl3N70wfWbBFe37zfzwjrSq/u694m89r7f71pradQdDqXJNb3uYrHCuZaHDx9xfn7G3SLnmWef5erqksGwYHd3l+l0QlYadG7ITI7GcHF5QfCO2jfM5pe8+MILWAtf+9rPcXJ8ioqK1jneeutN3nzjTU5OTvDBU+SFKCaVHHUpKDH9eur2mVzHhFReKwttUrnJ8XEhEHykqi45PTvjzp07vPD88zz11FM8cesWZ+fn3L9/zNnZKVW1QhudyNTO2r7FFltsscUWf7jxp175BcJsS3BtscVPOz4VyXX9+k2yzFIvV9R1S+3krn2IQZRDWlRd0vAWk7JHs2oqnDOYNvRh3ULErDOFjDXpb4XWOTEGfFTUbeCjj+9i84zBYECRi9VkMBySDwaUSlGOWspxzXBS4byESGulUNqQGUMInnff/4D3br+PUjIQWi0NU938pI3CWkXrxH6V55HZ7IrZ7JKr+TneVQxGJePxgCw3FKVhslOS5zmr1ZLhaCiWD6VZVUsm4zGXs4jN91EKqqYRS5DVqKCT1aQlBJLdLqRBxRNCncKGG6zNkO47MKbLodFkWQ65IssyqnrBzs4UYzKqqsaYDOdEsTMsR0SvufvBXY7vn/Po5JI7H92lKDOeefo5blx/mn/nf/Tn+ewLr1DmI5wLRC/jeutbadKMOjWyyQCvtJAJwa/VZTGGZGETtVZINqYksnjMuiP5M+kbrAmvTvmjlEIri0rtXijJy0kPQqFovaOuawkz1xqTSUC0zTKcd2itKDNZ3t1Q2w3A3nuMs6JsidKiF3zEtSlLSCkCgda3VKuaelVhjOXw8Iif+erXuHnrCe4fH/P3/v4/4P333ufk5CHzvuFOYYysi7IsKMqM4UAUgtPphFu3bvLiiy9w48Z1fOs4PnnAnTt3+eEP36Bt3WNh1Mak3LbUrNi2LVmWkec5VVWhlGJ/f5cXX3yJMi9pmpplteL09JSPPvqYDz+6x62jfa7mS3QiVaKHoKQFEQWtDyiViEKj2bRMweMEg46pZbGpe6LyMTJQ/MuPWUCFWNC4VtZOoEnPltr9ujwtklxHKRTSdNiGkIggscOpEMhzA1FjjO4JF73RWAqa2OXqxYjJbMogCrimxRMxuUWlEPzDwyNefvll9vb2uHPnDm+//SaXl5cIF6bT80tYt05kByEKpaZNWvdgs4w8yxiNhhityYsCYzRVVWOt6bOU6rqmadp0/qR9EpKGLqxz52wuhHVH5kAitFCyv6Mok2IEjRaradrvVeWpqhUXlzO65C2lhPwYDAaSFzgeUxQ51maMxqUE2peOs/NTmrqibYWUaltPdF4CyU26RnnJU9R5IoxS22aH9dqhf+1eharWKrcOHfHmvV9nbbFWKW2Gsz+mklNKbiJslFNY22WxyU2U7vx/fLvWofYxxj7fKi/LPlBffk72o9Ziq1VKU5YFxgw5PDwgzw2zqzm3b3/IbDbDGI3NNGVZsrMzZW9vV1TB5TDZICO2yCiHOWVpeeHZ5zFZzjAvuHv3LsPhkJ//+Z/nC5//PD966y3effcdjo+Pubi8ZNmsLaZ5XvQZgrKu1GPWRmstNq2bjlDUSmzh3T4zqU303ffe4/79++xOp1w7POLpp5/mqaeeoq4rzs/POb84ZbGY4dwGy7rFFltsscUWf0gRV1tp8xZb/EHApyK5qrqWu+taY8uCtpZfvHNryQ0YDcZaYvIrGxQYyfAyKIp8mIildSujeFgg4vuhyBiTflG3ZLkMMU3byh3rGKhbRzObM1+uegUPCryS1zN6Jbkkbslq1UhDVHSSixU8KrYoqfojBk+IHpsZssySZWI5GSmDLgK3DiZ8ZucG0+mA0aTA+QoI5FlOiFBXFUV5neFwyNnZIybTKdYaRqMR89UCQGxNWqN0BC0tcGgZwI0yyYKSMqKCIqLRJqcoMqwVO53EKSm8k4yYqqrX9jnnGY0sTbXg+PgRIRgO9o54+623+Oiju2S6ZFTusDM54LUXXuVP/fFnODw8Is8LgoNBOcJaGQzzLAcL3jtRcSTiAsAHGaZUjCnoXImiL6YWMKVpXSuDVafv8ZHYN91FsQmqtbogpPytbujsMmB88MmWpVBRJXIsoq0WW5oSos1q2x9HrwJeCXvTDXjOuT6/qLP59GH3JEuUVri2BSWPl+y2BqM1h3v7PPfl53nqmWdxwfODN97i1/7m3+b927dZzOYpRFtjbY7SBqOFOBgMSnZ2JuztTdnZmXDzxiGf//xn2dmdcOfjO/z2N/8hq9UKF9SaLElkRqfc69Qw3fYKAUAfZA2B11//Is899xyzyyt++7e/yYOHJ2htuby45IPbH3Lz6PM0zhNBzpvGCXnkPM4HrM16wkVOP9UP9ZCIxyCqOqNEkYUR0knaDJMyD5LqDQnF2lD3KEwiDyCEZLWKYjezxvDJlsWIRhkIMaBNhg8wLEtG06nY5WLA+5DkZTplRQkBpCLkWhNTQH8goiKE1qEyQCt8jEynE5595hlu3LhJVdV8//s/4N69ezjXkGWWEB3K6ESW0Oc+kdawVmJH817IGV/XrFYrVstFf/3K8xy0Is8yTLInDkejXl0klsU8Hf/Y2xmzzPbnRQiBxrXUq0oyyKI0NzaupW4aXNMQnCeESFu7nszQWpOZQhQ/maXIUqtiCFSrhro+T7xiR0RCPsgZDEtG4wFg2Nu3sm2uEXVlSIH6ThRWzjlR8XYkVnw8N0pUup4Y5ZqurQZ0f77LcktFJcZslFCs90W/Bj/xJ6QSkE7Ru4mOLO/X3wapBWvCXanuBsO6cVEBOmXGCYnoe+Vx97dzgfPzM/JcGnufePIWbdMyn8+5uLxksZjz6NEl5+cLyuIhZWbFWlxmlMOS3Z0p7zvHcr7AaMOTT77M2ekp5XBInuXcvPUk1w6PeP1LX+KjDz/k29/6Fu8mK2PTyGdYUeQ0TUPbNik/a93EKMdlTY4qFHYwQEdFVVdoXaJ0pG1bFPL36ekpJ48eYjNLUZQMBpIZ+OSTz5BZy+XVFVtsscUWW2yxxRZbbPEHAZ/OrjgcYfIcbcCajPbhGUU+YHcyJrdKWgKhz+pJvhVGwxGjcoDNMow1Sa2lU8ZIarRznhiEJPPeUTdLmrZmOV/IQKQ8SqcsIA0hitUntC0utLRtjXN1ymwJGC3Db5YZtImUZcbeMEcpzaAYMihyslwykmwmRNxkPGIwzMlyQ1QN66DupFTSjkFWpjwZIZuyTBq8jIlcv36NqhLFlmsbFosZRVHgnMeHKPbBqKiaVsK8bYa24F1kMZ8RYiQzOdYa2rrCWktTN2ijMFoGzszmqJhJ46EK3L17TLVqaapTju894sHxOb5RvPaZ13nphZ/lj37p32BnesCgGAlxkRQOeZ5jdNfYZgh4XOOomioNsZsWpO4uvuobt5J7iehjCudOdq2wtmxFH/phMrJWKEFH4IDWFptnPYkTvGyf907IkyDDNVEMYt6LHTM38m+DJjhH1dR454g6KWPi2hbVrckOIQTa9LwheIITVVjrGqwx7B/s8+wzz3J4dJ26dtz+8EO++V/9ZR6cnPDo9JTZfMl8VUOUzJo8EaOZzRmORkwnE6Y7E4aDAdbAtWt7/NIv/gI3n3iKt374Pd5/531q51HKJrUWqK4nUOv1UE4Xzr7e/s5a1ZEDP/zhG+zs7nJ4cI1XP/s53A8iH398h8v5knc+us/FquH+1QoVJHS8yyoLXsK9d8oSFQKRlnJYktuM1WLZZx+56Gk7e5eS/Cy8HDet+soA2SbdqatksI6sc6hIpMHVbNnnI3kUrj8ma9WXUolYVRodpT3zxo0jrt98gqjElgiiIPMu2WB9WiuJvHRISYXzUlTRNK2oaXTkyaducbB/SPCBB8f3uHP3LsvlAoVHJ+LWYGnbNgWwBwhJPQSoGMWGq0VRNhjkHB1dY3dnl9sffMjp2TnWOepqRdSRzGZpn+i1Giyp1kyXBRfBaJsaIoVoNInwwkprZGYzcpNLwYDRSV2kepVj8J4QI1YbaflbiY0yxIDRpKKMYSqeWLBYLFL4uhwna8R6GJoaT0AxxXuFb5WISXNRsxkD1EKCuwBGR8kMQxGVtAcqrVABfOuZrVa0KIq8JMuLnoDprMKislxbCHW/D9aFD78XydWRZZ2tEdYEVgf58djv8g6d8qy7vq1JTFHM9aSX2mx3TasuKaiqSkonTk5OKMsBo9GQ69ePMOYmTdOyXC5ZLhcsVytihKyy6NmKB8enlGXO7Y/vUeY5O3vXefvd98FkFKVld2+HshgymY556eVnmU41rz36PG+//SPe+dG7nJ2fc3F6IfyuhBmilSLL88cszc45uUFCYLGY9yqvGCN1Vcv1N9lCfUQ+R50EzlerirOzM7RW3Lh5g4P9fbbYYosttthiiy222OIPAj5luauRVkLEqgaK4D2ubdBYuQOeBhTvHcqKGalpajEgNWLryfKMLM/RSoOG4B1tqKlWS1q/oK5XLFeLNDhFtFUYA9oEjIlSE68hKIfOIqNcFFhZPkYpKAqbHhMZDkuKMmMyGVIUmeT4EJJaZT1EKS1ZWULgaUbTa1R1RZEXPHz0EK0yGciUxzsnhEoQq43Sqg9dXyxFvbVcLrn94W2KsmA0GiWLSUHbNNS1KHGyLIeBTg1zlm62v5rNiCGijcHmMty2zQrvInXlmF1VLOYNTe25vFjQzCPWFlzbeYGv/8lXODq6yWQwJssLyqLE2iwpFlgPhq7F0xKCDInOe0IipULorEHqxwZIheoJLa06hZaQhFZrYlLaQCCalMXUDZkhEryi9es8I9cGdKtxXpR8ATDWrLelI7r6wVYoNIMipEyl6D0uirKns0x2BMumZWfzbxdhtVpR1yuM1ly/foOnn3mK60fXcd5z9+5d/vav/z3e/+A2D04eMrua450Qb8pmaG2wpmBQllijmU4njEcjJpMJo9GQclCQGcNkPOL111/jqaefYblc8IMf/JBlVWNslt6b7/ctRAii6NoMzd4c9NcthEISPHz4kG9967f4ype/ws2bN1FK07ae73znu1wtlqyahrr1SS2phRjyMZUdKJTRjEZDDq7t88qrr7A33eG9d9/lrTfepK5rbJ5hgufWE0/w1M0u10kC/5UWN2njHa1zSTXXZWvJn1VVEaOoRaqqYrG6i44aayxFKbbOpq6ZzeeinkvqTpVsbfK3YrlaiX3PWgwKa42o/HKF0UYy25IyLxDxUnyJCw6lDM5LiLjSgS++/hrPPPUcTdPw4Ycfcbg/5ez8nPlsTtOIFc9aSxtbeT/pXJcg8dRml+zQQvZGjFYMy5yv/uyX+d53f8Dx8TE2s0L4Btev3a54QSstWUvJYik2PAOpFbIjXrSWY4ZSWJuhlO7PT9NZ9xQoHfu1YtWmxVGUUwqPio5BmTMeTdjbnZLnGVmWEWNgNptxdXUlxIaSa0/wXshIH1AhoBGix4eAT3lipGB+rYXUVkbITJ1C16tVxfnFBYu6kWtcerxKajaBWBq10qJ4s7rPwersid21ev1v01+rlNI45zGmU4kpYlzneSnV2dLVY+dSl27WKcLWtso1Ob+JT5L0Qs6JOrKuG2azOVkmNyOsNRRFweHhEUVRsFyumM/n1FVFjLBaNSyXj4DIr/7qX+Xjj+8zXy5559032N0ds79/jYM9KUrJ8oznnjngqSef4HOvfY4ffv+HfHD7NsvFkkikaRsiJIusHDefjgHKp7WmsFZIU++k4VZFIb6N0RRlwSDL0chnWduKUtAHT9PWPHr44Pf3K8IWW2yxxRZbbLHFFlv8hPGpSK68yMnzApfCgbWK2AyyQpNlGqMtNkvqAgcueqyFzEagwYWWphJSI87FQuhdAzi0ifjQYkzLeDfj2s2CIreEKBkwg7LAB89kkjEYFCm0mNQwl6GNwgepUS+LjK6SXmsZiG1SkAESHB2kFbFTEeCTcgTQxnI181TLiqIoaOpGrEcgPISXLBxrZKAJybJkjMVqy2AwpK5bDg+vQ4x945bWBp1rjM56pU7bSu5U8IosLyiKEt9q8kEhQdCtYrkKnJ8tWC4qLi+WzC5XFMWEp598jtc/8xy75b5Yv2zOtf0DBoOhDKfBJbdh7C17nWJmM4RZqcfJFGMS8ZeGyv7nAnRKhhAjPq5tpxFoCb2tsAuhXofTC1nlk6otxCAWVSRA2bWS1eZj7LLqHyO2IK5zoGJEofGhJapOwSNWKB3XurNOxdUNpJ1lsWkamrZlNBrx8suvcP36DbRWnJ2d8Y1v/jb37t/jzsd3uXf/mMurK8Q1mIiZYoCyYqMd5CW7OxOm0zGT8ZiyyBkMSvJclDtlkfPCs0/z6qufwYfIt377tzg9O0fbXAgC8f71x+GTJFZ3DDaPV5fLszms37tzB63gS18SouvVV5f8zu/8Tnq/TlKZlJHBX+nUPCgkwHPPP8cLLzzP7t4uOzs7GKXxrSM4z/n5OZOdKZ7I7t4emZEBua4r6qqmqmuatqVK+9M5J8c3EVwxRtpko/LeJxuoWJwdDuoal5QjxChNbnpttxPiT2O04e7dezgP2uRkJsNmVhRNqfjC6HTuap1UTonATuoVCEiGuueDd9/j9OQRxkgm4NNPPckLzz9HVdXMZnNOT8+Yza5oXNMr2qL30rLZZYfJwdlQC0aaumJ8/ZA/+a//Et/73ve5d++YohDbYlGWmHQuuUQItk72R9ecB6R2P1FM6pSRFukIeb0mweKaqImJrAGD90EC9VO2nFZC2osK08v7aqpklRQF4ng85uDggOefe4a8yJlfzLh7fJ+oJHci+IAOazWUnL8R7wJBRVSeEdNyViHlNAYhnbXWDAYDMFZsqEpuhGRZ1j+uKEqxveeFnOfeJcWi7PF1HmBndfc9CSrXKytEnForCR9XcyW94QZJtVZ2du2NMb2eXOTX+YD8Hj+zDt/fDLsH11uJO2XqYrFkMCgIIdI0LVVdUxQFg3KQSL7IYlkxHI5YLpcsFhc8OHlAnn3MoBwyHg0YDHOmk1329vYZjsd8+We+zMuvvMTf/6//AWcXlwyzgrzIJC+sa/pM67Jt2/59F0WWVGgNbetYrZZkeUZRFpRlATHSVDVN0+CcEJmdlXi5XLLFFltsscUW/6Lx2d/8s2T/YPrY16prkbf/B/+Xn9AWbbHFFn8Q8alIrqgcwYP3qe3MeIx1RFXho8K7iAuSPdX6BhdbnIqEYBmVAzIb0EktpXTAZAprZBDKc8NwOEWpwHQ6YjTOsNm6Ic+kAPmd3REQ5e5zUeKdZ7VaJuWOTjk/EiCfWcm9qeuGGD1t4xNR0A3doR9uQggUOhfbjHe0XjJnXKqPb12LSnlhoozQqKQccGlIjbHm4clDUIblciXNWKieDHNe7JlSQafQRmyDrg0sV0vu3XuENQUX5wvG4ymnp2eYkEGyso3Huzx762kGz03Y2bnGjaNbHB4cMSmHOBfEktU65vMFMYY+WL9rzRNLkO8JLaV0spMpYoCYCKzOLhRjJMtEcdS2TsKnu9DrSBosYxrEZSgShd/aJuQcfUB2DCn7KVmsorIS4K0MIvqSxj0XxHYVQhD1VLI2hmRdIwRR1qjUOha7XruQsoPC42RCIrZASITr169z8+ZNRqMRGMvDhw+5d/8+7777Hh999BEXl5c0dSoFSIoMrQx5VlKWQ/JBwWA0ZGc8YHcqJNdoOOhH5M76tL+zy3PPPovWih+9/SZvvPlWT8aR1IQxrkmtT1q0uq91BNcmudU14u3u7jKZSH5O0zScnDzg8vIqEau6V7WRFEORwOHhIdaKZS3GwHwxZ7lacvv2bVzT4JuW1XJF27YsFgsa77iazfqvtU2Daxx109B6Wfu+s49tZGt1C2Hz3zG9Lx20EJtq3dgn64g+SL5bowCrqub8/BKtM2xeYHQmJQ5aSBOtFEYpNApl5I+sK1GfGi3WLIicPrwQYkhJu+p4NKYsS5Q2WGNR1pCXA1RrWS4XVFWDa+o+w8hmEuxtTMovU1I44VzD8fFdnv7Zn+Vf++Vf5B/8g9/kanbBcDhgb2+fawcHhOR76wL+QdG2jqqWrCVrhfhYLitiiDgfqOuK1rVCKoUgSsqNIoXuvBHyWdoMRQ2ZVDxsEDRK7MZNI0H4i8Wc1XLBarVguRyyu7tLbizPPvs0w3FGUeTEdrYmihBrqfOBoEXVJdcEAIUKQjZ579FRiJ7xaEw5ApUs16RrRkiKoaIoCT6QZXmy2a2zszrydzN3qyf3ok9ElErnUkg3NSxgHwuk3ySs1vlcHXHcfT3wONaq1u51N7O91p8dMb322pIdQqCuG5bLpVhPjTRJtm2ydcZIloniWGvLeJyl9zgAhIzt1F8xgjb3yfOc8XjE7t4Bg7KgTWo6pSKqFVWrSc2Km1ZQrYUEtjajqiqKoiTLAkVR9GS8zSwxBDKTU5Y+3RgJmExuEFXVluTaYostttjiny9e/Ev/E5R7XDb97K9V6N/4zce+Zg4Pecn+O/9Ct+W/86f+a/43hz/8F/oaW2yxxb88fMrg+VO8kyZArWAwcBQDT146rJFslq5BEB1BQ2YM1sJkbMlzyMscY8FmiqLMKIpMBlSjKItcQuCtJsvFgLRcemwafrTKyHLN7OoKbzSZFVtktZpLpkyWpVYyJDcqhkTchDRgiFrCWoP3MmDFIBZKrTQxgPOeqnYURUFmrdhqtKZ1XvKIYgpHT1aPXr3iZLCZzRbE+JCLiwv29/cYlENi0BSFZBg534AS1VWngmqahuW84sP37rNago0lTz91wG7+FINiQJkPGQwH7O3ucm3/Gjs7e5TlEBU1beuYzWZ414WUdzk1Ae/XCqjklKJPGQsR6NoGhSAkWQgjyToHaC+h1iHZGdc6KXriiZCshsJgyBCoxHrXqb5CPyAKx6e05DmFEIne41P4eYgeHyPOt70iqG9HI6noOkuSMXh8L6xxKYzce0/TNqxWK7z35FnOznRHCKGdCdOdXQaDIY8ePeK99z/gnXff5fjBA2azOU0j9h2lLZm1lEVBOSjJ84LxcJKa6UYMxyOm45wizylzIQOU1pSDAcPBAIgMioLFcsHZW2/ywe0PiFFRlgNkyDWStYVYtrQxfbZSH6bdq7jk3cfgyfKM8WiEzXJ2d3coy7Vy7OTkAY8enXF5eYXzgaIc0raNZD2R1DxNxUsvvsDR9Wtp3TjOT09ZLJfMFwvqqur3cad4q9tka02B4z6pmkIiGLoVsUkmKNbvZTMTaUNA89h77XKQYgh9LhlpHWprKYdDTJahyVDapoZPKSXocuc762B0AbzC6IjxAaUcJuUPCWkW8cHTesfF1RznHqTQc814NGYwHIqyyGS0DiIGlRXUVcvsakbwDms01sh1qwuZt9ZwevoQm2X8wh/9Bb7wxS/ww+9/n8vLC2bmkjLPesK+LEvyIifL8mRts0SE+DBaU43qXqHUtg0hET0dMd2tCRA7eF03/fnSqcK69d+27TpUSu4GoIj9fq+qiuVywckDh7GW3ekeh4dHjMc3gEgTIyhDpiwxiiLMBU80ndJKDoAY2UEFuS5oJddWG0SdhRKVqOpWh4LcWrQCkxlUIqw3yanNZsTNP5vWwk3lY0dciW1Q94+RfbJJZD0eRq/StbOzNXbrdfNvec31WpdzYU2MddfeTcXY2hbpeiKwe5wxql831po+TyvP8pRVqJODWYjNpo2cnV/x6PSCGEUZhlLJKtygUslBURQURbLJp+cUK3GgqhqUavDOi1WVKITnBhm4VrQphjajbRzttl1xiy222GKLT4mDH7b8iTf/Tf7WZ3719/x+Ntc8+7//DuGfoBb2Dx/y/P/q4b+ITezxX7z0xd8XyfXh//qrPPO/+zYxtWZvscUWP534VCTXdK9lMhrIAKFiXz9fDDLJZLJQFGKdkIHAUZYF1uqkfkgqIb0edmOkH+5bt8BaqCsPFITgqasK54QIUEqxWlVcXFxijKFtpHJehkGf2r98r66KAZq67a2GXb6UMVZaDMP6Dr6xNg1MGoLCJAWEd4Hc5lhNCpyXAc4FR9PWvY1PGvYsh9duSGaNh7bxaOVR5JBrhsMR2miWi5rZoqZtHKgWqwcMiz1efm6Mq3L2J9d44sYz3Lr5lDQsZhbnHE1d0zpHs/L4eiH5aJ3Sh7WiobNhdRbB5G3qBzodouSypAGwa+0TpQ+gkl0KCezuhrJNS103CznvCK0Mmk7kYEk95iHEbqaW8G4QklCppPwKydakJWjee1waap13dAH2HYuiOmtR+mLt2nX+jNKEIEN+07QorZhOJ0ynu+zv7TMZj8nyjGVT8d4HH/DOO+/z4YcfSuB827KqxMKjlCKzliwryHMJ6h6NRkwnY/b2d9nf26UocsqyZDjMGRYlk8mYoijAaAmgzzLqesXF+QXff+MNUdh5z3g8AWX647LZDGc2j6VaKwa7rKGizCnLgt2dXXZ2ptKANhxweSk5ShcX5ywXC9rWYW3OeDTi4OCQB8f3k7JFp5LMyK0bR7z8yovUdUO9qlhWS67mS65mc5bLJXUjdsS6rpnPl1QPT0UF2A34qDVpyePWr+6c7kZi3XEr6bU78k6he0JUlFu6J0RVImQ1kh022pmQlYVkvIV16LyKdEwbQZEUiOl1dSIRFRitpPlVmRQk76U5VGmUCmgjKsfWOS6v5lxczqnrmvFgRDmStsHReJdiOMVHw4PjY5p6jlaiZOwaETMrDa3n3/kBrY+8/vrrHN24yaPTUx48OOH00SO8c31wfCTK9UubvnFxOCyTwkYxHI7IMinHGA5KBuWALM9QycrZrZl6VbFYraS4IURWqyVtKwpUn5SoKgpR3WfsKUUgYrTk4XnnUaGl8Y7L2Zz79x8wvzrhqSf+BIPRHsFXxGxMiKc4nwL+E2PdZ4SpSOi8xkrsy5O9CdcmExbLFReXF7ItJHIc+mvBcDBAaZ0aUOWavL7uRLk+hE5Furbu9tdsOlWVp64bBoMyfTaY3voa42ahhu8VYmulYRc4v17Tm2TipkDxMXViUr9u2iphTUQ+riiLtO1ayVXXTVLZ+nTtNqIezfNEYMvNljwXItUYyYCs65pyECgHA7kBkV7bWtNbUCeTCWVZAlBVS84v5hR5wdXskmq1wmZiX2wbsQ37pLjt6iSU0ozGI4jhsWzGLbbYYosttvj9YPDxFW+9cQs+83t///Dr91F5Bn+AxMJv/tt/kV/5P/4SfktybbHFTzU+Fcn1+pef4GBvymBYQPREPDaXrKvQKQYUWCP2hqquUFnE5BmtX6FjRvAQfUiEkYRgdwOLWAW74Vczn8/6wa2ua4IPSf2Q/VgQ8WajVFVVKKUYjUa0bdtX0xdFAaQBRUFeZEKydcOF0WRZRtM0ZFkm7ydIU6M2Cuc8rvW0TYvzISkhDEFHhoMRbevJJgOxuZmcelnz8ME5ZdlQFJbReIAxikeP5jx8cIUm49rBHp/7zGfYnV7j8OAmu+Nr+EZzfnpFU4slrG4busZKlUJnAmJp6QY+rTWGZB/aHMCU6hvhNjOqfPCPibJkEAuJpzBCFigFPkioNLK/Q1w/vzEmPc9GblTK6+msMiElBvX2yDQQ++BwneokiP0uhCBym0R2iKIqWde6tr40PPqkLAtJtQWKvMgx1jLOCw4OD3jq6ae5dnBIVVW8/faP+N3f/V3efucdZvM53jtiFEJQrKYS5C1kZcZoOGI6nbC7u8N0ZyIk1+4Ok+mILMsYDQcMhkOm4wnTyYThcMiqqbl//x73j++zWixE9dLtS6CNijxPg3kayr3vFCF+TUL21ivDYFAku9set554gtFwxMXFBcfH93n06BGtExWd1kKwWStB67u7uzz91FPc+fhDtBIlX0dS5UWGtZq6DhSFtIlOpjvcjKJw8l4sns45Ls6v+MY3fovz80tiXNvINlU03Tn1mE1xA516plu70FESG1CSNdXlWyVKkxgD0+kUayRvihBRWix6Wq0Vh5uKHCLJogUgtl1rDEbppMJMlsEodtGYLGdK6Z5YVcqwWK1YVEtOzwM3btzi8PCI69dvMJvNaJo6PY1cw5xrWdFQliUhVPy93/iH3P7wHp/9zEtMd/d5eHLKsq6JMZArTWaUBLFj6LKkhABxiehRXF7OHiM8dU8yJpJPpXZGpdF51hMbWilRGw4OyItCrpdK9aourTVZnrNaLpOtrma1WjG/uGTVNjgfGI0mDCdTxjvX2TsY4JolRA36GJdKR7rm0xAgqoiOAR9S/ls698tywDPPPAtKce/+PU5PTwk+UNXS/Cj5T47zi/N+HWlt6ex/3fmwPi/Wa7C7bnXnT2djDyFQVVXfJpin1kFrbW9h3lRxdZa+7vXXa0n2dqdwWm9Tsngncsu5kAhqsSLKU3WEWWcZl6uXcy5tY+yff5kUd8aImks731tC43yRlM4khVrGYDBgPB4zHA4ZDgdC3PrQ57w1TcPxgwfcuXsX70WxlWUGrXOUUpSFFD4Mh8NEBG7cKNHyudw0LctVJeSXd7SLP0ATyBZbbLHFFj8VOPvSHv/+L//aP/L78T85Iizu/kvcon92/M/ufo3o3D/5gVtsscVPFJ+K5Dq6PsDahkCLjw6tFU0bCLXDGIvRnRoK8sxgzACtFZk1yU5hsMbgXEvwXTivIctytDbyC7k1qXVOhqUQIM9zlBI7V4ihvzu/buciKXiafgDfJLfatmUwkKDfOmXfiJ3F9mSP0nJnfTwe0jQSitw2DePRiNVqhWtdUl1YvPMYNNZkLBZLzs/O2dlpcS6QZQVnZ+esViuef/JVPro848P3PkapSF23vPjiC7zwwku8+twR16/dYm+8g80K2qqmugzcO3+EUpbMZtg8Q9Lek5LAGHwXdNypqqAPP143fnX5PIlAZK080FqjktJGGy1B4m0DIsLCWNPbuowxmKJIVrLYN0h2pGTbtpCGSgDrPSqISqnpSEu1JrligNZ56qamaZv+fUAKvoe+CUzTERYyLHZqBwm8d1TLFRrFIC+SXS2yu7vLCy+9yBO3nsD5wAcffMBv/uZf5Y033+LBg2NWqyrtF9UHijdN1beh5bkMf+PRiMNr+zz15C1u3LzBZDJKComCnd0pN65fZ3dnymxR8+jkIT/4wQ+Yz+c0zm2EntvUPik2wU0yKMts3zjZ2dc2W+SMSc1s1w554sknGA4Kzk7PuX/vHg8ePOgH4qIo0Lrtj4tOgd5aG4aDkoOD/WQtSxll3mE18vopi6gLlg6hkdD/GFAxYHREWc3+/g43rx9yeXG+zqNT6jFi4HGof+TXuserjUdtPlolP5hzrl+3XYC3XB8SwZZUXJ2i73FSQsjXLojMaoPdCCTvCAqdctFQoIKE3aPExhxDJMsLaZ/zNUdHR9x84gkymzOfr4RoSa2Fcp0SEtbHQFU5lFYEBx+8f4eiyHnyyaeY7kfu33/Act4Q4wKrNWWRUxQ5eWZ6sqGua7IsYzqdUqe7hOtg881A/mQhjhGjoG1aTh89YnZ1xWKxTJZs368VndaYUqQChRxrMwZlye7uLnt7e+xPdjB5RlGWhBiZTAcMh0OICpuNgRzMkBgNIbSEdNEIicwJiahTG8fz7t073Ll3l/FkwuHRES+++CKTyQTvJE9qMZ9zfnHBvbt35aYI0lrZtb7GuCbIO3T2yG6fiPXP9tfBTtnVXaOccxL+XxRMJpM+p6+qqsdKErpSjt+rkVGpSAg65V3FPvcKlJDxSY3Yve9Na65Sa1VhluW9YrM7llXdPHb+W2tSjqO0E8v6lwXvnWexWDCfzVFaCFttNHlWUA6k7XU0HDIej8kzUf21TcNyOaeqfd+Aqo2oxfb295lOJ5RFTtM0VHXDcrHk4vKSPFkxy7JIFuwttthiiy22+P1j5//2Df5S+FP8e//Rf/J7fv+//xf+Cv/l3/8c/tHpv+Qt+3EsH4z4XlPxhbz8xz7u3a97opv9S9qqLbbY4p8Wn4rkms0u2dkVa1aoPeumPoXzLUSX1D3dgOFoGrELDgZDZldLJpMxYOSXdjolRJeXEmkdeO/S0GJSC5wMBa0PKBX6zJl1I1kuw1sK3XVOMrVWqxVZlklrnDFU1aq39GXJDlLXFbOrGVpr6rrm7t07zBdzMit17NeuXaeuK5yTzKjVqmK1WlEUA6bjXazJKMohJydnXJxfYW2GtTmXl5fY5hHP3PosX/n8dV548XmuHx2itWW1aqkWHt8oVleOLIvEmOFdi7WS27NYrDBGp3asrrGsU27ppA4Q8iIvC+oUJC2DlxAlEnQfUHE9KHdWw0js928IYluKRNpKlBUR8Gn/CgEggz1J0RCRDCSiPJdCETWYgBAQURECeILkbaXge9fGpKYQNZNSCh98ylPqCC2fbI7rwTMmhRhIwLxzjtxmZFnGa5//PC+/+gpV0/DO++/y//x//b95++0fcXp2JsUFPvbv3zkviiCVYbQlyxSj0ZDxaMjOzpTd3V0OD69x6+YRe7tTxuMx164dcHR0yGQy4eGjhzw4PuatN9/g7GJOaB1FUUqYc9nZfBJx5ENPQsp50YXuyzEMIaBRTEZjbly/zt7BAdPpBGsN89mC4+Njvv3bv81icYXRFpMXGJEn0TQtTS3KyY7YlbbL2GftSFaXWPLWIfYBY03fuuZcS0gB3qLMSYRKCPimxQe4ceOIDz74gFWzVm51g3pnzaJbKVo9RlzBuq2TLo1JrZVc4jiM6M6+phVsOKPWKs8u70s/9vVPKsmkXCFgM42KkjmlAKsNpnvtREgYbWicI7ZCThokyylGRds2RA+T6YRXXn2V1nlmVwvu3LlLXdeiMFXr1w1JgeRDIDohABvvee/2R5xfzbl+4zrXb91kPlswu7zk6vyU09NTjJbgb2MM1ooarxyULJcrIpEiz0FpjJa1ZYyWwoospxwMRMGkFNFHVCLp8rxIytSQrIGht3KKBbbFuRYV4QqknVNrtIv46GljoG5bXvv8SzzxxBMMygEkhZsPmqb1OO/ACtnoQwS9cW1JKr9AJM9LikFJ27a88847vPHGGz0RWxQ50+mUZ599ls985lWstbRtK2TgcsnDhw+5urrqbZebxNDm+ujIPyH00nmlDZvksnOetp3Ttm1vKVZKU1Wr/joKKlm3OxtjoGshFUVd99qhv0HT2fi60PvNHLFPniudGqy7fnvv8F4xHo97y/66vVNyzJRSOCfntUpqvH79h9Ri2cJqVXN51TVQdvZ5KTbRWjMcDhiNRgyHQ2KU5tSr2YzZfI5zTX+DaDKZMhiMemumEIKhLyrYYosttthii39e+C++9Cyh+skTXAAv/7u/xb/1f/6f8/6f+b/+Ix/zjWpr3d9iiz8o+FQk13g6phhkqCjEUm9xQ+7eRy/KG6MURWlpjMZg0MqiowwJxqyHLZJNqGlbYlNR1VVSWEhgsfeeIs9p2yoNsDKAW2ux1jIYDHr1TWdBEUvOirJcB+4aE6nrBVW1YjKZsFzCct7Q1jKgGl32yq88GzEZ5eRlRmYLYtBYPeBydsagLLh14yZHhwdk1vL+e7f53rd/yOVlzaOTFcPBNV7//Gf4k//6n+Lg4Bqs8l6B1LYti1MPOLGh2ZxoIm2bLDlK99lbeS518EJCFOnuu+8HqLVCwxKjp2kkh8mm+viikLvuYntaD50dKSgKJlFS5bm8TuMddVXRNE3/PKFTIdFto+TF+OAlDylKq6FGJULNi93NBbQyRBX7ZkbXZ7oojDUo3Q1+nuiFmEQrrNIEI8SX714jNV76tsUHz97OlK/+3Bd56bXXuHF4ne/94Pv8lb/+N/jtb32bhw8f4V0rJEWa1JzzEkrf5e1oS55njIZDdncm7OxMeOappzi8dkBR5Hz2s5/hyadu4bzj6mrGcrnkd3/3O5yfn9O2LZnNU3h+gbKGxkdU8L3qqG9bMwar1pRPDA6UYVnXWAWTyYjnnnmRGzevc/LwAXfufMhsLq/nnNhAtdJok0voutIQNVGaAiSHLK6LFSISvu9DQwiO0WiA1uCcnGtayfNleUnTtEkp1JUBJBJTxZTMFiUrymiGozEBJMA+JltlDGjT2VzXhQJda6Eo91KmWqcmTMe/+/+OIJL34fsBvyMDxD5qGI+nLFcrsrwg6Bq8IcbuWmLYVDc9DkUMUUjFEIlaCHkhy2LaVsCIYsYQMSYj+IjC0LDg1RdeRpsMX3k+/vBj5rOZtIjGKFZgraQ4wfl+vWmtiU5UisurC/AtTz31BIPREBekVEH+RJaLJcu6XivP7JIiT9Zpa2Xd4hIJJgpPaeUrGAwHFGWZFIOyX4tEanjniCFgjMaadcvmpgWPPgMqwyiFt4rAkmk55cHxCdPxCGs0kUQYIQS08wGlLQHQVhHwhKCArL8JYYzGO8/e/i5f+sqXmU6n3L17l/fee59Hj86FyJ8vWS5W3L1zjxijqCgnYw72Dzg6OuK5555LZJ1Y0c8vLrh//z4PHjzg8uKCqhLLo7WdMlH118eO+OqyEo0RYne1qlkuK0IIlKXkn1mbs1gsUu7XmhDuyCg5pimnUFtg3cprbda3DXdWW6U6gmudIdZdh+XvtDb7rEGxmXcqsccR+kD8TXul7JPwGOmVrjLpfUeaxvVWysViCTzqt8Fay3g85ubNm1y/fp3JZMJiseTOnbvcv3+f2WzWf9ZqrRkMRmyxxRZbbLHFp4Xy8MgvuGZ+/HNE7UwhfZb/NEA1inmoGOsfV3PdcXP+w+f/KLC1Km6xxR8EfCqSa7VckWeaGJ0QB84zGJS9PZAQqeuGa9f2UNqT5wZrsvRLugflJfRZieonKzQ2KkR5lJFVA9pEsjjn8DFii6IPR2/bFo0oVayxZJnYCsWuYjg9PaVJFkOQ4biqKlarlvl8RZEXLJct1aolAnW9tjQulgtWyxW7u7s4F9gbDGjbiHeRxbzi4nTOR5f3sdYwGgwYD6eU2ZSf+fwv8eorX+D55z5L8IbZbMmjR6ecPbiitMPHBq+1qsD1JNNmg2D3p65rnHNorVPlPI9Z2mCdHaOUomU97IgC4aJXvMQYMWng6xUW4RODl1KQSKcuv6YoCpq6XaurYujDxjOboZA8ndxKqH+zbFktq/V7S0RPSAqsZFokxev0dp7kCJIMGudprca3PqnBItVKLEUH1/b57Gtf4QtfeJ1r12/w0cd3+K3f+ibf+Iff5N3332c+X8i+bT2tb/thNYaIl9AgrM3IsxKlDbu7Yz7/uc/y8ovPc+PwGjdvXSfLLePpmA8+eJ9vfvMb6T2kPyjK4YgsKYVE+deKQmvDIhWC6/PiuvDpTWuf1poiz/Ftw5e+/GVeeuWzGK147/b73L1/DECWZZSDopfKaZPJTkvrx+Y5LtnQxLAb+hwzlQL4Y4h98LSQWaKiKsuSTNukpCkhEVqNdygdMFossiFotFPEoFjVC8mqUkYsUiTFFWC06dfRj+VyKZUUNeuvhX6yV920n9bGei1qZVFJsWZthtEZeVYQUXgiplfGPE4eCCnRqX3EumkUSQUFCk3Y2JaIKL2MAnREK4PTITWzRq4dHLK/dw2i4vLyivl8kRSm4I1KKkjZ95vWaa2kXEO20TBbrvjOd77DaDRBackHK4uC/WsHZFnOcrWgriUHcDgseeKJWzz/4rPs7e+TZTlVtWS1WDKbXbGcL2jqJpUlrLhaXqAU5CbZi1M2mzFiDV9Uq5T1ZFNOmd5Q4XVNmUmBFQLaapbLJc8//yx/9I/9CcbjHFwDxggRmgoDAgqMTsSmwkcpqnCt6226LkTuPzjh6jf+PijVkypPP/Ms5+fnNI00oJ6fn/dEVtPU3L37MXfvfizEXJTr/Wg0YjQec3BwwEsvvoC1GfO5FCVcXl5SVRXn5+csFouN66eorbRWeN+iVKQoMroMw6qquLhY9ETOZDL6xI0B318rve/UsL6/DouyVoit9TlPf57LEl9/bR3erh/b/8Z09l+LUmtL+OOn0o/bgLu8ts3Pln5td6rdjc8JurNAy/tfLBa8++67vPvuewyHA3Z2djg4OODnfu6rjJJN/+TkhA8//JCTk5Mfe/0ttthiiy22+Cdh8p9/g//2yb/H3/6//2c/9r2/8ju/xr/1lV/BHT/4CWzZj+PFf/8bfO3h/5Lv/E//T499/ZFf8T98+o/+hLZqiy22+KfBpyK5gpeQYWl8U4TgqFYV1lislQDr8e6Yg8M96npFXTkuLxZMp1MiLZOdMYPBkIuLC5TOGA6GKEjBxw2rVYU1BqJiOBhxvjrn6lLC54scQHFxeYFzjslkSlXVHB8f0zQN+/sHKKVpWwdRLH8X5wvaVoia2WxGCIHRaIRC4/1aESV3qgdcP9qT9iqlmF9VzK4qvvO7P8RViulon1s3n+Xzr32B559/nvFwBxUyvItcXq547617aC0qisIMiDGwWq2AzpK5zn7pvgZCXklrlultLpPJpH9MCOufDyHQtu1jg1hEVAY2z1EkQi3l2XRoN6rtu+3ohp4sNWy5jXDwGMG1m4oGydtBKzIjbXB1XaONwcfAcrmkWlXM51diwQldWyCSedbbhgIqSj6N80I3KN1Z+2SoXFUVq8WSEBwHBwd8+We/xOc//wV2d/e4/dFH/H9+9a/y7d/5XY6Pj1PzpljthACVzBongW8YZciLTBrKMmlCvHHjOs89/xy3bt1kf3fK/PKMQWl5/YuvkQ8mfOc7v83V/AqlDXlq7Qwkki+QQrE9WhvK0iZisGA0GuFTkH4fur/RBudcm0KiJbeuiYHTR4946ZVAQPPEU09zfnnFYrFIw67pyZ8O3SCtlCLbJDvRvSU0hEDwEWMzbt68yR/5+tf5/vd/wPnFBdVqhfeBv/KrfzWxjLFv+usaAq01GCOB5nlWgDZ8fOcuMdq0PnSyc63JLfk6SYFFv47ksaHffqWkHKF7X52xcfN9WqsxpkBZea+DXFouFRIaHzwoHrdNdvtFFC6qt/516pdNAkKbbr+l0HbZALx3uLYleJfOmYann/4cMWq8C3zwwW18p6ABVIAYdX9OKdr1eRpjapWMtEHC2C8vZ5yfX6KAMs84Ojrk2rUjRqMxq6ri7t271FVF61rmi7d55/13xLpYDphMxgyHA8bDIYNywM7uXlKx5thMlF1GQ5FUXllSNllrqaqKtm2p64q6rmjqOhH/qz6TKssyovNkmcG5CGjGkyFZXqQcRI+KjugbmtWK+XzJKnq8aYFA6wJZlmOUlZsZ3XXHOQZ1LXa50YjZbMbF+UVvB9w/OKAsSo6OrjMoSy4uLriazZJCTyX1p6KuG6qq5vTsnPfee19ugDjZbmMM4/GYJ598kldffZXJZMJoNOo/G+7evcuDBw9YLBYYYxgMBn3joPehv9531ugQYgpqz5JVUPK71hZI+fzTOrJJLG3ePOgIu47I70s5erJpTXIJSeUSOf3jBJfWJj02bpxr6jFLZEfSbRZBdNuyec2Q7ZDMxhD8Y+fFYrFiNlvw0UcfE2NkMBhIFt3Nm/yRP/LzOOf4j//jv8gWW2yxxRZb/PPCn37h5wnVTwfB1eHJv/Cb/Bt/4Ss/6c3YYost/hnxqUiuGCPLRZVsSmJLJARW9Yq6qdHWsLs74fj4PtZq5rOKs9M5i8WCLFeYYCjKQSILMhaXl2mQCWibBgIUF5dXWGNomob5YkEXIDwajZgMxwyHGQpFkRuefOJZuqwlIbgcRhdUqwVFUfDo4UOKoqCuJXzYmjKpPOjtGFmWcXl5ydnZBaen5xij+NwrX+LG/i5//Ov/Jgc7tyjyKatZw3y+opm3nM8c1niMzij0kKKUbYwhoIxCEfo8lG6Q2FRtbaq3xJ72441fne2mU6t1g013LDqSy6v42HOD7p/POYdRCqNNb9vZVIOtVqs+QF6UVqbPHOoe04V9hw3CAlLgfeuS9yyI2ibL+ky2LhBaKQn2jsHTtj4NjKTtFEXbYiFKrN3plJ/54ut88Ss/w+7BAQ8enPBb3/oW3/jGN3j//Q+4uprhnOuH0BhFUeadgwh5Js2Io5G0j42GI6aTMbs7U25ev87+3i7aKDxwevqQL7/+eT7/5S9TDkd893e+xXe/+wPq1ok1j0ZUKyH2Vs88z8QmioRRd0qK+Xz2WFaQZCApuhhuleyAvdLJaB48ekiIkVxrdnd30xqJ5LlJNkUhaFwiJTfVQuusKk8X49P92bS0funLX+YLX/gCzjvOzk4li+fykqurK2bzBYvFiqpqaNqGrvVRpYFaa0NE410kz6c0bU1nPExuLbRKyiBpb0Br26sG+/cahey01gqr+Yk11K0zleyUGgOZRlnNKCsIreTKKW0IaZ/LWhZFWneOibKzIxskf8xmxYZ6UKyRMcaUpxXEjKsNuRW/ZQgBlOapp59mZ7qPMYY3330brS15HkXlFl1S9giZqdW6AbFX1oTUPqkVRlnJUgtynnjvuHf/HucXp1w7vM7BtZu8+tnPcH5+zuXZKfPZJa6tcVVFbZcsri7IjCFLFm3Jn1pb2GKyTmbp3M6s7gmdwUAUOuPxiMGg5OjoKDVAyrm8XC7x3rOaL6ibBcuqoWk9k4mUhqAKlAFU5OT4mHfeeIOPb3+MGhQcPHmAMQpthADSQYn4sCdiLVXdcnE5Q2mxx+WFwjsh6Wezq14F3K3tnhSNEWs7gmfdpNhli5VF2R937z1vv/12T95nmTRNaq3Z2dnhj//xP8aNGzdo25Yf/ehHfPe73+Xk5KS3K26uQ8ludNR1tUEMrddyp9BqmjoRSzoRqmrj+905vyaBN6/bm6Krbt1buyaNO4JKSFP3Y8+7+ZzddaBrkuwe21uB9fqa0xOyG59HmyH4a2UarFZLPvzwNh9//BFt2/bPvcUWW2yxxRafFubv/g5/8smvYK8f8te+/TcA+G8+/TNE99NjVdxiiy3+1cKn+s21LEdoHbCZ6DG8MyilyXTEFmUKtPY4F8mMJrSa0WgkzVLaYsggGKJ3OCdBtyRLiGsCy9WSwaAkuIAtR4SYob1k8xwd7TEoC+bLJXc+foCEMFuU1hS5KB7yTJFn0LRw88Yz/MZv/F32dve48fQtdqYT2rbl0clDTh5+zOnpjHd/dMadjy65eeOAX/jFn+GXf+lfI4YBmjFPHd4kAvPZgovjBq3FAqiVpcxtb2dpWrGwdENjjJG2StlgvvrE3XffkxAuZebEpLrqsmeUkgyrddZR0r7E+GMDV//fRqGiSvlnMuyovEgBz8le1Cl9orShBZ8sgarFZgU+ESOdEqFpmv41Qgy4nkBTfRi8SWH/xgo5kRcWxTrUXF4vELzHB7E7dhlSVbIUKRW5df0GP/uVL/HaF77AzsEhd+/d4xu//S2+/4Mf8MH7H3B+cSEZUlUlmU3O07QOlMZ1w6ctmU6n7OzuMh4NuHnzOkf7B+zv7bK3tyNNhInIgwgBpuMhX/7qz2HykjsfvsPtD95nb28Xk2UYZfA+SGNiUnDYRKx1+T4xtZ25tqVNOUT9MKj0Y8NmCEHCndO+dT5y7/5DqlUFBPb29tjd3WO1qoAodkBhMTDpObM8J6S10iSFYkjMVqfm8F7IJO+lva4sc5o6Yqzmxo0bHB1do21TM2rjmM0WnJ6ecnZ2yXy+pKpqVlVF3TaJNJP1aI1hyCiRASqpRrr9idgY9aaNUAjOLoBca02IEReEAMqzHMPaNheTDQ6Vcq4QW2xe5DRR8oXKMqPMcnSRYY2R4R4hFLt2vUDAGo01CptUOsrqNekUorSFBi921E5N5oNkpgE7e9LSWRQjzs/POT89Bd+iusy5EPBRARajJHtNbaTla52yA5VCh5bM2nScAjF6vGsJ3uAdPHzwkNWi5tYTNznY24XW8dorz/Hw4X0Wixprc4xRtE4IdK0iWWYYFBkSRg5Wa2Ii+5qmIc9kvbi25qqpmM8u1wR4aj/trldCimXkeYY2CnRkMb9ieXnC0cEhR0eH3Lz5JIPRlBAyotYMdgZk4xKtk+VTa4wBkekJ6aSi0KUhhD48/vz8XKygwfcE097eHgqoVivyPKeuKkblAJtloEhlDUJAOS8KqiKdB0LkB7zzED2jYdmfa1eXl/gYOT095c6dO+zu7fHkE0/wyquv8ou/+ItczWa88cMf8tZbb3FyciJZe1nW33joAu3lBoPkYm0qo0Jq/e2UsJtkUae06ooevHdJAUp/LVfJpy3Xd09n6V1bIWOv5upz8zYUYZs3RDoisCu46Ek21iSYUpGqbjfsy2t0N0PWJKKmI7LFLq9ZrbbB81tsscUWW/wzIHjc/WN+5dU/BkB0Vz/hDdpiiy3+VcanIrmuLpZkOeSFDNFN3ZLlOQqN8y1KS1i1TMdLUX1oRUjDf1W3nN0/YWdnpycLJC9GcrfCYiU2OQx37txHK4uofWqa2jEaD6jqitWiYTweMyiHMsiiWc4qlqslTd3gQ8v9u/f48pe+gvee3/32m9z56BGZbXn6qSf58he/wrO/+DKD4pDMTCiKAhdWtE1NVUVUHDBf1LRtIxYSpWmdhKdrkTUQvOQebVa/dw1VTdOk92V6BZC0dG0+PuUsbQz6/V33ZD1Z32kXhUMIkRi64WUdiuy6gHYVUMl6GEOkDV7CxhPRpZRGaY1GoYw8zzCXJdDbGEXHs1boxIgPnrppN3LEZJDUvYVGNqptWxSh3/Y+zyZIcL53jrZ2uODZ3dvjpVde5guvf5Fr1/b54P33+Vt/5+/wo3ff5/TsjPOLS1ZVRVXVqSHR91Yw50MKM5f9meU5BwfXmEwmPPnkk3z2s69w8/oRuRHrXZbZvpExRnB1i8kDX/ra1zEG7n/8Lm++8UOKIieiqdsWF1o6m2XrJINO1etGwqLI+3Xgndgj21bUhH2LohYrUttKwyjQD5MgA2hdr8gLS5aJ5bFTBHUDbetcr1apVqtefdgRQl3oeNgYsDdVHHleQHqe9RAuxKlScu5Np1Oee27dQlc3DXXTUjc1dV0zv5rLdvvIcrWkrmppykyNmZFOaaKJyYoVUutfWuLr7U1WtI5sEgGYhmS57B6jjbTDWWMpihJbZOhco6zYDLWR64xKbXJd+5+sdd1xb2ktKnKzbmUk5WKZ2JG4EWUgao3O4OmnnkYBj04e8uGHt3EhkOV5ynZyG8/rIIb+vPXeU9d1rzIMIRBbUVmFpFg1WoPNCJ1KJ3aqmQ8wOid6z+c/+yw///Uvc/v2R1xezWmaFm10IiVncp1JxIW0+hmi1hAjZVH019Zu3XRrRinVF0ZAyuvT0jIZQpBGTpOxu7tP29b89b/2q9T1iuA9r3/xdW4cPsXl1QyvNJnSOBfQKWDeO9mPzrU9SZ/nJW3rmM/Fcj4cDrDWUBQZznvccokPAde2NKn1sG1bLs8vqJqawWDAaDRM1xjFYFBKNmPjpe5CW6L3GGtR2spCU6JutVZhVaeMcpw+OuHi/JTvff+7ou6cTnnttc/zZ//snwXg3Xff5Xd+53e4e/duTxQbYyBdBzPWge8g6tE6tT4KoWsIITIcDnqFHSBZk9kotX5CjL6/Zjon1kFjMmIi/9e2w47h6kiqx3PvPqkQg5jUfapX65qNDEfn2t5G2RFznQ2zPy8gPccnn/vHFWlbbLHFFlts8U8Df7Ult7bYYot/8fhUJNfyquLw+h6xdcSgUMFw8WiGUv9/9v7kyZbkSvPEfqpq0x18fO5vjBkRGBJAJpDIrKyqrKrMbpZUdbM4bbpFukW4aCH/quaCC3LFXbMprGIL2ZKVc1YCiRmBCCDivRfx5tGHO9ikqlwcVTW7DhTJ2LABETsQx/Nwv36vXTM1cz8/+77vaKpZRV5m5FmJwtPUNSozZMoIWOg823WLIqfZCkAwxtJ2Hev1OlkiijyjqpasVy1lIaHB8/keKEeezSnyBUUm+UfttmfVNjjnyLOCWbGHUS11veL505ds15Y719/g6x98i3/5z9/hjTt3WM6XFHmF7TxN7bGdoW49nhzrNIVSZFmFdxIanYWR9EqLiktpLUoIpUKDPVgJi0KmKUa7TMyrstalbBXnLH0/5Le4oKzorUxj8z5kFHlpTMSqMzQ2Y8siyP1/G5vWYBNMSgStUgh0zJRSo6YpPkNe5EldlplMguAT5BKIYTJNpSucE2VPXW/luPY9Skvj1PdulEcVlG1EhZtjsZjzzte/xPtf/jK37tzh7Oycn/zkJ/zkZz/j/v3POHv9mm0Imm9CExmDva11KURemYwiL5jNZuS5YT6fc/vmLd7/4EvcuHGD2aygbbbUTiY+RiVb23a4zrK+WPNf/W//a/YOlmy3G/7qr/+GzXqLzqLaQqXQa4E4I8VRAAZt28kEO4acqqv2IAGb7Fg/YciJms/nlNWMopijtWZ/f5+qqliv18O00DB0oRipTOLz9UGtpZQiD+BrvB1RmdL3PdoNakKlTFBtyPYIgIvb7Jk7Oc5t12Eyg7PRpic/33WWpqmp65a6EQjZBjDW9o6+6wXOxambPmR4aYX2TuB1GDYgDFnOpSzsI5OpBINjPlJvLXm0ZDrwvRfVidEoI/MgHR4dMuwcCh+AR4QkgzVSAsRk/3qBdd7TWZkcePfTT1kulxweXefdd97l4cOHvHzxgqbrMOHYSfZZUGYFkBRz8MaA140sYVER5Hr5WplX6dzo25bOdRijufPGDXJt+cZX3+ey3nL/04e0XY9aag7291mtVlxcXFDXW3lt61GZQL8IOuOU1HhNUCl8fzeg3AY1aVKmNm2wKfqgOhML849//CE/7H7Oy+eXbJuaVbOhdx1971ivVpAmqXZhfyjyvEgqoRh+r5RYimezGXlRUBYFRVmglGJvz9N1LWWek+dFsmuL1XJGVYm1cD6fc36+AqXFMmrl90jMKpTrH0klO9jxZI3HnLI/+7M/oyxLjo+P+eCDD/g3/+bfAPD48WM++ugjHj58JMrKcMyNMQne5llGZkwKcY+W+u22HsEjGQQRX1trE4Lwh8mg8/ksKHx9AmZDdtcAuuJ1Y2xTTnmJATZrDV03DMYgqDsFwmm0yRKka5omAfV4jRhnd12tXxd8P9VUU0011VRTTTXVVL+J9YUgV13bMJmwRymxMvSdw2iDdxrbKRpnMVrjrME7qPsanRl05uk6j9E5WheiEFmJNSszJWURJhFaS995lot95vM9tpuGppFQ5IvzdWoa26alKEqqYsZmveX1+Tkmy5iVM772lW/xL//kfW7eOMV1JU2zJVcz8IZ+q/C1E6BkAefoO2mepQkA5TuaJgAnPQAb5zw+WGT6TsKP5esuNRuxAd5ut6DVqOEdq3iGKYcKHabiBajSWzwjWBUnzyGtjihVAihRMt2szPLUvGQhGDttFxLyHrfXWYftRU2lFDglSq2oOGu82FJiw9P3fRjyp3A2Zn8FJZdB1DfWsm1qsGonYF8pz+HhIbffuMMbb7zB8bUTrPN8/vkD/vwv/4JP793n2bMXbDZburZjtVqBt3hEFdb1fZqMGBVpi9mccjZnb2+PveWCW7du8t47b7O3WDCbSVB213doBEiiBCC2bYezjgzNv/xX/5qjkxt0bc3PfvwTmrrHYegayRczCrzSwcqmdhq81FhasW4SlG6DjQiSjMgP4dJxjWw2G2Kwc1EUfPLJL1jMFxTljMePH9O2bWpiI5SJa8VaiwqWRGOMAExjErYYW2bjMYi2wjzPw3NK8P/4sbLFwYoa1U3OkWUmrUOjdZo2KcqxJc46ul7UdT7Yq6z3dG0b4KAA0bZt6XtL23f0dUfdtjivwgRVN4AoHyYhatkirTVlCBfPjEF5mayovQqwRiVLrDEmqRATVHJI1pySs0irEAgeIHL6UDqcW4D1NE2D7R1t4zi6do0PPviA97/8AWdnr3j6+AmXZ+egPVlR4EZW5SzL0udNI/Dd+KA40wacwmNxWs7zrpfMLrTHuB7noG5abt68zubVE2ZFxu1bJ8zzBR/+/CMeP3nKthH4u91uZfiD1uRZhrZ9gqcCnHt8b8X2ly4gKl1HxsA1z8JjdNx3g2VPVRqFTD109OwdLimWc6yTSbmZzqi3Na1tsQGAytqDNqg/4/EXyGfZ1i1d78iyBjxBtZfx8uWZZIlVJWVVYoxMML28XMnXyoJ5VVHmBQeHe1hnKfICpRVVWbFcLjk7O+P169fEIPemiUpQJ+dpVJfanrbtqeuazWbDs2fPmM1mHB0dcf36dX7/93+fP/7jP+bycsW9e/e5d/8+Z2dnYa2H7LUrtk+AsixEObpzLsZrvpw/dV2jtQyx2G4b4sRGgXHjGxDxeqOxdjeHa6zajAAuqsHiue+dC3lnMlRCG53yDJfLJW0rUzrjJN9xlt9VaH/15spUU0011VRTTTXVVFP9ptYXglyZKVmvtiyWFV3b0fUekxUoZehaS1NLbsxivqDv4119R29btnXN3mKPvDCSce61gLEesiynzEvKqsB7y+XlCu8k++n87JzDw2Owms8/f8jFy8tg24Lbt2/zO1//Jl//3feYzeYs5nv01nB0sA/OoBpNmVWYrMR2LuSLiAJGYFloFFQAWl7sVkqJ9cf2HQ6V7GcCq+JjST8vtrTY7GSApe8teIUxBc51IaQaGIGTmGmkGBoIk+V0Xcd8Pme7rYlBwGY0JStaFY0xYTpenpQv0vhLk9L7EGaPNE8YgQPeK/zojr3trChqGBQCUT1mrcXj8I7QKDq8EsTgXLTQQL9tsH1P07bsLfe5fecOb771BkfH10DBxeUFf/+97/HJJ/e4d+8ejx4/ZrvdBoWDNKTbusaHbY9ZXihFWVYSol3N2N/f5+hgn+s3rnPj+g1OT6+xXAgg9c7hcajeB1ja09sOk2XcOD1htliyWl2iCsWPf/x9nj5+wqtXZ/ROGMAweQ9gCHqGobGMn6ug6vAIWExQYGQj9WGhxDwupRSnp6fcvn2bmzdvopRiu1nz5OlzNpsNr1+f7SgqdDi+cSsiyNRakxmDHUGLYX2KeimCU7EsFjsgNr6HtI2AIkwNTGBsCLoXtZ4m28mMV6gCyqCSGlKASAMKxIrWp4bbA10va6p3otrpgw3YWhsmbEZoSFr3MRMNpTAoybYK55LSAnsHh1W00oLOC1QI7x9rU7RS2KA88jqoG4PSLdOZgObeU29XPH/astlcsL9/wLWjA64dHfLy5Utev3rF5eUl9WaTlJHOESyNFqW9bAMaF0LivQ8ZTxHQeYf2UBYF3mraukuT746Pjzg+PKSz8M7bb7DdrDjYm9N1PReXaxnA4Dxeac7Oz1mtRf4fp24mCBIzoHyYJAk70yxVUDlqIzZsARqy/r2Xa4OslQyTGZQGU3is7QCP0RlVWeCxSfXo8DgbwewA0cXWaGk6S9+L4qtto70RmqZFK0PbdJjVNp0HWabT1MiyKnny/DXHx/torTk5OaGalRitWK0s0FNWBlwm1+CiEAun1kmxJu+ZnQm0bduy2WzYbre8fPmS5XLJtWvXODo65u133uKNN+7w/PlzHjx8wPNnz+WGRVBqxWuGDxlW0VqesuKMIguTG8U5Hq9VHm9tyrIbn5fxOcXm2KXzYTzcIp6/gwV+UHw5J1ZeFBhnsNajzaA4LIoiWTIjeB9fD8aW6bEyd6qppppqqqmmmmqqqX7T6wtBrrYWNURbe6wD72RiWNd3bNoe2wsAOnu1Yb3eUJYlVTmnKEsuXp9RrzxFoTm+dhDG3hs627KpO5ptT5ZvMTn0XR8sUlvqbcOTzVPa2mF0wTe//kdcO77OcjFjPpuxv3/EwcERZVFRljOcM3hnqdua1veURSd33K0opLQG5TXKD9YPoxSYcWMYmvZgHYwRxEZraQBVaD6CFETEOx4U9LYXVZQS64jRJmS3hAYjBO3HBmQ3e0sUJtZZ8j6ndz24MMXQugQtlBqsjto6vLY7ShbvRK1ieyvAIahd0vvRYkFy1mEQNZdJGUbjwHQXFBEO52xSIsCQD9P2rVjMNNy6fYvbt9/g5PQ6WV5wcXHOhz//mIePH/PkyWOePHnC48dPE9yK09P6XvaJ2I18ACwZVTWnLEuWywXHR8fcuHGdm9evs7+cs7+/z2KxIM+zZFnqup5tvSbPMubzOfv7J+wfHFJWFcZIY9u2Nfc/u0/Xtrx+dUbXOeJ0szGocgHejcOeY6MXM3uuhj/HLJxoU7PWoo1hf3+fvb09jo6OuHbtGlmW8erVK87Ozmiahrquwz5xaQ2On1ePpirG6roObYxYZ+WBO99PoCxl/AzT38Y1bq5jQ3t1OmLMc4rfH0953H228DWvQ06XrEilBpOcrHyBc33fy7ELIdfe+QBOSRbfmPUV9EfyHNG2iA9Zc1HvSLAhguT2J6mWKAJ1eE8O+gADk4pMCZIyIuMTS6S3tM2Wpt2yXl2yWCw4Oj7m9PSEk5NrbFYbzl695vXr16zXqwAyDV5DEax6RuixnIva4p3FKIPTNqhmerq2S9vtvdjp9vZLUWhp2D9YcHJywI3TI+q64fXrcy4uV1yut3S9Yz6r2DT7nJ+f0zRNAh9pv4QnHlRsPkHBaIntnEWPBgcYbeQa4mxCmINIUa4hksEWP89QKihevcelaYDB+usE6DjnsQlii8qyay1Ky3rou56uC9bioIJt256m7ZIN8cXL18wfi9Jrb7nHbFayt7/HcjlPlsaqnKO1CWtVBUtesPjp3XMhWpCjvbdpmgDgGlarS46Pj5jN5ijl2Vsu8K6nbTvOz8/xQZE4hMHL2pVjK2o2G3IUo2Itz2Jmm6WnS/sxWp8jYBpfCyL7yjITrlUDgBpA1HDuXz23w9FOqq/ttk7we4DjwzkRy4X1MUGuqaaaaqqppppqqql+W+oLQS7jZzy8/xCTebT2FGUWrFgWrXOWi32KomS92UBVUBYl+/uHLJd7aFeC0rRtjetL6tZSNw113YidKsuoty2OhvlsgUIzLw843NO025Z3bt/hzhvv8f67X+ba8SllkeNsL7lAdUNd99TbFcZkEJRLeV7gg7IjNmjOyd13ya3eBTbju9dDYLxLDcxOUxHuiItdyqcmXiCXRQF2pGxxYQpY/Jl4990z5CfF19VmaHSyTGBgevzodfuQCeWD6mb8fgbVmYCzQYE2ND6xb/FByeFDPlJsdlBqcN4phdfgQ6ZQVzdkmeHoaJ9r1085PTnl5PpNtDZcXK74+S8+5Oc//zmf3r3Hq1evaJuWvpeAaRgAUtt2o8DkjDzPyLKMqpqxt7fH4eEB164dc+vGdW7euM7B/j4mKWccm/U65VQtF0uBScdHVGUZ8m8MdVPz8uJCbKSrLY21zGYVi8Ui5aMppWmaOrEixTCRMqqarlr8IuiKoe86wJwsz6mWSxbLJfv7++zv7ydQFPOUHj58yHq9xhhDWZYJksVjE19nrOyIwApGaT0hcHw3vUfUSi6oweLzjOHZr6ur62e8LePtGdukxj83fkz8N70nxsoZGWyQxZy7GEyfVI4mKOSyBCm0jPIL7sJ4Xg1qFR+2NbXpXuOcQCQdLHg6wEtnHSbAapk2Kig7y2IQuNhRbYC83nu6tuF1U7NeXXJyesLx0THXjo843D/k6OiYs/PXvHz5ksuLC6yXiZzeeQhTF5WR6ZPOKqwCl8LJRdGGk2mnGnjx4iUFSwyy/6rVAoXjYH+fveWCqsyZz0ry1+fUbcesKmj6Jcv5grre8uLly6SCVGHoBAwWVQVoNeBJ7316ry4MrUjfC0qwuI+jclMyCsN6DMQsDbVwLuR6DfBUbJlhGqgGrc1onQ/ZUs45ut7TtZ1AoL4PSjBL14ryz3lR5dZ1y+XlBqVgNquYzUqKopS8u9ksZPYVaCMqLudJE2cFsg/5eTEjbzabUdc1bduGc/Wc58+fM5/PBJ5qTRmskcfHRxRFRd/3bLdbVqsVTdPQhixB773YN9sepVVQ+4plPDJb5z1Ge1GRjlSYciHyIfNruC7EfaYUlGU5glyD4urq+Rgz6eRmygD75No7usGSjvigQo2QdLQkpppqqqmmmmqqqaaa6je6vhDkunV6h83FloODBVmuWS7nzGYleV4wny/Z2z+kqiratkOh6LqePC/RSnP7+hqdGc7OzthsNlxcvKZXsKgKZvM5VTGjblo6u+X48Bqzas7N67dptg1lPuP9L33AfH5I3/Vcnl9yFsKMsyzHmAyjM1FrEIULgmpcL9O7XPxj34OSVmxk89BBVTIAqHh3PjYBsUGP0CPLsgQ5rJNJa955lFVpqpVCYXvJjXHO0dGlr6sYWKwlE6coiqCIkG7SZIa2bcl1nl4PRO0Qs1SstfLaAWDFPJWmbiSXKoyCj4qzaC+U926C5VBsMzH0O8KzIYfM0dsuqBMcyjnKIufo+gmnpzd485232Ds6wFrPw8dP+clPfsInn3zK/fufsbpcUYdsIhga5KgSc06m+eV5QZ7nzGZzlosZRVlweHjA9ZMTTk9PuHbtiKODA2ZlQdPUrNc1mTHMFwvKg0NmVUVe5Ozt7Qs0mldsNxvOX7/i7PUrzs7PadqW2WyBiWAq2EuLIsd76HtHDPnfDX0mqR+01qMmU5HnOW2Y/FgURVKXLZdLFotFUJrlbDYbsbi9FhASgdZyuUxrbgyGxq+hQwZXrDGktHZQVUUY8es+YsXnuwqi1AiCXP3++DnG58qv+/n4GjDkPo3hq2z/7jRRTQRTAHpHvSJ8RmiUYlC0mdxIHp0frFpEO2mwg2lvQEs+WHjlAKwYgEAAlC408VHt44Q0YAPuVY6gZJRzY315ju0aDg6OuHbthPevv43W7/L61UseP33K8+dPuby8ZLut0eSC7nQIu3cu5Fm5pK7qmo5msxJ7HQVPnz4j8y30MKsKiuIsKBVbFvM5VXlMWRTgPZttQ9N1PHl2RrepOdhb8uLZczJt6GyPD9locb/GqXtjiBn1qPH6KMMegu00nArpGCfwqlJGoEfJgNWgilW5AC0VhzDE9+p8eM6xXTtOlRXwpvQwGTMNLnA+DJ0Qu6N1ogDru56mbZIVcrtt2W5aPJ6X/jVGG/I8x2QyedFonSAYKLE1htfP85yyLCnLkqIokspOMr3kRkrbyg2ZCMQODw+Zz+ccHMTfew0vXrzgxYuXrFYyBbPtespOstGauqHvbRrWEI+H1oSbHFGQGWGzDRbq8HtLxZsVcVKqbH+eD7/GozL46vUCoO9Nuv4O568eZXAFQBZgvYc0uESpX6fZnGqqqaaaaqqppppqqt+8+kKQ6713P+A73/pHXL9+QlEWzKoKbUSB45ynbtsADHr6Tu7C19uGzXZLkdc415GrGfWs5mB5RJZBlmsyk6FURtNKSO7pyXUWswVVOUehMDqn73rOX12I0iAoA3zvcL5LAdk+hJZLHyV362VSX5cAkDEanMJos5NLorVOU7GyMGUvNrZam2DTytKd9aIsyAsJbHbOUdehcQk2HmMM620NmVhYlBo1I3JjndTNheco85KqqlBKMZvNxCrkBlVPbGDiHXwQsNV0Ysnsuo4uBD3HF7LWBsil0v/iz40hlw9f8x66CNGcDRZCmRy5mC/YW+5x88Z13n3nHQ5PTllvGz7+xUf8/Xe/y49+9FNevnxJ1/WEXilkytikJoh5ZFpnCWwtFktms4qD/QMO9xfMZhU3b93gzu2bLBdiD+q7TgLinacocg4PDjk6OmLv6JDZbEbTtKxXK148e8HZxTl1s0WHwPXl3j4LL+Hf1kNf19T1AN+yLA+QS9RZOqguDEMO11jlZIyRwGZrqaoKrTWHh4e8cecOh0dHaK25vLzk+fPnnJ2d8fz5c9q2pSiKHbA6BqhjG+G4CRWljEpAZAwMU1YVXnLmohIxPKbve9qu2wFHV8HX8N6ikmoAV+NQ7XHjHL8mypGxrWr0nAxAOGYQDZlxYf2pqP4hqH50Osfi+vTBH6yMRqkwrW6kZlEEiEKYZplsyB4k7igEwluyLA/WsCHMX6aHysCAaPVVOtiZcznvfYJiDhVAcdd19O2aeltwfLTg5s1TvvG1d0EbPvvsM+5/do+nT57QbGXqX7SyoQaFTMSBXdNzcfaC7WYLSlFvG169Pkc7xVtv3mG7lbV6eXmJd4695R57e8tkq6uqklcvzlhdnJFnhjLPOTk9YVvXbOua1XqTlKnGGLESOrmGujB90SuwVqF1OO4mWh3DWovgMYuB53KcswAYnY0KOjk/lFKYLMN5K5N447XGD8AkTpyN6wUIGVUurUejxSYu12iTHhhzEuN1PV7r4znSNB1N29KFmwJt24RstM2gzszEQqiVRhvN5WrFq9evuXXrFrdv3eLgYJ+uE1vi5eUlZ69fE/P1zs7OePLkCW3b8sEHH/D1r3+dt99+mxs3bvL48SMePXrIixcv6C9WAmuRCYfW9hgjVnbQ9F2Lh7BG5D1H26iokYPSUaZ/4P0wyGK7rRGFlqjSjBkgcdyvY3WyADqVfp9FuJyHmzAwZPrtqLu8AMappppqqqmmmmqqqab6bagvBLneevtdlssl27qhXjdcrFu8kwlqTdOkLKq2kTvWtu/puo66rrlcr1iWc4zWlNWMxf4es5kocNJI+bKUHB5kHLvtxI7XIne9yTWqV7jeymTEAHFs73f+KJcGvWO7qVNDppXBeoezoXEO2Td1XQerCMmqI7k2EQqokH8i4EMGZVm63sJmm8bWA2lSlUCQDV3bBWwQmueomIl3zEM2kXPSGFZVycXZeXi2XTVPLGNMaiLjc7hg9VNoMpOjdUbTNnRtR9M2OG/FbhSVOc5ju9gQ+gAVOmwvdsnUsDpLkWv2Dw557733eOfd9zk6ugba8OjhQ/76f/h/8A/f+z737n9O3XTU23VSJsGQJRYbaa1FoZbnEiS/v7dgPl9weHDM/sGC/f0lp3sL9vf3OLlxnf2Dfdqm4dWrl2ybLYu9Pb70znscHR6zWCx4+eolTx5/zpMnT2nalswYyc7RGUVRiVpNG2yAPZ1tE2Bq2z7tUwnNllyztuuSQgUGuGWtFdtOgKB5ljGbzfjylz9gb2+fg4N9+t7y4sVzPvvscx4/fsx2u6EoRBlSFIUc+yuWx/j5uDkdcNM4QF5suPFz5xx5nhNtm0UAUQI1B2AptjBZ81mWJbA2zumKUC2Cn7EyKmV5xaD9HWVXNmx7XKNhwtu4VKC8ojoT5Urc82kbIDXh0tCHHC41KA/xCpMZMFosvEGd4wHrLWP1nVce0CiT4VCgDV6B83aALErhvAbv0Mh5Hg6SfD8MMNBKobxHe49H1E0qE8h38eoFl69f8OLxA77ylS/z3rvv8O1vfI0/+v3f5bPPP+PnH/6Shw8fcHl5GdbUoJiJULDPDMrvU5Y5Wkt+V9d57n72OduuIVOaa8fHHB0eUHct9vKCsiw5Oj7COcerV694481TvvWtb1I3LQ8ePmS2XFDNKi4v1zx8+ITHT5+yWl3Sd50odLQmU5movJKSL759hTHjwQuDGXbIdhsgrEK2WaswQiOsEx0GfMSbEgIlFSqKtRSDuosYxg7aKzl+arANW+cwWkCLt6JczbJMAKdz4B15poPiKaMsC5TeI1rAIwTuw++kvu9ZrbcBXlp839M0r3j9+pxPPrmLQiYlLhdLTk9PuXPnDm+++Q5N23D2+oyul+tl1zk+++wh9+8/4ODggDt3brO/v8fh4bGE7lsBol3XsVzOca7C2j5MWNSi3lMC8ERBK9MY5XOZZKsUwXIr+YVxWqMP13VRdKp0swYYJioGGB2uYjgHJtNpSrHk5jFakz4e6QTXUQr9H3c5TzXVVFNNNdVUU0011W9UKX9V1vFr6uLigoODA/7dv/2/sLfYY71Zs16tubi8AC9qGaUVbd9yeXmJC6qB7WbL/nJPcpS0QWeG2WxGVVaYXJrvaJ9ROlroFLYL4e3OUVUVnZXn80rRNV2a2DYOw412wwjbJDul3IFEscGP+Uhjq1W0acXPu6BKM0bTW5dGz8epfz4oUrquTRkvcVw7gDFZ+u/BWvbrJ2NdzXiKHzaEqQ8Wv189VB6warCFRVvPeBJgVKP4cEe+68RaKVMhxZZjrICCru9Zr9fkZc6Xv/w+v/P1b3D7jTdoO8e9e/f54Q9/yPf+4fs8ePCAtu3C/nZhnw92NO+HCXlKyYS/+XzObLFkMV8yq0rK0jCrSt57+10ODvY42J+zt1iwXCw4unaN5f5BaHQF0HTOsVlveProIefn55gsZ1ZVeA95WdDUAjX7MAABFF2Y+BjDk+OUSjuCTda5tP+iFU/UDj6prKIayVrL4aFAv3ffexdrHZ9++kueP3/Oy5cvaZoGrSUcWmCk2Tl2Y0VVhD9KqaCUiRMrh3UdG/M8z8O6MqFxbkM4e4Rx0vjG/5aJdXEd9QHOiQJtvM5SUL6P4G1Qb8T1FZU8bqQmjMAqni9RnRRLKYG5495YbHADjI5rPIK88bkarVKirAv5T17WAeE6occgLuQ9RRWb1mBtCL9XQQUWFTLKCNgYoo/S8SXZthxuB4YN+8VfeU/eO8nV8p5qVvKl977Et7/9e5RFQVlUPH70hB//5Cc8fvyY9foyHDeVYLWz8hx933N29oq2bdjfX1Dmmvmsom8k/+746IibN66zXC6C7TlMQAwA9vPPH3F0fIrJNI+fPma+mCO8OmNbN7x8KcMOtvWWPijzrkJPOX/6nWED4+tiPG7jShZVj8BEHwYoIGtgnCf46y2sQw6iUoosWALD9IAdRZHyMpgDrXe2ceda5z1KfKvp94T3YrdGERSQPmUBRpVi33VyvQi5WhEaJ4u3kevYbDbj4OAAYwyr1YY8z0fnp0qZXLPZjLfffptvfvPrnJ6esr9/EL4nz9m2La9fv6brPU3T0jQygbXrOjbrDa/PXvH8+XNWqxWr1Zq+79KAkZjbOFiM41kzQOssMzKVMuSQxZtO2sh1Pwbtx8zGYV9HzD4cn7at+T/8t/8t5+fn7O/vM9VU/1NV/Hv0T/lfk6n8f+rNmWqqqaaaaqqp/v9Yve/4M/67/69/k34hJdf9zx5I1ksY+y7ZWhe0bSdNhO+Zz2acXjvh4OCA44NjUSjlAjiarsNZz2a9xWRarHmZpm07ehtC1J3HW0fXthht2G4atk0tDU1myNSgMIg5VfEP9jzP08SoqLCKTf18PkcpJdlXSnKvohqhrus0lSw2ep2T6V/Gm51GMjUWSqOUSWoW7z1lWaUmq6pmzGbzlG8Sm8lx4xgtXrGR2mw2NE0TbFsRUon6I+Zk/bqsJacUnepElaV0aljiXXjnJIMqhvC3bRNUAhYf9pWrW5Q2fOlL7/KdP/pf8N6Xv4w2OU+ePObP/v2f83d/910+/uXdFCIfgcR2W2NdUFaYaEeTKWgxMy3PpTFcLucslktmswVHh/scH+1z/doRB/t7uL7j7bducXp6h2snp6AVjx894MHnn/Hq1Wu2dQ0KlsslRV5SVnNploMtqm07tM6wVmw81jqUMcnGCQGUWIfte3xovI0xWCc2oKjkkcymLjXIXWfZ39/n9q3bvPXWW9y+c4fV5SU//elPePDgIZeXl6nhLooyNdtt26P12AapEyTamaIY1kFsvp0TjZ8Kx9CEdR6bWWMUWpfEXCOBvcO6EOWcwQY4jM8T5CsKQ7RCQWyIAeeC+smniY2p1Y1KrAiGGdRZknEnABp/JYxeDQAutswqwI8IjQfAFy2SIYDf+zBEYrC4egfOeoyKFuCgk0wZTjEpyoPzaBQ4j8mGrxHUWDGLK73uWH2mFHiNj5lnHnQAC33fB8dhMPk6j+3FTquUYnVxyYc/+xk//cmPuHnzJt/8xu/y3nvv8a9u/c+4d/8+f/u3/4EnT54AJgHwqjIURclms+XiwlCWFRfnlxRlznpbs1fNcHgu1ht4/oKDpqEsC7RSlNWcqiw52tvj+bOX/PQnP+b6jVOaZsv7H7yLc55nz16RzWccH74HwNnZGZ8/eMizFy9omjbs8wywyUIo9sbhuMQpl1HcNVYBRoub8w7t4jp0KKNS/pcLUyYjVI43BrSR/ayNpgzXZmOG7Cg/ss96L/s7CzdFxsfcOhsURzJswagBlqaswxFclmsfw4RSJ8M3ooISSLlfcTJtG+yP3nsuLy6TFb4sy5G61u5k1/30pz/l449/zuHhEbPZLF23x9dzUOGGwRBYL/s7wmvZJ13bYpGJvZLfFnLMggIyWsPjsem6Ptwk2aIUZEHppU1Glg0ZYHFPxqm88ZwfW5//f7gXNtVUU0011VRTTTXVVL8R9YUg16tXrwYVhvVUZcWtW7co8oL5rGR/TybKOedkcp3tU1PhnEM1uTS0owbfOrGiKG0ocsneMrlkUnWN5BiVs0rUI3lOFhqRtm3Tc0ebRrQKRugVK4W0B6UXMLrDHxUtZmiclYRh60wTXRvKizorZngpNEZlFEWR7pTHUPikjlFDM9j3AgVjPo/YfLxEnYdtyDJDWe6Jvaa3YbqfDaxKMbYpugCWUGI/6boO5y0m03hHAhzOOlzviYHSonjrsa6X/K6m5ejoiO/80R/x7X/8x1y7fp1nT5/w3b//D/zf/u//jl/88i7nFytRBlkbJnJ14fVD1pYyYmdzFmMyyrJCaxLgKoqCqqrYWy65ffsmp6fX2d9bcHSwx5fee5drpze4dfMGJs94+fwZf/93f82rszNitlmeFeTLEgKU3Gw3kiMTwvCLokhKG7FiKQpTykQ7pUQNGDLilNNkVQ4iPhLQoKOaL08Qs+s68jzn+PiY27fv8M4771AWJQ8efM7/+P/6f/LkyRM8MuFsPp8BKqlFRMkSj5femZo4hE2PMrgAQih6Eq2EdaG0JlNiqUOJZVAglwo5X+VgMVJaAJly4UOez1157UGtNqhB4pRGcEkGFbN5GIEM2dQBwHp8ggtDvpKs/dg+eyRgXofwLaV2AdPwEXOFSOeo/OsgfC9+JPBidNpXErA+BMsrFTPLhlJaB1VjfA2ClVhLQ69IQeySpy7v/fLykv39ffIwYCAC0oP9ffb2l+ka5FzP5cUlSntOTk7QXmH7nsZaTk5O+M/+s3/Nz372M372sw+5vFyhtaLPxDrtvSIvSrzrMWaP9fqStoG+7SlyySrrupaXr14ym83Y21uyWLTcuXULay1vvfkmT5884dXLl1y/eY1ffvwRb73zDl/96gc8/PwJdz/9jLppKGcV169fZ7m3x/Og7ur7PsF8ldRvu9P8XMhlGttYU4ZT2LfGaDI12OaiejLmPSmGTC6lAoSPWV7BJtsFZdX4NQhqQpVF2+wwSTAG1JdlKUH1nRMQ6QYFmqwVI4phFc75MH3TGANOnjPacuNggri2AOaLebqWDzBK4HVRFGHgxIwsy9LvArmBsqXrWrZbUX3F7MeoiHQJssdsuTCV0Q42apRse993WNVTFDldNwyAEFvjCAYSbI7Bxqy1rEOloOu7NDgl7ueo9hS4RTr+4+Mw1VRTTTXVVFNNNdVUvw31hSCXx7BYiFVjsVgwn88xRkLavbUo16fx5Ov1Jih5Moq8CC/WJ6tK13VJVRNznNrtAK5iI+FWlzugzHv9H4U+QIIvaZtDgxLvoo+tLaBHEEmgTFQdNNsWBUn5pfyQp4W4ZUBbNptNAm6xIYkNcNc1o0Y9vHbIsDFAbx29F0VDtLh03XaAgqPmYmwliw16+n6w2/VdR99KA6MRmKG9p/fSwLbbmsvzC7y13Lx5ne/88R/zpa99nTfffpums3z44c/4P/6f/0/8h//w9zx9+py27ZMCJ040czZMOLNOJhVisd6RqYw8q5jP5iwXC7TRMoFzPuP05Bo3Tk+4fv0a77z3Fl/92jc4PDrm4OCI1flrnj15zIc//qEEtNuOxWLB9evX8T6oKfqe3vbYXoYAyKADWTvVcp7ADTFA3EPvwXgJ2heFQwCaId+m70VxEbPJttst1lqOj4959923uHXrJsfHxwA8evSIv/u7vxWwFVQWi+UezvkEB0SBFeGJGimudkPfx7BprOhLZ5hRoDIkrHrI6ep6yZLKjMEjSpgqqG6UMsnG6FwAWMaIvU+bAN78DlizztH1Qy5ZWlSItU/rgbjJW1KgIDfSwBsdodwAQkTNI9AhG71no6JibLjcyLRBiwqQYAxTxrbL8fof779h7ceTIbyvkL/lekdgi3JtGFmFHSEjKii9vHf4FKyt0nMpD1iHVorlbM7efM7h4QE3b95kuVyKEjTsx81my2q14tmzZyGPbctyuaTvZD0fHx+H6+Ka169fs9lsZG8byY9zAfbP5wJS6rrm+mJPrgt9i0KxXvfkuaaalfTW8+LFK168eMl2u6U0Jev1mmJRslfsc3F+yWq14uLsgsODexwdn3L9+iG//PQeH370czbbBpPl5GURYG2O97nYbZWh64eJn7JWLRqPUQqvFH08HplJsARICtp4DZT3KLbdMMIS2w+wDO/JtQnX2KDwsxYb0VlUoSrEvogXRVIAcpIfJzlem8t1WhfRgjisIxXUarvQRnLq4tofYFG0XcvL7qqadFQxKnCuxzqobcdmfZmu98bIYJOylCmYTb3l5OQaN2/e5Nnzp/R9lwYHQBbUaG6UaZjR9Za2adJzGe2BYgDV6de3wur4e03+e2xHjhZhR5ia2Vn63qF1n/ZBnucUmTxv18uwkeG6OuzPqaaaaqqppppqqqmm+k2vLwS5DvYPKEsZr35+fs75+XmYDh/vxAt4illYMNydzvOcLtjcYn5WnEwXc1piDXYSR9e36Wtio3HpOSMsSNk20Xo3snyIdbBiXW9TaLu1vdwRDxZCYwy+92Q2S5MJjc6w3pOHrJ+oEHCjRiTCtQjz8jzHKQcO+rrHmDw1B+OGMb1X3wFewrAzyHVGOS92pu21TZMUSxGmRNjVB5tjH6yHRhv6TrJksI6maWiahm1ds5gvuHXzBv/8n/0xv/PNb3J8eoP1esOndz/lv/vv/6/8zd/+PY8ePaJuOqJtTF5DmlIXcooGa5IEcWuVs5jNmM8XHO3vUVUV+/tLbt+6yenJMUcH+7z91pu8+dYb5GXJdrvlxZOnPHv4kAef3yfLMtrQ8GUmQ2lNXTdkeSHA0ASbFBrretquoSwr2TYcq9UqqSFihpZJVhz575ivY4whz/NkLxqvoS9/+cu8//775Lk0+pvNmh//+Ed88sknnJ9fhIa1xHsVGkQbMtfk2Ee7KwwNflyXV5vq2DjG1y7CtMwxgI0WPmutDDsYZbfF80qhcR6MiZA2TDoN73vgQF4GEI6Uj55B5RGP6QCBg1IwblOAZiB5SGNr4biU1hiuTnTbzXWK5+n4nIglj9dhCp3d2XfjaaLxejG+Zoyf3/a/Oq0ySNx+5TWj2iWpgtQI6pkItgXmnK8uOF9dcP/B50TrXFS5RbWQc46yLJnNZvR9T1EWNK2EwY/P62o2S9s2hnfR9ip5fHIu1m1H34mqtfcG6yx9n5Nl8v6fPn/O8f4Rz5494+bNU9abNVkegb1knt39/C7Ow+mNY26/dYezs0uePH3B4ydP8R7yomJ1eQ546roJEG/IMrTWESezjvf11eMZ13R6b5AyGg3DlMX4/ZgLNV4j3ouNEYJqL5djYpGAebleD1licf/FfTdeF8NAh4HjRjDmkdcZq4DjWvNO8r8UpJ8d3zyJMCnP4zkk0wmjbVjsh1tWq2F/vH59xsXFBbPZjNmswphMMh/rNl3Vm7oVtax3FMZgyiqpQ/M8Szc/RFEMEQjHgHuxKvtgxZfvj5We47Uej0P6nTn6vVkURVJFa62TAnqqqaaaaqqppppqqql+0+sLQa7NekNmDJmOIdwhzDdUB0HBZNMfyEACQ7kWJYo28q9RWqwmzmK9D02Kx/Zd+GM+TgvU9F0PSB5L0zfgZVKfRqOCmqTIC/Isx4agbuddgGXSvHZNyzZYNrIsw2gj1qVgb2r6BttbaX7KDNdbLi8u0EqTjRqMMi+IE+gAMpMlsNGEUfXehuwfa0Ow9K6iK1onLePwffm3C0oZpRVN3RKzu8Q5JnYbEEWK9YP1zGQyoev8/JzLs3MuLi74yle+wp/+iz8RgFOW9M7x4uyM//7f/g/8+V/+JQ8fPKKuG7oQ1o/SMomw64ZwGgjgRFQ6eVawXBbM5wuqas7econJNIf7y6DYOuW9d99msaiYlQVaeR58/ilt21FUM4qqRGnP7TffSEMLlA4DDNBs65qz1+fUdS15ba4LqgmP0qJQirbQpmkBJXk2YQ3FvBofLE6Luai98qIQmBYsp9E6en5+zi9+8Qs+/PDDpKiLzZ1A22r0WqSmTylFWZY7FtWxfXZstxpbFGPwe6rU4Kvhv8N545xH4RNk1EomcmZK8s88DueCUiNYjjzD1Exp8OVccsTwd5/UKNGWNQZKKcQ+ADljDDZlfslWxuD+qBq5am2K63wMDuT9D/tBrIU2rO3QlCvktbS8T6PTwhcrVfh6svwqWTsxr8z7EDbvR0ArPCZWPFaickHUlfH9K9mrIMqiOD1QaYUnBtfrMP1PzoerIG58bDOT4ZRLcLMPmU7JcuxC/qAxqLCufFjHcRfPZgv6vGC9uqSuPUVRUDc9eaYp8oKqKnn2/Dmvz89o+prcyPTPrMhxQFmV6HorU25X51R9x3yW88H7b/HmGzf53vd/wNnrF+R5CSGQ3VpH07Q77yUOAIjH2TlH3o9ys7SiD9ej+BilVFpfaE2mDfP5nLIsOT8/TzBzrIDKTYZX4JXYXAG8gjxMaDRK05oOM4LK6aaGk0mCuR7W3QCQR2o+53AIzMKI/d6O8qz0yIp71a43VhOOb9LITZh+R4U7Pg9kqqNjtVrTtm0aKFEViiwTe72e5cxmubiTEdto27ZYpVit1nRtS54XAq4CFI77bbCx7wLlCLaUkoy0pM4dA+7R2o3vIeYIxhtXU0011VRTTTXVVFNN9dtQX1DJtWSxmKcgdaNDUG/4Y1tF+BWaNxdyoXKjyWLDTVR6QFlJaHgMjo7fI/whLqAhk8wuEzJztMdkg02xdz24oYlOagmtUvaQNDRebDIhk2TbbIkTGGfFTABV02AyQ9u2bLYbAWchyD5OI5Og45h3EnKNwmQ017rU7KGhbrqgGHPEAOGolLHW0XY9veuS4gfYUXtorXfUb1dVQdHipIPy5fz8nO1atvub3/wm3/r2t3nn/fepTMF6s+XnH3/MX/3N3/CDn/yEZ0+fs95sEjyMioo+5MDI8QiNj3OApiwrCX0vSw4O9yjLUjK38oyTk2t853e/wc2bN9HKcXx0yKwqqGalBKcHy5Lsjw7tPQ5FWVbYepsgx2XT0DYdeV6QFyXWWdoQoCwTHFXK/qprmWToPWR5ngCTMYM6ZKyg0wGkee9pmobz8/PUAK5Wq/S+o+IrNrBxWuegCvFBPebYbre0AVJENV9sNscTLvsw2UzghUugM8/zQbWkDTFEPlmkAqSJ2UbOSsC2C/26dRYLKO+D4k8UQF0vAC8GosdA8bjGvPfkWYYK4f0QbFnIpMUxOIhrPJ6kEfxIUH709v0q0Bu/z7ielRpsmvL1XUDm1S4k8ozsYcSMsGH9E957vIj4+C8C6+KQCBPB1eiZBeRKQH0CLd4lW5yNjjnnUE4xKCj7lEMWny9e066CzZQVNwIP8f3HY2u0kcwk53a+H62LUdlVVRXOWcn4amqapiXPetquZ29Z8OabbzKbl7x8+ZKnT5+yt1hSVaIkUxiwiu16S9d2ZJkMhdjbP+Cf/eM/xFrHjz/8ZbBa1iwW8zCgwnF0dCRrfbOlDpNtBXgpnDbpv733mMLsnCtKa1RYL9ZaXD9MdRyr2OLPW2tBC3jJMlnv1oZjIgIsnJIbI+P9OT6v5BjZdGyG0PxhTWqtwQQbnrq6MnZtxVfVanG5J6WkG9Zx/L0wHEeI9mWdrMNQ1zJl0TnH6lJuKBijyPMMozVFUTKr4nCJAqc1ZVnQ95a6bpLiuCwr6roJNwki6PIpz0vuxaigHtU772GcmxbfX1yrAuT6dFzi60011VRTTTXVVFNNNdVven0hyHV64wZHR4c7oe7OOfquo206bNunP5j7vmc+H8a3d50oQuQusVjIXr06C42E2KzyPMMApsgpihIbGqBmW4ecKUAZdJguZW0nAe0BKBRFwXxekOVFagzlD3kl2SiINUNrhcLTdX0K5QWF1nmwoJVo7ciyPMGM2LTE7Y1Nk/eDnUeeR4dsJgmJjqqgCEakwVBkmSjBjMsQi0u0r2V4F5sKB95AyJZyboADzjqyPKNtWrarMzabDTdv3+I/+df/iq9981ss948wmebhg7t893s/4vvf/xF3797lxcsXbDabpNYYN3CpEUfUF84J8ClnFXlRkWcVWThOWVZgbc/J0SF3bt3k5s1T9g4qrK9Ba16cvRBFkhvUBdZ5eisT/pyTqZYC12yYfOipm5Dpk2VhamIrxyRYkUR9JdkyZSZACa3x1mI0KBxNvcX7AYJkWYZFhhUoJDh6u93w3nvvcePmLZRS3Lt3j/Pzc2BQRYynpMV9E49nbABV+IiKh6tqiqjqGoNLnULKXbIlAkMulPeDUiqomGI2ECoqihTGaAqVy4AD5yGTBjfLMnInYfzR1hWb1mgRiwqO8XPHKXPe+wSbZDJnhzaGpm4xWia5oVTK+or7Ob7PCC+aoIqL+7AqCwF5ssjkZ0aqGIFMEF5cdoV1kGkBub2lo0crsQ/G8095j/KiyooQTYC7l+tJUozpAJLk3NOIMizmQYHDikALH+15XlRz8h6j2ivAAgbQMSj5BhAalXgC3gYrptYuAD4Bmn3fodxg9Za1F/eHx3sZNpBlM+LAAY+iVS3WOZre0b9ec7FpuXnjGsfHJxzsH/H0+XPOVmscT9A+w5gcrTNcLyqtjduyWdfMlwvyvOAPv/O7/PiHGS9enQX4rXBOc3GxJk4bNSFDLUJxFdZbBJnxmhvhcDq28ViHCZVxXcQbE+NqeovJDPRRaRStgwIN47l5VS01BsvOhqD6BE2jJdKFY+ZlcEj4vSBh9tGWGI4dBDWdT8feja5nsm4CzLQOpcO11CP5V2GVxKm2EWjLdaTHOXBO4cK1v+0dfd8FJec25X4BzGZzUYBaR5FltLRkmcZkisWywmRlUhTH379d16W8SB8Um3Vdh8tKtHDGYSQ+qLJVGOxA2Gd9gNmj6aNTTTXVVFNNNdVUU031G1xfCHLZtqWrG+rNhjzL5Y/jaLnpOrzzlFVJs22pm4bMGPKiQKkMr7zYULxNUwql8RZrYte1WNthtMY4S2/FxpbnOToE79q+RxuDtVGRIzCrLCuqqhR1WFnilQTKbzfbZDNsW2keZvOKIi8wmcCrthGrnmQfOYwXa5LyUZnhQ4C4STBsyJ1RIZ8rKmQcMfgbkOl+3lPNZljbDw1YaKIMg2ogQpK2bdGZNH9d1+GtNMdaS1i6dRa8w9qO9XpFUeS8//6X+fbvf5vrN25SLZastxt++YPv8YMffJ+fffhTPn/wjPOLFU1T0weFj8C5CLkgZslE+FHkJVlWpCyqsqrIswIF5Lnm4HCPN9+4xZu3bnPz9ASlPWQR2AxKAev69HrxffZ9zJjJA4x0dK7HWeidTDjzzqGcBHHHYOuu6+jDejBaowPI6pxN8MYYTdsF2GrdoEBCHlvXNUVe8E//+J/y3ntfQinFD77/fbbb9RCUrQU2xooN/RhWwpBFVxZFssON1RGxxioeY4wALC8B2uMaq/TGDXx8/AADYuh1gIfehoEGKqmXiNZGI1PVCKH2sYkfygUVpkBGF+xeLgAeUUQJ+DFaoc1ITeg9ShOm55GOewQbEYZrramqKoGBTIsVzDsfQNGVrCwlrxdfRhEDlVx86+G5RjlQQUEZg/DFimjp+w5jigQ64jY673bUZ9F2mF5Ty+xTgQOyT8wovD7CFz1Sb/1KDlg6jtFaqRkD7Xg8IvyO9rAIiAaVm6ILVkeFqJSc92R5LlMZe4vOFNtNzaNHTzFYTk5OObl2iucaZ6+fsVpfUlUVs1KuRdt6K0DU9Xgc1axkMZux2Vzy7W99k1evX/H02XPOz9cC8E2W8rTGkMkxZETF/x6vgbR/A+iKH/Hmggr70SPTZT1AANkOn0CiihDWC6z0vduxCI/BIoANNwS0CmsWgZ7OuwA9Pdo7fBdgqovWPxXWphup8AjHcABsPvx+MGE/2JGyLK4/HYCr8/HYD0NRdiytWbZjLyZAqag99F6uW/H64bxjb2+P2azCOkvX9dR1zcYO8C2qUZfLZZiqKKrp2WxG0zTJlj3AWJes3k1Th5sLTfj+1WvGVFNNNdVUU0011VRT/ebWF4Jc5+cXCYTArs3BOfnDfLVai8XEWRqlqKzDZHKnu7VdaG5MaoIlZHoACB5F0/Z0Vv7ILwpLUze0IQ9pb3+fvDA7zURRCIyR7VK0bcN6vcE5x0wrbO8w2pAXWVKT1U1N17a0XYftxQ6XGmev0chd7uAOw/sOQsMR7SfOymO6vscHFYFHVAEAhCanrEo8Mpo+9EvB7qXQXieAMlYJRbsJ3qIQ9UbTNGw3W7TR3Lp1i3feeZc7b77JzRt3KPOczx884B/+xz/n7v17PH32lKdPnvDi5UuxxiRaQTpeoiQKqiIEEhR5jtaG5XyPPI+Aq6QoMmZVycH+PtdPr3Fy7YjjwwPm84r5vMIpR9fKNLCiMAGaiBIt2uAAlCGo42wCYEVe0FuHUw6vHcZkI5DIMGkzNGVd21IH+1bf9XgngFIULmH/jhvfoFZCKa5du8ZXvvJV3nrrLYzJ+NEPv8+rVy+D6kTvNOfjxjmu+aiUiOuo7/s0aCGq4sah6WMVWMzyis15AgNBeRFhXlwL8efHqhelFM6PbLRdJ5bF0et4Lw26VtIQ+2D5NHp4P0ppTJYFeBsAnVIwAm/OOTRgQ2OfZTlKS14RSmyjcUkNcDPmxw1+rpjPl2BQUJGoYLMVi3KATIg9Vp5TFCXG6DBkAYGJI1AwVo8JKDA716io6IyPjxlGKW9ODcAqfp6+4QYrZQR5Aj58eg7P8Fo78JNhDcrrR4A5hl8CPqKNrq7rnSEaUQUoOXyOPGQB6r6nazs8niIvadqGzsqUzbruyDJ48vwFs2rG3mLO3v5RUqnWTUPf97SN5NwprVivVjjvWFRr2npNph3XTw7Z25tz/94jXrx8Let49A7aruPw4ECssW0coCE5hLYdjnVUDUbwQlLrefreJvUj3uNUtPWJSlYkTsNadAHoytP4nfOkHakGo6XVo8hCgL0KhxTnYzz8kJWYNmnIB3PW7WRyxTUdQZRzEWSFawVxAmNAdtojRuJ4xB0gClY5xvG4W2CAZj6sL7liDVl9Nmbzhetc07Zst9vR78ASpYahF1VZMV/MUSg2mzVd3weL9mui2lHei8GYIryWQqkOrWccHx+xWIhd9vJyzfPnz5lqqqmmmmqqqaaaaqrfhvpCkGt1cUm93aC1CaqDoHBSKqmTlI5WB2lMmqaGhmB1kTvlsSEemsPBzuU9dE2N8oqm72j7Dtf1cjcahckNVVVRFCVVVTGfz0LzJxbCvu/JcrGgKBRFWUgTpobGqAvB6hFS9H2fVD8pJ8bHCW5iPxRbpB2pnzy296mJjdBi3MwrLU14U9fE/KwIWwivY53bgSgxkDhZvuo1SomVZG9vj7fffpvbb7zJG2++ydHhAV3f8dlnj/jhD3/IJ59+wr3PPgtgq05TBQV0BGXAWDUTmvvMGJQpyHNDWRbkJmM+X5JnOUWeU81Kjg6WXD854ubN61w7PkabEJLf1Wy30jh2bR8aT2m2+gAQxapTyLFIFs4e58OUSqdG+23YnzsKKK1TY+yDiimNu7dhqibgw2O1ESWFCyrDalbx1a9+jZOTE05PT1FK8bOf/oRf/vKXiC0LBktOn2DI2Jo0zpgagw2VGuFB0TP+2vhjbFu9qtwaw7X4eQRf8XkJmzm2mKqggonb61xUQimU11jvyDI5T611GDPAqHHOWFLajJQ3SmtR2wTVlqxZIb/xcUIACLlG8fyPU91UApRjiCXPF875tBtCa+8lA26w7RHgVVCkmCwoaHSCdBFypWfyg+pmvI+jkirCBNnqkLcV32fKGSNBjtETj4L644/9arbUGDhGOCJfGg+RAO/jNVEH+B+BYjYAtjhhNGxLZjKyYNXNso4szynyjKau2WxatDF0TcvqfM15bji+fsR8VlFWJfV2S1939LbDmEzUsVqmFW7XGxbzBZvVJfNlxWJe8KUvvcWNG6ecnZ3x9PlLLlfrdM1b5zl917HdbkcTS3ezq8Yh7NZafIRQ6f17emvJVbQie0yWo5QooHBxLQ3HYMg4ZOcc3bGVx3WYXsvLQlPDMQHJDPPx55wHJUBMG03EYcM5mZZAWmNJ7TWCpKLw08PXw89YG7PI4jU/ntMurF2f/t35HalIIBS8TAcOVsS45vKiTcDXaE3bNNT1FpBrSFVVKdctDieJ15CouLQ2Hi9CzuBYVTjVVFNNNdVUU0011VS/HfWFINd8uaAMqqk8l/HnMfC3rpsw8j6G+wbwEMJ1nXPUfbREiH1JVDECjwjNa2c9KkCvTBu6vkvWQK019bYRRYeKlkVCUyhBynmeM5spsmwIc1dqyCSKf9gXec6smqEU1HXDdruhadoQ2B2mIjqHtTo1rDbAFHlOyQNyfpguBiQFRoQiMDQJsYGAQe3hvE12NGcdOIvtWtpGgon35jkn169zevMO12/e5uT0OtVszuVqzQ9/8hEff/wRdz/5jLt373J+cZHUGqk5Cc2bKJxCwxYaIbIcYzLKWYXJKuazmYQfGy1NcVGwXMzZP9jj+rVDTo4P2dtbYjJF13Y0zuKdDcddjpWAPo93Ou1zpRRZ2A9122L7ELju+rCPJcemKkv6bnc6GYSsJxi8Tkoli49SAnGUycQ+h6ASax1N39K2Dcvlkq9+9Wt85Stfoaoqmrrml59+yscf/4K6aSnyIgGRCEuUcqNjNoCauJ5SQzkaGjD+3qDeMunr4ciH55fPfbDCjddEPEYJ7qVmNwAXK6oWk/bDeMKf3QFg0jRnaL2bqWNMlsCMSrt1sEul/R7eIwGCRWiqkCmfY8WWPIcP6Gh476KwkjU3BkKyHSbNvFNKbGNZpjBK4DJhmqJAHyTPLCrF2AWDUWUTgWlSBIUaWwYjAAnPkmxhijg19erESVlbRhuUcyEnKoL6eGxjbt4YhMQg8iGvbaz6k+vFIKDzflDtjUFdtGhG8JiPspfKUkLK8R7nZDiD0RlPHjylLAu8UrT7c/b2llSzBdb6JJLq+p5ZJedOU7fsLZc0dQ048qJgsdhjOZ9xsJyxXC54eXbO+dkF27qmbRuBzuE4ikJzgEJy3R7yxQQKgQ4TNiXbLeyf8FgfFEuMYE88hnKDQMtjghrYeYuzHq/khkskpun8shbnVNBGqeh1le8FoBjz1sIqCWt5bFUczs3x+h1UvW44D4IF04VjNeSCDdOIvY8q0RCqL7phIgAFnX5/tW38faNkHKkXtaLy4B145fHO0vfbdN7tgHUUVVlSVSW9s8wXiwQTAebzeZr8KBmXorZug1IsXp+7bgJdU0011VRTTTXVVFP9dtQXglzvf/A++3t7zBcL/Ch7qus6mrqhazusc/RdjzY6fL1OIbhNI38oaw2LxSJYriQ3ByWNd48Sa2Nd0zQNqo4Bv8HmFtQ867VAqfV6w2w2S2Pph2bLpQY9jj8viiLcsQ95KsagFOSZTNLLTC3Wn67H6cHiNChEVGp6tFbobLB/SQbQYHGLKi3vhrvx8Xlizg9KYW0vja3S1PWGvmlRCq6fHHN0fMJbb97i1p03Obx2nbrpePT4Cffu/5CPf/kpH3/8CZ8/fEizWQOkyYhdJ3f4Jb8rToLUKC3KNBOUIHlRSJ7ZbAZKs7dcovAs5zNOrh2xt1xweLDHwcE+y8WMPDdY52naGnxQ2pgMk2kyY8iyqAqS/ZDlmSiqgpqm6SQwuw95W731ZCZDeRudRLRNG/J5ckwWQtuJFqJRJhUa64LVSCmyPIeggLNWrDlFUXD9+g3efvttfud3vk5Z5Gy2Gz67d5ef//wjyY3LcgFGV9ZNrLEFcRx2PYYf44DtseJL1DnDcwG/Yl+0YQKp8x7jXFJFRsAVm+IIOJLi0TkwZqf5HguOpLMmwGM9qObC81R5kcLtFYQA7aGJTwtdSd6VZA4NdkMVoYIgCdkGdl9fp+1yvwK5opLrqnpN4F0413orof4+ArMreWijf2NFex9qBKDSfhlssxHAeRfea1y3qOHzK/ASO+wv4TBupLRRWO8EKEeYHEBcDBb38bggUyRRmkgrkrqS0TFgdD1BFHTxOSPxVQpRYiqoZiV5nsm0166jrGZorVmvGrZNQ9v17O/vU1YLsqwYAHWwxfVesr1622N6g/MCy+Pk0Fs3r3F87ZBnz1/w5OlzmqZD6zCJNMskJyzLroBMP3yMDoUKxzjmDWo9DFOIHEqpserL7yyw4Tob1FZBJTcsWwFm40j7pOzywRCYANduDc+djnaq+N5iPpvWiq73BD0ecSPDkhpgHlGZNZzDWabCIJS4L1R6z2Mr83gtScl558J6iDcxPOG6EF4/AtXNdst6vQHtWa/XeO8pikLO0VGWoPw+HG4yyJRGl7Izp5pqqqmmmmqqqaaa6rehvhDkOj6+xtHRIfsHBwGeDGG8UWlge7FjFGWF9471ai2huJs1eMkkcs4N8CM0i0TrRyaNx8XrMzabLXmejUbPqxTWLVbIZsdyWJblTq6RMWJtjAAjhl+3bSvBuk2Hsw7rLGUlgEwbgzYN3g42wvj8SfmiVMrLUUolpZYPE9JinpYyOjW+1tmkJIih6Nb2dG2Psz0eT9+LkuLGzet869vf5q2330NlOa9fn/OTn33Ezz/8GT/56c+4d/8+r88uaNsOrxTeWeKUsmjFjBO/UFDkBXmek2UyFTEvCrJMlGxVVVGWBcYoTk5OMEZx7eiQN27fYn+5oCizpKLx3kHXY7ShKKqUHZbnOXgoK7GGuqBwiCo/ax1dL5Mw86AETFO/IrQB1iE7Jir8tFfgBQiI9S2sNximrKnBBqi1pmtbvHPs7+/x1ltv88EHH3B6eopzjpevXvL40SM+/vhj6rphsVik6Y0RhI5thQlWhpw0yZAbIEtcD3GtXf05UeWotE4G295gf0qPlScd1EVBGeJDozmGbVFZNSgTd1VHER75AJqkEVbYCOuUKGfi98Ib+hVF2RgASPOdCG/4monfTO8hhoXHg6pGjXRUi8XPrwLAwOTS8w3gZ6x6GpRzSR00Uq5FIJSC7VW0oZHek1JhsqIPUxf9ADPDDkxQRQ3CtqRQjcoijyhp0vaPtiXaN7U2O0rPBFnCdiqt8HbY5/E9p+McXikJhUb2UllbFVmWiyIqyzBZzKnSvPXOO1xcXrJer7Fdz+uzFW1rWS7mzGcV87n8e35+Tt/L+YgO70mDdZa2a6GT7aqqinI24+aNa2gNL16e0zQd27pJuWd5WTKfz8J1qKfrWtl/4UZFXOdxTTPKnXLhOubSbgqKKOuHXZfOSUfXtphMh4UTFXoqWQjTAQzHRg0LXNRn8ethjf7HgFf8+fHXBlAd1ZYREsvBL4rh94P8vvDI5N1BEar1eCrnFVts2C9ZtguyY4mNXvafUy7YeQfr5vjxSikwoJDfs2lQjPe8bCXrUq7x4bqrw3EJ1yrvXIJnU0011VRTTTXVVFNN9ZteXwhyPXz8GJ0ZrPcsFnOyLKespMnSSgLX+74nLyq0lsb44PiUtq5Zry4DFOlp2z7ZIdq2wzlF3zu6vsUiTXhZlpRlhXP7bDYbXB/+yB4Ff4+zjWKT0jRNsEnK48/OzpJ9Kc/zpCqLuUDOebI8k2mRtg/KBulsm2D7i6Draj7TuOG01mJ0nhp3a22yA8WpXsNdd4/te7abDb7vRF2VF1y/fYd//qd/yttvv4ND8/Dh53z00S/53ve+x8e/+CUvX76kadoE0VJzj4Cu3vY7AeomE0BV5CVlUZEXBXlekuWSaTSfzyjLgqoq2N9b8vbbb1IUhjLLKYqMpqnZbHpR2WmZwKe1oigEJkZYGGFjnufJaiWHSgtYcZ4+hDzb3oIeoEUbmixjDG3XSWOvNKg4rTIMDchz8jLDWUvddhDUDjpAxnGO2cnJNX73d3+PO2+8gQnZYJerFT/72Yfcv38PpWRYwWw2o+97ikICvaMiC4ZmOg42iFAzDjiIj4s/F78ffzYemwFUKLQO9jytUkNqjBmsiSaG9I9K+xTE75wbgFJUeIR1HxK0wv4b7HNRX+KUD8dv2Mbxv+E/Rp8OVkbnJU/P+SGLTCeVzogCMaii4hbEKaFXJ4tGhVCEEs7HXKJBtaK1DsMEIigbQv2vqp7GQBJAG7m0eTWookZCm5DBFxp5vwsufAJhEAPFUeCNBj/KXhPKNQJiCmV289eihVJ+RgLGU24aco30/CpEifsu7VdFglnjt0MAXy5AER/WlM9znPPs7e2RZRmr1QrvLdttS103lEXO6bUjlssle3t79H3Pen2ZtjvZjMN+lGB3i8dhTM71k2vs7e3x4OFj8iKjKkr6XqySx8dH4drecn7uduxxKpCgaMscqx9j1l163x5RwwawmAZYBHYl2WTD+SfXXAUhxF12mx/sqLIA07V4vM7HNSjpdlWb48clhVWA+XIsxpMVhUpqFW9siM1wnG8VrwcSwB9tyUNeF8QhFANM11oFQNgPof2CjHfO37i9Y1VgFoGaHs67dHOg63CjgRkRFMo1T/bzVFNNNdVUU0011VRT/TbUF4Jci8USax3bbU1d18znc5zz5HlPnmXkeUGuc0xW0DUrimoJeLKiZP+oot6u8a4XywSwXouNYrPZBoWXBePY39snNxllWUqobp6TZ6IWUlomrbVNS9e1dAFwiB2yESUPYofsQigykFRj2gzTt3rb0zYt1oXmKsAVpRRVUaaGGXahB5BUTLG5AlGkRaUMwLZukqXE9v1IxWXDGPeak6Ml3/jm7/EH//hfcHR8jW3Tcu/Tu/zD9/6Bv/iLv+AXdz+jbhuBDSG0WKXtCXlWjIL/A/zJi0IshHlOrivyvBQ7Z56jjEwdm80qDg/3OD25xryqKIym0BrbddQbaQbzTGDQYjGnKApRn3U93luyTI5RhF2St9Yn8ONCiylWVINP1kOoqkoAV4CPs6oS6BVyvGJovEyAlBBppRWZyjCh6cuyDO8sWWboGgm5/9pXv8bXv/519vb3ZeqilfV2//59Hjx4iPODjW61WgGk45dlWbIsXs3fGit9rqq4ZAiDRsfsqtE6iY+J6q2YtSM/JwBMBxAWVWkwOLNUsLtprSULKsKCYAuTfW2TGjKCJskgGqnEAniK63Xc/I5tmGOAJmvaj4YChNe+cl2IkMZ7UAEIRdCWpk3G9zlS78TnjalkuwBO3odWCtvb0ODvwrkIrsc2U7HpulGYPXgn9rJhP8S8JC1ANSlr5Pz3Qb6lVYQCKmKEBNnHpURwuLPf4rrxfsQeVACAWqMI2UzJ7hnVa/J5DP0GcDiUF8uoCoA0Hnu5LoT92dm0X6KKzZiMsqzoexkC4pHzqe16njx7ztn5Oacnx5ycnOCRx9R1jdEZKOTGQziG1tlgSczJ8oJMa95+8zbXT69TFgUPPn/Ii9fntOFGQ5ZlYotDLO3WObSP6kuTrhNx7QgcBq3dAH2N2E8TKPQxd0+gjTZKzHtKBiSEAzhSuukEa1FihVdEFXEEgzK1Ma7tsapStklUxAlghTWQ9r8TxSYO0GObpMKHvDFnnShvfbQ7yprFE6Y87gLbeD5aG+3Dcmx7J+pDOY9le62zZMHyKKBtdP2I0HwMUYmnV1BfOpeuDfHcijeAxlNep5pqqqmmmmqqqaaa6rehvhDkunnzFlkmuSFiDSzwjgQ5jGkpy1mwnOU0TUfbStO03dZsNhcyvr5tWa1WrFarZFsDj8WjrcZZhc8ysmImjawy1HXDxfk53vYyPS/Y86JiAEiKkbIo8NahUeRBiRCzYpxz9K0Nf/grMp2T6TyAjWCJ1OaKXWiAA7Fpj5ArdvwKybKxvVhuTLCZeA84i3IO53q6piYzmg/efZPf+/Z3+OB3vk4126duan7wox/xF3/x1/zFX/41T188o+t6bFsn+DG2osAIBhAmgmmxZGZZRlbkyRpYZXPKvCQvTJigmLNYVFy/fsqtW7dYzOcURgf4Ydnb36OqitDkeDITpp05B0psMUVekGVD8HWWZezv73N5eYm1nq6z5FlGn5QIMnmts72oFpRkpFU6S3kvNjSg89mc+XyB9z5NiEyKPaNZ5BVtW2OMonOW7XZD2zf8yb/4T/jSe18myzS2a3He8/LVaz7++GMePHyEMRmLxUIatpEiL1ZUyMGg1EjAzrlkaYzHPyrArloJY4MaYVncRykTa9TIJiWWkqwyGBQY0jxL/o7so6HRHqtKvLP4BHlkKlycpikwRwV4YKQhznK6rh9sqG6YkjjeH8M2xImJJsGVXQUYSW3mnd15fx4ZKBCfTyEKHLIBaiWo6D1oRaZMeo4yz+jwKaNIzmM541IeE9FuKEAqWiuzsXImqcsU3sqkSLQEoXsCnIMAG+Ta0Yf9GPdNVAjJaefSfouggwANVKAISoN3BBBNUESaMO1UXs/6IfQ8riGQiXfDtMAhHzAeEx/WZrJ+e0IGn6zPuq7TDQKtHUVRJlgR91vvLJum5+HTV5xvau7cuMYy3yfPCpqmxQVlKIQAd+NxxuCcpWtrGSBhDLbZUM5L/uD3v8lHP/+EB48f09UNZrHg4OAQrQ31dsP5+Rn1tsYqeX8qWKDlumoD+Ol21LnjbKq45o0xGC+KsvhYm7COk1yvEGCvwpAEpTSKwVIsukCPYWQTjAA5XEvDhAPipE/ZBrHpx/xB5TV5BFZ4vBewn4ClUygrgwpyPRo+ANg+gEpPAs8JNjuXblyImlUnFVymFDoOjvBBuRdOROW9qA3TqRktm57O7arntBqs//F3nbWiFtXGoHS0Hnu8nyDXVFNNNdVUU0011VS/HfWFIJeAjCVNKwDh4vISZy1dL9MJ41jz8/Nz1mvJ4nLOpUbF21F4drhTnu6mI3e08Z622aK15qws0/Sntm3RyrPtWtbrNU3TAMMEuDzPsd6JpQdomiblc0U1QYRhWT40G1FdhXJ4XFB7ZVjrUjMfG/8xZFJaoRgmnDnnxNYW1EweyI3Fa8d2s6VpG27evMm3vvUv+MrXvsHhtes4r3j54gV/+3ff49/+23/H93/wYy4uVrKPjJF8M+dxoWmWhifm8wzKG+ccVVUmqGWMYT6fi5orz6gyAV/zecnh4T6Hh/scHx1yeu2E/f19jNbU27WoL/KCWVUym8XMrcF2Z4yAr+22liwr24dmXBQUz58/D9BSmqg25HO1bZfUf0WRc3h4KBldnUAl7xx5npEVJX3f0naKtmvTJMvY8HddBx6qakbfd6xWl3Rdw7e+9S3+0T/6JxRlCd7Stg1Kax49fsLPP/yQ58+fY7IiKL/cjiVwDJ7G4fLjnJyoKonHvgu2yqjIGKuixs1q/IiANSmXErwQVcsYXsAV65y/kjc1etygJhvUTYMKzaXzQlRfdrR+FXk4B0RhpSBOMxy9flrraXuGyX9X3+d4+yQPbgAz0cblg91SvgFt2+3AungtaNsmXW+iDVQgejgGRia7WhtVNgzHgqisGfbHoKYTyKMB7TQ+BIL7RK6iSspiGYHEAGN8UMhE6OUVydYVgSIhRwrryHWwF+qRZZIQWJ9yujzeDjDLmAi8+rhLJLCfAXyM4SDeY7sOS7B/akXb9ZRVMcAMp9g/2CMPFuPNRq7N1kq2U9s2XJxZtpeXzGdz3nrrDvPFHpu13IRQHppmi3V2x7atlcJby6NHj7i4uODzzz/HkXH7zm1OTluZxHh+SVGULPcWLPeWXF5e8vjxY9brdbo+R7uhc07y/UbnZvzeGPLbvkfj0doN50NQTHnvB2WlGs4L2f3x/JGbGU6Bygb1khsdoyBATTA8PiYqVZNl3RPUhHL+RCtkBHi963fOI1F8CuTHQGc7cpOHXEap+HtHe7EYGzzaRyhnwIBTYKImUcWZkHGb3fhtoEbB9xGMxvPhM2+PAAEAAElEQVQirsEIg22y5Aswj2rEq5bOqaaaaqqppppqqqmm+k2tLwS57t27y+HhHm3bU9c1bduzrbd0XUPbNAlqXVUb9b2oeQqdEe8oQ5g0FzKqhp/R1LVkYRV5QZafc/b6jLzIh+bBiRUlQou2EwWVUoqubemCvauqqhR0H1VBMQg9TpMihEMrtCiWtOS36Hxo5GMDNgYXyZ6lQed5aqB7K3f015sNL558jnOef/pP/yn/5J/9KW+88z5FOWO7WfHxhz/lL//yL/iLv/kh9+99xmazTdZDYxS2a7F9lxQBMdMnNiRKSW5ZUWRkeUVe5GTaUBQSMj+bzVLAeFUYjg73ODo6ZBFyuOZlAa7nxZMn0jgbsX51vePy8jJMORMbZJpKGfZFURQ479hut6Hpy3j58iXeW46Pj2nbjs1my6bestluqaoZRVGmgPrtektW5HS9qPLKIhfVBJI7JI/zqDxOGtTkudhhjTEYrXn18hKt4T//z//nfPDlr+G9pe9amZbo4OOf/5xPP/2U7XaLyYqk0koQxg2WqAjQxkMLxo2p2DTtzhqIyocdRdXoZ5LCJDzXWCEWXydaV+M2ZSO7EGpQmEiej4ANyc5xu4quIDiMOUAg50fK7SKCKoEmcf07Nwrud4P6Km5DXNMDYBCVTAQd8XyM7yVIouS9BmgwVrVFa1jMkRrDxLDjJPdpBATH0yjj/kRFtZiT8y88V9dbZOqmpSxzutHwCJ0PVlqcExDrg9IlAb2oODQ76yFl8QWlHD5ABT8OMx/y9rQRiN93Mn1QRToGKUQ92tScdxilw3mfpX0/fs8Jssd1MTo+csNArgt5UdD3HbOZ3BjwQRmWZQJRuk5ytWJ2Xtc1Qf0qUL/tLNt2xebjT5nPKm7dvE5ZVWw3G7TNkIGww/Wv7y0qQKakVNQZl6sLlss9jg/2mJcF56sVZ2ev5HdAUXLnzh1evnzJei1TYccDCMageZyBOA5oj0q2qKaV70teX8y4EqtiBJIhC872gzIKGT5gg5LMaDP8TlAQtVARml4Fvil70EoI/vimiO1jZmT49eqGGypi2A7XB6MwSjL6nNIMuuEoKhsUbVhH52zQ7Kp0fcYLAHWEKcBumLIar1OWeG4P58pgQYY8HxSp6YeJQy9sOi5TTTXVVFNNNdVUU03121BfCHI579DGYDIogl2xqkqs66nrLavLS7bb7Y7aJKmnMsO8nKXGxHtPWZSpMYgfTdOM8kocfdsyqwSQWGtFPaEGUCBAQKw6qQkOd9yjEmiwRI5BBOG/LV03WGFynafnGitrxjlNgNxhV+D9AMO6vufVq1e8fPmSxWLBf/lf/tf83u//AXtHp+Dhweef8Xd/81f8zd/8LZ98+imvzy55fb4N6oMibH8vSgPl8a4FVQQ4MYDDCIIWi4V8Xs3EOqfEaqm1pshzjo6OeOONN7h+bZ/95Zw8F6VHHBJQ5jmzopRttw7b9xidkxU5zvcSYq0MtoupSUqGC3R1mqoYVTMHBweACyqnjDzPKfHs7e+jlKZpWmnsGHJ1lsslbVNjjKYsK1rn6boGpXKyAA6jhStmrmmlyZTizTt3+JP/9F9SVhW4Htd3ZFnJerXie9//Po8ePQo2HFERmlEYNEiDHNfQbDYjy7Id5eF4emZscqN1ryzLtEbH6+qq2uEqGM2ybKe5HP971QI4VklFVU8MrV8s5un55bwZgqLHKpIYNB/VMHIOjxteFb13eL/bzI8hYHzeuHnjr4udkrBuZcKcDsqjAR4Nz4eXAG55PrejEul7S+8sWQLQsh/GUy2dc3TW0TsLeLRXCRSK4kfARNN2ZGHiXtM0dBtp0quqpMzytN8ENogdU5SkIRA8rI24bSljjVHQv1JBDTbAO8maijl9jt72v7IuwhPJFMO0fnbzvBLckFcXldNIWZqsqCG7y/cdfYDizlm6rk37K8syUakyTAD0XpFlBVpnaC0279VqDShWm4bXZ+ecnZ1xenLM7Vs3uXZyzGaz5vz8nO12m9ZzEUDOdrulKAoZpuE9L54+YrFYMJ8vWd64zmp/y/nFiqYRC+T+/j6LxYIXL15QFMVOll9cM/GYp0msPlplHbOiQKnhnGnbNikMnROFmkCuuD8NMQcsnGEoozHodKxBYBHBsjq+/sQ1P1YHW2tBD5lV1vbJNnlVZZgUakqOmXMuTKEF6/3O78Gr6sjeWnwv03qDNxaPfG6Uofd22GaG3xWBicqwGCvW5/HvtPEai2DZBbWlnEtmCPSflFxTTTXVVFNNNdVUU/2W1BeCXFplnL2WsO6u6yjLEu8V3imUN2QqozDSsPRdUBtlcoc/z3KZXKhUgiMRFIyVIVFlEK0h4z/Gx1lEUZmjIOVuWSfTtRTSJPvOi8JASdhyLKdI08i0ytDhtay19Faaw9gwJUWHt8QpVHVdY/se11u2W8kbe/70OVmm+da3vsH/7r/5b/jy176OMiXr9Yp//+d/zl/91V/x4x/9hKfPnlPXLV3X0/cd2kSVRo3yKjSjQa2BSTlAMDRLs9ksfeR5nvaZMYr5rOLOndu88cYdDvb3ODo4QDlLVYpdLzZtYnGsALF22qblcrMl04ZtvWF/f4/l3p4oHiK00AqjDXUtGWzVbMZ6IzbHuJ/63rGuzzk4OGC+WAiAAIoiZ1aVdG2PVwKWlNYsqpKmrilmFarryMMUr74bQpphsA6t1hf84Xe+xXf+4A8xWY7rGzyKzx8+4tmzF8kymV2BFGPrWmyWY202m53Gcgy3xoArPk9sxiO0io+LP5eeW6nQkAqQkAmTCpPglh4d22gpGrLApMGU7KH4NWk8s/Re+r4LcGV3e8drWClNiNrBjTKisiwbVC4j0DzYmJAAdx+zpgbbWARjwz5T8jFSZ0VrX1RYFkUBRiXlptaSoyWwS/aSHqnrVGq2VQIJAMqTQFUsNQoZz7XYFo3SeDzz+ZK2a3HWja4vksHnvCdO7DNaCzxTSgLNM5Ompkb1T3wtawmPG02dU6IOUz4ce2/wyo9UQcPnUYHnlAyT0CGvKalvwkCDsZop1lgFJ6xNY3WW9qE8fshP6zrJvIoKQFGrRetaHyze8n4JP1sUc3rnefHinM2mYW9vj5OTI05Or3N+dkETMr8cQ1i+tUERmeswrVFs5UVRUJUV7795E+c0P/3wLl1v6fuOZlOjPewfHHC5XgUgmwfo4tJwBrHWKpRTKAx9L5bcuAaKLEtgVa4hcUKjD3n/HqXE7mwYbKpxf49vtAzXDVGIGZMnJWPXxRsgAu1d16OMlmw3n65UKKVROg6icOH8BttburZH+QGcWgaVlJxbagfggYTsi/ovTBdtW7TRtCrCaXntqHxNa0SpoMDbtSnG61k6pwLElp+N1lgBhjApuaaaaqqppppqqqmm+u2pLwS5LlcXzKoZ5+cXbDYb+r4PWUsNSnmKYrdZGFtPyrLEdV1QmfiUv5KZTIKTMy12H6WGxjPkrOA9NgUIe7LMkIWcLRdCfbMsC8otsYBE9YrWGtv2codeKfqux3mC9UilEPGmadJ/j4N4lVJsNhu8k0YxNpf1tmZ1cY4xmm/87lf5J3/8v+eNN9+hKEu6tuPup3f5s7/8e/7qL/+SJ0+esFqv6dpOAp1DYLX3auc5x9a2PM8TRBxPcszznCzLqKqKvb09sSdmGmM0109P+b3f+yazqsQoRZYb5vMK7T1d2yaVXGyuVqtVym0qi4z33n07WPdayjKq5/rUTHedDdsvQGd1eSmTH5Wmazoa1ZDnOVVViXIhNFUuHL/e9mnq3eVqRV3X+N6yXC6CCkiBlul4Y2gT1SgffPAB3/mDP6QqS7QG1/fgHaaQSY337t0LqpXdXKkInuKxjvvAGDOCc/2OaugqXIiwJa6Lq4DrauM4Vp6MVWneK+qmI4s5c2EdDsqlIesqNpaecM50LsFdax2zWRVef7BYXp0EOeQ39cxm8xCYvQv5rBUQp4wW65wiWLAE+HgPyihwQybYVTvmjvok7P9ol/XekwUli7U2KczGAFJrTZamGA4AYgwXI7iMWUxRfSbnqRkpd3RS8IiipsdZGXjRNNvdhl0JnFNKY7QhKwu8IHCs1WHfdgnaaSUYPYV9X4GmKto+R3lJ6V/vQO0qdtACOC2S55TpTLK/lMJ62ZJoN2X071hBOL5RMP5eBBiyRkT5Fq89sp5lQEYc/HDr1i3q7Zb1ep1+xvYdl6sNq9WGi8tzrl074ujwkOOjIy4uznnx8jkHBwdhjUbLuKW3jt42UNeUZUnTNGzrLbPZkjtv3EQBT54+ZzYr0zUD78iMFriPqAN1sjFnKBS2k2EfTkV1oEDHNE1TKXRmxEee9oUPEFSAT28tJoTKj9Vacf8mSDuChuP9LZA0rP+o6gs1XgsRjMmU12B7DzdLnJXrodEGzwDWBLglgR/RlmvDPvB4bPim9Q6cBxumiaoI71z63bsz/Xc0JTI8+86aiZArbosOKsW49qeaaqqppppqqqmmmuq3ob4Q5Fqv1+RZLqHjXRdAl5V8ECUgJVkzPGTakOUZZVEKpMnyoaHMDEVsfPswnt7FkHrJzrXOY10f7mo7XHjOqP5QqARHirwQDZc2uN5jikymjaGDMsANf8T7QfEyQANpBCKgiDbLZJVxotzZbrdsNhuODw/4kz/95/zet7/F8mBf8q1ePOIHP/wpf/s3f8/dew94fbZmHWBg34c7/27IdoqWmDHkitMnvRcVWpoYGSxyVVUxn8/JQoi0947DxSHvvfceR8eH0HfUq5YsM7g+w7U1MUQ4z/P0OlrrFFLfdR37+8ukrhHrXsNqtR4scU7abe9hs94kKJgVeWqmq6qiqirW9ZaLiwts11OVJSbPUC4q+ESN0Na1fK8SABpVfDG/TYLoZduOjo/4yle/yvUbNyUPLECLvneYLOd73/17PvnkE9q2T++nD+8x2oS6rksgINpv4r5t2zY1fnVdA1ezhwYL1biiAixOgYuB12OQOLYAgSipmqYhM4Ny52oItLWDslHW5a6tKEIi54apg95nAfoMEEpe3wcFSsdcXI5JpRLLGEN0zkVoIjltQQmmZRuLaHcaAapxWLdWMpVtDJF0gNZJWRImW44B4vDexWrprNuBibEikJTBA3Hy3/g5RqoqlGRGKRUC7nWAl54IAryXvLmiKPBerj027LMQxpQm5wXsLjpLiXlKYfA7asErU1kFEARI4kcWsRTNJc/qnBe45xyt7QMIiXCMRD0Gi+qw38dTPX9djpXY7ES/ZEN2mskk204m4ZqU4ZVlGcvlEq01m82GtpFrltaKuul59OgpL1+8ZrlYUJY5+3uHAEl1i5V9VsdrrfJs24bc2aCgXZGZEm00167ts91u+fTuPfK8YLFY4FxOF9ZwlhdB7SfXbq0VeSmw1YY1ipKbI8oE5SCEY9yn7KtoJ/VobB+Us9bj1a5a0F/Zx+NBDBH6WOvSjRrvXVgQPu3n8c8rLXbZeF6GB8vvr3BIXVAvxuuSnOfxXAjb7xU6wCYRHYo1PloTtRcoitqdvGqtS+d0PK+LokjX7ljD+w7viQjcNGkSZ7D+TzXVVFNNNdVUU0011W96fTEl18UGvE75KfsHe9KUOgnQjs11DCqP8CCCg0wplFZkmQkWuNCMZRrlQ/MXlTOZobN9+qMeJdastq0xmQm5SRJk33fSVJjM4OwwJcs6ySppu3Zo3p3Du+FOeQoiDs2sBBdLAx2biM1mQ73dUJYF7733Hl/72te4c/s6B3sz5vN9uh4+f/CKf/8Xf813v/8DXr1+zdn5OW0rTbizXRjH7vB0eAaFjsL8ioorwhKZIKmYzRZJHRU/sixjsVjw7jvvcP1oSZ5n0HXoqmC5XHCwv4cxSmCKEttbG9RcESbGqX+z2YwyLxPc8B7KMgPfsKrXEnA/K+htT1O3ZEXOfDEHpSTY3Rh6ZXcUUYvFQqbGIY11BEHeeXAyF0xrTV4WVFVFWzd456iqGeA5Pz+j6zreeOMOX/7gAw7293j08HN++ctf8Lu/+02KPGPv4ISf//Qn3Lt3jxh+rUZQJQKgca5XfM/jhjR+bdwgRgVEnOqXBzUWkNZ/WZbpscMgg6HGqhDCfhArWJECc4wxYn1NiiQSgNFaC5QI5awLk9IYNbJBGbIDysYgS8BpnmdBOSa5Xn3fJugpx11MshHUoJRMAQzZQc57fIBI8fV2snq8gAc7gixKKbpest1UUNIB8n5HEDBakaPby1u3c06MIa88RxteQ8CHvAebFEkRtCfwwmD/VMqF649KoKnvrSgNfYQaGhwoNH0vmXPOOgiB8vFAXVX4xBorgRzRjhzAid3NRNoBfiMlnHVOpir6MG7C7wKXCFIioBlvQzyu4+mw8hCf1o93nq6P9vEsQKQBEiplmM8XYbJtg7U9eIWzcH6+5tXLM4xWnF4/4c6dW1hTsNlsMEa2YT6bByunp+v6oNbNsN7hmo1YyrMcazvefedtTk5OuP/ZZ9RNDYShBTisld8f3nuWC5kYa4xhvVqhA5jrrBy/Is+xzqMZ9mlmDHJfw2OdnHNJNTpSacV1OF5z8utIVIECyAdbrlw7gjpL7U5VTZBRjePrfVCm6dFETnldEyy0Qy7Xrr1aMWSvKaUwHvKikrw3o9PAg/F5r4KFNCob5XKT7QC9+D7HNufxNUTWVFSlTZlcU0011VRTTTXVVFP9dtQXglzW9rRNzXK5DOPqdVAjiaKha1qi6qTve1rbst6siVYjXUSwEP8gHz4XwCUqiszEfBVFnhdJdVPkJY0DvA5/dIdmwSlaZ8ncoHiJWUQyUdGnD7mTPoTID3e5fYBbHdttTW4UL1+94uLigls3b/LV3/0mH3zwAcfHxyzmM7Ki5OVlzXd/9H0++ugXfPzxpzx48Ijzy/OgyOrobZ+aWlI2T8jE8TKFyzsX1BqaopxRzRZoPaibMhTzuWRvzecVh4cHnJyccHp6wrVr17h18wbKdVRVxayssE6UdcvFImWf1U0Tsp10mo5njGG5XISvG8qikEBpFN462r6j7UQN0dYNTd2gjICjPM/ZBhuSNNQAWjK7sox5LgDN9j19b8kKAXMS/i4wpaoKvFLS6HY9XduSlxWbeg3ec+fOHe688Qan16/T9z2f3f+cFy9fMpstaFsBLB//8nvc/fRTULJOyrJKkPCqBRRIcDDCgbgORB12RZEzskmOVTLJejuCSmNINrYJXlXcSPC9l32gNdrosE8yYki1/PzwejEsX2tN57s0UTRNcguvNzSpZmS/kvU2m83JzJDLo5VkKfWdFXgRVFDOu52GN9lFvcNoQ+vaZKWNCrf4/uU8dAzB9gMQVEqRj5Roznu0H2xz3nu6kMGmtBYb6gjADedzsIj1dgAFSgCC8zI4IR9NAv11/w7W0AGGK63QPjb+Mt0UPF4FBWN8T9ZJ+LcXpVdEQs7ZkGWlkrV0KFG5/voKr48ou0L8mWSJeY93hCzB3UwueS8RyA5qJWM04HaugQJubdhGdmyUGpkkqINicHfiJfR9nGxaotB0rqNzPdYryAqst5xfrlGPn3JyfMzx8TXW9YrtdosmTOH0DrRMbxXFkgCpzCgcPavVijzPWK9LDg72OD46YlW3vD47k/wrlFgLgdmsShbqooxWXUvOyAarItATpWPrhlxHGLLkgJSN562XDy/XaK1GExDD/jDK7HwODOei0RRFzM2LUzsHm7G1Kh0zrTVGiSJWFwKo8jD4wIsfFmOydH7FPK+oqlLGoMK1JvcGk4m90QVCHJWT8QaSC5Ixa3sMAm8zlWG9lQw2wHq5Zno1zuUbprpG+DfVVFNNNdVUU0011VS/DfWFIJcJU6GkQTfDxENcyryyznJxccF2s8XjadoGo8NY9s4H1YtJDZq1fVLGiD1LYIy30rSpMAEu3qmOP+usT80+RBuJNNnKKDrbJyWXNhqtvdgirXwtNvA2TGH0QNe21E3N+dk5yzznzVu3eO9P/pQ7t25ycHTIYm+Ppmt58Ogxn977jLv3H/D55w94+vQZL168wvUe66PVLAarDM0TijSWfchAMWRBnbVYziiLgjzPwnvXLMuCa8dHLPdErXXt+Ihbt2+yXIpdqKoKfKdZLhdJUVTXNdu65tnz5wIXAzTUWqGzDI3YzNq+Z7PZikVxu6FtOgFjaV8LiHGdTSCzbVsyk1GVVcpB8yjyXNPbDtfL9Ly2FoVXnokVKjNimTTGgA9ZaM6x3dQUmRzT9WbD8fEht2/eYDab07Y19+9+iveOoiz50gcfANA0LU+efsZn9z+jtz1VWYWmUuw2UY13NaMowi8QG2oMIY/vdWxXGk8hG8OWIRdKJ+AztjqNocpVdY9Y1gbARmhkx98fmkyVlHUKkrVyDJB8+B5IaLrY9RQ2KkiU5EeVRQ5JUSRASCxWY4VQasujmDJMqFM4F21QsiaV1rKG3GjKX4RxV973eB8rJXZGrijOxvtdK1mj8XtmpMaL+94FyITyonJSMY4ogEEjod/OWcSkFl7LSBaS9WEQRaBKYkWLeYJKbINKYZUPEG2UhRWspwNzGo4Bo/cdS3bLyHo2ei7nAoD1I/VMypYKT5l8pMNzDscsKv/i+aqI6C093ej1IapHA8RjsKn11lKogq5tZU2qmAUo50JvHU3bBku6TNnVSmM9nF+s6Luek2vXODo+oKrmXF5eYHvLerUmywx5lokiSWm5PvddWFOynev1Cu89+/sH5JnmYLlH23a0bSdQ1vuQE9cENZp85HmepujKOcJOYP8Y2sTDI/vCJAiltU6DC0y2q3SS/aqTZXc46uHJgiJMGx2ski78viKt82Ha5jD0wXuP8REyRWAeB1HETL/h9cR6G9RXfrDnxvNcfpnIc41VWX4ErXU2Hgrh0+RP592OfTbeBCiKfIJbU0011VRTTTXVVFP91tUXm64YlCrrEFiug9VKFEshIyfYA9tOQrUzk+FC6LjtLZkxdJ384VwUotboWml48vDfkoflQ0aIKC5sDPNGiT1jBBOA9HNaa0whUC3ZENP0NimFWKJsLzlNtu/o+pbtZkNR5vzO17/KV959n5u3b3Pz9m3m8zmvzs748ONP+OXdT7l37z6ff/6Al89fsNls6a2jbXu0ygLAkjDiq+ACBluLNBEleV4lldbe3pKyyKiqkv29PfYPlizygoODPcoQtj6bVczLHO0trnM03uF6gTBx4mVd1+zv7SfroLIDjBAbjQ77S4Lwt9sNxhhm1QwVAtF7K4ClbRrWl2tRHeWSczarZnSdDcowE8K9Ld5bLIos88EWKgo+B7Qx/BhF3/Yk6OI8ddcxm1WcLPdZ7M1ZrdY8efKE89dnHB4c8tY7b3NyekqWFzx6/IgHDx/x6uXLtC3WWWzTyb4NYKjvbVqzsSK8ihbEsiwTDBsUfbu2nagGi7lHRVGkKaFx/46VXFdBVYRh8bW7TlR3Y5XZGF6lDK/RNoOm7VqqqkqqsszEbDrwfshkEvAVlRgy2bLvxSYcm2jJhBIrqZwfw4S8CEoGeDLkV+1OVyTZxcbXh/F7j+dl3AdXgeJVe9Q4ND+eI/F8jbYtoTaMAInAAOcjjBDbozEaeoHicTtkW8Su5hKcGv7fM4AjFezULqikIiwKr5je8/j6E68u8bin/QQBukm2lwoWbBVUNi7+qOzuK9az9Eqj/961jg0PU+m4+QRChp91I1iLFxVgDNA3JkNrEwLjXVrj+JjTZ6iqkiLPxJZq43lDAF1r2t6yabYc7O+z3Nsnywu221quE9rI5E0UvesHm3gmID0q+ep6i20tmdaYqqQsSrZNw2a7pe0Ge2V8PMhaKooi2OKHcy7ut/H1V/aLfOwGxHtMKb8rlN+FkeNj7YmQeFB8xoMQs7WUj3lcA5u8CsvHv7u6fvid4JXCdwNolvMhXgfCdkYTZDgvfZgiKpA3wDEfjjsD2Io3fcZgy+ODZTbtLVmzI6urH31/qqmmmmqqqaaaaqqpftPrC0GutmkkqLtp0h/xksVlU+MZA83zPMdZl4K9rbN46+m9xXcCxLqiSHfdY0hu6PxQStN3HX1QfriUeaIT/IKhed1plO1gg7K9lal+8XEoydvxYsvrmpbeNsxmFe+9+zZ33nyDt99+l7feeofO9jx6+JCf/vwjPr17j5/9/CM++eQuL1+9pm1qnI+ZRGJvidvoXdC2XbF+xea3KArKsqQsZ5TlgjzPWC4lUP5gf8HpyTWODw+ZzysWRcWsKqUpwZHnGbOqpCwLijLHeeg6l7K2mqYRiJMJkMmyjM22wXlCplmXGvA8z7C9pW178iKoANqGy8u19NtGs16tWK83zGYVVZ6hlKbterabLVmWU5ZFUBmAySP0tGH6YC95aHKgsE5goO16jFYBFFnmYd9fv3mTl2ev+fz+fc5ev2YxW3B4dIhzjrufforznhevXvLs+QsAZjNJUu+DGs+E7KG+H6BmtLmNm7a+75NFcazoGk/WHIOYHQXHGJaqwfo6Vn/Fz8eQK0KymPc2hlljxcjVr0crmu0Hu5lzHqcANSitINiUQgOutAoeJk/bNRR5CXpQAPmg/IDBJhdBV4RdQ0Oud/K3ds45rcXSFyBUfEzMz4r7zwXVZdy/8XnGwfVXlW8R+mk1WB8F5kGE4OPtka+KMioGwgs80CGAf7hORJihlUzcAy/ZTWOEZR1JNJTUUxEo7Db+8lq/CkZUgg3hOfzoFQJ53lEBjgCWqNCSDm0HXF0FN/G4QlDEaTU6noPSzqXXGRReES52fU+W5UEtq5NyKOV+eVHPdn1PXddh8mqw+RrYNi2rR0+4XK05OTlmMZ9zcno9AFe5ERIthpKlJqBGmOuQNaZ8L9MI8wKTG3qrmM9K0DOxn9tdCJzy/gKUHVuUxSIolj+l2DlHjTZDZlq8VtsBMI5tytG2q5TC4Yb8upGd1kfI7EEhij8v1HJnTcMu0BVlscd7C1oFq2GEcIOqLCq7PCT1GhAmKw6qvPiCzg0qQ601fRcU0wyQawzd0vVADfswnvfjc3Oqqaaaaqqppppqqql+k+sLQa7tZkNZ5DjvMUkhozBak+VZCAUe8mAkj0ejdPhvbPre1cBb72MujzRoqJC9E5riLqqS0BIUHe7UD3+ID5DN+h5jwpRG/G4wL4p222LbjizT7O3N2T+4yZtvv8Xvfftb3Lz9BuvVJY+ePePTTz/lpz/9kJ/99EM+//whq9Wa7aam63pQPqgQjChJnKe3ouCKyrao9pHpZVmCQkVZkGcZWVZQFhlFkbO3mHN6csy140Pu3L5JVRRkmWF/sUeRFwEiygRDyaaRrKhtvcU5JROzQkZaXTfkRcG2roOF1NF1ko/VdX2CO9IYxsPYs17LtMBtXZMVBTo0X2VVUVUzqqJMAfZlXiQlj1ZiGzKFofGi8lBO0Yc1MLaj9b1Fy6wwijynKErefudt3nn3XXpnOTs7Q2nNnTfe5K233mJ/f5979+7x8ccfUc2qoDqKEwxVmA6nMSYL9joNSB5bhEnjkPmYqTWEmEuNVUZjCAP8CvS6GvY9tqMNzfUAD+LPykABE17bURQlcfLh+PE70ENJhlucrOmcC4ACMpMlq5IAozCFTom9ypoMFSzDsYweIHFs1KU8LsCeQcE1qCHjw+I+HGeYjaRE6b2PwW58LxEUaK+TFVOgVORIu1bNtHa0xo/AhR89n/djK59P1ueudUnNmWW7ai6XpFOKhMa8l6wkRcoV8264FqF8sEIqIreI6rK4v0Rpc1XdtWvfTFsav7fz37vfV17gEVcfE7lJVH0FCDfY2+L2xM/lGGljyEbra5yLBqLAFcVoyHuzPTErLkiXiFMoszzHWUvXib3RaYGPvnVcrNZs6pobp6fsLRcC6Z3kOXZ9h9Gynjywc8MCsZYrJzcgbNfSdgLN54sls/mci8sLmqYFVdI2LUoL0JSbJIqmaVNOYFQRah2D1+V9RCWTdUNYvNJB6TRav9i4FuVY6ZSDRgLOCa6NgK33Ht/Luo35XgOAjucOgMZZKwbTcBMgvkZkq8MxDIpPF+Bvb9GZTtetaEKN/+fCVERRPgcQ5yzGm3S6yrlg0w2r+KoRPu6qzgawONVUU0011VRTTTXVVL/J9YUgl0Jyr4wCY2ITrkPmkthRdAi3Vki2DQGIaaWwnYxQN8EOBDFfR+wdETw4Jw2ICnfCPRrvBytGBDWxie27Pj1eoei7NilgskyHqYuiOJMcqR6jNTdu3eCb3/gmH3z16xwcH7OtWz578IRP797lh9//MX/3d3/Ho8eP2azXdEGBE4OotdZojKi2grohZgr1XZvggFKasqgoqxllKdZEk8m2Ga3IVM+yqrhxvM/XPniHosiYz3PKMufo+JhcF1grjeV8Pk85WJuNTKyUiYESum4ygWnVrKN3jtliId/PM9q+xWSaqtoDoO06bN+z3mxpm5as11SVoqwqjMnBSSDxcrmXAvk322CvaRoWiwUgAIhgHVOILcl2PbYT1Zj3MS9K7G0CHxVHR0fcuXObmzdvcXJ6nVcvX3L3/gOePn2KMRkffOUr3Lp9m7quqRZzDo6P6bqOtq4piirAqBywIRy+Tw1rlukEnWJw/FhNFAGr1pr5fL6jpBpPSBxPW4yTI2EXfkXblB6t3wjCRCEmtkLnbHhNUZeUZRWA8C40G79mBLjOeYoiT2vaOw8B3OBHVkOlkyJGK53ygozOpYH2PmXaRUVShDUR/gyQSVSa8v2gTlQDdJYmGKLqErycgwEiRBKXnp8AGca5YsEjqALoithHjxQmMfOsD1lYojbKEhTsrQvXj/BkzoFTQ66cUjKV0oepkQTlk9Ih0DzsIxP2R3TzMUzYk8Vt8LjAkwYljE4AI8L0QbEWEMOvv5YG5VdUqcXjnexlYT+m+Xwj8BjeGFHlQwI4QaGmw6sq2TaldYJxY5AaIWRc8zNTpcfIvznW7qoLldZkxpAHtU/eSSZW0zR47wUga8Nms+Xx4yeczSqOjw7Z31uG89aQq4ym61IuV4S8WZahtLyG1lBvatqul2PTt3RbS6Yc5WKG0zmvz87YbmUqqywDxXrdMp/n5LnG2jZAzjzYcBVKhX3gNdZ2O5bFCLNTPl/Yt/H6lWV5OifwPkwAtml9x/2ZDlFYXREOOmeTNdUpOa/wcaWKkss7uU5GxSZqsF2GX3g4Jevb9halHUZ+M2N0HAYjakYVtyCtI8mow/qk6EJpuXsiRzcBYGOGjELZP792GU811VRTTTXVVFNNNdVvXH0hyFVWBUUhjbtWsZnVIcRZhYltbfI+6TwjMxnrzZpobXKhWUWPJsKhCCYgCZE2GoVP+Tpd32GdTRlg6Ng4e/qup+7qtI2SsQXgUd5RZoaiNBgg14obJ0d89Wu/wwdf+zonN26BMqzXGz69d59P797n7/7+H/j77/0Dr589peu6pHoSS+Vg2XJ+mAgXvzdWcORFQZ6XFHnBfLYMzbo01nmuyYuMxazg+vEB777zNu9/6V1Orh1hjCIvCvb291mtV/he4XxHXdcSrBxsmArIi5yqqsRWuF4zXyzou4ZtvaXrOmxoupTRLPf28A5RdHWd7O3QDC/2llw7PmK1WlHNKpq227HhJPzgLZnJ0KUo9mJjar3AS4eXwH88eZYxC4oKgT2Kvu/wXlFkGV96923ef/8DegxPnz3le9/9Hmevz3j/g/c4ODwAHM+fP+X58xd8+uldzs7OZJpjVgCDpSYqfvoAP+KaGg8z2Gmi47plsBt2XSeg9kpgtTEmAQCtdbI1jtVMsTEe53JFaCWPlSY1Ai0pT1FkYR8P+U3j/LC0nSqEuUeAYjRt1+C8pQuPG0+TlPUHfdcj6iyBLZI95iUEPM9BhZy7oHKMcEdULQOAFmgr69wTs7lgUCgNrylgRd6ndqKKwgt48eISlvcYO/1ovwugScVG3g/vPwLKuG/GQNLhMYBXPu3LzAR1YlRkKbl+KK1F/RLeX6Z8AI6kY2CtZEF1zoYsL5cUUALugzWTwRaWMIBGwsmtw3qb1HRJcpX+P6hL/ehn1fBMSZkVPnfeCapQQ9j9FQ1YUnZ5ZNfu6Op0fG0BJGOlYdyfA/AajmW8pg2PiZRjsM/FdR/Pk7quaZpwjJQAyNVqQ9u2rNYrDvYP2N9bUJUFervFrjvKsqBt22Rx10rR9D2bWgBXVc1YLOdY17NttuR5QZZnFNWCosi5e/ceeV5wdnYZQvItm+2G42ov2HiF4XjvRta7IadqrLaM539cb2l1x7UUciWdd5I1aQdFsRkNpIg70XufcrBcUHEpBnWZnC4BQCoPRs4FP1oP6Z8Iho0MXdFaC7IKv2sFoHm01wN0C8fNBMt/puP1bzczUIdrlzzVYJV06TyIQw2mmmqqqaaaaqqppprqN7++EOTKy4KyqlLIr7VOFD94fAgeB7kTjXP0zpGbDBPuLpdFQb2td6xAHsmyanuH9w1KKcqiTI1PXdeiXEGgDk4a/gQbUORGpkDVTQBBGJzr5Y/53rNcHPG1r3yZ3/nG7/DWO+9Szpf0XnG52vDwwSO+/8Mf8d1/+AGffHqXFy9fSoC9s0H95eTf0V3tGDDcO5eCzmNTmGcZZVmyXC7JspI8L9DKBLWJpSwLDvb2uHP7Bl/7ygf8ztfe5/atG+RFzt7BEV294emz5zx98ZKu7ch0xmKxIB8FnudZTlEUqbm0zmPylqbrKMuKoiyp5nM2mw1FWeKtNLdt2+Mdwd5jmc1m5FmJUorDo0OatkkwLWX+hOa6rCqKvEAhNjmxB4lNaLaY07YtF5crslyatrZpAAFDXdPS9S0Ap6fX+c53vsPh0SFd2/PJ3Y/47ne/x+NHj/nf/C//V9x+8xblrOLup3e5e+8er1+f0bUty8UCpcLzBbtqbNRiePwYEg3TOuUYioVpsMKBqODGmV3xGMb9HC2GY5UVMExHZABlMcw+fk0mAaowEW431yZm7URlYrRVwgBx4uOVUpgsp6lr+nDM4jZFe2+ENPG8itZF54KiLMuoqhl5nrPZbMKEUZ8GOMjjB7Dkgwory7Ida3Lfd2RZnvaLc7t5ZcP5EVRRNkw69AplMgHUZsheGquhAjfDK8jCuo6gse97ur6jKEs5/wGdyfdzrcXa5iQ8PdrGvEKuG+FfUVUNx8s5j9fDOS37ParTQEYkhMchVjiDALKrWWpKKcnOUioBtwjvHEOIuPM+uv4SPFGokOEkP5vUM54wLCBkcY32r/cE225QJ4Wvq6CQTRZOlf4Zzucr2w2ktTfkVcXvD0BXPrfpXIs2YBV+rm1FPZvlBU3dAIoiL2V6rvVcrra0XU9dbznc32M2m3NtvqBtW87Pz1ivV2RZRtu2eByzqsK5WtaijkDO4b2labacnZ1xev0677/7LnVTh/MvY73Zslgs6domnZMRXjVNQ5aJunY8GCCek8PU29F56gQGO+Q8sH240WFkXY0heNxnIKzX9r3YEUN2XjxuY/u263p0niUgGlVi8fjE64DWOkxaDeuEMPDCOTJtwFlR1Kbj6zFGjltUmKpghYy8KlkvgyV3uEbFQQsmnH82xRBMNdVUU0011VRTTTXVb3p9IchVlRVZljGbzQCo6xa8NPJoMDrbUY5oo+lsz2a7DX+QK5pg5fPeJ2WMzgw4RdNIA9XHwOrwEce3b7bbpNyJYd7WWtq2DRMZBeDQ1GhtODk55g/+8A/5R3/0R2ShWX7x7AVnl/f4xSd3+Q/f+wEf/uJTNk1LUzdsLi/o6g0KR+OGu/zxjri10vBZBqVDbJRiZtJ8PqeqKsqyoixylDYYrdjf2+fWjeu88+Yd3nv3bU5OjlE4rO24e/8uVVmyWLzAebBeo0yBVZ6+aSirStQQl5d4kNeI+8A5mq5NEKdua7bbLXlZcLmSxrHe1BhtAEXXDtMEm7bl8nLNwcE+m82Gqqq4vLxM0/9MntGH3B1pZBu6tsU7z3w+T3CorrdsNhscyPa0otDIsoK+71mv1xweHvL1r3+d6yfXsH3Hg4cPuX//Pj/+8U+4eXqd/+q/+C+YL5dstmvOL8/45JNf8uLFS5wVu1G0mXZdz3yxGDXkLimnbAi8j8qUtm2TwuvqhL/4s2VZJgtoBFyxgR9nRPmg3BsH2Mf3D6SvJcXF6GtKgbX9KIfOst1uEsS5vLzcUYCN4YPkQLXEXBxn+9SQGq1QyX4YQEb8X1BclWUR3lcrGUvepel0JjOD/VAJcB5b2bQxEPeZUhQ6QqJB2afGDbPWxKhr7zzKhODsIEfpnQVnMUpjtCY3MqnP2yFQ21kXTIGDekUpRVGJRRUDyss6i2qopIKK8Nwo2l4gmthpRUaW5blANeOx1tFZh1HBAurFdmlxaJ1h+5YsZCD5YN0lKl8inE9qLo8DMp2Rm1yyCFH04ZiPbV8JLMT14d3O1+OxdBHMKZWgR1KEqfjZrhVygGdq53vj3LL/j/lgPqo3h9D+eG0dKxx1gp4CcUTNlSF5ihkmC9eXoCgl5CxuNhtevnyBUVAUBUdHx5ycHHN4eMx8tmCz2YTt6TC5pqhKLi9XOCyLRUU1n6GUZrPeMqsKLs5eQYDAb719m8/uf87B/oLNdoNWioODPWazGUVR0DRNGnYxZHHpdF0fXxfS+o/W5rh+rU2wNK45yRcT66DH41wfsihlqEHgxUmRLM+twk0hL1lzegSpg3pyrP4yWtZw1/VoPUxx9H0vNx3KCtd57EgdqES+llZJzE90zpFnebpeRrVoslhqfWUtiJJ3vH+mmmqqqaaaaqqppprqN7m+EOSid+SFYnV+waau0Up+3FmxRmEyCPYvCH8wW0eVi8Wss5ZZUQKDeiCqtLAO28kd86ZpUmMSbWfJEth2aAVeKWzI68rzApzn4vyMeV7wL/7FP+YP/vCfMD84AF2waXs+v/c5P//oYz766CM+u/8Zz549Y71eA0ODKZlLCuc1+A5xmgV7orU4Ox6p7kB5jMkoipLFfMFsNifPBSpobygyQ1UVXD+5xte+8mVu37lJVeZk2rG6fIExGfP5Ps5atq0D0yeQ0juL7RsuL1esNtudu/r12XlSKHRdhzKahck4OztLzdnm2QuaMA2zyEuKokxAZ7PZYP7f7P1nry1Lmt+J/cKkW2a748+1dcs1u7vaO4qcbnI4juQIIwiCIMwnk6AvIAECNNILGQgYAkOyyW6SYnV1dRXLX3fu8dstkzYi9OKJiJX7lubFfVcFrKdw6tyz91q5MsPk3s8//8YYmqZhtVqhlOLq7SU6Stm27Q1d17NerihsgTYl3gsTYLk6YZomumHIoFACZ05OVozeU1YNfddze33Dqq75/d/+bb7x7d/g9OIew9Dz2aef8u///b/jiy8+5/GDh/zTf/ZP2W5u+fFPf8R+27PZbfHeUxYNQxgio6CgKISV1rYtRVHkNTJnYcwZXslvK4GiifWV5rqqqtzIA9lHK70mjXdqeoF8vXBgsBRFkc8je0jN2FnpfXVd53/P5aDpPNPr099zGaIPAZtYIyr57Ihf0DQ5ATgyA0QfmD8hpgdqjfMiZ3MetImMrDKCdG5u4h49vXzAICbgUVNFCGRQQGubz1WpyNginkf0kTvI8gJ19CibM1kMihDBZAOEOBf5/pHGr+vz2KpCWGFWG2GJKo1SnmkaJfTAKawtUcrk8ZvChIvJokqpaLouc5x84kTupYgbH+fSepJUR0ySnyqMUmhto3RxkuBK78HI2EfBJ6iDdxoqgXMzYOtLmFPyXUuMM6UUIfqCqWjYrkLAhiS1DBlUCWTSUwzWPKTrpTXsgz/4ps0+U6YkAXKa5LMWglxnmookITXGMAwD7dTSD7KXUAZrD+dQFvbA+FKKruvY77d4oBvh8qZlu3/Bk0cPWC8XPH6wYnN7w22YsEXJOLUslsI0HQcBq9Gwb/eU61MyVc07fN/y+P45jx49om1bhtHTdSPDKCEjdVGxfrRmH5leNzc3XN5c5nNL45NYX3K/jeOtdJbtHe4z4rOX5Yoo0Crv0QS4+hBiAqn8rPD5/T4tcnQANxx+ZqrMXCMyEx1d26OiFNcrSd3044Qymu1+n+cnBB1BNU9iZAWvGAa5pxWFZQpeZLXOyYMmDizTw76Te1gOu9CHe9mxjnWsYx3rWMc61rGO9atcXwnkGrqBorAQkAbTaKqypCg04+TY9yNhCpiyENmJm+inwy/vepYUltISU4MmoI1G2yKbGM8lYOM4CivHWFw08t7vN1xfXqKC58MP3ud/9c//d/zxn/19Aj1tO6KLkh//9BP+p3/5l/zN3/wtl9c3vH37lv32huAPv7RbexiGAwuH+HfyeDqkTWltsGVNUVXUVUVdVlkCkxrAs7Mzfu83P+R3vvM7VHVFQFg7i8WCshLQz0SZU1mWuNispustbIExhouzezMG0MH7KwEq3nu6oef6+jpfhzGGsiwjo6yi78Y7YFRZlrmpq6qK3W7H+cUFxhr6vqcfB5HFKS3yNA/OC4ugKAqapsK5OkuXDj5lYs7sppFp7Hlw/x6/87u/y7vvvoP3js8++Sl/98OfcHNzzcuXL/jD3/t9fvd3fxc3jZyeX3B+fo9f/OK7B+Aqnl9V19njZooMiMS4ats2pyWmdTYHkBLglZrXZDavlDBL5s3d3FdtztCayxnTGM89o3KqZ2wak5Rwbp4+N8CfpomyLO8wvuYg3dwLLIQgSXb+bpphWmuJhaO1yo14+txhGOi6CEgpcwCfbTIgh6Fr5RgzyZKKVKH5/FoTJbdKoaNX9ThOAiopgXXmLDSR/k15bGVYQwYbEzgw9ycKIUTgMclRYbfbxD1qMEZl9htejOTV7BjG2HxOIRyS7/7/sey0usuYU1qM+n32y1IRDNKIpZEk9M2N24P3meWTQKnEopuiCb93U54XhcIaTQgz1s4sjTbN3XwNpjmw1uKiF5OOny9j5MVvLJ1HiIC9/5L8lYCcbchgZpY7R4YSEXeZyzHT9cr1yed5P+H9RAiek5MVXdfluVGoHC6Q7kNp/I05rNlpmmjbPW0Lu92OZVPy5NFDLi4uWCxXvH37lrGVBFiFwk2ekQmlNYumQRtJYnTOCbUPKCuL8yNVXVAuCtbnltvbW66urrne3rDb7gXorwowitVqBZCZlMAdL78QDkxduXZZw/P5cu7AbkxBDkkimuYwyzvna2fGipu8EzZz3NsJscpzhXg8yr4U2aRR4oUo60XFZNWUHHrw1dJKiZzd2vxDTQC2g+dlH5l6wB3QD2C/3zNNjq47JLQe61jHOtaxjnWsYx3rWL/K9ZVALu8dw9Dn1DYbDeKbumFVFIxvLtnc3mavrGA01shHNFXN29dvY7MmEhdtNN4HpklkT0rrDHAlkCs15nWUKw1Dz83VJbvtDQ8fXPBP/6t/zB/+8Z/w/nvvCcBWGFpX8zc/+Dv+5b/8N/yH736Prutp9x373U68jSZhg6XGJslzDrKg+RP79Mu/pSiKzCAqywprxYBda1guFzx4cI/79+/z4Ycf8Ed/9Ie8c28lMst9yxBZT0VZ4ryn63ppeqIkrYuJkE3TSAOl4ezsnN1mm4GTBJa0bcvt7W08L0PV1Dx48CCDMXrWZI7jSFFUjMM4Y91IQ17XNc45lsslzo/0bYtCsz45kfGI1z6NE8pll+4M7CyX4qkj8zIweXjx/Dn4kX/yT/4xDx8/oWoa+n7kk48/5Sc//glvr97i3cSf/emf8Bvf/g0ZA2v5q7/+az7//Bnee05OT/M1zNPPpnHMBvvzMUmyvgw0hHCHBZUAARmLIq+rOdgy9+cpyzKDiuk8hmEgSWzn3kZzgCoBV0lGm0CVJPman1Oam2ma8jHTn8QCS+BAXde4BLBGYG2K5ybAaDLMTueVlnKUEiPN8X7fxmu7u86naYosFBulieCm+HUlgOvoJvquy+MOCfBJjJ8v3y0OSW13WEmzMUtAXpLDpXUVwsEXSmsTJbEd4yjgoItrOZ37LwFYWoDjuefafM4O9zOfXy9eZrN1oBQ+SAoe6mD6nnzElFLimeUjqKFm3kkIM87j4xwcAiu+XPPz+TKbZi6vTcb6zglHhxmYIc5c3EFXTAwHScd1bsruXelcjDIsmyX90DP2IxgyUJvWfwJ107U5d1ib1hY45zFx3QzDkNfQFB9itG17YM9pQ1VV7Pf72dc0bhq52ezp+8+5vtnw9MlD7j24T1GVdG0LAbrtln4/8ODBA0nLjA8VRH7t8rne3m5kbRUlxhaU1nJxdkJTldy7OIOg6PuRk/UKpU9wzlFVFdvtNt8LDnsyEvBm45hYnmkcnZPXGqWY4ppOCYyZ1eV9Bibv+IChMFrjnSckb0BjMxtSgFgdrw/QyT/P0/dblDHRSy9hYxEAjYJK7yStcZocNgZ2hKDnQta8NtNcpEARMQiTVxaFYRyPxvPHOtaxjnWsYx3rWMf69aivBHKd3ztBW3nyP7nA5Dx917HvWknT8oE6GmOnBjR4+aX8+vYGpcFYLUbOeJz3WGuwNjb845CNnxPAkACHV69e8fr1a5Ym8Kd/8of8w7/4Cz76+kcsVgtQmrYbeXl9y1/99X/kL//1X/H55894fXVJ13ZM48g0DajgwQ+4aQRrsmwnyX5y6ll8Ip4au6qqKcsqMx+MEQ8aawyLRc35+RlPnz7m61/7gGZR0VQVt1cv2V2/5vTkFFsUkWXSsd1e5+a1Kkt8NDKuqopxHMU7xhgI8POf/xzvxITZWkvfizG8tWJGnxr77XbLMAysVqvMSqjrGmOEmQWaoT/4liUWWFVVmaHhppEpNosYjTUlKhmLR3nOMN4FepZLmStjNFXV8Or5C37nd36H3/x7v0FVWZwb+fTjX/CTn/2Cq8tbfPCsV0v+wT/4h1xcXGCLkmHo+df/+l/z6Wefs1iuWBQldgbmdX0v5tbOMU6TgIQzAOnAEjF3WDZzyWECuFJ1nRhVV1WVx6PruvzfiV2YGWqx4Z97c4EwANMfIKc8zuW6qXlM0sj9XtgkXdfl4yej/znzJV2jMUYa5mxsrbIZu9YaG5kX4zhFEE3PZJIhnk/0VOt7ttsNy8WCIgYXaK2o60rSD2MD7Z2XVLjoweWCi2BaArQS+BjlUDOGy0GKeACXBNAbZuy1gwfRHGg6jBeZtSR7JZ5jEMZaYlfOkxdN9A1z7gASyto80EcT+y0xrpLcLZmBJ5lYvBBEgZaAM+6AdmJKTvTNIr9/zsLywaOCzt5n8zULUQEa/yTGzxzYvTueAnIl8/DD+oxr3egDkyrWNGOFJbbTHDQb3cjN7Y0wooLDKnsHkPyyn5jWSQaq8jpP8mrvD+MwDD3jOGKtzcC8rFuN1ivW6zVd19F2Q17/q/Upbhq4vL7henvNw/sPuH/vHrYsJRXTWqZxoq5q2ralbfdUVQ1K7j9FkZJRPeM4MHpPYzSmKCmqJauTE4qiYLfb8/btJePUc3V1izGGp0+fslgsuLm54fb2NjNEtRYmq5x3HN87WE+YMZ8iqKUU5ksMwi8zhZVSTIOA7CqAJ+SE4tFNKH9gN6YACTwU1jK4CReiBBIiE1BYZs4HIM1dTBG1OkpmZc6KO95aB185rVUMETgcg7yWTU5cPdaxjnWsYx3rWMc61rF+1esrgVzXux2r1RrQeOVRWlFUFcGH+BQbSewrCrbbrYBcyYRea0xhCEGSmr7sWRQC2MJg9MA4BqwuGTvDs88/Zdjv+PC9R/xv/vl/z3f+4A9ZrddoAqYouLxu+du/+xH/7t9/l+//4Ce8ePmGbnMtjVC3i41zQIXI+MDJvzlIliK/QVgvCoKHsoiyQgW2KrDGUBjxAFpUFSfrBffvnfLue+/y3nvvUVcVWmtOT88oS2F7te2OL54/4/rmhrquaRYNycRbG0M39oztlEFBYRUIS6zve5EsVpKI13W3WGupovn/5KQBGaeRqijBKIZ2xCoBoDbXW243t1hj2bUt3gdOT09pmoau69hu99zcbFgulxHAUJlhcXt7y3otps3Zr2z0jKNIg3zwKKO53W2wpuD5xy/5+c9/wWpdc9KU/PzHP+T3/+D3aceBz754zn7Xs1wuefzwEe+++5Rut+P/8Zd/yXe+89u8evuWt2/fslotBXiIDXTXtnRdd0emiVK5YUxSo7knV5JiDsOAnkkUp9iIG60jmCqMujnDa56iOJcS5a8RGR4RbCwj0DdNE0P0J0uAQApUmMaRLjLd0rESoJXSMYFZAx1ljgnoiBSNlNznowROLJ1EwhUCWKujub5BaxPN/veM40hVFXm9lVVFUZaZqTOXaE3eZUYSiNeXivK8oixp3T4zW4w53DYS0wrIa1hrJfZUJo2ngE9KmTusrTmAcwAUXQQqhHUHAiaOkR2D0eIhlJgqwcf0Oh3P31NU9uD196XjH9hWh++rEFDRjizou+AmM/+qL/8tXlmgBA28A9oFBRYr95PIiNTR6yyQDOejhxkKgsIonT2znHModLwOhfNi3g5BQgIy2KLwKLSHSBy7s4bT9Wpj5OFCPJ4OWoBMQGuRgAdLNtTyHFhsAuj5w+d5mcuqMlhbkFI2i6ICPGVV3PHBq8oy71EBxxybzY7tdpv3y263p64bqqZmGPa8fHXJdttycrLidL3m9PSUcRjYbTdMQ4+tKhQSAuCcyP3GccIPkv5qtcX1I7t+RMf7tjaGwhY8uncmQO4gnnqvXrykqAq0hnfeecLkHG3b0e5bhmHC+wRYH8DYOdMzsxmBMYJXaV8bbcHF74Y8hBid7jdCxXMuSdUD3o+EID5gKu5dkf/GEAsfMEWJdxOFNYQg9wg3OVKEQ/AC3g79QTZNBPyD95JUejgd8dtzARXBUMcc2J/yA5JjHetYxzrWsY51rGMd61e9vhLI1e0H6lKe4A/DgDbxl3gSD4r8y7AwiMjNfNu2lLZgv9tL8tg0SrM8YxioAErXTNOOt2+e4aeBv/izP+A7f/AnPHnnCYWBerlis9nzySef87ff/0/88Ec/49kXL3j95i2b3Za+73FdS3o6nfx9EstAGFp3m8D0NWHGWGxlYxMnT/FtYSlsQV2V3L9/j3ffeZenTx6wWtUURcHjR49ZLhfCOlKKohJJovfw4MEjqkYSF4vCMgwj4zQxOmFt1WVJ09QELywfY8W7bLFsgEA39Dg/cnq25uTkBO89u90O58bMHOmGPgMtTV1n/xRrha1l65KqqgkEdvsdIQTWZ2uRSfYdzju6m30E5lru3btHVVW5yZE5VeLFFTwuGmff3Nzy6uVLQgi8++QR9+9f0O5blqsTnr14yWa/p217ttsd+80WqxRu6vHBc//+PT777HNevH4tnxHXy2pVSiM3BYzR0dfJU5YWWxSUZcU4HiRDCSxJQKHI2kbKsozJkOOBKROBpKJM0p2Qj5GOk0AognguZdCt76nrOoNahEAXWXfzppe4B1JDn1hzc6+wcRzpuo7FYpGvI8tjIwCUADrCIYVU1qvsqSmm1sl1mPw970OW31Z1jbUHNk5d13fYY4nldsebKlFVtMZqMeJXcIf1luTF8jmSsnhgu+W3A4l5pijLAmG+JKN3SIiMc+J9Jz5PMkeJiZiOUZWFgAjjiDZWmFszIDIRjjJDLBzM21PNWX9z+aB4ZznxOIrsHAFlxszmmfvlCRDhIggpQNSc/ZQYWSntLgUFCEcmGvEn3yUfUJHPJV+XsSAC8S4GPmTDLDQhOBIPLPmnBQJBBUmBTOPgU/KjlwMj4FjGWoxBKx2TI9N0hMzMOpB3AkoT0/nS9fvZ2Mq5ODfm/ZTWbJq/NPY+iMfcarXKKalaKfZty5DkyFVJYU2UmO+5ub7h4uKM87NTzu5fYOsSlGbsB6wSFmZZlgJcu8AYgZ08j9NEH+dPxZAHpTWPHt3n5vaWaRyznFwbQxGZsIvFgs3thq7rMzMxXbusp4PvmdYSOCDAroqAmELp+POGyBQMoIz4vRVxXZW6xE0HZl4KADgY4CswQQDKyNKqKpGKeuezD5tSCh3XmI/y2zTX1tr4M+ngVziXY2ulcFH2GGbXdKxjHetYxzrWsY51rGP9utVXA7n6ge12nz21yqJinEaGfoi/xB+e1oM0Wvv9HmutmBMvSsbJ472j7zpMlFsZo5ncxPXVJdM48PTJI/7L/+Kf8I1vf5t7985ZrU4wZc2nn3zK3/yb7/K3f/sDfv6LT7i8vGGz2bHbbhnGHhfNkAkSey4NyXSHtZIkPyHANAk7QimdZTVFUVLXFcvlgqoUkKguKx4/esijR49Yn6xZr1YsFjVVJc26gE6O7XZDWZbsrY0gxAEc2O12h3GxIv8QAEHTtT390EdZ1yQgTgjs21bYS4Wl7wcu317hg6PvBUQpY5KjMZa6qjDaiMfZOIoHV5TKeMRAWAzEo2fOKNI676QZXC6XmQlVVVVm3CRmXt8PbPc7nPfUTU3Xdfz4xz/h8aOHFMaKhMw5bFWjjebq+pbvfu97vHz5knv3Lnj88BF9t+fTz0ReeXYmSWjjKDK4whb4MGP9WUtJAkdk3ow1s7mbMgtLRdZWusYkp/Te32FOwaG5O7Aw5JoT0yTJBadpwk0T0/+M71Pf91R1nX2HkmfcvDlNctB0DtbaDCQmydc8PXQONiWWY2GFdTVN7o6cTYCrFNhgsv+UD8J4ahYLlBJmlLWW3W6XgQelVJa9JvkeQWFMEYFhkU/JOLs45qm5T83+wfxcPucAVifALcm7sucd6euH6zjI7pL8URG8l7GN45zuN0lO6F3yz0rgdMKMVGQ7KYbhIIP8sh8YCNCQJItKKWyhMUHTjQMhuMhYk3Q9YacJSy7JNZUS0JTIvEqV14kcmRDn02gT5dB3jckTCCF71x++okCjMcpE1tldlmHypFJRrpcOHCLDU6MJKmTwSjDFmNAqB0pmYuItplIipEYTJXCoDLSm8/HIWjzM9dxLLI3/QYKrgC7+t8jc3UE2OgmLVSvFcrGI7DUw1uIJ1PUCN4103cDrV2/Z7fYsFjUnJyc0TcN+u2O7u40sRgmq2O9bpnFiHAYWiwVVVeGcy/LkBEAba2kWBRcXJxRFxa5tefHSR/+8MUprNU2zoGkWWT7dtvt47xAW2zAMkXApcyvzkvaGrFOjtYCJTpiYCYhUIQKUEfwjsvVk/aU5l1kx1qJ1QAVPiPcUofOJ+F95kfiGGUgl4L4wBZUKWKvxPoK6IYGYwt46gFvkdTb3hMvfONaxjnWsYx3rWMc61rF+xesrgVzOeUbnGPoBMzmc90zTiIvJUylSfS41SkbGwzDQ95eMw4jWMLkR76DvHSoEVqslv/1bv8U3v/kNHr/zHvcfPOL07JRA4Bc//wX/9t/+NT/8wY/46SfPefnyFbvdFoJ4SY1DHxtnaShccNGsN/xSgyvMEpvBLQEeKsqyoq7l76qqODtbc3Z6wvvvvIsKgYf371MvGvqhx/uJcewxFhaLBcZ6yqrEtOLJopXGFgVjN9K1PbvtXkCGsuTs7EwYDMEz9AM6ya2MxlrDfr/PPjdNU+cUynEY2I2jMMtsQQieyY1oJWljdV1DlHE1Tc3tZnOQVSlQsdGSJjeCON4Js0ArjLZUVcU0TfSRkQZkf6nJObpeWFjaKBbLmt//g9/h5vKavhuYxgkfHMrAvt3jnOfD99/nd77z2zx48JDVqiEEz/XVhl/84mPQmm4YqIoyJ7zpoKKxtQAdc88bn1k46vD66NmW1lfyAErswbnZ+Ny/KYFc6dhz76F03UUhElUTG+KK6GcUAsvlMvvvAPlz03pPAE5i/sz9wZKn1jwEYM5YggPwAVDFa5GAhunOmBz8qQ6gVwj+jiE7+NywJlnm4Xvk8SGynkRWqDLbxBgbQT+Zj8QwC8FHcEtYQiF6Bx08qQ5AmABkImMUKWO4AwYcSFWH60+ssrOzM66vr+Uz3Ux6FwI6AkMuygqlIVfoOKfJ/Hzuy5XmSCsdmZAeH1J6oFzjHLADSMmACbBLJv8CIhxYYXfGlMjg0hHqyrLGuGwSCJFAouAze1Dp6NUUXHxdAr+Sf6DOYxnxpyxfTMwu+RyV5Y9ySQcXMBWRL7kfuJmHWjz7kOSgEWwJgXGaIsNLR2YsUS4o6+FOSEQEb7/80MNERmWSEyePv6qu5TXGAA4/TUxe0hSrqkYBQzcx9lu6/cC9B+cs6gZbSFiJMpowjozTSGkLxhj2kL0h4x5o21b2q1Ioncz8B8qq4NGjB7RtT9te0bZdvK9W+f3r9QqtFft9yzgOcS84cnpmGs8wY1IFzxTXTLr/KgRYnNwU2W2JORXnU5HXcvawUwod52FyXny5wuGeoQ/61dm95LAX84OeEOL6ive7uE9T+ILzXpbPbE0n6fKxjnWsYx3rWMc61rGO9etQXwnkars+JyAaY3Cx2U1/W6NZrVbsdjv6fQ9K0+5a1ssVU+dR5UTTFEzjwDCJT9P9B0948uRd3nn3fR4+fsyjR4+pmobtdsff/O3f8eMf/4Tvf//7/PhHP+Xlq1fcbvb0/SAMChWihMZFtkFMSUOadWlsQo63B5HwJXBLzNdLFosldV2xWCxYrZZcXNzjnXceslosOFufgnOcrNdUTc0UvABdwVNai0ZRVgKOnaxP0EbkXJI6aOj7AaMN69UJRWGx2tBudwwRCLRFgXeOYRhxzqOVwWhpkqpFjTYH3zLv97PGWtLTjLVUdY3Sh6j6BBBkMCV6thS2OIAULghnw0hjKsBWlFAFGSetgzDjAKMVlVUs12d89I1v0CwXXF6+4QebHbYocMMERuG8gCkffPA1Hj9+wtnFBUoprq6veP36Ff0wsliuqKpSjJKVpIGlZDJhvBCRgAMzRCMStHGcMDp67MRrLqJUsa6qCOZJM6gQACo1bY7ItEmMlgh6JnZX9i9KoFd8XTJ3TwyYxBwJIWAiI28OnMFd757kI5Y+6877ozRx7hkVXyxAQFFAAK0PAFMC5g7SuLj84/m6md+d1uLhZbTFO0kyncYpYSrZrD6MkzAhvUcbTXAQlIr7TOe1lprvuXwrsaqyvAoIQcAdAW00hAPbKzfVmfHFneuXRLmAtSKns8bmMIAE5GbJXEjXL99LQLw2NrJsVAY40xwbY3CjO9CpAMdBohVCAC+m31ppAadCwDuPn6KZe/qDkpQ753HxHI2RUIsQZI85Hxi9+LlFbWEchcMppHPLABSH8I1plH0djI5zkBhZgnD5GQVnhnFF9pUAxMHHr8f/U/H4+Q0hGfCDC8JUCkrlufHuboLmYc7kjwDvcr+YEkNLa1wED8UPUUCsBLDYuB8Sc3LRNITFAoJnHHqGlI5p5l5zW/ruLf3Yc3F+wb17F5RVg5smgm9xo8OUNeM4Ztlk2neJOZgYjZubDXVdM45TZNaWnCwbDCGa6juGvpc1aEX6eHZ2Rl3XbLZbnJtggMTuO4zN4d9pmfiYjiieemLy772X4JEoq0zBDgnAlgPIHzc5pnhMMaI/LKIQAl5DUOEOQK6+dD5zEPZOWEd6HXe2RFxPKkokj9LFYx3rWMc61rGOdaxj/XrUVwK5xnGg61RuIFKTnRkmi0qexAfoOwFNgvOM3QgTjO4WEypWTcPX3vsW733wIU8eP+Xhw0ecX9xnCoqXr6/46U//A5988jE/+clP+NnPfsGLFy/Y7zuGYWQaxR8l1R0JEiECbgfWFvE7CRgoCvHWqqqKqiyp65rlasHJyQn379/jwf37nJ2fc3F+Ip5EMUURoq9KZIIVGrTS4sM19AIeTCMFZW72XGwitTaRCaIZR2FKDcPAYHpaLcbCWguzpChK0Irddism6U6YDsYY6rqRZj8yOQCCD/R9L/5a0ah97okzDEOUnCnGaIKeWURAMk0GMniZJHAhBPp+in5qhnffecp7H37IO+99iDYGN02Y6Fem6sDohQH20de/yUcffcT52TmewMtXr/j88895+fIV4+AoqwqlDW4U03gU0UxbAC8TWVApwS0ZsWul5FqsgGJllCipCJAlEGuM4EAyb89Mpbgugj9I2VIDDGQ2T2KBuVlTmMYr0Y4S2y6tswQwpn0B3GESZXDF3fXxCbPzmDO5DmtaxXUUWYrJR8e57DuVrlFAXyfNN2TZprCuIoMvjmeScvp4HO884yD/VqWVudCarg8YLX4+Y/Q1mu+5xNJJ46cQsITZdabrGYdBQA6lMmg1N0dPY5CYWtM0sW9bAUIgg4Vp/9/1FdIReNH0Q0dRkMGtBPqma05JpmmMElNQJbZMnIQQwKuDj5f3LhvaJ1zJuwNTNHtjRVaO8wEVAnfBSFlTSquYqIcAanGOVUzZk9dolNFYb+Ss9CFVMUQmT4ggRzpvTQL0D7pIkQ2qAygXGX9ZIpvQr8ji8kFYaNpYHCIhlL0Uxz+Igb3SZOAq3Vd8lDIm4PcwZ9HHKgHBEQwcxxEV5zL5nmlMvFeZmAo7HsBjW1CvLOPoePP2CucDFxfnVEWFZmC5WJISRgP+kOgbhHVsrKEoBRT3TvZK37cYbdAEtPIsmwIWBbvdwHbbRbakou87Tk5OsdZydnbGdrvB+yCg8pcZw3FPJJZWYggKmSr5QcZ1MFv7BxbYIRRFBXDhIB88yGHJ97J+6PPPuHSsO75zMxB5Lo1O97R0HzuAzwfAn7j2jnWsYx3rWMc61rGOdaxfh/pKIBccGAepCU0gVwgeWmHa7Le7zAox2rDZXLJcLjlfL3ny9F2evvM+3/jmb/D46TsYY9lvNnzyi5/y8WfP+dsffsz3/uZ7PPviGfvdPnpUDfT9wDiNEA4m8nD37yRJmf/CngAfYwxVVdE0DSerJcumZrFYsl6vWK6WnJ2ecHZ2ztn5KVpr+n3PoBWr5QpjFVdX1wxDD5EJUFcVxmr6fmAYesry4B3UNA1aW5QeKQppqPphkLREa7I/VNvtmaaRxXJBZSrGcWCcBHSZvGOcRsQSx88kMalBDREwUbRtmxt4AcNq2rZDa03b7qOHzmF8jDGcnp7Fc5MmKXlfJdBrGPocHhBCYH1ywodf+zrvfvABk/M8/+I5l5dXTJNDK/HXKeqCx48f861vfZvFYsEwjrx5+5ZPP/2U16/f0PcDSsl1bLdbtBIWXGKWzH1gEqgyjGIibY2hKMvMqHLO0c/m/MtgVQK2CNKEJuAgGdCn/05rOoE3qQE8GKmrO2BW+l4Cd+beXgnISucyZ4/8sp8Wh3Ml4QyHRjKzL4JYlSdmkABWRhg80ZRchySziymUUVYoZvwTzk2EIGBEAu68d7jhAEQZfQByfWSoJXagdzIH4zRlz7I5kyxdx+EcY5Oc16pcn/Mu+qqpfO6JuSJ/iCzLCHBOjjDIvlIxWXDuf3XXZ0++nkDGEMJdD6YI3s5BufkczP8+AJDyv8lNBHVIY5UXKVDJDymCkNZGRlSMOVTCijLGYPP7osOWSlJGAdIyiDAH5iMwnvwCs2l9Wh+RReecw8b5MzMWGEDQB0AseYTJuoqOZPqwNm0E2FTwd9ZiYlSqxKLz5HTdeKYii9QKpTVmdo5i3G/zvxPbLXAIjUhrf5qEUSWMpCRN1ZmxppSiKipJsg1i/H95eUXX9dw7P+PZs2fUdcVqteD07GR2DQHnxevQekMIK6ZxxBYmyult/CzFbrelsAVlVcbgkQIXPP0wMrZ78eQKAWsL6rqRhxKI5yJBUobnPyNJgGNe5wegKa05CRpQ8V4f7twrtJLUzXT9aRzTA4p0L3GTE8n8l0MV5uD57F6Z5bTqEJYgD6hCZt3NAbD5PetYxzrWsY51rGMd61jH+lWurwxypV/CtdaZDQEioejankDL0HVURcHkR5arknsXD3n0+BHf+vbX+Hu/9XssT+7Tdx3X11e8fvOWTz/5jL/7/vf57vf+jk8/eyMMEy/MKOcmAlP8jdvdaaIz22UGIgC5sUpSsOVymc3Vz8/PuX92wvnJKadnp5ycnLBYNFEqAjoExr6nb3uKsmCvNbs9vLl8S2ELFnWDRjP0A75LqVqaoR8pypJxcBTWizdUUTKNE0VZ5MRD73xsgsEYMSK3xtDuW/btnpTmJvIYMbqeJkcIMR1MEf99aCB3uz3GGJpG0vqmyfHq1avM5LHWxCZfY4yNEs1FNj4fxymDAgmsub6+BuDJ06es1yecnKyxRcmbV6+53W74m+99j6vra5GDakthS957912+9Y1vQAhcX91we3vD58+ecXVzE1kMhq7rGUdJllw0jXhpxcYsN2jhwE4rom9PYjwl0Ch5SyklJuoJ8Ejzno7j4x8iSBVmfyfz+rmPVmKkjOMhoS15ByVmypdZTN77O6buc+N4ENZXAoTTZ2fGhI9eTAmYI6vZ8rETAygplMJM9qeUNLiC6R2YhpJgqCKoKEmVSUqrFJFNcwAETUxSdN7F1x8Yad5JqISejcUc2MtNfQRdFGTZaDLXJhDB5pSseJBozlksWimCD1kiFvzBU8tNB9AQDkw1MbY/MFjma8Bam035y7K4wzD6ZdnnXcDcGB3BhwgS6QR4pllSWU4Wp0OAm2nOLj2AEvJelceJ6OulwoF1ehdaisBfAg9n55aO74lebTr6wGmT/cjkWuIc+wPoKx59CsIvswe11gR1mNtUeX4j0GiiF1liiaXvGyMyxzvJgBweRpgoHx7inkjzP9+XAaiqGgFrTfawGoeRtuvYbncs65KTkxMIcH19w9B2/Pznv+D09JRvf/uEi4v7kTXl2e9F4m6NrIuhH8RHLLLwFs2CMd4LurbDV3IO3kPdLLh3cs7oHJeX1+InpzVXVzes12t5ALBec3l5iY3MtCHe31J4B1GiKGs3AcOR8RUUHo/R9pfG+46cNIUu5HUagcW8F2y+f89liXAAg5M/plIqM0K/zPxKe3D+MzWlYh7rWMc61rGOdaxjHetYvw71lUCuYegJEYCYS4Cyj1FkHSkFi6ZCrxq+89u/yT/4B39OvWjwwbLft7x6+wuePfuCH/7wP/E33/s+H3/8GbfbLX0/EiaPcyJTUzoZVoObxiwBCiE9lU5Mm8TiEACnKMrYUEsU/IMH9ynLkqZpePjwAe88eMCDe/dYLBaQJVwCVBgr17TTBYv1isk7hmni9Oyc9XKFdxNunOiHgX4chNVV13jvqWPaHghTZrPbcnV5xdnZGbe3G6qqIknHpCEBW4h3kLaG9clJbEjlqboPnu12x8FHpojXSfZwkfRGRdM0hBDYRMP5xWKR52axaHLSWAJxhmFgt9tmoKewBV3wBO9ZLJcU1vK1r3/ERx99nbbtePvmDT97/Yb9fsftZsP17S3rkxO6ruf25pY//qM/4sk7T9juOvbtFS+fv+Dt2zfC7CisyO6cyJyKqsBNEgzgvAClwoyoY+KjyNNMTF5z3kfvtQPLKoFiSbImYxvuNHpJRpQqfV3SIvucAJcYJakZnDeoCeTKaYdFkUGx+XvmLK9hGHLTno6TPYpmMr4pnt/cnys1oUnqmOVeyWPLGLxyjNH3SI4zkthMRWExJh1PDMKLooohEQeg1C7kuHItxHMCGxlTc9ZfURa4yMrMcqwv7/3cBAsol4znBeQRDpI1BUrNGVjM3hciFhbydYdJfIbkPCNoA5EhJo2+gNMqS3IT8DsHgoqiiHNNZDe2GbDTs/NI7D+lE3soptAZnYEW8RdLnkgKIT8lIEyYdwmp0ncCBRKIQDLR4jBih8osr/hdkX4mVp/MVZhJEfOb4t/OeYrCkgzi0+tUAgdV4sLJfTTJbo3WGXSbMyrTwghyIVGO6g+sLoShp7QiuAMrK/18mM/zgWFrMCYcfMO0wRgBKicnaasJfDHmsPdsAVWQ+/UwTLx6/ZblouH05Ixu3/LOO+8yjgOXl5ecnC45Pz+Na7SfMRAV4yTX7CZHXTcMYwzO8J5xGrGFxU2TAF9ewNHles3jxw8x2vLZ58+4vrpkvVoyjhM3MRhheXrKMIyw30dPw5aubQVcHpNcMXrc6UMwQlAJuD6gpekBhw8BhciJiTsqeGEQhhDB5whAZ/beDIRWHJhayX/PBzGTl/WZGJRzme7shoDsZX2UKx7rWMc61rGOdaxjHevXpL5auqKfKHWBLeSJtTXRPNc7TND4cYIwcnFvzZ/9/T/h27/xbepmwdXVJdc/ectmrPjBD37AX/3VX/Ps2TPatj2wiaYR7yQ9TxqhmQxtEo8SPwVhQoQgEqiZgXwZ/bWSd9Vi0WCtpiwLHty/4OzslG98/SMePriPJjBOI8YqqqrGu4q+77GFZRxGuqFn8C1hH6VdDqzWaOVxYWJwLUor1svVQRpVlDRVnZvG3W7HbrsBkKZaG5JXzDB09ENPU9eEIEwkaU6L2Ix5nBMARuSaY0yBHDNjaJr6LE9crVa8ffs2mt3rDKh0XUfTNEyTo207xnHK59t1XU5AKwpLXYokarPZMPYjT995yqJp+PSTT/j5z3/GF188xxYlD+7fp65r7l1c4CbH5evX7Pd7Pv7Fz/mrf/fv0Frz8uVL3nnnncyiC0DXCTBaVTXBS2MrbCxHu+9QqmcaJ7puH6+1xE0j+34gyTWHWeCBjkDQMAjQGCLboOs6COEO0CRyN51BSGMMr1+/ZrVcZoAsHSfJPiH6SI1jlrwBuWH03lOWZR5zrXVOpUzAWGI7JrlZYnMlr7S0vqcItqXrgBm7KUoQE+A29D0hJjkSwYhpkoQ3GVP5XpJ+qcg2EgBMACE5fmL5JGaQy83tXPonjC7F6D0mesEpdQC7EjtwzvxJvlPJjF7AKDmmI8SktsM+FilXvKTIGkv7OzXgktyYZLpJDpaSKQ9MqNTgi7Q5Xy5lVQorzBoWywWJZxUUTMHjETBr8gefL6ZJ7nHx/DQKfGSbaZ2lpCkVL60LrU0E1AwquDsyThXBuYRjzbl78zHUSke5skdHKE7wM3UHkECBtnJtyaNrmg7yZh3n33OQvaZxmleCMKYYKDBNE4WxOf3Rx/dqhJ2XJJDE+7OJay1wCGA4hJME8e1SiY0kzEGRIdsI1sj5WlNE2a3Pe1dCORI4LHZvya9ts9tzcrKiLEoWZcl2e8u+7RmdY5x8lCJalqsT+r7L0u6iKNht91RVI8bzQpeiLGoKWzFOI303gBoJ3tPudvHhREMYO8Z2w+my4mefvGWaJppmwYvnL7m5uQUUZ2fneKfp+om6Kpmcp923lFUZQVpwLuAjUPplAF8820zeT8FHluaXmHEhsrx0lJrm90Rz+qACmIOs+uBnKaBsiHOYeYQqDnIIWJuAUZv39LGOdaxjHetYxzrWsY71q15fzXh+8HjXEoI83R76lnHomaYRqz0ffvCY/+K/+G/55rd/C6UtkxtYre5RVBdsdp5/+6/+R7773e/y6aefstvt7jQuSfcjIFaSa7jcJCtFbmyl2SioqoqyLDMDpo4pg4smglzGsFjU/O7vfIfHjx5SRODBGFismixZUkqxCiJpNDHRbRgHFosVfd9zdXkTvZwmYMGFucBNnr4bhHUVG7bLq8vcjNR1xcXFBWVZ0zQNbduz3W4xZkFZFtR1RVkWbDZ7FosGEMZJAj+ur69jc6moFk32E2q3G/ZtS9u2LBYLHj54QN8Lwy4BLkVRZInmarUS/6q+5+bmhmmaqKoqN+RJ2mXi+N67d49/+I/+Md57rt6+pqhqyqqiWS65ud2grKFsap4/fw7Af/3P/hk3N9c8evSIoqz4/PPP+MUvfpFZWX3fs931WGtZLJaMw5jlY8OQvGeEqdN1e7TR+ODYt7sMGBkjvjfDMBCcp6oaqrJkt9/jvKduhKl2c3OT35MNxSNIZCIYKg1pw8nJSfZqK8uS1Yw99WUPmr7vub29zY3ier0GyKBZ27YAnJycHOQ9sblfLBZ5jbZtmz8zsZUSUJdYSEnaeAfwiHOaJHjp9flaS6I0z4gZeAjYQppd70U/aLQw6ZLfVQhRsohGsgXvepKlz5rLQyO5CWNU9HJLcqkkF0zNuoybUoEymXxn8O7Aykr/Tt55Cnl/JK3MWJEH9p2cYwJrxZNOztVHUDPOmxJgJx1HK83ox+ybZ5TBTS4DmlprHMJeKpMMMjhGP8o4KM0U5Lw1Uc7HXaDocM6SECnrfLyTnnnntdyVXKe/59K/O1JsjbDKZl8TWS5MaXwhg4FJziaMrOQPxZ15zcfK8zEzQJ+ltBICzjuROhodAwt8ZiANbpI9h87eThgxfMcY2dfei7Q23veNMUwRzE9A877dxxTK+Xh6tCY+JJjESB5LXdcMQ8f1zQ0KePzoAQ8ePuTy7Vs2m1vevHlDXVe88/QJT995ilKK169fsd/vsNbSD138ueKwRUEIUJQV1momJ/OvlWFyjsvrq/gAYS9gttGgFWfnJ3zx7Dltu6csK66vL3EusN/vKaOH4L5t6fqe1XJJ09RRPimAvdIHj0UbkzMPmyOxHjXa+8yS0xGoRyvGJJlOXpSRxemdQ6MwxUEGmdiPaY3u9/u4t4ss13demNSyrExmfybvxmMd61jHOtaxjnWsYx3rV72+Esilki+Wm7i9usVq+NqHH/J7v/c7fOtbX+dk3bDvRt68fsNqfUG9OOd//Bd/yf/z//X/5vt/931225abmxthp8Sn/To2iz42S1mBE0JkggSMUZGpolFavEcSiJGM3tfrFavVWgzU/cDjx4/5kz/6I9p2T1M3fPrxx1hjY5piQ1mVFIWYcyutxRTeGKbdyNnZGevViuvrW5FzeTEuLquCxWJBXZd452nbLjfe0zRhrMqG36enpwzTyOZ2x263YxhGmqZBKZF9dn0rn40AUV3X8fbt23gNFScnp9KAK8XtZpPBqcIWNHWgriqauuHm5gYbx6Yo5Pycc+x2O5qm4dWrVyyXS8qy5PT0NDPfnHO0bZsZRzdXV/y9v/cb/P4f/iFuHNlut/zoRz/KDRkIc63rOn78ox/x4P4D/uIv/oLLq0v+43/4/1KUJf0wcHZ2ln2Mghc/sbIoWa1WAupENpmY7weSl1ICOrquQ2kBnBIzLzXzVVkxjCNGa16/fs1isaBpGny8ltych0ARmSQQk9uiz1ZKbGwaAUED0EWQKsTXJvlWkng1TZOPVZYl1lo5zwgANE2TGVsJbAQyg2tuvg1kxldav2neEjMsyQQTCJykVuM4xsZ+yGl0iTmUMYzMkvL531prkX5qE4GXxOqQpldrE5lwB1+qOXNNKQVGZ5LHwRPuAKT5meeQfM3HxFGfTkrm2RgSKeQA5KncRIsf0CE5cc6ES+NqbTL392gt94dIPsmMSIUTBpQmpxcWpqDdtTgj3kQmsgklgfQu+JuAJhygo9eWSsl5UQYWpV8JMDTWxsQ+lyWj4tkXwSc/Y5wleWa4C5Wl8U8sSxBQzUUGjo+piC6Nd2Tf6FlKKvHzkmG5nxzaHEC57PeUPj9+plU6J5nOv9f3PSEBY7NzSHsiIJ/htc7MrOAhuHjuzjNMYx63RALTUZ44ThPBCyDcdT0+JsZO05TB3TTnid1qi4LVasVuv8NNI5eXl/zkpz/l6dOn3Lt/j2a/4PbmhmkaefP2Kt+bHj16gnMTr1694uREPBk///wWawvKoqJtW/quj8CwyfcCU1WRoafou46nT56w225ZLhs+/Np7OOe5fHvJycmSEDT379+nbVtevHxNWTU0zRJtLLebLYW1DONIARigjAb4YwS3kx/eAaA8gJjp55WLIJakXirxOosMsOAOUsbE1kpAp8xp2pMhXpOs4QSyWRNluUBhLUobvO841rGOdaxjHetYxzrWsX4d6iuBXGWp2W83rJYNf/4P/pw//bM/4d69C9zQ4caANaecntZ88cUb/of/6//I/+df/As++exTtu01UxgwTlhS4ifiRa6h1J1Gaw5ypV/EhbklPlsonT236rpmuVyyWDRRorjg4cMH/MHv/ibvPHnKy5cv2W4m3DjytQ+/RlmKEfwwdRirUVoxjAcp2sXFhQAsbmLaTzSLBV3XcXJaCwOk0JRVyb5t2W62DP2QpYTGGHa7XWQRCVgxecdu1zIOI3XdxMZViT/Y4oSqLHnz5pK3b99greX999/n9vY2Mw2M0RR1g4ngSWbaGGlyyrrCWENwnr7vM1MpgYDDMLCMkrzdbpeNnk30bzk5OWEYBrqu47/+p/+Up0+foo3lxz/4Plc317x+/ZqyLFmv1xRlRdv3bC93nF9c8L/87/47rq+vcd7zzW9/i7Isef3mDcMwZAkZCtbrFXVdA9B1HWVpsVYzDCNKQV1X2U9LGjN5rdZNlvXtolTo7OyMqq7ZbLYYa7l3/z4319cM3qNDYLlcZvlrrggkldGra+6RlRp9G0G/wh5kdwlYy01+CFH6OWUgaxgGyrKcgXYxAS5+VpJbTdNE27aZ0ZU+tygKdrsddV1nFldiGiVgC8hstPR+ILODiqKQ1ETnCMEzRSlfChpIflgS5CBJi8n4ehj6g6cYBy+mnLY3A+ZUBFKsNREIOqzHBOZ4f5A8yrGm3KzLeGqCd3HvuyydTWCf+PoJaDc37p83/OKt5fL1e+8Yx5BISgIijwPog2E7Gtwo41c11cGYOwjQZOLnEoIEHcT10TuHjgyX0Y13JJHzki+HO3MYgqw1a/VB/oiMYYiIXCawqjngR57ftA5dcPjImgsiCBQ5W7wPCMPOZAasQgCOfE+NCYXzNNFsMp6+lgIYFNk4XkeTdGMMprCHdTZNWXJHYnop8S+b5msgYikhSmOFmaQoTBH3usIWlrIqxctuHDg/P8NNE3VdAeSAjLZtcxKiUuJV5rynritCKCNY2dH1PZ9+9hnLesGTJ0+Ypombm5v4sGHP5eUVdV3z6NF9Hj+6R9d1LBbykGSz2dAPnTyoMJaiXESZucqgrTGWoiwh3kvqsqDvW05Ozlgs3uGTjz/D+8A49ux2W/GXnBzb7Y7Ly0uePn3Myckpi+Uiytg1CodRJu4lnyW8Osp20wOMtGfmhvDkvaUyq0/WpMy/mxzKpnUrrGiZc09ZFmid2MxRFsnBvy09MPDjdNgzxzrWsY51rGMd61jHOtaveH0lkGtRVfz3/9v/Nd/5nT/Amondbss4gi7O2LQd3/uP3+Pf/tu/4rvf/S4vX75kv98egBkUQQVcmHKTB4eGMZlJe6+j31GVf7lPIFJVVaAtTV1jtEgW66qkKiz3L055//33ePjgPu1uzxdffMFut+Pk5CQnrN1evhUgxIUIyIwsFsts0v7p5gVVVXBzvWMcB9w0sV6tud2Kafz56RndrhNgzIMtGspqSQiBy8tLrq5uI1AW6Ic+MkoMy6UAN7vdjvV6zdnZGcMw8MUXr9jv96xWK7bbHc69yg1x0ywpioI3b97kVKyh78VzCgFIFlVNsSq4vLxkuVxmgCQxiuDQIIUg8hTvPacna4w2vHr1ko++/nX+7B/+Z4z9lr/92+9x+faSZ8+es1qtePjwKWUpUpZPP/+cH/zdD+n3e54+fcL/5f/8f+InP/kxH334Nc7v3cNUFXXVYE3Bfr+nsCWrk3X+7LZt8U4at+VyyTBcUZYVISh2u5a6rrG2QClhMez3e4ZBGFaFFQBpu9lledmiabi9uQFgvRJvNGn4o3dNXDOJxWU4yN7mYEU6t3maIBxM6hOr6mCELU3vcrnMYzyX0s0lYAkwmsvDhmHIXmqJDZZYbIm9lq5lzvaaH3eecJiZbvG8RarkcZO/A5wYA+NwaFZ9CBS2nBmMB1RwwqRKZtaz85q6Xpy/Q/IKky3sXQS69GHM5iDg6ekpSikx+vfTHfAlgzhuYhim7OOklDvcN5TIE8fRU9clVVVI+qI2X0p8i4Bg/Cw3jBkoTHJDNzgMkthIsieL7DIdQZMpBhIsFgsKK5+VWF1JcifMRgFHtU3+ZJ5CG9wwgpHxTD5k6VoLo8W1KhxAOSAzCv1s3aU1mPbwwYtQpKFiFA8Gg/MTQTtclLQZYoJfNAwPgciwixBZ8Hgnflh+isCW96iQ2GYag4qyWQmFWC9XKKPZbDb4URhWLsh7TOaqCaCV2HxuHNFKo60BHdloTsZ86JNHXyWyyMg62u92kekl+2K7ayPgqTCmQClPVdY4P5e8KpYLAamSF+HVzTWXNzeslkvW6zUmBG5vNwxTy77v6MaOB2f3Wa8XXJzf5/r6msu3t7LflpWELABBZkXAbe8JyuOnEXygMJa+3aLCxObmLQrF08f3aZqGruspraKqLP3oaeqS8/NT6rqkbTfsdjtOTy8AjVYCSM/vGbIEZO7GcYTIsky+hGkPi7m8pPYSZJ8rAkZHcDO4KH2Ve2sygosvj+mNAlCmtZ7GVAuaSHAOPxySlI91rGMd61jHOtaxjnWsX+X6SiDXP/1v/lsePnxK324J1YLBL/n5Lz7mf/pX/5p/99f/gZcvX7HZbGjbNiaAhdzEp1/gU809YO56ASnKqsqgVgIlBASxkeVS0dQVq9WShw8f8OTxI+q6ou9abq6uUDqw3d1S1zXbnUjUTk5OuHf/nBACb96+ifI2ze3tLZvNJqcvdp0kD/Z9T/CBvh8x1nB7u6GpG2neI1sjycVubwXcun//gq7r6Htp8vddy9n5Od3Y4Z2wL64311xvrgGYxilf+2q1yiDMMAw8f/6c/X7PYrWgKAs2e2nglifLPIbKKnbdnn3fZdDDViV9TA/b9x1lWXJzuyH4QFmWjOPI9//uh/Rtxz/6R3/BH//Jn/Hi+XPO752x7/Z048DFg/s459i2e8bbgVevXnJ1dcViUfHO4wc8evyIDz78kP/8P/9H3N5uuL6+ZnGypou+Y0Vhsh/Nzc1VZkGt1kv6vme33xLwDGPPcrmkKC1FYaNkx9F1Hbe3t1kKmEAfYwyLCC5N00QAqggKJZCpqiqmacpgYFmWmQWR/GVAQIfEokrsqgQwzlk0af0mVlP6Wnp9Aq3S677MTkqfOW9gE/MreXQlmVwyrD8wnwQ4mCLgFeLn+7hmJufwiXGVXjtN2YhegZjaA6WSdD418/wZRpFNdtGMew70+QhEtzsBFuuq4ub2JgOCSbaZ5nmaMcC01hSlRRuF8xNaabq+xWhDGwFNYbkllpakkbrIljIRXAPyNaQxN5m1FmKiXIhAjBVWo4KysCgr940EigIHGWKIiZTWxMTRgmFQWUZa1yXeTziXfJIORvtaQ1laiGmLgciqGSdu9ruc6ur8GPEDnd87TCNGaVQ0klc6smq0YhqnDFIfzjd5gEcp52x9ZYxMkxMIq7oSYCkieId7bL5joDBYo3BKwA8fvcYT40oZgwd0YVFW5mB0jk28tnQeCVhNgGYGRUIgJgtgtDDtplEYcUanEARh45ZldN//Eost+Z8rFe9ptsif5b2sj6pYZi+8dG+w8WdFukfs9zt2+z1t27FYLLm4uMcwdExTz263A6fY7DY8efKQh48fo4zliy++YKmX6MIyjY5pmCAmUVpjGYcBF8H629sNpxfLzHobp5GuG2Lgx4L79++zXK158eo1L16+ErA+LLh374Lbmw3WaC4vr1BKfvadnJwAnq6ThxFV/Dk4Z5XmQIvIVlVKYY2h63q0VnnfaK2x+jCgec/oeRKqyqzXZCGQfL8SKyyJYOc/u491rGMd61jHOtaxjnWsX+X6SiDX93/4E9pBWEq/+PnH/Me/+R4ff/oZm82OcegZhz43dPLLOXeAgkRfcLE5T79MJ8lQURQ0i2X26rIxSbAoCsoEdlUFZaG5uDjlo699jUcPHhC8p+talnVDU9c8evKIcRy4vd1QFAVt19LuO3a7PavlipP1CU3dRM8fl4GK7XZLVYnXy3K5lsYsNjZNs6TtB8ZpwjuPd9HHxhY8efI0g13jOGYpoLaWsoh+SmHM177b79hutyiluH9xP4+Rc479fp99o7TWVGUVWQSBru8Y+5Fm0VDYguura9q2IyX9LZoFbprYdR3r1Rq8Z+h7JDbe8eaNgHs2GtL/5Cc/4cc/+QnXV1f8/f/sT7m53jBNnrbtxbDeB14+f0FRWr7+9Y/Ae/p9y/X1Nbsf/h0ffPAB4zCx2Wz52SefUFdNluptt1eUZUlK2Ov74RBh7wNnZ+csIoB1c3PD1fYaQqDvBybnWC4WFKUkkWVpYCHNbtf3lGXJvXv3GIaBm5sbWT9liYtm9wkkNUZ8qPq+z6Bp3/fs9/s7zK25NFCpQ3JjAqsSoJXYXKm5T2yhZJyd2FlzNheQ3zdPcUyVU0SjZCydj46+YPMmN7HVAsh4RM+g7XYrDWtkLSltmMYxe5WNg8ppd3OvnyR3MxHImcYRpTU2gknGGMqiwHlh/jx6+AjnXfb1KuMeDYTowTVlbZoxVpIXgZP1CX3f0zSLLE/NrDLvKYoy+5Ltd1v2+/0dYDGNf0oLNcZgtMXauVzLCFtIKWEqRTZfut7U6Cc/L+cU0+SwVnzJZO0KYJokmcZYEvH0sF4O5vCJgqW1oSznzLoDKHlgDup4C4wO/kGMvpNX/lzKKq+Xv7XSKHNIJjywpqKvFhpUNHU/fDseI/mszeSLSlL4fPBoHcEpDoweY7Qwg+J9GR0IcfxDBGnnY5r2SPAeW8q9ehpGvPazPSCf4RH/p5T8OZe6qvgwpIwm8JOTdZbB0ziOQz9gpwk3TeK1NwziJRbB1iSZPj+/yD8H5N48sFg0vPPOu7x89Zyb61veDD3tfsdqtWK1WvGNr38DYxT7dkvbtgzDSFlK6u1ut2foe66urqnrBV3Xcu/hqayV6INVlgIEisRaExScnC65uPctxtFxc73h5ctXdF3PZnMb16/h/v37wnYcRW6e1tg0pdCGgNE6h1KUEcw/AOoHoD4xIiWJVO4X1hZxj075CVOSR2bfvSh1VlpDArsTQmoO98pjHetYxzrWsY51rGMd61e5vhLI9eOffsq//Q/f4+XLN1xdXTEMPcFNeDdl4+nUkIoM6i54kIAtH4GwuTF3WRTydL8SZkgRm2etTfRhEQPwd58+4uzsFGM0u+0tH29vMZG1slgsuLm64vLmbQYrCluI+XJsWvf7HUYLE8P7wDCM2dD69PQU7yeeP3/ONAWK6Bd1+eaNgGIEFsslRmmmsUckSRYbJUvGGBaLht1uh9aK1XqVfbFMbEK6rqNuaqq6YowSkOQ5k8YpATBnZ2corSTx0Wrqps6suG4QMKVuKqwtWS1XbLYbhmFgHSWadioYp0n8YbbSyH3tax9QFgWEwNXVFWdnp3zw/rtsdltGN7Hd7mgWK/Zty9X1Fb/1nd+iLEtevnzJ9eUlVmsxba8Kri5v2O9btLYURc04DiyXC0C8tqborZO8wsZxxHmRdl1dXdG2+8zWskXBMPQoHYGg6HGV2Eog7KLJHTx/rq6uRJrmHGdnZ5mVkNhS4ziy2Wzouo71es0HH3xA27a8ffsWa20G5NrIZErNuosm9dK0llnumsCssiyzMXhRFHRdl8GJ+fsSqyKBYXOvsMTwSuMCB++pOYtMEth0xi0yEyMExug/ppUSsCRJNrWKRvM6psYF2nbHOI1ZDpnSEA/MpihljMl4ib2hlCS4FUWBqsQHShhIAlb0fS9rNILayRYohMS6itfpJmxho8dVYl+lfaMzyBeC+AxVdZ2lmWk+6qZBG4O1Po5LUlsdPIeSL9hc8vfl8QQoChPXkazHxLpybsJaQ8QGYoXMNhOPpvjVyLISAJ9onp+MvZNBu4BXmb+lFCnLUGAfhfIhe3MJ6BMBIR+TGYMCL1mRIWRhan69iPsi80ZbJp8SLrPDv6ydaJTvQ3p9lLoF8cpCKfJK0/F7M38veRhhCByYjF8eY+8dbjzMhXcus4OS2T6QQcg58GaMwRqTWYS4kNcHCNtIfm5YlDEUWmMi46nrhM16cnoqLC0EoNNac3IiQHkIgWGcuLq+4uL8gmu/pTc7Nts9/TByu9lydnbGvXtnOD9Sln0GgsST0bKIUkRQOSlRvB7HzIQchpFpEmBPGUVZWepapPU/+k8/ZhzkZ996teTR43t8/PEzui6GXwRhjYl88cBSDMFnhuP8/qTi3k/elek+me4pSVo89j1eK0IQ5pdSAsxOkwcf0GYmWwwBko+cVvneeKxjHetYxzrWsY51rGP9OtRXArn+zV/9VZbyOedQ+pCGhoLgptirSeM1TV4MnYHYJRJS424sVV1TFKUARcZSxCanbiqausZ7Bypwulrx3nvv8e6771DYgLU6s2GCT6wJxXK5YOgHykpkeVVdo5BGPBCoyop+6KUxzcl7VWagnJ2v2W33kW0DU3AYozg/P2O1XNCNA0qJGKgqxJQ4xCve7nco7zlvFjx+fBqpH56b/Za+3RNCEGmcgm6Q5ulkfUJVNPjgcePEs2fPsmxRF4rt7Yau76jrmi4ay6/XaxbLBXph6AdhR93ebqLHVRHBgkmkPhY+/cUv0ChOT04oS8V2c800TTSLBltqgoUhjPSbka7r0Cg211fsdhIv3+327Ldbbq6umMaJZrVCW4t3AaOFsVSWlqkdqZolXTdE0G5PCNA0AhqMUY4lYIjHaMNut2eaHCcnMv5XV9diDl6WjMMQmRSDgJ3GMPS9SN0AQmC33ZIS+K6jlKcsSwiBrm3FYyk2e13b8sMf/CCDX8baLHFMjXYCruam3GI4PWUD/CRNTE1fYu5tt9vsnZa+f5C46czQSqyU9O/E/EnATAJL5yyZMTLX5LIjKKA1VVmKN5hSdG2Pm0aMEXkdkGWNJsotvXOZDZICE7quy+NmIpg3TRM6skGSGT+AinI1ILPblLJ3rpcgu19rjU9+X0EM3QHCzDibCJwc5FGecRjknmIMU2TnqHhfSNevylKOFxl3CdTUSmGVeD/59NoZ4yi9Lt075GsRlIr+XFNMQCzrhv12F6WfmqKw2VQ9BDLDxhojRvleWEUeUesZYwkqRjsiYJGO30v6QDk3AZt0kpPNb87aoGMCqdfmLjgWcaCgFCGlK3qPx0dg8cAqI15jiKxZAcVkbENi9qHEq0wp0MkpPgKI6RgEQloLGVg8SODEsNzgIsuVoKJ3Gvk9RiuM0gSdJJsxtTPtP6XwAcaYwqq1Osg+VYg/d3Q8TTm3YRgkDTWeU2KVBWAYx8yKTWt0vx/Y7wcIgQePHuLCJPfaduRF+4o3b95Sliam6TaoIN5Yw9AzxdTH1XrNbr+l2w+UtsaNAZQDr6K3mWhNtbL4YWIMLVXV8PTRA9brExbrBc+efU7brrDGMPRyTwKdze4FyIKzszXBe8apZxoHAb/ifCRwOHlapvlK+KzgVT4yS+Vns3Mu71GltADaQaHMDPgm4LwTiSkKFe8pxzrWsY51rGMd61jHOtaven0lkOvy8jI+8c9+tdJIOw/ekdyos3eQP/h4qKDEW0VrjC3Q1gqjqaow2lIaS1WV0gQZQ1lazs4uOFmtMFqxXJQEN2DrirIsopTRCBMiNdmI2W7TyGvOz88jCHQTZXKeqirRRs+agEDbdvFJv8cWhtVqKY2b0dR1TZgcwQc8Ica8a7TR0oRaafDWdi0smSDJWtMwYuuCplnSNDWL5RLvvLBpmkbM0LXBTcLuUlrz6MljqrIiEOi7XkzfraapG2n2tMb7id12iy0sHthuN4Sgs6+TSCXX8bMUv/97v0dTL/j040+4ubnlZLWmLCsKU+AQZkPbtkzdxHq95u3btzx79ox79+4xjiM/+9nPePjwIQ8ePsw+UFNsHCfvmbzDhkDd1IyTw3sBsuq6it5LUYqkCpwTuVZRVCLXRHF6ehrngSwnDAiDy8bkQGZzNU6TyJmQxrgoS6qqYrffR5mPfD159MzNxxMoYWJjnmSJybMrydsSsJW9v2IjnfyyEnujLMssbUsgSgJ/5hLDuQE8SDO/3W5ZLpd3ALA5syWxNXRMrptmxwewWuevYQzJvyjyqHKKYmIVWWOiK7XsleRllpLUEqBnrc0sugRg6Cj5m8tq56b46bVwAC1MfF+WP6V7Qvx3AtwCEfjzkpAZQqCsKkJaaxE4zJ5ASjyZUAc2UQIJ7yYyQggi35MxTqmHSMpfvB8QQaQQLcZRYKxmchNFYQlBgLBxDGKcH8cqRHZX8J4pDPm6QpSVKSUsrcLaCMILO0bWZwI1E4tMZWNxuY+paMau8OnreR/IBaT7ngsOjRiMq+QdZU2eD601Lrg7azCz35LReAioyJiTdXBYqyEEVARKAhAisB0yg05AxRwo4EXObSJzKy37EGWayZRex/WrlBIWkQcX10dKBk2vTWw1kT7LzxUV7hrz2xkYW8X16+J4l7N02nQfaNsWguft5TVn52sePHzE5nbDdrNlHCemaaBZ1CwXC6ZB2G/d0OOibPP84pzVahFZcGJS39SNjIUC43VmJBMk5dBbx3q1QOtAYRUfvP8eTbPCmoLrmxt2u5ZpGtG6pCgq6ugBeX19Tdf3nJ2tefT4EbvtnrbtGceBaFZ25+fZ/I8wNmUvEpLnFtnPLrPk0qoyd9dOQLzjcLPE2mMd61jHOtaxjnWsYx3rV7i+Esjl3YhWIr0JBPzkD82adySDXkn0mvm/aI3RhsJW2KKkqEu0Mdlrq7QFdVnRNDWFVdR1xQfvv8fJyYrddktVFhSFxpgQJU+WshBAYxzFGLiIx1FoeSKuHPuuZb/bYawkMpZFGRkxmrZr7zR1uUGqSlarJX3XUzU1TdMwdIP43QDWTOIBMzl0XUYzZkVlpTHDCcNIrtuyWlUCXPmAcz4nwxVKPGmUdngnzfMiyhGnycUkuYqisOJ7ZFJjL023mGF7kfrZCmtF1vn61Wuu3l7y4Ycf8uTpO5ycnmBtyePHT/nsk4/Z3NzinGMYJ5GiDCN11VAuLLvdjg8/fJ+zs1PqZsFisWB9coIKgavrK25ut7F5UkyTR+tAXS8iWKLQ3qOUQSlNXTdZ5idAUfI0Ulmql4yst9ut+I9VVQY+kreTtXL9APu2JQSRjCZvmuTdtlwuReaUmCmRDRVCyGzCJI1NQFSSrlVVdUf+A0QDcZMZVsAd+WH6dwJalFLZA2wOvKT3JjBtHqQAZEAtyRvnIQ1Jxpt8r3wEcoroTZaTI02SH6ZUNvLeS7LcJHPKQJk6GLkrefGdpMYESLnIfpqmKZvc+9nX5kbpENk1SmWwy0UwK8ug4rGTlCyEwOyU70qt4rglNp+ZSSnTPCVgK43bwQ9NgD5QWJsYdeJDJayYkdzaK5H+uQyCCDBb2iIy3lQ8Z5Fd3gH3vEeF5Bc1Yz9FppEKs+RLEgjhM7goay6/U/aPZDDGL8anCQQ5VnxxkjXqxAxTAjBaDutH1lA08M8G+D5L2EIQThfhwPgJIRD0wbMtz2ucQ5m/w/UkwEzl64trR4k3XLqyrLJMssUIYN1hPBoBP33wwuhKjD8OckZZz+7AMFPiT5WZl3FPpL1cFEX2l5uzKpPH236/J+BR55qyqlmhGMYON42Mw8Tnb5/z5vVbTk5PefzkEcGJ59vFxQWb2xtCgNevX0dQ3h78riI4GIKAR8L23IhHnQJrtCR+jj3LZUNRWk5OBtq2o+sk4XMcp3xfMsbSti3OBxaLFW3bU5YV0zTImkbJPSDyqCUtEZQO8vBJKZn7eN/KgC8H/7ogyzb72CXgOFkHHOtYxzrWsY51rGMd61i/DvWVQC6CJ3iFi74z8ufu02MApUxkFIh0qoipiNbWFFVNVZfYQlgzhTEs6pqz01Pu37vg/GzFctmIcTuB0kLT1JSFpWlqqmbF7WZ78JVBiSeXMkwxF93aknFs6dqefduzWhW03YACxskzDiMuGhy74LPkrCxLnJtYrZbUZYW2RoyPnaewJYWdRI6lQwZLBMRxYEQGpWOzZpTI4gIwDDv27R6jTQYAExholaaqSnnarg+G5cYUGGtyc7dcrTBG07atGCAPPUGpzCYqyzW3t7d88ewZ1ijOTs/48IMPKI0Rj5oAVVWz0zuc8zJ2SqQx/TDQDlvef/8Dvvmtb9IPA1objNbs9ntevXjB7e2Grh9yAEBZVjHNSxggfpow1kZAIqb/TePMd6pHa5F2JZAEuONHlZgDWVLm3AGsguwnJWli0eB9zkRKcrEo9RFwVWfAxn7J8yo1vcMwZIAkreF5o5fOJzGtmqbJAF0697nf0xz8SteYgL0EjtTRcyq9N5mxz48z98HRxmDidZZRzplAEyJLJgE2ISTwIHpaRfZhiKBAal7dLPkxGdij5ql86jAO8bO+bKQ/P8/5+H2ZYZWAkARGGWPiaYcMghH/nQ3+4xpx04SKbJx07Ln/X9ozcJAoCvMnjb94VGmtwGumyGxKAFC6j5noVZTZVXEujYmG8VoxRdN9pbyA2hF0Uop8vgQIKq07j+Ygbw0JfIjj5dP8JS8zPAph2Mk9LvlRRXAp6xSJkszoVeV9BI5CBu2d86AEFNRZMnhgR6kQZY4HdaKAl+GQkjlnQImUMLEDFUnaltaMUlp80sKXJI0JCEvrJNxdL8nzKnmz4SNIE+dFXgshpART2YdzeXE+/gyAzMEGQIjefprD/UVry2jkHLa7HUTJZl3XmKZmGgb6bsB7GMaJfhhY1BWnpycyvuOAC4qu61gsl3mdm9m4zVlR4zhRGjGJd9PI6BxD76iXa6axZ71esF4v2e97ri6vGYcRawXYnbz8HNxstoyDPDQoC4sPDpR4nKHSuOUlks395X5I3oMuAoXeiZecMC5FSpoq+WhO03RMVzzWsY51rGMd61jHOtavTX0lkCuEmCJF+uX9wMxI7AppJnVuxo0p8tP0slpQ1RVFWWCMMJdOT9Y8vH+Pxw8fcnKyYrmoMEYL0ILiwYP7LBYNq6VIQbre51/UrbWY6A81jiNDBKtsUdD3wv4IHoyWp+Bd10m63jDkhEGlFPu2FzYViiqyzHyYcOPEdrPHTY6ydNlYuqxKisJKU+ak8eqGHlOVuFHkemM/0DlH4cQXKviALkzq+IDAODmGqcdaQ12VAnoVBVUIbHdbpr6ntAXWFngCbdvS98IiGscpsumcNGFlwcsXLzg7PeXs7JSh7xn6ntf7PZ98/oyrqxvcNAmrSRmRr2jFNDjGYeTR/Xt861vf5PTslHGYUFrz6vUrPvn4Y25vbhiGET1jbejC4Lz4toQo4zTWzJpiATsTsJMYCXNWlIugZGIqpdc0TSPrKsrpkm/QarUieM9uvxcD/7rOIE3yllIz5lRKGVNKCfiYQBPn8nkmZkfyC0vrCoSxlb4+B3SKKINMbLL0vgTofBmsGscx+0AlL6x56l9ijaVm8sBGOjBZUAd5oFIqS7G0MSSnJRmLlMAXIoOLmFAX8jEkWfCQeJfgYuZgQeyUkxF48koiMfJC8uUKmf2V3h+8P0jeAgzDmNkgCbdLrDTiWeRSKgIbYlOujaUoExNJ2FFay5qapjEbrgvTUhhC2SdrcpIKaRMDSO5dk/dZ7ibMsyg/RECBNK9eiZwrgQDJb8k5x+g9Lo5DYr8kOW1KnhRwzEfJYrqmxLJxWYpI9J4iSLCCNpYcuRgEWAIyIzGBRX7yWbbrJ4/SZJ8mFcdRwI6AJ0nVZA48CRxN434AorTSs2OEw/cTsyx/qs57HWRuBKSOkrfopZWOndbDl0FROWxk1AZhsQkLTKPiOIiJv6xhYy3B+Qg26sw2Sg8PElsq7es5YBfRsriXFFrXEBMf27aNc79isWgoyobzC0O9WDFOA9v9HqMVddMQfGC1WrHdS2rrerUWyXtcg0miGoLHI/6F2poY2qDo2x6lYJwm7Ngz9HuqylIUJU1l6ZuKsR8Yp5HNdouyhnv3ntLudwydIyDJv7aweDflPe1c+jl1F1ic+3V5HxNa4xLI8uqgQRMB55DHax6OcaxjHetYxzrWsY51rGP9qtdXkytGX5bc/CS8hoMkUUCNIv6xFLakLMUjq15IMt1q2VBXJfcvznn86CGPHtzj5GQNePb7LUPvWS2XrNengDSpzaJms7ml3e8w2lBXNbaw9N3Afidx7yomGF5fX2fwIDF+kscRcrp00UDY2IIxNttt27M+WdP2PVeXl2hj6No+S3VS46q0Ag3KGNbLJdYKs0hrzX7fstvt2O93FJWANaUtsl+Uc9J4T0Fkat2+FZNoW+KcY2l0bOCn2KxolBnZ71uur6+wtmCxWIDzTOPE1e0Vxmgu375mHCf+/j/6U87OL3jx/AsmF3jx4hUvXr6i7TqssZT2IH0bOwESv/bBh/zxH/0+w9jR7XeMg+Py8pIXb95ws9lgbUGFJJSp4HMzqb4kzUtsjgAYI6mWkjwoEfYgEqthmHITrrVhGMbI/BpyU+yjnEYzA4yCZxjFAD6BTUl2NPeLmjOvEiiUvp9YW3NpW0rwm0sY5z5bX/5a8uiCgxx3LqvKjI4IfCRQK4F4cx+rBHql5MV0nnO5UDYQhzvfS2CyUjqOV0AS80RSGryn7wbGUTyjmromBGmsvfeURSkStwzUqTuywiQXTOvW+4D4m0ujrI1OlkC5Mc4ARvq6P/hPGaMyCJMBV5IqLwJOEQSRdTXl+4p8pkeZAxMpmdwTQgSOPEpFs/p4Fs5J+t4wSGBAiIypKYIBwpaMrKsoq81G9toQgo4BBT6DPyCgiwtJKqkiqOLpYjBHXQlbVStDsOoghQw+nkOI0lqNCx6NjP/omHknKQnrCEnQGe6ARDn1LhqjJ9ZO2lsiRgwQU/qUVhk0Upk5FsHeeG3C6rsDO2apoVLCKwukhMw0/pElB1GOrfPxVZ6LWakv/Y1GaWG4JR8vYUP5CJ4i40jABUkDTABalgU7lw3uU0pnvh4ljNe0f1XyuRvHCPodJMohBHb7lmEURm+5aAha4VsJcOiHMf8ssbbk9KSE4Fkslmy3W6ZJmL0J1JR9oghKQg1MSAETcv5aa6ZxoK4KvBvoxoHgFcpPQGTDWs1ivUArqKuKump4/eYK5wOVtWitKApF38dgFO3zvWYOuGtNvvfJGg5Zqqqin58PPoPmwfvsq5ewzmMd61jHOtaxjnWsYx3rV72+slyRmZwhP/GPJtNF9LxKrI3EeGnqhrquWZ80FNby8MF9Hty/x73zcxZNg5tGrq9uqKqCaXJUdc1yuWK5XNJ1Lbe3t9xublktF6zXa/peEvyGcWDoR/q+j0buh6fWibWTWEF3jMQVDOPAfjeijRXvrbrGTxM3Nxu6scOq5HMERSGm+FprUIFxHEQaqTVt32GdgCnOe7qhZ9+12KKkqkrx16pqlCJ6rQz0w0g/9tFbS1NYQz+KwfBuW4qxvrWUZUU7DOzavTBnYnLg7XZD27Z473n+/Dl9u8N7z+/93u9TVRW3N9fc3Fzz+vUrGZvY5Ho/4b3CmJJxHLi6uuLp4ye8+957BAXOC1vs+vKan/3s5xDBF23VHWaSc46yqtC2ICULJimfD9FQ301MbrrDXPJOfMlSOmcIKs+Pcy76VAkAkr7mg4BQkxvphy4zhJKnVWJwLZdLjDFsNpvs95UAqMQSE8Dty1K0A/g093lK3lXp3J1zOclvLpMDRD44u847xs2ztZgkjiqyI4A7DIkEyM2N6/M1RClWalLn8jytfAbdpJKUzDOOcs4iZUostzGCdxofIrtKgfMHlptWBwBJG8MYz1c5cN5lo/4sFY3eYgoVmVMHvzHx7xoECPLu7jXFz0h0oMRmS2Bl9gyDCPbIWA7DkFkpmakTwaP0mUVh4jUHQhB/KgFgdWQKiQlRkmUF5PzSPIrfGbgopwtO5thG5l4Cg0JkaCmtaMqK4BxNsxDzeKLHVvRFIoI+dgYqeh9QRqNR6Gyydag09zKo6dMEcFSoaNYu6YMyponlFw3w01zG9ZJXifpl3yvvPSkiMrmJHSSJCVxSMg9KpIfOJ1lhBE3wh9REn9Zx/Hx18D4LkZFYaCPrIi4FkXULgzOxBOXcApNLYDAUhYlfF5mh9y4HTUzTIZQgS6HjvjUxrfMwyiH74hWF+LDt93vatuXk5ITlomEZwWsQYPLVq9fs9zvee/cdLi7OqeuaohBp+Ha7oygMTpSEFEUpQGAQYNdFsD8FYUzeYY2OLDKNNVZYeWFiuV7w9ScP2e327HY7ptHT9wLO7XYb+t7KOHAIjkgg3BwMlXt3vC/F+xkI6DqXcCuVeZ2RlSnn+ktA5bGOdaxjHetYxzrWsY71K1pfmcmV0pi0lpREY6K3li0oCmEviAl6QV3XlGWZk/Y++uAJdV2zaBaMfc/L588l7U6Lufi0aNh1W05PNddXnxFCoK4lce/kZMl2u2cYolm2VoRJDLVPTk4IBLooL1M+5Ea5LEuurq7YbrcZ/HDBURTiBaaNZXSO7W6P8o5hHNnud7z/zlOqusaaPjJqfEy7E4NfTWAcRJKY/I3u33+AAt5/7z3Oz865urlms9kQnKPrRnY7AaOKsqSsRSppY/qWGJMLY6XrBhSjNBdGU9qCs7Mzuq7j5auXkTEwsd/v+ehrX+Pi/Iw3b97wX/5X/xVlWfG9v/kuX3zxnGEYKIqCyXvqpmFRLwghcHl5yfX1Nc459ts9r1695u//6R/wjd/6Dp99/jk//dFP2Gw2LE5OhMXiPaUtMiiU5rUbxXh8sVjMpIjikdZ2HSEEFs2CoizwztO5jv1+xxQDC8qyZrPZ0DQVWmvOz0+5vrnNHkf5eMZkCZBD5lRr8SfTWhIwx3GMcs4+S/9SczcHshLgpLVmGQ3s9/s9Yu5sfglgSoDXNEkK5WKxuCNtTKBMYl0l8CmBbCl9MZneCysoZDAsySPTeaXzTl5DiZFWFIWAiU2DmyT8gAgWFdbCzPfpYMZuaBo5D5RI+sZpoq7qaCiv6Lo+stlMvgallKQxxuO1u52AAnNzd5Qkxs3OVUhDnnEMec6myeGc7MvFYiG+PxFwGMcRncDT2GhP8ZhZnqhNlpdNk8vSV++F5VPYguRtpVQEbDLIaChLAUfquhFpahA2qpucnH+QzyqtznMSvPjqtX1PFYG8vu8Zwyh+ewnoisDSNLmDn5GHtVlHJlMElycBRzwCdGmIZvU6Mscc+ANoJmsvEJxHK0OCYxyBoLgD3hDBn8zwSzdrpWJ6IlFGegD/5zLEBFwDBK2w2uY1jPd5XuAgVJSDeYIT0MtojZuE2QciazSRVUb8k0G2+CfdRzHCaLKR5TYPAZFEwMSSk3tsktkl2apIgCeMlrWmtYv70P2SL1z6dw4yiOswScDTpaV7SgiK29str1+/oWlqLs7PkcROw+QCi6X4IF5fX3Pv3j3Oz89ZLles1y2bzSYaxSf5sYzTNE40J0smNxAcaAOVKZn8iPMCqsYFwmJZc35xTl1btreOuirZTT2bzYbTs9PIjo2MMKXveAFWVZXB9/RAJFnGHaTVIaZ4yli4+F4d2a0J8PM+ZFD+WMc61rGOdaxjHetYx/pVr68EconRuDS7xhQUZUFZVlhbYo3FRDmfMYayEh+upik5Ozvlo48+4v3HF1xfX7FcLrHnpwz9iBsdBJW9m1arVXx6r7HWsFqeoM2hwX348AwfvaD6oRcz36JisVzQDQM3N9f40WWGT9/3mVXWNA2LxYJhGgjBY5T4DVXasGgWaKUoy4JhGjldrdlsNmy38mQ+ARPL5QKtY4IVITcUPgS2+x1lVTE5x/OXL7KnU3AeG4GqcRwZkpeTtQzRd6UwAhYVuuDFixdoFA8fPmQ/9PTjQFlXVHXFFy+e8+bNGy4uLvjt7/w2wYcIorX83/9v/wPvv/8+u92Os7MzttsNTdNgipKiLHHjRLvb0fdd/t5ut+Gjj77GD3/4n/ji5Uvevn2DVtLYN80CpQ192zIGMa5PrKbbzYZhEnbTycmJMJmMQSuF845xHKhKSS0cp1EAEO9ZrdYYYyP4I19r25a6rrm8vJSm3XmcF6CmLErET8ll1sH5+TnDMLDdblmtVtR1zdu3bzMbLDGFAHa7Hc45zs/PUUrlJMcEIPV9T9u2ks4Y5UzzJMRxHLMcMn1vHMVQv+s6xlHkS8MwYK3NbLFkkp/em43ylZjil2XJfr/PrKH0WQlgS+eSxjsdHw5sKxOZTnPvoTn7K8mhuq7LiXNt22KtpWmaKOMqGIaBcUw+UgL0TO7AuGrqGhX3EHAHDEygHAgIMmeZKSVppT56dNV1FcGog8QzhIDzSeqImHK7A5DjvaOMY+PchA/CWstpo8FntlBRyLqy0QNLmFJgjBL/rsgeymblRqOjybkPAaI5uQK8C5RlkYHSpmkyuArRyygcJJkQUEbAP5njXoAYI8b5RWEJEWxUIfLRovTQCG1HQKggHnWTG6NEWuXzCxGgS2MrEsHkoybnkBl4JOLYgYMz/2+tdGbkZfZhuOvp5ZwT6WcMCcjea/H6XZS2WWPwOmTmj/N31+IcOM4Mvvg6gFHNI0EPQQ7jNOHdlA3ptdb4aWIYegHcbJEB3RRyEYLHe5eTP+dS5SRvbttWpIlZlirJrm3bZj81rS0BqCILWWvNzc0tWsc9M45opelGAZ3armW73bJer2kWTQS8FuzblrZr8e7wuckgviortFX0u559u6daNCQWprGawll5mNK3LJY1q9WacXrNo8f34/1nZBhGLAFTqHwPmKchJr+/7XYLqLxnE6Cs9AEcNZF9l+YgAYPzZM1jHetYxzrWsY51rGMd61e9vhLIpVRBUVXUVYUyYvpubSGAl9EYBdZqqqqgLCxPnz7hyZMnfPS1r3F9dcU4jhkoWSwWdN1b2qGlritMZTGFptACUqxXJ+z3e66vr4VF0+0ic0X8vsRYXOPcRNe14rkSHMMwUhZNBicSePbo8SO881xd3Yi3ixOvGxONtX/245+CUhSlNE4qiNRxvV4zThPLyFbabLYCDlSVSE3Gie3NNiZsLQg+ZADBuZFt21KWFbUtscYw+onROZQ2qMGx32xYLpcoLeBOc9rw9OlTYe7sdiwWa243t/zwhz9g6Peslg3/iz/5fe5f3OPp03f54Jt/j5/99KdcXd3y4MEDum5CqQKlLE2z5vzinAB89sknTEPP2ekJjx/f46Ovv8/l1RWVLdlvbnn3G9/k+vqas/tP6LqOfrNhcp5pcvTjRFCaQmsmH0BpqrJmubAordm1O4y1LKua29tbkV15RZg81aI+mEITxJsmgnzj2LFeL+/IAvf7Pf04HAzZtZXm24svj/OOrm1RSnF6coIxhpubmwxMVVUlqZnOsdvv6aNBfWIMaaUY+p4AbLdbttstJyey1hLIkxq8BGClRq9pmjssrrTG5g10YmYlplIChFIznd6XfIJ2u11myiRg6+C1dfD1SnJIay3b7ZYyXqeYnTtKYyNpJmRwKJ1rUVRUlTSuVZRzJZZNVZb45FenDrKklGgZvMhyVfxvpcX/K4ELJoIWyXdLKZPPU67b3gFSgpcUN1ERism5sVE6CLHBvivlnGLKZrNYiIl/WUbwM12vMP6EneMoiyoyT3T0JxP/Luc9hS3wk4RBEMfXapXlzyDyU8GDklG9ozAW5TVTMvyXT2fyjsk7SlXgR4/zHmUUtqoE7NNgSyusLRCgCxkrPzmRGBotvoDeMfY9k5frxcMUJnwExxJAaIyNYynSwBAc6ksy8pCBsQguIdJBn0Y6JNkjeCUjpZKMcPZe59wBnFNqNuYhBj2EDNppFA7x43NuJPndzQHNDJpEVhqAUVo8ziJzbIwgGZEBCYdAB2sMZVyb6VwEBHUCMCK+Vb0R4HAaBdDRxjA4YV3aUn5+hMjQBOS+FdmdCQiaX+v8vjCMnmkKBOVRGKrlOe0wMF1uaXtPU/cU1lLYAmtqFrXs+bZvefD4AnxgGj11bWi7HbYoWBUnBCXBLjiFnwL7/T6b2d97cA/nWpqyZHGxoh8HrOnZeEfZlBht8X0v3mT+EO7hvYzryekpRBab3H+gsIcAmQSIJg8u5yeUMjFMxGfPtWMd61jHOtaxjnWsYx3rV72+EshVLRfUTZ0lPMLgSv5RBU1dslo1vPPOU37zN3+Duq7Ybbe8efUS7wPLRUEIjrOzE+q6xrmRqpJm1TnHfj+wjwbuZSEStMePH/P8+XMePHxIURZ8+ukz2nafpTfr9Zr79+9TliV93/PF82c8ePyA09NTbm9vubm5IYTAvttze3sbGzOR1FUZKIB333mX5XLBbren7zuWpydst1veXF2yWq0wpZjYu8iWuNncEkJgvV5jyoLTRRP9vjzbdo9Situra87OTwnTxOurG56+85QH9x5ys9lweXWJmyb6oQejszH9sxfPc0N1e3vLj374Yy4uzvjjP/kj/uiP/pCHDx4Qu1vQlp/86Ef87/+P/wceP37MaliJPDKel9aaF69e0O9bmrrm9//sT/l7v/mb2bvo5vYaP0z85b/6Sz7++OciUYxg3jAMdN1+BrrAfuvpu44pmnjvdju00VRNzWK55O2bN3Rdl02eSwKbN68zyJG8qPq+z55aie00RMPurusAosxVXpPYQgkYWi6Xdwzk922bZVe3t7eZsZTYHD4Ednu5ltVqxeRcZvz0fZ/ZXKvVCiCDo6nBXS6XGZhKzJT9fp9ZPkqpLGW01gpQF2WTVVVluaSAJ4qqqrJ09fz8/E664pzNlYAuHwJKa7a7Hc57rm9uePToUfSMMiJ3y6wuYbIYayjKIrPIUtpkAp8SsDaMI0pryqq6I2NTM8+sANgIqAH5fBKwaM1B6pglYnhQgWmK1xNlZ3Uj61yAh7knVJLEqcxYS/5CqRLjLb0/fd6cKeT8xDhFJpznkJQY5HWTk2a+KO3sOBPeO8ax5/T0NK/TENTBHisl+Y0T4zDS1LUYikd5l4rAYmGFARSmKQYoKIzVIjUMATcJGKMRoCftgfV6jVWa2+02MttkNEIImMSsQvySUsphdAIT0D8cEvXmCZ1pnJIPlkqatTD7K8pD0/uzp5y10Yj87tfTHvDOUUY2Vf48DaMXWec0TZLUGqWFSqkMiGotrDqjxHurMBatfGbSKaXw+jDviZl08JyaSAmA3pMliNYKAKaVlvXvW5Gtx/XrjBjIe+dQkTmslPqlsJLEvEz7ci4jliETZpZWcq8dhoF+HBlvbnj75k1mVC1XDffvn3Fx7xxlYH2y4s3L11hjGLoeFTQ2gvJWG2EXxrECkWzWdc2+7bDFgs8/+4zHTx5zcr6mKtYsFiWj89ze7jDmkIxZFFVmeeq4T9w0ZUZi3/f0bsyBDwmIVIlFN6Z7ron7hGMd61jHOtaxjnWsYx3r16K+Esi1XKyoygKjNWVZUMTUwLIsuXdxzgcfvMvZ2SlaBQiO/fYWoxUXF6cURUnAU1YVbd/TDQOn5+fcs5bb29soezvl5YuXKKXY3O44PTnBGkNdVWitqKuKjz76gM1mh1Y6AwpffPFFBjAImpcvXhJ8YOgHggsM40BVVayWK2HCbDbRYyswDG1Ogvzii8/p+56qbmhfjSxXSx49eEQIcH1zzTDIcYKHwpYogjQOtTC3qrIUX6iui9LGFafrNR5Ps1rw+vKSjz97homm2EpDVZYMXc9yseD8/Jw3b96w3WxpmoYnjx5TAH//z/6Mb3z7W1R1QfAjxpZsth1//df/mp/85Kd865vfYrlcQoCxH3nw4AHGGB49fsR3/+N3OVkt+e3f/E3eefKYrt3SDz27/Z7Pnn3BmzeXkqBYVTTNAhvTF3e7Pft9R9PUYqLtA9v9lsJaLu7dwzvPYrFgu9thlCQk7rc7Ts9OWTQLbm9vGfuB4D1GaVSUQiUwKiUkJiDoyZMnXF1dCaAYm7Mk7UuMqrquI/jW3fHkWkRvreR91XWdAGuRETU3mw7es9tuKSIQN44j2+2WqqoyMDU3dU+MjjlL6/b2Nnt9AVxeXmKMyYb6CRiEu+mE19fXGQAEad4TsyIBOwmMSuBf27bU0YdLAX3XsWiaDAQMwyCNLZKoZ4y9AygkyVkVATalFDr6hREZKswkZToBeYnthSRdGn1gCoV4PUkWmFISjdYiJZ4mbGXxSILjwVxf/KO895kVNvcSSz5bk5syeyaxf+6wiNIx4vklgMtYGxma0fBcSzKjUorClkCIJul3O/YkObS2oOv6w82xKmRsJkc3DtT1gqaqqYoyry0dpXx4T2GNMKUiI8bHpEDvD5+nVDwnFCoy93bDDh2EueYIWFuKp1X0SUrXPvdcmntruRgsAAeZ2VwuaCMwKHMnKZQhGWwFAc6c97hBgNg+Sv8SEKZUAtbyl/K+ShLeBHwZW2CNjLUKCRyLqbxECSWJYabxQXy2MttvckimajLJV3hPTJqVfyfgUxI7RcraNI34Lboxj4l3LsuOlZJACx33hY/Hv729pWmafG+5vb3NfpIZzIvjnZibs5VDCJ6F90zOsVousVbnAIbgPePkuL3ZEjysVmsUmtXqhJuba1lj2uLGiaIq8X5i6kecH+j7IT9Eurm5RRnFyaJhv9lhnkLf7whOs16t2XUti6bEOTEzdM7T9V0cM4RVFoF08UMT+a21OvsEpj2qvM+y4bQnheF6ZHId61jHOtaxjnWsYx3r16O+Esi1ahYYa2iamkVTR3+SiocPH/H06WMWdUG/31HXJXVVoFRBU9V0XUdVlXRDz6effsb9+/dpmoa23Ufz7jGyNAx1U9O2HcvVkmax4PLqin4YKHrLdrejLMsoEdPsW8/QD1Eyadnvu2ieO/HzX/ycvu95/PixsM8iU8o5MUhO/l9lWVLYIj/hHicBPT7//AWffvYZTdPc8UnSJj1lF4nH2E8477m5uYkJWwXnFxe8ePGCpmwYh4mytgzTQNd3GKVZNgsmL2mBp6dnBOS837x9S1lVrGMD+fDxI/7JP/nPOVs1tPsd/a5ncBMff/o53/+7/8SbN1dYW1I3BV3fUVUV73/4vkjvrObZF8/4wz/+Q77zW79NXRiU92ij2e13fP7FM376s59DUNTNksmPlGXBaiVssPPzM5qmyUljwhrS6MIwBYc2iqqoadZL3r59y3a7YRhG6mGg7TppqgpLOWNCmGgUvVgssnl9An02m002yhdW3x6AqqoETNtu2Ww2VFWVAZaqqjIzCQ4NWWJkJK+dKrKUEqBlojQyewTN/KaSTBEkwXG5XGbALbG1EtgJZH8ra2327pmmKTPN0n/PzfKTJCrJOJNHTmIn5qY8gmVN07CLQN0wDFR1nUGAJPNMAMjBYN8wORn3xN7xUe6VxkopxRABRIgm5FHSmb6f2B0+gk2JuTVntaT9IeNhUDHJMYNQBEI04E5+W1+WZWbZoY7XMvt+GpsQAmVZcn19ncfPGJOBLAgUhdzStE5+TAJOeBew1kgK4UyKl3zJ5LzlGvq+p4reevECIwtriibuiSkmSXmegEbB5DFFwTSNkUVo8KNHzY4NkuDoJxeT9wqCEmB2GkfKRZQ6Ty6OHUxhZjSvBcx0B4t5AaycALwhzmOWpOrE+DrMuRjWk/dlbWQtE5l+Qad5V9lLLyV0HsCnNDQHKV/2UYusMk+IclabwUrxeiODut55VDgki9qywE9hFp5wkO0mk3lm5zD3D/MRoMmJmfF65yBq2p/p2AlM3mw2rFbyECTtx/Te9P6UFpxkmDImsj82m418v1pxslhkRthms+F227Lbd9R1xcnJCffOzzg7E6bx7dUtxmr22x1BBzSaplkwuUC/2TIyMvYjy5MVwzCy2+yobMXN7SV1taLbt7S7PUWzQCl3WJtuwhixExgHSSNexHtZuscuy2W+hyVAPcGYic168DTjWMc61rGOdaxjHetYx/q1qK8EchVlNEcvCsrordU0Dd57AaMWNVVVsjo9R5ka8OzbEVuU7Pcd7777lKePn0Q5Wcd2u+Pm5opxmFiv11S2R4XANAxstjvGcWK320ma1PkZZdPQ7we809xubum7/o7ZblmWNHXDF69ecHp6SlFWeB8kQXESpkDb9ShjKcoapS3DOHFzu+V0PWGNgBDb7ZbdfseqaTg/P2Oz3zFNE+enp+LpEzxd39O1A2jF5AK2qHCDY79tWSxWlFVDAK53e6brka4XhkgIgdvtJoKFSzEcHwZKI+DGNA3sb9/yF3/+D/nGN75FsJbBOagaXr96zc9//gtevHjBMDiqqslNmjR6gapquLq6YZo87777lA8//BDvA93gxJPs+i0ff/wxn332GT54qrISNk5kXbRtTwgiLxqGSTzOyhqdpXQK0AzjxDC0MV3Q8+jh48yaaKNnVmoInZMggAQeVFWVgwGMMTn9K4E0SdKXGtnUdCbfKxs/o2kaNlthvc1fB9yRFc1ZQcvFAhOZDen70zRx//79LPVMX58fY5omqkpSIBeLRQRp29wcF0WRTe4Xi0VObhzHMf93MoFPxvRlWR4klfH6v8zWKYqCru2FnSVuZ9GnTDH0Ap6N3uE9s+bdxARLYQ4ppej7g6QzjU8yxZ83/UmqN5dspvMbh0FYWtFnaYihDsm/SwBmOb6ksCqcm7LUSf5OLLZiNr4usp1kDYvHlDoAI/H80nkURTWTiiY2mM7HS0BfYp+EoDJLL12j1smg3yBsIQFI5L2avnfoQlhsWimaCEyOo4AvLgSM0VRFQYjson6aqEtLe7uLn2vQhRVp3ow8FkIQIEnBhMejURpUEYE0J+OojGacPEqFO6mSad9khpsn++apoDHGZsaRnyQ9T+uMUOXAjLzPECDSEejG4Q7TcC5DxAcJ/oupkInRpxR4L2yuoDz4KDn0MpdEaaIcy+CCxzkBTgIweYd3wjgapgkVFGgxp5+zKoWhJddDBlbj3wqclz2mULN5PoCaNgYYpGvzIaCN3AeWaQ/v95Ig2zQyKu4QpJDA9XEcKYoihkEUBB+4d+8BwzCw23X0/YH96X0MKfETU+u43e55/vIFjx7e4969M5rlks3NNde3N8IgW5R0Xct+v6OwkvLoRs9q0VCWFe9//T1UqSmnms3mli6CsaVqKEuRa67XFcO6YbuVc8ms0nhvTR6H/TTmdS8SxxIX564ofPQU1AJ0HlTDxzrWsY51rGMd61jHOtavdH0lkGu9XlGWVZRJ9VhrsFbTG0VRGiZXQj/w5u0l676nKAw3V5fcv3+P9XKJMobXr19zfX3D6ekp/dAzTAPKaNDC/miaJWVZcXZxxtXVNYtlRdfvub65oWmW7G6FzbNcLKmrOjMyUmNb1RVnp+f0fc80CYNjv28BaXRtYVkt12w2O16/Fu8U8QcTmdnLly85Pz3lnXee4keXUwinaaJtOzbjBhvlLKenFZN3tF2HUoGytCgVuL6+ZHJOfF/iI/DlcoG1Jkp7JDmSEHDDyOn6BOeEMfXo4QP+2T//59SNNDVoMVb/2c9+xrNnz7i+vsmSqsWiialgYnx/clLx/PlznHP8+Z//OY8fP4yNiqVrWz5/9jkvnr9g3+4xhcFPnqADgxvohwHvPA8ePmC5WnJ9fcVmuxc2QDARLDRoYxinKUsCrbVUdY02hs32NoMmdV0zjD1uckzR8BkUtjDs24myKNFG0Q8dkxOZlLFNZCLIMXRkGqEUq/UaIANoieGU2DzALyUhJqZUYg0lwCaEQN/3lGXJarVivV5nfylAfLuibLAsy7y2QBgODx48uMPMSV5RybsryRrTZyVQqCxLzs7OMgAxN+ZOnkLp2sTnrszJZm5yUe5boI009cZqfJBxnSZ3YPtkM3KV5WRpHBJjJoEcc3bPgbUhAGU6XrqGYRioI7tviscCcvJa8m47yLyc+IOZJKHzaF3E44IkAqqYyCpjKRIr9UveZHeAv0rAQeI5K6UJCgaXgBZQ0efLGE1Z2ixjA48xFrRiGCfcMKDNYa4wmq7tIQSWhXjCoRQueHmdOLBLqqkK+doPAKXLY+fFRz1KFv0dkNEoIwy3EDA2ppJOYG2B8gHPJNIyLaBSmqc5OHtguIGN8ucQDmwnOfWAdyPBR0bSl9hJc0bUnDE3BzczkzX4KE1VEh4QwOMhvjZd7OQTC0/CNPDx+FFuqHQ8VnB3zmVyHj8lE/SU5hgw+sA+TOesIM+Z1xqlAkK0k7U794dL1wfcYUkqRU41LYoCbUxOWY25A5jZfklM0+RbVdU1RhO/bigKeeCz3d5Gb8KK9XqFMTXeT5GJaDBGsW87yl3LwwePWC0blssVw9gzjgPDsM1efs5N2EpRNJYpOFana5xzkeG6pO16tm/eUBYVSiuapor3REVdFyyXK9p2n5NgJdzjNu4pF+9TBq0NdW0zA1PCKHqGYcrsx2Md61jHOtaxjnWsYx3r16G+EsiVEgWHYaBp6tyIC2NHs9+3BO+j9Gbg/PyU8/Mz6lqeTr99c8lquea9d9+nqkpevHyZjYSdm3jz9jVGG7RWrE/XvP/+O9FI23N1c0vf9+w2e07Wa05OTjJDKKXidV1Hu2/Zp6fxdZ09cbRWUZJYxISskRCNnbfbDfvdlhAC984EILu5uUGHCKpY8Vvq9i0n6zW2LCKI5ggpDr6qGduO5XLFtt0B8mS8qipOT0/Y7fYRGPKANExGa8rFkna3Z7la8g//sz/j8dN30AR++tOfSpJYXfHv/v2/5+XLV3eafgEdOryfsjTviy++yP44f/VXf8U3v/l1tNZcX13Tdz390OfELmMMhREWgjA6ajHC73q8bxmGkcViKSl0QR1YN87hpkkkMLHZWi2X9H1P8BJLr42GIAwGpTUmiAyqrmpMBPqECSRgj9GG25vohdMsWCwW+fgqecXMfIm01tkwOs19MpxvmuYuy2W+2CMDJvn/JL+vuq5FxpjSyGZG+W3bZs+sBLTMExeXy2UGiBIAkT53LmtK67xtWwFjqyozcxIIl0CQOfAUgiTESanIZCojgKqZxjHLL9N7E7tRpkxlP6N5yiEc2DHp/OdeTiGEDLwleVNaszmBbSbjleuNjKFoEKW1ib5Jh+TH9LrkrSR7U9/5zK5r74CEqbIELxxkpvE7mT1ECGil0TZJKYnnk1g/Guc8VluqssSZSEmKPl7WWM5PG7qhx+iD77yKjK+QpG8c/MsSiCX3I5fXERy8pOYSwvkfFOgAQWvQmmkYGYeBoMVLaz6+aX5THdYIaC0yyXSvWyxq+n5AqRmrK8/TgSU3v6f8zwFcqbQSn7C0FsFjjcY7Hxl+RlJjE2j1JWmhMQZtNC4Ieyh95kFG6DKApbRGzeRyydfMaBNZW8I8C4Rk9CXsrwhIEhmMX147B4DLZBlqOjcB4jVV1cQwggPDsSgKTAR1UYq6lmAUgqeq6sw2TOOrVExUbFuaRQ1BZ8DTmCLK028YBse6rlksFlycn7Hf73ETaN2hjTD8zsqCoi5wk6z9/VYsAa6vb1idnjFMI7vtlqq0LJsChUcrWC6b6OsFi0XJZtPz5s3byHyVfQkwTQJGF2URAVUXvzafm8P1HetYxzrWsY51rGMd61i/yvWVQK6T1ZL1ySlaqwysaK2xxuC8p+skOv3i4pzVcsG9exdoPMPQcX5+yvMXl/T9wNu3VzgvT4ilaVYEFKUpKK3h8eMnDNNE3w/c3mx4+fIlzWqBLTQP7t9jGMbMSEnMoQQY7HY7VAQVlk3D2ekZ19HktyxLdtstIUBtCyYFm9s9Xd9x7+Kci4sLCmvFm8Qa6phQpawAaCFIrPvuzZ62bSnLEluVGbQwQRhufd+zXK3Q0e/n7du3sdFUUcYl3kVNVbNeLHjn3Xc5u7jgs08+4/vf/zvGcZS0L6Opm5qrq+tozH/CYrG4kyqXgIBXr15xe7tlt9tlwO/Zs8+yifJisZBGzjthYSCeOyrKtrq+J4QgqX8xia4oFhFIEPBqHCccZGlaaoSHcRSpHx5rLE3dgCKPWdM0LBYLtNKSJok03OM0UlUVhS7Qnc4AVvKR0iFQlKWYeEcmRWYPRRnbZrPh9PQ0s5TmptPJq0prTVGW2BDoo1wwpaYppaSxnL0+eQAls+nkD5QS18RjrsrA1Bw0SqyjJEtM0sQkVRSAuLkDIHTRwyyt53TcxFC0ka2W1lzyfCrLUpgX0xQlYwfvpoNHkjCqki/Y3HPsy+ytNPYJ7Ersr3QNydfMzNgxKq4FGS8VARciqOUxRppmYXHpDMokmZnIBuXfIQgglGSuc8nonHnkg3CCfBDPpzTnqENSHP5wDQBFYaMUVu5lwkpK4IdI0STZT+5FSim8m5jG6GkWxshGIvuTJaAmGf0LGDpj4+mCYXL0fQuQpbjW2kjvAmUi2ykErLYkc/60R0trGaeDFNfcAX6SZDP6fMVrdk5FRqNIVo1WUak4Cw+YHct7j1YaF1wGxNL30muTL5PSQBAQR0WPPR3RG6UjuKniNcU1lmEyrfCEyOADYlKkURqlDMaEHBgR5nMe/yQWGgq0UXfWqTHqAL56j9FJJnrYm+l80lyklEEJELCyhiDu94CxVhh2cW2VUS59R+rpJ6ZJWGHT5OJ6lmAWAZYn9rs9xhScn5/lxFqCjjJSw267ZxomrD7DaENdNTgnY21LOZZSiu3uhtOTcwpjafuWYRIfxXfee4ryCh29zoIXFrHcs5P8u2CxOKGJnpdt2wHi3Sd+dMJQS3tQlQXGrPLDAMIBXD3WsY51rGMd61jHOtaxfpXrK4FcbT8SdjuMNdB2wmRAmtSUsLZerZgmjw+gjeXsZM1+v+Pho0fs2pHC2piCNkVQ4cAMUEq8fILSbLc7nPMUdcU7770riX7es9/s2NzuYiOtKApDWVpQFUM/0Cxqhu7g06W1xk0TxlrKquJ2s0FFJkihCppmgTGWwpbsdi1D34uZ+LLh1atX9H3PYrkUY+AInhBE4qiNhQAKxXazoyws+66jKAvGYWTsJSWr7wfKspCm00NRGNw0cn21Y+w6bFXy5vKSruv40Y9/xNnZWUy8c1xfXXN6ekpVlBAC3b7NDWdRlLhx5MWL55ydnfP7v/d7fPLxxyhjubj4/7H3Z8+SZVd6J/bt6UzufseIyERmYqgCUAXUSLI4VbFlYhubErtE6UUPkkx60B8qsza2kd1t3Wxx6CIKIKoAJBI5xXAndz/TnvSw1trn3KxqyfJJgNlZZgFExvXrfvxM9+6ff8MNXr/+kkPSG3gfcDpRmLtzomiaV9Alo3IOfd9TuLhzUNCoa0fqrZjKwtezokeBWhJTjLQItFVRMNR1w013pMKYJ18Wh8ZYzjqinKnzmdrHqqpG8B6JF68hkGULOcPPM6ZxhGMAgpzxeDxSxlCMpT0wBioCEEuR0hpICU+PjxjGEQ1ngokK0Dn3rFVxrahaFFH0XF3XkVWPrY3FxsXnvkAz2uTnNql16+JaoSStkALxSjD46ntJCZU5j8sUGKNA+1JBQSsDYyVDSROojGTX1cqQ7dRQlhflu2sCFaD2O/C1TBvPdsOY+Boy8D5AK4YIHIqutCbbXk6cF5Y4l4vAk589rATBK40UyTo3zx5aqwIVY1xnL8m9QDM0ALyXQoIEYxKMtaQajAFBkUprfaxI/Tbzfk9cUGCglMEwjMiZrgFV1EusigLlg83joo4EFCuVZjRNjRTpHpAylThAU+6YcxXaugZ4HyBlzGFEVbcFSskU5Z6jZsswewqu1wSe6qZGVgrez4gr8CpB+evnK/AmZ0BpJLWcV0opbnjMyIGA5xKmTvctgUyidtOspP2qYoxejP8wkEx5VTqgCM6RBZE+uDCV4zyqRFCdD7CCRswEWHIOyCnDOA2fAoXG54yUYpHRkXoPwOr8NFoVJaFRGikGZIZsKgMRgX82LX9yyoAGJdtxy2PlLKl6cyqqvZQIViU2WAo4lX0tFmalFJyl92CMLconAo2Z94NGPw7oj1SgcnNzhWmckZE468oDMeDm+gY/+9kvYa3Fw8MT5tljd9Gh7WpozkCbJw/vZ/hI19Xl1QWQI7q2wTx4aj42in7+hkw5aTry/bgFKqCqFKp6B20yxpGVmZrvATlD8hidq4BM8NT7CFc32GabbbbZZpttttlmm9+E+VqQa5oDsplhAzVQkWOFFiDyC79WZMuYpgnzPKO/vYZSwM31DdtZSM00jmTlsBwCLlYuqTQ/HA4EXxTwjQ8+KJlA43nAxcWhWMOcIwBQVQ5KA3Xl0J89hqFHt9ux8ucFhnFEzgn73Q4hRl5gUYZX0zbYH/b0PE1Ntsi+x6k/c9YMKQASOZ6gOcOErHex5H9J7Tq9n1Ayd2QhrTXZy7q2wW9/5zu4uXmBmIEvv/gC948PePv2LWbv0e46gk1VhcZYWH6PMZJ6pHY1rLMIHKb/p3/6p3j56j1cX13iO9/+FqAMfKAF0dPTE0KIJUOraRoYY4s6SfKc6qbGPE0w3BJIyo8E7xdFRoyrEHDnME8TMtu0SGEWyuJ3nqcCnpRSmP1EDXecaRNChA8BgZVFddMgcvta4mZCCZgOMZYMKIVFsZRSQlVVZI/lDC0JTpc8K7CyI4SA4D3sjsL+7+7uClySxeswDEWh5b0vKo+maZBSKnBsrcwSW51AhTWAkP/+qi1MviavL/k763+TbK+maaAVME4zQvCYJlGLZcqtShEZCvMcn6tfcqJssxiKm6sfBoJzyKRKw5JhlGPEyDlFWpGiK4k6h5v46FoiaBlD4GvZkk0uJzhUZX9ncMtezpg9Qa159qyGiaQCygmKg7k56goqP99firOZCLrk1XmbyappCEoqMCj0c1GxEZwEQvAA8iq4nS2rfF7Ldkr+GWm56Jqd5+V76LhxqH4kmJJjIgtjJptkjLHsM2p+1MUmK3BWridtNNvXbFE3KQUYZwsQjqyUohB9vSiq+E9KsajZkMm+t7bpUUYZfw/YMqf4hfg9Gj6fyGqqWX33XLkj2y5AkO4PSwMhIEom8nj64BESqau01gTxBPTx/1Ekm0bKgUE6gS2xM0IAbAGfFJCvBVjJfgSQIud9sZov5YzImV/GkMUxc+A+WIWXc2IlK31I8/x6Bf2cUBrmK7ZRud4FOmYQTCcl2kzWSqXoQxBFOWt1Q5bG06lHigF1XVFxxtMTjAYejk8Y+gFOW/gQ6JrpB3S7Ds7VOJ+pzECUc9poKP7gA3xtGCuWZSqiyJmC8/3skSLo/qwA62pUFf/cDXxvDKzR5fcp1lGB+iFsyfPbbLPNNttss8022/xmzNfL5KocZH0ki76sMpzk56QM76mRL4SI87lHThFV5fBw8VBCfqeJLH1rSEBZPCO0oQXE4eISVd1gHEdAAQ93D/wLvoGtHUMNi7omYFNVFara0afzNdAPYFuXZ9gBTMOApmkxeY+J86C0XWf70ML0fD6jbcni1zSkxKiaGloREBJrm3UOdU32KmoWJLuRUgqn0wmHiw7OOnQdZQ31/RkqZ1xeXOLDDz/Czc0LzDHiF7/4BQ4XF3g6PuHlq1fY7XasNnHQvOAkGLioEmJMaJoa3/mt38L3v/vbpJxiW9mpH/Hxx78AINAjoeu6YpWS4yDPFUKANQZn71E3BFxm75ESKbgICiRME7VZ1lVVFnpVXZdmy9kLXElIOeHicIGMzC2YS0aWWM4EJh0OB6gVNBq8hwKKvS5wiLw0UcZ1Q1pKSAwnLKvLkBdFBbCoWqqqAkBB0SVsWutij5PnXM+61MAYymbruq5AQ8niknPoWd4SP99adSN2S/l32S6BY+tgfGDJLZLXIwsgWbH8CugEVjIJ9KHXS3DWkpUtp6LYCH65LnLOqOqK4HQUhZaCsYZD1mnRLNd9zpxulTNUVqyQAp9rKIoegQu0/+blPQJs0yJVmtKiH+NzI2Ukpcp715osv5TvFRl4LaCLD3d5vChEZb8uIDOReovBmfcBWku7IllyKWCbVE2Sl7VW9+WSJWX+BgwJYYDjMG/naox+RGVcAeTGihpPssFUUUaR7TeU/SjXrbWWVE15sd/FSN+ki7Xs+azvZcXapymjjO4EudgH6TxNbP98nmGn+Niv8+VSjJRFBoIs8hxfff2UF9tfhthipRAhFxibGdhEkGIoZ3puUhUuW7JsB4fjIyPFBGMl50xyxiApXXS+8nWY+Fy0xi6WzpSK0kqyqeR+Ut5SykgqIiVTrjO5Tr+q4sx5KeZIfB0LpKzrGm3dlIyupq5JqbXbQykNWzn04wilFSIS6qYmNWZabPhN3dAxyRmZlbNyTWpjUFWKoVTgD3HoXJmmmRosg4d1ZAOdE6kZ67qBV4HhZsY0Bco6E+C4Ok/E4rzNNttss80222yzzTa/7vO1IFddOw77TaURTCtSm1jjMI0zwjzBaMuL/6rYjvp+QNsRZMg5o+s6AEsznXMOh8MFAjfrnc49rKswnntorTH0Ayt3as7gCiWbS/KMAMDHCGiNjIzj6YRxGHE47KG1Rl3VZH2b5tKuV1c1gg84n8+Y5xnTTPDt9vYGbdfBcEC6giwgKWy9bVtohgnPW/hSASgXhz1lSmmL4/FEUIih3ief/Ao//elf4ermFkPfo+s6vLh9AR882+hoQTbNEy1m6ros5IzW6LoO3/r2t/GD3/0B5rFHsqRO+/yzz/DJZ5/jk08+KcDEOYJwkl0mWUtre9c4jUg54enpiWxmbO/MbEmSRY8ATQBo2/YZNDNGQytb1DQ5k5JonmcOmzcIDLtijHC8fVprjNO0tACyHXCtihIotKhxLEONWECptbZkWK3tXbIgpSKCXDK15Nwr0JLf19rquoZlEi5OygbaxpRSsTvKPgWWUHDZbvleCn1GeYxAmLVdcj3Sxun9zPBKFYAzjhOqihV9/PgCuTKQVMJuv2NVpUfT1AQcU4SzHJIvtkFtnoE3FIsXQRBtSa2SIt0BrLUw1vB+o/yhnAheispIa4VxHKAUAS1Tk+qLsrxUUf1A4ESm7bHKlMIJsS4u25YLMCWYRYBKlD7WOoaySwC4tQreJ7RtW2BhsSKmXL6frHq5qOC0puIHDVJSPVfpLWAFIOWqchUsQ/N5ngGrAGOePV62Sc4zum1J2D9ZICUTDAByiqQYy8v3C9xTrJqVbDKlFGCWJkqCiqwgNLztDCnX1wep11C2SUASVq+ZiUzx4crPrglpWpQcsZQpS++rajCAVWTFGMtKMM7gMsYAiRVxKbIybfU4tcpQSwkWppQXAAkw9GFGypQxphXBb3lPWmuoxBZmVmCRwpbhFhRBnkTZgoahWGSLuuyfNexajqOiIpGqelYyEGIkqMv7KiUL5yoMw4DHxyfEENG2Dan1NIod3jmLkBT6oYfSCnvOVCQbcUZdW8yJch6dc4gImEbPZQPgD1wAH6RsggB5znROQWn4aQSUhrMGQAXvKSdR7k3y/3Jf3mabbbbZZpttttlmm9+E+VqQS0Gj4gDnFHnBohX8HOEREbxHTgn9OGCYBoQYcdh1UDrj4fEJdw8e+/0OANBJFlblSqi1tRanfkCMkX65V8D5eIJzDvvdrqi9pPKclF1A21B9euUqUjc4hXbXoQ4RVhu8evUKYz9AG42nx0dWWzjsdjtUzmHIY1FFhODx8vZFsWfJJ/L90MOzRbOqKjQNQSfvQ1GMkbWJQMdu18G6CiEkeE9tjx9++AHmacKbN+/Qn89wzqHd7/Hq/fdwd3fHC6mMYehZ7UDqC2sIKAZWqXzjg/fxve9/H69evKQFoqvgQ8Dj4zv85Cc/xZt379APPZqaoE5UCcPQs3pLF0XSNE0F/jRtgxginp6OSCnh6uoKbdvifD6Xx9VVRVlXoODmtm0LzCHQ89wmdTqdONOFLC+U8bJYnLwx6IcBp9OpqLpkgThz86FnW5CAA7FGLtCRAEPTNPDzXFRCpQ0yLUBEAt89n6dV2yLGiGkc0e12ZJlahU9LCLZjRU1mCOW9h5GAfKWeZXtJ5hZA8EEx3BI4JtsrOV/UXplR1XWxVK6VbgTOCHSRBdOUVlPvSc0IpWA5tijnxFlookCyqGsFY04E2vg9ZLb+dVVXgIdkc4GtdwI0jNG8vxVyJquYdRzKHSOMYcWZoo2QUH85D5yjbZRjYg0paFgSVnLXFEjlRV8I/D4AID8DgIutbK1sJAhL6iyyAUa2X2a2u1HTo2G4uaj96LGB8wGX11JawzoHFOsjgTCFDBG6aK1RM1RUyGw9VJSBxPuvECR+v+XchLz/zJA0cHg6SgOpyRkxhQJzF5gqyitSdCnNNt4k8G0FwnhLkJ8rrwRmgs9txSoqyuRagNfyfPK19Mzqa7QtCi0CIn8zx0oyn2Q/yP2N9toCVQCyDma2/GUsSimCYwSyckrwHJKfGXLR8SO7Iu9ayFsXRaHOigGtLdZYAosoikexPIqVUUouxM4qIGutthToDrZsChjUwUNxzqA1Fprf8263ow8+uAxh17WoqwYxeAQfkDUBW2UU+nHklteAVy9eoG5adN0e4fhEp1RSbF8mJZYxBEz5DCiws1gOM8FwP5OaMyAAivIt5TCkROpjsXpviGubbbbZZpttttlmm9+U+VqQqx9muMCLQ7ExMTQxRsFqBVdZ+BwRQ8QwzhykXOGLN2/RtdUCObSFUhr9MOHp2HNOj2JbhSMVyDRAO4M5eoToUdUVQjgjhoRsIg77A66urpBzwsPjI2iFB6Ss4FyNkEZ859vfxjzPFJCOCOMMbm6uEUOC0YYXqbxAqYC2qnA+nzH0PeZ5wne+8x1U1iJfXEBrg7quuF6dvl9ZYJgjrHNo2h0pe8YR+/0B537AJ7/8FHXlcHND29n3PfphwOHigI8+/BBVZfH69QPG84gPP/wGco64vbnEeZjgXAULjbatcbjY43w6Yn/Y4/vf/10cLi7hxwnaWGRo/NVPf4o3b97giy9e4/LqEteX1wghEKCyAKkhFrXOOI44nc6YprnYYZx1uJaGyXHE6XTC8empKObEBicKpeA921QTKufgg8fx6YmVbh3lpgXF+5qBBwOnmqHO23fv0LUtxpnUdTfX18XSmjmXK4aAfppKI5pj8KjZ/uZDgAItZGtrcf/wUECQbKv3Hn1PwfuSR9V1HfrzGY+PjyX0fZ2rJYv4ApUCqdJyWtR63Y6O+TAMGIYBYl8UoCQwTBbCfd/jeDyiaRrUdU2NibwvpmlC07YE1UJAZMWW95Hy5YxlCycBIGpXpCBvxU1yShsoR2Hp8zzj/u6e1F2a7FpNo3B88jDWoHI1nCOros9z2VcpRrJeiXKI93WMkRQnvKAvtjBW+Mh9gBRRhtVuNbReFEzyeLtqCQQWyEHAKcHZCkZz4QFbo40mwAN+r5qhRowBMUUYztkbpxFGW1RVDSkwkP1f4KUxsFaX45xywjhNqOsKBkvDJtndGLzOAU1Tw1mHrMTCSk2JxmjOCDOIIaLrWgrYZ/URYoKKmS11ZB1FJqhqlAEYdEEbQIAjNIWoF+Xa2rbHqk4j/504rJ32IympImUmSsA+38fF0gfZNmRkpYEYoTl3bbHlMazVbCVNi4qrKEGjwELJ3MrIISIrRR+ErDmfktB72o6AjIQMK+eX1kiBt4e6GEs+mgKF5OfMVkUBYSoBSkAsq9s4vxBZ7JMZWdG9gqyoAsNBYf1aIcfE1wmHsfP9Z33fWytJgcXerEAK2ynMpR1WKYUcCS6mKLZKKVSQrDZSvqbzGSEGWKOhssYcAiIrupQCGlfjfB7xxes32O86uLrGfr/HOI7wrDQmxaSC4X2eIv0crqxFzBmS6QaAz/W6WH9TTLDQcKLYBJDSDETFQPW5wnSbbbbZZpttttlmm21+XedrQa7z+QkXF5dFOSFtbikF+iXeaBhIw5ci1VQMqOca0c/o2ldomhYXF5clH4c+oe5Bv4CDM2ciHh8IlnzwwQdwzqE/nfBw/4gdQwVSWhh4rkk/nXrc3z+i6zpc3b7APM/Y7/eo6xpPT0+4ubmBjzO3ztHCEaBFl7Ea80yfqDe2gQ8zjNU4HHZcBQ/c3t7gfD6j78/o+x5aGxwOF7DOolIoFjaAFkOff/45jscjgIynxxPmsYdxlNcSY4IG8PDwgPH1VOyap9MJbdtAZeD29hZPTydUdY1xmvHu44/xzW9+E7/929/Hbr9DigGuqjGHgNdvX+M///V/hjEGlzeXMNqgO1B4/cPxAde76wIRJESdFBoa3/jGHiFEWGcKhOmHHufzmY5zijBWc1ByVwBOSqksso7HI4ZhQMqkkGu7FkoBp/Op7GOlSWXTtjtaGDMEeu+99yiXy7mycHx8pONc1wQpBqXgQ4BOCdY5jBPts1fvvYeqqvD0+EhZM97j3Pew1mK3I8WgWKYoE62n7LaqQts0FGbvHA4XF0gpoe06zDM1TjrnUEsmWAiY5hkXFxcFRsVEzYNv377F7e0tjscjDocDqqpCz9uwbyhTbhgGssYWyympJCgQm46LhMIbY0pulZxLUBl1TW1n2iho44rKK8ZIIdSgYyVWNm0UqtotahvL4ds5wToDZx2UViV0vahXVhALACleFCle5O8ChuzqaxkgWx0Sg2+FEERVAgDUvCfnjxQFFNULQHCEVUlQme8zAcbStsREMCskhkMM4lIGL8pjUcEZ0GuTwmViGJGLGi1msvcqramlMUbUTQNtDHq2DxPEobZBP8+Y5rnYc421IDlrRPSBgYXm/DJqaqxrh2EYYRQFsYsFEnyUtNKAABW+L8zzSDdmSxlg0LkUHghUWgefi1Ix5AgYvbI0srKPj2tKS1uoZHXJfVRgZYJY6gIonz1Ba4IsmrPT4sq+u85d0wxzy7aFiAgCkWINLcM2vsVayHlffE55BIYqGkYxlEuRGjVZ2ZVACMxojdkHaE2FIILFsG4OzZxLphS8zgg5w2gAmtRaBqQ8QyTVZdaqwLycqb1V9qnWBufzCXVdo+8H1HVF1lpW2cp+DoFUeXVdwTnKU5umVBRl3a4DFNmq55nUuiEmzDOpsYKXplYFpTJUTGiqGinRBzr9OOBbH32Iw+GAc99jGkk5nVJGTBk5B6SU4SqHOXi+zRD4o+vOwlqNeZafWQopUjOtgYG2Fl1boapaBO8xjvP/j98Ottlmm2222WabbbbZ5tdjvhbkylnRos0YhBjRmpYXvLTo8T4izj0S57K0VYXRToDVaOoaKSsoZfD27buywPVsSyO7oqFfyucZH374IYZhLAuNru1gtOE6dGrAG1lt1DQtvvOd79ACOme8fvuOLI+9x67tlhwqVlBcXOwQY8I4jpwX5dHUtOAgpYxH03R4enrC6XRmOyUt1F6+fMVB+Z5UPQCqpsHD4xO897i/v8env/oVuq6DsxpXFxdILQEObR2quoa1lIEkzxnCEgyfMwX8V1WN3S5jOJ5xd3eHP/qjP8R3v/9dNHWNGBKQFc7jGb/4xS9w//SAcRjQdR0q61DXNZyxaOoatzc3qOuW4VpkiKjQdXt0HdiiokoOkCzumrpB0zQFvEhD48TZWQIpIofCU7ZPQgyBFBZa4eJwURa/I+dCHY9Haiisa1S8eC9ZSSFgHEfsug673a6cG87Seym5XKKmmmcEVmgZa7HrutIKuM7vknPNOVfgRd00uL+/h9Yah8OBLItdh6ZpMI0EGiSgfr/fkyotZ1QM45q6hvce19fXxfIpwfaOGyRFOSNZOsfjER2/N7FfivIPIAwgijKtFLIiuGI07X/LVjbJtMqJcrAyu84kQ8log5gi5okD9qsK0p4XQ+TnC6z80pzdRvBClHZkj40FTWhW34GVUYG3N3gPKMojUlohJ2AcJ75fSObUyqmmlrwvKFVgntgWMwgkiDUUAL93Cx88qsoh+aXRkKOiaN+kDGUUktbIMRdwxHoz9P3AIFUjBo+ccgkFR8pIIcIojbZpYJQmO5yxiDHAh0CNpjFgHCeyxrEVkqzTz5sHp3FC1TaIKUIZA1dZaOhS4CBB+BRIv0D3nJf9FiPnXaXlfF4XRix5c8s9OvlAaiydCyTJJXdMti8jhvgciPG5II8xDKeU4sD9Zz8HltD1eZ7LcRKbsBQgJPZ0JrZCiuJPQcEqssxaTeq6x/sHAArWOgQktE0LwxCKdwpf14AymhsGCQh3bUtglA4AQTdWLeWcye5tNHykXEAKdeesuQL/BDRGSEGDWFzlWslsVVSKYJYo6igXq15UZCmCe02K6tI5R/BTadR1DWtsseM2dQulqFBBayD7CCkC0NqQtTBMQKR72fXtDeZ5xps3b9E0NW5fvUTX1tDqiPN5QM7UXhpBAf1+9jgcOkS+B0izMNkt6cahlIJ1BlrZYhk2RiOrhA8/fA/9efzbfynYZpttttlmm2222WabX7P5WpCrbZti+VEalB2V5RNvQCvAKQ6S12TboXa8gIfHAePpiPPpjN1uB2MMjscjzuczWQbFsjWPuLy8xK7bI8aE16/fkkWNG83qtkHMGW/f3ZXsqGa3w/3TEZ9++ikOh8OSFxM8Mn86P44jfKLwbVmIKQ3s9l1ZpD0+PuLVzSu0uxZDP5FNbuhxf3+H999/n8KAgweQCVSZFjElJNBC6H/6n/4tLIfQV1UFlQN2uwZaaewPl4C2HMQ+IacM7QxleU0BVlnUDalcTqcTHk9nnE49/vIv/hP+7M/+MV6+egUFhfP5jPP5jLu7ezw9HXG4uETKEd/81kel/bBtqclLaeDi8gDvSeoiAeaSazUMA/r+hP1+D6U1qbFSgrMOVU3vIeeMYRwWuKKWhkax3J05X2yaR7IiaVVC80WdpRRQVRZNQwHKM+fpiNJKzgHD55eXHBiGBq6qYIvajfKOnhiYAaSEatu2LEglNwcg9VrXdaWFU6Dnzc3NMzURZJFrDLd6Ktzc3BRbYYwRitUmddMU5Z5kcsl5JBlzKWfoti0qLtm/SqlyrKQBULZvbY8UaCsAmBRQnqEjwQ5tyL6VlS6B2ykTSDmejgTnOLTeGA3rLFIiwEtQ1xV4KJBC8R+nF1AguWuLooUeI62BKWck3q/AEtAtryv/vkAQ8wyyrCelRdXDMVHwYS6gJzOAE4AqoLRuagJ7IcDppWGyrqvyurTIV4Cm5sOaW1Sr8xnH4xH9OJTtq4yFQoIxCnVTkZJomgDOBIwpIXIwefAe1li4qgLYXheCR9e2dL2zHTJzQDugMKcAHUjtVLuqQBZpg5RQ9sQArASPMwCUYgnJdlM5IyWUptuUKaOLLH6rMHEowCxQp0i5AGjDOXApLaqzzLlXagFcoshbH8N1sYWxDoG3tcBXvtbA0E6+31qLw8UFgo8llw+ySaswKGoB1Ig58f2Mtks5jfHcE4QFaD/kwN+lys8gQXXR070lxQifuCHVVmR3VKpcYyhlBouycZqmcm7uDwcgk/Vb9qVS9LNAAZTnBrL9aa3Q8H1ZSi5STgzlE5RcH8aiNmS1jSliHAcAGa/eew/Hx3tAa0wzZQqCVYf6/h5WKewPB7x4+QreT3h6eqB2YyTESIBYawfnatmbaJqaPqiRe05tIMUOPkTMPgAIADyejqe/cZ1us80222yzzTbbbLPNr+N8TbvimT/JVmXhq0CLask5SQrwKQJsHZqfIqpxRlNZ7C72sLYCQCosgkEO0zTieHzCMAzY7Q4IPmKeA+7uHtC0DfrhiFevXuF4POJ4eqKsFG6Re/36C/zkJ38JAPjmBx9iOB0xxQmVIyXSEALGcw+AQMJ+f0DmmnTnHFLMuHt3h7qu0bV7HJ/O+PLLL9FULbwPqGyNytaoXYMXNwfc39/jzZdvkJDx4r1X6PZ7/OhHP8ZPfvyX+OF3v4um0oh5wv/+z/8P6LpLfPLJx3g69nh4fMI0DNh1LW6vLxFSwjDMaPIF9nuF4Gf46BFyQFXVcABUCPg//cs/x83tNYahR9O0yFD47Ms3+Pjjn0NrhfvjA0wGIjKurq4QQsAwDHDa4PriEjkl2F2NnlVYFQOmeRihUsaL6xdwVYX7uzvEibKL2o5C6GMOBFYmUgO5pi5thNIWeDqdivXu8uIK3vuiyuqnAVdXVwRyIi3ixAI4r8PsQYqgoe/RNA3apsHEGVxaa+y4WWyapiUbisHU0+MjLi4u0DYNrDGkGEyprI211gVyCWjZ7XaIvCAfhoGgiLWcs6ZhjWF1iH4GdWSxK7BBrFtd1+Hq6qqAqwJ+V4HVAgbmeSbrLL83CcIX2LduNTPGYB4GsoyBLFC7XQetyPZqjAR+pwL7ckoEgJTCxeGAyHlhlxcXdNwYCKUYC4yTMHaxP0q+kCjNrLVFybOEtecCNGS/rNVFss/kfS0QYLl3rCHJOtNJMyAMrOIDUAoFqGzCQDv6e9M2AFiNEzKccQizLyBPQJio7QqQBGXF9ccew2kgOJoynCYlpPdzUZtRKLtGiB4AAcpxHEEKngpd0yFXGcM8kXUyibo1ozIaMAQ7KANJFDuG7apkvZN9EkKCMQ4hEIy2yUJzsPsabsUYiw1Waw0NA+sMpuCRFDjQnJRQEYkseZqUT4GzulRcjk3OGY4huwTQ07FeVHgxBEihgWTSyTGVEPaGG3VjXhpDxRKbfMDsA5Rm0AZq7ot0gAGT4UwNC4uMiHmixlZVssMArQnkJVa5KQ3MMSBkIIwTK6Y0EoMmsdiSeoxUbcZYKE3lGdoQ5PVsEyVVoAW4WTF5VjwqUyCwQHbZJvmAhs5nyqCUr6cY4WxFEE2R0lHAtlYGdWURoy8lJ9JES9cBKPMNGZ99+ikA4OryEtFnaG0xh4ScFd69fUQIAftuwNXVBbquxeXlDbpuj2kcMU8j7ee8hpQVpHFRa9qvyNRCmjNglIVBQt21fF/blFzbbLPNNttss8022/xmzNeCXCFEOLeoOoBF3aO0RsqASkuw+PpxddvAWIvZe1R1jW63RwjURvjy4iU++uY3cTqdYLRG3/eYpgG3t9cYpwmn04Cnpwfc39/j5vIK3/jGNxBixORn7HYH3N6+hFYKzlg8Pj4CrHhqmgavX7+G0waHw57yXrzH+dTzgtGwmqWG9wFKzWRXm2Y4V+Pq5hqXl5c4nU5QSuE89GTDu7zEME74j//+R/jRj/8SV9eXuLm6wm7XwFngv/oXf45vfueH+OxXn+Dzz17j/uEBUBpGaxxPPXxICCnhiy++wDzPmKYZV1dXuLm5Rk4ZQ9/jYtfhv/rn/yUuLq9QNR0UFB4fH/Hu/g6AwqtX7yMED62BeQ4wAIaBFt9936OtarLSpIxheoeZ84SqqkIIM4ZxhI8BX7z5At57XF5eoqoqvLt7B3Wv0HUdOoZLIQdM/Qw1LI2Fxhjc3NwU+2Hf92XR1/c99vt9ycWS/VfXNY7cpBhixDRN6IqCwcAxVHj7juysFau3YkqYGQSdzmfc3t6iqqoS2q60LnbPYSAlzm63e6YWeXp6wsXFRbF8lYZDTcclciZT0zTPso4kPwtAgXpizxSAFWPE8XgsjxOVkuyPYRgKaJHtltbK995775miZa0aEWCWckTddAhnT22VrOIKUXKrYoFVVOBH7ztwwHrLwA4pwVUVZrYYDkXNVcEufkIorWl/51zUKAJCRBXZskJN4BqAAroEhMl7kPOF7iGh/HdpoWQQJq/jV8+5hmqIYrHLGIaBv+4W6BgJvOScMAwjKRSVKtZQOWbPj1FkyyHdy47HE4dxZ86EE9Cg4Hm/IYsVEMg5YvYTQqL9cjgcCCQgQ+WIlEyxnc0M36ShUHKbjJNss1y2t6rqAnesdc+A4Vr9WJRdRiPk9GyfaU1NhCHMSJnKB1CaExWVFGRRzmVMk2Q3oYTzS+ssWfOew0s5lut/H+ahWCrbpmFASHltBVRmAsTWOMraitLISOfIGEQZFYAoOWTcqKuArAFEYJpGGKNRd2SrXnLKIkE9K0q05XhJeyIl4i/ndcqZLMJseQRyKVNYt0nKeSwTQdmUzjr0fU/2Us3nq+JMvUANmUpRMQtUhtIKbdcAGZhmLOeCJbugvJ7jn6+OFZjTNJUsPLJTxgJw5+Dx5t07VEeHpq7Q1DXqpiVgOY5wxvB1Q/eK04nKVbqOQLH38+q+B8zzhIjMP4+3TK5tttlmm2222WabbX4z5mtBLmtJBWS5atxaV5QE0laVYihZLesF+DzOmBSw3+8RY8TT04AYPAW3Vw4YRwTvUXU7aG3ws5/9nILNpwnvv/8+cgZevHiJ0+MjPv74Yyiufad8G1IxNWz5ub66wZs3b/H0dAKgcH//gKqqME0z5nlECAlXV9e4vNwhhIDz+QzKyKlxPg/44IOP8K1vfoTzuccvf/nLogoBMg6HA7788kv8xV/8CHf3j/joWx/ih7/3A5xPR6Sc8X/5v/8/UXcd/t2/+x/w0598jOjDkk3FsMZzRstHH3xIoMoaspbkjL/4D/8L/vQf/0P8/u/9Plzl8Hg64f/93/5r/Lt/9+/hXIVvvPcNtE0NYzQuLg44n0+AUggpFvtgjBHX1zdQAM6nM7TS2O32qJuGa8AMYAxcCNjvDwQjhxHXN9dw1hWAoKFL6yLV0yc+D0hx8Pj4WI7/5eUl2rZlC2SPcRwxjgQauq4D+FhZ52AZvIzDQBYjVoLIsbw4HEoo+1rBYrTG+++9h7Zt8fDwAK0UXrx4gXmaSh5WihFN29Kib57LYn2e57JtokYkqHFEU9e4vLoqLZwCbsTyKMdfLImyyG+aZrF88ePXOWDyZ2lSowXyPM84HA5lYS7KEGojpMwvUXjFGFG7CikmOFdBQVQYCTGQtbK0Oa6KDyiba9lWCc+2bMUU+55lMKb49ed5pjZF/t7ITXEC3gRWCvz725RYAkB2ux2cc8Xquba6yfPJlGY+LODcMGyjxsIVnNIGVZX4HFsW/aQ6c9DalH0qx0SsfQJ/CFxJSLiHL+Hxtjx+9DMQM4y1BIhiQmQFnXMWGiArntYwSqHrdsgZ3L5I+zmnCcZYhLDAusTW4RLmPntEzn9KKcGDct2ocVGjn3oE3n+yD+S49qx+XIfBr+2gBNwUtQfq5TgJzFFaQWdpX8xFtfW3Ke0sQyMBo5LNJ/tWaw1ja261TLDawkcGZxytFfgeYizl1qmowD0lCIEaVsFNfnVdkdUVvO9i5Aw2DW00OtchRr8AXlEJKpBSTXLiIrUr0jm/qCezVjCK7dAMc8HntXEW1jqyNZZ8OGoIlfdKr6VJiqdAij7kZf+DAvWhclG/zfOM3X4PaSyknLmK1KWsohTbp1ldI3VNJR8hBKToy/2o6zqM04jZk4VRYGkICePoYe2Atm5QuRZKJ7bzA5999hlqzqGU3DHuoAQyK0JTgM0Wu6aBzn/TVrzNNttss80222yzzTa/jvO1IFddVTDWwFgBWlwxnxLX0pNFSz5xlk+YAVqwxJTw+vVr3N7eoutaWuQpUrqMvHB68+Y1zuczdjuyl1WVw8PDPZ6enigjKiRYY2Erh7ppUFUcQj9MuH97jxgDtKMMmru7O8SYUVcUEr7b7bDfd1DK4nQ64ZNPfoUQAp6enngba9ze3qKuG/zlj3+M0+mEqqqw3+9x/3CP6+trnM5n3N/f40/+/t/BP/rH/xC2MvjZz3+OT3854b2X7+Nf/6t/g+O5x7uHe1qAOoeLiz2UyjAFYGRqspo95jngcHFA27WYpwF//uf/HH/y9/4E0Ue0hz1CTPijP/5jdN0eb9++Q44Jbdtg1+2gVEbbtABDJ7JIaWhtME0z/DwjBVLvVGw1nP2MaRoxz54X+xoxJewOO4zTCOss5idSljnn0A89q6MACe4RBY9zBDkPhwO893h6eirFACEEHA4HCsFnCCNB/dZaOubDgJRJMdOwRVEW8JEXlgKeBKRZaxF5MWespRBnhkKRF8F93xdlmSyAc84FaIrdK+dMUMk5PD4+Yp7nkqdGiqBcQFlpZWxbeO/LIv94PBZoJothWaRa3j6BOtZa3N/fo+u68nVRggnMEyi3tklKLpoxBrpymOeJG/7MMwgh1jpRjVlrEXjbRcU0ew8ARW1Tmi45mwdqyV1b52UJyFsDFoF48vqlyZH3d9/3BezJ+SKARR6ztjCKeuWroeayLd77oizTWnGbIQW4C9wt53TMBVyUwgK2iDonlkQP70mhIm2vztnyHgPbsHPKSJACA7pl5kyB/wkZgWEfAJzPI5IPS6Zaoh7AuDoOom4jSDRDKQ3nFqAq+5b2jS2W0AWSpWdqLVF2udW+kUkpcRFEAqChVAaQ2AYo6ixSl8EYUk9hUQPKObo+vjnnAqSlMbRASwUoZAYnnt87XRfaGGRWCZXMNmuhuLjEOYfKOWRtWAWlgEx2x5QSsub3ntnyiQgoanxUABDJApiRkVWi3CqGicicn4ZEULJaNZTmBAN6jNzflNYEf7Uu2YR0jPViDc4ZWWXEROe2nF9QQIhsc+TjofiPdWQ/9H4uIJky9kKxxxZVK7/G2sIIAPv9BaZpQEwJT8cnGGPQcEZgjBnaWCilEWJCTMA0HlHVNS4ONZ9HCq9eUXaX5L/VdQVoarGVMdbCMNibZ/+/+nvBNttss80222yzzTbb/DrN14Jc9As+fUI+TfRLel03XFGf2CJF9hpZSKqcYbTCOPTY71qkFHE+HxHDvCwmg8c8cyh8VlCpBNVAa4U4BbSuwTzNBZ7dvrhFZR2SD/C8uKWsE4unpx5ffvkFqV1iRG8MxnnAMJOqIMwzIrcDWmtxOOyw3++x3+95cT3BNkCVHFRUePPmHSrX4OHuHt/85of4s3/yT3C4vMQvfvFz/I//zf+Ip4cnat0zNbICZh9gtcV7L2/Rc1D8btdRWDIywjyVhsGOF/4GAb//e7+D7333d9iyFfD0+Rs8HZ9wf3ePt69f4+nxEYfDAdYanPszUqKcJqU1Dvs9no5POD8d4UNAurwEQCHoIWf4YUA8nopirapqylqZIwe693h8fETTNLC2wjjOCIEaAVOKrMqpysJXrGYPDw/PVHtroLNWJAkQyZwRNU8TbZv3mMaxwC7DKiRbVYghwBqDy8tLGIYTMUZkpdCwagxAWWRLKH9dVcVqmTNlO3luYcw50+uxCubm5gZD3wOgzKGoNR5Y7SVh8PL+xnEs4dan0wm7rkMlChDeDucczAo8eG5oVEqVLCzDqrR5nqFYCScQR/4uyhxZYKeUoK3FxGoOUXkopUqLndEanrdX9neKERGA1haVIzVjzgnTNHODHUPFmJDBYeOZ1vvWWTw8PODq6oqynaIvmV9JlFlYQFzmLCo/T8v2S9B1VswQCIQLjMOz709sGVOkkAJl/gVP788ah3EYEbllrq6a8v0pUqZQ8BExJBhDDajjMCGlWO5XBNCoKIGL5RjcJNR1g9PxjLqm/VRzY+bE5ypYCVWaKdmOVrLRQkJXd5iVgLMK3gf04wjnLKAUzqcTQorUvhkJMBgLhk+ZbJMwBQTGTFbeNaRTitRJRiloy7lYdoGNsn+Kcs1qpBCRFSn/1sBMLLfee6hMgJNaCDWU0WRvzKkEwCvNkEqx4o6/pjI1XmpYapQEQaKYKasxl5ZHPh007XyBR9oAIUckhmQKlG1mFCnNEgMoo6k5UymFmCK00UiR4ZIGMtJiJ2UIKU2l9DMqwxgCTon3IW2D5pIDttuS3K7AS2qZJLAJwzmUKgMRMMogxwydNRAByvU3pRlSfg6Ve2DOBZIDKKB3bf2V81opRfel1c84KFUac8dRsSKUznuAVFixKPvo/htSgjKUY1ZXNXaHHbTe4+nhsYBeUarRvUtBgX7ON02DtDp222yzzTbbbLPNNtts8+s8XwtyDX6C0QYzh/qm6DFPMwVBawCgT44pu6uCzpQZZRuDrm1Qr5oNZ+9Rawr5VtxoZ10FpIjLm0vUVQ1jDO7v72FrUld06DD0A4IPmKeZGgoZKORMAeAhBJjK4Rvf+AAphZKT1DQNnKsxjiMm7+GcRdN26LoWV9fXpCAAWcnqpoHWQNtcIoWMvv8M//1//z/g8uIC1ljcvbvH8dyjnwYYpfC97/42hmHANHnA0EKkMgb7wx4hSph7V1Qq2or9SuF8PMP7GX/6Z38fMQb8t//q3+Dv/t0/ARDx+vUbPJ1PePf2HWY/od23qNoKIQVMgYPZlUaOwORnfPY55WtVdYXdYY+3b97i9sUtQgwcrE35Q9J2F0MsrV5Jaez3+wJk7u/vASzqkHn2xaozzzM1aDqH4/FYYOXsPQLnc6WUsN/vS/5WxmLD2XUdHMMDCW8/n05o33sPTdOg79metQJqMUaczmfM84zbmxtoazECsKyCOPc95mkiMCpWRwApZ2SAzy/Kb7IMFsf7ezwdj6jrusCreZ5L0rY0j4mt0DmHcRzL+z2dz8/UNWvFGMCtbEAJsM+gBW1V13h6eqLFLQfsi4pMrJ2SL1SCxVnxMYjFk0fUV0UBxf8NRQ2ESxB+wDxTs+A49ojBw3td1JRiAcsZiCkgxwRtFDISpnnixX8s2ybnwVrpA4BtkxF1QwqfIABCUduf1pqaM3NG4MW9vA8BXdIoSfa3CuM0clYUgZEYI1KOUEojxkDnJiTYPeF0PmHX7VDXNU6nI7z3aBoC7CnS9kl139p6FlNA09bcTEote84ZAKQcMpVFjqnkVlGWVYTWBHByjgwzaN9PYYaxBmkMQNaIwcM4Dcdh5zEEBB+guPXSaAPAou9HVKYipRiDRAEhci5kVuAJkBQFrajB1rZZBVXO+XUxgpxTa3vjOu9LoIuCgl6ptWLmbVEKCYDTGj5y1lXK1OYIfh5FyIqy4ugQZtr1DE7o+rewfA6JIlKsldSQGdneZ6xFVnQckSk/TmNdcpAZfEUCT0ojYblGxY5ZgJtiuySW8P/1+SzZV5lcfGTn41PRWFbb8fGHQWm11OAMNAXksDSOyocDAvcIXpP6LEHsw6p8XSlFNnOQMo7ywyLqmpoR267lMP1lu0URqrWmD5mshdaKM+Ey5tnD2ksYGFxeX0FlKpU5nk/I/LMy56U1MoSAmJ4DuG222WabbbbZZptttvl1na8FuXzwMBVlkxhNYbsAoJWG0rSQVhkwmv49hIC6tmjbFtfXlwQb2DY1ThOACnVdoa4dZ5AY9OcRL168KKCgaRpe7BqkkGAv7DP1wTAOvBCoMYcZ4zxBWYPdbgetKQdKApKtJRtR5RyqyqFtG1jrMI0jZVcZDeSM/nzGw+MJbdvii08/xW7X4bd+69v4we/+AIfdDl98/iW++OJLnMczfvvb38aLW8oA63Z7jNOM0+mI/YsbxBBRuaqEUlvO9rHaQDuF4APqusJu1+L+/h7H4xNev77DX/zFf8KLF1fo+zOeHo8FKjWrYHSjDZRTvIgD7h8eoI1GpStcXl5inmeM04iHhwcorbDbdYhRYRwGWNvicNihP/fIkRbEXddRALm1JSherF2SEyULHsnPEiAjgf5+nilQ2loYrUvTYYwRCaTC0HXNzYBUTpB4f+8PBwDAue+LhW7m9sNiw2No8/DwsGRwsTVWrI5y3i3tcrT4m6epLPppH1h0bVuUZ2KFo0BsA7ByZg1xBLYpRcUGlpUZAEEGLfZDgNROrAhJKRUL526/p304jthzLhewBLTLIlVskqL6UEphnmecTqeS5yUNe3odhs0AI4ZQAKJdgQ9Sh5Cl1fuAnEe2kmm4zPBvGpEz2Yjn2WPoB7RdWxouIzczIlN7HHIuYdlU4LAs0ud5hlaa1D2KQE/ghkc5fiWMXmsGdrRoDyEiZ254NIYCyyMHxWeFFEMBZgJgjLVwzmGaJ0zzVNR6Rht473E6n1ktxpZXbcq2x5g4T4oUdOM4FgWL9x4xJ2ix00Z6P9YahhkZieGEsQbJexhjYa1B7ahBNOXEzX4EfbSi60sZDW0sMjJp19jyatxy7q1zp9YB/wAYmi920r8tIB3AM6BY8qv084wpuZ7XNkilUM7lck1hUTklflBKZPtTRiOlZR+rZQNKZlVOi3pJzntrLUHpTFgseI/A70tpBa0F/NBzGW0KPCPIiMXyp3Sxz2cO8M9YzlN5n2t7rITsL+qt5zlUmYH5swy6zDuAXztxVh708+eRc0Z+rsk+l/OIpWvU9OocjDbl+jdsIxUbMSkXE5+7CvM00z7he5b8fLTWUiFLVdH9ySh4P8Noh/OZ1Ku7tkXXtjgcLqCMQT/05YMOyjL0f+u+2GabbbbZZpttttlmm1/X+VqQ67BrUFUNLwgUKletbCxAhgWyogVbomrytiUwY6yDtWTTSjnj8uoSu90OHSucPGcFGa3R7boSXF7VFbq2Q0wEjKqqwuzJAnY6nZAyhbjv9xTMm7Fa8HIo9+XlJcMQ+uW9qmvK4NFkO5E8K501hmGA4wygn/zkL/Htjz7EP/iHfwINhV/8/GP85CefIPiEy8sL7C8pM+snP/4pMkhJdjwf0bYd6qrG6Cf44IuKwnuPIY64v7vH8XjE4bDHy9tb1E2D/e6AaZyhtcLT0wOqGri/e0BItMB0lUNVUyba+j1qrfF490D5X3WN/X6PqnKYvUfLdlEFtYJTZJ3yPmD2M3QGGlbdnE4nsgxyxouAFMlGksBxCUAWsCS2z2meEeYZdV3jwM1266ZCzeqVYRjouUNAxa+X2FYoi1CBPFgtFNdqE8UqBVGPgb8uSiEJ6pacH2vJLmatZRvsoq7z81xgmEAs5FwURbKfRdkg1sULzhyb55m2gbO6tDGwvBBOAJy1qDkIX2x+AmNEFSX5Zut/W79fsSBKrlUBDIky11JKmKeJGtqMQeKFruZF/zyOpVGSAIhkKxHQMtYgVpGgaSTw5IOHcxWHggckPpblOXg/kvWPygMS5xPJsZimGUYTHLXOQRsDJefDVxRgAm8on4iKLsQem9O6ORB0js/UgqhAGUgqkCXUWovgQ7k3aK0xzROQKVdQwKKo4LRWsNYxvKCML+csQjAF1IBfoyqgiexcVhsotgjmnMgyDc6XqhwAxcUNpKKBVqRrytS4mMmXWYCMUtT4mRnkrDPMZJtFPUTGPm4QxZJzJdebAC/Zx7L/BPLI82mGVj6Fcq2tLY3I3BLI1wVAFnS5NmNOEtdH6jzROykFazUFsOe8AlwJOQGac8GkrVSOhwAbZx2MsUhIBSIRaFLl3MsJ0CDrYdmPOZVtpVZHhSyKX3rTz1RtOktjrH62X2RfAIuiLAvIZDWsMqpsm/zMSYm2RcCgXu1Pyd+S46mUQkJCisuxo9INFDWWHFeAVHUCbJ11iCkiIxfIKbBQ9mlMCTZn+BCgIhjkEhijg5eQIpVo7HY7GGtwPp8xjmO598u9epttttlmm2222WabbX4T5usFz9fc3pciQohQij/ZBtmeNKsaBBK4ygJJQWuHlAkc3b54AWMM2naBKH3f07NojabrODcIcMYhQgAGQZSUErQiK0hbt+WX/trVyDHDGlZLsUWjbWmxQIuMxeKHDPiJwEFVO7iKmtW01jidzrAG+N53v4M/+sM/wHvvv8LQD7i6uYSrHGJMGGeP8zBgGkaM3O739v4dA4uKsoAMwanry2sAwDiNePf2Haw1+Pa3v4VXL1/AGg3vZ9pXWuH2xQ2ptTjsWiuNquIAcwY0ksFUcZuksxb73Y6AAwOBpqoQOJcqsUXIGgu3P2B/ONBiPGf4aUa722FaBQs754p9bm3Z67oOSqkSKL5WTlVVBeSMy4sL7Pd7tF0Hz3BMguIByr2qqwo1W/YUwODSU6C090X50bAtbw2b5Dm996hZbaNAtkUJJRe1RVGRZcr70t4TgOK2xXX2leX8Kq2WRlBZYIr6QhansjBv2rZAHjUMtPBWCsY5WkgDZWG7QJ8JF1xmIFYxAGV/C0Dz3qPl5xdgIa8lKhvJQKvrBjEE5JQJJGmNka28SinMnGMnFkjKdbMl7FopUljSvsvFyidh+zEE1Gxh0vx3BVavaYIyABh2OrLHGYLIzjlSnTAUs47Oz5wz5SsJtARtg16FcEtBgWEVl7x/UX05VgwmVsAoVvwppdC0DYfSuwIJxHKqDQdpZ95fitVtimCMVgRNCLhGGMuqspRKTqA2hu57KxCVMxBTLkotlRViTEiZUZxSZF+F8CHFKsVIbYB8D9SKwUxa2wUZAHKgeU6CuBhyMWhZh6KvQTjtPwJsagVlJH9JHlcAKsPxNezRvH9jpjyysr9BdteYIhVGACsIh3JNCviiWCt6P8qQkq5YKOWROdMHIwxqUk6guC16Rq1MsRUL4BOwlRKgNd8HALI1KkoJE9sj3UcDn/e8LUqvjuUCtsB2Sw3Fwfn0Na0NgWV5L6Iuc8u+jVjD2VyuE/k3YwwilmZSyVTLavk3+R7578DqrwzaBucqzJls1ta6co+a+edlCKT4tM7CWYtxmvkapPN5mp/g+gEXFweKFCCdHk7HIyQzE6tt2WabbbbZZpttttlmm1/n+VqQK2VF1erKAeNInx5nDvhNCippGEcLP1mUj/OMfpgQksLloUFdd9jtOjhn0PdnHI9HnM89vOcgeAYQmvO6nLXISkMplMbApm4KhGibtrSvKUXqsrZtMHkPyXg5Ho+4vb2FtQZKLYvjnCmIOKeMqqHso3EY4azF9dUeP/jdH8BVDufTgJQivvWd7yCGiDevX+PLN29hvEHd1tjvdggxYIwTrg6XUBnwgRRNLdtBRAlzeXGB73z72/j+97+Pq6tLPDw+4HR6YgtOxm6/g7EO/enEOULUJqk1QZhxHDkLyUE5Dc8ZYvKpewZlbkEB7Y4gYEwJyhigacim13UFGEzThLpuEGJE17Ul1ybGULJx9vtdacM0RqOqHAfUO25qJAVat9vh6uqqWEwBsgQpTYtKae5y1qJhZVPFIKuRRr7zGU3bonIOhvNrIlvi1qHsOVEzW1FBcbteCAFNXZcsMFqsgxeEz6GELLYzQGoozsDyK7vPWh1jrS2qNbG9Tgw427ZdVBRsDxIlmqj4BLrI8yqlihJMoMR6Mby2lH1V4bVuYhSVk3MVLOcESbaVqNOquobue/oea1n9RPDXWFvUiyklZD4Wonqa2Xo6zzOUtWi4mXAJOieworVGUzfIeVFlOesQ4KG1o0whPm6Q97GGKPr5veOruVHleDC4BMieaBVlTilQ0966pEC+X4CZ3CeogVAX2AJW/BgO3hbwZozljKcEpQxtH9vkAEWKK85lF1sccULFCptASictEEQVQAgBXSkDDDWyppwxy62KZNMzJQtKth/A6jyme3BRXuG5vUzz9Ufvm+6JYiIsCiWln31f8Kzqknt/YnUUv5a0D+Ykwf+pBNfTE7PNMRe8xc9HgNAwpCpfpQ2RFysW5SzHhp2B+ivvvwBDKXcsyk+20Ce6xwu0Uwy0tNJIia2uRtpMF6gnz5/Skg8HBmVQSnpREEOEdrpA2CQWzQKNDZATwUk+P2OKxZprnEXyHjpTSL8z/LNplY1GMJF2VAZIDQpSZcle1UrDB19Aa4oBno/h7Mly7CoLvdthGEYurkjF4qjGCT4EtG2Dyjk0dQOrDXzXws9zOR+22WabbbbZZpttttnm132+FuSavcccIqyilkVRPWS2W+RECivnaMEsForzMOL+8QlGvcJnn32J999/iaapcHd3t1IFKcQY4JzhX+yBEChDqGl2mOcJbdeSQsw5hBiQQkJVV2iaBufzGT54aKPRoMI0Ue5SXde4vr5C09Sw1qCqa1TOcZaJR98PSBlIMcOPI5Aifvd73+W8pAqvv3yNi8srVK7B6y9e44svPscwjHCVw8V+RxkoPsCHhBc3t7jY78ti3DWUcXU8HguQuL29xeFwABSgrYW1Dh9++G3K8erucDyeMc89cspoqoaUB5oUISEm+BARUoIBZSbFGBFiKHZPVzlYZ/H48MCwhewsFrxfkTFPU7HcVM4hxkAL3Uxh2jEGhlqAtZRdphQwjgMF2zP80Fqj61pUFalrLi8vC1yQPwBQGbPApRWMEOUVsCzEq7rGbrcr/y4qqMBlB2I1swxINGd7HQ4HPD48IADoWNVW4JHi5kKGHQLXYowwWBQVSimCV6tgd4FO60DnYiMUqKAoHDqEQJDLWkRZpDK0la/VdV1UVQJzJExe4FW1stQJSFpnaq0VN9IGKo+dZzoXJMMHMKgqB1dVsM6Vfe5Z1QW2VspCX2ld9pMxBsZaVPzvkrcm+90YQ8qdle3QOQuCBQm0C8j+pzWVUxC4oWBwUQdJGLfh51mrr8Q2K+BALGeZ1Vsle4ihauTzc33+iNVW1GsCKQXwEAxTBLOSBCyhgD6yhtkFqgiQBOVs5ZyRVeZSA0ttjHVTtmENPUQlVdRCinBYyot9LqYEw9HthhU7YueUa2UNX+XfJEMOeG7/FPC2BqiiwaV9mkHMaVETlW0FVuc/tRDqVci5bBO9nqG2QizP8wxMl/1BUEyzqgsMskQ9J9Amp1womwIXJEADyFw8oBZoCdo+2R7K5kKBzCllGP0c+K2Blmwv3QsWFdpzOymr+LSB5mICYw2UUUghFVUdXfeLuitlVqNq+vCBLIAVrKNrCWnJtjOOPoSRnDrZTilQKepZBtRyzqSUyj0Hq5wzeYeucuVc996jqWsYozCvlLOn0xlPT0c4Z3FxecDVxSW6XYdpmuDDFjy/zTbbbLPNNttss81vxnwtyHU+U4OgQubFqaGcLaNhtIUGhamzKQbyO3fdNBiGEXfvHihnKnhcX18ipcwZUhW8D6XKvO97+gV+ptytV++/h2EcaUEZEypXwVWuKAjOpzPlc8WEMAZMfQ8YjaZpcXl5iWma8Pj4iN1uj123g6stQvAYxxnzTE2Nd2/vUFcV/o//8l/g6uoC/+F/+RHevn2Hzz//HDc3N9Ba44svvgSQcXt7i7quMc4TrCOLWT2N6Oq22FuqhrZxGAacj2e8uH2BDz74AG/fvsUnv/wlFO+Xv/7pT/Hq1Xt4/eYt+mFC09QYhgFXN9fQ0Dj2R6RMaq66ItWYnylAexh6tF2LWjmESHbBGAOMNmxReUJd1bSYirIoNEXZFENERiQLoDGlOdAYi8vLCwCK2w0NpmkueUgUhBxL+Pzl5RW++OJz1NBIDHVE+eTYuhfiEowdGVjtdjtM00QNiTEisJJKVEY5Z/h5LkBGAJI2lEljjEHXdQVAZABtQ5Za70k1JNa3ivPERJVRcbuj1rrYHgu0whIEL4Bqt9shpYS+74saSzKy1k121loKnWfFlEAYyYYTVReAZ68lKjTJIhMQ1jQESwRarPPmJCMtZ/BiN5VjpLUp0ExAQ8XZXYb3n2yDqLFkAa2Vgq2qJV8Ii/rO8rGXfSjgi3Kzn1uuBMq1bYOUMqqqptZC7xG8x8iZbplVPIyXaBv4fVJu2FIqII9fh6sXYJSWQPG15XQNFItSKYhy1K4gUOY/ApZjOc8FjmTvUTmChmBbo1ionXVAQlH/eG7Ds4baPuU8EahgjAFYvae0BhIp/6qanltgWozpGZR5BqtWoGadtSXXS/k3AVckw1pUYMoAiOXYfXXWtsaUMhQS54mxjVYtAfgElIGwgmRrwCfyLsp3z+V6LK/N4Fjzcy05XWIYFBXmAvbkedbwWb43plhUTxkZKS4qTqeXEP9i0Sz7U/49icyMtyMjZ8W7kr5O+Wq8vaxqBlAAnbyGqO2sscgt4GfP7ZoeVptidRa1ppz/s/cwRheYtd6fcg+S87Nt23IsUkqYQR8mVE4sjLncV3JVIcYMBcoHpO/TfN5nPD4cMQ4z9ocOyKRo3GabbbbZZpttttlmm9+E+Vq/uVaO6s1TBAIvILSqyIKTKRsKSiGlCO9lMaYwTo+IOWGeA+qmwpdfvEWMEdfXF9Da4HQ64XQ6QWsKxA7e4+rqCsl7XF1dsgWEoEzbkFVx9nNZEFhrUTfU5HfdXsMag+P5DEDh4198AgB49d4rvHjxAt57nM9nIGf0fY+f/exn+PRXn+H9l6/wd/74D2CNwTT2gDL4+Je/Isji33COVc0Le+Dh8Yh5nmBrh67bYb+/gM4KTVXRe40BQz/gzZs3uDxc4sMPPii5RnVTo+1adF2Hj771Eb588xbvHu4ImtQWTVejrmt88smvMPkJL16+oJyvcSJYpUktMfsZO72D1gqNWxotswLaoZLJmwAAeR9JREFUtkPOZ1R1hcrZUmcv4eHGWAzjCPAiJ6ZIih/nMI5YLaQM5QqlyBlNlPWy23WUwxMDjNG4vr6GdTXZH5sGdVUh8/ZIw+U6V8pVFfI8Y7fbQSmF8+lUmhwlj2mepqIUEhVUXdclAB6gPC/JukopcasmLcDrpoFjBZQ8p6jCBGKRxSiXXDXFMGKaJrJLYVFTzfOMYRjw8uXLAlEqbk0suV0Mq0ThJQtw2c4QAs7nMyRny/O5LnlKomQSJZdkUMn2C9wp2URlkQ6QO3INcSgrT6ysXddh4pZPyVcDFqAg2zdxhpdAxLquYZ1DPwywxi4wQmtYBm9fbaezlq9lPu7zTGUDCqRyo0yr56+vVjat9XtbQ5R11ppzS/6QZG5VAsO0JojA8FT2n0BGURmKGkqghlICRGgbfQzodjuM04imaeFnD833uQxW7LAVcZon2iZrMPsZKVLTXs35YFVVIceAKKUA2gB6ZStkZRzA+WSacq5SBipbFXXPWsGllCrZWesmULmO1somLRZKPq/X+1jUcc/gEhblk6hrwQqz8rW0BOOnFAma5QU8yrkBcM5YzjBKAVoT7EwZCaQqVVqVzLOUE7dVLioulAyyCGfMokDFkqMnGXP0gqy0AzhfLUMrvQJ0AklRzi8oRSopUTWyUo9Uy7rYkIvVWRyceikBIPv+YltMicLdM0gJd3NzAwWFt+/eYhpGKMf3GN5sOua5qNwEvJ77HtdXV1w8oMs9TACfcw7Be2QAdU0/q/q+h1ak2ALoPN/v9wjew/uJFKRZLMcaxjhSFqaIYaDw+ZjCZlfcZpttttlmm2222eY3Zr4W5HKVg4aiBWSkJrxut0Nd1TCaFECWm9sS226maUKaAqwBbK0xR4/xfiC7YUq4vj5weLGGsxWMARpWX7mGbGNKKbx8+RKz93j75m2pTG/qGkppzNNE6qWYUNka93fvEFh50Lga0Uc8vL3H/f0doBScrfD551/gi88/Q105/L2/8wf4w9//fXz/e99F29T4xS9+jnGe8MFHH+Lt27cIwcNWDo/HJ4SccNU2OLQHTJPDNM0YhhHHeYZVGsZa7C+vcOonnO7f4v3bl/iDP/xDtAey4P3g934PSlOW1i9/8TEejk84Ho8ASNn03ouX6Dk7yVqLq9vrlV2FFoDzNEGlhA/ee4W6qjEFz9lbGdpZ3N/fYxpH7PYd9rsWWVFw9jrTKYSAzGqccRyhtaZ2LWNQVZJ5pqC1RQgT5jmUFsu6pgUPKZ0CxpEUeMPwhHmeqeGOFT+kclGoVoHpKSUEz8HfrPyy1mK329FCnpU91lrEicoCnHPQSmEaR0TONwsMViODi4Yzxwpk4jGabJmlCdF7NG1bQtjl/XddV0CCNQZ6ZRms6xrWGOx3u/J8omSz1hYVnAA0UXVZVkBN/D7kvwV8iSoKWKmL+PsE8CyqG1q9e86bk0X0Gkh47wtwA1Bee5qmYvXldTktiPl8gFLw/N4d56jFsAAZP88wmv69spba7CANchQErh3lD8k2CawTdZrYCJ2tikqOLIsEG5ApsF7C1eU9rW1bcl2s95ko3NYqrxjCM4Wd4fcgj895AXDy3IG/RylVcuXE7voP/uTvo6oq/OxnP8P9/T1iCLDWQSsLH3zJWUo+wRmLGAOQAGM1ga0Y4VPkIPSMHPn9GYOEXJRx8zzDx0DKsrAolmIMFICFxNZKx7ledN1brcq+EgXeWpmYckYSqMn/q7WCc4bu5fzvoo6LMcIoxVlYZCtVKyCXEqlqwfCQ8uc0Ir8X2Y/PACVndmVNWWYB1E5YW/dMnRYVNydGUW0tWXWilESm8xTGlXN0rZJUmRtTOWsuIyMbAWaUqaVyhk8Ed+Q90X0P1DQZcnk8EihEnrfR8XOlTKotA4NxoHZfa20pN6FtIaCWUkJTNXhx/QLTNOF8PMNph2nsS/vq8XyGqxyaukZOuZQdGL73arYQCwAFCPT7mcLkLStnBcI1rPbU/DM7Z7KEn7xHVTXQ2iAmAMrwMYzcPEy2ymkaYSxljW2zzTbbbLPNNttss81vwnwtyJVjgK4qVmmQ/SJGYJoDtIrQhn7ptpoCqKdpIFhlLBrXwFiFWlnOu9U4nUYgK7RdRbYzR4qiTz/7rCzYpNVPgqZdVSFG+kS6bkmNMo4jfc1Qnb12DTprcDo9wVQa2gDNrkZVt0gZ+Nlf/QQP9+/wZ3/69/FP/uyfICPj1XvvwZoKxmR8lD7Ej//Vf8ewQsPaGnd3b+GcxXvvvYRzDg8PDwSTuIo9Z2DqB1xdXcHWFXIKeP/9V/id3/4ejCWLXkoZPiV88qtP8fHHHyN6jzlMuL29hXMOjw+PSD7geDzizZs3FJavDIwzHHQsuT0AQMH8TdtCR0fqppTx7u4d6rrGbr/Dfr9DSpFto89VM8BSd0+ARqGqLJrmgJRon5L9hWyk2lBQ8uPTE8ZxxH6/J1DBtsH9fo/Hx3u0HeV3hUhqqKZpkEH5NVC2wM/kPbquIzsa2xvvHx7Kv0lIuJFgdOegeQFeVRXevn2Lw+GA8/n8LLz8GRBiQKEZGAnU2HO7oMCipmlwdXUFQPJyOLdqFRbv2UoIAMM4FmWWgKuqqkoekkAXyb8RALO2SgrcmqbpWeaUQC4BBJbtmcUyxyeb5X0h9qOZF/dQCo63LcaIfhgIZDGIIOvUKpMLBECccyVMXLZfa12a77z3rKh0lI2kGYvkTAvgjGewSBR740R5YU3bYvYeMY7lGAkI+eofUdrIPvhqDpnsT7F4ioqvqFk4d0vKDOZ5xvF0Kko+OXbrc2ZdLCCqLzl2OWf8+Mc/LgqwRVEmFs+EGClcn/5NjiUQQfekBMr30txCmEJc8sVY+SPH3SiNGCKapsE4jogxw08jgeMcS6ueXM+AQsKi2iqgh9VjwKJYew5HUzn/CIzGBQoajeDnclxJ8aiwRh0CxlImNRY4F0vO4a+OQCSZdXba+t4kj6HzQD87RkopdF1Xnk82RGnK8RJ7cmTboxb1qqjKOFuLHi8KpkVxRsdbFVViXtl/12qrjAxlDGLyBTZpp4u9EsxtU0yQ7zJGYRjO+OlPf1KUfc5ZpFSV86xpmwWS6aU0A0YjRo2B8/fKPma42dquQPY15Jf96YxGpcjS3g8DHNuR53mGAuj+slKLAhlt29KHEcio3PNjtc0222yzzTbbbLPNNr+u87Ugl7UGbdvA2RrBBcwxwseI2YeiOmjaGvV+D1drXLcXmKYJw2lCCBnjNFCeCxSm0ePtm4SLiwPef/8lqXR4wfXq5XtQSuHu7g4PD49AVoghYbffQ2uyo4QQcT4PvFCoMc8TzucTtAaMrdG2Dd5/7z1kEMRw1uGv/+pnGPsR//Dv/V38wR/8EC3Ds5QyfvSf/hPmecb11TV+9otf4Hzu8a1vfQsvX77Er371K9zfP+Dp6YiUaBGPlHF9dY3mRYPj8REhBLTvvaSFZPD48NV7+Pa3vgmrNbLWmGPEPM54uH/A2zdvcD73SCliv+uACFRNhfPTGe9ev4UxFi9vX6FpyOZkMjXoTfOMaZ5KHpV1Dn3f4/F0xjiOOJ+PGMcRh8MBOcdnqi0BBqJSyWzXvLu7Q9u2uL19UaBNShl13SBnCiNWSpUgba0UdNchsQVuZvhC1rsAZx1b/CijyFqHqtKYZ8MLYFWC52MIePfuHZq2xWG/x8wqJIFLtNhbMoGmcSwL+MvLS+x2u2cNhrIQLlCEn4NCxjNmhlrOuWIZTCnh4uLiWSaW7DNp+RQllzEGwXvUdY1hHItyTOCXQJF1LpAsXkWBtA6UXgMdsdsJGAOWZrWlNZNeX2uNkfeV1roEeOuVCkcUIM45WGMwsNJsZlumgA0BPprtWbKvRX0lQfkKBHJPxxP2h0NRphmtS5h9ShSeLTZPURplVnTSe1rgTIqpWA7XiiOtF3XauklSZr1v122VIQSM44gQAq6vrgr4q+tFESrnSVhBPnkugVxrVZhATFElKqXY0kvnYl2TGkbrVLKzpsnDWvp+BQBGk0WPA96tMciidIwRBqSAkuMuryNQMme67kidtain5Bj2/QilcgEn6yw2OffW279u95RrXv57nj2pe2JC5Vj5GCKMoSZLLflvIPteqT1EfnYN/m2zhpVfHQG8WmsiUxmkJFxlbdG+kcD9lVpT7IFalccaYwqgheLssJyglUGKAcY4gO8POdPPk6rAYY+cWb1m+AXYEqqVJhVyZIskNKyxHJqvuAkW/EEO7RtrXcnuo4y5EUopnM9DaTMUSy0Sq+isLqCQmlKBHEkBa41FVTtWtQW65qAwDhMmvhfJdV/u6QCsXRo166rhnw9iK+V70zDBWgrWjyFxIzHKhznbbLPNNttss80222zz6z5fC3IpZTGNHhM8LYKJZgApw2iD2tXQWaM/npBzhcPlHnXtMPYj+n5A9BFAQts4VLVGCvQJ8ueffYHzaY9vfPAK4zRheHigT/mVQtu2GKcJj09PuJgm7HcdQoiIidRCp/MAawxubq9wcXlA8ASCgp8wDgMuL69wf3/EF59/hpvLS/xv/uk/xKv3XmDsJ5yeejRNC2iNp8cexlr8v/6bf43b21t897u/jU8//RT393dsAfM4HPZoW7IxDecBx+MRChkXhwvcvnyBv/rZX+PTTz/F/+P/9n8FQoL3Ca6p4VPAeRjw7ss3OJ9PiDGgqh1iUPDTjFNMOB6PGPoBu65D0zRFKfVw94CYKWvKOIuqqmEML0gAzDFhmj2sc2jbFq5yePnyJYyxUNBQ2mAchpKpBTy3u7Vti/1+j5wTtUYydAhBQAst3M79uSjoqqrCzJY/u1I9OWdR1VUBM92uK7lTFIpOqrKJQYuxFi2rMiaGS5ILBbC6hRVSEvr+9PSErm25rIBgWFjZA7XWMCsrG4Ci5pF8L7EtNk1TXlOAh8AO+f91jlMIARmAdQ7SnSdgBUABD+t8LQEOAlDu7+8RYyyATEaAzRoCCGyTFkGBB2G1rXxhFtAn8EygndIa0yrIXhtDoem8vbJ/5H0KjBN4J9tAKikL5yy3nirOa4vwfn6mxLLcsDlOE/b7PQXHr6AMbaeG1nV53bLtrDhah8oL4FhniEm5wRrWSPj2MAzUGreycsr7Tymh5mZN2cfrXC8BQsWqyP8v56BswxqcLXlUsn2UFbU0jRI0dAxtM7f+SaZVaSKt8OycWCCcRjYaOUe2dqpSzKC1RtPVgMB3nvU5W6DPM4UUEGMo16Icg7quQIqsgKQVnLMMOhQpMlOEJHrFTIH7GYstUs6htSJrvU3rQgB5zfX3eu9hnEWOS2ECAVcq9FBKFfsynaNjOYfmMKO2DlLAIDbcnFNRewaQ1VlnC6QEZQ0rtZaiA2MscgxFochXKH89UQtnMX1S+LxxbFXOdO5Ebn80hsLx5ZpJKXNb7fMiAcnK8jEUGF9C5GNENhraGjS6pfcLysWUDDdrHYwFan5erXXJzitZX3xeuwKll/NZGk0z2/wlhzHFgMPhgL7vsc0222yzzTbbbLPNNr8J8zXtihnKaPjgAa1h+JNtbak63pmMygBZG2ht0Z8mWEWl74edQ5wtqElLw/uAaQpIaYbWCudhRD/OuL25QNNUsMag7joYrfHu3TuM44Rdt8c0zXCuxvnhAefzGc5ZHA4Et2LwmGcPiwzTNDBO4en4hM9+9Ut877e+jb/zB38EYxzmpKBcA9cYhJzx9u1b9NOEpzdvcHNzDa0VxnMPlTKSD8ghwkBBZ2A897Twjx5ziGhiRA2Ff/tv/2fsdzX+z//yv4aOEa6pYDLQdA1+8dP/jNev35RcrL7vkTI1SzptSoNfiAHDNKJqagwT5UTBGvRPZ/TnAU1To25baGdhDP3Rmb/XJ9S1xUWzR0oZ59MTAIXDYYd5HnE8nkruVc4Zp9MJTdPgcDhgv9+zYsKhbXc4n89szQqkyHIOWgMKBCjGYcAwDNjvdqicw+eff14C0ve7A/zsMQ4TxmEqgfGixquqCjEEzNOEaRwxTROFxSuFpq7R9z3mVdYVnXikiOnaFl3bQqlMDYoMf0JIuL+/x47PF2sMBt5GgVpivYsx4nQ6FYvb4XAoljexBAqgEsXPktVDoEkgIW1aLtspQEvUPqKi8Kz+Ejvdfr+HtRZPT0+4vLwsj1kXKQhUmaapNBvO81xAgSyApfVx3cqYcy55Wokho3MOicPaRR2n1gt7se9hgRGaLZ+ObXRaLZlXKWWKiMoKSmkYTaAncKNg3/fY7XZ07JSCs9TwOY0j5ZtZi6HvEViVuAZ7Wilq/FzZFgEUeJdyLsdItl9AjoBFswKAGaSEKllq41iUcQID5fmnaSpqP3n+kcP6RdlW7JazR87gjDWy65FKzmCOHkgZnhVsZEU0IHEbt/DxPk050PaGiH4YkbVC4yrap2BhUFqKEsTWSMebQYmPSAwjc8pQOaJjmOuco8y11Tm9qB4NUqJjGCODwRBhQM8TGNJlRVlVGoBVCtQumJFCLOcGAETiSpQpJucVeRqXhHcFpLjknxm+flTOMFBQISFzM6HVBkpnWKWRAfgYoDVQVWSbbasaOSuMYUZKEf0s19paoaYpV4xhnbKsOIsZzhkolVhhtTpWbJUUyrXOF9PchoickRRnmSkDYxSstty4KBAMfJwAYyjY/bntHMhqKRPQWsNpU86xkmsIuhassnDaIaZIbY5g0J5G5Eww3WpWHnI+XsoEURturq3ZLu34HiNFLDU3zkpWHr1xg8enE8bR/3/93WCbbbbZZpttttlmm21+XeZrQS4fA1S2aLoWGhQKrJWB0bbYnbSiPKfHBwoCbysHazR2uz2enk54ejphHGYAoqzI5dPu0+mEuqKcqbbbI0aPFy9fwFiywi1KiYiua3FxccEWsBmPjw+YJo+u20E1NSqt8fD2HZrG4c//xf8Otzc3aJodLTSRAa3w+PiAn/70p/j8889xcXHBKilNagat8PL99zBNE968eYOqbaDYAlM1Dfb1FRQU+uMRf/Wff4zf/cF38Y/+4T+AhkLKCrAOJkU8nihfi+yJSwiznyccIy0c5nnG5cUlrm6vaHGdMo79sagVmqaG5oaxEGcgeV4gg5QWxqBtdzBGYbfb4e7uDuM44PLymhshG1RVXdQvAj2kcn4YBjhnsNu1oGBroG1rhGBYZeIJZDDEAVAsfo+Pj3RueI/dfo+3796RPbSq2EKkcTqfcTweEUNA1y3qqZwTzucjco549eoVjsdHAGR/iylBM7xYW/jqusZ7773C3R0p7Ha7HU6nB1xc7FFVpK45nc+wxuBwOKyUaaSQEFC05Cotx0QgFLCoKwR0VPx+am5TFKC0bk6U51nbtpRSJdBfYNfanijgTNRgJQtL7GkcID57j8BKDM3nIUC2z7u7O0zjCFdVBaYZUWalhKquMQxk7W2aBsMwPANzlLfE8CpS5pPAIbGDWmNAyUsZ2qiiHgODMKgMaynLaZ0tFmNEDAE9K4aqukYGgSOBG6I8WUNEY0xRoMh+lteLnLkl3yuPWVveBBzudjtWFe7w7t07VEqVHLln1jalnu0PgODV+Xwu9z+xD0u7pwDD2U8wRqyQCTEGfP75Z/jwww/JQpozKmsBKMw+IKRIWXNaI8e4QB6lMPF5Xs6vnBAC6YaiD4AmYBORubyDrksYjWn0SPPEis0EP5ASLEwEs3Je9pO8X7LkEnDq2IbsrGUIzE2DlYOE35N6i+2rSgFKk5JSGWRkOEPnTeKsq5xYVUR9JAzOAmKKcJXjPcvXC9jGy/mBMUYgcTNhztBGl3sWstgk6bQoWVpsS4xzfAabxeppjIHC0kCpgaJQlfMghAAlaqjVlP2WqM2USGWCymqxT66cmIrbLLOmnK6kEkIMMNog61wsjkYt9441UJfzMfI9QCkK/4+IALeARkRknWG1KaAxsV05Z/pQqqlruqZYoZpSQvSeVIUAWi7ciHwuxjVws/ZZ7uE222yzzTbbbLPNNtv8us/Xglw1L+ZzTKh2Law1cLpCzkAM/It3SgghQkNhHia0VYXd7gCtEi4vDzgc9ri7u0d/nkDtfaYsRKw16PsJr798jRBmWKPw+HiPrm1xcdjBWofz+QRAszXMoOsaAA2OR1qsDEOPEGb81U9/gv/iz/4Rfu8HP6TgZp9RNWSlymHG3bt3+PTTX+Hu7h5VVaOuqfFungNOpzPmcaTw9t2e2gGbhjNoFLqmha0qvH3zFqeHe/zjf/T38Ud/949hXY2cQI1lUPiP//E/4PXrt3h8fMQ4TogxlbDfEDx2XYe6qumT90CWu6ZqcP94Ty1zPuDTTz9DW9W4uriE0rT8U9Yghojz+Yynh0e8e3jAixcvYK3B4+Mjuq6D1hrn8xnjOKGuq2fNcRK+LZY/UTv1/VBCu62l5shxnHjxB8zziJTIOinfa4wp6haxgXnvMQ4D2q7DzNlNdVVhyglPT0e8evWqtDC2bYem6cjOysoUBT6P+Pm996QAahqEEPDZZ59jZEVQCBHOVZyNpDgraUbwqkCpruvgvS/KLoFNxRL68FBgV9u2AFCUXusmvooh0vFI2WeiwqpYAQEs0EVAFYASWL7O5JL9Jdlfsm0ChpZw8AXIDcOAllv/hmFAw5a5pq6x3+3I2sUgT7KX5DXnaaI2Ut4egVBrZU8WdQr/P7WeEgiJWsNVNQEm50ihoxWMsaSaG0e0TYO2bcrzCQAUcGWtpTBwrUvDo6jr/Mp+KCHha6uiKJJE2SLHS4BT27blXBF4WNc1Hh8fCUXw4+uK7LRpVQpQ8b/JrEGw5NcJhFyr+3LKRf0Fvq6m6Yjdju5Vx+MJGtRyR8eE1EEJGX4coZBh1JLLphnspRiBmIrKTGu9mONSRowMQwGEkBBjptZbpZFyxDxRrhI1mhLIUFDQZsmLE4uk/LcxBgakGKPGwQRjLGIGlM5QGjDQSJGD2xVZ95ABozSs0WQpZMWWhqb7lVpC48GgXimFylZ/w06quF2TwtqLhqpYV7F6vFFATJInRaUZOXNDYCZlo9GUW6gMffgCPmaA1HAAwQdWisq/LCUEawVhzhlG6XIuKaWRFciuydeOMRbJUzvs0vSZ6cMfLbleKBZDx4qqr1py18B8bVPOmVocffDQhsoJcqJtt8bC+/kZMAYWG6L8d2JYbPQS0i/nrlxXoryMMWL2voDabbbZZpttttlmm222+U2YrwW53t09YL/fAxpkq0lUCx8jLfqcq6i1SSn4eYLRCqcT0LQOPnpcXu5xfX2F/X6Hzz77khVdi92oqio0dcMLFuBwcQFjFEOKGTc3N3jx4hYPD494ejqWhUFdVzgcPsDpdMaPfvSXcEbhH/2Dv4ePvvENpARoWyFrg8fjEfP4Bg9PD/j8889xOp3gXMU5WwrzHABkXF9f4Xg8wlgLHwNahiSuqqCNxv3jA96+foPGOfzxH/4BvvXtb6MfJvz43/8Ffutb30G3a6AcATFTbE4NXr9+DQBomwYqAefTCe2rFhf7C7RNCx98gUTOOaSccHV1hYvdDlYbysNZLVB9iAgx4OrqEm3bYp4nXFxcQqmM3a5lGxIdYgEI68ZAWfh0XQdjFPo+Y7frKE9pHHA+n0oWU0qkfBAlkliBRBGTc8b9/X1RToni5XQ6IaWEtm1R1w0/V0SMAQ8P9xiGHkop7Pc7znEaynMgRpjVglAWnqfjUwEG40gWQ2mDVEqjqupixQFQGscE+IiyS8CTMeaZ7W+x5KUCAuQ8HTnAvWP1g+HmQzmHxfpojEHTUHKXHK+2bXE6nVBVVdmmtm0RYyxNlPK6olSSbLoQApxzmHj7zAqIhBiLdRNAsVyO4/hs0bxWhMgUxQYvhr33iPNc9kNd1wUownvEEJCRkVj1cjweS3aXMaTOm6YJMcZig7XW0n1DtoH3R2bwCoDyqvg8Ujk/2+6vti2K4kZUcsBiJ5OvP7M/8qJ/t9uxl47gmmWFWN/3z15DXrfvSX359PSEGCPZahm+pZQwxQlgtVyMCW3bMMSe+JrSsFBFmRgDqZuQE3JMBAlZiZkE6lqDylRIfsnXkiy9NYgQ1R2gAGMArVC3DWywXPyQi0IMYtnLAmFp3ywtnrHk5n01e4yOaUammkUuN1As3mMYkzJCSoACnKbCBwl9T6z4lbbClEEB8EbAnub9Qg2JouYC2wCVQrGexnJ86YMWrTQpmRiMLqo8UrsF7zHOI1ymEHaK1aIWxnU4vsDdnDOss6gM2RVjYoinSV1GmVppuZ+mBMvNt8hADoHy1jRllwGAsrq8l5QilKFzjqyopIyU55Nzfa0E5ZObGm6Vhja67LsQA7TSpHQOBHct230zH3c/z6WQws8zhnHJMJMPJdYqyLquKccrJcznc/m3dd7bNttss80222yzzTbb/DrP14JcbVvTL+05UWgvf+KuQbaInDO17SlypxwOl4BV3D6XYXQFox0uDnv0l2fc5yd4n5BZ1+FjhB8G4JxxOlvkpHF5uYNWM65vrkiN00+o6hovX9UMSzJO5xF3H/8Kp8d7/PY3P8Dv/s730O52uLi4QrPb4+l4xts3b/Dw8IBpHPB4/4BxGHBxeYHr62tM04TT06mEMA/nAafziBA8pnHAe++/h/feewVA4fPPPsNnn3+Bi/0OP/jhD/DBNz+CqRv4mLHb73F5c4O+P+Ph/g5DP6HvB7x8+Qo5o4AladpKMaLbdYgxYJxGVJVFXTcYho6VNg4qT/AxISuNmAHvyeoTfMDQ96irCtZVOJ1O/Mm8RlU5VFWLlDLm2SMnspRZaym3iheCfd8jhoDq+hrjNKGuG17sUMtazoAxDlrTwl3+TWtT4IS0yGlNtpiSXVRVxaIoti5atJHio64bVFVG1+1wPJ4QY8L53KNpmuWxOTNUoUY6w5Yi60gB0XUdtUNyeP35fC7walZL+55YkaL35Rg3dV1yqxRowT8OAzwvFNeWrkWVsSgq1lZGgU4CdAR4raGM5FQJWBBVmPcR9/ePmCaP/f6iQAVjbFFjEYCljKq6qvD4+Ehwy3vMWpf9NK+yutbWPWdteVzwHpVz5e+lPTAEBG7787yf6rpGTgktw7qcGTCkhMfTCV3XoWF7pAJQMZyUgOu1WmgNnjKdWCtVDMFgOeZaKQx9Dx8C5+0RXEusbEr82HVbn7TwyWvI/pLjB6Ccs6Rks6wktVBKF/jbNC28JyDaj2NRHrVtuyiulIIRKBjomnB1Vb4ec0JVORiAHwcgRn49vdgGtSr5aGIttdpCg2x3ayWgXLNKkUIKUMgcWl85R5a3DIbqvhz7ogaMKLAqI7MKiaGeMnAAIt+JM0MSpRU0DD9Gc1MmK/3Y8q2VQsxUPtBUFUJkWMPOvSyZbYatfxlQZvVjJ0FelT6Q0IAymlonjUFWmZVPFNi/NJOSxTZmgob4ivLROsf7icARPQO/5Oo6/qpyCnxskAEFCiHLyGTVTGQZVUrDWkdWzZQRUyQFWs5SNAkJyVd8nATyKVF/pVTC6wXu5hzhfZAn4OdYzmGx7co9RmA9IjUvAhRsD5WLgo+OPbWeKk12z2kaUNct282P6Lqm3Je0MXB83ojyV+5D22yzzTbbbLPNNtts85swX+s313Gc0DkHrTIkkpesDQbQhqw0KfIiwGL0HioClXXIOWIYR+AeCGHC4bBHyhlPT2dMcyClA//JKcOaDl9++Qaaa+Hfvn3H4fIJ1zfXaNoW3nu8fv0ljo+PuDzs8IPf+S5+6zvfxsXVJeYQ4aoK0zzj4eEBv/z4F5S9YijzqeFP1/thgFIKTdtCa4OHxwd0ux0uLg4YhgFd16GuKFg6Bo/Hx0fcXF/hu7/9W7h98QLT7NHf3WEYRlxcXOAXv/wYf/mjvwRA4chD38OzfYwWOgp17dA0Nc7n80p9gJIJJeoUQGPCiHEcaNFWPr1XaNsG1hrsug6nMzVfXV1fAwArOMgaRCu7zHk5BnVTF0USZUktLWukFKH2vpgy2m5HqiqtUddg1ZPlhU9e2bpseS+iQhIbmaiWRA1EjWcajkFVBrgd0eLm9hZD35MtjReikstjWOkDpdA0DSmIOEcoxshB0Yv1KqfEIeAEFEMke2fOFPgvCqhhGEj5wK8hAGsd7i52OFFxASgLwGEYSk7QGuaI9VBsjGKTlCa7pmn4+0ekFGAMqTVSWvKhRFl2OJCtchwG9KzIE5hUgsXlWmRLsWyPtRbnvn8G6gg6076lPDZHGVGrbCrLcDDEWKCfKbbWHobhmewfUaOJ0rCuKgzjiHGa0LGVMLJaCbxYdwzXNB+vFCO0MYs6bZ6pXMJaUkNxW6QxSzC3QC6xKa4BgBzDdUaYlAsseUu53GMAyrgDEsEtTQo6Y82zLDVRxgCkCDMgJVBTN2xrpP1eaYNQAAmBEGM02Qv1At+KSoYVNOD3SPeQioLzjUHmexbY7ilKUWstBbnz+zfGwCiySFpri80vp1wC3SX4XnCKtaacc0oTmFEMRouSkZVUct9XmkBZ1oC1lEDFlKg0LqqSXQX5iQENxdd3BtQCKoG4UpstQ7BmUV6tr3doQHEhgrxMztRqqE1VFKzIAqe5hRSyD5iRgY53TPQeVGlTBKvYwEHu8hoM3jIKcFsA3ALXynmiNCV0JUF6KHbXmEjxprTBB994H5eXl5jGCW/fvcPT0xEp5WL7lfcj57hc6/vLC1iGXms7bU4UQJ9SgjPcGmso65Fy2FpoQ9ZjlVX5hUBUkjnn5TzaZpttttlmm2222Wab34D52r+5khtDAypRzohSUAwfcsow2sBoCv89nY/0qXWMsM7CPD7A6mvEFLDb7XB1eYGUMvzDI1ueSGGi2Obipxn9MEIpCrvumhpVRYu6T3/1KR4fH1A7i29+8Aq/9e2PcHNzjcvLayjj4ENEzBlffvkaf/3XP6dMJc6ooXZCw+oCxTk3HtM8IWcKN284/4qC31u8ffMO3k948eIFvvGN93BzdY2cgdkHKH6et2/f4Xg8sX2NrTZGl+Y7AR2y6B6GgRctBGSOxye2jSnO4rGY5gnTPENNYo2KaJoWXdfC8GI9plUFPatz1p/Ca6PRmJoXwARwAMnxVuiHnqAD2+HEzqgUWX4iPzhnwPuZsneQ6f2vAF4IZEOUQoASGM4jECWlhNP5TFlnVYWu6zi3RhfQKeea4vNLwJPYI2u2cLVti7dv3yIlCliXPJm1CkZgXAiB7Dgp4el4LMck5YyR4Yf3Hpbb6AQKac6vmeeZbWgLZJHcK8nWWYMV8PkjzyOqsnmVP9X3Z8yzwK+ElMAg8Tk8Ki1rX1EvCYhSmlolLYdLhxCQ+fHDMKCuqgJOBHTJ47QmlY5sk1Kq5KzFEJAYqlRsl/tqQL+cz6LEEkVI//hY7E8qsY2RoZYV6BUjFL+uZpVUBAGCbrcjmCY5WinB/C3KkrXVS/azWE3XYK9iy+vzkgAAyByYD74OKszztJQMsJ1O9s9aIWW0Jgsu5+1Za2ENHSOrDcI8Q2WCVNpSYQC75siyluk9SX7Z+v2Az/+UM4W801cZwBCwoRICQGddVH9aa6i8ZEkR4VHCn8BvvijKstys8PxPzvjK31Gy2Oiew2nyoGy2nFHgk1z3dK2ALYu0rxPEsrgUWYhyTBE5A0Ch97IPlBZFGqmiVKZGQ7rHyXlAMErxz6ISkC+vmxNSzPw6BLekLCBhna9F5RtgFZWo3ejpFXJWfO1Z5nSLgqxsb9n3i2KMHkNh9XK+kmIqlCbTEAj0aWMhZQE5o5zPcp3Jc8vfm7qGddzIOk4Yx4H2E58/3nvUriqQVq7/pqnpgxNRVobl/BPrqnxQsM0222yzzTbbbLPNNr8J87UglzGGAo412WaM5XBhYxBzRkoMVkBLGWMN5Yhohaqu4Jyl1r4oli2Hi0OHnBN6NyIGwPsIGGD2HhnA6dxjnkdcXhxQXTSAijg+PaE/HXF56PD9734H3/rWh7i+uoQzFs418ElhuL/H/dMRn376Gc7nM25urgmesXqIgIEq9rAYc8nd8T6wGqWGcxbjcEZ/OuJwccD3v/c9HA4HxBCADDjrYOsKsw/4+ONfAgCahlQ/ElYuYdU0uWQlEXwJyDl+BcS0SzaLEigxwVmHuqpQO1LrBASMDA6sc7i/vy/gQhaZZBV0JXNpyWkiS+LsZ/T9AGupbfF8Pj8LQB+HgSADKNS973uyJjVteQ8CYPr+XCCC2IDW9rmS42VtafgrC0EGXzlnTNNEz8PwRBZ25/MZTdPAag1nHWW/sZVrHEdShFlbAN04juX1J17USY6U7IvLy0uMw4B5mhCklYyP1FrNJeBQAs7XKqg14BIVxVeVYBLGLs87DAPGccT9/QO0phY456qi6JP9KJBLrJd1XXNxQXi2eLbGFIWRwJeQM0EkoCzC5fivg/RLIyFvu8AWOQ/WWVBxBR7WLZPFWsnvdd0SmdnGJVBQrGVfzc+yrNYzeclX0ppCwz2/3zWgWu/ndZujwDz5/3XO0ZKpldnGKjBCwVppNTQIIcLwcfFxaXwsNkDeBnkvxooSzAGGYGnMBIUUAJ8IAEEpgl2i9sl6UReppVVQzh3DGViUqcUqHpDayjIESYkaD62lhkOlqQFQ7j0xi1mPPYTlDF/ADAWSL3lbArfWsEZyqTIrOeU4KAY0ItpaZ77JCCRblFHLOSCUqqiz+JiFFArgkscqrYBM50TKS/YXQdpIoAp0LYLPFVVel96zaM6UAEDesJwSw2X6ui7vg+CZFvUX7xdpOCwWx/JenwMvAM/AFD2GjonWVNqirAUUcH//gNOJVLHz7CGK3DXkWgMuUS+O0wTNCk26VwWIDTjw/UOplSovL5ZqBYUcqb1yreYUa6SA+W222WabbbbZZptttvlNmK8FubRWsM6wUoEsMUopyhaJCdo5QKEEcmttUTmHy8Me1jlcH/aluc9ajRg9ul0DV1V4ejrjeBwAUOPZse9hrcPsaeFyPPZQMIDyMDrhe9/9Fr7/3d/C+++/Qtu10MrAGoe+n3D38IC//vkvcDydcTyecHE4UA5Rypgjwa1p6jEMtNCoKkdqIv6lfxwHWG3R7mv0/QkpzHj/vZf46KNv4ubmlqAFKPBXKY3zucfbt++opY8VHTFE7A9daWd7DrqWnCIwkLDW4OLiJcZxRNftkHLGMIyIsaIgbmTsuh12ux1b/sRiFVE3Deq6xv27O+wP+6KkEGWNYQCRcsI8zSVYGECBB0prTPOElCKsNRiHAdNMCiJZ/OScEGPg7C9am1q72MLEmieh5WtFQ2nEU6pkvsRIFlZpwotpaZ8U4CGLrGmaqMo+JdSWsp+894gpoWkaTPNMAeLGwFmDnHJRQBHM8zjsdxjHEfM04fLqqmynNCkGDocPIcCv3kNRNjgHBWpeHIYBfd+XcPlii+RjLABPwJcoIXa7XWk16/se8zzj4uICzrmixBH1hgAV2a+yABVYJMBHiRKHYVJZvDJwFjC3tjHK95agcb2Ez4sSylkLMOiap6ls9zqMH0CBTARIclnQHw4XBINpK+FcBWtNWZzLNokizfB7km2STC95zBqGrQsC1sou+V6BggI8wftl/ZxLRtay+BeYCWT05x5t28J2HeZphrWUxUS4KHN2Ftvg1BKS7wOBxXGa0LQtXW8M1oxRz9R4SZSLagnl11pj9hTmX9UEmJUSGA9WGtH9NcSAlCKQNbIGQTRR2hrDmXMEimJeLNMZCyAtuWBAycOTbZTrtljsVmotur+wcqo8gyoKInAb5AJ8FvXcAq0WaCnAJSWyrRrNRQTgF+a/K60pdJ4VjDknQXKstMqsVFtAnBwbuS4S73Pim8v7J3i1QECAbZrgAHqtkGIuClD6IOJ5o6Hc75/Bvvz839fNlpLTZrihcp57iKVylc5F+4PbFBVDPq00rLEYh7Fcd3KtVJWj/RkjrHVIiQoIyr0EC3yLwUPBsjpvuU5EkSvft80222yzzTbbbLPNNr/u87Ug18XlDvvDHiEGKBikmJFShJ89NKg1EEbj3J+hFYpdKqQM5QmWzN7j+uoKxirESIvavp/wmI5QyEVNkVNGSgohZAwp4M2bd3jXPOCb33yB733vt/B3//j3cXN5BaMMKtciZYXHc49f/epz/PJXn+Dt23cAOH/JByBl1HWDeZp52yiMVytqndIK6NoGWhHs2e/3OJ2e8HB/jx/+3g/xjfffh3U1YkxIKcO6GsZoHM8n/PznP8eZQ8X76UzwR6uysAhBrDpYYA8ArQN2TQMkhbbt0LZdUWbM3mOeJygF7HZ77PcHWLtAgeQTnHWoXIX9oULwHq9evKA2vEALdaM12rpG5SpUruLcpQnH4xHTNBdlQFVVZKucRrRNwy1/Ed4rNF1XQA7Zkmjh2TRNyXVZK3e01ri4uMA0Txj6HkoRwMogEODYNieWPWNMsVnWdV1UA6LMkUwYeTwAhBTh6gpzCDBGPwN1KSX4eS4L15SBGBcQMowDvJ/Qn8+Ypwl+nglshIBd16FyrqjwFOdOCTipnEMIAacTWVKxsmXJY8S+uQZJ0zRhnucFUPLjyWJ6YHuiZhuoWQGQRU0lC04Az6yC64ytBCzARykK6+fvkXbGtT3RsOovsIVQbJ5K69JoJ4CjADVemBtrSxGAvNcMIPsArQyQgaZuEG0ox0LsYGSTaooabA185DUAlGDtdaaWPJ5C+31ZhMvift0OuC4dkPNHr7ZZjoW89jRNJXDfGAPtI1JgRarhNsuUF1udUlBIZAUEOJCdoKNzDufTGbZuMM6e4UaGtapsg7yuXzXXCYDrOTOvbluGQ3GlmkIhThoKIWWEnKChoS0BE/D+8CEAWsFqx2olsQ4uEBpKQeulJRClEXGx8yGDcqOMhtWcyCgwCWvQpRBCLM8r4FOeOiX6mUHn0yq7iuVWWiukTHlVfHAgcVzFXpoTksC8hAKQCDQK0MTfOKcKunqmqFpg1Ff/Xz4IKIpMZUtwPxTl+ImyS47b+jlE4UahX2RfTXllz8yJfuYZgss5kh19fa1kyfnKBLnk3M45Q0PDOis7lm2wtN9DjAizJ9sqfz1z+H/mvSQ2xpwSlDGkZPWBr6WEEDzlWDJI3GabbbbZZpttttlmm9+E+VqQq2s7aGuhSrM5QYqqtbQQNqRsaJsazjlUdQVrLE6nE3RFUKiuG0ApdN0eTV0ByDD6HuPQY9c59MOMu/snOGvxxRevMQwjnNPoGofb2w/x+z/8If74j38Pl4c96rYBNf0lxJxx/3jEJ59+ik8++SVevnjBtiuFpBW0UQhhhqstmraFa6qyYB6GAcZWaNoO3W6Hp6cn3D/e43zu8af/xT+h8PSUgRiQs4KrK1hj8PT4hF/84hf47LPPEFPC7e0tTscj2cosqUgeHx9L3k61CgX33gOZIBwULfTOXNn+xRdfYLffI4SAtmlQcSaRtbbY1U6nE2xFKhXvA16/fo3rqyu8uXsHa20JBW/bDq6uCgR4Dtk0hr7H6XjE/nDAbrfDOPbohxHTNMF7j8PFJaxz6Pu+wB+AVE0lZLyuORcK2O079MMZUkKQkeEqgjhK6QLMDocDjsdjCfSWhT/lmeWyvQAKnOi6DrtdB8OtaVaJIixAa+DyYo8MavmztiqALO5zCdl3VYWrq0sAGvvDASklvHj5kkLpQQs/bUyBcbLvlVKYpgmP796hrusF3PB2C7ATlYYot2Shba3F4XDAPM9ouTRBlGBVZcvxr+saXbcr36e1KkBMrKhVVZVjGUJAyhmWwU+BUMYgeI9xHIvlsWlb+BBwdXWF89u32LOarcBBtj65r6hrUs5wHHxtrUXFuWZrqCR2xJwSXEXWq5QjQwBaViu9qFlkG58122FR2wgEkuKCtaJurY4TxZZ8bwYKDJTvlWMjME3yiL5qf1yDMaUUXrz3AsMwYBgGAJShldWiEBK4AqAAN+SMtuuopID3zTAMyDnj4uKCQeYCCwUy5pRRsdXWOYeri4sSQJ+SQCdRRBHMWSCUZIuR4id4uh4Sh46rCMQVgAkxIEV6PussalPDWMoP02yPI5Wb/N0ybMlFbRcjqTkzngeuZ0V2TIAVUJkyBwkKUQg/uKUT/L1ftXRn8RcqAms5rQPdSR2mEAt8ETBktEFMkcLw+fWjQFpjCjiCALC4AkYrRd8afIkVEgBiiuUaEIAlqjGZtU2RFFIBOUZobciSKK+lFFJMsJYbHNOiBhOVmVp9qBBBH/wgL+qrnDJqW9M9MlOuXFGMIReoJ2UUE6sDoRWscwizLwBdO4sUIhLDVK2Buq74mCzAfpttttlmm2222WabbX7d52tBrruHI5wbEYOHsxausgy0Klp4FejhyCrBdfBKKRwOB1RVi8vLK8To8ebNW1hjcHV1gZvrW1xdXWKaR8xzxs9//gnu7k74NH4BpQhefPTh+/jWtz9CTgpv3z4hJoWXVQsDjS/evMHnn3+Bp+MRIXp868OP8OLFC5zPZ8qpcgaTn3F7+wJNU6NtWozDgC9fv8bd3R2soUbD/nzGzc0tfvTLv0RWE/75P/vn2O0OmGeP4/GItqH8sHE44+H+EXd3jxjHGVVVo+1adF2HFy9uMY4DqsqWxZLALQEbxhgcDgfUVVWgzxQofJza2YC+J8tK07bo2hYAnqma5DnneUaYI66vr4uF5nQ8Yr/fUxC7IYin+PXP5zNijOhaytTas32uqihXrK5p0ZRTQtu26LodhY+zBUazSmAcR+x2u2J1GocBCgoXF5e4e/cOWgP7/YuikDHGclsXyjaIGmxtUZN9I1lQEviulOLQeFKCAGALFzUMThMpdZqmKdBJYMg8k6WraxvO0CL1AnLGPE2wDBYStxUiZ86dUwVWrYPOnbWoqwpKa9pv86KKk+MtuVai8LDW4ng8wnuP29tbxBix3+8RY8I4Ttjtdtjt9s9yzCj7i6QqAs6kFXMNPQX4SNmAPEdOCZZD4hP//fLysoAhAU01L65FsVVALB/z4D1BrhiRlFrUapKHxM8DANFEAKRyGaYBXdsVS1mMCXVd43g8IqWEw+HwzFpJx3SBDAL9ZP+vQWJd1+U9AAsITZlyyHLO5XvFeiWPkWtprQBb2hVdAR7RR6RIlrDKVSISeq4OSgkZGVYb+Gmm809pTMMI5xyapsEwDOU6l9cTgJv5XINaXjuwxbCu6qKS0npRqMm2i0VUa8owtNaikcy25Ms5JMwo5kTQCHRfbuoaTdPQeTWNKyubLf9fctgyXW9aS3kE2wfV86D6GEPZR89AUV6setSGKOpAyokj+yUXJmQKpjdaFxhXig3Agf0ps9o3IS+xXmTns9RWq6yB5u1XiksNUioqsbWdUY65HNe11VpmbQdcqw/lnJK/y7ElAK4ZIi/vn/LdpKwhFJUeVxIUm+YatmmtSYXMIEv+zNNM+wMJWlEmm6kcdOYyDM4zm1jtKe/LWIJaIQQkBeQYYbSBdprvm6IcFAvlFjy/zTbbbLPNNttss81vxny94HkAGoCrydKmNS12+mGiRU+SevaMQY8wzqBua1xcXsAYg/v7E+7uH6EQ0bY1orE4n3s0dYXrywtU9Ut88tlr3N3dY5o9Li86hBDw0Ucf4MXtDbS2eDwNaN49YZwjxikiJY8f/acf4a//6mf43d/5Ll68vMLp6YwYPdq2hjE30I5sTo+P94hxj3EcMU0jUo7Y7XcwxmH2Hp9/+Rr/87/79/jh7/0Q//Sf/nN07QHWOlyYCk23g3MWDw/3eHh8wOncF4iy3++hNC1enLNw7oBXt7c4j3MJGffeo2katC2Fyo/jiPPpBGdeEJywpijLbm5uSoh4wy13SVRJ/BjK2AItoKsWPgYMHCreVDUOuz3mGBBTgqsqTNNU4MJut4Of5wIRVMrY7S+gNT3+6ekRzlVo2x36fmA4M6OqHOquKwDqfKag+ZgSq30cpnGEqxzDLQJ2zlmGcgopLXlVwzDg8vKyLIjP5/OSowQCfefzGd4TAJQFuSwAm6bBOM6YZw9rHQOjmWFYLJlXilvYHp+e+PUCYqYge6UUHC/87h8eUFUVLi4untkm67rGNE3o+x77/b6oO9aQRRaku12HGFNpziSwR9sr7+3x8bFkiokKKQTP0CIjBP8MEqxVR13Xle3KmQABGDjFEGB430roftd1pKqsKlK1MBy5uLgoTZLrxT0gmUcK5itKpxAjXFUVZUhpemPljGboJ5DM8nEnxdkS4C/n7/9aZpLAOlE8Sri/QBBRZwEoqhr53hACerYcnvseCgSPBI6tbYrr4yZAQwDiOI4ImZVIyPCRjo81tsAV5FyaQI0xyApQWmHyM6mZFHB/fw+lUGBpXdN1IENgxEADz8CXnOtzDIgxl1ZQufbWsExrjbZrkRkoJ1ZQQd670ggRQIqQkgOjKQON4OjM+1Ahx4ScJZ9ssQhrrTjQPZTzVM4ZUnVpVJWj8w94VrSQY1zOPWsAUXSBlFfGGIQYYLK0RmrERKUbEuj/DEBpRQ2+KUEbjZxZAZYTxJ+XcgbiYulNOSGvYJSoNL0PEEXZkqFFajGUd7nYHQ1nyolSqnwP3xPkfCrXDcNAwypDOVcEXCuA1IHICEnKFejrkssVY4RKCsoo4Yn0/Ea2loCfKB6XfLAFHgukl1bfGCMQxbbK4FZlGEX5kJK1GEIEkOHc1y5i3mabbbbZZpttttlmm/+/zNeDXFrjYr9HSvTL+zBNmEMgS0wGdKLHxEyLiJ2qkUNAiBrnMWLfdqhqC6UMgS9t4EyFGA0ezx7920f87K8/QV3v8O7tp0BOiGHC2zdvsOtaXF3fYAoan31xh/Tpa9S1g3MaCgZ/54/+ADc3l9CVxuXVC7x9+xZ3d3e4vb3FbncAAKREi68vvvgMzllcX19htzsgZ4u/+unH+Iv/5a/wz/7Z/xbf+/538LO/eoMYv0TOAX/913+Nf/Nv/jv8+Z//16jrCvcP9/B+grUa+/0ebdvg6ekJKQW8ePGCbIAzqTpOpxO00ahUVRb38qfrWhwOe7x9+7YsJs/nE3IG9vsOr169gh8DvA94Oj5BaU1B2P+f9t7sybLrvPJbezjTPffenLMqqwAIIAWKpES1HZRbcnSE3faTX9pWPzjs/gcd4fB7R/gvUNPhltmUSEMkMRcKWZWVw53OuAc/fPvb52SRdhMRDguw9mKAVShk3jzDPrdy/3Kt9fHkw67FOFoMwwbL5RLaCbz3/Q9gLQEejAjRNgcboo+8Qc6yDCcnJwAQ+oO2FNnzHtvtliCEMxHkMXTw3kdAYYxBWZI76vj4GF9+8QVevXqNq6srcmcEcKdVBiU1hn6MMISdLVysziBrsVhiGKjk3FmLTOspsiklaO9IG8imaZDnGaqqQFEU0SXnHPDwsAn/Pcd6XWOxWMSNd5YVaJpNnKbJoOD09DTCBIYhHENjUAQgQqS+pyjgYlFBKYEsUxgGC8ChrivIMP3u+Pg4rD+K9RUM60CbzkM4Tv6YeUn8fEIgT/ebx/3mPVMIgCa6g4L6roOzFiYAQi6Dl0IgbutnQMtZC+d9nCAowsfleR43yxxNRPg4dvqx846PlUvz5+dQlmV0Vr0NmdhVxU4/LqifX5tpgMM05GDuBszyHBbkOlzVywiYhBDkSmQQwRFGTA6rcRynjrOwDhYF3S/TD+iaFt652Ekm+LU8MAwjfF5AeKDKi+C4ZDcUgbmuayIMjJPsHMWtAcS1No4jrLXIpEKZZXQ+fKyh6ok/RguJMl+gbfewALmtFHWGGePgxQSdpJQEhpzH0PVxaqSU5PgarIUQE3Dj50AEp5+z1BcYHXTwFGf0HsNgkOf0A4XYLRUcWmEIYXRwFbokd9No4RX1SNngTlKh9yvGIDkyKELZuwsQyk9xRFrPEtZZiOA4pVgj/Z3EhElKclbBe/SDiSXz8wi3EIB3BhASQqn43ue8g5aKSvx5eEF4Npwnh5gQMvQ9OnK+CXo9B2A0YwRjWtKPjOieTEMqyCkngzONoWwG2Flvm5h++OHDMyB1Fj+eHVhFSXDYeQ8ICW9G2GGENwSvFotFnBBrnIWzHoCL7trRmunecUdaUlJSUlJSUlJS0rdc32y6otJoWipDt2GDKqUAdbiH/h5LboE8I8eB8B5d0+Li/BS5VnDjiHq5gAobPa0yDOOAzWGHr69f4ef/x8/h3Ig8EwAMykrhx3/6x1ivziCQYX/osNvtsN88QEng/PwEp6crCOFwrk5xf3sHiB2stXjy5EmMALKrZ7vdUn+JzgChsd0d8Iv/8Ev823/7v+LZs+f43/73n+F//l/+J7z37gd49vwZ3nv3PXzy2Sf4l//1f4HT8xOKmdkaQ68wjgPu7u6QZRmqqsLp6emjuFPTHkIX2IjFYhEcWRZK59CZQj/0EHuJ/b7BYrGAcxZFUaHrOhRFhdev3wAOGIcRKkSf2PHEkwSrxQLeW6yPjmKEa2waZHkOnWXY7XbUk5QpKCUidFiv1zDGoO973N3dwkPErqWLy0sAdE97jgoGJxXH4wBEyDWOIzabDYqyxKKu0YUIIAAaYW8MbN8/6q5ieKS1xv39PaqqCm6tAkoJihFy31PYzElBkSmlsjhBjKZbtjGSSGDJYLlcoCiyEOnLMY49hBBo2y6cRwbAzSCERZZRfGi73aIoSiwWdQR6Qgia4tj3EfZMsUiDPKfzzfM8TlDs+x5aZzFOmGVZBCoA9ZKpUPjsvIdmgBWum3BUKl8GyDfvU0OIK/bDABv6tObTF+cAjO8FuS9ldFMxlGKIwdca3DeE0FEmqJdLBTjgPRVWK60nYBScVPOC+vmvHB9kgDSHdXwv+biBt6HD9FocteQIGl+TeWRMhOvHLjm+JvPr4qWAEwBn3TxCzE0rwAb4oMglZuEBJSGFhoCHyvQjBxlDOWk1mr5DFeLFjkcSzo6dnnMXjweg6KAKsFd5hyIvoKXA2LYRFrloGHJwJsQkvYVWAplS6LsmRvakBKAooua9h1cK1tFgBi9ocEOMwkn6x3kHZ0Z47zAMJsJIetZdvM4TlJzuEd1LRHAppECu8+A08xFwkTsJ8ELAOAe4sOadh1QEsCwojiwcFcnz1ETvPax30cXEMVEIRGhlvYPUigryQfHMqPB5vF69EFBKhB4qH52zXJpPbjYPzICqeASRyDVI6wC0RoQIUWoH6wAhPKQIhfAAhOTII2CcQ5nl4e8EByFAx+Om9zURuvuttY+6zyCApu+mYR1aRcgco47GwsLHHq7BjMh1BucJArNTtus6Wn9aI9MiVg4MfR+fOxu63ZKSkpKSkpKSkpK+C/pGkGscKc5G0RyKUmipICFgQdMKldbIQyeXGQdUVY310QpmHDF6YLEoUZUlrp48wXZ/wGazw+7Q4Ob2Fu1hj3/2kz/Bj370A/yzP/8RjB3Q9gZ///e/wW5ncXd7i5ev3mC338GYEXAWw9BDa+poub55wPFyhbIuQzzyHh9//DH2+z1++tOfRphR1zWWyxXadsQXX7+AtQ7/6l/9NyF+p/BXf/mXGEeDrt3j3//tz3D75g3+5X/5L+DdgK5tMI4dnDcQwkcHj7UjdrsDXr68DtMJHeplDXi6buMw4uTkBOM4Yrfbo21bHPYHWOtRlgXWa9r8D8OAJ0+eYBwt+n5A31AHUxb6hRjWFUWBrifH02pVk6PF0WTBk9NTPDw84OH+HvvdLmxKZYjoFCFiRD0reV7g6mqJ+/sH9GECIE28pE6wTGuUZYkyxCz5GrZtCxX6nu7v79G2LaQQOD07gwkOEwAxElhVFYwx6DqCAHNgMwdnu90eeU4RusN+D6UUqqp61BelQsyVQUNVVdjv9zTFkSetSYWy1PFcKXYDcN5nsajRth26rkdZltEZAwCLRY3Fon7kRGL3Fvc8sXOo6zpoTUX3NMFvKp0HppJ0F9w/fI/ZITOPRCopYUIEzYfnrAyT/qy1MarK5faDtShDlNIjuCitjZ1UzlEHlg8xN77+7BRjh1440EfRK3bHuOBYGYcBXmv0fY+qqh71ETEIZIBZ1/Wj9w0+f3a/zO89gxE+pnm3Gf/Dx8nHN/94/u8ccQQQgQUDNBb3ksngNuUxc85MHVlwPg5t8M7DWCreFgKQoImVAgJ2nJxRPUcDlSYnUThndszpAOXm150hKAAcrdZou5ZK30Ffx3mPTGk475BnBawnkBkG9VFENQxzkFLRvQ89XFIqHLouQj6tNIzp4zFTLJomumrFXV9AluU4HA50LhlF+eZpPO8RYnw+PPtc7D8vxA/H76YoHLG8t5xAPrjEQmTybaeVEKA4HabydyUlIMOUQRtK79lxCOrKo0mqdGN9XGdhLQVY5jyBaes98oyGdwzDEBxMNBWX1wuX3sf4oQhQUpFrjEY/itDhR91YfB3YuWank4MOU0KHvsfAXWdSzJxS0/sVv0dQNHbm2EQAbmG9EiDTIXIoAU/nbYcBozGQUqHIC7pOiqC1HQlkFlVJ7znOIVM6lPNPTkF4usZmnGBfUlJSUlJSUlJS0rdZ3why9cOA1WoZprZTDMaOA7zz1OOhVIizDIAATo7WWK1qCAEUeY7VeolFWaLIMry+eY2uH3Hz5h43N7cQAnhycYnz4wo//MH38O677+LFixdwVqEoFvjss8/x5RcvcWhbDMEtVuVURH57+4C27fHwsMMPf/B93G3u4L3HF198gbqusVqt8OrVKxhjyMFkLf7m3/0Mf/d3H2G3PeDq6gn+6j//z/Db336Efhiwu76G9xbX19dQSuJf/+u/hnUGXT9gGHtUVQ74HE3TPnKXeA/03Ad0OGCzfcDJyQnqekGxvjyjjhkB6rfK6TXOwyTI6+trXF09xTBQl9WbN2+QSepxMmaEsbSZn8OFoihQFDkuLy/R9z1ub2+x3e3omPIcxycn0DN3DPUjWXjvUFVlcLc1ABA7puZfAyHWJQS5wBhuFUWBPM9xf38fHQ3870JQ7w9FOStsNhvc3d3Fz2vbNsKOqqpi2T5FpzSGYUTbHKL7KU45lLydFTgc9tBao2ma6ArjGByFg/AICHEsC/DYbB5QVRWUkijLAlqr0Ik1658y4zT5bgaMjDFxqiCfJ4AIf6gcvgVPJDOGgJbWGuMwxN6euRMJCHGyAEHmPVjee9gAwpz3EGHTybDZOgepFMXnlKLYV3CAcS9X3/dYBEhJPURjdIHQ2jIxRsUwijfTih1Ls+mEfd9jv99jtV5Hh9m8M4nXztyBFTfnQjz62Dms4jXIjrm524nvZ7xW7EQKf54FWMHnwzEzXvfzz6dnVsV74a2FA8Efz04hEHyQoLXXti31RgUIw51FfK3463f7HmAXTPj6TdMQqBUixiGbhjr9VqsVBjMSSDAGQkkM4w5FTg6d/fYA7xyk1uQSEgJaZRTNExwnHgElQ1+WhjEj+r6D1uG58aA1HjieznQ4Rw8dwByDvLpehN47FwvoJzeeC+4i7uECqFuKoBFDcRkm+EnB8UoPF3q+lKRydOssrSmtgPB+wxMFtVBwnpxI5AaehgfY4ISCoMJ9D8TI4hAdig5kFPNx3RL8nbmypAh/+RHRyZSOkwmddQTE2C0GdlOBjlEwLPMwAaoVRQEzWiB0l9F1owMUCN1hgl1s9FrGjhTZ1/kjME69ZOwac+F8HPXuhWdrGAfC9UrCOBuvnVACKssgncIwDlCQMM5ACgXhpvedgp8TreKz5K3DMPYRRrO7OK6xpKSkpKSkpKSkpO+AvhHkMmZEP45QmgqCzTBCQCDPchrfbi289ViUOaoqw+Gww263w/NnT7A6WuHs7AwyjPpaL2r85rcf47NPfwtrRjx7cgE/7PHrX3+NFy+u8dd//d/iwx/+CW5u3qAsC3TdBkIMWFQSueUiYId9e8AwjuiHEevVAp989gXa5oCj4zWqaoGzs1NcXJ5hv99jGCSkAnbbBm3T4PiowsX5GoDHJx//A5x1+Pyzz3B+cYG/+E9/jN0Hz/Hs6XNcnJ2h7wfkMoPtHfYPLaQCiioPG0GBcTQ4HPa4uLiAMQanp6fo+x4CEtZYDP2IVlGH02hG9H2Pw/6AIi+w22ywXK/x5MkT0DQrDzMMeHrxFKOzaA4HNKHEXUpBBc9KxFiUMQ6ff/4FyrKCtR73dw/QWuH09AxD32E/7DGOJgIEFWJYXI6uNd2vvu+nvqSwGVotlwCAcRggpcR6vY5gid1yp6enaJoGTdPEsnXqArMhsqdxeXkZy+p3ux2yLKOojZS4vb2N/WBVVaBtLcqqwvroKE5gBBAB3DB00U3EkaqyLKPryDrEzRwgQuk5OS2EAMaxwe3tyxDZtDFSSNCtRN8P2G738Xox+KHeoSHG7qiPaxGfj7alkn6GHlySz8e3XC7Rti36roM1BmVRwAUHkQ4A0DoXXW0MZTjGyDCJp0h2XYcxTNrkDjO+d3mWIQtxzypcIwZyMfIIRAeUCkBz3o8Ve5dmHWUM4bIsi6APQIQ6XgBKqEdfg2OC7IphMdyanxu71uYuLnY8cdxyXr7OwG7eyyWlhGJHCqZIJhCibaDObueoqw7OI1caCgJmJEDW9R2yMH0QmOKVj47XOXJ3aQ0HAVgPaz0yCYhMIC8LSNB10zoP8E8jywSqiqBRnufo+wFZlkPrLIBowIRScGMcWtujqiSyjIYHOOMB6QFIQHqM1iEXCkJpyACqq6qGtT70J5JrTWLqa6MJheT8zHgIgCOI+vZ9QQAuEAxg+TpQX5QQVJ5PQwjohx8+PIMEBQW8nbrXrKMeRyUobgsu4g8/MPA+dHpJAScIGXkpAKjg4rJw1kBKjTE8W7wWyEUlIckiBYDgDUI/1xwsN20LrXSMgfNatdZMnVvy8bREhMmP4zhS/FOSc47i0m5yIIbi99AsD2f95GgTdIQOgFIZ9ab5edR3+juXo4sKHsJ5gl6S7pUHoLWEg8doqSDeewspfYB1gHe0PiUkvB8pOulngx68x2gM+nFAnuXxvYTeZ/v4bHNnYFJSUlJSUlJSUtK3Xd+seF6r8FPqaVqcdx7DOALDgDzTWFQLFHkGa03oqipRVQsIobDfH1CWFXbbB3z18gVeX7/Eu0+P8ezqEtWiQlHVePb8A5yfX+Lq6jn+/u9/hV//+td42DxgUWX40z/7Hvb7Hq9v7tC0AwZDQGC0I0Y7oB0aNF2H06MVjBlwcrKGkAQm8qyCNRo3r3e4u7uFUhnOzy+wWBS4vDzH0fEazlo8f+cSXdfj9PwC3//wB9g8bPDlixcQUmIcDcxo4T1QFSWKogybMmCz2UYXybRBEHGiHsdOePqZd7wRMVgfrbFc1tjvd9jv9+j7HkerNfb7HbaHA4qwCWvbltwWWEBnNBESoE3Ier3CdrvFZrOFlNS55b2FCJO7slzTlMOC4FLXdtCZwuFAcU/rFHSW4cVXX0XgcHpyQs6v4IRihxd3kAkhorvJOYeTkxM0TROdOA8PDwCAxWIBIUQAjQPW6zWqqkLbtri/v8disYidZUWRY7fbRndY13WP4moMl9gNtVwu4zFst9sIlY5CRxkDKhaDkzzPAwyg8mWeCjiOYwQr80mA7MYCaHNY1zW6rkPXUUccd4rxP/POqd1uNxU8G4O2beP5zt1I1trYo8TdbiI4s/i1hKKCcHZLVVVFoCA4S6KrEFPRulIKRYCF1lpo5+IUwThUgDNKwKNjYhljaL15HwvzdYASbgapPDzF7sQUWWPx0AEx+/N4XuHj27YFMJXc83pjxxQ7XvhzONrIDjt2+wg3udG4d8pYC2I2dE15HUkpASmRZRrS0tdUNsMwDMhCrx0fj7UW+xCjVdwNJQSk1sg1HedhOGB1zMMuXDyPIYDiPM+RZeS4morufYjQNuGaKHjvcHS0hvA0LMB7G5yYBCZ1loXpggLOGYT8GgFmbzEMA9r2EMC2QEj9wVqDrmviM+WcCeXjv9tv5r2HcFQwz7eSHU3WO1hLn8MwC4I/dzZYABNYZSeYEFMHnGcApdh5F04ldEsB5AqDoO4wKQFnp3L8vu8fdcxJSeCHj1aE3i6elIlQHJ9n0yAJfq05bJXs7AQ5J7WgKKw15PxT/PoArDPh/AikC7gIWQUExVjF5GaDIMg5jD1NMlWKusiiU4wdh3SsPGnSGYPRUUcXwVUPoSQ096o5cn5JKSG1ICAaJk6Cn2fv4RD8rkJAaIVclRO0dg5CSXhQR5hSenY9k5KSkpKSkpKSkr7d+kaQi7/R9d4jUzQdanQGQkpkWiFXMrhJJMoqR1UVWFQFdrsdpDoGvETXbdDst/jTH3+I/+pf/HMcHR+hXq0glcKhGfDbjz/Bv/vZz/D97/8xPv30Mzjn8MH772O1WqAscvztz3+JV69v0DYH9KOFVBpFkUEogb4fYM0WSngMJsfR8RHyfIHdrkPbDvjVLz/C9fUNrB3xR3/0Di4uTtF1B3R9h7/60V9iv2/wxx/+AM3hgL4bcf3y1SNnTVGUsIY2h95ZdF2Hul7ShikU3Esp6XgEUBSL6EqZx9C6roMQAucXF1jWC4zGoGlbfH39CgBBoaZt0bRtiO8NNB3PGNT1Asu6hlQKTUPHUtfLEJWkTXkRStDZeSQFRU9U6JyhzheD3W6Ppjng7PSMXFb7PUVOQ+yrD0BJax1dEOzmibAubCwZ5AGIgIg3dQzIGBoJIXB7exs3l8fHx8iyDG3bYrvdxo0qA4w8z1EU1CXWNBSt5AgiA7R5bI7ByNzh48J95Ol1DKUYQrADyBgT/9u8qHwew2PnyHzIAEcm+eM5UqeUwnK5jNMGu65D27a4uLjAZrOJ14hhkwzAhGOWU8l2UIBR7FrjrjLu3jJheqAEbezZxSSFiE4vjizya8Q2oJmLi+8BryNjDEVUQxS3yHPAE+CWMxeMDyBi3rHFvwoAbv6xfnL3AG8XmftHUxRjNxEmCMd6Ow6ZZ1l0H1ljkYXnQTkfwQP83NljoyOO+poozsvPKsdseb3G6OEwxPWjdBbL2rtDQwMKBMIUPWAch/j5/PrWkvsOjuJyDCSlVFT6DyDTCn6gXiWKBXK0kO9N6KoLYISexTxED+vgghOh3Jw/z0Frgu/cx0WvT1FgN4u10T8+Qhfqj3IReNOzE66vkhFCEiDy4euF8rMQB2RXmXOO1iXH+8JC5Ps8h8DUU0cTPak3SwVQOvUE8tqliYJutrYEHwG5rxwVtDs+ntka48/n55rhtgrrhl1xDDkBBrwI7qpwNkJAKQkpVBjeICNM5gJ7jm4Kz/fFwoWOLXrdxxFb7ibjvr4I0p2DDeX9UkzgkHr6Reg4EzNMFQryPd0nAYJuFC/109RPeIyjj8AxKSkpKSkpKSkp6bugbwS5rA0/IQ7jsrwPLgXPRdxUAA8pMBgD2QNVSUXdzgtcv3qFPJP48Z98gLOTNa6eP0W9XMMLia4bcH19g+vrO+z3LT7++FOUJU3Fy7VG3/bo2wHNoUGWaSyXFSoLeNDkMGvoZ9POGoxjiXEz4n55QFms8atffYTrVy+xXC7w7NkZAIHj4zXquoYQDtvNDv/h53+Hvh/x9OkVttstbSZDVOTp1RW6to2T9bx3IbJicTjQhCp2GC2XizA1T+D167u48e1DSXw7ex0hKPqy222xP5AD6u7uFstljaqsAAGMo42bLsF9QKH/h3uLCOgMOBz2ODk5CYDFoW0H9P2AxaLCaEZ0fY8sI8jT9z26rkVVUlfTw8MDmqah47IUF+QOITdz+DBEYrDAIIddSgyLOArJ/8yjbvv9Pjqk+OOGAAyAqRssxpsC0OI/J0ebeFR4nud57KliZwSDriG4o2gNk2ODY5lc/D53oHEUj2EV93HxNWCYNoaCfY7yvO0244/l/86vVdd1BHDsIGOX0NvXOsImP00hZDghBbmThBDo2paibOyeEdPkPyBMQ3UOwzg+Ol52cLHLip2IfK/4XvO/MzTkTbMxhp4TKWnDLEQYSjd1mUXYFWNcVL7Pjjc+1/k9nZ/DPD7Ja+BRhAyPpzBKKeEERyipx8qa4HTy02TYedRSahVidtNUSY538mvztWBnmdYaKi8gQwk5DwYoyxIqmzrBaE3n8RxobYReL2OgBEGqse9hvMdikWEIHXEMobRS8NZDgWOTmIYDOAuAytBpXQ4AqM9Jha4uMxrIcH05csg9WjEm6QnQsBMPCEMcwpTDYDOK/zhHZfgixB8hRIQ57Cai42M3lYCAjEX1MtSn898rAOKUwOnazTvcAKjpz+ljJgcWrxc+bg9EAEuHTCX9XvoIFOcwj67FFI22lrq0EO4bvyfIcF+c91AC8ZpBAkqI4JDiY3gc243vH+F/AiL0b82jlDxlVExOQ+fgDHWueVgo+ssDMjzbU1/h468dn8npIIIXO6xnKcghFhy7dK09/blUEZQnJSUlJSUlJSUlfVf0jSBXVeTItaZvlEWIYgjaZBnrYVUG77lU16AbHPqBQMAXn38OZ1r85T//Kb73vffhnYGHgodE34047DsM/RCiZBkeNg94UlxgvVxhu9lABafDerWClBJvbm/RdQOG0aHtBogA3Wha1wApczzc7bHbfILb2ztkWYbLy3NoHTbFSmC/31IZuANefX2N9fFx3OA754OTyUBspihVVVWQUsXpYn3fYxgGHA4H1HUNax3atkOWaXABshBU/ExuDkexwTwLUwo72vAGCFSVFZTUKArqzrp58wZKqvB1RexxGscRVVlCCInm0ODQ0LTBTGfY7/bwzkcwpHUWNnzkZzCGIl1VWcXoWZZlMX61WCxwfHyMvutC/42NAIbBB7uH3p4+mOc5mqaJzh+lFIwxOBwO8N4HADgV4Z+enkbH2TyWyO4MPXM18Z8PA4G7LNOw1qBp2jixsSxyGGNj9HC/30eQxkCJvw7FxBDPgWFYjFHN3Avs1OKPz7IsFpK/DX8ARFDGx82/8mACnuY23zzz72X4eBccM0LQFDopaKIau6IYegmAJu/NwCC7vOaASAqBLLjW2uAC9EB0isngJENwh7Bbp8hz4kPOwQao6QGIGYygY6P+I3aXeE8RSgYXOri/fABu/HFgl0k4Tr4/fL3nm/T5+cz//FH0EMEVFFwzLjSui/B/WsiZKyrANUl9T4OZnFU6TDd81FfFUIKoIFR4r6DOJwElJFSm45ADhi+TuEjcBwcVget9mEKaBfgtQPfLjAO8o8mZHEGdHEsOEgTmRjvCWgaRAbpAIAtTP2myIBlyCHxZdB2VjJdlAG9aUwwulvI7nuEHiOCwCo6uTNMkvmEYYI2Flw7SqQjevPNxfQnI4BSanGRzt5YL7qzoogpgSQT3F9U4OiqEF+QoFpig6xxuAYgRWoQfxkCwjwuxA08IKqiPDingEbSPsIfPx3PT1+M/pwmXCM4xCaEkZOjt8qGDi51ZvAb53HkNeMyeUTnBxQitfHhOMXV0Udl+QGWerof3Yna8AaAJch3zEfv42vR76zyUJkjrwmvzMVjnQxwVMHH6Y1JSUlJSUlJSUtK3W98Icnnn0Xc9/QQcPrg7aFOgBG8MaHMlIOGsx/WrV8i0wpMnlzg5eoo/+8mPsV4taUPjBYbRou9HbB42cM5jvVpA63UEHFVOccdxHCEhUJY5dKaQFxm6bsB+f8D9wwZ9PwBeQ2cZ+sHCux53t19iGHo8e36Oq2cXKHKNptlHV0m5WEAKga5p8OTyEsdnpwCARX2Ew76FMSNub28gBDl/imKCJBR70zgc9jE+s1zWMY602WxQL2v0XYemOYQydurQmUfjhnFEUZaQSsGE+B25LCy6nvpaqqpEFjbO7NbI85x6uYaBCpQDvBiGgRxeoM1ovVjETZ+UMsYerbWol8s42bBeEEQb+h7VooLWCh187KVx3gF2chnQNDmAHSHsGLFWou+74HYh8MIuNi7x5s09gbA8TGyUGAYbIRo5QURwB06dS1lGRdE0FZEAWNd11L9lbewCYkAzh0X8cVJKdF0Xe55408mfw5tAjmOVZRndWQxT+HzKskTTNDOHzuQ+YlcUO3/mjiW+hwwGeWqaCht3Fm/knXMEFqSEsTSZzofjlkohY6AmZZxwN45j/BgGTvyEsvOOzxMAfHBVMcRy1kYYxSDKB1JiTJg0yIAqnG8XwKhSCmVVQQcX1RxeAYj3zkQnkIzF47GLbOay4eO0EVJMTjd2+nFMlV9TZ5omIiJEJzkOKVXsXlNaQSuCO8aa+HoiWKW4T0lJctYIgckxF+bMSqVp6qV1OBwOGIYOq/UKmVYYrfsdx5CUUySQwZwJ73fsEOy7HijYeSjiWp27gdgNp7XGaGmYBcd3+f3aCxcjrbysGEDNI570mwBhvJ05svwUU48OXgZ0BMwIUBEMUUqGSCDi809uPcl/NdAr+QkkU9cYgSxjDJTXvwOuBNjtxOdFTiUhaAKoD1DJe3InCccdYlTqzqfo2AUnJIQSBH+C84l/FVJMpihBBy2lh3WeCuRnfV+xbD7EYx2D5gCuIt/ia+99BIUyOq78oyju3EX3yHHIXWMBCPrpIONzjfi6PE0XEZ6JMASB151SOjw7ZupWU4D3Mr6f8bpTs3uRlJSUlJSUlJSU9G3WN4JcbdtDaxtLilUYUa+DA2A0PaSQ6HsHby2cHSGlw/Onz/EXf/HnWNYLrI+O4Z1Duajw8LDB11/foO97bLc7chxIcnBUFQGRXOd4/vwZbm7ewBkDA4t1tcJVcYXtbo83b26R5Rk2mx2MIaCx7z1uXl/j4f4e69USF+MxjXf3HkpqGEMxtbIssVgssFws8OTJE0DJOCGQphOeoGl2uLy8iNMCD4c2Rv2o2LkNx+pR13WM5w0DOXZ4k6CzDDr0VjFsGkJPldYKxoyoFlXskmnaBtvtFuv1ClVVBmBiYYwguFJVGPoBbUudQacnJ3jYPKBpGxhroF2GIvRKGcubGBtBAcEEESNZ/TCgKHIUeY4swJe+p9J3KaYolBAgWCcVhmGKXSqlUdcLDMNI3U05OUiGoYcxI8qywmq1Cl1eNnQcOex2O1hrsFwuI+CwAVaRm0fBGNol0uQwFScucsyPwdUEO3yMcs4jgfv9PoIWLoLn0moAEWRxH9c8tsel533fR9iQhdgoA4NpomMoah+mHib+lXu7+HPEDEAKIWJkiEEXR1OnYvfJiRH7whC6hgK8lbNOK4YM7Fpj95RSCn0oVgfDjADJVHBQGSC4Mu2sp4f+Hc7BzkAJRzujG857ZOHzpFIw1sIG4IfgTIsBqwg7CAporSNsYhg2j+fyefM5cmyU3V8UzbTQOcVVdfiafF+8DtBWBpAiBbzzMGak2KF3GLoBi8UCUslw/jbC4iHAQ+89nJYodQbnHfpxxG6/Q9PsIZVEUeaQUse1yRCLAc48hsuutfmaYeA71wQ96HilFCiKPF4jchV5iABO3HSVgVCG7kCXoygothrjsqGHjNxUU3SOYFJw+ASXkvMe1pDrSikJD3KBKk3rRDoB62brVTg4L8h55sPrsltqBnUm19MENvm8+D1LShmjpVJJKC1pkqD3BGjCa3sOEjLDkyK6IWNUUMngFCNHMn0uEOOWMwBGbqepHF6Auq8AxKj+FK2kY/aztT13WiKsc35vePv+vg26HsEuwVMoZ+6qABD5vCb3IBM2EZ8dvsf8IewWpZ44fq/l9xiK1ersG32rkJSUlJSUlJSUlPSPpm/4nStFXqylMfHWOnRdD6CnkVjeQUsJa0ZI4XC0XuLHP/4BfvTDH+D0aIXBWAzDiMOhhb3d4s2bN/jq5UtYa7FerwDv0Q89BGgz+vTpU9jB4OTkGMfHx7h7c4vFosb66AhaZ2jaDlprrJZrOCew3x/QNC3atsXd/S3GsUeWH1FRfdfh9PQYRZ5hGBtIQRDj4vwC77/3Lrmh8ixO9JNCoSwrfPDB91CWBSjmZ9D3I4qiRN93IZaYhSLvKWJljMHZ2Tl0ppHnOVbLZXR/KEU9PCOAOsvIQWLoJ+ZVWWAYeiipsNlukGcZlss6bIAZltBxNE2DMRRSn5ycRAijdRaL2nWAG+SycgHikAstzwsM/UDXu6yw2WxwcnyMelnDGBNcOT3GAEKyWX+SlAx8OlTVIpzziDwvcDg00JpAHrtKtM5QlkUER0qpAOfouGijTxBmcvDI6HihTR45QwAbokbTZrAsSxwOhwCjNKwl11bTNI8mQa5Wq+iym4OvsixpSmHfP4pFsoOKnVp8f+n6ESDg2CG7teabVgaI84l1fA25VysPEIvjlRwx5FiVDo4trTUBtpmLKstzuABMrTHk2Jk5yOaQizuI2FnnvI/HLqWEDYX1Kri24hMffj+fUMnnF0FUcH5571GWJUWkAPRhwEIepl9qpeLz4WauNwSow4AYYirdn8fJ5sCHr/Pv6yCT4dii002E2JsI3WChJ28OEFj8NbvwrHI0jaN63GE2QSU61tBiDq1pvZlxhJRAvSwf3Xt2YM3PgdfCHJBUVfWo7J6eB46STZFY7wkw8xRX7wE7GijFscbJHeQcReSkfAxHnHXU9xfcP+wmjJ1UwfUkwD1QBJKMN5Cg11RawRhL8dDgXpuvHf4aHB2kmB/Bsyzj6KEDMHcE+0frjWGNUhSntMFJZ42NsVshZIjnBffXtJAj/IluRGvoOKSIgIjfbyIMC24ogl0iviaX/UPSuqLjdLPjnUBdjAfO7u/817c1gb3pePk9ZB7X/Z3Ybnh9G11vYX26aXAGH8McoE4OQzV79AVo2cpwTX7voSYlJSUlJSUlJSV96/SNIJdUIkRLPIoFTQ5kMOCtA6yFUoDWAucXp/jpT/8TXD29hBKAgELbNjg0LW5ubvHZZ59DCNrY53mGIi8wjgO0kjg5PoZ1Fuv1Ep/+9mMYQxO7Ds0eu6bD/f0WeZFDCImyKDEMBkoqKKUxDgbbzS3qRQ4gg/MGbefRDwa3dxsYMyLTBn/+kx/h+9//PuA9Xr1+Da0Vnlw9jSBmt92grmvUdYW7u4cw/a9D13XBMeZDMbTFbrdDXdfYbDYoywJFUeDNmzd4/s5zcjwFdxEwlY9bazE6B51pSAmcn58BALa7LTw8lssaRV5ASBGcRQR5pFTwHtQ7pXS4dhnG4JbKswzr9RpCUPSQAYxzFm3T4NAcsFgsUBQOwzigaZrokJGKNpsP9w9URmwtEOJARZ7D+SkeBgA6TJRTSqHvewhYWGtQFjU5H0IBPsCghxxyBA/UrNDbou+7AJ4oa7Ooa+oS8y64hEYMPcWzRmsghI8F7gCw2+2wXq9BEaLQARUipbxOLy8vMYwD9vt9BEnsQAIQO8a4aJ6io4hxS4Y4vKls2zbCGIZj7AZjZ44LHWfsRJoXuHPXFplXZg4QKSGBCIDaroswSioFFRxdCOegNMV0uVuLpybq4KBSATgaayHZUSYETawEYlQSwUnGAIpL7bl7jF1gVYjA8jXpAjSQIcaKAJyco468GAc1BjK4rgA82rRz7NEEN9pisYiupiGAVq1ULI3na8Xl9Xz9Y1G/FGGypMYwjODJePPSfACxMwwzoCClRF7kMGaMUMtaCyd8dJrS8xecY8ZBZRo5BNbrFayrcDgckOcFrLEQGYGRcRiRFznyPIcZR3hBkzR5cugc2sXYZYAPXC4/wV6Km9HzQa6cOYgEuNCfCA3BsQnGvA1eFBSdZ3BnxQmZwXnKEInddrR2FST3Q9HhwVh6f9NKx2Z8/rNMZBAKEF5gPkVQhBjivICeo5Ncks/Mi2Gqo9GBAKbz9o4cY7S2CAZ67+jvptBx5jxNaRSSJk5ScXuIcKoJDk2OUk/wh79YKIlHcCt655BlMrqI2b01Aar/e7jF4GreT8aa/3661iIC/kevxz1es891HEmlA4qfYw09JypMQo7vOeF1aFKjC6BPRjDOBf5JSUlJSUlJSUlJ33Z9I8gltIPyNGremhHWOfRDj67voLzDMlc4Xh/hgw8+wPPnV3hy+QQQAvmihlcCq/U5Xrx4jZubVzBmxPHxEer6LJZcW2NRFRmunl5iv9/DmRE//NGf4Pb2Fqenp7BuxM3dPe5ub1GVOZbLJYqiwJPzYzy5vMBnn3+J169fYRgbrE+Oqa+qoUl88ALeWKxXa/zRe+/iww8/xPn5OXa7HVbrNZRSOBxaCOHRtS3evLnFzc0brNZLLOsau90W290e3gOnJ6dYLmv0fY/9fo+u6+C9wPX1Nd5//33orMB+f8Bh30RA0LZtjDzOO6Csc1hUFfY7Os7jozN0XYeyLHF7e4um7bBYGPR9h65rwJtbpTS0znA4kHPi7PwMq+URRjNCCo1Xr17BGIOTkxPkmURZ5KhrA4BcIt55aJWhXizJSbeqICDRHFrkRQlrDPKcYoA6y2DM1IfUmxGZzqCkAKW+NJwF8qpAnhXRnUVxRYp9VSWBnkznBDNCVFNKibKo0DQNpGAgU0MKif2uoain1vCOr5dFnhXQKkNVLvBw/zW8d1jWNZrDAWbMoZTA8dEKEDQdsm1bAA59T9e4WpQEABcVzs/OQq/WAUPfh3MKm/ng1siyDGakGOYQoj1t10FJircy3KLhAFMfFztyAIKb2+0Wx8fHj8rvp+ho2HRKCRtcWOwiy7IMJlwHeA/rHIa+j4Xu/Pq8YWbHIF9fHozAx8jr0ASHBx+DUlQcbkNvG7uIsuBuzAIwhffItIYJziRryTkTi+tBoK0oijgNUQcQlWcZfHBziQDi4muEzXRZltGBo5VCPivxb5omTsRkdwqAGAPt+5428R4wA52/4e6qvIj3pu+7R84wKSXBYDCwd+jHboqyhhhrnFjpHIxxGDsamHB8fAyhJDJVYBgFXF5gtVxj8/AANwPbYJeOBzJF3Wnc48THMI4jlFYhjqcggutUiNB/qNiR5TAMNOGVI8AmOPbm5/X2NNS5M4h7xwiuAYCAFAreAcJLCCce9UpJEa67d1BSRxcbxf4k4B2csXCCJi1672l6pOTBBGGq4Px4gBjTFjK4z2ZwCIIq1p138JbWEhjgeUBJTfAPdlp/4Tmg6KYAnIAJcW26fwJKKFjL10aHzjZ2vVGZuwpwLQI5TH1bADtNRYRhJMZNAkJwmfvUrTfvAnzUOzaDYHOHIUMuGurggntPxWsjg0MuurvcNPhBOI9M0L1xcDDWwIPgl/D+UdeWDW5F4Si+Cwk4HhKQnFxJSUlJSUlJSUnfEX0jyJXLDDLLEb+VH8gZsshznBwt8fTyDGdnZzg/O4fSGrt9HwrCHfq+x8uXn6BtD4D3WK9XKMuS+q3aFuNooLXCu++8i6dPn+Jv/uZvsFwusd1u4/Q7ay2eXj3B/e0dzDiiLAqcnp4BQuDjTz/FL37x7/H0yRXuFjlO1zVu7wfIOkemC6yXK3Rdh6unV7i8PMdXX13jyy+/QlmWOD09wenpCV6+fIkywLPjoxUeHu5xcXqC3W6LMs9Qnp2EmFePzcagKEss6hpSKXR9j/OLC3LVaA2lNT797DM0TYOiKFDXdQQdHJ3z3qNtWywWC5Rlie12G91Dbdui6zosFjXevHmDcRxQ1zUWiwWapsN+v0dVLaZusK7BMhTJZ1mGr776Ct57vPjqS1xeXiLP8+hQGo3BbrfDmzdvkGUZiqLAer2Gcw739/eo6zrG77IsQx8mH9JNp1iPXNBGbBgH7A97OOfQ37UQAtjuNvDeY70+wvpoTe6IsJey1sXrMPQ9lNZ49uxZ7IWqFjWUomheHoYZ7A8HdB3FIIeBnFb90GFRV9gfdrDW0nUTRXRXmdFEF1yeF8EtQ/0yVVmhbRv0AzndjFWAAHRG8c793RuMo8VytcLJ8XEsrh+GAR7A1dUV9OEQhgJUaNs29rsdeEpegDvsDCyKAsfHxwAQHV08sZOnUPIaZ8jE1z8vChR5Hru24Bx0lsV/t+HeTIXSKna/7XY7CCGwXK2wXC4xGkPwz5i49nyIKOoA2voAkdgllRd0/dq2RZbnaLsOHkAeOueKAOnGccQYIAv3V213u9jRZqyFDUX7WQBGDPxGY2iTHY7t/v4eUkrUdR1hIUcZ+drN3XIc0eQ+NQA4HA7RkUdTW/P4sRw9ZcjAUNEYgyzLUJYl2rbF4XDA8fFx7F/jcwPIdWasgco0LDyEotgnhECxqAApkFcEjKuqJFebdWi7Nna/CS3jOoQQaPtucrU5HlZADkjvp9iiEA7OCWgtAxzTj86HYR7Ha2NZueeScyqU987HjivAh9dDeD+W8N6C2By5qYRw08RJIHbKhbItAIKuh3PwcnJZCaVATWH0dfyUAaRrqKh7bQhTTCfo5kPMEZBSQWUSdqRifJ6iKhl2egEz8qAEGgwgJPeUBZebBCAJyjlD5flkdrKhdN9ByuBeCw4vVnQczlxwQgDGDPQ1nA33KUT6Z44uXqe8hhgs87162501l3ME92SAkVooeGthIwSUUHpWYO8onhu/Pjyso2vG0fJh6CGlgtY0UIHeO1wotg+3Rvj4jLZd8/u+JUhKSkpKSkpKSkr61ukbQa6mGaByiqzt9zvYccB6tcTzZ09xeXmJoyPaSPfGQmYF+tFBaA936LHZbPH6+iW6rglgoMHRUXBQ7ffw3uPq6goA8NFHH2G9XuPp06dx0973Pdq2xa5p4KzBxdkFrp4+xdAN+Id/+AjdYYt/89//d6jqGm1PXUlv7u7RtC2yvAScwFcvvsJ+t8XLl9fIM42rq6d47733UVUF9vsdldAvF9hsNjhaH+O9996BFAL7/Q5KqwiDxtHi0LRoWup9qusamda4f3iAEAJ/94tfYLPZ4N333sN6vYaSEm9ubzGOI87Pz3FycgLvqHQ90xqr9Rq77RYnJyfo2har1YoK4M/PIYTD+dlRdAkYY5BnCkfrJVarJV6+fIlf/upLHB8d45133oUUAjc3N/jwwx/g4eGBNvQAuraFdS5ea44gvvPOO7i+vsZvf/MbAEC9XKLIc+gwUev29habzQZ916Oua5yfn8N7j6ZpYQMQuL+/x6vXr7FYlFjWNZarFdbrNW5ubnBzc0NTI+saeZZT2XnY0C+qCq9evowxwbZpUFYFDQboB0hJ0yKdd6iqEkWeh7JsgXE0eP36hhwUoWupbVq8ub3F2ekZ6mUVBgQMsJaijYBA03TIiwKZ1tgfDthtdxiGEcvlEi+/eomLiwtcvvcETdvi4WGDm5sbKiAPTgypFNq2xX6/f+SCYGDFJd4A4kRHIQQOB4qJMog6HA6oqiq+NoOxcRzhvY8RNi7VZ8cYFf0LqJlri11A7MqaT4DkiZu84TWhiL0sCtSLRfzvJsCIsixjVI47wzj2yufovcdquYQKriZnLYa+jwX1Kkx7FFKiLMtYjl8w6LI0BAKhb4xjhPxxWXCs8eAE7vF6GzLwPek6cmVRXHUChRx5ZHi1PxwiEORCe+BxkX+e5zgcaBrq3Pk075hCiJnqjArnu7ZD17TI8ixcD4ci07CjQZHlcGG6oECI8Fq6rmVRUA+aDAXmgs5dKY22aWOnm1Iai8USTXOIYIfOX6EodOzQ42Plf5dSTm46x4k/H6+xDcMvhM5CPNUFWEmOT+85UgoYM07uRsnrUMbBFMYYmJGmpUohIbWk6KPQ5ELyDuNofme9CilQKIp/QghoRVNZeVrvPPJHEUKKLgsxdeZRt1yGqbfQQ2cSAtM9ExLRWeadx2AGcqZJOUUKwwANEWKBED4Wz//+AQCAiFOFQVc3uOH4uGm5iEfnzWB3vvbma3H++/hcA4DkQQ8CxtAU3YfdA13boYMHudQWZYmyqJDpDFlRhvcQBSlKVGWJ45NT/OY3H4cY7DQVVikNiVlsOsAvOpb/h28MkpKSkpKSkpKSkr5F+oMgF38jfr/dQHqBoe9QVxWeP3+GZ+9c4fTsBHlWQkCj72nUepYD/dDh61evcP3119jt97i6OEVVlaFbBHFz6p2H1gp3d3fINdC2HS4uzvHFF1+iqkrkRYFxGDAMI4wdUdUVyqrCV19+hV/+8pfIswz/47/5H7BYlPjyy89xv/P41f/5EUbnYKzA5naDTz/9DG3TQECh1Ar1IsfD/Q5f6q8gJY1Yr+uKIoJNi743eHVzgydPLvGw3cA5h+VqBQiF3/z2t7i+foWzs3McHx9H583x8TE+//xzbLZb1MslqqrCfr+P06uUUvj666/x4sWL6BYpyxKffPJJ3KTmOcX5nHNYLpewZohuoLbtMY4GeZFjtaxxODRY1DV+8pM/g3MODw/3uL29DS4x6ofJ8wybzQaHwyGWsDOIWK/XcJ46n+rlEpeXFBO9u7/H0dERRQkD9OAI5Wq9wm63w+5hi/1+j+VyidV6heOTYzTNHlprlGWBruswjgPKskC2WiLPc3QdubDy4Ozx3kcnlDEGXd/j5s0NVqs1FosFtKaJlPf3d3h4uMfZ2Sl2e2C73eDi4gJ934USe3LcAEBVLXB3d0dTAsN5OudijG232wXgRtBwf9jj7v4OZVnCWIN+6HF9/TWapoXzgJIyFqg773FycoIvvvgCYyg2Pzo6QtOQy4EHAtR1jaIosN/vMQwDnj59iq7r4jrhwQBcwj8MQ3R80HTIaUPNHVmb7ZZipuFZZAcWl8bzGmGX0hzQzHu1ur7HUegVG43BLqxP3nwzROIeLH7+2SXF0M0DoWvORacbgDhBsGnbOIlyfixc2M+b/NihBUwxrr5HFRyNfO+awwFFKIvn6C8fL7vIbm5uHk0vFZIm/u0C5OLII/C7MG8eDWXwx5CRp0YyiMuyjECFtbBuiox6AWQAuqEHeo+HzQYAOfdOTk5idNV5h67vYcNrCSWpCy6U4g/DnvrUQhehhEBz2IfOKkFwzTmY8F7AkKLv+ng8zjkqwGcAhseOJFYbnJ9aKXRtB2AajjAEhxkBVQ/jTJwEaswIa8lhNob1Nww0yKLIy0cwsh9o/dBwjun1+d7x5zJ49N4i0znMGMBdZIsEd72zcHaI0AoQaLo+dErR8Y1AcHFNXWcA4loRQsBbFyfgAoALTqgI4Tz3eoWutNAxx9BKchdWiFo+KoGfaQ655sfzNvx6u5eN71U/9jgM+/Ce2mJR5bi4OMX3fvg+3n/+Fzg5Oqf3zIr+zinyCkVWQMkMfW/w5uYeX399jd/8+gXu7zb4/H6Hn//tFyiLAkWpoJSDlICXPK1Sxe5ErXSAYTIeT1LSP6ZiRB9jitEmJSUlJSX9ExON7/uPf08q/B/wXeuLFy/w7rvv/r9zZElJSUlJSUnfOX355Zd45513/rEPI+mfsNL3o0lJSUlJSUn/se9J/yDI5ZzDy5cvsVqtfu9PqpOSkpKSkpL+/ynvPXa7HZ49e/aoLD8p6f9rpe9Hk5KSkpKS/unqD/2e9A+CXElJSUlJSUlJSUlJSUlJSUlJSd9mpR/JJiUlJSUlJSUlJSUlJSUlJSV955UgV1JSUlJSUlJSUlJSUlJSUlLSd14JciUlJSUlJSUlJSUlJSUlJSUlfeeVIFdSUlJSUlJSUlJSUlJSUlJS0ndeCXIlJSUlJSUlJSUlJSUlJSUlJX3nlSBXUlJSUlJSUlJSUlJSUlJSUtJ3XglyJSUlJSUlJSUlJSUlJSUlJSV95/V/AYjMs8lSYGiQAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset = Dataset(x_train_dir, y_train_dir, classes=[\"car\"])\n", "# get some sample\n", "image, mask = dataset[0]\n", "visualize(\n", " image=image,\n", " cars_mask=mask.squeeze(),\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Augmentations\n", "Data augmentation is a powerful technique to increase the amount of your data and prevent model overfitting.\n", "If you not familiar with such trick read some of these articles:\n", "\n", " - [The Effectiveness of Data Augmentation in Image Classification using Deep Learning](http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf)\n", " - [Data Augmentation | How to use Deep Learning when you have Limited Data](https://medium.com/nanonets/how-to-use-deep-learning-when-you-have-limited-data-part-2-data-augmentation-c26971dc8ced)\n", " - [Data Augmentation Experimentation](https://towardsdatascience.com/data-augmentation-experimentation-3e274504f04b)\n", "\n", "Since our dataset is very small we will apply a large number of different augmentations:\n", "\n", " - horizontal flip\n", " - affine transforms\n", " - perspective transforms\n", " - brightness/contrast/colors manipulations\n", " - image bluring and sharpening\n", " - gaussian noise\n", " - random crops\n", "\n", "All this transforms can be easily applied with [**Albumentations**](https://github.com/albu/albumentations/) - fast augmentation library. For detailed explanation of image transformations you can look at [**kaggle salt segmentation exmaple**](https://github.com/albu/albumentations/blob/master/notebooks/example_kaggle_salt.ipynb) provided by [**Albumentations**](https://github.com/albu/albumentations/) authors." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.482545Z", "iopub.status.busy": "2024-08-23T11:30:06.482154Z", "iopub.status.idle": "2024-08-23T11:30:06.495170Z", "shell.execute_reply": "2024-08-23T11:30:06.494101Z", "shell.execute_reply.started": "2024-08-23T11:30:06.482503Z" }, "trusted": true }, "outputs": [], "source": [ "# training set images augmentation\n", "def get_training_augmentation():\n", " train_transform = [\n", " A.HorizontalFlip(p=0.5),\n", " A.ShiftScaleRotate(\n", " scale_limit=0.5, rotate_limit=0, shift_limit=0.1, p=1, border_mode=0\n", " ),\n", " A.PadIfNeeded(min_height=320, min_width=320, always_apply=True),\n", " A.RandomCrop(height=320, width=320, always_apply=True),\n", " A.GaussNoise(p=0.2),\n", " A.Perspective(p=0.5),\n", " A.OneOf(\n", " [\n", " A.CLAHE(p=1),\n", " A.RandomBrightnessContrast(p=1),\n", " A.RandomGamma(p=1),\n", " ],\n", " p=0.9,\n", " ),\n", " A.OneOf(\n", " [\n", " A.Sharpen(p=1),\n", " A.Blur(blur_limit=3, p=1),\n", " A.MotionBlur(blur_limit=3, p=1),\n", " ],\n", " p=0.9,\n", " ),\n", " A.OneOf(\n", " [\n", " A.RandomBrightnessContrast(p=1),\n", " A.HueSaturationValue(p=1),\n", " ],\n", " p=0.9,\n", " ),\n", " ]\n", " return A.Compose(train_transform)\n", "\n", "\n", "def get_validation_augmentation():\n", " \"\"\"Add paddings to make image shape divisible by 32\"\"\"\n", " test_transform = [\n", " A.PadIfNeeded(384, 480),\n", " ]\n", " return A.Compose(test_transform)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:06.496921Z", "iopub.status.busy": "2024-08-23T11:30:06.496543Z", "iopub.status.idle": "2024-08-23T11:30:07.590478Z", "shell.execute_reply": "2024-08-23T11:30:07.589517Z", "shell.execute_reply.started": "2024-08-23T11:30:06.496879Z" }, "trusted": true }, "outputs": [ { "data": { "image/png": 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize resulted augmented images and masks\n", "augmented_dataset = Dataset(\n", " x_train_dir,\n", " y_train_dir,\n", " augmentation=get_training_augmentation(),\n", " classes=[\"car\"],\n", ")\n", "\n", "# same image with different random transforms\n", "for i in range(3):\n", " image, mask = augmented_dataset[3]\n", " visualize(image=image, mask=mask.squeeze())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.592216Z", "iopub.status.busy": "2024-08-23T11:30:07.591752Z", "iopub.status.idle": "2024-08-23T11:30:07.606959Z", "shell.execute_reply": "2024-08-23T11:30:07.606000Z", "shell.execute_reply.started": "2024-08-23T11:30:07.592181Z" }, "trusted": true }, "outputs": [], "source": [ "CLASSES = [\"car\"]\n", "\n", "train_dataset = Dataset(\n", " x_train_dir,\n", " y_train_dir,\n", " augmentation=get_training_augmentation(),\n", " classes=CLASSES,\n", ")\n", "\n", "valid_dataset = Dataset(\n", " x_valid_dir,\n", " y_valid_dir,\n", " augmentation=get_validation_augmentation(),\n", " classes=CLASSES,\n", ")\n", "\n", "test_dataset = Dataset(\n", " x_test_dir,\n", " y_test_dir,\n", " augmentation=get_validation_augmentation(),\n", " classes=CLASSES,\n", ")\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)\n", "valid_loader = DataLoader(valid_dataset, batch_size=32, shuffle=False, num_workers=4)\n", "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create model and train" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.608742Z", "iopub.status.busy": "2024-08-23T11:30:07.608339Z", "iopub.status.idle": "2024-08-23T11:30:07.613493Z", "shell.execute_reply": "2024-08-23T11:30:07.612605Z", "shell.execute_reply.started": "2024-08-23T11:30:07.608680Z" }, "trusted": true }, "outputs": [], "source": [ "# Some training hyperparameters\n", "EPOCHS = 20\n", "T_MAX = EPOCHS * len(train_loader)\n", "OUT_CLASSES = 1" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.615277Z", "iopub.status.busy": "2024-08-23T11:30:07.614938Z", "iopub.status.idle": "2024-08-23T11:30:07.638455Z", "shell.execute_reply": "2024-08-23T11:30:07.637474Z", "shell.execute_reply.started": "2024-08-23T11:30:07.615245Z" }, "trusted": true }, "outputs": [], "source": [ "class CamVidModel(pl.LightningModule):\n", " def __init__(self, arch, encoder_name, in_channels, out_classes, **kwargs):\n", " super().__init__()\n", " self.model = smp.create_model(\n", " arch,\n", " encoder_name=encoder_name,\n", " in_channels=in_channels,\n", " classes=out_classes,\n", " **kwargs,\n", " )\n", " # preprocessing parameteres for image\n", " params = smp.encoders.get_preprocessing_params(encoder_name)\n", " self.register_buffer(\"std\", torch.tensor(params[\"std\"]).view(1, 3, 1, 1))\n", " self.register_buffer(\"mean\", torch.tensor(params[\"mean\"]).view(1, 3, 1, 1))\n", "\n", " # for image segmentation dice loss could be the best first choice\n", " self.loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True)\n", "\n", " # initialize step metics\n", " self.training_step_outputs = []\n", " self.validation_step_outputs = []\n", " self.test_step_outputs = []\n", "\n", " def forward(self, image):\n", " # normalize image here\n", " image = (image - self.mean) / self.std\n", " mask = self.model(image)\n", " return mask\n", "\n", " def shared_step(self, batch, stage):\n", " image, mask = batch\n", "\n", " # Shape of the image should be (batch_size, num_channels, height, width)\n", " # if you work with grayscale images, expand channels dim to have [batch_size, 1, height, width]\n", " assert image.ndim == 4\n", "\n", " # Check that image dimensions are divisible by 32,\n", " # encoder and decoder connected by `skip connections` and usually encoder have 5 stages of\n", " # downsampling by factor 2 (2 ^ 5 = 32); e.g. if we have image with shape 65x65 we will have\n", " # following shapes of features in encoder and decoder: 84, 42, 21, 10, 5 -> 5, 10, 20, 40, 80\n", " # and we will get an error trying to concat these features\n", " h, w = image.shape[2:]\n", " assert h % 32 == 0 and w % 32 == 0\n", "\n", " assert mask.ndim == 4\n", "\n", " # Check that mask values in between 0 and 1, NOT 0 and 255 for binary segmentation\n", " assert mask.max() <= 1.0 and mask.min() >= 0\n", "\n", " logits_mask = self.forward(image)\n", "\n", " # Predicted mask contains logits, and loss_fn param `from_logits` is set to True\n", " loss = self.loss_fn(logits_mask, mask)\n", "\n", " # Lets compute metrics for some threshold\n", " # first convert mask values to probabilities, then\n", " # apply thresholding\n", " prob_mask = logits_mask.sigmoid()\n", " pred_mask = (prob_mask > 0.5).float()\n", "\n", " # We will compute IoU metric by two ways\n", " # 1. dataset-wise\n", " # 2. image-wise\n", " # but for now we just compute true positive, false positive, false negative and\n", " # true negative 'pixels' for each image and class\n", " # these values will be aggregated in the end of an epoch\n", " tp, fp, fn, tn = smp.metrics.get_stats(\n", " pred_mask.long(), mask.long(), mode=\"binary\"\n", " )\n", " return {\n", " \"loss\": loss,\n", " \"tp\": tp,\n", " \"fp\": fp,\n", " \"fn\": fn,\n", " \"tn\": tn,\n", " }\n", "\n", " def shared_epoch_end(self, outputs, stage):\n", " # aggregate step metics\n", " tp = torch.cat([x[\"tp\"] for x in outputs])\n", " fp = torch.cat([x[\"fp\"] for x in outputs])\n", " fn = torch.cat([x[\"fn\"] for x in outputs])\n", " tn = torch.cat([x[\"tn\"] for x in outputs])\n", "\n", " # per image IoU means that we first calculate IoU score for each image\n", " # and then compute mean over these scores\n", " per_image_iou = smp.metrics.iou_score(\n", " tp, fp, fn, tn, reduction=\"micro-imagewise\"\n", " )\n", "\n", " # dataset IoU means that we aggregate intersection and union over whole dataset\n", " # and then compute IoU score. The difference between dataset_iou and per_image_iou scores\n", " # in this particular case will not be much, however for dataset\n", " # with \"empty\" images (images without target class) a large gap could be observed.\n", " # Empty images influence a lot on per_image_iou and much less on dataset_iou.\n", " dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n", " metrics = {\n", " f\"{stage}_per_image_iou\": per_image_iou,\n", " f\"{stage}_dataset_iou\": dataset_iou,\n", " }\n", "\n", " self.log_dict(metrics, prog_bar=True)\n", "\n", " def training_step(self, batch, batch_idx):\n", " train_loss_info = self.shared_step(batch, \"train\")\n", " # append the metics of each step to the\n", " self.training_step_outputs.append(train_loss_info)\n", " return train_loss_info\n", "\n", " def on_train_epoch_end(self):\n", " self.shared_epoch_end(self.training_step_outputs, \"train\")\n", " # empty set output list\n", " self.training_step_outputs.clear()\n", " return\n", "\n", " def validation_step(self, batch, batch_idx):\n", " valid_loss_info = self.shared_step(batch, \"valid\")\n", " self.validation_step_outputs.append(valid_loss_info)\n", " return valid_loss_info\n", "\n", " def on_validation_epoch_end(self):\n", " self.shared_epoch_end(self.validation_step_outputs, \"valid\")\n", " self.validation_step_outputs.clear()\n", " return\n", "\n", " def test_step(self, batch, batch_idx):\n", " test_loss_info = self.shared_step(batch, \"test\")\n", " self.test_step_outputs.append(test_loss_info)\n", " return test_loss_info\n", "\n", " def on_test_epoch_end(self):\n", " self.shared_epoch_end(self.test_step_outputs, \"test\")\n", " # empty set output list\n", " self.test_step_outputs.clear()\n", " return\n", "\n", " def configure_optimizers(self):\n", " optimizer = torch.optim.Adam(self.parameters(), lr=2e-4)\n", " scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_MAX, eta_min=1e-5)\n", " return {\n", " \"optimizer\": optimizer,\n", " \"lr_scheduler\": {\n", " \"scheduler\": scheduler,\n", " \"interval\": \"step\",\n", " \"frequency\": 1,\n", " },\n", " }\n", " return" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:07.642407Z", "iopub.status.busy": "2024-08-23T11:30:07.642034Z", "iopub.status.idle": "2024-08-23T11:30:09.551807Z", "shell.execute_reply": "2024-08-23T11:30:09.550754Z", "shell.execute_reply.started": "2024-08-23T11:30:07.642359Z" }, "trusted": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth\" to /root/.cache/torch/hub/checkpoints/resnext50_32x4d-7cdf4587.pth\n", "100%|██████████| 95.8M/95.8M [00:00<00:00, 127MB/s] \n" ] } ], "source": [ "model = CamVidModel(\"FPN\", \"resnext50_32x4d\", in_channels=3, out_classes=OUT_CLASSES)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-08-23T11:30:09.553779Z", "iopub.status.busy": "2024-08-23T11:30:09.553376Z", "iopub.status.idle": "2024-08-23T11:34:35.862427Z", "shell.execute_reply": "2024-08-23T11:34:35.861229Z", "shell.execute_reply.started": "2024-08-23T11:30:09.553732Z" }, "trusted": true }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Sanity Checking: | | 0/? [00:00" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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4gft15H0wq2fPec5zsNby1re+ddftv/7rv44QghtuuAEICwF79uzhox/96K7Hve1tb/sK3kGg3vZv/MZv7Lr9zW9+8wU9v9bmv3fS8OW47LLL7vM+fvd3f/c+FY2vdPsNDQ27ecELXoC1lp//+Z+/z33GmMln7JnPfCZJkvCbv/mbu77HLuQ74eu//us5evQob37zm+/zmd25rQv9XCul+NZv/Vbe97737aoSnz9/nve85z188zd/M9PT0192vxoeepqKxtc4z3ve83jDG97Ay172Mp785Cdz00038e53v3vX6gMEKbu3vvWtvOhFL+InfuInOHDgAO9+97snhjj1qoKUkne84x3ccMMNXHvttbzsZS/j0KFDnD59mg9/+MNMT0/zv/7X//o3f58NDY9UrrjiCt7znvfwohe9iKuuumriDO6959ixY7znPe9BSnmfPun749u//dt5+tOfzs/8zM9wzz338HVf93X8zd/8De973/t49atfvWt+4uUvfzn/7b/9N17+8pfzhCc8gY9+9KN88Ytf/Irfx3XXXceLXvQi3va2t7G5ucmTn/xk/vZv//Y+Vc8H4rLLLmN2dpbf+Z3fodfr0e12+cZv/MaJFPcD8fKXv5wf/uEf5t/9u3/Hs571LD73uc/xgQ98YFdFtt4/pRS/9Eu/xObmJlmW8YxnPOMB+8wbGhrun+uvv55XvOIV/OIv/iI33ngj3/qt30qSJNxxxx28973v5S1veQvf/d3fzeLiIj/5kz/JL/7iL/K85z2P5zznOXz2s5/lr/7qr+7z+bw3Ukp++7d/m2//9m/nuuuu42UvexkHDhzgtttu4+abb+YDH/gAEBY3IYhQPPvZz0YpxQtf+ML73eYb3/hGPvjBD/LN3/zN/OiP/ihaa97+9rdTFAW//Mu//NAepIYL52FSu2p4AO5P3rbb7d7ncddff72/9tpr73P7kSNH/HOf+9zJ33me+9e+9rX+wIEDvt1u+2/6pm/yn/jEJ/z111/vr7/++l3Pvfvuu/1zn/tc3263/eLion/ta1/r/+f//J8e8P/4j/+467Gf/exn/Xd913f5hYUFn2WZP3LkiH/BC17g//Zv//YhOAoNDV973Hnnnf5HfuRH/OWXX+5brZZvt9v+6quv9j/8wz/sb7zxxl2PfaDPvffe9/t9/5rXvMYfPHjQJ0nir7jiCv8rv/Iru2QavQ+S0z/4gz/oZ2ZmfK/X8y94wQv80tLSA8rbLi8v73p+/V107NixyW3j8di/6lWv8gsLC77b7fpv//Zv9ydPnrwgeVvvvX/f+97nr7nmGq+13iV1+0DfZ957b631//k//2e/Z88e3+l0/LOf/Wx/55133kfe1nvvf+/3fs9feumlXim1S0rz3t+LNff3PdjQ8LXAvWOJ++NLfc947/3v/u7v+sc//vG+3W77Xq/nH/OYx/if+qmf8mfOnJk8xlrrf+7nfm4SYzztaU/zX/jCF+7z+by3vG3Nxz72Mf+sZz3L93o93+12/WMf+1j/m7/5m5P7jTH+x3/8x/3i4qIXQuySur2/753PfOYz/tnPfrafmprynU7HP/3pT/f/8A//cEHH5oH2seFfhvC+mXppeGDe/OY385rXvIZTp07tksNraGhoaGhoaGho+FI0iUbDhPF4vGvwK89zHve4x2Gt/Re1XDQ0NDQ0NDQ0NPzfRzOj0TDhu77ru7j44ou57rrr2Nzc5A//8A+57bbbHlDirqGhoaGhoaGhoeGBaBKNhgnPfvazecc73sG73/1urLVcc801/NEf/RHf+73f+3DvWkNDQ0NDQ0NDw1cZTetUQ0NDQ0NDQ0NDQ8NDTuOj0dDQ0NDQ0NDQ0NDwkNMkGg0NDQ0NDQ0NDQ0NDzlNotHQ0NDQ0NDQ0NDQ8JBzwcPghy87Sn99E28tQmsSnWDKCmstWZbivacoCqSUgEdKBYDDo5MEAZiyxDuHi2MhAgFCIAQonSKkJGllaCUYbfXBe4I7C0RjavDhOXiPEAKl5cS1WghBt9fm2/7dc7j82ifiREqWzKG0YHZ2Bqkzjlx2AJUINgYl/WHJ6ZNn8VZz5RVHmDuwh7WtEe1OSlFVzM52kYlCS9jYrPBeMT0j8ULgHCgV9s3auEvxNxK8A2PDY5wFT/y3AzxIEW6rzI7bASnDv70P//YOrAMlwcTHIsA7j7WgtcA7Hx8vqCqPUuFYOOtDJinC6+gkvLY3HqUEHoG1Hu88RV7RbqcIJfDeYwxoHfbDmbAvCE+3JZjtCpT3JAKSeAwSKUi9xwEZgkSEcycQOBwekMTzFO+xuHC/9xjAIyi9ZVyWGAt5USCFDtdWXrF6Zo3XvfqnWFq/i6rMMUWBR6BkglaCssiZn5um2+uStjNOHj9Dq90Kx640DPtDSmOZ278XW1bMzkwxHlf4Mgd8vC49Uko8gqoy4D1SCLLOFAcPX8KpY3fjfYWLJ2xza4gxFSrRdKbn0VLwnd/3HZy6e40b/+HvcZQ475BCYJ3FGYfHo5SM50zirEUqiRQCISV4j3OW9fU+xnkWDhwA2cKYgvbUFI9/8pP4g1/76nQ5fZb8nod7FxoaGiIfdO99uHfhQdN8hzQ0PHK4kO+QC65ojDb74BxaaxQhiLUmRNDWOpRSCCEQQuA9eO/x3qOExDmHrSNpIWMSIvD4+DiB8y5E2MZijQch6+iWNMtod7sopQmxqg8BvQDrXExEJEmqecozr+ebnv4sxqUlLwwnTt9Nqw2DwTlMtYUb5mysjDm/NEBIxey+BdY3lrjjzru5++5TTHVadHpt5vf0sFaGbEAKpqYT2lOSmBsBIcAm/ggRciAvdhy0eJ9z8a3ExzkPbsfzBSGhkCImVfFvfHjszteptxvfPsJ7nJvkXtQvX9/vXHyaAOLjiOcIXydEhnY3wYvt1wvnhsm2pYQshUxDIqClIJPQIvytgQzoAJkABSg8kpDshLQGRHjnbB+mcI/zlsobSuPIS4tzPiZQFoFDSs/q0lnGwzVsWYGXSJ2GLTpDWVUY69gajJFKMTe/wPziAlqJcJHXxxHAK9JWmzRr4wmJlXMOH954SAAQaKURUoEQFKMhJ499Eeeq7T2XgjqTc85h8hwFzE53kcLisXjv4ut7BCGpxrt4XWxfPM65yekVQoCQk9euT7oHRCPd0NDQ0NDQ0PBVwgVXNLwUGDxpluKsw5YGRAienLMUhY2VhVBp2K5GOAQqBEkiRNs+rhJ7D857tJJIpXDGoqTCWEtd89CJpjc7S2VNWGG2NryGDDmSdx4vQWrJdU94HC/69y/l0JGjXHLJFjfdeYzDhw8z3e0w1T1AmrVY2tzkwOGjyFlHfzjm0ZcdZna+y+bymIXZKc6dW+bo1MGw4tyKVYu4L0oTkhvLJKKvE4JJsrEjmRA7ko5JuOjic+Jj4gL25D4hYxJCrCbYcFs4ftvJxM7kYqdumKi3J3xIXnYkOD7GxXWlREpPWRranZAkmgqkComLEiIEtc4jpEBK6GaCbgodCQpBAmgBynuk9+hYpamTikngjEDuypRiIrPjP+MsxkNZuVCJUZ40TbA2VBW8c0zNtpGZRIzAVRaURKFjshXKSXlRsNkfMb/Xs2//fs6cOIGrQiVNSInEY70DA946fJ0Ah50Ke+jBeRePa7jGPR5nDUIIpAwfGx+rNr35HvloTJkP6XYT2lnG7MxUeO+x+iSlCBW8OrHwHmctVoRaj5Qh4QmVqPoC276AvLchcfMOa7eTnYaGhoaGhoaGRyoXXNHoTs/QanXpzM4hVYLWCVprpBQgQGqFVAofqxph5T8mE9aF6Hln8CQEUimUkqG1Rni88BRFTlmWMWAX6CQFwBqD8yEo3dkqJZVAKcXCvj086SlPY/ncFrfceDvDDZhKZlleOc1tt93Exuoap86dY3m9z213n8MUisU98wxLx+L+QwglGAxKuu0250+v4ar4OmISPW8TW5/q4F2oHffHWNo5iIWbGGwyqURATBDqVitACI+NLVCTagOEZC4uYwvhcSYcgzo+3pmATJ5nY7tWfacQqLAphBeT/XDGIXFIIcP+xcdIPFL67cpNfA+JhqoKL6QJP4pQyUh2lFsm1ZYdP2HXPC42Urm4YestFofxsaXOO6QAFX+kD5UN5wxzCzPM758nbWt0SyC1JWlpZKaRaYrMEkCxud5nbWUtJrzhGhRSIoUMSYML2V5RFAjnUVqCFBhrwjHAoxNN2skoq2pSEbLexbTIxeRLooTk8CUXcenVl9PqZOTjgrvvOIVwOy8YMUnC63MModXNOYdzZlIBNMZijMNUBudcSHG8ixWPkNhkmXrgD2pDQ0NDQ0NDwyOEC65olHlBkmiKwTAEj1JSVbE6ISVZllGWJaY0yBica63i3EBoIYHYUhXbRLRWYa1bMOl5N9bgiUGcCpWQ0Xg8CcLEjmRFiLAS3Jtqce01V6MTy+bWaRb27uPU0m0IFN/0hMegrCWvHK09h7jprpN4YdjaWCdp70GnCidh8dA+zp1eQ2YtpqdSls+ssXBwASdF3a1171xiwqSKYUNysbPCsHO+ZOc8R/23r5OY+DOpYgi2X7eudMRtCb9dFQltUPWMRqyS1LcLP4nynfeoSeLnkUpgjCNrp2G77EwKwtyHkCDjzIbzgq2BxxqPM4J9U5DE96gIrUZ1MgHbzVH1dIafrOeLyfFzQOlCJSxUEEJrkRRxSz7MTXjv8N6h221e+EP/njvv/hxeWFpZyo2fuJmLr7iE2z97K5dffSkffO/7GWyNWDq3Qtpqx7aomMDFY+6cR2pBu9MiH5c45zHWUxmLkhIpE7yDSy+7nLuOHWO0tkmWaup5IocnpCzhv6npaRYP7AchOH3Xcf7ub/6OSw5fsZ0ceJCTZ4SWKylEuLa8wFqLUOE+Z2PbmHeTC8l7F3Ndi9QSJZtEo6GhoaGhoeGRzwUnGiIGRbsWasM9QEgg7o8vaQd4Qf3mMWTduTR+L5zzVJWhKg2eUE1wDrSSVMaQj8dYJMqEIV6t6lmSOpANsx5hxTsGoirOY1zILtbU+/dgniR2/bqfP+5/e/WMygVs+kvjL3yH6wKPfBDHRexILB5om1/6+bsZj0b0t7bwWKosoSwKBv0BZVEy6A8ms0B1MP+lNugnwy/3sx8iJE3WbPfJbSeNux9cJ8/GGLzzaJ2QpMm9N7d9HPy9bwivJb7sURUX+JlpaGhoaGhoaHj4ueBEQycJ48GI6YVZhht9vLOhr9yFleeyKCfVDRF726011L1DQmwPIwi53bEfZjhkUN9xlkQn2Dica61Fa02n02Fc5GilsM7GPRJ477DWMxzl3Hrb7Tz+iU9ipneQVjrNxYcPcWp1hU9+5lakc1x2yWWc3zwOFQitmZ6bo9XSiEShNKyeWaLb6qFVWNnee2gOp+rhXSYxXh1s76T+U+2sKIRDsGteQ4gw82HsdutVHKFgMhccZ+BrpapQmYivE9WuJpWK+CI790nK2JJV917VbToijGFrEaoUeEgSyTiv6HTTydB4XZUQsamurn5IAdNdgXSCuTRUMWrCWfaoHXWR3TnXdh1DxGpAHS8nIlStpJA4BN5bnBdh/T+23wkR2p7Ggy3+9F1/zJmTd1EUJQ6LFAm3/NPNOOu49cZb8aUlTRP27V+k3WnFmYq4rZi0SiVwHvJxEY+ZQHuB0wohJNY5tFLc/cUvkvdHpGmy3bbnZXzvfnKs+pubrK+ucubYSbRKeOa3PYNyZLn9thtjdWjn42OCE1ulwrGujxIorcCG6k7dtieEDOfXSZx1GFt/BhoaGhoaGhoaHrlcuOrU1iZlMWKwto4zFcYYTGWwLgS03lmctYgYQEG9AixCdUB66jHnOtiy1mJtkDgVPvwkSUqSpHHuwFOVJc45VJT/lIgd2/dY67DWsbG8xoc/9AH27JviskcdIfcbrGydZe++w1x62ZW0pjocPrCXvfMzXHP5PqQqWV5eJZWOU/ccA+dptRVFVbL34CwiysZuy0DtPh677orzFzsfUsvUUj+ulqXdceSFigkG4L1AKbFdMdjROjUZFvcgk3BMVYz0xeQ475j3UOH1RX2n91jq1/fbSZOSgMQ6O0kswgiHwNrt1633oahCa5gTgorwY4ESqHalEtznRxDaq+oWoiDW5FFCIZFoqWJCIHEerBcYF15LSIFUmvXVLTaWNyjGBlM6nFFUeYUrDbas8GUFOGbnpplbmIvteD5Wr1xsz/Ih6RWSrNXCS4k1LlQiVJ13C6wx2NKSJElMnAVK1O1PMs6NOKx3nLz7BMe/eIwyr8haKYcu2YcTO5OB7c+EkmKSiDoXEnMp9USxTSmF1opEJxOZ3ZBo1Q1qgrLcMcDe0NDQ0NDQ0PAI5cJbpxBB1rasQAik1lgblt2VVOhEMx6PEUKGKoWMrgmidk0QMTjf7r8Pdwd/AW8FSkocYThXlAAeawz9jXV0kuCM2Q5mY5lAShnnIzy33nQL7/rdt/O87/1eTp8b4NEcv+MWrrjqCFUxZnZuL1dfehmbGxuc3xjR6qR84fO3c/yLx5ifXyBJ2+w/tA+LAOOpSo9AopOw36aCNAWlxER5Smwv4gfVqLqyEDYxoU5KhGQiUSpFqG7oeBaE2J2s1BWQnQpTcsd9Ls5ubA+Uh32QKiQLQkbpWxX3jZBIOBsGrb0XJJmmLCvSRMaV9fA8W1ddVFiRtxZGhQcvUBJSQvLiZKhoaCHQO87pzoYpH4e/7+2jUf/nAS1Cq1qSSCprEQLKsoq+LAKEZNwv8GXYR6U01hqctTEYDwe53W7Rm2oh8Jw7cwbrTDxP28mBlrVnxb2OeUzuQrInJ21YPmaRKsmCUpQLiQkyKHONtkZIJWi1p/FeMxrnrG+OwjuNybafXPsh6VEqnFylVJTWDUkIUZHN1lnq5HxLHCHxUuqCP7YNDQ0NDQ0NDQ8bF646NdMDKamsCcOwMqy+IkApibV2kkRIsd324rxDRoWeWPqYrDRD3RoTAjsvJagY8NYmDniqsiIfjoLsZ1xdrluNZNSH9d5hKssn//6TfOh//wX4HCEsl156BZIOhw5dTrs7x0BAq6fYs9DCu4r++ib79h3mmkddxaWXH2ZclPQ3xqwu90lUVF6ynsFmST5y25WLeycZnqjqFI5X/RhErAK47cfV9gs7n79z2NuznUCIna9TS9Wyo1ogxHb1BHYlKp5Q2ah9JOrX9zHrqbtztNaMR9Wk1StUU8Tk3/XQelFBaUMClbtQySgIfxsgB0ZA4cECwQFj2znDT37vTkMEHikUidCkWtFKVVzpj14SSJyTLB44RKc3h0oU3hucKfEEc8gkSUiUotdt44xhbWWZ9dV1jPO7zxcE3418RJGPw3uUAinVDrU0cDiMs/jYw9bpTXPJFVcjZbLdtebqiyHILassxQFb/RFSaARyUqEJCbePiUcoR0m5fSZVLddMnZC4+No7kzd2+7Q0NDQ0NDQ0NDyCufClUSkRcfW13WpR5uVEKcraoK6TJAlpllEWRVg9ji50UoTech8jvlqediLH6kOwa51DifDbA0olJDqhLAuMtTu8CAIeH1Z+42yIkop8XPK3//tvufvuczzhm5/OzPwCswtTDItNpJScPjlgcX6Ok6dOs766yezsXi658jAmSegPR1QmBHtaZfT7eXxvEqU1U1MhCdrZqlS3FwkRKgfGbc9o1O1LSgVX70mg67e9LMQk2IeqYpJBhGrQ9o8Xu59DvY17V03ifV54bF3diK/rJ8mImMTIgpD1JJmiLCpa7WTbf4PgHE7cF2uhrKCywRVcRs8P44OqVe7AeuioYNxX7/52DWO7haqe5PCEpGQiius9iUpAeKzVCKGonEFoQWu6S6vbpjpbUVaW6ApCmiRUZU6SJSAFg8GI/tmtyTSIFjK2joXtV6XFVh4caKEwsbWqfqPW1hWG4N7darV51GMfi9QZ4o4v7qiAhPkPDyFJTjTOOjrtFt1eGy/VtklizNbEjgqf0BLvHDKaXToX5IJD5S8m587go7M4QqCUZKbXuaCPbENDQ0NDQ0PDw8mFt05ZH5KBytBKMhKV0N/cnMxaeO+ZnpmhtCbMXcRKRb38bo0N7SsxpAyD42IyGFtVFQhIE0Vptp2TrTWh4hGX5UOP/fbKv/Ue6YmeA4AXlIXgzLFjnDp8kLWtEVPtNs5YLr38Ig4cPszqusNWFZce2M+Ry6+gzFrkxoKDbiul20uCAZxqMdgqaWUZ3Sk5kbqVhIC6jjjrAN/DpEa0q53Jb/8tZQjYdzx99/hHrIDUj5kMksf7bL3NHRPpzoltbwax3WoVEo56R3zc1vbKOiL6ceggNVwWNhjbxXkB7zzOhjY4GxPCyngGY0E3CzviHOSFRWuJVmCtx2ehLUkSWrTwYZZDCh8dw2sLvHBf4TxWhHTExNcTKlQatisCCo8iS1pgXTAUVCo41UuBTMOEelUZwFMZR9JuIb1AiDDQHo6/IE0SchPbtryhLCvwjrKsSNIkVBq8QGuFSlpcee2j2XPoIEunl4LvRZSbrfvhQrUhJAHeOLwQTE11UEpS1v4pUqB1gjEGZywOgdKSJEkwzgavGbY/D3VSXjuWu5gICQHTndaFfmwbGhoaGhoaGh42LjjRGPYHdNptqrykqkpkkkwmnq1zQTUKh6kqkiSJARlxdT4Ed0VZhARDyDAg7msfPzFJPrY21kMyEYcePKEsIKQMwZb3secntg2xHcRDXOG3luHmgFO338P/50degOp0Kcsx99x1J2V1hu50h/HA8g3fch2tbnCfzkdjFvbO44Vkc6MPMkFIi9KKbk+iku32JIjVAblDYSruh6irFDJUMe7NdtK0e793/l0nLXXL0uRxfvfjnQtKV7WHxmRonHoCYsdgfpzVgLDPk8eKoETlPKRZSllW6FTHRG+7BSx0/4RV96IEbz1ZGhKuqgLlQCvwzqN0aKNSsUXMOU/lIFGQijDfYbwPszBAYT25cWgVFMu0FMHYLpxdPBJjQ3AuZRrUqQTgLGmSkY/HzM5MkxcFuGhrJyRaJ5NkVtXuiQSjSKk8+XhEZarQ2ic0xjicdSQ6RSpJkqQcvfoa9l90KAT/zm6fhHhiBCGpIia8RVFyfmWdluwEV3XvsfF6lsJNni6EDAIIIogghHPt8ELFCyj04UkZh3rqtjzn2Bz073thNTQ0NDQ0NDQ8wrjgRMN5T1FWeAFplmFjf0+dIDjnGA2HeOtJ0hQvRJToBGMqamdkAI+Lw8n1KrMkaWWYclu7yMWExCGwPhieSaIJXS2jq1TobffBRyOs2EcnaeD08bs4cfKf2X/lYyhLz1Oe9iRuvf0O1pZHPOX6b2aMIalKtvol0icsrWzS7+fsX1yk3U1Dq5gAlYTWHkHYvqsHq12IpOvxk7ryUOcFO4P/e7da1fdbu93SVD9uYu/g4liGA+Kch5okI7WL+PYsBYB12/uB3FHd2PGYuv0qjhdMXkvJ0AJU5gad6Mlw+ERdK1ZInA+zGraM248KUcZCIiXWe0oLWoWqSFX6WJ0RGOGplKCsfDRqDM8rjEdbh4oHclyETM57QWUFlRFYFEKnYTjdWNrtjHw0ptvrTvYVCSK680mhMN6h4jURZoVC0iomCUhInuo2QK2TeIwci/v3cujIIbwSGFOysb6E96HUZKJTPfW1AHgpsA62NoYks91Q8Ykn01kbzA6dn7TTCQHG2CgDTewcC48VdTYaP19h2iWYVg5H4wv92DY0NDQ0NDQ0PGxceKJhLdl0j9KUbKxvhPab6HwMgPe42B6VtTMoyyDD6VxY0RVhZTs8NgRmqvYP8GC9w4lgdmbrykeiyLpTjAcDfGyhEjFqrpWNhAjDxpNonzpY95RFxXve8Q7+n6c9i8dffwN/9cG/gzLhSU/9Fu48e57ZQYtLL9rH1YcWWTXQN5as1UW3JB5Ht6UZlqGCouL+F2VoD9JJSIlkDBpdVGmaBO73qnTsZOeQ95eqbtQJQvDFCFWSnYkKMBkQtzbsR606VW/D7Xi8B3ChhUrJ7X10jugELtCJwhpLVVVkWTrxeKirGgioPEgb5lGcC8E6AqzxyFRQ5CBS8HgqC0UV1LuKWslKgjF+okRQVpbgEO9xUuArT2EAL3DWUVUe4xQWj0pThPdkaYIUoDNNK0upygLhbawABN8NhMRHVaq6QmRjG5MT4d9KhBkiGzS04o/eft9YvPHcfdutnLjrrnC/kGgdnMJNleMNCB1mT5x3lMaxvjWKylGhCiekjO1ohlhzQUBwBSckPz7OcIiYrHkRWgO9C27kxjm0s/Q3m4pGQ0NDQ0NDwyOfC080jGU0GCI8mKraNhnbMURb1yuGg0FYUUbgYgUCJFKGQdtdQXj87Ss7aQ2pKyFKStrthGIYfSDqx8aKRqITUBopBDqBqsxxk+pJWJWu+gW3fPrTXHrFo9k7d5gnXf8UZvbOsbwxgKriC3eeZnSkYGtYYWhx5KJFSBK6LUVeObQA5T3GhCDeVh6tRJgRiB0usaU/BI87JFPrlqad1Yyd1QfYnXQYs6OlyYHXTJKySQuTj7mFF9uVlB2qU5NjRJSoldttafWGvPOxNCJAxlRxx5C70pqqNGH13YtJguNFCI+d39FyRVjFVwpwodJR728hQrXCGI9SHl/5ibITHkRc3a+sR8uw+l8CwjhM5ZAqVABKaxFC43FIHfYnyxKKYsz8/GxoaXKh10vIbUWrWlpWCYGN4b0gBP3TM3OYwQrCO1ysyAVzPzkZ3h4Nx5TWcuLOOzh+113g62Q2muzFE1dLFISk12Oswbjg16GlwoX63+QCkPGk+kl1Q8Wq4GTPt6WGa/EED3hBt9XF79RNbmhoaGhoaGh4hPIgBPk9pihxzoZgTogQ4AuJMXGV1kdjNBckb4UAXG1M5re9JEQtSesnfhveu5AYlC4GZw5TlWyuroVqiQ/DsiJ6ETjrqCjBGHozM4g0QYwlxXCIn7SehKB47fwyn/jQ+/mB//if8ZljaXOFsjSUo5yt9TGfPHk7+w8e4JrrDpJ1WygBCk9VGLwQVB5aqQTlKKqKTjejjPlSXcXYWZ3Y6W+xs3IxaWNiO6FwbttHo261qisZUuwYBpdMFtxF7a3BdpVDBAGm4JmxcxsqJkO1f4esZVXD39aB0nHA3cRziCDJEsrKoLUOwa4PyYqPbvAyBr8SMHFuxnqoYsxfmuBX4X2o9jjAOBckkb0HQ6wS2VC5EB5rw3a9D1WqT33ig+yZ7XJmaQmtFPPzC4yr8ygpKMZjZmemSJSkLB1SyBiQx1qBErQ7Gb4UCKogKSxCm1672wJjKa1BqXCtgsa5ICoQni7JR0NO3HkHd9x6B0IpDh69gnP33IWviiAbLGQ0XbQoryavH5TKgkqadSEpDOfcx2s5tk7J4LQOMfmrP2mhz2ziCmmdQSvFwp4FLrnsMFo3iUZDQ0NDQ0PDI58LTjS01kgpKIvQTy4Iq79pK8ONhngfVoS3VaHE5O8k0TgXEou6ZUTEgFWEjcUExaFVQrvbZTjsh5lvD3biJ1C7PIcFXmODU8NgcwuVJegkod3tMB4OcdYhY5DsgLvvvIO/ft//jyc/57tpdxeZ6kxz8cEDLOzZx3SrTW9KceLcOp1BzvR0xnBc0Ekyup0W8y2J8pZ+XnFgthUXtj3SCzShrSXMq4jtFiO/o5Uq/r1TecqYexn13WuWY0eL/kRBCojGd7GKYeOwdTzmEGYQQkLjJy1kO9uewmD9tuqUkjtmDBATV3KlBM4JnDNonYQ2MVMPv8cNSibtY/XNQrBLEtfHnRYehNs+Ps4HdaZaf2rHWj7eee669Rbe/bbfphxvUJQlriyRSlEMc4T0KDSzs1M4LyjyEVArkqnJcSvKAmndRKpXIJiZncUZw+bSebIkmRyj7UH6oPSEF4xGOTd/5gsY65he3MPc3r2cv/s4UE4S3pgm4LwLLXwOnDW4aoSnjFWSWMlDTM61j/vrvEfUSl++ThJlnAUJVRGlUg5ddDHze+YZbJzljptv+bKf14aGhoaGhoaGh5sLTjS8c7ja4S0GtiqRVFVO6EMPwZMQIs5e1IFfHUXH+Yko4Sl2TCfXbT1hNdsyGg3ibQI7MbKrjfxCC09YJA4+CdZWiFKgdUrlHFq3qGwRkg0pEc5RjQv+8YMf5Iorr+RJz/kucuO5+/RxWmmbfXMHGTm4+OAcUggGo5I93TaZt3S1w/c3sAL2T01TqdDb31Ng8WjACNj0UUGrrhLEINxUIHVsedq54K7uv9Jx75mNemB8kmxE8SRZu33H22VUwLLU1RSBVH7SmiYEE9UpdpgHxi614BYe5zZqw8Uk1eTjMp5vEWVfw+Osrcfj/aRVrQ67t2dpYhKkxPa92zkI24nRdmOciApLd3zudjZPnqOoRgjnsbFCggqJZ6/TJs1Szi2FilddrRDSg5dID95apFAIEZJYlWjmFxc4d+YsiZThWEvAiyilC7W0rJOAsZjSkrZbSK/ptDsxoQolK+d9rEQopFdIBN1uj7y/ybnjx/DWb7ecRd3hbRdzJopg9WdESjA2HE9rHUJq9uxd5FGPeQxJBnfdeiPnTpyIF1pDQ0NDQ0NDwyObC040bHRIdj4Er4lOcT6a9blYwaiVf2B7iR6wccW2pg5kZXyOtTYM7wJSKqRKsDZHa0W716W/sRXalJQOA75VRVykRgkFeJytAMdUr8dgOEJIQZWPJ/4DEkE+HPMn73wnIm1x8dVPYH5hHwmeYyfWmJ/vIqfatFLN4dkOLTOmynOqjT575uZpJVlYgfchWJQIih1T36nzQaGKHR4bDvACH+NoF6sAUbEXCAnHJJFg9+9JslG3YMl4wnZUPaQEpcW2GV89LxGPL7FyMQn+fVDkkjKE/tb6yTxJnaD46IgugTTRmNKgUz2pdHgfZjfqMyx37WdMMFwI/rF1ohEOh6orB96jhQjJQD0oPbnPMpW1cZXDGovyYUA6bWUURU631cZ6x7lz5xmPKjqdDrWTeUxlo1CACtdmfXw8nDtzmqR2AffheDnrJyWfyXESHmPDZIfWKUopetNdlJQ4VEyWwVqDswalJEpDa0rxxc9/nosuv5wibWNKM3GMd+xukVJSRv+YkHk4R2ytCkPvR49czqGLL2Krv8JNn/kC4/4QnaQk2YPoeGxoaGhoaGhoeJiQX/4hAR+TDB8HAHSWoLMUpAoxtXN452PlI0S9IYDdGcTVTsq1NCtxZVfEbUO72yPptPBAZSr66xvRGwGkCkGkFKCVCqvQSpB1uzipGAyHbG2uI4UPXglJgnUWb13YhnVsLK/y0f/9F9jRCs7lLB6cY26hR5EbtJdMe8ceDSYfstCb5uK9B0iVisGrI8MzhSAT0AKy+NPGIY1BGIOuKkRhobShLcZ5ROVxlceWHh885XZVG6SMqlF+dytVfRxj19PkpAnCjIOStSlevU2PFB4xkXSNCYdgMq4/qXjEtqnJQHvs+ar/ljFZ0YnClBZVJyqufo1wXhHbw+iTFq36/BITiPqEizoDY1JVCRUVNxEW8IDO0mhOGFrfEq2xtqLTTmm1WxRFxfLqFjJJkEpOklwbzVuc9yDVZDYEAdYYRHAOnAys318VBuq2vpg4S4VSin17ZkiSdLs2JwDpsd5hnSUvxqytrFPlFVKF4RhBHPIW2xK6Pr5WZUKV0PtgumidR8iwnwuLCxw4NM+dt3+emz71aYpBTpa1aWUttGoSjYaGhoaGhoZHPhecaDgXXKOlECRJgsfjymqSBOwcJHDOhdVg56ICkw8zHHFbwSFcxGTCxiBPIYSkqgry0Sj2/YvQ1kJY/RVeoESQAa2iw7JxDle/rveMx2PKoiRNU7JOB6nT6JMQfByc99x96+387z/+Q9xoi9u/eIrB+ibtTNBfPcf5M/ewtHyGbqcNUmJwoW1LCDSSBIWPlYtEbP+0laCjJS3pUa5E2wrKAjfOqYY51XCEGeVBE9Z7nI3JgN9WpqqHwXcOc997oFxGx23c9mxEfbsQoJPtQXG/I2FQO7YRkpb4QnHOQ+6ostQDF7Vhn1DhfFWVCcmB2w7KRVRQEpI4aRHncARBMECJ6P3hSZRACYfAoaVA+nBd1PdPnOKRSKkRyHDepUBlCuGg123j8VjnUFKHa0kwyb6C87kIz1MepI/GkdtKUc6FNit8eF8hGRK7ZjXqcy4IiYFxDuGjw7gnziTFgXAREmrpBUnSwSFw1iKRoVoSMyqpwjZlnF2qz4UxFusczlqKogQhGG5t8bl/+jRLJ0+jpSJNQ1VFSonSTaLR0NDQ0NDQ8MjnQVU0amlSYwzVOMdWFfgoSxsrEyFAkzFWDWZo9eq2VAqlQ9uK0mFIQUmBkppWpwMC8mKMr0pgewXew6RSYoltWNEvASSmsmgp6Uz1kDrBFAXeWVqtFkmSgAhVFERY6c7znJs+9Wlu+tRHufTIPqY7bVqtBGzJvrl5WlmbRLew3iOFwitFiWCMo8BjgBIwbLfLCyCVgkRJOpmirWEqFbRkRUKBsjmiHCGqio5wZN6SeTB5MHIrijgsHysGQnqM8XXn0yS5EH47YTCxWFTPeEhZJwDBbVrHzikvdiQSfntWolbHqhMaDzgxOeKTeRIPpC1FWUXt2jrDqe/f2e4lw7kSEqTwpAraKWQatAySuBpPEksoMr4xhUHG5Mc5Q5olk2upNz2DKQzz89MkiaKyVXgfKrrGe4G3HiVlcACPlR3lQEgddlJKPBKHR4lQIelOTU1mJUQsGbm6+jYx9pOoRGK9YzTsk0Qp2rpSEw5XeC9KSWSSggiKV6oe1PEgZLCclFFlLQych2vSWhsM/ZxDSoXWmmKU440na7VRWqMSHQUZ5GS2o6GhoaGhoaHhkcwFL43Wi93e10GoI8tamKrC2TKuGtemfD5KqPrJTICKgaX32+1VtSqV844yDpV7Bza24ygkSZpiqioGipBoRRVlUp0xJEnCzJ551ldX0a0MZSrwMOxv0fbBMFDWA+zxnTjrKMY5f/qHf8CRSy/iwCVXc/cd5/nGR12JlJB22gglkASTOOqAHoHBI6mdE4jOz6HCIQEtFK1UQHQTz1PFsCzJhaOynsqNyTf7WGdJp2ewg5Kx0FROkrUSlJIU44Kspakqj9USpdSkrcrV1QS//XeQhSXMg0STCxerDNb6OHAtJhUT60G62O5UFzZsrKhYsAacqs+nmPiEKK0pxxUiUbjgBhGkjMOFEZKLeJ0oKUgktKQnzQTGOKrCxmHocP3gIXi/x6qXMzgvsc6iU42WwQm8KHN63Ta96Q7D4Rhvt9uaPAIvQfqw83WCi5DMzM3S7w+gAum3K0NpmjK/by/luMAO3KSSM/GEEX7ydz07IoBOt41WKlZeQpWvnlsRQoSWKC2RQjDub+FsThBIqD9BfjL4Xs+lSKlw3uCsJU3TyectVGzCwLlUIYHy3lNVVWgFa2hoaGhoaGh4hHPhiYYQO3rawypsWRZR3UjhTLCvFnWzv4hzG3Wg7hw2rh6HRfGwJRcDzGBMEfpypFTBtTnRdKe6bG5tTtp/bGWwxuKswcdIeTTYwhpDPhwF87MMTFkx7PdRsdcn9MmHVhcpBK4ybCyt8ftv/k3+2y//Ct/yhMfhZAjg2kmGEx7hHamXwScCQuCLmFQxnN9WnmrFKo4kJEpWCCTQyjRae9qpYlwaVtc3OXvuPFVlmJ2bx8qMYSVodbqUVQsnDP31jRCwqoSpqR4IhXPJpLe/riY4D9aE42gNCO0xFdSlhu0h9NjeVFd2HEGil6A2FZ7vEVpgCUmHEyC9x7hQKXBVkFp1CMy4Imun+NiSJrzAeh+ONSDxZNrT1gQJLi9CchlLKM56PI6gLxDnFryNj5FYExLJNMvIiyFKCLq9DuPxiLW1TZK0hUCE6gUybF+AELW8l2RuzyKlrchHW7Sji7eSgkRpFvbuJe10KMYFEF3BXZDiChWD7T4z7y0qJpZKhvar2lnd2AovbKgKeQ9OTK7rjY0+Zlzgo4yvjxWMOjGVQuCRIBwCT5qGJNM5HxMXHWadhEQoibEWU1bheDczGg0NDQ0NDQ1fBVy4vC1BdrNWLvL4YNRXzxMQAlIpRZDBDTdNnhNWcuMYbZii3TEM7kNPTd2SEo3K6pkLfDBBcyIGpSG6jS0olnF/GDZtbQheJSitcFUtPxoGd53zeBFWiiHElqePneJ//Pe389P/9f9l756DSCFx1qKVoizGDCpLu9NBCYme7Ht4c5aQWCjkpKIR3mAw/Kubv7pK01ICYR3jVDPTbXPqxEk2TYVudyl9i1arwyjfYnV5mW67zWClT5pmJGhG+ZiqmKIehpiamUInKgad2/MYzhLbroKxoXXbXh7ehwSgHsz2XkxasWqZW+PrHCaqStWPdYTjOFEJc5jKhsTFeVz8jZI4H2ZWptJwSoelpRwLTGUnZn/WblezghyTZzhcJ9FBfzcfjtncOoNKHX7smJmfxVSW5XPL6CSN8l3hQCslJ611QeJWoLWmPd1l6ex50iTZHkZHMDUzQ7c3TWlMdOau5yUUUrh6PAWpFMK6KFTgcFXJqdNnKWwFeEpbYaoK72RI0qSnKnOM03jv6PV6jK1iPKrqSyIOj8vJ7Ez4HLnJZwpCou6cC61WXmKsw1UV1nucE1SVQ+9QcGtoaGhoaGhoeKRy4YlGbHuSojbic5OWku3f2w7eEFptrDWTnvY6SK+Hw0P7SWydcW5SDXHeIYVCa0VVlnjrwxCsUlhrd21HS02SZuR5Ac5jKhMCziT0uuNCu4nWCik0QknyskA4h8JjKs8nP/4p3vfeP+EVP/JjKKkwzpMISdaaQrR3B3U7reWUEKiJBpSf3C92Psp7NCHwn0o1ZStBzM1w/swZhPeY8YgkVawtnWYwzlFekjuLt5Yiz7HlGIHlzPFjJEmC0ilSSGYWenWHD86FGfOwKwJrg4KVsw6VyO1B5zgW4F09W7Bd2ajPH/FcCgjHHYJqFn4SmKepxhqDlCommiEpdNaFgW8F1oVqSFlarJdhAFvvuI6kwAtHogXlYJOP/uWfMTM9Q6Ict33xDu6544vk4wHddoYQMM4LrBekSYJUoQ3MwCSTkoRkyEqQ1rF6LriJ1wG+d2EfvZJY4dnc3KC/uQEetJTRzTsE+lqF6pSFyXt0xnL8+FkqY8JcRVVRlSCkQkgF3gZVK6HwXobPgQotXt5bYrmP2tMjeHDYSVKhtJzoKbg4k2OdxVQmnBOdIdOUViulM9W90I9tQ0NDQ0NDQ8PDxoPqwaiDtjRNyYs89LTX6jx18O9hsoSuwjBv3es+wW8HtXVJRGsdlHecmyQy49EQj0DrBJXoSfuN9xapw5CvShOm5+fxG5u0WxmMc2xRYCoThqARkyH0JMlIkhSpFflwgJQhEB0Oc/74j9/L1Y+6gmc/69tJlMZ7S+lCkqSERis9UVXaaTa4LVMEjqCApajlU8N/Dk9ZlZw4c5KZ3jTejOl1WqysrZK1pxkPlxnkJaPSMNObR7U1VTkmLwqSNGFjcxVTlHSmZhByRLc3xdR0B+cVwvvJMLhzcWW8Tuxim4/wQXnJhi6yqHgVKxYuLq/7yakJMsV4nLVhtsaabZ8OKfGxncqUVVBAispR7cSTKUdbezSSvAztbd4Fr5QgEBwcsK2Jczo4bv7cZ/nsJ/8+tMdpwbE7j7O1vkVHJ6RJmAPy1qF1QpJleFdXU1wI8gnVMlHPVjiH1iHBEvH6klKGc2gca6urnD15hlRL0iTdvripWwPlpMUpqGk5kqxLWZSYKp9M6Cglo+FhkFPWUkGa4QcCY6qYCMZkI/6vI1TFnBBIuaNyJOLgeDxvYX4lqGEplaGyLp3pOaROkOz4LDU0NDQ0NDQ0PEJ5UMPgIS51GBvbQWI/ej1k6+MsBgi00kzNTDPsDzBlATtUjBB1K852a5WxJrbUgFI6BGqEVhhnDVVh0TpBa42JjngiSilZglN4WRUIqUjaXRBQluNgsOfBeo+wBp2lJFmKMRmuMoClKArW1/r82pvexJ7FBS67+lo2N/vMz87TSdoopeIxENtvYBf19IqIXuVQ229LBFujLfKiYGV5mcFgi6KsmJ3ucPbsSTrdDnk+ZrA+QLenKfIRutXi/PmzWFOhk5TzJ08z3ZumUDn5eICUkpnZeXQm8XFGY3Js45546uTDx9a16ODt6qoLkwqHsMF4cWcbko9VCqyLalAiliTq3CTMRVhbBUM7CVMtRUdbWjooTvnSIHWQhh27sKrvbDA9zBKF8JBpz6f/7hPcddMdVGVBd7rHaDBGy4TZ2XmKfBQqAMaGfduR40klSFstKHOkEzHHismGrUsz4RwQr8+t9Q3yczmmsrTnZ8Ixii1+Mg5fC7fd2lXPxCgtuOfOO8miX4cQEiktxpjt869UmJ8QPrSWxcShHgi30WcGQPrtIXutFEqqiRKVQFBVFdYJdNYja0/TmZvd9blpaGhoaGhoaHikc8HytlDHbbWZ2Y7B5Pq+WssTh7UVxWgUBr3Zbpep/5voqcZnSK1j0iFpd3tk7SlqjwOpFMggMepqh3LrsJVBAFoKnKnodjpIFaSPlFZIGSyyrbWYqqIocqqqIE1SOt0eSic4Fxygh6Mx99xznre85a0sLS2TtacZjnJyU4U2F4hNUQKLm6x4T7IsmCQatSqVJHhvbKyvs7K0TKYSNtY3Obz/EDMzM1R5QVnkFMMhc3NzXH7l5eTFmM2NVapxjkYyGgyYX9iLA7QMyVWn02W4tcXa+TMUw0GYTYmVDAtRBirs28TvIp4aVxc86tmaKO3qa+tsvzMx2U4cQ+Li8dEMEOFjC5PH2wqJQWBIkuCVkUhPry2Y7Un2zGr2zCjmOorZlme6BYszmrm2YyqFbisBINEJ9SV07WMey8z8YpgdsY76klFCImMlTHgRznedvMZzRPS2UFIyPTOL1ApjLdaZ4LNSGlAq+KtE9SvqAptzofqyw4HQVYbRYIzJ7eQ6V0qiVJzf8NsGlKFKJjGVCW1+EGeMLC7OpggpJ0pUQgiU0rGVMDrCCIFMu3RmF5maW2RqbhaZqLr7iknPXENDQ0NDQ0PDI5gHlWjAdiAGfqKkg9827aurHOAZj4axb31HwBuDwnrouG5rmepNI5Wm05lidnGBpNMCIZFa0+520UlKmrViUCYmZm1lXrCxvII3hvFghPOOqixxMQnxPvTvSwDvGfeH5IMhUknSLAvu0d5jrCHPSz71iRt55+/9HpkUJGmG9Z5hOSY3JUNbMbIVDolFYDyTH1+3vcR3H1wTBAq4ePEA7Syl3W2xsbbB0vlzbAw22NjcIM8rvFBs5Tkr6ysMB31Wz5/FmBytJKmUXPWoq1FJwmhzg6lOl3w4YHN1iXxzjWKwFYzkdlY04u9aaWtnp5eIMr+hcy22SHkPQoYKRwyOJ5aK0VvCR8M64YNcsCTMMrRTRSos0x1JNwHpLQKLVo4sFSTK4O0IYcdgxySyoJsYpjKPokIJR5rqMPujwkzO3MICF19+lKIMczc+GkAiQagY6vsd78X50CIVB6tDxUUxt2cP3empiWt9PeittCZJ0zjSItieiA8BvIxiAfU8Es6TtNoIoVBSIKUJx1KGJFjGAW5nHVJJtEqZnlkkSToTCVuBmBju1dv3k1Kgi0lPGELPpmZp9WZpTU2RdBKsHZH3l9lcOcHy2Ts5c+zmB/uxbWhoaGhoaGj4N+dBzWjUbSlhBTxUHwLbbSaCGEDtaEcBcFFrtQ7FQ297MHbwzjEa9AFHt9fFViXVeARA1mqTZi2MMZiqCsFljM+kECityTpd7HBIFX0NtFYYbJznCH3xMu6LEJLxeIxMNd3eFCJJGG1thmFhCfm45P1/+Tc87rrH8bzv+F5M7K4XSuO8J5GKsn6fMYmxQBIPpqJusYoNVN5jfcXc9Azrx9e55MglbGxucnrlPP1hjl9epxIppStYWdtAC8mgPyDLWpMqDVowNzvNcNMyPd2lMiC9Y3NlA2sEU3N7EMn2WLpzIFVIrhIVZIGdCAPixoDBk+iQYFT1Sj7byV/dHGbjbE34AXBo4UnSIBHca2k6iSBTKc6UdFJFXhmUFqHKUpahAmXCDIeXYaBc+orxoGA86iNlQuVGZO0WRV6BrWh3W6yuLdHfWpvMinhnw+yIBaRA6NAGplWKESOc8GBjQqQUs7MzTM/NUObFpIRTqztJIVFSBz+RHdWde1/DQsbkWEp0ohFyR2sZPswKSUuiPWUZ2vd0mnHkyquYm+9y25nTQX3MR4+R+Hlx9b4ICd7EgXARE6CMQX+dsihx3lHkY0xR4GxIZCbzTw0NDQ0NDQ0Nj3C+YkH+0Gojdw15O2tDWwgitHiw3fc+Gf4WuzYSfuGpigKEYHNjHe891hhSrdi7dy9OCpwxDPt9vPAkiZ6oVeE9PjorSxFcy4PaksYZg5Lb1RQpFWmnTT4aM9zcImu16PSmMaZivLUZgnQh2NoY8NbfeBt7D1/MY5/wZKiTJakYE93AnSeJ7ToVIZAvgQ51shERHq0URZUzPTPNyto6i3v38sVjJxAq4eSJU7RnF0izLhsra8xOT5GmGb2ZGVIluezqK9kY54zLgjRtB7+KRFJWlpm5RRYPHwyKRTIkF/U8uJKQZILpDGZSME4wqGAsIRWCma4gU+C8ZHVgGebRv8QKtPBkicQYi7E+zA5Iz1wX5jspWocWsp4OUrbGGrxOqKxBYdAqQcbV+SR6UVSmwDnP+vI6b3/rm1jfXKIqKypTsbk8xMSx+d50j7Iy3P6F26nGY7wxWGMmswtKJVjC9HtvukeqBbl3WOJMSjz+KtM4Zxn2++H5RFPDiJRqRyVje5ZFShmdC12Y8/Dh/cuYZJhaEhhPsOdwUbIXBI5WJ0Gqirtuu4nRYJNOO0MrPal0ee8nZnxShf2tVdn6G5uMBgNsZbav7x0fldB+WKeTDQ0NDQ0NDQ2PbB5EorEd3AQhn91dV0KEXnkpFUma4qyNAaqYDLr6KOspJs/Zfn6dg0jCanIVW3iKMsdIGVap9+xlOOzjrEFFZ2WtM8rCIIXAlFVYbY6T0ELuaN4XIgaHCp0lWOFZXVll34H9tNtthhsbOGtJ0hRrLSdPnOHXf/VX+dU3X8r+AweRCEbOI71nY3OTZGoarWQwtpNBTUlFlaE2Idi13pKbgrX+GjfddhvOeXoz02yNhyzsP8Qdd5/EGstgc5PZuZT9ew/SH/ZJW5I9i3s5c/oE/dGA/tYQnEO3WpTlGCVb9GZmOXLFpZTWozOFtbWUKijvaSvBVAs6CrK6Vc15hAvVjFYafDeU9MxNSbqtcPStCcZ0rSRMKislGA8rUqHYP52RCAjNY1DZKpRJvCXVSZDNtQbhNdZ78iqIBhhjMZUFIbjtplv457//Z4pqHL0/LFOdHsY7jh45CgJW19ZwSIROKEYjyrxA6jCnoxOFd5Ks3WZ6fpblc2dpZQkTF0WCZLIpSlbHy5w7fZYsS5F15SZW2pTWSBxg43UrIQb3or4ggdrJXortGSSPQ0tHkniKKFMrhMZVJZvnz7C5sow1hm67E5SuYqJdu7sHKd5QFbHWMBzm5PkoGFHGKlwos8hYZalHgepKWZNoNDQ0NDQ0NDzyeRAzGn7yOwyu+h23e4htNmmaRlnVWK0QIJREKTlpwakDr10dIJPZAU9VlkAIGAf9Pmack2YZKk1I2x2kzpA6Jcla7D18kLQd5jm6vWla0z2SVhZCSOuwMfGoW17y4RAqE2VZPStnzmLzAkHos6+qKvw2lhs//Tl+6y1vZn04YEhQrjq1ss4HP3EjG+OKkQcjBWNg4D1DPGuVYeA9A2dYGm5xfG2J8xtbWJEwKh233nYX//Dxf2Rjc4P+KKcyFdJJur1Z9hw+xMKhg1jnaPemGOVjjn3xDvobG6wsnWM0GjHYGlKZgpn5KUbFGJUogvN2WDPXArotQTsVpAoSEZzMx8YHZahMkCrBcOzYGjv6hSPMWjtaKbRSsNZgrEUKR6os012YnZIo4bAESVqHxfggWZvnORBa1oo8p6zK6BxvGecVeRESDmc9N3/mZopRSZlXWFPRaneprKGVpVz92GsnwXSSZQiVYK3HRr+JOJgRPP6KitWz50ikBh9bu4QEF9qs1tdWOXn3cYpxCfgwHyGii7gnVC2UnlzV9XyRrJ3tJzd7HC4IGSiDcyXOCZx1mMpgKkNVeaoqVE9WT58G62i1WiilJtU870Mu5AgD6NZZxvmYzY1NBv0tqsrEYxiU1bZnmsKMh9QZKumQZNO02nMX/rFtaGhoaGhoaHiYuHB5WyGZyHiKnUlGXOf1nna3h261yAdboVdeCIQDYf1kZgOY9L5Pkg0hkEKQJOlENahetC3HBVnaRgpJtztFb3aO5XNnqcZjxqOcc2dOMzU9E6U/QxsLUpGmGd4abFmBsGH1OK7oh+HhUH1xzjIYDoPfhwwDuZWxZGmCKQ1/8/6/4sjVV3L987+Hk2dWaXWnuOqaa1C9jNLBbV+4mXzY59rHfwOlkyyfXadDRZZI7rjzdiSWIh9RjCvK0rC0ssH6ygbrwxLhJf3BJtML+3A4hsNNxoMthBDc+NlPkol2kEC1JQsLi4yGWww31yln5/jC1ga9uTmuesxjSdpdUiWQwiPiTAZAUXmcgrKCvLDoJBjnVaVFJxolg5N3acGaEMRXhQn+EhqwFqygpRRa1Gfch9kaPMY5cldRRc+R0SgM2efjHB0rQ6WxmNKgdQi6H3vd4/jrP/8zSjvCe4EzJSCZ6rYZjDZZ31yNho1EdbEqGt6p4CxO8GWpqoq0lSKUiObi9SxHsAUZj0sqI4Jpn1c71KiC9wVS4lxot8PVqmJxgD5c8EBoRQuqUUFuWagkVkKgKCqsU1jrqCqDd4JWu4VO09AuJeVkW2H2Q+Cto8gLiiL8OBfmLtIkQWlNZSxStkhaKUmaodMOSZKi0xSpEpIkRagHreHQ0NDQ0NDQ0PBvzoMz7ItGeiHZ2HmbD7K00z2clPh+3aoToj4pgtmdc25bcSq2GU0aQeJ2Qg96CPtq+dHxeESej+lvbZB22jjrmJ6Zo9VpBx8D4fA+pSpLbG7CFr3HOYtKJFUeR7qVJE0zqspMWr98vYItUlSqyNIW+WhIVYX2rK2NLd71O79Hd36Rw9d8A3N7FshHfbCWrf6Q3/+d36NVFVxx9bUUyRSlSzh7fplMCU6fWmNt+TxFPmJ+bo7+cMi4PyYvYbBxjrk9+6mMwaMYbvU5cfIeZqfnabenIXccufxyvnj3rezfu59xMWSwukaiE8q8oJ212Dh3nrU9p6hcyaEDh8i6s1jvKZygKAhzJJnGGE9VOrSNUsDxeFeVxbrgYRKSMosUnqluRqYFJY6WTtBCIHw4hs67OAsRpIaLylKaipW1NT79yU/y2Mc/DpUk9Nc38E5gnMeUlspYjDXsv+QAnekZBufXgyJZ6ul221hjuOkzN1LlJTrrIJDRoNFirUMqR6Y1qU6oXJCXRYSBdRCToW4IiayQEp1lpDqJnhc7ZomcCaW8WOGYDIAD1jkmHUreo0RwBpdC4qLogY8D2c4Hs8R6vkNpHX6URkUDvjqpts7hqorBcMhoNArKbT5UWpIkQQiBdZ7O1DR79h8mbU/hpAYfBu+jDEP8zD2YT21DQ0NDQ0NDw8PDg6hohCBr0jceB72FlJPqxWBjgzRrk2ZtTFXgrEEKkCpIetowshu3B3VCUPsJdKa65EWFsyGYk0qhVUqSpngnKfKcNPXMzEyTtdsIJWgnXax1tKylynO01JS2wpqSKJQUZHJFCAhNVaFV7a9hkFIglcZbh0wSWt02adpiY3WZqqqQSrJ65jy/+6u/wpOe8230xzm2rHDOsLa2zrk7T7DQm8X0+6QHe6TtlIXFA2ytr+BUwpXXXsf66jpb60tIoUhbHVTWYVwYNjc38cYxPTXN6sYawksOXnwUlWXcfdM/0+pqXJHjXMHm2gbaK/bv20eqIW0JbJpy5+f/ifm5OToXHyLVHmcMqZJMxYqFBZwGo4OftJSQKEWiBVs4nIOkLUhkC6SgisH9uAyD9UKEVqskZI7kppy4txtrUUqRlyWDzT7d3gxFUUJlGI8LqsoGg8XKIbUK/hTCoROJlJBqRbudopXEGoOULabm5imNxXswZYWpDFoqlFIcOnyItbX1MFziPcJ62plGAGVZhYrEDrVaJQUqSfAi+lw4iyC0YCVKkSSKclROKnK726fCwRIiJqk6xXmFcxUKhxAVSjqsdWSZop4hn0gD11U7EWR4TVUxHAwZj8dhDgPQWqN1gnWerJUxt7DIgUOH6c5MMRhWlDZcs75WKfZi14xTQ0NDQ0NDQ8MjmQtONHwd7cTh1HrGuu5nT9I2ew9chJOwsbqCdBXehbmH4J48mUgOgdtkuDX8eO9DYItAiJAI9KZmSdptkk7CzMwc/a0BVVVQVCWVNQgp6HTCnIBzHqEVnc4UibMMtjbxwsX9DP3v3oExIbhWSqG0CkpClcGWJaIqabVazC3swQvB+tI5vAu+HMe/eDfnTv4+3dlZQCGkREbvhQ1Tsbyywp4DB/HWIhUU5ZjReExpSrrzPe6542YG/Q16swvkVcXswgJLS2eZnZ1hZX0JW3mOHLmMwWhMubHCqRP3kHUTls+cwpqC6d4C13zdY1lbP0cra2PKMc6V9DodDh3Yy+r5s+zfH4LrbpIwK0EnYegdfFypF0gRvMsVjqQNuTEkWlOanP6oZGMwYjzOybKURGqyRKPiILQUnrwI8xgCgdaSNE0YDUe0Oh3KqqQ/6IOQFEWYwzh55jiXX3El2rYRwnPrLV9gc30ZrTRZIoNogDQIrUi7U8FYcdhHpR3KsoyVMHDOsrG+SjHOaaUZHmi1W+xZXGB9bQOowvubBOKhXU9KjYgSsja2SIFAasVonKO9x0ZvkGiXF5TS3PZ2QsVCkSQtMiXxZhCcVIRHhYND7e/nnJv8SClD0uM8ZVlRlsVEQjck6QohFDOzM+w9dIiZPQthcF1KlHQI5xEyJvVR6YroH9LQ0NDQ0NDQ8EjnQSQabuKBAWwHc/EP40rG1QBnHdV4iLPBN0NKiUozpLGYKoiY7vIsmCQcMNjqk6QpCE+atJjbt5fe4iJrK8usrqzQm56m3W1TVWVo/Skr8sGQdm+aTq+LUJJinNNRmqnpGZaXzmGrElFVKBGcolUrC3K4wsW5kdqcTeKtY7C5hRSgdUpnaprh1iYei3CecpSjRJ+k0wWpogmgpRyP2Vpa4hIBvZkWJ88vc/ettyJsyfnzZ9naWMMbw8LsHqTWbG31Kf2YTpaBsuTjAa2sw9d9/TVsbI645eabuPa6J3Ly7rtoZV2slSAkg/GYleV1VvwSKdDrtCmLkqVz57no4ovwVc6Js6eY6bRQF12EsgnTWQcto/YtAhOHkUugMiV5UVIZyfrWgPPLfTa3RpRViRCQJZreVBcZJYWVEBhThe3hUcKTpi2Wzq+Cs6yurFGUhpXlZdqdNoOtAR/7yEc58qqLOX76BFUx5E/e/T/IiyFT3S6jwYhUKrSGTGmkhLyoQoCNw1RldMuWWOsoxwVa6TBroSS96S46iapO1JLLtU8FobShJDb6jggp8RK8cRRFQVVYZFJfj+AkKCEmalh1S5XzjiQR7D+wl82lEyAtOPBOYE1si7IOKZMgXxsrPbXogRSCTrtFkmrycUGe5wgh6XR77Nl/gIX9i7RabbwKFUJJSEAELlRHPBPX8ODabr/yT3xDQ0NDQ0NDw78RD9qwr04K6mpGfZu3jsHaOtYGB+l63kJEWVkbA7cdAxoT5artx4KJSYROFdZWOFuwZ+8ettZTNjfW6c106fV6OC/o2y3KskCVY6y39Gbm6HS6oUVHgmqleGe2DdgIbtdeCHSa4EyQNhVChAFhoZiamWFrawOFojs7Fyoow8Fk2LwoSkSSohIm6lrOC9ZOnWCfhKqtOCc9115+GWtry+R5wch49hw6SKfVYrM/ZGl1BWcNWmtWlpeZnV9EYlhfWsJKmJlqs/fgIY6fPMbePXsZj0va7Q6333YrwlZob+nOZLSzFkcuvoheu0uq4dSpu1k5d57epUdZXV/GOse6Suh2Ouxd2BuSKecpncFYQ380ZmtQsNkfsbq+RVlY8qIEBNZYhn5Evz8mTRTtLCVLgzM2aYoQHuMslc0RKgmD3SgQmsp42iTccvMtYD0f+ZsPcdcdd3P6xD2cO3maVCfoRIc2J6VAhhmE+hoRQlCMxpT5CO89WkV/C+9x1mEx4KCyhlMnT5LnJVLqcN6dJ02TMNfgXRictiFhUCr0N3kcKkko8uC54gnyawricPZusqyNlDDc2iAfDWm1wvC5MR5rJUpprDDbn40dSlPee4QWSCXJZIJWGp2l9Kbn2LP/IJ3pbpgjiTNE254vIQl3UTENZynKIabKsVX5oD7kDQ0NDQ0NDQ0PBw/aRyO2socWKil33d+bnaOyhlG/D7F9BCnwlcF5Fwe+QxvOxOhvx2RrmPfweAejwZCqOkO/36fdbpN22rTbLUxeMLCOJGsxOz/LqK8pihxrcvp2hVani3EOZwxmnGNNrKJ4h/MCrNt+H0qGQNO70EsvFcFHXFAUY6bENFk3YzTsIwkKRkolGGtxwiOUDspIwnP8nrsZbCxx5x1f5MrLruTSRx+hLA+wtLnJ+QMLHL/7i1x+0UEOHbqOP/+bMTfe+BlGGxscuexKimKEkFOsryyxMRiStDOO3XE77VaL6dlpDl+yh+Fwk06h0alk1N9idW1MPh7Tm53h8KEDrKwss7W6isBzz7FjdNptpqd7CAdXXnUlw3yM1wkrm33KssRUjuXVTUZ5yXAwxlYWqSTe2jjPEBIJn3u8ifMdpFgpcDaPw9OeohzjnSFNJEma0Gq1mJrqoZRm5cwK6xvL+Nssw7VNzt59CnBMzU1hEQitwjUhHGVVop0L7t7e4R2h182LONMgGA7H9HpToVpQlqwsr7G5us7cwjwqzgpZEYe1Y5Wi221jlMOMSwQeZ4I/S3DiVgghEc4iEVEKOSQJToQ2tOn5OdJ2m6WTJxhubNLrpiH6dxIhsqCSKwwqOq3vJFTvQtualOEYqkSyd36WfRdfglYpFoFUKjjdS4HzUdNLyiByICDf2qS/cYaiGONqqd+GhoaGhoaGhkc4D9oZvB7iFnK7jWoiduscaZJQSDlxqPaeMDQsJEK4KIa7PRBeuyLHkfDQShUTmKoqGQ8HtJIEbyyj/oB80EcqycLiIt6YICvqg6RpnudsbWzR6XSoyhJT5DHYE1HWdLtty1UGoWTwVpAquj1DMRyjpMKnCavnl8jabZI0Ae+ROkEkKpgFOonxBonAlCU3fuaf+I3f+GU+9n/+gf/v//s6rv66r0MngoN75tm3MEMrsZw+eYLBeMjFhxa55fOak2fPsn//IosHL0a1egzygtW1LSpT4o1BZ+3g70GBtyXFsI9RnsOHDjG/sJdOJ2Mw3OCLdx5jMNhieekcEkEiFdd9w9dz1x13cNGBg9x0680cPHgI2epSlo58VDIeF6yvbTIaVxhTkeigfuQ85FUJHlT0rMjHJcJ7nKnQUkTPkdieJKGdJUipUDI4ticqQUnBxsYaG+urbGyssLW6RlUV9KZ69Psjkk4LCO1sWDepim1LHSu8dxhr0ciQSHjH/J45hsMxjDyDzWG4VqJSVHDvDq1h3gW/l7LIISYXO+eL6uvW7/BvYTKXATiPVAKpBGdOnsBZR7fTQmsBzmC9RmpJIk2UShZY44IqlxcToz8hw/uZtOlJgUo0aZrgvUILgdYJAk9Z5ZjoLVJfs1I6qnFBNTYooZB4bDOi0dDQ0NDQ0PBVwININHZUIcI496QaIQkDqltra+gsDfKxMAkavQStFKUNgXndrhT6zd3ErVkIgZCKNGtRVcFjoCpLls+fRWlNp9dDakUxGrG2skynN41UCpUkCKWZ7s2Q6yHeOhKd0G53sc4ihQIgaWVYa1CVQWqNtxa8wwmibGvd6gVKJwgvKccVSqdBUSiqV0EMUJ3HCw/Ocdutd3L3XfeQZRknjh9HTCWcObfElY+6hmPHTzDc2GCz3+eyyy9jcOYcW1sDhv0Rd9xyJydOnOXotY+jOrfEwuIhytKwvrbKdK/HqZNDsqU2WkgS6ahGYy45so/ZuXnOnj+NwDAcVVSFYG1tzHBzg5mZHn/xp/+LrN1ha2TwFpbWRuw7fDHtrIOpgiytThNaKJK0SytRKCUxhaFyFuc9WnpUmtDfGkVpYjkZpvbSB3UmGc6zcT4a8KWUecra6hrLp8/QH6xjqgIctNodlJSUZYUSaiIB67yLg9HgNSgU5TDHWYtSIiYZQckpa6eMhuPYWiRQKsX6OIztfVAXi1U3CZjSIL2LhnkinG/rY2KiwIQM0xFVpwgzFdY5yqIkH+ekUpG02lGtrAAD3kd1KgH5qKIoLUoHBaztal2Y/Qk+HR4rBcoppAjVHOFqNbSSjZUlVpaXccAV1z6aWtBWCoFIEnS7w3BrnaroN8PgDQ0NDQ0NDV8VfAUVjTgs631cSQ4Jh1DQarcRSlNVFu9NCLJEDBRtNNOLq8q1/GcI7bYNALVOmF5YYGtjE28trW6L/sY63tiQwCQKIRVlUSHkiOmFPUzNTAd5VCWYnp9nsLFJkiZ0k/A4Zw2j0YjpXg8vJatLSxBnNZwQSCQ2zo+oNEG40E4lpcJR7ay3ACK8D8L7USq0e1nvMZVF+IpPfPJTVKnn1Okl7rzzGEq2OLz/INNTs/zV+/+ao1dchUgShNQcvfox3H3HHfQ3h2RZm/nFfZTiHOMTQ9bPnefopUdZ3H+AYX8dpWD+4CHOLa8yGI+RDoq8pBwN8VKyb3GeraxFWeYM+wVpOo0pIUk0q8t9jD1De6qHEBItFc6FuYJ2lqJ1CIjBkujggp6lCdPTXTCGsrI4QjuRlsH0Tsp47myYwdna3OIxX3c1vqy489ZlxsNB8AkxcOToUYaDPqYsQMZqFrVvikPFxDXMYjiG/f7Ez6JOAq0xLC8tY4yPSaGEOHQdI3ycEwhJ0JqN58pFVSlBnJuoz6eQscLlgCjBbNxk2Np7T5LWKlAyGAf6MP8TrmOLtVVstxJY61CSyVwSLviNEFun8NFbRkiEUBhTsb61xtrSefobm1hjSVrtsG0l8dhwAKRAao1zIFQYym9oaGhoaGhoeKTzoJzBJ87ek+Bs23/AeSjKnFanjVICYwXOW4SXeC1j8cPjfZCjJa5ATzwMEOAFHsdw1AfhSBLF9OwMo9EIX5mJLGjS7qCEAuEZj4bMLs4jtWK4tYWSBb3Z6SDDKgRCKRKlmNIJxhkSqenNzjHc3EBpgUFiiiokPB6MsQilQxDrLEqCtR4ZfUO8iGmR8+ANWIVONcITvSAUt938RQ5dcgnDUcXW5ipXXH05F191lMFwiMUzysfoRDIuxzjt2XNwP/lgnSOXXQaJItUZRy65kls+/88Ibzl97gxLJ09wyaVXkPUsp8+epdPpAJ5Bv4+wYa7ElhUHLjrC0tnz9GYsew7sC+Z5W1vkozAwv6A0WqfIJJxDrSXOlBgrkUpgfYU3YGwwWGylCalOsMYhJWglmO61UUIEiWApKfICJzzFgb2YqkArePx11/Jn07OMl0ckScKhSw5x2+dvpqrK7XYnHyoMIooGhLqTpIxzL0miscbgnEcJQVmUnD5+hvnFRep2OyUFxHYuH4tsUgR1KSEFXgmEFbEtyiN9kAQgGu9ZF7cUW52krI0jY+KjQtUtxPseY0qE0nhrcJagOmWI1Z5g2ieVQumE+cX9OCtYXz4PIiQNnvBepPecuecEqyvnqIo8XncSWdmwH16FFiwpUEoGI8BEI02KVA96faChoaGhoaGh4d+cB2XYV8837FSLqluepBSYqmLUD4GjVgrnQi+/MxY9kfuE4E2wI1Fhu9VE64RW1mVQRB8M5yYT6N46fGWDmpBWk+dtLK+F1Wcd5EX7gy3SJJ0M/ro6GXKAg263R6fTZWtzjWo0REuFEcFrI9EJxrrJULizoXffiZhYuCif6iVpnGmoShMe4z0OzzjPsV6wsLiH2YVFllbX+bM/+zO21jfZu7DAlddcw+bqGtJaNs+epdVtcf78efLBJcy5vUzPzJH3R0hcHAD2XPOYb+T82gqnb7wZcPRmevS3+iSpotedQXjB7NwMQiWM8hFJpugPNsnHQ7rtHocuPoJOsjD8Lg2JTlBKkSUKKfxkAFviMdYFRS4vyccFe+Z6lCZlazgkTRKyVJFpzfR0l6lOB18ZxlUFeY7Pxwy2VsIci3RkiSZNWqysrOBxpGlCaRzeh8QuyO6GFf+pTpvV1fUopQxKaUwVvFWsrZ3iffSoUBArFT6en+Cjsd3e56OLed3uFdqzQpkkFLDqgQ05UUTTaYopK+pRIgEkSUo7S+kPNnG+FVRzlcN5hUVQ2RLnPDqVIQGTgoOXXMyBiy7FWY+SCWvLpxCE68RaS2kMW5tDqtyEa0xAohKkklGFKyRS3taGfSJUoZQKSloNDQ0NDQ0NDY9wLjzRmLQ6bScb20mGRETlHO/BmQpfT93K4IBclcVE1pYdyQrEIVkfhsBbrRatqSmG4xG2LNhYX0f50MqDFAgtcXlFMR6B99iqYHpmhm5vmtFgSJplVFVOkY9pdbtgHfm4INEJU72p0IJjg3LP9PwcDk8xHIVZCw/GlDgnkTIEo66qwDmsEMi6fz/OFNg4uO6Mj7MmYZh5NM5Js4R77jnGeJTjkQy2NkmTjFW/yc2fv4UD+/ezes8Jjl5+OTffdCOzvS52PCRTFciEw5ddyuf/8SNBEUoVbIzXSbVm78GLufv2mxlsbjA1NctwPKIah8ShN9ujyEesnT9Hr5uRjxM21jaYn7O0Oh1m5/cgUGgJUrigFKVqE+xg5idxKLXdUtRuaTqdlJbXGFvQ7bSZnupiqwolPXk1JJUpWkN/c4Of/7VfYWtzjbIybK70ufqaR7F0do2VpeWwXULViDqUFqAIltqry+cxlUHphE6rDUIE076oBlUThqzDPgovg/22jxWMOmf19eUWTBuFqtWrgoGhFIK5+Xk2l0bgbKhWOcd4OEZrvT1nkaT0ZufI+wOUyhCiwlKhRIJKNd5BmmmMcaG9CkHW6bCwd384plpy4Mh+hhsrVGZI7B5ESkXW7uKxFIMhXhpUolFJEj9nfkd7mUAhQhVPhipHQ0NDQ0NDQ8MjnQclb7szuYB6XiO0g4QW+WDkFtpIYjtK7EsPMrK7h1i3h8uhni2XWtPt9VhdWsJ5Qac3y6ZZjYZlHmss+WiEiBO/plKcPXWCfQcO1Mq16CwD5xn2+2RZxuzcbHAEJ6heSSWoSgsesjSjHI9wHpQLQ75hxdjirCNJUzwhsFVx1dsai3EuJBzeo0RwCjelxTnPYDTmE3//CfYcOISSKdOzs0x1psnzksFgk83+FmdPnyYvxhy76w5Wzi9RTU9z/TMvpb++ghOKdm8GY8aMBmsIrVg6Aa3OFKNxjisd3V6P9tQUs9k8WXuKsjCsrWwxznPWVlexZY/SOrpTPWYXF8jznHw4RE2B7kZZVoK6EyIMUUuhSJMg+VoWFaUxaAX5eESapnRbLfCOqqqoioI8H4fqjTX0B0PuOXGW43edwRRjrLVorWm1W6DA5IZWGuSAa5UwKWrlMo9zwUMjTTQWOHzZUYabQ9ZXV7EuJAY7r5nQ/gRW+CDLS6yShELUZBaoboPyJswUeTxSKdq9LomSWFOSaBmFCELS7LxDIpFJC9maonAKkXWReHzhkIAzNrRBJQpVxeQ7JjUz8/OoJMGYCqkEWbdNtzfLxvo4OH17H8wjdYrKMkRRgQwCBDpJQiKFip8fF7w/hAgzQ15OTCYbGhoaGhoaGh7JPOhEQ05aUeKtQuCsAyxCRU+ARGFNFVpXPBRFHuRGYaJGVT+3dnQWHkBSFmNG/Q2wFVorOlM9+sMRwpSYosAaj0QidFS8kgqbl5w7eYrp2TmSJMULSbs3Q2ot+WjI+vo6rVZGUZbYqiJrtWi1WsEPo6zwxk7UsRwgfPRaUMGl2btguEZcYXa27u+3pDqJbV0WooeDEw5jPeOioLIFi4f3056aZl97iuXlJe658zYq40izFt2pKbJ2C60TRuMRm/0t8JaudrSVJd9aZ3FxkY50aFdy5OjFLPw/h8mt4+Yv3IyQmsNHj1KVhnI44PSpexgNx1hTcfElV+DxrK8skaYJ+xb2kLYynDU4K7E6ytTGVjQXo/QkgVYrIfUaEBhraUmBVsGhezgYUpYlVWW459gJBoMtSmOgCkPOtYpYq91iY2Nz8jfObw9uSzlpg0JKpJKhlUkqBAKVJrgoWzu5TmAyS6Gi0IBUAp0k0SfFTa4r65kE6vGfCBEeu+/wRZgi5/zZ4yhCy1gUh0KpUOJJp6YhaSPTDJWkOJGDkThb4aTAIahMCd5QFgZrBTrxaKWZnpulLAqO3347nek2hy+9gu7CDOubS0Ea17k6q0bpDKFzEunRWqOkjkaDoVCDjyIE8ZjhgppWQ0NDQ0NDQ8MjnQtvnVIy9qL4SeC305DAWYcUDpTCGhMKGwhsXKkWkonqFJN1bCZbEULgnWOwtUWe5yilqLxnONjAlnnwvZCKrJWS5/nEN0EiUFlKWZZsrK5QVSUHLj5C0mmTAk44yuEQbxyJ0HgcpjRs5ZsoqdBJStpuI5RACAUiBMDeB9M/GXfSGlNPwYdER4Kt1YykxMngOl0PFq+vrdKZnuLMqTPMzixw2dXXUhUVi/v34b1neXmVylra7TZKJaRZxmBrk/179+JdxbkTp1BAojTPf+63ccexY4zznLmZDCWGrJ4/z76982GexFraU1OsLJ+lv7WJlIr9Bw9x9MrLKEYFVTkmzTI63TZaBsf1PAdnDbaqcN6TaBUDfUiT4KytkiS0FFnLKC9wLlSUrLVY67B4ZufnKMqCza1BbDkKiePM7CzeWdI0odvtUJkiKClZG+Y3BKhEY6sK2WrhBSipOHTkMk4dP8Hq0grrK8uxikb0oYjCyjJUzrx3QelLWlQioidKlIWNbuNpkiEQmKof3L+lYGtjlWqcx/OvQ0ISK2pCJagsC8pSVGjdohj3KfIB5bgPxRBjw8C2856yMJgqvGaShiHwtN3h3MkzbKyusrkhmdu7l6npKbTUGGditUUgtUD64D8ilUMgg9s8IeGtH4cM1zqS0D4om1SjoaGhoaGh4ZHPBScaSZriXYGP0p/bg7SeJEtJ0zQEoM4g4rC3dTZUQZBhybiOFtkeJq+pk5ckSWm3p8jHI5wzrJ0/H128NVqp0Icft1Ob8WXtdnADrwz9jQ1MWTG9Z4Gs1WKU54y3NljctxedtEljG4oWEiFDgClTSX8ztLRIIakqi0MglMZWJdjwnuulZBHN42pZ11QlwctDRulbB2eOH2dhzwIzszNcedVVnD57DpxhYXEvs/OzXH71NXx+fQM8HDp0kJnpKY5echFnls4jhUQnCdY5Zmd6qEyyunaeTmuKU2fOMB7nXHL51ayuD9i7uIfKjDl/6izleMiBQ0cohiMcgtOnjrP/wAHSqWnmZmZpdaei0V4a3qcJAbOxhlIF40PnLForpJIopdFpG5UkKBdbxITC2DwOaIeAeGZ+nps+fzM3ffZTFMUQnWg6vS4ba2t4AVmrje5v7DLMq1vqpFYQq0R7Lz7A/P4DnDx2nMGwT1mU2+IDPsxAWGe381vn0FKilSaYQQZJWF8nxPG6MoWhbtKqigrnHFmWIYVAijBgLoQHqZGtlNFoyHg0xlqDKSucMUF6VkG7FXxAEEHcwFahBU/p4I+RZSkCyWB9EyEFRV6wevYcB49cBiicsxhr8M5vt2vV1ZxwcTE5SDH5VkJQRlle4evPUENDQ0NDQ0PDI5sLTjSyVgtTlLUqbeyVF9uKOFmKy8ugvmTDaq+IkrBaJ6FKED0NdpYyRO1SISRSCNIsY8/B/Zw6cQLKMCNhjI3bUkgdjOWUCMO7XjmcDa1PSauNcYaiyFk6eYqZ+TlaMzMUecnq+RXm9uylPTVFkqVYY6nyMWVZ4LwnTVp4a0nbGaowjIfD4HngwiC6lGLSslJXLUJyJDCmotOdwlSSsizwHirruO2Wm0nTjOr6p7CwuMio3+fMPcdZvGg/rY7GO8uZM2cYDDbJkotY3+xTOcX+fYu0uzm92Tl6MzP8r//9Abpph6OXXcTK+jpStLBOcuSyq3DWcdddt3P+1Dn2XXyYfDRiZs8s3ekZNldWEEhmFmaReITQUdlLo5TGWo+RPqhxRTUvvKeqQn9Ooj1t1Ub1ZrA6mCgKW2KtCtUQ46kMlMZz+p5znDt2Ci01vV6PfFygpEZIgUoVZVmRRqUwpROMMWitJmZ8Ojgisrx0jtIUVGWBMxUQ5x/CEv/kerN1VUAIlFTBuVy47YA9OAnS6UxhE8twPZ+Y/IWTKEBLrHMIrUEl5HnOeH2dfDTCVGbSupUmKakSaK3itarwXmJNNOmrEycP7U43zLdUFVIlWJuzubrOoUtApxnWhgTFEVzDQ4VC4YPUQHi/tUO5j0Ptce5EKYGvdifoDQ0NDQ0NDQ2PVC58RsPvMDKLq8X1ynFZFFgB7XaHJM0ox8G5eTwe432obEgpEcITxZ3iNgERXca9A6Eo85y1tdUoRSpJsjbGjMBapApeDzBZ1MY5hzEm+iiEIBYhcNawtbmOk5B1OiRZB+uD47MZDlFRJnRqehpjTNxa2GqSGpRSDPp9XFnF+ZF7S/uG6E8rjbOGIh9v997HVfXR1ohSF9xx623svexylpbPsH9+L8tnlxlsbiGV5PKrr+GfP/UJPvfZGzly5aO48rGP49P/+HGuuPwSpmbneezXP47PfOZzPPobn8SpcyfIki77Lz5Mq9ulNT1DUZbs2bufQxdfSn9ri3MnT9JuZ0xN9ZDWk2Yp3guqylGWJSAxlUNp8EIgRJQLjtq/AoH0MgzbA7IypFIyNTvHuN/H5AKZgXAZ0lqMgUxpFhb3U5WG3swUQgkQCiEq1pdWUGknJKhSgHXBFzAmb85ZvABbwZnjpxiOSxIJtiypqmoif6yUDKpRDowxpFrHFqJgvOcReGdxDqQKHh97Dx+iM9Xm3PGTeG9xPhgNbhsvhnNelYZ8uElVFGQtRbuVUMmgTialR+koZew83omJ6IHW4XdlSlotjRSetJ0xzsehMOE1HsEoz8E7tNYUcWBdwKQFyrPdtocgSOXWUtIAUVpaCY1thsEbGhoaGhoavkq44ERjPBpNvC9gd9AthCBRilaWUJSG6blZnPTkZTEJ0q21OyohMGmfiv8rVIJQCicEJi/QAlSSMrdnD16sUw37GGeRucdbh4nyn0ontKd6DAfD4LkhgkKR0hpTVZhxAVJRigKdJLRch/bMHqRwmKoEZ9BpMkk2lFRIHecTkoTN1RVc7rYVgAgrzmGBPQwgO6AsKtT/n70/j7YkXc86sd83xLCHM+dcWcMdda8EaEQSQkjQGNNG3dAGmrYBuZkMBhro1YvGqw29bC8DNjZgm15YBtttDGa128zCBiQhIUsgXd1Bd55rrsqqzDyZZ9pTRHyT/3i/iH2yJKEsSVdVJcVT61SeYe/YMe1z3vd7n8FYyqqiDRv6/I4QIj/xI/+ayac/yepiwW/7976Lb/6mb+Sf/fPvwyjF4fVr7BxcZXnvHg8ePGDy6susGs9HP/EZTi4W/NiP/2uuXn+SdTKcnTY8/Z6bYCzL9YYvPvslClNw5fpNzs/OeOWF5/DdmqOnbpOUZrqzS9OuaE9PaaoalKaspkzqCSr1a/+aECUfJGUnr1JrUIYQI3eef5b44peZ7e4wmx1maluNtQqd4BMf/yif//RHeO2FV1FG0bYd66ZlvrMjTURCwua0XJMYApoI2uBDYHc2xbVtLqolLJHgSXl/+ntMa73NXoE8DdNDmF6fl5FEc44tC4zVvPr8c+iYMNbIBCxGyPa1yQfOz0UTpLViUk+o6ylaBzabNV0XiFEcrXz0GG0xpiKhiM4TYsA7R1Fs30bGWlzn0EVBDE4smH0kEFHGCPULoZxpJSn3KqkcECgtfAxgSoUSFp7ca9kRTZJOxonGiBEjRowYMeLtjzchBleDGDq7h8r3c1hf8J7VYkk1mQpf3WwfpLVGVtLdEIzGpcmGMQatjaxKJ9isVhhl0MYSs+g2kR2SjMouRWL/GWOg7Vr5fiKvjneovEpsjKULjhTAuZaoPDF6bFljlaFrVyTXoVTfDIlOA5VA5+PWoEIiZMcjoXsJXz7GIIGAshRNTGKfWlYV3jtiiKxXK9abDSolLh4e81VP3eDht/xqPvXhD/Ppj32Y6NbYsuC9zzzBnTsvcf3aVR48PGO9eInm1PGBr/217B0ecHTtOs4HHh7fI8TI8SuvcOX6Lc4enuCd5+T+fa5eO+SVl16grGq0SmyWK1znMEaxf7jP9VvPUN+YApqQEkVVoNoIRo4hpoSLiaI0KFXz5U89z3r5kMl8wq//7/4WYvLcee0FFqfHXJwv+Dc/8IPcfeVLaDTXblxnuVpik5JzGSMhRHwEFwI2T7as1iLsr2quXDnk+P4xCY22ltJW0G6ESqcNIdsi+xBkYoTcOzq3SdE7YnAYK7bKKkZIgXazZrNcDonuva2uypa6tiyJMVGWLdpoJpMp9aTO2g1H8B7vu3yPmsH22FQVxpaE1uNdC4D3mZalZSLhfcCYgk4Fos6WyDHgg3vEGlqzDbsECaTUiOtZzJql/JPt+0iNEo0RI0aMGDFixDsDj99ooOl9QnvHKaFwRKEhKYUtC1CJ1XpNiFEoIbmjGGhXl0L7pOnIAmqVckGYsIXoMpJ33LvzCtEHgvcYa7GlRTktj81BbqFtcT6gjaGoSuHAh4BK4NqGrmlIJNqLBauzE45u3ODo+m2Kqqas5gQ2+G6dmygpkm1hcnCdGrQl/b7HnjoGkPn0kURIHtc1TKYTuqYlmQIVXW4+CnxwvPDSK/z17/ke1iFSFgW7kxl3nnuei+WC115/jaZxXLl6g3WlWZ0f88TNW8x3JnziQz/GzZs32J1VeAdf+NwXOdjf5fDKPs7J60fnaBpH8LBYXbBeLzg8OKKua5rNBZuVI8RI03XUuhCL2RDQRTlY9KqUUBrK6YzJbJfv+j2/l+gcs8pQVVIwT0pNt7eDLWe8/LnnufviF1FGs2o2GKWJSmNLC96jgMIaQtvRxMisKlEaisISleLi4kKaOyuOS0oXBLVBG5vPsDR3b1zDVyiKsqSezYQCJf8jJUkZ76cEyliKqhYBdtNJQ5vF5W27wVaWWT1nNp8x2dllUlek4LHljO7VF1EhAIakEkaD0QldGpnAdRofHLFTNBvPZBpzI6rRtkArj9Ulu/t7VKbKzmmZLhVSfleJwD5eel/FbAXdR4IopYck8z6fcMSIESNGjBgx4u2Ox2402qZlmEFknoqsrEp2RlFUTCYzCWlO0DaNTAZ6lyq9tbRVl8WsOVehD/gT0bfQXtxmTWo9OgmNRCslDUiMJK0HvYfSGlsolLbCoddWXJucE2qKNegYUUoTfeT+K69wcfyQq0/eZrp7ROcdm4sF9bQWGpHeTl+qyYRutZJk8uHYt7SvkB2plBExc1TQbMSVaTo/oF2vaNuVnIcQefHFl3n57l2+7hu/BWNKrj1xi5/8yY/TNS2f/sTn+IZv/07uvf4a5xcbFmfntAeHfOhHfogQIgbPxVnNxfk5h0fXePml57l3fI/bt9/NfH+Xd73/3RgzoV2vWG3WbBYLms0alxLJe+Y7ivV6yWQ9YzbfwRhLUppyUmOLakg310XBbHeX+e4hurDslIrr84LgGhJw5coBi/MLjC2Z7FRAoiitWN/6QEJjrc0C6UjoOqEQOUeqCmbTGZP5nNfvHnN8dsHB4Z40OCi0MgRtxbEsBFKeaPR3TEziEhVRFJOam7dvcfZwQbs+R/dJgPn+LOsJ9XxOVIZ5YVmcnLBer0TX0zkKZYgqYo3h2s2bKFNgrYEUCWh2ltdoNyvaZoPSkZRCvicD5Uzu9ZASzvnBg00pZB+AalIz33uSwyu7Qp/KblO9tslaQ+f6Zlx4UhL7l0Mwc9CgaFCyxiX1d96IESNGjBgxYsTbG4/daBjAX9ZYZFscnWlMurDowpIU+NaRIpRVjSIQSXjnSUgN9mifIR2LztvUOYTNJ1DWYpVML7x3OWE8YFCoGMQVqCiZ7u6xaTZSzGsgKbS2JAO6MKiuG6xptdHEkGjWK+6+8AK7V5eYoubk+D57e3uU9UTC+5LYvpKpUMF7Sa9GWFXkhPQ+/8PkVXSwpCSORc1mQfAOjSJ2HpUC69UGGyKvvfYaq9WSz3/qs0zmc07uJdrNhuP7x9x95QWeeOZdaF1wenpK1AXXbtxktWp55YWX2T885JWXXoJYUNQVD44f8OwXP8fNm7ehKulaz/UbtzjYu8ZiecpkVjOf7bG8OGVvb861a/t84KvfgzIFq8bhfaDzER8DkDC2oK4rduYVO5OCqxPDjlHEZAgojAYbHD/+Y/+GT3/8xygnFSFGCl2gbMIoOzSBXetYL++SoqRoK8AWBtd1RC+ahuF2QqE1OfUdQmLb4GUU1mKtlcusNeW0hrNzIgmjJNxOabFDLuYzkikhKUxZs3t4xPnZGclHtNKUk5LZ3hHr5YqiLIiqwPmWzXqFLWtm+1cgBbpuJYV+1MQIrmsp6grXNsgQTqFUoiwNSiVCFNtjYyBGx91XLjh+7RWazQqlEEodnqQT6Ag2TziS6FKCd6DioG9SJCKapEHpAqPM475tR4wYMWLEiBEj3jI8dqNR1xWr1WqrzdBqWGkOIVIXlrZtKGxBWRRi0VlX2EKxXC5pN00ORvvpeR9VNcFUJSFEuka47zY3McpoFGHIRUhZYIsCne1yDTlsT6nM2SfTcSR1Ovo02Ohaq1HG4r3j4Z07TOa7VEVJSoakhI8fU8JoQ+e6nDG43e8YY9adCGUJLXQXSBiR+RJ9h3eb7Sp3yh8Kko9cPDjBuY4YE916gwoBoxTr1YqugS9/9nM0ywuUbzm68SReaV5+9lkKZaiqGdeeuI0pCk5Ojrl4+JDTe3e5dfvd7B4e0XSeru3YOTzE4bAannj6Jpv1AU2zZHd/Skwty4slm9Zjy5Lp7iE+aaw1BNcSu4Z2ecZBvYtJJWsfKbRYu84KxevnD/hv//bfQqdIPZmwWW8ye0nsWvtJVQxxCPGrqikhJo6PT+icQ6lSkuVTJKaACh6rkUmGD4/cH6rXB+XmLqZIWVa89NxzuHWDsdLkGiVaD11OMdUeVTUhhEAXHEYXaCvaGUhcu/0k+9dv8eDVu4Dh9P59zh4e40Lgqfe9F1NaYnKQoOsCRkdcF4lJEdwEncgZHnKBxW5XMlaMMTSLFecn90gpMNuZY/ugS6WGxmoQeCed6V/iaaAz7TBm9ylNwmjNbHfnZ3wPjRgxYsSIESNGvJ3w2I1GSH2GAdtCKYu8tTGUk1poMzlbonf9WTdrgpeicO3DEDjQNwzDhERvJwSmKESc7ToJ1AMMWgrSIKvuWmuighgCzXpJQmGMpbAl0TgIkTTY6iq0FR58T3FS1mA7LWJjpHiMKVAUJcVkgg+edrmAtBXlGiWOP0lthfH9OYkhSKqzinKIprdyVeisQQlJ6FNKKVaLc6J3nNy/hzEGtGQ0TCZTnnr/V/GFz3wSt9lQlpb53h6nJw9wTcPBE09xcPMmTdfRLC84vXef0/v32b9yROdbVosLNps1J8ev86TVFMZy7fohzXrDZDbDFJbXXz3m+P4SpQqstaAVO4cNs6MrVLNDqumE1HUoHTm52LBarSF01Bbq0uI2Df/47/8DHr52F2sr3GYNQcTQJiWSyXoLJZSymODqrVvs7R9x/NqLuDaitEUbQzQyfVL53IaQJGFdSW5FyM5N9OLowlJOJhA85yen0jSiWCyWGGvZPzyi3Dmgmu1QTWpSSCSV6DaRzaYhhCCp8oVmfriPUondgz1WF6fce+V1mm5NPamySNtiTI0xK4wKKIw0r2iKoiBaSwoyzUoROpdYLTecnZxLXky0uDaACqJRyg04PX0qJ35HZBqmrBHnq5xmnhSE3pctRdpmRehWg/vZiBEjRowYMWLE2xmPT52y9qcwwzVCGyIlQucoqhIfAj56rDG4piM0HbOdXbqupWlaVBIWer8C3IfeqayL0Nqws7dDChHnCpzrcJ00Dj4GjOmzB7LUPCWc68SJSgvlSSmFrQpCJzoLScMGFBRVITSuGKWglY3RrTeEriP4jr1r1yjrKcx2IEVMYUm56emdrVJ234JM/0rI9EVJXohPSUS9SQIMIQ5J0J13lDFilGH38AgfEvfvvEKMgWa9Zr4/pSxL0Iqjqzc4Pzvj/p0X2dk5pGnWnNx7ncOrN7DK0qwaqsqCsawultTVnNJaWhTL83NmO7tM6hJlCmbzGXO1w9n9+yTvMMZkHURic74ApUkJZvO5FM85PTx0DhU9G7ehsPC5j3+Cf/Uvvp8YIk3oMGVJiB1d11FqRVUZsQ5OYjVcTibs7O0KFU31zaoeRM8piW5HKQYqnrU2N4Fbul5Rljz51JO0TUdIcRBKu9YRE0xnO3g0qVmxaVcEF2g3K3zT4juP947QNUynFbaq8F3gtZe+zJPPPIUyBjudopMjGY1GozDYeoZen0kTmZB7Jilx8iqsZHZYQ/AR7zx3X32NpmkxZcFktpddyRyq31+ycL930FKS+VEXU4pCpmybZkm1MxcdVL63YkioZBA236PTnhEjRowYMWLEiLcjHt91ShuUtUJpScIbTyJWwBiLMZK74DpHaDs6pQhtRzmpqepStoEW7nm2sX2Efh8iSstKsQuOGALRe8qiAhTNap3tPQ0hei4RlzKZP9C5NVgnoc+pT2xWKGOAhO6LvaSIQZLKQZG0IsZIaByuadhcnHN04yb13hFJa7qmIXpPSjFrdrNjkJJphTgGJYqyJIBMOqKIn3u9RyJJsrkWnULXdWhtObyyz8nFknI6Y3F2RmGg3SzRKmC0JurEvZefpbQl9azGWsNmteJBep3Z7i6ogC0Lknd0oeHk7D5u4+gaoUYlEovVnN3dfZrNGmvLbC8cqCqxeHXBk1LAdx2FMRKAWBSEEIldQ6U8Knpc07J2Df/ov/l74pdUlBAhRp9dmUq0TcTo8a5BAbOdHa7cvM2LX/4SpIDVGqMVGIOPYGK+j4YJl9CtgndDKGLvPBVD4OH9Y6qqwhibJ0oJCsuktqwWFzT37wrVSEuwnQjMpWlIfYOZFFU95eH9E45fe52j61fQRYmtS6yrcjMk90REmh9jpNFOXpy5QvAoq9HRM50o1mvZT8kOidm6Vg+ULxKi4yGKvW+UCVkMkELH4uJMROHGMp1PpTnt3x8aMOJkpUZ/2xEjRowYMWLEOwRvQqNRs1mvcV6oS4O1q9YYLfzyFECjKesJy+VCbGJTwnWS8JxUIvlAljcItSZPHLq2ZffgABc8ISaquiYZi1GaznfiYkRCFRbtPQaGyUFSCqM1wfucQ4BMOWJukJJQm1KKuE1HUgpblESXA/6MzhkICWMMvvPce+UVdlcrTFXhXUeKIQuchS7lk3wtGSBCFeq6DoyhyBkQxtrBdSv11sBZ6Oy9J8aOn/yJn+D6rdsU9YTF2SlnD46xk10uzi9w3nHnheeZT/aY3Tjg4evHTHdmFNUUHwOvv/wiVmkmOzuszs5YLc6Zzna4evMm1k6oJxV1VfP6q/dY7S+5ces2HR7XBZq2BQpsIbqYtGlomw5TWK7cfILQblDG4LsWqwLBd6ASP/ajP8rrr76K0lrC55RBKymqrTGo5EhdAyGClryR1XrJ8mLBbJ4TwpVkW6gYCTlrIxq5DsYKhQyjJXQxSJBiTNJMBh9IE9W3mPjO0fkgzk9ai5tWUefm17KV12ja9ZrlxUOZjFlLu2kIKXF+csr+0TWsttiiJGVLYtH3VCgKFK00w0FcoUIIqM4BIW8/olSS+0SRczVUzliRr7WRY+uds8R9KtMBI0QVMVzScWRdz0DXM3LeUhrF4CNGjBgxYsSItz8eu9HouhZiZMjb6y1HY6TrWtJKQtC0Mfjgcd5R1xUxBpZn56QQJUytpx3lVdmU8w9sYXFRNByVKXCbhm7TUJYlO7s7rNSK4BzFZCKOQz4Qk8cqhS1F46CMwhbyXNV6aUyiJ3lHioGktDxOISvmSqGs6CpUbpZ0YTBaEYLn/OQB9WSKAbGI7cUaSMOQSKgg9roK2aaKgWhLyAVn6n2Ae52BEjpM8p6UEsvTM65eu8HO3hHRFCQ073n/+1it17z+/Bcpi4prT7+LvcN9bDnh4vycGFqCDzRn58wODlgtLqTALiuCa1menTCd76G1oW021NOaB/eO2azW7B9dY3dnj7Rp6LqGPggRHwgpcfzqHZZnF+wd7XNweIBrW1R0BLfmwf37/ND/95+jINv/alKfVN0nW6ckDVhRYrTm4uyCB/cfEolZH6MHvUXuNNFZPK4ArEJZy2Q+RxuDe9DlyVO2ftUKTCnnOwl1yhiY7sypZ3OqyRx0LdknvRI/JrHcrWe0XUPslrJt30FUNMsN+ooBbVFYkvJidgA5tFEa0v6aozUYLTkXMdE2ogXSWqY5Cj1oMSCJqDvmKVcQHVMv/EaBLStCbx6gFGbQQg3OCzmvZZs9M2LEiBEjRowY8XbHYzcaF+cXpBCGwL6UVdAKKbRjiGwWSyIS6laVJTElyrLAlhW+czRdS/I9Z+oyb0o474lGijLjZdXbWIqqEvtcoxHaf0RphS4tFvk5CpqmJYVAUGQr1YIQPT4EWR3ODYa2Wpx+VObJDyvvUZyy8pRCGY0yRc404JEGo1+lJpEtR7MDV4JExLmO5CX/ISI6BVn0zsV2Dl4TypesYl+9cYs7Xz7FVhNOzk7YPTxCo7n1xNNcu/0kZ6cnrI5PKCYVKikWpw85uHaV67ffxWq1QlsZE3lTYKylrCfYqmJ5foprNuzu7xMCnB4fU1pLWU1IFGTzLpQxOVU7kZzj4sFDaTA6x2Zxzqd+8sf51Ic/jNtsUKWFTsnPo5fiWCtWywtm0wplLPtXrrFZLWmbZnBj2hbIou3RSpOMQRmT6WX9yr8i5TBBo7WI6FGU9YxqfsBs/yq+2aDOFtjSUE1q9o+OmO3uoW3NYuXxw+QtoaOkumtVorUZ0rojCWUsznuUkeRvrTUp6C1rSev+DpCbIDeVCoOxBcolnI/k/EhpOlX/ytt7XAYyQjtMIaJSEnpVzBM1a8AJ3Ss73Q55GuRGS+f/Yj8SHDFixIgRI0aMeBvj8cXgSuEGx6itzWxSEe87bLKUpdBwjNZMZzNAinBtrbgQLTUpeXofJ9kGgJIVcCs0nLKuWS6X1FVFSInlYkkMSQpT59FKLGvJegmdZEVeuO9qWDHOUWjiAASkGHFNg9EWU1eAEbGtSllIbtC2kH0KEUwkBvkgxCw011vXoCSy9hgi2xIakooiAmbbkPRIfWOjZK0brTk+vsvBE0+x3qwpJzUP7h5T1bU0R6XFuw6tFddu3qIoCy7OT7n36pLZdIfOt+wc7NNuGrQxHB7tcPLwGG0Uq+WCtm2IuiCGxN7BVZbLU1znUNNaGoCkQMuxmCzO964jucTpfY8m8dKXn+WHvvefE7s1V27cEFG/NigdcrEc2bSdTHSouH77Ng9Pzug2a0lUVxqSJsQk1Datc+CiRsVASnLNtDJMtGXpHV3bT9Akm2K6u8/s6Dr7V69RVhWtLkj6ntzERnFweICtZviYUFoa4q5ZSgZGSNiypphMUcoCRhT92qJMlZtQNTiqkcg0vATeEyPEqEgq5SGDwlpLVdbEBDpbJWsLVol7GSlmVza1bTKyXa1R4EIgqoKkRHRPNgqgNy9IkikjN5U0q0IpU480MCNGjBgxYsSIEW9XPHajkVIcJhGPBO7FmNO6ye5OiqIscV1LVdeEGFBRD7a2j27zkutQ5vmbwrLarDHWiIUoCe8CKikmkxplZYW56zq884TGQYwYWwKKejohBI9zDkLAaCui3hilkE6IwDdKwa91prn0Uw4SWltZ+Y6ybYFkHqQ+FTxz7FWMdF0nQl+tIJLFxwwJ6I/OboTuo62hix2ByOnJCZ/8iX+NiolJPeH0/IL15hxIdF3L6ckpu9MZk/kM1zl8CBTWMpnMOHvwgHq2pms7qknFwdEBVV2zWa/Y2T1gOptRFJbCWGyhadZr3HxKpXdQSdKuU0hYq3PzpFivNqQQKEqL0dJ8da3nYG9P3JaMIXkpfgOR4DpIEWvkvG02GzbrDTopjBKdQhCxgbSYSZqsCPgUKbOYHgXnD4/pms2gZSiqinl9xGTvKtO9XbmezmcrYcmssNkaeHF+RkoJw4yL9ZLz49dxzQUhRuYHRxxOZ0DWEyWh61XTmv3DA2m0tEYr2a+UEiE4XLMRyhMQc3MclSIaEbQrKxbBzonzVdyOv6TJ1pqYZIKilCJ5MayNIWexDBohgxZSoDw3C9ITomlRudGQcMCROjVixIgRI0aMePvjsRsN6C1l+88ZPo8xTw9idmWKAaUUIUbKssS1rRShKrtVpX4LAqWkEbGFpW1bgvcUk5r1Zi2uQSgikca1dBcbUhSr2LIoKaoarTQhJbz3rJdLirKksAVDbwRDoVbUlRSKJKLzoI3kY+QGJPpIUkHE3NqSVKZA5cZiaDDyCnhPcVExShaCEs3HI+dN5QV0FCEBzlMkJF8jJqJSrM/PKMuS5cUJMQVe/MIXiCSOrl3HK8vZ2UNOzo9R2vDg3n1oG5bn58SEuEVVE1znObl/TNs21NOSojQU5SS7UgWsUdR1TVmVVNaglbhxpRgx5JyPmHDtOlsNz+hCyxc/92mqSYGt6kHQ3l/HrlmjQkBpzZWrVwmN4/z0XM6plqJcZQqQSilbBEdUMsPqfX992myBLNa2gagt0705CUXbLNisT3He0W42krESPXZnRtc6Lk7Oee3Oaxwc7TE7nKKNTLWa5YqQItOd3UFfJHoQhY8OHzoeHt+lnFjpgJSWdHvv6LqGiCMqJQnlicF4QAOmtOLYJV5QkgHS6ypSQunhU1IIQ9L8oPUY5BYyrQtaZ81Mn+2Shn0FmQT152rEiBEjRowYMeLtjse3tx3SjNmu6ufvZVn38H3vRFzdnl/kXARNYS3BeyD9FC1rSonVakmKnpAiRlt86zDWYAtZwU4h4Jy4V5VVTVmWlFWJKUtZhU6Jtuly05OI+bUAmU6keCnDI9uOZiG00lvbW2PNoLVQAfojNNYQ3LbRgC21pafc9OeiLwhTjIO+Q6GyaFz0IGJfmi49T9O5jk9/+Cf4Vd/2nWxuPcPLX/wM6+UFZb1LjFFSrqNnUliu3nofy/WSejqlWa/YuzKnKCum8znOR87PlrjuVa7euMFczaknNfWkZDqpCd0Go/cpyqw/iDltPdvxFtZI5kRwnD084fnPfZqdWS26iqxZKAqLb1uS86AUewcHbNaNpGXXlThZZTpQP/0Q0XemseW8kxQDwXUUhUUBdVnJufER33Ycn75CDH7Ineh19VopTGFRCZarNfPDSFFMQVuhOGmDUlYem0DltG1jDCToNmuaixXeJY4Xp1y/dVMaXpVy5kWSbI1yApu1NIm50dYomfTkC57y+4MoNL7MxpOP3I56H4g+DmLu4V5RBqMiRml8bqtjEL2Q7sXs9DQ9OaejQmPEiBEjRowY8U7Am2o0Lv/baxT6yq8vsMW9KQ2rscE5olcE54cC/adsm0QKgdVqzWw+lwRra2iblq7ZEHzEWkNd1dnZSnI7UFl/kVOllZGmRgOmsBAcoRPtBipJfgPgXdjmGmgtxWIfdxCj2NJqDSGI/FZrfJ9QnY81ZX2GUjJx6UXyUjzHbRhhP+1JSehneutABblI1WrrUhQCq4szEgljtDQXKXL96XcRg+e5z3+G27eeYn5wRLlccn7+kLZpWC0usGWNsYayLsHucHH/Ae1mzdH1a+zt77G7t8Nsd4eT+3fZ2ZfJweLijNOTY249cRtril53DCTaZs2HfvSHKFQk0dsYy341mw3RdZRlSVFPcJ2TwL7CCK0pKBF2K335QqNzUJ93DlsqYvAQPMGKKNyHgFaKrt2wWa2IMWKNpMD3YmqtpPHL4zOcC0StUbaQ5gLIdlHi2ETMOh2d3aQSJw+OWa82lPUuPgRUDEJcyoW8ygYCMY+jyqqW5+f7wVg5V8l7VOrds0RrobP2KMbt3C/GRNt1VGUJiBGBKvI0L+s4cucx3F/idBXFqCu7eUnbMSaDjxgxYsSIESPe/njsRiOGbRrxI80GW70CubDOj6JfzldKURQF3jl8LsIvNx29Y2dRV5iiYL1eY5VoBrTVTObToUjVWpOMJiQRaMfQC9SlMLTGyvQhBJJRWB1wUVapTVlQ2BxE5z2+c4QU0Do3FlHcjfqwNNFv5ON5RGixne5cHs4keg1CGqhlWkkT0U+EjDIDtap3r4oxyqp7kvRr7z3tekX0nsXFBYc3niCElpe/9CWuHF6h2pnjvaOaTTgorxFREBLT+YRyOsU3HuVlIrRaN3D8gJRgNj+lriagC9pNh9EFX/rCs7z64pc4+E371LN9jJJGTRH51Mc+ysNXX2E+n0vDlbMlvHO06zW2KJjND+ico1kuMWWRbW61TIQgO3oJ7e5yQrtzHlMUlKW4VJlyAsrgugU6BUxRMpmKsYBSSprOLk+ser1DjEKxSlGcyayFtBV1y2uJ7kRyVALaaOrplNnODutVI9kuSZzS0JIFUuzsUJQVutkQldz3ISV824jzGoqaGbYo5R5RUE8rUsq0pwQp+kyZkwPub5+IwqIIIWLLXpORpz1oQtaupOFuApISwXg/Oxz7jBEjRowYMWLEOwBveqLx033dNxlFVZJCHIqtvqGQkLGtV84bJxv9833XsfKe+XyHoixpGsl5SCnlVfIgrk1JJhgGJdOHlCTQTypMFGK3qnRBMhadcqOkZBphjMaYEmutBNflBGeZbmh0DlrzPgq1J4uHh44oH1OMwqNX/flIslJttDRE/bGJE1U+1je2KCmhcgh0SnBxfs6tFHHOoVBcvX6LoDXPfebTPHHzCWZXrnFxcU5onUx+CktdV5IdgkwRqqqgKhTt5hxlS6qq4OjKIVVZSqGsFE2zoa5rnrz9FCl01JMdEUmTCNFxfnLCi5//AqY/X7mR0xq6tkFrja0qWtfRrNZy/NpkahVISW1QSQ0lc4ii5VAJijx5ssZi6ylBGbrNhna5zFMlg7aGwtiBNgUyNdNKXJ9iCLkBlsmJsfLYPuhuS1SDmEK+jzT1ZCL2xZcKd2lYpLE8W54z2Z2L1TE6T6zkMTqlvoMkEdHWYq00XyHI9AqjMUbnkMl81ZVkvEhg47YhyQdFn4GulExf1HAvi3ZINOy5qf4ZJoMjRowYMWLEiBFvJ/ycG43L3++dcWJMGGNRVgThIU9BlNKkS1a2j44HtkV6CAEdE6vlYijUq+kUY8SlCSMTAmWExw5CkUoxyUQiU7ZUCL3qN7v7GGxZQwrgvTj3aCVuVUY0GaYwmfueiFFWmLWKdF5tm4W0bRNiLtgH2kvWqCjypCKmywcoj8kZC32TsZ3sSAFMJLt7gW9alIK7d1+l7ToKZTB1TYqBaT1BTSasV0tIBq0SMQWaxQVGRZwPtE2LjpGjowO0NRwe7A4NgzU7VJOaqrYodrhy5apQkQBSwjUtn/roR2jXS1IKg/sRKtK1DqM1sytX2TQNm8UiayFCnoRkaliUgpssAiclNssl8/kMYwy7O4esFguW64bFcklvKyuTJCnvrSnYbNaDI1lRVrRtQ1KI01NKaGPkGijQyhCTh0GTrfL+xC2dDU3yAbdpZJKkZHoTfIQUiDESXMS1LVpXGAy2KMW+1ihU0qBlu8E7UgiSzeLzNUxKqFT5tZVK2SmqT6dP6GByg3rJWkHJtEeTnan6TJP+FkJhlJbtjRONESNGjBgxYsQ7AI/vOpVX86Wm3joFgRRB2shqcNt1RCW2pr0KIWaBd0+b6TULPS67OMUYiC5KQ6E1IYe+FWVBWZaYoqSgGJ6TgjhExZD3SRsUgRBFPKsQS1txxMoZIHkawVDqKYqilLMRE947vA/ZblSOd9jfvBrdf38r933jlCZvPzdRSWUylTKQw93iMNxQl/bNiNi4LAkx0G3WrM7PufnUuzg9O8VcnNMs1xRGLF1b31HYgp2dPc4vLogqMZnOc1K1pmtbmtOGZ+PnODi6yo0bNygLQ1kamX6UFqMV1vZaG8NLzz3P8Wt3iDFnP8SENUroUsZQTqasmoa2aTBFQYoKdSnpWmvJOOnPPWRnsnxNtTGyoq80s9mEye4OSZUi3NaGFCLr8xNS6PA+UhYF2ubJQ7x0XpGGows+09D6hijTjLS6JIfJkwg0y+WKopWpVYoyhUoxoXTC2AKjSyA3PNqgjcUWBaYTDVBKEH2LdyobVSlhCqZISjJdCSGwOHuIVjKdiVEsmbXW2BgfSfpWKqH7LBciPsr92x+jzk2L9Gtx0PeMGDFixIgRI0a8nfH4jUYv/oaBPtRDa8XBwSExRs5PT4e0ba01IShJTM5ZG5KlcFmfsdV7DALq/DW5AEw+4tqOjVpL9kFVSfp1WaKqioQ0B23jmO3vC/VES+pyCpGospUsOW3Z5IlHbjqEupSyXkNRVAXBB6JPklKdsh1p2k4tLp2YYTJxmT7VH1s/mRE3oURMPjNvNIN1aRaDpwS+c6TksVact+6+9LIE1G02KG1YtS1FUVPWJYfXrrNpGmLwzHd2mKU9yFauO/sli7OHONcwnU5xrXga+eA53NslISGMKdfkRda1+K7lC5/9DO1mPYiPFYngOqJzzA72cT7SXawwhSX47NCEJiHJ1lpJurfRCZfZZpqcuA5inesj1hh29nfZObzCqpGJllIyKVien4r9sDEka9D9ec2Tilym07adUItCkslZTPmYeo1Gn5shInClIHqPKgp6UpfKzaMycv9pY/I1VSRtiEiavDEFIJoQ7yNKOYy1pIhYG+frGb1MLlanJ0MDGWMgxYDWmqIoSMGjbG4g8jRHqawPMln3pIS6FaIfNBtaGTkvI0aMGDFixIgRb3M8dqMRQhgEzsDQFICsYLuuJSTJA5BBQO/CBCkFXBceKdB/qiD80a+HyYHqqSjSFFhr8c6xXi4x1mLLkrKSID+SOAaF2Gd1KKICpRMpr84npSSNepjORLTSWDToS00CYIyCmMleMeXVdIafb0XhabC0fSMuNyAoUEqKUWMN3ueCMesJkkoE7/jy5z+LwhBiolCKw6s3mM52iSFw+5n3cnZ2wmxac3D9OsXiAtdsMLbAd4EUE67bsHd9H1JgdXbCzv4eyXdMplOsEfeoqEFn4yatobCa5D0f+/Efo11diNNTlGvXuQ6lEkVV45xnuVxTFJrYj6hyEyYaD40yoizoNq1oVsoK5QLeBxaLlazmK7DVhP2rh5BpRefHx8TQsbO/l8+nOImJX1RPy8p3lsrf1GrYXt+w9SJ8rQ3VZAdFZDKZ5vMsJ1xfEvT3ZgYmO1Qp5bPF8SVdDtI/y+vLMYcQMnVLJiMgieL9PRRiTx3M93Zm1DkfcCFsJ2VJXKmiF7phVVqstkCQ6QmQVCIqTTGdY+e7P+29NmLEiBEjRowY8XbC47tOZeoL9KLVrc5AW0uIUnglnalKl+hK2lgp5t7gONU3K0pbTFHQNZvBUueyo1W/6qy0whSFJIIHKcpc17FZrVFaM9/dI4VAt1pjiiJPK6QADklW8LVSJDRE4boPc45cRPYr0ykpQhYQi/Ns7wCU9ysXqgMNS8m+an4qvawXs8tLyfEYbWTKkTwq9nx+eZ12vc6iYSjrmqZtUMZwcHiVajJhHnZZXpwyW10gidyG+c6ceOZpvKPrHCpF9vd2wDfYQrNsHIvVEqVhd3cPqy0qImKGKEWv6zo++ZFPsLw4paorxF5VJi9lVZGUYdO0W+pOyqSxrGRX+aNZLlmenTGZzaCcErtOLGRzV6MKgzIWZUtWFys6n9B2RrfZ4F3DbGdOVU9wsaVbOnQSelFENBkphuwMpgYthtgVg6oqtNpeo5gCvlujVCQED3lCJU2UzEXEjlbuTZ0bjBQT0fR6CEVSRl4/x0fqSxqgFBUpapzrLrmubUl10lDkU5anbIQ43NMqiu6o3pmzu7vDdD7D2AKtYqYaakBLMKQxYMvHfduOGDFixIgRI0a8ZXjsRsMYWYEfqE75+71Ium9EeocpFRPJSNEtguuEj2FbcKX0yETElqU0Gjw6LblMrdJaZUrM9nvyEVCZUtJtWk4fPERpKGyJ7bUd1hK1Glhf+lKxHGMgDeSULS0s5QK2bxJ6GftleldKUqhGrdBJVtcfscXNiKkn+wi1qG3bYeW7V5P0K9wp60SsMezs7XN47SZN23ClKui6PmXdcnF+gTWWEDzz/T2qyZQudJRFSQyOsqzY2dmRacVkSrta0xYlKTiS1mCyCN851qsV56dndE1LVZXi+hQkC77MaeJnF0vKqhTtjHdCLcpFtNYK13VE31Boi7UlRT0FU9E1LfP5nP0rR3hVgCogJbqmJSaHsRVKGawtxW5WGbQRvc/WrUwNOSnBh3yNLgmoUyKFRLtZkeb7QpFL4NuO4DuCsdJ0AlEpirrCRTm+XthdOEf0DUoZQoiUlR6aqoHW108hVBryNrquw3s/NNYD+lspC0WksYnEFIjRyzEpaU1taTnY26GuK8np0JqitPgAKgclaiO0K6OLx33bjhgxYsSIESNGvGV47Ebj8oRhaARy0RW8J3QOEAqMBJpZmTrksYEWpfMwsdhuSw/6CKEiXZqUaP1IU4FSRBQeGQz0xJ2eZpNih28XYknrA75zWGdxrRymrSrKukYZQzKK3nM2ppzorHpBcaZ95UbAkOkreb9Vnoxc3i+TxPkII83GZQ1LjDGnYkPSQsHSRlbpB+9XtroOehqPkqDDvaMDujbg2jWN79AYlJIJUug85bQQPn9dks4CSXck72kT0nDYmqvXDllvNuztTojBk3RBQhGSp/Me5wNf/tKXSASMtSgl05bCGlKKLFebXC9rSIHUh9QlCMnjvWO1WFBPaijkwlycneKdNE3z+YS9K1dYbCJd15B8RBkNTqY7PiEZe2jQGh/lvkr9hCtnc6TYi+YVOubMiSSOUaAzkyo3xCqhNfgkhgR9YrhRht2jQ5w7IUSPT4nggyTTuyZTmYJoyvvMP+FM5cmTylMNRdc0uLYlxXBpUtdP9Po7iazfkO2ElIhBplxJRRKRST1hVk+xVo4/xoTrHDF6gg/y/WRl38Ko0RgxYsSIESNGvP3xpjQagu3KLlxa6SUNKdApgc+Plzo6ZVF0L6rOW1Jscw/6rV/adswrucOqMlLYalQO7yO/Xrr04dEmp33HJEni2tI1a2KMdE0jyeKFoSwqeWzfWOSiX2hRKadJp0emF/3rwaN0KhWjPC9rBmR/GXIeSFsdR0IyKcRAKA5i5AGZrpVcx8nD+zz72U+wt3+F0wR1XaOqisXDB+wdXSWkRNOtmO/sUE3npBhpVku6nSn7B1doSbTOoZYBYwzRiyYlpYDziRg9LniOj4/51E9+XFbXtZFwvJRpVY1BF8Ug0Il9MnVKdK7DdS0m6zWapiFtNsz39plPdkhJzm+zWbA8XxBSyfGrr6J1Yu/omgi3tYGQNSzaoJCcDCC7QgW5txQUVQ1KKHoheGlW8vnLM4Z8f8kEbCj66SdSMunoNo2I35ND60jrPPOyxFQV+DUqOaFiDToUhntX7hk5B13XEbwfmm65f9k+/hJEpqMxylBPp5TWMq0L9FSCJCHR5CmOd57FyQIXHM45knds1uuh8R4xYsSIESNGjHi74/FdpzI9Req4Lfe855mjlSQ0620h1DvuyOfZxafn86ut9WgiSaHJNmG8D8ITqlRezVaSQ6Hz9/piT2tNiJIfobo+qyKhdHY6UlomCiqvgMeI2zjcpgUFRhtMUYA1WGMgZTcmq4mdJyaVjXIvTSkG3n26dG5S5tNvJzI9tain6Gil88q6CHxVRHj3CQl8y8ceo+RXqBhoF0vS7j7eJ2JZ0pyf025a7t95FaymKgua/R269YYYPN4FlucX7B8cYsuC1WKJwnLtylV817JYb+i6lrqesLhYcnZyzhc//Wk2q5U0FEZRVhVaGy5OT7BViTKa5Fy2vJVVft82JO+IrssanSgp39Zw4+YTpHpGu3E437HpNviUJJfCWtHb2IKQQi6eE8oacJHoI13b4r2s6FsKmRjEROdaUvSY7V0mORfZdQpjRKQeE0RFTGIZG1MieQ++IxJ5/dVXiSHk7AvR2ShdoOyUxCYH6Omc2C75GxoLJsiUI2q6psM37XC9e3OA+IamtP9ZvispqorZ3i7dZk2hE65rWSzPcZsN7aah2WzYbNbZUUymLZpL748RI0aMGDFixIh3AB6fOgXZNSmLlrPPf4JMZ8l0qiyKFrvWfuqxzaPYai8Y/o3e46LYpKI0xshuGWOwRYEtLK5z+Oj7zQyNiqxayyq2915WuS9tPCnJbUBpKW77fZGSkBQiSSeazVooKkWBMRZd2mzfGvJkg0sr1n3z0Ieu9aMQttqMXqsyTHyk2JS2pF9mz+coynY0MgFxScLajBVty3qz5v7rr1PNdkBDWU+5cvM6zaZhvV6xvljy7Oc+x7Vbt+mcrP53bSvUm376U1rJr0iK+/fvE5xnd3+X1WrDqy+8xPGdV0RAr0QDobWha1rRY8SI7a93jBJ413W0TUvwnrbt0NowPzhksn8FFHRRoVzg4d1XibGjms5QxpKSQtttPoWmFFF+Ehtk0ad4EWlr3c9OSCFm2pYSy+IUtzkk+V5TZquh6e9VaVQNOsHm4pyLkzNS8KAV1XRGPZtjtGUy38mhk4GoElHJ9C3ma6ltQVnXaJOIydFtvEwa2NodD3qStL0fdD8RSXlipsC1HS899xzdpsN3Lb7riNm0QCYxYq+rYm6OYtomy18yZRgxYsSIESNGjHg7401rNIzRA0edJC5ARVFiMCQtXPQE+OQzTUVyDYZF3d7VRyrCwflnUFwkCS9TyshEQIELOSk5ZlG1yqMQU0ij0nPWlRLXq9iX87ngLDTlpB4KwhjFejSlCDqirYUYiaGj6ycJjUxNjDHEvJLfHxvbvaVfZX7k+9KJEfpGw4qPbOwbtQgmZ3f0E51ed9A3K1u1iizOry4WlNUEEmzWG0LXYWyB1eJW9ODu69y6rZjs7nPy8JiQSlarFbO9PablHFsYlus1Lz/7PIvzhzStQysIzvPg/t1BaKyAZrmW7ApVSAsVoowwYsQ7h+88KSWqakZrHLPJHGsqCqOpqpoYAt4njJXrraOnKCq0tqSoMMYQktjwGmtIKqKVkWT06AndihSchL0jlrXedeiyFuG9yjQ6xBLWhY7ObajLWqYTIRFIJJ0g6WxYEDl/eJ8ITA/2sVVNWZToPCVDWdlvt8IaxaSaElxHMgmP2CwXkwkkj183hNYTXBhsbXuOVk+z62dY26lX/n6KONdyevf+oEHSOrtPIfuqTKaAhUBIGlQcbHtlAjNONUaMGDFixIgRb3+8CerUFpedp4wxzHZ3wIqTkFiPBoz3uK4jBHHiSZcde3qkbZGmtSYGcX8KIUj2BQmtyBMJEVtHBUkrdFVQTWq0MqzPz9AJmVrkTfcOVf3+GmOkmNOGFBMmRlKKYrsqT8jce5nWpPzzEHwuENMj+5yGQ7g0pbn0vZ5Gg5IphcqFet9MRfoV7O3q9LBSnXo9QMIHT4piqrq6OGe1XuG9E52K0TJ1UACB5eKCm4dXseuKqp4QkuhfqnqCD4EHd+/z8P4xi7MTWaEvC1wrGoO6luDDfqrTBY8ybPUjMeG8BNSFCJt1i1Ke3aMDytkeMSW65ZLl6Rmr5Qk3nnwSbWUqhLYobWg3S9rO0zYbumZJtz5nd/+IoihRyqAShLah7TYk71BBKEMxetFpJBFOa6XESjklXHAopXBtQzWTiUCMcZgzoRQm64NiiuxfvYqdzklJYXITE4JMUGIUkXs1mTKdznh4/4S2a1kvVqQoehDvWnQKpJyD0V/ny6GOvUZkmKogeqPYN57DPdnT7DTmMuWQPMHI7lPEuL3f8jGNGDFixIhfZAxsiUu5WWmrWRwxYsRPxWM3Gv00gpSkIchFlnOOkwcPMEUhblPGYIsSU5QUZcVsR8LFnOto182g27hM/5BE5DB83btASYEb8+QjYlROX86uTyklbFVRz2Y0y8W2uNeyShx7ukpSoDLLPeUQQK1QGAqlCN7n+YF8KJVADGfpKVa9tHfQjbBtKAa6TP/9/mcpAXnVWkyQhtdPKcnXl3j3YuEreQn9SVdJpkgxBLrNRsIiQMIRc4aHIhFSZLlcMJlUHBwcUZUlk8l0ON8JODk5ZblYsFyspAEi4Z0jOA+TmpgS9WTC8uI8Z44IzccWExaLCyKJTbNGJc1kPsMUkvfguo71xXkOr9N457POJorDFwbvHKen93GrFTF4lEJCHidTYtiRfYwdzm3YNA1GiaFAIqETOa9C9Bw6Wy0P51hpCBGbwClISIOVohH9Sz8ViZGgFIUyQmkaGgKL6zzt6oz1coHS0C7XXJxf0G3WONeJBbO1KCXTONfJMaiUTQGGBqP/W6SGaypfqUvaIvWGBjVT8d7QtBq0UN+0Jubwvt5S95c7zNd8Fat37zH5F58guY747V9H+dxdMIbzb7nN/Pklq2fm7Pzwl9h8y3upX1vgd2vssmPx3h12vnyBevku4fT0rT6UESNGvM2hrEXv75FuX2fx3h3WVzVuR0EEu4HyIjG755h87nXCvWOS697qXR7xTsfP9Hf+HdjQPv5EY1jQl5XboejuC6kYSTGKk0/XEaJw6hWgrRR71hjJb+ipQnnTKlNIRFiu0UbsVZVWFEXJzs4OVVVSVBW6mlGUU2IMNF1Ds16zNIp2sxkmKjoXc3npOGskejqSItAXf9uf9x9a6ewmJS1DWU1oNptMXRnWyYeV5YFONRSR24IzdyOPNlU602OUJqUoK9eq17kw6E4eOe25jo3EHO5HnrjID2OKJBL3797l9Vdf5vYz72W9WooWohCbYUOiLEtW6yWbZkPyEXQ+Hy4SXKSa1jSdF6coFYkJNk2LrSpA431gd++QkMXrm+WSEEvKyc5wb4iuRFzKdD/ByWJ8Yy1N8ATXyXW2lrPThzjnmc7mbBYn+PwLuhdUq6ybSEn0NArZdt+YbnVBgNbYohiupbWG+XzOJjTEBLOdXcpS6GeSuKeIWrbTbjrOH9wnhFaa1MkETUQbzbSaDfoUjaJrWrEo7rUgiW1z2F9npR5pINWlxkap7fSj15H0uiMRpeevh5tUPlFc1oD8EoY2kJvU7td/LXe/peK/99s/9MhD/p29f8J31mf8xeNvpo2WP3D0PfyXL/9WatPyvc98Dz/aWH5N3fLXTj/A7939p3yyO+JJe8bdMOc3TgI/uDH85OYZ7rZ7/MNPfANf9T0N+oXX4OiA8KXn3qIDHzFixNsK2qBnU9IHnuG53zbn63/DF/lT1/8B7y5O2Ml2mQFYRM1L/oD//Uu/ide+75u4/f1n8IXniU3zlu7+iHcoLk/OLkVCPPIzeMfUAm9ioqF6yfMWmRLVr7IO+RJKRNd9AyEC6YguC7RWhNC7Kl1auc/0qaKwWFtgbMHu7i52OuWJWzd4+pmnsfNdLlaJ2c4uurC0rcM1DWcn93npi5/j1RefY32xkCIyW9yiswIkF/gp8/tTiuKC1WdeJKFKxZSGIlIbgylLaJs81Ej97j7CvR/WpVP+NxeRQzOWqTw670Pvy5vIFr55XDS4cA03T297Kxz9SMiZHr3YPk9HlNjBFrYk+shsd442OgchWs5OTrj32h3u3bmT09dF5GxSIIYExYzWaayLKK2zdWyC1FHPptiioiwrdFmANjSLDRAkPyXk02xMPs9WaGpDw6VIEdr1kma5JAZpYAiR4FuhbrUdrtvQNDI9KMsCleSYQghE73NooiSvh5/y5kqEGFDWUFcTpvWE2C0I7QbXNiQSpiiY7e2DMSLU78+5SmgjNLS9o32skYmI957OwbSUxinGrAfZtDJpSTJVMvqnud6P7BlbBVL/mv1dk5sR76VRLKyIwPsYxxQi0fvcxKifdvvvaCiFrir5/Kvexd1vPyDU8NW//Qt8/vg6v/s9H+W/v/vXeH8x+xk2UPMXr38qfz7hH773B/Lnml8/iUDBf374HDDnpm2BCV+DNJa/cRL4jRNpKP7KzZ/kR34DfKG9xQ17xl/6s/8j5v/vDzFixIhfArhUtKnLi3iDAcflx2qUMaKTMwZ95ZDTb32Cxf/ggv/6a/8631QFKlUAP/V30gfLlt/0ge/lxfeu+Yu//d/lI3/vG3jy7z6Hv3f/HVMQjhjxlcDjTzRyQ6EHXrkaqCsq04/67/eVuMrZB33NqYAYt7qJ/mM6mzHfneNax3w+w1jDZDolpsRkOuPK9eu0CR4en4Kq2NufsVpv8D7StC3Yind98GuYHezywue/xHp5gUpR8h6S0INSSP0itlCzVJYTp5StdPsV6DQ0UEr1DUu2pb1UNl5m3mzLyW3jMZSdeVvkYhVNXulneF2t1JDJkTJVrBcSxxiJ0Q3n2acw/N7UWn5xxqQGkfny4hxFoqwn2MKyPDvny5/5DGcnJ2xWi+wkJcWzc54QZTvJeebKSvCfkkLcB89sd4f5fB/XdRKi1zYoFJ60TUzvNTZJaFxVVcmxh4TVinWzYXm2IPh2oBilPIUhJtoQ8N4TY8QaLXMn9SilTiktep8o95VQmPqzrNjb32c+naCUplkuuHhwzPnxPXy3JqaA9h7vOqE/ZSqVvnRvlpOSqpoQvaOqCjarFacPNrkhBZIi+DA4TfUjhxD7m1umW8O9cWkC2J+fvoHtFU79te71JsYIpcso2b+mE1cv2WrfnP40fxzfQVBVhZ7UcOWQL/3R6/wXv+UfY1Tk3eWH+I760gPf1X/yMzUZv7D4jhq+o34NgI//2R/hQ1/6OuKnvig5MiNGjHhnQQttVpUFalKjZlPi3gx/MMHNLKHS2HWgWHp041A+EitLmBa4ucXNNH6iePD1iT/wG36YP7j/Ua6ZKUYV/9aXNUrznmLO33zyR3j5T/4Lfvt3/CGu/NVb2J/4/DjdGPH4GFadH81Xe6fisRuNwhaZjbQNx+sP/HLY3GDnmhJ9zgXkrA2lsdZQTir5JZAUMUT2rl0h6cSkqpkf7FIUFZO6QmmDNQX3Ty4ILNG2YjpTrC4WqLJkubhA6YKynnJ6ukHrKc988Ffw+gtf5vz4LrHzqFJTGE0yKa9gSOAfucEgZdtUrTG2RClyRoMaitKqqknWk/oCONOBAEiRZrMhxm1ac0/JyjyoR0XiMZG0lilGTMNEIily0F+fyaFEWD+kjMctNUvJfhij5cuUiF1k4wPnJw+58+JL7B8eYHb3eXB8jwf379EulngvlqxlVWOUpksJjCZFTzGtCQqhnRnLbDbHrVeUVYXzAd95VGHltbXGpEjUmY6W81FUUpBtamPs91OLKYBvCVm43etSettgBaKTUdIJphjxUUwEehG1IVOo+kh4ALQ0YymyvDhltVjg2hbfBpqmk2lJcKQUwHXcf+01rt66jZkV+XmJpNUQCkiQN3bXNnjXkoh0bSenP0DbdtvGIyNmh7DhPh/ETHkPldxN8dKTRJ8j959GZbMAOe6ogKjw3uOdzxRE2WYfIqnUO/cXzvp7b/E/fc8/Z0c3jzYWbyP8z69+jg//o0/yH/+tP8XTf+HDYjE8YsSItz+UQpUl5uoV3FNXWDwz4eJpzfopz8ET5/yKq8/xgdk9rhfnrGPFAz/nuNvhrJtgdWBuO3Zsw57ZMDUtv276JX5FqajU/E3thlGadxVzPvRNf4f/zV/7Wv7J93wnN/5fnyOcX7yjC8YRv4j4JXSfPHajEbODkaxEX6b3XDofwwqxFMdaSw7GbD5DGVnRnU6nlPOJWLWicJ2jKAxlWTCZTNDWEENkvW7Zme2wWLW0vsMUJbbS1IUU38F1HBzuE02FLkrmV6/wxU9+lnmh2Du8jtGKh/fvS3FKLtaUrIDH3gkrSHCe0gZjLKmAFIOsMBNRMULX5kLZ5RVrOT6xJhWRdg6CyOfgkjh8OD/CyxcOvqQzpMu0spjQyhCVH4rWFCUrQla99aWJy3CaCSH2Lwl526uLJZ/56Ec5ONrna77hm1icLvBNe6mgTrjO5deJaCJ1HaiqBN4TFezs7aJsTbtayca1jJEvz2366Y9RipipSz0FLDhH22woqxmb1ZLNajVY9qaYBvH0GxQv/dmSKdClD+nZ4tDQvjFJnpQ4vfeAqq6zfgV81+RJwbYJ9k3D5uKMajqBZGQ/8qKBLSxFNeX84et0zRrvHT54YkjEECVPpTdB6Pc0N0Pk6cvlhmM7w2NoylP/o0ybMkWJMYYiKlzXDKGS7WYjdCljsYXCmoIQE9E7zDt4mgHwI7/yH73Vu/BY+Oaq4Mf+0F/m29Sf5uk/PzYbI0a8raEUyhbo/T3cB27z4n9nwvTrH/Ibb3+Kb50/x/vK+xxqz0xpKmUp1DbyNSJW9JGf+ru1UsUjj32zqFTB/+zKp9n5Txr+bvrNXP8HXyKcnI2T0hG/rPD4ORoxDJxygN5hqS/WescppRQhRKwtmO/vUVQTtIXOOUxKNN6zW005uHLAbDZjud6wvFhiCwu6EF2Egda3bJwjREXXOqpoSTpK0Ueia1rqSUnEEYIGW/Dk+9/Dq5/7Ejff8z5eeSEx7zrWy7OhIOyj1ZSSZqMfSvT0nBC8pIQboTIZLfSh6L3kbFwO7gO0UUMhq3vdxeWG41KBmWLEGEOvxYiDUDprO0TEIUGI/dc9Jae/Bqlf3dYD/UiaFXl8UgnnHO7klGa95ujGHU7u3id6aZ6ErJSIKWC0/PLUSjI+2vWK5IO4eE0jOpJ3IO+zEpF6khEKITuCJRI+dDSLc6rplOA869U5ZW0pqwkXJw+J3gEpC7y3IvbMZNveY/2ULCXKqsK7gFKBvvkwWnQnxhY45/Deo7XCGItSRuhtCXxwOO9zszfE7hGTZ71ZsRMCmmx5GyPBd/jW49crlmfndK4leGkqYpAGZ7Cv5dLFyXTCvvW4rNLQunc5S4MLlRoOL1HWNU9/4ANoVfHql5+jbRuMlUDDEBOmMBRFjakKDg6v0DSezcUFKbhHmp0RXzkcmCk/9gf/Mt/G2GyMGPG2gTYDQwItEwx99YjVB65y75sLbvzaO/xvn/kBvqG6z762VKpAU2LUzzRCNV9R8VuhDH/84Iu4/8Twd+e/idv/9B4cnxCXqzfnTqXUVmdisqNirycBkvMk70nOP7r4OWLEW4w3MdEA3bPFraWoZTXW2AJjLWVVMplNaZqGs+MTJrMp090dbFVRliWu66iskfC9FNg0a1Zdy2bVER3oLqLoMFpRWENRFCxXDW0X0aaUUjFThlzOhJjOJphCk4gYBdQ19cEBBs+Np95FcB0xONaLhUwkVL/CjByHMaQgAX0xJQh5TSOzcyRV26JtIHTbFYjBgjQXvpebi75Qli9zJ6N4ZJW+34bK20gpEV3o+47tSj4MK+Y95OveyWpL1UlZbOBdh8LgQ+LOy6/QLDegFcEFwqW09j40MSlxEN7Z2UWbgoRCa0tMaUirTmmI3CYlhXMd0bd0TYt3G9qHp8R2DSnQrBuInugdp3dfx7drlEpbCti2KkeCCtUg1rucz1LVE7puQQxZ9E6iKEtxNWtbYhB9g8l/dBKRznWkGOk6/wi3UfW2Awl856Rx1BCdw7UbNuslzXpNeiM1K+9jr6Xoz3e/LTEX2OZi9Je6P4beGKGf8MWed5mNBorphG4T2KxFCxJDIMRM2dOawlpAs1ouIWq0UaDtoyOdEV9RDM1G+tMjjWrEiLcBzHueZvPuQxa3C9yuYvlU5Jlf+Rrf/cQ/5ddPn+WmKamUxbxJutNXEpUq+M8Ov8Cv+KOv8Ge+7XeQPv5Bbv3rhvITzxEulj/zhCNPaszRAe49Nzn9qgnr64r2MBF2AmbHMZs1WBPYtCWbs5rybsHel+Hwc0vM3VPS+QVxvd5mP40Y8YuMNycG14qd/X32rlyh3puzWW+EOx8C9aQmaU0bAjv7e9R1xeGVAyKK2WxKUrA8O0erSLORBO6kRINRViVRJ0LrIWqKlAidZ3mxBlUwmZYkJVSjmALaKGxZcvbghJ2jQ/Z2Kvb2Ku6dbbh5+xYvfuYz3H7XkwTX0K2XNM2GzjXg+4IxZ1sEJOchcokW1WtwIloXmKIEBdYWFIWlKErquqasK8rpFJSRHInlQgTTncsWpQW6LNFKsTi9T+hECNY3D32In8m6iz4jIotABp3HoHdRW9ehlLIQuxdMZy1D//xqNsP7wIPX7koqORBCIqVtaGK/zd2jI4ypKesaZQq6tiOmSCDgY6YKpXw+kqFdrVicnaFSwIdtQW+NErqS26DQrJcLEeEn+QU6iL8zJIxRDQX50FylhIqRi4sFvpPGoRe/t02Tn6cz3S07XclGcE4mT+Jy9ugv1N7hLAVPcBtCVKyXC9qmkf3MzmCobGOcxy0Kle1W+xuj3/9+spXyNEvOU29N670jKYtSCqNSpuOlgV4m1zxT+XI4JEZRlDVts6Yo5K1pbYFrWoqqlubUlo80USO+8jgwU6FRpT/NU3/+J37GosDefoLuXdeGr1/892t2P/jwTb3W2XOHfNXfeEj4/Jd/Xvs8YsQvVTz7B67z637Dp/mPrvwEN+yCGyawp8vsBvX2aS7eiEIZ/t3Jml/9zX+TT3ztPn/u1/wH2L/9QQ5+5EXCyel2EpGhbIE+3Gf1q5/h5X8Pfte3fJjfsf8RnjQtU20oMJIJdWklMpJwKfAgdvzQ+t187/2v5ePPvY+DD5dc+/AF+uX7xIsLea2RvjXiFwmP3WhYo6nrCXtXj6hnE+qqpLSGZr3BR8+maZhManYmE2ZXr3Dy8CGQ2KxWzKYVKFm5d21HURTs7u2gjCWisntUR3CJgGNSaS7OlsSo0UboWSGv5BsSyUd29nd5cOd1qtkUV5dUqmZiFT5p9q/dYH2+4tZTT9Guz3HdhuA6XHY9Skh+hUsJnSJFUYGR8DttzeD+U5YlpiwwegetLLYssoOQvLFD8MTkMcWEr/mmr8Y7xysvPMdqucKUO9jJhOQC68UF0bV5QT9PPfLvhpiiCLCVyg5SIhDuV823wpdt4ayUpigrsVzNzNKkcvGeJwchRrzvsEGcpJSymTMG6EzPImFtAcoiYYGZphUj2kjhv1ouhD4UArPpDqvzU2maer5WbsKMVjSbNRqIKeC7VgTW9JOdPvSwT8fOovJMOQtZED00Yt4DUXI54lYbIc2App5Nme8fipuYkVmbc44UIhcPjkmpo28ot1oJOX8pBGKIdM1GrHPZXo9+rCRTrviI6cFAc+ub0jxpSUmjyK5bYbudFGO+LvIUrTR9YF/PwipsgSksaZPYrNZo3RGChzYRvIN2Q1GUrC/a3HR9BWf8I35GHJgp/+0f+Kv8mX/6B0kf/ywA5uiQ9Te/hzvf7Xjm+kN+/fXP8Z8efO/wnJ8Tt/sb4Xu/a8p/9o//Y977536S1La/UIcwYsQvCfy+7/ohfv/+x7hiJpkSpX/2J71NYJTmSE/49nrFX/7g3+MP/47vRqVn2P9IRTo9kwaAvAD6zC3u/MZD3vUfPMf/8anv5YOlzs3Uz+x8ZZDfO0/pkt+3e5/v3vk+lu9u+cx3Vvxf738nP/zZr2LvEyVHn22pXj5BNZ38cbKGVBaixwwBtVyT1hti245NyYifNx670dDGcOX6FY6ODpjt76G14vThKVobtDbs7u7ifEfnAw8fPsQ1HZv1mumkklV3BbPdHWxZUE0mlEXBatNKoyFREZkuYmg3LV3ngBKlhd+jVMSobFfbeZQFXRYsT8+o6oK2nVBaTeoShzeu8PynP8fuwZz5wSHXbt+mXW8Incd3HcpojLHZSUvcn6QA1oOFKRHWq/UgxNZaDwVo6ovXJAVrWc/Y3d3nyXc/Sdfd4sVnn6MoCsqiIoQNh4dXiXE/03oa2rZBa0VVTdDGUk+mJKCsa8qi4OH912k3m8E+1nUdrtlkYbtQ2FSSuBCxaVUkbYjOk1Si7TpiJAco9knoIio3RSHFs5HGL0URxGvEntX7htY1NOuW1dlJdlWCoihoVyuCa/PC/lbwHIIjJj1QlPrTo7Vi60i2tXm9TCGTfYjD1yFkLVC+H+p6yma95PKEQilFUdXUszkJLdMV77FlIcd7YujjFbdQw34F32XaVyuPT72+AlTfnKg+ayU+IssYHpsYHNZIZP1OYju1uTSZepT7lqceCU3KqQ5JtEna0LYiYo8xCLUvQbtpMHnntTWY4vEHkSN+4fCryppv+K8/zQ//pW9Du0T7+0/5yDf8X97wqJ+7cLTHb52t+c2/+6/xwWt/hIPDJWdfPmT3eY2bg/v6JV9/+1W++Hc/wOQk0v6eE9I/O+LG73qJ/XLDhz72fr75G7/Mhz/6fr71m74IwI9/+n088QMK20Tq7/v4SP8a8Y7G79//WLaafec0GJdhlGaqSr6p7Pgr3/D3+F/O/n1efuYJdl+8SbEQCnW7b7j7GwJ/6tv+Gb9797Mc6cnP6XiN0uypCb+2hm9+8odZPvF9PP/vWH50/X5+7PQ9XHQ1KWn2qjUH5ZqJcWxCwfOLK7x0fJ3w2pSdFzT7zzqmz5/CvWPRloy/Q0a8Caj0mMrS9331V3PrqSewVYEtCparFcuzC/YO9jk7PSUmRddJd2yN4eBgD2sN6xx2N9/fkxXcumKxWmLQtK2E5qGQNHGfUCnQLTfEoNBFhbEl1XRGPanZmZVUkxJbV9i6IoTAg9de513veRdXruxiJzUPV4qgLPfuvE7hWnYPp7z+8ovcfeVl7j7/Mu1mJXQWa0hJCmGdkFVlxG4383lAGcm9MJJqLc2GFkcoZAIRWnGkunn7KX7VN3wthzeP+MiHPopfNTz59HvBGGKM+ODZ3z2gcRvuvfYKr7z8skw5XBh4/6YoePLpp7BVgUqaw2u3uDh/SPAN9159Bd8G7t15BZUCtqpFQI9YswYf8+q8wpSS5B2D5EagSjSBQByaF2MtCtjdP8KUNQTPZrVEaQm3O394At6jtMF7h7GW4B0hRJTRWFtSTuZ479gszimKmhA6cuIdpJ7GtJ0I9EV4yOJ7rfWgXRkmGlmknpLsY1lP6JqG4Dv6ZsXagp2jq+weXiFESCpBjATncM5xfv+YrlkOad06DTIQbGGZ70xpug7fdjJhSOqSjWyewOR9jpdpSlkQfrmBUeqSRmf43vYRW3k4w/4bW1BPpzzz1V9D18Fzn/4UpJaDK0ecn17QNRsSkbKeoE1BXU9omlbu0RDZOzrguc988nHetm87xLvve6t34ZctlrHhf3X8rfzkyZMUf7QkPPsi9unbPPh1t9h9qUH/6KfGlctfZviB+Pfe6l1403Cvv+cd22S8EW1y3AstP7p5mk+tn+R+u4MmcbM+53fuffTSFOMXHyFFIollbHneS3PyL48/yOc/8TQ3/3Vi99MPpfFYbUjejfqPX6Z4nN8hj700+lVf/QHunz6kuTjH+0BRlKQEDx+cEH2kmNTUtcZqKeKbrqM5XzOdTJjMZpRlxbSuWXctbdOhohLqVHYvIgaM0rRNR/TICC8lcYGyhqIQUW2IkbDZUBaWsrJMZxOWZ+fYQjNPiugMysLR0RGvfumL3Li+Rz2bce3GLS4enuOcE5tSpbFFgTYaqzTJaGJS4CW3QRlN0magSfVaBVBYW1JMajabjaRMp8jy4oLFYslTz9zimfe8l+e/+AVee/V5do8OiClxcbpEKcO1J64x3Z+y2Kx4eOdO1o7LpMI3jheefZabt27QdR5tC05PjmWCYgqeeOo23jl81zDb3SNpxWq5xHeOEBuUkemGaAa0NG7kYEANIC5KVms558D5yUOKekK72ZB8RzWpies10TuhVWlDdI7CWur5Hi6I+5EtC/av3GB5ccZmcUbXbFBWUZZTSBC6IPkgMWSnKGm4+oYiRqFj9QL6IcBxKMtFa6GtTCeGMEWyjWFREEPEO4dznTg5VRXKWGxV45o1CgkQfCR3Iom9LyllFzBFDFvhfSZvZW1MT42Sc3d57/oRRxqcyBjulX76st3vvvlQj/wu9j4QQp5uGIUtCoqixLUbEr0o3FCWFUqXVLsz3Lqh86NGY8Sbx1zX/KXrn4Drn+Af/H92+WJzk/fWP8zvmp/zqa7h//Hw2/jYf/mrqX/wUyNdYsTbFr9UmgwQkfgTxvA753f5bbM7hPy3w6CYqLeWFmaUxiC00W808I3Vi/ynBy+yfm/Hx3+r5b95+Gv4/mc/gP7ylN3n4fAzC/QLrxFOTsemY8QjeOxG47XX7+BVGmg0tiyFj58D1YrCEkPAGmk01ss1JCmW6smEsipYNRuWqw3JJ1JUpCgUpeg9mkSKDt+5zGe3kGTVu7KaolBgROxkk0JHj1GWnf0d7r1yl2pao0xJMjU6BKalYb47YfnwhJ3pnGax5ujGdZr1Eh0SmK3bUYyJFALalqjSDgnoyhhCLohVpiKhEskAREIjOoSYoGsaHhwf8+orU977/g/w+p07tKsV81nNsutooufi7Iyjm1d45qmbRP9BPrZecXLvWPQIXmhP0RacPnjIzVvXefYzn6SeVCitWS4WLF+/S1FXhBg5Pz0jxigNX4ArV67gEhL01jTEENg7vAop0jSNOGhFacJC1xE7h9aaoiqp6pqUIu2qY7VcoI0d3JFkdV9TljXTvQN89LSbhhQD9+48m62BczEfFd1qmUXuZLGzND46n0vRguRJBrD19oVt8Z6L9BhZnZ1dssHNVLKmoTk/Y1LXLE8eslkvILtSGWtRKZFUQuV6PPUeYtkeUGcbZheTTK2yFaAaphjy+TCp6N2i8j486l6W258+ZvwSLrGtRCfTO1VlMwBNBGVkatQ2nNw/JngPCYySNPp6Ngdrczo8KG0JYwE44ueJ3zG/gPnF8PWvKmv+ys2f5BN//cf5WPM0f+Ff/jbe/6c/MWpERoz4CkMK+rducvFmMdUlv7aGb73147Q3f5T1tzvuBc0PrT7A//kL387R33kvsx/4LHG1eqt3dcTbBI/daExmM6q6xBQFXdfRtY6yqgjOZ1tO4RaGGImdiHjnVUUELs7OsOs1TedwrRf+vTHoJIJbYzRWwWrZiTMU0sUro9HWYo1BobHaDBahCujahhgSi4sFZw8foooCU1uIEY/mxo0jXv/Ss1y5dZWrVw9pmzWz3T0WD44lIC/2q9pQlRVJWaICqzUx5XyNmAYbWmUt127exJaGhw8eiOdvEL2EaztOH57yqeUZeztzbty8zoO793j15TsEZWibjgUX3HvtPot7r7JztMd7PvB+Lk5PabpOrOc0qBBpNy0nJw/xvmO9dFRlCTERQqRQBhe80LecJ/qOGBUXFx47nWOKiipC124whaHbeOZ7u0Qg+MDq5JQQwiDCVkYTfWR374CLEPEXZ8QUqOoaYwp8iEJt847Naim6gRjZrBuxBs4FcVGWdM6TMpXLWglrVCpgjEYbjcGKtCWKsxT5vKZ4eYV+OwG4vCiy1VAAKRKDo2k2ONeiYiDGSBc8oNCFxRhDiO5SjsUWWmvC5UyUS31OL/aW4VWv08gTD9ULui/lybxhD4dN5f3v3cOGRkP1zYboP5QyaK3xKRKDH9y5tBLr5PnODj4kOifHpoCqqh7zXTtixJvD11UVX1fd5Wu+6//E/+Jv/z74yKff6l0aMWLE2xC91mRKyRUDX1O+xB/8li/zv3vv1/HPZ9/B/t//+LhQMQJ4Mzka0RG8xvvAer0i+cji5BTvQrZblWlDCkI7KsoSpR0aKMuKEFoRe5eWkMC1nuDBKiiNEYqMEitRjYTSaGMwhUFbMFrEvL2Vm3cBjWG1WHF89zUujl/nyc2S60+/j6qq0NoQXYtLQvWZ7e9xcLTP8uyI5cU5YdOiKnFZWF9c0K1WTHZ2SbaQkjEi+pHsEKSMoqpL6tmUzm/w3mfaTEAhIXib1YbNsuNDP/yjPPGupzGIRqFZLVHa0rUbHt57wHOvv8Dufs3htetMqpLu7CJ7RxXYBPO64onbz7BYfAbftkxnM+J6A8aALShsSbtaolNCZZtc30VQa3St2KyXGBXZLBbSMGiFIWGspq1KUheHYjkkSRgPIYpAPk+mFNA0Lb5rhcXmPc3igkQkK7WzUF+CCJUxWMC5TkTqCqq6omkadNZtpLAVfYvgOxf7DN/a5oLkBkCK8kdnHv3PiQljLUbPaDcbgu9IKIraiu7CdVm4ni5NIbLNbko57f4RxcXQdMhrpjzMED1O75T1Rrvh3lNrcK7Nzx9akZRQcesk1gcAkkAnJQ2m2mo/tIKQICmFC44UQOXJWkzpF0BuPGLEvx3ft/iV8IkvvtW7MWLEiHcQprrkjx9+hL//O7+Ow4/cJnz5+ZFGNeLxG43VesP52QVd01IUpVCGgqyya6VBSyCZbztsUeC6Tla6raVbbaRwNBqlxdmpbTqqwlLOpnQt7F894urVivXS4WMgKkVZTDFlIY5KWqMRa1mtNJ3zlNowKSueePI2Lz77JZ799Gd57c5rTOsp733/+0gaitJy//4xT88mXLtxjbPTBbuLq5y99vq22A4hf5xSznfAVjJFyW5ZSiu0NtTVhKbd0LmOoppgqg0milg6dA7vOsBzcnJC6zsOjq7y1FNP8NlPfo4rT1zn4uIClQydC5yfnHN2ckbXddiyJpJQSaF8x+bkhId37kiD5iLOideUtYVMCwqL0+A7LwWuNkSfiNHjfUddTpjN52hbiP4jIpQvpCkoZlOqupK8hxgxJoAKOWE7WwrHhA+S6A2Krmvza0lZbYzO9q+gC4sta2hbcezKhb1zPqeBi6uSijo7UQWUMmIFG7Ng2+RcjGwnezlYUBK1zSDElockvPdUZUnbeYwtCMGjUiQ4n5+vhuf01rl9gru8XiJ4Ef+r/rWQ6QP0045LjYiMU/IX6ZHn9Pv1RvpUP8Hpn5OAGBL6kmvH8IxLgYVaixVus26Y1BOhhRlLu2kluG/EiK8QPt+t+Vf/xa+lch95q3dlxIgR7zBMVcG7Dk/odq+81bsy4m2Cx240lhcLWeUPkXa9ICVZvbZFQV1WdJsW17ncgERUL7YV7ghaa6wxg0WsUuIA1KxWaGOIbcPBlSNuXp+BtYSkMabER3AukpTCaE1pxQY0KaE2zfbmvG++w3Rnhxe++CVW52csjo/pFgt2rh8SG8dqtaa2lmc++F4Ojg5ZLxs2Fwu69QKtLRpLVIkQHKnr0BjRNOhcRGYrWFuV2SLXoaKiKCdE3xG8FzF3EK2Jj57NekMI97j99C2Ugc3DB1y7eYvGgTaWPsnau4jrPLooSCrifEsMgddffpFy7wCtLZvVBquNFNwx4r00Yj5br+pclNdmQj3boSgrlDGgEjopghL3iJgis/kMRSAFcc6qjcW7BqylmtVYe0DwgfWmQXtHyI1E9EKNi5eCBQMJtMKUJbMrT7B8eErarKVR8JEQHMZo0WpoKdpLa+mcZG0orWWSk7U8fRMAW+cm0T4oUkx5ipTQCrQydE1DCkmaKSXNDEnn9kIctlSCIaRRK9mfYVLS07QYzAekgZBtxDfsy+V9G2QbqbexVagUGWx1e3pV36yoRxd2UpK0dK0KQA9fK2PRBgjQLFeELjKpJwBMJjV1WVCU40xjxFcGbXL8lh/6k3zV93/8jS3ziBEjRvyseMl7Pv+hd/G+l56V/LMRv+zx2I2GX3V45wbrzxACwXti52VikXnywGDXKp9rrNUSgqe1rHAjK7Y+BJzrqOuaoizRWlNaja1KjC0JytCFhDYRbYUOVZRlfn4WF2uNIvDM+7+ao+tPcHLvDs16g+s86+WGZrGkbVqef+4FWud4/fgBeMn0WC3OUGjquqJtZSU8eI8uJdshKCMut1G0J6cPzrIlrpeiEIihX0Hf0n5C8EQSXdvy6Y9/muBaXrnzOvePH6CrmisHu9x++klefPklHrx+n6qUol1rTcIQlVBrnOtQypJioPOJsrBEl3BtK5OAPuAun88+IyPEQPK5iE45NdxIoR1ihJDQZFtXlTBFJdSuzhGdQ2vDZD6jqiuc9zINiFK8x5x7EXwg5AI8OIfRGl3WUignl1fyo4QAKkUK29A9nelDwry6xDXiUoORnalUzgVMeexQZrezqBRWSX/gOo9RimKStQsx5hwWyaww2uBjIimhPpFE8A3SeIDsh9y/0hFsmxHopzrb3cwJ84/sehqmJpck4MM1Grai+jwPRYgBa7LyIskUqa4rQkj46EjR45o158f38TFhVaJrPU07iuxGfGXwrR/9bj7wJz6fAzNHjBgx4vFxGtb8kS/+Ht7z95fE09O3endGvE3w+BON88WwItsv8EpDIUWYiILNpQf0hZnCu4D38VIitMIBbdNRliVlURJiwrWOrg5MdkrKuiZi8ZtI0p6QlDhUuUhVFRRVQUgJnxSokkTk2pNPMj84oNusaNYrzk4vWC+WBC/hbMfHJyxOz9kslsx2dyinE7rzJXVdD1SbzjXEVlNP9zCmEJ2GBhIE31ugakiBGD3dZp1XphU6RUxh8D5JhoQ2nJ+c0rUd3nt011IazYMHD+i6BhcjZVXxxLvfxfHxMeenZ6iqRqsaRZKmx0BIgRATsfWkNogrltIimVeiZSmrEqVFqB19IKpERPj8MUZUiBgryeZR5+8lMLaAsiakSHAtrumEamQMRivqogCTk7KFWIQCAlC2NZGIazvWZ/dxXRLhdzJSg2uICow1hBDwzmGNza5L29RtlcMQL6d/941qzBMBH4Lcf8YSnRfnM22opjWnDzfSoPQuU4MVrtyjQttyqCSWsiSFzfbIopeQ55jcPPRNx5AMzqUdylkafe+hHsnZyP/m94fO1oRDwB8MlDxFzmyxQSYYuYF3bUeKClIgpAApoJ1murPHZt2InsaPq0QjfmHw5+7/Sn78wbv4uoNXOXNTjv4P09EtZsSIEW8KLgWedS1/7Mv/Q8r/9QF8/FNjqN+IAW9CDH7pi6TJnJT8dV9+CvrMBoYcgV58m4YV3xADWivWqxUX5xe8cucuZSlWq7OdXabzOUU9w1QTdClFtE4KqxKzSUVZWgndMxatC0kY35SUkynlZBdta2y9S3fYslkv2KwWtIsLYl6mXi+XaKOHbIcYpLDUiAtS26yZWAtYVNIixI2eGJyIzENLihGVk8NTSLSbNUUouHXzCW6/511EpXnttdd4/ZU7GBdRytMsL1BYfOskd0ElXnvxJbGUBWxdEYMndi0peJSWJGuNzfvqMUaoRlVdy3kpCkzWOGilUUYNygEdezFzwmqhBLkoFJ+QIqFrwHUEF4iIW1SRU6qdd3RtA4C2BmsL2a4xQi6rK7kNvGKzPsMow2RaYcxU7pmccO2DJzgvzlnOXUoLh95CV2mZuFzma/TTDa2EzBRTIAWHthpl5dotL85BIcfe07QuaSuMFvqeyvoKa600AP3rIqLzmPptGPm+kqZEpkXbBPAURUyeokx2yBOj2H+eIjH2FKxt89QL0vtjN7mxkmZrm9ARvMvPlMY85u1PZzMuFkuC9/hmdPIY8QuDZaio/qMln3kYgSWGn3yrd2nEiBHvILgU+MHNlD/2Y/9j3v1/B/tvPjM2GSMewWM3GgPtpefRS5jEwD8fSOt5lVoYKJmKkiHr4Qw0mZQQwbF3UmimKDqOGAhdy3q9oW0a2maTaUqBFPsEaYXu3YCUEYtcayjqKbqoKOspk9mMyc4O9XTK4a3bGBVZXJxx56WXefWF53FNg7Ua77vcOG2dgGLbsnYPJZsj04VilEmFIhFVEhqZTqTgSdERPRRa7HxffPZLlJMZLgSMtVy5JQ5Td195jc478BGlNVopEgHoMAqid0TfQXBZjyE6C63AZ0tUn587nc6FbpXpQWVdQ0j45GUqkRSm0HTOoVLMGhKNQp6jlRmUCIVVpGjEecoWkCIqenRQWeTvJEkbmXbYskBbi1aGyXxCneotw0j1tC2ZDoQQCKXP9Kkolnc9hU4ZlFZ5pV/uknR5SoDQ74wxoBVJKayVeyX4gHcdKTueaXpqUhZ+5wR4pTQe+cVnyxLvHMF72WaelKSU8H2AYNYUxRjBaKHn5emd2NtqlEkoK7bLA7TYNSuAuA3ziyHSti3Rh8G1KgEheEKMGPoJYBwaG5I0QjF5gg9sVkusNgTlB7rXiBE/X/yHBx/mzz/xu+HhyVu9KyNGPBZCir+kQvve6fhUF/jj//T381V/65z0hedJrnurd2nE2wxvotEISDFkeur5JZHrNtl50N4qfSlrYMtZl8eonJEWB22DViIWL5TCrZYsTh7gnZeiMAZxAlLSXPQ0mz5t+TJnRSmFtQXOS6ZDYQuhFpUlZV1R1hMmkwlPPf0Um8WSk5NT1qtldibSoDTee0CTQguuQ+kCxZaqBBqdQxFSFOG71rInrttwcd6xWi0kANBqfEw0KuG7BlMYYs7NsEVB0lqaFiTnQgQIudDOBbOyRs5X/k/rHCiopQkgyb9t24p8QMtpiSkRIujsyKRTEPG1yvSvnsqWkG0QpLkJHltY6vmc4BzdpkGFiMrahBiCJImTc04qi9aiwRkoUAoRaCsRUmhrRCydEtWkJvQzsCTTCrEIluMTMwH5g0KmWSmlsD1RLyqxU84ifJ2QkYRwyqTBYauJ0EOjAH3jO9w5adsQp0yjgoGFRQqK+Ib7XGud3be2jQ0waJB0dkhDW2ksVAAvGqayrkSf5Pv3U8x20AXaiNlCjPGSxkk0Havlkv0rV2m7Nr8XR4z4uSOkyCZ1/N7v/5O8/1Mffqt3Z8SIx0ZPCR7x1iKkyCc6z3/4z/4EH/yv7uJffAXGMNkRPw3e1ERD9+nHl114ElKt6Z4esnXlge2QY7DuTBLoFoJQp7Q2WCTALaw3tG1HpxVdCpmfr4eGJqY0uE/1DkT5VS7lH0RQBVeuX8c5x/LiguA9G+fYLBcopQkpUVhDUU+YTKfs7u2yWCzYLNcSbBezWDlmSpg226YmT25UPjBNRBcakpE06SyKVxEp+mOSQLmuxWtDjB5FRGPobVzFvCjhg896j61bkUpqEGGLU5PClpVML5SIRwatTNZdIMoMOXcpYvP16Js7tEGZ3q1KtpFUIuWSP3pP64M0akVJNZ3LNes2BB9R2sgqfFQSjrhpiUnco7TR2KLAZIqSTBYSSemtA5QCE2NP7iKkRFJRMiVSxOgEylAYaRpCjETnaJqG6XRC8IGu64gx9DfXT7lXH20KJOcj5SwKnR9DLuh7MXdvxdvTnoCB6qS1WO8mBdFLU5FyY9Q3ykopOU4tTakyMv2IMUryPOI41utrUqZWKaNEyE+QxsxvJ4gxqSzfSJjCoosS3a4f9207YsRPi6//yO/h1l+wfOALnyf+7A8fMWLEiAEuBf7VpuaP/ZM/xgf/q9fwL70yMFVGjHgjHrvRUErnwvFRJx0p8vUb8gakIZEV420Cc8xFuDQDQm0RmknMBXXCxYCKEPWWiZNIUixmLrzWeqC2qG2HMdSbXduyvDhnb38fP6lZr1aSykzmvIdAFyNVPWGxuIAIRWHY2d9FaUPbdqzXG4LLhbsyUnjqPE1BKD8kD0EEu72rUiJlOo7HYnNQW5QANiPC58l0SrtqxMVLKwmdMyIGVrlh6HuaXtsSgpzHMjcZ1pZ5uiH0q34JXGlFVFLUKpKs8CuyHiEQgYQU9VsHJdiqBHKhnqcXrmtxSmHLQiYc3tM1rSSZZ92DTpDQuODx3uG6FoXQoUxRYItyu/KvJJsEbWXyoFReodeYhFznQpLZ5fCFbqS1pVS1TBJ0xBSW2mqxXB6E39kuV8t2kt+urmilSUkaDWMMxCgOXLnJIG1F5Co3cJfvbX2J8tTbDMN2MhL7iZ8KRC8tm9Gy7iZOX9IUNYvlMHVrmw3eJ7xzQyo4PU2vv6czQnAszs+oygldfLSxGjHizeAHN4brf6UmfWS0sB0xYsSbx5dcxx/5V/8TPvg3jscmY8TPisduNLYUpUw9ydoI+UvVF7NqaCgkgTnkz/uF7IRRoh3wJFRKeQqQJJMBSFHoUSlkGo1KJC1FsNF6CNfTOfegL1577YcUpyISXl6cY4tiu/95KlGW5eB0tLu7z8XpGd550VNkUfXhwT4nJxd4L6vMIli22ElFUViI0G4WeB9JoZNjyZOFlJDCUXUSQhc9MSp0NChrKKzBlJrgnEyKkpbtxCiPvTQOijGvkitQylAUhVj8Xlq17y2DjdaZ5S/nMOZGJcSIsQaXszBUkqTqSEIpiZzTeZKSyBawvdUrUvi6tsO3UFrLZD4nxYRrNnjnc1+ZKIothah38Qo+sG4WoKAqK0xhUdbIhICEiimnVpCnIqCyY5ZGiZ1w1kaUdYVKksUxKLhh0JnI4eb9dQHXOXQWyMu5yTbApSU4T/RCe+uf2zer2xRxOad9Gyb3d8yNrxp622Gol2nD/cQkpoD0nfn9kETkLZfIosni7+iRFPI8UcmNTQw5hT13nc1iidndCsxHjHgzeNUv+f9tnuZv/JnfweRHR7rUiBEj3jwehBV/5Avfzfv+liM+/9LYZIz4WfH41KkYiCiM3dKUgEwjApAVYnFG6oYV3z7JWyE0Ip0SJmX3nYFXNbyKFGyZihJBKFNA8B5r7daJJwt3ByerS9vpV6BtUbF/eMTF+SnRe7SxxJiEzp/g7OwEhaaqKjAWFRPBB6L3WGM4ONrn5ORMwupSABVRUdGuNnjnSL4jhU6E0zDoDLYJ2kEyHFJ/nhRGaTbrNelSEZk10Bhj6LyTFfqUbV9TImpFWZQUZUFVT4ZVSH1JJ5DyRkQToYgpuySRiAHK2uKUy0J2oQQlrTD9VCkXuUlv6WFpOLfyv0CicR0uT2K0sZRlSYiR0Dp0bpLScG1zoW4NMUbatiFt5EfGWqECaRHyD3Q0tpOFRMKgUIPLmVDMMrPs0u1zKb9CZVesfK+UZYkpSlCKzjuiksIfpagmU8kE6Tpi8NkFLN/PqddlDCfgkanZcL9e+rz/oe4bzhhz05geuT8T5AZcwgZjCPTuV/TUK23xqSNk8wG5jo7Nain34ogRbxLf8Y/+NB/4888xOR6bjBHvXMi8d1Rp/GIipMhFbPiiq/hDn/jD3PorBfrDnxvdpUY8Ft6Eva0nZnqT1gmlhXsPOhdICe87cYWiT2/W2ZUnobNuISITh6RyI6I0Ol3SWPQvmBKSXZ1XlBUi0lZq+7he3zAIw7cwxlAUlq5t6G1GZRJixc42BqwpmM5m+BD7wYysVifYtA1Xru1yeP19vPjcC2wuzlE+4puLfveAfkiihpKz339hJYkdrdKK4Ux4CTrsV65VLiNJYiGro5VpiHBnSIgmpSgL6kmd6WJvoKqRMEmTVF6RT9LYJQ2G7ISkZTvhktBcdAoM4nGUllC7XpSdty5Cd5l6RAWBiAoQQyIaQzmtmUwmdOsNXduSsiAspTBQ5NCZemfz9IKEc54QWlKKFEamNaawQlETNblMOXqjs35/ohJNSd/UkRutlPIERHba2uznpJSI0XPjVpZlPj5DQmHLEqImhN6mFnnNgVq1bX4e/fpR9Lkg9KL4bHm7bTIUPR0LVKYRKnFZi0HoXYgeRGkjoZDqckZ5NhTQ4x/ZEW8OP7zRPPV9gXB8/FbvyogRPy80yVOp4md/4IhfEKxjx189+VX83/7Nd3D0McPTP/ga/qVXh7/zI0b8bHj8iUZelRVr1YDRgUAWc1t50xcoalPK40gkrSmMwfkuC5Ezlx6wSUsatrrUZOR6LKY40KGi2hbzpDToMi5z5mFLd7msM3BdS9d1KKXytCUAbjgmrTXOORGWx7R9nbzKf/bwATfqmq/71d/AJz/8EdbnZ0NTIK+ZiKmn1mz3UylNNZlgtWG9XlEUhZh1RZkM6QQ+sV1Bz9SwXqys+u2jKIoCXUlyus86DZ2pO/06fn/+dJ5GKES4n7K+I/VL//3JUTI56icDMU8ORH+Q7Vm1RufHRCIqXhohpP4fWbEPixWtNdRVxXwywTtHs1ptJyJKksiT6otxBmtirUS+EGNkvV6DkmMuqiIX3mpIMe9pcSl3Hkmly4dEQiY5SiVKWxDLggCoS1oeH8RZShsj1CxlkItjMVmnEWOmsaVEys5WKaZM7Yrb48q0qv6xSiGi89xcJBhCFEHOga1K6umUzjnQisJW0oJm21xQgwZJznMc3iM9pc6HcRVpxL8dL/slv+nv/Od0h4E/8ev+JX/zH/5mnv5nP/5W79aIET9v3AuRqQoUalxw+UpjHTv+y3vfyr/5q9/MB//l88Szc3w75jiNeHN4ExqNLWLIdA+lsLZEqYKYErU26BDxOq/Oa50zIDROBymmyLakSag7WyGvGhqHnoSitWg5Llvl9gXjI0LwjKHZyI2IrCr3blmywp7iJQpLUqyXbgiKU7lJiFpjjMWHxJ0XX+bh/eN+7iANQX6c7rUhSksxrFKmvoi2wDk/BLn1/UkIl4pSJQnVoEneD45IGHF/MsZQlAXlbJKtXhkaoSQK7txYaNA6/zg3aPk4o3QPUqRq0TXoTB9L+ecqQRBldp4OJFTM7KGhABZhvJxiLdqGS6v60XnWnVj21pOanb0DurahaxuhuZGGSUmeg0HKPlfaZFG8wXtP17YyiVKgtcXYAlsW4uIERHw+R9Bz62RCofqhAYUtMLu728ZGa6bTqUx8+gcNtCjRpah+n7QeNBgphIGel5KIuoeAvnwvxKikI867I05TYaBh6X5bOUwwpkQgiqi+0ChjUN4Pr6Py/b6lkKlBCyK32+ghP+Lfju/62B/mmT/746AU328OeTp86K3epREjfkHww+v3cX3nOeZUY57GVxAhRf6fi2f40b/2LRz9o08S1qPb4YifG95Eo9EX7FJE92Jf2BZgbZBV80JbKVi1JhqDKQsp5I2hKEpW9+9mQa2E4/XozX5CBFLMgXLbgvYyXSWlJEnO2X3qciMigX460+wVKWnUG4o1yBIJevp/dmHqMy2Cp9IVPnjOTk6IIQ1OT31joZQIlbfbzOcjRHxy+dUSIQVS0iJwj2GwgkVFko8430qToQ2mqrN2QWOspSikiVNpO2mhnwzklf2oNCrKArusyOfzl/MjjCTayTXoj1NOomw7AkqoXTFfZ2lMQOZWKlOylDSO2pB0IgQ/FMPSAQR8F1m4Fm2FCjXd3cVozXq1wTVrdEyPXIP+mvXNWGEKrLaEgX4l+SCbtsFoPQjK+0yTfiKjdE/hE0tcbWQS4vM0RimozSQX/GHbs9FbFauerZX/eKlBs6IvCf3V0ABsm+MQozSQfVNJbiiDf0TSkZRGa0PwDouiXa/+/+29a7BlSXYW9q3Mvfd53FtV/Z7umZ6XZnpetqRBlpDBBkOAbcJhI2PACEQAIrAjMJggzB9DBDYKE7bBGBM2lm1MEBIODIqQsEAihCRGSEjMQzNCI8372ZpRP6a7p7vrce957J2Zyz/WWpm5z73VU9XTPVVdld9M9b33nH32zp07b9X68vvWWtilE6QoJY9Jd+jIk7i2VM2QogCFMM2TpBoazmK3HeQbVeUaGu4U/NCT345/8+1fxDv7CI9GNF4tvJi2+B/e/x/gPT/1OEIjGQ1fB26YaCxXXQl6ImO/L4FgjNL1OVpZTq2s03VH8N0A9h6slhV0PbTFBKDGmMgp766LXcre1epD56gZgCb1ouRoWEdocg7OexxdOMbmdIswnv8PbVFEKAdvKUmCLgCcXrsG770muAeAOsB1+lkbf52dYSVqkyoYlarApqiIqiLVqaSaEsco8wNC5zv4foDrvCSpMxDHvSgYpiikJJ9LrI38pCQwRDSCJy/5NOI1AruUc0LSLMq3Zn3yUyIhf6aGULVLb/MMJiUYSjDzk4KJC0J2pgkhSO+LxWKJYbXC6ugY++0G426DGKacOyPn1jnSa3lv80xwLuYcm+3pKUBAp9W3OuuRoeV4zcOmvEEIoRfyRQ5qkdM8CVJVjLWXiGXMmH1MmWlty3NWhUqT3SNH9M6h7wuBY2aEEJCCz/Y21s/WKlwCIYVJE/Bdtqv5nOSPTPpiinlMjWY0vBR+ddzh/n+yvNXDaGh4VfBrH389fu6Rd+Lhi5/Evc41C9WrhJ/cvAlv+AmP+NxXb/VQGl7juGGisVh0ICUA00Rwo+yyphgwpYgeJJWUHDBxQiICxSQ2nXGHcdwjjHtsOAEpShlWBkbtDs4kKa9m15GgUxq6WWBHRKVJm5IKs1CJtcUiZilbOnQd/PEFXL18iogA8b+bCsPVxrCEmM51Ug0I2s8AhBC0elCKmmsgxyeivKMtgakDyIhP0spFGlwCoBi0fG0J+tnGASjXUasNORyvj0FEODmRruWwhHeWnXFEVTDMz6QJ2wRpvkfaQFHuw+XzWikvJj2OKoWBZCaGrtexeMCJvSsFsctFU2WidF63MQG59Z90BNfchcQR2/EE+9Mt+sUCiwtHuHC0xni6wW5zCk4h28py1alMGGUSvTa+s9wFTgkcE7anG3jnpEs6EXzXixLUd2KtIrVV2XNmhofkDlm2S2K9hua8RG0kKK6qslaKJgbNeQGIpYIaO0lAN57DIPRdj6T/AJbcJM7V2BJrLhKArus0cd4pYUwwYxiIpNkfld+Bhobr4bd/4ruw+PMXcOkjzSrVcGdi/ZTDpzaP4NrxJ3DJtQpUrwY2acT//aXfgoufeCFv6DY0vFzcMNHIicsgECXtopyKnUcTfFmTVhnA9vQadqfX1OojO/aAWFGYoInGGs+R9aAoXZlNKdC4POczM5UgzCCBoe3aS0LvlSvXcO+996PrN5imQgzymbNSYtcRf5FZhcqZ1SvPESlZMO+ylx6QgN+SfgEAEXKv3mcSVHz3Ony7gu7QO9+J558TTjenuQKSr7Plc2DPsx10V82Z5SUQUI6JakVjJTeuLl2LPLlEwDROYqUiSYTe76GJ46zKihIpKrMDaL6H5ShEacLoZGGAkbDfbjCNewzLBZarIxwtB4Rph/3pFjyFikxxfp5kOgNHEDG6zgGQ+/CWsK3EVYgNQHtSAuLhvfTtkGaNGuQntVZlZuB0oqQ6l1UeU6MZrEM72f1XOpbTOczPUmWnukpVtmh5KvYockhwQIoAElKUsXOQimjTFIQ0eSe9WHTtlB4lDQ1zfHY6Bf7ag+CPfORWD6Wh4VXD0ZMJv/zVN+C5+1d4nZ/QwbdcjVcYH58Iz/3s6/HmJz9egoiGhpeJGyYaXQek5NUiY7vwJdCPGlACQgS0YGwO7gt50F4NsMC1lKYtwRpmPwPWrRnif4nWbbwMIWqzveybjxEp7XH1ymX5vCMN/kvAb7vkeSddz082zhTzLrJzXkvDql+fUw5c5WcqOR+qMhBJv4hpGtUXJsGs5GV7QMfZuQ7kpQTrsFig6wfs93s5zrnKglXUj0zKNAjOr6XsHhLrGXMus7tYdMruIqyKk+V75EBWbUVCpixHBsWKRADgMpkBlGCqKmUPppRlLdWiyEmwv99uMe328IsOy6MVLjxwP8ZrO4y7LUIYM6mUnBmXrUy2BoxweqcVnjRvwXkZb0oRKTLCOIF5L6TDeXR9B+88vLDVrKpJB3WbNyoKixHSpBckh0gMVnWqcw5J7V/SO4W0yaCoIq4T4iimOiUv+nCIIFWunFTW6vReuO+QOIFVmXH2HIMoeZLa0/7ibyiInPAv9w7f+6N/Dm//6Q/f6uE0NLyquPS5U3zh4w/h5x59Fx7tfhkL3zdN4xXEE+EEf/iDfwpv/+FnEK9du9XDabgDcMNEwyo5Ba02Za8BEqhGyoYTee/g6+HreoIzbPnQGlIne+c8jLxjfPZzNdEAEk5PT+HVbkMaEBcLivqb8sg4v05mz9JAXHb4XVEM7BMVaRHUO9mysx5UpZh5/6GWLwf4TrqRkxMSFdMe3s6bZMN9NjcHOSuox8GljCyR2MOkrC9rcjGQKruT5ZVYvoURSRmk5txYnI1CGk15sCpXds+1Dcup4iOxtQcxZ7UrISKNCWGa0A8DhmGB4/vvxX4cMZ5ugWkP6/JtKoEW4K2elqowJsXojr/rHNjJfacUpev7NGLck6oEHt0wwHd9tmc5OCROQj4Syr0Qg7RvBcPIrQN5PcaCf7PRmXpRN1NkLSGcl7uuFyqTlXReJT/DYbUe5HNJKpclH0Dax4TmS7/hLsd//uu/FU9/7yN4+6eaXarhzkf39Iu4/2PH+JH3vBe/6d2fw31ubHkarwA2acTjIeJPf+578Ob/07Wu3w2vGG6CaHhYZ2wJ/pCDf5MVLAzMXzWoPMyhyCShIiqH5zD7iV3DEsAzgamIRU14kvXDAOV+CEl3u6HKRH1eVGfN1CMrFS5bg7wHAgdAE97NM1/uJXuU1F7ESEwISYJ45piVAYPvelDXwXmPbrGEow7TtFcCQtkGJnnOQnrIOcS8o11bvGqSINdxrME4DghWVgwY4AjtVKEqCUR9cVrJyq6R7FlqZSqzEHEeSR6EnEIrJulzyaWA9eD8nDhhnLbYbyf4bofleoWjSxfBYEybHfabra63qJYtfVbOfFucSbDZ+FjVI695G73vEJMQvRAiphCUMO/hO4euG9D1Q1bYcm8X52SeXAIlUXl8lt2U0Kk/jSBd1guT5KwCATKPrMoaEeUyy6zd4c2OxZK8gUixkBIQvB+kFkH7i7/hAN928Uv4J19pDcwa7g7wlau49LkNHv/4A/jnj74H77rvw1hjuNXDek3j6XCC//2F34S//7FvxyM/OmD5kY8jtWp1Da8QbrKPBsF5AhCLn/4A5q2vw6EZsajfq85xxndekxINmHMH6Oqz5fMaBGflQoJK8dh4OLaSpXIfVJVbKiVa/UytsBM77cwdY6xIVCr2HkAC8fyDNZJLmrPg4ShJLwxLXtccAqflf5dHa4xjQIqSk5HzP8hyFLRykprSLI+EzUeEPLD8c+nDUKlPQM5hIcAi5azkWOldYr3TKpCeP9NK2bHr2bzp66nKKch9Jyq2lVLM5Y1TnBDjhDSNUnFrtcLiwhGG9QphP2LcbhDDPksmda4NjMRU163XTyl53KHz2jkxSV5EnCaM+xFEThLCfQfvHbq+k7wMAiik8rwpd+GAlXC2XCWpkCaSg1nOistJnxopeTlY46z3ISSOwVzUPkJRtazEdMPdiZ/dOnzrcIJ7/Tq/9mNf+RbQ/sVbOKqGhm8c0n6P/pkruPT5Nd73lXfi91z6V7jfpZan8TIQOeHJuMEf/cz3YPsDj+CdH30R/OXPI7Vytg2vIG5C0YDmFmiegjUNs2AJJfyxYCwHU2Rdp7mQDbPdmLUGleJRW4K0ChBmu/FVyFsHngxttMZaplaSqRMAdi7bVSzolRur9QzMvqtJh+sHdEvGuDmdJ33nY6tSuzpeCxa97yTAn0bAbEcgOPLwzgMJ2Fw7zXYjMGbVtSwIZWZocSIJ7q+jbDhTWqxRnM7NmeoRqpywdQoHSUlbZqTIIK/NAJWgsdOci6xAUSEZ+nnVRbIqJV9Kon/Nzoi80qaS2B45IYwRU5iw226xWK0wLBa4cHS/NAA83SLsRyS25HGfLW3Qqxf1S3SanEsDrU9CDuwcnGMw93Apqk0pgqc9pr2sTd910suk65SoeLARLlvgMc5kvJzTwuWZZZseEziR9jvR0SqDKDSu2MDIK0njpL8HOqeNZ9yV+LaP/AE8/F8z3vH/fBF/4xFJ+H7f1mP7v7wBy82Tt3h0DQ3fICQGtjusvprwxDP34tffdg/e3Z+2PI2bROSEz047/IGP/md44G+uce8HPoa43WmBkoaGVw43TDQSc7Z7WPMwQfHo2z6+WZfIPOtaltQCevHOp6xC2IcyOalQ51LYTn6xP4lKMFc2Uv5qwat0cmYJLFErKKaS6MhsRzxfF3pncvzQrzBia0fMRlsaGBaFxZFUJUoMTSQ3i4z1cZA/Kary4UruQyYPOXiVAN67+V+nJZG+BKCHFjUL9BMndL0oM3WSvpE9my9WS1KMrPYnIRwM1hq2B0pCniexQ+VGi2physTNRkr6fGYkQ9WD4ohCmCbEMGF/6jGsVlivj7C8d41p3GO/32DaboVc6hhNzJHrC/mrxR4bA7H1y5CvTgmoFBMAODFiiOCUMI17TONe1q/36Lpeks4Z2Q6W1zGXK50j9umzTIgqxtQKmH1RLUROmqr7cv5cBbHh7sHJx+9D/OQH8L6//5vxrf/2Y7j6lQt4+9+bsPz5X7zVQ2to+MbBEeC9FAscHU7TAgnX0Mrc3hy+HDb445/6o3jgb66x+IVPIO12t3pIDXcobphobE4jUpSk7CkEsO4W174d603hXAdyHVIM4ne3yk5su9pyvKQhVMFTDmpLCF9bo+RTNNsVh+2Ug9Smg+zZN2VDPs8VNzjcibdgvKIKNlaSnJQYgiQBa+A/z/Go78OSgFl3o7U/SIpSscl7tfAMGI7WYALCbifHQ5OSdVzea69uHaQMX3t2GJhLLgQ0f4Ih5WhJaSALUYwhwg99tlJZ40CqiAMBooRYQrQpT2bhsrwNBtgVVarMcfUMCTnJmWxM+jpY809Yq2Y5eaaWLC45EnLukALi6Sn2uy2WyxUW6zWO7r0XuHAB43aP3XYLDhMsx0JXo1YUU5LHZbmWRoP6xOweWAiVcw5u8Pr8AlJMCDEAIWLaCelw3sH3vZTPdR7qnsrr0Wx+oiwVMgRAcm5SNdb8/BLqwzKFrM57loo33C249Hn5+vq/9n7gfyY8zG0tNNx9cIsF4sP34so3edz70GXc4zaaZdhwozhJO/zlp38X4g89hOUvfwZxv7/VQ2q4g3ETRGMCAE3MNpIB1IFPibsZFqkmZiCYymCBkkScXd/nAJ9zUAvdbbZdfOTrSlWfUuWptlsRSPMOqGwvE2nAL7Yh56scDAKYpYHfYaBcV1RKpnLECOYxqw0vvblczQnEV8+ORMHwkpvR9VLxaBxHsXMR5z4kpgJIkH42mJBEa2kh57Ith+2W88OgvD9eKnH55HW33AhdRcDy/Js6wtX7rJ3HRQFim2KYZarYhjKpTOXzJcdGn59wqDxXlAmoPb4EJJfvLXEEMbA/3WDcbtEvByzXawxHa/TLFab9DtvTU6RpAldlo9gSxY3kpDk5Kmu3aB4lUV3UDpd7WQApSpfuGBNi2INI8juc8/C9h++8zDcSiCoVIj8bLY1MZd5mK4dr9aVeR5TXRIsvG9oiaLhbQfffixffcwGbb93iP3rDF/Bwdw1AK4ZwI5g44sW0w59/8t/Hx7//m/HAT34B8cUr7e+ThlcVN5kMrpYXsx0dRtu6WCUXoMpZsE7fSDnIM+uQ7foTaWUoc/pzQKpzCg7sQMZZWHeuQWKJsWNiSnINB3BMVXnZqjFg3XCvtvhwpVjY+zECLGQlccw2spyHkAdqSc9ypRSjBP3OA+RU3XFIKWK/OYV6jZC1FFNgkKDRaCY95IVEJYglzcHK6lZlZy2w1+eUVHsiAlIISEMP13WI41jK4Nqcm03HXichNZQlBjkuNwdkluHqGFNK8ixQjdlepzJX0EpL5ccoZXyB0mMFDNK8BCFUUuY46XOOmx22uz26ocd6dYR+ucTxYsC422PcbpDCKHkNrNWeyMGTA1OqCgYcKAqmYtVPtFbSSEoRO+qUS0chDSlhCiNCLOWXHRFcL3MtjSkZzvlMqFLuRSPz4kBaJcxEt0I6pdO8dIMHORDNFZmGhoaGuwWn734Iz/7miD/4r/0SftuFT+FhH+Fa1SkAkneRwJg4Ys8BExiRGU/FAR/cvg0/+vR78fhH34C3/uM97vvIryJuNo1kNLzquAmiwao6lCCntg8dWomK8QOZEMyPY4QwwqxGzjl4DeaICDFqArIRmjro1yDYdslr5cO+cZoxzVZjtFJMbHwlJ6Eae3mxVC8iUUu879TYpH0S6l/QanwwaxExgjYShCZeu64vBKYmKVyUB0tWsPwJK6VLOmbvXLGQZVlBz3WQ46FHIjeiS5LnkchLgI/57r7RHOtfQtVzZB0nqwdKqRSQbF1QXif21Uoby0fTbIx2ZnZWyrjMfyY/2iwvsVXwIkRVdJCAsBtxbTehXy4wrFbolyus1kfacfwU435UkpgQzZpUr1LC/DmiqGcHR2alLfcR8R6ouq0zJ6QYEUMQmh2C5HV4IZjsOK9xm22C014es1nOD0MIjia721JuDfvuCux5woJkp/Yk7UAtR7OhAS++o8c7HvsyfuuFz+Bt/YtYtB4a2KQRvzp6/PjV9+JDz78Fjz9zP/iZJfyW4AJw9ARwzxdGLL78It7x3KcQr55of62GhlcfN54MnkqQeDYIE8zJhm7PKtOQYHfuoyRI7oYkQWuyMUsZ11S2yq9jc1F7jOaAzIJlVn++s07SqZR0dT4H0zYKC6BnMSAh7+TbeUKYZDeeU7m1g9yT0t9DTyJdN0C+h+8HHF+8gP1uhzCOpj/AQTtaW5BdZAU4p6Vw6xyS7DKivPMdYTv/BOIk9+OkewPZs9HeE53rMGEq4zdSAMCREZ6YH6NND5MasVQROFSZkLlX1fOEkUvlMkrFJVKFAtCmjJzEyqbXsb4dl9N7jAAASM5JREFUJvRkqqp2pmy50+PG3R7TOIKIsDw6wmq9xvreBYZxwu70GsbdLueiHK4V4xql/tlc5cjLg2zWGdDSt6zrlEAgL9aprpO+HSlGpBgxhqBkeYR3Us1KShvLHKYYQdopXvajVM2I2ovE6bMn67/R/Mh3Mqxx1u/9wT+H/cMTaBXx4E8v8MCPfRItNGi423H6Bsa/fs9TeNhfxZoAD1H571a68eVwgj/z+O/F4z/6NrzuQ6cYvnIF77j2JHi3A5LEAjwFcIxSYr8pGA3fYLwM69TZRXpoQTGQOxu4nTmGtARpjLrjrToF0cyjft41rSKVBa5yQskNIEdw3mlg7EAU55vFuute77jn9/Lg5AfntIqS9tEAuUIGNPx3RDkoJpAE+Zof4JzHsFxhWB9JL46kO/KwkFGUiZwbkUWKMrJC9JxYrfL1hTw4PQ1b40CyzzGc5qlwYhAiun7QhO+q5Kx93gJrsoB2HpxnZaRkPwusYpZenIxgKOEq5E6fbVTapEnghNIpW6lPVrfK7RTFyyxw9nJCAkV5b3PtGvbbDVbrNZarFdaX7sNiPWLa7TDtdgBP1SIo5WrPq3t2WKzAxmGkQ86TbBBKpjyc78BebFUJjBQTUgwIIUmzQBC8IzjfaXI552ebe5mQZJu4ShliOhxhw2sRvzru8F0//V8C0+HfPsDFz3Z49B8/hTf/2odmpSYbyWhoANKDI96yfB4X3IQlOfTks533bsOVtMX3fvYPgf/HB/H6D30M6eQEoRGJhtsMN9FHo/win19xqfjTS7lZ2xWex6QStKasArB9bwebTUvPfXZ3uagcVllo5q0ngvc9Fqs12Dlcu/wiyHpnUG15srwN+6RVjKI8RoBKHoLuKGdCUN3n7L5J7C6m1PiuQzcMSHHCNO7BGmgW1aNKgs82MeTmhMQ5JhY1JVEeY6JCx6yztOWsWDUuCVBZPgfCcr3CbtwjhopA2HyzA0gqW8nOuly8/qsrj1vHJ00cKVf4IlVZMmE0JYCNGBb1iKE9WRLrGPRGyen9U14TTnNUGABzVHqW4NgMYqVhYxgTTsM1bE836JcrLFdLrC9eQjo6wrTfYr/ZIoyqUJWVOVujh+pdJkBZ+ajuVy1V8tmkZAyAI3iSSlbsPCInye1IESEmsVepauZ9D9d5mY+ZkkNAKo0aD+1fDa89fNdP/Bm847/48HV3F1tP3oaG83F8zwav6y9jTcCCuru24lTkhL9/9e3Y/J3X455f+GgrT9tw2+KmiEYhDFqa1pXAHJAkZ/PRk+Y1yE68dGHOsRkVsmDJywY+55rVK+f+u2we+XKYJJp3izW486CrV5FIy59Ke+2yQ14FjBVVAUlsB1FbAnzXyX07ApPPu9ukwSC5rhAjV/JOjCTtdltJiIYkhp+n18xUluJMQspWJNJKVBXxqwLiQpgsNtfkdy5zFFLAFCZ0nUd0ZRfergGmHDADqZonLopHpTRYp+r6U3WOiBmScg6GzrQ8L60qpfdjIbwQG9Kysxb0izojKpmSH/2TxJ91hpTGKP1aYoyY9lssFgsM6xUWFy5gsT7CfrfH/uQEcRqV9JbeK4fk9uxa1CdCRW0qVdOq5HwbPwDn1CbnHZA8EieEFMExgWNEShEUHMh5OCL4zhcFzea8bVa95jFxxOI53ywMDQ0vA8s+4Mjt0RPBkxTSuBu7gp/wHn/9V34nHvv5JxAayWi4jXHDRKP0qED281uOAbNVkiqN4OYVfDjv8tZGGH3n3OBpbrE6e8Dh+7KTjYrs6O42OnhHCASpQmRdsAlguBwAizWlJKoD0sAtRWksR97BsQPHJMEu+eo2UrYOOSjRIIL34tdnhvRhADKJsJ1659TexYSINJOA81iqoNeC6cPvjXAA0M7lyME/a7lWckBkxna7Qd/36HyHmAKq+B+AVGpKqjIZYZRTu3ydTEUSNMivnqcMFpJzgEwurfmfqQ6mXAFGWpU0xZR7rIhFy5oocn42dc+M0r9jxlnzeylFxDFiHEe4zQaL9Qrr9RrL9RLDcoFp3GN3coKw3wOx3Lc9g1kVMq1UZmVyz67FikSbIgXjXgkEDylQQIDzcN6BegCJEUMApwQOExIYMQgRc76TAgBOS/U2SeM1jafjFm/6ic2tHkZDw2sS3iX0iFJY4y7G5yeP43+xRnz207d6KA0NL4mbSgY3gpGrPWUjiVpadEceKLu65WeBHFIFowfBWr0zL6/Xn5Yz5CBXg8hynF5Bu4WnOKHjAaX2EjKpqEaTiYkF0NbJnJMk5zrdOQnOgdnLLr4SGvm8le+VxHY4oO8HLJZrDIsB29OTapwuB88liIVE7HY+hvZ/KM6eHFTbHBzsuB8Gn0Uxkl4OiYUEEiekaYJfLkCdlySxfA7WHIGyi0+gTIacEUsjP7aDz0YaylhInxTZecnWCFcVuzjPOyfpXG7XICIpAesAYuuZYgTKyGm1Bq06FTivT1N+cjJ6gpQVvnqC6WSD5dEKy6ML6BdL9P0C426HzeXLiHESdaGyxdl95eRxmqth5dlWT2lmMWRYBbQ8N8zwdh4vhIJDQEohd7MXZWaPoES27wd0/m5Ne7wz8KbuGF/8fSu87QO3eiQNDa89LLsAf5eX+I6c8IPP/1t43fsvI43jrR5OQ8NL4saTwTWwkyCwfs3Ig/xnvrN9toP2dU9f2QiyhaZ6pY6n6x39+rOz5OkY8+5013UIwaL2uQ/f7C/2xzkn5V+Z8x8y75ESKiMihtwvQoPSrvdYrVZYro9wenKCaZqq+0s6VSW5m5SgdLqDL70fCukSoiDBuuQ+8My6lewBcFWMlvTzzgL0yhjGECsbgK5zCCHYqPTJ1U+AiwsqmdWNxGakViaGVkNylG1S8tGD525rg1m5R3l+OV+H9GPE2j0bVTxfdTPPliy7d/3iSO5ZlSjPDqwFB6x/CrMQ59OTE+x2O6yOjrBaH2OxXIHuYYy7Lab9DjGk+TrMJFi7pCihLYQx4czKPbTHmNJh6p6O36qCUdchsfZqiUnUmBizijKxkI6G1y72PGH53N1n9WhoeCVw1I/w4K994B2Mq2mHH//Qt+Fdj3+qWTAbbnvcMNGwIO167xZBYd7Nu97zrULd2fe2O84H783YRbWLb/0tilZC+Wz2kRgjYkrwDCzWFzDu90jYz7z2Zv0icrJLrLvLUwgSfKeYVReJfeWrO7AyAQBrb4uu67Ber9F3C5xevoL9OOa8BhurFI3ShO1kMpElSVeBvZb8TVXob+MoU6TvcFE9HGtDP02Ut2o1DgCc9KFgZnjfYRwnJAiBEWeSVoA6oHtEdgICqvth1s7nLPNZl7215ybvcen/wCXZ3w4zsmXXpUyayoKwID+xFfOt3szBt+ThyGNzSERZPSKd3kxWmBDGgGvjVew2e6yPj7FYLOGHAYtwjO3JNYT9VuaW7XkXgjQnujb2stpJmQRntmTjnefg5HdiykoJsTSwJO/hPYOVcKSUkGL7h+U1j/YIGxpeFqboseM+N6Nz8pc6ANwVuRqREz68v4SHf54QT05v9XAaGr4mbuq30nb9zwPPgix7ca5SHH5vu/bMfBDWIr9Wk5bDwZzN4yifsEA2hJAJRPko5YCx8x5d5zMBCSEgTAExpllgPE3h4B4PqmGpFYhTxHazwdUrL2K/24A4gFyx0iSuKmydIUmc55KJs1HJE8E7B09mCHKgJFv/xlPyOA6/5yT1oyzAjgzEhGkn1a8mDV6tLUnOfSDK95tYjpE+GiU3oliLXLY/gY0CFLsRCLJbbzV4lbQ4R2pDI5BXK5rdoykb9n5OHKdicbM5tIC+yBo6JoZUFpNEa2hPEvJyXVepVdO4x9XLL+L05BoQGb4fcHTffVjfdx/8YqEd3SmTB7u1sp4oP8P5c6jXZ3k2M5KKAkurdyRlkR057bnh4e1rUzReE5g44m9def2Z1xfUY/vw3W39aGh4ubiyX+L5eIzTxNL9mqOUEAdLn4g7GJs04gthi//2c78b93zkmVn564aG2xU33UejoNrV1x/FBcJn2MhMpTDkz1HuAZG+Rvyke8Ezq1S2alUeG9aoOUwj2FcBJ6RTODmCIyEXzouvPqaEGKZsU7EBW6AYphGu67KNyVd9FCxJWBKnGcyxmpe6MpfxEZ4F4DLy0gHd1AWCKw3uoME6EThoQE3aAR31fFAhblQqZ1kFJ2LJfYgxgjzBE0rdbTLSYD+6nOeRfU16XJ1LI/k7nL/PBNDVpY71o04C6aSqgnADApOWb9V5KcnuZe5SErXCGikWladWFpAT6gufk+uInUozdqjMuxTOYqQUsLl6gt3pFsNqheXRCsNyjX5YYtrvEHY7hP1OFAVOqBv8lWcwbx5Z41ABKTlMc1KWq19ZRWZ9z/rCtIZ9ty/2PGGTJnzfs78V/+JvfwfWzyZ87//6/ehb9+KGhlcEz371Ij78urfiQX8Vb+kv4wKN2k/DwcGhR+mrcScoHJETTniPX9jdi7/yhd+DZz78MN70T7dIv/bxWz20hoYbwo1bp87kWhRdonQQQCEZrD0hroecYyDdoMtnruMqyPaquX1lHtRpsAcLuI1wTDlIB2lDNZNnGIghIFoHzdmOdJ0HkhBDgO9lyhJbh2oZCx+MZVYNCrYrTwBF1MFl3athXgFLrm+iCmlCNjTvgmzHv7K0nb0+MvmRhG6W+3SirHjvtCqWVIJivV8uUymBubOu7apczQLlavbrNULn3RuXxobZbSTzGNmqhlXEqjpXVgG0JG99zfqeZ+OxlIlKVWCG5mzYEKqyu6pwcIjYn5xg2mzghwH9agE/9BguXMSwXGEaR8Rxj6C2uNJzo7ZVHayDg3mqj6mrZtmaqd1nuZSw5vPE2Haxbjds0oj//qv/Bn7sB38LXv/PL4OefA4PPvcB+Me+CT9y8gC++8KL+diTtMPFz732A6CGhluB7okF/sU9b0NPEe89+jLe0L2Ie9wWaxewpIA1EZbkhdzza5dsGMH4wO4e/Def+QNI//ABPPRzX8HRMx9HOt2Am5rR8BrBTSkahwHTmd1qO07ePO8EVSQppWBdkv3ZhBz3zy1U1Wc1Lp3bsHKMJlV9iKQPQdd3SACm3VbGZp269Q8AcIqYQtAgG7BkcUvyzddnUw8sNk4g8iCSIHxYLdEvjjDuNxh3W0BVk2yn0nPnylFKOogZHFPenbc5FXVIOkSbJad0nzY1hJBiEgJCslufPw8A2rckaSAcOWrwKufhlJCY4IclIgNpChrgptwx3HqCJMRMLgDtZ5HnQgmEc6oOcZmzFLMKVVWmLeTL7F+mbIAQJyUcTtZHYtYSw1WitZKHwyIAhQzpsQRYOWP7oKwxJYCsqfkVGcl1tRIjpogYIsbdFt5Ld/d+WKBfrNAv14jTHtN2izBN0u3dmX0K4AMJv6JDIKoLCWiiuq4VIXoEIp6NvS6zWxciaLi12KQR/9Pz78WP/OBvw6N/7/N4+Jn3Y/bkz1G3nooRR8+0IKGh4eXgwpeAy8t78VPpXfjSA/fhHcfP4pHhMh7sruIet8H9/hQP+hEXKGFBnBWO1xLhmDji8bDDX3riP8RHf+LdeNNPXgN98lcRN5tz/05paLidcZMN+26sgtRLnCTbQABUwevcXDW7xuyXinLwdbgrnANyRYwJ5B04JXjv54E8JFk8BCljWu8ez65fjZfIwTnKATYgO+Pr4wtYXTjGuJe8juzVr/I55rfDQLLE75nxJ9+v0RNiU06Qw2yzUM2mRXs6cKoSoWFJ86naLdceETrffefR9QuEMAHTJASqziWAfl7nm5zP91NyQGRQ0rQud+6wT2cmaHdpze2sdK757kxJyXYpaMNHY5Zzj5zyiNJlnKuxSGJ94bT13GdSmubrqqg5cyabIFatMImitacN/NCBOg8Pgu96EBz2+50QP1NLZo4oympZrbrl9WAEtJaSYLa3WikiEPk8hw23Hv/JZ38P3H+6wyNffT/Oow7pS0/iL/6j78YHf8eH8eM/8x0AgPs/BtzzDz/4jR1oQ8MdggtPBKSux8l0Dz5yeY3P3/cAXn/xKh5ZXcV9wyneungOjy2+gtf7a7jfT1gjoSepJHm7E449T3gu7vEzm7fgL33od+NN/8DjLR/5HNKLLyKFcKuH19DwsnBTisbXohgWKFp0d4aYHNipmAhMpDkCnINDO9eciJRRWKBbn9q+TSzBdooR3nfoux7OecQ0SfCt3cpjjGpBKbvi2bZUKzUkidhwmoxMBO86MAGL1RKL42OEAJxeu4oYJuQQMtlYy3w4crkR3uy2aE43yOZNrUSJSh6L2b9k99saDpbjq817LZMrL3IV0Dto8nZkUO+QQirmN4bameYWKid1cgvB0+dD0CTwLEdVCheXruQJSXfiC6lMnHKOCXlRWfKUaINIsPUysV4ltpRKjkjmPFlJsPKz9cqQm0mJ8weM7LA+K6aSY2K3w9X5SRWftE/gnT5CYiQCKAtOSuyoZFLYHDAsU0g+67zL65EATSUpumAZPxfWkW11Dbcaj/3sH8Pb/8oe8atPXvcYnka87S98GJ9dr/G2q61xRkPD14vFczscLxwAj93pAlcu97h8zzEev3AfLq53ePLSPdhcXGBaPomEy7jPBawdY0kdHG7PXKmJI74ctvirz/y7+Olf/Ba87l8S3v0LTyA+/RXERjAaXuO4OesUUIK0+o2aPByQjJlHvfrecg6swRqA0nbh0KI1M1SdpxLkPeF8XD/0oN4jjkmUBicBfu5JcE51ilrRICckyKvlSt7UAJ2AfrnA+sIxAGBzcqpKgwXj9aRZYraVeSVYCdsc2zO06Xbpm+G8y70unHPadE5gHv2S0FzGbVOQd8rzNcr3CdKjYrfdoRuWep4qP2OGrAXl89TlXVO9I59Y+VjZwT889jBMNitQvbMPJX0zFcqUEVb1IY8pUw99pbyTEkO6llvIP1fOStJ8YWi5OLNepygu9pn6GFWJnJQ8dn0n59M1k6YIzoqZ2qGyICOE2EgdyrRV88a5GMDBUzjzlBq+sfi+596Dx77vBPEzn/+ax3IIiFevfgNG1dBw58Nf22HxQo/UEfxI6DYO47UBuws9NscrjMFj5Scc+x2WNKGna+g5wSPqhlW6rVSNiSM+MQb8wY/8STzyfw1410c/j3TlGsLUGvE13Bm46WTw+mv1ZvHJVwTjvERYqiK2smM8Vz7qc8wD3PPHVe0dw5olMAjOdUjYIoUJzksGtViL5M/cuoT8s/NerDFdL7kLTl4zRaHrOiyWR0iJsLl2GdM4CiFRu895uSs8G295Ib/H5XvZvC8qT4pROmar9Ktll8r5WCtt2ZynOcmTPhc275RzUmKKSBzRDwvphh0nGJmSsRXSdFi2dWaly8eXe3YavMv9aH5BsgaCmtSv92SJ6KaIkFqZLL+EuJ4PzvepDwRImtehxzFUrSFL0reGhTpnWhM4n0OfK4NzwrVZzCJb8QFXrFXgYl9T8sgkc7xcrdEvlyDnkVJADBHTbos0TeAYRMnIvxdFnbIlYbaxOkHc5ts0lvN+Dxq+cXjf1uODf+RbkT7zqVs9lIaGuw8xwU0Jfp/QbRySR/67cqIOm90Cp2HAPvWIZtm9hcP9Wng6bvGnP/NH8PDfXmDx/k8ibrdnNlsbGl7LeNnlba+Xq3FIRs4k7F4vWD3n3PXObu23nx8+39W2HeG+78HOoet6JCa4ziFMIQe5c99V2XWHqhkSsANEXnfYCbbfz5ww7ffY73ZIIUhXcI7ls1XAfVjBqtyDHOOcE7tNto4RkIQ4lF3sqpxsZbdiDbxNTSn5BdabhMrOvgwGRsrs2aQU0XU+B745nwClMWKBqQOcE8IBq+5ln4WSBLuezWPdpK48s0JqqAT/+tnS/Zvz/aL62e612O0qdYNSPm/Ka1CPonnTRSa9ngX55SxZYcrXZVuLOsdJbGfOO8QQcHpyAr/fY1gs0S8HDIsBw9CBOSGMI8btFnGaVA0pY6f8fKqfs5BTKU7GShtuGf7MR78bj37s07d6GA0NdydSAk0Rfs/ofXEHsCOwB/abHlenJTZpwMQdIs+V7NsJe57wdy9/O8Z/8Dpc/MAnJNm7oeEOw8uqOnVuGddzjj3c2T98zz7/Uu/Xf0FYYFtDLEklaDWNIqYk/RG8l0pNfafnSjmYnicgS8I3E4G6ToO+BNd5LfQkTdQkOAfCuJ9ZwpxzcMOAOEUgBvhh0C7OmgdilqqDXWoraWuBZA4m9V7kewabaqM9IUi5ElmlIyVPciqaB8f2vR6ZiUhKiFPAYrVEN/QYdwHgBMfINKd+hpQqFYqshweyWmJ2NCsvbD1CjAzNy9/K/TlH0m9Dd/eT2omIzUckx0qlrpTVnGxBA0CVoiXXYHAmi/NyxWaDktyMJGOkoqwAlMv5Wg6Hq21tmcQxwCWx0OnMphQRxoQ4jdjvOqxXa3RDD+oc+vUK/XKJsNtjv90h7EckjoVUoFK1yH7XCtGQ27A8l4ZbhXc++Cw2znJ7GhoavpGgEOH2Ad3JBDd5uMnDNtCICXE94Ml7LuGL6wfwuv4K7nEbHLktFqT5frdRnsaXwoi/87P/Dt79vl9HuHbtVg+noeFVwc0lg59DKs4jGfV75yofL+H9KHapvJ98nWPmpT/zNeXCGHd7rIalJIJzkE7a9fmI8o6/kQznOrFIoagFVmLWSr3azrPZb6zUaEpA2gcwA44JUcvmZjsTW6bJebv5rMF6scfI/8t9kl6LwOAogS4nwHsJPOvk6OzIgSgyWZngfPYc2E7TiGG5kgR3LiqUERiYpwuQ+r46yY7KenDOkr7NZlSsSPVG0qE9DiT5JpQAOD8jIXIQAc64BSuBcpLXk8Ra5TXBPKWY59pBmhKyqReuNDUEoIW0iopkE1wqRh0oC3pMMV8V1erQSuhImxESEMcJm3gC8g6L9RKLYSnlkJdr9IsVxs0G280JYgxF/cnzgzyXh7bBl/qda3j18bkfewyvD++/1cNoaLg7MQXQZo8OAJ86+G0HNw3odg7TiQPY4fLiIn65exQLF9BfiFjSU+gxwTvZMLxdqMaPX/tmvPGnGfErzza7VMMdi5tWNG6mxO31SIYlNmdLC8rOuQVYzKYyzH//XurauYcDLDjzALQ86xRzrDmzv1felUQM5ggHpyTCgl8H33XSUyJGtdekKmgnYRosKgo5ly012UGUe5XKxbIywFVHbJjNiVUVULrFSkZiCablWCn/SqQlcJlljEEJkoXD9p6ezBGptUn7ZyDBWjNIrpxVkuJsvSKimcohKkDK92NjVZkC9gIRRFWA8AZTnZiVNIFl7hxma0uOL3v8nKoCvzkK1+dZkU4QIdnzObAZ5aC9XBmUqnM5kp/teFsnVaWqmJU6Xb95vDKGnAyfAKaEGBmIQJwmjN0e/dBjuVqBnEO/XMItOuw3G4T9XtQvPuiubusly//tH6NbDb5dopSGhrsQvNuBUgKNk7gJrnl0VwYsVj3SosPi6oBu2+HFaw/gn15b4atvPMa1e5f45uWv443dBvfpv3W3unv4xBH/8In34uKnv6oVKxsa7kzcdI7GyyUZ2UqFrxUqlbKehwrKfCe37C/Xr+TvU9CgO4E4aaA4J0nOuVzxyJETy1SFZJVKCYghghDLC87BW8wLqLWnvl/b8ZaRmY2IqtdSDp6LaiAWLa+BdwKS0hVGbgKY8jzwWXKljfpKuVq1DrEUVnVQEqHz0XWDkAntAVICesx7TbAG09ni5arnU9u+kuaY8Ozx5ApPUBqlahGMmNVPkyvikJ+VL5YlHZAlnBOEICFV+RNVUF6SwovCYqqL5XAws2QMZmWIKqJTJoFVTXFWfncu2QAMeCIk7XySdN2nlJDGHfb6Z7lcoV8McJ3H+tJF6Ua+3WHc7YAQ8hzUqkbD7YHdt2wA54HWmbeh4RsOHicgRmAcZRPIEajv4bsOfujhT1bwuyP0Jz1OrhzjA5u34fk3H+GZBy7h24++iHf1X8V9LmFBnfxdXf3dWm8HvtoE5Om4xfMfeBgXnvqVpmY03NG4qYZ9QAl4rldR6nqKx3nJ4DkhF8XyVMN29y0Wz8dQCaLLtwzLuxDrkPRmoAAAhIiou9CmnhSbVrY/xSj2JM3FIGLt9my74xpgOwko69KshAOLi7MeDanMC6MkbQNiA0oW2JYkdUbSJuVypKtsPVU0rq8RwJrDwJJEPrPZVPOqtalAxm3Igb1D5FTrM3nOSXtX5FwSre4kQXCSxt5s86hBOTOgFbByigVs7mS+RWXhXM44EMPVNivrTA5VQbgkpzO0M7aSJQaDkpw7UTHNG/mDqhvz5cX5viQBA1n5QZorTKzzls1XpiahapJoBNYBkZNJUPk55yaF+s9YmiI24RRuu8NytcSwXIBB6JZruGEBngLCuMO435Xl3v4detVwJW1xTIsbDixWv7xuJKOh4VZhmsDRAZhyfyH2HuQ90Hdw+xGrkNDt1uhPB/SnAz5/+VF8+Y334mMPvx6//f7P4L3LL+Eet8eaInoCBpJMPw9CTw4ODh7Sc+rVIB8TR/xvX/0teNNPXENqCeANdzhedtUp4CzZOOyhcd731zuPHlC9Wu+mS7SVg/nq2LOnLB6eNI6gJAndU+K59Ue+ORBFklnl5Xrew/tOeyFw3sV3ThqtMaPqDVGi1Tr4JipdneuddrkX7dTtoIRFy7pW1hlnNiWU84iwUWxlRIDvvOaFSIDNSQJe6cFhVaU435/zLj8PYoJ3XgxFWXLRilW6g5/HUpMRuEL08rRpIjUL2bNnUj9j0VYo35t4jkjzGyR3Itk5qsfqQMovlAgBsDwOPaTcU/VfB+lDkmBlDq2DtxEfTQIHIdUsjer1y9rDQ+9Wb/gw78TK8pYStTo8olyiWBNakFLEJoyY9oPYqLoenghY9OiGDt1ygXG3QxwngGJWzxq+PlxJW1xJEZ+bLuFP/Mwfx30f7nDx9z+Ff/ae/+9rBhKfnU7x4K+0+vYNDbcKHALU41tejAnsAmgcJYcjRvT7EReurrF4cYnVcz1On7qAX37DEb7wTQ/gNz7yKN68fEGSxf0GR24Ph4Slm3CP2+ECjVgSsCRXiIeqH19vd/HICc/ELX74F78D7/7sZxCbmtFwh+Om+mgc/lxXJDqPZAAvYbUiq5AkQZdT+8z8HGo7Ed/LjGCcf8rSfyDFhHG7QTcs0S8W4DFJTW2rVCQxZt55JiKQl4Rw7z36foHl0TFSSNhtTxHjJJYm3aY3sgEgl6e1/ggM2YWvcwgsMs4BuRwE50w1YTtYS+QikyscBrM6X9D7YQAhTDpfyLkQQMmpyWVec5UuqQo1TRMWQ0LX9/C+A2JArbHI11K+NlfesmA/2+F0Z1//Nwv4bXef52uB7blm9YOz8uMclOBoqdzi6EKuMIKSa2GtH8l5gE2BSnpNq0YmlbJq+5fNkU58qZSV16bev37OSvUiP+uUFTEhd7ak588TDCSWYgGSwA/EJCV8p90e4xjgXYdhscCwWsB1Hn5wWPYdwjgh7raY9uNMxWu4OfzSfsTv/7k/iXs+OOD46Yjj9z+Odzz/r4AU8cL0m3Dy3+1xiVYveY5Pjw9i8YHP3NZ1+Rsa7mSI9fcwpyEK+XAEjgkUI2g/wp9usbqyxOLZJY6fXGPzxICrz9yHn3rLRQz37nDfxVNcHPY46vdY+oAL/Q5vXL6IB7pruK87wf3+BPe7DRYU4IlxgRgXXIceHg4OPd18wtaWR/yzzTfhgV/0SCenr8ykNDTcxrh5RUN3oM9UEMpv33gUVFdKqntu1MiVmw7OX1f7mV1T2AgYwDTu4PseTA4pRd1B9yWfoArkiaCB3grDcoV+uUIIAZvdRj/r8uY7c8oJ0nlgSefB2fxYg7dqvFltQSYkcm/ONBEhHxrMq4cnB9yUWYQF5RJ95/dtLlWV8FpBK1UlXq1fhV3fQUr+9f0C3WKBuE3a1FDOl4u36m6OJUNbpan8XHIojzzW3BsDpYN4ripmIlBFOOvCWxyVPhBy8A9IsrupQ0nv32xeVmSA2Xa7Sk5QVJWrzruwZG6uyBKYKnHK+pAkcO7LkScY1giQzW4FzPJabDlHrqgRldcZcm+JgZQCOAXEaUSY9hhWK/ihBxGh7wf0nYfvJ4RxhzgFNNwY9jzhh649gr/0we/CY98/4rGPfDTbnmrz04Mf/CqupIhLt0/D4IaGhvNwXdtiAkcAiLLZNE6g7RY46UEvOCyeXWLx5RUufv4YuwdXGC+tMR4f4dkFIfVA6oC4AKYLjHicwMuIbh1w8cIGR8OERRfwluMX8C3HT+CbFs/gDf4KHu12uOSWN0w4NmnET24ewvf9zH+Md//8M4ih/V3ecOfjxhUNzG0bN5Ocer0+GxJcUs7VuG6Oh6kEFcnICkp1/kMLFlsfC3IIMZkjSneENaisun4jEbzvMayOADiMm2tI06jXJ0SHTCJmyc52znocJD0VeEZIOI93pgbFpPGtvV8a+xkpkC19CbIlyE9wjpCi9KLgpEnYOofeexmnJX5nixeKeYsBBEYcRwlmF0uMu31WE1KKYFTERxULrsr2gu1+ynPNpV61HLCpH/WzAkxccsqdzJak9jEAuWpWkvs1ZUA9UjpHWhmLy/rJqhGsk3fK5EtEHS6ESytikavK8doaZACcKgIh81ucd5Ttc6XPRuaSmczqCCqvb7mm7zymKeZXiRhxmnAaApz3WKyW6IceIId+sYAfBoT2j9N18XevPoAnxvsBAD/wye/EpZ86woM//Ak8dvLRl86ruEHy9le/8LtwPD75Coy0oaHhFUX1b4vYqwgcAIyT/NtxugVevAx6psP68x3W/QDqvPTaIokF4D14OYCHDtx7xIVHWlxA6h0mB3zi4hvwkQe+FduHGPtHAr7j3V/EH37dB/DexbN4wA3oyZ+xVkW1eJ3wHj9++ij+4k/+Przr71xDevzL3+gZami4Jbi5ZHBTICqLEoDZL7jhXCJSKxOwDfuzyojlKJRKRmfVizqH4bARXP7KjGm3he8mUAh6vdLwjJxYZfq+R0oJznXoFguAGScnlxHGnfj2wWBiON1y58ToOj/LpbD5ENuQ7FCjngIblrESUzRyEH5InOpAuOQHkJMKUblnBRzyGy7CYW5nY22ABy7XoDx/EsCHcQKtE7x36IYe0ziWYDsTAMgYLKA+ywXPtfTk3Xt7X1UaYk2vZjuI87wApfoHa7K8t3l1gGNoQ796CYrCYGWBbR2ZT62oK/VaEypiTfjkuoc3p2sQCUQ+lwGORlzZ1pSQEHtmloJOKnKp9lGIp0wowlg/Ewc29YcZkRN2JxFj59EvBvR9DziXe73cyfjR02M87K/gzd0Wf+W53wYA+OlfeyeO/9HFl/zcA+/7EsLTzwAA3sofk3m8gevxU8/gez/7h/C+9/zjlzzuqa/ci8emL93ILTQ0NNxK5H98ogrc+m/KFICtKuWHuRba6BW6SdbZ90pE1t7j3mEAH62QLq3xpfe8A3/2O9+O3/gbPofvfugX8Z7hGdzn5N8rpy6ACYxrifGzm7fjL//c78ZjP7QHPv1FIUMNDXcBbtw6VeVgoA6uMVc6rgsLro0ooFIo7BL50HnwnvM5DpQPO2amgFQBfOSowZ0kc8N7ub7ufhvpYBZ7yuLoGP3QY3d6imm3BStZsJwHs8UwM2KABrC622+KhAbO5uXPFZmqnXKGdf8u5ViLGoJsMwKVylz5GWTFgLRile6GE4OS7orr7nyMEZa8Dk08t3sgPZ/8hegQpghyDt55BLUZgUpytlXbqucg35ft8s/IpQb+VLQFUz8cuUrByG0Jyxo7WBvGT8x+RjYH1gPDl8Uj50nl35jE5ST5ttzs5+pLHnuSrn6zNZYVD/tA5kaVFsOqjpjSY2sCYgPTSdD17CrSVpXgtXEykJAQR+ngHvoe3dCj67+uGg63FO/4gT+JoycI6d97Ecd/7yIoAZe/5wTf8rqnAAAf/Og70F92eOsPXwU6h/HSgP59/wpgxqP4xNc8/8v9pzttNnj8qQeA91z/mJO0wz0fGlrFqYaG1yIq4iE/EnDONsSZLdKKjGQb7wse9JTHA09ewD2feQif/+g78V/9hsfw+rc9h2974NfRk5x3Yo/L0wpfvPIAvvKJh/C2HxvRfeJxxP3+Fb65hobbFy8vYrkJ2xSAGUGYn4ZnxxwSlvNK6FoQer1cEOYDlSQxIknCrgNpcAqN9xIceXRDj+VyDdcNOL0mJAMcc+AOQHIGqK6yxOjUysTJ/PrFTmM2GsrBqRCcqksFiCkHpXl+lLBYUrW8UKxj1ueCq3TUnKTtSo8NszDJrZZ+HUSsios0HoxMiGC4/R7dMGgOhJAU7zyswwUnBjsH6TQedRxCIVxV7reQI7KhI0fkRPp3tgXtxUpWlJLSrd2StjO5AUCJkYzYGAHVqDwrQTB7mZARs3oZybQqVfmZwaiQyA/MhRw4WLWz8tzLjdHsXyXKvVOKjSoPXM+lkmAmo0Y2ASWYbLNqQpSqiI4Qxj3CNL6mFY23/oUPyDffX147+hHgef3+MXwIQJnW/hs2sq+Nj489Hv7hz9+QQtLQ0HCb40bjGC6/8bnQVQhgIqTdHvTiZTz0uTVe976LiPcd45MXv1n+yYsMSgw3Rlw83ePSC48jPv+iNOe72RiqoeE1jK9/a/TAM3NeYjjNDudzCYTtWp97iYPjzg7hbHL6ocpBdEh0rOmcE1UCwGazwW5zCmLpLW02pGgBe6o6cxPlQFHsVR5WmlbuXcaREsM7r9fTeDuTCLk55wgpaW8G7YnhdLfbYtmc02KBaJX3kSwYhZZX1cR36/Ehdh0tu6pBvZViJfJASpjCBD8MErM7B6KEGINYhjRATpHhfJEAstUNZfffepBI2V5VXcjl5npFEUszxYrM+mTJ+tVD52oN1AUEyNQms4YlyH3K2bUfh17CUZ43axboIMnhpYN6aUBoV7TcDFuhRnnsvDPFJZOQeuT2O8DVutSjVZFhky+MkBkprNe4JpwzM0JqkvurgeXnlph+R3xZlWQaGhruMjADHMH7KArF5cvAr4nAPj/sxuybDQ13Km64xkoOvapgPduf6teq3Ir82Sq4tZ/PnL8iB/PPS9BtdpvD89XnnAeJJVgka8Jnu9rOAV527hNH7LYbXH3xRYwnV4A0gc1yRZjVuHZ2byA41hCfEyJHJI4lRiSIMd/JtZKqFJzkT6oC/RKwS+CdwHDswEmrHSXbHbdAVYJpsJSBVfN/3gl3teqSkm7BcBWgEyyp3FQHECHECHKEYRjQdX15TjnCl2bIpNYjIiFPUjFJ++dVa0UG4eCc13uU8J/yuCQIJy5EwfIuWHueyHlSIVtyJ9JlO6XcwM++iudWlRQ5sSbZC+GR5Pykqk+xKiVIZaissnC5D4bcW4KW2TU+YGtOiV5+pmZRY7lO+SPntJ/rZ1me83w+hKjKvYYYEWOUn6f2z9argbf+wJfx1194F07SDi/GDSaO2CTpmfGF6QTf8y//BHi3u8WjbGhouC1hFujDPw0NdzluWtGo8zKyMpEtKyXov5kytzXOfq5YYqpc4dmxZ6pOlQMk8HYO7CxJmHJ8jYojWSFabwEoyv51Sik3lyPddU4soXAuW5uPL7vWMmyn6keJXs2iM68ChUxihEgAufqUq0hV9vVTNeNFPZGd8lS6rhN069/usyRhMwAkhvOSfxHGEf1yAQDw3sORy4EulFh1XQfnHGKMcM5nBcd6Q1QXKjv8KESw5MXosTUhVYuWVJey5yTKEytJQVX6N1eZOlTRWOQNjlYOtwwtqUJjykSxQTE4EbwpF1yOYC05DFNPUDVfnKlz9QIt6kfps5GNUMbv8vykxHruoloZ0ZDPmpp2PlFv+PoRfv0J/PM/9hvxQ9/xO3HhiYivfKfH+iuEy98c8I4f2OHtv/irSC0/o6GhoaGh4YZxc0TjJWxSAgt96+DxfLvT+aefsYjy1QIwWAWg6tzkcvA/M0YRSZCpth37jKkjtuMMloRqy2IgT9rZ2WlpXIK3oJ4ZrCKQ5SMUexiJepB9+gB5rVZhCdh6LxYoM9uOO/IuOZzs/pfEa5MnrAKXzjJZ8K7XtDbazDkXJYHlfKmQocRJeEdiJBKVZlgt4YceiAnjfoLveqQQEaHleatYPSXtUE0kZKTqkg5VG5xz2XJkQbYxOyNUrERNcjw0mztV68u8U+TE5yof1PsnVHwpf0bsRQyXVMUw5cfmyj5QqWWHq9OoQJmxisCykiFUDwGlCaA9HLsO6WccrLGgPv9MJvSKlTpH+VX7xuahrKvyZsMrDf6lT+DBX5Lv3/Lj8vWhWzechoaGhoaG1zRuimjUQf5ZzN9h6wJN579/cOKigpy3W1u/fuDft3HZtXIZ05wQUeUzMIGQwGqpsWExi8LAaveRXAHZDbdxURVgyleNdG13m606UwlebTcaurNebWArmVGrjL7hnNNSutpbhKwhnBI4axRnPn+1+ThIA0HOu/mlnGuKVffyxIWTmA2MCbuTDfzQo+8HxBhAnUeInOfBdtjrnf7ynLUjOjgTEM7BPWBNBQHo+GR+uFoPUhVMEsUzVaWXWDGkKpOSAcltKevTlKUy3U4tTbpGHNQrxVlx0psBO+TjinpXE+eaDB+8VqYfORUjrwMWi5kqRNfLU7pex2lO5pG7ceLe0NDQ0NDQ0HArcVNEg6/zfU0ObA/edqvl7dKgrrakGBwqEnNgf8p8oN7RtfdyzkT5HOn2eyYaWhHJKv5kgnHwOQZrwnLKQaJZf5KNwuxXzLm/hd1v3rnPfny9rnPgXGlJLE25U7ZFpEmi/xwM6wRZ6VyzDNlcIolSkMNp53NuQD23jjycF5uXZwJ5h8Qa6EICbOu8jhgR4gYAwUUH56R5YQgAtPdD0jLB9hxTZEnQcFYhysiXzApRnfgv85H5H/yMTDjHuaKHaUTaZD0H36VDu/RSTymp2lQlThOESJr9CAAlzuuLwEBMeaEaSSAlYPUCo0q1gJGmihgaZoSBVfNIcj+sr9mTOcxTqvhIPjYdKBuiKpV5bUyjoeEOAhH8Qw8iPvPsrR5JQ0NDwyuOG04Gvx6Yy/5xDrdqFeHg2HxI9WeW93HmCtUrVFVu0j9wBHZ6LSMepmRY851qnHaV7O3n0jMhcRK7EaBNw12pm21RMYsKQKqemC0raqCflAgAhXwQFVJUwmidE505AgHaX85RGfe8q3ghEvXdpBjlc5B7hpMmgxGa2UAeoA4xMayPhXSek+19sjwS1rMzYxgG+L7TROuDZ2f3w8KREgCkQnQK4ahUnVplgiZAZ/XFFDBlgnwwV05IEjmvpKYoVUVtkTwHa2aYn3mVwyGdunVkVJQTgKucm7KWWcmm2bwOtJycQ5FSQggB0xQQQkQICSEkRH2Pq7HQwTmE+EVVdZKMQ1+LUQoTgCKs5G5NKBsaGl5bcOs13Le+GwDw7J/6zeje/EaQ9+BHHrjFI2toaGh4dXBz1qnqewt1in+dZz/PAvwDlQIHnz0/cDLFoXw/b/anO/yaHO3q0WlwzgCICc57uM4LqQgRiZ1agsSO4jVwrcPAnNOQTIXQ5nyJ8y4zq41JY1Xd/CZzOOUgWyqrWvCb6ulC3vnnMq9nK2mlTHSkepbahSBWKKlaBA38JanYOe21kWRmUooaaPvqGgznAOc9mB2AKOV4OwfqpaqUdw5hChqRG0HTuaGUSQ85pwqEzWvhsMwo3VVVDbKGiRZAF17hdF6BkrRdkVDLcUgJRGI1y0nhZPkqZb5m60ItZbkwgOaEENmVSNWbYp8SdcKejIwkKWFLRgxSWb+ZQLKpOnXCuqwdUZTEKlbWPhffFVtvDcoLpSaWjWg0NLw2QaslNo8eY/ErwEP/x4cQtLgAf/STt3hkDQ0NDa8ObphozAMoZMuRvqgvlVwL2x3nWRTJ1Zmqc8+IyIEVCvOPEEqZW0K9q10NiyCBr+/gXI/j4wvwix6bq1exnTZiy3d1g1+L8jkHmBpi5r4TCUIYLLF49hloQjAX4cOGPXfVFJuOY2k8Z9fMhKyaotyTQ8/DKZUSrpr7kEmJ7pwTSHJCiHR3XytGWS5F3smvGuMRhExwhPMO/aJH13VIDDjn4ZyVyBWVZk4m7bliHiCbdcxrlSnLCScnc5+K8lHnVNh5i6XKZduSzbsF99qeZPY8JF1/nv8ws+7Z+fVhUVWNjBnSV4NctSYJQEKM2l1eS+SWRP752OvnbCpZtl2Z78o+mw5/F+oxo5rL8r0954aGhtce4vMvYPFPXpAfWgWzhoaGuwA3Xd62ViFKEvY5rwEARDmwXftsc8kiSO2dn/1nri3I1m8hFaQBK2zXuw695FXvBwzLFZZHR3DUIYwjEpPYgVKQXWlTStSuUpMcUS3s+qhUAeTtboIE0rmPA0t9IedL0zpAjFHSO8IBSGWzn6UkrSVuyz1WuRjVPZnVyHPSMrY6f2w7/YDvOlg1rTBNQKUIOO0bQpE1dUUsU448pK1FABHQDQP8YiEqEAjDegXaCpGx6lL1c7Ou20UgUNsTF9UJ2m3clAIGwPqacw6JEgi9Lg2pFsaptlfJM3GOQKz+MqpK19akFqVRIbiQlmznMp5ma0en3pEHEsBRLE9hmmblgpNVtKKqyzpXJLCe7fzsbHWy/h4A2R9nRFaHX1Pw+nfovJLNL1WSoaGhoaGhoaHhdsHL6wxOmpvAhy/PlQnboc1WlAMbS/UDrIoTlVfmSbd6kpLrwBZfzsAAXNdhWC3R9QNADrvNBrvtCTgE6WJdjbAOCrmOGrm6A7Zk5zLCbBPKCdJe1ZSq/0GtTuhuOBxK1adsDsrsSRQD7VNh8yTN8LTT9hmLmoSz0v9BqysRwZHPgbQlajvSQr6m+Dg5xhEwDAsQGL0f4F0nOQf7gGmaEMcJ0ER1zTpB0upMzu5Xx0SONAeEJKeCpJRt7oeh9+K9R6dzkByDqIOF6kRavdZyLCBWMJV15BkSxLKU+5OoLYrLz0xV/xKb77qfir6VQkQKCWGcEMZ9IZ72jPW5OqMTnLKSlVWwbPeqkc4oE2zkJ+drmFWqfHKugqWysLN609DQ0NDQ0NBw++OGiYZZTuY/V+9XUX9WCvSnHGxx9a6+MLPa5PM6lHTiCtlPrwEgqh10iM2nXy7QL5foFyuQI0zjDpvtNXAMQk0YKHWMSpBoaokFj6YSIAfqEpgypfye7egLXUiaH0HZg2+BobTDcPCuSkbOlhiJ2HMRWLueEwuUBd9Od/Qt1GdmtTE56eStU5qSF0IRI2IIYBTykjjBUcxzGTnBuy7neRADcRwxOiFrrnPw3CFME5zrgKg0zRMcO8CreuQkUZtcB+9FNXJ9B9/38H0H7zycExsbkygWfTfkccEqSTkP770QEp0PplKtS8iHdGFPKWLc7zGNI0KckPYj4jQhxYgYA1IICCnAmbJh6kfSPJ2YME0BcZLPcYqzvB1yhbS42drm3K+jWAbtecrPVr62Jhfl9yRBGjHOLVfnlQ0+s/wbx2hoaGhoaGh4DeGmk8FvJNap9AZIGdXyRm0PYWUrVBENw0zvyLapYtnRN/Ixru+wXK0xrFYg8mAGpt0O+5MTIAZtScFFtTD7DAFgV9QNtRWBqMp7kBGZBgHdZc9j5yJ/FMWkygNAUTCKZMOZQORCwAzEmGbkSb4HpHKU2ZI4kxFHhJhivpmUAmII0j9DY2HnPbqhl6fhvNyj2pOIHFIY4ZyDgwNch5CAQTuqdwsH8k5IgXfoF0t0iyWWwxJdt0Q3DPKnX8C5XoiC61Sd8SDvq1XhSq6B2d/MLpUVHgeCk2Z7IFjPQiOXee7YygFHJB6BOCFOAeO0xenVyzi5chndNIJjBIeEFBPiNGLc7zHudrmiEymppCqKFy5alK+cK5OVNFFW6mXI1c/2mfN6ZfCM6J5jjarvU+emfA8lYmc/09DQ0NDQ0NBwu+GGiYark7MPYK6oHB8RqkZ08mYOt6hqf5bN8nbuomrI2/PeG4cBVg7GvcPi6AjdMEiwjYQ4RmxProBDrD7v1P5DWrWJqt4JpIE8SqM+7cGRg2ORNMqNaedpIxlMxXOfkDC3w0gn7kyr9BqVcSpnm+ccA+eqINsmVl5wLN20QwiwHhvOSTfzEAKYJWQHS/lb5k5UA3aSdE6ErpdEbdd79H2PYbFAtzpCv1xisTrCsFij6wd03QDyPch3mhyupEX7dAi58EKGmKE+qmy3mlngYIdIcjqxZipUSo+tNZFaWBsNZt9W6beh8++xgHMRnQfcMKEfLsFRj921y9idbHFy7Sp22xOkaQ/mAEYsq0xJW1GzAEZSaxPnZ1dXhzpPbbiR14w0mn3qEPaScy4n3YvVSxUjYFayuaGhoaGhoaHhdsbNNeybeclLsGNxZN1BecZI7L2SNVtIAjToBmvcbp2mTQU4CMqoUgqIQN5jdXQM53u1pIidab/dgGNFIPQ8jrTfhEbvCSz5BDYW0s7ggFh6oJWDKHulwNrCuut6xBCyjSrPA1cBoeUIVNWaoO8Ro5R6tX4dytpI2ES2bMl5K7sYa/K1M/UliDoRo1i/vIPrenjnQY7QLRcY+gF9P2BYLbFYr9EPSwzDEYblGv1iCSYHhoP3A3zXA0Twrs8KiHNeLU4OUUu+is1JCZkG7cmp/QvFQudYbFBC8OS+g04YsfTAILLO2az8IknSerawVUqZrhWpACV9KMCMyAlhz7j63A7PP/UEpv0JOO3BHLMSUhOIxFZRy15DuQqn84N6ew72uMobWamr3zNyYa947zKJJjKiVhpNyvo3+1hOJy/Lg88ZU0NDQ0NDQ0PDbYabytFAZVs6U/mGLclWf6wUDVZrTE0SLPpiSICarwHknX7oNefXqexSzmE4WsN1HSzZOEwBYRwRwwTSwBaQZGzSa1tyde4WDsxLopoFisqutASAmmydJB9jHKNakTgHhOWr5E+wK43askpS3ZtZiyT/gqTxHQFwHk7JjfEV6ZtRrD7ea3dvZnR9h2G5wvrSRawv3ouje+7BYnUJruvhyMG5PpMF5zqAPFJi9H0H7wfpLp5Ei+n6Pidyg8XOxGB4R4iREaPtzCckJnEZMYM0MGcogeOyDqKqEsyQe4yiaiQGUmQhC1WwbyqDJ8rWJav+RABiCIhTAAGYplF+DgHTfkQa93j68S8ijs8CPFbkIR0yA1xPobB1eDNwpjaZxc1RVicsQd6UCiGMHqWPCJWv4OzcKi06OL/HZ2+ioeHlw3k886e+E6//fz+N+PwLt3o0DQ0NDQ13EF5G1anr7aZWgbrlFei+bt3UL9uQGNkyU3WAyEqFVeKxkqWmSrCqId57LJdL+MVCgju9VgwB4ziKUiBRrXroORMds0wl4nztWO205+hePijEITkwJTCxJCXbuZQU5e4GwgL0dkVZIJggUipmSWMOl4lGJnBdB+9KTw5RXQgUE7wDOifkxPc9+sWA5foI/XKN1dEFLFeX4PsFQB2c75XIOHSkBIMkgdtradtpCuj7Do46JJCWxE0AdeAEBAYoaS6EK/OT1Ru1i0kRJinhmqtQcen3IHnP1etaujaxdvJOCZwkwZsjIwZTKBJiTEghYBpHSfCeRkzbHWIYEcOI3e4UHEZwCpjGvShOaYuUdkhpD9NB8io9yJs4a8dDXnOHHLe2gBEVixNUhYDzuRu8JbUnNvJJ1ZISySOkupwxsuKXxQ8lc/k36SaJT0PDDSFFPPy3fglxv7/VI2loaGhouMPwMoiGREDzXVWzfbj5UZLFnKlJ7uxtPnU9xl63ADY35LMdf9sZJifVkLz0f/C+0915lh35EJFiyDaaxCk33Mt9HwC1PxVzy8wOlm0uJrzk5IHZXnKuKpVHrp+vbDk5gV2bBzKTdrSWk7uuE9uYWbtQXnPeidrQdfDDEkM/YLFcY1iuRKXoBvhuAd/18L6Hd53MFWlKtVqmch4MpASudS9PKQJqlWKIKhJClLyQMYITY1I7FBjS5JBlchKVymEWAFs1JYKUi4UDEgcgApETYpDKUdNe1IeUWJSIacK03yHFgDSN2G+38v64RRi3QiCmPULcAxyQ4gSCVIhKKcgzFgZUsR/LDamC+Oo512TjPNIh3MHyXqSzvCWuey+d1a08MMgJ0cqkRBSYRCQVyNhWXdIG71oEwdaU2RHZlLdUbFaHxKLxjIZXCdxIRkNDQ0PDq4CX10fj68bc3X7W614HheXnXI0p/zk8bwn7r6+73Ciuf5aXOv+NYNZyrQpQc95JttwImfJdB9fpV69/nM8qhbNO1krssiqEPHlaGtWMXec6iPQrzyxyyAF5+ex8IvRs2Z6kljhWdUiVDM6vSQ8KUS+iNMgLEZykJG0Mk5TlDZOQjxTAMQBJk7g5gsn6WHA1EL5OIH7e6vraqJtRlrU3f70szno+zq6Nikeci2ytu+lRNjQ0NDQ0NDTcvrhJokHVd1VH7+LzyIdR9bMpFjmIrXIyzH6UrSoSm+ad3jQFAIRI8ax1arWSBni6e0zkROWgAKYI6x1uQ7FgzrqKA5LDYe/li6MoGdYwzq6BXFmKq8T1OrSsPp/Pw0hhgpGApCpBDACxQ40URpD3GBNjp9apqLkbnrQ7dtehGwb0ywHL9TGG5RGW62MslxfhugVAHs53M+sUOQ+v1ilHUso2IqmpRxridZ0HkEDegxiiaGh3dK4IReJqJZh1yum5yAGdQ0IC6/LKPeeYsThaIXHSpG+tiGXWKSUeIUbom4gxIk0Re1U9wrjHtN0hhREh7LHbb4A4geOEaRrliaYdOI1IPN+lrVWmPPwDdUMS0oUI2bqeEM5YrFyVf1Fbp8jJ2sqKEsuaMkWkVsAqw6BOpSbXW+EBcjk/pVpaDQ2vPL4WG25oaGhoaHgZuEmiMf+H6Lyd23xkzscAmMuOu72e7SKMXGXKMl/TQSAoJ0l5Z17sOQG7zQYLR+g6aQQH70Fdh2ExYNzvpJSrsQy2JGrKeRVWRpWZ4clK2sruPSPBysZKBShJiobmFthuulmwLJk8e+n1HjmmYo8Bsp1JksKjVsaS1wkEjhEpBQAe5NS2pMF9ZGCiBDeNmDan2DrCVXoO1ml7uVrD9R2OLt2Ho4v3oF9egPOSDE6uy431nPMAvAT7XQffaTK45rR4coDzGHIyuGgZnkiSunUepc+I9rqoksEjpHGhzC1AHXJuB0NVjQhEZjB3VWCfHxVgRM6R9ATRz8WU4ADEaUKcxCI3TXukEBGmCeN+jzTt8ZVf+zRoehbAmOOnnO8wIw3zNc1cVnbOk7FnWiFGyS+pibMk9Fv3elcpU6I6sXO59wkD6Mgr6RR7nS5xECgng5d+M9yIRsOrA+fx9J/9Tjz6dz+D+NXnb/VoGhoaGhruINww0bAA+rzytvLj9ZqTQXa92V44a5syKYM0WD8sbztjMNV1UozYn57CHR8DnQNxQt936BwhxYA4jiUI1ERsIxi5cR8jv14SuVl3oJ3u4Eu1omS7+s7BEeA7jxQi2FQQ1F+1y3TifA91qVqbIE5BdxNFfbFkdXDQkq+aOF/tONqONwcNwAmI4x5xnDDudnj2y18GeVE9OteJqrFYYBgG9MMCw2qF5WqFzsrbLlbww1IT3Alu7LXbOMG5HqRKCnSX3pGTsTkljXBKiISsOdFIhByayKUES8oJeyTHcLYUGGCWpPiUVFGocmuoN1WqkqUsUZ9ZE8mlzHDkiLDbgzHihacZYTwB0ghGzIn+2QLGwLzaVW0NK8S0Jhz5ueVvyyesSV9KDKFbtmTLmrfywIflbaElk22dOFUMnXYor1Uzaoyj4ZVEinjkr78f8VaPo6GhoaHhjsPNdQa/zk6wBG2lTKgF1HK4WpByoKWfsWBNd8Zr1IrG4ft5BPr5FCK2J6dYXXTofC/n7zwWqzU2YQKpomB0I+nucDK7FkMbwqktCwnOWdnRlC9qJMgSoAnF9gOgJAOzkoxk86VvaBDqnAPrTni+D7MWOZcD9wQGkpAKU3skabz2gUn1JlFetFKV96K6hIQp7BF4DxDQLUfsLYB1BPKErgpwh8WgDfuO0a9Wkni+WKPrBnjfg7oe5DRRnToAasHyTisseSQn4TFpwz7SxnegUoXMmRLkhFC6HESXRHVHVoJYuUpKtoyQmyaqrQ35OQohCInQdQH3PnyM1fqN2Fzd4OTKVey3J0hxD3AEc4J1AnfsC4HTczMnWC2xzEerDO6aK2Jmx6KinlTruf69YAZCiAe/S5Mt9UJAtFGfs++dl/vM+TcNDQ0NDQ0NDbc3bphopEppMNTahtmGAJiVv+Q+UPUpLtYbOcDlsxw6hPmAvMwVFQlAyXukGLE7PcVqfQS39HBwoGGB1YWL2J9c0yZ2ujNMSmRIA0uzcVVjY/WwcKwasxFrR3GU3XQy5aKoL4XSuLIDrwTByEq2D0H6RNgppYcGaSCsFapsXqje02bEJLYir7kYNnneebBPSDHlzX+x7uh1SMrOOniEaQTBIYQR085jAwK5F8GOMCwHIVzE4JjgvIfrPPrlCt1iieWwQtevRCVZLOC6BZzrxZ5FXsgDSfWrMotWoph03o1YWQNDJRyYd0SnLH1Ae6GYupEghYUnUYBiwDRucXL5BVy78gLiNAI94+ieNZZHC8Rpj3G3xbjbIcZJP2/BPTJ5ETWGy5pMZvdDXh8zmxxwJvg/q+7xbP0efq4QlUJwM1FHISFC6hrRaGhoaGhoaLj9QdyK8zc0NDQ0NDQ0NDQ0vMJwX/uQhoaGhoaGhoaGhoaGm0MjGg0NDQ0NDQ0NDQ0Nrzga0WhoaGhoaGhoaGhoeMXRiEZDQ0NDQ0NDQ0NDwyuORjQaGhoaGhoaGhoaGl5xNKLR0NDQ0NDQ0NDQ0PCKoxGNhoaGhoaGhoaGhoZXHI1oNDQ0NDQ0NDQ0NDS84mhEo6GhoaGhoaGhoaHhFcf/DxnXydGfHaGWAAAAAElFTkSuQmCC", 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", 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4rlP9XRuGwQte8AKuv/76k0aJp6am+MIXvsBznvMcCoXCU26X9vTTIxq/5K655hre/e53c+2113LRRRexfft2Pv/5z5909wE6pew+8pGP8NrXvpY//uM/ZnBwkM9//vNLDXFO3FWQUvLpT3+aq6++mk2bNnHttdcyPDzM+Pg4t9xyC4VCga9//es/9/3UtGerNWvW8IUvfIHXvva1rFu3bqkzuFKKgwcP8oUvfAEp5ePmST+Rl7zkJVxxxRX8+Z//OYcOHeKMM87gpptu4vrrr+dtb3vbSfkT1113HX/5l3/JddddxznnnMNtt93Gnj17fuL92LJlC6997Wv52Mc+RrVa5aKLLuK73/3u40Y9n8yqVasolUp84hOfIJ/Pk81mOf/885dKcT+Z6667jt/7vd/jN37jN3j+85/Pww8/zI033njSiOyJ7TMMgw984ANUq1Ucx+HKK6980nnmmqY9scsuu4w3v/nNvP/972fbtm284AUvwLIs9u7dy5e+9CX+7u/+jle84hX09vbyJ3/yJ7z//e/nmmuu4UUvehFbt27lW9/61uN+nz9MSsnHP/5xXvKSl7BlyxauvfZaBgcH2b17Nzt37uTGG28EOjc3oVOE4qqrrsIwDF7zmtc84Trf+973cvPNN/Oc5zyHP/iDP8A0TT75yU8SBAEf/OAHn96DpJ26Z6jalfYknqi8bTabfdzrLrvsMrVp06bHLR8dHVUvfvGLlx77vq/e8Y53qMHBQeV5nrr44ovV3XffrS677DJ12WWXnfTeAwcOqBe/+MXK8zzV29ur3vGOd6gvf/nLClD33HPPSa/dunWrevnLX666u7uV4zhqdHRUvepVr1Lf/e53n4ajoGm/fPbt26d+//d/X61evVq5rqs8z1Pr169Xv/d7v6e2bdt20muf7HevlFL1el29/e1vV0NDQ8qyLLVmzRr1V3/1VyeVaVSqU3L6t3/7t1WxWFT5fF696lWvUtPT009a3nZmZuak9584Fx08eHBpWbvdVm9961tVd3e3ymaz6iUveYk6evToKZW3VUqp66+/Xm3cuFGZpnlSqdsnO58ppVSSJOp//I//oXp6elQmk1FXXXWV2rdv3+PK2yql1Kc+9Sm1cuVKZRjGSaU0f/i8eMITnQc17ZfBD19LPJEfdZ5RSql/+Id/UGeffbbyPE/l83l12mmnqT/90z9VExMTS69JkkT9xV/8xdI1xuWXX6527NjxuN/nD5e3PeGOO+5Qz3/+81U+n1fZbFadfvrp6u///u+Xno/jWP3RH/2R6u3tVUKIk0rdPtF556GHHlJXXXWVyuVyKpPJqCuuuELdddddp3RsnmwbtZ+OUEpnvWhP7kMf+hBvf/vbOXbs2Enl8DRN0zRN0zTtR9GBhrak3W6flPjl+z5nnnkmSZL8VFMuNE3TNE3TtF89OkdDW/Lyl7+c5cuXs2XLFqrVKp/73OfYvXv3k5a40zRN0zRN07QnowMNbclVV13Fpz/9aT7/+c+TJAkbN27ki1/8Iq9+9auf6U3TNE3TNE3TfsHoqVOapmmapmmapj3tdB8NTdM0TdM0TdOedjrQ0DRN0zRN0zTtaacDDU3TNE3TNE3TnnannAz+ipe8gv2799BoLiAdhfITDAQIkJZFs90CwDAtyt1ddPfmiKOQYqaE60gmj0xj2YKcbYIwKPSVaFSbWDZsWTvG6Rs2EDRTjszMUxwZZPnG01m2Yh0H9x1nbGw5QyN9pECSKKSUHDs2g+taTM3MUZufI5/PcOaWTRw+NsXqlcNIBKCoN9vsPTjJ/Xdtx3UDzjr/AprNKp/+yCd4/1/+b+59eDsHHpnknEtOY2hoAGmAMuB9//v9FPCZOD7N//j//U9Wr1/DY3uOEbZ98qUMbb/F8pFhCoUsApBC8Il//FfWLl/GlVddSaMV4QcBjmPwve/fysP37OLFV19FOwqZnapje5LTt6zD9kx6usoIFGmSYhqS8YlJBvr7sEyLVCkMKQiCkIVKhYH+vqUu3T8oSVLmFyoU8jls2wJ4wtdpPzutVpsP/+0nue2O71GtVxhatgI/CMnnbEzTImg1iKI2cZyAkkgpyWQ8/uS//zFbzjgPKY0nXbdSiiSJuP3Om7nx29/kL9//sZ/jnj19ni9f+UxvgqZpi25Ov/RMb8KPTZ9DNO3Z41TOIac8onHs0MOYVpOe3jyu5TI01Ee5XKDUlcN0JMViDsc1cTyDZrPO9PE6xWyJwA+o15sIoSi4GSQgTEmz3iTrmpy5fiXnbj6dQq6bAEVuqIdVW85hcGQFlp1BGQHChK3bHuWxPYc5sOcoc7MVGrUGC9MLECrmZhfYvGkdlm2SptBohgAoBJmsh1QpWx+4h3PO2kJXV4lqvYFhmEwcn2HlyhXMNY5z6MBhoraP6VhYpoHnZpiemUWlBtWZGtVKi/vveRgMqMzPs3x4kHIphyENlJJUq20sw6JaazK/UCdNQ0gjapUWywdGyZYNHnrkQcbnjhGoBc7YsorhgR56SkUsKbCkxLFMDCnpKpeYm6sgBBiyEyzYjo1SnYAjimKSJOUH8/gNQ9JVLhKGIWma/ph/KtrTwfNcLjzreeTcIWzbpN1s0arXmJma5fj4FO1WTMYrYUgbUDiuYPMZI6xcuRYpf/RPUQiBYVpcfunV/H9//p6fzw5pmqZpmqb9FE55RCPjlWm3W6RJShzFHJuYRcUJXt6j1mpBokgB/ASBSaM+T+QHrFo5gmcniLZApSHS8/AyDpZUnL9lMz25EjXf5NjBozjFLKtPuxiVlBhbsYYjx+bYtH4jfX3dLF82SKvtMzExRa1ZwfJitj/0KJZjEyYRew9MYpomkR8zTQV3eQ9hFFGp1nnw3p30L8uxe88RVsQGhmmTKsHQQA/T9Sau59Df20PgRxSVZP/eY8zNTTM3NUUuW+Cmb3+H2VaAtJrkCiaDvavoKhcBWKg02LtvAts2mJmdZ3z/EdasW4ebz2KakMvlWLlmBdnSNdx7z4Oct+kcjEzMg1sfoee5XTieg+Dk0YdcNsvsXBU/THBsA4FAANmsx9x8ha6uMiQJUkpsy1x6r2EYZDIe7XaA57lIKdCDGj8/QggyTg7PzlMqFWg2F7AtE9uyMUxBGAYkiQQU2azL0PAAb/6dP6FY6AKe+osSCBCCXK77Z74vmqZpmqZpP61TDjRmZ6ewHRclBE2/TRIpLFPSavskYYQQijhVKKUwRYRtWPh+QL3WoNCVIZtzSZUgTgVJq01Pfx+zcwG7H9uFsGwG1vYzuHI123btYGzNevYcOsq2bbu48IKzOD5bJU5SavUm/T1dJEKSc0127TxEV1eJNetHyXoFZmbnODJ7iPvunWRsbB3lXg/HtZmdn+QFz3s+drbAmlXD3HL3OFEUcPDgcXLlAkPDQ9xx230MDPRQqbU5/+JNnHbaem45+hhzsxOodSvZsHGEA4clo8uW41g2C/N1xo/N0mg3WLVqmEw2RypDEtnm+Ow44XEouEXOO78f2zYw5Ainnd7iy1/7Nr/2wudy7jlb2PnYfsaWj1Au5TFNiWLxYhLIZT1UGpOmcmlUI5/L0fYDTENimp1pNj9Ym1gAUkpc16bVbuO6DqZh6ClUP0dprDBNl+58iYxnU6vVsByLbCbL3OwC2VwG32/gZXIUC114bkF/P5qmaZqm/VI65UDDcW2CMARpopREyhTbsanXm6i0M01JCIGUYIjOXdswDJAGZEyT2CnQIsZRCRvHxvDcLH4U0TNUonfVKM+/5tXMzIScd1E3mAYHDh7ngnM3MzxQxjYtlIJ9gc9CpcHQUA+NeoNWs0VXsUTGzbJzz2EG+3q44MJz2bwl4NC+cTZvWscDD+zF9STILAO9A+zYeawzKhMHJGbE/du34uShWhvn7OesZThVDC0bwHw4Ty7nEciEOIRmNaKYzbNv7xHafowpFa5tMdDXh9+ISdM2Q329yN4yF55/AYa02HfwEDfdeCdXXnk++bzHGRs3c/ToEW7+3nd447Wvo/e0HnbvPkAQpiiVMDzUgxCdO+O5bIZGq43nOkvfgRCCQj5Ho9GiVMqDEI+7Dy6EwDAMctkMURx3cjz0hezPTWQ0CdJJLAy8TI4giDAMCyFtQOI6HqYRgBKAsRQwapqmaZqm/bI55UCjM71H0rkShh9u83fiWrZz6dsJOlCdJFYhQEpBGqeoJCGO405ya5oghSTwA5I4xDIlqYoh7SR8J0lKmqYoQKE6/1ad3ARpLD6/uCGWaaIUpGlKEsfIxVEAyzKJ4gQhOtti2+bStiuliKMEqVKUgjAMUakiSdPOJypQaYqQAssyaIcK13UIwpQ4DomlxHFMDCERi5/XCbYECEUQBDi2hWEYnXWplMAPcRwHY3H7kzjpHDN5cjCQKoV8ggChczxPLXDQnRh//tIEBCaCqJNDs/Ql/MC3sRhMCiEe9zvSNE3TNE37ZXHKgYbvh1i2g5ASSUqCIvRDXNfB931AkSpFmipSIZGYOI4LStKIY6JmDSENUuGwd+84ff0hQ33DTE/NUp0/zL/NfYzVG07n6KTPyrWb2LBxLQ9u24XteFim2bkoT2Cgp0QziMhlPLq6S1i2oN6ssWblEOMTU9x1x3aOH5ll1ep1PLJ9N5YjSWNJGFSYmIL1q8c4On0Yy3LxZIZLL7iAu++6j/7elVQmQ2p1n1L+OGFYpdloEzZChEzAjKg2a5x+xjpWrfQI/IDJiXkOHDrGihUDFLMljh2fpjW/wPdvu4NESQa6+7niuedjGIJ6o8Wje3YyfrjFS194BcdnpphfqLNm5QoKuQzSEEuXokopmq02uWyGJFVLU6dS1Zk+1ttdXnodLF60/sB74zihHQRkPPcJgxXtZ8cRWWzZi1JHaLdb+GEb1xCd4gCktP0WURSilEOaRiRJDDhPtVpN0zRN07RfOKccaPQPDNJqtUmTmJyXIQhiVJTgei6JUpAqDFi8W2uiUkUmmyGXzxKmEe1mgGnYGBkTXJuF2gJjy/u48Ow12E4Xx2drNI/NcNH5l2LYZdaMDuNZHqaKGejpJk1Tmr7LkSMTxElCmoY0qhVCv82Ro+OcdfY55DJ5xpav4fSNZzAy0k2SxDQabYYGRrnxpps5/+xLcYwMpXweQ1oMDpaZbwccOzrO+ReezVD/IH0jvRw6MM6OHXsxlKSvbxkZZ5DDBytMTB7h0Eg/g7399PZ0sWbtMAND3ezbP8n0TANLeGSshJGhUZycR5rGzFQqOLbN5NQ4+/Yc5GUvfS4RTXbtPMgLnnsZpm0tJm3/V7CQpopGvUUhX1gKMgDqtQZpmhBGMcRgnEgG/4HvKU1TgjAkl/EQQupk8J8zISGKfapzCzQbDUzLIgxCoqhTCa3ZaCGEotVqUKnOUm/M4Xmd70rTNE3TNO2XySlf3VQqs7RbVRKVIAzBYH+Jnu4cGcegmHPI5x0cCzKegedKevu6WL16hCiKCGJFI1CYpo3wfYJGm2Y75v5tu9m5fy+eFbJhZS8lF3Y88B2ieJJdj+1AGDE7dj3KwcMTPLh1F0eOTOMYHsODw1iyyDlnn8/aNRvwHJeVy/tYNdaH7Rr09pewTInr2HR1F9i0ZTW12YRVK4fo6SsRBAGmKZiZq5IkMX47ZGp6HsM2COKQgeXd9PT20tvXD5bFc19wBRdccDqO1U27AXv2HGR8cpoojvE8h00bRxldPkBvT4ktZ5/O6NgIwwM9DPb2YGBy6MARvvaVb9CotLl76/3ce/cjbNq0ASElcRIvTglbHBFSinqjSTbr4tgn5u8rlEpptdv0dnfh2BaubZ1UcQogimKazRae5y5OzXma/1q0H0kpRcOv0QoqVKsNcvle4sTCDxSBb2CZGSwrC0CzGXDs2AR//5EPsLAwy6lMdFMo0jRhdu7oz3hPNE3TNE3TfnqnHGis33wRhtnD/HyLIA6ZmJihUmlQrTSJQ0W91iYKE+JQUCyWKHd7zFVn8TIO2WwGIQwW2k0iIIkSMvkMrSBmx/4j3PHQA1Qb89gpqGqbfVsfoDp3nLDdwJZZhJKcfeZGVq0aYXBZP/lCBi/nkitnSaRiYKCfrQ/vIvBDTEOQ9U4M1CiqtQbSMDnnggu59/6HqNUbdBXzRFHIYH83e/ccYKRnlNVrx3CzGeIgJkkV7Vabvr4epEzIljLk8i5nnbMR0zDo6ulhemaO2fkaURKjVEKh4BHFEZ5nU8h5KGUQpQLHM9l/dD+Jb3H+OeexvH8ZvaUR9j02zvjkHHOVGlHSqdgVRhFxnLBQqVAuFUmVIkk7F6BBECKlwLJMDKPT7O0Hg4w4TqhUa7h6utQzplFvcds936IdHieKYkzHxsvl6O3voXegjGkn1BuzJGmAEBCFsH/vLLt37yBJkh+5bqUUQdDmG9/8d973vv/9c9ojTdM0TdO0n9wpT51at2U5w6Mj3HfXNo5P7aUZLWCbnYtegcKybEzbpa+/jJAh+/YfIk4E6Qj0lItgChJp0IgSjDQhnl3AkBKExa4jx0mU4qpLL0agiBQc2r6NKAwwrS6azSoLlTzSMinkPUypyHg2fb0Fsp7NxKTNwkKVHbv3s2bV6OIWK4IoodEMGFsxTKsV8O2vf51rilmqlXmkYRAlAY8+vJ/fedMrqfltenpyxImiHURY0sDNOKRJTD6fwbYMUhWwevUw4xNzWJZNvdbEMiSO62EikNLAMAya7QBpGJQKHg8+/BAT+xc458zzKZTynDG2BqUkYRgRhAFhlDAzU0FKhWFIKvMVCoUsqYI0inEWu3zPV6p0lYtPmAiepilz8wuUSkUs85S/Uu1pdmjvJHsfPUKSRqjUwLIspqamqcy3gASxWKzAMExMw8T1XFQr4d///Xo2bTqHYrHwBGtVJEnC/v2P8M1vfpOpKZ9zz/q1n/euaZqmaZqm/dhO+ar0tltuY/Pp67nuD1/N5//1q+x8+D7CKMYEDEtSLOUoll2arQXqNZ80BhUr0lAR+jF+EFIoZBHCwm+2SJophmli2xLbsXjs0CRJejvrlq9g1dByclHCxJ4dmOXlpMpg8+b1CNnJY2i1IzIZB9M0yWUdDEuwbNkAs3MLPLrrAH19RQZ6u0mEpK+3jB/GrBwbJpfrotWsIyxJFNb46jeuZ6RvEDtr0pfrwjElYRQiSWm1m8yOH0YoQZokJImi2fbZumM7m9afhmkaeI7D+PhxZMNneLgX3/exXYuM6zC7UOHBPdu5566tvO6Vr0MYBqbVabhmWxJpWCgJ5bIDKOI4pdlsopSiv69nKaAQQtBstYnCCNd5fNKwUoqFSo18PodtmUvlv/SYxs9XEITcdc9d1MNp/DglVQnVyjxB0MRvLgYa4r8aMxpS4vgRjuPQ3T1MNpt53DqVUtTq89x08zf4/vdvZ8XyMzj3/E1kCvbPee80TdM0TdN+fKccaJTzJbbev5VWo05Pn4frZjAzklzeo6unQKNR4dDBwwTtFMOQZLJZQiJcw8KzDTzDoN1o4mRsTNckCmNMaeC32xgiJp/PcfjYPI1Gm3IhR1exiy7fYuf2+3GFDeo8pJCLFZl8clkXAZimgWtb9PeWEEJQLhWYmZlj6yN7mV+osmH9KspdBbpLWTZtXsNDDz1AcWCA8cljPHjnTt7zv1/BQrVKb3en27IfhGQcCynAEoKUDJlcniiFrlIXBw82SJOUVAikVKxaOUyl0uD41AxRHKJUm/HjR9m37zFu/OadPPfS55OkMSkpjuswPVuhWMiCEGTczmhFs+UjBRw6eIy1a1cipFwKFJIkYWpqhqGh/pO+D6UUYRQzOzuP49h4rrNUTlVIHWb8vLVqMdvuaNBst1EqxXBNxo+OY5pgWzZpGhMnKUJIDKPzd9wp3yw47/xzMU8aiVJEUcg9936fb97wNZIky6bNVzAwMEw2nyFRwTO2n5qmaZqmaafqlAONOKoxMFjm6JEJyqUcp582ytz8PAuVCvv2ThKFiigGZUgiBY1mm0I+SypSGs0GaZJgmuZS34s4UWBIPNsh8EPy2QTLtDg6Ps83v3cX5525ESFsjDhl/2N7aTabFAr5pb4ajm0hBJhS0my2CKISA/1lHNskmx2iUMhRKuao1hocn5zj4OEJBof7+L//9l22XGAQthOuufoF1INZpqeq7N69j+dceA5JohCkJElCX08PIyMbKZTLCAXLlw/R39uL32oDCj8MsSyTYjGH49kEQcS/f/FzjK44gzTJ8bKXvILlowPM1eYolYpMTc+Ry2ZYqCjyuSypBZYJtmVy6OA4o6MjuJ7DYotwlFJMHJ+iVCri2DZCCNI0JQwj5is1ojCkXCqRz2eI4wQpJVLq0YxnQjbvsH716ew/fhdhOkkcJ5im7ASBntfp/SJMgjAgTRMUKY7j0V3uYf26VUDn+1Yo9u7dybdv/Ap7H5ugv38tK1atoqunG9OUpGnYmXKoaZqmaZr2LHfKgcbsfEQmCKnX6izMzNBqtWhWA1qtgGSx+3ScJiAEUkpiEhIBsYSkFUKqSOMYpST5nIuVtQiCgEAlmIbBQq2BbQo8N4dCsnP3Pob7hlm/fjOhVeaBB3dy9tmbUdIgn3WXyr4qAKWoVmrkh/uQQAx0dRWwJPR2FyiVC4RByOTkHF09I1SmF+gqFdix60Hu23EHq1euZWT4NG699R7OOnszTd8iCEJSS7Bx8xYcxyaOE+I4IZtzKJc9avU2lmXRDgKSOGbbIw+zsDBOb3GMKy+7Escpsm7jCKZpcGD8EBnPo7+vTG93Fy2/c7EYBBHNdsz4kUm6u0t0dRVO2qcjRyeQUlIuFZam3ARByO7dBxkdHWagr5sgjAjDeDFBXJxyMz/t6WU5kuc8bzW3PziClDVko0WQxpimgRAKIRVJGiJkiinBMExyORvbEUC6OE1qge9+92vc/L3bKJVGWDZ2OstGxujuKSNkSpLEpComCNvP9O5qmqZpmqY9pVMONAYHBxifPo7fihlbNcKRw8cJGilSxp2m1gpMIUhRCEClELYjyqUCrUodyxZEQUgYJbSCkCCIyOdyWJZHqmChWiPnOqS0iVTMfB2qjYBsIUv3YBnbtpmvNUhQLOvv7WyUUsRJSiaXI/BDDClIO5+OlLDYDpw4UTiOzYrRfs45bwvf+87N+FHMOWddiJ3p5b67b+flL1vHvgMTjB85Ts9AiVpzgeHyEKvWryEIIoTsrNM0O3eTc1mHKAk5eHAf37rxZtaMbsaxeli5ehmxSli5rAvLNoiiCNe1mZ2bIUkV+XwBx7GQi7kacws+hmnS09NNEETESUIm4zJ5fJowDFm1cnSxg3RnJGhqeoahoV4Mw6BWa2KYJtmMhdR3uZ9RQggKXRIvI6m2EioLFcLIJ44737/j2IRJgkoEhhRYliSM2sRxzL4Dj9BoHuffvvB5arWUYn4Z5a4+1qxZTyaTIU3jzlSrtM3U8cPc8I2v8mtXv/iZ3mVN0zRN07Qf6ZQDja7eHrKlbo5PzDM1c4Rlw10cjWKSNKHtB50LXSVAgSENhAlSClSaks+YyNSgGoZkTIO2HxD5CU3aFMp5XMfA7C7RqrdotSIMZeAVMpiux+59k/zGBasxHZfpqXkyWY85u0EYhXieS7sVUi5kGZ+Y6cQVgLEY+LD42DIEhlBMz9UZW7EMadh0dRWxXRgZ62H/kQF279rF2eedy67dB/nGt76DCNpkMx7dfQXCJODwwcPkvDyZfJZ6fY6777mNXY/sYqC4krHlGzjrzNO458H7abfr9PX0UChmaTRaPLh1G66Tobvcz8hIH4YUSNHpeVGrt5idnWd4sBfLkpimhaVM5ucrTB2fZ/NpaxFSLnVYmJ1boO3H2JbCcSwcxyJNO8dZe+YZBjSrbRYqs51O74HAdbM4jo1SCgOJMEUnxyeR+G1FHC3wyX/4OI7tkMn0k8uX6e0bZt3GjZiWBJViSEW7Nc93bv4GN910E/OzzWd6VzVN0zRN057SKQcaoyuHOXzgOKefsYrtj/js27+TDRvGOHTQZPr4HEkcI+jc2T1RHcpAEDXbCENhCIXtmLT9CNt0CGSMFBbtlo9jZBBJiudYoASmIYnbLcg4SMMgU7AZGh1GCkE+nyGKU+pNged6FHM5XFsyeXwO3+/0sUg5Mf0IFAJDpMxV6mQ8m65SjiSOqNZmCQNFb08X5110Dnfc/F1WrBnD9WyCIASlSI2Ab974JRaqDRq1Nq5jYlkuD967jeV9q7nk7Gs4+7zTSaRCmJAmAVKYZLNZLMsgCGBifIKurm5KxRKtVpNWu02xUEApqNdqNJtNcrkVS8d59569zE5XOe+8MzAN2dkHldJoNjl46CAbN2wkk+k05EuShCiK8YzHV6PSfv6q822SME8hX8YP2kujGZ2eJ5I0jQGwPBvbtgkCHzAI2i6OVcCys4yOrmLF2EqETDvNHBOf3bu28dX/+DL79x1C4NDbs+yZ3VFN0zRN07RTcMqBxte+9W02rV1HT89yfL9NrRKwe9d+ysU8V1xxHju272V2dn5x+k+EUimu2elebUk6SeBS4HgWSjmcd9G5zE5NMTN1ED+MIU0QKbieTTuOEUph1dsElkksFT1deY6Nz9Ddlcd1LLIZm5Yf4doShKCrK8/xqVnGRgeRCELVGdlQacrcQgPHtjqN9IBNp5/J1gfvRaWKvQcOksYKP5C0Gj65YoF2q4FUirmpgErZoVwaYOWKIj0DRSDFTgd41atfiJt18cOAfDZDve5TW2gwdtEYQRhTqTSZnJoiqsdU0hnctWtQCAI/4cjCOEfHxxkZXsZpm9chROf4VKtVZo7Pc+45Z2JbnRKmYRRSqdcZP3Sc4YEhshl36TsJgpA0TQEdaDwbLF/TS/9AH60FByMKsZ0MjuvSajWBFJWmGIYijhXtto/rZegq9+B5ORy3wNDwGF7Www+auNKhWjnOjTdcz5133EurqchluykW8zSblWd6VzVN0zRN057SKQca7UDy2N5DxLFgYHCI44cP026FtOpzBL6PY3ssXzbE8ck52kGbNEnoKhSJQh8/DMl4HqaUxEmKaXtcduVzKHf18o2v/CdbH74XQ6SoOMHN2HiLd4GTOMb1HKanKlRrdUxLEkURjmMTRDGubXbyF4CuYp4dE/sZGx0EwBCKIIqZr7Yo5lwyrg0I4lRxycVncePX+2iHDY4dPs7FF5zF7IbVTB8/zvq+bo5PTeC4BoYDL3/Nixgc6u4kWQvYs+8gG9QYhXJ+MQ9E0fYDCgWXQrGIl82SyTsoGbN9932cfeFmjo6Pc8cdt7Jq7WriUJJx8mxYu5Hlo30EUUijFbJQqXLvPQ/xvCsvwcvYzFcWmJg8zvxCnTiIWbd2JUPDA//1hShFnCTYlvX0/kVoPzGFQqkYz3OIowZJmtBsNbHtzshcq94mDAMcxyaXK5PJZXHcLMVyN4NDQ0gDkiQmDOs8cP/3uPFb32bq+DyWmWVgoIcwaDIxcZgwDJ/pXdU0TdM0TXtKp95GWimCJEUIjyRu41o2zShCSBM/SGi1Zjhj8zqWLx9ix8691Jtt7IyFSGI8aSKFJIpjEALPMbEcl7YR8KprX0P7H2H66EFa1eOIOMW0LEgVhmWBtNh8+jqOHZ0im8/RDmKUNLBMo5MDspiL4bo2hiGoVOoUSnmq1SZ+ENPTlccwZWc6lVKAIpPLYFoOj+y4n+c9/9fo7S2xev0qHrjzXkbGVlKdr2IYkqydpbevdFIlp8pci+XLhpFGZ3qY69jMt2oo1WnSVipnieOI//zPr9GqBvhzu3hk50MMDvSzMNPgmpc9n5Fly4nSlIValZmZOer1Fn4z4OILz8PNeOzZf5AkSZDKIGtn2bBlFZmMS5p2kt8t00AI8P0Ay9SBxrOFEGDaFmlLYZouWRdSQDg20jSJaw0y2RzZXI5MJoPrZeju7aerp5tUgJAxE+N7uOP2W9mz+wAoi1KhD9M0mJuboNlsYRoOAwOjz/SuapqmaZqmPaVTDjSk4YCQmJaBMCStVhPHdQgDnyiMiULYuWs/51+wibUrB6jXA2qtNqEyUCrBCmLavk+sBKYdMTDUx9CatTy2Yytv/O3Xcfct9/DI/fcwO3uQMAyxTZM4jrHSmFa9wabT1nPoyAR79x9kYKAPwzTIZzzyWQ8QpMBAfx/7Dx1l5aoVKAQDvcVOovRikDQzt4CUglYQIoXgwfsf4txzLiJRiu5ykXY7JPAbNOp1onqV3MZOrkWnC97ivyQMDHaDUktJ2sVCjrbvI6QEA3bte4xlA8vZ9PzTuOe+uykVhhkdWUurXueLn7seJ2OxctUojpOhf3CQYj5PvpDl4NEjtFs+Rw5NkqawYnSIzaevAATNto9jW/8VZAQhuVy20w1ce1YQEizLwHUdDBOE7RCnKcIwqNcblEvlpb4amUyOck8PuXwBIRWt1jwP3PsQO7fvIGjHFPJlXNehWqvQaNQxDJPe3gF6enqxbF1hTNM0TdO0Z79Tvko1DAvDgM5sIYUUgiSKsSwTVIplQa0ecP9Du1k20I1lWLRbAaXuAkkYkqZQyGZotgPCdoRpOJR7S5xx9vk8tnM7faOD/OaZ1/LPn/wE9bkJpGUTA0oIkrTTbXxsbJj8TIbxyVlsUxDmPJQqkc1m8IOEQjHP4WMTgMI0Dar1JvVGi1qjjZdxaTaazE1XKPX3kMm5DHYP8f1bbmXzltModQ2wfuMq7r3vHpKwRT6fZ6FS48ChcbL5HMV8BikEaRphGMZSkJGkKUIIbNchTds8unsbd961nZG+QcYnjnPhRRdw6cVXoow2rXaTu25PEUowPT3P1NQORkaGOe300/H9kHq1TWu+SU9fL8MjA9iL3b5t28R1OiMXnSTwlChOcRwLw9AXnacqSRKmZqcpFUtYpkmSpjiWvVSSWfDT9SGRQpDxPNzYJvFjVKCQAoJWm4xtYtsOrm1TyBcpd/VieJIoarBvz2Ns37aV6kKLjJenu6ubKG4zOTlOqiCf72JkZJhcPkcQ+AjdklHTNE3TtF8ApxxoCAlpmpKkKe12G9OQBFFIPpNDJQpFgJOazM+3MIXJqpUrKGQjbCmJDIE0BKmSuLZJ1rUxSJCkmK7N2o2bMCyXiWN7yXYVqM8fx/cDpG1hmyaWaQIKiaCvt0xPT5latc5Cpcajuw4jjISB3jL1po/j2ExNzzEyPEiSCgq5HN3lEoZpUHUdhDKxHZNm02d4sB/l9vLxj3+EM8++iNi3uPeuhwkaNdxSBse2Wb5siHYQEcUpKk2o1Zs0W20ynotlmRhSMDk1RZiETM9McO93Q37/D/6A3oEesnkXJOw5sJ87vn83QTNh88bTKBbzJDFEawPq9RpzMw3KXWVOP20NuaxLrCKiMKZYyCIEJ138KiBOU0zTJElSlCF1k75ToJTi4e37+MinPsHvvOn1bH1oH+1ogVf/2sv51D9/lssuv5ArLrzkp/qMTuf2hCRJkYZJHLdJ0wTPdbFsE9e2yRe7yeZypCJk6sgRtj/8MNPHp7FNl65yN0oppmcmSJOEXK5MX98AhWKeTM4l42VQaRYh0qfpqGiapmmapv3s/BhTpwxUKoiTFJVCEnca5LWaLTK5LJa0CcKQrONSr7eoVRcolTzSKKEdppCmOJaFbdvEIub49DjDlWW4Xo4gChkcHuTggX1Ylol0HFSaksQJUTv8rzu4ovM/hoByuUCplGdsxTDVeotdu/YyN7dAruCwMJ9gW4rB/kFMyyVWCbVqnQMHjzC6bIT+/jJd/QMYYZ03Xvdb/PVffQLChGte9EJ6BvvYvvN+pFSYZgLEOJYkjGOCMKCrlCdVKX7cZr7RYGZmmp27dnL/3VuJG4oXX3c1j+zZxtHvTdCsNQnaEb29/Tz38ueyYfMYrbZPoZAlTVOqlQaTk7PYlsXQcC+u5yCl6HT6NuVSmeAfpNIUP4yxDIM0SRBC52icKhE79DjDuHaWeN4Cx6ReSRh/LCA4Xz31Cn4EpRRpGhEls+QKRSrVBkLWyLgetmPjOi7lcjeOazM7N8GORx5k8ugkQhj0dPcjhGBhYRa/7WNaFsuWr6BYLFMq53FdD4Eim/WI44Barfo0HRFN0zRN07SfnR9j6pSBYRl4rk3NlMSxIkpiUpUiDYPu3i6iMKZarWI7JuOTc6wYGaC3t4dG9SiWbSEMibRMbDdDtVLj2KH99Pb1kgqPvt6BxaZ/UPA8oiQmimPyhSK5Yv4J79qf6JidyXpsWL+We+7eTmW2TqPWYP+eI4ytXs7k1EGWjY7QXRphw7qV5HJZUsB2HGQc091d5Ld+61X8zd98gNXrlrN971YEIa5VojK/wCf/4VN0dfWRzeeYOHqU7q4ye/bvYOeurcxMTdFYCPDbCZc85zIq03PcfdfDbDnjbF76wnNxHZdcNgtCUSznkIbAw0MIgWEYlMoFpmfncTM2mWynbG0QdbpA24YBdC5gF/PdAWgHIa1mi56uEqZjoQczTp1SEciIJA2wvJAgbmNYMaYdEcc/aSUnhVIwNTXBDTfcwMT8I2CbpHFKuVhCmgael6G7uws/qPPI1gfY89gefD+mVOzCdW2q1Xnq9QZCGJ0E8e4uBof6KRSKWGanfHMu5xFHMdVqnVQPaGiapmma9gvglAMNpSTEEfWFeYIgIFaCOAWlBI1GG8OukKoEKU2arZDQT5jN1hkcHsByDCLfJ3YtRCqxhMHms86nUpvn0KFjFAsFSsU8QkZEvk+URrgZCzM28YM2lvvkd+2TVIEC27G5/IqzCcMIx7aYX6hz6OAEaWuWo3tmeKy9h5e/4uUoPAwh8VyboFan0Wyz6Yw1XHThC/ib//MhUichDkMqzQp9w4NE0Ty1lk+1IRgfn+DBh47TbiTk3S42rr6AjVedwdCyfhZqdR56cAevf+0rGF0zSpIqTEOSpoqJySnKRh6AlBQwUEpRqzcYGurHsS2EEMRxQhjFZF0bFKRpQrPdJk6hnM8SRjETx2fp7upCSqGDjB9DkiQ8vOMe9h+8m498dBtpKIkinwOH76fSaHPHXQ1Wrxxkw5rNp7xOpRRhGLB123185StfZXqmRvfgAFGUdJrxCSgUi2SzGfbt38UjDz3A/FyVbLZAX2+GVqvKxMQ0oLAsC8/1yGRcerq7OlPzTEkUxSRpjFIhtuWQyWSI4+hnd6A0TdM0TdOeJqdesihVKAmZYjdh2Jk2lUpBGCcIJalVm4RBiJQmCEkQJzRbAbv3HKCUy6CiGBElpCJBJSmFUo5la0c5eugIh/fsJCUibFSJ/QDPtEijFITAsSyi4OQLqxOTXBYLQWEanT7kpgG23cnn6Okt0d1TYNMZazEE3HnXg3zkY5/i1b/xYpaPrWV+oYaXxhQKGSzL4HW/+Wvs3r2bBx74FlIaVBbaPHjfLvbumCNRCj9aIJ91eOHVv8GGzRvZs3s/UdpkNjzAqvIgQ2OryJe7cLMuCIGUgjTt9NhwHPvk7VeKqek5UIK+vq7Fuf0p1XqjE3AJgUIxNTfP/EKVkf4hJmfn2f7ITractplS3tV5GT8GpWBifJZvffN7tOptavNVgiTCQDJxeBppGsxOz9GVv4F1/21jZ2TtR65PoVTK4SOH+PZN3+C++x7Ec0uMjKwgilNaLR/bsejqLlOrznPL977Bgf2HsQyb3r4+Ar/N8aljJHGCbXemE9qWRcbz8FwXx7Hx/Ta2bXcKDVh2p4wzgiSJSZLk53LcNE3TNE3TfhqnngyehBi2QSafoz5vYJsGUgAoTMciUQmWa6OUQkYCQ1q4to2JIJfNkLVsgnZIFMcIFkcoDEHf4ABRGNFs1mjUfOIowl+oYWcdUtMgSeLFZPDHU0qRJCmGNJCyE3gcGR+nv7cHadr4gcKxJX4Us/n0dezYvod//uxXWbdpBYW8S2V+BgnMVhvsP3CYK59/JdseuY/QnyaNIn7jZa/iiuddSZIqjo83mJudZL42xQ3f/Dqvec1raTR8vn/rXThyG5c/93IgQRoS6FTlajTbKAXZbOa/jqOAWr2BUtDTU16cE6VotQOyGa9TzSvt9PtoNgO6i2WQiunjs6wZG6Ovt6yDjB+b4tihGeamGjSjGmnaGeFwHafTq0VIVAr79hwkimMc237yNSlFvV7lO9/9Frfe9n1m51oMDCyjXCoRJxEqjjCtFMsSPPTA7ex5bA/1WhuJhee5VCtVojDGkC5eTuI6FpZlkiQJUZzgSgPHyxKFPnEc4zg22WyGVqtFHMekaUK9Xvs5HjtN0zRN07SfzCkHGmHQRpguqUoJw3Ax+TUlTVNQkMQJ5mLvi06OgSRnOxRdh4mjE2QyOVzHxhIOjpfBtO3F90Vkc1lWrFzBI488jB+HWI5Nyw9IBHQVTITxxBfWndEMSRSnGLJzgf61r32X3/j1F9Lf34sQCmkYRO2E/QcmeMMbfp0oivngX3+YiWO76M8VqNQq7D06jSNtLrhoM13/UqIxW0N4NqZo09VbQkiD8y8+EyklrWab279/H9+/7ftc/aLn8/t/+Fvs3nmQ799yD1HcwA/aNJotsl6m01DPtsg6HgDNdpvQjzqjFMMDmEZnJGO+UkdKSTbjohSkKUzNzoISZLMue/buZ2hwiIH+bh1k/IQyXo6sU6YeHSFRCtOySRGkUUxqGLiOQ7Fc+pGd1qMo5NFHt/Pl//wShw+NkyqDlSvWkslkCMI2URjQblY5fPggO3bspFEPyXgeuXwOlCCOUyzbwXU9FCmuZRBGAWmisEwHaWbIZLswTBvDULiOTaoSoijAskyq1RqNRhs/iH+OR07TNE3TNO0nc8qBRhJHJLHdmTOepKhOakQn4FCdcqupSkmSpLNcSNqBTylr4xgGlmnhuBYqFmTzHq7nYCwmf6PANAxiAdKwSWljWRZSQbsdIsXJU1lOTJlKkpQwSYnjpDPdJY0ZXb6M7u4y7SAh45jU2wE7dx5g2VA32UKG/QePcfHFV/D97wr2P/Z9PvOZT7J89Xoufc6lhElIs13Ftk1ajRZpbJHJ53AcAyE6ORHZnMe5F5xBoVDmvrsfYWCom9XrxugeyHP9V1rcc/dtPOfyywl8n2a7iZM6GAaEcUwYxMzPVlmzZhTTNIiThIWFGtlMZ25+J4hQzM1Oc+jgQZaNjHDHnXezcsVKyuUCotN7UOdm/JiEEJS7suSzWWppBsuwMAyDMAxJU4VpmoRx3BmRUOpxwZxSioWFOW741te55dbbaTUDMl6e0RVjGJZBFLep1RfYtXM7ex7bQ7MRkMsVKZc8arUF4maMYdhYtoXnedi2jUoTLMOiVm+Ty9sUigMML1tNT18f1co4UdApk5skMYqUMEgIgnhxBE/3TtE0TdM07dnv1MvbCkkcxaBSoigiTjp3VQ0psS2LII5IlUIYBkql+FFIkMQkQmE7DhPHj5PJe2S9DJmgyvzMJMPZMaIoxDAk0jDwvCymbWIlFghBvdmmVC6RL+Yftz2CzmiGYUgSwyBKEh47sJ//vP4fGR7Kcvrp5yAkNFttPNtBSYOJqVkmxqep1iqs3jDGow/fxsxEyNEjd7MwM4mTsWg3KmQKeawoZNWmUbbteoD777mTF1z+fEaWL6e7u5dCIcPZ563HcgRRFLJ/7ziWbWEaHq16yj987F8YGe2lt68fzytQKORIYiiXS4yNLScMIyYnZ0jTlP7+bjzv5JyLPXv3MzI4wqO7drNx/UZGRjoVuVIFoBCKTsdz7ZQFfkAcx2SyGSI/wLQchFC02n7n2CtFvdYmThLsH7iQD8OQe+69g6997XrGJ6dJY5Pu7j6Gh4dRJNSqc+zd9xg7tu+kVq2TzxXp6SlQq1WYm2vgODblcje9vf1YtsSQBmEYEycJjm0zN9dgcGSMDaefRbmrH+KI6al9pHFEHAVEUYRl2RRLZYRoE8eLTTI1TdM0TdOe5U75iuWiCy7i8NEpRApp2sm1SJUiiiIUEIQhSZp0qictFmNNkoQgjsjaFkO9PVT8gFzGod2a5zs3fY2X/sarEKkFSiEQuLZLq90mFydYpsC2LNqtJr7vY7uPnzcvxGKfCQP27NvDZ/7hw8TNGp/9p7/nut/5I9Zv2sKju/eyYmQ5PT1lvn/b7SwcX2BsbJQ77z5AuVziVa+9huPHA7Y/vJ0HHvoOaRziByGmbfHlr/wHoys3smxwHQ/cvYfvf+9+gqhJvpDj8ssvo2+gl5npCoViF/1D/aRGxPrTR9h05jpuveV29u7ZwwuvfgHDg4PkCln8IGTHozvp7uqlp7uLbNZDSrk0SqGUot5oIBC0gxae5ZHJZEhThRAKpEDfy/7JZDMejuMSYGMIgeVZuKaH07aQmBgtyHgexg+UFZ6eOc6/f/Hz3H3PfSSJQaIky0aG6e7pxg9bjI8fYuuDDzJ+bArHydJd7qHtN5mcnEEpRblcYtWqVbhup7ljEARYlkUmI5ivVLA8iWlLlq1YRu9IL6HfZGr6IKaVUm92CiAIITvFFQKfZrOBlAL7R+SQaJqmaZqmPVuccqCxbu0wZ551GpOTEzy2PcQwTJIoIk5TiBMswwTV6bfhmp0RBlda2KkgaIcYpsRIUwxpYcsck8eq/PM//ROnbTyNtevPQJBiWxYqAT+MkLZDKlIMoUjiJ66yo+g0sNv6yEP86+c+RdyYoyvv0m7W+fDfvp8rnvdSMvkRvLUO1VqdNHHZsHY9bt5GIIiCiFtvvYtlY2O85W2/xWc/6/LPn/04rh0xPz9D3h1hxfINdPfYXH31K2i1Q1zHZv9jR3jggYc4fPSbDA70U6lWiVXMxOEj/MunPsfg8GqGh1aybGAdD927i3vv3MZcbYZyOcsVlz+f3p4yrueQpCkIUKkiTWH8+AQ3f/dmugt9zM9XeO5zLyeb9YgW814MBKDL2v4kbMckjmKOjB+j1W5gWJ2RtzhJMISBEDAyugYhBL7f5qGt9/Pv//ffmJycR6UCKQ1WrlhBJu8xMzvB9u3b2bPrMfx2SMbLIGXK8alxABzbJpvLMTw0QKmUx7JsoijCdR26e7qpVpq0W1NIIwZS2kGNHQ/dy/iRo9impL+7jOs6SGnQaDQIgs6oX3d3F2mqaDQaz+zB1DRN0zRNOwWnHGg8dNfNnHHeOeSyGUqlIscdlyCKOh3DBaQSlISElDCKSYSkFUZgGHiOw/x8Dcu0iaOQcpfLhRefzSOP7OXA3kN09/ZT7u3CNiQZ1yFo15hfqJPNZTEE8ENN6+C/gox7H7yHL37hn7HSiNgwCeKYKEyQmNx7x+287JWvwURxaGKG2sI8F529kVw5y/Q/zxNEEeecexFDy/r5p8/9I1Ozkzi2oLuUo1Fp8JY/egtnnn8mX/7yN7n55lvYuGkdq1aNseXc9Ww5dz2V+To7t+8lX/DY9sAuDnYf5r//6Z9T6umi2a7x8LZtnDawHtOwmZme5f4HHuLL//EfPProNjaffjrd5f7OaFDcZs++fex8cB+b1p/GaRvPYPmKAeI0IUkVtmX+SieBp6lienqO3t7y0ojDj8u0JV5WEgaKdquTm6EEKATSiDFNiyDw2bN3J1//2vVs3fYwaSKJE0XGy7N8dBlh2OKhB7az69GdNOotTNPCti3afgulwLZdbNvEtm0yXoZcPo/rOjiOR6PRwMt0RrDiOEJKiW3ZoAQ7tm7H84rkc12US2VarSpp6mMYBpmMR6FQIIo6U7+CICBJdB8NTdM0TdOe/U450Ni3+wDbtu5i2dgyVqwcozG/jD27d6MwEIbEdRxarSYAQlqYKsXN2FgGxEmE7RpYhqRer5Ip5Dh66Aj9/f0IKRHColar0/JbxFGEUmAZVqccbqgQP5T8qpQiSRNuu/M2bvrmVzBVQqVSwbQN/CBGxYpM3sXLmXzrW19moTJFf/86Nq1fQ29fkcMTs4yNrWfrHffSagc4bpYj++sc2LcbS5ooIJfJs2zFMLmCx0WXnkPYCjh2dJr5+TpbtmzAdV2cjM3o6iFq1Robtqzh1jtvZPuj21io1Dh68DDnnncRGSvP5OQUEofnXHBFp6LU9Dhf+Y8bWL9+Ha6bY3Z2HhUKXv7S36B7sAvTlFi2gaEkUohf6SADOtPKcrnsU/a3eCKdpnoh9zxwO410inbQQinxXx3XDYkpJVIZHDy0l3e/512Eoew0qExTunv76ekuc+Twfh7eto3p6Xksy8RxXOLAxzMNhvoHkaZDu9kgSWPiBBA2npfF8zJIKZYaLErZya+p1WpYbif3yPOKrFx7OivGVuPZHg/e/d1OvlPaKa6QJAlxnGCaBqb55FWxNE3TNE3Tnk1OOdBwzQxu0ePw3sMc2HMM13YxUtUpx6oUS2WooDPKEaeAApVgConnuIRhjDRspiamGT96kF979W+yY9djHNi3h9Xr19FqNpGGgeV6RHFAxrJxHBfLspYqTaEUURzz7Ztv4J67b4E0wLZMpGFQqzQ7pUOtlJbfwm7bmJbNTd/6Jus2jPM7v/P7ndEWpRhbMUIiYkgDHNPkpS95Ie97z10EYZuFBUGxVMTNWqRpwuBAH7t2PMZzLjmX6el57r77QXp6i6xYMcqevY9hWzY7H32UenUBE5tLn3MZ6kLBkWOH6R/qZsWaUVqNJrlcjnw+SxhG3HTTLRw5coDlywr0dg1x8SXnIxyo1Wp0d5U6+S9xgqMTfwHwPKczzQy1OLL11MGXUorx8SP8879+im996yYmJhYIwhiRLr5ACEzTwHNtSoUuSsUuWg0FRoJpGPT2DZKomNtuu4UjBw+RJArHtoiiEIuUwe4yvd1FssUifio4dLAFuBSLJfr6higW8yRJRKVSI00V0hDEUUgUBtimhWXaCCnZdMYZrNt8Lq7rETTbGIaJabqYUqKAVrtFGIQUiwWkZLF5n6ZpmqZp2rPbKV/Ftut1pGFiKAlp545sutjBGgVhFGGYJmmaEgcRpIokSBBKYCDJeS4yY1LxfTALVGsJd99+G+XeAkePjPPozodJ4zaGKQmJMAyDVCmcjIvtWMRJAnTuTn/pP/+Nh7fdhyQlbDbIeB7ZjIMhUlQqQJrksjnmqgvksnlMw+Hwgb18/GN/xSte+UYst0y+kCGNEjKeS1c5y7Ll/bT9iDiFMAhoiCrve9+fs3x0JaedtgUvm+XA4d0sGx7lzDM3c+Dgfr769a+wb/8ejMjgiue+gB0P7mTl6hUsHxsGDLoHupieniUKI7p6SkxPz3Pg0AFsx2R2fopisZv16zZQLJWo1qt02SUa9Tblcok46STI/6qPZpwghGD82ARHju7nzC3nUq1V6O/rRwiJShVRHHYCUiFRSuH7bW677Xt87vP/zPZHdrOwUCNa7DYvDQMhDCzbpVjMUyzk8DKZpWaLruXieR4HD+1h/76DVKqVxUpfEt8P6OkqMlguYFkmKQrb9TCliWV79Hb3snL96RQKZWaO7cFvNbAsizSFNBU0my1SpZCmJI07uU6ZQgbHszAMi0S1MCwL0zBJkphmswUIMhkXz3Px/QDf95/pr0PTNE3TNO0pnXKgYRiSJE1RqSII/E6CMopEKVSqFqeiKFSaIqSB6uQ5I4TAMCRhGAERUZKwYmyAi59zCbv2bOeBB+9m2eBqxo9O0qz7xGGIkpBGMbZjU6lVOLD/MKllUKvO8f3bbuLgvn0ErSZd3YVOj4PKwuLnmEjDJJPPYDs2Q3mHKAhptxJsWzI7dZy//7u/4jde/Woq8xG+X+ORR+7l4kvOR5qCTMYjCAzafhvXznDR2VdRKJfZu+soU9PT+EGNam0aw4Te0iBGkqW/fyVHq8d48KH7qLfmqNYXWKhU6OnuoVDIUq/XKZUKHDl6hCBs02jVWDjSYNO601AiZe3GFYsjNn1MTE6zfNkAhtnp8eAtdlo/kf39qxxyCAF7HjvCvfffj0izfPPb3+Av3vX/YRhwfHKWj37s47z1bb9Hb08vs7Oz/O2HPsCDDz7A0SOTVKoN0pROcGG6lMpdkKbk8lkyGRfHsZGmASgsy6HdbvHII9uYnZ3DMDtFDpIkJZPJsHx0BVnHJG5Uma818fI95Mv9RHFANltgeGSE7t4SSRIhZUo25+E4Ns1GCyEN0jTFDwKCIKTVVigVIwQIKZFSIqQkDEISI1icamXgOA62Y1GpVgnDkDAKn+mvQ9M0TdM07SmdcqCR9TykIRBCoZKUJI6oRSFhnHbuugtI0rRzcSQUpIo4DplZqFAoFCjlMgRBiEgVflBnZEUPiVhPV2GI7oEeatUZvIxDFKWEzTae2+mS7ZoO/X29NMIG//eLX2fi4B5yWY++5UPU6jXCICXjZXEzbqcnAoow8mm2GwgUJhZ+o4lp5DFsk6Ad8c+f+RRbzrmEWMXs2b6Tnbt3s+uxI8RJTKmYo1FPWbdqPQuNOtlujzde9xqmJ6sUCzmq1So333QLQ8vKjK1YTU9fD/l8lpu+fRu3fecudj2ymwOHDrHljNMYGBphfmGWx3Y/RhILhFQUMl0sO20V+WIW32+xb/9+Nqxfx/HpGRzHJl/I0Wj5HJ+ZI5fN0F0uYhnGr3aUAdRrDR64/z5SQqQhiJKEMApwpYffDqguRCSpYur4cT716U9y+213MTk5RavlE8cKwzQodnXT1z+ESmNMwHEsDNtEGgLSlDiIOXLgAMenpjoBsxAEQUQumyXjZRgaHqBYKpFEEUfHp1i74TRWrF1LGgYklUksS9IOmhx4bDvzcwsM9XchRUKzEdJq+XieS5IkGFJiLU6dUmkTKQ0Mw0QJMIRJFKS0kjqGITFNizAMaLfbQGebklgng2uapmma9ux3yoFGtVrFdiyiKF7s5G1iyBghFaYwkEKAVIvTpxKEUvSVynRlLaotn3k/IJPxsITJ7t17+eB73s0F55/Nb/72m3FyZb759ZuYPLIfIRUSie/7CGzA58j4Ab58/ReZPXaEXNYhTVLm5+cxTItidzf1yjxhHHcu1oDID0gRSEMSxSFd3T0Eccjc9DxCSGIluOfOm3FMqNQD9u46yKbNG4nTGNNM8Ns+p522hWUr1rHlrHVIaZAtOLg5i2yxl//nTa/kq1/9Og889CBDwz1cdtmlnHPh6Xzzm31sOfscJo7Ncc8dj5AkWxkdW855F55DJpPFMMTiFDBBq+XTbrcIg4SvfeMGLr7oAnp7u5ACoiihp6uMZRioVIGOM5ifr7J33x6m5g+z/9AuGhWfv/34+/nNV76BG27+Bn4yxT33fZ9bbryRu+/bytzcAmEYLSZRe/T2DZPJZYlCn2zGxrXtzvQ8Or1gFmbnOHbsGMliI0pQWKbF8uXL6OkqMT01T7GYpVTMEkcpQyOjBCHkSzlaC4o4TlAqZu/uvRQKXfT2DGKZFlFYp9VqUas18Ba7v5+YDpfEEQhIVYphSJQSmKaJaZqkGItJ4AG2bXcCjCTtFAqwdB8NTdM0TdOe/U450GgFMcowkKYJRkoSCwxpYKgYRAqLc+PTNEGakiiOQUAp72FYAr8V02i0SJHkXIfWfJXbv/095qaOcsVLXsbZ52zin7dtQwqwLAtTQBSGtPw5vvivHyURglgq/CShlMtTq1YgVWCbZPMFpJA0m3VM26SQ76JaqdGsN7Etk5SYZqOFZTkYpoQgQEoT0xIsXzZIGARU6xXSOMTxbFCKgeF+pJtQqVYxbROkAqFAGHgZm2Ujo6xdu5JtW3dy/VduYGR0ENOCZStG2HTGJiYnp8l4Wer1WucutimWci6UUkRRQBBETE9VkMplfnaBUqnI9NwC83MVVq8cI5txdI4GneTn7950Fzt2bCWKfY4dmcA0JEeP7ONbX/3W0t/insd2MD83z/xCm87cPYmbKTIwMsay4UEW5mYxDBvPMxF0Rtzq9Tqzs/MsLFQIQx/LNPBsi4H+Xkpd3WSLJVzLYnp6AWOx672UKblSlii0MR0LpToBpDAMenuHOG3LefT1D7B3x4Od6VBCYhgmUdQmDGLiOAQBjmdjVA2k7OQjic5cQ1KVEsedjHXLshYrZwWYpiRJFEmis8E1TdM0TXv2O/VkcCGIoxjHtokRJFKQoEikAgGSdKkiUBJ1ksCbYUw7SRGGST5nk4ktWu2IxACRpAz0lDm89xAfes/7WL1+NaOrN9J91nnce/cdqCTGMm1CP6QyO4/puqRRgpX1SJKYNI7JZTO4noPvtwjjAD/wsZWD3wzxAx9hSCynU9knTWJSBFGc4NkutuvguCbbd9xNYgYcnT0AaUCaeiAEbsZh2bJlncR2IE4U9UaTvp5ums0mpVKRdrvNpZedz9Gjkzy0bRtzs1X27N7N5jPOwHYspJFSKheYnp4nDEO6u8s0Wy0mJqZRClatGmX9+tUcn5zn6LEJbv7ebWzYsJ7R5cvIZ10Q4nH9Q341Keq1Nq1WRJLGRHGCStPOlCcUruNgmopKpUK9XidVnQZ7hUIXI8tXEqmIZrOG61mYhgAVU6lWmJ6eIvAjDMMiTVNMw2C4u4tyMYebcSmV82SKhcVco87/iMW/ISkNhBSoxSaVhmGAgtXrV9E92IVlWoAkSRJs28a2Y5QyOo0rzRRpdHIykiQhTRPCMKTTIkSRJClJkgIpcRzjOPZi9/jO6/VfhKZpmqZpvwhOOdAQGQcMg9Q0aTaahGlKjEJIA2lI0sWcZYFALt5NxjaJDRPbNQkaTVIpyGRdlEhp1hParTaGNFnRN0Brpsatu77FWReei2cJas02kWFiZz1EnNKotchk8p27x2mCtF3CKKA2XSNJFXGQQKIwPYtCd5lWu0mzXiEhIUFiWvbixV9KFIXUqlUsQ1Krttj32KPM3n0PUgmiOCZOFN+++WauW7WGTM4jCAJs2wWVIqTg8KGjjK1cxtTxaRrNJr19XSxbvoxSOYsUBjt37OXoscP09/exbPkIg0O9VCp1vn3jd8lmspx+xma6u8ud4woMDnVTKGWZme3ClBamaRAlirbfJus5mD9hk7pfFkIIeru68bwcYQymlRIEAYZhYNs2YRgwMztFHEekqcKwLLq7BujtH8KPfBxTADFCdqo+zUwdp1qtkqYplmlRb1Qpl0qMDA5i41OrtTC8ArliH45rEoad5nme65KmKUkKlm2TRAYoicTAbwWEfgRSYEgT0/UIwxTfD3AcCykNTMMiVjFxFCFSRavZIooj4ijCMkwMaZLKzmiF5zkEgQ90plNFcae/TBwni8GGpmmapmnas9spBxq205nCIU2DbCEHrYAoiQnDAJGKk5qpSQEIiJKIhIQwgUSatPw2rmUjU4Xj2mQ9jyRWRO0QiaK3VGbvY3uxJfQUs8RJikoi4jgiWyxQLhcI2gmVhQphGBCHTVASy3HwMhkSkZCqhDBqE4QBiRK4ponfblMoFLEck4X5BaJE4WYKxGqClh90pqukiiTs9Njo6+rhnLMuZHqmSn+/IElT8rk8cRwThRHVao04SbBdi2q1QldXF4VClmw2Q39vDyvWrmbdwmoO7D/M1PEFjo9XyOVsLr7oQgr5PFPTM4RhiG3bJKnCNCSFfIZSIYNS0PRDGu2ArGNj/ARN6n4ZmaYBqUIKhTAkntfpst1sNpmfn+1UPUtBGia9fcvo6x+g3WqS9xxcz0SlKRPHjjE7N4tlWhiGQRCE5HI5lo+OUiqVyLgu+x/bw8Doas4893xmJ6cIo/pi128LaQgswyQII0zLQBoCRUoYBYAiVQkpnT4yhmmSz+dJUwvTNBGCxamFnepVSbKACuPODC8hsG270yRwscpYkiYYhgQEURQhhSSKIpI0JU3TpzhamqZpmqZpz7xTDjQa9Tqe55HIhDAKUSi6ussEfqealGEYi1NLFkloBAEhMQM9GWoLIVZqYpo2Bim+7xMmCYVcFlRKveHj5ruZm5km05XBiHykkBTyOaJsBlyb6WNHKJZ7KHcXO7kPgcJwHJpNnyAIKJVLgKC6UMM0TRzLoV2rkwiFSBXIDMVCgVgpgjAiCCLCIGZ+poqKUoQwEEqxesVq+gb6yOU85uaqrFgxglIppmlSqVQZGRkim8kQ+D6BH+D7Ae3Ap16vnqhES6pSlo8Ok81mOtWNjBMBg6Cvr5uJqWks06Knq4wyJClgGbLTBduzF3sfCnSKBoCgUMiT+tAIm0gpcTyXSqVKrVolTRVCGHi5AkNDI3hejjgM6CrnEKRUFuaYmZnFb/ugBO22j23brF+/gb6+XrJZr9P/JY7JdPUgbA9cB9MySAOIk5Q0TWg0W2Q9j7m5BcqmiVJZ0sU+lalKMaVYalppmgZJmpAmKaGK8BdHYDoUfhBgWZ2JcVEUEicREhvTkMjF6Vm2bSKFJE4SpJS0Wu1Ovs+v+AiXpmmapmm/GE450Kg32tTqTQwpMQwD07Jptdr4vk+apshELlbUWewHkCqCOGFoWZn+ksFZW4aozMccOTRDqxEicBCmRTuIMaTCcDP8P79zHXOtNrff/D0O7X4E10rBkAhTUq9WMKQkCJrErQZhGOM323hGp7qUY1pkMxnaoU+axqQJuF4Gt1QmjNvMzM5SDAu4rkM7ioiCCNd1qMsmrmOTKB9DGp0LexmxfuNKZhda7N9/kOHBPupJSpIkTE9Ps3nzRkCRyWbZu3cfxUKRNE6IgpDpmVmGx1YgBbgZh3qjhuf1Aiwldtu2xejIEK12SLXeJIhCMp5HGIQYpkFPVxGh5+EvEQLyxSxeNkPq5IjjmJnpGdp+u3OUpEWu2M3wsuUkSYwQCcWCRxi0mJgYx2/7GKaJYVqE7Ta9vb2sWrWKcrmEEArL6uRY1GudkrJSGlimuZi4n1KtVAiCkDhKCESA52YQyE4fmSQiDIOl0QYhIE1TBIKFhRrNRoNcLkOpUFgcjYip1xudKYYSlEpBKQQKpRKSJCJVaadYgWnRarUAiOMY0zSxbZso0uVtNU3TNE179jvlQOMFV72YrQ89xPTx4/htH0ULKSWm2ZnuIVSCAAzDBCUwDPBsE0WDoZEhTj+jl/Ej04yuHKa6INi7a4pGNcYQgjRWpLEiSGqsWD1MsesaZo9dyI6tD3N033Yw2gTtTs8O6j62aRDHEVIY+HW/M3qR9wjbDYJ2G0NC4LepLlTIOCaGIcg4Js16FZV6VKpNTMNCqgTPdZCWxEos0rhz57hSnePTn/o7fvMNb2bV2CoOHjxCX38vhUKOWqNOkqYcO3iE3p5uSqUSju2y/dFHybou+XyOer1BqiCT8ajW6tRqdQrFfOcCdDGhGMBxbTKe3Un+TVN8w8A0ZOdZoVN+T1BKoUSAtBJiP2Rmdp4gCIFORaeuvgEGB0cIw4BC1gVijh09RL1WQ0oDz8syMDBIoVjCb/t09xTJ5TrlhoMgJE0TTFNgWhau52BIszO6JQVJkiBEJ4i2TEkad5K0pRRksh4WBn67TdvvTCUUKkXRyeXJeFkMWSSOfdpBp8N3GIaEYSdfJFUJSqUoldCoLdBuhzRqdRQ+iBQ/8InjGCEllm0jEMRxrEc0NE3TNE37hXDKgcbadWsol0vsenQXx44dZXZ6Gt9v4To2hZyJ64BtGUgJhtHJ2XBNk7Rd4eD+EMcJ6OsrsmJFP/V6QH+/wfxcyqFDNSaPzGPZDl/54r/RvayXM887H6+UYctzTiemzbZHHkFYBtIySGK1WMnHQCmFIQ2kYeC3QnxCSCKkELiWgYVNmgCGSdxqkMs4tP02YdMnkhGe49IyIoJ2gN8O6SoUiFSClILjE8f5p3/8KK961W9RKI0yPTPLju3bWbV6FXOz81QWFhgZHmTlyhVUKjUsy8aPQgzLxPd9TNMCoFDIU6nUEFKSzXrAUqNvTvT7Nk0DAwPTNBef/+UPMeI47gSL0sAw5GK/C5MkSTqd0hePgVKKyYkJvv7N/6BSO87M7CxBGIKSGNKir3+Y7oEBkjigmHWp1mocnzhCFMUY0iCbdbjsissYHl5GZWGBhfl5HNdEqc50KMuSKEBKSZomJIsjB1JCmiREYYRhdkbpDKNThhYgTROKXS4P3Xtnp+qxFEghSdO4ExihiOOIublZICGOOyNiQgqElIACJVAoqpUqszPTxIkiDiJUmqBQKKXIZrOLoxmdylhKKZ0MrmmapmnaL4RTDjTypSzV+jyFsscyc4Bi2WVhfh5DhJQKiowrsW0D05SYhkJicMbGtVz6nM08vP1Rdm4/wn7PYnxijs2bljO6PMuq9Q44NfoH+kh9i8njTWaPHOfbR66nf3gI0zJJVUImnyNQnYurWMX4YUwsHExDEvgxRtS5450kMeVCBhXHnR4bSYJj2himwCzm8cMWcZSQyWbx/RDTlOTzOZRKMGWMaVmkSeeic36hQqXR5MMfej+XXHEFCwstXKvEihWjmIbB6Wds7vT7sExmZ2dZvnyEru5eatUqvQODVOt1lFI4tk1/fw+NZmspsbszM18gxclBh/wVCDBOeOyx/Wzddi+2lWHt2jHuv38rl15yKbt27+TXfu1lQGcK0u7du/jgB/+SBx98iPn5eaLFXBrbybJsdCWWY0PURoqEifFOJ/AkUsRxTHd/Nxs2bqJQLnF8dpokTBBGimVJ0hSSpNPPxLZtms0WfruFQCGEJE1ToqhTxcpxHQzDIIxiJII0TWk227SDKQ7vn2Dt+lEajYQ4jEjSBJWqTgnmJCGOY6TsfOMKgSkNkjhGqRRBJ5BCJUBCGgXUK/MoGaLiTlChlMKyLHw/WHqsRzQ0TdM0TftFcMqBhikktm3iOiZpYgEWrl1EpAG5fILrdOa6SwlSKkxhks8bLFvZS//yS7n9uw9z5Mgk99y1j0e27mPF6CAr1wyRdU1K2Sy5fIZlq4oszCccPjjP5OHDtIOQIIJQWIQqxTQNHMfCdmyklKg0RZgWGJ3pW1JYNEOIw06DvDSOSFwBaYI0HVp+TBgoXFfSjkNkKhka6OskclsN8gWPaj2l3qyTCgFBhN9SfPU/rmf5shVcfsULWLVqBZMT05S7SxCDaXXmzacqpdls0tXTg2V17j7PzM7R1dWFaRqdzs6LvRpQJ4KNk/2qxBknGtC1223qjToLlRK1aoVqfYFGs9rJsUnh9jtu5aMf+QhHj05QrTWIY4WUJvliiZ6BEQxpEkctGpU5Wu0mru0x0D+AigVB3GbF2AiGKXj00d2ccda5xGFEdS4kjmOSJFkcHVBUKlWiMKHZaneCAFIknYpPhiEJggBIQaVEcUIURQghKRWyoAyEIbAti1QlJFEnx6IzqpGAAtM08f2o890LRZouhppCoVTM3PQU7VaLhdk5/HYb23SwLYmUEEVRZ2qgYxPHnWDbcZxn8uvTNE3TNE07JaccaERhiOPaeJ5HV1eRY+MRBimmUBQLKabZKT1qmAIpBQYSL+vQajQZGu3H6iuSM0tsXDbM/MQk23dNsu2RIxQLLmecsRov10XvUJlyb4ZcUbExHWLvnimOjtdYaMUgbJRKiJMEkJimTRh3EtFNzMVpVJIojIiimKTtIxS0WgHCkCh8VNKpHiqNhEK+0Knuk4TYns30oVlqtQqtdtjpeWDa5EsFkjgiCkL2+3tYObaK48fXsHx0jPZiErxlmUjDIIo7U2aENGi3fbq7uvB9nziKMQ0Dz3WoVGp0dZX4ryyNX01BEPBvX/w39ux5GITizju/S7PR4pE99xO3Y/qH+zh26Bhf+MIXqVY7OTGu4xEngnJXD329g7SiFo25Kebn51AKSsUu1q1fz3nnn8P05Cx3330XuayHadvYToZmOyTr2kRhhGlCkiT4vo/v+4sjBYIwjAjDBGlI4qjT8T6KI+q1BkEQMDMzRz6fJ0lS7KWgAZJY0ajViaOIOIpRaUIQBkgDHNclTTtJ/mmSYEiJaZmLjf9ilJJMT8zieU1yuQIDy0Zot5vUG/NIIbFsa/GoKUxTopSi3W4+o9+fpmmapmnaqTjlQANS0jjCMg1cz8K2LZLYwBQGjiOxTIUUIIVabKQBlikwDIPpiXnsYg8HD03jY9CaNujqHqOv14F2jb17j/Pwzv0M9g5x7jlnsnb9AIqIoZFR5uYDDh0LeXjnEWbnAoR0sGxjsfeA05nekiiUSgmDECHSxfn9giCKsS0LFUOcdAIB2zERUtJsNFGpotVu0Wy1CPyIXCZHyXDIFYuExJ31+xLPslESDo8f5aabbuL1r3stXr7Ipz71CS65+EI2bNhCEse0Wi0MIahUKnR3d9Pd1cVCtUacJmS8TrM33/fxvMVcjZ/BF/qLYHJihu3b9jBfmesEjirFMAQTk/NIafDhj3yE2lyVWq0FqtP9O5cv0Dc4ijAkM3MTtJp16tUqjuNxzgUXsmHdaUhCJqenCIKYIAhIVIqhFNKUIDvBYBzHWJZFkqS02z5RFC1OTWqjFLiuQ7m7zN7dO2lUpxBpZwQiSRRxnBKFcec9KkUt1rKVwsS0bIRhEqedXi6t6gKQLI5uSeK4TbJYJjeKU5Dgejl6eobp7u4mX8iSqpjZ6eNUqwtYtoVpGAghcRybwA+I44Qg8BFC91bRNE3TNO3Z75QDjVqjhu+3MS0T328t9pWQGBgYhsIwFKYESEEoBCb5XJZyTxnbkPRV26wedJgI6hTP76fdaDIlIxpzTUbdYbq7UvbuHWfTRo87vv8YA/2CkeHlZMyA09dabNqwhVvunCeICkxOHaPdqnSq9iwmyEohcBwbRGcOu++H2MLAFAK5WMIpTVKkYRIFEWGcEkch1Vqj0wPBtCjmc6g47VSiSjp3jxOVYtk2ljQIg5Bjhw7yl+/9C0bWjXHffQ9x4LHt/NYb3wRKdpK6DZNmrcGKFVkMo9MHpFqvU63W6ekud4KgxaZsTzR96ldBqVSikCtSrU8uHoNOQ70oTBAGRKGJVeil4MVEUUAURGTdPI16k7mpozRbdaQwkcJkw8Z1XHDxhbQbIft278HN5shlc1iWgwIc26UVxyAgiRParRZxLDEME8dxOwHq4ihYmqakKmZycpK9Ox+l3OWRsS0s01rKjRCik8h+IpgVCGzLxDBN0hTSBJIkIgqbBH6dNI1gcRqWbTtAQppGWKbJuvWrcR2b2elpHn10L416E0MaFMplbNvDNA0GB3uZmZknjDoBThjpqlOapmmapv1iOPXO4KZNaHS6HBcKBebmZlCJxJQSw1SYVifHQKWdueciBcM0QSa4eRclmpQKeZiPIWPQtjzmwxbhslVYQ714ZsqAUabYVaatRrj1e9/hhc9bSRIr+ksxvjJJhcnysTGOjh+j0WhiWhLLNDpN00SnelO77SNEjBQSaRjESYztOsRB5+Leb7eRKXj/f/b+9Mmy9L7vxD7Pcra75lZZe3VVd1d3YyF2bgJJcJFMUmbImpmYibBf2GFHjC2/GHsc1njCYSv8B0yEHTG2ZyyNQxMzIY0kStRCaANJkRBIiQTRABpo9FrVtVdl5Z53O9uz+cVzMhuQCLIgAiRMnk9HAYWsrJt5857beH7n912KglYpkqREBEEQMChyrHMsy4o00dRti1SKVdNQpAmL433UxgYf/eBH+Z2vvopZVuzVDf/1f/1fcf36C1hXEwJMxtMzw4UUgvFwyOb6Gm1rzg6JwYf4Kc9gzAgh4Lth6v/fEqkCAe8879x6i53dx2cN8iprGQyHeBejYJXUnJycYKxleukCDIa44GlXJfMn+zy9d4/F8QFKKl544SWU0DR1xQvPX0elko2tTUbDAlnkIGM0bUBS1xVCJyilSbXEhzjQWOlomoa2jYZvpTRtazHOkBcFUqRkadptPxxCyrPvXQhB2zTEDGKBUKLzlTgOD3Z58+stx/uH2KomTQVSdR0eTctolAEC5xa8/rXXMMaSZQXnzp3n8uWbOB9AeyajMWVZ8/DRHvOTJQFDmiZopc82KT09PT09PT093898B9KpwKAoaJqS6doa0cwam6uVEkgV7+8GEaUuStJtHABnaU3N/LimtQ20loFKubCxyQPv0UVC5QPy0kV+8Tf+FZc3J0wvP09ZO7bPX+e3fvPXGQ0bZk8defGIqjpBhIBp4kaDILAEbBsoVxUqQDYooDNht8ZEbb7SpDrBGU9AUNUNbWNQQuIFLJZLJutTbNVgq4rRZIRHoJNAcJ4PvvwBPvPn/jz/9B/9Qy5ub2GqmoOjY1praeu3ydUAnShc8BhjifItwyDPEN3Gpa4bpEy/44QpYx1Z8h28XN8HeO/ZefqEw4Nj/sr/7f/KYnkUW+V9oMgneB/wwWNrg3GOuqoYjyZsbp4jKSZY73m6eMTdt96lWhySJpqPf+oH+cQP/hA7D5/w3rtvI4CLly6xMTnPm1/9IiCw1uG9ZzFf0iaK4do6SZohXEMgROlTmsRkKO9pjY2bChe7WaQQBB/w1iHSOGgEH1OijLXdswvoJEFK3X3N2KGx++gps6Mlk8mYJElJU0mSJCzmJYvFgqJYQ0iBdZ7BaMLV5y4xHI5BOpaLBftPd6kXJ2RpwtqFi1y4/iLZ7oy9nfcwraFu6ljy19PT09PT09Pzfc4zn1wHgyHWNyRlwtr6BCmj6VsqATJ0GqDo0wgBCIGqXuFsS/AJKMHu4SH5UKOlZDFfkQ9TCqnwleHDl67CuqC9/gIL22BOnvKN19/ko9kLVGbKleEVquXr/Oav/gbWWMZrY6JWKyCE6BqdJdWiwraW0TTFC/AidhzkwxwpJM60JEUBBJJU40P8b+cMh8cz0kEWhxepWJQVAon3AYFguZzx6lc+T6IFt9+7z/MvPk+WF9x/8ADvHaOtKTtPHzJdP4/WsXzPa01rHFkaDcB5llLVLUrGweNZ5FNCCPIzU/B3j9YY7t67ixSCJEkYjycMh6OzdmtjDIlOyLtB6VmJqVItUkp+7XOfYzo9z+ygZNXUOBfbs5UwhBCo65rlYoGUksl0g0/+8Ke5fP46ST6AAFkduL++zVEwXLt2mavXLmOsQ0gf050E5IMChMSZgByAtw4lNHkmybMcbwMvvniDr3/5SyihEDp026W4xZBSYk3s9DA2dFI7hQkBpRPSAEF4nLMkaY7wiuACg7zgk5/6BM5V5OmQGzc3yNIc5wwox9OdE/J8hJKxvT4vhgSRMRyPuPHCFoPpOA6mJoD31Ks57WzJMEnQjePk4SPmyxkESVMvY7KVbc+6PHp6enp6enp6vp959lvkItC0FUIEWnMqG4mH4KgoiYefUw+CEJL9gzmz2ZK1tRGNtTxYHJOLlCJLON49ogwNF86dQ2QFovVM18YkqWTlhtQisGSHh19fsiY2cSeCn/2RH8W24LtD4Mlsxs7xHruHu5SmIk0Uk80p1jqSVGOcwxpPCGCMJThPmiaYrg3audjw3LgG6SXWBzzghSDPU4SId55da1BIXnnpBX7ny6/zwtVL/Pyf/3nSfEhrf5fReMy9hw8JSvDX//pf5ed+/hc4f36LNElIk3+jhE9IijzleL7C+ECRpSgp/0AFVTg7XHY1f98FBZWSkkExom4ajAmkaYZS0U9gnWM+X/Lee/f5wR/8GIMif8ZHDXjv+fy//HV+4MMf5e2330Kkb6FSTy6KLlY2pj6tVivKsmQ8HnP9+vN8+KOf4PqLr/DoyVOCbPDGUlclV69d5eLldcbDnOA9T57sMMx19OI0MTVKJxqlNEKAVAqdJAyHAwgSmSQ8evyI0XCI0hrhA01TA+KsK8N515msY/awc54Q5FlzOBA/zzra1jDIB4yHgtLMuffeuwzHBYvVjJ0nD6irEoRiY3OTJC3QWcFmsYG1EqQnCMnasCArRmxtnWM63aQuF9j6Syz3MhJalFIkMhBqgxeSwaDAGoNONH/waNrT09PT09PT88fPMw8a+/t7HJ8coqXn5CgneAdeILVC4mMpWQhd8ZxESoE1gVu37vPuO2+yEOtY1zDKM7SumNs5781hr/CcZ4tc54S2Yf/2He6+fpfDO09p5xXSC2QArTTjomBzbYNzFy8xnqyxNRjxytXnMddv8vDogPf2HrPrjnFZ52toGpq6xfsoNdFKUddNNOWGcJZAZIIjyzMmkwmLRYUzDp8mCElsOtcS4T0XL26is4zt7Q0unh8R0iHWeRaLo9hz4Bwns4q//bf/JojAz/3s/+RM13+KAISUTMZDAKR8No9GVdcUeQ5868Dxh0EpxdUrl37PP0t0wfBygfcB07bwjIOGc57lcs7iZMHXvvp1fvd3X8PREnwsukNKcIG6anE2cPPFl/nwxz7K1rmLWG8oBhkCSV2VnBwdYZ1F5ylmFdA6GrBlojDW0ZqGelWB99jW0JiGG+dvYCqHwGFaC0EwKAKz2Yz14ZAsy1iVDVIqrI0Ga6V03HY5hwseISVaaxKpSbSirOI1JJVGCMloPKLIE9746qssT+YEFEmmMI1junaOK88NGU3XUTKhNQ3GOrzXDMZj1jc3mKxtUgyG0a8j4jXQNCuUKojGcRd7XDyExiC0QOc5kNC0sWCwp6enp6enp+f7nWceNBKdY1qPCZb33rtHtVqiNSCjdEnSJSmF0Elv4NpzF/mRT/8wdVXy93/zddw4ZdlWXMk0awlc3BqRbxWsjha8/pVbHPz2W9S7M0RIycYT5GiEKjJs8NTGcLQ/4+7DHUZvvcULV6/ywnMvMBgMGY+G/PB0jZfPZ7y+94QvHdznxNaYpiVJ064TodsqCEHbtt0mRqKkoGkcIpOsjUc8fbrP+uYGrWuQIer18zzHd1IgrRTOObwM/J2/+Tf52Ic+wI5UPD48oaoqpAgsFjN+8e/8LdbW1vnRH/mJf2vYANAyGpPjuBA4XVh8O4nSO+/e4iMf/hBCxO1H6DRX3/t729+Z9Xh3d5df//XPMT9ZIOURTdMgdRxAkJLBYIRtLR//xA+TaM3W9jZXrj/H3fceUgwTpNJ472jaOv58BOhUY08MVVkyHHuK0ZjV0RGCKPFaLOYsT2qsbQkhpjoFQhwmjKesKjaljLHGq5K2bRBCdhstSZomWBsL/Hwb/RvXrl3HuROUgixNzzotBoOck+MTrI1N8lsXLhHQ1I3jxVdeoG4MZblgsVhijWa6tsHzL1xnNJ5SDPKuJDDG3Nb1EikEzjuUiINfAHwIqADSi1j6JyRaaZCCo6Mlly5vfo9e656enp6enp6e7x7P7tEYFkzHAxItKOsVR0IivEUROsmJQgQQ3oMHaQXlQcu7X7lHMIbm3ozRkcO0jifWMFy16OMHyNuK59JzFH7K3oUXWV3JYXsdxhlBSwI+bkoCBOtpFiXH93f42q332Ns94GMvv8Lkxgu4LGHrpRt88uEG7dccn3/8DjpLkEqigoxejCQhIQUhMG1LoiVWCmQQeOcQSjAc5jhnkahoCJaCREqUVuR5TC+KkaaKUTHhpZs3qNtHLE2gaRoEHiEUx0cl/81f+6solfCDn/rRfyuSVAiQxIhb708TpX7vn733gbffucWHPvBKLG0DQvDIP4I+BRGefZgJIbC/d8hrr32V1tSEIEmyNH6vWqB0gRCCT3zqB5mdnPCRH/xh3vjaLc4/d4X1rS0aUwGx2E7L+HMeT8cQPEdPH2NsTIXS3pPoFKXjkLD/dBdcgk4UhweHpCojIGnblizL8SpFKkVw0NRN9MUISJIUaw1SxsN+2xqscWgZeO6Fbd75xlMmozFJohFCY52jaQyD8YTRaEJWjEjSgqY1BN2wd7hkUIy5du1DrG9tMxpPCd0gYU1N01Q4Y3DOIXWCVAmEQJYXtKEBoHUeb2NYgo/FNKTTMcPpGCk1x4eLzoDe09PT09PT0/P9zTOfWHbeeJXyZE6WSYKH7HhOsAZ7ZFntS4QXYAK+dQQTwAi++LsHvMqXEAFc0KRB0imsEALmBNJE8dyLKTdeuMnzWxu0ARY+sNvU3F3MmNX1mS5dSc1kY8SFzZeYXLvA43/9ZV5/+22u3rjB9NMfwawVXPnYi3x8lHP3c4c8dgukjC3crWkJwZMkCSFA0AnDQUa5WOAJWGc5mc3AB3SI6UJCCYoso2kaMhWTiJRWJEmClJDlCXmRMihS6qomTzQ/9VM/ya/9yr/geH7MfLbil/7e30eIhE998lNIpb7l0H7qZ1Fn8qnf+0jvvOPBg4fRwKw1eDhba3TTSWy3DlgX78ynSfLdicL9Drcmn/+N3+Z3f/erQJQgZdmA6WQT4wKrcsGVK1d5+PA+jx894sd+5jNsbZ3Dh0CapbhggLilOH0+3setRpqlZFkcrNIso5rNu4hgydraOhe2n+NrX/oiPsQ0tNO/76xF6GhmP00T1lrHwknnqGuB87FLQ0jJ1voad99+neOTE4bDEUqv4awnywcMR2tM19bRSUrdtJwsKvJcsrmxzfPPX2A4mYD3tKZkujGNnS2tIdgGU5YkeU6SZqRCIXS8DkUIIEFqi+picF0TcDiCkAQbaJcldaHRKmH93JhVVf/hX9eenp6enp6enu8xzzxovParv4n2Mh5efZRKee86X4ZAEJAy/nkQkiAh6WQ+IggQLUGAlBIpJCHEO/WZ0kx39pFaMRoMKIYjNvMBm+OCUQj8ztGSSjiCAC0Uk8mYIAL51pTzP/JR9v/lq1S5Zq499999j8lozIVP3eTa16/w4O43WJm4ZVAqxbQBY1pSLcmLjOAdxjm6p4GtY+eGzhKCEggpz2RDbRvN5FopEi0RQhKcxBqLUrF5HARFPuSHP/Upvv7GN9jb32cwHPP6m3dojeMjP/BhiqKI8aThfYO3lDEC9dvNBc5aVsslJyfHFIOYmCW6//Tes1it0EqxKkt2nu6ytbnB5QsX/lAXxvt8BysNYDoYIILEC0miE0ajMa2pqMqGwSDn6OAp3kteePEF8nREmheAQKlOOuTBB48LHmsdi8WcvaePSHT0MkghGI8GHO/HQcY5S1pkyCTBuwAq4ESI12cI1G1NrgrybIBpGwbDAWCRUhFCIMvymDwlNMZ59g5neO8IBEY6wUlFbeDazZfwNoBM0OmIK+c2WFtfJxuMUVIxX8xRqeLoyTFNc0KaCKqmYX5ygneeK9dfQakUqQPWOEJwWGOixlAFEKqTiaWUc0HpGoqQgBRgLPWqxoaKIMG29g96GXp6enp6enp6/th55kHjqSzAx7vBQmiqaonWEmstzno8AukBEXDeghSomOGED7FfQ4h4eBddy/L29gWyNKMOmp3jho15w2YyZ71IGA4GbA5GfPryee63hqbIaSVnA0rAoicFfmMNPxzR+kCiEh4+fALec+nFG6R33sTpBB9cTPFJEow1BDxNVVGVJb7zlEwGAyaTEU3b4p0FAsFHbwh0B2gZWFufEILFeYtOFN57ms6/Yb2nqipGkzE/+Mkf5J1b7yKkYnP7Arfu7/Hlr/33PH/1MkU+INEJZVnSti1b587x6U//2Lf92RtrWZYlR8dHbG9v433czBACzjkmXSTtsCg4t7HxXS31O90MPCtCCUKA6XTK2voax0dHHB0fUxQZg+Eaz994kY2NiySjnCQfMD85YW/3AS994ENnfhVCIFhHqhN2j09o64Z0oLvvx7O3t4/WGiXl2RAkRBzIrHVoAsgQfec2mrxffOF5vvbq73YdG5YklbStwzlYlTWNsbTGcHw8i9e0sxwdzZDZgPNb1xivbbGxsUUxHCGU5PHDu1TVjMF0hDcNbb1gJRucb2iaigd3biOUwrnAxcvXkFpSrZbkue6GUwXeE5xDqwQldTSca0VpWlIdKESKFgqXSOqmASQi12wOJ9+117enp6enp6en53vFMw8aH/upH2fvwS46SfBS8PD+fTY3E4LwLOYl5arFe8iyBPC0JuBMQOuENMljaZ6xaCVJswzvHFdffpmtc1ucP3+O+WzFoBjz+OkOd2f75IcL1o6WbKQJm4nii7ff4c3DQ4yNDczRvCt45fJzvLU65N7vfpGXbt7kxZdeQATP5NJm1OAHkCrDO0/A4328Q6+1jg3S3R37yWhIliWsyhIfQGcabx0Ez2QyQUlIioK6bkmTtU7jrxgMMlzwKC3wwcemaKHwvuXFF2/y5rvvMZ8fMV4/T5okfOnVV/GuRElJUxt8CFy+dIkf+qEf/rZpQs455qsFs9niTE4EdMb76O04HS6+283h327I+HYfd86xde4848mAh48e0dQ1y+WS4WTAz//CL/Daa9/g9be+zo//zE8hEGxuXcDbCUe7u6xtrONdS+j+EUownk4ZDApWs10QAe/BeQjWYayB4PEhYJoWQmA4HHU+nDiVntve5tpzNznefcjR/g61icPI4mCPtjXI4DFtS7Mq46/ZjNaYaERXkvF4zHMvvIAjoFKJSEA4Q7CBcjFjdrJPU9ekScrQrXHuwlXquxXLao41hhBA5y8CkqQLEgidSUQlGp3lCCWoFktcaMkyTZCSVbsiV4qhzhHGo3WKI1CkGcOd+Xf1Ne7p6enp6enp+V7wzIPG7GSfNEtI0gwXAlmekWQS6ypG44TBIInegFSjtKCqPbOjGiFSLlzYRmeax08OaKsKoSRaSnSakmYZxWBA3Tg2L2yxffkC3nlWyxWHu3vs7u6w3N3h4f6M1SpQty2z2QnOO7TSvHT1BvcP97n96lfY2HiVq9cu88rLN5kMR3gFuNiV0LYtaRa3AAEwJiYTWesYDoZsbm6wqlcgBFmeYpxFSAE2blG01rR1Q57nBCBVSWyLFvDm229ycLhiPJ6glOy2AB7jPG3bsre7y3mRMx5NeOkDn+TBvXdo6wXGCxAJx7OWxWLBxrfZRljrWMyWLObz6CtoGpRS+BC3Llr/0ZuDrbUIYhHd/v4eEBBS4DEMBhl3790BEU343jmyLOXLX3mNqqxQWqO0xgcIwXG0t8Puw8fkg4xrN28SgouPJ2A8GtE0FctZjE2OP3NBXdcYa5FKU9cNc2YIJblwfpuT/RnWemwA6wPfePN15scnzBcrggDhHG1V0dYV1WyObRqC8wTnoWnBmnidSEG9mHFyfIwUkrk7pK2X6DRlsnae490FxlSILlzAmJa6qqibhtHoHBub50jTnLyYRNlgopFKkEkVN3/O4JoFs/1ddh4+4HD/PRbzfTyG4SAn0SkogcgTkjxFBk9ee/Tx4o/89e7p6enp6enp+U555hPq3pNdNje2gQwpYkzpdKPg8cMd5rOK4GB9raAocqy38cBVG5QSNHXF1evP0bSW1SqjqWrytIh+DaVRUnTxrgHvDEIIxtMhk+kL+Jsv8t6d9/jivX+AJ7Yzexs17g5YrBYMyhU6zZisnWNvb8aDB18geIdQGlvXZFmKEFA3DVLKzmAco0Mb68nTwNraBDtzhNmqO8BDmiSMh2OSNKWuS5o23o2WCLIsJ00zttansVG6bjCFY3f3EVevXKOqwDtPYyzOBRpTIqpAUmSMJmsc1Se88sp1bt2+z8a5TY7nKwbDIarzazgX5V5CCLRS3Lhyg8lkivOeqq5w3jEZj6lbS/G9ujp+H05TtJqm4Z9/7h8DngcPHvHGN97iyc4ejXGoRKF0gdYVRVYgVUaiDEIJCB7nHc1qyf03vkEi4eSwYTqdwGTtLCZZJQrtE7RK4uHc2q5KRKClIgjBwc4+x+GAH/jID+DLkkf33kXgWVYVh7fei3KptqVaLWlXFbauMHWLCF0LeIc1huCiYSeEAB5W8wXbF8/z5O5D2mZFcDVpXlDkGQfBoxKNlDmD4YD1c5dI0wEvvvwRdJKQaIEQAS881hradsVqOeNw/zF7Tx+zv7fLoBBsThWhNWwMDaMkY3t9i9nBHJoAUiISTZoqQDPRU0Yvn/tjeMV7enp6enp6er4znnnQkFIgpUIpjQserRV5nqN1RggN1lqkLljbHNO2hrpyrJYBgeTa1Ut8/KOvEJAcHS042DsiONGFJsXHhZg2pKQiILpEoZg+lGcp9ukhNDUDAoUL1FKwVALXWJQNZAcn7M4WZFsbpJMR+3sHbK6PCIROtmWQQjEajvAu4LGUpSEET2sNT3Z2KK1BKAneEZyjDQYbPJk3ZDoa27M0RYTAarlkMCxwLt7ZF1ITQuBLX3qVB/fu8cEPfBitFARPmiSslivyYgDe8/TxE37r87/Gz/35P8elC5u8996b/KvfXmN9OuT6tct8+EMf4eDwkPW1NdI0Jc9zfu7n/izXnrv6/vaik1AlyR9P1OlpstPdO3f53Od+FSECOzsHHJ/MyLKCD37oo6xvbrP/dI9vvPYqWifRIyMESsafiwgBU5fYxRFlu6QYDxFYnDUEIeN1ISSqG7rAIpQkzzJWCKQUbKyvUS1XPH7wEG8bVtWKLMlJdIJrDIdPnuKrmrauwTvWhpKmbchTjfOSoBLKsiL4KKuL6VQx3CAIwWI+p65KdJrhrCMZDFjfPIdOc67f/AB5UVAtW9JMkWYFAod3lqo65mhxxMnxAU93HnF4uM/8+Ii6KnHW0LQtAcG1G9uMC0USYumlTjyJtWgFBouwCrdqmLWGYjjg3Aef59ylG38sr3lPT09PT09Pz3fCsw8aQiJELBQLgJLxbruSCq0T8AKlJEpJ0kyjNJ1hN2E8HZAXOZPpGOc8k9GQ1bJBKYkUceAIPsqNAhJxWnRArItLBVz0EBxILwneMdOClZAombBRDHllfZu2rDjcWbD7aBc5yEjPb3Lp8lXm82VMlbKWc+e2+fmf+3l+98tf4jd/81dxzkGAummRWqG1ipIW5/FAaBzg0CKnrdsoofIWT8D5Fudt/LwuqUjrjDv37rPzeIeLl69G/4SUDIoBxhgeP3nItcsX+NhHPskXfuO3+NCHXmZzY4uvf+3LXHvuOsE5Nje3SdP0bKio6orf/Ne/wc7TG/zsz/4szhqe7D1lMpkyGo0p8uLMr/Hd5vfyYoSuVf3Xfu1X+OXP/jKPHz9lsVwRgNFknZ/66T/H1esv8PXX34h+leBw3iGVjINcAJAgNEFIlITL188xubCOlQ5JTOIKgELgTFewKCTT6ZTRIONExnFgb3efk3feY2tzk2KQMxjklGWDsYaRzmlWJcq0hKYlkYIXr63z9MERpRHUQbAyLUEEkkSjUkljDKaKEjoELBdLHJ6N7fP4NjA9NyVJUrw7DTqwBFVzdDRjdrTH4f5TDvd3OTk5plytaNsm9mYkmiTLSYuCRA4YJSlIyXA6JE1qfFciqBRdyEDAB7ASvBKkeYpSCluXVIc73/XXuaenp6enp6fnu82zDxpKxk4KIZDEPgOtEnSiUUrjVWxiFlKiRECp2KKdJBpCQCeSREuCNxAko1GBlEB4v0PC+4CSXRN1CGeGXheg7AaQEAJeCKrucyrb0roYZTqSmlGquaZHvDfWvP34EYkSTKZTQgDnGvb2HvHk6R2yInnfmCshiBjXK4WgcQEhY6KUThLSNMUYQ5okKKVwxkWfR+sBGU3JMkpvLm5vUa0WWGN54423uPr8y93XdijrqOqSn/nxz3D9+iV+5OCHOT7a5+Gjh7SNoW0aDk7m/Nqv/zo/81M/GQ/5Atq2Zbls2H16wONHj2jbhiRNmUynCCFomposy79nw8Y3E0JgsZjz2c9+lv/hb/1NDg+O8AGEUGSDEdPNc9y4eZO1tS22NnY42NnBexe7Qjo5FA6E8wRvKKYpz3/qOlV9AkmgrktSNYyFfVLEsj/5vuH98OCA27dvsz6ZMhyN2d8/YTDMyPKM5XKJkorZbMFyMacYZCRZRrWMDdzee6yzCOlwLg7KiRAkiSZNMwIB41009Pson6qrimKQkaU5+092kIua1WLJ0d5T9vaecHx0SLVaUJU1VVUikEzWJqgkoZiMyZnEPpEiJ8kLVJIgtSJNMwAK7ZFuD+EszjkSrXBGIKRAEt+g3nuE9zRtzcPX32J35xj+y//79/R17unp6enp6en5w/LMg4YQMvY/QDQBC4lEonWCkAqURyqFFIogHABKxmHDWY+QiuBaksQgZYqx7qyjwncGbR/AeY9AxMNe3BVgpeCJ8rjUd3fYBU6AFwFHYBUcb2tHYVomDkZohBBkKgVncE1DIgWZiAlDv/q5z9Ga+HVCgNYYmqZBpTnWBYRMUCqQiQRHoKoapsMhs0XFD//Ij0LTsKr3EDKhqhqsNRhvWRwe8AMffJlHITaax40NWOfihsQ5EKC0ZDKdMp1O+cCHPsDTnV3+7i/9Xd67/S7D0Rqz1ZwvfOE3uHrlOTY213n05DFBGvYPnnLr9jsUecFzz93gZH6MVgmDIro0vjeDxvsbDe89t2/f4m/97b/FF7/4RQ4Pjtg+f5G6aVksS3Sao5RmY32K94GnT3eREoQMKKli7wpxQDXOI02LaVqs0iyWAWM9hycHbF+eEGSGVBJrWw6e7lBXFWkisNYzHg5YrpasqpLj4xOSbANjWhKtCQicjddNVRmsddGg7xzBB6wVZHlC4hKCtUgbcEHQtAZPQCcpUmmc9AQJrW348m9/nrpsONjbw3vLaDhk5+ETjLPkg5zLN57jcjbk6cE+As35K+cwzuM9BKWRSpEkCiV1LHtMNBiLcZYkNLjaICXIQPQgeQjeEwggAlJLbAh4G2gTkE3fo9HT09PT09Pz/c8zDxpKaYSMB2cP8Y6rAq1lTHOSAp0kxGKD+EtIjZCxCC0EaK3DhYCgRcoEQYwmDSFKpLqAVnwISCFwzkfDuJQMt9bBRc+FEAHrPPP5EqQkiEB6cYvMlTS15+HuIaaF4aigyNf5oQ9e5SPPZSxKz5feuMU7TyqkArMfjcVSKLK04OrzrxDI4h10GTBtQ5rlhCA5OXjE7cePebC3y/PXb6B1ymd+8qdJBxYpY1u3s+4sEleKKAs7xXtH8BKJYD6b42zLZDIhz3N+7Md/jMPjAz7/hS9w784trl17gVu3H3Hr1gPu3r3F090nCCHJEs1bb77Of/gf/kdI9YCNzXUGg4I00axN10nT7/5W4/RVqeuaz3/h83z2s5/lrTffYjQac/W5FxiOBiyXNcuyQUiJzlKWyyWzkzm7Tx/hjMN6iQ1RXmadQwRDWS6o65qT4yOKdIt8kHG0s0NVN7hzDSpztHVNuVzw5OFD6nJG01Q468mLAeVqSQiBVVmzvrWG85AlGXXTsCorlEqZHc1wxpMnCWhBU1oeP13hvGfZtKhEo4Y5SZLjA0w3JqRBUr79NqPNCUlR4J3j1utvIgWUVcPFG1c4t3mOLMuZzRZkoyEbly4zTDPkcIxDUIwHFCEQvMOfxg4Tr6flfEXTVAjnMFXFIBeMtcUHTwjRJ+K87xpoBCiJzhKMd/ggqVNBcn76XXt9e3p6enp6enq+VzzzoPELv/AX+c3f/NcIEf0aWitUIjufhmCQZ6SpPmu3lgKkECgpcfFGNtZ5qsYju7QfXXxT6XR432AMUSJ/mrqUFQkvv/ICztS0rcUHR7mqWCxWcfshYTLKuUyA1CKOAw+9pakNs/kJ6uV1rqyv4yZDFu0uX3p7BxckbdtGw3GIX6s1DciAFhrXeqxtqUxLYx2r+YKjwxWf+OgHWBwf8vDJPh/+8MsMR+dJk5REa7ROODo8jOVxnfcjeH/2y1kL3jMYDnFGo7XGGMNyteTP/JlP8+67t9jePsdP/MSfIU0zRoOc927d5v/9X/2/KMsVsrsjXpUV2cWU3ac77O3vs7W5jdaKT3z8k5w/f+kPfVG8/xpEqdzOzg6f+5V/zpe+9GXu3r3L+fMX+bEf/wmePD2krpc05hjvIdGSjY01/vnnPsedd29RLZexvDHA22+9y907d3E+DmLL5SGXL12lWS0RraZdLFBSkQjJG6+9hipyvPd465jPTtBpQlnVeOvwIsG6GH2b6AHlvObe6hHWtLRNS/Ca04HVhUDrQvQITQbk4xEOQSIl4/GQEByXLz/H7u4hW9tTZo/2kUim0wmTzQ28dRhjcG2DlJrVbMndkzlNUzMcT9i4eJ5sOCCEKI9qTEu5WsZkq7LE2BitbI2lrVfYNiZbZWlC2xjk9pC1rQRv4rUiRSdRVArwhNbRzhaka2OMgKZtsMPvrTyup6enp6enp+e7wTMPGm+//XWEiIV078s6JDrVFFlyJg+BgDcO31okGkRsc9YqZX06xXtPtapoant25z0ebLvNRvBdGpVEaxmbwANYZ6KsJDiapqZpy7OhJqqpJF7nZBhSHMprpFDUdcvJSc2bv72DqzT2UoY14FVMbZKdDMx7x+1338QYDwGSJEGoaOAwFopE4q0lzcZcu3yJ565epS6PCe2cmzeuUwzWufPeexyfzAkugBDoREdJWHe49kFgnSWRGpnEzynLEu883nvG4xFf/uK/YjBMGU23uXrlKi+/8iI//dOf4V984bdIkxTpDUeHBzx343nqxuKMpywrlJQ8fPiQ9fVNjLEMBgOklN/xBWGt5b07d7h48TxFXvD1r3+Nz372swTgyePHrK2tcfOVD3Dl+ovsH87xPmNr+zwHB3O8q3nj669xfHjIaDBCaoFwEidhtVixmFmsiTKhoydP8fOSaj6jrJaMxwVXrl+nbSc82X/K9UsbHB7PaELDhcvnSAdjnj56zHI2B29ZG2kunlvnzoN96sqQaE2WFWiZYpqAkIHgHGJQMFQDnv/gTYRKuHnzJm+89g2y6RiUYL5/iNSKvMhZX1+n3p/jCVR1jV4tSZOUXCvaVU2WSPYPjvA+Jo2trW2yODiinB2Bs5SLkrIso/zPB6yxcdhxllRrrIvBAZlOaKsGEQJZkiFkTDTTWhKkACMQrozxu7lCjQpK6wCJ0ip6gnp6enp6enp6vs955kHjvdt3mU43IYizjoPTjYUMAUVASqiOVrSrEmk1SigUsZNi+9x5Pv6xT7Fc1Tx4/IS9pwe8/eZtAr4ruAvdgEEnIfFnQwfEu+s60UgZmw+MqTmVaDnnotxEiqh/l4DzMY5XBEwbKJ5/keVhy7J6hE4VzgmctfGuufe0TUuSRm9GohVJqsF7ts9f4sLV53nnzTexXvPPvvA7DLTnJ376x9FqzNHMooSiLCvyPOenfvpneHDnDo8fP+Z4No/DUAjs7++iuujU4N3ZEOBDwAfHarVkfTLmz/7Ej/Nrv/5rvPiBj3Dj+nW+9OqXaZua0XiEtY52WTIax26P5XxJXTeMx4E0TZnP5yxXURo0GAy+44shBHjnnXf4q3/tr/LpT/8Qden4R//4s6xWJefObbG+vkk+zBmtTQhSI5RkUVY8enAHIR3e1CgECsF8dkzbtJSrkrap8T6+XMHHMr6vfPUrfOj5K2yuTbl8ZYvBsODGlS3evnvI5Nwa87bmQ688x4MnhwStiUuxTs6WKrSUHOw+Resc5wWhCxWIaU1xcxSEYzAsSIMjK3KCVGxsbaKERicJ5y5uc/h4l6OjI5yNPhIpY9qUcw5rLZPJlEk+4sGTh7RpglaK0dqENM0ospzjvQNCsCgJ5arEOstoPMIFhwiO9eka4/GYp0+fkuUp165fR3rPu+++y3BYsLV1Ee8M9x+8h8Dz8oc/SJZpZk/fI8g5uEBb1zgESIVEYUz7Hb+2PT09PT09PT1/1DzzoOE9nZcCgpQEIfDOM50OKNKUelmibItftowvbnHyaE6wniATjDGAx/kWIR1JIrl48Ty337kXD4hdWR8i4AldoZ7DexlN6C5QLspoBva+OxRmQEwlst4jRUA6S2hb8A4bks5QrtBofup//j8lGW/z3/23/yXBfoXGGOqqBhQuBOZlzWCQkyQKraBtKgIC0ZmZvQugC/YePUYLw2D7EijJanZC6yVKaXSSsraxwcbGGhcuXmR3b5+nR7NOLuPRaUJrTUzm0jJG9+oEKRXHR8dYU7N1ZYu/+Bd+jpMqpjR95id+kulkwuvv3ML5OHWdHB9x6923KJclq3LFaDwgTRO2z59nd3+Pa1evfYsMDfg3tkeAiKb+f5M333ibW+/e4oMfvImW6yTJkHykGRQD8IJA6Ez+MJlMmC+W3Hv4kLWNCZBycnDAMEu4cvkq5zY3ePRoh1u33mUwKMiLnOBjyV+1nHPx0jZ5nlEMcmxraRqBRTBf1SSqYGdRI/ICSdw4ia7HRSnJ3sEcnGcw2QSRU5YrVitPwCOI8qM0TdnaOsfyYJ+jgyM2trfJi5w0z8i04t7du7SmZciItm0YDAYxthkRJVOtwQRPXhRoESN3dRpjnpM0Jc1zdFJx7doV2qrl7nt3uXztEpevXuG9W3coV3NuvvQiSqccHByRaMn2+W3aqkYJDUIzWdvEGY8x90E6VDJEKoVWKYnScXshBCpLCKIbTP2/03u9p6enp6enp+ePlO+o7S2ctWjEISP4gE4081lJPkypnpywdnGDWWsoSw8+SqCc9+zsxK6FJE2pqgaCwuNAnEqm6GRG75+iTjcnEA95xrQkWRZThAhxCOmOy9I1qFDTrlZIHzcVqZRkWUbdlvyT/+r/QUjWacwBqZbULSRK44LAeU/TtGSJjib0IGlbT9Mabt+6xe07d8l0TlmWNGUNyrE1XmNneYIYDvDfVDjonENqSVYUbF+8wOOD4xhtG7N8OTk5wnvPZDTGWoPozNtZlrK5eY75ouLtd7/O+sXrlFVFkkheeOF5nr96jbffvR3v2DtDmij2qxUhOE6Oj2nqlstXrjEYKcqyZD6fs7m+QZqmlFUZBwXAOktrWg72D7h29RrW2c4XIM9+zlrF1C5kYLQ2pF2Y2B3iPHk24MG9h8znS7yFr3zla5RVxdCOSbOEjXOb/Nmf+DOotGCYZ4gguXPnFjqRFEUciB7cf4RzFq0kSoizxKVl2SBDYDQak02HmCBjMzgSb2LE8mAwwDlwDhKtmc1nPP/iBR7eWzCZTOPhXwm0jgPDcDhgdSi5fPkyL7z0ClVVEYJnbW3Kqm1QSiGl5MaN65zbPsd9+W70SJxehN6RCc8o1+jRhEvr5xmPxyilMK1hMSu5dOkyxwfHJEnKdH2N6doaRTGgrpaMJ9M4IEmFVpJBluNbi7c+hgeEKEMUSgIeqSSuewsEJVB5gR5kmDp+r0h5Fo3b09PT09PT0/P9zHfUoyEQCBGlUoLYO3EqbzImoEYDXKpZ7ZUEJ9BacOq9ePRolwuXHM5almXNZDSJvRg+QNcAHjcbAiECIThCkIBCCkma5STJAESI0pEmSo7opFeqbUkygStGWI6x3pHgETKghSUNK45vzRm+QLzjrQROCISI7dNaKoSSGOdYVRVpkhOCwViPxhM0aKlBKIosYWs65d07d5ktZ11yVZR8IQUuhLNBKBZABLQSKB1Tux48usO58x+hyHO0Upw7d4733n2XPM/Z3Zuxe7AiG9e41mBaw3gy5i/9pf8Nv/T3/h6/8S9+haqq2NvfY3f3Cec2t5nNlwxHE8aTKSfzOW+9/Xl+4ed/Aa013nv+6T/7x/zFv/DvobTmH/yDv8/R8S4PHz7if/ef/Kf8jb/xN7h+4xpr03UODg55/PgJk+mEd955l9F4nd29HUzrGCcpNz/0EfCCNEvJ8gyQTEcb7IhHVHWNQDIapOSDglVZsVrNqJsVpm1ZLQPWxNSp+WpBocGYhkRrbOWwTYwAXl9fI3n6mFQI8AJjovxqOBzTrmqMsaRphs6S6P1JJEmmGI/GDMcFWZ6TpZoiLxgMB9i6RQfJ9sULXLx8EddGqdvm+S2ODmcMBwN+6Ic/Tp5mNNWS4NrYexKiWGs1m3FvtaBsAul6RtsYmrRkMp1SrixIUFlGmuXRwC0FQUjyYkgxXIsboM5UHyOiA94RCy6lQAtB243wUojoK0ERlIyenqqJvpYixVgLWtG2vUejp6enp6en5/ufZx40XBdRe2oE9z6wmC8IBOra4YxDSkFzUrNctAgJzoMKsFzVvPa1t5FveJqqIc8TsrQgz6Zk6YhwtsU4lfrIrn3aY0yDEILhcIRzDU3TYI3vkqxit0fwDisU1jYgUxbO0waJL2vmJws8a7zwwTXmWyl3qtusmpKT2QJnDFJB8ArjHMtVDUCaJkgtyERKCAFn46EzzTKKwQCdBow1/OgnP8nRyRF//7XXSZSkqSqMMeRp2nWBvJ/cJLqYXq0U33jzqxzP32A4HPEDH/oJfLjKxz75Cb7w+V9nOF7n45/4FOPJGkWRR/OvaRkMBvz4p3+YN77+Ko8ePeLe/QcIEZhO1hBCYkyD95ZBkXP/3j2++tqrFHlBkma8995t/vVv/xb7h/t86ctf5v69u3jn+Mv/2f+R5aLht3/7d1EqFiZWVYNSCQdv3GZ2coJOM7Y2LjMsRqxvbaH1OCZoBYdtawajEc+/8DI7D2/Rtg3HC/idf/3bzGczLl48z8sf+gAf/sjH+Cf/+LNY18ZBociQHg6eLBkkFT4ohBRYoWkDHO7uMQ6BGy/cpC1LjpY1wcXkrlNPjVIa5wze+fj8reHc9gYvvfRCZ6xOyNKMW2/fxgePTCRKC3CxuDHLFNevX+Vw9zH7e49oqiXz42OOjp/GhneVEPCUVcPCOlKRMCmGHB0tWC6PuXzlCkcHJ0gpohdIK5ROQETDNjIWBPrgEYBSstucNTHFyjmC91RVhXNgrEEJT13XSJHGdvRAlHIlGmTc/jjxrVu/np6enp6enp7vV569R0NqfDjtvYhld3mRY4yhyBVWOXTiSVLNYqFp29h3IZUizTTjyYB8kHH/7hNEEw+pSTIGBSAJp3KREPDOIaUiEDg8PGSyNkHIeIc7BDA24GRsDQ8+0FpPi2B2Msc6x74PGA+jfIDaVLxxf5dfeVVx8fIV3rx7RJ4XqGWFDwJnTKf9h8EwP/MtOOdwJm5tEAGlFBevXmF/vuLx3bf57//6f8vVG1e5dP4CBSUvPF9w555gpBoK7fGFxM6jEV6I2KMQnMP7wKoqqeoFTb3L3/ulN1mVhk985Ec5PDRsrV/g9t173HzpBvkwxZgG0zaMx2NGw5w81YTgMDZ6Xh7cu0+WZnhn2Hv6lDffuc39+495642/TsDjhWS5WPDqq1+JvSQ6pTEOby117RAElEyjfEqBkoKtzXXKpkGpgmK0TiZzjPEslyu8UBS5JtUCKQNtWzEeDHlkYHNzBAF2Hj8hSTQvvXSDz3zm01SV5dd+9VfwtY2Gfq2QAvaO5yQmFuMprdk/njEejSikRgYIzjOeTlkeHcZGeinxxmKlBrquFe84OjnGWosNnvlywfHxMVILhqOcsl5gfcvtt95kNTsgUZqDoye8+jv/kmFWsJgfcevtJUoGnPWUVYkLFusNKdlZelieZqRpynA4xJgKgUd2WwrvLUhBmkgI4LyhKVeYtsQ2Nd4YslThcBwd7lCXNZO1HKUkd27fRirYmIxItOLw6R6DYgx4ggCVaESR4ANoEQ3hSoRv8y7t6enp6enp6fn+4dmlUzJKNtrWMxzmKKUoBhluWZOkijzLkapGKsV0WlAuV53uP9CuVpTHgXx4nulavCPuTbzTK0IcLgLvJ0/5EKC7a719bpuqreI3qzU6y2hXJUEKvIDTfnGHpFUJTUiYqxbvHB7BcDwiSS/wK68d8fyy4e67DSEovI9VdEJGeZdWijRJqeqKLMsQCExTUtcNznmydMjh4T6taXiyd8Q//Ae/Bt7yP/65H0Ut99idHSGDZt085dJAshonPHKGt/IEQvRGCKU6iZigrloSDU+fPqWpW1790m/xmZ/897j+3Iv88j/7Rdr6hPH6Nod7h7z80gtnPo+2aSnyIYPxkKeP7+Byx8nJMcjA669/g9t37rGYnaBkoG0ty6qJX7s7qLd1gw8xwjcET5YlONcyGIxYLCsODpfs7B6yfek82+evkOebzE+O2X26S7v4bdbXN9HDIXv7OxzuPeXOvUesVitGwwEXLtzg/PkLfP1rX+PytWus6oa33n0HZwNBQFmWWGtx1jKZjpFKkqkElSZoHaOQFTLGKBM7Iw4PDxBdY3z0wFiEd1hjEcSm94d37zAerrM2XePu4yc0dUkgkOyBcAFrDO+9e5s7t98hU5qttS2U8RwdPaWtatoioCR4FyNpcTH5QCBQKiaFaa3RSrGxsc5qKajKFVpLxoOMo0d3IHiuXZwgQsXBw9usDTxb4zXa2WMIlitbCltb2sOH5HiurQtCus68dEgJ060pIsm48dIrPLn/gPneUUxXW5aIRiKnIzzxvVLkxXf1XwI9PT09PT09Pd8LnnnQSNMCCLHgDhEbo4MjGIc1gWKc4AIEbxmPM/aTBc56RAC7aJCbQxCBJJE45zAnS8JoSggx7pQg8D7KQoIPOFxn1AUIVKYhVRKtM4wx+M6tKxDgLc45Kj1g3pbxYOshOIsSOePNdcrVirqF2arsvod4+FVSoaREyJSmaREIqqrGWUfbWoKPhnRjLda0KKkYDtfJ0gG+MZgKfGtonEWIhFu3HzIvWqYDzVaRcn5zE6QCkaE1aJXgbEtTA5ng8uUbrCdDZlXgd3/7S0xGa6xNR4zGCaN8jb2DY9r2DQ6PjpnPjmiswzmHUIHWGlpraZ1nUbZ84423onwsOBaLmrpp8V1xnXUxEjVPC7SIhuLZyQlZKrHGkgbPuQtXGK9/iMf3X0ckI+q2YnZ8B1sbHj25R4XhJFEcWcOqLUHCqEhQXpFlCSHE4SbLs1gs6AKzZcXO4z2UUl0vStxCZWkGDWRCo4RGSBW7U6SMRuum5Whvn6pcMRwO2J6uI4UEFa8/IQRaapSK0cYuWOZNSZCeyXhIWTasDQuWe0eYxnLu0uU4cLma+w8eUAwygrdYKymXEp0AQeIt3bWlcN4RfCxzlN1AvJ451poF7mDFIFvH1i0sj2i8gy4RKkiJ8h7nLY31WO/wwdGualaLirVCMRqk1GwilKapWsrEoIzDSQgudAOpJRmk5OtDrFQY6/AiYL6Lze89PT09PT09Pd8rvoPUqUCSaNI0o9s/4LxHa43SiiQVYONBUysYjxJW8xZR1owmE2SasKxXODyZVjDMwcco29PHC6GTKnVJTN57nI9DQdO0yDRFyjiI2K6PIXo0AkJoXJExnzdY10m8gNVqycndu/im5eTkBCEEbWuw1naPYxFSdrIwEWVOAqQ8LSaMB8y2qTk+2mM2q1nf2CJJ18BbdJKzmQ5YyzO+djAnLwqKYYaShuCiV4A0IVUKKT1JkhCEIknH5IXk+HiBMIKTeUXdSBalQWvNfD6nqWum0wnWGmazinfeucds6ShXxyi96uRmmrZtOVrsszGpGBQZi8WKYjBioNM45ITAarkgz3IGRY5UAtAcHh2zqltC3fJ4/z6bLxcsasPh4Ql6nFA9fUhmA4EBSkhEG9vNpVI4FzhZrrh0cZs0tSglGI1HbKyv8+RJik40bWt5unuER6KTNP4sAIIgSVK8MkjUmVEaKXCASDQhBHZ3n0IIZFlK8k0dKk1dk+iY9CVlTGlSMsHYQFUbWunQOqd2EuM0wTvmsxlrG+fJdMqT+/dJUsVoVGAXbZRHaQUoAlGep6SEzg8hAKni10uwCFeyc1KRnhtijAMNJwuLDx6tFEIHdPAkIg4ezgogdnRoFb06PgRa22KcIAB1UzOdjNnc3OTp7fvRexIAHzB1i0+zKCf0Ln7Nnp6enp6enp7vc74D6VR0L3R5SrGsT0lUKjBVG5N0hEBpgWlaQlMiG4/KhzAa4QSYdomQAiFy8mlCcHRt3/794r5Oew90Xg2J64YKY1vSKM9HxyNhHFacI01ThEqxITYp+O6OcmtbTOsQUnO8v0eaJISgkcQ4VyHicwldkzPEJm/vfBe320m6vMHXM+YnLaN8ROOyzhMy4dHK8nQV8EgWIWeSF0w3B+wdzHA6QQuJJxC8wFmHo+X1r+1y6fKQxcpR5XCwN+Pi+TX+yS//XZLM4QDjakb5gGq1oqqWHM/m2JDi0ZwcHpClmuF4zOHhCbu7u4yKFC3pShUFHk+iJd56hoMRSiXYdoX3DcX4PNZ7yllFU7doEo5vvYuTQ2yw7D56SOZqhM5QIiHNUkJbIqVikCaUTiOlJssyXN0yzBOyLCUET7laIZRka3PCxa0LHB6coKUiSbLO0+ARSsYBI15coDRCBvDE/goUrnUI4bGm5fj4iDTRsX0+WIQAG2I6WZIo8kHBhWuXaf1lLILJaMj6cMTvfO4LcTtVLRFhxMc++SM8fuchjTVcXB8zm+9jvUOgoxTPB4QX0a8iNF603UbIIaTEeIFWOaNxhtcJw3FGojVDl+KMRUlJUeQIHFq4M8+P9466zMnyOg7AzrMyK1alJYRAFmJT+ez4hMYF6BLLnPO4qgWhCFLilcTa3gze09PT0/N9jhAxjbG7UYePXtV4YOz508IzDxpNUyOLAk/0VUgEUkiEVOTDBGTA22guznJNMsyoGotNNC54/Jl/NcqusjSlqboPhtOGjtAd+uNBygVQQaM6mYzwNrZLdxXiodt8gGA4HNIaT5qkTCdTDg8O8D7gnENrzWg0wdgW07Z477HO4n00nZ+W2wUvsNZijCFJEk5rybWOd9jzNKUYpNS147g65qUXn2dWztjIpyRphuUud44sj2YLZl99yMnxjA9+8sepmhrZDVIXr1xiNLwS73LLEi8cXjrqpmY8zHny5Bi7PGFlYNVYDu/e4UM3r3F8vOTjH/sAX/nyv+Lo8Al10xLCkL29R1jfsP2hG7C0CJmwubVJ3bQI70l0gvMWgiCVEusliBTR1kyyFL89ZaE85r0DRqMBByeGrc1zPN29j08l22sXqZaOREmElIQuxlfKeBCOd+Yt1A1lWTHMGxBQVw0hxAjXNJUxZjjRcVNEHDSQXaO3IEYm+4AI4K2jbGPCmJRE2VtVY9oWJSQkCcbas0b56KnwZFlGay0iCGSaoLMEYw0mOATRdD6ZTJAoVlXZWTFCN5T6+PziJUqSJHHLchrpTPzcg9JTL1NUIhjKjGxjg93dp1TWkOohZVmjWoPAkyQqDuch4L2gLAUnJw3eB5SSjMYFT/b2UErw3I1NJlsXuffwIUE4RPeeUHmKHhccL0qU1kidkqbfUf1NT09PT0/PHx1CgJCIRCPHI9hYw49zZGXgyS5+uSJY+8f9Xfb8EfHMJ5akk7OEEPP+EbEhOZbmddfV6Z37ELceXmq0kIgQcCFuCTQJpbW4ICAkUV/vfJx0u06O0/jO0xI5ISR5luCMw3uPThSj0Yj7Iv55CII8GzAYKE5O5qxK120PfOfFMCDCWdTs6QFVShUn7m6DkmiFEIEkUWRpRlk11HUdtxtCYYLjZF4zO65Q6RoIybysyLVHu7gdGU0nSCV5fLBAjzZBJZTlgltvfpX1tXV+9uf+RyQ64eKVBbOT2xhXkQSBNQFnGrROmM0qNi4+z9pkwm++9lX2HtzBo3ny5CEPH9zHufhzaFtDvVqxXhTIdMRkO/ZyOOdo6grvA5sbWyxdSVMvmR0dkdCSSWhYQRA0TUMQDi0hVYoQWhazGavVEj3cxIYhVtYgYs/F0WJBtjZFKh1lQEgQktY6lssVzWrZbaIS5vOK+6t7PHj4mKBkHOKcx3hYrkoGHoTqZFPQ9ZFInA+YbrskpEBIFa8JKTHOR3kdIW7CusK7pqmiQVwqRPD44BAyIIMH7zHWY50HXGzW1tGYL6SMnTAhIL0gCAHC411Ap3SSKoEgmtEXlWVROtJcMdApDgky+lzyYkhoPMY7Ep2yqmraumJtuoaQirqZcXQyx1lPkqasbWwDgrpuuX/vLmvntsmcZjDdIHAPQsBUFdZaRpMRQie0IrC5vfmHfuP39PT09PR8TzgdMiYTmh+4xuOfTFEfmlPurvHi3xqSvH4HN1/2m40/JTzzoKGzDFxAIc4kTkpJkiSJnQDBAYJYoHc6eESxFQ6kj8V7CSlJKkh1Smtjk7bt7k5778+2CKfDgHM29ja4rtRPSBINOvUIEYcMKSRZkjIYjRkODtB61ZnLuy0FlrJconUapVjOnXVbCGKikfOOPC+ApOvwcF1fg8RahxCxCbpuK1wI5ImkMQ121TA/fMxwPMVah/OQ5hkohdSxn6NtGoQzvPv229y5fZsPf+QjjNZGtMZTVw06s5yczNjbywgiIzjBhXPn2VzfYFAMmC9mJEnObGlRakjwhrJaoLptiPIONfK0puH8tSvMF3O8N2yfv0RWpAQpeLizQ1EMUMUGxtY83HmISHJefvkTvPmlV2nqkvF0CsD62jrL8iB2pDQOgsQYG6NvN87BsKCtZu9vNTqvS1XWDCYjgvXxuVtL7cEjkEKTFwW0lkAs/QvlMl5biSbJC4J3hKalyHOcSrtBIiZMBQJK62gUt4Ysy/BN7D1JkpQ0zSkGI9qmJSiBtQ7r48CrkLFR3XoWyxXWGSZrY4RV+O6a9j5gncVZF6/fzrdD8EiVxMEZQdO0rMqaoFKMcTx48A6ZdigB4FFa05qASlNa59g/3kWnOUmadEM4Z+WUpx0cUkqkUCRJSq4yRJqD0nghQSiEjvG2OIfF0jTfrbd/z59YTiULgPjQTQ4/voauA9N/+BpiOoHxEH/vYSxM/QP+z77+hR+i2lRs/dPb+ONjbv0Xn+IHf+hdTv73lwivfuOP4tn09PT8cfLNASThD45XFzL++0fkGYurKWuf2uevvPSP+Tv7P8TtL3yQ9dsFYrk6qzXo+ZPNsxf2mRaBigVksivKw+K9IYTurnSQxKkiSsylVLHhmxhDG6xF65TgQSaK4KMhG2JvhfAB593ZhiGEgCd+TClFbSBNFEokWGdiyo+LA44OguAdWRZYLfdBGJRKUSqhrmw0gBuL0grnLSG4znMSxyNBNIkT6zloW4P3FudsLFtzDolCehmfm5C0psXWDcFYzm9f5J1330UJCNZgjUELQblYcHx8xM7jp2xsbjEYTNjYOseFy+d5cK8kMKdsWlbVklvvtVy7+QGu3fggwSp+/V9+gaf7h3zyEx+jrlvkcMrLH/44zlre+sbr3Lv9JsZ6is0J+8czgrMIpTk42CNJNNONTXSacfvO67Rtw6d+9Ee4cPEy77z+NXa//hWE0Kj8Fscnc2QwcRPg4+tUJAkNHmQgeE/TlCRtS+1cbJLrYohdZ+YH0RnqY2qUAFoXcBZUNoBljRSAiHGuidbxtQ0e7T1KJwSvaJsaax1ZltG4QHCW4C0OwXg0ZnP7Mns7u6xWR9E3JFU3qEKWD3BlhcET2sB8VWK8JyBJs4ymbHjz62/RNhWqlcxnS5wPSA86ydD5gFmYAdFTkag0tnTjESLKwHyIqVCmia3trrGoXDPKYkKa1pp8PMLZJefWzrFaVswXC4pBTlacelRaAu5s4FBSkKTpWbM4IsTCPhGfXxDQti1BCBpnsKJvBu/59vgf+xh3/pLgf/3R3wLgU4Nf5GcKR+lb/tpfeYmxvMeaKrndnOevfuXHufm/+Mq3faz9/+2P8sv/5/+CK3rEjU//x3zg/wJ/7S/8N/xM4Xj+P/lfcfN/qfq7kj09f5IRAqEThJKETqYcXPee/zZDR/AB0d04Fh4ao/la+RzvzbaQJjzTsNLzJ4dnHjSEkF3b9amfwSOQ8eMiSk/eb/YOnSwlypJ8iBdeYxqaumY1a5FJwoXzF8/M1rLT+4fTjYb3BCFQ3UNqFVN7XPCUVUvWeQZ8AGsdSEVAkqVDnDPfJC+qYlGdVKDiENDZOuIBudN9Oe8xxqG7DY3t2sBPNy1SSryHqNoHZwPzWcUAwbmtTYIAY1qqsmIVAk3VIoWkLlfce+82dx8+YrZccenCFX7rC7+FF4atzQFeS5482uHxw4fsCMXapWusZQVP9g74yQ/+AF/6+tf4V1/8Ii/cuMGjR0/Y2DqHD4JV3VA3LePxkAdHu6gkJ/jA7t4OGxtb3L1zlzt3/xFBCCaTKXXr+dprr3Hvzn1m+7ukSU5eFORZxmQypq1WXdoWeKmQOsG3NTaf4XyL8w0meDKZYJ0FBUqrM/+Kc5aqqjjpNl6EgNQJzlisB6kEUihSpUl0RhAxLQolsMbRVCuUjPG3VVmRKk2wELxDKYG1nsFwwsbWBVaLhuXiiLZtIESPjTEGY9vY0yGiSXy2WJ6lWVlrmB2dEJyl9g0jPaF2HudBWE9Ttxwvl9R13ZVGxuHChW4zJ+TZe0CquAmRISAJNKsleXCMszVqkXXXnmAxX3Lu/Db4uH2rlrMYzRxcjM4lIKXA2rgdLI+PCMmSbLAixNYMTGPAOUSRIaQgdBG3PT3fDv3VW/z7Hyz5zzdvfcvHBzLlP12/9/4HRnN+4+pL/H5XUzoP/MvqOcayIpk01B9/jr998CNcOv+r/J9+8HP8o/w5fFl+T55HT0/P94B/Mx799zv0SxUlUFkW/7e1BNfdpHW/zw0G7/CNJ+wfsPV5QfNom38+/QzFiSV75z7u+OT3//s9f6L4jlKnTtdccRPgY0KPUEgV74J772MkaPAE7+LDi+gZ8E6Bh2r/hKoBnyZsn/PA6YPGpCjvXXegi0QZlozFeiLgAzStYZxNCUJwej89HQ7Q+YD19W3G4w0ODuZRdhViO7kPAWeixMuH+DinJXaJ1vGQKwJVXcYDprVnshbnHF6ADb7btni8c9HTYcGFgPVxmKlbw+HhEW3bohPJyeyYx4/uUVUlRwGeu3aDJM1wwZAPcqqqxNk45Agcb7z2OlkxJC2GvHLjeVIpePRkh/XhEJ1IRNMwGI2wTYWQGql0V2Lnma6tA4GN9Q3cNcliMSPNC9bWp+ik4PDokGq1QCeKmy++glQSGwRXrlzk4b178fUMgaptWZQl1ja0RUW1KrG2RaYa7y2JCwil8dZjjHnfvG8dy+USKR3L5YLhYIBOCpxscN5hXY2QkiTLKJdlp6qLh3VnTEz5Ch6tJca2iJAAcUgQAfZ2HnHvzi3apkUp0DrFmBaCoDWW23feJZ+sgVR466mt62RwFmkVtnIY02CDoy4r0rzAOY9wgsFkTBA1i6PjM9met/5sEPXdYCq6gbJIMnzoPB3eE+oGp5aQ5JTLkrpcEERAK836+jpJoinnnuEox7n07LnKbsU8GI8IQmBESqgbgoiBC8PxiDBIKdsmmsOlOvOv9PT8XvjVit1m8nv+2Z5bkQnJVMbSx3ffvMJNHn/bx1r/pdf4H77wo6AkN5s97NNdHn9lg//Dc/8xy+sjhuUXvyfPoaen53tE54kUMqZtxgOd/7cHDqmQeYYoctAaXKwe4HRAEBL4Pf7eKSEQmgb7eIdk/4BESoKLUuh+C/qni2ceNMrVgiIfEm/rKwSya/E+NeR6hIxa91M9ktYxilOogAyCTKZUPuCsw4Zorj1LDophtd01G84GBIiPZazDGYvUDmsNy5MVwcWBJoQQm8LxJEnKoJgAAqU0xjQkiUSILhc3aKxzZx4OncjOfB5QUpIPBxCgBKz1nWm8u5sdwNn4xhoUEi3jsHF0NGe2bJAq6vrrpsbaFmcUaqBishIBYw1VXVKuVtx86SWWzRGr+YKLF85x9Gif1ckc01Z413K0f8A/+cW/w+DcFo8eP0K3DdsXL5Djee21LxGEYmP7POvTEefPb1OWJRsbGzgfSNKC85cKLsqLpGmOThRSadbWx5RVSZEPMU3NYn4SY31tzfM3X8QFQWnAWI/rivVAIGRCwOLylLpt0Sici/6VLi4MJVU8TJclea6oqxJ3coQICcY4nHXoJA5iy+MDtEwhOFpnMMGjcPHAHjw2jTI27yw65vVSVzVKCZaLOQHBYDCI35uIoVPOO1bLFelwxOmYmqZJHGD8+9caAfI8Y1WWMaZZBrQSPPfiy9x++zGCJwD4bqfgvI/yrSxHKcWNmy9y5dp1siLDW8ve0z28UDQhAysQOg4IzsJ0bUzTNpRlCSFQlhVpqmhaj/cyhiMkCflwwHhtwrlrV2mNxi1OEHgIAlPVeNxZt4hKE5RU34W3fs+fZH7zGy/Dc1/4lo+Z4PjM//c/QxrwH1/wH9x8DbXx+xt+fF3jHz76lo+5g0M4OGT45e/6t93T0/O9JngQ8ky2fkY0vb7/+1N8gNYQrCW0LTgX/+7vNZz8XnTbjfe/fr+R/9PGMw8aWTaId3CJspj306HiBRdC9/sOITqNOcSPeyiKAcch+iEE8e/74GPqD3Gylp185rQoz3fTszEteI8mNn7Pj+d4a+PF3klQgnNIFZ9S3KrEPzbGkOho/vb4b/JoiK43ITY3GxMLA7MsR2mFde/H6HofYreCDsgW5suKRdnwsVe2mDnJslqhVTRHB++61mmDMSZqG4mxsE/3drl65SrHJ0fUds7lq9epvUFqTes8s0VNEAEpNAfliktskKcJdVtRHh6SLkrGeYaTmjTLuHrpPEmasrG5iXcW5wW1sbQ2HtIzHaU+QnQN11KxXM0psoLtCxdZzk8g5HgfMM6hkoTjg2OOlKI2AWQgLVJmyxPSNKVxjvEgw7ZVlJg5G3tORCylQyh0kkffDoEkgXpVsVitEMBiafCuZVRoQvAoxJnJW3aN8JW30W9CTOMaDIYsF0voChXpUqaapsU5i3USFWxsDu9+zj4EXGMxbUvwHonoBmGLMS1S6/fTq1z8s/nxDGcaQvAYa0idReskDtHd4DVZG1FkQwKBg90DHBKHwnuF9wkZktFkRNPEIsgolfJ462K7vI2yu7zIYw8I0a/inCUpCoJS2Lno2s4d1ltc4/FCYJ1D5imyN9D1/AF89KUHZ7//peWE//yX/2fkB5Ib/583cPMlQim+MtzgJnfp7y329PwpIgTOlCT/1se/9XOCsd0NwPCtA8a/+fnP9DV7/rTy7IPGYERVVXhAEmKZRofvDuJAvBNLiBGhHiCWnSEhYFFC4rwhKH1WlOcREDyi69g423CcDjNKdD4A3/UcnK78JAHRubkFQQpc27IslzEa1NquhdlFqZT3UcPfmYdFLEcAYvpPlmd45zDWxK/VvcG89wghqeqGtWlBYwzLOiBEwBpP1dQYY0l0waqsqFuLtZamqVFNSTEoYsmg1gwGOctyiXUt+UCTmMCsLGmaGuscSkYDus5Fd9e8ZW08RAaPx1GvVly9/hyr1uAJJGmGlALTtkgpaI3pNpzx+/bO0/j4Z1JJ8iLHBx+N7M6itWaxmBN8TEwq0gy5sU6aacpyyXy5oFqucLahrgQheFrhMF2XhrUe4wNaBhrT0jQGIQT5yBOcx4koV5NScnR0SLVcMRjktHVNFkAJieoGTxHiBiHLNNPpCIgm6OWqBAJSaZrWEHw8qEspcTZeNUoqEq0RQUDnpTn1/5z+S04qgbcBW7ckA4kLARE8beuQUsdwAhdjkLVOEAiMadGJjhtj7zBNhXS+23bYbgABKwKtFQhjCbKiacpuwxyHbOc8xlja1iKERkhJXTfUVc1ytUAPUqSA0DTEnC6BEwJ0QpCwXMZBbZCnNKb9d3/H9/yp4L2jTXbskh//xb/My//Px7zw4IsQwtlQEbzDnfTXUU/Pn0qe5eAfAsG0hD57pOcPyTMPGqZp4oDROaL9qWSp086ILuY0dJr7096BTgkFBKw1eBc3CUq/L/8IQhBEOCtLi9uRzgkSG+I4LelDxISeuq7PPjf42JUgpGa5XFJWKyCQJrGnw/vO5yFAKUnwsTsh9oGoLlFIorVGJEns6tAJUhp8VUXju4CmrVkuLT7kBAFCBJZliW1aFHET01RVNLZby6ppkElClqbRMO/d2YFfSoUygcOvvU2zPkQLzWQ84dLF86xWS0ajUfy5EqU9Ssd2zfGli5Am5EkSB4yuXNBZQ5JojLOA7n72IbZeC0lrLGmWorWkyDLquqWuaryzKJngsLR1vJsvlWRtMmY8HFCWFee2NhgNB1hraOoGGzz5cEAhx2eJT9E701A1DdZb1p1Dhnj3Q3avW5Szqe5Of0si6Fq5o38n+JhCBTJugbxnMBzG8sFuk2tMHOLSLENJhZMORKDICpIQELWBRAMeb+MmQiA6M38crKSQjNKcJMmompqmtrz5ldcoV0fdBRkHtdP0s7Pr1xje+sYbuKbl/OWL5MUobueEIqiYwFU3Da0zgMM7ARJc23aGckGeD+KGRUqUkGSDIVevXeLSlReZPTnCV1UsuPQBJ8G1BhEU4/G488LE7UtPz+/H5f/oNv/Bv/+XeenX38Pu7/9xfzs9PT09PX9KefaK4eC/5fAVPJjWI7U/kzlJIXAugAsEK/DeALo7ZEPQmtC0gOyM36fxZ9HD4Z1Dqi6pqjtU0enqIZq3rWk4f36bnZ2ncXES3efM9o8ZTyYsl3PqtoZAbIU2DcEHkjSJ6UWto20M0ktc8AgRH79tDW7uo/xGxMcNnVzGdevC4CDY6OWIsjDFqmwYFEOmkwk7B4fs7OwwGhY417JaLQkYUhUo8oIsS9FSMMySzvBsKLY3WZuMuWo8i8WCLEu4fOkmR8fHXfFdvBsOmjwfsLV9Id59Dx5rTddk3pBoTVlVuCBBuGi6cp66KsmylOAF1aqkKHKUklESlAaaJmB9wPkuws67LmLWoJOUC+e3YyxtJzMzbUvbxnQnaxu89+R5jlSq2yoonHUoJOc3NggB9irDMMtZv3oV6yx1U2OaGjtb4b3HtAYpJL6LyG2alnw0wIfoa/Fdz4X3rutTEd2WKUqMpNIIPPffvo1SKWmWkecZQgjaaoUuUjY21glBIoyhXZQ0yxV1VaFHA6RQnOzsI7vmeYA0SRFSYpvYJC+yjBAC8+MZ85MjlssZF69exeE7n4c6k3VpqRjkefQxeY8TFq0VLknAR3N6ohOcdaRZwpUbN5huXuXp27eRlcCJOLQQYseIHGQ4KbHOxdhknXyX3v49f1IJpmX8d36nl0X19PT09Pyx8uyFfUlCu1oRkgwI2OApS4OUBglgPKE1NIsKby2NCXgR8HiEsHgnqGdV7NBQiiA6L0ZnKArB4wmIIHhfriRIdYKX0V9gib6PLNWMRqd3k6Pspm0a2rphsVh02nuwJrZAexvY2lojKRLuvXc7yq6k4P1023i3uW1NtyEJ8WDYHYJDCAgVTclKSaajKX6lGA4GBHEczc4+/tJakyYJ585tsb6+xmx+wvlL51jOF1y+cJ7tc+dIkoQkSTiezyjygs31dU7KFXW9oshTpmtTZrN59Fy4mOx18eIVXrz5CkmaIYiG5rquODo+omkapJS0NoCK0jYRQ7rOfhanA9tqtWI8HqN19JJoPaRpDW1DTOQC0mJIGqAql9FvomKZXNptfbI8xzmHMYaBc0wm6128bJQ1tW3LcrmIZm6lMW3NydEBz129iveaVGtqqTg+XqBIUMjY6C27QVX4bpgxhAB1HZ+flII0TaMMqZPAGWMIGKRUrFZlHGhD3BwJIUiFQglBNV9QTCakeXpWMDksJkzW1hhNp2iZUDU1T+u7CBFlSiEEpNbkaQIqDqA6TeKQ1g05wbtuA6LO/Ejee2zwKELXyxJbzBOdxOFYiE7C52itoV6uWNsI6ExQ16toRPcuronE+zI4pRQ2uLMI6J6enp6enp6e72eeedCYz05QWneyJkmmc9paxXjb2KiBkJBMNhBSkAlBUFGLLqRCaY2Wgu2L1wgybjRs0xLoGpgRUVKiNe7sHCW6dIRY+he3IAHT1rR1hSAeSE9bw60xLJbLM5O5lApjBdZ5jo4OSfMUKTXGNZ35XJy1M3fVGmeNujLJQaao1BGCxTUlWVagdMP5i9cweyWDTFPPDsFHw7ezLVmqWV+fdGb3gGlq1idrTMZjNrY2mIxGBAFBRD+CcZa6aVEqFt1ZZ2nbBuss1jmm0ylXrj3H1WvXmUzXUFqTpSmmNSwWC44Oj5hOx1R1izEe6UUsRwxxyDLGMJ2uUVfxsB58YD5fsLa2DiLQNjVZmsZCxLaOyUkhmvIn61sE76jLJVW5wlqD0po8z9FaolW8HmJvRGx4HxSD2KoeHE8ePyXPc+bzBXVVUVYlxljqqqJualoCZbBkDqQIyCCxXb+E87GhWypJkiicdzStiVscF6OTT4dEpRQqTZAyQdlACFGWJ2X0bLi2Ynl0xOLwCOcDrWnIpMa0hsOdXeaHxyRJEsv0jMELqNuGpJPuGSnxJh7wsywnSWPDPHiSNErydJd6BgIp4tbrNJUj+DhEG2Nom5q2bXDOxSho5ynrGpVlHO4fc//217l242ocFr3HWxNlfsOC4XhEO5t1eVg9PT09PT09Pd/fPLsZPM+6Ert43zbPMwaD4VmU7akH4ps15Oo0FlapOGwoiRBdjK33LNpoHA5dilXgNFI1Iruc5yACVVWznC9I0pbFfElV1/GQS8B4y7xc0ViD0ooL5y8gPWeRpkma0poWIWMDuez6M0LnLYkpQ5bgY1qVc45itIXRIwaZZjE/wVZlZ0aPpuRTCZkQgrzI8N7jbPSLpIlGhq5EztnYdeEdWZKgkxTXHRSFlBgbez3ObW0xm81iLK6zJImmKHKuX3+e6y++SJ4PgDgolKuSxXzG4eEBWquuubuFIGNChAjd9+PwQVKWK5wN8TDdpXodHR8xmUyYjiccHZ4glCJNElofsG00pCMFUkiywZg0G+FsSdvWzOcznPOkacpwOEKH6AlJvCd1jra1eO8w1tPMlggk08ka89kc30mHlJBMN9aQyM4345FBoL2NQ5aJ0jrX2k46Fg/qzsVNwOkdfqUUxWBw1nkSREycIoiugyXGE3B6fXW/R0BwsQ/FtYb69MMuDqnW2NjU3f3MRffPqS9JCEGiEzY2NuNjeI91Bnsa7+yhizmIW7IQPSJSEvtQpKcoUurKRO9MkuBtjAHuriwUAlmk2ESxXC1Ylit0lpCm6b/Tm72np6enp6en54+SZx401rbOcbC7HzcaQuKtYblcAu9vAk4RQnzLQfy0GO/9/y3P5CAhpLE4Brr+g9NBI+4YRACk4mDviCePHsYQn25YwMbBxFjL7TffAKUIbYtvDJkxLIkeBqWi4fs0+cc5033tUwVViB+3PrZeI3C2RieKYAXCrmibGucMUgiUCuRJIFMN+UQzKdap6ookTWMTdYht6LYrIGzbNpbXqRTrHF6KLrHJoZREJglJJ82S3bZnbW2NK9euc+HiJUaj2AviXUyLmh8fU5UrDg72mU5G8S6/cTFFq0vkct1WRyNRUmJci+0GByWiP2V+MmM8GpLmGXVTI4SgyAucdrSmjelb3uKDRAqFygYUaUGWjWmamtXqhKOjQxKtGQ4G5F17aJ53B3gfB8iBd90wF8t6WtPSNhXGeZQEqTRZkaO1whhD6j1ax0vzVKJljCFNVmilzprbT5vkpYoGbxGIQ20ikEGihMbUNSI4Og0TMvgo9YP3JUidzEwCDpABlBBopTpTvcd3l7cIAufBOEdTN6RSkWmNTjO0HiO1jk3zzmF9lJO1nawM52maBtsKPILGBWyAnbt30DLF+orR2hiUwhHir8YhvCdVGi9j90aint1a1dPT09PT09Pzx8Uzn1iU1mcJUxBojQHze+eeffOQ8c0fO/vVRdLmWU5MloqfE4hRuaemWikFQcbkpcJ5Rqv2/Y4NATaKbNBYxnuHTCxo69EhsONaToaCRVURCEzGExCSsiwJ3sayOREP9aq7y990JmcpJYkSZCrEGEhT09Y1ddVEszFzrmzUCCxKZmiVdebdLuJXxcBWYS2dp5fr12+gtMKLuMmIXzsA7iyGVilNkqSMRlO2ts6zsbVFlg+wNsqpjOlSn0zD7t5TBoMBSZpQVc2ZVyWWJ76Pd566amIPCESZmJCozlBelhWj0ZC1tfPs7u3ju0SkNMuQWmGNIbiA73pHpBSoLGOQZRTDIc60lOWKVbnCLZcUec5gOEDrBH96Ou88OFkI+G4r5Doje1XXrFZLVst5lC55T9vG7/d0e2G/SZp1OiAiFEomTCfrDMdjDg4PsLbuYpcFWmqQXWCyiP0bMX0sDhgx6Yxvqn7ptmpd4IFSUZqVpmmXrBW3GqcbNyEkvmlYLlcxZSyEKBNUcbMipEBoTZLlZGnCsEhRozWkVl0zfWxDN63Beo+tK0bjHO83SFRC6+NwoxONTBQ2eFyIEcTO9s3gPT09PT09Pd//PPOg4Tu9uUAgkTR1Q9vWyLOozSgNEd1B77TpWwqJEN3fU3SbBeKhWiu8c3gbi9NCkGfDRugM2kJo8IZhkKybmKPqRSwO3E1dvIuNZOhh3XrcaZeMCKgkIbFxYyKk6royCggOa6K3Q6iYYJTohDSNJm0gDhxNBQiyNGVtfQNjPUpbqsUOSaKjDl8ofFCdaTtKa8qyIXjHcrXE+8ByVZKmOT6ADgLXGoyxVFXd9Vgs8D4wmawxnYy5dOkKg9EYrXU0hNuAMw5Tt5TLFfv7e9Rlyfb57bitMO7sMI2Md92RKk5jgPOBLMs6n0z01cSDe/S+HJ3MqJvYteGsi/IypZBCkiQpDhOL+YzDyXg6F133iNAZ+VCRZAWSQNtWnBwf471nPJ4wHA6jBMoJRIjpTCEESBJCCOR5wXg8wRpD0zSsViuaxgCBNC1QhTrrVTlNyfWnUjvfRR5j0TJuAEQglgUKEUsZRQwyEBC3Vd2gp7qBIITu75zORCL6gnxXFhmCp6kN4+EAZPS/eDzeW0Lw6C6BzDuHdxbpbExfEzEtrGZ2NgTSDbcBSJI0brISTZqmJAKKyYhzmxvUZcXq6T4EyIsUQ8C18TWWQlKW1b/zG76np6enp6en54+KZx407DfdRY1mXUuWyWiGlTHiM5zOGAGqquXkuEKKDCUFFy9tcvMDU3YeH/HO209IdMb6+rR7wHiX2HuPCKIrAHz/znx8XHHWHu67ssD4fXh8gJXWeBGogqUSgbnKGY5GFMO4AbHWkucZeZ7StC1Hh4fxETr5TVEUbA5H8W50J/WKMinB+tokGp8dICXeWdrg3z9ACsHRUUx/aprmLJo2hDg8eB9Ts7I8j8/TRH/B+fPbsU9DKeq6iX8uNcezGYvlinK1ig3nIZAkKcPhIEbWti3nzp9HKkVjHVk+QCoduza+SZZmuzQsgThr8HZSxdjVEJONVKLIsxxjDSrRXL1yhZ0nO6zKFeI0xjfRCCloQ4x67VYn8WAvRJQLSUmwDp0KxklO8IaqLDk+Po6xvjqlKIpu2xNHlbi10mihUEqT5QXjybSTRlmci0OPd1GCFrcZ/iwUwDmHCx5nDUUnvYoDQow8hjhA5MOCJI3DRlXVLMoV4401pBCs5suzfoqzWGMCxrTRVB5Cl0YWvnVT1F03wfu4yegufdn9IsThXIQQ/SNCgIkG/4CgrZr4MwWCUvEaxcfCQCkQXdt6vVjipCR0A0lMPlP09PT09PT09Hy/88yDRrVadVX08U6yUpq1zZQsj23gQoL3llMnbFp6ymWKlBlaJ0ynBefPjZgMBONhysUrm9y708b9iJQoGc2zBAXhdBMSi/wccKwCO5qYqNSZzz2apDvw3lGmE9crrIsRq1dHI3SSELzn4OAApRR5MUBVJUcH+wROTbrvJyedFhEqpZACvBdo5WhdYLE0BFR3N1xxqrsJIWCdR0hFawxTpdFZjlIaKSXj8RClFN4HtI7yKESUEfnO8xK6DUGaF0zXpwyynPlsRggwmUwJBGbzOXW1JC9GlK1Fys7ZomQcgELcI50+H+di3KyXAtdtPdCgRLcJkGBaQ+Nj14gPnsfWcn77PKv7S5yzBBn9LVIp0jTDdo3igQA+Gq1FF0UslESptPtZQpaPyQcjjGkoV0vKqmQ0GpGlKUHILuFLInzcVJ16JqRSJEqTwdlAZE9lVNa933ruHXSDR/REdBHJzncRtgHbttR1Q7mswXmsMagkoZgWSC/I0gHORQ9I27b4uiKE2BfibfTaZGkKxMO9EgItFFpK6KRUQobYvxJiD0gcojwigO4GP7o2SiVVl7IWum0c4KLfRHofn0/wKCHBB5LBAJ0n/7/2/uxJkuQ68wV/upiZu8eee1ahAHSDBIgCQIAg730aGZm5f/a89H0dkWY3iaoCQIBYqiqzcovIWH0z02UezlF1T3T3ncRtSiMpoh8lWYmMCF/MLUTOp+dbCGMglSK/1FKnGhoaGhoaGj58vH+Pht8lFlkLxkjZnXPFiyHDU2Y3MGbtc8hZ0oKurm65vbxh3CZ+8+vnrJYLzk4Xe8bwXNvFq78jZUiJe48eEqetJAohJ8pvXr+ubeH3Hj9kvljQdZY3b97y8ps3YFw9Fc8aZ+usxTmPU1Mx2sWBgcPDQ2mO1u+RlyD9DRiYH4mcSDT4ctoPMixeXl/z8uULDg4POTiSjo+oFRar9bZ6IxiDtpwjr8dYnDd0/cB8Mefs/j2mELl4+5b5MOPw6Jjl3R2XV1dsp1FJkC0OfC0Y3PlfcpJT9jAF9RPInxSTbDYw4k/QFCYSEiWrrdw31zds1htABuwxhKorskYibdFeiJRkpEaTmLI11cdhul7kRCngup7Ts/ukGNhs1tzcXJNTlo3TbAFACJJARqaGA8j7MyL58o7oEzEpyVBZk7VS6phSEt9OTBi9l+pnnwI5JqZxZNqOHBmL63u2qw23V7d4a+i8p7OObQmNSomoBvyNFgdC5pP/+B3u3TxktVmStysCRWJlyOg2LkpBXwlIKElVGFN7TcrGzGDIUdKpbKYmZUlSlbzmlKMQPmuRnLXWo9HQ0NDQ0NDw4eO9icb/8f/+P/g//9N/Yrta7+I3ncP5qIOoISVTpSfGOPVrZDmJngzTJuKt5/XFG77+8pbvfPt7KkEx2ixudeiXWFxrZDj33vHwwT06K6bpnGUQvLq6hCybgPl8znwx4Gfglg6sI8eA9UIUyiYmxCi9Bkm2IkWGklPGeelSAKtejyxbA/WBiP4/EZIMuEkHRWNlILUYDuYHhFHM5gYhBKVEzvmdxEbGUitDKomT0xMODo54e3HJZr3i7Ow+88WCi4u3XN9eMU0iXZNY3khOBksmWzXaZ4Mtpno1NGfAaGGcM0KgrJNTdWNEwhRTxuWdhCwn2XwMsxmffPIx5+cXXF1dS9iqBXA4A7ZsEWIgaQJUMnISX9+lNVjjSckwhUhOMAxz5vMZ43Zks9lwfXVN1/ccHx0ym81EkhWlqyXtndyLgd2I38f7+nqlLTxqj4vBq26pfL3Kr1Kim82lqVsftzvsOXYdaTuRpkAcR7Kz6qUwHB4c6qZjyxQCdze3LA6OefzkAb7/mOf/+nturm4lZjnnwv10uyHvY5e4Zkg51u+z+lskfiQxiexoq3RySIFlJI6J5IWUZiNxwA0NDQ0NDQ0NHzreP9725ISh71kvVxhkM2C1O8NIMitWI1pTANhF2krCUuT4+AjvZnz+mxeEYNWasXsJJYY2GeqgDOCdw3tpZE5W5E02W4zVkCHdnDhn6XvLfNFhbWI2eBaLOSElri+vtPvAk4rfRIfREqUqBm+JJ90fGnf9Hnk38Kp0qkT1hmkiRjn1RnssQPT7zjo9nd5gncNpzGzOEm/78NEjMpkX3zwn58yTJ4+Zz2a8ePENt3dLQgi1mC4hiUXWenK5vkq8ykl8zkkXPeVEPZFywJiOFLUTomyM/uTU3Wi87na75fnzb/jkk4+xxnFxcYkxMhSbPRO10b6SEKMuPjLZybC8b5AXf3onXRPjhHeOw6NjDo8Mq9VKUqOmwOJgwdHRMV03qCSt9EpQG9yzxkftIpOLjM3odibodiTjraXve2mjz6VfJLDdbuWzPPZ0nadzjhQitzc3nE4TWUMONpsN4zjhnOXu9pYYv2Z5fMLRyQnb9RLJDZMKPWcNJCEPpUSyGNdBGtuteo2ixhzX657ru9QtoNx/R6dnLMPEarlmG0aGxYKjw4P3/bVtaGhoaGhoaPiL4b2JRj/McL5TszFgHDkbyJIqJeGpMiHJAFhiQE3V85MlzrQMYTFZct71WljrkeF9NwxbbVgeup6hlxjZlIIUvFlPUimSBVw2DLOeg+MOyHS+Y5jNGAx88u1PGPqBxWLGcrnmj3/8gwytevo/DANPHj3Cd30djiVWduTu7o4QI944MJKYNZvNdsQoJW5vbskZhqGn7/t3TuPRgj45kZdBsuu7Gvu6Xi2x1jLrOznVT4lXL1/w5tUrxrB/Kp4lDcp5nEsY5yT5KkqcKmnnXSgSHWe9XC9kePXeE1PAGilENK7E4mYlJLoRCYnl3ZLf//6PPH3yEev1hvV6VV+/NY6QMy4nMB3oVsdkyAliTup3Qf6bRBJFzuQE6xAgboUcdj0P7j8khMDd8o5vvnlBSpGDg0Pund1jGIZq6M9JtgdRo26dc8xn80p+skWSunSLEWOsSVTWe4bOQ0osDg4JIRDCxDSOhBCZQsR2Hd5aNZ1njk9O2Gw2BE3j2qyWkCJvz19zd3PLQTfgjRU5oUq+coSUVSalRNVqspUzFmuEXEixYBZinbMUCMpvDSmKjPDt+RX0ns53Qmhi5Pb65n/iV76hoaGhoaGh4X8N3ptoWGvUAA2o4TiVTotsyAlyUolSlfAABryXITmEyDSF6jOQk9u0O/XOSbYiUYY2bdwAa/BdRz/0Kn3xqs+3RD0lthlsCOA7grHEbLF+YL44xFrHYpE1zjSRWDGFqC3a0uAdQmDcSuRtTKmmbFlrOTk5qaSivFYhEioLi4EU5fsn7RYp0p3Dw0O6rsNapz8jEp+cM9fXV+ScGMeJs7NTri6vCDHy8ccfY6zn//H//H/x+S9/w8X5S8hiwDfW4azKoKyYtI3KbnTnQmnFtsXrYCxG/S8xRoyxRALgatzsNI1Mk/gkvPOyqUny2p5/81zelzyokB5j6IYB13WQoU8aU6xEIBVvSEl/UuIVUwLt0ihpUjFFxknIQdcvOOnmrJa3LJd33Nxc0fmOJ0+esjiYY+jYaqN82cqM06iSMlSCJ5suMWNLolNMibiZmDZbMpLi1ffyJ88X9f5cre5YLVfEGDBGiIr3Hu+9JEypzyLGIGTRGU0iKyZt2d7Ihkn7OyifRfG2FDphhJXp70pKct1i0k2QgX4xIzvLdjvqJkvIZkNDQ0NDQ0PDh44/o0dDZUJJOiqiJukklX2kVMiC0T9lLrXMZk5iSHMip6hDt2wxjMnVjJ2NJDAZla2UgsAMTHFieXeHpIRmMolYjbvgYsJvJ8Lo2KaObDwhxtpV0fdCUkKMbLajaPfDJL0ZxhBCYLPZMD+QhKhe/73o/MdxlOugQ2LSpKCSVrRaLzFGyMPQ92QMXddJu3SMWN9hrTzPNE6kFPHesVgcEULgN7/9F44OT2VrEwMmJ5Z3l2w3SyETVhpMJA3LieJJpUpGtWt6Tg4k7fgQtT+5lNbp17NuVTrP0dGRJCVZg/UOdMhNesI+aceJc4aDw2OGfuDi4qJuD9S/XV9XSglSwmp6Us4lllY+rEJSjDGYwVYjt2wXhGxYYzhYDDjnpPU8BN6cv2b55R2L+SHHp2ccLBY4K6lXGOl2kZtmF0SAEroig5PgKClKDFMihLUGAsAwDPR9z/HxGQcHx3VrNoVAmCbGaSQG6c7IKWGHgU67QGLOkroWlVAZMN5XUzpkgm7dcu35KL9Z5V4XCVbMmTEG2RplmJHZTkF9RYk4TQxHR3/O73hDQ0NDQ0NDw18E7080Sq2yauGdtVq2l9SDISeyxuQqR9q3AeScSNFoopPTXUVUciKnu0aScfVnTI0EtfVpDUHjZxO7TYg1BrcN9MmwWkXGtCAaj3eOruswxrDZbIghME4jN9dX9YS99GaEGLSJOhDUZFxOzMupvO+6WvSWnGMcR0kr6joW8zlPn37EfD5nsVgwn8/rIFricMdJuiU2ZsM0jXjvefPmDRfnFwxDx8re8dHTJ5g4MZsN/PEPf2B5dys9DUlMwOJNEWJRrlk2+2burMO6xTqNVsXIe1BS56wjk9luR8bxHOukf0KM7TL8l2cA5H12HdvNmqdPnuC95+WLl7vY1rLpMRnrhCiGsDP3xxgl6UrlTNmo5Vlfr0TEWpz3WOeEjCVEmuV6Zv3A09nAZr3l9vaW8/PXvDWWw4NDDo8PMca9Y5I2ep+W/50ztb07k8sSgpwN3jliSkIYt1uJCLaGrnM47xlcx2wmG48iSyv30TRNhGncESn1jxTyghM5WyarTE7JeJRrkLU2vmxJDBDUvI6VLd3yZonpOmaLOZvtyGq7JpTHb2hoaGhoaGj4gPHeRCPqgChDPpL3HwM5ZqKRoQ0MJdq2nJo7Z6pZedzCwaKnHyyb7UZ6GrR7ABAJkHoEbBkS9f9Z6zBO+guMg2hUZpWFvHTWEW+XhLcd24NDsrFsNhvsrXmH+EgEbCROAeNEWpRiJAbLxcUFDwDnfY2v9c7jhq5KtwoxKZuOGuWrp/XeO5XadPI4wDhNjKsVm81WPAXzOeO45csvvySlxL379+g6z6NHDxjHLf1swfnFFW+vb8HpWb1RPZDNZBP1OVUupOQrJtkYaUwWtVDOyDUWvws69AJOiZbkY+lj7oiidU5M8NYQJjFY//a3v+Wv/uqvcO4jnn39XB7PGvks7M5U7rwlx7JZEP4YU6pxuBh9bmfwBvX87Mccywn+NE1sxoQ1jn5+wOPDI3IKbDdb7m6XvHnzBu8ci8WC4+MjTRgr8rYgKWjaFG71Hkgxiackl9SwVJ/XaDdGGANjmlT651VmNeC9q8QghEgIstEat1vGcUuMQbs+VBJVSIh20BgDhCSE2RgcEt8bR7mfyqrKGiGDeejZhEC+u2O1nsSr0no0GhoaGhoaGv4d4L2JBrB3yp0wSCJQTFZPqPVkGiBL4pA1VorgkngX7pYjvvN89PE9fvOr14hf2e7SkVTfjpIN0LZwJyVuznkd2ORUWJKbZDDsZ3MWyXLLQGSB6w92PQpZitpCHCEmrq+uJArVSWNz7x1D55k2S6bNDDsbJOo2W2JKjNst2RhmuqlwTmRQXdeVAmrtc0C3DTLYb5dLtuPINI5Ya6pf4+uvv+L5s2csDg45Pj5mNus5Oz1ls11zeHjIq/O33N5tNdlKonVzNpSlksXUtKiUk5btFfN9iYEtojOjQ70kXYkvQIrkdnGquUYAl8ijnFOVsJkkYbzTFOk6z2//9bf8h+9+l+9+97t8+ccv5ZOLkSSuct0giAcHa0pliPy/lIhBTOLiCZGNjLVdTfCibFfq/0kM7xSk5bzrDL6f8ejxIZjMZr3i7cVbXr96TcqZ+WLBYrFgNptVf4wt2xolUEI8yiZNt0GUBC2Rnvmscr6c2W42bHLGeYd3nm7ocd7jfEffD+RFud8C4zTtNiQxqIk97zZPJmGzbl80J4FOemqMsHmCZCCQ1fSObmHGadptTBoaGhoaGhoaPmC8N9GYxkkJgJ5KG8tmHfGDxXiHNFJHORnPlhQdGUOdXQ2s1pkpjCy3I2f3TjBG/ATlhDuRyTlKL0VOWkZXEqhkwA7AFCIxleI7GYqnGNiGiXGY49wcMxyTk0bOZo08TYFxu8amwNMH9+kGj+9kwO2dp+872bxsRiY70XUDh0dz+vkC33U6oFqViJlqeoes2vtMDolpGkkxVmP4fD7n6OiIm5sbvvjiC7bbDffu32eYzTk7PcZby3az4uBwwfNvXjNG1flrOWCJCrZWtwZQrmw1gcu1QjcYFkwS74wBVGZGlrI+qzGxyjkwJoOxOGslHlY3GqWCEXbpYZIOZvnd7/7A97//ff7qr7/H7//wh5rutItnLd4b3XZkAEs2IkmLKQtJy5nycqyR+wpNaBKZmBTaxaQt6jlDykxJrrNxMPQDn3z720yT+Hhubm54e35O13Wcnp1xcnKq0r1AxlRJlzFizEevSSoyPGcqWYtRN2zWyvOnzHYb2IyjvmbZYg2zgc47fOroh4GoRvcUoxCPcWScZPORM9i8i2/OgO0sxnqs77EhEGIgTKNE31pDCLEa56VnpKGhoaGhoaHhw8Z7E43rt5esVyuRglhNyMmw3hiwEZtlGDJWmqFjEJN2sqVLwHJ9sxGvwmg4Ob6Hc14N4TLxxhDpe6+n6zJtVimVtUwxEEpJXIpq1pZIVbK8rvnBjOPDA15YT4wj0zhpFGoUKVe/4PHTI2bDQD/0zOZzrPPo2bYMlOUE2VghF85J7GtMSHWD2Q17pnRtQIypGpqjNl2fnZ7SdR2//8Mf+Oabbzg8OODw8Ii+H/j4o0cslysZbP3As2evCUm3CDr4u9IoXjcCICm7RmfuMuDL9qj8X6EgVQ5E8WiIxCoZizEOm8r2qMQS77ZJ6jWH+lgQQsRZkRP99re/5cH9+3TeE6ZQW8prO7zZSYeMAesNJvsae2uTJepnWYoLi2SrxBsb58A53F50b84SFyuvKjGlRNhOOGs5Pjvj/oP7rFcrrq6uuL294fzNG2ldPz3j4PAQMYTvBvYih5OllBYz6nWoDfIm0zuJJJ6mpBsm2SaNo8ilRJYVNThB7qeoEjDnO2bOYa2kdGWk78RZKx0kalqvLeJ597kkLRmcxpH1dvN/93e9oaGhoaGhoeF/Kd6baPyf/+n/w/X1HZ3vKUexzlus8yqdMkgLhh46x1AWFRgjp9HjiJbjFS9Dr49eNPVZNwYSAwtCZopUKOlQ3zlHRDoJ0CjVzd2S2dDjvOPAANtLQmcwwaskSErdit4+ZRinCHZkvujour4avUs3BrDbJLAzt8vWRQzeZaMQJj2xHidyzhws5sznc65vbvjyyy/Zbrc8ePCAvu85PDzg/r1T3pyfc+/eMev1ljdvrknZYawI+VPO2JyJKYrER8sQpXVaZFSU6w2ytdEB3xrR2eiYqnKpTMoBEWKZnUHa7HpPUsrynpRh5Jy1+0Kt50mIQwgTJYr45ctXNXmKUuZXdi4qSarekLK5cBafVP5mDTk6Yg4Sv5sCJkcMtl77Mrg7Z6v3pHpN9M7LyFZr3E5Yk5n1Ax99/C2sheXyjlevXvHq9UvMG8vx8Smnp6d475SE7rwq1aNidz0uIu3TtLEsJvOUkBVMzGqGV5mfcZr0JVsZeYxdHG3Xld+fpFs56DrZkmWNvrVm9zrk87QYm3GuY5jN2Wwa2WhoaGhoaGj48PHeRGO9lqI97+W0XZcIUr5nHIlEimL8NVhiQisCMmJ5ldSdFKlm3RIfW/wFcvq9OxHOiD9A2rSdehXCLuWHTDay9TDGkmYzVtuJ6+0KM46QC5HJeGcZhkG1+wspvqsEYidNKpIap10F5Wtl6KssK2dyiqw2670ujcjQ9xwdHZNS5NnXX3F+fsFsPuPo8BDnLU+fPgIcb87f8PTJA96cX3J5tdK0IzSqtqQkyX9LzK9Bkp12opuSsIQW1iX9k9Ugrh4ONaoDVfZl1U+RksiRSsdI8WjsP371BBi0GFBK+MSn4mv/RpIv6DWUjUl5PfUaqlTJWeQN48k+45IO3inJdiOXk/w9Iz+7z6JKxXImR7m3rBeSk2Jms4lsthusM/R+4Nvf/g+EMLK8u+Pq+ppnX3/JfLHg6PCQxWLBMHT6Pi0hJk0aS+Qkn4Gxvl7rMAXx5GBINlcS5b2RzVr5h3Ifq18mxShGdL0nc+7f8SLV6GQtpZTtWNJEql0U8LtlkA0NDQ0NDQ0NHybeP3WqJCzpDCoDqAyxcpJe0nWMJCNR5lXp2BBjsdXmaX3QKq9Rv4GeHGcy1noxwyIpQev1hvM35ySV2mRgO24lzpXM0mVsb0XXblYczjpc51ks5tKL0fc4Z2salLWuDt9lgJQyO1P/7N6nDNpVYmOtyKP09DmpZGY+nzOfzXjz5g0vXnxDjJF79+7hvafrO7773U+4fHtDCEuePDnjxYs33C23oOlRcrWEcchjaykf9WLKdTW56pqM9o6U1Cmjr7mYqsWhburnYQCTMtikp+j1I1U6syMZdagvcjL9phLXOo6TbhkMIUa63vHkyVNubm64vb3Dmqwn/Srnsk7fC7shvCRcWQvGg80k47Q1W0lTvWf2OleqPIyaPmZ0C+MMoLG1mcRmTGxSkJSn7oAHDxeEOBHCSIyBm5sryInDxYKQMpvthPedGP+90+tbfg+kqDLGqOR0dx+Vz8PqvZ7Z+53JmWQtdDv/Sk1ow+yic98hIpkYE+M4EoL0zhBgMZ+/z69sQ0NDQ0NDQ8NfFO/foxH3kqFUrhRz0uFcdeUpq6G3DFMqczFyMpxMlFNuu/NepCgG8Kwn7zmrWTdLJ4HBkMgsV2u+/vJrUpzqoCYSEik52zpHh2V7d83y+jk5rMCcMJsNzGZzZrN53VLsthM7krGLqd3Fu3rvdxsG3XjEGFmt1qQU2Gy2gJi9j4+PWa1WPHv2jJvra/qh5+DwkClMnJ4e88m3PuLFyzf0vef+vQO++voN602qp+EyvCci4jsp1ogYIsaJrEe2CFZKEvc2MPKjWTsbqP0ZqUSqkslOSEhKMEYpCxTvjMHaRLbiR9k3GqeY3hmIE+KjyEm/Zi3TNDGfz/HOs9mu6HvPj3/0Y37zL7/l8u1bNZ8LkUopqAndyusqcbxqfCeXvhaLNVk2Yln8EVlldAaDdZpFlQzRmJ23g73PMsv2JWPlPbld2WKIkUSHcQ7rJHHLOUcy4C0czbVLxBiG+ZxBG+nX6zUpRjF9eyebGddBEi9HSdGSuObEMAy797Qf46x+m2SE9MUQpMQyOrByz8cYSCFgrafvLX3XE2JktVwSi4avoaGhoaGhoeEDxnsTjVIGV8zb+xryctKcdWiWQ3Q1K6dEzjLUWlfMrWCqxIQqnUIlUN53pCgFZlkzXa11cgpevn/vxD6lxGq95m65YrOVxKecEn3XM5vNZIiskh49BddT+rKt2N9g7ORSMhw754kxsFqNElWaMzFOWGs5Pj7GOcebN+d8880zDEb1/yK1+dYnHzMMA3/849c8vH9MzJk/fPmKbciYrH4Lomr/u5q65DWONu8lRJX0KfbK6cqGoaY8sRuoc5bWdtH+5/oYWb8n5awRutrkXr+nDMP2HXNyGZD3tx7GGsZxZL44wMeB3//u9zjr+f4P/ppf//LXvL28pCaHkUny8kVCVAoe95OxgJz1s7JWfiYnbC73SSJGlcth6uvN5f3qa973Rli720AA9X5A7y+RaOm9itwPThO/prhmvRnxTvpRjk4XOO8JIbBcLhnHkRREMtcNPZlM3G4Yx5HNel03ITkZnN9tzqQ8UO833YSgJMVkav+JMeqTSUJsjk9O3pG2NTQ0NDQ0NDR8qHhvomFs/RsGcNYy5YnSc2DI4OW02pDFKGuynMxGLSVTj4fN5dTc7hEWQ84B1AAtAUtZGpwxpBi4vr4lp1EGUSs9DZ3vMAbZMsTAMMy59/Ahz795xuAc89mMTF2zyEmyRrQWL8Y+CbGa8lRFLcay2azZbDbSsRAC1loWBwcsFgu2mw0vXrzg7dsLrDXM5gtSSvjO8+1PPgIyX3/1Nd/+1gO22y0vz1fk7MSjUEgXls6aarw2VuJWLWB9iYaVyFVdfwhxiGqal0uNVSN9TAnvPBZLNkkkQ2avABGVgamWylBaxndkZecxYEdmktwIxuyulwz2ifVqRdcPxBj5zW9+g/OGH/zwr/mXX/8r5+fnFFGWxZBC3L0elUZZuzulL9uuHZGVV5hJtWdCNhx7Xh6jwq9M3Y6V11g8Kvst9PX5rMXq8zggiIkIMLK/SYlxEygm+67zHBwccnh4wGx2wHaz5fbmVgih9xzMZzx4KMWLby+u2Ky38r69hhukJD4b3fallMWebyAm9cLoaywbkBKOIBLD3DwaDQ0NDQ0NDf8u8GcV9pXYpQxqwFaKoQNfypmadRRFRZ/eUXlkdhKpcoJclhNlUMygXgnUnJwzOKeN3JTo0HKiLYPm0eEBZ2ennJyc0vcD5xfnBD3FDioHMjpsylvZeTGcc/VEnH2PhsqPvPcsFguWyyXWGpUKOW5vbnj29des12tOT084PD7i6uqah/cf8OTpA9brNTdX1/zH7zzi+nbFm4s7IT3GqUlbeyk0TUkXDmI+1i1DiLoxQsidTvtCDawMnzutv8p2VIqV93cPu8isnadAr+O+zK2epO9/3nsLpJx27RpVyhUTxiaYMrPZgvV6yW/+5bd8+ukP+Zsf/pBffvEF5xcXIg1LuslxIvFK8qKl4FETr7IzmLQ/UAuTqm3xOpDr6K7yr5LEtZMnlfdkdixZXrsay1HigTE16clpG7rRa2kosjlDSpkQAtfX11zfXDOFAFkkW8N8xszN6IehSvRiTGynkc57+n7g8PBQ7kfdhtzc3hGmqZhjtNCvfEzapaI3htM0L1k0ORoaGhoaGhoaPnS8N9GQ4VdSjdSejAOchWQMxjicpkzFWKQ2MszFmDAm6OA3Ya2XHo69QTIjJWx9jpStSSZBtsQcOD464u9//hNSjBqvKgPzZrvFWc9ffe8/0g+zSmQ++dbHjNuxylNE7hRq10XXdVVWBYXoyHQ6jRN3d3fEOL0Tc7uYD8znR4xj4PWr17x9+5acE2f3zkgpM40T3/nkWzx+eMar8wu2my3f/tZTzi+veHOxrGbsbFKVO+UkmxusIVvqBsFZkTyRkkbbyggtoU07goV6E0IIIjFyFmMsMUcZXLW6PMawk7fV95t3G5ycxRthd8lOO5mcfkZ5dy2yludJQhSQDCEFclrhrGW7Gfn1r/+FTz/9lB/8zQ/Iv/wNl2/PKYlRtfzPO2IW4iBRvkX+hcbtai9HVUnp60slNMBWWd+OS+0KFI2xSo7KQmtXKJgR8kn5++6blNtYkXfViONIIdbl2qSciBnWqyWr5ZLXL1+rZCvjvePBg/scKMHY3K24vHjLer3GOsuDh/c5PjnBOstqteLm6prb61vG7SQkxxrpSTGWMIkXCiMxww0NDQ0NDQ0NHzrem2hI8k3EuVSHtISETCVExpNzBpv1pFg7EpI0hmvurXYq7GQx4pMIlA6BUjBXYlf1wJmu89y7d4+cJvVtSCfHze0dN9e33N5ek29vICdSgtXqjhgy529e45wXY66RONv91J8QAt77OrBCxjrD0dEBm82G1WpFSonZMOB9x2az5fnzb7i9u2UYBk5PH7BcrpjPZzx98pB7p0d89dU3dH3Ht58+4NmrCy5v1pUcxKRbA7vbDpWT9TIQI55gie4tQ3Eu7dRgbK7pUmW7ULwxO0+FRqFGGd6LxCjn+hMUQqc/qsO/fG1felRlPXuDvPy4fj3vSOMUJrz3+K5nuwn8/g+/5wff/wHf/u4nTHFDDEmTxyJy28T6eeecGUPEeTG/G71fpHV8t5ExVvwbKSfd6GjaFmVTY/eumxixoXh7ii9CvzUV+ZheDeu0gVy+niyVpBpkk1fSyUgGciGBpXNkVywYU2C6nLi5ueXo+Iiz0zMeffSE7XbL24sLzt+c8/bigtOzUx4+esSTx0/YbjZcX15xcfGW5d2tpJvp71LnHcZ1e/K2hoaGhoaGhoYPF+/v0dDW55giLmnBW07kqFKanKQjQdOPylTqvcUYIRve+Br9iSlG3KjGbymos6n4AZKcUFuRyoRxy831JSkEQopEbQiPIWCs582bN+SUWAxzut7TOUvfeTrnOTw8wvquKFTk/SDSra7vNUlqRVBjeFYvRk6SHDSbzcg58+rVK27v7kgxcnJ8xOLgkGmKPHhwj6eP7xNz4re/+4p7J4ccHM353devWK0CCXkPxqq5OyeMasqkaNwgtE1kXNKLbdUwLIM85Zro4F0HZks9Wd+ZiU2NI7baZWGMavyNkddirAzmuXhTTLWxQEkPMzU611qrn6sW0+k9URLInNNIWm3L9p2l846UMjc31zx8+Iif//znTFPEOI/vxVA9jSOkzGazZbXdMG02rDcb7q5v2G5HAJwz9ZOrW5aEvDenMqOszeIUc3shs+hWYrepMVn8Ham+L+rwXt6XGEUMthjtlZBkTZjS/6FSNogpy+eq/h7jNGkrJrbTxHr9houLixqH65zj5PSEo+NDnLOEccPb9Vp+j0icnB5zenrC7GBB30uy1Xa7Zblc7XpNGhoaGhoaGho+YPxZG40pTFhriMkhVgrZQojEI0HUKNSUsClhka4N62VuC+OISF86YswkI9GeZXAuJt8qy0kyzCUSm/XIH//4rKZVlVP5zjuOjg6xWIZFx8Fsju9UDoUnG8t6O9Jj6Loer70PZSMQxpG75bIO5mWI7rqObhjwXcfd3R1XV1fc3t3gnef45JhpnJjGkQcP7vPwwT3eXl1xeXnN08endNbx5VevWW/lJNpkGUQNSeNeUZVWSZaiTPh724VUB9vSUl16FcoRfM4Zmy0xhOqnKK3m78b3mtpLYbIlWyrhS1kLAE0xWZcZ2+ykRFAZ2n5bdumTeCeFDJFppRRYHCyIIfP82QtWyxUff+tb9P2MkBLr1ZqcMtuNJjStVqw2G6ZxyzhObLfSkVI3TXa3ZSkbB0ngMmJ6Tyoly6b6WIw2ku82GeU9yHV2mvS086LkypHL91rtNKmyLBVZGVm34Iyp10bKFc2OCOpzRGdwSuZSlKSzaZrYrDfc3S05Pj7i9OyU05MjrLUsV0uuri65vrzmzfk5i4MD7t+7x+HRESenp60ZvKGhoaGhoeHfBf4MM7ik7pQOBYyFKNuLnI2mFkniUZwCcQqUNCljdnKdaTuS00hK4IcZKUamIFG2UoInOviUog6upV064b0lpYw3hr7ryGQ63+Os4+R4QTd0ON/jnMc7T86GbCxe6sz1ZN/VwTjphuDw8JDtdstqtZKL0nU450khcv72ktu7O0KKzOcHHB8dMU0Ts2HGt771EQeLnmdfPSPkzHe//YTtes2XL87ZbCPZilTK6mbAWqdXQT0Zaog31smf4n93qvbJcpovnoRc3eLG7JKyilwqRTGti0E8qUxNdiNZX0NKWTcelhhkA1Aatq1KqoxxpPIzLktK1J6UqNjMo3acGDUsZ4wW9CnRSYnNekPX92xiZLlacn5xwfe+91c8eviIm6sr/vU3v2U7jnJtdHhPJVUpaxEhSZVPO5ZgTaY0/1UJVB34c714SVOair+jyMbE32F2res1Ba3E8Irp3iBfshlSTcUy2CxMxNpMKQYv2zyb5e8pWbJ0pcvVKZ6aItvT1zFOE2/eXHD59grfeQ4ODzk8PuT49B6z+aEYz6+vWC2XWGsZZjMODg7e/9e2oaGhoaGhoeEvhD9DOmWqgTvr6bhOylWyk2MkT0EKzDQZJ00TYco4rwMcBuMsTk/po/oIQPT6MQSwVuQ9GDVkR5wzPHhwH0PGGUPvPa5zWCckYpjNmS8W+H6A3fhZTb7WWonknSZub2/3ms61tRpL13V1WF/eLbm+fEuIEecdRwdHHB4dsd1uOTk55qMnD4kh8Ps/PmOxmPPtR6dcXd/w8vUtU91CiATH6Ol6SWiqXou9LUH5r7W2JiAZECKnpAF9N6mc5qMEzhgwCesMXd8TQqifWyFUzrn6PKUjxGo0rRjBVZqUk3ZIGN0G7JXMURdPVXpWfBDls9z33hR2MpvNWK/WXG9u+eUvf8n6P6z4zne+g7OWX//qX9iMK0Jit90R18bOs2NtlSQ5ayuhyOx8Lbneo6mmSlUZlG5ayuvEivQPlaQZTTRLKZXQqroJKklkzprqS7K6d7K5pKAhGxWV+jnXSRlhITDqX7JIcIK1Kq+yXqSDypFCiFxfXXN5dS1kKMvmqXiHUgyEuzturq7e99e2oaGhoaGhoeEvhvcmGrPZXNJvQpDhKFP9FSmN6iMA33mykWGYlBjHRMoGn7OU3/lBB6xEyolpGtlu11KKF4JEl9bht3gNIs45Hj9+jLEGZy1e06LGcdSmZ8N6OzEznr7v6+C4M03DFAJ3d3dM0wSIIVn08h7ve/q+h5x58fIFy7slnbf0Q8d8vsA68RQ8ffqIB/cOefX6govzGx4+OOH+vSO+eXHB2+slY8y7eNpacIj6M1QSla1uOWS4tvo11HhdvBE7h7cM/P3Q8+jhI4x1LJdLLi8v67ZH3o9IkdBkpyJ78t7vWsKN+CaMbnj2ZVAgqVZZTdYYuyM0uRo4EL/IrqwRdiWKxZ5ulDDEFFlvNvjO45wnTIE//P6P5JT49ne/y4//9sd88fkXrNYropJWV+J3oaaCGVs4QK7kaNfjkUlGZErGqBQMlB0ZlYZByVqW2FpDLuQqRn0cqlwsauJVSU6jbEdyUqJS7ivxzDjnJOWLvEcQlBwWiVbKGLszzhtj8c6SnWyIyiav9LnEstXL8lpOT0+5d+8ei8XifX9tGxoaGhoaGhr+YnhvohFjrIV1ICfs0zSy3UgajtUkoFwULHpy6ztPnCaKfzXrUX4d6GIgTJMSg/iO5t9aSwwTOcvzGo1yHfqefpgRQ8D5budHsFYGNH2y/ehaa2VjcXJywmaz4e7uDpEhWTpt8d6sV5xfXDBNE85bjk9O6PoBZy2d73j65D7eG/7wxxes1hs+fnzGweHAH756xd1yJNVhXGQ7qrAhQzUVO6Pn4XnPVIyewGvsa0YH2+KH2Evqur65ZhwDoXhbdKguTeeF5KDRrqWvo1yDyhXYDdmFbCUlHqVFW7iiGJwh1XSock33r3tRN6UU5cTflAbyTE4ByPRdB8ERp8gf//AV528v6Zzcgs5Yool7Eq3yGo36gsSbk9h5IvYbyl1tMS8yKSUtGEK53tYSg5jlNbBL7jVNBDMGkhJFp/6ZWDc6ck2dMbVQUFmZ3PsYkXMl/WesysCEEHnvpTMDIX56EdXLYbDO1nsipUgs6VoU70zm5uaW25tbhtnsvX5nGxoaGhoaGhr+knhvorHdbtluNzt/Q46EaSLqNB1MkSntEoKqQXia2BJJOeLNluwsJoH1Do4ORZ4UIxJxK0TGGKOdFxPb9YqLN6/BZE5Pz5gv5ljrcM5J2g/vDr7lFH4cx3eM0SlGxmliHEfRu/c9fdcTxpE3b14yxhFrHYv5nMXBAu87YkocHs559OCM9XrN7798w6zr+P53H5NT5suv33C7mtQPkcVsTtHpl9hVHX9TlvesiUUxiTncG4fJpTNCh/OyHVCZWgZ57dOoPRSAKdHB1Gsm240iOwoYY7F7sil021ClQvrqgsa3RnaSqN1mpWxA9j7TnIlKPgji5TB+rwiEnYANJSjbcctsmJO9IYSR29tbTMrEnFSJ9+69kzKEaaLzXqRf+vrlUuY9omVwZZug5vtcu0GopvIEYI1G5e48L5UFa5t9qt+v901Kdbth9t5ZkY+VmGGjpKEU/bk9gljI9O5z2vNtVMJklLQ49q9hrnJFCDEy3S3/R7+mDQ0NDQ0NDQ0fDN6baEgng5inQSX940TcxprcU4Yn2HkDcpYivqA+i+wyUU+eXedx8xmbzQbn3N4pvTzfernk/Pw143rNydExvu+ZQmK6ucO6nURqvxnbGCmvW6/XLJc6kO2ddBtj6LqOXg3fK5UghRgw1jKbzTg8PGSaAn3veHDvhPtnx7w6v+DNm2tODud89OSM1XrLs5eXbLYlvlYM0LJZkGHe6nBem8b1usQUa7GeNbvT9TK0iqJHtyPVX1DeICTK5oJ33r9c86xm8BJb63abh73XoJdFCvKKSRpIobw23Urk4umQLYskPcnwbrI2k+vnD1IAiEHM5jlriaBTg3hmnKZaljiFQMpBuUKRbu06MLx3Yo63RszzqCRM3DsYnNrQ3x3gxVshNqKQI7UfZG+wN+yM9DsCoLG2dvd5FNnSftlh+d79PpZiaAekuTtn8XM4pwEHKuLaI8RgtLxRkshM0h3N3mORwXkhHvvbpIaGhoaGhoaGDx3vTTTCJK3arpbeQXSG0HliUc/r/CPpRjLcxRCIRk67ozVMJpJ14OrITFNgHLf0fUcIE9M0sl6vubu5Ztpu6bqOgwf3AUPMBm8tXefJGcZxy/65ubyGzDiO5JwZhkHIi9RM03We3vdYJ6bst9cXbLdbjDMcLg4YZnNNq8ocHR3x6MEps5nnD18+4+Zuw+NHpzy+d8j521tevLkhRgneKh0VqZTHqbekEgwjMbLWOYKSgJil/dtbt5NLqWRpHyntonyhGLhliE1acvinPyOFdbvrsisj3PsWY/B+JzUr1X11Y6Fbj92Gqga7yhBvkM1A/bI0wpdtibFWYn1DJKeEs1ZO46eRGAMnR6dEEttJTfkS1wTW1LSonQPdECZpIHdKgjKJnMQEH6P64Y3Ve1TTsLwlZ0Mum49c+Qx1S4P6S4x8X0LItJcdjWwn1AsiBGgnV6tET8lGNqVjRMmi0Wuvz71PTPTDhFRKGamPKd9XNlsG46rdRElHQ0NDQ0NDQ8OHj/cnGkoCUpaBXebihHNZT8VLD0Um5qiJQEI4ymAVgv5dZSApBLxzbNcDnbPc5czq9hZj4fDwgO7sFOc6sg7uEslKba8uA7b5k8G61+Sl5XJJDBHvHNY7Zl2P9x2Xl5fSnZEi1hkWh4ecnp6yWctmZTEfeHj/lJQCf/jyJZvNxHc+fsDJyZxvXrzlzdslSYsFZR62Ox/EnnQokcW0Xt6znpA7ayXpKCdiKgO9XucpYL2Tx9k7oQcd8v9EqmONIcdCNspJfdmoiMegDra5SLJM/fsYokqx9L3YsoUR2U8Iofo1nBFSZKyRsjy000TJkNNBO9U2eIOzjqzt7xL4JF6c27sbDg4P6fOMcb2pg3TZDGmToX7ORklEqDI0a7UHPGsvib6/kthF2Sjp9c0Un4VI2HKRTskKQUiDJm0VYzhGiE0xacs1VqN/TiSD0hGqH2b3qpWIZJWVGXaEJP8JeUTjbs1u60QuaWVVMEYi/7eksqGhoaGhoaHhA8V7Ew1rDc5Zpmnk7cWqDn7eeyLaTVAaqKuOnzoA76RUCacmae88pEiaRtLYY4ZBJFLDAEYGqqin5TL4laNfUz0G0zS905Qsg66YxzvnOJz3dJ3HGEk8enP+RiRV1jD0HccnxwzzOdM0sZgtODmZc3p2zNX1DS9eXuCd5Tuf3Gc+6/jjl6+5Xo56fJ5V+6/SJJM1jCiRYpahMu08FLUcTi3N1lgdgqmeAGMMzhvdH+ydblOGZSUz+l6rAMfa3XSLnNCLYTntncjvZEMpJ2y2NfK3OtbNbomAXPb6s0ViBepDiJFsZLBO7H/uqW4eYtylk3VO0q6mScoFU54w6yWdm+G8J6ax06QKZQAATONJREFUbkMKc0glzSllcGKU9p2XRnmVNKWozwkYbN2kpCyfQ5GwpVRkaJJQpR9FNVwLR0iVhAnhyBh9fqN+DmN29x95n6zotVGCiCn+GvnETf3ZQvrKtaQSo3ItRdWVd8+nkq79cIOGhoaGhoaGhg8d7000nLEMXY8BNqq/N8bWjgbbiY68GJJNLtIX8USEECS9qeuYDaLRHwYp18tqKDfWcnV7i9ts6bueYRiwbicVEQmQnJhvtxvW67VE1WZ2yUfIxmAx9CxmA0Mnp9O3dzdcvL0jpoTrPMZa+tmcEBJmHJnNZjx5eMLioOfZN284f3vD4cHAx49PSSnxr394yXI91e1FkJVGNf6q5F/K3DSCN8VIClHSuJyebptCkoQ85Sh+i3Er8jHp2shkI10kFkthFvsn5WUzsTsl37s+OWGS0TQpW0me+CuchiVJhKvOxLppyFJQp+lMxashfSaR0geCegmMsdpjIXHGYi2RE/4QYk2v8s4Ro5i+nXUiq4uJaTPh5p6jkyOWt0vGaQtKwvY4pVxLfeNFilVHbZUnxZhIOWKSEDGrRFR8F7s285iitLkU47wSwiKG2nkxdPtTtjvIhq74SUq/RyYLqQpKUkpkrr7GbKivIcekmw1pdEc3HfvSNZAUXrm26uexWe4Ddmb8hoaGhoaGhoYPHe9NNLLJGJOxOXO8WDABpWDOWYkDLUZx6yzOOZyzlSg4a/G+wzmJky1xrOJdUN+HMfT9QD+byYCM6O+Ltl22FVG6M0LQgr2O7XZbW76HvqOzjr7zkDM3N0tu7+6wVlKMrO/wnWeYzaRN2zkO5jOePD7FOvjlr//AZh15dP+Ij5+ccrNa8fWLGzYbMe3KYFhOqHXIh+qbcN6TkyRoWWM07jer7Ec2CAaEeFiL0WK7rvf1JLvKZbIYk5112pYtn0XSAdlYU+VYVftvwPddNYQrK0EG59LVsDco54TByhLBZB2qcz2RNwaNvRWvibFGtgw5E6OcvpeOCeOkLT6TJTaXspVKGGvwVpwPYdzKFsoLYew6z3yxgHVmDKNuZPI72zBtwyBmaZG3SnLFrF1iguV1qO8b42wlG7WssEQIlw2HblF2pAQRMmV5WzkICUgar2utVfGUAZVPJSUm+psi16Je93KJZDMV9TFyLH0fOzlUrjxGnz+JkZ4sQQxQ5F/v+1vb0NDQ0NDQ0PCXw5/VDG6c4+DwUAf0QiYcxlCbp0U2Y/Bdh1MTsrMaT2pFs2+sqZ0OYhx2ak7uKgEZR5FlOWf2BjEZyuZzKSwbx5HVcimFa1b8C+N2ws0c2zFwe3tDVmLS9R3OOxLQ9T3OOYZh4OhgwaOHxyyXd3zz8pJxgocPDnl075g3l9c8f3VNCGqARs25RmRkO+2SbGRCCDq4myq3sdZRVF8WQ4hJ/RBK3JwkJ6HXwvuOlJIUFSIn3zEEPS2XoTXq0LqLz9UKhySRueV6pRirrr/IbmKIpKgEz6jkyKTaTm2d+EMycopeSIn8fCRHGGPAWoc1Vt5PlqhZZ8r7NvjO1/hcctkeKNkzsgGT5UhktV6ymBkWBweYlWG72cq9YhyJTJxC7Wjpul6K7FCyobHIxbAtq509w/We07psSPb7P0qalleJVNLSQGMNNhc/RsY6Q0ro65fHTQghyjHWddN+vLOpEqoifUuy4shKEnPS+0kN6VZztFIiGm0sz9RtjivkKjWm0dDQ0NDQ0PDh472JxtHxMdbKybdF5UHO4b2vg5TzHu8cxlis9TJc6+l3DdrRU2prd63TRZoCMgSWQrMywO7/vUhHttut9GEY6Dovz+09vReZ1sXFW7bbDdbJKL5ernhw/yHOSyyu856zk0POThZcXt/yzcsLUoInD085O5lxeb3ixZslIYrJ11hbpS/GoN4AOb236n8oPgYhPUVbXxq6d8VtxXRcth1Ok6cS0pMg18TU0j/5hx2v2TcEF19EKsZpPbmvfScpYIxnmmL9HETaNNWTfucsKckmKpMZldh0nWHS/ofiEQEZ0GPQ7hBjiDlJP8ifmJyNNYSkqwGT5WYzuW66Ygp1MxSmLdZZFosDUkiMYcKYRI5ChqxugsI0UWNudfkiz6WX2e7IQe24UBN72e4UAlDeiylbqT9ZFVhrVaZViCH6HuV9mKxyqpS1LbyQMru71nrt0NeSdLO027+oDK02wVPDAWTJtCOhZaPyp6+zoaGhoaGhoeFDxHsTjdPTU4qmHKjSE2utpCRV0iDn69YKCYkxvmPWrqlTHj1Z11NvRT2NT4nVaiXxs4adVEkHduccg/ZoeCUZOWeurq+YpsB23MgpsOsYhoFDa+n6nn4Y6DrH4/unHM47nr264O3VCjB89PiIk8MZl5d3vDq/I6SdEduQcdrlUCRU6HuRE+dd+pFscVztf0AL4lISmVhp+i4EpbR3w54B2xqcK64TWzcq+z4A2Bm/rRbapVz8E2Zv2+RxTrwAWTcUi8UcMASN4QX5urOGoespCWIil7KEJL4bGZypciJ02LZezN9lKC5yq84YAipL0gN9yBCitIHnRAyJZEYimdkwo591hG0gk/C959vf/g7z2YyLi7e8fv1GYmhzltJHW2RRuhlISQzV9V6U5ytyqP14Wadxw9lIb4Wx4pkR35HIBYsXpRIDTZbKMRKjvEfxshS+YevnIrJC9P4vWx0o5vsqtooJvNPly056lZJI5yxy7cqL2P22NDQ0NDQ0NDR8uHh/j0bxdlN8CXISbq3TYc9irdGBv5TFidSjkADYbTRE559wlA2IVVOvSJ1ub2+rFKk8t8FgjVW/h6vbk77vWC1XrNZrtlsxiHddT993HB0d6/OL3n0+dDy6f4y1ht99/YrrmxWHBzMe3TthNrO8eHXF9d1WSgVzliI/Y8S0Xk6093wRBa6kcoUg0iADUwwi/0ligra2TIq7MsPSS+H0veySpdwuqlWvXYxRtw/yeOWDMZoc9afFfOUz2KVByXPLqyhxvLbKiWRjZesmwBiDdfJfb101socQSCnRD4MM3/r9YjYX/4Nz8n5jzoiFItcBOWcIWTpZSheIMYa4jYQQWBwsmM9mNVFsNvQ8ffqEhw8eMutnfPnVH6XoLkMy2qSds8Ypp0q6SmJXlTJZ/ptrVOVIdSdUIopLohVYdt4V2TwksErw7M5sLxuLnbdFfEFV2PbO8ybdyuRy34spQ66n2aVU1Xtfr1XZ1jQ0NDQ0NDQ0fOh4b6LR94MOynXuVe16GfiLXKUYt2ONdhViYSmlZ8XLYYwMy+UUeLvdstmsGUeJOu26YqxWkuIcnevwXobyoe/BZN5eXHB7d6vDWCaEiHNJNP/GkrF47zk8GHh474jNdsvXX7/lbrnl6HDg6eNjnM08e3nN5fVWOjJy2CNRO6KTsaQYsM5LU7oaviVlKWFKypNuGsI24JwjpkS0Mrg7dl0Vxhhc8awokRMTtalN0OUou9OytnLdhAjsIk93G5/iKqYOpnVTkgxZuzKcvjExoNu6jYh68u6sle9XvVWR9DgrjeZJtyE5J4xYMDRGFm0M3yM6e/eSAXDUxDKcwcbMdhy198TT9wPedizXS774/Fc453j85DH/4XvfBZP5+uvnhDxJO3kOIpmyDpcthiQDfpb0rmG2YNqO1Ghg3bjk6q8v9y21+bzctypIA7vb4hUpl3NF/2YgxboB2Tfil3demtwLURfZVdT4XLTjXD47WSRZYGd4x4JNRqRUtTikoaGhoaGhoeHDxXsTDTCEEPdOWGVQzimRnSVME9MU9GRXPblKIEqbc03+UXN4MRmXgdpa0ejP5zNSSmy3I9MoiVLeObquo0yB87mceF+8vWC1XIo8xRgwlocPH5YzbJz3WGM4OVlw/+yA65slXz8/J4TE6fGCjx4fkFPkq5fX3NxsVB5TWr7zTo6UI8Y42apYDyUzSE/SRV4j5WzOS9+IRMp6vQau+i1K9GktyoPqHTClFE6E+/te5krQCoErQ3yR9SRNsDKupCdB1fSUMdeKLCtpYlQi74zrez6MGCVGuBj3pSxx11y+kyuJabt4Z/blbcUPUmJ3yxal67ymjcm1DUmGfu962YJl2I4jne/o+p4YAr/5l98yzAZOjk/47ne/wzRNPHv+tXgsnN9tRlRiFkPU8AHLOI6kGNSULi/KGktMqXo7ZClk9uRdauSPEXKSeN4Yd30o2WDYa1zftQZinalkE1PS1VCCrL8DKrlLKcmCxOinqNesGP3Ltd5bnlXC39DQ0NDQ0NDwIeO9icab1y/q1sL7Dtj5AGazmSRQWYt3XpKL9rTz+0MoUDcF5TH25SwinZqYxi0hBryxIhPKhmk74nxHmCJ3t28YVSZlnMEZz+LgoA6nXiN0e2d4eO+Y+Xzg1atLXry+JKXM2fGch6czttuRF2/uWG8j2Yj0CU1l6jq7SzdKGWN3p8lFalSavmPR7udECCVNyO0RLDnFLj0SJZ2oeECyNdXInVLCIf9bPBQyqNcthTFV8lTkVcnIdQ3TBCSVrRkysnWIKUhBIpJGJTYJJRKuFHUUw7EhBZVDIZsLZy3TFPBWtkPGZFKU0/zS0WFtpqQslZP/4tUwyZJIkkylBmftyMOlTEgR5z2YzBSmojCj9x3JWbbThs9+8Rk//vGPOTk54a/++q+IKfLixQvZIOQ904ORdnqXHNiSTCUbHONc9cRIzC1796kM+VGb1kskbRn0U1LvibHKCbIWjWv0rTFalKikESvyP+3pGEftnLFWpYeuekqSSvKU8b17f2AkSjjKvda84A0NDQ0NDQ3/HvBnNIPbPVKQsa5jNgzMF/PqwZDhVgbVnHcD+f7Pl+jWkiBVWrzLJmScJlKIssFwDqcN2oWY3NzecnHxVsrtnESaxpAYhoG+09javiMDi1nHo4fHWANfPXvJpW4snjw84tHZEbfLNc9fXTEGQ9XRpITzrsbFAmQjpXNOU5li3JPDWEtMEu1aWqGr/0G3AdZK2lCMUbYNUAdyY42cfJeEKd0E2HJaXv0W4n8o3xuNGIxNkeckHeophC3hrKfkGzlN8rJWNisxRqyx9H0vDd4loYnSQ7GTyYUg8i8hDlIiJ3IpMbzLZyzXIoTwjj/CeacDtMXs+U5inOQaaMFeImNVzhSjelhSoksdi8MFZrJsNyO//OJX/OgnP+L05IRPf/Qp3nuef/UMYyyu92AM07jFGHmuWqqhr91oNG7BzoSvkr+4ax2v5KVI2nSzU/s2lFAZJZsYhKgCXdcJKXdeZWKxygpjjEpc6ksDtIMl510yVX3duz6NGOOOGDY0NDQ0NDQ0fMB4/2ZwJ8O7d475fM5sNq9m7xiLVEYGoxDK6X6uBuYyNG02GzabTTVUlxN6cpHVdPSdFzMsWmxmpFvg7u6OiYkxjPhevBfzYcb9k2NNuEoy4GE4ORx49PCY7Rj4/VevWa62eGd58uCYs5O5FvFdMk4ZkCFe/BK6hciQtZHaWC2LixGMSH2cdeJ12JMloUQqG31f1mI0XldOuy0kI83Q5dqFiO86SXSikBT25Ee6RQl6kO32kr/KkJwSJjusyYScmDYjXdeRcqin9eQ9CVNKdEo8xA8jJGmapt0mSj0k4qPwlTiYLPKwmJBJ2yBSIZPrZiHDjrSUXgoyOSTGFMm6ibHGgLd6I4oMzxpD70srdiKEic16Q9/3zOYz1psNX3z+GT/72c84Ojrhb37wQ5xxPHv+jHE7Yp2V+89o1lSKErGcZMvRdb6+LpF92UoyQhD5nXPSNF5DAHKJtpVbJWdt89br5rwXIqGbkGo016JFId9Ozd27z9Xa8l8l8fq7JlsRLWWsWy55PuulW6ShoaGhoaGh4UPHexONw6OjWtBXNhdlWNs/FYZ3tx9d15FSZJom1qtVPfE12smxX6zmrWwxLLKpMNZiLYzbDXfLJavVijEHYows5guOT0+ZLw64vbmBbOiHAWcNZ8eHPLi3YLVc87s/vORuM3Ew7/nko3sczDy3t1u+fnXFFKTtG5NVTqTm5UQdBL1uB1LhEuybnpMcSyMGaZImFqkUylgHWRKhikdAkmmFVHjnyE5arWVRkbWPQvwFBjH/Jo2l9b6r0a3C0VS2ZKya3kUStW/WnkKQ7Y919SS9EI4dQaI+jpi49X3ogF1K+ErpX0m9Kl6GGCK+F6IXYqTvu1oql/f8KM47bJb0JLLKgdh1qoSUxCyRZcNkjHhbJJY36bYnc3Nzyy8++4y/+9nPmQ1z/vr7f0VMgS//+CUpJ7rea6SsEohx2qVCaTIWCFlI7PWgOGn9FrIh3pWsjfNFwuSsJRnonK+fg7WGlISUlGuWUibbvCstBG1y39+S6PUtvzfIViTqPbUjadpdg7Zu7K9BGhoaGhoaGho+ULw30ZjPF5U81NNgTZsSc3J+Z/4pEqvtdstqtSaGCPvEQmVDVtvCHbZKUmKMdN6SQmC5WrHerCWRKEWG2YxpDGQ1p9/d3TCfzcAYOu94fO+Iw4Oe128v+eblFVOInB4vePLwiFnnOL9cc351xzQlQMhO6TygnCRXlM6ItNuWyNtSmZKcxltjqlbfGotF5FLF4BtDIMWM76TE0GTVzdgSLZvqiTVRkqRCMcgbGW4jiYTIpcppunUOl50arXeSoEL0nHXQix8gAxhbN0w5Q1RTucs7k3JOcS86N9VG7hCkndtpc3jaI5XW2dqV4r0ToznqbbAlbUnG5HKtSwLUNAUwDpud+EamQGfFz5EAYxIWw7jdylaj7xkNXF9e84t//md+/vO/Y5jN+Zsf/hAMfP3sWZXlOaeEw1IJsrGQglyLSqQQQrTbRpQsKilQdMVfVN6PBWLWiFuNa9b44iIDBFM7TYoMyxi5fhLD+65vSTwj8vk67zBRrgHG4Iw00seYaixuQ0NDQ0NDQ8OHjj8jdWrnWSiDYhkud4Zn+SN69KSbjEDXdXRdJ70KMbHZbHSoh856Ouc0plSNzk4kVne3d2w2axLSxTGbzTk9PeO73/0PXF5eQc7MhzkYx2zoeHT/gFlv+fL5K968vQPg/skBjx8cYSy8vrjj/FK2KkW6BDJwW+2JkGFUpTtatCZswKgERhwFbm94tDrAp5zx2kWR9WS6mImt08EzJzkVVw9C2Vw4HR5zitQGaPU+FNlSDFGlWOwN62rY1u/BOsZpkkNz9crY2jotMbblfdfeP/V9OO/V2yDvJ0SRdUGi7yUpKsQAUTZVhWhhTD1vJ5u6KCnlhhKjS/U3xJhUqiYRxjFFyDqEm6St6tTTfknGSmy2ib7vGfqBoR+4ubnhF//8GT//h7/He89ff//7xJR4/uwZpRW93J9lu1GStESOJ+ZuUtmwWVKO1WxfOkRyVt+GGrUpiVIJyLGa+DFGPp9sdduUqydFpHMae1w2JLX2vXhEbJUJltcgzx/V0F66Tv6c39qGhoaGhoaGhr8M3ptoFFJREqTKZkIIhNVthtFth8U5i3Me73tQc+t6Lf4MayTpqHdeTm+zmIG9d4QQub66Zjtu2Y5bYkp47xjmMxaLQ6YQubh4S9f3zOdzjIHDxcDTh2eQJn7/1RuubtcYLKfHc57cP8KZzIvzO84v1zVydJwiOUVNpypysIizouEXH4WQkCIdEm0/gCXHQIwZazuyFUmSd46UoqQslc6QvX6KVFKEiqyKkrolpmkow/jeJKmyJjEc6+k7VON6Yp/oiZa/67x8X/FGpKgyNLvzkejAWx7KWCskAkhZtk/e+5ooVaRavvRixER2alzXEkZZk2SMM0KySlTrnp8hBNl+dUMnDd9FEoVsLlIJVTKyZXC+q/4HEL9D13VYZ7HOc3H5ln/6r/+Vn/70p/S+4wff/wExRF69fiVGc1dIYiTGUF+mtNkbUhapXsxJPmvAW0tQs7xRD4d8jEmStlBpV7a7z8fsekV0TVa9HVbld+XaJZWwFR+OsQaTTZXCSTRuqh4O2XJ40N9BUvNoNDQ0NDQ0NHz4+DPM4LtSvnqy+o7UREzM5eS4kA4Qw/H19TUhTOI7SJHNas3xySnDbCbyIOdZr1es1xvWmzVjmCQ21BiG+YLHTz5mnALL2xsOj47E0+GETNw7XbAdN/zxq5fcrSasNTw8O+Dh2QJjMs9eXfP2dkRDgurrL8lP1qCbid37KAOjw5BMUnO6ntwbwDgxQhsxfNeIXo1FRQlSlduUmCHkGpEzUY3kxlhQuVHZZpCoBmYL1fuR2G+jVqOzEgqTjTZMl3hVIylT+8lK7ORv9fWqCRtjpDeiyK5QolDSt9THUdK05HpqVGyWr8sQbHG+NG4Dpalbr0m9Htbi9T3lkLBeZGAxBYwSujBJ4pWxQlKsgRgCGF99JRdv3/L5F1/wt3/7twzDwI9+9CnOOb558Y1sndR3A+LDsEr6Dg4OGMcgsrCoPhUNLrDaZyIxt0kJqviIQkjkpPe9ksqSoFVIoFGvTGYnFwwx4r2r180Yg3e60TESLCDXLKsRv/yeifTNOHVqNJ7R0NDQ0NDQ8O8A7000yqmtVZ1+OaEuQ2aR+BRjc0n2uVsumcYt1hp69TjM/MB8Psd7kVQtl3es12tCCEzjVPXwvuuYzeY41/H8+Qtm8xlHh4cyoDp4cO+A46OB2+WKP371lmkSic/DswPODmfkmPnm4pa3N1tCTO+8j+IxSVq2l8uwl3eDvJCAkkIkp+HCLyQG1aiRPAPo6XOG2vRdNESFFJClLds5RwxRE53kdD4GSVgqRmabpD8kmkicIsY7eY6UKJ0a1ov52KoHIxs0+lQH/5xqpGo5mc9GYnRDjFhntGDQ6WNkslFzOrv3XyNZteFcUq4kuarIkUClWnpST0riS/EdYxgJWV533/VyLeO74QEpJ6Yw1Xssp0wMk3xGsRAc2cDEHDFJ4nRzlK3TNy++wTrLj3/8Y7rO8zc//AHbccP5+TlhHIUMoGTIWZy13C3vMFlIjGw1Mpmkzehm9zMlHQ2RfcUYmMJUPRjDMFSvitFNRpGHFTJuDCKrsuwxBXk85TBaYuh0i2Rq4EI1mSvRbV7whoaGhoaGhn8PeG+ioXM3OaMN4XKSPU2T+jHKyfZu47Fer9THIB6FvuurF6P3UvC3Wa1YrdasNuudNj9n+qHn9OxUPdOO05NZbUTunOOjRycs5o4Xry95+fqWEBLWwr2TE86OeqYQef7qhtvVRM5WJSwZa1w1TceUVNZUBEmyiQkhVK9Bec4YxW+RNEmr64faB2KNnNj7riNMQYzvHnCGHCWZKmuCUs4JksMbR3Z66q+DaNd1OnAKGYopkJTIiB0gvRO1aupnkslWkokclkgm5VhNw0IEZED2SmzKcwGkEGuMbyUNxsh1UfmT0ZQka42arNPeZkS2NvqDpCg+lJwS43YLJuM0dnaaJmJK1ZNirRXSZi05igW8EDaU0EkpohCCMUwytBuDA5HnOdluvHz5itlszl//9ffo+56f/vSnfP7Z57x++QZjd59hjHGXEIa8R+csWPkMy4CfcyQZKyRMmJuazC05eyVrsnUp0rWu6yQlylmcM7rlKBKqkmqFfnDFm/HuptA52WaIdE0+ZGsMhhKV25hGQ0NDQ0NDw4eP9yYam+3mHdNripFxHKupuZzSloHLWOh8SWWS6FeTRf9uvWe73rAeR8btyHbaSrqPc0zTxP2ze5w9uM/19TVDP9B1HRjYbrecHC34+PEZXW95/vKKN5d3Uk5nMk8fnnB6NDCFxLOXl9ytIzFJ5pJVg4MMkDAMPSFEtmHEl7HWFMmRdENgLN7LNsN7V7sVStLWzpyrcqQQayJTnIL6IpycWGv/hWXPhADUtC6zMwQ767QMzqifRVOe1MtiQq4RtqgMSLKxsqZJCXkpCVlWY2lzdtX7UJhj2SAYo8Zo9RTIdTL0vpMNhjIO68pr2pGNnBM5luSl8rpk2C6FjM45+q4jhEinsqxuGHRzEcAakhEyZY0hhoBBiJmxBuslJcoqYZCGev1MErV48Pe/+x2YzPf++nsM8xk/+smPCdMvuLg8V3M85LQz/seaMlYM4hppq/8aY5BeC93xSGpUkcVpAaKTz2aaJlIMdF0v1yTJ4ybdZFVJnpI3CSGw9XPXahNJl9KtRpUhltepm4+GhoaGhoaGhg8d7000Fot5PXndrDdMSjJqlwZQTN87f4CYab21klTkpbn59vaW7WZLyqlKiIah12SpGbPFghcvXjGfD/R9h7EOby2nD0746NEJZPjqmzdcvBVzd+8tTx4dc3TQk2Lmm9fXXN5ssNap0XiXkLVPkjCGznd1oMv6frwX+VBKWbc3QDZ7UqEkkbUZ2WaYrM3NchWKp0BGxKyt17ot0E2Fs1ZIkCZCxaT6/WIixlRil6JsRHLOuHIibg1BPQqFPJTT8VKAaK34TEIIeO9ZLBaycVHTt7wByDHKIGyKIVmJS0yMecIgrdUxBcBiHFXKk4qkp6QklcFavQY5J22OFymVMWK8dlaTukqXBuCMxM/GEPG+ryQo5Vgjc531QqkSjGOQzzgmJSuSGPb73/+O+XzGJ598Qtd1/O3P/pbPPvsFF2/OyQYpfHQWrH72SshSUuO8s+o1ybX7ohCE0tZe/CfOe9lAYap5Xgio25nujUYnm1zJn7yX3cbJ2CzPD0o2TXX9G40HExkctc+koaGhoaGhoeFDxp8lndpsNqxXq9rjkGJUo+2uxdhaQ993YrDV1B5rLH3v2azXrNfSibHebkB17c45htmchw8fYqzj7dtLhr7j+PgYkB6JeycHPLi/YBwjz15ccH23IcTIrPc8eXTEwcKzHidevbllvZG4WhDJT2151nQk4y3GaZypDrrSRK0RqCqTSUkM1lkvQDH6Jj1Nl6Qgt5MgFflVTpikPo2cpWIv7xuqi/dB59mkp/P1JH1XjocOq+Vr1lhwVk7nU65DLoix21nLOI1yeq5DdPE2jOO4G+ytZZomxtWI9xbX93LCr/G6KWoil7H1cw0aAyskTORWWVO5pIkcUgqURvHSDVKIqJClJF4TYBqF8FgHOZeOD4d11E6KEHUzUqhsSnjrAJHtWZfwWKac8X2Hs5btOPHrX/0aZx1Pnz5lNuv5yU9+wmf/9AsuLi/pfbcrzzOQdRtUvCGd90zbEVz5np20aZ/MeeeEEKJbCWtqR0mRlcnmxQuBAnJUXwq7BDGnBNMaTevS34tKOvVesSq9c12TTjU0NDQ0NDR8+HhvonH59u07PgxQb4B2UnhN87HqS3BY4pRwGnV6s7lhvd4SwsQ2jHR9R7ZyCjyNMpz+67/+AWsMj588ZH58rOTAcP/eIQ/O5mw2I3989pa71UQMifnQ8dHjQ2Yzx2o98fLNDett2sm3jK0n/UXeBCVtSmQzUQmRmKt3zddFnVK2AylnTCpeDwta1HZwMGOzHVWSI4O0c06GdTnGFo7hrLRJG0OIk8h/kMHd6Ol11mhgo2Ztk+VahjThvJf3oYQkxb34U2vlVD5lYgrkmDDO6vdYNRS7ej0NcgrvrMQGS2IVkKJ4K3LWlKhMNLLhyTnjrWPcjvV/W91QOe/Ui5MrKclJTuxd2SqpIV58Elaq6fTayqguvRVBN09C5jq99kWyZneSL41U9mroTzFCsHTe0/c90zTxxRe/ZD5fcHZ2xnw+54c/+pR//sUvuLx4Swri7ygt8EaSf8kma89LZuh6tuoJKfdCSeAqJu0pBiGKalpP+vmX0IQQQv0dsqgZHNnaFDlUka4Vclg8GMZQPRvlH/rB7/wwDQ0NDQ0NDQ0fMP6Mwj49xVdD+HY71qFLp2Qx5cYsEauaphNTZjOOEAPbccsUJ5IVGZLN0PczHj484e3lJfNFz+npGbOZ9GPMBsfj+yccLDwXV7d8/fySKQhxODxwPH14xLz33G0Dz19esxlF+lRiRd+RMhnIWeRRIKXcqNeguiUKgUoihdrp51XPH5VEqMfCWsNmuyFn8UqgJuXic8gJMDq0x0QwEe+kAVtK2yQBKmlDt9XtTZYf01NuW0kGCAHZGYj1+UrTdBKC4buuDsXlRL2eyMeERbY81uvzaTqVrR+lyKGcd5oiJQQqQ5XDGXHGK1HadYWg/g8hdxZjnRq6wRqPtZkc1bOi0bC5mrLFtF6kajFHMdOb3fXaxb86jHUYLMPQsV6vmcYJZx2d6yEZ0hT5r//4T/z4Jz/i0eNHHB4d8pO//Qmf/fMvuL2+K95/ym6hlPKVZKoxjDhva8N4TlkSw2pRJXRGWtuxYLLBmyKZ2l1/yGridpRdhrFWZHDsfE2FpJTHLq/Nd07DCfT7m3SqoaGhoaGh4d8B3ptonJycAplxmlguVzuZizF4TZCKk5xYi1TEsVmvZaOQMlMIGG/p+xnbcWSzXmvykeHLr77i3r17HB8fixzFGI4XM54+PsZ6JFnqzQ3TlIlx4sHpAR89PMb5zOXtxDevrtlOocaPpphx3hJCoBTTFZP2TrK065QwmkRVTuKt93Rdp+3gQVKfYqjFflGL5ox6EnKKJG3WttYRUtACaZHjJLIMh/XfypZEE6as2cmvNOI0pwRWiIjLTrscxHhvddi1VoQ7ZSCNOTL4TrYoZJzzNbWplu91XgiMEgyJt/Vywq4Dt9+Pu92PugXts5hqVDGwM1Ynaf421tJbqz4W3dyUjVKWNCshRQ6LXIOIIYzSdVL8Ls46ki3xw6kGBpTYWQNMuiGLIdL1HSknwiRSODBM48Svfvkr+pnn9N59jo4O+dnf/YwvPv+C68srYohkt2tJScimpvSsyHNn7fWQ7RxQyVvZSkj6mPhbpkliefu+l62JEq8Uc/UDGesqwTTIZ+G0Mdxq/C5GtzoI0fOdeF3COP7f+V1vaGhoaGhoaPhfivcmGlMILJdLtpsNAJIGKqfgKUZICUfxZ0TG7cgUJhmMopxCeyfejcPDBScnZ2y3I9vtyL1795gNQ32u+ycLHt0/IJnMl8/fcn5xS0yGvrPcPznkyYNDnM1c3U58/eqK7TaKeVZ9GMZK6zNeBvpYTbmo0Zt3CYMO7jGqQTdrv0HataH/Nz+j3gUZTvWkH91OGHZyKDUUZySVCO+VnCTQUjbvxPwb1b9RkrxKaWAKiUzC2GIENnXQjSnincc6L+85ifHd+5KllXGdDqs7y75ExW4Dvca5YoxIqFSOVUrqim8l5/IeDdY4vT7y/UmTorIO5iFMQijsbjtSXNBOez5CiBj9TEzKJJJE4MZIRuJvQwxCbmr8sMia5ot53fI4J6V/EfEMBSaRYal0KcbIze0d//iP/8Tf/8Pfc3pywuJgwU9+8mM+/8XnvD2/0OhYcEYM9kJOnW4xZENTzOjG7fo83umSUSlViIG+7+tGab/l3ejKKMZU5U9Zt4EY8J17x+gtDehOY5+tXgfzzu9KQ0NDQ0NDQ8OHivcmGhcXFzpMOTo9cfbei7dBdeQmw/L2lu0kSUUhjGLS9R3ee1bLFcfHxzx6+Jib2zvWqzVHJ4d43wPQe8vj+8ecnczZbEe+fH7J5e1aexkSj87OOD6cYQycX6548eaOKRaTtKmGXGNyJRYgDeLGWmJKIhmytg7KVk+WnbM7U7UxIidSr4KK52vykHee0oMQQgQ0dUqHSlujUsUsXwr6vDXEKeC9JGmRMk5L+LBOUpegphzFlEhBT7pLKhZJtf4l5UhaxbMar5ORKF/pOZE9RAoB65wYtmMCJx6DfuhFChblWqXkSDGTcyRGiaR1nVPCJPKvjEiaUkzVVB2DlPl1Xcc4SjP7FCackaK5adrinZAdrL4+MuvNBmNkcI4hamqVxahMyKDX2hhiynTdIFualMmTFA6CkIAYJR3M2UzIAZMNne1wnWOwhs1yyz/+f/+R/+1//wdOTo6Zz+d8+uNP+eKzz7m6vtYtUaCIAWPQgkb1g1hr6TpPCJFpGrWgT0zpORtSlE3GbhOmcjg1z0talXwmlUxo5HHOEhvsvZdtkkr2CrG11tRtmPxpHo2GhoaGhoaGDx/vTTQs4LpOUoOy9gaMEzFGDhYLpvVmb+DLhBiQaE4ZmLfrEdd5Vus1//Kbf+X46JDTeyd46wHLydGMB6dzFjPH26tbnr28ZrmWxz8+nPHowRHHc/EMvHq74vxiSUi5xqQWAlHkUECNiwVDiIGMUY+EpkMZHWgNsvko3odpko1A0v4NPZEvA3AZvMsGpWwwYkwE7dzIyRJCxDqNNI0J11nxZFTnuCYJaRGbDO1qLu66dxKjZLvh1AoiG4ZpmnDWkowQhxQTXd+jWiW9/vL55RQJWq5X26oRn0QYA8VeMY4TzokvAbKSCyFSxov/oDddPa1PcWc0DyFCNpKaax3WODCZoR8wVuNjMZANXdfvvCNkuq5IjHZED2DSrYYYrOWxs0bPZo0By8imIcdIMdxkMuM00nedbHtSZL1Z8stffs5Pf/pT5vM58/mMH376Q/75n3/B5fUVXSepVakY8VMiaTxUjJGUY7n1iTlhEc+Fs47kyvZM7kWU6GaMyqos1kpYglWSmEvZoTH4zstrV/+P+N53m49yD5RG+4aGhoaGhoaGDx3vTTRmnWU29+Q0Mu97Ig7nPd+8eM319TUHs4FtGNmMG4lsRbYFYdxydHTMg4efEHPm8uotDx7O6PpZNRXfO1nw+P6CTOab81tevrlFahEMh/MFTx8dMRsMIWReXS65vNkQsyFhsCqNcrqxKEO2Vy1/SJOkGqkPI0Y1Z+/5HQoxgeIfN/Wx0l50b9IYXGMNMYthW9VN1QjurK9GaWdLvwi61Uh16wFGPRfqtZCYJyESnSPEJAWHzooHJGb9GRlWsZIUtYtcha6TE/Y0id+ieEZ25XGypcG5ShRCiuzCnDJdr9cjq5wqmyoDQyNbvbOlOL1Kg8pz5JwwWUgaVj0qOVdje0yBcRv2jNhGChkplXiW0gxujAz9Sf0NvlNTf5IBPAWVtdlcPR3i0YkSG6ubJNNZ+r5ns46cv3nLZ599zqeffsp8vuDg4JCf/OQn/Jf/8l+4u7vD+Y4UI5GI8xJfa4whG407Rsif1XjhnCEx6VsRUuGdw9iy1cj0vVyPECeGYVGJnlPfjFy/0lqfGMdxT+InF6pEIk/TxN367n1/bRsaGhoaGhoa/mJ4b6Jx//4BnbcYO2ecEnGS+FRvEwE5vd9ut6ScmWKS4dEa7j24T9/PeP3mDWGaOD07oe9nZAydszx9dI97Jz3jNPLV87ecX66YQoScOT2e8+T+IYM3jFPk2UuJr00paZ9aJlvqiXpOpclCBmITjab9iLRKBmUZ6pw3GDXq7jwKOx6QU1Tfg8iuUkq1P8Rq4lQIAWscKVtttHY4BylH6VQwnRZlSGiQ9EGA9UIe5KTe6DZm19VgUoIE2VpCihCjeh5EohbJVcYmsjEQclQesxTB7RrAc07ii9CkVKvEzOr2IsW4i7TVLoeUM1lJxjSO+OJ9UV+JPIdcT2n/tnUDVCJwU4pK1tD4W5jNOsYQKeV3mUwKQT4zHLY+Dgx+IIRQSwQx0nwetWOi7zr1tsjqYdczIuwpBilHtM5xcHTIdrvl/Pyczz//nJ/+7GcMXc/BwQF///d/z69+9SveXl7iOrkexYtDKoZ9uTliSsSQNcJ3R1RLd0vK4lcqr8EYcJ3DdRJlPJvNVHYom78wBayzeO9IJULZSc+JM363pVE51WKx+LN+yRsaGhoaGhoa/hKw7/uN2xHuVpnlCjZr2KyWTOMtd6s7rpdL7rZrkjGiW0+RofN89PQph0cn3NzeMk0jh4eH5Ahhisx6x3e+9YDTI89yteV3f3zD+eWSKQRSmjg5Gnj68JihN2xi5KsX11zdblSaJZuAGCPTOBGmqOVvcvLr7K4PoyQUhSjDc9dZXGd38iGVInnnVHOPdkNIIpYkTokp2DvpoUCzmIbe0/cDJIk2NTmTY5RNR4IUItMUIEHMkaimX9H/SGJUDDJAx5DqMD3FJElVOUEUY3LOulXJsUq9avkfSMJWEulOyIkwjeQYxEDuLc47hlnP0PdYa3DO0HdayJd3Ld+yC8lsxu0uISlD73zt6hCpljZZ592fGnacEup1rylS1jkSElObMTqUG03Okm6Sznd1q5OitMaHaYKcJcWLTNbkJusNs3lPJpJCFF8LVj9H7Z6whuxk8B/HkTBFetdhjeXNm3N++atfElLEect8MecHf/MDZrOBGCYhhUn9MFA3MgYlWsbSOUdKQV4zGWu1j6PE2RpJpyrXsXMWZ4wEJYyBbKAfBhYHC/q+r4q3rpf+EPECad5Xhmk7yhYrlivd0NDQ0NDQ0PDh4r03GimKbGW12TBNW1Jck0IkRcNmu6Xr5aFySpycnfHg4UM224mL83NmvefeyalEvFrL8eGMjx4e0XWGy6sVz1+95W49krJo108PD3l0dkDvLesp8vL8lu0Y8X7X1yDdFgmjvQVO5UA7g26RFJVmax0cNQ0IKwZe0byXgU68CKX/oLSfF6mMfJOebGcZeqc46msS2Q9WGsJtLsN0lhLDJKfxRj0JMe9LZmzV4htrMSkxTSNTRjwGxuomwtRrXGJec5bo4JwzFmnszimprEukNtZK7GzpwMhZNhEpRfF7FEdyktZwYy3DMIg3IYTqezFVQZV087O7tjXGVzcU8u9OWsq1OdxZt7dlkW2KwTCFiFe/jNcSwCI6204j3rnahWK9U8KlZndr8d3OH0FEk7Bi/foYpF/DYLGdZ7aYk+6WnL8+51e/+iU//vGP8d5xcCibjc8//5zLyystXiybLFks9V1fZXopRjrvtTxQ7itrbY0Sdkp4SuCAyKEM4xiYpomFmTNNkxBGLSAsccwpJUIIDLNZ/cxzFt9JISANDQ0NDQ0NDR8y3ntiMSawXm+4Xa1IMTB0kSkaQsykELGa8394fMYUEr/97e+xOXN6/5SDg0OylcH4wcmCh/cPMNbw4vU1L95cyem3sQze8PjhCUczj3OwHCeev7xls5WEoaKR35XRJT0xLsPcREpZ26Vz7TAwOqQVQgLSl1GiSZOJOCUsIi8ypAhd51XaFDUqNas0Sk28ajL3XmJNnfNkq9sJIp3tyAZizljn0eIMkqUSGVUD4YxKZcQ/raf7AEKuXOfEbG3Qjgvq/7fW1ffiEL9CdpaEtnYj1xdja0pWCCo3MobsXd1OmNLyDSQSfpAuDmJS+ZeYxH3nSXH/fRhIEWMdnTNMIch1MxbTScdGtcJYCylXj4w3lmr6cLJDcDh5ft2GWB3eQ9iTYqlkKpuoHSklLjYzaa8KQDdIT8g2bIkpMu8WzOcH3C6XPHv2nK7r+Ju/+RustcznM3706ad88cUXXF5e1tjgIo/a6qDvvBMDt76nWMieE0/M0Peg6Weg96kXYtgNTrpmcq5ElixyMGOLqV08HGXrhvUYJ90kJSCgoaGhoaGhoeFDxnsTjevrt4SYWa+3gJywdv1MBy7L8fEJDx49JOF4/fo1zhkePXhANwxgPL2Hjx6ecnw0I6XAizc3vHh1JfGs1ki07b0DTg+EkCxXI89e3zBFGb4OFgvW6zXb7UjORjoXQqpDfym+yznX0r+s3QoU860rmvkSN7ozZo/TJG3lVjQ/XScDd5H7pJIEZMQMTkJPsDUhyWifBobed7JZ0JN99roSpGlbezKsyIVSDGw2mxprKiSKapafYsQgJYRZY49M1sfKuzZ02bp4Uoq7CN8gJ+tiYpfvNVYkbrs8I+mjyGpSSTkJOVJzc2lTF3+8/Px2O0ofRDFz69tMMdENA5vNlpzVt2KMpC7p9c+GGvs7TZN+BFo+SPGpgPOO3jumGHSzsr9NQQhFnGrniNEmc6ASw/ImrfUYK43o47jFZqNJV4Gvv/qKoe/53ve+h8lwcHDAp59+ymef/YLlco3XQkN5XEvKCasEsBQw9kOPsWreVvmetdIFgsbXllCpoe8Zx6nep8CuTBKRmjlTpH+66cq5ppB12ovS0NDQ0NDQ0PAh472JxjhOrNaTyEQsGDcjZRn2vfdsxsDzF6/ZrDfMZgMfffRRHWbnM89HD49YzD13mxXPX1xzc7dBFEyJofc8un/M4VyGwcubNS/Pl2zHhNG0n5vbJeUgt2wz+r6rqT0lShclFCnkKkcxapYGtHjN1KK50jpdEpWM9TVGyljxZyRQ34duVbIhxQCaACWH1pJMFFPCGyvFhTHiu06SjxBJVdL27BAifV8SluRFGmclNlWlSMYKeckJCHEXWWtdTbMqxMRmaRq3xhJiIoyTbEY0NlbSkjyuc5IGFoNE2VqLzdKJMoWgPQ5GzO2IIbxshjzg+47tOBKCvH/jpLIwqd8ixsh6vRZfQnZSfBe0sFG0YzWuNoSoMiM9+U8iK8v6XuSaGkkKy4mu8+ptMZoIBU6jjaPGLlvj1NhusU7kYCki3hn13IRRmrs774hBNmC///0f8L7j6UdPMRgWBws+/dGP+Od//mc2mw3ZWPp+IMSpmtydc0LarGwo5vMZ2+2GcdxiPKAlix5L1g2FB33fTu/aklxWkqgMYRJC2PVd3V6kGLHeYtR839DQ0NDQ0NDwoeP9PRoag2qs1VZm8Sp861sf43zH9fUt11dX3L93xmJxQDaWlCJnx0d89OCYfsicX93x7MUV2zGrHSBydDjj4f0jhsFgbObics3L81tiln4M5+0u7SemKn/yvqsEQvoVxI+B/j2rLMU5g1FZUhlwAd02UM3l9URfjdyIugfnPBZJAYpRSxSsyqeyIYUshCcnTbhyTGEkZ4exlmk7ytAcVe5jASe+jcRO1mWdqz6SYvTOGi1rs5TBGTXbV/8JgJVehbJ1QDcR4u0QClDSrqYYsbpF2G43It9Rz0kIoQ7lcUqYnME6sJJmZY3BatGgMbYW8OUi38oQdJh3TqVRJDVGB5EEmayN6GLTKB4azd6t2wDZHgipEwqjPRrGYr3Tj6A0bztCjvhOtw5qbDdKloa+k+1LjpCt9J4EHe6tZZjN6HNmGid+85t/JefMJ598QkqZo6ND/vanf8sXn3/BarmW+8lKelRKma6Tokkhs9KB0vd99fEYstp25N7KSeKF3+lG0S1ObVRXuZfTvpdxHFValbU4UHw/DQ0NDQ0NDQ0fOt7fVWot/eCJyWKM5+BwweHBAeM4cXFxyXq95P79e8xmc2KGzmYe3Dvi/ukBxga+eXXHN6+vGScZIp2Fs+MFD+8d4roMxnJ+ueb87R3bMaJHt1gkLlZ1KppaJCTA6LwlyUjSEG3VOxH1ZDwEMEbkP9kY0iQdDt4VORVY25UHojRqk2SrIdIpGSJjSjgjQ6YvhKUM/EYSr5L6OajFcvo6jaU0lpMl1UrSmaLImlS2ZDLEEIkpMVMjsLWWfjYTgqF9GKCcJxcrR0lwyphkxKNgMt3QE2MkAE6yYbHWMhtmGGMYw4jLtvZDSAWKGNSdtUjNobjAwzSpudyStYAPMiFpVC2m+mdk+pZr3A89U5hk8DZC0rJM+gTdMIn8SYoKcxYfi+0cjBM4SxglcSnp83VdV6VpzlpyyNhsIRn1dhg64wkxyfbDGGlAxxBTgCBEJqeM73pSgvXyjt/+9vcM/ZyHj+5jjOHevTP+7uc/4/PPPmOz2RJjwjnxEDmnHp8sZGqzHglR+jf62UDKELZS/riLYM7knNhuN3RdXyV8nTd43+n9l+pn4QopcaXsJDWPRkNDQ0NDQ8O/C7w30YjR4DrP4Dt810O23Nwsubm54mAx5+jhQ6z1ZDIH857H94+Yzx2b7ZaLqzXnb2+ZghiCOweP7h1zdjIweAjZ8uzlW65uRsRSIcO8c3KaLqf8SbYYqC8j6ykvWQzcZVBHdPyi9d+1VltriSGqqVheh7XazJ2S+A2MlUHPgPNeBtgS1WpgGAZyTGzHEaOPW5rPc5ZB33kZ2o21TGNQX4gMpSkZ/bvXejo58S4biZRT9TLkvThXrMSklhJBZ5Rsad+GIevX5H3iDBanQ6pseLxuBEpqF8h/xU+S9DOO8r69k7hYY3AIrZHtjry3ksrVdUX2JPI52ThlYiydGhJpm5JsomIIhBDw3a5ZnJwZJ0mFwmRyCLqpKuWAnkQG76U13YlMqXg5IBMnMZ53vmOMAd87pjGQYmKaAvP5IDe7k6b4BIQw0XWdpD6pxMz7I1brFZ9/8Tk/7X7Co0ePMCROT0742c9+ymeff85qtRHSZEqRodyjnV436yz9MIAxjONWTOEgW6qc1VOT5TNXf421lnEchYSlzDDopgRJ8JpCAHKV1OEa0WhoaGhoaGj48PH+E4txzGYLHj95irGWb755zs3NNYeHBxweHmN0K3B8MOPjx6ccHDg225HnL694+UZM3VKwZnly/4TTox5rDGO2PHtzzdXtKK3VKkuSwjiUAJg6XEpDsnobcjFcWzCuejRylkHOWZXKqNG2pPikKIbvoIMoxuA6GXRrXKtzDMNcdgc2SxRrzkzTKANiTlUr7zRSV8d3QJKmsJCtIZks3RY5kEikHEgpyKZGh/CcISbxJhjrMM4Rs8TgWqwmaUkfR8qRbBIpR2IKegJu6kBfZW56rZwV+Zcp8bP6uncxvhBj2Pld1BsSQqglgClHNpst0xTEExGjeFzI4vMw5Y90kiSN6BWyJ/Iu6zz9MKMkdqFG6tnQ6aCuciwrXpocIsvVimmccN4D0mRO3hu6c8Z48L3FDxbj5DrGEOkHTz90snnRRvcafaWblzFMhDgJeVVp2zht+dWvfs3d3Z32sljund3jp3/7U05PT1gs5vS9tplnMeKnlEgm0w/yHu7ulozbsZrek8rfpknTqbR8se878cTopqLr5B4NMVBSh/uup+86vBX/Se17aWhoaGhoaGj4gPHeRGNxcETXDbx5c85queTx4wecnh4zDEONk713tuDJkxOMy1zfbPny+SWXd6PU2yUp6Xv84JijQzF9r8aJL5+/5fpmxGg1Ws5yOi8nzSLh2fe+Ji1Ok4FWiISxmr6UdwlJpd1aQpq0f0Ifv+t71eiLKSMXj4BuCgwWi2G7WUmXRExMYVK9vJ6oZxnEd3p7HdxTImbqSbVV4uK9p+97vLcy9GfxGaARtzFFTbASKZXNuyK7mCMhTMQYhGipD6VG9cqLxziLcWUAR6VjE+N2W9OjMrskJvR6ZN2yeO9rqhVkTGkNV6JW+izKtsLsPc6uAdzsrrOmI42jDNgF4jWR7RFZ/CDGoMO1wdqyAYoMs4G+l/LEru9EspYghUQKYvp2xmONx+BwrsMo6ZzGwDQF3WDJoJ+NwXc93snnsZjP9LkCGPF05Jx4+/aC//yf/zO3t3f12jx8cJ8ff/pDZn2vhX2dyp3kmoUxsF5tyFECAUDukaSyPpHqObquE3JcPwhTu0pK8hgU/5BsiJKW9BkM2/XmfX9tGxoaGhoaGhr+YnhvohFjYLVaslzesljM6bqhtjs7Z3j88IgHZwfkmLi8XvHs1RV3y0k6AXLk8GDgyaMTjhYdzhvWU+TrF1dc32yYpsR2nDSFytahSzwZMnDHlKSYTiU1IpWSly/DvNFeCJg0irTIa8ogWORKOaX6NTFClwboXFucS0FaiXxVt4U+rtGB28nJvsbcyhifgIR3tnomZNBNSoayEgPptSgN2ClJUlRO8py202I6tVuLzCaSUiTEVL8Pq1KzKASlmqGRBChjDb4XU7e8l0zKsm2IMdQiwnEc2Y6jGLB1qC0bJr3M5BTZbtaEINKoRGY7jsSUCEkkZzGKB6aQICkKdPR9R85RNyeBHNOuhC6EuoWJSVret9tR3psEVUkZn9hmmEYhTilnkTFZuZyZhHGGEMT4HyZJ0ZJmdK99I7Z+fiCm/LJdcNYyzAYODhacnZ2yXN7xn//xv3Jzd0fU+OSHDx7wNz/4gfa2JLabdfVdGMTIvl6vWa9X+nsTxchvxXC/Xq9YrVcsl3esVivWmy1g3gkDSElI1DRO6vtJjOOkHSiGsJ3+nN/xhoaGhoaGhoa/CP6MeNstKSVOTk4AjdjMiWHWc/90weHcEqYtF1cbLq7WTEH15xjmvePh2YL5IMPccjPxzatrVutJNxS5NkJLW3XCacKVtU7SkowSCpX+5JTFJ2El/jaESTsOkPjWEHB2185tDJgkRmxTHkcDfBypnrhL8hE6PGaMEWlQzmLkTSnhOpHNOCfJWCChUqWVnKwRuIj0yWgDdjFZR20oL43QmN3PZhKWTNSUqRgC/dBjTV87KIgZ60UalvV7nLE7qZOV53PWlI5A3fxYcpTG6c555U2yRRFpGrWBu5YZanu6MRCAru8loStLE3dpwRb5lsP7Qr6kNK/vu9rGHsvGwjnGcWSaJuaLBcZaMLrFMYAXX0YIUUmSXGBZmMimJRttcQ+ZTjcxEhWc6QaPSRnve8Y4Ab5uE8o9qcxTtlh212FBznReNiiL+ZyUJn7zL7/i53//9+LxMJknT5+w2qz57BefabqWfH4xi/E8qiE8hiBSKCQkoOu6mnrmtTXdO1d/t4wxLBYL2YSpsb5sOax2wexkhA0NDQ0NDQ0NHzbee2LZbLYcHx/XLUDOicNFz5NHJ3QeYsy8Or/j+nZLSla7DDJHRzMenx3Q9xJ7ut1Enr+4YjOGWjJnS/FcLAVlWQdvQwixynWMsYQp6FYiaoxt0PQjtLBPdO2lM2GcpioJSrG0U8sWJmXAZEmSqsZxkTuVjYcz8pjWWCJBZVNiQi4RuZX46EYl57IBkeRW5yTqN0eJmpXXIyRDgpvkuWyJ3E1By9xg6Lz4BzTtqPRskLNsLGS1I5IvNWuX15FikqK4bMhTJBnt4siZST0Z0qy+az8vcqhimDfGqrnbgTFM00gIVuU/Rl9vIkwT3mcwGplrpd07A533THESSRNGCKQDr8lcKarJ28rXsYa+c+Ss5vwkBXimeiLEdC33iK2xuruCu6z3lWFwHSWKuWzJrJPPKqmXo2yxjJWo2ZTFhH54dMQ4jlxe3fBf/ss/8YPv/zX37p+ScuLb3/6Eznt+/at/4eb2Bsga/WyFHAGb9YYQxYvjnXzmfSemeeMNCYnl9V4IUKqEGjV8Z92c5CpNCyG2wr6GhoaGhoaGfxd4b+lUOU1954fNruNCUpCyDvI6+BtwOsSVCbZKh959dFC51LtlZP+9vgCz97XyfPuQf1Au8D8H8+4z/o+w72v4c54z/zd/2T1GIUzvfP3/zwt558vF8/zO9foffG/5jv/Oay9ERv6+/+/vPtY7l9u8+/j7L3/vVti9ycx//z3+jz5+3cSUx/u/fAN/8l7+9B7ef5L/3jXRACyiei2KLA1DTZna/Q5oktjeI79zT5f/1A/XFFb8znX+H+N/9oZuaGhoaGhoaPhfh/feaHSdZ71ec3x8VE+6b5cb4svM/bMFBzPH04eHdH7N2+uNnsRGrm62TGPk8aNjZn1iMfN89PiEb15ds9mKdCoZ6RDwXae6/aSn8Q7vLCGMoP0WcppvMNapIVmkO2ESo3Q2hpzKtkJO0zMqw9J+C2Os+g6EqIhnQ7Tw1may+hbIhqjJSGIYN9KT0Plq5g4pysZCiihky5F1cQDVqC7xrWI2DzHJFqf0ZxQylqWgznlXh9NpCnRDL68vyQOnmLDe7hX15Z0nwxjxVaBN3VlO6J0XD0COGdh1QVjdtvxpN4MxVt9Drn4GMXj3teMhp0TW197rv1tjq3QqamdH8Y+IdMqCk6brECP9bNAOEDX9l/etXSJD10mPSYwY9aXEFKVlPYvHZT4Mcr2s1QuvHRkGphBqxHHZcqVkgKgyJClbzGjamRGvT06Z5d0SYzKnp6f8wz/8nNlsEIO+tTz/6jn/9F//iRgS1jjZNqVEiplpHKv/pOs7jf6VazxOQVOl5J71ztfgA9m6qNxNtzMh7HeUOLrO7dLBGhoaGhoaGho+YLz3RmM2k0jP62uRiVhrMdaxGSOvLpbcbTPdMOPxw1OePj5hsfBVbrMaI28uV2xGSZQ6Wng+/uiU2UwNutYRYiQniXbNSAt3TpGco0bWyjZENiLaYYAYip21dH0HRrTsIQacF7O2eDTUc1GIiCYt5ZggJWJKqp+31fhdehKytcTitdCo0xylzyIWuRTiPUkxgTaNZyMkwqnhOyODb9Io2qxSnTKQh3EiTAGT5Guu64QcqcwqpEDQKNvSfi7GYZHXiBfE47oO67x4N5TIWJXhZDLGG/zgZVjPZWA1aloeCSESYzFtl9cq190kdiZyjawNMWCtU6JiCSHVz2GYDeJVUC9K3/VCuHKm73uGoVdJW6r+k5gzYQyEcWdUJ0G2Rq6FMaQpYRK1cC+EIPcPsi2YtoEYMpvNWJ3sWaVmOclzZN1KxGrQl/eIRsuGGFiuluRs+MHf/BBfpWKGN6/e8Otf/wbne7LRvpAir5siBkOYpJMEIz0rpZF9s9kwjiPrzUalaBPDMKMfBvphIKTIFALbcRJDvMoHx3Gsn/d+gldDQ0NDQ0NDw4eK995oeOfp5guMWbNcrpnPe6wVMhFC5uXrW0KM3D895P7ZAUPnePH6muVGBsDbuw1xCjy8f8TBzHPQO7719JTXF0s2G0nTicV3AaqXp/ovnHNYdAtQTt9LH4QO3ZmMw2rCkZwCy0mxRtDC3mNLJ0dMCe+dpEUZIyTCZox3kKKYffPOi1E8AHJ6nRiGQUzbMSprs2SMbi20k8NqWR6I6RlTOyysEz8FIePUS2JUk4+RrUlOu21O+VOTtLTx3DrdgigpK5uIlKJsRTovTg7dOpiuxAkL2ei6ToZ93YSkFMWrIp5/eWfO0dtOBvQgyVp9J30oGUhRDPI5ZUKMGmErA34ImWEYcE6vo7PY7KSnovNgHFNUb8ogXoeYIhlDNhZnjTjuM/TDIBG/KWG7TiV7uZrUve9IIeKcJ4wRvCU7/R5NnhLy46vBPycw3jBGiTEex5Hj42N+/nd/x+nxsZQkpszl9RW/+vWvawP8bD4nhIBBfC3WWWazuZA/3RSVYkDZIvlqoN833eeUMM7VzZIvGyjkvnBIkEBKEd83j0ZDQ0NDQ0PDh4/33mgsV7eEMPLo0UPmiwWvX59zc3PDOG5lUM9wfr7k1ZtbyIazkznf/fiM04MeiyRBrbYTL89vWa7kZPlw6PnOR/c4OuqQ/mn1dRgjZmNrtcNBX6zG2KaciDrM1iHRON2OWB0orSb2SMSsHGCLLGYaR3KWfowSwQpUSUo2csI9zA40rcjSOU/fd8Qo7dZAHRiBd4mAQYdCGW5Tykwhsh0nac8mk40aE7IkYVkjpl+LpEMlI6f4KSe89XRdrzIfamt3SbwyyL/lmMhRYnDLVsb7nmGY6XbHVs9BjRDWa17eV0ndAtn4iEFZvnfSXocSyZtzdW+IQVvTrtDnDmESQ/sg8qGyESh9IyLPsrplAm99vV4xZgyWzXrLOI2YnAnTJFsJA8ZbrBLEmAOZiDFZCwwj2WSGWUfXd3JfgPRxAGEaiSEwThOr1YYYZSuUs2GjcbOnp2f8w9//A2dnp6QkW563l1d89stfst5uiCSmGFQOJh+C7z2LgznWWYLeF53vNC1NjeaavoVBSxUBs/N27CRUVPJonVXpmxDIYTF731/bhoaGhoaGhoa/GN4/JzMl1usVMYgk5OHDx6SUuLm5wQL90JOxXF2vSDHx5OEx83nPx09O6N+ueHt1RzaW7Rh5cX7NE3vK6aFl7jKfPD7hy3DJ7XIiJ1P7BFSNI0NuCHRajiYEQlKJQE7lJT4W7V4wKv+J4H0dlIOecltnIUvikrFSJhijDPyZDDESjWW7XauXAqLJ+M7RDwPb7VYswcqApLBQk6ZMFvZmLSkEfUkaNWukIdqajmyTysLklD0ZIQel4TuniDcdyRgSCe97SYayTrYcgDW5voai6TfW4PaaxHNOxCRxq3WArbGpTiRD+rgipcqViHjvSUa3HMbRzbr6XNaa3cZCnzvlpCQhYDUpTGRZQizCNBHCRNf1xCTyu5CykFXXYUjawG6lP8U7Fk48NiFIupj1klSWdetjjCEFLcTLjhQS3SAt4tuNbCfmi/kudYqyCMvVo+Fch7VeNjwJrHd8//vf5/TsRIhMyixXK37xi19we7eUDUaNmZXPq/Mei2EaJ4bZnOOjQzabDVC8KgZjuloeKMWOcu/Ji0JJSGQYBnwnv5rWWvVwJLwTX1KOpSemoaGhoaGhoeHDhcnvxjw1NDQ0NDQ0NDQ0NDT8T+O9pVMNDQ0NDQ0NDQ0NDQ3vi0Y0GhoaGhoaGhoaGhr+zdGIRkNDQ0NDQ0NDQ0PDvzka0WhoaGhoaGhoaGho+DdHIxoNDQ0NDQ0NDQ0NDf/maESjoaGhoaGhoaGhoeHfHI1oNDQ0NDQ0NDQ0NDT8m6MRjYaGhoaGhoaGhoaGf3M0otHQ0NDQ0NDQ0NDQ8G+O/x+bZ9rDzKCvzAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "images, masks = next(iter(test_loader))\n", "with torch.inference_mode():\n", " model.eval()\n", " logits = model(images)\n", "pr_masks = logits.sigmoid()\n", "for idx, (image, gt_mask, pr_mask) in enumerate(zip(images, masks, pr_masks)):\n", " # Number of samples visualized\n", " if idx <= 4:\n", " plt.figure(figsize=(10, 5))\n", " plt.subplot(1, 3, 1)\n", " plt.imshow(image.numpy().transpose(1, 2, 0))\n", " plt.title(\"Image\")\n", " plt.axis(\"off\")\n", "\n", " plt.subplot(1, 3, 2)\n", " plt.imshow(gt_mask.numpy().squeeze())\n", " plt.title(\"Ground truth\")\n", " plt.axis(\"off\")\n", "\n", " plt.subplot(1, 3, 3)\n", " plt.imshow(pr_mask.numpy().squeeze())\n", " plt.title(\"Prediction\")\n", " plt.axis(\"off\")\n", " plt.show()\n", " else:\n", " break" ] } ], "metadata": { "kaggle": { "accelerator": "gpu", "dataSources": [], "dockerImageVersionId": 30762, "isGpuEnabled": true, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "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": "3.10.14" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/convert_to_onnx.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/convert_to_onnx.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# to make onnx export work\n", "!pip install onnx onnxruntime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See complete tutorial in Pytorch docs:\n", " - https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import onnx\n", "import onnxruntime\n", "import numpy as np\n", "\n", "import torch\n", "import segmentation_models_pytorch as smp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create random model (or load your own model)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model = smp.Unet(\"resnet34\", encoder_weights=\"imagenet\", classes=1)\n", "model = model.eval()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Export the model to ONNX" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# dynamic_axes is used to specify the variable length axes. it can be just batch size\n", "dynamic_axes = {0: \"batch_size\", 2: \"height\", 3: \"width\"}\n", "\n", "onnx_model_name = \"unet_resnet34.onnx\"\n", "\n", "onnx_model = torch.onnx.export(\n", " model, # model being run\n", " torch.randn(1, 3, 224, 224), # model input\n", " onnx_model_name, # where to save the model (can be a file or file-like object)\n", " export_params=True, # store the trained parameter weights inside the model file\n", " opset_version=17, # the ONNX version to export\n", " do_constant_folding=True, # whether to execute constant folding for optimization\n", " input_names=[\"input\"], # the model's input names\n", " output_names=[\"output\"], # the model's output names\n", " dynamic_axes={ # variable length axes\n", " \"input\": dynamic_axes,\n", " \"output\": dynamic_axes,\n", " },\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# check with onnx first\n", "onnx_model = onnx.load(onnx_model_name)\n", "onnx.checker.check_model(onnx_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Run with onnxruntime" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[array([[[[-1.41701847e-01, -4.63768840e-03, 1.21411584e-01, ...,\n", " 5.22197843e-01, 3.40217263e-01, 8.52423906e-02],\n", " [-2.29843616e-01, 2.19401851e-01, 3.53053480e-01, ...,\n", " 2.79466838e-01, 3.20288718e-01, -2.22393833e-02],\n", " [-3.12503517e-01, -3.66358161e-02, 1.19251609e-02, ...,\n", " -5.48991561e-02, 3.71140465e-02, -1.82842150e-01],\n", " ...,\n", " [-3.02772015e-01, -4.22928065e-01, -1.49621412e-01, ...,\n", " -1.42241001e-01, -9.90390778e-02, -1.33311331e-01],\n", " [-1.08293816e-01, -1.28070369e-01, -5.43620177e-02, ...,\n", " -8.64556879e-02, -1.74177170e-01, 6.03154302e-03],\n", " [-1.29619062e-01, -2.96604559e-02, -2.86361389e-03, ...,\n", " -1.91345289e-01, -1.82653710e-01, 1.17175849e-02]]],\n", " \n", " \n", " [[[-6.16237633e-02, 1.12350248e-01, 1.59193069e-01, ...,\n", " 4.03313845e-01, 2.26862252e-01, 7.33022243e-02],\n", " [-1.60109222e-01, 1.21696621e-01, 1.84655115e-01, ...,\n", " 1.20978586e-01, 2.45723248e-01, 1.00066036e-01],\n", " [-2.11992145e-01, 1.71708465e-02, -1.57656223e-02, ...,\n", " -1.11918494e-01, -1.64519548e-01, -1.73958957e-01],\n", " ...,\n", " [-2.79706120e-01, -2.87421644e-01, -5.19880295e-01, ...,\n", " -8.30744207e-02, -3.48939300e-02, 1.26617640e-01],\n", " [-2.62198627e-01, -2.91804910e-01, -2.82318443e-01, ...,\n", " 1.81179233e-02, 2.32534595e-02, 1.85002953e-01],\n", " [-9.28771719e-02, -5.16399741e-05, -9.53909755e-03, ...,\n", " -2.28582099e-02, -5.09671569e-02, 2.05268264e-02]]]],\n", " dtype=float32)]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create sample with different batch size, height and width\n", "# from what we used in export above\n", "sample = torch.randn(2, 3, 512, 512)\n", "\n", "ort_session = onnxruntime.InferenceSession(\n", " onnx_model_name, providers=[\"CPUExecutionProvider\"]\n", ")\n", "\n", "# compute ONNX Runtime output prediction\n", "ort_inputs = {\"input\": sample.numpy()}\n", "ort_outputs = ort_session.run(output_names=None, input_feed=ort_inputs)\n", "ort_outputs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verify it's the same as for pytorch model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exported model has been tested with ONNXRuntime, and the result looks good!\n" ] } ], "source": [ "# compute PyTorch output prediction\n", "with torch.inference_mode():\n", " torch_out = model(sample)\n", "\n", "# compare ONNX Runtime and PyTorch results\n", "np.testing.assert_allclose(torch_out.numpy(), ort_outputs[0], rtol=1e-03, atol=1e-05)\n", "\n", "print(\"Exported model has been tested with ONNXRuntime, and the result looks good!\")" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/dpt_inference_pretrained.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/dpt_inference_pretrained.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# make sure you have the latest version of the libraries\n", "!pip install -U segmentation-models-pytorch\n", "!pip install albumentations matplotlib requests pillow" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import requests\n", "import numpy as np\n", "import albumentations as A\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import segmentation_models_pytorch as smp\n", "\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading weights from local directory\n" ] } ], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "# More checkpoints can be found here:\n", "checkpoint = \"smp-hub/dpt-large-ade20k\"\n", "\n", "# Load pretrained model and preprocessing function\n", "model = smp.from_pretrained(checkpoint).eval().to(device)\n", "preprocessing = A.Compose.from_pretrained(checkpoint)\n", "\n", "# Load image\n", "url = \"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg\"\n", "image = Image.open(requests.get(url, stream=True).raw)\n", "\n", "# Preprocess image\n", "image = np.array(image)\n", "normalized_image = preprocessing(image=image)[\"image\"]\n", "input_tensor = torch.as_tensor(normalized_image)\n", "input_tensor = input_tensor.permute(2, 0, 1).unsqueeze(0) # HWC -> BCHW\n", "input_tensor = input_tensor.to(device)\n", "\n", "# Perform inference\n", "with torch.inference_mode():\n", " output_mask = model(input_tensor)\n", "\n", "# Postprocess mask\n", "mask = torch.nn.functional.interpolate(\n", " output_mask, size=image.shape[:2], mode=\"bilinear\", align_corners=False\n", ")\n", "mask = mask[0].argmax(0).cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot results\n", "plt.figure(figsize=(12, 6))\n", "\n", "plt.subplot(121)\n", "plt.axis(\"off\")\n", "plt.imshow(image)\n", "plt.title(\"Input Image\")\n", "\n", "plt.subplot(122)\n", "plt.axis(\"off\")\n", "plt.imshow(mask)\n", "plt.title(\"Output Mask\")\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/save_load_model_and_share_with_hf_hub.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import segmentation_models_pytorch as smp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save to local directory and load back" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading weights from local directory\n" ] } ], "source": [ "model = smp.Unet()\n", "\n", "# save the model\n", "model.save_pretrained(\"saved-model-dir/unet/\")\n", "\n", "# load the model\n", "restored_model = smp.from_pretrained(\"saved-model-dir/unet/\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Save model with additional metadata" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "model = smp.Unet()\n", "\n", "# save the model\n", "model.save_pretrained(\n", " \"saved-model-dir/unet-with-metadata/\",\n", " # additional information to be saved with the model\n", " # only \"dataset\" and \"metrics\" are supported\n", " dataset=\"PASCAL VOC\", # only string name is supported\n", " metrics={ # should be a dictionary with metric name as key and metric value as value\n", " \"mIoU\": 0.95,\n", " \"accuracy\": 0.96,\n", " },\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "library_name: segmentation-models-pytorch\n", "license: mit\n", "pipeline_tag: image-segmentation\n", "tags:\n", "- model_hub_mixin\n", "- pytorch_model_hub_mixin\n", "- segmentation-models-pytorch\n", "- semantic-segmentation\n", "- pytorch\n", "languages:\n", "- python\n", "---\n", "# Unet Model Card\n", "\n", "Table of Contents:\n", "- [Load trained model](#load-trained-model)\n", "- [Model init parameters](#model-init-parameters)\n", "- [Model metrics](#model-metrics)\n", "- [Dataset](#dataset)\n", "\n", "## Load trained model\n", "```python\n", "import segmentation_models_pytorch as smp\n", "\n", "model = smp.from_pretrained(\"\")\n", "```\n", "\n", "## Model init parameters\n", "```python\n", "model_init_params = {\n", " \"encoder_name\": \"resnet34\",\n", " \"encoder_depth\": 5,\n", " \"encoder_weights\": \"imagenet\",\n", " \"decoder_use_batchnorm\": True,\n", " \"decoder_channels\": (256, 128, 64, 32, 16),\n", " \"decoder_attention_type\": None,\n", " \"in_channels\": 3,\n", " \"classes\": 1,\n", " \"activation\": None,\n", " \"aux_params\": None\n", "}\n", "```\n", "\n", "## Model metrics\n", "```json\n", "{\n", " \"mIoU\": 0.95,\n", " \"accuracy\": 0.96\n", "}\n", "```\n", "\n", "## Dataset\n", "Dataset name: PASCAL VOC\n", "\n", "## More Information\n", "- Library: https://github.com/qubvel/segmentation_models.pytorch\n", "- Docs: https://smp.readthedocs.io/en/latest/\n", "\n", "This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)" ] } ], "source": [ "!cat \"saved-model-dir/unet-with-metadata/README.md\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Share model with HF Hub" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1d6fe9d868c24175aa5f23a2893a2c21", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
BCHW\n", "input_tensor = input_tensor.to(device)\n", "\n", "# Perform inference\n", "with torch.inference_mode():\n", " output_mask = model(input_tensor)\n", "\n", "# Postprocess mask\n", "mask = torch.nn.functional.interpolate(\n", " output_mask, size=image.shape[:2], mode=\"bilinear\", align_corners=False\n", ")\n", "mask = mask[0].argmax(0).cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot results\n", "plt.figure(figsize=(12, 6))\n", "\n", "plt.subplot(121)\n", "plt.axis(\"off\")\n", "plt.imshow(image)\n", "plt.title(\"Input Image\")\n", "\n", "plt.subplot(122)\n", "plt.axis(\"off\")\n", "plt.imshow(mask)\n", "plt.title(\"Output Mask\")\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/upernet_inference_pretrained.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/upernet_inference_pretrained.ipynb)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# make sure you have the latest version of the libraries\n", "!pip install -U segmentation-models-pytorch\n", "!pip install albumentations matplotlib requests pillow" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ubuntu/projects/segmentation_models.pytorch/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "import requests\n", "import numpy as np\n", "import albumentations as A\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import segmentation_models_pytorch as smp\n", "\n", "from PIL import Image" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preprocessing:\n", " Compose([\n", " Resize(p=1.0, height=512, width=512, interpolation=1, mask_interpolation=0),\n", " Normalize(p=1.0, mean=(123.675, 116.28, 103.53), std=(58.395, 57.12, 57.375), max_pixel_value=1.0, normalization='standard'),\n", "], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True)\n" ] } ], "source": [ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "# More checkpoints can be found here:\n", "# https://huggingface.co/collections/smp-hub/upernet-67fadcdbe08418c6ea94f768\n", "checkpoint = \"smp-hub/upernet-swin-tiny\"\n", "\n", "# Load pretrained model and preprocessing function\n", "model = smp.from_pretrained(checkpoint).eval().to(device)\n", "preprocessing = A.Compose.from_pretrained(checkpoint)\n", "print(\"Preprocessing:\\n\", preprocessing)\n", "\n", "# Load image\n", "url = \"https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg\"\n", "image = Image.open(requests.get(url, stream=True).raw)\n", "\n", "# Preprocess image\n", "image = np.array(image)\n", "normalized_image = preprocessing(image=image)[\"image\"]\n", "input_tensor = torch.as_tensor(normalized_image)\n", "input_tensor = input_tensor.permute(2, 0, 1).unsqueeze(0) # HWC -> BCHW\n", "input_tensor = input_tensor.to(device)\n", "\n", "# Perform inference\n", "with torch.inference_mode():\n", " output_mask = model(input_tensor)\n", "\n", "# Postprocess mask\n", "mask = torch.nn.functional.interpolate(\n", " output_mask, size=image.shape[:2], mode=\"bilinear\", align_corners=False\n", ")\n", "mask = mask[0].argmax(0).cpu().numpy()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot results\n", "plt.figure(figsize=(12, 6))\n", "\n", "plt.subplot(121)\n", "plt.axis(\"off\")\n", "plt.imshow(image)\n", "plt.title(\"Input Image\")\n", "\n", "plt.subplot(122)\n", "plt.axis(\"off\")\n", "plt.imshow(mask)\n", "plt.title(\"Output Mask\")\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "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": "3.10.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: licenses/LICENSES.md ================================================ LICENSES for specific files =========================== The majority of the code is licensed under the [MIT License](LICENSE). However, some files are licensed under different terms. Please check each file for file-specific license. **Component-Specific Licenses** - NVIDIA License * Applies to the Mix Vision Transformer (SegFormer) encoder * This is for non-commercial use only * [segmentation_models_pytorch/encoders/mix_transformer.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/mix_transformer.py) * [LICENSE_nvidia](LICENSE_nvidia.md) - Apple License * Applies to the MobileOne encoder * [segmentation_models_pytorch/encoders/mobileone.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/mobileone.py) * [LICENSE_apple](LICENSE_apple.md) - BSD 3-Clause License * Applies to several encoders and the DeepLabV3 decoder * [segmentation_models_pytorch/encoders/_dpn.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/_dpn.py) * [segmentation_models_pytorch/encoders/_inceptionresnetv2.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/_inceptionresnetv2.py) * [segmentation_models_pytorch/encoders/_inceptionv4.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/_inceptionv4.py) * [segmentation_models_pytorch/encoders/_senet.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/_senet.py) * [segmentation_models_pytorch/encoders/_xception.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/_xception.py) * [segmentation_models_pytorch/decoders/deeplabv3/decoder.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/decoders/deeplabv3/decoder.py) - Apache-2.0 License * Applies to the EfficientNet encoder * [segmentation_models_pytorch/encoders/_efficientnet.py](https://github.com/qubvel/segmentation_models.pytorch/blob/main/segmentation_models_pytorch/encoders/_efficientnet.py) ================================================ FILE: licenses/LICENSE_apache.md ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION, LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER COMM ERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. ================================================ FILE: misc/generate_table.py ================================================ import os import segmentation_models_pytorch as smp from tqdm import tqdm encoders = smp.encoders.encoders WIDTH = 32 COLUMNS = ["Encoder", "Pretrained weights", "Params, M", "Script", "Compile", "Export"] FILE = "encoders_table.md" if os.path.exists(FILE): os.remove(FILE) def wrap_row(r): return "|{}|".format(r) header = "|".join([column.ljust(WIDTH, " ") for column in COLUMNS]) separator = "|".join( ["-" * WIDTH] + [":" + "-" * (WIDTH - 2) + ":"] * (len(COLUMNS) - 1) ) print(wrap_row(header), file=open(FILE, "a")) print(wrap_row(separator), file=open(FILE, "a")) for encoder_name, encoder in tqdm(encoders.items()): weights = "
".join(encoder["pretrained_settings"].keys()) model = encoder["encoder"](**encoder["params"], depth=5) script = "✅" if model._is_torch_scriptable else "❌" compile = "✅" if model._is_torch_compilable else "❌" export = "✅" if model._is_torch_exportable else "❌" params = sum(p.numel() for p in model.parameters()) params = str(params // 1000000) + "M" row = [encoder_name, weights, params, script, compile, export] row = [str(r).ljust(WIDTH, " ") for r in row] row = "|".join(row) print(wrap_row(row), file=open(FILE, "a")) ================================================ FILE: misc/generate_table_timm.py ================================================ import timm from tqdm import tqdm def check_features_and_reduction(name): encoder = timm.create_model(name, features_only=True, pretrained=False) if not encoder.feature_info.reduction() == [2, 4, 8, 16, 32]: raise ValueError def has_dilation_support(name): try: timm.create_model(name, features_only=True, output_stride=8, pretrained=False) timm.create_model(name, features_only=True, output_stride=16, pretrained=False) return True except Exception: return False def valid_vit_encoder_for_dpt(name): if "vit" not in name: return False encoder = timm.create_model(name) feature_info = encoder.feature_info feature_info_obj = timm.models.FeatureInfo( feature_info=feature_info, out_indices=[0, 1, 2, 3] ) reduction_scales = list(feature_info_obj.reduction()) if len(set(reduction_scales)) > 1: return False output_stride = reduction_scales[0] if bin(output_stride).count("1") != 1: return False return True def make_table(data): names = data.keys() max_len1 = max([len(x) for x in names]) + 2 max_len2 = len("support dilation") + 2 max_len3 = len("Supported for DPT") + 2 l1 = "+" + "-" * max_len1 + "+" + "-" * max_len2 + "+" + "-" * max_len3 + "+\n" l2 = "+" + "=" * max_len1 + "+" + "=" * max_len2 + "+" + "-" * max_len3 + "+\n" top = ( "| " + "Encoder name".ljust(max_len1 - 2) + " | " + "Support dilation".center(max_len2 - 2) + " | " + "Supported for DPT".center(max_len3 - 2) + " |\n" ) table = l1 + top + l2 for k in sorted(data.keys()): if "has_dilation" in data[k] and data[k]["has_dilation"]: support = "✅".center(max_len2 - 3) else: support = " ".center(max_len2 - 2) if "supported_only_for_dpt" in data[k]: supported_for_dpt = "✅".center(max_len3 - 3) else: supported_for_dpt = " ".center(max_len3 - 2) table += ( "| " + k.ljust(max_len1 - 2) + " | " + support + " | " + supported_for_dpt + " |\n" ) table += l1 return table if __name__ == "__main__": supported_models = {} with tqdm(timm.list_models()) as names: for name in names: try: check_features_and_reduction(name) has_dilation = has_dilation_support(name) supported_models[name] = dict(has_dilation=has_dilation) except Exception: try: if valid_vit_encoder_for_dpt(name): supported_models[name] = dict(supported_only_for_dpt=True) except Exception: continue table = make_table(supported_models) print(table) with open("timm_encoders.txt", "w") as f: print(table, file=f) print(f"Total encoders: {len(supported_models.keys())}") ================================================ FILE: misc/generate_test_models.py ================================================ import os import torch import tempfile import huggingface_hub import segmentation_models_pytorch as smp HUB_REPO = "smp-test-models" ENCODER_NAME = "tu-resnet18" api = huggingface_hub.HfApi(token=os.getenv("HF_TOKEN")) def save_and_push(model, inputs, outputs, model_name, encoder_name): with tempfile.TemporaryDirectory() as tmpdir: # save model model.save_pretrained(f"{tmpdir}") # save input and output torch.save(inputs, f"{tmpdir}/input-tensor.pth") torch.save(outputs, f"{tmpdir}/output-tensor.pth") # create repo repo_id = f"{HUB_REPO}/{model_name}-{encoder_name}" if not api.repo_exists(repo_id=repo_id): api.create_repo(repo_id=repo_id, repo_type="model") # upload to hub api.upload_folder( folder_path=tmpdir, repo_id=f"{HUB_REPO}/{model_name}-{encoder_name}", repo_type="model", ) for model_name, model_class in smp.MODEL_ARCHITECTURES_MAPPING.items(): if model_name == "dpt": encoder_name = "tu-test_vit" model = smp.DPT( encoder_name=encoder_name, decoder_readout="cat", decoder_intermediate_channels=(16, 32, 64, 64), decoder_fusion_channels=16, dynamic_img_size=True, ) else: encoder_name = ENCODER_NAME model = model_class(encoder_name=encoder_name) model = model.eval() # generate test sample torch.manual_seed(423553) sample = torch.rand(1, 3, 256, 256) with torch.inference_mode(): output = model(sample) save_and_push(model, sample, output, model_name, encoder_name) ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ['setuptools>=77'] build-backend = 'setuptools.build_meta' [project] name = 'segmentation_models_pytorch' description = 'Image segmentation models with pre-trained backbones. PyTorch.' readme = 'README.md' requires-python = '>=3.10' license = "MIT" license-files = ["LICENSE"] authors = [{name = 'Pavel Iakubovskii', email = 'qubvel@gmail.com'}] classifiers = [ 'Programming Language :: Python', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy', ] dependencies = [ 'huggingface-hub>=0.24', 'numpy>=1.21.2', 'pillow>=8.4', 'safetensors>=0.3.1', 'timm>=0.9', 'torch>=1.11', 'torchvision>=0.12', 'tqdm>=4.42.1', ] dynamic = ['version'] [project.optional-dependencies] docs = [ 'huggingface-hub', 'six', 'sphinx', 'sphinx-book-theme', ] test = [ 'gitpython', 'packaging', 'pytest', 'pytest-cov', 'pytest-xdist', 'ruff>=0.9', 'setuptools', ] [project.urls] Homepage = 'https://github.com/qubvel-org/segmentation_models.pytorch' [tool.ruff] extend-include = ['*.ipynb'] fix = true [tool.setuptools.dynamic] version = {attr = 'segmentation_models_pytorch.__version__.__version__'} [tool.setuptools.packages.find] include = ['segmentation_models_pytorch*'] [tool.pytest.ini_options] markers = [ "logits_match", "compile", "torch_export", "torch_script", ] [tool.coverage.run] omit = [ "segmentation_models_pytorch/utils/*", "**/convert_*", ] ================================================ FILE: requirements/docs.txt ================================================ huggingface-hub==1.7.1 six==1.17.0 sphinx==9.1.0 sphinx-book-theme==1.2.0 ================================================ FILE: requirements/minimum.old ================================================ huggingface-hub==0.24.0 numpy==1.21.2 pillow==8.4.0 safetensors==0.3.1 timm==0.9.0 torch==1.11.0 torchvision==0.12.0 tqdm==4.42.1 Jinja2==3.0.0 ================================================ FILE: requirements/required.txt ================================================ huggingface_hub==1.7.1 numpy==2.4.3 pillow==12.1.1 safetensors==0.7.0 timm==1.0.25 torch==2.10.0 torchvision==0.25.0 tqdm==4.67.3 ================================================ FILE: requirements/test.txt ================================================ gitpython==3.1.46 packaging==26.0 pytest==9.0.2 pytest-xdist==3.8.0 pytest-cov==7.0.0 ruff==0.15.7 setuptools==82.0.1 ================================================ FILE: scripts/models-conversions/dpt-original-to-smp.py ================================================ import cv2 import torch import albumentations as A import segmentation_models_pytorch as smp MODEL_WEIGHTS_PATH = r"dpt_large-ade20k-b12dca68.pt" HF_HUB_PATH = "qubvel-hf/dpt-large-ade20k" PUSH_TO_HUB = False def get_transform(): return A.Compose( [ A.LongestMaxSize(max_size=480, interpolation=cv2.INTER_CUBIC), A.Normalize( mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), max_pixel_value=255.0 ), # This is not correct transform, ideally image should resized without padding to multiple of 32, # but we take there is no such transform in albumentations, here is closest one A.PadIfNeeded( min_height=None, min_width=None, pad_height_divisor=32, pad_width_divisor=32, border_mode=cv2.BORDER_CONSTANT, value=0, p=1, ), ] ) if __name__ == "__main__": # fmt: off smp_model = smp.DPT(encoder_name="tu-vit_large_patch16_384", classes=150, dynamic_img_size=True) dpt_model_dict = torch.load(MODEL_WEIGHTS_PATH, weights_only=True) for layer_index in range(0, 4): for param in ["running_mean", "running_var", "num_batches_tracked", "weight", "bias"]: for block_index in [1, 2]: for bn_index in [1, 2]: # Assigning weights of 4th fusion layer of original model to 1st layer of SMP DPT model, # Assigning weights of 3rd fusion layer of original model to 2nd layer of SMP DPT model ... # and so on ... # This is because order of calling fusion layers is reversed in original DPT implementation dpt_model_dict[f"decoder.fusion_blocks.{layer_index}.residual_conv_block{block_index}.batch_norm_{bn_index}.{param}"] = \ dpt_model_dict.pop(f"scratch.refinenet{4 - layer_index}.resConfUnit{block_index}.bn{bn_index}.{param}") if param in ["weight", "bias"]: if param == "weight": for block_index in [1, 2]: for conv_index in [1, 2]: dpt_model_dict[f"decoder.fusion_blocks.{layer_index}.residual_conv_block{block_index}.conv_{conv_index}.{param}"] = \ dpt_model_dict.pop(f"scratch.refinenet{4 - layer_index}.resConfUnit{block_index}.conv{conv_index}.{param}") dpt_model_dict[f"decoder.reassemble_blocks.{layer_index}.project_to_feature_dim.{param}"] = \ dpt_model_dict.pop(f"scratch.layer{layer_index + 1}_rn.{param}") dpt_model_dict[f"decoder.fusion_blocks.{layer_index}.project.{param}"] = \ dpt_model_dict.pop(f"scratch.refinenet{4 - layer_index}.out_conv.{param}") dpt_model_dict[f"decoder.projection_blocks.{layer_index}.project.0.{param}"] = \ dpt_model_dict.pop(f"pretrained.act_postprocess{layer_index + 1}.0.project.0.{param}") dpt_model_dict[f"decoder.reassemble_blocks.{layer_index}.project_to_out_channel.{param}"] = \ dpt_model_dict.pop(f"pretrained.act_postprocess{layer_index + 1}.3.{param}") if layer_index != 2: dpt_model_dict[f"decoder.reassemble_blocks.{layer_index}.upsample.{param}"] = \ dpt_model_dict.pop(f"pretrained.act_postprocess{layer_index + 1}.4.{param}") # Changing state dict keys for segmentation head dpt_model_dict = { name.replace("scratch.output_conv", "segmentation_head.head"): parameter for name, parameter in dpt_model_dict.items() } # Changing state dict keys for encoder layers dpt_model_dict = { name.replace("pretrained.model", "encoder.model"): parameter for name, parameter in dpt_model_dict.items() } # Removing keys, value pairs associated with auxiliary head dpt_model_dict = { name: parameter for name, parameter in dpt_model_dict.items() if not name.startswith("auxlayer") } # fmt: on smp_model.load_state_dict(dpt_model_dict, strict=True) # ------- DO NOT touch this section ------- smp_model.eval() input_tensor = torch.ones((1, 3, 384, 384)) output = smp_model(input_tensor) print(output.shape) print(output[0, 0, :3, :3]) expected_slice = torch.tensor( [ [3.4243, 3.4553, 3.4863], [3.3332, 3.2876, 3.2419], [3.2422, 3.1199, 2.9975], ] ) torch.testing.assert_close( output[0, 0, :3, :3], expected_slice, atol=1e-4, rtol=1e-4 ) # Saving transform = get_transform() transform.save_pretrained(HF_HUB_PATH) smp_model.save_pretrained(HF_HUB_PATH, push_to_hub=PUSH_TO_HUB) # Re-loading to make sure everything is saved correctly smp_model = smp.from_pretrained(HF_HUB_PATH) ================================================ FILE: scripts/models-conversions/segformer-original-decoder-to-smp.py ================================================ import torch import argparse import requests import numpy as np import huggingface_hub import albumentations as A import matplotlib.pyplot as plt from PIL import Image import segmentation_models_pytorch as smp def convert_state_dict_to_smp(state_dict: dict): # fmt: off if "state_dict" in state_dict: state_dict = state_dict["state_dict"] new_state_dict = {} # Map the backbone components to the encoder keys = list(state_dict.keys()) for key in keys: if key.startswith("backbone"): new_key = key.replace("backbone", "encoder") new_state_dict[new_key] = state_dict.pop(key) # Map the linear_cX layers to MLP stages for i in range(4): base = f"decode_head.linear_c{i+1}.proj" new_state_dict[f"decoder.mlp_stage.{3-i}.linear.weight"] = state_dict.pop(f"{base}.weight") new_state_dict[f"decoder.mlp_stage.{3-i}.linear.bias"] = state_dict.pop(f"{base}.bias") # Map fuse_stage components fuse_base = "decode_head.linear_fuse" fuse_weights = { "decoder.fuse_stage.0.weight": state_dict.pop(f"{fuse_base}.conv.weight"), "decoder.fuse_stage.1.weight": state_dict.pop(f"{fuse_base}.bn.weight"), "decoder.fuse_stage.1.bias": state_dict.pop(f"{fuse_base}.bn.bias"), "decoder.fuse_stage.1.running_mean": state_dict.pop(f"{fuse_base}.bn.running_mean"), "decoder.fuse_stage.1.running_var": state_dict.pop(f"{fuse_base}.bn.running_var"), "decoder.fuse_stage.1.num_batches_tracked": state_dict.pop(f"{fuse_base}.bn.num_batches_tracked"), } new_state_dict.update(fuse_weights) # Map final layer components new_state_dict["segmentation_head.0.weight"] = state_dict.pop("decode_head.linear_pred.weight") new_state_dict["segmentation_head.0.bias"] = state_dict.pop("decode_head.linear_pred.bias") del state_dict["decode_head.conv_seg.weight"] del state_dict["decode_head.conv_seg.bias"] assert len(state_dict) == 0, f"Unmapped keys: {state_dict.keys()}" # fmt: on return new_state_dict def get_np_image(): url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" image = Image.open(requests.get(url, stream=True).raw) return np.array(image) def main(args): original_checkpoint = torch.load(args.path, map_location="cpu", weights_only=True) smp_state_dict = convert_state_dict_to_smp(original_checkpoint) config = original_checkpoint["meta"]["config"] num_classes = int(config.split("num_classes=")[1].split(",\n")[0]) decoder_dims = int(config.split("embed_dim=")[1].split(",\n")[0]) height, width = [ int(x) for x in config.split("crop_size=(")[1].split("), ")[0].split(",") ] model_size = args.path.split("segformer.")[1][:2] # Create the model model = smp.create_model( in_channels=3, classes=num_classes, arch="segformer", encoder_name=f"mit_{model_size}", encoder_weights=None, decoder_segmentation_channels=decoder_dims, ).eval() # Load the converted state dict model.load_state_dict(smp_state_dict, strict=True) # Preprocessing params preprocessing = A.Compose( [ A.Resize(height, width, p=1), A.Normalize( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], max_pixel_value=1.0, p=1, ), ] ) # Prepare the input image = get_np_image() normalized_image = preprocessing(image=image)["image"] tensor = torch.tensor(normalized_image).permute(2, 0, 1).unsqueeze(0).float() # Forward pass with torch.inference_mode(): mask = model(tensor) # Postprocessing mask = torch.nn.functional.interpolate( mask, size=(image.shape[0], image.shape[1]), mode="bilinear" ) mask = torch.argmax(mask, dim=1) mask = mask.squeeze().cpu().numpy() model_name = args.path.split("/")[-1].replace(".pth", "").replace(".", "-") model.save_pretrained(model_name) preprocessing.save_pretrained(model_name) # fmt: off plt.subplot(121), plt.axis('off'), plt.imshow(image), plt.title('Input Image') plt.subplot(122), plt.axis('off'), plt.imshow(mask), plt.title('Output Mask') plt.savefig(f"{model_name}/example_mask.png") # fmt: on if args.push_to_hub: repo_id = f"smp-hub/{model_name}" api = huggingface_hub.HfApi() api.create_repo(repo_id=repo_id, repo_type="model") api.upload_folder(folder_path=model_name, repo_id=repo_id) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--path", type=str, default="weights/trained_models/segformer.b2.512x512.ade.160k.pth", ) parser.add_argument("--push_to_hub", action="store_true") args = parser.parse_args() main(args) ================================================ FILE: scripts/models-conversions/upernet-hf-to-smp.py ================================================ import re import torch import albumentations as A import segmentation_models_pytorch as smp from huggingface_hub import hf_hub_download, HfApi from collections import defaultdict DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") # fmt: off CONVNEXT_MAPPING = { r"backbone.embeddings.patch_embeddings.(weight|bias)": r"encoder.model.stem_0.\1", r"backbone.embeddings.layernorm.(weight|bias)": r"encoder.model.stem_1.\1", r"backbone.encoder.stages.(\d+).layers.(\d+).layer_scale_parameter": r"encoder.model.stages_\1.blocks.\2.gamma", r"backbone.encoder.stages.(\d+).layers.(\d+).dwconv.(weight|bias)": r"encoder.model.stages_\1.blocks.\2.conv_dw.\3", r"backbone.encoder.stages.(\d+).layers.(\d+).layernorm.(weight|bias)": r"encoder.model.stages_\1.blocks.\2.norm.\3", r"backbone.encoder.stages.(\d+).layers.(\d+).pwconv(\d+).(weight|bias)": r"encoder.model.stages_\1.blocks.\2.mlp.fc\3.\4", r"backbone.encoder.stages.(\d+).downsampling_layer.(\d+).(weight|bias)": r"encoder.model.stages_\1.downsample.\2.\3", } SWIN_MAPPING = { r"backbone.embeddings.patch_embeddings.projection": r"encoder.model.patch_embed.proj", r"backbone.embeddings.norm": r"encoder.model.patch_embed.norm", r"backbone.encoder.layers.(\d+).blocks.(\d+).layernorm_before": r"encoder.model.layers_\1.blocks.\2.norm1", r"backbone.encoder.layers.(\d+).blocks.(\d+).attention.self.relative_position_bias_table": r"encoder.model.layers_\1.blocks.\2.attn.relative_position_bias_table", r"backbone.encoder.layers.(\d+).blocks.(\d+).attention.self.(query|key|value)": r"encoder.model.layers_\1.blocks.\2.attn.\3", r"backbone.encoder.layers.(\d+).blocks.(\d+).attention.output.dense": r"encoder.model.layers_\1.blocks.\2.attn.proj", r"backbone.encoder.layers.(\d+).blocks.(\d+).layernorm_after": r"encoder.model.layers_\1.blocks.\2.norm2", r"backbone.encoder.layers.(\d+).blocks.(\d+).intermediate.dense": r"encoder.model.layers_\1.blocks.\2.mlp.fc1", r"backbone.encoder.layers.(\d+).blocks.(\d+).output.dense": r"encoder.model.layers_\1.blocks.\2.mlp.fc2", r"backbone.encoder.layers.(\d+).downsample.reduction": lambda x: f"encoder.model.layers_{1 + int(x.group(1))}.downsample.reduction", r"backbone.encoder.layers.(\d+).downsample.norm": lambda x: f"encoder.model.layers_{1 + int(x.group(1))}.downsample.norm", } DECODER_MAPPING = { # started from 1 in hf r"backbone.hidden_states_norms.stage(\d+)": lambda x: f"decoder.feature_norms.{int(x.group(1)) - 1}", r"decode_head.psp_modules.(\d+).(\d+).conv.weight": r"decoder.psp.blocks.\1.\2.0.weight", r"decode_head.psp_modules.(\d+).(\d+).batch_norm": r"decoder.psp.blocks.\1.\2.1", r"decode_head.bottleneck.conv.weight": r"decoder.psp.out_conv.0.weight", r"decode_head.bottleneck.batch_norm": r"decoder.psp.out_conv.1", # fpn blocks are in reverse order (3 blocks total, so 2 - i) r"decode_head.lateral_convs.(\d+).conv.weight": lambda x: f"decoder.fpn_lateral_blocks.{2 - int(x.group(1))}.conv_norm_relu.0.weight", r"decode_head.lateral_convs.(\d+).batch_norm": lambda x: f"decoder.fpn_lateral_blocks.{2 - int(x.group(1))}.conv_norm_relu.1", r"decode_head.fpn_convs.(\d+).conv.weight": lambda x: f"decoder.fpn_conv_blocks.{2 - int(x.group(1))}.0.weight", r"decode_head.fpn_convs.(\d+).batch_norm": lambda x: f"decoder.fpn_conv_blocks.{2 - int(x.group(1))}.1", r"decode_head.fpn_bottleneck.conv.weight": r"decoder.fusion_block.0.weight", r"decode_head.fpn_bottleneck.batch_norm": r"decoder.fusion_block.1", r"decode_head.classifier": r"segmentation_head.0", } # fmt: on PRETRAINED_CHECKPOINTS = { "convnext-tiny": { "repo_id": "openmmlab/upernet-convnext-tiny", "encoder_name": "tu-convnext_tiny", "decoder_channels": 512, "classes": 150, "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, }, "convnext-small": { "repo_id": "openmmlab/upernet-convnext-small", "encoder_name": "tu-convnext_small", "decoder_channels": 512, "classes": 150, "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, }, "convnext-base": { "repo_id": "openmmlab/upernet-convnext-base", "encoder_name": "tu-convnext_base", "decoder_channels": 512, "classes": 150, "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, }, "convnext-large": { "repo_id": "openmmlab/upernet-convnext-large", "encoder_name": "tu-convnext_large", "decoder_channels": 512, "classes": 150, "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, }, "convnext-xlarge": { "repo_id": "openmmlab/upernet-convnext-xlarge", "encoder_name": "tu-convnext_xlarge", "decoder_channels": 512, "classes": 150, "mapping": {**CONVNEXT_MAPPING, **DECODER_MAPPING}, }, "swin-tiny": { "repo_id": "openmmlab/upernet-swin-tiny", "encoder_name": "tu-swin_tiny_patch4_window7_224", "decoder_channels": 512, "classes": 150, "extra_kwargs": {"img_size": 512}, "mapping": {**SWIN_MAPPING, **DECODER_MAPPING}, }, "swin-small": { "repo_id": "openmmlab/upernet-swin-small", "encoder_name": "tu-swin_small_patch4_window7_224", "decoder_channels": 512, "classes": 150, "extra_kwargs": {"img_size": 512}, "mapping": {**SWIN_MAPPING, **DECODER_MAPPING}, }, "swin-large": { "repo_id": "openmmlab/upernet-swin-large", "encoder_name": "tu-swin_large_patch4_window12_384", "decoder_channels": 512, "classes": 150, "extra_kwargs": {"img_size": 512}, "mapping": {**SWIN_MAPPING, **DECODER_MAPPING}, }, } def convert_old_keys_to_new_keys(state_dict_keys: dict, keys_mapping: dict): """ This function should be applied only once, on the concatenated keys to efficiently rename using the key mappings. """ output_dict = {} if state_dict_keys is not None: old_text = "\n".join(state_dict_keys) new_text = old_text for pattern, replacement in keys_mapping.items(): if replacement is None: new_text = re.sub(pattern, "", new_text) # an empty line continue new_text = re.sub(pattern, replacement, new_text) output_dict = dict(zip(old_text.split("\n"), new_text.split("\n"))) return output_dict def group_qkv_layers(state_dict: dict) -> dict: """Find corresponding layer names for query, key and value layers and stack them in a single layer""" state_dict = state_dict.copy() # shallow copy result = defaultdict(dict) layer_names = list(state_dict.keys()) qkv_names = ["query", "key", "value"] for layer_name in layer_names: for pattern in qkv_names: if pattern in layer_name: new_key = layer_name.replace(pattern, "qkv") result[new_key][pattern] = state_dict.pop(layer_name) break # merge them all for new_key, patterns in result.items(): state_dict[new_key] = torch.cat( [patterns[qkv_name] for qkv_name in qkv_names], dim=0 ) return state_dict def convert_model(model_name: str, push_to_hub: bool = False): params = PRETRAINED_CHECKPOINTS[model_name] print(f"Converting model: {model_name}") print(f"Downloading weights from: {params['repo_id']}") hf_weights_path = hf_hub_download( repo_id=params["repo_id"], filename="pytorch_model.bin" ) hf_state_dict = torch.load(hf_weights_path, weights_only=True) print(f"Loaded HuggingFace state dict with {len(hf_state_dict)} keys") # Rename keys keys_mapping = convert_old_keys_to_new_keys(hf_state_dict.keys(), params["mapping"]) smp_state_dict = {} for old_key, new_key in keys_mapping.items(): smp_state_dict[new_key] = hf_state_dict[old_key] # remove aux head smp_state_dict = { k: v for k, v in smp_state_dict.items() if "auxiliary_head." not in k } # [swin] group qkv layers and remove `relative_position_index` smp_state_dict = group_qkv_layers(smp_state_dict) smp_state_dict = { k: v for k, v in smp_state_dict.items() if "relative_position_index" not in k } # Create model print(f"Creating SMP UPerNet model with encoder: {params['encoder_name']}") extra_kwargs = params.get("extra_kwargs", {}) smp_model = smp.UPerNet( encoder_name=params["encoder_name"], encoder_weights=None, decoder_channels=params["decoder_channels"], classes=params["classes"], **extra_kwargs, ) print("Loading weights into SMP model...") smp_model.load_state_dict(smp_state_dict, strict=True) # Check we can run the model print("Verifying model with test inference...") smp_model.eval() sample = torch.ones(1, 3, 512, 512) with torch.inference_mode(): output = smp_model(sample) print(f"Test inference successful. Output shape: {output.shape}") # Save model with preprocessing smp_repo_id = f"smp-hub/upernet-{model_name}" print(f"Saving model to: {smp_repo_id}") smp_model.save_pretrained(save_directory=smp_repo_id) transform = A.Compose( [ A.Resize(512, 512), A.Normalize( mean=(123.675, 116.28, 103.53), std=(58.395, 57.12, 57.375), max_pixel_value=1.0, ), ] ) transform.save_pretrained(save_directory=smp_repo_id) if push_to_hub: print(f"Pushing model to HuggingFace Hub: {smp_repo_id}") api = HfApi() if not api.repo_exists(smp_repo_id): api.create_repo(repo_id=smp_repo_id, repo_type="model") api.upload_folder( repo_id=smp_repo_id, folder_path=smp_repo_id, repo_type="model", ) print(f"Conversion of {model_name} completed successfully!") if __name__ == "__main__": print(f"Starting conversion of {len(PRETRAINED_CHECKPOINTS)} UPerNet models") for model_name in PRETRAINED_CHECKPOINTS.keys(): convert_model(model_name, push_to_hub=True) print("All conversions completed!") ================================================ FILE: segmentation_models_pytorch/__init__.py ================================================ from . import datasets from . import encoders from . import decoders from . import losses from . import metrics from .decoders.unet import Unet from .decoders.unetplusplus import UnetPlusPlus from .decoders.manet import MAnet from .decoders.linknet import Linknet from .decoders.fpn import FPN from .decoders.pspnet import PSPNet from .decoders.deeplabv3 import DeepLabV3, DeepLabV3Plus from .decoders.pan import PAN from .decoders.upernet import UPerNet from .decoders.segformer import Segformer from .decoders.dpt import DPT from .base.hub_mixin import from_pretrained from .__version__ import __version__ # some private imports for create_model function from typing import Optional as _Optional import torch as _torch _MODEL_ARCHITECTURES = [ Unet, UnetPlusPlus, MAnet, Linknet, FPN, PSPNet, DeepLabV3, DeepLabV3Plus, PAN, UPerNet, Segformer, DPT, ] MODEL_ARCHITECTURES_MAPPING = {a.__name__.lower(): a for a in _MODEL_ARCHITECTURES} def create_model( arch: str, encoder_name: str = "resnet34", encoder_weights: _Optional[str] = "imagenet", in_channels: int = 3, classes: int = 1, **kwargs, ) -> _torch.nn.Module: """Models entrypoint, allows to create any model architecture just with parameters, without using its class """ try: model_class = MODEL_ARCHITECTURES_MAPPING[arch.lower()] except KeyError: raise KeyError( "Wrong architecture type `{}`. Available options are: {}".format( arch, list(MODEL_ARCHITECTURES_MAPPING.keys()) ) ) return model_class( encoder_name=encoder_name, encoder_weights=encoder_weights, in_channels=in_channels, classes=classes, **kwargs, ) __all__ = [ "datasets", "encoders", "decoders", "losses", "metrics", "Unet", "UnetPlusPlus", "MAnet", "Linknet", "FPN", "PSPNet", "DeepLabV3", "DeepLabV3Plus", "PAN", "UPerNet", "Segformer", "DPT", "from_pretrained", "create_model", "__version__", ] ================================================ FILE: segmentation_models_pytorch/__version__.py ================================================ __version__ = "0.5.1.dev0" ================================================ FILE: segmentation_models_pytorch/base/__init__.py ================================================ from .model import SegmentationModel from .modules import Conv2dReLU, Attention from .heads import SegmentationHead, ClassificationHead __all__ = [ "SegmentationModel", "Conv2dReLU", "Attention", "SegmentationHead", "ClassificationHead", ] ================================================ FILE: segmentation_models_pytorch/base/heads.py ================================================ import torch.nn as nn from .modules import Activation class SegmentationHead(nn.Sequential): def __init__( self, in_channels, out_channels, kernel_size=3, activation=None, upsampling=1 ): conv2d = nn.Conv2d( in_channels, out_channels, kernel_size=kernel_size, padding=kernel_size // 2 ) upsampling = ( nn.Upsample(mode="bilinear", scale_factor=upsampling, align_corners=True) if upsampling > 1 else nn.Identity() ) activation = Activation(activation) super().__init__(conv2d, upsampling, activation) class ClassificationHead(nn.Sequential): def __init__( self, in_channels, classes, pooling="avg", dropout=0.2, activation=None ): if pooling not in ("max", "avg"): raise ValueError( "Pooling should be one of ('max', 'avg'), got {}.".format(pooling) ) pool = nn.AdaptiveAvgPool2d(1) if pooling == "avg" else nn.AdaptiveMaxPool2d(1) flatten = nn.Flatten() dropout = nn.Dropout(p=dropout, inplace=True) if dropout else nn.Identity() linear = nn.Linear(in_channels, classes, bias=True) activation = Activation(activation) super().__init__(pool, flatten, dropout, linear, activation) ================================================ FILE: segmentation_models_pytorch/base/hub_mixin.py ================================================ import torch import json from pathlib import Path from typing import Optional, Union from functools import wraps from huggingface_hub import ( PyTorchModelHubMixin, ModelCard, ModelCardData, hf_hub_download, ) MODEL_CARD = """ --- {{ card_data }} --- # {{ model_name }} Model Card Table of Contents: - [Load trained model](#load-trained-model) - [Model init parameters](#model-init-parameters) - [Model metrics](#model-metrics) - [Dataset](#dataset) ## Load trained model ```python import segmentation_models_pytorch as smp model = smp.from_pretrained("") ``` ## Model init parameters ```python model_init_params = {{ model_parameters }} ``` ## Model metrics {{ metrics | default("[More Information Needed]", true) }} ## Dataset Dataset name: {{ dataset | default("[More Information Needed]", true) }} ## More Information - Library: {{ repo_url | default("[More Information Needed]", true) }} - Docs: {{ docs_url | default("[More Information Needed]", true) }} This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) """ def _format_parameters(parameters: dict): params = {k: v for k, v in parameters.items() if not k.startswith("_")} params = [ f'"{k}": {v}' if not isinstance(v, str) else f'"{k}": "{v}"' for k, v in params.items() ] params = ",\n".join([f" {param}" for param in params]) params = "{\n" + f"{params}" + "\n}" return params class SMPHubMixin(PyTorchModelHubMixin): def generate_model_card(self, *args, **kwargs) -> ModelCard: model_parameters_json = _format_parameters(self.config) metrics = kwargs.get("metrics", None) dataset = kwargs.get("dataset", None) if metrics is not None: metrics = json.dumps(metrics, indent=4) metrics = f"```json\n{metrics}\n```" tags = self._hub_mixin_info.model_card_data.get("tags", []) or [] tags.extend(["segmentation-models-pytorch", "semantic-segmentation", "pytorch"]) model_card_data = ModelCardData( languages=["python"], library_name="segmentation-models-pytorch", license="mit", tags=tags, pipeline_tag="image-segmentation", ) model_card = ModelCard.from_template( card_data=model_card_data, template_str=MODEL_CARD, repo_url="https://github.com/qubvel/segmentation_models.pytorch", docs_url="https://smp.readthedocs.io/en/latest/", model_parameters=model_parameters_json, model_name=self.__class__.__name__, metrics=metrics, dataset=dataset, ) return model_card @wraps(PyTorchModelHubMixin.save_pretrained) def save_pretrained( self, save_directory: Union[str, Path], *args, **kwargs ) -> Optional[str]: model_card_kwargs = kwargs.pop("model_card_kwargs", {}) if "dataset" in kwargs: model_card_kwargs["dataset"] = kwargs.pop("dataset") if "metrics" in kwargs: model_card_kwargs["metrics"] = kwargs.pop("metrics") kwargs["model_card_kwargs"] = model_card_kwargs # set additional attribute to be able to deserialize the model self.config["_model_class"] = self.__class__.__name__ try: # call the original save_pretrained result = super().save_pretrained(save_directory, *args, **kwargs) finally: self.config.pop("_model_class", None) return result @property @torch.jit.unused def config(self) -> dict: return self._hub_mixin_config @wraps(PyTorchModelHubMixin.from_pretrained) def from_pretrained( pretrained_model_name_or_path: str, *args, strict: bool = True, **kwargs ): config_path = Path(pretrained_model_name_or_path) / "config.json" if not config_path.exists(): config_path = hf_hub_download( pretrained_model_name_or_path, filename="config.json", revision=kwargs.get("revision", None), ) with open(config_path, "r") as f: config = json.load(f) model_class_name = config.pop("_model_class") import segmentation_models_pytorch as smp model_class = getattr(smp, model_class_name) return model_class.from_pretrained( pretrained_model_name_or_path, *args, **kwargs, strict=strict ) def supports_config_loading(func): """Decorator to filter special config kwargs""" @wraps(func) def wrapper(self, *args, **kwargs): kwargs = {k: v for k, v in kwargs.items() if not k.startswith("_")} return func(self, *args, **kwargs) return wrapper ================================================ FILE: segmentation_models_pytorch/base/initialization.py ================================================ import torch.nn as nn def initialize_decoder(module): for m in module.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_uniform_(m.weight, mode="fan_in", nonlinearity="relu") if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance( m, (nn.BatchNorm2d, nn.LayerNorm, nn.GroupNorm, nn.InstanceNorm2d) ): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) def initialize_head(module): for m in module.modules(): if isinstance(m, (nn.Linear, nn.Conv2d)): nn.init.xavier_uniform_(m.weight) if m.bias is not None: nn.init.constant_(m.bias, 0) ================================================ FILE: segmentation_models_pytorch/base/model.py ================================================ import torch import warnings from typing import TypeVar, Type from . import initialization as init from .hub_mixin import SMPHubMixin from .utils import is_torch_compiling T = TypeVar("T", bound="SegmentationModel") class SegmentationModel(torch.nn.Module, SMPHubMixin): """Base class for all segmentation models.""" _is_torch_scriptable = True _is_torch_exportable = True _is_torch_compilable = True # if model supports shape not divisible by 2 ^ n set to False requires_divisible_input_shape = True # Fix type-hint for models, to avoid HubMixin signature def __new__(cls: Type[T], *args, **kwargs) -> T: instance = super().__new__(cls, *args, **kwargs) return instance def __init__(self): super().__init__() self._is_encoder_frozen = False def initialize(self): init.initialize_decoder(self.decoder) init.initialize_head(self.segmentation_head) if self.classification_head is not None: init.initialize_head(self.classification_head) def check_input_shape(self, x): """Check if the input shape is divisible by the output stride. If not, raise a RuntimeError. """ if not self.requires_divisible_input_shape: return h, w = x.shape[-2:] output_stride = self.encoder.output_stride if h % output_stride != 0 or w % output_stride != 0: new_h = ( (h // output_stride + 1) * output_stride if h % output_stride != 0 else h ) new_w = ( (w // output_stride + 1) * output_stride if w % output_stride != 0 else w ) raise RuntimeError( f"Wrong input shape height={h}, width={w}. Expected image height and width " f"divisible by {output_stride}. Consider pad your images to shape ({new_h}, {new_w})." ) def forward(self, x): """Sequentially pass `x` trough model`s encoder, decoder and heads""" if not ( torch.jit.is_scripting() or torch.jit.is_tracing() or is_torch_compiling() ): self.check_input_shape(x) features = self.encoder(x) decoder_output = self.decoder(features) masks = self.segmentation_head(decoder_output) if self.classification_head is not None: labels = self.classification_head(features[-1]) return masks, labels return masks @torch.inference_mode() def predict(self, x): """Inference method. Switch model to `eval` mode, call `.forward(x)` with `torch.inference_mode()` Args: x: 4D torch tensor with shape (batch_size, channels, height, width) Return: prediction: 4D torch tensor with shape (batch_size, classes, height, width) """ if self.training: self.eval() x = self(x) return x def load_state_dict(self, state_dict, **kwargs): # for compatibility of weights for # timm- ported encoders with TimmUniversalEncoder from segmentation_models_pytorch.encoders import TimmUniversalEncoder if isinstance(self.encoder, TimmUniversalEncoder): patterns = ["regnet", "res2", "resnest", "mobilenetv3", "gernet"] is_deprecated_encoder = any( self.encoder.name.startswith(pattern) for pattern in patterns ) if is_deprecated_encoder: keys = list(state_dict.keys()) for key in keys: new_key = key if key.startswith("encoder.") and not key.startswith( "encoder.model." ): new_key = "encoder.model." + key.removeprefix("encoder.") if "gernet" in self.encoder.name: new_key = new_key.replace(".stages.", ".stages_") state_dict[new_key] = state_dict.pop(key) # To be able to load weight with mismatched sizes # We are going to filter mismatched sizes as well if strict=False strict = kwargs.get("strict", True) if not strict: mismatched_keys = [] model_state_dict = self.state_dict() common_keys = set(model_state_dict.keys()) & set(state_dict.keys()) for key in common_keys: if model_state_dict[key].shape != state_dict[key].shape: mismatched_keys.append( (key, model_state_dict[key].shape, state_dict[key].shape) ) state_dict.pop(key) if mismatched_keys: str_keys = "\n".join( [ f" - {key}: {s} (weights) -> {m} (model)" for key, m, s in mismatched_keys ] ) text = f"\n\n !!!!!! Mismatched keys !!!!!!\n\nYou should TRAIN the model to use it:\n{str_keys}\n" warnings.warn(text, stacklevel=-1) return super().load_state_dict(state_dict, **kwargs) def train(self, mode: bool = True): """Set the module in training mode. This method behaves like the standard :meth:`torch.nn.Module.train`, with one exception: if the encoder has been frozen via :meth:`freeze_encoder`, then its normalization layers are not affected by this call. In other words, calling ``model.train()`` will not re-enable updates to frozen encoder normalization layers (e.g., BatchNorm, InstanceNorm). To restore the encoder to normal training behavior, use :meth:`unfreeze_encoder`. Args: mode (bool): whether to set training mode (``True``) or evaluation mode (``False``). Default: ``True``. Returns: Module: self """ if not isinstance(mode, bool): raise ValueError("training mode is expected to be boolean") self.training = mode for name, module in self.named_children(): # skip encoder if it is frozen if self._is_encoder_frozen and name == "encoder": continue module.train(mode) return self def _set_encoder_trainable(self, mode: bool): for param in self.encoder.parameters(): param.requires_grad = mode for module in self.encoder.modules(): # _NormBase is the common base of classes like _InstanceNorm # and _BatchNorm that track running stats if isinstance(module, torch.nn.modules.batchnorm._NormBase): module.train(mode) self._is_encoder_frozen = not mode def freeze_encoder(self): """ Freeze encoder parameters and disable updates to normalization layer statistics. This method: - Sets ``requires_grad = False`` for all encoder parameters, preventing them from being updated during backpropagation. - Puts normalization layers that track running statistics (e.g., BatchNorm, InstanceNorm) into evaluation mode (``.eval()``), so their ``running_mean`` and ``running_var`` are no longer updated. """ return self._set_encoder_trainable(False) def unfreeze_encoder(self): """ Unfreeze encoder parameters and restore normalization layers to training mode. This method reverts the effect of :meth:`freeze_encoder`. Specifically: - Sets ``requires_grad=True`` for all encoder parameters. - Restores normalization layers (e.g. BatchNorm, InstanceNorm) to training mode, so their running statistics are updated again. """ return self._set_encoder_trainable(True) ================================================ FILE: segmentation_models_pytorch/base/modules.py ================================================ from typing import Any, Dict, Union import torch import torch.nn as nn try: from inplace_abn import InPlaceABN except ImportError: InPlaceABN = None def get_norm_layer( use_norm: Union[bool, str, Dict[str, Any]], out_channels: int ) -> nn.Module: supported_norms = ("inplace", "batchnorm", "identity", "layernorm", "instancenorm") # Step 1. Convert tot dict representation ## Check boolean if use_norm is True: norm_params = {"type": "batchnorm"} elif use_norm is False: norm_params = {"type": "identity"} ## Check string elif isinstance(use_norm, str): norm_str = use_norm.lower() if norm_str == "inplace": norm_params = { "type": "inplace", "activation": "leaky_relu", "activation_param": 0.0, } elif norm_str in supported_norms: norm_params = {"type": norm_str} else: raise ValueError( f"Unrecognized normalization type string provided: {use_norm}. Should be in " f"{supported_norms}" ) ## Check dict elif isinstance(use_norm, dict): norm_params = use_norm else: raise ValueError( f"Invalid type for use_norm should either be a bool (batchnorm/identity), " f"a string in {supported_norms}, or a dict like {{'type': 'batchnorm', **kwargs}}" ) # Step 2. Check if the dict is valid if "type" not in norm_params: raise ValueError( f"Malformed dictionary given in use_norm: {use_norm}. Should contain key 'type'." ) if norm_params["type"] not in supported_norms: raise ValueError( f"Unrecognized normalization type string provided: {use_norm}. Should be in {supported_norms}" ) if norm_params["type"] == "inplace" and InPlaceABN is None: raise RuntimeError( "In order to use `use_norm='inplace'` the inplace_abn package must be installed. Use:\n" " $ pip install -U wheel setuptools\n" " $ pip install inplace_abn --no-build-isolation\n" "Also see: https://github.com/mapillary/inplace_abn" ) # Step 3. Initialize the norm layer norm_type = norm_params["type"] norm_kwargs = {k: v for k, v in norm_params.items() if k != "type"} if norm_type == "inplace": norm = InPlaceABN(out_channels, **norm_kwargs) elif norm_type == "batchnorm": norm = nn.BatchNorm2d(out_channels, **norm_kwargs) elif norm_type == "identity": norm = nn.Identity() elif norm_type == "layernorm": norm = nn.LayerNorm(out_channels, **norm_kwargs) elif norm_type == "instancenorm": norm = nn.InstanceNorm2d(out_channels, **norm_kwargs) else: raise ValueError(f"Unrecognized normalization type: {norm_type}") return norm class Conv2dReLU(nn.Sequential): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, padding: int = 0, stride: int = 1, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): norm = get_norm_layer(use_norm, out_channels) is_identity = isinstance(norm, nn.Identity) conv = nn.Conv2d( in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=is_identity, ) is_inplaceabn = InPlaceABN is not None and isinstance(norm, InPlaceABN) activation = nn.Identity() if is_inplaceabn else nn.ReLU(inplace=True) super(Conv2dReLU, self).__init__(conv, norm, activation) class SCSEModule(nn.Module): def __init__(self, in_channels, reduction=16): super().__init__() self.cSE = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, in_channels // reduction, 1), nn.ReLU(inplace=True), nn.Conv2d(in_channels // reduction, in_channels, 1), nn.Sigmoid(), ) self.sSE = nn.Sequential(nn.Conv2d(in_channels, 1, 1), nn.Sigmoid()) def forward(self, x): return x * self.cSE(x) + x * self.sSE(x) class ArgMax(nn.Module): def __init__(self, dim=None): super().__init__() self.dim = dim def forward(self, x): return torch.argmax(x, dim=self.dim) class Clamp(nn.Module): def __init__(self, min=0, max=1): super().__init__() self.min, self.max = min, max def forward(self, x): return torch.clamp(x, self.min, self.max) class Activation(nn.Module): def __init__(self, name, **params): super().__init__() if name is None or name == "identity": self.activation = nn.Identity(**params) elif name == "sigmoid": self.activation = nn.Sigmoid() elif name == "softmax2d": self.activation = nn.Softmax(dim=1, **params) elif name == "softmax": self.activation = nn.Softmax(**params) elif name == "logsoftmax": self.activation = nn.LogSoftmax(**params) elif name == "tanh": self.activation = nn.Tanh() elif name == "argmax": self.activation = ArgMax(**params) elif name == "argmax2d": self.activation = ArgMax(dim=1, **params) elif name == "clamp": self.activation = Clamp(**params) elif callable(name): self.activation = name(**params) else: raise ValueError( f"Activation should be callable/sigmoid/softmax/logsoftmax/tanh/" f"argmax/argmax2d/clamp/None; got {name}" ) def forward(self, x): return self.activation(x) class Attention(nn.Module): def __init__(self, name, **params): super().__init__() if name is None: self.attention = nn.Identity(**params) elif name == "scse": self.attention = SCSEModule(**params) else: raise ValueError("Attention {} is not implemented".format(name)) def forward(self, x): return self.attention(x) ================================================ FILE: segmentation_models_pytorch/base/utils.py ================================================ import torch @torch.jit.unused def is_torch_compiling(): try: return torch.compiler.is_compiling() except Exception: try: import torch._dynamo as dynamo # noqa: F401 return dynamo.is_compiling() except Exception: return False ================================================ FILE: segmentation_models_pytorch/datasets/__init__.py ================================================ from .oxford_pet import OxfordPetDataset, SimpleOxfordPetDataset __all__ = ["OxfordPetDataset", "SimpleOxfordPetDataset"] ================================================ FILE: segmentation_models_pytorch/datasets/oxford_pet.py ================================================ import os import torch import shutil import numpy as np from PIL import Image from tqdm import tqdm from urllib.request import urlretrieve class OxfordPetDataset(torch.utils.data.Dataset): def __init__(self, root, mode="train", transform=None): assert mode in {"train", "valid", "test"} self.root = root self.mode = mode self.transform = transform self.images_directory = os.path.join(self.root, "images") self.masks_directory = os.path.join(self.root, "annotations", "trimaps") self.filenames = self._read_split() # read train/valid/test splits def __len__(self): return len(self.filenames) def __getitem__(self, idx): filename = self.filenames[idx] image_path = os.path.join(self.images_directory, filename + ".jpg") mask_path = os.path.join(self.masks_directory, filename + ".png") image = np.array(Image.open(image_path).convert("RGB")) trimap = np.array(Image.open(mask_path)) mask = self._preprocess_mask(trimap) sample = dict(image=image, mask=mask, trimap=trimap) if self.transform is not None: sample = self.transform(**sample) return sample @staticmethod def _preprocess_mask(mask): mask = mask.astype(np.float32) mask[mask == 2.0] = 0.0 mask[(mask == 1.0) | (mask == 3.0)] = 1.0 return mask def _read_split(self): split_filename = "test.txt" if self.mode == "test" else "trainval.txt" split_filepath = os.path.join(self.root, "annotations", split_filename) with open(split_filepath) as f: split_data = f.read().strip("\n").split("\n") filenames = [x.split(" ")[0] for x in split_data] if self.mode == "train": # 90% for train filenames = [x for i, x in enumerate(filenames) if i % 10 != 0] elif self.mode == "valid": # 10% for validation filenames = [x for i, x in enumerate(filenames) if i % 10 == 0] return filenames @staticmethod def download(root): # load images filepath = os.path.join(root, "images.tar.gz") download_url( url="https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz", filepath=filepath, ) extract_archive(filepath) # load annotations filepath = os.path.join(root, "annotations.tar.gz") download_url( url="https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz", filepath=filepath, ) extract_archive(filepath) class SimpleOxfordPetDataset(OxfordPetDataset): def __getitem__(self, *args, **kwargs): sample = super().__getitem__(*args, **kwargs) # resize images image = np.array( Image.fromarray(sample["image"]).resize((256, 256), Image.BILINEAR) ) mask = np.array( Image.fromarray(sample["mask"]).resize((256, 256), Image.NEAREST) ) trimap = np.array( Image.fromarray(sample["trimap"]).resize((256, 256), Image.NEAREST) ) # convert to other format HWC -> CHW sample["image"] = np.moveaxis(image, -1, 0) sample["mask"] = np.expand_dims(mask, 0) sample["trimap"] = np.expand_dims(trimap, 0) return sample class TqdmUpTo(tqdm): def update_to(self, b=1, bsize=1, tsize=None): if tsize is not None: self.total = tsize self.update(b * bsize - self.n) def download_url(url, filepath): directory = os.path.dirname(os.path.abspath(filepath)) os.makedirs(directory, exist_ok=True) if os.path.exists(filepath): return with TqdmUpTo( unit="B", unit_scale=True, unit_divisor=1024, miniters=1, desc=os.path.basename(filepath), ) as t: urlretrieve(url, filename=filepath, reporthook=t.update_to, data=None) t.total = t.n def extract_archive(filepath): extract_dir = os.path.dirname(os.path.abspath(filepath)) dst_dir = os.path.splitext(filepath)[0] if not os.path.exists(dst_dir): shutil.unpack_archive(filepath, extract_dir) ================================================ FILE: segmentation_models_pytorch/decoders/__init__.py ================================================ ================================================ FILE: segmentation_models_pytorch/decoders/deeplabv3/__init__.py ================================================ from .model import DeepLabV3, DeepLabV3Plus __all__ = ["DeepLabV3", "DeepLabV3Plus"] ================================================ FILE: segmentation_models_pytorch/decoders/deeplabv3/decoder.py ================================================ """ BSD 3-Clause License Copyright (c) Soumith Chintala 2016, All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ from collections.abc import Iterable, Sequence from typing import List, Literal import torch from torch import nn from torch.nn import functional as F __all__ = ["DeepLabV3Decoder", "DeepLabV3PlusDecoder"] class DeepLabV3Decoder(nn.Module): def __init__( self, in_channels: int, out_channels: int, atrous_rates: Iterable[int], aspp_separable: bool, aspp_dropout: float, ): super().__init__() self.aspp = ASPP( in_channels, out_channels, atrous_rates, separable=aspp_separable, dropout=aspp_dropout, ) self.conv = nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False) self.bn = nn.BatchNorm2d(out_channels) self.relu = nn.ReLU() def forward(self, features: List[torch.Tensor]) -> torch.Tensor: x = features[-1] x = self.aspp(x) x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class DeepLabV3PlusDecoder(nn.Module): def __init__( self, encoder_channels: Sequence[int], encoder_depth: Literal[3, 4, 5], out_channels: int, atrous_rates: Iterable[int], output_stride: Literal[8, 16], aspp_separable: bool, aspp_dropout: float, ): super().__init__() if encoder_depth < 3: raise ValueError( "Encoder depth for DeepLabV3Plus decoder cannot be less than 3, got {}.".format( encoder_depth ) ) self.aspp = nn.Sequential( ASPP( encoder_channels[-1], out_channels, atrous_rates, separable=aspp_separable, dropout=aspp_dropout, ), SeparableConv2d( out_channels, out_channels, kernel_size=3, padding=1, bias=False ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) scale_factor = 4 if output_stride == 16 and encoder_depth > 3 else 2 self.up = nn.Upsample( mode="bilinear", scale_factor=scale_factor, align_corners=True ) highres_in_channels = encoder_channels[2] highres_out_channels = 48 # proposed by authors of paper self.block1 = nn.Sequential( nn.Conv2d( highres_in_channels, highres_out_channels, kernel_size=1, bias=False ), nn.BatchNorm2d(highres_out_channels), nn.ReLU(), ) self.block2 = nn.Sequential( SeparableConv2d( highres_out_channels + out_channels, out_channels, kernel_size=3, padding=1, bias=False, ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: aspp_features = self.aspp(features[-1]) aspp_features = self.up(aspp_features) high_res_features = self.block1(features[2]) concat_features = torch.cat([aspp_features, high_res_features], dim=1) fused_features = self.block2(concat_features) return fused_features class ASPPConv(nn.Sequential): def __init__(self, in_channels: int, out_channels: int, dilation: int): super().__init__( nn.Conv2d( in_channels, out_channels, kernel_size=3, padding=dilation, dilation=dilation, bias=False, ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) class ASPPSeparableConv(nn.Sequential): def __init__(self, in_channels: int, out_channels: int, dilation: int): super().__init__( SeparableConv2d( in_channels, out_channels, kernel_size=3, padding=dilation, dilation=dilation, bias=False, ), nn.BatchNorm2d(out_channels), nn.ReLU(), ) class ASPPPooling(nn.Sequential): def __init__(self, in_channels: int, out_channels: int): super().__init__( nn.AdaptiveAvgPool2d(1), nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), ) def forward(self, x: torch.Tensor) -> torch.Tensor: size = x.shape[-2:] for mod in self: x = mod(x) return F.interpolate(x, size=size, mode="bilinear", align_corners=False) class ASPP(nn.Module): def __init__( self, in_channels: int, out_channels: int, atrous_rates: Iterable[int], separable: bool, dropout: float, ): super(ASPP, self).__init__() modules = [ nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), ) ] rate1, rate2, rate3 = tuple(atrous_rates) ASPPConvModule = ASPPConv if not separable else ASPPSeparableConv modules.append(ASPPConvModule(in_channels, out_channels, rate1)) modules.append(ASPPConvModule(in_channels, out_channels, rate2)) modules.append(ASPPConvModule(in_channels, out_channels, rate3)) modules.append(ASPPPooling(in_channels, out_channels)) self.convs = nn.ModuleList(modules) self.project = nn.Sequential( nn.Conv2d(5 * out_channels, out_channels, kernel_size=1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU(), nn.Dropout(dropout), ) def forward(self, x: torch.Tensor) -> torch.Tensor: res = [] for conv in self.convs: res.append(conv(x)) res = torch.cat(res, dim=1) return self.project(res) class SeparableConv2d(nn.Sequential): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, bias: bool = True, ): dephtwise_conv = nn.Conv2d( in_channels, in_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=in_channels, bias=False, ) pointwise_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) super().__init__(dephtwise_conv, pointwise_conv) ================================================ FILE: segmentation_models_pytorch/decoders/deeplabv3/model.py ================================================ from collections.abc import Iterable from typing import Any, Literal, Optional from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import DeepLabV3Decoder, DeepLabV3PlusDecoder class DeepLabV3(SegmentationModel): """`DeepLabV3`__ implementation from "Rethinking Atrous Convolution for Semantic Image Segmentation" Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_channels: A number of convolution filters in ASPP module. Default is 256 encoder_output_stride: Downsampling factor for last encoder features (see original paper for explanation) decoder_atrous_rates: Dilation rates for ASPP module (should be an iterable of 3 integer values) decoder_aspp_separable: Use separable convolutions in ASPP module. Default is False decoder_aspp_dropout: Use dropout in ASPP module projection layer. Default is 0.5 in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. upsampling: Final upsampling factor. Default is **None** to preserve input-output spatial shape identity aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **DeepLabV3** .. __: https://arxiv.org/abs/1706.05587 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", encoder_output_stride: Literal[8, 16] = 8, decoder_channels: int = 256, decoder_atrous_rates: Iterable[int] = (12, 24, 36), decoder_aspp_separable: bool = False, decoder_aspp_dropout: float = 0.5, in_channels: int = 3, classes: int = 1, activation: Optional[str] = None, upsampling: Optional[int] = None, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() if encoder_output_stride not in [8, 16]: raise ValueError( "DeeplabV3 support output stride 8 or 16, got {}.".format( encoder_output_stride ) ) self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, output_stride=encoder_output_stride, **kwargs, ) if upsampling is None: if encoder_depth <= 3: scale_factor = 2**encoder_depth else: scale_factor = encoder_output_stride else: scale_factor = upsampling self.decoder = DeepLabV3Decoder( in_channels=self.encoder.out_channels[-1], out_channels=decoder_channels, atrous_rates=decoder_atrous_rates, aspp_separable=decoder_aspp_separable, aspp_dropout=decoder_aspp_dropout, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels, out_channels=classes, activation=activation, kernel_size=1, upsampling=scale_factor, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "deeplabv3-{}".format(encoder_name) def load_state_dict(self, state_dict, *args, **kwargs): # For backward compatibility, previously Decoder module was Sequential # and was not scriptable. keys = list(state_dict.keys()) for key in keys: new_key = key if key.startswith("decoder.0."): new_key = key.replace("decoder.0.", "decoder.aspp.") elif key.startswith("decoder.1."): new_key = key.replace("decoder.1.", "decoder.conv.") elif key.startswith("decoder.2."): new_key = key.replace("decoder.2.", "decoder.bn.") state_dict[new_key] = state_dict.pop(key) return super().load_state_dict(state_dict, *args, **kwargs) class DeepLabV3Plus(SegmentationModel): """`DeepLabV3+`__ implementation from "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation" Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name encoder_output_stride: Downsampling factor for last encoder features (see original paper for explanation) decoder_atrous_rates: Dilation rates for ASPP module (should be an iterable of 3 integer values) decoder_aspp_separable: Use separable convolutions in ASPP module. Default is True decoder_aspp_dropout: Use dropout in ASPP module projection layer. Default is 0.5 decoder_channels: A number of convolution filters in ASPP module. Default is 256 in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **DeepLabV3Plus** .. __: https://arxiv.org/abs/1802.02611v3 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: Literal[3, 4, 5] = 5, encoder_weights: Optional[str] = "imagenet", encoder_output_stride: Literal[8, 16] = 16, decoder_channels: int = 256, decoder_atrous_rates: Iterable[int] = (12, 24, 36), decoder_aspp_separable: bool = True, decoder_aspp_dropout: float = 0.5, in_channels: int = 3, classes: int = 1, activation: Optional[str] = None, upsampling: int = 4, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() if encoder_output_stride not in [8, 16]: raise ValueError( "DeeplabV3Plus support output stride 8 or 16, got {}.".format( encoder_output_stride ) ) self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, output_stride=encoder_output_stride, **kwargs, ) self.decoder = DeepLabV3PlusDecoder( encoder_channels=self.encoder.out_channels, encoder_depth=encoder_depth, out_channels=decoder_channels, atrous_rates=decoder_atrous_rates, output_stride=encoder_output_stride, aspp_separable=decoder_aspp_separable, aspp_dropout=decoder_aspp_dropout, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels, out_channels=classes, activation=activation, kernel_size=1, upsampling=upsampling, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "deeplabv3plus-{}".format(encoder_name) ================================================ FILE: segmentation_models_pytorch/decoders/dpt/__init__.py ================================================ from .model import DPT __all__ = ["DPT"] ================================================ FILE: segmentation_models_pytorch/decoders/dpt/decoder.py ================================================ import torch import torch.nn as nn from segmentation_models_pytorch.base.modules import Activation from typing import Optional, Sequence, Union, Callable, Literal class ReadoutConcatBlock(nn.Module): """ Concatenates the cls tokens with the features to make use of the global information aggregated in the prefix (cls) tokens. Projects the combined feature map to the original embedding dimension using a MLP. According to: https://github.com/isl-org/DPT/blob/cd3fe90bb4c48577535cc4d51b602acca688a2ee/dpt/vit.py#L79-L90 """ def __init__(self, embed_dim: int, has_prefix_tokens: bool): super().__init__() in_features = embed_dim * 2 if has_prefix_tokens else embed_dim out_features = embed_dim self.project = nn.Sequential( nn.Linear(in_features, out_features), nn.GELU(), ) def forward( self, features: torch.Tensor, prefix_tokens: Optional[torch.Tensor] = None ) -> torch.Tensor: batch_size, embed_dim, height, width = features.shape # Rearrange to (batch_size, height * width, embed_dim) features = features.view(batch_size, embed_dim, -1) features = features.transpose(1, 2).contiguous() if prefix_tokens is not None: # (batch_size, num_prefix_tokens, embed_dim) -> (batch_size, 1, embed_dim) prefix_tokens = prefix_tokens[:, :1].expand_as(features) features = torch.cat([features, prefix_tokens], dim=2) # Project to embedding dimension features = self.project(features) # Rearrange back to (batch_size, embed_dim, height, width) features = features.transpose(1, 2) features = features.view(batch_size, -1, height, width) return features class ReadoutAddBlock(nn.Module): """ Adds the prefix tokens to the features to make use of the global information aggregated in the prefix (cls) tokens. According to: https://github.com/isl-org/DPT/blob/cd3fe90bb4c48577535cc4d51b602acca688a2ee/dpt/vit.py#L71-L76 """ def forward( self, features: torch.Tensor, prefix_tokens: Optional[torch.Tensor] = None ) -> torch.Tensor: if prefix_tokens is not None: batch_size, embed_dim, height, width = features.shape prefix_tokens = prefix_tokens.mean(dim=1) prefix_tokens = prefix_tokens.view(batch_size, embed_dim, 1, 1) features = features + prefix_tokens return features class ReadoutIgnoreBlock(nn.Module): """ Ignores the prefix tokens and returns the features as is. """ def forward(self, features: torch.Tensor, *args, **kwargs) -> torch.Tensor: return features class ReassembleBlock(nn.Module): """ Processes the features such that they have progressively increasing embedding size and progressively decreasing spatial dimension """ def __init__( self, in_channels: int, mid_channels: int, out_channels: int, upsample_factor: int, ): super().__init__() self.project_to_out_channel = nn.Conv2d( in_channels=in_channels, out_channels=mid_channels, kernel_size=1, ) if upsample_factor > 1.0: self.upsample = nn.ConvTranspose2d( in_channels=mid_channels, out_channels=mid_channels, kernel_size=int(upsample_factor), stride=int(upsample_factor), ) elif upsample_factor == 1.0: self.upsample = nn.Identity() else: self.upsample = nn.Conv2d( in_channels=mid_channels, out_channels=mid_channels, kernel_size=3, stride=int(1 / upsample_factor), padding=1, ) self.project_to_feature_dim = nn.Conv2d( in_channels=mid_channels, out_channels=out_channels, kernel_size=3, padding=1, bias=False, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.project_to_out_channel(x) x = self.upsample(x) x = self.project_to_feature_dim(x) return x class ResidualConvBlock(nn.Module): def __init__(self, feature_dim: int): super().__init__() self.conv_1 = nn.Conv2d( in_channels=feature_dim, out_channels=feature_dim, kernel_size=3, padding=1, bias=False, ) self.batch_norm_1 = nn.BatchNorm2d(num_features=feature_dim) self.conv_2 = nn.Conv2d( in_channels=feature_dim, out_channels=feature_dim, kernel_size=3, padding=1, bias=False, ) self.batch_norm_2 = nn.BatchNorm2d(num_features=feature_dim) self.activation = nn.ReLU() def forward(self, x: torch.Tensor) -> torch.Tensor: residual = x # Block 1 x = self.activation(x) x = self.conv_1(x) x = self.batch_norm_1(x) # Block 2 x = self.activation(x) x = self.conv_2(x) x = self.batch_norm_2(x) # Add residual x = x + residual return x class FusionBlock(nn.Module): """ Fuses the processed encoder features in a residual manner and upsamples them """ def __init__(self, feature_dim: int): super().__init__() self.residual_conv_block1 = ResidualConvBlock(feature_dim) self.residual_conv_block2 = ResidualConvBlock(feature_dim) self.project = nn.Conv2d(feature_dim, feature_dim, kernel_size=1) self.activation = nn.ReLU() def forward( self, feature: torch.Tensor, previous_feature: Optional[torch.Tensor] = None, ) -> torch.Tensor: feature = self.residual_conv_block1(feature) if previous_feature is not None: feature = feature + previous_feature feature = self.residual_conv_block2(feature) feature = nn.functional.interpolate( feature, scale_factor=2, align_corners=True, mode="bilinear" ) feature = self.project(feature) return feature class DPTDecoder(nn.Module): """ Decoder part for DPT Processes the encoder features and class tokens (if encoder has class_tokens) to have spatial downsampling ratios of [1/4, 1/8, 1/16, 1/32, ...] relative to the input image spatial dimension. The decoder then fuses these features in a residual manner and progressively upsamples them by a factor of 2 so that the output has a downsampling ratio of 1/2 relative to the input image spatial dimension """ def __init__( self, encoder_out_channels: Sequence[int] = (756, 756, 756, 756), encoder_output_strides: Sequence[int] = (16, 16, 16, 16), encoder_has_prefix_tokens: bool = True, readout: Literal["cat", "add", "ignore"] = "cat", intermediate_channels: Sequence[int] = (256, 512, 1024, 1024), fusion_channels: int = 256, ): super().__init__() if not ( len(encoder_out_channels) == len(encoder_output_strides) == len(intermediate_channels) ): raise ValueError( "encoder_out_channels, encoder_output_strides and intermediate_channels must have the same length" ) num_blocks = len(encoder_out_channels) # If encoder has prefix tokens (e.g. cls_token), then we can concat/add/ignore them # according to the readout mode if readout == "cat": blocks = [ ReadoutConcatBlock(in_channels, encoder_has_prefix_tokens) for in_channels in encoder_out_channels ] elif readout == "add": blocks = [ReadoutAddBlock() for _ in encoder_out_channels] elif readout == "ignore": blocks = [ReadoutIgnoreBlock() for _ in encoder_out_channels] else: raise ValueError( f"Invalid readout mode: {readout}, should be one of: 'cat', 'add', 'ignore'" ) self.projection_blocks = nn.ModuleList(blocks) # Upsample factors to resize features to [1/4, 1/8, 1/16, 1/32, ...] scales scale_factors = [ stride / 2 ** (i + 2) for i, stride in enumerate(encoder_output_strides) ] self.reassemble_blocks = nn.ModuleList() for i in range(num_blocks): block = ReassembleBlock( in_channels=encoder_out_channels[i], mid_channels=intermediate_channels[i], out_channels=fusion_channels, upsample_factor=scale_factors[i], ) self.reassemble_blocks.append(block) # Fusion blocks to fuse the processed features in a sequential manner fusion_blocks = [FusionBlock(fusion_channels) for _ in range(num_blocks)] self.fusion_blocks = nn.ModuleList(fusion_blocks) def forward( self, features: list[torch.Tensor], prefix_tokens: list[Optional[torch.Tensor]] ) -> torch.Tensor: # Process the encoder features to scale of [1/4, 1/8, 1/16, 1/32, ...] processed_features = [] for i, (feature, prefix_tokens_i) in enumerate(zip(features, prefix_tokens)): projected_feature = self.projection_blocks[i](feature, prefix_tokens_i) processed_feature = self.reassemble_blocks[i](projected_feature) processed_features.append(processed_feature) # Fusion and progressive upsampling starting from the last processed feature processed_features = processed_features[::-1] fused_feature = None for fusion_block, feature in zip(self.fusion_blocks, processed_features): fused_feature = fusion_block(feature, fused_feature) return fused_feature class DPTSegmentationHead(nn.Module): def __init__( self, in_channels: int, out_channels: int, activation: Optional[Union[str, Callable]] = None, kernel_size: int = 3, upsampling: float = 2.0, ): super().__init__() self.head = nn.Sequential( nn.Conv2d( in_channels, in_channels, kernel_size=kernel_size, padding=1, bias=False ), nn.BatchNorm2d(in_channels), nn.ReLU(inplace=True), nn.Dropout(p=0.1, inplace=False), nn.Conv2d(in_channels, out_channels, kernel_size=1), ) self.activation = Activation(activation) self.upsampling_factor = upsampling def forward(self, x: torch.Tensor) -> torch.Tensor: head_output = self.head(x) resized_output = nn.functional.interpolate( head_output, scale_factor=self.upsampling_factor, mode="bilinear", align_corners=True, ) activation_output = self.activation(resized_output) return activation_output ================================================ FILE: segmentation_models_pytorch/decoders/dpt/model.py ================================================ import warnings from typing import Any, Optional, Union, Callable, Sequence, Literal import torch from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders.timm_vit import TimmViTEncoder from segmentation_models_pytorch.base.utils import is_torch_compiling from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import DPTDecoder, DPTSegmentationHead class DPT(SegmentationModel): """ `DPT`__ is a dense prediction architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks It assembles tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combines them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks Note: Since this model uses a Vision Transformer backbone, it typically requires a fixed input image size. To handle variable input sizes, you can set `dynamic_img_size=True` in the model initialization (if supported by the specific `timm` encoder). You can check if an encoder requires fixed size using `model.encoder.is_fixed_input_size`, and get the required input dimensions from `model.encoder.input_size`, however it's no guarantee that information is available. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution. encoder_depth: A number of stages used in encoder in range [1,4]. Each stage generate features smaller by a factor equal to the ViT model patch_size in spatial dimensions. Default is 4. encoder_weights: One of **None** (random initialization), or not **None** (pretrained weights would be loaded with respect to the encoder_name, e.g. for ``"tu-vit_base_patch16_224.augreg_in21k"`` - ``"augreg_in21k"`` weights would be loaded). encoder_output_indices: The indices of the encoder output features to use. If **None** will be sampled uniformly across the number of blocks in encoder, e.g. if number of blocks is 4 and encoder has 20 blocks, then encoder_output_indices will be (4, 9, 14, 19). If specified the number of indices should be equal to encoder_depth. Default is **None**. decoder_readout: The strategy to utilize the prefix tokens (e.g. cls_token) from the encoder. Can be one of **"cat"**, **"add"**, or **"ignore"**. Default is **"cat"**. decoder_intermediate_channels: The number of channels for the intermediate decoder layers. Reduce if you want to reduce the number of parameters in the decoder. Default is (256, 512, 1024, 1024). decoder_fusion_channels: The latent dimension to which the encoder features will be projected to before fusion. Default is 256. in_channels: Number of input channels for the model, default is 3 (RGB images) classes: Number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - **classes** (*int*): A number of classes; - **pooling** (*str*): One of "max", "avg". Default is "avg"; - **dropout** (*float*): Dropout factor in [0, 1); - **activation** (*str*): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits). kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Specify ``dynamic_img_size=True`` to allow the model to handle images of different sizes. Returns: ``torch.nn.Module``: DPT .. __: https://arxiv.org/abs/2103.13413 """ # fails for encoders with prefix tokens _is_torch_scriptable = False _is_torch_compilable = True requires_divisible_input_shape = True @supports_config_loading def __init__( self, encoder_name: str = "tu-vit_base_patch16_224.augreg_in21k", encoder_depth: int = 4, encoder_weights: Optional[str] = "imagenet", encoder_output_indices: Optional[list[int]] = None, decoder_readout: Literal["ignore", "add", "cat"] = "cat", decoder_intermediate_channels: Sequence[int] = (256, 512, 1024, 1024), decoder_fusion_channels: int = 256, in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() if encoder_name.startswith("tu-"): encoder_name = encoder_name[3:] else: raise ValueError( f"Only Timm encoders are supported for DPT. Encoder name must start with 'tu-', got {encoder_name}" ) if decoder_readout not in ["ignore", "add", "cat"]: raise ValueError( f"Invalid decoder readout mode. Must be one of: 'ignore', 'add', 'cat'. Got: {decoder_readout}" ) self.encoder = TimmViTEncoder( name=encoder_name, in_channels=in_channels, depth=encoder_depth, pretrained=encoder_weights is not None, output_indices=encoder_output_indices, **kwargs, ) if not self.encoder.has_prefix_tokens and decoder_readout != "ignore": warnings.warn( f"Encoder does not have prefix tokens (e.g. cls_token), but `decoder_readout` is set to '{decoder_readout}'. " f"It's recommended to set `decoder_readout='ignore'` when using a encoder without prefix tokens.", UserWarning, ) self.decoder = DPTDecoder( encoder_out_channels=self.encoder.out_channels, encoder_output_strides=self.encoder.output_strides, encoder_has_prefix_tokens=self.encoder.has_prefix_tokens, readout=decoder_readout, intermediate_channels=decoder_intermediate_channels, fusion_channels=decoder_fusion_channels, ) self.segmentation_head = DPTSegmentationHead( in_channels=decoder_fusion_channels, out_channels=classes, activation=activation, kernel_size=3, upsampling=2, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "dpt-{}".format(encoder_name) self.initialize() def forward(self, x): """Sequentially pass `x` trough model`s encoder, decoder and heads""" if not ( torch.jit.is_scripting() or torch.jit.is_tracing() or is_torch_compiling() ): self.check_input_shape(x) features, prefix_tokens = self.encoder(x) decoder_output = self.decoder(features, prefix_tokens) masks = self.segmentation_head(decoder_output) if self.classification_head is not None: labels = self.classification_head(features[-1]) return masks, labels return masks ================================================ FILE: segmentation_models_pytorch/decoders/fpn/__init__.py ================================================ from .model import FPN __all__ = ["FPN"] ================================================ FILE: segmentation_models_pytorch/decoders/fpn/decoder.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from typing import List, Literal class Conv3x3GNReLU(nn.Module): def __init__(self, in_channels: int, out_channels: int, upsample: bool = False): super().__init__() self.upsample = upsample self.block = nn.Sequential( nn.Conv2d( in_channels, out_channels, (3, 3), stride=1, padding=1, bias=False ), nn.GroupNorm(32, out_channels), nn.ReLU(inplace=True), ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.block(x) if self.upsample: x = F.interpolate(x, scale_factor=2.0, mode="bilinear", align_corners=True) return x class FPNBlock(nn.Module): def __init__( self, pyramid_channels: int, skip_channels: int, interpolation_mode: str = "nearest", ): super().__init__() self.skip_conv = nn.Conv2d(skip_channels, pyramid_channels, kernel_size=1) self.interpolation_mode = interpolation_mode def forward(self, x: torch.Tensor, skip: torch.Tensor) -> torch.Tensor: x = F.interpolate(x, scale_factor=2.0, mode=self.interpolation_mode) skip = self.skip_conv(skip) x = x + skip return x class SegmentationBlock(nn.Module): def __init__(self, in_channels: int, out_channels: int, n_upsamples: int = 0): super().__init__() blocks = [Conv3x3GNReLU(in_channels, out_channels, upsample=bool(n_upsamples))] if n_upsamples > 1: for _ in range(1, n_upsamples): blocks.append(Conv3x3GNReLU(out_channels, out_channels, upsample=True)) self.block = nn.Sequential(*blocks) def forward(self, x): return self.block(x) class MergeBlock(nn.Module): def __init__(self, policy: Literal["add", "cat"]): super().__init__() if policy not in ["add", "cat"]: raise ValueError( "`merge_policy` must be one of: ['add', 'cat'], got {}".format(policy) ) self.policy = policy def forward(self, x: List[torch.Tensor]) -> torch.Tensor: if self.policy == "add": output = torch.stack(x).sum(dim=0) elif self.policy == "cat": output = torch.cat(x, dim=1) else: raise ValueError( "`merge_policy` must be one of: ['add', 'cat'], got {}".format( self.policy ) ) return output class FPNDecoder(nn.Module): def __init__( self, encoder_channels: List[int], encoder_depth: int = 5, pyramid_channels: int = 256, segmentation_channels: int = 128, dropout: float = 0.2, merge_policy: Literal["add", "cat"] = "add", interpolation_mode: str = "nearest", ): super().__init__() self.out_channels = ( segmentation_channels if merge_policy == "add" else segmentation_channels * 4 ) if encoder_depth < 3: raise ValueError( "Encoder depth for FPN decoder cannot be less than 3, got {}.".format( encoder_depth ) ) encoder_channels = encoder_channels[::-1] encoder_channels = encoder_channels[: encoder_depth + 1] self.p5 = nn.Conv2d(encoder_channels[0], pyramid_channels, kernel_size=1) self.p4 = FPNBlock(pyramid_channels, encoder_channels[1], interpolation_mode) self.p3 = FPNBlock(pyramid_channels, encoder_channels[2], interpolation_mode) self.p2 = FPNBlock(pyramid_channels, encoder_channels[3], interpolation_mode) self.seg_blocks = nn.ModuleList( [ SegmentationBlock( pyramid_channels, segmentation_channels, n_upsamples=n_upsamples ) for n_upsamples in [3, 2, 1, 0] ] ) self.merge = MergeBlock(merge_policy) self.dropout = nn.Dropout2d(p=dropout, inplace=True) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: c2, c3, c4, c5 = features[-4:] p5 = self.p5(c5) p4 = self.p4(p5, c4) p3 = self.p3(p4, c3) p2 = self.p2(p3, c2) s5 = self.seg_blocks[0](p5) s4 = self.seg_blocks[1](p4) s3 = self.seg_blocks[2](p3) s2 = self.seg_blocks[3](p2) feature_pyramid = [s5, s4, s3, s2] x = self.merge(feature_pyramid) x = self.dropout(x) return x ================================================ FILE: segmentation_models_pytorch/decoders/fpn/model.py ================================================ from typing import Any, Optional from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import FPNDecoder class FPN(SegmentationModel): """`FPN`__ is a fully convolution neural network for image semantic segmentation. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_pyramid_channels: A number of convolution filters in Feature Pyramid of FPN_ decoder_segmentation_channels: A number of convolution filters in segmentation blocks of FPN_ decoder_merge_policy: Determines how to merge pyramid features inside FPN. Available options are **add** and **cat** decoder_dropout: Spatial dropout rate in range (0, 1) for feature pyramid in FPN_ decoder_interpolation: Interpolation mode used in decoder of the model. Available options are **"nearest"**, **"bilinear"**, **"bicubic"**, **"area"**, **"nearest-exact"**. Default is **"nearest"**. in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **FPN** .. __: http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_pyramid_channels: int = 256, decoder_segmentation_channels: int = 128, decoder_merge_policy: str = "add", decoder_dropout: float = 0.2, decoder_interpolation: str = "nearest", in_channels: int = 3, classes: int = 1, activation: Optional[str] = None, upsampling: int = 4, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() # validate input params if encoder_name.startswith("mit_b") and encoder_depth != 5: raise ValueError( "Encoder {} support only encoder_depth=5".format(encoder_name) ) self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = FPNDecoder( encoder_channels=self.encoder.out_channels, encoder_depth=encoder_depth, pyramid_channels=decoder_pyramid_channels, segmentation_channels=decoder_segmentation_channels, dropout=decoder_dropout, merge_policy=decoder_merge_policy, interpolation_mode=decoder_interpolation, ) self.segmentation_head = SegmentationHead( in_channels=self.decoder.out_channels, out_channels=classes, activation=activation, kernel_size=1, upsampling=upsampling, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "fpn-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/linknet/__init__.py ================================================ from .model import Linknet __all__ = ["Linknet"] ================================================ FILE: segmentation_models_pytorch/decoders/linknet/decoder.py ================================================ import torch import torch.nn as nn from typing import Any, Dict, List, Optional, Union from segmentation_models_pytorch.base import modules class TransposeX2(nn.Sequential): def __init__( self, in_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() conv = nn.ConvTranspose2d( in_channels, out_channels, kernel_size=4, stride=2, padding=1 ) norm = modules.get_norm_layer(use_norm, out_channels) activation = nn.ReLU(inplace=True) super().__init__(conv, norm, activation) class DecoderBlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() self.block = nn.Sequential( modules.Conv2dReLU( in_channels, in_channels // 4, kernel_size=1, use_norm=use_norm, ), TransposeX2(in_channels // 4, in_channels // 4, use_norm=use_norm), modules.Conv2dReLU( in_channels // 4, out_channels, kernel_size=1, use_norm=use_norm, ), ) def forward( self, x: torch.Tensor, skip: Optional[torch.Tensor] = None ) -> torch.Tensor: x = self.block(x) if skip is not None and skip.shape[1] != 0: x = x + skip return x class LinknetDecoder(nn.Module): def __init__( self, encoder_channels: List[int], prefinal_channels: int = 32, n_blocks: int = 5, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() # remove first skip encoder_channels = encoder_channels[1:] # reverse channels to start from head of encoder encoder_channels = encoder_channels[::-1] channels = list(encoder_channels) + [prefinal_channels] for i in range(1, len(channels) - 1): # Transformer-style encoders may expose a 0-channel placeholder for the # missing 1/2-scale skip. Keep the decoder stream non-empty and just # skip feature fusion at that stage. if channels[i] == 0: channels[i] = channels[i - 1] self.blocks = nn.ModuleList( [ DecoderBlock( channels[i], channels[i + 1], use_norm=use_norm, ) for i in range(n_blocks) ] ) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: features = features[1:] # remove first skip features = features[::-1] # reverse channels to start from head of encoder x = features[0] skips = features[1:] for i, decoder_block in enumerate(self.blocks): skip = skips[i] if i < len(skips) else None x = decoder_block(x, skip) return x ================================================ FILE: segmentation_models_pytorch/decoders/linknet/model.py ================================================ import warnings from typing import Any, Dict, Optional, Union, Callable from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import LinknetDecoder class Linknet(SegmentationModel): """`Linknet`__ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial resolution (skip connections) which are used by decoder to define accurate segmentation mask. Use *sum* for fusing decoder blocks with skip connections. Note: This implementation by default has 4 skip connections (original - 3). Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_use_norm: Specifies normalization between Conv2D and activation. Accepts the following types: - **True**: Defaults to `"batchnorm"`. - **False**: No normalization (`nn.Identity`). - **str**: Specifies normalization type using default parameters. Available values: `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`. - **dict**: Fully customizable normalization settings. Structure: ```python {"type": , **kwargs} ``` where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation. **Example**: ```python decoder_use_norm={"type": "layernorm", "eps": 1e-2} ``` in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **Linknet** .. __: https://arxiv.org/abs/1707.03718 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() if encoder_name.startswith("mit_b"): raise ValueError( "Encoder `{}` is not supported for Linknet".format(encoder_name) ) decoder_use_batchnorm = kwargs.pop("decoder_use_batchnorm", None) if decoder_use_batchnorm is not None: warnings.warn( "The usage of decoder_use_batchnorm is deprecated. Please modify your code for decoder_use_norm", DeprecationWarning, stacklevel=2, ) decoder_use_norm = decoder_use_batchnorm self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = LinknetDecoder( encoder_channels=self.encoder.out_channels, n_blocks=encoder_depth, prefinal_channels=32, use_norm=decoder_use_norm, ) self.segmentation_head = SegmentationHead( in_channels=32, out_channels=classes, activation=activation, kernel_size=1 ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "link-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/manet/__init__.py ================================================ from .model import MAnet __all__ = ["MAnet"] ================================================ FILE: segmentation_models_pytorch/decoders/manet/decoder.py ================================================ from typing import Any, Dict, List, Optional, Union import torch import torch.nn as nn import torch.nn.functional as F from segmentation_models_pytorch.base import modules as md class PABBlock(nn.Module): def __init__(self, in_channels: int, pab_channels: int = 64): super().__init__() # Series of 1x1 conv to generate attention feature maps self.pab_channels = pab_channels self.in_channels = in_channels self.top_conv = nn.Conv2d(in_channels, pab_channels, kernel_size=1) self.center_conv = nn.Conv2d(in_channels, pab_channels, kernel_size=1) self.bottom_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) self.map_softmax = nn.Softmax(dim=1) self.out_conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1) def forward(self, x: torch.Tensor) -> torch.Tensor: batch_size, _, height, width = x.shape x_top = self.top_conv(x) x_center = self.center_conv(x) x_bottom = self.bottom_conv(x) x_top = x_top.flatten(2) x_center = x_center.flatten(2).transpose(1, 2) x_bottom = x_bottom.flatten(2).transpose(1, 2) sp_map = torch.matmul(x_center, x_top) sp_map = self.map_softmax(sp_map.view(batch_size, -1)) sp_map = sp_map.view(batch_size, height * width, height * width) sp_map = torch.matmul(sp_map, x_bottom) sp_map = sp_map.reshape(batch_size, self.in_channels, height, width) x = x + sp_map x = self.out_conv(x) return x class MFABBlock(nn.Module): def __init__( self, in_channels: int, skip_channels: int, out_channels: int, interpolation_mode: str = "nearest", use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", reduction: int = 16, ): # MFABBlock is just a modified version of SE-blocks, one for skip, one for input super().__init__() self.hl_conv = nn.Sequential( md.Conv2dReLU( in_channels, in_channels, kernel_size=3, padding=1, use_norm=use_norm, ), md.Conv2dReLU( in_channels, skip_channels, kernel_size=1, use_norm=use_norm, ), ) reduced_channels = max(1, skip_channels // reduction) self.SE_ll = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(skip_channels, reduced_channels, 1), nn.ReLU(inplace=True), nn.Conv2d(reduced_channels, skip_channels, 1), nn.Sigmoid(), ) self.SE_hl = nn.Sequential( nn.AdaptiveAvgPool2d(1), nn.Conv2d(skip_channels, reduced_channels, 1), nn.ReLU(inplace=True), nn.Conv2d(reduced_channels, skip_channels, 1), nn.Sigmoid(), ) self.conv1 = md.Conv2dReLU( skip_channels + skip_channels, # we transform C-prime form high level to C from skip connection out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.conv2 = md.Conv2dReLU( out_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.interpolation_mode = interpolation_mode def forward( self, x: torch.Tensor, skip: Optional[torch.Tensor] = None ) -> torch.Tensor: x = self.hl_conv(x) x = F.interpolate(x, scale_factor=2.0, mode=self.interpolation_mode) attention_hl = self.SE_hl(x) if skip is not None: attention_ll = self.SE_ll(skip) attention_hl = attention_hl + attention_ll x = x * attention_hl x = torch.cat([x, skip], dim=1) x = self.conv1(x) x = self.conv2(x) return x class DecoderBlock(nn.Module): def __init__( self, in_channels: int, skip_channels: int, out_channels: int, interpolation_mode: str = "nearest", use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() self.conv1 = md.Conv2dReLU( in_channels + skip_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.conv2 = md.Conv2dReLU( out_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.interpolation_mode = interpolation_mode def forward( self, x: torch.Tensor, skip: Optional[torch.Tensor] = None ) -> torch.Tensor: x = F.interpolate(x, scale_factor=2.0, mode=self.interpolation_mode) if skip is not None: x = torch.cat([x, skip], dim=1) x = self.conv1(x) x = self.conv2(x) return x class MAnetDecoder(nn.Module): def __init__( self, encoder_channels: List[int], decoder_channels: List[int], n_blocks: int = 5, reduction: int = 16, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", pab_channels: int = 64, interpolation_mode: str = "nearest", ): super().__init__() if n_blocks != len(decoder_channels): raise ValueError( "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format( n_blocks, len(decoder_channels) ) ) # remove first skip with same spatial resolution encoder_channels = encoder_channels[1:] # reverse channels to start from head of encoder encoder_channels = encoder_channels[::-1] # computing blocks input and output channels head_channels = encoder_channels[0] in_channels = [head_channels] + list(decoder_channels[:-1]) skip_channels = list(encoder_channels[1:]) + [0] out_channels = decoder_channels self.center = PABBlock(head_channels, pab_channels=pab_channels) # combine decoder keyword arguments kwargs = dict( use_norm=use_norm, interpolation_mode=interpolation_mode ) # no attention type here blocks = [ MFABBlock(in_ch, skip_ch, out_ch, reduction=reduction, **kwargs) if skip_ch > 0 else DecoderBlock(in_ch, skip_ch, out_ch, **kwargs) for in_ch, skip_ch, out_ch in zip(in_channels, skip_channels, out_channels) ] # for the last we dont have skip connection -> use simple decoder block self.blocks = nn.ModuleList(blocks) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: features = features[1:] # remove first skip with same spatial resolution features = features[::-1] # reverse channels to start from head of encoder head = features[0] skips = features[1:] x = self.center(head) for i, decoder_block in enumerate(self.blocks): skip = skips[i] if i < len(skips) else None x = decoder_block(x, skip) return x ================================================ FILE: segmentation_models_pytorch/decoders/manet/model.py ================================================ import warnings from typing import Any, Dict, Optional, Union, Sequence, Callable from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import MAnetDecoder class MAnet(SegmentationModel): """`MAnet`__: Multi-scale Attention Net. The MA-Net can capture rich contextual dependencies based on the attention mechanism, using two blocks: - Position-wise Attention Block (PAB), which captures the spatial dependencies between pixels in a global view - Multi-scale Fusion Attention Block (MFAB), which captures the channel dependencies between any feature map by multi-scale semantic feature fusion Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in decoder. Length of the list should be the same as **encoder_depth** decoder_use_norm: Specifies normalization between Conv2D and activation. Accepts the following types: - **True**: Defaults to `"batchnorm"`. - **False**: No normalization (`nn.Identity`). - **str**: Specifies normalization type using default parameters. Available values: `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`. - **dict**: Fully customizable normalization settings. Structure: ```python {"type": , **kwargs} ``` where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation. **Example**: ```python decoder_use_norm={"type": "layernorm", "eps": 1e-2} ``` decoder_pab_channels: A number of channels for PAB module in decoder. Default is 64. decoder_interpolation: Interpolation mode used in decoder of the model. Available options are **"nearest"**, **"bilinear"**, **"bicubic"**, **"area"**, **"nearest-exact"**. Default is **"nearest"**. in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **MAnet** .. __: https://ieeexplore.ieee.org/abstract/document/9201310 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", decoder_channels: Sequence[int] = (256, 128, 64, 32, 16), decoder_pab_channels: int = 64, decoder_interpolation: str = "nearest", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() decoder_use_batchnorm = kwargs.pop("decoder_use_batchnorm", None) if decoder_use_batchnorm is not None: warnings.warn( "The usage of decoder_use_batchnorm is deprecated. Please modify your code for decoder_use_norm", DeprecationWarning, stacklevel=2, ) decoder_use_norm = decoder_use_batchnorm self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = MAnetDecoder( encoder_channels=self.encoder.out_channels, decoder_channels=decoder_channels, n_blocks=encoder_depth, use_norm=decoder_use_norm, pab_channels=decoder_pab_channels, interpolation_mode=decoder_interpolation, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels[-1], out_channels=classes, activation=activation, kernel_size=3, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "manet-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/pan/__init__.py ================================================ from .model import PAN __all__ = ["PAN"] ================================================ FILE: segmentation_models_pytorch/decoders/pan/decoder.py ================================================ from collections.abc import Sequence from typing import Literal, List import torch import torch.nn as nn import torch.nn.functional as F class ConvBnRelu(nn.Module): def __init__( self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = True, add_relu: bool = True, interpolate: bool = False, ): super(ConvBnRelu, self).__init__() self.conv = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=bias, groups=groups, ) self.activation = nn.ReLU(inplace=True) self.bn = nn.BatchNorm2d(out_channels) self.add_relu = add_relu self.interpolate = interpolate def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.conv(x) x = self.bn(x) if self.add_relu: x = self.activation(x) if self.interpolate: x = F.interpolate(x, scale_factor=2.0, mode="bilinear", align_corners=True) return x class FPABlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, upscale_mode: str = "bilinear" ): super().__init__() self.upscale_mode = upscale_mode if self.upscale_mode == "bilinear": self.align_corners = True else: self.align_corners = False # global pooling branch self.branch1 = nn.Sequential( nn.AdaptiveAvgPool2d(1), ConvBnRelu( in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, ), ) # middle branch self.mid = nn.Sequential( ConvBnRelu( in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, ) ) self.down1 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2), ConvBnRelu( in_channels=in_channels, out_channels=1, kernel_size=7, stride=1, padding=3, ), ) self.down2 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2), ConvBnRelu( in_channels=1, out_channels=1, kernel_size=5, stride=1, padding=2 ), ) self.down3 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2), ConvBnRelu( in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1 ), ConvBnRelu( in_channels=1, out_channels=1, kernel_size=3, stride=1, padding=1 ), ) self.conv2 = ConvBnRelu( in_channels=1, out_channels=1, kernel_size=5, stride=1, padding=2 ) self.conv1 = ConvBnRelu( in_channels=1, out_channels=1, kernel_size=7, stride=1, padding=3 ) def forward(self, x: torch.Tensor) -> torch.Tensor: _, _, height, width = x.shape branch1_output = self.branch1(x) branch1_output = F.interpolate( branch1_output, size=(height, width), mode=self.upscale_mode, align_corners=self.align_corners, ) middle_output = self.mid(x) x1 = self.down1(x) x2 = self.down2(x1) x3 = self.down3(x2) x3 = F.interpolate( x3, size=(height // 4, width // 4), mode=self.upscale_mode, align_corners=self.align_corners, ) x2 = self.conv2(x2) x = x2 + x3 x = F.interpolate( x, size=(height // 2, width // 2), mode=self.upscale_mode, align_corners=self.align_corners, ) x1 = self.conv1(x1) x = x + x1 x = F.interpolate( x, size=(height, width), mode=self.upscale_mode, align_corners=self.align_corners, ) x = torch.mul(x, middle_output) x = x + branch1_output return x class GAUBlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, interpolation_mode: str = "bilinear", ): super(GAUBlock, self).__init__() self.interpolation_mode = interpolation_mode self.align_corners = True if interpolation_mode == "bilinear" else None self.conv1 = nn.Sequential( nn.AdaptiveAvgPool2d(1), ConvBnRelu( in_channels=out_channels, out_channels=out_channels, kernel_size=1, add_relu=False, ), nn.Sigmoid(), ) self.conv2 = ConvBnRelu( in_channels=in_channels, out_channels=out_channels, kernel_size=3, padding=1 ) def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: """ Args: x: low level feature y: high level feature """ height, width = x.shape[2:] y_up = F.interpolate( y, size=(height, width), mode=self.interpolation_mode, align_corners=self.align_corners, ) x = self.conv2(x) y = self.conv1(y) z = torch.mul(x, y) return y_up + z class PANDecoder(nn.Module): def __init__( self, encoder_channels: Sequence[int], encoder_depth: Literal[3, 4, 5], decoder_channels: int, interpolation_mode: str = "bilinear", ): super().__init__() if encoder_depth < 3: raise ValueError( "Encoder depth for PAN decoder cannot be less than 3, got {}.".format( encoder_depth ) ) encoder_channels = encoder_channels[2:] self.fpa = FPABlock( in_channels=encoder_channels[-1], out_channels=decoder_channels ) if encoder_depth == 5: self.gau3 = GAUBlock( in_channels=encoder_channels[2], out_channels=decoder_channels, interpolation_mode=interpolation_mode, ) if encoder_depth >= 4: self.gau2 = GAUBlock( in_channels=encoder_channels[1], out_channels=decoder_channels, interpolation_mode=interpolation_mode, ) if encoder_depth >= 3: self.gau1 = GAUBlock( in_channels=encoder_channels[0], out_channels=decoder_channels, interpolation_mode=interpolation_mode, ) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: features = features[2:] # remove first and second skip out = self.fpa(features[-1]) # 1/16 or 1/32 if hasattr(self, "gau3"): out = self.gau3(features[2], out) # 1/16 if hasattr(self, "gau2"): out = self.gau2(features[1], out) # 1/8 if hasattr(self, "gau1"): out = self.gau1(features[0], out) # 1/4 return out ================================================ FILE: segmentation_models_pytorch/decoders/pan/model.py ================================================ from typing import Any, Callable, Literal, Optional, Union import warnings from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import PANDecoder class PAN(SegmentationModel): """Implementation of `PAN`__ (Pyramid Attention Network). Note: Currently works with shape of input tensor >= [B x C x 128 x 128] for pytorch <= 1.1.0 and with shape of input tensor >= [B x C x 256 x 256] for pytorch == 1.3.1 Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name encoder_output_stride: 16 or 32, if 16 use dilation in encoder last layer. Doesn't work with ***ception***, **vgg***, **densenet*`** backbones.Default is 16. decoder_channels: A number of convolution layer filters in decoder blocks decoder_interpolation: Interpolation mode used in decoder of the model. Available options are **"nearest"**, **"bilinear"**, **"bicubic"**, **"area"**, **"nearest-exact"**. Default is **"bilinear"**. in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. upsampling: Final upsampling factor. Default is 4 to preserve input-output spatial shape identity aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **PAN** .. __: https://arxiv.org/abs/1805.10180 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: Literal[3, 4, 5] = 5, encoder_weights: Optional[str] = "imagenet", encoder_output_stride: Literal[16, 32] = 16, decoder_channels: int = 32, decoder_interpolation: str = "bilinear", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, upsampling: int = 4, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() if encoder_output_stride not in [16, 32]: raise ValueError( "PAN support output stride 16 or 32, got {}".format( encoder_output_stride ) ) upscale_mode = kwargs.pop("upscale_mode", None) if upscale_mode is not None: warnings.warn( "The usage of upscale_mode is deprecated. Please modify your code for decoder_interpolation", DeprecationWarning, stacklevel=2, ) decoder_interpolation = upscale_mode self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, output_stride=encoder_output_stride, **kwargs, ) self.decoder = PANDecoder( encoder_channels=self.encoder.out_channels, encoder_depth=encoder_depth, decoder_channels=decoder_channels, interpolation_mode=decoder_interpolation, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels, out_channels=classes, activation=activation, kernel_size=3, upsampling=upsampling, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "pan-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/pspnet/__init__.py ================================================ from .model import PSPNet __all__ = ["PSPNet"] ================================================ FILE: segmentation_models_pytorch/decoders/pspnet/decoder.py ================================================ from typing import Any, Dict, List, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from segmentation_models_pytorch.base import modules class PSPBlock(nn.Module): def __init__( self, in_channels: int, out_channels: int, pool_size: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() if pool_size == 1: use_norm = "identity" # PyTorch does not support BatchNorm for 1x1 shape self.pool_size = pool_size self.pool = nn.Sequential( nn.AdaptiveAvgPool2d(output_size=(pool_size, pool_size)), modules.Conv2dReLU( in_channels, out_channels, kernel_size=1, use_norm=use_norm ), ) def forward(self, x: torch.Tensor) -> torch.Tensor: height, width = x.shape[2:] if torch.jit.is_scripting(): # TorchScript path: use standard AdaptiveAvgPool2d via self.pool x = self.pool(x) elif torch.onnx.is_in_onnx_export(): # ONNX export path: AdaptiveAvgPool2d is often problematic during export. # Using F.interpolate with 'area' mode provides the same mathematical result # (average pooling) while being more robustly supported. x = F.interpolate(x, size=(self.pool_size, self.pool_size), mode="area") x = self.pool[1](x) # use only ConvRelu block from pool else: x = self.pool(x) x = F.interpolate(x, size=(height, width), mode="bilinear", align_corners=True) return x class PSPModule(nn.Module): def __init__( self, in_channels: int, sizes: Tuple[int, ...] = (1, 2, 3, 6), use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() self.blocks = nn.ModuleList( [ PSPBlock( in_channels, in_channels // len(sizes), size, use_norm=use_norm, ) for size in sizes ] ) def forward(self, x): xs = [block(x) for block in self.blocks] + [x] x = torch.cat(xs, dim=1) return x class PSPDecoder(nn.Module): def __init__( self, encoder_channels: List[int], use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", out_channels: int = 512, dropout: float = 0.2, ): super().__init__() self.psp = PSPModule( in_channels=encoder_channels[-1], sizes=(1, 2, 3, 6), use_norm=use_norm, ) self.conv = modules.Conv2dReLU( in_channels=encoder_channels[-1] * 2, out_channels=out_channels, kernel_size=1, use_norm=use_norm, ) self.dropout = nn.Dropout2d(p=dropout) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: x = features[-1] x = self.psp(x) x = self.conv(x) x = self.dropout(x) return x ================================================ FILE: segmentation_models_pytorch/decoders/pspnet/model.py ================================================ import warnings from typing import Any, Dict, Optional, Union, Callable from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import PSPDecoder class PSPNet(SegmentationModel): """`PSPNet`__ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* and *Spatial Pyramid* (decoder). Spatial Pyramid build on top of encoder and does not use "fine-features" (features of high spatial resolution). PSPNet can be used for multiclass segmentation of high resolution images, however it is not good for detecting small objects and producing accurate, pixel-level mask. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name psp_out_channels: A number of filters in Spatial Pyramid decoder_use_norm: Specifies normalization between Conv2D and activation. Accepts the following types: - **True**: Defaults to `"batchnorm"`. - **False**: No normalization (`nn.Identity`). - **str**: Specifies normalization type using default parameters. Available values: `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`. - **dict**: Fully customizable normalization settings. Structure: ```python {"type": , **kwargs} ``` where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation. **Example**: ```python decoder_use_norm={"type": "layernorm", "eps": 1e-2} ``` psp_dropout: Spatial dropout rate in [0, 1) used in Spatial Pyramid in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. upsampling: Final upsampling factor. Default is 8 to preserve input-output spatial shape identity aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **PSPNet** .. __: https://arxiv.org/abs/1612.01105 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_weights: Optional[str] = "imagenet", encoder_depth: int = 3, psp_out_channels: int = 512, decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", psp_dropout: float = 0.2, in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, upsampling: int = 8, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() psp_use_batchnorm = kwargs.pop("psp_use_batchnorm", None) if psp_use_batchnorm is not None: warnings.warn( "The usage of psp_use_batchnorm is deprecated. Please modify your code for decoder_use_norm", DeprecationWarning, stacklevel=2, ) decoder_use_norm = psp_use_batchnorm self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = PSPDecoder( encoder_channels=self.encoder.out_channels, use_norm=decoder_use_norm, out_channels=psp_out_channels, dropout=psp_dropout, ) self.segmentation_head = SegmentationHead( in_channels=psp_out_channels, out_channels=classes, kernel_size=3, activation=activation, upsampling=upsampling, ) if aux_params: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "psp-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/segformer/__init__.py ================================================ from .model import Segformer __all__ = ["Segformer"] ================================================ FILE: segmentation_models_pytorch/decoders/segformer/decoder.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from typing import List from segmentation_models_pytorch.base import modules as md class MLP(nn.Module): def __init__(self, skip_channels: int, segmentation_channels: int): super().__init__() self.linear = nn.Linear(skip_channels, segmentation_channels) def forward(self, x: torch.Tensor): batch, _, height, width = x.shape x = x.flatten(2).transpose(1, 2) x = self.linear(x) x = x.transpose(1, 2).reshape(batch, -1, height, width) return x class SegformerDecoder(nn.Module): def __init__( self, encoder_channels: List[int], encoder_depth: int = 5, segmentation_channels: int = 256, ): super().__init__() if encoder_depth < 3: raise ValueError( "Encoder depth for Segformer decoder cannot be less than 3, got {}.".format( encoder_depth ) ) if encoder_channels[1] == 0: encoder_channels = [ channel for index, channel in enumerate(encoder_channels) if index != 1 ] encoder_channels = encoder_channels[::-1] self.mlp_stage = nn.ModuleList( [MLP(channel, segmentation_channels) for channel in encoder_channels[:-1]] ) self.fuse_stage = md.Conv2dReLU( in_channels=(len(encoder_channels) - 1) * segmentation_channels, out_channels=segmentation_channels, kernel_size=1, use_norm="batchnorm", ) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: # Resize all features to the size of the largest feature target_size = [dim // 4 for dim in features[0].shape[2:]] features = features[2:] if features[1].size(1) == 0 else features[1:] features = features[::-1] # reverse channels to start from head of encoder resized_features = [] for i, mlp_layer in enumerate(self.mlp_stage): feature = mlp_layer(features[i]) resized_feature = F.interpolate( feature, size=target_size, mode="bilinear", align_corners=False ) resized_features.append(resized_feature) output = self.fuse_stage(torch.cat(resized_features, dim=1)) return output ================================================ FILE: segmentation_models_pytorch/decoders/segformer/model.py ================================================ from typing import Any, Optional, Union, Callable from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import SegformerDecoder class Segformer(SegmentationModel): """`Segformer`__ is simple and efficient design for semantic segmentation with Transformers Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_segmentation_channels: A number of convolution filters in segmentation blocks, default is 256 in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. upsampling: A number to upsample the output of the model, default is 4 (same size as input) aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **Segformer** .. __: https://arxiv.org/abs/2105.15203 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_segmentation_channels: int = 256, in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, upsampling: int = 4, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = SegformerDecoder( encoder_channels=self.encoder.out_channels, encoder_depth=encoder_depth, segmentation_channels=decoder_segmentation_channels, ) self.segmentation_head = SegmentationHead( in_channels=decoder_segmentation_channels, out_channels=classes, activation=activation, kernel_size=1, upsampling=upsampling, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "segformer-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/unet/__init__.py ================================================ from .model import Unet __all__ = ["Unet"] ================================================ FILE: segmentation_models_pytorch/decoders/unet/decoder.py ================================================ from typing import Any, Dict, List, Optional, Sequence, Union import torch import torch.nn as nn import torch.nn.functional as F from segmentation_models_pytorch.base import modules as md class UnetDecoderBlock(nn.Module): """A decoder block in the U-Net architecture that performs upsampling and feature fusion.""" def __init__( self, in_channels: int, skip_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", attention_type: Optional[str] = None, interpolation_mode: str = "nearest", ): super().__init__() self.interpolation_mode = interpolation_mode self.conv1 = md.Conv2dReLU( in_channels + skip_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.attention1 = md.Attention( attention_type, in_channels=in_channels + skip_channels ) self.conv2 = md.Conv2dReLU( out_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.attention2 = md.Attention(attention_type, in_channels=out_channels) def forward( self, feature_map: torch.Tensor, target_height: int, target_width: int, skip_connection: Optional[torch.Tensor] = None, ) -> torch.Tensor: feature_map = F.interpolate( feature_map, size=(target_height, target_width), mode=self.interpolation_mode, ) if skip_connection is not None: feature_map = torch.cat([feature_map, skip_connection], dim=1) feature_map = self.attention1(feature_map) feature_map = self.conv1(feature_map) feature_map = self.conv2(feature_map) feature_map = self.attention2(feature_map) return feature_map class UnetCenterBlock(nn.Sequential): """Center block of the Unet decoder. Applied to the last feature map of the encoder.""" def __init__( self, in_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): conv1 = md.Conv2dReLU( in_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) conv2 = md.Conv2dReLU( out_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) super().__init__(conv1, conv2) class UnetDecoder(nn.Module): """The decoder part of the U-Net architecture. Takes encoded features from different stages of the encoder and progressively upsamples them while combining with skip connections. This helps preserve fine-grained details in the final segmentation. """ def __init__( self, encoder_channels: Sequence[int], decoder_channels: Sequence[int], n_blocks: int = 5, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", attention_type: Optional[str] = None, add_center_block: bool = False, interpolation_mode: str = "nearest", ): super().__init__() if n_blocks != len(decoder_channels): raise ValueError( "Model depth is {}, but you provide `decoder_channels` for {} blocks.".format( n_blocks, len(decoder_channels) ) ) # remove first skip with same spatial resolution encoder_channels = encoder_channels[1:] # reverse channels to start from head of encoder encoder_channels = encoder_channels[::-1] # computing blocks input and output channels head_channels = encoder_channels[0] in_channels = [head_channels] + list(decoder_channels[:-1]) skip_channels = list(encoder_channels[1:]) + [0] out_channels = decoder_channels if add_center_block: self.center = UnetCenterBlock( head_channels, head_channels, use_norm=use_norm, ) else: self.center = nn.Identity() # combine decoder keyword arguments self.blocks = nn.ModuleList() for block_in_channels, block_skip_channels, block_out_channels in zip( in_channels, skip_channels, out_channels ): block = UnetDecoderBlock( block_in_channels, block_skip_channels, block_out_channels, use_norm=use_norm, attention_type=attention_type, interpolation_mode=interpolation_mode, ) self.blocks.append(block) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: # spatial shapes of features: [hw, hw/2, hw/4, hw/8, ...] spatial_shapes = [feature.shape[2:] for feature in features] spatial_shapes = spatial_shapes[::-1] features = features[1:] # remove first skip with same spatial resolution features = features[::-1] # reverse channels to start from head of encoder head = features[0] skip_connections = features[1:] x = self.center(head) for i, decoder_block in enumerate(self.blocks): # upsample to the next spatial shape height, width = spatial_shapes[i + 1] skip_connection = skip_connections[i] if i < len(skip_connections) else None x = decoder_block(x, height, width, skip_connection=skip_connection) return x ================================================ FILE: segmentation_models_pytorch/decoders/unet/model.py ================================================ import warnings from typing import Any, Dict, Optional, Union, Callable, Sequence from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import UnetDecoder class Unet(SegmentationModel): """ `U-Net`__ is a fully convolutional neural network architecture designed for semantic image segmentation. It consists of two main parts: 1. An encoder (downsampling path) that extracts increasingly abstract features 2. A decoder (upsampling path) that gradually recovers spatial details The key is the use of skip connections between corresponding encoder and decoder layers. These connections allow the decoder to access fine-grained details from earlier encoder layers, which helps produce more precise segmentation masks. The skip connections work by concatenating feature maps from the encoder directly into the decoder at corresponding resolutions. This helps preserve important spatial information that would otherwise be lost during the encoding process. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in decoder. Length of the list should be the same as **encoder_depth** decoder_use_norm: Specifies normalization between Conv2D and activation. Accepts the following types: - **True**: Defaults to `"batchnorm"`. - **False**: No normalization (`nn.Identity`). - **str**: Specifies normalization type using default parameters. Available values: `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`. - **dict**: Fully customizable normalization settings. Structure: ```python {"type": , **kwargs} ``` where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation. **Example**: ```python decoder_use_norm={"type": "layernorm", "eps": 1e-2} ``` decoder_attention_type: Attention module used in decoder of the model. Available options are **None** and **scse** (https://arxiv.org/abs/1808.08127). decoder_interpolation: Interpolation mode used in decoder of the model. Available options are **"nearest"**, **"bilinear"**, **"bicubic"**, **"area"**, **"nearest-exact"**. Default is **"nearest"**. in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: Unet Example: .. code-block:: python import torch import segmentation_models_pytorch as smp model = smp.Unet("resnet18", encoder_weights="imagenet", classes=5) model.eval() # generate random images images = torch.rand(2, 3, 256, 256) with torch.inference_mode(): mask = model(images) print(mask.shape) # torch.Size([2, 5, 256, 256]) .. __: https://arxiv.org/abs/1505.04597 """ requires_divisible_input_shape = False @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", decoder_channels: Sequence[int] = (256, 128, 64, 32, 16), decoder_attention_type: Optional[str] = None, decoder_interpolation: str = "nearest", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() decoder_use_batchnorm = kwargs.pop("decoder_use_batchnorm", None) if decoder_use_batchnorm is not None: warnings.warn( "The usage of decoder_use_batchnorm is deprecated. Please modify your code for decoder_use_norm", DeprecationWarning, stacklevel=2, ) decoder_use_norm = decoder_use_batchnorm self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) add_center_block = encoder_name.startswith("vgg") self.decoder = UnetDecoder( encoder_channels=self.encoder.out_channels, decoder_channels=decoder_channels, n_blocks=encoder_depth, use_norm=decoder_use_norm, add_center_block=add_center_block, attention_type=decoder_attention_type, interpolation_mode=decoder_interpolation, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels[-1], out_channels=classes, activation=activation, kernel_size=3, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "u-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/unetplusplus/__init__.py ================================================ from .model import UnetPlusPlus __all__ = ["UnetPlusPlus"] ================================================ FILE: segmentation_models_pytorch/decoders/unetplusplus/decoder.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from typing import Any, Dict, List, Optional, Union, Sequence from segmentation_models_pytorch.base import modules as md class DecoderBlock(nn.Module): def __init__( self, in_channels: int, skip_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", attention_type: Optional[str] = None, interpolation_mode: str = "nearest", ): super().__init__() self.out_channels = out_channels self.is_empty = out_channels == 0 self.interpolation_mode = interpolation_mode if self.is_empty: self.conv1 = None self.attention1 = None self.conv2 = None self.attention2 = None return self.conv1 = md.Conv2dReLU( in_channels + skip_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.attention1 = md.Attention( attention_type, in_channels=in_channels + skip_channels ) self.conv2 = md.Conv2dReLU( out_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.attention2 = md.Attention(attention_type, in_channels=out_channels) def forward( self, x: torch.Tensor, skip: Optional[torch.Tensor] = None ) -> torch.Tensor: x = F.interpolate(x, scale_factor=2.0, mode=self.interpolation_mode) if self.is_empty: height, width = x.shape[2:] return x.new_empty(x.shape[0], 0, height, width) if skip is not None: x = torch.cat([x, skip], dim=1) x = self.attention1(x) x = self.conv1(x) x = self.conv2(x) x = self.attention2(x) return x class CenterBlock(nn.Sequential): def __init__( self, in_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): conv1 = md.Conv2dReLU( in_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) conv2 = md.Conv2dReLU( out_channels, out_channels, kernel_size=3, padding=1, use_norm=use_norm, ) super().__init__(conv1, conv2) class UnetPlusPlusDecoder(nn.Module): def __init__( self, encoder_channels: Sequence[int], decoder_channels: Sequence[int], n_blocks: int = 5, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", attention_type: Optional[str] = None, interpolation_mode: str = "nearest", center: bool = False, ): super().__init__() if n_blocks != len(decoder_channels): raise ValueError( f"Model depth is {n_blocks}, but you provide `decoder_channels` for {len(decoder_channels)} blocks." ) # remove first skip with same spatial resolution encoder_channels = encoder_channels[1:] # reverse channels to start from head of encoder encoder_channels = encoder_channels[::-1] # computing blocks input and output channels head_channels = encoder_channels[0] self.in_channels = [head_channels] + list(decoder_channels[:-1]) self.skip_channels = list(encoder_channels[1:]) + [0] self.out_channels = decoder_channels if center: self.center = CenterBlock( head_channels, head_channels, use_norm=use_norm, ) else: self.center = nn.Identity() # combine decoder keyword arguments kwargs = dict( use_norm=use_norm, attention_type=attention_type, interpolation_mode=interpolation_mode, ) blocks = {} for layer_idx in range(len(self.in_channels) - 1): for depth_idx in range(layer_idx + 1): if depth_idx == 0: in_ch = self.in_channels[layer_idx] skip_ch = self.skip_channels[layer_idx] * (layer_idx + 1) out_ch = self.out_channels[layer_idx] else: out_ch = self.skip_channels[layer_idx] skip_ch = self.skip_channels[layer_idx] * ( layer_idx + 1 - depth_idx ) in_ch = self.skip_channels[layer_idx - 1] blocks[f"x_{depth_idx}_{layer_idx}"] = DecoderBlock( in_ch, skip_ch, out_ch, **kwargs ) blocks[f"x_{0}_{len(self.in_channels) - 1}"] = DecoderBlock( self.in_channels[-1], 0, self.out_channels[-1], **kwargs ) self.blocks = nn.ModuleDict(blocks) self.depth = len(self.in_channels) - 1 def forward(self, features: List[torch.Tensor]) -> torch.Tensor: features = features[1:] # remove first skip with same spatial resolution features = features[::-1] # reverse channels to start from head of encoder # start building dense connections dense_x = {} for layer_idx in range(len(self.in_channels) - 1): for depth_idx in range(self.depth - layer_idx): if layer_idx == 0: output = self.blocks[f"x_{depth_idx}_{depth_idx}"]( features[depth_idx], features[depth_idx + 1] ) dense_x[f"x_{depth_idx}_{depth_idx}"] = output else: dense_l_i = depth_idx + layer_idx cat_features = [ dense_x[f"x_{idx}_{dense_l_i}"] for idx in range(depth_idx + 1, dense_l_i + 1) ] cat_features = torch.cat( cat_features + [features[dense_l_i + 1]], dim=1 ) dense_x[f"x_{depth_idx}_{dense_l_i}"] = self.blocks[ f"x_{depth_idx}_{dense_l_i}" ](dense_x[f"x_{depth_idx}_{dense_l_i - 1}"], cat_features) dense_x[f"x_{0}_{self.depth}"] = self.blocks[f"x_{0}_{self.depth}"]( dense_x[f"x_{0}_{self.depth - 1}"] ) return dense_x[f"x_{0}_{self.depth}"] ================================================ FILE: segmentation_models_pytorch/decoders/unetplusplus/model.py ================================================ import warnings from typing import Any, Dict, Sequence, Optional, Union, Callable from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import UnetPlusPlusDecoder class UnetPlusPlus(SegmentationModel): """`U-Net++`__ is a fully convolution neural network for image semantic segmentation. Consist of *encoder* and *decoder* parts connected with *skip connections*. Encoder extract features of different spatial resolution (skip connections) which are used by decoder to define accurate segmentation mask. Decoder of Unet++ is more complex than in usual Unet. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_channels: List of integers which specify **in_channels** parameter for convolutions used in decoder. Length of the list should be the same as **encoder_depth** decoder_use_norm: Specifies normalization between Conv2D and activation. Accepts the following types: - **True**: Defaults to `"batchnorm"`. - **False**: No normalization (`nn.Identity`). - **str**: Specifies normalization type using default parameters. Available values: `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`. - **dict**: Fully customizable normalization settings. Structure: ```python {"type": , **kwargs} ``` where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation. **Example**: ```python decoder_use_norm={"type": "layernorm", "eps": 1e-2} ``` decoder_attention_type: Attention module used in decoder of the model. Available options are **None** and **scse** (https://arxiv.org/abs/1808.08127). decoder_interpolation: Interpolation mode used in decoder of the model. Available options are **"nearest"**, **"bilinear"**, **"bicubic"**, **"area"**, **"nearest-exact"**. Default is **"nearest"**. in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **Unet++** .. __: https://arxiv.org/abs/1807.10165 """ _is_torch_scriptable = False @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", decoder_channels: Sequence[int] = (256, 128, 64, 32, 16), decoder_attention_type: Optional[str] = None, decoder_interpolation: str = "nearest", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() if encoder_name.startswith("mit_b"): raise ValueError( "UnetPlusPlus is not support encoder_name={}".format(encoder_name) ) decoder_use_batchnorm = kwargs.pop("decoder_use_batchnorm", None) if decoder_use_batchnorm is not None: warnings.warn( "The usage of decoder_use_batchnorm is deprecated. Please modify your code for decoder_use_norm", DeprecationWarning, stacklevel=2, ) decoder_use_norm = decoder_use_batchnorm self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = UnetPlusPlusDecoder( encoder_channels=self.encoder.out_channels, decoder_channels=decoder_channels, n_blocks=encoder_depth, use_norm=decoder_use_norm, center=True if encoder_name.startswith("vgg") else False, attention_type=decoder_attention_type, interpolation_mode=decoder_interpolation, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels[-1], out_channels=classes, activation=activation, kernel_size=3, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "unetplusplus-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/decoders/upernet/__init__.py ================================================ from .model import UPerNet __all__ = ["UPerNet"] ================================================ FILE: segmentation_models_pytorch/decoders/upernet/decoder.py ================================================ from typing import Any, Dict, Union, Sequence, List import torch import torch.nn as nn import torch.nn.functional as F from segmentation_models_pytorch.base import modules as md class PSPModule(nn.Module): def __init__( self, in_channels: int, out_channels: int, sizes: Sequence[int] = (1, 2, 3, 6), use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() self.blocks = nn.ModuleList( [ nn.Sequential( nn.AdaptiveAvgPool2d(size), md.Conv2dReLU( in_channels, out_channels, kernel_size=1, use_norm=use_norm, ), ) for size in sizes ] ) self.out_conv = md.Conv2dReLU( in_channels=in_channels + len(sizes) * out_channels, out_channels=out_channels, kernel_size=3, padding=1, use_norm="batchnorm", ) def forward(self, feature: torch.Tensor) -> torch.Tensor: _, _, height, width = feature.shape pyramid_features = [feature] for block in self.blocks: pooled_feature = block(feature) resized_feature = F.interpolate( pooled_feature, size=(height, width), mode="bilinear", align_corners=False, ) pyramid_features.append(resized_feature) fused_feature = self.out_conv(torch.cat(pyramid_features, dim=1)) return fused_feature class LayerNorm2d(nn.LayerNorm): def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.permute(0, 2, 3, 1) # to channels_last normed_x = nn.functional.layer_norm( x, self.normalized_shape, self.weight, self.bias, self.eps ) normed_x = normed_x.permute(0, 3, 1, 2) # to channels_first return normed_x class FPNLateralBlock(nn.Module): def __init__( self, lateral_channels: int, out_channels: int, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() self.conv_norm_relu = md.Conv2dReLU( lateral_channels, out_channels, kernel_size=1, use_norm=use_norm, ) def forward( self, state_feature: torch.Tensor, lateral_feature: torch.Tensor ) -> torch.Tensor: # 1. Apply block to encoder feature lateral_feature = self.conv_norm_relu(lateral_feature) # 2. Upsample encoder feature to the "state" feature resolution _, _, height, width = lateral_feature.shape state_feature = F.interpolate( state_feature, size=(height, width), mode="bilinear", align_corners=False ) # 3. Sum state and encoder features fused_feature = state_feature + lateral_feature return fused_feature class UPerNetDecoder(nn.Module): def __init__( self, encoder_channels: Sequence[int], encoder_depth: int = 5, decoder_channels: int = 256, use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", ): super().__init__() if encoder_depth < 3: raise ValueError( "Encoder depth for UPerNet decoder cannot be less than 3, got {}.".format( encoder_depth ) ) # Encoder channels for input features starting from the highest resolution # [1, 1/2, 1/4, 1/8, 1/16, ...] for num_features = encoder_depth + 1, # but we use only [1/4, 1/8, 1/16, ...] for UPerNet encoder_channels = encoder_channels[2:] self.feature_norms = nn.ModuleList( [LayerNorm2d(channels, eps=1e-6) for channels in encoder_channels] ) # PSP Module lowest_resolution_feature_channels = encoder_channels[-1] self.psp = PSPModule( in_channels=lowest_resolution_feature_channels, out_channels=decoder_channels, sizes=(1, 2, 3, 6), use_norm=use_norm, ) # FPN Module # we skip lower resolution feature maps + reverse the order # [1/4, 1/8, 1/16, 1/32] -> [1/16, 1/8, 1/4] lateral_channels = encoder_channels[:-1][::-1] self.fpn_lateral_blocks = nn.ModuleList([]) self.fpn_conv_blocks = nn.ModuleList([]) for channels in lateral_channels: block = FPNLateralBlock( lateral_channels=channels, out_channels=decoder_channels, use_norm=use_norm, ) self.fpn_lateral_blocks.append(block) conv_block = md.Conv2dReLU( in_channels=decoder_channels, out_channels=decoder_channels, kernel_size=3, padding=1, use_norm=use_norm, ) self.fpn_conv_blocks.append(conv_block) num_blocks_to_fuse = len(self.fpn_conv_blocks) + 1 # +1 for the PSP module self.fusion_block = md.Conv2dReLU( in_channels=num_blocks_to_fuse * decoder_channels, out_channels=decoder_channels, kernel_size=3, padding=1, use_norm=use_norm, ) def forward(self, features: List[torch.Tensor]) -> torch.Tensor: """ Args: features (List[torch.Tensor]): features with: [1, 1/2, 1/4, 1/8, 1/16, ...] spatial resolutions, where the first feature is the highest resolution and the number of features is equal to encoder_depth + 1. """ # skip 1/1 and 1/2 resolution features features = features[2:] # normalize feature maps for i, norm in enumerate(self.feature_norms): features[i] = norm(features[i]) # pass lowest resolution feature to PSP module psp_out = self.psp(features[-1]) # skip lowest features for FPN + reverse the order # [1/4, 1/8, 1/16, 1/32] -> [1/16, 1/8, 1/4] fpn_lateral_features = features[:-1][::-1] fpn_features = [psp_out] for i, block in enumerate(self.fpn_lateral_blocks): # 1. for each encoder (skip) feature we apply 1x1 ConvNormRelu, # 2. upsample latest fpn feature to it's resolution # 3. sum them together lateral_feature = fpn_lateral_features[i] state_feature = fpn_features[-1] fpn_feature = block(state_feature, lateral_feature) fpn_features.append(fpn_feature) # Apply FPN conv blocks, but skip PSP module for i, conv_block in enumerate(self.fpn_conv_blocks, start=1): fpn_features[i] = conv_block(fpn_features[i]) # Resize all FPN features to 1/4 of the original resolution. resized_fpn_features = [] target_size = fpn_features[-1].shape[2:] # 1/4 of the original resolution for feature in fpn_features: resized_feature = F.interpolate( feature, size=target_size, mode="bilinear", align_corners=False ) resized_fpn_features.append(resized_feature) # reverse and concatenate stacked_features = torch.cat(resized_fpn_features[::-1], dim=1) output = self.fusion_block(stacked_features) return output ================================================ FILE: segmentation_models_pytorch/decoders/upernet/model.py ================================================ from typing import Any, Dict, Optional, Union, Callable from segmentation_models_pytorch.base import ( ClassificationHead, SegmentationHead, SegmentationModel, ) from segmentation_models_pytorch.encoders import get_encoder from segmentation_models_pytorch.base.hub_mixin import supports_config_loading from .decoder import UPerNetDecoder class UPerNet(SegmentationModel): """`UPerNet`__ is a unified perceptual parsing network for image segmentation. Args: encoder_name: Name of the classification model that will be used as an encoder (a.k.a backbone) to extract features of different spatial resolution encoder_depth: A number of stages used in encoder in range [3, 5]. Each stage generate features two times smaller in spatial dimensions than previous one (e.g. for depth 0 we will have features with shapes [(N, C, H, W),], for depth 1 - [(N, C, H, W), (N, C, H // 2, W // 2)] and so on). Default is 5 encoder_weights: One of **None** (random initialization), **"imagenet"** (pre-training on ImageNet) and other pretrained weights (see table with available weights for each encoder_name). For ``tu-`` encoders, set to **True** to download pretrained weights or **None** for random initialization. The pretrained variant is defined in the encoder name decoder_pyramid_channels: A number of convolution filters in Feature Pyramid, default is 256 decoder_segmentation_channels: A number of convolution filters in segmentation blocks, default is 64 decoder_use_norm: Specifies normalization between Conv2D and activation. Accepts the following types: - **True**: Defaults to `"batchnorm"`. - **False**: No normalization (`nn.Identity`). - **str**: Specifies normalization type using default parameters. Available values: `"batchnorm"`, `"identity"`, `"layernorm"`, `"instancenorm"`, `"inplace"`. - **dict**: Fully customizable normalization settings. Structure: ```python {"type": , **kwargs} ``` where `norm_name` corresponds to normalization type (see above), and `kwargs` are passed directly to the normalization layer as defined in PyTorch documentation. **Example**: ```python use_norm={"type": "layernorm", "eps": 1e-2} ``` in_channels: A number of input channels for the model, default is 3 (RGB images) classes: A number of classes for output mask (or you can think as a number of channels of output mask) activation: An activation function to apply after the final convolution layer. Available options are **"sigmoid"**, **"softmax"**, **"logsoftmax"**, **"tanh"**, **"identity"**, **callable** and **None**. Default is **None**. aux_params: Dictionary with parameters of the auxiliary output (classification head). Auxiliary output is build on top of encoder if **aux_params** is not **None** (default). Supported params: - classes (int): A number of classes - pooling (str): One of "max", "avg". Default is "avg" - dropout (float): Dropout factor in [0, 1) - activation (str): An activation function to apply "sigmoid"/"softmax" (could be **None** to return logits) kwargs: Arguments passed to the encoder class ``__init__()`` function. Applies only to ``timm`` models. Keys with ``None`` values are pruned before passing. Returns: ``torch.nn.Module``: **UPerNet** .. __: https://arxiv.org/abs/1807.10221 """ @supports_config_loading def __init__( self, encoder_name: str = "resnet34", encoder_depth: int = 5, encoder_weights: Optional[str] = "imagenet", decoder_channels: int = 256, decoder_use_norm: Union[bool, str, Dict[str, Any]] = "batchnorm", in_channels: int = 3, classes: int = 1, activation: Optional[Union[str, Callable]] = None, upsampling: int = 4, aux_params: Optional[dict] = None, **kwargs: dict[str, Any], ): super().__init__() self.encoder = get_encoder( encoder_name, in_channels=in_channels, depth=encoder_depth, weights=encoder_weights, **kwargs, ) self.decoder = UPerNetDecoder( encoder_channels=self.encoder.out_channels, encoder_depth=encoder_depth, decoder_channels=decoder_channels, use_norm=decoder_use_norm, ) self.segmentation_head = SegmentationHead( in_channels=decoder_channels, out_channels=classes, activation=activation, kernel_size=1, upsampling=upsampling, ) if aux_params is not None: self.classification_head = ClassificationHead( in_channels=self.encoder.out_channels[-1], **aux_params ) else: self.classification_head = None self.name = "upernet-{}".format(encoder_name) self.initialize() ================================================ FILE: segmentation_models_pytorch/encoders/__init__.py ================================================ import json import timm import copy import warnings import functools from torch.utils.model_zoo import load_url from huggingface_hub import hf_hub_download from safetensors.torch import load_file from .resnet import resnet_encoders from .dpn import dpn_encoders from .vgg import vgg_encoders from .senet import senet_encoders from .densenet import densenet_encoders from .inceptionresnetv2 import inceptionresnetv2_encoders from .inceptionv4 import inceptionv4_encoders from .efficientnet import efficient_net_encoders from .mobilenet import mobilenet_encoders from .xception import xception_encoders from .timm_efficientnet import timm_efficientnet_encoders from .timm_sknet import timm_sknet_encoders from .mix_transformer import mix_transformer_encoders from .mobileone import mobileone_encoders from .timm_universal import TimmUniversalEncoder from .timm_vit import TimmViTEncoder # noqa F401 from ._preprocessing import preprocess_input from ._legacy_pretrained_settings import pretrained_settings __all__ = [ "encoders", "get_encoder", "get_encoder_names", "get_preprocessing_params", "get_preprocessing_fn", ] encoders = {} encoders.update(resnet_encoders) encoders.update(dpn_encoders) encoders.update(vgg_encoders) encoders.update(senet_encoders) encoders.update(densenet_encoders) encoders.update(inceptionresnetv2_encoders) encoders.update(inceptionv4_encoders) encoders.update(efficient_net_encoders) encoders.update(mobilenet_encoders) encoders.update(xception_encoders) encoders.update(timm_efficientnet_encoders) encoders.update(timm_sknet_encoders) encoders.update(mix_transformer_encoders) encoders.update(mobileone_encoders) def is_equivalent_to_timm_universal(name): patterns = [ "timm-regnet", "timm-res2", "timm-resnest", "timm-mobilenetv3", "timm-gernet", ] for pattern in patterns: if name.startswith(pattern): return True return False def get_encoder(name, in_channels=3, depth=5, weights=None, output_stride=32, **kwargs): if name.startswith("timm-"): warnings.warn( "`timm-` encoders are deprecated and will be removed in the future. " "Please use `tu-` equivalent encoders instead (see 'Timm encoders' section in the documentation).", DeprecationWarning, ) # convert timm- models to tu- models if is_equivalent_to_timm_universal(name): name = name.replace("timm-", "tu-") if "mobilenetv3" in name: name = name.replace("tu-", "tu-tf_") if name.startswith("tu-"): if not isinstance(weights, (type(None), bool)): warnings.warn( f"For 'tu-' (timm universal) encoders, `encoder_weights` should be set to True " f"to download pretrained weights or None for random initialization. " f"The pretrained variant is defined in the encoder name i.e 'tu-.'. " f"Got encoder_weights='{weights}'.", UserWarning, ) name = name[3:] encoder = TimmUniversalEncoder( name=name, in_channels=in_channels, depth=depth, output_stride=output_stride, pretrained=weights is not None, **kwargs, ) return encoder if name not in encoders: raise KeyError( f"Wrong encoder name `{name}`, supported encoders: {list(encoders.keys())}" ) params = copy.deepcopy(encoders[name]["params"]) params["depth"] = depth params["output_stride"] = output_stride EncoderClass = encoders[name]["encoder"] encoder = EncoderClass(**params) if weights is not None: if weights not in encoders[name]["pretrained_settings"]: available_weights = list(encoders[name]["pretrained_settings"].keys()) raise KeyError( f"Wrong pretrained weights `{weights}` for encoder `{name}`. " f"Available options are: {available_weights}" ) settings = encoders[name]["pretrained_settings"][weights] repo_id = settings["repo_id"] revision = settings["revision"] # First, try to load from HF-Hub, but as far as I know not all countries have # access to the Hub (e.g. China), so we try to load from the original url if # the first attempt fails. weights_path = None try: hf_hub_download(repo_id, filename="config.json", revision=revision) weights_path = hf_hub_download( repo_id, filename="model.safetensors", revision=revision ) except Exception as e: if name in pretrained_settings and weights in pretrained_settings[name]: message = ( f"Error loading {name} `{weights}` weights from Hugging Face Hub, " "trying loading from original url..." ) warnings.warn(message, UserWarning) url = pretrained_settings[name][weights]["url"] state_dict = load_url(url, map_location="cpu") else: raise e if weights_path is not None: state_dict = load_file(weights_path, device="cpu") # Load model weights encoder.load_state_dict(state_dict) encoder.set_in_channels(in_channels, pretrained=weights is not None) if output_stride != 32: encoder.make_dilated(output_stride) return encoder def get_encoder_names(): return list(encoders.keys()) def get_preprocessing_params(encoder_name, pretrained="imagenet"): if encoder_name.startswith("tu-"): encoder_name = encoder_name[3:] if not timm.models.is_model_pretrained(encoder_name): raise ValueError( f"{encoder_name} does not have pretrained weights and preprocessing parameters" ) settings = timm.models.get_pretrained_cfg(encoder_name).__dict__ else: all_settings = encoders[encoder_name]["pretrained_settings"] if pretrained not in all_settings.keys(): raise ValueError( "Available pretrained options {}".format(all_settings.keys()) ) repo_id = all_settings[pretrained]["repo_id"] revision = all_settings[pretrained]["revision"] # Load config and model try: config_path = hf_hub_download( repo_id, filename="config.json", revision=revision ) with open(config_path, "r") as f: settings = json.load(f) except Exception as e: if ( encoder_name in pretrained_settings and pretrained in pretrained_settings[encoder_name] ): settings = pretrained_settings[encoder_name][pretrained] else: raise e formatted_settings = {} formatted_settings["input_space"] = settings.get("input_space", "RGB") formatted_settings["input_range"] = list(settings.get("input_range", [0, 1])) formatted_settings["mean"] = list(settings["mean"]) formatted_settings["std"] = list(settings["std"]) return formatted_settings def get_preprocessing_fn(encoder_name, pretrained="imagenet"): params = get_preprocessing_params(encoder_name, pretrained=pretrained) return functools.partial(preprocess_input, **params) ================================================ FILE: segmentation_models_pytorch/encoders/_base.py ================================================ import torch from typing import Sequence, Dict from . import _utils as utils class EncoderMixin: """Add encoder functionality such as: - output channels specification of feature tensors (produced by encoder) - patching first convolution for arbitrary input channels """ _is_torch_scriptable = True _is_torch_exportable = True _is_torch_compilable = True def __init__(self): self._depth = 5 self._in_channels = 3 self._output_stride = 32 @property def out_channels(self): """Return channels dimensions for each tensor of forward output of encoder""" return self._out_channels[: self._depth + 1] @property def output_stride(self): return min(self._output_stride, 2**self._depth) def set_in_channels(self, in_channels, pretrained=True): """Change first convolution channels""" if in_channels == 3: return self._in_channels = in_channels if self._out_channels[0] == 3: self._out_channels = [in_channels] + self._out_channels[1:] utils.patch_first_conv( model=self, new_in_channels=in_channels, pretrained=pretrained ) def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: """Override it in your implementation, should return a dictionary with keys as the output stride and values as the list of modules """ raise NotImplementedError def make_dilated(self, output_stride): if output_stride not in [8, 16]: raise ValueError(f"Output stride should be 16 or 8, got {output_stride}.") stages = self.get_stages() for stage_stride, stage_modules in stages.items(): if stage_stride <= output_stride: continue dilation_rate = stage_stride // output_stride for module in stage_modules: utils.replace_strides_with_dilation(module, dilation_rate) ================================================ FILE: segmentation_models_pytorch/encoders/_dpn.py ================================================ """PyTorch implementation of DualPathNetworks Ported to PyTorch by [Ross Wightman](https://github.com/rwightman/pytorch-dpn-pretrained) Based on original MXNet implementation https://github.com/cypw/DPNs with many ideas from another PyTorch implementation https://github.com/oyam/pytorch-DPNs. This implementation is compatible with the pretrained weights from cypw's MXNet implementation. """ import torch import torch.nn as nn import torch.nn.functional as F from collections import OrderedDict class CatBnAct(nn.Module): def __init__(self, in_chs, activation_fn=nn.ReLU(inplace=True)): super(CatBnAct, self).__init__() self.bn = nn.BatchNorm2d(in_chs, eps=0.001) self.act = activation_fn def forward(self, x): x = torch.cat(x, dim=1) if isinstance(x, tuple) else x return self.act(self.bn(x)) class BnActConv2d(nn.Module): def __init__( self, in_chs, out_chs, kernel_size, stride, padding=0, groups=1, activation_fn=nn.ReLU(inplace=True), ): super(BnActConv2d, self).__init__() self.bn = nn.BatchNorm2d(in_chs, eps=0.001) self.act = activation_fn self.conv = nn.Conv2d( in_chs, out_chs, kernel_size, stride, padding, groups=groups, bias=False ) def forward(self, x): return self.conv(self.act(self.bn(x))) class InputBlock(nn.Module): def __init__( self, num_init_features, kernel_size=7, padding=3, activation_fn=nn.ReLU(inplace=True), ): super(InputBlock, self).__init__() self.conv = nn.Conv2d( 3, num_init_features, kernel_size=kernel_size, stride=2, padding=padding, bias=False, ) self.bn = nn.BatchNorm2d(num_init_features, eps=0.001) self.act = activation_fn self.pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.act(x) x = self.pool(x) return x class DualPathBlock(nn.Module): def __init__( self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, groups, block_type="normal", b=False, ): super(DualPathBlock, self).__init__() self.num_1x1_c = num_1x1_c self.inc = inc self.b = b if block_type == "proj": self.key_stride = 1 self.has_proj = True elif block_type == "down": self.key_stride = 2 self.has_proj = True else: assert block_type == "normal" self.key_stride = 1 self.has_proj = False if self.has_proj: # Using different member names here to allow easier parameter key matching for conversion if self.key_stride == 2: self.c1x1_w_s2 = BnActConv2d( in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=2 ) else: self.c1x1_w_s1 = BnActConv2d( in_chs=in_chs, out_chs=num_1x1_c + 2 * inc, kernel_size=1, stride=1 ) self.c1x1_a = BnActConv2d( in_chs=in_chs, out_chs=num_1x1_a, kernel_size=1, stride=1 ) self.c3x3_b = BnActConv2d( in_chs=num_1x1_a, out_chs=num_3x3_b, kernel_size=3, stride=self.key_stride, padding=1, groups=groups, ) if b: self.c1x1_c = CatBnAct(in_chs=num_3x3_b) self.c1x1_c1 = nn.Conv2d(num_3x3_b, num_1x1_c, kernel_size=1, bias=False) self.c1x1_c2 = nn.Conv2d(num_3x3_b, inc, kernel_size=1, bias=False) else: self.c1x1_c = BnActConv2d( in_chs=num_3x3_b, out_chs=num_1x1_c + inc, kernel_size=1, stride=1 ) def forward(self, x): x_in = torch.cat(x, dim=1) if isinstance(x, tuple) else x if self.has_proj: if self.key_stride == 2: x_s = self.c1x1_w_s2(x_in) else: x_s = self.c1x1_w_s1(x_in) x_s1 = x_s[:, : self.num_1x1_c, :, :] x_s2 = x_s[:, self.num_1x1_c :, :, :] else: x_s1 = x[0] x_s2 = x[1] x_in = self.c1x1_a(x_in) x_in = self.c3x3_b(x_in) if self.b: x_in = self.c1x1_c(x_in) out1 = self.c1x1_c1(x_in) out2 = self.c1x1_c2(x_in) else: x_in = self.c1x1_c(x_in) out1 = x_in[:, : self.num_1x1_c, :, :] out2 = x_in[:, self.num_1x1_c :, :, :] resid = x_s1 + out1 dense = torch.cat([x_s2, out2], dim=1) return resid, dense class DPN(nn.Module): def __init__( self, small=False, num_init_features=64, k_r=96, groups=32, b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), num_classes=1000, test_time_pool=False, ): super(DPN, self).__init__() self.test_time_pool = test_time_pool self.b = b bw_factor = 1 if small else 4 blocks = OrderedDict() # conv1 if small: blocks["conv1_1"] = InputBlock(num_init_features, kernel_size=3, padding=1) else: blocks["conv1_1"] = InputBlock(num_init_features, kernel_size=7, padding=3) # conv2 bw = 64 * bw_factor inc = inc_sec[0] r = (k_r * bw) // (64 * bw_factor) blocks["conv2_1"] = DualPathBlock( num_init_features, r, r, bw, inc, groups, "proj", b ) in_chs = bw + 3 * inc for i in range(2, k_sec[0] + 1): blocks["conv2_" + str(i)] = DualPathBlock( in_chs, r, r, bw, inc, groups, "normal", b ) in_chs += inc # conv3 bw = 128 * bw_factor inc = inc_sec[1] r = (k_r * bw) // (64 * bw_factor) blocks["conv3_1"] = DualPathBlock(in_chs, r, r, bw, inc, groups, "down", b) in_chs = bw + 3 * inc for i in range(2, k_sec[1] + 1): blocks["conv3_" + str(i)] = DualPathBlock( in_chs, r, r, bw, inc, groups, "normal", b ) in_chs += inc # conv4 bw = 256 * bw_factor inc = inc_sec[2] r = (k_r * bw) // (64 * bw_factor) blocks["conv4_1"] = DualPathBlock(in_chs, r, r, bw, inc, groups, "down", b) in_chs = bw + 3 * inc for i in range(2, k_sec[2] + 1): blocks["conv4_" + str(i)] = DualPathBlock( in_chs, r, r, bw, inc, groups, "normal", b ) in_chs += inc # conv5 bw = 512 * bw_factor inc = inc_sec[3] r = (k_r * bw) // (64 * bw_factor) blocks["conv5_1"] = DualPathBlock(in_chs, r, r, bw, inc, groups, "down", b) in_chs = bw + 3 * inc for i in range(2, k_sec[3] + 1): blocks["conv5_" + str(i)] = DualPathBlock( in_chs, r, r, bw, inc, groups, "normal", b ) in_chs += inc blocks["conv5_bn_ac"] = CatBnAct(in_chs) self.features = nn.Sequential(blocks) # Using 1x1 conv for the FC layer to allow the extra pooling scheme self.last_linear = nn.Conv2d(in_chs, num_classes, kernel_size=1, bias=True) def logits(self, features): if not self.training and self.test_time_pool: x = F.avg_pool2d(features, kernel_size=7, stride=1) out = self.last_linear(x) # The extra test time pool should be pooling an img_size//32 - 6 size patch out = adaptive_avgmax_pool2d(out, pool_type="avgmax") else: x = adaptive_avgmax_pool2d(features, pool_type="avg") out = self.last_linear(x) return out.view(out.size(0), -1) def forward(self, input): x = self.features(input) x = self.logits(x) return x """ PyTorch selectable adaptive pooling Adaptive pooling with the ability to select the type of pooling from: * 'avg' - Average pooling * 'max' - Max pooling * 'avgmax' - Sum of average and max pooling re-scaled by 0.5 * 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim Both a functional and a nn.Module version of the pooling is provided. Author: Ross Wightman (rwightman) """ def pooling_factor(pool_type="avg"): return 2 if pool_type == "avgmaxc" else 1 def adaptive_avgmax_pool2d(x, pool_type="avg", padding=0, count_include_pad=False): """Selectable global pooling function with dynamic input kernel size""" if pool_type == "avgmaxc": x = torch.cat( [ F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad, ), F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding), ], dim=1, ) elif pool_type == "avgmax": x_avg = F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad, ) x_max = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding) x = 0.5 * (x_avg + x_max) elif pool_type == "max": x = F.max_pool2d(x, kernel_size=(x.size(2), x.size(3)), padding=padding) else: if pool_type != "avg": print( "Invalid pool type %s specified. Defaulting to average pooling." % pool_type ) x = F.avg_pool2d( x, kernel_size=(x.size(2), x.size(3)), padding=padding, count_include_pad=count_include_pad, ) return x class AdaptiveAvgMaxPool2d(torch.nn.Module): """Selectable global pooling layer with dynamic input kernel size""" def __init__(self, output_size=1, pool_type="avg"): super(AdaptiveAvgMaxPool2d, self).__init__() self.output_size = output_size self.pool_type = pool_type if pool_type == "avgmaxc" or pool_type == "avgmax": self.pool = nn.ModuleList( [nn.AdaptiveAvgPool2d(output_size), nn.AdaptiveMaxPool2d(output_size)] ) elif pool_type == "max": self.pool = nn.AdaptiveMaxPool2d(output_size) else: if pool_type != "avg": print( "Invalid pool type %s specified. Defaulting to average pooling." % pool_type ) self.pool = nn.AdaptiveAvgPool2d(output_size) def forward(self, x): if self.pool_type == "avgmaxc": x = torch.cat([p(x) for p in self.pool], dim=1) elif self.pool_type == "avgmax": x = 0.5 * torch.sum(torch.stack([p(x) for p in self.pool]), 0).squeeze( dim=0 ) else: x = self.pool(x) return x def factor(self): return pooling_factor(self.pool_type) def __repr__(self): return ( self.__class__.__name__ + " (" + "output_size=" + str(self.output_size) + ", pool_type=" + self.pool_type + ")" ) ================================================ FILE: segmentation_models_pytorch/encoders/_efficientnet.py ================================================ """model.py - Model and module class for EfficientNet. They are built to mirror those in the official TensorFlow implementation. """ # Author: lukemelas (github username) # Github repo: https://github.com/lukemelas/EfficientNet-PyTorch # With adjustments and added comments by workingcoder (github username). import torch from torch import nn from torch.nn import functional as F import re import math import collections from functools import partial from typing import List, Optional # Parameters for the entire model (stem, all blocks, and head) GlobalParams = collections.namedtuple( "GlobalParams", [ "width_coefficient", "depth_coefficient", "image_size", "dropout_rate", "num_classes", "batch_norm_momentum", "batch_norm_epsilon", "drop_connect_rate", "depth_divisor", "min_depth", "include_top", ], ) # Parameters for an individual model block BlockArgs = collections.namedtuple( "BlockArgs", [ "num_repeat", "kernel_size", "stride", "expand_ratio", "input_filters", "output_filters", "se_ratio", "id_skip", ], ) # Set GlobalParams and BlockArgs's defaults GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields) BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields) class MBConvBlock(nn.Module): """Mobile Inverted Residual Bottleneck Block. Args: block_args (namedtuple): BlockArgs, defined in utils.py. global_params (namedtuple): GlobalParam, defined in utils.py. image_size (tuple or list): [image_height, image_width]. References: [1] https://arxiv.org/abs/1704.04861 (MobileNet v1) [2] https://arxiv.org/abs/1801.04381 (MobileNet v2) [3] https://arxiv.org/abs/1905.02244 (MobileNet v3) """ def __init__( self, block_args: BlockArgs, global_params: GlobalParams, image_size=None ): super().__init__() self._has_expansion = block_args.expand_ratio != 1 self._has_se = block_args.se_ratio is not None and 0 < block_args.se_ratio <= 1 self._has_drop_connect = ( block_args.id_skip and block_args.stride == 1 and block_args.input_filters == block_args.output_filters ) # Pytorch's difference from tensorflow bn_momentum = 1 - global_params.batch_norm_momentum bn_eps = global_params.batch_norm_epsilon # Expansion phase (Inverted Bottleneck) input_channels = block_args.input_filters expanded_channels = input_channels * block_args.expand_ratio if self._has_expansion: Conv2d = get_same_padding_conv2d(image_size=image_size) self._expand_conv = Conv2d( input_channels, expanded_channels, kernel_size=1, bias=False ) self._bn0 = nn.BatchNorm2d( expanded_channels, momentum=bn_momentum, eps=bn_eps, ) else: # for torchscript compatibility self._expand_conv = nn.Identity() self._bn0 = nn.Identity() # Depthwise convolution phase kernel_size = block_args.kernel_size stride = block_args.stride Conv2d = get_same_padding_conv2d(image_size=image_size) self._depthwise_conv = Conv2d( in_channels=expanded_channels, out_channels=expanded_channels, groups=expanded_channels, # groups makes it depthwise kernel_size=kernel_size, stride=stride, bias=False, ) self._bn1 = nn.BatchNorm2d( expanded_channels, momentum=bn_momentum, eps=bn_eps, ) image_size = calculate_output_image_size(image_size, stride) # Squeeze and Excitation layer, if desired if self._has_se: squeezed_channels = int(input_channels * block_args.se_ratio) squeezed_channels = max(1, squeezed_channels) Conv2d = get_same_padding_conv2d(image_size=(1, 1)) self._se_reduce = Conv2d( in_channels=expanded_channels, out_channels=squeezed_channels, kernel_size=1, ) self._se_expand = Conv2d( in_channels=squeezed_channels, out_channels=expanded_channels, kernel_size=1, ) # Pointwise convolution phase output_channels = block_args.output_filters Conv2d = get_same_padding_conv2d(image_size=image_size) self._project_conv = Conv2d( in_channels=expanded_channels, out_channels=output_channels, kernel_size=1, bias=False, ) self._bn2 = nn.BatchNorm2d( num_features=output_channels, momentum=bn_momentum, eps=bn_eps, ) self._swish = nn.SiLU() def forward(self, inputs: torch.Tensor, drop_connect_rate: Optional[float] = None): """MBConvBlock's forward function. Args: inputs (tensor): Input tensor. drop_connect_rate (bool): Drop connect rate (float, between 0 and 1). Returns: Output of this block after processing. """ # Expansion and Depthwise Convolution x = inputs if self._has_expansion: x = self._expand_conv(inputs) x = self._bn0(x) x = self._swish(x) x = self._depthwise_conv(x) x = self._bn1(x) x = self._swish(x) # Squeeze and Excitation if self._has_se: x_squeezed = F.adaptive_avg_pool2d(x, 1) x_squeezed = self._se_reduce(x_squeezed) x_squeezed = self._swish(x_squeezed) x_squeezed = self._se_expand(x_squeezed) x = torch.sigmoid(x_squeezed) * x # Pointwise Convolution x = self._project_conv(x) x = self._bn2(x) # Skip connection and drop connect if self._has_drop_connect: # The combination of skip connection and drop connect brings about stochastic depth. if drop_connect_rate is not None and drop_connect_rate > 0: x = drop_connect(x, p=drop_connect_rate, training=self.training) x = x + inputs # skip connection return x class EfficientNet(nn.Module): """EfficientNet model. Args: blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks. global_params (namedtuple): A set of GlobalParams shared between blocks. References: [1] https://arxiv.org/abs/1905.11946 (EfficientNet) Example: >>> import torch >>> from efficientnet.model import EfficientNet >>> inputs = torch.rand(1, 3, 224, 224) >>> model = EfficientNet.from_pretrained('efficientnet-b0') >>> model.eval() >>> outputs = model(inputs) """ def __init__(self, blocks_args: List[BlockArgs], global_params: GlobalParams): super().__init__() if not isinstance(blocks_args, list): raise ValueError("blocks_args should be a list") if len(blocks_args) == 0: raise ValueError("block args must be greater than 0") self._global_params = global_params self._blocks_args = blocks_args # Batch norm parameters bn_mom = 1 - self._global_params.batch_norm_momentum bn_eps = self._global_params.batch_norm_epsilon # Get stem static or dynamic convolution depending on image size image_size = global_params.image_size Conv2d = get_same_padding_conv2d(image_size=image_size) # Stem in_channels = 3 # rgb out_channels = round_filters(32, self._global_params) self._conv_stem = Conv2d( in_channels, out_channels, kernel_size=3, stride=2, bias=False ) self._bn0 = nn.BatchNorm2d(out_channels, momentum=bn_mom, eps=bn_eps) image_size = calculate_output_image_size(image_size, 2) # Build blocks self._blocks = nn.ModuleList([]) for block_args in blocks_args: # Update block input and output filters based on depth multiplier. block_args = block_args._replace( input_filters=round_filters( block_args.input_filters, self._global_params ), output_filters=round_filters( block_args.output_filters, self._global_params ), num_repeat=round_repeats(block_args.num_repeat, self._global_params), ) # The first block needs to take care of stride and filter size increase. self._blocks.append( MBConvBlock(block_args, self._global_params, image_size=image_size) ) image_size = calculate_output_image_size(image_size, block_args.stride) if block_args.num_repeat > 1: # modify block_args to keep same output size block_args = block_args._replace( input_filters=block_args.output_filters, stride=1 ) for _ in range(block_args.num_repeat - 1): self._blocks.append( MBConvBlock(block_args, self._global_params, image_size=image_size) ) # image_size = calculate_output_image_size(image_size, block_args.stride) # stride = 1 # Head in_channels = block_args.output_filters # output of final block out_channels = round_filters(1280, self._global_params) Conv2d = get_same_padding_conv2d(image_size=image_size) self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False) self._bn1 = nn.BatchNorm2d( num_features=out_channels, momentum=bn_mom, eps=bn_eps ) # Final linear layer self._avg_pooling = nn.AdaptiveAvgPool2d(1) if self._global_params.include_top: self._dropout = nn.Dropout(self._global_params.dropout_rate) self._fc = nn.Linear(out_channels, self._global_params.num_classes) self._swish = nn.SiLU() def extract_features(self, inputs): """Use convolution layer to extract feature. Args: inputs (tensor): Input tensor. Returns: Output of the final convolution layer in the efficientnet model. """ # Stem x = self._swish(self._bn0(self._conv_stem(inputs))) # Blocks for idx, block in enumerate(self._blocks): drop_connect_rate = self._global_params.drop_connect_rate if drop_connect_rate: # scale drop connect_rate drop_connect_rate *= float(idx) / len(self._blocks) x = block(x, drop_connect_rate=drop_connect_rate) # Head x = self._swish(self._bn1(self._conv_head(x))) return x def forward(self, inputs): """EfficientNet's forward function. Calls extract_features to extract features, applies final linear layer, and returns logits. Args: inputs (tensor): Input tensor. Returns: Output of this model after processing. """ # Convolution layers x = self.extract_features(inputs) # Pooling and final linear layer x = self._avg_pooling(x) if self._global_params.include_top: x = x.flatten(start_dim=1) x = self._dropout(x) x = self._fc(x) return x ################################################################################ # Help functions for model architecture ################################################################################ # GlobalParams and BlockArgs: Two namedtuples # round_filters and round_repeats: # Functions to calculate params for scaling model width and depth ! ! ! # get_width_and_height_from_size and calculate_output_image_size # drop_connect: A structural design # get_same_padding_conv2d: # Conv2dDynamicSamePadding # Conv2dStaticSamePadding # get_same_padding_maxPool2d: # MaxPool2dDynamicSamePadding # MaxPool2dStaticSamePadding # It's an additional function, not used in EfficientNet, # but can be used in other model (such as EfficientDet). def round_filters(filters, global_params): """Calculate and round number of filters based on width multiplier. Use width_coefficient, depth_divisor and min_depth of global_params. Args: filters (int): Filters number to be calculated. global_params (namedtuple): Global params of the model. Returns: new_filters: New filters number after calculating. """ multiplier = global_params.width_coefficient if not multiplier: return filters # TODO: modify the params names. # maybe the names (width_divisor,min_width) # are more suitable than (depth_divisor,min_depth). divisor = global_params.depth_divisor min_depth = global_params.min_depth filters *= multiplier min_depth = min_depth or divisor # pay attention to this line when using min_depth # follow the formula transferred from official TensorFlow implementation new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor) if new_filters < 0.9 * filters: # prevent rounding by more than 10% new_filters += divisor return int(new_filters) def round_repeats(repeats, global_params): """Calculate module's repeat number of a block based on depth multiplier. Use depth_coefficient of global_params. Args: repeats (int): num_repeat to be calculated. global_params (namedtuple): Global params of the model. Returns: new repeat: New repeat number after calculating. """ multiplier = global_params.depth_coefficient if not multiplier: return repeats # follow the formula transferred from official TensorFlow implementation return int(math.ceil(multiplier * repeats)) def drop_connect(inputs: torch.Tensor, p: float, training: bool) -> torch.Tensor: """Drop connect. Args: input (tensor: BCWH): Input of this structure. p (float: 0.0~1.0): Probability of drop connection. training (bool): The running mode. Returns: output: Output after drop connection. """ assert 0 <= p <= 1, "p must be in range of [0,1]" if not training: return inputs batch_size = inputs.shape[0] keep_prob = 1 - p # generate binary_tensor mask according to probability (p for 0, 1-p for 1) random_tensor = keep_prob random_tensor += torch.rand( [batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device ) binary_tensor = torch.floor(random_tensor) output = inputs / keep_prob * binary_tensor return output def get_width_and_height_from_size(x): """Obtain height and width from x. Args: x (int, tuple or list): Data size. Returns: size: A tuple or list (H,W). """ if isinstance(x, int): return x, x if isinstance(x, list) or isinstance(x, tuple): return x else: raise TypeError() def calculate_output_image_size(input_image_size, stride): """Calculates the output image size when using Conv2dSamePadding with a stride. Necessary for static padding. Thanks to mannatsingh for pointing this out. Args: input_image_size (int, tuple or list): Size of input image. stride (int, tuple or list): Conv2d operation's stride. Returns: output_image_size: A list [H,W]. """ if input_image_size is None: return None image_height, image_width = get_width_and_height_from_size(input_image_size) stride = stride if isinstance(stride, int) else stride[0] image_height = int(math.ceil(image_height / stride)) image_width = int(math.ceil(image_width / stride)) return [image_height, image_width] # Note: # The following 'SamePadding' functions make output size equal ceil(input size/stride). # Only when stride equals 1, can the output size be the same as input size. # Don't be confused by their function names ! ! ! def get_same_padding_conv2d(image_size=None): """Chooses static padding if you have specified an image size, and dynamic padding otherwise. Static padding is necessary for ONNX exporting of models. Args: image_size (int or tuple): Size of the image. Returns: Conv2dDynamicSamePadding or Conv2dStaticSamePadding. """ if image_size is None: return Conv2dDynamicSamePadding else: return partial(Conv2dStaticSamePadding, image_size=image_size) class Conv2dDynamicSamePadding(nn.Conv2d): """2D Convolutions like TensorFlow, for a dynamic image size. The padding is operated in forward function by calculating dynamically. """ # Tips for 'SAME' mode padding. # Given the following: # i: width or height # s: stride # k: kernel size # d: dilation # p: padding # Output after Conv2d: # o = floor((i+p-((k-1)*d+1))/s+1) # If o equals i, i = floor((i+p-((k-1)*d+1))/s+1), # => p = (i-1)*s+((k-1)*d+1)-i def __init__( self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True, ): super().__init__( in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias ) self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 def forward(self, x): ih, iw = x.size()[-2:] kh, kw = self.weight.size()[-2:] sh, sw = self.stride oh, ow = ( math.ceil(ih / sh), math.ceil(iw / sw), ) # change the output size according to stride ! ! ! pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) if pad_h > 0 or pad_w > 0: x = F.pad( x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2] ) return F.conv2d( x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, ) class Conv2dStaticSamePadding(nn.Conv2d): """2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size. The padding mudule is calculated in construction function, then used in forward. """ # With the same calculation as Conv2dDynamicSamePadding def __init__( self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs, ): super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs) self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 # Calculate padding based on image size and save it assert image_size is not None ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size kh, kw = self.weight.size()[-2:] sh, sw = self.stride oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) if pad_h > 0 or pad_w > 0: self.static_padding = nn.ZeroPad2d( (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2) ) else: self.static_padding = nn.Identity() def forward(self, x): x = self.static_padding(x) x = F.conv2d( x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, ) return x def get_same_padding_maxPool2d(image_size=None): """Chooses static padding if you have specified an image size, and dynamic padding otherwise. Static padding is necessary for ONNX exporting of models. Args: image_size (int or tuple): Size of the image. Returns: MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding. """ if image_size is None: return MaxPool2dDynamicSamePadding else: return partial(MaxPool2dStaticSamePadding, image_size=image_size) class MaxPool2dDynamicSamePadding(nn.MaxPool2d): """2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size. The padding is operated in forward function by calculating dynamically. """ def __init__( self, kernel_size, stride, padding=0, dilation=1, return_indices=False, ceil_mode=False, ): super().__init__( kernel_size, stride, padding, dilation, return_indices, ceil_mode ) self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride self.kernel_size = ( [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size ) self.dilation = ( [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation ) def forward(self, x): ih, iw = x.size()[-2:] kh, kw = self.kernel_size sh, sw = self.stride oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) if pad_h > 0 or pad_w > 0: x = F.pad( x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2] ) return F.max_pool2d( x, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode, self.return_indices, ) class MaxPool2dStaticSamePadding(nn.MaxPool2d): """2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size. The padding mudule is calculated in construction function, then used in forward. """ def __init__(self, kernel_size, stride, image_size=None, **kwargs): super().__init__(kernel_size, stride, **kwargs) self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.stride self.kernel_size = ( [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_size ) self.dilation = ( [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation ) # Calculate padding based on image size and save it assert image_size is not None ih, iw = (image_size, image_size) if isinstance(image_size, int) else image_size kh, kw = self.kernel_size sh, sw = self.stride oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) if pad_h > 0 or pad_w > 0: self.static_padding = nn.ZeroPad2d( (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2) ) else: self.static_padding = nn.Identity() def forward(self, x): x = self.static_padding(x) x = F.max_pool2d( x, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode, self.return_indices, ) return x ################################################################################ # Helper functions for loading model params ################################################################################ # BlockDecoder: A Class for encoding and decoding BlockArgs # efficientnet_params: A function to query compound coefficient # get_model_params and efficientnet: # Functions to get BlockArgs and GlobalParams for efficientnet class BlockDecoder(object): """Block Decoder for readability, straight from the official TensorFlow repository. """ @staticmethod def _decode_block_string(block_string): """Get a block through a string notation of arguments. Args: block_string (str): A string notation of arguments. Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'. Returns: BlockArgs: The namedtuple defined at the top of this file. """ assert isinstance(block_string, str) ops = block_string.split("_") options = {} for op in ops: splits = re.split(r"(\d.*)", op) if len(splits) >= 2: key, value = splits[:2] options[key] = value # Check stride assert ("s" in options and len(options["s"]) == 1) or ( len(options["s"]) == 2 and options["s"][0] == options["s"][1] ) return BlockArgs( num_repeat=int(options["r"]), kernel_size=int(options["k"]), stride=[int(options["s"][0])], expand_ratio=int(options["e"]), input_filters=int(options["i"]), output_filters=int(options["o"]), se_ratio=float(options["se"]) if "se" in options else None, id_skip=("noskip" not in block_string), ) @staticmethod def decode(string_list): """Decode a list of string notations to specify blocks inside the network. Args: string_list (list[str]): A list of strings, each string is a notation of block. Returns: blocks_args: A list of BlockArgs namedtuples of block args. """ assert isinstance(string_list, list) blocks_args = [] for block_string in string_list: blocks_args.append(BlockDecoder._decode_block_string(block_string)) return blocks_args def efficientnet_params(model_name): """Map EfficientNet model name to parameter coefficients. Args: model_name (str): Model name to be queried. Returns: params_dict[model_name]: A (width,depth,res,dropout) tuple. """ params_dict = { # Coefficients: width,depth,res,dropout "efficientnet-b0": (1.0, 1.0, 224, 0.2), "efficientnet-b1": (1.0, 1.1, 240, 0.2), "efficientnet-b2": (1.1, 1.2, 260, 0.3), "efficientnet-b3": (1.2, 1.4, 300, 0.3), "efficientnet-b4": (1.4, 1.8, 380, 0.4), "efficientnet-b5": (1.6, 2.2, 456, 0.4), "efficientnet-b6": (1.8, 2.6, 528, 0.5), "efficientnet-b7": (2.0, 3.1, 600, 0.5), "efficientnet-b8": (2.2, 3.6, 672, 0.5), "efficientnet-l2": (4.3, 5.3, 800, 0.5), } return params_dict[model_name] def efficientnet( width_coefficient=None, depth_coefficient=None, image_size=None, dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True, ): """Create BlockArgs and GlobalParams for efficientnet model. Args: width_coefficient (float) depth_coefficient (float) image_size (int) dropout_rate (float) drop_connect_rate (float) num_classes (int) Meaning as the name suggests. Returns: blocks_args, global_params. """ # Blocks args for the whole model(efficientnet-b0 by default) # It will be modified in the construction of EfficientNet Class according to model blocks_args = [ "r1_k3_s11_e1_i32_o16_se0.25", "r2_k3_s22_e6_i16_o24_se0.25", "r2_k5_s22_e6_i24_o40_se0.25", "r3_k3_s22_e6_i40_o80_se0.25", "r3_k5_s11_e6_i80_o112_se0.25", "r4_k5_s22_e6_i112_o192_se0.25", "r1_k3_s11_e6_i192_o320_se0.25", ] blocks_args = BlockDecoder.decode(blocks_args) global_params = GlobalParams( width_coefficient=width_coefficient, depth_coefficient=depth_coefficient, image_size=image_size, dropout_rate=dropout_rate, num_classes=num_classes, batch_norm_momentum=0.99, batch_norm_epsilon=1e-3, drop_connect_rate=drop_connect_rate, depth_divisor=8, min_depth=None, include_top=include_top, ) return blocks_args, global_params def get_model_params(model_name, override_params): """Get the block args and global params for a given model name. Args: model_name (str): Model's name. override_params (dict): A dict to modify global_params. Returns: blocks_args, global_params """ if model_name.startswith("efficientnet"): w, d, s, p = efficientnet_params(model_name) # note: all models have drop connect rate = 0.2 blocks_args, global_params = efficientnet( width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s ) else: raise NotImplementedError( "model name is not pre-defined: {}".format(model_name) ) if override_params: # ValueError will be raised here if override_params has fields not included in global_params. global_params = global_params._replace(**override_params) return blocks_args, global_params ================================================ FILE: segmentation_models_pytorch/encoders/_inceptionresnetv2.py ================================================ import torch import torch.nn as nn class BasicConv2d(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d( in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False, ) # verify bias false self.bn = nn.BatchNorm2d( out_planes, eps=0.001, # value found in tensorflow momentum=0.1, # default pytorch value affine=True, ) self.relu = nn.ReLU(inplace=False) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Mixed_5b(nn.Module): def __init__(self): super(Mixed_5b, self).__init__() self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(192, 48, kernel_size=1, stride=1), BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2), ) self.branch2 = nn.Sequential( BasicConv2d(192, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1), ) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(192, 64, kernel_size=1, stride=1), ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class Block35(nn.Module): def __init__(self, scale=1.0): super(Block35, self).__init__() self.scale = scale self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(320, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1), ) self.branch2 = nn.Sequential( BasicConv2d(320, 32, kernel_size=1, stride=1), BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1), BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1), ) self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Mixed_6a(nn.Module): def __init__(self): super(Mixed_6a, self).__init__() self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2) self.branch1 = nn.Sequential( BasicConv2d(320, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1), BasicConv2d(256, 384, kernel_size=3, stride=2), ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Block17(nn.Module): def __init__(self, scale=1.0): super(Block17, self).__init__() self.scale = scale self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(1088, 128, kernel_size=1, stride=1), BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)), ) self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1) self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x out = self.relu(out) return out class Mixed_7a(nn.Module): def __init__(self): super(Mixed_7a, self).__init__() self.branch0 = nn.Sequential( BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2), ) self.branch1 = nn.Sequential( BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 288, kernel_size=3, stride=2), ) self.branch2 = nn.Sequential( BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1), BasicConv2d(288, 320, kernel_size=3, stride=2), ) self.branch3 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class Block8(nn.Module): def __init__(self, scale=1.0, noReLU=False): super(Block8, self).__init__() self.scale = scale self.noReLU = noReLU self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(2080, 192, kernel_size=1, stride=1), BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)), BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)), ) self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1) if not self.noReLU: self.relu = nn.ReLU(inplace=False) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) out = self.conv2d(out) out = out * self.scale + x if not self.noReLU: out = self.relu(out) return out class InceptionResNetV2(nn.Module): def __init__(self, num_classes=1001): super(InceptionResNetV2, self).__init__() # Special attributs self.input_space = None self.input_size = (299, 299, 3) self.mean = None self.std = None # Modules self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2) self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1) self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1) self.maxpool_3a = nn.MaxPool2d(3, stride=2) self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1) self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1) self.maxpool_5a = nn.MaxPool2d(3, stride=2) self.mixed_5b = Mixed_5b() self.repeat = nn.Sequential( Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), ) self.mixed_6a = Mixed_6a() self.repeat_1 = nn.Sequential( Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), Block17(scale=0.10), ) self.mixed_7a = Mixed_7a() self.repeat_2 = nn.Sequential( Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), ) self.block8 = Block8(noReLU=True) self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1) self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False) self.last_linear = nn.Linear(1536, num_classes) def features(self, input): x = self.conv2d_1a(input) x = self.conv2d_2a(x) x = self.conv2d_2b(x) x = self.maxpool_3a(x) x = self.conv2d_3b(x) x = self.conv2d_4a(x) x = self.maxpool_5a(x) x = self.mixed_5b(x) x = self.repeat(x) x = self.mixed_6a(x) x = self.repeat_1(x) x = self.mixed_7a(x) x = self.repeat_2(x) x = self.block8(x) x = self.conv2d_7b(x) return x def logits(self, features): x = self.avgpool_1a(features) x = x.view(x.size(0), -1) x = self.last_linear(x) return x def forward(self, input): x = self.features(input) x = self.logits(x) return x ================================================ FILE: segmentation_models_pytorch/encoders/_inceptionv4.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class BasicConv2d(nn.Module): def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d( in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False, ) # verify bias false self.bn = nn.BatchNorm2d( out_planes, eps=0.001, # value found in tensorflow momentum=0.1, # default pytorch value affine=True, ) self.relu = nn.ReLU(inplace=True) def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Mixed_3a(nn.Module): def __init__(self): super(Mixed_3a, self).__init__() self.maxpool = nn.MaxPool2d(3, stride=2) self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2) def forward(self, x): x0 = self.maxpool(x) x1 = self.conv(x) out = torch.cat((x0, x1), 1) return out class Mixed_4a(nn.Module): def __init__(self): super(Mixed_4a, self).__init__() self.branch0 = nn.Sequential( BasicConv2d(160, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1), ) self.branch1 = nn.Sequential( BasicConv2d(160, 64, kernel_size=1, stride=1), BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)), BasicConv2d(64, 96, kernel_size=(3, 3), stride=1), ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) out = torch.cat((x0, x1), 1) return out class Mixed_5a(nn.Module): def __init__(self): super(Mixed_5a, self).__init__() self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2) self.maxpool = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.conv(x) x1 = self.maxpool(x) out = torch.cat((x0, x1), 1) return out class Inception_A(nn.Module): def __init__(self): super(Inception_A, self).__init__() self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(384, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), ) self.branch2 = nn.Sequential( BasicConv2d(384, 64, kernel_size=1, stride=1), BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1), BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1), ) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(384, 96, kernel_size=1, stride=1), ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class Reduction_A(nn.Module): def __init__(self): super(Reduction_A, self).__init__() self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2) self.branch1 = nn.Sequential( BasicConv2d(384, 192, kernel_size=1, stride=1), BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1), BasicConv2d(224, 256, kernel_size=3, stride=2), ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Inception_B(nn.Module): def __init__(self): super(Inception_B, self).__init__() self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1) self.branch1 = nn.Sequential( BasicConv2d(1024, 192, kernel_size=1, stride=1), BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)), ) self.branch2 = nn.Sequential( BasicConv2d(1024, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)), BasicConv2d(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)), BasicConv2d(224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)), ) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(1024, 128, kernel_size=1, stride=1), ) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class Reduction_B(nn.Module): def __init__(self): super(Reduction_B, self).__init__() self.branch0 = nn.Sequential( BasicConv2d(1024, 192, kernel_size=1, stride=1), BasicConv2d(192, 192, kernel_size=3, stride=2), ) self.branch1 = nn.Sequential( BasicConv2d(1024, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)), BasicConv2d(256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)), BasicConv2d(320, 320, kernel_size=3, stride=2), ) self.branch2 = nn.MaxPool2d(3, stride=2) def forward(self, x): x0 = self.branch0(x) x1 = self.branch1(x) x2 = self.branch2(x) out = torch.cat((x0, x1, x2), 1) return out class Inception_C(nn.Module): def __init__(self): super(Inception_C, self).__init__() self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1) self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) self.branch1_1a = BasicConv2d( 384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1) ) self.branch1_1b = BasicConv2d( 384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) ) self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1) self.branch2_1 = BasicConv2d( 384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0) ) self.branch2_2 = BasicConv2d( 448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1) ) self.branch2_3a = BasicConv2d( 512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1) ) self.branch2_3b = BasicConv2d( 512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0) ) self.branch3 = nn.Sequential( nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False), BasicConv2d(1536, 256, kernel_size=1, stride=1), ) def forward(self, x): x0 = self.branch0(x) x1_0 = self.branch1_0(x) x1_1a = self.branch1_1a(x1_0) x1_1b = self.branch1_1b(x1_0) x1 = torch.cat((x1_1a, x1_1b), 1) x2_0 = self.branch2_0(x) x2_1 = self.branch2_1(x2_0) x2_2 = self.branch2_2(x2_1) x2_3a = self.branch2_3a(x2_2) x2_3b = self.branch2_3b(x2_2) x2 = torch.cat((x2_3a, x2_3b), 1) x3 = self.branch3(x) out = torch.cat((x0, x1, x2, x3), 1) return out class InceptionV4(nn.Module): def __init__(self, num_classes=1001): super(InceptionV4, self).__init__() # Special attributs self.input_space = None self.input_size = (299, 299, 3) self.mean = None self.std = None # Modules self.features = nn.Sequential( BasicConv2d(3, 32, kernel_size=3, stride=2), BasicConv2d(32, 32, kernel_size=3, stride=1), BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1), Mixed_3a(), Mixed_4a(), Mixed_5a(), Inception_A(), Inception_A(), Inception_A(), Inception_A(), Reduction_A(), # Mixed_6a Inception_B(), Inception_B(), Inception_B(), Inception_B(), Inception_B(), Inception_B(), Inception_B(), Reduction_B(), # Mixed_7a Inception_C(), Inception_C(), Inception_C(), ) self.last_linear = nn.Linear(1536, num_classes) def logits(self, features): # Allows image of any size to be processed adaptiveAvgPoolWidth = features.shape[2] x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth) x = x.view(x.size(0), -1) x = self.last_linear(x) return x def forward(self, input): x = self.features(input) x = self.logits(x) return x ================================================ FILE: segmentation_models_pytorch/encoders/_legacy_pretrained_settings.py ================================================ pretrained_settings = { "resnet18": { "imagenet": { "url": "https://download.pytorch.org/models/resnet18-5c106cde.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, }, "ssl": { "url": "https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, }, "swsl": { "url": 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"https://github.com/qubvel/segmentation_models.pytorch/releases/download/v0.0.2/mit_b5.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], } }, "mobileone_s0": { "imagenet": { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileone/mobileone_s0_unfused.pth.tar", "input_space": "RGB", "input_range": [0, 1], } }, "mobileone_s1": { "imagenet": { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileone/mobileone_s1_unfused.pth.tar", "input_space": "RGB", "input_range": [0, 1], } }, "mobileone_s2": { "imagenet": { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileone/mobileone_s2_unfused.pth.tar", "input_space": "RGB", "input_range": [0, 1], } }, "mobileone_s3": { "imagenet": { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileone/mobileone_s3_unfused.pth.tar", "input_space": "RGB", "input_range": [0, 1], } }, "mobileone_s4": { "imagenet": { "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "url": "https://docs-assets.developer.apple.com/ml-research/datasets/mobileone/mobileone_s4_unfused.pth.tar", "input_space": "RGB", "input_range": [0, 1], } }, } ================================================ FILE: segmentation_models_pytorch/encoders/_preprocessing.py ================================================ import numpy as np def preprocess_input( x, mean=None, std=None, input_space="RGB", input_range=None, **kwargs ): if input_space == "BGR": x = x[..., ::-1].copy() if input_range is not None: if x.max() > 1 and input_range[1] == 1: x = x / 255.0 if mean is not None: mean = np.array(mean) x = x - mean if std is not None: std = np.array(std) x = x / std return x ================================================ FILE: segmentation_models_pytorch/encoders/_senet.py ================================================ """ ResNet code gently borrowed from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py """ from collections import OrderedDict import math import torch.nn as nn class SEModule(nn.Module): def __init__(self, channels, reduction): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0) self.sigmoid = nn.Sigmoid() def forward(self, x): module_input = x x = self.avg_pool(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.sigmoid(x) return module_input * x class Bottleneck(nn.Module): """ Base class for bottlenecks that implements `forward()` method. """ def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: residual = self.downsample(x) out = self.se_module(out) + residual out = self.relu(out) return out class SEBottleneck(Bottleneck): """ Bottleneck for SENet154. """ expansion = 4 def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None): super(SEBottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes * 2, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes * 2) self.conv2 = nn.Conv2d( planes * 2, planes * 4, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False, ) self.bn2 = nn.BatchNorm2d(planes * 4) self.conv3 = nn.Conv2d(planes * 4, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.se_module = SEModule(planes * 4, reduction=reduction) self.downsample = downsample self.stride = stride class SEResNetBottleneck(Bottleneck): """ ResNet bottleneck with a Squeeze-and-Excitation module. It follows Caffe implementation and uses `stride=stride` in `conv1` and not in `conv2` (the latter is used in the torchvision implementation of ResNet). """ expansion = 4 def __init__(self, inplanes, planes, groups, reduction, stride=1, downsample=None): super(SEResNetBottleneck, self).__init__() self.conv1 = nn.Conv2d( inplanes, planes, kernel_size=1, bias=False, stride=stride ) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d( planes, planes, kernel_size=3, padding=1, groups=groups, bias=False ) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.se_module = SEModule(planes * 4, reduction=reduction) self.downsample = downsample self.stride = stride class SEResNeXtBottleneck(Bottleneck): """ ResNeXt bottleneck type C with a Squeeze-and-Excitation module. """ expansion = 4 def __init__( self, inplanes, planes, groups, reduction, stride=1, downsample=None, base_width=4, ): super(SEResNeXtBottleneck, self).__init__() width = math.floor(planes * (base_width / 64)) * groups self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1) self.bn1 = nn.BatchNorm2d(width) self.conv2 = nn.Conv2d( width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False, ) self.bn2 = nn.BatchNorm2d(width) self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.se_module = SEModule(planes * 4, reduction=reduction) self.downsample = downsample self.stride = stride class SENet(nn.Module): def __init__( self, block, layers, groups, reduction, dropout_p=0.2, inplanes=128, input_3x3=True, downsample_kernel_size=3, downsample_padding=1, num_classes=1000, ): """ Parameters ---------- block (nn.Module): Bottleneck class. - For SENet154: SEBottleneck - For SE-ResNet models: SEResNetBottleneck - For SE-ResNeXt models: SEResNeXtBottleneck layers (list of ints): Number of residual blocks for 4 layers of the network (layer1...layer4). groups (int): Number of groups for the 3x3 convolution in each bottleneck block. - For SENet154: 64 - For SE-ResNet models: 1 - For SE-ResNeXt models: 32 reduction (int): Reduction ratio for Squeeze-and-Excitation modules. - For all models: 16 dropout_p (float or None): Drop probability for the Dropout layer. If `None` the Dropout layer is not used. - For SENet154: 0.2 - For SE-ResNet models: None - For SE-ResNeXt models: None inplanes (int): Number of input channels for layer1. - For SENet154: 128 - For SE-ResNet models: 64 - For SE-ResNeXt models: 64 input_3x3 (bool): If `True`, use three 3x3 convolutions instead of a single 7x7 convolution in layer0. - For SENet154: True - For SE-ResNet models: False - For SE-ResNeXt models: False downsample_kernel_size (int): Kernel size for downsampling convolutions in layer2, layer3 and layer4. - For SENet154: 3 - For SE-ResNet models: 1 - For SE-ResNeXt models: 1 downsample_padding (int): Padding for downsampling convolutions in layer2, layer3 and layer4. - For SENet154: 1 - For SE-ResNet models: 0 - For SE-ResNeXt models: 0 num_classes (int): Number of outputs in `last_linear` layer. - For all models: 1000 """ super(SENet, self).__init__() self.inplanes = inplanes if input_3x3: layer0_modules = [ ("conv1", nn.Conv2d(3, 64, 3, stride=2, padding=1, bias=False)), ("bn1", nn.BatchNorm2d(64)), ("relu1", nn.ReLU(inplace=True)), ("conv2", nn.Conv2d(64, 64, 3, stride=1, padding=1, bias=False)), ("bn2", nn.BatchNorm2d(64)), ("relu2", nn.ReLU(inplace=True)), ("conv3", nn.Conv2d(64, inplanes, 3, stride=1, padding=1, bias=False)), ("bn3", nn.BatchNorm2d(inplanes)), ("relu3", nn.ReLU(inplace=True)), ] else: layer0_modules = [ ( "conv1", nn.Conv2d( 3, inplanes, kernel_size=7, stride=2, padding=3, bias=False ), ), ("bn1", nn.BatchNorm2d(inplanes)), ("relu1", nn.ReLU(inplace=True)), ] # To preserve compatibility with Caffe weights `ceil_mode=True` # is used instead of `padding=1`. layer0_modules.append(("pool", nn.MaxPool2d(3, stride=2, ceil_mode=True))) self.layer0 = nn.Sequential(OrderedDict(layer0_modules)) self.layer1 = self._make_layer( block, planes=64, blocks=layers[0], groups=groups, reduction=reduction, downsample_kernel_size=1, downsample_padding=0, ) self.layer2 = self._make_layer( block, planes=128, blocks=layers[1], stride=2, groups=groups, reduction=reduction, downsample_kernel_size=downsample_kernel_size, downsample_padding=downsample_padding, ) self.layer3 = self._make_layer( block, planes=256, blocks=layers[2], stride=2, groups=groups, reduction=reduction, downsample_kernel_size=downsample_kernel_size, downsample_padding=downsample_padding, ) self.layer4 = self._make_layer( block, planes=512, blocks=layers[3], stride=2, groups=groups, reduction=reduction, downsample_kernel_size=downsample_kernel_size, downsample_padding=downsample_padding, ) self.avg_pool = nn.AvgPool2d(7, stride=1) self.dropout = nn.Dropout(dropout_p) if dropout_p is not None else None self.last_linear = nn.Linear(512 * block.expansion, num_classes) def _make_layer( self, block, planes, blocks, groups, reduction, stride=1, downsample_kernel_size=1, downsample_padding=0, ): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( nn.Conv2d( self.inplanes, planes * block.expansion, kernel_size=downsample_kernel_size, stride=stride, padding=downsample_padding, bias=False, ), nn.BatchNorm2d(planes * block.expansion), ) layers = [] layers.append( block(self.inplanes, planes, groups, reduction, stride, downsample) ) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes, groups, reduction)) return nn.Sequential(*layers) def features(self, x): x = self.layer0(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) return x def logits(self, x): x = self.avg_pool(x) if self.dropout is not None: x = self.dropout(x) x = x.view(x.size(0), -1) x = self.last_linear(x) return x def forward(self, x): x = self.features(x) x = self.logits(x) return x ================================================ FILE: segmentation_models_pytorch/encoders/_utils.py ================================================ import torch import torch.nn as nn def patch_first_conv(model, new_in_channels, default_in_channels=3, pretrained=True): """Change first convolution layer input channels. In case: in_channels == 1 or in_channels == 2 -> reuse original weights in_channels > 3 -> make random kaiming normal initialization """ # get first conv for module in model.modules(): if isinstance(module, nn.Conv2d) and module.in_channels == default_in_channels: break weight = module.weight.detach() module.in_channels = new_in_channels if not pretrained: module.weight = nn.parameter.Parameter( torch.Tensor( module.out_channels, new_in_channels // module.groups, *module.kernel_size, ) ) module.reset_parameters() elif new_in_channels == 1: new_weight = weight.sum(1, keepdim=True) module.weight = nn.parameter.Parameter(new_weight) else: new_weight = torch.Tensor( module.out_channels, new_in_channels // module.groups, *module.kernel_size ) for i in range(new_in_channels): new_weight[:, i] = weight[:, i % default_in_channels] new_weight = new_weight * (default_in_channels / new_in_channels) module.weight = nn.parameter.Parameter(new_weight) def replace_strides_with_dilation(module, dilation_rate): """Patch Conv2d modules replacing strides with dilation""" for mod in module.modules(): if isinstance(mod, nn.Conv2d): mod.stride = (1, 1) mod.dilation = (dilation_rate, dilation_rate) kh, kw = mod.kernel_size mod.padding = ((kh // 2) * dilation_rate, (kh // 2) * dilation_rate) # Kostyl for EfficientNet if hasattr(mod, "static_padding"): mod.static_padding = nn.Identity() ================================================ FILE: segmentation_models_pytorch/encoders/_xception.py ================================================ """ Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch) @author: tstandley Adapted by cadene Creates an Xception Model as defined in: Francois Chollet Xception: Deep Learning with Depthwise Separable Convolutions https://arxiv.org/pdf/1610.02357.pdf This weights ported from the Keras implementation. Achieves the following performance on the validation set: Loss:0.9173 Prec@1:78.892 Prec@5:94.292 REMEMBER to set your image size to 3x299x299 for both test and validation normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299 """ import torch.nn as nn import torch.nn.functional as F class SeparableConv2d(nn.Module): def __init__( self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False, ): super(SeparableConv2d, self).__init__() self.conv1 = nn.Conv2d( in_channels, in_channels, kernel_size, stride, padding, dilation, groups=in_channels, bias=bias, ) self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias) def forward(self, x): x = self.conv1(x) x = self.pointwise(x) return x class Block(nn.Module): def __init__( self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True, ): super(Block, self).__init__() if out_filters != in_filters or strides != 1: self.skip = nn.Conv2d( in_filters, out_filters, 1, stride=strides, bias=False ) self.skipbn = nn.BatchNorm2d(out_filters) else: self.skip = None rep = [] filters = in_filters if grow_first: rep.append(nn.ReLU(inplace=True)) rep.append( SeparableConv2d( in_filters, out_filters, 3, stride=1, padding=1, bias=False ) ) rep.append(nn.BatchNorm2d(out_filters)) filters = out_filters for i in range(reps - 1): rep.append(nn.ReLU(inplace=True)) rep.append( SeparableConv2d(filters, filters, 3, stride=1, padding=1, bias=False) ) rep.append(nn.BatchNorm2d(filters)) if not grow_first: rep.append(nn.ReLU(inplace=True)) rep.append( SeparableConv2d( in_filters, out_filters, 3, stride=1, padding=1, bias=False ) ) rep.append(nn.BatchNorm2d(out_filters)) if not start_with_relu: rep = rep[1:] else: rep[0] = nn.ReLU(inplace=False) if strides != 1: rep.append(nn.MaxPool2d(3, strides, 1)) self.rep = nn.Sequential(*rep) def forward(self, inp): x = self.rep(inp) if self.skip is not None: skip = self.skip(inp) skip = self.skipbn(skip) else: skip = inp x += skip return x class Xception(nn.Module): """ Xception optimized for the ImageNet dataset, as specified in https://arxiv.org/pdf/1610.02357.pdf """ def __init__(self, num_classes=1000): """Constructor Args: num_classes: number of classes """ super(Xception, self).__init__() self.num_classes = num_classes self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False) self.bn1 = nn.BatchNorm2d(32) self.relu1 = nn.ReLU(inplace=True) self.conv2 = nn.Conv2d(32, 64, 3, bias=False) self.bn2 = nn.BatchNorm2d(64) self.relu2 = nn.ReLU(inplace=True) # do relu here self.block1 = Block(64, 128, 2, 2, start_with_relu=False, grow_first=True) self.block2 = Block(128, 256, 2, 2, start_with_relu=True, grow_first=True) self.block3 = Block(256, 728, 2, 2, start_with_relu=True, grow_first=True) self.block4 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block5 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block6 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block7 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block8 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block9 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block10 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block11 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True) self.block12 = Block(728, 1024, 2, 2, start_with_relu=True, grow_first=False) self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1) self.bn3 = nn.BatchNorm2d(1536) self.relu3 = nn.ReLU(inplace=True) # do relu here self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1) self.bn4 = nn.BatchNorm2d(2048) self.fc = nn.Linear(2048, num_classes) # #------- init weights -------- # for m in self.modules(): # if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) # elif isinstance(m, nn.BatchNorm2d): # m.weight.data.fill_(1) # m.bias.data.zero_() # #----------------------------- def features(self, input): x = self.conv1(input) x = self.bn1(x) x = self.relu1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu2(x) x = self.block1(x) x = self.block2(x) x = self.block3(x) x = self.block4(x) x = self.block5(x) x = self.block6(x) x = self.block7(x) x = self.block8(x) x = self.block9(x) x = self.block10(x) x = self.block11(x) x = self.block12(x) x = self.conv3(x) x = self.bn3(x) x = self.relu3(x) x = self.conv4(x) x = self.bn4(x) return x def logits(self, features): x = nn.ReLU(inplace=True)(features) x = F.adaptive_avg_pool2d(x, (1, 1)) x = x.view(x.size(0), -1) x = self.last_linear(x) return x def forward(self, input): x = self.features(input) x = self.logits(x) return x ================================================ FILE: segmentation_models_pytorch/encoders/densenet.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import re from torchvision.models.densenet import DenseNet from ._base import EncoderMixin class DenseNetEncoder(DenseNet, EncoderMixin): def __init__(self, out_channels, depth=5, output_stride=32, **kwargs): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride del self.classifier def make_dilated(self, *args, **kwargs): raise ValueError( "DenseNet encoders do not support dilated mode " "due to pooling operation for downsampling!" ) def forward(self, x): features = [x] if self._depth >= 1: x = self.features.conv0(x) x = self.features.norm0(x) x = self.features.relu0(x) features.append(x) if self._depth >= 2: x = self.features.pool0(x) x = self.features.denseblock1(x) x = self.features.transition1.norm(x) x = self.features.transition1.relu(x) features.append(x) if self._depth >= 3: x = self.features.transition1.conv(x) x = self.features.transition1.pool(x) x = self.features.denseblock2(x) x = self.features.transition2.norm(x) x = self.features.transition2.relu(x) features.append(x) if self._depth >= 4: x = self.features.transition2.conv(x) x = self.features.transition2.pool(x) x = self.features.denseblock3(x) x = self.features.transition3.norm(x) x = self.features.transition3.relu(x) features.append(x) if self._depth >= 5: x = self.features.transition3.conv(x) x = self.features.transition3.pool(x) x = self.features.denseblock4(x) x = self.features.norm5(x) features.append(x) return features def load_state_dict(self, state_dict): pattern = re.compile( r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$" ) for key in list(state_dict.keys()): res = pattern.match(key) if res: new_key = res.group(1) + res.group(2) state_dict[new_key] = state_dict[key] del state_dict[key] # remove linear state_dict.pop("classifier.bias", None) state_dict.pop("classifier.weight", None) super().load_state_dict(state_dict) densenet_encoders = { "densenet121": { "encoder": DenseNetEncoder, "params": { "out_channels": [3, 64, 256, 512, 1024, 1024], "num_init_features": 64, "growth_rate": 32, "block_config": (6, 12, 24, 16), }, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/densenet121.imagenet", "revision": "a17c96896a265b61338f66f61d3887b24f61995a", } }, }, "densenet169": { "encoder": DenseNetEncoder, "params": { "out_channels": [3, 64, 256, 512, 1280, 1664], "num_init_features": 64, "growth_rate": 32, "block_config": (6, 12, 32, 32), }, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/densenet169.imagenet", "revision": "8facfba9fc72f7750879dac9ac6ceb3ab990de8d", } }, }, "densenet201": { "encoder": DenseNetEncoder, "params": { "out_channels": [3, 64, 256, 512, 1792, 1920], "num_init_features": 64, "growth_rate": 32, "block_config": (6, 12, 48, 32), }, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/densenet201.imagenet", "revision": "ed5deb355d71659391d46fae5e7587460fbb5f84", } }, }, "densenet161": { "encoder": DenseNetEncoder, "params": { "out_channels": [3, 96, 384, 768, 2112, 2208], "num_init_features": 96, "growth_rate": 48, "block_config": (6, 12, 36, 24), }, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/densenet161.imagenet", "revision": "9afe0fec51ab2a627141769d97d6f83756d78446", } }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/dpn.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torch.nn.functional as F from typing import List, Dict, Sequence from ._base import EncoderMixin from ._dpn import DPN class DPNEncoder(DPN, EncoderMixin): _is_torch_scriptable = False _is_torch_exportable = True # since torch 2.6.0 def __init__( self, stage_idxs: List[int], out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._stage_idxs = stage_idxs self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride del self.last_linear def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.features[self._stage_idxs[1] : self._stage_idxs[2]]], 32: [self.features[self._stage_idxs[2] : self._stage_idxs[3]]], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] if self._depth >= 1: x = self.features[0].conv(x) x = self.features[0].bn(x) x = self.features[0].act(x) features.append(x) if self._depth >= 2: x = self.features[0].pool(x) x = self.features[1 : self._stage_idxs[0]](x) skip = F.relu(torch.cat(x, dim=1), inplace=True) features.append(skip) if self._depth >= 3: x = self.features[self._stage_idxs[0] : self._stage_idxs[1]](x) skip = F.relu(torch.cat(x, dim=1), inplace=True) features.append(skip) if self._depth >= 4: x = self.features[self._stage_idxs[1] : self._stage_idxs[2]](x) skip = F.relu(torch.cat(x, dim=1), inplace=True) features.append(skip) if self._depth >= 5: x = self.features[self._stage_idxs[2] : self._stage_idxs[3]](x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("last_linear.bias", None) state_dict.pop("last_linear.weight", None) super().load_state_dict(state_dict, **kwargs) dpn_encoders = { "dpn68": { "encoder": DPNEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/dpn68.imagenet", "revision": "c209aefdeae6bc93937556629e974b44d4e58535", } }, "params": { "stage_idxs": [4, 8, 20, 24], "out_channels": [3, 10, 144, 320, 704, 832], "groups": 32, "inc_sec": (16, 32, 32, 64), "k_r": 128, "k_sec": (3, 4, 12, 3), "num_classes": 1000, "num_init_features": 10, "small": True, "test_time_pool": True, }, }, "dpn68b": { "encoder": DPNEncoder, "pretrained_settings": { "imagenet+5k": { "repo_id": "smp-hub/dpn68b.imagenet-5k", "revision": "6c6615e77688e390ae0eaa81e26821fbd83cee4b", } }, "params": { "stage_idxs": [4, 8, 20, 24], "out_channels": [3, 10, 144, 320, 704, 832], "b": True, "groups": 32, "inc_sec": (16, 32, 32, 64), "k_r": 128, "k_sec": (3, 4, 12, 3), "num_classes": 1000, "num_init_features": 10, "small": True, "test_time_pool": True, }, }, "dpn92": { "encoder": DPNEncoder, "pretrained_settings": { "imagenet+5k": { "repo_id": "smp-hub/dpn92.imagenet-5k", "revision": "d231f51ce4ad2c84ed5fcaf4ef0cfece6814a526", } }, "params": { "stage_idxs": [4, 8, 28, 32], "out_channels": [3, 64, 336, 704, 1552, 2688], "groups": 32, "inc_sec": (16, 32, 24, 128), "k_r": 96, "k_sec": (3, 4, 20, 3), "num_classes": 1000, "num_init_features": 64, "test_time_pool": True, }, }, "dpn98": { "encoder": DPNEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/dpn98.imagenet", "revision": "b2836c86216c1ddce980d832f7deaa4ca22babd3", } }, "params": { "stage_idxs": [4, 10, 30, 34], "out_channels": [3, 96, 336, 768, 1728, 2688], "groups": 40, "inc_sec": (16, 32, 32, 128), "k_r": 160, "k_sec": (3, 6, 20, 3), "num_classes": 1000, "num_init_features": 96, "test_time_pool": True, }, }, "dpn107": { "encoder": DPNEncoder, "pretrained_settings": { "imagenet+5k": { "repo_id": "smp-hub/dpn107.imagenet-5k", "revision": "dab4cd6b8b79de3db970f2dbff85359a8847db05", } }, "params": { "stage_idxs": [5, 13, 33, 37], "out_channels": [3, 128, 376, 1152, 2432, 2688], "groups": 50, "inc_sec": (20, 64, 64, 128), "k_r": 200, "k_sec": (4, 8, 20, 3), "num_classes": 1000, "num_init_features": 128, "test_time_pool": True, }, }, "dpn131": { "encoder": DPNEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/dpn131.imagenet", "revision": "04bbb9f415ca2bb59f3d8227857967b74698515e", } }, "params": { "stage_idxs": [5, 13, 41, 45], "out_channels": [3, 128, 352, 832, 1984, 2688], "groups": 40, "inc_sec": (16, 32, 32, 128), "k_r": 160, "k_sec": (4, 8, 28, 3), "num_classes": 1000, "num_init_features": 128, "test_time_pool": True, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/efficientnet.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch from typing import List, Dict, Sequence from ._base import EncoderMixin from ._efficientnet import EfficientNet, get_model_params class EfficientNetEncoder(EfficientNet, EncoderMixin): def __init__( self, out_indexes: List[int], out_channels: List[int], model_name: str, depth: int = 5, output_stride: int = 32, ): if depth > 5 or depth < 2: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) blocks_args, global_params = get_model_params(model_name, override_params=None) super().__init__(blocks_args, global_params) self._out_indexes = out_indexes self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride self._drop_connect_rate = self._global_params.drop_connect_rate del self._fc def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self._blocks[self._out_indexes[1] + 1 : self._out_indexes[2] + 1]], 32: [self._blocks[self._out_indexes[2] + 1 :]], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] if self._depth >= 1: x = self._conv_stem(x) x = self._bn0(x) x = self._swish(x) features.append(x) depth = 1 for i, block in enumerate(self._blocks): drop_connect_prob = self._drop_connect_rate * i / len(self._blocks) x = block(x, drop_connect_prob) if i in self._out_indexes: features.append(x) depth += 1 if not torch.jit.is_scripting() and depth > self._depth: break features = features[: self._depth + 1] return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("_fc.bias", None) state_dict.pop("_fc.weight", None) super().load_state_dict(state_dict, **kwargs) efficient_net_encoders = { "efficientnet-b0": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b0.imagenet", "revision": "1bbe7ecc1d5ea1d2058de1a2db063b8701aff314", }, "advprop": { "repo_id": "smp-hub/efficientnet-b0.advprop", "revision": "29043c08140d9c6ee7de1468d55923f2b06bcec2", }, }, "params": { "out_channels": [3, 32, 24, 40, 112, 320], "out_indexes": [2, 4, 8, 15], "model_name": "efficientnet-b0", }, }, "efficientnet-b1": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b1.imagenet", "revision": "5d637466a5215de300a8ccb13a39357df2df2bf4", }, "advprop": { "repo_id": "smp-hub/efficientnet-b1.advprop", "revision": "2e518b8b0955bbab467f50525578dab6b6086afc", }, }, "params": { "out_channels": [3, 32, 24, 40, 112, 320], "out_indexes": [4, 7, 15, 22], "model_name": "efficientnet-b1", }, }, "efficientnet-b2": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b2.imagenet", "revision": "a96d4f0295ffbae18ebba173bf7f3c0c8f21990e", }, "advprop": { "repo_id": "smp-hub/efficientnet-b2.advprop", "revision": "be788c20dfb0bbe83b4c439f9cfe0dd937c0783e", }, }, "params": { "out_channels": [3, 32, 24, 48, 120, 352], "out_indexes": [4, 7, 15, 22], "model_name": "efficientnet-b2", }, }, "efficientnet-b3": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b3.imagenet", "revision": "074c54a6c473e0d294690d49cedb6cf463e7127d", }, "advprop": { "repo_id": "smp-hub/efficientnet-b3.advprop", "revision": "9ccc166d87bd9c08d6bed4477638c7f4bb3eec78", }, }, "params": { "out_channels": [3, 40, 32, 48, 136, 384], "out_indexes": [4, 7, 17, 25], "model_name": "efficientnet-b3", }, }, "efficientnet-b4": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b4.imagenet", "revision": "05cd5dde5dab658f00c463f9b9aa0ced76784f40", }, "advprop": { "repo_id": "smp-hub/efficientnet-b4.advprop", "revision": "f04caa809ea4eb08ee9e7fd555f5514ebe2a9ef5", }, }, "params": { "out_channels": [3, 48, 32, 56, 160, 448], "out_indexes": [5, 9, 21, 31], "model_name": "efficientnet-b4", }, }, "efficientnet-b5": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b5.imagenet", "revision": "69f4d28460a4e421b7860bc26ee7d832e03e01ca", }, "advprop": { "repo_id": "smp-hub/efficientnet-b5.advprop", "revision": "dabe78fc8ab7ce93ddc2bb156b01db227caede88", }, }, "params": { "out_channels": [3, 48, 40, 64, 176, 512], "out_indexes": [7, 12, 26, 38], "model_name": "efficientnet-b5", }, }, "efficientnet-b6": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b6.imagenet", "revision": "8570752016f7c62ae149cffa058550fe44e21c8b", }, "advprop": { "repo_id": "smp-hub/efficientnet-b6.advprop", "revision": "c2dbb4d1359151165ec7b96cfe54a9cac2142a31", }, }, "params": { "out_channels": [3, 56, 40, 72, 200, 576], "out_indexes": [8, 14, 30, 44], "model_name": "efficientnet-b6", }, }, "efficientnet-b7": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/efficientnet-b7.imagenet", "revision": "5a5dbe687d612ebc3dca248274fd1191111deda6", }, "advprop": { "repo_id": "smp-hub/efficientnet-b7.advprop", "revision": "ce33edb4e80c0cde268f098ae2299e23f615577d", }, }, "params": { "out_channels": [3, 64, 48, 80, 224, 640], "out_indexes": [10, 17, 37, 54], "model_name": "efficientnet-b7", }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/inceptionresnetv2.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torch.nn as nn from typing import List from ._base import EncoderMixin from ._inceptionresnetv2 import InceptionResNetV2 class InceptionResNetV2Encoder(InceptionResNetV2, EncoderMixin): def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride # correct paddings for m in self.modules(): if isinstance(m, nn.Conv2d): if m.kernel_size == (3, 3): m.padding = (1, 1) if isinstance(m, nn.MaxPool2d): m.padding = (1, 1) # for torchscript, block8 does not have relu defined self.block8.relu = nn.Identity() # remove linear layers del self.avgpool_1a del self.last_linear def make_dilated(self, *args, **kwargs): raise ValueError( "InceptionResnetV2 encoder does not support dilated mode " "due to pooling operation for downsampling!" ) def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] if self._depth >= 1: x = self.conv2d_1a(x) x = self.conv2d_2a(x) x = self.conv2d_2b(x) features.append(x) if self._depth >= 2: x = self.maxpool_3a(x) x = self.conv2d_3b(x) x = self.conv2d_4a(x) features.append(x) if self._depth >= 3: x = self.maxpool_5a(x) x = self.mixed_5b(x) x = self.repeat(x) features.append(x) if self._depth >= 4: x = self.mixed_6a(x) x = self.repeat_1(x) features.append(x) if self._depth >= 5: x = self.mixed_7a(x) x = self.repeat_2(x) x = self.block8(x) x = self.conv2d_7b(x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("last_linear.bias", None) state_dict.pop("last_linear.weight", None) super().load_state_dict(state_dict, **kwargs) inceptionresnetv2_encoders = { "inceptionresnetv2": { "encoder": InceptionResNetV2Encoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/inceptionresnetv2.imagenet", "revision": "120c5afdbb80a1c989db0a7423ebb7a9db9b1e6c", }, "imagenet+background": { "repo_id": "smp-hub/inceptionresnetv2.imagenet-background", "revision": "3ecf3491658dc0f6a76d69c9d1cb36511b1ee56c", }, }, "params": {"out_channels": [3, 64, 192, 320, 1088, 1536], "num_classes": 1000}, } } ================================================ FILE: segmentation_models_pytorch/encoders/inceptionv4.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torch.nn as nn from typing import List from ._base import EncoderMixin from ._inceptionv4 import InceptionV4 class InceptionV4Encoder(InceptionV4, EncoderMixin): def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride self._out_indexes = [2, 4, 8, 14, len(self.features) - 1] # correct paddings for m in self.modules(): if isinstance(m, nn.Conv2d): if m.kernel_size == (3, 3): m.padding = (1, 1) if isinstance(m, nn.MaxPool2d): m.padding = (1, 1) # remove linear layers del self.last_linear def make_dilated(self, *args, **kwargs): raise ValueError( "InceptionV4 encoder does not support dilated mode " "due to pooling operation for downsampling!" ) def forward(self, x: torch.Tensor) -> List[torch.Tensor]: depth = 0 features = [x] for i, module in enumerate(self.features): x = module(x) if i in self._out_indexes: features.append(x) depth += 1 # torchscript does not support break in cycle, so we just # go over all modules and then slice number of features if not torch.jit.is_scripting() and depth > self._depth: break features = features[: self._depth + 1] return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("last_linear.bias", None) state_dict.pop("last_linear.weight", None) super().load_state_dict(state_dict, **kwargs) inceptionv4_encoders = { "inceptionv4": { "encoder": InceptionV4Encoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/inceptionv4.imagenet", "revision": "918fb54f07811d82a4ecde3a51156041d0facba9", }, "imagenet+background": { "repo_id": "smp-hub/inceptionv4.imagenet-background", "revision": "8c2a48e20d2709ee64f8421c61be309f05bfa536", }, }, "params": { "out_channels": [3, 64, 192, 384, 1024, 1536], "num_classes": 1001, }, } } ================================================ FILE: segmentation_models_pytorch/encoders/mix_transformer.py ================================================ # --------------------------------------------------------------- # Copyright (c) 2021, NVIDIA Corporation. All rights reserved. # # Licensed under the NVIDIA Source Code License. For full license # terms, please refer to the LICENSE file provided with this code # or visit NVIDIA's official repository at # https://github.com/NVlabs/SegFormer/tree/master. # # This code has been modified. # --------------------------------------------------------------- import math import torch import torch.nn as nn import torch.nn.functional as F from functools import partial from typing import Dict, Sequence, List from timm.layers import DropPath, to_2tuple, trunc_normal_ class LayerNorm(nn.LayerNorm): def forward(self, x: torch.Tensor) -> torch.Tensor: if x.ndim == 4: batch_size, channels, height, width = x.shape x = x.view(batch_size, channels, -1).transpose(1, 2) x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) x = x.transpose(1, 2).view(batch_size, channels, height, width) else: x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) return x class Mlp(nn.Module): def __init__( self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features self.fc1 = nn.Linear(in_features, hidden_features) self.dwconv = DWConv(hidden_features) self.act = act_layer() self.fc2 = nn.Linear(hidden_features, out_features) self.drop = nn.Dropout(drop) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x: torch.Tensor, height: int, width: int) -> torch.Tensor: x = self.fc1(x) x = self.dwconv(x, height, width) x = self.act(x) x = self.drop(x) x = self.fc2(x) x = self.drop(x) return x class Attention(nn.Module): def __init__( self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, sr_ratio=1, ): super().__init__() assert dim % num_heads == 0, ( f"dim {dim} should be divided by num_heads {num_heads}." ) self.dim = dim self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim**-0.5 self.q = nn.Linear(dim, dim, bias=qkv_bias) self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias) self.attn_drop = nn.Dropout(attn_drop) self.proj = nn.Linear(dim, dim) self.proj_drop = nn.Dropout(proj_drop) self.sr_ratio = sr_ratio if sr_ratio > 1: self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio) self.norm = LayerNorm(dim) else: # for torchscript compatibility self.sr = nn.Identity() self.norm = nn.Identity() self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x: torch.Tensor, height: int, width: int) -> torch.Tensor: batch_size, N, C = x.shape q = ( self.q(x) .reshape(batch_size, N, self.num_heads, C // self.num_heads) .permute(0, 2, 1, 3) ) if self.sr_ratio > 1: x_ = x.permute(0, 2, 1).reshape(batch_size, C, height, width) x_ = self.sr(x_).reshape(batch_size, C, -1).permute(0, 2, 1) x_ = self.norm(x_) kv = ( self.kv(x_) .reshape(batch_size, -1, 2, self.num_heads, C // self.num_heads) .permute(2, 0, 3, 1, 4) ) else: kv = ( self.kv(x) .reshape(batch_size, -1, 2, self.num_heads, C // self.num_heads) .permute(2, 0, 3, 1, 4) ) k, v = kv[0], kv[1] attn = (q @ k.transpose(-2, -1)) * self.scale attn = attn.softmax(dim=-1) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(batch_size, N, C) x = self.proj(x) x = self.proj_drop(x) return x class Block(nn.Module): def __init__( self, dim, num_heads, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=LayerNorm, sr_ratio=1, ): super().__init__() self.norm1 = norm_layer(dim) self.attn = Attention( dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio, ) # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.mlp = Mlp( in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop, ) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x: torch.Tensor) -> torch.Tensor: batch_size, _, height, width = x.shape x = x.flatten(2).transpose(1, 2) x = x + self.drop_path(self.attn(self.norm1(x), height, width)) x = x + self.drop_path(self.mlp(self.norm2(x), height, width)) x = x.transpose(1, 2).view(batch_size, -1, height, width) return x class OverlapPatchEmbed(nn.Module): """Image to Patch Embedding""" def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768): super().__init__() img_size = to_2tuple(img_size) patch_size = to_2tuple(patch_size) self.img_size = img_size self.patch_size = patch_size self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1] self.num_patches = self.H * self.W self.proj = nn.Conv2d( in_chans, embed_dim, kernel_size=patch_size, stride=stride, padding=(patch_size[0] // 2, patch_size[1] // 2), ) self.norm = LayerNorm(embed_dim) self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.proj(x) x = self.norm(x) return x class MixVisionTransformer(nn.Module): def __init__( self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512], num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=LayerNorm, depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1], ): super().__init__() self.num_classes = num_classes self.depths = depths # patch_embed self.patch_embed1 = OverlapPatchEmbed( img_size=img_size, patch_size=7, stride=4, in_chans=in_chans, embed_dim=embed_dims[0], ) self.patch_embed2 = OverlapPatchEmbed( img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0], embed_dim=embed_dims[1], ) self.patch_embed3 = OverlapPatchEmbed( img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1], embed_dim=embed_dims[2], ) self.patch_embed4 = OverlapPatchEmbed( img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2], embed_dim=embed_dims[3], ) # transformer encoder dpr = [ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) ] # stochastic depth decay rule cur = 0 self.block1 = nn.Sequential( *[ Block( dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[0], ) for i in range(depths[0]) ] ) self.norm1 = norm_layer(embed_dims[0]) cur += depths[0] self.block2 = nn.Sequential( *[ Block( dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[1], ) for i in range(depths[1]) ] ) self.norm2 = norm_layer(embed_dims[1]) cur += depths[1] self.block3 = nn.Sequential( *[ Block( dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[2], ) for i in range(depths[2]) ] ) self.norm3 = norm_layer(embed_dims[2]) cur += depths[2] self.block4 = nn.Sequential( *[ Block( dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale, drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer, sr_ratio=sr_ratios[3], ) for i in range(depths[3]) ] ) self.norm4 = norm_layer(embed_dims[3]) # classification head # self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity() self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, nn.Linear): trunc_normal_(m.weight, std=0.02) if isinstance(m, nn.Linear) and m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, LayerNorm): nn.init.constant_(m.bias, 0) nn.init.constant_(m.weight, 1.0) elif isinstance(m, nn.Conv2d): fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels fan_out //= m.groups m.weight.data.normal_(0, math.sqrt(2.0 / fan_out)) if m.bias is not None: m.bias.data.zero_() def init_weights(self, pretrained=None): pass def reset_drop_path(self, drop_path_rate): dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))] cur = 0 for i in range(self.depths[0]): self.block1[i].drop_path.drop_prob = dpr[cur + i] cur += self.depths[0] for i in range(self.depths[1]): self.block2[i].drop_path.drop_prob = dpr[cur + i] cur += self.depths[1] for i in range(self.depths[2]): self.block3[i].drop_path.drop_prob = dpr[cur + i] cur += self.depths[2] for i in range(self.depths[3]): self.block4[i].drop_path.drop_prob = dpr[cur + i] def freeze_patch_emb(self): self.patch_embed1.requires_grad = False @torch.jit.ignore def no_weight_decay(self): return { "pos_embed1", "pos_embed2", "pos_embed3", "pos_embed4", "cls_token", } # has pos_embed may be better def get_classifier(self): return self.head def reset_classifier(self, num_classes, global_pool=""): self.num_classes = num_classes self.head = ( nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() ) def forward_features(self, x: torch.Tensor) -> List[torch.Tensor]: outs = [] # stage 1 x = self.patch_embed1(x) x = self.block1(x) x = self.norm1(x).contiguous() outs.append(x) # stage 2 x = self.patch_embed2(x) x = self.block2(x) x = self.norm2(x).contiguous() outs.append(x) # stage 3 x = self.patch_embed3(x) x = self.block3(x) x = self.norm3(x).contiguous() outs.append(x) # stage 4 x = self.patch_embed4(x) x = self.block4(x) x = self.norm4(x).contiguous() outs.append(x) return outs def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = self.forward_features(x) # x = self.head(x) return features class DWConv(nn.Module): def __init__(self, dim=768): super(DWConv, self).__init__() self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim) def forward(self, x: torch.Tensor, height: int, width: int) -> torch.Tensor: batch_size, _, channels = x.shape x = x.transpose(1, 2).view(batch_size, channels, height, width) x = self.dwconv(x) x = x.flatten(2).transpose(1, 2) return x # --------------------------------------------------------------- # End of NVIDIA code # --------------------------------------------------------------- from ._base import EncoderMixin # noqa E402 class MixVisionTransformerEncoder(MixVisionTransformer, EncoderMixin): def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.patch_embed3, self.block3, self.norm3], 32: [self.patch_embed4, self.block4, self.norm4], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: # create dummy output for the first block batch_size, _, height, width = x.shape dummy = torch.empty( [batch_size, 0, height // 2, width // 2], dtype=x.dtype, device=x.device ) features = [x, dummy] if self._depth >= 2: x = self.patch_embed1(x) x = self.block1(x) x = self.norm1(x) x = x.contiguous() features.append(x) if self._depth >= 3: x = self.patch_embed2(x) x = self.block2(x) x = self.norm2(x) x = x.contiguous() features.append(x) if self._depth >= 4: x = self.patch_embed3(x) x = self.block3(x) x = self.norm3(x) x = x.contiguous() features.append(x) if self._depth >= 5: x = self.patch_embed4(x) x = self.block4(x) x = self.norm4(x) x = x.contiguous() features.append(x) return features def load_state_dict(self, state_dict): state_dict.pop("head.weight", None) state_dict.pop("head.bias", None) return super().load_state_dict(state_dict) mix_transformer_encoders = { "mit_b0": { "encoder": MixVisionTransformerEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mit_b0.imagenet", "revision": "9ce53d104d92d75aabb00aae70677aaab67e7c84", } }, "params": { "out_channels": [3, 0, 32, 64, 160, 256], "patch_size": 4, "embed_dims": [32, 64, 160, 256], "num_heads": [1, 2, 5, 8], "mlp_ratios": [4, 4, 4, 4], "qkv_bias": True, "norm_layer": partial(LayerNorm, eps=1e-6), "depths": [2, 2, 2, 2], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, }, }, "mit_b1": { "encoder": MixVisionTransformerEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mit_b1.imagenet", "revision": "a04bf4f13a549bce677cf79b04852e7510782817", } }, "params": { "out_channels": [3, 0, 64, 128, 320, 512], "patch_size": 4, "embed_dims": [64, 128, 320, 512], "num_heads": [1, 2, 5, 8], "mlp_ratios": [4, 4, 4, 4], "qkv_bias": True, "norm_layer": partial(LayerNorm, eps=1e-6), "depths": [2, 2, 2, 2], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, }, }, "mit_b2": { "encoder": MixVisionTransformerEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mit_b2.imagenet", "revision": "868ab6f13871dcf8c3d9f90ee4519403475b65ef", } }, "params": { "out_channels": [3, 0, 64, 128, 320, 512], "patch_size": 4, "embed_dims": [64, 128, 320, 512], "num_heads": [1, 2, 5, 8], "mlp_ratios": [4, 4, 4, 4], "qkv_bias": True, "norm_layer": partial(LayerNorm, eps=1e-6), "depths": [3, 4, 6, 3], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, }, }, "mit_b3": { "encoder": MixVisionTransformerEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mit_b3.imagenet", "revision": "32558d12a65f1daa0ebcf4f4053c4285e2c1cbda", } }, "params": { "out_channels": [3, 0, 64, 128, 320, 512], "patch_size": 4, "embed_dims": [64, 128, 320, 512], "num_heads": [1, 2, 5, 8], "mlp_ratios": [4, 4, 4, 4], "qkv_bias": True, "norm_layer": partial(LayerNorm, eps=1e-6), "depths": [3, 4, 18, 3], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, }, }, "mit_b4": { "encoder": MixVisionTransformerEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mit_b4.imagenet", "revision": "3a3454e900a4b4f11dd60eeb59101a9a1a36b017", } }, "params": { "out_channels": [3, 0, 64, 128, 320, 512], "patch_size": 4, "embed_dims": [64, 128, 320, 512], "num_heads": [1, 2, 5, 8], "mlp_ratios": [4, 4, 4, 4], "qkv_bias": True, "norm_layer": partial(LayerNorm, eps=1e-6), "depths": [3, 8, 27, 3], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, }, }, "mit_b5": { "encoder": MixVisionTransformerEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mit_b5.imagenet", "revision": "ced04d96c586b6297fd59a7a1e244fc78fdb6531", } }, "params": { "out_channels": [3, 0, 64, 128, 320, 512], "patch_size": 4, "embed_dims": [64, 128, 320, 512], "num_heads": [1, 2, 5, 8], "mlp_ratios": [4, 4, 4, 4], "qkv_bias": True, "norm_layer": partial(LayerNorm, eps=1e-6), "depths": [3, 6, 40, 3], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/mobilenet.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torchvision from typing import Dict, Sequence, List from ._base import EncoderMixin class MobileNetV2Encoder(torchvision.models.MobileNetV2, EncoderMixin): def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride self._out_indexes = [1, 3, 6, 13, len(self.features) - 1] del self.classifier def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.features[7:14]], 32: [self.features[14:]], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] depth = 0 for i, module in enumerate(self.features): x = module(x) if i in self._out_indexes: features.append(x) depth += 1 # torchscript does not support break in cycle, so we just # go over all modules and then slice number of features if not torch.jit.is_scripting() and depth > self._depth: break features = features[: self._depth + 1] return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("classifier.1.bias", None) state_dict.pop("classifier.1.weight", None) super().load_state_dict(state_dict, **kwargs) mobilenet_encoders = { "mobilenet_v2": { "encoder": MobileNetV2Encoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mobilenet_v2.imagenet", "revision": "e67aa804e17f7b404b629127eabbd224c4e0690b", } }, "params": {"out_channels": [3, 16, 24, 32, 96, 1280]}, } } ================================================ FILE: segmentation_models_pytorch/encoders/mobileone.py ================================================ # # For licensing see accompanying LICENSE file. # Copyright (C) 2022 Apple Inc. All Rights Reserved. # import copy from typing import List, Optional, Tuple, Dict, Sequence import torch import torch.nn as nn import torch.nn.functional as F from . import _utils as utils from ._base import EncoderMixin __all__ = ["MobileOne", "reparameterize_model"] class SEBlock(nn.Module): """Squeeze and Excite module. Pytorch implementation of `Squeeze-and-Excitation Networks` - https://arxiv.org/pdf/1709.01507.pdf """ def __init__(self, in_channels: int, rd_ratio: float = 0.0625) -> None: """Construct a Squeeze and Excite Module. :param in_channels: Number of input channels. :param rd_ratio: Input channel reduction ratio. """ super(SEBlock, self).__init__() self.reduce = nn.Conv2d( in_channels=in_channels, out_channels=int(in_channels * rd_ratio), kernel_size=1, stride=1, bias=True, ) self.expand = nn.Conv2d( in_channels=int(in_channels * rd_ratio), out_channels=in_channels, kernel_size=1, stride=1, bias=True, ) def forward(self, inputs: torch.Tensor) -> torch.Tensor: """Apply forward pass.""" b, c, h, w = inputs.size() x = F.avg_pool2d(inputs, kernel_size=[h, w]) x = self.reduce(x) x = F.relu(x) x = self.expand(x) x = torch.sigmoid(x) x = x.view(-1, c, 1, 1) return inputs * x class MobileOneBlock(nn.Module): """MobileOne building block. This block has a multi-branched architecture at train-time and plain-CNN style architecture at inference time For more details, please refer to our paper: `An Improved One millisecond Mobile Backbone` - https://arxiv.org/pdf/2206.04040.pdf """ def __init__( self, in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, inference_mode: bool = False, use_se: bool = False, num_conv_branches: int = 1, ) -> None: """Construct a MobileOneBlock module. :param in_channels: Number of channels in the input. :param out_channels: Number of channels produced by the block. :param kernel_size: Size of the convolution kernel. :param stride: Stride size. :param padding: Zero-padding size. :param dilation: Kernel dilation factor. :param groups: Group number. :param inference_mode: If True, instantiates model in inference mode. :param use_se: Whether to use SE-ReLU activations. :param num_conv_branches: Number of linear conv branches. """ super(MobileOneBlock, self).__init__() self.inference_mode = inference_mode self.groups = groups self.stride = stride self.kernel_size = kernel_size self.in_channels = in_channels self.out_channels = out_channels self.num_conv_branches = num_conv_branches # Check if SE-ReLU is requested if use_se: self.se = SEBlock(out_channels) else: self.se = nn.Identity() self.activation = nn.ReLU() if inference_mode: self.reparam_conv = nn.Conv2d( in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True, ) else: self.reparam_conv = nn.Identity() # Re-parameterizable skip connection self.rbr_skip = ( nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None ) # Re-parameterizable conv branches rbr_conv = list() for _ in range(self.num_conv_branches): rbr_conv.append(self._conv_bn(kernel_size=kernel_size, padding=padding)) self.rbr_conv = nn.ModuleList(rbr_conv) # Re-parameterizable scale branch self.rbr_scale = None if kernel_size > 1: self.rbr_scale = self._conv_bn(kernel_size=1, padding=0) def forward(self, x: torch.Tensor) -> torch.Tensor: """Apply forward pass.""" # Inference mode forward pass. if self.inference_mode: return self.activation(self.se(self.reparam_conv(x))) # Multi-branched train-time forward pass. # Skip branch output identity_out = 0 if self.rbr_skip is not None: identity_out = self.rbr_skip(x) # Scale branch output scale_out = 0 if self.rbr_scale is not None: scale_out = self.rbr_scale(x) # Other branches out = scale_out + identity_out for module in self.rbr_conv: out += module(x) return self.activation(self.se(out)) def reparameterize(self): """Following works like `RepVGG: Making VGG-style ConvNets Great Again` - https://arxiv.org/pdf/2101.03697.pdf. We re-parameterize multi-branched architecture used at training time to obtain a plain CNN-like structure for inference. """ if self.inference_mode: return kernel, bias = self._get_kernel_bias() self.reparam_conv = nn.Conv2d( in_channels=self.rbr_conv[0].conv.in_channels, out_channels=self.rbr_conv[0].conv.out_channels, kernel_size=self.rbr_conv[0].conv.kernel_size, stride=self.rbr_conv[0].conv.stride, padding=self.rbr_conv[0].conv.padding, dilation=self.rbr_conv[0].conv.dilation, groups=self.rbr_conv[0].conv.groups, bias=True, ) self.reparam_conv.weight.data = kernel self.reparam_conv.bias.data = bias # Delete un-used branches for para in self.parameters(): para.detach_() self.__delattr__("rbr_conv") self.__delattr__("rbr_scale") if hasattr(self, "rbr_skip"): self.__delattr__("rbr_skip") self.inference_mode = True def _get_kernel_bias(self) -> Tuple[torch.Tensor, torch.Tensor]: """Obtain the re-parameterized kernel and bias. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L83 :return: Tuple of (kernel, bias) after fusing branches. """ # get weights and bias of scale branch kernel_scale = 0 bias_scale = 0 if self.rbr_scale is not None: kernel_scale, bias_scale = self._fuse_bn_tensor(self.rbr_scale) # Pad scale branch kernel to match conv branch kernel size. pad = self.kernel_size // 2 kernel_scale = torch.nn.functional.pad(kernel_scale, [pad, pad, pad, pad]) # get weights and bias of skip branch kernel_identity = 0 bias_identity = 0 if self.rbr_skip is not None: kernel_identity, bias_identity = self._fuse_bn_tensor(self.rbr_skip) # get weights and bias of conv branches kernel_conv = 0 bias_conv = 0 for ix in range(self.num_conv_branches): _kernel, _bias = self._fuse_bn_tensor(self.rbr_conv[ix]) kernel_conv += _kernel bias_conv += _bias kernel_final = kernel_conv + kernel_scale + kernel_identity bias_final = bias_conv + bias_scale + bias_identity return kernel_final, bias_final def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]: """Fuse batchnorm layer with preceeding conv layer. Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95 :param branch: :return: Tuple of (kernel, bias) after fusing batchnorm. """ if isinstance(branch, nn.Sequential): kernel = branch.conv.weight running_mean = branch.bn.running_mean running_var = branch.bn.running_var gamma = branch.bn.weight beta = branch.bn.bias eps = branch.bn.eps else: assert isinstance(branch, nn.BatchNorm2d) if not hasattr(self, "id_tensor"): input_dim = self.in_channels // self.groups kernel_value = torch.zeros( (self.in_channels, input_dim, self.kernel_size, self.kernel_size), dtype=branch.weight.dtype, device=branch.weight.device, ) for i in range(self.in_channels): kernel_value[ i, i % input_dim, self.kernel_size // 2, self.kernel_size // 2 ] = 1 self.id_tensor = kernel_value kernel = self.id_tensor running_mean = branch.running_mean running_var = branch.running_var gamma = branch.weight beta = branch.bias eps = branch.eps std = (running_var + eps).sqrt() t = (gamma / std).reshape(-1, 1, 1, 1) return kernel * t, beta - running_mean * gamma / std def _conv_bn(self, kernel_size: int, padding: int) -> nn.Sequential: """Construct conv-batchnorm layers. :param kernel_size: Size of the convolution kernel. :param padding: Zero-padding size. :return: Conv-BN module. """ mod_list = nn.Sequential() mod_list.add_module( "conv", nn.Conv2d( in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=kernel_size, stride=self.stride, padding=padding, groups=self.groups, bias=False, ), ) mod_list.add_module("bn", nn.BatchNorm2d(num_features=self.out_channels)) return mod_list class MobileOne(nn.Module, EncoderMixin): """MobileOne Model Pytorch implementation of `An Improved One millisecond Mobile Backbone` - https://arxiv.org/pdf/2206.04040.pdf """ def __init__( self, out_channels: List[int], num_blocks_per_stage: List[int] = [2, 8, 10, 1], width_multipliers: Optional[List[float]] = None, inference_mode: bool = False, use_se: bool = False, depth=5, in_channels=3, output_stride=32, num_conv_branches: int = 1, ) -> None: """Construct MobileOne model. :param num_blocks_per_stage: List of number of blocks per stage. :param num_classes: Number of classes in the dataset. :param width_multipliers: List of width multiplier for blocks in a stage. :param inference_mode: If True, instantiates model in inference mode. :param use_se: Whether to use SE-ReLU activations. :param num_conv_branches: Number of linear conv branches. """ if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__() assert len(width_multipliers) == 4 self.inference_mode = inference_mode self.in_planes = min(64, int(64 * width_multipliers[0])) self.use_se = use_se self.num_conv_branches = num_conv_branches self._depth = depth self._in_channels = in_channels self._out_channels = out_channels self._output_stride = output_stride # Build stages self.stage0 = MobileOneBlock( in_channels=self._in_channels, out_channels=self.in_planes, kernel_size=3, stride=2, padding=1, inference_mode=self.inference_mode, ) self.cur_layer_idx = 1 self.stage1 = self._make_stage( int(64 * width_multipliers[0]), num_blocks_per_stage[0], num_se_blocks=0 ) self.stage2 = self._make_stage( int(128 * width_multipliers[1]), num_blocks_per_stage[1], num_se_blocks=0 ) self.stage3 = self._make_stage( int(256 * width_multipliers[2]), num_blocks_per_stage[2], num_se_blocks=int(num_blocks_per_stage[2] // 2) if use_se else 0, ) self.stage4 = self._make_stage( int(512 * width_multipliers[3]), num_blocks_per_stage[3], num_se_blocks=num_blocks_per_stage[3] if use_se else 0, ) def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.stage3], 32: [self.stage4], } def _make_stage( self, planes: int, num_blocks: int, num_se_blocks: int ) -> nn.Sequential: """Build a stage of MobileOne model. :param planes: Number of output channels. :param num_blocks: Number of blocks in this stage. :param num_se_blocks: Number of SE blocks in this stage. :return: A stage of MobileOne model. """ # Get strides for all layers strides = [2] + [1] * (num_blocks - 1) blocks = [] for ix, stride in enumerate(strides): use_se = False if num_se_blocks > num_blocks: raise ValueError("Number of SE blocks cannot exceed number of layers.") if ix >= (num_blocks - num_se_blocks): use_se = True # Depthwise conv blocks.append( MobileOneBlock( in_channels=self.in_planes, out_channels=self.in_planes, kernel_size=3, stride=stride, padding=1, groups=self.in_planes, inference_mode=self.inference_mode, use_se=use_se, num_conv_branches=self.num_conv_branches, ) ) # Pointwise conv blocks.append( MobileOneBlock( in_channels=self.in_planes, out_channels=planes, kernel_size=1, stride=1, padding=0, groups=1, inference_mode=self.inference_mode, use_se=use_se, num_conv_branches=self.num_conv_branches, ) ) self.in_planes = planes self.cur_layer_idx += 1 return nn.Sequential(*blocks) def forward(self, x: torch.Tensor) -> List[torch.Tensor]: """Apply forward pass.""" features = [x] if self._depth >= 1: x = self.stage0(x) features.append(x) if self._depth >= 2: x = self.stage1(x) features.append(x) if self._depth >= 3: x = self.stage2(x) features.append(x) if self._depth >= 4: x = self.stage3(x) features.append(x) if self._depth >= 5: x = self.stage4(x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("linear.weight", None) state_dict.pop("linear.bias", None) super().load_state_dict(state_dict, **kwargs) def set_in_channels(self, in_channels, pretrained=True): """Change first convolution channels""" if in_channels == 3: return self._in_channels = in_channels self._out_channels = tuple([in_channels] + list(self._out_channels)[1:]) utils.patch_first_conv( model=self.stage0.rbr_conv, new_in_channels=in_channels, pretrained=pretrained, ) utils.patch_first_conv( model=self.stage0.rbr_scale, new_in_channels=in_channels, pretrained=pretrained, ) def reparameterize_model(model: torch.nn.Module) -> nn.Module: """Return a model where a multi-branched structure used in training is re-parameterized into a single branch for inference. :param model: MobileOne model in train mode. :return: MobileOne model in inference mode. """ # Avoid editing original graph model = copy.deepcopy(model) for module in model.modules(): if hasattr(module, "reparameterize"): module.reparameterize() return model mobileone_encoders = { "mobileone_s0": { "encoder": MobileOne, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mobileone_s0.imagenet", "revision": "f52815cf0ad29278a9860c9cd5fabf19f904bedf", } }, "params": { "out_channels": [3, 48, 48, 128, 256, 1024], "width_multipliers": (0.75, 1.0, 1.0, 2.0), "num_conv_branches": 4, "inference_mode": False, }, }, "mobileone_s1": { "encoder": MobileOne, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mobileone_s1.imagenet", "revision": "5707a98852b762cd8e0c43b5c8c729cd28496677", } }, "params": { "out_channels": [3, 64, 96, 192, 512, 1280], "width_multipliers": (1.5, 1.5, 2.0, 2.5), "inference_mode": False, }, }, "mobileone_s2": { "encoder": MobileOne, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mobileone_s2.imagenet", "revision": "ddc3db8fa40d271902c7a8c95cee6691f617d551", } }, "params": { "out_channels": [3, 64, 96, 256, 640, 2048], "width_multipliers": (1.5, 2.0, 2.5, 4.0), "inference_mode": False, }, }, "mobileone_s3": { "encoder": MobileOne, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mobileone_s3.imagenet", "revision": "da89b84a91b7400c366c358bfbf8dd0b2fa4dde2", } }, "params": { "out_channels": [3, 64, 128, 320, 768, 2048], "width_multipliers": (2.0, 2.5, 3.0, 4.0), "inference_mode": False, }, }, "mobileone_s4": { "encoder": MobileOne, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/mobileone_s4.imagenet", "revision": "16197c55d599076b6aae67a83d3b3f70c31b097c", } }, "params": { "out_channels": [3, 64, 192, 448, 896, 2048], "width_multipliers": (3.0, 3.5, 3.5, 4.0), "use_se": True, "inference_mode": False, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/resnet.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch from typing import Dict, Sequence, List from torchvision.models.resnet import ResNet from torchvision.models.resnet import BasicBlock from torchvision.models.resnet import Bottleneck from ._base import EncoderMixin class ResNetEncoder(ResNet, EncoderMixin): """ResNet encoder implementation.""" def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride del self.fc del self.avgpool def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.layer3], 32: [self.layer4], } def forward(self, x: torch.Tensor) -> list[torch.Tensor]: features = [x] if self._depth >= 1: x = self.conv1(x) x = self.bn1(x) x = self.relu(x) features.append(x) if self._depth >= 2: x = self.maxpool(x) x = self.layer1(x) features.append(x) if self._depth >= 3: x = self.layer2(x) features.append(x) if self._depth >= 4: x = self.layer3(x) features.append(x) if self._depth >= 5: x = self.layer4(x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("fc.bias", None) state_dict.pop("fc.weight", None) super().load_state_dict(state_dict, **kwargs) resnet_encoders = { "resnet18": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnet18.imagenet", "revision": "3f2325ff978283d47aa6a1d6878ca20565622683", }, "ssl": { "repo_id": "smp-hub/resnet18.ssl", "revision": "d600d5116aac2e6e595f99f40612074c723c00b2", }, "swsl": { "repo_id": "smp-hub/resnet18.swsl", "revision": "0e3a35d4d8e344088c14a96eee502a88ac70eae1", }, }, "params": { "out_channels": [3, 64, 64, 128, 256, 512], "block": BasicBlock, "layers": [2, 2, 2, 2], }, }, "resnet34": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnet34.imagenet", "revision": "7a57b34f723329ff020b3f8bc41771163c519d0c", }, }, "params": { "out_channels": [3, 64, 64, 128, 256, 512], "block": BasicBlock, "layers": [3, 4, 6, 3], }, }, "resnet50": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnet50.imagenet", "revision": "00cb74e366966d59cd9a35af57e618af9f88efe9", }, "ssl": { "repo_id": "smp-hub/resnet50.ssl", "revision": "d07daf5b4377f3700c6ac61906b0aafbc4eca46b", }, "swsl": { "repo_id": "smp-hub/resnet50.swsl", "revision": "b9520cce124f91c6fe7eee45721a2c7954f0d8c0", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 6, 3], }, }, "resnet101": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnet101.imagenet", "revision": "cd7c15e8c51da86ae6a084515fdb962d0c94e7d1", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 23, 3], }, }, "resnet152": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnet152.imagenet", "revision": "951dd835e9d086628e447b484584c8983f9e1dd0", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 8, 36, 3], }, }, "resnext50_32x4d": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnext50_32x4d.imagenet", "revision": "329793c85d62fd340ae42ae39fb905a63df872e7", }, "ssl": { "repo_id": "smp-hub/resnext50_32x4d.ssl", "revision": "9b67cff77d060c7044493a58c24d1007c1eb06c3", }, "swsl": { "repo_id": "smp-hub/resnext50_32x4d.swsl", "revision": "52e6e49da61b8e26ca691e1aef2cbb952884057d", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 6, 3], "groups": 32, "width_per_group": 4, }, }, "resnext101_32x4d": { "encoder": ResNetEncoder, "pretrained_settings": { "ssl": { "repo_id": "smp-hub/resnext101_32x4d.ssl", "revision": "b39796c8459084d13523b7016c3ef13a2e9e472b", }, "swsl": { "repo_id": "smp-hub/resnext101_32x4d.swsl", "revision": "3f8355b4892a31f001a832b49b2b01484d48516a", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 23, 3], "groups": 32, "width_per_group": 4, }, }, "resnext101_32x8d": { "encoder": ResNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/resnext101_32x8d.imagenet", "revision": "221af6198d03a4ee88992f78a1ee81b46a52d339", }, "instagram": { "repo_id": "smp-hub/resnext101_32x8d.instagram", "revision": "44cd927aa6e64673ffe9d31230bad44abc18b823", }, "ssl": { "repo_id": "smp-hub/resnext101_32x8d.ssl", "revision": "723a95ddeed335c9488c37c6cbef13d779ac8f97", }, "swsl": { "repo_id": "smp-hub/resnext101_32x8d.swsl", "revision": "58cf0bb65f91365470398080d9588b187d1777c4", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 23, 3], "groups": 32, "width_per_group": 8, }, }, "resnext101_32x16d": { "encoder": ResNetEncoder, "pretrained_settings": { "instagram": { "repo_id": "smp-hub/resnext101_32x16d.instagram", "revision": "64e8e320eeae6501185b0627b2429a68e52d050c", }, "ssl": { "repo_id": "smp-hub/resnext101_32x16d.ssl", "revision": "1283fe03fbb6aa2599b2df24095255acb93c3d5c", }, "swsl": { "repo_id": "smp-hub/resnext101_32x16d.swsl", "revision": "30ba61bbd4d6af0d955c513dbb4f557b84eb094f", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 23, 3], "groups": 32, "width_per_group": 16, }, }, "resnext101_32x32d": { "encoder": ResNetEncoder, "pretrained_settings": { "instagram": { "repo_id": "smp-hub/resnext101_32x32d.instagram", "revision": "c9405de121fdaa275a89de470fb19409e3eeaa86", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 23, 3], "groups": 32, "width_per_group": 32, }, }, "resnext101_32x48d": { "encoder": ResNetEncoder, "pretrained_settings": { "instagram": { "repo_id": "smp-hub/resnext101_32x48d.instagram", "revision": "53e61a962b824ad7027409821f9ac3e3336dd024", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": Bottleneck, "layers": [3, 4, 23, 3], "groups": 32, "width_per_group": 48, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/senet.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch from typing import List, Dict, Sequence from ._base import EncoderMixin from ._senet import ( SENet, SEBottleneck, SEResNetBottleneck, SEResNeXtBottleneck, ) class SENetEncoder(SENet, EncoderMixin): def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride # for compatibility with torchscript self.layer0_pool = self.layer0.pool self.layer0.pool = torch.nn.Identity() del self.last_linear del self.avg_pool def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.layer3], 32: [self.layer4], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] if self._depth >= 1: x = self.layer0(x) features.append(x) if self._depth >= 2: x = self.layer0_pool(x) x = self.layer1(x) features.append(x) if self._depth >= 3: x = self.layer2(x) features.append(x) if self._depth >= 4: x = self.layer3(x) features.append(x) if self._depth >= 5: x = self.layer4(x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("last_linear.bias", None) state_dict.pop("last_linear.weight", None) super().load_state_dict(state_dict, **kwargs) senet_encoders = { "senet154": { "encoder": SENetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/senet154.imagenet", "revision": "249f45efc9881ba560a0c480128edbc34ab87e40", } }, "params": { "out_channels": [3, 128, 256, 512, 1024, 2048], "block": SEBottleneck, "dropout_p": 0.2, "groups": 64, "layers": [3, 8, 36, 3], "num_classes": 1000, "reduction": 16, }, }, "se_resnet50": { "encoder": SENetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/se_resnet50.imagenet", "revision": "e6b4bc2dc85226c3d3474544410724a485455459", } }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": SEResNetBottleneck, "layers": [3, 4, 6, 3], "downsample_kernel_size": 1, "downsample_padding": 0, "dropout_p": None, "groups": 1, "inplanes": 64, "input_3x3": False, "num_classes": 1000, "reduction": 16, }, }, "se_resnet101": { "encoder": SENetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/se_resnet101.imagenet", "revision": "71fe95cc0a27f444cf83671f354de02dc741b18b", } }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": SEResNetBottleneck, "layers": [3, 4, 23, 3], "downsample_kernel_size": 1, "downsample_padding": 0, "dropout_p": None, "groups": 1, "inplanes": 64, "input_3x3": False, "num_classes": 1000, "reduction": 16, }, }, "se_resnet152": { "encoder": SENetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/se_resnet152.imagenet", "revision": "e79fc3d9d76f197bd76a2593c2054edf1083fe32", } }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": SEResNetBottleneck, "layers": [3, 8, 36, 3], "downsample_kernel_size": 1, "downsample_padding": 0, "dropout_p": None, "groups": 1, "inplanes": 64, "input_3x3": False, "num_classes": 1000, "reduction": 16, }, }, "se_resnext50_32x4d": { "encoder": SENetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/se_resnext50_32x4d.imagenet", "revision": "73246406d879a2b0e3fdfe6fddd56347d38f38ae", } }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": SEResNeXtBottleneck, "layers": [3, 4, 6, 3], "downsample_kernel_size": 1, "downsample_padding": 0, "dropout_p": None, "groups": 32, "inplanes": 64, "input_3x3": False, "num_classes": 1000, "reduction": 16, }, }, "se_resnext101_32x4d": { "encoder": SENetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/se_resnext101_32x4d.imagenet", "revision": "18808a4276f46421d358a9de554e0b93c2795df4", } }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": SEResNeXtBottleneck, "layers": [3, 4, 23, 3], "downsample_kernel_size": 1, "downsample_padding": 0, "dropout_p": None, "groups": 32, "inplanes": 64, "input_3x3": False, "num_classes": 1000, "reduction": 16, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/timm_efficientnet.py ================================================ import torch import torch.nn as nn from typing import List, Dict, Sequence from functools import partial from timm.models.efficientnet import EfficientNet from timm.models.efficientnet import decode_arch_def, round_channels from timm.layers.activations import Swish from ._base import EncoderMixin def get_efficientnet_kwargs( channel_multiplier=1.0, depth_multiplier=1.0, drop_rate=0.2 ): """Create EfficientNet model. Ref impl: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-b0': (1.0, 1.0, 224, 0.2), 'efficientnet-b1': (1.0, 1.1, 240, 0.2), 'efficientnet-b2': (1.1, 1.2, 260, 0.3), 'efficientnet-b3': (1.2, 1.4, 300, 0.3), 'efficientnet-b4': (1.4, 1.8, 380, 0.4), 'efficientnet-b5': (1.6, 2.2, 456, 0.4), 'efficientnet-b6': (1.8, 2.6, 528, 0.5), 'efficientnet-b7': (2.0, 3.1, 600, 0.5), 'efficientnet-b8': (2.2, 3.6, 672, 0.5), 'efficientnet-l2': (4.3, 5.3, 800, 0.5), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage """ arch_def = [ ["ds_r1_k3_s1_e1_c16_se0.25"], ["ir_r2_k3_s2_e6_c24_se0.25"], ["ir_r2_k5_s2_e6_c40_se0.25"], ["ir_r3_k3_s2_e6_c80_se0.25"], ["ir_r3_k5_s1_e6_c112_se0.25"], ["ir_r4_k5_s2_e6_c192_se0.25"], ["ir_r1_k3_s1_e6_c320_se0.25"], ] model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier), num_features=round_channels(1280, channel_multiplier, 8, None), stem_size=32, round_chs_fn=partial(round_channels, multiplier=channel_multiplier), act_layer=Swish, drop_rate=drop_rate, drop_path_rate=0.2, ) return model_kwargs def gen_efficientnet_lite_kwargs( channel_multiplier=1.0, depth_multiplier=1.0, drop_rate=0.2 ): """EfficientNet-Lite model. Ref impl: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite Paper: https://arxiv.org/abs/1905.11946 EfficientNet params name: (channel_multiplier, depth_multiplier, resolution, dropout_rate) 'efficientnet-lite0': (1.0, 1.0, 224, 0.2), 'efficientnet-lite1': (1.0, 1.1, 240, 0.2), 'efficientnet-lite2': (1.1, 1.2, 260, 0.3), 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), 'efficientnet-lite4': (1.4, 1.8, 300, 0.3), Args: channel_multiplier: multiplier to number of channels per layer depth_multiplier: multiplier to number of repeats per stage """ arch_def = [ ["ds_r1_k3_s1_e1_c16"], ["ir_r2_k3_s2_e6_c24"], ["ir_r2_k5_s2_e6_c40"], ["ir_r3_k3_s2_e6_c80"], ["ir_r3_k5_s1_e6_c112"], ["ir_r4_k5_s2_e6_c192"], ["ir_r1_k3_s1_e6_c320"], ] model_kwargs = dict( block_args=decode_arch_def(arch_def, depth_multiplier, fix_first_last=True), num_features=1280, stem_size=32, fix_stem=True, round_chs_fn=partial(round_channels, multiplier=channel_multiplier), act_layer=nn.ReLU6, drop_rate=drop_rate, drop_path_rate=0.2, ) return model_kwargs class EfficientNetBaseEncoder(EfficientNet, EncoderMixin): def __init__( self, stage_idxs: List[int], out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._stage_idxs = stage_idxs self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride del self.classifier def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.blocks[self._stage_idxs[1] : self._stage_idxs[2]]], 32: [self.blocks[self._stage_idxs[2] :]], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] if self._depth >= 1: x = self.conv_stem(x) x = self.bn1(x) features.append(x) if self._depth >= 2: x = self.blocks[0](x) x = self.blocks[1](x) features.append(x) if self._depth >= 3: x = self.blocks[2](x) features.append(x) if self._depth >= 4: x = self.blocks[3](x) x = self.blocks[4](x) features.append(x) if self._depth >= 5: x = self.blocks[5](x) x = self.blocks[6](x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("classifier.bias", None) state_dict.pop("classifier.weight", None) super().load_state_dict(state_dict, **kwargs) class EfficientNetEncoder(EfficientNetBaseEncoder): def __init__( self, stage_idxs: List[int], out_channels: List[int], depth: int = 5, channel_multiplier: float = 1.0, depth_multiplier: float = 1.0, drop_rate: float = 0.2, output_stride: int = 32, ): kwargs = get_efficientnet_kwargs( channel_multiplier, depth_multiplier, drop_rate ) super().__init__( stage_idxs=stage_idxs, depth=depth, out_channels=out_channels, output_stride=output_stride, **kwargs, ) class EfficientNetLiteEncoder(EfficientNetBaseEncoder): def __init__( self, stage_idxs: List[int], out_channels: List[int], depth: int = 5, channel_multiplier: float = 1.0, depth_multiplier: float = 1.0, drop_rate: float = 0.2, output_stride: int = 32, ): kwargs = gen_efficientnet_lite_kwargs( channel_multiplier, depth_multiplier, drop_rate ) super().__init__( stage_idxs=stage_idxs, depth=depth, out_channels=out_channels, output_stride=output_stride, **kwargs, ) def prepare_settings(settings): return { "mean": settings.mean, "std": settings.std, "url": settings.url, "input_range": (0, 1), "input_space": "RGB", } timm_efficientnet_encoders = { "timm-efficientnet-b0": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b0.imagenet", "revision": "8419e9cc19da0b68dcd7bb12f19b7c92407ad7c4", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b0.advprop", "revision": "a5870af2d24ce79e0cc7fae2bbd8e0a21fcfa6d8", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b0.noisy-student", "revision": "bea8b0ff726a50e48774d2d360c5fb1ac4815836", }, }, "params": { "out_channels": [3, 32, 24, 40, 112, 320], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.0, "depth_multiplier": 1.0, "drop_rate": 0.2, }, }, "timm-efficientnet-b1": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b1.imagenet", "revision": "63bdd65ef6596ef24f1cadc7dd4f46b624442349", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b1.advprop", "revision": "79b3d102080ef679b16c2748e608a871112233d0", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b1.noisy-student", "revision": "36856124a699f6032574ceeefab02040daa90a9a", }, }, "params": { "out_channels": [3, 32, 24, 40, 112, 320], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.0, "depth_multiplier": 1.1, "drop_rate": 0.2, }, }, "timm-efficientnet-b2": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b2.imagenet", "revision": "e693adb39d3cb3847e71e3700a0c2aa58072cff1", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b2.advprop", "revision": "b58479bf78007cfbb365091d64eeee369bddfa21", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b2.noisy-student", "revision": "67c558827c6d3e0975ff9b4bce8557bc2ca80931", }, }, "params": { "out_channels": [3, 32, 24, 48, 120, 352], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.1, "depth_multiplier": 1.2, "drop_rate": 0.3, }, }, "timm-efficientnet-b3": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b3.imagenet", "revision": "1666b835b5151d6bb2067c7cd67e67ada6c39edf", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b3.advprop", "revision": "70474cdb9f1ff4fcbd7434e66560ead1ab8e506b", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b3.noisy-student", "revision": "2367bc9f61e79ee97684169a71a87db280bcf4db", }, }, "params": { "out_channels": [3, 40, 32, 48, 136, 384], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.2, "depth_multiplier": 1.4, "drop_rate": 0.3, }, }, "timm-efficientnet-b4": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b4.imagenet", "revision": "07868c28ab308f4de4cf1e7ec54b33b8b002ccdb", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b4.advprop", "revision": "8ea1772ee9a2a0d18c1b56dce0dfac8dd33d537d", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b4.noisy-student", "revision": "faeb77b6e8292a700380c840d39442d7ce4d6443", }, }, "params": { "out_channels": [3, 48, 32, 56, 160, 448], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.4, "depth_multiplier": 1.8, "drop_rate": 0.4, }, }, "timm-efficientnet-b5": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b5.imagenet", "revision": "004153b4ddd93d30afd9bbf34329d7f57396d413", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b5.advprop", "revision": "1d1c5f05aab5ed9a1d5052847ddd4024c06a464d", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b5.noisy-student", "revision": "9bc3a1e5490de92b1af061d5c2c474ab3129e38c", }, }, "params": { "out_channels": [3, 48, 40, 64, 176, 512], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.6, "depth_multiplier": 2.2, "drop_rate": 0.4, }, }, "timm-efficientnet-b6": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b6.imagenet", "revision": "dbbf28a5c33f021486db4070de693caad6b56c3d", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b6.advprop", "revision": "3b5d3412047f7711c56ffde997911cfefe79f835", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b6.noisy-student", "revision": "9b899ea9e8e0ce2ccada0f34a8cb8b5028e9bb36", }, }, "params": { "out_channels": [3, 56, 40, 72, 200, 576], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.8, "depth_multiplier": 2.6, "drop_rate": 0.5, }, }, "timm-efficientnet-b7": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b7.imagenet", "revision": "8ef7ffccf54dad9baceb21d05b7ef86b6b70f4cc", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b7.advprop", "revision": "fcbc576ffb939c12d5cd8dad523fdae6eb0177ca", }, "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-b7.noisy-student", "revision": "6b1dd73e61bf934d485d7bd4381dc3e2ab374664", }, }, "params": { "out_channels": [3, 64, 48, 80, 224, 640], "stage_idxs": [2, 3, 5], "channel_multiplier": 2.0, "depth_multiplier": 3.1, "drop_rate": 0.5, }, }, "timm-efficientnet-b8": { "encoder": EfficientNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-efficientnet-b8.imagenet", "revision": "b5e9dde35605a3a6d17ea2a727382625f9066a37", }, "advprop": { "repo_id": "smp-hub/timm-efficientnet-b8.advprop", "revision": "e43f381de72e7467383c2c80bacbb7fcb9572866", }, }, "params": { "out_channels": [3, 72, 56, 88, 248, 704], "stage_idxs": [2, 3, 5], "channel_multiplier": 2.2, "depth_multiplier": 3.6, "drop_rate": 0.5, }, }, "timm-efficientnet-l2": { "encoder": EfficientNetEncoder, "pretrained_settings": { "noisy-student": { "repo_id": "smp-hub/timm-efficientnet-l2.noisy-student", "revision": "cdc711e76d1becdd9197169f1a8bb1b2094e980c", }, "noisy-student-475": { "repo_id": "smp-hub/timm-efficientnet-l2.noisy-student-475", "revision": "35f5ba667a64bf4f3f0689daf84fc6d0f8e1311b", }, }, "params": { "out_channels": [3, 136, 104, 176, 480, 1376], "stage_idxs": [2, 3, 5], "channel_multiplier": 4.3, "depth_multiplier": 5.3, "drop_rate": 0.5, }, }, "timm-tf_efficientnet_lite0": { "encoder": EfficientNetLiteEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-tf_efficientnet_lite0.imagenet", "revision": "f5729249af07e5d923fb8b16922256ce2865d108", }, }, "params": { "out_channels": [3, 32, 24, 40, 112, 320], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.0, "depth_multiplier": 1.0, "drop_rate": 0.2, }, }, "timm-tf_efficientnet_lite1": { "encoder": EfficientNetLiteEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-tf_efficientnet_lite1.imagenet", "revision": "7b5e3f8dbb0c13b74101773584bba7523721be72", }, }, "params": { "out_channels": [3, 32, 24, 40, 112, 320], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.0, "depth_multiplier": 1.1, "drop_rate": 0.2, }, }, "timm-tf_efficientnet_lite2": { "encoder": EfficientNetLiteEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-tf_efficientnet_lite2.imagenet", "revision": "cc5f6cd4c7409ebacc13292f09d369ae88547f6a", }, }, "params": { "out_channels": [3, 32, 24, 48, 120, 352], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.1, "depth_multiplier": 1.2, "drop_rate": 0.3, }, }, "timm-tf_efficientnet_lite3": { "encoder": EfficientNetLiteEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-tf_efficientnet_lite3.imagenet", "revision": "ab29c8402991591d66f813bbb1f061565d9b0cd0", }, }, "params": { "out_channels": [3, 32, 32, 48, 136, 384], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.2, "depth_multiplier": 1.4, "drop_rate": 0.3, }, }, "timm-tf_efficientnet_lite4": { "encoder": EfficientNetLiteEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-tf_efficientnet_lite4.imagenet", "revision": "91a822e0f03c255b34dfb7846d3858397e50ba39", }, }, "params": { "out_channels": [3, 32, 32, 56, 160, 448], "stage_idxs": [2, 3, 5], "channel_multiplier": 1.4, "depth_multiplier": 1.8, "drop_rate": 0.4, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/timm_sknet.py ================================================ import torch from typing import Dict, List, Sequence from timm.models.resnet import ResNet from timm.models.sknet import SelectiveKernelBottleneck, SelectiveKernelBasic from ._base import EncoderMixin class SkNetEncoder(ResNet, EncoderMixin): def __init__( self, out_channels: List[int], depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(**kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride del self.fc del self.global_pool def get_stages(self) -> Dict[int, Sequence[torch.nn.Module]]: return { 16: [self.layer3], 32: [self.layer4], } def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [x] if self._depth >= 1: x = self.conv1(x) x = self.bn1(x) x = self.act1(x) features.append(x) if self._depth >= 2: x = self.maxpool(x) x = self.layer1(x) features.append(x) if self._depth >= 3: x = self.layer2(x) features.append(x) if self._depth >= 4: x = self.layer3(x) features.append(x) if self._depth >= 5: x = self.layer4(x) features.append(x) return features def load_state_dict(self, state_dict, **kwargs): state_dict.pop("fc.bias", None) state_dict.pop("fc.weight", None) super().load_state_dict(state_dict, **kwargs) timm_sknet_encoders = { "timm-skresnet18": { "encoder": SkNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-skresnet18.imagenet", "revision": "6c97652bb744d89177b68274d2fda3923a7d1f95", }, }, "params": { "out_channels": [3, 64, 64, 128, 256, 512], "block": SelectiveKernelBasic, "layers": [2, 2, 2, 2], "zero_init_last": False, "block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}}, }, }, "timm-skresnet34": { "encoder": SkNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-skresnet34.imagenet", "revision": "2367796924a8182cc835ef6b5dc303917f923f99", }, }, "params": { "out_channels": [3, 64, 64, 128, 256, 512], "block": SelectiveKernelBasic, "layers": [3, 4, 6, 3], "zero_init_last": False, "block_args": {"sk_kwargs": {"rd_ratio": 1 / 8, "split_input": True}}, }, }, "timm-skresnext50_32x4d": { "encoder": SkNetEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/timm-skresnext50_32x4d.imagenet", "revision": "50207e407cc4c6ea9e6872963db6844ca7b7b9de", }, }, "params": { "out_channels": [3, 64, 256, 512, 1024, 2048], "block": SelectiveKernelBottleneck, "layers": [3, 4, 6, 3], "zero_init_last": False, "cardinality": 32, "base_width": 4, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/timm_universal.py ================================================ """ TimmUniversalEncoder provides a unified feature extraction interface built on the `timm` library, supporting both traditional-style (e.g., ResNet) and transformer-style models (e.g., Swin Transformer, ConvNeXt). This encoder produces consistent multi-level feature maps for semantic segmentation tasks. It allows configuring the number of feature extraction stages (`depth`) and adjusting `output_stride` when supported. Key Features: - Flexible model selection using `timm.create_model`. - Unified multi-level output across different model hierarchies. - Automatic alignment for inconsistent feature scales: - Transformer-style models (start at 1/4 scale): Insert dummy features for 1/2 scale. - VGG-style models (include scale-1 features): Align outputs for compatibility. - Easy access to feature scale information via the `reduction` property. Feature Scale Differences: - Traditional-style models (e.g., ResNet): Scales at 1/2, 1/4, 1/8, 1/16, 1/32. - Transformer-style models (e.g., Swin Transformer): Start at 1/4 scale, skip 1/2 scale. - VGG-style models: Include scale-1 features (input resolution). Notes: - `output_stride` is unsupported in some models, especially transformer-based architectures. - Special handling for models like TResNet and DLA to ensure correct feature indexing. - VGG-style models use `_is_vgg_style` to align scale-1 features with standard outputs. """ from typing import Any import timm import torch import torch.nn as nn class TimmUniversalEncoder(nn.Module): """ A universal encoder leveraging the `timm` library for feature extraction from various model architectures, including traditional-style and transformer-style models. Features: - Supports configurable depth and output stride. - Ensures consistent multi-level feature extraction across diverse models. - Compatible with convolutional and transformer-like backbones. """ _is_torch_scriptable = True _is_torch_exportable = True _is_torch_compilable = True def __init__( self, name: str, pretrained: bool = True, in_channels: int = 3, depth: int = 5, output_stride: int = 32, **kwargs: dict[str, Any], ): """ Initialize the encoder. Args: name (str): Model name to load from `timm`. pretrained (bool): Load pretrained weights (default: True). in_channels (int): Number of input channels (default: 3 for RGB). depth (int): Number of feature stages to extract (default: 5). output_stride (int): Desired output stride (default: 32). **kwargs: Additional arguments passed to `timm.create_model`. """ # At the moment we do not support models with more than 5 stages, # but can be reconfigured in the future. if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__() self.name = name # Default model configuration for feature extraction common_kwargs = dict( in_chans=in_channels, features_only=True, output_stride=output_stride, pretrained=pretrained, out_indices=tuple(range(depth)), ) # Ｎot all models support output stride argument, drop it by default if output_stride == 32: common_kwargs.pop("output_stride") # Load a temporary model to analyze its feature hierarchy try: with torch.device("meta"): tmp_model = timm.create_model(name, features_only=True) except Exception: tmp_model = timm.create_model(name, features_only=True) # Check if model output is in channel-last format (NHWC) self._is_channel_last = getattr(tmp_model, "output_fmt", None) == "NHWC" # Determine the model's downsampling pattern and set hierarchy flags encoder_stage = len(tmp_model.feature_info.reduction()) reduction_scales = list(tmp_model.feature_info.reduction()) if reduction_scales == [2 ** (i + 2) for i in range(encoder_stage)]: # Transformer-style downsampling: scales (4, 8, 16, 32) self._is_transformer_style = True self._is_vgg_style = False elif reduction_scales == [2 ** (i + 1) for i in range(encoder_stage)]: # Traditional-style downsampling: scales (2, 4, 8, 16, 32) self._is_transformer_style = False self._is_vgg_style = False elif reduction_scales == [2**i for i in range(encoder_stage)]: # Vgg-style models including scale 1: scales (1, 2, 4, 8, 16, 32) self._is_transformer_style = False self._is_vgg_style = True else: raise ValueError("Unsupported model downsampling pattern.") if self._is_transformer_style: # Transformer-like models (start at scale 4) if "tresnet" in name: # 'tresnet' models start feature extraction at stage 1, # so out_indices=(1, 2, 3, 4) for depth=5. common_kwargs["out_indices"] = tuple(range(1, depth)) else: # Most transformer-like models use out_indices=(0, 1, 2, 3) for depth=5. common_kwargs["out_indices"] = tuple(range(depth - 1)) timm_model_kwargs = _merge_kwargs_no_duplicates(common_kwargs, kwargs) self.model = timm.create_model(name, **timm_model_kwargs) # Add a dummy output channel (0) to align with traditional encoder structures. self._out_channels = ( [in_channels] + [0] + self.model.feature_info.channels() ) else: if "dla" in name: # For 'dla' models, out_indices starts at 0 and matches the input size. common_kwargs["out_indices"] = tuple(range(1, depth + 1)) if self._is_vgg_style: common_kwargs["out_indices"] = tuple(range(depth + 1)) self.model = timm.create_model( name, **_merge_kwargs_no_duplicates(common_kwargs, kwargs) ) if self._is_vgg_style: self._out_channels = self.model.feature_info.channels() else: self._out_channels = [in_channels] + self.model.feature_info.channels() self._in_channels = in_channels self._depth = depth self._output_stride = output_stride def forward(self, x: torch.Tensor) -> list[torch.Tensor]: """ Forward pass to extract multi-stage features. Args: x (torch.Tensor): Input tensor of shape (B, C, H, W). Returns: list[torch.Tensor]: List of feature maps at different scales. """ features = self.model(x) # Convert NHWC to NCHW if needed if self._is_channel_last: features = [ feature.permute(0, 3, 1, 2).contiguous() for feature in features ] # Add dummy feature for scale 1/2 if missing (transformer-style models) if self._is_transformer_style: B, _, H, W = x.shape dummy = torch.empty([B, 0, H // 2, W // 2], dtype=x.dtype, device=x.device) features = [dummy] + features # Add input tensor as scale 1 feature if `self._is_vgg_style` is False if not self._is_vgg_style: features = [x] + features return features @property def out_channels(self) -> list[int]: """ Returns the number of output channels for each feature stage. Returns: list[int]: A list of channel dimensions at each scale. """ return self._out_channels @property def output_stride(self) -> int: """ Returns the effective output stride based on the model depth. Returns: int: The effective output stride. """ return int(min(self._output_stride, 2**self._depth)) def load_state_dict(self, state_dict, **kwargs): # for compatibility of weights for # timm- ported encoders with TimmUniversalEncoder patterns = ["regnet", "res2", "resnest", "mobilenetv3", "gernet"] is_deprecated_encoder = any( self.name.startswith(pattern) for pattern in patterns ) if is_deprecated_encoder: keys = list(state_dict.keys()) for key in keys: new_key = key if not key.startswith("model."): new_key = "model." + key if "gernet" in self.name: new_key = new_key.replace(".stages.", ".stages_") state_dict[new_key] = state_dict.pop(key) return super().load_state_dict(state_dict, **kwargs) def _merge_kwargs_no_duplicates(a: dict[str, Any], b: dict[str, Any]) -> dict[str, Any]: """ Merge two dictionaries, ensuring no duplicate keys exist. Args: a (dict): Base dictionary. b (dict): Additional parameters to merge. Returns: dict: A merged dictionary. """ duplicates = a.keys() & b.keys() if duplicates: raise ValueError(f"'{duplicates}' already specified internally") return a | b ================================================ FILE: segmentation_models_pytorch/encoders/timm_vit.py ================================================ from typing import Any, Optional import timm import torch import torch.nn as nn from .timm_universal import _merge_kwargs_no_duplicates def sample_block_indices_uniformly(n: int, total_num_blocks: int) -> list[int]: """ Sample N block indices uniformly from the total number of blocks. """ return [ int(total_num_blocks / n * block_depth) - 1 for block_depth in range(1, n + 1) ] def validate_output_indices( output_indices: list[int], model_num_blocks: int, depth: int ): """ Validate the output indices are within the valid range of the model and the length of the output indices is equal to the depth of the encoder. """ for output_index in output_indices: if output_index < -model_num_blocks or output_index >= model_num_blocks: raise ValueError( f"Output indices for feature extraction should be in range " f"[-{model_num_blocks}, {model_num_blocks}), because the model has {model_num_blocks} blocks, " f"got index = {output_index}." ) def preprocess_output_indices( output_indices: Optional[list[int]], model_num_blocks: int, depth: int ) -> list[int]: """ Preprocess the output indices for the encoder. """ # Refine encoder output indices if output_indices is None: output_indices = sample_block_indices_uniformly(depth, model_num_blocks) elif not isinstance(output_indices, (list, tuple)): raise ValueError( f"`output_indices` for encoder should be a list/tuple/None, got {type(output_indices)}" ) validate_output_indices(output_indices, model_num_blocks, depth) return output_indices class TimmViTEncoder(nn.Module): """ A universal encoder leveraging the `timm` library for feature extraction from ViT style models Features: - Supports configurable depth. - Ensures consistent multi-level feature extraction across all ViT models. """ # prefix tokens are not supported for scripting _is_torch_scriptable = False _is_torch_exportable = True _is_torch_compilable = True def __init__( self, name: str, pretrained: bool = True, in_channels: int = 3, depth: int = 4, output_indices: Optional[list[int]] = None, **kwargs: dict[str, Any], ): """ Initialize the encoder. Args: name (str): ViT model name to load from `timm`. pretrained (bool): Load pretrained weights (default: True). in_channels (int): Number of input channels (default: 3 for RGB). depth (int): Number of feature stages to extract (default: 4). output_indices (Optional[list[int] | int]): Indices of blocks in the model to be used for feature extraction. **kwargs: Additional arguments passed to `timm.create_model`. """ super().__init__() if depth < 1: raise ValueError(f"`encoder_depth` should be greater than 1, got {depth}.") # Output stride validation needed for smp encoder test consistency output_stride = kwargs.pop("output_stride", None) if output_stride is not None: raise ValueError("Dilated mode not supported, set output stride to None") if isinstance(output_indices, (list, tuple)) and len(output_indices) != depth: raise ValueError( f"Length of output indices for feature extraction should be equal to the depth of the encoder " f"architecture, got output indices length - {len(output_indices)}, encoder depth - {depth}" ) self.name = name # Load a timm model encoder_kwargs = dict(in_chans=in_channels, pretrained=pretrained) encoder_kwargs = _merge_kwargs_no_duplicates(encoder_kwargs, kwargs) self.model = timm.create_model(name, **encoder_kwargs) if not hasattr(self.model, "forward_intermediates"): raise ValueError( f"Encoder `{name}` does not support `forward_intermediates` for feature extraction. " f"Please update `timm` or use another encoder." ) # Get all the necessary information about the model feature_info = self.model.feature_info # Additional checks model_num_blocks = len(feature_info) if depth > model_num_blocks: raise ValueError( f"Depth of the encoder cannot exceed the number of blocks in the model " f"got {depth} depth, model has {model_num_blocks} blocks" ) # Preprocess the output indices, uniformly sample from model_num_blocks if None output_indices = preprocess_output_indices( output_indices, model_num_blocks, depth ) # Private attributes for model forward self._num_prefix_tokens = getattr(self.model, "num_prefix_tokens", 0) self._has_cls_token = getattr(self.model, "has_cls_token", False) self._output_indices = output_indices # Public attributes self.output_strides = [feature_info[i]["reduction"] for i in output_indices] self.output_stride = self.output_strides[-1] self.out_channels = [feature_info[i]["num_chs"] for i in output_indices] self.has_prefix_tokens = self._num_prefix_tokens > 0 self.input_size = self.model.pretrained_cfg.get("input_size", None) self.is_fixed_input_size = self.model.pretrained_cfg.get( "fixed_input_size", False ) def _forward_with_prefix_tokens( self, x: torch.Tensor ) -> tuple[list[torch.Tensor], list[torch.Tensor]]: intermediate_outputs = self.model.forward_intermediates( x, indices=self._output_indices, intermediates_only=True, return_prefix_tokens=True, ) features = [output[0] for output in intermediate_outputs] prefix_tokens = [output[1] for output in intermediate_outputs] return features, prefix_tokens def _forward_without_prefix_tokens(self, x: torch.Tensor) -> list[torch.Tensor]: features = self.model.forward_intermediates( x, indices=self._output_indices, intermediates_only=True, ) return features def forward( self, x: torch.Tensor ) -> tuple[list[torch.Tensor], list[Optional[torch.Tensor]]]: """ Forward pass to extract multi-stage features. Args: x (torch.Tensor): Input tensor of shape (B, C, H, W). Returns: tuple[list[torch.Tensor], list[torch.Tensor]]: Tuple of feature maps and cls tokens (if supported) at different scales. """ if self.has_prefix_tokens: features, prefix_tokens = self._forward_with_prefix_tokens(x) else: features = self._forward_without_prefix_tokens(x) prefix_tokens = [None] * len(features) return features, prefix_tokens ================================================ FILE: segmentation_models_pytorch/encoders/vgg.py ================================================ """Each encoder should have following attributes and methods and be inherited from `_base.EncoderMixin` Attributes: _out_channels (list of int): specify number of channels for each encoder feature tensor _depth (int): specify number of stages in decoder (in other words number of downsampling operations) _in_channels (int): default number of input channels in first Conv2d layer for encoder (usually 3) Methods: forward(self, x: torch.Tensor) produce list of features of different spatial resolutions, each feature is a 4D torch.tensor of shape NCHW (features should be sorted in descending order according to spatial resolution, starting with resolution same as input `x` tensor). Input: `x` with shape (1, 3, 64, 64) Output: [f0, f1, f2, f3, f4, f5] - features with corresponding shapes [(1, 3, 64, 64), (1, 64, 32, 32), (1, 128, 16, 16), (1, 256, 8, 8), (1, 512, 4, 4), (1, 1024, 2, 2)] (C - dim may differ) also should support number of features according to specified depth, e.g. if depth = 5, number of feature tensors = 6 (one with same resolution as input and 5 downsampled), depth = 3 -> number of feature tensors = 4 (one with same resolution as input and 3 downsampled). """ import torch import torch.nn as nn from torchvision.models.vgg import VGG from torchvision.models.vgg import make_layers from typing import List, Union from ._base import EncoderMixin # fmt: off cfg = { "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], "D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"], "E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"], } # fmt: on class VGGEncoder(VGG, EncoderMixin): def __init__( self, out_channels: List[int], config: List[Union[int, str]], batch_norm: bool = False, depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(make_layers(config, batch_norm=batch_norm), **kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride self._out_indexes = [ i - 1 for i, module in enumerate(self.features) if isinstance(module, nn.MaxPool2d) ] self._out_indexes.append(len(self.features) - 1) del self.classifier def make_dilated(self, *args, **kwargs): raise ValueError( "'VGG' models do not support dilated mode due to Max Pooling" " operations for downsampling!" ) def forward(self, x: torch.Tensor) -> List[torch.Tensor]: features = [] depth = 0 for i, module in enumerate(self.features): x = module(x) if i in self._out_indexes: features.append(x) depth += 1 # torchscript does not support break in cycle, so we just # go over all modules and then slice number of features if not torch.jit.is_scripting() and depth > self._depth: break features = features[: self._depth + 1] return features def load_state_dict(self, state_dict, **kwargs): keys = list(state_dict.keys()) for k in keys: if k.startswith("classifier"): state_dict.pop(k, None) super().load_state_dict(state_dict, **kwargs) pretrained_settings = { "vgg11": { "imagenet": { "url": "https://download.pytorch.org/models/vgg11-bbd30ac9.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg11_bn": { "imagenet": { "url": "https://download.pytorch.org/models/vgg11_bn-6002323d.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg13": { "imagenet": { "url": "https://download.pytorch.org/models/vgg13-c768596a.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg13_bn": { "imagenet": { "url": "https://download.pytorch.org/models/vgg13_bn-abd245e5.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg16": { "imagenet": { "url": "https://download.pytorch.org/models/vgg16-397923af.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg16_bn": { "imagenet": { "url": "https://download.pytorch.org/models/vgg16_bn-6c64b313.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg19": { "imagenet": { "url": "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, "vgg19_bn": { "imagenet": { "url": "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth", "input_space": "RGB", "input_size": [3, 224, 224], "input_range": [0, 1], "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "num_classes": 1000, } }, } vgg_encoders = { "vgg11": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg11.imagenet", "revision": "ad8b90e1051c38fdbf399cf5016886a1be357390", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["A"], "batch_norm": False, }, }, "vgg11_bn": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg11_bn.imagenet", "revision": "59757f9215032c9f092977092d57d26a9df7fd9c", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["A"], "batch_norm": True, }, }, "vgg13": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg13.imagenet", "revision": "1b70ff2580f101a8007a48b51e2b5d1e5925dc42", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["B"], "batch_norm": False, }, }, "vgg13_bn": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg13_bn.imagenet", "revision": "9be454515193af6612261b7614fe90607e27b143", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["B"], "batch_norm": True, }, }, "vgg16": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg16.imagenet", "revision": "49d74b799006ee252b86e25acd6f1fd8ac9a99c1", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["D"], "batch_norm": False, }, }, "vgg16_bn": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg16_bn.imagenet", "revision": "2c186d02fb519e93219a99a1c2af6295aef0bf0d", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["D"], "batch_norm": True, }, }, "vgg19": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg19.imagenet", "revision": "2853d00d7bca364dbb98be4d6afa347e5aeec1f6", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["E"], "batch_norm": False, }, }, "vgg19_bn": { "encoder": VGGEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/vgg19_bn.imagenet", "revision": "f09a924cb0d201ea6f61601df9559141382271d7", }, }, "params": { "out_channels": [64, 128, 256, 512, 512, 512], "config": cfg["E"], "batch_norm": True, }, }, } ================================================ FILE: segmentation_models_pytorch/encoders/xception.py ================================================ from typing import List from ._base import EncoderMixin from ._xception import Xception class XceptionEncoder(Xception, EncoderMixin): def __init__( self, out_channels: List[int], *args, depth: int = 5, output_stride: int = 32, **kwargs, ): if depth > 5 or depth < 1: raise ValueError( f"{self.__class__.__name__} depth should be in range [1, 5], got {depth}" ) super().__init__(*args, **kwargs) self._depth = depth self._in_channels = 3 self._out_channels = out_channels self._output_stride = output_stride # modify padding to maintain output shape self.conv1.padding = (1, 1) self.conv2.padding = (1, 1) del self.fc def make_dilated(self, *args, **kwargs): raise ValueError( "Xception encoder does not support dilated mode " "due to pooling operation for downsampling!" ) def forward(self, x): features = [x] if self._depth >= 1: x = self.conv1(x) x = self.bn1(x) x = self.relu1(x) x = self.conv2(x) x = self.bn2(x) x = self.relu2(x) features.append(x) if self._depth >= 2: x = self.block1(x) features.append(x) if self._depth >= 3: x = self.block2(x) features.append(x) if self._depth >= 4: x = self.block3(x) x = self.block4(x) x = self.block5(x) x = self.block6(x) x = self.block7(x) x = self.block8(x) x = self.block9(x) x = self.block10(x) x = self.block11(x) features.append(x) if self._depth >= 5: x = self.block12(x) x = self.conv3(x) x = self.bn3(x) x = self.relu3(x) x = self.conv4(x) x = self.bn4(x) features.append(x) return features def load_state_dict(self, state_dict): # remove linear state_dict.pop("fc.bias", None) state_dict.pop("fc.weight", None) super().load_state_dict(state_dict) xception_encoders = { "xception": { "encoder": XceptionEncoder, "pretrained_settings": { "imagenet": { "repo_id": "smp-hub/xception.imagenet", "revision": "01cfaf27c11353b1f0c578e7e26d2c000ea91049", }, }, "params": {"out_channels": [3, 64, 128, 256, 728, 2048]}, } } ================================================ FILE: segmentation_models_pytorch/losses/__init__.py ================================================ from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE from .jaccard import JaccardLoss from .dice import DiceLoss from .focal import FocalLoss from .lovasz import LovaszLoss from .soft_bce import SoftBCEWithLogitsLoss from .soft_ce import SoftCrossEntropyLoss from .tversky import TverskyLoss from .mcc import MCCLoss __all__ = [ "BINARY_MODE", "MULTICLASS_MODE", "MULTILABEL_MODE", "JaccardLoss", "DiceLoss", "FocalLoss", "LovaszLoss", "SoftBCEWithLogitsLoss", "SoftCrossEntropyLoss", "TverskyLoss", "MCCLoss", ] ================================================ FILE: segmentation_models_pytorch/losses/_functional.py ================================================ import math import numpy as np from typing import Optional import torch import torch.linalg as LA import torch.nn.functional as F __all__ = [ "focal_loss_with_logits", "softmax_focal_loss_with_logits", "soft_jaccard_score", "soft_dice_score", "wing_loss", ] def to_tensor(x, dtype=None) -> torch.Tensor: if isinstance(x, torch.Tensor): if dtype is not None: x = x.type(dtype) return x if isinstance(x, np.ndarray): x = torch.from_numpy(x) if dtype is not None: x = x.type(dtype) return x if isinstance(x, (list, tuple)): x = np.array(x) x = torch.from_numpy(x) if dtype is not None: x = x.type(dtype) return x def focal_loss_with_logits( output: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, alpha: Optional[float] = 0.25, reduction: str = "mean", normalized: bool = False, reduced_threshold: Optional[float] = None, eps: float = 1e-6, ) -> torch.Tensor: """Compute binary focal loss between target and output logits. See :class:`~pytorch_toolbelt.losses.FocalLoss` for details. Args: output: Tensor of arbitrary shape (predictions of the model) target: Tensor of the same shape as input gamma: Focal loss power factor alpha: Weight factor to balance positive and negative samples. Alpha must be in [0...1] range, high values will give more weight to positive class. reduction (string, optional): Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum' | 'batchwise_mean'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: :attr:`size_average` and :attr:`reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override :attr:`reduction`. 'batchwise_mean' computes mean loss per sample in batch. Default: 'mean' normalized (bool): Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf). reduced_threshold (float, optional): Compute reduced focal loss (https://arxiv.org/abs/1903.01347). References: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/loss/losses.py """ target = target.to(dtype=output.dtype, device=output.device) logpt = F.binary_cross_entropy_with_logits(output, target, reduction="none") pt = torch.exp(-logpt) # compute the loss if reduced_threshold is None: focal_term = (1.0 - pt).pow(gamma) else: focal_term = ((1.0 - pt) / reduced_threshold).pow(gamma) focal_term[pt < reduced_threshold] = 1 loss = focal_term * logpt if alpha is not None: loss *= alpha * target + (1 - alpha) * (1 - target) if normalized: norm_factor = focal_term.sum().clamp_min(eps) loss /= norm_factor if reduction == "mean": loss = loss.mean() if reduction == "sum": loss = loss.sum() if reduction == "batchwise_mean": loss = loss.sum(0) return loss def softmax_focal_loss_with_logits( output: torch.Tensor, target: torch.Tensor, gamma: float = 2.0, reduction="mean", normalized=False, reduced_threshold: Optional[float] = None, eps: float = 1e-6, ) -> torch.Tensor: """Softmax version of focal loss between target and output logits. See :class:`~pytorch_toolbelt.losses.FocalLoss` for details. Args: output: Tensor of shape [B, C, *] (Similar to nn.CrossEntropyLoss) target: Tensor of shape [B, *] (Similar to nn.CrossEntropyLoss) reduction (string, optional): Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum' | 'batchwise_mean'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: :attr:`size_average` and :attr:`reduce` are in the process of being deprecated, and in the meantime, specifying either of those two args will override :attr:`reduction`. 'batchwise_mean' computes mean loss per sample in batch. Default: 'mean' normalized (bool): Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf). reduced_threshold (float, optional): Compute reduced focal loss (https://arxiv.org/abs/1903.01347). """ log_softmax = F.log_softmax(output, dim=1) loss = F.nll_loss(log_softmax, target, reduction="none") pt = torch.exp(-loss) # compute the loss if reduced_threshold is None: focal_term = (1.0 - pt).pow(gamma) else: focal_term = ((1.0 - pt) / reduced_threshold).pow(gamma) focal_term[pt < reduced_threshold] = 1 loss = focal_term * loss if normalized: norm_factor = focal_term.sum().clamp_min(eps) loss = loss / norm_factor if reduction == "mean": loss = loss.mean() if reduction == "sum": loss = loss.sum() if reduction == "batchwise_mean": loss = loss.sum(0) return loss def soft_jaccard_score( output: torch.Tensor, target: torch.Tensor, smooth: float = 0.0, eps: float = 1e-7, dims=None, ) -> torch.Tensor: assert output.size() == target.size() jaccard_score = soft_tversky_score(output, target, 1.0, 1.0, smooth, eps, dims) return jaccard_score def soft_dice_score( output: torch.Tensor, target: torch.Tensor, smooth: float = 0.0, eps: float = 1e-7, dims=None, ) -> torch.Tensor: assert output.size() == target.size() dice_score = soft_tversky_score(output, target, 0.5, 0.5, smooth, eps, dims) return dice_score def soft_tversky_score( output: torch.Tensor, target: torch.Tensor, alpha: float, beta: float, smooth: float = 0.0, eps: float = 1e-7, dims=None, ) -> torch.Tensor: """Tversky loss References: https://arxiv.org/pdf/2302.05666 https://arxiv.org/pdf/2303.16296 """ assert output.size() == target.size() if dims is not None: output_sum = torch.sum(output, dim=dims) target_sum = torch.sum(target, dim=dims) difference = LA.vector_norm(output - target, ord=1, dim=dims) else: output_sum = torch.sum(output) target_sum = torch.sum(target) difference = LA.vector_norm(output - target, ord=1) intersection = (output_sum + target_sum - difference) / 2 # TP fp = output_sum - intersection fn = target_sum - intersection tversky_score = (intersection + smooth) / ( intersection + alpha * fp + beta * fn + smooth ).clamp_min(eps) return tversky_score def wing_loss( output: torch.Tensor, target: torch.Tensor, width=5, curvature=0.5, reduction="mean" ): """Wing loss References: https://arxiv.org/pdf/1711.06753.pdf """ diff_abs = (target - output).abs() loss = diff_abs.clone() idx_smaller = diff_abs < width idx_bigger = diff_abs >= width loss[idx_smaller] = width * torch.log1p(diff_abs[idx_smaller] / curvature) C = width - width * math.log(1 + width / curvature) loss[idx_bigger] = loss[idx_bigger] - C if reduction == "sum": loss = loss.sum() if reduction == "mean": loss = loss.mean() return loss def label_smoothed_nll_loss( lprobs: torch.Tensor, target: torch.Tensor, epsilon: float, ignore_index=None, reduction="mean", dim=-1, ) -> torch.Tensor: """NLL loss with label smoothing References: https://github.com/pytorch/fairseq/blob/master/fairseq/criterions/label_smoothed_cross_entropy.py Args: lprobs (torch.Tensor): Log-probabilities of predictions (e.g after log_softmax) """ if target.dim() == lprobs.dim() - 1: target = target.unsqueeze(dim) if ignore_index is not None: pad_mask = target.eq(ignore_index) target = target.masked_fill(pad_mask, 0) nll_loss = -lprobs.gather(dim=dim, index=target) smooth_loss = -lprobs.sum(dim=dim, keepdim=True) # nll_loss.masked_fill_(pad_mask, 0.0) # smooth_loss.masked_fill_(pad_mask, 0.0) nll_loss = nll_loss.masked_fill(pad_mask, 0.0) smooth_loss = smooth_loss.masked_fill(pad_mask, 0.0) else: nll_loss = -lprobs.gather(dim=dim, index=target) smooth_loss = -lprobs.sum(dim=dim, keepdim=True) nll_loss = nll_loss.squeeze(dim) smooth_loss = smooth_loss.squeeze(dim) if reduction == "sum": nll_loss = nll_loss.sum() smooth_loss = smooth_loss.sum() if reduction == "mean": nll_loss = nll_loss.mean() smooth_loss = smooth_loss.mean() eps_i = epsilon / lprobs.size(dim) loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss return loss ================================================ FILE: segmentation_models_pytorch/losses/constants.py ================================================ #: Loss binary mode suppose you are solving binary segmentation task. #: That mean yor have only one class which pixels are labled as **1**, #: the rest pixels are background and labeled as **0**. #: Target mask shape - (N, H, W), model output mask shape (N, 1, H, W). BINARY_MODE: str = "binary" #: Loss multiclass mode suppose you are solving multi-**class** segmentation task. #: That mean you have *C = 1..N* classes which have unique label values, #: classes are mutually exclusive and all pixels are labeled with theese values. #: Target mask shape - (N, H, W), model output mask shape (N, C, H, W). MULTICLASS_MODE: str = "multiclass" #: Loss multilabel mode suppose you are solving multi-**label** segmentation task. #: That mean you have *C = 1..N* classes which pixels are labeled as **1**, #: classes are not mutually exclusive and each class have its own *channel*, #: pixels in each channel which are not belong to class labeled as **0**. #: Target mask shape - (N, C, H, W), model output mask shape (N, C, H, W). MULTILABEL_MODE: str = "multilabel" ================================================ FILE: segmentation_models_pytorch/losses/dice.py ================================================ from typing import Optional, List import torch import torch.nn.functional as F from torch.nn.modules.loss import _Loss from ._functional import soft_dice_score, to_tensor from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE __all__ = ["DiceLoss"] class DiceLoss(_Loss): def __init__( self, mode: str, classes: Optional[List[int]] = None, log_loss: bool = False, from_logits: bool = True, smooth: float = 0.0, ignore_index: Optional[int] = None, eps: float = 1e-7, class_weights: Optional[List[float]] = None, ): """Dice loss for image segmentation task. It supports binary, multiclass and multilabel cases Args: mode: Loss mode 'binary', 'multiclass' or 'multilabel' classes: List of classes that contribute in loss computation. By default, all channels are included. log_loss: If True, loss computed as `- log(dice_coeff)`, otherwise `1 - dice_coeff` from_logits: If True, assumes input is raw logits smooth: Smoothness constant for dice coefficient (a) ignore_index: Label that indicates ignored pixels (does not contribute to loss) eps: A small epsilon for numerical stability to avoid zero division error (denominator will be always greater or equal to eps) class_weights: List of weights for each class. If not ``None``, the loss for each class is multiplied by the corresponding weight. Only supported for multiclass and multilabel modes. Weights do not need to be normalized. Shape - **y_pred** - torch.Tensor of shape (N, C, H, W) - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) Reference https://github.com/BloodAxe/pytorch-toolbelt """ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super(DiceLoss, self).__init__() self.mode = mode if class_weights is not None and mode == BINARY_MODE: raise ValueError("class_weights are not supported with mode=binary") if classes is not None: assert mode != BINARY_MODE, ( "Masking classes is not supported with mode=binary" ) classes = to_tensor(classes, dtype=torch.long) self.classes = classes self.from_logits = from_logits self.smooth = smooth self.eps = eps self.log_loss = log_loss self.ignore_index = ignore_index self.class_weights = ( to_tensor(class_weights, dtype=torch.float) if class_weights is not None else None ) def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: assert y_true.size(0) == y_pred.size(0) if self.from_logits: # Apply activations to get [0..1] class probabilities # Using Log-Exp as this gives more numerically stable result and does not cause vanishing gradient on # extreme values 0 and 1 if self.mode == MULTICLASS_MODE: y_pred = y_pred.log_softmax(dim=1).exp() else: y_pred = F.logsigmoid(y_pred).exp() bs = y_true.size(0) num_classes = y_pred.size(1) dims = (0, 2) if self.mode == BINARY_MODE: y_true = y_true.reshape(bs, 1, -1) y_pred = y_pred.reshape(bs, 1, -1) if self.ignore_index is not None: mask = y_true != self.ignore_index y_pred = y_pred * mask y_true = y_true * mask if self.mode == MULTICLASS_MODE: y_true = y_true.reshape(bs, -1) y_pred = y_pred.reshape(bs, num_classes, -1) if self.ignore_index is not None: mask = y_true != self.ignore_index y_pred = y_pred * mask.unsqueeze(1) y_true = F.one_hot( (y_true * mask).to(torch.long), num_classes ) # N,H*W -> N,H*W, C y_true = y_true.permute(0, 2, 1) * mask.unsqueeze(1) # N, C, H*W else: y_true = F.one_hot(y_true, num_classes) # N,H*W -> N,H*W, C y_true = y_true.permute(0, 2, 1) # N, C, H*W if self.mode == MULTILABEL_MODE: y_true = y_true.reshape(bs, num_classes, -1) y_pred = y_pred.reshape(bs, num_classes, -1) if self.ignore_index is not None: mask = y_true != self.ignore_index y_pred = y_pred * mask y_true = y_true * mask scores = self.compute_score( y_pred, y_true.type_as(y_pred), smooth=self.smooth, eps=self.eps, dims=dims ) if self.log_loss: loss = -torch.log(scores.clamp_min(self.eps)) else: loss = 1.0 - scores # Dice loss is undefined for empty images with no classes # So we set the contribution of any channel without true pixels to zero # NOTE: A better workaround would be to use loss term `mean(y_pred)` # for this case, however it will be a modified jaccard loss mask = y_true.sum(dims) > 0 loss *= mask.to(loss.dtype) if self.classes is not None: loss = loss[self.classes] return self.aggregate_loss(loss) def aggregate_loss(self, loss: torch.Tensor) -> torch.Tensor: """Aggregate per-class losses into a single scalar. Args: loss: Per-class loss tensor of shape (C,) Returns: Scalar loss value """ if self.class_weights is not None: weights = self.class_weights.to(loss.device) # If classes filter is applied, slice weights accordingly if self.classes is not None: weights = weights[self.classes] return (loss * weights).sum() / weights.sum() return loss.mean() def compute_score( self, output, target, smooth=0.0, eps=1e-7, dims=None ) -> torch.Tensor: return soft_dice_score(output, target, smooth, eps, dims) ================================================ FILE: segmentation_models_pytorch/losses/focal.py ================================================ from typing import Optional, List from functools import partial import torch from torch.nn.modules.loss import _Loss from ._functional import focal_loss_with_logits, to_tensor from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE __all__ = ["FocalLoss"] class FocalLoss(_Loss): def __init__( self, mode: str, alpha: Optional[float] = None, gamma: Optional[float] = 2.0, ignore_index: Optional[int] = None, from_logits: bool = True, eps: float = 1e-7, reduction: Optional[str] = "mean", normalized: bool = False, reduced_threshold: Optional[float] = None, class_weights: Optional[List[float]] = None, ): """Compute Focal loss Args: mode: Loss mode 'binary', 'multiclass' or 'multilabel' from_logits: If True, assumes input is raw logits eps: Small value used for numerical stability when converting probabilities to logits . alpha: Prior probability of having positive value in target. gamma: Power factor for dampening weight (focal strength). ignore_index: If not None, targets may contain values to be ignored. Target values equal to ignore_index will be ignored from loss computation. normalized: Compute normalized focal loss (https://arxiv.org/pdf/1909.07829.pdf). reduced_threshold: Switch to reduced focal loss. Note, when using this mode you should use `reduction="sum"`. class_weights: List of weights for each class. If not ``None``, the loss for each class is multiplied by the corresponding weight. Only supported for multiclass and multilabel modes. Weights do not need to be normalized. Shape - **y_pred** - torch.Tensor of shape (N, C, H, W) - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) Reference https://github.com/BloodAxe/pytorch-toolbelt """ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super().__init__() if class_weights is not None and mode == BINARY_MODE: raise ValueError("class_weights are not supported with mode=binary") self.mode = mode self.from_logits = from_logits self.ignore_index = ignore_index self.reduction = reduction self.eps = eps self.focal_loss_fn = partial( focal_loss_with_logits, alpha=alpha, gamma=gamma, reduced_threshold=reduced_threshold, reduction=reduction, normalized=normalized, ) self.class_weights = ( to_tensor(class_weights, dtype=torch.float) if class_weights is not None else None ) def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: if not self.from_logits: y_pred = torch.clamp(y_pred, self.eps, 1 - self.eps) if self.mode in {BINARY_MODE, MULTILABEL_MODE}: # inverse sigmoid y_pred = torch.log(y_pred / (1 - y_pred)) elif self.mode == MULTICLASS_MODE: # convert softmax probabilities to log-space y_pred = torch.log(y_pred) if self.mode == BINARY_MODE: y_true = y_true.reshape(-1) y_pred = y_pred.reshape(-1) if self.ignore_index is not None: # Filter predictions with ignore label from loss computation not_ignored = y_true != self.ignore_index y_pred = y_pred[not_ignored] y_true = y_true[not_ignored] loss = self.focal_loss_fn(y_pred, y_true) elif self.mode in {MULTILABEL_MODE, MULTICLASS_MODE}: num_classes = y_pred.size(1) # Filter anchors with -1 label from loss computation if self.ignore_index is not None: not_ignored = y_true != self.ignore_index class_losses = [] for cls in range(num_classes): if self.mode == MULTICLASS_MODE: cls_y_true = (y_true == cls).long() else: cls_y_true = y_true[:, cls, ...] cls_y_pred = y_pred[:, cls, ...] if self.ignore_index is not None: cls_y_true = cls_y_true[not_ignored] cls_y_pred = cls_y_pred[not_ignored] class_losses.append(self.focal_loss_fn(cls_y_pred, cls_y_true)) class_losses = torch.stack(class_losses) # shape (C,) if self.class_weights is not None: weights = self.class_weights.to(class_losses.device) loss = (class_losses * weights).sum() / weights.sum() else: loss = class_losses.mean() return loss ================================================ FILE: segmentation_models_pytorch/losses/jaccard.py ================================================ from typing import Optional, List import torch import torch.nn.functional as F from torch.nn.modules.loss import _Loss from ._functional import soft_jaccard_score, to_tensor from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE __all__ = ["JaccardLoss"] class JaccardLoss(_Loss): def __init__( self, mode: str, classes: Optional[List[int]] = None, log_loss: bool = False, from_logits: bool = True, smooth: float = 0.0, ignore_index: Optional[int] = None, eps: float = 1e-7, class_weights: Optional[List[float]] = None, ): """Jaccard loss for image segmentation task. It supports binary, multiclass and multilabel cases Args: mode: Loss mode 'binary', 'multiclass' or 'multilabel' classes: List of classes that contribute in loss computation. By default, all channels are included. log_loss: If True, loss computed as `- log(jaccard_coeff)`, otherwise `1 - jaccard_coeff` from_logits: If True, assumes input is raw logits smooth: Smoothness constant for dice coefficient eps: A small epsilon for numerical stability to avoid zero division error (denominator will be always greater or equal to eps) class_weights: List of weights for each class. If not ``None``, the loss for each class is multiplied by the corresponding weight. Only supported for multiclass and multilabel modes. Weights do not need to be normalized. Shape - **y_pred** - torch.Tensor of shape (N, C, H, W) - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) Reference https://github.com/BloodAxe/pytorch-toolbelt """ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super(JaccardLoss, self).__init__() self.mode = mode if class_weights is not None and mode == BINARY_MODE: raise ValueError("class_weights are not supported with mode=binary") if classes is not None: assert mode != BINARY_MODE, ( "Masking classes is not supported with mode=binary" ) classes = to_tensor(classes, dtype=torch.long) self.classes = classes self.from_logits = from_logits self.smooth = smooth self.ignore_index = ignore_index self.eps = eps self.log_loss = log_loss self.class_weights = ( to_tensor(class_weights, dtype=torch.float) if class_weights is not None else None ) def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: assert y_true.size(0) == y_pred.size(0) if self.from_logits: # Apply activations to get [0..1] class probabilities # Using Log-Exp as this gives more numerically stable result and does not cause vanishing gradient on # extreme values 0 and 1 if self.mode == MULTICLASS_MODE: y_pred = y_pred.log_softmax(dim=1).exp() else: y_pred = F.logsigmoid(y_pred).exp() bs = y_true.size(0) num_classes = y_pred.size(1) dims = (0, 2) if self.mode == BINARY_MODE: y_true = y_true.reshape(bs, 1, -1) y_pred = y_pred.reshape(bs, 1, -1) if self.ignore_index is not None: mask = y_true != self.ignore_index y_pred = y_pred * mask y_true = y_true * mask if self.mode == MULTICLASS_MODE: y_true = y_true.reshape(bs, -1) y_pred = y_pred.reshape(bs, num_classes, -1) if self.ignore_index is not None: mask = y_true != self.ignore_index y_pred = y_pred * mask.unsqueeze(1) y_true = F.one_hot( (y_true * mask).to(torch.long), num_classes ) # N,H*W -> N,H*W, C y_true = y_true.permute(0, 2, 1) * mask.unsqueeze(1) # N, C, H*W else: y_true = F.one_hot(y_true, num_classes) # N,H*W -> N,H*W, C y_true = y_true.permute(0, 2, 1) # N, C, H*W if self.mode == MULTILABEL_MODE: y_true = y_true.reshape(bs, num_classes, -1) y_pred = y_pred.reshape(bs, num_classes, -1) if self.ignore_index is not None: mask = y_true != self.ignore_index y_pred = y_pred * mask y_true = y_true * mask scores = soft_jaccard_score( y_pred, y_true.type(y_pred.dtype), smooth=self.smooth, eps=self.eps, dims=dims, ) if self.log_loss: loss = -torch.log(scores.clamp_min(self.eps)) else: loss = 1.0 - scores # IoU loss is defined for non-empty classes # So we zero contribution of channel that does not have true pixels # NOTE: A better workaround would be to use loss term `mean(y_pred)` # for this case, however it will be a modified jaccard loss mask = y_true.sum(dims) > 0 loss *= mask.float() if self.classes is not None: loss = loss[self.classes] if self.class_weights is not None: weights = self.class_weights.to(loss.device) if self.classes is not None: weights = weights[self.classes] return (loss * weights).sum() / weights.sum() return loss.mean() ================================================ FILE: segmentation_models_pytorch/losses/lovasz.py ================================================ """ Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License) """ from __future__ import print_function, division from typing import Optional import torch import torch.nn.functional as F from torch.nn.modules.loss import _Loss from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE try: from itertools import ifilterfalse except ImportError: # py3k from itertools import filterfalse as ifilterfalse __all__ = ["LovaszLoss"] def _lovasz_grad(gt_sorted): """Compute gradient of the Lovasz extension w.r.t sorted errors See Alg. 1 in paper """ p = len(gt_sorted) gts = gt_sorted.sum() intersection = gts - gt_sorted.float().cumsum(0) union = gts + (1 - gt_sorted).float().cumsum(0) jaccard = 1.0 - intersection / union if p > 1: # cover 1-pixel case jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] return jaccard def _lovasz_hinge(logits, labels, per_image=True, ignore=None): """ Binary Lovasz hinge loss logits: [B, H, W] Logits at each pixel (between -infinity and +infinity) labels: [B, H, W] Tensor, binary ground truth masks (0 or 1) per_image: compute the loss per image instead of per batch ignore: void class id """ if per_image: loss = mean( _lovasz_hinge_flat( *_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore) ) for log, lab in zip(logits, labels) ) else: loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore)) return loss def _lovasz_hinge_flat(logits, labels): """Binary Lovasz hinge loss Args: logits: [P] Logits at each prediction (between -infinity and +infinity) labels: [P] Tensor, binary ground truth labels (0 or 1) ignore: label to ignore """ if len(labels) == 0: # only void pixels, the gradients should be 0 return logits.sum() * 0.0 signs = 2.0 * labels.float() - 1.0 errors = 1.0 - logits * signs errors_sorted, perm = torch.sort(errors, dim=0, descending=True) perm = perm.data gt_sorted = labels[perm] grad = _lovasz_grad(gt_sorted) loss = torch.dot(F.relu(errors_sorted), grad) return loss def _flatten_binary_scores(scores, labels, ignore=None): """Flattens predictions in the batch (binary case) Remove labels equal to 'ignore' """ scores = scores.reshape(-1) labels = labels.reshape(-1) if ignore is None: return scores, labels valid = labels != ignore vscores = scores[valid] vlabels = labels[valid] return vscores, vlabels # --------------------------- MULTICLASS LOSSES --------------------------- def _lovasz_softmax(probas, labels, classes="present", per_image=False, ignore=None): """Multi-class Lovasz-Softmax loss Args: @param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1). Interpreted as binary (sigmoid) output with outputs of size [B, H, W]. @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1) @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. @param per_image: compute the loss per image instead of per batch @param ignore: void class labels """ if per_image: loss = mean( _lovasz_softmax_flat( *_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), classes=classes, ) for prob, lab in zip(probas, labels) ) else: loss = _lovasz_softmax_flat( *_flatten_probas(probas, labels, ignore), classes=classes ) return loss def _lovasz_softmax_flat(probas, labels, classes="present"): """Multi-class Lovasz-Softmax loss Args: @param probas: [P, C] Class probabilities at each prediction (between 0 and 1) @param labels: [P] Tensor, ground truth labels (between 0 and C - 1) @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. """ if probas.numel() == 0: # only void pixels, the gradients should be 0 return probas * 0.0 C = probas.size(1) losses = [] class_to_sum = list(range(C)) if classes in ["all", "present"] else classes for c in class_to_sum: fg = (labels == c).type_as(probas) # foreground for class c if classes == "present" and fg.sum() == 0: continue if C == 1: if len(classes) > 1: raise ValueError("Sigmoid output possible only with 1 class") class_pred = probas[:, 0] else: class_pred = probas[:, c] errors = (fg - class_pred).abs() errors_sorted, perm = torch.sort(errors, 0, descending=True) perm = perm.data fg_sorted = fg[perm] losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted))) return mean(losses) def _flatten_probas(probas, labels, ignore=None): """Flattens predictions in the batch""" if probas.dim() == 3: # assumes output of a sigmoid layer B, H, W = probas.size() probas = probas.reshape(B, 1, H, W) C = probas.size(1) probas = torch.movedim(probas, 1, -1) # [B, C, Di, Dj, ...] -> [B, Di, Dj, ..., C] probas = probas.reshape(-1, C) # [P, C] labels = labels.reshape(-1) if ignore is None: return probas, labels valid = labels != ignore vprobas = probas[valid] vlabels = labels[valid] return vprobas, vlabels # --------------------------- HELPER FUNCTIONS --------------------------- def isnan(x): return x != x def mean(values, ignore_nan=False, empty=0): """Nanmean compatible with generators.""" values = iter(values) if ignore_nan: values = ifilterfalse(isnan, values) try: n = 1 acc = next(values) except StopIteration: if empty == "raise": raise ValueError("Empty mean") return empty for n, v in enumerate(values, 2): acc += v if n == 1: return acc return acc / n class LovaszLoss(_Loss): def __init__( self, mode: str, per_image: bool = False, ignore_index: Optional[int] = None, from_logits: bool = True, ): """Lovasz loss for image segmentation task. It supports binary, multiclass and multilabel cases Args: mode: Loss mode 'binary', 'multiclass' or 'multilabel' ignore_index: Label that indicates ignored pixels (does not contribute to loss) per_image: If True loss computed per each image and then averaged, else computed per whole batch Shape - **y_pred** - torch.Tensor of shape (N, C, H, W) - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) Reference https://github.com/BloodAxe/pytorch-toolbelt """ assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super().__init__() self.mode = mode self.ignore_index = ignore_index self.per_image = per_image def forward(self, y_pred, y_true): if self.mode in {BINARY_MODE, MULTILABEL_MODE}: loss = _lovasz_hinge( y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index ) elif self.mode == MULTICLASS_MODE: y_pred = y_pred.softmax(dim=1) loss = _lovasz_softmax( y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index ) else: raise ValueError("Wrong mode {}.".format(self.mode)) return loss ================================================ FILE: segmentation_models_pytorch/losses/mcc.py ================================================ import torch from torch.nn.modules.loss import _Loss class MCCLoss(_Loss): def __init__(self, eps: float = 1e-5): """Compute Matthews Correlation Coefficient Loss for image segmentation task. It only supports binary mode. Args: eps (float): Small epsilon to handle situations where all the samples in the dataset belong to one class Reference: https://github.com/kakumarabhishek/MCC-Loss """ super().__init__() self.eps = eps def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: """Compute MCC loss Args: y_pred (torch.Tensor): model prediction of shape (N, H, W) or (N, 1, H, W) y_true (torch.Tensor): ground truth labels of shape (N, H, W) or (N, 1, H, W) Returns: torch.Tensor: loss value (1 - mcc) """ bs = y_true.shape[0] y_true = y_true.reshape(bs, 1, -1) y_pred = y_pred.reshape(bs, 1, -1) tp = torch.sum(torch.mul(y_pred, y_true)) + self.eps tn = torch.sum(torch.mul((1 - y_pred), (1 - y_true))) + self.eps fp = torch.sum(torch.mul(y_pred, (1 - y_true))) + self.eps fn = torch.sum(torch.mul((1 - y_pred), y_true)) + self.eps numerator = torch.mul(tp, tn) - torch.mul(fp, fn) denominator = torch.sqrt( torch.add(tp, fp) * torch.add(tp, fn) * torch.add(tn, fp) * torch.add(tn, fn) ) mcc = torch.div(numerator.sum(), denominator.sum()) loss = 1.0 - mcc return loss ================================================ FILE: segmentation_models_pytorch/losses/soft_bce.py ================================================ from typing import Optional import torch import torch.nn.functional as F from torch import nn __all__ = ["SoftBCEWithLogitsLoss"] class SoftBCEWithLogitsLoss(nn.Module): __constants__ = [ "weight", "pos_weight", "reduction", "ignore_index", "smooth_factor", ] def __init__( self, weight: Optional[torch.Tensor] = None, ignore_index: Optional[int] = -100, reduction: str = "mean", smooth_factor: Optional[float] = None, pos_weight: Optional[torch.Tensor] = None, ): """Drop-in replacement for torch.nn.BCEWithLogitsLoss with few additions: ignore_index and label_smoothing Args: ignore_index: Specifies a target value that is ignored and does not contribute to the input gradient. smooth_factor: Factor to smooth target (e.g. if smooth_factor=0.1 then [1, 0, 1] -> [0.9, 0.1, 0.9]) Shape - **y_pred** - torch.Tensor of shape NxCxHxW - **y_true** - torch.Tensor of shape NxHxW or Nx1xHxW Reference https://github.com/BloodAxe/pytorch-toolbelt """ super().__init__() self.ignore_index = ignore_index self.reduction = reduction self.smooth_factor = smooth_factor self.register_buffer("weight", weight) self.register_buffer("pos_weight", pos_weight) def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: """ Args: y_pred: torch.Tensor of shape (N, C, H, W) y_true: torch.Tensor of shape (N, H, W) or (N, 1, H, W) Returns: loss: torch.Tensor """ if self.smooth_factor is not None: soft_targets = (1 - y_true) * self.smooth_factor + y_true * ( 1 - self.smooth_factor ) else: soft_targets = y_true loss = F.binary_cross_entropy_with_logits( y_pred, soft_targets, self.weight, pos_weight=self.pos_weight, reduction="none", ) if self.ignore_index is not None: not_ignored_mask = y_true != self.ignore_index loss *= not_ignored_mask.type_as(loss) if self.reduction == "mean": loss = loss.mean() if self.reduction == "sum": loss = loss.sum() return loss ================================================ FILE: segmentation_models_pytorch/losses/soft_ce.py ================================================ from typing import Optional from torch import nn import torch import torch.nn.functional as F from ._functional import label_smoothed_nll_loss __all__ = ["SoftCrossEntropyLoss"] class SoftCrossEntropyLoss(nn.Module): __constants__ = ["reduction", "ignore_index", "smooth_factor"] def __init__( self, reduction: str = "mean", smooth_factor: Optional[float] = None, ignore_index: Optional[int] = -100, dim: int = 1, ): """Drop-in replacement for torch.nn.CrossEntropyLoss with label_smoothing Args: smooth_factor: Factor to smooth target (e.g. if smooth_factor=0.1 then [1, 0, 0] -> [0.9, 0.05, 0.05]) Shape - **y_pred** - torch.Tensor of shape (N, C, H, W) - **y_true** - torch.Tensor of shape (N, H, W) Reference https://github.com/BloodAxe/pytorch-toolbelt """ super().__init__() self.smooth_factor = smooth_factor self.ignore_index = ignore_index self.reduction = reduction self.dim = dim def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: log_prob = F.log_softmax(y_pred, dim=self.dim) return label_smoothed_nll_loss( log_prob, y_true, epsilon=self.smooth_factor, ignore_index=self.ignore_index, reduction=self.reduction, dim=self.dim, ) ================================================ FILE: segmentation_models_pytorch/losses/tversky.py ================================================ from typing import List, Optional import torch from ._functional import soft_tversky_score from .constants import BINARY_MODE, MULTICLASS_MODE, MULTILABEL_MODE from .dice import DiceLoss __all__ = ["TverskyLoss"] class TverskyLoss(DiceLoss): """Tversky loss for image segmentation task. Where FP and FN is weighted by alpha and beta params. With alpha == beta == 0.5, this loss becomes equal DiceLoss. It supports binary, multiclass and multilabel cases Args: mode: Metric mode {'binary', 'multiclass', 'multilabel'} classes: Optional list of classes that contribute in loss computation; By default, all channels are included. log_loss: If True, loss computed as ``-log(tversky)`` otherwise ``1 - tversky`` from_logits: If True assumes input is raw logits smooth: ignore_index: Label that indicates ignored pixels (does not contribute to loss) eps: Small epsilon for numerical stability alpha: Weight constant that penalize model for FPs (False Positives) beta: Weight constant that penalize model for FNs (False Negatives) gamma: Constant that squares the error function. Defaults to ``1.0`` class_weights: List of weights for each class. If not ``None``, the loss for each class is multiplied by the corresponding weight. Only supported for multiclass and multilabel modes. Weights do not need to be normalized. Return: torch.Tensor: loss """ def __init__( self, mode: str, classes: Optional[List[int]] = None, log_loss: bool = False, from_logits: bool = True, smooth: float = 0.0, ignore_index: Optional[int] = None, eps: float = 1e-7, alpha: float = 0.5, beta: float = 0.5, gamma: float = 1.0, class_weights: Optional[List[float]] = None, ): assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} super().__init__( mode, classes, log_loss, from_logits, smooth, ignore_index, eps, class_weights, ) self.alpha = alpha self.beta = beta self.gamma = gamma def aggregate_loss(self, loss: torch.Tensor) -> torch.Tensor: """Aggregate per-class losses into a single scalar, raised to the power of gamma. Args: loss: Per-class loss tensor of shape (C,) Returns: Scalar loss value """ if self.class_weights is not None: weights = self.class_weights.to(loss.device) if self.classes is not None: weights = weights[self.classes] return ((loss * weights).sum() / weights.sum()) ** self.gamma return loss.mean() ** self.gamma def compute_score( self, output, target, smooth=0.0, eps=1e-7, dims=None ) -> torch.Tensor: return soft_tversky_score( output, target, self.alpha, self.beta, smooth, eps, dims ) ================================================ FILE: segmentation_models_pytorch/metrics/__init__.py ================================================ # fmt: off from .functional import ( get_stats, fbeta_score, f1_score, iou_score, accuracy, precision, recall, sensitivity, specificity, balanced_accuracy, positive_predictive_value, negative_predictive_value, false_negative_rate, false_positive_rate, false_discovery_rate, false_omission_rate, positive_likelihood_ratio, negative_likelihood_ratio, ) __all__ = [ "get_stats", "fbeta_score", "f1_score", "iou_score", "accuracy", "precision", "recall", "sensitivity", "specificity", "balanced_accuracy", "positive_predictive_value", "negative_predictive_value", "false_negative_rate", "false_positive_rate", "false_discovery_rate", "false_omission_rate", "positive_likelihood_ratio", "negative_likelihood_ratio", ] ================================================ FILE: segmentation_models_pytorch/metrics/functional.py ================================================ """Various metrics based on Type I and Type II errors. References: https://en.wikipedia.org/wiki/Confusion_matrix Example: .. code-block:: python import segmentation_models_pytorch as smp # lets assume we have multilabel prediction for 3 classes output = torch.rand([10, 3, 256, 256]) target = torch.rand([10, 3, 256, 256]).round().long() # first compute statistics for true positives, false positives, false negative and # true negative "pixels" tp, fp, fn, tn = smp.metrics.get_stats(output, target, mode='multilabel', threshold=0.5) # then compute metrics with required reduction (see metric docs) iou_score = smp.metrics.iou_score(tp, fp, fn, tn, reduction="micro") f1_score = smp.metrics.f1_score(tp, fp, fn, tn, reduction="micro") f2_score = smp.metrics.fbeta_score(tp, fp, fn, tn, beta=2, reduction="micro") accuracy = smp.metrics.accuracy(tp, fp, fn, tn, reduction="macro") recall = smp.metrics.recall(tp, fp, fn, tn, reduction="micro-imagewise") """ import torch import warnings from typing import Optional, List, Tuple, Union __all__ = [ "get_stats", "fbeta_score", "f1_score", "iou_score", "accuracy", "precision", "recall", "sensitivity", "specificity", "balanced_accuracy", "positive_predictive_value", "negative_predictive_value", "false_negative_rate", "false_positive_rate", "false_discovery_rate", "false_omission_rate", "positive_likelihood_ratio", "negative_likelihood_ratio", ] ################################################################################################### # Statistics computation (true positives, false positives, false negatives, false positives) ################################################################################################### def get_stats( output: Union[torch.LongTensor, torch.FloatTensor], target: torch.LongTensor, mode: str, ignore_index: Optional[int] = None, threshold: Optional[Union[float, List[float]]] = None, num_classes: Optional[int] = None, ) -> Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor]: """Compute true positive, false positive, false negative, true negative 'pixels' for each image and each class. Args: output (Union[torch.LongTensor, torch.FloatTensor]): Model output with following shapes and types depending on the specified ``mode``: 'binary' shape (N, 1, ...) and ``torch.LongTensor`` or ``torch.FloatTensor`` 'multilabel' shape (N, C, ...) and ``torch.LongTensor`` or ``torch.FloatTensor`` 'multiclass' shape (N, ...) and ``torch.LongTensor`` target (torch.LongTensor): Targets with following shapes depending on the specified ``mode``: 'binary' shape (N, 1, ...) 'multilabel' shape (N, C, ...) 'multiclass' shape (N, ...) mode (str): One of ``'binary'`` | ``'multilabel'`` | ``'multiclass'`` ignore_index (Optional[int]): Label to ignore on for metric computation. **Not** supported for ``'binary'`` and ``'multilabel'`` modes. Defaults to None. threshold (Optional[float, List[float]]): Binarization threshold for ``output`` in case of ``'binary'`` or ``'multilabel'`` modes. Defaults to None. num_classes (Optional[int]): Number of classes, necessary attribute only for ``'multiclass'`` mode. Class values should be in range 0..(num_classes - 1). If ``ignore_index`` is specified it should be outside the classes range, e.g. ``-1`` or ``255``. Raises: ValueError: in case of misconfiguration. Returns: Tuple[torch.LongTensor]: true_positive, false_positive, false_negative, true_negative tensors (N, C) shape each. """ if torch.is_floating_point(target): raise ValueError( f"Target should be one of the integer types, got {target.dtype}." ) if torch.is_floating_point(output) and threshold is None: raise ValueError( f"Output should be one of the integer types if ``threshold`` is not None, got {output.dtype}." ) if torch.is_floating_point(output) and mode == "multiclass": raise ValueError( f"For ``multiclass`` mode ``output`` should be one of the integer types, got {output.dtype}." ) if mode not in {"binary", "multiclass", "multilabel"}: raise ValueError( f"``mode`` should be in ['binary', 'multiclass', 'multilabel'], got mode={mode}." ) if mode == "multiclass" and threshold is not None: raise ValueError( "``threshold`` parameter does not supported for this 'multiclass' mode" ) if output.shape != target.shape: raise ValueError( "Dimensions should match, but ``output`` shape is not equal to ``target`` " + f"shape, {output.shape} != {target.shape}" ) if mode != "multiclass" and ignore_index is not None: raise ValueError( f"``ignore_index`` parameter is not supported for '{mode}' mode" ) if mode == "multiclass" and num_classes is None: raise ValueError( "``num_classes`` attribute should be not ``None`` for 'multiclass' mode." ) if ignore_index is not None and 0 <= ignore_index <= num_classes - 1: raise ValueError( f"``ignore_index`` should be outside the class values range, but got class values in range " f"0..{num_classes - 1} and ``ignore_index={ignore_index}``. Hint: if you have ``ignore_index = 0``" f"consirder subtracting ``1`` from your target and model output to make ``ignore_index = -1``" f"and relevant class values started from ``0``." ) if mode == "multiclass": tp, fp, fn, tn = _get_stats_multiclass( output, target, num_classes, ignore_index ) else: if threshold is not None: output = torch.where(output >= threshold, 1, 0) target = torch.where(target >= threshold, 1, 0) tp, fp, fn, tn = _get_stats_multilabel(output, target) return tp, fp, fn, tn @torch.inference_mode() def _get_stats_multiclass( output: torch.LongTensor, target: torch.LongTensor, num_classes: int, ignore_index: Optional[int], ) -> Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor]: batch_size, *dims = output.shape num_elements = torch.prod(torch.tensor(dims)).long() if ignore_index is not None: ignore = target == ignore_index output = torch.where(ignore, -1, output) target = torch.where(ignore, -1, target) ignore_per_sample = ignore.view(batch_size, -1).sum(1) tp_count = torch.zeros(batch_size, num_classes, dtype=torch.long) fp_count = torch.zeros(batch_size, num_classes, dtype=torch.long) fn_count = torch.zeros(batch_size, num_classes, dtype=torch.long) tn_count = torch.zeros(batch_size, num_classes, dtype=torch.long) for i in range(batch_size): target_i = target[i] output_i = output[i] mask = output_i == target_i matched = torch.where(mask, target_i, -1) tp = torch.histc(matched.float(), bins=num_classes, min=0, max=num_classes - 1) fp = ( torch.histc(output_i.float(), bins=num_classes, min=0, max=num_classes - 1) - tp ) fn = ( torch.histc(target_i.float(), bins=num_classes, min=0, max=num_classes - 1) - tp ) tn = num_elements - tp - fp - fn if ignore_index is not None: tn = tn - ignore_per_sample[i] tp_count[i] = tp.long() fp_count[i] = fp.long() fn_count[i] = fn.long() tn_count[i] = tn.long() return tp_count, fp_count, fn_count, tn_count @torch.inference_mode() def _get_stats_multilabel( output: torch.LongTensor, target: torch.LongTensor ) -> Tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.LongTensor]: batch_size, num_classes, *dims = target.shape output = output.view(batch_size, num_classes, -1) target = target.view(batch_size, num_classes, -1) tp = (output * target).sum(2) fp = output.sum(2) - tp fn = target.sum(2) - tp tn = torch.prod(torch.tensor(dims)) - (tp + fp + fn) return tp, fp, fn, tn ################################################################################################### # Metrics computation ################################################################################################### def _handle_zero_division(x, zero_division): nans = torch.isnan(x) if torch.any(nans) and zero_division == "warn": warnings.warn("Zero division in metric calculation!") value = zero_division if zero_division != "warn" else 0 value = torch.tensor(value, dtype=x.dtype).to(x.device) x = torch.where(nans, value, x) return x def _compute_metric( metric_fn, tp, fp, fn, tn, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division="warn", **metric_kwargs, ) -> float: if class_weights is None and reduction is not None and "weighted" in reduction: raise ValueError( f"Class weights should be provided for `{reduction}` reduction" ) class_weights = class_weights if class_weights is not None else 1.0 class_weights = torch.tensor(class_weights).to(tp.device) class_weights = class_weights / class_weights.sum() if reduction == "micro": tp = tp.sum() fp = fp.sum() fn = fn.sum() tn = tn.sum() score = metric_fn(tp, fp, fn, tn, **metric_kwargs) elif reduction == "macro": tp = tp.sum(0) fp = fp.sum(0) fn = fn.sum(0) tn = tn.sum(0) score = metric_fn(tp, fp, fn, tn, **metric_kwargs) score = _handle_zero_division(score, zero_division) score = (score * class_weights).mean() elif reduction == "weighted": tp = tp.sum(0) fp = fp.sum(0) fn = fn.sum(0) tn = tn.sum(0) score = metric_fn(tp, fp, fn, tn, **metric_kwargs) score = _handle_zero_division(score, zero_division) score = (score * class_weights).sum() elif reduction == "micro-imagewise": tp = tp.sum(1) fp = fp.sum(1) fn = fn.sum(1) tn = tn.sum(1) score = metric_fn(tp, fp, fn, tn, **metric_kwargs) score = _handle_zero_division(score, zero_division) score = score.mean() elif reduction == "macro-imagewise" or reduction == "weighted-imagewise": score = metric_fn(tp, fp, fn, tn, **metric_kwargs) score = _handle_zero_division(score, zero_division) score = (score.mean(0) * class_weights).mean() elif reduction == "none" or reduction is None: score = metric_fn(tp, fp, fn, tn, **metric_kwargs) score = _handle_zero_division(score, zero_division) else: raise ValueError( "`reduction` should be in [micro, macro, weighted, micro-imagewise," + "macro-imagesize, weighted-imagewise, none, None]" ) return score # Logic for metric computation, all metrics are with the same interface def _fbeta_score(tp, fp, fn, tn, beta=1): beta_tp = (1 + beta**2) * tp beta_fn = (beta**2) * fn score = beta_tp / (beta_tp + beta_fn + fp) return score def _iou_score(tp, fp, fn, tn): return tp / (tp + fp + fn) def _accuracy(tp, fp, fn, tn): return (tp + tn) / (tp + fp + fn + tn) def _sensitivity(tp, fp, fn, tn): return tp / (tp + fn) def _specificity(tp, fp, fn, tn): return tn / (tn + fp) def _balanced_accuracy(tp, fp, fn, tn): return (_sensitivity(tp, fp, fn, tn) + _specificity(tp, fp, fn, tn)) / 2 def _positive_predictive_value(tp, fp, fn, tn): return tp / (tp + fp) def _negative_predictive_value(tp, fp, fn, tn): return tn / (tn + fn) def _false_negative_rate(tp, fp, fn, tn): return 1 - _sensitivity(tp, fp, fn, tn) def _false_positive_rate(tp, fp, fn, tn): return 1 - _specificity(tp, fp, fn, tn) def _false_discovery_rate(tp, fp, fn, tn): return 1 - _positive_predictive_value(tp, fp, fn, tn) def _false_omission_rate(tp, fp, fn, tn): return 1 - _negative_predictive_value(tp, fp, fn, tn) def _positive_likelihood_ratio(tp, fp, fn, tn): return _sensitivity(tp, fp, fn, tn) / _false_positive_rate(tp, fp, fn, tn) def _negative_likelihood_ratio(tp, fp, fn, tn): return _false_negative_rate(tp, fp, fn, tn) / _specificity(tp, fp, fn, tn) def fbeta_score( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, beta: float = 1.0, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """F beta score""" return _compute_metric( _fbeta_score, tp, fp, fn, tn, beta=beta, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def f1_score( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """F1 score""" return _compute_metric( _fbeta_score, tp, fp, fn, tn, beta=1.0, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def iou_score( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """IoU score or Jaccard index""" # noqa return _compute_metric( _iou_score, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def accuracy( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Accuracy""" return _compute_metric( _accuracy, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def sensitivity( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Sensitivity, recall, hit rate, or true positive rate (TPR)""" return _compute_metric( _sensitivity, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def specificity( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Specificity, selectivity or true negative rate (TNR)""" return _compute_metric( _specificity, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def balanced_accuracy( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Balanced accuracy""" return _compute_metric( _balanced_accuracy, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def positive_predictive_value( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Precision or positive predictive value (PPV)""" return _compute_metric( _positive_predictive_value, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def negative_predictive_value( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Negative predictive value (NPV)""" return _compute_metric( _negative_predictive_value, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def false_negative_rate( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Miss rate or false negative rate (FNR)""" return _compute_metric( _false_negative_rate, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def false_positive_rate( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Fall-out or false positive rate (FPR)""" return _compute_metric( _false_positive_rate, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def false_discovery_rate( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """False discovery rate (FDR)""" # noqa return _compute_metric( _false_discovery_rate, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def false_omission_rate( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """False omission rate (FOR)""" # noqa return _compute_metric( _false_omission_rate, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def positive_likelihood_ratio( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Positive likelihood ratio (LR+)""" return _compute_metric( _positive_likelihood_ratio, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) def negative_likelihood_ratio( tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str] = None, class_weights: Optional[List[float]] = None, zero_division: Union[str, float] = 1.0, ) -> torch.Tensor: """Negative likelihood ratio (LR-)""" return _compute_metric( _negative_likelihood_ratio, tp, fp, fn, tn, reduction=reduction, class_weights=class_weights, zero_division=zero_division, ) _doc = """ Args: tp (torch.LongTensor): tensor of shape (N, C), true positive cases fp (torch.LongTensor): tensor of shape (N, C), false positive cases fn (torch.LongTensor): tensor of shape (N, C), false negative cases tn (torch.LongTensor): tensor of shape (N, C), true negative cases reduction (Optional[str]): Define how to aggregate metric between classes and images: - 'micro' Sum true positive, false positive, false negative and true negative pixels over all images and all classes and then compute score. - 'macro' Sum true positive, false positive, false negative and true negative pixels over all images for each label, then compute score for each label separately and average labels scores. This does not take label imbalance into account. - 'weighted' Sum true positive, false positive, false negative and true negative pixels over all images for each label, then compute score for each label separately and average weighted labels scores. - 'micro-imagewise' Sum true positive, false positive, false negative and true negative pixels for **each image**, then compute score for **each image** and average scores over dataset. All images contribute equally to final score, however takes into accout class imbalance for each image. - 'macro-imagewise' Compute score for each image and for each class on that image separately, then compute average score on each image over labels and average image scores over dataset. Does not take into account label imbalance on each image. - 'weighted-imagewise' Compute score for each image and for each class on that image separately, then compute weighted average score on each image over labels and average image scores over dataset. - 'none' or ``None`` Same as ``'macro-imagewise'``, but without any reduction. For ``'binary'`` case ``'micro' = 'macro' = 'weighted'`` and ``'micro-imagewise' = 'macro-imagewise' = 'weighted-imagewise'``. Prefixes ``'micro'``, ``'macro'`` and ``'weighted'`` define how the scores for classes will be aggregated, while postfix ``'imagewise'`` defines how scores between the images will be aggregated. class_weights (Optional[List[float]]): list of class weights for metric aggregation, in case of `weighted*` reduction is chosen. Defaults to None. zero_division (Union[str, float]): Sets the value to return when there is a zero division, i.e. when all predictions and labels are negative. If set to “warn”, this acts as 0, but warnings are also raised. Defaults to 1. Returns: torch.Tensor: if ``'reduction'`` is not ``None`` or ``'none'`` returns scalar metric, else returns tensor of shape (N, C) References: https://en.wikipedia.org/wiki/Confusion_matrix """ fbeta_score.__doc__ += _doc f1_score.__doc__ += _doc iou_score.__doc__ += _doc accuracy.__doc__ += _doc sensitivity.__doc__ += _doc specificity.__doc__ += _doc balanced_accuracy.__doc__ += _doc positive_predictive_value.__doc__ += _doc negative_predictive_value.__doc__ += _doc false_negative_rate.__doc__ += _doc false_positive_rate.__doc__ += _doc false_discovery_rate.__doc__ += _doc false_omission_rate.__doc__ += _doc positive_likelihood_ratio.__doc__ += _doc negative_likelihood_ratio.__doc__ += _doc precision = positive_predictive_value recall = sensitivity ================================================ FILE: segmentation_models_pytorch/utils/__init__.py ================================================ import warnings from . import train from . import losses from . import metrics __all__ = ["train", "losses", "metrics"] warnings.warn( "`smp.utils` module is deprecated and will be removed in future releases.", DeprecationWarning, ) ================================================ FILE: segmentation_models_pytorch/utils/base.py ================================================ import re import torch.nn as nn class BaseObject(nn.Module): def __init__(self, name=None): super().__init__() self._name = name @property def __name__(self): if self._name is None: name = self.__class__.__name__ s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name) return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower() else: return self._name class Metric(BaseObject): pass class Loss(BaseObject): def __add__(self, other): if isinstance(other, Loss): return SumOfLosses(self, other) else: raise ValueError("Loss should be inherited from `Loss` class") def __radd__(self, other): return self.__add__(other) def __mul__(self, value): if isinstance(value, (int, float)): return MultipliedLoss(self, value) else: raise ValueError("Loss should be inherited from `BaseLoss` class") def __rmul__(self, other): return self.__mul__(other) class SumOfLosses(Loss): def __init__(self, l1, l2): name = "{} + {}".format(l1.__name__, l2.__name__) super().__init__(name=name) self.l1 = l1 self.l2 = l2 def __call__(self, *inputs): return self.l1.forward(*inputs) + self.l2.forward(*inputs) class MultipliedLoss(Loss): def __init__(self, loss, multiplier): # resolve name if len(loss.__name__.split("+")) > 1: name = "{} * ({})".format(multiplier, loss.__name__) else: name = "{} * {}".format(multiplier, loss.__name__) super().__init__(name=name) self.loss = loss self.multiplier = multiplier def __call__(self, *inputs): return self.multiplier * self.loss.forward(*inputs) ================================================ FILE: segmentation_models_pytorch/utils/functional.py ================================================ import torch def _take_channels(*xs, ignore_channels=None): if ignore_channels is None: return xs else: channels = [ channel for channel in range(xs[0].shape[1]) if channel not in ignore_channels ] xs = [ torch.index_select(x, dim=1, index=torch.tensor(channels).to(x.device)) for x in xs ] return xs def _threshold(x, threshold=None): if threshold is not None: return (x > threshold).type(x.dtype) else: return x def iou(pr, gt, eps=1e-7, threshold=None, ignore_channels=None): """Calculate Intersection over Union between ground truth and prediction Args: pr (torch.Tensor): predicted tensor gt (torch.Tensor): ground truth tensor eps (float): epsilon to avoid zero division threshold: threshold for outputs binarization Returns: float: IoU (Jaccard) score """ pr = _threshold(pr, threshold=threshold) pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels) intersection = torch.sum(gt * pr) union = torch.sum(gt) + torch.sum(pr) - intersection + eps return (intersection + eps) / union jaccard = iou def f_score(pr, gt, beta=1, eps=1e-7, threshold=None, ignore_channels=None): """Calculate F-score between ground truth and prediction Args: pr (torch.Tensor): predicted tensor gt (torch.Tensor): ground truth tensor beta (float): positive constant eps (float): epsilon to avoid zero division threshold: threshold for outputs binarization Returns: float: F score """ pr = _threshold(pr, threshold=threshold) pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels) tp = torch.sum(gt * pr) fp = torch.sum(pr) - tp fn = torch.sum(gt) - tp score = ((1 + beta**2) * tp + eps) / ((1 + beta**2) * tp + beta**2 * fn + fp + eps) return score def accuracy(pr, gt, threshold=0.5, ignore_channels=None): """Calculate accuracy score between ground truth and prediction Args: pr (torch.Tensor): predicted tensor gt (torch.Tensor): ground truth tensor eps (float): epsilon to avoid zero division threshold: threshold for outputs binarization Returns: float: precision score """ pr = _threshold(pr, threshold=threshold) pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels) tp = torch.sum(gt == pr, dtype=pr.dtype) score = tp / gt.view(-1).shape[0] return score def precision(pr, gt, eps=1e-7, threshold=None, ignore_channels=None): """Calculate precision score between ground truth and prediction Args: pr (torch.Tensor): predicted tensor gt (torch.Tensor): ground truth tensor eps (float): epsilon to avoid zero division threshold: threshold for outputs binarization Returns: float: precision score """ pr = _threshold(pr, threshold=threshold) pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels) tp = torch.sum(gt * pr) fp = torch.sum(pr) - tp score = (tp + eps) / (tp + fp + eps) return score def recall(pr, gt, eps=1e-7, threshold=None, ignore_channels=None): """Calculate Recall between ground truth and prediction Args: pr (torch.Tensor): A list of predicted elements gt (torch.Tensor): A list of elements that are to be predicted eps (float): epsilon to avoid zero division threshold: threshold for outputs binarization Returns: float: recall score """ pr = _threshold(pr, threshold=threshold) pr, gt = _take_channels(pr, gt, ignore_channels=ignore_channels) tp = torch.sum(gt * pr) fn = torch.sum(gt) - tp score = (tp + eps) / (tp + fn + eps) return score ================================================ FILE: segmentation_models_pytorch/utils/losses.py ================================================ import torch.nn as nn from . import base from . import functional as F from ..base.modules import Activation class JaccardLoss(base.Loss): def __init__(self, eps=1.0, activation=None, ignore_channels=None, **kwargs): super().__init__(**kwargs) self.eps = eps self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return 1 - F.jaccard( y_pr, y_gt, eps=self.eps, threshold=None, ignore_channels=self.ignore_channels, ) class DiceLoss(base.Loss): def __init__( self, eps=1.0, beta=1.0, activation=None, ignore_channels=None, **kwargs ): super().__init__(**kwargs) self.eps = eps self.beta = beta self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return 1 - F.f_score( y_pr, y_gt, beta=self.beta, eps=self.eps, threshold=None, ignore_channels=self.ignore_channels, ) class L1Loss(nn.L1Loss, base.Loss): pass class MSELoss(nn.MSELoss, base.Loss): pass class CrossEntropyLoss(nn.CrossEntropyLoss, base.Loss): pass class NLLLoss(nn.NLLLoss, base.Loss): pass class BCELoss(nn.BCELoss, base.Loss): pass class BCEWithLogitsLoss(nn.BCEWithLogitsLoss, base.Loss): pass ================================================ FILE: segmentation_models_pytorch/utils/meter.py ================================================ import numpy as np class Meter(object): """Meters provide a way to keep track of important statistics in an online manner. This class is abstract, but provides a standard interface for all meters to follow. """ def reset(self): """Reset the meter to default settings.""" pass def add(self, value): """Log a new value to the meter Args: value: Next result to include. """ pass def value(self): """Get the value of the meter in the current state.""" pass class AverageValueMeter(Meter): def __init__(self): super(AverageValueMeter, self).__init__() self.reset() self.val = 0 def add(self, value, n=1): self.val = value self.sum += value self.var += value * value self.n += n if self.n == 0: self.mean, self.std = np.nan, np.nan elif self.n == 1: self.mean = 0.0 + self.sum # This is to force a copy in torch/numpy self.std = np.inf self.mean_old = self.mean self.m_s = 0.0 else: self.mean = self.mean_old + (value - n * self.mean_old) / float(self.n) self.m_s += (value - self.mean_old) * (value - self.mean) self.mean_old = self.mean self.std = np.sqrt(self.m_s / (self.n - 1.0)) def value(self): return self.mean, self.std def reset(self): self.n = 0 self.sum = 0.0 self.var = 0.0 self.val = 0.0 self.mean = np.nan self.mean_old = 0.0 self.m_s = 0.0 self.std = np.nan ================================================ FILE: segmentation_models_pytorch/utils/metrics.py ================================================ from . import base from . import functional as F from ..base.modules import Activation class IoU(base.Metric): __name__ = "iou_score" def __init__( self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs ): super().__init__(**kwargs) self.eps = eps self.threshold = threshold self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return F.iou( y_pr, y_gt, eps=self.eps, threshold=self.threshold, ignore_channels=self.ignore_channels, ) class Fscore(base.Metric): def __init__( self, beta=1, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs, ): super().__init__(**kwargs) self.eps = eps self.beta = beta self.threshold = threshold self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return F.f_score( y_pr, y_gt, eps=self.eps, beta=self.beta, threshold=self.threshold, ignore_channels=self.ignore_channels, ) class Accuracy(base.Metric): def __init__(self, threshold=0.5, activation=None, ignore_channels=None, **kwargs): super().__init__(**kwargs) self.threshold = threshold self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return F.accuracy( y_pr, y_gt, threshold=self.threshold, ignore_channels=self.ignore_channels ) class Recall(base.Metric): def __init__( self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs ): super().__init__(**kwargs) self.eps = eps self.threshold = threshold self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return F.recall( y_pr, y_gt, eps=self.eps, threshold=self.threshold, ignore_channels=self.ignore_channels, ) class Precision(base.Metric): def __init__( self, eps=1e-7, threshold=0.5, activation=None, ignore_channels=None, **kwargs ): super().__init__(**kwargs) self.eps = eps self.threshold = threshold self.activation = Activation(activation) self.ignore_channels = ignore_channels def forward(self, y_pr, y_gt): y_pr = self.activation(y_pr) return F.precision( y_pr, y_gt, eps=self.eps, threshold=self.threshold, ignore_channels=self.ignore_channels, ) ================================================ FILE: segmentation_models_pytorch/utils/train.py ================================================ import sys import torch from tqdm import tqdm as tqdm from .meter import AverageValueMeter class Epoch: def __init__(self, model, loss, metrics, stage_name, device="cpu", verbose=True): self.model = model self.loss = loss self.metrics = metrics self.stage_name = stage_name self.verbose = verbose self.device = device self._to_device() def _to_device(self): self.model.to(self.device) self.loss.to(self.device) for metric in self.metrics: metric.to(self.device) def _format_logs(self, logs): str_logs = ["{} - {:.4}".format(k, v) for k, v in logs.items()] s = ", ".join(str_logs) return s def batch_update(self, x, y): raise NotImplementedError def on_epoch_start(self): pass def run(self, dataloader): self.on_epoch_start() logs = {} loss_meter = AverageValueMeter() metrics_meters = { metric.__name__: AverageValueMeter() for metric in self.metrics } with tqdm( dataloader, desc=self.stage_name, file=sys.stdout, disable=not (self.verbose), ) as iterator: for x, y in iterator: x, y = x.to(self.device), y.to(self.device) loss, y_pred = self.batch_update(x, y) # update loss logs loss_value = loss.cpu().detach().numpy() loss_meter.add(loss_value) loss_logs = {self.loss.__name__: loss_meter.mean} logs.update(loss_logs) # update metrics logs for metric_fn in self.metrics: metric_value = metric_fn(y_pred, y).cpu().detach().numpy() metrics_meters[metric_fn.__name__].add(metric_value) metrics_logs = {k: v.mean for k, v in metrics_meters.items()} logs.update(metrics_logs) if self.verbose: s = self._format_logs(logs) iterator.set_postfix_str(s) return logs class TrainEpoch(Epoch): def __init__(self, model, loss, metrics, optimizer, device="cpu", verbose=True): super().__init__( model=model, loss=loss, metrics=metrics, stage_name="train", device=device, verbose=verbose, ) self.optimizer = optimizer def on_epoch_start(self): self.model.train() def batch_update(self, x, y): self.optimizer.zero_grad() prediction = self.model.forward(x) loss = self.loss(prediction, y) loss.backward() self.optimizer.step() return loss, prediction class ValidEpoch(Epoch): def __init__(self, model, loss, metrics, device="cpu", verbose=True): super().__init__( model=model, loss=loss, metrics=metrics, stage_name="valid", device=device, verbose=verbose, ) def on_epoch_start(self): self.model.eval() def batch_update(self, x, y): with torch.inference_mode(): prediction = self.model.forward(x) loss = self.loss(prediction, y) return loss, prediction ================================================ FILE: tests/__init__.py ================================================ ================================================ FILE: tests/base/test_freeze_encoder.py ================================================ import torch import segmentation_models_pytorch as smp def test_freeze_and_unfreeze_encoder(): model = smp.Unet(encoder_name="resnet18", encoder_weights=None) def assert_encoder_params_trainable(expected: bool): assert all(p.requires_grad == expected for p in model.encoder.parameters()) def assert_norm_layers_training(expected: bool): for m in model.encoder.modules(): if isinstance(m, torch.nn.modules.batchnorm._NormBase): assert m.training == expected # Initially, encoder params should be trainable model.train() assert_encoder_params_trainable(True) # Freeze encoder model.freeze_encoder() assert_encoder_params_trainable(False) assert_norm_layers_training(False) # Call train() and ensure encoder norm layers stay frozen model.train() assert_norm_layers_training(False) # Unfreeze encoder model.unfreeze_encoder() assert_encoder_params_trainable(True) assert_norm_layers_training(True) # Call train() again — should stay trainable model.train() assert_norm_layers_training(True) # Switch to eval, then freeze model.eval() model.freeze_encoder() assert_encoder_params_trainable(False) assert_norm_layers_training(False) # Unfreeze again model.unfreeze_encoder() assert_encoder_params_trainable(True) assert_norm_layers_training(True) def test_freeze_encoder_stops_running_stats(): model = smp.Unet(encoder_name="resnet18", encoder_weights=None) model.freeze_encoder() model.train() # overridden train, encoder should remain frozen bn = None for m in model.encoder.modules(): if isinstance(m, torch.nn.modules.batchnorm._NormBase): bn = m break assert bn is not None orig_mean = bn.running_mean.clone() orig_var = bn.running_var.clone() x = torch.randn(2, 3, 64, 64) _ = model(x) torch.testing.assert_close(orig_mean, bn.running_mean) torch.testing.assert_close(orig_var, bn.running_var) ================================================ FILE: tests/base/test_modules.py ================================================ import pytest from torch import nn from segmentation_models_pytorch.base.modules import Conv2dReLU def test_conv2drelu_batchnorm(): module = Conv2dReLU(3, 16, kernel_size=3, padding=1, use_norm="batchnorm") assert isinstance(module[0], nn.Conv2d) assert isinstance(module[1], nn.BatchNorm2d) assert isinstance(module[2], nn.ReLU) def test_conv2drelu_batchnorm_with_keywords(): module = Conv2dReLU( 3, 16, kernel_size=3, padding=1, use_norm={"type": "batchnorm", "momentum": 1e-4, "affine": False}, ) assert isinstance(module[0], nn.Conv2d) assert isinstance(module[1], nn.BatchNorm2d) assert module[1].momentum == 1e-4 and module[1].affine is False assert isinstance(module[2], nn.ReLU) def test_conv2drelu_identity(): module = Conv2dReLU(3, 16, kernel_size=3, padding=1, use_norm="identity") assert isinstance(module[0], nn.Conv2d) assert isinstance(module[1], nn.Identity) assert isinstance(module[2], nn.ReLU) def test_conv2drelu_layernorm(): module = Conv2dReLU(3, 16, kernel_size=3, padding=1, use_norm="layernorm") assert isinstance(module[0], nn.Conv2d) assert isinstance(module[1], nn.LayerNorm) assert isinstance(module[2], nn.ReLU) def test_conv2drelu_instancenorm(): module = Conv2dReLU(3, 16, kernel_size=3, padding=1, use_norm="instancenorm") assert isinstance(module[0], nn.Conv2d) assert isinstance(module[1], nn.InstanceNorm2d) assert isinstance(module[2], nn.ReLU) def test_conv2drelu_inplace(): try: from inplace_abn import InPlaceABN except ImportError: pytest.skip("InPlaceABN is not installed") module = Conv2dReLU(3, 16, kernel_size=3, padding=1, use_norm="inplace") assert len(module) == 3 assert isinstance(module[0], nn.Conv2d) assert isinstance(module[1], InPlaceABN) assert isinstance(module[2], nn.Identity) ================================================ FILE: tests/conftest.py ================================================ def pytest_addoption(parser): parser.addoption( "--non-marked-only", action="store_true", help="Run only non-marked tests" ) def pytest_collection_modifyitems(config, items): if config.getoption("--non-marked-only"): non_marked_items = [] for item in items: # Check if the test has no marks if not item.own_markers: non_marked_items.append(item) # Update the test collection to only include non-marked tests items[:] = non_marked_items ================================================ FILE: tests/encoders/__init__.py ================================================ ================================================ FILE: tests/encoders/base.py ================================================ import pytest import unittest import torch import segmentation_models_pytorch as smp from functools import lru_cache from tests.utils import ( default_device, check_run_test_on_diff_or_main, requires_torch_greater_or_equal, ) class BaseEncoderTester(unittest.TestCase): encoder_names = [] # some tests might be slow, running them only on diff files_for_diff = [] # standard encoder configuration num_output_features = 6 output_strides = [1, 2, 4, 8, 16, 32] supports_dilated = True # test sample configuration default_batch_size = 1 default_num_channels = 3 default_height = 64 default_width = 64 # test configurations in_channels_to_test = [1, 3, 4] depth_to_test = [3, 4, 5] strides_to_test = [8, 16] # 32 is a default one def get_tiny_encoder(self): return smp.encoders.get_encoder(self.encoder_names[0], encoder_weights=None) @lru_cache def _get_sample(self, batch_size=None, num_channels=None, height=None, width=None): batch_size = batch_size or self.default_batch_size num_channels = num_channels or self.default_num_channels height = height or self.default_height width = width or self.default_width return torch.rand(batch_size, num_channels, height, width) def get_features_output_strides(self, sample, features): height, width = sample.shape[2:] height_strides = [height // f.shape[2] for f in features] width_strides = [width // f.shape[3] for f in features] return height_strides, width_strides def test_forward_backward(self): sample = self._get_sample().to(default_device) for encoder_name in self.encoder_names: with self.subTest(encoder_name=encoder_name): # init encoder encoder = smp.encoders.get_encoder( encoder_name, in_channels=3, encoder_weights=None ).to(default_device) # forward features = encoder.forward(sample) self.assertEqual( len(features), self.num_output_features, f"Encoder `{encoder_name}` should have {self.num_output_features} output feature maps, but has {len(features)}", ) # backward features[-1].mean().backward() def test_in_channels(self): cases = [ (encoder_name, in_channels) for encoder_name in self.encoder_names for in_channels in self.in_channels_to_test ] for encoder_name, in_channels in cases: sample = self._get_sample(num_channels=in_channels).to(default_device) with self.subTest(encoder_name=encoder_name, in_channels=in_channels): encoder = smp.encoders.get_encoder( encoder_name, in_channels=in_channels, encoder_weights=None ).to(default_device) encoder.eval() # forward with torch.inference_mode(): encoder.forward(sample) def test_depth(self): sample = self._get_sample().to(default_device) cases = [ (encoder_name, depth) for encoder_name in self.encoder_names for depth in self.depth_to_test ] for encoder_name, depth in cases: with self.subTest(encoder_name=encoder_name, depth=depth): encoder = smp.encoders.get_encoder( encoder_name, in_channels=self.default_num_channels, encoder_weights=None, depth=depth, ).to(default_device) encoder.eval() # forward with torch.inference_mode(): features = encoder.forward(sample) # check number of features self.assertEqual( len(features), depth + 1, f"Encoder `{encoder_name}` should have {depth + 1} output feature maps, but has {len(features)}", ) # check feature strides height_strides, width_strides = self.get_features_output_strides( sample, features ) self.assertEqual( height_strides, self.output_strides[: depth + 1], f"Encoder `{encoder_name}` should have output strides {self.output_strides[: depth + 1]}, but has {height_strides}", ) self.assertEqual( width_strides, self.output_strides[: depth + 1], f"Encoder `{encoder_name}` should have output strides {self.output_strides[: depth + 1]}, but has {width_strides}", ) # check encoder output stride property self.assertEqual( encoder.output_stride, self.output_strides[depth], f"Encoder `{encoder_name}` last feature map should have output stride {self.output_strides[depth]}, but has {encoder.output_stride}", ) # check out channels also have proper length self.assertEqual( len(encoder.out_channels), depth + 1, f"Encoder `{encoder_name}` should have {depth + 1} out_channels, but has {len(encoder.out_channels)}", ) def test_invalid_depth(self): with self.assertRaises(ValueError): smp.encoders.get_encoder(self.encoder_names[0], depth=6) with self.assertRaises(ValueError): smp.encoders.get_encoder(self.encoder_names[0], depth=0) def test_dilated(self): sample = self._get_sample().to(default_device) cases = [ (encoder_name, stride) for encoder_name in self.encoder_names for stride in self.strides_to_test ] # special case for encoders that do not support dilated model # just check proper error is raised if not self.supports_dilated: with self.assertRaises(ValueError, msg="not support dilated mode"): encoder_name, stride = cases[0] encoder = smp.encoders.get_encoder( encoder_name, in_channels=self.default_num_channels, encoder_weights=None, output_stride=stride, ).to(default_device) return for encoder_name, stride in cases: with self.subTest(encoder_name=encoder_name, stride=stride): encoder = smp.encoders.get_encoder( encoder_name, in_channels=self.default_num_channels, encoder_weights=None, output_stride=stride, ).to(default_device) encoder.eval() # forward with torch.inference_mode(): features = encoder.forward(sample) height_strides, width_strides = self.get_features_output_strides( sample, features ) expected_height_strides = [min(stride, s) for s in height_strides] expected_width_strides = [min(stride, s) for s in width_strides] self.assertEqual( height_strides, expected_height_strides, f"Encoder `{encoder_name}` should have height output strides {expected_height_strides}, but has {height_strides}", ) self.assertEqual( width_strides, expected_width_strides, f"Encoder `{encoder_name}` should have width output strides {expected_width_strides}, but has {width_strides}", ) @pytest.mark.compile def test_compile(self): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") sample = self._get_sample().to(default_device) encoder = self.get_tiny_encoder() encoder = encoder.eval().to(default_device) torch.compiler.reset() compiled_encoder = torch.compile( encoder, fullgraph=True, dynamic=True, backend="eager" ) if encoder._is_torch_compilable: compiled_encoder(sample) else: with self.assertRaises(Exception): compiled_encoder(sample) @pytest.mark.torch_export @requires_torch_greater_or_equal("2.4.0") def test_torch_export(self): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") sample = self._get_sample().to(default_device) encoder = self.get_tiny_encoder() encoder = encoder.eval().to(default_device) if not encoder._is_torch_exportable: with self.assertRaises(Exception): exported_encoder = torch.export.export( encoder, args=(sample,), strict=True, ) return exported_encoder = torch.export.export( encoder, args=(sample,), strict=True, ) with torch.inference_mode(): eager_output = encoder(sample) exported_output = exported_encoder.module().forward(sample) for eager_feature, exported_feature in zip(eager_output, exported_output): torch.testing.assert_close(eager_feature, exported_feature) @pytest.mark.torch_script def test_torch_script(self): sample = self._get_sample().to(default_device) encoder = self.get_tiny_encoder() encoder = encoder.eval().to(default_device) if not encoder._is_torch_scriptable: with self.assertRaises(RuntimeError, msg="not torch scriptable"): scripted_encoder = torch.jit.script(encoder) return scripted_encoder = torch.jit.script(encoder) with torch.inference_mode(): eager_output = encoder(sample) scripted_output = scripted_encoder(sample) for eager_feature, scripted_feature in zip(eager_output, scripted_output): torch.testing.assert_close(eager_feature, scripted_feature) ================================================ FILE: tests/encoders/test_batchnorm_deprecation.py ================================================ import pytest import torch import segmentation_models_pytorch as smp from tests.utils import check_two_models_strictly_equal @pytest.mark.parametrize("model_name", ["unet", "unetplusplus", "linknet", "manet"]) @pytest.mark.parametrize("decoder_option", [True, False, "inplace"]) def test_seg_models_before_after_use_norm(model_name, decoder_option): torch.manual_seed(42) with pytest.warns(DeprecationWarning): model_decoder_batchnorm = smp.create_model( model_name, "mobilenet_v2", encoder_weights=None, decoder_use_batchnorm=decoder_option, ) model_decoder_norm = smp.create_model( model_name, "mobilenet_v2", encoder_weights=None, decoder_use_norm=decoder_option, ) model_decoder_norm.load_state_dict(model_decoder_batchnorm.state_dict()) check_two_models_strictly_equal( model_decoder_batchnorm, model_decoder_norm, torch.rand(1, 3, 224, 224) ) @pytest.mark.parametrize("decoder_option", [True, False, "inplace"]) def test_pspnet_before_after_use_norm(decoder_option): torch.manual_seed(42) with pytest.warns(DeprecationWarning): model_decoder_batchnorm = smp.create_model( "pspnet", "mobilenet_v2", encoder_weights=None, psp_use_batchnorm=decoder_option, ) model_decoder_norm = smp.create_model( "pspnet", "mobilenet_v2", encoder_weights=None, decoder_use_norm=decoder_option, ) model_decoder_norm.load_state_dict(model_decoder_batchnorm.state_dict()) check_two_models_strictly_equal( model_decoder_batchnorm, model_decoder_norm, torch.rand(1, 3, 224, 224) ) ================================================ FILE: tests/encoders/test_common.py ================================================ import pytest import segmentation_models_pytorch as smp from tests.utils import slow_test @pytest.mark.parametrize( "encoder_name_and_weights", [ ("resnet18", "imagenet"), ], ) @slow_test def test_load_encoder_from_hub(encoder_name_and_weights): encoder_name, weights = encoder_name_and_weights smp.encoders.get_encoder(encoder_name, weights=weights) ================================================ FILE: tests/encoders/test_pretrainedmodels_encoders.py ================================================ import segmentation_models_pytorch as smp from tests.encoders import base from tests.utils import RUN_ALL_ENCODERS class TestDPNEncoder(base.BaseEncoderTester): encoder_names = ( ["dpn68"] if not RUN_ALL_ENCODERS else ["dpn68", "dpn68b", "dpn92", "dpn98", "dpn107", "dpn131"] ) files_for_diff = ["encoders/dpn.py"] def get_tiny_encoder(self): params = { "stage_idxs": [2, 3, 4, 6], "out_channels": [3, 2, 70, 134, 262, 518], "groups": 2, "inc_sec": (2, 2, 2, 2), "k_r": 2, "k_sec": (1, 1, 1, 1), "num_classes": 1000, "num_init_features": 2, "small": True, "test_time_pool": True, } return smp.encoders.dpn.DPNEncoder(**params) class TestInceptionResNetV2Encoder(base.BaseEncoderTester): encoder_names = ( ["inceptionresnetv2"] if not RUN_ALL_ENCODERS else ["inceptionresnetv2"] ) files_for_diff = ["encoders/inceptionresnetv2.py"] supports_dilated = False class TestInceptionV4Encoder(base.BaseEncoderTester): encoder_names = ["inceptionv4"] if not RUN_ALL_ENCODERS else ["inceptionv4"] files_for_diff = ["encoders/inceptionv4.py"] supports_dilated = False class TestSeNetEncoder(base.BaseEncoderTester): encoder_names = ( ["se_resnet50"] if not RUN_ALL_ENCODERS else [ "se_resnet50", "se_resnet101", "se_resnet152", "se_resnext50_32x4d", "se_resnext101_32x4d", # "senet154", # extra large model ] ) files_for_diff = ["encoders/senet.py"] def get_tiny_encoder(self): params = { "out_channels": [3, 2, 256, 512, 1024, 2048], "block": smp.encoders.senet.SEResNetBottleneck, "layers": [1, 1, 1, 1], "downsample_kernel_size": 1, "downsample_padding": 0, "dropout_p": None, "groups": 1, "inplanes": 2, "input_3x3": False, "num_classes": 1000, "reduction": 2, } return smp.encoders.senet.SENetEncoder(**params) class TestXceptionEncoder(base.BaseEncoderTester): supports_dilated = False encoder_names = ["xception"] if not RUN_ALL_ENCODERS else ["xception"] files_for_diff = ["encoders/xception.py"] ================================================ FILE: tests/encoders/test_smp_encoders.py ================================================ import segmentation_models_pytorch as smp from functools import partial from tests.encoders import base from tests.utils import RUN_ALL_ENCODERS class TestMobileoneEncoder(base.BaseEncoderTester): encoder_names = ( ["mobileone_s0"] if not RUN_ALL_ENCODERS else [ "mobileone_s0", "mobileone_s1", "mobileone_s2", "mobileone_s3", "mobileone_s4", ] ) files_for_diff = ["encoders/mobileone.py"] class TestMixTransformerEncoder(base.BaseEncoderTester): encoder_names = ( ["mit_b0"] if not RUN_ALL_ENCODERS else ["mit_b0", "mit_b1", "mit_b2", "mit_b3", "mit_b4", "mit_b5"] ) files_for_diff = ["encoders/mix_transformer.py"] def get_tiny_encoder(self): params = { "out_channels": [3, 0, 4, 4, 4, 4], "patch_size": 4, "embed_dims": [4, 4, 4, 4], "num_heads": [1, 1, 1, 1], "mlp_ratios": [1, 1, 1, 1], "qkv_bias": True, "norm_layer": partial(smp.encoders.mix_transformer.LayerNorm, eps=1e-6), "depths": [1, 1, 1, 1], "sr_ratios": [8, 4, 2, 1], "drop_rate": 0.0, "drop_path_rate": 0.1, } return smp.encoders.mix_transformer.MixVisionTransformerEncoder(**params) class TestEfficientNetEncoder(base.BaseEncoderTester): encoder_names = ( ["efficientnet-b0"] if not RUN_ALL_ENCODERS else [ "efficientnet-b0", "efficientnet-b1", "efficientnet-b2", "efficientnet-b3", "efficientnet-b4", "efficientnet-b5", "efficientnet-b6", # "efficientnet-b7", # extra large model ] ) files_for_diff = ["encoders/efficientnet.py"] ================================================ FILE: tests/encoders/test_timm_ported_encoders.py ================================================ from tests.encoders import base from tests.utils import RUN_ALL_ENCODERS class TestTimmEfficientNetEncoder(base.BaseEncoderTester): encoder_names = ( ["timm-efficientnet-b0"] if not RUN_ALL_ENCODERS else [ "timm-efficientnet-b0", "timm-efficientnet-b1", "timm-efficientnet-b2", "timm-efficientnet-b3", "timm-efficientnet-b4", "timm-efficientnet-b5", "timm-efficientnet-b6", "timm-efficientnet-b7", "timm-efficientnet-b8", "timm-efficientnet-l2", "timm-tf_efficientnet_lite0", "timm-tf_efficientnet_lite1", "timm-tf_efficientnet_lite2", "timm-tf_efficientnet_lite3", "timm-tf_efficientnet_lite4", ] ) files_for_diff = ["encoders/timm_efficientnet.py"] class TestTimmGERNetEncoder(base.BaseEncoderTester): encoder_names = ( ["timm-gernet_s"] if not RUN_ALL_ENCODERS else ["timm-gernet_s", "timm-gernet_m", "timm-gernet_l"] ) def test_compile(self): self.skipTest("Test to be removed") class TestTimmMobileNetV3Encoder(base.BaseEncoderTester): encoder_names = ( ["timm-mobilenetv3_small_100"] if not RUN_ALL_ENCODERS else [ "timm-mobilenetv3_large_075", "timm-mobilenetv3_large_100", "timm-mobilenetv3_large_minimal_100", "timm-mobilenetv3_small_075", "timm-mobilenetv3_small_100", "timm-mobilenetv3_small_minimal_100", ] ) def test_compile(self): self.skipTest("Test to be removed") class TestTimmRegNetEncoder(base.BaseEncoderTester): encoder_names = ( ["timm-regnetx_002", "timm-regnety_002"] if not RUN_ALL_ENCODERS else [ "timm-regnetx_002", "timm-regnetx_004", "timm-regnetx_006", "timm-regnetx_008", "timm-regnetx_016", "timm-regnetx_032", "timm-regnetx_040", "timm-regnetx_064", "timm-regnetx_080", "timm-regnetx_120", "timm-regnetx_160", "timm-regnetx_320", "timm-regnety_002", "timm-regnety_004", "timm-regnety_006", "timm-regnety_008", "timm-regnety_016", "timm-regnety_032", "timm-regnety_040", "timm-regnety_064", "timm-regnety_080", "timm-regnety_120", "timm-regnety_160", "timm-regnety_320", ] ) def test_compile(self): self.skipTest("Test to be removed") class TestTimmRes2NetEncoder(base.BaseEncoderTester): encoder_names = ( ["timm-res2net50_26w_4s"] if not RUN_ALL_ENCODERS else [ "timm-res2net50_26w_4s", "timm-res2net101_26w_4s", "timm-res2net50_26w_6s", "timm-res2net50_26w_8s", "timm-res2net50_48w_2s", "timm-res2net50_14w_8s", "timm-res2next50", ] ) def test_compile(self): self.skipTest("Test to be removed") class TestTimmResnestEncoder(base.BaseEncoderTester): default_batch_size = 2 encoder_names = ( ["timm-resnest14d"] if not RUN_ALL_ENCODERS else [ "timm-resnest14d", "timm-resnest26d", "timm-resnest50d", "timm-resnest101e", "timm-resnest200e", "timm-resnest269e", "timm-resnest50d_4s2x40d", "timm-resnest50d_1s4x24d", ] ) def test_compile(self): self.skipTest("Test to be removed") class TestTimmSkNetEncoder(base.BaseEncoderTester): default_batch_size = 2 encoder_names = ( ["timm-skresnet18"] if not RUN_ALL_ENCODERS else [ "timm-skresnet18", "timm-skresnet34", "timm-skresnext50_32x4d", ] ) files_for_diff = ["encoders/timm_sknet.py"] ================================================ FILE: tests/encoders/test_timm_universal.py ================================================ import unittest import warnings import segmentation_models_pytorch as smp from tests.encoders import base from tests.utils import has_timm_test_models # check if timm >= 1.0.12 timm_encoders = [ "tu-resnet18", # for timm universal traditional-like encoder "tu-convnext_atto", # for timm universal transformer-like encoder "tu-darknet17", # for timm universal vgg-like encoder ] if has_timm_test_models: timm_encoders.insert(0, "tu-test_resnet.r160_in1k") class TestTimmUniversalEncoder(base.BaseEncoderTester): encoder_names = timm_encoders files_for_diff = ["encoders/timm_universal.py"] class TestTimmUniversalEncoderWeightsWarning(unittest.TestCase): """Test that tu- encoders emit appropriate warnings for encoder_weights.""" def test_string_weights_emits_warning(self): with self.assertWarns(UserWarning): smp.encoders.get_encoder("tu-resnet18", weights="imagenet") def test_true_weights_no_warning(self): with warnings.catch_warnings(): warnings.simplefilter("error") smp.encoders.get_encoder("tu-resnet18", weights=True) def test_none_weights_no_warning(self): with warnings.catch_warnings(): warnings.simplefilter("error") smp.encoders.get_encoder("tu-resnet18", weights=None) ================================================ FILE: tests/encoders/test_timm_vit_encoders.py ================================================ import timm import torch import pytest from segmentation_models_pytorch.encoders import TimmViTEncoder from segmentation_models_pytorch.encoders.timm_vit import sample_block_indices_uniformly from tests.encoders import base from tests.utils import ( default_device, check_run_test_on_diff_or_main, requires_torch_greater_or_equal, requires_timm_greater_or_equal, ) timm_vit_encoders = ["vit_tiny_patch16_224"] @requires_timm_greater_or_equal("1.0.0") class TestTimmViTEncoders(base.BaseEncoderTester): encoder_names = timm_vit_encoders tiny_encoder_patch_size = 224 default_height = 224 default_width = 224 files_for_diff = ["encoders/dpt.py"] num_output_features = 4 default_depth = 4 output_strides = None supports_dilated = False depth_to_test = [2, 3, 4] def get_tiny_encoder(self) -> TimmViTEncoder: return TimmViTEncoder( name=self.encoder_names[0], pretrained=False, depth=self.default_depth, in_channels=3, ) def get_encoder(self, encoder_name: str, **kwargs) -> TimmViTEncoder: default_kwargs = { "name": encoder_name, "pretrained": False, "depth": self.default_depth, "in_channels": 3, } default_kwargs.update(kwargs) return TimmViTEncoder(**default_kwargs) def test_forward_backward(self): for encoder_name in self.encoder_names: sample = self._get_sample().to(default_device) with self.subTest(encoder_name=encoder_name): # init encoder encoder = self.get_encoder(encoder_name).to(default_device) # forward features, prefix_tokens = encoder.forward(sample) self.assertEqual( len(features), self.num_output_features, f"Encoder `{encoder_name}` should have {self.num_output_features} output feature maps, but has {len(features)}", ) if encoder.has_prefix_tokens: self.assertEqual( len(prefix_tokens), self.num_output_features, f"Encoder `{encoder_name}` should have {self.num_output_features} prefix tokens, but has {len(prefix_tokens)}", ) # backward features[-1].mean().backward() def test_in_channels(self): cases = [ (encoder_name, in_channels) for encoder_name in self.encoder_names for in_channels in self.in_channels_to_test ] for encoder_name, in_channels in cases: sample = self._get_sample(num_channels=in_channels).to(default_device) with self.subTest(encoder_name=encoder_name, in_channels=in_channels): encoder = self.get_encoder(encoder_name, in_channels=in_channels).to( default_device ) encoder.eval() # forward with torch.inference_mode(): encoder.forward(sample) def test_depth(self): cases = [ (encoder_name, depth) for encoder_name in self.encoder_names for depth in self.depth_to_test ] for encoder_name, depth in cases: sample = self._get_sample().to(default_device) with self.subTest(encoder_name=encoder_name, depth=depth): encoder = self.get_encoder(encoder_name, depth=depth).to(default_device) encoder.eval() # forward with torch.inference_mode(): features, _ = encoder.forward(sample) # check number of features self.assertEqual( len(features), depth, f"Encoder `{encoder_name}` should have {depth} output feature maps, but has {len(features)}", ) # check feature strides height_strides, width_strides = self.get_features_output_strides( sample, features ) encoder_out_indices = sample_block_indices_uniformly(depth, 12) feature_info = timm.create_model(model_name=encoder_name).feature_info output_strides = [ feature_info[i]["reduction"] for i in encoder_out_indices ] self.assertEqual( height_strides, output_strides, f"Encoder `{encoder_name}` should have output strides {output_strides}, but has {height_strides}", ) self.assertEqual( width_strides, output_strides, f"Encoder `{encoder_name}` should have output strides {output_strides}, but has {width_strides}", ) # check encoder output stride property self.assertEqual( encoder.output_strides, output_strides, f"Encoder `{encoder_name}` last feature map should have output stride {output_strides[depth - 1]}, but has {encoder.output_stride}", ) # check out channels also have proper length self.assertEqual( len(encoder.out_channels), depth, f"Encoder `{encoder_name}` should have {depth} out_channels, but has {len(encoder.out_channels)}", ) def test_invalid_depth(self): with self.assertRaises(ValueError): self.get_encoder(self.encoder_names[0], depth=0) with self.assertRaises(ValueError): self.get_encoder(self.encoder_names[0], depth=25) def test_invalid_out_indices(self): # out of range with self.assertRaises(ValueError): self.get_encoder(self.encoder_names[0], depth=1, output_indices=-25) with self.assertRaises(ValueError): self.get_encoder(self.encoder_names[0], depth=3, output_indices=[1, 2, 25]) # invalid length with self.assertRaises(ValueError): self.get_encoder( self.encoder_names[0], depth=2, output_indices=[ 2, ], ) def test_dilated(self): pytest.skip("Dilation is not supported for ViT encoders") @pytest.mark.compile def test_compile(self): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") encoder = self.get_tiny_encoder() encoder = encoder.eval().to(default_device) sample = self._get_sample( height=self.tiny_encoder_patch_size, width=self.tiny_encoder_patch_size ).to(default_device) torch.compiler.reset() compiled_encoder = torch.compile( encoder, fullgraph=True, dynamic=True, backend="eager" ) if encoder._is_torch_compilable: compiled_encoder(sample) else: with self.assertRaises(Exception): compiled_encoder(sample) @pytest.mark.torch_export @requires_torch_greater_or_equal("2.4.0") def test_torch_export(self): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") sample = self._get_sample( height=self.tiny_encoder_patch_size, width=self.tiny_encoder_patch_size ).to(default_device) encoder = self.get_tiny_encoder() encoder = encoder.eval().to(default_device) exported_encoder = torch.export.export( encoder, args=(sample,), strict=True, ) with torch.inference_mode(): eager_output = encoder(sample) exported_output = exported_encoder.module().forward(sample) for eager_feature, exported_feature in zip(eager_output, exported_output): torch.testing.assert_close(eager_feature, exported_feature) @pytest.mark.torch_script def test_torch_script(self): pytest.skip( "Encoder with prefix tokens are not supported for scripting, due to poor type handling" ) ================================================ FILE: tests/encoders/test_torchvision_encoders.py ================================================ import segmentation_models_pytorch as smp from tests.encoders import base from tests.utils import RUN_ALL_ENCODERS class TestResNetEncoder(base.BaseEncoderTester): encoder_names = ( ["resnet18"] if not RUN_ALL_ENCODERS else [ "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnext50_32x4d", "resnext101_32x4d", "resnext101_32x8d", "resnext101_32x16d", "resnext101_32x32d", "resnext101_32x48d", ] ) files_for_diff = ["encoders/resnet.py"] def get_tiny_encoder(self): params = { "out_channels": [3, 64, 64, 128, 256, 512], "block": smp.encoders.resnet.BasicBlock, "layers": [1, 1, 1, 1], } return smp.encoders.resnet.ResNetEncoder(**params) class TestDenseNetEncoder(base.BaseEncoderTester): supports_dilated = False encoder_names = ( ["densenet121"] if not RUN_ALL_ENCODERS else ["densenet121", "densenet169", "densenet161"] ) files_for_diff = ["encoders/densenet.py"] def get_tiny_encoder(self): params = { "out_channels": [3, 2, 3, 2, 2, 2], "num_init_features": 2, "growth_rate": 1, "block_config": (1, 1, 1, 1), } return smp.encoders.densenet.DenseNetEncoder(**params) class TestMobileNetEncoder(base.BaseEncoderTester): encoder_names = ["mobilenet_v2"] if not RUN_ALL_ENCODERS else ["mobilenet_v2"] files_for_diff = ["encoders/mobilenet.py"] class TestVggEncoder(base.BaseEncoderTester): supports_dilated = False encoder_names = ( ["vgg11"] if not RUN_ALL_ENCODERS else [ "vgg11", "vgg11_bn", "vgg13", "vgg13_bn", "vgg16", "vgg16_bn", "vgg19", "vgg19_bn", ] ) files_for_diff = ["encoders/vgg.py"] def get_tiny_encoder(self): params = { "out_channels": [4, 4, 4, 4, 4, 4], "config": [4, "M", 4, "M", 4, "M", 4, "M", 4, "M"], "batch_norm": False, } return smp.encoders.vgg.VGGEncoder(**params) ================================================ FILE: tests/models/__init__.py ================================================ ================================================ FILE: tests/models/base.py ================================================ import os import pytest import inspect import tempfile import unittest from functools import lru_cache from huggingface_hub import hf_hub_download import torch import segmentation_models_pytorch as smp from tests.utils import ( has_timm_test_models, default_device, slow_test, requires_torch_greater_or_equal, check_run_test_on_diff_or_main, ) class BaseModelTester(unittest.TestCase): test_encoder_name = ( "tu-test_resnet.r160_in1k" if has_timm_test_models else "resnet18" ) files_for_diff = [r".*"] # should be overriden test_model_type = None # test sample configuration default_batch_size = 1 default_num_channels = 3 default_height = 64 default_width = 64 compile_dynamic = True @property def model_type(self): if self.test_model_type is None: raise ValueError("test_model_type is not set") return self.test_model_type @property def hub_checkpoint(self): return f"smp-test-models/{self.model_type}-tu-resnet18" @property def model_class(self): return smp.MODEL_ARCHITECTURES_MAPPING[self.model_type] @property def decoder_channels(self): signature = inspect.signature(self.model_class) # check if decoder_channels is in the signature if "decoder_channels" in signature.parameters: return signature.parameters["decoder_channels"].default return None @lru_cache def _get_sample(self, batch_size=None, num_channels=None, height=None, width=None): batch_size = batch_size or self.default_batch_size num_channels = num_channels or self.default_num_channels height = height or self.default_height width = width or self.default_width return torch.rand(batch_size, num_channels, height, width) @lru_cache def get_default_model(self): model = smp.create_model(self.model_type, self.test_encoder_name) model = model.to(default_device) return model def test_forward_backward(self): sample = self._get_sample().to(default_device) model = self.get_default_model() # check default in_channels=3 output = model(sample) # check default output number of classes = 1 expected_number_of_classes = 1 result_number_of_classes = output.shape[1] self.assertEqual( result_number_of_classes, expected_number_of_classes, f"Default output number of classes should be {expected_number_of_classes}, but got {result_number_of_classes}", ) # check backward pass output.mean().backward() def test_in_channels_and_depth_and_out_classes( self, in_channels=1, depth=3, classes=7 ): kwargs = {} if self.model_type in ["unet", "unetplusplus", "manet"]: kwargs = {"decoder_channels": self.decoder_channels[:depth]} if self.model_type == "dpt": kwargs = {"decoder_intermediate_channels": self.decoder_channels[:depth]} model = ( smp.create_model( arch=self.model_type, encoder_name=self.test_encoder_name, encoder_depth=depth, in_channels=in_channels, classes=classes, **kwargs, ) .to(default_device) .eval() ) sample = self._get_sample(num_channels=in_channels).to(default_device) # check in channels correctly set with torch.inference_mode(): output = model(sample) self.assertEqual(output.shape[1], classes) def test_classification_head(self): model = smp.create_model( arch=self.model_type, encoder_name=self.test_encoder_name, aux_params={ "pooling": "avg", "classes": 10, "dropout": 0.5, "activation": "sigmoid", }, ) model = model.to(default_device).eval() self.assertIsNotNone(model.classification_head) self.assertIsInstance(model.classification_head[0], torch.nn.AdaptiveAvgPool2d) self.assertIsInstance(model.classification_head[1], torch.nn.Flatten) self.assertIsInstance(model.classification_head[2], torch.nn.Dropout) self.assertEqual(model.classification_head[2].p, 0.5) self.assertIsInstance(model.classification_head[3], torch.nn.Linear) self.assertIsInstance(model.classification_head[4].activation, torch.nn.Sigmoid) sample = self._get_sample().to(default_device) with torch.inference_mode(): _, cls_probs = model(sample) self.assertEqual(cls_probs.shape[1], 10) def test_any_resolution(self): model = self.get_default_model() sample = self._get_sample( height=self.default_height + 3, width=self.default_width + 7, ).to(default_device) if model.requires_divisible_input_shape: with self.assertRaises(RuntimeError, msg="Wrong input shape"): output = model(sample) return with torch.inference_mode(): output = model(sample) self.assertEqual(output.shape[2], self.default_height + 3) self.assertEqual(output.shape[3], self.default_width + 7) @requires_torch_greater_or_equal("2.0.1") def test_save_load_with_hub_mixin(self): # instantiate model model = self.get_default_model() model.eval() # save model with tempfile.TemporaryDirectory() as tmpdir: model.save_pretrained( tmpdir, dataset="test_dataset", metrics={"my_awesome_metric": 0.99} ) restored_model = smp.from_pretrained(tmpdir).to(default_device) restored_model.eval() with open(os.path.join(tmpdir, "README.md"), "r") as f: readme = f.read() # check inference is correct sample = self._get_sample().to(default_device) with torch.inference_mode(): output = model(sample) restored_output = restored_model(sample) self.assertEqual(output.shape, restored_output.shape) self.assertEqual(output.shape[1], 1) # check dataset and metrics are saved in readme self.assertIn("test_dataset", readme) self.assertIn("my_awesome_metric", readme) @slow_test @requires_torch_greater_or_equal("2.0.1") @pytest.mark.logits_match def test_preserve_forward_output(self): model = smp.from_pretrained(self.hub_checkpoint).eval().to(default_device) input_tensor_path = hf_hub_download( repo_id=self.hub_checkpoint, filename="input-tensor.pth" ) output_tensor_path = hf_hub_download( repo_id=self.hub_checkpoint, filename="output-tensor.pth" ) input_tensor = torch.load(input_tensor_path, weights_only=True) input_tensor = input_tensor.to(default_device) output_tensor = torch.load(output_tensor_path, weights_only=True) output_tensor = output_tensor.to(default_device) with torch.inference_mode(): output = model(input_tensor) self.assertEqual(output.shape, output_tensor.shape) is_close = torch.allclose(output, output_tensor, atol=5e-2) max_diff = torch.max(torch.abs(output - output_tensor)) self.assertTrue(is_close, f"Max diff: {max_diff}") @pytest.mark.compile def test_compile(self): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") sample = self._get_sample().to(default_device) model = self.get_default_model() model = model.eval().to(default_device) if not model._is_torch_compilable: with self.assertRaises((RuntimeError)): torch.compiler.reset() compiled_model = torch.compile( model, fullgraph=True, dynamic=self.compile_dynamic, backend="eager" ) return torch.compiler.reset() compiled_model = torch.compile( model, fullgraph=True, dynamic=self.compile_dynamic, backend="eager" ) with torch.inference_mode(): compiled_model(sample) @pytest.mark.torch_export def test_torch_export(self, eps=1e-5): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") torch.manual_seed(42) sample = self._get_sample().to(default_device) model = self.get_default_model() model.eval() exported_model = torch.export.export( model, args=(sample,), strict=True, ) with torch.inference_mode(): eager_output = model(sample) exported_output = exported_model.module().forward(sample) self.assertEqual(eager_output.shape, exported_output.shape) torch.testing.assert_close(eager_output, exported_output, rtol=eps, atol=eps) @pytest.mark.torch_script def test_torch_script(self): if not check_run_test_on_diff_or_main(self.files_for_diff): self.skipTest("No diff and not on `main`.") sample = self._get_sample().to(default_device) model = self.get_default_model() model.eval() if not model._is_torch_scriptable: with self.assertRaises(RuntimeError): scripted_model = torch.jit.script(model) return scripted_model = torch.jit.script(model) with torch.inference_mode(): scripted_output = scripted_model(sample) eager_output = model(sample) self.assertEqual(scripted_output.shape, eager_output.shape) torch.testing.assert_close(scripted_output, eager_output, rtol=1e-3, atol=1e-3) ================================================ FILE: tests/models/test_deeplab.py ================================================ from tests.models import base class TestDeeplabV3Model(base.BaseModelTester): test_model_type = "deeplabv3" files_for_diff = [r"decoders/deeplabv3/", r"base/"] default_batch_size = 2 class TestDeeplabV3PlusModel(base.BaseModelTester): test_model_type = "deeplabv3plus" files_for_diff = [r"decoders/deeplabv3plus/", r"base/"] default_batch_size = 2 ================================================ FILE: tests/models/test_dpt.py ================================================ import pytest import inspect import torch import segmentation_models_pytorch as smp from tests.models import base from tests.utils import ( slow_test, default_device, requires_torch_greater_or_equal, ) class TestDPTModel(base.BaseModelTester): test_encoder_name = "tu-vit_tiny_patch16_224" files_for_diff = [r"decoders/dpt/", r"base/"] default_height = 224 default_width = 224 # should be overriden test_model_type = "dpt" compile_dynamic = False @property def decoder_channels(self): signature = inspect.signature(self.model_class) return signature.parameters["decoder_intermediate_channels"].default @property def hub_checkpoint(self): return "smp-test-models/dpt-tu-test_vit" @slow_test @requires_torch_greater_or_equal("2.0.1") @pytest.mark.logits_match def test_load_pretrained(self): hub_checkpoint = "smp-hub/dpt-large-ade20k" model = smp.from_pretrained(hub_checkpoint) model = model.eval().to(default_device) input_tensor = torch.ones((1, 3, 384, 384)) input_tensor = input_tensor.to(default_device) expected_logits_slice = torch.tensor( [3.4166, 3.4422, 3.4677, 3.2784, 3.0880, 2.9497] ) with torch.inference_mode(): output = model(input_tensor) resulted_logits_slice = output[0, 0, 0, 0:6].cpu() self.assertEqual(expected_logits_slice.shape, resulted_logits_slice.shape) is_close = torch.allclose( expected_logits_slice, resulted_logits_slice, atol=5e-2 ) max_diff = torch.max(torch.abs(expected_logits_slice - resulted_logits_slice)) self.assertTrue(is_close, f"Max diff: {max_diff}") ================================================ FILE: tests/models/test_fpn.py ================================================ import segmentation_models_pytorch as smp from tests.models import base class TestFpnModel(base.BaseModelTester): test_model_type = "fpn" files_for_diff = [r"decoders/fpn/", r"base/"] def test_interpolation(self): # test bilinear model_1 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bilinear", ) assert model_1.decoder.p2.interpolation_mode == "bilinear" assert model_1.decoder.p3.interpolation_mode == "bilinear" assert model_1.decoder.p4.interpolation_mode == "bilinear" # test bicubic model_2 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bicubic", ) assert model_2.decoder.p2.interpolation_mode == "bicubic" assert model_2.decoder.p3.interpolation_mode == "bicubic" assert model_2.decoder.p4.interpolation_mode == "bicubic" ================================================ FILE: tests/models/test_linknet.py ================================================ import torch import segmentation_models_pytorch as smp from tests.models import base class TestLinknetModel(base.BaseModelTester): test_model_type = "linknet" files_for_diff = [r"decoders/linknet/", r"base/"] def test_timm_transformer_style_encoder(self): model = smp.Linknet("tu-convnext_atto", encoder_weights=None).eval() with torch.inference_mode(): output = model(torch.rand(1, 3, 256, 256)) assert output.shape == (1, 1, 256, 256) ================================================ FILE: tests/models/test_manet.py ================================================ import segmentation_models_pytorch as smp from tests.models import base class TestManetModel(base.BaseModelTester): test_model_type = "manet" files_for_diff = [r"decoders/manet/", r"base/"] def test_interpolation(self): # test bilinear model_1 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bilinear", ) for block in model_1.decoder.blocks: assert block.interpolation_mode == "bilinear" # test bicubic model_2 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bicubic", ) for block in model_2.decoder.blocks: assert block.interpolation_mode == "bicubic" ================================================ FILE: tests/models/test_pan.py ================================================ import pytest import segmentation_models_pytorch as smp from tests.models import base class TestPanModel(base.BaseModelTester): test_model_type = "pan" files_for_diff = [r"decoders/pan/", r"base/"] default_batch_size = 2 default_height = 128 default_width = 128 def test_interpolation(self): # test bilinear model_1 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bilinear", ) assert model_1.decoder.gau1.interpolation_mode == "bilinear" assert model_1.decoder.gau1.align_corners is True assert model_1.decoder.gau2.interpolation_mode == "bilinear" assert model_1.decoder.gau2.align_corners is True assert model_1.decoder.gau3.interpolation_mode == "bilinear" assert model_1.decoder.gau3.align_corners is True # test bicubic model_2 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bicubic", ) assert model_2.decoder.gau1.interpolation_mode == "bicubic" assert model_2.decoder.gau1.align_corners is None assert model_2.decoder.gau2.interpolation_mode == "bicubic" assert model_2.decoder.gau2.align_corners is None assert model_2.decoder.gau3.interpolation_mode == "bicubic" assert model_2.decoder.gau3.align_corners is None with pytest.warns(DeprecationWarning): smp.create_model( self.test_model_type, self.test_encoder_name, upscale_mode="bicubic", ) assert model_2.decoder.gau1.interpolation_mode == "bicubic" ================================================ FILE: tests/models/test_psp.py ================================================ from tests.models import base class TestPspModel(base.BaseModelTester): test_model_type = "pspnet" files_for_diff = [r"decoders/pspnet/", r"base/"] default_batch_size = 2 ================================================ FILE: tests/models/test_segformer.py ================================================ import torch import pytest import segmentation_models_pytorch as smp from tests.models import base from tests.utils import slow_test, default_device, requires_torch_greater_or_equal class TestSegformerModel(base.BaseModelTester): test_model_type = "segformer" files_for_diff = [r"decoders/segformer/", r"base/"] @slow_test @requires_torch_greater_or_equal("2.0.1") @pytest.mark.logits_match def test_load_pretrained(self): hub_checkpoint = "smp-hub/segformer-b0-512x512-ade-160k" model = smp.from_pretrained(hub_checkpoint) model = model.eval().to(default_device) sample = torch.ones([1, 3, 512, 512]).to(default_device) with torch.inference_mode(): output = model(sample) self.assertEqual(output.shape, (1, 150, 512, 512)) expected_logits_slice = torch.tensor( [-4.4172, -4.4723, -4.5273, -4.5824, -4.6375, -4.7157] ) resulted_logits_slice = output[0, 0, 256, :6].cpu() is_equal = torch.allclose( expected_logits_slice, resulted_logits_slice, atol=1e-2 ) max_diff = torch.max(torch.abs(expected_logits_slice - resulted_logits_slice)) self.assertTrue( is_equal, f"Expected logits slice and resulted logits slice are not equal.\n" f"Max diff: {max_diff}\n" f"Expected: {expected_logits_slice}\n" f"Resulted: {resulted_logits_slice}\n", ) ================================================ FILE: tests/models/test_unet.py ================================================ import segmentation_models_pytorch as smp from tests.models import base class TestUnetModel(base.BaseModelTester): test_model_type = "unet" files_for_diff = [r"decoders/unet/", r"base/"] def test_interpolation(self): # test bilinear model_1 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bilinear", ) for block in model_1.decoder.blocks: assert block.interpolation_mode == "bilinear" # test bicubic model_2 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bicubic", ) for block in model_2.decoder.blocks: assert block.interpolation_mode == "bicubic" ================================================ FILE: tests/models/test_unetplusplus.py ================================================ import segmentation_models_pytorch as smp import torch from tests.models import base class TestUnetPlusPlusModel(base.BaseModelTester): test_model_type = "unetplusplus" files_for_diff = [r"decoders/unetplusplus/", r"base/"] def test_timm_transformer_style_encoder(self): model = smp.UnetPlusPlus("tu-convnext_atto", encoder_weights=None).eval() with torch.inference_mode(): output = model(torch.rand(1, 3, 256, 256)) assert output.shape == (1, 1, 256, 256) def test_interpolation(self): # test bilinear model_1 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bilinear", ) is_tested = False for module in model_1.decoder.modules(): if module.__class__.__name__ == "DecoderBlock": assert module.interpolation_mode == "bilinear" is_tested = True assert is_tested # test bicubic model_2 = smp.create_model( self.test_model_type, self.test_encoder_name, decoder_interpolation="bicubic", ) is_tested = False for module in model_2.decoder.modules(): if module.__class__.__name__ == "DecoderBlock": assert module.interpolation_mode == "bicubic" is_tested = True assert is_tested ================================================ FILE: tests/models/test_upernet.py ================================================ import pytest from tests.models import base class TestUnetModel(base.BaseModelTester): test_model_type = "upernet" files_for_diff = [r"decoders/upernet/", r"base/"] default_batch_size = 2 @pytest.mark.torch_export def test_torch_export(self): super().test_torch_export(eps=1e-3) ================================================ FILE: tests/test_base.py ================================================ import torch import tempfile import segmentation_models_pytorch as smp import pytest def test_from_pretrained_with_mismatched_keys(): original_model = smp.Unet(classes=1) with tempfile.TemporaryDirectory() as temp_dir: original_model.save_pretrained(temp_dir) # we should catch warning here and check if there specific keys there with pytest.warns(UserWarning): restored_model = smp.from_pretrained(temp_dir, classes=2, strict=False) assert restored_model.segmentation_head[0].out_channels == 2 # verify all the weight are the same expect mismatched ones original_state_dict = original_model.state_dict() restored_state_dict = restored_model.state_dict() expected_mismatched_keys = [ "segmentation_head.0.weight", "segmentation_head.0.bias", ] mismatched_keys = [] for key in original_state_dict: if key not in expected_mismatched_keys: assert torch.allclose(original_state_dict[key], restored_state_dict[key]) else: mismatched_keys.append(key) assert len(mismatched_keys) == 2 assert sorted(mismatched_keys) == sorted(expected_mismatched_keys) ================================================ FILE: tests/test_losses.py ================================================ import pytest import torch import segmentation_models_pytorch as smp import segmentation_models_pytorch.losses._functional as F from segmentation_models_pytorch.losses import ( DiceLoss, JaccardLoss, SoftBCEWithLogitsLoss, SoftCrossEntropyLoss, TverskyLoss, MCCLoss, FocalLoss, ) def test_focal_loss_from_logits_false_multiclass(): torch.manual_seed(0) input_logits = torch.tensor( [[0.0, 10.0, 0.0], [10.0, 0.0, 0.0], [0.0, 0.0, 10.0]] ).float() target = torch.tensor([1, 0, 2]).long() # Convert to probabilities input_probs = torch.softmax(input_logits, dim=1) loss_logits = smp.losses.FocalLoss( mode="multiclass", from_logits=True, )(input_logits, target) loss_probs = smp.losses.FocalLoss( mode="multiclass", from_logits=False, )(input_probs, target) # They should be close (not exact due to constant shift issue) assert torch.isfinite(loss_probs) assert torch.isfinite(loss_logits) assert abs(loss_logits - loss_probs) < 0.2 def test_focal_loss_with_logits(): input_good = torch.tensor([10, -10, 10]).float() input_bad = torch.tensor([-1, 2, 0]).float() target = torch.tensor([1, 0, 1]) loss_good = F.focal_loss_with_logits(input_good, target) loss_bad = F.focal_loss_with_logits(input_bad, target) assert loss_good < loss_bad def test_softmax_focal_loss_with_logits(): input_good = torch.tensor([[0, 10, 0], [10, 0, 0], [0, 0, 10]]).float() input_bad = torch.tensor([[0, -10, 0], [0, 10, 0], [0, 0, 10]]).float() target = torch.tensor([1, 0, 2]).long() loss_good = F.softmax_focal_loss_with_logits(input_good, target) loss_bad = F.softmax_focal_loss_with_logits(input_bad, target) assert loss_good < loss_bad @pytest.mark.parametrize( ["y_true", "y_pred", "expected", "eps"], [ [[1, 1, 1, 1], [1, 1, 1, 1], 1.0, 1e-5], [[0, 1, 1, 0], [0, 1, 1, 0], 1.0, 1e-5], [[1, 1, 1, 1], [1, 1, 0, 0], 0.5, 1e-5], ], ) def test_soft_jaccard_score(y_true, y_pred, expected, eps): y_true = torch.tensor(y_true, dtype=torch.float32) y_pred = torch.tensor(y_pred, dtype=torch.float32) actual = F.soft_jaccard_score(y_pred, y_true, eps=eps) assert float(actual) == pytest.approx(expected, eps) @pytest.mark.parametrize( ["y_true", "y_pred", "expected", "eps"], [ [[[1, 1, 0, 0], [0, 0, 1, 1]], [[1, 1, 0, 0], [0, 0, 1, 1]], 1.0, 1e-5], [[[1, 1, 0, 0], [0, 0, 1, 1]], [[0, 0, 1, 0], [0, 1, 0, 0]], 0.0, 1e-5], [[[1, 1, 0, 0], [0, 0, 0, 1]], [[1, 1, 0, 0], [0, 0, 0, 0]], 0.5, 1e-5], ], ) def test_soft_jaccard_score_2(y_true, y_pred, expected, eps): y_true = torch.tensor(y_true, dtype=torch.float32) y_pred = torch.tensor(y_pred, dtype=torch.float32) actual = F.soft_jaccard_score(y_pred, y_true, dims=[1], eps=eps) actual = actual.mean() assert float(actual) == pytest.approx(expected, eps) @pytest.mark.parametrize( ["y_true", "y_pred", "expected", "eps"], [ [[1, 1, 1, 1], [1, 1, 1, 1], 1.0, 1e-5], [[0, 1, 1, 0], [0, 1, 1, 0], 1.0, 1e-5], [[1, 1, 1, 1], [1, 1, 0, 0], 2.0 / 3.0, 1e-5], ], ) def test_soft_dice_score(y_true, y_pred, expected, eps): y_true = torch.tensor(y_true, dtype=torch.float32) y_pred = torch.tensor(y_pred, dtype=torch.float32) actual = F.soft_dice_score(y_pred, y_true, eps=eps) assert float(actual) == pytest.approx(expected, eps) @pytest.mark.parametrize( ["y_true", "y_pred", "expected", "eps", "alpha", "beta"], [ [[1, 1, 1, 1], [1, 1, 1, 1], 1.0, 1e-5, 0.5, 0.5], [[0, 1, 1, 0], [0, 1, 1, 0], 1.0, 1e-5, 0.5, 0.5], [[1, 1, 1, 1], [1, 1, 0, 0], 2.0 / 3.0, 1e-5, 0.5, 0.5], ], ) def test_soft_tversky_score(y_true, y_pred, expected, eps, alpha, beta): y_true = torch.tensor(y_true, dtype=torch.float32) y_pred = torch.tensor(y_pred, dtype=torch.float32) actual = F.soft_tversky_score(y_pred, y_true, eps=eps, alpha=alpha, beta=beta) assert float(actual) == pytest.approx(expected, eps) @torch.inference_mode() def test_dice_loss_binary(): eps = 1e-5 criterion = DiceLoss(mode=smp.losses.BINARY_MODE, from_logits=False) # Ideal case y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, 1, -1) y_true = torch.tensor(([1, 1, 1])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1) y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1) y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) # Worst case y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1) y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1) y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1) y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) @torch.inference_mode() def test_tversky_loss_binary(): eps = 1e-5 # with alpha=0.5; beta=0.5 it is equal to DiceLoss criterion = TverskyLoss( mode=smp.losses.BINARY_MODE, from_logits=False, alpha=0.5, beta=0.5 ) # Ideal case y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, 1, -1) y_true = torch.tensor(([1, 1, 1])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1) y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1) y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) # Worst case y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1) y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1) y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1) y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) @torch.inference_mode() def test_binary_jaccard_loss(): eps = 1e-5 criterion = JaccardLoss(mode=smp.losses.BINARY_MODE, from_logits=False) # Ideal case y_pred = torch.tensor([1.0]).view(1, 1, 1, 1) y_true = torch.tensor(([1])).view(1, 1, 1, 1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, 1, -1) y_true = torch.tensor(([1, 0, 1])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, 1, -1) y_true = torch.tensor(([0, 0, 0])).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) # Worst case y_pred = torch.tensor([1.0, 1.0, 1.0]).view(1, 1, -1) y_true = torch.tensor([0, 0, 0]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) y_pred = torch.tensor([1.0, 0.0, 1.0]).view(1, 1, -1) y_true = torch.tensor([0, 1, 0]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, eps) y_pred = torch.tensor([0.0, 0.0, 0.0]).view(1, 1, -1) y_true = torch.tensor([1, 1, 1]).view(1, 1, 1, -1) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, eps) @torch.inference_mode() def test_multiclass_jaccard_loss(): eps = 1e-5 criterion = JaccardLoss(mode=smp.losses.MULTICLASS_MODE, from_logits=False) # Ideal case y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) y_true = torch.tensor([[0, 0, 1, 1]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) # Worst case y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) y_true = torch.tensor([[1, 1, 0, 0]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) # 1 - 1/3 case y_pred = torch.tensor([[[1.0, 0.0, 1.0, 0.0], [0.0, 1.0, 0.0, 1.0]]]) y_true = torch.tensor([[1, 1, 0, 0]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0 - 1.0 / 3.0, abs=eps) @torch.inference_mode() def test_multilabel_jaccard_loss(): eps = 1e-5 criterion = JaccardLoss(mode=smp.losses.MULTILABEL_MODE, from_logits=False) # Ideal case y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) # Worst case y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) y_true = 1 - y_pred loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) # 1 - 1/3 case y_pred = torch.tensor([[[0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0]]]) y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0 - 1.0 / 3.0, abs=eps) @torch.inference_mode() def test_soft_ce_loss(): criterion = SoftCrossEntropyLoss(smooth_factor=0.1, ignore_index=-100) y_pred = torch.tensor( [[+9, -9, -9, -9], [-9, +9, -9, -9], [-9, -9, +9, -9], [-9, -9, -9, +9]] ).float() y_true = torch.tensor([0, 1, -100, 3]).long() loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0125, abs=0.0001) @torch.inference_mode() def test_soft_bce_loss(): criterion = SoftBCEWithLogitsLoss(smooth_factor=0.1, ignore_index=-100) y_pred = torch.tensor([-9, 9, 1, 9, -9]).float() y_true = torch.tensor([0, 1, -100, 1, 0]).long() loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.7201, abs=0.0001) @torch.inference_mode() def test_binary_mcc_loss(): eps = 1e-5 criterion = MCCLoss(eps=eps) # Ideal case y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.0, abs=eps) # Worst case y_pred = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [0.0, 0.0, 1.0, 1.0]]]) y_true = 1 - y_pred loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(2.0, abs=eps) # 1 - 1/3 case y_pred = torch.tensor([[[0.0, 1.0, 1.0, 0.0], [0.0, 1.0, 1.0, 0.0]]]) y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) # Trivial classifier case #1. y_pred = torch.tensor([[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]) y_true = torch.tensor([[[1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.0, 0.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1.0, abs=eps) # Trivial classifier case #2. y_pred = torch.tensor([[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]]]) y_true = torch.tensor([[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(1 + 0.5, abs=eps) # Trivial classifier case #3. y_pred = torch.tensor([[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]]) y_true = torch.tensor([[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]]) loss = criterion(y_pred, y_true) assert float(loss) == pytest.approx(0.5, abs=eps) @torch.inference_mode() def test_class_weights_uniform_equivalent_to_no_weights_multiclass(): """Uniform class_weights should produce the same loss as no weights (multiclass).""" eps = 1e-5 torch.manual_seed(0) y_pred = torch.randn(2, 3, 4, 4) y_true = torch.randint(0, 3, (2, 4, 4)) for loss_cls in [DiceLoss, JaccardLoss, TverskyLoss]: loss_no_w = loss_cls(mode=smp.losses.MULTICLASS_MODE)(y_pred, y_true) loss_uniform = loss_cls( mode=smp.losses.MULTICLASS_MODE, class_weights=[1.0, 1.0, 1.0] )(y_pred, y_true) assert torch.allclose(loss_no_w, loss_uniform, atol=eps), ( f"Uniform weights should be equivalent to no weights for {loss_cls.__name__}" ) @torch.inference_mode() def test_class_weights_uniform_equivalent_to_no_weights_multilabel(): """Uniform class_weights should produce the same loss as no weights (multilabel).""" eps = 1e-5 torch.manual_seed(0) y_pred = torch.randn(2, 3, 4, 4) y_true = torch.randint(0, 2, (2, 3, 4, 4)).float() for loss_cls in [DiceLoss, JaccardLoss, TverskyLoss]: loss_no_w = loss_cls(mode=smp.losses.MULTILABEL_MODE)(y_pred, y_true) loss_uniform = loss_cls( mode=smp.losses.MULTILABEL_MODE, class_weights=[1.0, 1.0, 1.0] )(y_pred, y_true) assert torch.allclose(loss_no_w, loss_uniform, atol=eps), ( f"Uniform weights should be equivalent to no weights for {loss_cls.__name__}" ) @torch.inference_mode() def test_class_weights_nonuniform_changes_loss_multiclass(): """Non-uniform class_weights should change the loss value (multiclass).""" torch.manual_seed(0) y_pred = torch.randn(2, 3, 4, 4) y_true = torch.randint(0, 3, (2, 4, 4)) for loss_cls in [DiceLoss, JaccardLoss, TverskyLoss]: loss_no_w = loss_cls(mode=smp.losses.MULTICLASS_MODE)(y_pred, y_true) loss_weighted = loss_cls( mode=smp.losses.MULTICLASS_MODE, class_weights=[1.0, 2.0, 0.5] )(y_pred, y_true) assert not torch.allclose(loss_no_w, loss_weighted, atol=1e-6), ( f"Non-uniform weights should change the loss for {loss_cls.__name__}" ) @torch.inference_mode() def test_class_weights_scale_invariant_multiclass(): """Scaling all weights by a constant should not change the loss (multiclass).""" eps = 1e-5 torch.manual_seed(0) y_pred = torch.randn(2, 3, 4, 4) y_true = torch.randint(0, 3, (2, 4, 4)) for loss_cls in [DiceLoss, JaccardLoss, TverskyLoss]: loss_w = loss_cls( mode=smp.losses.MULTICLASS_MODE, class_weights=[1.0, 2.0, 0.5] )(y_pred, y_true) loss_w_scaled = loss_cls( mode=smp.losses.MULTICLASS_MODE, class_weights=[10.0, 20.0, 5.0] )(y_pred, y_true) assert torch.allclose(loss_w, loss_w_scaled, atol=eps), ( f"Loss should be scale-invariant w.r.t. class_weights for {loss_cls.__name__}" ) @torch.inference_mode() def test_class_weights_binary_mode_raises(): """class_weights should raise an error when used with binary mode.""" for loss_cls in [DiceLoss, JaccardLoss, TverskyLoss]: with pytest.raises(ValueError): loss_cls(mode=smp.losses.BINARY_MODE, class_weights=[1.0, 2.0]) @torch.inference_mode() def test_focal_class_weights_uniform_equivalent_to_no_weights(): """Uniform class_weights should produce a loss equivalent to no-weights loss.""" eps = 1e-5 torch.manual_seed(0) y_pred = torch.randn(2, 3, 4, 4) y_true = torch.randint(0, 3, (2, 4, 4)) loss_no_w = FocalLoss(mode=smp.losses.MULTICLASS_MODE)(y_pred, y_true) loss_uniform = FocalLoss( mode=smp.losses.MULTICLASS_MODE, class_weights=[1.0, 1.0, 1.0] )(y_pred, y_true) assert torch.allclose(loss_no_w, loss_uniform, atol=eps) @torch.inference_mode() def test_focal_class_weights_scale_invariant(): """Scaling all weights by a constant should not change FocalLoss.""" eps = 1e-5 torch.manual_seed(0) y_pred = torch.randn(2, 3, 4, 4) y_true = torch.randint(0, 3, (2, 4, 4)) loss_w = FocalLoss(mode=smp.losses.MULTICLASS_MODE, class_weights=[1.0, 2.0, 0.5])( y_pred, y_true ) loss_w_scaled = FocalLoss( mode=smp.losses.MULTICLASS_MODE, class_weights=[10.0, 20.0, 5.0] )(y_pred, y_true) assert torch.allclose(loss_w, loss_w_scaled, atol=eps) ================================================ FILE: tests/test_preprocessing.py ================================================ import numpy as np import segmentation_models_pytorch as smp # noqa def _test_preprocessing(inp, out, **params): preprocessed_output = smp.encoders.preprocess_input(inp, **params) assert np.allclose(preprocessed_output, out) def test_mean(): inp = np.ones((32, 32, 3)) out = np.zeros((32, 32, 3)) mean = (1, 1, 1) _test_preprocessing(inp, out, mean=mean) def test_std(): inp = np.ones((32, 32, 3)) * 255 out = np.ones((32, 32, 3)) std = (255, 255, 255) _test_preprocessing(inp, out, std=std) def test_input_range(): inp = np.ones((32, 32, 3)) out = np.ones((32, 32, 3)) _test_preprocessing(inp, out, input_range=(0, 1)) _test_preprocessing(inp * 255, out, input_range=(0, 1)) _test_preprocessing(inp * 255, out * 255, input_range=(0, 255)) def test_input_space(): inp = np.stack([np.ones((32, 32)), np.zeros((32, 32))], axis=-1) out = np.stack([np.zeros((32, 32)), np.ones((32, 32))], axis=-1) _test_preprocessing(inp, out, input_space="BGR") def test_preprocessing_params(): # check default encoder params params = smp.encoders.get_preprocessing_params("resnet18") assert params["mean"] == [0.485, 0.456, 0.406] assert params["std"] == [0.229, 0.224, 0.225] assert params["input_range"] == [0, 1] assert params["input_space"] == "RGB" # check timm params params = smp.encoders.get_preprocessing_params("tu-resnet18") assert params["mean"] == [0.485, 0.456, 0.406] assert params["std"] == [0.229, 0.224, 0.225] assert params["input_range"] == [0, 1] assert params["input_space"] == "RGB" ================================================ FILE: tests/utils.py ================================================ import os import re import timm import torch import unittest from git import Repo from typing import List from packaging.version import Version has_timm_test_models = Version(timm.__version__) >= Version("1.0.12") default_device = "cuda" if torch.cuda.is_available() else "cpu" YES_LIST = ["true", "1", "y", "yes"] RUN_ALL_ENCODERS = os.getenv("RUN_ALL_ENCODERS", "false").lower() in YES_LIST RUN_SLOW = os.getenv("RUN_SLOW", "false").lower() in YES_LIST RUN_ALL = os.getenv("RUN_ALL", "false").lower() in YES_LIST def slow_test(test_case): """ Decorator marking a test as slow. Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them. """ return unittest.skipUnless(RUN_SLOW, "test is slow")(test_case) def requires_timm_greater_or_equal(version: str): timm_version = Version(timm.__version__) provided_version = Version(version) return unittest.skipUnless( timm_version >= provided_version, f"timm version {timm_version} is less than {provided_version}", ) def requires_torch_greater_or_equal(version: str): torch_version = Version(torch.__version__) provided_version = Version(version) return unittest.skipUnless( torch_version >= provided_version, f"torch version {torch_version} is less than {provided_version}", ) def check_run_test_on_diff_or_main(filepath_patterns: List[str]): if RUN_ALL: return True try: repo = Repo(".") current_branch = repo.active_branch.name diff_files = repo.git.diff("main", name_only=True).splitlines() except Exception: return True if current_branch == "main": return True for pattern in filepath_patterns: for file_path in diff_files: if re.search(pattern, file_path): return True return False def check_two_models_strictly_equal( model_a: torch.nn.Module, model_b: torch.nn.Module, input_data: torch.Tensor ) -> None: for (k1, v1), (k2, v2) in zip( model_a.state_dict().items(), model_b.state_dict().items() ): assert k1 == k2, f"Key mismatch: {k1} != {k2}" torch.testing.assert_close( v1, v2, msg=f"Tensor mismatch at key '{k1}':\n{v1} !=\n{v2}" ) model_a.eval() model_b.eval() with torch.inference_mode(): output_a = model_a(input_data) output_b = model_b(input_data) torch.testing.assert_close(output_a, output_b)